Characterizing vulnerabilities to climate change across the United States.

Climate change will cause a range of related risks, including increases in infectious and chronic disease, intensified social and economic stresses, and more frequent extreme weather events. Vulnerable groups will be disproportionately affected due to greater exposure to climate risks and lower ability to prepare, adapt, and recover from their effects. Better understanding of the intersection of vulnerability and climate change risks is required to identify the most important drivers of future climate risks and effectively build resilience and deploy targeted adaptation efforts. Incorporating community stakeholder input, we identified and integrated available public health, social, economic, environmental, and climate data in the United States (U.S.), comprising 184 indicators, to develop a Climate Vulnerability Index (CVI) composed of four baseline vulnerabilities (health, social/economic, infrastructure, and environment) and three climate change risks (health, social/economic, extreme events). We find that the vulnerability to and risks from climate change are highly heterogeneous across the U.S. at the census tract scale, and geospatially cluster into complementary areas with similar climate risks but differing baseline vulnerabilities. Our results therefore demonstrate that not only are climate change risks both broadly and variably distributed across the U.S., but also that existing disparities are often further exacerbated by climate change. The CVI thus lays a data-driven, scientific foundation for future research on the intersection of climate change risks with health and other inequalities, while also identifying health impacts of climate change as the greatest research gap. Moreover, given U.S. government initiatives surrounding climate and equity, the CVI can be instrumental in empowering communities and policymakers to better prioritize resources and target interventions, providing a template for addressing local-scale climate and environmental justice globally.

Predictors of residential mobility and its impact on air pollution exposure among children diagnosed with early childhood leukemia.

Epidemiology studies relying on one address to assign exposures over time share common methodological limitations in failing to account for mobility that may introduce potential exposure misclassification. Using Texas birth certificate and cancer registry data, we identified predictors of residential mobility among mothers of children diagnosed with early childhood leukemia in Texas from 1995 to 2011. We used U.S. Environmental Protection Agency (EPA) National Air Toxics Assessment data to estimate residential levels of benzene and 1,3-butadiene based on addresses at birth and diagnosis and applied mixed-effects ordinal logistic regression models to evaluate differences in exposure classification between the two time periods. In total, 55% of children moved from time of birth to diagnosis, although they generally did not move far (median distance moved was 8 km). Predictors of mobility, at delivery, included younger age, being unmarried and living in neighborhoods with high benzene levels, and, at diagnosis, increasing child's age and living in neighborhoods with low poverty rates. We observed that the odds of being assigned to a higher exposure quartile at diagnosis relative to the time of birth decreased by 31% for 1,3-butadiene (OR = 0.69, 95% CI 0.59-0.82) and by 12% for benzene (OR = 0.88, 95% CI 0.75, 1.05).

Air toxics and early childhood acute lymphocytic leukemia in Texas, a population based case control study.

BACKGROUND: Traffic exhaust, refineries and industrial facilities are major sources of air toxics identified by the U.S. Environmental Protection Agency (U.S. EPA) for their potential risk to human health. In utero and early life exposures to air toxics such as benzene and 1,3-butadiene, which are known leukemogens in adults, may play an etiologic role in childhood leukemia that comprises the majority of pediatric cancers. We conducted a population based case-control study to examine individual effects of benzene, 1,3-butadiene and polycyclic organic matter (POM) in ambient residential air on acute lymphocytic leukemia (ALL) diagnosed in children under age 5 years in Texas from 1995-2011. METHODS: Texas Cancer Registry cases were linked to birth records and then were frequency matched by birth month and year to 10 population-based controls. Maternal and infant characteristics from birth certificates were abstracted to obtain information about potential confounders. Modelled estimates of benzene, 1,3-butadiene and POM exposures at the census tract level were assigned by linking geocoded maternal addresses from birth certificates to U.S. EPA National-Scale Air Toxics Assessment data for single and co-pollutant statistical analyses. Mixed-effects logistic regression models were applied to evaluate associations between air toxics and childhood leukemia. RESULTS: In adjusted single pollutant models, odds of childhood leukemia among mothers with the highest ambient air exposures compared to those in the lowest quartile were 1.11 (95 % CI: 0.94-1.32) for POM, 1.17 (95 % CI: 0.98-1.39) for benzene and 1.29 (95 % CI: 1.08-1.52) for 1,3-butadiene. In co-pollutant models, odds ratios for childhood leukemia remained elevated for 1,3-butadiene but were close to the null value for benzene and POM. CONCLUSIONS: We observed positive associations between 1,3-butadiene and childhood leukemia in single and co-pollutant models whereas effect estimates from single pollutant models were diminished for benzene and POM in co-pollutant models. Early life exposure to 1,3-butadiene rather than benzene or POM appears to increase early childhood risk of acute lymphocytic leukemia.