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Open Access Highly Accessed Research article

Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty

Pieter H van Baal12*, Peter M Engelfriet3, Rudolf T Hoogenveen1, Marinus J Poos4, Catharina van den Dungen4 and Hendriek C Boshuizen1

Author Affiliations

1 Expertise Centre for Methodology and Information Services, National Institute for Public Health and the Environment Antonie van Leeuwenhoeklaan, 9 Bilthoven, 3720 BA, The Netherlands

2 Institute of Health Policy & Management/institute for Medical Technology Assessment, Erasmus University, Rotterdam, Burgemeester Oudlaan, 50 Rotterdam, 3000 DR, The Netherlands

3 Centre for Prevention and Health Services Research, National Institute for Public Health and the Environment, Antonie van Leeuwenhoeklaan, 9 Bilthoven, 3720 BA, The Netherlands

4 Centre for Public Health Forecasting, National Institute for Public Health and the Environment, Antonie van Leeuwenhoeklaan, 9 Bilthoven, 3720 BA, The Netherlands

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BMC Public Health 2011, 11:163  doi:10.1186/1471-2458-11-163

Published: 15 March 2011

Abstract

Background

Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined to estimate incidence and prevalence of chronic diseases and to explore the role of uncertainty when comparing diseases.

Methods

Incidence and prevalence counts, specified by gender and age, of 18 chronic diseases from 5 GPRNs in the Netherlands from the year 2007 were used as input. Generalized linear mixed models were fitted with the GPRN identifier acting as random intercept, and age and gender as explanatory variables. Using predictions of the regression models we estimated the incidence and prevalence for 18 chronic diseases and calculated a stochastic ranking of diseases in terms of incidence and prevalence per 1,000.

Results

Incidence was highest for coronary heart disease and prevalence was highest for diabetes if we looked at the point estimates. The between GPRN variance in general was higher for incidence than for prevalence. Since uncertainty intervals were wide for some diseases and overlapped, the ranking of diseases was subject to uncertainty. For incidence shifts in rank of up to twelve positions were observed. For prevalence, most diseases shifted maximally three or four places in rank.

Conclusion

Estimates of incidence and prevalence can be obtained by combining data from GPRNs. Uncertainty in the estimates of absolute figures may lead to different rankings of diseases and, hence, should be taken into consideration when comparing disease incidences and prevalences.

Keywords:
incidence; prevalence; Monte Carlo simulation; uncertainty