Long-term nighttime aircraft noise exposure and risk of hypertension in a prospective cohort of female nurses.

There is growing interest in cardiometabolic outcomes associated with nighttime noise, given that noise can disturb sleep and sleep disturbance can increase cardiometabolic risk such as hypertension. However, there is little empirical research evaluating the association between nighttime aircraft noise and hypertension risk. In this study, we expand on previous work to evaluate associations between nighttime aircraft noise exposure and self-reported hypertension incidence in the Nurses' Health Studies (NHS/NHSII), two US-wide cohorts of female nurses. Annual nighttime average aircraft sound levels (Lnight) surrounding 90 airports for 1995-2015 (in 5-year intervals) were modeled using the Aviation Environmental Design Tool and assigned to participants' geocoded addresses over time. Hypertension risk was estimated for each cohort using time-varying Cox proportional-hazards models for Lnight dichotomized at 45 dB (dB), adjusting for individual-level hypertension risk factors, area-level socioeconomic status, region, and air pollution. Random effects meta-analysis was used to combine cohort results. Among 63,229 NHS and 98,880 NHSII participants free of hypertension at study baseline (1994/1995), we observed 33,190 and 28,255 new hypertension cases by 2014/2013, respectively. Although ∼1% of participants were exposed to Lnight ≥45 dB, we observed an adjusted hazard ratio (HR) of 1.10 (95% CI: 0.96, 1.27) in NHS and adjusted HR of 1.12 (95% CI: 0.98, 1.28) in NHSII, comparing exposure to Lnight ≥45 versus <45 dB(A). In meta-analysis, we observed an adjusted HR of 1.11 (95% CI: 1.01, 1.23). These results were attenuated with adjustment for additional variables such as body mass index. Our findings support a modest positive association between nighttime aircraft noise and hypertension risk across NHS/NHSII, which may reinforce the concept that sleep disturbance contributes to noise-related disease burden.

A comparison of population-level exposure and equity tradeoffs across strategies to reduce fine particulate matter emissions from transportation sources in the northeastern US.

BACKGROUND: Many climate mitigation policies to reduce transportation emissions have public health benefits related to ambient air pollution. However, few health analyses consider the equity implications of alternative policies. Equity can be conceptualized in many different ways that may be relevant to communities, decision-makers, and other stakeholders. OBJECTIVES: To evaluate alternative transportation emissions reduction scenarios across the northeastern United States considering population exposure reductions and multiple equity constructs. METHODS: We developed four quantitative indicators reflecting equity constructs that aligned with stakeholder perspectives, including racial/ethnic exposure inequities, proportion of benefits in environmental justice communities, distribution of benefits among participating states, and rural/urban share of benefits. We analyzed numerous transportation emissions reduction scenarios for directly emitted fine particulate matter (primary PM2.5) covering 12 Northeast states and the District of Columbia. We used the Community Multiscale Air Quality model with the decoupled direct method to estimate the reduction in population-weighted primary PM2.5 exposure and the impact on equity for each scenario. RESULTS: Scenarios that yielded greater reductions in population-weighted primary PM2.5 exposure generally emphasized emissions reductions in urban areas or states with large urban centers, with a more than threefold difference in benefits across scenarios. The higher exposure-benefit scenarios typically also had greater reductions in racial/ethnic exposure inequities but led to higher between-state or rural/urban inequality. Scenarios that targeted uniform percentage emission reductions from light or heavy-duty trucks best addressed rural/urban inequalities but led to the smallest reductions in racial/ethnic inequity. CONCLUSION: There are intrinsic tradeoffs among equity constructs, where focusing resources on distributing benefits across states or between urban and rural populations could come at the expense of less reduction in racial/ethnic exposure inequities or in environmental justice communities. Future health benefits analyses should incorporate multiple equity indicators that reflect different stakeholder perspectives and articulate the underlying constructs and tradeoffs.

Healthcare workers' experiences protecting themselves and their families during the COVID-19 pandemic in 2020-2021.

We characterized experiences and strategies used by frontline healthcare workers to prevent severe-acute-respiratory-syndrome-related coronavirus transmission at work and to household members during the coronavirus disease pandemic. Alongside an online questionnaire (n = 234), remote semi-structured interviews (n = 23: 15 clinicians, 8 non-clinicians) were conducted in 2021. Mitigation challenges and facilitators were identified from data to represent experiences as a process considering the before, during, and after work shifts. Journey mapping was utilized to visually describe how healthcare workers experienced the stages of the work environment, leaving work, commuting home, and the home environment, and strategies implemented to stay safe. Major facilitators included the uptake of coronavirus disease vaccines and testing, information regarding virus transmission, and adequate personal protective equipment. The most critical challenges identified included a lack of designated areas for end-of-day disinfection, changing rooms, showers, and lockers in the leaving work stage. Psychosocial and environmental factors must be considered in future hospital pandemic preparations.

Development and evaluation of two educational sessions on take-home lead exposure prevention for construction workers and their families.

Take-home exposures occur when workers accidentally bring workplace contaminants home. Regular job responsibilities may expose construction workers to lead, which extends to their households via the take-home pathway. The present study aimed to develop and evaluate 2 educational sessions addressing take-home lead exposure tailored to construction workers and their families. Educational materials on take-home lead exposure and prevention strategies were designed following guidance from US government institutions and experts on construction work, lead exposure, and educational interventions. The educational materials were pilot-tested with construction workers and their family members during in-person or online sessions in English or Spanish. Changes in knowledge of take-home lead exposure were assessed through pre- and post-testing and open-ended feedback was collected from both participants and session facilitators. The study sample comprised 44 participants, including 33 workers and 11 family members. Among all participants, 81% were male, 46% were Hispanic or Latino, and the average age was 29 years. Post-test scores (µ = 93%, SD = 10%) were higher than pre-test scores (µ = 82%, SD = 19%), and younger participants (<30 years) were more likely to have a lower pre-test score compared to older participants (≥30 years). Overall, feedback from participants and facilitators was positive, indicating appropriate duration, appealing visuals, and ease of engagement through the training activities. Effective public health education for lead-exposed construction workers and their families is needed to reduce lead exposure disparities, especially among children of workers. Interventions must recognize that take-home exposures are not isolated to occupational or home environments.

Aircraft noise exposure and body mass index among female participants in two Nurses' Health Study prospective cohorts living around 90 airports in the United States.

OBJECTIVE: Aircraft noise exposure is linked to cardiovascular disease risk. One understudied candidate pathway is obesity. This study investigates the association between aircraft noise and obesity among female participants in two prospective Nurses' Health Study (NHS and NHSII) cohorts. METHODS: Aircraft day-night average sound levels (DNL) were estimated at participant residential addresses from modeled 1 dB (dB) noise contours above 44 dB for 90 United States (U.S.) airports in 5-year intervals 1995-2010. Biennial surveys (1994-2017) provided information on body mass index (BMI; dichotomized, categorical) and other individual characteristics. Change in BMI from age 18 (BMI18; tertiles) was also calculated. Aircraft noise exposures were dichotomized (45, 55 dB), categorized (<45, 45-54, ≥55 dB) or continuous for exposure ≥45 dB. Multivariable multinomial logistic regression using generalized estimating equations were adjusted for individual characteristics and neighborhood socioeconomic status, greenness, population density, and environmental noise. Effect modification was assessed by U.S. Census region, climate boundary, airline hub type, hearing loss, and smoking status. RESULTS: At baseline, the 74,848 female participants averaged 50.1 years old, with 83.0%, 14.8%, and 2.2% exposed to <45, 45-54, and ≥55 dB of aircraft noise, respectively. In fully adjusted models, exposure ≥55 dB was associated with 11% higher odds (95% confidence interval [95%CI]: -1%, 24%) of BMIs ≥30.0, and 15% higher odds (95%CI: 3%, 29%) of membership in the highest tertile of BMI18 (ΔBMI 6.7 to 71.6). Less-pronounced associations were observed for the 2nd tertile of BMI18 (ΔBMI 2.9 to 6.6) and BMI 25.0-29.9 as well as exposures ≥45 versus <45 dB. There was evidence of DNL-BMI trends (ptrends ≤ 0.02). Stronger associations were observed among participants living in the West, arid climate areas, and among former smokers. DISCUSSION: In two nationwide cohorts of female nurses, higher aircraft noise exposure was associated with higher BMI, adding evidence to an aircraft noise-obesity-disease pathway.

Multiple prenatal exposures and acute-care clinical encounters for asthma among children born to mothers living near a Superfund site.

Prenatal exposures are associated with childhood asthma, and risk may increase with simultaneous exposures. Pregnant women living in lower-income communities tend to have elevated exposures to a range of potential asthma risk factors, which may interact in complex ways. We examined the association between prenatal exposures and the risk of childhood acute-care clinical encounters for asthma (hospitalizations, emergency department visits, observational stays) using conditional logistic regression with a multivariable smoothing term to model the interaction between continuous variables, adjusted for maternal characteristics and stratified by sex. All births near the New Bedford Harbor (NBH) Superfund site (2000-2006) in New Bedford, Massachusetts, were followed through 2011 using the Massachusetts Pregnancy to Early Life Longitudinal (PELL) Data System to identify children aged 5-11 years with acute-care clinical asthma encounters (265 cases among 7787 children with follow-up). Hazard ratios (HRs) were higher for children living closer to the NBH site with higher umbilical cord blood lead levels than in children living further away from the NBH site with lower lead levels (P <.001). HRs were higher for girls (HR = 4.17; 95% CI, 3.60-4.82) than for boys (HR = 1.72; 95% CI, 1.46-2.02). Our results suggest that prenatal lead exposure in combination with residential proximity to the NBH Superfund site is associated with childhood asthma acute-care clinical encounters. This article is part of a Special Collection on Environmental Epidemiology.

Evaluating COVID-19 Risk to Essential Workers by Occupational Group: A Case Study in Massachusetts.

Occupational exposure to SARS-CoV-2 varies by profession, but "essential workers" are often considered in aggregate in COVID-19 models. This aggregation complicates efforts to understand risks to specific types of workers or industries and target interventions, specifically towards non-healthcare workers. We used census tract-resolution American Community Survey data to develop novel essential worker categories among the occupations designated as COVID-19 Essential Services in Massachusetts. Census tract-resolution COVID-19 cases and deaths were provided by the Massachusetts Department of Public Health. We evaluated the association between essential worker categories and cases and deaths over two phases of the pandemic from March 2020 to February 2021 using adjusted mixed-effects negative binomial regression, controlling for other sociodemographic risk factors. We observed elevated COVID-19 case incidence in census tracts in the highest tertile of workers in construction/transportation/buildings maintenance (Phase 1: IRR 1.32 [95% CI 1.22, 1.42]; Phase 2: IRR: 1.19 [1.13, 1.25]), production (Phase 1: IRR: 1.23 [1.15, 1.33]; Phase 2: 1.18 [1.12, 1.24]), and public-facing sales and services occupations (Phase 1: IRR: 1.14 [1.07, 1.21]; Phase 2: IRR: 1.10 [1.06, 1.15]). We found reduced case incidence associated with greater percentage of essential workers able to work from home (Phase 1: IRR: 0.85 [0.78, 0.94]; Phase 2: IRR: 0.83 [0.77, 0.88]). Similar trends exist in the associations between essential worker categories and deaths, though attenuated. Estimating industry-specific risk for essential workers is important in targeting interventions for COVID-19 and other diseases and our categories provide a reproducible and straightforward way to support such efforts.

Ambient air pollution and rate of spontaneous abortion.

Spontaneous abortion (SAB), defined as a pregnancy loss before 20 weeks of gestation, affects up to 30% of conceptions, yet few modifiable risk factors have been identified. We estimated the effect of ambient air pollution exposure on SAB incidence in Pregnancy Study Online (PRESTO), a preconception cohort study of North American couples who were trying to conceive. Participants completed questionnaires at baseline, every 8 weeks during preconception follow-up, and in early and late pregnancy. We analyzed data on 4643 United States (U.S.) participants and 851 Canadian participants who enrolled during 2013-2019 and conceived during 12 months of follow-up. We used country-specific national spatiotemporal models to estimate concentrations of particulate matter <2.5 µm (PM2.5), nitrogen dioxide (NO2), and ozone (O3) during the preconception and prenatal periods at each participant's residential address. On follow-up and pregnancy questionnaires, participants reported information on pregnancy status, including SAB incidence and timing. We fit Cox proportional hazards regression models with gestational weeks as the time scale to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association of time-varying prenatal concentrations of PM2.5, NO2, and O3 with rate of SAB, adjusting for individual- and neighborhood-level factors. Nineteen percent of pregnancies ended in SAB. Greater PM2.5 concentrations were associated with a higher incidence of SAB in Canada, but not in the U.S. (HRs for a 5 µg/m3 increase = 1.29, 95% CI: 0.99, 1.68 and 0.94, 95% CI: 0.83, 1.08, respectively). NO2 and O3 concentrations were not appreciably associated with SAB incidence. Results did not vary substantially by gestational weeks or season at risk. In summary, we found little evidence for an effect of residential ambient PM2.5, NO2, and O3 concentrations on SAB incidence in the U.S., but a moderate positive association of PM2.5 with SAB incidence in Canada.

Simulating Energy Use, Indoor Temperatures, and Utility Cost Impacts Amidst a Warming Climate in a Multi-family Housing Model.

Rising ambient temperatures due to climate change will impact both indoor temperatures and heating and cooling utility costs. In traditionally colder climates, there are potential tradeoffs in how to meet the reduced heating and increased cooling demands, and issues related to lack of air conditioning (AC) access in older homes and among lower-income populations to prevent extreme heat exposure. We modeled a typical multi-family home in Boston (MA) in the building simulation program EnergyPlus to assess indoor temperature and energy consumption in current (2020) and projected future (2050) weather conditions. Selected households were those without AC (no AC), those who ran AC sometimes (some AC), and those with sufficient resources to run AC always (full AC). We considered stylized cooling subsidy policies that allowed households to move between groups, both independently and in conjunction with energy efficiency retrofits. Results showed that future weather conditions without policy changes yielded an increase in indoor summer temperatures of 2.1 °C (no AC), increased cooling demand (range: 34-50%), but led to a decrease in net yearly total utility costs per apartment (range: - $21 to - $38). Policies that allowed households to move to greater AC utilization yielded average indoor summer temperature decreases (- 3.5 °C to - 6.2 °C) and net yearly total utility increases (range: + $2 to + $94) per apartment unit, with greater savings for retrofitted homes primarily due to large decreases in heating use. Our model results reinforce the importance of coordinated public policies addressing climate change that have an equity lens for both health and climate goals.

Air pollution and fecundability in a North American preconception cohort study.

BACKGROUND: Animal and epidemiologic studies indicate that air pollution may adversely affect fertility. However, the level of evidence is limited and specific pollutants driving the association are inconsistent across studies. METHODS: We used data from a web-based preconception cohort study of pregnancy planners enrolled during 2013-2019 (Pregnancy Study Online; PRESTO). Eligible participants self-identified as female, were aged 21-45 years, resided in the United States (U.S.) or Canada, and were trying to conceive without fertility treatments. Participants completed a baseline questionnaire and bi-monthly follow-up questionnaires until conception or 12 months. We analyzed data from 8,747 participants (U.S.: 7,304; Canada: 1,443) who had been trying to conceive for < 12 cycles at enrollment. We estimated residential ambient concentrations of particulate matter < 2.5 µm (PM2.5), nitrogen dioxide (NO2), and ozone (O3) using validated spatiotemporal models specific to each country. We fit country-specific proportional probabilities regression models to estimate the association between annual average, menstrual cycle-specific, and preconception average pollutant concentrations with fecundability, the per-cycle probability of conception. We calculated fecundability ratios (FRs) and 95% confidence intervals (CIs) and adjusted for individual- and neighborhood-level confounders. RESULTS: In the U.S., the FRs for a 5-µg/m3 increase in annual average, cycle-specific, and preconception average PM2.5 concentrations were 0.94 (95% CI: 0.83, 1.08), 1.00 (95% CI: 0.93, 1.07), and 1.00 (95% CI: 0.93, 1.09), respectively. In Canada, the corresponding FRs were 0.92 (95% CI: 0.74, 1.16), 0.97 (95% CI: 0.87, 1.09), and 0.94 (95% CI: 0.80, 1.09), respectively. Likewise, NO2 and O3 concentrations were not strongly associated with fecundability in either country. CONCLUSIONS: Neither annual average, menstrual cycle-specific, nor preconception average exposure to ambient PM2.5, NO2, and O3 were appreciably associated with reduced fecundability in this cohort of pregnancy planners.

Characterizing temporal trends in populations exposed to aircraft noise around U.S. airports: 1995-2015.

BACKGROUND: Aircraft noise is a key concern for communities surrounding airports, with increasing evidence for health effects and inequitable distributions of exposure. However, there have been limited national-scale assessments of aircraft noise exposure over time and across noise metrics, limiting evaluation of population exposure patterns. OBJECTIVE: We evaluated national-scale temporal trends in aviation noise exposure by airport characteristics and across racial/ethnic populations in the U.S. METHODS: Noise contours were modeled for 90 U.S. airports in 5-year intervals between 1995 and 2015 using the Federal Aviation Administration's Aviation Environmental Design Tool. We utilized linear fixed effects models to estimate changes in noise exposure areas for day-night average sound levels (DNL) of 45, 65, and a nighttime equivalent sound level (Lnight) of 45 A-weighted decibels (dB[A]). We used group-based trajectory modeling to identify distinct groups of airports sharing underlying characteristics. We overlaid noise contours and Census tract data from the U.S. Census Bureau and American Community Surveys for 2000 to 2015 to estimate exposure changes overall and by race/ethnicity. RESULTS: National-scale analyses showed non-monotonic trends in mean exposed areas that peaked in 2000, followed by a 37% decrease from 2005 to 2010 and a subsequent increase in 2015. We identified four distinct trajectory groups of airports sharing latent characteristics related to size and activity patterns. Those populations identifying as minority (e.g., Hispanic/Latino, Black/African American, Asian) experienced higher proportions of exposure relative to their subgroup populations compared to non-Hispanic or White populations across all years, indicating ethnic and racial disparities in airport noise exposure that persist over time. SIGNIFICANCE: Overall, these data identified differential exposure trends across airports and subpopulations, helping to identify vulnerable communities for aviation noise in the U.S. IMPACT STATEMENT: We conducted a descriptive analysis of temporal trends in aviation noise exposure in the U.S. at a national level. Using data from 90 U.S. airports over a span of two decades, we characterized the noise exposure trends overall and by airport characteristics, while estimating the numbers of exposed by population demographics to help identify the impact on vulnerable communities who may bear the burden of aircraft noise exposure.

Associations between long-term aircraft noise exposure, cardiovascular disease, and mortality in US cohorts of female nurses.

There is limited research examining aircraft noise and cardiovascular disease (CVD) risk. The objective of this study was to investigate associations of aircraft noise with CVD among two US cohorts, the Nurses' Health Study (NHS) and Nurses' Health Study II (NHSII). Methods: Between 1994 and 2014, we followed 57,306 NHS and 60,058 NHSII participants surrounding 90 airports. Aircraft noise was modeled above 44 A-weighted decibels (dB(A)) and linked to geocoded addresses. Based on exposure distributions, we dichotomized exposures at 50 dB(A) and tested sensitivity of this cut-point by analyzing aircraft noise as categories (<45, 45-49, 50-54, ≥55) and continuously. We fit cohort-specific Cox proportional hazards models to estimate relationships between time-varying day-night average sound level (DNL) and CVD incidence and CVD and all-cause mortality, adjusting for fixed and time-varying individual- and area-level covariates. Results were pooled using random effects meta-analysis. Results: Over 20 years of follow-up, there were 4529 CVD cases and 14,930 deaths. Approximately 7% (n = 317) of CVD cases were exposed to DNL ≥50 dB(A). In pooled analyses comparing ≥50 with <50 dB(A), the adjusted hazard ratio for CVD incidence was 1.00 (95% confidence interval: 0.89, 1.12). The corresponding adjusted hazard ratio for all-cause mortality was 1.02 (95% confidence interval: 0.96, 1.09). Patterns were similar for CVD mortality in NHS yet underpowered. Conclusions: Among participants in the NHS and NHSII prospective cohorts who generally experience low exposure to aircraft noise, we did not find adverse associations of aircraft noise with CVD incidence, CVD mortality, or all-cause mortality.

Associations between Aircraft Noise Exposure and Self-Reported Sleep Duration and Quality in the United States-Based Prospective Nurses' Health Study Cohort.

BACKGROUND: Sleep disruption is linked with chronic disease, and aircraft noise can disrupt sleep. However, there are few investigations of aircraft noise and sleep in large cohorts. OBJECTIVES: We examined associations between aircraft noise and self-reported sleep duration and quality in the Nurses' Health Study, a large prospective cohort. METHODS: Aircraft nighttime equivalent sound levels (Lnight) and day-night average sound levels (DNL) were modeled around 90 U.S. airports from 1995 to 2015 in 5-y intervals using the Aviation Environmental Design Tool and linked to geocoded participant residential addresses. Lnight exposure was dichotomized at the lowest modeled level of 45 A-weighted decibels [dB(A)] and at multiple cut points for DNL. Multiple categories of both metrics were compared with <45 dB(A). Self-reported short sleep duration (<7 h/24-h day) was ascertained in 2000, 2002, 2008, 2012, and 2014, and poor sleep quality (frequent trouble falling/staying asleep) was ascertained in 2000. We analyzed repeated sleep duration measures using generalized estimating equations and sleep quality by conditional logistic regression. We adjusted for participant-level demographics, behaviors, comorbidities, and environmental exposures (greenness and light at night) and examined effect modification. RESULTS: In 35,226 female nurses averaging 66.1 years of age at baseline, prevalence of short sleep duration and poor sleep quality were 29.6% and 13.1%, respectively. In multivariable models, exposure to Lnight ≥45 dB(A) was associated with 23% [95% confidence interval (CI): 7%, 40%] greater odds of short sleep duration but was not associated with poor sleep quality (9% lower odds; 95% CI: -30%, 19%). Increasing categories of Lnight and DNL ≥45 dB(A) suggested an exposure-response relationship for short sleep duration. We observed higher magnitude associations among participants living in the West, near major cargo airports, and near water-adjacent airports and among those reporting no hearing loss. DISCUSSION: Aircraft noise was associated with short sleep duration in female nurses, modified by individual and airport characteristics. https://doi.org/10.1289/EHP10959.

Assessing the impact of aircraft arrival on ambient ultrafine particle number concentrations in near-airport communities in Boston, Massachusetts.

Aircraft emissions contribute to overall ambient air pollution, including ultrafine particle (UFP) concentrations. However, accurately ascertaining aviation contributions to UFP is challenging due to high spatiotemporal variability along with intermittent aviation emissions. The objective of this study was to evaluate the impact of arrival aircraft on particle number concentration (PNC), a proxy for UFP, across six study sites 3-17 km from a major arrival aircraft flight path into Boston Logan International Airport by utilizing real-time aircraft activity and meteorological data. Ambient PNC at all monitoring sites was similar at the median but had greater variation at the 95th and 99th percentiles with more than two-fold increases in PNC observed at sites closer to the airport. PNC was elevated during the hours with high aircraft activity with sites closest to the airport exhibiting stronger signals when downwind from the airport. Regression models indicated that the number of arrival aircraft per hour was associated with measured PNC at all six sites, with a maximum contribution of 50% of total PNC at a monitor 3 km from the airport during hours with arrival activity on the flight path of interest (26% across all hours). Our findings suggest strong but intermittent contributions from arrival aircraft to ambient PNC in communities near airports.

Influence of geospatial resolution on sociodemographic predictors of COVID-19 in Massachusetts.

PURPOSE: When studying health risks across a large geographic region such as a state or province, researchers often assume that finer-resolution data on health outcomes and risk factors will improve inferences by avoiding ecological bias and other issues associated with geographic aggregation. However, coarser-resolution data (e.g., at the town or county-level) are more commonly publicly available and packaged for easier access, allowing for rapid analyses. The advantages and limitations of using finer-resolution data, which may improve precision at the cost of time spent gaining access and processing data, have not been considered in detail to date. METHODS: We systematically examine the implications of conducting town-level mixed-effect regression analyses versus census-tract-level analyses to study sociodemographic predictors of COVID-19 in Massachusetts. In a series of negative binomial regressions, we vary the spatial resolution of the outcome, the resolution of variable selection, and the resolution of the random effect to allow for more direct comparison across models. RESULTS: We find stability in some estimates across scenarios, changes in magnitude, direction, and significance in others, and tighter confidence intervals on the census-tract level. Conclusions regarding sociodemographic predictors are robust when regions of high concentration remain consistent across town and census-tract resolutions. CONCLUSIONS: Inferences about high-risk populations may be misleading if derived from town- or county-resolution data, especially for covariates that capture small subgroups (e.g., small racial minority populations) or are geographically concentrated or skewed (e.g., % college students). Our analysis can help inform more rapid and efficient use of public health data by identifying when finer-resolution data are truly most informative, or when coarser-resolution data may be misleading.

Application of probabilistic methods to address variability and uncertainty in estimating risks for non-cancer health effects.

Human health risk assessment currently uses the reference dose or reference concentration (RfD, RfC) approach to describe the level of exposure to chemical hazards without appreciable risk for non-cancer health effects in people. However, this "bright line" approach assumes that there is minimal risk below the RfD/RfC with some undefined level of increased risk at exposures above the RfD/RfC and has limited utility for decision-making. Rather than this dichotomous approach, non-cancer risk assessment can benefit from incorporating probabilistic methods to estimate the amount of risk across a wide range of exposures and define a risk-specific dose. We identify and review existing approaches for conducting probabilistic non-cancer risk assessments. Using perchloroethylene (PCE), a priority chemical for the U.S. Environmental Protection Agency under the Toxic Substances Control Act, we calculate risk-specific doses for the effects on cognitive deficits using probabilistic risk assessment approaches. Our probabilistic risk assessment shows that chronic exposure to 0.004 ppm PCE is associated with approximately 1-in-1,000 risk for a 5% reduced performance on the Wechsler Memory Scale Visual Reproduction subtest with 95% confidence. This exposure level associated with a 1-in-1000 risk for non-cancer neurocognitive deficits is lower than the current RfC for PCE of 0.0059 ppm, which is based on standard point of departure and uncertainty factor approaches for the same neurotoxic effects in occupationally exposed adults. We found that the population-level risk of cognitive deficit (indicating central nervous system dysfunction) is estimated to be greater than the cancer risk level of 1-in-100,000 at a similar chronic exposure level. The extension of toxicological endpoints to more clinically relevant endpoints, along with consideration of magnitude and severity of effect, will help in the selection of acceptable risk targets for non-cancer effects. We find that probabilistic approaches can 1) provide greater context to existing RfDs and RfCs by describing the probability of effect across a range of exposure levels including the RfD/RfC in a diverse population for a given magnitude of effect and confidence level, 2) relate effects of chemical exposures to clinical disease risk so that the resulting risk assessments can better inform decision-makers and benefit-cost analysis, and 3) better reflect the underlying biology and uncertainties of population risks.

A science-based agenda for health-protective chemical assessments and decisions: overview and consensus statement.

The manufacture and production of industrial chemicals continues to increase, with hundreds of thousands of chemicals and chemical mixtures used worldwide, leading to widespread population exposures and resultant health impacts. Low-wealth communities and communities of color often bear disproportionate burdens of exposure and impact; all compounded by regulatory delays to the detriment of public health. Multiple authoritative bodies and scientific consensus groups have called for actions to prevent harmful exposures via improved policy approaches. We worked across multiple disciplines to develop consensus recommendations for health-protective, scientific approaches to reduce harmful chemical exposures, which can be applied to current US policies governing industrial chemicals and environmental pollutants. This consensus identifies five principles and scientific recommendations for improving how agencies like the US Environmental Protection Agency (EPA) approach and conduct hazard and risk assessment and risk management analyses: (1) the financial burden of data generation for any given chemical on (or to be introduced to) the market should be on the chemical producers that benefit from their production and use; (2) lack of data does not equate to lack of hazard, exposure, or risk; (3) populations at greater risk, including those that are more susceptible or more highly exposed, must be better identified and protected to account for their real-world risks; (4) hazard and risk assessments should not assume existence of a "safe" or "no-risk" level of chemical exposure in the diverse general population; and (5) hazard and risk assessments must evaluate and account for financial conflicts of interest in the body of evidence. While many of these recommendations focus specifically on the EPA, they are general principles for environmental health that could be adopted by any agency or entity engaged in exposure, hazard, and risk assessment. We also detail recommendations for four priority areas in companion papers (exposure assessment methods, human variability assessment, methods for quantifying non-cancer health outcomes, and a framework for defining chemical classes). These recommendations constitute key steps for improved evidence-based environmental health decision-making and public health protection.

Inequities in COVID-19 vaccine and booster coverage across Massachusetts ZIP codes after the emergence of Omicron: A population-based cross-sectional study.

BACKGROUND: Inequities in Coronavirus Disease 2019 (COVID-19) vaccine and booster coverage may contribute to future disparities in morbidity and mortality within and between Massachusetts (MA) communities. METHODS AND FINDINGS: We conducted a population-based cross-sectional study of primary series vaccination and booster coverage 18 months into the general population vaccine rollout. We obtained public-use data on residents vaccinated and boosted by ZIP code (and by age group: 5 to 19, 20 to 39, 40 to 64, 65+) from MA Department of Public Health, as of October 10, 2022. We constructed population denominators for postal ZIP codes by aggregating census tract population estimates from the 2015-2019 American Community Survey. We excluded nonresidential ZIP codes and the smallest ZIP codes containing 1% of the state's population. We mapped variation in ZIP code-level primary series vaccine and booster coverage and used regression models to evaluate the association of these measures with ZIP code-level socioeconomic and demographic characteristics. Because age is strongly associated with COVID-19 severity and vaccine access/uptake, we assessed whether observed socioeconomic and racial/ethnic inequities persisted after adjusting for age composition and plotted age-specific vaccine and booster coverage by deciles of ZIP code characteristics. We analyzed data on 418 ZIP codes. We observed wide geographic variation in primary series vaccination and booster rates, with marked inequities by ZIP code-level education, median household income, essential worker share, and racial/ethnic composition. In age-stratified analyses, primary series vaccine coverage was very high among the elderly. However, we found large inequities in vaccination rates among younger adults and children, and very large inequities in booster rates for all age groups. In multivariable regression models, each 10 percentage point increase in "percent college educated" was associated with a 5.1 (95% confidence interval (CI) 3.9 to 6.3, p < 0.001) percentage point increase in primary series vaccine coverage and a 5.4 (95% CI 4.5 to 6.4, p < 0.001) percentage point increase in booster coverage. Although ZIP codes with higher "percent Black/Latino/Indigenous" and higher "percent essential workers" had lower vaccine coverage (-0.8, 95% CI -1.3 to -0.3, p < 0.01; -5.5, 95% CI -7.3 to -3.8, p < 0.001), these associations became strongly positive after adjusting for age and education (1.9, 95% CI 1.0 to 2.8, p < 0.001; 4.8, 95% CI 2.6 to 7.1, p < 0.001), consistent with high demand for vaccines among Black/Latino/Indigenous and essential worker populations within age and education groups. Strong positive associations between "median household income" and vaccination were attenuated after adjusting for age. Limitations of the study include imprecision of the estimated population denominators, lack of individual-level sociodemographic data, and potential for residential ZIP code misreporting in vaccination data. CONCLUSIONS: Eighteen months into MA's general population vaccine rollout, there remained large inequities in COVID-19 primary series vaccine and booster coverage across MA ZIP codes, particularly among younger age groups. Disparities in vaccination coverage by racial/ethnic composition were statistically explained by differences in age and education levels, which may mediate the effects of structural racism on vaccine uptake. Efforts to increase booster coverage are needed to limit future socioeconomic and racial/ethnic disparities in COVID-19 morbidity and mortality.