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Genetic association studies have identified loci including the choline transporter SLC44A2 as a potential regulator of thrombosis. Here the authors report that loss of SLC44A2 impairs platelet activation and thrombosis in mice via a reduction of mitochondrial ATP production.

Introduction: Many trans women of color communities experience high HIV seroprevalence, extreme poverty, high rates of victimization and substance use, and poor mental health. Greater knowledge of trans women of color social capital may contribute toward more effective services for this marginalized population. Methods: These data come from a mixed-methods study that examined trans/gender-variant people of color who attended transgender support groups at harm reduction programs in NYC. The study was conducted from 2011 to 12, total N = 34. The qualitative portion was derived from six focus group interviews. Results: Two support groups stood out as exhibiting very strong alternative kinship structures. One group was comprised of immigrant trans Latinas, and the other group were trans women of African descent living with HIV. Both groups demonstrated ample cultivation of "trust capital" in the form of "thick trust" (bonding capital) and "thin trust" (bridging/linking capital) both inside and outside/beyond the support groups. Thick trust included the cultivation of intimacy, support in primary romantic relationships, and community leadership. Thin trust included networking with a variety of organizations, increased educational opportunities, and cultural production. Discussion: Participants "opened up to social capital" through the process of trusting as a series of (1) risks; (2) vulnerabilities; and (3) reciprocities. A solid foundation of thick trust resulted in a social, psychological, and emotional "base." Upon this foundation, thin trust was operationalized resulting in positive material, economic, and quality-of-life outcomes, leading to an expanded space of capabilities. [ABSTRACT FROM AUTHOR]

Background: The ways in which prescription drug monitoring programs (PDMPs) have been integrated into primary care practice remain understudied, and research into physician utilization of PDMPs in states where PDMP use is mandated remains scant.Objectives: To characterize primary care physician perspectives on and utilization of a mandatory PDMP in New York City.Methods: We conducted face-to-face, in-depth interviews with primary care physicians who reported that they currently prescribed opioid analgesic medication. We used a thematic analytic approach to characterize physician perspectives on the PDMP mandate and physician integration of mandatory PDMP use into primary care practice.Results: Primary care providers demonstrated a continuum of PDMP utilization, ranging from consistent use to the specifications of the mandate to inconsistent use to no use. Providers reported a range of perspectives on the purpose and function of the PDMP mandate, as well as a lack of clarity about the mandate and its enforcement.Conclusion: Findings suggest a need for increased clinical and public health education about the use of PDMPs as clinical tools to identify and treat patients with potential substance use disorders in primary care. [ABSTRACT FROM AUTHOR]

Background and Aims: In light of the accelerating drug overdose epidemic in North America, new strategies are needed to identify communities most at risk to prioritize geographically the existing public health resources (e.g. street outreach, naloxone distribution efforts). We aimed to develop PROVIDENT (Preventing Overdose using Information and Data from the Environment), a machine learning‐based forecasting tool to predict future overdose deaths at the census block group (i.e. neighbourhood) level. Design Randomized, population‐based, community intervention trial. Setting: Rhode Island, USA. Participants: All people who reside in Rhode Island during the study period may contribute data to either the model or the trial outcomes. Intervention: Each of the state's 39 municipalities will be randomized to the intervention (PROVIDENT) or comparator condition. An interactive, web‐based tool will be developed to visualize the PROVIDENT model predictions. Municipalities assigned to the treatment arm will receive neighbourhood risk predictions from the PROVIDENT model, and state agencies and community‐based organizations will direct resources to neighbourhoods identified as high risk. Municipalities assigned to the control arm will continue to receive surveillance information and overdose prevention resources, but they will not receive neighbourhood risk predictions. Measurements The primary outcome is the municipal‐level rate of fatal and non‐fatal drug overdoses. Fatal overdoses will be defined as unintentional drug‐related death; non‐fatal overdoses will be defined as an emergency department visit for a suspected overdose reported through the state's syndromic surveillance system. Intervention efficacy will be assessed using Poisson or negative binomial regression to estimate incidence rate ratios comparing fatal and non‐fatal overdose rates in treatment vs. control municipalities. Comments The findings will inform the utility of predictive modelling as a tool to improve public health decision‐making and inform resource allocation to communities that should be prioritized for prevention, treatment, recovery and overdose rescue services. [ABSTRACT FROM AUTHOR]

Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016–2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R 2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality. [ABSTRACT FROM AUTHOR]

Background Evidence suggests that individuals with history of substance use disorder (SUD) are at increased risk of COVID-19, but little is known about relationships between SUDs, overdose and COVID-19 severity and mortality. This study investigated risks of severe COVID-19 among patients with SUDs. Methods We conducted a retrospective review of data from a hospital system in New York City. Patient records from 1 January to 26 October 2020 were included. We assessed positive COVID-19 tests, hospitalizations, intensive care unit (ICU) admissions and death. Descriptive statistics and bivariable analyses compared the prevalence of COVID-19 by baseline characteristics. Logistic regression estimated unadjusted and sex-, age-, race- and comorbidity-adjusted odds ratios (AORs) for associations between SUD history, overdose history and outcomes. Results Of patients tested for COVID-19 (n  = 188 653), 2.7% (n  = 5107) had any history of SUD. Associations with hospitalization [AORs (95% confidence interval)] ranged from 1.78 (0.85–3.74) for cocaine use disorder (COUD) to 6.68 (4.33–10.33) for alcohol use disorder. Associations with ICU admission ranged from 0.57 (0.17–1.93) for COUD to 5.00 (3.02–8.30) for overdose. Associations with death ranged from 0.64 (0.14–2.84) for COUD to 3.03 (1.70–5.43) for overdose. Discussion Patients with histories of SUD and drug overdose may be at elevated risk of adverse COVID-19 outcomes. [ABSTRACT FROM AUTHOR]