Methodological considerations in screening for cumulative environmental health impacts: lessons learned from a pilot study in California.

Int J Environ Res Public Health

Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, 1515 Clay Street, 16th Floor, Oakland, CA 94612, USA.

Published: August 2012

AI Article Synopsis

  • Polluting facilities and hazardous sites tend to be located in low-income communities of color, which already experience multiple health stressors, highlighting a need to factor socioeconomic status into risk assessments.
  • A pilot study introduces a screening method that assesses cumulative impacts by evaluating pollution burdens alongside community characteristics, aiming to highlight areas needing more focus for environmental justice.
  • The method evaluates five components—exposures, public health effects, environmental effects, sensitive populations, and socioeconomic factors—while addressing challenges in data integration and methodological choices to ensure the robustness of the assessment.

Article Abstract

Polluting facilities and hazardous sites are often concentrated in low-income communities of color already facing additional stressors to their health. The influence of socioeconomic status is not considered in traditional models of risk assessment. We describe a pilot study of a screening method that considers both pollution burden and population characteristics in assessing the potential for cumulative impacts. The goal is to identify communities that warrant further attention and to thereby provide actionable guidance to decision- and policy-makers in achieving environmental justice. The method uses indicators related to five components to develop a relative cumulative impact score for use in comparing communities: exposures, public health effects, environmental effects, sensitive populations and socioeconomic factors. Here, we describe several methodological considerations in combining disparate data sources and report on the results of sensitivity analyses meant to guide future improvements in cumulative impact assessments. We discuss criteria for the selection of appropriate indicators, correlations between them, and consider data quality and the influence of choices regarding model structure. We conclude that the results of this model are largely robust to changes in model structure.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3499854PMC
http://dx.doi.org/10.3390/ijerph9093069DOI Listing

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