IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v13y2021i1d10.1007_s12561-020-09282-3.html
   My bibliography  Save this article

Joint Spatial Modelling of Childhood Morbidity in West Africa Using a Distributional Bivariate Probit Model

Author

Listed:
  • Ezra Gayawan

    (Federal University of Technology)

  • Funmilayo Adenike Fadiji

    (African Institute for Mathematical Sciences)

Abstract

Fever and cough are early symptoms of infectious and non-infectious diseases among children. They have been studied using different univariate regression techniques, but there is evidence that the two health outcomes are correlated. This study aims at jointly estimating the spatial variability in the levels of, and correlation between the two outcomes in multiple West African countries. We adopt a structured additive distributional biprobit model allowing for every parameter of the response distribution, in this case, means and correlation, to be simultaneously related to covariates. Results show that regions with huge burden of fever are also having burden of cough among children. In particular, there appears a tie in the pattern of the illnesses that transcends geographical boundaries particularly among regions of Ivory Coast, Burkina-Faso, Ghana and Mali. In addition, the correlations between the two outcomes are significant for children of rich households and those who possess mosquito bed nets.

Suggested Citation

  • Ezra Gayawan & Funmilayo Adenike Fadiji, 2021. "Joint Spatial Modelling of Childhood Morbidity in West Africa Using a Distributional Bivariate Probit Model," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 56-76, April.
    • Handle: RePEc:spr:stabio:v:13:y:2021:i:1:d:10.1007_s12561-020-09282-3
    DOI: 10.1007/s12561-020-09282-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-020-09282-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12561-020-09282-3?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Habibov, Nazim N., 2011. "On the socio-economic determinants of antenatal care utilization in Azerbaijan: evidence and policy implications for reforms," Health Economics, Policy and Law, Cambridge University Press, vol. 6(2), pages 175-203, April.
    2. Nadja Klein & Thomas Kneib & Stephan Klasen & Stefan Lang, 2015. "Bayesian structured additive distributional regression for multivariate responses," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 64(4), pages 569-591, August.
    3. Smith, Michael & Kohn, Robert, 2000. "Nonparametric seemingly unrelated regression," Journal of Econometrics, Elsevier, vol. 98(2), pages 257-281, October.
    4. Brezger, Andreas & Lang, Stefan, 2006. "Generalized structured additive regression based on Bayesian P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 967-991, February.
    5. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
    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. Alexander März & Nadja Klein & Thomas Kneib & Oliver Musshoff, 2016. "Analysing farmland rental rates using Bayesian geoadditive quantile regression," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 43(4), pages 663-698.
    2. Klein, Nadja & Denuit, Michel & Lang, Stefan & Kneib, Thomas, 2013. "Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape," LIDAM Discussion Papers ISBA 2013045, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Simon N. Wood & Natalya Pya & Benjamin Säfken, 2016. "Smoothing Parameter and Model Selection for General Smooth Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1548-1563, October.
    4. Alexander Silbersdorff & Kai Sebastian Schneider, 2019. "Distributional Regression Techniques in Socioeconomic Research on the Inequality of Health with an Application on the Relationship between Mental Health and Income," IJERPH, MDPI, vol. 16(20), pages 1-28, October.
    5. Belitz, Christiane & Lang, Stefan, 2008. "Simultaneous selection of variables and smoothing parameters in structured additive regression models," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 61-81, September.
    6. Marra, Giampiero & Radice, Rosalba, 2017. "Bivariate copula additive models for location, scale and shape," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 99-113.
    7. Kneib, Thomas & Silbersdorff, Alexander & Säfken, Benjamin, 2023. "Rage Against the Mean – A Review of Distributional Regression Approaches," Econometrics and Statistics, Elsevier, vol. 26(C), pages 99-123.
    8. Samson B. Adebayo & Ezra Gayawan, 2022. "A Bivariate Analysis of the Spatial Distributions of Stunting and Wasting Among Children Under-Five in Nigeria," Journal of Development Policy and Practice, , vol. 7(1), pages 31-52, January.
    9. Nikolaus Umlauf & Nadja Klein & Achim Zeileis, 2017. "BAMLSS: Bayesian Additive Models for Location, Scale and Shape (and Beyond)," Working Papers 2017-05, Faculty of Economics and Statistics, Universität Innsbruck.
    10. Maike Hohberg & Francesco Donat & Giampiero Marra & Thomas Kneib, 2021. "Beyond unidimensional poverty analysis using distributional copula models for mixed ordered‐continuous outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1365-1390, November.
    11. Thaden, Hauke & Klein, Nadja & Kneib, Thomas, 2019. "Multivariate effect priors in bivariate semiparametric recursive Gaussian models," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 51-66.
    12. Paul F. V. Wiemann & Thomas Kneib & Julien Hambuckers, 2024. "Using the softplus function to construct alternative link functions in generalized linear models and beyond," Statistical Papers, Springer, vol. 65(5), pages 3155-3180, July.
    13. Stefan Lang & Nikolaus Umlauf & Peter Wechselberger & Kenneth Harttgen & Thomas Kneib, 2012. "Multilevel structured additive regression," Working Papers 2012-07, Faculty of Economics and Statistics, Universität Innsbruck.
    14. Muschinski, Thomas & Mayr, Georg J. & Simon, Thorsten & Umlauf, Nikolaus & Zeileis, Achim, 2024. "Cholesky-based multivariate Gaussian regression," Econometrics and Statistics, Elsevier, vol. 29(C), pages 261-281.
    15. Umlauf, Nikolaus & Adler, Daniel & Kneib, Thomas & Lang, Stefan & Zeileis, Achim, 2015. "Structured Additive Regression Models: An R Interface to BayesX," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i21).
    16. Nadja Klein & Michel Denuit & Stefan Lang & Thomas Kneib, 2013. "Nonlife Ratemaking and Risk Management with Bayesian Additive Models for Location, Scale and Shape," Working Papers 2013-24, Faculty of Economics and Statistics, Universität Innsbruck.
    17. Nadja Klein & Torsten Hothorn & Luisa Barbanti & Thomas Kneib, 2022. "Multivariate conditional transformation models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 116-142, March.
    18. Thomas Kneib & Nadja Klein & Stefan Lang & Nikolaus Umlauf, 2019. "Modular regression - a Lego system for building structured additive distributional regression models with tensor product interactions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 1-39, March.
    19. Thomas Kneib & Nikolaus Umlauf, 2017. "A Primer on Bayesian Distributional Regression," Working Papers 2017-13, Faculty of Economics and Statistics, Universität Innsbruck.
    20. Klein, Nadja & Denuit, Michel & Lang, Stefan & Kneib, Thomas, 2014. "Nonlife ratemaking and risk management with Bayesian generalized additive models for location, scale, and shape," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 225-249.

    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:spr:stabio:v:13:y:2021:i:1:d:10.1007_s12561-020-09282-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.