What factors explain the differences in morbidity estimations among general practice registration networks in the Netherlands? A first analysis (original) (raw)
2008, European Journal of General Practice
Dungen, C. van den, Hoeymans, N., Gijsen, R., Akker, M. van den, Boesten, J., Brouwer, H., Smeets, H., Veen, W.J. van der, Verheij, R., Waal, M. de, Schellevis, F., Westert, G. What factors explain the differences in morbidity estimations among general practice registrations networks in the Netherlands? A first analysis.
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BMC Public Health, 2011
Background: General practice based registration networks (GPRNs) provide information on morbidity rates in the population. Morbidity rate estimates from different GPRNs, however, reveal considerable, unexplained differences. We studied the range and variation in morbidity estimates, as well as the extent to which the differences in morbidity rates between general practices and networks change if socio-demographic characteristics of the listed patient populations are taken into account. Methods: The variation in incidence and prevalence rates of thirteen diseases among six Dutch GPRNs and the influence of age, gender, socio economic status (SES), urbanization level, and ethnicity are analyzed using multilevel logistic regression analysis. Results are expressed in median odds ratios (MOR). Results: We observed large differences in morbidity rate estimates both on the level of general practices as on the level of networks. The differences in SES, urbanization level and ethnicity distribution among the networks' practice populations are substantial. The variation in morbidity rate estimates among networks did not decrease after adjusting for these socio-demographic characteristics. Conclusion: Socio-demographic characteristics of populations do not explain the differences in morbidity estimations among GPRNs.
What went and what came? Morbidity trends in general practice from the Netherlands
European Journal of General Practice, 2008
Background: Fourty years of morbidity registration in general practice is a milestone urging to present an overview of outcomes. This paper provides insight into the infrastructure and methods of the oldest practice-based research network in the Netherlands and offers an overview of morbidity in a general practice population. Changes in morbidity and some striking trends in morbidity are presented. Methods: The CMR (Continuous Morbidity Registration) collects morbidity data in four practices, in and around Nijmegen, the Netherlands. The recording is anchored in the Dutch healthcare system, which is primary care based, and where every citizen is listed with a personal GP. Trends over the period 1985Á2006 are presented as a three year moving average. As an indicator for 20-year prevalence trends we used the annual percentage change (APC). We restricted ourselves to morbidity, which is presented to the family physician on a frequent basis (overall prevalence rates 1.0/1000/year). Results: The age distribution of the CMR population is comparable to the general Dutch population. Overall incidence figures vary between 1500/1000 ptyrs (men) and 2000/1000 ptyrs (women). They are quite stable over the years, whereas overall prevalence figures are rising gradually to 1500/2500 ptyrs (men) and 2000/3500 ptyrs (women). Increase in prevalence rates for chronic conditions is diffuse and gradual with a few striking exceptions. Conclusion: For morbidity patterns, the CMR database serves as a mirror of general practice. Practice-based research networks are indispensable for the development and maintenance of general practice as an academic discipline.
Quality aspects of Dutch general practice-based data: a conceptual approach
Family Practice, 2013
Background. General practice-based data, collected within general practice registration networks (GPRNs), are widely used in research. The quality of the data is important but the recording criteria about what type of information is collected and how this information should be recorded differ between GPRNs.
BMC Public Health, 2011
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.
Background Within the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners’ (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model categorizes individuals based on their complete health profile, taking into account age, gender, diagnoses and medication. However, the ACG system was developed with non-Dutch data. Consequently, for wider implementation in Dutch general practice, the system needs to be validated in the Dutch healthcare setting. In this paper we show the results of the first use of the ACG system on Dutch GP data. The aim of this study is to explore how well the ACG system can distinguish between different levels of GP ...