# Health risky behaviors among rural-to-urban migrant workers in China: prevalence, patterns, and association with distal and proximal factors

## TL;DR

This study examines risky health behaviors in rural-to-urban migrant workers in China and identifies factors that predict these behaviors.

## Contribution

The study develops and validates a predictive model for health risky behaviors among migrant workers using distal and proximal factors.

## Key findings

- The study found a high prevalence of health risky behaviors among rural-to-urban migrant workers in China.
- Key predictors of risky behaviors include work burnout, abuse/neglect experiences, and adulthood poverty.
- The developed nomogram model showed good predictive accuracy with an AUC of 0.77 in the training set.

## Abstract

Health Risky Behaviors (HRBs) pose a significant public health challenge, particularly among migrant workers in China who face unfavorable living and working conditions. This study aimed to investigate the prevalence and characteristics of HRBs in rural-to-urban migrant workers, as well as explore factors associated with HRBs from both distal and proximal perspectives.

A cross-sectional survey involving 2,065 rural-to-urban migrant workers was conducted. Participants completed a structured questionnaire assessing HRBs, distal factors (school dropout, peer victimization, physical neglect/abuse, emotional neglect/abuse) and proximal factors (work burnout, parent-child conflict, adulthood poverty, divorce intention, core self-evaluation). Logistic regression analysis was utilized to identify predictors of HRBs, leading to the development and validation of a prediction model (nomograms) for HRBs among migrant workers. The model's performance was assessed using metrics such as the area under the curve (AUC), calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).

Significant predictors of HRBs included gender, school dropout, peer victimization, abuse/neglect experiences, work burnout, parent-child conflict, adulthood poverty, divorce intention, and core self-evaluation. The developed nomogram showed promising predictive accuracy with an AUC of 0.77 for the training set and 0.76 for the validation set. The calibration curve demonstrated good alignment with the diagonal, and the DCA illustrated the model's utility across different threshold ranges.

This study highlighted a high prevalence of HRBs among migrant workers in China, and the predictive tool developed can be instrumental in informing targeted interventions and policies to address and manage HRBs effectively among this population.