Skip to main content

Advertisement

SpringerLink
Log in

A Flexible Bayesian Model for Estimating Subnational Mortality

  • Published:
Demography

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. Throughout this article, we refer to the Y px as principal components for simplicity, even though technically Y px is the pth vector of principal component loadings.

  2. Data on age-specific mortality rates are available through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) tool (https://wonder.cdc.gov/).

  3. Personal communication to Magali Barbieri by the Division des statistiques régionales, locales et urbaines, INSEE (May 28, 2013). We chose to use French départements data because at the time of writing, data for all U.S. counties were not readily available.

  4. See shiny.demog.berkeley.edu/monicah/French/.

References

  • Alkema, L., & New, J. R. (2014). Global estimation of child mortality using a Bayesian B-spline bias-reduction method. Annals of Applied Statistics, 8, 2122–2149.

    Article  Google Scholar 

  • Alkema, L., Raftery, A. E., Gerland, P., Clark, S. J., Pelletier, F., Buettner, T., & Heilig, G. K. (2012). Probabilistic projections of the total fertility rate for all countries. Demography, 48, 815–839.

    Article  Google Scholar 

  • Barbieri, M. (2013). Mortality in France by département. Population, 68, 375–417.

    Article  Google Scholar 

  • Bijak, J. (2008). Bayesian methods in international migration forecasting. In J. Raymer & F. Willekens (Eds.), International migration in Europe: Data, models and estimates (pp. 253–281). Chichester, UK: John Wiley & Sons.

    Google Scholar 

  • Bravo, J. M., & Malta, J. (2010). Estimating life expectancy in small population areas. In EROSTAT–European Commission (Ed.), Work session on demographic projections: EUROSTAT methodologies and working papers (2010 ed., pp. 113–126). Luxembourg City, Luxembourg: Publications Office of the European Union.

  • Chetty, R., Stepner, M., Abraham, S., Lin, S., Scuderi, B., Turner, N., . . . Cutler, D. (2016). The association between income and life expectancy in the United States, 2001–2014. JAMA, 315, 1750–1766.

  • Congdon, P. (2009). Life expectancies for small areas: A Bayesian random effects methodology. International Statistical Review, 77, 222–240.

    Article  Google Scholar 

  • Congdon, P. (2014). Estimating life expectancies for US small areas: A regression framework. Journal of Geographical Systems, 16, 1–18.

    Article  Google Scholar 

  • Currie, J., & Schwandt, H. (2016). Mortality inequality: The good news from a county-level approach. Journal of Economic Perspectives, 30(2), 29–52.

    Article  Google Scholar 

  • Dwyer-Lindgren, L., Bertozzi-Villa, A., Stubbs, R. W., Morozoff, C., Kutz, M. J., Huynh, C., . . . Flaxman, A. D. (2016). US county-level trends in mortality rates for major causes of death, 1980–2014. JAMA, 316, 2385–2401.

  • Ezzati, M., Friedman, A. B., Kulkarni, S. C., & Murray, C. J. L. (2008). The reversal of fortunes: Trends in county mortality and cross-county mortality disparities in the United States. PLoS Medicine, 5, e66. doi:10.1371/journal.pmed.0050066

    Article  Google Scholar 

  • Gelman, A., & Rubin, D. (1992). Inference from iterative simulation using multiple sequences. Statistical Science, 7, 457–511.

    Article  Google Scholar 

  • Girosi, F., & King, G. (2008). Demographic forecasting. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Jarner, S. F., & Kryger, E. M. (2011). Modelling adult mortality in small populations: The SAINT model. ASTIN Bulletin, 41, 377–418.

    Google Scholar 

  • Kindig, D. A., & Cheng, E. R. (2013). Even as mortality fell in most US counties, female mortality nonetheless rose in 42.8 percent of counties from 1992 to 2006. Health Affairs, 32, 451–458.

    Article  Google Scholar 

  • King, G., & Soneji, S. (2011). The future of death in America, Demographic Research, 25(article 1), 1–38. doi:10.4054/DemRes.2011.25.1

  • Kulkarni, S. C., Levin-Rector, A., Ezzati, M., & Murray, C. J. (2011). Falling behind: Life expectancy in US counties from 2000 to 2007 in an international context. Population Health Metrics, 9, 16. doi:10.1186/1478-7954-9-16

    Article  Google Scholar 

  • Lee, R. D., & Carter, L. R. (1992). Modeling and forecasting U.S. mortality. Journal of the American Statistical Association, 87, 659–675.

    Google Scholar 

  • Macintyre, S., Ellaway, A., & Cummins, S. (2002). Place effects on health: How can we conceptualise, operationalise and measure them? Social Science & Medicine, 55, 125–139.

    Article  Google Scholar 

  • Plummer, M. (2003, March). JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling. Paper presented at the Third International Workshop on Distributed Statistical Computing, Vienna, Austria. Retrieved from https://www.r-project.org/conferences/DSC-2003/Proceedings/

  • Raftery, A. E., Chunn, J. L., Gerland, P., & Sevcikova, H. (2013). Bayesian probabilistic projections of life expectancy for all countries. Demography, 50, 777–801.

    Article  Google Scholar 

  • Raftery, A. E., Li, N., Sevcikova, H., Gerland, P., & Heilig, G. K. (2012). Bayesian probabilistic population projections for all countries. Proceedings of the National Academy of Sciences of the USA, 109, 13915–13921.

  • Schmertmann, C., Zagheni, E., Goldstein, J., & Myrskyla, M. (2014). Bayesian forecasting of cohort fertility. Journal of the American Statistical Association, 109, 500–513.

    Article  Google Scholar 

  • Sharrow, D. J., Clark, S. J., Collinson, M. A., Kahn, K., & Tillman, S. M. (2013). The age pattern of increases in mortality affected by HIV: Bayesian fit of the Heligman-Pollard Model to data from the Agincourt HDSS field site in rural northeast South Africa. Demographic Research, 29(article 39), 1039–1096. doi:10.4054/DemRes.2013.29.39

    Article  Google Scholar 

  • Srebotnjak, T., Mokdad, A. H., & Murray, C. J. L. (2010). A novel framework for validating and applying standardized small area measurement strategies. Population Health Metrics, 8, 26. doi:10.1186/1478-7954-8-26

    Article  Google Scholar 

  • U.S. Census Bureau. (2015). County population totals tables (Vintage 2014). Retrieved from https://www.census.gov/programs-surveys/popest/data/tables.2014.html

Download references

Acknowledgments

This study was made possible by an award from the Center on the Economics and Demography of Aging (National Institute on Aging grant P30-AG012839) at the University of California, Berkeley. However, any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors alone and do not necessarily represent the official views of the National Institute on Aging. The authors would like to thank two anonymous reviewers for helpful comments on an earlier version of this manuscript.

Author information

Authors and Affiliations

  1. Department of Demography, University of California, Berkeley, 2232 Piedmont Avenue, Berkeley, CA, 94720-2120, USA

    Monica Alexander & Magali Barbieri

  2. Department of Sociology, University of Washington, Seattle, 211 Savery Hall, Box 353340, Seattle, WA, 98195-3340, USA

    Emilio Zagheni

  3. Institut National d’Études Démographiques, 133 Boulevard Dabout, 75020, Paris Cedex, France

    Magali Barbieri

Authors
  1. Monica Alexander
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emilio Zagheni
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Magali Barbieri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Monica Alexander.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alexander, M., Zagheni, E. & Barbieri, M. A Flexible Bayesian Model for Estimating Subnational Mortality. Demography 54, 2025–2041 (2017). https://doi.org/10.1007/s13524-017-0618-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13524-017-0618-7

Keywords

Search

Navigation