Abstract
Background Two required inputs to mathematical models of sexually transmitted infections are the average duration in epidemiological risk states (e.g., selling sex) and the average rates of sexual partnership change. These variables are often only available as aggregate estimates from published cross-sectional studies, and may be subject to distributional, sampling, censoring, and measurement biases.
Methods We explore adjustments for these biases using aggregate estimates of duration in sex work and numbers of reported sexual partners from a published 2011 survey of female sex worker in Eswatini. We develop adjustments from first principles, and construct Bayesian hierarchical models to reflect our mechanistic assumptions about the bias-generating processes.
Results We show that different mechanisms of bias for duration in sex work may “cancel out” by acting in opposite directions, but that failure to consider some mechanisms could over- or underestimate duration in sex work by factors approaching 2. We also show that conventional interpretations of sexual partner numbers are biased due to implicit assumptions about partnership duration, but that unbiased estimators of partnership change rate can be defined that explicitly incorporate a given partnership duration. We highlight how the unbiased estimator is most important when the survey recall period and partnership duration are similar in length.
Conclusions While we explore these bias adjustments using a particular dataset, and in the context of deriving inputs for mathematical modelling, we expect that our approach and insights would be applicable to other datasets and motivations for quantifying sexual behaviour data.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
The study was supported by: the Natural Sciences and Engineering Research Council of Canada (NSERC CGS-D); the Ontario Ministry of Colleges and Universities and the University of Toronto (QEII‐GSST); the Canadian Institutes of Health Research Foundation Grant (FN-13455); the National Institute of Allergy and Infectious Diseases (R01AI170249).
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
We used only aggregate data from the following published study: Stefan Baral et al. "Reconceptualizing the HIV epidemiology and prevention needs of female sex workers (FSW) in Swaziland". PLOS ONE 9.12 (2014) e115465. http://doi.org/10.1371/journal.pone.0115465
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Footnotes
Funding The study was supported by: the Natural Sciences and Engineering Research Council of Canada (NSERC CGS-D); the Ontario Ministry of Colleges and Universities and the University of Toronto (QEII-GSST); the Canadian Institutes of Health Research Foundation Grant (FN-13455); the National Institute of Allergy and Infectious Diseases (R01AI170249).
Data & Code https://github.com/mishra-lab/hidden-bias-sex-data
Data Availability
All analysis code is available on GitHub, from which all results can be reproduced.