IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v33y2024i4d10.1007_s10260-022-00678-7.html
   My bibliography  Save this article

Hierarchical Bayes small area estimation for county-level health prevalence to having a personal doctor

Author

Listed:
  • Andreea Erciulescu

    (Westat)

  • Jianzhu Li

    (Financial Industry Regulatory Authority)

  • Tom Krenzke

    (Westat)

  • Machell Town

    (Centers for Disease Control and Prevention)

Abstract

The complexity of survey data and the availability of data from auxiliary sources motivate researchers to explore estimation methods that extend beyond traditional survey-based estimation. The U.S. Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System (BRFSS) collects a wide range of health information, including whether respondents have a personal doctor. While the BRFSS focuses on state-level estimation, there is demand for county-level estimation of health indicators using BRFSS data. A hierarchical Bayes small area estimation model is developed to combine county-level BRFSS survey data with county-level data from auxiliary sources, while accounting for various sources of error and nested geographical levels. To mitigate extreme proportions and unstable survey variances, a transformation is applied to the survey data. Model-based county-level predictions are constructed for prevalence of having a personal doctor for all the counties in the U.S., including those where BRFSS survey data were not available. An evaluation study using only the counties with large BRFSS sample sizes to fit the model versus using all the counties with BRFSS data to fit the model is also presented.

Suggested Citation

  • Andreea Erciulescu & Jianzhu Li & Tom Krenzke & Machell Town, 2024. "Hierarchical Bayes small area estimation for county-level health prevalence to having a personal doctor," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(4), pages 1171-1191, September.
    • Handle: RePEc:spr:stmapp:v:33:y:2024:i:4:d:10.1007_s10260-022-00678-7
    DOI: 10.1007/s10260-022-00678-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-022-00678-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10260-022-00678-7?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Raghunathan, Trivellore E. & Xie, Dawei & Schenker, Nathaniel & Parsons, Van L. & Davis, William W. & Dodd, Kevin W. & Feuer, Eric J., 2007. "Combining Information From Two Surveys to Estimate County-Level Prevalence Rates of Cancer Risk Factors and Screening," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 474-486, June.
    2. Torabi, Mahmoud & Rao, J.N.K., 2014. "On small area estimation under a sub-area level model," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 36-55.
    3. Ryan Janicki, 2020. "Properties of the beta regression model for small area estimation of proportions and application to estimation of poverty rates," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(9), pages 2264-2284, May.
    Full references (including those not matched with items on IDEAS)

    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. Jae Kwang Kim & Zhonglei Wang & Zhengyuan Zhu & Nathan B. Cruze, 2018. "Combining Survey and Non-survey Data for Improved Sub-area Prediction Using a Multi-level Model," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 175-189, June.
    2. K. Shuvo Bakar & Nicholas Biddle & Philip Kokic & Huidong Jin, 2020. "A Bayesian spatial categorical model for prediction to overlapping geographical areas in sample surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 535-563, February.
    3. Masaki,Takaaki & Newhouse,David Locke & Silwal,Ani Rudra & Bedada,Adane & Engstrom,Ryan, 2020. "Small Area Estimation of Non-Monetary Poverty with Geospatial Data," Policy Research Working Paper Series 9383, The World Bank.
    4. Song Cai & J.N.K. Rao, 2022. "Selection of Auxiliary Variables for Three-Fold Linking Models in Small Area Estimation: A Simple and Effective Method," Stats, MDPI, vol. 5(1), pages 1-11, February.
    5. Lu Chen & Luca Sartore & Habtamu Benecha & Valbona Bejleri & Balgobin Nandram, 2022. "Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs," Stats, MDPI, vol. 5(3), pages 1-18, September.
    6. Linda J. Young & Lu Chen, 2022. "Using Small Area Estimation to Produce Official Statistics," Stats, MDPI, vol. 5(3), pages 1-17, September.
    7. Corral, Paul & Henderson, Heath & Segovia, Sandra, 2025. "Poverty mapping in the age of machine learning," Journal of Development Economics, Elsevier, vol. 172(C).
    8. Andrew Lawson & Anna Schritz & Luis Villarroel & Gloria A. Aguayo, 2020. "Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example," IJERPH, MDPI, vol. 17(5), pages 1-20, March.
    9. Lu Chen & Balgobin Nandram, 2023. "Bayesian Logistic Regression Model for Sub-Areas," Stats, MDPI, vol. 6(1), pages 1-23, January.
    10. Newhouse David, 2020. "Discussion of “Small area estimation: its evolution in five decades”, by Malay Ghosh," Statistics in Transition New Series, Statistics Poland, vol. 21(4), pages 45-50, August.
    11. Laura Marcis & Domingo Morales & Maria Chiara Pagliarella & Renato Salvatore, 2023. "Three-fold Fay–Herriot model for small area estimation and its diagnostics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(5), pages 1563-1609, December.
    12. Newhouse,David Locke & Merfeld,Joshua David & Ramakrishnan,Anusha Pudugramam & Swartz,Tom & Lahiri,Partha, 2022. "Small Area Estimation of Monetary Poverty in Mexico Using Satellite Imagery and Machine Learning," Policy Research Working Paper Series 10175, The World Bank.
    13. Schmid, Timo & Bruckschen, Fabian & Salvati, Nicola & Zbiranski, Till, 2016. "Constructing socio-demographic indicators for National Statistical Institutes using mobile phone data: Estimating literacy rates in Senegal," Discussion Papers 2016/9, Free University Berlin, School of Business & Economics.
    14. Kevin Watjou & Christel Faes & Yannick Vandendijck, 2020. "Spatial Modelling to Inform Public Health Based on Health Surveys: Impact of Unsampled Areas at Lower Geographical Scale," IJERPH, MDPI, vol. 17(3), pages 1-19, January.
    15. Giancarlo Manzi & David J. Spiegelhalter & Rebecca M. Turner & Julian Flowers & Simon G. Thompson, 2011. "Modelling bias in combining small area prevalence estimates from multiple surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 31-50, January.
    16. Camilla Salvatore, 2023. "Inference with non-probability samples and survey data integration: a science mapping study," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 83-107, April.
    17. Harding, Lee & Iachan, Ronaldo & Martin, Kelly & Deng, Yangyang & Middleton, Deirdre & Moser, Richard & Blake, Kelly, 2022. "State and regional estimates using seven cycles of pooled nationally representative HINTS data," Social Science & Medicine, Elsevier, vol. 297(C).
    18. Corral Rodas,Paul Andres & Henderson,Heath Linn & Segovia Juarez,Sandra Carolina, 2023. "Poverty Mapping in the Age of Machine Learning," Policy Research Working Paper Series 10429, The World Bank.
    19. Erciulescu Andreea L. & Cruze Nathan B. & Nandram Balgobin, 2020. "Statistical Challenges in Combining Survey and Auxiliary Data to Produce Official Statistics," Journal of Official Statistics, Sciendo, vol. 36(1), pages 63-88, March.
    20. Christopher K. Wikle & Scott H. Holan, 2015. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 901-903, September.

    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:spr:stmapp:v:33:y:2024:i:4:d:10.1007_s10260-022-00678-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.