Evidence map›Paper›PMID 38519938›Full record

ArticleHealth research policy and systems2024

Multimorbidity healthcare expenditure in Belgium: a 4-year analysis (COMORB study).

Phuong Bich Tran, Georgios F Nikolaidis, Emmanuel Abatih, Philippe Bos, Finaba Berete, Vanessa Gorasso, Johan Van der Heyden, Joseph Kazibwe, Ewan Morgan Tomeny, Guido Van Hal and 2 more

Open access · goldAbstract read
In one paragraph

Article in Health research policy and systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

7 citing papers in PubMed, 6 citations in OpenAlex.

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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

12 authors at 6 institutions in 3 countries.

Phuong Bich TranDepartment of Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium. Phuong.tran@uantwerpen.be.ORCID http://orcid.org/0000-0003-2119-998X
Georgios F NikolaidisIQVIA Ltd, 37 North Wharf Road, London, W2 1AF, UK.
Emmanuel AbatihDepartment of Applied Mathematics, Computer Sciences and Statistics, Ghent University, Ghent, Belgium.
Philippe BosDepartment of Sociology, University of Antwerp, Antwerp, Belgium.
Finaba BereteDepartment of Epidemiology and public health, Brussels, Belgium.
Vanessa GorassoDepartment of Epidemiology and public health, Brussels, Belgium.
Johan Van der HeydenDepartment of Epidemiology and public health, Brussels, Belgium.
Joseph KazibweDepartment of Clinical Sciences, Lund University, Malmö, Sweden.
Ewan Morgan TomenyDepartment of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK.
Guido Van HalDepartment of Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium.
Philippe BeutelsCentre for Health Economics Research & Modelling Infectious Diseases (CHERMID), University of Antwerp, Antwerp, Belgium.
Josefien van OlmenDepartment of Family Medicine and Population Health, University of Antwerp, Antwerp, Belgium.
University of Antwerp · BEInstitut Scientifique de Santé Publique · BEGhent University · BEIQVIA (United Kingdom) · GBLiverpool School of Tropical Medicine · GBLund University · SE

Funding

Universiteit Antwerpen BOF-DOCPRO
6 · The paper itself

Abstract

backgroundThe complex management of health needs in multimorbid patients, alongside limited cost data, presents challenges in developing cost-effective patient-care pathways. We estimated the costs of managing 171 dyads and 969 triads in Belgium, taking into account the influence of morbidity interactions on costs.

methodsWe followed a retrospective longitudinal study design, using the linked Belgian Health Interview Survey 2018 and the administrative claim database 2017-2020 hosted by the Intermutualistic Agency. We included people aged 15 and older, who had complete profiles (N = 9753). Applying a system costing perspective, the average annual direct cost per person per dyad/triad was presented in 2022 Euro and comprised mainly direct medical costs. We developed mixed models to analyse the impact of single chronic conditions, dyads and triads on healthcare costs, considering two-/three-way interactions within dyads/triads, key cost determinants and clustering at the household level.

resultsPeople with multimorbidity constituted nearly half of the study population and their total healthcare cost constituted around three quarters of the healthcare cost of the study population. The most common dyad, arthropathies + dorsopathies, with a 14% prevalence rate, accounted for 11% of the total national health expenditure. The most frequent triad, arthropathies + dorsopathies + hypertension, with a 5% prevalence rate, contributed 5%. The average annual direct costs per person with dyad and triad were €3515 (95% CI 3093-3937) and €4592 (95% CI 3920-5264), respectively. Dyads and triads associated with cancer, diabetes, chronic fatigue, and genitourinary problems incurred the highest costs. In most cases, the cost associated with multimorbidity was lower or not substantially different from the combined cost of the same conditions observed in separate patients.

conclusionPrevalent morbidity combinations, rather than high-cost ones, made a greater contribution to total national health expenditure. Our study contributes to the sparse evidence on this topic globally and in Europe, with the aim of improving cost-effective care for patients with diverse needs.

Indexed as

Health ExpendituresJoint DiseasesBelgiumDelivery of Health CareHealth Care CostsHumansLongitudinal StudiesMultimorbidityRetrospective StudiesBelgiumChronic diseasesCost analysisDisease interactionHealthcare expenditureIntegrated careMultimorbidityNoncommunicable diseases

Identifiers

PMID38519938
PMCPMC10960468
OpenAlexW4393079919

What OpenQuestion holds

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LicenceCC BY
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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.