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Objective <p>To 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.</p> Methods <p>We 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.</p> Results <p>In 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.</p> Conclusion <p>This 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月27日

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

摘要

Objective

To 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.

Methods

We 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.

Results

In 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.

Conclusion

This 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.