ZIP Code Tabulation Area Research Articles

Spatial regression identifies socioeconomic inequality in multi-stage power outage recovery after Hurricane Isaac

Power outages are a common outcome of hurricanes in the USA with potentially serious implications for community wellbeing. Understanding how power outage recovery is influenced by factors such as the magnitude of the outage, storm characteristics, and community demographics is key to building community resilience. Outage data are a valuable tool that can help to better understand how hurricanes affect built infrastructure and influence the management of short-term infrastructure recovery process. We conduct a spatial regression analysis on customers experiencing outages and the total power recovery time to investigate the factors influencing power outage recovery in Louisiana after Hurricane Isaac. Our interest was in whether infrastructure damage and recovery times resulting from a hurricane disproportionately affect socioeconomically vulnerable populations and racial minorities. We find that median income is a significant predictor of the time it takes to restore 50%, 80%, and 95% of the total outages within a ZIP Code Tabulation Area, even after controlling for hurricane characteristics and total outages. Higher income geographies and higher income adjacent geographies experience faster recovery times. Our findings point to possible inequities associated with income in power outage recovery prioritization, which cannot be explained by exposure to outages, storm characteristics, or the presence of critical services such as hospitals and emergency response stations. These results should inform more equitable responses to power outages in the future helping to improve overall community resilience.

Small Area Forecasting of Opioid-Related Mortality: Bayesian Spatiotemporal Dynamic Modeling Approach

Opioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation. The overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts. We obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts' 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas. Bayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019. Our Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy.

Evolution of COVID-19 Health Disparities in Arizona.

COVID-19 burdens are disproportionally high in underserved and vulnerable communities in Arizona. As the pandemic progressed, it is unclear if the initial associated health disparities have changed. This study aims to elicit the dynamic landscape of COVID-19 disparities at the community level and identify newly emerging vulnerable subpopulations. Findings from this study can inform interventions to increase health equity among minoritized communities in the Southwest, other regions of the US, and globally. We compiled biweekly COVID-19 case counts of 274 zip code tabulation areas (ZCTAs) in Arizona from October 21, 2020, to November 25, 2021, a time spanning multiple waves of COVID-19 case growth. Within each biweekly period, we tested the associations between the growth rate of COVID-19 cases and the population composition in a ZCTA including race/ethnicity, income, employment, and age using multiple regression analysis. We then compared the associations across time periods to discover temporal patterns of health disparities. The association between the percentage of Latinx population and the COVID-19 growth rate was positive before April 2021 but gradually converted to negative afterwards. The percentage of Black population was not associated with the COVID-19 growth rate at the beginning of the study but became positive after January 2021 which persisted till the end of the study period. Young median age and high unemployment rate emerged as new risk factors around mid-August 2021. Based on these findings, we identified 37 ZCTAs that were highly vulnerable to future fast escalation of COVID-19 cases. As the pandemic progresses, vulnerabilities associated with Latinx ethnicity improved gradually, possibly bolstered by culturally responsive programs in Arizona to support Latinx. Still communities with disadvantaged social determinants of health continued to struggle. Our findings inform the need to adjust current resource allocations to support the design and implementation of new interventions addressing the emerging vulnerabilities at the community level.

Influence of social deprivation index on in-hospital outcomes of COVID-19

While it is known that social deprivation index (SDI) plays an important role on risk for acquiring Coronavirus Disease 2019 (COVID-19), the impact of SDI on in-hospital outcomes such as intubation and mortality are less well-characterized. We analyzed electronic health record data of adults hospitalized with confirmed COVID-19 between March 1, 2020 and February 8, 2021 from the INSIGHT Clinical Research Network (CRN). To compute the SDI (exposure variable), we linked clinical data using patient’s residential zip-code with social data at zip-code tabulation area. SDI is a composite of seven socioeconomic characteristics determinants at the zip-code level. For this analysis, we categorized SDI into quintiles. The two outcomes of interest were in-hospital intubation and mortality. For each outcome, we examined logistic regression and random forests to determine incremental value of SDI in predicting outcomes. We studied 30,016 included COVID-19 patients. In a logistic regression model for intubation, a model including demographics, comorbidity, and vitals had an Area under the receiver operating characteristic curve (AUROC) = 0.73 (95% CI 0.70–0.75); the addition of SDI did not improve prediction [AUROC = 0.73 (95% CI 0.71–0.75)]. In a logistic regression model for in-hospital mortality, demographics, comorbidity, and vitals had an AUROC = 0.80 (95% CI 0.79–0.82); the addition of SDI in Model 2 did not improve prediction [AUROC = 0.81 (95% CI 0.79–0.82)]. Random forests revealed similar findings. SDI did not provide incremental improvement in predicting in-hospital intubation or mortality. SDI plays an important role on who acquires COVID-19 and its severity; but once hospitalized, SDI appears less important.

Colorectal Cancer Screening Disparities Among Race: A Zip Code Level Analysis

BackgroundColorectal cancer (CRC) screening can prevent disease by early identification. Existing disparities in CRC screening have been associated with factors including race, socioeconomic status, insurance, and even geography. Our study takes a deeper look into how social determinants related to zip code tabulation areas affect CRC screenings. Materials and MethodsWe conducted a retrospective cross-sectional study of CRC screenings by race at a zip code level, evaluating for impactful social determinant factors such as the social deprivation index (SDI). We used publicly available data from CDC 500 Cities Project (2016-2019), PLACES Project (2020), and the American Community Survey (2019). We conducted multivariate and confirmatory factor analyses among race, income, health insurance, check-up visits, and SDI. ResultsIncreasing the tertile of SDI was associated with a higher likelihood of being Black or Hispanic, as well as decreased median household income (P < .01). Lower rates of regular checkup visits were found in the third tertile of SDI (P < .01). The multivariate analysis showed that being Black, Hispanic, lower income, being uninsured, lack of regular check-ups, and increased SDI were related to decreased CRC screening. In the confirmatory factor analysis, we found that SDI and access to insurance were the variables most related to decreased CRC screening. ConclusionOur results reveal the top 2 factors that impact a locality's CRC screening rates are the social deprivation index and access to health care. This data may help implement interventions targeting social barriers to further promote CRC screenings within disadvantaged communities and decrease overall mortality via early screening.

Can Hybrid In-Person and Virtual Care Delivery Models Increase Telehealth Access to Vulnerable Populations in the Post–COVID-19 Era?

Background The COVID-19 pandemic dramatically increased telehealth use; however, it simultaneously widened the digital health divide. Several studies in New York City (NYC) demonstrated that communities with the highest COVID-19 prevalence also had the lowest telehealth use. Moreover, the social distancing measures used to curb COVID-19 led to isolation, loneliness, and the avoidance of needed in-person care. Mobile integrated health programs utilizing both in-person components and virtual components can overcome social determinants that limit access to telehealth. Our mobile integrated health program, community teleparamedicine (CTP), employs care managers and community paramedics to engage patients with chronic illness and facilitate telehealth visits with emergency physicians. Objective We aimed to (1) describe the CTP cohort with respect to demographics and self-reported loneliness and (2) compare CTP visit locations to COVID-19 case rates by zip code. Methods Demographics were collected at CTP enrollment, and patients completed the UCLA Loneliness Scale (a summed score of 7 or higher indicates significant loneliness). Patients’ experiences with CTP were assessed by using anonymous postvisit surveys with a 5-point Likert scale. We presented descriptive statistics of survey results. CTP patients’ home addresses were compared to modified zip code tabulation area data on cumulative rates of COVID-19 cases per 100,000 people (NYC Department of Health’s COVID-19 GitHub repository). Results From January 2021 to February 2022, a total of 275 patients enrolled in CTP. These patients had an average age of 70 years, 49% were female, 53% were non-White, 16% were Hispanic or Latino, 42% resided in Manhattan, 30% resided in Brooklyn, 20% resided in Queens, and 8% resided in Bronx. Further, 1 in 5 patients reported significant loneliness; 37% lived alone; 61% were single, divorced, or widowed; and 70% did not have a home health aide. Only 8% did not own a computer, 4% did not have internet access, and 23% did not use a smartphone. Between April 2019 and October 2021, nearly 25% of NYC zip codes with CTP usage were among the top 50 zip codes with the highest cumulative rates of COVID-19 cases. CTP patients were satisfied with this hybrid model of virtual care; at least 95% of patients found that it was easy to connect to the physician, were satisfied with the care provided, felt less anxious about needing to return to the hospital, and agreed that CTP was easier than going to the hospital. Conclusions The hybrid in-person and virtual CTP program, which was specifically designed to address known barriers to accessing telehealth, successfully reached NYC communities that traditional telehealth programs did not, of which many experienced the highest rates of COVID-19. Our initial results indicate positive patient experiences; however, further qualitative research is needed to fully understand if facilitated telehealth also reduces isolation and loneliness as well as improves health and well-being. Conflicts of Interest None declared.

Geographic Access to Pediatric Cancer Care in the US

Although access to pediatric cancer care has implications for use of such care and patient outcomes, little is known about the geographic accessibility of pediatric cancer care and how it may vary by population characteristics across the continental US. To estimate the travel time to pediatric cancer care settings in the continental US, identify potential disparities among subgroups of children and adolescents and young adults (AYAs), and identify areas needing improved access to pediatric cancer care. This cross-sectional study collected data from August 1 to December 1, 2021. Pediatric oncologists' service locations in 2021 served as the pediatric cancer care settings, data for which were scraped from 2 websites containing information about health professionals. Demographic characteristics for younger children and AYAs aged 0 to 21 years were obtained from the 2015 to 2019 American Community Survey 5-year estimates. Data were analyzed from January 1 to April 31, 2022. The main outcome was the travel time from geographic centroids of zip code tabulation areas to the nearest pediatric oncologist. The median (IQR) travel times for each demographic subgroup were estimated. Per capita pediatric oncologist supply was calculated by dividing the total number of pediatric oncologists for each state or US Census division by its population. Of the 90 498 890 children and AYAs included in the study, 63.6% were estimated to travel less than 30 minutes and 19.7% to travel between 30 and 60 minutes (for a total of 83.3%) to the nearest pediatric oncologist. Median (IQR) travel times were longest for the American Indian or Alaska Native pediatric population (46 [16-104] minutes) and residents of rural areas (95 [68-135] minutes), areas with high deprivation levels (36 [13-72] minutes), and the South (24 [13-47] minutes) and Midwest (22 [11-51] minutes) compared with the general population of children and AYAs. The pediatric oncologist supply was lowest in Wyoming (0 oncologists per 100 000 pediatric population) and highest in Washington, DC (53.3 oncologists per 100 000 pediatric population). Pediatric oncologist supply across Census divisions was lowest in the Mountain division (3.3 oncologists per 100 000 pediatric population) and highest in the New England division (8.1 oncologists per 100 000 pediatric population). Results of this study showed that most children and AYAs in the continental US had adequate access to pediatric cancer care, although disparities existed among racial and ethnic groups and residents in rural areas, areas with high deprivation levels, and some Southern and Midwestern states. Reducing these disparities may require innovative approaches, such as expanding the capabilities of local facilities and creating partnerships with adult oncology centers and primary care physicians.

Spatial access to Emergency General Surgery (EGS) services and EGS bypass behaviours in California

ABSTRACT Hospital bypass behaviours (i.e. people go to faraway hospitals instead of close ones) of Emergency General Surgery (EGS) patients may cause treatment delays and worsened health outcomes. This study aimed to find the associations between EGS bypass behaviours and spatial access to EGS services as well as other factors at the individual level and ZIP Code Tabulation Area (ZCTA) level in California. We used a gravity model to calculate spatial access to EGS services at the ZCTA level. A Bayesian hierarchical spatial model was used to access associated variables of EGS bypass while accounting for spatial autocorrelation. Results show that better spatial access to EGS hospitals was associated with lower likelihood of EGS bypass. Other factors such as rural–urban status, health insurance type and race/ethnicity were also related to EGS bypass behaviours. Besides, people with similar EGS bypass behaviours seemed to cluster together due to spatial effect. Our results have important implications for EGS resource allocation, utilization and EGS disparity alleviation.

Abstract A054: Disparities in the availability of neuro-oncology clinical providers and infrastructure in the United States

Abstract BACKGROUND: Barriers to clinical trial access have significant implications for patient care. Motivated by previous work which identified geographical and socioeconomic disparities in neuro-oncology trials’ accessibility in the United States (US), we assessed the role of these factors on the availability of neuro-oncology providers and member institutions of trial collaboratives and community outreach programs. METHODS: US Census data on population and geography and the Neighborhood Atlas’ Area Deprivation Index (ADI) were obtained. Data on UCNS-certified neuro-oncology subspecialists, NRG Oncology Main Member institutions, and NCI Community Oncology Research Program (NCORP) sites were obtained and mapped to 5-digit zip code tabulation areas (ZCTAs) using Google Geocode. Linear and logistic regression and spatial analyses were conducted in R using the spdep and spatialreg libraries, to assess the role of geography, disadvantage, and population on infrastructure accessibility. RESULTS: We identified 316 neuro-oncologists, 130 NRG institutions, and 963 NCORP sites, in 0.6%, 0.4%, and 2.2% of all ZCTAs in the US (N = 33120), respectively. Neuro-oncologists and NRG institutions were more likely to exist in more populated (OR=1.86, p&amp;lt;0.001; OR=1.80, p&amp;lt;0.001) and less-disadvantaged (OR=0.98, p&amp;lt;0.001; OR=0.98, p&amp;lt;0.001) regions, reflecting urban-rural and socioeconomic disparities. Moreover, neuro-oncologists were more likely to exist in geographically adjacent regions (OR=320.594, p&amp;lt;0.001), while NRG institutions were geographically more broadly distributed (OR=1.49, p=0.859). Where existent, a greater number of neuro-oncologists served less-disadvantaged regions (β=-0.014, p=0.021), while the number of NRG institutions was more equitably distributed overall (Moran’s I data=0.002, p=0.394; Moran’s I residuals=0.002, p=0.489; population: β=0.004, p=0.778; ADI: β=-0.0001, p=0.919). On the other hand, NCORP sites were more likely to exist in areas of greater disadvantage (OR=1.01, p=0.001), reflecting the NCORP mission to reach more diverse communities. However, NCORP sites were more likely to exist and were more prevalent among more populated (OR=2.79, p&amp;lt;0.001; β=0.076, p=0.007) and geographically adjacent regions (OR=8.23, p&amp;lt;0.001; ρ=0.120, p&amp;lt;0.001), reflecting urban-rural disparities. CONCLUSION: The identified disparities in availability of neuro-oncologists and institutions capable of hosting clinical trials may explain the previously reported disparities in trial sites. Disparities in neuro-oncologist availability suggest the need for neuro-oncology specific measures involving telehealth and local oncologists. The more equitable geographic and socioeconomic distribution of NRG institutions, and the more equitable socioeconomic distribution of NCORP sites reaffirm the importance of the mission and ability of such initiatives to bridge geographic and socioeconomic barriers. Finally, the existence of disparities across all infrastructure levels raises the concern that these disparities reflect broader disparities in access to neuro-oncology care. Citation Format: Yeonju Kim, Terri S. Armstrong, Mark R. Gilbert, Orieta Celiku. Disparities in the availability of neuro-oncology clinical providers and infrastructure in the United States [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr A054.

Critical access medication for opioid use disorder (MOUD) treatment facilities in the continental United States

Research objectiveMedication opioid use disorder (MOUD) treatment is the first-line approach to the treatment of opioid use disorder (OUD). This analysis seeks to identify “critical access” MOUD facilities that ensure geographic access for MOUD patients. Using public-source data and spatial analysis, we identify the top 100 “critical access” MOUD units across the continental U.S. Study designWe use locational data from SAMHSA's Behavioral Health Treatment Services Locator and DATA 2000 waiver buprenorphine providers. We identify the closest MOUDs to each ZIP Code Tabulation Area (ZCTA)’s geographic centroid. We then construct a difference-in-distance metric by computing the difference in this distance measure between closest and second-closest MOUD, multiplied by ZCTA population, ranking MOUDs by difference-distance scores. Population studiedAll listed MOUD treatment facilities and all listed ZCTA's across the continental U.S., and all listed MOUD providers proximate to these areas. Principal findingsWe identified the top 100 critical access MOUD units in the continental United States. Many critical providers were in rural areas in the central United States, as well as a band extending east from Texas to Georgia. Twenty-three of the top 100 critical access providers were identified as providing naltrexone. Seventy-seven were identified as providing buprenorphine. Three were identified as providing methadone. ConclusionsSignificant areas of the United States are dependent on a single critical access MOUD provider. Implications for policy or practicePlace-based supports may be warranted to support MOUD treatment access in areas dependent upon critical access providers.

Socioeconomic deprivation and cardiometabolic risk factors in individuals with type 1 diabetes: T1D exchange clinic registry

AimsSocial determinants of health (SDOH) influence cardiovascular health in the general population; however, the degree to which this occurs in individuals with type 1 diabetes (T1D) is not well understood. We evaluated associations among socioeconomic deprivation and cardiometabolic risk factors (hemoglobin A1c, low-density lipoprotein, blood pressure, body mass index, physical activity) in individuals with T1D from the T1D Clinic Exchange Registry. MethodsWe evaluated the association between the social deprivation index (SDI) and cardiometabolic risk factors using multivariable and logistic regression among 18,754 participants ages 13 – 90 years (mean 29.2 ± 17) in the T1D Exchange clinic registry from 6,320 zip code tabulation areas (2007–2017). ResultsSDI was associated with multiple cardiometabolic risk factors even after adjusting for covariates (age, biological sex, T1D duration, and race/ethnicity) in the multivariable linear regression models. Those in the highest socially deprived areas had 1.69 (unadjusted) and 1.78 (adjusted) times odds of a triple concomitant risk burden of poor glycemia, dyslipidemia, and hypertension. ConclusionsPersistent SDOH differences could account for a substantial degree of poor achievement of cardiometabolic targets in individuals with T1D. Our results suggest the need for a broader framework to understand the association between T1D and adverse cardiometabolic outcomes.

Geospatial analysis of patients' social determinants of health for health systems science and disparity research.

Geospatial analysis of patients' social determinants of health for health systems science and disparity research.

Concentric regulatory zones failed to halt surging COVID-19: Brooklyn 2020.

We relied on reports of confirmed case incidence and test positivity, along with data on the movements of devices with location-tracking software, to evaluate a novel scheme of three concentric regulatory zones introduced by then New York Governor Cuomo to address an outbreak of COVID-19 in South Brooklyn in the fall of 2020. The regulatory scheme imposed differential controls on access to eating places, schools, houses of worship, large gatherings and other businesses within the three zones, but without restrictions on mobility. Within the central red zone, COVID-19 incidence temporarily declined from 131.2 per 100,000 population during the week ending October 3 to 62.5 per 100,000 by the week ending October 31, but then rebounded to 153.6 per 100,000 by the week ending November 28. Within the intermediate orange and peripheral yellow zones combined, incidence steadily rose from 28.8 per 100,000 during the week ending October 3 to 109.9 per 100,000 by the week ending November 28. Data on device visits to pairs of eating establishments straddling the red-orange boundary confirmed compliance with access controls. More general analysis of device movements showed stable patterns of movement between and beyond zones unaffected by the Governor's orders. A geospatial regression model of COVID-19 incidence in relation to device movements across zip code tabulation areas identified a cluster of five high-movement ZCTAs with estimated reproduction number 1.91 (95% confidence interval, 1.27-2.55). In the highly populous area of South Brooklyn, controls on access alone, without restrictions on movement, were inadequate to halt an advancing COVID-19 outbreak.

Association of Environmental Factors with Age-Related Macular Degeneration using the Intelligent Research in Sight Registry.

Investigate associations of natural environmental exposures with exudative and nonexudative age-related macular degeneration (AMD) across the United States. Database study. Patients aged ≥ 55 years who were active in the IRIS Registry from 2016 to 2018 were analyzed. Patients were categorized as nonexudative, inactive exudative, and active exudative AMD by International Classification of Diseases 10th Revision and Current Procedural Terminology (CPT) codes. Patients without provider-level ZIP codes matching any ZIP code tabulation area were excluded. Environmental data were obtained from public sources including the US Geological Survey, National Renewable Energy Laboratory, National Oceanic and Atmospheric Administration, and Environmental Protection Agency. Multiple variable, mixed effects logistic regression models with random intercepts per ZIP code tabulation area quantified the association of each environmental variable with any AMD versus non-AMD patients, any exudative AMD versus nonexudative AMD, and active exudative AMD versus inactive exudative and nonexudative AMD using 3 separate models, while adjusting for age, sex, race, insurance type, smoking history, and phakic status. Odds ratios for environmental factors. A total of 9 884 527 patients were included. Elevation, latitude, solar irradiance measured in global horizontal irradiance (GHI) and direct normal irradiance (DNI), temperature and precipitation variables, and pollution variables were included in our models. Statistically significant associations with active exudative AMD were GHI (odds ratio [OR], 3.848; 95% confidence interval [CI] with Bonferroni correction, 1.316-11.250), DNI (OR, 0.581; 95% CI, 0.370-0.913), latitude (OR, 1.110; 95% CI, 1.046-1.178), ozone (OR, 1.014; 95% CI, 1.004-1.025), and nitrogen dioxide (OR, 1.005; 95% CI, 1.000-1.010). The only significant environmental associations with any AMD were inches of snow in the winter (OR, 1.005; 95% CI, 1.001-1.009) and ozone (OR,1.011; 95% CI, 1.003-1.019). The strongest environmental associations differed between AMD subgroups. The solar variables GHI, DNI, and latitude were significantly associated with active exudative AMD. Two pollutant variables, ozone and nitrogen dioxide, also showed positive associations with AMD. Further studies are warranted to investigate the clinical relevance of these associations. Our curated environmental dataset has been made publicly available at https://github.com/uw-biomedical-ml/AMD_environmental_dataset.

Abstract 255: Association Of Hospital Market Area Dominance With Out-of-hospital Cardiac Arrest Survival To Admission

Introduction: Access to care has been measured by geographic distance, transport time or provider density. For emergency care sensitive conditions, like out-of-hospital cardiac arrest (OHCA), market area coverage may be associated with short-term outcomes due to the need for access to definitive care. Our objective was to evaluate the association of market area dominance with survival to hospital admission. Methods: ED-treated OHCA were identified in age-eligible Medicare fee-for service claims using ICD 9/10 codes and ED charges. Hospital market shares were defined as the cumulative proportion of all OHCA claims treated at the hospital of interest within a group of ZIP code tabulation areas (ZCTAs) centered around the hospital. Market share was calculated using sequential addition of ZCTAs. Hospital Market Areas (HMAs) included all ZCTAs where the cumulative market share exceeded a threshold (&lt;50% 50-74% 75-89% ≥90%). Survival to admission was determined using in-patient claims and vital status data. The primary predictor, maximum HMA threshold, is the highest market share proportion covering that ZCTA. A ZCTA with a 90% max HMA has one hospital treating 90% of OHCA case in that ZCTA or closer. Relative risk of survival was calculated using modified Poisson regression with hospital random effect and patient characteristics (age, sex, race, comorbidity). Results: Of the 194,914 claims treated at 4,436 hospitals between 2013 and 2015, 45% were female, 16% Black, 77% White, and 50% from large metro areas. 28% of claims were from a ZCTA with &lt;50% max HMA (an area where no single hospital treats the majority of OHCA). Relative risk of survival to admission decreased as the max HMA increased [RR[95%CI] 50-74%: 0.97[0.95, 0.99]; 75-89%: 0.94 [0.91, 0.98]; ≥90%: 0.90 [0.83, 0.98]. Small and non-metro areas were associated with lower survival likelihood. Interactions were not significant between max HMA and small metro areas; non-metro areas saw a greater reduction in survival likelihood for ZCTAs with max HMAs &gt;75%. Conclusion: For OHCA, market dominance was associated with lower likelihood of survival to hospital admission. The likelihood of survival decreased as market dominance increased. Further study is needed to develop strategies to mitigate this disparity.

Urban-rural disparities in geographic accessibility to care for people living with HIV

ABSTRACT In the United States, people living with HIV (PLWH) in rural areas fare worse along the HIV care continuum than their urban counterparts; this may be due in part to limited geographic access to care. We estimated drive time to care for PLWH, focusing on urban-rural differences. Adult Medicaid enrollees living with HIV and their usual care clinicians were identified using administrative claims data from 14 states (Medicaid Analytic eXtract, 2009–2012). We used geographic network analysis to calculate one-way drive time from the enrollee’s ZIP code tabulation area centroid to their clinician’s practice address, then examined urban-rural differences using bivariate statistics. Additional analyses included altering the definition of rurality; examining subsamples based on the state of residence, services received, and clinician specialty; and adjusting for individual and county characteristics. Across n = 49,596 PLWH, median drive time to care was 12.8 min (interquartile range 26.3). Median drive time for rural enrollees (43.6 (82.0)) was nearly four times longer than for urban enrollees (11.9 (20.6) minutes, p < 0.0001), and drive times exceeded one hour for 38% of rural enrollees (versus 12% of urban, p < 0.0001). Urban-rural disparities remained in all additional analyses. Sustained efforts to circumvent limited geographic access to care are critical for rural areas.

Socioeconomic Deprivation, Sleep Duration, and Mental Health during the First Year of the COVID-19 Pandemic.

The coronavirus disease 2019 (COVID-19) has had a rapid and sustained negative impact on sleep and mental health in the United States with disproportionate morbidity and mortality among socioeconomically deprived populations. We used multivariable and logistic regression to evaluate the associations among sleep duration, mental health, and socioeconomic deprivation (social deprivation index) in 14,676 Ohio residents from 1101 zip code tabulation areas from the 2020 Behavioral Risk Factor Surveillance System (BRFSS) survey. Higher socioeconomic deprivation was associated with shorter sleep and poorer mental health after adjusting for covariates (age, sex, race, education, income, and body mass index) in the multivariable linear regression models. Those in the highest socioeconomically deprived areas had 1.6 and 1.5 times higher odds of short sleep (duration < 6 h) and poor mental health (>14 poor mental health days), respectively, in the logistic regression models. Previous researchers have focused on limited socio-environmental factors such as crowding and income. We examined the role of a composite area based measure of socioeconomic deprivation in sleep duration and mental health during the first year of COVID-19. Our results suggest the need for a broader framework to understand the associations among socioeconomic deprivation, sleep duration, and mental health during a catastrophic event.

Species distribution modeling of Aedes aegypti in Maricopa County, Arizona from 2014 to 2020

Background:Aedes aegypti mosquitoes transmit dengue, yellow fever, Zika, and chikungunya viruses. Their range has recently been expanding throughout the world, including into desert regions such as Arizona in the southwestern United States. Little is understood about how these mosquitoes are surviving and behaving in arid environments, habitat that was previously considered inhospitable for the vector. The goal of this study is to create quarterly species distribution models based on satellite imagery and socioeconomic indicators for Ae. aegypti in Maricopa County, Arizona from 2014 to 2020.Methods: Trapping records for Ae. aegypti in Maricopa County, Arizona from 2014 to 2020 were split into 25 quarterly time periods. Quarterly species distribution models (Maxent) were created using satellite imagery-derived vegetation and moisture indices, elevation, and socioeconomic factors (population density, median income) as predictors. Maps of predicted habitat suitability were converted to binary presence/absence maps, and consensus maps were created that represent “core” habitat for the mosquito over 6 years of time. Results were summarized over census-defined zip code tabulation areas with the goal of producing more actionable maps for vector control.Results: Population density was generally the most important predictor in the models while median income and elevation were the least important. All of the 25 quarterly models had high test area under the curve values (&amp;gt;0.90) indicating good model performance. Multiple suburban areas surrounding the Phoenix metropolitan core area were identified as consistent highly suitable habitat.Conclusion: We identified long term “core” habitat for adult female Ae. aegypti over the course of 6 years, as well as “hotspot” locations with greater than average suitability. Binary maps of habitat suitability may be useful for vector control and public health purposes. Future studies should examine the movement of the mosquito in this region over time which would provide another clue as to how the mosquito is surviving and behaving in a desert region.

Impact of Radiation Oncology Advanced Alternative Payment Model on Black Americans

<h3>Purpose/Objective(s)</h3> In January 2023, the Centers for Medicare and Medicaid Services (CMS) will implement the Radiation Oncology (RO) Alternative Payment Model (APM) to reform how cancer patients receive radiotherapy (RT) and standardize payments for services. The CMS asserts that the purpose of this model is to "reduce Medicare expenditures while preserving or enhancing the quality of care for Medicare beneficiaries." The RO Model requires mandatory participation for 16 cancer types and encompasses RT providers from "randomly" selected zip codes that includes approximately 30% of all eligible Medicare recipients for these RT claims, with a stated goal of saving $230 million over the initial 5 years the model is to be in place. The purpose of this study is to analyze the potential impact of this model on already existing demographic disparities in cancer care between APM-affected zip codes and unaffected zip codes. <h3>Materials/Methods</h3> Demographic data from the agency for Healthcare Research and Quality and data from Zip Code Tabulation Areas (ZCTAs) for affected zip codes were used to examine demographics within affected and unaffected zip codes. Specific social determinants of health (SDOH) variables such as education, food stamps, median household income, and Gini coefficient were also examined. Data collected from the Surveillance, Epidemiology, and End Results (SEER) Program was used to analyze the difference in 5-year cancer incidence based on race and gender. Each individual APM diagnosis code was analyzed according to race and gender in order to determine the number of people impacted for the 16 different cancer types included within the RO Model. <h3>Results</h3> In total, 20.2% more black persons (1.56 million) and 1.9% fewer (878,000) non-Hispanic white (NHW) persons are included in APM zip codes compared with a randomly-selected U.S. population. There was no significant difference in SDOH variables between affected and unaffected zip codes; however, there is a higher population density (1990 vs. 1273 per sq. mile) and average population (16,000 vs. 10,000 per ZCTA) in APM zip codes. When examining all cancer diagnoses, 7.9% more NHW persons (480.3 per 100,000) than black persons (445.2) were diagnosed with cancer; however, when looking at only APM cancers, 17% more black persons (391.5 per 100,000) than NHW persons (334.5 per 100,000) were diagnosed. A breakdown of race and gender disparities showed 17.4% more black men than NHW men were included in the APM diagnosis codes when compared with a randomly-selected U.S. population. Likewise, NHW women had 15.2% (456.6 per 100,000) more cancer diagnoses than black women (396.4), but when looking at only APM cancer diagnoses, that gap narrowed to 7.2% (319.2 NHW vs. 297.7 black). <h3>Conclusion</h3> These data indicate that the RO APM Model is heavily imbalanced in terms of the number of black persons mandated to participate and further optimization of this model is needed before widespread adoption.

Racial and Ethnic Cardiometabolic Risk Disparities in the Type 1 Diabetes Exchange Clinic Registry Cohort

ObjectiveTo determine whether individuals from a historically underrepresented racial group have a higher cardiometabolic risk than historically represented individuals with type 1 diabetes (T1D) considering socioeconomic deprivation. MethodsWe used the multivariable logistic and linear regression models to examine socioeconomic deprivation (upper 10th percentile) by race/ethnicity interaction for each cardiometabolic risk factor and cardiometabolic risk burden score, respectively, across 6320 zip code tabulation areas. We also determined the age-adjusted prevalence of low, moderate, and high cardiometabolic risks defined as 0, 1 to 2, and 3 or more risk factors for hypertension, obesity, dyslipidemia, and off-target glycemia for non-Hispanic White (n = 15 746), non-Hispanic Black (n = 1019), Hispanic (n = 1115), and other (n = 887), respectively. ResultsThe sample comprised 18 767 adolescents and adults with T1D. Those identifying as non-Hispanic Black were more likely to have a high cardiometabolic risk profile, including a 4.5-fold increase in the odds of off-target glycemia, a twofold increase in the odds of systolic hypertension, and 0.29 (unadjusted) and 0.46 (adjusted) increases in a higher cardiometabolic risk burden compared with non-Hispanic White individuals (P < .01). Those identifying as Hispanic had a 3.4-fold increase in the odds of off-target glycemia but were less likely to be overweight/obese or have systolic hypertension compared with non-Hispanic White. However, the lower likelihood of overweight/obesity and hypertension did not persist after considering covariates. ConclusionThere is a need to investigate additional determinants of racially/ethnically underrepresented cardiometabolic health, including structural racism and implicit bias in cardiometabolic care for individuals with T1D.