Temporal Models for Demographic and Global Health Outcomes in Multiple Populations: Introducing the Normal-with-Optional-Shrinkage Data Model Class

TL;DR

This paper introduces the NOS data model class, a statistical framework that combines multiple data sources with varying reporting issues to improve estimates of demographic and health indicators, accounting for uncertainties and outliers.

Contribution

The paper presents the normal-with-optional-shrinkage (NOS) data model class, a novel approach for integrating diverse data sources with robust outlier handling in demographic and health estimates.

Findings

The NOS model improves estimates of contraceptive use globally.

It effectively accounts for sampling errors and observational uncertainties.

The model demonstrates robustness to outliers in survey data.

Abstract

Statistical models are used to produce estimates of demographic and global health indicators in populations with limited data. Such models integrate multiple data sources to produce estimates and forecasts with uncertainty based on model assumptions. Model assumptions can be divided into assumptions that describe latent trends in the indicator of interest versus assumptions on the data generating process of the observed data, conditional on the latent process value. Focusing on the latter, we introduce a class of data models that can be used to combine data from multiple sources with various reporting issues. The proposed data model accounts for sampling errors and differences in observational uncertainty based on survey characteristics. In addition, the data model employs horseshoe priors to produce estimates that are robust to outlying observations. We refer to the data model class as the normal-with-optional-shrinkage (NOS) set up. We illustrate the use of the NOS data model for the estimation of modern contraceptive use and other family planning indicators at the national level for countries globally, using survey data.