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

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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.

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References

  • Battese G, Harter R, Fuller W (1988) An error-components model for prediction of county crop areas using survey and satellite data. J Am Stat Assoc 83:28–36

    Article  Google Scholar 

  • Berkowitz Z, Zhang X, Richards T, Nadel M, Peipins L, Holt J (2018) Multilevel small-area estimation of colorectal cancer screening in the United States. Cancer Epidemiol Biomark Prev 27(3):245–253

    Article  Google Scholar 

  • Berkowitz Z, Zhang X, Richards T et al (2019) Multilevel regression for small-area estimation of mammography use in the United States. Cancer Epidemiol Biomark Prev 28(1):32–40

    Article  Google Scholar 

  • Browne W, Draper D (2006) A comparison of Bayesian and likelihood based methods for fitting multilevel models. Bayesian Anal 1(3):473–514

    Article  MathSciNet  MATH  Google Scholar 

  • Cadwell B, Thompson T, Boyle J, Baker L (2010) Bayesian small area estimation of diabetes prevalence by U.S. county, 2005. J Data Sci 8:173–188

    Google Scholar 

  • Erciulescu A, Cruze N, Nandram B (2020) Statistical challenges in combining survey and auxiliary data to produce official statistics. J Off Stat 36(1):63–88

    Article  Google Scholar 

  • Erciulescu A, Opsomer J (2019) A model-based approach to predict employee compensation components, In: Joint statistical meetings proceedings, Government Statistics Section, American Statistical Association, July 27–August 1; Alexandria, pp 1601–1623

  • Fabrizi E, Ferrante MR, Trivisano C (2016) Hierarchical Beta regression models for the estimation of poverty and inequality parameters in small areas. In: Analysis of poverty data by small area methods. Wiley, pp 299–314

  • Fay R, Herriot R (1979) Estimates of income for small places: an application of James–Stein procedures to census data. J Am Stat Assoc 74(366a):269–277

    Article  MathSciNet  Google Scholar 

  • Fuller W, Goyeneche J (1998) Estimation of the state variance component. (Unpublished manuscript)

  • Gabler S, Häder S, Lahiri P (1999) A model based justification of Kish’s formula for design effects forweighting and clustering. Surv Methodol 25:105–106

    Google Scholar 

  • Gelman A (2006) Prior distributions for variance parameters in hierarchical models (Comment on an article by Browne and Draper). Bayesian Anal 1(3):515–534

    Article  MathSciNet  MATH  Google Scholar 

  • Holt J, Matthews K, Lu H et al (2019) Small area estimates of populations with chronic conditions for community preparedness for public health emergencies. Am J Public Health 109(S4):S325–S331

    Article  Google Scholar 

  • Janicki R (2020) Properties of the beta regression model for small area estimation of proportions and applicationto estimation of poverty rates. Commun Stat Theor Methods 49(9):2264–2284

    Article  MATH  Google Scholar 

  • Kish L (1965) Survey sampling. Wiley, New York

    MATH  Google Scholar 

  • Krenzke T, Mohadjer L, Li J, et al (2020) Program for the international assessment of adult competencies (PIAAC): state and county estimation methodology report. Tech. Reports NCES2020225, U.S. Department of Education, Rockville: Westat. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2020225

  • Lahiri P, Suntornchost J (2015) Variable selection for linear mixed models with applications in small areaestimation. Sankhya B 77(2):312–320

    Article  MathSciNet  MATH  Google Scholar 

  • Liu B, Parsons V, Feuer E et al (2019) Small area estimation of cancer risk factors and screening behaviors in U.S. counties by combining two large national health surveys. Prev Chronic Dis 16:E119:190013

    Article  Google Scholar 

  • Pierannunzi C, Xu F, Wallace R et al (2016) A methodological approach to small area estimation for the Behavioral Risk Factor Surveillance System. Prev Chronic Dis 13:E91:150480

    Article  Google Scholar 

  • Polson N, Scott J (2012) On the half-Cauchy prior for a global scale parameter. Bayesian Anal 7(4):887–902

    Article  MathSciNet  MATH  Google Scholar 

  • Raghunathan T, Xie D, Schenker N et al (2007) Combining information from two surveys to estimate county-level prevalence rates of cancer risk factors and screening. J Am Stat Assoc 102:474–486

    Article  MathSciNet  MATH  Google Scholar 

  • Torabi M, Rao J (2014) On small area estimation under a sub-area level model. J Multivar Anal 127:36–55

    Article  MathSciNet  MATH  Google Scholar 

  • Watanabe S (2013) A widely applicable Bayesian information criterion. J Mach Learn Res 14:867–897

    MathSciNet  MATH  Google Scholar 

  • Wieczorek J, Hawala S (2011) A bayesian zero-one inflated beta model for estimating poverty in us counties. In: Proceedings of the American statistical sssociation, section on survey research methods. American Statistical Association, Alexandria, VA

  • Zhang Z, Holt J, Lu H et al (2014) Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the Behavioral Risk Factor Surveillance System. Am J Epidemiol 179(8):1025–1033

    Article  Google Scholar 

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Acknowledgements

This work was conducted under a CDC-Westat project. The authors thank Carol Pierannunzi, the CDC’s main contact for the project, for helpful discussions and comments. Dr. Li contributed to this work while she was a Senior Statistician at Westat. Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Funding

The work described in this paper was conducted under contract with the Centers for Disease Control and Prevention (CDC Contract #HHSD2002013M53968B Order #75D30120F09442). The BRFSS data are confidential to CDC so cannot be shared.

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Authors and Affiliations

  1. Westat, 1600 Research Blvd, Rockville, MD, 20850, USA

    Andreea Erciulescu & Tom Krenzke

  2. Financial Industry Regulatory Authority, 9509 Key W Ave, Rockville, MD, 20850, USA

    Jianzhu Li

  3. Population Health Surveillance Branch, Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA, 30333, USA

    Machell Town

Authors
  1. Andreea Erciulescu
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  2. Jianzhu Li
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  3. Tom Krenzke
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  4. Machell Town
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Correspondence to Andreea Erciulescu.

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Appendices

Appendix A Auxiliary data pool

See Table 5.

Table 5 Auxiliary data pool
Full size table

Appendix B STAN code

1.1 Model specification

figure a

1.2 Model fit

figure b

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Erciulescu, A., Li, J., Krenzke, T. et al. Hierarchical Bayes small area estimation for county-level health prevalence to having a personal doctor. Stat Methods Appl (2022). https://doi.org/10.1007/s10260-022-00678-7

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  • DOI: https://doi.org/10.1007/s10260-022-00678-7

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