Multivariate analyses of social-behavioral factors with health insurance coverage among Asian Americans in California

Abstract

This study aimed to estimate the prevalence of uninsurance among California adults and Asian Americans, and to examine the associations of social-behavioral variables with uninsurance. A total of 24,136 adults (aged 18–64) including 2,060 Asian Americans were selected from the combined 2013–2014 California Health Interview Survey. Weighted univariate and multivariate logistic regression analyses were used to estimate the associations of potential factors with uninsurance. To evaluate the relationship of independent variables, the oblique principal component cluster analysis (OPCCA) was used to classify 9 variables into disjoint clusters. For Whites, African Americans, Latinos, and Asians, the prevalence of uninsurance was 8.5%, 10.3%, 24.7%, and 12.6%, respectively. Among Asians, the prevalence of uninsurance was 15.5%, 9.2%, 6.2%, 20.8% and 12.1% for Chinese, Filipinos, Japanese, Koreans, and Vietnamese, respectively. In the whole sample, multivariate logistic regression analysis revealed that being male, non-citizen, lower education, higher poverty, and current smoking were associated with uninsurance. Among Asians, compared to Koreans, being Filipinos and Vietnamese were associated with lower odds of being uninsured; meanwhile being male, non-citizen, lower education, and higher poverty were significantly associated with increased odds of uninsurance. Elder age groups and current smoking were significantly associated with increased odds of uninsurance in bivariate analysis; however, such associations disappeared after adjusting for other factors. Nine independent variables were divided into 2 clusters, where the variables in the same cluster were strongly correlated but had weak correlations with the variables in the other cluster. In conclusion, there are differences in the prevalence of uninsurance between Asians and Whites, and among Asian subgroups. Being male, non-citizen, lower education, higher poverty and current smoking were positively significantly associated with uninsurance.