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Background: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-level factors, and SUD treatment engagement., Methods: This was a secondary analysis of the Global Appraisal of Individual Needs (GAIN) dataset and United States Census Bureau data utilizing random forest machine learning and generalized linear mixed modelling. Our sample (N = 15,873) included all people entering SUD treatment at GAIN sites from 2006 to 2012. Predictors included an array of demographic, psychosocial, treatment-specific, and clinical measures, as well as environment-level measures for the neighborhood in which patients received treatment., Results: Greater odds of treatment engagement were predicted by adolescent age and psychiatric comorbidity, and at the neighborhood-level, by low unemployment and high population density. Lower odds of treatment engagement were predicted by Black/African American race, and at the neighborhood-level by high rate of public assistance and high income inequality. Regardless of the degree of treatment engagement, individuals receiving treatment in areas with high unemployment, alcohol sale outlet concentration, and poverty had greater substance use and related problems at baseline. Although these differences reduced with treatment and over time, disparities remained., Conclusions: Neighborhood-level factors appear to play an important role in SUD treatment engagement. Regardless of whether individuals engage with treatment, greater loading on social determinants of health such as unemployment, alcohol sale outlet density, and poverty in the therapeutic landscape are associated with worse SUD treatment outcomes., Competing Interests: Declaration of competing interest David Eddie is on the scientific advisory boards of mental-healthcare companies ViviHealth and Innerworld and is a partner in Peer Recovery Consultants. The remaining authors declare that they have no known potential competing financial or personal interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Biodiversity continues to decline despite protected area expansion and global conservation commitments. Biodiversity losses occur in existing protected areas, yet common methods used to select protected areas ignore postimplementation threats that reduce effectiveness. We developed a conservation planning framework that considers the ongoing anthropogenic threats within protected areas when selecting sites and the value of planning for costly threat-mitigating activities (i.e., enforcement) at the time of siting decisions. We applied the framework to a set of landscapes that contained the range of possible correlations between species richness and threat. Accounting for threats and implementing enforcement activities increased benefits from protected areas without increasing budgets. Threat information was valuable in conserving more species per spending level even without enforcement, especially on landscapes with randomly distributed threats. Benefits from including threat information and enforcement were greatest when human threats peaked in areas of high species richness and were lowest where human threats were negatively associated with species richness. Because acquiring information on threats and using threat-mitigating activities are costly, our findings can guide decision-makers regarding the settings in which to pursue these planning steps., (© 2023 Society for Conservation Biology.)

Wildlife trafficking, whether local or transnational in scope, undermines sustainable development efforts, degrades cultural resources, endangers species, erodes the local and global economy, and facilitates the spread of zoonotic diseases. Wildlife trafficking networks (WTNs) occupy a unique gray space in supply chains-straddling licit and illicit networks, supporting legitimate and criminal workforces, and often demonstrating high resilience in their sourcing flexibility and adaptability. Authorities in different sectors desire, but frequently lack knowledge about how to allocate resources to disrupt illicit wildlife supply networks and prevent negative collateral impacts. Novel conceptualizations and a deeper scientific understanding of WTN structures are needed to help unravel the dynamics of interaction between disruption and resilience while accommodating socioenvironmental context. We use the case of ploughshare tortoise trafficking to help illustrate the potential of key advancements in interdisciplinary thinking. Insights herein suggest a significant need and opportunity for scientists to generate new science-based recommendations for WTN-related data collection and analysis for supply chain visibility, shifts in illicit supply chain dominance, network resilience, or limits of the supplier base.

Objective: This study used machine learning (ML) to test an empirically derived set of risk factors for marijuana use. Models were built separately for child welfare (CW) and non-CW adolescents in order to compare the variables selected as important features/risk factors., Method: Data were from a Time 4 (Mage = 18.22) of longitudinal study of the effects of maltreatment on adolescent development (n = 350; CW = 222; non-CW = 128; 56%male). Marijuana use in the past 12 months (none versus any) was obtained from a single item self-report. Risk factors entered into the model included mental health, parent/family social support, peer risk behavior, self-reported risk behavior, self-esteem, and self-reported adversities (e.g., abuse, neglect, witnessing family violence or community violence)., Results: The ML approaches indicated 80% accuracy in predicting marijuana use in the CW group and 85% accuracy in the non-CW group. In addition, the top features differed for the CW and non-CW groups with peer marijuana use emerging as the most important risk factor for CW youth, whereas externalizing behavior was the most important for the non-CW group. The most important common risk factor between group was gender, with males having higher risk., Conclusions: This is the first study to examine the shared and unique risk factors for marijuana use for CW and non-CW youth using a machine learning approach. The results support our assertion that there may be similar risk factors for both groups, but there are also risks unique to each population. Therefore, risk factors derived from normative populations may not have the same importance when used for CW youth. These differences should be considered in clinical practice when assessing risk for substance use among adolescents., Competing Interests: The authors have declared that no competing interests exist.

Early liver transplantation (LT) for alcohol-associated hepatitis (AH) is the fastest growing indication for LT, but prediction of harmful alcohol use post-LT remains limited. Among 10 ACCELERATE-AH centers, we examined psychosocial evaluations from consecutive LT recipients for AH from 2006 to 2017. A multidisciplinary panel used content analysis to develop a maximal list of psychosocial variables. We developed an artificial intelligence model to predict post-LT harmful alcohol use. The cohort included training (N = 91 among 8 centers) and external validation (N = 25 among 2 centers) sets, with median follow-up of 4.4 (IQR 3.0-6.0) years post-LT. In the training set, AUC was 0.930 (95%CI 0.862-0.998) with positive predictive value of 0.891 (95%CI 0.620-1.000), internally validated through fivefold cross-validation. In the external validation set, AUC was 0.692 (95%CI 0.666-0.718) with positive predictive value of 0.82 (95%CI 0.625-1.000). The model identified specific variables related to social support and substance use as highly important to predict post-LT harmful alcohol use. We retrospectively developed and validated a model that identified psychosocial profiles at LT predicting harmful alcohol use post-LT for AH. This preliminary model may inform selection and post-LT management for AH and warrants prospective evaluation in larger studies among all alcohol-associated liver disease being considered for early LT., (© 2022 The Authors. American Journal of Transplantation published by Wiley Periodicals LLC on behalf of The American Society of Transplantation and the American Society of Transplant Surgeons.)

Background: The United States (US) continues to grapple with a drug overdose crisis. While opioids remain the main driver of overdose deaths, deaths involving psychostimulants such as methamphetamine are increasing with and without opioid involvement. Recent treatment admission data reflect overdose fatality trends suggesting greater psychostimulant use, both alone and in combination with opioids. Adolescents and young adults are particularly vulnerable with generational trends showing that these populations have particularly high relapse rates following treatment., Methods: We assessed demographic, psychosocial, psychological comorbidity, and environmental factors (percent below the poverty line, percent unemployed, neighborhood homicide rate, population density) that confer risk for opioid and/or psychostimulant use following substance use disorder treatment using two complementary machine learning approaches-random forest and least absolute shrinkage and selection operator (LASSO) modelling-with latency to opioid and/or psychostimulant as the outcome variable., Results: Individual level predictors varied by substance use disorder severity, with age, tobacco use, criminal justice involvement, race/ethnicity, and mental health diagnoses emerging at top predictors. Environmental variabels including US region, neighborhood poverty, population, and homicide rate around patients' treatment facility emerged as either protective or risk factors for latency to opioid and/or psychostimulant use., Conclusions: Environmental variables emerged as one of the top predictors of latency to use across all levels of substance use disorder severity. Results highlight the need for tailored treatments based on severity, and implicate environmental variables as important factors influencing treatment outcomes., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Urban water services, including drinking water supply and wastewater treatment, are highly energy dependent, contributing to the challenges described under the water-energy nexus. Both future climate change and decentralized water system adoptions can potentially influence the energy use of the urban water services. However, the trend and the extent of such influences have not been well understood. In this study, a modeling framework was developed to quantify both the separate and the combined influences of climate change and decentralization on the life cycle energy use of the urban water cycle, using the City of Boston, MA as a testbed. Two types of household decentralized systems were considered, the greywater recycling (GWR) systems and the rainwater harvesting (RWH) systems. This modeling framework integrates empirical models based on multilinear regression analysis, hydrologic modeling, water balance models, and life cycle assessment to capture the complex interactions among centralized water services, decentralized water system adoptions, and climate parameters for cumulative energy demand (CED) assessment, considering all residential buildings in Boston. It was found that climate change alone will slightly increase the energy use of the centralized systems towards the end of the century, due to the cancelation effect amongst changes in water quality, flow rate, and space and water heating demand. When decentralization is considered alone, we found economically viable decentralized systems may not necessarily produce energy savings. In fact, RWH adoptions may increase energy use. When climate change and decentralization are combined, they will increase the water yield and cost savings of the decentralized systems, while reducing the energy use from the centralized systems. When the centralized systems are further added into the picture, the CED of the entire urban water cycle is projected to increase by 0.9% or 2.3% towards the end of the century under climate change if GWR or RWH systems are adopted by respective cost saving positive buildings., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Spatial optimization approaches that were originally developed to help conservation organizations determine protection decisions over small spatial scales are now used to inform global or continental scale priority setting. However, the different decision contexts involved in large-scale resource allocation need to be considered. We present a continuous optimization approach in which a decision-maker allocates funding to regional offices. Local decision-makers then use these funds to implement habitat protection efforts with varying effectiveness when evaluated in terms of the funder's goals. We illustrate this continuous formulation by examining the relative priority that should be given to different counties in the coterminous United States when acquiring land to establish new protected areas. If weighting all species equally, counties in the southwest United States, where large areas can be bought cheaply, are priorities for protection. If focusing only on species of conservation concern, priorities shift to locations rich in such species, particularly near expanding exurban areas facing high rates of future habitat conversion (e.g., south-central Texas). Priorities for protection are sensitive to what is assumed about local ecological and decision-making processes. For example, decision-makers who doubt the efficacy of local land protection efforts should focus on a few key areas, while optimistic decision-makers should disperse funding more widely. Efforts to inform large-scale conservation priorities should reflect better the types of choice that decision-makers actually face when working over these scales. They also need to report the sensitivity of recommended priorities to what are often unstated assumptions about local processes affecting conservation outcomes., (© 2020 by the Ecological Society of America.)

Sea level rise in the United States will lead to large scale migration in the future. We propose a framework to examine future climate migration patterns using models of human migration. Our framework requires that we distinguish between historical versus climate driven migration and recognizes how the impacts of climate change can extend beyond the affected area. We apply our framework to simulate how migration, driven by sea level rise, differs from baseline migration patterns. Specifically, we couple a sea level rise model with a data-driven model of human migration and future population projections, creating a generalized joint model of climate driven migration that can be used to simulate population distributions under potential future sea level rise scenarios. The results of our case study suggest that the effects of sea level rise are pervasive, expanding beyond coastal areas via increased migration, and disproportionately affecting some areas of the United States., Competing Interests: The authors have declared that no competing interests exist.

Development of sustainable and resilient water infrastructure is an urgent challenge for urban areas to secure long-term water availability and mitigate negative impacts of water consumption and urban development. A hybrid system that combines centralized water infrastructure and household decentralized water facilities, including rainwater harvesting and greywater recycling, may be a solution to more sustainable and resilient water management in urban areas. Understanding household and community preferences for decentralized water facilities is important to inform the design and ultimately the promotion and adoption of such systems. In this study, we conducted a discrete choice experiment, via Amazon Mechanical Turk, to collect data on household choices of different decentralized water facility designs in two U.S. cities, Atlanta, Georgia and Boston, Massachusetts. Based on the responses to the choice experiment, we then developed a latent-class choice model to predict households' preferences of decentralized system design features and examine the influence of socioeconomic and personal characteristics on heterogeneous class membership. We identified six major classes of preferences in Atlanta and Boston, respectively, and evaluated how readily each class is likely to choose a decentralized water facility. Atlanta and Boston have some classes sharing similar preferences for decentralized water systems, but the socioeconomic and personal characteristics of these classes in the two cities are different. We found that the early adoption of decentralized water facilities is positively related to neighbors' adoptions and pressure of water scarcity increases households' willingness to share a decentralized facility. The visualization of spatial distribution of the classes highlighted early demand of decentralized water facilities is likely to emerge in low-property-value communities, which creates a unique opportunity for introducing decentralized water facilities during water infrastructure renovations. Our study provides a framework through citizen engagement to understand social demand and to inform the promotion of decentralized water facilities., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Ecological distance-based spatial capture-recapture models (SCR) are a promising approach for simultaneously estimating animal density and connectivity, both of which affect spatial population processes and ultimately species persistence. We explored how SCR models can be integrated into reserve-design frameworks that explicitly acknowledge both the spatial distribution of individuals and their space use resulting from landscape structure. We formulated the design of wildlife reserves as a budget-constrained optimization problem and conducted a simulation to explore 3 different SCR-informed optimization objectives that prioritized different conservation goals by maximizing the number of protected individuals, reserve connectivity, and density-weighted connectivity. We also studied the effect on our 3 objectives of enforcing that the space-use requirements of individuals be met by the reserve for individuals to be considered conserved (referred to as home-range constraints). Maximizing local population density resulted in fragmented reserves that would likely not aid long-term population persistence, and maximizing the connectivity objective yielded reserves that protected the fewest individuals. However, maximizing density-weighted connectivity or preemptively imposing home-range constraints on reserve design yielded reserves of largely spatially compact sets of parcels covering high-density areas in the landscape with high functional connectivity between them. Our results quantify the extent to which reserve design is constrained by individual home-range requirements and highlight that accounting for individual space use in the objective and constraints can help in the design of reserves that balance abundance and connectivity in a biologically relevant manner., (© 2019 Society for Conservation Biology.)

In real physical systems the underlying spatial components might not have crisp boundaries and their interactions might not be instantaneous. To this end, we propose δ -MAPS; a method that identifies spatially contiguous and possibly overlapping components referred to as domains , and identifies the lagged functional relationships between them. Informally, a domain is a spatially contiguous region that somehow participates in the same dynamic effect or function. The latter will result in highly correlated temporal activity between grid cells of the same domain. δ -MAPS first identifies the epicenters of activity of a domain. Next, it identifies a domain as the maximum possible set of spatially contiguous grid cells that include the detected epicenters and satisfy a homogeneity constraint. After identifying the domains, δ -MAPS infers a functional network between them. The proposed network inference method examines the statistical significance of each lagged correlation between two domains, applies a multiple-testing process to control the rate of false positives, infers a range of potential lag values for each edge, and assigns a weight to each edge reflecting the magnitude of interaction between two domains. δ -MAPS is related to clustering, multivariate statistical techniques and network community detection. However, as we discuss and also show with synthetic data, it is also significantly different, avoiding many of the known limitations of these methods. We illustrate the application of δ -MAPS on data from two domains: climate science and neuroscience. First, the sea-surface temperature climate network identifies some well-known teleconnections (such as the lagged connection between the El Nin õ Southern Oscillation and the Indian Ocean). Second, the analysis of resting state fMRI cortical data confirms the presence of known functional resting state networks (default mode, occipital, motor/somatosensory and auditory), and shows that the cortical network includes a backbone of relatively few regions that are densely interconnected., Competing Interests: The authors declare no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Conservation biologists recognize that a system of isolated protected areas will be necessary but insufficient to meet biodiversity objectives. Current approaches to connecting core conservation areas through corridors consider optimal corridor placement based on a single optimization goal: commonly, maximizing the movement for a target species across a network of protected areas. We show that designing corridors for single species based on purely ecological criteria leads to extremely expensive linkages that are suboptimal for multispecies connectivity objectives. Similarly, acquiring the least-expensive linkages leads to ecologically poor solutions. We developed algorithms for optimizing corridors for multispecies use given a specific budget. We applied our approach in western Montana to demonstrate how the solutions may be used to evaluate trade-offs in connectivity for 2 species with different habitat requirements, different core areas, and different conservation values under different budgets. We evaluated corridors that were optimal for each species individually and for both species jointly. Incorporating a budget constraint and jointly optimizing for both species resulted in corridors that were close to the individual species movement-potential optima but with substantial cost savings. Our approach produced corridors that were within 14% and 11% of the best possible corridor connectivity for grizzly bears (Ursus arctos) and wolverines (Gulo gulo), respectively, and saved 75% of the cost. Similarly, joint optimization under a combined budget resulted in improved connectivity for both species relative to splitting the budget in 2 to optimize for each species individually. Our results demonstrate economies of scale and complementarities conservation planners can achieve by optimizing corridor designs for financial costs and for multiple species connectivity jointly. We believe that our approach will facilitate corridor conservation by reducing acquisition costs and by allowing derived corridors to more closely reflect conservation priorities., (© 2016 Society for Conservation Biology.)