Small Area Estimation of Fertility in Low- and Middle-Income Countries

TL;DR

This paper develops small-area estimation methods to produce accurate, high-resolution fertility estimates in low- and middle-income countries, addressing data sparsity and guiding localized health interventions.

Contribution

It introduces Bayesian hierarchical models and other techniques for subnational fertility estimation using survey data, improving spatial granularity and reliability.

Findings

District-level fertility estimates for Madagascar are generated.

Model-based approaches outperform direct estimates in accuracy.

Spatiotemporal smoothing enhances estimate reliability.

Abstract

Accurate fertility estimates at fine spatial resolution are essential for localized public health planning, particularly in low- and middle-income countries (LMICs). While national-level indicators such as age-specific fertility rates (ASFR) and total fertility rate (TFR) are often reported through official statistics, they lack the spatial granularity needed to guide targeted interventions. To address this, we develop a framework for subnational fertility estimation using small-area estimation (SAE) techniques applied to birth history data from household surveys, in particular Demographic and Health Surveys (DHS). Disaggregation by geographic area, time period, and maternal age group leads to significant data sparsity, limiting the reliability of direct estimates at fine scales. To overcome this, we propose a suite of methods, including direct estimators, area-level and unit-level Bayesian hierarchical models, to produce accurate estimates across varying spatial resolutions. The model-based approaches incorporate spatiotemporal smoothing and integrate covariates such as maternal education, contraceptive use and urbanicity. Using data from the 2021 Madagascar DHS, we generate district-level ASFR and TFR estimates and evaluate model performance through cross-validation.