Reduction in Urinary Arsenic with Bottled-water Intervention

The study was conducted to measure the effectiveness of providing bottled water in reducing arsenic exposure. Urine, tap-water and toenail samples were collected from non-smoking adults residing in Ajo (n=40) and Tucson (n=33), Arizona, USA. The Ajo subjects were provided bottled water for 12 months prior to re-sampling. The mean total arsenic (μg/L) in tap-water was 20.3±3.7 in Ajo and 4.0±2.3 in Tucson. Baseline urinary total inorganic arsenic (μg/L) was significantly higher among the Ajo subjects (n=40, 29.1±20.4) than among the Tucson subjects (n=32, 11.0±12.0, p<0.001), as was creatinine-adjusted urinary total inorganic arsenic (μg/g) (35.5±25.2 vs 13.2±9.3, p<0.001). Baseline concentrations of arsenic (μg/g) in toenails were also higher among the Ajo subjects (0.51±0.72) than among the Tucson subjects (0.17±0.21) (p<0.001). After the intervention, the mean urinary total inorganic arsenic in Ajo (n=36) dropped by 21%, from 29.4±21.1 to 23.2±23.2 (p=0.026). The creatinine-adjusted urinary total inorganic arsenic and toenail arsenic levels did not differ significantly with the intervention. Provision of arsenic-free bottled water resulted in a modest reduction in urinary total inorganic arsenic

Establishing a proactive safety and health risk management system in the fire service

BACKGROUND: Formalized risk management (RM) is an internationally accepted process for reducing hazards in the workplace, with defined steps including hazard scoping, risk assessment, and implementation of controls, all within an iterative process. While required for all industry in the European Union and widely used elsewhere, the United States maintains a compliance-based regulatory structure, rather than one based on systematic, risk-based methodologies. Firefighting is a hazardous profession, with high injury, illness, and fatality rates compared with other occupations, and implementation of RM programs has the potential to greatly improve firefighter safety and health; however, no descriptions of RM implementation are in the peer-reviewed literature for the North American fire service. METHODS: In this paper we describe the steps used to design and implement the RM process in a moderately-sized fire department, with particular focus on prioritizing and managing injury hazards during patient transport, fireground, and physical exercise procedures. Hazard scoping and formalized risk assessments are described, in addition to the identification of participatory-led injury control strategies. Process evaluation methods were conducted to primarily assess the feasibility of voluntarily instituting the RM approach within the fire service setting. RESULTS: The RM process was well accepted by the fire department and led to development of 45 hazard specific-interventions. Qualitative data documenting the implementation of the RM process revealed that participants emphasized the: value of the RM process, especially the participatory bottom-up approach; usefulness of the RM process for breaking down tasks to identify potential risks; and potential of RM for reducing firefighter injury. CONCLUSIONS: As implemented, this risk-based approach used to identify and manage occupational hazards and risks was successful and is deemed feasible for U.S. (and other) fire services. While several barriers and challenges do exist in the implementation of any intervention such as this, recommendations for adopting the process are provided. Additional work will be performed to determine the effectiveness of select controls strategies that were implemented; however participants throughout the organizational structure perceived the RM process to be of high utility while researchers also found the process improved the awareness and engagement in actively enhancing worker safety and health.This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at [email protected]

The use of proactive risk management to reduce emergency service vehicle crashes among firefighters

Introduction: Emergency service vehicle crashes (ESVCs), including rollovers and collisions with other vehicles and fixed objects, are a leading cause of death among U.S. firefighters. Risk management (RM) is a proactive intervention to identifying and mitigating occupational risks and hazards. The goal of this study was to assess the effect of RM in reducing ESVCs. Methods: Three fire departments (A, B and C), representing urban and suburban geographies, and serving medium to large populations, participated in facilitated RM programs to reduce their ESVCs. Interventions were chosen by each department to address their department-specific circumstances and highest risks. Monthly crash rates per 10,000 calls were calculated for each department an average of 28 months before and 23 months after the start of the RM programs. Interrupted time series analysis was used to assess the effect of the RM programs on monthly crash rates. Poisson regression was used to estimate the number of crashes avoided. Economic data from Department A were analyzed to estimate cost savings. Results: Department A had a 15.4% (P = 0.30) reduction in the overall monthly crash rate immediately post-RM and a 1% (P = 0.18) decline per month thereafter. The estimated two-year average cost savings due to 167 crashes avoided was $253,100 (95$192,355 - $313,885). Department B had a 9.7% (P = 0.70) increase in the overall monthly crash rate immediately post-RM and showed no significant changes in their monthly crash rate. Department C had a 28.4% (P = 0.001) reduction in overall monthly crash rate immediately post-RM and a 1.2% (P = 0.09) increase per month thereafter, with an estimated 122 crashes avoided. Conclusions: RM programs have the potential to reduce ESVCs in the fire service and their associated costs; results may vary based on the interventions chosen and how they are implemented. Practical applications: Risk management may be an effective and broadly implemented intervention to reduce ESVCs in the US fire service. (C) 2019 The Author(s). National Safety Council and Elsevier Ltd.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Pulmonary Biomarkers Based on Alterations in Protein Expression after Exposure to Arsenic

OBJECTIVE: Environmental exposure to arsenic results in multiple adverse effects in the lung. Our objective was to identify potential pulmonary protein biomarkers in the lung-lining fluid of mice chronically exposed to low-dose As and to validate these protein changes in human populations exposed to As. METHODS: Mice were administered 10 or 50 ppb As (sodium arsenite) in their drinking water for 4 weeks. Proteins in the lung-lining fluid were identified using two-dimensional gel electrophoresis (n = 3) or multidimensional protein identification technology (MUDPIT) (n = 2) coupled with mass spectrometry. Lung-induced sputum samples were collected from 57 individuals (tap water As ranged from ~ 5 to 20 ppb). Protein levels in sputum were determined by ELISA, and As species were analyzed in first morning void urine. RESULTS: Proteins in mouse lung-lining fluid whose expression was consistently altered by As included glutathione-S-transferase (GST)-omega-1, contraspin, apolipoprotein A-I and A-IV, enolase-1, peroxiredoxin-6, and receptor for advanced glycation end products (RAGE). Validation of the putative biomarkers was carried out by evaluating As-induced alterations in RAGE in humans. Regression analysis demonstrated a significant negative correlation (p = 0.016) between sputum levels of RAGE and total urinary inorganic As, similar to results seen in our animal model. CONCLUSION: Combinations of proteomic analyses of animal models followed by specific analysis of human samples provide an unbiased determination of important, previously unidentified putative biomarkers that may be related to human disease

Arsenic Exposure Is Associated with Decreased DNA Repair in Vitro and in Individuals Exposed to Drinking Water Arsenic

The mechanism(s) by which arsenic exposure contributes to human cancer risk is unknown; however, several indirect cocarcinogenesis mechanisms have been proposed. Many studies support the role of As in altering one or more DNA repair processes. In the present study we used individual-level exposure data and biologic samples to investigate the effects of As exposure on nucleotide excision repair in two study populations, focusing on the excision repair cross-complement 1 (ERCC1) component. We measured drinking water, urinary, or toenail As levels and obtained cryopreserved lymphocytes of a subset of individuals enrolled in epidemiologic studies in New Hampshire (USA) and Sonora (Mexico). Additionally, in corroborative laboratory studies, we examined the effects of As on DNA repair in a cultured human cell model. Arsenic exposure was associated with decreased expression of ERCC1 in isolated lymphocytes at the mRNA and protein levels. In addition, lymphocytes from As-exposed individuals showed higher levels of DNA damage, as measured by a comet assay, both at baseline and after a 2-acetoxyacetylaminofluorene (2-AAAF) challenge. In support of the in vivo data, As exposure decreased ERCC1 mRNA expression and enhanced levels of DNA damage after a 2-AAAF challenge in cell culture. These data provide further evidence to support the ability of As to inhibit the DNA repair machinery, which is likely to enhance the genotoxicity and mutagenicity of other directly genotoxic compounds, as part of a cocarcinogenic mechanism of action

DNA methylation among firefighters

Firefighters are exposed to carcinogens and have elevated cancer rates. We hypothesized that occupational exposures in firefighters would lead to DNA methylation changes associated with activation of cancer pathways and increased cancer risk. To address this hypothesis, we collected peripheral blood samples from 45 incumbent and 41 new recruit nonsmoking male firefighters and analyzed the samples for DNA methylation using an Illumina Methylation EPIC 850k chip. Adjusting for age and ethnicity, we performed: 1) genome-wide differential methylation analysis; 2) genome-wide prediction for firefighter status (incumbent or new recruit) and years of service; and 3) Ingenuity Pathway Analysis (IPA). Four CpGs, including three in the YIPF6, MPST, and PCED1B genes, demonstrated above 1.5-fold statistically significant differential methylation after Bonferroni correction. Genome-wide methylation predicted with high accuracy incumbent and new recruit status as well as years of service among incumbent firefighters. Using IPA, the top pathways with more than 5 gene members annotated from differentially methylated probes included Sirtuin signaling pathway, p53 signaling, and 5' AMP-activated protein kinase (AMPK) signaling. These DNA methylation findings suggest potential cellular mechanisms associated with increased cancer risk in firefighters.US Federal Emergency Management Agency Assistance to Firefighters Grant program [EMW-2014-FP-00200]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Imputation methods for addressing missing data in short-term monitoring of air pollutants

Monitoring of environmental contaminants is a critical part of exposure sciences research and public health practice. Missing data are often encountered when performing short-term monitoring (<24 h) of air pollutants with real-time monitors, especially in resource-limited areas. Approaches for handling consecutive periods of missing and incomplete data in this context remain unclear. Our aim is to evaluate existing imputation methods for handling missing data for real-time monitors operating for short durations. In a current field-study, realtime PM2.5 monitors were placed outside of 20 households and ran for 24-hours. Missing data was simulated in these households at four consecutive periods of missingness (20%, 40%, 60%, 80%). Univariate (Mean, Median, Last Observation Carried Forward, Kalman Filter, Random, Markov) and multivariate time-series (Predictive Mean Matching, Row Mean Method) methods were used to impute missing concentrations, and performance was evaluated using five error metrics (Absolute Bias, Percent Absolute Error in Means, R2 Coefficient of Determination, Root Mean Square Error, Mean Absolute Error). Univariate methods of Markov, random, and mean imputations were the best performingmethods that yielded 24-hour mean concentrations with the lowest error and highest R2 values across all levels of missingness. When evaluating error metrics minute-by-minute, Kalman filters, median, and Markov methods performed well at low levels of missingness (20-40%). However, at higher levels of missingness (60-80%), Markov, random, median, and mean imputation performed best on average. Multivariate methods were the worst performing imputation methods across all levels of missingness. Imputation using univariate methods may provide a reasonable solution to addressing missing data for short-term monitoring of air pollutants, especially in resource-limited areas. Further efforts are needed to evaluate imputation methods that are generalizable across a diverse range of study environments. (C) 2020 Elsevier B.V. All rights reserved.24 month embargo; published online: 3 May 2020This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]