Introduction to epidemiology Manjinder Sandhu Group leader, Genetic Epidemiology

1. Introduction to epidemiology Manjinder Sandhu Group leader, Genetic Epidemiology Wellcome Trust Sanger Institute University Lecturer in Epidemiology, NCD research group, Department of Public Health and Primary Care, University of Cambridge

2. Epidemiology • — “the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to control of health problems” (Last 2001)

3. “Aging and obesity increase the odds that a person will develop Type 2, or adult-onset diabetes, the most common form.” “Rates of colorectal cancer have been declining in recent years, by about 2 percent annually, thanks mostly to increased screening.” “Women who took an epilepsy drug during their pregnancy are at greater risk of having children with lower IQ, according to a study.”

4. What do we want to know? • — what’s the scale of the problem? • — prevalence of disease • — incidence of disease • — and what’s being done about it (therapeutic and preventative)? • — determinants of disease

5. How do we quantify disease in populations • — define individuals with disease • — define the population at risk • — use an appropriate measure of disease frequency • — limitations

6. Defining a disease: what’s a case? The trajectory of disease Health Outcome Disease onset Symptoms Diagnosis Treatment Seek care At the population level, definitions may be based on statistical, clinical, prognostic or operational considerations

7. Type 2 diabetes • — rapidly increasing prevalence • — defined by both clinical and prognostic criteria • — based on cut-offs for circulating levels of blood glucose

8. Changing definition of type 2 diabetes and glucose intolerance • — WHO 1985 • — WHO 1999 • — ADA 1997 • — ADA 2003 (lowering cut-off for IFG) • — implications for disease burden

9. And who is at risk? • — To appropriately measure disease frequency, we need to know who is at risk • — Demarcation of the population at risk • — In the context of a sample of the population, are they representative? • — Have you included all the population at risk — as defined?

10. Estimating disease frequency Total prevalence of diabetes, 1995 King et al, Diabetes Care, 1998

11. Measures of disease frequency • — ratios • — proportions • — prevalence, incidence • — risks, rates, odds • — all functions of numerators (cases) and denominators (population at risk or those at risk but disease free)

12. Ratios and proportions • — ratios: dimensionless — the relative magnitudes of two quantities (usually expressed as a quotient) (A/B) • — proportions: a ratio that relates the part (the numerator) to the whole (the denominator) — numerator always part of the denominator (A/A+B)

13. Disease prevalence • — The prevalence of a disease in a population is defined as: • — the total number of cases (existing cases) of the disease in the population at a given time • OR • — the total number of cases in the population, divided by the number of individuals in the population • — It is a proportion and is usually expressed as a percentage

14. Incidence • — The incidence of a disease in a population is defined as: • — the total number of NEW cases of the disease in a population at risk of the disease in a defined time period • OR • — the total number of NEW cases in the population, divided by the total number of individuals at risk of the disease in the population • — Again, it is a proportion (RISK) and can be expressed as a percentage

15. Odds of disease • Provides an alternative way to express a probability (likelihood of an event) • Risk = A / N • Odds = A / (N-A) • Odds = probability / (1+odds) • Probability = odds / (1-odds)

16. Risk and odds • — Risk is relatively easy to understand – number of events over number of possible events • — Odds is defined as the number of events to the number of non-events (six to one — a risk of 86%) • — An odds of 0.2 to 1 is a risk of 17% • — However, the odds has properties that make it very useful in epidemiology

17. Rate • — velocity at which new cases of a particular disease (or outcome of interest) occur in a population at risk for the disease • Calculated as • Number of individuals developing disease over specified time period • Sum of the “disease-free” time experienced by study participants at risk of disease

18. Measures of association

19. How do we measure association? • — measure disease frequency in groups of interest — for example, those taking the intervention and those taking the placebo • — or those exposed to a risk factor and those unexposed • — compare and quantify comparisons • — NB. Equality of comparisons (see session on validity)

20. What are we asking? • — “How strong is the association between bone density and risk of hip fracture?” • — “How more likely are people with low bone density to fracture their bones compared with those with high bone density?”

21. Measures of association • — Relative — ratios • — Absolute — differences • — Risks, Odds and Rates

22. Risk ratio Risk in exposed (Re) = a/(a+b) Risk in exposed (Ru)= c/(c+d) Risk ratio = Re/ Ru

23. Rate ratio Rate in exposed (Re) = a/b Rate in exposed (Ru)= c/d Rate ratio = Re/ Ru

24. Odds ratios • — for the case control study design, cannot directly measure risk or rate • — artificially sampled case and control populations, which does no reflect the population rate or risk of disease • — can use the odds ratio to approximate the risk ratio

25. Odds ratio Odds of a case being exposed (Re) = a/b Odds of a control being exposed (Ru)= c/d Odds ratio = Re/ Ru = (a/b)/(c/d) = ad/bc

26. Odds and risk ratios • — when the disease is rare the odds ratio will approximate the risk ratio odds of disease in a population = incidence x duration) • — however, if the prevalence of disease is high (high initial risk), the odds ratio can under- or overestimate the risk ratio

27. Analytical study designs: individual level data • — case control • — cross-sectional • — cohort • — experimental

28. Case control studies • — comparing a group of individuals with disease and a group without disease with respect to the factor of interest (exposure/treatment) • — retrospective or prospective • — involves the specific selection of a “case” group and a “control” group

29. Case control studies

30. Cross-sectional studies • — In this study design, a sample of a reference population is examined at a given point in time • — defining a “cohort” • — individuals are classified in to disease and exposure levels • — estimate the relation between exposure and disease prevalence

31. Cross-sectional studies

32. Advantages of case control studies • — Inexpensive (relatively) • — Appropriate design for rare diseases • — Relatively fewer participants • — Fast (do not need follow-up for disease development)

33. Disadvantages of case control studies • — Selection problems for cases and controls • — Potential for biases • — No information on temporality • — Uses odds ratio as measure of association (except study designs based on incidence sampling) • — matching

34. Cohort studies • — In this study design, a sample of a reference population is examined at a given point in time • — defining a “cohort” • — Individuals are classified in to exposure levels • — Study participants are followed over time and assessed for the development of disease

35. Cohort studies

36. Establish the sample • Measure the predictive variables • Follow up the cohort • Measure the outcome variables Cohort studies The present The future Disease No Disease population Risk positive Risk negative Disease No Disease

37. Advantages of cohort studies • — Can measure temporal association between exposure and disease • — Generally less susceptible to biases • — Can estimate incidence of disease • — Can assess the association between multiple exposures and diseases

38. Disadvantages of cohort studies • — Could be biased with respect to case ascertainment • — large number of participants required and followed through time (hence expensive and can be logistically difficult) • — Susceptible to changing diagnostic practices • — Attrition and generalisability

39. Epidemiology in Sub-Saharan Africa Manjinder Sandhu Group leader, Genetic Epidemiology Wellcome Trust Sanger Institute University Lecturer in Epidemiology, NCD research group, Department of Public Health and Primary Care, University of Cambridge

40. aetiology Synthesis Discovery science Translation clinical utility public health Pragmatism

41. What is epidemiology? “The study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems” Last. Dictionary of Epidemiology

42. Emergence of modern epidemiology — Framingham Heart Study (1949) — Atomic Bomb Survivors Study (1950) — British Doctors Study (1951)

43. What is the scope of epidemiology? — To define the burden of disease — To search for causes of disease — To identify measures that prevent, treat and control disease

44. 30Kbase 27Kbase 110Kbase The future of epidemiology? — Genomic/molecular epidemiology — Mega-trials and studies — Meta-analyses — Large scale international collaborations

45. NCDs and public health in Sub-Saharan Africa • Disease estimates and projections • Reliability of data • Public health planning • Capacity and infrastructure

46. Global demographic transitions Raleigh, BMJ, 1999

47. Epidemiological transition in Sub-Saharan Africa • The World Health Organisation (WHO) projects that over the next ten years the continent will experience the largest increase in death rates from cardiovascular disease, cancer, respiratory disease and diabetes • This chronic disease burden is attributed to multifaceted factors including increased life expectancy, changing lifestyle practices, poverty, urbanisation and globalisation • There are limited health infrastructure and resources to cope with the joint burden of infectious and non-infectious chronic diseases

48. The burden of disease in Sub-Saharan Africa

49. Diseases in Sub-Saharan Africa Noncommunicable diseases, such as hypertension, heart disease, diabetes and stroke and injuries represented 27% of the total burden of disease in SSA Surveys in South Africa, Cameroon, Congo, Eritrea and Mozambique have found a very high prevalence levels of risk factors for these diseases. By adopting broad prevention plans African countries could achieve 10 more healthy life years for their people. Cape Town, 2006 (Mayosi et al, Lancet, 2009)

50. NCDs and cardiometabolic risk factors in African populations Incidence of NCDs is increasing worldwide Changing lifestyles • Ageing populations • Improved public health systems Projected diabetes cases in Africa • Studies in western populations have identified several susceptibility loci • Ethnic specific differences in risk may have genetic and/or environmental mechanisms • Suggested interrelation between infection and metabolic disease • HCV/HBV/HHV8 and diabetes • Diabetes and TB • HIV/HAART and cardiovascular risk pofiles 20M 15M 10M 2000 2030 Year