BackgroundGlyphosate (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.MethodUrine 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.ResultsThe 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).ConclusionsThis 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.

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.

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.

ObjectiveIn this paper we used Boosted Regression Tree analysis coupled with environmental factors gathered from satellite data, such as temperature, elevation, and precipitation, to model the niche of Dengue Fever (DF) in Colombia.IntroductionDengue Fever (DF) is a vector-borne disease of the flavivirus family carried by the Aedes aegypti mosquito, and one of the leading causes of illness and death in tropical regions of the world. Nearly 400 million people become infected each year, while roughly one-third of the world’s population live in areas of risk. Dengue fever has been endemic to Colombia since the late 1970s and is a serious health problem for the country with over 36 million people at risk. We used the Magdalena watershed of central Colombia as the site for this study due to its natural separation from other geographical regions in the country, its wide range of climatic conditions, the fact that it includes the main urban centers in Colombia, and houses 80% of the country’s population. Advances in the quality and types of remote sensing (RS) satellite imagery has made it possible to enhance or replace the field collection of environmental data such as precipitation, temperature, and land use, especially in remote areas of the world such as the mountainous areas of Colombia. We modeled the cases of DF by municipality with the environmental factors derived from the satellite data using boosted regression tree analysis. Boosted regression tree analysis (BRT), has proven useful in a wide range of studies, from predicting forest productivity to other vector-borne diseases such as Leishmaniosis, and Crimean-Congo hemorrhagic fever. Using this framework, we set out to determine what are the differences between using presence/absence and case counts of DF in this type of analysis?MethodsWe combined data on Dengue fever cases downloaded from the Instituto Nacional de Salud (INS) Programa SIVIGILA INS site with population data downloaded from the 2005 General Census administered by the National Administrative Department of Statistics (Departamento Administrativo Nacional de Estadística, DANE) and projected to 2012–2014 levels. We acquired remote sensing data from the National Aeronautics and Space Administration (NASA) data servers for each day of the study period. Imagery for each environmental variable was composited to reduce the effects of cloud cover and to match the ISO Week Date format reporting of the case data. We aggregated these weekly composite images for each variable using GIS to create annual minimum, maximum, and mean for a raster cell. These data were further aggregated to the municipality level using the GIS, again for minimum, maximum, and mean. Land use and elevation were only downloaded for one period given they change very little over time. The BRT analysis was conducted twice: once using the Bernoulli family of presence/absence and again using the Poisson family of actual case counts. In the first analysis (Bernoulli), any municipality reporting one or more cases of DF in the year was coded as having disease “presence”, while all others were coded as not having disease “absence”. The BRT model was run, using a twenty-five percent hold out of the data as a testing set, for each year. In the second analysis (Poisson), the only change to the models consisted of replacing the presence/absence data with the actual cases of reported DF within the municipality. The Poisson family was chosen in the model since the count data were highly skewed.ResultsWe calculated RMSE and Pearson r values for each of the three years. The Poisson model out-performed the Bernoulli model across all years. The RMSE values were considerably lower for the Poisson model compared to the Bernoulli model, reflecting a better model fit. The Pearson r values were higher for the Poisson model compared to the Bernoulli model, again across all three years. We created maps to compare Cases with the Poisson and the Bernoulli results. The maps shown in the figure reflect the results for 2012. The left panel represents the cases per 10,000 population per square kilometer for each municipality. The dark green color represents very low ratios of DF, while the red color reflects a higher incidence of DF. All maps used the same classification as the reported cases map for comparison, with an additional symbol (black) used for values outside the reported cases range.ConclusionsUsing actual reported case data and the Poisson function within the BRT functions created by Elith et al. and the gbm package in R, we show that the differences between using presence/absence and case counts of DF in a BRT analysis gives a clearer picture of the spatial distribution of DF. By using readily available and freely accessible data, we have shown that practitioners both within and outside of Colombia can quickly create accurate maps of annual DF incidence. The methods described here could also be extended to other regions and diseases, making it useful to a wide range of end users.