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Background: Empirical studies have demonstrated associations between ten original adverse childhood experiences (ACEs) and multiple health outcomes. Identifying expanded ACEs can capture the burden of other childhood adversities that may have important health implications., Objective: We sought to identify childhood adversities that warrant consideration as expanded ACEs. We hypothesized that experiencing expanded and original ACEs would be associated with poorer adult health outcomes compared to experiencing original ACEs alone., Participants: The 11,545 respondents of the National Longitudinal Surveys (NLS) and Child and Young Adult Survey were 48.9 % female, 22.7 % Black, 15.8 % Hispanic, 36.1 % White, 1.7 % Asian/Native Hawaiian/Pacific Islander/Native American/Native Alaskan, and 7.5 % Other., Methods: This study used regression trees and generalized linear models to identify if/which expanded ACEs interacted with original ACEs in association with six health outcomes., Results: Four expanded ACEs-basic needs instability, lack of parental love and affection, community stressors, and mother's experience with physical abuse during childhood -significantly interacted with general health, depressive symptom severity, anxiety symptom severity, and violent crime victimization in adulthood (all p-values <0.005). Basic needs instability and/or lack of parental love and affection emerged as correlates across multiple outcomes. Experiencing lack of parental love and affection and original ACEs was associated with greater anxiety symptoms (p = 0.022)., Conclusions: This is the first study to use supervised machine learning to investigate interaction effects among original ACEs and expanded ACEs. Two expanded ACEs emerged as predictors for three adult health outcomes and warrant further consideration in ACEs assessments., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: To operationalize an intersectionality framework using a novel statistical approach and with these efforts, improve the estimation of disparities in access (i.e., wait time to treatment entry) to opioid use disorder (OUD) treatment beyond race., Data Source: Sample of 941,286 treatment episodes collected in 2015-2017 in the United States from the Treatment Episodes Data Survey (TEDS-A) and a subset from California (n = 188,637) and Maryland (n = 184,276), states with the largest sample of episodes., Study Design: This retrospective subgroup analysis used a two-step approach called virtual twins. In Step 1, we trained a classification model that gives the probability of waiting (1 day or more). In Step 2, we identified subgroups with a higher probability of differences due to race. We tested three classification models for Step 1 and identified the model with the best estimation., Data Collection: Client data were collected by states during personal interviews at admission and discharge., Principal Findings: Random forest was the most accurate model for the first step of subgroup analysis. We found large variation across states in racial disparities. Stratified analysis of two states with the largest samples showed critical factors that augmented disparities beyond race. In California, factors such as service setting, referral source, and homelessness defined the subgroup most vulnerable to racial disparities. In Maryland, service setting, prior episodes, receipt of medication-assisted opioid treatment, and primary drug use frequency augmented disparities beyond race. The identified subgroups had significantly larger racial disparities., Conclusions: The methodology used in this study enabled a nuanced understanding of the complexities in disparities research. We found state and service factors that intersected with race and augmented disparities in wait time. Findings can help decision makers target modifiable factors that make subgroups vulnerable to waiting longer to enter treatment., (© 2021 The Authors. Health Services Research published by Wiley Periodicals LLC on behalf of Health Research and Educational Trust.)

Abstract: The quantification of carbon fluxes between the terrestrial biosphere and the atmosphere is of scientific importance and also relevant to climate-policy making. Eddy covariance flux towers provide continuous measurements of ecosystem-level exchange of carbon dioxide spanning diurnal, synoptic, seasonal, and interannual time scales. However, these measurements only represent the fluxes at the scale of the tower footprint. Here we used remotely sensed data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to upscale gross primary productivity (GPP) data from eddy covariance flux towers to the continental scale. We first combined GPP and MODIS data for 42 AmeriFlux towers encompassing a wide range of ecosystem and climate types to develop a predictive GPP model using a regression tree approach. The predictive model was trained using observed GPP over the period 2000–2004, and was validated using observed GPP over the period 2005–2006 and leave-one-out cross-validation. Our model predicted GPP fairly well at the site level. We then used the model to estimate GPP for each 1km×1km cell across the U.S. for each 8-day interval over the period from February 2000 to December 2006 using MODIS data. Our GPP estimates provide a spatially and temporally continuous measure of gross primary production for the U.S. that is a highly constrained by eddy covariance flux data. Our study demonstrated that our empirical approach is effective for upscaling eddy flux GPP data to the continental scale and producing continuous GPP estimates across multiple biomes. With these estimates, we then examined the patterns, magnitude, and interannual variability of GPP. We estimated a gross carbon uptake between 6.91 and 7.33PgCyr−1 for the conterminous U.S. Drought, fires, and hurricanes reduced annual GPP at regional scales and could have a significant impact on the U.S. net ecosystem carbon exchange. The sources of the interannual variability of U.S. GPP were dominated by these extreme climate events and disturbances. [Copyright &y& Elsevier]

Various approaches are used to subdivide large areas into regions containing streams that have similar reference or background water quality and that respond similarly to different factors. For many applications, such as establishing reference conditions, it is preferable to use physical characteristics that are not affected by human activities to delineate these regions. However, most approaches, such as ecoregion classifications, rely on land use to delineate regions or have difficulties compensating for the effects of land use. Land use not only directly affects water quality, but it is often correlated with the factors used to define the regions. In this article, we describe modifications to SPARTA (spatial regression-tree analysis), a relatively new approach applied to water-quality and environmental characteristic data to delineate zones with similar factors affecting water quality. In this modified approach, land-use-adjusted (residualized) water quality and environmental characteristics are computed for each site. Regression-tree analysis is applied to the residualized data to determine the most statistically important environmental characteristics describing the distribution of a specific water-quality constituent. Geographic information for small basins throughout the study area is then used to subdivide the area into relatively homogeneous environmental water-quality zones. For each zone, commonly used approaches are subsequently used to define its reference water quality and how its water quality responds to changes in land use. SPARTA is used to delineate zones of similar reference concentrations of total phosphorus and suspended sediment throughout the upper Midwestern part of the United States. [ABSTRACT FROM AUTHOR]

This article investigates the existence of multiple regimes in the U.S. economy during the 19231991 period. A technique known as regression tree analysis is applied to search for splits in the data, if any exist, rather than choosing a splitting point a priori as has been done in previous work. Using this technique, strong evidence for the existence of nonlinear behavior of U.S. output is found over this period. Monte Carlo results are presented to assess the significance of the regime changes that are found. [ABSTRACT FROM AUTHOR]

Certified organic dairies are required to utilize pastures for a portion of forage intake. Pasture species composition, management, and soil fertility are known to influence milk production, but have not been studied concurrently. We evaluated agronomic and management variables on 20 organic dairies in the upper midwestern United States to determine factors associated with potential pasture milk production. At each farm, two pastures were sampled just prior to grazing in June and September for species composition, productivity, and forage nutritive value. Soil samples and management information were collected in October. Potential milk production was calculated based on forage productivity, cell wall concentration and digestibility, and estimated dry matter intake by a 500 kg cow. A regression tree prioritized the factors associated with potential milk production. Improved legume cover exceeding 40% in June increased potential milk production by 97%. Non-improved grass cover <70% in June and September increased potential milk production by >75%. Maintaining residual sward height at 9 cm or greater throughout the year was also associated with increased milk production. Soil fertility explained little of the variability in milk production. Our results suggest prioritizing management of residual height and improved legume and non-improved grass cover are critical for high milk production from organic pastures in the Upper Midwest. • Of the species measured, the cover of improved legumes and non-improved grasses were most associated with milk production. • The sole management factor found to be associated with greater potential milk production was residual sward height. • Slope of the pasture was the only soil characteristic found to have a significant effect on potential milk production. [ABSTRACT FROM AUTHOR]

An approach to identify causal components of complex air pollution mixtures was explored. Rats and mice were exposed by inhalation 6 h daily for 1 week or 6 months to dilutions of simulated downwind coal emissions, diesel and gasoline exhausts and wood smoke. Organ weights, hematology, serum chemistry, bronchoalveolar lavage, central vascular and respiratory allergic responses were measured. Multiple additive regression tree (MART) analysis of the combined database ranked 45 exposure (predictor) variables for importance to models best fitting 47 significant responses. Single-predictor concentration-response data were examined for evidence of single response functions across all exposure groups. Replication of the responses by the combined influences of the two most important predictors was tested. Statistical power was limited by inclusion of only four mixtures, albeit in multiple concentrations each and with particles removed for some groups. Results gave suggestive or strong evidence of causation of 19 of the 47 responses. The top two predictors of the 19 responses included only 12 organic and 6 inorganic species or classes. An increase in red blood cell count of rats by ammonia and pro-atherosclerotic vascular responses of mice by inorganic gases yielded the strongest evidence for causation and the best opportunity for confirmation. The former was a novel finding; the latter was consistent with other results. The results demonstrated the plausibility of identifying putative causal components of highly complex mixtures, given a database in which the ratios of the components are varied sufficiently and exposures and response measurements are conducted using a consistent protocol.

The National Environmental Respiratory Center Program was initiated as an experiment to explore strategies for identifying the components of complex air pollution mixtures that cause health effects associated statistically with air pollution. A strategy involving multivariate analysis of a composition-concentration-response database was adopted. A novel database was created by exposing rodents daily for up to six months to one of four combustion-related mixtures and measuring respiratory, cardiovascular and general toxicological responses after one week or six months of exposure. The mixtures included multiple concentrations of diesel and gasoline engine exhaust, hardwood smoke and simulated downwind coal combustion emissions. After reporting the biological effects of each mixture and comparing effects among them, 47 significant effects were selected for multiple additive regression tree analysis to identify putative causal components. Although the four mixtures provided a database marginally sufficient for the analysis, the results suggested the putative causes of 19 significant effects with acceptable confidence. This article describes and critiques the Program and its strategy. The integrated results are presented in two accompanying papers, and mixture-specific results were presented in preceding papers, which are cited. The experiment demonstrated the potential utility of the general approach and identified certain cause-effect relationships for confirmatory studies. A follow-up study provided support for causation by the components implicated for one of those relationships. The advantages and disadvantages of the Program's management and funding strategies are discussed.