Revisiting assumptions about age-based mixing representations in mathematical models of sexually transmitted infections

Caleb W Easterly, Fernando Alarid-Escudero, Eva Enns, Shalini L Kulasingam

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND: Sexual mixing between heterogeneous population subgroups is an integral component of mathematical models of sexually transmitted infections (STIs). This study compares the fit of different mixing representations to survey data and the impact of different mixing assumptions on the predicted benefits of hypothetical human papillomavirus (HPV) vaccine strategies.
METHODS: We compared novel empirical (data-driven) age mixing structures with the more commonly-used assortative-proportionate (A-P) mixing structure. The A-P mixing structure assumes that a proportion of sexual contacts - known as the assortativity constant, typically estimated from survey data or calibrated - occur exclusively within one's own age group and the remainder mixes proportionately among all age groups. The empirical age mixing structure was estimated from the National Survey on Sexual Attitudes and Lifestyles 3 (Natsal-3) using regression methods, and the assortativity constant was estimated from Natsal-3 as well. Using a simplified HPV transmission model under each mixing assumption, we calibrated the model to British HPV16 prevalence data, then estimated the reduction in steady-state prevalence and the number of infections averted due to expanding HPV vaccination from 12- through 26-year-old females alone to 12-year-old males or 27- to 39-year-old females.
RESULTS: Empirical mixing provided a better fit to the Natsal-3 data than the best-fitting A-P structure. Using the model with empirical mixing as a reference, the model using the A-P structure often under- or over-estimated the benefits of vaccination, in one case overestimating by 2-fold the number of infections prevented due to extended female catch-up in a high vaccine uptake setting.
CONCLUSIONS: An empirical mixing structure more accurately represents sexual mixing survey data, and using the less accurate, yet commonly-used A-P structure has a notable effect on estimates of HPV vaccination benefits. This underscores the need for mixing structures that are less dependent on unverified assumptions and are directly informed by sexual behavior data.
Original languageEnglish (US)
Pages (from-to)5572-5579
Number of pages8
JournalVaccine
Volume36
Issue number37
DOIs
StatePublished - Sep 5 2018

Bibliographical note

Funding Information:
Mr. Easterly received financial support for this study from the Howard Hughes Medical Institute Data Science Fellowship through Macalester College . Dr. Alarid-Escudero received financial support for this study by a Doctoral Dissertation Fellowship from the Graduate School of the University of Minnesota as part of the doctoral program. Dr. Enns is supported by a grant from the National Institute for Allergy and Infectious Diseases of the National Institutes of Health under award number K25AI118476 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding agencies had no role in the design of the study, interpretation of results, or writing of the manuscript. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report.

Publisher Copyright:
© 2018 Elsevier Ltd

Keywords

  • Human papillomavirus
  • Mathematical modelling
  • Sexual behavior
  • Sexual mixing
  • Sexually transmitted infections

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