Lidia Mladenova Georgieva Doctor of Philosophy

RISK FACTOR SURVEILLANCE FOR CORONARY HEART DISEASE IN SOFIA, BULGARIA

Lidia Mladenova Georgieva Doctor of philosophy

2001

CONTENTS

Declaration of originality and word length ……………………………………………14 Acknowledgements…………………………………………………………..…………..15 List of relevant publications and presentations…………………………………..…….16 Summary………………………………………………………………………………….18

SECTION I – INTRODUCTION AND METHODS 1. CHAPTER - INTRODUCTION...... 19 1.1 EPIDEMIOLOGY OF CARDIOVASCULAR DISEASES ...... 19 1.1.1 Definitions of terms...... 19 1.1.2 Brief history of epidemiological investigation of IHD and stroke risk factors21 1.1.3 Current stage of knowledge about risk factors for cardiovascular disease and measuring problems...... 22 1.1.3.1 Blood pressure...... 22 1.1.3.2 Cholesterol ...... 27 1.1.3.3 Anthropometrical measurements ...... 31 1.1.3.4 Smoking ...... 31 1.1.3.5 Alcohol...... 34 1.1.3.6 Physical activity...... 38 1.1.3.7 Diet...... 39 1.1.3.8 New and candidate risk factors for CVD...... 43 1.2 THEORY AND PRACTICE OF RISK FACTOR SURVEILLANCE FOR VASCULAR DISEASES 43 1.2.1 International experience in risk factor surveillance...... 43 1.2.1.1 WHO MONICA...... 43 1.2.1.2 The Health Survey for England ...... 45 1.2.1.3 The Dietary and Nutritional Survey of British Adults...... 46 1.2.1.4 USA the first National Health and Nutrition Examination Survey...... 47 1.2.1.5 Risk Factor Prevalence Study in Australia ...... 47 1.3 VASCULAR DISEASES IN BULGARIA...... 48 1.3.1 Background ...... 48 1.3.2 Demographic and mortality trends in Bulgaria...... 49 1.3.3 Mortality trends for chronic diseases in Bulgaria...... 54 1.3.4 Current knowledge about risk factors for cardiovascular diseases in Bulgaria 59 1.3.4.1 Primary data sources ...... 59 1.3.4.2 Summary of findings...... 64 1.3.5 Shortcomings in available risk factor evidence for Bulgaria ...... 68 1.3.5.1 Study populations...... 68 1.3.5.2 Data collection...... 68 1.3.5.3 Interpretation of results ...... 69 1.3.6 How Sofia differs from the rest of Bulgaria...... 69 1.4 NEED FOR CARDIOVASCULAR RISK FACTOR SURVEILLANCE IN BULGARIA ...... 70 1.5 THE AIMS OF THE THESIS ...... 73 2. CHAPTER - METHODS ...... 75 2.1 STUDY DESIGN AND IMPLEMENTATION: ISSUES AND CHOICES ...... 75 2.1.1 Origin, funding and organization of parent study ...... 75 2.1.1.1 Original ideas planned ...... 75

2.1.1.2 Further refinement of proposal ...... 76 2.1.1.3 Management structure of SHS ...... 77 2.1.2 Statistical power and sample size ...... 78 2.1.3 Selection of target population...... 79 2.1.3.1 Definition of target population...... 79 2.1.3.2 Selection of administrative districts (obstinae)...... 80 2.1.3.3 Sampling of subjects ...... 82 2.1.4 Ethical clearance ...... 83 2.2 MANAGEMENT OF PRIMARY DATA COLLECTION...... 83 2.2.1 Selection and staffing of survey centers...... 83 2.2.1.1 Training staff...... 84 2.2.2 Recruitment ...... 85 2.2.2.1 Arranging appointments...... 85 2.2.3 Subject flow within survey centers...... 86 2.3 MEASUREMENTS ...... 88 2.3.1 Height and weight measurements ...... 88 2.3.1.1 Height...... 88 2.3.1.2 Weight...... 88 2.3.2 Measurement of waist and hip circumferences...... 88 2.3.2.1 Quality control of physical measurements...... 89 2.3.3 Blood pressure measurements ...... 89 2.3.3.1 Quality control of blood pressure measurement ...... 90 2.3.4 Blood sample collection...... 93 2.3.4.1 Quality control of blood analyses ...... 94 2.4 QUESTIONNAIRE...... 98 2.4.1 Overall structure...... 98 2.4.2 The recording process...... 98 2.4.3 The food frequency questionnaire...... 99 2.4.4 Alcohol ...... 100 2.4.5 The smoking behavior questionnaire ...... 100 2.4.6 Physical activity ...... 100 2.4.7 Quality control of the interview ...... 101 2.5 DATA ANALYSES AND PRESENTATION ...... 101 2.5.1 Data entry and editing ...... 101 2.5.2 Data presentation...... 101 2.5.3 Standardisation ...... 102 2.5.4 Data analyses...... 103 2.5.4.1 Social-demographic variables...... 103 2.5.4.2 Alcohol...... 104 2.5.4.3 Smoking ...... 105 2.5.4.4 Physical activity...... 106

SECTION II – RESULTS 3. CHAPTER: STUDY POPULATION...... 107 3.1 RECRUITMENT...... 107 3.2 PARTICIPATION BY AGE AND SEX ...... 109 3.3 MARITAL STATUS...... 109 3.4 SOCIO-ECONOMIC CHARACTERISTICS ...... 111 3.4.1 Formal schooling ...... 111 3.4.2 Employment status ...... 113 3.5 REPRESENTATIVENESS OF THE SAMPLE ...... 121 3.6 CONCLUSIONS AS TO REPRESENTATIVENESS ...... 123

4. CHAPTER: ANTHROPOMETRY...... 125 4.1 ASSESSMENT OF DATA QUALITY...... 125 4.2 HEIGHT ...... 125 4.3 WEIGHT ...... 128 4.4 BODY MASS INDEX ...... 130 4.5 WAIST-HIP RATIO ...... 132 4.6 COMPARISON WITH MONICA ...... 135 5. CHAPTER: BLOOD PRESSURE...... 138 5.1 SYSTOLIC BLOOD PRESSURE ...... 138 5.2 DIASTOLIC BLOOD PRESSURE ...... 143 5.3 BLOOD PRESSURE CATEGORIES ...... 148 5.4 ASSESSMENT OF DATA QUALITY...... 150 5.4.1 Digit preference ...... 150 5.4.2 Proportion of odd readings...... 150 5.4.3 Proportion of identical duplicate readings...... 150 5.4.4 Proportion of second readings exceeding first readings:...... 151 5.4.5 Mean difference between first and second readings: ...... 151 5.4.6 Implications of data quality ...... 151 5.5 ASSESSMENT OF BLOOD PRESSURE DISTRIBUTION ...... 154 5.5.1 Estimates of central tendency ...... 154 5.5.2 90th centile...... 154 5.5.3 Proportion reporting antihypertensive treatment...... 156 5.5.4 External comparison...... 158 5.5.4.1 MONICA...... 158 5.5.5 Potential effect of data quality in external comparisons ...... 161 6. CHAPTER: BLOOD LIPIDS ...... 165 6.1 INTRODUCTION...... 165 6.2 RESPONSE TO BLOOD TESTS ...... 166 6.3 TOTAL CHOLESTEROL CONCENTRATION...... 168 6.4 HIGH DENSITY LIPOPROTEIN CHOLESTEROL CONCENTRATION...... 170 6.5 RATIO TOTAL CHOLESTEROL/HIGH DENSITY LIPOPROTEIN CHOLESTEROL ...... 172 6.6 TRIGLYCERIDES ...... 174 6.7 EXTERNAL COMPARISON ...... 176 6.7.1 MONICA ...... 176 7. CHAPTER: SMOKING ...... 179 7.1 OUTLINE ...... 179 7.2 SMOKING STATUS BY AGE AND SEX...... 180 7.3 SMOKING BEHAVIOR OF CURRENT SMOKERS ...... 182 7.4 SMOKING BEHAVIOR OF EX-SMOKERS ...... 190 7.5 PASSIVE SMOKING...... 195 7.6 COMPARISON WITH OTHER POPULATIONS...... 197 7.6.1 MONICA ...... 197 7.6.2 Stage of evolution of the smoking epidemic...... 201 8. CHAPTER: DIET, ALCOHOL AND PHYSICAL ACTIVITY ...... 205 8.1 OUTLINE ...... 205 8.2 DIET...... 205 8.3 ALCOHOL ...... 221 8.4 PHYSICAL ACTIVITY ...... 231

9. CHAPTER: RISK FACTOR LEVELS BY SOCIO-DEMOGRAPHIC CHARACTERISTICS...... 238 9.1 INTRODUCTION...... 238 9.2 RISK FACTORS DISTRIBUTION BY LEVEL OF EDUCATION...... 239 9.2.1 Blood pressure by level of education ...... 239 9.2.2 Total serum cholesterol by level of education ...... 241 9.2.3 BMI by level of education ...... 242 9.2.4 Smoking by level of education...... 245 9.2.5 Alcohol consumption by level of education...... 246 9.2.6 Physical activity by level of education...... 248 9.3 RISK FACTOR DISTRIBUTIONS BY OTHER SOCIO-ECONOMIC CHARACTERISTICS ...252 9.3.1 Blood pressure by other socio-economic characteristics ...... 252 9.3.2 Total serum cholesterol by other socio-economic characteristics ...... 260 9.3.3 Body Mass Index by other socio-economic characteristics...... 264 9.3.4 Smoking behaviour by other socio-economic characteristics ...... 267 9.3.5 Alcohol consumption by other socio-economic characteristics...... 270 9.4 REGRESSION ANALYSES OF RELATIVE STRENGTH AND INDEPENDENCE OF RISK FACTOR ASSOCIATIONS WITH SPECIFIC SOCIO-DEMOGRAPHIC CHARACTERISTICS...... 273 9.4.1 Regression analyses of associations of systolic blood pressure with other risk factors and socio-demographic characteristics...... 274 9.4.1.1 Relationships with age and sex ...... 274 9.4.1.2 Relationship with age, sex and BMI ...... 274 9.4.1.3 Relationship with age, sex and alcohol...... 275 9.4.1.4 Relationship with age, sex, education and socio-economic variables ...275 9.4.2 Regression analyses of associations of total serum cholesterol with other risk factors and socio-demographic characteristics...... 279 9.4.3 Assessment of associations of smoking with socio-demographic characteristics...... 281 10. CHAPTER: RISK FACTOR COMBINATIONS...... 286 10.1 DEFINITIONS OF HIGH VALUES OF RISK FACTORS...... 286 10.1.1 Introduction...... 286 10.1.2 Definition of high values of risk factors...... 287 10.2 COMBINATION OF RISK FACTORS...... 288 10.2.1 Proportion with high levels of the each of the four main cardiovascular disease risk factors separately ...... 288 10.2.2 Combinations of risk factors...... 292 10.3 THE PREVALENCE OF RISK FACTORS IN HIGH RISK SUBGROUPS...... 296 10.3.1 Risk factors in those with high blood pressure ...... 296 10.3.2 Risk factors in those with hypercholesterolaemia ...... 297 10.3.3 Risk factors in current cigarette smokers ...... 297 10.3.4 Risk factors in those who were obese...... 298

SECTION III - DISCUSSIONS AND IMPLICATIONS 11. CHAPTER: POTENTIAL SIGNIFICANCE AND LIMITATIONS OF THE SHS AND SUMMARY OF MAIN FINDINGS...... 299 11.1 LIMITATIONS OF THE SHS...... 299 11.2 POTENTIAL SIGNIFICANCE OF THE SHS ...... 301 11.3 SUMMARY OF MAIN FINDINGS ...... 301 11.3.1 Summary of data quality assessment ...... 301 11.3.2 Summary of findings...... 302

11.3.2.1 Blood pressure...... 302 11.3.2.2 Blood lipids...... 303 11.3.2.3 Anthropometrical measurements ...... 304 11.3.2.4 Smoking ...... 304 11.3.2.5 Diet...... 305 11.3.2.6 Alcohol...... 306 11.3.2.7 Physical activity...... 307 11.3.2.8 Risk factor combinations ...... 307 12. CHAPTER: INTERPRETATION OF FINDINGS IN RELATION TO REPORTED MORTALITY ...... 309 12.1 BLOOD PRESSURE...... 309 12.2 BLOOD LIPIDS...... 310 12.3 BODY MASS INDEX ...... 311 12.4 SMOKING...... 312 12.5 DIET, ALCOHOL AND PHYSICAL ACTIVITY ...... 313 12.6 COMPARISON WITH MONICA - COMPLEX ASSESSMENT ...... 315 13. CHAPTER: LESSONS LEARNED AND RECOMMENDATIONS FOR THE CONDUCT OF FUTURE STUDIES ...... 320 13.1 SUMMARY OF METHODOLOGICAL ISSUES ...... 320 13.1.1 Study design ...... 320 13.1.2 Staffing of studies...... 320 13.1.3 Management of fieldwork...... 321 13.1.4 Analysis and reporting in ways that help interpretation ...... 321 13.1.5 Specific measurement issues ...... 321 13.1.5.1 Blood pressure measurements...... 321 13.1.5.2 Blood lipids and anthropometrical measurements...... 322 13.1.6 Behavioral Factors: physical exercise, smoking, alcohol ...... 322 13.1.6.1 Physical activity...... 322 13.1.6.2 Diet...... 323 13.1.6.3 Smoking ...... 323 13.1.6.4 Alcohol...... 323 13.2 OUTSTANDING QUESTIONS ...... 324 13.2.1 Surveillance-related...... 324 13.2.2 Aetiologic issues...... 325 14. APPENDIXES ...... 327

TABLES

TABLE 1.1: DIASTOLIC BLOOD PRESSURE (MEASURED ON A SINGLE OCCASION) AND CORONARY HEART DISEASE, SELECTED STUDIES ...... 25 TABLE 1.2: DIASTOLIC BLOOD PRESSURE (MEASURED ON A SINGLE OCCASION) AND STROKE, SELECTED STUDIES ...... 26 TABLE 1.3: CHOLESTEROL LEVEL (MEASURED ON A SINGLE OCCASION) AND CORONARY HEART DISEASE, SELECTED STUDIES ...... 29 TABLE 1.4: CHOLESTEROL LEVEL (MEASURED ON A SINGLE OCCASION) AND STROKE, SELECTED STUDIES...... 30 TABLE 1.5: CIGARETTE SMOKING AND CORONARY HEART DISEASE, SELECTED STUDIES...... 33 TABLE 1.6: CIGARETTE SMOKING AND STROKE, SELECTED STUDIES ...... 33 TABLE 1.7: ALCOHOL CONSUMPTION AND CORONARY HEART DISEASE, SELECTED STUDIES ....36 TABLE 1.8: ALCOHOL CONSUMPTION AND STROKE, SELECTED STUDIES ...... 37 TABLE 1.9: THE MAIN CANDIDATE METHODS FOR DIETARY ASSESSMENT IN POPULATION HEALTH SURVEYS...... 40 TABLE 1.10: TRENDS IN CRUDE MORTALITY RATES* PER 1000, IN BULGARIA 1970 - 1995, TOTAL, URBAN AND RURAL BY SEX ...... 51 TABLE 1.11: TRENDS FOR CRUDE MORTALITY BY CAUSE PER 100 000 POPULATION, BULGARIA 1985-1995, BOTH SEXES COMBINED ...... 53 TABLE 1.13: CURRENT STATE OF REPORTING FINDINGS ABOUT RISK FACTORS FOR CORONARY HEART DISEASES IN BULGARIA...... 60 TABLE 1.14: SUMMARY OF MAIN FINDINGS ABOUT RISK FACTORS FOR CHD IN BULGARIA ..65 TABLE 2.1: ESTIMATES OF PRECISION (STANDARD ERROR AND 95% CONFIDENCE INTERVAL) FOR THE MAJOR RISK FACTOR ESTIMATES FOR TARGET CELL SIZES OF 50, 100 AND 200 SUBJECTS USING STANDARD DEVIATIONS FROM THE HUNGARIAN MONICA COHORTS FOR AGE 55-64...... 79 TABLE 2.2: SUMMARY OF SUBJECTS FLOW WITHIN SURVEY CENTRES ...... 86 TABLE 3.1: NUMBERS OF FINAL PARTICIPANTS IN EACH AGE/SEX STRATUM AND THE RATIO TO THE ORIGINAL SAMPLE SIZE OF 310 (620 FOR BOTH SEXES COMBINED)...... 109 TABLE 3.2: DISTRIBUTION OF STUDY POPULATION BY MARITAL STATUS...... 110 TABLE 3.3: EDUCATIONAL CATEGORIES...... 111 TABLE 3.4: DISTRIBUTION OF STUDY POPULATION BY SCHOOLING ...... 112 TABLE 3.5: DISTRIBUTION OF STUDY POPULATION BY EMPLOYMENT STATUS, MALES ...... 114 TABLE 3.6: DISTRIBUTION OF STUDY POPULATION BY EMPLOYMENT STATUS, FEMALES.....115 TABLE 3.7: DISTRIBUTION OF STUDY POPULATION BY TYPE OF EMPLOYMENT...... 116 TABLE 3.8: DISTRIBUTION OF STUDY POPULATION BY HOME OWNERSHIP ...... 118 TABLE 3.9: DISTRIBUTION OF STUDY POPULATION BY PERSONS PER BEDROOM*...... 119 TABLE 3.10: DISTRIBUTION OF STUDY POPULATION BY REPORTED PURCHASING POWER IN COMPARISON WITH PREVIOUS YEAR* ...... 120 TABLE 3.11: COMPARISON OF MARITAL STATUS WITHIN AGE AND SEX STRATA BETWEEN SOFIA HEART STUDY 1994 POPULATION AND CENSUS DATA CITY OF SOFIA REGION, 1992...... 122 TABLE 3.12: DISTRIBUTION WITHIN AGE-STRATA BY YEARS OF FORMAL EDUCATION, SHS POPULATION COMPARED TO CENSUS DATA FOR CITY OF SOFIA REGION, 1992...... 124 TABLE 4.1: DISTRIBUTION OF HEIGHT WITHIN TEN-YEAR AGE GROUPS WITH MEAN, STANDARD DEVIATIONS AND 10TH, 50TH AND 90TH PERCENTILES, MALES ...... 126 TABLE 4.2: DISTRIBUTION OF HEIGHT WITHIN TEN-YEAR AGE GROUPS WITH MEAN, STANDARD DEVIATIONS AND 10TH, 50TH AND 90TH PERCENTILES, FEMALES ...... 127 TABLE 4.3: DISTRIBUTION OF WEIGHT BY AGE GROUP AND SEX...... 129 TABLE 4.4: DISTRIBUTION OF BODY MASS INDEX BY AGE GROUP AND SEX...... 131 TABLE 4.5: DISTRIBUTION OF WAIST HIP RATIO BY 10 YEAR AGE GROUP, WITH MEANS, STANDARD DEVIATIONS AND 10TH, 50TH, 90TH PERCENTILES, MEN ...... 133

TABLE 4.6: DISTRIBUTION OF WAIST HIP RATIO BY 10 YEAR AGE GROUPS, WITH MEANS, STANDARD DEVIATIONS AND 10TH, 50TH, 90TH PERCENTILES, WOMEN ...... 134 TABLE 5.1: DISTRIBUTION OF SYSTOLIC BLOOD PRESSURE*, WITHIN 10 YEAR AGE GROUPS, WITH MEANS, STANDARD DEVIATIONS AND 10TH, 50TH, 90TH PERCENTILES, MALES...... 139 TABLE 5.2: DISTRIBUTION OF SYSTOLIC BLOOD PRESSURE*, WITHIN 10 YEAR AGE GROUPS, WITH MEANS, STANDARD DEVIATIONS AND 10TH, 50TH, 90TH PERCENTILES, FEMALES ..140 TABLE 5.3: DISTRIBUTION OF DIASTOLIC BLOOD PRESSURE*, WITHIN 10 YEAR AGE GROUPS, WITH MEANS, STANDARD DEVIATIONS AND 10TH, 50TH, 90TH PERCENTILES, MALES...... 144 TABLE 5.4: DISTRIBUTION OF DIASTOLIC BLOOD PRESSURE*, WITHIN 10 YEAR AGE GROUPS, WITH MEANS, STANDARD DEVIATIONS AND 10TH, 50TH, 90TH PERCENTILES, FEMALES ..145 TABLE 5.5: DISTRIBUTION OF SUBJECTS (EXCLUDING THOSE REPORTING A DIAGNOSIS OF DIABETES) BY BLOOD PRESSURE CATEGORIES, WITHIN 10-YEAR AGE GROUPS BY SEX.149 TABLE 5.6: FREQUENCY DISTRIBUTION OF POOLED VALUES OF THE TWO CONSECUTIVE READINGS OF SBP, FOR READINGS IN RANGE 138-172, BOTH SEXES COMBINED...... 155 TABLE 6.1: DISTRIBUTION OF STUDY POPULATION BY WHETHER BLOOD SAMPLE OBTAINED (‘FULL PARTICIPATION’)...... 167 TABLE 6.2: TOTAL SERUM CHOLESTEROL CONCENTRATION: DISTRIBUTION WITHIN AGE GROUPS, MALES...... 168 TABLE 6.3: TOTAL SERUM CHOLESTEROL CONCENTRATION: DISTRIBUTION WITHIN AGE GROUPS, FEMALES ...... 169 TABLE 6.4: HIGH DENSITY LIPOPROTEIN CHOLESTEROL CONCENTRATION (HDL): DISTRIBUTION WITHIN AGE GROUPS WITH MEANS, 10TH, 50TH AND 90TH CENTILES, MALES ...... 170 TABLE 6.5: HIGH DENSITY LIPOPROTEIN CHOLESTEROL CONCENTRATION (HDL): DISTRIBUTION WITHIN AGE GROUPS WITH MEANS, 10TH, 50TH AND 90TH CENTILES, FEMALES ...... 171 TABLE 6.6: RATIO TOTAL CHOLESTEROL/HIGH DENSITY LIPOPROTEIN CHOLESTEROL CONCENTRATION (HDL): DISTRIBUTION WITHIN AGE GROUPS WITH MEANS, 10TH, 50TH AND 90TH CENTILES, MALES...... 172 TABLE 6.7: RATIO TOTAL CHOLESTEROL/HIGH DENSITY LIPOPROTEIN CHOLESTEROL CONCENTRATION (HDL): DISTRIBUTION WITHIN AGE GROUPS WITH MEANS, 10TH, 50TH AND 90TH CENTILES, FEMALES...... 173 TABLE 6.8: TRIGLYCERIDES CONCENTRATION: DISTRIBUTION WITHIN AGE GROUPS WITH MEANS, 10TH, 50TH AND 90TH CENTILES, MALES...... 174 TABLE 6.9: TRIGLYCERIDES CONCENTRATION: DISTRIBUTION WITHIN AGE GROUPS WITH MEANS, 10TH, 50TH AND 90TH CENTILES, FEMALES ...... 175 TABLE 7.1: SMOKING, BY AGE AND SEX ...... 181 TABLE 7.2: CURRENT REGULAR CIGARETTE SMOKERS: AVERAGE REPORTED NUMBER OF CIGARETTES SMOKED PER DAY, BY AGE AND SEX ...... 183 TABLE 7.3: CURRENT CIGARETTE SMOKERS: AGE AT START SMOKING BY AGE AND SEX STRATA ...... 185 TABLE 7.4: CURRENT CIGARETTES SMOKERS: WHETHER INFORMANTS REPORTED HAVING TRIED TO CHANGE THEIR SMOKING BEHAVIOUR, BY AGE STRATA, MALES ...... 187 TABLE 7.5: CURRENT CIGARETTES SMOKERS: WHETHER INFORMANTS REPORTED HAVING TRIED TO CHANGE THEIR SMOKING BEHAVIOUR, BY AGE STRATA, FEMALES...... 188 TABLE 7.6: CURRENT CIGARETTES SMOKERS: MEAN DURATION OF SMOKING, BY AGE AND SEX ...... 189 TABLE 7.7: EX-SMOKERS: REASONS SHOWED BY INFORMANT FOR CHANGING THEIR SMOKING BEHAVIOUR BY AGE...... 191 TABLE 7.8: EX-SMOKERS: MAXIMUM NUMBER OF CIGARETTES EVER SMOKED PER DAY FOR AS LONG AS A YEAR...... 192 TABLE 7.9: EX-SMOKERS: YEARS OF SMOKING BY AGE AND SEX STRATA...... 193

TABLE 7.10: MEAN DURATION OF SMOKING FROM EX-SMOKERS BY AGE AND SEX...... 194 TABLE 7.11: PASSIVE SMOKING: REPORTED HOURS EXPOSED TO TOBACCO SMOKE BY AGE AND SEX STRATA ...... 196 TABLE 8.1: REPORTED SPECIAL DIET BY SEX AND AGE GROUPS...... 206 TABLE 8.2: REPORTED DIETARY HABITS OF SHS POPULATION: MEAT AND MEAT PRODUCTS, FISH, EGGS, MILK AND MILK PRODUCTS, MALES...... 208 TABLE 8.3: REPORTED DIETARY HABITS OF SHS POPULATION: MEAT AND MEAT PRODUCTS, FISH, EGGS, MILK AND MILK PRODUCTS, FEMALES...... 209 TABLE 8.4: REPORTED DIETARY HABITS OF SHS POPULATION BY SEX: PASTRY AND CORN FOODS ...... 210 TABLE 8.5: REPORTED DIETARY HABITS OF SHS POPULATION: FRUIT AND VEGETABLES, MALES...... 212 TABLE 8.6: REPORTED DIETARY HABITS OF SHS POPULATION: FRUIT AND VEGETABLES, FEMALES...... 213 TABLE 8.7: REPORTED SOFT DRINKS CONSUMPTION BY AGE GROUPS, MALES ...... 214 TABLE 8.8: REPORTED SOFT DRINKS CONSUMPTION BY AGE GROUPS, FEMALES...... 215 TABLE 8.9: REPORTED BREAD CONSUMPTION BY AGE GROUPS AND SEX ...... 217 TABLE 8.10: REPORTED COOKING FAT CONSUMPTION BY AGE GROUPS, MALES ...... 218 TABLE 8.11: REPORTED COOKING FAT CONSUMPTION BY AGE GROUPS, FEMALES...... 219 TABLE 8.12: REPORTED SALT USE BY SEX AND AGE GROUPS...... 220 TABLE 8.13: REPORTED DRINKING STATUS OF SHS POPULATION BY AGE AND SEX...... 223 TABLE 8.14: CURRENT DRINKERS: MEAN ALCOHOL CONSUMPTION BY TYPE OF ALCOHOL, AGE AND SEX ...... 224 TABLE 8.15: CURRENT DRINKERS: MEAN DAILY ALCOHOL CONSUMPTION BY FREQUENCY OF DRINKING WITHIN AGE - SEX STRATA ...... 225 TABLE 8.16: DISTRIBUTION OF DRINKING LEVEL WITHIN AGE GROUPS, MEN...... 227 TABLE 8.17: DISTRIBUTION OF DRINKING LEVEL WITHIN AGE GROUPS, WOMEN ...... 228 TABLE 8.18: REPORTED PHYSICAL ACTIVITY DURING PAID WORK BY SEX WITHIN AGE STRATA ...... 232 TABLE 8.19: PHYSICAL ACTIVITY IN LEISURE TIME BY SEX WITHIN AGE STRATA ...... 233 TABLE 8.20: “DO YOU PRACTISING ANY SPORT IN YOUR LEISURE TIME?” BY SEX WITHIN AGE STRATA ...... 234 TABLE 8.21: HOW MANY STREETS DO YOU CROSS DAILY? ...... 236 TABLE 8.22: REPORTED CHANGE IN THE LEVEL OF PHYSICAL ACTIVITY BY SEX WITHIN AGE STRATA ...... 237 TABLE 9.1: SYSTOLIC BLOOD PRESSURE LEVELS BY LEVEL OF EDUCATION WITHIN AGE AND SEX STRATA...... 240 TABLE 9.2: DIASTOLIC BLOOD PRESSURE LEVELS BY LEVEL OF EDUCATION WITHIN AGE AND SEX STRATA...... 241 TABLE 9.3: SERUM TOTAL CHOLESTEROL BY LEVEL OF EDUCATION WITHIN SEX AND AGE STRATA ...... 243 TABLE 9.4: BODY MASS INDEX BY LEVEL OF EDUCATION WITHIN AGE AND SEX STRATA ....244 TABLE 9.5: CIGARETTES SMOKING STATUS BY LEVEL OF EDUCATION WITHIN AGE AND SEX STRATA ...... 246 TABLE 9.6: ALCOHOL CONSUMPTION BY LEVEL OF EDUCATION WITHIN AGE AND SEX STRATA ...... 248 TABLE 9.7: PHYSICAL ACTIVITY DURING WORKING TIME BY LEVEL OF EDUCATION WITHIN AGE AND SEX STRATA...... 250 TABLE 9.8: PHYSICAL ACTIVITY DURING LEISURE TIME BY LEVEL OF EDUCATION WITHIN AGE AND SEX STRATA ...... 251 TABLE 9.9: SYSTOLIC BLOOD PRESSURE BY MARITAL STATUS WITHIN SEX AND AGE STRATA ...... 253

TABLE 9.10: SYSTOLIC BLOOD PRESSURE BY HOME OWNERSHIP AND PERSON PER ROOM WITHIN SEX AND AGE STRATA ...... 254 TABLE 9.11: SYSTOLIC BLOOD PRESSURE BY EMPLOYMENT STATUS WITHIN SEX AND AGE STRATA ...... 255 TABLE 9.12: DIASTOLIC BLOOD PRESSURE BY MARITAL STATUS WITHIN SEX AND AGE STRATA ...... 257 TABLE 9.13: DIASTOLIC BLOOD PRESSURE BY HOME OWNERSHIP AND PERSON PER ROOM WITHIN SEX AND AGE STRATA ...... 258 TABLE 9.14: DIASTOLIC BLOOD PRESSURE BY EMPLOYMENT STATUS WITHIN SEX AND AGE STRATA ...... 259 TABLE 9.15: TOTAL SERUM CHOLESTEROL BY MARITAL STATUS WITHIN SEX AND AGE STRATA ...... 261 TABLE 9.16: TOTAL SERUM CHOLESTEROL BY HOME OWNERSHIP AND PERSONS PER ROOM WITHIN SEX AND AGE STRATA ...... 262 TABLE 9.17: TOTAL SERUM CHOLESTEROL BY EMPLOYMENT STATUS WITHIN SEX AND AGE STRATA ...... 263 TABLE 9.18: BODY MASS INDEX BY MARITAL STATUS WITHIN SEX AND AGE STRATA ...... 265 TABLE 9.19: BODY MASS INDEX BY EMPLOYMENT STATUS WITHIN SEX AND AGE STRATA.266 TABLE 9.20: SMOKING BEHAVIOUR BY MARITAL STATUS WITHIN SEX AND AGE STRATA ....268 TABLE 9.21: SMOKING BEHAVIOUR BY EMPLOYMENT STATUS WITHIN SEX AND AGE STRATA ...... 269 TABLE 9.22: ALCOHOL CONSUMPTION LEVELS BY MARITAL STATUS WITHIN SEX AND AGE STRATA ...... 271 TABLE 9.23: ALCOHOL CONSUMPTION LEVELS BY EMPLOYMENT STATUS WITHIN SEX AND AGE STRATA ...... 272 TABLE 9.24: REGRESSION COEFFICIENTS AND 95% CI FOR ASSESSMENT OF ASSOCIATIONS OF SYSTOLIC BLOOD PRESSURE WITH SPECIFIC SOCIO-DEMOGRAPHIC CHARACTERISTICS SEPARATELY ...... 277 TABLE 9.25: REGRESSION COEFFICIENTS AND 95% CI FOR ASSESSMENT OF ASSOCIATIONS OF SYSTOLIC BLOOD PRESSURE WITH SPECIFIC SOCIO-DEMOGRAPHIC CHARACTERISTICS COMBINED...... 278 TABLE 9.26: REGRESSION COEFFICIENTS AND 95% CI FOR ASSESSMENT OF ASSOCIATIONS OF TOTAL SERUM CHOLESTEROL WITH OTHER RISK FACTORS AND SOCIO-DEMOGRAPHIC CHARACTERISTICS...... 281 TABLE 9.27: ODDS RATIOS AND 95% CI FOR ASSESSMENT OF ASSOCIATIONS OF SMOKING WITH SOCIO-DEMOGRAPHIC CHARACTERISTICS ADJUSTED FOR AGE AND SEX...... 283 TABLE 9.28: ODDS RATIOS AND 95% CI FOR ASSESSMENT OF ASSOCIATIONS OF SMOKING WITH SOCIO-DEMOGRAPHIC CHARACTERISTICS ADJUSTED FOR AGE AND SEX AND OTHER SIGNIFICANT SOCIO-DEMOGRAPHIC CHARACTERISTICS ...... 284 TABLE 10.1: PROPORTION WITH ‘HIGH’ LEVELS OF THE EACH OF FOUR CVD RISK FACTORS (BLOOD PRESSURE, CHOLESTEROL, CIGARETTE SMOKING STATUS AND BMI) BY AGE AND SEX...... 290 TABLE 10.2: PROPORTION WITH 'HIGH' LEVELS OF THE EACH OF THE FOUR MAIN CVD RISK FACTORS (BLOOD PRESSURE, CHOLESTEROL, CIGARETTE SMOKING STATUS AND BMI) BY AGE FOR ALL ADULTS ...... 291 TABLE 10.3: DISTRIBUTION OF 4 RISK FACTORS (HIGH BLOOD PRESSURE, HIGH CHOLESTEROL, SMOKING AND HIGH BMI) BY AGE GROUP AND SEX...... 294 TABLE 10.4: COMBINATION OF RISK FACTORS BY AGE AND SEX...... 295 TABLE 12.1: COMPLEX ASSESSMENT OF RISK FACTOR DISTRIBUTION MONICA BASELINE DATA AND SHS AGE 35-64, MEN...... 318 TABLE 12.2: COMPLEX ASSESSMENT OF RISK FACTOR DISTRIBUTION MONICA BASELINE DATA AND SHS AGE 35-64, WOMEN...... 319

FIGURES

FIGURE 1.1: BIRTH RATE, MORTALITY RATE AND NATURAL GROWTH PER 1000 POPULATION, BULGARIA 1925 – 1995 ...... 50 FIGURE 1.2: LIFE EXPECTANCY AT BIRTH, IN YEARS, FOR BULGARIA, UK, EU AVERAGE AND COUNTRIES FROM CENTRAL AND EASTERN EUROPE (CEE), EXCLUDING THE FORMER USSR, AVERAGE, 1970-1997...... 52 FIGURE 1.3: STANDARDISED DEATH CERTIFICATION RATES OF ISCHAEMIC HEART DISEASE, 0- 64 AGE, FOR BULGARIA, UK, EU AVERAGE AND CEE AVERAGE, 1970-1997 PER 100000 ...... 55 FIGURE 1.4: STANDARDISED DEATH CERTIFICATION RATES OF CEREBROVASCULAR DISEASES, 0-64 AGE, FOR BULGARIA, UK, EU AVERAGE AND CEE AVERAGE, 1970-1997 PER 100000...... 56 FIGURE 1.5: STANDARDISED DEATH CERTIFICATION RATES OF TRACHEAS, BRONCHUS AND LUNG CANCER, 0-64 AGE, FOR BULGARIA, UK, EU AVERAGE AND CEE AVERAGE, 1970- 1997 PER 100000 ...... 58 FIGURE 1.6: CONSUMPTION OF FRESH FRUIT AND VEGETABLES, BULGARIA, 1965-1994...... 67 FIGURE 1.7: SEASONAL CHANGES IN CONSUMPTION OF FRESH FRUITS AND VEGETABLES IN 1992, TOWNS AND VILLAGES, WINTER (DECEMBER-FEBRUARY), SPRING (MARCH-MAY), SUMMER (JUNE-AUGUST), AUTUMN (SEPTEMBER-NOVEMBER)...... 67 FIGURE 2.1: MAP OF THE DISTRICTS (OBSTINAE) OF SOFIA CITY REGION (OBLAST GRAD SOFIA) INCLUDED IN THE TARGET POPULATION, WITH THE 8 SAMPLED DISTRICTS SHOWN IN DARK OUTLINE...... 81 FIGURE 3.1: FLOWCHART OF RECRUITMENT PROCESS SHOWING NUMBERS ORIGINALLY SAMPLED, NUMBERS OF REPLACEMENT SUBJECTS AND FINAL PARTICIPATION...... 108 FIGURE 4.1: AGE STANDARDISED BMI: MONICA BASELINE DATA AND SHS, AGES 35-64, MEN...... 136 FIGURE 4.2: AGE STANDARDISED BMI: MONICA BASELINE DATA AND SHS AGE 35-64, WOMEN ...... 137 FIGURE 5.1: FREQUENCY DISTRIBUTION OF SYSTOLIC BLOOD PRESSURE (SBP)* BY SEX ....141 FIGURE 5.2: SYSTOLIC BLOOD PRESSURE*: VALUES FOR THE 10TH, 50TH, AND 90TH CENTILES BY AGE GROUP AND SEX ...... 142 FIGURE 5.3: FREQUENCY DISTRIBUTION OF DIASTOLIC BLOOD PRESSURE (DBP)* BY SEX ..146 FIGURE 5.4: DIASTOLIC BLOOD PRESSURE (DBP)* : VALUES FOR THE 10TH, 50TH, AND 90TH CENTILES BY AGE GROUP AND SEX ...... 147 FIGURE 5.5: PROPORTION REPORTING ANTIHYPERTENSIVE TREATMENT IN RELATION TO MEDIAN SYSTOLIC BLOOD PRESSURE, AGE-STANDARDISED VALUES FOR AGES 35-64, MONICA BASELINES SURVEYS AND SHS*, MALES ...... 157 FIGURE 5.6: PROPORTION REPORTING ANTIHYPERTENSIVE TREATMENT IN RELATION TO MEDIAN SYSTOLIC BLOOD PRESSURE, AGE-STANDARDISED VALUES FOR AGES 35-64, MONICA BASELINES SURVEYS AND SHS*, FEMALES...... 158 FIGURE 5.7: AGE-STANDARDISED MEDIAN SYSTOLIC BLOOD PRESSURE: MONICA BASELINE DATA AND SHS, AGES 35-64, MEN ...... 159 FIGURE 5.8: AGE-STANDARDISED MEDIAN SYSTOLIC BLOOD PRESSURE MONICA BASELINE DATA AND SHS AGE 35-64, WOMEN...... 160 FIGURE 5.9: AGE-STANDARDISED MEDIAN DIASTOLIC BLOOD PRESSURE MONICA BASELINE DATA AND SHS AGE 35-64, MEN...... 163 FIGURE 5.10: AGE-STANDARDISED MEDIAN DIASTOLIC BLOOD PRESSURE MONICA BASELINE DATA AND SHS AGE 35-64, WOMEN...... 164 FIGURE 6.1: AGE-STANDARDISED MEDIAN TOTAL CHOLESTEROL MONICA BASELINE DATA AND SHS AGE 35-64, MEN ...... 177

FIGURE 6.2: AGE-STANDARDISED MEDIAN TOTAL CHOLESTEROL MONICA BASELINE DATA AND SHS AGE 35-64, WOMEN ...... 178 FIGURE 7.1: PROPORTION OF CURRENT SMOKERS MONICA BASELINE DATA AND SHS AGE 35-64, MEN...... 199 FIGURE 7.2: PROPORTION OF CURRENT SMOKERS MONICA BASELINE DATA AND SHS AGE 35-64, WOMEN ...... 200 FIGURE 7.3: EVOLUTION OF CIGARETTE EPIDEMIC: % EVER SMOKING BY CENTRAL BIRTH YEAR, MALES, BULGARIA AND ENGLAND...... 203 FIGURE 7.4: EVOLUTION OF SMOKING EPIDEMIC: % EVER SMOKING BY CENTRAL BIRTH YEAR, FEMALES, BULGARIA AND ENGLAND ...... 204 FIGURE 8.1: CURRENT DRINKERS: RATIO OF ALCOHOL CONSUMED ON WEEKEND DRINKING DAY AND WORK DRINKING DAY...... 230

APPENDIXES

APPENDIX 14-1: THE 1992 CENSUS POPULATIONS OF THE OBSTINAE OF OBLAST GRAD SOFIA BY SEX...... 327 APPENDIX 14-2: PROTOCOL OF ETHICAL APPROVAL...... 328 APPENDIX 14-3: MEASUREMENT PROTOCOLS FOR BLOOD PRESSURE, WEIGHT, HEIGHT, WAIST, HIP AND BLOOD SAMPLE COLLECTION ...... 329 APPENDIX 14-4: PROTOCOLS FOR EXTERNAL QUALITY CONTROL RIQAS TESTS...... 334 APPENDIX 14-5: QUESTIONNAIRE ...... 335 APPENDIX 14-6: STUDY POPULATION BY SEX AND 5 YEARS AGE GROUP ...... 344 APPENDIX 14-7: DISTRIBUTION OF STUDY POPULATION BY MARITAL STATUS WITHIN TEN YEAR AGE GROUPS AND SEX...... 345 APPENDIX 14-8: DISTRIBUTION OF WEIGHT BY AGE AND SEX...... 346 APPENDIX 14-9: BODY MASS INDEX...... 347 APPENDIX 14-10: PROPORTION OVERWEIGHT OR OBESE (BMI > 25) BY AGE AND SEX ...... 348 APPENDIX 14-11: DISTRIBUTION OF SYSTOLIC BLOOD PRESSURE BY FIVE YEARS AGE GROUPS AND SEX ...... 349 APPENDIX 14-12: MEDIAN SYSTOLIC BLOOD PRESSURE FOR PERSONS AGED 35-64 OF AGE- STANDARDISED VALUES FOR SHS WITH PUBLISHED VALUES FOR MONICA POPULATIONS MEN ...... 350 APPENDIX 14-13: MEDIAN SYSTOLIC BLOOD PRESSURE FOR PERSONS AGED 35-64 OF AGE- STANDARDISED VALUES FOR SHS WITH PUBLISHED VALUES FOR MONICA POPULATIONS, WOMEN ...... 351 APPENDIX 14-14: SMOKING, BY AGE AND SEX ...... 352

Declaration of originality and word length

I hereby declare that my thesis entitled Risk Factors Surveillance for Coronary Heart Diseases in Sofia, Bulgaria is entirely my own work (except where it is indicated otherwise). This thesis is based on original research conducted in Sofia and called Sofia Heart Study (SHS).

This thesis contains less than 80,000 words.

Date ...... Signed ......

Acknowledgments

I would like to thank my supervisor Dr John Powles for all his help and encouragement over the last 6 years. I would also like to thank Professor Nick Day for his support provided.

Thanks also due to Professor Dr Chudomir Nachev and Associated Professor Dr Eli

Shipkovenska for their advice and encouragement through the whole period.

I would like to acknowledge the support of all the staff working on Sofia Heart Study.

I am grateful to Eng. Gencho Genchev who helps me a lot in numerous occasions.

As a part time candidate I especially valued the daily support of all my colleagues in the

Department of Social medicine and Public Health in Sofia.

Most importantly, I sincerely thank Marin, my husband, for his unfailing confidence in my ability to complete the task. I also enjoyed the sympathy and constant understanding of my child, Ivailo, who know well the demands of study.

I was supported by Institute of Public Health, Department of Community medicine and

Biostatistics Unit in Cambridge, UK, which made possible this work.

There is not room to mention all the people by name who have helped me, or to describe in detail the help that people gave me, over the past 6 years. This does not mean that I have forgotten the help I received.

List of relevant publications and presentations in International conferences

I. Publications

1. Georgieva L, Powles J et al. A Protective Effect Of Fruit And Vegetables In Coronary

Heart Disease: A Case Control Study From Sofia, Bulgaria. Proceedings of the first

Bulgarian international Symposium on Cardiovascular diseases and pediatric trauma,

Sofia, Bulgaria, September 11, 1995, 67-75

2. Shipkovenska E., Nachev Ch, Georgieva L, Powles J et al. Cardiovascular Risk Factors

In A Representative Sample Of Sofia, Bulgaria. Proceedings of the first Bulgarian international Symposium on Cardiovascular diseases and pediatric trauma, Sofia, Bulgaria,

September 11, 1995, 119-129

3. Georgieva L, Powles J, Ness A et al. Fruit and vegetables and ischaemic heart disease in eastern Europe: A hospital-based case control study in Sofia, Bulgaria. Central

European J. of Public Health, 1999; 7: 87-91.

4. Georgieva L. Epidemiological studies – types. New Public Health – part I,

Aquagraphics Ltd 1997; 1: 174.

5. Georgieva L. Descriptive epidemiological studies. New Public Health – part I,

Aquagraphics Ltd, 1997; 1: 175-186.

6. Grancharova G, Georgieva L, Velkova A. Epidemiological studies, implications in prophylactic activities. New Public Health – part I, Sofia, Aquagraphics Ltd 1997; 1: 193-

204.

7. Georgieva L, Glutnikova Z. Epidemiology and Public health. New Public Health – part I, Sofia, Aquagraphics Ltd 1997; 1: 204-209.

8. Georgieva L. Epidemiological aspects of chronic socially important diseases - coronary heart diseases. New Public Health – part II, Sofia, Aquagraphics Ltd 1997; 2:

188-193.

9. Georgieva L, Chambers J et al. My Health, My School - manual for health promotion in schools - edited by Georgieva L., Sofia, IK Alex Soft, 1997, p. 152

10. Shipkovenska E, Nachev Ch, Georgieva L, Vasilevski N. Methodological approaches for studying cardiovascular risk factors in Sofia. Social medicine 1994; 2: 25-27.

II. Presentations

1. Georgieva L. Risk Factors for Ischaemic Heart Disease. First International Symposium on Cardiovascular Prevention and Pediatric Traumatology, Sofia, September 11, 1995, 18.

2. Georgieva L, Salchev P. Health changes in Bulgaria during the period of transition.

Conference on “Health Development in Central and Eastern Europe after transition” - organized by World Bank, WHO, EC, Warsaw, Poland, 11-13 May, 1998, Poster presentation.

Summary

Introduction: Cardiovascular diseases place a heavy burden on the Bulgarian population.

Standardized death certification rates for cerebrovascular disease in Bulgaria in the age group 0-64 were about six times higher than in England in 1994. Rates in males rose to

1994. Prior to 1994 no risk factor surveys had been conducted to international standards for design, implementation and reporting.

Methods: An age and sex stratified sample of 3100 people (1550 males and 1550 females) aged 25 - 74 was drawn from the 1992 Census listing for Sofia. The participation ratios were 0.62 for males and 0.66 for females. Subjects were invited during 1994 to their local polyclinics where anthropometrical indices and blood pressure were measured, a blood sample drawn and questionnaire administered.

Results: Means and proportions for ages 35 – 64 standardised to the world standard population, were for males and females respectively: Systolic blood pressure – 132 mmHg and 129 mmHg, Diastolic blood pressure – 86 mmHg and 85 mmHg, Total serum cholesterol – 5.0 mmol/l and 5.0 mmol/l, Body Mass Index – 26.2 kg/m2 and 25.8 kg/m2, regular smokers - 36% and 29%. Smoking prevalence was much higher at younger than older ages and it was lower in those with least education.

Data quality was problematic especially for blood pressure. Biases were judged likely to make measured pressures higher then true pressures.

Conclusions

The risk factor findings offer little explanation for reported mortality trends, especially the very high stroke mortality. Cohort studies are needed to clarify the causes of the high incidence of stroke in Bulgaria. Repeated surveillance studies will be needed and for these, greater attention will need to be given to achieving and maintaining quality in data collection and especially for blood pressure.

SECTION I – INTRODUCTION AND METHODS

1. Chapter - Introduction

This chapter describes the background for this thesis and the main aims of the Survey.

Section 1.1 gives definition of terms used and summarized the knowledge about aetiology and major known risk factors for cardiovascular diseases

Section 1.2 gives international experience in risk factors surveillance

Section 1.3 describes current stage of knowledge about vascular diseases in Bulgaria

Section 1.4 states the needs for this Study in Bulgaria.

Section 1.5 gives the aims of the thesis

1.1 Epidemiology of cardiovascular diseases

1.1.1 Definitions of terms

In this thesis I shall use ‘cardiovascular diseases (CVD)’ as a convenient shorthand for atherosclerotic vascular diseases namely ischaemic heart disease, stroke and peripheral vascular disease. I shall not, for example, be intending to include valvular disease of the heart.

Atheroma of the arteries causes coronary, cerebral and peripheral arterial disease, collectively called cardiovascular diseases.

Atheroma involves the formation of plaques that thicken the walls of arteries of various sizes, owing mainly to the deposition of lipids and the formation of fibrous tissue. The patchy thickening results in narrowing of lumen of the arteries, often complicated by occlusive thrombosis, with consequent loss of blood supply, chronic ischaemia and the infarction of various organs and tissues.

One of the consequences of the atherosclerosis includes coronary heart disease (CHD).

The coronary arteries supplying the myocardium are often severely affected. Severe atheromatous narrowing, particularly of more than one major coronary artery, can result in angina pectoris and myocardial infarction.

Ischaemic heart disease (IHD) is defined by a joint International Society and Federation of Cardiology and World Health Organisation task force as “myocardial impairment due to an imbalance between coronary blood flow and myocardial requirements caused by changes in the coronary circulation”1. Ischaemia due to non-coronary disease such as aortic stenosis is thus excluded.

Another consequence is cerebrovascular disease, often called stroke. Stroke is defined by the World Health Organisation as “rapidly developed clinical signs of focal or global disturbance of cerebral function, lasting more than 24 hours or until death, with no apparent non vascular cause”2.

The three main classes of stroke are ischaemic stroke, intracerebral haemorrhage, and subarachnoid haemorrhage.

Not all strokes are a result of atheromatous disease. In the Western countries around 80% of strokes are caused by ischaemia. Some ischaemic strokes are a result of thrombosis in situ, whereas others are caused by emboli lodging in the cerebral circulation. Estimates of the proportion of ischaemic strokes due to emboli vary between 10 and 60%. These emboli may arise from the heart or the large arteries of the neck. The remaining 20% of strokes are due to haemorrhage, either intra cerebral or subarachnoid. Intracerebral haemorrhage is thought to result from bleeding due to rupture of damaged arteries or rupture of microaneurysms in the arteries3.

The term risk factor is used in various ways. The following distinctions among risk factors for CHD and stroke are worth making: inherent biological traits such as age and

20 sex that cannot themselves be altered; physiological characteristics that predict future occurrence of CHD and stroke (e.g., blood pressure, serum cholesterol concentration, fibrinogen, body mass index, blood sugar); behaviors (e.g., diet, smoking, alcohol consumption, oral contraceptive use) that maybe associated with CHD and stroke because of their links with characteristics such as blood pressure or serum cholesterol, or via other mechanisms; social characteristics such as social class or ethnic groups that mark out differences in rate of occurrence of disease without telling us reason, whether due to differences in behavior, to other social and cultural factors, or to genes; and environmental features that may be physical (e.g., temperature) psychosocial, or biological4.

1.1.2 Brief history of epidemiological investigation of IHD and stroke risk factors

Most of the research leading to identification of the major risk factors and elucidation of their role in the aetiology of the atherosclerotic diseases was performed in the nineteenth and twentieth centuries.

At the beginning of the Framingham study in 1948 application of an epidemiological approach to gain knowledge about the causes of cardiovascular disease was novel.

However this prospective epidemiological investigation has accumulated information on the incidence of cardiovascular disease, close to pathogenesis, the chain of circumstances leading to its occurrence. The Framingham study has successfully identified and documented several classes of contributors to cardiovascular disease. These include atherogenic personal attributes, living habits that promote these, signs of preclinical disease, and host susceptibility to these influences. The established atherogenic factors include the blood lipids, blood pressure, glucose intolerance and fibrinogen5.

A review published in 1981 presented prospective data from more than 65 cohorts in 23 countries, showing remarkable expansion of epidemiology research in this field. Most of

21 the studies have dealt with only three of the four established major risk factors, i.e. blood pressure, serum cholesterol and cigarette smoking, but not diet6.

Among the within-population prospective investigations reporting data on total cholesterol, blood pressure and smoking, one is of special value because of its enormous sample size of

361 662 men aged 35-57 years at baseline, who were screened by standardised methods for the Multiple Risk Factor Intervention Trial (MRFIT) 7.

1.1.3 Current stage of knowledge about risk factors for cardiovascular disease and measuring problems

1.1.3.1 Blood pressure

High blood pressure is a major modifiable risk factor for cardiovascular diseases through reduction in obesity, salt intake, anti-hypertensive medication8,9,10. The higher the blood pressure, the higher is the risk of both stroke and coronary events. There is no threshold at which the risk associated with high blood pressure begins; it increases as blood pressure rises11. People with high blood pressure have three to four times the risk of developing coronary heart disease and as much as seven times the risk of developing stroke as people with normal blood pressure. In absolute terms (although not necessarily in relative), high blood pressure is a much greater risk factor for cardiovascular diseases in elderly people than in younger people. Among those with mild hypertension the risk of major cardiovascular events ranges from less than 1% at the age of 25-34 years to more than 30% at the age 65-7412. The blood pressure tends to rise with age but the increase varies considerably between individuals, thus, the dividing line between normotension and hypertension is arbitrary. It is defined as the level of blood pressure above which intervention has been shown to reduce the risk of harmful health consequences13.

A combined analysis of nine major prospective observational studies of 420 000 individuals showed a graded relation between diastolic pressure and stroke and coronary

22 heart disease 11. Risk grows steadily as diastolic pressure level grows, even in the normal range. In a meta-analysis of drug treatment for hypertension, incidence of stroke increased by 46% and CHD by 29% for every 7.5 mmHg increase in diastolic blood pressure.

Importantly randomized trials have shown that reduction of raised blood pressure prevented stroke. An overview of 14 treatment trials in 37 000 hypertensive patients let to the conclusion that an average blood pressure reduction of 5.8 mmHg resulted in a 42% reduction in stroke incidence14.

Although diastolic component of blood pressure was emphasized in most trials, CHD and stroke risk is also clearly related to level of systolic pressure.

Illustrative findings for relative risks for CHD and stroke with baseline measurements of blood pressure on a single occasion are given in Table 1.1 and Table 1.2 respectively.

Measurement problems

It is well known that blood pressure varies within the individual all the time and this variability has implications for assessing risk of mortality for cardiovascular diseases.

Additionally measurements are subject to substantial random fluctuations (due partly to the measurements process and partly to some real but temporary deviations from an individual’s usual blood pressure).

When one is considering the contribution of blood pressure to individual risk, then it is the individuals’ usual BP ( rather than causal BP measured imprecisely in a single measurement), which should be of prime interest. However in the context of risk factor surveillance it is conventional only to measure BP on a single occasion (casual BP). This gives a reasonable estimate of average BP, but substantially exaggerates the dispersion of the underling usual BP. In the context of the surveillance there are two implications that need attention:

First, it is always important to be clear whether one is discussing the distribution of casual or usual BP.

Second, the conventional cut-points applied to surveillance data to classify and also measure the prevalence of hypertension, are generally derived from context where usual

BP is intended (e.g. clinical assessment). Such cut-points will exaggerate the prevalence of usual BP above the chosen cut-points e.g. about half of those with BP above 160/95 mmHg on single occasion will have usual levels below 11.

Table 1.1: Diastolic blood pressure (measured on a single occasion) and coronary heart disease, selected studies Diastolic blood Study RR∗ pressure

76 [mmHg] 0.4 84 [mmHg] 0.2 MRFIT screeners, M, age 91 [mmHg] 0.3 35-57 years, n=350 977 15 98 [mmHg] 0.7 105 [mmHg] 1.2

76 [mmHg] 0.4 Chicago Heart Association, 84 [mmHg] 0.8 M+W, age 35-64, n=22 777 91 [mmHg] 1.5 16 98 [mmHg] 2.0 105 [mmHg] 2.9

76 [mmHg] 0.7 84 [mmHg] 0.9 Whitehall, M, age 40-64, 91 [mmHg] 1.1 n=16372 17 98 [mmHg] 1.8 105 [mmHg] 2.6

76 [mmHg] 0.6 84 [mmHg] 0.9 Puerto Rico, M, age 45-64, 91 [mmHg] 1.5 n=8158 18 98 [mmHg] 1.6 105 [mmHg] 3.1

76 [mmHg] 0.7 84 [mmHg] 1.0 Honolulu, M, age 45-68, 91 [mmHg] 1.3 n=7317 19 98 [mmHg] 1.6 105 [mmHg] 2.5

76 [mmHg] 0.7 84 [mmHg] 0.8 LRC Prevalence, M+W, age 91 [mmHg] 1.8 25-84, n=4674 20 98 [mmHg] 2.2 105 [mmHg] 5.0

76 [mmHg] 0.5 84 [mmHg] 0.8 Framingham, M+W, age 91 [mmHg] 1.3 40-69, n=4641 21 98 [mmHg] 2.0 105 [mmHg] 2.1

76 [mmHg] 0.8 84 [mmHg] 0.9 Western Electric, M, age 91 [mmHg] 1.1 40-59, n=2025 22 98 [mmHg] 1.3 105 [mmHg] 1.8

76 [mmHg] 0.8 84 [mmHg] 1.0 People Gas, M, age 40-59, 91 [mmHg] 1.4 n=1402 23 98 [mmHg] 1.6 105 [mmHg] 2.0

∗ The data as they are given on a log scale. Disease risks in each category relative to risk in whole study population 11

Table 1.2: Diastolic blood pressure (measured on a single occasion) and stroke, selected studies Diastolic blood Study ∗ pressure RR

76 [mmHg] 0.5 84 [mmHg] 0.8 MRFIT screeners, M, age 91 [mmHg] 1.5 35-57 years, n=350 977 15 98 [mmHg] 2.3 105 [mmHg] 5.7

76 [mmHg] 0.5 Chicago Heart 84 [mmHg] 0.3 Association, M+W, age 91 [mmHg] 1.7 35-64, n=22 777 16 98 [mmHg] 2.5 105 [mmHg] 4.1

76 [mmHg] 0.3 84 [mmHg] 0.6 Whitehall, M, age 40-64, 91 [mmHg] 1.0 n=16372 17 98 [mmHg] 3.9 105 [mmHg] 3.8

76 [mmHg] 0.6 84 [mmHg] 1.2 Honolulu, M, age 45-68, 91 [mmHg] 1.2 n=7317 19 98 [mmHg] 2.6 105 [mmHg] 4.0

76 [mmHg] 0.4 84 [mmHg] 0.8 Framingham, M+W, age 91 [mmHg] 1.2 40-69, n=4641 21 98 [mmHg] 1.6 105 [mmHg] 4.5

76 [mmHg] 0.3 84 [mmHg] 0.8 Western Electric, M, age 91 [mmHg] 1.7 40-59, n=2025 22 98 [mmHg] 1.6 105 [mmHg] 2.6

76 [mmHg] 0.6 84 [mmHg] 0.9 People Gas, M, age 40- 91 [mmHg] 1.5 59, n=1402 23 98 [mmHg] 2.2 105 [mmHg] 2.0

∗ The data as they are given on a log scale. Disease risks in each category relative to risk in whole study population 11

1.1.3.2 Cholesterol

The strong positive relationship between cholesterol and coronary heart disease (CHD)

comes from several lines of epidemiological evidence:

• Between populations - Seven Countries Study24

• Migration study - Ni Hon San25

• Within a single population - British Regional Heart Study26, Framingham Heart Study27,

Honolulu28, Multiple Risk Factor Intervention Trail29,30.

The total cholesterol represents that contained in atherogenic particles (mainly LDL),

together with that in cardio protective particles (HDL). Reduced HDL-cholesterol is a very

strong risk factor for CHD.

HDL promotes the transport of extrahepatic cholesterol back to the liver for elimination.

This ‘reverse cholesterol transport’ may explain the strong association of a reduced HDL

with CHD, and a high HDL being cardioprotective. The predictive power of a reduced

HDL-cholesterol for CHD, independent of other risk factors, has been demonstrated in

several epidemiological studies involving different populations31,32.

The measurement of total cholesterol includes both atherogenic (LDL) and

cardioprotective (HDL) fractions which are independently related to the risk of CHD. A

ratio of total cholesterol/HDL-cholesterol is considered to be better indicator of CHD. In

epidemiological studies the risk of CHD has been shown to markedly increase when the

ratio is >4.533.

Serum triglyceride has an important role in the pathogenesis of atherosclerosis. The

association of serum triglyceride and CHD has been shown in unvaried analyses in the

Framingham Study, PROCAM and Helsinki Heart studies. There is also evidence that

combination of an elevated serum triglyceride with either a reduced HDL or elevated LDL

is strongly associated with CHD for men and women34.

The risk of coronary heart disease is substantially higher when high cholesterol levels are associated with other risk factors, such as high blood pressure, smoking and obesity14, 35.

The relation between serum cholesterol and total stroke incidence is not evident. Serum cholesterol concentration is strongly related to death from non-haemorrhagic stroke, but there was an inverse association between total cholesterol levels and risk of haemorrhagic stroke36. The Prospective Studies Collaboration meta-analysis of cohort studies found no relationship at all between serum cholesterol and total stroke37.

Relative reduction of stroke risk by 19% to 32% was associated with statin treatment, as well as in meta-analysis of statin monotherapy trials38,39,40. Furthermore, there appears to be considerable reduction of stroke incidence in coronary heart disease patients41.

Illustrative findings for relative risks for CHD and stroke with baseline measurements of cholesterol on a single occasion are given in Table 1.3 and Table 1.4 respectively.

Table 1.3: Cholesterol level (measured on a single occasion) and coronary heart disease, selected studies Study Risk factor Results Comments

Age Log. Reg. Coeff. MRFIT screenees (i.e. 35-39 0.0111 persons screened for Cholesterol 40-45 0.0084 6 years MRFIT), M, age 35-57 (mg/dl) 45-49 0.0078 follow-up years, n=325,384 42 50-54 0.0078 55-57 0.0068

Multivariate regression coefficients Framingham Study, Controlled Men Women Sample of for SBP, [mmol/l] 50-64y 65-79y 50-64y 65-79 Massachusetts town, glucose and Total cholesterol 0.67 0.27 2.51 0.98 M+W, age 30-62years, cigarette LDL cholesterol 0.66 0.25 2.19 1.13 n=520943 smoking HDL cholesterol -0.93 -0.18 -1.05 -1.11 Triglycerides 0.24 -0.18 0.48 -0.69

From Cox proportional hazard Honolulu Heart Study, [mg/dl] Multivariate relative risk models incl. Japanese-Americans in Total cholesterol 1.15 age, SBP, Hawaii, M, age 46-65, LDL cholesterol 1.12 BMI, n=7591, 5 years HDL cholesterol 0.64 cigarette follow-up44 Triglycerides 1.01 smoking, alcohol and history of diabetes

Men aged 40-54 With no Albany, New York, M, years previous age 38-55 years, Logistic regression coefficient Serum history of n=1,910; 33 stroke 0.0052 cholesterol heart deaths 45 (mg/dl) disease

Men aged 40-54 With no Western Electric, years previous Chicago, Employees Logistic regression coefficient Serum history of >2 years in company, 0.0035 cholesterol heart M46 (mg/dl) disease

Table 1.4: Cholesterol level (measured on a single occasion) and stroke, selected studies Study Risk factor Results Comments

Cholesterol Logistic regression MRFIT screening, M, (mg/dl) coefficient 5 years follow- age 35-57 years, 1* 0.0023 up n=325,384 47 2** 0.0021 * category 1 includes all patients in white racial group ** category 2 excludes those participants who reported previous hospitalization for heart attack or who were taking medication for diabetes

1.1.3.3 Anthropometrical measurements

Body mass index (BMI) has consistently been found to be associated with all-cause and cause-specific mortality rates48,49,50. In particular, increased BMI (≥ 28 kg/m2) is associated with increased mortality rates from cardiovascular disease51. The most extensive data on the relationship of BMI to blood pressure with other life-style factors considered are from the international cooperative INTERSALT study52,53. It was estimated from the

Framingham study data that for each 10% reduction in weight in men there should be a

20% reduction in CHD incidence, and for every 10% increase in weight a 30% increase in coronary incidence54. 20 years later data from British Regional Heart Study on 7100 middle-aged British men provided evidence to support the second statement but not the first55.

Abdominal adiposity, represented by an elevated waist/hip circumference ratio was found to be associated with elevated levels of several cardiovascular risk factors56,57,58 and has been suggested to be even better predictor of mortality than BMI59.

1.1.3.4 Smoking

Tobacco smoking is the largest single external and, therefore, avoidable cause of death from cardiovascular diseases and cancer. Most studies60,61 have demonstrated a dose- response effect, with the amount smoked and duration of regular smoking contributing to the increased risk of disease. About half of tobacco related excess deaths in smokers are due to cardiovascular diseases and two thirds of these to coronary heart disease. Regular cigarette smoking doubles the calculated risk of overall cardiovascular death62.

The combined effect of smoking with other risk factors, such as elevated blood pressure, elevated serum cholesterol level and physical inactivity, is known to increase in a multiplicative way, the risk of developing cardiovascular diseases.

It has been estimated that cigarettes are the cause of deaths of one in two of their persistent users, and that approximately half a billion people currently alive - 8% of the world's population - could eventually be killed by tobacco if current smoking patterns persist.

Despite this pandemic, tobacco consumption continues and is increasing in many countries especially in Southern and Eastern Europe63.

Illustrative findings for relative risks for CHD and stroke with smoking are given in Table

1.5 and Table 1.6 respectively.

Table 1.5: Cigarette smoking and coronary heart disease, selected studies Study Risk factor Results Comments

For number of 5 years follow- cigarettes smoked Logistic regression up MRFIT screening, per day coefficient Estimated for M, age 35-57 years, 1* 0.0222 fixed age, DBP n=325,384 47 2** 0.0250 and serum cholesterol

United Kingdom, OR Early 1990s cases 13926, responses to a Smokers/nonsmokers Age 30-59 Age 60-79 controls 32389, M postal 3.85 2.37 and F, age 30-7964 questionnaire

* category 1 includes all patients in white racial group ** category 2 excludes those participants who reported previous hospitalization for heart attack or who were taking medication for diabetes

Table 1.6: Cigarette smoking and stroke, selected studies

Study Risk factor Results Comments

For number of 5 years follow- cigarettes smoked Logistic regression up MRFIT screening, M, per day coefficient Estimated for age 35-57 years, 1* 0.0260 fixed age, DBP n=325,384 47 2** 0.0287 and serum cholesterol

14 countries Current longitudinal cross- smokers/non- RR=4 national analysis65 smokers

Case-control study, RR Subarachnoid M+W, age 35-64, Smokers/nonsmokers Men Women haemorrhagic cases=115, 3 4.7 stroke controls=158666

The Copenhagen City Heart Study, W, age Smokers/nonsmokers RR = 1.4 ≥35, n=706067 * category 1 includes all patients in white racial group ** category 2 excludes those participants who reported previous hospitalization for heart attack or who were taking medication for diabetes

1.1.3.5 Alcohol

There appears to be a negative association between moderate alcohol consumption and risk of coronary heart disease68, this protective effect can be achieved at a low consumption levels and can not be important for men under 35 years of age and premenopausal women.

In a 22 years follow-up of the Framingham study was reported that frequent drinkers were less likely to die of CHD than abstainers69. This protective affect of alcohol consumption for CHD was confirmed later from many ecological, cohort and case-control studies70, 71, 72.

Despite the consistency of the findings, some have argued that the association may be due, at least partly, to the use of reference group of non-drinkers, which may include heavy drinkers who deny their alcohol intake or people who have stopped because of illness73, 74.

Therefore E. Rimm et al examined prospectively, with control for diet and other risk factors the relation of alcohol consumption to risk of CHD and provide strong evidence for hypothesis that alcohol intake is inversely associated with CHD68.

Many studies have examined the relation between drinking and stroke. Most cohort studies suggested that drinkers have modestly elevated risk of total stroke compared to nondrinkers. Some studies reported evidence for U-shaped association between level of alcohol intake and total stroke with reduced risk for men reported ≤ 2 drinks per day and for women ≤ 1 drink per day 45, 75, 76. This association was independent of several potential confounders in the Framingham study and of 14 risk factors in the Nurses’ Health study.

Other studies77 found alcohol consumption to be associated with increased risk of stroke and high blood pressure. Of the studies that specifically addressed ischaemic stroke, one found an independent U-shaped association 76, others found no significant association78,79,80. By contrast most of the studies on haemorrhagic stroke found evidence for a positive dose – response association with alcohol intake 76, 78, 80, 81, 82, 83 and one reported for no significant association79.

Illustrative findings for relative risks for CHD and stroke with alcohol consumption are given in Table 1.7 and Table 1.8 respectively.

An estimated overall effect of alcohol consumption is an increase in total mortality84. The relationship appears to be J-shaped85.

Both alcohol consumption and alcohol related problems, although at a high level, are now stable or decreasing in a number or Western European countries86 and after an increase in the most countries of Central and Eastern Europe, there is evidence for declining in the second half of the 90s 87, 88.

Table 1.7: Alcohol consumption and coronary heart disease, selected studies Study Risk factor/category RR Comments

SD/day Men Women Framingham Study, 0 1.21 1.03 Controlled for Sample of <0.5 1.09 1.08 cigarettes/day, Massachusetts town, 0.6-1.0 1.07 0.70 SBP, age, M+W, age 30-62 1.1-1.6 0.91 0.74 relative weight, years, n=520970 1.7-3.3 0.70 0.77 lipoproteins 3.4+ 0.87 0.78

SD/day1 Drinkers/abstainers Controlled for Honolulu Heart Study, 0 1.00 age, SBP, Japanese-Americans <0.4 0.67 cholesterol, in Hawaii, M, age 46- 0.4-0.8 0.85 relative weight, 65, n=7591, 5 years 0.9-2.1 0.32 cigarettes/day, follow-up71 2.2-3.3 0.24 carbohydrates 3.4+ 0.52

SD/day Controlled for Whitehall Study, 0 2.1 age, smoking London, civil <0.7 1.0 habit, DBP, servants, M, age 40- 0.7-2.5 1.5 cholesterol, 64, n=1422 85 2.6+ employment status

Nondrinkers/drinkers Mortality in 13 Busselton, Australia Men Women years (%), M+W, age ≥40 years, Non-smokers 1.16 1.67 controlled for n=2,20972 Smokers 2.77 1.48 smoking

Western Electric, Higher Chicago, Employees Inverse trend up to 5 mortality in 6+ >2 years in company, drinks per day (not M46 significant)

Drinkers/nondrinkers Controlled for Puerto-Rico, Urban & Sudden CHD death 0.72 age, cigarette rural population Nonsudden CHD 1.3 smoking, sample, M, age 35-79, death exercise, n=915089 Nonfatal MI 0.7 urban/rural, income

Do not have Yugoslavia CVD, Daily drinkers more detailed 0.58-0.76 Bosnia & Croatia, M75 Less often information on alcohol dosage

1 SD – one standard drink is equivalent to 13.6 g absolute alcohol 2 p<0.05

Table 1.8: Alcohol consumption and stroke, selected studies Reference Study RR Comments category

Birmingham, Alabama, M+W, age 50-69 years Nondrinkers 1.0 No adjustment 10,876 persons, 87 Drinkers 1.7 strokes 81

California, Kaiser- Nondrinkers 1.0 Controlled for Permanente M+W, mean ≤ 2 dr/day3 1.1 age, sex, race, age 45 years, n=8,060; 3-5 dr/day 1.2 smoking 121 hospitalizations90, 91 ≥6 dr/day 1.3

Busselton, Australia M+W, age ≥40 years, Nondrinkers 1.0 No adjustment n=2,209; 72 stroke Drinkers 0.9 deaths72

Copenhagen, Denmark Not daily 1.0 M+W, age ≥35 years, drinkers No adjustment 1.3 n=13,088; 295 “strokes”92 Daily drinkers

Albany, New York, M, Nondrinkers 1.0 Controlled for age 38-55 years, n=1,910; <2 dr/day 0.3 sex 33 stroke deaths 45 ≥2 dr/day 0.9

Nondrinkers 1.0 USA, Nurses’ Health <0.1 dr/day 0.9 Study, W, age 34-59 Controlled for 0.1-0.4 dr/day 0.6 years, n=87,526; 120 age, sex 0.5-1.2 dr/day 0.6 strokes76 ≥1.3 dr/day 0.9

Nondrinkers 1.0 Japan, physicians, M, age Exdrinkers 2.3 Controlled for ≥25 years, n=5,135; 230 Occas. drinkers 1.1 age, sex, stroke deaths93 <3.6 dr/day 1.2 smoking ≥3.6 dr/day 1.7

Controlled for age, sex, 1-hr Nondrinkers 1.0 Honolulu, Hawaii, M, age glucose, <1 dr/day 1.2 45-69 years, n=7,895; smoking, BMI, 1-2.7 dr/day 1.3 290 strokes94 uric acid, HTN, >2.7 dr/day 1.5 cholesterol, haematocrit

3 one standard drink is equivalent average to 12 g ethanol

1.1.3.6 Physical activity

Physical activity is a complex behaviour, which is defined as "bodily movement accomplished by muscle power and energy expenditure"95. Physical inactivity has been associated with increase risk of coronary heart disease96,97, stroke, elevated blood pressure, and osteoporosis. Physical inactive people are twice as likely to develop cardiovascular diseases as physically active people. On the other hand physical activity has a well- documented protective effect. It can reduced the risk of coronary heart disease98, stroke, lower blood pressure99, improve the lipoprotein profile, that is, increase the level of HDL and decrease that of LDL100, improve the balance between energy intake and expenditure and promote weight loss and thus preventing obesity101.

Problems in measuring physical activity

Physical activity can be broadly divided to activity associated with paid work and other, non- work activity. Non-work or leisure time physical activity is commonly regarded as taking three main forms: sports, games and keep-fit exercises; getting about (walking), cycling, stair- climbing; home activities102. These areas of physical activity should be covered in the questionnaire. Since physical activity may show considerable variation from week to week, the chances for mis-classifying individuals will be reduced if data are collected over a longer period, but this requirement must be balanced against the increasing problems of accurate recall as the reference period is extended. Development work for the U.K. National Fitness

Survey indicated that four weeks was the longest period for which accurate information of the required details could be collected relying on respondents' memories and that this period providing a fairly stabile picture of individuals' current activity103. Physical activity tends to show a great deal of seasonal variation. Studies addressing the lifestyle factors should take this into account and the questionnaires should be modified and standardised accordingly.

1.1.3.7 Diet

Diet is believed to be a major factor in the aetiology of cardiovascular disease104, but there is still considerable scientific uncertainty about the relationship between specific dietary components and cardiovascular disease risk and epidemiological doubts about the adequacy of the classic diet-heart hypotheses105.

Many constituents of diet are associated with health risk, but it is their relative proportions that matter. Increased risk has been associated with high proportion of dietary fat and particularly certain saturated fats, low energy turnover and salt intake. Reduced risk has been associated with a high intake of antioxidants such as vitamin C and E106. The main uncertainty is not about the presence of protective constituents in plant foods, but about which are most important.

Problems in measuring diet

Diet evaluation is an important adjunct to anthropometric, clinic, and biochemical assessment.

It provides a description of the dietary background that may serve to explain observed chronic disease prevalence and can suggest appropriate interventions. It is difficult however, accurately to quantify dietary intake in the context of large scale surveys and to infer that dietary patterns obtained by assessment techniques are indicative of long- term dietary habits.

The main candidate methods are:

1. 24 hour recall

2. A diet diary (for return by mail after the survey)

3. Food frequency questionnaire

4. Question dietary practices in selected area

5. Biological markers

Advantages and disadvantages of each method are given in Table 1.9.

Random and systematic sources of error in measuring diet have been enumerated107, 108 and include errors due to the respondent, the interviewer, data coding and processing, and the

39 Table 1.9: The main candidate methods for dietary assessment in population health surveys Advantages Disadvantages Comments 24 hour recall Does not reselect foods for Does not characterise the usual diet of 30-40 min. to administrate + inclusion. individuals (due to day variability) labour intensive + post coding Need to conduct survey on all week Readily adjusted to Bulgarian days conditions Diet diary Does not reselect foods Needs careful explanation to Would need feasibility study Characterised current diet of respondents for Bulgarian populations individuals. Substantial proportion of incomplete returns or non-returns Food frequency Labour efficiency (eliminates Reselects foods needs to be developed Would need considerable questionnaire post-coding) and tested for local populations developmental work within Can be designed for self- Bulgaria completion Questions dietary Can be incorporated in main Needs prior developmental study to practices in questionnaires establish which practices best predict food selected areas and nutrient intakes of interest. Biological Measurement error may be Only evidence for limited number of Adaptable to Bulgarian markers reduced dietary constituents e.g. S. Ferritin, Plasma conditions Objective measures can be vit C, Urinary Na, K, Subcutaneous compared with literature adipose -carotene nutrient database. As far as the recall errors are of the greatest importance, it is important to understand how recall errors arise and how they can be controlled. Cognitive processes used in surveys requiring recall have been studied. Four main stages in the cognitive process have been delineated: 1) question comprehension, 2) information retrieval, 3) estimation/judgement, 4) response formulation109. If at first stage, the respondent does not understand the question, the question will either be skipped or a nonsensical answer provided.

At this and subsequent stages factors such as intelligence, education and personal ability may influence recall accuracy since the individual must be able to understand the question and know how to report consumption frequencies.

If the question has been understood, the second cognitive stage involves attempting to retrieve from memory the relevant information regarding whether a food item was eaten in the past.

Problems with interference are encountered at this stage. It occurs when previously or newly acquired knowledge inhibits the retrieval of the desired information from memory. As the time interval is increased over which diet should be recalled, interference will also increase110.

At the third stage when the respondent makes a judgement regarding the retrieved information if he can’t remember consuming a food item, two types of error may occur. The first type of omission may occur when food items actually eaten are not identified. The second type is errors of commission resulting when foods not eaten are identified as having been consumed.

The rate of omission depends, in part, upon the salience of the food item to the respondent.

Since more frequent events are generally more salient, the most frequently consumed items should be more adequately recalled. However, foods never consumed are salient items, and, therefore, are also recalled more accurately and reliably.

The fourth cognitive task is response formulation. Respondents may report what they believe they should eating rather than what they actually ate, and may even distort their responses unconsciously.

Having in mind these possibilities for bias it is important to make an attempt to improve the questionnaire design and interviewing techniques that would minimize problems occurring at

41 each stage of the cognitive process used in reporting past diet. Simple wording of the questions has been found to improve questionnaire and, thereby overall recall ability111.

Question comprehension may also be improved by adapting questionnaires to a form, which facilitates recall, for example, by structuring them by meals112. In particular, recent research has shown that dietary recall can be improved by using a so-called "cognitive interview" 113.

This type of interview begins by placing the subject in the same psychological environment for which diet is being recalled.

A recall calendar could be designed in order to assist subjects in recreating their life situation for the period of time of which they report their dietary habits. At the stage of deciding whether a food item was in fact consumed, the respondent make a judgment regarding their recalled food intake and estimates the frequency and the portion size of this consumption.

24-hour diet recall is a useful tool to measure the current diet. This has the advantages that recall bias is minimum. The 24 hr diet recall also provides a good measure of the intake i.e.; the actual portion size. A possible drawback seems to be the daily variability in diet; a diet recalled for a weekend or a special day may be very different from the usual diet. It is the usual diet that is conceptually relevant for individual assessment but day-to-day variability does not detract from the methods ability to characterise the diet of groups.

Dietary data may be analysed and reported as foods (frequency and quantity of consumption) or as nutrients (quantity consumed). Nutrient values may be obtained by chemical analysis or from national standard food tables. The most common method used in large-scale studies is the calculation of intake from standard food tables on the basis of data collected by an interview or from diaries. On the other hand as the food tables are the means of values obtain from chemical analyses, this method is particularly suitable for the processing of information on large number of individuals, especially when time, money and personal are limited. Food tables should be judged according to the nutrients of interest and the goals of the investigation.

They should be prepared and supplemented where necessary by chemical analyses of samples

42 of local foods and with data from commercial food processing forms and local recipes.

Furthermore chemical analyses are helpful in securing compatibility of data from studies of different populations.

1.1.3.8 New and candidate risk factors for CVD

In addition to the established risk factors for CVD, there is growing evidence that newly emerging factors, like apolipoproteins, hyperinsulinaemia, thrombogenic risk factors, hyperhomocysteinaemia114, inflammation115, vascular reactivity, ventilatory function116, dietary sodium and potassium117, saturated fatty acids, sex hormones118, associated with CVD should be regarded as candidate causal risk factors and investigated to clarify their contribution119.

1.2 Theory and practice of risk factor surveillance for vascular diseases

1.2.1 International experience in risk factor surveillance

International examples are given by the Health Survey for England 1991 and 1994, National

Health and Nutrition Examination Surveys (NHNES) in USA, Risk Factor Prevalence Study

(RFPS) in Australia, the WHO MONICA multi centre international collaborative project and the Dietary and Nutritional Survey of British Adults (DNSBA) in U.K. (The selection of surveys discussed has been partly influenced by access to their main reports).

1.2.1.1 WHO MONICA

The WHO MONICA Project is a multi centre international collaborative project coordinated by the World Health Organization. Its objectives have been to measure trends in cardiovascular mortality and morbidity and to assess the extend to which these trends are related to changes in risk factor levels and medical care, measured at the same time in different countries in defined communities. Thirty-nine collaborating centers from 26 countries of

Europe, North America, and the Western Pacific collaborate in this project, using a

43 standardised protocol and covering a population of approximately 10 million men and women aged 35 - 64.

One of the two main null hypotheses to be tested by the MONICA was:

Among the Reporting Units there is no relationship between 10 - year trends in serum

cholesterol, blood pressure, and cigarette consumption, and 10 - year trends in coronary heart

incidence rates. In order to test the hypotheses, four sets of data are required: event rates, case

fatality rates, medical care and population risk factors. Two risk factor surveys, at the

beginning and the end of event registration, were obligatory, and third in the middle of the

study period were strongly recommended. The time schedule of the MONICA Project was

the following. All full members of the project started event registration by 1 October 1984.

The registration lasted until the end of 1994.

Blood pressure was measured on the right arm, with the subject in a sitting position and after

a minimum of 5 minutes of rest. In ten collaborating centers random zero devices were used,

and simple mercury sphygmomanometers were used in the rest of the centers. To consecutive

observations of systolic blood pressure and diastolic blood pressure were recorded to the

nearest 2 mmHg. DBP was read at the beginning of Korotkoff phase V. The mean value of

two readings was used for the analysis.

A venous blood sample was drawn with the subject in a sitting position, with limited use of

tourniquet. It was recommended that total cholesterol should be determined on the day of

blood collection. If not, samples should be stores after centrifugation, at 4oC for up to 4 days.

The enzymatic method was recommended for cholesterol determination and used by 25

collaborating centers.

Height and body weight were measured with the subject in a standing position without shoes

and heavy outer garments.

Data on smoking were obtained through a standard questionnaire. The responders have been

classified into following categories: regular cigarette smokers, other current smokers, ex-

smokers and non-smokers. The number of cigarettes smoked per day per regular or

occasional cigarette smoker was used to characterize the current degree of exposure to

cigarette smoke.

The methodology developed by the WHO MONICA Project is being used more and more

inside and outside the project for a variety of purposes120,121,122.

1.2.1.2 The Health Survey for England

The 1991 Health Survey for England was commissioned by the Department of Health from

OPCS. It is the first in a new series of annual health surveys designed to monitor trends in the nation's health. The aims of the 1991 Health Survey included obtaining information on important aspects of health relevant to cardiovascular disease and nutrition.

The Survey attained a nationally representative sample of 3242 adults (aged 16 or over) living in private households in England. This was the first official survey of this type to include people aged 65 and over and provides important information on the health of elderly people.

Fieldwork was divided into two elements: at the first visit each adults was asked to participate in a health and socioeconomic interview, have measurements of their height, weight taken and agree to have a nurse visit them at home. At this visit, subjects were asked to give details of any prescribed medicines they were taking, and to have their blood pressure, demi-span, and waist and hip circumferences measured. Those aged 18 and over were also asked to provide a blood sample for analysis of total cholesterol, haemoglobin and ferritin. The sample was selected using a multi-stage random probability design. A total of 3242 adults completed a full interview123.

The 1994 Health Survey for England was the fourth in this series of surveys designed to monitor trends in the nation's health. It was commissioned by the Department of Health and carried out by the Joint Health Surveys Unit of Social and Community Planning

Research and of the Department of Epidemiology and Public health at University College,

London.

The principal focus of the 1994 Survey was on cardiovascular disease and associated risk factors. The aims were: to provide annual data about the nation's health; to estimate the proportion of the specified health conditions; to examine the prevalence of risk factors associated with those conditions; to examine differences between population subgroups; to assess the frequency with which combinations of risk factors occur; to monitor progress towards two Health of the Nation targets related to blood pressure and obesity. 15809 adults aged 16 and over were interviewed. Interviewing was carried out throughout the year in order to deal with seasonal variation. Interviewers not only asked questions but also made certain measurements. Their visit was followed by another visit, by a nurse, who made further measurements, including blood pressure measurements, and blood sample collection124.

1.2.1.3 The Dietary and Nutritional Survey of British Adults

The Dietary and Nutritional Survey of British Adults has been requested and commissioned jointly by the Ministry of Agriculture, Fisheries and Food and the Department of Health.

The main aim of the study has been to provide detailed information on the current dietary behaviour and nutritional status of the adult population living in private households in Great

Britain. Additional the survey has been intended to provide a database of the food, drink and nutrient intake of the population against which future changes in dietary behaviour could be measured, to provide anthropometric, haematological and biochemical measures of nutritional status which could also subsequently be used to monitor changes, to identify the characteristic of individuals at increased risk of coronary heart disease. A national representative sample of

2197 adults aged 16 to 64 was attained. Fieldwork has been distributed in four waves over a

12 - month period, to allow for seasonality. Interviewing has been carried out over the period

1986 to 1987. The survey comprised a short interview, a seven - day weighed dietary record

(for which a payment of £10 per subject was made to encourage participation), anthropometric

46 and blood pressure measurements, the taking and analysis of 20 ml sample of blood, and the analysis of a specimen of urine taken from a 24 - hour collection125.

1.2.1.4 USA the first National Health and Nutrition Examination Survey

In the USA the first National Health and Nutrition Examination Survey (NHANES I) program was conducted from 1971 - 75 on a sample of the U. S. population 1 - 74 years old, sponsored by the Department of Health and Human Services. The main aim of the study was measuring and monitoring the nutritional status and health of the U.S. population over time.

The four kinds of data collected to make this nutritional assessment were: dietary intake information, haematological and biochemical tests, body measurements, and clinical assessments. NHANES II examinations have been conducted from 1976 to 1980. The data from this latter survey provide the first look at changes in the health and nutritional status of the population over time. In Hispanic NHANES subjects, examinations have been conducted from 1982 to 1984126. NHANES III has recently been completed.

1.2.1.5 Risk Factor Prevalence Study in Australia

The Risk Factor Prevalence Studies in Australia were conducted in 1980, 1983, 1990 and

1993 with men and women aged 25 - 64 years from the State capital cities (no rural regions was included). One of the main purposes of the RFPS was to investigate whether there has been a favourable trend in the level of factors, which increase the risk of heart and blood vessel disease, such as cigarette smoking, high blood cholesterol and high blood pressure. The surveys provided data, which can be used in the planning and the monitoring of community prevention programs and research into the treatment of cardiovascular disease127.

1.3 Vascular diseases in Bulgaria.

1.3.1 Background

Bulgaria is situated on the Balkan Peninsula in Europe. The population of Bulgaria was

8,459,763 people, according to the last Census in 1992, of which 4,151,638 men and

4,308,125 women, with 67.6% urban, and 32.4% rural population128.

Sofia is the capital of Bulgaria situated in the west part of the country. The population of

Sofia - city is 1,188,563 with 95.7% urban and 4.3% rural. The region (oblast) Sofia city has 24 administrative obstini (districts). Six obstini (districts) are predominantly rural and

18 - urban.

Major socio-economic changes have taken place since 1989 in Bulgaria, related to the reorientation of the economics of the country from central planning to free market which has led to the liberalizing of prices and decreasing the real income of the population. The transition from a planned to a market economy has not been easy in Bulgaria. Macroeconomic performance has been worse than the average for Central and Eastern Europe. Output has dropped by more and inflation has been higher than other countries in the region. As a result, households have seen a sharper contraction in their standard of living. It is only in 1998 when output has begun to recover and, simultaneously, inflation has been controlled that there are prospects for reversing this decline in living standards. Promoting a recovery in living standards through continuing these positive macroeconomic trends, accompanied by structural reforms and programs to reduce poverty, is now one of the greatest challenges confronting the

Government. According to the analysis of the World Bank over 36% of the Bulgarian population is living in poverty, and the residents of Sofia city show higher than average poverty rates. Not surprisingly, poor people allocate a larger amount of their budget to food and consumed larger amounts of cheaper staple grain commodities129. Due to lack of health education directed towards chronic diseases prevention, there is no awareness among the general population of the importance of a healthy lifestyle.

1.3.2 Demographic and mortality trends in Bulgaria

There has been tendency of continuously decreasing trend of natural population growth in

Bulgaria from 18.2/1000 in 1925 to -5.0/1000 in 1995. This tendency resulted mainly from decrease of birth rate (from 39/1000 in 1925 to 8.6/1000 in 1995), upward trend in mortality (from 8.1/1000 in 1960 to 13.6/1000 in 1995) and migration changes (Figure

1.1). The proportion of aged persons (over 60 years of age) was rapidly growing from 19.8

% in 1990 to 22% in 1996.

Figure 1.1: Birth rate, mortality rate and natural growth per 1000 population, Bulgaria 1925 – 1995

45.0

40.0 35.0 Birth rate 30.0 Mor tality 25.0 Natural grow th 20.0

15.0

10.0

5.0

0.0 -5.01925 1930 1940 1950 1960 1970 1980 1985 1990 1992 1993 1994 1995 -10.0

Source: National Statistical Institute, Bulgaria130

The total unstandardised mortality increased from 7/1000 in 1970 to 10.7/1000 in 1995 in urban areas and from 11.4/1000 in 1970 to 19.9/1000 in 1995, in rural areas (Table 1.10).

In the last two decades significant decreases in adult male life expectancy of Bulgaria have been observed (from 69.13 years in 1970 to 67.22 years in 1994). For 1994 Bulgarian male life expectancy at birth was 67.22 compared with United Kingdom 74.18, EU average 74.01 and CEE average - 67.68 (Figure 1.2). Observed differences are due mainly to chances of eventually dying from vascular diseases. Differences for females are less pronounced in absolute terms, but greater in proportional terms131.

There has been a continuously increasing trend of reported mortality from chronic diseases in

Bulgaria from the 1960s to around 1990s132.

Cardiovascular diseases topped the list of crude mortality and their (not age standardised) rate increased from 612.3/1000 in 1980 to 766.6/1000 in 1990 and to 867.6/1000 in 1995 (Table

1.11)130.

Table 1.10: Trends in crude mortality rates* per 1000, in Bulgaria 1970 - 1995, total, urban and rural by sex

Year Total population Urban Rural male femal all male female all male female all e 1970 9.7 8.5 9.1 7.4 6.5 7 12.2 10.6 11.4 1980 12.1 10 11.1 9.1 7.3 8.2 17.1 14.4 15.7 1985 13.4 10.7 12 9.7 7.6 8.7 20.2 16.5 18.3 1990 13.9 11 12.5 10.7 8.2 9.4 20.3 16.8 18.6 1995 15.4 12 16.3 12.3 9.1 10.7 21.9 18.1 19.9

Source: National Statistical Institute, Bulgaria133 * Crude mortality rates were used even though not very informative, because age standardised or even age specific rates were not available.

Figure 1.2: Life expectancy at birth, in years, for Bulgaria, UK, EU average and countries from Central and Eastern Europe (CEE), excluding the former USSR, average, 1970-1997

060101 Life expectancy at birth, in years, males

75 Male 74

66 1970 1975 1980 1985 1990 1995 2000

Bulgaria CEE average United Kingdom EU average

060101 Life expectancy at birth, in years, females

81 80 79 Female 78 77 76 75 74 73 72 71 1970 1975 1980 1985 1990 1995 2000

Bulgaria CEE average United Kingdom EU average

Source: Health for All Data Base, European Region, WHO, Regional office for Europe, updated June 1999

Table 1.11: Trends for crude mortality by cause per 100 000 population, Bulgaria 1985-1995, both sexes combined Causes of death 1985 1990 1995 Infectious diseases 7.1 6.1 7.3 Cancer 164.9 173.6 192.0 Metabolic, Immunological, endocr. 17.3 21.2 27.2 and eating disorders Mental disorders 3.1 2.3 3.7 Blood diseases 0.8 0.7 1.2 Neurological diseases 6.0 6.4 8.0 Circulatory diseases 721.1 766.6 867.6 Respiratory system diseases 92.3 74.0 62.9 Gastrointestinal diseases 36.7 37.7 42.7 Genito-urinary tract diseases 20.9 19.8 16.3 Congenital malformations 6.5 5.8 4.6 Trauma and poisoning 63.5 63.9 65.8 Unclear conditions 52.1 61.8 59.9

Source: National Statistical Institute, Bulgaria 133

1.3.3 Mortality trends for chronic diseases in Bulgaria

The commonest certified cause of death in Bulgaria is cardiovascular diseases (CVD) of which ischaemic heart disease is the largest single component in males and stroke in females.

Figure 1.3 shows age standardised death certification rates of ischaemic heart diseases by sex in Bulgaria, United Kingdom, EU average and CEE average in the age groups 0 - 64), in 1970

- 1994. The age standardised rate of death certificated ischaemic heart disease in this age group for both sexes are about 2-fold higher in Bulgaria (100.14/100000 for men and

24.08/100000 for women in 1994) than in EU average (47.66/100000 for men and

10.42/100000 for women in 1994).

Figure 1.4 shows the change in age standardised death rates for cerebrovascular disease mortality aged 0 - 64, by sex between 1970 - 1994 in Bulgaria in comparison to United

Kingdom, EU average and CEE average. The Central and East European countries in general have shown an increase in the mortality rates (especially for men in Bulgaria from

45.96/100000 in 1970 to 78.41/100000 in 1994) compared to the Western European countries, which experienced a substantial decline (for men from 27.58/100000 in 1970 to 13.36/100000 in 1994). Age standardised death rates of cerebrovascular disease in Bulgaria in these age groups are about six times higher than in England and EU average.

Significant changes have been observed in the trends in cerebrovascular mortality.

The determinants of these trends in Bulgaria are not known.

Figure 1.3: Standardised death certification rates of ischaemic heart disease, 0-64 age, for Bulgaria, UK, EU average and CEE average, 1970-1997 per 100000

090201 SDR, ischaemic heart disease, 0-64/100000,mal

160 Male 140

120

100

40 1970 1975 1980 1985 1990 1995 2000

Bulgaria CEE average United Kingdom EU average

090201 SDR, ischaemic heart disease,0-64/100000,fem.

40 Female 35

10 1970 1975 1980 1985 1990 1995 2000

Bulgaria CEE average United Kingdom EU average

Source: Health for All Data Base, European Region, WHO, Regional office for Europe, updated June 1999

Figure 1.4: Standardised death certification rates of cerebrovascular diseases, 0-64 age, for Bulgaria, UK, EU average and CEE average, 1970-1997 per 100000

090301 SDR, cerebrovascular diseases,0-64/100000,mal

80 Male 70

10 1970 1975 1980 1985 1990 1995 2000

Bulgaria CEE average United Kingdom EU average

090301 SDR, cerebrovascular diseases,0-64/100000,fem

35 Female

5 1970 1975 1980 1985 1990 1995 2000

Bulgaria CEE average United Kingdom EU average

Source: Health for All Data Base, European Region, WHO, Regional office for Europe, updated June 1999

Figure 1.5 shows age standardised death certification rates of cancer of the trachea, bronchus and lung by sex in Bulgaria United Kingdom, EU average and CEE average in the age groups

0 - 64), in 1970 - 1994. The age-standardised death rates of the above cancers in this age group has been increasing only about half as rapidly (since1970) in Bulgarian males as in males in the Central and East European countries as a whole. Bulgaria reaching 41.89/100000 for men and 5.26/100000 for women in 1994. The rates are higher in Bulgaria than in EU average (29.87/100000) for men, but substantially lower in Bulgarian women than those in the

UK (12.43/100000), EU average (6.98/100000) and CEE average (8.10/100000) in 1994.

Aditionally, it should be noted that rates for males are still increasing in Bulgaria, whereas they have started to decline in the UK, EU and CEE.

These data are very important in interpreting the contribution of tobacco exposure to mortality trends in Bulgaria.

Figure 1.5: Standardised death certification rates of tracheas, bronchus and lung cancer, 0- 64 age, for Bulgaria, UK, EU average and CEE average, 1970-1997 per 100000

100201 SDR, trach/bronch/lung cancer,0-64/100000,mal

45 Male

20 1970 1975 1980 1985 1990 1995 2000

Bulgaria CEE average United Kingdom EU average

100201 SDR, trach/bronch/lung cancer,0-64/100000,fem

14 Female

4 1970 1975 1980 1985 1990 1995 2000

Bulgaria CEE average United Kingdom EU average Source: Health for All Data Base, European Region, WHO, Regional office for Europe, updated June 1999

1.3.4 Current knowledge about risk factors for cardiovascular diseases in

Bulgaria

1.3.4.1 Primary data sources

The scientific environment in Bulgaria was not suitable for public health research programmes directed towards chronic diseases in the last decades. However many small studies have been undertaken by various departments and organisations in the country. Most of their reports are not published in international refereed journals, but they contribute to the knowledge about the public health policies and health behavior of the community. Moreover they are the only source of such information.

No national system of repeated risk factor surveys has been instituted in Bulgaria. Several risk factor surveys have been conducted, but the credibility of their reported findings has not been established by adequate description of their methods (see below). Such sources as are available all have serious limitations. These are summarized in Table 1.13.

Table 1.13: Current state of reporting findings about risk factors for coronary heart diseases in Bulgaria. Written account located BG CINDI BG CINDI "Sofia1"* "Sofia2"* Case Nationwide Multistage giving study 1984* study1994* control nutrition Nationwide study for survey, Survey 1997* IHD 1994* 1997-98* Methods: Eligibility criteria for - + - - + + + inclusion Recruitment methods - + - - + + + Participation rate - + - - + + + Measurement protocols - + - - + + + Findings: Tabulation of age and sex + + - - + + + specific findings for categorical data Description of - + - - + + + distributions for continuous variables

- → No information located + → Information located * For identity and description of studies, see text.

60 i. Bulgarian CINDI Survey.

CINDI stands for ‘countrywide integrated non communicable disease intervention’ programme and was coordinated by the World Health Organization, European Office.

Bulgaria was included in this programme and as part of it, in 1984, 711,000 persons, from four adjacent regions - Gabrovo, Sliven, Veliko Turnovo and Stara Zagora were screened for chronic diseases risk factors. Information about smoking habits and practices related to salt consumption were obtained by interview. In addition height, weight and blood pressure were measured. No reports have been found of eligibility criteria, sampling procedure, recruitment methods and participation rates. However, it is common knowledge among informed persons in Bulgaria that measurement protocols were poorly developed and were not effectively implemented. The findings of this study have been reported in an unsystematic manner, and are still appearing.

In 1994 Bulgaria again participated in CINDI with 2363 persons from the age group 25-64 living in the region Veliko Tarnovo being surveyed. The methodology and findings of this study were recently published in Health promotion bulletin of the National Centre for Health

Knowledge134. ii. Sofia Risk Factor Study No1 is the name I shall employ to describe a study conducted during the period 1988 - 1990 by Ch. Nachev, E. Shipkovenska (Higher Medical Institute,

Clinic of Internal Diseases), F. Ribarova, S. Shishkov (National Centre of Hygiene, Ecology and Nutrition), on a sample of 1154 persons aged 15 - 64 years in the capital - Sofia135. The stated aims were: to measure the distribution of blood pressure, the frequency of arterial hypertension among the population of Sofia, to characterize the average daily food intake with special emphasis on lipids and proteins and to assess association between nutrition and hypertension. The investigators measured height and weight and performed electrocardiogram

(ECG), and physical examination on each participant. In addition they took blood samples to measure cholesterol. A food frequency questionnaire was included. So far, no written

61 description of the sampling methods or measurement protocols of this study has been located.

Graphical presentations have been prepared of some of the findings. iii. Sofia Risk Factor Study No2 is the name I shall employ to describe a study conducted by

Ch. Nachev and E. Shipkovenska from Higher Medical Institute, Clinic of Internal Diseases.

The study was carried out in 1992 on a sample of approximately 1000 persons aged 15 - 64.

Out of the total of 27 polyclinics in Sofia, the sample was chosen from 25. No written report has been found of sampling methods and participation rate. The aim of the study was to measure changes in risk factors through the transition period in order to compare them with mortality changes. Graphical presentations have been made of some of the findings. v. Case control study for risk factors for IHD was carried out in Central clinical hospital

Sofia in 1994. Cases (n=155) were patients aged 45 to 69, with confirmed diagnosis of IHD, admitted to the cardiology unit and controls (n=154) were patients admitted for minor elective surgery. Measurements were made of blood pressure, height and weight and a blood sample taken around 3 days after admission. Subjects were invited before discharge and asked about the frequency with which they consumed fresh foods, their smoking and drinking habits and physical activity. The detailed methodology and results were presented and published elsewhere136, 137. Although not a surveillance study, risk factor distribution in the controls could be suggestive of community levels.

The principal investigator of this study is the author of this thesis. vi. Multistage nationwide survey 1997. Multistage nationwide survey was conducted in

May 1997 and designed to be representative of the population of Bulgaria aged 18 years and older. A two-stage random sampling procedure was used. 1550 persons were interviewed face-to-face. The questionnaire collected data on a range of variables related to lifestyle, health status, household income, and socioeconomic status, use of health services and total expenditure of health care. The detailed methodology and results from

62 multivariate analysis of data on patterns of tobacco smoking and alcohol consumption and their relationship with sociodemographic factors were published elsewhere138,139. vii. Other studies. A few other reports have been located to health related behaviour of

Bulgarian population.

Dietary data. Apart from the above sources this comes from National food balance sheets and from diet surveys on specific populations.

The national household budget survey system was established in 1951 and the surveys have been conducted annually by the National Institute of Statistics since 1953. The surveys cover whole country, the urban and rural population and the major socio-economic strata. At present, 2508 households are selected randomly through a two-stage sampling procedure from

271 urban and 147 rural settlements. The quantities of purchased, home-produced or bartered foods, as well as foods consumed away from the household, are recorded all the year (National

Institute of Statistics, 1995b)140.

National food balance sheets are known to be prepared for Bulgaria but apparently have not been published regularly. They are based on the collecting reports of the FAO. Such sheets attempt to estimate the amount of food "disappearing" for human consumption. They provide very indirect and crude measure of consumption. They tend to overestimate actual consumption, because do not consider the amount of the food thrown away or used as a food for dogs, cats etc.

Petrova St. et al141 from National Centre of Hygiene and Medical Ecology, Department of

Nutrition, Sofia, reported to the Preparatory meeting on ICN for Central and East European

Countries 1-4 April 1992 (unpublished paper), a study conducted in 1991 on a rural population (360 persons from 3 villages) and an urban population (500 persons in Sofia). The main aim of this study, as stated, was to elicit self-reported changes in diet over the transition

63 period (1989 - 1991). Respondents were contacted by telephone. Sampling methods and participation rates have apparently not been reported.

Nation-wide nutrition surveys were conducted throughout Bulgaria in April – May 1997 and

March 1998 by the National Centre of Hygiene, Medical Ecology and Nutrition and GALLUP

- BBSS.

Sampling used a multistage random probability design with quotes for age-sex groups. The effective sample comprised 2757 respondents, demographically representative for the

Bulgarian population over 1 year of age. Respondents with urban residence were 69.1%; males were 49.5% within the studied sample. Pregnant women were not included and will be a subject of special survey.

Data were obtained by in-house face-to-face interviews. Information for major socio- economic characteristics of the responders' households was collected. Dietary data were obtained using 24-hour recall. All days of the week were proportionally included and in this way any day-of-the week effects on food and/or nutrient intakes were taken into account. The quantities of foods consumed were determined by description of foods and beverages consumed in household measures or average portion size.

1.3.4.2 Summary of findings

Table 1.14 presents a summary of the main findings about risk factors for CHD in Bulgaria

Table 1.14: Summary of main findings about risk factors for CHD in Bulgaria Study Study Blood pressure Cholesterol BMI Smoking Alcohol Comments population High prevalence of Hypertension: M+W, age 25- smoking in those SBP≥140 and BG CINDI Mean TSC = 5.28 64 years, Hypertension 21% - aged 20-30, - study 1984 mmol/l DBP≥90 [mmHg] n=711000 marked sex difference Mean BMI for M – Mean SBP for M - 27 kg/m2 and for M+W, age 25- TSC > 6.2 mmol/l Regular smokers Current drinkers: BG CINDI 134, W – 129; W – 26.8 kg/m2; 64 years, in 27.5% in M and 50% in M and 24% 90.5% in M and study 1994 DBP – M – 85, W BMI>25 kg/m2 in n=2363 23.1% in W in W 62.2% in W – 81 [mmHg] 66% of M and 56% of W M+W, age 15- Hypertension: Hypertension: "Sofia1", 1988- TSC > 6.7 mmol/l 64 years, Age 15-64 – - - - SBP≥160 and 1990 in 8.9% n=1154 21.2% DBP≥95 [mmHg] M+W, age 15- Hypertension: Hypertension: "Sofia2", 1992 64 years, Age 45-54 – 30%, - - - - SBP≥160 and n=1000 age 55-64 – 40% DBP≥95 [mmHg] M+W, age 45- Mean SBP for Case control Mean TSC for Mean BMI for 69 years, controls = 136.8, Current smokers - Current drinkers study for IHD controls 6.33 controls - 26.5 cases=155, DBP = 80.4 46.8% in controls controls: 59% 1994 mmol/l kg/m2 controls=154 [mmHg] 57% of M and Multistage Prevalence of 13.6% of W drink M+W, age ≥18 nationwide - - - smoking 38.4% in at least weekly; years, n=1550 survey 1997 M and 16.7% in W abstainers -32% of M and 65% of W

Diet data about Bulgaria

Data from National household budget survey tend to suggest for Bulgaria141:

• The consumption of meat and meat products tended to increase until 1990, when meat

consumption was 100 g/daily per capita and that of meat products was 49 g/capita/day. In the

next few years of the economic transition a significant reduction of total meat consumption

occurred - in 1994 it had reached 70 g/day/.

• Fish consumption is traditionally low. The average daily intake per capita has been around 7-

10 g since 1965.

• The consumption of milk and yogurt has decreased by 52.3% from 1990 to 1994 due to the

reduced availability and high prices.

• Total added fat consumption has increased slightly from 47 g/daily per capita 1975 to 54

g/daily per capita. The use of sunflower oil in cooking is traditionally high 37-40 g/daily per

capita during the past 20 years.

• There has been a trend for reduced consumption of fresh fruits and vegetables since the 1970s.

There are great seasonal changes in their availability reflected by their presence in the diet of

the Bulgarian population (Figures 1.6 and 1.7).

• Bread and bakery products are traditionally consumed in large quantities (over 400 g/daily per

capita) but especially high (496 g/daily per capita) in the critical year 1991.

Figure 1.6: Consumption of fresh fruit and vegetables, Bulgaria, 1965-1994

250

200

150

100

50 Consumtion (g/day/capita) 0 65 70 75 80 85 90 91 92 93 94 Years Fresh fruit Vegetables

Source: S. Petrova, Nutritional policy: Bulgaria 141.

Figure 1.7: Seasonal changes in consumption of fresh fruits and vegetables in 1992, towns and villages, winter (December-February), spring (March-May), summer (June-August), autumn (September-November).

140 120 100 wtr 80 spr 60 sm 40 atm 20 Consumtion (g/day/capita) 0 Town Village Town Village

Fresh fruit Vegetables

Source: S. Petrova, Nutritional policy: Bulgaria 141.

• There has been a tendency to lower the consumption of sugar, sweets and pastry with the

onset of economic difficulties.

• The alcohol consumption was moderate during the 1960s - 1980s; in 1985 to 1989 the

apparent annual intake of alcoholic beverages was in the range of 4.9 - 5.5 litres per capita

(expressed as ethanol). The consumption of all alcoholic beverages has shown a tendency to

decrease (3.7 l per capita) to 1994.

• The reported average salt intake is 20 g/day/person.

In survey of 500 persons in Sofia during 1991 Stefka Petrova et al141 found that 75% of the

investigated people reported constricted food expenses, with the consumption of milk and

dairy products, meat and meat products, fresh fruits and vegetables restricted, while the intake

of bread and legumes had increased. The survey on the "rural" population described as "360

person from 3 villages" showed no changes in nutrition habits in comparison with the period

before 1991.

1.3.5 Shortcomings in available risk factor evidence for Bulgaria

Risk factor studies conducted so far in Bulgaria suffer from the following limitations:

1.3.5.1 Study populations

None of the studies carried out before 1994 reported any of the following: clear definition of

eligible source population, sampling frame employed, methods of sampling; recruitment

procedures and participation rates. Hence the representativeness of the study populations is

uncertain.

1.3.5.2 Data collection

For data obtained by interview, the methods of interview and data collection are important

elements in a surveillance study. They could be sources of random and systematic errors due

to the interviewer, the respondent, data coding and processing. Most of the studies conducted

in Bulgaria suffer from lack of detailed description of these procedures. For data obtained by

68 measurement (anthropometric data, blood pressure, blood constituents) measurement protocols have typically not been reported and nor have the results of the quality control procedures.

1.3.5.3 Interpretation of results

The inadequacies explained in the above two sections raise serious questions about the validity and generalisability of the results coming from these studies.

1.3.6 How Sofia differs from the rest of Bulgaria

It is important to what extent the conclusions drawn from SHS are going to be valid for the whole of Bulgaria. The differences in various characteristics of Sofia and the rest of Bulgaria can affect the generalisability of the results.

In 1994 Sofia had a population of 1,188,563, which was about 14% of the population of

Bulgaria (8,443,600). The male population in the city of Sofia was 48.5%, while the proportion of male population for whole country was 49.0%.

Age structure of the population of Sofia had the same tendency of a higher proportion of people in older age groups and a lower proportion of population in the younger age groups, as in the rest of Bulgaria. For example, in the age group over 60 the proportion is 19.2% for Sofia and 20.5% for the whole country128.

The structure of the population according to the marital status in the city of Sofia was close to that of Bulgaria. The biggest difference was in the proportion of married people, which was with 4% lower in Sofia (50.9%) than the whole country (54.9%)128.

At the end of 1994, 62.6% of the population of Sofia had eleven or more years of education, while for the whole country this proportion was 41.3%128. The observed difference is due mainly to migration process and the stable tendency of aging of the rural population. Another important reason is the fact that Sofia as the administrative and political center of Bulgaria attracts people with high levels of education.

The proportion of unemployed people in Sofia was 5.3%, while for Bulgaria this percentage was 12.8 in 1994.

The supply with health care in Sofia is better than in the rest of the country. In Sofia there are

24 medical doctors per 10000 people, while in the whole country there are 22/10000. In primary health care the difference is even bigger, 9.6 physicians per 10000 population for

Sofia and 5.1/10000 for Bulgaria. Additionally, there is higher concentration of specialists in

Sofia than in the rest of the country.

The crude mortality rate from cardiovascular diseases is more than two-fold higher in rural

(1351.0/100000), than in urban areas (638.4/100000) of Bulgaria. As there are no data available about age standardised rates one does not know how much of the difference is an artifact of age structure. Additionally, insufficiency of specialists in rural areas could reflect in incorrect registration of unclear conditions as CVD mortality. Presumably however, the age standardised mortality rates of vascular diseases in Sofia are lower, perhaps moderately lower than the national rates.

We expect that Sofia differs from Bulgaria in a number of behavioral factors like alcohol consumption, smoking habits, diet, stress etc. Unfortunately there are only very limited data available about the distribution of those factors among Bulgarian population and they are not always comparable, because of different methodology used, age groups, lack of standardisation etc. The data available are given in Table 1.14.

1.4 Need for Cardiovascular risk factor surveillance in Bulgaria

Bulgaria and other East European countries are undergoing major social, political and economic changes. The predominantly secluded society is fast moving towards a western lifestyle. We are now in the transition period. Like all facets of life, the health sector is also undergoing major changes. Bulgaria has one of the highest reported stroke mortality rates in the world. Colon cancer and breast cancer rates are on the increase.

Given the social and financial situation in the country, epidemiological studies will face resource limitations and logistic problems. The present financial constraints in Bulgaria cause difficulties in setting up surveillance programmes. Surveillance programmes are labour intensive (not such a major problem) but also needs a substantial financial input for equipment and biochemistry.

The cost of conducting surveillance must be compared with the cost of not doing it. The latter costs include reduced productive life due to disease that can be more readily prevented when the distribution of their causes in the population has been measured. In the absence of a sense of how health determinants are distributed in the population, substantial amounts of money can be spent on relatively inefficient preventive measures - e.g. drug treatment of hypertension.

Conclusions

1. Cardiovascular diseases have emerged as the major public health problem in Bulgaria.

Primary prevention of risk factors seems to be the most powerful strategy to control this

epidemic. The role of risk factor surveillance is paramount in this context. Prevention

strategies will be most effective when based on valid estimates of the magnitude of the

problem and the distribution of the risk factors in the community. A National Health Survey

with special attention on CVD risk factors can give rise to data, which are essential for the

planning and implementation of control programmes. Unless this is undertaken at the earliest

it may be too late to control the epidemic.

2. Primary prevention i.e., preventing the disease-causing exposures, is in principle, the

best strategy for controlling cardiovascular diseases. The well-known risk factors - smoking,

raised blood pressure and hypercholesterolaemia can be changed to a certain extend by dietary

and life style modification based on research findings.

3. Limitations of a "high risk" strategy for the prevention of cardiovascular disease have

been well demonstrated: For example many strokes occur in persons who do not meet the

criteria for arterial hypertension. Even with active programmes to treat hypertension and other

risk factors it is unlikely that the CVD epidemic will be controlled. There is some evidence

that effective control requires shifting the whole population distribution in a favourable

direction10.

4. The World Bank's development report for 1993 has provided policy guidelines for the

rational utilization of resources. In the health sector it envisages the redistribution of financial

outlay form tertiary care into prevention and primary care in the community142. The bank has

emphasized the need for re-orientation towards chronic disease prevention in Bulgaria143.

Bulgaria can import knowledge about how risk factors are related to disease, but cannot

import information about the population distribution of risk factors in Bulgaria. Hence the

72 importance of achieving a better understanding of the prevalence of known risk factors for

CVD in Bulgaria.

Furthermore, there are features of the pattern of the cardiovascular mortality in Bulgaria – notably the very high mortality from stroke, which remain poorly explained. National surveillance data can provides the context for focussed investigations into aetiological questions requiring local answers.

1.5 The aims of the thesis

Against this background the aims of this thesis (and of the study to which it relates) are:

1. Measure the distribution of the principal cardiovascular risk factors in a representative

sample of the Sofia population in a way that permits comparison with data published for

representative samples in other countries (e.g. MONICA).

2. To explore associations between environmental exposures, behavioural factors and

intermediate outcomes (such as blood pressure, blood cholesterol concentration etc.).

3. To assess the frequency with which particular combinations of risk factors are found

and in which groups these combinations most commonly occur

4. To make external comparisons – for example with the MONICA data from other

countries

5. To consider implications for the further development of risk factor surveillance within

Bulgaria in the light of both the methodological and substantive aspects.

6. To make a preliminary assessment of the relationships between risk factor

distributions and the level of mortality attributed to IHD and stroke and to specify the most

important aetiological questions required from further local investigations.

7. To provide data, which can be used in the planning and monitoring of public health

programmes directed towards Bulgarians’ most serious public health problem.

8. To identify more specifically those aspects of the cardiovascular disease profile in

Sofia which remain poorly explained by the major risk factors and to identify questions

73 that need to be investigated further in order to better understand why these diseases are so common in Bulgaria.

2. Chapter - Methods

Section 2.1. of this chapter summarizes the objectives of the study and describes the considerations of study design, funding, management structure and organization, the statistical power and selection of target population.

Section 2.2. describes the management of primary data collection. Selection and staffing of survey centers, recruitment and subject flow within survey centers, and ethical clearance.

Section 2.3. describes the protocols for the anthropometrical measurements and for the measurement of blood pressure and venepuncture and quality control of the data collection.

Section 2.4. describes the questionnaire and coding.

Section 2.5. describes the procedure for data entry and presentation and data analyses.

Procedures for quality control are discussed in sections 2, 3 and 4.

2.1 Study design and implementation: issues and choices

2.1.1 Origin, funding and organization of parent study

2.1.1.1 Original ideas planned

The need for epidemiological investigation of epidemic of cardiovascular diseases in

Bulgaria was recognized by the researchers in the Department of Internal Diseases,

Medical University, Sofia.

In 1992 - 1993 when the Bulgarian Heart Study (BHS) was planned by the organizational committee (see below), originally there were five main parts that were considered:

1. Surveillance of routine heath and demographic data for City of Sofia Region (Oblast

Grad Sofia).

2. Surveillance of risk factors of a representative sample from City of Sofia Region

(Oblast Grad Sofia).

3. Register and follow up of cases with cardiovascular diseases.

4. To conduct an intervention program for cardiovascular risk factors

5. To follow up a representative sample for changes in risk behavior in each 5 years.

2.1.1.2 Further refinement of proposal

After further considerations the purposes were refined as follows:

1. To determine the prevalence of cardiovascular diseases risk factors in a representative sample of the population in Sofia.

2. To assess the association of risk factors with ethnicity.

3. To assess the frequency with which particular combinations of risk factors are found, and in which groups they predominate.

4. To provide data that could be used in the planning and monitoring of public health programmes directed towards cardiovascular diseases.

5. To provide baseline data on which base to prepare an intervention program in a randomized groups of the population.

6. Follow up of the changes by caring out surveillance studies on each five years intervals in: smoking behavior, dietary habits, alcohol consumption, physical activity, serum cholesterol concentrations, HDL, triglyceride, blood pressure

7. Follow up of the changes in mortality rates of cardiovascular diseases over 10 years144 as indicated by the vital statistics system.

The changes in political and economical situation in Bulgaria made impossible some parts of the initial proposal. An intervention could not be financed, and investigation of risk factor differences by ethnic group was not politically acceptable.

In 1993 a grant application was put by Department of Internal Diseases, Medical

University, Sofia to Medical Services Corporation International (MSCI) (address: 1716

Wilson Boulevard, Arlington, Virginia 22209, USA, represented in Bulgaria: 149 Georgi

S. Rakovski St., 4th floor, No 10, Sofia) for financing the second component of the original

BHS proposal - ‘Screening and risk factor surveillance of a representative sample from

Oblast Grad Sofia’ (Grant No EUR-0037-G-00-1083-00). The Project was funded and the fieldwork was finally undertaken during 1994 (January to December). This part of the overall proposal was named Sofia Heart Study 1994 (SHS).

This thesis is based on the data collected as part of Sofia Heart Study.

2.1.1.3 Management structure of SHS

The management committee of SHS was based in the Department of Internal Diseases in

Medical University, Sofia with the following members;

Prof. Ch. Nachev (Chairman), Assoc. Prof. E. Shipkovenska (Director of the study),

Assoc. Prof. F. Ribarova, Dr. N. Vasilevski, Dr. L. Georgieva and Assoc. Prof. M. Vukov.

External advisors: Prof. N. Day and Dr. J. Powles - University of Cambridge, UK.

Management team: Prof. E. Shipkovenska, Assoc. Prof. F. Ribarova, Dr. N. Vasilevski, Dr.

L. Georgieva, Dr. T. Tulevski, Dr. A. Gudev

The author of this thesis was a member of the management team with the following responsibilities:

1. Involved in writing up Project application

2. Design and developing the questionnaire about;

• basic demographic characteristics

• socioeconomic characteristics

• smoking behavior

• alcohol consumption

• diet

• physical activities

• health knowledge

3. Developed the protocols for blood pressure, blood sampling, height, weight, waist and hip measurements.

4. Involved in organization, and coordination of data collection

5. Developed the model for data entering in the statistical program SPSS

6. Involved in data entering

7. Involved in data analyses

8. Translation of all documents (protocols, questionnaire etc.) from Bulgarian into

English and vice versa.

9. Developed and self-published the booklet with all basic documents about the study.

A broad decision was taken by the management team to follow MONICA protocols (where it is possible), adapted for Bulgaria145.

2.1.2 Statistical power and sample size

For reasons of efficiency, an age and sex stratified sampling procedure was chosen.

The target number of participating subjects in each age/sex cell was chosen after examination of the expected levels of precision given the standard deviations of the main risk factors for the 55 to 64 year age stratum in the Hungarian MONICA cohorts (Table

2.1).

Table 2.1: Estimates of precision (standard error and 95% confidence interval) for the major risk factor estimates for target cell sizes of 50, 100 and 200 subjects using standard deviations from the Hungarian MONICA cohorts for age 55-64146.

SD (from For n=50 For n=100 For n=200 Hungary Monica) for age 55-64 SE 95%CI SE 95%CI SE 95%CI (+ or -) (+ or -) (+ or -) Total cholesterol 1.20 0.17 0.34 0.12 0.24 0.09 0.17 (mmol/l) Systolic BP 21.00 3.00 5.88 2.11 4.14 1.49 2.92 (mmHg) Diastolic BP 12.00 1.71 3.36 1.21 2.36 0.85 1.67 (mmHg) Smoking 0.07 0.14 0.05 0.10 0.03 0.07 (prevalence of 0.4) After reviewing the above data a target size of 200 subjects per cell was adopted. To achieve this number of participants, a target sample size for invitees of 310 was calculated assuming a 65% participation rate – based on experience in other MONICA cohorts147 and local knowledge.

2.1.3 Selection of target population

2.1.3.1 Definition of target population

The target population was restricted to Bulgarian citizens, aged 25 to 74 years with usual residence within selected administrative districts of Sofia. Students, non-citizens and other temporary residents were not eligible.

The national capital, Sofia, was chosen as the target population for reasons of logistical convenience. (It was appreciated that death rates for Sofia are below the rational average, so the risk factor estimate were expected to be more favorable than the national average.)

The region (oblast) of Sofia City is comprised of 24 administrative districts (obstinae). Six of these (Iskar, Witosha, Nowi Iskar, Kremikowci, Pancharevo, Bankja) with a 1992 census population of 207,416 include substantial rural areas and were excluded from the target population for this reason.

The target population thus comprised 18 of 24 administrative districts of Sofia (Sredec

Krasno selo, Vazragdane, Oboriste, Serdika, Podujane, Slatina, Lozenec, Izgrev, Triadica,

Krasna poljana, Ilinden, Nadegda, Mladost, Studentska, Ovcha Kupel, Lulin, Wrabnitca) with 82.6% of the population of City of Sofia Region (Oblast Grad Sofia)148

The 1992 census populations of the obstinae are given in Appendix14.1.

2.1.3.2 Selection of administrative districts (obstinae)

To reduce the number of survey centers to a more convenient number a table of random numbers was used to select 8 of the 18 administrative districts in the target population.

Those selected were Lulin, Serdica, Vazragdane, Oboriste, Sredec, Triadica, Ovcha Kupel, and Mladost. These and all the obstinae of which they are a chosen sample are shown on the map (Figure 2.1).

Figure 2.1: Map of the districts (obstinae) of Sofia City Region (Oblast Grad Sofia) included in the target population, with the 8 sampled districts shown in dark outline.

2.1.3.3 Sampling of subjects

Although official population registries exist it was considered unlikely that population lists could be conveniently obtained from that source under prevailing administrative conditions.

Gallup International was therefore approached. Gallup maintains it’s own population registry based on the Census listing from 1992, updated from Local Governmental

Registers. As Gallup International is a commercial company which is doing most of the everyday sociological surveys in Bulgaria it keeps on of the most up to date population register. This fact is very important for achieving a higher response rate for the study. Both the official registries of residents and Gallup use the ‘ESGROUN’ system for personal Ids.

This includes date, month and year of birth, as well as personal identification number and addresses.

Gallup International were asked to:

1. Select from their data base all residents of the 8 sampled districts.

2. Draw a random sample of 310 males and 310 females in each of the five 10-years age groups in the age range 25-74.

3. Provide a machine-readable file and hard copy lists of the samples drawn, including name, address and ESGROUN of the subjects selected sorted by district, age and sex.

Each survey center was able to work with the list for its district, and all lists were available in the main survey center - Clinic of Internal diseases, Medical University.

In order not to under-represent the more mobile part of the population, it was decided that in cases where sampled subjects were no longer resident as listed another subject of the same sex and age-group was to be invited from the same residential block - selected according to a fixed rule (first next door neighbour of the same sex-age group).

2.1.4 Ethical clearance

Ethical approval for the survey as a whole was obtained from the Medical Ethics

Committee in Bulgaria [Address: 1 “Georgy Sofiisky” St, Sofia; Chairperson Prof. Ch.

Nachev]. A copy of the protocol of the ethical approval is in Appendix14.2.

Protocols for procedures associated with blood sampling sought to ensure that they were safe and acceptable to the subjects and laboratory workers.

2.2 Management of primary data collection.

2.2.1 Selection and staffing of survey centers

There was usually one polyclinic (primary health care unit) per administrative district. In 3 districts there were 2 polyclinics and both were included in the survey as survey centers.

Additionally the Clinic for Internal diseases in Medical University was used as a survey center.

Major constraints in staffing a research study such as this in Bulgaria are that:

1. Persons in existing posts would not agree to participate in short term research project, if it required them to resign their existing post.

2. There is no experience of persons being given leave without pay so that their job can be preserved while they are employed on research projects.

As a consequence it was not possible to employ any senior staff full time on the project.

All were engaged part-time in addition to national full-time jobs.

At that time, Bulgarian primary health care was functioning on the regional principal

(general practitioner (GP) doctors and nurses were responsible for providing primary health care for the population from a defined region). All regional GPs and nurses from the selected regions were recruited as study stuff.

27 doctors, 56 nurses, 9 laboratory workers, and 34 interviewers were included in the study staff.

Interviewers were recruited not from the staff of the polyclinics, but from the nurses and students practicing and specializing in the Clinic for Internal diseases in Medical

University at that time.

Everyone from the study staff was appointed to work for the study for four hours, three days per week.

The very large number of the staff involved in the study is a potential source of bias. In order to minimize this bias we pay special attention to training of the staff.

2.2.1.1 Training staff

The staff of the study was divided in to four groups:

• managers and interviewers

• primary care medical doctors

• nurses

• laboratory doctors and nurses

Everyone from the staff undertook a specific theoretical and practical 2 days training course on the following topics:

• representing the study aims and objectives for participants

• interviewing and completing the questionnaire

• measuring blood pressure

• laboratory techniques for blood sample collection

• anthropometric measurements

During the training process special attention was paid to the importance of the accuracy of all measurements and information collected.

Some of this was common for all categories of staff e.g. explaining the rationale of the study, why standardisation of methods is necessary, difference from clinical procedures etc.

Some of the training time was specific to the category of staff involved e.g. standardisation of blood pressure measurements and recording of current medications for physicians, interview training for interviewers, recruitment procedures for appointment secretaries.

Everyone was provided with a set of guidelines to follow.

Routine supervision and control of the work of both interviewers and nurses was carried out during all data collection by the management staff (see above) of the study.

2.2.2 Recruitment

2.2.2.1 Arranging appointments

Each selected person was visited personally at home by his or her regular polyclinic nurse4. Wherever possible an appointment was made for the participant to visit a study center, otherwise a schedule with working days and hours of the survey centers was provided. When the person was not at home a letter of information was posted in order to inform them about the aims of the study and to ask him/her to participate in it.

During the nurse visit or in the letter sent every selected person were informed about the aims of the survey, all procedures which are going to be undertaken, and was asked not to smoke and drink coffee 30 minutes before the visit in the polyclinic, as well as to fast for

12 hours.

Two weeks before the planned attendance date, those with telephones were contacted and encouraged further to join the study. For those who agreed an appointment was scheduled.

Those without telephones were visited in the evenings and offered an appointment.

9 subjects were scheduled for each survey day for each survey center, 3 were asked to turn up at 8.00, 3 at 9.00 and 3 at 9.30. If a participant came to one of the study centers without being in the schedule, he or she was still given opportunity to participate in the study.

If during the nurse visit, or telephone call it was found out that the person had moved or no longer lived at this address, the combination name/address was determined to be no longer valid and to require a ‘replacement’ procedure. The selected person was replaced by a person from the same residential block (or if not available first next door neighbour) from the same age sex group, who interviewers found to meet the criteria.

The protocol specified two repeat phone calls and two repeat visits to be undertaken before the subject is recorded as 'uncontactable'.

For each listed name from the sample list that failed to become a participant, the reasons for this failure were recorded e.g.

• 'Listed number contacted, but sampled person, reported to be no longer resident.'

• 'Listed person contacted but declined to participate'.

• 'Listed person agreed to participate but failed to appear for scheduled appointment and declined further appointment' etc.

2.2.3 Subject flow within survey centers

Table 2.2: Summary of subjects flow within survey centres

Stations Data collection Staff Planned time in station

Registration Registration nurse-secretary 10 min

Rooms for interview interviewers (3) 1 hour interviews (3)

Physician’s office BP, medical history, physician (1) 20 min (1) medication, physical examination, ECG

anthropometrics nurse 10 min

Survey center blood taking laboratory technician 10 min laboratory (1).

4 Nurse who is not employed especially for the study, but together with the general practitioner doctor she is pesponsible for providing everyday health care of definite population.

The visit had four stages:

1.Registration

Every participant was registered by a nurse secretary and was given the questionnaire form

together with the personal ID for the study.

2.Interview

The interviewer asked the informant questions and fulfilled the first part of the

questionnaire including: socio-demographic factors, smoking behavior, alcohol

consumption, diet, physical activities health knowledge and stress.

3.Visit to physician and nurse

3.1. The physician made a standardised clinical examination of the cardiovascular system,

then filled in the part of the questionnaire about family history. Medical history was taken

asking about diagnosis of specific conditions plus an examination to detect any major signs

or indications of the following CVD: stable angina pectoris, unstable angina pectoris, post

infarct angina pectoris, silent ischaemia, acute myocardial infarction, IHD - dysrythmia

form, IHD with a diffuse left ventricular dysfunction (IHD dilative myocardiopathy),

symptomless latent IHD.

3.2. The physicians measured blood pressure as part of this examination

3.3. The nurse take measurements: height, weight, waist, hip and record them in the

questionnaire form

When a person with CVD symptoms or diagnosis was discovered, he or she was

interviewed with questionnaire form 2 (The data from this are not going to be analysed in

this thesis).

4.Visit in laboratory

A blood sample of 20 ml, was taken by laboratory workers.

2.3 Measurements

Protocols for measurements of blood pressure, height, weight, waist, hip and blood sample obtained are given in Appendix14.3

2.3.1 Height and weight measurements

The equipment

Medical stadiometer plus scales (beam balance).

2.3.1.1 Height

The height was measured with the subject in a standing position without shoes and heavy outer garments, with flat feet, feet together, the back as straight as possible and his/ her arms hanging loosely by his/ her side. The head was straight in horizontal position. The nurse placed the measuring arm on the informant's head. The informants were asked to breathe deeply in and stretch to their fullest height when the measurements were taken.

The nurse asked the informants to step forwards. The nurse read the height value at eye level. The measurement was recorded to the nearest millimeter.

2.3.1.2 Weight

Protocol

The weight was measured with the subject in a standing position. The informant was asked to remove shoes, any heavy outer garments such as jackets and cardigans, heavy jewelry and loose money and keys from the pockets. The value of the weight was read by the nurse and was recorded in kilograms.

2.3.2 Measurement of waist and hip circumferences

Waist was defined as the mid-point between the lower rib and the upper margin of the iliac crest.

Hip was defined as the widest circumference around the buttocks below the iliac crest.

Waist-hip ratio (WHR) is defined as the waist circumference divided by hip circumference.

Equipment

Plastic tape calibrated in millimeters.

Protocol

On the first visit the nurse asked the informant not to wear tight clothing for the visit in the polyclinic. The informant was standing in a relaxed manner, weight balanced on both feet and the feet about 25-30 cm apart with arms hanging loosely at the sides. The nurse was sitting on a chair by the informant while taking the measurements. The nurse passed the meter around the body of the informant.

Measuring waist circumference

Position of the meter was on the midway between the lower rib margin and the iliac crest.

The meter should not cause indentation. The nurse ensured that the meter is horizontal.

The measurement was taken at the end of a normal expiration.

Measuring hip circumference

The nurse placed the meter around the hips at the position yielding the maximum circumference over the buttocks. The nurse ensured that the meter is horizontal. The informant was stand without contracting the gluteal muscles.

Measurements were recorded to nearest millimeter.

2.3.2.1 Quality control of physical measurements

The equipment used for the physical measurements was calibrated in the beginning of the study and checked regularly (every week) during all data collection by the people from

Main Medical Physics Laboratory, Sofia.

2.3.3 Blood pressure measurements

The equipment

A conventional mercury sphygmomanometer (Riva-Rochi) was used.

Protocol

The physician checked whether the participant kept the requirements:

• have not smoked and drink coffee 30 min ago

• have not take antihypertensive medicine in the day of examination

The blood pressure was measured after 5 min in which the participant was seated and

relaxed. Physician asked the informant to remove jumper, cardigan etc. If the informant

wearing a sleeve this was rolled up, but it should not restrict the circulation of the blood.

The informant was sitting in a chair with the right arm resting on any suitable support to

bring the antecubital fossa to approximately heart level. The correct cuff was chosen and

placed on the right arm place. The criteria used to select the correct cuff size were as

follows:

Circumference of the armpit Cuff size

Less than 33 cm 12 cm x 23 cm

33 – 41 cm 15 cm x 33 cm

More than 41 cm 18 cm x 36 cm

The lower end of the cuff was about 2 cm above the elbow crease. The cuff was tight

enough to admit two fingers between cuff and arm. Longer cuffs designed to cover the

whole circumference of the arm were used for the measurement of blood pressure on obese

subjects. The diastolic blood pressure was measured by listening to the characteristic

Korotkoff sound phase five. Two consecutive blood pressure measurements were

performed within two minutes. The two readings of the systolic (SBP) and diastolic (DBP)

blood pressure were recorded to the nearest 2 mmHg.

2.3.3.1 Quality control of blood pressure measurement

The equipment used for measurement of blood pressure was calibrated prior to the study in the

Main Medical Physics Laboratory than every week additional check and calibrating was done.

When blood pressure is measured in population studies it is liable to measurement error

from multiple sources - type of instrument, choice of protocol, observer training, and skills

90 of the observer as well as accurate reading and recording of data. The assessment of blood pressure measurement quality is problematic because there is no generally accepted and reproducible ‘gold standard’ to which individual blood pressure measurements can be related.

Standardised approaches to the assessment of data are few. The MONICA project has proposed a standardised assessment of BP measurement quality that can potentially be applied to any population blood pressure survey. They identify three major areas for the assessment: procedures of quality assurance, procedures of quality control and quality indicators contained in blood pressure value recordings149.

For the Australian National Risk Factors Survey S. Bennett150 developed a blood pressure measurement assessment technique using the following indicators:

1. Deviation from study protocol: last digit preference for zero; proportion of odd readings

(odd final digit).

2. Variation in measurement technique: proportion of identical duplicate readings; proportion of second readings exceeding first readings; mean difference between first and second readings.

Two common manifestations of measurement error in epidemiological studies are last digit preference for zero and the proportion of identical readings. Preferences for zero as the last digit when recording blood pressure using normal mercury sphygmomanometers is well documented151,152. It effects the shape of the distribution curve153 and reduces the power of statistical tests - making it more difficult to assess the relation between blood pressure and other potential risk factors150. The use of the proportion of identical measurements as an indicator of accuracy of the measurements is less well documented. High proportions my cause a shift in the entire blood pressure distribution153.

It was first intended to record identifiers for study centres and the observer taking the

blood pressure measurements in order to be able to use them for quality assessment

analyses, but this was not implemented.

All physicians received prior training in blood pressure measurement according to the

standard procedure, which followed WHO MONICA protocol.

All physical examinations took place during the morning, because of lipid fasting

requirements, and this helped control for time-of-day effect on blood pressure154. Blood

pressure measurements were taken after registration and completion of a questionnaire and

5 minutes of rest, which gave respondents time to become familiar with the surrounding

and to relax.

The quality of blood pressure measurement has been assessed in term of deviation from the

study protocol and variation in measurement technique between the two consecutive

readings. The indicators used to examine the quality of the data were the following:

Deviation from study protocol:

• last digit preference for zero

• proportion of odd readings (odd final digit)

Variation in measurement technique:

• proportion of identical duplicate readings

• proportion of second readings exceeding first readings

• mean difference between first and second readings

Last digit preference for zero: The distribution of the last digits of all single BP readings

was computed. Its deviation from the theoretically expected uniform distribution, i.e. from

equal frequency for each of the possible five even digits 0, 2, 4, 6, and 8, was determined.

A Digit Preference Score (DPS) was calculated from the formula: DPS = 100* (χ2 /

d.f..*N)1/2, where N is the number of observations, χ2 is the chi-square-statistic for the test

of homogeneity between the observed and the expected distributions of last digits, and d.f.

are the respective degree of freedom (e.g. in the case of five even last digits d.f. = 4). The

DPS ranges from 0 to 100. It is low for high agreement with the ideal for non-preference

and rises consistently with loss of such agreement.

Proportion of odd readings (odd final digit): the proportions of BP readings ending with

odd values were computed from single systolic and diastolic blood pressure measurements.

Proportion of identical duplicate readings: The proportion of identical first and second

readings of DBP and SBP are calculated for participants of the study.

Hypertensive categories: Due to an over-sight in the design of the questionnaire, the

question concerning antihypertensive treatment was asked together with the question about

antidiabetic drugs. We therefore excluded diabetics from the analyses, even we admittedly

creating a possible bias e.g. if diabetics were more likely to be hypertensive.

The participants were classified into one of the following categories:

• ‘Treated normotensives’: Systolic blood pressure <160 and Diastolic blood pressure <95; reporting treatment for hypertension • ‘Treated hypertensives’: Systolic blood pressure >159 and/or Diastolic blood pressure >94; reporting treatment for hypertension • ‘Untreated hypertensives’: Systolic blood pressure >159 and/or Diastolic blood pressure >94; not reporting treatment for hypertension • ‘Untreated normotensives’: Systolic blood pressure <160 and Diastolic blood pressure <95; not reporting treatment for hypertension We accept as a risk factor ‘hypertension’ = Treated normotensives + Treated hypertensives

+ Untreated hypertensives and participants without risk factor ‘hypertension’ = Untreated

normotensives.

2.3.4 Blood sample collection

The taking of the blood sample was the last item of data collection

Informants eligible to provide a blood sample

Only informants who have agreed to give a blood sample and have not any (self reported)

disorders of bleeding were eligible to provide a blood sample.

Requirements for taking a blood sample for serum cholesterol concentration

• record whether taking oral contraceptive, antilipid drugs, b-blockers

• have not eaten for 12 hours

• relaxed for at least 10 min.

Technique

For obtaining the blood samples Vacutainer SST5 Gel and Clot Activator, (Becton Dickinson

6510-4 No24-EXP) was used.

The blood sample was taken from either arm with the subject in the sitting position. It was

preferred that tourniquet not be used. When the tube was full, it was withdrawn from the

Vacutainer holder and invert at once.

With subjects that were difficult to bleed, a maximum of two attempts were made to obtain a

specimen, before abandoning the procedure. A label was stuck on the tube with the subjects

study ID number (code).

All samples were taken between 8 and 10 a.m. and were transported on wet ice to the central

Clinical Laboratory Outpatient Clinic of Department of Internal Diseases within a two-hour

period of time by a courier. At all times the samples were kept out of direct sunlight, and in a

cool place but not frozen. All measurements are in units conforming to SI system (mmol/l).

Results from blood sample analyses were send to the Central survey center in the department

of Internal Diseases.

2.3.4.1 Quality control of blood analyses

Serum total cholesterol values are subject to environmental and biological variability and

other variability caused by different sampling, storage and analytical procedures.

Comparability and correctness of cholesterol measurement were control from various

factors during the whole measurement procedure. They are divided by the MONICA

Project recommendations155 into two stages:

1. The preanalytic stage - measurement conditions, blood drawing and handling, and storage before analysis

2. The analytic stage - the use of different methods, standards or analysers.

Fasting status

Food intake before blood sampling seems to have little if any effect on total serum cholesterol156. All blood samples were obtained after asking the respondents to fast for 12 hours. Blood collection was the first thing in the morning, after registration, in order to avoid compliance.

Seasonal variation

Some studies showed that higher cholesterol levels are measured in winter than in summer.

Differences of 0.2 mmol/l in the mean levels between June and December were observed in the placebo group of the Lipid Research Clinics Trail157. The blood samples for SHS were obtained at an approximately constant rate from January to December, so we would not expect a bias overall from seasonal variations.

Posture during sampling

Blood specimens for SHS were obtained with the respondent in sitting position.

Tourniquet use

According to earlier experience, prolonged tourniquet use is associated with cholesterol values higher than those obtained by blood drawing without tourniquet use. Levels increase by 10-15% after 10 minutes of tourniquet use, and by 2-5% after two minutes.

Tourniquet use up to one minute is not associated with a significant increase in cholesterol levels. Well-trained technicians need less than one minute for one vial158. The blood sample for SHS were obtained by well trained laboratory workers who were previously advised not to use tourniquet when possible, so we would not expect a bias to increase the levels of cholesterol.

5 VACUTAINER is a trade mark of BECTON DICKINSON and Company, Rutherford, N.J., 07070, USA

Quality control of the analyses

The analyses were performed using the enzymatic method on a biochemical analyzer

Cobas Mira5. All analyses were carried out according to Standard Operating Procedures under the supervision of Assoc. Prof Dishlianova.

Principle of analyzing cholesterol enzymatic (CHOD-PAP method).

Cholesterol and its esters were released from lipoproteins by detergents. Cholesterol esterase hydrolyzed the esters. In the subsequent enzymatic oxidation by cholesterol oxidase, H2O2 was formed. This was converted into a colored quinonimine in a reaction with 4-aminfantipyrine and phenol catalyzed by peroxidase159.

Quality Control / Calibration

Aqueous standards were used to calibrate the procedure or an appropriate serum calibrator.

Serum control with known normal and elevated values was run routinely for quality control.

Calculation

(A=Absorbance)

A()patient × Concentration of standard = Cholesterol A()standard

For external quality control RIQAS tests were sent in Randox laboratories LTD, Ardmore,

Diamond Road, Crumlin, County/Antrum, United Kingdom BT29 4QY. Protocols are given in Appendix14.4. The difference between our results and the ‘consensus mean’ is expressed as a Target Score (TS) using the following mathematical formulae160:

(Result − Method Mean)×100 V = Method Mean

 3.16×TCV  TS = log10  ×100  V 

TS values are in the range 10 to 120 and are interpreted as follows:

< 40 unacceptable

41 - 50 need for improvement

51 - 70 acceptable

71 - 100 good

101 - 120 excellent

Target Coefficient of Variation (TCV) for cholesterol is 5.0.

On the base of the attached protocols in Appendix 14.4 we calculated TS for our results and receive TS1 = 88.7 and TS2 = 97.9. Both results falls into ‘good’ precision category.

All tests show good precision of cholesterol measurements on Levey-Jennings plot as well

(CV<5).

HDL - cholesterol

Principle

When serum was combined with the polyethylene glycol reagent, all beta-lipoproteins

(LDL and VLDL) were precipitated. The HDL fraction (alpha-fraction) remained in the supernatant. The supernatant was then treated as a sample and assayed for cholesterol by an enzymatic method. The value obtained was the HDL cholesterol value.

Calibration Quality Control

The test was calibrated with a serum based calibrator or an aqueous cholesterol standard.

Control serums with known HDL values were run routinely to monitor the validity of the procedure.

Triglycerides - GPO

Triglycerides in the sample were hydrolysed by lipase to glycerol and fatty acids. The glycerol was then phosphorylated by adenosine-5-triphosphate (ATP) to glycerol-3- phosphate (G3P) and adenosine-5-diphosphate in a reaction catalyzed by glycerol kinase

(GK). Glycerol-3-phosphate was then converted dihydroxyacetone phosphate (DAP) and hydrogen peroxide by glycerophosphate oxidase (GPO). The hydrogen peroxide then

97 reacted with 4-aminoantipyrine (4-AAP) and 3-hydroxy-2,4,6-Tribomobenzoic acid

(TBHB) in a reaction catalyzed by peroxidase to yield a red colored quinoneimine dye.

The intensity of the color produced is directly proportional to the concentration of triglycerides in the sample.

Quality control for triglyceride measurements was not performed.

2.4 Questionnaire

2.4.1 Overall structure

The questionnaire (Appendix14.5) was designed to be completed within 60 minutes. It consisted of 70 questions, divided in the following main groups:

• questions covering demographic variables, such as date of birth, sex, marital status,

address and occupation.

• physical activity

• dietary habits

• alcohol consumption

• smoking behavior

• stress

• health knowledge and behavior

• general health status

• symptoms of CVD (the people with this symptoms were interviewed further with

questionnaire 2)

• family history of CVD

• medicines being taken (for high blood pressure, oral contraceptive, diabetes etc.).

2.4.2 The recording process

1. The personal study ID was recorded by the nurse-register during the registration.

2. The interviewer recorded the information on questions in the first part of the questionnaire including: sociodemographic factors, smoking behavior, alcohol consumption, diet, physical activities health knowledge and stress.

3. The physician recorded the information on the questions concerning family history and symptoms of CVD, as well as blood pressure measurements.

4. The nurse recorded the height, weight, waist and hip measurements.

2.4.3 The food frequency questionnaire

Questions on dietary habits, in the form of a modified "food frequency" questionnaire and covering, in addition items such as: salt and sugar intake, fat consumption, dietary regimen were asked. Because of the difficulties of measuring aggregate diet intake efficiently, recourse is often made to obtain information on dietary practices that are considered likely to be predictive of overall dietary intakes. This was the strategy adopted here.

The food frequency questionnaire consists of 30 questions. Most of them describe a category of foods with a opportunity to choose 1 of six answers: one time per day, almost every day, several times per week, one day of the week, one or a few times per month, rare or never. The foods in the list were chosen after consulting Assoc. Prof. Ribarova from National Nutrition

Institute. The food list was based on:

1. Relative importance of food in the Bulgarian diet

2. Assumed relevance to cardiovascular diseases

The food items included in the list are classified in the following categories: meat and meat products, fish, pastry, corn foods, eggs, milk and milk products, fresh fruits, canned fruits, fresh vegetables and canned vegetables. Separate questions were asked about kind of bread and kind of fat usually used, salt added and soft drinks consumption.

The last question from the food frequency questionnaire is about a special diet.

2.4.4 Alcohol

The questions about alcohol consumption were developed by author, based on assumptions about typical for Bulgarian drinking behavior. It consisted of 8 questions. The participants were asked about how often they usually drank alcohol. If they were drinker, they were asked about the kind of alcohol and how their drinking habits differ during the week and the weekend, as well as did they have changed their drinking behavior in comparison to

1990 and if ‘yes’, why. Informants were asked separately about quantity of different type of drinks (bear, wine, spirits, other alcohol drinks) drank during the working days and weekend days.

The nondrinkers were asked whether they have always been nondrinkers, or they gave up, and if they gave up, how many ears ago and how many drinks they usually drank per week.

2.4.5 The smoking behavior questionnaire

The questionnaire about smoking behavior is based on a standard MONICA questionnaire161. It consists of 11 questions. The first question require information on current smoking behavior of the participant, dividing them to regular, nonsmokers and occasional (less than 1 cigarette per day).

The smokers were asked about how many cigarettes they smoke per day, the brand of cigarettes, giving up and quit attempts, and history of smoking behavior like age at starting.

Non-smokers were asked whether they have ever smoked and if ‘yes’, the age at start and the age at stop smoking as well as the maximum number of cigarette ever smoked per day and the reasons for stopping smoking.

Everyone was asked about the hours per day exposed to tobacco smoke.

2.4.6 Physical activity

There were six questions concerning physical activity. The participants were asked about self-assessment of their physical activity in work time. Physical activity in leisure time was

100 assessed with the standard question “How often have you been physically active for at least 20 minutes in which you puff and sweat?”. Questions were asked about sporting and number of streets crossed daily.

2.4.7 Quality control of the interview

During the whole process of interviewing the completeness of the forms and missing data was monitored.

2.5 Data analyses and presentation

2.5.1 Data entry and editing

The data were entered in statistical package SPSS for Windows, version 5.0 by the author and the statistician of the study Assoc. Prof. M. Vukov.

In order to minimize the transcription errors we made visual check by comparing the source data with the hard copy of the data file before storing it. Additionally a double data entry was done. Frequency distribution was made for all variables, comparing with plausibility ranges and odd values were investigated and corrected. Additional quality control was made when complete, spot checks were made of subsamples of stored data against the original documents. This was undertaken to various degrees at various stages and after all the handling has been completed and the final database was established.

2.5.2 Data presentation

Results are generally shown for 10, 5 years age groups: 25-29, 30-34, 35-39, 40-44, 45-49,

50-54, 55-59, 60-64, 65-69, and 70-74. When age has been cross-tabulated with sex and other variables, because of the small numbers within each age-sex group results are sometime presented in 5, 10 years age groups: 25-34, 35-44, 45-54, 55-64, 65-74.

Where a comparison with the data from the MONICA study was intended an additional presentation of data for the age range 35 to 64 standardised to the world standard population (by 5 year age groups) has been given162.

101

Data were presented in terms of central tendencies and dispersions for continuous variables.

The median is often more useful for reviewing the distribution of a variable. This is because a few individuals, with extreme values can pull the mean ‘off centre’. The median is derived by listing all scores sequentially and taking the middle score. If it was an even number, the halfway point between the two values surrounding the middle was taken.

In addition of the mean or median, as summary statistics of central tendency the standard deviation was reported as summary statistics of variability among the individuals in a group of population. The standard deviation is a measure of the spread of data about their mean. It is calculated from the observations in the group as:

n 2 ∑()xi − x SD = i=1 n −1

To account for sampling error in the estimation of the mean standard errors of the mean were reported. The standard error (SE) is a measure of the accuracy of the sample mean as an estimation of the population mean.

SD SE = n

A percentile is a number that indicates the percentages of the distribution that is equal to or below that number. For summarizing non-normal distributions, percentiles (e.g. P10, P50,

P90) may be more appropriate as they do not assume symmetry.

2.5.3 Standardisation

For the purpose of comparison with MONICA populations we used aged standardisation.

1. A weighting variable “stwt3564” was created.

Each individual in a given age-sex stratum was assigned a weight equal to the age stratum weight divided by number of study individuals in the same age-sex stratum.

The world standard population weights that we used for the age groups 35-64 were162:

102

35-44 age group - 12/31

45-54 age group - 11/31

55-64 age group - 8/31

These strata weights were divided by the number of study subjects in each age-sex stratum to give the weight for each subject-specific to his or her age-sex stratum.

Analyses were restricted to those aged 35 to 64.

For the purpose of comparison with other populations we used the same procedure of standardisation with the following world standard population weights for 25-74 age groups:

25-34 age group - 14/50

35-44 age group - 12/50

45-54 age group - 11/50

55-64 age group - 8/50

65-74 age group - 5/50

SPSS procedures were then used to calculate means and standard deviations using weighted rather than unweighted data163. Similarly the procedure ‘Frequency’ was used to calculate centiles including the median using weighted data164.

Median, 10th and 90th percentile values were used to compare the distribution of particular risk factors with other studies’ populations.

2.5.4 Data analyses

Initially all data were tabulated by age and sex. Means and standard deviations were calculated for numerical variables. Statistical analyses were performed using statistical package SPSS for Windows, version 5.0.

2.5.4.1 Social-demographic variables

As a social economical characteristic person per room was used. Person per room is equal to number of persons in household divided by number of bedrooms.

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2.5.4.2 Alcohol

Alcohol was expressed in grams. Conversion from measures by volume assumed a specific gravity of 0.78 g/ml. alcohol(g) = alcohol(ml) / 100 x ki(%) x 0.78 , where ki(%) is specific percents of alcohol per 100 ml for different kind of drinks and 0.78 is alcohol density.

165 ki(%) for beer is 4.5%, for wine 11%, for spirits 40% and for other drinks - 12% .

Using this formula we received:

• for 1 bottle of beer (500 ml) 17.55 g alcohol166

• for a glass of wine (200 ml) 17.6 g alcohol167

• for spirits (100 ml) 31.2 g alcohol168 and

• for other drinks (per 100 ml) - 9.36 g alcohol169.

As we collected information about alcohol consumption separately for week and weekend days we created two variables: alcwgr (alcohol consumed per week days) and alwendgr

(alcohol consumed per weekend days), where alcwgr = No of bottles of beer x 17.55 + No of glasses of wine x 17.6 + No of glasses of spirits x 31.2 + No of glasses of other alcohol drinks x 9.36

In the same way we compute ‘alwendgr’ using the quantity of alcohol reported to be consumed during the weekend.

Then we computed the alcohol consumption per drinking day using the formula:

5× alcwgr + 2× alwendgr alcdaily = 7

To each participant was allocated coefficients according to the reported frequency of alcohol consumption:

Frequency of alcohol consumption Coefficient (alccoeff)

non drinkers 0

104

less then 1 day per week 0.5

1-2 days per week 1.5

3-6 days per week 4.5

every day 7

Finally we received mean alcohol consumption per drinking plus nondrinking day using the formula:

alcdaily × alccoeff almean = 7 and mean alcohol consumption during the weekend:

alcwendgr × alcoefwe almeanwe = 2

As a measure of peak drinking we use the ratio of alcohol consumed on weekend drinking day and work drinking day:

alwendgr alcratio = ×100% alcwgr

According to their drinking status participants were divided to: lifetime non-drinkers, past drinkers and current drinkers.

Current drinkers were divided in six groups according to the quantity of mean alcohol consumption over all days: less then 5 g/day, 5-10 g/day, 10-20 g/day, 20-40 g/day, 40-60 g/day and more then 60 g/day.

2.5.4.3 Smoking

According to reported smoking status participants were divided to: current smokers

(including occasional smokers, who smokes less then one cigarette per day), ex-smokers and never-smokers. For the purpose of the comparison with MONICA populations were used the following categories: regular cigarette smokers, if they reported they smoked cigarettes every day; other current smokers, if were not cigarettes smokers but reported that they smoked cigarettes occasionally (less that one per day); ex-smokers, if they

105 reported they had smoked cigarettes regularly in the past but did not smoke currently; non-smokers, if they reported that they were not current smokers and had never smoked cigarettes regularly.

The number of cigarettes smoked per day per regularly or occasionally cigarette smokers was used to characterize the current degree of exposure to cigarette smoke.

To assess the proportion of ex-smokers quit ratio was calculated:

ex - smokers Quit ratio = current + ex - smokers

Duration of smoking for current smokers was calculated by subtracting the age at start smoking from the age in the moment of the interview. Duration of smoking for ex-smokers was calculated by subtracting the age at start smoking from the age at distinguish smoking.

2.5.4.4 Physical activity

According to self-assessment of their physical activity at work participants were divided in four categories: very light, light, moderate and heavy physical activity.

Physical activity in leisure time was presented as “puffing and sweating for at least 20 minutes” every day; at least 3 times weekly; at least once weekly and less then once weekly.

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SECTION II – RESULTS

3. Chapter: Study population

3.1 Recruitment

From the total sample list of 3100 persons provided by GALLUP International, 37 (0.03%) had an inappropriate address, 209 (6.7%) were recorded as ‘uncontactable’ e.g. the nurse was unable to make contact with anyone in the house, 12 were too ill to participate, 774 persons (24.9%) refused to participate when called on by the nurse and 59 (1.9 %) agreed to participate, but did not come. No information is available for the refusers (apart from age and sex) (Figure 3.1).

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Figure 3.1: Flowchart of recruitment process showing numbers originally sampled, numbers of replacement subjects and final participation.

Sample drawn 3100 (m-1500/f-1500)

Replacement subjects Valid address Invalid addresses 37 3063 37 (m-20/f-17) (m-1530/f-1533) (m-20/f-17)

Contacted Uncontactable 2891 209 (m-1449/f-1405) (m-81/f-128)

Able to participate Too ill 2879 12 (m-1441/f-1401) (m-8/f-4)

Agreed to participate Refused 2105 774 (m-992/f-1053) (m-449/f-348)

Participated in interview No shows 1996 59 (m-964/f-1032) (m-28/f-21)

Participants with blood analyses No blood 1696 Collection - 300 (m-761/f-935) (m-203/f-97)

m – male, f - female

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3.2 Participation by age and sex

Table 3.1 shows subjects participating in the Study by age and sex and the ratio to the original sample size. The participation ratios were 0.62 for males and 0.66 for females, or

0.64 overall. The response ratios were lower in the youngest and in the oldest age groups for males and for females.

Table 3.1: Numbers of final participants in each age/sex stratum and the ratio to the original sample size of 310 (620 for both sexes combined).

Males Females Total Age Sample Partici- Response Sample Partici- Respon- Sample Partici- Response groups drawn pants ratio drawn pants se ratio drawn pants ratio 25-34 310 196 0.63 310 185 0.59 620 381 0.61 35-44 310 209 0.67 310 219 0.70 620 428 0.69 45-54 310 198 0.63 310 234 0.75 620 432 0.69 55-64 310 191 0.61 310 205 0.66 620 396 0.63 65-74 310 170 0.54 310 189 0.60 620 359 0.57 Total 1550 964 0.62 1550 1032 0.66 3100 1996* 0.64 *Includes total of exactly 300 subjects from whom no blood sample was obtained.

The overall participation ratio (participants/(original sample - invalid addresses + replacements)) was 0.64 - close to expectations. This includes the 300 subjects, who refused to give blood sample, and their data are still analysed in this thesis, except the blood lipids, where they are indicated as missing cases.

Distribution of the study population by 5 years age groups and sex is given in

Appendix14.6.

3.3 Marital status

The distribution of the study population by marital status is given in detail in Table 3.2.

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Table 3.2: Distribution of study population by marital status Age groups 25 - 34 35 - 44 45 - 54 55 - 64 65 - 74 Males married 60.7 81.2 80.8 78.4 78.2 never married 34.2 7.7 6.6 5.8 2.4 divorced 5.1 9.1 9.6 6.8 5.3 widowed 0 1.9 3.0 8.9 14.1 missing 0 0 0 0 0 Females married 71.1 72.1 75.2 66.3 46.8 never married 20.1 8.7 3.8 3.4 3.7 divorced 8.2 16.4 12.0 9.8 7.4 widowed 0.5 2.7 9.0 20.5 42.0 missing 0 0 0 0 0

110

The percentage single was higher among men (11.5%), but the percentage widowed was three times higher among the females (14.5%) than among the males (5.3%). The distribution of the study population by age groups and sex is given in Appendix14.7.

3.4 Socio-economic characteristics

No generally accepted class scheme was available for Bulgaria in the period of transition.

The main socio-economic characteristics measured were - formal schooling, employment status, type of employment, home ownership, person per bedroom, reported change in purchasing power since 1993.

The distribution of study population by characteristics is given for men and women separately.

3.4.1 Formal schooling

Table 3.3 explains the educational categories used.

Table 3.3: Educational categories Educational categories used Explanation of categories More than 12 years University 9-12 years Colleges (gymnasium) 5-8 years Secondary school (pro-gymnasium) Less than 5 years Primary school No education No diploma for completed primary school

Table 3.4 shows the distribution of study population by years of formal schooling.

The highest proportion of participants had 8 - 12 years of education and the proportion of males who had received university education were slightly higher than women. There are only a few respondents with no schooling mainly in older age groups. The proportion with tertiary education is high both for males (35.4%) and for females (24.8%).

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Table 3.4: Distribution of study population by schooling Age 25-34 35-44 45-54 55-64 65-74 Total Males More than 12 years 66 78 91 56 50 341 % 33.7 37.3 46.0 29.6 29.4 35.4 9-12 years 119 121 90 98 76 504 % 60.7 57.9 45.5 51.9 44.7 52.4 5-8 years 11 10 16 30 37 104 % 5.6 4.8 8.1 15.9 21.8 10.8 Less than 5 years 0 0 1 5 7 13 % 0 0 0.5 2.6 4.1 1.4 No education 0 0 0 0 0 0 % 0 0 0 0 0 0 Total 196 209 198 189 170 962 % 20.4 21.7 20.6 19.6 17.7 100 Females More than 12 years 46 66 65 56 22 255 % 24.9 30.1 28.0 27.3 11.7 24.8 9-12 years 132 146 145 113 107 643 % 71.4 66.7 62.5 55.1 56.9 62.5 5-8 years 7 7 21 34 44 113 % 3.8 3.2 9.1 16.6 23.4 11.0 Less than 5 years 0 0 1 2 12 15 % 0 0 0.4 1.0 6.4 1.5 No education 0 0 0 0 3 3 % 0 0 0 0 1.6 0.3 Total 185 219 232 205 188 1029 % 18 21.3 22.5 19.9 18.3 100 Number of Missing Observations: 5

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3.4.2 Employment status

Table 3.5 and Table 3.6 present the distribution of study population by employment status separately for males and females.

As we were expecting eventual different pattern in risk factors in those working “shift work” (because of the specific nature of their working pattern), they were classified in a separate category.

The biggest proportion had a full time job. A considerable percentage were people working on shift work and only about 5% reported to be unemployed on the time of the Study. Only

2.2% of females reported staying at home as a housewife.

More than half of the employed respondents were on a governmental job. 9% of the females and 23.5% of males reported being self-employed (Table 3.7).

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Table 3.5: Distribution of study population by employment status, males

Age

25-34 35-44 45-54 55-64 65-74 Total Males full time 94 137 114 62 9 416 % 48.5 65.9 57.6 33.2 5.3 43.5 part time 33 23 20 14 1 91 % 17 11.1 10.1 7.5 0.6 9.5 shift work 32 35 32 14 0 113 % 16.5 16.8 16.2 7.5 0 11.8 unemployed 22 11 12 5 0 50 % 11.3 5.3 6.1 2.7 0 5.2 retired -ill 1 1 9 5 3 19 % 0.5 0.5 4.5 2.7 1.8 2 home duties 0 0 1 5 1 7 % 0 0 0.5 2.7 0.6 0.7 fully retired 0 1 9 81 153 244 % 0 0.5 4.5 43.3 90.5 25.5 student 12 0 1 1 2 16 % 6.2 0 0.5 0.5 1.2 1.7 Total 194 208 198 187 169 956 % 20.3 21.8 20.7 19.6 17.7 100

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Table 3.6: Distribution of study population by employment status, females

Age

25-34 35-44 45-54 55-64 65-74 Total Females full time 97 139 133 23 5 397 % 53 64.1 57.3 11.3 2.7 38.9 part time 10 7 10 6 0 33 % 5.5 3.2 4.3 3 0 3.2 shift work 54 54 41 9 1 159 % 29.5 24.9 17.7 4.4 0.5 15.6 unemployed 14 14 19 3 0 50 % 7.7 6.5 8.2 1.5 0 4.9 retired -ill 0 0 10 6 5 21 % 0 0 4.3 3 2.7 2.1 housewife 2 2 5 7 6 22 % 1.1 0.9 2.2 3.4 3.2 2.2 fully retired 0 1 14 149 169 333 % 0 0.5 6 73.4 90.9 32.6 student 6 0 0 0 0 6 % 3.3 0 0 0 0 0.6 Total 183 217 232 203 186 1021 % 17.9 21.3 22.7 19.9 18.2 100 Number of Missing Observations: 19

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Table 3.7: Distribution of study population by type of employment

Age

25-34 35-44 45-54 55-64 65-74 Total Males unemployed* 24 11 20 63 103 221 % 12.2 5.3 10.1 33 60.6 22.9 governmental job 103 132 126 98 57 516 % 52.6 63.2 63.6 51.3 33.5 53.5 self-employed** 69 66 52 30 10 227 % 35.2 31.6 26.3 15.7 5.9 23.5 Total 196 209 198 191 170 964 Females unemployed* 14 11 34 106 126 291 % 7.6 5 14.5 51.7 66.7 28.2 governmental job 138 176 185 90 59 648 % 74.6 80.4 79.1 43.9 31.2 62.8 self-employed** 33 32 15 9 4 93 % 17.8 14.6 6.4 4.4 2.1 9 Total 185 219 234 205 189 1032 Number of Missing Observations: 0

* unemployed - includes students, retired and housewife

** ‘self-employed’ - includes working for private employers

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Table 3.8 shows the distribution of study population by home ownership; 88% of the studied population was living in their own houses (mainly apartments).

Table 3.9 describes the study population by persons per bedroom (number of person in household/number of bedrooms). Half of the observed population live in households with less than 1 person per bedroom, about 45% live in houses with 1-2 persons per bedroom and only 5% of the people live in houses with more than 2 persons per room.

Table 3.10 shows the self-assessed purchasing power in comparison with 1993. About

40% of the respondents described their purchasing power as much smaller in comparison with a year ago and only 7.8% of males and 4.3% from females assessed their purchasing ability as larger. The highest percentage of people with much smaller purchasing power was among the olders age group. This is what we would expect, as those are retired people with insufficient governmental support and no chances for additional work.

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Table 3.8: Distribution of study population by home ownership Age Total 25-34 35-44 45-54 55-64 65-74 Males owners 150 184 180 174 150 838

% 77.7 88.0 91.8 92.6 90.4 88.0 renters 43 25 16 14 16 114 % 22.3 12.0 8.2 7.4 9.6 12.0 Total 193 209 196 188 166 952

Females owners 148 192 212 186 165 903

% 80.4 88.5 92.2 92.5 88.7 88.7 renters 36 25 18 15 21 115 % 19.6 11.5 7.8 7.5 11.3 11.3 Total 184 217 230 201 186 1018

Number of Missing Observations: 26

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Table 3.9: Distribution of study population by persons per bedroom* Person per Age bedroom 25-34 35-44 45-54 55-64 65-74 Total Males < 1 73 72 72 104 110 431 % 41 39.3 42.4 61.9 69.6 50.3 1 - 2 92 102 94 58 42 388 % 51.7 55.7 55.3 34.5 26.6 45.3 > 2 13 9 4 6 6 38 % 7.3 4.9 2.4 3.6 3.8 4.4 Total 178 183 170 168 158 857 Females < 1 63 73 87 112 115 450 % 38 40.1 43.3 63.3 64.2 49.7 1 - 2 91 97 101 59 54 402 % 54.8 53.3 50.2 33.3 30.2 44.4 > 2 12 12 13 6 10 53 % 7.2 6.6 6.5 3.4 5.6 5.9 Total 166 182 201 177 179 905 Number of Missing Observations: 234 (11.7%)

* → number of persons in household / number of bedrooms

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Table 3.10: Distribution of study population by reported purchasing power in comparison with previous year* Age 25-34 35-44 45-54 55-64 65-74 Total Males larger 22 21 15 15 2 75 % 11.2 10.1 7.6 7.9 1.2 7.8 the same 42 49 33 23 8 155 % 21.4 23.7 16.8 12.2 4.7 16.2 smaller 72 79 90 80 59 380 % 36.7 38.2 45.7 42.3 34.9 39.7 much smaller 60 58 59 71 100 348 % 30.6 28 29.9 37.6 59.2 36.3 Total 196 207 197 189 169 958 Females larger 12 16 4 8 4 44 % 6.5 7.3 1.7 3.9 2.1 4.3 the same 44 18 19 15 1 97 % 23.9 8.3 8.2 7.4 0.5 9.5 smaller 77 103 113 90 70 453 % 41.8 47.2 48.9 44.3 37 44.2 much smaller 51 81 95 90 114 431 % 27.7 37.2 41.1 44.3 60.3 42 Total 184 218 231 203 189 1025

Number of Missing Observations: 13

*Response to question: “ How do you assess your abilities to buy anything in comparison with 1993?”

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3.5 Representativeness of the sample

The representativeness of the sample was assessed by comparing age and sex specific distributions for two potentially informative variables on which comparable data was available from the 1992 Census - marital status and years of formal schooling (Table 3.11).

Unfortunately Census data were not available for exactly the same geographic area as the study sought to represent but rather for a population vary slightly (The study sought to present the “urban” part of the population of the City of Sofia Region (Oblast Grad Sofia)).

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Table 3.11: Comparison of marital status within age and sex strata between Sofia Heart Study 1994 population and census data City of Sofia Region, 1992 Age groups Unweighted 25 - 34 35 - 44 45 - 54 55 - 64 65 - 74 mean Males married SHS 60.7 81.2 80.8 78.4 78.2 75.9 census 66.4 81.1 83.3 84.8 80.8 79.3 never married SHS 34.2 7.7 6.6 5.8 2.4 11.3 census 29.6 10.1 6.0 4.2 3.6 10.7 divorced SHS 5.1 9.1 9.6 6.8 5.3 7.2 census 3.2 7.7 8.3 6.1 4.5 6.0 widowed SHS 0 1.9 3.0 8.9 14.1 5.6 census 0.1 0.5 1.5 4.3 10.9 3.5 missing SHS 0 0 0 0 0 0 census 0.5 0.6 0.9 0.5 0.4 0.7 Females married SHS 71.1 72.1 75.2 66.3 46.8 66.3 census 74.5 77.4 75.0 67.5 47.9 67.9 never married SHS 20.1 8.7 3.8 3.4 3.7 7.9 census 17.0 6.9 4.7 3.4 3.9 7.2 divorced SHS 8.2 16.4 12.0 9.8 7.4 10.8 census 7.4 13.0 8.3 10.0 7.7 9.3 widowed SHS 0.5 2.7 9.0 20.5 42.0 14.9 census 0.6 2.1 6.7 18.6 40.1 13.6 missing SHS 0 0 0 0 0 0 census 0.5 0.6 0.5 0.4 0.4 0.5

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The male SHS population was slightly less likely to be married compared to the census population (unweighted means of age strata SHS - 75.9%, census - 79.3%). While the marital status distributions of the female population were closely comparable (unweighted means of age strata SHS - 66.3%, census - 67.9%).

Census data on formal schooling was not available separately by sex.

For comparison, weighted means of the two sexes were calculated from the SHS data for each age stratum using as weights the proportions of each sex in the comparison (Census) population (Table 3.12).

The population with more than 8 years of education is significantly over-represented in the

SHS. Higher participation by the more educated is a common experience. A participation proportion around 64% leaves scope for selection effects for attributes associated with educational level. It is worth noting however that we have not tended to find substantially more adverse levels of risk factors in those with less education (see, for example, results for cholesterol concentration and smoking). Thus it is unlikely that under-representation of those with less schooling is seriously biasing our findings towards more favourable levels for attributes such as cholesterol concentration and smoking than those actually existing in the source population. Above age 35 the proportion with more than 8 year schooling is 7-

10% higher in the SHS population.

3.6 Conclusions as to representativeness

The SHS population is closely representative of the source population in respect of marital status. Although a quite close match of Sofia population, SHS has a significant overrepresentation of persons with more than 8 years schooling especially in age groups above age 35. For variables strongly correlated with schooling, “schooling - adjusted” estimates for the source population should also be prepared. Most observed associations with schooling are however weak.

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Table 3.12: Distribution within age-strata by years of formal education, SHS population compared to census data for City of Sofia Region, 1992

Age groups Weighted Schooling mean* 25-34 35-44 45-54 55-64 65-74

Census propn male 0.490.47 0.48 0.46 0.45 More SHS male 94.4 95.2 91.4 81.5 74.1 87.3 than 8 female 96.2 96.8 90.5 82.4 68.6 86.9 years standardised 95.3 96.0 90.9 82.0 71.1 87.1 Census both sexes** 92.3 88.9 81.0 68.7 58.1 78.0

4 - 8 SHS male 5.6 4.8 8.6 18.5 25.9 12.7 years female 3.8 3.2 9.5 17.6 29.8 12.8 standardised 4.7 4.0 9.1 18.0 28.0 12.8 Census both sexes 6.8 10.2 18.1 30.0 40.2 21.1

Less than SHS male 0 0 0 0 0 0 4 years female 0 0 0 0 1.6 0.3 standardised 0 0 0 0 0.9 0.2 Census both sexes 0.4 0.3 0.4 0.8 1.4 0.7 * all age strata

** Census data only available for both sexes combined

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4. Chapter: Anthropometry

4.1 Assessment of data quality

Full details of the protocols for carrying out the anthropometric measurements are given in

Appendix14.3 and are summarised in Chapter 2 Methods.

All respondents to the Sofia Heart Study were eligible for the anthropometric measurements but pregnant women were excluded from the measurements of weight. The nurse did not attempted to measure height and weight of the people who were not able to stand upright unsupported.

Examination for digit preferences shows a moderate overrepresentation on zeros (17.7%, compared to 10% expected) for height and a slight overrepresentation on 0 (15.9%), 5 and even numbers for weight.

4.2 Height

Height was obtained from 960 (99.59%) of men and 1026 (99.42%) women.

Measurements were missing mainly in the oldest age group.

The mean height was 175.7 cm among men and 163.1 cm among women. The mean height decreased with increasing age. This decrease in height with age is likely to reflect both the loss of height with age and accelerated growth in the younger generations.

The distribution of height by age is shown in Tables 4.1 and 4.2.

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Table 4.1: Distribution of height within ten-year age groups with mean, standard deviations and 10th, 50th and 90th percentiles, males

Height Age Total [m] 25-34 35-44 45-54 55-64 65-74 Less than 1.60 1 0 1 4 2 8

% 0.5 0 0.5 2.1 1.2 0.8

1.60 - 1.64 5 3 6 11 10 35

% 2.6 1.4 3.0 5.8 5.9 3.6

1.65 - 1.69 12 16 19 31 25 103

% 6.2 7.7 9.6 16.3 14.7 10.7

1.70 - 1.74 34 50 50 57 52 243

% 17.4 24.0 25.4 30.0 30.6 25.3

1.75 - 1.79 67 65 66 45 57 300

% 34.4 31.3 33.5 23.7 33.5 31.3

More than 1.79 76 74 55 42 24 271

% 39.0 35.6 27.9 22.1 14.1 28.2

Total 195 208 197 190 170 960

Mean 177.5 177.2 175.8 174.0 173.4 175.7

S. D. 6.09 6.11 6.32 7.04 6.14 6.54

Percentiles

10th 170 170 168 165 166 168

50th 178 177 176 174 174 176

90th 185 184 184 184 181 184

Number of Missing Observations: 4

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Table 4.2: Distribution of height within ten-year age groups with mean, standard deviations and 10th, 50th and 90th percentiles, females

Height Age Total [m] 25-34 35-44 45-54 55-64 65-74

Less than 1.60 25 52 52 63 78 270

% 13.5 23.7 22.2 31.0 42.2 26.3

1.60 - 1.64 69 65 81 58 66 339

% 37.3 29.7 34.6 28.6 35.7 33.0

1.65 - 1.69 53 60 61 45 30 249

% 28.6 27.4 26.1 22.2 16.2 24.3

1.70 - 1.74 28 29 34 20 10 121

% 15.1 13.2 14.5 9.9 5.4 11.8

1.75 - 1.79 6 12 6 16 1 41

% 3.2 5.5 2.6 7.9 .5 4.0

More than 1.79 4 1 0 1 0 6

% 2.2 0.5 0 0.5 0 0.6

Total 185 219 234 203 185 1026

Mean 164.8 164.1 163.6 163.2 159.7 163.1

S. D. 5.79 5.91 5.77 6.80 6.16 6.31

Percentiles

10th 158 157 156 154 152 155

50th 164 164 164 163 160 164

90th 172 172 170 173 168 171

Number of Missing Observations: 6

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4.3 Weight

Weight was obtained of 98.96% men and 99.03% women.

The mean weight of men was 81.74 kg and mean weight of women was 69.59 kg.

The mean weight peaks between 35 and 55 in males and then falls, and among females weight increases to the age of 64, then decreased in the oldest age group.

The distribution of weight by 10-year age groups and sex is shown in Table 4.3.

The distribution of the weight by 5 years age groups and sex is given in Appendix14.8

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Table 4.3: Distribution of weight by age group and sex Weight Age [kg] 25-34 35-44 45-54 55-64 65-74 All ages Men less than 60 4 4 0 2 4 14 % 2.06 1.96 0.00 1.05 2.35 1.47 60 - 69 25 21 16 23 23 108 % 12.89 10.29 8.16 12.11 13.53 11.32 70 - 79 67 48 61 72 58 306 % 34.54 23.53 31.12 37.89 34.12 32.08 80 - 89 60 64 67 48 55 294 % 30.93 31.37 34.18 25.26 32.35 30.82 90 or more 38 67 52 45 30 232 % 19.59 32.84 26.53 23.68 17.65 24.32 Total 194 204 196 190 170 954 Mean 80.65 83.90 83.34 81.13 79.24 S. D. 11.71 12.91 11.50 12.59 9.86 Percentiles 10th 67 68 70 68 67 68 50th 80 82 81 79 79 80 90th 90 100 100 100 92 98 Women less than 60 82 64 44 16 28 234 % 44.32 29.36 19.05 7.88 15.14 22.90 60 - 69 64 68 62 60 65 319 % 34.59 31.19 26.84 29.56 35.14 31.21 70 - 79 28 44 63 57 53 245 % 15.14 20.18 27.27 28.08 28.65 23.97 80 - 89 4 20 42 45 28 139 % 2.16 9.17 18.18 22.17 15.14 13.60 90 or more 7 22 20 25 11 85 % 3.78 10.09 8.66 12.32 5.95 8.32 Total 185 218 231 203 185 1022 Mean 62.89 68.06 70.96 74.83 70.65 S. D. 12.17 15.35 12.78 12.32 10.96 Percentiles 10th 52 52 55 60 58 54 50th 61 66 70 75 69 68 90th 75 90 87 92 86 87 Number of Missing Observations: 20

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4.4 Body mass index

Body mass index (BMI) is defined as weight (kg)/height (m2). BMI was calculated for all those informants for whom a valid height and weight measurements was recorded. BMI is a continuous variable but for convenience of tabulation, the index can be broken down into categories to represent degrees of fatness. The categories used in this thesis accord with those adopted by the Royal College of Physicians170.

20 or less -’underweight’; over 20 to 25 - ‘desirable’; over 25 to 30 - ‘overweight’; over 30

- ‘obese’.

The mean BMI was 26.5 kg/m2 among men and 26.2 kg/m2 among women. Among men the BMI increased with age to the age group 45-54, then declined slightly. Among women,

BMI increased to age group 55-64 then decreased in the age group 65-74. More than half

(63.7%) of men and 53.4% of women had a BMI of more than 25 and so would be classified as overweight. The proportion of the obese people (BMI > 30 kg/m2) increases from 10.3% in the age group 25-34 to 22.1% in the age group 55-64 and then fallen down to 9.4% in the age group 65-74 for men. Among women we found the same tendency of steep increasing of the proportion of the obese from 5.4% in the age group 25-34 to 35.0% in the age group 55-64 and then fallen down to 24.5% in the age group 65-74.

The distribution of BMI by 10-year age groups and sex is shown in Table 4.4.

The distribution of BMI by 5-year age groups and sex is given in Appendix14.9.

The distribution of BMI as a risk factor (BMI > 25 kg/m2) is given in Appendix14.10.

Means substantially exceeded medians indicating the positive skew of the distribution.

In younger and middle-aged groups, changes in weight and BMI are mainly due to change in fatness. In older groups, changes in weight and BMI can be due to true changes in fatness, weight loss due to disease or loss of lean body mass.

130

Table 4.4: Distribution of Body Mass Index by age group and sex BMI Age All ages [kg/m2] 25-34 35-44 45-54 55-64 65-74 Men less than 20 5 4 2 2 1 14 % 2.6 2.0 1.0 1.1 0.6 1.5 20 - 25 incl. 88 62 55 72 55 332 % 45.4 30.4 28.1 37.9 32.4 34.8 25 - 30 incl. 81 105 104 74 98 462 % 41.8 51.5 53.1 38.9 57.6 48.4 greater than 30 20 33 35 42 16 146 % 10.3 16.2 17.9 22.1 9.4 15.3 Total 194 204 196 190 170 954 Mean 25.59 26.64 27.00 26.85 26.32 26.49 S. D. 3.39 3.60 3.61 4.27 2.86 3.62 Percentiles 10th 21.38 22.28 22.95 22.13 22.75 22.40 50th 25.16 26.27 26.30 25.95 26.19 25.96 90th 30.42 31.64 31.07 33.60 29.73 31.38 Women less than 20 26 26 13 5 0 70 % 14.1 11.9 5.6 2.5 0 6.9 20 - 25 incl. 118 100 83 53 52 406 % 63.8 45.9 35.9 26.1 28.3 39.8 25 - 30 incl. 31 54 86 74 87 332 % 16.8 24.8 37.2 36.5 47.3 32.5 greater than 30 10 38 49 71 45 213 % 5.4 17.4 21.2 35.0 24.5 20.9 Total 185 218 231 203 184 1021 Mean 23.14 25.24 26.50 28.20 27.65 26.17 S. D. 4.21 5.28 4.48 4.98 3.87 4.94 Percentiles 10th 19.36 19.58 21.36 22.04 22.94 20.57 50th 22.41 24.22 26.03 27.68 27.08 25.51 90th 27.64 33.22 31.86 34.81 32.49 32.81 Number of Missing Observations: 21

131

4.5 Waist-hip ratio

The mean waist was 92.8 cm among men and 83.5 cm among women.

The mean hip was 104.1 cm among men and 106.5 cm among women.

Waist-hip ratio (WHR) is a measure of the tendency for fat to be deposited abdominally, rather than ‘peripherally’. There is some evidence that the location of adipose tissue may be more important than its amount in determining the risk to health of increased fat. A study in Sweden has shown a significant positive association between the WHR and occurrence of stroke and coronary heart disease for both men and women and it was a strong predictor of cardiovascular disease morbidity than was BMI171. WHR was calculated for all those informants for whom a valid waist and hip measurement was recorded. The mean WHR calculated for men was 0.89, and for women 0.78.

The distribution of WHR by 10-year age groups is shown in Table 4.5 and Table 4.6.

132

Table 4.5: Distribution of Waist Hip Ratio by 10 year age group, with means, standard deviations and 10th, 50th, 90th percentiles, men

Age WHR All ages 25-34 35-44 45-54 55-64 65-74 < 0.750 8 9 3 10 10 40 % 4.2 4.5 1.6 5.5 6.0 4.3 0.750-0.799 19 13 12 11 7 62 % 9.9 6.5 6.5 6.1 4.2 6.7 0.800-0.849 19 35 26 27 24 131 % 9.9 17.5 14.0 14.9 14.4 14.1 0.850-0.899 49 42 51 46 54 242 % 25.5 21.0 27.4 25.4 32.3 26.1 0.900-0.949 50 66 61 53 44 274 % 26.0 33.0 32.8 29.3 26.3 29.6 0.950-0.999 23 23 24 23 19 112 % 12.0 11.5 12.9 12.7 11.4 12.1 1.000-1.049 16 10 5 5 8 44 % 8.3 5.0 2.7 2.8 4.8 4.8 > 1.049 8 2 4 6 1 21 % 4.2 1.0 2.2 3.3 .6 2.3 Total 192 200 186 181 167 926

Mean 0.90 0.89 0.89 0.89 0.89 0.89 S. D. 0.09 0.07 0.07 0.08 0.7 0.08 Percentiles 10th 0.78 0.79 0.81 0.79 0.79 0.79 50th 0.90 0.90 0.90 0.89 0.89 0.90 90th 1.02 0.97 0.97 0.97 0.96 0.98

Number of Missing Observations: 38

133

Table 4.6: Distribution of Waist Hip Ratio by 10 year age groups, with means, standard deviations and 10th, 50th, 90th percentiles, women

Age WHR All ages 25-34 35-44 45-54 55-64 65-74 < 0.750 114 104 82 33 28 361 % 62.3 47.9 35.2 16.4 15.5 35.6 0.750-0.799 37 56 63 47 46 249 % 20.2 25.8 27.0 23.4 25.4 24.5 0.800-0.849 13 31 53 67 52 216 % 7.1 14.3 22.7 33.3 28.7 21.3 0.850-0.899 11 14 22 37 40 124 % 6.0 6.5 9.4 18.4 22.1 12.2 0.900-0.949 5 8 9 14 13 49 % 2.7 3.7 3.9 7.0 7.2 4.8 0.950-0.999 1 4 2 2 2 11 % .5 1.8 .9 1.0 1.1 1.1 1.000-1.049 1 0 1 1 0 3 % .5 0 .4 .5 0 .3 > 1.049 1 0 1 0 0 2 % .5 0 .4 0 0 .2 Total 183 217 233 201 181 1015

Mean 0.75 0.76 0.78 0.81 0.81 0.78 S. D. 0.07 0.08 0.08 0.07 0.06 0.08 Percentiles 10th 0.67 0.67 0.70 0.72 0.73 0.69 50th 0.73 0.75 0.78 0.81 0.81 0.78 90th 0.85 0.86 0.87 0.89 0.89 0.88

Number of Missing Observations: 17

134

4.6 Comparison with MONICA

For the aims of comparison with MONICA data we are using age-standardised values for the age group 35-64 years.

Comparing age-standardised median BMI with those from the MONICA172 we found that for men Sofia (26.2 kg/m2) ranks equal 21st (together with Vaud/Frebourg) out of 43

(range 23.4 - 27.7 kg/m2). For females MONICA populations range is from 24.4 kg/m2 to

29.8 kg/m2, and Sofia females (25.8 kg/m2) ranks equal 20th - 22 (together with Catalonia and Ghent) out of 43 (Figures 4.1, 4.2).

The age-standardised 90th centiles of BMI for the MONICA populations varied between

27.6 kg/m2 to 32.7 kg/m2 for men and between 29.5 kg/m2 to 36.5 kg/m2 for women and

SHS population ranks 39 out of 43 with 32.2 kg/m2 for men, and ranks 26 out of 43 with

33.3 kg/m2 for women. However in SHS women obesity peaks sharply in late middle age, with a prevalence of 35% at ages 55-64 (Table 4.4).

135

Figure 4.1: Age standardised BMI: MONICA baseline data and SHS, ages 35-64, men

bmim

kg/m2 30

21 CHIN-BE POL-TARN NZ-AUK DEN-GLO UK-GLAS AUS_PER FR-HG UK-BEL IT-BRIA US-STAN BE-LU CH-VAUD AUS NEW BE-GHE FRG-RHE BUL-SOF FRG-BRE BE-CHA POL-WAR FIN-KUO FIN-TUR FIN-NK CH-TICIN FRG-AUU CZ-CZ FRG-AUR FR-BASR USSR-KAU

Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990 136

Figure 4.2: Age standardised BMI: MONICA baseline data and SHS age 35-64, women

bmiw kg/m2 30

21 US-STAN DEN-GLO FR-HG NZ-AUK AUS_PER CHIN-BE CH-VAUD CH-TICIN FRG-RHE IT-BRIA AUS NEW UK-BEL BE-LU FRG-AUU BE-GHE FIN-TUR FRG-BRE UK-GLAS FR-BASR FRG-AUR BUL-SOF FIN-KUO BE-CHA FIN-NK POL-WAR CZ-CZ POL-TARN USSR-KAU

Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990 137

5. Chapter: Blood pressure

Valid blood pressure readings were obtained for the total of 1996 respondents. Results presented are the mean of two readings unless otherwise indicated.

5.1 Systolic blood pressure

The distribution of systolic blood pressure (SBP) by age is presented in Table 5.1 for males and Table 5.2 for females. The age-standardised mean SBP for men 25-74 years old was

132.7 mmHg, which is slightly higher than that for women 128.4 mmHg. There was a well-marked effect of age on mean levels of SBP in both sexes. Among men, mean SBP increased from 126 mmHg in those aged 25-34 to 143 mmHg in those aged 65-74. Among women, mean SBP increased from 116 mmHg in those aged 25-34 to 147 mmHg in those aged 65-74. Therefore, although younger women had lower SBP than men, the rise in SBP across age groups was steeper in women, so that by age group 65-74 women had a higher mean SBP than men.

Systolic blood pressure was approximately normally distributed in men but slightly skewed to the right in women (Figure 5.1 and 5.2).

138

Table 5.1: Distribution of systolic blood pressure*, within 10 year age groups, with means, standard deviations and 10th, 50th, 90th percentiles, males

Age 25-34 35-44 45-54 55-64 65-74 All ages less than 100 2 5 7 2 2 18 % 1.0 2.4 3.5 1.0 1.2 1.9 100-109 22 12 10 5 5 54 % 11.2 5.7 5.1 2.6 2.9 5.6 110-119 33 40 23 21 8 125 % 16.8 19.1 11.6 11.0 4.7 13.0 120-129 63 60 36 26 18 203 % 32.1 28.7 18.2 13.6 10.6 21.1 130-139 44 42 38 46 32 202 % 22.4 20.1 19.2 24.1 18.8 21.0 140-149 20 25 42 39 53 179 % 10.2 12.0 21.2 20.4 31.2 18.6 150-159 10 10 20 12 15 67 % 5.1 4.8 10.1 6.3 8.8 7.0 160-169 2 9 18 21 27 77 % 1.0 4.3 9.1 11.0 15.9 8.0 170+ 0 6 4 19 10 39 % 0 2.9 2.0 9.9 5.9 4.0 Total 196 209 198 191 170 964 Mean 126 129 135 140 143 134 S. D. 13.7 18.3 19.3 19.6 17.9 18.9 Percentiles 10th 109 110 112 115 120 112 50th 124 126 136 138 142 134 90th 144 152 160 168 164 161 *mean of two readings

139

Table 5.2: Distribution of systolic blood pressure*, within 10 year age groups, with means, standard deviations and 10th, 50th, 90th percentiles, females

Age 25-34 35-44 45-54 55-64 65-74 All ages less than 100 28 20 12 4 0 64 % 15.1 9.1 5.1 2.0 0 6.2 100-109 34 40 21 7 2 104 % 18.4 18.3 9.0 3.4 1.1 10.1 110-119 55 49 29 12 7 152 % 29.7 22.4 12.4 5.9 3.7 14.7 120-129 36 46 44 29 9 164 % 19.5 21.0 18.8 14.1 4.8 15.9 130-139 19 34 45 36 48 182 % 10.3 15.5 19.2 17.6 25.4 17.6 140-149 9 12 40 43 50 154 % 4.9 5.5 17.1 21.0 26.5 14.9 150-159 1 10 11 24 30 76 % .5 4.6 4.7 11.7 15.9 7.4 160-169 2 6 17 30 22 77 % 1.1 2.7 7.3 14.6 11.6 7.5 170+ 1 2 15 20 21 59 % .5 .9 6.4 9.8 11.1 5.7 Total 185 219 234 205 189 1032 Mean 116 121 133 143 147 132 S. D. 15.67 17.86 22.74 22.15 17.03 22.73 Percentiles 10th 96 101 104 118 130 104 50th 114 120 132 142 144 131 90th 136 144 162 169 170 162 *mean of two readings

140

Figure 5.1: Frequency distribution of systolic blood pressure (SBP)* by sex

Men

0 <100 100-109 110-119 120-129 130-139 140-149 150-159 160-169 >170 SBP [mmHg]

25 Women

0 <100 100-109 110-119 120-129 130-139 140-149 150-159 160-169 >170 SBP [mmHg]

*mean of two readings

141

Figure 5.2: Systolic blood pressure*: values for the 10th, 50th, and 90th centiles by age group and sex Men SBP [m m Hg]

180

160

140

120

100

0 25-34 35-44 45-54 55-64 65-74 Age group

Women SBP [m m Hg]

180

160

140

120

100

0 25-34 35-44 45-54 55-64 65-74 Age group *mean of two readings

142

5.2 Diastolic blood pressure

The distribution of DBP by age is presented in Table 5.3 for males and Table 5.4 for females.

The mean age-standardised DBP for adults 25-74 years old was 84.9 mmHg in men and

83.6 mmHg in women.

We found a constant rise in mean levels of DBP according to age for both men and women, up until the age of 65. After this age there is a different pattern of DBP distribution in men and women. There is a decline in the females’ DBP from 90.82 mmHg to 87.98 mmHg, while the males DBP demonstrate quite stable levels.

Diastolic blood pressure was not normally distributed in men and women (Figure 5.3 and

5.4)

143

Table 5.3: Distribution of diastolic blood pressure*, within 10 year age groups, with means, standard deviations and 10th, 50th, 90th percentiles, males

Diastolic blood pressure Age All ages [mmHg] 25-34 35-44 45-54 55-64 65-74 < 60 0 0 5 0 1 6 % 0 0 2.5 0 0.6 0.6 60 - 64 10 8 3 6 1 28 % 5.1 3.8 1.5 3.1 0.6 2.9 65 - 69 13 3 3 5 3 27 % 6.6 1.4 1.5 2.6 1.8 2.8 70 - 74 25 22 23 15 12 97 % 12.8 10.5 11.6 7.9 7.1 10.1 75 - 79 32 29 7 12 12 92 % 16.3 13.9 3.5 6.3 7.1 9.5 80 - 84 50 55 46 35 32 218 % 25.5 26.3 23.2 18.3 18.8 22.6 85 - 89 36 47 35 50 47 215 % 18.4 22.5 17.7 26.2 27.6 22.3 90 - 94 19 11 28 25 26 109 % 9.7 5.3 14.1 13.1 15.3 11.3 95 - 99 8 12 12 12 17 61 % 4.1 5.7 6.1 6.3 10.0 6.3 100 - 104 0 11 22 13 9 55 % 0 5.3 11.1 6.8 5.3 5.7 105 - 109 0 5 7 7 6 25 % 0 2.4 3.5 3.7 3.5 2.6 110+ 3 6 7 11 4 31 % 1.5 2.9 3.5 5.8 2.4 3.2 Total 196 209 198 191 170 964 Mean 81 85 87 88 89 85 S. D 9.46 11.05 12.29 11.78 10.28 11.28 Percentiles 10th 68 72 72 72 74 72 50th 82 84 86 87 86 85 90th 92 100 103 103 102 101 *mean of two readings

144

Table 5.4: Distribution of diastolic blood pressure*, within 10 year age groups, with means, standard deviations and 10th, 50th, 90th percentiles, females

Diastolic blood pressure Age All ages [mmHg] 25-34 35-44 45-54 55-64 65-74 < 60 11 9 0 3 0 23 % 5.9 4.1 0 1.5 0 2.2 60 - 64 19 11 14 2 0 46 % 10.3 5.0 6.0 1.0 0 4.5 65 - 69 13 4 2 4 1 24 % 7.0 1.8 0.9 2.0 0.5 2.3 70 - 74 35 46 22 13 13 129 % 18.9 21.0 9.4 6.3 6.9 12.5 75 - 79 20 20 16 12 14 82 % 10.8 9.1 6.8 5.9 7.4 7.9 80 - 84 43 45 56 35 43 222 % 23.2 20.5 23.9 17.1 22.8 21.5 85 - 89 26 45 37 35 42 185 % 14.1 20.5 15.8 17.1 22.2 17.9 90 - 94 8 14 30 26 34 112 % 4.3 6.4 12.8 12.7 18.0 10.9 95 - 99 5 7 15 19 22 68 % 2.7 3.2 6.4 9.3 11.6 6.6 100 - 104 4 8 16 25 11 64 % 2.2 3.7 6.8 12.2 5.8 6.2 105 - 109 0 6 10 6 5 27 % 0 2.7 4.3 2.9 2.6 2.6 110+ 1 4 16 25 4 50 % .5 1.8 6.8 12.2 2.1 4.8 Total 185 219 234 205 189 1032 Mean 77 81 87 90 88 85 S. D. 11.0 11.8 12.9 13.3 9.2 12.8 Percentiles 10th 62 66 71 74 76 70 50th 78 82 86 89 86 84 90th 88 97 105 110 101 102 *mean of two readings

145

Figure 5.3: Frequency distribution of diastolic blood pressure (DBP)* by sex Men 25

0 < 60 60-64 65-69 70-74 75-79 80-84 85-89 90-94 95-99 100- 105- 110+ 104 109 DBP [ m m Hg]

Women 25

0 < 60 60-64 65-69 70-74 75-79 80-84 85-89 90-94 95-99 100- 105- 110+ 104 109 DBPDBP [mmHg] [m m Hg]

*mean of two readings

146

Figure 5.4: Diastolic blood pressure (DBP)* : values for the 10th, 50th, and 90th centiles by age group and sex

Men 120

100

60 DBP [mmHg] 40

0 25-34 35-44 45-54 55-64 65-74 Age group

Women

120

100

60 DBP [mmHg] 40

0 25-34 35-44 45-54 55-64 65-74 Age group

*mean of two readings

147

5.3 Blood pressure categories

On the bases of blood pressure readings and participants’ reports about whether they currently taking any antihypertensive drugs we classified them in to one of the following categories:

1. Normotensive, treated (systolic blood pressure <160 and diastolic blood pressure <95; reporting treatment for hypertension)

2. Hypertensive, treated (systolic blood pressure >159 and/or diastolic blood pressure >94; reporting treatment for hypertension)

3. Hypertensive, untreated (systolic blood pressure >159 and/or diastolic blood pressure

>94; not reporting treatment for hypertension)

4. Normotensive, untreated (systolic blood pressure <160 and diastolic blood pressure <95; not reporting treatment for hypertension)

The distribution of informants in these categories by age and sex is presented in Table 5.5.

As subjects were only asked about medication for hypertension or diabetes the proportion reporting anti-hypertensive medication was estimated excluding those reporting a diagnosis of diabetes (n = 135).

'The proportions of both males and females in category 4 decrease with age. All other categories increase with age (Table 5.5).

148

Table 5.5: Distribution of subjects (excluding those reporting a diagnosis of diabetes) by blood pressure categories, within 10-year age groups by sex

Age Total 25-34 35-44 45-54 55-64 65-74 Males Normotensive treated* 8 18 26 22 31 105 % 4.2 8.9 13.8 13.6 20.4 11.7 Hypertensive treated** 7 26 26 34 33 126 % 3.7 12.8 13.8 21.0 21.7 14.0 Hypertensive untreated*** 5 12 21 15 14 67 % 2.6 5.9 11.1 9.3 9.2 7.5 Normotensive untreated**** 171 147 116 91 74 599 % 89.5 72.4 61.4 56.2 48.7 66.8 Total 191 203 189 162 152 897 Females Normotensive treated* 4 22 41 30 49 146 % 2.2 10.5 18.3 17.3 30.2 15.3 Hypertensive treated** 7 16 45 56 41 165 % 3.8 7.7 20.1 32.4 25.3 17.3 Hypertensive untreated*** 5 8 14 15 9 51 % 2.7 3.8 6.3 8.7 5.6 5.4 Normotensive untreated**** 168 163 124 72 63 590 % 91.3 78.0 55.4 41.6 38.9 62.0 Total 184 209 224 173 162 952

Number of Missing Observations: 147 * Systolic blood pressure <160 and Diastolic blood pressure <95; reporting treatment for hypertension ** Systolic blood pressure >159 and/or Diastolic blood pressure >94; reporting treatment for hypertension *** Systolic blood pressure >159 and/or Diastolic blood pressure >94; not reporting treatment for hypertension **** Systolic blood pressure <160 and Diastolic blood pressure <95; not reporting treatment for hypertension

149

5.4 Assessment of data quality

5.4.1 Digit preference

The derivation of Digit Preference Score was discussed in Section 2.3.3.1. of the Methods chapter.

The MONICA project defined three categories of Digit Preference Score (DPS): if systolic and diastolic DPS < 10 (optimal); if at least one of the two DPSes was between 10 and 20 and neither above 20 (satisfactory), and if either systolic or diastolic or both DPS > 20

(unsatisfactory). The DPS calculated for data from SHS is about 50, so the data are in the third category with unsatisfactory quality.

5.4.2 Proportion of odd readings

Proportions of Odd Readings (POR) were grouped in three classes by the MONICA

Project153: less than 5% odd readings (optimal); 5% to < 10% odd readings (satisfactory), and 10% or more odd readings (unsatisfactory). Data from SHS has about 1% odd readings in both systolic and diastolic measurements. They are classified in the first category as data with optimal quality.

5.4.3 Proportion of identical duplicate readings

MONICA Project defined the following classes of Proportions of Identical Results (PIR): good - less than 33% identical results in duplicate SBP or DBP; intermediate - at least one proportion of identical results for either SBP or DBP between 33% and 50% and none above 50%; poor- at least one proportion of identical results for either SBP or DBP above

50%. Data from SHS had 51% odd readings, so they are just over the threshold of poor quality for this criterion.

150

5.4.4 Proportion of second readings exceeding first readings:

Data from SHS had 19% of SBP and 18% of DBP where the second reading exceeded the first reading.

5.4.5 Mean difference between first and second readings:

The mean difference based on the observed data was 1.0 mmHg (SD=0.61) for DBP and

1.7 mmHg (SD = 1.8) for SBP.

5.4.6 Implications of data quality

The proportion of incomplete measurements (PIM) is a strong indicator of validity problems in terms of population representativeness. SHS achieved complete measurements of blood pressure in all participants.

The proportion of odd readings (POR) was perceived as pointing to problems with blood pressure measurement precision. Last digit preference (LDP), an indicator for biased blood pressure readings, may indicate reduced validity of blood pressure results. The most common bias was the preference of recording zero as the last digit. Preference for zero as the last digit when recording blood pressure affects the shape of the distribution curve153 and reduces the power of statistical tests (by reducing precision) thereby making it more difficult to assess associations between blood pressure and other potential risk factors152. It has been found that a preference for zero generally has no appreciable effect on the means and the standard deviations, but percentiles and proportions above certain cut-off points could be increased150. As the data from SHS are classified as ‘poor’ according to the digit preference, the 90th centiles could be biased upwards.

In general low grading in PIM, POR and LDP scores is indicative of problems with proper observer training and quality control, and hence of deficiencies in the prerequisites for high quality blood measurements149.

Given the high biological variability of blood pressure with moment-to-moment variation, higher proportions of identical readings suggests lower accuracy; i.e. the observer was

151 unduly influenced by the first reading when taking the second reading, measurements were taken without due attention to accuracy, or perhaps even that blood pressure was being measured once but recorded twice.

High prevalence of identical duplicate measurements may cause a shift in the entire blood pressure distribution to higher values. This affects means, percentiles, and prevalence alike, and is of particular relevance for systolic blood pressure results153. Some studies found that the prevalence of identical duplicate measurements was significantly and inversely correlated with the mean difference between the duplicate measurements. The

Australian Risk Factor Survey data have been used to model this relationship. They found that absolute differences of around 30 percentage points between centres or surveys in the prevalence of identical duplicate measures would bias estimates of differences in mean systolic blood pressure by 0.85 mmHg. They did not find a similar effect for the diastolic blood pressure150.

There is no evidence of any association, in the Sofia study population, between the proportion of identical duplicate measurements and the age, sex, body mass index or socio- economic status of the respondent. It seems that the occurrence of identical duplicate measures is more attributable to the person taking the blood pressure measurement than to the person whose blood pressure is measured.

The data from SHS were found to be just over the threshold of the poor quality for that criterion, so they might be affected by it. We would expect an upward bias across the entire BP distribution.

There is no gold standard for the proportion of second readings exceeding the first reading.

In the Australian survey it varies between 9% and 54% in different survey centres150.

Having 19% of cases where the second reading exceeded the first in the SHS data, we would assume the SHS data are of moderate quality.

152

The mean difference has been advocated as an important indicator for measurement quality, which should be monitored periodically throughout data collection173. It could reflect a systematic bias by the observer or a faulty sphygmomanometer but these effects are difficult to substantiate. An association between the proportion of identical duplicate readings and the absolute rise of the mean difference between first and second reading is to be expected, as the two indicators are obviously related150.

In conclusion we can expect:

• Lack of bias coming from proportion of incomplete measurements

• Reduced validity of blood pressure results and reduced power of statistical tests to

assess associations between blood pressure and other potential risk factors, little

effect on the means and the standard deviations, but possible increased upper

percentiles as a result of slightly high proportion of odd readings.

• Shift in the entire blood pressure distribution to slightly higher values, with effects

on means, percentiles, and prevalence alike, particularly for systolic blood pressure

as a result of high prevalence of identical duplicate measurements.

• As the degree to which the proportion of second readings exceeding the first

reading can be considered normal or acceptable is not clearly defined and SHS data

were assumed to be of moderate quality, we would not expect a bias coming from

this data quality criterion.

The direction of any measurement bias, if present, is thus likely to be modestly

upwards. Measurement bias is therefore an implausible explanation for the measured

blood pressure levels being ‘lower than expected’ — given, for example, the high

stroke mortality. There is however, a generally increased uncertaintly due to concerns

about the quality of the blood pressure measurement. This implies that in future

studies, particularly those addressing disease at an individual level, careful attention

must be paid to staff training and ongoing validation.

153

5.5 Assessment of blood pressure distribution

5.5.1 Estimates of central tendency

The median is to be preferred to the mean to represent the central tendency as it is less affected by the proportion receiving treatment or by digit preference.

The age standardised162 median for SBP for age stratum 35-64 was 132 mmHg for males and 129 mmHg for females. The corresponding age standardised means were 134 mmHg for men and 131 mmHg for women. Bias from digit preference could affect the means but is unlikely to affect the medians.

5.5.2 90th centile

After age-standardisation for the age group 25-74 10% of the values for SBP exceeded 159 mmHg for men and 160 mmHg for women.

Inspection of the curve of the mean of two consecutive readings over the full range of SBP suggests that the observers took less care when recording high values — because the lower half of the distribution is smoother than the upper half. Inspection of the contributing single readings for SBP shows an unexpected over-allocation on 144 mmHg and 164 mmHg as well as on 140, 150 and 160 mmHg. The rounding to 144 mmHg and 164 mmHg seems to be downwards to 140 and 150 and from both sides. The distribution of readings between 155 and 165 does not confirm to any readily interpretable pattern but there is some suggestion of a net rounding up to 160. The frequency distribution of pooled values of single SBP readings for the range 138 to 172 is given in Table 5.6.

Inspection of the curve of the mean of two consecutive readings of DBP suggests that the observers took less care with the low values than the high — with the curve being less smooth over its lower half. Inspection of the distribution of the contributing single readings for DBP shows a substantial tendency to round up to 70 mmHg and 80 mmHg.

154

Conclusion: The 90th centiles of both SBP and DBP might be biased by measurement error. The direction of bias could be downwards in the case of SBP as a result of rounding down to 164 and 144 mmHg. The smoother nature of the upper part of the DBP distribution suggests that any bias is likely to be small.

Table 5.6: Frequency distribution of pooled values of the two consecutive readings of SBP, for readings in range 138-172, both sexes combined. SBP Frequency 138 124 140 192 142 124 144 231 145 3 146 74 148 62 150 115 152 65 154 29 155 1 156 30 158 25 160 91 162 80 164 91 165 3 166 21 168 35 170 37 172 28

155

5.5.3 Proportion reporting antihypertensive treatment

The proportion reporting treatment for high blood pressure was very high. To examine this further, this proportion was compared with that in MONICA populations (Figures 5.5, 5.6).

The Sofia Heart Study is a clear outlier on this variable, making it likely that there is a bias towards either over-treatment or over-reporting antihypertensive therapy.

We tested the assumption that misclassification of treatment status was present as random noise — that is independent of actual blood pressure. On this assumption those misclassified as being on treatment would be drawn proportionately from those with high and low blood pressures. What we observed by comparing the data from SHS with

MONICA data was that the high proportion of those reporting treatment was mainly associated with substantially lower proportions of those with high blood pressure not reporting treatment, but not with a higher proportion of those with lower blood pressures reporting treatment. Conclusion: If there is a bias towards over-reporting medication it appears to be mainly present in those with higher blood pressures. This could be the delayed results from the previous existence of easily accessible health care and the system of ‘dispenserisation’ of hypertensive people, or that a high proportion of hypertensives do consider themselves ‘on treatment’ - perhaps independent, to some extent, from the regularity, with which they take medications. In the present period of the crisis in the health care system (difficult access to medical staff, no requirements for prescriptions for drugs) actual consumption of antihypertensive treatment is unlikely to be so high.

This being so, the classification schema based on both blood pressure and treatment status could be seriously misleading when used to compare with other populations.

Therefore external comparisons of blood pressure levels are likely to be less biased when based on recorded pressures only - ignoring the reported treatment.

156

Figure 5.5: Proportion reporting antihypertensive treatment in relation to median systolic blood pressure, age-standardised values for ages 35-64, MONICA baselines surveys and SHS*, males

Men 30% Sof

20 Ber

DDR Rhe New Cze Nor Nov Sta Kau Tur Kuo Mal Kar TarPer Hal Bas 10 ChaHauNor Mos WarFri Ghe AucLux Gla Bri Vau Nov Bei Tic Aug Glo NovBelAug Mos Bre Cat

0 ON_TREAT 120 130 140 150 mmHg SBP

Excluding subjects reporting a diagnosis of diabetes Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990

157

Figure 5.6: Proportion reporting antihypertensive treatment in relation to median systolic blood pressure, age-standardised values for ages 35-64, MONICA baselines surveys and SHS*, females.

% Women 40

Sof

DDR Nov Ber Kau Mal 20 CzeNov New Tar Mos Kar Mos Hal NorKuo Ghe Rhe Bas Lux NovBri Fri PerChaAuc War CatSta Nor HauBeiAug Tur Tic AugBel 10 GloVau Bre Gla

0 ON_TREAT 110 120 130 140 150 mmHg SBP

* Excluding subjects reporting a diagnosis of diabetes Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990

5.5.4 External comparison

5.5.4.1 MONICA

For the aims of comparison with MONICA data we are using age-standardised values for the age group 35-64 years.

Comparing age standardised median SBP’s with those from the MONICA172 we found that for men Sofia (132 mmHg) ranks 23 out of 43 (range 121 - 145 mmHg). For females, the

MONICA populations range from 117 mmHg to 143 mmHg, and Sofia females (129 mmHg) ranks 18 out of 43 (Figures 5.7, 5.8).

158

Figure 5.7: Age-standardised median systolic blood pressure: MONICA baseline data and SHS, ages 35-64, men

sbpm mmHg 145

140

135

130

125

120

115 CZ-CZ BE-LU FIN-NK IT-BRIA FR-HG FIN-TUR UK-BEL NZ-AUK BE-GHE CHIN-BE BE-CHA CH-TICIN FIN-KUO BUL-SOF US-STAN FRG-RHE FR-BASR FRG-BRE DEN-GLO CH-VAUD UK-GLAS FRG-AUR FRG-AUU AUS_PER AUS NEW POL-WAR POL-TARN USSR-KAU

Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990

159 Figure 5.8: Age-standardised median systolic blood pressure MONICA baseline data and SHS age 35-64, women

sbpw mmHg 160

140

120

100

0 BE-LU FIN-NK FR-HG IT-BRIA CZ-CZ UK-BEL FIN-TUR BE-GHE CHIN-BE FIN-KUO BUL-SOF BE-CHA NZ-AUK CH-TICIN FRG-RHE FRG-BRE FR-BASR FRG-AUR FRG-AUU US-STAN DEN-GLO AUS NEW UK-GLAS AUS_PER CH-VAUD USSR-KAU POL-WAR POL-TARN

Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990 160 Comparing age standardised median DBP with those from the MONICA172 we found that the median DBP of the MONICA populations for men varies from 74 mmHg to 91 mmHg and with 86 mmHg Sofia ranks 24 out of 43. For females MONICA populations range is from 72 mmHg to 89 mmHg, and Sofia females (85 mmHg) ranks 33 out of 43 (Figures

5.9, 5.10).

The age-standardised 90th centiles of SBP for the MONICA populations varied between

146 mmHg and 171 mmHg for men and between 144 mmHg and 176 mmHg for women and the SHS population was found to be in the middle of those ranges (rank 21 out of 43 with 161 mmHg for men, and rank 22 out of 43 with 161 mmHg for women).

A similar ranking was observed for the age-standardised 90th centile of DBP in SHS men

(ranking 29 out of 43 with a value of 102 mmHg) compared to MONICA populations, which ranged from 90 mmHg to 108 mmHg. However, with an age-standardised 90th centile of the DBP of 104 mmHg, Sofia women ranked in the upper end (40 out of 43) when compared to the MONICA populations that were between 87 mmHg to 109 mmHg.

5.5.5 Potential effect of data quality in external comparisons

The most robust BP parameter for external comparison is the median because it is relatively resistant to the effects of digit preference. It is also little affected by the proportion on treatment. On this parameter the Sofia population occupies an intermediate position relative to the MONICA baseline studies.

Whether the distribution is more skewed to the right in the SHS is more difficult to assess, as the 90th centile could be more vulnerable to bias. The 90th centile for DBP is possibly safer to compare, as the data quality appears better for the upper curve of the DBP distribution than the SBP distribution.

In comparison to the MONICA populations, the SHS ranks higher on the 90th centiles for

DBP than it does for the median suggesting either that

161 a) the distribution is more ‘skewed to the right’ in Sofia (for example because of less adequate control of hypertension) or b) the estimates for the 90th centiles are biased upward by measurement error.

Thus, the data on blood pressure, when used for external comparison must be interpreted with caution for the following three reasons: Firstly, because there are data quality issues in the measurement of BP, secondly, because the sample is biased towards higher educational groups and thirdly, because the data from the survey are for Sofia only and BP distribution may differ in the Bulgarian population as a whole.

There are, however, some tentative implications. First that there is clearly a high prevalence of reported antihypertensive treatment, some of which may be real and some due to over-reporting. Secondly, in this admittedly not fully representative sample, DBP is somewhat higher than in MONICA countries, on average – especially for women. Finally, careful attention to quality of blood pressure measurement is indicated for future studies.

The slightly high DBP observed is presumably a contributor to the high cardiovascular and cerebrovascular disease rates in the country. With better measurement and a more representative sample, it might be possible to better quantify the amount of disease attributable to high blood pressure.

162

Figure 5.9: Age-standardised median diastolic blood pressure MONICA baseline data and SHS age 35-64, men

dbpm mmHg 92

70 CZ-CZ BE-LU IT-BRIA FIN-NK FR-HG FR-HG FIN-TUR NZ-AUK UK-BEL CHIN-BE BE-GHE CH-TICIN BE-CHA BUL-SOF FIN-KUO FR-BASR FRG-RHE US-STAN FRG-BRE UK-GLAS DEN-GLO CH-VAUD FRG-AUU FRG-AUR AUS_PER AUS NEW POL-WAR POL-TARN USSR-KAU

Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990

163

Figure 5.10: Age-standardised median diastolic blood pressure MONICA baseline data and SHS age 35-64, women

dbpw mmHg 88

66 BE-LU FIN-NK FR-HG IT-BRIA CZ-CZ UK-BEL BE-GHE FIN-TUR CHIN-BE FIN-KUO BE-CHA NZ-AUK BUL-SOF CH-TICIN FRG-RHE FRG-BRE FR-BASR US-STAN FRG-AUR DEN-GLO FRG-AUU AUS_PER AUS NEW UK-GLAS CH-VAUD USSR-KAU POL-WAR POL-TARN

Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990 164 6. Chapter: Blood lipids

6.1 Introduction

This chapter presents results for the total serum cholesterol, HDL and tryglicerides analyses (mmol/l) carried out on the blood samples taken from the informants in SHS as part of their visit to the survey centres. For the purpose of the analyses, the total cholesterol results have been grouped into the following conventionally accepted categories174:

Level of total cholesterol Description less than 5.2 mmol/l desirable 5.2 - less than 6.5 mmol/l mildly elevated 6.5 - less than 7.8 mmol/l moderately elevated 7.8 mmol/l or more severely elevated

HDL was grouped into the following categories:

Level of HDL cholesterol less than 1.0 mmol/l 1.0 - less than 1.3 mmol/l 1.3 - less than 1.9 mmol/l 1.9 mmol/l or more

The ratio of total cholesterol and HDL was calculated and grouped into the following categories:

Ratio of total cholesterol to HDL cholesterol less than 3.5 3.5 - less than 5.5 5.5 - less than 7.5 7.5 - less than 9.5 9.5 or more

165

Tryglicerides were grouped into the following groups:

Level of triglycerides less than 1.0 mmol/l 1.0 - less than 1.6 mmol/l 1.6 mmol/l or more

The observed blood lipids were presented by age, sex and selected characteristics of the population.

6.2 Response to blood tests

Blood samples were obtained from 1696 informants (761 men and 935 women). This presents 85.0% response rate from 1996 participants (54.7% from the original sample of

3100). In some circumstances (like blood collection), a low response rate may be difficult to avoid. This low response rate creates the possibility of substantial non-response or selection bias in the study sample for blood measurements and therefore biased estimates of blood-based variables175.

Informants, who were pregnant, had a clotting disorder or taking lipid lowering drugs were ineligible to give a blood sample.

Full details of the response to the blood sample by age group and sex is given in Table 6.1.

Those least likely to have blood samples were young males (aged 25-34). For males over

35, around 80% had blood samples taken. For females of all ages, the rate was 80-90%.

166

Table 6.1: Distribution of study population by whether blood sample obtained (‘full participation’) Age Total 25-34 35-44 45-54 55-64 65-74 Males Survey population 116 179 163 158 145 761 with full participation Total survey 196 209 198 191 170 964 population % with full 59.2 85.6 82.3 82.7 85.3 78.9 participation Females Survey population 158 201 222 195 159 935 with full participation Total survey 185 219 234 205 189 1032 population % with full 85.4 91.8 94.9 95.1 84.1 90.6 participation

167

6.3 Total cholesterol concentration

Table 6.2: Total serum cholesterol concentration: distribution within age groups, males Age mmol/l Total 25-34 35-44 45-54 55-64 65-74 less than 5.2 (desirable) No 84 100 81 90 73 428 % 72.4 55.9 49.7 57 50.3 56.2 5.2 - less than 6.2 (mildly elevated) No 23 46 55 44 55 223 % 19.8 25.7 33.7 27.8 37.9 29.3 6.5 - less than 7.8 (moderately elevated) No 7 21 19 19 11 77 % 6 11.7 11.7 12 7.6 10.1 7.8 or more (severely elevated) No 2 12 8 5 6 33 % 1.7 6.7 4.9 3.2 4.1 4.3 Total 116 179 163 158 145 761 Mean 4.79 5.34 5.34 5.26 5.28 5.23 S. D. 1.11 1.46 1.25 1.25 1.47 1.36 Percentiles 10th 3.52 3.79 3.95 4.00 3.61 3.79 50th 4.64 5.00 5.20 5.07 5.18 5.01 90th 6.19 7.50 7.06 6.85 6.79 6.85 Number of Missing Observations: 203

168

Table 6.3: Total serum cholesterol concentration: distribution within age groups, females Age mmol/l Total 25-34 35-44 45-54 55-64 65-74 less than 5.2 (desirable) No 119 137 121 85 56 518 % 75.3 68.2 54.5 43.6 35.2 55.4 5.2 - less than 6.2 (mildly elevated) No 27 51 64 63 53 258 % 17.1 25.4 28.8 32.3 33.3 27.6 6.5 - less than 7.8 (moderately elevated) No 10 9 25 37 31 112 % 6.3 4.5 11.3 19 19.5 12 7.8 or more (severely elevated) No 2 4 12 10 19 47 % 1.3 2 5.4 5.1 11.9 5.0 Total 158 201 222 195 159 935 Mean 4.64 4.77 5.31 5.55 5.88 5.23 S. D. 1.30 1.35 1.41 1.27 1.47 1.43 Percentiles 10th 3.22 3.48 3.77 4.06 4.17 3.64 50th 4.36 4.57 5.14 5.36 5.70 5.03 90th 6.32 6.12 6.94 7.26 8.30 7.01 Number of Missing Observations: 97

We found a tendency for mean levels of TSC among females to increase with age. Among males it was slightly higher in the age range 35 to 54. In tables 6.2 and 6.3 are presented mean and SD of total serum cholesterol levels by age and sex. After age standardisation mean TSC for males remains 5.2 mmol/l and for females become 5.1 mmol/l.

43.8% from males and 44.6% from females had at least mildly elevated total cholesterol (≥

5.2 mmol/l). 14.5% from males and 17.0% from females had more or equal to 6.5 mmol/l

TSC, which is considered as risk factor176,177.

169

6.4 High density lipoprotein cholesterol concentration

Table 6.4: High density lipoprotein cholesterol concentration (HDL): distribution within age groups with means, 10th, 50th and 90th centiles, males Age mmol/l 25-34 35-44 45-54 55-64 65-74 All ages

<1 33 57 60 49 34 233

% 28.4 31.8 36.8 31.0 23.3 30.6 1.0 - 1.3 38 71 57 43 49 258

% 32.8 39.7 35.0 27.2 33.6 33.9 1.3 - 1.9 39 39 41 55 53 227

% 33.6 21.8 25.2 34.8 36.3 29.8 >1.9 6 12 5 11 10 44

% 5.2 6.7 3.1 7.0 6.8 5.8 Total 116 179 163 158 146 762

Mean 1.24 1.17 1.16 1.24 1.28 1.22

S. D. 0.46 0.38 0.37 0.43 0.38 0.41

Percentiles

10th 0.76 0.73 0.74 0.75 0.81 0.78

50th 1.17 1.14 1.11 1.14 1.23 1.15

90th 1.77 1.68 1.61 1.82 1.80 1.76

Number of Missing Observations: 202

170

Table 6.5: High density lipoprotein cholesterol concentration (HDL): distribution within age groups with means, 10th, 50th and 90th centiles, females Age mmol/l 25-34 35-44 45-54 55-64 65-74 All ages < 1 21 40 37 26 27 151 % 13.3 20.1 16.7 13.3 17.0 16.2 1.0 - 1.3 46 53 63 62 52 276 % 29.1 26.6 28.4 31.8 32.7 29.6 1.3 - 1.9 67 86 95 91 63 402 % 42.4 43.2 42.8 46.7 39.6 43.1 > 1.9 24 20 27 16 17 104 % 15.2 10.1 12.2 8.2 10.7 11.1 Total 158 199 222 195 159 933 Mean 1.46 1.36 1.40 1.38 1.35 1.39 S. D. 0.49 0.43 0.46 0.37 0.43 0.44 Percentiles 10th 0.92 0.88 0.86 0.97 0.82 0.89 50th 1.36 1.33 1.35 1.36 1.30 1.34 90th 2.10 1.92 2.02 1.87 1.92 1.93 Number of Missing Observations: 99

Table 6.4 and Table 6.5 present the distribution of HDL in males and females within age groups. There was a tendency for HDL levels to be higher in the youngest age group in female subjects. The mean age-standardised values were 1.21 mmol/l for males and 1.40 mmol/l for females.

171

6.5 Ratio Total cholesterol/High density lipoprotein cholesterol

Table 6.6: Ratio Total cholesterol/High density lipoprotein cholesterol concentration (HDL): distribution within age groups with means, 10th, 50th and 90th centiles, males Age mmol/l 25-34 35-44 45-54 55-64 65-74 All ages < 3.5 38 44 33 42 42 199 % 32.8 24.6 20.2 26.6 29.0 26.1 3.5 - 5.4 58 78 75 74 77 362 % 50.0 43.6 46.0 46.8 53.1 47.6 5.5 - 7.4 13 39 44 33 21 150 % 11.2 21.8 27.0 20.9 14.5 19.7 7.5 - 9.4 3 8 5 5 2 23 % 2.6 4.5 3.1 3.2 1.4 3.0 > 9.5 4 10 6 4 3 27 % 3.4 5.6 3.7 2.5 2.1 3.5 Total 116 179 163 158 145 761 Mean 4.32 5.00 4.99 4.78 4.43 4.74 S. D. 1.91 2.18 1.80 2.31 1.78 2.03 Percentiles 10th 2.32 2.85 2.98 2.51 2.72 2.70 50th 4.04 4.59 4.79 4.68 4.12 4.47 90th 6.74 7.39 6.97 7.17 6.43 6.88 Number of Missing Observations: 203

172

Table 6.7: Ratio Total cholesterol/High density lipoprotein cholesterol concentration (HDL): distribution within age groups with means, 10th, 50th and 90th centiles, females Age mmol/l 25-34 35-44 45-54 55-64 65-74 All ages < 3.5 96 103 90 71 40 400 % 60.8 51.8 40.5 36.4 25.2 42.9 3.5 - 5.4 46 71 91 89 72 369 % 29.1 35.7 41.0 45.6 45.3 39.5 5.5 - 7.4 15 19 30 28 36 128 % 9.5 9.5 13.5 14.4 22.6 13.7 7.5 - 9.4 1 1 8 2 7 19 % 0.6 0.5 3.6 1.0 4.4 2.0 > 9.5 5 3 5 4 17 % 2.5 1.4 2.6 2.5 1.8 Total 158 199 222 195 159 933 Mean 3.47 3.87 4.15 4.39 4.79 4.13 S. D. 1.39 1.82 1.64 2.12 2.16 1.89 Percentiles 10th 2.00 2.15 2.50 2.58 2.69 2.38 50th 3.12 3.44 3.83 3.99 4.49 3.75 90th 5.50 5.92 6.43 6.28 7.16 6.22 Number of Missing Observations: 99

Tables 6.6 and 6.7 show the distribution of the ratio total cholesterol/HDL for men and women in ten years age groups. In males the ratio tended to be lowest in the youngest age group and highest at ages 35-44. In females the ratio tended to increase with age.

173

6.6 Triglycerides

Table 6.8: Triglycerides concentration: distribution within age groups with means, 10th, 50th and 90th centiles, males Age mmol/l 25-34 35-44 45-54 55-64 65-74 All ages

< 1 28 33 27 29 33 150

% 24.1 18.4 16.7 18.4 22.8 19.7 1.0 - 1.6 40 56 39 59 66 260

% 34.5 31.3 24.1 37.3 45.5 34.2 > 1.6 48 90 96 70 46 350

% 41.4 50.3 59.3 44.3 31.7 46.1 Total 116 179 162 158 145 760

Mean 1.61 2.11 2.25 1.81 1.55 1.89

S. D. 0.89 1.71 2.03 1.21 0.87 1.49

Percentiles

10th 0.65 0.84 0.79 0.78 0.78 0.78

50th 1.40 1.60 1.75 1.46 1.41 1.54

90th 2.82 4.25 3.97 3.30 2.65 3.36

Number of Missing Observations: 204

174

Table 6.9: Triglycerides concentration: distribution within age groups with means, 10th, 50th and 90th centiles, females Age mmol/l 25-34 35-44 45-54 55-64 65-74 All ages

< 1 83 92 69 42 16 302

% 52.5 45.8 31.1 21.5 10.1 32.3 1.0 - 1.5 37 55 69 52 46 259

% 23.4 27.4 31.1 26.7 29.1 27.7 > 1.5 38 54 84 101 96 373

% 24.1 26.9 37.8 51.8 60.8 39.9 Total 158 201 222 195 158 934

Mean 1.20 1.24 1.44 1.67 1.96 1.49

S. D. 0.69 0.70 0.72 0.92 1.37 0.93

Percentiles

10th 0.58 0.61 0.75 0.79 0.96 0.70

50th 0.97 1.09 1.24 1.57 1.69 1.28

90th 2.14 2.06 2.45 2.62 3.20 2.48

Number of Missing Observations: 98

Tables 6.8 and 6.9 show the distribution of the triglycerides concentration for males and females. Among men triglycerides concentration increases to the age 45-54 and then decreases. Among women it increases with age.

175 6.7 External comparison

6.7.1 MONICA

For the aims of comparison with MONICA data we are using age-standardised values for the age group 35-64 years.

Comparing age standardised median total cholesterol with those from the MONICA178 we found that for men Sofia 5.0 mmol/l ranks 2 out of 43 (range 4.1 - 6.4 mmol/l). For females

MONICA populations range is from 4.2 mmol/l to 6.4 mmol/l, and Sofia females 5.0 mmol/l ranks 2 out of 43, as well (Figures 6.1, 6.2).

The age-standardised 90th centiles of total cholesterol for the MONICA populations varied between 5.4 mmol/l to 8.1 mmol/l for men and between 5.5 mmol/l to 8.3 mmol/l for women and the SHS population was found to be at the lower end of those ranges (ranked equal 9th together with Rhein-Neckar Region, out of 43 with 7.2 mmol/l for men, and equal

4th (together with Bas-Rhin and Warsaw), out of 43 with 6.8 mmol/l for women).

The results of the present study show that hypercholesterolaemia is not a common risk factor in Bulgaria. When our data are compared with data from MONICA project it shows that median total cholesterol in Sofia is lower than in the most MONICA countries.

Comparing the 90th centile Bulgaria is again in the range with low levels of total cholesterol concentration.

The Sofia population appears to have a total cholesterol distribution shifted to the left.

Neither the central tendency, nor the 90th centiles levels seems to explain the high prevalence of cardiovascular disease in Bulgaria.

However, when using this data for external comparison with other studies and for explaining the prevalence of cardiovascular diseases in Bulgaria the selective nature of the respondents in the SHS need to be considered, as the cholesterol distribution in urban population may differ from this in rural. It should also be borne in mind that the non- responders in this study may have higher cholesterol levels than the responders.

176

Figure 6.1: Age-standardised median total cholesterol MONICA baseline data and SHS age 35-64, men

cholm mmol/l 7

0 CZ-CZ BE-LU FIN-NK IT-BRIA FR-HG NZ-AUK UK-BEL FIN-TUR CHIN-BE CH-TICIN BE-GHE BE-CHA BUL-SOF FIN-KUO US-STAN FR-BASR FRG-RHE FRG-BRE FRG-AUR FRG-AUU DEN-GLO UK-GLAS CH-VAUD AUS_PER AUS NEW POL-TARN POL-WAR USSR-KAU

Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990

177

Figure 6.2: Age-standardised median total cholesterol MONICA baseline data and SHS age 35-64, women

cholw mmol/l 7

0 FIN-NK BE-LU FR-HG IT-BRIA CZ-CZ BE-GHE FIN-TUR UK-BEL CHIN-BE FIN-KUO BUL-SOF NZ-AUK BE-CHA CH-TICIN FRG-RHE FRG-BRE FR-BASR US-STAN FRG-AUR FRG-AUU DEN-GLO UK-GLAS AUS NEW AUS NEW AUS_PER CH-VAUD POL-WAR POL-WAR USSR-KAU POL-TARN

Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990

178

7. Chapter: Smoking

7.1 Outline

SHS included a section about smoking in order to provide an estimate of the proportion of the population exposed to this risk factor and to examine the relationship between smoking and other risk factors, as well as to compare it with the prevalence of this risk factor in other survey populations. The analyses presented in this chapter are concerned with cigarettes smoking only because cigar and pipe smoking are very rarely used in Bulgaria.

Section 7.2 describes smoking status of SHS population by age and sex.

Section 7.3 describes smoking behaviour of current smokers.

Section 7.4 describes reasons for changes in smoking behaviour of ex-smokers.

Section 7.5 describes reported passive smoking by age and sex.

Section 7.6 compares reported smoking behavior of SHS population with other populations.

179

7.2 Smoking status by age and sex

Table 7.1 presents the distribution of participants according to their reported cigarette smoking behavior by age and sex. Data shows that the proportion of men, who were current smokers were slightly higher than that for women in all age groups, 38.3% compared with 33.3%. The prevalence of cigarette smoking tended to decrease with age for both sexes.

A larger proportion of men than women were ex-cigarette smokers (26.5% compared with

10.9%). Among men there was a tendency of increasing in the proportion of ex-smokers with age, while among women there was a clear decrease of ex-smokers with age.

Women were more likely than men never to have regularly smoked cigarettes (54.5% and

33.9% respectively). This was the case in all but the youngest age group; the proportion of men and women who had never regularly smoked cigarettes was similar to the age of 35-

44. There is a tendency of sharp decrease in never smokers from older to younger age groups. Among males the percentages of never smokers decreases from 45.3% in the age group 65-74 to 24.0% in age group 25-34. Among females, even more dramatically, the percentages of never smokers decrease from 83.6% in the oldest age group to 28.6% in the youngest age group.

The quit ratio (ex to current plus ex smokers) among males is almost twice as high as among females (0.41 and 0.25 respectively). It increases with age in both sexes, but much more steeply among males.

Current smokers include regular smokers and other current smokers (occasional). The occasional smokers were only 4.2% from males and 5.8% from females (Appendix14.14).

180

Table 7.1: Smoking, by age and sex Age Total 25-34 35-44 45-54 55-64 65-74

Males current smokers 115 112 75 44 23 369 % 58.7 53.6 37.9 23.0 13.5 38.3 ex-smokers 30 42 60 55 68 255 % 15.3 20.1 30.3 28.8 40.0 26.5 never smokers 47 54 61 88 77 327 % 24.0 25.8 30.8 46.1 45.3 33.9 not stated 4 1 2 4 2 13 % 2.0 0.5 1.0 2.1 1.2 1.3 Total 196 209 198 191 170 964 quit ratio* 0.21 0.27 0.44 0.56 0.75 0.41

Females current smokers 106 108 73 40 17 344 % 57.3 49.3 31.2 19.5 9.0 33.3 ex-smokers 26 32 25 16 13 112 % 14.1 14.6 10.7 7.8 6.9 10.9 never smokers 53 78 131 142 158 562 % 28.6 35.6 56.0 69.3 83.6 54.5 not stated 0 1 5 7 1 14 % 0 0.5 2.1 3.4 0.5 1.4 Total 185 219 234 205 189 1032 quit ratio 0.20 0.23 0.26 0.29 0.43 0.25

* ex to current plus ex smokers

181

7.3 Smoking behavior of current smokers

Current regular cigarette smokers have been subdivided into three categories based on their daily cigarette consumption: ‘heavy’ smokers - those who reported smoking 20 or more cigarettes a day; ‘moderate’ smokers - those who smoke 10-19 cigarettes a day; and ‘light’ smokers - those who smoke 9 or fewer cigarettes a day.

Table 7.2 shows average number of cigarettes smoked per day by age and sex. The average number of cigarettes smoked per day was higher for men than for women. Men smoked an average of 19.1 cigarettes a day, compared with an average of 13.0 cigarettes for women.

Women of all ages smoked fewer cigarettes a day than men. The average number of cigarettes smoked per day was lowest for informants in the youngest and the oldest age group. Among men the average number of cigarettes smoked per day rose with age from

18.0 for the youngest age group, peaked at an average of 22.2 for those aged 45-54, and than fell with age to an average of 14.5 per day for men in the oldest age group. These patterns were not found for women. Among women the difference between age groups in

the average number of cigarettes smoked were not statistically significant.

In all age groups, men were more likely than women to smoke heavily. The proportion of heavy smokers peaked for both men and women among those aged 45-54 (66.7% of men and 37.3% of women) and fell to 36.8% of men and 7.7% of women aged 65-74. The proportion of ‘light’ smokers increases with age in both sexes and it is only 9.8% for males and 24.3% for females.

182

Table 7.2: Current regular cigarette smokers: average reported number of cigarettes smoked per day, by age and sex Cigarettes Age Total per day 25-34 35-44 45-54 55-64 65-74 Males Light (1 - 9) 8 10 4 6 4 32 % 7.6 9.9 6.1 16.2 21.1 9.8 Moderate (10 - 19) 41 34 18 11 8 112 % 39.0 33.7 27.3 29.7 42.1 34.1 Heavy (> 20) 56 57 44 20 7 184 % 53.3 56.4 66.7 54.1 36.8 56.1 Total 105 101 66 37 19 328 Females Light (1 - 9) 16 24 15 9 5 69 % 17.0 26.7 25.4 32.1 38.5 24.3 Moderate (10 - 19) 53 45 22 13 7 140 % 56.4 50.0 37.3 46.4 53.8 49.3 Heavy (> 20) 25 21 22 6 1 75 % 26.6 23.3 37.3 21.4 7.7 26.4 Total 94 90 59 28 13 284

183

Table 7.3 presents reported age at starting to smoke by age and sex. Data shows that in general males start smoking younger than women. The highest proportion of both sexes

(62.6% from males and 43.6% from females) started smoking between 16 and 20. We found out that the proportion of respondents from youngest age group started smoking earlier between 10-15 years is much higher than those in other age groups. The fact that some of the younger non-cigarette-smoking respondents will take up cigarette smoking later in their life introduces a potential complication into the analysis. It seems clear that older women started regularly smoking cigarette at a later age than younger women.

Although the start of smoking may have been memorable for many of those concerned, memory error cannot be ruled out, and may affect different sex age groups differently.

184

Table 7.3: Current cigarette smokers: age at start smoking by age and sex strata

Age at start Age Total smoking 25-34 35-44 45-54 55-64 65-74 Males 10-15 42 17 9 4 4 76 % 36.8 15.5 12.0 9.3 18.2 20.9 16-20 66 73 50 26 13 228 % 57.9 66.4 66.7 60.5 59.1 62.6 21-25 6 16 9 8 5 44 % 5.3 14.5 12.0 18.6 22.7 12.1 26-30 0 2 4 3 0 9 % 0 1.8 5.3 7.0 0 2.5 >30 0 2 3 2 0 7 % 0 1.8 4.0 4.7 0 1.9 Total 114 110 75 43 22 364 Females 10-15 14 0 0 3 0 17 % 13.6 0 0 8.8 0 5.3 16-20 68 39 24 7 1 139 % 66.0 37.9 36.4 20.6 7.7 43.6 21-25 19 39 20 5 5 88 % 18.4 37.9 30.3 14.7 38.5 27.6 26-30 2 17 13 10 3 45 % 1.9 16.5 19.7 29.4 23.1 14.1 >30 0 8 9 9 4 30 % 0 7.8 13.6 26.5 30.8 9.4 Total 103 103 66 34 13 319

185

Tables 7.4 and 7.5 shows whether current cigarette smokers report having tried to change their smoking behaviour, by age. Results shows that 42.9% of males and 48.9% from females had never tried to give up smoking. About half of the current smokers from both sexes tried to quit smoking during the year before the onset to the study (46.6% from males and 55.0% from females). 55% of the respondents had changed the brand of the cigarettes they usually smoke, over the past few years, most of them to a lower or similar tar brand.

Only 8.2% of males and 4.9% of females reported changing the brand of cigarettes to a higher tar brand.

Table 7.6 presents mean duration of smoking by age and sex. Mean duration of smoking for males is higher than those for women in all age groups

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Table 7.4: Current cigarettes smokers: whether informants reported having tried to change their smoking behaviour, by age strata, males Age Total 25-34 35-44 45-54 55-64 65-74

Have you ever tried to give up smoking? Yes 62 64 40 27 17 210 % 53.9 57.1 53.3 62.8 73.9 57.1 No 53 48 35 16 6 158 % 46.1 42.9 46.7 37.2 26.1 42.9 Total 115 112 75 43 23 368

Have you tried to quit smoking in the last year? Yes 51 60 26 21 11 169 % 44.3 53.6 36.6 50.0 47.8 46.6 No 64 52 45 21 12 194 % 55.7 46.4 63.4 50.0 52.2 53.4 Total 115 112 71 42 23 363

Over the past few years have you change the brand of cigarettes you usually smoke? No 60 56 45 23 14 198 % 52.6 50.9 64.3 60.5 63.6 55.9 Yes, to a 17 20 7 5 5 54 lower tar brand % 14.9 18.2 10.0 13.2 22.7 15.3 Yes, to a 25 27 11 9 1 73 similar tar brand % 21.9 24.5 15.7 23.7 4.5 20.6 Yes, to a 12 7 7 1 2 29 higher tar brand % 10.5 6.4 10.0 2.6 9.1 8.2 Total 114 110 70 38 22 354

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Table 7.5: Current cigarettes smokers: whether informants reported having tried to change their smoking behaviour, by age strata, females

Age Total 25-34 35-44 45-54 55-64 65-74

Have you ever tried to give up smoking?

Yes 52 56 33 17 10 168 % 49.5 53.8 47.8 47.2 66.7 51.1 No 53 48 36 19 5 161 % 50.5 46.2 52.2 52.8 33.3 48.9 Total 105 104 69 36 15 329 Have you tried to quit smoking in the last year? Yes 51 60 40 18 12 181 % 48.6 57.7 58.8 48.6 80.0 55.0 No 54 44 28 19 3 148 % 51.4 42.3 41.2 51.4 20.0 45.0 Total 105 104 68 37 15 329 Over the past few years have you change the brand of cigarettes you usually smoke? No 58 55 37 20 9 179 % 55.8 53.4 53.6 55.6 64.3 54.9 Yes, to a 22 34 19 10 0 85 lower tar brand % 21.2 33.0 27.5 27.8 0 26.1 Yes, to a 17 11 11 3 4 46 similar tar brand % 16.3 10.7 15.9 8.3 28.6 14.1 Yes, to a 7 3 2 3 1 16 higher tar brand % 6.7 2.9 2.9 8.3 7.1 4.9 Total 104 103 69 36 14 326

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Table 7.6: Current cigarettes smokers: mean duration of smoking, by age and sex Age Total 25-34 35-44 45-54 55-64 65-74 Males Mean (years) 13.2 21.2 30.2 39.4 54.4 24.7 S. D. 3.86 5.46 5.86 7.76 7.02 12.76 Percentiles 10th 9 16 23 31 48 11 50th 13 21 30 39 50 22 90th 18 26.7 36 46.5 67.2 41 Females Mean 11.5 17.3 26.1 36.3 42.9 20.8 S. D. 4.93 7.20 10.38 13.31 18.86 13.10 Percentiles 10th 6 10 12.4 20.4 9.8 8 50th 12 17 26 37 46 17 90th 16 25 42.4 57.9 66.6 40.5

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7.4 Smoking behavior of ex-smokers

One quarter of men and one tenth of women respondents of SHS reported to be ex- smokers. Ex-smokers were asked about the reasons for changing their smoking behaviour.

36.2% from males and 28.8% from females reported they stopped smoking because of ill health. 26.5% from both sexes give up smoking because their health might be affected.

About 10% from ex-smokers reported that they quit smoking because of economical reasons (Table 7.7).

Ex-smokers were asked about the maximum number of cigarettes ever smoked per day for as long as a year. Results show (Table 7.8) that more than half of men who gave up smoking use to be heavy smokers. 45% of female informants who were now ex-cigarette smokers said that they used to smoke less than 10 cigarettes per day. As for current smokers, men who were ex-smokers were more likely than women to have been heavy smokers: 52.4% of men who had stopped smoking cigarettes had been heavy smokers compared with 29.5% of women ex-smokers. It seems that men are both more likely than women to have stopped smoking regularly and to have been heavy smokers.

Ex-smokers were also asked about the year of start smoking and the year of stop smoking.

We calculated how many years they smoked before they gave up (Table 7.9). Half of the male informants who were now ex-smokers said that they have been smoking for more than 20 years before they gave up compared with 19.6% of women ex-smokers. This may reflect a real change over time in the type of smokers who gave up smoking, but it may reflect the difficulties of recall over a longer period also.

Table 7.10 presents mean duration of smoking by age and sex. As for current smokers mean duration of smoking for males is higher than those for women in all age groups.

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Table 7.7: Ex-smokers: reasons showed by informant for changing their smoking behaviour by age Age Total 25-34 35-44 45-54 55-64 65-74 Males

Why did you stop smoking?

Because ill health 3 17 21 28 25 94 % 9.4 39.5 33.9 49.1 37.9 36.2 Because my health 8 6 19 15 21 69 might be affected % 25.0 14.0 30.6 26.3 31.8 26.5 Because of 4 8 6 3 6 27 economical reasons % 12.5 18.6 9.7 5.3 9.1 10.4 Other reasons 17 12 16 11 14 70 % 53.1 27.9 25.8 19.3 21.2 26.9 Total 32 43 62 57 66 260

Females

Why did you stop smoking? Because ill health 3 11 10 5 5 34 % 13.0 29.7 34.5 31.3 38.5 28.8 Because my health 5 16 4 4 2 31 might be affected % 21.7 43.2 13.8 25.0 15.4 26.3 Because of 2 3 3 4 3 15 economical reasons % 8.7 8.1 10.3 25.0 23.1 12.7 Other reasons 13 7 12 3 3 38 % 56.5 18.9 41.4 18.8 23.1 32.2 Total 23 37 29 16 13 118

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Table 7.8: Ex-smokers: maximum number of cigarettes ever smoked per day for as long as a year Age Total 25-34 35-44 45-54 55-64 65-74 Males

Heavy (≥20) 29 57 64 39 47 236 % 33.3 55.3 60.4 54.2 57.3 52.4 Moderate 17 22 12 12 19 82 (10-19) % 19.5 21.4 11.3 16.7 23.2 18.2 Light (0-9) 41 24 30 21 16 132 % 47.1 23.3 28.3 29.2 19.5 29.3 Total 87 103 106 72 82 450 Females Heavy (≥20) 22 34 15 9 9 89 % 27.5 34.3 23.1 23.1 47.4 29.5 Moderate 22 26 19 8 2 77 (10-19) % 27.5 26.3 29.2 20.5 10.5 25.5 Light (0-9) 36 39 31 22 8 136 % 45.0 39.4 47.7 56.4 42.1 45.0 Total 80 99 65 39 19 302

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Table 7.9: Ex-smokers: years of smoking by age and sex strata Age Years Total 25-34 35-44 45-54 55-64 65-74 Males

1-5 8 9 2 2 6 27

% 30.8 22.5 3.5 3.6 8.6 10.8 5-10 12 6 5 5 5 33 % 46.2 15.0 8.8 8.9 7.1 13.3 11-20 6 19 20 13 7 65 % 23.1 47.5 35.1 23.2 10.0 26.1 21-30 0 6 25 15 16 62 % 0 15.0 43.9 26.8 22.9 24.9 >30 0 0 5 21 36 62 % 0 0 8.8 37.5 51.4 24.9 Total 26 40 57 56 70 249

Females 1-5 7 6 2 3 2 20 % 35.0 20.0 8.0 21.4 15.4 19.6 5-10 8 8 5 0 0 21 % 40.0 26.7 20.0 0 0 20.6 11-20 4 16 14 4 3 41 % 20.0 53.3 56.0 28.6 23.1 40.2 21-30 0 0 4 5 3 12 % 0 0 16.0 35.7 23.1 11.8 >30 1 0 0 2 5 8 % 5.0 0 0 14.3 38.5 7.8 Total 20 30 25 14 13 102

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Table 7.10: Mean duration of smoking from ex-smokers by age and sex Age Total 25-34 35-44 45-54 55-64 65-74 Males Mean 7.8 12.7 21.6 26.6 29.6 22.3 S. D. 4.47 7.24 8.11 10.91 14.09 12.79 Percentiles 10th 2 3 10.3 10.8 7.2 4 50th 8 14 23 28 32 21 90th 13.5 22 30 40.6 46.2 40 Females Mean 6.1 11.1 13.9 20.0 24.7 14.1 S. D. 3.56 5.03 7.30 11.73 13.91 10.10 Percentiles 10th 1 4.7 3.5 2.6 1.8 3 50th 6.5 11 14.5 20 23 12 90th 11 19.3 27.1 37.8 43 29.8

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7.5 Passive smoking

The respondents of SHS were asked about how many hours per day they are exposed to tobacco smoke. Table 7.11 presents those results by age and sex. The data shows that in general women were less exposed to tobacco smoke than men (57.2% and 67% respectively). Younger age groups are exposed to tobacco smoke for longer than older age groups.

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Table 7.11: Passive smoking: reported hours exposed to tobacco smoke by age and sex strata Age Hours Total 25-34 35-44 45-54 55-64 65-74 Males 0 23 49 65 77 104 318 % 11.7 23.4 32.8 40.3 61.2 33.0 1-2 46 32 40 46 44 208 % 23.5 15.3 20.2 24.1 25.9 21.6 3-5 42 56 38 34 9 179 % 21.4 26.8 19.2 17.8 5.3 18.6 6-10 57 55 40 25 12 189 % 29.1 26.3 20.2 13.1 7.1 19.6 >10 28 17 15 9 1 70 % 14.3 8.1 7.6 4.7 .6 7.3 Total 196 209 198 191 170 964 Females 0 36 65 93 121 127 442 % 19.5 29.7 39.7 59.0 67.2 42.8 1-2 35 39 51 31 37 193 % 18.9 17.8 21.8 15.1 19.6 18.7 3-5 40 45 42 25 15 167 % 21.6 20.5 17.9 12.2 7.9 16.2 6-10 52 48 37 21 6 164 % 28.1 21.9 15.8 10.2 3.2 15.9 >10 22 22 11 7 4 66 % 11.9 10.0 4.7 3.4 2.1 6.4 Total 185 219 234 205 189 1032

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7.6 Comparison with other populations

7.6.1 MONICA

For the aims of comparison with MONICA data we are using age-standardised values for the age group 35-64 years.

Figures 7.1 and 7.2 shows age standardised proportion of current smokers MONICA baseline data and SHS, age 35-64.

Comparing age standardised proportion of regular cigarette smokers with those from the

MONICA179 we found that for men (36.0%), Sofia ranks 17th out of 45 (range for centres:

23.7% - 58.8%). For females MONICA populations range from 2.7% to 50.2%: Sofia females at 28.9% are ranked 36th out of 45.

The age-standardised proportion of other current (occasional) smokers for the MONICA populations varied between 0.5% to 24.9% for men and between 0.0% to 6.0% for women and SHS population found to be at the middle of the of this range (ranks 23 out of 45 with

4.6%) for men, and ranks at the end of the range (45 out of 45 with 6.9%) for women.

Comparing age standardised proportion of ex-smokers with those from the MONICA we discovered that SHS population ranks on 20th place out of 45 for men with 26.2% in the range from 4.4% to 35.9%, and on 33rd place for women (11.7% in the range from 1.1% to

24.6%). Men never-smokers ranks on 37th place with 33.2% in the range from 18.1% to

38.4%, and women ranks equal 8th with 52.5% in the range from 36.2% to 94.5%.

The age-standardised mean number of cigarettes smoked per day by cigarette smokers for the MONICA populations varied between 13 and 27 for men and between 7 and 20 for women and SHS population found to be between 15th and 21st place of this range with 18 for men, and ranks between 10th and 15th place of the range with 11 for women.

Comparing age standardised median number of cigarettes smoked per day by cigarette smokers with those from the MONICA172 we found that for men Sofia (20) ranks between

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12th and 42nd out of 43 (range 11-25). For females MONICA populations range is from 5 to 21, and Sofia females (10) ranks between 10-22 out of 43.

The age-standardised 90th centiles of number of cigarettes smoked per day by cigarette smokers for the MONICA populations varied between 20 to 40 for men and between 15 to

31 for women and SHS population found to be in the middle of those ranges (ranks 27-28 out of 43 with 35 for men, and ranks between 6-20 out of 43 with 20 for women).

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Figure 7.1: Proportion of current smokers MONICA baseline data and SHS age 35-64, men

smokm % 70

0 CZ-CZ BE-LU FIN-NK IT-BRIA FR-HG FIN-TUR NZ-AUK UK-BEL BE-GHE BE-CHA CHIN-BE CH-TICIN BUL-SOF FIN-KUO FR-BASR FRG-RHE US-STAN FRG-BRE CH-VAUD FRG-AUU FRG-AUR DEN-GLO UK-GLAS AUS_PER AUS NEW POL-TARN POL-WAR USSR-KAU

Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990 199

Figure 7.2: Proportion of current smokers MONICA baseline data and SHS age 35-64, women

smokef % 60

0 FIN-NK FIN-NK BE-LU FR-HG IT-BRIA IT-BRIA CZ-CZ CZ-CZ FIN-TUR FIN-TUR BE-GHE BE-GHE UK-BEL CHIN-BE CHIN-BE FIN-KUO FIN-KUO BE-CHA BE-CHA NZ-AUK NZ-AUK BUL-SOF CH-TICIN FRG-BRE FRG-BRE FRG-RHE FRG-RHE FR-BASR FR-BASR FRG-AUR FRG-AUR FRG-AUU FRG-AUU US-STAN DEN-GLO AUS_PER AUS_PER AUS NEW UK-GLAS CH-VAUD CH-VAUD USSR-KAU POL-WAR POL-TARN

Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990 200 7.6.2 Stage of evolution of the smoking epidemic

The results of the present study showed that:

1. There are not very high proportions of current smokers.

2. There is not big difference between the proportions of current smokers in both sexes.

3. There is a substantial difference by sex in the proportion of never smokers.

4. Relatively low proportion of people giving up smoking.

5. Higher prevalence in upper socio-economic groups (more educated) - an ‘early phase’ phenomenon.

6. Relatively high age at onset for older cohorts (possibly due to social conservatism).

7. Consistency with lung cancer mortality in males (not very high in older age groups, rising sharply in middle age)62.

Analysing the main characteristics of smoking epidemic in Bulgaria it seems that Bulgaria is one of the last European countries to go through the cigarette epidemic. To explore this hypothesis we made a plot: Percentages ever smoking by central birth year comparing

England and Bulgaria (Figure 7.3 and Figure 7.4). We observed about four decades' difference in developing of smoking epidemic between Bulgaria and England. For England it is considered to be one of the few countries, which had experienced all four stages of the cigarette epidemic, and is now enjoying declines in smoking-attributable mortality at least among men. With continuous rising prevalence of smoking among men reaching a peak of

74%, prevalence of smoking among women typically lags behind that of males, but increasing rapidly, relatively low proportion of ex-smokers, higher smoking prevalence among people with high education, Bulgaria could be described as a country in second stage of the smoking epidemic63.

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Distribution of the risk factor smoking found in SHS cannot explain the mortality up to now, but we can expect increasing of mortality attributable to smoking. Still figures are not that high like EC and US in 50s.

202 Figure 7.3: Evolution of cigarette epidemic: % ever smoking by central birth year, males, Bulgaria and England

90 80 70 60 50 Bulgaria 40 England 30 20 10 0 1924 1934 1944 1954 1964 1974

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Figure 7.4: Evolution of smoking epidemic: % ever smoking by central birth year, females, Bulgaria and England

30 Bulgaria

20 England

1924 1934 1944 1954 1964 1974

Source: Health Survey for England and SHS

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8. Chapter: Diet, alcohol and physical activity

8.1 Outline

Section 8.2 of this chapter describes the reported frequency of consumption of a range of foods and beverages.

Section 8.3 presents the analyses of the reported alcohol consumption of SHS population.

Section 8.4 presents the results on reported physical activity.

8.2 Diet

The participants in SHS were asked how often certain foods (26) are usually eaten. This method is design to measure the ‘usual’ diet of the individuals. It does not measure the changes in diet that may have occurred in the past. It yields an average estimate of the individual’s recalled intake, which is subject to substantial reporting error180,181,182.

Therefore the results obtained by this method must be interpreted with caution.

Table 8.1 presents results from the question “Are you on a special diet?” 84% of males and

81.2% of females reported not being on any diet. Only 2% from both sexes were on a slimming diet suggested by a doctor. 5.4% from males and 7.7% from females were on a slimming diet prescribed by themselves. The highest proportion of them was in the age group 35-64 (7.2% from males and 11.4% from females). About 5% from respondents were on diabetic diet. The proportion of people reporting a diabetic diet was about 2% until the age 55 then it increased to more than 10% in the oldest age groups. Only 1% from both sexes were vegetarians.

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Table 8.1: Reported special diet by sex and age groups Age Total 25-34 35-44 45-54 55-64 65-74 Males No 168 183 176 152 128 807 % 86.2 87.6 89.3 80.0 75.3 84.0 Slimming diet, 1 5 3 6 4 19 suggested by your doctor % 0.5 2.4 1.5 3.2 2.4 2.0 Slimming diet, 11 15 11 4 11 52 prescribed by yourself % 5.6 7.2 5.6 2.1 6.5 5.4 Diabetic diet 5 2 5 20 18 50 % 2.6 1.0 2.5 10.5 10.6 5.2 Other medical 2 4 2 6 7 21 diet % 1.0 1.9 1.0 3.2 4.1 2.2 Vegetarian 8 0 0 2 2 12 diet % 4.1 0 0 1.1 1.2 1.2 Total 195 209 197 190 170 961 Females No 160 181 196 156 138 831 % 87.0 82.6 84.5 78.0 73.4 81.2 Slimming diet, 4 3 5 5 3 20 suggested by your doctor % 2.2 1.4 2.2 2.5 1.6 2.0 Slimming diet, 16 25 17 11 10 79 prescribed by yourself % 8.7 11.4 7.3 5.5 5.3 7.7 Diabetic diet 2 4 5 24 22 57 % 1.1 1.8 2.2 12.0 11.7 5.6 Other medical 1 4 7 4 12 28 diet % 0.5 1.8 3.0 2.0 6.4 2.7 Vegetarian 1 2 2 0 3 8 diet % 0.5 0.9 0.9 0 1.6 0.8 Total 184 219 232 200 188 1023 Number of Missing Observations: 12

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Table 8.2 and Table 8.3 present the results on reported dietary habits of SHS population by sex for consumption of main protein sources: meat and meat products, fish, eggs, milk and milk products.

With respect to meat consumption we found that 17.6% reported eating pork every day or almost every day. The next most preferred meat is chicken. Beef and lamb are very rarely consumed. The results show that more than half of the population reported eating fish rarely or never.

Almost 20% of the participants eat eggs every day or almost every day.

More than half of the population consumed milk and milk products every day or almost every day.

We did not observed marked sex differences of consumption of main protein sources, apart from meet products, which are more frequently consumed by males than females.

One quarter of the respondents report eating pastries every day and another quarter - almost every day (Table 8.4). The most commonly eaten pastry in Bulgaria is so called

'banitza' (salty paste made of flour, fat, fatter cheese etc., baked in an oven), which is the usual breakfast. Males consumed pastry an corn foods more offen tham females.

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Table 8.2: Reported dietary habits of SHS population: meat and meat products, fish, eggs, milk and milk products, males One Almost Several One day One or a Rare or Mis- Products time per every time per of the few times Total never sing day day week week per month Meat and meat products Pork 46 158 146 283 169 160 2 964 % 4.8 16.4 15.1 29.4 17.5 16.6 0.2 100 Beef 8 48 67 253 263 323 3 964 % 0.8 5.0 7.0 26.2 27.3 33.5 0.2 100 Lamb 2 9 15 58 154 724 2 964 % 0.2 0.9 1.6 6.0 16.0 75.1 0.2 100 Chicken 15 48 115 392 250 142 2 964 % 1.6 5.0 11.9 40.7 25.9 14.7 0.2 100 Other meat 66 204 143 199 110 240 2 964 products % 6.8 21.2 14.8 20.6 11.4 24.9 0.2 100 Fish River fish 6 13 27 111 200 605 2 964 % 0.6 1.3 2.8 11.5 20.7 62.8 0.2 100 Sea fish 5 5 20 153 305 474 2 964 % 0.5 0.5 2.1 15.9 31.6 49.2 0.2 100 Eggs 36 166 189 351 127 93 2 964 % 3.7 17.2 19.6 36.4 13.2 9.6 0.2 100 Milk and milk products Milk 164 231 130 133 91 213 2 964 % 17.0 24.0 13.5 13.8 9.4 22.1 0.2 100 Yoghurt 255 342 178 86 49 52 2 964 % 26.5 35.5 18.5 8.9 5.1 5.4 0.2 100 Cheeses 267 373 171 92 35 24 2 964 % 27.7 38.7 17.7 9.5 3.6 2.5 0.2 100

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Table 8.3: Reported dietary habits of SHS population: meat and meat products, fish, eggs, milk and milk products, females One Almost Several One day One or a Rare or Mis- Products time per every time per of the few times Total never sing day day week week per month Meat and meat products Pork 35 111 150 322 203 210 1 1032 % 3.4 10.8 14.5 31.2 19.7 20.3 0.1 100 Beef 9 25 28 226 279 464 1 1032 % 0.9 2.4 2.7 21.9 27.0 45.0 0.1 100 Lamb 1 5 6 46 127 846 1 1032 % 0.1 0.5 0.6 4.5 12.3 82.0 0.1 100 Chicken 16 40 82 409 299 185 1 1032 % 1.6 3.9 7.9 39.6 29.0 17.9 0.1 100 Other meat 59 153 122 209 124 364 1 1032 products % 5.7 14.8 11.8 20.3 12.0 35.3 0.1 100 Fish River fish 6 5 11 108 209 692 1 1032 % 0.6 0.5 1.1 10.5 20.3 67.1 0.1 100 Sea fish 8 8 15 129 368 503 1 1032 % 0.8 0.8 1.5 12.5 35.7 48.7 0.1 100 Eggs 34 151 173 388 148 137 1 1032 % 3.3 14.6 16.8 37.6 14.3 13.3 0.1 100 Milk and milk products Milk 193 281 127 134 79 217 1 1032 % 18.7 27.2 12.3 13.0 7.7 21.0 0.1 100 Yoghurt 259 376 150 105 53 88 1 1032 % 25.1 36.4 14.5 10.2 5.1 8.5 0.1 100 Cheeses 299 410 131 117 30 44 1 1032 % 29.0 39.7 12.7 11.3 2.9 4.3 0.1 100

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Table 8.4: Reported dietary habits of SHS population by sex: pastry and corn foods One Almost Several One day One or a Rare or Mis- Products time per every time per of the few times Total never sing day day week week per month Males Pastry1 256 273 139 132 66 96 2 962 % 26.5 28.3 14.4 13.7 6.9 10.0 0.2 100 Corn 63 163 129 221 118 268 2 962 foods2 % 6.5 16.8 13.4 23.0 12.3 27.8 0.2 100

Females

Pastry1 235 207 160 175 109 145 1 1031 % 22.7 20.1 15.5 17.0 10.6 14.1 0.1 100 Corn 56 133 118 229 137 358 1 1031 foods2 % 5.4 12.1 11.4 22.2 13.3 34.7 0.1 100 1 Includes different kinds of sweaty and salty paste made of flour, fat, cheese or chocolate etc., baked in an oven.

2 Includes various grain plants rye, oats, wheat etc.

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We found that more than 40% reported eating fresh fruits from ‘one day of the week’ to rarely or never (Table 8.5, Table 8.6). Green vegetables and salads, as well as carrots, tomatoes, onion and garlic were reported to be frequently consumed (more than half of the participants reported to eat them every day or almost every day). (Other evidence shows that the consumption of fruit and vegetables is highly seasonal in Bulgaria. Fieldwork for this study was conducted mainly between March and October.)

We found that younger people consumed more juices than people from older age groups, in both sexes (Tables 8.7, 8.8).

Coffee consumption decreases with age as well, while tea consumption increases with age, but more than half of the population does not drink tea at all.

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Table 8.5: Reported dietary habits of SHS population: fruit and vegetables, males. One Almost Several One day One or a Rare Mis- Products time per every time per of the few times or Total sing day day week week per month never Fresh fruits 147 229 166 176 137 107 2 964 % 15.2 23.8 17.2 18.3 14.2 11.1 0.2 100 Canned 30 109 147 204 148 324 2 964 fruits % 3.1 11.3 15.2 21.2 15.4 33.6 0.2 100 Fresh 236 258 142 148 61 117 2 964 vegetables % 24.5 26.8 14.7 15.4 6.3 12.1 0.2 100 Potatoes 61 251 269 256 55 70 2 964 % 6.3 26.0 27.9 26.6 5.7 7.3 0.2 100 Green 228 323 142 126 61 82 2 964 vegetables and salads % 23.7 33.5 14.7 13.1 6.3 8.5 0.2 100 Carrots and 219 254 130 170 83 106 2 964 tomatoes % 22.7 26.3 13.5 17.6 8.6 11.0 0.2 100 Onion 231 320 144 122 55 90 2 964 % 24.0 33.2 14.9 12.7 5.7 9.3 0.2 100 Garlic 125 271 142 180 93 151 2 964 % 13.0 28.1 14.7 18.7 9.6 15.7 0.2 100 Beans, len- 16 78 174 416 170 108 2 964 tils, peas % 1.7 8.1 18.0 43.2 17.6 11.2 0.2 100 Canned 27 125 149 247 184 230 2 964 vegetables % 2.8 13.0 15.5 25.6 19.1 23.9 0.2 100

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Table 8.6: Reported dietary habits of SHS population: fruit and vegetables, females. One Almost Several One day One or a Rare or Mis- Products time per every time per of the few times Total never sing day day week week per month Fresh fruits 188 260 149 149 137 148 1 1032 % 18.2 25.2 14.4 14.4 13.3 14.3 0.1 100 Canned 55 130 135 173 129 408 2 1032 fruits % 5.3 12.6 13.1 16.8 12.5 39.5 0.2 100 Fresh 242 291 148 114 63 172 2 1032 vegetables % 23.4 28.2 14.3 11.0 6.1 16.7 0.2 100 Potatoes 69 276 271 292 45 77 2 1032 % 6.7 26.7 26.3 28.3 4.4 7.5 0.2 100 Green 263 339 183 95 49 101 2 1032 vegetables and salads % 25.5 32.8 17.7 9.2 4.7 9.8 0.2 100 Carrots and 213 310 162 126 62 157 2 1032 tomatoes % 20.6 30.0 15.7 12.2 6.0 15.2 0.2 100 Onion 273 353 156 85 45 118 2 1032 % 26.5 34.2 15.1 8.2 4.4 11.4 0.2 100 Garlic 138 226 162 150 101 253 2 1032 % 13.4 21.9 15.7 14.5 9.8 24.5 0.2 100 Beans, len- 23 57 105 395 251 199 2 1032 tils, peas % 2.2 5.5 10.2 38.3 24.3 19.3 0.2 100 Canned 62 134 145 221 206 262 2 1032 vegetables % 6.0 13.0 14.1 21.4 20.0 25.4 0.2 100

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Table 8.7: Reported soft drinks consumption by age groups, males Soft Age Total drinks 25-34 35-44 45-54 55-64 65-74 per day Juices 0 41 41 78 75 108 343 % 20.9 19.6 39.4 39.3 63.5 35.6 1 - 2 77 91 75 85 49 377 % 39.3 43.5 37.9 44.5 28.8 39.1 ≥ 3 78 77 45 31 13 244 % 39.8 36.8 22.7 16.2 7.6 25.3 Total 196 209 198 191 170 964 Coffee 0 34 42 64 80 96 316 % 17.3 20.1 32.3 41.9 56.5 32.8 1 - 2 70 82 81 83 63 379 % 35.7 39.2 40.9 43.5 37.1 39.3 ≥ 3 92 85 53 28 11 269 % 46.9 40.7 26.8 14.7 6.5 27.9 Total 196 209 198 191 170 964 Tea 0 144 146 122 108 80 600 % 73.5 69.9 61.6 56.5 47.1 62.2 1 - 2 44 58 65 80 85 332 % 22.4 27.8 32.8 41.9 50.0 34.4 ≥ 3 8 5 11 3 5 32 % 4.1 2.4 5.6 1.6 2.9 3.3 Total 196 209 198 191 170 964

214

Table 8.8: Reported soft drinks consumption by age groups, females Soft Age drinks Total 25-34 35-44 45-54 55-64 65-74 per day Juices 0 31 69 92 108 121 421 % 16.8 31.5 39.3 52.7 64.0 40.8 1 - 2 88 108 109 79 55 439 % 47.6 49.3 46.6 38.5 29.1 42.5 ≥ 3 66 42 33 18 13 172 % 35.7 19.2 14.1 8.8 6.9 16.7 Total 185 219 234 205 189 1032 Coffee 0 19 34 52 83 105 293 % 10.3 15.5 22.2 40.5 55.6 28.4 1 - 2 74 114 131 103 79 501 % 40.0 52.1 56.0 50.2 41.8 48.5 ≥ 3 92 71 51 19 5 238 % 49.7 32.4 21.8 9.3 2.6 23.1 Total 185 219 234 205 189 1032 Tea 0 125 114 130 105 97 571 % 67.6 52.1 55.6 51.2 51.3 55.3 1 - 2 54 96 92 94 86 422 % 29.2 43.8 39.3 45.9 45.5 40.9 ≥ 3 6 9 12 6 6 39 % 3.2 4.1 5.1 2.9 3.2 3.8 Total 185 219 234 205 189 1032

215

As we expected, bread consumption was very high (Table 8.9). The white bread was preferable and about half of male population reported to eat five or more slices of bread per day. Mean reported consumption of bread was 3.37 slices of white bread and 1.06 slices of brown bread per day. More than 70% of SHS population does not eat brown bread at all.

The respondents of SHS study were asked what kind of cooking fat they usually use. They were given opportunity to choose more than one answer. The results are shown in Tables

8.10 and 8.11. More than 90% from both sexes reported that the oil is their usual type of cooking fat. The consumption of butter decreases with age. Lard is relatively less popular especially among female respondents (only 10% of them reported to use it usually). A quarter of the survey population consumed margarine.

In attempt to assess salt consumption we asked the participants whether they usually add salt to their meal (Table 8.12). 11.2% from males and 6.8% from females said that they add salt before taste and this percentage decreases with age in both sexes. 30% from both sexes reported that they taste the meal and then usually add salt. About 35% from both sexes taste and then rarely add salt. Only 24.3% from males and 27.7% from females never add salt to their meal, and the percentage of these people increases with increasing of age.

216

Table 8.9: Reported bread consumption by age groups and sex Bread Age Total slices/day 25-34 35-44 45-54 55-64 65-74 Males White 0 29 25 38 46 55 193 % 14.8 12.0 19.2 24.1 32.4 20.0 1 - 2 16 13 23 15 4 71 % 8.2 6.2 11.6 7.9 2.4 7.4 3 - 4 44 69 52 47 44 256 % 22.4 33.0 26.3 24.6 25.9 26.6 ≥ 5 107 102 85 83 67 444 % 54.6 48.8 42.9 43.5 39.4 46.1 Total 196 209 198 191 170 964 Brown 0 148 175 146 141 113 723 % 75.5 83.7 73.7 73.8 66.5 75.0 1 - 2 9 10 10 10 4 43 % 4.6 4.8 5.1 5.2 2.4 4.5 3 - 4 15 11 14 20 19 79 % 7.7 5.3 7.1 10.5 11.2 8.2 ≥ 5 24 13 28 20 34 119 % 12.2 6.2 14.1 10.5 20.0 12.3 Total 196 209 198 191 170 964 Females White 0 32 56 67 78 48 281 % 17.3 25.6 28.6 38.0 25.4 27.2 1 - 2 57 40 37 20 23 177 % 30.8 18.3 15.8 9.8 12.2 17.2 3 - 4 71 88 98 73 79 409 % 38.4 40.2 41.9 35.6 41.8 39.6 ≥ 5 25 35 32 34 39 165 % 13.5 16.0 13.7 16.6 20.6 16.0 Total 185 219 234 205 189 1032 Brown 0 153 163 167 126 134 743 % 82.7 74.4 71.4 61.5 70.9 72.0 1 - 2 14 25 27 17 3 86 % 7.6 11.4 11.5 8.3 1.6 8.3 3 - 4 14 25 27 48 34 148 % 7.6 11.4 11.5 23.4 18.0 14.3 ≥ 5 4 6 13 14 18 55 % 2.2 2.7 5.6 6.8 9.5 5.3 Total 185 219 234 205 189 1032

217

Table 8.10: Reported cooking fat consumption by age groups, males Kind of Age cooking Total fat 25-34 35-44 45-54 55-64 65-74 Oil Yes 176 194 188 185 159 902 % 89.8 92.8 94.9 96.9 93.5 93.6 No 20 15 10 6 11 62 % 10.2 7.2 5.1 3.1 6.5 6.4 Total 196 209 198 191 170 964 Butter Yes 83 72 71 46 46 318 % 42.3 34.4 35.9 24.1 27.1 33.0 No 113 137 127 145 124 646 % 57.7 65.6 64.1 75.9 72.9 67.0 Total 196 209 198 191 170 964 Lard Yes 42 30 30 28 34 164 % 21.4 14.4 15.2 14.7 20.0 17.0 No 154 179 168 163 136 800 % 78.6 85.6 84.8 85.3 80.0 83.0 Total 196 209 198 191 170 964 Margarine Yes 60 47 49 58 39 253 % 30.6 22.5 24.7 30.4 22.9 26.2 No 136 162 149 133 131 711 % 69.4 77.5 75.3 69.6 77.1 73.8 Total 196 209 198 191 170 964

218

Table 8.11: Reported cooking fat consumption by age groups, females Kind of Age Total cooking fat 25-34 35-44 45-54 55-64 65-74 Oil Yes 176 219 228 196 179 998 % 95.1 100.0 97.4 95.6 94.7 96.7 No 9 0 6 9 10 34 % 4.9 0 2.6 4.4 5.3 3.3 Total 185 219 234 205 189 1032 Butter Yes 61 62 69 49 47 288 % 33.0 28.3 29.5 23.9 24.9 27.9 No 124 157 165 156 142 744 % 67.0 71.7 70.5 76.1 75.1 72.1 Total 185 219 234 205 189 1032 Lard Yes 17 17 21 15 33 103 % 9.2 7.8 9.0 7.3 17.5 10.0 No 168 202 213 190 156 929 % 90.8 92.2 91.0 92.7 82.5 90.0 Total 185 219 234 205 189 1032 Margarine Yes 44 68 80 66 46 304 % 23.8 31.1 34.2 32.2 24.3 29.5 No 141 151 154 139 143 728 % 76.2 68.9 65.8 67.8 75.7 70.5 Total 185 219 234 205 189 1032

219

Table 8.12: Reported salt use by sex and age groups

Age Total 25-34 35-44 45-54 55-64 65-74

Males

Add before 29 30 22 17 10 108 taste

% 14.8 14.4 11.2 8.9 5.9 11.2

Taste and 61 65 65 53 36 280 usually add

% 31.1 31.1 33.0 27.9 21.3 29.1

Taste and 62 71 71 71 64 339 rarely add

% 31.6 34.0 36.0 37.4 37.9 35.3

Never add salt 44 43 39 49 59 234

% 22.4 20.6 19.8 25.8 34.9 24.3

Total 196 209 197 190 169 961

Females

Add before 21 18 15 11 5 70 taste

% 11.4 8.2 6.5 5.5 2.7 6.8

Taste and 57 85 77 53 41 313 usually add

% 31.0 38.8 33.2 26.5 21.8 30.6

Taste and 69 76 76 66 70 357 rarely add

% 37.5 34.7 32.8 33.0 37.2 34.9

Never add salt 37 40 64 70 72 283

% 20.1 18.3 27.6 35.0 38.3 27.7

Total 184 219 232 200 188 1023

Number of Missing Observations: 12

220

8.3 Alcohol

SHS collected information about informants’ drinking habits to examine the relationship between alcohol consumption and other cardiovascular diseases risk factors. We were not able to collect detailed information about previous drinking patterns but did ask about the reasons for which individuals had cut down or stopped drinking. To obtain this information the subjects were asked to recall a 'typical week' of alcohol consumption in frequency of drinking days (which could include more than one drinking occasions) and the type of alcohol. The conversion factors and methods used to convert beer, wine and spirits to gram ethanol are described in Chapter 2: Methods.

Table 8.13 presents the reported drinking status of SHS population by age and sex. Almost half of women (48.6%) and 15.7% of men were lifetime non-drinkers. Among women the proportion of lifetime non-drinkers increases with age from 38.8% in the age group 25-34 to 73.4% in the age group 65-74, while among men we did not find the same tendency.

We find out that the percentages of past drinkers were higher among the youngest age group (25-34) in both sexes (9.8% for men and 6.4% for women) and then decreased in the age group 35-44. From the age group 45-54 the percentage of those who gave up drinking increases with age to 16.8% for men and 10.8% for women in the age group 65-74.

Three quarters (74.8%) of men and 45.6% of women were current drinkers. Among men the proportion of current drinkers increases from 76.7% in the age group 25-34 to 78.4% in the age group 45-54 and then decreases to 65.2% in the age group 65-74. Among women the proportion of current drinkers decreases steeply from 62.8% in the age group 25-34 to

15.8% in the age group 65-74.

We asked participants about the type of alcohol they usually drink. The results of the mean alcohol consumption by the type of alcohol are presented in Table 8.14. The preferable type of alcohol for both sexes and all age groups were spirits (concentrates), which corresponds to Bulgarian habits to drink predominantly rakia (schnapps).

221

Additionally men were more likely than women to drink spirits. The mean quantity (in grams per day) of alcohol which comes from spirits consumed by men were two to three times higher than those among women. The second preferable type of alcohol was beer for men but wine for women, as we expected as well. Among males 59.2% of the alcohol consumed comes from spirits, 27.2% from beer, 12.1% from wine and 1.8% from others.

Among females the corresponding proportions of the alcohol consumed comes from:

49.1% spirits, 23.1% beer, 25.3% wine and 2.5% others. We did not find a clear dependency from age in both sexes.

We calculated mean daily alcohol consumption by number of drinking days. The results are presented in Table 8.15. Mean number of drinking days per week was two times more among males in all age groups than among females. Additionally the mean quantity of alcohol consumption per drinking day was as well twice higher among men than women.

Among men it peaks between age of 35-54 and then fallen down, and among women the mean alcohol consumed per drinking day decreases with increasing the age. The mean alcohol consumed increases with increasing the number of drinking occasions.

222

Table 8.13: Reported drinking status of SHS population by age and sex

Drinking Age Total status 25-34 35-44 45-54 55-64 65-74 Males Life time non 26 34 25 30 28 143 drinkers % 13.5 17.1 13.2 17.1 18.1 15.7 Past drinkers 19 10 16 16 26 87 % 9.8 5.0 8.4 9.1 16.8 9.5 Current drinkers 148 155 149 129 101 682 % 76.7 77.9 78.4 73.7 65.2 74.8 Total 193 199 190 175 155 912 Females Life time non 53 78 103 93 116 443 drinkers % 30.8 38.6 49.8 54.1 73.4 48.6 Past drinkers 11 7 7 11 17 53 % 6.4 3.5 3.4 6.4 10.8 5.8 Current drinkers 108 117 97 68 25 415 % 62.8 57.9 46.9 39.5 15.8 45.6 Total 172 202 207 172 158 911

Missing values 173.

223

Table 8.14: Current drinkers: mean alcohol consumption by type of alcohol, age and sex Age Consumption in 25-34 35-44 45-54 55-64 65-74 Total current drinkers Mean Mean Mean Mean Mean Mean [g/day] [g/day] [g/day] [g/day] [g/day] [g/day] Males Mean number of 2.8 2.9 2.7 2.1 2.3 2.6 drinking days per week Mean from spirits 34.2 43.7 42.0 40.0 30.3 38.6 % 55.1 57.0 61.3 56.9 73.2 59.2 Mean from beer 19.4 22.1 16.7 19.5 7.1 17.7 % 31.2 28.9 24.4 27.7 17.1 27.2 Mean from wine 6.6 9.6 9 9.4 3.2 7.9 % 10.6 12.5 13.1 13.4 7.7 12.1 Mean from others 1.8 1.2 0.8 1.4 0.8 1.2 % 2.9 1.6 1.2 2.0 1.9 1.8 Mean per drinking 62.1 76.6 68.5 70.3 41.4 65.4 day Mean per drinking 29.3 40.5 35.8 29.7 15.2 31.4 + non drinking day Females Mean number of 1.3 1.4 1.2 1.4 1.0 1.3 drinking days per week Mean from spirits 18.2 19.9 12.5 12.3 8.7 15.7 % 47.4 51.7 49.4 48.2 40.3 49.1 Mean from beer 9.1 7.8 6.9 6.0 5.2 7.4 % 23.7 20.3 27.3 23.5 24.1 23.1 Mean from wine 9.8 10.2 5.2 7.1 5.7 8.1 % 25.5 26.5 20.6 27.8 26.4 25.3 Mean from others 1.2 0.7 0.8 0.1 0.8 0.8 % 3.1 1.8 3.2 0.4 3.7 2.5 Mean per drinking 38.4 38.5 25.3 25.5 21.6 32.0 day Mean per drinking 10.2 9.8 5.4 8.7 5.5 8.4 + non drinking day

224 Table 8.15: Current drinkers: Mean daily alcohol consumption by frequency of drinking within age - sex strata Number of Age drinking 25-34 35-44 45-54 55-64 65-74 days per Mean No Mean No Mean No Mean No Mean No week [g/day] [g/day] [g/day] [g/day] [g/day] Males < 1 2.8 36 3.0 31 2.2 41 2.5 49 2.4 31 1 - 2 13.4 64 13.7 73 13.0 65 16.5 54 9.1 48 3 - 6 43.8 35 64.3 32 81.6 19 60.6 22 28.2 8 7 85.6 26 116.6 31 97.9 32 134.5 13 51.4 16 Females < 1 1.9 65 1.9 68 1.3 61 1.3 50 0.8 25 1 - 2 10.3 45 11.1 50 7.5 42 6.4 15 7.4 7 3 - 6 29.5 9 29.4 7 21.4 7 35.1 3 90.1 1 7 130.5 3 57.9 7 28.8 2 63.4 6 25.1 1 Missing values 16

225

Average daily intakes of alcohol by age group from men and women are shown in Tables

8.16, 8.17.

73.9% (710) from male responders and 44.7% (455) from female were current drinkers.

Data collected show that men were more likely than women to drink and to drink more than the recommended sensible maximum. 21% from men and 28.5% from women reported to drink less than 5 g per day. The proportion of women who drink decreases with increasing the age. Additionally the quantity of alcohol consumption among women decreases with age as well. Only 4.8% reported to drink more than 20 g per day from which 0.6% drink more than 60 g per day. We did not find the same tendency for men.

There is not a clear age dependency of alcohol consumption, except of decreasing of proportions that drink more than 40 g per day in the oldest 65-74 age group. 11.6% of male responders reported to drink more than 60 g per day. Their proportion peaks between the age 35-54 and then fallen down.

26.1% of males and 55.3% of females reported that they did not consume alcohol and they were qualified as non-drinkers (Table 8.16, 8.17). They were additionally asked whether they had been lifetime non-drinkers or they gave up drinking (Table 8.13). (Some of them did not respond to this question, which is the reason for the differences between the numbers of non-drinkers and the sum of lifetime non-drinkers and past drinkers.)

226

Table 8.16: Distribution of drinking level within age groups, men Alcohol Age groups Total 25-34 35-44 45-54 55-64 65-74 [g/day] Non drinkers 40 45 46 52 68 251

% 20.4 21.5 23.4 27.5 40.0 26.1 < 5 38 33 42 49 40 202 % 19.4 15.8 21.3 25.9 23.5 21.0 5-10 22 29 19 18 22 110 % 11.2 13.9 9.6 9.5 12.9 11.4 10-20 30 37 33 22 15 137 % 15.3 17.7 16.8 11.6 8.8 14.3 20-40 23 17 18 21 15 94 % 11.7 8.1 9.1 11.1 8.8 9.8 40-60 17 16 8 10 5 56 % 8.7 7.7 4.1 5.3 2.9 5.8 > 60 26 32 31 17 5 111 % 13.3 15.3 15.7 9.0 2.9 11.6 Total 196 209 197 189 170 961

Missing values 3

227

Table 8.17: Distribution of drinking level within age groups, women Alcohol Age groups Total 25-34 35-44 45-54 55-64 65-74 [g/day] non drinkers 63 85 127 129 160 564

% 34.2 39.2 54.7 64.8 85.6 55.3 < 5 71 75 68 53 23 290 % 38.6 34.6 29.3 26.6 12.3 28.5 5-10 16 20 19 4 1 60 % 8.7 9.2 8.2 2.0 0.5 5.9 10-20 21 19 12 4 56 % 11.4 8.8 5.2 2.0 5.5 20-40 7 11 5 4 2 29 % 3.8 5.1 2.2 2.0 1.1 2.8 40-60 4 5 1 4 14 % 2.2 2.3 0.4 2.0 1.4 > 60 2 2 1 1 6 % 1.1 0.9 0.5 0.5 0.6 Total 184 217 232 199 187 1019 Missing values 13

228

We observed peak drinking by calculating the ratio of alcohol consumed on weekend drinking day and work drinking day. The results are shown in Figure 8.1. The proportion of alcohol consumed on weekend day among males increases with increasing the mean daily consumption in those who consumed less than 10 g per day to 165% and then decreases to 93.8% in those drinking more than 60 g per day (probably because they drink more regularly). Among women the ration increases from 120.1% in those drinking less than 5 g alcohol per day to 129.3% in those drinking 10 to 20 g alcohol per day, than falls to 103% in those drinking between 20 and 40 g alcohol per day, peeks to 124.5% in those drinking 40-60 g alcohol per day and then drop to 57.7% in those drinking more than 60 g alcohol per day.

Finally the ratio falls under 100% only in those drinking more than 60 g alcohol per day, in both sexes, which means that only they drink more on a work drinking day than on weekend drinking day.

229

Figure 8.1: Current drinkers: ratio of alcohol consumed on weekend drinking day and work drinking day

180 165

160

133.5 140 125.9 129.3 120.1 123.9 122.5 124.5 118.2 120 103

100 93.8 % 80 57.7 60

0 < 5 5 - 10 10 - 20 20 - 40 40 - 60 > 60

male female g/day

230

8.4 Physical activity

It has been recognised for sometime that moderate levels of physical activity can help to prevent cardiovascular disease, although there is continuing debate about the precise nature of the association and the level of activity with which health benefits occurred. A section of physical activity was included in SHS in order to provide information on the level of activity reached by the adult population and on how this varied between age groups.

The participants in SHS were asked for their self-assessment of the level of their physical activity during working time (paid work). The results are shown in Table 8.18. About 20% in all age groups (except the oldest one in which percentage increases to 34%) in both sexes reported to have “almost no” physical activity. 38% from males and 44% from females find the level of their physical activity during work “moderate”. We observed increasing of proportion among men reporting “hard” physical activity to the age 45-54 and than this proportion decreases. The proportion of those men reported “almost nothing” physical activity increases after the age of 44. Only 9.5% from males and 4.3% from females assessed their physical activity during work as “hard”.

In an attempt to assess physical activity in leisure time, participants were asked how often they are physically active for at least 20 minutes in which they “puff and sweat” (Table

8.19). Among men, 20% answered “every day” and the rest (80%) are evenly distributed between the three categories “at least 2-3 times weekly”, “at least once weekly” and “less than once weekly”, and there was no clear age dependency except in the oldest age group

65-74, which shows a decrease in their physical activity in leisure time. Among women we found an equal distribution between the four categories (about 25% in each) with no clear age dependent tendency.

Results of the question “Do you practice any sports during your leisure time?” are presented in Table 8.20. Only 15.6% from males and 9.7% from females reported that they

231 took part in sport and their proportion decreases with increasing the age from 26% in the age group 25-34 to 8.3% in the age group 65-74. Of females 90.3% reported they do not engage in sport. The proportion of females not engaging in sport increases with age from

81.6% in the youngest age group to 96.8% in the oldest.

Table 8.18: Reported physical activity during paid work by sex within age strata

Age Total 25-34 35-44 45-54 55-64 65-74

Males

Almost 39 34 39 41 54 207 nothing

% 20.0 16.3 20.0 22.8 34.6 22.1

Light 55 70 54 52 49 280

% 28.2 33.5 27.7 28.9 31.4 29.9

Moderate 87 84 73 71 44 359

% 44.6 40.2 37.4 39.4 28.2 38.4

Hard 14 21 29 16 9 89

% 7.2 10.0 14.9 8.9 5.8 9.5

Total 195 209 195 180 156 935

Females

Almost 44 41 50 46 59 240 nothing

% 23.8 18.8 21.6 24.1 33.5 24.0

Light 56 63 61 40 59 279

% 30.3 28.9 26.3 20.9 33.5 27.8

Moderate 80 109 106 90 55 440

% 43.2 50.0 45.7 47.1 31.3 43.9

Hard 5 5 15 15 3 43

% 2.7 2.3 6.5 7.9 1.7 4.3

Total 185 218 232 191 176 1002

Number of Missing Observations: 59

232

Table 8.19: Physical activity in leisure time by sex within age strata

Age Total 25-34 35-44 45-54 55-64 65-74

Males

Every day 40 45 43 33 25 186

% 20.4 21.8 22.1 18.4 15.7 19.9

2-3 times 55 63 49 38 29 234 weekly

% 28.1 30.6 25.1 21.2 18.2 25.0

At least once 59 55 54 58 40 266 weekly

% 30.1 26.7 27.7 32.4 25.2 28.4

Less than once 42 43 49 50 65 249 weekly

% 21.4 20.9 25.1 27.9 40.9 26.6

Total 196 206 195 179 159 935

Females

Every day 39 66 56 61 35 257

% 21.5 30.7 24.6 31.6 19.7 25.8

2-3 times 51 55 58 44 34 242 weekly

% 28.2 25.6 25.4 22.8 19.1 24.3

At least once 38 58 66 35 46 243 weekly

% 21.0 27.0 28.9 18.1 25.8 24.4

Less than once 53 36 48 53 63 253 weekly

% 29.3 16.7 21.1 27.5 35.4 25.4

Total 181 215 228 193 178 995

Number of Missing Observations: 66

233

Table 8.20: “Do you practising any sport in your leisure time?” by sex within age strata Age Total 25-34 35-44 45-54 55-64 65-74 Males

Yes 51 36 25 24 14 150

% 26.0 17.2 12.6 12.6 8.3 15.6 No 145 173 173 166 155 812

% 74.0 82.8 87.4 87.4 91.7 84.4 Total 196 209 198 190 169 962

Females Yes 34 23 25 12 6 100

% 18.4 10.5 10.7 5.9 3.2 9.7 No 151 196 208 191 182 928

% 81.6 89.5 89.3 94.1 96.8 90.3 Total 185 219 233 203 188 1028

Number of Missing Observations: 6

234

The participants in SHS were asked how many streets they usually cross daily (i.e. how many street blocks they usually walk) (Table 8.21). 30.8% from males and 45.8% from females answered that they usually cross between 1 and 5 streets per day and their proportion increases with age, steeper among women. The people who reported that they cross more than 20 streets per day were only 10.6% of men and 5.9% of women and the percentage decreases with age in both sexes.

Respondents were asked about the change of the level of their physical activity during the last year. More than 40% from both sexes said that they decrease their physical activity during the last year and only 8% reported that their physical activity was increased (Table

8.22).

235

Table 8.21: How many streets do you cross daily? Age Total 25-34 35-44 45-54 55-64 65-74 Males

1-5 46 55 54 53 60 268

% 25.0 30.1 30.2 30.5 39.7 30.8 6-10 60 65 73 67 40 305

% 32.6 35.5 40.8 38.5 26.5 35.0 11-20 44 40 35 47 40 206

% 23.9 21.9 19.6 27.0 26.5 23.7 >20 34 23 17 7 11 92

% 18.5 12.6 9.5 4.0 7.3 10.6 Total 184 183 179 174 151 871

Females 1-5 66 86 97 92 93 434

% 38.2 41.7 44.1 49.7 56.7 45.8 6-10 58 69 80 56 48 311

% 33.5 33.5 36.4 30.3 29.3 32.8 11-20 29 38 32 28 20 147

% 16.8 18.4 14.5 15.1 12.2 15.5 >20 20 13 11 9 3 56

% 11.6 6.3 5.0 4.9 1.8 5.9 Total 173 206 220 185 164 948

Number of Missing Observations: 177

236

Table 8.22: Reported change in the level of physical activity by sex within age strata

Age Total 25-34 35-44 45-54 55-64 65-74

Males

Increased 29 19 19 14 3 84

% 14.8 9.1 9.8 7.5 1.8 8.8

No change 106 124 103 86 52 471

% 54.1 59.6 53.4 46.0 31.0 49.5

Decreased 61 65 71 87 113 397

% 31.1 31.3 36.8 46.5 67.3 41.7

Total 196 208 193 187 168 952

Females

Increased 27 18 19 10 2 76

% 14.8 8.3 8.3 5.0 1.1 7.5

No change 102 111 91 91 60 455

% 56.0 50.9 39.6 45.7 32.1 44.8

Decreased 53 89 120 98 125 485

% 29.1 40.8 52.2 49.2 66.8 47.7

Total 182 218 230 199 187 1016

Number of Missing Observations: 28

237

9. Chapter: Risk factor levels by socio-demographic characteristics

9.1 Introduction

As there is no generally accepted system for coding occupations to social class in Bulgaria we are using years of full-time education as the main socio-economic characteristic. For the purpose of this analyses, because of the small numbers of respondents having no education (n=3), primary school (4 years of education) (n=28) and 5-8 years of education

(n=217) the above three categories were combined in one with “less than 9 years of education”, yielding a total of 3 categories of education level.

Because education differences are confounded by age, data are generally presented using 3 age strata: 25-44, 45-64 and 65-74.

Distribution of the risk factors by level of education is described in section 9.2 of this chapter.

In SHS are available data on marital status, employment status, home ownership and persons per room, which we are using additionally as socio-economic characteristics.

Results are presented in section 9.3.

In section 9.4 the relative strength and independence of risk factor associations with specific socio-demographic characteristics are assessed by regression analysis.

Because of the lack of previous work in this area and the lack of locally validated measures for socio-economic ranking, the analyses reported in this chapter are of an exploratory kind. Little attention is paid to formal significance testing because of the very large number of comparisons involved.

238

9.2 Risk factors distribution by level of education

9.2.1 Blood pressure by level of education

We observed distribution of SBP categories by level of education (Table 9.1). We found that the mean and median SBP and 90th centiles increases with decreasing the level of education in both sexes, in all age groups, (except in the age group 45-64, males, where

SBP decreases with decreasing the level of education). For DBP (Table 9.2) we observed decreasing of mean DBP with decreasing the level of education for men in the age group

25-64, while in the age group 65-74 men with education 9-12 years had the highest DBP.

Among women we did not find a clear dependency of DBP from the level of education except in the age group 65-74 where DBP decreases with decreasing of the level of education. Overall, the women with less than 9 years of education had the highest mean

DBP (87.7 mmHg), followed by women with more than 12 years of education (85.2 mmHg) and those having 9-12 years of education (84.4 mmHg).

There is thus a lack of consistency in the relation of blood pressure to educational level. If vascular risk is more strongly related to systolic pressure than to diastolic pressure, then the tendency of these pressures to be higher in those with less schooling may be the finding of greatest importance.

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Table 9.1: Systolic blood pressure levels by level of education within age and sex strata

SBP Level of education Total* [mmHg] > 12 years 9 - 12 years Less than 9 years Males [mmHg] [mmHg] [mmHg] [mmHg] Age 25-44 Mean 126.0 128.6 129.8 127.7 SE mean 1.5 1.0 3.4 0.8 Median 124.0 126.0 132.0 125.0 Age 45-64 Mean 138.8 137.0 135.8 137.6 SE mean 1.8 1.3 2.7 1.0 Median 138.0 137.0 135.5 138.0 Age 65-74 Mean 139.8 144.3 144.3 143.0 SE mean 2.2 2.2 2.8 1.4 Median 142.0 141.0 144.5 142.0 All ages Mean 133.6 134.1 137.9 134.4 SE mean 1.1 0.8 1.8 0.6 Median 132.0 132.5 138.0 134.0 Base Age 25-44 144 240 21 405 Age 45-64 147 188 52 387 Age 65-74 50 76 44 170 All ages 341 504 117 962 Females Age 25-44 Mean 116.1 119.8 119.9 118.7 SE mean 1.5 1.0 6.1 0.8 Median 116.0 118.0 114.8 117.8 Age 45-64 Mean 138.8 136.4 142.6 137.9 SE mean 2.1 1.4 2.9 1.1 Median 137.0 136.0 140.0 137.0 Age 65-74 Mean 145.7 146.1 148.6 146.8 SE mean 3.2 1.8 2.0 1.2 Median 149.0 143.0 146.0 144.0 All ages Mean 129.4 130.8 142.9 132.0 SE mean 1.4 0.9 1.8 0.7 Median 126.0 130.0 144.0 131.5 Base Age 25-44 112 278 14 404 Age 45-64 121 258 58 437 Age 65-74 22 107 59 188 All ages 225 643 131 1029 *Includes cases where there was no data for level of education; Missing observations 5

240

Table 9.2: Diastolic blood pressure levels by level of education within age and sex strata

DBP Level of education Total* [mmHg] > 12 years 9 - 12 years Less than 9 years Males [mmHg] [mmHg] [mmHg] [mmHg] Age 25-44 Mean 83.1 82.9 81.9 83.0 SE mean 0.8 0.7 2.0 0.5 Median 83.5 83.0 86.0 84.0 Age 45-64 Mean 88.5 87.2 83.4 87.2 SE mean 1.0 0.9 1.4 0.6 Median 87.0 86.0 86.0 86.0 Age 65-74 Mean 86.6 88.0 87.8 87.5 SE mean 1.2 1.2 1.8 0.8 Median 86.0 88.0 86.5 86.5 All ages Mean 86.0 85.3 84.8 85.5 SE mean 0.6 0.5 1.0 0.4 Median 85.0 84.8 86.0 85.0 Base Age 25-44 144 240 21 405 Age 45-64 147 188 52 387 Age 65-74 50 76 44 170 All ages 341 504 117 962 Females Age 25-44 Mean 78.7 79.6 78.9 79.4 SE mean 1.0 0.7 4.5 0.6 Median 80.0 80.0 80.0 80.0 Age 45-64 Mean 90.2 87.8 91.0 88.9 SE mean 1.2 0.8 1.7 0.6 Median 88.0 86.0 90.0 88.0 Age 65-74 Mean 90.9 88.3 86.5 88.0 SE mean 2.41 0.92 1.02 0.68 Median 87.0 87.0 86.0 86.0 All ages Mean 85.2 84.4 87.7 85.0 SE mean 0.8 0.5 1.0 0.4 Median 84.0 84.0 87.0 84.0 Base Age 25-44 112 278 14 404 Age 45-64 121 258 58 437 Age 65-74 22 107 59 188 All ages 225 643 131 1029 *Includes cases where there was no data for level of education; Missing observations 5

9.2.2 Total serum cholesterol by level of education

The distribution of the TSC by level of education, sex and age strata is shown in Table 9.3.

The mean total cholesterol among men 25-44 and 65-74 years old decreases with

241 decreasing of the level of education, while among those 45-64 years old it does not show dependency from the level of education. Overall the mean total cholesterol decreases slightly from 5.3 mmol/l in men with more than 12 years of education to 5.2 mmol/l in men with less than 9 year of education. The proportion of men with cholesterol higher than

6.5 mmol/l increases from 13.3% in those with more than 12 years of education to 16.7% in those with less than 9 year of education.

Among women 25-44 years old mean TSC decreases from 4.9 mmol/l in those with more than 12 years of education to 4.5 mmol/l in those with less 9 years of education. In the age group 45-64 women with more than 12 years of education and 9-12 years of education had equal levels of mean TSC and those with less than 9 years of education had higher mean

TSC (5.6 mmol/l). In the age group 65-74 mean TSC increases with decreasing the level of education from 5.5 mmol/l in those with more than 12 years of education to 6.0 mmol/l in women with less than 9 years of education. Overall, the mean serum cholesterol in women with more than 12 years of and 9-12 years education was 5.2 mmol/l and it was lower than in women with less than 9 years of education (5.7 mmol/l). The proportion of women with

TSC more than 6.5 mmol/l was much higher in those with lowest educational level

(26.4%) than in those with more than 9 years education (about 15%).

9.2.3 BMI by level of education

Table 9.4 presents BMI by level of education within sex and age strata. We did not find a clear dependency between BMI and the level of education among males, while among females mean BMI decreases with increasing the level of education, from 27.8 kg/m2 in those less than 9 year of education to 25.0 kg/m2 in those with highest educational level.

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Table 9.3: Serum total cholesterol by level of education within sex and age strata

TSC Level of education Total [mmol/l] > 12 years 9 - 12 years Less than 9 years Males [mmol/l] [mmol/l] [mmol/l] [mmol/l] Age 25-44 Mean 5.2 5.1 4.9 5.1 SE mean 0.1 0.1 0.3 0.1 Median 5.0 5.0 4.5 4.9 Age 45-64 Mean 5.3 5.3 5.4 5.3 SE mean 0.1 0.1 0.3 0.1 Median 5.1 5.1 5.1 5.1 Age 65-74 Mean 5.4 5.2 5.1 5.3 SE mean 0.2 0.2 0.2 0.1 Median 5.5 5.0 5.1 5.2 All ages Mean 5.3 5.2 5.2 5.2 SE mean 0.1 0.1 0.2 0.1 Median 5.1 5.0 4.9 5.0 Base Age 25-44 119 163 13 295 Age 45-64 130 152 38 320 Age 65-74 45 67 33 145 All ages 294 382 84 760 Females Age 25-44 Mean 4.9 4.7 4.5 4.7 SE mean 0.2 0.1 0.3 0.1 Median 4.6 4.4 4.6 4.5 Age 45-64 Mean 5.4 5.4 5.6 5.4 SE mean 0.1 0.1 0.2 0.1 Median 5.3 5.2 5.5 5.2 Age 65-74 Mean 5.5 5.9 6.0 5.9 SE mean 0.3 0.2 0.2 0.1 Median 5.2 5.8 5.7 5.7 All ages Mean 5.2 5.2 5.7 5.2 SE mean 0.1 0.1 0.1 0.1 Median 5.0 5.0 5.4 5.0 Base Age 25-44 104 247 8 359 Age 45-64 120 244 51 415 Age 65-74 19 88 51 158 All ages 243 579 110 932 Missing observations 304

243

Table 9.4: Body mass index by level of education within age and sex strata

BMI Level of education Total [kg/m2] > 12 years 9 - 12 years Less than 9 years Males [kg/m2] [kg/m2] [kg/m2] [kg/m2] Age 25-44 Mean 25.9 26.2 26.6 26.1 SE mean 0.3 0.2 0.9 0.2 Median 25.7 25.7 26.5 25.7 Age 45-64 Mean 26.6 27.2 26.9 26.9 SE mean 0.3 0.3 0.6 0.2 Median 26.1 26.5 25.2 26.2 Age 65-74 Mean 26.1 26.8 25.9 26.3 SE mean 0.4 0.4 0.4 0.2 Median 26.1 26.5 25.5 26.2 All ages Mean 26.2 26.7 26.4 26.5 SE mean 0.2 0.2 0.3 0.1 Median 25.9 26.2 25.5 26.0 Base Age 25-44 143 234 21 398 Age 45-64 146 187 51 384 Age 65-74 50 76 44 170 All ages 339 497 116 952 Females Age 25-44 Mean 23.6 24.7 21.7 24.3 SE mean 0.4 0.3 0.8 0.2 Median 22.6 23.5 21.9 23.1 Age 45-64 Mean 26.1 27.5 29.0 27.3 SE mean 0.4 0.3 0.6 0.2 Median 25.7 27.0 28.6 27.0 Age 65-74 Mean 26.3 27.6 28.2 27.6 SE mean 0.6 0.4 0.5 0.3 Median 25.9 26.7 27.6 27.1 All ages Mean 25.0 26.3 27.8 26.2 SE mean 0.3 0.2 0.4 0.2 Median 24.2 25.6 27.5 25.5 Base Age 25-44 111 278 14 403 Age 45-64 120 254 58 432 Age 65-74 20 106 57 183 All ages 251 638 129 1018 Missing observations 26

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9.2.4 Smoking by level of education

We observed the relation between smoking behavior and level of education of the participants in SHS. We ran two-sided Pearson Chi-square test, separately for men and women, which shows statistically significant (p < 0.05) relation between smoking behaviour and level of education, for both sexes.

Table 9.5 presents cigarette-smoking status by level of education, age and sex. Among males 25-44 years old we observed decreasing the proportion of current smokers with increasing the level of education from 66.7% in those with less than 9 year of education to

54.9% in those with more than 12 years of education, which might due partly to the increasing proportion of ex-smokers with increasing the level of education in the same age group. Among women aged 25-64 the highest proportion of current smokers was among those with 9-12 years of education. In the age group 65-74 the proportions of current smokers and never smokers women increase with increasing the level of education.

Overall we found that the people with 9-12 years of education has the highest proportion of current smokers (41.5% for males and 37.6% for females) followed by people with more than 12 years of education (38.1% for men and 35.7% for women). The participants with less than 9 years of education had the highest proportion of never smokers (45.3% for men and 87.8% for women). Among males those with 9 years of education had the highest proportion of ex-smokers (29.9%), while among women the proportion of ex-smokers decreases with decreasing the level of education from 16.5% in those with more than 12 years of education to 3.1% in those with less than 9 years of education. The quit ratio was the lowest in those with 9 to 12 years of education (0.38 for males and 0.21 for females).

The highest quit ratio among men we found in those with less than 9 years of education

(0.55), while among women it was in those with more than 12 years of education (0.32).

The complex relations between education level and smoking prevalence reflect the dynamics of a smoking epidemic that is at a relatively early stage of evolution.

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Table 9.5: Cigarettes smoking status by level of education within age and sex strata Level of education Total More than 12 years 9 - 12 years Less than 9 years Males % % % % Age 25-44 Current smokers 54.9 55.8 66.7 56.0 Ex-smokers 21.5 15.8 14.3 17.8 Never smokers 22.9 26.7 19.0 24.9 Age 45-64 Current smokers 29.9 34.0 19.2 30.5 Ex-smokers 25.9 31.4 34.6 29.7 Never smokers 43.5 32.4 46.2 38.5 Age 65-74 Current smokers 14.0 14.5 11.4 13.5 Ex-smokers 42.0 43.4 31.8 40.0 Never smokers 44.0 39.5 56.8 45.3 All ages Current smokers 38.1 41.5 24.8 38.3 Ex-smokers 26.4 25.8 29.9 26.5 Never smokers 34.9 30.8 45.3 34.0 Not stated 0.6 2.0 0 1.2 Base Age 25-44 144 240 21 405 Age 45-64 147 188 52 387 Age 65-74 50 76 44 170 All ages 341 504 117 962 Quit ratio 0.41 0.38 0.55 0.41 Females Age 25-44 Current smokers 47.3 55.4 50.0 53.0 Ex-smokers 17.0 13.3 14.3 14.4 Never smokers 35.7 30.9 35.7 32.4 Age 45-64 Current smokers 27.3 29.5 6.9 25.9 Ex-smokers 18.2 6.6 3.4 9.4 Never smokers 52.1 60.9 87.9 62.0 Age 65-74 Current smokers 22.7 11.2 0.0 9.0 Ex-smokers 4.5 11.2 0.0 6.9 Never smokers 68.2 77.6 100.0 83.5 All ages Current smokers 35.7 37.6 8.4 33.4 Ex-smokers 16.5 10.3 3.1 10.9 Never smokers 46.3 50.7 87.8 54.3 Not stated 1.6 1.4 0.8 1.4 Base Age 25-44 112 278 14 404 Age 45-64 121 258 58 437 Age 65-74 22 107 59 188 All ages 255 643 131 1029 Quit ratio 0.32 0.21 0.27 0.25 Number of Missing Observations: 5

9.2.5 Alcohol consumption by level of education

Table 9.6 presents the reported alcohol consumption in grams per day by level of education, sex and age strata. Among males aged 25-44 and 65-74 we found increasing the proportion of non-drinkers with decreasing the level of education. Overall the highest

246 proportion of non-drinkers (45.3%) was among men with less than 9 years of education and it was two-fold higher than among those with University and 9-12 years of education

(23.5%). The highest proportion of males drinking more than 60 g per day was among those with 9 to 12 years of education (13.5%) followed by males with less than 9 years of education 11.1% and 8.5% in those with more than 12 years of education.

Among females the proportion of non-drinkers increases with decreasing the level of education in all age groups from 42.7% in those with more than 12 years of education to

84.5% in those with less than 9 years of education. The proportion of women drinking more than 40 grams per day in the age group 25-44 increases with decreasing the level of education, while in the age group 45-74 it is highest among those with more than 12 years of education.

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Table 9.6: Alcohol consumption by level of education within age and sex strata Alcohol Level of education Total [g/day] > 12 years 9 - 12 years Less than 9 years Males % % % % Age 25-44 Non drinkers 19.4 21.3 28.6 21.0 1 – 60 70.1 62.1 57.1 64.7 > 60 10.4 16.7 14.3 14.3 Age 45-64 Non drinkers 24.7 21.4 42.3 25.5 1 – 60 66.4 64.2 44.2 62.3 > 60 8.9 14.4 13.5 12.2 Age 65-74 Non drinkers 32.0 35.5 56.8 40.0 1 – 60 66.0 63.2 36.4 57.1 > 60 2.0 1.3 6.8 2.9 All ages Non drinkers 23.5 23.5 45.3 26.1 1 – 60 67.9 63.0 43.6 62.4 > 60 8.5 13.5 11.1 11.5 Base Age 25-44 144 240 21 405 Age 45-64 146 187 52 385 Age 65-74 50 76 44 170 All ages 340 503 117 960 Females Age 25-44 Non drinkers 31.3 38.5 50.0 36.9 1 – 40 67.0 57.8 42.9 59.9 > 40 1.8 3.6 7.1 3.2 Age 45-64 Non drinkers 48.8 59.9 80.4 59.4 1 – 40 47.9 39.3 19.6 39.2 > 40 3.3 0.8 0 1.4 Age 65-74 Non drinkers 68.2 82.9 96.6 85.5 1 – 40 27.3 17.1 3.4 14.0 > 40 4.5 0 0 0.5 All ages Non drinkers 42.7 54.4 84.5 55.3 1 – 40 54.5 43.7 14.7 42.7 > 40 2.7 1.9 0.8 2.0 Base Age 25-44 112 275 14 401 Age 45-64 121 252 56 429 Age 65-74 22 105 59 186 All ages 255 632 129 1016

Number of Missing Observations: 20

9.2.6 Physical activity by level of education

Tables 9.7 and 9.8 present physical activity separately during working time and during leisure time by level of education by sex and age strata. As could be expected we found

248 that the proportion of those reported to have ‘hard’ physical activity during their working time increases with decreasing the level of education in all age groups in both sexes from

4.1% for men and 1.2% for women in those with more than 12 years of education to 18.9% for men and 11.3% for women with less than 9 years of education. The proportion of those reporting to have ‘almost nothing’ physical activity during their working time was highest in the age group 25-64 in those with more than 12 years of education in both sexes, but in the age group 65-74 this proportion was highest among those with less than 9 years of education in both sexes again.

The participants in SHS were asked how many times weekly they ‘puff and sweat’ for at least 20 minutes during their leisure time. Among males the lowest proportion of those who reported to be physically active in their leisure time every day we found in people with more than 12 years of education (15.6%). This proportion increases with decreasing the level of education in the age group 25-64 and just the opposite in the age group 65-74 it increases with increasing the level of education. 32.1% from males with more than 12 years of education reported to be physically active at least once weekly which could means that they using mainly weekends.

Among females the proportion of those who reported to be physically active in their leisure time every day increases with decreasing the level of education in the age group 25-44 and it is highest among those with less than 9 years of education in all age groups. The proportion of women reporting to be physically active during their leisure time less than once weekly was highest among those with 9-12 years of education.

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Table 9.7: Physical activity during working time by level of education within age and sex strata

Level of education Total > 12 years 9 - 12 years Less than 9 years Males % % % % Age 25-44 Almost nothing 27.3 12.1 23.8 18.1 Light or moderate 68.5 77.9 52.4 73.3 Hard 4.2 10.0 23.8 8.7 Age 45-64 Almost nothing 32.2 14.6 14.0 21.4 Light or moderate 63.7 71.3 60.0 66.8 Hard 4.1 14.0 26.0 11.8 Age 65-74 Almost nothing 28.6 31.3 47.5 34.6 Light or moderate 67.3 62.7 45.0 59.6 Hard 4.1 6.0 7.5 5.8 All ages Almost nothing 29.6 15.7 27.9 22.2 Light or moderate 66.3 73.4 53.2 68.4 Hard 4.1 10.9 18.9 9.4 Base Age 25-44 143 240 21 404 Age 45-64 146 178 50 374 Age 65-74 49 67 40 156 All ages 338 485 111 934 Females Age 25-44 Almost nothing 22.3 20.9 14.3 21.1 Light or moderate 75.9 76.5 78.6 76.4 Hard 1.8 2.5 7.1 2.5 Age 45-64 Almost nothing 25.2 23.9 12.7 22.8 Light or moderate 73.9 69.7 63.6 70.1 Hard .9 6.4 23.6 7.1 Age 65-74 Almost nothing 18.2 34.7 36.4 33.1 Light or moderate 81.8 62.2 63.6 65.1 Hard 3.1 1.7 All ages Almost nothing 23.3 24.3 23.4 23.9 Light or moderate 75.5 71.6 65.3 71.8 Hard 1.2 4.2 11.3 4.3 Base Age 25-44 112 277 14 403 Age 45-64 115 251 55 421 Age 65-74 22 98 55 175 All ages 249 626 124 999 Missing observations 63

250

Table 9.8: Physical activity during leisure time by level of education within age and sex strata Level of education Total More than 12 years 9 - 12 years Less than 9 years Males % % % % Age 25-44 Every day 14.7 23.8 35.0 21.1 2-3 t/weekly 27.3 31.8 15.0 29.4 ≥ 1 t/weekly 32.2 25.5 35.0 28.4 < 1 t/weekly 25.9 18.8 15.0 21.1 Age 45-64 Every day 16.0 19.0 38.0 20.4 2-3 t/weekly 21.5 25.7 18.0 23.1 ≥ 1 t/weekly 34.0 28.5 24.0 30.0 < 1 t/weekly 28.5 26.8 20.0 26.5 Age 65-74 Every day 17.4 16.9 11.9 15.7 2-3 t/weekly 28.3 15.5 11.9 18.2 ≥ 1 t/weekly 26.1 19.7 33.3 25.2 < 1 t/weekly 28.3 47.9 42.9 40.9 All ages Every day 15.6 21.1 27.7 19.9 2-3 t/weekly 24.9 27.2 15.2 24.9 ≥ 1 t/weekly 32.1 25.8 29.5 28.5 < 1 t/weekly 27.3 26.0 27.7 26.7 Base Age 25-44 143 239 20 402 Age 45-64 144 179 50 373 Age 65-74 46 71 42 159 All ages 338 489 112 934 Females Age 25-44 Every day 22.0 27.8 35.7 26.5 2-3 t/weekly 27.5 26.7 21.4 26.8 ≥ 1 t/weekly 27.5 22.3 35.7 24.2 < 1 t/weekly 22.9 23.1 7.1 22.5 Age 45-64 Every day 28.8 26.1 33.9 27.9 2-3 t/weekly 20.3 25.3 26.8 24.1 ≥ 1 t/weekly 24.6 24.1 21.4 23.9 < 1 t/weekly 26.3 24.5 17.9 24.1 Age 65-74 Every day 20.0 16.0 26.3 19.8 2-3 t/weekly 30.0 20.0 14.0 19.2 ≥ 1 t/weekly 35.0 20.0 31.6 25.4 < 1 t/weekly 15.0 44.0 28.1 35.6 All ages Every day 25.1 25.2 30.7 25.9 2-3 t/weekly 24.3 25.1 20.5 24.3 ≥ 1 t/weekly 26.7 22.7 27.6 24.3 < 1 t/weekly 23.9 27.0 21.3 25.5 Base Age 25-44 109 273 14 396 Age 45-64 118 245 56 419 Age 65-74 20 100 57 177 All ages 247 618 127 992 Missing observations 70

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9.3 Risk factor distributions by other socio-economic characteristics

9.3.1 Blood pressure by other socio-economic characteristics

Data in Table 9.9 shows mean and median SBP according to marital status, age and sex.

We found that never married participants had the lowest level of mean SBP in both sexes

(129.8 mmHg for men and 122.2 mmHg for women), since the widowed experienced the highest mean SBP again in both sexes (140.6 mmHg for men and 141.0 mmHg for women).

Table 9.10 presents mean and median SBP by home ownership and person per room, age and sex.

The participants who owned their own home had higher levels of mean SBP than those who did not have one in both sexes.

We observed a tendency of increasing the mean levels of systolic blood pressure with decreasing the number of persons living in one room from 131.6 mmHg to 136.0 mmHg in men and from 122.9 mmHg to 135.1 mmHg in women.

Table 9.11 shows systolic blood pressure by employment status within sex and age strata.

As we can expect we found he lowest mean SBP among students and the highest levels of mean SBP among fully retired participants. Respondents working on full time job were with higher mean SBP than those working part time, in all age groups in both sexes

(excluding only males aged 25-44).

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Table 9.9: Systolic blood pressure by marital status within sex and age strata Marital status Never Separate or Total Married Widowed married divorced Males [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] Age 25-44 Mean 126.9 127.7 129.5 121.8 127.7 SE mean 1.6 1.0 2.5 6.9 0.8 Median 124.0 124.0 130.0 118.5 125.0 Age 45-64 Mean 135.9 138.3 133.7 135.6 137.5 SE mean 3.3 1.1 3.1 4.9 1.0 Median 136.0 138.0 130.0 136.0 137.5 Age 65-74 Mean 152.2 140.7 157.1 148.4 143.0 SE mean 11.6 1.5 8.3 3.6 1.4 Median 157.0 142.0 144.0 148.5 142.0 All ages Mean 129.8 134.7 134.0 140.6 134.4 SE mean 1.6 0.7 2.1 3.0 0.6 Median 127.0 134.0 132.0 141.0 134.0 Base Age 25-44 83 272 45 4 404 Age 45-64 24 300 41 23 388 Age 65-74 4 133 9 24 170 All ages 111 705 95 51 962 Females Age 25-44 Mean 116.2 119.3 116.9 133.9 118.8 SE mean 2.2 1.0 1.9 6.1 0.8 Median 114.0 118.0 116.0 132.0 118.0 Age 45-64 Mean 135.9 138.8 135.1 136.6 137.9 SE mean 6.0 1.3 3.1 3.0 1.1 Median 134.0 137.5 134.0 138.0 137.0 Age 65-74 Mean 139.6 149.2 146.1 145.2 146.8 SE mean 5.8 2.0 3.4 1.8 1.2 Median 137.0 148.0 148.0 142.0 144.0 All ages Mean 122.2 131.9 128.3 141.0 132.0 SE mean 2.3 0.9 1.9 1.7 0.7 Median 120.0 130.0 126.0 140.0 131.5 Base Age 25-44 56 279 61 7 403 Age 45-64 16 302 58 63 439 Age 65-74 7 85 17 79 188 All ages 79 666 136 149 1030 Missing observations 4

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Table 9.10: Systolic blood pressure by home ownership and person per room within sex and age strata Home ownership Persons per room Yes No Total < 1 1 – 2 > 2 Total Males [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] Age 25-44 Mean 128.0 126.9 127.8 127.6 127.4 128.1 127.5 SE mean 0.9 2.3 0.8 1.2 1.3 3.2 0.8 Median 125.0 125.0 125.0 124.0 125.0 130.0 125.0 Age 45-64 Mean 137.7 136.1 137.6 138.4 136.8 137.6 137.6 SE mean 1.0 3.6 1.0 1.5 1.5 5.4 1.1 Median 137.0 138.5 138.0 138.0 136.5 143.0 137.5 Age 65-74 Mean 142.4 144.0 142.6 143.2 141.9 134.6 142.5 SE mean 1.4 6.7 1.4 1.6 3.2 9.7 1.4 Median 142.0 143.0 142.0 142.0 141.5 129.5 142.0 All ages Mean 134.7 131.7 134.3 136.0 132.6 131.6 134.3 SE mean 0.6 2.0 0.6 0.9 1.0 2.8 0.6 Median 134.0 127.0 134.0 135.0 132.0 131.0 134.0 Base Age 25-44 334 68 402 145 194 22 361 Age 45-64 354 30 384 176 152 10 338 Age 65-74 150 16 166 110 42 6 158 All ages 838 114 952 431 388 38 857 Females Age 25-44 Mean 119.3 115.2 118.7 119.8 118.7 116.0 118.9 SE mean 0.9 2.1 0.9 1.4 1.3 3.5 0.9 Median 118.0 114.0 117.0 118.0 118.0 114.0 118.0 Age 45-64 Mean 138.6 131.3 138.0 139.5 138.2 122.7 138.1 SE mean 1.2 4.3 1.1 1.6 1.9 4.0 1.2 Median 138.0 126.0 137.0 140.0 134.0 124.0 136.0 Age 65-74 Mean 146.9 145.2 146.7 145.7 149.4 139.7 146.5 SE mean 1.3 3.9 1.3 1.7 1.9 6.7 1.3 Median 144.0 146.0 144.0 143.0 148.5 135.5 144.0 All ages Mean 132.8 125.3 132.0 135.1 130.6 122.9 132.4 SE mean 0.8 2.1 0.7 1.0 1.2 2.7 0.8 Median 132.0 122.0 131.5 135.5 128.0 122.0 132.0 Base Age 25-44 340 61 401 136 188 24 348 Age 45-64 398 33 431 199 160 19 378 Age 65-74 165 21 186 115 54 10 179 All ages 903 115 1018 450 402 53 905 Missing observations for ‘home owners’ 26, for ‘person per room’ 234

254

Table 9.11: Systolic blood pressure by employment status within sex and age strata Employment status Retired, Full Part Shift Unem- House- Fully Total because Student time time work ployed wife retired of ill Males [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] Age 25-44 Mean 128.2 129.6 126.8 125.1 121.7 - - 125.5 127.7 SE mean 1.1 3.0 1.7 2.5 15.6 - - 2.7 0.8 Median 125.0 124.0 125.0 125.0 123.0 - - 124.5 125.0 Age 45-64 Mean 137.0 121.8 136.3 134.5 132.5 - 146.8 114.0 137.5 SE mean 1.5 2.4 2.7 4.4 4.4 - 1.9 10.0 1.0 Median 137.0 118.0 137.5 132.0 133.0 - 144.0 114.0 138.0 Age 65-74 Mean 140.9 134.0 - - 135.3 - 143.7 - 143.3 SE mean 4.5 - - - 18.2 - 1.5 - 1.4 Median 142.0 134.0 - - 151.0 - 142.0 - 142.0 All ages Mean 132.2 126.7 130.6 128.3 131.3 - 144.8 123.9 134.4 SE mean 0.9 2.1 1.5 2.3 4.4 - 1.2 2.7 0.6 Median 132.0 124.0 129.0 127.0 133.0 - 143.0 124.0 134.0 Base Age 25-44 231 56 67 33 3 0 0 12 402 Age 45-64 176 34 46 17 14 0 90 2 379 Age 65-74 9 1 0 0 3 0 153 0 166 All ages 416 91 113 50 20 0 243 14 947 Females Age 25-44 Mean 119.6 117.1 118.7 112.2 144.0 115.8 - 117.8 118.7 SE mean 1.2 2.8 1.5 3.4 - 8.6 - 6.0 0.9 Median 113.0 118.0 118.0 111.2 144.0 115.0 - 119.5 117.8 Age 45-64 Mean 131.4 129.8 134.0 125.4 146.0 128.7 147.4 - 137.8 SE mean 1.6 5.9 3.3 5.4 8.3 8.3 1.6 - 1.1 Median 131.0 125.0 131.0 121.0 143.5 123.0 144.0 - 137.0 Age 65-74 Mean 135.0 - 150.0 - 153.2 142.3 147.1 - 146.8 SE mean 6.9 - - - 6.0 2.8 1.3 - 1.2 Median 138.0 - 150.0 - 155.0 140.5 144.0 - 144.0 All ages Mean 124.4 123.2 123.7 118.0 147.5 130.1 147.3 117.8 132.0 SE mean 1.0 3.3 1.6 3.2 6.1 5.1 1.0 6.0 0.7 Median 122.0 122.0 122.0 116.0 144.0 134.5 144.0 119.5 131.0 Base Age 25-44 236 17 108 28 1 4 0 6 400 Age 45-64 156 16 50 22 16 12 163 0 435 Age 65-74 5 0 1 0 5 6 169 0 186 All ages 397 33 159 50 22 22 332 6 1021

Missing observations 28

255

Table 9.12 presents diastolic blood pressure by marital status within sex and age strata.

We found that never married participants had the lowest level of mean DBP (82.8 mmHg for men and 79.4 mmHg for women) since the widowed experienced the highest mean

DBP (87.3 mmHg for men and 87.5 mmHg for women). In the age group 45-64 the highest mean DBP was found among married participants (87.7 mmHg for men and 89.8 mmHg for women).

The mean DBP was higher in those who have their own homes than those who did not own homes in the age group 25-44 in both sexes. In the age group 65-74 we observed just the opposite that the people who did not own their home experienced higher levels of mean

DBP than those who own homes.

Male participants who live with 1 to 2 persons per room experienced the highest level of mean DBP in all age groups, but it does not differ substantially from those living with more than 2 and less than 1 persons per room. Among women we observed a tendency of increasing the mean levels of diastolic blood pressure with decreasing the number of persons living in one room (Table 9.13).

Table 9.14 shows diastolic blood pressure by employment status within sex and age strata.

The highest mean DBP was found among fully retired people among men (89.2 mmHg) and retired because of ill among women (90.6 mmHg). The lowest levels of DBP were found among men working part time (81.5 mmHg) and among unemployed women (77.9 mmHg).

256

Table 9.12: Diastolic blood pressure by marital status within sex and age strata

Marital status Never Separate or Total Married Widowed married divorced Males [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] Age 25-44 Mean 81.9 83.3 82.8 86.2 83.0 SE mean 1.2 0.6 1.5 7.9 0.5 Median 82.0 84.0 83.0 83.5 84.0 Age 45-64 Mean 84.6 87.7 86.1 84.8 87.2 SE mean 2.2 0.7 2.1 2.7 0.6 Median 84.0 87.0 85.0 85.0 86.0 Age 65-74 Mean 90.0 86.0 94.1 89.8 87.5 SE mean 7.1 0.8 2.7 2.7 0.8 Median 86.0 86.0 92.0 88.0 86.5 All ages Mean 82.8 85.8 85.3 87.3 85.5 SE mean 1.0 0.4 1.2 1.8 0.4 Median 82.0 86.0 84.0 86.0 85.0 Base Age 25-44 83 272 45 4 404 Age 45-64 24 300 41 23 388 Age 65-74 4 133 9 24 170 All ages 111 705 95 51 962 Females Age 25-44 Mean 76.7 80.2 77.3 86.3 79.3 SE mean 1.5 0.7 1.5 4.3 0.6 Median 77.0 80.0 78.0 85.0 80.0 Age 45-64 Mean 85.9 89.8 86.1 87.1 88.8 SE mean 3.5 0.8 1.8 1.6 0.6 Median 86.0 88.0 84.0 86.0 88.0 Age 65-74 Mean 85.9 88.5 87.4 87.9 88.1 SE mean 3.7 1.0 2.7 1.0 0.7 Median 84.0 86.0 86.0 87.0 86.5 All ages Mean 79.4 85.6 82.3 87.5 85.0 SE mean 1.4 0.5 1.2 0.8 0.4 Median 80.0 84.8 83.0 87.0 84.0 Base Age 25-44 56 279 61 7 403 Age 45-64 16 302 58 63 439 Age 65-74 7 85 17 79 188 All ages 79 666 136 149 1030 Missing observations 4

257

Table 9.13: Diastolic blood pressure by home ownership and person per room within sex and age strata Home ownership Persons per room Yes No Total < 1 1 – 2 > 2 Total Males [mmH [mmH [mmHg [mmH [mmH [mmH [mmHg g] g] ] g] g] g] ] Age 25-44 Mean 83.4 81.0 83.0 82.4 82.8 82.1 82.6 SE mean 0.6 1.2 0.5 0.8 0.8 2.5 0.5 Median 84.0 82.0 84.0 83.0 83.0 84.5 83.0 Age 45-64 Mean 87.2 88.0 87.3 86.7 87.9 86.6 87.2 SE mean 0.6 2.0 0.6 0.9 0.9 3.0 0.6 Median 86.0 87.5 86.5 86.0 88.0 88.5 86.5 Age 65-74 Mean 87.4 87.8 87.4 88.0 88.3 76.9 87.7 SE mean 0.8 3.5 0.8 0.9 1.9 4.4 0.8 Median 86.0 88.5 86.0 87.0 86.5 74.5 86.0 All ages Mean 85.7 83.8 85.5 85.6 85.4 82.5 85.4 SE mean 0.4 1.1 0.4 0.5 0.6 1.8 0.4 Median 85.0 84.0 85.0 85.0 85.8 84.0 85.0 Base Age 25-44 334 68 402 145 194 22 361 Age 45-64 354 30 384 176 152 10 338 Age 65-74 150 16 166 110 42 6 158 All ages 838 114 952 431 388 38 857 Females Age 25-44 Mean 79.8 76.6 79.3 79.0 79.8 78.4 79.4 SE mean 0.6 1.6 0.6 1.0 0.9 2.1 0.6 Median 80.0 78.0 80.0 80.0 80.0 79.0 80.0 Age 45-64 Mean 89.4 82.2 88.9 89.5 89.2 79.3 88.8 SE mean 0.7 2.4 0.6 1.0 1.0 2.8 0.7 Median 88.0 82.0 88.0 88.0 86.0 80.0 87.5 Age 65-74 Mean 87.8 88.7 88.5 86.6 88.2 87.9 SE mean 0.7 2.1 0.7 0.9 1.1 2.2 0.7 Median 86.0 89.0 86.0 87.0 86.0 87.5 86.0 All ages Mean 85.5 80.4 84.9 86.1 84.4 80.6 85.0 SE mean 0.4 1.2 0.4 0.6 0.6 1.5 0.4 Median 85.0 82.0 84.0 86.0 84.0 82.0 84.0 Base Age 25-44 340 61 401 136 188 24 348 Age 45-64 398 33 431 199 160 19 378 Age 65-74 165 21 186 115 54 10 179 All ages 903 115 1018 450 402 53 905 Missing observations for ‘home owners’ 26, for ‘person per room’ 234

258

Table 9.14: Diastolic blood pressure by employment status within sex and age strata Employment status Retired, Full Part Shift Unem- House- Fully Total because Student time time work ployed wife retired of ill Males [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] [mmHg] Age 25-44 Mean 83.6 83.1 82.5 79.9 77.0 - - 83.8 83.0 SE mean 0.7 1.8 1.0 1.6 4.6 - - 1.9 0.5 Median 84.0 82.0 83.0 82.0 77.0 - - 83.5 84.0 Age 45-64 Mean 87.5 78.7 85.8 85.2 85.5 - 91.9 74.0 87.3 SE mean 0.9 1.4 1.8 2.5 3.1 - 1.3 12.0 0.6 Median 87.5 79.0 84.5 86.0 87.0 - 88.5 74.0 86.0 Age 65-74 Mean 91.1 88.0 - - 80.0 - 87.7 - 87.7 SE mean 2.6 0 - - 7.6 - 0.8 - 0.8 Median 92.0 88.0 - - 85.0 - 87.0 - 87.0 All ages Mean 85.4 81.5 83.9 81.7 83.7 - 89.2 82.2 85.5 SE mean 0.6 1.2 1.0 1.4 2.6 - 0.7 2.0 0.4 Median 85.0 80.0 84.0 82.2 85.0 - 88.0 83.5 85.0 Base Age 25-44 231 56 67 33 3 0 0 12 402 Age 45-64 176 34 46 17 14 0 90 2 379 Age 65-74 9 1 0 0 3 0 153 0 166 All ages 416 91 113 50 20 0 243 14 947 Females Age 25-44 Mean 80.2 77.2 79.1 74.3 102.0 70.0 - 80.8 79.4 SE mean 0.8 2.4 1.0 2.6 0 7.8 - 3.3 0.6 Median 81.0 80.0 79.0 73.5 102.0 65.0 - 81.5 80.0 Age 45-64 Mean 87.1 82.2 86.9 82.5 90.9 79.9 92.8 - 88.7 SE mean 1.0 3.9 2.0 3.4 3.8 4.2 0.9 - 0.6 Median 86.0 83.5 85.5 81.5 90.0 76.0 90.0 - 88.0 Age 65-74 Mean 82.4 - 89.0 - 87.0 88.7 88.2 - 88.0 SE mean 1.6 - 0 - 4.0 4.1 0.7 - 0.7 Median 82.0 - 89.0 - 89.0 89.5 87.0 - 86.5 All ages Mean 83.0 79.6 81.6 77.9 90.6 80.5 90.4 80.8 84.9 SE mean 0.6 2.2 1.0 2.2 3.0 3.1 0.6 3.3 0.4 Median 84.0 80.0 82.0 76.5 90.0 76.5 88.0 81.5 84.0 Base Age 25-44 236 17 108 28 1 4 0 6 400 Age 45-64 156 16 50 22 16 12 163 0 435 Age 65-74 5 0 1 0 5 6 169 0 186 All ages 397 33 159 50 22 22 332 6 1021 Missing observations 28

259

9.3.2 Total serum cholesterol by other socio-economic characteristics

Table 9.15 presents total serum cholesterol by marital status within sex and age strata. The lowest levels of mean TSC were found among never married in both sexes (4.7 mmol/l, all ages). The highest levels of mean TSC among male were in those who were separate or divorced (5.5 mmol/l) and among widowed women (5.6 mmol/l). Across age groups we did not found clear dependency of TSC from marital status.

Total serum cholesterol by home ownership and persons per room within sex and age strata is presented in Table 9.16. We did not find a clear dependency of TSC from home ownership.

We observed TSC by employment status (Table 9.17). The lowest level of mean TSC we found among students in both sexes (4.4 mmol/l for men and 4.2 mmol/l for women), followed by retired because of ill for men (5.0 mmol/l) and those working on shift work for women (4.7 mmol/l). The highest level of TSC were found among unemployed men (5.4 mmol/l) and retired because of ill women (6.2 mmol/l).

260

Table 9.15: Total serum cholesterol by marital status within sex and age strata

Marital status Never Separate or Total Married Widowed married divorced Males [mmol/l] [mmol/l] [mmol/l] [mmol/l] [mmol/l] Age 25-44 Mean 4.4 5.3 5.2 5.2 5.1 SE mean 0.2 0.1 0.3 0.7 0.1 Median 4.4 5.0 4.8 5.6 4.9 Age 45-64 Mean 5.4 5.3 5.4 4.6 5.3 SE mean 0.4 0.1 0.2 0.4 0.1 Median 5.0 5.1 5.4 4.1 5.1 Age 65-74 Mean 4.7 5.2 7.0 5.4 5.3 SE mean 0.2 0.1 0.7 0.4 0.1 Median 4.7 5.1 6.5 5.5 5.2 All ages Mean 4.7 5.3 5.5 5.0 5.2 SE mean 0.2 0.1 0.2 0.2 0.1 Median 4.5 5.1 5.2 4.8 5.0 Base Age 25-44 51 212 28 3 294 Age 45-64 15 256 31 18 320 Age 65-74 2 118 8 17 145 All ages 68 586 67 38 759 Females Age 25-44 Mean 4.3 4.8 4.8 4.0 4.7 SE mean 0.1 0.1 0.1 0.7 0.1 Median 4.2 4.5 4.8 3.8 4.5 Age 45-64 Mean 5.7 5.4 5.6 5.4 5.4 SE mean 0.4 0.1 0.2 0.2 0.1 Median 5.3 5.2 5.6 5.3 5.2 Age 65-74 Mean 5.0 6.0 5.5 5.8 5.9 SE mean 0.5 0.2 0.5 0.2 0.1 Median 5.3 5.9 5.0 5.6 5.7 All ages Mean 4.7 5.2 5.2 5.6 5.2 SE mean 0.2 0.1 0.1 0.1 0.1 Median 4.6 5.0 4.9 5.4 5.0 Base Age 25-44 42 257 54 6 359 Age 45-64 15 291 49 62 417 Age 65-74 5 77 15 62 159 All ages 62 625 118 130 935 Missing observations 2

261

Table 9.16: Total serum cholesterol by home ownership and persons per room within sex and age strata Home ownership Persons per room Yes No Total < 1 1 – 2 > 2 Total Males [mmol/ [mmol/ [mmol/ [mmol/ [mmol/ [mmol/ [mmol/ l] l] l] l] l] l] l] Age 25-44 Mean 5.2 5.0 5.1 5.2 5.3 4.6 5.2 SE mean 0.1 0.2 0.1 0.1 0.1 0.2 0.1 Median 5.0 4.6 5.0 4.8 5.0 4.6 5.0 Age 45-64 Mean 5.3 5.3 5.3 5.3 5.4 5.1 5.3 SE mean 0.1 0.2 0.1 0.1 0.1 0.4 0.1 Median 5.1 5.2 5.1 5.1 5.1 4.9 5.1 Age 65-74 Mean 5.3 5.5 5.3 5.3 5.3 4.9 5.3 SE mean 0.1 0.5 0.1 0.2 0.2 0 0.1 Median 5.2 5.1 5.2 5.1 5.4 4.9 5.2 All ages Mean 5.2 5.2 5.2 5.2 5.3 4.8 5.3 SE mean 0.1 0.2 0.1 0.1 0.1 0.2 0.1 Median 5.0 4.9 5.0 5.0 5.1 4.8 5.0 Base Age 25-44 253 39 292 102 138 16 256 Age 45-64 294 24 318 138 129 9 276 Age 65-74 131 10 141 101 32 1 134 All ages 678 73 751 341 299 26 666 Females Age 25-44 Mean 4.8 4.5 4.7 4.6 4.7 4.6 4.7 SE mean 0.1 0.2 0.1 0.1 0.1 0.3 0.1 Median 4.5 4.4 4.5 4.6 4.4 4.4 4.4 Age 45-64 Mean 5.4 5.1 5.4 5.6 5.3 4.8 5.4 SE mean 0.1 0.2 0.1 0.1 0.1 0.2 0.1 Median 5.2 4.9 5.2 5.3 5.2 4.8 5.2 Age 65-74 Mean 5.8 6.7 5.9 5.8 5.9 6.4 5.8 SE mean 0.1 0.3 0.1 0.2 0.2 0.5 0.1 Median 5.6 6.5 5.7 5.6 5.8 6.6 5.7 All ages Mean 5.2 5.1 5.2 5.3 5.1 5.0 5.2 SE mean 0.1 0.2 0.1 0.1 0.1 0.2 0.1 Median 5.0 4.9 5.0 5.1 4.9 4.7 5.0 Base Age 25-44 311 46 357 120 168 19 307 Age 45-64 385 24 409 194 150 16 360 Age 65-74 138 19 157 96 45 8 149 All ages 834 89 923 410 363 43 816 Missing observations for ‘home owners’ 322, for ‘person per room’ 514

262

Table 9.17: Total serum cholesterol by employment status within sex and age strata Employment status Retired, Full Part Shift Unem- House- Fully Total because Student time time work ployed wife retired of ill Males [mmol/l [mmol/l [mmol/l [mmol/l [mmol/l] [mmol/l [mmol/l [mmol/l [mmol/l ] ] ] ] ] ] ] ] Age 25-44 Mean 5.2 5.0 5.1 5.1 4.6 - - 4.3 5.1 SE mean 0.1 0.3 0.2 0.3 0.4 - - 0.3 0.1 Median 5.0 4.8 5.0 4.6 4.6 - - 4.5 4.9 Age 45-64 5.2 Mean 5.4 0.3 5.3 5.7 5.1 - 5.2 4.9 5.3 SE mean 0.1 5.0 0.2 0.5 0.2 - 0.1 0.2 0.1 Median 5.2 5.2 5.4 4.5 - 5.1 4.9 5.1 Age 65-74 Mean 4.6 5.0 - - 5.1 - 5.3 - 5.3 SE mean 0.4 0 - - 0.4 - 0.1 - 0.1 Median 4.9 5.0 - - 5.1 - 5.3 - 5.2 All ages Mean 5.3 5.1 5.2 5.4 5.0 - 5.3 4.4 5.2 SE mean 0.1 0.2 0.1 0.3 0.2 - 0.1 0.3 0.1 Median 5.0 5.0 5.1 5.2 4.7 - 5.1 4.6 5.0 Base Age 25-44 186 30 51 16 2 0 0 9 294 Age 45-64 153 19 35 11 14 0 79 2 313 Age 65-74 8 1 0 0 2 0 131 0 142 All ages 347 50 86 27 18 0 210 11 749 Females Age 25-44 Mean 4.7 5.2 4.6 4.9 6.3 3.9 - 4.2 4.7 SE mean 0.1 0.4 0.1 0.3 0 0.4 - 0.4 0.1 Median 4.6 5.1 4.3 4.8 6.3 4.0 - 4.1 4.5 Age 45-64 Mean 5.3 5.0 5.1 5.3 5.8 4.2 5.7 - 5.4 SE mean 0.1 0.3 0.2 0.5 0.4 0.2 0.1 - 0.1 Median 5.1 5.2 5.0 5.0 5.6 4.3 5.5 - 5.2 Age 65-74 Mean 4.9 - 4.7 - 8.5 4.2 5.8 - 5.9 SE mean 0.3 - 0 - 0.1 0.3 0.1 - 0.1 Median 5.2 - 4.7 - 8.5 7.1 5.7 - 5.7 All ages Mean 5.0 5.1 4.7 5.1 6.2 4.9 5.8 4.2 5.2 SE mean 0.1 0.3 0.1 0.3 0.4 0.4 0.1 0.4 0.1 Median 4.8 5.2 4.4 4.9 6.4 4.5 5.6 4.1 5.0 Base Age 25-44 216 12 100 20 1 3 0 4 356 Age 45-64 152 13 46 18 16 8 161 0 414 Age 65-74 5 0 1 0 3 4 146 0 159 All ages 373 25 147 38 20 15 307 4 929 Missing observations 318

263

9.3.3 Body Mass Index by other socio-economic characteristics

Table 9.18 presents BMI by marital status within sex and age strata. The lowest levels of mean BMI were found among widowed men (25.3 kg/m2), followed by never married

(25.6 kg/m2). The highest mean BMI was found among married males (26.7 kg/m2).

Among females the lowest mean BMI was in never married (23.3 kg/m2). The highest BMI we found in widowed women (27.4 kg/m2).

BMI by employment status within sex and age strata is presented in Table 9.19. The lowest

BMI was found among students in both sexes (25.5 kg/m2 for male and 21.1 kg/m2 for female), closely followed by unemployed participants (25.8 kg/m2 in men and 23.8 kg/m2 in women). Among men the highest BMI was found in those who were fully retired (27.0 kg/m2), while among women with highest BMI were those retired because of ill (28.9 kg/m2) followed by fully retired (28.2 kg/m2).

264

Table 9.18: Body Mass Index by marital status within sex and age strata Marital status Never Separate or Total Married Widowed married divorced Males [kg/m2] [kg/m2] [kg/m2] [kg/m2] [kg/m2] Age 25-44 Mean 25.2 26.4 26.3 25.8 26.1 SE mean 0.4 0.2 0.5 1.5 0.2 Median 25.0 25.8 26.1 25.8 25.7 Age 45-64 Mean 27.4 27.2 26.2 24.5 26.9 SE mean 1.0 0.2 0.4 0.8 0.2 Median 26.1 26.4 25.9 24.0 26.2 Age 65-74 Mean 25.0 26.4 26.4 26.0 26.3 SE mean 1.7 0.2 0.7 0.6 0.2 Median 24.5 26.2 26.2 25.1 26.2 All ages Mean 25.6 26.7 26.3 25.3 26.5 SE mean 0.4 0.1 0.3 0.5 0.1 Median 25.1 26.2 26.1 25.0 26.0 Base Age 25-44 82 267 44 4 397 Age 45-64 23 298 41 23 385 Age 65-74 4 133 9 24 170 All ages 109 698 94 51 952 Females Age 25-44 Mean 22.0 24.7 24.1 25.8 24.3 SE mean 0.4 0.3 0.5 2.2 0.2 Median 21.2 23.6 23.2 23.1 23.1 Age 45-64 Mean 25.8 27.6 26.3 27.1 27.3 SE mean 1.0 0.3 0.7 0.6 0.2 Median 25.4 27.2 25.2 26.4 27.0 Age 65-74 Mean 27.7 27.6 27.1 27.8 26.2 SE mean 2.4 0.4 0.9 0.5 0.3 Median 26.7 27.0 25.8 27.3 27.1 All ages Mean 23.3 26.4 25.4 27.4 26.2 SE mean 0.5 0.2 0.4 0.4 0.2 Median 22.2 25.7 24.6 23.4 25.5 Base Age 25-44 56 278 61 7 402 Age 45-64 16 297 58 63 434 Age 65-74 7 82 17 77 183 All ages 79 657 136 147 1019 Missing observations 25

265

Table 9.19: Body Mass Index by employment status within sex and age strata Employment status Retired, Full Part Shift Unem- House- Fully Total because Student time time work ployed wife retired of ill Males [kg/m2] [kg/m2] [kg/m2] [kg/m2] [kg/m2] [kg/m2] [kg/m2] [kg/m2] [kg/m2] Age 25-44 Mean 26.2 26.2 26.0 25.6 26.5 - - 25.6 26.3 SE 0.2 0.4 0.5 0.5 2.2 - - 0.9 0.6 mean Median 25.7 25.8 25.8 25.0 24.8 - - 25.5 26.8 Age 45-64 Mean 26.7 25.6 27.1 26.2 26.6 - 28.4 24.7 23.6 SE 0.3 0.5 0.5 0.8 1.2 - 0.5 0.2 0.9 mean Median 26.1 25.1 26.5 26.2 27.7 - 27.6 24.7 23.5 Age 65-74 Mean 29.2 27.3 - - 25.7 - 26.2 - 26.4 SE 1.2 0 - - 0.7 - 0.2 - 2.2 mean Median 28.8 27.3 - - 26.4 - 26.0 - 28.3 All ages Mean 26.5 26.0 26.4 25.8 26.4 - 27.0 25.5 24.7 SE 0.2 0.3 0.4 0.4 0.9 - 0.2 0.8 0.8 mean Median 25.9 25.7 26.1 25.4 26.4 - 26.4 25.5 25.0 Base Age 25-44 227 55 66 32 3 0 0 12 395 Age 45-64 174 34 45 17 14 0 90 2 376 Age 65-74 9 1 0 0 3 0 153 0 166 All ages 410 90 111 49 20 0 243 14 937 Females Age 25-44 Mean 24.3 23.2 24.9 22.8 27.9 24.5 - 21.1 24.6 SE 0.3 0.8 0.4 0.7 0 4.2 - 0.6 1.8 mean Median 23.1 22.8 24.0 22.5 27.9 20.4 - 21.4 24.3 Age 45-64 Mean 26.6 25.6 26.3 25.2 28.8 23.7 28.8 - 31.4 SE 0.3 1.1 0.8 0.8 1.6 1.2 0.4 - 2.4 mean Median 26.4 25.9 25.4 25.1 29.3 22.7 28.1 - 30.9 Age 65-74 Mean 26.6 - 26.6 - 29.3 27.2 27.7 - 25.6 SE 2.1 - 0 - 1.0 1.1 0.3 - 1.0 mean Median 26.4 - 26.6 - 28.8 26.2 27.1 - 25.6 All ages Mean 25.2 24.4 25.3 23.8 28.9 24.8 28.2 21.1 27.5 SE 0.2 0.7 0.4 0.5 1.2 1.0 0.2 0.6 1.5 mean Median 24.2 24.2 24.4 22.8 28.8 24.9 27.6 21.4 26.6 Base Age 25-44 236 17 107 28 1 4 0 6 399 Age 45-64 152 16 50 22 15 12 163 0 430 Age 65-74 5 0 1 0 5 6 165 0 182 All ages 393 33 158 50 21 22 328 6 1011 Missing observations 48

266 9.3.4 Smoking behaviour by other socio-economic characteristics

Table 9.20 presents smoking behaviour of SHS population by marital status within sex and age strata. We found that males living separate or divorced have the highest proportion of current smokers (50.5%). Widowed men had the highest proportion of never smokers

(54.9%). Among females the highest proportion of current smokers (46.8%) we found in single women, followed by those who were divorced (41.9%). The widowed women were most likely to be never smokers (66.4%). The quit ratio was highest among widowed participants (0.48 for men and 0.28 for women).

Among males the highest proportion of current smokers we found in those working on shift work (52.2%) followed by unemployed (52.0%). The lowest proportion of current smokers (14.4%) as well as the highest quit ratio (0.72) was found among fully retired men. Among females most likely to be current smokers were students (50%), followed by those working on shift work (48.4%). The highest proportion of never smokers was found among fully retired people (79%), followed by housewives (68.2). The lowest quit ratio

(0.08) was among unemployed women (Table 9.21).

267

Table 9.20: Smoking behaviour by marital status within sex and age strata Marital status Never Married Separate or Widowed Total married divorced Males % % % % % Age 25-44 Current smokers 44.6 57.4 73.3 25.0 56.2 Ex-smokers 18.1 18.8 11.1 0 17.6 Never smokers 34.9 22.8 15.6 75.0 25.0 Age 45-64 Current smokers 20.8 30.0 36.6 39.1 30.7 Ex-smokers 29.2 30.3 31.7 17.4 29.6 Never smokers 50.0 38.0 31.7 43.5 38.4 Age 65-74 Current smokers 50.0 14.3 0 8.3 13.5 Ex-smokers 50.0 40.6 55.6 29.2 40.0 Never smokers 0 43.6 44.4 62.5 45.3 All ages Current smokers 39.6 37.6 50.5 23.5 38.4 Ex-smokers 21.6 27.8 24.2 21.6 26.4 Never smokers 36.9 33.2 25.3 54.9 34.0 Not stated 1.8 1.4 0 0 1.2 Base Age 25-44 83 272 45 4 404 Age 45-64 24 300 41 23 388 Age 65-74 4 133 9 24 170 All ages 111 705 95 51 962 Quit ratio 0.35 0.43 0.32 0.48 0.41 Females Age 25-44 Current smokers 53.6 52.0 55.7 71.4 53.1 Ex-smokers 10.7 14.3 19.7 0 14.4 Never smokers 35.7 33.3 24.6 28.6 32.3 Age 45-64 Current smokers 31.3 21.2 36.2 36.5 25.7 Ex-smokers 6.3 8.9 10.3 11.1 9.3 Never smokers 56.3 66.6 51.7 52.4 62.2 Age 65-74 Current smokers 28.6 5.9 11.8 10.1 9.0 Ex-smokers 0 4.7 11.8 8.9 6.9 Never smokers 71.4 89.4 70.6 81.0 83.5 All ages Current smokers 46.8 32.1 41.9 24.2 33.4 Ex-smokers 8.9 10.7 14.7 9.4 10.9 Never smokers 43.0 55.6 41.9 66.4 54.4 Not stated 1.3 1.7 1.5 0 1.4 Base Age 25-44 56 279 61 7 403 Age 45-64 16 302 58 63 439 Age 65-74 7 85 17 79 188 All ages 79 666 136 149 1030 Quit ratio 0.16 0.25 0.26 0.28 0.25 Number of Missing Observations: 4

268

Table 9.21: Smoking behaviour by employment status within sex and age strata Employment status Retired, Full Part Shift Unem- House- Fully Stu- Total because time time work ployed wife retired dent of ill Males % % % % % % % % % Age 25-44 Current smokers 56.7 46.4 65.7 54.5 33.3 0 0 41.7 56.0 Ex-smokers 18.2 16.1 19.4 18.2 33.3 0 0 8.3 17.9 Never smokers 24.2 32.1 14.9 27.3 33.3 0 0 50.0 24.9 Age 45-64 Current smokers 36.4 23.5 32.6 47.1 14.3 0 18.9 0 30.1 Ex-smokers 31.3 23.5 30.4 17.6 42.9 0 31.1 0 30.1 Never smokers 31.8 47.1 34.8 35.3 42.9 0 48.9 100.0 38.5 Age 65-74 Current smokers 22.2 0 0 0 33.3 0 11.8 0 12.7 Ex-smokers 44.4 0 0 0 0 0 41.8 0 41.0 Never smokers 33.3 100.0 0 0 66.7 0 45.1 0 45.2 All ages Current smokers 47.4 37.4 52.2 52.0 20.0 0 14.4 35.7 38.0 Ex-smokers 24.3 18.7 23.9 18.0 35.0 0 37.9 7.1 26.8 Never smokers 27.6 38.5 23.0 30.0 45.0 0 46.5 57.1 33.9 Not stated 0.7 5.5 0.9 0 0 0 1.2 0 1.3 Base Age 25-44 231 56 67 33 3 0 0 12 402 Age 45-64 176 34 46 17 14 0 90 2 379 Age 65-74 9 1 0 0 3 0 153 0 166 All ages 416 91 113 50 20 0 243 14 947 Quit ratio 0.34 0.33 0.31 0.26 0.64 - 0.72 0.17 0.41 Females Age 25-44 Current smokers 55.1 58.8 51.9 46.4 0 25.0 0 50.0 53.3 Ex-smokers 14.4 11.8 16.7 7.1 0 25.0 0 16.7 14.5 Never smokers 30.5 29.4 30.6 46.4 0 50.0 100.0 33.3 32.0 Age 45-64 Current smokers 28.2 12.5 42.0 40.9 31.3 25.0 17.8 0 26.0 Ex-smokers 11.5 25.0 10.0 0 12.5 8.3 6.7 0 9.4 Never smokers 59.0 62.5 44.0 50.0 43.8 66.7 73.6 0 62.1 Age 65-74 Current smokers 40.0 0 0 0 0 16.7 8.3 0 9.1 Ex-smokers 0 0 0 0 0 0 7.7 0 7.0 Never smokers 40.0 0 100.0 0 100.0 83.3 84.0 0 83.3 All ages Current smokers 44.3 36.4 48.4 44.0 23.8 22.7 12.9 50.0 33.6 Ex-smokers 13.1 18.2 14.5 4.0 9.5 9.1 7.2 16.7 11.0 Never smokers 41.8 45.5 35.2 48.0 57.1 68.2 79.0 33.3 54.2 Not stated 0.8 0 1.9 4.0 9.5 0 0.9 0 1.3 Base Age 25-44 236 17 108 28 1 4 0 6 400 Age 45-64 156 16 50 22 16 12 163 0 435 Age 65-74 5 0 1 0 5 6 169 0 186 All ages 397 33 159 50 22 22 332 6 1021 Quit ratio 0.23 0.33 0.23 0.08 0.29 0.29 0.36 0.25 0.25 Number of missing observations: 28

269

9.3.5 Alcohol consumption by other socio-economic characteristics

Table 9.22 shows alcohol consumption levels by marital status within sex and age strata.

We found that the widowed participants from both sexes tended to be non-drinkers (35.3% from males and 70.5% from females). The highest proportion of men drinking between

1and 60 g alcohol per day was found among married (63.3%) respondents. Separate or divorced males (16.8%) were most likely to drink more than 60 g alcohol per day. Among females the highest proportion (53.2%) of those drinking between 1and 40 g alcohol per day was never married and less likely to drink 1 - 40 g alcohol per day were widowed women (28.1%).

Table 9.23 presents alcohol consumption levels by employment status within sex and age strata. The highest proportion of non-drinkers among males was in those retired because of ill (45.0%), followed by fully retired (38.0%) and unemployed (38.0%). The lowest proportion of non-drinking males (7.1%) was among students. Male students were most likely to drink between 1 and 60 g alcohol per day (85.7%). Men working part time and shift work were most likely to drink more than 60 g alcohol per day (20.9% and 20.4% respectively). Among women we found that 100% from students were non-drinkers, but this is based only on 6 persons. Less likely to drink between 1 and 40 g alcohol per day were retired because of ill (15%) and fully retired (21.6%) women. The highest proportion of women drinking more than 40 g alcohol per day was among housewives (4.5%).

270

Table 9.22: Alcohol consumption levels by marital status within sex and age strata Marital status Never Separate or Total Married Widowed married divorced Males % % % % % Age 25-44 Non drinkers 25.3 19.5 22.2 25.0 21.0 1 – 60 61.4 66.9 55.6 75.0 64.6 > 60 13.3 13.6 22.2 0 14.4 Age 45-64 Non drinkers 33.3 24.2 24.4 34.8 25.4 1 – 60 58.3 62.4 61.0 65.2 62.2 > 60 8.3 13.4 14.6 0 12.4 Age 65-74 Non drinkers 100.0 40.6 11.1 37.5 40.0 1 – 60 0 57.9 88.9 50.0 57.1 > 60 0 1.5 0 12.5 2.9 All ages Non drinkers 29.7 25.5 22.1 35.3 26.1 1 – 60 58.6 63.3 61.1 58.8 62.3 > 60 11.7 11.2 16.8 5.9 11.6 Base Age 25-44 83 272 45 4 404 Age 45-64 24 298 41 23 386 Age 65-74 4 133 9 24 170 All ages 111 703 95 51 960 Females Age 25-44 Non drinkers 41.1 37.0 29.5 57.1 36.8 1 – 40 55.4 59.1 70.5 42.9 60.0 > 40 3.6 4.0 0 0 3.3 Age 45-64 Non drinkers 37.5 59.3 66.1 59.7 59.4 1 – 40 62.5 39.4 32.1 38.7 39.2 > 40 0 1.3 1.8 1.6 1.4 Age 65-74 Non drinkers 85.7 90.6 82.4 80.5 85.5 1 – 40 14.3 9.4 17.6 18.2 14.0 > 40 0 0 0 1.3 0.5 All ages Non drinkers 44.3 54.0 51.5 70.5 55.3 1 – 40 53.2 43.8 47.8 28.1 42.8 > 40 2.5 2.3 0.7 1.4 2.0 Base Age 25-44 56 276 61 7 400 Age 45-64 16 297 56 62 431 Age 65-74 7 85 17 77 186 All ages 79 658 134 146 1017 Missing observations 19

271

Table 9.23: Alcohol consumption levels by employment status within sex and age strata Employment status Retired, Full Part Shift Unem- House- Fully Stu- Total because time time work ployed wife retired dent of ill Males % % % % % % % % % Age 25-44 Non drinkers 20.8 21.4 14.9 36.4 33.3 0 0 0 20.6 1 – 60 66.7 55.4 65.7 57.6 66.7 0 0 91.7 64.9 > 60 12.6 23.2 19.4 6.1 0 0 0 8.3 14.4 Age 45-64 Non drinkers 20.0 20.6 21.7 41.2 42.9 0 34.8 50.0 25.7 1 – 60 68.0 61.8 56.5 41.2 42.9 0 59.6 50.0 61.8 > 60 12.0 17.6 21.7 17.6 14.3 0 5.6 0 12.5 Age 65-74 Non drinkers 22.2 0 0 0 66.7 0 39.9 0 39.2 1 – 60 77.8 100.0 0 0 33.3 0 56.9 0 57.8 > 60 0 0 0 0 0 0 3.3 0 3.0 All ages Non drinkers 20.5 20.9 17.7 38.0 45.0 0 38.0 7.1 25.9 1 – 60 67.5 58.2 61.9 52.0 45.0 0 57.9 85.7 62.4 > 60 12.0 20.9 20.4 10.0 10.0 0 4.1 7.1 11.6 Base Age 25-44 231 56 67 33 3 0 0 12 402 Age 45-64 175 34 46 17 14 0 89 2 377 Age 65-74 9 1 0 0 3 0 153 0 166 All ages 415 91 113 50 20 0 242 14 945 Females Age 25-44 Non drinkers 35.9 47.1 31.5 44.4 0 50.0 0 100.0 36.8 1 – 40 61.5 47.1 63.9 55.6 0 25.0 100.0 0 59.9 > 40 2.6 5.9 4.6 0 0 25.0 0 0 3.3 Age 45-64 Non drinkers 44.8 50.0 65.3 76.2 80.0 50.0 68.3 0 59.1 1 – 40 53.2 50.0 34.7 23.8 20.0 50.0 29.8 0 39.5 > 40 1.9 0 0 0 0 0 1.9 0 1.4 Age 65-74 Non drinkers 80.0 0 0 0 100.0 100.0 86.2 0 86.4 1 – 40 20.0 0 100.0 0 0 0 13.2 0 13.0 > 40 0 0 0 0 0 0 0.6 0 0.5 All ages Non drinkers 39.9 48.5 41.8 58.3 85.0 63.6 77.2 100.0 55.3 1 – 40 57.8 48.5 55.1 41.7 15.0 31.8 21.6 0 42.7 > 40 2.3 3.0 3.2 0 0 4.5 1.2 0 2.0 Base Age 25-44 234 17 108 27 1 4 0 6 397 Age 45-64 154 16 49 21 15 12 161 0 428 Age 65-74 5 0 1 0 5 6 167 0 184 All ages 393 33 158 48 21 22 328 6 1009 Missing observations 42

272

9.4 Regression analyses of relative strength and independence of risk factor associations with specific socio-demographic characteristics

In order to examine the relative strength and independence of risk factor associations with specific socio-demographic characteristic we ran multiple regression in SPSS version 8.0 using the formula:

Y = b0 + b1X1 + b2X2 + … + bnXn

Where bo is the constant in the equation, X1 is the first independent variable and b1 is the regression coefficient associated with it, X2 is the second independent variable and b2 is the regression coefficient associated with it etc.

As in simple regression, the dependent variable Y is a numerical measure, the traditional multiple model calls for the independent variables to be numerical measures as well.

However most of our socio-demographic (independent) variables were not numerical, so we used the procedure called dummy coding, which allows us to include nominal variables in a regression equation in a straightforward manner183. The models were specified so that for each categorical variable there was one reference category and one or more other categories entered as 'dummy variables'. So, for example:

SBP = f(age, educat2, educat3),

Where SBP and age are both continuous variables and education is coded as follows:

Educat1 (More than 12 years of education) is the reference category

If the subject is university educated then the value of variable educat1 is 1 but it is not entered into the equation. The value of educat2 and educat3 is 0 in each case.

If the subject is in the middle schooling category then educat2 = 1 and the other 2 variables take values of zero etc.

In order to assess the relative strength and independence of categorical risk factors

(smoking and alcohol consumption) with specific socio-demographic characteristics we used a two-sided Chi-squared test and logistic regression.

273

9.4.1 Regression analyses of associations of systolic blood pressure with

other risk factors and socio-demographic characteristics

To assess the association between SBP and other characteristics multiple regression

models were explored with SBP treated successively as a function of:

1. Age, sex;

2. Age, sex and BMI (linear and categorical);

3. Age, sex and alcohol consumption (linear and categorical);

4. Age, sex, BMI and alcohol;

5. Age, sex, education and socio-economic variables;

6. Age, sex, BMI, alcohol, education and other socio-economic variables.

Categorical variables were dealt with by the use of dummy variables in the standard

regression procedure using SPSS version 8.0.

9.4.1.1 Relationships with age and sex

We checked the correlation between SBP and age (Pearson’s correlation coefficient

rxy = 0.44; p<0.001) and by visual inspection of a scatter plot.

Then we checked for differences in slope by sex – by adding sex and age into the

regression model (for this purpose male was coded as 0 and female as 1). We found that

the SBP increases with 0.67 mmHg with increasing the age with one year (p < 0.001).

Females had about 3 mmHg lower SBP than males and the coefficient was significant (p <

0.01).

9.4.1.2 Relationship with age, sex and BMI

Assessing the relationship of SBP with BMI we treated BMI as both a continuous and a

categorical variable (e.g. ≤ 25.0, >25 -≤30, >30 kg/m2) to see which gives a better fit. With

continuous BMI we tested two models:

sbp = sex, age, bmi

274 and sbp = sex, age, bmi, bmi2

The coefficient of BMI2 was not significant, so the relation is not quadratic. We found that with increasing of BMI with 1 kg/m2 SBP increases with 1.5 mmHg (p<0.001). SBP of overweight participants (BMI between 25 and 30 kg/m2) was with 8.9 mmHg (p<0.001) higher than in those with BMI less than 25 kg/m2, and SBP of the obese people (BMI more than 30 kg/m2) was with 15.5 mmHg (p<0.001) higher than in those with BMI less than 25 kg/m2 (Table 9.24).

9.4.1.3 Relationship with age, sex and alcohol

Similarly to BMI assessing the relationship of SBP with alcohol consumption we treated alcohol as both a continuous and a categorical variable (non-drinkers (reference category); drinking less than 60 g per day for male and 40 g per day for female; drinking more than

60 g per day for male and 40 g per day for female) to see which gives a better fit. The significant model was only this with categorical alcohol consumption. We found that SBP is 2.4 mmHg (p<0.05) lower in those who drink less than 60 g alcohol per day (among men) and less than 40 g per day (among women) than in non-drinkers. Those who drink more than 60 g per day for male and 40 g per day for female had SBP with 1.8 mmHg lower than non-drinkers but the coefficient was not significant (Table 9.24).

After including in the regression equation BMI and alcohol together the relationship of

SBP with BMI and alcohol remains significant with almost the same coefficients.

9.4.1.4 Relationship with age, sex, education and socio-economic variables

To assess the relative association between SBP and specific socio-demographic characteristics initially we run multiple regression equation in which we included as independent variables each of the socio-demographic characteristics separately: education, marital status, home ownership, person per room and employment status (dummy

275 variables). The results are shown in Table 9.24. We did not find significant statistical correlation of SBP with education, marital status and home ownership. The results shows that the people living with more than 2 person per room had 4.33 mmHg lower SBP than those living with less than 1 person per room, but the 95% CI is very large (-8.4 to -0.25 mmHg). From employment status categories we found statistically significant dependence of SBP only with fully retired people who had 8.14 mmHg higher than those fully employed.

After including in the model all independent variables the coefficient for BMI and fully retired people remains significant, and the coefficients for alcohol consumption and person per room more than 2 lose significance (Table 9.25).

In exploratory model, which include BMI and alcohol in one, the sign for two category of alcohol consumption still remains negative.

Analyzing the magnitudes of standardised regression coefficients (Table 9.25) we can conclude that for SBP the most important role from the observed independent variables plays BMI, followed by age, being fully retired and then sex.

276

Table 9.24: Regression coefficients and 95% CI for assessment of associations of systolic blood pressure with BMI, alcohol and specific socio-demographic characteristics separately Variable Coefficients B 95% CI Constant 99.73*** 96.68 - 102.78 Age 0.57*** 0.51 - 0.63 Female -2.28** -3.89 - -0.67 Overweight 8.87*** 7.06 - 10.68 Obese 15.49*** 13.22 - 17.77 Constant 104.49*** 100.68 - 108.30 Age 0.65*** 0.59 - 0.71 Female -3.54*** -5.29 - -1.78 ≤ 60/40 g per day alcohol -2.40* -4.25 - -0.55 > 60/40 g per day alcohol -1.79 -5.44 - 1.86 Constant 101.33*** 97.95 - 104.71 Age 0.66*** 0.60 - 0.73 Female -2.94** -4.61 - -1.27 9-12 years of education 0.90 -0.98 - 2.78 Less than 9 years of ed. 1.86 -1.02 - 4.73 Constant 102.19*** 98.47 - 105.91 Age 0.68*** 0.61 - 0.74 Female -2.69** -4.38 - -1.01 Married -0.89 -3.85 - 2.08 Separate or divorced -1.81 -5.50 - 1.87 Widowed -1.48 -5.60 - 2.64 Constant 102.25*** 99.01 - 105.49 Age 0.66*** 0.60 - 0.72 Female -2.843** -4.51 --1.17 Non-homeowner -1.75 -4.37 - 0.87 Constant 102.39*** 98.73 - 106.05 Age 0.66*** 0.59 - 0.72 Female -2.40** -4.16 - -0.65 1-2 persons per room 0.15 -1.70 - 1.99 > 2 persons per room -4.33* -8.40 - -0.25 Constant 110.54*** 106.26 - 114.82 Age 0.46** 0.36 - 0.55 Female -3.47*** -5.15 - -1.79 Part time employed -2.71 -6.26 - 0.84 Shift work -0.46 -3.03 - 2.12 Unemployed -3.86 -7.75 - 0.02 Retired because of ill 5.61 -0.30 - 11.52 Housewife -2.44 -10.48 - 5.60 Fully retired 8.14*** 5.28 - 10.99 Student -1.03 -9.40 - 7.34 * - p<0.05; ** - p<0.01; *** - p<0.001

277

Table 9.25: Regression coefficients and 95% CI for assessment of associations of systolic blood pressure with BMI, alcohol and specific socio-demographic characteristics combined Variable Coefficients B Stand. Coeff. B 95% CI Constant 110.48*** 104.97 - 115.98 Age 0.37*** 0.25 0.27 - 0.47 Female -2.83** -0.07 -4.72 - -0.94 Overweight 8.42*** 0.20 6.46 - 10.37 Obese 14.78*** 0.27 12.32 - 17.23 ≤ 60/40 g per day alcohol -1.54 -0.04 -3.49 - 0.40 > 60/40 g per day alcohol -1.28 -0.02 -5.07 - 2.50 9-12 years of education 0.04 0.001 -1.93 - 2.02 Less than 9 years of ed. 0.69 0.01 -2.35 - 3.72 Married -1.48 -0.03 -4.67 - 1.71 Separate or divorced -1.00 -0.02 -4.87 - 2.86 Widowed -2.56 -0.04 -6.89 - 1.76 Non-homeowner -0.19 -0.003 -2.97 - 2.59 1-2 persons per room 0.16 0.004 -1.67 - 1.99 > 2 persons per room -3.56 -0.04 -7.64 - 0.52 Part time employed -2.15 -0.025 -5.88 - 1.58 Shift work 0.09 0.000 -2.69 - 2.71 Unemployed -3.69 -0.04 -7.75 - 0.36 Retired because of ill 5.55 0.04 -0.79 - 11.89 Housewife -2.38 -0.01 -10.73 - 5.98 Fully retired 7.17*** 0.16 4.16 - 10.19 Student -0.55 -0.003 -9.13 - 8.03 * - p<0.05; ** - p<0.01; *** - p<0.001

278

9.4.2 Regression analyses of associations of total serum cholesterol with other risk factors and socio-demographic characteristics

To assess the association between cholesterol and other risk factors and characteristics multiple regression models were run with cholesterol as a function of:

1. Age, sex;

2. Age and BMI (linear and categorical);

3. Age and smoking;

4. Age and alcohol consumption (linear and categorical);

5. Age, education and socio-economic variables;

6. Age, BMI and marital status.

We checked for differences in slope by age and sex – by adding sex and age into the regression model (for this purpose male was coded as 0 and female as 1). We found that the total serum cholesterol increases with 0.02 mmol/l with increasing the age with one year (p < 0.001). Females had about 0.01 mmol/l higher TSC than males but the coefficients was not significant (p > 0.05), so we excluded sex from the following regression models.

Assessing the relationship of TSC with BMI we treated BMI as both a continuous and a categorical variable (e.g. ≤ 25.0, >25 - ≤30, >30 kg/m2).

We found that with increasing of BMI with 1 kg/m2 TSH increases with 0.05 mmol/l

(p<0.001). TSH of overweight participants (BMI between 25 and 30 kg/m2) was with 0.49 mmol/l (p<0.001) higher than in those with BMI less than 25 kg/m2, and TSC of the obese people (BMI more than 30 kg/m2) was with 0.52 mmol/l (p<0.001) higher than in those with BMI less than 25 kg/m2 (Table 9.26).

We did not find a significant relationship of TSC with smoking and alcohol consumption.

To assess the relative association between TSC and specific socio-demographic characteristics we run multiple regression equation in which we included as independent

279 variables each of the socio-demographic characteristics separately: education, marital status, home ownership, person per room and employment status (dummy variables). The results are shown in Table 9.26. We did not find significant statistical correlation of TSC with education, employment status, persons per room and home ownership. The results shows that the married participants had 0.34 mmol/l (p<0.01) higher TSC than never married, separate or divorced had 0.44 mmol/l (p<0.01) TSC higher than never married and widowed 0.27 mmol/l TSC higher than never married but coefficients was not significant (p>0.05).

After including in the model all significant independent variables (age, BMI and marital status) all coefficients remains very similar and significant (Table 9.26).

Analyzing the magnitudes of standardised regression coefficients we can conclude that for

TSC the most important role from the observed independent variables is played by BMI, followed by age and marital status.

280

Table 9.26: Regression coefficients and 95% CI for assessment of associations of total serum cholesterol with other risk factors and socio-demographic characteristics Variable Coefficients B 95% CI Constant 4.11*** 3.85 - 4.37 Age 0.02*** 0.02 - 0.03 Female 0.01 -0.12 - 0.14 Constant 3.07*** 2.66 - 3.49 Age 0.02*** 0.01 - 0.02 BMI 0.05*** 0.03 - 0.06 Constant 4.06*** 3.81 - 4.30 Age 0.02*** 0.01 - 0.02 Overweight 0.49*** 0.34 - 0.63 Obese 0.52*** 0.34 - 0.70 Constant 4.03*** 3.74 - 4.32 Age 0.02*** 0.02 - 0.03 Current smokers 0.08 -0.06 - 0.23 Constant 4.16*** 3.88 - 4.42 Age 0.02*** 0.02 - 0.03 9-12 years of education -0.05 -0.19 - 0.10 Less than 9 years of ed. 0.01 -0.23 - 0.23 Constant 3.85*** 3.55 - 4.16 Age 0.02*** 0.02 - 0.03 Married 0.34** 0.08 - 0.59 Separate or divorced 0.44** 0.13 - 0.75 Widowed 0.27 -0.06 - 0.60 Constant 3.85*** 3.55 - 4.16 Age 0.02*** 0.02 - 0.03 Overweight 0.49*** 0.34 - 0.63 Obese 0.52*** 0.34 - 0.70 Married 0.34** 0.08 - 0.59 Separate or divorced 0.44** 0.13 - 0.75 Widowed 0.27 -0.06 - 0.60 * - p<0.05; ** - p<0.01; *** - p<0.001

9.4.3 Assessment of associations of smoking with socio-demographic characteristics.

In attempt to assess the association of smoking with socio-demographic factors we ran two-sided Pearson Chi-square test. We found statistically significant association of smoking with level of education and marital status for both sexes and with home ownership and employment status only for male. Analyzing the Cramer's coefficients of contingency we can suggest that the power of association of smoking with level of education and marital status is bigger in women than in men. Among male the higher

Cramer's coefficient was with employment status (Cramer’s V = 0.228).

281

To further assess the association between smoking and other characteristics logistic regression models were explored with smoking treated as a function of:

1. Age, sex;

2. Age, sex and education;

3. Age, sex and marital status;

4. Age, sex and home ownership;

5. Age, sex and persons per room;

6. Age, sex and employment status.

7. Age (continuous and categorical), sex and all significant socio-economic characteristic.

Smoking was coded: 1 for current smokers and 0 for all others. We used the standard logistic regression procedure of SPSS version 8.0.

We found that probability to be a smoker decreases with age (OR = 0.94***, 95% CI:

0.93-0.95). Females had lower probability to be a smoker than males (OR=0.82*, 95% CI:

0.67-0.99).

To assess the relative association between smoking and specific socio-demographic characteristics initially we run logistic regression models in which we included as independent variables each of the socio-demographic characteristics separately: education, marital status, home ownership, person per room and employment status. The results are shown in Table 9.27. We did not find significant statistical association of smoking with persons per room and home ownership. The results show that the people with less than 9 year education had lower probability to be a smoker than those with more than 12 years of education (OR=0.55**, 95% CI: 0.37 - 0.83). Widowed, separate or divorced and married participants had higher probability to be a smoker than never married. Students, fully retired and those working in shift work had lower probability to smoke than that working full time. After including in the logistic regression model all significant variables the OR-s

282 remain very similar, only being married and being student lost their significance (Table

9.28).

Table 9.27: Odds ratios and 95% CI for assessment of associations of smoking with socio- demographic characteristics adjusted for age and sex Variable OR 95% CI Age 0.942*** 0.934 – 0.949 Male 1.000 Female 0.820* 0.674 – 0.999 Education More than 12 years 1.000 9-12 years 1.123 0.903 – 1.397 Less than 9 years 0.552** 0.370 – 0.825 Persons per room < 1 1.000 1 – 2 0.929 0.744 – 1.161 > 2 0.636 0.387 – 1.056 Home ownership Yes 1.000 No 1.027 0.756 – 4.395 Marital status Never married 1.000 Married 1.536* 1.088 – 2.168 Separate or divorced 2.264*** 1.476 – 3.472 Widowed 2.326** 1.396 – 3.877 Employment status Full time 1.000 Part time 0.600* 0.399 – 0.902 Shift work 1.088 0.819 – 1.445 Unemployed 0.948 0.616 – 1.460 Retired because of ill 0.590 0.272 – 1.278 Housewife 0.664 0.231 – 1.912 Fully retired 0.554** 0.385 – 0.799 Student 0.389* 0.153 – 0.989 * - p<0.05; ** - p<0.01; *** - p<0.001

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Table 9.28: Odds ratios and 95% CI for assessment of associations of smoking with socio- demographic characteristics adjusted for age and sex and other significant socio- demographic characteristics Variable OR 95% CI Age 25-34 1.000 Age 35-44 0.705* 0.525 – 0.947 Age 45-54 0.366*** 0.270 – 0.497 Age 55-64 0.250*** 0.166 – 0.376 Age 65-74 0.136*** 0.077 – 0.240 Male 1.000 Female 0.770* 0.625 – 0.950 More than 12 years 1.000 9-12 years of education 1.157 0.923 – 1.450 Less than 9 years of ed. 0.592* 0.389 – 0.900 Never married 1.000 Married 1.232 0.868 – 1.749 Separate or divorced 1.870** 1.212 – 2.886 Widowed 2.143** 1.267 – 3.623 Full time 1.000 Part time employed 0.639* 0.422 – 0.966 Shift work 1.131 0.847 – 1.510 Unemployed 1.017 0.656 – 1.578 Retired because of ill 0.545 0.248 – 1.199 Housewife 0.630 0.217 – 1.830 Fully retired 0.540** 0.349 – 0.835 Student 0.529 0.203 – 1.378 * - p<0.05; ** - p<0.01; *** - p<0.00

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Summary:

This exploratory assessment of socio-economic differences in risk factors in Sofia has yield the following provisional conclusions:

1. Unlike finding in many Western populations, there is little tendency for smoking

prevalence to increase with decreasing socio-economic status. On the contrary, in the

approximately 12% of respondents with less then 9 years of schooling, there were

high proportion of never smokers and low proportion of current smokers, except in

those younger then 45.

2. The blood pressure data does not reveal any consistent gradient by level of

education.

3. Cholesterol concentration fail to show any systematic pattern in relation to

education.

4. Subjects older then 45 and with less then 9 years of schooling show a distinctive

pattern of alcohol use. Over 80% of females in this category were non-drinkers.

Among men they tend to be more likely heavy or non-drinkers, then all other

educational groups.

5. In relation to marriage, although unmarried persons have generally found to have

higher mortality, these data do not show them to have higher levels of risk factors.

6. The logistic regression analysis of smoking confirmed the strong relation with age,

and lower prevalence in those with less education.

7. In relation to comparison using home ownership and persons per room as indices of

material resources, it was found surprisingly, that blood pressure tended to be lower in

those who did not own their houses and in those living in more crowded conditions.

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10. Chapter: Risk factor combinations

10.1 Definitions of high values of risk factors

10.1.1 Introduction

The previous chapters have described each of the observed risk factors for cardiovascular disease separately: BMI, casual blood pressure, casual cholesterol level, smoking, diet, alcohol consumption and physical activity. Their relationship with age, level of education and other socio-economical characteristics were presented. In order to assess the extent to which cardiovascular disease risk is concentrated in certain individuals, the association of risk factors in individuals was examined.

Because blood pressure and cholesterol vary within an individual over time it is well known that the repeated measurement should be taken before individuals are classified as having or not having a particular 'risk factor'184,185. The repeated measurements within a single visit offer only small gains for increased precision in determining an individual's underlying mean level when compared with the gains in repeated measurements over longer periods of time. Although individuals should only be classified into a particular category of blood pressure or cholesterol on the basis of repeated readings on different occasions, it has been conventional, in the context of surveillance studies to classify them on the base of a measurement on a single occasion (‘casual’ levels). This has the practical advantage, in relation to this study, of being able to make external comparison with

MONICA. Unless specified otherwise ‘blood pressure’ and ‘cholesterol ’refer to their casual values.

Section 10.2 of this chapter describes what proportion of men and women and all adults have ‘high’ values on each of these risk factors, first for each risk factors separately and then for various combinations of these risk factors.

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Section 10.3 focuses on the prevalence of co-existing risk factors among specific high risk sub-groups.

Section 10.4 presents the complex assessment of comparison of MONICA baseline data and SHS data.

For the analyses in this chapter, high levels on each of the observed risk factors need to be defined. The definitions of high levels adopted in the following analyses are given below.

The data on diet, alcohol and physical activity were not use in these analyses.

It should be noted that some of the prevalence estimates presented here are based on relatively small sample sizes and should therefore be interpreted with caution. Sample sizes are shown in the tables. Additionally association observed in a cross-sectional study of this type should be interpreted cautiously, as the direction of the association is unknown.

10.1.2 Definition of high values of risk factors

Blood pressure level

High casual blood pressure was defined as currently taking medication for hypertension, or having a mean of two SBP readings of 160 mmHg or above, or a mean DBP of 95 mmHg or above.

Cholesterol level

As in MONICA reports, for the purpose of this section raised casual total cholesterol was defined as having total cholesterol of 6.5 mmol/l or above when measured on a single occasion.

Smoking status

Smoking status is split in two groups, current cigarette smokers (includes current regular and occasionally) and non-smokers. The latter includes ex-regular cigarette smokers as well as those who had never smoked cigarettes regularly.

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Body mass index

In presentation of the prevalence of other risk factors among values high-risk sub-groups as overweight and obese were accepted those with BMI > 25 kg/m2.

10.2 Combination of risk factors

10.2.1 Proportion with high levels of the each of the four main cardiovascular disease risk factors separately

Tables 10.1 and 10.2 shows for each of the four risk factors separately, the proportions with high levels by age for men, women and all adults. The percentages shown in these tables are based on all those for who each of the individual risk factors was measured; thus the bases for each risk factor vary as not every one had their blood pressure or cholesterol measured.

More than one third of men (36.2%) and 41.3% for women had high blood pressure. The proportion with high blood pressure increases steadily with age in both sexes. Among men, the proportion with high blood pressure range from 10.2% of those aged 25-34 to 55.7% of those aged 65-74. Among women, the proportion of high blood pressure increased even more steeper from 8.6% in the age group 25-34 to 66.1% in the age group 65-74.

14.5% from males and 17% from females had cholesterol levels more than 6.5 mmol/l. The proportion of all adults with high levels of cholesterol increased steadily with age. Among men we observed decreasing of proportion with high levels of cholesterol from 18.4% in the age group 35-44 to 11.7% in the age group 65-74. Among women the proportion with high levels dropped from 7.6% in the age group 25-34 to 6.5% in the age group 35-44 and then increased with age from 16.7% in those aged 45-54 to 31.4% in those aged 65-74.

More than one third of men (38.4%) and more than one forth of women (27.7%) were current cigarette smokers. The proportion of current smokers decreased steadily with age in both sexes. Among men the proportion of current smokers ranged from 58.7% in the age

288 group 25-34 to 13.5% in the age group 65-74. Among women, the proportion of current smokers decreased from 50.8% of those aged 25-34 to 6.9% of those aged 65-74.

Almost two thirds of all men (63.7%) and more than half of women (53.4%) were overweight or obese. The proportion of those overweight or obese increased with age in all adults. Among men this tendency was less clear. However the proportion of overweight or obese men increased from 52.1% in those aged 25-34 to 70.9% in those aged 45-54, then dropped to 61.1% in the age group 55-64 and increased again to 67.1% in the age group

65-74. Among women the proportion of overweight and obese increased from 22.2% in the age group 25-34 to 71.7% in the age group 65-74.

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Table 10.1: Proportion with ‘high’ levels of the each of four CVD risk factors (blood pressure, cholesterol, cigarette smoking status and BMI) by age and sex. Risk factor Age 25-34 35-44 45-54 55-64 65-74 Total Males Percentage with high level on each factor High blood pressure 10.2 26.9 40.9 51.1 55.7 36.2 High cholesterol 7.8 18.4 16.6 15.2 11.7 14.5 Current cigarette smoker 58.7 53.6 37.9 23.0 13.5 38.4 BMI over 25 [kg/m2] 52.1 67.6 70.9 61.1 67.1 63.7 Bases High blood pressure 9 33 27 24 17 110 High cholesterol 32 79 82 68 72 333 Current cigarette smoker 115 112 75 44 23 369 BMI over 25 [kg/m2] 101 138 139 116 114 608 Females Percentage with high level on each factor High blood pressure 8.6 22.5 45.9 63.1 66.1 41.3 High cholesterol 7.6 6.5 16.7 24.1 31.4 17.0 Current cigarette smoker 50.8 41.1 25.3 13.9 6.9 27.7 BMI over 25 [kg/m2] 22.2 42.2 58.4 71.4 71.7 53.4 Bases High blood pressure 16 49 107 128 123 423 High cholesterol 12 13 37 47 50 159 Current cigarette smoker 94 90 59 28 13 284 BMI over 25 [kg/m2] 41 92 135 145 132 545

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Table 10.2: Proportion with 'high' levels of the each of the four main CVD risk factors (blood pressure, cholesterol, cigarette smoking status and BMI) by age for all adults Risk factor Age 25-34 35-44 45-54 55-64 65-74 Total Percentage with high level on each factor High blood pressure 9.4 24.6 43.6 57.3 61.2 38.8 High cholesterol 7.7 12.1 16.6 20.1 22.0 15.9 Current cigarette smoker 58.0 51.4 34.3 21.5 11.2 35.8 BMI over 25 [kg/m2] 37.5 54.5 64.2 66.4 69.5 58.4 Bases High blood pressure 36 105 188 225 216 770 High cholesterol 21 46 64 71 67 269 Current cigarette smoker 221 220 148 84 40 713 BMI over 25 [kg/m2] 142 230 274 261 246 1153

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10.2.2 Combinations of risk factors

Table 10.3 presents the number of four risk factors: high blood pressure, high cholesterol, smoking and BMI by age and sex.

Although there are many other factors, which also contributed to cardiovascular disease risk, information on the prevalence of these four major risk factors and their co-occurrence provides a simplified useful summary of cardiovascular disease risk of the population, and of the scope for cardiovascular disease prevention.

It must be emphasised that the relative importance of each of these four major risk factors is not the same, and therefore it cannot be assumed that all those with a given number of risk factors are at the same or similar risk of cardiovascular disease. In addition, this approach does not take into account the actual amount by which each factor is raised.

Overall, only 13.0% of men and 17.4% of women did not have any of the four major risk factors. This proportion decreased unsteadily from 17.5% in men aged 25-34 to 12.6% in men aged 65-74. Among women the percentage with none of those factors is 23.4% in the age group 25-34, than peaked to 27.0% in the age group 35-44 and after that decreased to

8.5% in the age group 65-74.

33.7% of men and 34.2% of women had one risk factor, 37.3% of men and 32.1% of women had two risk factors, 14.6% of men and 13.9% of women had three risk factors, and 1.5% of men and 2.4% of women had four risk factors. The distribution of the number of risk factors across the age groups was similar in men and women. We did not observed clear age dependency of this distribution (excluding women with two or three risk factors where the proportion of those with risk factors increases with age).

Table 10.4 presents the percentage of informants with various combinations of the four major risk factors.

The most common of the risk factors combinations with two risk factors in the age group

25-44 in both sexes was being overweight and smoker. After the age 44 to the age 74,

292 again in both sexes, the most frequent combination was to be overweight and to have high blood pressure.

The most common combinations of three risk factors among both sexes between 25-54 was to be overweight, smoker and to have high blood pressure. In the age group 55-74 the most frequent combination was to be overweight, to have high cholesterol and to have high blood pressure among both sexes. The highest proportion of co-occurrence of being overweight, smoker, have high cholesterol and high blood pressure, is in the age group 35-

54 among men and in the age group 45-64 among women.

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Table 10.3: Distribution of 4 risk factors (high blood pressure, high cholesterol, smoking and high BMI) by age group and sex Number of Age risk factors 25-34 35-44 45-54 55-64 65-74 Total Males None 20 20 21 18 18 97 % 17.5 11.5 13.0 11.6 12.6 13.0 1 risk factor 47 64 42 52 47 252 % 41.2 36.8 25.9 33.5 32.9 33.7 2 risk factors 33 50 72 62 62 279 % 28.9 28.7 44.4 40.0 43.4 37.3 3 risk factors 14 35 23 21 16 109 % 12.3 20.1 14.2 13.5 11.2 14.6 4 risk factors 0 5 4 2 0 11 % 0 2.9 2.5 1.3 0 1.5 Total 114 174 162 155 143 748 Females None 37 54 37 19 13 160 % 23.4 27.0 17.1 9.9 8.5 17.4 1 risk factor 84 73 71 47 39 314 % 53.2 36.5 32.7 24.6 25.5 34.2 2 risk factors 34 47 70 78 66 295 % 21.5 23.5 32.3 40.8 43.1 32.1 3 risk factors 3 21 33 38 33 128 % 1.9 10.5 15.2 19.9 21.6 13.9 4 risk factors 0 5 6 9 2 22 % 0 2.5 2.8 4.7 1.3 2.4 Total 158 200 217 191 153 919 Number of Missing Observations: 329

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Table 10.4: Combination of risk factors by age and sex Men Women Combination of risk factors Age Age 25-34 35-44 45-54 55-64 65-74 Total 25-34 35-44 45-54 55-64 65-74 Total % % % % % % % % % % % % High BMI and smoker 32.0 34.8 27.6 11.7 10.6 23.8 11.9 23.4 15.2 13.4 7.1 14.5 High BMI and high cholesterol 6.1 14.4 14.2 10.8 6.2 10.8 1.9 5.0 10.5 20.7 25.6 12.5 High BMI and high BP 8.8 23.2 35.2 40.2 40.7 29.2 3.2 16.6 33.0 54.5 48.6 31.1 Smoker and high cholesterol 3.3 12.8 4.4 2.2 0 4.8 3.4 4.2 6.9 5.9 3.2 4.8 Smoker and high BP 6.1 15.9 13.1 9.5 6.0 10.3 4.9 14.2 14.7 11.9 5.4 10.6 High cholesterol and high BP 0 4.5 8.0 10.2 8.4 6.5 0 4.5 11.3 18.1 22.3 11.2 High BMI and smoker and high 4.4 11.5 3.7 2.6 0 4.7 0.6 3.5 3.7 5.7 2.6 3.4 cholesterol High BMI and smoker and high 4.6 12.8 11.7 6.9 4.8 8.3 1.1 9.7 9.2 10.0 3.9 7.0 BP High BMI and high cholesterol 0 4.0 6.8 8.3 5.6 5.2 0 4.0 8.7 17.2 17.5 9.4 and high BP High cholesterol and high BP 0 3.4 3.1 1.3 0 1.7 0 3.0 4.1 5.2 2.6 3.1 and smoker High cholesterol, high BP, high 0 2.9 2.5 1.3 0 1.5 0 2.5 2.8 4.7 1.3 2.4 BMI and smoker Bases 196 209 198 191 170 964 185 219 234 205 189 1032

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10.3 The prevalence of risk factors in high risk subgroups

The purpose of this section is to present a summary of the prevalence of other risk factors within various high risk groups which could be the scope for reducing cardiovascular disease risk through target intervention and secondary prevention.

10.3.1 Risk factors in those with high blood pressure

In the fifth report of the National Commission of Hypertension League in the guidelines for managing hypertension is emphasize the importance of reducing other risk factors in hypertensives, because these factors are known to further increase cardiovascular disease risk in those with hypertension186. Previous studies have found high levels of other risk factors in those with hypertension187. In SHS population in those 156 men and 175 women with high blood pressure (a raised blood pressure but not on any treatment for high blood pressure at that time) we found the following:

Risk factors in those with high blood pressure Men Women 78.0 % had BMI > 25 kg/m2 75.9 % had BMI > 25 kg/m2 26.9 % were current cigarette smokers 28.7 % were current cigarette smokers 17.2 % had cholesterol ≥ 6.5 mmol/l 28.0 % had cholesterol ≥ 6.5 mmol/l

The participants with high blood pressure had a higher prevalence of raised BMI and cholesterol than the whole population for males and a higher prevalence of raised BMI and cholesterol for females (Table 10.1). We did not find cigarette smoking to be more prevalent in those with high blood pressure than in the whole population in men and women. There is considerable scope for cardiovascular disease risk factors reduction in hypertensives, particularly in relation to body weight and cholesterol.

We explored the prevalence of these risk factors in those who were already known to be hypertensive, as among this group there has already be an opportunity for target reduction of other risk factors. To examine the level of other risk factors in known hypertensives, current treatment for high blood pressure was used as a proxy measure of detected

296 hypertension. There were 191 men and 248 women reported to be on treatment for hypertension. Among them we found the following picture:

Risk factors in those on treatment for high blood pressure Men Women 82.5 % had BMI > 25 kg/m2 76.3 % had BMI > 25 kg/m2 29.8 % were current cigarette smokers 23.4 % were current cigarette smokers 14.9 % had cholesterol ≥ 6.5 mmol/l 24.6 % had cholesterol ≥ 6.5 mmol/l

The data observed, suggest that the high prevalence of CVD risk factors in participants with high blood pressure was similar to those who already being treated for hypertension, so they are not confound.

10.3.2 Risk factors in those with hypercholesterolaemia

From 1696 participants with valid cholesterol measurement obtained, 110 men and 159 women were having a total cholesterol level of 6.5 mmol/l or above and were defined as hypercholesterolaemic. In those people we found the following prevalence of other risk factors:

Risk factors in those with hypercholesterolaemia Men with hypercholesterolaemia Women with hypercholesterolaemia 74.3 % had BMI > 25 kg/m2 73.9 % had BMI > 25 kg/m2 38.2 % were current cigarette smokers 30.8 % were current cigarette smokers 44.5 % had high blood pressure 65.4 % had high blood pressure (of these 46.9 % were not on treatment) (of these 45.2 % were not on treatment)

Compared with the whole SHS population, high blood pressure and raised BMI were more prevalent in men and women with hypercholesteroaemia. The prevalence of cigarette smoking was similar in men and women with and without hypercholesterolaemia (Table

10.1).

10.3.3 Risk factors in current cigarette smokers

The prevalence CVD risk factors in current cigarette smokers (369 male and 344 female) is given below:

Risk factors in current cigarette smokers

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Men who were smokers Women who were smokers 62.9 % had BMI > 25 kg/m2 43.4 % had BMI > 25 kg/m2 14.9 % had cholesterol ≥ 6.5 mmol/l 15.2 % had cholesterol ≥ 6.5 mmol/l 26.8 % had high blood pressure 31.4 % had high blood pressure (of these 42.4 % were not on treatment) (of these 46.3 % were not on treatment)

Being overweight was less common among current smoking women, but not among men.

The percentage of informants with raised total cholesterol was similar in current smokers and non-smokers. The prevalence of high blood pressure was higher among non-smokers than in smokers (Table 10.1, 10.2).

10.3.4 Risk factors in those who were obese

The prevalence of other CVD risk factors among 146 men and 213 women who were obese (BMI > 30 kg/m2) was as follows:

Risk factors in those who were obese Men who were obese Women who were obese 33.6 % were current cigarette smokers 21.1 % were current cigarette smokers 15.6 % had cholesterol ≥ 6.5 mmol/l 22.3 % had cholesterol ≥ 6.5 mmol/l 57.5 % had high blood pressure 71.4 % had high blood pressure (of these 51.2 % were not on treatment) (of these 44.7 % were not on treatment)

It is not surprising that we found a higher prevalence of high blood pressure and high cholesterol in those who were obese. This interpretation have to be make with caution because there is still a debate as to whether obesity is an independent risk factor for CVD or its effect is mediated in part trough raised blood pressure and raised cholesterol. The prevalence of smoking for both sexes was lower in those who were obese compared to those who were not (Table 10.1)

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Section III - Discussions and Implications

11. Chapter: Potential significance and limitations of the SHS and summary of main findings

11.1 Limitations of the SHS

Before discussing the results, the limitations of the study must be considered. Although the overall sample was relatively large, the small size of certain groups reduced the precision of estimates for these groups, and rouse the possibility of selection bias. The issues of selection and measurement error bias are potentially important. In relation to the risk factors of greatest interest the potential effects of selection bias have been assessed as follows (bearing in mind the uncertainties that remain due to the possibilities of selection on unmeasured attributes):

Blood pressure: Systolic pressures in the source population may be underestimated as they tend to be higher in those with less education – who were under-represented in the study population. However a bias in this direction is unlikely to extend to diastolic pressures because their gradient in relation to education was found to be the opposite.

Blood cholesterol concentration: Levels in the source population are unlikely to be substantially underestimated by levels in the study population because persons with less education (who were under-represented) were not found to have significantly higher levels.

Smoking prevalences: These bear a complex, age and sex dependent relationship to educational levels making a summary of the potential effects of selection biases difficult.

In any survey such as this, one has to bear in mind the possibility of under-representation of smokers, heavy drinkers or others with high-risk lifestyle factors. The particular demographics of smoking in Bulgaria, however, with higher smoking rates in those with higher educational levels, suggest that overall in the SHS, smokers may be over-

299 represented. There may, however, be some slight under-representation within educational categories.

The potential effects of measurement biases are most important for blood pressure where there was evidence of substantial problems with the quality of the data. These are discussed further under Section 11.3.1 below.

When using the results from SHS for external comparison, or especially when considering the relevance of the findings for explaining national mortality patterns, one should consider the fact that this study is restricted to the capital city.

As we mentioned in Section 1.4.6. the city of Sofia differs from the rest of Bulgaria in a number of characteristics, which could affect the generalisibility of the results. The fact that Sofia as the administrative and political center of Bulgaria has a high proportion of people with high levels of education may make its smoking prevalence higher than in the country as a whole, especially in the case of females. Additionally the population in Sofia would have been most exposed to Western influences and especially tobacco and alcohol advertising, which can increase their tobacco and alcohol consumption.

The lower proportion of unemployed people in Sofia, especially in combination with higher educational level leads to higher socio-economical status of this population than in the rest of Bulgaria. High socio-economic status has been shown to be associated with lower CVD morbidity and mortality188, 189.

The better access to health care and the higher concentration of specialist in Sofia than in the rest of the country could reflect in higher registration of CVD morbidity and mortality.

On the other hand, insufficiency of supply of health care and specialists in rural areas of

Bulgaria could be a reason for both increased CVD morbidity and incorrect registration of unclear conditions as CVD mortality. This could be one of the reasons for the higher CVD mortality reported in rural regions of Bulgaria.

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11.2 Potential significance of the SHS

In the conditions of deep economic crisis and lack of opportunity for carrying out research, as well as insufficiency of well prepared specialists in the field of epidemiology at that time, with a health care system going through the transition, it was a challenge to carry out the Sofia Heart Study. It was the first study (together with CINDI – Veliko Tarnovo) with a well-defined sampling procedure and use of standardised measurement protocols, to assess the existing prevalence of risk factors among the urban Bulgarian population.

Additionally, it is worth noting the temporal coincidence of the SHS with the period of highest mortality in Bulgaria.

11.3 Summary of main findings

From this study emerges the thesis that the risk factor profile of urban Bulgarian population does not explain the high morbidity and mortality from cardiovascular diseases in the country.

11.3.1 Summary of data quality assessment

The most important data quality issues to emerge were those associated with blood pressure measurement. Achieving standardised measurements appears to be much more difficult than previously believed by Bulgarian workers. Care was taken to specify the nature of the measurement problems and to estimate the likely direction and importance of any resulting biases. We found a high digit preference score in our blood pressure data. This is unlikely to have substantially affected the estimated means or standard deviations. Inspection of the curve of the mean of two consecutive readings of blood pressure suggest possible downwards bias for the 90th centile as a result of rounding down for SBP values in this range (though probably not for diastolic) (Section 5.5.2). Rounding up to the systolic cut-point of 160 mmHg may have slightly increased the proportion classified as hypertensive. The data were found to be just over the suggested threshold for the “proportion of identical duplicate measurements”. This could have caused a small (less than 1 mmHg) upward bias across the entire systolic BP distribution. It was concluded that

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the potential effects of measurement errors were to make the estimated values slight overestimates of the true values (Section 5.4.6). Seasonal variation of arterial blood pressure has been demonstrated in large studies of both hypertensive and normotensive populations190,191. As the blood pressure measurements of SHS were taken mainly between March and October (with no air conditioning available) we would expect a moderate shift downwards of entire distribution of blood pressure. The blood pressure measurements have been taken by physicians, who may be less prepared to follow a standardised protocol when taking it. We found a likely over-reporting of hypertensive treatment, which will have biased the hypertension prevalence estimates, and impaired their international comparability.

11.3.2 Summary of findings

Results are reported for age 25 to 74.

11.3.2.1 Blood pressure

Findings in relation to the distribution of casual blood pressures found in SHS 1994:

• The mean age-standardised SBP for ages 25 to 74 was 132.7 mmHg for men and 128.4

mmHg for women.

• although younger women had SBP lower than men, the rise in SBP across the age groups

was steeper in women.

• 90th centiles for SBP exceeded 159 mmHg for men and 160 mmHg for women.

• the mean DBP was 84.9 mmHg for men and 83.6 mmHg for women.

• DBP rises with age in both sexes up until the age of 65, after this it declines in women and

rises slightly in men.

• 90th centiles for DBP exceeded 99 mmHg for men and 100 mmHg for women.

• 25,7% of males and 32,6% of females reported to be on treatment for hypertension (though

the validity of these data is uncertain).

• the proportion with casual blood pressures above the conventional cutpoints of

160/90mmHg increases steadily with age in both sexes. Among men it rises from 10.2% in

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those aged 25-34 to 55.7% in those aged 65-74. Among women the increase is even

steeper from 8.6% to 66.1% respectively.

On the basis of the regression models between SBP and other attributes it was found that:

• SBP increases on average 0.67 mmHg per year of age and with 1.5 mmHg per kg/m2 of

BMI.

• SBP is on average 2.4 mmHg lower in those who drink less then 60 g of alcohol per day

(among men) and less than 40 g daily (among women) than in non drinkers.

• No consistent and statistically significant correlation was found between SBP and

education, marital status or home ownership.

11.3.2.2 Blood lipids

• The mean age-standardised casual TSC for age range 25 to 74 was 5.2 mmol/l for men and

5.1mmol/l for women.

• 14.5% of males and 17.8% of females had casual TSC equal or more than 6.5 mmol/l.

• There was a tendency for mean TSC to increase with age among women, but not among

men.

• The mean age-standardised values of HDL were 1.2 mmol/l for men and 1.4 mmol/l for

women.

• The ratio TSC/HDL among men is lowest in the age group 25-34, peaked in the age group

35-44 and after that decreases with age, while among women it increases steadily with age.

• The mean age-standardised values of fasting casual triglycerides were 1.9 mmol/l for men

and 1.4 mmol/l for women.

On the basis of the regression models between TSC and other attributes it was found that:

• TSC increases on average by 0.02 mmol/l per year of age and by 0.05 mmol/l per kg/m2 of

BMI.

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• TSC was not, statistically, significantly associated with smoking, alcohol, education,

employment status, persons per room or home ownership.

11.3.2.3 Anthropometrical measurements

• The mean height was 175 cm among men and 163cm among women

• The mean weight of men was 81.7 kg and mean weight of women was 69.6 kg.

• The mean age–standardised BMI was 26.4 kg/m2 among men and 25.6 kg/m2 among

women.

• The mean age–standardised WHR was 0.89 for men and 0.77 for women.

• Mean BMI decreases with increasing the level of education from 27.8 kg/m2 in those with

less then 9 years of education to 25.0 kg/m2 in those with highest education among

females, but not among males.

• Almost two thirds of men (63.7%) and more then half of women (53.4%) were overweight

or obese (BMI>25 kg/m2).

• The obese (BMI>30 kg/m2) participants had higher prevalence of high blood pressure and

high cholesterol than the whole study population.

11.3.2.4 Smoking

• The prevalence of current smokers was 38.3% for males and 33.3% for females and

decreased strongly with increasing age in both sexes.

• 26.5% of males and 10.9% of females were ex-smokers. Among men there was a tendency

of an increasing proportion of ex-smokers with age, while among women the proportion

decreased with age.

• The quit ratio (ex to current plus ex-smokers) is almost twice as high among males than

among females (0.41 and 0.25 respectively) and it increases with age in both sexes, but

much more steeply among males.

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• 43% of male and 49% of female smokers had never tried to give up smoking.

• Male smokers smoked an average of 19, and female smokers-13 cigarettes per day.

• The proportion of heavy smokers (more than 20 cigarettes per day) was 56% for male and

26% for female smokers.

• The commonest ages for starting to smoke is between 16 and 20 (63% from male and 44%

from female). The proportion of people who started smoking earlier (between 10 and

15years) is much higher in youngest age group.

• 36% of male and 29% of female ex-smokers reported that stopped smoking because of ill

health. 27% from both sexes gave up smoking because their health might be affected and

about 10% from ex-smokers reported that they quit smoking because of economic reasons.

• People with less then 9 years of education had a lower probability to be a smoker than

those with higher education. Widowed, separated or divorced and married people had a

higher probability to be a smoker then never married.

11.3.2.5 Diet

• Relatively few report being on special diets192.

• Bread is the main staple (3.4 slices of white and 1.1 slices of brown bread mean daily),

supported by potatoes.

• Pork and chicken are the most popular meats but consumption frequencies are relatively

low.

• Dairy products, especially yoghurt, are major sources of animal protein.

• Reported consumption of vegetables is not low (but other evidence suggests that this is

likely to be highly seasonal141).

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• Older respondents report only modest consumption of coffee and tea and very little juice.

Juice and coffee are more frequently reported by those less than middle aged.

• Oil (in practice, mainly sunflower oil) is the main type of separated fat used.

• Reported practices do not suggest high salt use within the household, in contrast with some

previous studies.

11.3.2.6 Alcohol

• The prevalence of non-drinking rises with age and more so in women, from 20% in

younger males to 40% in older males and 86% in older females. Most female non-drinkers

are lifetime non-drinkers. About half of the older male non-drinkers are ex-drinkers.

• The main source of alcohol is spirits (‘rakia’) (56.6% of total) followed by beer (26.1% of

total). Despite the reputation of Bulgarian wine, little is consumed in this urban population,

accounting for only 15.2% of alcohol consumed.

• Overall drinking is relatively concentrated temporally, with drinking males reporting an

average of less than 3 drinking days per week and an average consumption on those days

of 65 g. The pattern of beverage type and the distribution of drinking across the days of the

week are relatively consistent across age and sex strata i.e. heavy weekend drinking is not

concentrated in younger ages as in some other countries e.g. England193.

• In relation to risks of ischaemic heart disease and ischaemic stroke: 30% of males and 67%

of females aged over 45 lack the protection found in various studies provided by modest

consumption of alcohol.

• In relation to overall health risks: 17% of males report an average consumption in excess

of 40 g/d (classified as ‘hazardous’ by some authorities194,195,196 and 12% in excess of 60

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g/d (classified as ‘harmful’ by some authorities). For females the respective percentages

and thresholds are 5% (20 g/d) and 2% (40 g/d).

• In relation to intoxication-related risks: the relatively concentrated nature of consumption

implies substantial potential for intoxication-related harms including road traffic injuries.

11.3.2.7 Physical activity

• Low proportions report participating in sports (about 10%).

• Μale respondents were generally more likely to report walking and playing sport during

their leisure time, but women were more likely than men to report vigorous exercises

(‘puffing and sweating’) during their leisure time.

• Almost half of both sexes reported decreasing over the last year their physical activity and

this proportion increases with age.

11.3.2.8 Risk factor combinations

The following estimates are based on the four factors as follows: BP, TSC, BMI and

smoking.

• Overall 13% of men and 17.4% of women did not have any of the four major risk factors.

• 33.7% of men and 34.2 % of women had one risk factor.

• 37.3% of men and 32.1% of women had two risk factors.

• 14.6% of men and 13.9% of women had 3 risk factors.

• 1.5% of men and 2.4% of women had four risk factors.

• The most common risk factor combination in the age group 25-44 in both sexes was being

overweight and smoker. After the age of 44 to the age of 74 again in both sexes the most

frequent combination was to be overweight and to have high blood pressure.

• The highest proportions with all 4 risk factors was observed in the age group 35-54 among

men and in the age group 45-64 among women.

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• The participants with high blood pressure had a higher prevalence of raised BMI and

cholesterol then the whole study population.

• The prevalence of CVD risk factors in participants with high blood pressure was similar to

those who reported being treated for hypertension.

• High blood pressure and raised BMI were more prevalent in men and women with

hypercholesterolaemia.

• The prevalence of high blood pressure and high cholesterol were higher in those who were

obese.

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12. Chapter: Interpretation of findings in relation to reported mortality

12.1 Blood pressure

The levels of mean SBP and DBP found in the present survey are consistent with SBP and

DBP levels reported in other studies conducted in Bulgaria132, 134,141.

Comparing age standardised median SBP and DBP in men with those of MONICA populations, the SHS population occupies intermediate levels (23rd out of 43 countries and

24th out of 43 respectively). For females the ranking is 18th out of 43 countries for SBP and

33rd out of 43 for DBP. A similar ranking was observed for the age standardised 90th centiles for SBP in both sexes (21st out of 43 for men and 22nd out of 43 for women). The less favorable ranking for age-standardised 90th centiles of DBP of SHS (29th out of 43) for men and 40th out of 43 for women when compared to MONICA populations could be contributed to by measurement error or may reflect a real effect.

A questionable finding is the high proportion (about two fold higher then those reported from MONICA populations) of participants reporting anti-hypertensive treatment. A possible explanation could be the high proportion of subjects who claim they are on treatment, but not taking into consideration the regularity of the medication taken. Many

Bulgarians appear to interpret high blood pressure as being analogous to acute infection, stop the medication when the ‘symptoms’ disappear. Such subjects could report they are

‘on treatment’.

Although we are not able to compare our data on prevalence of hypertensive treatment with other studies conducted in Bulgaria this observation is consistent with the opinion of leading specialists, with even higher percentages reported (56%)197. Additionally the above mentioned discontinuation of treatment and beginning again when the symptoms reappear,

309 without systematic follow up of blood pressure levels by a specialist may increase risk at a given average blood pressure level198.

Bearing this in mind, we may conclude that real antihypertensive drug consumption in

Bulgaria remains uncertain and the effectiveness of hypertension detection and control needs further investigation.

Even after allowance is made for the potential effects of selection and measurement biases, neither the central tendency, nor the 90th centile levels of blood pressure found in the SHS seem to give a reasonable explanation for the high heart disease and cerebrovascular event mortality rates of the Bulgarian population.

12.2 Blood lipids

The blood lipid levels measured in the SHS population are similar to those reported for other Bulgarian populations of uncertain representativeness134, 141.

As a potential source of bias could be considered the lack of registration in the SHS of lipid lowering drugs, although their use is not popular at all among Bulgarian population, because there is no systematic cholesterol screening and the lipid lowering drugs are extremely expensive.

Comparison of median age standardised values of TSC for the age group 35-64 of SHS with those in MONICA populations shows that the Sofia population is at the lower end (2 out of 43 countries) for both sexes. Comparing the 90th centile, Sofia is again in the range with low levels of TSC (9th and 4th out of 43 for men and women respectively).

An elevated risk of CHD among persons with a low HDL cholesterol level, even in combination with relatively low levels of total cholesterol or LDL cholesterol has been seen in several cohorts199, 200, 201. If both total cholesterol and HDL cholesterol concentrations are available it is worth using a combined lipid score. When compared with other populations202, 203 and European Atherosclerosis Society guidelines204 the present

310 study does not show the observed population to have either low levels of HDL or an elevated TSC/HDL ratio.

The results of the present study show that hypercholesterolaemia does not appear to be a common risk factor for the observed population. Neither the central tendency nor the 90th centiles levels of TSC, nor the levels of HDL cholesterol and TSC/HDL cholesterol can explain the high prevalence of cardiovascular diseases in Bulgaria.

Since ischaemic stroke accounts for 70-80% of total stroke205 in Western populations it is unlikely that the low average TSC concentration could be a reason for the high stroke mortality in Bulgaria, unless the proportion of ischaemic stroke is much lower than expected. The inverse association between total serum cholesterol levels and risk of haemorrhagic stroke36 needs to be borne in mind in future investigations of stroke aetiology in Bulgaria.

12.3 Body Mass Index

The association which we find between BMI and blood pressure and total cholesterol, which remained after adjustment for age and sex, is consistent with the findings of other studies48,50, 206.

Even though, we find two thirds of the men and more than half of the women in the observed population to be overweight or obese, when median BMI was compared with

MONICA populations, SHS ranks 21st out of 43 countries for both sexes. Our data are consistent with the prevalence of obesity and mean BMI measured in other Bulgarian populations 132, 134.

Neither the BMI, nor the waist/hip ratio found in our study differ substantially from those reported from countries which have much lower CVD morbidity and mortality and experience additional decline207.

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12.4 Smoking

The overall prevalence of smoking and relatively low proportion of people giving up which we find in the present study does not differ substantially from those reported from previous studies in Bulgaria132, 134, although we have not been able to determine the nature of the samples used or their representativeness. We did not find a big difference between age-standardised smoking prevalence of both sexes while ‘CINDY1994’ reported for almost twice higher prevalence of smoking among men than among women. This may partly be explained by our higher quit rate in men and a continious increase in smoking among women. Balabanova D. et al138 reported for overall prevalence of smoking 38.4% among men and 16.7% among women, strongly associated with age in a sample “not entirely representative of the Bulgarian population” aged 18 years and older. They also reported that smoking was “more common in Sofia and, for women, in cities and towns than in villages” which could be explanation of differences from our findings. The sex differences in smoking behavior, which we found, are probably due to the fact that our sample is representative of the urban population in Sofia only, and their smoking habits might differ from the rest of Bulgaria.

In consistency with Balabanova et al we found that the prevalence of smoking is much higher among the young than the old. This may affect cause-specific and age-specific mortality, with smokers dying in middle age of smoking-related causes. This is complicated by smokers giving up smoking, as they get older. At any rate, those born in more recent years are more likely to become addicted to tobacco, and it is likely that there will be further increases in smoking-related deaths.

We found that smoking was most common among those with higher education, which is similar to Balabanova’s observations (statistically significant only for women). As our sample is likely to over-represent the people with higher education this might additionally bias upwards the prevalence found for smoking.

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Comparison with age- standardised data for MONICA populations shows that the SHS population occupies intermediate position for males and upper end for females, for the age- standardised prevalence of regular cigarette smokers. Strong cohort effects are, however, hidden by those averages with higher prevalence at younger ages in Bulgaria.

Higher prevalence of smoking in more educated people, which we found is noteworthy and it is probably a typical ‘early phase‘ phenomenon for the smoking epidemic.

The relatively late age of starting smoking in the older cohorts can be expected to reduce the age-specific mortality rates in those cohorts compared to those observed at a similar age in populations which start smoking earlier.

The observed four decade difference in developing of the smoking epidemic between

Bulgaria and England (Chapter 7, Figures 7.3 and 7.4) suggests that smoking cannot explain the high mortality in older cohorts up to now, but we can expect a rapid increase of mortality attributable to smoking, first among middle to older aged men and after a couple of decades, among middle aged women.

12.5 Diet, alcohol and physical activity

Liberalization of prices decreasing the subsidies of production of agriculture products and foods and decreasing the real income of the population has led to an increase in the proportion of income spent on food and a shifting of consumption to cheaper foods - sometimes of doubtful quality. This has increased the risk of unbalanced nutrition especially in some social groups. The apparent, through poorly documented, changes in the lifestyle of the population of

Bulgaria, along with variability in the availability of different foods and the permanent increase of prices are likely to be producing substantial changes in eating habits.

Diet may influence CHD and stroke by affecting blood pressure or serum cholesterol. The low levels of serum cholesterol and levels of blood pressure that were not notably high, which we

313 found in SHS could partly be due to the reported reduction of total of meat and meat products141 consumption after 1990 in result of economic transition.

Another suggestion is that consumption of fruit and vegetables may protect against CHD and stroke106. Our findings do not show low consumption of fresh fruit and vegetables, but other studies in Bulgaria141 as well as irregular availability suggest that this is likely to be highly seasonal.

The data does not show high salt use, but this should be interpreted cautiously until 24- hour urine collections are available on representative samples. Previous studies in Bulgaria have suggested high salt intakes.

The greatest problem facing alcohol research is the lack of a reference standard with which to validate self-reported drinking208. The reliability of self-reported alcohol consumption can be good209, but most alcohol research faces the so called “coverage problem” due to response error (i.e. forgotten or deliberately concealed drinking), which probably affects the SHS data on alcohol, as well.

Our findings about prevalence of alcohol consumption differ substantially from those reported by Balabanova et al139. They showed much lower prevalence of alcohol consumption for women in Bulgaria as a whole, but at the same time they reported a “clear urban-rural gradient, with very much lower frequencies (of alcohol consumption) in villages among women”. Thus, the urban population in the SHS my explain some of the difference.

The present study found that alcohol drinking is relatively concentrated in a few days in the week (2.6 mean number of drinking days per week for men and 1.3 for women), thus

Bulgarian drinking culture differs substantially from the ‘Mediterranean’ pattern where the main beverage is wine and consumption is spread over a large number of occasions per week210. Comparison between the SHS and the Russian Longitudinal Monitoring Survey shows an almost 50% lower daily mean alcohol consumption for the Sofia population

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(31.4 g/day for men and 8.4 g/day for women) than in Russia (59.6 g/day for men and

14.5 g/day for women)211, which suggests that the urban Bulgarian population is much less exposed to the harmful effect of alcohol consumption above the moderate amount, with which the risk of fatal stroke and other adverse health effects are associated212.

The enormous gravity of the problem in food supply, quality and safety combined with a lack of information about healthy diet, the background of poverty and a high rate of diet related diseases points to the need for further investigation of Bulgarian nutrition and drinking behaviour.

The very low proportion of people who play sports in Sofia was expected, and it is due to: from one side lack of facilities for playing sport and, from the other side, where such facilities are available the prices are too high to be acceptable and affordable for the population.

12.6 Comparison with MONICA - complex assessment

The purpose of this section is to compare the prevalence of the measured CVD risk factors in Bulgaria with those in MONICA populations.

For the aims of comparison with MONICA data we are using age-standardised values for the age range 35 to 64 years.

The comparison for each risk factor separately is given in the previous chapters 4, 5, 6 and

As in MONICA213 the proportions of persons with 0-3 risk factors were used to present the prevalence of different risk-factor combinations in those study populations for which data on three major risk factors (hypertension, cholesterol more than 6.5 mmol/l and smoking) were available. One risk factor was present (score 1) if a participant was classified either as a smoker or into categories I-III for blood pressure, or had a total cholesterol level ≥ 6.5

315 mmol/l. Every combination of two risk factors present was scored 2. The presence of all three risk factors was scored 3.

It must be emphasized that the relative importance of each of those major risk factors is not the same, and therefore it cannot be assumed that all those with a given number of risk factors are at the same or similar risk of CVD. In addition this approach does not take into account the actual amount by which each factor is raised.

However, comparing age-standardised proportions of two risk factors with those from the

MONICA214 we found that for men Sofia (20.0%) ranks 7th out of 28 (range 15.8% to

42.6%). For females MONICA populations range from 4.6% to 37.9%, and Sofia females

(18.9%) rank 19th out of 28 (Tables 12.1, 12.2).

Conclusions:

1. We can be reasonably confidant that the total serum cholesterol levels in Bulgaria are towards the lower end of the distribution in MONICA cohorts in the early to mid

1980s.

2. The relative position of the blood pressure distribution for Bulgaria can only be estimated with caution due to problems with data quality both in the BP measurements and in the quantity of relevant medication. However, given that the likely measurements biases are in the upward direction we can note that for 3 out of 4 measurements values lie approximately in the 60th –80th centiles of the MONICA distributions. The exception is

DBP in females, which is toward the top of its distribution. The significance of this is unclear.

3. In relation to smoking, ranking of age-standardised prevalence are misleading, because they do not consider the country’s position in the evolution of the smoking epidemic. Bulgarian males have not been notably heavy smokers in the past. The first post- war cohorts of heavier smokers are now showing rising lung cancer mortality in middle

316 age and could contribute heavily to the burden of tobacco attributed mortality over the coming decades.

In conclusion we should consider the recent findings of the MONICA project, which reports that changes in the classical risk factors seem to only partly explain the variation in population trends in CHD leaving the paradox that the factors that have well-established ability to explain differences in risk between individuals provide a very inadequate explanation of differences between our population and others215, 216. Therefore, the need to explore role of additional factors in Bulgaria emerges. Special attention is due to the new and candidate risk factors, like dietary sodium and potassium117, seasonal deficits of fresh fruit and vegetables, respiratory function116, oral contraceptive118, hyperhomocysteinemia114, inflammation115 and their casual relation with CHD and stroke.

The fact that this relatively low cholesterol population has a relatively high prevalence of overweight adds further inquiry to the questions of diet and physical activity.

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Table 12.1: Complex assessment of risk factor distribution MONICA baseline data and SHS age 35-64, men Risk factors Proportion Ranks Countries Proportion of Proportion of Median total with two risk current smokers Median BMI Median SBP Median DBP hypertension cholesterol factors 1 CHIN-BE 61.1 23.4 126.0 85.0 24.6 4.1 15.8 2 NZ-AUK 34.3 25.4 131.0 81.0 20.1 5.7 16.8 3 CH-TICIN 43.7 26.8 131.0 80.0 18.9 5.5 17.0 4 FRG-RHE 38.2 26.2 128.0 82.0 22.0 5.7 17.6 5 US-STAN 43.2 25.6 127.0 82.0 23.4 5.3 18.7 6 AUS NEW 35.0 26.0 129.0 83.0 25.7 5.7 19.6 7 BUL-SOF 40.2 26.2 132.0 86.0 38.1 5.0 20.0 8 AUS_PER 36.0 25.5 131.0 85.0 25.1 5.8 21.1 9 POL-TARN 59.0 25.2 130.0 85.0 27.8 5.3 21.9 10 BE-GHE 51.8 26.1 126.0 79.0 13.0 6.1 22.7 11 FRG-AUR 37.8 27.3 133.0 85.0 21.6 6.1 24.4 12 IT-BRIA 47.0 25.5 136.0 88.0 31.1 5.6 24.8 13 USSR-KAU 41.7 27.5 134.0 88.0 30.4 5.9 25.1 14 FR-HG 43.1 25.5 130.0 85.0 25.7 5.9 26.0 15 CH-VAUD 38.5 25.9 130.0 81.0 18.0 6.3 27.3 16 UK-BEL 45.7 25.5 132.0 83.0 23.7 5.9 27.8 17 FR-BASR 41.2 27.3 143.0 91.0 42.2 5.5 28.2 18 FRG-BRE 46.7 26.3 139.0 84.0 26.0 6.0 28.5 19 FRG-AUU 39.9 26.8 134.0 84.0 27.7 6.2 28.9 20 POL-WAR 59.9 26.4 140.0 89.0 37.5 5.5 29.8 21 BE-CHA 55.7 26.3 129.0 81.0 19.5 6.1 30.8 22 BE-LU 51.0 25.6 131.0 78.0 15.5 6.4 31.4 23 FIN-TUR 38.9 26.5 140.0 87.0 35.5 6.1 32.3 24 DEN-GLO 61.1 25.4 125.0 80.0 14.9 6.2 32.9 25 FIN-NK 37.0 26.7 143.0 88.0 39.4 6.3 37.2 26 CZ-CZ 48.4 27.1 135.0 85.0 32.4 6.3 37.9 27 FIN-KUO 43.4 26.4 145.0 90.0 45.3 6.2 41.5 28 UK-GLAS 62.2 25.4 134.0 87.0 32.0 6.2 42.6 Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990

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Table 12.2: Complex assessment of risk factor distribution MONICA baseline data and SHS age 35-64, women Risk factors Proportion Ranks Countries Proportion of Proportion of Median total with two risk current smokers Median BMI Median SBP Median DBP hypertension cholesterol factors 1 CHIN-BE 19.0 23.9 127.0 80.0 21.5 4.2 4.6 2 CH-TICIN 28.0 24.2 126.0 77.0 17.0 5.2 9.4 3 IT-BRIA 20.6 24.3 131.0 83.0 24.6 5.5 11.2 4 BE-GHE 25.2 25.2 117.0 74.0 16.6 5.9 12.1 5 POL-TARN 11.9 27.3 131.0 86.0 34.4 5.4 12.6 6 US-STAN 37.1 23.5 120.0 79.0 16.8 5.2 13.0 7 USSR-KAU 5.0 29.3 132.0 84.0 30.6 6.0 13.8 8 FRG-AUR 15.4 25.8 128.0 80.0 19.3 5.9 13.9 9 FR-HG 18.9 23.6 125.0 80.0 17.6 5.7 14.2 10 NZ-AUK 27.4 23.7 123.0 76.0 18.1 5.7 14.5 11 FRG-RHE 24.9 24.3 123.0 78.0 17.4 5.7 14.8 12 FR-BASR 17.5 25.6 133.0 87.0 32.1 5.4 14.9 13 AUS_PER 22.5 23.8 122.0 78.0 19.1 5.7 15.4 14 BE-CHA 23.9 26.0 123.0 77.0 15.6 5.8 15.6 15 FRG-AUU 21.5 25.1 128.0 80.0 19.4 6.0 16.4 16 POL-WAR 34.7 26.8 138.0 84.0 30.9 5.5 17.2 17 AUS NEW 23.3 24.5 125.0 79.0 25.1 5.6 17.4 18 BE-LU 17.9 25.1 127.0 77.0 19.6 6.3 18.1 19 CH-VAUD 28.3 24.0 123.0 77.0 14.0 6.0 18.3 20 BUL-SOF 35.4 25.8 129.0 85.0 41.2 5.0 18.9 21 FIN-TUR 22.5 25.2 133.0 81.0 24.2 6.0 20.7 22 UK-BEL 35.3 24.8 129.0 79.0 20.3 6.0 22.1 23 FRG-BRE 29.3 25.5 135.0 82.0 23.3 6.0 23.6 24 FIN-NK 14.1 26.2 141.0 84.0 34.7 6.2 23.7 25 FIN-KUO 14.3 25.9 143.0 85.0 37.7 6.2 24.6 26 CZ-CZ 25.1 27.2 133.0 83.0 31.3 6.3 27.8 27 DEN-GLO 49.6 23.5 121.0 76.0 12.6 6.1 28.2 28 UK-GLAS 52.0 25.5 131.0 82.0 25.4 6.4 37.9 Source: WHO MONICA Project, MONICA manual, CVD unit WHO, CH-1211 Geneva 1990

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13. Chapter: Lessons learned and recommendations for the conduct of future studies

13.1 Summary of methodological issues

This was the first attempt in Bulgaria to perform a surveillance study according to international standards. As previously noted (chapter 12), problems were experienced with data quality. It is therefore important to summarize the lessons learned for improving future studies. Many of the remark below apply to epidemiological study of disease (case- control, cohort and cross-sectional) as well as to risk factor surveys.

13.1.1 Study design

The process of study design should in retrospect, have been more explicit and sequential – starting with the broad study questions, framing them in operational terms and devising a study design to address them.

The existence of an accessible population register in Bulgaria has proved to be a big advantage in selecting a representative sample. Contingency plans should be drawn up to cope with (possibly selective) shortfalls in compliance.

13.1.2 Staffing of studies

Although in this study it was only possible to use people in existing posts, this can complicate the quality control. With dedicated study staff this problem would have been probably considerably smaller. It seems to be more easily achievable now with the high unemployment rate in Bulgaria. A small number of core staff included in the survey will make easier to follow the standard procedure.

Training should include detailed monitoring of actual performance so that inadequate performances can be detected and corrected.

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13.1.3 Management of fieldwork

Continuous monitoring of fieldwork is essential including collection of ancillary data to

assist quality control, e.g.

• identifiers for persons involved in the data collection at each stage of the study

• identifiers for the equipment used (sphygmomanometers, lipid analyzer).

Regular calibration of equipment used and continuous checks on data quality during data

collection.

It is important to have an active system for monitoring the quality of the data during

collection.

13.1.4 Analysis and reporting in ways that help interpretation

A difficulty in surveying past literature on risk factor levels in Bulgaria is that the study

populations have often been poorly identified (see Section1.1.5). It is therefore desirable to

follow international practice (for example American Journal of Epidemiology) in

identifying study name and years of data collection in captions for tables and figures.

Reports should distinguish between casual and usual values of time-varying attributes,

such as blood pressure.

Findings should be published in the international journal literature to make them accessible

and to obtain the benefit of international peer review.

13.1.5 Specific measurement issues

13.1.5.1 Blood pressure measurements

We experienced substantial problems with the quality of our blood pressure data. It is

therefore important to learn how others have managed to minimize those problems.

Since blood pressure is not only variable within an individual, but also subject to systematic

observer-bias, instruments bias, seasonal effect, and effect of the physical, and physiological

321 circumstances in which the measurements were made, it is important to adopt internationally standard procedures and keep quality control of measurements throughout the study. Also whenever possible, it is desirable to have more than one blood pressure measurement per subject.

The use of random zero machines (when possible) to avoid digit preference is recommended.

Careful pre-testing and validation of questions to elicit medication use is essential to avoid upward bias in estimates of hypertension prevalence.

13.1.5.2 Blood lipids and anthropometrical measurements

Relative to the international quality standards total serum cholesterol and anthropometric measurements were satisfactory.

Obesity seems to be a problem. BMI is the conventional, but not necessarily the method to measure it.

An alternative is bioelectrical impedance, which is relatively simple and inexpensive as well as accurate104. Additionally BMI should be augmented by other body measures like waist-hip ratio and skinfold thickness measures.

13.1.6 Behavioral Factors: physical exercise, smoking, alcohol

13.1.6.1 Physical activity

It is increasingly being recognized that measuring of physical activity is very difficult, therefore questionnaire measurements should have limited objectives.

Precise measuring of energy expenditure should be the subject of separate, specially designed investigations e.g. individually calibrated heart rate monitoring217.

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13.1.6.2 Diet

On a practical basis Bulgaria is facing a change in the food market. Foodstuffs are not available on a stable basis. This rapidly fluctuating food intake imposes further limitations on dietary data collection. Therefore it is very important to regularly monitor the nutrition situation in the country.

In Bulgaria there are available food composition tables, but they are not up to date with the recent changes of food availability.

Dietary trends are being monitored by national surveys using the 24-hour recall method. A more complete dietary picture might be obtained by having each subject complete several 24- hours records at random intervals or in different seasons.

13.1.6.3 Smoking

Because of the uncertain representativeness and validity of past smoking surveys in Bulgaria it is difficult to monitor trends. In order to do so in the future, it will be best to adopt an internationally standardised measure - e.g. the MONICA smoking questionnaire and to pay due attention to sampling.

13.1.6.4 Alcohol

The choice of instrument to measure alcohol consumption should be guided by the relevant methodological literature. A provisional choice might be a 7-day recall, supplemented, for infrequent drinkers, by additional questions covering a longer reference period. As well as yielding a measure of average daily (or weekly) intake it should also yield a measure of the frequency of episodes of "peak drinking" (e.g. of more than 5 drinks).

Recent studies found that the prevalence estimate for hazardous and harmful drinking differs in a dramatic way depending on the techniques used to assess alcohol use218.

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13.2 Outstanding questions

While surveillance studies such as the Sofia Heart Study have an important role and can contribute to an assessment of risk factor distributions and, when repeated, to trends, their ability to explore causal links is limited. However, several findings deserve to be examined more fully even though the discussion has to be somewhat tentative.

In the view of the limited availability of internationally accessible validation studies on death certification in Bulgaria, published mortality data should be interpreted with caution.

However the striking pattern of Bulgaria, ranking very much higher for stroke than for

IHD, is likely to be correct.

Thus the reported mortality pattern is consistent with a population placed more at risk by its levels of blood pressure than by its cholesterol concentrations. However the question remains as to how Bulgaria’s high mortality rates from stroke are to be explained.

Some specific issues needing further investigation:

13.2.1 Surveillance-related

1. The validity of the nationally available cause of death data for Bulgaria219 needs

further study –perhaps in the context of cohort studies.

2. Future risk factor surveys need to give pro-active attention to achieving and

maintaining internationally acceptable standards of data quality, using experience

from MONICA and elsewhere. This is especially true for blood pressure and blood

cholesterol measurements. Repeat measurement is desirable.

3. Studies using internationally comparable methodology, of the effectiveness of

ascertainment of high blood pressure, and of its management and control by both

physicians and patients.

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4. The extent of regional variation in risk factors needs to be assessed by

standardised methods. Differences by settlement size – cities, towns, and villages –

should also be explicitly addressed.

5. Low socio-economic status and lack of social support220, 221 has been shown to be

associated with high cardiovascular disease morbidity and mortality and low life

expectancy in a large number of studies in many industrialized countries188, 189.

Dimensions of socio-economic inequalities are unclear in this transitional society

and future work is required to construct appropriate scales for social class and other

psychosocial characteristics, to use in future surveys.

6. Given that mortality rates have been declining since 1995, a repeat survey should

be conducted in Sofia – perhaps in 2004 after a 10-year lapse.

13.2.2 Aetiologic issues

From the point of view of Western Europe, the observed population has a very distinctive and interesting risk factor profile with low cholesterol levels, high levels of overweight and blood pressures not particularly higher than in other populations with much lower stroke mortality rates.

1. Emerging findings from this and similar studies (such as CINDI – Veliko

Tarnovo) should be used in combination with diet survey data to develop

hypotheses about the causes of high mortality from all vascular diseases combined

and from stroke (where the relative excess compared to western rates is much

greater) and studies designed to test these hypotheses should be performed with the

assistance of international funds available for such purposes.

2. The distinctive risk factor profile of the observed population seems to be an

indication for studies involving the combined effect of alcohol, cholesterol and

overweight on heart disease risk.

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3. Since low cholesterol levels have been observed to be a risk factor for non-

ischaemic stroke, studies of the relationship between cholesterol levels and risk by

type of stroke should be a priority. Given the low cholesterol levels and relatively

high rates of obesity, studies of physical activity may also be productive.

4. Further research identifying new and candidate risk factors and their

aetiological roles is needed. Special consideration should be given to dietary

sodium and potassium, seasonal deficits of fresh fruits and vegetables, respiratory

function (FEV1), and inflammation, especially in relation to stroke. The association

between CHD and apolipoproteins, hyperinsulinemia, thrombogenic risk factors,

hyperhomocysteinaemia, infections, vascular reactivity, dietary antioxidants and

sex hormones continues to require study.

The importance of such studies is based, to a considerable extent, on the potential generalisibility of the findings to other middle and low income countries with cool winters that are less advanced in the processes of social and economic modernization. For such countries, findings from Bulgaria might provide better guidance as to how their vascular epidemics should be dealt with than recent findings from countries where the decline in vascular mortality is now well advanced.

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14. APPENDIXES

Appendix 14-1: The 1992 census populations of the obstinae of Oblast Grad Sofia by sex Obstinae Males Females Total Sredec 18211 22893 41104 Krasno selo 36267 40871 77138 Vazragdane 18765 21600 40365 Oboriste 15871 19184 35055 Serdika 21879 23380 45259 Podujane 25605 27204 52809 Slatina 27156 29443 56599 Lozenec 18103 20212 38315 Izgrev 14464 16051 30515 Triadica 27994 32574 60568 Krasna poljana 27803 30317 58120 Ilinden 16748 18354 35102 Nadegda 33910 36927 70837 Iskar 31616 33054 64670 Mladost 49850 52238 102088 Studentska 24036 23813 47849 Vitosha 18887 19597 38484 Ovcha Kupel 18162 18850 37012 Lulin 55466 58741 114207 Wrabnitca 19576 20192 39768 Novi Iskar 14648 14608 29256 Kremikovci 22099 21623 43722 Pancharevo 11608 11448 23056 Bankya 4047 4181 8228 Total 572771 617355 1190126

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Appendix 14-2: Protocol of ethical approval

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Appendix 14-3: Measurement protocols for blood pressure, weight, height, waist, hip and blood sample collection Measurement protocols for weight, height, waist and hip

Purpose

The obesity is important risk factor for CVD. Many people do not know their height, weight and other body measurements or if they know them they are not accurate. Therefore it is necessary to obtain some measurements in this Survey.

The equipment

Medical stadiometer (beam balance).

Height

1. The nurse asks the informant to remove shoes in other to obtain as accurate as possible recording of height.

2. The informant should be standing on the stadiometer with flat feet, feet together. The back must be as straight as possible and his/her arms hanging loosely by his/ her side.

3. The head should be straight in horizontal position.

4. The nurse places the measuring arm on the informant's head. The informants should breath deeply in and stretch to his/her fullest height when the measurements is taken. The nurse asks the informants to step forwards. If the measurements has been done correctly the informant will be able to step off the stadiometer without having to duck.

5. The nurse read the height value at eye level. The measurements should be recorded in centimeters and to the nearest millimeter.

Weight

1. The nurse asks the informant to remove shoes, any heavy outer garments such as jackets and cardigans, heavy July and loose money and keys from the pockets.

2. The nurse ask the informant to stand on the stadiometer with feet together and arms hanging loosely at the side and head facing forwards.

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3. The scale will take a short time to stabilized its reading. The value of the weight is read by the nurse and should be recorded in kilograms and grams.

Measurement of waist and hip circumferences

Purpose

The waist- hip ratio is an indicator of the distribution of body fat. Analyses suggested that this ratio could be a predictor of health risk like the body mass index.

Equipment

Plastic meter calibrated in millimeters

Protocol

On the first visit the nurse asked the informant not to wear tight clothing for the visit in the polyclinic.

1 The nurse ensure that the informant do, not wear tight clothing and that all heavy outer garments have been removed,

2. The informant should be standing in a relaxed manner, weight balanced on both feet and the feet about 25-30 cm apart with arms hanging loosely at the sides.

3. The nurse is sitting on a chair by the informant while taking the measurements. The nurse passes the meter around the body of the informant.

Measuring the waist circumference

Position of the meter is on the midway between the lower rib margin and the iliac crest.

The meter should not cause indentation. The nurse ensures that the meter is horizontal.

Take the measurement at the end of a normal expiration. Record this on the schedule.

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Measuring hip circumference

The nurse places the meter around the hips at the position yielding the maximum circumference over the buttocks. The nurse ensures that the meter is horizontal. The informant should stand without contracting the gluteal muscles.

Protocol of blood pressure measurements

Purpose

High blood pressure is a risk factor for CVD.

The equipment

Mercury sphygmomanometer.

Protocol

1. The physician ensures that the participant kept the requirements:

- have not smoked and drink coffee 30 min ago

- have not take antihypertensive medicine in the day of examination

2. The blood pressure should be measure after 5 min in which the participant seat relaxed.

3. Ask the informant to remove jumper, cardigan etc. If the informant wearing a sleeve this should be rolled up, but it should not restrict the circulation of the blood.

4. The informant should be sitting in a chair with the right arm resting on any suitable support to bring the antecubital fossa to approximately heart level.

5. Choose the correct cuff and place it on the right arm place. The lower end of the cuff should be about 2 cm above the elbow crease. The cuff should be tight enough to admit two fingers between cuff and arm.

6. The diastolic blood pressure is measured by listening to the characteristic Korotkoff sound phase five.

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7. Two consecutive blood pressure measurements are performed within two minutes. 8.

When high blood pressure is measured for the first time the participant is invited for an other measurement 1-2 weeks after.

Protocol for blood sample collection

1. Requirements for taking a blood sample for serum cholesterol concentration

- register thoses who take oral contraceptives, antilipid medications, b-blockers

- have not eaten for 12 hours

- physical and psychologically relaxed for at least 10 min.

- the serum should be tested within two hours / at least for HDL /

2. The equipment

Tourniquet, alcohol swabs, cotton wool balls, rubber gloves, preventive dressing, laboratory address labels, vacutainer holder, vacutainer needles, vacutainer tubes SST with gel and clot activator.

3. Positioning the informant for taking the sample

The blood sample may be taken from either arm. The informant should be seated in a chair with sleeve rolled up to expose the antecubital fossa. Never take the sample with the informant standing up.

4. Technique

Preparation

1. It is preferable that you do not use the tourniquet. If this is not possible, place the tourniquet around the upper arm. Fully extend the elbow and look a suitable vein. If necessary lower the arm of the chair to allow the vein to few. Do not ask the informant to clench the fist. If you a re using the tourniquet do not leave it for longer than two minutes.

2. Wipe the side of the vein whit an alcohol swab.

Taking the sample.

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3. Grasp the informant's arm at the elbow to control the natural tendency to pull the arm away.

4. Enter the vein in a smooth continuous motion.

5. Holding the hub of the needle take the plane tub and insert it into the holder.

6. If you have use the tourniquet immediately release it.

7. If no blood is obtained try pulling the needle back and advancing again at a different angle.

8. When the tub is full, withdraw it from the vacutainer holder and invert it at once.

Cleaning up.

9. Place the cotton wool boll over the venepuncture, remove the needle quickly and immediately apply firm pressure with the cotton boll. Ask the informant to continue to press firmly for three minutes.

10. Stick the label on the tube.

11. Check on the venepuncture and affix an adhesive dressing.

12. Put the tubes into postal container

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Appendix 14-4: Protocols for external quality control RIQAS tests

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Appendix 14-5: Questionnaire

ID ______Background information

1*. a/ Name, Forename, surname...... ………………………. b/ Address and telephone number...... …………..…….. ______

2*. Ethnicity...... …...... ______3*. Sex . male...... …………...... 1 female.……….…………………...... 2 ______Date of birth...... ______5*. What is your marital status? single...... 1 married...... 2 living separate, but not divorced...... 3 divorced...... 4 widowed...... 5 6*. What is your education? A. high...... 1 two years of high education...... …. 2 11-12 years of education....…...... 3 primary school...... 4 without education...... 5 B. Specialty...... 7*. What is your employment status? full time job...... 1 part time job...... 2 full time job in shift work...... 3 unemployed, seeking work /how many years/.………. 4 unemployed, because sick or disabled of illness...... 5 housewife...... 6 wholly retired from employment...... 7 full time student...... 8 part time student...... ………...... 9 8*. A. What is your main occupation /incl. housewife, retired and unemployed/?...... B. For how many years?...... …...... ………………………...... ______9*. What is your job now?

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governmental...... … 1 self-employed...... 2 10*. How do you assess your abilities to buy anything in comparison with 1993 ? greater than 1993...... …. 1 no changes...... 2 less than 1993...... 3 much less than 1993...... 4 11*. Do you have greater difficulties in paying your bills for taxes, electricity, water, heating and others in comparison with 1993? yes...... 1 no...... 2 no changes...... 3 12*. In what kind of house you living? A. your own house...... 1 rent house...... 2 B. How many people you living in your family?...... No. 3 C. How many rooms you have in your house? ...... No. 4

Physical activity 13*. What is the level of your physical activity during your paid work? Almost nothing...... 1 light...... 2 moderate...... 3 heavy...... 4 14*. How often you are physically active for at least 20 minutes in which “puff and sweat” in your leisure time? Every day...... … 1 At least 2-3 dais weekly...... 2 At least once weekly...... 3 Less than once weekly……...... 4 15*. Do you sport during your leisure time? Yes...... 1 No...... 2 If the answer is “Yes”: What kind of sport you practicing..……...... 1 How many times weekly...... ….... 2 Mean duration..……..……………………..minutes 3 How many months during the year...... …...... 4 16. Are you performing some of the following activities in your leisure time? walking /at least 20 minutes daily/ 1 going upstairs ...... number of floors ...... 2 cycling /at least two times weekly/ 3 gardening and farming /at least two times monthly/ 4 tourism /at least one-two times monthly/ 5 non of the above activities 6 17*. How many streets do you cross daily ?...... ………………………. ______18*. Do you think that during the last year your physical activity is:

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Increased...... ….…...... 1 Remained the same...... …... 2 Decreased...... …...... 3

Dietary habits

Please, try to give the most appropriate answer! How many times you usually eat the following foods? one time almost several one day one or a rare or per day every times per of the few times never day day week per month 19*. Meat and meat products a/ pork 1 2 3 4 5 6 b/ beef 1 2 3 4 5 6 c/ lamb 1 2 3 4 5 6 d/ chicken 1 2 3 4 5 6 e/ other meat products 1 2 3 4 5 6 20*. Fish a/ river fish 1 2 3 4 5 6 b/ sea fish 1 2 3 4 5 6 21*. Pastry 1 2 3 4 5 6 22*. Corn foods 1 2 3 4 5 6 23*. Eggs 1 2 3 4 5 6 ...No. 24*. Milk and milk products a/ milk 1 2 3 4 5 6 b/ yogurt 1 2 3 4 5 6 c/ cheeses 1 2 3 4 5 6 25*. Fresh fruits 1 2 3 4 5 6 26*. Canned fruits 1 2 3 4 5 6 27*. Fresh vegetables 1 2 3 4 5 6 a/ potatoes 1 2 3 4 5 6 b/ green vegetables and salads 1 2 3 4 5 6 c/ carrots and tomatoes 1 2 3 4 5 6 d/ onion 1 2 3 4 5 6 e/ garlic 1 2 3 4 5 6 f/ beans, lenis, peas 1 2 3 4 5 6 28*. Canned vegetables 1 2 3 4 5 6

29*. How many cups of soft drinks you have per day? juices...... No coffee...... …...... No 337

tea...... …...... No 30*. What kind of bread do you usually eat? white...... loaf per day brown...... loaf per day 31*. What kind of fat do you usually use ? oil...... 1 butter...... 2 lard...... 3 margarine...... 4 32*. Do you usually add salt to your meal ? add before taste...... ….... 1 taste and than usually add...... …..... 2 taste and rarely add...... 3 never add salt...... 4 33*. Are you on a special diet ? no ...... ….... 1 slimming diet, suggested by your doctor…...... 2 slimming diet, prescribed by yourself...... 3 diabetic diet...... …..... 4 other medical diet/ specify ...... ………..../ 5 vegetarian diet...... ….... 6

Alcohol consumption

34*. Do you drink alcohol now ? no...... …...... 1 less than ones per week...... 2 one or two days per week...... 3 tree to six days per week...... 4 every day...... 5 35*. What kind of alcohol you usually drink ? beer...... ….. 1 wine...... 2 concentrate alcohol drinks...... …...... 3 mixed...... 4 36*. What quantity of alcohol you usually drink: A. During the working days of the week? beer...... bottles /500 ml/ wine...... …...No glasses/200 ml/ concentrates...... No glasses/100 ml/ other alcohol drinks...... No glasses/100 ml/ B. During the weekend days? beer...... bottles /500 ml/ wine...... No glasses/200 ml/ concentrates...... No glasses/100 ml/ other alcohol drinks...... No glasses/100 ml/ 37*. Do you drink more or less in comparison with 1990 ? less than 1990 ...... …...... 1

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more than 1990 ...... …...... 2 no changes...... ………...... ….... 3 I gave up...... …...... 4 38*. Specify the reasons for changing your habits of alcohol consumption...... …………………………………………………………. 39*. If you are non-drinker, have you been always a non-drinker or did you give up? always a non drinker...... 1 I gave up ...... years ago....…...... …... 2 ______40*. Before you stop drinking how many drinks you have usually drunk per week?...... drinks per week...... ______

Smoking habits 41*. Do you smoke cigarettes now? yes, regularly...... 1 no / go to question ¹ 49 /...... …...... 2 occasionally / usually less than one cigarette per 3 day/...... 42*. On the average, about how many cigarettes do you now smoke a day?....No...... ……...... ______43*. What brand of cigarettes do you usually smoke ...... …………………….... ______44*. How old were you when you began to smoke cigarettes?...... years……...... ______45*. Have ever tried to give up smoking ? yes...... 1 no...... 2

46*. Have you tried to quit smoking in the last year ? yes...... … 1 no...... 2 47*. Over the past few years have you changed the brand of cigarettes you usually smoke ? no...... ….... 1 yes, to a lower tar brand...... ….. 2 yes, to a similar tar brand...... …...... 3 yes, to a higher tar brand...... 4 If yes specify the kind of cigarettes, which you smoke now...... …...... ______48*. Why did you change the brands of the cigarettes? because of ill health...... 1 because my health might be affected...... 2 because of economical reasons...... 3 other reasons...... 4 49*. Did you ever smoke cigarettes ? 339

yes, regularly...... …... 1 no, never /go to question 52/...... 2 occasionally...... 3 50*. What is the maximum number of cigarettes you ever smoked per day for as long as a year?....No...... ………...... ______51*. A. How old were you when you began to smoke cigarettes ? ...... years old...... B. How old were you when you stopped smoking cigarettes ? ... .years old...…...... C. Why did you stop smoking ? because of illness...... …...... …………….. 1 because you did not want to damage your health...... 2 because of economical reasons...... ……………...... 3 other...... ……………………...... 4 D. If you stopped smoking over the last year when was this: less than one month ago...... 1 one to six months ago...... 2 7-12 months ago...... 3 52*. About how many hours per day you are exposed to tobacco smoke (hours)...... ______Stress 53. How often were you strongly irritated for reason Almost never 1 of little troubles in your life during the last two Rarely 2 weeks? Sometimes 3 Often 4 54. What was the level of the stress that you feel as a I did not feel 1 whole, during the last two weeks? stress /go to Think about the stress as a feeling, disturbance and/or question 56/ tense due to events, which changing your usual Low 2 lifestyle. Moderate 3 High 4

55. Which from the following areas of your life were cause for the stress you experienced? a/ work/school Yes No b/ health condition – yours and yours relatives and friends Yes No c/ attitudes within the family and between friends Yes No d/ relations with neighbors and friends Yes No e/ financial/possession problems Yes No f/ life condition in home/town Yes No g/ “unpleasant” events /death, crash, steal, violence, problems with the authorities, judge etc. Yes No h/ “pleasant” events /wedding, bearing of child, financial or possess benefit, holiday etc. Yes No i/ community-political situation Yes No j/ others ...... Yes No 56. What was the affect of the stress on your health during the last year? Almost nothing 1 Weak 2 340

Moderate 3 Strong 4 57. Did you have one or more of the following complaints during the last two weeks? 1. Nervousness or trembling “from inside” Yes No 2. Pains in the heart or in the chest Yes No 3. Suicide thinks Yes No 4. Sudden stress without reason Yes No 5. Feeling of loneness Yes No 6. Sadness, pain Yes No 8. Lack of interest for everything Yes No 9. Feeling of fear from something Yes No 10. Sickness or heaviness in the stomach Yes No 11. Difficulties in getting sleep Yes No 12. Difficulties in breathing Yes No 13. Warm or cold waves Yes No 14. Stiff or tingle of different parts of the body Yes No 15. Feeling for hopelessness in the future Yes No 16. Weakness in some parts of the body Yes No 17. The feeling for strenuous Yes No 18. Attacks of fear or panic Yes No 19. Extreme strenuous, because of which you cannot stay on a single place Yes No 20. Feeling of uselessness Yes No

Health knowledge

58. How do you assess the level of your knowledge about the harm caused from smoking, low physical activity, overweight? Completely enough 1 Not enough 2 I don’t now almost nothing 3 I am not interested in this 4 I cannot reply 5

59. Do you know to have some of the following diseases? 1. Hypertension Yes No 2. Myocardial infarction Yes No 3. Stroke Yes No 4. Diabetes Yes No 5. Ischaemic heart disease Yes No 6. Defect of the heart Yes No ______

60. Do you think that the risk of getting one of the above diseases would decrease if: 1. You work sensible and get rest Yes No 2. Give up smoking Yes No 3. Not eat fat foods Yes No 4. Not eat salty foods Yes No 5. Decrease your weight Yes No 6. Increase your walking distances Yes No 7. Eat more often fruits and vegetables Yes No

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8. Restrict or stop eating sweets Yes No 9. Eat more often fish Yes No 10. Get additionally vitamins A, C, E Yes No 11. I can not reply Yes No 61. In case that you reply more than 7 time with “Yes” to the previous question, tell the reason for not doing it. 1. I do not have enough knowledge Yes No 2. I do not think that this is important Yes No 3. It is difficult for me to change my habits Yes No 4. I do not see health staff to have healthy lifestyle Yes No 5. I think that this is not influence on me Yes No 6. I think that I have a healthy lifestyle Yes No 62. In case that a middle aged person has strong pain in the chest for more than half an our he/she have to: Relax until the pain is gone 1 Take a painkiller 2 Call a medical doctor 3 Call immediately emergency 4 I can not reply 5

Health status and family history

63*. Did your mother or father have heart diseases before they were 60 years old? yes, father...... years old...... 1 yes, mother ...... years old...... 2 no...... 3 I do not know...... 4 64*. Did any of your brothers or sisters have heart diseases before they were 60 years old? yes, ...... years old...... …...... 1 no...... …...... 2 I do not know...... 3 I have not any brothers and sisters..…...... 4 65*. Are you now taking any medications for high blood pressure or diabetes? yes...... 1 no...... 2 If yes, what?...... ______66*. Are you regularly taking any other medical treatment at present / oral contraceptive, β - blockers, heparin, antilipaemics/? yes...... 1 no...... 2 If yes, what ?....……...... …...... …...... How long are you taking them?..…...... ______Symptoms of cardiovascular

67*. Have you ever had any pain or discomfort in your chest or pain in either leg on walking? yes...... 1 no...... 2

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68*. Are you troubled by shortness of breath when hurrying on level ground or walking up a slight hill? yes...... 1 no...... 2 69*. Did you have a feeling of heart stopping when hurrying on level ground or walking up a slight hill? yes...... 1 no...... 2 70*. Did you have a feeling of quick heart beating when hurrying on level ground or walking up a slight hill? yes...... 1 no...... 2

Measurements* Height...... cm Weight...... in kg /to the nearest 200 g/ Waist...... cm Hip...... cm Blood pressure / I measurement / s. d. Blood pressure / II measurement/ s. d.

Diseases

Cardioscleroses* Yes No Hypertension heart Yes No Coronary heart disease* Yes No Total arrhythmia Yes No Extrasystolies, arrhythmias Yes No Rheumatic heart defect Yes No Past talking disorders Yes No Temporary disorder of blood circulation of the brain Yes No Latent disorder of blood circulation of the brain Yes No Stroke/previous/* Yes No Diabetes* Yes No

Laboratory measurements:

Total serum cholesterol*…...... Triglycerides*...…...... HDL cholesterol*…...... Vitamin "Å"...... …………...... Potassium...... Sodium...... Creatinine...... Urea......

Thank you for your cooperation!

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Appendix 14-6: Study population by sex and 5 years age group Respondents Age group Male Female Total (N) (N) (N) 25-29 108 85 193 30-34 88 100 188 35-39 95 99 194 40-44 114 120 234 45-49 102 129 231 50-54 96 105 201 55-59 89 99 188 60-64 102 106 208 65-69 103 115 218 70-74 67 74 141 Total 964 1032 1996

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Appendix 14-7: Distribution of study population by marital status within ten-year age groups and sex. Age 25-34 35-44 45-54 55-64 65-74 Total Males never married 67 16 13 11 4 111 % 34.2 7.7 6.6 5.8 2.4 11.5 married 108 164 152 148 133 705 % 55.1 78.8 76.8 77.9 78.2 73.3 separate not divorced 11 5 8 1 0 25 % 5.6 2.4 4 0.5 0 2.6 divorced 10 19 19 13 9 70 % 5.1 9.1 9.6 6.8 5.3 7.3 widowed 0 4 6 17 24 51 % 0 1.9 3 8.9 14.1 5.3 Total 196 208 198 190 170 962 % 20.4 21.6 20.6 19.8 17.7 100 Females never married 37 19 9 7 7 79 % 20.1 8.7 3.8 3.4 3.7 7.7 married 123 156 173 129 85 666 % 66.8 71.2 73.9 62.9 45.2 64.7 separate not divorced 8 2 3 7 3 23 % 4.3 0.9 1.3 3.4 1.6 2.2 divorced 15 36 28 20 14 113 % 8.2 16.4 12 9.8 7.4 11 widowed 1 6 21 42 79 149 % 0.5 2.7 9 20.5 42 14.5 Total 184 219 234 205 188 1030 % 17.9 21.3 22.7 19.9 18.3 100 Number of Missing Observations: 4

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Appendix 14-8: Distribution of weight by age and sex

Weight Age [kg] 25- 30 - 35 - 40 - 45 - 50- 55- 60- 65- 70- All 29 34 39 44 49 54 59 64 69 74 ages Men less than 60 2 2 3 1 0 0 0 2 1 3 14 60 - 69 11 14 7 14 7 9 13 10 15 8 108 70 - 79 42 25 25 23 39 22 36 36 34 24 306 80 - 89 33 27 26 38 36 31 20 28 36 19 294 90 or more 20 18 34 33 20 32 20 25 17 13 232 Total 108 86 95 109 102 94 89 101 103 67 954 Women less than 60 47 35 36 28 30 14 6 10 16 12 234 60 - 69 27 37 35 33 37 25 35 25 36 29 319 70 - 79 7 21 14 30 32 31 29 28 35 18 245 80 - 89 2 2 6 14 19 23 19 26 16 12 139 90 or more 2 5 7 15 10 10 8 17 9 2 85 Total 85 100 98 120 128 103 97 106 112 73 1022

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Appendix 14-9: Body Mass Index

Age BMI All 2 25- 30- 35- 40- 45- 50- 55- 60- 65- 70- [kg/m ] ages 29 34 39 44 49 54 59 64 69 74 Men less than 20 4 1 2 2 0 2 0 2 0 1 14 20 - 25 incl. 52 36 26 36 33 22 35 37 30 25 332 25 - 30 incl. 41 40 55 50 55 49 38 36 63 35 462 greater than 11 9 12 21 14 21 16 26 10 6 146 30 Total 108 86 95 109 102 94 89 101 103 67 954 Women less than 20 16 10 20 6 10 3 4 1 0 0 70 20 - 25 incl. 56 62 50 50 48 35 29 24 30 22 406 25 - 30 incl. 9 22 17 37 45 41 37 37 51 36 332 greater than 4 6 11 27 25 24 27 44 31 14 213 30 Total 85 100 98 120 128 103 97 106 112 72 1021 Number of Missing Observations: 21

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Appendix 14-10: Proportion overweight or obese (BMI > 25) by age and sex Age 25-34 35-44 45-54 55-64 65-74 Total Males BMI>25 101 138 139 116 114 608

% 52.1 67.6 70.9 61.1 67.1 63.7

BMI<25 93 66 57 74 56 346

% 47.9 32.4 29.1 38.9 32.9 36.3 Total 194 204 196 190 170 954

Females BMI>25 41 92 135 145 132 545

% 22.2 42.2 58.4 71.4 71.7 53.4 BMI<25 144 126 96 58 52 476

% 77.8 57.8 41.6 28.6 28.3 46.6 Total 185 218 231 203 184 1021

Number of Missing Observations: 21

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Appendix 14-11: Distribution of systolic blood pressure by five years age groups and sex

SBPM Age All [mmHg] 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 ages Men < 100 0 2 3 2 4 3 1 1 2 0 18 100-109 13 9 6 6 4 6 4 1 5 0 54 110-119 18 15 19 21 11 12 12 9 5 3 125 120-129 34 29 29 31 25 11 12 14 11 7 203 130-139 25 19 18 24 25 13 24 22 18 14 202 140-149 11 9 11 14 19 23 16 23 33 20 179 150-159 7 3 5 5 7 13 4 8 9 6 67 160-169 0 2 2 7 6 12 12 9 14 13 77 170+ 0 0 2 4 1 3 4 15 6 4 39 Total 108 88 95 114 102 96 89 102 103 67 964 Women < 100 17 11 14 6 9 3 3 1 0 0 64 100-109 13 21 19 21 15 6 5 2 2 0 104 110-119 22 33 24 25 20 9 5 7 7 104 120-129 18 18 23 23 29 15 22 7 6 3 164 130-139 9 10 14 20 21 24 10 26 28 20 182 140-149 5 4 3 9 15 25 25 18 25 25 154 150-159 0 1 0 10 7 4 10 14 16 14 76 160-169 1 1 1 5 6 11 13 17 16 6 77 170+ 0 1 1 1 7 8 6 14 15 6 59 Total 85 100 99 120 129 105 99 106 115 74 1032

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Appendix 14-12: Median systolic blood pressure for persons aged 35-64 of age- standardised values for SHS with published values for MONICA populations men City SBP % on treatment Catalonia 121 3.30 Ghent 126 8.40 Glostrup 125 6.50 Luxembourg Province 131 8.50 Vaud 130 7.50 Ticino 131 6.70 Charleroi 129 8.90 Auckland 131 8.10 Northern Sweden 131 9.00 Augsburg (rural) 133 5.90 Rhein - Neckar Region 128 13.80 Novi Sad 132 12.40 Stanford 127 11.70 Belfast 132 6.20 Beijing 126 7.10 Perth 131 9.80 Newcastle 129 13.20 Haute-Garonne 130 9.40 Bremen 139 5.70 Moscow (intervention) 134 5.20 Augsburg (urban) 134 7.10 Tarnobrzeg Voivodship 130 9.90 Novosibirsk (control) 131 6.00 Kaunas 134 12.00 Brianza Area 136 8.40 Glasgow 134 8.20 Czechoslovakia 135 13.50 Malta 136 11.30 Halle County 137 9.80 Friuli 140 9.40 Turku 140 11.40 Novosibirsk (intervention) 132 7.40 Moscow (control) 133 9.10 Berlin Lichtenberg 139 19.30 Warsaw 140 9.00 DDR MONICA (other surveys) 142 15.20 Karl-Marx-Stadt County 138 10.70 North Karelia 143 13.40 Bas-Rhin 143 10.10 Kuopio Province 145 11.70 Sofia - SHS 144 27.40

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Appendix 14-13: Median systolic blood pressure for persons aged 35-64 of age- standardised values for SHS with published values for MONICA populations, women City SBP % on treatment Glostrup 121 7.90 Catalonia 118 10.60 Vaud 123 7.80 Charleroi 123 12.10 Ghent 117 14.60 Stanford 120 10.50 Ticino 126 9.40 Rhein-Neckar Region 123 13.80 Haute-Garonne 125 10.00 Auckland 123 11.80 Northern Sweden 126 11.20 Perth 122 12.60 Augsburg (rural) 128 9.60 Augsburg (urban) 128 8.70 Luxembourg Province 127 13.20 Belfast 129 8.70 Beijing 127 9.60 Bremen 135 8.40 Turku 133 10.30 Brianza Area 131 13.10 Newcastle 125 17.60 Glasgow 131 6.00 Novi Sad 132 21.20 Moscow (intervention) 133 15.10 Friuli 136 12.80 Kaunas 132 20.40 Warsaw 138 12.60 Czechoslovakia 133 19.30 Novosibirsk (control) 131 13.00 Bas-Rhin 133 14.30 Berlin-Lichtenberg 135 21.80 Tarnobrzeg Voivodship 131 16.80 North Karelia 141 15.20 Karl-Marx-Stadt County 138 16.30 Halle County 138 15.10 Malta 138 20.20 Moscow (control) 134 17.00 Kuopio Province 143 15.50 Novosibirsk (intervention) 133 19.10 DDR MONICA (other surveys) 140 22.30 Sofia - SHS 144 34.60

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Appendix 14-14: Smoking, by age and sex Age Total 25-34 35-44 45-54 55-64 65-74 Males regular smokers 105 101 66 37 19 328 % 53.6 48.3 33.3 19.4 11.2 34.0 other current 10 11 9 7 4 41 smokers % 5.1 5.3 4.5 3.7 2.4 4.2 non-smokers 81 97 123 145 147 593 % 41.3 46.4 62.1 75.9 86.5 61.5 not stated 0 0 0 2 0 2 % 0 0 0 1 0 0.2 Total 196 209 198 191 170 964 Females regular smokers 94 90 59 28 13 284 % 50.8 41.1 25.2 13.7 6.9 27.5 other current 12 18 14 12 4 60 smokers % 6.5 8.2 6 5.9 2.1 5.8 non-smokers 79 111 160 162 171 683 % 42.7 50.7 68.4 79 90.5 66.2 not stated 0 0 1 3 1 5 % 0 0 0.4 1.5 0.5 0.5 Total 185 219 234 205 189 1032

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5 Kannel WB. The Framingham experience. Coronary heart disease epidemiology. From aetiology to public health. Edited by Marmot M. Oxford University Press 1992; 67-82.

6 NHLBI (Working Group on Arteriosclerosis of the National Hard Lung and Blood Institute). Arteriosclerosis 1981, Vol. 2.

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