The influence of population characteristics on variation in general practice based morbidity estimations

Dungen, Catharina van den; Hoeymans, Nancy; Boshuizen, Hendriek C.; Akker, Marjan van den; Biermans, M. C. J.; Boven, Kees van; Brouwer, Henk J.; Verheij, Robert; Waal, R.M.W. de; Schellevis, F.G.; Westert, Gert P.

doi:10.1186/1471-2458-11-887

Cited by 15 publications

(16 citation statements)

References 11 publications

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“…We did not adjust for other characteristics of the populations, such as socioeconomic status, ethnicity, and degree of urbanization. In an earlier study, we already showed that these characteristics did not explain any of the variance between networks [3]. The same was found for differences between GPs and general practices.…”

Section: Discussionmentioning

confidence: 50%

“…We and others [3, 4] looked at differences between registration network within the same country and found large differences that could not be fully explained. When comparing registration-based data between countries, we expected similar differences in the organization of data collection as between networks in the Netherlands.…”

Section: Discussionmentioning

confidence: 83%

“…These differences could not be explained by differences in characteristics of the patient population or in the characteristics of the general practice [3, 4]. Most likely, differences in registration procedures contribute to this variation.…”

Section: Background and Goalmentioning

confidence: 99%

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Estimating incidence and prevalence rates of chronic diseases using disease modeling

Boshuizen¹,

Poos²,

Akker³

et al. 2017

Popul Health Metrics

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BackgroundMorbidity 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.MethodsData from five Dutch GP registration networks and data on four chronic diseases (chronic obstructive pulmonary disease [COPD], diabetes, heart failure, and osteoarthritis of the knee) were used. A joint model (DisMod model) was fitted using all information on morbidity (incidence and prevalence) and mortality in each network, including a factor for misclassification of prevalent cases as incident cases.ResultsThe observed estimates vary considerably between networks. Using disease modeling including a misclassification term improved the consistency between prevalence and incidence rates, but did not systematically decrease the variation between networks. Osteoarthritis of the knee showed large modeled misclassifications, especially in episode of care-based registries.ConclusionRegistries that code episodes of care rather than disease generally provide lower estimates of the prevalence of chronic diseases requiring low levels of health care such as osteoarthritis. For other diseases, modeling misclassification rates does not systematically decrease the variation between registration networks. Using disease modeling provides insight in the reliability of estimates.

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Section: Discussionmentioning

confidence: 50%

Section: Discussionmentioning

confidence: 83%

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Estimating incidence and prevalence rates of chronic diseases using disease modeling

Boshuizen¹,

Poos²,

Akker³

et al. 2017

Popul Health Metrics

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“…[29] The differences between the cohorts in Amsterdam and Eindhoven may partly be explained by methodological differences (the shorter existence of SMILE (fewer active problems) and coding agreements (fewer MUS, but more psychological codes in SMILE)), different populations of general practitioners (shorter experience in coding problems in SMILE) and patient factors (more stable population; more females in SMILE). However, socio-demographic characteristics of populations cannot explain the differences in morbidity estimations among these cohorts [30].…”

Section: Discussionmentioning

confidence: 95%

Predictability of Persistent Frequent Attendance in Primary Care: A Temporal and Geographical Validation Study

et al. 2013

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BackgroundFrequent attenders are patients who visit their general practitioner exceptionally frequently. Frequent attendance is usually transitory, but some frequent attenders become persistent. Clinically, prediction of persistent frequent attendance is useful to target treatment at underlying diseases or problems. Scientifically it is useful for the selection of high-risk populations for trials. We previously developed a model to predict which frequent attenders become persistent.AimTo validate an existing prediction model for persistent frequent attendance that uses information solely from General Practitioners’ electronic medical records.MethodsWe applied the existing model (N = 3,045, 2003–2005) to a later time frame (2009–2011) in the original derivation network (N = 4,032, temporal validation) and to patients of another network (SMILE; 2007–2009, N = 5,462, temporal and geographical validation). Model improvement was studied by adding three new predictors (presence of medically unexplained problems, prescriptions of psychoactive drugs and antibiotics). Finally, we derived a model on the three data sets combined (N = 12,539). We expressed discrimination using histograms of the predicted values and the concordance-statistic (c-statistic) and calibration using the calibration slope (1 = ideal) and Hosmer-Lemeshow tests.ResultsThe existing model (c-statistic 0.67) discriminated moderately with predicted values between 7.5 and 50 percent and c-statistics of 0.62 and 0.63, for validation in the original network and SMILE network, respectively. Calibration (0.99 originally) was better in SMILE than in the original network (slopes 0.84 and 0.65, respectively). Adding information on the three new predictors did not importantly improve the model (c-statistics 0.64 and 0.63, respectively). Performance of the model based on the combined data was similar (c-statistic 0.65).ConclusionThis external validation study showed that persistent frequent attenders can be prospectively identified moderately well using data solely from patients’ electronic medical records.

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“…The prevalence of osteoarthritis, for example, ranges from 10 to 60 per 1000 person years between GPRNs. Overall, prevalence figures estimated from GPRNs show more variation than incidence Figures [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks?

et al. 2014

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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.

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The influence of population characteristics on variation in general practice based morbidity estimations

Cited by 15 publications

References 11 publications

Estimating incidence and prevalence rates of chronic diseases using disease modeling

Estimating incidence and prevalence rates of chronic diseases using disease modeling

Predictability of Persistent Frequent Attendance in Primary Care: A Temporal and Geographical Validation Study

Do practice characteristics explain differences in morbidity estimates between electronic health record based general practice registration networks?

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