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Applied Epidemiology: Poplhlth 304

Applied Epidemiology: Poplhlth 304. Simon Thornley. Josh Knight. Tutor and PhD candidate. Course co-ordinator and PhD candidate. Staff. Epidemiology can take you places. http://gameauland.com/that-sugar-film-teaser/. Introductions Why study epidemiology? My story Course outline

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Applied Epidemiology: Poplhlth 304

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  1. Applied Epidemiology: Poplhlth 304

  2. Simon Thornley Josh Knight Tutor and PhD candidate • Course co-ordinator and PhD candidate Staff

  3. Epidemiology can take you places... http://gameauland.com/that-sugar-film-teaser/

  4. Introductions • Why study epidemiology? • My story • Course outline • Assessment :-( Outline

  5. A bit about you... • Pairs, 5 mins; report back one thing about the person that stands out and why they are interested in a career in health. • A bit about me... • Lectures and course material made available at... http://flexiblelearning.auckland.ac.nz/poplhlth304/ • Also on CECIL Introductions

  6. My career

  7. Why?

  8. What is epidemiology?

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

  10. Who gets sick and why? • Once we've found out why, what can we do about it? • Sounds simple, but many fish hooks • Many reasons it goes wrong • “Wrong”, by Freedman Detective work

  11. Our girth is expanding

  12. Why?

  13. Do some numbers help?

  14. Asthma

  15. What causes asthma?

  16. Culprit?

  17. Is sugar a confounder?

  18. Statistics • Health promotion • Easy, requires effort • Static, rather constantly evolving What epidemiology isn't

  19. Skills are transferable to any subject • Mercury on cognitive development? • How smoking cessation drugs work? • Who gets CVD? • Why cyclists crash? • Effect of alcohol on injury? • Smoking policy in prisons? • Who gets gout and diabetes? • Find out what is working and what is not • Stop wasting money, do no harm Why study epidemiology?

  20. Practical aspects of analytical epidemiology • Understand different study designs • Analysis of data using statistical software • (R commander). • Health and social progress What we will cover?

  21. The course in a nutshell

  22. Is change in exposure distribution temporally related with change in disease distribution? Aim • does exposure cause disease? • does drug treat disease? Statistical power calculation (type-1, type-2 error, prevalence of disease in unexposed, minimum detectable effect) Design study Can I randomise? • Ethical? • Clinical equipoise? Randomised study Report (RR) Yes No? Epidemiology in a nutshell Observational study Rare disease? One outcome? Case-control (report OR) Rare Exposure? Many outcomes? Cohort (report RR) Define case and exposure status

  23. Check missing data, duplicates, data range, bivariate scatterplots and lowess curves Are there systematic differences between exposure and unexposed groups (confounding) Yes (shouldn’t be in RCT!) Are they adjusted for in the analysis if confounders? Population divided by exposure status? What population is the study sample drawn from? Is it representative of underlying population or is there likely selection bias? Table 1

  24. Check data distributions Transform? Outcome variable? Continuous Categorical Report ‘crude’ or univariate measures of association (OR/RR/HR) Chi-square or Fisher exact test if cell counts <5 t-test Confounders? Review scientific literature… is there likely to be a • “Shared common cause of exposure and disease”? Logistic regression and or stratification Multiple linear regression Results: Analysis If difference between crude and adjusted >10%, then Statistical evidence of confounding Report adjusted measures of association (OR/RR)

  25. Estimate OR/RR and 95% C. I. Hypothesis likely false Is there an association between exposure and outcome? Is P <0.05 or 95% CI for measure of association contain null value (1)? No Consider type-2 error; confounding, bias, other studies Yes Exposure is associated with disease Is there another explanation? Confounding Bias Type-1 error (consider strength of association) Interpret study results Information (recall) Shared common cause of exposure and disease? Selection (survivor; loss to follow up, hosp. controls) How does my study compare with others? Regression or stratified analysis Could study design be improved?

  26. Is the association I have detected causal? Bradford Hill criteria Temporality: (cohort study? Not cross sectional or case-control which do not separate exposure and disease) Strength of association: (odds ratio or relative risk, does it indicate >50% increase) Dose response: is there increasing association with increased exposure? Biological plausibility: (are there any laboratory studies to support your assertions?) Consistency: (do other studies using different methods, with different groups come up with similar findings?) Experimental evidence: (Any randomised studies?) Analogy: (Any similar findings from related fields of science?) Specificity: Is exposure to the cause reliably followed by disease? Also: are there any other competing explanations? Are there any studies which shed light on these? If not then… Discussion Calculate Risk difference, NNT and PPAR. Yes (on balance) Exposure causes disease

  27. Designing Clinical Research. Hulley, SB. Lippincott, 3rd edition. • Alternatives available (from library) Many free e-books available. • A Pocket Guide to Epidemiology. David G. Kleinbaum, Kevin M. Sullivan, Nancy D. Barker. 2007, Springer. Textbook

  28. First 3 weeks – Basic epidemiology, study design, effect measures • Weeks 3 to 6 – Error in epidemiology and its remedies; bias, confounding, measurement error • Weeks 7 to 11 – Application of epidemiology; Asthma, vitamin D, CVD risk prediction, ethics, grants. Lecture outline

  29. 3 Assignments: • Assignment 1 – 10% • Due by 4pm Wednesday 19th March 2014 • Assignment 2 – 10% • Due by 4pm Wednesday 9th April 2014 • Assignment 3 – 30% • Due by 4pm Wednesday 21st May 2014 • Final exam – 50% • Principles and application Assessment

  30. A story... me as a medical student We use epidemiology every day without thinking about it...

  31. Start with belief (does she like me?) • Collect data (does she want to spend time with me? If so, how often) • Analyse and interpret data.... • Which hypothesis is most likely after collecting the data? • She does or she doesn't like me... • Consider other data... “does she like someone else?” • Action • Proposal? Epidemiology

  32. Happy ending...

  33. ? Questions?

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