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 noninstitutionalized 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.
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.
BackgroundGeneral practice based registration networks (GPRNs) provide information on population health derived from electronic health records (EHR). Morbidity estimates from different GPRNs reveal considerable, unexplained differences. Previous research showed that population characteristics could not explain this variation. In this study we investigate the influence of practice characteristics on the variation in incidence and prevalence figures between general practices and between GPRNs.MethodsWe analyzed the influence of eight practice characteristics, such as type of practice, percentage female general practitioners, and employment of a practice nurse, on the variation in morbidity estimates of twelve diseases between six Dutch GPRNs. We used multilevel logistic regression analysis and expressed the variation between practices and GPRNs in median odds ratios (MOR). Furthermore, we analyzed the influence of type of EHR software package and province within one large national GPRN.ResultsHardly any practice characteristic showed an effect on morbidity estimates. Adjusting for the practice characteristics did also not alter the variation between practices or between GPRNs, as MORs remained stable. The EHR software package ‘Medicom’ and the province ‘Groningen’ showed significant effects on the prevalence figures of several diseases, but this hardly diminished the variation between practices.ConclusionPractice characteristics do not explain the differences in morbidity estimates between GPRNs.Electronic supplementary materialThe online version of this article (doi:10.1186/s12875-014-0176-7) contains supplementary material, which is available to authorized users.
BackgroundEstimates 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.MethodsIncidence 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.ResultsIncidence 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.ConclusionEstimates 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.
The quality of general practice-based data can be considered on five content-based aspects. These aspects determine the quality of recording.
RegistratiemarktNaar gebruik leveren sociale en medische organisaties ieder voor zich aparte gegevens in jaarverslagen en rapporten. Men verzamelt cijfers over hoe burgers wonen en welk inkomen zij hebben. Over het aantal jongeren dat de school voor gezien houdt, het aantal mensen dat overlast geeft, zich thuis of buiten gewelddadig of crimineel gedraagt en de cel ingaat, en hoeveel er aankloppen bij sociale dienders, formulierenbrigadiers en schuldhulpverleners. Apart en moeilijk te vinden zijn cijfers over het aantal mensen dat op een voedselbank zit, bij wie de deurwaarder op de stoep staat, dat hun huis wordt uitgezet, dat aan de poort van de maatschappelijke opvang staat of buiten slaapt. En er zijn aparte cijfers over hoeveel mensen gebruikmaken van een ambulance, huisarts, fysiotherapeut, tandarts, ziekenhuis, verpleeghuis, verslavingszorg en geestelijke gezondheidszorg, en meer in publieke of private zorg.De geïsoleerde presentatie van al deze cijfers volgen aanbod en geldstromen meer dan dat de mens, diens problemen en het zorgeffect worden beschreven. Een sociaal medische monitor ontbreekt en daarmee inspanningsresultaten en de grenzen van de publieke liefdadigheid. Tot slotMet deze 'klinische les van de straat' beoog ik het ambacht van de sociaal medische armenzorg onder de aandacht te brengen om haar uit de marge te halen. Voor het integreren van de mens in sociale en medische exclusie zal men de armendokter moeten reanimeren en gereedschap moeten bieden. Voor een betere en goedkopere armenzorg is het ambacht van de generalist in de praktijk onontbeerlijk. Het gaat om het leren integreren van kennis en ervaring naar mens, probleem en sociaal medische zorg.Voor reanimatie en gereedschap voor de armendokter adviseer ik een academische werkplaats van de straat. Om in de praktijk de zorg voor te doen, als meester gezel, met de beste helper aan de poort. Om de zorg zodanig te leren organiseren dat helpers met voldoende gereedschap op pad kunnen worden gestuurd met een duidelijke boodschap: ga kijken, luisteren en verzamelen, dan kunnen we gezamenlijk oordelen, een plan maken en samen met betrokkenen uitvoeren. Zorg moet duidelijk en eenvoudig zijn. Grenzen stellen en resultaten tellen. Leren integreren begint op straat. AbstractThe art of social medical care for the poor and homeless In a clinical lesson of street medicine, the doctor for the poor is resuscitated. The need for basic and integrated care is described by a process of social medical decay. The art of social medical care deserves academic tools, as a basis for knowledge and experience, to prevent the most vulnerably people lose their home.
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