STATISTICAL-ANALYSIS OF PASSIVE SURVEILLANCE DISEASE REGISTRY DATA

Passive surveillance disease data involve a registry of individuals who are at risk of disease and who are not under active follow up. The most serious limitations with such data are incomplete ascertainment of cases of disease and little or no follow-up information on patient vital status. This paper considers whether it is possible to estimate disease risk from such data and, if not, what additional information is required. In general, relative risks based on passive surveillance data will be biased even under the assumption that the probability of disease reporting and the hazard of death from other causes are the same for all individuals in the registry. However if the disease is rare, this bias is negligible. Methods are developed for estimating absolute disease incidence rates by combining passive surveillance data with a cohort study. Analytic approaches are proposed for the situations when death rates from all other causes are known and also unknown, and it is found that there is little loss in efficiency even if death rates are not known. There are considerable gains in efficiency for estimating absolute disease incidence rates by supplementing a cohort study with passive surveillance registry data compared to using the cohort study alone, especially if the exposure is rare and the cohort study is small relative to the size of the registry. Intuitively, the cohort data provides information about absolute rates of disease, while the passive surveillance data provides information about relative risks. The methods are applied to a registry of patients with an artificial heart valve that is at risk of breaking.