Glyphosate is the most widely applied herbicide worldwide. Glyphosate biomonitoring data are limited for agricultural settings. We measured urinary glyphosate concentrations and assessed exposure determinants in the Biomarkers of Exposure and Effect in Agriculture (BEEA) study. We selected four groups of BEEA participants based on self-reported pesticide exposure: recently exposed farmers with occupational glyphosate use in the last 7 days (n = 98), farmers with high lifetime glyphosate use (>80th percentile) but no use in the last 7 days (n = 70), farming controls with minimal lifetime use (n = 100), and nonfarming controls with no occupational pesticide exposures and no recent home/garden glyphosate use (n = 100). Glyphosate was quantified in first morning void urine using ion chromatography isotope-dilution tandem mass spectrometry. We estimated associations between urinary glyphosate concentrations and potential determinants using multivariable linear regression. Glyphosate was detected (≥0.2 µg/L) in urine of most farmers with recent (91 %) and high lifetime (93 %) use, as well as farming (88 %) and nonfarming (81 %) controls; geometric mean concentrations were 0.89, 0.59, 0.46, and 0.39 µg/L (0.79, 0.51, 0.42, and 0.37 µg/g creatinine), respectively. Compared with both control groups, urinary glyphosate concentrations were significantly elevated among recently exposed farmers (P < 0.0001), particularly those who used glyphosate in the previous day [vs. nonfarming controls; geometric mean ratio (GMR) = 5.46; 95 % confidence interval (CI): 3.75, 7.93]. Concentrations among high lifetime exposed farmers were also elevated (P < 0.01 vs. nonfarming controls). Among recently exposed farmers, glyphosate concentrations were higher among those not wearing gloves when applying glyphosate (GMR = 1.91; 95 % CI: 1.17, 3.11), not wearing long-sleeved shirts when mixing/loading glyphosate (GMR = 2.00; 95 % CI: 1.04, 3.86), applying glyphosate exclusively using broadcast/boom sprayers (vs. hand sprayer only; GMR = 1.70; 95 % CI: 1.00, 2.92), and applying glyphosate to crops (vs. non-crop; GMR = 1.72; 95 % CI: 1.04, 2.84). Both farmers and nonfarmers are exposed to glyphosate, with recency of occupational glyphosate use being the strongest determinant of urinary glyphosate concentrations. Continued biomonitoring of glyphosate in various settings is warranted.

Asthma is a heterogeneous disease with varying severity and subtypes. Recent reviews of epidemiologic studies have identified cleaning and disinfecting activities (CDAs) as important risk factors for asthma-related outcomes among healthcare workers. However, the complexity of CDAs in healthcare settings has rarely been examined. This study utilized a complex survey dataset and data reduction approaches to identify and group healthcare workers with similar patterns of asthma symptoms, and then explored their associations with groups of participants with similar patterns of CDAs. Self-reported information on asthma symptoms/care, CDAs, demographics, smoking status, allergic status, and other characteristics were collected from 2030 healthcare workers within nine selected occupations in New York City. Hierarchical clustering was conducted to systematically group participants based on similarity of patterns of the 27 asthma symptom/care variables, and 14 product applications during CDAs, separately. Word clouds were used to visualize the complex information on the resulting clusters. The associations of asthma health clusters (HCs) with exposure clusters (ECs) were evaluated using multinomial logistic regression. Five HCs were identified (HC-1 to HC-5), labelled based on predominant features as: "no symptoms", "winter cough/phlegm", "mild asthma symptoms", "undiagnosed/untreated asthma", and "asthma attacks/exacerbations". For CDAs, five ECs were identified (EC-1 to EC-5), labelled as: "no products", "housekeeping/chlorine", "patient care", "general cleaning/laboratory", and "disinfection products". Using HC-1 and EC-1 as the reference groups, EC-2 was associated with HC-4 (odds ratio (OR)=3.11, 95% confidence interval (95% CI)=1.46-6.63) and HC-5 (OR=2.71, 95% CI=1.25-5.86). EC-3 was associated with HC-5 (OR=2.34, 95% CI=1.16-4.72). EC-4 was associated with HC-5 (OR=2.35, 95% CI=1.07-5.13). EC-5 was associated with HC-3 (OR=1.81, 95% CI=1.09-2.99) and HC-4 (OR=3.42, 95% CI=1.24-9.39). Various combinations of product applications like using alcohols, bleach, high-level disinfectants, and enzymes to disinfect instruments and clean surfaces captured by the ECs were identified as risk factors for the different asthma symptoms clusters, indicating that prevention efforts may require targeting multiple products. The associations of HCs with EC can be used to better inform prevention strategies and treatment options to avoid disease progression. This study demonstrated hierarchical clustering and word clouds were useful techniques for analyzing and visualizing a complex dataset with a large number of potentially correlated variables to generate practical information that can inform prevention activities.

Residents of agricultural areas experience pesticide exposures from sources other than direct agricultural work. We developed a quantitative, active ingredient-specific algorithm for cumulative (adult, married lifetime) non-occupational pesticide exposure intensity for spouses of farmers who applied pesticides in the Agricultural Health Study (AHS). The algorithm addressed three exposure pathways: take-home, agricultural drift, and residential pesticide use. Pathway-specific equations combined (i) weights derived from previous meta-analyses of published pesticide exposure data and (ii) information from the questionnaire on frequency and duration of pesticide use by applicators, home proximity to treated fields, residential pesticide usage (e.g., termite treatments), and spouse's off-farm employment (proxy for time at home). The residential use equation also incorporated a published probability matrix that documented the likelihood active ingredients were used in home pest treatment products. We illustrate use of these equations by calculating exposure intensities for the insecticide chlorpyrifos and herbicide atrazine for 19,959 spouses. Non-zero estimates for >/=1 pathway were found for 78% and 77% of spouses for chlorpyrifos and atrazine, respectively. Variability in exposed spouses' intensity estimates was observed for both pesticides, with 75th to 25th percentile ratios ranging from 7.1 to 7.3 for take-home, 6.5 to 8.5 for drift, 2.4 to 2.8 for residential use, and 3.8 to 7.0 for the summed pathways. Take-home and drift estimates were highly correlated (>/=0.98), but were not correlated with residential use (0.010.02). This algorithm represents an important advancement in quantifying non-occupational pesticide relative exposure differences and will facilitate improved etiologic analyses in the AHS spouses. The algorithm could be adapted to studies with similar information.

Objectives: Use of cleaning and disinfecting products is associated with work-related asthma among healthcare workers, but the specific levels and factors that affect exposures remain unclear. The objective of this study was to evaluate the determinants of selected volatile organic compound (VOC) exposures in healthcare settings. Methods: Personal and mobile-area air measurements (n = 143) from 100 healthcare workers at four hospitals were used to model the determinants of ethanol, acetone, 2-propanol, d-limonene, alpha-pinene, and chloroform exposures. Hierarchical cluster analysis was conducted to partition workers into groups with similar cleaning task/product-use profiles. Linear mixed-effect regression models using log-transformed VOC measurements were applied to evaluate the association of individual VOCs with clusters of task/product use, industrial hygienists' grouping (IH) of tasks, grouping of product application, chemical ingredients of the cleaning products used, amount of product use, and ventilation. Results: Cluster analysis identified eight task/product-use clusters that were distributed across multiple occupations and hospital units, with the exception of clusters consisting of housekeepers and floor strippers/waxers. Results of the mixed-effect models showed significant associations between selected VOC exposures and several clusters, combinations of IH-generated task groups and chemical ingredients, and product application groups. The patient/personal cleaning task using products containing chlorine was associated with elevated levels of personal chloroform and alpha-pinene exposures. Tasks associated with instrument sterilizing and disinfecting were significantly associated with personal d-limonene and 2-propanol exposures. Surface and floor cleaning and stripping tasks were predominated by housekeepers and floor strippers/waxers, and use of chlorine-, alcohol-, ethanolamine-, and quaternary ammonium compounds-based products was associated with exposures to chloroform, alpha-pinene, acetone, 2-propanol, or d-limonene. Conclusions: Healthcare workers are exposed to a variety of chemicals that vary with tasks and ingredients of products used during cleaning and disinfecting. The combination of product ingredients with cleaning and disinfecting tasks were associated with specific VOCs. Exposure modules for questionnaires used in epidemiologic studies might benefit from seeking information on products used within a task context.

We assessed the effect of metaboreceptor activation on whole-body evaporative heat loss (WB-EHL) in twelve men (24+/-4 years) in the early-to-late-stages of a 60-min exercise recovery in the heat. Metaboreceptor activation induced by 1-min isometric-handgrip (IHG) exercise followed by 5-min forearm ischemia to trap metabolites increased WB-EHL by 25-31% and 26-34% during the ischemic period relative to IHG-Only and Control (natural recovery only) respectively throughout recovery. We show that metaboreceptor activation enhances WB-EHL in recovery.

On April 25, 2017, the Sinoe County Health Team (CHT) notified the Liberia Ministry of Health (MoH) and the National Public Health Institute of Liberia of an unknown illness among 14 persons that resulted in eight deaths in Sinoe County. On April 26, the National Rapid Response Team and epidemiologists from CDC, the World Health Organization (WHO) and the African Field Epidemiology Network (AFENET) in Liberia were deployed to support the county-led response. Measures were immediately implemented to identify all cases, ascertain the cause of illness, and control the outbreak. Illness was associated with attendance at a funeral event, and laboratory testing confirmed Neisseria meningitidis in biologic specimens from cases. The 2014-2015 Ebola virus disease (Ebola) outbreak in West Africa devastated Liberia's already fragile health system, and it took many months for the country to mount an effective response to control the outbreak. Substantial efforts have been made to strengthen Liberia's health system to prevent, detect, and respond to health threats. The rapid and efficient field response to this outbreak of N. meningitidis resulted in implementation of appropriate steps to prevent a widespread outbreak and reflects improved public health and outbreak response capacity in Liberia.

OBJECTIVES: Trichloroethylene, a chlorinated solvent widely used for metal degreasing, is classified by the International Agency for Research on Cancer as a kidney carcinogen. Other chlorinated solvents are suspected carcinogens, most notably the cleaning solvent perchloroethylene, although it is unclear whether they are associated with kidney cancer. We investigated kidney cancer associations with occupational exposure to 6 chlorinated solvents (trichloroethylene, perchloroethylene, 1,1,1-trichloroethane, carbon tetrachloride, chloroform, and methylene chloride) within a case-control study using detailed exposure assessment methods. METHODS: Cases (n=1217) and controls (n=1235) provided information on their occupational histories and, for selected occupations, on tasks involving potential exposure to chlorinated solvents through job-specific interview modules. Using this information, an industrial hygienist assessed potential exposure to each solvent. We computed ORs and 95% CIs for different exposure metrics, with unexposed participants as the referent group. RESULTS: 1,1,1-trichloroethane, carbon tetrachloride, chloroform, and methylene chloride were not associated with kidney cancer. Among jobs with high exposure intensity, high cumulative hours exposed to perchloroethylene was associated with increased risk, both overall (third tertile vs unexposed: OR 3.1, 95% CI 1.3 to 7.4) and after excluding participants with ≥50% exposure probability for trichloroethylene (OR 3.0, 95% CI 0.99 to 9.0). A non-significant association with high cumulative hours exposed to trichloroethylene was observed (OR 1.7, 95% CI 0.8 to 3.8). CONCLUSIONS: In this study, high exposure to perchloroethylene was associated with kidney cancer, independent of trichloroethylene. Additional studies are needed to further investigate this finding.

BACKGROUND: Increased pesticide concentrations in house dust in agricultural areas have been attributed to several exposure pathways, including agricultural drift, para-occupational, and residential use. OBJECTIVE: To guide future exposure assessment efforts, we quantified relative contributions of these pathways using meta-regression models of published data on dust pesticide concentrations. METHODS: From studies in North American agricultural areas published from 1995-2015, we abstracted dust pesticide concentrations reported as summary statistics (e.g., geometric means (GM)). We analyzed these data using mixed-effects meta-regression models that weighted each summary statistic by its inverse variance. Dependent variables were either the log-transformed GM (drift) or the log-transformed ratio of GMs from two groups (para-occupational, residential use). RESULTS: For the drift pathway, predicted GMs decreased sharply and nonlinearly, with GMs 64% lower in homes 250 m versus 23 m from fields (inter-quartile range of published data) based on 52 statistics from 7 studies. For the para-occupational pathway, GMs were 2.3 times higher (95% confidence interval [CI]: 1.5-3.3; 15 statistics, 5 studies) in homes of farmers who applied pesticides more versus less recently or frequently. For the residential use pathway, GMs were 1.3 (95%CI: 1.1-1.4) and 1.5 (95%CI: 1.2-1.9) times higher in treated versus untreated homes, when the probability that a pesticide was used for the pest treatment was 1-19% and ≥20%, respectively (88 statistics, 5 studies). CONCLUSION: Our quantification of the relative contributions of pesticide exposure pathways in agricultural populations could improve exposure assessments in epidemiologic studies. The meta-regression models can be updated when additional data become available.

BACKGROUND: Women living in agricultural areas may experience high pesticide exposures compared to women in urban or suburban areas due to their proximity to farm activities. OBJECTIVE: Our objective was to review the evidence in the published literature for the contribution of nonoccupational pathways of pesticide exposure in women living in North American agricultural areas. METHODS: We evaluated the following nonoccupational exposure pathways: para-occupational (i.e., take-home or bystander exposure), agricultural drift, residential pesticide use, and dietary ingestion. We also evaluated the role of hygiene factors (e.g., house cleaning; shoe removal). RESULTS: Among 35 publications identified (published 1995-2013), several reported significant or suggestive (p<0.1) associations between para-occupational (n=19) and agricultural drift (n=10) pathways and pesticide dust or biomarker levels, while three observed that residential use was associated with pesticide concentrations in dust. The four studies related to ingestion reported low detection rates of most pesticides in water; additional studies are needed to draw conclusions about this pathway's importance. Hygiene factors were not consistently linked to exposure among the 18 relevant publications identified. CONCLUSIONS: Evidence supported the importance of para-occupational, drift, and residential use pathways. Disentangling exposure pathways was difficult because agricultural populations are concurrently exposed to pesticides via multiple pathways. Most evidence was based on measurements of pesticides in residential dust, which are applicable to any household member and are not specific to women. An improved understanding of nonoccupational pesticide exposure pathways in women living in agricultural areas is critical for studying health effects in women and for designing effective exposure-reduction strategies.