Challenges in estimating HIV prevalence trends and geographical variation in HIV prevalence using antenatal data: insights from mathematical modelling

Johnson, Leigh; Kubjane, Mmamapudi; Eaton, Jeffrey

doi:https://www.dx.doi.org/10.1371/journal.pone.0242595

File(s)

pone.0242595.pdf (2.19 MB)

Published version

Author(s)

Johnson, Leigh

Kubjane, Mmamapudi

Eaton, Jeffrey

Type

Journal Article

Abstract

HIV prevalence data among pregnant women have been critical to estimating HIV trends and geographical patterns of HIV in many African countries. Although antenatal HIV prevalence data are known to be biased representations of HIV prevalence in the general population, mathematical models have made various adjustments to control for known sources of bias, including the effect of HIV on fertility, the age profile of pregnant women and sexual experience.<h4>Methods and findings</h4>We assessed whether assumptions about antenatal bias affect conclusions about trends and geographical variation in HIV prevalence, using simulated datasets generated by an agent-based model of HIV and fertility in South Africa. Results suggest that even when controlling for age and other previously-considered sources of bias, antenatal bias in South Africa has not been constant over time, and trends in bias differ substantially by age. Differences in the average duration of infection explain much of this variation. We propose an HIV duration-adjusted measure of antenatal bias that is more stable, which yields higher estimates of HIV incidence in recent years and at older ages. Simpler measures of antenatal bias, which are not age-adjusted, yield estimates of HIV prevalence and incidence that are too high in the early stages of the HIV epidemic, and that are less precise. Antenatal bias in South Africa is substantially greater in urban areas than in rural areas.<h4>Conclusions</h4>Age-standardized approaches to defining antenatal bias are likely to improve precision in model-based estimates, and further recency adjustments increase estimates of HIV incidence in recent years and at older ages. Incompletely adjusting for changing antenatal bias may explain why previous model estimates overstated the early HIV burden in South Africa. New assays to estimate the fraction of HIV-positive pregnant women who are recently infected could play an important role in better estimating antenatal bias.

Date Issued

2020-11-20

Date Acceptance

2020-11-05

Citation

PLoS One, 2020, 15 (11), pp.1-22

URI

http://hdl.handle.net/10044/1/85387

URL

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242595

DOI

https://www.dx.doi.org/10.1371/journal.pone.0242595

ISSN

1932-6203

Publisher

Public Library of Science (PLoS)

Start Page

1

End Page

22

Journal / Book Title

PLoS One

Volume

15

Issue

11

Copyright Statement

© 2020 Johnson et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.

License URL

http://creativecommons.org/licenses/by/4.0/

Sponsor

National Institutes of Health

UNAIDS

Medical Research Council (MRC)

Bill & Melinda Gates Foundation

Identifier

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242595

Grant Number

1R03AI125001-01A1

2017/778519

MR/R015600/1

2018/837458-0 PO202048522

Subjects

Science & Technology

Multidisciplinary Sciences

Science & Technology - Other Topics

POPULATION-BASED SURVEYS

SUB-SAHARAN AFRICA

CONTRACEPTIVE USE

PREGNANT-WOMEN

INFECTED WOMEN

SOUTH-AFRICA

PREVENTION

FERTILITY

HIV/AIDS

COHORT

Adolescent

Adult

Female

HIV Infections

Humans

Infectious Disease Transmission, Vertical

Middle Aged

Models, Theoretical

Pregnancy

Pregnancy Complications, Infectious

Prenatal Care

Prevalence

Rural Population

South Africa

Surveys and Questionnaires

Young Adult