The influence of population characteristics on variation in general practice based morbidity estimations
1 National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA, Bilthoven, the Netherlands
2 Department Tranzo, Faculty of Social and Behavioural Sciences, Tilburg University, Tilburg, the Netherlands
3 Department of General Practice, School for Public Health and Primary Care (Caphri), Maastricht University, Maastricht, the Netherlands
4 Department of Primary and Community Care, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
5 General Practitioner, Franeker, the Netherlands
6 Department of General Practice, Academic Medical Centre/University of Amsterdam, Amsterdam, the Netherlands
7 Netherlands Institute for Health Services Research (NIVEL), Utrecht, the Netherlands
8 Department of Public Health and Primary Care, Leiden University Medical Centre, Leiden, the Netherlands
9 Department of General Practice/EMGO Institute for health and care research, VU University Medical Centre, Amsterdam, the Netherlands
10 Scientific Institute for Quality of Healthcare (IQ healthcare), Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands
11 Department of General Practice, Katholieke Universiteit, Leuven, Belgium
BMC Public Health 2011, 11:887 doi:10.1186/1471-2458-11-887Published: 24 November 2011
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.
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).
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.
Socio-demographic characteristics of populations do not explain the differences in morbidity estimations among GPRNs.