Climate change-related hazards and disasters, known to adversely impact physical and mental health outcomes, are also expected to result in human migration above current levels. Environmentally-motivated migration and displacement may lead to the disruption of existing social ties, with potentially adverse consequences for mobile populations as well as their family members who remain in places of origin. We propose that the disruption of social ties is a key mechanism by which climate-related migration may negatively impact mental health, in particular. Existing social ties may provide social and material resources that buffer mental health stressors related to both prolonged and acute climate events. Preparation for such events may also strengthen these same ties and protect mental health. Communities may leverage social ties, first to mitigate climate change, and second, to adapt and rebuild post-disaster in communities of origin. Additionally, social ties can inform migration decisions and destinations. For example, scholars have found that the drought-motivated adaptive migration of West African Fulbe herders only occurred because of the long-term development of social networks between migrants and non-migrants through trade and seasonal grazing. On the other hand, social ties do not always benefit mental health. Some migrants, including those from poor regions or communities with no formal safety net, may face considerable burden to provide financial and emotional resources to family members who remain in countries of origin. In destination communities, migrants often face significant social marginalization. Therefore, policies and programs that aim to maintain ongoing social ties among migrants and their family and community members may be critically important in efforts to enhance population resilience and adaptation to climate change and to improve mental health outcomes. Several online platforms, like Refugee Start Force, serve to integrate refugees by connecting migrants directly to people and services in destination communities. These efforts may increasingly draw upon novel technologies to support and maintain social networks in the context of population mobility due to climatic and other factors.

Background and objectives

The sex ratio of human birth cohorts predicts the health and longevity of their members. Most literature invokes natural selection in support of the argument that heritable tendencies to produce male or female offspring induce oscillation in the sex ratio and its sequelae. Tests of the argument remain exceedingly rare because they require vital statistics describing many generations of a population both unaffected by migration and exposed to an exogenous stressor virulent enough to change the sex ratio at birth. We contribute to the literature by using time-series modeling to detect oscillation in the best data currently available for such a test.

Methodology

We apply rigorous time-series methods to data describing Sweden from 1751 through 1830, a period when the population not only aged in place without migration, but also exhibited the effects of an Icelandic volcanic eruption including a historically low secondary sex ratio. That very low sex ratio should have induced oscillation if heritable mechanisms appear in humans.

Results

We detected oscillation in the ratio but not that predicted by heritable tendencies to produce males or females. We found peak-to-trough oscillation at 14 rather than the approximately 32 years expected from the heritable tendencies argument.

Conclusions and implications

Our findings suggest that mechanisms other than perturbation of heritable tendencies to produce males or females induce oscillation in the human secondary sex ratio. These other mechanisms may include reproductive suppression and selection in utero.

Lay summary

The male to female ratio in human birth cohorts predicts longevity but its variation over time remains unexplained. We test the long-held theory that the ratio oscillates due to heritable tendencies to produce males or females. We find oscillation, but it appears due to social processes rather than heritable mechanisms.

Anxiety-inducing life events increase the risk of motor vehicle crashes. We test the hypothesis that earthquakes, known to increase anxiety in the population, also increase the incidence of motor vehicle crashes. Our study took place in Oklahoma, USA where wastewater injection resulted in increased induced seismicity between 2010 and 2016. We identified dates of earthquakes ≥ magnitude 4 (a level felt by most people) with data from the U.S. Geologic Survey. The Oklahoma Highway Safety Office provided county-level monthly vehicle crash counts. We defined high, medium, and low earthquake exposure counties based on the location of earthquake epicenters. Using time-series analyses, we evaluated the association between monthly counts of ≥magnitude 4 earthquakes and motor vehicle crashes by exposure group. Earthquakes ≥ magnitude 4 took place in 38 of 84 study months, and a monthly average of 5813 (SD = 384) crashes occurred between 2010 and 2016. In high-exposure counties, we observed an additional 39.2 motor vehicle crashes per each additional ≥ magnitude 4 earthquake in the prior month (SE = 11.5). We found no association between the timing of ≥magnitude 4 earthquakes and motor vehicle crashes in the medium or low exposure counties. With a binary earthquake exposure variable, we found a 4.6% (SE = 1.4%) increase in motor vehicle crashes in the high exposure counties in the month following 1 or more ≥magnitude 4 earthquakes. Consistent with our hypothesis, there was no association between earthquakes of magnitude ≤ 2.5 and motor vehicle crashes in the high-exposure counties. This novel evidence of an association between induced earthquakes in Oklahoma and motor vehicle crashes warrants future research given the high economic and social costs of such vehicle crashes.

Introduction

Redlining, a racist mortgage appraisal practice of the 1930s, established and exacerbated racial residential segregation boundaries in the United States. Investment risk grades assigned >80y ago through security maps from the Home Owners' Loan Corporation (HOLC) are associated with current sociodemographics and adverse health outcomes. We assessed whether historical HOLC investment grades are associated with 2010 greenspace, a health-promoting neighborhood resource.

Objectives

We compared 2010 normalized difference vegetation index (NDVI) across previous HOLC neighborhood grades using propensity score restriction and matching.

Methods

Security map shapefiles were downloaded from the Mapping Inequality Project. Neighborhood investment risk grades included A (best, green), B (blue), C (yellow), and D (hazardous, red, i.e., redlined). We used 2010 satellite imagery to calculate the average NDVI for each HOLC neighborhood. Our main outcomes were 2010 annual average NDVI and summer NDVI. We assigned areal-apportioned 1940 census measures to each HOLC neighborhood. We used propensity score restriction, matching, and targeted maximum likelihood estimation to limit model extrapolation, reduce confounding, and estimate the association between HOLC grade and NDVI for the following comparisons: Grades B vs. A, C vs. B, and D vs. C.

Results

Across 102 urban areas (4,141 HOLC polygons), annual average ±standard deviation (SD) 2010 NDVI was 0.47 (±0.09), 0.43 (±0.09), 0.39 (±0.09), and 0.36 (±0.10) in Grades A-D, respectively. In analyses adjusted for current ecoregion and census region, 1940s census measures, and 1940s population density, annual average NDVI values in 2010 were estimated at -0.039 (95% CI: -0.045, -0.034), -0.024 (95% CI: -0.030, -0.018), and -0.026 (95% CI: -0.037, -0.015) for Grades B vs. A, C vs. B, and D vs. C, respectively, in the 1930s.

Discussion

Estimates adjusted for historical characteristics indicate that neighborhoods assigned worse HOLC grades in the 1930s are associated with reduced present-day greenspace. https://doi.org/10.1289/EHP7495.

Lay persons and policy makers have speculated on how national differences in the imposition of social distancing to reduce SARS CoV-2 (severe acute respiratory syndrome coronavirus 2) infection has affected non-COVID-19 deaths. No rigorous estimation of the effect appears in the scholarly literature. We use time-series methods to compare non-COVID-19 deaths in Norway during its 9 weeks of mandated social distancing to those expected from history as well as from non-COVID-19 deaths in relatively less restricted Sweden. We estimate that 430 fewer Norwegians than expected died from causes other than COVID-19. We argue that failing to account for averted non-COVID-19 deaths will lead to an underestimate of the benefits of social distancing policies.

BACKGROUND: This study capitalized on coal and oil facility retirements to quantify their potential effects on fine particulate matter (PM2.5) concentrations and cardiorespiratory hospitalizations in affected areas using a generalized synthetic control method. METHODS: We identified 11 coal and oil facilities in California that retired between 2006 and 2013. We classified zip code tabulation areas (ZCTA) as exposed or unexposed to a facility retirement using emissions information, distance, and a dispersion model. We calculated weekly ZCTA-specific PM2.5 concentrations based on previously estimated daily time-series PM2.5 concentrations from an ensemble model, and weekly cardiorespiratory hospitalization rates based on hospitalization data collected by the California Department of Health Care Access and Information. We estimated the average differences in weekly average PM2.5 concentrations and cardiorespiratory hospitalization rates in four weeks after each facility retirement between the exposed ZCTAs and the synthetic control using all unexposed ZCTAs (i.e., the average treatment effect among the treated [ATT]) and pooled ATTs using meta-analysis. We conducted sensitivity analyses to consider different classification schemes to distinguish exposed from unexposed ZCTAs, including aggregating outcomes with different time intervals and including a subset of facilities with reported retirement date confirmed via emission record. RESULTS: The pooled ATTs were 0.02 μg/m3 (95% confidence interval (CI): -0.25 to 0.29 μg/m3) and 0.34 per 10,000 person-weeks (95%CI: -0.08 to 0.75 per 10,000 person-weeks) following the facility closure for weekly PM2.5 and cardiorespiratory hospitalization rates, respectively. Our inferences remained the same after conducting sensitivity analyses. CONCLUSIONS: We demonstrated a novel approach to study the potential benefits associated with industrial facility retirements. The declining contribution of industrial emissions to ambient air pollution in California may explain our null findings. We encourage future research to replicate this work in regions with different industrial activities.

Methane superemitters emit non-methane copollutants that are harmful to human health. Yet, no prior studies have assessed disparities in exposure to methane superemitters with respect to race/ethnicity, socioeconomic status, and civic engagement. To do so, we obtained the location, category (e.g., landfill, refinery), and emission rate of California methane superemitters from Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) flights conducted between 2016 and 2018. We identified block groups within 2 km of superemitters (exposed) and 5-10 km away (unexposed) using dasymetric mapping and assigned level of exposure among block groups within 2 km (measured via number of superemitter categories and total methane emissions). Analyses included 483 superemitters. The majority were dairy/manure (n = 213) and oil/gas production sites (n = 127). Results from fully adjusted logistic mixed models indicate environmental injustice in methane superemitter locations. For example, for every 10% increase in non-Hispanic Black residents, the odds of exposure increased by 10% (95% confidence interval (CI): 1.04, 1.17). We observed similar disparities for Hispanics and Native Americans but not with indicators of socioeconomic status. Among block groups located within 2 km, increasing proportions of non-White populations and lower voter turnout were associated with higher superemitter emission intensity. Previously unrecognized racial/ethnic disparities in exposure to California methane superemitters should be considered in policies to tackle methane emissions.

Background

The Icelandic volcano Laki erupted from June 1783 through January 1784. It produced 122 megatons of sulfur dioxide, particulate matter, and acid rain and contributed to one of the coldest winters on record in Western Europe. Although uncontrollable volcanic eruptions continue, few studies have investigated their perinatal health implications.

Methods

Using the Human Mortality Database, we assessed the association between the Laki event and the secondary sex ratio, infant mortality rates, and the number of births from 1751 to 1800 with time-series models that controlled for temporal trends.

Results

The secondary sex ratio decreased 3% below expected levels in 1784 (95% CI = -4%, -1%). Both female and male infant mortality rates exceeded expectation in 1785, by 54% (95% CI = 25%, 83%) and 37% (-1%, 74%), respectively. We observed little change in female live births but a reduction in male live births in 1784.

Conclusions

Our findings are consistent with the hypothesis that the large-scale Laki volcanic eruptions of 1783-1784 resulted in adverse perinatal health outcomes in Sweden.

Background

Debate over "social distancing" as a response to the pandemic includes the claim that disrupting clinical and public health programming dependent on human-to-human contact increased non-COVID-19 deaths. This claim warrants testing because novel pathogens will continue to emerge. Tests, however, appear frustrated by lack of a convention for estimating non-COVID-19 deaths that would have occurred had clinical and public health programming during the pre-vaccine pandemic remained as efficacious as in the pre-pandemic era. Intending to hasten the emergence of such a convention, we describe and demonstrate "new-signal, prior-response expectations" suggested by research and methods at the intersection of epidemiology and process control engineering.

Methods

Using German data, we estimate pre-pandemic public health efficacy by applying Box-Jenkins methods to 271 weekly counts of all-cause deaths from December 29 2014 through March 8 2020. We devise new-signal, prior-response expectations by applying the model to weekly non-COVID-19 deaths from March 9 2020 through December 26 2020.

Results

The COVID-19 pandemic did not coincide with more non-COVID-19 deaths than expected from the efficacy of responses to pre-pandemic all-cause deaths.

Conclusions

New-signal, prior-response estimates can contribute to evaluating the efficacy of public health programming in reducing non-COVID-19 deaths during the pre-vaccine pandemic.

Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source-specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source-specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data-Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on-road vehicles, power plants, industrial sources, and wildfires. We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate. Evaluation is important but difficult-since source-specific exposure is difficult to observe, the most direct evaluation methods involve comparisons with alternative models.