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Analyzing disparity trends for health care insurance coverage among non-elderly adults in the US: evidence from the Behavioral Risk Factor Surveillance System, 1993–2009

Author

Listed:
  • Shireen Assaf

    (ICF International)

  • Stefano Campostrini

    (Ca’ Foscari University of Venice)

  • Cinzia Di Novi

    (University of Pavia
    Health, Econometrics and Data Group, University of York (UK). LCSR, National Research University Higher School of Economics, Russian Federation)

  • Fang Xu

    (Northrop Grumman Corporation)

  • Carol Gotway Crawford

    (Rollins School of Public Health Emory University)

Abstract

Objective To explore the changing disparities in access to health care insurance in the United States using time-varying coefficient models. Data Secondary data from the Behavioral Risk Factor Surveillance System (BRFSS) from 1993 to 2009 was used. Study design A time-varying coefficient model was constructed using a binary outcome of no enrollment in health insurance plan versus enrolled. The independent variables included age, sex, education, income, work status, race, and number of health conditions. Smooth functions of odds ratios and time were used to produce odds ratio plots. Results Significant time-varying coefficients were found for all the independent variables with the odds ratio plots showing changing trends except for a constant line for the categories of male, student, and having three health conditions. Some categories showed decreasing disparities, such as the income categories. However, some categories had increasing disparities in health insurance enrollment such as the education and race categories. Conclusions As the Affordable Care Act is being gradually implemented, studies are needed to provide baseline information about disparities in access to health insurance, in order to gauge any changes in health insurance access. The use of time-varying coefficient models with BRFSS data can be useful in accomplishing this task.

Suggested Citation

  • Shireen Assaf & Stefano Campostrini & Cinzia Di Novi & Fang Xu & Carol Gotway Crawford, 2017. "Analyzing disparity trends for health care insurance coverage among non-elderly adults in the US: evidence from the Behavioral Risk Factor Surveillance System, 1993–2009," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(3), pages 387-398, April.
    • Handle: RePEc:spr:eujhec:v:18:y:2017:i:3:d:10.1007_s10198-016-0806-1
    DOI: 10.1007/s10198-016-0806-1
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    References listed on IDEAS

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    1. Paul Contoyannis & Andrew M. Jones & Nigel Rice, 2004. "The dynamics of health in the British Household Panel Survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(4), pages 473-503.
    2. Andrew M. Jones & Nigel Rice & Paul Contoyannis, 2012. "The Dynamics of Health," Chapters, in: Andrew M. Jones (ed.), The Elgar Companion to Health Economics, Second Edition, chapter 2, Edward Elgar Publishing.
    3. Teresa Bago d'Uva & Eddy Van Doorslaer & Maarten Lindeboom & Owen O'Donnell, 2008. "Does reporting heterogeneity bias the measurement of health disparities?," Health Economics, John Wiley & Sons, Ltd., vol. 17(3), pages 351-375, March.
    4. Shireen Assaf & Stefano Campostrini & Fang Xu & Carol Gotway Crawford, 2016. "Analysing behavioural risk factor surveillance data by using spatially and temporally varying coefficient models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(1), pages 153-175, January.
    5. Light, Donald W., 2011. "Historical and comparative reflections on the U.S. national health insurance reforms," Social Science & Medicine, Elsevier, vol. 72(2), pages 129-132, January.
    6. Jennifer L Kohn, 2012. "What is Health? A Multiple Correspondence Health Index," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 38(2), pages 223-250.
    7. Jianhua Z. Huang, 2002. "Varying-coefficient models and basis function approximations for the analysis of repeated measurements," Biometrika, Biometrika Trust, vol. 89(1), pages 111-128, March.
    8. Sudano, Joseph J. & Baker, David W., 2006. "Explaining US racial/ethnic disparities in health declines and mortality in late middle age: The roles of socioeconomic status, health behaviors, and health insurance," Social Science & Medicine, Elsevier, vol. 62(4), pages 909-922, February.
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    Cited by:

    1. Di Novi, Cinzia & Marenzi, Anna, 2019. "The smoking epidemic across generations, genders, and educational groups: A matter of diffusion of innovations," Economics & Human Biology, Elsevier, vol. 33(C), pages 155-168.

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    More about this item

    Keywords

    Health insurance; Disparities; Health surveillance data; Temporal trends; P-splines; Varying coefficient model;
    All these keywords.

    JEL classification:

    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality

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