Patient Location and Disparities in Access to Fertility Preservation for Women With Gynecologic or Breast Cancer.

OBJECTIVE: To assess the effect of geographic factors on fertility-sparing treatment or assisted reproductive technology (ART) utilization among women with gynecologic or breast cancers. METHODS: We conducted a cohort study of reproductive-aged patients (18-45 years) with early-stage cervical, endometrial, or ovarian cancer or stage I-III breast cancer diagnosed between January 2000 and December 2015 using linked data from the California Cancer Registry, the California Office of Statewide Health Planning and Development, and the Society for Assisted Reproductive Technology. Generalized linear mixed models were used to evaluate associations between distance from fertility and gynecologic oncology clinics, as well as California Healthy Places Index score (a Census-level composite community health score), and ART or fertility-sparing treatment receipt. RESULTS: We identified 7,612 women with gynecologic cancer and 35,992 women with breast cancer. Among all patients, 257 (0.6%) underwent ART. Among patients with gynecologic cancer, 1,676 (22.0%) underwent fertility-sparing treatment. Stratified by quartiles, residents who lived at increasing distances from gynecologic oncology or fertility clinics had decreased odds of undergoing fertility-sparing treatment (gynecologic oncology clinics: Q2, odds ratio [OR] 0.76, 95% CI, 0.63-0.93, P=.007; Q4, OR 0.72, 95% CI, 0.56-0.94, P=.016) (fertility clinics: Q3, OR 0.79, 95% CI, 0.65-0.97, P=.025; Q4, OR 0.67, 95% CI, 0.52-0.88, P=.004), whereas this relationship was not observed among women who resided within other quartiles (gynecologic oncology clinics: Q3, OR 0.81 95% CI, 0.65-1.01, P=.07; fertility clinics: Q2, OR 0.87 95% CI, 0.73-1.05, P=.15). Individuals who lived in communities with the highest (51st-100th percentile) California Healthy Places Index scores had greater odds of undergoing fertility-sparing treatment (OR 1.29, 95% CI, 1.06-1.57, P=.01; OR 1.66, 95% CI, 1.35-2.04, P=.001, respectively). The relationship between California Healthy Places Index scores and ART was even more pronounced (Q2 OR 1.9, 95% CI, 0.99-3.64, P=.05; Q3 OR 2.86, 95% CI, 1.54-5.33, P<.001; Q4 OR 3.41, 95% CI, 1.83-6.35, P<.001). CONCLUSION: Geographic disparities affect fertility-sparing treatment and ART rates among women with gynecologic or breast cancer. By acknowledging geographic factors, health care systems can ensure equitable access to fertility-preservation services.

Social determinants of health predict readmission following COVID-19 hospitalization: a health information exchange-based retrospective cohort study.

Introduction: Since February 2020, over 104 million people in the United States have been diagnosed with SARS-CoV-2 infection, or COVID-19, with over 8.5 million reported in the state of Texas. This study analyzed social determinants of health as predictors for readmission among COVID-19 patients in Southeast Texas, United States. Methods: A retrospective cohort study was conducted investigating demographic and clinical risk factors for 30, 60, and 90-day readmission outcomes among adult patients with a COVID-19-associated inpatient hospitalization encounter within a regional health information exchange between February 1, 2020, to December 1, 2022. Results and discussion: In this cohort of 91,007 adult patients with a COVID-19-associated hospitalization, over 21% were readmitted to the hospital within 90 days (n = 19,679), and 13% were readmitted within 30 days (n = 11,912). In logistic regression analyses, Hispanic and non-Hispanic Asian patients were less likely to be readmitted within 90 days (adjusted odds ratio [aOR]: 0.8, 95% confidence interval [CI]: 0.7-0.9, and aOR: 0.8, 95% CI: 0.8-0.8), while non-Hispanic Black patients were more likely to be readmitted (aOR: 1.1, 95% CI: 1.0-1.1, p = 0.002), compared to non-Hispanic White patients. Area deprivation index displayed a clear dose-response relationship to readmission: patients living in the most disadvantaged neighborhoods were more likely to be readmitted within 30 (aOR: 1.1, 95% CI: 1.0-1.2), 60 (aOR: 1.1, 95% CI: 1.2-1.2), and 90 days (aOR: 1.2, 95% CI: 1.1-1.2), compared to patients from the least disadvantaged neighborhoods. Our findings demonstrate the lasting impact of COVID-19, especially among members of marginalized communities, and the increasing burden of COVID-19 morbidity on the healthcare system.

Assessment of a SARS-CoV-2 wastewater monitoring program in El Paso, Texas, from November 2020 to June 2022.

The border city of El Paso, Texas, and its water utility, El Paso Water, initiated a SARS-CoV-2 wastewater monitoring program to assess virus trends and the appropriateness of a wastewater monitoring program for the community. Nearly weekly sample collection at four wastewater treatment facilities (WWTFs), serving distinct regions of the city, was analyzed for SARS-CoV-2 genes using the CDC 2019-Novel coronavirus Real-Time RT-PCR diagnostic panel. Virus concentrations ranged from 86.7 to 268,000 gc/L, varying across time and at each WWTF. The lag time between virus concentrations in wastewater and reported COVID-19 case rates (per 100,00 population) ranged from 4-24 days for the four WWTFs, with the strongest trend occurring from November 2021 - June 2022. This study is an assessment of the utility of a geographically refined SARS-CoV-2 wastewater monitoring program to supplement public health efforts that will manage the virus as it becomes endemic in El Paso.

Association of Social Vulnerability and COVID-19 Mortality Rates in Texas between 15 March 2020, and 21 July 2022: An Ecological Analysis.

BACKGROUND: Despite the key role of social vulnerability such as economic disadvantage in health outcomes, research is limited on the impact of social vulnerabilities on COVID-19-related deaths, especially at the state and county level in the USA. METHODS: We conducted a cross-sectional ecologic analysis of COVID-19 mortality by the county-level Minority Health Social Vulnerability Index (MH SVI) and each of its components in Texas. Negative binomial regression (NBR) analyses were used to estimate the association between the composite MH SVI (and its components) and COVID-19 mortality. RESULTS: A 0.1-unit increase in the overall MH SVI (IRR, 1.27; 95% CI, 1.04-1.55; p = 0.017) was associated with a 27% increase in the COVID-19 mortality rate. Among the MH SVI component measures, only low socioeconomic status (IRR, 1.55; 95% CI, 1.28-1.89; p = 0.001) and higher household composition (e.g., proportion of older population per county) and disability scores (IRR, 1.47; 95% CI, 1.29-1.68; p < 0.001) were positively associated with COVID-19 mortality rates. CONCLUSIONS: This study provides further evidence of disparities in COVID-19 mortality by social vulnerability and can inform decisions on the allocation of social resources and services as a strategy for reducing COVID-19 mortality rates and similar pandemics in the future.

Examining neighborhood-level hot and cold spots of food insecurity in relation to social vulnerability in Houston, Texas.

Food insecurity is prevalent and associated with poor health outcomes, but little is known about its geographical nature. The aim of this study is to utilize geospatial modeling of individual-level food insecurity screening data ascertained in health care settings to test for neighborhood hot and cold spots of food insecurity in a large metropolitan area, and then compare these hot spot neighborhoods to cold spot neighborhoods in terms of the CDC's Social Vulnerability Index. In this cross-sectional secondary data analysis, we geocoded the home addresses of 6,749 unique participants screened for food insecurity at health care locations participating in CMS's Accountable Health Communities (AHC) Model, as implemented in Houston, TX. Next, we created census-tract level incidence profiles of positive food insecurity screens per 1,000 people. We used Anselin's Local Moran's I statistic to test for statistically significant census tract-level hot/cold spots of food insecurity. Finally, we utilized a Mann-Whitney-U test to compare hot spot tracts to cold spot tracts in relation to the CDC's Social Vulnerability Index. We found that hot spot tracts had higher overall social vulnerability index scores (P <0.001), higher subdomain scores, and higher percentages of individual variables like poverty (P <0.001), unemployment (P <0.001), limited English proficiency (P <0.001), and more. The combination of robust food insecurity screening data, geospatial modeling, and the CDC's Social Vulnerability Index offers a solid method to understand neighborhood food insecurity.

Spatial Patterns of COVID-19 Vaccination Coverage by Social Vulnerability Index and Designated COVID-19 Vaccine Sites in Texas.

Equitable access to the COVID-19 vaccine remains a public health priority. This study explores the association between ZIP Code−Tabulation Area level Social Vulnerability Indices (SVI) and COVID-19 vaccine coverage in Texas. A mixed-effects, multivariable, random-intercept negative binomial model was used to explore the association between ZIP Code−Tabulation Area level SVI and COVID-19 vaccination coverage stratified by the availability of a designated vaccine access site. Lower COVID-19 vaccine coverage was observed in ZIP codes with the highest overall SVIs (adjusted mean difference (aMD) = −13, 95% CI, −23.8 to −2.1, p < 0.01), socioeconomic characteristics theme (aMD = −16.6, 95% CI, −27.3 to −5.7, p = 0.01) and housing and transportation theme (aMD = −18.3, 95% CI, −29.6 to −7.1, p < 0.01) compared with the ZIP codes with the lowest SVI scores. The vaccine coverage was lower in ZIP Code−Tabulation Areas with higher median percentages of Hispanics (aMD = −3.3, 95% CI, −6.5 to −0.1, p = 0.04) and Blacks (aMD = −3.7, 95% CI, −6.4 to −1, p = 0.01). SVI negatively impacted COVID-19 vaccine coverage in Texas. Access to vaccine sites did not address disparities related to vaccine coverage among minority populations. These findings are relevant to guide the distribution of COVID-19 vaccines in regions with similar demographic and geospatial characteristics.

Understanding Pediatric Surgery Cancellation: Geospatial Analysis.

BACKGROUND: Day-of-surgery cancellation (DoSC) represents a substantial wastage of hospital resources and can cause significant inconvenience to patients and families. Cancellation is reported to impact between 2% and 20% of the 50 million procedures performed annually in American hospitals. Up to 85% of cancellations may be amenable to the modification of patients' and families' behaviors. However, the factors underlying DoSC and the barriers experienced by families are not well understood. OBJECTIVE: This study aims to conduct a geospatial analysis of patient-specific variables from electronic health records (EHRs) of Cincinnati Children's Hospital Medical Center (CCHMC) and of Texas Children's Hospital (TCH), as well as linked socioeconomic factors measured at the census tract level, to understand potential underlying contributors to disparities in DoSC rates across neighborhoods. METHODS: The study population included pediatric patients who underwent scheduled surgeries at CCHMC and TCH. A 5-year data set was extracted from the CCHMC EHR, and addresses were geocoded. An equivalent set of data >5.7 years was extracted from the TCH EHR. Case-based data related to patients' health care use were aggregated at the census tract level. Community-level variables were extracted from the American Community Survey as surrogates for patients' socioeconomic and minority status as well as markers of the surrounding context. Leveraging the selected variables, we built spatial models to understand the variation in DoSC rates across census tracts. The findings were compared to those of the nonspatial regression and deep learning models. Model performance was evaluated from the root mean squared error (RMSE) using nested 10-fold cross-validation. Feature importance was evaluated by computing the increment of the RMSE when a single variable was shuffled within the data set. RESULTS: Data collection yielded sets of 463 census tracts at CCHMC (DoSC rates 1.2%-12.5%) and 1024 census tracts at TCH (DoSC rates 3%-12.2%). For CCHMC, an L2-normalized generalized linear regression model achieved the best performance in predicting all-cause DoSC rate (RMSE 1.299%, 95% CI 1.21%-1.387%); however, its improvement over others was marginal. For TCH, an L2-normalized generalized linear regression model also performed best (RMSE 1.305%, 95% CI 1.257%-1.352%). All-cause DoSC rate at CCHMC was predicted most strongly by previous no show. As for community-level data, the proportion of African American inhabitants per census tract was consistently an important predictor. In the Texas area, the proportion of overcrowded households was salient to DoSC rate. CONCLUSIONS: Our findings suggest that geospatial analysis offers potential for use in targeting interventions for census tracts at a higher risk of cancellation. Our study also demonstrates the importance of home location, socioeconomic disadvantage, and racial minority status on the DoSC of children's surgery. The success of future efforts to reduce cancellation may benefit from taking social, economic, and cultural issues into account.

Leveraging a health information exchange for analyses of COVID-19 outcomes including an example application using smoking history and mortality.

Understanding sociodemographic, behavioral, clinical, and laboratory risk factors in patients diagnosed with COVID-19 is critically important, and requires building large and diverse COVID-19 cohorts with both retrospective information and prospective follow-up. A large Health Information Exchange (HIE) in Southeast Texas, which assembles and shares electronic health information among providers to facilitate patient care, was leveraged to identify COVID-19 patients, create a cohort, and identify risk factors for both favorable and unfavorable outcomes. The initial sample consists of 8,874 COVID-19 patients ascertained from the pandemic's onset to June 12th, 2020 and was created for the analyses shown here. We gathered demographic, lifestyle, laboratory, and clinical data from patient's encounters across the healthcare system. Tobacco use history was examined as a potential risk factor for COVID-19 fatality along with age, gender, race/ethnicity, body mass index (BMI), and number of comorbidities. Of the 8,874 patients included in the cohort, 475 died from COVID-19. Of the 5,356 patients who had information on history of tobacco use, over 26% were current or former tobacco users. Multivariable logistic regression showed that the odds of COVID-19 fatality increased among those who were older (odds ratio = 1.07, 95% CI 1.06, 1.08), male (1.91, 95% CI 1.58, 2.31), and had a history of tobacco use (2.45, 95% CI 1.93, 3.11). History of tobacco use remained significantly associated (1.65, 95% CI 1.27, 2.13) with COVID-19 fatality after adjusting for age, gender, and race/ethnicity. This effort demonstrates the impact of having an HIE to rapidly identify a cohort, aggregate sociodemographic, behavioral, clinical and laboratory data across disparate healthcare providers electronic health record (HER) systems, and follow the cohort over time. These HIE capabilities enable clinical specialists and epidemiologists to conduct outcomes analyses during the current COVID-19 pandemic and beyond. Tobacco use appears to be an important risk factor for COVID-19 related death.

Examining Social Vulnerability and the Association With COVID-19 Incidence in Harris County, Texas.

Studies have investigated the association between social vulnerability and SARS-CoV-2 incidence. However, few studies have examined small geographic units such as census tracts, examined geographic regions with large numbers of Hispanic and Black populations, controlled for testing rates, and incorporated stay-at-home measures into their analyses. Understanding the relationship between social vulnerability and SARS-CoV-2 incidence is critical to understanding the interplay between social determinants and implementing risk mitigation guidelines to curtail the spread of infectious diseases. The objective of this study was to examine the relationship between CDC's Social Vulnerability Index (SVI) and SARS-CoV-2 incidence while controlling for testing rates and the proportion of those who stayed completely at home among 783 Harris County, Texas census tracts. SARS-CoV-2 incidence data were collected between May 15 and October 1, 2020. The SVI and its themes were the primary exposures. Median percent time at home was used as a covariate to measure the effect of staying at home on the association between social vulnerability and SARS-CoV-2 incidence. Data were analyzed using Kruskal Wallis and negative binomial regressions (NBR) controlling for testing rates and staying at home. Results showed that a unit increase in the SVI score and the SVI themes were associated with significant increases in SARS-CoV-2 incidence. The incidence risk ratio (IRR) was 1.090 (95% CI, 1.082, 1.098) for the overall SVI; 1.107 (95% CI, 1.098, 1.115) for minority status/language; 1.090 (95% CI, 1.083, 1.098) for socioeconomic; 1.060 (95% CI, 1.050, 1.071) for household composition/disability, and 1.057 (95% CI, 1.047, 1.066) for housing type/transportation. When controlling for stay-at-home, the association between SVI themes and SARS-CoV-2 incidence remained significant. In the NBR model that included all four SVI themes, only the socioeconomic and minority status/language themes remained significantly associated with SARS-CoV-2 incidence. Community-level infections were not explained by a communities' inability to stay at home. These findings suggest that community-level social vulnerability, such as socioeconomic status, language barriers, use of public transportation, and housing density may play a role in the risk of SARS-CoV-2 infection regardless of the ability of some communities to stay at home because of the need to work or other reasons.

Assessing the Proximity Relationship of Walk-in Clinics and Primary Care Physicians.

This article evaluates the spatial relationship between primary care provider clinics and walk-in clinics. Using ZIP code level data from Harris County, Texas, the results suggest that primary care physicians and walk-in clinics are similarly located at lower rates in geographic areas with populations of lower socioeconomic status. Although current clinic location choices effectively broaden the gap in primary care access for the lower income population, the growing number of newly insured individuals may make it increasingly attractive for walk-in clinics to locate in geographic areas with populations of lower socioeconomic status and less competition from primary care physicians.

Preterm birth and its associations with residence and ambient vehicular traffic exposure.

BACKGROUND: Preterm birth (PTB) is a multifactorial disorder, and air pollution has been suggested to increase the risk of occurrence. However, large population studies controlling for multiple exposure measures in high-density settings with established commuter patterns are lacking. OBJECTIVE: We performed a geospatial analysis with the use of a publicly available database to identify whether residence during pregnancy, specifically with regard to exposure to traffic density and mobility in urban and suburban neighborhoods, may be a contributing risk factor for premature delivery. STUDY DESIGN: In our cohort study, we analyzed 9004 pregnancies with as many as 4900 distinct clinical and demographic variables from Harris County, Texas. On the basis of primary residency and occupational zip code information, geospatial analysis was conducted. Data on vehicle miles traveled (VMT) and percentages of inhabitants traveling to work were collected at the zip code level and additionally grouped by the three recognized regional commuter loop high-density thoroughfares resulting from two interstate/highway belts (inner, middle, and outer loops). PTB was categorized as late (34 1/7 to 36 6/7 weeks) and early PTB (22 1/7 to 33 6/7 weeks), and unadjusted odds ratios (OR) and adjusted ORs were ascribed. RESULTS: PTB prevalence in our study population was 10.1% (6.8% late and 3.3% early preterm), which is in accordance with our study and other previous studies. Prevalence of early PTB varied significantly between the regional commuter loop thoroughfares [OR for inner vs outer loop: 0.58 (95% confidence interval, 0.39-0.87), OR for middle vs outer loop, 0.74 (0.57-0.96)]. The ORs for PTB and early PTB were shown to be lower in gravidae from neighborhoods with the highest VMT/acre [OR for PTB, 0.82 (0.68-0.98), OR for early PTB, 0.78 (0.62-0.98)]. Conversely, risk of PTB and early PTB among subjects living in neighborhoods with a high percentage of inhabitants traveling to work over a greater distance demonstrated a contrary tendency [OR for PTB, 1.18 (1.03-1.35), OR for early PTB, 1.48 (1.17-1.86)]. In logistic regression models, the described association between PTB and residence withstood and could not be explained by differences in maternal age, gravidity or ethnicity, tobacco use, or history of PTB. CONCLUSION: While PTB is of multifactorial origin, the present study shows that community-based risk factors (namely urban/suburban location, differences in traffic density exposure, and need for traveling to work along high-vehicle density thoroughfares) may influence risk for PTB. Further research focusing on previously unrecognized community-based risk factors may lead to innovative future prevention measures.

Geospatial analysis of food environment demonstrates associations with gestational diabetes.

BACKGROUND: Gestational diabetes mellitus (GDM) is one of most common complications of pregnancy, with incidence rates varying by maternal age, race/ethnicity, obesity, parity, and family history. Given its increasing prevalence in recent decades, covariant environmental and sociodemographic factors may be additional determinants of GDM occurrence. OBJECTIVE: We hypothesized that environmental risk factors, in particular measures of the food environment, may be a diabetes contributor. We employed geospatial modeling in a populous US county to characterize the association of the relative availability of fast food restaurants and supermarkets to GDM. STUDY DESIGN: Utilizing a perinatal database with >4900 encoded antenatal and outcome variables inclusive of ZIP code data, 8912 consecutive pregnancies were analyzed for correlations between GDM and food environment based on countywide food permit registration data. Linkage between pregnancies and food environment was achieved on the basis of validated 5-digit ZIP code data. The prevalence of supermarkets and fast food restaurants per 100,000 inhabitants for each ZIP code were gathered from publicly available food permit sources. To independently authenticate our findings with objective data, we measured hemoglobin A1c levels as a function of geospatial distribution of food environment in a matched subset (n = 80). RESULTS: Residence in neighborhoods with a high prevalence of fast food restaurants (fourth quartile) was significantly associated with an increased risk of developing GDM (relative to first quartile: adjusted odds ratio, 1.63; 95% confidence interval, 1.21-2.19). In multivariate analysis, this association held true after controlling for potential confounders (P = .002). Measurement of hemoglobin A1c levels in a matched subset were significantly increased in association with residence in a ZIP code with a higher fast food/supermarket ratio (n = 80, r = 0.251 P < .05). CONCLUSION: As demonstrated by geospatial analysis, a relationship of food environment and risk for gestational diabetes was identified.