Estimating incidence and prevalence rates of chronic diseases using disease modeling

Hendrike C Boshuizen,Kees Van Boven,Nancy Hoeymans,Marion C J Biermans,Joke C Korevaar,Marjan Van Den Akker,Marinus J J C Poos,Margot W M De Waal

doi:10.1186/s12963-017-0130-8

Abstract

BackgroundMorbidity estimates between different GP registration networks show large, unexplained variations. This research explores the potential of modeling differences between networks in distinguishing new (incident) cases from existing (prevalent) cases in obtaining more reliable estimates.MethodsData from five Dutch GP registration networks and data on four chronic diseases (chronic obstructive pulmonary disease [COPD], diabetes, heart failure, and osteoarthritis of the knee) were used. A joint model (DisMod model) was fitted using all information on morbidity (incidence and prevalence) and mortality in each network, including a factor for misclassification of prevalent cases as incident cases.ResultsThe observed estimates vary considerably between networks. Using disease modeling including a misclassification term improved the consistency between prevalence and incidence rates, but did not systematically decrease the variation between networks. Osteoarthritis of the knee showed large modeled misclassifications, especially in episode of care-based registries.ConclusionRegistries that code episodes of care rather than disease generally provide lower estimates of the prevalence of chronic diseases requiring low levels of health care such as osteoarthritis. For other diseases, modeling misclassification rates does not systematically decrease the variation between registration networks. Using disease modeling provides insight in the reliability of estimates.

Highlights

Morbidity estimates between different general practitioner (GP) registration networks show large, unexplained variations
An important measure for population health is the morbidity in the population: what are the most important diseases and how are disease patterns changing over time? Registries in general practice are key sources for morbidity estimates, especially if all people are registered in a general practice and the general practitioner (GP) is the gatekeeper of health care
Prevalence and incidence data from “episode of care” registries based on data that include GP visits from 2 year before baseline yield consistent estimates for diabetes, chronic obstructive pulmonary disease (COPD), and heart failure

Summary

Introduction

Morbidity estimates between different GP registration networks show large, unexplained variations. Registries in general practice are key sources for morbidity estimates, especially if all people are registered in a general practice and the general practitioner (GP) is the gatekeeper of health care In this case, the population registered in general practices is representative of the whole population outside of long term health care facilities. If Previous research has shown that morbidity estimates between different general practice registration networks vary considerably [2]. These differences could not be explained by differences in characteristics of the patient population or in the characteristics of the general practice [3, 4]. The method to distinguish new cases (incidence) from existing cases

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