Evidence map›Paper›PMID 39280510›Full record

ArticleCureus2024

Unraveling the Tapestry of Depression: A Cross-Sectional Study.

Rohankumar Gandhi, Ilesh Kotecha, Kaushikkumar R Damor, Yogesh Murugan

Abstract read
In one paragraph

Article in Cureus, 2024. 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.

Rohankumar GandhiCommunity and Family Medicine, Shri Meghaji Pethraj (MP) Shah Government Medical College, Jamnagar, IND.
Ilesh KotechaCommunity and Family Medicine, Shri Meghaji Pethraj (MP) Shah Government Medical College, Jamnagar, IND.
Kaushikkumar R DamorCommunity Medicine, Gujarat Medical Education and Research Society (GMERS) Medical College, Rajpipla, IND.
Yogesh MuruganFamily Medicine, Guru Gobind Singh Government Hospital, Jamnagar, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAn often-occurring and severely disabling mental illness that mostly affects older people living in urban slums is depression. Developing successful therapies requires an understanding of the complex interactions between the different factors that contribute to depression in this susceptible population.

objectivesThis study aimed to find the prevalence of depression and identify the factors associated with depression in the geriatric population aged ≥60 years in the study area during the study period in Gujarat, India.

methodsThis study was carried out among 450 participants aged ≥60 years. Face-to-face interviews and standardized assessment tools, including the Geriatric Depression Scale (GDS) for depression and the Mini-Cog test for cognitive impairment, were used to collect data on depression levels, sociodemographic characteristics, behavioral factors, medical conditions, life events, and psychiatric history. Statistical analyses, including chi-squared tests, were performed to assess the associations.

resultsSignificant associations were found between various factors and depression levels, which were lower education (11.11% severe depression among non-literate vs. 2.11% among literate, p<0.001) and widowhood (11.56% severe depression among widowed vs. 4.53% among married, p<0.001), which were linked to higher depression severity. Behavioral risk factors like short sleep duration (<6 hours at night: 21.71% severe depression, p<0.001), tobacco snuffing (16.24% severe depression, p<0.001), and lack of physical activity (28.71% severe depression, p<0.001) were strongly associated with increased depression. Medical conditions such as hypertension (10.36% severe depression, p<0.001) and stressful life events like family conflicts (16.67% severe depression, p<0.001) exhibited strong associations. A personal history of depression (38.82% severe depression, p<0.001) was a potent predictor.

conclusionsThe study highlights the multifaceted nature of depression in the geriatric population of the study area, underscoring the necessity of all-encompassing measures to tackle the recognized possible risk factors. The results provide valuable insights for developing targeted prevention strategies, healthcare policies, and support systems to enhance the mental well-being of this vulnerable population.

Indexed as

depressionelderlymental healthrisk factorssociodemographic factorsurban slums

Identifiers

PMID39280510
PMCPMC11402483

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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.