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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. The authors find within-day, seasonal, and regional differences in county-level power outages from 2018–2020. Outages commonly co-occur with climate events. Counties in the south and Michigan faced high social and medical vulnerabilities and outages. [ABSTRACT FROM AUTHOR]

Introduction In 2019, there were nearly 50,000 opioid-related deaths in the USA.[[1]] The impact of the opioid overdose crisis has not been equally distributed, however, with wide variation across sociodemographic groups, regions of the country, and by opioid type [[2]-[4]]. Discussion We observed an alarming and sustained acceleration of opioid-related mortality within the non-Hispanic Black population of Washington DC that far exceeded the level and rate of increase within the non-Hispanic white population, and which was driven by recent increases in synthetic opioid deaths. In the majority of areas, synthetic opioid-related mortality was higher than heroin-related mortality (36 or 71% of states and 69 or 89% of sub-state areas) indicating synthetic opioids are permeating the drug supply outside of fentanyl-adulterated heroin. Specifically, the opioid-related mortality rate among the white population was dramatically higher than among the Black population (Palm Beach Florida: 51.1 per 100,000 vs 5.8 per 100,000), driven by high synthetic opioid-related mortality in the white population (45.7 per 100,000). [Extracted from the article]

The ubiquity of smartphones, with their increasingly sophisticated array of sensors, presents an unprecedented opportunity for researchers to collect longitudinal, diverse, temporally-dense data about human behavior while minimizing participant burden. Researchers increasingly make use of smartphones for "digital phenotyping," the collection and analysis of raw phone sensor and log data to study the lived experiences of subjects in their natural environments using their own devices. While digital phenotyping has shown promise in fields such as psychiatry and neuroscience, there are fundamental gaps in our knowledge about data collection and non-collection (i.e., missing data) in smartphone-based digital phenotyping. In this meta-study using individual-level data from six different studies, we examined accelerometer and GPS sensor data of 211 participants, amounting to 29,500 person-days of observation, using Bayesian hierarchical negative binomial regression with study- and user-level random intercepts. Sensitivity analyses including alternative model specification and stratified models were conducted. We found that iOS users had lower GPS non-collection than Android users. For GPS data, rates of non-collection did not differ by race/ethnicity, education, age, or gender. For accelerometer data, Black participants had higher rates of non-collection, but rates did not differ by sex, education, or age. For both sensors, non-collection increased by 0.5% to 0.9% per week. These results demonstrate the feasibility of using smartphone-based digital phenotyping across diverse populations, for extended periods of time, and within diverse cohorts. As smartphones become increasingly embedded in everyday life, the insights of this study will help guide the design, planning, and analysis of digital phenotyping studies. [ABSTRACT FROM AUTHOR]

University administrators and mental health clinicians have raised concerns about depression and anxiety among Ph.D. students, yet no study has systematically synthesized the available evidence in this area. After searching the literature for studies reporting on depression, anxiety, and/or suicidal ideation among Ph.D. students, we included 32 articles. Among 16 studies reporting the prevalence of clinically significant symptoms of depression across 23,469 Ph.D. students, the pooled estimate of the proportion of students with depression was 0.24 (95% confidence interval [CI], 0.18–0.31; I2 = 98.75%). In a meta-analysis of the nine studies reporting the prevalence of clinically significant symptoms of anxiety across 15,626 students, the estimated proportion of students with anxiety was 0.17 (95% CI, 0.12–0.23; I2 = 98.05%). We conclude that depression and anxiety are highly prevalent among Ph.D. students. Data limitations precluded our ability to obtain a pooled estimate of suicidal ideation prevalence. Programs that systematically monitor and promote the mental health of Ph.D. students are urgently needed. [ABSTRACT FROM AUTHOR]

For most pathogens, transmission is driven by interactions between the behaviours of infectious individuals, the behaviours of the wider population, the local environment, and immunity. Phylogeographic approaches are currently unable to disentangle the relative effects of these competing factors. We develop a spatiotemporally structured phylogenetic framework that addresses these limitations by considering individual transmission events, reconstructed across spatial scales. We apply it to geocoded dengue virus sequences from Thailand (N = 726 over 18 years). We find infected individuals spend 96% of their time in their home community compared to 76% for the susceptible population (mainly children) and 42% for adults. Dynamic pockets of local immunity make transmission more likely in places with high heterotypic immunity and less likely where high homotypic immunity exists. Age-dependent mixing of individuals and vector distributions are not important in determining spread. This approach provides previously unknown insights into one of the most complex disease systems known and will be applicable to other pathogens. Phylogeographic analyses can provide broad descriptions of the spread of pathogens between populations, but are limited by incomplete sampling. Here, the authors develop an inference framework that reconstructs sequential transmission events and use it to characterise dynamics of dengue in Thailand. [ABSTRACT FROM AUTHOR]

Over 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of dengue fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that geographically-distant provinces strongly connected by human travel have more highly correlated dengue incidence than weakly connected provinces of the same distance, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems. [ABSTRACT FROM AUTHOR]

Very little information exists with regard to sex party behaviors in young men who have sex with men (YMSM), often defined as men ranging in age from 13 to 29 years. The current analysis examines sex party attendance and behavior in a sample of 540 emergent adult gay, bisexual, and other YMSM in New York City, ages 18-29 years. Findings indicate that 8.7% (n = 47) of the sample had attended a sex party 3 months prior to assessment. Sex party attendees reported that parties included both HIV-positive and HIV-negative men; attendees also reported unprotected sex and limited access to condoms and lubricant. As compared with those who did not attend sex parties, those who did indicated significantly more lifetime and recent (last 3 months) casual sex partners, drug use (both number of different drugs used and total lifetime use), psychosocial burden (history of partner violence and number of arrests), and total syndemic burden (a composite of unprotected anal sex, drug use and psychosocial burden). These results indicate that while only a small percentage of the overall sample attended sex parties, the intersection of both individual risk factors coupled with risk factors engendered within the sex party environment itself has the potential to be a catalyst in the proliferation of the HIV/AIDS epidemic in urban settings. Lastly, given that sex parties are different than other sex environments, commercial and public, with regard to how they are accessed, public health strategies may need to become more tailored in order to reach this potentially highly risky group. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1007/s11524-021-00573-8 B Correction to: J Urban Health (2021) 98:589-595 b https://doi.org/10.1007/s11524-021-00573-8 Due to an error during the production process, the abstract and key words were missing from this article as originally published. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Correction to: Racial/Ethnic Disparities in Opioid-Related Mortality in the USA, 1999-2019: the Extreme Case of Washington DC. [Extracted from the article]