Leicester Warwick Medical School Health and Disease in Populations Cohort Studies Paul Burton

Lecture Objectives 1.Describe the logical basis of, and the practical problems involved in, cohort studies of disease incidence 2.Compare incidence rates or mortality rates between two groups of individuals within a cohort by calculating the incidence rate ratio (IRR) (internal comparisons)

Lecture Objectives 3.Compare disease incidence or mortality in a study cohort with that in a reference population using standardisation methods ( e.g. the SMR) (external comparisons) 4.Describe the factors determining the precision of an estimated relative risk Relevant reading in Prescribed book : Farmer R, Miller D. Chapter 5 pp 47-55

Investigating the aetiology (cause) of a disease Comparison of incidence rates between groups So far we have focused on ‘natural experiments’ often based on routine data Examples: leukaemia  nuclear plant Skin cancer  living in Brighton Mortality rates in different social classes But there are serious limitations on what can realistically be achieved

What would be an ideal study? Basic scientific method: compare ‘like with like’ Two identical groups, differing only in exposure status Differences can then reasonably be attributed to that exposure.

What would be an ideal study? A problem How to get two identical groups differing only in exposure status, when exposure status is linked to many other characteristics? e.g. Smoking is linked to: alcohol, type of occupation, ethnicity, social class, exercise levels....

What would be an ideal study? The ideal solution an experiment (force equality) Next best randomisation (later in module) Next best a cohort study (measure and record factors that may determine inequality)

A simple cohort study with a ‘within cohort comparison’ e.g. Does smoking cause asthma? Recruit disease-free individuals Classify into exposed versus unexposed e.g. current smokers versus non-smokers Follow up over time and: (a) Count the person years ‘at risk’ (p-y) (b) Count how many develop asthma (d) (c) Calculate incidence rate (IR = d/p-y)

A simple cohort study with a ‘within cohort comparison’ Do this for each exposure group separately: IR SMOKERS = d SMOKERS / p-y SMOKERS IR NON-SMOKERS = d NON-SMOKERS / p-y NON-SMOKERS Relative risk = Incidence rate ratio: IRR = IR SMOKERS / IR NON-SMOKERS

A worked example 1,000 children followed from birth to age 5 yrs 300 at least one parent smoked in the home 700 neither parent smoked in the home p-y SMOKE = 300  5 pyr = 1,500 p-y p-y NON-SMOKE = 700  5 pyr = 3,500 p-y

A worked example Smoke exposed, 75 diagnosed asthma Smoke unexposed, 105 diagnosed asthma IR SMOKERS = 75/1,500 = 50 per 1,000 p-y IR NON-SMOKERS = 105/3,500 = 30 per 1,000 p-y

A worked example IRR = e.f. = e.f. = % CI: 1.667÷1.35 to  1.35 i.e to 2.25

Advantages over looking at routine data You can study exposures/personal characteristics which aren’t collected routinely Opportunity to obtain more detailed information on outcomes and exposures Ability to collect additional data on confounding variables

Prospective cohort studies All cohort studies involve prospective follow up. That is: Recruit and define exposure status in disease free individuals Follow up  count p-y and d But, may collect data starting in the future ( e.g. start follow-up in 2003). This is a conventional prospective cohort study.

‘Historical’ or ‘retrospective’ cohort study Alternatively, may collect follow-up data starting in the past: e.g. recruit and define exposure status in disease free individuals from 1990 using historical records Follow up  count p-y and d (possibly using historical records) Start of study 2003 but start of follow-up 1990 This is a historical cohort study

Exposure data may be binary or in several categories or continuous Lung cancer death rates per 100,000 p-y

Comparisons can be made internally or against an external reference population Comparison of sub-cohorts (internal comparison)

Comparisons can be made internally or against an external reference population IRR = r +  r - Random variation: Internal comparisons: If either subcohort is small, e.f. large d + =3, d - =10  e.f. = 3.73 d + =3, d - =10,000  e.f. = 3.17 d + =30, d - =10,000  e.f. = 1.44

External comparisons

Calculating expected cases Usually cohorts observed over long periods People age during the study Rates in reference population change during the study

Extending the SMR approach Calculate separate ‘number of expected cases (or deaths)’ for each age group in each different calendar time period e.g. age in period : obtain reference population’s age-specific rates for each calendar period from routine sources multiply these rates by appropriate cells' person- years to estimate the expected cases (or deaths) in each cell

Extending the SMR approach Expected deaths are then summed over all cells (i.e. over all age groups and all periods) Can also add additional classification variables: e.g. age-sex specific rates at each calendar time period. But limited to variables which are recorded in the routine data! Lexis diagram

Deaths from IHD and p-y exposure in test population O= = 994

Rates from reference population E= = SMR = O/E = 994/ = or % CI = 55.8 ×/÷ = (52.4, 59.4)

Precision External comparisons:

Precision Internal comparisons:

External comparisons Useful when not possible to use subcohorts but Often limited data for reference population Often no incidence data Make do with mortality data Study and reference populations may not be comparable - selection bias The ‘healthy worker effect’ Many occupational cohorts yield SMRs well below 100%: Employment restricted to healthy individuals

Problems with cohort studies Large and resource intensive Take a long time (historical cohorts take less time) Rigorous definitions of disease and exposure Expensive Intensive/invasive investigation Difficulty avoiding high drop out Results take a long time Ethical dilemmas Can become politically charged Not very good for rare diseases Difficulty with confounding (especially unknown confounders)

Why do cohort studies at all? Detailed and prospective assessment of exposure, outcomes and confounders a huge scientific benefit (historical cohorts can be less good) Wish to study a range of different outcomes Wish to study a rare exposure Conditions which fluctuate with age Randomly Systematically