Estimating the prevalence of anemia rates among children under five in Peruvian districts with a small sample size

TL;DR

This study evaluates the effectiveness of basic and spatial Fay-Herriot models in estimating anemia prevalence among Peruvian children under five, especially in districts with small sample sizes, and explores optimal neighbor selection methods.

Contribution

It demonstrates how spatial Fay-Herriot models can improve anemia prevalence estimates in small samples and investigates neighbor selection impacts.

Findings

Spatial models improve estimates in small samples

Neighbor selection affects inference accuracy

Method provides reliable estimates for policy use

Abstract

In this paper we attempt to answer the following question: ``Is it possible to obtain reliable estimates for the prevalence of anemia rates in children under five years in the districts of Peru?'' Specifically, the interest of the present paper is to understand to which extent employing the basic and the spatial Fay-Herriot models can compensate for inadequate sample size in most of the sampled districts, and whether the way of choosing the spatial neighbors has an impact on the resulting inference. Furthermore, it is raised the question of how to choose an optimal way to define the neighbours. We present an illustrative analysis using the data from the Demographic and Family Health Survey of the year 2019, and the National Census carried out in 2017.