RESUMO
After a chemical fire, an investigation assessed health effects by using syndromic surveillance to monitor emergency department (ED) visits, a general health survey to assess the general public, and a first responders health survey to assess first responders. A total of four separate multivariable logistic regression models were developed to examine associations between reported exposure to smoke, dust, debris, or odor with any reported symptom in the general public. Syndromic surveillance identified areas with increased ED visits. Among general health survey respondents, 45.1% (911 out of 2,020) reported at least one symptom. Respondents reporting exposure to smoke, dust, debris, or odor had 4.5 (95% confidence interval (CI) [3.7, 5.5]), 4.6 (95% CI [3.6, 5.8]), 2.0 (95% CI [1.7, 2.5]), or 5.8 (95% CI [4.7, 7.3]) times the odds of reporting any symptom compared with respondents not reporting exposure to smoke, dust, debris, or odor, respectively. First responders commonly reported contact with material and being within 1 mi of the fire ≥5 hr; 10 out of 31 of first responders reported at least one symptom. There was high symptom burden reported after the fire. Results from our investigation might assist the directing of public health resources to effectively address immediate community needs and prepare for future incidents.
RESUMO
Critical to identifying the risk of environmentally driven disease is an understanding of the cumulative impact of environmental conditions on human health. Here we describe the methodology used to develop an environmental burden index (EBI). The EBI is calculated at U.S. census tract level, a finer scale than many similar national-level tools. EBI scores are also stratified by tract land cover type as per the National Land Cover Database (NLCD), controlling for urbanicity. The EBI was developed over the course of four stages: 1) literature review to identify potential indicators, 2) data source acquisition and indicator variable construction, 3) index creation, and 4) stratification by land cover type. For each potential indicator, data sources were assessed for completeness, update frequency, and availability. These indicators were: (1) particulate matter (PM2.5), (2) ozone, (3) Superfund National Priority List (NPL) locations, (4) Toxics Release Inventory (TRI) facilities, (5) Treatment, Storage, and Disposal (TSD) facilities, (6) recreational parks, (7) railways, (8) highways, (9) airports, and (10) impaired water sources. Indicators were statistically normalized and checked for collinearity. For each indicator, we computed and summed percentile ranking scores to create an overall ranking for each tract. Tracts having the same plurality of land cover type form a 'peer' group. We re-ranked the tracts into percentiles within each peer group for each indicator. The percentile scores were combined for each tract to obtain a stratified EBI. A higher score reveals a tract with increased environmental burden relative to other tracts of the same peer group. We compared our results to those of related indices, finding good convergent validity between the overall EBI and CalEnviroScreen 4.0. The EBI has many potential applications for research and use as a tool to develop public health interventions at a granular scale.
