IDEAS home Printed from https://ideas.repec.org/a/aph/ajpbhl/10.2105-ajph.2005.077743_6.html
   My bibliography  Save this article

Small-area estimation of health insurance coverage for California legislative districts

Author

Listed:
  • Yu, H.
  • Meng, Y.-Y.
  • Mendez-Luck, C.A.
  • Jhawar, M.
  • Wallace, S.P.

Abstract

Objectives. To aid state and local policymakers, program planners, and community advocates, we created estimates of the percentage of the population lacking health insurance in small geographic areas of California. Methods. We modeled probabilities of uninsurance using state-level, multiyear data from the US Census Bureau's Current Population Survey. The models were applied to population projection data from a commercial vender (Claritas). We updated the population data using the most recently available census and survey data to reflect intercensual population changes. Legislative district boundary files were merged into the updated population projections. Finally, calibration ensured the consistency and stability of the estimates when they were aggregated. Results. Health insurance coverage among nonelderly persons varied widely across assembly districts, from 10% to 44%. The utility of local-level estimates was most apparent when the variations in subcounty uninsured rates in Los Angeles County (19%-44%) were examined. Conclusions. Stable and useful estimates of health insurance rates for small areas such as legislative districts can be created through use of multiple sources of publicly available data.

Suggested Citation

  • Yu, H. & Meng, Y.-Y. & Mendez-Luck, C.A. & Jhawar, M. & Wallace, S.P., 2007. "Small-area estimation of health insurance coverage for California legislative districts," American Journal of Public Health, American Public Health Association, vol. 97(4), pages 731-737.
  • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2005.077743_6
    DOI: 10.2105/AJPH.2005.077743
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.2105/AJPH.2005.077743
    Download Restriction: no

    File URL: https://libkey.io/10.2105/AJPH.2005.077743?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

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


    Cited by:

    1. Hongjian Yu & Yueyan Wang & Jean Opsomer & Pan Wang & Ninez A. Ponce, 2018. "A design‐based approach to small area estimation using a semiparametric generalized linear mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1151-1167, October.

    More about this item

    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:aph:ajpbhl:10.2105/ajph.2005.077743_6. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Christopher F Baum (email available below). General contact details of provider: https://www.apha.org .

    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.