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The Relationship between Cognitive Impairment and Social Vulnerability among the Elderly: Evidence from an Unconditional Quantile Regression Analysis in China

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  • Junkai Zhao

    (Business School, Sichuan University, Chengdu 610064, China)

  • Xinxin Zhang

    (Business School, Sichuan University, Chengdu 610064, China)

  • Zongmin Li

    (Business School, Sichuan University, Chengdu 610064, China)

Abstract

As the global proportion of the elderly population has been growing rapidly, it has become important to better understand the holistic social factors involved in cognitive impairment in the elderly. To investigate the relationship between social vulnerability and cognitive impairment in the elderly, this study applied an unconditional quantile regression model on open source health survey data in China. It was used to estimate the relationship for full sample and subsamples divided by different levels of a specific covariate. It was found that the cognitive impairment had a positive association with social vulnerability, and this relationship is stronger at the higher cognitive impairment quantiles. The cognitive impairment of females and elderly who took less exercise; had lower self-rated health; had greater incidences of depression, chronic diseases, and physical limitations; and consumed less fruit and vegetables, milk and tea were more related to social vulnerability. These results provide some insights into the strategies that could be used by the elderly to decrease the risk of cognitive impairment.

Suggested Citation

  • Junkai Zhao & Xinxin Zhang & Zongmin Li, 2019. "The Relationship between Cognitive Impairment and Social Vulnerability among the Elderly: Evidence from an Unconditional Quantile Regression Analysis in China," IJERPH, MDPI, vol. 16(19), pages 1-12, September.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:19:p:3684-:d:272333
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    References listed on IDEAS

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    1. Je-Liang Liou, 2019. "Effect of Income Heterogeneity on Valuation of Mortality Risk in Taiwan: An Application of Unconditional Quantile Regression Method," IJERPH, MDPI, vol. 16(9), pages 1-15, May.
    2. Tu N. Nguyen & Patrice Ngangue & Tarek Bouhali & Bridget L. Ryan & Moira Stewart & Martin Fortin, 2019. "Social Vulnerability in Patients with Multimorbidity: A Cross-Sectional Analysis," IJERPH, MDPI, vol. 16(7), pages 1-9, April.
    3. Mensi, Walid & Hammoudeh, Shawkat & Reboredo, Juan Carlos & Nguyen, Duc Khuong, 2014. "Do global factors impact BRICS stock markets? A quantile regression approach," Emerging Markets Review, Elsevier, vol. 19(C), pages 1-17.
    4. Bijan J. Borah & Anirban Basu, 2013. "Highlighting Differences Between Conditional And Unconditional Quantile Regression Approaches Through An Application To Assess Medication Adherence," Health Economics, John Wiley & Sons, Ltd., vol. 22(9), pages 1052-1070, September.
    5. Johanna Catherine Maclean & Douglas A. Webber & Joachim Marti, 2014. "An Application of Unconditional Quantile Regression to Cigarette Taxes," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 33(1), pages 188-210, January.
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