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Additive interaction between heterogeneous environmental quality domains (air, water, land, sociodemographic and built environment) on preterm birth
Citation:
Grabich, S., K. Rappazzo, C. Gray, J. Jagai, Y. Jian, L. Messer, AND D. Lobdell. Additive interaction between heterogeneous environmental quality domains (air, water, land, sociodemographic and built environment) on preterm birth. Frontiers in Public Health. Frontiers, Lausanne, Switzerland, 4:232, (2016).
Impact/Purpose:
This manuscript is a methodologic study which expands on a previously published study from our group investigating Environmental Quality Index and preterm birth (Rappazzo et al. (2015)). The associations between environmental quality and preterm birth in the United States, 2000-2005: a cross-sectional analysis. Environmental Health 14 (1), 50). The Environmental Quality Index (EQI), created originally through SHC projects 1.2.2.3 (continues with new projects 2.64 and 2.62) was used as a measure of overall environmental quality in this study. The research is intended to support EPA and external community efforts to better understand the relationship between environmental exposures and health effects, and to provide information that can be used by communities to make decisions.
Description:
BACKGROUND 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 2000-2005.METHODS: 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 (PD) and 95% confidence intervals (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 (i.e. the “net effect”) to show departure from additive interaction for the all U.S counties. Analyses were also performed for subgroupings by four urban/rural strata. RESULTS: 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 between 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. CONCLUSIONS: Observed antagonist 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.
