Evidence map›Paper›PMID 40097950›Full record

ArticleBMC geriatrics2025

Utilizing machine learning to identify fall predictors in India's aging population: findings from the LASI.

Mrinmoy Pratim Bharadwaz, Jumi Kalita, Anandita Mitro, Aditi Aditi

Abstract read
In one paragraph

Article in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

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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

1 citing paper in PubMed.

  1. 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

4 authors.

Mrinmoy Pratim BharadwazRWE/HEOR/ES, Axtria India Pvt. Limited, Pune, Maharashtra, India.ORCID http://orcid.org/0000-0001-9124-4323
Jumi KalitaDepartment of Statistics, Lalit Chandra Bharali College, Guwahati, Assam, India.ORCID http://orcid.org/0000-0003-4936-0042
Anandita MitroDepartment of Economics and Finance, Bits Pilani, Hyderabad, India.ORCID http://orcid.org/0009-0005-3217-5336
Aditi AditiDepartment of Survey Research and Data Analytics, International Institute for Population Sciences, Mumbai, 400088, India. aditi.chaudhary72@gmail.com.ORCID http://orcid.org/0000-0003-2750-5685

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDepression has a detrimental effect on an individual's mental and musculoskeletal strength multiplying the risk of fall incidents. The current study aims to investigate the association between depression and falls in older adults using machine learning (ML) approach and identify its various predictors.

methodsData for the study was derived from the Longitudinal Ageing Study in India, (LASI) conducted in 2017-18 for people aged 45-years and above. The study was carried out on 44,066 individuals. Depression was measured using the CIDI-SF scale. Bivariate cross-tabulations were used to study the prevalence of falls. And its association with depression and other independent factors were assessed using the novel ML, the Conditional inference trees (CIT) method.

resultsAround 10.8 percent of older adults had fall incidents. CIT model predicted region to be a significant predisposing factor for an older adult to experience falls. Multimorbidity, depression, sleep problems, and gender were other prominent factors. The model predicted that, among depressed older adults, falls incidents were around 80 percent higher than non-depressed.

conclusionsAn association between falls and depression was observed. Depressive symptoms were associated with an increased risk of falls, even after controlling for other co-factors. The CIT method leveraged us to select the most important variables to predict falls with great precision. To prevent and manage falls among the expanding and diverse older-aged population, a multilevel and cross-sectoral approach is required. Mental health, especially depression, should be dealt with greater precautions. Public health enthusiasts should focus on the physical as well as mental health of the country's older adult population.

Indexed as

Accidental FallsAgingDepressionMachine LearningAgedAged, 80 and overFemaleHumansIndiaLongitudinal StudiesMaleMiddle AgedRisk FactorsConditional Inference treesDepressionFallsIndiaLASIMachine learningOlder adults

Identifiers

PMID40097950
PMCPMC11912680

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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.