Evidence map›Paper›PMID 36113890›Full record

SynthesisBMJ global health2022

Community prevalence and dyad disease pattern of multimorbidity in China and India: a systematic review.

Xinyi Zhang, Asutosh Padhi, Ting Wei, Shangzhi Xiong, Jie Yu, Pengpeng Ye, Wenijng Tian, Hongru Sun, David Peiris, Devarsetty Praveen and 1 more

Abstract readSystematic Review
In one paragraph

Synthesis in BMJ global health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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, 1 synthesis or guideline pooled it.

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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

11 authors.

Xinyi ZhangSchool of Public Health, Harbin Medical University, Harbin, Heilongjiang, China.
Asutosh PadhiThe George Institute for Global Health, India, Hyderabad, Telangana, India.
Ting WeiThe George Institute for Global Health, China, Beijing, China.
Shangzhi XiongThe George Institute for Global Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
Jie YuThe George Institute for Global Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
Pengpeng YeThe George Institute for Global Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
Wenijng TianSchool of Public Health, Harbin Medical University, Harbin, Heilongjiang, China.
Hongru SunSchool of Public Health, Harbin Medical University, Harbin, Heilongjiang, China.
David PeirisThe George Institute for Global Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.ORCID 0000-0002-6898-3870
Devarsetty PraveenThe George Institute for Global Health, India, Hyderabad, Telangana, India.
Maoyi TianSchool of Public Health, Harbin Medical University, Harbin, Heilongjiang, China maoyi.tian@hrbmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDriven by the increasing life expectancy, China and India, the two most populous countries in the world are experiencing a rising burden of multimorbidity. This study aims to explore community prevalence and dyad patterns of multimorbidity in China and India.

methodsWe conducted a systematic review of five English and Chinese electronic databases. Studies involving adults 18 years or older at a community level, which reported multimorbidity prevalence and/or patterns were included. A modified Newcastle-Ottawa Scale was used for quality assessment. Despite large heterogeneity among reported studies, a systematic synthesis of the results was conducted to report the findings.

resultsFrom 13 996 studies retrieved, 59 studies met the inclusion criteria (46 in China, 9 in India and 4 in both). The median prevalence of multimorbidity was 30.7% (IQR 17.1, 49.4), ranging from 1.5% to 90.5%. There was a large difference in multimorbidity prevalence between China and India, with median prevalence being 36.1% (IQR 19.6, 48.8) and 28.3% (IQR 8.9, 56.8), respectively. Among 27 studies that reported age-specific prevalence, 19 studies found multimorbidity prevalence increased with age, while 8 studies observed a paradoxical reduction in the oldest age group. Of the 34 studies that reported sex-specific prevalence, 86% (n=32) observed a higher prevalence in females. The most common multimorbidity patterns from 14 studies included hypertensive diseases combined with diabetes mellitus, arthropathies, heart diseases and metabolic disorders. All included studies were rated as fair or poor quality.

conclusionMultimorbidity is highly prevalent in China and India with hypertensive diseases and other comorbidities being the most observed patterns. The overall quality of the studies was low and there was a lack of representative samples in most studies. Large epidemiology studies, using a common definition of multimorbidity and national representative samples, with sex disaggregation are needed in both countries. PROSPERO REGISTRATION NUMBER: CRD42020176774.

Indexed as

HypertensionMultimorbidityAdultChinaComorbidityFemaleHumansIndiaMalePrevalencearthritisepidemiologyhypertensionpublic healthsystematic review

Identifiers

PMID36113890
PMCPMC9486196

What OpenQuestion holds

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Registered trials

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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.