Evaluating distributional regression strategies for modelling self-reported sexual age-mixing
- Imperial College London, United Kingdom
- 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).
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AHRI.PIP.Men's General Health.All.Release 2020-07AHRI Data Repository, doi: 10.23664/AHRI.PIP.RD04-99.MGH.ALL.202007.
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AHRI.PIP.Women's General Health.All.Release 2020-07AHRI Data Repository, doi: 10.23664/AHRI.PIP.RD03-99.WGH.ALL.202007.
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Haiti Enquête Mortalité, Morbidité et Utilisation des Services 2016-2017 - EMMUS-VI [Dataset]The DHS Program, Haiti: Standard DHS, 2016-17.
Article and author information
Author details
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.
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).
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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Evaluating distributional regression strategies for modelling self-reported sexual age-mixing
eLife 10:e68318.
https://doi.org/10.7554/eLife.68318
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