Zip Code Level Research Articles

Abstract 4141487: Social Determinants of Health and Outcomes in Patients with Hypertrophic Cardiomyopathy in the Sarcomeric Human Cardiomyopathy Registry (SHaRe)

Introduction: Area-based social determinants of health (SDOH) are associated with higher risk for acquired heart disease. However, the impact of area-based SDOH on clinical outcomes in patients with hypertrophic cardiomyopathy (HCM) has not been previously studied. Hypothesis: Residing in an area with lower median household income and a higher social deprivation index (SDI) are independently associated with adverse outcomes in patients with HCM. Methods: This study included 4,483 adult patients with HCM from five Sarcomeric Human Cardiomyopathy Registry (a multi-center prospective registry of patients with HCM) sites in the US followed for a median [IQR] of 4.0 [1.0-8.5] years. The patients’ residential address was geocoded at the zip code level to characterize median household income and SDI. SDI is based on various social factors (such as income and education), with a higher score reflecting more deprivation. Multivariate models, adjusting for sex, age, hypertension and body mass index were used to determine the independent association of household income and SDI on ventricular arrhythmias (VA), heart failure (HF), and an overall composite outcome (VA, HF, atrial fibrillation, death, and stroke). Results: Median age at diagnosis was 48.3 [32.0-60.4] years with 42.0% female. Median household income was $90,000 [60,000-110,000] and median SDI was 25 [10-55]. Adjusted hazard ratios per $10,000 higher income were 0.96 (95% CI 0.93-1.00, p=0.03) for VA, 0.91 (95% CI 0.90-0.93, p <0.01) for HF, and 0.95 (95% CI 0.94-0.96, p <0.01) for the composite outcome. Adjusted hazard ratios per 10 units higher SDI (more deprived) were 1.07 (95% CI 1.03-1.12, p<0.01) for VA, 1.06 (95% CI 1.04-1.08, p <0.01) for HF, and 1.05 (95% CI 1.03-1.06, p <0.01) for the composite outcome. The associations of SDI and income on clinical outcomes were similar regardless of sarcomere gene variant status. Conclusions: Residing in an area with lower median household income and higher SDI were independently associated with adverse outcomes in patients with HCM. These findings extend observations from acquired cardiovascular disease and highlight the need to address adverse SDOH to reduce health disparities and improve outcomes in patients with HCM.

Assessing demographic vulnerability and weather impacts on utility disconnections in California.

When a household is disconnected from their electric utility service the consequences can be severe, including accumulation of debt, the inability to maintain comfortable temperatures, and in the most extreme cases, homelessness or mortality. While the survey-based literature on utility disconnections has yielded important findings about which households are most likely to experience a utility shutoff, only a few existing studies have used data from utility companies themselves. In this analysis, we utilize zip-code level data from four of California's largest utility providers to measure sociodemographic disparities in disconnections, in addition to the impact of adverse weather. We find that zip codes with a higher share of vulnerable households, especially Black and Hispanic households and households with young children, face a higher number and rate of utility disconnections, even after controlling for an extensive set of factors that are commonly thought to explain higher rates of energy insecurity, including income, housing characteristics, and energy costs. Our analysis also suggests that sociodemographic and weather disparities in disconnections differ between utility types. We conclude by discussing the implications of the findings for research and policy, including the impact of regulation, utility provider practices, and more extreme weather driven by climate change.

Effects of fine particulate matter from wildfire and non-wildfire sources on emergency-department visits in people who were housed and unhoused in San Diego County (CA, USA) during 2012–20: a time-stratified case–crossover study

Being unhoused can increase vulnerability to adverse health effects due to air pollution. We aimed to quantify changes in emergency-department visits during and after exposure to wildfire-specific and non-wildfire particulate matter 2·5 μm or less in diameter (PM2·5) in San Diego County (CA, USA) in people who were both unhoused and housed. For this time-stratified case-crossover study, we used data on exposure to wildfire-specific PM2·5 in California and individual-level data for people admitted to the emergency departments of two hospitals (UC San Diego Health emergency departments at La Jolla and Hillcrest, San Diego) in San Diego County between July 1, 2012, and Dec 31, 2020. People with a postcode outside of San Diego County were excluded. Demographic information was age group, race or ethnicity, and transport to the emergency department. Wildfire-specific PM2·5 concentration at the postcode level was previously estimated using an ensemble model that combined multiple machine-learning algorithms and explanatory variables obtained via data on 24-h mean PM2·5 concentrations from the US Environmental Protection Agency Air Quality System. Conditional logistic regression models were applied, adjusting for specific humidity, wind velocity, and maximum temperature extracted from the US Gridded Surface Meteorological Dataset. Housing status was established by registration staff or triage nurses on arrival at the emergency department. For people who were unhoused, exposure was defined based on the weighted mean PM2·5 concentration at the city level proportional to the number of people who were unhoused in each specific city across urban centres in San Diego County. For people who were housed, we used residence postcode to measure exposure. We assessed the association between PM2·5 from wildfire and non-wildfire sources and emergency-department visits in people who were housed and unhoused. There were 587 562 emergency-department visits at the two hospitals, 76 407 (13·0%) of which were by people who were unhoused. People who were housed had a higher exposure to overall PM2·5 (24-h mean over the study period of 9·904 mg/m3, SD 3·445) and non-wildfire PM2·5 (9·663, 2·977) than people who were unhoused (9·863, 3·221; 9·557, 2·599). However, people who were unhoused had a higher exposure to wildfire-specific PM2·5 (0·305, 1·797) than people who were housed (0·240, 1·690). Overall PM2·5 exposure was associated with increased odds of emergency-department visits for both people who were housed (odds ratio 1·003, 95% CI 1·001-1·004 per 1 μg/m3 PM2·5 for 0-3 days after exposure) and people who were unhoused (1·004, 1·000-1·008 for 0-3 days after exposure). We found that non-wildfire PM2·5 was associated with emergency-department visits among people who were housed (1·003, 1·002-1·005 for 0-3 days after exposure) and wildfire-specific PM2·5 was associated with emergency-department visits in people who were unhoused (1·006, 1·001-1·011 for 0-3 days after exposure). People who were unhoused in San Diego County were more likely to visit emergency departments after exposure to increased wildfire-specific PM2·5. As the intensity and frequency of wildfires increase, understanding risk factors for vulnerable populations, such as people who are unhoused, is crucial to develop effective adaptation strategies. US National Institutes of Health, National Institute on Aging.

Estimation of HIV prevalence at the ZIP code-level in Atlanta, Georgia: Bayesian prediction modeling using passive surveillance data and social determinants of disease spreading

ObjectiveThis study aims to predict the number of undiagnosed HIV cases at the ZIP Code-level in Atlanta, Georgia, based on publicly available information. Study designStatistical modeling. MethodsWe fitted a Bayesian hierarchical Binomial model to county-level estimates of the passive-surveillance-system. The denominator was the true total HIV cases arising from a Negative Binomial distribution. The trial probability, known as ascertainment probability, depended on socio-economic determinants of HIV, retained via feature-selection algorithms. Data were obtained from CDC's HIV report for End of the HIV Epidemic and the American Community Survey. The prediction model was assessed out-of-sample in Georgia counties. We combined socio-economic data with the posterior predictive distribution of the coefficients to predict the mean ascertainment probability and total HIV cases at the ZIP Code-level. These estimates were spatially smoothed and aggregated at the county-level for secondary validations. ResultsThe county-level model showed good mixing properties and predictive accuracy. The mean ascertainment probability calibrated to the ZIP Code-level varied from 78.4% (95% credible interval: 24.4%–99.3%) to 93.8% (95%CI: 80.6%–99.8%). Further, the predicted undiagnosed HIV cases ranged between 12 (95%CI: 6–19; ZIP Code 30322) to 1603 (95%CI 1209–1968; ZIP Code 30318). ConclusionsOur findings provide a more complete picture of the relative burden of HIV across ZIP codes. Such information can be used by Local Health Departments to identify underserved areas and allocate resources accordingly. Furthermore, our methodological approach can be applied to complement the information obtained from passive surveillance, especially when more resource-intensive approaches are not available or are unfeasible to employ.

Quantifying post-disaster community well-being: A case study of Hurricane Harvey

Natural hazards such as hurricanes affect various aspects of the community members’ lives and their post-disaster well-being by causing significant disruptions in the key community activities in the immediate recovery phase. Furthermore, natural hazards leave behind short-term socio-economic impacts such as stress, anxiety, huge recovery expense, and lack of affordable housing. There is a need for incorporating both immediate and short-term impacts of natural hazards when measuring disaster recovery. This study aims to address this need by introducing community well-being as the metric for the recovery of communities from natural disasters. From this perspective, community resilience is defined as the capability of community to reach its pre-disaster state of well-being, in a timely and efficient manner. The study leverages Bottom-Up Spillover Theory and the existing literature to introduce a community well-being model. This model quantifies how the functionality of infrastructure systems can affect various aspects of community well-being based on 6 domains, 17 sub-domains, and 51 indicators. The indicators were quantified using survey data and 211-call data for the City of Houston, and data on the impact of Hurricane Harvey at the zip code level. The results showed that various dimensions of well-being were affected heterogeneously and followed different recovery patterns. The proposed framework can serve decision makers as a dashboard for identifying the well-being domains and sub-domains that should be addressed to enhance post-disaster well-being in the immediate-to short-term. Furthermore, the study introduces the phone call data as an inexpensive and timely replacement for multiple rounds of survey questionnaires for quantifying community well-being.

Syndemic geographic patterns of cancer risk in a health-deprived area of England

ObjectivesThis study aims to analyse the geographical co-occurrence of cancers and their individual and shared risk factors in a highly deprived area of the North West of England to aid the identification of potential interventions. Study designAn ecological study design was employed and applied at postcode sector level in the Morecambe Bay region. MethodsA novel spatial joint modelling framework designed to account for large frequencies of left-censored cancer data was employed. Nine cancer types (breast, colorectal, gynaecology, haematology, head and neck, lung, skin, upper gastrointestinal, urology) alongside demographic, behavioural factors and socio-economic variables were included in the model. Explanatory factors were selected by employing an accelerated failure model with lognormal distribution. Post-processing included principal components analysis and hierarchical clustering to delineate geographic areas with similar spatial risk patterns of different cancer types. Results15,506 cancers were diagnosed from 2017 to 2022, with the highest incidence in skin, breast and urology cancers. Factors such as age, ethnicity, frailty and comorbidities were associated with cancer risk for most of the cancer types. A positive geographical association was found mostly between the colorectal, haematology, upper GI, urology and head and neck cancer types. That is, these cancers had their largest risk in the same areas, similarly to their lowest risk values. The spatial distribution of the risk and cumulative risk of the cancer types revealed regional variations, with five clusters identified based on cancer type risk, demographic and socio-economic characteristics. Rural areas were the least affected by cancer and the urban area of Barrow-in-Furness was the area with the highest cancer risk, three times greater than the risk in the surrounding rural areas. ConclusionsThis study emphasizes the utility of joint disease mapping by geographically identifying common or shared factors that, if targeted, could lead to reduced risk of multiple cancers simultaneously. The findings suggest the need for tailored public health interventions, considering specific risk factors and socio-economic disparities. Policymakers can utilize the spatial patterns identified to allocate resources effectively and implement targeted cancer prevention programmes.

Wildland Fire-Related Smoke PM 2.5 and Cardiovascular Disease ED Visits in the Western United States.

The impact of short-term exposure to fine particulate matter (PM 2.5 ) due to wildland fire smoke on the risk of cardiovascular disease (CVD) remains unclear. We investigated the association between short-term exposure to wildfire smoke PM 2.5 and Emergency Department (ED) visits for acute CVD in the Western United States from 2007 to 2018. ED visits for primary or secondary diagnoses of atrial fibrillation (AF), acute myocardial infarction (AMI), heart failure (HF), stroke, and total CVD were obtained from hospital associations or state health departments in California, Arizona, Nevada, Oregon, and Utah. ED visits included those that were subsequently hospitalized. Daily smoke, non-smoke, and total PM 2.5 were estimated using a satellite-driven multi-stage model with a high resolution of 1 km. The data were aggregated to the zip code level and a case-crossover study design was employed. Temperature, relative humidity, and day of the year were included as covariates. We analyzed 49,759,958 ED visits for primary or secondary CVD diagnoses, which included 6,808,839 (13.7%) AFs, 1,222,053 (2.5%) AMIs, 7,194,474 (14.5%) HFs, and 808,396 (1.6%) strokes. Over the study period from 2007-01-01 to 2018-12-31, the mean smoke PM 2.5 was 1.27 (Q1: 0, Q3: 1.29) µg/m 3 . A 10 µg/m 3 increase in smoke PM 2.5 was associated with a minuscule decreased risk for AF (OR 0.994, 95% CI 0.991-0.997), HF (OR 0.995, 95% CI 0.992-0.998), and CVD (OR 0.9997, 95% CI 0.996-0.998), but not for AMI and stroke. Adjusting for non-smoke PM 2.5 did not alter these associations. A 10 µg/m 3 increase in total PM 2.5 was linked to a small increased risk for all outcomes except stroke (OR for CVD 1.006, 95% CI 1.006-1.007). Associations were similar across sex and age groups. We identified an unexpected slight lower risk of CVD ED visits associated with short-term wildfire smoke PM 2.5 exposure. Whether these findings are due to methodological issues, behavioral changes, or other factors requires further investigation.

Young adults, particularly young women, account for an increasingly large share of Dutch mental healthcare expenditure over the period between 2015 and 2021.

There is increasing concern over the mental distress of youth in recent years, which may impact mental healthcare utilisation. Here we aim to examine temporal patterns of mental healthcare expenditures in the Netherlands by age and sex in the period between 2015 and 2021. Comprehensive data from health insurers in the Netherlands at the 3-number postal code level were used for cluster weighted linear regressions to examine temporal patterns of mental healthcare expenditure by age group (18-34 vs 35-65). The same was done for medical specialist and general practitioner costs. Additionally, we examined interactions with gender, by adding the interaction between age, year and sex to the model. Mental healthcare costs for younger adults (18-34) were higher than those for older adults (35-65) at all time points (β = 0.22, 95%-CI = 0.19; 0.25). Furthermore there was an increase in the strength of the association between younger age and mental healthcare costs from β = 0.22 (95%-CI = 0.19; 0.25) in 2015 to β = 0.37 (95%-CI = 0.35; 0.40) in 2021 (p < 0.0001) and this was most evident in women (p < 0.0001). Younger age was associated with lower general practitioner costs at all time points, but this association weakened over time. Younger age was also associated with lower medical specialist costs, which did not weaken over time. Young adults, particularly young women, account for an increasing share of mental healthcare expenditure in the Netherlands. This suggests that mental distress in young people is increasingly met by a response from the medical system. To mitigate this trend a public mental health approach is needed.

6676 Exposure to Neighborhood Violence Rewires Glucocorticoid Receptor Binding and Drives Proliferation and Invasiveness in Lung Tumor Samples

Abstract Disclosure: H. Heath: None. J. Yoo: None. S. Akter: None. A. Jain: None. V. Sharma: None. H. McGee: None. A. Soliman: None. A. Mohamed: None. A.K. Matthews: None. R. Winn: None. Z. Madak Erdogan: None. S. Kim: None. Background: Despite the lower rates and frequency of smoking, Black men experience a higher incidence of lung cancer compared to white men. This health disparity is even more prevalent in Black men from Chicago, suggesting local neighborhood factors impacting tumorigenesis. Preliminary data indicate that, compared to white men, Black men are more likely to reside in neighborhoods with high levels of violence and have elevated levels of hair cortisol. Objective: To understand the link between lung tumorigenesis and exposure to neighborhood violence, our objective is to investigate the chromatin recruitment of the receptor for cortisol, called the glucocorticoid receptor (GR), and to correlate this recruitment with target gene expression levels. Methods: Utilizing CUT &amp; RUN, we identified the gene binding sites of GR using 15 lung tumor and corresponding healthy tissue samples from lung cancer patients living in Chicago. GR recruitment to chromatin was correlated with the neighborhood homicide rate obtained using patients’ zip codes. Spatial transcriptome profiles for these 15 lung tumor samples were obtained from the Space Ranger pipeline and gene expression hot spots of GR target genes were visualized using Loupe Browser (10x Genomics). Results: GR recruitment to regulatory regions of chromatin increases with greater zip code level violence rates. In patients from high violence neighborhoods, tumor samples had higher magnitude of binding compared to normal neighboring tissue. In tumor samples from patients living in high violence compared to low violence neighborhoods, GR binding is seen in genes that are associated with increased tumor aggressiveness. Hot-spot analysis of these genes found strong co-expression of genes associated with proliferation and invasion in samples from high violence but not low violence neighborhoods. Conclusion: Exposure to neighborhood violence may impact tumor biology via increased GR recruitment to genes associated with proliferation and invasion. Presentation: 6/2/2024

Social Determinants of Health and Timing of Tracheostomy for Severe Bronchopulmonary Dysplasia

To estimate the association of ZIP code-level social determinants of health (SDoH), specifically household income, education level, and unemployment rate, with post-menstrual age (PMA) at tracheostomy placement in patients with severe bronchopulmonary dysplasia (BPD). This was a retrospective observational study of infants born <32 weeks' gestation and discharged from a Children's Hospitals Neonatal Consortium newborn intensive care unit. Patients were diagnosed with severe BPD and received tracheostomies before discharge. Maternal ZIP code at admission was linked to that ZIP code's SDoH via the 2021 US Census Bureau 5-year data. Unadjusted and adjusted analyses were completed with separate models fit for each SDoH marker. 877 patients received tracheostomies at median (interquartile) 48 (44-53) weeks' PMA. In multivariable models, patients in the highest education groups received tracheostomies earlier (OR: 95% CI = 0.972: 0.947-0.997, p=0.031), and non-Hispanic Black patients received tracheostomies later compared with non-Hispanic White patients (OR: 1.026; 95% CI =1.005-1.048, p=0.017). For household income and unemployment, the PMA at tracheostomy did not differ by SDoH or race. For all three models, male sex, small for gestation status, and later PMA at admission were associated with later PMA at tracheostomy. For each SDoH marker, significant inter-center variation was noted as several centers had independently increased PMA at tracheostomy. Education at the ZIP code-level influenced PMA at tracheostomy after adjusting for patient and clinical factors. Adjusted for each SDoH studied, significant differences were noted among centers. Factors leading to the decision and timing of neonatal tracheostomy need further evaluation.

Ambient Air Pollution Exposure and Outcomes in Patients Receiving Lung Transplant

Elevated ambient fine particulate matter (PM2.5) air pollution exposure has been associated with poor health outcomes across several domains, but its associated outcomes among lung transplant recipients are poorly understood. To investigate whether greater PM2.5 exposure at the zip code of residence is associated with a higher hazard for mortality and graft failure in patients with lung transplants. This retrospective cohort study used panel data provided by the United Network for Organ Sharing, which includes patients receiving transplants across all active US lung transplant programs. Adult patients who received lung transplants between May 2005 and December 2016 were included, with a last follow-up of September 10, 2020. Data were analyzed from September 2022 to May 2023. Zip code-level annual PM2.5 exposure was constructed using previously published North American estimates. The primary outcome was time to death or lung allograft failure after lung transplant. A gamma shared frailty Cox proportional hazards model was used to produce unadjusted and adjusted hazard ratios (HRs) to estimate the association of zip code PM2.5 exposure at the time of transplant with graft failure or mortality. Among 18 265 lung transplant recipients (mean [SD] age, 55.3 [13.2] years; 7328 female [40.2%]), the resident zip code's annual PM2.5 exposure level was greater than or equal to the Environmental Protection Agency (EPA) standard of 12μg/m3 for 1790 patients (9.8%) and less than the standard for 16 475 patients (90.2%). In unadjusted analysis, median graft survival was 4.87 years (95% CI, 4.57-5.23 years) for recipients living in high PM2.5 areas and 5.84 years (95% CI, 5.71-5.96 years) for recipients in the low PM2.5 group. Having an annual PM2.5 exposure level greater than or equal to the EPA standard 12 μg/m3 was associated with an increase in the hazard of death or graft failure (HR, 1.11; 95% CI, 1.05-1.18; P < .001) in the unadjusted analysis and after adjusting for covariates (HR, 1.08; 95% CI, 1.01-1.15; P = .02). Each 1 μg/m3 increase in exposure was associated with an increase in the hazard of death or graft failure (adjusted HR, 1.01; 95% CI, 1.00-1.02; P = .004) when treating PM2.5 exposure as a continuous variable. In this study, elevated zip code-level ambient PM2.5 exposure was associated with an increased hazard of death or graft failure in lung transplant recipients. Further study is needed to better understand this association, which may help guide risk modification strategies at individual and population levels.

Understanding the zero-emission vehicle market spatial diffusion and its determinants from 2019 to 2022 using spatial econometric models

Due to the rapid rise in the acceptance of zero-emission vehicles (ZEVs), much research has been conducted on the market evolution of alternative fuel technology. The research reported in this paper builds on prior studies by incorporating several new directions. Firstly, the spatial distribution of ZEV purchases in California is explored, then seven models (two nonspatial models and five spatially explicit models) are developed to predict regional ZEV sales. The predictions are based on fundamental and widely accessible characteristics such as infrastructure and demographic statistics. Next, a spatial econometric model known as the “spatial error model” is chosen to distribute ZEV sales from their original postal code (ZIP) level to the more often utilized US census tract level. This allows for carrying out a comprehensive analysis that includes studying the correlation of ZEV sales with local incentives and disadvantaged communities. Results reveal that ZEV sales in California increased significantly between 2019 and 2022, as spatial autocorrelation decreased. Vulnerable groups were less likely to have access to ZEVs, particularly during the initial stage of the ZEV market penetration in 2019. As expected, regions with considerable ZEV sales tended to also experience substantial purchase rebates and lower disadvantaged population, especially in 2019.

Changes in cancer diagnoses and stage distribution during the second year of the COVID-19 pandemic in the US.

130 Background: During 2020, the first year of the COVID-19 pandemic, cancer diagnoses decreased, especially early-stage disease, with largest decreases in medically underserved populations. It is unknown whether this pattern continued during 2021, the second year of the COVID-19 pandemic. With newly released cancer surveillance data, this study examined nationwide changes in adult cancer diagnoses and stage distribution from 2019 to 2021 in the US. Methods: Adults aged ≥18 years who were newly diagnosed with first primary malignant cancers between 2019 and 2021 from 50 US states and the District of Colombia were identified from the National Cancer Database. We calculated monthly cases, stage distribution, and adjusted odds ratios (aOR) of stage I vs. stage II–IV cancer and stage IV vs. stage I–III cancer diagnoses in 2021 compared with 2019, controlling for sociodemographic and clinical factors and stratified by cancer type. Results: We identified 864,430 individuals diagnosed in 2019, 780,478 diagnosed in 2020, and 819,791 diagnosed in 2021. The proportion of diagnoses at stage I decreased from 39.6% in 2019 to 31.1% in April 2020, and were 39.2% overall in 2021. The decrease in stage I diagnoses was mirrored by an increase in stage IV diagnoses. The pattern was most prominent for female breast, lung, colorectal, and prostate cancers, and melanoma. Overall, compared to 2019, we found a 5.98% decrease in stage I diagnoses and a 4.55% decrease in stage IV diagnoses in 2021 (Table). Lower odds of diagnosis at stage I were most prominent among individuals who were male, aged 18-44, Hispanic, from Mid-Atlantic region, and for the cancers of melanoma, non-Hodgkin lymphoma, prostate, esophagus, and cervical cancer. Higher odds of diagnosis at stage IV in 2021 vs. 2019 were most prominent among individuals who were Hispanic, diagnosed with cancers of prostate, thyroid, liver, esophagus and cervix. Conclusions: Decreases in cancer diagnoses and proportion of early-stage diagnoses continued in 2021 in the US. The proportion of late-stage diagnoses were lower in 2021 than 2020 but have not returned to pre-pandemic levels. Changes in stage I distribution among major cancer sites 2021 vs. 2019. 2019 2021 2021 vs. 2019 Cases Cases Percent Change AdjustedOR* (95%CI) All Cancers 341971 321514 -5.98% 0.978 (0.972-0.985) Female breast 116828 116780 -0.04% 1.023 (1.007-1.038) Prostate 26323 21012 -20.18% 0.826 (0.809-0.843) Lung cancer and bronchus 28258 25966 -8.11% 1.033 (1.012-1.053) Colon and rectum 14258 12992 -8.88% 0.942 (0.918-0.968) Melanoma 21005 19542 -6.97% 0.936 (0.907-0.966) Data source: National Cancer Database 2019-2021. *Stage I vs. II-IV, models adjusted for age group, sex, race/ethnicity, zip-code level social deprivation index, comorbidity score, state-level COVID-19 age adjusted mortality rate, MSA, state, insurance status.

Extreme Heat Events and Emergency Department Visits among Older Adults in California from 2012-2019.

Background and Objectives: Extreme heat events are increasing with climate change impacting human health. This study investigates the impact of extreme heat events on Emergency Department (ED) utilization by older adult patients. Materials and Methods: We conducted a study of all 324 non-federal hospital EDs in California during an 8-year period from data extracted from the California Department of Health Care Access and Information (HCAI). The study utilized a time-stratified case-crossover design to investigate ED visited in patients aged 65 years and older during 1-day and 2-day heat wave events. Extreme heat temperatures were measured and weighted using historical data at the zip code level at the 95th, 97.5th, and 99th percentiles 2012 through 2019. Conditional logistical regression was used to estimate the odds of ED visits during extreme heat events compared to non-extreme heat days. Stratified analyses by age and comorbidity status were conducted. Results: During the study period, 8,744,001 of ED visits among older patients were included in the study analysis. Odds ratios (OR) increased for during 1-day heat events (95th percentile (OR = 1.023, 95%CI: 1.020, 1.027), 97.5th percentile (OR = 1.030, 95%CI: 1.025, 1.035), 99th percentile (OR = 1.039, 95%CI: 1.032, 1.058)) and more so with 2-day heat wave events (95th percentile (OR = 1.031, 95%CI: 1.026, 1.036), 97.5th percentile (OR = 1.039, 95%CI: 1.031, 1.046), 99th percentile (OR = 1.044, 95%CI: 1.032, 1.058)). Older patients with three or more comorbidities had the highest odds of ED visits (OR = 1.085, 95%CI: 1.068, 1.112) at the 99th percentile. Conclusions: Our findings indicate that ED visits increase for older patients during extreme heat events, particularly with event intensity and duration. Older patients with at least one comorbidity were at greater risk.

Abstract B135: Association of residential economic and racial segregation with metastatic disease at diagnosis in pediatric patients with solid tumors

Abstract Background: Disparities in pediatric cancer outcomes by race and ethnicity may be influenced by neighborhood characteristics, particularly residential economic and racial segregation. Prior to a new cancer diagnosis, pediatric patients are vulnerable to structural barriers embedded in their neighborhoods. In particular, residential economic and racial segregation may impede access to initial care for early cancer detection, increasing the likelihood of presenting with distant metastatic disease at diagnosis that can translate to excess morbidity and mortality. This study assesses the association of residential economic and racial segregation as measured by the Index of Concentration at the Extremes (ICE) with the likelihood of presenting with distant metastatic disease (as compared to local or locoregional disease) among children newly diagnosed with cancer. Methods: We studied a cohort of pediatric patients diagnosed with solid tumors at Children’s Healthcare of Atlanta between 2010 and 2016. Patients’ ZIP codes were linked to the ICE, which was constructed at the ZIP code level using American Community Survey 2012-2016 five-year estimates. The ICE was categorized into tertile 1 (most deprived) through tertile 3 (most privileged). Multivariable logistic regression models were used to assess the association between residential economic and racial segregation, defined by tertiles of the ICE, and the likelihood of presenting with distant metastatic disease, as determined by initial cancer staging. All models adjusted for patient- and disease-level covariates, including age, sex at birth, health insurance type, and cancer type. Results: Among 829 patients identified in our analytic sample, 27.6% had distant metastatic disease at diagnosis. Living in the most deprived (ICE tertile 1) versus the most privileged (ICE tertiles 2/3) neighborhoods was significantly associated with a higher likelihood of presenting with distant metastatic disease at diagnosis (adjusted odds ratio [OR] = 1.46; 95% confidence interval [CI] = 1.03 to 2.07; p=0.03). Additionally, non-Hispanic Black patients living in the most deprived neighborhoods were more likely to have distant metastatic disease at diagnosis compared to non-Hispanic White patients living in the most privileged neighborhoods (adjusted OR = 1.83; 95% CI = 1.15 to 2.92; p=0.002). Conclusion: This study provides new insights into how residential economic and racial segregation is associated with metastatic disease at diagnosis among children presenting with a new solid tumor. Our findings highlight the need for geographically targeted interventions to improve community care, resource allocation and investment, and public policies aimed at supporting this vulnerable patient population. Citation Format: Alexandra Cathcart, Sharon M. Castellino, Lu Zhang, Karen Wasilewski-Masker, Andrew L. Hong, Heeju Sohn, Xu Ji. Association of residential economic and racial segregation with metastatic disease at diagnosis in pediatric patients with solid tumors [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B135.

Abstract A127: Enhancing partnerships to address cancer screening disparities: community engagement strategies in a colorectal cancer screening navigation trial

Abstract Intro While researchers are expected to address study participants’ social determinants of health (SDOH), little to no empirical work is dedicated to the science of how to advance partnerships to redress health disparities. The aims of the study are to describe engagement methods to recruit community based organizations (CBOs) in a clinical trial designed to test the navigation of patients through colorectal cancer (CRC) screening while also creating linkages to help address patients’ SDOH. Methods Federally qualified health center (FQHC) staff invited CBO representatives to participate in the trial’s formative assessment components, food box provision, and/or delivery of CRC screening education. This engagement relied on relationships built through the team’s facilitation of a community coalition- a network that already existed in the FQHC. Study records were reviewed to enumerate and tally engagement approaches over the course of the study’s first 2 years. Results Seven CBOs provided a letter with the grant’s submission: 4 CBOs expressed interest in participating in an advisory board; 2 CBOs committed to providing a food box to patients identified with food insecurity; and 1 CBO committed to delivering community CRC screening education. Of the 7 CBOs, 6 completed a key informant interview; 2 provided zip-code level expertise to define study neighborhoods; and 2 developed formal agreements to provide food boxes to intervention participants. There was a change in leadership with 4 of the 7 CBOs, which likely explains the missing interview. Of the 2 partners that committed to food box provision, at the time of implementation- 1 of these no longer had a food pantry function as the CBO had taken this role on during the COVID pandemic’s peak. A neighboring site, however, continues to provide this service and has stepped in to provide food boxes for the study. Similarly, the CBO that had committed to providing community CRC education no longer has capacity to do so and a new partner was engaged to deliver this health education. At the end of grant year 1, to identify additional CBOs that could supply food boxes in other study neighborhoods, staff emailed 2 CBOs that were recommended during interviews, however, we received no response. Half-way through grant year 2, staff reengaged the dedicated CBO engagement team and requested additional e-introduction to new CBO partners that could address patients’ SDOH in these other study neighborhoods. Six CBOs have been approached with 1 interview completed to date. One of these year 2 CBOs provided zip-code level expertise to the study’s analytical team while another CBO is developing a formal agreement to provide food boxes in the other study areas. Conclusions Having a group dedicated to CBO engagement has allowed the research team to conduct outreach and partner with CBOs to co-deliver the intervention to address patients’ SDOH. Additional work is needed to consider the transitions in CBOs which impact initial commitments and timeline in order to anticipate and address these challenges early in a study. Citation Format: Anne Escaron, Joanna Garcia, Adriana Aranda, Jacqueline Macias-Sandoval, Dina Cruz Quintanilla, Crystal Gomez, Elva Arredondo, Gloria Coronado. Enhancing partnerships to address cancer screening disparities: community engagement strategies in a colorectal cancer screening navigation trial [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A127.

Evaluating the Predictive Accuracy of Socioeconomic Metrics on Heart Failure Risk and Outcomes in Maryland.

Introduction Annually, a significant number of Americans are hospitalized due to heart failure (HF), marking it as an important contributor to morbidity and mortality. It also poses a substantial financial burden and leads to considerable losses in productivity. Socioeconomic disparities may intensify the risk of hospital admissions following HF and worsen patient outcomes. Objective This study investigates the predictive accuracy of different socioeconomic metrics on the risk and outcomes of HF in Maryland. Methodology To evaluate the predictive accuracy of various socioeconomic metrics on the risk of HF, we utilized data from the Maryland State Inpatient Database. Our retrospective analysis covered hospital admissions for HF from 2016 to 2020, correlating these with poverty indicators derived from U.S. Census data at the zip code level with socioeconomic metrics like race/ethnicity, insurance, household median income, and neighborhood distress (Distressed Communities Index (DCI)). Multivariate logistic regression models adjusted for confounders and isolated the impact of socioeconomic factors. Result During the study period, a total of 389,220 cases of HF were reported in the Maryland State Inpatient Database (SID). The majority of these patients were White individuals (56.8%) and female (51.1%), with a median age of 73 years (interquartile range (IQR) 62-82 years). The in-hospital mortality rate was 5.1%, while rates of atrial fibrillation, cardiac arrest, and prolonged hospital stay were 34.4%, 0.3%, and 48.4%, respectively. The studied socioeconomic metrics showed varying predictive power for the risk of HF-related admissions and selected outcomes, with the highest predictive accuracy for neighborhood distress on the risk of HF (AUC = 0.53, 95% CI 0.530-0.532), atrial fibrillation (AUC = 0.479, 95% CI 0.477-0.480), cardiac arrest (AUC = 0.511, 95% CI 0.498-0.525), prolonged hospital stays (AUC = 0.531, 95% CI 0.530-0.532), and mortality (AUC = 0.499, 95% CI 0.496-0.502). Conclusions The Distressed Communities Index demonstrates significant predictive power for assessing the risk of hospital admissions following HF and outcomes among individuals with HF, exceeding factors like insurance, race/ethnicity, and household median income.

Analyzing Fatal Police Shootings: The Roles of Social Vulnerability, Race, and Place in the U.S.

IntroductionSocial vulnerability, race, and place are three important predictors of fatal police shootings. This research offers the first assessment of these factors at the zip code level. MethodsThe 2015-2022 Mapping Police Violence and Washington Post Fatal Force Data (2015-2022) were used and combined with the American Community Survey (2015-2022). The social vulnerability index (SVI) was computed for each zip code by using indicators suggested by CDC, then categorized into low-, medium-, and high-SVI. The analytical file included police officers who fatally shot 6,901 individuals within 32,736 zip codes between 2015-2022. Negative Binomial Regression (NBRG) models were run to estimate the association between number of police shootings and zip code SVI, racial composition, and access to guns using 2015-2022 data. ResultsMoving from low-SVI to high-SVI revealed the number of fatal police shootings increased 8.3 times, with the highest increases in Blacks (20.4 times), and Hispanics (27.1 times). The NBRG showed that moderate-, and high-SVI zip codes experienced higher fatal police shootings by 1.97, and 3.26 times than low-SVI zip codes; zip code racial composition, working age population, number of violent crimes, number of police officers and access to a gun, were other predictors of fatal police shootings. ConclusionsSocial vulnerability and racial composition of a zip code are associated with fatal police shooting, both independently and when considered together. What drives deadly police shootings in the United States is not one single factor, but rather complex interactions between social-vulnerability, race, and place that must be tackled synchronously. Action must be taken to address underlying determinants of disparities in policing.

Impact of Warehouse Expansion on Ambient PM2.5 and Elemental Carbon Levels in Southern California's Disadvantaged Communities: A Two‐Decade Analysis

AbstractOver the past two decades, the surge in warehouse construction near seaports and in economically lower‐cost land areas has intensified product transportation and e‐commerce activities, particularly affecting air quality and health in nearby socially disadvantaged communities. This study, spanning from 2000 to 2019 in Southern California, investigated the relationship between ambient concentrations of PM2.5 and elemental carbon (EC) and the proliferation of warehouses. Utilizing satellite‐driven estimates of annual mean ambient pollution levels at the ZIP code level and linear mixed effect models, positive associations were found between warehouse characteristics such as rentable building area (RBA), number of loading docks (LD), and parking spaces (PS), and increases in PM2.5 and EC concentrations. After adjusting for demographic covariates, an Interquartile Range increase of the RBA, LD, and PS were associated with a 0.16 μg/m³ (95% CI = [0.13, 0.19], p &lt; 0.001), 0.10 μg/m³ (95% CI = [0.08, 0.12], p &lt; 0.001), and 0.21 μg/m³ (95% CI = [0.18, 0.24], p &lt; 0.001) increase in PM2.5, respectively. For EC concentrations, an IQR increase of RBA, LD, and PS were each associated with a 0.021 μg/m³ (95% CI = [0.019, 0.024], p &lt; 0.001), 0.014 μg/m³ (95% CI = [0.012, 0.015], p &lt; 0.001), and 0.021 μg/m³ (95% CI = [0.019, 0.024], p &lt; 0.001) increase. The study also highlighted that disadvantaged populations, including racial/ethnic minorities, individuals with lower education levels, and lower‐income earners, were disproportionately affected by higher pollution levels.

Estimating primary substation boundaries and the value of mapping Great Britain's electrical network infrastructure

Localised data aggregation in many countries including Great Britain (GB) is typically performed at a geographical level with polygon boundaries that have a robust and trusted governance system in place. This means there is confidence in a process to create a set of polygons that have unique identifiers coupled to geographical areas, and the ability to have these updated through a defined code of practice. Examples found across many countries are in the delivery of post, such as post codes and zip codes, and of the definition of census areas and municipal boundaries. The confidence in these boundaries allows different data to be aggregated by third parties, which itself provides greater amounts of data over comparable geographical areas to enhance wider analysis and decision making. As helpful as these polygons are for certain types of analyses, they are not specifically defined for energy systems analysis. Here we combine publicly available datasets published from the six regional electricity Distribution Network Operators of GB to produce a new geospatial dataset with 4436 unique polygons defining the areas served by electrical primary substations. An example demonstrating the value of these polygons is given to link these polygons with postcode level open government datasets on domestic energy consumption (2015–2020) from the Department of Energy Security and Net Zero (DESNZ). The resulting new data reflects energy statistics aggregated to the geographical areas served by each primary electrical substation across Great Britain. The significant value of the generated data is demonstrated by the quantification of the domestic annual heating demand presently met by natural gas across different primary substation areas which varies from an average of 5100 kWh to 28,900 kWh per property. This highlights how the rate of electrical capacity expansion will have to differ by geography under an electrified heating scenario. Therefore, we believe there is a compelling argument for all countries to set up processes to create and update polygons that have a meaningful relationship to energy systems. This would allow more accurate energy systems analysis to be performed, ultimately leading to an accelerated or potentially lower cost transition to a net zero world.