Policy makers need valid epidemiological information about the incidence and prevalence rates of diseases in the population to formulate public health policy. Every four years, the Dutch Public Health Status and Forecasts Report presents an overview of the population's health status using key public health indicators such as (healthy) life expectancy, morbidity rates and health determinants [1
]. In this report general practice based data are used to estimate the population's morbidity in terms of incidence and prevalence rates of many diseases.
Using data generated by general practice registration networks (GPRNs) to estimate morbidity has many advantages, especially in countries with a strong primary care system, like the United Kingdom and the Netherlands [3
]. In these countries, all non-institutionalized residents are listed with a single general practitioner (GP), which makes a precise determination of the population at risk possible.
GPRNs put a lot of effort in building a reliable database. GPs, who belong to the same GPRN, are expected to use uniform recording methods and classification systems to record diseases. Furthermore, GPRNs systematically check the data to assure quality. Still, GPRNs differ from each other on several aspects. For example, there are GPRNs that include all morbidity presented in general practice, 'episode based' registries, while others only record chronic or very serious conditions into their database, also called 'problem based' registries [4
In a previous paper, we identified possible explanations for differences in morbidity rates among Dutch GPRNs and categorized them into four types of factors, health care system, methodology, practice/practitioner characteristics and patient characteristics. Until now, the contribution and mechanisms of these factors on the differences in morbidity estimation among GPRNs are not fully understood [3
]. To improve the usability of GPRN data for morbidity estimations of the total national population these aspects need to be investigated.
In this paper we investigate the effect of differences in patient characteristics on variation in morbidity estimations among GPRNs. Age, gender, socio-economic status (SES), urbanization level and ethnicity affect the probability to be diagnosed with a certain disease. For example, 65 percent of the people in low socio-economic class is chronically ill compared to nearly 40 percent of the people in the highest socio-economic class [1
]. There is reason to believe that the distribution of population characteristics varies among GPRNs, because some networks only operate in urban areas, while others operate in both urban and rural areas [4
]. Furthermore, most networks operate in a specific region, while immigrants are not equally spread across the Netherlands [6
Before investigating the effect of socio-demographic characteristics on the variation in morbidity among GPRNs, we studied the variation between networks and practices. We assume that for diseases with more ambiguous diagnostic criteria (e.g. depression) the variation among networks and among practices is larger than for diseases with clear diagnostic criteria (e.g. diabetes mellitus) [7
]. For diseases with disease-free periods (e.g. dermatitis, depression), we expect more variation in prevalence rates than in incidence rates [8
]. These differences result from difficulties in determining the ending of an episode in the registration. An episode starts when a GP records information about a patient's health, from contact with the patient or from information about the patient's condition from other health care providers, in the patient's medical record. On the other hand, a GP does not receive information when a disorder is cured [10
In summary, the goal of this paper is to study the variation among general practices and networks in incidence and prevalence rates of a selection of diseases. To gain more insight in possible explanations for these differences in morbidity rates, we investigate the influence of population characteristics. We hypothesize that adjusting for differences in age, gender, SES, urbanization level, and ethnicity among networks will reduce the variation among networks and therefore partly explain the differences in morbidity estimations among GPRNs.