Suchergebnisse

A simulation model of tuberculosis (TB) transmission among hospital employees is described. A hypothetical cohort of 1000 workers was divided into low‐, medium‐, and high‐risk groups. The number of TB patients admitted daily was treated as a Poisson random variable. A patient imparted a daily infection risk that was identical for all workers within a risk group but that varied between risk groups. In some scenarios, infected employees were assigned a daily risk of developing TB disease. If disease developed, the individual remained on the job for 3 calendar weeks and imparted a substantial infection risk to 25 close contacts. Simulations were run over 5‐year intervals. Cumulative infection incidence increased over time and with more TB patients admitted. Given a scenario in which there were 600, 300, and 100 susceptibles in the low‐, medium‐, and high risk groups, respectively, 50 TB patients admitted annually and accounting for disease among infected employees, at 5 years there were approximately 100 primary infections (due to infection by patients), 40 secondary infections (due to infection by diseased coworkers), five primary disease cases, and two secondary disease cases. The input parameter values and simulation outcomes were reasonably consistent with the sparse information reported in the literature.

Cooking and heating remain the most energy intensive activities among the world's poor, and thus improved access to clean energies for these tasks has been highlighted as a key requirement of attaining the major objectives of the UN Millennium Development Goals. A move towards clean energy technologies such as biogas systems (which produce methane from human and animal waste) has the potential to provide immediate benefits for the control of neglected tropical diseases. Here, an assessment of the parasitic disease and energy benefits of biogas systems in Sichuan Province, China, is presented, highlighting how the public health sector can leverage the proliferation of rural energy projects for infectious disease control. Methodology/Findings: First, the effectiveness of biogas systems at inactivating and removing ova of the human parasite Schistosoma japonicum is experimentally evaluated. Second, the impact of biogas infrastructure on energy use and environmental quality as reported by surveyed village populations is assessed, as is the community acceptance of the technology. No viable eggs were recovered in the effluent collected weekly from biogas systems for two months following seeding with infected stool. Less than 1% of ova were recovered viable from a series of nylon bags seeded with ova, a 2-log removal attributable to biochemical inactivation. More than 90% of Ascaris lumbricoides ova (used as a proxy for S. japonicum ova) counted at the influent of two biogas systems were removed in the systems when adjusted for system residence time, an approximate 1-log removal attributable to sedimentation. Combined, these inactivation/removal processes underscore the promise of biogas infrastructure for reducing parasite contamination resulting from nightsoil use. When interviewed an average of 4 years after construction, villagers attributed large changes in fuel usage to the installation of biogas systems. Household coal usage decreased by 68%, wood by 74%, and crop waste by 6%. With reported energy savings valued at roughly 600 CNY per year, 2–3 years were required to recoup the capital costs of biogas systems. In villages without subsidies, no new biogas systems were implemented. Conclusions: Sustainable strategies that integrate rural energy needs and sanitation offer tremendous promise for long-term control of parasitic diseases, while simultaneously reducing energy costs and improving quality of life. Government policies can enhance the financial viability of such strategies by introducing fiscal incentives for joint sanitation/sustainable energy projects, along with their associated public outreach and education programs.

AbstractFew studies have assessed how the intersection of social determinants of health and environmental hazards contributes to racial disparities in COVID-19. The aim of our study was to compare COVID-19 disparities in testing and positivity to cumulative environmental health impacts, and to assess how unique social and environmental determinants of health relate to COVID-19 positivity in Seattle, King County, WA, at the census tract level. Publicly available data (n = 397 census tracts) were obtained from Public Health–Seattle & King County, 2018 ACS 5-year estimates, and the Washington Tracking Network. COVID-19 testing and positive case rates as of July 12, 2020, were mapped and compared to Washington State Environmental Health Disparities (EHD) Map cumulative impact rankings. We calculated odds ratios from a series of univariable and multivariable logistic regression analyses using cumulative impact rankings, and community-level socioeconomic, health, and environmental factors as predictors and having ≥ 10% or < 10% census tract positivity as the binary outcome variable. We found a remarkable overlap between Washington EHD cumulative impact rankings and COVID-19 positivity in King County. Census tracts with ≥ 10 % COVID-19 positivity had significantly lower COVID-19 testing rates and higher proportions of people of color and faced a combination of low socioeconomic status–related outcomes, poor community health outcomes, and significantly higher concentrations of fine particulate matter (PM2.5). King County communities experiencing high rates of COVID-19 face a disproportionate cumulative burden of environmental and social inequities. Cumulative environmental health impacts should therefore systematically be considered when assessing for risk of exposure to and health complications resulting from COVID-19.

Abstract
Legal commercial cultivation and processing of cannabis is a rapidly growing industry in multiple countries. However, to date little effort has been made to characterize and identify the various occupational hazards that workers may be facing in the cannabis production industry, including airborne contaminants that may affect the human respiratory system. In the current study, we quantified occupational exposures to particulate matter (PM) and volatile organic compounds (VOCs) in various task zones of two indoor cannabis facilities in Washington State. Full-shift (8-h) area measurements of PM and VOCs were collected in each task zone. Measurement devices were placed near the employee's work area in order to attempt to estimate the personal exposure to the contaminants. In each task zone we measured particle number concentration, particle mass concentration (PMC), cumulative size distribution of the particles, and total terpene mass concentrations. The mean PMCs were greater in task zones that required the employees to manipulate the cannabis plants and materials. The arithmetic mean PMC for the trim task was 60 µg m−3, preroll task was 45 µg m−3, grow task was 42 µg m−3, and the referent office area was 27 µg m−3. When comparing each task zone PMC to the office referent PMC, the trim task, and the preroll task were significantly higher than the referent group (P-values both &lt;0.05). The arithmetic mean terpene mass concentration for the trim task was 36 mg m−3, preroll task was 9.9 mg m−3, grow task was 15 mg m−3, and for the office referent space was 4.9 mg m−3. Compared with the office space, only the trim task area had significantly elevated terpene mass concentrations (P-value &lt;0.01). We observed a weak but statistically significant correlation between PMC and total terpene mass concentrations (rho = 0.42, P &lt; 0.02). Overall, we observed that exposures to respiratory hazards were highest in task zones where cannabis plants and material were manipulated by workers, including the trim, preroll, and the grow task areas. These observations can help inform the employer of the task zones where exposure to respiratory hazards are the highest, and where it may be beneficial to deploy control measures to reduce worker exposures.

Traditionally, microbial risk assessors have used point estimates to evaluate the probability that an individual will become infected. We developed a quantitative approach that shifts the risk characterization perspective from point estimate to distributional estimate, and from individual to population. To this end, we first designed and implemented a dynamic model that tracks traditional epidemiological variables such as the number of susceptible, infected, diseased, and immune, and environmental variables such as pathogen density. Second, we used a simulation methodology that explicitly acknowledges the uncertainty and variability associated with the data. Specifically, the approach consists of assigning probability distributions to each parameter, sampling from these distributions for Monte Carlo simulations, and using a binary classification to assess the output of each simulation. A case study is presented that explores the uncertainties in assessing the risk of giardiasis when swimming in a recreational impoundment using reclaimed water. Using literature‐based information to assign parameters ranges, our analysis demonstrated that the parameter describing the shedding of pathogens by infected swimmers was the factor that contributed most to the uncertainty in risk. The importance of other parameters was dependent on reducing thea priorirange of this shedding parameter. By constraining the shedding parameter to its lower subrange, treatment efficiency was the parameter most important in predicting whether a simulation resulted in prevalences above or below non outbreak levels. Whereas parameters associated with human exposure were important when the shedding parameter was constrained to a higher subrange. This Monte Carlo simulation technique identified conditions in which outbreaks and/or nonoutbreaks are likely and identified the parameters that most contributed to the uncertainty associated with a risk prediction.

Abstract
Pesticide spray drift represents an important exposure pathway that may cause illness among orchard workers. To strike a balance between improving spray coverage and reducing drift, new sprayer technologies are being marketed for use in modern tree canopies to replace conventional axial fan airblast (AFA) sprayers that have been used widely since the 1950s. We designed a series of spray trials that used mixed-effects modeling to compare tracer-based drift volume levels for old and new sprayer technologies in an orchard work environment. Building on a smaller study of 6 trials (168 tree rows) that collected polyester line drift samples (n = 270 measurements) suspended on 15 vertical masts downwind of an AFA sprayer application, this study included 9 additional comparison trials (252 tree rows; n = 405 measurements) for 2 airblast tower sprayers: the directed air tower (DAT) and the multi-headed fan tower (MFT). Field-based measurements at mid (26 m) and far (52 m) distances showed that the DAT and MFT sprayers had 4–15 and 35–37% less drift than the AFA. After controlling for downwind distance, sampling height, and wind speed, model results indicated that the MFT [−35%; 95% confidence interval (CI): −22 and −49%; P &lt; 0.001] significantly reduced drift levels compared to the AFA, but the DAT did not (−7%; 95% CI: −19 and 6%; P = 0.29). Tower sprayers appear to be a promising means by which to decrease drift levels through shorter nozzle-to-tree canopy distances and more horizontally directed aerosols that escape the tree canopy to a lesser extent. Substitution of these new technologies for AFA sprayers is likely to reduce the frequency and magnitude of pesticide drift exposures and associated illnesses. These findings, especially for the MFT, may fit United States Environmental Protection Agency's Drift Reduction Technology (DRT) one-star rating of 25–50% reduction. An 'AFA buyback' incentive program could be developed to stimulate wider adoption of new drift-reducing spray technologies. However, improved sprayer technologies alone do not eliminate drift. Applicator training, including proper sprayer calibration and maintenance, and application exclusion zones (AEZs) can also contribute to minimizing the risks of drift exposure. With regard to testing DRTs and establishing AEZs, our study findings demonstrate the need to define the impact of airblast sprayer type, orchard architecture, sampling height, and wind speed.

AbstractPesticide spray drift represents an important cause of crop damage and farmworker illness, especially among orchard workers. We drew upon exposure characteristics from known human illness cases to design a series of six spray trials that measured drift from a conventional axial fan airblast sprayer operating in a modern orchard work environment. Polyester line drift samples (n = 270; 45 per trial) were suspended on 15 vertical masts downwind of foliar applications of zinc, molybdenum, and copper micronutrient tracers. Samples were analyzed using inductively coupled plasma mass spectrometry and resulting masses were normalized by sprayer tank mix concentration to create tracer-based drift volume levels. Mixed-effects modeling described these levels in the context of spatial variability and buffers designed to protect workers from drift exposure. Field-based measurements showed evidence of drift up to 52 m downwind, which is approximately 1.7 times greater than the 30 m (100 ft) 'Application Exclusion Zone' defined for airblast sprayers by the United States Environmental Protection Agency Worker Protection Standard. When stratified by near (5 m), mid (26 m), and far (52 m) distances, geometric means and standard deviations for drift levels were 257 (1.8), 52 (2.0), and 20 (2.3) µl, respectively. Fixed effect model coefficients showed that higher wind speed [0.53; 95% confidence interval (CI): 0.35, 0.70] and sampling height (0.16; 95% CI: 0.11, 0.20) were positively associated with drift; increasing downwind distance (−0.05; 95% CI: −0.06, −0.04) was negatively associated with drift. Random effects showed large within-location variability, but relatively few systematic changes for individual locations across spray trials after accounting for wind speed, height, and distance. Our study findings demonstrate that buffers may offer drift exposure protection to orchard workers from airblast spraying. Variables such as orchard architecture, sampling height, and wind speed should be included in the evaluation and mitigation of risks from drift exposure. Data from our study may prove useful for estimating potential exposure and validating orchard-based bystander exposure models.

Abstract
Driven by climate change, wildfires are increasing in frequency, duration, and intensity across the Western United States. Outdoor workers are being exposed to increasing wildfire-related particulate matter and smoke. Recognizing this emerging risk, Washington adopted an emergency rule and is presently engaged in creating a permanent rule to protect outdoor workers from wildfire smoke exposure. While there are growing bodies of literature on the exposure to and health effects of wildfire smoke in the general public and wildland firefighters, there is a gap in knowledge about wildfire smoke exposure among outdoor workers generally and construction workers specifically—a large category of outdoor workers in Washington totaling 200,000 people. Several data sources were linked in this study—including state-collected employment data and national ambient air quality data—to gain insight into the risk of PM2.5 exposure among construction workers and evaluate the impacts of different air quality thresholds that would have triggered a new Washington emergency wildfire smoke rule aimed at protecting workers from high PM2.5 exposure. Results indicate the number of poor air quality days has increased in August and September in recent years. Over the last decade, these months with the greatest potential for particulate matter exposure coincided with an annual peak in construction employment that was typically 9.4–42.7% larger across Washington counties (one county was 75.8%). Lastly, the 'encouraged' threshold of the Washington emergency rule (20.5 μg m−3) would have resulted in 5.5 times more days subject to the wildfire rule on average across all Washington counties compared to its 'required' threshold (55.5 μg m−3), and in 2020, the rule could have created demand for 1.35 million N-95 filtering facepiece respirators among construction workers. These results have important implications for both employers and policy makers as rules are developed. The potential policy implications of wildfire smoke exposure, exposure control strategies, and data gaps that would improve understanding of construction worker exposure to wildfire smoke are also discussed.

Wildfires have frequently occurred in the western United States (US) during the summer and fall seasons in recent years. This study measures the PM(2.5) infiltration factor in seven residences recruited from five dense communities in Seattle, Washington, during a 2020 wildfire episode and evaluates the impacts of HEPA-based portable air cleaner (PAC) use on reducing indoor PM(2.5) levels. All residences with windows closed went through an 18-to-24-hour no filtration session, with five of seven following that period with an 18-to-24-hour filtration session. Auto-mode PACs, which automatically adjust the fan speed based on the surrounding PM(2.5) levels, were used for the filtration session. 10-second resolved indoor PM(2.5) levels were measured in each residence's living room, while hourly outdoor levels were collected from the nearest governmental air quality monitoring station to each residence. Additionally, a time-activity diary in minute resolution was collected from each household. With the impacts of indoor sources excluded, indoor PM(2.5) mass balance models were developed to estimate the PM(2.5) indoor/outdoor (I/O) ratios, PAC effectiveness, and decay-related parameters. Among the seven residences, the mean infiltration factor ranged from 0.33 (standard deviation [SD]: 0.06) to 0.76 (SD: 0.05). The use of auto-mode PAC led to a 48%−78% decrease of indoor PM(2.5) levels after adjusting for outdoor PM(2.5) levels and indoor sources. The mean (SD) air exchange rates ranged from 0.30 (0.13) h(−1) to 1.41 (3.18) h(−1) while the PM(2.5) deposition rate ranged from 0.10 (0.54) h(−1) to 0.49 (0.47) h(−1). These findings suggest that staying indoors, a common protective measure during wildfire episodes, is insufficient to prevent people's excess exposure to wildfire smoke, and provides quantitative evidence to support the utilization of auto-mode PACs during wildfire events in the US.

The Imperial County Community Air Monitoring Network (the Network) is a collaborative group of community, academic, nongovernmental, and government partners designed to fill the need for more detailed data on particulate matter in an area that often exceeds air quality standards. The Network employs a community-based environmental monitoring process in which the community and researchers have specific, well-defined roles as part of an equitable partnership that also includes shared decision-making to determine study direction, plan research protocols, and conduct project activities. The Network is currently producing real-time particulate matter data from 40 low-cost sensors throughout Imperial County, one of the largest community-based air networks in the United States. Establishment of a community-led air network involves engaging community members to be citizen-scientists in the monitoring, siting, and data collection process. Attention to technical issues regarding instrument calibration and validation and electronic transfer and storage of data is also essential. Finally, continued community health improvements will be predicated on facilitating community ownership and sustainability of the network after research funds have been expended. https://doi.org/10.1289/EHP1772

Conventional regulatory air quality monitoring sites tend to be sparsely located. The availability of lower-cost air pollution sensors, however, allows for their use in spatially dense community monitoring networks, which can be operated by various stakeholders, including concerned residents, organizations, academics, or government agencies. Networks of many community monitors have the potential to fill the spatial gaps between existing government-operated monitoring sites. One potential benefit of finer scale monitoring might be the ability to discern elevated air pollution episodes in locations that have not been identified by government-operated monitoring sites, which might improve public health warnings for populations sensitive to high levels of air pollution. In the Imperial Air study, a large network of low-cost particle monitors was deployed in the Imperial Valley in Southeastern California. Data from the new monitors is validated against regulatory air monitoring. Neighborhood-level air pollution episodes, which are defined as periods in which the PM(2.5) (airborne particles with sizes less than 2.5 μm in diameter) hourly average concentration is equal to or greater than 35 μg m(−3), are identified and corroborate with other sites in the network and against the small number of government monitors in the region. During the period from October 2016 to February 2017, a total of 116 episodes were identified among six government monitors in the study region; however, more than 10 times as many episodes are identified among the 38 community air monitors. Of the 1426 episodes identified by the community sensors, 723 (51%) were not observed by the government monitors. These findings suggest that the dense network of community air monitors could be useful for addressing current limitations in the spatial coverage of government air monitoring to provide real-time warnings of high pollution episodes to vulnerable populations.