Showing papers by "Edmund Seto published in 2020"
TL;DR: Fully adjusting for meteorology, this study shows that the COVID-19 responses were associated with much more reductions in traffic-related UFPs than PM2.5 in the Seattle region, in contrast to the reverse trend from the direct empirical data comparison.
97 citations
TL;DR: Investigation of the association between perceived change in physical activity or exercise and mental health outcomes over the short-term in response to COVID-19 mitigation strategies in a sample of adult twins found a perceived decrease in physicalActivity or exercise was associated with higher stress and anxiety levels.
Abstract: Background Physical distancing and other COVID-19 pandemic mitigation strategies may have unintended consequences on a number of health behaviors and health outcomes. The purpose of this study was to investigate the association between perceived change in physical activity or exercise and mental health outcomes over the short-term in response to COVID-19 mitigation strategies in a sample of adult twins. Methods This was a cross-sectional study of 3,971 identical and same-sex fraternal adult twins (909 pairs, 77% identical) from the community-based Washington State Twin Registry. Participants in this study completed an online survey examining the impact of COVID-19 mitigation on a number of health-related behaviors and outcomes, administered between March 26 and April 5, 2020. In the present study, the exposure was perceived change in physical activity or exercise. The outcomes were levels of perceived anxiety and stress. Results More twin pairs reported a decrease in physical activity levels (42%) than those reporting no change (31%) or increased physical activity levels (27%). A perceived decrease in physical activity or exercise was associated with higher stress and anxiety levels. However, the physical activity–stress relationship was confounded by genetic and shared environmental factors. On the other hand, the physical activity–anxiety relationship held after controlling for genetic and shared environmental factors, although it was no longer significant after further controlling for age and sex, with older twins more likely to report lower levels of anxiety and females more likely to report higher levels of anxiety. Conclusions Strategies to mitigate the COVID-19 pandemic may be impacting physical activity and mental health, with those experiencing a decrease in physical activity also having higher levels of stress and anxiety. These relationships are confounded by genetic and shared environmental factors, in the case of stress, and age and sex, in the case of anxiety.
83 citations
TL;DR: An approach to metropolitan region-specific calibration models for low-cost sensors that can be used with caution for exposure measurement in epidemiological studies are described.
81 citations
TL;DR: The findings suggest that individuals’ mental health may be associated with changes in alcohol use during the COVID-19 pandemic, where twins with higher levels of stress and anxiety were more likely to report an increase in alcohol consumption.
Abstract: The novel coronavirus (COVID-19) has impacted the lives of people worldwide since being declared a pandemic on March 11, 2020. Social restrictions aimed at flattening the curve may be associated with an increase in stress and anxiety, which may increase the use of alcohol as a coping mechanism. The objective of this study was to examine if stress and anxiety were associated with changes in alcohol use in a sample of adult twins. Twins allowed us to control for genetic and shared environmental factors that would confound the alcohol - mental health relationship. Twins (N = 3,971; 909 same-sex pairs) from the Washington State Twin Registry (WSTR) completed an online survey examining several health-related behaviors and outcomes and their self-reported changes due to COVID-19. About 14% of the respondents reported an increase in alcohol use. We found an association between both stress and anxiety and increased alcohol use, where twins with higher levels of stress and anxiety were more likely to report an increase in alcohol consumption. The associations were small and confounded by between-family factors and demographic characteristics. However, there was no significant difference in stress or anxiety levels between non-drinkers and those who reported no change in alcohol use. Our findings suggest that individuals' mental health may be associated with changes in alcohol use during the COVID-19 pandemic.
63 citations
TL;DR: Investigating how exposure to one's everyday natural outdoor environments over one week influenced mood among residents of four European cities found increasing evidence of psychological and mental health benefits of exposure to natural outdoor environment, especially among urban populations such as those included in this study.
47 citations
TL;DR: The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with high spatiotemporal resolutions based on statistical models.
44 citations
TL;DR: In this paper, a modified model was designed to measure burnout, with exhaustion and disengagement among unskilled construction workers taken into consideration, and partial least squares structural equation modeling was applied to analyze data collected at the task and individual levels.
39 citations
TL;DR: This study quantifies the presence and concentrations of PAHs with low molecular weight and higher molecular weight, i.e., smaller and larger than Pyrene, in combustion-generated PM using excitation-emission matrix (EEM) fluorescence spectroscopy.
Abstract: Analysis of particulate matter (PM) is important for the assessment of human exposures to potentially harmful agents, notably combustion-generated PM. Specifically, polycyclic aromatic hydrocarbons (PAHs) found in ultrafine PM have been linked to cardiovascular diseases and carcinogenic and mutagenic effects. In this study, we quantify the presence and concentrations of PAHs with lower molecular weight (LMW, 126 < MW < 202) and higher molecular weight (HMW, 226 < MW < 302), i.e., smaller and larger than Pyrene, in combustion-generated PM using excitation-emission matrix (EEM) fluorescence spectroscopy. Laboratory combustion PM samples were generated in a laminar diffusion inverted gravity flame reactor (IGFR) operated on ethylene and ethane. Fuel dilution by Ar in 0% to 90% range controlled the flame temperature. The colder flames result in lower PM yields however, the PM PAH content increases significantly. Temperature thresholds for PM transition from low to high organic carbon content were characterized based on the maximum flame temperature (Tmax,c ∼ 1791 to 1857 K) and the highest soot luminosity region temperature (T*c ∼ 1600 to 1650K). Principal component regression (PCR) analysis of the EEM spectra of IGFR samples correlates to GCMS data with R2 = 0.988 for LMW and 0.998 for HMW PAHs. PCR-EEM analysis trained on the IGFR samples was applied to PM samples from woodsmoke and diesel exhaust, the model accurately predicts HMW PAH concentrations with R2 = 0.976 and overestimates LMW PAHs.
27 citations
TL;DR: In this article, the authors assessed community factors that were associated with COVID-19 testing and test positivity at the census tract level for the Seattle, King County, Washington region at the summer peak of infection in July 2020.
Abstract: Individual-level Coronavirus Disease 2019 (COVID-19) case data suggest that certain populations may be more impacted by the pandemic. However, few studies have considered the communities from which positive cases are prevalent, and the variations in testing rates between communities. In this study, we assessed community factors that were associated with COVID-19 testing and test positivity at the census tract level for the Seattle, King County, Washington region at the summer peak of infection in July 2020. Multivariate Poisson regression was used to estimate confirmed case counts, adjusted for testing numbers, which were associated with socioeconomic status (SES) indicators such as poverty, educational attainment, transportation cost, as well as with communities with high proportions of people of color. Multivariate models were also used to examine factors associated with testing rates, and found disparities in testing for communities of color and communities with transportation cost barriers. These results demonstrate the ability to identify tract-level indicators of COVID-19 risk and specific communities that are most vulnerable to COVID-19 infection, as well as highlight the ongoing need to ensure access to disease control resources, including information and education, testing, and future vaccination programs in low-SES and highly diverse communities.
19 citations
TL;DR: It is suggested that biomass burning is an important contributor to PM2.5 in the wintertime, and emissions associated with diesel and soot are important contributors in the fall; however, the variety of emissions sources and combustion conditions present in this region may limit the utility of traditional interpretations of aethalometer data.
19 citations
TL;DR: A method for identifying PM source using excitation emission matrix (EEM) fluorescence spectroscopy and a machine learning algorithm and found the algorithm was effective in some cases but would require a training data set containing more samples to be more broadly applicable.
TL;DR: The feasibility of in-situ calibration of instruments for fleet vehicle-based mobile monitoring of ultrafine particles (UFPs) and black carbon (BC) by comparing rendezvous vehicles' measurements is examined and the extension of this approach to an instrumented fleet of mobile monitoring vehicles is discussed.
Abstract: This study examines the feasibility of the in situ calibration of instruments for fleet vehicle-based mobile monitoring of ultrafine particles (UFPs) and black carbon (BC) by comparing rendezvous vehicle measurements. Two vehicles with identical makes and models of UFP and BC monitors as well as GPS receivers were sampled within 140 m of each other for 2 h in total during winter in Seattle, Washington. To identify an optimal intervehicle distance for rendezvous calibration, 6 different buffers within 0-140 m for UFP monitors and 5 different buffers within 0-90 m for BC monitors were chosen, and the results of calibration were compared against a reference scenario, which consisted of mobile colocation measurements with both sets of the UFP and BC monitors deployed in one of the vehicles. Results indicate that the optimal distances for rendezvous calibration are 10-80 m for UFP monitors and 0-30 m for BC monitors. In comparison with the mobile colocation calibration, the rendezvous calibration shows a normalized root mean squared deviation of 6-14% and a normalized mean absolute deviation of 4-8% for these monitors. Criteria for applying a rendezvous calibration approach are presented, and an extension of this approach to an instrumented fleet of mobile monitoring vehicles is discussed.
TL;DR: The spatiotemporal pattern of exposures is consistent with the precision agriculture framework and is foundational to addressing equity in rural agricultural settings and inform efforts about highest risk areas, times of year, and data availability in rural areas.
Abstract: Objectives: To evaluate the combined burden of heat and air quality exposure in Washington State agriculture by (1) characterizing the spatiotemporal pattern of heat and PM2.5 exposures during wildfire seasons; (2) describing the potential impact of these combined exposures on agricultural worker populations; and (3) identifying data gaps for addressing this burden in rural areas. METHODS We combined county-level data to explore data availability and estimate the burden of heat and PM2.5 co-exposures for Washington agricultural workers from 2010 to 2018. Quarterly agricultural worker population estimates were linked with data from a weather station network and ambient air pollution monitoring sites. A geographical information system displayed counties, air monitoring sites, agricultural crops, and images from a smoke dispersion model during recent wildfire events. RESULTS We found substantial spatial and temporal variability in high heat and PM2.5 exposures. The largest peaks in PM2.5 exposures tended to occur when the heat index was around 85°F and during summers when there were wildfires. Counties with the largest agricultural populations tended to have the greatest concurrent high heat and PM2.5 exposures, and these exposures tended to be highest during the third quarter (July-September), when population counts were also highest. Additionally, we observed limited access to local air quality information in certain rural areas. CONCLUSION Our findings inform efforts about highest risk areas, times of year, and data availability in rural areas. Understanding the spatiotemporal pattern of exposures is consistent with the precision agriculture framework and is foundational to addressing equity in rural agricultural settings.
TL;DR: A network of over 40 air sensors in Imperial County, CA, which is delivering real-time data to local communities on levels of particulate matter, was developed by comparing the low-cost sensor readings to regulatory monitors for 4 years of operation (2015–2018) on a network-wide basis.
Abstract: Air monitoring networks developed by communities have potential to reduce exposures and affect environmental health policy, yet there have been few performance evaluations of networks of these sensors in the field. We developed a network of over 40 air sensors in Imperial County, CA, which is delivering real-time data to local communities on levels of particulate matter. We report here on the performance of the Network to date by comparing the low-cost sensor readings to regulatory monitors for 4 years of operation (2015–2018) on a network-wide basis. Annual mean levels of PM10 did not differ statistically from regulatory annual means, but did for PM2.5 for two out of the 4 years. R2s from ordinary least square regression results ranged from 0.16 to 0.67 for PM10, and increased each year of operation. Sensor variability was higher among the Network monitors than the regulatory monitors. The Network identified a larger number of pollution episodes and identified under-reporting by the regulatory monitors. The participatory approach of the project resulted in increased engagement from local and state agencies and increased local knowledge about air quality, data interpretation, and health impacts. Community air monitoring networks have the potential to provide real-time reliable data to local populations.
TL;DR: 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.
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 < 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.
TL;DR: Because wildfire smoke episodes are likely to continue impacting the Pacific Northwest in years to come, continued preparedness and mitigations to reduce exposures to wildfire smoke are necessary to avoid this excess health burden.
Abstract: Major wildfires that started in the summer of 2020 along the west coast of the U.S. have made PM2.5 concentrations in cities in this region rank among the highest in the world. Regions of Washington were impacted by active wildfires in the state, and by aged wood smoke transported from fires in Oregon and California. This study aims to assess the population health impact of increased PM2.5 concentrations attributable to the wildfire. Average daily PM2.5 concentrations for each county before and during the 2020 Washington wildfire episode were obtained from the Washington Department of Ecology. Utilizing previously established associations of short-term mortality for PM2.5, we estimated excess mortality for Washington attributable to the increased PM2.5 levels. On average, PM2.5 concentrations increased 91.7 μg/m3 during the wildfire episode. Each week of wildfire smoke exposures was estimated to result in 87.6 (95% CI: 70.9, 103.1) cases of increased all-cause mortality, 19.1 (95% CI: 10.0, 28.2) increased cardiovascular disease deaths, and 9.4 (95% CI: 5.1, 13.5) increased respiratory disease deaths. Because wildfire smoke episodes are likely to continue impacting the Pacific Northwest in future years, continued preparedness and mitigations to reduce exposures to wildfire smoke are necessary to avoid this excess health burden.
TL;DR: It is important for clinicians to know that if the patient has type 1 diabetes and is smoking, a preemptive action to treat high glycated hemoglobin levels should not necessarily be treatment intensification due to the risk of hypoglycemia.
Abstract: Background:The prevalence of smoking and diabetes is increasing in many developing countries. The aim of this study was to investigate the association of smoking with inadequate glycemic control an...
19 Feb 2020
TL;DR: Using food recall data collected from over 8000 Americans, Machine Learning was used to classify a person’s demographic characteristics based on the foods they consumed in a 24-h period to provide tailored recommendations for healthier food options based on low-calorie and low-fat options.
Abstract: Using food recall data collected from over 8000 Americans, we used Machine Learning to classify a person’s demographic characteristics (age, gender, race, and income) based on the foods they consumed in a 24-h period. The best-performing models predicted gender correctly 61%, race 44%, and age 43% of the time on independent validation data. The model was subsequently used to provide tailored recommendations for healthier food options based on low-calorie and low-fat options for specific food groups that are typically consumed by other Americans that match the person’s demographics. This system is part of a larger Smart Human-Centered System that assists users in recording the foods they consume, recognizes nutritional content of the food, offers tailored recommendations for consuming healthier foods, and tracking behavior change over time.
TL;DR: Overall, it is 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.
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 <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 <0.01). We observed a weak but statistically significant correlation between PMC and total terpene mass concentrations (rho = 0.42, P < 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.
01 Sep 2020
TL;DR: This work used machine learning to evaluate and compare generalized linear, stochastic gradient boosting, random forest, and neural network model performance on predicting cardiovascular risk factors, such as hypertension, body mass index, and total cholesterol level on 5,992 adults in the US National Health and Nutrition Examination Survey (NHANES).
Abstract: Cardiovascular disease is a major global health burden. Machine learning may be used on big data from national surveys to develop models that predict various cardiovascular risk factors. We used machine learning to evaluate and compare generalized linear, stochastic gradient boosting, random forest, and neural network model performance on predicting cardiovascular risk factors, such as hypertension, body mass index, and total cholesterol level on 5,992 adults in the US National Health and Nutrition Examination Survey (NHANES). The highest accuracy of 73% was found for predicting hypertension status, using a random forest model on a combination of demographic, diet and physical activity behavior, and mental state predictor variables. We demonstrate the use of the machine learning model through the development of an Application Programming Interface (API), which is called by a mHealth smartphone app and web interface. This work has promise for future intervention studies that assess how users respond to feedback on cardiovascular risk predictions, and which could evaluate improvements in costeffective cardiovascular healthcare.
TL;DR: Preliminary results suggest robust reproducibility (r = 0.99) and limits of detection appropriate for longer-term (~1–3 months) monitoring in households that use solid fuels, and suggest high precision.
Abstract: We propose a low-cost passive method for monitoring long-term average levels of light-absorbing carbon air pollution in polluted indoor environments Building on prior work, the method here estimates the change in reflectance of a passively exposed surface through analysis of digital images To determine reproducibility and limits of detection, we tested low-cost passive samplers with exposure to kerosene smoke in the laboratory and to environmental pollution in 20 indoor locations Preliminary results suggest robust reproducibility (r = 099) and limits of detection appropriate for longer-term (~1–3 months) monitoring in households that use solid fuels The results here suggest high precision; further testing involving “gold standard” measurements is needed to investigate accuracy