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Obtaining cancer risk factor prevalence estimates in small areas: combining data from two surveys

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  • Michael R. Elliott
  • William W. Davis

Abstract

Summary. Cancer surveillance research requires accurate estimates of risk factors at the small area level. These risk factors are often obtained from surveys such as the National Health Interview Survey (NHIS) or the Behavioral Risk Factors Surveillance System (BRFSS). The NHIS is a nationally representative, face‐to‐face survey with a high response rate; however, it cannot produce state or substate estimates of risk factor prevalence because the sample sizes are too small and small area identifiers are unavailable to the public. The BRFSS is a state level telephone survey that excludes non‐telephone households and has a lower response rate, but it does provide reasonable sample sizes in all states and many counties and has publicly available small area identifiers (counties). We propose a novel extension of dual‐frame estimation using propensity scores that allows the complementary strengths of each survey to compensate for the weakness of the other. We apply this method to obtain 1999–2000 county level estimates of adult male smoking prevalence and mammogram usage rates among females who were 40 years old and older. We consider evidence that these NHIS‐adjusted estimates reduce the effects of selection bias and non‐telephone coverage in the BRFSS. Data from the Current Population Survey Tobacco Use Supplement are also used to evaluate the performance of this approach. A hybrid estimator that selects one of the two estimators on the basis of the mean‐square error is also considered.

Suggested Citation

  • Michael R. Elliott & William W. Davis, 2005. "Obtaining cancer risk factor prevalence estimates in small areas: combining data from two surveys," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 595-609, June.
  • Handle: RePEc:bla:jorssc:v:54:y:2005:i:3:p:595-609
    DOI: 10.1111/j.1467-9876.2005.05459.x
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    Cited by:

    1. Anika Rasner & Joachim R. Frick & Markus M. Grabka, 2013. "Statistical Matching of Administrative and Survey Data," Sociological Methods & Research, , vol. 42(2), pages 192-224, May.
    2. 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.
    3. 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.
    4. Marissa B. Reitsma & Sherri Rose & Alex Reinhart & Jeremy D. Goldhaber-Fiebert & Joshua A. Salomon, 2024. "Bias-Adjusted Predictions of County-Level Vaccination Coverage from the COVID-19 Trends and Impact Survey," Medical Decision Making, , vol. 44(2), pages 175-188, February.
    5. Takis Merkouris, 2010. "Combining information from multiple surveys by using regression for efficient small domain estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(1), pages 27-48, January.
    6. Rasner, Anika & Frick, Joachim R. & Grabka, Markus M., 2013. "Statistical Matching of Administrative and Survey Data: An Application to Wealth Inequality Analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 42(2), pages 192-224.

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