Abstract

Environmental exposures often occur in tandem; however, epidemiological research often focuses on singular exposures. Statistical interactions among broad, well-characterized environmental domains have not yet been evaluated in association with health. We address this gap by conducting a county-level cross-sectional analysis of interactions between Environmental Quality Index (EQI) domain indices on preterm birth in the Unites States from 2000 to 2005. The EQI, a county-level index constructed for the 2000-2005 time period, was constructed from five domain-specific indices (air, water, land, built, and sociodemographic) using principal component analyses. County-level preterm birth rates (n = 3141) were estimated using live births from the National Center for Health Statistics. Linear regression was used to estimate prevalence differences (PDs) and 95% confidence intervals (CIs) comparing worse environmental quality to the better quality for each model for (a) each individual domain main effect, (b) the interaction contrast, and (c) the two main effects plus interaction effect (i.e., the "net effect") to show departure from additivity for the all U.S. counties. Analyses were also performed for subgroupings by four urban/rural strata. We found the suggestion of antagonistic interactions but no synergism, along with several purely additive (i.e., no interaction) associations. In the non-stratified model, we observed antagonistic interactions, between the sociodemographic/air domains [net effect (i.e., the association, including main effects and interaction effects) PD: -0.004 (95% CI: -0.007, 0.000), interaction contrast: -0.013 (95% CI: -0.020, -0.007)] and built/air domains [net effect PD: 0.008 (95% CI 0.004, 0.011), interaction contrast: -0.008 (95% CI: -0.015, -0.002)]. Most interactions were between the air domain and other respective domains. Interactions differed by urbanicity, with more interactions observed in non-metropolitan regions. Observed antagonistic associations may indicate that those living in areas with multiple detrimental domains may have other interfering factors reducing the burden of environmental exposure. This study is the first to explore interactions across different environmental domains and demonstrates the utility of the EQI to examine the relationship between environmental domain interactions and human health. While we did observe some departures from additivity, many observed effects were additive. This study demonstrated that interactions between environmental domains should be considered in future analyses.

Highlights

  • Environmental exposures such as pollutants, social factors, and built environment likely have a collective influence on health; epidemiological research often focuses on singular exposures

  • While the Environmental Quality Index (EQI) allows researchers to consider multiple environmental constructs simultaneously, no published studies have examined potential interactions between environmental domains in the assessment of human health. We address this current gap in the literature by conducting a county-level cross-sectional analysis of interactions between EQI domain indices on preterm birth (PTB) in the Unites States from 2000 to 2005

  • Results are presented as prevalence differences (PDs) and 95% confidence interval (CI) comparing worse environmental quality to the better quality for each model for (a) each individual domain main effect, (b) the interaction contrast, and (c) the two main effects plus interaction effect to show departure from additive interaction. This net effect is the cumulative association between the two interacting domains on PTB prevalence

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Summary

Introduction

Environmental exposures such as pollutants, social factors, and built environment likely have a collective influence on health; epidemiological research often focuses on singular exposures. This may be in part due to the complexity of measuring multiple environmental factors in tandem [1, 2]. Some research, including air pollution studies, has used indices or decomposition methods such as principal component analysis (PCA) to assess the simultaneous impact of different pollutants [3].

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