Evidence map›Paper›PMID 41211494›Full record

ArticleIndian journal of psychiatry2025

Unravelling cognitive decline among elderly in rural communities: A cross-sectional study.

Pratyaksha Pandit, Reema Kumari, Adarsh Tripathi, Prabhakar Mishra, Sugandhi Sharma

Abstract read
In one paragraph

Article in Indian journal of psychiatry, 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

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

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

5 authors.

Pratyaksha PanditDepartment of Community Medicine, Uttar Pradesh University of Medical Sciences, Saifai, Uttar Pradesh, India.
Reema KumariDepartment of Community Medicine and Public Health, King George's Medical University, Lucknow, Uttar Pradesh, India.
Adarsh TripathiDepartment of Psychiatry, King George's Medical University, Lucknow, Uttar Pradesh, India.
Prabhakar MishraDepartment of Biostatistics and Health Informatics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India.
Sugandhi SharmaDepartment of Community Medicine, Uttar Pradesh University of Medical Sciences, Saifai, Uttar Pradesh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Dementia is a major cause of disability among older adults. The global burden of dementia is expected to triple by 2050, with developing countries, witnessing a sharp increase. Early detection and management through screening programs can mitigate the progression of cognitive decline. Objectives: This study aimed to determine the prevalence and predictors of cognitive impairment among older adults residing in rural areas. Methods: An analytical cross-sectional study was conducted among older individuals residing in rural communities. A three-stage cluster sampling method was employed to select 350 participants. Cognitive screening was performed using the Hindi Mental State Examination, with a score ≤23 indicating cognitive impairment. Data on socio-demographics, clinical parameters, and functional abilities were collected and analyzed using SPSS version 26. Results: The prevalence of cognitive impairment (CI) was 24.9% ( Conclusion: Cognitive impairment is the highly prevalent among the elderly in rural areas, with multiple socio-demographic and behavioral factors contributing to its occurrence. Implementing routine cognitive screening and promoting physical activity, social engagement, and healthy lifestyle practices are essential for early detection and intervention.

Indexed as

Cognitive impairmentelderlyHindi mental state examination (HMSE)prevalencerural healthscreening

Identifiers

PMID41211494
PMCPMC12594064

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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.