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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. [ABSTRACT FROM AUTHOR]

Background: Schistosomiasis infection, contracted through contact with contaminated water, is a global public health concern. In this paper we analyze data from a retrospective study reporting water contact and schistosomiasis infection status among 1011 individuals in rural China. We present semi-parametric methods for identifying risk factors through a comparison of three analysis approaches: a prediction-focused machine learning algorithm, a simple maineffects multivariable regression, and a semi-parametric variable importance (VI) estimate inspired by a causal population intervention parameter. Results: The multivariable regression found only tool washing to be associated with the outcome, with a relative risk of 1.03 and a 95% confidence interval (CI) of 1.01-1.05. Three types of water contact were found to be associated with the outcome in the semi-parametric VI analysis: July water contact (VI estimate 0.16, 95% CI 0.11-0.22), water contact from tool washing (VI estimate 0.88, 95% CI 0.80-0.97), and water contact from rice planting (VI estimate 0.71, 95% CI 0.53- 0.96). The July VI result, in particular, indicated a strong association with infection status - its causal interpretation implies that eliminating water contact in July would reduce the prevalence of schistosomiasis in our study population by 84%, or from 0.3 to 0.05 (95% CI 78%-89%). Conclusions: The July VI estimate suggests possible within-season variability in schistosomiasis infection risk, an association not detected by the regression analysis. Though there are many limitations to this study that temper the potential for causal interpretations, if a high-risk time period could be detected in something close to real time, new prevention options would be opened. Most importantly, we emphasize that traditional regression approaches are usually based on arbitrary pre-specified models, making their parameters difficult to interpret in the context of realworld applications. Our results support the practical application of analysis approaches that, in contrast, do not require arbitrary model pre-specification, estimate parameters that have simple public health interpretations, and apply inference that considers model selection as a source of variation. [ABSTRACT FROM AUTHOR]

Vehicle counts and truck percentages are important input variables in both noise pollution and air quality models, but the acquisition of these variables through fixed-point methods can be expensive, labor-intensive, and provide incomplete spatial sampling. The increasing availability and decreasing cost of high spatial resolution imagery provides an opportunity to improve the descriptive ability of traffic volume analysis. This study describes an object-based classification technique to extract vehicle volumes and vehicle type distributions from aerial photos sampled throughout a large metropolitan area. We developed rules for optimizing segmentation parameters, and used feature space optimization to choose classification attributes and develop fuzzy-set memberships for classification. Vehicles were extracted from street areas with 91.8 percent accuracy. Furthermore, separation of vehicles into classes based on car, medium-sized truck, and buses/heavy truck definitions was achieved with 87.5 percent accuracy. We discuss implications of these results for traffic volume analysis and parameterization of existing noise and air pollution models, and suggest future work for traffic assessment using high-resolution remotely-sensed imagery. [ABSTRACT FROM AUTHOR]

During the Escherichia coli O157:H7 outbreak in 2006 in the United States, the primary strategy to prevent illness was to advise consumers not to eat spinach. No widespread warnings were issued about preventing person-to-person (secondary) transmission. A disease transmission model, fitted to the current data, was used to investigate likely reductions in illnesses that could result from interventions to prevent secondary transmission. The model indicates that exposure to contaminated spinach occurred early in the outbreak and that secondary transmission was similar to that in previous E. coli outbreaks (≈12%). The model also suggests that even a modestly effective strategy to interrupt secondary transmission (prevention of only 2%-3% of secondary illnesses) could result in a reduction of ≈5%-11% of symptomatic cases. This analysis supports the use of widespread public health messages during outbreaks of E. coli O157:H7 with specific advice on how to interrupt secondary transmission. [ABSTRACT FROM AUTHOR]

Background: Environmental racism, community stressors, and age-related susceptibility play a significant role in environmental inequality. The goal of this article was to use an inequality index (II) to assess the level of equality in environmental threats and hazards based on race, poverty, and age in Washington State. Methods: Using the Washington Environmental Health Disparities Map, we quantified the level of disproportionate burdens on communities with greater populations of people of color, people in poverty, children younger than 5, and people older than 65 using 3 cumulative environmental indices and 10 individual environmental indicators. Results: Census tracts with a higher proportion of people of color and those with people living below 185% federal poverty levels were found to be disproportionately burdened by environmental threats (II = -0.175 and II = -0.167, respectively, p  < 0.001). Individual environmental indicators were found to disproportionately burden communities of color and low-income communities. Children younger than 5 were also disproportionately burdened by cumulative environmental indices (II = -0.076, p  < 0.001) and individual indicators. Our analysis did not show disproportionate burden of environmental health threats based on the proportion of people older than 65 (II = 0.124, p  < 0.001). Discussion: The disproportionate burden of the cumulative environmental threats on communities of color and low-income communities in this study corroborates similar analyses. These findings can be applied in policy and regulatory actions to correct the distributive environmental disparities. Conclusion: We found much higher burdens among historically marginalized communities and children who are more susceptible to environmental threats and hazards., Competing Interests: No competing financial interests exist., (Copyright 2021, Mary Ann Liebert, Inc., publishers.)

Background: Physical activity can be affected by both meteorological conditions and surrounding greenness, but few studies have evaluated the effects of these environmental factors on physical activity simultaneously. This multi-city comparative study aimed to assess the synergetic effects of apparent temperature and surrounding greenness on physical activity in four European cities. Specifically, we aimed to identify an interaction between surrounding greenness and apparent temperature in the effects on physical activity., Methods: Data were collected from 352 adult residents of Barcelona (Spain), Stoke-on-Trent (United Kingdom), Doetinchem (The Netherlands), and Kaunas (Lithuania) as part of the PHENOTYPE study. Participants wore a smartphone for seven consecutive days between May-December 2013 and provided additional sociodemographic survey data. Hourly average physical activity (Metabolic Equivalent of Task (MET)) and surrounding greenness (NDVI) were derived from the Calfit mobile application collecting accelerometer and location data. Hourly apparent temperature was calculated from temperature and relative humidity, which were obtained from local meteorological stations along with other meteorological covariates (rainfall, windspeed, and sky darkness). We assessed the interaction effects of apparent temperature and surrounding greenness on hourly physical activity for each city using linear mixed models, while adjusting for meteorological, demographic, and time-related variables., Results: We found significant interactions between apparent temperature and surrounding greenness on hourly physical activity in three of four cities, aside from the coastal city of Barcelona. Significant quadratic effects of apparent temperature were found in the highest level of surrounding greenness for Stoke-on-Trent and Doetinchem, with 4% decrease in median MET observed for a 10°C departure from optimal temperature (15.2°C and 14.6°C, respectively). Significant linear effects were found for higher levels of surrounding greenness in Kaunas, whereby an increase of 10°C was associated with ∼4% increase in median MET., Conclusion: Apparent temperature and surrounding greenness interacted in the effect on hourly physical activity across three of four European cities, with varying effect between cities. While quadratic effects of temperature suggest diminishing levels of physical activity in the highest greenness levels in cities of temperate climates, the variation in surrounding greenness between cities could be further explored, particularly by looking at indoor-outdoor locations. The study findings support the need for evidence-based physical activity promotion and urban design., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Regulatory monitoring networks are often too sparse to support community-scale PM 2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM 2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM 2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM 2.5 concentrations at a 1-km spatial resolution using three different PM 2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM 2.5 prediction with an increase of cross-validation (CV) R 2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM 2.5 . The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Communities across Washington State have expressed the need for neighborhood-level information on the cumulative impact of environmental hazards and social conditions to illuminate disparities and address environmental justice issues. Many existing mapping tools have not explicitly integrated community voice and lived experience as an integral part of their development. The goals of this project were to create a new community-academic-government partnership to collect and summarize community concerns and to develop a publicly available mapping tool that ranks relative environmental health disparities for populations across Washington State. Using a community-driven framework, we developed the Washington Environmental Health Disparities Map, a cumulative environmental health impacts assessment tool. Nineteen regularly updated environmental and population indicators were integrated into the geospatial tool that allows for comparisons of the cumulative impacts between census tracts. This interactive map provides critical information for the public, agencies, policymakers, and community-based organizations to make informed decisions. The unique community-academic-government partnership and the community-driven framework can be used as a template for other environmental and social justice mapping endeavors.

Background: Traffic-related noise is a growing public health concern in developing and developed countries due to increasing vehicle traffic. Epidemiological studies have reported associations between noise exposure and high blood pressure, increased risk of hypertension and heart disease, and stress induced by sleep disturbance and annoyance. These findings motivate the need for regular noise assessments within urban areas. This paper assesses the relationships between traffic and noise in three US cities., Methods: Noise measurements were conducted in downtown areas in three cities in the United States: Atlanta, Los Angeles, and New York City. For each city, we measured ambient noise levels, and assessed their correlation with simultaneously measured vehicle counts, and with traffic data provided by local Metropolitan Planning Organizations (MPO). Additionally, measured noise levels were compared to noise levels predicted by the Federal Highway Administration's Traffic Noise Model using (1) simultaneously measured traffic counts or (2) MPO traffic data sources as model input., Results: We found substantial variations in traffic and noise within and between cities. Total number of vehicle counts explained a substantial amount of variation in measured ambient noise in Atlanta (78%), Los Angeles (58%), and New York City (62%). Modeled noise levels were moderately correlated with measured noise levels when observed traffic counts were used as model input. Weaker correlations were found when MPO traffic data was used as model input., Conclusions: Ambient noise levels measured in all three cities were correlated with traffic data, highlighting the importance of traffic planning in mitigating noise-related health effects. Model performance was sensitive to the traffic data used as input. Future noise studies that use modeled noise estimates should evaluate traffic data quality and should ideally include other factors, such as local roadway, building, and meteorological characteristics., (Copyright © 2014 Elsevier Inc. All rights reserved.)

While child and adolescent obesity is a serious public health concern, few studies have utilized parameters based on the causal inference literature to examine the potential impacts of early intervention. The purpose of this analysis was to estimate the causal effects of early interventions to improve physical activity and diet during adolescence on body mass index (BMI), a measure of adiposity, using improved techniques. The most widespread statistical method in studies of child and adolescent obesity is multi-variable regression, with the parameter of interest being the coefficient on the variable of interest. This approach does not appropriately adjust for time-dependent confounding, and the modeling assumptions may not always be met. An alternative parameter to estimate is one motivated by the causal inference literature, which can be interpreted as the mean change in the outcome under interventions to set the exposure of interest. The underlying data-generating distribution, upon which the estimator is based, can be estimated via a parametric or semi-parametric approach. Using data from the National Heart, Lung, and Blood Institute Growth and Health Study, a 10-year prospective cohort study of adolescent girls, we estimated the longitudinal impact of physical activity and diet interventions on 10-year BMI z-scores via a parameter motivated by the causal inference literature, using both parametric and semi-parametric estimation approaches. The parameters of interest were estimated with a recently released R package, ltmle, for estimating means based upon general longitudinal treatment regimes. We found that early, sustained intervention on total calories had a greater impact than a physical activity intervention or non-sustained interventions. Multivariable linear regression yielded inflated effect estimates compared to estimates based on targeted maximum-likelihood estimation and data-adaptive super learning. Our analysis demonstrates that sophisticated, optimal semiparametric estimation of longitudinal treatment-specific means via ltmle provides an incredibly powerful, yet easy-to-use tool, removing impediments for putting theory into practice.

Point-prevalence recording of the distribution of tropical parasitic diseases at village level is usually sufficient for general monitoring and surveillance. Whilst within-village spatial patterning of diseases exists, and can be important, mapping infected cases in a household-by-household setting is arduous and time consuming. With the development of low-cost GPS-data loggers (< £40) and available GoogleEarth(TM) satellite imagery, we present a field-applicable method based on crowdsourcing for rapid identification of infected cases (intestinal schistosomiasis, malaria and hookworm) by household. A total of 126 mothers with their 247 preschool children from Bukoba village (Mayuge District, Uganda) were examined with half of these mothers given a GPS-data logger to walk home with, returning the unit the same day for data off-loading, after which, households were assigned GPS coordinates. A satellite image of Bukoba was annotated with households denoting the infection status of each mother and child. General prevalence of intestinal schistosomiasis, malaria and hookworm in mothers and children was: 27.2 vs 7.7%, 28.6 vs 87.0% and 60.0 vs 22.3%, respectively. Different spatial patterns of disease could be identified likely representing the intrinsic differences in parasite biology and interplay with human behaviour(s) across this local landscape providing a better insight into reasons for disease micro-patterning., (Crown Copyright © 2011. Published by Elsevier Ltd. All rights reserved.)

Social interaction and physical interconnections between populations can influence the spread of parasites. The role that these pathways play in sustaining the transmission of parasitic diseases is unclear, although increasingly realistic metapopulation models are being used to study how diseases persist in connected environments. We use a mathematical model of schistosomiasis transmission for a distributed set of heterogeneous villages to show that the transport of parasites via social (host movement) and environmental (parasite larvae movement) pathways has consequences for parasite control, spread and persistence. We find that transmission can be sustained regionally throughout a group of connected villages even when individual village conditions appear not to support endemicity. Optimum transmission is determined by an interplay between different transport pathways, and not necessarily by those that are the most dispersive (e.g. disperse social contacts may not be optimal for transmission). We show that the traditional targeting of villages with high infection, without regard to village interconnections, may not lead to optimum control. These findings have major implications for effective disease control, which needs to go beyond considering local variations in disease intensity, to also consider the degree to which populations are interconnected.

The real exposure to many of the tropical diseases is difficult to assess at the individual-level due to problems of recall, self-reported diaries, personnel requirements, and altered behaviour related to observation. We present a study in an area endemic for Schistosoma japonicum in which global positioning system (GPS) receivers were used for personal time-activity monitoring to assess water-contact associated with schistosomiasis transmission. The study subjects were equipped with a vest with an embedded GPS receiver for 8-hour periods. The resulting data were used to create hourly time-activity maps, which were subsequently used in interviews to ascertain the timing and location of the water-contacts. Based on a sample of twenty-four 8-hour person-days we found that individuals averaged 1.4 +/- 1.2 water-contacts per day, and were surprisingly mobile, with 39% of the participants having spent time out of the village (0.8 +/- 1.4 hours outside of village). Such mobility suggests the need for further research into social patterns that may facilitate the spread of parasites, and contribute to sustained transmission. We present an assessment of the accuracy of cheaper commercially- available GPS units that have shown promise in such applications. We feel that a speed-filtering method is effective in managing measurement errors commonly encountered during personal activity monitoring with GPS. We conclude that personal GPS units can help reduce recall problems associated with other methods of assessing water-contact, and that they offer valuable insights into time-activity patterns that influence schistosomiasis transmission.