51. Methodological considerations in screening for cumulative environmental health impacts: lessons learned from a pilot study in California
- Author
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George V. Alexeeff, Laura Meehan August, Lara Cushing, John B. Faust, and Lauren Zeise
- Subjects
Health, Toxicology and Mutagenesis ,Population ,lcsh:Medicine ,Pilot Projects ,Risk Assessment ,Sensitivity and Specificity ,California ,Article ,Environmental health ,Humans ,education ,environmental justice ,Environmental justice ,community health ,education.field_of_study ,cumulative impacts ,cumulative risk assessment ,Geography ,Impact assessment ,business.industry ,lcsh:R ,Environmental resource management ,Public Health, Environmental and Occupational Health ,Environmental exposure ,Environmental Exposure ,Disparate system ,Socioeconomic Factors ,Data quality ,Community health ,Environmental Pollutants ,Public Health ,Psychology ,business ,Risk assessment ,Environmental Health - 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.
- Published
- 2012