A Bayesian analysis of current duration data with reporting issues: an application to estimating the distribution of time-between-sex from time-since-last-sex data as collected in cross-sectional surveys in low- and middle-income countries

TL;DR

This paper introduces a Bayesian statistical method to estimate the distribution of time-between-sex using cross-sectional survey data, addressing reporting issues and limited timing information common in low- and middle-income countries.

Contribution

It develops a flexible Bayesian current duration model that accounts for reporting issues and data limitations in estimating sexual activity patterns from survey data.

Findings

Effective estimation of TBS distribution from TSLS data.

Addresses reporting issues like rounding and unit differences.

Provides a detailed Bayesian modeling framework.

Abstract

Aggregate measures of family planning are used to monitor demand for and usage of contraceptive methods in populations globally, for example as part of the FP2030 initiative. Family planning measures for low- and middle-income countries are typically based on data collected through cross-sectional household surveys. Recently proposed measures account for sexual activity through assessment of the distribution of time-between-sex (TBS) in the population of interest. In this paper, we propose a statistical approach to estimate the distribution of TBS using data typically available in low- and middle-income countries, while addressing two major challenges. The first challenge is that timing of sex information is typically limited to women's time-since-last-sex (TSLS) data collected in the cross-sectional survey. In our proposed approach, we adopt the current duration method to estimate the distribution of TBS using the available TSLS data, from which the frequency of sex at the population level can be derived. Furthermore, the observed TSLS data are subject to reporting issues because they can be reported in different units and may be rounded off. To apply the current duration approach and account for these data reporting issues, we develop a flexible Bayesian model, and provide a detailed technical description of the proposed modeling approach.