Measuring the vulnerability of the Uruguayan population to vector-borne diseases via spatially hierarchical factor models

TL;DR

This paper introduces a novel spatially hierarchical factor model to accurately assess Uruguay's population vulnerability to vector-borne diseases, accounting for regional hierarchy and data aggregation effects.

Contribution

It develops a new class of spatially hierarchical models that improve vulnerability estimation by explicitly modeling regional hierarchy and comparing with traditional aggregation methods.

Findings

The proposed model outperforms standard approaches in accuracy.

Data aggregation can distort vulnerability rankings.

Hierarchical modeling captures regional differences more effectively.

Abstract

We propose a model-based vulnerability index of the population from Uruguay to vector-borne diseases. We have available measurements of a set of variables in the census tract level of the 19 Departmental capitals of Uruguay. In particular, we propose an index that combines different sources of information via a set of micro-environmental indicators and geographical location in the country. Our index is based on a new class of spatially hierarchical factor models that explicitly account for the different levels of hierarchy in the country, such as census tracts within the city level, and cities in the country level. We compare our approach with that obtained when data are aggregated in the city level. We show that our proposal outperforms current and standard approaches, which fail to properly account for discrepancies in the region sizes, for example, number of census tracts. We also show that data aggregation can seriously affect the estimation of the cities vulnerability rankings under benchmark models.