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

Nianshuo Wang; Ifeoma Ozodiegwu; Shaoqing Gong; Kesheng Wang; Xin Xie; Nianshuo Wang; Ifeoma Ozodiegwu; Shaoqing Gong; Kesheng Wang; Xin Xie

doi:10.3934/QFE.2019.3.473

Quantitative Finance and Economics

2019, Volume 3, Issue 3: 473-489. doi: 10.3934/QFE.2019.3.473

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Research article

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

1.
College of Finance and Statistics, Hunan University, Changsha, Hunan Province, China
2.
Department of Biostatistics and Epidemiology, College of Public Health, East Tennessee State University, Johnson City, TN 37614, USA
3.
School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, China
4.
Department of Family and Community Health, School of Nursing, Health Sciences Center, West Virginia University, Morgantown, WV 26506, USA
5.
Department of Economics and Finance, College of Business and Technology, East Tennessee State University, Johnson City, TN 37614, USA

Received: 02 May 2019 Accepted: 15 July 2019 Published: 26 July 2019
JEL Codes: C12, C30, C38, G22, I12, I13, I14, I18

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.
- uninsurance,
- ethnicity,
- citizenship,
- socioeconomic factors,
- smoking,
- Asian,
- weighted logistic regression,
- variable cluster analysis
Citation: Nianshuo Wang, Ifeoma Ozodiegwu, Shaoqing Gong, Kesheng Wang, Xin Xie. Multivariate analyses of social-behavioral factors with health insurance coverage among Asian Americans in California[J]. Quantitative Finance and Economics, 2019, 3(3): 473-489. doi: 10.3934/QFE.2019.3.473

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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.

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