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.

Nathan Ford and co-authors discuss global priorities in the provision of HIV prevention and treatment services.

OBJECTIVE: A recent HIV outbreak in a rural network of persons who inject drugs (PWID) underscored the intersection of the expanding epidemics of opioid abuse, unsterile injection drug use (IDU), and associated increases in hepatitis C virus (HCV) infections. We sought to identify US communities potentially vulnerable to rapid spread of HIV, if introduced, and new or continuing high rates of HCV infections among PWID. DESIGN: We conducted a multistep analysis to identify indicator variables highly associated with IDU. We then used these indicator values to calculate vulnerability scores for each county to identify which were most vulnerable. METHODS: We used confirmed cases of acute HCV infection reported to the National Notifiable Disease Surveillance System, 2012-2013, as a proxy outcome for IDU, and 15 county-level indicators available nationally in Poisson regression models to identify indicators associated with higher county acute HCV infection rates. Using these indicators, we calculated composite index scores to rank each county's vulnerability. RESULTS: A parsimonious set of 6 indicators were associated with acute HCV infection rates (proxy for IDU): drug-overdose deaths, prescription opioid sales, per capita income, white, non-Hispanic race/ethnicity, unemployment, and buprenorphine prescribing potential by waiver. Based on these indicators, we identified 220 counties in 26 states within the 95th percentile of most vulnerable. CONCLUSIONS: Our analysis highlights US counties potentially vulnerable to HIV and HCV infections among PWID in the context of the national opioid epidemic. State and local health departments will need to further explore vulnerability and target interventions to prevent transmission.