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Spatial Non-stationarity in Opioid Prescribing Rates: Evidence from Older Medicare Part D Beneficiaries

Author

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  • Seulki Kim

    (University at Albany, State University of New York)

  • Carla Shoff

    (Centers for Medicare & Medicaid Services)

  • Tse-Chuan Yang

    (University at Albany, State University of New York)

Abstract

Previous research that examined spatial patterns of opioid prescribing rates and factors associated with them has mainly relied on a global modeling perspective, overlooking the potential spatial non-stationarity embedded in these associations. In this study, we investigate whether there are spatially non-stationary associations between opioid prescribing rates and key characteristics of older Medicare Part D beneficiaries and their prescribers using several data sources from the Centers for Medicare and Medicaid Services. All measures are aggregated to the ZIP code-level and a total sample size of 18,126 ZIP codes is included in the analyses. Our descriptive results from geographically weighted regression and the Monte Carlo significance test suggest that most of the associations between the characteristics of beneficiaries and prescribers and opioid prescribing rates are spatially non-stationary. Our findings not only challenge the conventional analytic approach by highlighting the importance of a local modeling perspective in opioid prescribing research, but also offer nuanced insight into how opioid prescribing rates are related to possible determinants across space.

Suggested Citation

  • Seulki Kim & Carla Shoff & Tse-Chuan Yang, 2021. "Spatial Non-stationarity in Opioid Prescribing Rates: Evidence from Older Medicare Part D Beneficiaries," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 40(2), pages 127-136, April.
    • Handle: RePEc:kap:poprpr:v:40:y:2021:i:2:d:10.1007_s11113-019-09566-7
    DOI: 10.1007/s11113-019-09566-7
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    References listed on IDEAS

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    1. Antonio Páez & Takashi Uchida & Kazuaki Miyamoto, 2002. "A General Framework for Estimation and Inference of Geographically Weighted Regression Models: 1. Location-Specific Kernel Bandwidths and a Test for Locational Heterogeneity," Environment and Planning A, , vol. 34(4), pages 733-754, April.
    2. Grubesic, Tony H., 2008. "Zip codes and spatial analysis: Problems and prospects," Socio-Economic Planning Sciences, Elsevier, vol. 42(2), pages 129-149, June.
    3. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    4. Stephen Matthews & Tse-Chuan Yang, 2012. "Mapping the results of local statistics," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 26(6), pages 151-166.
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    Cited by:

    1. Sauer, Jeffery & Stewart, Kathleen, 2023. "Geographic information science and the United States opioid overdose crisis: A scoping review of methods, scales, and application areas," Social Science & Medicine, Elsevier, vol. 317(C).

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