IDEAS home Printed from https://ideas.repec.org/a/dem/demres/v47y2022i11.html
   My bibliography  Save this article

A probabilistic model for analyzing summary birth history data

Author

Listed:
  • Katherine Wilson

    (University of Washington)

  • Jon Wakefield

    (University of Washington)

Abstract

Background: There is an increasing demand for high-quality subnational estimates of under-5 mortality. In low- and middle-income countries, where the burden of under-5 mortality is concentrated, vital registration is often lacking, and household surveys, which provide full birth history data, are often the most reliable source. Unfortunately, these data are spatially sparse so data are pulled from other sources to increase the available information. Summary birth histories represent a large fraction of the available data and provide numbers of births and deaths aggregated over time, along with the mother’s age. Objective: Specialized methods are needed to leverage this information, and previously the Brass method and variants have been used. We wish to develop a model-based approach that can propagate errors and make the most efficient use of the data. Further, we strive to provide a method that does not have large computational overhead. Contribution: We describe a computationally efficient model-based approach that allows summary birth history and full birth history data to be combined into analyses of under-5 mortality in a natural way. The method is based on fertility and mortality models that allow smoothing over time and space, with the possibility for including relevant covariates associated with fertility and/or mortality. We first examine the behavior of the approach on simulated data before applying the model to household survey and census data from Malawi.

Suggested Citation

  • Katherine Wilson & Jon Wakefield, 2022. "A probabilistic model for analyzing summary birth history data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(11), pages 291-344.
    • Handle: RePEc:dem:demres:v:47:y:2022:i:11
    DOI: 10.4054/DemRes.2022.47.11
    as

    Download full text from publisher

    File URL: https://www.demographic-research.org/volumes/vol47/11/47-11.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.4054/DemRes.2022.47.11?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. Kristensen, Kasper & Nielsen, Anders & Berg, Casper W. & Skaug, Hans & Bell, Bradley M., 2016. "TMB: Automatic Differentiation and Laplace Approximation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i05).
    2. Andrea Verhulst, 2016. "Child mortality estimation: An assessment of summary birth history methods using microsimulation," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 34(39), pages 1075-1128.
    3. Katie Wilson & Jon Wakefield, 2021. "Child mortality estimation incorporating summary birth history data," Biometrics, The International Biometric Society, vol. 77(4), pages 1456-1466, December.
    4. Jon Pedersen & Jing Liu, 2012. "Child Mortality Estimation: Appropriate Time Periods for Child Mortality Estimates from Full Birth Histories," PLOS Medicine, Public Library of Science, vol. 9(8), pages 1-13, August.
    5. Eoghan Brady & Kenneth Hill, 2017. "Testing survey-based methods for rapid monitoring of child mortality, with implications for summary birth history data," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-10, April.
    6. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    7. Jon Wakefield, 2004. "Ecological inference for 2 × 2 tables (with discussion)," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 385-445, July.
    8. Leontine Alkema & Jin Rou New & Jon Pedersen & Danzhen You & all members of the UN Inter-agency Group for Child Mortality Estimation and its Technical Advisory Group, 2014. "Child Mortality Estimation 2013: An Overview of Updates in Estimation Methods by the United Nations Inter-Agency Group for Child Mortality Estimation," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-13, July.
    9. Jon Wakefield, 2004. "Ecological inference for 2 × 2 tables," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 385-425, July.
    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. Sebastien J-P. A. Haneuse & Jonathan C. Wakefield, 2007. "Hierarchical Models for Combining Ecological and Case–Control Data," Biometrics, The International Biometric Society, vol. 63(1), pages 128-136, March.
    2. Irene L. Hudson & Linda Moore & Eric J. Beh & David G. Steel, 2010. "Ecological inference techniques: an empirical evaluation using data describing gender and voter turnout at New Zealand elections, 1893–1919," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 185-213, January.
    3. Shuai Shao & Göran Kauermann, 2020. "Understanding price elasticity for airline ancillary services," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(1), pages 74-82, February.
    4. Jonathan Wakefield & Taylor Okonek & Jon Pedersen, 2020. "Small Area Estimation for Disease Prevalence Mapping," International Statistical Review, International Statistical Institute, vol. 88(2), pages 398-418, August.
    5. Puig, Xavier & Ginebra, Josep, 2014. "A cluster analysis of vote transitions," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 328-344.
    6. Rob Eisinga, 2009. "The beta‐binomial convolution model for 2×2 tables with missing cell counts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(1), pages 24-42, February.
    7. Katie Wilson & Jon Wakefield, 2021. "Child mortality estimation incorporating summary birth history data," Biometrics, The International Biometric Society, vol. 77(4), pages 1456-1466, December.
    8. Antonio Forcina & Davide Pellegrino, 2019. "Estimation of voter transitions and the ecological fallacy," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 1859-1874, July.
    9. Sebastien J.‐P. A. Haneuse & And Jonathan C. Wakefield, 2008. "The combination of ecological and case–control data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 73-93, February.
    10. Carolina Plescia & Lorenzo De Sio, 2018. "An evaluation of the performance and suitability of R × C methods for ecological inference with known true values," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 669-683, March.
    11. Nathan Kallus & Xiaojie Mao & Angela Zhou, 2022. "Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination," Management Science, INFORMS, vol. 68(3), pages 1959-1981, March.
    12. D. James Greiner & Kevin M. Quinn, 2009. "R×C ecological inference: bounds, correlations, flexibility and transparency of assumptions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 67-81, January.
    13. Arie ten Cate, 2014. "Maximum likelihood estimation of the Markov chain model with macro data and the ecological inference model," CPB Discussion Paper 284.rdf, CPB Netherlands Bureau for Economic Policy Analysis.
    14. Fatih Chellai, 2021. "Determinants of Under-Five Child Mortality in Arab Countries. Are the Effects Homogeneous Across Birth Order and Among Countries?," European Review of Applied Sociology, Sciendo, vol. 14(23), pages 34-49, December.
    15. Beh, Eric J., 2010. "The aggregate association index," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1570-1580, June.
    16. 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.
    17. Xiaohui Chang & Rasmus Waagepetersen & Herbert Yu & Xiaomei Ma & Theodore R. Holford & Rong Wang & Yongtao Guan, 2015. "Disease risk estimation by combining case–control data with aggregated information on the population at risk," Biometrics, The International Biometric Society, vol. 71(1), pages 114-121, March.
    18. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.
    19. Shreosi Sanyal & Thierry Rochereau & Cara Nichole Maesano & Laure Com-Ruelle & Isabella Annesi-Maesano, 2018. "Long-Term Effect of Outdoor Air Pollution on Mortality and Morbidity: A 12-Year Follow-Up Study for Metropolitan France," IJERPH, MDPI, vol. 15(11), pages 1-8, November.
    20. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.

    More about this item

    Keywords

    Bayesian hierarchical model; Brass method; Malawi; spatial smoothing; temporal smoothing;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

    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:dem:demres:v:47:y:2022:i:11. 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: Editorial Office (email available below). General contact details of provider: https://www.demogr.mpg.de/ .

    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.