investigator traces back in time to classify past exposures in the cohort and then tracks the cohort forward in time to ascertain the rate of disease. Retrospective cohort studies are commonly performed in occupational health. They often focus on disease mortality rates because of the relative ease of determining vital status of individuals and the availability of death certificates to determine the cause of death.
For comparison purposes, cohort studies often use general population mortality rates (age, sex, race, time, and cause specific) because it may be difficult to identify a suitable control group of unexposed workers. The observed number of deaths among workers (from a specific cause such as lung cancer) is compared with the expected number of deaths. The expected number is calculated by taking the mortality rate in the general population and multiplying it by the number of person-years6 of follow-up for the workers. The ratio of observed to expected deaths (which, by convention, is often multiplied by 100) produces a standardized mortality ratio (SMR). An SMR greater than 100 generally suggests an elevated risk of dying in the exposed group. Further, as discussed below many cohort studies refine their measures of health outcomes by using an internal comparison group, which may differ in exposure level but may otherwise be more similar to the cohort than the general population. Many of the studies of uranium workers are retrospective cohort studies (see Chapter 4).
The major problem with using general population rates for comparison with occupational cohorts is the “healthy-worker effect” (see Chapter 2; Monson, 1990), which arises when an employed population experiences a lower mortality rate than the general population, which consists of a mix of healthy and unhealthy people. The healthy-worker effect is usually due to lower cardiovascular and trauma deaths. A population with elevated external traumatic causes of death (e.g., Gulf War veterans), however, may be different from many occupational populations.
In calculating the SMR, the denominator (expected deaths) is derived from general population figures rather than from an otherwise comparable group of unexposed workers (which may be unavailable). The “artificially” higher denominator for expected deaths in the general population lowers the SMR, thereby underestimating the strength of the association between exposure to the agent and the cause of death. In other words, the healthy-worker effect introduces a bias that diminishes the true disease–exposure relationship.
To counter the influence of the healthy-worker effect, some studies divide the worker population into different groups, based on their levels of exposure to the agent being studied. Searching for dose–response relationships within the worker population itself is a way of reducing the potential bias introduced by the use of population controls. The problem, of course, is that measurements of dose may be imprecise or unavailable, particularly if the exposures occurred decades