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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 477087
Title Is it possible to increase the reliability of estimated incidence and prevalence rates by disease modelling?
Author(s) Boshuizen, H.C.; Hoeymans, N.
Source The Lancet 381 (2013)Suppl.2. - ISSN 0140-6736 - p. S20 - S20.
DOI http://dx.doi.org/10.1016/S0140-6736(13)61274-X
Department(s) Chair Nutrition and Disease
Biometris (WU MAT)
PE&RC
Publication type Abstract in scientific journal or proceedings
Publication year 2013
Abstract Background In order to monitor the impact of health policy, morbidity estimates must be timely and reliable. Registries in general practice (GP) are key sources for morbidity estimates, as the general practitioner is the gatekeeper of health care. However, morbidity estimates between different GP registration networks vary considerably, and these differences could not be explained by characteristics of the patient population. The aim of this research is to see whether these differences can be explained by variation between networks in the accuracy of distinguishing new (incident) cases from existing (prevalent) cases, and if so, whether modelling this enables more reliable estimates. Methods We used 2010 data from five Dutch GP registration networks and data on four chronic diseases (chronic obstructive pulmonary disease, diabetes, heart failure, and osteoarthritis). We fitted a joint model (DisMod) using all information on morbidity (incidence and prevalence) in each network as well as mortality rates in those with and without the disease. The model assumed stable incidence and survival probabilities over time and included a misclassification factor that allows for misclassification of a percentage of prevalent cases as incident cases. We fitted this model by maximum likelihood and compared the prevalence and incidence estimates for each network with the observed prevalence and incidence estimates. Findings Osteoarthritis of the knee showed large misclassifications (between 2 and 24%), especially in episode-based registrations. Before taking this misclassification into account, prevalence rate estimates from different networks were 1·0–2·8%, while they were 1·3–3·9% afterwards. Incidence rates changed from 2·4–4·5 per 1000 person-years before taking misclassification into account, to 0·8–3·0 per 1000 person-years afterwards. Also, for the other diseases, including a misclassification term mostly affected incidence estimates (lowering them) but did not systematically decrease the variation between prevalence and incidence estimates from different networks. Interpretation Episode-based registers cannot reliably deliver first incidence rates for chronic diseases requiring low levels of professional health care. For the other diseases, modelling misclassification rates does not systematically decrease the variation between registration networks, and only modestly influences estimates. However, such a modelling exercise gives qualitative insight into the reliability of estimates.
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