Health care utilisation amongst Shenzhen migrant workers: does being insured make a difference?

BMC Health Serv Res. 2009 Nov 21;9:214. doi: 10.1186/1472-6963-9-214.


Background: As one of the most populous metropolitan areas in the Pearl River Delta of South China, Shenzhen attracts millions of migrant workers annually. The objectives of this study were to compare health needs, self-reported health and healthcare utilisation of insured and uninsured migrant workers in Shenzhen, China, where a new health insurance scheme targeting at migrant workers was initiated.

Methods: A cross-sectional survey using multi-staged sampling was conducted to collect data from migrant factory workers. Statistical tests included logistic regression analysis were used.

Results: Among 4634 subjects (96.54%) who responded to the survey, 55.11% were uninsured. Disease patterns were similar irrespective of insurance status. The uninsured were more likely to be female, single, younger and less educated unskilled labourers with a lower monthly income compared with the insured. Out of 1136 who reported illness in the previous two weeks, 62.15% did not visit a doctor. Of the 296 who were referred for inpatient care, 48.65% did not attend because of inability to pay. Amongst those who reported sickness, 548 were insured and 588 were uninsured. Those that were insured, and had easier access to care were more likely to make doctor visits than those who were uninsured.

Conclusion: Health care utilisation patterns differ between insured and uninsured workers and insurance status appears to be a significant factor. The health insurance system is inequitably distributed amongst migrant workers. Younger less educated women who are paid less are more likely to be uninsured and therefore to pay out of pocket for their care. For greater equity this group need to be included in the insurance schemes as they develop.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • China
  • Cross-Sectional Studies
  • Female
  • Health Expenditures / statistics & numerical data
  • Health Services / statistics & numerical data*
  • Humans
  • Insurance, Health / statistics & numerical data*
  • Logistic Models
  • Male
  • Socioeconomic Factors
  • Surveys and Questionnaires
  • Transients and Migrants / statistics & numerical data*
  • Young Adult