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Rationale: Avoiding excess health damages attributable to climate change is a primary motivator for policy interventions to reduce greenhouse gas emissions. However, the health benefits of climate mitigation, as included in the policy assessment process, have been estimated without much input from health experts. Objectives: In accordance with recommendations from the National Academies in a 2017 report on approaches to update the social cost of greenhouse gases (SC-GHG), an expert panel of 26 health researchers and climate economists gathered for a virtual technical workshop in May 2021 to conduct a systematic review and meta-analysis and recommend improvements to the estimation of health impacts in economic-climate models. Methods: Regionally resolved effect estimates of unit increases in temperature on net all-cause mortality risk were generated through random-effects pooling of studies identified through a systematic review. Results: Effect estimates and associated uncertainties varied by global region, but net increases in mortality risk associated with increased average annual temperatures (ranging from 0.1% to 1.1% per 1°C) were estimated for all global regions. Key recommendations for the development and utilization of health damage modules were provided by the expert panel and included the following: not relying on individual methodologies in estimating health damages; incorporating a broader range of cause-specific mortality impacts; improving the climate parameters available in economic models; accounting for socioeconomic trajectories and adaptation factors when estimating health damages; and carefully considering how air pollution impacts should be incorporated in economic-climate models. Conclusions: This work provides an example of how subject-matter experts can work alongside climate economists in making continued improvements to SC-GHG estimates.

We greatly appreciate the alternative framing and call to action posed by Ivey, Pruitt, and Garcia (1) in response to our commentary. (2) We agree that the available evidence strongly [...]

BACKGROUND: Recently enacted environmental justice policies in the United States at the state and federal level emphasize addressing place-based inequities, including persistent disparities in air pollution exposure and associated health impacts. Advances in air quality measurement, models, and analytic methods have demonstrated the importance of finer-scale data and analysis in accurately quantifying the extent of inequity in intraurban pollution exposure, although the necessary degree of spatial resolution remains a complex and context-dependent question. OBJECTIVE: The objectives of this commentary were to a) discuss ways to maximize and evaluate the effectiveness of efforts to reduce air pollution disparities, and b) argue that environmental regulators must employ improved methods to project, measure, and track the distributional impacts of new policies at finer geographic and temporal scales. DISCUSSION: The historic federal investments from the Inflation Reduction Act, the Infrastructure Investment and Jobs Act, and the Biden Administration's commitment to Justice40 present an unprecedented opportunity to advance climate and energy policies that deliver real reductions in pollution-related health inequities. In our opinion, scientists, advocates, policymakers, and implementing agencies must work together to harness critical advances in air quality measurements, models, and analytic methods to ensure success. https://doi.org/ 10.1289/EHP13063, Introduction Twenty-three states and the federal government have enacted environmental justice policies since the 1990s, but only recently have significant resources been allocated at both state and federal levels. Currently, [...]

BackgroundEnvironmental heat exposure is a public health concern. The impacts of environmental heat on mortality and morbidity at the population scale are well documented, but little is known about specific exposures that individuals experience.ObjectivesThe first objective of this work was to catalyze discussion of the role of personal heat exposure information in research and risk assessment. The second objective was to provide guidance regarding the operationalization of personal heat exposure research methods.DiscussionWe define personal heat exposure as realized contact between a person and an indoor or outdoor environment that poses a risk of increases in body core temperature and/or perceived discomfort. Personal heat exposure can be measured directly with wearable monitors or estimated indirectly through the combination of time-activity and meteorological data sets. Complementary information to understand individual-scale drivers of behavior, susceptibility, and health and comfort outcomes can be collected from additional monitors, surveys, interviews, ethnographic approaches, and additional social and health data sets. Personal exposure research can help reveal the extent of exposure misclassification that occurs when individual exposure to heat is estimated using ambient temperature measured at fixed sites and can provide insights for epidemiological risk assessment concerning extreme heat.ConclusionsPersonal heat exposure research provides more valid and precise insights into how often people encounter heat conditions and when, where, to whom, and why these encounters occur. Published literature on personal heat exposure is limited to date, but existing studies point to opportunities to inform public health practice regarding extreme heat, particularly where fine-scale precision is needed to reduce health consequences of heat exposure. https://doi.org/10.1289/EHP556.

This study investigated changes in glycemic control following a small increase in time spent outdoors. Women participants with type 2 diabetes (N = 46) wore an iBUTTON temperature monitor and a pedometer for 1 week and recorded their morning fasting blood glucose (FBG) daily. They went about their normal activities for 2 days (baseline) and were asked to add 30 min of time outdoors during Days 3-7 (intervention). Linear mixed effects models were used to test whether morning FBG values were different on days following intervention versus baseline days, and whether steps and/or heat exposure changed. Results were stratified by indicators of good versus poor glycemic control prior to initiation of the study. On average, blood glucose was reduced by 6.1 mg/dL (95% CI - 11.5, - 0.6) on mornings after intervention days after adjusting for age, BMI, and ambient weather conditions. Participants in the poor glycemic control group (n = 16) experienced a 15.8 mg/dL decrease (95% CI - 27.1, - 4.5) in morning FBG on days following the intervention compared to a 1.6 mg/dL decrease (95%CI - 7.7, 4.5) for participants in the good glycemic control group (n = 30). Including daily steps or heat exposure did not attenuate the association between intervention and morning FBG. The present study suggests spending an additional 30 min outdoors may improve glycemic control; however, further examination with a larger sample over a longer duration and determination of mediators of this relationship is warranted., Author(s): Molly B. Richardson [sup.1] , Courtney Chmielewski [sup.2] , Connor Y. H. Wu [sup.3] , Mary B. Evans [sup.4] , Leslie A. McClure [sup.5] , Kathryn W. Hosig [sup.1] [...]

ABSTRACT To reduce possible ecological impact from oil spill events, the chemical dispersant Corexit 9500A (C-9500A) is approved for widespread application. Interactions with changing environmental factors such as temperature are [...]

Machine learning was applied to predict evacuation rates for all census tracts affected by Hurricane Laura. The evacuation ground truth was derived from cellular telephone–based mobility data. Twitter data, census data, geographical data, COVID-19 case rates, the social vulnerability index from the Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR), and relevant weather and physical data were used to do the prediction. Random forests were found to perform well, with a mean absolute percent error of 4.9% on testing data. Feature importance for prediction was analyzed using Shapley additive explanations and it was found that previous evacuation, rainfall forecasts, COVID-19 case rates, and Twitter data rank highly in terms of importance. Social vulnerability indices were also found to show a very consistent relationship with evacuation rates, such that higher vulnerability consistently implies lower evacuation rates. These findings can help with hurricane evacuation preparedness and planning as well as real-time assessment. Significance Statement: This study evaluates the usefulness of Twitter data, COVID-19 case rates, and the social vulnerability index from the Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry in predicting evacuation rates during Hurricane Laura, in the context of other relevant geographic, and weather-related variables. All three are found to be useful, to different extents, and this work suggests important directions for future research in understanding the reasons behind their relevance to predicting evacuation rates. [ABSTRACT FROM AUTHOR]