Evidence map›Paper›PMID 33632770›Full record

ArticleBMJ global health2021

Medical costs and out-of-pocket expenditures associated with multimorbidity in China: quantile regression analysis.

Yang Zhao, Rifat Atun, Kanya Anindya, Barbara McPake, Tiara Marthias, Tianxin Pan, Alexander van Heusden, Puhong Zhang, Nadila Duolikun, John Lee

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

Article in BMJ global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
6.5field-weighted citation impact, top 2% 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

36 citing papers in PubMed, 53 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

10 authors at 4 institutions in 5 countries.

Yang ZhaoThe George Institute for Global Health at Peking University Health Science Centre, Beijing, China Wzhao@georgeinstitute.org.cn.ORCID 0000-0002-6011-5948
Rifat AtunDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.ORCID 0000-0002-1531-5983
Kanya AnindyaThe Nossal Institute for Global Health, The University of Melbourne, Melbourne, Victoria, Australia.ORCID 0000-0001-6832-0412
Barbara McPakeThe Nossal Institute for Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
Tiara MarthiasThe Nossal Institute for Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
Tianxin PanMelbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
Alexander van HeusdenThe Nossal Institute for Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
Puhong ZhangThe George Institute for Global Health at Peking University Health Science Centre, Beijing, China.
Nadila DuolikunThe George Institute for Global Health at Peking University Health Science Centre, Beijing, China.
John LeeThe Nossal Institute for Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
The University of Melbourne · AUPeking University · CNHarvard University · USUniversitas Gadjah Mada · ID

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveMultimorbidity is a growing challenge in low-income and middle-income countries. This study investigates the effects of multimorbidity on annual medical costs and the out-of-pocket expenditures (OOPEs) along the cost distribution.

methodsData from the nationally representative China Health and Retirement Longitudinal Study (CHARLS 2015), including 10 592 participants aged ≥45 years and 15 physical and mental chronic diseases, were used for this nationally representative cross-sectional study. Quantile multivariable regressions were employed to understand variations in the association of chronic disease multimorbidity with medical cost and OOPE.

resultsOverall, 69.5% of middle-aged and elderly Chinese had multimorbidity in 2015. Increased number of chronic diseases was significantly associated with greater health expenditures across every cost quantile groups. The effect of chronic diseases on total medical cost was found to be larger among the upper tail than those in the lower tail of the cost distributions (coefficients 12, 95% CI 6 to 17 for 10th percentile; coefficients 296, 95% CI 71 to 522 for 90th percentile). Annual OOPE also increased with chronic diseases from the 10th percentile to the 90th percentile. Multimorbidity had larger effects on OOPE and was more pronounced at the upper tail of the health expenditure distribution (regression coefficients of 8 and 84 at the 10th percentile and 75th percentile, respectively).

conclusionMultimorbidity is associated with escalating healthcare costs in China. Further research is required to understand the impact of multimorbidity across different population groups.

Indexed as

Health ExpendituresMultimorbidityAgedChinaCross-Sectional StudiesHumansLongitudinal StudiesMiddle AgedRegression Analysishealth economicshealth policyhealth systems

Identifiers

PMID33632770
PMCPMC7908909
OpenAlexW3130230332

What OpenQuestion holds

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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.