Evidence map›Paper›PMID 28381229›Full record

ArticlePopulation health metrics2017

Estimating incidence and prevalence rates of chronic diseases using disease modeling.

Hendrike C Boshuizen, Marinus J J C Poos, Marjan van den Akker, Kees van Boven, Joke C Korevaar, Margot W M de Waal, Marion C J Biermans, Nancy Hoeymans

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Population health metrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.0field-weighted citation impact, top 23% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 2 countries.

Hendrike C Boshuizen, PO box 1 3720 BA, Bilthoven, The Netherlands. Hendriek.boshuizen@rivm.nl.
Marinus J J C Poos, PO box 1 3720 BA, Bilthoven, The Netherlands.
Marjan van den AkkerDepartment of Family Medicine, Maastricht University, P.O. Box 616, 6200, MD, Maastricht, The Netherlands.
Kees van BovenDepartment of Primary and Community Care, Radboud university medical center, Internal post ELG 117, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
Joke C KorevaarNetherlands Institute for Health Services Research (NIVEL), Otterstraat 118-124, P.O. Box 1568, 3500 BN, Utrecht, The Netherlands.
Margot W M de WaalDepartment of Public Health and Primary Care, Leiden University Medical Center, Postal zone V-0-P, PO-box 9600, 2300 RC, Leiden, The Netherlands.
Marion C J BiermansDepartment of Primary and Community Care, Radboud university medical center, Internal post ELG 117, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
Nancy Hoeymans, PO box 1 3720 BA, Bilthoven, The Netherlands.
Netherlands Environmental Assessment Agency · NLRadboud University Medical Center · NLDepartment of Public Health · USLeiden University Medical Center · NLNetherlands Institute for Health Services Research · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Chronic DiseaseDiabetes MellitusFemaleGeneral PracticeHeart FailureHumansIncidenceMaleModels, StatisticalNetherlandsOsteoarthritis, KneePrevalencePulmonary Disease, Chronic ObstructiveBurden of diseaseGeneral practice registrationGeneral practitionerIncidence-prevalence-mortality modelMorbidityPublic health

Identifiers

PMID28381229
PMCPMC5382408
OpenAlexW2604743511

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.