Evaluating distributional regression strategies for modelling self-reported sexual age-mixing

  1. Timothy M Wolock  Is a corresponding author
  2. Seth Flaxman
  3. Kathryn A Risher
  4. Tawanda Dadirai
  5. Simon Gregson
  6. Jeff Eaton
  1. Imperial College London, United Kingdom
  2. Biomedical Research and Training Institute, Zimbabwe

Abstract

The age dynamics of sexual partnership formation determine patterns of sexually transmitted disease transmission and have long been a focus of researchers studying human immunodeficiency virus. Data on self-reported sexual partner age distributions are available from a variety of sources. We sought to explore statistical models that accurately predict the distribution of sexual partner ages over age and sex. We identified which probability distributions and outcome specifications best captured variation in partner age and quantified the benefits of modelling these data using distributional regression. We found that distributional regression with a sinh-arcsinh distribution replicated observed partner age distributions most accurately across three geographically diverse data sets. This framework can be extended with well-known hierarchical modelling tools and can help improve estimates of sexual age-mixing dynamics.

Data availability

Data from the Demographic and Health Surveys are available from the DHS Program website (https://dhsprogram.com/data/available-datasets.cfm). Data from the Africa Centre Demographic Information System are available on request from the AHRI website (https://data.ahri.org/index.php/home). Data from the Manicaland study were used with permission from the study investigators (http://www.manicalandhivproject.org/manicaland-data.html).

The following previously published data sets were used
    1. Gareta D
    2. Dube S
    3. Herbst K
    (2020) AHRI.PIP.Men's General Health.All.Release 2020-07
    AHRI Data Repository, doi: 10.23664/AHRI.PIP.RD04-99.MGH.ALL.202007.
    1. Gareta D
    2. Dube S
    3. Herbst K
    (2020) AHRI.PIP.Women's General Health.All.Release 2020-07
    AHRI Data Repository, doi: 10.23664/AHRI.PIP.RD03-99.WGH.ALL.202007.

Article and author information

Author details

  1. Timothy M Wolock

    Department of Mathematics, Imperial College London, London, United Kingdom
    For correspondence
    t.wolock18@imperial.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5898-1014
  2. Seth Flaxman

    Department of Mathematics, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2477-4217
  3. Kathryn A Risher

    Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9588-1693
  4. Tawanda Dadirai

    Manicaland Centre for Public Health Research, Biomedical Research and Training Institute, Harare, Zimbabwe
    Competing interests
    The authors declare that no competing interests exist.
  5. Simon Gregson

    Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Jeff Eaton

    Faculty of Medicine, School of Public Health, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

Funding

Bill and Melinda Gates Foundation (OPP1190661,OPP1164897)

  • Kathryn A Risher
  • Simon Gregson
  • Jeff Eaton

Medical Research Council (MR/R015600/1)

  • Simon Gregson
  • Jeff Eaton

National Institute of Allergy and Infectious Diseases (R01AI136664)

  • Jeff Eaton

Engineering and Physical Sciences Research Council (EP/V002910/1)

  • Seth Flaxman

Imperial College London (President's PhD Scholarship)

  • Timothy M Wolock

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Talía Malagón, McGill University, Canada

Ethics

Human subjects: We conducted secondary analysis of previously collected anonymised data in compliance with each data producer's use requirements. Procedures and questionnaires for standard DHS surveys have been reviewed and approved by the ICF International Institutional Review Board (IRB). The Manicaland study was approved by the Medical Research Council of Zimbabwe and the Imperial College Research Ethics Committee. The Africa Centre Demographic Information System PIP surveillance study was approved by Biomedical Research Ethics Committee, University of KwaZulu-Natal, South Africa (BE290/16).

Version history

  1. Received: March 11, 2021
  2. Accepted: June 23, 2021
  3. Accepted Manuscript published: June 24, 2021 (version 1)
  4. Version of Record published: July 7, 2021 (version 2)

Copyright

© 2021, Wolock et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. Timothy M Wolock
  2. Seth Flaxman
  3. Kathryn A Risher
  4. Tawanda Dadirai
  5. Simon Gregson
  6. Jeff Eaton
(2021)
Evaluating distributional regression strategies for modelling self-reported sexual age-mixing
eLife 10:e68318.
https://doi.org/10.7554/eLife.68318

Share this article

https://doi.org/10.7554/eLife.68318

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