Residents' educational attainment and preventive care utilization in China

Int J Health Care Qual Assur. 2018 Feb 12;31(1):41-51. doi: 10.1108/IJHCQA-01-2017-0001.

Abstract

Purpose China launched a comprehensive health reform in 2009 to improve healthcare quality. Because preventive care utilization in China has not been frequently discussed, the purpose of this paper is to focus on the association between education level and preventive care before and after the initiation of the reform. Education has been referred to as the best health outcome indicator and China's educational reform has been progressive, such as the health reform. Design/methodology/approach The authors analyzed data from four China Health and Nutrition Surveys (CHNS): 2004 ( n=9,617); 2006 ( n=9,527); 2009 ( n=9,873); and 2011 ( n=9,430). Variables were selected based on Andersen's healthcare utilization model (predisposing, enabling and need factors). Multivariable logistic regression models, odds ratios (ORs) and 95 percent confidence intervals (95 percent CI) were conducted and reported. Findings In the adjusted multivariable logistic regression models, the authors found that general education was associated ( p<0.05) with access to preventive care in 2004, 2009 and 2011, but not in 2006. Individuals with higher education had higher ORs for utilizing preventive care, compared with lower education (primary school education or none). Practical implications Policy implications include providing educational protocols regarding preventive care's significance to residents educated at lower level schools, especially younger individuals. Originality/value To the authors' knowledge, this is the first comparative assessment on education level and preventive care utilization before and after the implementation of the Chinese health reform.

Keywords: China; Education; National health service; Policy; Prevention; Public health service.

MeSH terms

  • Academic Success
  • Adult
  • Age Factors
  • Aged
  • China
  • Female
  • Health Care Reform
  • Health Status
  • Humans
  • Logistic Models
  • Male
  • Middle Aged
  • Preventive Health Services / statistics & numerical data*
  • Regression Analysis
  • Sex Factors
  • Socioeconomic Factors