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A Flexible Bayesian Model for Estimating Subnational Mortality

Author

Listed:
  • Monica Alexander

    (University of California)

  • Emilio Zagheni

    (University of Washington)

  • Magali Barbieri

    (University of California
    Institut National d’Études Démographiques)

Abstract

Reliable subnational mortality estimates are essential in the study of health inequalities within a country. One of the difficulties in producing such estimates is the presence of small populations among which the stochastic variation in death counts is relatively high, and thus the underlying mortality levels are unclear. We present a Bayesian hierarchical model to estimate mortality at the subnational level. The model builds on characteristic age patterns in mortality curves, which are constructed using principal components from a set of reference mortality curves. Information on mortality rates are pooled across geographic space and are smoothed over time. Testing of the model shows reasonable estimates and uncertainty levels when it is applied both to simulated data that mimic U.S. counties and to real data for French départements. The model estimates have direct applications to the study of subregional health patterns and disparities.

Suggested Citation

  • Monica Alexander & Emilio Zagheni & Magali Barbieri, 2017. "A Flexible Bayesian Model for Estimating Subnational Mortality," Demography, Springer;Population Association of America (PAA), vol. 54(6), pages 2025-2041, December.
    • Handle: RePEc:spr:demogr:v:54:y:2017:i:6:d:10.1007_s13524-017-0618-7
    DOI: 10.1007/s13524-017-0618-7
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    References listed on IDEAS

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    Cited by:

    1. Qian Lu & Katja Hanewald & Xiaojun Wang, 2021. "Subnational Mortality Modelling: A Bayesian Hierarchical Model with Common Factors," Risks, MDPI, vol. 9(11), pages 1-21, November.
    2. Carl P. Schmertmann & Marcos R. Gonzaga, 2018. "Bayesian Estimation of Age-Specific Mortality and Life Expectancy for Small Areas With Defective Vital Records," Demography, Springer;Population Association of America (PAA), vol. 55(4), pages 1363-1388, August.
    3. Zoe Gibbs & Chris Groendyke & Brian Hartman & Robert Richardson, 2020. "Modeling County-Level Spatio-Temporal Mortality Rates Using Dynamic Linear Models," Risks, MDPI, vol. 8(4), pages 1-15, November.
    4. Bernard Baffour & James Raymer, 2019. "Estimating multiregional survivorship probabilities for sparse data: An application to immigrant populations in Australia, 1981–2011," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(18), pages 463-502.
    5. Samuel J. Clark, 2019. "A General Age-Specific Mortality Model With an Example Indexed by Child Mortality or Both Child and Adult Mortality," Demography, Springer;Population Association of America (PAA), vol. 56(3), pages 1131-1159, June.
    6. Florian Bonnet, 2020. "Computations of French lifetables by department, 1901–2014," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 42(26), pages 741-762.
    7. Gonzaga, Marcos Roberto & Queiroz, Bernardo L & Monteiro da Silva, José H C & Lima, Everton & Júnio, Walter P. Silva & DIOGENES, VICTOR HUGO DIAS & Flores-Ortiz, Renzo & da Costa, Lilia Carolina Carne, 2022. "Estimation and projection of probabilistic age- and sex-specific mortality rates across Brazilian municipalities between 2010 and 2030," OSF Preprints egrc9, Center for Open Science.
    8. Herbert Susmann & Monica Alexander & Leontine Alkema, 2022. "Temporal Models for Demographic and Global Health Outcomes in Multiple Populations: Introducing a New Framework to Review and Standardise Documentation of Model Assumptions and Facilitate Model Compar," International Statistical Review, International Statistical Institute, vol. 90(3), pages 437-467, December.
    9. Jie Liu & Qiu Tang & Wei Qiu & Jun Ma & Junfeng Duan, 2021. "Probability-Based Failure Evaluation for Power Measuring Equipment," Energies, MDPI, vol. 14(12), pages 1-16, June.
    10. Alexander, Monica, 2022. "Decomposing dimensions of mortality inequality," SocArXiv uqwxj, Center for Open Science.
    11. Monica Alexander & Kivan Polimis & Emilio Zagheni, 2022. "Combining Social Media and Survey Data to Nowcast Migrant Stocks in the United States," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(1), pages 1-28, February.
    12. Tom Wilson & Irina Grossman & Monica Alexander & Phil Rees & Jeromey Temple, 2022. "Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(3), pages 865-898, June.
    13. Queiroz, Bernardo L & Gonzaga, Marcos Roberto & Nogales, Ana Maria & Torrente, Bruno & de Abreu, Daisy Maria Xavier, 2019. "Life expectancy, adult mortality and completeness of death counts in Brazil and regions: comparative analysis of IHME, IBGE and other researchers estimates of levels and trends," OSF Preprints pj3sx, Center for Open Science.
    14. Alexander, Monica, 2018. "Deaths without denominators: using a matched dataset to study mortality patterns in the United States," SocArXiv q79ye, Center for Open Science.

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