ArticleBMC family practice2014
Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks?
Article in BMC family practice, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Implications of Data Extraction and Processing of Electronic Health Records for Epidemiological Research: Observational Study.Journal of medical Internet research · 2025Observational
- Antihypertensive medication classes and risk of incident dementia in primary care patients: a longitudinal cohort study in the Netherlands.The Lancet regional health. Europe · 2024Article
- Estimating disease prevalence from drug utilization data using the Random Forest algorithm.European journal of public health · 2019Article
- Estimating Morbidity Rates Based on Routine Electronic Health Records in Primary Care: Observational Study.JMIR medical informatics · 2019Article
- Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse.Journal of medical Internet research · 2018Article
- Estimating incidence and prevalence rates of chronic diseases using disease modeling.Population health metrics · 2017Article
- Dementia incidence trend over 1992-2014 in the Netherlands: Analysis of primary care data.PLoS medicine · 2017Article
- Improving the quality of EHR recording in primary care: a data quality feedback tool.Journal of the American Medical Informatics Association : JAMIA · 2017Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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