The Association between Replacement Drivers and Depressive Symptoms

Jongmin Lee, Heejoo Park, Juyeon Oh, Juho Sim, Chorom Lee, Yangwook Kim, Byungyoon Yun () and Jin-Ha Yoon ()
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Jongmin Lee: Department of Occupational Health, Graduate School of Public Health, Yonsei University, Seoul 03722, Republic of Korea
Heejoo Park: Department of Business Administration and Data Science, CHA University, 120 Haeryong-ro, Donggyo-dong, Pocheon-si 11160, Republic of Korea
Juyeon Oh: Department of Public Health, Graduate School, Yonsei University, Seoul 03722, Republic of Korea
Juho Sim: Department of Public Health, Graduate School, Yonsei University, Seoul 03722, Republic of Korea
Chorom Lee: Department of Preventive Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
Yangwook Kim: Department of Public Health, Graduate School, Yonsei University, Seoul 03722, Republic of Korea
Byungyoon Yun: Department of Preventive Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
Jin-Ha Yoon: Department of Preventive Medicine, Yonsei University College of Medicine, Seoul 03722, Republic of Korea

IJERPH, 2022, vol. 20, issue 1, 1-12

Abstract: A replacement driver is a type of gig worker who provides driving services to the target point with the drunk driver’s own car. This study aimed to examine the association of replacement drivers (ref: paid workers) with depressive symptoms. Information on replacement drivers was collected through online/offline surveys. Data from the 8th Korea National Health and Nutrition Examination Survey were applied to construct the control group. The Patient Health Questionnaire-9; ≥5 points was defined as depressive symptoms. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were calculated by performing multivariable logistic regression analysis. The mean age of replacement drivers was 56.11. The prevalence of depressive symptoms in replacement drivers and controls were 49.63% and 12.64%, respectively. Replacement drivers showed a higher association with depressive symptoms than paid workers (aOR 7.89, 95% CI [5.53–11.26]). This relationship was prominent in the older, low-education, and low-income groups. Linear discriminant analysis was the most effective in predicting depressive symptoms among the machine learning models. Using the replacement driver feature increased the AUC values of the models. Given the strong association between depressive symptoms and replacement drivers, in-depth studies to establish guidelines to prevent mental diseases among replacement drivers are required.

Keywords: replacement driver; gig worker; depressive symptoms; paid workers; machine learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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