A Non-Clinical Referral Tool to Help Identify Problematic Child Sexual Behavior: Development, Training, and Initial User Feedback.

Early identification of children and youth who engage in problematic sexual behavior is important for all parties involved, such as children who exhibit and are impacted by the behavior. There are several reliable and valid identification tools that can be used to recognize problematic sexual behavior in children and youth (PSB-CY) in clinical practice; however, professionals who work with children in non-clinical settings (i.e., child development centers, youth programs, and schools) often have limited resources and tools when they encounter PSB-CY. This paper describes the development, content, and user feedback of a referral tool (RT) that was designed to help identify incidents of PSB-CY for use with military agencies and schools. Specifically, the RT was designed to help professionals, who may have observed or who may have been made aware of sexual behaviors in children and youth, organize their observations of the behavior in alignment with evidence-based information about PSB-CY and consistently document these occurrences. The RT guides users in determining if the observed behavior is normative, cautionary, or problematic and promotes informed decisions about whether the behavior needs to be referred to those who have experience using clinical tools for further review and the identification of next steps for supporting the children and families involved. Early adopters provided feedback on the use of the RT. The feedback suggested that the tool was user-friendly, understandable, and helpful as they made objective decisions about how to identify and handle referrals of PSB-CY.

Mortality associated with wildfire smoke exposure in Washington state, 2006-2017: a case-crossover study.

BACKGROUND: Wildfire events are increasing in prevalence in the western United States. Research has found mixed results on the degree to which exposure to wildfire smoke is associated with an increased risk of mortality. METHODS: We tested for an association between exposure to wildfire smoke and non-traumatic mortality in Washington State, USA. We characterized wildfire smoke days as binary for grid cells based on daily average PM2.5 concentrations, from June 1 through September 30, 2006-2017. Wildfire smoke days were defined as all days with assigned monitor concentration above a PM2.5 value of 20.4 µg/m3, with an additional set of criteria applied to days between 9 and 20.4 µg/m3. We employed a case-crossover study design using conditional logistic regression and time-stratified referent sampling, controlling for humidex. RESULTS: The odds of all-ages non-traumatic mortality with same-day exposure was 1.0% (95% CI: - 1.0 - 4.0%) greater on wildfire smoke days compared to non-wildfire smoke days, and the previous day's exposure was associated with a 2.0% (95% CI: 0.0-5.0%) increase. When stratified by cause of mortality, odds of same-day respiratory mortality increased by 9.0% (95% CI: 0.0-18.0%), while the odds of same-day COPD mortality increased by 14.0% (95% CI: 2.0-26.0%). In subgroup analyses, we observed a 35.0% (95% CI: 9.0-67.0%) increase in the odds of same-day respiratory mortality for adults ages 45-64. CONCLUSIONS: This study suggests increased odds of mortality in the first few days following wildfire smoke exposure. It is the first to examine this relationship in Washington State and will help inform local and state risk communication efforts and decision-making during future wildfire smoke events.

A Monte Carlo method for summing modeled and background pollutant concentrations.

Air quality analyses for permitting new pollution sources often involve modeling dispersion of pollutants using models such as AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model). Representative background pollutant concentrations must be added to modeled concentrations to determine compliance with air quality standards. Summing 98th (or 99th) percentiles of two independent distributions that are unpaired in time overestimates air quality impacts and could needlessly burden sources with restrictive permit conditions. This problem is exacerbated when emissions and background concentrations peak during different seasons. Existing methods addressing this matter either require much input data, or disregard source and background seasonality, or disregard the variability of the background by utilizing a single concentration for each season, month, hour-of-day, day-of-week, or wind direction. Availability of representative background concentrations are another limitation. Here the authors report on work to improve permitting analyses, with the development of (1) daily gridded, background concentrations interpolated from 12-km CMAQ (Community Multiscale Air Quality Model) forecasts and monitored data. A two-step interpolation reproduced measured background concentrations to within 6.2%; and (2) a Monte Carlo (MC) method to combine AERMOD output and background concentrations while respecting their seasonality. The MC method randomly combines, with replacement, data from the same months and calculates 1000 estimates of the 98th or 99th percentiles. The design concentration of background + new source is the median of these 1000 estimates. It was found that the AERMOD design value (DV) + background DV lay at the upper end of the distribution of these one thousand 99th percentiles, whereas measured DVs were at the lower end. This MC method sits between these two metrics and is sufficiently protective of public health in that it overestimates design concentrations somewhat. The authors also calculated probabilities of exceeding specified thresholds at each receptor, better informing decision makers of new source air quality impacts. The MC method is executed with an R script, which is available freely upon request. IMPLICATIONS: Summing representative background pollutant concentrations with air dispersion model output using a Monte Carlo method that respects the seasonality of each provides for more robust and scientifically defensible air quality analyses in support of permit applications. This work provides applicants a method to demonstrate compliance with National Ambient Air Quality Standards and avoid emission controls that might be based on overly conservative analyses. It also calculates the probability of exceeding the standard, allowing regulators to make more informed permitting decisions.

Neighborhood-Scale Spatial Models of Diesel Exhaust Concentration Profile Using 1-Nitropyrene and Other Nitroarenes.

With emerging evidence that diesel exhaust exposure poses distinct risks to human health, the need for fine-scale models of diesel exhaust pollutants is growing. We modeled the spatial distribution of several nitrated polycyclic aromatic hydrocarbons (NPAHs) to identify fine-scale gradients in diesel exhaust pollution in two Seattle, WA neighborhoods. Our modeling approach fused land-use regression, meteorological dispersion modeling, and pollutant monitoring from both fixed and mobile platforms. We applied these modeling techniques to concentrations of 1-nitropyrene (1-NP), a highly specific diesel exhaust marker, at the neighborhood scale. We developed models of two additional nitroarenes present in secondary organic aerosol: 2-nitropyrene and 2-nitrofluoranthene. Summer predictors of 1-NP, including distance to railroad, truck emissions, and mobile black carbon measurements, showed a greater specificity to diesel sources than predictors of other NPAHs. Winter sampling results did not yield stable models, likely due to regional mixing of pollutants in turbulent weather conditions. The model of summer 1-NP had an R(2) of 0.87 and cross-validated R(2) of 0.73. The synthesis of high-density sampling and hybrid modeling was successful in predicting diesel exhaust pollution at a very fine scale and identifying clear gradients in NPAH concentrations within urban neighborhoods.

A multi-level examination of how the organizational context relates to readiness to implement prevention and evidence-based programming in community settings.

Prior theoretical and empirical research suggests that multiple aspects of an organization's context are likely related to a number of factors, from their interest and ability to adopt new programming, to client outcomes. A limited amount of the prior research has taken a more community-wide perspective by examining factors that associate with community readiness for change, leaving how these findings generalize to community organizations that conduct prevention or positive youth development programs unknown. Thus for the current study, we examined how the organizational context of the Cooperative Extension System (CES) associates with current attitudes and practices regarding prevention and evidence-based programming. Attitudes and practices have been found in the empirical literature to be key indicators of an organization's readiness to adopt prevention and evidence-based programming. Based on multi-level mixed models, results indicate that organizational management practices distinct from program delivery may affect an organization's readiness to adopt and implement new prevention and evidence-based youth programs, thereby limiting the potential public health impact of evidence-based programs. Openness to change, openness of leadership, and communication were the strongest predictors identified within this study. An organization's morale was also found to be a strong predictor of an organization's readiness. The findings of the current study are discussed in terms of implications for prevention and intervention.