Small Area Estimation Methods for Multivariate Health and Demographic Outcomes using Complex Survey Data

TL;DR

This paper introduces multivariate small area estimation models for complex survey data to improve the accuracy of health and demographic indicators at fine geographical scales, aiding targeted policy interventions.

Contribution

It develops shared component models that jointly estimate multiple related outcomes from complex survey data, enhancing estimate precision over univariate methods.

Findings

Models outperform univariate approaches in simulations.

Application to Kenyan survey data shows improved estimates.

Joint modeling benefits are demonstrated across health indicators.

Abstract

Improving health in the most disadvantaged populations requires reliable estimates of health and demographic indicators to inform policy and interventions. Low- and middle-income countries with the largest burden of disease and disability tend to have the least comprehensive data, relying primarily on household surveys. Subnational estimates are increasingly used to inform targeted interventions and health policies. Producing reliable estimates from these data at fine geographical scales requires statistical modeling, and small area estimation models are commonly used in this context. Although most current methods model univariate outcomes, improved estimates may be attained by borrowing strength across related outcomes via multivariate modeling. In this paper, we develop classes of area- and unit-level multivariate shared component models using complex survey data. This framework jointly models multiple outcomes to improve accuracy of estimates compared to separately fitting univariate models. We conduct simulation studies to validate the methodology and use the proposed approach on survey data from Kenya in 2014; first, to jointly model height-for-age and weight-for-age in children, and second, to model three categories of contraceptive use in women. These models produce improved estimates compared to univariate and naive multivariate modeling approaches.