IDEAS home Printed from https://ideas.repec.org/p/ven/wpaper/201414.html
   My bibliography  Save this paper

Analyzing Disparities 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

    (Department of Statistical Sciences, University Of Padua)

  • Stefano Campostrini

    (Department of Economics, University Of Venice C� Foscari)

  • Cinzia Di Novi

    (Department of Economics, University Of Venice C� Foscari)

  • Fang Xu

    (Northrop Grumman Information Systems)

  • Carol Gotway Crawford

    (Department of Biostatistics and Bioinformatics Rollins School of Public Health Emory University)

Abstract

Access to health care in the United States remains greatly disproportionate across socioeconomic groups. It is not known, however, whether the disparities between the socioeconomic categories are increasing or decreasing. This analysis used a well-established non-parametric technique, employing time-varying coefficient models applied to data from the 1993 to 2009 US Behavioral Risk Factor Surveillance System (BRFSS). The analysis was able to show the changes in the odds ratios of having no health insurance plan for variables of interest over time, therefore highlighting the changes in the disparities between the categories of a variable over time. While other studies have attempted to show the changes in health insurance coverage by socioeconomic groups in different time periods, there is no study to date that has shown these changes as a smooth function with time, therefore providing a clearer picture of the changes in these disparities. The results of this analysis show, for instance, that when compared with individuals with a college education or greater, those with less than a high school education showed a steady increase in the odds ratios for having no health insurance. The same trend seems applicable although in a less-clear way to Hispanics and Non-Hispanic black race-ethnicities, compared with non-Hispanic whites (the reference race category). As measures of the Affordable Care Act are being gradually implemented, studies are needed to provide baseline information about health care access disparity, in order to gauge any changes in health care access over time; BRFSS can be a useful data source in accomplishing this task.

Suggested Citation

  • Shireen Assaf & Stefano Campostrini & Cinzia Di Novi & Fang Xu & Carol Gotway Crawford, 2014. "Analyzing Disparities Trends for Health Care Insurance Coverage Among Non-Elderly Adults in the US: Evidence from the Behavioral Risk Factor Surveillance System, 1993-2009," Working Papers 2014: 14, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2014:14
    as

    Download full text from publisher

    File URL: http://www.unive.it/pag/fileadmin/user_upload/dipartimenti/economia/doc/Pubblicazioni_scientifiche/working_papers/2014/WP_DSE_assaf_campostrini_dinovi_xu_crawford_14_14.pdf
    File Function: First version, anno
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Scott R Sanders & Michael R Cope & Paige N Park & Wesley Jeffery & Jorden E Jackson, 2020. "Infants without health insurance: Racial/ethnic and rural/urban disparities in infant households’ insurance coverage," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-13, January.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Jacobson, Jerry Owen & Robinson, Paul & Bluthenthal, Ricky N., 2007. "A multilevel decomposition approach to estimate the role of program location and neighborhood disadvantage in racial disparities in alcohol treatment completion," Social Science & Medicine, Elsevier, vol. 64(2), pages 462-476, January.
    3. Arturo Vargas Bustamante & Claudio A. Méndez, 2016. "Regulating self-selection into private health insurance in Chile and the United States," International Journal of Health Planning and Management, Wiley Blackwell, vol. 31(3), pages 219-234, July.
    4. Ye, Mao & Lu, Zhao-Hua & Li, Yimei & Song, Xinyuan, 2019. "Finite mixture of varying coefficient model: Estimation and component selection," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 452-474.
    5. Wang, Qihua & Zhang, Riquan, 2009. "Statistical estimation in varying coefficient models with surrogate data and validation sampling," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2389-2405, November.
    6. Hui Ding & Mei Yao & Riquan Zhang, 2023. "A new estimation in functional linear concurrent model with covariate dependent and noise contamination," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(8), pages 965-989, November.
    7. Tsai, Guei-Feng & Qu, Annie, 2008. "Testing the significance of cell-cycle patterns in time-course microarray data using nonparametric quadratic inference functions," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1387-1398, January.
    8. Olga Klopp & Marianna Pensky, 2013. "Sparse High-dimensional Varying Coefficient Model : Non-asymptotic Minimax Study," Working Papers 2013-30, Center for Research in Economics and Statistics.
    9. Lan Xue & Hua Liang, 2010. "Polynomial Spline Estimation for a Generalized Additive Coefficient Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 26-46, March.
    10. Tang Qingguo & Cheng Longsheng, 2008. "M-estimation and B-spline approximation for varying coefficient models with longitudinal data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(7), pages 611-625.
    11. Qiu, Jia & Li, Degao & You, Jinhong, 2015. "SCAD-penalized regression for varying-coefficient models with autoregressive errors," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 100-118.
    12. Megha Swami & Hugh Gravelle & Anthony Scott & Jenny Williams, 2018. "Hours worked by general practitioners and waiting times for primary care," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1513-1532, October.
    13. Xiaochao Xia & Hao Ming, 2022. "A Flexibly Conditional Screening Approach via a Nonparametric Quantile Partial Correlation," Mathematics, MDPI, vol. 10(24), pages 1-32, December.
    14. Wang-Li Xu & Li-Xing Zhu, 2008. "Goodness-of-fit testing for varying-coefficient models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 68(2), pages 129-146, September.
    15. Shujie Ma & Yanyuan Ma & Yanqing Wang & Eli S. Kravitz & Raymond J. Carroll, 2017. "A Semiparametric Single-Index Risk Score Across Populations," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1648-1662, October.
    16. Yiqiang Lu & Riquan Zhang, 2009. "Smoothing spline estimation of generalised varying-coefficient mixed model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(7), pages 815-825.
    17. Liu, Hefei & Song, Xinyuan & Zhang, Baoxue, 2022. "Varying-coefficient hidden Markov models with zero-effect regions," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    18. Zhang, Riquan & Li, Guoying, 2007. "Averaged estimation of functional-coefficient regression models with different smoothing variables," Statistics & Probability Letters, Elsevier, vol. 77(4), pages 455-461, February.
    19. Chin-Tsang Chiang, 2005. "Comparisons between simultaneous and componentwise splines for varying coefficient models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(4), pages 637-653, December.
    20. Jianbo Li & Minggao Gu & Tao Hu, 2012. "General partially linear varying-coefficient transformation models for ranking data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1475-1488, January.

    More about this item

    Keywords

    USA; big data; disparities; health plan; health surveillance data; P-splines; temporal trends; varying coefficient model.;
    All these keywords.

    JEL classification:

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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ven:wpaper:2014:14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Geraldine Ludbrook (email available below). General contact details of provider: https://edirc.repec.org/data/dsvenit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.