Quality aspects of Dutch general practice-based data: a conceptual approach.

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. OBJECTIVE: We aim to identify aspects that describe the quality of general practice-based data in the Netherlands. METHODS: To investigate the quality aspects, we used the method of concept mapping, a structured conceptualization process for a complex multi-dimensional topic. We explored the ideas of representatives from 10 Dutch GPRNs on the quality of general practice-based data in five steps: preparation, generation of statements, structuring, representation and interpretation. In a brainstorm session, 10 experts generated statements about good data quality from general practice, which we completed with information from the literature. In total, 18 experts participated in the ranking and clustering of the statements. These results were analysed using ARIADNE software, using a combination of principal component analysis and cluster analysis techniques. Finally, the clusters were labelled based on their content. RESULTS: A total of 72 statements were analysed, which resulted in a two-dimensional picture with six clusters, 'complete health record', 'coding of information', 'episode oriented recording', 'diagnostic validity', 'recording agreements' and 'residual category'. CONCLUSIONS: The quality of general practice-based data can be considered on five content-based aspects. These aspects determine the quality of recording.

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.

What factors explain the differences in morbidity estimations among general practice registration networks in the Netherlands? A first analysis.

BACKGROUND: Information on the incidence and prevalence of diseases is a core indicator for public health. There are several ways to estimate morbidity in a population (e.g., surveys, healthcare registers). In this paper, we focus on one particular source: general practice based registers. Dutch general practice is a potentially valid source because nearly all non-institutionalized inhabitants are registered with a general practitioner (GP), and the GP fulfils the role as "gatekeeper". However, there are some unexplained differences among morbidity estimations calculated from the data of various general practice registration networks (GPRNs). OBJECTIVE: To describe and categorize factors that may explain the differences in morbidity rates from different GPRNs, and to provide an overview of these factors in Dutch GPRNs. RESULTS: Four categories of factors are distinguished: "healthcare system", "methodological characteristics", "general practitioner", and "patient". The overview of 11 Dutch GPRNs reveals considerable differences in factors. CONCLUSION: Differences in morbidity estimation depend on factors in the four categories. Most attention is dedicated to the factors in the "methodology characteristics" category, mainly because these factors can be directly influenced by the GPRN.