Census Block Research Articles

Trends in socioeconomic disparities in urban heat exposure and adaptation options in mid-size U.S. cities

There is ample evidence that Environmental Justice communities experience high levels of extreme heat. However, it is unknown how disparities in urban heat exposure and adaptation options change over time. This study investigates socioeconomic disparities over time in urban heat exposure and adaptation options in eight mid-sized Northeastern cities. We ask: How were socioeconomic factors associated with heat exposure and adaptation options over time? We analyzed disparities at the census block group level and census block level, respectively. At the census block group level, we ran spatial regression models between socioeconomic variables, including race, income, gender, and age, and heat exposure and adaptation variables, including land surface temperature, normalized different vegetation index (NDVI), tree cover, and air conditioning ownership rate. We found that: Low median household income is always associated with high LST and low NDVI from 1990 to 2020; Low percentage of females is always associated with high LST and low NDVI from 1990 to 2020. High percentage of POC is associated with high LST in 2010 and 2020, but not in 1990 and 2000; Low median household income and low percentage of elderly are associated with lower tree cover; High percentage of POC, low percentage of elderly, and low median household income are associated with lower AC rates. In analysis at the census block level by city, we found that disparities in urban heat exposure between predominantly POC and predominantly white communities increased in most cities during 1990-2020. Predominantly POC communities consistently have lower vegetation cover over time in most cities. Disparities in vegetation cover per unit area increased in most cities, whereas disparities in vegetation cover per capita decreased in most cities. Our findings of the trends in disparities in heat exposure and adaptation are useful for forecasting disparities in the future. These findings also suggest that interventions should prioritize cities with increasing disparities in heat exposure and adaptation.

The Impact of Socioeconomic Factors on 90-day Postoperative Readmissions and Cost in Shoulder Arthroplasty Patients

Understanding the impact of Social Drivers of Health (SDOH) on shoulder arthroplasty is pivotal for the development of equitable value-based payment models that enhance the quality of patient care. This investigation aims to understand the influence of Area Deprivation Index (ADI) on hospital admissions, readmissions, and associated costs post-shoulder arthroplasty. We conducted an analysis using US Medicare claims data from 2019 to 2021, identifying patients who received shoulder arthroplasty in either an inpatient or outpatient setting using Current Procedural Terminology (CPT) codes. Our primary outcomes were 90-day unplanned readmissions and post-procedure 90-day care costs. The ADI was our primary exposure variable, calculated at the census block level. Our analysis adjusted for multiple factors using a stepwise modeling approach including patient demographics, 29 Elixhauser comorbid conditions, Medicaid-dual eligibility, and end-stage renal disease status using logistic regression models. A total of 145,435 patients were included in our study. The average age of patients in our cohort was 74.5 years old, with 59.5% being female. Patients in the high ADI group had a higher readmission rate than the lowest ADI group (8.5% vs. 6.0%, p<0.001).When controlling for confounding factors, there was an indepdentent association between high ADI and readmission (OR: 1.28 [95% CI 1.12, 1.46]). Overall, we saw a dose-dependent relationship between ADI and readmission, with the association growing stronger as ADI increased. Conversely, we found a negative association with ADI and high-costs. (High ADI Group OR: 0.80 [95% CI 0.70, 0.91]). Patients in the high SES group had higher healthcare contact days during the 90-day follow-up period, with a median of 16 visits (IQR 8, 23), compared to 13 visits (IQR 6, 22) in the middle SES group and 10 visits (IQR 5, 20) in the low SES group (p<0.001). Socioeconomic disparities significantly influence the outcomes of primary shoulder arthroplasty, as indicated by higher readmission rates for low SES patients. Notably, our analysis shows a strong, independent association between ADI and readmission. Moreover, patients with higher SES incurred greater costs within a 90-day period potentially due to increased resource utilization.

Upstream Oil and Gas Production and Community COVID-19 Case and Mortality Rates in California, USA.

Higher concentrations of ambient air pollutants, including PM2.5 and NO2, and other pollutants have been found near active oil and gas wells and may be associated with adverse COVID-19 outcomes. We assessed whether residential exposure to nearby oil and gas production was associated with higher rates of the respiratory infection COVID-19 and related mortality using a population-based ecological study in California. Using gridded population estimates, we estimated area-level exposure to annual average oil and gas production volume from active wells within 1kilometer (km) of populated areas within census block groups from 2018 to 2020. We geocoded confirmed cases and associated deaths to assess block group case and mortality rates from COVID-19 from February 2020 to January 2021. We fit hierarchical Poisson models with individual and area covariates (e.g., age, sex, socioeconomic disadvantage), and included time and other interactions to assess additional variation (e.g., testing, reporting rates). In the first 4months of the study period (February-May 2020), block groups in the highest tertile of oil and gas production exposure had 34% higher case rates (IRR: 1.34 95% CI: 1.20, 1.49) and 55% higher mortality rates (MRR: 1.52 95%: CI: 1.14, 2.03) than those with no estimated production, after accounting for area-level covariates. Over the entire study period, we observed moderately higher mortality rates in the highest group (MRR: 1.16 95%: CI: 1.01, 1.33) and null associations for case rates.

Modeling evacuation activities amid compound hazards: Insights from hurricane Irma in Southeast Florida

Given the destructive nature of hurricanes in tropical regions, pre-disaster evacuation has emerged as a critical approach for hurricane preparedness. Nevertheless, the compounding effects of natural hazards and the outbreak of infectious diseases, such as Covid-19, significantly challenge hurricane evacuation management. To investigate emergency responses under compound hazards, this study develops an activity-based model to measure the evacuation behaviors of individuals, using Hurricane Irma as a case study. Four scenarios are designed, including a single hurricane hazard, Hurricane Irma compounded with a pandemic like Covid-19, Hurricane Irma compounded with flood damage to the transportation network, and a combination of all these hazards. The metropolis-hasting algorithm is utilized to generate a population with socioeconomic attributes, which is then allocated to census block groups covering Palm Beach, Broward, Miami-Dade, and Monroe Counties in Florida. Datasets from multiple sources are used to measure evacuation decisions, which are subsequently simulated using MATSim. The results highlight the potential impacts of compound hazards on transportation systems, including increased congestions in scenarios involving compounded hurricanes and floods, especially between 10 a.m. and 7p.m. Moreover, a higher proportion of socially vulnerable populations is observed in scenarios involving compounded hurricanes and pandemics, particularly in the Key West area. The developed model could be further applied to measure the indirect impacts of natural hazards on transportation systems.

Inequalities in emergency service accessibility: spatial analysis of urban infrastructure

PurposeThis study aims to investigate the relationship between emergency response times and crash severity in New York City, focusing on spatial disparities and their implications. It examines how these disparities impact disadvantaged neighborhoods, particularly regarding traffic safety and emergency service accessibility.Design/methodology/approachThe research uses comprehensive spatial analysis techniques, including hotspot mapping, network analysis for travel time modeling, local bivariate correlation analysis and service area calculations. It maps crash data alongside emergency facility locations, considering peak-hour traffic. The Area Deprivation Index (ADI) is integrated to evaluate socioeconomic factors influencing accessibility. This approach provides a nuanced understanding of how emergency response times correlate with crash severity at the census block level, accounting for socioeconomic disparities.FindingsThis study reveals significant disparities in emergency response times across New York City. In high-poverty, predominantly minority areas, response times are 2–3 min longer than average, correlating with a 15% increase in severe injury rates. Over 20% of neighborhoods show correlations between response times and crash severity, with positive linear (5.51%), negative linear (10.72%), concave (2.44%) and convex (2.80%) relationships. Blocks with positive linear relationships have a mean ADI rank of 3.918. During peak hours, 69.7% of Manhattan blocks show negative relationships, the highest among boroughs.Originality/valueThis research highlights spatial justice issues in urban emergency response systems, emphasizing the need for localized, data-driven planning and infrastructure adjustments. By integrating the ADI, the multifaceted approach reveals the complex dynamics of socioeconomic factors and emergency service accessibility that have not yet been investigated in diverse urban communities.

Equity, travel, and park visitation in 10 US metro areas: A smartphone mobility study

Urban planners and urban forestry researchers use various methods to evaluate how well different populations access the benefits of parks, often by assessing park distribution in relation to the social and economic conditions of surrounding neighbourhoods. However, without precise visitation data, it remains unclear whether proximity to parks translates into actual use. This study analyses the social and spatial factors influencing park visitation using real-world data across multiple urban and ecological contexts. We utilised smartphone mobility to capture visitation counts for over 14,000 parks across 10 US metropolitan areas, representing populations from more than 26,000 census block groups (CBGs). By integrating census data with spatial models, we examined the relationships between park visitation, proximity of parks to visitors' home neighbourhoods, and socio-economic variables such as race and ethnicity. Our analysis revealed that park visitation increases with proximity to visitors' home neighbourhoods. We also found positive associations between visitation rates and factors such as population density, median income, and park coverage in visitors' home neighbourhoods. Additionally, we identified significant correlations between park visitation and the racial and ethnic composition of these neighbourhoods. These findings confirm the assumption that closer proximity to parks enhances visitation rates. We recommend that park visitation data be considered alongside distributional analyses when planning for park accessibility.

Persistent Environmental Injustice due to Brake and Tire Wear Emissions and Heavy-Duty Trucks in Future California Zero-Emission Fleets.

The adoption of zero-emission vehicles (ZEVs) offers multiple benefits for the climate, air quality, and public health by reducing tailpipe emissions. However, the environmental justice implications of the nonexhaust emissions from future ZEV fleets for near-roadway communities remain unclear. Here, we model the on-road fine particulate matter (PM2.5) emissions across all California counties and assess the near-roadway exposure disparities at the census block group level in the Los Angeles County in 2050, when almost all passenger vehicles are projected to be ZEVs. We found that promoting zero-emission heavy-duty trucks generates more air quality benefits for disadvantaged communities than light-duty passenger vehicles. Persistent disparities in near-roadway PM2.5 levels, however, exist due to the remaining brake and tire wear emissions and increased truck traffic in disadvantaged communities. We recommend implementing fleet-specific ZEV policies to address brake and tire wear emissions and optimizing freight structures to address these persistent environmental justice issues in California.

A bottom-up nationwide analysis of sectoral land use reveals spatial heterogeneity across the United States

The high degree of urbanization and economic development in the United States has intensified competition for land resources, leading to land use conflicts and environmental challenges. Addressing these issues first requires an exploration of high-resolution, sector-specific land use patterns. However, existing land classification studies rarely provide spatially explicit data on urban land use across all sectors at a national level. This study aims to fill this gap by analyzing land use areas across various economic sectors in the United States, particularly focusing on urban impervious surfaces. Using a bottom-up approach, we created a full-sector land use inventory, integrating Points of Interest (POI) data, road networks, building characteristics, and geographic information processing techniques. Our primary innovation lies in the development of a spatially explicit database that details sector-specific land use areas nationwide. On this robust foundation, we further explored the heterogeneity of sectoral land use eco-efficiency (SLUEE) among different sectors and states, providing supplementary insights into spatial disparities in urban land use. Our findings reveal significant spatial heterogeneity, with service sectors dominating land use and exhibiting higher SLUEE compared to goods-producing industries. Key sectors such as Professional, Scientific, and Technical Services (441064.58 ha), and Administrative and Support Services (420148.34 ha) are prominent across states according to land use area, reflecting the country's service-oriented economy. Additionally, regional disparities in SLUEE are evident, with jurisdictions like Washington D.C., New York, and California demonstrating more efficient land use. Compared with traditional land use evaluations, our model effectively offers higher spatial resolution, down to the census block level, to sectoral land use evaluation. Building on this robust database, our SLUEE analysis has uncovered notable heterogeneity between sectors and states, offering new insights into cross-sectoral and inter-state land use dynamics that can inform and promote sustainable urban planning.

From roads to roofs: How urban and rural mobility influence building energy consumption

Understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

Socio-spatial inequalities in presence of primary care physicians and patients' ability to register: A simulated-patient survey in the Paris Region

ObjectivesWe studied the socio-spatial inequalities of three types of general practitioner (GP) density in the Paris metropolitan area: the density of GPs present (that is, practising) in the census block and of those registering new patients for office visits and, separately, for house calls. Study designAn exhaustive simulated patient survey enabled us to determine the number of GPs practising in the Paris metropolitan region accepting new patient registration for continuing care at their office and/or for house calls. MethodsWe measured at the level of a census block three types of GP densities: density of GPs present, density of GPs registering new patients at their office and density of GP registering new patients for house calls. We compared the association between the social deprivation level and each density measurement overall, then by stratifying for the population density of the census block. ResultsIn 2017–2018, we contacted 8171 physicians (87.6 % of the GPs in the region). Although more than 55 % of Ile-de-France census blocks had (at least) one practising GP, fewer than 40 % had one willing to register a new patient for ongoing office care, and fewer than 20 % for home care. Regardless of the GP density considered, it decreased as the census block's deprivation index rose. However, these inequalities were more marked for registration than for presence and in the most densely populated blocks. ConclusionsIn conclusion, the indicators of GPs' mere presence appear to minimise the socio-spatial disparities associated with access to registration.

Uncovering disparities in water-based outdoor recreation using cell phone mobility data

Abstract Outdoor recreation plays a pivotal role in improving people’s physical and mental health, serving as a popular form of entertainment and a significant economic contributor. Limited access to these resources not only exacerbates health disparities but also deprives underserved areas of essential benefits like stress relief and community bonding, both of which are crucial for enhancing overall quality of life. This paper provides one of the first detailed analyses of water-based recreation at over 61 000 inland and coastal sites across the United States. We aim to explore disparities in recreational behavior across race, ethnicity, income, and socioeconomic status. Using Advan cellphone data from more than 70 million outdoor trips, representing 215 000 census block groups, we find that communities of color, rural areas, and socioeconomically disadvantaged groups are significantly underrepresented in water-based recreational visits. Despite living similar distances from recreational sites, these groups show notably different patterns in travel distance for water-based recreation. Additionally, we find Native Americans from underserved areas have to travel 3–5 times longer distances than other groups for water-based recreation. Our findings show that the extensive and frequent cellphone mobility data could reveal policy-relevant patterns especially those made by underserved Americans often overlooked in traditional household surveys.

Equitable stormwater utility fees: an integrated analysis of environmental, socioeconomic and infrastructure factors at the community scale

Abstract Municipalities worldwide implement stormwater management programs to mitigate the hydrological and water quality impacts of stormwater runoff. Stormwater utility fees (SWUF) are often used to fund such important programs by collecting revenue from residential and commercial properties. However, existing SWUFs often solely rely on the estimate of impervious surfaces and do not consider other environmental, infrastructure, and socioeconomic factors in the generation and effects of stormwater runoff. This study is the first attempt to propose a reconstruction of SWUFs from the perspectives of social equity and environmental justice. The method aims to address disparities in fee rates among residential parcels, focusing on helping economically disadvantaged communities. It integrates drainage service, potential contribution to non-point source pollution, and socioeconomic status through two alternative schemes. The two schemes allocate fees based on combined rankings of the three factors at the level of census block groups. The proposed method was applied to 88 180 residential parcels in Corpus Christi, Texas, a mid-sized coastal community. The results suggest that over 70% of the disadvantaged communities would benefit from the reconstructed SWUFs without affecting the targeted funding for stormwater infrastructure. This method builds on publicly available datasets and offers an adaptive framework for other municipalities to incorporate additional factors or datasets, representing an exploratory step toward achieving more equitable stormwater management practices.

Neighborhood Socioeconomic Disadvantage and Acute Care Utilization in Washington State Medicaid: A Retrospective Cohort Study.

Neighborhood disadvantage has been associated with potentially preventable acute care utilization among Medicare beneficiaries, but this association has not been studied in a Medicaid population, which is important for informing more equitable care and policies for this population. To describe the association between Area Deprivation Index (ADI) and acute care utilization (including potentially preventable utilization) among Medicaid beneficiaries in Washington State. Retrospective cohort study of 100% Medicaid claims. Mixed effects logistic regression was applied to estimate the association between state-level ADI decile and acute care utilization, adjusting for age, sex, self-identified race and ethnicity, Charlson Comorbidity Index, primary spoken language, individual Federal Poverty Level, homelessness, and rurality. Standard errors were clustered at the Census block group level. 1.5 million unique adult Medicaid beneficiaries enrolled for at least 11months of a calendar year during the period 2017-2021. Binary measures denoting receipt of ED visits, low-acuity ED visits, hospitalizations in a calendar year. Increasing levels of neighborhood socioeconomic disadvantage (by ADI decile) were associated with greater odds of any ED visits (adjusted odds ratio (aOR) 1.07, 95% confidence interval (CI) 1.06-1.07), low-acuity ED visits (aOR 1.08, CI 1.08-1.08), and any hospitalizations (aOR 1.02, CI 1.02-1.02). Among Medicaid beneficiaries, greater neighborhood socioeconomic disadvantage was associated with increased acute care utilization, including potentially preventable utilization. These findings signal potential barriers to outpatient care access that could be amenable to future intervention by health systems and payers.

Green spaces and flood vulnerability in urbanising regions: a case study in the Philadelphia metropolitan area

ABSTRACT Flooding continues to be one of the most significant natural hazards, disproportionately impacting vulnerable populations in urbanising regions. This study aims to examine the relationship between green space density and flood vulnerability in the Philadelphia Metropolitan Area by integrating social and geophysical data. The research focuses on identifying areas where high flood vulnerability coincides with low green space availability, as these are critical regions for targeted intervention. Using social vulnerability indicators such as income, age, and housing characteristics, combined with geophysical factors like soil permeability and drainage density, the study assesses flood vulnerability across census block groups. Local Indicators of Spatial Association (LISA) and discriminant analysis were employed to detect spatial clusters with elevated flood risks and low green space density. Results reveal that these high-risk clusters are concentrated in densely urbanised areas such as Norristown, where lower-income and elderly populations are overrepresented. These findings reinforce broader environmental justice concerns, as socially vulnerable communities face compounded risks from both inadequate flood protection and limited access to green spaces. The study emphasises the need for integrated flood management, combining green infrastructure with structural interventions, to guide urban planners and policymakers in reducing flood risks and promoting environmental equity.

A ten-year overview of cancer genetic family history screening in Georgia's Latina population.

Population-based cancer genetic family history (FH) screening to identify families at high risk for BRCA-associated cancers has been endorsed by national public health policies. This report aimed to describe the utilization of FH screening services from 2013 to 2022 according to rurality and socioeconomic deprivation among Latinas in Georgia. Women who attended a medical appointment at participating Georgia Public Health Clinics were invited to complete FH screening. Screening results and participant zip code were reviewed. Area deprivation index (ADI) was measured at the census block group level and dichotomized (more deprived and less deprived). Rurality was measured through Rural-Urban Commuting Area (RUCA) codes and dichotomized (urban and rural). The ADI and RUCA codes were linked to participant data by zip code to characterize FH utilization among the Latina community. Of the 9,330 adult Latinas in Georgia that completed cancer genetic FH screening, 9,066 (97.17%) women screened negative, and 264 (2.83%) screened positive (i.e., FH suggestive of higher risk for carrying BRCA1/2 mutations compared to the general population). Screening completion was higher among Latinas in urban areas (n = 7,871) compared to rural areas (n = 1,459). Screening completion was also higher in more socially deprived areas (n = 5,207) compared to less socially deprived areas (n = 4,123). Georgia's FH screening program reached Latinas across Georgia, particularly those living in urban, socially deprived areas. To ensure equitable cancer genetic screening dissemination, future efforts should prioritize tailored outreach in rural regions and comprehensive evaluations to identify key determinants of screening trends among Georgia's Latina population.

Higher levels of neighborhood deprivation and risk for incident colorectal cancer and mortality outcomes: A systematic review and meta-analysis.

131 Background: Significant geographic and racial disparities exist for colorectal cancer (CRC) incidence and mortality in the United States (US), which may be caused by neighborhood-level deprivation and limited access to preventative cancer screening. We completed the first systematic review and meta-analysis of neighborhood-level deprivation in relation to CRC incidence, mortality, and screening among individuals from the US. Methods: A systematic literature review was completed to identify original research articles from PubMed, Scopus, and Web of Science. Eligible studies reported associations between deprivation at the census block or tract level using a validated deprivation index (e.g. the Area Deprivation Index or Yost Index) and CRC incidence, mortality, or screening outcomes in a US-based sample. A fixed-effects meta-analysis was completed using inverse-variance weighting to estimated hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) for CRC incidence, mortality, and screening outcomes, comparing the highest vs. lowest quantiles of neighborhood-level deprivation. All analyses were completed using the meta package in R. Results: In total, 19 studies were included in the meta-analysis. Living in a neighborhood in the highest quantile of deprivation was associated with significantly higher risk for incident CRC (HR 1.37, 95% CI 1.34-1.41, seven studies). In stratified analysis, positive associations with incident CRC were observed for White (HR 1.43, 95% CI 1.39-1.47, three studies), Black (HR 1.18, 95% CI 0.96-1.44, three studies), and Asian/Pacific Islander individuals (HR 1.18, 95% CI 1.08-1.28, two studies). For individuals diagnosed with CRC, higher deprivation was associated with elevated risk for overall mortality (HR 1.11, 95% CI 1.10-1.13, four studies) and CRC-specific mortality (HR 1.17, 95% CI 1.15-1.19, seven studies). Higher deprivation was associated with lower completion of recommended screening for CRC, including colonoscopy, sigmoidoscopy, or fecal occult blood testing (OR 0.76, 95% CI 0.74-0.79, four studies). Conclusions: Individuals who reside in neighborhoods with high levels of deprivation have significantly elevated risk for CRC incidence and mortality compared to individuals from low deprivation neighborhoods. Targeting healthcare resources to highly deprived neighborhoods to increase uptake of preventative screening may help to address racial and geographic disparities for CRC.

Examination of Water Use and Social Vulnerability: A Comparison of Four Communities in Alachua County, Florida

It is projected that about 30% of the world’s major cities will face severe water stress and urban drought from 2050 if climate change continues and has the anticipated effects (Florke et al, 2018; www.wrcwebsite.azurewebsites.net). Socio-economic drought, meaning water shortage in urban life, can have significant effects on a city’s inhabitants, including health and quality of life. Social and economic factors play an important role in planning and decision making of a society (Zhang et al, 2020). The analysis investigated the relationship between socio-economic factors and the use of potable water, its associated impact on aquifer recharge, and potentially identifying major factors influencing water demand and resource sustainability. The Centers for Disease Control and Prevention’s Social Vulnerability Index (SVI) and other measures were used for evaluating socio-economic factors. However, since SVI showed weak relationship with water use in the county and the neighborhoods, other socio-economic measures were assessed. Past research had found a correlation between water use and population, GDP, per capita income, electricity usage and irrigated land areas (Alacoma et al., 2007, www.usf.uni-kassel.de/watclim). Therefore, socio economic measures such as population density, household size, per capita income, and poverty rate and irrigation (with and without) potable water were analyzed to evaluate the relationship between water use patterns and these factors across the county and in the communities. The western communities had newer development and less parcels compared to the eastern with much older construction, which impacted their water use. Oakmont with separate reclaimed irrigation meter had lesser usage on potable water. Tioga on the other hand irrigated with potable water. Yearly SVI on a census tract level for the specific years of the study and other socio-economic measures such as population, per capita income, poverty, and household size were used in the analysis. The analysis was performed on four communities in Alachua County, Florida; two at the west and two at the east of the county. Socio-economic measures and physical features were evaluated for communities in the four locations to evaluate whether there is a relationship between water use patterns, spatial characteristics of development such as percent impervious and runoff, and measures of SVI. Spatial and basic statistics was used for this analysis. The western part of the county has had more intensive development in recent years compared to the eastern part of the county. The eastern communities have a high SVI and low potable water use per capita compared to the western communities. The number of households and population were the primary drivers of potable water use per census block. Percent impervious surface and runoff volume did not show any significant. relationship with a community’s SVI. At the parcel level, statistically significant differences were found between communities. For example, potable water use per parcel was lower in communities with high SVI. Keywords: water sustainability, urban water management, social vulnerability index, socio-economic factors, potable water use, aquifer recharge, reclaimed water use, runoff, imperviousness

The impact of neighborhood socioeconomic deprivation on treatment and survival for patients with metastatic gastric cancer: An incidence-based retrospective cohort study.

110 Background: Studies have shown that socioeconomic deprivation (SED) is associated with reduced treatment and overall survival (OS) for gastric cancer. However, these studies have typically used large geographic areas for assessment of SED and aggregated all stages. Using a statewide cancer registry, the Area Deprivation Index, a granular measure of SED, was used to assess receipt of treatment and OS for metastatic gastric cancer patients. Methods: Using the incidence-based Florida Cancer Data System, gastric adenocarcinoma patients diagnosed from 2007-2015were identified (n=8,304). The Area Deprivation Index (ADI) is a composite measure of SED and validated dataset that ranks neighborhoods (census block groups) from 1-100 (higher scores=higher deprivation). Demographic, tumor, and treatment variables were assessed using descriptive statistics, regression, and survival analysis. Results: Overall, 3,050 stage IV patients met inclusion criteria; 53.9% were male and the median age was 67. When assessed by SED quartile, there were significant differences by race, ethnicity, insurance, and receipt of chemotherapy (p&lt;0.05). For the cohort, 55.3% of patients received chemotherapy. However, 61.3% of the lowest deprivation cohort received chemotherapy compared to 50.4% in the highest deprivation cohort (p&lt;0.001). After adjustment for potential confounders, the lowest deprivation quartile had increased odds of chemotherapy compared to the highest deprivation quintile (OR 1.52; 95% CI,1.20-1.92). Of patients who received chemotherapy, the median time in months to initiation was 1.1 (interquartile range 0.6-1.7). Median OS was 5.8 months. The median OS for the lowest and highest SED quartiles was 7.5 and 5.1 months, respectively (p&lt;0.001). For patients who received and did not receive chemotherapy, median OS was 10.0 and 1.9 months, respectively. The median OS of the lowest and highest SED quartiles for those who received chemotherapy was 11.3 and 8.8 months, respectively (p&lt;0.001). Conclusions: In an incidence-based, statewide cancer registry examining metastatic gastric cancer, just over half of patients received chemotherapy and median OS was poor. Additionally, higher SED was associated with lower odds of chemotherapy and worse OS. These data suggest that SED impacts metastatic gastric cancer outcomes and future studies are needed to understand the multilevel factors driving disparities. Lowest Deprivation Low Deprivation High Deprivation Highest Deprivation p-value N=752 N=780 N=747 N=718 Chemotherapy 0.003 No 262 (34.8%) 315 (40.4%) 306 (41.0%) 314 (43.7%) Yes 461 (61.3%) 424 (54.4%) 410 (54.9%) 362 (50.4%) Unknown 29 (3.9%) 41 (5.3%) 31 (4.1%) 42 (5.8%)

The impact of neighborhood socioeconomic deprivation on clinical outcomes for locally advanced and metastatic hepatocellular carcinoma: An incidence-based retrospective cohort study.

108 Background: Prior studies show that socioeconomic status impacts hepatocellular carcinoma (HCC) outcomes. However, these have used larger geographic areas such as zip codes or counties for assessment. Using a statewide cancer registry the Area Deprivation Index (ADI), a granular measure of socioeconomic deprivation (SED), was used to assess receipt of treatment and overall survival (OS) for locally advanced and metastatic HCC. Methods: The incidence-based Florida Cancer Data System was used to identify stage III/IV HCC patients diagnosed from 2007-2015. The ADI was used to measure SED and is a validated dataset that ranks neighborhoods (census block groups) from 1-100 (higher scores = higher deprivation) based on income, education, housing, and other socioeconomic factors. Demographic, tumor, and treatment variables were assessed using descriptive statistics, regression plots, and survival analysis. Chemotherapeutic treatment (CT) included receipt of chemotherapy and/or chemoembolization. Results: The study identified 2,534 eligible patients. Males comprised 80.6% of the cohort. The median age was 63 years. By SED quartile, there were significant differences by age, race, insurance, and receipt of chemotherapy (p&lt;0.05) but no difference by gender, ethnicity, stage, or survival. Overall, 39.1% received chemotherapeutic treatment. However, 41.0% of the lowest-deprivation patients received CT compared to 32.1% in the highest-deprivation cohort (p&lt;0.01). After adjustment for potential confounders, the lowest deprivation quartile had increased odds of CT compared to the highest deprivation quartile (OR 1.47; 95% CI,1.15-1.87). The median OS for the cohort was 4.0 months and was 4.6 and 3.3 months, respectively, for the lowest and highest deprivation quartiles (p&lt;0.01). For patients who received and did not receive CT, the median OS was 8.0 and 2.2 months, respectively (p&lt;0.01). However, there was no difference in median OS by SED for those who received CT (p=0.14). Conclusions: In an incidence-based, statewide cancer registry, the median OS for locally advanced and metastatic HCC remains poor and most patients do not receive CT. While there were differences in receipt of CT by SED, when patients did receive CT, median OS was similar across SED quartiles. These data suggest that SED may impact receipt of treatment and efforts are needed to understand and address treatment disparities. Lowest Deprivation Low Deprivation High Deprivation Highest Deprivation p-Value N=624 N=650 N=589 N=629 Chemotherapeutic treatment 0.001 No 347 (55.6%) 345 (53.1%) 339 (57.6%) 411 (65.3%) Yes 256 (41.0%) 283 (43.5%) 235 (39.9%) 202 (32.1%) Unknown 21 (3.4%) 22 (3.4%) 15 (2.5%) 15 (2.6%) Vital Status 0.93 Alive 41 (5.7%) 44 (5.9%) 43 (6.0%) 44 (6.5%) Dead 682 (94.3%) 695 (94.1%) 673 (94.0%) 632 (93.5%)

Navigating stroke care: Geospatial assessment of regional stroke center accessibility: Geospatial Assessment of Stroke Centers

IntroductionReducing time between stroke onset and hospital intervention is crucial for positive outcomes in stroke patients. While EMS utilization decreases time to intervention, many US regions are not within timely proximity to an advanced-care-capable stroke center (ASC), defined as a comprehensive or thrombectomy-capable center. This study aims to utilize geographic methodology to identify regions in Wisconsin with both high stroke mortality and low physical accessibility to certified stroke centers (SCs), particularly ASCs. MethodsGeocoded mortality records for stroke death between 2015 and 2020 were accessed from the Wisconsin Department of Health Services. Indirectly age-standardized mortality ratios (SMRs) were estimated continuously across Wisconsin using adaptive spatial filtering and mortality records at the census block group level; the surface was then averaged by census tract for tract level SMRs. Addresses for SC locations within Wisconsin and bordering states were collected, and drive times from Wisconsin census tract centroids to the nearest SC subtypes were estimated. Drive times and mortality ratios were evaluated at the tract level alongside Rural-Urban Commuting Area (RUCA) codes. Spatial error regression modeling was used to determine RUCA classifications with the highest stroke risk independent of accessibility to stroke centers. ResultsApproximately 50%, 68%, and 78% of Wisconsin residents resided within 30, 45, and 60 minutes of an ASC, respectively. Median drive time from census tract centroids to the nearest ASC were highest for rural tracts (M=90 minutes, IQR=68-115) compared to small-town (M=82 minutes, IQR=49-113), micropolitan (M=53 minutes, IQR=43-77), and metropolitan tracts (M=19 minutes, IQR=11-35; p<0.001). Clusters of high stroke SMRs were found in urban centers as well as rural areas irrespective of county declinations. Spatial regression modeling suggested small-town census tracts had the highest SMR irrespective of physical accessibility to care and spatial correlation. In small-town census tracts >45 minutes from the nearest ASC, the median stroke SMR was 1.12 (IQR=0.94-1.40) with 226,000 residents and 150 stroke deaths per year. ConclusionSmall-town areas are associated with both long drive distance to ASC locations and high stroke mortality. Geographical analyses reveal apparent stroke care deserts and may inform strategic allocation of emergency medicine resources and coverage.