Evidence map›Paper›PMID 41454401›Full record

ArticleBMC psychology2025

Occupational and psychosocial risk factors for depression among uninsured middle-aged food delivery riders.

Haiyi Long, Yanni Yang, Liaoyue Chen, Shaoting Luo, Haiyun Lai

Abstract read
In one paragraph

Article in BMC psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Haiyi Long *Department of Management, Shanghai University of Engineering Science, Songjiang District, Shanghai, Shanghai, PR China.
Yanni Yang *Department of Pediatric Orthopedics, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang, Liaoning, 110004, PR China.
Liaoyue ChenKey Laboratory of Health Ministry for Congenital Malformation, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China.
Shaoting LuoDepartment of Pediatric Orthopedics, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang, Liaoning, 110004, PR China.
Haiyun LaiDepartment of Pediatric Orthopedics, Shengjing Hospital of China Medical University, 36 Sanhao Street, Heping District, Shenyang, Liaoning, 110004, PR China. Laihy0504@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo assess the prevalence of depression among uninsured middle-aged food delivery riders, to identify occupational and psychosocial determinants, and to develop a predictive nomogram for early risk detection.

methodsWe conducted a cross-sectional survey of 1,333 uninsured riders aged 40-59 years in China between January 2022 and December 2024. Depressive symptoms were evaluated using the CESD-10 scale. Data on sociodemographic, behavioral, health, and occupational characteristics were collected. Predictors were identified through least absolute shrinkage and selection operator (LASSO) regression and entered into a multivariable logistic regression to construct a predictive model. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, Brier scores, and decision curve analysis.

resultsIn total, 516 riders (38.7%) met the criteria for depression. Riders with depression were more often female and rural residents and reported poor self-rated health. Key occupational risk factors included frequent near-miss traffic events, higher algorithmic pressure, adverse weather exposure, and a greater number of customer complaints. Protective factors include male sex, better self-rated health, and greater organizational justice. The predictive nomogram demonstrated strong discrimination (AUC 0.828 in the training cohort and 0.853 in the test cohort) and satisfactory calibration.

conclusionThis study developed and validated one of the first nomograms to predict depression in uninsured middle-aged food delivery riders. The model underscores the critical role of occupational stressors and psychosocial resources and provides a practical tool for risk identification.

Indexed as

DepressionMedically UninsuredAdultChinaCross-Sectional StudiesFemaleHumansMaleMiddle AgedNomogramsPrevalenceRisk FactorsDepressionFood delivery ridersGig economyOccupational riskPredictive modelPsychosocial factors

Identifiers

PMID41454401
PMCPMC12853888

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Registered trials

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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.