Long-term Impact of Tropical Cyclones on Disease Exacerbation Among Children with Asthma in the Eastern United States, 2000-2018.

BACKGROUND: Tropical cyclones are associated with acute increases in mortality and morbidity, but few studies have examined their longer-term health consequences. We assessed whether tropical cyclones are associated with a higher frequency of symptom exacerbation among children with asthma in the following 12 months in eastern United States counties, 2000-2018. METHODS: We defined exposure to tropical cyclones as a maximum sustained windspeed >21 meters/second at the county center and used coarsened exact matching to match each exposed county to one or more unexposed counties. We used longitudinal, de-identified administrative claims data to estimate the county-level, monthly risk of experiencing at least one asthma exacerbation requiring medical attention among commercially insured children aged 5-17 with prior diagnosis of asthma. We used a difference-in-differences approach implemented via a Poisson fixed effects model to compare the risk of asthma exacerbation in the 12 months before versus after each storm in exposed versus unexposed counties. RESULTS: Across 43 tropical cyclones impacting the eastern United States, we did not observe evidence of an increase in the risk of symptom exacerbation in the 12 months following the storm (random-effects meta-analytic summary estimate: risk ratio = 1.03 [95% confidence interval = 0.96, 1.10], I2 = 17%). However, certain storms, such as Hurricane Sandy, were associated with a higher risk of symptom exacerbation. CONCLUSIONS: These findings are consistent with the hypothesis that some tropical cyclones are detrimental to children's respiratory health. However, tropical cyclones were not associated in aggregate with long-term exacerbation of clinically apparent asthma symptoms among a population of children with commercial health insurance.

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.

Planning to Reduce the Health Impacts of Extreme Heat: A Content Analysis of Heat Action Plans in Local United States Jurisdictions.

Objectives. To examine commonalities and gaps in the content of local US heat action plans (HAPs) designed to decrease the adverse health effects of extreme heat. Methods. We used content analysis to identify common strategies and gaps in extreme heat preparedness among written HAPs in the United States from jurisdictions that serve municipalities with more than 200 000 residents. We reviewed, coded, and analyzed plans to assess the prevalence of key components and strategies. Results. All 21 plans evaluated incorporated data on activation triggers, heat health messaging and risk communication, cooling centers, surveillance activities, and agency coordination, and 95% incorporated information on outreach to at-risk populations. Gaps existed in the specific applications of these broad strategies. Conclusions. Practice-based recommendations as well as future areas of research should focus on increasing targeted strategies for at-risk individuals and expanding the use of surveillance data outside of situational awareness. (Am J Public Health. 2023;113(5):559-567. https://doi.org/10.2105/AJPH.2022.307217).

Survey of extreme heat public health preparedness plans and response activities in the most populous jurisdictions in the United States.

BACKGROUND: Increasingly frequent and intense extreme heat events (EHEs) are indicative of climate change impacts, and urban areas' social and built environments increase their risk for health consequences. Heat action plans (HAPs) are a strategy to bolster municipal EHE preparedness. The objective of this research is to characterize municipal interventions to EHEs and compare U.S. jurisdictions with and without formal heat action plans. METHODS: An online survey was sent to 99 U.S. jurisdictions with populations > 200,000 between September 2021 and January 2022. Summary statistics were calculated to describe the proportion of total jurisdictions, as well as jurisdictions with and without HAPs and in different geographies that reported engagement in extreme heat preparedness and response activities. RESULTS: Thirty-eight (38.4%) jurisdictions responded to the survey. Of those respondents, twenty-three (60.5%) reported the development of a HAP, of which 22 (95.7%) reported plans for opening cooling centers. All respondents reported conducting heat-related risk communications; however, communication approaches focused on passive, technology-dependent mechanisms. While 75.7% of jurisdictions reported having developed a definition for an EHE, less than two-thirds of responding jurisdictions reported any of the following activities: conducting heat-related surveillance (61.1%), implementing provisions for power outages (53.1%), increasing access to fans or air conditioners (48.4%), developing heat vulnerability maps (43.2%), or evaluating activities (34.2%). There were only two statistically significant (p ≥ .05) differences in the prevalence of heat-related activities between jurisdictions with and without a written HAP, possibly attributable to a relatively small sample size: surveillance and having a definition of extreme heat. CONCLUSIONS: Jurisdictions can strengthen their extreme heat preparedness by expanding their consideration of at-risk populations to include communities of color, conducting formal evaluations of their responses, and by bridging the gap between the populations determined to be most at-risk and the channels of communication designed to reach them.

Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts.

BACKGROUND: Associations between community-level risk factors and COVID-19 incidence have been used to identify vulnerable subpopulations and target interventions, but the variability of these associations over time remains largely unknown. We evaluated variability in the associations between community-level predictors and COVID-19 case incidence in 351 cities and towns in Massachusetts from March to October 2020. METHODS: Using publicly available sociodemographic, occupational, environmental, and mobility datasets, we developed mixed-effect, adjusted Poisson regression models to depict associations between these variables and town-level COVID-19 case incidence data across five distinct time periods from March to October 2020. We examined town-level demographic variables, including population proportions by race, ethnicity, and age, as well as factors related to occupation, housing density, economic vulnerability, air pollution (PM2.5), and institutional facilities. We calculated incidence rate ratios (IRR) associated with these predictors and compared these values across the multiple time periods to assess variability in the observed associations over time. RESULTS: Associations between key predictor variables and town-level incidence varied across the five time periods. We observed reductions over time in the association with percentage of Black residents (IRR = 1.12 [95%CI: 1.12-1.13]) in early spring, IRR = 1.01 [95%CI: 1.00-1.01] in early fall) and COVID-19 incidence. The association with number of long-term care facility beds per capita also decreased over time (IRR = 1.28 [95%CI: 1.26-1.31] in spring, IRR = 1.07 [95%CI: 1.05-1.09] in fall). Controlling for other factors, towns with higher percentages of essential workers experienced elevated incidences of COVID-19 throughout the pandemic (e.g., IRR = 1.30 [95%CI: 1.27-1.33] in spring, IRR = 1.20 [95%CI: 1.17-1.22] in fall). Towns with higher proportions of Latinx residents also had sustained elevated incidence over time (IRR = 1.19 [95%CI: 1.18-1.21] in spring, IRR = 1.14 [95%CI: 1.13-1.15] in fall). CONCLUSIONS: Town-level COVID-19 risk factors varied with time in this study. In Massachusetts, racial (but not ethnic) disparities in COVID-19 incidence may have decreased across the first 8 months of the pandemic, perhaps indicating greater success in risk mitigation in selected communities. Our approach can be used to evaluate effectiveness of public health interventions and target specific mitigation efforts on the community level.

Ozone-related asthma emergency department visits in the US in a warming climate.

Ozone exposure is associated with higher risk of asthma-related emergency department visits. The meteorological conditions that govern ozone concentration are projected to be more favorable to ozone formation over much of the United States due to continued climate change, even as emissions of anthropogenic ozone precursors are expected to decrease by 2050. Our goal is to quantify the health benefits of a climate change mitigation scenario versus a "business-as-usual" scenario, defined by the United Nations Intergovernmental Panel on Climate Change Representative Concentration Pathways (RCPs) 4.5 and 8.5, respectively, using the health impact analytical program Benefits Mapping and Analysis Program - Community Edition (BenMAP - CE) to project the number of asthma ED visits in 2045-2055. We project an annual average of 3100 averted ozone-related asthma ED visits during the 2045-2055 period under RCP4.5 versus RCP8.5, with all other factors held constant, which translates to USD $1.7 million in averted costs annually. We identify counties with tens to hundreds of avoided ozone-related asthma ED visits under RCP4.5 versus RCP8.5. Overall, we project a heterogeneous distribution of ozone-related asthma ED visits at different spatial resolutions, specifically national, regional, and county levels, and a substantial net health and economic benefit of climate change mitigation.

Increased Risk of Opioid Overdose Death Following Cold Weather: A Case-Crossover Study.

BACKGROUND: The United States is in the midst of an opioid overdose crisis. Little is known about the role of environmental factors in increasing risk of fatal opioid overdose. METHODS: We conducted a case-crossover analysis of 3,275 opioid overdose deaths recorded in Connecticut and Rhode Island in 2014-2017. We compared the mean ambient temperature on the day of death, as well as average temperature up to 14 days before death, to referent periods matched on year, month, and day of week. RESULTS: Low average temperatures over the 3-7 days before death were associated with higher odds of fatal opioid overdose. Relative to 11°C, an average temperature of 0°C over the 7 days before death was associated with a 30% higher odds of death (odds ratio: 1.3; 95% confidence interval, 1.1, 1.5). CONCLUSIONS: Low average temperature may be associated with higher risk of death due to opioid overdose.

Modeling health and well-being measures using ZIP code spatial neighborhood patterns.

Individual-level assessment of health and well-being permits analysis of community well-being and health risk evaluations across several dimensions of health. It also enables comparison and rankings of reported health and well-being for large geographical areas such as states, metropolitan areas, and counties. However, there is large variation in reported well-being within such large spatial units underscoring the importance of analyzing well-being at more granular levels, such as ZIP codes. In this paper, we address this problem by modeling well-being data to generate ZIP code tabulation area (ZCTA)-level rankings through spatially informed statistical modeling. We build regression models for individual-level overall well-being index and scores from five subscales (Physical, Financial, Social, Community, Purpose) using individual-level demographic characteristics as predictors while including a ZCTA-level spatial effect. The ZCTA neighborhood information is incorporated by using a graph Laplacian matrix; this enables estimation of the effect of a ZCTA on well-being using individual-level data from that ZCTA as well as by borrowing information from neighboring ZCTAs. We deploy our model on well-being data for the U.S. states of Massachusetts and Georgia. We find that our model can capture the effects of demographic features while also offering spatial effect estimates for all ZCTAs, including ones with no observations, under certain conditions. These spatial effect estimates provide community health and well-being rankings of ZCTAs, and our method can be deployed more generally to model other outcomes that are spatially dependent as well as data from other states or groups of states.

Warm season ambient ozone and children's health in the USA.

BACKGROUND: Over 120 million people in the USA live in areas with unsafe ozone (O3) levels. Studies among adults have linked exposure to worse lung function and higher risk of asthma and chronic obstructive pulmonary disease (COPD). However, few studies have examined the effects of O3 in children, and existing studies are limited in terms of their geographic scope or outcomes considered. METHODS: We leveraged a dataset of encounters at 42 US children's hospitals from 2004-2015. We used a one-stage case-crossover design to quantify the association between daily maximum 8-hour O3 in the county in which the hospital is located and risk of emergency department (ED) visits for any cause and for respiratory disorders, asthma, respiratory infections, allergies and ear disorders. RESULTS: Approximately 28 million visits were available during this period. Per 10 ppb increase, warm-season (May through September) O3 levels over the past three days were associated with higher risk of ED visits for all causes (risk ratio [RR]: 0.3% [95% confidence interval (CI): 0.2%, 0.4%]), allergies (4.1% [2.5%, 5.7%]), ear disorders (0.8% [0.3%, 1.3%]) and asthma (1.3% [0.8%, 1.9%]). When restricting to levels below the current regulatory standard (70 ppb), O3 was still associated with risk of ED visits for all-cause, allergies, ear disorders and asthma. Stratified analyses suggest that the risk of O3-related all-cause ED visits may be higher in older children. CONCLUSIONS: Results from this national study extend prior research on the impacts of daily O3 on children's health and reinforce the presence of important adverse health impacts even at levels below the current regulatory standard in the USA.

Short term exposure to low level ambient fine particulate matter and natural cause, cardiovascular, and respiratory morbidity among US adults with health insurance: case time series study.

OBJECTIVE: To estimate the excess relative and absolute risks of hospital admissions and emergency department visits for natural causes, cardiovascular disease, and respiratory disease associated with daily exposure to fine particulate matter (PM2.5) at concentrations below the new World Health Organization air quality guideline limit among adults with health insurance in the contiguous US. DESIGN: Case time series study. SETTING: US national administrative healthcare claims database. PARTICIPANTS: 50.1 million commercial and Medicare Advantage beneficiaries aged ≥18 years between 1 January 2010 and 31 December 2016. MAIN OUTCOME MEASURES: Daily counts of hospital admissions and emergency department visits for natural causes, cardiovascular disease, and respiratory disease based on the primary diagnosis code. RESULTS: During the study period, 10.3 million hospital admissions and 24.1 million emergency department visits occurred for natural causes among 50.1 million adult enrollees across 2939 US counties. The daily PM2.5 levels were below the new WHO guideline limit of 15 µg/m3 for 92.6% of county days (7 360 725 out of 7 949 713). On days when daily PM2.5 levels were below the new WHO air quality guideline limit of 15 µg/m3, an increase of 10 µg/m3 in PM2.5 during the current and previous day was associated with higher risk of hospital admissions for natural causes, with an excess relative risk of 0.91% (95% confidence interval 0.55% to 1.26%), or 1.87 (95% confidence interval 1.14 to 2.59) excess hospital admissions per million enrollees per day. The increased risk of hospital admissions for natural causes was observed exclusively among adults aged ≥65 years and was not evident in younger adults. PM2.5 levels were also statistically significantly associated with relative risk of hospital admissions for cardiovascular and respiratory diseases. For emergency department visits, a 10 µg/m3 increase in PM2.5 during the current and previous day was associated with respiratory disease, with an excess relative risk of 1.34% (0.73% to 1.94%), or 0.93 (0.52 to 1.35) excess emergency department visits per million enrollees per day. This association was not found for natural causes or cardiovascular disease. The higher risk of emergency department visits for respiratory disease was strongest among middle aged and young adults. CONCLUSIONS: Among US adults with health insurance, exposure to ambient PM2.5 at concentrations below the new WHO air quality guideline limit is statistically significantly associated with higher rates of hospital admissions for natural causes, cardiovascular disease, and respiratory disease, and with emergency department visits for respiratory diseases. These findings constitute an important contribution to the debate about the revision of air quality limits, guidelines, and standards.

Impacts of the choice of distance measurement method on estimates of access to point-based resources.

BACKGROUND/OBJECTIVE: Lack of access to resources such as medical facilities and grocery stores is related to poor health outcomes and inequities, particularly in an environmental justice framework. There can be substantial differences in quantifying "access" to such resources, depending on the geospatial method used to generate distance estimates. METHODS: We compared three methods for calculating distance to the nearest grocery store to illustrate differential access at the census block-group level in the Atlanta metropolitan area, including: Euclidean distance estimation, service areas incorporating roadways and other factors, and cost distance for every point on the map. RESULTS: We found notable differences in access across the three estimation techniques, implying a high potential for exposure misclassification by estimation method. There was a lack of nuanced exposure in the highest- and lowest-access areas using the Euclidean distance method. We found an Intraclass Correlation Coefficient (ICC) of 0.69 (0.65, 0.73), indicating moderate agreement between estimation methods. SIGNIFICANCE: As compared with Euclidean distance, service areas and cost distance may represent a more meaningful characterization of "access" to resources. Each method has tradeoffs in computational resources required versus potential improvement in exposure classification. Careful consideration of the method used for determining "access" will reduce subsequent misclassifications.

Heat-related mortality in U.S. state and private prisons: A case-crossover analysis.

Rising temperatures and heatwaves increase mortality. Many of the subpopulations most vulnerable to heat-related mortality are in prisons, facilities that may exacerbate temperature exposures. Yet, there is scare literature on the impacts of heat among incarcerated populations. We analyzed data on mortality in U.S. state and private prisons from 2001-2019 linked to daily maximum temperature data for the months of June, July, and August. Using a case-crossover approach and distributed lag models, we estimated the association of increasing temperatures with total mortality, heart disease-related mortality, and suicides. We also examined the association with extreme heat and heatwaves (days above the 90th percentile for the prison location) and assessed effect modification by personal, facility, and regional characteristics. There were 12,836 deaths during summer months. The majority were male (96%) and housed in a state-operated prison (97%). A 10°F increase was associated with a 5.2% (95% CI: 1.5%, 9.0%) increase in total mortality and a 6.7% (95% CI: -0.6%, 14.0%) increase in heart disease mortality. The association between temperature and suicides was delayed, peaking around lag 3 (exposure at three days prior death). Two- and three-day heatwaves were associated with increased total mortality of 5.5% (95% CI: 0.3%, 10.9%) and 7.4% (95% CI: 1.6%, 13.5%), respectively. The cumulative effect (lags 1-3) of an extreme heat day was associated with a 22.8% (95% CI: 3.3%, 46.0%) increase in suicides. We found the greatest increase in mortality among people ≥ 65 years old, incarcerated less than one year, held in the Northeast region, and in urban or rural counties. These findings suggest that warm temperatures are associated with increased mortality in prisons, yet this vulnerable population's risk has largely been overlooked.

Does choice of outdoor heat metric affect heat-related epidemiologic analyses in the US Medicare population?

Outdoor air temperature is associated with increased morbidity and mortality. Other thermal indices theoretically confer greater physiological relevance by incorporating additional meteorological variables. However, the optimal metric for predicting excess deaths or hospitalizations owing to extreme heat among US Medicare beneficiaries remains unknown. Methods: We calculated daily maximum, minimum, and mean outdoor air temperature (T), heat index (HI), wet-bulb globe temperature (WBGT), and Universal Thermal Climate Index (UTCI) for populous US counties and linked estimates with daily all-cause mortality and heat-related hospitalizations among Medicare beneficiaries (2006-2016). We fit distributed-lag nonlinear models for each metric and compared relative risks (RRs) at the 99th percentile. Results: Across all heat metrics, extreme heat was statistically significantly associated with elevated risks of morbidity and mortality. Associations were more pronounced for maximum daily values versus the corresponding minimum for the same metric. The starkest example was between HImax (RR = 1.14; 95% confidence interval [CI] = 1.12, 1.15) and HImin (RR = 1.10; 95% CI = 1.09, 1.11) for hospitalizations. When comparing RRs across heat metrics, we found no statistically significant differences within the minimum and maximum heat values (i.e., no significant differences between Tmax/HImax/WBGTmax/UTCImax or between Tmin/HImin/WBGTmin/UTCImin). We found similar relationships across the National Climate Assessment regions. Conclusion: Among Medicare beneficiaries in populous US counties, daily maximum and mean values of outdoor heat are associated with greater RRs of heat-related morbidity and all-cause mortality versus minimum values of the same metric. The choice of heat metric (e.g., temperature versus HI) does not appear to substantively affect risk calculations in this population.

Warm-season temperatures and emergency department visits among children with health insurance.

High ambient temperatures have become more likely due to climate change and are linked to higher rates of heat-related illness, respiratory and cardiovascular diseases, mental health disorders, and other diseases. To date, far fewer studies have examined the effects of high temperatures on children versus adults, and studies including children have seldom been conducted on a national scale. Compared to adults, children have behavioral and physiological differences that may give them differential heat vulnerability. We acquired medical claims data from a large database of commercially insured US children aged 0-17 from May to September (warm-season) 2016-2019. Daily maximum ambient temperature and daily mean relative humidity estimates were aggregated to the county level using the Parameter-elevation Relationships on Independent Slopes dataset, and extreme heat was defined as the 95th percentile of the county-specific daily maximum temperature distribution. Using a case-crossover design and temperature lags 0-5 days, we estimated the associations between extreme heat and cause-specific emergency department visits (ED) in children aged <18 years, using the median county-specific daily maximum temperature distribution as the reference. Approximately 1.2 million ED visits in children from 2489 US counties were available during the study period. The 95th percentile of warm-season temperatures ranged from 71 °F to 112 °F (21.7 °C to 44.4 °C). Comparing 95th to the 50th percentile, extreme heat was associated with higher rates of ED visits for heat-related illness; endocrine, nutritional and metabolic diseases; and otitis media and externa, but not for all-cause admissions. Subgroup analyses suggested differences by age, with extreme heat positively associated with heat-related illness for both the 6-12 year (odds ratio [OR]: 1.34, 95% confidence interval [CI]: 1.16, 1.56) and 13-17 year age groups (OR: 1.55, 95% CI: 1.37, 1.76). Among children with health insurance across the US, days of extreme heat were associated with higher rates of healthcare utilization. These results highlight the importance of individual and population-level actions to protect children and adolescents from extreme heat, particularly in the context of continued climate change.

Calculating access to parks and other polygonal resources: A description of open-source methodologies.

Public health studies routinely use simplistic methods to calculate proximity-based "access" to greenspace, such as by measuring distances to the geographic centroids of parks or, less frequently, to the perimeter of the park area. Although computationally efficient, these approaches oversimplify exposure measurement because parks often have specific entrance points. In this tutorial paper, we describe how researchers can instead calculate more-accurate access measures using freely available open-source methods. Specifically, we demonstrate processes for calculating "service areas" representing street-network-based buffers of access to parks within set distances and mode of transportation (e.g., 1-km walk or 20-minute drive) using OpenRouteService and QGIS software. We also introduce an advanced method involving the identification of trailheads or parking lots with OpenStreetMap data and show how large parks particularly benefit from this approach. These methods can be used globally and are applicable to analyses of a wide range of studies investigating proximity access to resources.

Examining the Optimal Placement of Cooling Centers to Serve Populations at High Risk of Extreme Heat Exposure in 81 US Cities.

OBJECTIVE: Although extreme heat can impact the health of anyone, certain groups are disproportionately affected. In urban settings, cooling centers are intended to reduce heat exposure by providing air-conditioned spaces to the public. We examined the characteristics of populations living near cooling centers and how well they serve areas with high social vulnerability. METHODS: We identified 1402 cooling centers in 81 US cities from publicly available sources and analyzed markers of urban heat and social vulnerability in relation to their locations. Within each city, we developed cooling center access areas, defined as the geographic area within a 0.5-mile walk from a center, and compared sociodemographic characteristics of populations living within versus outside the access areas. We analyzed results by city and geographic region to evaluate climate-relevant regional differences. RESULTS: Access to cooling centers differed among cities, ranging from 0.01% (Atlanta, Georgia) to 63.2% (Washington, DC) of the population living within an access area. On average, cooling centers were in areas that had higher levels of social vulnerability, as measured by the number of people living in urban heat islands, annual household income below poverty, racial and ethnic minority status, low educational attainment, and high unemployment rate. However, access areas were less inclusive of adult populations aged ≥65 years than among populations aged <65 years. CONCLUSION: Given the large percentage of individuals without access to cooling centers and the anticipated increase in frequency and severity of extreme heat events, the current distribution of centers in the urban areas that we examined may be insufficient to protect individuals from the adverse health effects of extreme heat, particularly in the absence of additional measures to reduce risk.

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.

Missing Race and Ethnicity Data among COVID-19 Cases in Massachusetts.

Infectious disease surveillance frequently lacks complete information on race and ethnicity, making it difficult to identify health inequities. Greater awareness of this issue has occurred due to the COVID-19 pandemic, during which inequities in cases, hospitalizations, and deaths were reported but with evidence of substantial missing demographic details. Although the problem of missing race and ethnicity data in COVID-19 cases has been well documented, neither its spatiotemporal variation nor its particular drivers have been characterized. Using individual-level data on confirmed COVID-19 cases in Massachusetts from March 2020 to February 2021, we show how missing race and ethnicity data: (1) varied over time, appearing to increase sharply during two different periods of rapid case growth; (2) differed substantially between towns, indicating a nonrandom distribution; and (3) was associated significantly with several individual- and town-level characteristics in a mixed-effects regression model, suggesting a combination of personal and infrastructural drivers of missing data that persisted despite state and federal data-collection mandates. We discuss how a variety of factors may contribute to persistent missing data but could potentially be mitigated in future contexts.

Environmental equity and COVID-19 experiences in the United States: Results from three survey waves of a nationally representative study conducted between 2020-2022.

Certain environmental exposures, such as air pollution, are associated with COVID-19 incidence and mortality. To determine whether environmental context is associated with other COVID-19 experiences, we used data from the nationally representative Tufts Equity in Health, Wealth, and Civic Engagement Study data (n=1785; three survey waves 2020-2022). Environmental context was assessed using self-reported climate stress and county-level air pollution, greenness, toxic release inventory site, and heatwave data. Self-reported COVID-19 experiences included willingness to vaccinate against COVID-19, health impacts from COVID-19, receiving assistance for COVID-19, and provisioning assistance for COVID-19. Self-reported climate stress in 2020 or 2021 was associated with increased COVID-19 vaccination willingness by 2022 (odds ratio [OR] = 2.35; 95% confidence interval [CI] = 1.47, 3.76), even after adjusting for political affiliation (OR = 1.79; 95% CI = 1.09, 2.93). Self-reported climate stress in 2020 was also associated with increased likelihood of receiving COVID-19 assistance by 2021 (OR = 1.89; 95% CI = 1.29, 2.78). County-level exposures (i.e., less greenness, more toxic release inventory sites, more heatwaves) were associated with increased vaccination willingness. Air pollution exposure in 2020 was positively associated with likelihood of provisioning COVID-19 assistance in 2020 (OR = 1.16 per µg/m3; 95% CI = 1.02, 1.32). Associations between certain environmental exposures and certain COVID-19 outcomes were stronger among those who identify as a race/ethnicity other than non-Hispanic White and among those who reported experiencing discrimination; however, these trends were not consistent. A latent variable representing a summary construct for environmental context was associated with COVID-19 vaccination willingness. Our results add to the growing body of literature suggesting that intersectional equity issues affecting likelihood of exposure to adverse environmental conditions are also associated with health-related outcomes.

Wet-Bulb Globe Temperature, Universal Thermal Climate Index, and Other Heat Metrics for US Counties, 2000-2020.

Epidemiologic research on extreme heat consistently finds significant impacts on human morbidity and mortality. However, most of these analyses do not use spatially explicit measures of heat (typically assessing exposures at major cities using the nearest weather station), and they frequently consider only ambient temperature or heat index. The field is moving toward more expansive analyses that use spatially resolved gridded meteorological datasets and alternative assessments of heat, such as wet-bulb globe temperature (WBGT) and universal thermal climate index (UTCI), both of which require technical geoscientific skills that may be inaccessible to many public health researchers. To facilitate research in this domain, we created a database of population-weighted, spatially explicit daily heat metrics - including WBGT, UTCI, heat index, dewpoint temperature, net effective temperature, and humidex - for counties in the conterminous United States derived from the ERA5-Land gridded data set and using previously validated equations and algorithms. We also provide an R package to calculate these metrics, including gold-standard algorithms for estimating WBGT and UTCI, to facilitate replication.