Glyphosate (GLY) is the most heavily used herbicide worldwide but the extent of exposure in human pregnancy remains unknown. Its residues are found in the environment, major crops, and food items that humans, including pregnant women, consume daily. Since GLY exposure in pregnancy may also increase fetal exposure risk, we designed a birth-cohort study to determine exposure frequency, potential exposure pathways, and associations with fetal growth indicators and pregnancy length. Urine and residential drinking water samples were obtained from 71 women with singleton pregnancies living in Central Indiana while they received routine prenatal care. GLY measurements were performed using liquid chromatography-tandem mass spectrometry. Demographic and survey information relating to food and water consumption, stress, and residence were obtained by questionnaire. Maternal risk factors and neonatal outcomes were abstracted from medical records. Correlation analyses were used to assess relationships of urine GLY levels with fetal growth indicators and gestational length. The mean age of participants was 29 years, and the majority were Caucasian. Ninety three percent of the pregnant women had GLY levels above the limit of detection (0.1 ng/mL). Mean urinary GLY was 3.40 ng/mL (range 0.5–7.20 ng/mL). Higher GLY levels were found in women who lived in rural areas (p = 0.02), and in those who consumed > 24 oz. of caffeinated beverages per day (p = 0.004). None of the drinking water samples had detectable GLY levels. We observed no correlations with fetal growth indicators such as birth weight percentile and head circumference. However, higher GLY urine levels were significantly correlated with shortened gestational lengths (r = − 0.28, p = 0.02). This is the first study of GLY exposure in US pregnant women using urine specimens as a direct measure of exposure. We found that > 90% of pregnant women had detectable GLY levels and that these levels correlated significantly with shortened pregnancy lengths. Although our study cohort was small and regional and had limited racial/ethnic diversity, it provides direct evidence of maternal GLY exposure and a significant correlation with shortened pregnancy. Further investigations in a more geographically and racially diverse cohort would be necessary before these findings could be generalized.

Disinfected water is the major source of haloacetic acids (HAAs) in humans, but their inter- and intra-individual variability for exposure and risk assessment applications is under-researched. Thus, we measured HAAs in cross-sectional and longitudinal urine and water specimens from 17 individuals. Five regulated HAAs—mono-, di-, and trichloroacetic acid (MCAA, DCAA, and TCAA) and mono- and dibromoacetic acid (MBAA and DBAA)—and one unregulated HAA—bromochloroacetic acid (BCAA)—were measured. Urinary DCAA, MBAA, DBAA, and BCAA levels were always below the limits of detection (LOD). Measured levels and interindividual variability of urinary MCAA were higher than urinary TCAA. Longitudinal urinary specimens showed MCAA levels peaked in after-shower specimens, while TCAA levels remain unchanged. Correlation between urinary MCAA and TCAA was moderate but statistically significant. The prevalence of MCAA and TCAA in urine suggest they can be considered as biomarkers of HAA. Peak urinary MCAA in post-shower specimens suggest MCAA captures short-term exposure via dermal and/or inhalation, while urinary TCAA captures long-term exposure via ingestion. However, further research is warranted in a large pool of participants to test the reliability of MCAA as exposure biomarker.

Dengue fever (DF), a vector-borne flavivirus, is endemic to the tropical countries of the world with nearly 400 million people becoming infected each year and roughly one-third of the world’s population living in areas of risk. The main vector for DF is the Aedes aegypti mosquito, which is also the same vector of yellow fever, chikungunya, and Zika viruses. To gain an understanding of the spatial aspects that can affect the epidemiological processes across the disease’s geographical range, and the spatial interactions involved, we created and compared Bernoulli and Poisson family Boosted Regression Tree (BRT) models to quantify the overall annual risk of DF incidence by municipality, using the Magdalena River watershed of Colombia as a study site during the time period between 2012 and 2014. A wide range of environmental conditions make this site ideal to develop models that, with minor adjustments, could be applied in many other geographical areas. Our results show that these BRT methods can be successfully used to identify areas at risk and presents great potential for implementation in surveillance programs.

Previous research on Dengue Fever have involved laboratory tests or study areas with less diverse temperature and elevation ranges than is found in Colombia; therefore, preliminary research was needed to identify location specific attributes of Dengue Fever transmission. Environmental variables derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellites were combined with population variables to be statistically compared against reported cases of Dengue Fever in the Río Magdalena watershed, Colombia. Three-factor analysis models were investigated to analyze variable patterns, including a population, population density, and empirical Bayesian estimation model. Results identified varying levels of Dengue Fever transmission risk, and environmental characteristics which support, and advance, the research literature. Multiple temperature metrics, elevation, and vegetation composition were among the more contributory variables found to identify future potential outbreak locations.

Climate change is intensifying flooding in the Great Lakes watershed, threatening critical infrastructure and limiting access to emergency health services. Existing U.S. flood risk tools, such as FEMA’s National Risk Index and newer models, provide valuable coverage but often emphasize economic impacts while overlooking community-level vulnerabilities. To address this gap, we developed the Community Resilience and Adaptation Spatial Infrastructure Database (CRASID). The CRASID tool combines flood risk, land use, emergency service accessibility, critical infrastructure, and sociodemographic indicators into a composite risk index. This study applies CRASID across six case study areas in the western Great Lakes—four overlapping areas in two urban metro areas (Cleveland, Detroit) and two non-overlapping rural areas—to assess model applicability and identify key drivers of flood-related risk. Statistical methods included three predictive models: principal components regression, backward stepwise regression, and boosted regression trees, with boosted regression trees achieving the strongest performance in predicting risk. Findings reveal that rural, floodplain-based communities with high concentrations of vulnerable populations are disproportionately at risk due to limited access to emergency services. While urban areas generally exhibit greater resilience, they also contain localized pockets of elevated vulnerability. These results underscore the importance of a community-centric approach, shifting focus away from primarily economic measures. By highlighting where and for whom risks are most significant, CRASID offers policymakers and communities a novel framework for planning, adaptation, and resilience-building in the face of climate-driven flooding. This people-focused approach provides actionable insights to enhance preparedness and protect public health across diverse Great Lakes communities.