IDEAS home Printed from https://ideas.repec.org/p/osf/osfxxx/egrc9.html
   My bibliography  Save this paper

Estimation and projection of probabilistic age- and sex-specific mortality rates across Brazilian municipalities between 2010 and 2030

Author

Listed:
  • Gonzaga, Marcos Roberto
  • Queiroz, Bernardo L

    (Universidade Federal de Minas Gerais, Brazil)

  • Monteiro da Silva, José H C
  • Lima, Everton
  • Júnio, Walter P. Silva
  • DIOGENES, VICTOR HUGO DIAS
  • Flores-Ortiz, Renzo
  • da Costa, Lilia Carolina Carneiro
  • Junior, Elzo Pereira Pinto
  • Ichihara, Maria Yury

Abstract

Background: Small area age- and sex-specific mortality rates are useful measures for population projections, health, economic, and social planning. Mortality rate estimation in small areas can be difficult due the low number of events/exposure. If a country’s mortality registration has problems, such as incomplete information, then estimating mortality rates can be even more difficult. Previous studies in Brazil have combined demographic and statistical methods to overcome these issues. These approaches depend on a gold standard for age-specific mortality rates and do not estimate uncertainties. We estimated age- and sex-specific mortality rates for all 5,565 Brazilian municipalities in 2010, and forecasted mortality rates between 2010 and 2030. Methods: We used the Tool for Projecting Age-Specific Rates Using Linear Splines (TOPALS) and a Bayesian model to estimate age- and sex-specific mortality rates in all Brazilian municipalities in 2010 while incorporating two types of uncertainties: low exposure and incomplete coverage of death counts. We adapted the Lee-Carter model to forecast age- and sex-specific mortality rates between 2010 and 2030 for all municipalities. Results: The proposed methodology was robust in adjusting for the mortality age profile and in estimating mortality rate uncertainties at the municipal level. The forecasted mortality rates indicated a convergence in life expectancy at birth, and variability of age at death across Brazil’s municipalities, with a persistent sex differential. Conclusion: We estimated and forecasted mortality rates in small areas with limited and incomplete death counts, and high mortality heterogeneity. The methodological approach applied could be useful for countries with death data quality problems similar to Brazil. Our results incorporated the main sources of uncertainty in estimating age- and sex-specific mortality rates and could be used as an important input for policy planning at the municipal level.

Suggested Citation

  • 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.
    • Handle: RePEc:osf:osfxxx:egrc9
    DOI: 10.31219/osf.io/egrc9
    as

    Download full text from publisher

    File URL: https://osf.io/download/6329e6836c24010acb50ab88/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/egrc9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Joseph E. Potter & Carl P. Schmertmann & Renato M. Assunção & Suzana M. Cavenaghi, 2010. "Mapping the Timing, Pace, and Scale of the Fertility Transition in Brazil," Population and Development Review, The Population Council, Inc., vol. 36(2), pages 283-307, June.
    3. 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.
    4. Ronald Lee & Timothy Miller, 2001. "Evaluating the performance of the lee-carter method for forecasting mortality," Demography, Springer;Population Association of America (PAA), vol. 38(4), pages 537-549, November.
    5. Matt Ruther & Stefan Leyk & Barbara Buttenfield, 2017. "Deriving Small Area Mortality Estimates Using a Probabilistic Reweighting Method," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1299-1314, November.
    6. Renato Assunção & Carl Schmertmann & Joseph Potter & Suzana Cavenaghi, 2005. "Empirical bayes estimation of demographic schedules for small areas," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 537-558, August.
    7. Jesús-Adrián Alvarez & José Manuel Aburto & Vladimir Canudas-Romo, 2020. "Latin American convergence and divergence towards the mortality profiles of developed countries," Population Studies, Taylor & Francis Journals, vol. 74(1), pages 75-92, January.
    8. Ronald Lee, 2000. "The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications," North American Actuarial Journal, Taylor & Francis Journals, vol. 4(1), pages 80-91.
    9. Pinheiro, Pedro Cisalpino & Queiroz, Bernardo L, 2018. "Regional Disparities in Brazilian Adult Mortality: an analysis using Modal Age at Death (M) and Compression of Mortality (IQR)," OSF Preprints t2ey3, Center for Open Science.
    10. Nico Keilman, 2019. "Mortality shifts and mortality compression in period and cohort life tables," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(40), pages 1147-1196.
    11. Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 575-594, August.
    12. Roger J. Marshall, 1991. "Mapping Disease and Mortality Rates Using Empirical Bayes Estimators," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(2), pages 283-294, June.
    13. Vladimir Canudas-Romo, 2008. "The modal age at death and the shifting mortality hypothesis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 19(30), pages 1179-1204.
    14. Andrés Villegas & Steven Haberman, 2014. "On the Modeling and Forecasting of Socioeconomic Mortality Differentials: An Application to Deprivation and Mortality in England," North American Actuarial Journal, Taylor & Francis Journals, vol. 18(1), pages 168-193.
    15. Eduardo Arriaga, 1984. "Measuring and explaining the change in life expectancies," Demography, Springer;Population Association of America (PAA), vol. 21(1), pages 83-96, February.
    16. Fabio Divino & Viviana Egidi & Michele Antonio Salvatore, 2009. "Geographical mortality patterns in Italy," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 20(18), pages 435-466.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lanza Queiroz, Bernardo & Lobo Alves Ferreira, Matheus, 2021. "The evolution of labor force participation and the expected length of retirement in Brazil," The Journal of the Economics of Ageing, Elsevier, vol. 18(C).
    2. 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.
    3. Adrian Raftery & Jennifer Chunn & Patrick Gerland & Hana Ševčíková, 2013. "Bayesian Probabilistic Projections of Life Expectancy for All Countries," Demography, Springer;Population Association of America (PAA), vol. 50(3), pages 777-801, June.
    4. Carlo Maccheroni & Samuel Nocito, 2017. "Backtesting the Lee–Carter and the Cairns–Blake–Dowd Stochastic Mortality Models on Italian Death Rates," Risks, MDPI, vol. 5(3), pages 1-23, July.
    5. Nan Li & Ronald Lee & Patrick Gerland, 2013. "Extending the Lee-Carter Method to Model the Rotation of Age Patterns of Mortality Decline for Long-Term Projections," Demography, Springer;Population Association of America (PAA), vol. 50(6), pages 2037-2051, December.
    6. Bergeron-Boucher, Marie-Pier & Vázquez-Castillo, Paola & Missov, Trifon, 2022. "A modal age at death approach to forecasting mortality," SocArXiv 5zr2k, Center for Open Science.
    7. Péter Vékás, 2020. "Rotation of the age pattern of mortality improvements in the European Union," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(3), pages 1031-1048, September.
    8. Queiroz, Bernardo L & Lima, Everton & Freire, Flávio & Gonzaga, Marcos Roberto, 2017. "Temporal and spatial estimates of adult mortality for small areas in Brazil, 1980-2010," OSF Preprints jk67t, Center for Open Science.
    9. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    10. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    11. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2023. "Thirty years on: A review of the Lee–Carter method for forecasting mortality," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1033-1049.
    12. Søren Kjærgaard & Yunus Emre Ergemen & Marie-Pier Bergeron Boucher & Jim Oeppen & Malene Kallestrup-Lamb, 2019. "Longevity forecasting by socio-economic groups using compositional data analysis," CREATES Research Papers 2019-08, Department of Economics and Business Economics, Aarhus University.
    13. Marie-Pier Bergeron-Boucher & Søren Kjærgaard & James E. Oeppen & James W. Vaupel, 2019. "The impact of the choice of life table statistics when forecasting mortality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(43), pages 1235-1268.
    14. Christina Bohk-Ewald & Marcus Ebeling & Roland Rau, 2017. "Lifespan Disparity as an Additional Indicator for Evaluating Mortality Forecasts," Demography, Springer;Population Association of America (PAA), vol. 54(4), pages 1559-1577, August.
    15. Debón, A. & Chaves, L. & Haberman, S. & Villa, F., 2017. "Characterization of between-group inequality of longevity in European Union countries," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 151-165.
    16. Henrik Brønnum-Hansen & Juan Carlos Albizu-Campos Espiñeira & Camila Perera & Ingelise Andersen, 2023. "Trends in mortality patterns in two countries with different welfare models: comparisons between Cuba and Denmark 1955–2020," Journal of Population Research, Springer, vol. 40(2), pages 1-28, June.
    17. 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.
    18. de Jong, Piet & Tickle, Leonie & Xu, Jianhui, 2020. "A more meaningful parameterization of the Lee–Carter model," Insurance: Mathematics and Economics, Elsevier, vol. 94(C), pages 1-8.
    19. repec:hum:wpaper:sfb649dp2009-015 is not listed on IDEAS
    20. Ahbab Mohammad Fazle Rabbi & Stefano Mazzuco, 2021. "Mortality Forecasting with the Lee–Carter Method: Adjusting for Smoothing and Lifespan Disparity," European Journal of Population, Springer;European Association for Population Studies, vol. 37(1), pages 97-120, March.
    21. David Blake & Marco Morales & Enrico Biffis & Yijia Lin & Andreas Milidonis, 2017. "Special Edition: Longevity 10 – The Tenth International Longevity Risk and Capital Markets Solutions Conference," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 84(S1), pages 515-532, April.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:osfxxx:egrc9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://osf.io/preprints/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.