Construction of an environmental quality index for public health research

Lynne C Messer,Kristen M Rappazzo,Jyotsna S Jagai,Danelle T Lobdell

doi:10.1186/1476-069x-13-39

Abstract

BackgroundA more comprehensive estimate of environmental quality would improve our understanding of the relationship between environmental conditions and human health. An environmental quality index (EQI) for all counties in the U.S. was developed.MethodsThe EQI was developed in four parts: domain identification; data source acquisition; variable construction; and data reduction. Five environmental domains (air, water, land, built and sociodemographic) were recognized. Within each domain, data sources were identified; each was temporally (years 2000–2005) and geographically (county) restricted. Variables were constructed for each domain and assessed for missingness, collinearity, and normality. Domain-specific data reduction was accomplished using principal components analysis (PCA), resulting in domain-specific indices. Domain-specific indices were then combined into an overall EQI using PCA. In each PCA procedure, the first principal component was retained. Both domain-specific indices and overall EQI were stratified by four rural–urban continuum codes (RUCC). Higher values for each index were set to correspond to areas with poorer environmental quality.ResultsConcentrations of included variables differed across rural–urban strata, as did within-domain variable loadings, and domain index loadings for the EQI. In general, higher values of the air and sociodemographic indices were found in the more metropolitan areas and the most thinly populated areas have the lowest values of each of the domain indices. The less-urbanized counties (RUCC 3) demonstrated the greatest heterogeneity and range of EQI scores (−4.76, 3.57) while the thinly populated strata (RUCC 4) contained counties with the most positive scores (EQI score ranges from −5.86, 2.52).ConclusionThe EQI holds promise for improving our characterization of the overall environment for public health. The EQI describes the non-residential ambient county-level conditions to which residents are exposed and domain-specific EQI loadings indicate which of the environmental domains account for the largest portion of the variability in the EQI environment. The EQI was constructed for all counties in the United States, incorporating a variety of data to provide a broad picture of environmental conditions. We undertook a reproducible approach that primarily utilized publically-available data sources.

Highlights

A more comprehensive estimate of environmental quality would improve our understanding of the relationship between environmental conditions and human health
The Environmental Quality Index was constructed for all counties in the United States and incorporates a wide variety of data to provide a broad picture of environmental conditions in the United States
Future development of the environmental quality index (EQI) includes assessing the consequences of the variable choices through sensitivity analyses, updating for 2006–2010, and exploring other levels of spatial aggregation

Summary

Introduction

A more comprehensive estimate of environmental quality would improve our understanding of the relationship between environmental conditions and human health. Polluted environments have contributed to harmful exposures associated with human morbidity [1,2,3,4,5]. The empirical characterization of environmental conditions, is challenging because the non-residential ambient environment comprises an almost uncountable array of complex mixtures, which are difficult to quantify simultaneously. The effect of the surrounding environment on human morbidity is more broadly understood to include exposures such as socioeconomic deprivation, access to healthy food, highway safety, etc. Environment often encompasses traditional exposure like pollutants, chemicals, and water quality, as well as other non-genetic exposures such as the built environment, nutrition, and socioeconomic climate. Still other work includes entire environmental domains to estimate non-residential ambient conditions (e.g., socioeconomic deprivation) [10,11]. If ever, are multiple environmental domains combined, even though we know humans are exposed to these multiple environmental domains simultaneously

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Discussion

Conclusion