Substance use disorder is a growing public health challenge in the United States. People who use drugs may be more vulnerable to ambient heat due to the effects of drugs on thermoregulation and their risk environment. There have been limited population-based studies of ambient temperature and drug-related morbidity. We examined short-term associations between daily ambient temperature and emergency department (ED) visits for use or overdose of amphetamine, cocaine and opioids in California during the period 2005 to 2019. Daily ZIP code-level maximum, mean, and minimum temperature exposures were derived from 1-km data Daymet products. A time-stratified case-crossover design was used to estimate cumulative non-linear associations of daily temperature for lag days 0 to 3. Stratified analyses by patient sex, race, and ethnicity were also conducted. The study included over 3.4 million drug-related ED visits. We found positive associations between daily temperature and ED visits for all outcomes examined. An increase in daily mean temperature from the 50th to the 95th percentile was associated with ED visits for amphetamine use (OR = 1.072, 95% CI: 1.058, 1.086), cocaine use (OR = 1.044, 95% CI: 1.021, 1.068 and opioid use (OR = 1.041, 95% CI: 1.025, 1.057). Stronger positive associations were also observed for overdose: amphetamine overdose (OR = 1.150, 95% CI: 1.085, 1.218), cocaine overdose (OR = 1.159, 95% CI: 1.053, 1.276), and opioid overdose (OR = 1.079, 95% CI: 1.054, 1.106). In summary, people who use stimulants and opioids may be a subpopulation sensitive to short-term higher ambient temperature. Mitigating heat exposure can be considered in harm reduction strategies in response to the substance use epidemic and global climate change.

Historic systemic racism, segregation, and White-dominated political institutions in the US have created a public health crisis for communities of color, which includes a higher burden of air pollution. Although outdoor air quality hbas improved, addressing environmental injustices remains a priority. Combining high-resolution ground-level NO2 conentrations, fine-scale sociodemographic information and source-specific emission, we find that racial/ethnic minorities are exposed to higher ambient NO2 levels in the US, and disparities have persisted and worsened between 2000 and 2016. Although not the only source, traffic-related emissions are a major contributor. The primary contribution of this work is identifying the source-specific contribtions to NO2 exposure disparities, enabling targeted actions to reduce environmental injustices among racia/ethinic groups

As wildland fires become more frequent and intense, fire smoke has significantly worsened the ambient air quality, posing greater health risks. To better understand the impact of wildfire smoke on air quality, we developed a modeling system to estimate daily PM2.5 concentrations attributed to both fire smoke and nonsmoke sources across the contiguous U.S. We found that wildfire smoke has the most significant impact on air quality in the West Coast, followed by the Southeastern U.S. Between 2007 and 2018, fire smoke contributed over 25% of daily PM2.5 concentrations at ∼40% of all regulatory air monitors in the EPA’s air quality system (AQS) for more than one month per year. People residing outside the vicinity of an EPA AQS monitor (defined by a 5 km radius) were subject to 36% more smoke impact days compared with those residing nearby. Lowering the national ambient air quality standard (NAAQS) for annual mean PM2.5 concentrations to between 9 and 10 μg/m3 would result in approximately 35–49% of the AQS monitors falling in nonattainment areas, taking into account the impact of fire smoke. If fire smoke contribution is excluded, this percentage would be reduced by 6 and 9%, demonstrating the significant negative impact of wildland fires on air quality.

Neonatal mortality and morbidity are often caused by preterm birth and lower birth weight. Gestational diabetes mellitus (GDM) and gestational hypertension (GH) are the most prevalent maternal medical complications during pregnancy. However, evidence on effects of air pollution on adverse birth outcomes and pregnancy complications is mixed. Singleton live births conceived between January 1st, 2000, and December 31st, 2015, and reached at least 27 weeks of pregnancy in Kansas were included in the study. Trimester-specific and total pregnancy exposures to nitrogen dioxide (NO2), particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5), and ozone (O3) were estimated using spatiotemporal ensemble models and assigned to maternal residential census tracts. Logistic regression, discrete-time survival, and linear models were applied to assess the associations. After adjustment for demographics and socio-economic status (SES) factors, we found increases in the second and third trimesters and total pregnancy O3 exposures were significantly linked to preterm birth. Exposure to the second and third trimesters O3 was significantly associated with lower birth weight, and exposure to NO2 during the first trimester was linked to an increased risk of GDM. O3 exposures in the first trimester were connected to an elevated risk of GH. We didn’t observe consistent associations between adverse pregnancy and birth outcomes with PM2.5 exposure. Our findings indicate there is a positive link between increased O3 exposure during pregnancy and a higher risk of preterm birth, GH, and decreased birth weight. Our work supports limiting population exposure to air pollution, which may lower the likelihood of adverse birth and pregnancy outcomes.

Background: There is a long tradition in environmental health of using frameworks for evidence synthesis, such as those of the U.S. Environmental Protection Agency for its Integrated Science Assessments and the International Agency for Research on Cancer Monographs. The framework, Grading of Recommendations Assessment, Development, and Evaluation (GRADE), was developed for evidence synthesis in clinical medicine. The U.S. Office of Health Assessment and Translation (OHAT) elaborated an approach for evidence synthesis in environmental health building on GRADE. Methods: We applied a modified OHAT approach and a broader “narrative” assessment to assess the level of confidence in a large systematic review on traffic-related air pollution and health outcomes. Discussion: We discuss several challenges with the OHAT approach and its implementation and suggest improvements for synthesizing evidence from observational studies in environmental health. We consider the determination of confidence using a formal rating scheme of up- and downgrading of certain factors, the treatment of every factor as equally important, and the lower initial confidence rating of observational studies to be fundamental issues in the OHAT approach. We argue that some observational studies can offer high-confidence evidence in environmental health. We note that heterogeneity in magnitude of effect estimates should generally not weaken the confidence in the evidence, and consistency of associations across study designs, populations, and exposure assessment methods may strengthen confidence in the evidence. We mention that publication bias should be explored beyond statistical methods and is likely limited when large and collaborative studies comprise most of the evidence and when accrued over several decades. We propose to identify possible key biases, their most likely direction, and their potential impacts on the results. We think that the OHAT approach and other GRADE-type frameworks require substantial modification to align better with features of environmental health questions and the studies that address them. We emphasize that a broader, “narrative” evidence assessment based on the systematic review may complement a formal GRADE-type evaluation. https://doi.org/10.1289/EHP11532

Background: Compared to many environmental risk factors, the relationship between pollen and asthma is understudied, including how associations may differ by pollen type and between subgroups, and how associations may be changing over time. Objectives: We evaluated the association between ambient pollen concentrations and emergency department (ED) visits for asthma and wheeze in Atlanta, Georgia during 1993–2018. We estimated overall associations for 13 individual pollen taxa, as well as associations by decade, race, age (5–17, 18–64, 65+), and insurance status (Medicaid vs non-Medicaid). Methods: Speciated pollen data were acquired from Atlanta Allergy & Asthma, a nationally certified pollen counting station. ED visit data were obtained from individual hospitals and from the Georgia Hospital Association. We performed time-series analyses using quasi-Poisson distributed lag models, with primary analyses assessing 3-day (lag 0–2 days) pollen levels. Models controlled for day of week, holidays, air temperature, month, year, and month-by-year interactions. Results: From 1993 to 2018, there were 686,259 ED visits for asthma and wheeze in the dataset, and the number of ED visits increased over time. We observed positive associations of asthma and wheeze ED visits with nine of the 13 pollen taxa: trees (maple, birch, pine, oak, willow, sycamore, and mulberry), two weeds (nettle and pigweed), and grasses. Rate ratios indicated 1–8% increases in asthma and wheeze ED visits per standard deviation increases in pollen. In general, we observed stronger associations in the earliest period (1993–2000), in younger people, and in Black patients; however, results varied by pollen taxa. Conclusions: Some, but not all, types of pollen are associated with increased ED visits for asthma/wheeze. Associations are generally higher in Black and younger patients and appear to have decreased over time.

Background: There are limited data on post-hospital discharge clinic attendance rates and outcomes among patients with diabetic foot ulcers (DFUs). Methods: Retrospective study of patients hospitalized with a DFU from 2016–2019 in a large public hospital. We measured rates and predictors of clinic attendance with providers involved with DFU care within 30 days of hospital discharge (“30-day post-discharge clinic attendance”). Log-binomial regression was used to estimate risk ratios (RR) and 95% confidence intervals (CI). Results: Among 888 patients, 60.0% were between 45–64 years old, 80.5% were Black, and 24.1% were uninsured. Overall, 478 (53.8%) attended ≥1 30-day post-discharge clinic appointment. Initial hospital outcomes were associated with clinic attendance. For example, the RR of 30-day post-discharge clinic attendance was 1.39 (95%CI 1.19–1.61) among patients who underwent a major amputation compared to patients with DFUs without osteomyelitis and did not undergo an amputation during the initial hospitalization. Among 390 patients with known 12-month outcome, 71 (18.2%) had a major amputation or died ≤12 months of hospital discharge. Conclusion: We found a low post-discharge clinic attendance and high post-discharge amputation and death rates among patients hospitalized with DFUs. Interventions to increase access to outpatient DFU care are needed and could prevent amputations.

Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable subpopulations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. We extend critical window variable selection (CWVS) to the multipollutant setting by introducing CWVS for mixtures (CWVSmix), a hierarchical Bayesian method that combines smoothed variable selection and temporally correlated weight parameters to: (i) identify critical windows of exposure to mixtures of time-varying pollutants, (ii) estimate the time-varying relative importance of each individual pollutant and their first order interactions within the mixture, and (iii) quantify the impact of the mixtures on health. Through simulation we show that CWVSmix offers the best balance of performance in each of these categories in comparison to competing methods. Using these approaches, we investigate the impact of exposure to multiple ambient air pollutants on the risk of stillbirth in New Jersey, 2005–2014. We find consistent elevated risk in gestational weeks 2, 16–17, and 20 for non-Hispanic Black mothers, with pollution mixtures dominated by ammonium (weeks 2, 17, 20), nitrate (weeks 2, 17), nitrogen oxides (weeks 2, 16), PM2.5 (week 2), and sulfate (week 20). The method is available in the R package CWVSmix.

Dust storms are increasing in frequency and correlate with adverse health outcomes but remain understudied in the United States (U.S.), partially due to the limited spatio‐temporal coverage, resolution, and accuracy of current data sets. In this work, dust‐related metrics from four public areal data products were compared to a monitor‐based “gold standard” dust data set. The data products included the National Weather Service (NWS) storm event database, the Modern‐Era Retrospective analysis for Research and Applications—Version 2, the EPA's Air QUAlity TimE Series (EQUATES) Project using the Community Multiscale Air Quality Modeling System (CMAQ), and the Copernicus Atmosphere Monitoring Service global reanalysis product. California, Nevada, Utah, and Arizona, which account for most dust storms reported in the U.S., were examined. Dichotomous and continuous metrics based on reported dust storms, particulate matter concentrations (PM10 and PM2.5), and aerosol‐type variables were extracted or derived from the data products. Associations between these metrics and a validated dust storm detection method utilizing Interagency Monitoring of Protected Visual Environments monitors were estimated via quasi‐binomial regression. In general, metrics from CAMS yielded the strongest associations with the “gold standard,” followed by the NWS storm database metric. Dust aerosol (0.9–20 μm) mixing ratio, vertically integrated mass of dust aerosol (9–20 μm), and dust aerosol optical depth at 550 nm from CAMS generated the highest standardized odds ratios among all metrics. Future work will apply machine‐learning methods to the best‐performing metrics to create a public dust storm database suitable for long‐term epidemiologic studies.

Household air pollution from solid cooking fuel use during gestation has been associated with adverse pregnancy and birth outcomes. The Household Air Pollution Intervention Network (HAPIN) trial was a randomized controlled trial of free liquefied petroleum gas (LPG) stoves and fuel in Guatemala, Peru, India, and Rwanda. A primary outcome of the main trial was to report the effects of the intervention on infant birth weight. Here we evaluate the effects of a LPG stove and fuel intervention during pregnancy on spontaneous abortion, postpartum hemorrhage, hypertensive disorders of pregnancy, and maternal mortality compared to women who continued to use solid cooking fuels. Pregnant women (18–34 years of age; gestation confirmed by ultrasound at 9–19 weeks) were randomly assigned to an intervention (n = 1593) or control (n = 1607) arm. Intention-to-treat analyses compared outcomes between the two arms using log-binomial models. Among the 3195 pregnant women in the study, there were 10 spontaneous abortions (7 intervention, 3 control), 93 hypertensive disorders of pregnancy (47 intervention, 46 control), 11 post postpartum hemorrhage (5 intervention, 6 control) and 4 maternal deaths (3 intervention, 1 control). Compared to the control arm, the relative risk of spontaneous abortion among women randomized to the intervention was 2.32 (95% confidence interval (CI): 0.60, 8.96), hypertensive disorders of pregnancy 1.02 (95% CI: 0.68, 1.52), postpartum hemorrhage 0.83 (95% CI: 0.25, 2.71) and 2.98 (95% CI: 0.31, 28.66) for maternal mortality. In this study, we found that adverse maternal outcomes did not differ based on randomized stove type across four country research sites.