Evidence map›Paper›PMID 35820748›Full record

ArticleBMJ open2022

Identifying non-communicable disease multimorbidity patterns and associated factors: a latent class analysis approach.

Parul Puri, Shri Kant Singh, Sanghamitra Pati

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
3.3field-weighted citation impact, top 7% 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

19 citing papers in PubMed, 21 citations in OpenAlex.

  1. Asthma and Multimorbidity Amongst Ethnic Minority Groups in High Income Countries.Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology · 2026
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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

3 authors at 2 institutions in 1 country.

Parul PuriDepartment of Survey Research and Data Analytics, International Institute for Population Sciences, Mumbai, Maharashtra, India parulpuri93@gmail.com.ORCID 0000-0001-6272-837X
Shri Kant SinghDepartment of Survey Research and Data Analytics, International Institute for Population Sciences, Mumbai, Maharashtra, India.
Sanghamitra PatiDepartment of Health Research, Indian Council of Medical Research Chandrasekharpur, Bhubaneswar, Orissa, India.
International Institute for Population Sciences · INIndian Council of Medical Research · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIn the absence of adequate nationally-representative empirical evidence on multimorbidity, the existing healthcare delivery system is not adequately oriented to cater to the growing needs of the older adult population. Therefore, the present study identifies frequently occurring multimorbidity patterns among older adults in India. Further, the study examines the linkages between the identified patterns and socioeconomic, demographic, lifestyle and anthropometric correlates.

designThe present findings rest on a large nationally-representative sample from a cross-sectional study. SETTING AND

participantsThe study used data on 58 975 older adults (45 years and older) from the Longitudinal Ageing Study in India, 2017-2018. PRIMARY AND SECONDARY OUTCOME MEASURES: The study incorporated a list of 16 non-communicable diseases to identify commonly occurring patterns using latent class analysis. The study employed multinomial logistic regression models to assess the association between identified disease patterns with unit-level socioeconomic, demographic, lifestyle and anthropometric characteristics.

resultsThe present study demonstrates that older adults in the country can be segmented into six patterns: 'relatively healthy', 'hypertension', 'gastrointestinal disorders-hypertension-musculoskeletal disorders', 'musculoskeletal disorders-hypertension-asthma', 'metabolic disorders' and 'complex cardiometabolic disorders'. Additionally, socioeconomic, demographic, lifestyle and anthropometric factors are significantly associated with one or more identified disease patterns.

conclusionsThe identified classes 'hypertension', 'metabolic disorders' and 'complex cardiometabolic disorders' reflect three stages of cardiometabolic morbidity with hypertension as the first and 'complex cardiometabolic disorders' as the last stage of disease progression. This underscores the need for effective prevention strategies for high-risk hypertension group. Also, targeted interventions are essential to reduce the burden on the high-risk population and provide equitable health services at the community level.

Indexed as

HypertensionMetabolic DiseasesMusculoskeletal DiseasesNoncommunicable DiseasesAgedCross-Sectional StudiesHumansLatent Class AnalysisMultimorbidityDemographyEpidemiologyNutritionPUBLIC HEALTHSOCIAL MEDICINE

Identifiers

PMID35820748
PMCPMC9277367
OpenAlexW4285098737

What OpenQuestion holds

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LicenceCC BY-NC
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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.