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

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

Using registries in general practice to estimate countrywide morbidity in The Netherlands.

OBJECTIVE: Examining the possibility of using data from registries in general practice in order to present morbidity figures concerning a broad range of major diseases for the Dutch population. STUDY DESIGN: Qualitative and quantitative analysis of registered diagnoses. METHODS: Quantitative data from six registries were obtained. In addition, information about the registration process was obtained and discussed with representatives of the registries. Subjects for discussion were the general characteristics of the registries and disease-specific rules. RESULTS: Some important differences exist in the characteristics of the registries and the disease-specific coding rules for computing incidence and prevalence. However, for most diseases the rules of two or more registries corresponded with each other, so that a selection of registries that measured the occurrence of a particular disease in a similar way could be made. Nevertheless, for some age categories rather large differences between registries were observed. The best estimates for the whole country were calculated as the average incidence and prevalence of the selected registries. CONCLUSIONS: Data that were originally obtained during patient care can be made usable for public health policy purposes. To further improve the quality of data and to increase the usefulness of these data for public health policy purposes, more efforts are required

Estimating incidence and prevalence rates of chronic diseases using disease modeling.

Morbidity 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