Enhancing the use of family planning service statistics using a Bayesian modelling approach to inform estimates of modern contraceptive use in low- and middle-income countries

TL;DR

This paper introduces a Bayesian hierarchical model that incorporates service statistics-derived estimates of modern contraceptive use to improve the accuracy and timeliness of family planning indicator estimates in low- and middle-income countries.

Contribution

It presents a novel Bayesian approach to integrate EMU data with survey data, accounting for uncertainty and enhancing mCPR estimation accuracy.

Findings

Improved predictive performance of family planning indicators.

Inclusion of EMU data enhances monitoring of contraceptive use.

Validated approach with country case studies.

Abstract

Monitoring family planning indicators, such as modern contraceptive prevalence rate (mCPR), is essential for family planning programming. The Family Planning Estimation Tool (FPET) uses survey data to estimate and forecast family planning indicators, including mCPR, over time. However, sole reliance on large-scale surveys, carried out on average every 3-5 years, can lead to data gaps. Service statistics are a readily available data source, routinely collected in conjunction with service delivery. Various service statistics data types can be used to derive a family planning indicator called Estimated Modern Use (EMU). In a number of countries, annual rates of change in EMU have been found to be predictive of true rates of change in mCPR. However, it has been challenging to capture the varying levels of uncertainty associated with the EMU indicator across different countries and service statistics data types and to subsequently quantify this uncertainty when using EMU in FPET. We present a new approach to using EMUs in FPET to inform mCPR estimates, using annual EMU rates of change as input, and accounting for uncertainty associated with the EMU derivation process. The approach also considers additional country-type-specific uncertainty. We assess the EMU type-specific uncertainty at the country level, via a Bayesian hierarchical modelling approach. Validation results and anonymised country-level case studies highlight improved predictive performance and provide insights into the impact of including EMU data on mCPR estimates compared to using survey data alone. Together, they demonstrate that EMUs can help countries monitor progress toward their family planning goals more effectively.