SPATIAL PATTERNS OF TUBERCULOSIS IN THE PUNJAB, 1990-2005
PA.ZX Dissertation
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Submitted by: Muhammad Shafqaat Aojum Roll No. H EC-02
June 20tl
DEPARTMENT OF GEOGRAPHY UNIVERSITY OF THE PUNJAB, LAHORE Abstract The present research is an attempt to explore the occurrence and clustering of tuberculosis patterns in the Punjab, Pakistan. The Punjab, which is the largest province of Pakistan, is selected to examine the patterns of TB from 1990 to 2005. Higher disease rates are found in big cities in 1990. South Punjab was found severely affected throughout the study period. Kulldorff Spatial Scan Test also identified disease dusters in major cities. Moreover, the diseases clusters have shifted from central and north Punjab to the south Punjab during this period. The disease proportion is found higher in females than males. Low income, larger families, illiteracy, and over crowdedness are found important factors in the disease patterns. The knowledge about the disease such as symptoms, causes and precautions is found very poor in the patients. The analysis of healthcare services revealed that the accessibility, time, and cost are important issues for the poor patients. Inequality in the distribution of healthcare services in various districts of the Punjab province is a major concern which is verified by the use of techniques such as Lorenz curve and Gini index.
ii 1
Dedicated to
My parents,
For everything, forever 1 ACKNOWLEDGEMENT Glorified is ALLAH with all praise due to Him, the Greatest is free from imperfection. I praise to Allah the most gracious, most merciful who blessed me to obtain formal education from school up to PhD level. The writing of this dissertation has been one of the most significant academic challenges 1 ever came across. Without the support, guidance, and patience of the people around me, thisstudy would not have been completed. I would like to express my heart felt thanks towards my research supervisor, Prof, Dr. Abdul Ghaffar, Chairman, Department of Geography, University of the Punjab, who has been a constant source of inspiration at every stage of work. His knowledge, expertise, mentorship, and co-operation were paramount in providing me guidance and help at every stage of this research work, 1 am grateful to Dr Darkhshan Badar and Dr Tehseen from DOTS program, Mr, Farooq Ahmad from HMIS, and all the statistical officers in Health department, Punjab, for providing me data and guidance to carryout this research work. I would also like to thank Prof. Sibqat ullah Tahir, Shoaib Khalid, Mrs. Fariha shoaib, and Omar bin Talib from GCUF for their co¬ operation and help. 1 acknowledge with thanks the services and assistance from Mr. Azhar Rasheed (Librarian} and Husnain in the department library who were always kind and supportive. It is not possible to not to pay my sincere gratitude to my friends Tanveer Zafar, Aftab Karim, Omar Riaz, Qais Ullah, Usman Feroz, and particularly Hafiz irfan for their valuable discussion from time to time on various aspects of my research and career. I can never forget to appreciate my friends in UK Samee, Wasee, Farooq, Bob, Amer jalal, and K, L Sunny for their consistent encouragement and cordial cooperation. Finally, with tremendous love, dignity, and honour, I am indeed grateful to my father, Muhammad Hussain Malik and my loving mother, both of them always inspired me with their hard work and commitment and brought success for me through their prayers, l am also obliged to my brothers, Muhammad Sohail and Muhammad Shahzad; sisters, and other family members for their support and encouragement and especially to cute kids in my family Rohaan
iii Farhat, Aaliyan Shahbaz, and Mahad Sohail for making me smile during the tiresome and exhaustive research work. In the end I would like to pay special tribute to Higher Education Commission, Government of Pakistan, for providing financial support which has allowed me to focus on my work to complete my Ph.D.
Muhammad Shafqaat Anjum
iv DECLARATION
[, Muhammad Shafqaat Anjum. hereby declare that this dissertation is my own work and all the sources have been quoted and acknowledged by means of complete references.
Mi lhafqaat Anjum
v TABLE OF CONTENTS
Abstract u Acknowledgement iii Declaration v Table of Contents Vi List of Tables ix List of Figures xi
CHAPTER 1 INTRODUCTION 1.1 Introduction i 1.2 Research Background 3 1.3 Aims of the study 4 1 .4 Research Objectives 4 1.5 Study Area 4 1,6 Dissertation Structure 8 CHAPTER 2 DATA SOURCES AND METHODLOGY 2,1 Introduction 9 2.2 Data Collection 9 2.2.1 Primary Data 9 2,2.2 Secondary Data 10 2.3 Methodology 11 2,3.1 Data Analysis and Visualization 11 2.3.2 Standardized Morbidity and Mortality Ratio 12 2.3.3 Disease Sex Ratio 13 2.3.4 Disease Clustering 14 2.3.5 Analysis of Healthcare Facilities 15 2.4 Limitations of Data 16 CHAPTER 3 LITERATURE REVIEW 3.1 Medical Geography 19 3.2 Development of Medical Geography 22 3.3 Medical Geography and Geographic Information System 28 3.4 Disease Mapping 30
vi 3.5 Disease Clustering 31 3.6 Ecological Analysis 33 3.7 Analysis of Healthcare Facilities 34 3.8 Literature Review with special reference to Tuberculosis 35 CHAPTER 4 TUBERCULOSIS AND ITS DISTRIBUTION IN THE WORLD 4.1 Tuberculosis 40 4.2 Transmission of Infection 42 4.3 Etiology 43 4.4 Patterns of Tuberculosis Distribution in the World 43 4.5 Disease situation in Pakistan 47 4.6 Role of World Health Organization to combat disease 48 4,7 Directly Observed Treatment Short Course (DOTS) 49 4.8 Summary 50 CHAPTER 5 SPATIAL PATTERNS OF TUBERCULOSIS IN THE PUNJAB 5.1 Introduction 51 5.2 Spatial Patterns of Tuberculosis in the Punjab in 1990 52 5.3 Spatial Patterns of Tuberculosis in the Punjab in 1995 58 5.4 Spatial Patterns of Tuberculosis in the Punjab in 2000 63 5.5 Spatial Patterns of Tuberculosis in the Punjab in 2005 68 5.6 Changes in SMRs from 1990 to 2005 in the Punjab 75 5.7 Smear Positive TB cases 78 5.8 Mortality among TB Patients 83 5.9 TB and Gender 88 5.10 Targets and achievements in the Punjab against TB 94 5.11 Disease Clustering 96 5.11.1 Identification of Disease clusters in the Punjab from 1990 to 2005 97 5.12 Summary 113 CHAPTER 6 ANALYSIS OF RISK FACTORS AND HEALTHCARE FACILITIES 6.1 Introduction 114 6.2 Demographic and Socio-economic conditions of TB Patients 115
vii 6.2.1 Demographic Characteristics of TB Patients 115 6.2.2 TB and illiteracy 119 6.2.3 Economic Conditions ofTB Patients in the Punjab 120 6.2.4 Tuberculosis and Household Income 124 6.2.5 Tuberculosis and Smoking 127 6.2.6 TB Patients and the number of family member Infected 127 6,2.7 Knowledge about Symptoms, Precautions, and Duration of Treatment 127 6.2,8 Tuberculosis and Diet 131 6,2.9 Duration of Treatment and Patient’s satisfaction and trust 131 6.2.10 Drug Intake Missed 131 62.1 1 Tuberculosis and awareness campaign by NTP 132 6.3 Analysis of Healthcare Facilities 132 6.3.1 Healthcare service centers in the Punjab for TB Patients 133 6.3.2 Spatial Distribution of Healthcare services in the Punjab 134 6.3.3 Efficiency and Effectiveness of Healthcare services 143 6.3.4 Accessibility to Health services 149 6.3.5 Spatial Distribution and Inequalities in Health servicesl55 6.3.6 Lorenz Curve and Gini Index 157 6.4 Summary 168 CHAPTER 7 SUMMARY AND CONCLUSION 7.1 Summary and Conclusion 169 7.2 Suggestions 174 7.3 Further Research 175 REFERENCES 177 APPENDICES 195 A. Questionnaire for TB Patients 196 B. Questionnaire for Healthcare Professionals 199
Yin LIST OF TABLES
Tabic Title Page 4.1 Estimated TB burden in 22 countries with highest TB burden in the world 45 5.1 Tuberculosis cases and SMR in the Punjab, 1990 54 5.2 Tuberculosis cases and SMR in the Punjab, 1995 59 5.3 Tuberculosis cases and SMR in the Punjab, 2000 64 5.4 Total Number of Tuberculosis Cases{All Forms) Notified in Punjab 2005 70 5.5 Tuberculosis cases and SMR in the Punjab, 2005 71 5.6 A Comparison of SMRs in the Punjab, from 1990 to 2005 76 5.7 Proportion of Smear Positive cases among total patients 80 5.8 Total deaths among TB patients and Standardized Mortality Ratio 85 5.9 TB and Gender differences in the Punjab in 2005 89 6.1 Age groups of TB patients in the Punjab 115 6,2 Age and Sex Composition and Family Size of TB Patients in the Districts of the Punjab 118 6.3 Monthly Household income of TB patients in the Punjab 125 6.4 Diagnostic and Treatment centers for TB Patients in the Punjab 135 6.5 Number of outdoor doctors and patients at health care centers in the Punjab 143 6.6 Ranking the funds and facilities of DOTS program by Healthcare professionals 144 6.7 Dissatisfactionofhealthcare professionals regarding treatment regimen recommended by DOTS Program at healthcare centers in the Punjab 147 6.8 Travel time taken by the tuberculosis patients for one way trip to access to health care centers in the Punjab 151 6.9 Cost per visit for the tuberculosis patients to access to health care centers in the Punjab 154 6.10 Distributionand inequality of health facilities in the Punjab 156 6.11 Cumulative Proportions of Population and Healthcare Facilities in the Punjab 2005 159 6.12 Cumulative Proportions of TB Cases and Healthcare Facilities in the Punjab 2005 162
ix 6.13 Gint Index of the Inequality in Total Population and Health Services in the Punjab 165 6.14 Gini Index of the Inequality in TB Cases and Health Services in the Punjab167
x LIST OF FIGURES Figure Title Page
Figure 1.1: Study Area: Punjab province in Pakistan 06 Figure 1.2: Map showing districts in the Punjab province 07 Figure 4,1: Estimated global prevalence, mortality and incidence rates, 1990-2005 44 Figure 4.2: Estimated TB incidence rates in the world in 2005 46 Figure 4.3: Estimated number of new TB cases in the world in 2005 47 Figure 5.1: Standardized TB morbidity ratio in the Punjab in 1990 55 Figure 5.2: Distribution of population and notified cases in the Punjab in 1990 56 Figure 5.3: Standardized TB morbidity ratio in the Punjab in 1990 57 Figure 5.4: Standardized TB morbidity ratio in the Punjab in 1 995 60 Figure 5.5: Distribution of population and notified TB cases in the Punjab in 1995 6! Figure 5.6: Standardized TB morbidity ratio in the Punjab in 1995 62 Figure 5,7: Standardized TB morbidity ratio in the Punjab in 2000 65 Figure 5.8: Distribution of population and notified TB cases in the Punjab in 2000 66 Figure 5.9: Standardized TB morbidity ratio in the Punjab in 2000 67 Figure 5.10: Standardized TB morbidity ratio in the Punjab in 2005 72 Figure 5.11: Distribution of population and notified TB cases in the Punjab in 2005 73 Figure 5.12: Standardized TB morbidity ratio in the Punjab in 2005 74 Figure 5.13: A comparison of SMRs from 1990 to 2005 76 Figure 5.14: Proportion of Smear positive patients among total notified TB cases 81 Figure 5.15: SMR smear positive TB cases in the Punjab in 2005 82 Figure 5.16: Standardized TB mortality ratio in the Punjab in 2005 86 Figure 5.17: Standardized TB mortality ratio in the Punjab in 2005 87 Figure 5.18: Proportion of male and female TB patients in the Punjab in 2005 92 Figure 5.19: Disease sex ratio in the Punjab in 2005 93 Figure 5,20: Disease control targets and achievements in the Punjab 95
xi Figure 5.21: Clusters of Tuberculosis in the Punjab in 1990 101 Figure 5.22: Clusters of Tuberculosis in the Punjab in 1995 105 Figure 5.23: Clusters ofTuberculosis in the Punjab in 2000 109 Figure 5.24: Clusters of Tuberculosis in the Punjab in 2005 112 Figure 6.1: TB patients: Employed, Unemployed and housewives 122 Figure 6.2: Percentage of patients employed in different sectors of economy 123 Figure 6.3: Monthly household income of TB patients 126 Figure 6,4: Knowledge about precautions and duration of treatment 130 Figure 6.5: Distribution of healthcare facilities in the Punjab in 2005 141 Figure 6.6: Ranking funds and facilities provided by DOTS program 145 Figure 6.7: Satisfaction of healthcare professionals with treatment regimen in the Punjab 148 Figure 6.8: Travel time to access health services in the Punjab 152 Figure 6,9: Lorenz curve showing population and health facilities in the Punjab in 2005 160 Figure 6.10: Lorenz curve showing TB cases and health facilities in the Punjab in 2005 163
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CHAPTER 1
INTRODUCTION
The application of concepts and techniques of the discipline of geography to health related issues is considered as medical geography (Meade & Earickson 2000). The geographical approach to study disease patterns is very effective to identify the population at risk and potential geographical factors associated with the disease. The spatial distribution of diseases has been studied from ages. The earlier accounts of the relationship between disease and the local environment arc attributed to Hippocrates (480 BC) who focused on the way of life of individuals and portrayed the effect of winds, waters and soil, and the location of the cities in relation to the Sun on the occurrence of disease (Hino et al., 2005; Light 1944). In modem times, Snow (1854) identified the Broad Street pump as the source of an intense cholera outbreak in London through a dot map by plotting the location of cholera deaths and the water pumps (Nobre & Carvalho, 1995). Nowadays, spatial variations in disease rates are well known and their study is a fundamental aspect of epidemiology, sociology and medical geography. The representation of maps of disease data is a basic tool in the analysis of regional variation of diseases (Maheswaran & Craglia, 2004). The geographical and statistical techniques are applied to find the probable clusters of disease. The next step from the perspective of geography is to study the demographic, socio-economic and environmental factors which might be associated with disease. In the recent years, the addition of analysis of health services with reference to access and equity has not only broadened the scope of medical geography but also completed the picture of disease incidence analysis and control on regional basis. The combination of all these analyses were designated as the holistic 'geography of health’ which has gained enormous fascination recently (Kearns & Moon 2002; Pearce 2003; Brown et al. 2010). All these aspects are being combined in the present study which covers following four themes: Disease mapping is the first step to understand and portray spatial distribution of health and disease related problems. The maps provide a quick visual summary of complex distribution and path of disease, highlight the areas of high risk, and provide the basis for an effective policy of health resource allocation. 2
Disease clustering investigations may prove useful in analyzing whether the observed events follow any systematic pattern as opposed to being distributed at random over the study region. Cluster of disease may occur in space, in time or in both. Clusters may be defined as the foci of particularly high incidence (Marshall, 1991). Disease clustering identification is important in public health surveillance, where it is vital to assess whether a disease map is clustered and where the clusters are located. Disease clustering investigations might be used to generate ideas and hypotheses regarding disease etiology, but perhaps also to calm public fears of a local excess. Disease clustering investigations are considered more significant in case of outbreaks and infectious diseases (Sabel & Loytonen 2004). The nature of the environment and socio-economic status can be influential both for infectious and non infectious diseases. In the former, it can provide ideal conditions for diseases to flourish. In the latter, it can be harmful or be deficient in the elements needed for healthy living (Lloyd, 2002). It is of great significance in the analysis of the geographical distribution of disease in relation to explanatory covariatcs, usually at an aggregated spatial level. Many issues relating to disease mapping and clustering have their roots in the ecology of disease and to the incorporation of covariates which may be demographic, social and environmental in nature. The geography of healthcare is a new field of study as compared to disease mapping and disease ecology. Meade & Earickson (2000) described that healthcare studies focus on spatial distribution of healthcare centers, their accessibility, utilization and planning, inequity is another issue which is equally important with reference to service providers and consumers. Disease control targets are difficult to achieve without a proper planning of resources. The planning is necessary in the provision of facilities and resources at health service centers and also to establish new healthcare centers to maintain equity among the areas and to focus the areas which have a dire need of them. In the present research, the analysis of healthcare is carried out by studying the available resources at the centers, the effectiveness of these resources and centers, and to determine whether there is equity among the districts of the Punjab in the provision of healthcare services. Thus the present study is an attempt to identify the spatial patterns of tuberculosis in the Punjab from 1990 to 2005 through disease mapping and analysis, to highlight the ureas which might be considered as disease clusters, to analyze the risk factors 3
associated with tuberculosis, and to examine the effectiveness and inequality in the provision of healthcare resources for TB patients in the Punjab. 1.2 Research Background Tuberculosis (TB) is a chronic infectious disease caused by mycobacterium tuberculosis. TB remained one of the largest causes of death from infectious diseases worldwide. Tuberculosis (TB) is primarily an illness of the respiratory system and attacks the lungs, which may also affect the kidneys, bones, lymph nodes, and brain. It is a disease which accounts for high morbidity and mortality rates worldwide. Each year, 8 million people develop active TB and 3 million die throughout the world. According to WHO estimates Pakistan ranks sixth amongst 22 countries with highest tuberculosis burden, and contributes 43% of disease burden in Eastern Mediterranean Region (WHO Country office in Pakistan 2007). More than 250,000 persons acquire active TB disease in Pakistan every year, about two third of which belong to productive age group (WHO, 2006), Thus, to study the spatial patterns of disease, tuberculosis is chosen for study due to its higher incidence in Pakistan. TB is contagious and spreads through the air, if not treated, each person with active TB infects on average 10 to 15 people each year. TB is a disease of poverty, deprivation and social disadvantage; virtually all TB deaths occur in the developing world, affecting mostly young adults in their most productive years, TB especially affects the most vulnerable, such as the poorest and malnourished (WHO, 2006). Half of all untreated TB cases are considered fatal (Padilla, 2006; Heinsohn, 2004). In such a horrifying situation, it is the need of the hour to study the disease from every aspect of medical, social and environmental sciences. Research from the perspective of medical science is satisfactorily conducted by a number of medical research centers, institutions, councils and medical universities on disease etiology and pathology. But the spatial, demographic and social issues are never addressed properly in Pakistan to study the spatial pattern of tuberculosis. This particular research is aimed to study the distribution, patterns and trends of tuberculosis (TB) and to identify the fragile regions where the disease is causing high level of devastation as being clustered. The present research also covers the study of socio¬ economic and demographic factors associated with disease and an analysis of the provision of health services to control TB. 4
13 Aim of the study Research in the field of Medical Geography is very sparse in Pakistan, However recently some scholars have conducted spectacular work in medical geography in Pakistan (Kazmi 2001; All et al 2004; Anne et at 2009; ) and very few studies with a focus on tuberculosis at a local and smaller scale (Khan cl al. 2000; Hussain et al. 2003), However there is still a great room for the work on national, provincial and district level. The present study is being conducted to provide a general picture of the spatial patterns of tuberculosis in the Punjab from 1990 to 2005 at a provincial level, to examine the possible clusters of disease, to study the socio-economic conditions and risk factors associated with TB, and an analysis of health services provided for the patients. L4 Research Objectives The proposed research is designed to cover four themes: disease mapping, disease clustering, analysis of risk factors, and the analysis of health services. These four themes are at the heart of medical and health geography. To cover these themes and to conduct this research following four objectives have been established:
> To explore disease mapping and spatial analysis of tuberculosis in the Punjab; > To examine spatio-temporal patterns and clustering of disease; > To analyze the spatial patterns of disease with respect to demographic and socioeconomic factors; To map health services and to study the efficiency, effectiveness and equity of the provision of health facilities for tuberculosis; 1.5 Study Area The study area of this research is the Punjab province of Pakistan. Punjab lies between 27*.40' to 34*.0l' north latitudes and 69\20' to 75\20' east longitude. It is the most densely populated province of Pakistan with 56 per cent share in the total population of the country. Punjab is the second largest province of Pakistan having an area of 205,344 square kilometers which constitutes about 26 per cent of the total area of Pakistan. The province consists of 8 administrative divisions and 35 districts (Punjab Government, 2006). The number of districts was increased from 34 to 35 in July 2005 by declaring Nankaana as a new district. But the data about population and disease is not available for Nankana as a separate district for the year 2005 so the 5
maps, data, and analysis used in this research would be based on the administrative division of the Punjab as was in June 2005 i.e. 34 districts. The Punjab province had a total population of 73,621,000 with the population density of 359 persons per kilometer according to 1998 census (Government of Pakistan, 1998) while according to the estimates of 2005 the total population of the province is 86,084,000 (Punjab Government, 2006). The climate of Punjab is continental with significant temperature fluctuations both seasonal and diurnal with significant aridity. Aridity and continentality are the salient features of this area. Excluding the northeastern parts the rainfall is Jess than 10 inches and is concentrated in four months, June to September. The annual range of temperature is high over the whole area (Ahmad 1951). The temperature decreases from south to north. Monsoon, in summer, is the major cause of the most of rainfall in the Punjab. The rainfall is heavier in the northern and north eastern part of the province which gradually decreases towards south. The winter rainfall is the result of western disturbances, which is heavier in northern and western areas and decreased towards south and east. In Punjab 69% households live in houses with 2 to 4 rooms while 25 % are living in houses with only one room. Only 6% households have 5 or more rooms (Government of Pakistan 2006). Literacy rate in the Punjab is 46.6% according to 1998 census. It is estimated to be 55.2% in 2005-06. The literacy rate is higher among males (65%) as compared to females (44%) in the Punjab during 2005-06 (Bureau of Statistics 2007). According to the data for the year 2005, there are 308 hospitals and 41 T.B clinics which overall contain 47,469 beds in the Punjab (Government of the Punjab, 2007), The total number of healthcare centers in the Punjab particularly for TB patients, where DOTS program has been implemented, is 451 which include hospitals, TB clinics and centers, Rural Health Centers and basic health units also. 6
Study Area: Punjab province in Pakistan
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milt#OHUI Kl*n / r \J i ,! fUJanpw cry , > / # 0 100 200 300 \ Km - 1:7 500 000
Figure 1.1 Study Area: Punjab Province District Map of the Punjab Province
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Cnaiwai Jneluiti Mianwaii Gujr fd Staflffli Kfxishab, Sargotfia Narcwd Bhatfor Shefchupura Jfiang XAFaiiaiaoaa layyafi Kasur Ckans fSarÿsi GtWi Khanewal ‘afcpatran venari Rajanpu- Bahawa-pur Rahim Yar Khan' 50 25 0 50 100 150 200 250 Kilometers 1:5.000.000 figure 1.2 Map showing districts in the Punjab Province 8 1.6 Dissertation Structure Chapter 1 presents a general introduction to the present research, four themes in this research, research objectives, and background. A general introduction of the study area (Punjab province) is also included in this chapter. Chapter 2 gives an account about methodology and data sources. It describes both primary and secondary data sources which are being used for the present research. There are also illustrated different cartographic and statistical techniques to achieve the objectives mentioned in the first chapter, Chapter 3 is concerned with the review of relevant literature. It provides an insight into different studies and researches conducted in the field of medical geography with special reference to disease mapping, disease clustering, analysis of demographic and socio-economic factors, and the study of health services regarding tuberculosis. Chapter 4 focuses on the introduction of Tuberculosis and its etiological aspects. It also provides information regarding the general geography of the study area i.e, Punjab province. There are also highlighted the efforts by World Health Organization (WHO) to control this disease with the help of DOTS strategy. In Chapter 5, there are shown the spatial patterns of disease in all the districts of the Punjab from 1990 to 2005 using secondary data. The disease rates are compared and the disease sex ratio is calculated. In the second section disease clusters are being identified and shown on maps using statistical and cartographic techniques and software. Chapter 6 is concerned with the relationship of disease with the demographic and socio-economic factors. The issues related to accessibility, cost, and the facilities provided at healthcare centers are also mentioned in this chapter. The second section of this chapter presents an analysis of equity among the districts of the Punjab regarding healthcare services. Chapter 7 provides a summary of the research and the conclusion which are drawn from this study. There are also described suggestions in the light of the conclusions and the way forward for further research, 9 CHAPTER 2 DATA SOURCES AND METHODOLOGY 2.1 Introduction The present research which is designed lo study the spatial patterns of tuberculosis in the Punjab encompasses the disease incidence patterns, disease mapping, and identification of disease clusters, the relationship of socio-economic factors with TB, and an analysis of health facilities with reference to tuberculosis. Both primary and secondary data collection methods and a variety of analytical techniques arc used to cover all these issues. To study the disease patterns of incidence and clustering, secondary data is being used from year 1990 to 2005. For the purpose of analysis and comparison, a five year interval was selected and the data for the years 1990, 1995, 2000, and 2005 were collected* The data about the number of TB patients in districts is collected from various government agencies and departments which are briefly discussed in data collection section. As the districts vary greatly in terms of total population; standardization technique was used to study disease patterns and to compare the districts. The clustering of disease was identified using KulldorfTs spatial scan test. Then to study socio-economic conditions and demographic characteristics of TB patients, their knowledge and behavior about disease, and to analyze the accessibility to health services, primary data was collected through a survey conducted in all 34 districts of the Punjab, In this survey, patients suffering from tuberculosis were interviewed with the help of a structured questionnaire. Another questionnaire was used (o gain information regarding drug and diagnostic facilities provided at healthcare centers and lo judge the effectiveness of treatment regimen for tuberculosis. 2.2 Data collection To achieve all the objectives of this research which are mentioned in the previous chapter, both primary and secondary data was collected, 2.2.1 Primary data Primary data was collected from the patients suffering from tuberculosis and health service professionals through structured interviews and questionnaire. A total of 1020 10 TB patients were interviewed in all 34 districts of the Punjab province on selected health service centers. The respondents were randomly selected from different health centers with a total of 30 respondents from each district. The questionnaire, to be filled by interviewing TB patients, developed in such a way to cover personal. demographic, socio-economic and a number of disease related aspects along with the accessibility options. The first part of the questionnaire consists of questions related to age, gender, marital status, literacy, employment status, income, household size, and the number of rooms in house. Next section of the questionnaire deals with disease related variables like smoking habits, knowledge of disease symptoms and adoption of precautions, disease status, drug intake, sharing of blankets and utensils, and the sources of information for the patients about the disease control and to prevent the spread of disease. The third section of the questionnaire contains questions to analyze healthcare services, facilities and accessibility. Another questionnaire survey was conducted to get information from health service professionals about the facilities provided at health centers and to analyze the effectiveness of treatment strategy being employed by the National Tuberculosis Control Program (NTP), Government of Pakistan, in collaboration with World Health Organization. The information was collected from all 451 hospitals, TB centers, rural health centers and dispensaries throughout the Punjab province which are registered by NTP, Government of Pakistan under Directly Observed Treatment Short course (DOTS) strategy. 2.2.2 Secondary' data Secondary data used for this research is related to the number of TB patients during 1990 to 2005, percentage of smear positive patients, population data for all the districts of the Punjab for the purpose of standardization, and health service centers located in the province. The data regarding the number of TB patients in the districts was collected from three departments: * Office of the Director General Health Punjab • Health Management Information System Punjab (HM1S), office of the Director General Health Punjab • National Tuberculosis Control Program (NTP), Ministry of Health, Government of Pakistan The data regarding the number of Tuberculosis patients for year 1990 and 1995 was taken from the office of the Director General (Health) Punjab. The said office collects n data through District Health Officers who are responsible to collect data from all the hospitals, TB centers and rural health centers from their respective districts. The data is then compiled for whole of the Punjab province in the office of the Director General (health) in the provincial capital. In 1997, for the purpose of improvements in data compilation and analysis, Health Management Information System (HMIS) was introduced by the Government of Pakistan in the Punjab province. The data which is being used in the present research for year 2000 regarding TB patients was obtained from HMIS. World Health Organization (WHO) started DOTS strategy in Pakistan in 1995, Systematic recording and reporting of TB patients and data compilation is one of the five key elements in DOTS strategy which is being managed in Pakistan by National Tuberculosis Control Program, Government of Pakistan. The data for the year 2000 under DOTS was limited to very few districts; however, in year 2005 as the DOTS coverage was 100% so a complete range of data was available. The data in 2005 has more details regarding smear positive cases, mortality, and gender which facilitated further analyses. The application of standardization techniques for analyzing disease patterns, and comparison among districts requires population data also. For this purpose, secondary data for the total population in the districts was collected. For year 1990, the data regarding total population was obtained from Punjab Development Statistics 1991 published by Bureau of Statistics, Government of the Punjab. In 1998, population census was conducted in Pakistan; so for the purpose of standardization for year 1995 and 2000, mid year 1998 population census data was used. For the year 2005 estimated population counts was used provided by NTP, Government of Pakistan. The data about health service centers in the Punjab was also collected from the same agency. For the purpose of comparison the overall data about the demographic and socio economic conditions of the Punjab and Pakistan was collected from Population census reports, Statistical yearbooks, Pakistan Demographic Survey and the websites created by government of Pakistan and other reliable agencies. 23 Methodology 23.1 Data Analysis and Visualization As mentioned earlier, the present study focuses on the analysis of patterns of disease and disease mapping, disease clustering, analysis of demographic, socio-economic, 12 and other disease related factors, and the analysis of health services distribution, facilities and inequalities. The data collected from both primary and secondary sources was used to carry out these analyses. First of all, disease patterns were identified in the districts of the Punjab from 1990 to 2005 by comparing disease rates. Then the districts with higher clusters of disease were detected using geographical techniques. The patterns of the rate of disease and clustering were shown on the maps using ArcGIS software version 9.2. The next step was to study the factors which were associated or influenced the occurrence and control of the disease. The final step was the analysis of health care services for TB patients which was conducted by studying the diagnostic and treatment facilities available there and to find the accessibility and equality of the areas in health service provision. The detailed account of different techniques and methods which were used is as follows: 2.3.2 Standardized Mortality and Morbidity Ratio To study the patterns of disease, the maps which show only the recorded cases of disease are not useful without keeping an eye on the population from which these cases arise. Lawson & Williams (2001) suggested a comparison of observed cases to expected counts that are in accordance with underlying population is a good measure to study disease variation. This is usually known as Standardized Morbidity Ratio (SMR). Similarly standardized mortality ratio can also be calculated with observed and expected counts of deaths. In the present research the area under study is the Punjab province which comprises 34 districts in 2005. The total estimated population of the districts range from slightly less than one million in district Hafizabad to 7.3 million in Lahore in 2005. There is also a great variety in the number of tuberculosis cases in each district. Thus standardization is an ideal technique to compare districts with each other and to the whole of the Punjab province. The data for the years 1990, 1995, 2000, and 2005 were analyzed using the following formula to calculate Standardized Mortality Ratio (SMR) for each district. o(/) SMR(i) = m 0(i) is observed number of cases in a given period of time in area i. (i=l,2,3....n) 13 E(i) = 0*N(i) And ±o where E(i) is expected number of cases in district / N(i) is the total population in district i 0(j) is the observed number of cases in the districts of Punjab province N(j) is the totai population in all districts of the Punjab province As SMR is a ratio between observed and expected occurrences in an urea so the SMR for a district equal to l implies that it has exactly the same rate of TB cases as in the overall Punjab province. The district having an SMR value greater than 1 has higher notified TB cases. When the SMR value crosses 2 it entails that the district has observed TB cases more than two times than the rate for Punjab and vice versa. Thus the districts are being compared with the Punjab province as well as with other districts. Similarly standardised mortality ratio is being calculated however the data regarding the number of deaths of TB patients is available only for year 2005, Thus the spatial patterns arc being identified by comparing the rate for districts for selected years and displaying on the map of Punjab province for the purpose of visualization, 2JJ Disease Sex Ratio The method of disease sex ratio was devised to find the gender differences among TB patients in the Punjab. Sex ratio is defined as the ratio between males and females in a human population (Chaudhry 1996). It is calculated by dividing the number of males in a population by the number of females in the same population and the result is expressed in percentage. Disease sex ratio is defined as the number of male patients to every 100 female patients. It is calculated in the following way 14 Total number of maleTB patients Disease sex ratio *100 — Total number of female TB patients A value of disease sex ratio greater than 100 implies that there are more male patients than females while in case of disease sex ratio less than 100 would reveal that the female patients arc more in number than male patients. 23.4 Disease Clustering Clusters of disease are the foci of particularly high incidence. There are a variety of techniques to identify clusters of high incidence such as Pearson’s chi-square statistic, K function, Openshaw’s GAM and Kernel estimates (Sabel & Loytonen 2004). In the present research, to identify the clusters of tuberculosis patients in the Punjab, Kulldorffs Spatial Scan Statistic is being used. This method is particularly gaining increasing attention in recent studies of disease clustering in developed countries (Sabel & Loytonen 2004). The method has been used in a number of studies to identify the clustering of events (KulldorfTf 1997; Sohel et al. 2010; Robertson & Nelson 2010). This method is a significant one to analyze area based data adjusting for a heterogeneous population density among areas. It is used to detect clusters in a temporal, spatial and space-time setting. The idea behind this method is to impose a circular window on the map and lets the center of the circle move over the study area so that at each position the window includes different sets of neighboring administrative areas (Sabel & Loytonen 2004). The software which is being used to perform this test is SaTScan 7.0.3. This software was developed by Martin Kulldorff to analyze spatial, temporal and space-time data using the spatial, temporal, or space- time scan statistics (Kulldorff 2006). To identify most likely and secondary clusters of TB cases in the Punjab, number of TB cases, total population, and coordinate files were used as input in SaTScan. The analysis was ‘Purely Spatial* using ‘Poisson Probability Model’. Such analyses were performed for the Punjab province For all the selected years. The primary and secondary dusters in the Punjab province were shown on maps using ArcGIS9.2. To understand the relationship of different socioeconomic and demographic factors with the disease, correlation technique was used. The effectiveness of the efforts of National Tuberculosis Control Program (NTP) regarding the drugs, diagnostics, and media campaign for the awareness of the masses were judged by calculating proportions and percentages. 15 2.3.5 Analysis of Healthcare Facilities One of the most important investigations in this research was the analysis of healthcare facilities for tuberculosis patients. This analysis has two aspects: firstly, to identify the facilities, effectiveness, and accessibility to these healthcare centers; and secondly, to evaluate the equity of healthcare facilities in different districts of the Punjab. The primary data, collected from the patients and healthcare professionals provided information regarding the available facilities and the effectiveness of drug regimen. The accessibility to health services was measured using the data for travel time and cost per visit. Travel time is being used as a measure as the patients don't know exactly the distance in kilometers and they travel using different modes of transportation. The inequality study was conducted using Lorenz curve and Gini index. Lorenz curve and Gini index are closely associated with each other and normally used in combination with each other. Lorenz curve is a graphical representation of cumulative measures of phenomenon or objects. It is a rank order of groups or individuals with the most disadvantaged on the left hand while the y-axis shows the cumulative proportion of health service centers. The straight line in the graph is considered the line of perfect equality while the curve depicts the departure from equal distribution of services to the population. The further the Lorenz curve is from equality line, the greater the degree of inequality. The area between the curve and the line of equality expressed as a proportion of the total area beneath 45° degree line of equality provide a mathematical value to measure inequality which is called Gini index. It can take a value from 0 (perfect evenness) to 1 (maximum possible unevenness) and provides a standardized value to reflect the relative unevenness of distribution. The mathematical formula used to find Gini index is: N o=i-2(orw+d;X<*M-«0 i-0 Where aX and oY are cumulative proportions of values on X and Y axis and N shows the number of observations. These techniques were used to compare the number of healthcare facilities with the proportional population and the number of patients in the districts of the Punjab. 16 2.4 Limitations of Data In the present research the disease patterns are being identified in whole of the Punjab province on district level. The aggregate number of TB patients and other data related to TB is only available on district level but not on smaller units like Tehsils and towns. The implementation of DOTS by the National Tuberculosis Control Program with the collaboration of World Health Organization (WHO) achieved the goal of 100% coverage in the Punjab in 2005. Data collection and maintenance is one of the five elements of DOTS strategy. So the data regarding the number of male and female patients, cure rate, case detection rates, and the proportion of smear positive and smear negative patients is only available for the year 2005. Health Management Information System (HMIS) maintained data before the implementation of DOTS Program but that were the total number of patients without any oilier details. So the detailed analysis of smear positive and gender differences male and female are only available for the year 2005. Another limitation regarding the data is the non availability of data from some private hospitals and clinics as they don’t maintain and provide concerned data. Thus the combination of primary and secondary data and all the methods mentioned above presented a general picture of incidence and clustering of tuberculosis in the Punjab from 1990 to 2005; socio-economic and other related factors associated with the disease, and a detailed analysis of effectiveness and equity in the provision of health services. Sources of Data Collection Primary Secondary Data Data Field Office of DG Health Managment TB Survey Information System DOTS Health Punjab (HMIS) Program I I Total number of Total number of TB Healthcare TB Patients in the TB Patients in the 1095 Total number Patients Professionals Punjab in 1990 & Punjab in 2000 Information of about healthcare TB Patients centers fo TB in the patients in the Punjab Punjab in 2005 Information about age, sex, smear positive, Mortality, and Case detection rate Data Analysis Disease Disease Analysis of Socio-economic Analysis of Mapping Clustering conditions and Risk Factors Healthcare facilities Spatial Distribution Kulldorff Statistical techniques Disease TB of TB Patients in the Spatial Scan e.g rates, ratio, & ratio Mortality Punjab 1990-2005 sex Statistic correlation * r ' f Standardized Standardized Spatial distribution Accessibility of Drug, Diagnosis, Equity of of healthcare services healthcare & treatment healthcare morbidity ratio mortality ratio in the Punjab services facilities services T T T I Lorenz Curve Data analysis Tables, Graphs, Maps: using Microsoft Word, & Gini Index » & visualization Excel, Satscan, Map Info, and ArcGIS 19 CHAPTER 3 LITERATURE REVIEW 3.1 Medical Geography Medical Geography uses the concepts and techniques of the discipline of geography to investigate health-related topics. It is an integrative, multistranded subdiscipline that has room within its broad scope for a wide range of specialist contribution (Meade & Earickson 2005). The integration of the history and geography of diseases is essential to provide a true picture of epidemiology and disease patterns as they are today. May (1952) defined medical geography as “the study of the distribution of manifested and potential diseases over the earth ‘s surface and of factors which contribute to disease (pathogens)followed by the study of the correlations which may exist between pathogens and environmental factors (geogens) ”. McGlashan (1972) explained medical geography as a "borderline discipline," referring to the conceptual overlap between geographic approaches to explaining health problems and medicine as a science. Medical Geography is also defined as; the study of geographical aspects of health and the provision of health care. It covers the study of the spatial distribution of human disease and causes of death, together with the factors of the environment conductive to human health and sickness (Clark 1985). John & Michael (1995) described medical geography as “the study of the spatial incidence of disease, morbidity and mortality; the environment as it effects human health; and the spatial organization of health care the provision of medical centers, clinics, hospitals etc”. According to Goodall (1987) medical geography is the study of human health, including the provision of health care. It covers the description of the spatial distribution of morbidity and mortality and considers possible causative relationship between sickness, disease and death and local variation in environmental conditions. De Blij & Murphy (2003) considers medical geography as the study of health in a geographic context. Many diseases have their origin in the environment They have source areas, diffuse through populations along identifiable routes, and form clusters within the populations. Mapping disease patterns can produce insights into relationships between disease and environment. Association between natural 20 environments and contagious diseases are of special interest to medical geographers. since geography deals with natural (physical) as well as human problems. Medical geographers also concern themselves with the location of health care facilities for people who need them. A more recent interest is with the spatial aspects of the organization of health services, especially the identification of optimum locations for health care facilities. Medical Geography is closely associated with public health and epidemiology as it tries to find spatial epidemiology or incidence of disease. Medical geography is also concerned with actions of the healthcare system in a spatial setting (Gatrell 2002). Medical geography has been acknowledged as a sub discipline in geography since the 1950s. (Meade and Earickson 2005). A synthesis of all the above definitions shows that there are two main areas of study within the subdiscipline of medical geography. The first is concerned with disease patterns and mortality rates and the relationship between ill-health and environment. The second stream addresses the location, accessibility, and utilization of health services. The first branch has been referred to as 'geographical epidemiology’, ‘geographic pathology', ‘disease ecology’, or ‘disease geography’ (Mayer 1982; Kearns 1995; Pringle 1996; Kearns and Moon 2002) while labels for the second tradition include ‘health care geography’, ‘geography of medical care*, ‘geography of health service provision’ (Jones and Moon 1987; Pringle 1996; Kearns and Moon 2002). The present research on spatial patterns of Tuberculosis is an attempt to cover these two areas of medical geography. One of the prime concepts of geography as a science is that it is concerned with the relationship between people and their environment. In the specific subfield of medical geography the interactions between human health within a variety of cultural systems and a diverse biosphere are brought into spotlight. As far as geographic method is concerned, the main tool is spatial analysis (Arbona & Crum 2007). The differences in disease rates among the areas indicate causation and the effect of environment on disease and health. The variation in different aspects of disease distribution and causation provide the basis for public health planning. Meade & Earickson (2005) described that medical geography is both an ancient perspective and a new specialization. As illustrated by the quote from Hippocrates (460-377 B.C.), he was familiar with the importance of cultural-environmental interactions more than 2,000 years ago. This ecological perspective on disease and health continued to be philosophically important, even dominant, until the emergence 21 of germ theory. Leeuwenhoek’s invention of the microscope and subsequent discovery of microbes created a basis for theories on disease causation (Brachman 2003), Eventually, Robert Koch’s announcement of the germ theory of disease in the late nineteenth century caused a radical change in medicine and the study of disease in all disciplines. However, the idea of pathogenic places remained prominent in popular thinking for many decades {Meade and Earickson 2000). Still today, medical geography and disease mapping tries to determine the relationship between environmental factors and disease patterns even when investigating infectious diseases (Jones and Moon 1991). In the 19th century came the discoveries of Louis Pasteur heralding a whole new science of bacteriology and immunology. Medical geography had to face a ban-age of criticism regarding its scope as the notion prevailed that the diseases are caused by germs and there was hardly any scope left for environment -disease interaction. Despite this, some scholars continued to work in medical geography, including a French Leon Poincare, who published his paper entitled Prophylaxis and medical geography, in 1884 with emphasis that even after Pasteur’s germ theory, the scope of medical geography will not be undermined. Poincare argued that medical geography is far from being a novelty. To combat a disease successfully, data are needed, the most important ones being provided by geography; to protect ourselves from an enemy, we must first of all know of its location, the site and extent of its permanent territory, the areas it would most frequently invade, the areas it could threaten, its possible routes and stopping places and, finally, the weaker border spots to be defended. It can be said that the strategy of prophylaxis must be based on the knowledge of medical geography (Izhar 2004). In modem times, John Snow identified the Broad Street pump as the source of an intense cholera outbreak in London in 1854 by plotting the location of cholera deaths on a dot-map (Nobre & Carvalho, 1995). With the passage of time the data collection techniques were improved and with the help of modem techniques and sophisticated computer technologies paved the way for a better understanding of patterns of diseases, interaction of ecological risk factors and the location and effectiveness of health facilities. 22 3.2 Development of Medical Geography Medical geography has been said to have its origins in Hippocrates' treatise on airs, waters, and places, more than 2000 years ago. In his work, Hippocrates pointed out that the effects of seasons, winds, water, ground, as well as the life and lifestyle of the inhabitants should be considered while studying the health of individuals (Light 1944; Pringle 1 996; Meade and Earickson 2000}. Ltyods (1978) observed that the treatise is a manual whose chief purpose is to help the itinerant doctor to anticipate the different types of diseases that are likely to occur in cities with different geographical and physical conditions. According to Barrett (2000) as the Hippocratic view was holistic; it laid the foundation for the idea that the integrative view observed that where people Jived, and how they Lived, would result in regional variations of disease patterns. So that, by accepting the holistic position and recognizing the role that geography played in disease determination, Hippocrates laid the foundation for medical geography as well as geographical medicine because in establishing the geographical aspects of disease, be also recognized the medical implications of geography. In his book titled Prognosis he described three climatic regions: torrid, temperate, and frigid, studied their geographical conditions and the methods of forecasting diseases. Barren (2000) also reviewed ancient Chinese and Indian medical literature, which revealed that the basic association between disease and geography was believed in both societies. In Chinese medicine the work that most closely parallels the Hippocarlic collection, in terms of fundamental historical importance, is the Huang Ti Nei Ching. Su Wen, and its second part, Ling Shu Ching before 2000 B.C. Lee (1980) comments that in ancient Chinese medicine it was believed that human body is really a reflection of the geography of ancient china. The theory of the Five Elements relates disease symptoms to geographical locations. The five elements are metal, wood, water, fire, and soil. In the Indian ancient medicine Caraka SamhUa and the Susrvta Samhita presented almost the same ideas as expressed by Hippocrates (Barrett 2000). As the power, sovereignty, and development of Greek civilization declined, the knowledge that the Greeks had gained was transferred to other societies. Immediate heirs of Greek medicine were the Alexandrian School in Egypt and Greco-Roman School in Rome, One result of the conquests of Alexander the Great was the founding of Alexandria (331 B.C.) by the Macedonian general Ptolemy. Ptolemy made a particular effort to obtain as many ancient manuscripts as possible for the Great 23 library and especially to gather the most important Greek works from the earlier centuries. From about 156 B.C. onwards, Roman expansion incorporated the Aegean societies into its domain, and with the shift of power, the focus of medical thinking also moved to Rome{Garrison 1929). From the perspective of medical geography, the most significant developments in the Roman period occurred in the three hundred years between the time of Lucretius (99-55 B.C.) and Galen (130-200 A.D.) (Barrett 2000). Among the ideas developed was the observation that geographical factors had a bearing on many diseases. Later the rise and spread of Islam and Arabic scholars contributed much more to medicine and geography. Rhazes (860-932) and Avicenna (980-1037) showed an appreciation of the geography of disease and worked on the selection of appropriate sites for hospitals (Barrett 2000). The period of European history referred to as the Age of Exploration began in the 1400s and extended, depending on the interpreter, into the 1600s or 1700s. During this period in 16th century there emerged works which addressed the relationship of disease and geography. The impact was of two types: (1) accounts of diseases in the new lands that would be a threat to explorers and ultimately colonists and that heightened an awareness of the geographical distribution of disease, and (2) observations about traditional medicine in other lands, and in particular the discovery of substances that could be used as remedies. Jacob Bontius writing in 1629 emphasized that there is a relationship between location, wind direction, and time of day in which the winds blow. In addition, the travelers1 diaries and accounts had also revealed association between location and occurrence of diseases. As described by Barrett (2000) has quoted Francis Drake who considered the disease of malaria due to bad air (mal-aria) which caused hundreds of deaths on his ship. A sequence of the voyages of exploration and exploitation was that the geographical distribution of diseases was changed fundamentally. As Europeans sailed to different parts of the world, they carried with them some of the endemic diseases from Eurasia and when they transported slaves from Africa, provided the room for infections to move to other hospitable regions. The consequence of this great geographical shifting of diseases was profound. As improvements in ship design reduced the sailing time between ports, an increasing number of diseases were transported from region to region. The diffusion of diseases is one the greatest consequences of the European exploration of the world. 24 Between 1705 and 1795, the first medical geographies on a global scale were written. The earliest of the significant works was by Hoffmann (1660-1742). In 1705 he wrote A Dissertation Concerning Diseases Peculiar to Certain People and Regions. His work addresses geographical associations with diseases, nutrition, and medical care, He claimed that excessive use of milk products by the Dutch results in body stones, and that excessive diarrhea in India and Indonesia results from the consumption of ‘Indian fruit’ (HoQfnann 1750). James Lind (1716-94) in his treatise Essay on Diseases Incidental to Europeans in Hot Climate divided the world into four major regions: Europe and North America, Africa, the East Indies, and the West Indies and provided the information about the diversity of geography and prevalent disease conditions in those areas. During 18U| century a number of scientists have put emphasis on this perspective of health as an interaction between environment and man. The potential contribution of geographical methods to the study of disease processes was first emphasized by the pioneer of public health in Europe, Johann Peter Frank (1745-1821) in his book. System emer vollstandigen medizinischen Polizey, published in 1779 (Lawson and Williams 2001). Early development of the discipline of medical geography may further be traced back to medical research and a number of German physicians in the 18th and 19th centuries, e.g, Finke, Schnurrer, Fuchs, and Muhry (Paul 1985), Following the Hippocratic tradition, Finke tried to identify the association between the geographical location of disease and the prevailing physical, social, and cultural aspects of the environment. It cannot be denied that his work deserves the name of Medical Geography (Brown & Moon 2004). During 1792-1795, Leonhard Finke published a book entitled Versuch emer ailgemeinen medicinisch-praktischen Geographic. With this book, Finke was the first person to provide a comprehensive description of medical geography. He divided the globe into a number of zones, each consisting of 10 degrees of latitude. He then described the medical geography for all of the countries within each zone. The descriptions consisted of the geographical position of the country, its soil type, and peculiarities of air, ways of life, customs and habits of the inhabitants, and in particular their food preferences; the range of diseases prevalent and the local treatments adopted (Lawson and Williams 2001; Rosen 1953). Bewell (1996) described that Finke created the first cartographic images of the world divided by disease. The map he made showed the areas of the world according to disease that were endemic and epidemic to each region. Barren (2000) believes and argued that 25 the first disease map of the world was prepared by Finke. He declares that endemic diseases were portrayed first rather than the epidemics and Finke was among the pioneers to identify relationships between disease and geography. He claimed that there was an earlier disease map than Seaman's 1798 spot map of yellow fever in New York and Schnuirer’s world disease map of 1827. Barrett (2000) describes that the first substantive response to Finke’s medical geography was the Schnurrer's book Geographical Nosology or the Theory of the Variations in Diseases in different regions of the Earth in connection with the Physical Geography and Natural History of Man published in 1 813. Some scholars admit Shnurrer’s work as the pioneer efforts in making world map of diseases (Jarcho 1970a; Camerini 1993; Wonders 1996). Others consider that that the catalyst for medical mapping was the spread of cholera from India to Europe (Gilbert, 1958; Jarcho, 1970; Robinson, 1982). It started as an epidemic in the Ganges Delta in 1817 which later diffused to South East Asia and west to Europe as a pandemic. England and Germany were the victims of it by 1831 and later it spread to North America and other parts of the world. The rapid flow and the horrible nature of cholera prompted research in this field and a large number of books as well as maps were introduced on the successive epidemics. Jarcho (1970) identified 36 scholars who published maps of cholera between 1820 and 1836, Thus, the 18"1 to 19th century physicians who first used the term “medical geography" and who struggled in dozens of works to describe and organize the avalanche of new information about human diseases, cultures, and environments, were continuing the holistic Hippocratic tradition (Meade & Earickson 2005). Their descriptions are being rediscovered and reevaluated by geographers and other scientists once again concerned with disease ecology (Barrett, 1980). Draak (2005) described that among the earliest examples of disease mapping are the maps for yellow fever in New York by Seaman and by Pascalis at the end of the 18th century. The first medical map in Germany published by Berghaus in 1847, the map by Rothenburg of cholera cases in Hamburg in 1832, and Acland’s map of cholera cases in Oxford in 1849{Paul 1985; Pringle 1996; Shaw et al. 2002). The most famous work in the 19th century was the studies of cholera carried out by John Snow from 1831 to 1854 in UK, An epidemic of cholera first hit England in late 1831, it was thought to be spread by "miasma in the atmosphere." The disease hit four times between 1831 and 1854. The studies conducted by john snow in 1854 in London revealed that cholera was spread by contaminated water. His ideas were 26 accepted neither by authorities nor the rest of the medical professionals (Frerichs 2007). Snow successfully employed the geographic by mapping the afflicted London water sources in 1854 (Gilbert, 1958; Meade et al., 1988; Schoiten & de Lepper, 1991; Croner et al., 1996; Moore and Carpenter, 1999; Lang. 2000). The clustering of cholera in the vicinity of water pumps supported Snow’s hypothesis that cholera was water-borne disease. This pioneering effort to utilize explanations of disease etiology subsequently led to the widespread use of maps to examine the geographic distribution of disease {Cliff & Haggett, 1988). There is a lot of controversy about all the aspects of the story of Dr John Snow and Cholera outbreak in London (Mcleod 2000). However, almost all the scholars and historians accept that Snow was pioneer to declare that cholera was water borne disease and he showed the cholera deaths due to an outbreak in Broad Street region of London on a map which is also presented in his book entitled lOn the Mode of Communication ofCholera. In 1860s the first German edition of the famous *Handbook of Geographical and Historical Pathology’ of August Hirsch was published (Light 1944). In the closing years of 1850s the French chemist Louis Pasteure (1822-95) and the British Naturalist Charles Darwin (1809-82) proposed theories that had impact on all the way down the ladder of knowledge significantly affecting both medical geography and geographical medicine. (Barrett 2000). Pasteure articulated the idea of Germ Theory of human disease. One of the consequences of the development of this theory was the fading of the idea of study of physical geography to discover the laws of disease and medicine. Although it is considered that with the advent of germ theory, medical geography was discouraged but Barrett (2000) argues that some of his experiments and writings reveal that he believed that there was geography of germs. Dubos (1960) strongly criticized the followers of Pasture who emphasized only one aspect of his leaching, and have lost the view of his broader philosophy of disease causation and to study the environment and the role of the risk factors in the evolution of disease. Later the American Climatological Association was founded in 1884 for the study of climatology and hydrology and of diseases of respiratory and circulatory organs. At the advent of 20115 century Davidson (1892) Haviland (1892) and Clemow (1903) remarkably added to the literature of medical geography. Sigerist (1933) proposed the founding of an international journal and an atlas of distribution of disease. Barrett (2000) identifies three trends in the development of medical geography during the 27 twenty five year period between first and Second World War environmental determinism which focuses that environment controls the human activities; the second trend was development in geographical medicine with foci on geographical pathology and gco-medicine. The third trend was renewed efforts to develop medical geography with foci on traditional methods and concepts of medical geography and a new direction that incorporated discoveries in bacteriology and entomology and considered them from geographical perspective. Since the Second World War, there has been a renewed interest in disease mapping (a ‘renaissance of medical cartography') (Paul 1985). Rodenwaldt and Jusatz published the three volumes of the German Welt-Seuchen Atlas. National atlases of mortality have been produced in the UK, Japan, and the USA (Meade and Earickson 2000}. The American Geographical Society published, under direction of May, the world atlas of disease and between 1952 and 1961, May (1950) constructed a theoretical framework to identify disease patterns by correlating geographical factors (geogens) and pathogens responsible for disease. The atlas of disease was published as a series of 17 cartographic maps between 1950 and 1955. May tried to put in spotlight the cartographic techniques to study disease patterns and urged medical geographers to contribute to WHO global public health initiatives (Brown & Moon 2004). May expressed the common interests of both geographers and medical practitioners when he formulated his conceptual approach and defined these as medical geography (May 1952; 1 958). During the late 1960s the quantitative revolution in geography resulted in an Increasing interest of human geographers in mapping the spatial geometry of observed human activities and existence, which led to ‘spatial science’ (Andrews 2002, Cloke 1991). The linkage between health/disease with G1S and remote sensing began with just a smattering of interest in the 1980s (Donald et al. 2000). Tanaka ei al. ( i 981) compared the changing patterns of population and health facilities distribution in Tokyo suburb between 1965 and 1975. McGlashan (1983) conducted a survey in central Africa of 55 disease and 20 environmental factors that have been associated with diseases. In the late twentieth and early twenty-first century, the tradition continues with cartographic techniques being used to analyze the various infection rates and demographics of the HIV/AIDS epidemic across the world from the African savanna to the American heartland (Meade and Earickson 2000; Smallman-Raynor 1995; Lam and Liu 1994). Thrift et al, (2000) analyzed that spatial analyses such as diffusion studies as conducted by Smallman-Raynor and Cliff, 1998, 1998a; Cliff and 28 Haggett, 1998 proved to be a hallmark of British health geography research (Smyth and Thomas, 1996) as do the investigation of the stability of epidemic modelling systems (Thomas, 1999) and the multi-level modeling of health-related behaviors and outcomes (Shouls et al.t 1996; Duncan et al., 1996; Congdon et al., 1997; Bullen et al,, 1997; Duncan et al., 1998). A variety of techniques from simple ecological mapping to multilevel modeling studies have examined conditions of disease and environmental exposure in late 20th century. Thrift et al (2000) also mentioned that the socioeconomic status and disease rates among different social groups arc also a great concern to which many geographers focused (Haynes et al, 1996, 1997). Empirically, multi-level modeling research has been particularly valuable in focusing on the relative importance of compositional and contextual effects in determining health variations between different geographical areas (Langford and Bcntham, 1997; Duncan et al., 1996, 1998). The history shows that the earlier medical geography was more related to the effect of environment on disease and human behavior in a generalized manner. During 19th century the focus changed from general to specific disease and risk factors as in case of cholera& water. As germ theory was known to the world so the focus was more on factors that contribute to diseases. In recent years disease mapping, spatial analysis, identification of disease clusters, the study of environmental risk factors and the analysis of health care facilities with modem GIS techniques are in spotlight. 3.3 Medical Geography and Geographic Information System Geographic Information Systems (GIS) are computer based systems for the integration and analysis of geographic data. GIS is a computer system for capturing, storing, querying, analyzing, and displaying geospatial data (Chang 2008). GIS is an automated system for capturing, storage, retrieval, analysis and display of spatial data (Van Beurden &. de Lepper 1995; Boelaert et al. 1998). Because of the visual power, GIS maps can become metaphors for the social and environmental conditions that are contained in a geographical space. The role of GIS in public health is potentially great. As computer technology continues to transform our ability to gather, analyze and map health data, new roles of GIS may emerge (Cromely & McLafferty 2002). GIS is particularly well suited for studying the associations between location, environment and disease because of its spatial analysis and display capabilities (Clarke et al. 1996). Recently GIS has been used in the surveillance and monitoring of 29 vector borne disease (Glass et al 1995; Beck et al 1994), water borne diseases (Dangendorf 2002) , environmental health (Martin el al 1991; Briggs 1997), modeling exposure to electromagnetic fields (Wartenberg 1993), quantifying radioactivity hazards in neighborhood (Gardner 1993) analysis of disease control policy and planning (Tempaiski 1994), and health services availability, accessibility and management analysis (Lovett 2004). Sui (2007) described that the past decade has witnessed a wide range of applications of GiS in public health and medical geography. It includes the analysis of the distribution of particular diseases, disease clusters, investigation of disease ecologies, and disease diffusion through time and space. Macro level geographic analyses often help to identify possible causal factors in pathogenesis (Mayer 1983, 1992), Case studies related to infectious diseases include SARS (Lai et al, 2004), AIDS (Khalakdina et al. 2003), cholera (Ali et al. 2002), tuberculosis (Tanser and Wilkinson 1999; Kistemann et al. 2002; Nunes 2007), hepatitis C (Trooskin et al. 2005), and schistomomiasis (Xu et al 2006; Yang et al, 2005). GIS have been applied at prevention, surveillance, and monitoring stages of infectious diseases. Since the early 1990s, the Center for Disease Control and Prevention (CDC) carried out several GIS based analyses to investigate the occurrence of infectious diseases due to environmental factors (Croner et al. 2000). Along with the study of infectious diseases GiS has also been applied to probe Into chronic diseases such as breast cancer (Selvh et al. 1998), prostate cancer (Mather 2006), cervical cancer (Barry and Breen 2004), leukaemia (Badrinath et al. 1999), and leprosy (World Health Organization 2005a, b). Respiratory health risks, pediatric asthma, and their relationship to socio-economic variables have been reported in (he literature (Guidry and Margolis 2005; Kimes et al. 2004; Maaniay 2007). The automated construction of maps resulting from technical advancements makes use of the full range of possibilities offered by GIS (O’Dwyer 1998). GIS allows analysis of data created by global positioning systems (GPS) to provide a powerful tool for the analysis and visualization of areas of high disease prevalence and the monitoring of control efforts. Kistemann (2004) argues that it has now become quite common to apply GIS, sometimes combined with remote sensing to provide ecological data. The introduction of user friendly software for remote sensing and GIS are the ideal tools to collect, organize, and use this large quantity of space-related 30 information. Remote sensing data are increasingly used for disease risk mapping, surveillance or monitoring, particularly of vector borne diseases (Beck et ai. 2000). During the last two decades, efforts to integrate the time dimension into geographical- epidemiological GIS models have been intensified. Peuquet and Duan (1995) used this technique to develop GIS model which integrates the dimension of time by means of an ‘event-based spatiotemporal data model' (Loytonen 1998). It is an interesting task to study the relation between time and space related processes of disease development and thus to find disease patterns. GIS technology must have the potential to improve the integration of the time dimension. Geographic information systems proved to be very effective in the allocation and planning of healthcare services, and particularly in community healthcare needs assessment (Melnick 2002). With the integration of GIS and socio-economic data fiom census, it is possible to study the spatial patterns of health outcomes in relation to the socioeconomic characteristics of diseases, gaps in healthcare provision, and monitoring the impacts of healthcare policies. GIS applications are also found in the study of access to healthcare services (Gesler el ai. 2004; Mitchella et al. 2002). GIS also has the potential to improve the decision making regarding the allocation of resources to facilitate the establishment of preventive health services and to control the burden of disease 3.4 Disease Mapping Mapping is a way of thinking about abstract things in a graphic and practical way (Koch 2005). Disease mapping is the first step towards understanding spatial aspects of health related problems. Lawson et al. (2001) declared that disease map is simply a collection of disease objects in their geographic association. As readers can have an overview of the spatial patterns of certain subjects through mapping; disease mapping has become a prominent part of medical geography (Pyle, 1979). The representation and analysis of maps of disease incidence data is a basic tool in the analysis of regional variation in public health. Distributions can be shown by using various cartographic symbols: points, lines and patterns. It provides estimates of the incidence of a disease and relative risks across a geographical study area depending upon the level of detail in ihe data. Applications for such methods lie in health services resource allocation and in disease atlas construction. Associative analyses can then be deduced statistically and visually after examining disease maps. Many countries 31 expanded or updated their national atlases while others published new ones during 1990s (Walter 2000). Le et a). (1996) prepared an atlas of cancer incidence in Canada. Geographical patterns were identified of disease incidence arc identified along with the criteria for the selection of cancer sites. Pickle el al (1996) showed age adjusted rates by sex and race in US mortality atlas, The data are shown as risks relative to the national averages, with categories defined by a combination of percentiles and statistical significance. An earlier US publication (Devine et al 1991) investigated injury mortality covering deaths such as homicide, falls and drowning. Several European countries have recently published disease maps ad some present new methodological points. Zatonski et al. (1993) worked on cancer mortality atlas of Poland. He used geographical centriod smoothing method with weights inversely proportional to the distance from point being smoothed and directly proportional to population. Other European national atlases appeared in Switzerland (Schurer et al. 1997), Estonia (Barburin et al 1997), Norway (Cancer registry), and Spain (Ortega et al 1996). The Europeans also have made considerable progress in the field of international disease mapping (Smarts et al 1992: WHO 1997). The worldwide distribution of AIDS lias been examined by Smallman-Raynor (1992). This atlas also contains maps of disease diffusion. Cliff & Haggett (1988) discussed different statistical and cartographic methods for the analysis and visualization of disease data and prepared an atlas of disease distribution in the world which includes the mapping of AIDS, tuberculosis, smallpox, influenza and measles. Disease mapping is not only concerned with the visualization and spatial patterns of the disease but it provides basis for more complex aspects of disease, environment and healthcare, 3.$ Disease Clustering According to the Centers for Disease Control USA “clustering is any unusual aggregation of health events, real or perceived.’1 Waller el al (2006) described that a spatial disease cluster may be defined as an area with an unusually elevated disease incidence rate. The basic interest in analyzing disease patterns is in determining whether the observed events exhibit any systematic pattern as opposed to being distributed at random over the study region. Wartenbcrg et al (1993) consider cluster detection studies to be a form of pre-epidemiology, placing them in an investigative niche prior to confirmatory epidemiological studies. Testing for clustering is 32 generally aimed at tackling two issues. First, is there a general tendency for clustering to occur and, if so, where? Second, do clusters occur in specific areas, e.g. near suspected environmental hazards? This may lead to examination of potential environmental hazards. There are a variety of techniques to identify clusters, however here the most sophisticated and widely used arc being discussed in a comprehensive way along with their applications in different regions of the world to find disease clusters. Sabel et al (2004) quoted Openshaw’s Geographical Analysis Machine (GAM) whose performance was compared with several exploratory geographical methods to indentify patterns spatially and temporally. GAM worked well with spatially distributed data and correctly identified temporal clustering. However, Openshaw’s method has been heavily criticized in the literature, largely due not only to the multiple testing problems but also due to the dependency of the test. Sabel et al (2003) used the Spatial Scan Statistic method in SaTScan software to analyze the impact of residential migration between places of birth and death for the rare neurological disease ALS in Finland for deaths between 1985 and 1995. The significant clusters were identified in two areas of the south and southeast of the country. Bilhel (1990) proposed probability density estimation techniques of which kernel estimation is the most widely trusted and understood. Bithell s ideas have been applied and developed by Kelsall and Diggle (1995) and Sabel (1999). Sabel et al. (2000) extended them to deal with the temporal components. They also investigated space time interaction by calculating density estimates of certain interval of time and then sequenced them together in an animation to obtain an understanding of the time lag of the etiology of the disease. Wheeler (2007) compared different cluster detection techniques to study the incidence of childhood leukemia in Ohio USA by geocoding the individual cases from 1996 to2003 using GIS, He then analyzed the clusters using K function, Cuzick and Edward’s method, and the kernel intensity function to test for significant global clustering and the kernel intensity function and Kulldorffs spatial scan statistic in SaTScan to test for significant local clusters. The spatial scan method in SatScan concluded that there were no significant local clusters. While the kernel intensity function method yielded statistically significant clusters in areas of central, southern, and eastern Ohio. 33 3.6 Ecological Analysis Relationships between the environment and health are intrinsically geographical. Ecological studies are based on the principle of the spatial linkage of health data and risk factors. The exposure of a population to a risk factor is evaluated by means of some risk factor level at the place of residence (Kistemann et al 2004). Ecological studies may examine associations in terms of place, time or both. Some common examples are descriptive studies on the variation in health between populations. These studies include geographical correlation studies for example the examination of association between socio economic deprivation and mortality. The second type of studies which are more common are the studies about the occurrence of disease in relation to spatially defined exposures such as nuclear installations, high voltage power lines and exposure surfaces such as air pollution. Morgenstem (1995) proposed a classification for variables in ecological studies: environmental, aggregate, and global. Environmental measures refer to physical characteristics of an area for which there is an equivalent measure at the individual level (e.g drinking magnesium water). Aggregate measures are summary' measures of variable derived at the individual level and grouped to the areal level (e.g. percentage of smokers), A global measure refers to an attribute of the place which is a contextual attribute, for example motorcycle helmet laws and social capital. Similarly the analysis in ecological studies may also be classified as individual level or at the ecological or group level. Wolf (2002) studied the association between outdoor air pollution and Chronic Rhino sinusitis (CRS) disease in Cologne, Germany. He studied 1435 patients from different city districts who were treated during 1990 and 1999. For the purpose of analysis, age specific rates of the patients were calculated which were then weighted according to mean age distribution of population in the city. He then collected information regarding air pollution, socio-economic and demographic composition of the districts. Regression analysis revealed a consistent statistical effect of pollution on the prevalence of CRS in the population which was exposed to above average rate of air pollution. Maautay (2005) applied GIS to identify spatial correspondence between Asthma and air pollution in Bronx, New York. In this study air pollution, exposure to toxic lands around power generating facilities, sludge processing plants, and waste disposal 34 points were considered in relation with asthma disease. It was found that people living near noxious land uses were up to 66% more likely to be hospitalized for asthma. Ecological studies are aimed to assess heterogeneity and spatial structure of health outcomes in an exploratory manner. It searches for clues to etiology by examining the relationship between health outcomes and socio-economic, environmental and genetic risk factors. This enterprise is sometimes known as hypothesis generation. Ecological correlation studies focus the association between health outcomes and sets of variables defined corresponding to geographical areas typically to industrial, agricultural, radioactive, and environmental pollutants (Elliot et at 2000). As ecological studies are conducted on aggregated disease and exposure data, the results drawn might be subject to the so called ecological fallacy which could only be excluded by a subsequent epidemiological study at the individual level (Morgenstem, 1998; Robinson, 1950). 3,7 Analysis of Healthcare Facilities The principle of equal access to health services for those in equal need should be the aim for the basis of provision of health care facilities in every country. Nevertheless, health services are inevitably located in particular places and are therefore more accessible to nearby residents than those living farther away {Lovett et al. 2004). Variations in proximity are, obviously only one element of accessibility to health services (Ricketts et al 1994), but the physical difficulties of overcoming distances tend to be particularly important in rural regions. Another aspect of interest in the analysis of healthcare would be the study of data regarding facilities e.g. number of beds, number of doctors, nursing and support staff, provision of diagnostic test and machines, bed occupancy rate, and population per doctor etc. Jones et al. (1997) studied the accessibility to health services and deaths from asthma in local districts of England and Wales, from 1988 to 1992 with the help of regression analysis. They concluded that asthma deaths were raised with the increase in distance from major health services. Lovett et al (2001) applied GIS to examine the accessibility of patients to general practitioners by public and private transport in parts of UK. For this purpose they collected data regarding patients, from patient registers, road network and bus routes, and integrated them in ArcGIS. It was concluded that only 10% residents traveled through a car more than 10 minutes while 13% of the population could not reach general medical services by daily bus. In the remote rural 35 areas the lowest levels of personal mobility and highest health needs indicators were found. Martin (2002) reviewed different approaches which were used in earlier works to measure access to health care services in south west of England. He incorporated private and public transport routes, which were available from new public transport information system, with hospital locations. Beere et al. (2006) analyzed the accessibility and travel time from maternity units in New Zealand considering different models and preferred Least Cost Path Analysis (LCPA) to assess the travel time from the health facilities in their research. The boundary data, census figures, road network, address point and location of maternity units were combined and it was concluded that a total of 58,461 people (1,57% of total population) live farther than 60 minutes from the nearest maternity unit. The west coast has the highest percentage of people under serviced. It was suggested that LCPA can be applied to various health services at different spatial and temporal scales. 3.8 Literature Review with Special Reference to Tuberculosis Kistemann et al. (2002) studied the spatial patterns of tuberculosis incidence in Cologne (Germany). They investigated the relationship between tuberculosis and several potential risk factors in the city of Cologne using geographical and statistical methods. They observed strong ecological correlations of the disease with the variables related to economic conditions and immigration status. All the data related to TB patients was geographically referenced. Due to the non availability of a deprivation index, a variety of variables were used to represent socio-demographic and socio-economic conditions of the patients which might be associated with tuberculosis. The results indicated that highest concentration occurred within the densely populated inner urban areas. They argued that the temporal changes in disease incidence showed that overall disease incidence was reduced but the elderly population didn't show any significant change. The comparison of TB incidence rates with housing, demographic and economic conditions revealed a statistically significant correlation between incidence rate and multiple dwelling, unemployment rate and non- German immigrants. Walls & Shingadia (2003) studied the morbidity and mortality of infants and young children due to tuberculosis in different regions of the world, in Europe they found remarkably high rate of tuberculosis in Eastern Europe as compared to western parts. 36 In Sub-Saharan Africa, pediatric cases make up a substantially higher proportion of overall TO cases than in other African countries. Higher notification rates in adults in china and India pose a myriad danger for infants. In Pakistan the rates are guessed to be higher but no data available for childhood TO. They found that factors such as overcrowding, poverty and the HIV epidemic have all contributed to the resurgence of tuberculosis globally. Mycobacterium tuberculosis infects millions of children worldwide every year, still a lot of data escapes recording in this regard for most of the world. Buxbaum et al. (2000) studied the patterns of geographic variation of the tuberculosis in South Carolina USA. The data was collected from the hospitals for 46 South Carolina counties from 1985-1995. Then the spatial variations in disease rates were calculated by county, region, and rural urban status using the analysis of variance technique. It was found that the disease rates were significantly higher in the coastal region. They concluded that association of disease with geographical regions involved environmental factors. They emphasized the need for further analysis to study the relationship of risk factors and the disease. Munch et al. (2003) investigated various risk factors and places of transmission of tuberculosis using GIS in the suburbs of Cape Town. They used spatial epidemiological techniques of exploratory disease mapping. The distribution of all selected cases was shown by dot density maps. The correlation between selected disease cases and crowding, unemployment and local drinking places was calculated using Poisson regression. Point pattern analysis and spatial statistics indicated clusters of cases in certain areas. There was found significant linkage of tuberculosis notifications with unemployment, overcrowding and number of local drinking places per enumerator sub-district. Moonan et al (2004) applied GIS analysis to identify the areas of tuberculosis transmission and incidence in USA between 1993 and 2000. They identified areas of tuberculosis transmission by linking GIS technology with molecular surveillance. For this purpose they collected data on newly occurring IB cases in Tarrant County. The addresses of the patients were geocoded using ArcView 4.0. There was an average 5.9 cases per 100,000 persons in the county while the two specific zip codes showed 94.3 and 55.2 cases per 100,000. It was observed that these areas were found to be low in socio-economic status, high unemployment rate, homelessness, drug use and low 37 quality housing conditions. They concluded that CIS technologies may prove very effective to enhance targeted screening and control efforts to combat tuberculosis. Khan et al. (2000) studied the cultural constraints associated with tuberculosis and recognized the problems involved with treatment of the disease in Pakistan. They selected a sample of 36 TB patients keeping in view the age, treatment stage and rural urban status. Those patients were interviewed to get information regarding their family, cultural aspects, and knowledge and attitude towards TB. The researchers concluded that most of the patients are very poor and have little knowledge about the disease while the doctors also don’t bother to update their patients. The inefficiency of health care institutions causes default and incomplete treatment in patients. Other reasons which are related to default cases is inability to access to health services due to the higher amount of time and money involved. The lack of knowledge and some cultural factors cause a stigma in patients which discourages long term and regular treatment. It was suggested to create awareness among people and also to improve health facilities in rural areas, Tanser & Wilkinson (1999) applied GIS and GPS technologies to analyze the access to tuberculosis heath care facilities in South Africa, They used Maplnfo software to digitize topographic maps of the district Hlabisa. To acquire spatial data, GPS positioning technique was used for 16583 homesteads and TB treatment centers and the remaining 7741 were digitized from Aerial photographs. Then tuberculosis data and raster images were analyzed in Maplnfo 5. They overlaid the data regarding all homesteads on distance images of 1991, 1996, and 1998. The results showed that the health care units increased from 37 in 1991 to 147 in 1996. The average distance from homestead to supervisor in tuberculosis program decreased from 2.3km in 1991 to 1.5km in 1996. The mean distance between hospital and homestead was 29,7 km which fell from 5,5km to 4.6km between 1991 and 1996. They suggested that GIS/GPS could play a vital role in tuberculosis control. This technology might help rational development of community based care by providing maps, locating potential supervision points, and focusing on areas of particular need. Hussain et al. (2003) studied the prevalence and risk factors associated with mycobacterium tuberculosis in prisoners in North Western Frontier Province, Pakistan, A cross-sectional study design was used to estimate the prevalence of latent MTB infection in the five central prisons in NWFP during July-September 2001. A stratified random sample of 425 male prisoners was taken who were tested for 38 tuberculosis and were interviewed through a structured questionnaire. The data was entered and analyzed in Epilnfo 6.04 software. The analysis of data showed that 48% <204/425) male prisoners were diagnosed with positive tuberculin test The multiple logistic regression revealed that the age, education level, smoking status and a higher population density were statistically significant risk factors. They suggested routine screening of patients, reduction of overcrowding, and awareness regarding smoking and other risk factors associated with disease. Weiss et al (2006) found that, globally, higher number of men is diagnosed with tuberculosis (TB) as compared to women. WHO found a 74% excess of sputum smear-positive males over females in case notification for the year 2002 (WHO, 2004). To study the gender differences in TB a WHO sponsored research was conducted in four countries: Bangladesh, India, Malawi, and Columbia. The data regarding sex ratio, respiratory symptoms, sputum submission and positivity, treatment outcomes was collected. The results showed that fewer women were found suspected to TB in India and Bangladesh, equal ratio of male and female in Malawi and more women were identified as suspected to TB in Columbia. The data also revealed that the dropout ratio was higher for females during diagnosis stage while the males were less interested to complete the treatment. The financial impact of illness caused stressful situations for men and the delay in seeking care in men was also due to livelihood factors. The females faced problems to complete the treatment due to their domestic responsibilities and social barriers in accessibility. Weiss el al (2006) concluded that the reasons for the gender differences in TB may point out that fewer women in the population have active TB; fewer women abend health care facilities, or a low rate of smear positive b overall in women. They suggested that DOTS programmers should keep an eye on gender related variations and barriers while planning for policies to control the disease. Shetty et al. (2006) evaluated potential socio-demographic risk factors for tuberculosis in South India. The data was collected through a questionnaire survey regarding marital status, religion, employment and occupation, household size, number of rooms in the house, separate kitchen, and use of biomass fuel was collected. They concluded that TB was associated with low education level, not liavmg separate kitchen, diabetes, urbanization and changing economic climate b Banglore. 39 Farchi et al. (2008) studied the patterns of incidence, prevalence and hospitalization of tuberculosis in Italy from 1997-2003. Data regarding TB patients was collected from different sources. It was concluded that incidence of TB declined from 15/100000 to 11 during 1997 to 2003, However the number of cases in foreign immigrants increased significantly during this period. They suggested a focus especially on immigrants to control tuberculosis. A thorough study of the origin and development of medical geography, the research with modem techniques undertaken in this specific field revealed that the whole story’ is woven around four themes: Disease mapping or patterns, disease clustering, ecological analysis and the analysis of healthcare facilities. An analysis of the whole of the above motioned literature shows that every study contains one or more of these themes. The present research to study the spatial patterns of tuberculosis is an attempt to cover all the four themes. The spatial patterns of tuberculosis will depict the disease incidence in different areas of Punjab. It will also indicate the areas which are severely hit by disease. This research would uncover the areas which have clusters of disease. These clusters will be then analyzed keeping an eye on environmental and social aspects. Furthermore, clustering will also be helpful to find the areas which require more health facilities as compared to facilities already available. Thus it would be helpful while planning for future health and disease prevention policy 40 CHAPTER 4 TUBERCULOSIS AND ITS DISTRIBUTION IN THE WORLD 4.1 Tuberculosis Tuberculosis (TB) is a chronic infectious disease caused by mycobacterium of the tuberculosis complex, mainly Mycobacterium tuberculosis (Wyangaarden et al 1988; Park 2001; Kumar & Cark 1987). TB remained largest single cause of death from any infectious disease worldwide. During the Industrial Revolution of the 18th and 19* centuries, the disease was known as the whiteplague. It was the major cause of death in young people all over the world. TB was considered as a hereditary disease rather than contiguous till mid 19th century. Also, until the mid 20* century, there was no cure for TB. For many people, a diagnosis of TB was a slow death sentence (CDC 2008). In 1993, World Health Organization (WHO) declared tuberculosis to be a global emergency {Nunes 2007). Tuberculosis (TB) is primarily an illness of the respiratory system and attacks the lungs, but which may also affect the kidneys, bones, lymph nodes, and brain. The disease is caused by Mycobacterium tuberculosis, a rod-shaped bacterium. Symptoms of TB include coughing, chest pain, shortness of breath, loss of appetite, weight loss, fever, chills, and fatigue. Children and people with weaker immune systems are the most susceptible to TB. Half of all untreated TB cases are fatal (Padilla, 2006; Hcinsohn, 2004). Today despite great progress in its treatment and control, it remains a myriad medical problem in many developing and underdeveloped countries. Currently, TB is the leading cause of mortality among infectious diseases worldwide, and 95% of TB cases and 98% of deaths due to TB occur in developing countries (Rajeswari et al 1999; Small 1996; Raviglione el al 1997). About one third of the world population is estimated to be infected with Mycobacterium tuberculosis. The poor and developing countries of Africa, Asia and Latin America are the most severely affected (Sudre et al 1992), It doesn't mean that industrialized and rich countries are the safe places against tuberculosis. During seven years from 1985 to 1992 there was a 20% increase of TB in the USA (Reichman, 1996). Many European 41 countries which were experiencing a decline in TB patients; faced the resurgence after 1980s (Raviglione et a! 1993), especially in the areas of Eastern Europe and the former Soviet Union {Bums et ah, 1994). TB is contagious and spreads through the air, if not treated, each person with active TB infects on average 10 to IS people each year. TB is a disease of poverty; virtually all TB deaths occur in the developing world, affecting mostly young adults in their most productive years. TB especially affects the most vulnerable, such as the poorest and malnourished. Each year. 8 million people worldwide develop active TB and 3 million die {WHO 2006). Co- infection with the HiV virus was recognized as early as 1985; a large portion of TB patients in currently reported cases are co-infected with HIV in western and African counties (Rieder 1989). Tuberculosis is a major problem in patients with the Acquired Immunodeficiency Syndrome AIDS, Tuberculosis is the most common disease and the leading cause of death in people living with HIV and AIDS. As HIV attacks and weakcjts the immune system of the body so the probability of being sick, by TB increases in the persons who are infected with TB bacilli. At the end of 2007, approximately 33.2 million persons were living with HIV infection in the world (WHO 2006). Since 1990. TB infection rates have increased 4-fold in countries that are severely affected by HfV (UNAIDS 2008), Fortunately AIDS prevalence in Pakistan is not touching very alarming figures as compared to other high tuberculosis burden countries due to religious and cultural constraints on some major ways of HIV transmission. The situation is worst in African countries where the percentage of prevalence of HIV in TB cases is very high. These figures for some high TB burden countries are: 68% in Zimbabwe, 60% in South Africa, 48% in Mozambique, 36% in Tanzania, 29% in Kenya, 21% in Ethiopia, 17% in Brazil, 8.5% in Thailand, 5.2% in India, 0.9% in China and Indonesia, 0.6% in Pakistan, and only 0.1% in Bangladesh and Philippines (WHO 2006). The diagnosis of pulmonary tuberculosis in most of the countries of the world is through sputum examination for the identification of M. tuberculosis. Whenever tuberculosis is suspected at least three specimens of sputum are collected within two days and examined by microscopy (NTP 1999). Along sputum examination, chest x- rays are extremely helpful in evaluating the extent of pulmonary tuberculosis and following its progression and response to therapy. Tuberculin test is also used to 42 diagnose tuberculosis but it is considered less valuable as a diagnostic tool (Cook 1996). The goal for the treatment of tuberculosis is to cure individual patients and to minimize the transmission of infection. Thus successful treatment has the benefit both for individual patient and the family as well as community in which patient resides (CDC 2006). There are four recommended regimens for treating patients with tuberculosis. Each regimen has an initial intensive phase of 2 months followed by a continuation phase of 4 to 7 months. Clearly, we do have drugs at hand to treat tuberculosis. However, the prolonged treatment time of up to 6 months with a combination of three different drugs renders chemotherapy unfeasible in developing countries and frequently results in the failure of patient compliance worldwide, which in turn fosters the development of multi drug resistant MDR TB which is more complex and difficult to cure. It is essential to monitor and supervise the treatment with great care particularly during the smear positive stage (Kaufmann & Hahn 2003). 4.2 Transmission of Infection Tuberculosis infection is transmitted entirely by the aerial route. It is most commonly transmitted to other persons by someone suffering from infectious pulmonary tuberculosis, by means of infected droplet nuclei. These persons aerosolize bacilli as droplet nuclei while speaking, coughing, and sneezing or otherwise forcibly expelling air from the respiratory tract (Warren & Mahmoud 1990). Tiny droplets dry rapidly, attach themselves to fine dust particles and the smallest of them may then remain suspended in the air for several hours (NTP 1999). Mycobacteria cannot sustain in the presence of ultraviolet light including daylight so the transmission is always an indoor event. Thus infection occurs almost exclusively through the respiratory system by inhalation of tubercle bacilli. Tuberculosis spreads from the primary lung lesion to other parts of the body through the blood stream, lymphatic and bronchial systems or by direct extension, and in this way may affect any organ. The evolution of disease depends primarily upon the immune status of the host. According to WHO estimates each person with active TB can infect on average 10 to 15 people a year (WHO 2008). 4.3 Etiology The micro-organism that causes tuberculosis belongs to the genus Mycobacterium, which is classified in the family Mycobacteriaceae (Wyngaarden 1988). There are 43 more than thirty recognized species in the genus Mycobacterium (Runyon el al 1974). M. tuberculosis is a type of mycobacteria. Mycobacteria can cause a variety of diseases. Some mycobacteria are called tuberculous mycobacteria because they cause TB or diseases similar to TB. These mycobacteria are M. tuberculosis, M. bovis, and M. africanum. Other mycobacteria are called nontuberculous mycobacteria because they do not cause TB. One common type of nontubcrculous mycobacteria is M. avium complex. Nontubcrculous mycobacteria are not usually spread from person to person (CDC 2008). M. tuberculosis was first identified by Robert Koch in 1892. M. tuberculosis has an important feature in its ability to lie dormant for many years (Cook 1996). 4.4 Patterns of Tuberculosis Distribution in the World Tuberculosis (TB) continues to be one of the most devastating and widespread infections in the world. Every year, about 8 million people develop TB disease and 3 million people die of the disease. In fact, among people older than 5 years of age, TB disease is the leading cause of death around the world (CDC 2008). According to World Health Organization (WHO 2007) there were estimated 8.8 million new incident cases in 2005, of which almost 4 million were smear positive. In developing countries, about three-quarters of all tuberculosis cases and deaths are concentrated in the economically productive age group of the population (15-59 years) (Kochi, 1991). Tuberculosis is one the humanity’s most ancient plagues whose traces were found in Egyptian mummies. In technologically advanced countries, tuberculosis has been declining in incidence and prevalence for at least the last hundred years. In these areas it has become largely a disease of the urban poor with three-fourths of clinical cases arising in the elderly (Warren & Mahmoud 1990). Home (1996) has quoted an article published in 1983 which pointed out that forecasts for the complete eradication of tuberculosis within a generation has been made for nearly a century, but it seems likely that several more generations will come and go before the complete disappearance of the disease. This proved prophetic because in 1985 USA observed for the first time a deviation from the expected logarithmic decline in tuberculosis, However with much organized efforts by WHO; the disease incidence, prevalence, and mortality rates have declined on a global level. Prevalence was already moving towards a decline by 1990, mortality peaked before the year 2000, and incidence has begun to fall since 2003 (Figure 4.1). In Africa, HIV caused a smaller increase in 44 prevalence but added much to incidence and mortality. In addition in Asia, the analyses suggest that DOTS has reduced prevalence more than incidence or mortality (WHO 2007). minuted glo bil prmktot, i*«Ulrty ind lidfentt ritei,1990-200$. Mofettw different udo on r-n«. u 1*-, ]i: jj 8 £ 1M- M0- I" ! S v- I j » I 1‘ ria. •i i i i *1 I [ 1 “I -I 1 NM—IMS—XW—MB mo i«s m-»i m JM m Source: WHO Report 2007 Global Tuberculosis Control Figure 4.1 Estimated global prevalence, mortality' and incidence rates, 1990-2005 Although the statistics are encouraging as they show that TB burden may be falling globally, but the decline is not fast enough to meet the targets set by the Stop TB Partnership to reduce the rate of prevalence and deaths up to 50% of from 1990 to 2015. WHO has divided the world into six regions for which data collection, analysis and health and disease indicators are available individually. The regions are the African Region (AFR), the Region of the Americas (AMR), the Eastern Mediterranean Region (EMR), the European Region (EUR), the South- East Asia Region (SEAR) and the Western Pacific Region (WPR) (Dye et al 1999). The Region of the Americas and the South-East Asia and Western Pacific regions are on track to reach these targets while the African, Eastern Mediterranean and European regions are not yet (WHO 2007). Out of six WHO regions five have experienced stability or decline in per capita TB incidence in 2004 but it grew 0.6% per year globally. The exception is the African Region, where TB incidence was still rising, following the spread of HIV. Since 1990, both HIV prevalence and TB incidence have been increasing more slowly each year and, by 2005, both indicators were falling. In Eastern Europe (mostly countries of the former Soviet Union), incidence per capita increased during the 1990s but has fallen in the last decade (WHO 2007). New smear¬ positive case detection rates by DOTS programs in 2005 were 35% in European, 44% in Eastern Mediterranean regions, 65% in the Region of the Americas, 64% in the South-East Asia Region, and 76% in the Western Pacific Region. Only the Western 45 Pacific Region met the 2005 target. Apart from the global division of regions as mentioned above it is more easy to understand and interesting to study the patterns of tuberculosis on country basis. WHO identified 22 countries with highest tuberculosis burden. These highest burden countries HBCs account for approximately 80% of the estimated number of new cases arising worldwide each year (WHO 2007). India, China, and Indonesia occupy top three positions among HBCs. Pakistan ranks 6th according to the estimates for the year 2005, With the exception of Brazil all the countries in HBCs are from Asia or Africa, rhere are six Asian and four African countries in top ten countries having high tuberculosis burden (Table 4.1). Among the 15 countries with the highest estimated TB incidence rates, 12 are in Africa (WHO 2007). Table 4.1: Estimated TB burden in 22 countries with highest TB burden in the world tillnut+d T1 burl**. 2005 MV NET. watt Mil Mil HOSES* pffouim PR HUMID ’UIWMO MMDIH P8 PER HOW POP PH PUS n*h poptutui HO* HOMO PM WKtrtM ft 1 liwfu noun IKJ M ijj 75 7299 799 722 29 5.2 2 Giiru mssu \m MO «i 45 2 777 2tt 205 14 0.5 I turn su ID w W 5*4 242 92 41 U 4 Niger* nine m MJ W 127 704 534 100 74 MI in m w m 102 57$ 404 46 47 91 4 PMcitw K7BS 286 81 129 C 441 297 » V 9.4 7 SoutMfrlu 47432 285 W0 116 MS 242 511 8 71 51 ( OH* 77 431 266 Hi HJ 152 |J3 546 56 7} n 9 9M#«H <3054 243 291 109 1|1 174 ISO 39 47 0.1 U hitii MI* MI M m m m 41 140 n 11 M&nj* 17549 20$ 3* W Jtl Ml 42 73 17 12 lustijoFMcniion 143202 170 IIS 74 S3 214 ISO 28 20 02 13 noHia $428 us m is n m as n 21 10 14 UR hnurili 8729 131 142 147 M 496 29 75 29 15 lunl 196405 IH 40 *44 24 142 74 IS 15 M 16 Uputt 79416 04 W 44 I* 161 559 26 91 8 64 273 91 142 41 47 171 204 12 19 7J6 U Rtewktw 19792 *4 447 8 US 111 S97 24 124 so N HpMDTdr 50519 * 171 8 74 14 17D t 15 7,1 17019 71 491 72 24$ 92 611 17 18 69 21 Cwfcfc U071 71 504 72 224 99 703 12 92 4,0 72 tfglrainun 29HJ M J» 21 76 14 288 10 IS 0.0 HÿlurfeKMatiies 4*454*2 70» 174 1117 77 71544 2B 1265 II Afl 73S08J ~2529 743 1088 147 3771 511 544 74 21 m 999757 75J 79 157 8 441 50 49 55 7# [MR 541 704 $45 104 253 47 811 163 112 21 2.1 tut 812795 445 50 199 27 525 60 66 7.4 46 SUft 14*529 Wi 1)1 1739 fl 4809 290 SO 31 19 m 17522*3 1927 110 £46 3414 206 295 v 10 Cbbel 6441751 IB11 134 )«2 W 44 KZ 717 1577 24 n B h*+*i4tmh md4m IBO3« naMtupd B-Him Source: WHO Report 2007 Global Tuberculosis Control A study of global incidence of TB estimates for the year 2005 reveals that the countries of south and east Africa were found to be severely hit by the disease. South 46 Africa, Zimbabwe, Namibia, Tanzania, Kenya Uganda, Zaire, and Cambodia are found in this category, then comes the Asian countries especially with larger populations like India, China, Russia, Pakistan, Bangladesh, Indonesia, Vietnam, Philippines, and from Africa countries like Niger, Sudan, Somalia and parts of centra) and south America. While the countries of North America, parts of South America, Western Europe, Middle East and Australia have a lower incidence of tuberculosis as shown in Figure 4.2, T» r*ir%, a* T * c £ tÿ> r -V; T >T r» > rb !ÿ! J /-a * , a B» _ »«w 'YJ Source: WHO Report 2067 Global Tuberculosis Control Figure 4,2 Estimated TH incidence rate in the world in 2005 In terms of estimated number of new TB patients India and China are found to be major contributors due to high burden of disease and larger populations. After these two, Pakistan, Brazil, Russia, South Africa, Uganda, Zaire, Ethiopia, and Nigeria appear as the countries with large number of new TB patients. Then comes Kazakhstan, Iran, Saudi Arabia, Yemen, turkey, Poland, Egypt, Algeria, USA, Mexico, Peru and Argentina where the annual number of new TB cases is less than 100 000, However the countries of Western Europe, Scandinavia, Canada and Australia are the countries with estimated least number of new TB patients in 2005 (Figure 4,3). 47 btbitotH rtwib*f J at n*wfl :j f f 7 rt i 6 ! hiMwmi 4* f" 1 IMW I 1 ICftVPW I |HMMtf# II 0 / Source; WHO Report 2007 Global Tuberuifo&is Control Figure 43 Estimated Number of New TB cases in the world in 2005 Estimates of the case detection rates for individual countries suggest that 67 countries met the 70% target by the end of 2005. China and India have improved to a great deal in terms of case detection in recent years, but these two countries still have an estimated 28% of all undetected new smear-positive cases in 2005. However, in 2005, Nigeria had succeeded China as the second largest reservoir of undetected cases. These three countries are among eight that together accounted for 59% of all cases not detected by DOTS programs in 2005, The increase in detection rate of new smear positive cases was slower from 1995 to 2001 and then improved to much from 2002 to 2005. The higher number of smear positive cases which reported in the South-East Asia and Western Pacific regions from 2002 to 2005 appeared as a sudden increase from 2002 to 2005 on a global level, TB control and monitoring efforts are well achieved in 21st century. By the end of 2006, data regarding case notification and treatment outcomes was collected from almost 99.9% population of the world. 4.5 Disease Situation in Pakistan According to WHO estimates for the year 2005 Pakistan ranks sixth amongst 22 countries with highest tuberculosis burden (WHO 2007), and contributes 43% of 48 disease burden in Eastern Mediterranean Region (WHO 2006). More than 250,000 persons acquire active TB disease in Pakistan every year, about two third of which belong to productive age group (WHO, 2006). Estimated incidence rate for Pakistan for 2005 was found 181/ 100 000 people per year while smear positive incidence is 82/ 100 000. TB prevalence rate was estimated 263 cases per 100 000 of population while the estimated mortality rate was found 34 deaths per 100 000 of population. The notification rate of TB patients was found to be 110/ 100 000 people per year during year 2005. To cure TB patients directly observed treatment short course therapy (DOTS) was introduced in Pakistan during 1990s. DOTS coverage was 2% in 1995 and reached to 100% in 2005. 4.6 Role of World Health Organization to Combat Disease As mentioned earlier, WHO declared tuberculosis a global emergency in 1993. As a result efforts were augmented to cure and stop tuberculosis in the last decade of 20th century. In 1994 internationally accepted strategy to control tuberculosis named DOTS was introduced (WHO 1994). It was decided by the World Health Assembly that 70% case detection rate and 85% cure rate would be achieved by the year 2000 which was later extended to 2005. These targets were narrowly missed globally as there was 60% case detection rate and 84% treatment success rate in 2005 (WHO 2007). The major progress towards global tuberculosis control was observed during 1995 to 2005 largely due to the firm commitment of WHO and implementation of DOTS strategy especially in high burden countries (WHO 2006a). Globally, more than 20 million patients had been treated through DOTS by 2004 and more than 16 million of them had been cured. Sub-Saharan Africa is the only region where TB mortality and incidence is not declining yet while other WHO regions showed decline or stability (2006a). WHO specified two goals to be achieved to 2005: a case detection rate of 70% and to treat successfully 85% of detected smear positive cases. These targets were achieved in the Western Pacific Region, and the treatment success exceeded 85% in the South-East Asia Region. Twenty-six countries achieved both targets, including China, Philippines and Viet Nam; 67 countries achieved at least 70% case detection in 2005, and 57 countries reported a treatment success of 85% or more in the 2004 cohort, WHO has proposed that the global burden of tuberculosis would be reduced by 50% relative to 1990 levels by 2015. It has been planned to decrease the prevalence to 155 per 100,000 and deaths to 14 per 100, 000 by year 49 2015. It is also targeted to bring TB incidence rate to less than one case per million population per year by 2050 (WHO 2006a). However Dye et al. (2005) has alarmed that current rates of progress are insufficient to allow the targets of halving TB mortality and prevalence by 2015 to be achieved. With the strenuous efforts of WHO, the funds available for TB control have increased enormously since 2002, reaching US$ 2.0 billion in 2007. Now the focus of WHO is to pursue high quality DOTS expansion and enhancement, to contribute to strengthen health system, to engage public and private sector in TB care and promoting research to develop diagnostics, drugs and vaccines. 4.7 Directly Observed Treatment Short Course (DOTS) Directly observed treatment short course (DOTS) is proven to be the most effective treatment strategy to cure and stop Tuberculosis. The objectives of the DOTS strategy are to decrease the risk of infection, reduce morbidity and the transmission of infection, and prevent TB deaths. According to WHO (2008), DOTS remains at the heart of the slop TB strategy. The basic components of DOTS are described below The first element is political commitment and adequate funding to stop TB. The role of national government is vital in this regard. Political commitment is essential not only to strategic action plans, technical support, trained manpower, and legislation but also to develop national and international partnership against the disease. Adequate funding may also be considered as a part of political commitment. The funding is essential both for the treatment of existing patients and to improve the conditions for the people who are at risk. As TB has close relation with poverty so the availability of adequate funds for national control programs arc necessary from the state as well as from the international agencies especially for the poor nations of Africa and Asia. The second element of DOTS is case detection through quality assured bacteriology. Bacteriology is the most widely used method of TB case detection using sputum smear microscopy, culture and drug susceptibility tests. For this purpose properly equipped standardized diagnostic laboratories should be maintained. The third element of DOTS strategy is standardized treatment, with supervision and patient support. The success of treatment of TB not only depends on proper medicine but also on the supervision of regularity of drug intake. It is also necessary to identify and remove any physical, financial, social and cultural barriers to the patient for the access to treatment services. The fourth element of DOTS is an effective drug supply and so management system. Treatment centers should be provided with drugs which would be delivered to patients free of charge and an effective management system should be maintained for supply and demand. The fifth element of DOTS is monitoring and evaluation system and impact measurement. It includes the complete recording and reporting system and a regular data collection and analysis. These data should be reported regularly to national TB control programs and other agencies to keep an eye on the epidemic and its treatment. The total number of countries implementing DOTS was 187 in 2005. During the last decade DOTS coverage experienced a steady increase in tenns of number of countries 4.8 Summary Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis. It has caused a large number of deaths throughout the world especially in poor and developing countries. Pakistan is one the countries with highest tuberculosis burden. Due the devastation due to TB in many countries of the world it was declared as a global emergency in 1993. World Health Organization (WHO) launched DOTS program to combat the disease. The strategy, funds, and resources provided through DOTS program contributed much to control the disease but still the situation is worst in Asia and Africa. 51 CHAPTER 5 SPATIAL PATTERNS OF TUBERCULOSIS IN THE PUNJAB 5.1 Introduction The basic interest in analyzing disease patterns is in determining whether the observed events exhibit any systematic pattern as opposed to being distributed at random over the study region (Waller 2006). The availability of geographically indexed health and population data, geographical information systems, and statistical methodology, have enabled the rational inquiry of spatial variation in disease risk (Elliott 2000). The geographical distribution of the majority of spatially referenced communicable disease data can only be described in terms of areal units. Area based representations describe counts of data associated with, and delimited by, specific geographical zones or areas, commonly predefined for administrative purposes (Atkinson & Molesworth 2000). The spatial patterns of disease can be observed by the recording of disease by geographical areas. However, the simple dot maps of disease may be misleading without reference to underlying population (Lawson & Williams 2001). The present research is an attempt to study areal variations and patterns of tuberculosis in all the districts of Punjab province during 15 years from 1990 to 2005. Punjab is the most populated province of Pakistan with an area of 205,344 square kilometers and a population of 73,621,000 individuals according to 1998 census, There are 34 districts in the Punjab majority of which have a population more than one million. Thus for the convenience in analysis, the data about the disease with an interval of five years is being used i.e, 1990, 1995, 2000, 2005. The counts for TB patients are available for all the districts. As districts vary greatly in population so the analysis of disease patiems and comparison among districts is not possible without reference to the underlying populations. For this purpose, Standardized Morbidity Ratio (SMR) is being used. These SMR and counts of the patients are used to study the spatial patterns of tuberculosis for 1990, 1995, 2000 and 2005 which are being discussed in first section of this chapter. The national TB program achieved 100% DOTS coverage against tuberculosis in the Punjab in 2005. So the data for year 2005 52 has more detail as compared to the past due to better recording strategies and emphasis of national and international agencies on data collection. Thus the data for the year 2005 provides information about gender, mortality, and cure rate which are being analyzed later in this chapter. The last section of this chapter describes the identification of clusters of disease in the Punjab during 1090 to 2005. 5.2 Spatial Patterns of Tuberculosis in the Punjab in 1990 In Punjab the ratio of observed patients in 1990 is higher than years 1995, 2000, and 2005. There were observed 57,344 TB cases in 1990 which are more in number than 1995 and 2000. The highest number of patients was found in Faisalabad district, followed by Lahore district. These two districts arc the most populated districts of the Punjab. However in terms of total population, Lahore district ranks first and Faisalabad is holding second position in Punjab in 1990 (Table 5.1), Areas of northern and central Punjab seemed to be more affected by disease. The map showing distribution of population and notified cases (Figure 5.2) gives a better comparison among districts. Generally, the districts with higher population have higher number of TB patients such as Faisalabad, Lahore, Gujranwala and Sialkot. The map also shows that districts Rajanpur and Mianwali have higher number of patients as compared to their population. In Pakistan there were an estimated notification rate was found to be 140 cases per 100,000 population per year in 1990 (WHO Database 2008). To compare the data from the province and districts with overall country data, expected counts were computed from the above mentioned notification rate of Pakistan. These expected and observed counts were compared to find Standardized Morbidity Ratio SMR on the basis of total notified in Pakistan in 1990. It is interesting to note that the overall SMR value for Punjab is remarkably lower than Pakistan which means that in 1990, Punjab was better than other three provinces in TB vulnerability as it had lesser proportion of patients. The SMR was higher than 1 in Faisalabad, Jhelum, Lahore, Mianwali, Rahiinyar Khan, and Rajanpur districts. Other districts having SMR lesser than 1, lowered the overall SMR of Punjab province. The total number of observed TB patients in Punjab, 57344, is the sum of the number of observed patients in all 30 districts. As the TB cases in every district appear from different populations so the comparison of the counts of patients only would not be useful. To study the spatial distribution of tuberculosis in districts SMR were calculated on the basis of population size for individual districts. These are shown in 53 Table 5.1. The districts which have SMR value greater than 1 are those which have comparatively more patients than their share according to their population and average for whole of the Punjab. Jhclum. Faisalabad and Mianwali have an alarmingly high SMR in the Punjab, while Shcikhupura, Rawalpindi, and Okara have lower SMRs (Figure 5.1). Other districts with higher SMR are Rajanpur, Lahore, Rahimyar Khan and Bahawalpur. The spatial variation in patient counts, population and SMR are shown in Figure 5.2 and 5.3. 54 Table 5.1: Tuberculosis cases and SMR in fhe Punjab, 1990 SMR Expected Expected SMR with based on Estimated Observed Cases with counts reference Pup Sue reference with DISTRICT* Population cases to with notified 1990 to Pakistan reference reference to Pakistan Punjab Punjab Attach 1036000 1321 !4-50 0.9108 975.523 1 1.3541 Bahavmmaanr 1811000 1942 2535 0.7660 1705,2823 1.1388 BaJuiwulpur 2028000 2667 2839 0.9393 1909.6148 1.3966 _Bil;lkk:lr 913000 498 1278 0.3896 859.7033 0.5793 Chakwul 883000 534 1236 0.4320 331.4546 0.6422 DXT. kilim 1353000 1 134 1894 0.5987 1274.0182 0.8901 1- Liisakih.td 3301 5664 1 .4655 3809.8134 2.1788 Gujrimwiitfl 3 546000 3362 4964 0.6772 3339.0010 1.0069 Gtijrni 2695000 1821 3773 0.4826 2537.6785 0.7176 JEuuin 2545000 2323 3563 0.6520 2396.4348 0,9694 Jhcium 742000 1825 1039 1.7568 698.6855 2.6120 Kasur 2006000 798 2808 0.2841 1838.8991 0.4225 Khancvxal 1 789000 1357 2505 0.5418 1684,5665 0.8055 Khushnb 77] OIK) 817 1079 0.7569 725.9926 1.1254 Lahurt- 4973000 7376 6962 1.0594 4682.6994 1.5752 Lajyah 925000 897 1295 0.6927 87 1 .0028 1.0298 Miaow ail 866000 1477 1212 1.2182 815.4470 1.3113 Multan 3623000 1367 5072 0.2695 34It.5061 0.4007 Muzzafiir Gat h 2150000 921 3010 0.3060 2024.4930 0.4549 Qkaru 2005000 560 2807 0.1995 1887,9574 0.2966 TakpiUlan 613000 769 858 0.896 1 577.2159 1.3323 Rahim var Khun 2471000 3492 3459 1.0094 2326.7545 1.5008 Rapinpirr 909000 1432 1273 1.1253 855.9368 1.6730 Rawalpindi 2642000 517 3699 0.1398 2487.7723 0.2078 Sa hiwill 2347000 2660 3286 0,3095 2209,993! 1.2036 Saraudlui 2375000 1083 3325 0.3257 2236.3586 0.4843 Sheikhuplira 2736000 314 3830 0.0820 2576.2851 0.1219 Sialkot 3168000 2631 4435 0.5932 2983,0669 0.8820 T.T.Sitieh 1183000 1525 1656 0.9208 1113,9420 1.3690 Velum 1749000 1623 2449 0.6628 1646.9015 0.9855 PUNJAB 611899000 57344 85259 0,6726 57344.000 1,0000 •The number of districts in the Punjab province were 30 in 1990 Source: SMRs computed from the data obtained from lire office of the D(i Health, Punjab Figure 5.1 Standardized TB Morbidity Ratio in the Punjab in 1990 3.0000 2 5000 20000 m I 1.5000 Vi fUl A 1.0000 05000 00000 2 at DISTRICTS 'J' ' y i Distribution of Population and Notified TB cases in the Punjab in 1990 > v t 4-s *• * ; / GUW 4* ** kSi a ““ ***4 'ÿ• “ * * *j 4>'* omt— R *. .J ** >; * - .. r.S;.*V i* * ITBctMblM 1 D« 50 TB Case* i of I PS 613000-1000000 1000001 - 2000000 50 25 0 50 100 150 200 250 2000001 - 3000000 3000001 Kilometers 4000000 4000001 - 5000000 1 5.000.000 Figure 5.2 Distribution of population and notified TB cases in the Punjab in 1990 Standardized TB Morbidity Ratio in the Punjab in 1990 v i 4-% * Sargodtw twmrtl* J #"Ofcara Dm i Rahim ¥*r Khan 0.1210-0.5000 0.5001 * 1.0000 1.0001 1 5000 50 25 0 50 100 150 200 250 - 1.5001-2.0000 20001 - 2.5000 1:5.000.000 2.5001 - 3.0000 Figure 5-3 Standardized TB Morbidity Ratio in the Punjab in 1990 58 5.3 Spatial Patterns of Tuberculosis in the Punjab in 1995 In 1993. World Health Organization declared tuberculosis a global emergency and started organized efforts to combat the disease. Directly observed treatment short course strategy was started all over the world. During 1995, DOTS strategy was in infancy in Pakistan with 2% coverage. Due to unknown reasons the total number of TB cases was probably under reported in the last five years of 20th century. The archives of Health Department, Government of Punjab province and WHO TB database both show lesser number of patients. In Punjab, the notified cases were amazingly low as compared to the cases notified in 1990. The total number of cases notified was 27,448. District Rahimyar Khan, is on top in total number of notified cases. To analyze the spatial distribution of TB patients in all 34 districts of the Punjab, Standardized Morbidity Ratio (SMR) were calculated on the basis of total number of cases in the districts and overall in Punjab and total population in the districts. The total number of TB counts and SMR are shown in Table 5,2 and Figure 5.4. The table shows that Khushab, Rahimyar Khan, Bahawalpur and Attock experienced an SMR more than 2. This implies that the number of TB patients observed there were more than double than their expected share in TB burden in the province. Such areas should be focused in national TB control programs. Along with these districts, Bhakkar, Jhelum. Chakwal and Mianwali are also among districts with higher SMR in 1995. Two big cities, Lahore and Faisalabad, in Punjab have lower SMR in 1995 while they were on top in 1990. Districts Faisalabad, Lahore, Rawalpindi, and Sheikhupura which are the big cities and among most populated areas had a lower SMR than 0,4. The spatial patterns are displayed in the maps in figures 5.5 and 5.6. Figure 5.5 shows number of patients in the form of a dot map which is overlaid on a choropleth map of total population in the districts to understand the spatial distribution of the disease. Figure 5.6 shows the distribution of SMR among districts in the Punjab province. 59 Tabic 5.2: Tuberculosis cases and SMR in the Punjab, 1995 K.ipectcd Ca.se SMR bail'll on Cases Population NotificiJ with Pop Size i(h DISTRICT Notified (All 1998 reference to reference to Forms) 1995 Punjab Punjab Attock 1274935 1013 475,3301 2.13115 Rahawalnagar 2061447 1201 768.563 1.562657 Babawalpur 2433091 1954 907.1219 2.154066 Blmkkar 1051456 758 392.011 1 1.933619 Cimksvnl 1083725 739 404.0419 1.829018 D.G. khan 1643118 513 612.5986 0,837416 Faisnlabnd 5429547 346 2024.281 0.170925 Cujramsala 3400940 913 1267.962 0.720053 (iiijrat 2048008 711 763.5525 0.931174 llaljzabnd 832980 417 310.5574 1 .342747 Jliang 2834545 670 1056.795 0.633993 Jbdum 936957 653 349.3228 1.869331 Kasur 2375875 504 885.7902 0.568983 Kbunewal 2068490 391 771.1888 0.507009 Kbusbiih 90571 1 982 337.6735 2.908135 fÿatiore frj 187-15 659 2355.798 0.279735 Unyyah 1 120951 521 417.9207 1 .246648 Lodlirau 1171800 311 436.8786 0.711868 M. B. DIN 1160552 171 432.685 0.395207 Minntvali 1056620 684 393.9364 1.736321 Multan 3116851 1436 1162.046 1.235751 Muzmfnr Garb 2635903 694 982.7356 0.706192 Nnruwal 1265097 365 471.6622 0.773859 Okara 2232992 816 832.5196 0.980157 Pakpattan 1286680 620 479.709 1.29245 Rahim yar Khan 3141053 3006 1171.069 2.566885 Rnjanptir 1103618 705 411.4585 1.713417 Rawalpindi 33639] I 428 1254.157 0.341265 SiiliUsat 1843 1 9-1 830 637.1924 1.207813 Sargodha 2665979 1336 993.9488 1.344134 Slicikhupura 3321029 478 1238.169 0.386054 Sialkot 2723481 760 1015.387 0.748483 T. T. Singh 1621593 753 604.5736 1.245506 Vcbari 2090416 1110 779.3634 1.424239 PUNJAB 73621290 27448 27448 I Source: SMRs computed from the data obtained from the office of the DG Health, Punjab Figure 5.4 Standardized TB Morbidity Ratio in tbe Punjab in 1995 3.00 2.50 ' 2 00 (-= f=i as £ 1-50 1.00 o 0.50 0.00 JMMJHLL DISTRICTS § Distribution of Population and Notified TB cases in the Punjab in 1995 N •**Alto* *ÿ *. / Rjwslpindl w r* ' Ch***• s X ' * kfmil *1 * •% *ÿ * * **V' •ft Sftrpodrift* * rtl-a •i * I " ft BhMar ft ft ft Jhang U-I*3L» ' * V >. Jot»«7*k5 RT Mo.’.iltj*i}arh Ghÿl xjun ft ft ft, * 1 ft lyy'V a •• •• ./ÿj ft ft sl ilftM * * ft * ..N _ •. » #* N Bshÿalpur* « • wifwyaitewÿ • ft •ft ih •••ft Notified TB cases In 1995 ft 1 Dot 50 TB case* Population 1998 832960 1500000 1600001 3000000 3000001 4500000 0 25 50 100 150 200 250 300 - 4600001 Kilometers -6000000 8000001 -7500000 1:5.000.000 Figure 5JS Distribution of Population and Notified TB cases in the Punjab in 1995 Standardized TB Morbidity Ratio in the Punjab in 1995 N w E V Rawalpindi S jn&an Gnr« ftfandi SahaUWn SUM 7 Narwal tihatka. Sfwtthupurji Irnharn< flJwv Faiutatud itotMjaavf Wtizadafgarb Chau Khan SdHnl / Dn MM.'UTV.H PilUpaBsn VWwt Multan Lodhran Banawatwpnr Standardized Morbidity Ratio ~ f ] 0.1708-0 5000 r~ I 0-3001 - 1.0000 1.0001-1.5000 1.6001 -2.0000 0 25 50 100 150 200 250 300 2.0001 - 26000 Kilometers 2.6001 - 30000 1:5,000,000 Figure 5.6 Standardized TB Morbidity Ratio in the Punjab in 1995 63 5.4 Spatial Patterns of Tuberculosis in the Punjab in 2000 Tuberculosis control programs developed to much extent all over the world till year 2000. The coverage of DOTS by WHO was only 9% in Pakistan till that time. Such a slow progress in five years to control a rapidly spreading disease is of course not acceptable. During this time, treatment and data recording was being arranged by the department of heath, government of Pakistan with the cooperation of national and international agencies. The data collected by DOTS was from very few areas so the data which is being used in this research is collected from Health management information system (HMIS) by the department of health, government of the Punjab. The data for year 2000 almost followed the same pattern as was in year 1995; however the number of cases is considerably higher. There were observed a total number of 41,953 TB cases in the Punjab in year 2000. Again, the highest number of cases was found in district Rahimyar Khan as was the case in 1995. Figure 5.8 shows the observed TB cases in all 34 districts along with their distribution of population. The map shows that the focus of disease has been changed from Lahore and Faisalabad to north and northeast. If we compare the dot density of disease cases with population distribution of districts, Khushab, Rahimyar Khan, Rajanpur, Mianwali and Vehari are facing much disease burden as compared to their position in population distribution (Figure 5.8), The spatial patterns of tuberculosis in 2000 are studied with the help of Standardized Morbidity Ratio (SMR) which is based on observed and expected counts in all 34 districts of the Punjab. These are shown in Table 5.3 and Figure 5.7. The table shows that the areas of north eastern Punjab are the least severely effected by TB. The most urbanized and thickly populated districts Faisalabad, Sheikhupura, Lahore, and Rawalpindi have a low SMR which is less than 0.5. Okara, Pakpattan, Layyah, Sialkot and Sargodha has an SMR around 1 which means that they have almost the same rate of disease as being observed in overall Punjab province. During year 2000 the highest SMR was found in Khushab which crossed 2,75. Other districts with higher SMR are Mianwali, Rajanpur, Bhakkar, Vehari and Gujranwala (Figure 5.9). 64 Table 5.3: Tuberculosis cases and SMR in the Punjab, 2000 Casts Expected Cases SMR with DISTRICT Population 1998 Notified (A!) with reference reference to Forms)2000 Puojab Attock 1274935 593 726.5201 0.81622 Bahawalnagar 206)447 1584 1174.713 1.348414 Bahawalpur 2433091 2299 1386.494 1.658139 Bhakkar 1051456 1183 599.1709 1.974395 Chakwal 1083725 992 617.5593 1.606323 I).G. khan 1643118 622 936.3287 0.664297 Faisalabad 5429547 721 3094021 0.23303 Gujrnnwala 3400940 3380 1938.021 1.744047 Gnjrat 2048008 1330 1167,055 1,139621 Hafijabad 832980 390 474.6726 0,821619 Jfhan* 2834545 837 1615.262 0.518182 936957 821 533.9238 1.537673 Kasur 2375875 910 1353.889 0.672138 Khanewa) 2068490 922 1178.726 0.7*22 Khusliab 905711 1430 516.1183 2.770683 Lahore 6318745 1339 3600.729 0.371869 Layyah 1120951 641 638.7725 1.003487 Lodhran 1171800 378 667.7488 0,566081 M, B. DIN 1 160552 979 661.3391 1.48033 Mianwali 1056620 1339 602.1136 2.223833 Multan 3116851 2206 1776.134 1.242024 Mnzzafar Garh 2635903 732 1502.066 0.487329 Narowai 1265097 1058 720.9139 1.467582 Qkara 2232992 1073 1272.468 0.843243 Pakpattan 1286680 710 733.213 0.968341 Rahim yar Khan 3141053 3548 1789.925 1.982206 Rajanpur 1103618 1284 628.8953 2.041675 Rawalpindi 3363911 811 1916.92 0.423074 Sahiwal 1843194 1208 1050.342 1.150102 Sargodha 2665979 1724 1519.205 1.134804 Sheikhupura 3321029 528 1892.484 0.278998 Sialkot_ 2723481 1712 1551.972 1.103113 T. T. Singh 1621593 416 924.0627 0.450186 Vehari 2090416 2253 1191.221 1.891337 PUNJAB 73621290 41953 41953 1 Source: SMRs computed from the data obtained from HM1S, Punjab Figure 5.7 Standardized TB Morbidity Ratio in the Punjab in 2000 3 00 / 2 60 1 200 1.50 ivt 1,00 050 000 i HllIlllllH|H3IMl|H»|ll|lMlliih h:l' DISTRICTS 9| Lfl Distribution of Population and Notified TB cases in the Punjab in 2000 . *Attodt V r- t * % IN'S/-. Oialw*j. s # 4 A" • *- d i *% ’ * « ’ It*: — Mi W/1 * • # ' |» * r--w *., .Uw* * *V • \ T(**4 % / •1 • • er; • /C ’ V • I** • * •Jk ill Yar wW *, In 2000 • 1 Dot « $0 TB Ctm Poputettoo of PunM«19M 0 25 50 100 150 200 250 300 1500001 •3000000 Kilometers 3000001 - 4500000 1:5,000,000 0000301 7500000 Figure 5-8 Distribution of Population and Notified TB cases in the Punjab in 2000 Standardized TB Morbidity Ratio in the Punjab in 2000 N S Allock Rawalpindi Cliakwal Jhnium Mares SahauM* Saipodh* I tkfrenwala Bhakkar Y SfwWiupura Lahore Jwg / ->Ka*u Layyah TO«Takar*h CÿJ Ohara mmm MM Whan Lodhren Rahkn var Khan Standardized TB Morbidity Ratio 0.2330 - 0.5000 05001-10000 1.0001-1 5000 1.6001 -2.0000 0 25 50 100 150 200 250 300 2.0001 2.5000 Kilometers - 2.5001 - 3.0000 1:5.000,000 Figure 5.9 Standardized TB Morbidity Ratio in the Punjab in 2000 68 5.5 Spatial Patterns of Tuberculosis in the Punjab in 2005 In 2005 DOTS strategy achieved 100% coverage in Pakistan. The diagnostic and treatment facilities provided through this strategy are, no doubt,, excellent. Along with these facilities, data recording was also improved greatly. Now the data indicates gender differences, different forms of disease {smear positive, negative, retreatment, extra pulmonary TB), and health facilities. According to this data the total number of notified cases in the Punjab in 2005 is 60,363 out of which there are 30,420 females and 29,943 males. The maximum number of reported cases was found in two districts of southern Punjab. Rahimyar khan is on top with 4150 patients, followed by Bahawalpur with 3607 recorded TB cases. Districts Rahimyar Khan, Jhang, Multan, and Gujranwala have a higher number of smear positive cases as shown in table 5.4. The table also shows that there are 5777 extra pulmonary TB cases found in 2005 in Punjab. During 2005, highly populated districts of north Punjab have considerable disease burden except Lahore and Faisalabad as shown in disease and population distribution map in figure 5.11. One of the drawbacks of a dot map is visual illusion affected by area. As the districts Bahawalpur, Bhakkar and Rajanpur have higher tuberculosis rate in actual but due to their larger areas the dots showing disease cases seemed to be lesser in number falsely. This problem can be overcome through a map showing standardized rates as in Figure 5.12. In 2005, according to WHO database, there is a notification rate of 87 per 100,000 population per year. To compare the notification rates from 34 districts of the Punjab with the rate estimated in Pakistan, expected counts were calculated based on the notification rate for Pakistan and estimated populations in the districts in 2005. Then Standardized Morbidity Ratio (SMR) was determined with reference to Pakistan in Punjab and all 34 districts. SMR for the Punjab province is 0.8101 which means that Punjab has a lower rate than the average for Pakistan. It implies that other provinces are adding more than Punjab to the disease burden in Pakistan, This situation was prevailing also in 1990 although at that time the share of the Punjab to disease burden in Pakistan was lower than that in 2005. The districts which have higher SMR are Bahwalpur, Rajanpur, Mandi Bahauddin, and Vehari. Out of 34 districts, 22 experienced a lower rate as compared to average for whole of the country. Three districts of south Punjab: Rajanpur, Rahimyar Khan, and Bahawalpur experienced high SMR with reference to Pakistan in 1990 which continues in 2005. To study the spatial patterns of tuberculosis in 69 different districts of the Punjab and to compare them within the province, expected counts were calculated on the basis of total cases, total population and estimated district population in 2005. With the help of these expected counts, SMRs were calculated which are give in table 5,5. The analysis of these SMR shows that Bahwalpur, Rajanpur, MB. Din, Vchart, Bhakkar, and Rahimyar Khan are the districts with high SMR. Three most populated districts Faisalabad, Lahore, and Rawalpindi are among the districts with the lowest SMR as shown in Figure 5.10. 70 Table 5.4: Total Number of Tuberculosis Cases (All Forms) Notified in Punjab 2005 Extra Pulmonary Smear Positive Smear Negative Total Total District TB Grand Total M F T M F T M F T M F Attock 136 129 265 540 719 1259 50 78 128 726 926 1652 Bahawalnagar 390 360 750 660 643 1303 83 86 169 1133 1089 2222 Bohawalpur 520 412 932 1163 1172 2335 160 130 340 1843 1764 3607 Bhakkar 152 198 350 379 523 902 80 87 167 611 808 1419 Chakwal 118 126 244 465 511 976 33 31 64 616 668 1284 D.G. khan 53 38 9! 522 699 1221 81 85 166 656 822 1478 Fflisalabad 232 199 431 354 345 699 32 38 70 618 582 1200 Gujranwala 553 549 1102 728 88) 1609 189 271 460 1470 1701 3171 Gujral 535 462 997 508 499 1007 119 156 275 1162 1117 2279 Hafizabad 49 47 96 274 271 545 27 47 74 350 365 715 Jhang 584 547 1131 184 143 327 89 90 179 857 780 1637 Jhelum 65 42 107 173 156 329 19 11 30 257 209 466 Kasur 104 101 205 507 584 1091 65 60 US 676 745 1421 Khanewal 409 396 805 572 581 1153 43 54 97 1024 1031 2055 Khushab 48 41 89 197 180 377 23 23 46 268 244 512 Lahore 164 144 308 413 419 832 170 285 455 747 848 1595 Layyah 295 316 611 227 321 548 54 39 93 576 676 1252 Lodhran 82 70 152 98 113 211 22 23 45 202 206 408 M, B, DIN 164 179 343 547 602 1149 66 71 137 777 852 1629 Mianwali 50 45 95 160 141 301 1 0 l 211 186 397 Multan 645 486 1131 637 717 1354 134 174 308 1416 1377 2793 Muzzafar Garb 345 290 635 815 971 1786 86 103 189 1246 1364 2610 Narowal 143 105 248 264 299 563 43 45 88 450 449 899 Okara 249 189 438 642 524 1166 55 69 124 946 782 1728 Pakpahan 102 94 196 525 576 not 10 15 25 637 685 1322 RahimyarKhan 1093 953 2046 982 872 1854 144 106 250 2219 1931 4150 Kajanpur 214 209 423 639 615 1254 It 10 21 864 834 1698 Rawalpindi 235 221 456 245 346 591 175 219 394 655 786 1441 Sohiwal 342 256 598 299 206 505 23 19 42 664 481 1145 Sargcdha 301 282 583 1267 1234 2501 169 180 349 1737 1696 3433 Sheikupura 308 271 579 372 421 793 34 65 99 714 757 1471 Sialkot 469 393 862 642 731 1373 139 216 355 1250 1340 2590 T, T. Singh 233 225 458 583 550 1133 88 82 170 904 857 1761 Vehari 434 366 800 919 962 1881 108 134 242 1461 1462 2923 PUNJAB 9816 8741 18557 17502 18527 36029 2625 3152 5777 29943 30420 60363 Source: Provincial TB control Program, Punjab (NTP) 71 Table 5.5: Tuberculosis cases and SMR in the Punjab, 2005 Observed Expected SMR Expected Estimated SMR with Cases Cases with with ref enscs with DISTRICT Population reference Notified reference to ref. to 2005 to Punjab 2005 to Pak PaJdsluii Punjab Atruck_ 2932 1652 1290 ] .2805 1045 1.5806 JlBhawaiaasar 2830560 2222 2463 0.9023 1995 LI 133 liahiiwalpur 2398670 3607 2087 1 .7284 1691 2.1336 Uhiikkai' 1223 M8 1419 1064 1.3335 862 1 ,6460 Chnkwal 126 1 033 1284 1097 lJ 704 889 1.4447 DAL khan 1911575 1478 1663 0.8887 1347 1.0970 I'liisalalifld 6315992 1200 5495 0.2184 4451 0.2696 Cujranwala 3956290 3171 3442 0.92 1 3 2788 1.1372 Ciijrat 2382433 2279 2073 1.0995 1679 1.3573 Hufkabad 968777 715 843 0.8483 683 1.0472 J Liang 3297088 1637 2868 0,5707 2324 0.7045 Jhrlum 1090009 466 948 0,4914 768 0,6066 Kasur 2763449 1421 2404 0.5910 1948 0,7296 Khunew al 2406247 2055 2093 0.9816 1696 1,2117 Khushab 1054289 512 917 0.5582 743 0.6890 Lahore 7350797 1595 6395 0.2494 5181 0,3079 Lamh 1304331 1252 1135 1,1033 919 1.3619 Loti bran 1362782 408 1186 0.3441 960 0.4248 M. R DIN 1349793 1629 1174 1.3872 951 1.7 1 23 MinJiwall 1229643 397 1070 0.3711__867 0.4581 Multan 3626146 2793 3155 0.8853 2556 1,0929 Mtiyzafar Curb 3066530 2610 2668 0.9783 2161 1.2076 Norwal M72!08 899 1281 0.7019 1038 0.8665 Okara 2597837 1728 2260 0,7646 1831 0,9438 Pnkpattan 1497004 1322 1302 1.015 r 1055 L2530 Kali ini yarKluin 3654291 4150 3179 1.3053 2576 1.6113 Ruianpur 1283765 1698 1117 1.5203 905 1.8767 Rawalpindi 39)2992 1441 3404 0.4233 2758 0.5225 ' Sahiwal 2144298 1145 1866 0.6138 1511 0.7576 Sareojtia 3101168 3433 2698 1.2724 2186 1.5707 Slmkwpara 3363200 1471 3361 0.4377 2723 0.5403 Siulkot 3168279 2590 2756 0.9396“ 2233 11599 T.T. Singh 1 886679 1761 1641 1.0729 1330 1.3243 Veilart 2432225 2923 2116 1,3814 1714 1,7052 PUNJAB 85646360 60363 74512 0.8101 60363 1.0000 Source: SMIls computed from the data obtained from NTP Figure 5.10 Standardized Morbidity Ratio in the Punjab in 2005 2.50 2.00 T £3 1 50 ' iss 1.00 L 0.50- OOOJ* IlMlMliffl UU DISTRICTS i -J i J Distribution of Population and Notified TB cases in the Punjab in 2005 N E * *V S * . * - Miinwar.. ’ # - T* is i J *a Tii * r . .1 ft ' p .* JlWOfl SJ i : •ÿ >5 •* t s* v # * o # + . **t1' s ~jy ****** ?TV; s \5T *• ,«V i In 30M • 1 Dot = H TB Cam Estimated Population 2000 0W777 -1600000 S0 25 0 50 100 150 200 250 16OO0C1 - 3000000 Kilometers 1;5,000,000 Figure 5.11 Distribution of Population and Notified TB cases in the Punjab in 2005 Standardized TB Morbidity Ratio in tbe Punjab in 2005 4s <, A { jn i. ( Dufcwtf NMMI KMtf ''•* •<*.*ÿ»* r Standardized TB AAorNdHy Ratio 02606-0.5000 05001-1.0000 0 2550 100 150 200 250 300 10001 15000 1 5001 - 2.0000 1:5000,000 2.0001-25000 Figure 5.12 Standardized TB Morbidity Ratio in the Punjab in 2005 75 5.6 Changes in SMRs from J990 to 2005 in the Punjab In the previous section, standardized morbidity ratios (SMR) are compared within districts of the Punjab province. The focus was more on spatial patterns rather than temporal. As geography is a study of areal variations that take place from time to time so this section would bring the temporal changes to spotlight. For this purpose, SMR which are calculated for the districts for the years 1990, 1995, 2000, and 2005 separately are now being compared. This comparison expresses the changes in individual districts in disease pattern that occurred from 1990 to 2005. There was found a great diversity in Standardized Morbidity Ratios (SMRs) that were experienced by districts in 15 years. Table 5,6 describes the changes that are experienced by every district in fifteen years of study period; from!990 to 2005, As shown in table 5.6 the largest city of Punjab, Lahore, had a higher SMR in 1990 but later there was found a sharp decrease till 2005, This trend may be due to improvement in health facilities in provincial capital and increased awareness among people. The second largest district in population in Punjab, Faisalabad, also followed the same trend. Some districts observed very high SMRs throughout the study period 1990 to 2005 (Figure 5,13). All of them are in southern Punjab: Rahimyar Khan, Bahawalpur, Rajanpur, Vehari, Bahawalnagar. Multan, Khushab, and Mianwali may also be placed in this category but they experienced a lower SMR in 2005. Gujranwala, Bhakkar, Gujrai, M.B Dm, Sargodha, and Sahiwal arc the districts which experienced high SMRs except in the last decade of study period. While Jhang, Jhelum, and Lodhran observed low SMRs in the last decade. Three districts Rawalpindi, Kasur, and Sheikhupuia may be categorized as the districts which always experienced very low SMR throughout the study period. The data showed that the residents of these three districts are less likely to be infected with tuberculosis. Among the above mentioned spatio-tempral patterns in disease rates; an issue which is of serious concern is the occurrence of higher disease rates in most of the disfrics of southern areas of the Punjab from 1990 to 2005. The reasons are not only the poverty and unawareness due to illelracy which is comparatively high in these areas but also the lack of interest by the policy makers and health service planners and the administration. 76 Table 5.6: A Comparison of SMRs in the Punjab, from 1990 to 2005 SMR Districts 1990 1995 200(1 2065 Atlock 1354 J 2.13115 o.sifi:: 1 5806 Balmwuluaam' 1,1388 1.562657 1 .348414 Lt 138 13ahanalpur 1,3666 2. 1 54066 1.658139 2.1336 EShukkar 0,5793 1.933619 1 .974395 1.6460 Cbnkwal 0.6422 1,8290 1 8 1 .606323 1 .4447 IXG. Idiau 0,890! 0.837416 0.664297 1 .0970 Faisiifubiid 2.1788 0.170925 0,23303 0.2696 Guiraiiwahi 1.0069 0.720053 1.744047 1.1372 Giijral 0.7176 0.931 174 1 . 1 3962 1 1.3573 Hafi /.a bad 1.342747 0.821619 1.0472 Jhitnii 0.9496 0.633993 0.518132 0,7045 Jliclum 2.6120 1.869331 1.537673 0.6066 Kasur 0.4225 0.568983 0.672138 0,7296 KhancwaJ 0.S055 0.507009 0.7822 1.2117 Klmsliiib 1.1254 2.908135 2.770683 0.6S9Q Lahore 1.5752 0.279735 0.371869 0,3079 Lay,yah 1.0298 1.246648 1.003487 1,3619 Lodi) ran 0.71 1868 0.566081 0.4248 M.H, Din 0.3 35000 » 3 0000 — 2.5000 2.0000 QL z 1 oooo 0.5000 7 0 0000 t s o i|| fjfil $ illil*" <5 i ° 1 DISTRICTS Year 2005 Year 2000 Year 1995 Year 1990 3 78 5.7 Smear Positive Tuberculosis Cases A patient with at least 2 initial sputum smear examinations positive for AFB; or one sputum examination AFB positive and radiographic abnormalities consistent with active pulmonary tuberculosis as determined by a healthcare professional; or one sputum specimen AFB+ and culture positive for M. tuberculosis is considered as a smear positive patient (WHO 2006). Smear positive patients are considered to have greater bacterial load and thus have the capacity to spread infection (Godoy et al. 2004). So they are more dangerous for the people around them as tubercle bacilli spreads through air during cough or sneeze. Such cases should be treated on a priority basis and a complete record about of the patient is kepi to monitor till the patients become smear negative. In case of the Punjab the data is available about the proportion of smear positive in total notified cases from 1990 to 2005. In Punjab the percentage of smear positive cases in total observed cases has increased during 1990 to 2005. It remained almost stable in the last decade of 20th century. However there is a sharp increase during 2000 to 2005 as shown in figure 5.14. It has not been exceeded titan 21% in Punjab province (overall) till 2000 but in 2005 it was observed 31 % which is alarming for a developing country which has a larger population and trapped in poverty. However one reason for the abrupt increase of smear positive cases may also be the improvement in case detection, diagnosis, and recording techniques in recent years. As shown in the Table 5.7, the district wise time series data regarding the proportion of smear positive cases demonstrates a great diversity among districts from 1990 to 2005. In 1990, the highest percentage of smear positive cases was observed in Sargodha, Jhang, and Multan as 26, 26, and 24 respectively. Districts Okara, JheLum, and D.G Khan had lowest proportion of smear positive cases in 1990. In 1995, Khanewal, and Attock joined with Sargodha, Jhang, and Multan as the districts with higher smear positive cases. It was not even reduced in year 2000 in Jhang, Sargodha and Multan. Along with these three, a proportion of 30% of smear positive cases were observed in Sahiwai, 27% b Lahore, and 28% in Hafizabad were found in year 2000. The lowest proportion was detected in some districts of Northern Punjab: Rawalpindi, M.B. Dm, and Gujrat. The data for the year 2005 is marked with comparatively high proportion in almost all districts of Punjab. Some districts observed extremely high rates of smear positive cases in 2005. District Jhang is on top of the list with 69% smear positive cases followed by Sahiwai with 79 52% cases in 2005. Other districts with higher proportion of smear positive patients are Rahimyar Khan, Layyah, Gujrat, Multan, Khancwal and Faisalabad. The districts which showed a lower percentage were D.G. Khan, Kasur, Attock, Sargodha and Hafizabad. In Pakistan an overall proportion of 34% smear positive among all the cases notified was observed in 2005 (NTP 2006), For the purpose of analysis, on the basis of this proportion, expected proportion of smear positive cases were determined for the Punjab and all the districts. The standardized morbidity rates (SMR) are then calculated which showed that the Punjab province has overall a lower proportion as compared to the proportion for whole the country. However some districts of the Punjab have very high SMR like Jhang which has almost two times the proportion in Pakistan. Bahwainagar, Sialkot, Rawalpindi has a standardized rate of almost 1, while majority of the districts have SMR values less than l which shows they have a lower proportion of smear positive cases as compared to the proportion for whole of the country (Figure 5.15). A very high rate of smear positive cases in recent years is very serious issue which should be addressed on emergency basis. These smear positive patients are the disease sources to infect healthy people around them. Although better diagnostic facilities and techniques have improved smear positive case detection which is one of the factors for such a high proportion of smear positive cases as compared to the past Now it is the responsibility of policy makers to take necessary actions without wasting any time with special focus on areas which are more vulnerable. 80 Table 5.7: Proportion of Smear Positive Cases among Total Patients Percentage uf Smear Positive Cases SMRS+ Districts among Total Case* Observed _ with ref. lo 1990J 1995 2000 H~2005 Pnk 2005 Attack 21 25 17 16 0.4718 Bahawalnagar 19 II 19 34 0.9927 B»b""|p'ir 24 23 15 26 0.7600 Hhakknr 18 19 13 25 0.7254 Chakwal 21 13 18 19 0.5589 D.G, Khan 16 16 13 6 0,1811 Paisalabad 23 22 21 36 1,0564 Gujranwflla 20 16 14 35 1.0221 Gujrat 23 23 13 44 1.2867 22 28 13 0.3949 Jhang 26 27 24 69 2.0321 Jheluitt 15 19 21 23 0.6753 Kflsur 19 14 18 14 0.4243 Khanewal 23 27 23 39 1.1521 Khushab 18 19 19 17 0,5113 Lahore 22 21 27 19 0.5680 Lawah 20 16 16 49 1.4354 Lodbran 14 16 37 1.0957 MB. Din 20 13 21 0.6193 MlanwaU 22 20 25 24 0.7038 Multan 24 26 29 40 1.1910 Muz/afar Garb 19 17 26 24 0.7156 Nurowal 22 21 28 0.8114 Okara 16 20 15 25 0.7455 Pakpattan 16 18 17 15 0.4361 Katiim var Khan 22 25 25 49 1.4500 Rajanpur 18 19 17 25 0.7327 Rawalpindi 19 19 12 32 0.9307 Sahiwal 22 25 30 52 1.5361 Sargodha 26 27 25 17 0.4995 Sheikhuptira 18 17 19 40 1.1754 Sialkot 22 22 22 33 0.9789 T. 23 26 20 26 0.7649 Vcbari 21 23 24 27 0.8050 [Punjab j 21 21 20 31 0.9042 Source: SMRs computed from the data obtained from NTP 81 Percentage of Smear Positive among Total Notified TB Cases 1990-2005 35 JO M * 1 B< 20 -Y 15 8a: id 10 y' 5 o I MO 1905 2000 2005 YEARS Figure 5.14 Proportion of Smear Positive patients among total notified TB cases SMR Smear Positive TB cases in the Punjab in 2005 N Anode RawiUprxJl w ( SltakXsr Hp SMktiupm Inhere Ka*ur l L#yyah Jn-; Ota,.1 Wuinflarÿarh Ghazi f Wwi"“M MuOan Lattnn RajaniMr Bahawaipw Raton Ya/ Wtan SMR Smear positive cases 0 1811 -0.6000 E 0.5001 - 1.0000 10001-1.5000 0 25 50 100 150 200 250 300 1.5001 -2.0000 Kilometers 2 0001 -2,5000 1:5,000,000 Figure 5.15 SMR Smear Positive TB cases in the Punjab in 2005 S3 5.8 Mortality among TB Patients Pakistan has an estimated rate of 37 deaths per 100,000 individuals of population due to tuberculosis in 2005 (WHO 2007}. This rate is higher than India and China which occupy top two positions of 22 high tuberculosis burden countries. Due to effective tuberculosis control program with the sponsorship of World Health Organization, TB mortality has been reduced to a great extent as compared to the situation in the past. TB is now considered a completely curable disease so most the deaths are caused by none or incomplete and irregular treatment. During treatment, in Punjab, there were 1225 deaths recorded in 2005 which is 2% of the cases notified. It should be kept in mind that these data doesn't represent the overall mortality due to tuberculosis. These are actually the number of deaths among the patients who were registered as TB patients. To analyze and compare the patterns of deaths of the patients in different districts of the Punjab, expected number of the deaths in all districts were calculated on the basis of total number of TB deaths notified in the Punjab by using the following formula: E(i) = e X and ±OU) 6 = — 5>0) J=I Where Eft) is expected number of deaths in district / N(j) is the total number of patients in district i Oft) is the observed number of deaths in the districts of Punjab province Nft) is the total number of patients in all districts of the Punjab province These expected counts were compared with actual counts to find standardized mortality ratio SMR to identify the districts where the death rate for tuberculosis patient was high during treatment. The following formula was used 0(0 SMR( 0= E(i) Where Oft) is observed number of deaths in a given period of time in area i. (i=l,2l3.,„n) Eft) is expected number of deaths in district i 84 Table 5-8 shows the number of deaths and in 34 districts in 2005. The highest ratio of observed deaths to expected counts was found in district Multan, followed by M.B. Din, and Vehari (Figure 5,17). The number of deaths in Multan district is more than double than the average for all districts of the Punjab (Figure 5.16). In such cases health services accessibility and effectiveness should be revisited, it is beyond doubt that TB is a curable disease. So the significant differences in death rate only in two or three districts may be due to flaws in health service provision and implementation of DOTS. District TB control administration should be advised to focus on these issues. The lowest number deaths of TB patients were observed in Lahore district which is the largest district in population. Lahore is the provincial capital so it is the most developed among other districts with highest literacy rate. These factors may contribute to improved public awareness about the need for the completion of treatment. 85 Table 5.8: Total Deaths among TB Patients and Standardized Mortality Ratio Observed Expected Cases Patients died aul deaths with SM.K Districts Notined(All Observed/ tola) reference to Forms) or Expected notified Punjab 2005 I Attock 1652 32 33.525$ 0.954497 1 Bahawalnagar 2222 38 45.09302 I 0.842702 Bahuwatpur 3607 97 73.20006 1.325136 Bhukkar 1419 34 28.79703 .180677 1 Chakwal 1284 11 26.05735 | 0.422146 D.G. Khan 1178 41 29.99437 1.366923 Fafcalabad 1 200 22 24.35267 0.903392 1 Gujranwala 3171 78 64.35192 1.212085 Gujrat 2279 I_60 46.24977 1-297304 Hafumbatl 715 19 14.51013 1.30943 Jhang 1637 38 33.221 1 1.143851 Jhclum 466 10 9.456952 1.057423 Knsur 1421 25 28.83762 0.866923 Khanewal 2055 19 41.70394 0.455592 Khnshab 512 13 10.39047 1.251146 Lahore 1595 3 32.36875 0.092682 Lavvah 1252 IS 25.40795 0.70844 Lotlliraa 408 10 8.279907 1.207743 M.B. Din 1629 65 33.05874 1.966197 Mianwali 397 9 8.056674 1.117086 Multan 2793 136 56.68083 2.3994 Muzzafar Garh 2610 23 52.96705 0.434232 Nartmal 899 16 18.24421 0.876991 Okaru 1728 28 35.06784 0.798452 Pakpaltaa 1322 28 26.82852 1.043665 Rahim yarKhan 4150 42 84.21964 0.498696 Rajanpur 1698 23 34.45902 0.667459 Rawalpindi 1441 31 29.24349 1.060065 Suhhval t 1145 28 23.2365 i.205001 Sargmlha 3433 41 69.66892 0.588498 Sheikhupura 147) 34 29.85231 1.13894 Sialkot 2590 26 52.56117 0.494662 T.T. Singh 1761 28 35.73754 0.78349 Vchari 2923 99 59.31904 1 1.668941 1 Punjab___60363 1225_ 1225 1 Source: SMRs computed from the data obtained from NTP Figure 5.16 Standardized Mortality Ratio in the Punjab in 2005 2,50 2,00 1.50 =: Z [4=7 /; fL 1, 1.00 i n 0.50 0.00 T s3 5 Mil 111 o fjljl ill! ? i I s s £ III!3 sH ° 5 1 i r ?i!ii DISTRICTS s Standardized TB Mortality Ratio in the Punjab in 2005 N E r s at Mum J.V- - • __ Suggdhd Fi t** t«k sr& can A Wwi Y* Krtjn Standardized Modality Ratio 0.0927 - 0.5000 0.5001-10000 1.0001 1.5000 0 2550 100 150 200 250 300 - 1.5001 - 20000 1:5.000.000 2.0001 - 2 5000 Figure 5.17 Standardized TB Mortality Ratio in tbc Punjab in 2005 88 5.9 TB and Gender A number of studies worldwide demonstrated that in many countries the number of male patients is higher as compared to female patients. A gender based study of tuberculosis was conducted in Bangladesh, India, Malawi, and Columbia. It was found that there were fewer female patients who were suspected for TB in India and Bangladesh. However there were equal number of males and females in Malawi and more women were identified with suspected TB in Colombia (Weiss et al, 2006). It was also observed that the males as compared to females are more in number to dropout before the successful completion of treatment. The reasons that are considered for the excess of males with TB may be that fewer women in population have active TB, fewer women attend health clinics for treatment due to gender specific barriers and cultural constraints, or fewer women are diagnosed with smear positive sputum (Uplekar et al 2001). A gender specific research about tuberculosis by World Health Organization (WHO 2004) suggests that prior to adolescence, the males and females have a minor difference in TB infection rates on the basis of gender. After the age of fifteen years, men begin to overtake women in their rates of infection. Moreover, as they grow older, men have a higher probability to switch over to disease from infection. The difference may be attributed to the fact that the men may be more exposed to other people with infectious TB, due to their greater social interaction outside the home. Other factors like smoking, alcoholism, migration, war and in some cases, imprisonment may also play their role to worsen the situation, However, the rate of progression from infection to disease is significantly higher for women of reproductive age than for men of the same age. But in the present study the data revealed that the number of female patients are little more than male patients in the Punjab, In Punjab, the social barriers and constraints for women are not much different as in the case of India and Bangladesh as described above in the research by Weiss et al. (2006) but the situation regarding disease and gender was found very much different. It is really surprising that there were found more female TB patients notified as compared to male patients in Punjab in 2005. It is interesting to mention that the sex ratio, according to 1998 census, in Punjab depicted more than 107 males to every 100 females. The disease sex ratio was found 98,4 in 2005. The data for the year 1990, 1995, and 2000 does not contain any detail about the sex ratio of the patients, so the 89 figures for year 2005 are being used Table 5.9 provides a detailed comparison of the total male and female TB cases notified in 2005 and sex ratio according to 1998 census for all of the districts of the Punjab, Table 5.9: TB and Gender differences in the Punjab in 2005 Notified TB Disease Sex District cases in 2005 Sex Ratio Total 1998 Male Female Ratio Attock 726 926 1652 78.4 99.6 Bahawalrmjjar 1133 1089 2222 104 107.4 Bahawnipur 1843 1764 3607 104.5 110.8 lihakkar 611 808 1419 75.6 107.1 Chakwai 616 668 1284 92.2 91.6 D.G. khan 656 822 1478 79.8 108.2 FaLsalabatl 618 582 1200 106,2 108.6 Gujramvala 1470 1701 3171 86.4 108.6 Guirat 1162 1117 2279 104 100.4 Hafizsbad 350 365 715 95.9 108.4 857 780 1637 109.9 108.4 Jkeluru 257 209 466 123 99.8 Kasur 676 745 1421 90.7 109.9 Khancwal 1024 1031 2055 99.3 107,7 Khushab 268 244 512 109.8 99.4 Lahore 747 848 1595 88.1 111.3 576 676 1252 85.2 106.8 Lodhran 202 206 408 98.1 108.3 M. B. DIN 777 852 1629 91.2 104.9 Miatnvali 211 186 397 113.4 100.8 Multan 1416 1377 2793 102.8 1 10.4 Muzzafar Garh 1246 1364 2610 91.3 108.7 Narciwal 450 449 899 100.2 101.2 Okara 946 782 1728 121 109.6 Pukpattau 637 685 1322 93 108 I RahimyarKhan 2219 1931 4150 114.9 108.8 Kajanpnr 864 834 1698 103.6 111.1 Rawalpindi 655 786 1441 83.3 104.9 Sahiwal 664 481 1145 138 107.2 Surgod! 1737 1696 3433 102.4 106.2 Shcikupura 714 757 1471 94.3 108.6 Sialkot 1250 1340 2590 93.3 105.2 T-T.Singh 41 904 857 1761 105.5 105.3 Vehari 1461 1462 2923 99.9 107.7 PUNJAB I 29943 30420 60363 98.4 107.2 Source: Data about notified cases in 2005 obtained from NTP 90 TolaJ of maleTB palienls Disease ratiosex xlOO = Total number of femaleTB patients In Punjab the total number of cases notified shows that there are more than 98 male patients to every 100 female patients. Although this difference is not much significant but when we compare it with the total number of males and females in the province, it appears a remarkable difference. The actual sex ratio in the Punjab is slightly more than 107 while the disease sex ratio of 98.4 lags far behind of it. The actual sex ratio according to 1998 census reveals that majority of the districts including all the big cities like, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Jhang, „ Kasur, Okara, Bahawalpur have a significant higher proportion of males as compared to females. Chakwal is the only district with considerable higher proportion of females while Khushab, Mianwali, Narowal, Gujrat and attock have almost similar number of males and females. The disease sex ratio reveals a lot of diversity among districts that ranges from 138 in Sahiwal to 75.6 in Bhakkar as shown in figure 5.19. Amongst 34 districts of the Punjab, 18 have a disease sex ratio less than 100, while the total number of districts below 107.2 (actual sex ratio of the Punjab) arc 27. It clearly expresses that female notification rate outnumbers males throughout the Punjab. There are only 7 districts in Punjab where the proportion of male TB patients is higher than the proportion of male in the total population. These are Sahiwal, Okara, Jhelum, Jhang, Khushab, Mianwali and Rahimyar Khan. The districts which have maximum number of female patients in proportion to males are Attock, Bhakkar, D.G Khan, Gujranwala, Lahore, Layyah, and Rawalpindi (Figue 5,18). There was also found a great disparity between existing sex ratio and disease sex ratio within individual districts. Attock, Bhakkar, D.G. Khan, Gujranwala, Lahore, Muzafar garh, and Rawalpindi are the districts where the proportion of female patients is remarkably high as compared to actual proportion of male and female in individual districts. In Chakwal, Faisalabad, Jhang, Narowal, and Toba Tek Singh the disease sex ratio was found almost in accordance with their respective actual sex ratios. In contrast to majority of the world regions the higher number of female TB notification in Punjab is a very serious threat. It is a fact that due to social obstructions, females have a comparatively lesser access to health facilities in the Punjab. For a disease like TB a longer period of treatment makes the situation worst for them. A logical conclusion in these circumstances may be drawn as the actual 91 number of female TB patients in the population is much more than the notified cases, When this conclusion is joined with the fact that the actual number of males is more than females in Punjab, it indicates towards a more horrible picture. Here an argument may be raised that this higher number of TB notification in females may be a result of a success of the DOTS strategy which has gained 100% coverage in Punjab in 2005. Bui if it was the case then the number of notified males should be higher as the proportion of males is higher in the population. Secondly, if it was true then overall detection rate should also be raised. Thus the higher proportion of females in notified cases should be considered as a serious concern because the chances of spread of disease are raised due to their role at home. Women spend more time at home as compared to males. They are in close contact with children especially under age of 5 years. They spend much of their time in kitchen and in close contact with utensils being used by the family. Literacy rate is lower in females as compared to males in the Punjab. Lack of necessary care while coughing and sneezing may cause risk for the entire family especially children. This scenario calls for serious concern and an improvement in policy to combat the disease with special reference to gender differences. Percentage of Male and Female TB Patients in the Punjab in 2005 N r S f r / / v j rPÿY J -j jJ J V x \ 50 25 0 50 100 150 200 250 % Male TB Patients H % Fema4e TB Patienta 1:5,000,000 Figure 5.18 Proportion of Mule and Female TB patients in the Punjab in 2005 Disease Sex Ratio in the Punjab in 2005 N % LAi M rafizatMd Giijf Bhaktuv Lxft*t 0*r»GhtoKW jflr WfwU -SS- Disease Sex Ratio Illy, I 76 - 80 81-100 50 25 0 50 100 150 200 250 101 - 120 Kilometers 121 140 1:5,000,000 - Figure 5.19 Disease Sex Ratio in the Punjab in 2005 94 5.10 Targets and Achievements in the Punjab against TB In 1991, ihe World Health Assembly (WHA) proposed that each National Tuberculosis Control Program (NTP) should achieve two objectives by the year 2000: to treat successfully 85% of detected smear-positive cases, and to succeed to reach 70% case detection rate (WHO 1994). During the early 1990s, the essential, basic methods for TB diagnosis and treatment were integrated into Directly Observed Treatment Short course (DOTS) strategy by WHO, DOTS became the internationally recommended approach to TB control. In spite of implementation of this program during the 1990s, the global targets were not achieved by 2000, and the target year was deferred to 2005 (Dye 2007), WHO (2007) declares that these targets were narrowly missed globally in 2005 with 60% case detection and 84% cure rate. According to WHO database there was a 37% case detection rate in Pakistan. Like Pakistan, the targets were also not achieved in the Punjab province. The rates observed here in Punjab are loo low than the targets set by WHO and the actual rates achieved by many other regions of the world. In Punjab, there was observed a 25% case detection rate of infectious TB cases in 2005 which is almost one third of the targets set by WHO. However an overall case detection rate for all types of cases was 40%. The cure rate in the Punjab is 64% which seems not a very bad performance against the target of 85% by WHO but indicates towards failure when compared with global rate of 84% in 2005 (Figure 5.20). 95 Disease Control Targets by WHO and Achievements in the Punajb in 2005 90 80 70 e 60 I! 0» 1 50 8 40 £ 30 1 20 10 0id Smear Positive Cure rate case detection Targets by WHO Achievemnts in Punjab i Figure 5.20 Disease control targets and achievements 96 5.11 Disease Clustering Clusters of health events arise whenever there is an excess of cases in space, in time or in both (Jacquez et at 1996). These are foci of particularly high incidence. Knox (1989) suggested that a cluster is a geographically bounded group of occurrences of sufficient size and concentration to be unlikely to have occurred by chance. Disease clustering investigations may prove most useful in the study of outbreaks and infectious diseases. Diggle (2000) declares that clustering is a departure from complete spatial randomness which is in a sense the rejection of hypothesis that cases occur independently of each other. This invites an interpretation in terms of genetic susceptibility or infectious transmission. As far as tuberculosis is concerned, it is not hereditary but the people who are in close contact with the patient are most likely to get infected. To study TB clusters on a broader level (ie province and districts) is useful to identify the variation in different districts in term of infectiousness of disease and secondly to identify how appropriate the present health facilities are, and where should be the focus in future. There are a variety of techniques, to identify clustering, which are discussed earlier in literature review. For the present study KulldorfFs Spatial Scan Statistic is being used. SaTScan is the software which is being used to identify Tuberculosis clusters in the Punjab. The scan statistic is a spatial, temporal, or spatiotemporal cluster detection method for aggregated data. The scan test is being applied on selective years: 1990, 1995, 2000, and 2005 to cover the whole time period for the present research. For this purpose SaTScan is provided with the number of cases in specific year, total population, and a coordinate file which provides geographic coordinate for each location ID. The analysis is done using Poisson Probability model because according to KulldorfF (2006) the Poisson model is used when the background population reflects a certain risk mass such as total persons lived in an area. The cases are then included as part of the population count. The software presents the results in an output file describing the location IDs for most likely and secondary clusters. These results are shown on the map of Punjab province, using ArcGIS 9.2, for a clear understanding of spatial location of clusters. The detailed output files from SaTScan are also provided. 97 5.11.1 identification of disease clusters in the Punjab from 1990 to 2005 In 1990 there was an overall high rate of tuberculosis in all the districts of the Punjab province. An analysis for identification of clusters using Kulldorff test through SaTScan yielded two districts: Lahore and Faisalabad (Figure 5.21). An analysts of TB patients’ data for 1995 through SaTScan using Kulldorff test identified that three districts of south Punjab: Bahwalpur, Rahimyar Khan, and Rajanpur form a most likely cluster in the Punjab. The secondary clusters were identified in northwestern and eastern parts of the Punjab province as shown in Figure 5.22. The districts that form secondary clusters are: Attock, Mianwali, Bhakkar, Chakwal, Khushab, Jhelura, Bahwalnugar, Pakpattan, Okara, Vehari, Sahiwal and Toba Tek Singh. During year 2000 as far as mostly likely cluster are concerned, there was no change from 1995 results. Again Bahwalpur, Rahimyar Khan, and Rajanpur were the districts where most likely cluster was found. In terms of secondary clusters, the cluster from northwestern Punjab spread towards east and north of punjab including districts Sialkot, Gujranwala, Gujrat, M.B Din, and Sargodha to the 1995 cluster districts Jhelum, Chakwal, Mianwali, Bhakkar, and Khushab (Figure 5.23). The situation on eastern side of Punjab was improved during these five years. Eastern cluster reduced to Bahwalanagar and Vehari only. Tuberculosis data for year 2005 showed a high rate of disease in many districts of the Punjab. The results from Kulldorff spatial scan statistic revealed that whole of the south Punjab and some parts of central Punjab were identified as most likely cluster. This major cluster consists of 13 districts: Bahwalnagar, Bahawalpur, Rahimyar Khan, Rajanpur, Muzzafargarh, Multan, Lodhran, Vehari, Khancwal, T.T Singh, D.G Khan, Layyah, and Bhakkar. In terms of secondary clusters, the situation was not different from 2000. The secondary cluster was found in Chakwal, Jhelum, Mandi Bahauddin, Sargodha, Hafizabad, Gujrat, Gujranwala, and Sialkot districts as shown in Figure 5.24. A comparison of all the years under study depicted that south Punjab was found to be severely affected throughout 15 years. Districts in northern and eastern parts of the Punjab were also found as clusters of tuberculosis disease. However areas in central Punjab remained less affected with tuberculosis. These cluster areas should be focused in future disease control and prevention policy. 98 SaTScan v7.0.3 Program run on: Mon March 14 13:15:27 2009 Purely Spatial analysis scanning for clusters with high rates using the Poisson model. SUMMARY OF DATA Study period 1990/1/1 - 1990/12/31 Number of locations,.....: 30 Total population : 60899000 Total number of...... cases....: 57344 Annual cases / 100000....: 94.2 MOST LIKELY CLUSTER 1.Location IDs included.: 16 Coordinates / radius..: (31.326869 N, 73.132892 E) / 0.00 km Population .: 4046000 Number of cases : 8301 Expected cases... 3809.81 Annual cases / 100000.: 205.3 Observed I expected...: 2.179 Relative risk... .: 2.378 Log likelihood ratio..: 2167.489629 Monte Carlo rank : 1/1000 P-value .:...... 0.001 SECONDARY CLUSTERS 2.Location IDs included.: 13 Coordinates / radius..: (31.512344 N, 74.410600 E)/ 0.00 km Population .: 4973000 Number of cases 7376 Expected cases... 4682.70 Annual cases / 100000.: 148.4 Observed / expected.,,; 1.575 Relative risk. : 1.660 Log likelihood ratio..: 728.112732 Monte Carlo rank : 1/1000 P-value. :...... 0.001 3.Localion IDs included,: 21 Coordinates / radius.,: (32.872486 N, 73.421408 E)/ 0.00 km Population 742000 Number of cases, 1825 Expected cases... 698.69 99 Annual cases / 1Q0DOO.: 246. 1 Observed t expected...: 2.612 Relative risk : 2.665 Log likelihood ratio,,: 637.203641 Monte Carlo rank 1/1000 P-value .: 0.001 4.Location IDs included.: 0, 3, 1 Coordinates l radius,.: (28.524156 N, 70.598083 E) / 125.15 km Population :5408000 Number of cases : 7591 Expected cases ...... : 5092.31 Annual cases / ...... 100000.: 140.5 Observed lexpected...: 1 .491 Relative risk : 1.566 Log likelihood...... ratio..: 592.600277 Monte Carlo rank : 1/1000 P-value. .:...... 0.001 PARAMETER SETTINGS Input Case File : F:\KulldorfTTest\S.tesl3\Slest90\TB90.txt Population File : F:\KuJldorfFTest\S.test3VStest90\Pop90.txt Coordinates File : F:\Kulldorff Test\S.test3\Siest90\trialp31.txl Time Precision : None Start Date : 1990/1/1 End Date : 1990/12/31 Coordinates : Latitude/Longilude Analysts Type of Analysis : Purely Spatial Probability Model : Poisson Scan for Areas with : High Rates Number of Replications : 999 Output Results File : F:\KulldorffTest\S.test3\Stest90\result90.txt Data Checking Study Period Check : Check to ensure that cases and controls are within the Study Period. Geographical Coordinates Check : Check to ensure that all locations in the case, control and population files are present in the coordinates file. Neighbors File Use Neighbors File : No 100 Spatial Window Maximum Spatial Cluster Size : 50% of population at risk Window Shape : Circular Inference Early Termination : No Report Critical Values : No Iterative Scan :No Clusters Reported Criteria for Reporting Secondary Clusters : No Geographical Overlap Run Options Processor Usage : All Available Processors Logging Analysis : Yes Suppress Warnings : No Program completed : Mon Sep 14 13:15:30 2009 Total Running Time : 3 seconds Ousters of Tuberculosis in the Punjab in 1990 N w E Anotx U f S \ Ch*kwa) jrwtum MLanwsk Sialkol Kjiuahab / / SargodhA Guimnti r- V Bhaklurr JtwnC /ÿ s-MT~ Kasur Layyah I l Toba T*k W.„... SaWwal Dura Gha*i Khan Kharwwnl Pakpatlan JV>f vwwri / Uuksn . . / ss l f : y ftaianpu y\ / BatitwaJpur Rahim Yif Khan / Clusters of disease NocSstefisund 50 25 0 50 100 150 200 250 Secondary Cluster* Kilometers Moat tea*dute* 1:5,000,000 Figure 5.21 Clusters of Tuberculosis in the Punjah in 1990 102 SaTScan v7.0.3 Program run on: Sun Mar 08 00:09:53 2009 Purely Spatial analysis scanning for dusters with high rates using the Poisson model. SUMMARY OF DATA Study period 1995/1/1 - 1995/12/3 1 Number of locations : 34 Total population ...... : 73621290 Total number of...... cases....: 27448 Annual cases / 100000....: 37.3 MOST LIKELY CLUSTER I.Location IDs included.; 0, 3, 1 Coordinates /radius..: (28.524156 N, 70.598083 E)/ 125.15 km Population .: 6677762 Number of cases. : 5665 Expected cases...... 2489.65 Annual cases / 100000.: 84.9 Observed / expected...: 2.275 Relative risk, : 2.607 Log likelihood...... ratio..: 1693.364128 Monte Carlo rank : 1/1000 P-value...... 0.001 SECONDARY CLUSTERS 2.Location IDs included.: 30, 29, 31, 27, 28 Coordinates / radius..: (32.748836 N, 71.525453 E) / 1 1 7.82 km Population .: 5372447 Number of cases 4176 Expected cases... 2002.99 Annual cases / 100000.: 77.8 Observed / expected...: 2.085 Relative risk : 2.280 Log likelihood ratio..: 990.695209 Monte Carlo rank : 1/1000 P-value .: 0.001 3.Location IDs included,: 6, 11, 12, 2, 5, 15 Coordinates / radius..: (30378894 N, 73.215325 E) / 86.66 km Population : 1 1 136322 Number of cases. :5330 Expected cases...... : 4151.92 103 Annual cases / 100000.: 47.9 Observed / expected...: 1.284 Relative risk : 1 .352 Log likelihood...... ratio..: 183.551946 Monte Carlo rank : I /1000 P-value. .:...... 0.001 4,Location IDs included.: 25 Coordinates / radius..: (32,872486 N, 73.421408 E)/ 0.00 km Population : 936957 Number of cases. : 653 Expected cases ...... : 349,32 Annual cases /...... 100000.: 69.7 Observed / expected,..; 1.869 Relative risk : 1.891 Log likelihood...... ratio..: 1 06.534942 Monte Carlo rank : 1/1000 P-value .:...... 0.001 5.Location IDs included.: 21 Coordinates / radius.,; (32.089375 N, 73.565664 E) / 0.00 km Population : 832980 Number of cases. 417 Expected cases... 310.56 Annual cases / 100000.: 50.1 Observed /expected...: 1.343 Relative risk. .: 1.348 Log likelihood ratio..: 16.663619 Monle Carlo rank : 1/1000 P-value...... 0.001 PARAMETER SETTINGS Input Case File : F:\KulldorfFTcst\S.test3\Stest95\TB95.txt Population File : F:\KulldorftTest\S.test3\Stest95\Popl998-95.txt Coordinates File : F:\Kulldorff TesrtS.test3\Stest95\p31.txt Time Precision : None Start Date : 1995/1/1 End Date : 1995/12/31 Coordinates : Latitude/Longitude Analysis Type of Analysis : Purely Spatial Probability Model : Poisson Scan for Areas with : High Rates Number of Replications :999 Output 104 Results File : F:\KulIdorfTTcst\S.tesO\Stest95\result_pu95.txt Data Checking Study Period Check : Check to ensure that cases and controls are within the Study Period. Geographical Coordinates Check : Check to ensure that all locations in the case, control and population files are present in the coordinates file. Neighbors File Use Neighbors File : No Spatial Window Maximum Spatial Cluster Size : 50% of population at risk Window Shape : Circular Inference Early Termination : No Report Critical Values : No Iterative Scan : No Clusters Reported Criteria for Reporting Secondary Clusters : No Geographical Overlap Run Options Processer Usage : All Available Proccessors Logging Analysis : Yes Suppress Warnings : No Program completed : Sun Mar 08 00:09:57 2009 Total Running Time : 4 seconds Clusters of Tuberculosis in the Punjab in 1995 K Attack 5 L Fi _" Gh«l Khtai - Khanowal PjiUpaiUin 4ÿjrfr~ Clutter* of dluaw Noi bund SO 25 0 50 100 150 200 250 Secondary Ctartan Kilometers Moat likfiiy duMiK 1:5.000,000 Figure 5.22 Clusters ofTuberculosis in the Punjab in 1995 106 SaTScan v7.G.3 Program run on: Sun Mar 08 00:03:23 2009 Purely Spatial analysis scanning for clusters with high rates using the Poisson model. SUMMARY OF DATA Study period .: 2000/1/1 -2000/12/31 Number of locations : 34 Total population ...... : 73621290 Tolal number of...... cases....; 41953 Annual cases / 100000....: 56.9 MOST LIKELY CLUSTER 1.Location IDs included.: 0, 3, 1 Coordinates / radius..: (28.524156N, 70.598083 E) / 125.15 ton Population : 6677762 Number of cases : 7131 Expected cases ...... : 3805.3 1 Annual cases/...... 100000.: 106.6 Observed / expected...: 1 .874 Relative risk : 2.053 Log likelihood...... ratio..: 1302331255 Monte Carlo rank ; 1/1000 P-value :...... 0.001 SECONDARY CLUSTERS 2,Location IDs included.: 29, 30, 24, 28, 31 Coordinates / radius..: (32.274847 N, 72.040658 E) / 95.87 km Population : 6763491 Number of cases : 6668 Expected cases...... : 3854,17 Annual cases / 100000.: 98.4 Observed / expected...: 1.730 Relative risk. : 1.868 Log likelihood...... ratio,,; 947.898159 Monte Carlo rank : 1/1000 P-value ...... 0.001 3,Location IDs included.: 22, 25, 23, 20, 33 Coordinates / radius..: (32.769444 N, 74.019044 E) / 69.14 km Population .: 10269938 Number of cases. 8222 Expected cases... 5852.31 107 Annual cases / 100000.: 79.9 Observed / expected...: 1.405 Relative risk : 1.504 Log likelihood...... ratio,.: 505.134998 Monte Carlo rank : 1/1000 P-value...... :...... 0.001 4.Location IDs included.: 2, 5 Coordinates / radius..: (29.719431 N, 73.029850 E)/ 69.50 km Population... : 4151863 Number of cases. : 3837 Expected cases,,, ...... ; 2365.93 Annual cases / 100000.: 92,2 Observed / expected...: 1 .622 Relative risk : 1.684 Log likelihood...... ratio..: 411.869983 Monte Carlo rank : 1/1000 P-value. .:...... 0.001 PARAMETER SETTINGS Input Case File : F:\KulldorfFTest\S.test3\Stest00VTB2000.txt Population File : F:\KulldorffTest\S.test3\Stest00\Popl998-2000.txt Coordinates Fite : F:\KulIdorfFTest\S.test3\Stest00\p31.txt Time Precision : None Start Date : 2000/1/1 End Date ; 2000/12/31 Coordinates : Lalitude/Longitude Analysis Type of Analysis : Purely Spatial Probability Model : Poisson Scan for Areas with ; High Rates Number of Replications : 999 Output Results File : F:\KulldorffTest\S.test3\Stcst00\result_pu2000.txt Data Checking Study Period Check : Check to ensure that cases and controls are within the Study Period, Geographical Coordinates Check : Check to ensure that all locations in the case, control and population files are present in the coordinates file. Neighbors File Use Neighbors File : No 108 Spatial Window Maximum Spatial Cluster Siare : 50% of population at risk Window Shape : Circular Inference Early Termination : No Report Critical Values : No Iterative Scan : No Clusters Reported Criteria for Reporting Secondary Clusters ; No Geographical Overlap Run Options Processer Usage : AJ1 Ava i I able Processors Logging Analysis : Yes Suppress Warnings : No Program completed : Sun Mar 08 00:03:27 2009 Total Running Time ; 4 seconds Clusters of Tuberculosis in tbe Punjab in 2000 \ < E 4s <**ÿ< U#KW Jwi\ mm L*nrM tot* TtkSUp* OMr* D*i Gma Wwi —Pÿp#n*n Chstvn of MOM Muty duUK 1 5,000,000 Figure 5.23 Clusters of Tuberculosis in the Punjab in 2000 no SaTScan v7.0.3 Program run on: Sat Mar 07 23:37:21 2009 Purely Spatial nnalysis scanning for clusters with high rates using the Poisson model. SUMMARY Oi DATA Study period. 2005/1/1 -2005/12/31 Number onocations 34 Total population : 85646360 Total number ot...... cases,,..: 60363 Annual cases / 100000...,: 70-5 MOST LECEI Y CLUSTER 1.Location IDs included.: 3. 0, 8. 10, 32, 4. 1,7, 9, 5.2, 15. 28 Coordinates radius..: (29.224833 N, 70.082878 E) 300,82 km Population,...... ,: 29386949 Number of cases, ,: 28376 Expected cases....,,,.: 20711.73 Annua! eases / 100000.: 96.6 Observed / expected...: 1.370 Relative risk : 1.698 Log likelihood...... ratio,.: 2063.401960 Monte Carlo rank..,.,,; 1/1000 P-value,., ,: 0.001 SECONDARY CLUSTERS 2T ocation IDs included.: 23, 21,25, 22, 33, 24. 31, 20 Coordinates / radius,,: (32.460322 N, 73.48323 1 E) / 102,54 km Population ,: 17277782 Number of cases, : 15567 Expected eases...... ,J 12177.27 Annual cases / 100000.: 90.2 Observed!expected...: 1,278 Relative risk,, 1.375 Log hkehhood ratio.,: 555,375804 Monte Carlo rank : 1/1000 P-value, :...... 0.001 PARAMETER SETTINGS Input Ill Case File : F:\KulldorffTest\S.test3\tbo5.txt Population File : F:\KulldorffTest\S.test3\Pupop20Q5.pop.txt Coordinates File : F:\KulldorfF Test\S.test3\p3l.txt Time Precision : None Start Date : 2005/1/1 End Date : 2005/12/31 Coordinates : Latitude/Lotigitude Analysis Type of Analysis : Purely Spatial Probability Model : Poisson Scan for Areas with : EErgh Rates Number of Replications : 999 Output Results File : F:\KulldorfTTest\S.test3'resultjutxl Data Checking Study Period Check : Check to ensure that cases and controls are within the Study Period. Geographical Coordinates Check : Check to ensure that all locations in the case, control and population files are present in the coordinates file. Neighbors File Use Neighbors File : No Spatial Window Maximum Spatial Cluster Size : 50% of population at risk Wmdow Shape : Circular Inference Early Termination : No Report Critical Values : No Iterative Scan : No Clusters Reported Criteria for Reporting Secondary Clusters : No Geographical Overlap Run Options Processer Usage : All Available Proccessors Fogging Analysis : Yes Suppress Warnings : No Program completed : Sat Mar 07 23:37:26 2009 Total Running Time : 5 seconds Clusters of Tuberculosis in the Punjab in 2005 N Atto<* S JM JhtMn h. Mianwit II G<*at MencS Banauddm SLtiktf KtuahoD Marowdl SKDOdhB Halubtd <**ÿ*»ÿ* Shefttiupura Uhw Jhws .y Kmur ' Okan iSahlwal "ÿ1 Clutter* or Figure 5.24 Clusters of Tuberculosis in Ihe Punjab in 2005 113 5.12 Summary Tuberculosis is one the major infectious disea ses found in all districts of the Punjab. The spatio-temporal patterns reveal that big cities had a higher rate of disease in 1990 but later on due to improvements in diagnosis, treatment and increased health facilities in these areas the disease burden was reduced. But the areas that were less developed especially in north western and south of the Punjab remained severely affected by tuberculosis. The clusters of disease were found especially in three districts of south Punjab throughout the study period. The proportion of patients with infectious TB was found to be increased in recent years however improvements in diagnostic techniques and centers facilitated progress towards case detection rate. In spite of a number of social barriers for females, the number of female patients is higher than males while the males are more in total population. It depicts that actual number of female patients is much more than male patients in the Punjab which is against the general trend of gender differences in TB disease in the world. There is also a need for the study of etiological factors along with epidemiological aspects in this regard. Punjab is far beyond the targets set by WHO for 2005 for the case detection and treatment of tuberculosis. Now it is the need of the hour to focus on the areas which are vulnerable due to disease rather than to serve only urbanized, metropolis and politically important areas. 114 CHAPTER 6 ANALYSIS OF RISK FACTORS AND HEALTHCARE FACILITIES 6.1 Introduction Tuberculosis is an infectious disease caused by tubercle bacillus. However, among all infectious diseases, tuberculosis has a unique characteristic to be considerably influenced by a number of demographic, socio-economic and ecological factors in its incidence, prevalence and spread. Thus the study of spatial patterns of tuberculosis in the Punjab may remain incomplete without an analysts of demographic and socio¬ economic aspects related to disease. After visualizing patterns of disease and analyzing associated factors, the next step is to study healthcare resources. The objective of complete control of a disease is not possible to achieve without efficient healthcare resources. It includes physicians and facilities at heath centers, accessibility to health services, and equity in health resource distribution. The general patterns of observed disease cases and identification of disease clusters on district level throughout the Punjab province are discussed in detail in the previous chapter. To gain an insight into socio-economic and living conditions of TB patients, disease related aspects and an analysis of accessibility and spatial distribution of healthcare facilities primary data was collected through structured questionnaire survey from TB patients and health service professionals. The respondents were confirmed TB patients and health service professionals in two separate surveys. TB patients were selected randomly at selected health centers in all districts with a total of 30 respondents in each district. The total number of TB patients surveyed in the Punjab province was 1020. Another questionnaire survey was conducted at all health centers for tuberculosis registered by Provincial Tuberculosis Control Program, Punjab. These include district and tehsil headquarter hospitals, TB climes, centers and hospitals and Rural Health Centers (RHC). The data was compiled and summarized with a variety of techniques to make inferences. Different variables were summarized using percentage, proportion, and measure of central tendency. Correlation technique was also employed to study the relation among variables. The data was analyzed with 1 15 different techniques to measure accessibility of health facilities and an examination of equity in health service provision. This chapter consists of two main sections. The first part deals with demographic, socio-economic and other disease related factors aloog with living conditions of TB patients and the utilization of health facilities. The second part is concerned with the analysis of healthcare facilities and the information from health centers about the utilization and provision of facilities. It also deals with the views from health services professionals about the effectiveness of drug regimen, followed under DOTS program, in this particular area and environment. Moreover, it includes the analysis of the accessibility and spatial distribution of healthcare facilities along with the measure of inequality of healthcare facilities within the districts with reference to the total population and the number of TB patients. 6.2 Demographic and Socio-economic Conditions of TB Patients 6.2.1 Demographic Characteristics of TB patients As described earlier a large sample of 1020 patients was selected from Punjab province to analyze the general demographic characteristics and to observe some social aspects of lives of TB patterns. Out of total 1020 patients almost half (50.5%) were females while the proportion of males was 49.5%. Almost the same ratio of males and females was found in the data collected by NTP regarding total number of TB patients in the Punjab in 2005. During the survey, the age of the respondents were recorded and then classified into three categories for the purpose of analysis; Jess than 15 years, 15-64, and 65 and more. The number of patients and their percentage in each age group is given below: Table 6.1: Age group ofTB Patients in the Punjab Percentage of Number (TIB Percentage of persons total Age Group patients patients population suneyed im Less than 15 166 16.3 42.5 15- 64 643 63 53.5 65 and More 211 20.7 4.0 Total 1020 100 100 Source: Data collected through questionnaire survey 116 Pakistan is experiencing a high birth rate so the proportion of persons less than 15 years of age comprises 42.5% of total population while 53.5% are the persons between 15-64 age group according to 1998 census. The percentage share of TB patients in 15-64 age group is 63%. Kochi (1991) found that almost 75% of all tuberculosis cases and deaths in developing countries are concentrated in economically productive age group (15-59 years). In Pakistan the above mentioned ratio is less as compared to other developing countries as described by Kochi (1991), but the point to be considered is Pakistan has achieved 100% DOTS coverage in 2005 and still such a high proportion of adults are infected with disease. There were 166 patients (16%) who were less than 15 years of age while the actual proportion of this age group is 42% in total population. To compare the proportion of TB patients in different age groups with the proportion of total persons in the population, following formula was used to compute 95% confidence interval. Sample j ±1-96 * P W n Where = Proportion of persons in the Punjab who have TB n = size of sample The proportion of person in age group less than 15 was 0,163 with a range of 0.1403- 0.1856 (95% confidence interval). The proportion of the same age group in total population is 0.425. Thus the proportion of TB patients having age less than 1 5 years is significantly less than the proportion of persons in the total population. Similarly, when the proportion of TB patient of age group 15-64 compared with the share of this age group in total population, it was observed that TB patients were significantly higher in this age group than expected. The proportion of TB patients was 0.63 with a range 0,6004-0.6596 (95% C.I,} as compared to actual share of population of 0.53. The age group l65 years and more’ also showed a significantly higher proportion as TB patients with a proportion of 0.207 ranging from 0.1821 to 0.2318 {95 C.I,) as compared to proportional share of 0.04 in total population. A relatively lower proportion of TB patients in persons less than 15 years of age may be due to better immune system and improvements in facilities in BCG vaccination in last two decades. A significantly higher proportion in elderly is of course due to weaker immune system and ageing. However a significantly higher proportion in the age 117 group 15-64 is alarming. The cultural, social, and economical patterns of the country reveal that this age group is actually the working class and feeds other two age groups of children and elderly. The males are the earning hands of the family and females are responsible for looking after the children. It can be said that the persons in this age group are more in contact with others in society and at home. Thus a family with a person infected with TB in this age group not only faces financial problems but also at more risk to be infected. Most of the people in this group are married so their counterparts are at highest risk. Among total 1020 TB patients nearly one fourth are never married (single), while 649 patients (63%) are married. The proportion of widowed is more than 10%, most of them are elderly males and females and less than 0.5% of TB patients were reported to be separated. Another demographic characteristic which is important in tuberculosis is the number of children at home. Children are at high risk due to weaker immune system and close contact with their mothers and elderly persons at home. In Punjab, joint family system is commonly prevailing throughout the province. So the number of children at home is likely to be higher. For a better assessment about the children at risk, in this survey, TB patients were asked about the total number of children under 5 years of age at home rather than only considering the number of children of the patients although they arc no doubt at more risk. Out of total 1020 patients, 141 (almost 14%) have no child under 5 years of age. Almost 22% and 20% were the patients who have one or two children at home respectively. Nearly 44% of the patients had 3 or more children at their homes. It shows that the number of children exposed to mycobacterium tuberculin is very high. A better idea of persons around TB patients can’t be gained without considering household size. In Pakistan the average household size was found to be 6.8 (Pakistan Demographic Survey 2005). In the present research, average household size was calculated on district level which is shown in table 62. The table shows that average household size of TB patients in the Punjab was found to be 6.9. It shows that the disease is concentrated mostly in the larger families and there are more people on risk. Districts Layyah, Narowal, Gujranwala, Rahimyar Khan and Multan have higher household size as compared to other districts. Overall higher household size of TB patients, of course, increases the chances of risk to population. 118 Table 6.2: Age and Sex Composition and Family Size ofTB Patients in the Districts of the Punjab Average the patients Age of household Female DISTRICT Male Less 65 & size of TB 15-64 than 15 Above patients Attock __ 14 16 5 21 4 6.4 Biihawalnagar 15 15 5 20 5 6.5 Bahaw a1pur 16 14 4 20 6 6.8 14 16 6 19 5 6.9 Chakwal 15 15 4 21 5 6.0 D.G. khan 14 16 5 19 6 7.0 Faisalabad 15 15 4 19 7 7.0 Gujranwala 14 16 4 20 6 7.3 Gujrat 1J 17 3 22 5 6.9 Hafizabad 16 14 5 18 7 6.9 Jhang 17 13 5 18 7 6.6 Jhcluni 15 15 7 19 4 6.5 Kasur 14 16 3 21 6 7 Khanewal 14 16 6 16 8 7.2 Khushab 17 13 7 18 5 6.3 Lahore 14 16 3 19 8 6.9 Layyah 13 17 5 19 6 7.6 Lodhrau 16 14 4 17 9 7.1 M. B. DIN 12 18 7 16 7 7.2 Mianwali 17 13 5 19 6 6.8 Multan 15 15 4 17 9 7.3 Muzzafar Garh 14 16 6 19 5 7.1 Narmval 15 15 4 21 5 7.5 Okara 17 13 7 19 4 6.8 Pakpattan 15 15 5 IS 7 6.5 Rahim yar Khan 17 13 6 16 8 7.2 Rajanpur 16 14 4 20 6 7.3 Rawalpindi 12 18 2 22 6 6.3 Sahtwal 15 15 5 18 7 7.0 Sargodha 13 17 7 15 8 7.1 Sheikupura 15 15 3 21 6 7,0 Sialkot 14 16 6 19 5 7.2 T. T, Singh 15 15 5 18 7 6.8 Vehart 16 14 5 19 6 7.1 PUNJAB 504 516 166 643 211 6.9 Source: Data collected through questionnaire survey 119 Tuberculosis is closely associated wilh poverty and overcrowding. Drucker et al (1994) studied spatio-temporal patterns in childhood TB in Bronx in 1970 to 1990. They found that TB cases were associated with residential overcrowding. A large number of studies focus on overcrowding and TB in many parts of the world. Large families having one or two rooms and an infected member are at higher risk. In Punjab, there are 25% households which are Jiving in the houses with only one room while almost 68% families are residing in the houses with two rooms (Pakistan demographic survey 2005). The percentage share of TB patients who are living in a house with single room is almost 44% which is significantly higher than the actual proportion of single room houses in the Punjab (25%) while 50% patients are living in houses with 2 to 4 rooms. Here it can be concluded that the persons living in houses with only one room are more likely to develop TB than those who are not as the proportion of patients from single room houses is very high as compared to actual ratio of single room ratio in the population. Smear positive patients release a large number of bacteria while coughing and sneeze in the room. Thus other family members and children sleeping in the room breathe in the same air which causes the spread of disease. No doubt tuberculosis control program cannot build seperate rooms for them but they may educate patients about the risk and necessary care to reduce the chances of disease multiplication. 6.2.2 TB and Illiteracy TB is considered a disease of poverty. Poverty and illiteracy go hand in hand in many less developed and developing countries. Pakistan is among the countries with low literacy rate. In Punjab the overall literacy rate is 51% according to estimates for the year 2005 (Pakistan Demographic Survey 2005). The literacy rate in females is comparatively low. The literacy rate in TB patients unveiled a horrible situation. Among the sample of 1020 TB patients from all the districts of the Punjab, there were 76% patients who found to be illiterate. Only 241 patients (24%) were able to read and write and many of them (87%) had 5 years of schooling or less. To study whether the literacy rate in TB patients is significantly low than the over all ratio for the province, the proportion of literacy in both cases were compared. The proportion of literacy ratio in TB patients is 0.24 with a range of 0.2138 to 0.2662 (95% C.I.) is significantly lower than the over all proportion of 0.51 in the Punjab. It shows that the disease cases arc found more in illiterate population. At this point it might be 120 interesting to analyze if there is any correlation between illiteracy and tuberculosis. The following formula was used to study correlation among TB cases and illiteracy. n E ZxVi ~ E Xj E Vi r = There was found a positive correlation with a correlation co-efficient of 0.22 between illiteracy and Standardized TB Morbidity rates in 34 districts of the Punjab. Low literacy rate affects both the patient and people around her or him regarding treatment and spread of disease. The patients who were illiterate lacked knowledge about disease and were less likely to take precautions to control the spread of disease. Among 1020 TB patients a strong correlation was observed between illiteracy and patient's knowledge about disease. The correlation coefficient was found to be 0.8709 which depicts that the patients who were illiterate have no knowledge about the symptoms, precautions and control of TB disease. A lack of knowledge about symptoms and precautions of disease causes carelessness towards the practice of taking precautions to block the spread of disease. The correlation coefficient between illiteracy and the number of patients taking no precautions regarding disease was 0.7213 which is also very high. Generally the literacy rate was found higher in big cities and urban areas as compared to rural areas. The female patients bad low literacy rate as compared to males. Over all the higher number of female TB patients; their close contact with family and children, and low literacy rate may play a combined effect to a horrible situation regarding disease. 6.2.3 Economic Conditions of Tuberculosis Patients in the Punjab Tuberculosis is a disease which is caused by airborne microorganism, Mycobacterium tuberculosis. However, the spread of tuberculosis is caused by mycobacterium as well as socio-economic conditions prevailing there (Hannum & Larson 2001). TB is more common in poor communities due to overcrowding, poor ventilation, malnutrition and with a combination of other diseases like AIDS (TB alert 2005). So it is considered the classic example of a social disease, i,e,, one that affects the poorest strata of society (Knopf 1914; Enarson et al 2003; Romaszko et at. 2008). Thus poverty is not 121 only a cause of tuberculosis but also an outcome due to the longer period of treatment and patient's inability to work due to weakness. In other words, poverty not only predisposes one to TB but also TB can increase poverty. Unemployment is one the parameters to study the economic conditions and material deprivation, Whitehead (1992) describes that unemployment may contribute to poor health and the people in poor health are at more risk to be unemployed. In Pakistan the unemployment rate is found to be 6.2% and in the Punjab it was 6.0% of the total population of working age group in year 2005-6 (Government of Pakistan 2007). In the present research, out of total number of 1020 patients, 149 persons i,e. 15% of the total patients were unemployed. The proportion of housewives who are also a part of dependent population was 42% of the sample. Almost 83% female patients of the total are housewives and only 17% of them are employed (Figure 6.1). Thus if the percentage of unemployed patients may be calculated with reference to working male and female population (504+86) in the survey, the situation is terrible as the proportion of unemployed patients crosses 25%. The survey results in the present research show that the rate of unemployment was found to be 23% to the total population of working age group ie 1 5-64 years of age. Now if this figure is examined in the context of big picture of larger family size, higher dependency ratio and a lower per capita income, the severity and seriousness of the issue might be felt more properly. To examine the share of employed persons in different sectors of economy who were found prey to tuberculosis, the patients were asked about their profession. Out of total 397 employed persons, 237 (60%) were involved in agriculture. Agriculture is the major sector of economy having the highest number of employed persons in Pakistan i.e 43% as compared to industry (21%) and the service sector with (36%) share in the total employment (Government of Pakistan 2007). So the proportion of persons involved in agriculture and being TB patients was higher than the actual share of this sector of economy in total employment. Secondly, there were 20% patients who were working in the field of industry. The third major profession which was observed in the patients was the laborers in the field of construction which has a share of 8%. Among all the employed patients, both trade and transport has a share of almost 5% each. The service sector of economy has a share of less than 2% patients (Figure 6.2). It shows that the people engaged in service sector were less likely to be effected by tuberculosis. The reason might the comparatively higher level of literacy and income of the people involved in services. Figure 6.1 TB Patients: Employed, Unemployed, and Housewives 100% 90% 80% 70% Housewife 60% Student b 50% Seif b errployed Unemployed Brrptoyed 30% I :_l __ I20% 10% 111111111!*s Is »jf!,tt:> to 123 Percentage of Patients Employed in Different Sectors of Economy 4% 5% 8% 2% 60% 21% D AÿtctAure •hfeatry Service* Construction •Trantportadori D Trade Figure 6.2 Percentage of patients employed in different sectors of economy 124 6.2.4 Tuberculosis and household Income As mentioned above, tuberculosis and poverty are closely related with each other. A large number of TB patients in Pakistan as well as in the world are poor and deprived. Among 1020 tuberculosis patients. 557 were living with extremely poor economic conditions. They had a household income of less than Rs.5000 from all the sources which is less than $ 2.75 daily. Almost 40% surveyed patients has no other earning hand and only one individual in the family feeds all other dependents. The average household size of TB patients in this survey was found to be almost 7. Thus a proportion of 55% households with an income of less than Rs. 5000 with a larger family were prey to severe poverty (Table 6.3). Another 44% of the patients had household income between Rs. 5000- 10,000 as shown in Figure 6.3. To understand this situation internationally, it might be described that 99% of TB patients in Pakistan had an average daily household income of 5 US dollars from all the sources with an average household size of almost 7 individuals. This situation is really alarming. No doubt, Pakistan is a developing country and the economy is in crises particularly in recent years. It is necessary to compare the average household income in the Punjab with the household income of TB patients to portray the full picture of economy and tuberculosis in the Punjab. According to Pakistan Demographic Survey (2005) there are only 20% households having their monthly income (ess than Rs. 5000 while a proportion of 80% has less than Rs.!0, 000. While in case of TB patients the percentages in these two income groups are 55% and 99% respectively. To compute 95% confidence interval (C.L) for the proportion of TB patients with a monthly household income less than Rs, 5000, the following method is being used: Ip temple I . v n -0.55) 0.55 ±1.96 055x0 V IO: Thus the proportion of patients with monthly household income less than Rs. 5000 is 0.55 with a range of 0.53442 - 0.56558 (95% C.I.). It is significantly higher than the proportion of 0.20 for whole of the Punjab, Here it can be concluded that tuberculosis is found in higher proportions in low income groups of the society. Although 59% individuals in the survey had more than one earning hand but still due to low income and high inflation rates majority of the patients were found very poor. 125 Table 6.3: Monthly Household Income of TB Patients in the Punjab Total Household Income per Month DISTRICT Less than Less than Less than Rs. 5000 Rs. 10000 Rs. 20000 Attock 19 tl Bahawalnagar 13 17 Buhawulpur 9 21 Bhakkar 14 16 Chakwal 17 13 D.G. khan IS 12 Faisalabad 9 20 1 Gujranwala 10 20 Guirat 14 16 Halm)bad 19 11 Jhang 21 9 Jhelum 17 13 Kasur 16 14 Khsnewul 15 14 1 Khushab 20 10 Lahore 10 18 2 Layyah 21 9 Lodhran 17 13 M. B. DIN 15 15 Mianwali 19 11 Multan 13 16 1 Muzzafar Garb 22 8 Narowal 18 11 1 Okura 17 13 Pa k paltan 16 13 1 Rahim yar Khan 14 14 2 Rajunpur 21 9 Rawalpindi 16 13 1 Sahiwal 18 12 Sarfiodba 20 9 I Sheikupura 16 14 Sinlkot 14 16 T. T. Singh 19 11 Vcharl 20 10 PUNJAB 557 452 11 Source: Data collected Lhrough questionnaire survey 126 Monthly Household Income of TB Patients 600 500 ft 1 400 £ •300 a I 200 100 ? 0 <10000 >10000 Tout beone per (R*> Figure 6.3 Monthly household income of TB patients 127 6.2.5 Tuberculosis and Smoking Smoking is considered an important risk factor for pulmonary tuberculosis which also hinders the proper cure of disease (Kolappan &. Gopi 2002; Maurya et aL 2002) In the present study it was not possible to conduct a case control study about the correlation of tuberculosis and smoking in all the districts of the Punjab. However the proportion of tuberculosis patients were identified who were smokers. Out of total 1020 TB patients there were found 134 patients who were smokers. This ratio is 17% of total patieots. In Pakistan, the habit of smoking is normally restricted to males. No female in the survey was observed to be a smoker. So the actual ratio of patients who smoke is calculated keeping in view male population only. Thus it was found that almost one third of male patients (34%) were regular smokers. 6.2.6 Tuberculosis Patients and the Number of Family Member Infected As TB is a contagious disease so the people around a smear positive TB patient are at higher risk especially the family members. As described in the previous section that a large number of TB patients are living in large families and in over-crowded conditions. The average household size of TB patients in this survey was found to be 6.9. Titus the chances of being infected are higher towards family members. Any family member who had tuberculosis in the past especially elderly might be a cause of disease transfer to other family member. The family history of tuberculosis, overcrowding, and household exposure to a TB case is a significant risk factor for TB (Hill et at (2006). In the present study of tuberculosis in the Punjab, there were found 1 15 cases (11%) out of total 1020 patients who had a TB case in the past or al present in the family. Further studies should be conducted in this regard to measure the effect of such exposure, 6.2.7 Knowledge about Symptoms, Precautions and Duration of Treatment Tuberculosis is an infectious disease which is closely associated with social and cultural factors in its incidence and spread. Thus the knowledge about the symptoms, precautions and treatment is very necessary for the patient and people around them. Pakistan has a great disadvantage in this regard due to illiteracy and lack of public awareness. In an earlier study in Pakistan it was found that only 10% patients were aware of the symptoms and risk of the disease (Khan et al (2000). The sample size selected in that study was very small. In the present research, a comparatively large 128 sample size of more than one thousand patient was selected from all over the Punjab. Due to the progress in urbanization and public awareness, the situation was not much gloomy in 2005. Although it has improved but still not a very good ratio of patients have clear idea about the symptoms and precautions of TB. There were found only 309 patients (30%) who were aware of all these things. A large majority (70%) was still not unaware and causing a great risk for a large healthy population around them. The rural females and elderly were the most ignorant about these aspects of disease. Not only is the lack of knowledge but carelessness making the situation worst in the Punjab regarding tuberculosis. The patients who are aware of precautions to be taken are not strictly following them. It was observed that 794 patients (78%) were not taking any precautions while coughing and sneezing, which are the most important sources of spreading disease. As mentioned above, there were 30% patients who had the knowledge about the risk of disease but only 22% were taking precautions seriously. Although a large number of patients who are smear negative are not infectious but the control of disease is not possible until smear positive patients arc provided with information and advised strictly to follow them so that the infection is blocked to attack healthy people. Another aspect of disease knowledge is the information about duration of treatment. Knowledge about the exact duration discourages default and multiple drug resistant (MDR) tuberculosis and reduces mortality due to incomplete treatment. Many of the patients know that tuberculosis takes long time to be completely curable. More than 61% patients in the survey were aware of the duration of treatment. Still there are almost 400 (39%) patients among total 1020 who arc unaware of the duration of treatment (Figure 6.4). As discussed earlier the family members around a tuberculosis patient are at higher risk due to their permanent exposure. In this regard, the patients were asked about sharing of beds, blankets and utensils. Some studies suggest that there is no need to separate eating utensils of TB patients (NTP 1999). However other researchers considered it and studied about the sharing of blankets and utensils (Hussain et al. 2003). In the present research, patient’s behavior regarding the sharing of beds, blankets and utensils is being studied. It is important to consider these aspects to assess the number of persons who are at higher risk such as spouse and children under three years of age. in case of blankets if they are shared, the patient and the child share the same air inside the blanket and the coughing and sneezing in this situation is more critical especially with reference to mother and child. It was found in this 129 research that 86% patients informed that they share blankets with a family member or a child. As the majority of the patients arc very poor. They have lesser number of rooms so they share blankets with other family members. Many of them were married female sharing it with their children. Similarly, (he female patients who were housewives were not taking much care about sharing utensils. However in case of utensils there were 40% patients using their separate utensils while 60% were not. In both of these cases of sharing utensils and blankets, the ratio of carelessness was higher in rural areas while the residents of urban areas and the literate persons were comparatively more careful in this regard. 130 Knowledge about Precautions and Duration of Treament 9 Yes No Yes No Yes No Knowledge about disease Taking precautions Know ledge about expected symptoms and precautions | duration of treatment Figure 6.4UPatient's knowledge about precautions and duration of treatment 131 6.2.8 Tuberculosis and Diet A balanced diet is necessary for healthy living. The probability of disease incidence increases in the people living on poor diet. A balanced and healthy diet is very necessary especially for TB patients to recover from weakness. Doctors normally advise meal, milk, eggs and fruits to the patients. But unfortunately, as TB is a disease which affects the poor strata of the society more severely, so majority of the patients normally don’t afford it. In this research it was found that only one fourth of total patients declared that they were using the balanced diet advised by the doctor. A large number of patients (64%) were not using the appropriate diet due to poverty. There were almost 10% patients who declared that the doctor had never mentioned about any specific diet. However, actually in most cases patients didn’t use Ihe proper diet as they were unable to afford it. 6.2.9 Duration of Treatment and Patients Satisfaction and Trust In order to know about the stage at which patients were in the Punjab. They were asked about the time that has passed since they are taking medicine. In all the districts of the Punjab, 230 patients (23%) were in the first month of their treatment, the same proportion of the patients were in 3rd or 4* month of their treatment. The proportions of the patients in the later stages of the treatment (5-6 month) and (more than 6 months) were lesser than other categories with a percentage of almost 15 in each category. Among these 1020 patients who were surveyed, 725 (71%) were satisfied with the treatment however 29% patients were not. Majority of unsatisfied patients were in 3rd or 4th month of their treatment. Upon inquiring about the reason, many of them declared that the change in drug regimen after 2 months caused the disease to come back. So they don’t think they are improving any more although the improvement was enormous in the first two months of treatment. Further details in this regard are being discussed in next section about the drug regimen and health service professional’s views and reservations about the drug regimen. 6.2.10 Drug Intake Missed An irregularity in the drug intake is one of the serious problems which may lead to failure of disease control program and the development of drug resistant tuberculosis. Here in Pakistan due to lack of awareness about disease there were many patients who had an irregular behavior towards drug intake. In the present study in the Punjab there 132 were 60% patients who were very regular and never missed drug intake. A proportion of 25% of the patients admitted that the drug intake was missed once a month while 10% missed it twice in a month. There were 51 patients (5%) in this survey who were missing it more frequently during a month. The carelessness in this regard was found mainly due to two reasons: firstly, the patients who were in the later stages of their treatment didn’t bother sometime as they have no symptoms of the disease anymore; secondly, the patients (especially females) faced problems to access the health service facility at regular intervals to get the drugs. 6.2.11 Tuberculosis and Awareness Campaign by NTP National Tuberculosis Control Program (NTP) launches media campaign through print and electronic media to create awareness among people about symptoms, treatment, and control of tuberculosis. In this research, patients were asked about the source from which they get information to study which media may prove more effective. The results from this survey showed that the media campaign by NTP is not much effective. Out of total 1020 patients, 857 patients (84%) were not aware of any advertisement or program by NTP for public awareness. Only 16% of patients confirmed that they found such information on media. Out of these 163 patients who gained some information through media, majority of them (67%) found it through television. Other sources mentioned by patients were newspapers and radio. As described earlier, literacy rate in Pakistan and particularly in TB patients is very low. All the people don’t have an access to newspapers, radio or TV. So a large majority was unaware of any campaign. Among the patients, mostly literate and residents of urban areas were getting information provided by NTP. The most effective source of media was television which was also useful to illiterate patients. NTP should review its policy regarding the dissemination of information as the present policy is not proving much effective to provide information where it is required. 6.3 Analysis of Healthcare Facilities Louis & Thomas (2001) described that health care includes formal, institutionalized care, along with alternative therapies, self care, and any other activities designed to prevent the onset of disease, treat illness, improve the quality of life and/or preserve health. The geography of health care covers the spatial properties, utilization, functions, efficiency, accessibility and planning of health care services. Health care 133 refers to society’s arrangements for improving the health status of population, individually or collectively. The social, economic and political characteristics of a society arc among the determinants for the provision and utilization of healthcare facilities. Health care studies in geography focused on the accessibility, utilization, distribution and inequalities issues (Meade & Earickson 2000; Cromely & McLafferty 2002; Maheswaran & Craglia 2004; Brown et ai 2010). In this section three aspects of health care services are discussed: the first part is concerned with the description of facilities provided in health centers, for example, X- rays, diagnostic tests, doctors and medicines under DOTS program; the second part includes the analysis of accessibility of health services and the last section presents an analysis of inequalities in the provision of health services within the districts and high and low incidence areas. 6.3.1 Healthcare Service Centers in the Punjab for TB Patients Health care service centers of tuberculosis are available in all the districts of the Punjab in public sector. The health service delivery system in the Punjab comprises both public and private sector centers. The present study is focused on the efficiency and effectiveness of DOTS program which is running in collaboration with World Health Organization and Government of Pakistan. So the analysis of health service centers here addresses only the public sector health services. No new health centers are setup through DOTS program but actually the DOTS program is initialed and applied on the network of health centers which are already working under the auspices of Government of Pakistan and the Government of the Punjab. Moreover, all the health centers of the network are not covered under DOTS. There are specific health centers in every district where this program is initiated. It is true that some TB patients, who can afford, are under treatment in private clinics but the data is not available from those clinics and secondly they are not facilitating patients through DOTS. So the poor and the most vulnerable prefer those public sector health facilities where DOTS program is underway. These centers are normally classified as primary and secondary health services. The primary healthcare centers provide diagnostic facilities and treatment to outdoor TB patients while the secondary healthcare centers have also specialized wards for tuberculosis patients along with the drug and diagnostic facilities to outdoor patients. Punjab public health sector has been organized into primary health care (BHU, RHC) and secondary health care (THQ & 134 DHQ) levels. A Basic Health Unit (BHU) which normally has a catchment population of up to 25,000 provides free of cost drug supply to TB patients. As there are no diagnostic facilities in a BHU so the patients with respiratory symptoms are referred to diagnostic centers. A Rural Health Center (RHC) provides diagnostic and treatment facilities to TB patients. It normally serves a population of 100,000 people. Tuberculosis sputum smear diagnostic tests and X-ray along with treatment facilities for TB patients are provided at RHC. The secondary level health services which include Tehsil Headquarter Hospitals (THQ) and District Headquarters Hospitals (DHQ) cover population of 0.5 to 1 million and 1 to 3 million respectively. These hospitals also deal with the referrals from lower levels and provide complete diagnostic and treatment services. A DHQ has more specialized services i.e. Chest specialists and in some cases a specialized ward for TB patients. Along with these health care facilities there are specialized TB clinics and Chest hospitals in the Punjab which provide drugs and diagnostics to TB patients. To study the utilization and the efficiency of health services for TB patients, the diagnostic and treatment facilities were analyzed through questionnaire survey. 6.3.2 Spatial Distribution of Healthcare Services in the Punjab There are 451 diagnostic and treatment centers are working in the Punjab under DOTS program. These include BHU, RHC, THQ, DHQ, TB Clinics and Hospitals (Figure 6.5). Most of the patients are treated as outdoor patients. However some severe cases are referred to District Headquarter Hospitals and TB Hospitals which have specialized wards for TB patients. Following is the complete list of healthcare service centers which are designated as diagnostic and treatment centers by NTP in all the districts of the Punjab (Table 6.4). 135 Table 6.4: Diagnostic and Treatment centers for TB Patients in the Punjab Attock DHQ Hospital Attock RHC Bahtar THQ Hospital Fateh Jang THQ Hospital Hassanabdal THQ Hospital Jand RHC Pomoil THQ Hospital Hazro RHC Rangoo THQ Hospital Pindi Ghcb RHC Maghian BHU Tarap Bahawalnagar DHQ Hospital Bahawalnagar RHC Donga Bonga RHC Madrissa THQ Hospital Chishtian RHC Shchr Farid RHC 6 /G THQ Hospital Fort Abbas RHC M S Ganj RHC Mclcodganj THQ Hospital Minchinabad RHCMarot RHC Dahranwala RHC Khichiwala THQ Hospital Haroonabad RHC Faqirwali Bahawalpur TBC Bahawalpur RHC Laia Sohanra RHC Khanqah Sharif RHC Dera Bakhha Bahawal Victoria Hospital THQ Hospital Ahmed Pur East RHC Mubarik Pur RHC Uch Sharif RHC Channi Goth THQ Hospital Khair Pur TBC Hasil pur RHC Qaiam Pur Tamewali RHC Chuna Wala THQ hospital Yazman RHC Head Rajkan RHC Khudri Banglaw Bhakkar DHQ Hospital Bhakkar RHC Behai THQ Hospital Kullurkot RHC Jandanwaia RHC Darya Khan BHU Sami Muhajir RHC Dullaywala THQ Hospital Mankcra Chakwal DHQ Hospital, Chakwal RHC Balkasar RHC Dhouman RHC Munday RHC Dhudhial THQ Hospital Chua Saidan Shah RHC Bhuchal Kalan THQ Hospital Tala Gang RHC Tamntan RHCLawa RHC Jhatla D.G. Khan DHQ Hospital D.G Khan RHC Choti Zerin RHC Qadir Abad RHC Sarwar Wali RHC Kot Chutta THQ Hospital Taunsa RHC Vehawa RHC Tibbi Qasierani RHC Shah Sadar Din Civil Hospital Sakhi RHC Shahdin Lund Civil Hospital Fort Manro Sarwar_ 136 Faisalabad Govi. Dispensary Allied Hospital Faisalabad DHQ Hospital Faisalabad Peoples colony no.2 Govt General Hospital RHC GMA Chak 30 JB RHC Dajkote RHC Chak Jhuinra Social Security Hospital Hospital Faisalabad WAPDA RHC Lundianwala RHC Khumcnwaia RHC Satiana RHC Chak 65 GB THQ Hospital Jaranwala RHC Muridwala RHC Kanjwani RHC Mamoon Kan jan THQ Hospital Samundri THQ Hospital RHC Pindi S Musa Tandlianwala Gujranwala ALRaees Hospital Allama Iqbai Hospital DHQ Hospital Gujraimala RHC Emin Abad RHC QiJaDidar Singh RHC Rasool Nagar TB Clinic Gujranwala RHC Dhonkel RHC Alimad Nagar RHC Gakhar RHC Ali Pur Chatlha THQ Hospital Kamoke THQ Hospital Noshera RHC Wahndo BHU Tatlewali Vikmn TBC Wazimhad Gujrat DHQ Hospital Gujrnt RHC K un i oh RHC Tanda RHC Paulat Nagar Civil Hospital J.P. Jattan RHC ShadiwaJ RHC Sarai Alamgir THQ Hospital Kliarian RHC Malka Hans RHC Dinga RHC Laiamusa RHC Pindi Sultan Pur Hafizabad DHQ Hospital Hafizabad Govt TB Clinic Hafizabad RHC Wanike Tarn THQ Hospital Pindi RHC Kaleki Mandi RHC Sukheki Mandi Bhattian RHC Jala Pur Bhattian Jhang DHQ Hospital Jhang RHC Mochiwaia RHC Mukhiana City Hospital Jhang RHC Bagh RHC Kot Shakir RHC Shah Jcevna RHC Rodu Sultan RHC Havel Sheikh Rajoo THQ Hospital Chiniot RHC Chak No. 14/J.B. RHC Bhowana RHC Ahmed Nagar TB Clinic Chiniot RHC Lallan RHC Baram THQ Shorkot RHC Ahmad PurSial RHC Garh Maharaja RHC Haveli BahadarShah B7 Jhtluni DHQ Hospital Jhclum RHC Kbalass Pur MCH Canter Sadar Chok THQ Hospital Pind Dadan RHC RHC Jalal Khan Lilia PUT Sharif RHC Pit* THQ Hospital Sohawa RJ-IC Domeli Kasur DHQ Hospital Kasur RHC Mustafa Abad RHC Chanda Singh Wala RHC Khurnn RHC Raja Jang RHCKRK Golab Devi Hospital Kasur THQ Hospital Chunian RHC Kangan Pur Branch RHC Allahabad RHC Changa Manga THQ Hospital Pattoki RHC Hal I a RHC Phool Nagar RHC Habib Abad Khancwjil DHQ Hospital Khanewal RHC Kacha Khuh THQ Hospital Jehanian THQ Hospital Kabir Wala RHC Abdul Hakim RHC Sarai Sadhu THQ Hospital Mian RHC Talumba Chanun_ Khushab DHQ Johar Abad THQ Hospital Khushab RHC Padhrar THQ Hospital Noor Pur Thai RHC Khabeki RHC Rods RHC Mitha Tiwana RHC Hadali THQ Hospital Noghehra Lahore Jinnah Hospital Lahore Cantoament General Hosp. Civil Hosptia] Shahdra Federal Medical Center Ghurki Trust Hospital Diagnostic Clinic Lohari Kot Khawaja Saeed Hospital Yakki Gate Hospital Govt Disp Baja Line Govt Disp Dharampura Govt Wahdat Colony Hospital Mian Munshi Hospital Infectious disease Hospital Mayo Hospital Mozang Hospital Gang Ram Hospital Services Hospital Gowal Mandi Hospital Diagnostic Centre Sodiwal Sanat Nagar Dispencery RHC Awan Daiwala RHC Burki Punjab University health center RJ 1C Chounu Gulab Devi Hopital Model Town Hospital Lahore General Hospital RHC Kahna RHC Raiwind Shaikhzayed Hospital RHC Manga Mandi Layyah DHQ Hospital Layyah RHC Kot Sultan RHC Chok Azam THQ Hospital Chobara THQ Hospital K L Esan RHC Fateh Pur 138 Lodhran DHQ Hospital Lodhran RHC M. Aali RHC 53 M THQ Hospital Duniyapur RHC Gogran RHC 231 WB THQ Hospital Kehror Paka M. B. DIN DHQ Hospital M.B. Din RHC MOOR RHC Chailianwala RHC Kuthiala Sheikhan THQ Hospital Phalia RHC Pahrianwal i RHC Jokalian RHC Bhckomore RHC Maiikwal RHC Miana Gondal TB Hospital Halal e Ahmr Vliatiwali DHQ Hospital Mianwali RHC Mochh RHC Daood Khel RHC Wan Bhachran RHC Chakrala TTTQ Hospital Isa Kliel THQ Hospital Kala Bagh RHC Trag RHC Kamar Mashani RHC Kundian_ RHC Hafiz Wala Multan RHC Makdum Rashid RHC Ayyazabad Maral RHC Qadar Pur Rawan RHC Martian Pur RHC Shar Shah Nbhtar Hospital Civil Hosptial Multan THQ Hospital Shujaabad RHC Matotli RHC KoUi Nijbat THQ Hospital Jala Pur Perwala Muzzafur Garh DHQ Hospital Muzzafar garh RHC Basecra RHC Rangpur RHC Rohilanwali RHC Shahjamal RHC Khangarh THQ Hospital Kot Addu RHC Sinawan RHC Gujrat RHC D.D.Panah RHC Chok sarwar shaheed THQ Hospital Alipur RHC Shahar Sultan RHC Jatoi RHC Khairpur Sadat RHC Seet Pur Social Security Hospital Muzzafar garh Narowal DHQ Hospital Narowal RHC Bado Malhi RHC Zafarwal THQ Hospital Shakar RHC Sankhatara RHC Qila Ahmed Abad Garh RHC Lasser JCalan RHC Shah Gharib RHC Kot Naina) Okara Additional DHQ Hospital DHQ Hospital Okara RHC Shah Bor Okara 139 RHC Govern RHC Renala RHC Aklitnr Abad RHC Bama Bala THQ Hospital Papal pui RHCHuira Shah Mokeem RHC HaveJi Lakba RHC Baseer Pur RHC Ballack RHC Wasaway Wala RHC Mandi Ahmadabad Pakpattan DHQ Hospital Pakpaitaa RHC Malka Huns RHC Bunga Hayat THQ Hospital Arifwala RHC 163 EB RHC Qaboola Rahim yar Khan Shiekh Zyed Hospital RHC T Saway Khan RHC Koi Samaba RHC M. W. Oureshian RHC Rajan Fur Kalan RHC Mnnthar THQ Hospital Sadiqabad RHC 173-P RHC Sanjar Pur RHC Ahmad Pur Lamma RHC Nawaza Abad RHC Jamal Din Waii THQ Hospital Liaqatpur RHC Allahabad RHCTMPanah RHC Khan Bela RHC Feroza RHC Pacca Larran TB Clinic Khanpur RHC Sell]a RHC Batjho Bahar RHC Zahirpir RHC Nawan Kot Rajanpur DHQ Hospital Rajanpur RHC Kot Milhan RHC Fazil Pur RHC Muhammad Pur THQ Hospital Rojhan RHC Bangla Icha THQ Hospital Jam Pur RHC Dajal RHC Harand Rawalpindi RHC Khyban-E-Sir Holy Family Hospital DHQ Hospital Rawalpindi Rwp Syed RHC Chontra RHC Bagga Sheikhan BHU Remat Abad BHU Hayal Sharif THQ Hosptial Taxiia THQ Hospital Gujar Khan RHC Qazian RHC Mandra RHC Daultala THQ Hospital Murree RHC Kalar Syedan RHC Kotli Sattian RHC Lehtara THQ Hosipial Kalmla RHC Fhagwari AI Mustafa trust Hospital Samli sanitarium Muree Federal Govt TB Center Rawalpindi__ Leprosy center Rawalpindi Sahiwal SHQ Hospital Sahiwal GHA Qayyum Hospital RHC 55/5 L RHC Noor Shah RHC 112/6 L RHC Harappa THQ Hospital Chicha RHC 120/9 L RHC Kasowal Walni RHC 45/12L RHC 96/12L RHC Ghazj Abad RHC 8/11L 140 Sargodha DHQ Hospital Sargodha TB Hospital Sargodha THQ Hospital 90/SB RHC Bbagtanwala RHC 46/SB RHC 104/NB THQ Hospital Silanwali RHC Farooqa THQ Hospital Sahiwal THQ Hospital Shah Pur RHC Jhawarian RHC Bhera THQ Hospital Bhalwal RHC Miani RHC Lalyani RHC Kot Momin RHC Midh Ranjha RHC Bhabra RHC Muaznm Abad Sheikhupura | DHQ hospital Sheikhupura TB Hospital Sheikhupura RHC Jandial Sher Khan RHC Farooq Abad RHC Kharianwla THQ Hospital Muridke RHC Narang Mandi RHC Sharqpur Sharif RHC Kala Shah Kaku THQ Nankana RHC Warburton RHC Syedwala RHC Bucheki RHC More Khunda RHC Rehanwala RHC Safdarabad RHC Khanqah Dogran RHC Sangla Hill RHC Shahkot RHC Mananwala Sialkot DHQ Allama Iqbal Govt Sardar Begum RHC Kahlian Hospital. Sialkot Hospital, Sialkot RHC Kotli Lohanui TB Hospital Daska THQ Hospital Daska RHC Satrah RHC Jamke Cheema RHC Begowala RHC Sambarial THQ hospital Pasroor RHC Chawinda RHC Klass Wala Chest Hospital Sialkot T. T. Singh DHQ Hospital T.T Singh Govt TB Clinic T.T Singh RHC 316 G/B RHC Rajana THQ Hospital Kamalia RHC 740 GB RHC Pir Mahal THQ Hospital Goira RHC 338 Nay Lahore Vehari DHQ Hospital Vehari RHC Luddan RHC Machinal RHC 56 WB RHC 87 WB THQ Hospital Burewala RHC Sahooka RHC Gaggo THQ Hospital Mailsi RHC Tibba Sultan Pur RHC Jalla Jcem 141 Distribution of TB Health Facilities in the Punjab 2005 5 JH-;pm fifil y * r'2'to , 0 -- DHCVLwputttMpfejIi O o %xk-8 THOHHPW TBCMH we SHU + 0 50 100 150 200 Km Figure 6.5: Healthcare facilities for TB patients in the Punjab 2005 142 6.3.3 Efficiency and Effectiveness of Healthcare Services The number and distribution of health facilities is not enough for a complete analysis. It is also necessary to investigate the facilities and health professionals available at healthcare centers and to determine the pressure on these resources. To understand the burden of patients on healthcare services and health care professionals in the Pupjab, the data was collected regarding the number of daily outdoor patients and different facilities provided at these health centers. For the purpose of analysis, a daily patient doctor ratio was calculated for every district. This patient doctor ratio shows the average number of patients that a doctor checks daily in outdoor. It provides information about the work pressure on health service professionals and the competition among patients to use a particular facility. A higher patient doctor ratio indicates that those doctors may not focus on every individual case and secondly it also discourages the patients to visit for eight or nine months as they have to wait for long hours and they feel that doctors are not taking much care of their disease situation. Table 6.5 provides a summary of number of health facilities and patient doctor ratio in all districts of the Punjab. 143 Table 6.5: Number of outdoor doctors and patients at health care centers in the Punjab No. Average DISTRICT of Health No. of No. of outdoor Patient / Centers daily patients Doctors Doctor Ratio Attock 11 1770 29 61.03 Bahawalnagar 15 4390 51 86.08 Bahavvalpur 16 2390 38 62.90 Bhakkar 8 1185 17 69.70 Chakwal 11 1505 29 51.90 D.G. khan 12 880 27 32.6 Faisalabad 20 4045 66 61.28 Gujramvala 16 2400 45 53.33 Gujrat 12 2060 40 51.50 Hafizabad 7 855 16 53.43 Jhang 20 3445 60 57.41 Jhelum 9 1365 28 48.75 Kasur 15 2010 42 47.85 Khanewal 8 1730 38 45.52 Khushab 9 1270 20 63.50 Lahore 31 5975 92 64.94 Layvah 6 1125 19 59.21 Lodhran 7 980 18 54.44 MB. Din 11 1355 29 46.72 Mianwali 11 1435 26 55.19 Multan 11 1865 32 58.28 Muzzafar Garli 17 2370 56 42.32 Narowal 9 1250 23 54.34 Okara 13 1845 37 49.86 Pakpattan 6 900 14 64.28 Rahim yar Khan 23 2660 62 42.90 M >ur 9 1450 26 55.76 Rawalpindi 22 3955 67 59.02 Sahiwal 13 1240 33 37.57 Sargodha 19 3100 70 44.28 Sheikhupura 20 2855 65 43.92 Sialkot 14 1900 37 5135 T. T. Singh 9 1880 32 58.75 Vehari 11 1990 42 47.38 PUNJAB 451 71430 1326 Source; Data collected through questionnaire survey Mean of Patient doctor ratio = 54.038 Standard deviation » 9.905 144 The table shows that there is a great variety among districts regarding patient doctor ratio. The highest daily patient doctor ratio was found in two bordering districts of the Punjab, Bahwalnagar and Bhakkar. The average for whole of the Punjab is 54 patients daily per doctor with a standard deviation of almost 10 patients. In all the health centers of the Punjab where DOTS program is being operated, the number of chest specialists is very few. They arc only available at District headquarters hospitals (DHQ) or TB hospitals. In all the basic health units BHU and rural health centers (RHC) there is no chest specialist. Majority of the Tehsil headquarters hospitals (THQ) are also without a specialized doctor in chest diseases and tuberculosis. These medical officers were trained through a short training course to treat tuberculosis patients. To get better results it is suggested that at least one specialized doctor in chest diseases should be appointed at all the centers where DOTS program is underway. Apart from the lack of specialized human resources, a large number of health service professionals were found to be not satisfied by the funds and facilities provided by DOTS program to treat TB patients. The following information presented in Table 6.6 was found from health service professionals to rank the availability' of funds and facilities to cope with the present disease situation in the Punjab. Table 6,6: Ranking the funds and facilities of DOTS program by Healthcare professionals Rank Number of healthcare centres _ Very Poor 8 Unsatisfactory 77 Satisfactory 301 Good 59 Excellent 6 Source: Data collected through questionnaire survey 145 Ranking Funds and Facilities provided by DOTS program 350 S I 300 250 200 150 100 50 0 Very Poor UhetUactory itfactory Good Figure 6.6 Ranking funds and facilities by healthcare professionals at healthcare centers in the Punjab provided by DOTS program 146 The results showed that almost 67% of health professionals ranked the funds and facilities as satisfactory while 13% declared it good as shown in Figure 6.6. But there were very few who consider them excellent. A proportion of 1 7% health professionals declared them unsatisfactory while 3% think that they are very poor. Thus a proportion of one fifth of the total health professionals are not considering it adequate which shows that there is still a great room for improvement. Another important inquiry about the effectiveness of the treatment through DOTS was carried out at health centers which revealed some alarming facts. According to the guidelines provided by DOTS program the treatment regimen which is being followed for a new TB case consists of two stages. The first one is Initial intensive phase of first two months during which four drugs (Ri/ampicin, Isoniazid Pyrazinamide and Ethambutoi) are prescribed and provided. This phase is observed and administered very strictly and carefully. Ri/ampicin is considered the most important drug to cure tuberculosis. According the guidelines provided by DOTS, Ri/ampicin is withdrawn after two months. This particular drug is not continued in the next Continuation phase of 6 months. This is not acceptable to many health professionals. A large number of the health service professionals who are in direct contact with the patients not satisfied with this treatment regimen. According to them, this change of drugs after two months caused the reappearance of signs of tuberculosis. According to them, the patients feel that their disease is back. The doctors consider that such situation is not only discouraging the patients but also lead towards multi drug resistant tuberculosis which is much more dangerous than ordinary tuberculosis disease. They believe that, no doubt, experts in W.H.O. have devised these methods with great care but there might be variations in the situation from one country to another due to environmental, biological and cultural differences. They consider that the withdrawal of the drug named Ri/ampicin after two months may not suit to the patients here in Pakistan. Out of total 451 health centers covered by NTP through DOTS program, there were 154 centers where the doctors dealing with tuberculosis patients declared that they are not satisfied with the treatment regimen recommended by DOTS program. These comprise one third proportion of the total number of health facilities (Figure 6.7). The highest ratio of dissatisfaction of health professionals were found in districts Pakpattan, Narowal, M.B, Din, and Bahwal Nagar while the doctors in Lodhran, Rawalpindi and Jhelum are more satisfied than other districts in the Punjab as shown in Table 6.7 147 Table 6.7: Dissatisfaction of Health Professionals Regarding Treatment Regimen Recommended by DOTS Program at Healthcare Centers in the Punjab Total Number No. of centers where DISTRICT of Health doctors are not satisfied Centers with treatment regimen Altock 11 6 Bahawalnagar 15 9 Bahawalpur 16 6 Dhakkar 8 3 Chakwal 11 3 D.G. khan 12 6 Faisalabad 20 8 Gujranwala 16 3 Gujrat 12 4 HaGzabad 7 3 Jhang 20 6 Jheluin 9 1 Kasur 15 3 Khanewal 8 3 Khushab 9 3 Lahore 31 12 Layyah 6 1 Lodhran 7 1 M.B. Din 11 7 Mianwali 11 4 Multan 11 3 Muzzafar Garh 17 5 Narowal 9 6 Okara 13 4 Pakpattan 6 4 Rahim yar Khan 23 4 Rajanpiir 9 3 Rawalpindi 22 3 Sahiwal 13 6 Sargodha 19 8 Sheikhupura 20 3 Sialkot 14 7 T.T. Singh 9 2 Vehari 11 4 PUNJAB 451 154 Source: Data collected through questionnaire survey 148 Satisfaction of Health Professionals with Treatment Regimen in the Punjab 34% 66% Yes No Figure 6.7 Satisfaction of healthcare professionals with treatment regimen 149 The health service professionals administering the DOTS program have a key role in the eradication of disease but the dissatisfaction and lack of trust of such a large proportion of health professionals raises a serious concern in this regard especially if it steers towards MDR TB. The effectiveness of treatment method and the development of MDR TB are beyond the scope of the field of health geography and also of this research; so these issues should be studied by the medical and biological scientists in detail on a priority basis to investigate if the concerns of the health professionals are real or perceived. 6.3.4 Accessibility to Health Services Access is a multidimensional concept in health services analysis that describes people’s ability to use health services when and where they are needed (Aday & Anderson 1981). People's access to health services is rooted in their daily activity patterns in time and space (Cromely & McLafferty 2002). The constraints in time and space have a central role in shaping access to health care. A fundamental concept of health care utilization patterns is distance decay, or the tendency for interaction with service facilities to decrease with increasing distance (Cromely & McLafferty 2002). As the cost and nuisance of travel increases, the tendency to use a health service decreases. Another factor in distance decay is the declining awareness and familiarity, with increasing distance, about the facilities provided at a particular health center, Haynes et. al. (1999) argued that severe conditions and emergencies discourage distance decay if the nearest health service facility doesn’t fulfill the required function. Tuberculosis is a disease which doesn’t normally appear with acute emergency situations and the long term treatment for six to nine months is more likely to be influenced by distance decay. Generally travel for health care is strongly affected by demographic, cultural and socioeconomic characteristics such as income, occupation, age, gender, and mode of transportation etc. Cromely & McLafferty (2002) quoted Bashshur et al (1971) who proved that people whose mobility is limited due to low incomes, age, or poor access to transportation are more sensitive to distance, and thus more likely to use the nearest healthcare provider. The simplest way to measure accessibility is to calculate average distance from the population in need of service to the health service facility. But the modes of transportation, physical and social barriers vary greatly individual to individual who are at the same distance. Another problem in the present research is 150 people’s inability to assess the distance in kilometers due to illiteracy and lack of private transport. To overcome all these problems, travel time is being considered as a tool to analyze the accessibility. Cromely & McLafferty (2002) also advocated that travel time provides a better indication of geographical barriers to health services than does travel distance, since it incorporates access to transportation, As described earlier, tuberculosis takes six to nine months to be completely cured. So the frequency of visits to health service centers should also be considered along with travel time. Income and cost to visit health care service centers is an important factor in all rich and poor countries. Tuberculosis is a disease of poverty. In a developing country like Pakistan, the long term treatment of tuberculosis and meager resources for health services makes the situation pitiable for the poor patients of tuberculosis. In the present research, out of total1020 surveyed TB patients 567 persons (56%) had to travel one hour for a one way trip to health center. One third of total surveyed patients (33%) were travelling for two hours to reach a hospital or rural health center (RHC), While there were 114 patients who were travelling up to 3 hours for their checkup. They found no hospital or health center near their place of residence. It is really very difficult for almost half of the TB patients in the Punjab to travel 2 or 3 hours one way trip for nine months. The table and figures show that almost 40 to 50% patients in all the districts have to travel 2, 3 or more hours for a one way trip to access health services. Table 6.8 shows the travel time for all the surveyed TB patients in the Punjab. The variable of the travel along with number of patients can be expressed as a frequency distribution (Figure 6,8). 151 Table 6.8: Travel time taken by the tuberculosis patients for one way trip to access to health care centers in the Punjab {Hours) DISTRICT Travel Time Less than I Less than 1 3 or more Attock 19 10 1 Bahawalnag.tr 17 11 2 Bahawalpur 19 9 2 Bhakkar 16 ID 4 Cliakwal 17 10 3 U.G, khan 16 1) 3 Faisalabnd IS 10 2 Gujranwala 15 12 3 Gujrat 14 It 5 Haftzabad 16 12 2 Jhang 14 13 3 Jlielum 17 9 4 Kasiir 19 8 3 Khanewal 13 14 3 KJiushab 15 12 3 Lahore IS 7 5 I.ayyah 16 10 4 Lodhran IS 8 4 MB. Din 9 16 5 Mianwait 17 9 4 Multan 14 11 5 Mtizzafar Garli 15 10 5 Narowal 20 7 3 Okara 15 13 2 Pakpattan 17 S 5 Rahim yar Khan 17 10 3 Rajanpur 16 II 3 Rawalpindi 19 7 4 Sahiwal 20 8 2 Sargodha 19 7 4 Sheikhupura 18 9 3 Sialkol 17 9 4 T. T. Singh 20 8 2 Vehuri 17 9 4 PUNJAB 567 339 114 Source: Data collected through questionnaire survey 152 Travel Time to Access Health Services in the Punjab f 000 A 900 |*00 y *S MO 1 XI 200 s-I 100 0 iHitani *M«hsn2 3a rmr* Time (Konrs) Figure 6.8 Travel time to access healthcare services in the Punjab 153 Out of total TB patients, almost one fourth are elderly and more than half of all TB patients are females. To travel for such long distances for these two groups of the society is really hard. Majority of the patients (84%) normally visit the hospital or RHC once in a fortnight to get their medicine for ten or 15 days. Such long distances visits may sometimes be cancelled or postponed due to weather or other social problems. This it may cause a break in regular intake of medicine as 40% of the patients missed drug intake once in a month or more frequently. The situation regarding accessibility to health service centers becomes more complicated when observed with the cost per visit. Majority of the patients are very poor. Almost 24% of the patients spend less than Rs. 20 per visit while 771 patients in the survey (76%) have to spend between Rs 20 and 60 per visit. Majority of them spend Rs. 20 to 40 per visit as shown in Table 6,9. One can realize that these complications in accessibility to hospitals and health centers may cause a high dropout ratio and a low case detection ratio as it was observed only 40% in whole of the Punjab in 2005. 154 Table 6.9: Cost per visit for the tuberculosis patients to access to health care centers in the Punjab Cost (Rupees) DISTRICT Less than 20 21-40 41-60 Attock_ 9 19 2 Bahawalnagar 7 21 2 Bahawalpur 6 21 3 Bhakkar 5 22 3 Cbakvval 8 19 3 D.G.khan 9 19 2 Paisalabad 6 16 8 Gujranwala 5 20 5 Gujrat 7 17 6 Hafizabad 7 18 5 Jhang 9 14 7 Jhelum 8 16 6 Kasur 7 19 4 Khanewal 10 18 2 Khushab 8 18 4 Lahore 4 13 13 Layyah 8 17 5 Lodhran 6 19 5 M.B, Din 5 20 5 Mianwali 7 17 6 Multan 8 13 9 Muzzafar Garh 9 18 3 Narowal 8 17 5 Okara 6 20 4 Pakpattan 9 17 4 Rahim yar Khan 6 16 8 Rajanpur 11 17 2 Rawalpindi 8 12 10 Sahiwal 6 18 6 Sargodha 7 14 9 Sheikhupura 9 11 10 Sialkol 6 16 8 T. T. Singh 8 18 4 Vehari 7 19 4 PUNJAB 249 589 182 Source: Data collected through questionnaire survey 155 6.3.5 Spatial Distribution and Inequalities in Health Service Centers Location of health services is a key factor affecting accessibility and utilization of healthcare services. Due to the location and accessibility issues some areas arc better served while others remain underserved. Certain political issues and public health policies by the state also determine if the areas would have enough health facilities or lack of it. State plays its role in healthcare provision and organization because uncontrolled free market forces will hinder the fair distribution of health services that meets the needs of the whole population (Curtis 2004), This may be of particular benefits to poor groups in the population that might not otherwise be able to afford services. In the present research our focus is on the health services provided by the state as National Tuberculosis Control Program (NTP) is carrying out DOTS program against tuberculosis on some of these public health service centers. As mentioned earlier, these include District headquarter hospitals (DHQ), Tehsil headquarter hospitals (THQ), Rural health centers (RHC), Basic health units (BHU), District TB and chest hospitals, and TB clinics. It is important to note that all the healthcare centers in all the districts of the Punjab are not equally included and chosen for the implementation of DOTS program. So the issue of inequality of equal access to healthcare system becomes more complex. An analysis of the number of healthcare centers in different districts shows there is a great diversity among the districts regarding the provision of treatment facilities for TB patients. Lahore, Rawalpindi, Faisalabad and Sheikhupura have higher number of treatment centers as compared to the districts of Layyah, Pakpattan, Hafizabad and Bhakkar which are at the bottom of continuum. When the number of diagnostic and treatment centers is analyzed in comparison to the total population of the districts it might be inferred that the distribution of health centers seemed to be balanced to some extent. For example, Lahore, Faisalabad, Gujranwala, Rawalpindi and Sheikhupura are top five most populated districts in the Punjab. While the highest number of health centers are found in Lahore, Rahimyar Khan, Rawalpindi, Faisalabad and Sheikhupura. But the districts with lowest population in the Punjab are Hafizabad, Khushab, Jhclum, and Bhakkar while the lowest number of treatment centers was found in Layyah, Pakpattan, Hafizabad, and Lodhran (Table 6.10). 156 Tabic 6.10: Distribution and Inequality of health facilities in the Punjab Total Number DISTRICT of Health Estimated Standardized Centers Population 2005 Morbidity Rate Attock 11 14*2932 1.5806 Baliawalnagar 15 2830560 1.1138 Bahawaipur 16 2398670 2.1336 Bhakkar 8 1223148 1.6460 Chakwal 11 1261033 1.4447 D.G. khan 12 1911575 1.0970 Faisalabad 20 6315992 0.2696 Gujramvala 16 3956290 1.1372 Gujrat 12 2382433 1.3573 Hafizabad 7 968777 1.0472 Jhang 20 3297088 0.7045 Jhclum 9 1090009 0.6066 Kasur 15 2763449 0.7296 Khanewal 8 2406247 1.2117 Khushab 9 1054289 0.6890 Lahore 31 7350797 0.3079 Layyah 6 1304331 1.3619 Lodhran 7 1362782 0.4248 M.B. Din 11 1349793 1.7123 Mianwall 11 1229643 0.4581 Multan 11 3626146 1.0929 Muzzafar Garh 17 3066530 1.2076 Narowal 9 1472108 0.8665 Okara 13 2597837 0.9438 Pakpattan 6 1497004 1.2530 Rahim yar Khan 23 3654291 1.6113 Rajanpur 9 1283765 1.8767 Rawalpindi 22 3912992 0.5225 Sahiwal 13 2144298 0.7576 Sargodha 19 3101168 1.5707 Sheikhupura 20 3863200 0.5403 Sialkot 14 3168279 1.1599 T. T. Singh 9 1886679 1.3243 Veliari 11 2432225 1.7052 PUNJAB 451 85646360 1.0000 Source: Data collected through questionnaire survey 157 It is important to note that the number of diagnostic and treatment centers is higher in the districts with higher ranks regarding size of population, but it does not mean that they are in accordance with the actual count of population. The number of health centers and the people being served in every district are not in proportion with each other. An analysis with the help of Gini index would reveal the disparities, which is being discussed in next section. No doubt, the government has tried to set up health centers in accordance with the size of population to be served (although this principle is not completely followed). However, TB disease in recent years has not followed this tradition i.e. the disease rates are not higher in the top five most populated districts of the Punjab. The standardized morbidity rate (SMR) of the year 2005 is minimum in the districts of Lahore, Faisalabad, Rawalpindi and Sheikhupura (ranging from 0.26 to 0.54) while they are on top in terms of number of diagnostic and treatment centers established by NTP. The districts Rajanpur, M.B. Din, Vehari, Bhakkar and Attock which have a standardized morbidity rate of greater than 1.50 are having least number of healthcare centers. This show’s the inequality and an unjust distribution of healthcare services in the Punjab. For further analysis of inequality among different districts, the most commonly used measures of Lorenze curve and Gini Index are used. 6.3.6 Lorenz Curve and Gini Index Gini index and Lorenz curve as important tools to measure the level of inequality in the distribution of health resources. Rickets et al, (1994) elaborated the application of Gini index to study the level of inequality in the geographic distribution of physicians on national and state level. A number of scholars suggested Lorenz curve and Gini index to measure health and health service inequalities among individuals and groups (Le Grand & Robin 1986; lllsley & Grand 1987; Wagstaff et al. 1991; Brown & Malcom 1994; Chang & Halfon 1997; Bemdt et. al. 2002). Lorenz curve and Gini index are closely associated with each other and normally used in combination with each other. Lorenz curve is a graphical representation of cumulative measures of phenomenon or objects. In the present situation it is used to compare cumulative proportions of population and health centers (Table 6.11), and secondly, the number of TB cases and health centers. As the figure 6.9 & 6.10 depict that x-axis is a rank order of groups or individuals with the most disadvantaged on the left hand while the y-axis shows the cumulative proportion of health service centers. The straight line in 158 the graph is considered the line of perfect equality while the curve depicts the departure from equal distribution of services to the population. The further the Lorenz curve is from equality line, the greater the degree of inequality. The area between the curve and the line of equality expressed as a proportion of the total area beneath 45° degree line of equality provide a mathematical value to measure inequality which is called Gini index. It can take a value from 0 (perfect evenness) to 1 (maximum possible unevenness) and provides a standardized value to reflect the relative unevenness of distribution. The mathematical formula used to find Gini index is: o=i f-0 Where oX and oY are cumulative proportions of values on X and Y axis and N shows the number of observations. All the districts are not equally served by the health facilities provided by National Tuberculosis Control Program. These healthcare facilities were plotted against the estimated total population of year 2005 in the Punjab in a Lorenz curve (Figure 6.9). The cumulative proportions of population are shown on x-axis while the cumulative proportions of health services are placed at y-axis. The proportional share of 34 districts in the Punjab is shown as a curved path starting from least poorly served to the better one. The curve shows the departure from the line of perfect equality. As stated earlier, the inequality is not much significant when population and the number of health facilities are compared. The Lorenz curve shows that half of the population in the Punjab is served by 40% of the share of health facilities. 159 Table 6.11: Cumulative Proportions of Population and Healthcare Facilities in the Punjab 2005 Cumulative Cumulative Estimated Healthcare Proportion Proportion DISTRICT Population facilities of of Health 2005 2005 Population facilities Multan 3626146 0.042339 11 0.02439 Faisalabad 6315992 0.116084 20 0.068736 Khuiiewal 2406247 0.144179 8 0.086474 Pakpattan 1497004 0.161658 6 0.099778 Gujranuata 3956290 0.207851 16 0.135255 Lahore 7350797 0.293679 31 0.203991 Sialkot 3168279 0.330671 14 0.235033 Vehari 2432225 0.359070 11 0.259423 Layyah 1304331 0.374299 6 0.272727 T.T. Singh 1886679 0.396328 9 0.292683 Okara 2597837 0.426660 13 0.321508 Gujrat 2382433 0.454477 12 0.348115 Lodhran 1362782 0.470389 7 0.363636 Shcikupura 3863200 0.515495 20 0.407982 Bahawalnagar 2830560 0.548544 15 0.441241 Kasur 2763449 0.580810 15 0.474501 Muzzafar Garh 3066530 0.616615 17 0.512195 Rawalpindi 3912992 0.662303 22 0.560975 Sahhval 2144298 0.687339 13 0.589800 Jhang 3297088 0.725836 20 0.634146 Narowal 1472108 0.743024 9 0.654102 Sargodha 3101168 0.779233 19 0.696230 D.G. khan 1911575 0.801552 12 0.722838 Rahim yar Khan 3654291 0.844220 23 0.773836 Bhakkar 1223148 0.858501 8 0.791574 Bahawalpur 2398670 0.886508 16 0.827051 Rajanpur 1283765 0.901497 9 0.847006 HaHzahad 968777 0.912808 7 0.862527 Attock 1482932 0.930123 11 0.886918 M. B, DIN 1349793 0.945883 11 0,911308 Jhclum 1090009 0.958610 9 0.931264 Khushab 1054289 0.970919 9 0.951219 Chakwal 1261033 0.985643 11 0.975610 Mianwali______1229643 _1.000000 11 1.000000 Source: National Tuberculosis Control Program 160 Lorenz Curve 100 *' 4 4 4 4 4 4 0.80 4 5 4 i 4 £ 4 * ® 0 60 j J i * * l04° 4 I ui * 020 * 0 00 0.00 020 0 40 0-60 0 60 100 CumutaOv* Proportion of Popubtion Figure 6.9 Lorenz Curve showing Population and Health facilities in the Punjab 2005 161 The larger the population of an area the higher the number of health centers is considered a general rule of equal access to health services. But this general principle is not applicable in certain situations when there are clusters of disease in some areas. So the rationale of equal access and utilization of health services will be based on the availability of resources to those who actually need them. As described in the previous chapter, the focus of tuberculosis in recent years has moved towards the southern parts of the Punjab. During 1990's, no doubt, the big cities were severely hit by the disease but the disease has moved towards smaller cities of southern Punjab in recent years. So in this situation, the big cities of north and central Punjab will have excessive resources and the areas of south Punjab might be under served. To analyze this situation, Lorenz curve is also being plotted between the cumulative proportion of health facilities and the number of cases appeared in year 2005 (Figure 6.10). 162 Table 6.12: Cumulative Proportions of TB Cases and Healthcare Facilities in the Punjab 2005 Observed Cumulative Healthcare Cumulative DISTRICT Cases Proportion of (TB) Proportion facilities of Cases 2005 Health 2005 Facilities Vchari 2923 0.048424 11 0.02439 Klianewal 2055 0.082468 8 0.042128 Multan 2793 0.128738 11 0.066519 Bahawalpur 3607 0.188493 16 0.101995 Pakpattan 1322 0.210394 6 0.115299 Layyah 1252 0.231135 6 0.128603 Gujranwala 3171 0.283667 16 0.16408 T. T. Singh 1761 0.312841 9 0.184035 Gujrat 2279 0.350596 12 0.210643 Rajanpur 1698 0.378726 9 0.230598 Sialkot 2590 0.42)633 14 0.261641 Sargodha 3433 0.478505 19 0.303769 Rahim yar Khan 4150 0.547256 23 0.354767 Bhakkar 1419 0.570764 8 0.372505 Muzzafar Garh 2610 0.614002 17 0.410199 Attock 1652 0.64137 11 0.43459 Bahawalnagar 2222 0.678181 15 0.467849 M. B. DIN 1629 0.705167 11 0.492239 Okara 1728 0.733794 13 0.521064 D.G. khan 1478 0.758279 12 0.547672 Chakwal 1284 0.779551 11 0.572062 Hafizabad 715 0.791396 7 0.587583 Narowal 899 0.806289 9 0.607539 Kasur 1421 0.82983 15 0.640798 Sahhval 1145 0.848798 13 0.669623 Jliang 1637 0.875918 20 0.713969 Sheikupura 1471 0.900287 20 0.758315 Rawalpindi 1441 0.924159 22 0.807095 Faisalabad 1200 0.944039 20 0.851441 Lodhran 408 0.950798 7 0.866962 Khushab 512 0.95928 9 0.886918 Jhelum 466 0.967 9 0.906873 Lahore 1595 0.993423 31 0.97561 Mianwali 397 1 11 1 Source: National Tuberculosis Control Program 163 Lorenz curve 1 00 4 4 0 60 Ie. i * ? 060 4 1 J ZI A £ j o |0 40 4 4 I3 > i 020 4 4 4 4 % 0.00 / 000 - 020 040 060 060 100 CumutBtiw Proportion of (timber of TB Cases Figure 6.10 Lorenz Curve showing TB Cases and Health facilities in the Punjab 2005 164 This curve shows more deviation from the central line of equality as compared to the previous one. This curve shows that almost 40% of total cases are being served with only 20% health centers while up to 30% of total TB cases which includes all the districts of south Punjab, have a 60% share of health facilities. Only 10% of total TB cases are enjoying a 25% share of health services. These include more urbanized districts of Sheikhupura, Rawalpindi, Faisalahad, and Lahore. In order to calculate the mathematical value of the difference between the curve and the diagonal line of equality, and to compare both of the above mentioned Lorenz curves we calculate Gini index denoted by G 1-0 The following tables (6.13 & 6.14) provide the necessary calculations to be used in this formula. 165 Table 6.13: Gini Index of the Inequality in Total Population and Health Services in the Punjab Cumulative Cumulative Proportion Proportion A. B. DISTRICT A x B of of Health OYM+UY, oX,.,-X( Population services .Multan 0.042339 0.02439 0.02439 0.042339 0.001033 Faisalabad 0.116084 0.068736 0.093126 0.073745 0.006868 Khanewal 0.144179 0.086474 0.155210 0.028095 0.004361 Pakpattan 0.161658 0,099778 0.186252 0,017479 0.003255 Gujranwala 0.207851 0.135255 0.235033 0.046193 0.010857 Lahore 0.293679 0.203991 0.339246 0.085827 0.029117 Sialkot 0.330671 0.235033 0.439024 0.036993 0.016241 Vchari 0.359070 0.259423 0.494456 0.028398 0.014042 Layyah 0.374299 0272727 0.532150 0.015229 0.008104 T. T. Singh 0.396328 0.292683 0.565410 0.022029 0.012455 Okara 0.426660 0.321508 0.614190 0.030332 0.01863 Gujrat 0.454477 0.348115 0.669623 0.027817 0.018627 I.odhran 0.470389 0.363636 0.711751 0.015912 0.011325 Sheikupura 0.515495 0.407982 0.771618 0.045106 0.034805 Bahawalnagar 0.548544 0.441241 0.849223 0.033049 0.028066 Kasur 0.580810 0.474501 0.915742 0.032266 0.029547 (Muzzafar Garh 0.616615 0.512195 0.986696 0.035805 0.035328 Rawalpindi 0.662303 0.560975 1.073170 0.045688 0.049031 Sahiwal 0,687339 0.589800 1.150776 0.025037 0.028812 Jhang 0.725836 0.634146 1.223946 0.038497 0.047118 Narowal 0.743024 0.654102 1.288248 0.017188 0.022143 Sargodha 0.779233 0.696230 1.350332 0.036209 0.048894 D.G. khan 0.801552 0.722838 1.419068 0,022319 0.031673 Rahim yar Khan 0.844220 0.773836 1.496674 0.042667 0.063859 Bhakkar 0.858501 0.791574 1.565410 0.014281 0.022356 Bahawalpur 0.886508 0.827051 1.618625 0.028007 0.045332 Rajanpnr 0.901497 0.847006 1.674057 0.014989 0.025093 Hafizabad 0.912808 0.862527 1.709534 0.011311 0.019337 Attock 0.930123 0.886918 1.749445 0.017315 0.030291 M. B. DIN 0.945883 0.911308 1.798226 0.015760 0.02834 Jlielum 0.958610 0.931264 1.842572 0.012727 0.02345 Khushab 0,970919 0.951219 1.882483 0.012310 0.023173 Chakwal 0.985643 0.975610 1.926829 0.014724 0.02837 Mianwali 1.000000 1.000000 1.975609 0.014357 0.028364 0.848296 Source: computed from the data obtained from NTP, Pakistan 166 Gini Index for the proportion of the population in the Punjab and the number of health facilities is: N i~0 G= 1-0.848296 G = 0.151704 The distribution of health services and the total population in different districts of the Punjab has a gini index value of 0.151704. 167 Table 6.14; Gini Index of the Inequality in TB Cases and Health Services in the Punjab Cumulative Cumulative Proportion Proportion A, B. DISTRICT A x B of TB of Health OYM+OYJ Cases services Vehari 0.048424 0.02439 0.02439 0.048424 0.001181 Khanewal 0.082468 0.042128 0.066518 0.034044 0.002265 Multan 0.128738 0.066519 0.108647 0.04627 0.005027 Baliawalpur 0.188493 0.101995 0,168514 0.059755 0.01007 Pakpattan 0.210394 0.115299 0.217294 0.021901 0.004759 Layyah 0,231135 0.128603 0.243902 0.020741 0.005059 Cujranwaln 0.283667 0.16408 0.292682 0.052532 0.015375 T. T. Singh 0.312841 0.184035 0.348115 0.029174 0.010156 Gujrat 0.350596 0.210643 0.394678 0.037755 0.014901 Rnjanpur 0.378726 0.230598 0.441241 0.02813 0.012412 Siatkot 0.421633 0.261641 0.492239 0.042907 0.021121 Sargodha 0.478505 0.303769 0,56541 0.056873 0.032156 Rahim yar Khan 0.547256 0.354767 0.658536 0.068751 0.045275 Bhakkar 0.570764 0.372505 0.727272 0.023508 0.017097 Muzzafar Garh 0.614002 0.410199 0.782705 0.043238 0.033843 Altock 0.64137 0.43459 0.844789 0.027368 0.02312 Bahawatnagar 0.678181 0.467849 0.902439 0.036811 0.033219 M. B. DIN 0.705167 0.492239 0.9600S8 0.026987 0.02591 Okara 0.733794 0.521064 1.013303 0.028627 0.029008 D.G. khan 0.758279 0.547672 1.068736 0.024485 0.026168 Chakwal 0.779551 0.572062 1.119733 0.021271 0.023818 Hafizabad 0.791396 0.587583 1.159645 0.011845 0.013736 Narowal 0.806289 0.607539 1.195121 0.014893 0.017799 ICasur 0.82983 0.640798 1.248337 0.023541 0.029387 Sahivyal 0.848798 0.669623 1.310421 0.018969 0.024857 Jliang 0.875918 0.713969 1.383592 0.027119 0.037522 Sheikupura 0.900287 0.758315 1.472283 0.024369 0.035878 Rawalpindi 0.924159 0.807095 1.56541 0.023872 0.03737 Faisaiabad 0.944039 0.851441 1.658536 0.01988 0.032971 t.odhran 0.950798 0.866962 1.718403 0.006759 0.011615 Khushab 0.95928 0.886918 1.75388 0.008482 0.014876 Jhelum 0.967 0.906873 1.793791 0.00772 0.013848 Lahore 0.993423 0.97561 L.882483 0.026423 0.049742 Mianwali i 1 1.975609 0.006577 0.012993 0.724533 Source: computed from the data obtained from NTP. Pakistan m G=1-IK-ÿki-\ ax,) (-0 G = 1- 0.724533 G = 0. 275467 The value of the Gini Index for the number of TB cases and the health service facilities in the Punjab is 0.275467 which is fairly large value and shows that there is a significant inequality between the health services provided and their actual need in the districts. The government and other agencies involved in controlling disease should have to focus on the areas where there are more patients to provide more health facilities to control the disease. 6.4 Summary An analysis into the demographic characteristics of TB patients reveals that the incidence is higher in working and productive age group of 15-64 years. This situation is creating social and economic problems for patients as well as their families. Due to larger family size, illiteracy, unemployment and clustering of disease in low income groups of the society is emerging as a health concern and social problem. This picture becomes grim as the majority of patients arc not taking precautions and some of them arc dissatisfied with the treatment method and regimen. Even a large proportion of health professionals have certain reservations about treatment regimen. The accessibility barriers and higher cost per visit for poor patients discourages the completion of treatment in continuation stage of the disease. The spatial distribution of health facilities is also not in accordance with the distribution of TB patients in the Punjab. 169 CHAPTER 7 SUMMARY AND CONCLUSION The present study of spatial distribution of Tuberculosis is planned to cover four themes which arc at the heart of medical and health geography; disease mapping, disease clustering, analysis of risk factors, and the analysis of healthcare facilities. TB is an infectious disease which has a devastating effect throughout the world. Pakistan ranks sixth amongst the countries with highest TB burden. The DOTS strategy was launched in Pakistan during 1990’s with the collaboration of World Health Organization to fight the disease effectively. DOTS coverage was 2% in 1995 and 100% in Pakistan in 2005 but still the disease is not under control. This study was conducted in the Punjab province which is the largest province of Pakistan with 56% share in total population of the country. The province is divided into 34 districts having a total population more than 86 million according to the estimates for the year 2005. For the present study both primary and secondary data were collected; which were then analyzed and visualized using statistical and geographical techniques, methods, and software. Disease patters are studied to understand whether the observed events exhibit any systematic pattern or they are randomly distributed. In the year 1990, the highest number of TB patients was found in Faisalabad followed by Lahore. These two districts held the top two positions regarding total population at that time. The districts of Jehlum, Faisalabad, and Mianwali observed the Standardized Morbidity Ratio (SMR) more than 2 at that time. The number of TB patients was probably under reported in 1995 as the total number of patients is remarkably lower in 1995 as compared to 1990. the districts of Khushab, Bahwalpur, Rahimyarm Khan, and Attock have SMR more than 2 which implies that the number of patients were more than double than their expected share in TB burden in the province. The areas of north western, and south Punjab are more affected. In 2000, the higher SMR was found in Khushab, Mianwali, Rajanpur, and Bhakkar. The most urbanized and thickly populated districts: Faisalabad, Sheikhupura, Lahore, and Rawalpindi have a low SMR. During the year 2005, higher SMR was found in Bahawalpur, Rajanpur, M.B. 1 70 Din, and Vehari. Three districts of south Punjab: Rajanpur, Rahimyar Khan, and Bahawalpur experienced high SMR in 1990 which continued up to 2005. Geography is the study of spatial variations that take place from lime to time. In order to understand the spatial patterns of TB front 1990 to 2005; it was found that the larger cities of Lahore and FaisaJabad had a higher SMR in 1990 but later there was observed a sharp decrease till 2005. The reason might be the better living standard. higher literacy, more health facilities and the focus of government and semi- govermnent agencies to control the disease. The areas of southern Punjab experienced a higher SMR throughout the study period especially the districts of Rahimyar Khan, Bahawalpur, Rajanpur, Vehari, and Bahawalnagar. The situation was better in the areas of Rawalpindi, Kasur, and Sheikhupura throughout the study period as they experienced low SMR. Smear positive patients are considered to have greater bacterial load and the capacity to spread infection till they become smear negative. The proportion of smear positive was one fifth in the last decade of 20th century. In 2005, it was found 31% which is alarming. However one reason for the abrupt increase of smear positive cases may also be the improvement in case detection, diagnosis, and recording techniques in recent years. Some districts observed extremely high rates of smear positive cases in 2005. District Jhang is on top of the list with 69% smear positive cases followed by Sahiwal with 52% cases in 2005. Other districts with higher proportion of smear positive patients arc Rahimyar Khan, Layyah, Gujrat, Multan, Khanewal and Faisalabad. It is the responsibility of policy makers to take necessary actions without wasting any time with special focus on areas which are more vulnerable. Earlier studies about gender differences in the incidence of TB in India and Bangladesh showed that there were fewer female patients as compared to males. The social, economic, and cultural constraints for women in the Punjab are not much different from India and Bangladesh but the number of female patients was found higher than males in many districts and whole of the Punjab province. It is interesting to note that the sex ratio in the Punjab, according to 1998 census, showed that there are more than 107 males to every 100 females. The disease sex ratio was found 98.4 in 2005 implying that there are a surprisingly larger number of females as TB patients. It clearly expresses that female notification rate outnumbers males throughout the Punjab. There are only 7 districts in the Punjab where the proportion of male TB patients is higher than the proportion of males in the total population. In 371 contrast to majority of the world regions the higher number of female TB notification in the Punjab is a very serious threat. It is a fact that due to social obstructions, females have a comparatively lesser access to health facilities in the Punjab. For a disease like TB a longer period of treatment makes the situation worst for them, A logical conclusion in these circumstances may be drawn that the actual number of female TB patients in the population is much more than the notified cases. When this conclusion is joined with the fact that the actual number of males is more than females in Punjab, it indicates towards a more horrible picture. Thus the higher proportion of females in notified cases should be considered as a serious concern because the chances of spread of disease are raised due to their role at home. Women spend more time at home as compared to males. They are in close contact with children especially under age of 5 years. They spend much of their time in kitchen and in close contact with utensils being used by the family. Literacy rate is lower in females as compared to males in the Punjab. A lack of necessary care while coughing and sneezing may cause risk for the entire family especially children. This scenario calls for serious concern and an improvement in policy to combat the disease with special reference to gender differences. World Health Organization claimed to achieve the targets of 85% cure rate and 70% case detection rate worldwide up to 2005. WHO declared that 84% cure rate and 60% case detection rate was achieved worldwide. But in case of Pakistan the case detection rate is only 37% in 2005. The situation is not much better in the Punjab province with a case detection rate of only 40%. The cure rate in the Punjab province is only 64% in 2005 which is too low as compared to 84% for the rest of the world. These figures indicate that there are some flaws in the planning and management to fight the disease in Pakistan, Clustering is any unusual aggregation of health events. Clusters of disease arise where there is an excess of cases in space or time. To identify the clusters of tuberculosis in the Punjab, KulldorfTs Spatial Scan Statistic was used with the help of SaTScan software. Disease clusters were found in the districts of Faisalabad and Lahore in 1990 and in the districts of Bahawalpur, Rahimyar Khan, and Rajanpur of south Punjab in 1995. During year 2000 the situation was almost the same regarding most likely clusters while the secondary clusters occupied the areas of northern and northwestern Punjab. During the year 2005, the most likely clusters remained in the areas of south Punjab and also included the areas of central Punjab. A comparison of 172 all the years under study depicted that the efforts to control TB seemed to be successful in big cities of the Punjab while the areas of south Punjab was found to be severely affected since 1995, The areas of the south Punjab arc considered less developed with low literacy rate as compared to the areas of northern Punjab. The clusters of disease are occupying those areas since last 15 years but the areas are seemed to be ignored in this regard. Tuberculosis is an infectious disease which is considerably influenced by a number of demographic and socio-economic factors in its incidence, prevalence, and spread. In a questionnaire survey conducted for the present research, it was found that TB patients were significantly higher in age group 15-64 years. The cultural, social, and economic patterns of the country reveal that this group is the working class and feeds children and elderly. Most of the people are married in this group thus their counterparts are at higher risk. The average household size of TB patients was found to be slightly less than 7, Majority of the patients had more than two children at home which were on higher risk due to weaker immune system. A number of studies emphasized on over¬ crowding as an important risk factor in TB incidence and spread. In the Punjab there are 25% households which are living in a house with only one room while almost 68% are residing in houses with two rooms. The percentage share of TB patients living in a house with single room is 44% while 50% are living in houses with 2 to 4 rooms. Thus it may be concluded that the persons living in houses with only one room are more likely to develop TB than those who are not as the proportion of patients from single room houses is very high as compared to actual ratio of single room ratio in the population. No doubt, NTP is not responsible to build separate rooms for TB patients at home but it may educate patients about the risk and necessary care to reduce the chances of disease multiplication. In the Punjab the overall literacy rate is 51% according to estimates for the year 2005 but the literacy rate in TB patients was found to be only 24%. Among the literate persons more than 87% patients have less than 5 years of schooling. A positive correlation exists between illiteracy and SMR in 34 districts of the Punjab. TB is a disease which largely affects the poorest strata of society. Poverty is not only a cause but also an outcome due to longer period of treatment and patient’s inability to work. More than half of the patients had a monthly household income less than Rs. 5000. Almost 99% patients belong to families with a monthly income of less than Rs. 1000. To understand this situation internationally, it might be described that 99% of 173 TB patients in Pakistan had an average daily household income of 5 US dollars from all the sources with an average household size of almost 7 individuals. The unemployment rate in TB patients in the Punjab was 23%. With these economic conditions, the vulnerability of TB patients and their families might be well understood. Thus these patients were not able to afford a healthy diet and there were greater chances of dropout before completing the treatment. Family history of TB is one of the risk factors for other family members. Out of total 1020 patients, a proportion of 11% declared that they had another family member infected at home presently or in the past. Further studies should be conducted in this regard to measure the effect of such exposure. More than 70% patients don’t have any information regarding the symptoms, precautions and risk of the disease diffusion. The rural females and elderly were the most ignorant in this regard. Not only is the lack of knowledge but carelessness making the situation worst in the Punjab regarding tuberculosis. It was observed that 78% patients were not taking any precautions while coughing and sneezing, which are the most important sources of spreading disease. Almost 84% patients were unaware of any media campaign by NTP to educate about the symptoms and precautions of TB. Due to lower literacy rate and other social and cultural habits, the only media which was found to be the most effective was the Television. Almost one third of the total patients were not satisfied with the treatment. Majority of them were in the continuation stage. It was found through further inquiry that they were dissatisfied with the change in drug regimen after 2 months. An irregularity in the drug intake is one of the serious problems which may lead to failure of disease control program and the development of drug resistant tuberculosis. In the present study it was discovered that 25% patients admitted the drug intake missed once a month while 10% missed it twice a month. One of the reasons was the problems regarding easy access to healthcare services. There are 45 1 diagnostic and treatment centers operating in the Punjab under DOTS program. These include BHU, RHC, THQ, DHQ, TB Clinics and Hospitals. The number of doctors, particularly the chest specialists, is very low when compared with the number daily patients at these health centers, A large majority of doctors doesn’t consider the funds, facilities, and drug regimen by DOTS program in accordance with the present requirements of patients. The issue of drug regimen is particularly an alarming one. According to the guidelines provided by DOTS program the treatment regimen which is being followed for a new TB case consists of Initial Intensive phase 174 and continuation phase. During the continuation stage an important drug 1Rifampicin' is withdrawn after initial intensive stage. This is not acceptable to almost one third of health professionals out of total 451 TB healthcare centers. According to them, they have observed in the field that the withdrawal of 1Rifampicin' increases the chances of multi drug resistant MDR TB and also discourages the patients to complete the treatment. They declared that this particular drug regimen may not be appropriate in case of Pakistan as the epidemiological and etiological conditions might be different. The health service professionals administering the DOTS program have a key role in the eradication of disease but the dissatisfaction and lack of trust of such a large proportion of health professionals raises a serious concern in this regard especially if it steers towards MDR TB. The accessibility issue is a crucial one for TB due to longer treatment, frequent visits, higher cost of travel, and poor economics of the patients. All these issues cause great problems for the poor patients and discourage the completion of treatment, thus increasing the danger of MDR TB. The location of healthcare facilities should be planned in such a manner that the patients would gel the treatment very near to their homes. Location of health services is a key factor affecting accessibility and utilization of healthcare services. The number ot health centers in different districts of the Punjab is not in accordance with their need. The general pattern of healthcare services shows that the number of health facilities is higher in the cities which are on higher rank with respect to population. This pattern might be justified in early 1990s. But later on, the disease trend has been changed. Now the number of patients is higher in many districts which are on lower rank with respect to population. But the decision makers have not followed this trend while planning for the location to set up new healthcare centers. They are still following the traditional approach and are more interested in big cities due to political or other reasons while those cities are not on top with respect to the total number of TB cases. A quantitative analysis with the help of Lorenz curve and Gini Index also confirmed that there is a greater disparity among the districts when the number of health services and recorded cases were compared. Suggestions As TB is an infectious disease so the public awareness regarding the sources of spread of disease and treatment is a crucial issue to control the disease. NTP is not only responsible for the provision of drugs and facilities but also to disseminate 175 information. A major hurdle in the awareness is the low literacy rate which is particularly low in TB patients. It was found that television is tire most effective media in these circumstances. NTP should review its awareness policy as the present policy is not proving much effective. The gender differences regarding TB in the province yielded alarming results as females arc more affected by the disease. Smear positive female patients are more dangerous due to their role at home, Tire policies should be revised to focus these gender issues in disease notification. The cure rate and case detection rate of TB is very low in the Punjab as compared to rest of the world. There is a need to find the flaws in the policy making and management. As the clusters of disease were found in the districts of southern Punjab since last fifteen years so the focus of disease controlling efforts should be diverted to those areas. It will not only help particularly to control and eradicate the disease but also to reduce the inequalities within the regions in the same province. Poor economic conditions increase the chances of incidence of disease and dropout before the completion of treatment. Government should take multifaceted steps to control the disease and to reduce the risk factors of the disease. A large proportion of healthcare professionals expressed their reservations about the present drug regimen policy by considering it a risk factor to develop MDR TB in some cases. Such a serious issue should be addressed on priority basis by conducting further research in this regard. Accessibility to health services is one of the important issues. Any impediments in this regard may lead to MDR TB or dropout. So the patients should be served very near to their homes. Otherwise high expenditure on drugs and other facilities may not prove good results. While planning healthcare centers the patterns of disease incidence arc seemed to be ignored. The number of healthcare facilities is still higher in some big cities while the rural areas and some less developed cities have higher concentration of disease and patients. Further Research The present research presented the spatial patterns of tuberculosis in the Punjab province from 1990 to 2005 with the help of patient counts and morbidity and 176 mortality rates. There were Found disease clusters in certain areas of the Punjab. These areas should be further analyzed to find the reasons for clusters to reside there since 1995. The data for the year 2005 showed surprisingly higher proportion of female TB patients. 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Yes No 197 Total Income per month: Less than Rs. 5000 Less than Rs.10000 Less than Rs.20000 More than Rs. 20000 Information regarding Disease Do you smoke? Yes Non Do you know the symptoms and precautions of the disease you have? Yes No Are you taking precautions while coughing and sneeze? Yes No How much time has passed while taking medicine of Tuberculosis? < 1 month 1*2 months 3-4 months 5-6 months more than 6 months Are you satisfied with the treatment? Yesa NoC If not, why? Do you know the expected duration of treatment of you disease? Yes O No Due to any reason how many times the drug intake is missed? Never Once in a month Twice in a month More frequently Do you use prescribed diet with medicine i.c. meat and milk? YesO NoG Never mentioned by doctor Is there any family member already infected by this disease? Yes No Cured Alive with disease O Dead Do you share blankets and bed with any other person? Yes NoD Do you share utensils with others? Yes No: Have you ever read or heard about symptoms and cure of tuberculosis in newspapers, radio or TV? Yesa Non If yes, from where you got the information most frequently? Newspapers Radio TV Other People Others_ 198 Health facilities Accessibility to health services: Fare per visit: Rs. Time consumed per visit: Frequency of visit to health services Once in a month Once in a fortnight Once in a week More frequently Are the drugs available free of cost? Yes Non Are the laboratory tests and X-Ray facilities provided free of cost? Yes No 199 B. Questionnaire for Healthcare Professionals CONFIDENTIAL Department of Geography University of the Punjab To be filled by Health Service Officials: Name of the Healthcare service centre: District: _ Average number of daily patients: Number of doctors available: Full Time Visiting How many chest specialists are there in the institution? __ Are there facilities available for the diagnosis of Tuberculosis? Tuberculin Test Lab X-Ray Unit Are the tests and X-Ray facilities provided free of cost for all TB patients? Yes No Are the drugs provided in time by DOTS Program? Yes Non Is there any specialized ward for TB patients? Yes U No If yes, how many beds are there in the ward? _ How would you rank the funds and facilities provided by DOTS Program? Very Poor unsatisfactory D satisfactory good excellent Are you satisfied with the treatment regimen? Yes No