Annals of Epidemiology

Infant mortality (IM), or death prior to the first birthday, is a key public health metric that increases with neighborhood structural inequities. However, neighborhood exposures shift as communities undergo gentrification, a pattern of neighborhood change defined by increasing affluence (in wealth, education, and housing costs). Gentrification has inconsistent associations with infant health outcomes like IM, which may be due to differing relationships between its composite measures and such outcomes. We designed a retrospective cohort analysis of all births and deaths from 2010-2019 across 4 metropolitan areas in Michigan to determine how gentrification and its neighborhood-change components are associated with risk of IM, using multilevel multivariable logistic regression models. Among 672,432 infants, 0.52% died before 1 year. IM was not associated with gentrification. Census tracts with greater increases in income and education had lower rates of IM, but tracts with greater increases in rent costs had higher rates of IM. In unadjusted models, odds of IM were 40% and 15% lower for infants living in tracts in the top quartile increase in household income and college completion, respectively, compared to infants from tracts with the least amount of change. Odds of IM were also increased 29% in infants from tracts with the most increases in rent, though these differences were attenuated when adjusting for individual social factors. Indicators of increasing community affluence have opposing relationships with IM. Policies and interventions that address rising housing costs may reduce IM.

PurposeLow-dose computed tomography lung cancer screening is effective for reducing lung cancer mortality. It is critical to understand the lung cancer screening practices for screen-eligible individuals living in Alabama and Georgia where lung cancer is the leading cause of cancer death. High lung cancer incidence and mortality rates are attributed to high smoking rates among underserved, low income, and rural populations. Therefore, the purpose of this study: (1) to define sociodemographic and clinical characteristics of patients who were screened for lung cancer at an Academic Medical Center (AMC) in Alabama and a Safety Net Hospital (SNH) in Georgia. MethodsA retrospective cohort study of patient electronic health records who received lung cancer screening between 2015 to 2020 was performed to identify the study population and outcome variable measures. Chi-square tests and Student t-tests were used to compare screening uptake across patient demographic and clinical variables. Bivariate and multivariate logistic regressions determined significant predictors of lung cancer screening uptake. ResultsAt the AMC, 67,355 were identified as eligible for LCS and 1,129 were screened. In bivariate analyses, there were several differences between those who were screened and those who were not screened. Screening status in the site at Alabama varied significantly by age (P<0.01), race (P<0.001), marital status (P<0.01), smoking status (P<0.01) health insurance (P<0.01), median income (P<0.01), urban status (P<0.01) and distance from UAB (P<0.01). Those who were screened were more likely to have lesser comorbidities (2.31 vs. 2.53; P<0.001). At the SNH, 11,011 individuals were identified as screen-eligible and 500 were screened. In the site at Georgia, screening status varied significantly by race (P<0.01), health insurance (P<0.01), and distance from site (P<0.01). At the AMC, the odds of being screened increased significantly if the individual was a current smoker compared to former smoker (OR=3.21; P<0.01). At the SNH, the odds of being screened for lung cancer increased significantly with every unit increase in co-morbidity count (OR = 1.12; P=0.01) ConclusionThe study provides evidence that LCS has not reached all subgroups and that additional targeted efforts are needed to increase lung cancer screening uptake. Furthermore disparity was noticed between adults living closer to screening institutions and those who lived farther.

IntroductionSocial determinants of health (SDoH), defined as nonmedical factors that impact health outcomes, have been associated with fatal crash occurrences. Road users who live in communities with negative SDoH may be at increased risk of crash-related mortality, and the risks may be further heightened among geriatric road users and in rural areas. We evaluated the relationship between the county-level measure of SDoH and county-level fatal crash counts among geriatric and non-geriatric road users living in rural, suburban, and urban areas. MethodsFor this ecological study, we pooled data from Fatality Analysis Reporting System (2018 to 2020) and the U.S. Census Bureau (2019 data) and limited our analyses to the 3,108 contiguous US counties. The outcome measures were county-level fatal crash counts involving (1) geriatric (65 years and older) road users (2) non-geriatric road users, and (2) the general population. The predictor variable was the Multidimensional Deprivation Index (MDI), a score that measures the five domains of SDoH - economic quality, healthcare access, education, community, and neighborhood quality. We defined the MDI as a three-level categorical variable: at or below the national average, within two-fold of the national average, and higher than two-fold of the national average. We controlled for county-level demographics and crash characteristics. We performed a Bayesian spatial Poisson regression analysis using Integrated Nested Laplace Approximations and reported the crash fatality rate ratios (plus 95% Credible Intervals (CrI)). ResultsThe median (Q1, Q3) standardized mortality rate ratios among geriatric and non-geriatric road users were 1.3 (0.6, 2.5) and 1.6 (0.9, 2.7), respectively. A total of 283 (9.1%) and 806 (15.9%) counties were classified as very highly deprived and highly deprived, respectively. Clusters of counties with high deprivation rates were identified in the Southern states. Counties classified as very highly deprived and highly deprived had 40% (95% CrI: 1.24 - 1.57) and 25% (95% CrI: 1.17 - 1.34) increased geriatric fatality crash rate ratios and this pattern of association persisted in suburban and urban areas. Also, counties classified as very highly deprived and highly deprived had 42% (95% CI: 1.27 - 1.58) and 32% (95% CI: 1.23 - 1.38) increased fatality crash rate ratios among all road users and this pattern persisted in suburban and urban areas. Counties with more than four-fold increased fatality rate ratios were located commonly in Texas, Oklahoma, Nevada, and Utah. ConclusionDespite older adults being less frequent road users, county-level deprivation measures of the SDoH are equally associated with geriatric and non-geriatric crash-related fatal rate ratios. Policies that improve county-level SDoH may reduce the county-level fatal rate ratios equally among geriatric and non-geriatric road users.

ImportanceThis study assessed the longitudinal impact of new COVID-19 cases when a mask ordinance was implemented in 2 of a 5-county Midwestern U.S. metropolitan region over a 3-month period of time. Reduction in case growth was significant and reduced infection inequities by race and population density. ObjectiveThe objective of this study was to assess the impact that a mandatory mask wearing requirement had on the rate of COVID-19 infections by comparing counties with a mandatory policy with those neighboring counties without a mandatory masking policy. DesignThis was a quasi-experimental longitudinal study conducted over the period of June 12-September 25, 2020. SettingThis study was a population-based study. Data were abstracted from local health department reports of COVID-19 cases. ParticipantsRaw cases reported to the county health departments and abstracted for this study; census-level data were synthesized to address county-level population, income and race. Intervention(s) (for clinical trials) or Exposure(s) (for observational studies)The essential features of this intervention was an instituted mask mandate that occurred in St. Louis City and St. Louis County over a 12 week period. Main Outcome(s) and Measure(s)The primary study outcome measurement was daily COVID-19 infection growth rate. The mask mandate was hypothesized to lower daily infection growth rate. ResultsOver the 15-week period, the average daily percent growth of reported COVID-19 cases across all five counties was 1.81% ({+/-}1.62%). The average daily percent growth in incident COVID-19 cases was similar between M+ and M- counties in the 3 weeks prior to implementation of mandatory mask policies (0.90% [{+/-}0.68] vs. 1.27% [{+/-}1.23%], respectively, p=0.269). Crude modeling with a difference-in-difference indicator showed that after 3 weeks of mask mandate implementation, M+ counties had a daily percent COVID-19 growth rate that was 1.32 times lower, or a 32% decrease. At 12 weeks post-mask policy implementation, the average daily COVID-19 case growth among M- was 2.42% ({+/-}1.92), and was significantly higher than the average daily COVID case growth among M+ counties (1.36% ({+/-}0.96%)) (p<0.001). A significant negative association was identified among counties between percent growth of COVID-19 cases and percent racial minorities per county (p<0.001), as well as population density (p<0.001). Conclusions and RelevanceThese data demonstrate that county-level mask mandates were associated with significantly lower incident COVID-19 case growth over time, compared to neighboring counties that did not implement a mask mandate. The results highlight the swiftness of how a mask ordinance can impact the trajectory of infection rate growth. Another notable finding was that following implementation of mask mandates, the disparity of infection rate by race and population density was no longer significant, suggesting that regional-level policies can not only slow the spread of COVID-19, but simultaneously create more equal environment. Key PointsO_ST_ABSQuestionC_ST_ABSHow are local mask ordinances associated with growth of COVID-19 cases among adjacent counties? FindingsEcological longitudinal analysis reveals a significant slowing of daily COVID-19 case growth after mask ordinance implementation among counties. MeaningLocal-level policy of mask ordinances are shown to be an effective COVID-19 mitigation strategy even within locations of diverse populations.

Birth defects are a leading cause of infant mortality in the United States, but little is known about causes of many types of birth defects. Spatiotemporal disease mapping to identify high-prevalence areas, is a potential strategy to narrow the search for potential environmental and other causes that aggregate over space and time. We described the spatial and temporal trends of the prevalence of birth defects in North Carolina during 2003-2015, using data on live births obtained from the North Carolina Birth Defects Monitoring Program. By employing a Bayesian space-time Poisson model, we estimated spatial and temporal trends of non-chromosomal and chromosomal birth defects. During 2003-2015, 52,524 (3.3%) of 1,598,807 live births had at least one recorded birth defect. The prevalence of non-chromosomal birth defects decreased from 3.8% in 2003 to 2.9% in 2015. Spatial modeling suggested a large geographic variation in non-chromosomal birth defects at census-tract level, with the highest prevalence in south-eastern North Carolina. The strong spatial heterogeneity revealed in this work allowed to identify geographic areas with higher prevalence of non-chromosomal birth defects in North Carolina. This variation will help inform future research focused on epidemiologic studies of birth defects to identify etiologic factors.

ImportancePersistent racial and ethnic disparities in breast and prostate cancer mortality are well documented. Most prior studies emphasize between-group differences and rely on population averages or single composite measures of social disadvantage, which can obscure high-need communities within groups. How socio-behavioral determinants of health vary within groups across local gradients of cancer mortality remains incompletely characterized. A framework that combines race- and cancer-specific mortality with local, domain-level socio-behavioral profiles may help identify where burden is greatest and which specific barriers warrant prioritization. ObjectiveTo determine how socio-behavioral risk relates to breast and prostate cancer mortality within racial and ethnic groups and to characterize domain-specific behavioral profiles across low-, moderate- and high-mortality counties to inform targeted, equity-oriented cancer control strategies. DesignCross-sectional study of U.S. counties. Setting United States, county-level analysis. Participants3,141 U.S. counties, stratified within Non-Hispanic White, Non-Hispanic Black, and Hispanic populations. ExposuresCounty-level socio-behavioral determinants of health measured using a composite index comprising seven domains: community solidarity; education, health literacy, and digital connectivity; quality of care; housing and environmental risk; economic livelihoods; lifestyle behaviors; and touchpoints with care. Main outcomes and measuresRace/ethnicity-specific, age-adjusted breast and prostate cancer mortality rates (2018-2022) and county-level socio-behavioral risk scores. Counties were grouped into mortality tertiles within each race/ethnicity-by-cancer-stratum. ResultsAcross groups, higher socio-behavioral risk was associated with higher breast and prostate cancer mortality. For breast cancer, socio-behavioral risk increased monotonically across mortality tertiles for all groups, with the largest within-group increases among Hispanic and Non-Hispanic Black women. For prostate cancer, risk generally increased across mortality tertiles for all groups. Although Hispanic populations had lower population-average mortality, high-mortality Hispanic counties exhibited pronounced risk in lifestyle behaviors, economic livelihoods, and touchpoints with care. Domain patterns associated with high mortality varied by race, ethnicity, and cancer type, with touchpoints with care and economic livelihoods consistently prominent. Conclusions and relevanceWithin-group heterogeneity in socio-behavioral risk is substantial across U.S. counties. Linking population-specific, domain-level socio-behavioral profiles to cancer mortality may support more precise and equity-oriented cancer control strategies than reliance on group averages or composite indices. Key pointsO_ST_ABSQuestionC_ST_ABSWithin racial and ethnic groups, how do socio-behavioral determinants of health vary across US counties with low, moderate, and high breast and prostate cancer mortality? FindingsIn this cross-sectional study, higher county-level socio-behavioral risk was associated with higher breast and prostate cancer mortality across racial and ethnic groups. Race/ethnicity-specific, domain-level profiles revealed within-group heterogeneity, including persistently elevated risk among Non-Hispanic Black populations and pronounced domain-specific gaps in high-mortality Hispanic counties. MeaningLinking population-specific socio-behavioral profiles to local cancer mortality can guide more precise and equity-oriented prioritization of intervention domains and geographies than reliance on group averages or composite indices.

IntroductionThe Affordable Care Act (ACA), whose major provisions were Medicaid expansion, state insurance exchanges, and allowing dependents to remain on their parents insurance until age 26, was implemented fully in 2014. Increased access to care might have improved access to lung/bronchus cancer diagnosis, treatment, and outcomes. We hypothesized differential changes in lung/bronchus mortality among Mississippi (which did not expand Medicaid) and New York (that did expand Medicaid). MethodsWe utilized the CDC Wonder database to compare lung/bronchus cancer mortality rates in Mississippi, New York, and the United States as a whole, comparing such rates 5 years prior (2009-2013) to 5 years after (2015-2019) ACA enactment. Statistical significance was deemed at p <0.05. ResultsIn the 5 years prior to ACA implementation (2009-2013), there was no statistically-significant difference between Mississippi, New York, or the total U.S. in the percent of population dying from lung/bronchus cancer (p >0.7). In the 5 years following ACA implementation (2015-2019), there was a statistically-significant decrease between Mississippi, New York, and the total U.S. in the percent of population dying from lung/bronchus cancer (p <0.002). Comparing the years 2009-2013 (5 years prior to ACA) and the years 2015-2019 (5 years after ACA), there was a statistically-significant difference in the decrease in percent of population dying from lung and bronchus cancers in New York, Mississippi, and the total United States (p <0.002), with New York having the greatest percent decline. In New York, the percentage of mortalities from lung and bronchus cancers decreased from 0.004691% to 0.004088% (p <0.0001). In Mississippi, the percentage of mortalities from lung and bronchus cancers decreased from 0.006537% to 0.006282% (p = 0.0061). In the total US, the percentage of mortalities from lung and bronchus cancers decreased from 0.05097% to 0.0473% (p <0.0001). ConclusionState-level participation in the ACAs Medicaid expansion was associated with disproportionate improvement in lung/bronchus mortality compared with non-participation and with the U.S. in total.

BackgroundThe COVID-19 pandemic has exacerbated health injustices in the U.S. driven by racism and other forms of structural violence. Research has shown the disproportionate impacts of COVID-19 morbidity and mortality in the most marginalized communities. ObjectivesWe examined the associations between COVID-19 cumulative incidence (CI) and case-fatality risk (CFR) and the CDCs Social Vulnerability Index (SVI), a composite score assessing historical marginalization and thus vulnerability to disaster events. MethodsUsing county-level data from national databases, we used population density, Gini index, percent uninsured, and average annual temperature as covariates, and employed negative binomial regression to evaluate relationships between SVI and COVID-19 outcomes. Optimized hot spot analysis identified hot spots of COVID-19 CI and CFR, which were compared in terms of SVI using logistic regression. ResultsAs of 2/3/21, 26,452,031 cases of and 448,786 deaths from COVID-19 had been reported in the U.S. Negative binomial regression showed that counties in the top SVI quintile reported 13.7% higher CI (p<0.001) than those in the bottom SVI quintile. Additionally, each unit increase in a countys SVI score was associated with a 0.2% increase in CFR (p<0.001). Logistic regression analysis showed that counties in the lowest SVI quintile had significantly greater odds of being in a CI hot spot than all other counties, yet counties in the highest SVI quintile had 63% greater odds (p=0.008) of being in a CFR hot spot than counties in the lowest SVI quintile. ConclusionWe demonstrated a significant relationship between SVI and CFR, but the relationship between SVI and CI is complex and warrants further investigation. SVI may help elucidate unequal impacts of COVID-19 and guide prioritization of vaccines to communities most impacted by structural injustices.

The national COVID-19 conversation in the US has mostly focused on urban areas, without sufficient examination of another geography with large vulnerable populations: the suburbs. While suburbs are often thought of as areas of uniform affluence and racial homogeneity, over the past 20 years, poverty and diversity have increased substantially in the suburbs. In this study, we compare geographic and temporal trends in COVID-19 cases and deaths in Louisiana, one of the few states with high rates of COVID-19 during both the spring and summer. We find that incidence and mortality rates were initially highest in New Orleans. By the second peak, trends reversed: suburban areas experienced higher rates than New Orleans and similar rates to other urban and rural areas. We also find that increased social vulnerability was associated with increased positivity and incidence during the first peak. During the second peak, these associations reversed in New Orleans while persisting in other urban, suburban, and rural areas. The work draws attention to the high rates of COVID-19 cases and deaths in suburban areas and the importance of metropolitan-wide actions to address COVID-19. RegistrationN/A Funding sourceNIH (DP5OD26429) and RWJF (77644) Code and data availabilityCode for replication along with data is available here: https://github.com/alinasmahl1/COVID_Louisiana_Suburban/.

Pulmonary embolism (PE) is a sudden blockage of lung arteries, usually caused by a blood clot that travels from the deep veins of the legs. As the world becomes more sedentary and lifestyle diseases emerge, deaths from PE are expected to rise in the next 20 years. For instance, the United States records annual deaths of 60 per 100,000 people. The degree to which these deaths are affected by demographic, socioeconomic and environmental predisposing factors as well as how they vary across time and space remains an open science question. In this paper, we conduct a detailed statistical and spatial-temporal study PE mortality counts across US counties from 2005 to 2022. Our study shows that study shows that PE mortality is not randomly distributed in space and time but concentrated in most counties in Arkansas, Mississippi, Kansas, Missouri, Oklahoma, Louisiana, Nebraska, Tennessee, and Texas. We also established that age is a statistically significant predictor (mean coefficient of 0.52) of PE mortality especially in counties of Mississippi, Kansas, Missouri, Tennessee, Illinois, Kentucky, Texas and Virginia. Our results thus provide empirical support for prioritizing regionally targeted PE prevention policies. Furthermore, the adopted county-level analysis uncovered granular geographic patterns that are usually obscured in state or national level analysis. Our study thus provides actionable evidence to support geographically tailored strategies aimed at reducing mortality by pinpointing counties with consistently elevated PE mortality risk at different timescales.

ObjectiveThe aim of this study was to describe differences and trends in ATV-related hospitalizations for urban and rural-dwelling youth in Canada. MethodsWe conducted a cross-sectional study using administrative hospital abstract data all patients admitted for an ATV-related injury to hospitals in 9 provinces in Canada between 2002 and 2019. The primary exposure was rural residence, defined by postal code. Rural-urban comparisons were stratified by age group: children (<16 years), adolescents (16-20 years) and adults (>21 years). The primary outcome was the incidence of any hospitalization, secondary outcomes were head injury, fractures, crush injury and spinal cord injury.. ResultsAmong 34,390 patients with complete data, 17% were children younger than 16 yrs and 14% were adolescents 16-20 yrs; 78% of children and 85% of adolescents were male, and 47% lived rurally. The incident rate ratio (IRR) for being hospitalized for an ATV-related injury was 5-fold higher for rural children (5.59; 95% CI: 5.30-5.88) and adolescents (5.16; 95% CI: 4.88, 5.47) compared to urban children and adolescents, respectively. The 5-fold higher IRR was also evident for ATV-related fractures among rural children and adolescents. Adolescents had a particularly higher risk for ATV-related crush injuries (IRR: 10.43; 95% CI: 5.74-18.96) and spinal cord injuries (IRR: 5.21; 95% CI: 3.33-8.15) while children were at higher risk of ATV-related head injuries (IRR: 6.55; 95% CI: 5.76-7.46) compared to urban dwelling youth. ConclusionsIn Canada, rural children and adolescents were at a very elevated risk of ATV injuries compared to those living in urban centres.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSPrior research has identified higher rates of COVID-19 mortality among people of color (relative to non-Hispanic whites) and populations in high-poverty neighborhoods (relative to wealthier neighborhoods). It is unclear, however, whether non-Hispanic whites in high-poverty neighborhoods experience elevated mortality, or whether people of color living in wealthy areas are relatively protected. Exploring socioeconomic position in combination with race/ethnicity can lead to a more detailed understanding of the specific processes that result in COVID-19 inequities. Methods and FindingsWe used census and individual-level mortality data for the non-Hispanic white, non-Hispanic Black, and Hispanic/Latinx populations of Cook County, Illinois, USA. We excluded deaths related to nursing homes and other institutions. We calculated age and gender-adjusted mortality rates by race/ethnicity, census tract poverty quartile, and age group (0-64 and [&ge;]65 years). Within all racial/ethnic groups, COVID-19 mortality rates were greatest in the highest-poverty quartile and lowest in the lowest-poverty quartile. The mortality rate for younger non-Hispanic whites in the highest-poverty quartile was 13.5 times that of younger non-Hispanic whites in the lowest-poverty quartile (95% CI: 8.5, 21.4). For young people in the highest-poverty quartile, the non-Hispanic white and Black mortality rates were similar. Among younger people in the lowest-poverty quartile, non-Hispanic Black and Hispanic/Latinx people had mortality rates nearly three times that of non-Hispanic whites. For the older population, the mortality rate among non-Hispanic whites in the highest-poverty quartile was less than that of lowest-poverty non-Hispanic Black and Hispanic/Latinx populations. ConclusionsOur findings suggest racial/ethnic inequalities in COVID-19 mortality are partly, but not entirely, attributable to the higher average socioeconomic position of non-Hispanic whites relative to the non-Hispanic Black and Hispanic/Latinx populations. Future research on health equity in COVID-19 outcomes should collect and analyze individual-level data on the potential mechanisms driving population distributions of exposure, severe illness, and death.

ObjectivesMultiple myeloma (MM) is the second most common hematologic malignancy in the U.S.; however, the etiology is poorly understood. We investigated social determinants of health (SDoH) associated with MM incidence and survival among low-income Black and White participants in the Southern Community Cohort Study (SCCS). MethodsThe SCCS enrolled participants aged 40-79 years from 12 Southeastern states. We examined associations between SDoH (residential racial segregation, neighborhood deprivation, population density, persistent poverty, and rurality) geocoded to zip code, with MM incidence and all-cause mortality using multivariable Cox regression analyses. Additional stratified analyses examined MM incidence by obesity status. ResultsAmong 74,294 participants, there were 162 MM cases, 133 self-identified as Black individuals and 29 self-identified as White individuals. Living in the highest vs. lowest deprivation areas was associated with 2-fold increased MM risk (HR:2.24; 95% CI:1.01-4.95). Among MM cases, those living in the least vs. most residentially segregated areas had 2-fold increased mortality (HR:2.21; 95% CI:1.03-4.74). Among participants who were not obese, those who lived in the most densely populated areas had a reduced risk of MM compared to those who lived in the least densely populated areas (HR:0.31; 95% CI: 0.14-0.67); and those who lived in urban vs. rural areas had a reduced risk (HR:0.52; 95% CI: 0.32-0.85). DiscussionSDoH factors including neighborhood deprivation and residential racial segregation could influence MM risk and survival among low-income populations. ConclusionSDOH factors should be considered when developing strategies to reduce overall MM burden, and disparities among people with lower socioeconomic backgrounds.

Childrens risk of exposure to carbon monoxide (CO) increases after disasters, likely due to improper generator use during power outages. Here, we evaluate the impact of outages on childrens CO-related emergency department (ED) visits in New York State (NYS). We leveraged power outage data spanning 2017-2020 from the NYS Department of Public Service for 1,865 power operating localities (i.e., communities) and defined all-size and large-scale power outage hours. All-size outage hours affected [&ge;]1% of customers, and large-scale outage hours affected [&ge;]20%. We identified CO poisoning using diagnostic codes among those aged <18 between 2017 and 2020 using the Statewide Planning and Research Cooperative System (SPARCS), an all-payer reporting system in NYS. We linked community power outage exposure to patients using the population-weighted centroid of their block group of residence. We estimated the impact of power outages on CO poisoning using a time-stratified case-crossover study design with conditional logistic regression, controlling for daily relative humidity, mean temperature, and total precipitation. Analyses were stratified by urban and rural communities. From 2017-2020, there were 917 pediatric CO poisoning ED visits in NYS. Most cases (83%) occurred in urban region of the state. We observed an association statewide between all-size and large-scale outages and CO ED visits on the index day and the following two days before a return to baseline on lag day 3. Four hours without power increased the odds of a pediatric CO poisoning ED visit by [&ge;]50% for small-scale and [&ge;]150% for large-scale outages, and associations were stronger in urban versus rural areas. While CO poisoning is a relatively rare cause of pediatric ED visits in NYS, it can be deadly and is also preventable. Expanded analyses of the health impacts of outages and advocacy for reliable energy access are needed to support childrens health in a changing climate

Background: Lung cancer in never-smokers is rising, with a substantial proportion harboring the EGFR mutation. While fine particulate matter (PM2.5) is a recognized risk factor, other intervenable pollutants and built environmental factors remain unknown. Objectives: To identify urban characteristics associated with EGFR-mutant (vs. wild-type) lung cancer using high-resolution spatiotemporal data. Methods: We analyzed 2,699 lung cancer patients with documented EGFR status treated at a high-volume academic medical center in New York City. Patient residential addresses were linked to high-resolution (300m x 300m) 5-year cumulative exposures to 3 air pollutants and 26 urban features. We developed Light Gradient Boosting Machine (LightGBM) models to classify EGFR status, comparing a basic clinical model with established predictors (Asian, female, never-smoking status, and adenocarcinoma histology) to an extended model with additional urban factors. Predictive performance was assessed based on discrimination (AUC). Results: We included 2,699 patients, of whom 54.1% were female and 25.8% self-identified as Asian, 11.2% as Black, and 7.4% as Hispanic; and 29% had EGFR-mutated cancer. The extended model showed modest improvements in discrimination (AUC: 0.775 [95% CI, 0.739-0.809] vs. 0.768 [0.723-0.811]), compared to the clinical model. Newly identified factors for EGFR-mutant status included black carbon (BC), nitrogen dioxide (NO2), proximity to airports, reduced access to public transportation, elevated noise levels, and lead exposure. Conclusions: Traffic-related pollutants (BC, NO2) from diesel engines and motor vehicles, and proximity to airports, were among the novel spatiotemporal features associated with EGFR-mutant lung cancer. These results may inform policy interventions.

Compared to previous studies that have typically used a single summary score, we aimed to construct a multidomain neighborhood environmental vulnerability index (NEVI) to characterize the magnitude and variability of area-level factors with the potential to modify the health effects of environmental pollutants. Using the Toxicological Prioritization Index framework and data from the 2015-2019 U.S. Census American Community Survey and the 2020 CDC PLACES Project, we quantified census tract-level vulnerability overall and in 4 primary domains (demographic, economic, residential, and health status), 24 subdomains, and 54 distinct area-level features for New York City (NYC). Overall and domain-specific indices were calculated by summing standardized feature values within the subdomains and then aggregating and weighting subdomains within equally-weighted primary domains. In citywide comparisons, NEVI was correlated with both the Neighborhood Deprivation Index (r = 0.91) and the Social Vulnerability Index (r = 0.87) but provided additional information on features contributing to vulnerability. Vulnerability varied spatially across NYC, and hierarchical cluster analysis using subdomain scores revealed six patterns of vulnerability across domains: 1) low in all, 2) primarily low except residential, 3) medium in all, 4) high demographic, economic, and residential 5) high economic, residential, and health status, and 6) high demographic, economic and health status. Created using a tool that offers flexibility for theory-based construction, NEVI provided detailed metrics of vulnerability across domains that can inform targeted research and public health interventions aimed at reducing the health impacts from environmental exposures across an urban center.

IntroductionFirearms are the leading cause of death in US children and adolescents, but little is known about whether legal policies may be responsible. MethodsWe conducted difference-in-differences analysis on CDC WONDER data before and after McDonald v. Chicago, the landmark 2010 Supreme Court decision on firearms regulation. States were divided into three groups, based on legal actions taken before and since 2010, most permissive, permissive, and restricted. Firearm mortality trends before (1999-2010) and after (2010-2023) were determined and compared across the three groups for all intents and by intent (homicide and suicide). Within the most permissive state grouping, pediatric firearm mortality by 2013 urbanicity and by observed race and ethnicity were conducted. For each US state, pre-and- post 2010 all-intent pediatric firearm mortality incident rates were compared. ResultsThere were 7130 excess pediatric firearms deaths in states with more permissive regulatory regimes than those with stricter frameworks, as well as higher rates of homicide and suicide. Non-Hispanic Black populations were disproportionately affected by these trends. Four states (California, Maryland, New York, and Rhode Island) had decreased pediatric firearm mortality after 2010, all of which were in the restrictive firearms law group. ConclusionStates with more permissive firearm laws have experienced greater pediatric firearm mortality during the post-McDonald v. Chicago era. KEY POINTS QuestionDid states enacting permissive firearm laws after 2010--when McDonald v. Chicago was decided by the United States Supreme Court--subsequently experience higher rates of pediatric firearm mortality? FindingsDifference-in-difference analysis found that state groups that enacted more permissive firearm laws after 2010 experienced >7,100 firearm deaths in children and adolescents ages 0-17 between 2010-2023 compared to restrictive law-enacting states, of which most (77.5%) were homicides. In the permissive states groups, increases occurred in all urbanicities. The largest increase occurred in non-Hispanic Black children and adolescents. Four states had statistical decreases in pediatric firearm mortality during the study period, all of which were in states which enacted restrictive firearm policies. MeaningPermissive firearm laws contributed thousands of excess firearm deaths among children living in states with permissive policies. Future work should focus on determining which types of laws conferred the most harm and which the most protection.

IntroductionPrevious studies have evaluated comorbidities and sociodemographic factors individually or by type but not comprehensively. This study aims to analyze the influence of a wide variety of factors in a single study to better understand the big picture of their effects on case-fatalities. MethodsCounty-level comorbidities, social determinants of health such as income and race, measures of preventive healthcare, age, education level, average household size, population density, and political voting patterns were all evaluated on a national and regional basis. Analysis was performed through Generalized Additive Models and adjusted by CCVI. ResultsFactors associated with reducing COVID-19 case fatality rates were mostly sociodemographic factors such as age, education and income, and preventive health measures. Obesity, minimal leisurely activity, binge drinking, and higher rates of individuals taking high blood pressure medication were associated with increased case fatality rate in a county. Political leaning influences case case-fatality rates. Regional trends showed contrasting effects where larger household size was protective in the Midwest, yet harmful in Northeast. Notably, higher rates of respiratory comorbidities such as asthma and COPD diagnosis were associated with reduced case-fatality rates in the Northeast. Increased rates of CKD within counties were often the strongest predictor of increased case-fatality rates for several regions. ConclusionOur findings highlight the importance of considering the full context when evaluating contributing factors to case-fatality rates. The spectrum of factors identified in this study must be analyzed in the context of one another and not in isolation.

ImportanceCOVID-19 racial disparities have gained significant attention yet little is known about how age distributions obscure racial-ethnic disparities in COVID-19 case fatality ratios (CFR). ObjectiveWe filled this gap by assessing relevant data availability and quality across states, and in states with available data, investigating how racial-ethnic disparities in CFR changed after age adjustment. Design/Setting/Participants/ExposureWe conducted a landscape analysis as of July 1st, 2020 and developed a grading system to assess COVID-19 case and death data by age and race in 50 states and DC. In states where age- and race-specific data were available, we applied direct age standardization to compare CFR across race-ethnicities. We developed an online dashboard to automatically and continuously update our results. Main Outcome and MeasureOur main outcome was CFR (deaths per 100 confirmed cases). We examined CFR by age and race-ethnicities. ResultsWe found substantial variations in disaggregating and reporting case and death data across states. Only three states, California, Illinois and Ohio, had sufficient age- and race-ethnicity-disaggregation to allow the investigation of racial-ethnic disparities in CFR while controlling for age. In total, we analyzed 391,991confirmed cases and 17,612 confirmed deaths. The crude CFRs varied from, e.g. 7.35% among Non-Hispanic (NH) White population to 1.39% among Hispanic population in Ohio. After age standardization, racial-ethnic differences in CFR narrowed, e.g. from 5.28% among NH White population to 3.79% among NH Asian population in Ohio, or an over one-fold difference. In addition, the ranking of race-ethnic-specific CFRs changed after age standardization. NH White population had the leading crude CFRs whereas NH Black and NH Asian population had the leading and second leading age-adjusted CFRs respectively in two of the three states. Hispanic populations age-adjusted CFR were substantially higher than the crude. Sensitivity analysis did not change these results qualitatively. Conclusions and RelevanceThe availability and quality of age- and race-ethnic-specific COVID-19 case and death data varied greatly across states. Age distributions in confirmed cases obscured racial-ethnic disparities in COVID-19 CFR. Age standardization narrows racial-ethnic disparities and changes ranking. Public COVID-19 data availability, quality, and harmonization need improvement to address racial disparities in this pandemic. Key PointsO_ST_ABSQuestionC_ST_ABSWhat are the racial-ethnic disparities in COVID-19 case fatality ratios (CFR) across states after adjusting for age? FindingsWe conducted direct standardization among 391,991 COVID-19 cases and 17,612 deaths from California, Illinois and Ohio to compare age-adjusted CFR across race-ethnicities. The racial-ethnic disparities in CFR narrowed and the ranking changed after age standardization. MeaningAge distributions in confirmed cases obscured racial-ethnic disparities in COVID-19 CFR.

ObjectiveTo estimate coronavirus disease 2019 (COVID-19) mortality rates among individuals incarcerated in U.S. state prisons by race and ethnicity (RE). DesignRetrospective population-based analysis SettingData from state-level Departments of Corrections (DOCs) from March 1 through October 1, 2020. ParticipantsPublicly available data collected by Freedom of Information Act requests representing adults in the custody of US state DOCs. Main OutcomesCumulative COVID-19 death and custody population data. Crude RE-specific cumulative death rates per 1,000 persons, by state and in aggregate, using RE-specific custody population on March 1, 2020, as the denominator. Rate ratios (RR) and 95% confidence intervals (95%CI) compared state-level and aggregate cumulative age-adjusted mortality rates as of 10/01/2020 by RE, with White individuals as reference group. ResultsOf all COVID-related deaths in U.S. prisons through October 2020, 23.35% (272 of 1165) were captured in our analyses. The average age at COVID-19 mortality was 63 years (SD=10 years) and was significantly lower among Black (60 years, SD=11 years) compared to White adults (66 years, SD=10 years; p<0.001). In age-standardized analysis, COVID-19 mortality rates were significantly higher among Black (RR=1.93, 95% CI: 1.25-2.99), Hispanic (RR=1.81, 95% CI: 1.10-2.96) and those of Other racial and ethnic groups (RR=2.60, 95% CI: 1.01-6.67) when compared to White individuals. ConclusionsAge-standardized mortality rates were higher among incarcerated Black, Hispanic and those of Other RE groups compared to their White counterparts. Greater data transparency from all carceral systems is needed to better understand populations at disproportionate risk of COVID-19 morbidity and mortality.