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BASIC CONCEPTS in EPIDEMIOLOGY Introduction BASIC CONCEPTS IN EPIDEMIOLOGY Introduction Hayley Coleman AIMS LO 8 To develop an understanding of research methodology and critical appraisal of the research literature 8a Research techniques Demonstrate an understanding of basic research methodology including both quantitative and qualitative techniques 8b Evaluation and critical appraisal of research Assess the importance of findings, using appropriate statistical analysis Cover: Study Types, Basic Epi and Statistics Disclaimer: I am not a statistician or academic, I have one hour STUDY DESIGNS Case-control studies Cohort studies Cross-sectional studies Geographical/Ecological studies Randomized controlled trials CASE-CONTROL STUDIES Start with identification of a group of cases (individuals with a particular health outcome) in a given population and a group of controls (individuals without the health outcome) to be included in the study. CASE CONTROL STUDIES Advantages Cost- effective relative to cohort study Efficient for study of rare disease or one with long latency Quick and fairly inexpensive Allows examination of multiple exposures Disadvantages Prone to selection/recall and observer bias Only examine one outcome Poor choice for rare exposures Temporal sequence may be hard to determine COHORT STUDY Group of individuals exposed to risk factor and group , unexposed to risk factor are followed over time (often years) to determine the occurrence of disease. Incidence of disease in the exposed group is compared with the incidence of disease in the unexposed group. Exposed No exposure group case No case case No case COHORT STUDY Advantages Multiple outcomes can be measured for 1 exposure and can look at multiple exposures Can delineate temporal relationship Good for rare exposures Can measure incidence and prevalence Disadvantages ▪ Costly and time consuming ▪ Prone to confounding ▪ Prone to loss to follow-up- bias ▪ Knowledge of exposure may bias assessment of outcome ▪ Being in study may alter participants behaviour ▪ Poor choice if rare disease CROSS-SECTIONAL STUDIES Examines relationship between disease (or health related state) and other variable of interest (i.e. exposure) as they exist in a defined population at a single point in time or over a short period of time (e.g. 1 year) Provide snap-shot in time Used to assess disease burden or health needs Can be descriptive and analytical CROSS-SECTIONAL STUDIES Advantages; Quick, easy, cheap Multiple outcomes and exposures measured Good for assessing burden/ planning services Good for generating hypotheses Disadvantages; Cant determine temporal relationship No good if disease rare/short duration Unable to measure incidence Bias due to low response and recall ECOLOGICAL STUDIES – USES Population level Risk Factor Hypothesis generation New connections, new ideas Are there factors which genuinely operate at the population level? Effect modification? Determinants of exposure to risks? Context The individualistic fallacy? ECOLOGICAL STUDY: GEOGRAPHICAL ECOLOGICAL STUDY: TIME TRENDS Time ASSOCIATION NOT CAUSATION ASSOCIATION VERSUS CAUSATION Association as a result of: Bradford-Hill criteria: (J Roy Soc Med 1965:58:295-300) Chance (random error) Strength of the association Bias (systematic error) Consistency of findings Specificity of the association. Confounding Temporal sequence of association Causal link Biological gradient. Coherence Experiment. Confounders: unable to adjust for confounders due to lack of data Bias: Data on exposure and outcome may be collected in different ways or using different definitions over time or in different places Ecological fallacy: Assuming that group level associations between outcome and exposure also apply at individual level lead to ecological fallacy ( or ecological bias) Loss to attrition/Migration of populations EPIDEMIOLOGY MEASURES OF DISEASE FREQUENCY Prevalence Incidence MEASURING OF FREQUENCY OF OUTCOMES Risk Odds Rates WHAT IS A RATE? Allows comparisons between populations of different sizes and/or at different times Strictly speaking a rate expresses a time interval e.g per year no. of events in population (numerator ) rate = no. of people in population (denominat or) Person Place Time PREVALENCE • A measure of the occurrence of all cases of disease in a population existing cases of disease in population prevalence = number of people in population • Case definition • Ascertainment (registers, surveys) • Point v period v lifetime prevalence • Suitable for chronic disease TYPES OF PREVALENCE Point prevalence The proportion of the population with a disease at any specific point in time Period prevalence The proportion of the population with a disease at any point during a defined period Lifetime prevalence The proportion of the population who have, or have had, a disease during their lifetime Better measure for chronic, relapsing conditions INCIDENCE Measure of the number of new cases of a disease (or other health outcome of interest) that develops in a population at risk during a specified time period. 2 incidence measures: Risk ( or cumulative Incidence) or Rate Number of NEW cases occurring over a given period of time in the population at risk (free of disease) at the beginning of the time period . INCIDENCE RATE - take into account the sum of the time that each person remained under observation and at risk of developing the outcome under investigation. Incidence Rate = 푵풖풎풃풆풓 풐풇 풏풆풘 풄풂풔풆풔 풐풇 풅풊풔풆풂풔풆 풊풏 풂 품풊풗풆풏 풕풊풎풆 풑풆풓풊풐풅 푻풐풕풂풍 풑풆풓풔풐풏−풕풊풎풆 풂풕 풓풊풔풌 풅풖풓풊풏품 풕풊풎풆 풑풆풓풊풐풅 student 10 student 9 student 8 student 7 student 6 student 5 student 4 student 3 student 2 student 1 0 2 4 6 8 10 RELATIONSHIP BETWEEN INCIDENCE & PREVALENCE CRUDE AND ADJUSTED RATES Crude rate applies to the total population Specific rates can be calculated for sub-groups Adjusting takes account of certain characteristics or factors in the population (potential confounders) This is useful because we know for example that death rates for various conditions differ markedly by age Standardisation allows you to compare populations with a different age profile total number of deaths crude death rate = size of population number of deaths in age range age specific death rate = size of population in age range STANDARISATION Allows you to ‘age standardise’ your data (adjusts your data for the confounder of age) Comparisons of health outcomes between groups or across time periods, where age structures differ, require techniques that adjust for variations in the age structure of populations (From Naing 2000) STANDARDIZATION Indirect and direct methodology available Direct Standardization Indirect Standardization Use when comparing several To determine if disease incidence is higher or population groups or several lower in one are only ( compares to standard) time periods Use if age specific rates for the population groups are not available or unreliable Use if rare event and thus deaths in population groups are small DIRECT AGE STANDARDISATION You will need: - your population broken down in age bands - death rates for each group in your population (deaths/total population) “ standard population” with same age bands Instructions Apply your age-band specific death rates to the age bands of the standard population → “Expected deaths” ( if your population had the same age distribution as the standard population) ASDR Preferred EUROPEAN STANDARD POPULATION INDIRECT AGE STANDARDISATION You will need: - “standard age related rates “ Your population broken down by age bands What you do: Apply the “standard rates” to your population Your answer gives expected deaths in each age group SMR: The ratio of the number of events observed in the study population to the number that would be expected if the study population had the same age-sex specific rates as the standard population 푂푏푠푒푟푣푒푑 푛푢푚푏푒푟 표푓 푑푒푎푡ℎ푠 SMR = 푒푥푝푒푐푡푒푑 푛푢푚푏푒푟 표푓 푑푒푎푡ℎ푠 EXAMPLE SMR = Observed number of deaths (O) X 100% Expected number of deaths (E) SMR = 160 = 1.6 X 100 = 160 100 MEASURES OF MORTALITY Mortality rates often used as proxy for disease occurrence routinely counted and readily available Poor proxy for diseases with great morbidity but little mortality chronic diseases infectious diseases STATISTICS HYPOTHESIS TESTING Step 1: what is our hypothesis? Null hypothesis = simplest position = no difference For example: are smoking rates higher in group A or B? H0 = no difference Alternative: There is a difference Step 2: gather data Step 3: decide on statistical test and calculate test statistic Step 4: P value from the test statistic Step 5: interpret – accept or reject H0 SIGNIFICANCE Statistical significance ≠ clinical significance Statistical significance = is p-value below α? Clinical significance: is the effect important enough to act upon? Need to consider what difference (e.g. fall in blood pressure) would change clinical practice. Sample size shaped by the clinical difference needed, power and statistical significance. Nb if a small difference would be clinically significant you’d need a more precise measure and a larger sample. HYPOTHESIS TESTING ERRORS Four possible outcomes: TIPS P value 0.05 Type 1 error Power 80% Type 2 error WHICH TEST? COMMON VALUES Standard deviation (s or σ): The Standard Deviation is a measure of how spread out numbers are. Its symbol is σ (sigma) - it is the square root of the Variance. - Spread Confidence Intervals: 95% certain that the true value lies between Precision ODDS RATIO Odds ratio (OR) is used in case-control studies to estimate strength of association between exposure and outcome.
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