Programmaleider Zorgdata en het Lerend Zorgsysteem; bijzonder hoogleraar 'Transparantie in de zorg vanuit patiëntenperspectief', Tranzo, Tilburg University
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The influence of population characteristics on variation in general practice based morbidity estimations.
Dungen, C. van den, Hoeymans, N., Boshuizen, H.C., Akker, M. van den, Biermans, M.C.J., Boven, K. van, Brouwer, H.J., Verheij, R.A., Waal, M.W.M. de, Schellevis, F.G., Westert, G.P. The influence of population characteristics on variation in general practice based morbidity estimations. BMC Public Health: 2011, 11(887)
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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. (aut. ref.)
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. (aut. ref.)
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