Texas Counties Research Articles

Physician Deserts: Navigating the Texas Terrain of Provider Supply and Demand with GIS Mapping

Background: Rural health disparities in Texas impact population health due to limited healthcare access, insurance, and transportation challenges, especially in medically underserved areas. A shortage of specialists in rural regions worsens these issues, leading to increased morbidity and mortality rates. Objective: Our research aimed to address a knowledge gap by investigating the availability of three medical specialists—cardiologists, pulmonologists, and endocrinologists—in rural counties of Texas and identifying areas where access to healthcare is limited. Methods: Utilizing data from regional, state, and federal sources, the analysis geocoded specialist locations and created GIS maps to visualize the distribution of specialists across Texas’s 254 counties. Physician demand was calculated by considering disease incidence and population size, resulting in a county-level physician availability index to highlight areas with shortages. Results: Our findings demonstrate a significant deficiency of cardiologists in 196 counties when considering a maximum reasonable travel distance of 50 miles. Comparable deficiencies were observed for pulmonologists and endocrinologists, with western rural counties predominantly comprising the deficiency areas for each specialty. These results emphasize a significant rural–urban disparity concerning access to the three investigated health specialists. Conclusions: Addressing geographic disparities can reduce health inequities, improve rural healthcare access, and promote a more equitable healthcare system across Texas. Solutions may include incentives for specialists to work in underserved areas, expanded telemedicine services, and transportation assistance.

COVID-19 public health restrictions and opioid overdoes: a summative content analysis of emergency medical services records in three Texas counties

BackgroundWith the broad recognition of increased U.S. opioid overdose (OOD) rates between 2020 and 2021, media, public health, and healthcare organizations have raised significant concerns over the emergence of a simultaneous COVID-19-opioid “twindemic.” Research in this area has explored the possible relationships between negative externalities associated with the pandemic and/or COVID-19 public health interventions and increased risks for opioid use and overdose alongside diminished outcomes following OOD events.MethodsThe study offers a summative content analysis of Emergency Medical Service (EMS) responses to opioid overdose (OOD) events before and after the institution of COVID-19 restrictions. Specifically, the study investigates three Texas counties to evaluate changing OOD rates, patient demographics, and OOD event features. The analysis uses a previously validated machine learning tool to identify OOD events and conducted a summative content analysis of identified events.ResultsA total of 1170 OOD responses events were identified in the three-county dataset. This includes 874 in Travis County, 242 in El Paso County, and 54 in Williamson County. Each county experienced modest changes in EMS calls for OOD events between the pre-restriction and public health restriction time periods. Travis County’s OOD event rate declined from 454 to 420. El Paso’s increased from 103 to 139, and Williamson County’s increased from 23 to 31. These changes were not significant as percentage of possible OOD events or based on by-month comparison. The notable differences between pre-restriction and public health restriction periods were significant decreases in documentation of patient race/ethnicity in Travis and Williamson Counties, significant decreases in housing insecurity and use alone in Travis County, and an increase in transport refusal after treatment in the field in Travis County.ConclusionsUltimately the results presented here problematize prevailing analyses about the so-called opioid-COVID-19 “twindemic.” The data further support emerging trends about substantial geographic variation and show some ways that COVID-19 mitigation measures may have improved conditions for some populations, particularly in terms of housing security. Additionally, the results presented here indicate that further attention should be paid to the effects of first responder stress on documentation quality.

A validated NICU database: recounting 50 years of clinical growth, quality improvement and research.

The importance of a Neonatal Intensive Care Unit (NICU) database lies in its critical role in improving the quality of care for very preterm neonates and other high-risk newborns. These databases contain extensive information regarding maternal exposures, pregnancy complications, and neonatal care. They support quality improvement (QI) initiatives, facilitate clinical research, and track health outcomes in order to identify best practices and improve clinical guidelines. The Parkland Memorial Hospital NICU database was originally part of the Maternal and Neonatal Data Acquisition, Transmission and Evaluation project funded by the Robert Wood Johnson Foundation to assess perinatal-neonatal care in Dallas County Texas, 1977-1982. Clinical data points were defined, transcribed and validated in 1977; revalidation has occurred multiple times. Data are prospectively extracted from health records of high-risk neonates among >11,000 births annually. The database contains clinical information on >50,000 neonates, including all initially admitted to the NICU regardless of gestational age or birthweight and since 10/03/2011, all neonates admitted for observation and transferred to the term newborn nursery. The database has provided the basis for QI studies and research designed to assess and improve neonatal care. We discuss the history, evolution, administration, impact on neonatal outcomes, and future directions of our database. IMPACT: A single neonatal intensive care unit (NICU) database was designed for prospective data collection, validated and maintained for 46yrs. This database has supported quality improvement assessment, original clinical research, education and administrative requirements and impacted clinical neonatal care.

An agent-based model of post-disaster recovery in multilayer socio-physical networks

Post-disaster recovery involves returning infrastructure to pre-disaster states or close, and humans back to communities or close, after disasters like hurricanes and earthquakes. It enables us to comprehend complex relationships between humans and infrastructure after natural hazards. Existing studies have identified individual recovery, which focuses on personal or household-level restoration, and systematic recovery, which focuses on broader community recovery processes. However, they have not described how individual recoveries contribute to the overall recovery. To close the gap, we propose an agent-based model (ABM), called Disaster Infrastructure Recovery Simulator (DIRSim). We apply DIRSim to analyze the recovery of five counties in Texas following Hurricane Harvey in 2017. Specifically, we construct a three-layer network, composed of a human layer, a social infrastructure layer, and a physical infrastructure layer, using mobile phone location data and point-of-interest foot traffic data. Based on this three-layer network, we develop the ABM to simulate how evacuated users return to their homes, and social and physical infrastructure recover using a household survey. By implementing the ABM, we unveil the heterogeneity in recovery dynamics regarding geographical locations, housing types, and income levels. Moreover, simulation results across nine scenarios quantify the positive recovery effects of social and physical infrastructure policies.

Machine Learning Algorithms for Water Quality Management Using Total Dissolved Solids (TDS) Data Analysis

Our research project specifically focuses on evaluating groundwater quality in six West Texas counties. We aim to determine whether environmental changes have any impact on the levels of Total Dissolved Solids (TDS) in the water supplied to the public. To achieve this goal, we will be utilizing advanced machine learning algorithms to analyze TDS levels and create geospatial maps for each year between the 1990s and 2010s. To ensure the accuracy of our data, we have gathered information from two trusted sources: the Texas Water Development Board (TWDB) and the Groundwater Database (GWDB). We have analyzed the TDS and other elemental analyses from TWDB–GWDB lab reports and compared them with the quality cutoff set by the World Health Organization (WHO). Our approach involves a thorough examination of the data to identify any emerging patterns. The machine learning algorithm has been successfully trained and tested, producing highly accurate results that effectively predict water quality. Our results have been validated through extensive testing, highlighting the potential of machine learning approaches in the fields of environmental research. Overall, our findings will contribute to the development of more effective policies and regulations in predicting groundwater quality and improving water resource management in Texas. Therefore, this research provides important information for groundwater protection and the development of plans for water resource use in the future.

Prevalence of Congenital Anomalies Before and After Implementation of a Novel Wastewater Treatment Technology in Texas: An Augmented Synthetic Control Method Analysis Utilizing Data from a Population-Based Birth Defects Registry

Objective and ApproachDirect potable reuse (DPR) involves adding purified wastewater that has not passed through an environmental buffer into a water distribution system. This technology may address growing demand for water in many population centers, but there are no studies of health outcomes in populations receiving DPR-treated drinking water. Our objective was to determine whether the prevalence of congenital anomalies increased in the period following introduction of DPR-treated water to certain public water systems in Texas. We obtained data on all cases with congenital anomalies regardless of pregnancy outcome during 2003-2017 from the population-based Texas Birth Defects Registry. Maternal demographic and residential data were obtained and a reference population of all livebirths in Texas was identified by linkage to birth and fetal death records. The augmented synthetic control method was used to model county-level changes in prevalence of congenital anomalies (expressed per 10,000 livebirths) after the adoption of DPR by four Texas counties in mid-2013. County-level data on maternal age, education, ethnicity, and rural-urban status were included as covariates. Results: We observed increased prevalence of all congenital anomalies collectively (average treatment effect in the treated [ATT] = 53.6) and congenital heart disease (ATT = 287.3) during the years 2014-2017. Neural tube defects prevalence was unchanged. ConclusionsWe found evidence that the prevalence of congenital anomalies overall and congenital heart disease specifically increased during the years 2014-2017. ImplicationsAdditional research into reproductive and developmental outcomes among people in areas supplied with DPR-treated water is warranted to inform water policy decisions.

Tracing Trajectories: Qualitative Visualizations of Migrant Death in South Texas, 1993–2020

Executive Summary This paper explores how qualitative information may be used to enhance understanding and inform recommendations for improved accounting of migrant deaths along the Texas-Mexico border. While border crossing related deaths affect jurisdictions throughout the US’s Southwestern border states and Northern Mexican states, Texas has unique challenges that merit specific examination. In short, the management and investigation of unidentified migrant decedents in Texas is severely fragmented and often noncompliant with Texas Criminal Code Procedure. I explore a way to improve accounting of migrant deaths leveraging qualitative information, local newspaper articles from South Texas and Northern Mexico, by using descriptive summaries coupled with Sankey Diagrams and a programmatic technique, qualitative spatial representation (QSR). QSR enabled me to identify under-recognized stakeholders (South Texas locals, Mexican consulates) and under-supported counties (i.e., those with migrant deaths that do not share a border directly with Mexico). I found that local English-language newspapers obscured the prevalence of migrant deaths and that their narrative tone of “business as usual” normalized the occurrence of such deaths. The Spanish-language articles better represented the diversity of agencies and individuals that were involved in the various aspects of migrant remains management and forensic investigation, most notably residents of South Texas themselves (or situational participants who first found the remains) and Mexican consulates. Finally, I noted that the collaborations among Texas counties and between Texan and Mexican jurisdictions, when they were described in the newspapers, highlighted that the phenomenon of migrant deaths penetrates beyond the dividing line of the Rio Grande/Río Bravo River. Migrant deaths must be accounted for in a way that reflects a regional experience in which people may perish as far as ~100 miles into Texas’ interior. Considering these observations, I have two policy recommendations. First, the Office of the Texas Governor should establish funds distinct from the Operation Lone Star program for the management of unidentified human remains. Currently, grants through the Operation Lone Star Program are the only funds available in Texas to support identification of migrant decedents. However, this program is explicitly designed to further border security operations in the state of Texas, which can contradict efforts around recovery and identification. The documentation and forensic investigation of the unidentified deceased can be eclipsed or neglected under the larger deterrence aims of Operation Lone Star. Second, to improve accounting and increase documentation of migrant deaths, a Regional Identification Center should be established in South Texas. Not every county in Texas has or has access to a Medical Examiner’s office, including the means to transport and pay for autopsy or other forensic services. The Center would provide training, store extra equipment such as mobile refrigerated morgues, and hire personnel to inventory cases, and report information to the state (e.g., vital statistics) and to foreign consulates. A Regional Identification Center would counteract the challenges highlighted in the local newspaper summaries and QSR by decreasing the isolation of counties experiencing migrant deaths and serve as a documentary and communication hub for stakeholders in Texas and Mexico.

The Texas Landscape: Accounting for Migrant Mortality and the Challenges of a Justice of the Peace Medicolegal System

Executive Summary This paper details the structural and resource challenges in Texas related to identifying migrant decedents, investigating their deaths, repatriating them, and adhering to legal and ethical requirements in addressing this humanitarian tragedy. While actors working on migrant decedent investigations in Arizona can map and provide accurate counts of migrant deaths, this is not yet possible for Texas cases. Texas’ mixed Medical Examiner/Justice of the Peace medicolegal system suffers from fragmentation across county jurisdictions, lack of resources, and minimal access to investigative tools for transnational families. These challenges produce a landscape where unidentified presumed migrants may structurally disappear (e.g., buried in temporarily marked graves as unidentified persons with no investigation or case tracking). The article highlights the work of Operation Identification (OpID), a humanitarian project formed to assist border counties with recovering, identifying, and repatriating migrant decedents. OpID’s extensive community outreach and collaboration with governmental and nongovernmental partners in the United States and Latin America have improved practices in some Texas counties. However, systemic change is still needed to address this humanitarian disaster. The article proposes that presumed migrant decedents be managed using a disaster victim identification (DVI) approach, which prioritizes identification, rather than how and why someone dies. It also proposes the establishment of regional Migrant Identification Centers (MICs) to streamline identification and repatriation efforts, while ensuring compliance with Texas law by Justices of the Peace (JPs). Centralization, the article argues, can lead to more accurate counts of migrant deaths and lay the groundwork for greater resources. The article also supports increased access to national databases including the National Combined DNA Indexing System (CODIS) and the National Missing and Unidentified Persons System (NamUs). It argues that transnational families of missing persons be afforded expanded access to investigative tools (e.g., NamUs)

The Border Patrol’s Migrant Death Undercounting in South Texas

Executive Summary For the past 25 years, the Border Patrol has tracked migrant deaths along the US-Mexico border. For nearly the same amount of time, it has also faced criticisms that it failed to capture the true number of migrant deaths in its tally. This article focuses on these undercounting criticisms and asks two questions: (1) How many documented migrant death cases are left out of Border Patrol’s official data? And (2) what factors lead to the Border Patrol’s migrant death undercounting? In particular, the article focuses on three South Texas counties: Brooks County, Kenedy County, and Maverick County. To answer the research questions, this article relies on comparative data analysis. In particular, it compares two person-level datasets: the Border Patrol’s dataset on migrant deaths from 2009 to 2017 and county-level records from the Brooks County Sheriff’s Office, the Kenedy County Sheriff’s Office, and Maverick County Justices of the Peace over the same period. It then attempts to match each county-level record to a recorded death in the Border Patrol’s dataset. Using this process, the article quantifies migrant death undercounting in South Texas, highlights geographic and temporal trends, and tracks the uncounted cases’ specific characteristics. From 2009 to 2017, this comparative data analysis confirmed that the Border Patrol was undercounting migrant deaths across the three South Texas counties. Specifically, the article finds that the Border Patrol failed to include 139 cases, which totaled 19 percent of the counties’ 749 recorded migrant deaths during the study period. This undercounting ranged from 16 percent in Brooks County to 24 percent in Maverick County and 29 percent in Kenedy County, with fluctuating rates over time. The uncounted cases also had specific characteristics. In particular, they were more likely to be skeletal remains, lack an identification, and be discovered by an external entity. These characteristics highlight the various factors behind the Border Patrol’s undercounting, such as issues with the Border Patrol’s migrant death definition, inconsistent data collection from external entities, and the agency’s low prioritization of migrant death tracking. To address and remedy the Border Patrol’s migrant death undercounting requires tackling each underlying factor. First and foremost, this article recommends that the Border Patrol fully train its agents on the agency’s migrant death definition and ensure consistent and standardized outreach to external entities. Further, it recommends that the Border Patrol improve its migrant death count’s accuracy through additional operational changes. These proposed changes include making “accurate migrant death counts” an official objective for the Border Patrol’s Missing Migrant Program, prioritizing a two-way information-sharing process with county-level officials, retroactively including missed migrant deaths in the official count, and publishing more detailed person-level data on migrant deaths.

Comparative Study of Risk Factors Associated with Normal Cognition and Cognitive Impairment in Rural West Elderly Texans.

Alzheimer's disease (AD) is related to one or more chronic illnesses, which may develop cognitive decline and dementia. Cognitive impairment is increasing, and public health officials must address risk factors for AD to improve the health of rural West Texas communities. The purpose of this study was to explore the sociodemographic and chronic disease risk factors related to cognitive impairment among elderly adults living in Cochran, Parmer, and Bailey counties in rural West Texas. Statistical methods such as Pearson's chi-squared, proportion tests, univariate binary logistic regression, and a multivariable logistic regression were utilized to analyze data. SPSS software was used to detect the significant relationship between cognitive impairment and risk factors. Summary statistics were obtained for sociodemographic and chronic diseases by using cross-tabulation analysis and comparing the county respondents with proportion tests. A univariate binary logistic regression method was utilized and found that age group 60-69, anxiety, depression, diabetes, hypertension, and cardiovascular disease were significantly associated with cognitive impairment. Using a multivariable logistic regression approach, it was found that Bailey County (age group 60-69) had a higher likelihood (p = 0.002) of cognitive impairment than Parmer (p = 0.067) and Cochran counties (p = 0.064). The risk of females (p = 0.033) in Parmer County was 78.3% lower compared to males in developing AD. Identifying significant risk factors for cognitive impairment are important in addressing issues of geographic variations and integrating such factors may guide relevant policy interventions to reduce cognitive impairment incidence in rural communities within West Texas.

Limitations and considerations of using composite indicators to measure vulnerability to natural hazards

Vulnerability assessment plays a critical role in disaster management and requires the consideration of multiple dimensions that involve both the built and social environments. A common approach to address this problem is the use of composite indicators, which offer a simplified method to combine information across different dimensions and facilitate decision making. However, composite indicators present limitations in the context of hazard vulnerability. This study investigates the source of these limitations and provides ways to overcome shortcomings in the interpretation of composite vulnerability indicators. To conduct this investigation, a composite indicator is developed to assess the vulnerability of power and transportation infrastructure, while considering social vulnerability, to capture community hazard vulnerability. Using a case study of Harris County in Texas, we investigate the disparities in outcomes resulting from different calculation methods, such as sub-indicator weighting. The case study shows that the value of the indicator is not consistent across different calculation methods. Additionally, weighting the sub-indicators plays an important role in the value of the indicator. Combining infrastructure and social factors is found to be misleading in the interpretation of hazard vulnerability, and the use of bivariate maps is proposed to better distinguish between infrastructure and social vulnerabilities.

Cooking Well with Diabetes: A Healthy Cooking School for Diabetes Prevention and Management.

One in ten Americans suffers from type 2 diabetes, which, if not managed well, can result in severe complications, disability, and premature death. Diabetes education classes can play a pivotal role in providing practical education on diabetes and self-care behaviors, with a particular emphasis on dietary management, which is often regarded as the most demanding diabetes self-care behavior. The Texas A&M AgriLife Extension Service developed Cooking Well with Diabetes (CWWD), a four-week interactive diabetes education series, with each week consisting of a lecture on healthy eating coupled with cooking lessons featuring diabetes-friendly recipes. The current study aimed to examine the effectiveness of CWWD in improving the frequency of healthy food preparation and consumption of program participants. Secondary data from 2017 to 2023 was analyzed involving 1574 adults from 59 predominantly rural Texas counties. Data from self-reported pre and post evaluations showed improvements in healthy food preparation and consumption behaviors. The curriculum enabled Extension Educators to introduce healthful dietary behaviors to a diverse group of clients. The curriculum can be adapted by Extension Educators in other states reaching a broader audience. The findings will inform future research aimed at planning and implementing successful diabetes education programs.

FRAP 2.0: a community engagement method applied to cervical cancer disparities among Hispanic women

focused Rapid Assessment Process (fRAP) 2.0 is a community engagement approach combining geospatial mapping with rapid qualitative assessment in cyclical fashion within communities to capture multifactorial and multilevel features impacting...

Ensemble Machine Learning on the Fusion of Sentinel Time Series Imagery with High-Resolution Orthoimagery for Improved Land Use/Land Cover Mapping

In the United States, several land use and land cover (LULC) data sets are available based on satellite data, but these data sets often fail to accurately represent features on the ground. Alternatively, detailed mapping of heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences among scenes, landscape heterogeneity, and computational limitations. Various machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study were to evaluate bagging (Random Forest, RF), boosting (Gradient Boosting Machines [GBM] and extreme gradient boosting [XGB]), and stacking ensemble ML models. We used these techniques on a time series of Sentinel 2A data and NAIP orthoimagery to create a LULC map of a portion of Irion and Tom Green counties in Texas (USA). We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. We then compared accuracy based on random cross-validation without accounting for spatial autocorrelation and target-oriented cross-validation accounting for spatial structures of the training data set. Comparison of random and target-oriented cross-validation results showed that autocorrelation in the training data offered overestimation ranging from 2% to 3.5%. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base learners. We show that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use/land cover classification using geographic object-based image analysis.

Two Counties in Crisis: Measuring Political Change in Reconstruction Texas

Robert Dillard’s Two Counties in Crisis explores three decades of political culture in two divergent Texas counties. Collin County, a frontier county largely home to emigrants from other Southern states, is contrasted against Harrison County, a cotton growing county with 145 planters who each owned more than 20 enslaved peoples. Dillard claims that these counties, and Texas more broadly, did not neatly align with the political culture of the rest of the Confederacy. In a combination of political science and history, Dillard argues that a culture of backlash and resistance to federal authority defined Texas state politics in the Civil War era. While the author’s claims of exceptionalism are unconvincing and unsubstantiated, Two Counties in Crisis demonstrates the contentious state level politics of Reconstruction.

Direct potable reuse and birth defects prevalence in Texas: An augmented synthetic control method analysis of data from a population-based birth defects registry.

Direct potable reuse (DPR) involves adding purified wastewater that has not passed through an environmental buffer into a water distribution system. DPR may help address water shortages and is approved or is under consideration as a source of drinking water for several water-stressed population centers in the United States, however, there are no studies of health outcomes in populations who receive DPR drinking water. Our objective was to determine whether the introduction of DPR for certain public water systems in Texas was associated with changes in birth defect prevalence. We obtained data on maternal characteristics for all live births and birth defects cases regardless of pregnancy outcome in Texas from 2003 to 2017 from the Texas Birth Defects Registry and birth and fetal death records. The ridge augmented synthetic control method was used to model changes in birth defect prevalence (per 10,000 live births) following the adoption of DPR by four Texas counties in mid-2013, with county-level data on maternal age, percent women without a high school diploma, percent who identified as Hispanic/Latina or non-Hispanic/Latina Black, and rural-urban continuum code as covariates. There were nonstatistically significant increases in prevalence of all birth defects collectively (average treatment effect in the treated = 53.6) and congenital heart disease (average treatment effect in the treated = 287.3) since June 2013. The estimated prevalence of neural tube defects was unchanged. We estimated nonstatistically significant increases in birth defect prevalence following the implementation of DPR in four West Texas counties. Further research is warranted to inform water policy decisions.

A Real-Time Prediction Approach to Deep Soil Moisture Combining GNSS-R Data and a Water Movement Model in Unsaturated Soil

Deep soil moisture data have wide applications in fields such as engineering construction and agricultural production. Therefore, achieving the real-time monitoring of deep soil moisture is of significant importance. Current soil monitoring methods face challenges in conducting the large-scale, real-time monitoring of deep soil moisture. This paper innovatively proposes a real-time prediction approach to deep soil moisture combining GNSS-R data and a water movement model in unsaturated soil. This approach, built upon surface soil moisture data retrieved from GNSS-R signal inversion, integrates soil–water characteristics and soil moisture values at a depth of 1 m. By employing a deep soil moisture content prediction model, it provides predictions of soil moisture at depths from 0 to 1 m, thus realizing the large-scale, real-time dynamic monitoring of deep soil moisture. The proposed approach was validated in a study area in Goodwell, Texas County, Oklahoma, USA. Predicted values of soil moisture at a randomly selected location in the study area at depths of 0.1 m, 0.2 m, 0.5 m, and 1 m were compared with ground truth values for the period from 25 October to 19 November 2023. The results indicated that the relative error (δ) was controlled within the range of ±14%. The mean square error (MSE) ranged from 2.90 × 10−5 to 1.88 × 10−4, and the coefficient of determination (R2) ranged from 82.45% to 89.88%, indicating an overall high level of fitting between the predicted values and ground truth data. This validates the feasibility of the proposed approach, which has the potential to play a crucial role in agricultural production, geological disaster management, engineering construction, and heritage site preservation.

Abstract 2157: Evaluating cervical and lung cancer risk among Hispanic and Non-Hispanic Black women in three urban Texas counties

Abstract This study examines if there is a statistically significant difference in the risk of being diagnosed with cervical and lung cancer between Hispanic and Non-Hispanic Black (NHB) women in Tarrant, Dallas, and Bexar county. Pulling from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program, 2000-2020 trends in cancer diagnosis were measured by age-adjusted annual percent change (APC). Data was sorted by county and race/ethnicity and statistically insignificant measures were removed. APC measures were ranked relative to counties across Texas and the US. APC measures with 95% confidence intervals for Hispanic and NHB women in Tarrant, Dallas, and Bexar county were compared for a significant difference. Results from a previous study indicated that the slowest decreases in cervical cancer rates are found among Hispanic and African-American women in Tarrant and Dallas County. We built upon this study to assess if there was a statistically significant difference in the risk of developing cervical and lung cancer between these two groups. In Tarrant County (TC), the cervical cancer APC of Hispanic women was -1.8 [-3.4,0.1] and NHB women was -1.2 [-3.7, 1.3]. In Dallas County, the cervical cancer APC of Hispanic women was -2.1 [-3.4, -0.8] and NHB women was -1.8 [-3, -0.5]. This indicates that for both cities, Hispanic women saw a greater decrease in the APC in cervical cancer rates than NHB women. In Tarrant County (TC), the lung cancer APC of Hispanic women was -2.7 [-4.5, -0.9] and NHB women was -2.1[-3.1, -1.1]. In Dallas County, the lung cancer APC of Hispanic women was -2.3 [-4.2, -0.4] and NHB women was -1.8 [-2.5, -1]. This indicates that for both cities, while Hispanic women saw a greater decrease in the APC in lung cancer rates than NHB women, the decrease is not statistically significant due to the overlap of the confidence intervals. Even though the confidence interval (CI) overlaps indicates insignificance, the APC can still provide valuable information regarding diagnostic trends seen in urban cities. The results showed that there is no significant difference between these two groups, suggesting that other county-level factors may play a role in diagnostic disparities seen between these two racial groups. Citation Format: Megan L. Chu, Irene E. Jayesh. Evaluating cervical and lung cancer risk among Hispanic and Non-Hispanic Black women in three urban Texas counties [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2157.

Modeling and characterization of shallow aquifer water based on ion concentrations to salinity variations using multivariate statistical approach

Water chemistry data (1965–2015) from the Chicot and Evangeline aquifers in Nueces and Kleberg counties of Texas were analyzed to assess the hydrogeochemical processes based on variations in ion concentrations affecting groundwater salinity levels in those coastal aquifers. A further aim of this study was to identify a proxy using major-ion concentrations to predict groundwater salinity. Correlation analysis suggests that the hydrogeochemical processes operating in these aquifers differ significantly. In principal component analyses, the first three principal components explain 91% and 94.8% of the total variabilities of the variation of groundwater chemistry in the Evangeline and Chicot aquifers, respectively. The rotated factors (factors 1, 2, and 3) using the varimax rotation method implied that depth control and salinity variations predominantly cause the highest variabilities in water chemistry in the Chicot and Evangeline aquifers in the study area. Results showed depth control on water quality parameters was more pronounced in the Evangeline aquifer compared to the Chicot aquifer. Stepwise elimination of the least significant predictors to identify the proxy for groundwater salinity revealed chloride (Cl) could be the most significant predictor to estimate groundwater salinity variations in both aquifers. However, regression models generated from 75% of the training datasets predicted total dissolved solid (TDS) variations with 78% and 43% accuracies in Chicot and Evangeline aquifers, indicating that Cl can be considered the proxy for the Chicot aquifer only but not suitable for the Evangeline aquifer in the study area.

Genetic and Pathogenic Variability among Isolates of Sporisorium reilianum Causing Sorghum Head Smut.

Sporisorium reilianum, the causal agent of sorghum (Sorghum bicolor (L.) Moench) head smut, is present in most sorghum-producing regions. This seed replacement fungal disease can reduce yield by up to 80% in severely infected fields. Management of this disease can be challenging due to the appearance of different pathotypes within the pathogenic population. In this research, the genetic variability and pathogenicity of isolates collected from five Texas Counties was conducted. Due to the lack of available space, 21 out of 32 sequenced isolates were selected and evaluated for virulence patterns on the six sorghum differentials, Tx7078, BTx635, SC170-6-17 (TAM2571), SA281 (Early Hegari), Tx414, and BTx643. The results reveal the occurrence of a new pathotype, 1A, and four previously documented US pathotypes when the 21 isolates were evaluated for virulence patterns on the differentials. The most prevalent was pathotype 5, which was recovered from Brazos, Hidalgo, Nueces, and Willacy Counties, Texas. This pathotype was followed by 1A and 6 in frequency of recovery. Pathotype 4 was identified only from isolates collected from Hidalgo County, while pathotype 1 was from Burleson County, Texas. It appeared that the previous US head smut pathotypes (2 and 3) are no longer common, and the new pathotypes, 1A, 5, and 6, are now predominant. The phylogenetic tree constructed from the single-nucleotide polymorphism (SNP) data through the neighbor-joining method showed high genetic diversity among the tested isolates. Some of the diverse clades among the tested isolates were independent of their sampled locations. Notably, HS37, HS49, and HS65 formed a clade and were classified as 1A in the virulence study, while HS 61 and HS 66, which were collected from Nueces County, were grouped and identified as pathotype 5.