Evidence map›Paper›PMID 40205377›Full record

ArticleBMC psychiatry2025

Development and validation of a depression risk prediction model for rural elderly living alone.

Shasha Gao, Huijun Zhang

Abstract readValidation Study
In one paragraph

Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

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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

2 authors.

Shasha GaoSchool of Nursing, Jinzhou Medical University, Jinzhou, Liaoning, P. R. China.
Huijun ZhangDepartment of Nursing, Jinzhou Medical University, No. 40, Section 3, Jinzhou City, Liaoning Province, P. R. China. 13904069606@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDepression is a prevalent psychological issue among rural elderly individuals living alone, severely impacting their physical and mental health.

objectiveTo develop and validate a depression risk prediction model for rural elderly living alone based on the health ecological model, providing a scientific basis for early intervention.

methodsUsing data from the 2011 China Health and Retirement Longitudinal Study (CHARLS), we included 1,221 participants. Thedataset was randomly stratified into a training set (70%) and a validation set (30%). Predictors were screened via univariate analysis, followed by multivariate logistic regression to construct the nomogram model. Statistical analysis was performed using R Studio 4.4.1.Ten-fold cross-validation was used to assess the model's stability. Model performance was evaluated using the Receiver Operating Characteristic (ROC) curve, with the Area Under the Curve (AUC) calculated, along with calibration plots, the Hosmer-Lemeshow test, and Decision Curve Analysis (DCA).

resultsSelf-rated health, pain, frailty, nighttime sleep duration, poor sleep quality, life satisfaction, and visit frequency were identified as independent predictors of depressive symptoms. The model demonstrated excellent discrimination (AUC = 0.85 [95% CI: 0.83-0.88] in the training set and 0.83 [95% CI: 0.78-0.87] in validation), good calibration (Hosmer-Lemeshow test p = 0.47), and high clinical utility (net benefit > 10% in DCA).

conclusionThe nomogram provides a reliable and intuitive tool for early screening of depressive symptoms in rural elderly individuals living alone, supporting targeted interventions. CLINICAL TRIAL NUMBER: Not applicable.

Indexed as

DepressionIndependent LivingRural PopulationAgedAged, 80 and overChinaFemaleHumansLongitudinal StudiesMaleMiddle AgedNomogramsRisk AssessmentRisk FactorsDepressionLiving alonePredictive modelRural elderly

Identifiers

PMID40205377
PMCPMC11983750

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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.