The Geography of Injuries in Trauma Systems: Using Home as a Proxy for Incident Location.

INTRODUCTION: Effective trauma system organization is crucial to timely access to care and requires accurate understanding of injury and resource locations. Many systems rely on home zip codes to evaluate geographic distribution of injury; however, few studies have evaluated the reliability of home as a proxy for incident location after injury. METHODS: We analyzed data from a multicenter prospective cohort collected from 2017 to 2021. Injured patients with both home and incident zip codes were included. Outcomes included discordance and differential distance between home and incident zip code. Associations of discordance with patient characteristics were determined by logistic regression. We also assessed trauma center catchment areas based on home versus incident zip codes and variation regionally at each center. RESULTS: Fifty thousand one hundred seventy-five patients were included in the analysis. Home and incident zip codes were discordant in 21,635 patients (43.1%). Injuries related to motor vehicles (aOR: 4.76 [95% CI 4.50-5.04]) and younger adults 16-64 (aOR: 2.46 [95% CI 2.28-2.65]) were most likely to be discordant. Additionally, as injury severity score increased, discordance increased. Trauma center catchment area differed up to two-thirds of zip codes when using home versus incident location. Discordance rate, discordant distance, and catchment area overlap between home and incident zip codes all varied significantly by geographic region. CONCLUSIONS: Home location as proxy for injury location should be used with caution and may impact trauma system planning and policy, especially in certain populations. More accurate geolocation data are warranted to further optimize trauma system design.

ZIP Code-Level Estimation of Air Quality and Health Risk Due to Particulate Matter Pollution in New York City.

Exposure to PM2.5 is associated with hundreds of premature mortalities every year in New York City (NYC). Current air quality and health impact assessment tools provide county-wide estimates but are inadequate for assessing health benefits at neighborhood scales, especially for evaluating policy options related to energy efficiency or climate goals. We developed a new ZIP Code-Level Air Pollution Policy Assessment (ZAPPA) tool for NYC by integrating two reduced form models─Community Air Quality Tools (C-TOOLS) and the Co-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA)─that propagate emissions changes to estimate air pollution exposures and health benefits. ZAPPA leverages custom higher resolution inputs for emissions, health incidences, and population. It, then, enables rapid policy evaluation with localized ZIP code tabulation area (ZCTA)-level analysis of potential health and monetary benefits stemming from air quality management decisions. We evaluated the modeled 2016 PM2.5 values against observed values at EPA and NYCCAS monitors, finding good model performance (FAC2, 1; NMSE, 0.05). We, then, applied ZAPPA to assess PM2.5 reduction-related health benefits from five illustrative policy scenarios in NYC focused on (1) commercial cooking, (2) residential and commercial building fuel regulations, (3) fleet electrification, (4) congestion pricing in Manhattan, and (5) these four combined as a "citywide sustainable policy implementation" scenario. The citywide scenario estimates an average reduction in PM2.5 of 0.9 µg/m3. This change translates to avoiding 210-475 deaths, 340 asthma emergency department visits, and monetized health benefits worth $2B to $5B annually, with significant variation across NYC's 192 ZCTAs. ZCTA-level assessments can help prioritize interventions in neighborhoods that would see the most health benefits from air pollution reduction. ZAPPA can provide quantitative insights on health and monetary benefits for future sustainability policy development in NYC.

Community Racial Composition and Hospitalization Among Patients Receiving In-Center Hemodialysis.

RATIONALE & OBJECTIVE: Community racial composition has been shown to be associated with mortality in patients receiving maintenenance dialysis. It is unclear whether living in communities with predominantly Black residents is also associated with risk for hospitalization among patients receiving hemodialysis. STUDY DESIGN: Retrospective analysis of prospectively collected data from a cohort of patients receiving hemodialysis. SETTING & PARTICIPANTS: 4,567 patients treated in 154 dialysis facilities located in 127 unique zip codes and enrolled in US Dialysis Outcomes and Practice Patterns Study (DOPPS) phases 4 to 5 (2010-2015). EXPOSURE: Tertile of percentage of Black residents within zip code of patients' dialysis facility, defined through a link to the American Community Survey. OUTCOME: Rate of hospitalizations during the study period. ANALYTIC APPROACH: Associations of patient-, facility-, and community-level variables with community's percentage of Black residents were assessed using analysis of variance, Kruskal-Wallis, or χ2/Fisher exact tests. Negative binomial regression was used to estimate the incidence rate ratio for hospitalizations between these communities, with and without adjustment for potential confounding variables. RESULTS: Mean age of study patients was 62.7 years. 53% were White, 27% were Black, and 45% were women. Median and threshold percentages of Black residents in zip codes in which dialysis facilities were located were 34.2% and≥14.4% for tertile 3 and 1.0% and≤1.8% for tertile 1, respectively. Compared with those in tertile 1 facilities, patients in tertile 3 facilities were more likely to be younger, be Black, live in urban communities with lower socioeconomic status, have a catheter as vascular access, and have fewer comorbid conditions. Patients dialyzing in communities with the highest tertile of Black residents experienced a higher adjusted rate of hospitalization (adjusted incidence rate ratio, 1.32; 95% CI, 1.12-1.56) compared with those treated in communities within the lowest tertile. LIMITATIONS: Potential residual confounding. CONCLUSIONS: The risk for hospitalization for patients receiving maintenance dialysis is higher among those treated in communities with a higher percentage of Black residents after adjustment for dialysis care, patient demographics, and comorbid conditions. Understanding the cause of this association should be a priority of future investigation.

ZIP Code-Level Estimates from a Local Health Survey: Added Value and Limitations.

We assessed the added value and limitations of generating directly estimated ZIP Code-level estimates by aggregating 5 years of data from an annual cross-sectional survey, the New York City Community Health Survey (n = 44,886) from 2009 to 2013, that were designed to provide reliable estimates only of larger geographies. Survey weights generated directly-observed ZIP Code (n = 128) level estimates. We assessed the heterogeneity of ZIP Code-level estimates within coarser United Hospital Fund (UHF) neighborhood areas (n = 34) by using the Rao-Scott Chi-Square test and one-way ANOVA. Orthogonal linear contrasts assessed whether there were linear trends at the UHF level from 2009 to 2013. 22 of 37 health indicators were reliable in over 50% of ZIP Codes. 14 of the 22 variables showed heterogeneity in ≥4 UHFs. Variables for drinking, nutrition, and HIV testing showed heterogeneity in the most UHFs (9-24 UHFs). In half of the 32 UHFs, >20% variables had within-UHF heterogeneity. Flu vaccination and sugary beverage consumption showed significant time trends in the largest number of UHFs (12 or more UHFs). Overall, heterogeneity of ZIP Code-level estimates suggests that there is value in aggregating 5 years of data to make direct small area estimates.

Zip Codes May Not Be an Adequate Method to Risk Adjust for Socioeconomic Status Following Total Joint Arthroplasty at the Individual Surgeon Level.

BACKGROUND: Increasing consumerism in healthcare has included a push toward the ranking of individual surgeons. These rankings rely on the adjustment of patient outcomes based on individual patient risk. Socioeconomic status (SES) has been identified as an important variable impacting patient outcomes following total joint arthroplasty, and patient zip code has been proposed as a proxy. Our study attempts to determine if zip code is an acceptable proxy for SES within a single surgeon's practice. METHODS: Using public zip code and Geographic Information Systems (GIS) tax map data, we compared the real estate holdings of 244 patients undergoing total joint arthroplasty from an individual hip and knee arthroplasty surgeon's practice within an academic medical center over a 14-month period. An independent t-test was used to compare GIS data with the average home value within a given zip code. A Pearson correlation coefficient was calculated between GIS values and average home value per zip code. RESULTS: In a sample of 244 patients, mean home value calculated from GIS data was $335,993 (standard deviation [SD] $246,549), and $243,663 with zip code data (SD $84,731). The Pearson correlation coefficient was 0.411 (P < .001). There was a significant difference between mean home values calculated from zip code data and GIS data (P < .001). Using zip code estimates would have mischaracterized home value, as defined as greater than or less than 1 SD, in 15% of patients. CONCLUSION: Although there was some relationship between zip code and real estate holdings, the correlation is only moderate in strength and a substantial number of outliers were present. Given the sample size at the individual surgeon level, we question whether zip code can be used as a proxy for SES risk adjustment for the purposes of surgeon ranking.

Estimating a Large Travel Time Matrix Between Zip Codes in the United States: A Differential Sampling Approach.

Estimating a massive drive time matrix between locations is a practical but challenging task. The challenges include availability of reliable road network (including traffic) data, programming expertise, and access to high-performance computing resources. This research proposes a method for estimating a nationwide drive time matrix between ZIP code areas in the U.S.-a geographic unit at which many national datasets such as health information are compiled and distributed. The method (1) does not rely on intensive efforts in data preparation or access to advanced computing resources, (2) uses algorithms of varying complexity and computational time to estimate drive times of different trip lengths, and (3) accounts for both interzonal and intrazonal drive times. The core design samples ZIP code pairs with various intensities according to trip lengths and derives the drive times via Google Maps API, and the Google times are then used to adjust and improve some primitive estimates of drive times with low computational costs. The result provides a valuable resource for researchers.

Direct Drug Targeting into Intracellular Compartments: Issues, Limitations, and Future Outlook.

Intracellular compartment drug delivery is a promising strategy for the treatment of diseases. By this way, medicines can delivered to particular intracellular compartments. This maximizes the therapeutic efficacy and safety of medicines, particularly of anticancer and antiviral drugs. Intracellular compartment drug delivery is either indirectly by targeting of cell nucleus as central compartment of the cell or directly through the targeting of compartments itself. Drugs or nanoshuttles labeled with compartment's localization signal represent a smart tactic for subcellular compartment targeting. There are several boundaries prevent the arrival of shuttles to the specified intracellular compartments. These boundaries include selective permeability of biomembranes, efflux transporters, and lysosomes. The utilization of specific ligands during design of drug delivery nanoshuttles permits the targeting of specified intracellular compartment. Therefore drugs targeting could correct the diseases associated organelles. This review highlights the direct targeting of the medicines into subcellular compartment as a promising therapeutic strategy.

Residential Area Life Expectancy: Association With Outcomes and Processes of Care for Patients With ESRD in the United States.

BACKGROUND: The effects of underlying noncodified risks are unclear on the prognosis of patients with end-stage renal disease (ESRD). We aimed to evaluate the association of residential area life expectancy with outcomes and processes of care for patients with ESRD in the United States. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: Adult patients with incident ESRD between 2006 and 2013 recorded in the US Renal Data System (n=606,046). PREDICTOR: The primary exposure was life expectancy in the patient's residential county estimated by the Institute for Health Metrics and Evaluation. OUTCOMES: Death, placement on the kidney transplant wait list, living and deceased donor kidney transplantation, and posttransplantation graft loss. RESULTS: Median life expectancies of patients' residences were 75.6 (males) and 80.4 years (females). Compared to the highest life expectancy quintile and adjusted for demographic factors, disease cause, and multiple comorbid conditions, the lowest quintile had adjusted HRs for mortality of 1.20 (95% CI, 1.18-1.22); placement onto the waiting list, 0.68 (95% CI, 0.67-0.70); living donor transplantation, 0.53 (95% CI, 0.51-0.56); posttransplantation graft loss, 1.35 (95% CI, 1.27-1.43); and posttransplantation mortality, 1.29 (95% CI, 1.19-1.39). Patients living in areas with lower life expectancy were less likely to be informed about transplantation, be under the care of a nephrologist, or receive an arteriovenous fistula as the initial dialysis access. Results remained consistent with additional adjustment for zip code-level median income, population size, and urban-rural locality. LIMITATIONS: Potential residual confounding and attribution of effects to individuals based on residential area-level data. CONCLUSIONS: Residential area life expectancy, a proxy for socioeconomic, environmental, genetic, and behavioral factors, was independently associated with mortality and process-of-care measures for patients with ESRD. These results emphasize the underlying effect on health outcomes of the environment in which patients live, independent of patient-level factors. These findings may have implications for provider assessments.

Prevalence of Emergency Department Patients Presenting with Heroin or Prescription Opioid Abuse Residing in Urban, Suburban, and Rural Jefferson County.

BACKGROUND: Heroin and prescription opioid abuse in the United States is exhibiting a growing geographic ubiquity. OBJECTIVES: This study characterizes the changing geographic distribution of patients presenting with heroin and prescription opioid abuse to one urban emergency department (ED). METHODS: A retrospective review of patients with heroin and prescription opioid abuse from 2009-2014 was conducted in one adult urban ED. The primary outcome was the prevalence of heroin and prescription opioid patients residing in urban, suburban, and rural ZIP codes over time. RESULTS: From 2009 to 2014, 2695 patients presented for either heroin (N = 1436; 53%) or prescription opioid (N = 1259; 47%) abuse from 32 Jefferson County ZIP codes. Of the 32 ZIP codes, 15 were urban (47%), 13 suburban (41%), and 4 rural (12%). The prevalence of heroin patients (per 10,000 population) increased in each ZIP code from 2009-2014. After 2011, prescription opioid prevalence decreased in urban and suburban ZIP codes but increased in rural ones. Using segmented regression analysis, the increase in patients who used heroin residing in all ZIP codes and the decrease in patients abusing prescription opioids residing in urban areas was statistically significant after 2011. CONCLUSION: From 2009 to 2014, there was an increasing trend in ED patients using heroin who resided in urban, suburban, and rural ZIP codes. There was an increasing trend in prescription opioid prevalence in all regions from 2009 to 2011. After 2011, prescription opioid prevalence decreased in urban and suburban ZIP codes but not rural ZIP codes.

Efficient Proximity Computation Techniques Using ZIP Code Data for Smart Cities †.

In this paper, we are interested in computing ZIP code proximity from two perspectives, proximity between two ZIP codes (Ad-Hoc) and neighborhood proximity (Top-K). Such a computation can be used for ZIP code-based target marketing as one of the smart city applications. A naïve approach to this computation is the usage of the distance between ZIP codes. We redefine a distance metric combining the centroid distance with the intersecting road network between ZIP codes by using a weighted sum method. Furthermore, we prove that the results of our combined approach conform to the characteristics of distance measurement. We have proposed a general and heuristic approach for computing Ad-Hoc proximity, while for computing Top-K proximity, we have proposed a general approach only. Our experimental results indicate that our approaches are verifiable and effective in reducing the execution time and search space.

Use of Medicaid and housing data may help target areas of high asthma prevalence.

OBJECTIVE: To determine if there was a significant difference between mold contamination and asthma prevalence in Detroit and non-Detroit Michigan homes, between newer and older homes, and if there is a correlation between mold contamination and measures of Medicaid use for asthma in the 25 Detroit zip codes. METHODS: Settled dust was collected from homes (n = 113) of Detroit asthmatic children and from a representative group of Michigan homes (n = 43). The mold contamination for each home was measured using the Environmental Relative Moldiness Index (ERMI) scale and the mean ERMI values in Detroit and non-Detroit homes were statistically compared. Michigan Medicaid data (13 measures related to asthma) in each of the 25 zip codes in Detroit were tested for correlation to ERMI values for homes in those zip codes. RESULTS: The mean ERMI value (14.5 ± 8.0) for Detroit asthmatic childrens' homes was significantly (Student's t-test, p < 0.001) greater than the mean ERMI value (2.1 ± 6.2) for the non-Detroit homes. Detroit homes > 60 years old had significantly (p = 0.01) greater mean ERMI values than Detroit homes ≤ 60 years old (15.87 vs. 11.25). The percentage of children that underwent spirometry testing for their persistent asthma (based on Medicaid data) was significantly, positively correlated with the mean ERMI values of the homes in the 25 zip codes. CONCLUSIONS: Applying Medicaid-use data for spirometry testing and locating a city's older housing stock might help find foci of homes with high ERMI values.

Protein targeting via mRNA in bacteria.

Proteins of all living organisms must reach their subcellular destination to sustain the cell structure and function. The proteins are transported to one of the cellular compartments, inserted into the membrane, or secreted across the membrane to the extracellular milieu. Cells have developed various mechanisms to transport proteins across membranes, among them localized translation. Evidence for targeting of Messenger RNA for the sake of translation of their respective protein products at specific subcellular sites in many eukaryotic model organisms have been accumulating in recent years. Cis-acting RNA localizing elements, termed RNA zip-codes, which are embedded within the mRNA sequence, are recognized by RNA-binding proteins, which in turn interact with motor proteins, thus coordinating the intracellular transport of the mRNA transcripts. Despite the rareness of conventional organelles, first and foremost a nucleus, pieces of evidence for mRNA localization to specific subcellular domains, where their protein products function, have also been obtained for prokaryotes. Although the underlying mechanisms for transcript localization in bacteria are yet to be unraveled, it is now obvious that intracellular localization of mRNA is a common mechanism to spatially localize proteins in both eukaryotes and prokaryotes. This article is part of a Special Issue entitled: Protein trafficking and secretion in bacteria. Guest Editors: Anastassios Economou and Ross Dalbey.

Residential zip code influences outcomes following hospitalization for acute pulmonary embolism in the United States.

Socioeconomic status (SES) as reflected by residential zip code may adversely influence outcomes for patients with acute pulmonary embolism (PE). We sought to analyze the impact of neighborhood SES on in-hospital mortality, use of thrombolysis, implantation of inferior vena cava (IVC) filters and cost of hospitalization following acute PE. We used the 2003-2011 Nationwide Inpatient Sample (NIS) for this analysis. All hospital admissions with a principal diagnosis of acute PE were identified using ICD-9 codes. Neighborhood SES was assessed using median household income of the residential zip code for each patient. Over this 9-year period, 276,484 discharges with acute PE were analyzed. There was a progressive decrease in in-hospital mortality across the SES quartiles (p-trend <0.001). The incidence of in-hospital mortality across quartiles 1-4 was 3.8%, 3.3%, 3.2%, and 3.1%, respectively. Despite low rates of thrombolytic utilization in this cohort, we observed a progressive increase in the rate of thrombolysis utilization across the SES quartiles (1.5%, 1.6%, 1.7%, 2.0%; p-trend <0.001). There was no significant difference in the use of IVC filters across the SES quartiles (p-trend=0.9). The mean adjusted cost of hospitalization among quartiles 2, 3, and 4, as compared to quartile 1, was significantly higher by $1202, $1650, and $1844, respectively (p-trend<0.001). In conclusion, patients residing in zip codes with lower SES had increased in-hospital mortality and decreased utilization of thrombolysis following acute PE compared to patients residing in higher SES zip codes. The cost of hospitalization for patients from higher SES quartiles was significantly higher than those from lower quartiles.

Social and Medical Determinants of Cardiometabolic Health: The Big Picture.

Cardiometabolic diseases, including diabetes and heart disease, account for >12 million years of life lost annually among Black adults in the United States. Health disparities are geographically localized, with ~80% of health disparities occurring within ~6000 (16%) of all 38,000 US ZIP codes. Socio-economic status (SES), behavioral and environmental factors (social determinants) account for ~80% of variance in health outcomes and cluster geographically. Neighborhood SES is inversely associated with prevalent diabetes and hypertension, and Blacks are four times more likely than Whites to live in lowest SES neighborhoods. In ZIP code 48235 (Detroit, 97% Black, 16.2% unemployed, income/capita $18,343, 23.6% poverty), 1082 Medicare fee-for service (FFS) beneficiaries received care for type 2 diabetes (T2D) and coronary artery disease (CAD) in 2012. Collectively, these beneficiaries had 1082 inpatient admissions and 839 emergency department visits, mean cost $27,759/beneficiary and mortality 2.7%. Nationally in 2011, 236,222 Black Medicare FFS beneficiaries had 213,715 inpatient admissions, 191,346 emergency department visits, mean cost $25,580/beneficiary and 2.4% mortality. In addition to more prevalent hypertension and T2D, Blacks appear more susceptible to clinical complications of risk factors than Whites, including hypertension as a contributor to stroke. Cardiometabolic health equity in African Americans requires interventions on social determinants to reduce excess risk prevalence of risk factors. Social-medical interventions to promote timely access to, delivery of and adherence with evidence-based medicine are needed to counterbalance greater disease susceptibility. Place-based interventions on social and medical determinants of health could reduce the burden of life lost to cardiometabolic diseases in Blacks.

Multilevel built environment features and individual odds of overweight and obesity in Utah.

Based on the data from the Behavioral Risk Factor Surveillance System (BRFSS) in 2007, 2009 and 2011 in Utah, this research uses multilevel modeling (MLM) to examine the associations between neighborhood built environments and individual odds of overweight and obesity after controlling for individual risk factors. The BRFSS data include information on 21,961 individuals geocoded to zip code areas. Individual variables include BMI (body mass index) and socio-demographic attributes such as age, gender, race, marital status, education attainment, employment status, and whether an individual smokes. Neighborhood built environment factors measured at both zip code and county levels include street connectivity, walk score, distance to parks, and food environment. Two additional neighborhood variables, namely the poverty rate and urbanicity, are also included as control variables. MLM results show that at the zip code level, poverty rate and distance to parks are significant and negative covariates of the odds of overweight and obesity; and at the county level, food environment is the sole significant factor with stronger fast food presence linked to higher odds of overweight and obesity. These findings suggest that obesity risk factors lie in multiple neighborhood levels and built environment features need to be defined at a neighborhood size relevant to residents' activity space.

RNA localization in bacteria.

One of the most important discoveries in the field of microbiology in the last two decades is that bacterial cells have intricate subcellular organization. This understanding has emerged mainly from the depiction of spatial and temporal organization of proteins in specific domains within bacterial cells, e.g., midcell, cell poles, membrane and periplasm. Because translation of bacterial RNA molecules was considered to be strictly coupled to their synthesis, they were not thought to specifically localize to regions outside the nucleoid. However, the increasing interest in RNAs, including non-coding RNAs, encouraged researchers to explore the spatial and temporal localization of RNAs in bacteria. The recent technological improvements in the field of fluorescence microscopy allowed subcellular imaging of RNAs even in the tiny bacterial cells. It has been reported by several groups, including ours that transcripts may specifically localize in such cells. Here we review what is known about localization of RNA and of the pathways that determine RNA fate in bacteria, and discuss the possible cues and mechanisms underlying these distribution patterns.

Disaggregation of Green Space Access, Walkability, and Behavioral Risk Factor Data for Precise Estimation of Local Population Characteristics.

BACKGROUND: Social and Environmental Determinants of Health (SEDH) provide us with a conceptual framework to gain insights into possible associations among different human behaviors and the corresponding health outcomes that take place often in and around complex built environments. Developing better built environments requires an understanding of those aspects of a community that are most likely to have a measurable impact on the target SEDH. Yet data on local characteristics at suitable spatial scales are often unavailable. We aim to address this issue by application of different data disaggregation methods. METHODS: We applied different approaches to data disaggregation to obtain small area estimates of key behavioral risk factors, as well as geospatial measures of green space access and walkability for each zip code of Allegheny County in southwestern Pennsylvania. RESULTS: Tables and maps of local characteristics revealed their overall spatial distribution along with disparities therein across the county. While the top ranked zip codes by behavioral estimates generally have higher than the county's median individual income, this does not lead them to have higher than its median green space access or walkability. CONCLUSION: We demonstrated the utility of data disaggregation for addressing complex questions involving community-specific behavioral attributes and built environments with precision and rigor, which is especially useful for a diverse population. Thus, different types of data, when comparable at a common local scale, can provide key integrative insights for researchers and policymakers.

Lung Cancer Prevalence in Virginia: A Spatial Zipcode-Level Analysis via INLA.

BACKGROUND: Examining lung cancer (LC) cases in Virginia (VA) is essential due to its significant public health implications. By studying demographic, environmental, and socioeconomic variables, this paper aims to provide insights into the underlying drivers of LC prevalence in the state adjusted for spatial associations at the zipcode level. METHODS: We model the available VA zipcode-level LC counts via (spatial) Poisson and negative binomial regression models, taking into account missing covariate data, zipcode-level spatial association and allow for overdispersion. Under latent Gaussian Markov Random Field (GMRF) assumptions, our Bayesian hierarchical model powered by Integrated Nested Laplace Approximation (INLA) considers simultaneous (spatial) imputation of all missing covariates through elegant prediction. The spatial random effect across zip codes follows a Conditional Autoregressive (CAR) prior. RESULTS: Zip codes with elevated smoking indices demonstrated a corresponding increase in LC counts, underscoring the well-established connection between smoking and LC. Additionally, we observed a notable correlation between higher Social Deprivation Index (SDI) scores and increased LC counts, aligning with the prevalent pattern of heightened LC prevalence in regions characterized by lower income and education levels. On the demographic level, our findings indicated higher LC counts in zip codes with larger White and Black populations (with Whites having higher prevalence than Blacks), lower counts in zip codes with higher Hispanic populations (compared to non-Hispanics), and higher prevalence among women compared to men. Furthermore, zip codes with a larger population of elderly people (age ≥ 65 years) exhibited higher LC prevalence, consistent with established national patterns. CONCLUSIONS: This comprehensive analysis contributes to our understanding of the complex interplay of demographic and socioeconomic factors influencing LC disparities in VA at the zip code level, providing valuable information for targeted public health interventions and resource allocation. Implementation code is available at GitHub.

The Implications of Socioeconomic Status by ZIP Code on Maternal-Fetal Morbidity and Mortality in San Antonio, Texas.

Introduction Over the past 20 years, the number of pregnancy-related fatalities in the United States has been on the rise. Increases in maternal and fetal mortality have been attributed to low socioeconomic status (SES). This raises the question of whether all geographical locations are proportionally affected by this upward trend in pregnancy-related fatalities. San Antonio is one of the largest cities in the United States and is known for its economic segregation. This study aims to compare the maternal and fetal health outcomes of mothers from diverse socioeconomic backgrounds in San Antonio, Texas. Methods To analyze the relationship between pregnancy-related mortality rates and SES in San Antonio, Texas, the International Classification of Diseases (ICD)-10 codes for maternal and fetal demise and their associated risk factors were identified. The ICD-10 codes were used to compare the health outcomes of pregnant women from the highest SES ZIP Code (78255, median income $124,397) to women from the lowest SES ZIP Code (78207, median income $25,415) using the Texas Inpatient Public Use Data File for 2016, which contains information on 93-97% of all hospital discharges in San Antonio, Texas. Results Notably, pregnant women from the high SES ZIP Code were admitted to the hospital from clinics or a physician's office (68.8%), while pregnant women from the low SES ZIP Code were admitted to the hospital from non-healthcare facilities like home or workplace (62.5%). In addition, a greater percentage of patients from the low SES ZIP Code were Black (4.3% vs 1.3%) or Hispanic (88.5% vs 35.1%). Compared to women from the high SES ZIP Code, women from the low SES ZIP Code experienced more fetal deaths and a higher prevalence of maternal and fetal risk factors such as obesity (47.6% vs 32.5%), asthma (1.7% vs 1.3%), hypertension (0.8% vs 0%), substance abuse (0.5% vs 0%), diabetes mellitus (9.8% vs 7.8%), preeclampsia (7.7% vs 2.6%), and multiple C-sections (35.5% vs 28.6%). Finally, fetal mortality rates were higher in the low SES ZIP Code (1.1% vs 0%). Although there were no statistically significant maternal or fetal mortality differences between the ZIP Codes, the trend suggests that women's health outcomes in San Antonio are not equitable. Discussion Analysis reveals disproportionate health outcomes for women in south San Antonio. Further investigation is warranted to better understand the role social and medical factors play in these results. Investigating the relationship between SES and pregnancy-related mortality can help to better inform healthcare providers and identify ways to improve women's health outcomes in San Antonio, Texas.

The association between local area immigrant fraction and prevalence of cardiovascular diseases in the United States: an observational study.

Background: Local area immigrant fraction is strongly and positively correlated with local life expectancy in the United States. The aim of the study was to determine the relationship between local area immigrant fraction and local prevalence of coronary heart disease (CHD) and stroke. Methods: Cross-sectional study design, with ZIP code as the unit of observation. Demographic data was obtained from the American Community Survey, and linked to indicators of health access (e.g., insurance, annual check-ups, cholesterol screening), obesity, behavior (smoking, exercise), and cardiovascular outcomes data from the 2020 Population Level Analysis and Community Estimates. Multivariable regression and path analyses were used to assess both direct and indirect relationships among variables. Findings: CHD prevalence was lower in the second (3.9% relative difference, 95% CI: 3.1-4.5%), third (6.5%, 95% CI: 5.8-7.1%), and fourth (14.8%, 95% CI: 14.1-15.8%) quartiles of immigrant fraction compared to the lowest (p-trend <0.001). These effects remained robust in multivariable analysis following adjustment for indicators of access, obesity, and behavioral variables (p-trend <0.0001). For stroke, only the highest quartile demonstrated a significant difference in prevalence (2.1%, 95% CI: 1.2-3.0% with full adjustment). In CHD path analysis, ∼45% of the association of immigrant fraction was direct, and ∼55% was mediated through lower prevalence of deleterious behaviors (e.g., smoking). In stroke path analysis, the effect was entirely mediated through indirect effects. Interpretation: In the United States, ZIP codes with higher immigrant fractions have lower prevalence of cardiovascular diseases. These associations are partially mediated through differences in health behaviors at the community level. Funding: NIH (K08CA252635, P30AG0059304, K24HL150476), Stanford University, Rutgers University.