1. Measures of disease frequency Simon Thornley

2. Measures of Effect and Disease Frequency Aims – To define and describe the uses of common epidemiological measures of disease frequency and effect

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

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

5. Results: Analysis Continuous Categorical 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”? Multiple linear regression Logistic regression and or stratification Outcome variable? Check data distributions Transform? Report adjusted measures of association (OR/RR) Report ‘crude’ or univariate measures of association (OR/RR/HR) If difference between crude and adjusted >10%, then Statistical evidence of confounding

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

7. Discussion 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… Yes (on balance) Exposure causes disease Calculate Risk difference, NNT and PPAR.

8. How is a disease diagnosed? Limitations?

9. Methods Clinical – History (symptoms) – Physical examination (physical signs) – Laboratory tests – Radiology – Microbiology Epidemiology – Hospital discharge codes, drug use, lab use, attendance at outpatient clinics, diagnosis codes, autopsy.

10. Proportions vs Rates Proportions – concerned with (disease) states – Useful for assessing health status of population – Planning health care services – Quality of care – Screening for asymptomatic disease Rates – concerned with (disease) events – Useful for assessing causation

11. Incidence and Prevalence ↑Incidence ↑Excess Death rate Emigration Immigration Prevalence

12. Populations Closed – No new members – Loses members to death – Approx when prevalence assessed over short time frame Open – Gains new members – Immigration – Birth – Emigration – Long time period

13. Prevalence The proportion with disease (at given instant) Adult Population Diabetes

14. Adult Population Incidence Proportion/ Cumulative incidence/Attack rate Adult Population 2010 (no diabetes) Diabetes Year 2014 per year 0 0.5 Incidence proportion= 0.5 Incidence rate impossible to calculate (once diseased, stop being “at risk”).

15. Incidence Rate Death Get disease

17. Incidence rate Average rate of disease occurrence over time interval Assumes rate doesn’t change over time Reciprocal of waiting time Years until disease Usually only first event counted; even if biologically, second event may be independent.

18. Prevalence, incidence and duration of disease If disease is rare:

19. Duration of disease E.g. Systemic lupus erythematosis Prevalence 0.5% Incidence 6 to 35/100,000 people per year – Say 20.5/100,000 Duration of disease = 0.005 /(20.5/100,000) – 24 years

20. Prevalence CHD (men) in NZ

21. Some questions You are about to design a programme to measure how many people with diabetes are taking appropriate drugs. Would the population of interest consist of prevalent or incident cases of diabetes?

22. Incidence proportion is: a) Useful for assessing the health status of the population b)The proportion of initially disease free people who, over a specified period of time, develop disease. c) Measures survivors only. d) A rate. e) Measured at one point in time.

23. Prevalence is: a) Useful for assessing the health status of the population b)The best measure to use when identifying risk factors for a disease c) Unaffected by deaths in the population d)Unaffected by migration e) A rate.

24. Comparisons Prevalence One point in time; easy to measure Proportion or % Affected by many factors apart from those causing disease. Numerator: count of people with disease Denominator: count of total population at risk No time component Incidence Involves time; difficult to measure Measured as either rate or proportion Less affected by other influences. Numerator: count of people who develop disease during follow-up Denominator: – (prop.) People at risk and disease free – (rate) Person-time at risk.

25. Summary Prevalence (among population, proportion with historic diagnosis of disease). Incidence proportion (disease free, follow, proportion that develop disease in defined period) Incidence rate (disease free, follow, number of cases that develop disease divided by time at risk).