This paper describes how to estimate the prevalence of chronic diseases in a population using data from contact registrations in general practice with a limited time length. Instead of using only total numbers of observed patients adjusted for the length of the observation period, we propose the use of (i) the time of the first contact of patients, (ii) the joint total numbers of patients and contacts, and (iii) the sets of patients in distinct time intervals, to generate prevalence rate estimates. The three new prevalence rate estimators have been developed assuming either a homogeneous or a parameterized heterogeneous patient population. Systematic and stochastic components of the estimators have been analysed by cross-validation for five chronic diseases using data from the Dutch 'Study on Chronic Conditions'. The results show that the first two estimators work well for diseases with a relatively structured visiting behaviour, such as hypertension and diabetes mellitus, assuming a time-constant contact rate and homogeneous patient population. For diseases such as ischaemic heart disease, chronic non-specific respiratory diseases and osteoarthritis, that do not satisfy these assumptions, the methods generally result in underestimations.
Copyright 2002 John Wiley & Sons, Ltd.