Bayesian Statistical Modeling in Action for Estimation and Forecasting in Low- and Middle-income Countries: The Case of the Family Planning Estimation Tool

TL;DR

This paper describes the Bayesian statistical model behind the Family Planning Estimation Tool, which provides estimates and forecasts of contraceptive use in low- and middle-income countries, incorporating recent updates and validation results.

Contribution

It introduces recent improvements to the FPET Bayesian model, including handling contraceptive transitions and error-prone survey data, and evaluates its performance.

Findings

FPET is reasonably well calibrated.

Model effectively incorporates survey and service statistics data.

Recent updates improve estimation accuracy.

Abstract

The Family Planning Estimation Tool (FPET) is used in low- and middle-income countries to produce estimates and short-term forecasts of family planning indicators, such as modern contraceptive use and unmet need for contraceptives. Estimates are obtained via a Bayesian statistical model that is fitted to country-specific data from surveys and service statistics data. The model has evolved over the last decade based on user inputs. In this paper we summarize the main features of the statistical model used in FPET and introduce recent updates related to capturing contraceptive transitions, fitting to survey data that may be error prone, and the use of service statistics data. We assess model performance through a validation exercise and find that FPET is reasonably well calibrated. We use our experience with FPET to briefly discuss lessons learned and open challenges related to the broader field of statistical modeling for monitoring of demographic and global health indicators.