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Identification of the Differential Effect of City-Level on the Gini Coefficient of Health Service Delivery in Online Health Community

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  • Hai-Yan Yu

    (School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    Department of Statistics, Eberly College of Science, The Pennsylvania State University, University Park, PA 16802, USA)

  • Jing-Jing Chen

    (School of Economics and Management, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)

  • Jying-Nan Wang

    (College of International Finance and Trade, Zhejiang Yuexiu University of Foreign Languages, Shaoxing 312000, China)

  • Ya-Ling Chiu

    (College of International Business, Zhejiang Yuexiu University of Foreign Languages, Shaoxing 312000, China)

  • Hang Qiu

    (School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
    Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China)

  • Li-Ya Wang

    (Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China)

Abstract

Inequality of health services for different specialty categories not only occurs in different areas in the world, but also happens in the online service platform. In the online health community (OHC), health services often display inequality for different specialty categories, including both online views and medical consultations for offline registered services. Moreover, how the city-level factors impact the inequality of health services in OHC is still unknown. We designed a causal inference study with data on distributions of serviced patients and online views in over 100 distinct specialty categories on one of the largest OHCs in China. To derive the causal effect of the city-levels (two levels inducing 1 and 0) on the Gini coefficient, we matched the focus cases in cities with rich healthcare resources with the potential control cities. For each of the specialty categories, we first estimated the average treatment effect of the specialty category’s Gini coefficient (SCGini) with the balanced covariates. For the Gini coefficient of online views, the average treatment effect of level-1 cities is 0.573, which is 0.016 higher than that of the matched group. Similarly, for the Gini coefficient of serviced patients, the average treatment effect of level-1 cities is 0.470, which is 0.029 higher than that of the matched group. The results support the argument that the total Gini coefficient of the doctors in OHCs shows that the inequality in health services is still very serious. This study contributes to the development of a theoretically grounded understanding of the causal effect of city-level factors on the inequality of health services in an online to offline health service setting. In the future, heterogeneous results should be considered for distinct groups of doctors who provide different combinations of online contributions and online attendance.

Suggested Citation

  • Hai-Yan Yu & Jing-Jing Chen & Jying-Nan Wang & Ya-Ling Chiu & Hang Qiu & Li-Ya Wang, 2019. "Identification of the Differential Effect of City-Level on the Gini Coefficient of Health Service Delivery in Online Health Community," IJERPH, MDPI, vol. 16(13), pages 1-18, June.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:13:p:2314-:d:244310
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    References listed on IDEAS

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