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The ongoing climate change crisis presents challenges to the global public health system. The risk of gastrointestinal illness (GI) related hospitalization increases following extreme weather events but is largely under-reported and under-investigated. This study assessed the association between precipitation and GI-related hospital admissions in four major cities in Texas. Daily data on GI-related hospital admissions and precipitation from 2004 to 2014 were captured from the Texas Department of State Health Services and the National Climate Data Center. Distributed lagged nonlinear modeling approaches were employed to examine the association between precipitation and GI-related hospital admissions. Results showed that the cumulative risk ratios (RRs) of GI-related hospital admissions were elevated in the 2 weeks following precipitation events; however, there were differences observed across study locations. The cumulative RR of GI-related hospitalizations was significantly higher when the amount of daily precipitation ranged from 3.3 mm to 13.5 mm in Dallas and from 6.0 mm to 24.5 mm in Houston. Yet, substantial increases in the cumulative RRs of GI-related hospitalizations were not observed in Austin or San Antonio. Age-specific and cause-specific GI-related hospitalizations were also found to be associated with precipitation events following the same pattern. Among them, Houston depicted the largest RR for overall GI and subgroup GI by age and cause, particularly for the overall GI among children aged 6 and under (RR = 1.35; 95 % CI = 1.11, 1.63), diarrhea-caused GI among children aged 6 and under (RR = 1.38, 95 % CI = 1.13, 1.69), and other-caused GI among children age 6 and under (RR = 1.46; 95 % CI = 1.12, 1.80). The findings underscore the need for public health interventions and adaptation strategies to address climate change-related health outcomes such as GI illness associated with extreme precipitation events., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Although PM 2.5 (fine particulate matter with an aerodynamic diameter less than 2.5 µm) is an air pollutant of great concern in Texas, limited regulatory monitors pose a significant challenge for decision-making and environmental studies., Objective: This study aimed to predict PM 2.5 concentrations at a fine spatial scale on a daily basis by using novel machine learning approaches and incorporating satellite-derived Aerosol Optical Depth (AOD) and a variety of weather and land use variables., Methods: We compiled a comprehensive dataset in Texas from 2013 to 2017, including ground-level PM 2.5 concentrations from regulatory monitors; AOD values at 1-km resolution based on images retrieved from the MODIS satellite; and weather, land-use, population density, among others. We built predictive models for each year separately to estimate PM 2.5 concentrations using two machine learning approaches called gradient boosted trees and random forest. We evaluated the model prediction performance using in-sample and out-of-sample validations., Results: Our predictive models demonstrate excellent in-sample model performance, as indicated by high R 2 values generated from the gradient boosting models (0.94-0.97) and random forest models (0.81-0.90). However, the out-of-sample R 2 values fall within a range of 0.52-0.75 for gradient boosting models and 0.44-0.69 for random forest models. Model performance varies slightly across years. A generally decreasing trend in predicted PM 2.5 concentrations over time is observed in Eastern Texas., Impact Statement: We utilized machine learning approaches to predict PM 2.5 levels in Texas. Both gradient boosting and random forest models perform well. Gradient boosting models perform slightly better than random forest models. Our models showed excellent in-sample prediction performance (R 2  > 0.9)., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

This study investigated refugee access to primary care physicians (PCP) in San Antonio, Texas. Catholic Charities of San Antonio (CCSA) is the primary agency responsible for connecting refugees to a PCP. Data on refugees were collected from CCSA between May to September 2013 (N = 547). PCPs information was accessed at the Texas Medicaid and Healthcare Partnership (TMHP) website. The 2SFCA method was used in geographic information systems (GIS) to analyze the ratio of healthcare providers relative to refugees within varying walking distances. The highest concentration of accessibility was at 20 min distance in the Medical Center area. The highest concentration of accessibility at all walking distances were also in the Medical Center area. The univariate and multivariate analyses did not result in significant findings for the association between demographic variables and the accessibility scores. These findings recommend building new and more relationships with healthcare providers where PCPs access is low.

To date, there is no clear standard to monitor drug treatment for canine Chagas disease. We used 2 real-time PCR (rtPCR) assays targeting Trypanosoma cruzi kinetoplast DNA (kDNA) and nuclear satellite DNA (nDNA) to detect T. cruzi in canine whole blood. Samples were collected randomly from 131 untreated dogs with unknown T. cruzi infection status in Texas. The kDNA-based rtPCR was slightly more sensitive (diagnostic sensitivity of kDNA = 49% vs. nDNA = 44%; p = 0.5732) but slightly less specific (diagnostic specificity of kDNA = 96% vs. nDNA = 97%; p > 0.9999) than the nDNA-based rtPCR. However, the differences in sensitivity and specificity between the nDNA- and kDNA-based rtPCR assays were not statistically significant. Using the nDNA- and kDNA-based qualitative rtPCR assays to monitor parasitemia from 137 itraconazole- and amiodarone-treated cases with nDNA- and kDNA-based PCR-positive baselines showed that the PCR positive rate decreased to 0% in 30 d. Using kDNA-based quantitative rtPCR to monitor normalized T. cruzi DNA copies in 4 representative dogs demonstrated that drug treatment could reduce parasite loads within 7-30 d. The kDNA-based qualitative rtPCR may be used for routine parasitemia screening of drug-treated Chagas-positive dogs, whereas nDNA-based qualitative rtPCR may be used for confirmation.

Asthma is one of the most prevalent chronic diseases in the United States of America (USA), and many of its risk factors have so far been investigated and identified; however, evidence is limited on how spatial disparities impact the disease. The purpose of this study was to provide scientific evidence on the location influence on asthma in the four states of southwestern USA (California, Arizona, New Mexico and Texas) which, together, include 360 counties. The Behavioral Risk Factor Surveillance System database for these four states covering the period of 2000 to 2011 was used in this analysis, and a Bayesian structured additive regression model was applied to analyse by a geographical information system. After adjusting for individual characteristics, socioeconomic status and health behaviour, this study found higher odds associated with asthma and a likely cluster around the Bay Area in California, while lower odds appeared in several counties around the larger cities of Texas, such as Dallas, Houston and San Antonio. The significance map shows 43 of 360 counties (11.9%) to be high-risk areas for asthma. The level of geographical disparities demonstrates that the county risk of asthma prevalence varies significantly and can be about 19.9% (95% confidence interval: 15.3-25.8) higher or lower than the overall asthma prevalence. We provide an efficient method to utilise and interpret the existing surveillance data on asthma. Visualisation by maps may help deliver future interventions on targeted areas and vulnerable populations to reduce geographical disparities in the burden of asthma.