Power outages threaten public health. While outages will likely increase with climate change, an aging electrical grid, and increased energy demand, little is known about their frequency and distribution within states. Here, we characterize 2018–2020 outages, finding an average of 520 million customer-hours total without power annually across 2447 US counties (73.7% of the US population). 17,484 8+ hour outages (a medically-relevant duration with potential health consequences) and 231,174 1+ hour outages took place, with greatest prevalence in Northeastern, Southern, and Appalachian counties. Arkansas, Louisiana, and Michigan counties experience a dual burden of frequent 8+ hour outages and high social vulnerability and prevalence of electricity-dependent durable medical equipment use. 62.1% of 8+ hour outages co-occur with extreme weather/climate events, particularly heavy precipitation, anomalous heat, and tropical cyclones. Results could support future large-scale epidemiology studies, inform equitable disaster preparedness and response, and prioritize geographic areas for resource allocation and interventions.

Purpose of Review: The volume of public health environmental justice (EJ) research produced by academic institutions increased through 2022. However, the methods used for evaluating EJ in exposure science and epidemiologic studies have not been catalogued. Here, we completed a scoping review of EJ studies published in 19 environmental science and epidemiologic journals from 2018 to 2021 to summarize research types, frameworks, and methods. Recent Findings: We identified 402 articles that included populations with health disparities as a part of EJ research question and met other inclusion criteria. Most studies (60%) evaluated EJ questions related to socioeconomic status (SES) or race/ethnicity. EJ studies took place in 69 countries, led by the US (n = 246 [61%]). Only 50% of studies explicitly described a theoretical EJ framework in the background, methods, or discussion and just 10% explicitly stated a framework in all three sections. Among exposure studies, the most common area-level exposure was air pollution (40%), whereas chemicals predominated personal exposure studies (35%). Overall, the most common method used for exposure-only EJ analyses was main effect regression modeling (50%); for epidemiologic studies the most common method was effect modification (58%), where an analysis evaluated a health disparity variable as an effect modifier. Summary: Based on the results of this scoping review, current methods in public health EJ studies could be bolstered by integrating expertise from other fields (e.g., sociology), conducting community-based participatory research and intervention studies, and using more rigorous, theory-based, and solution-oriented statistical research methods.

Under long-standing threat of seasonal influenza outbreaks, it remains imperative to understand the drivers of influenza dynamics which can guide mitigation measures. While the role of absolute humidity and temperature is extensively studied, the possibility of ambient ozone (O₃) as an environmental driver of influenza has received scant attention. Here, using state-level data in the USA during 2010–2015, we examined such research hypothesis. For rigorous causal inference by evidence triangulation, we applied 3 distinct methods for data analysis: Convergent Cross Mapping from state-space reconstruction theory, Peter-Clark-momentary-conditional-independence plus as graphical modeling algorithms, and regression-based Generalised Linear Model. The negative impact of ambient O₃ on influenza activity at 1-week lag is consistently demonstrated by those 3 methods. With O₃ commonly known as air pollutant, the novel findings here on the inhibition effect of O₃ on influenza activity warrant further investigations to inform environmental management and public health protection.

In the United States, severe weather-driven power outages are increasing in frequency and duration, likely with health consequences. Previous studies examined individual severe weather events (e.g., heatwaves) and focused on large outages in metropolitan areas. Here, we described nationwide spatiotemporal patterns of individual (e.g., tropical cyclone alone) and simultaneous (e.g., tropical cyclone + anomalous heat) severe weather events co-occurring with 8+ hour outages. We used hourly county-level PowerOutage.us data from 2018–2020 to define 8+ hour outages as whenever the daily proportion of customers without power was ≥0.1% for ≥8 continuous hours. We conducted analyses at the daily and county (county-day) level and identified county-days with severe weather events, including anomalous cold, anomalous heat, anomalous precipitation, snowfall, tropical cyclones, and wildfire. Of 1,657 counties with reliable outage data, 1,205 (72.7%) experienced an 8+ hour outage co-occurring with an individual severe weather event, and 904 (54.6%) with multiple simultaneous severe weather events. Anomalous precipitation events co-occurring with outages were the most common, affecting 1,170 (70.6%) counties. These outages concentrated along the Gulf Coast, the Northeast, Michigan, and Southern California. Co-occurrence with anomalous heat happened the second most frequently, affecting 839 (50.6%) counties, mostly in Southeastern states. Among all county-days with a severe weather event, tropical cyclones–though rarer and primarily affecting the Eastern Seaboard–co-occurred with an 8+ hour outage 24% of the time. On the West Coast, wildfires were increasingly likely to co-occur with weather-related outages from 2018–2020. Among multiple simultaneous weather events, 8+ hour outages co-occurred with anomalous precipitation-anomalous heat on 1,155 county-days in 40 states, anomalous precipitation-tropical cyclone on 705 county-days in 24 states, and anomalous cold-snowfall on 259 county-days in 27 states. Our results can help guide efforts to strengthen the electricity grid, prepare communities for multi-hazard events, and allocate resources for adaptation and recovery.

The San Francisco Department of Public Health initiated naloxone prescribing at 6 safety net clinics. We evaluated this intervention, demonstrating that naloxone prescribing from primary care clinics is feasible and acceptable. To evaluate acceptability of naloxone dispensing to patients prescribed opioids among pharmacists serving clinics participating in a naloxone intervention. We surveyed 58 pharmacists from November 2013 through January 2015 at pharmacies that serviced San Francisco safety net clinics. Surveys collected information on demographics, experiences in dispensing naloxone, and interest in prescriptive authority. We conducted descriptive analyses and assessed bivariate relationships. Most respondents were staff (56.9%) or supervising pharmacists (34.5%). Most (92.9%) were aware their pharmacy stocked naloxone and 86.8% felt it should be prescribed to some or all patients on long-term opioids. Most (82.1%) dispensed naloxone at least once in the past 12 months. More than half were comfortable providing naloxone education. Nearly half (43.4%) indicated they would want authority to furnish without a prescription. Over half (55.2%) reported no problems dispensing. The common problem was insufficient naloxone knowledge. Only 12% reported more than one problem in dispensing naloxone, which was associated with being uncomfortable with educating patients (P = .03). Naloxone dispensing was acceptable among pharmacists. Their most cited problem was insufficient naloxone education. This may be resolved with improved instructional materials, incentives for patient education, or mandatory training.