This cross-sectional study of respondents to the National Survey on Drug Use and Health evaluates rates of medication for opioid use disorder receipt among people with need for treatment as well as associated sociodemographic characteristics.

HCV care coordination interventions that include screening, education and active linkage to care in MMT settings are likely cost-effective at a conventional $100,000/QALY threshold for both HCV mono-infected and HIV co-infected patients.

Physical distancing measures to curb COVID-19 transmission introduced mental health and economic stressors, possibly impacting problematic drinking. This cross-sectional study examines mental health and economic stressors early in the COVID-19 pandemic which may be associated with heavy alcohol use and increased alcohol use. We administered an online survey of U.S. adults via social media April 5 to May 5, 2020. High-risk drinking was defined by WHO risk drinking levels, a daily average of ≥4 drinks for men and ≥3 drinks for women. Participants reported retrospective assessments of increased alcohol use if their past-week alcohol consumption exceeded their past-year average weekly alcohol consumption. We used logistic regression to assess possible covariates of high-risk drinking and increased alcohol use. Among 2175 participants, 10% ( n = 222) reported high-risk drinking, and 36% ( n = 775) reported increased alcohol consumption. In multivariable analysis, high-risk drinking was significantly associated with household job loss (OR = 1.41, 95%CI = (1.06, 1.88)) and depressive symptoms (OR = 1.05, 95% CI = (1.02, 1.07)), and women had higher odds of high-risk drinking than men (OR = 2.37, 95% CI = (1.32, 4.69)). Previous mental health diagnosis was not significantly associated with high-risk drinking during the pandemic (OR = 1.31, 95% CI = (0.98, 1.76)) in univariable analysis. High-risk drinkers were almost six times as likely to report retrospective assessments of increased alcohol consumption, controlling for mental health and economic stressors (OR = 5.97, 95% CI = (4.35, 8.32)). Findings suggest a need for targeted interventions to address the complex mental health and economic stressors that may increase alcohol consumption and high-risk drinking during and after the pandemic.

Sphingosine 1-phosphate receptor 1 (S1P 1 ) is a G protein-coupled receptor (GPCR) for a versatile lysophospholipid mediator sphingosine 1-phosphate (S1P) ( 1, 2 ). S1P levels are high in blood and lymph, but low in the interstitial tissue fl uid, thereby forming a gradient important for immune cell traffi cking ( 3 ). S1P, which is poorly soluble in aqueous solutions, is bound by specifi c chaperones. For example, plasma S1P is bound by albumin ( 4 ) or apoM ( 5 ), a specifi c apolipoprotein found in HDL. The S1P-S1P 1 signaling system is critical for sprouting angiogenesis ( 6-8 ) and vascular permeability ( 2, 9-11 ). In addition, part of the vasoprotective, anti-infl ammatory, and anti-atherosclerotic effects associated with HDL is attributed to its cargo, apoM-S1P ( 12-15 ). The S1P-S1P 1 signaling system also plays an important role in immune cell traffi cking ( 3 ). The gradient of S1P concentration between interstitial fl uids and lymph is a basis that allows lymphocytes to egress from secondary lymphoid organs to the circulation via the chemotactic activity of S1P 1 ( 3, 16 ).Previous studies have accumulated signifi cant functional and structural information on S1P 1 ( 1, 2 ). The Abstract Sphingosine 1-phosphate receptor 1 (S1P 1 ), an abundantly-expressed G protein-coupled receptor which regulates key vascular and immune responses, is a therapeutic target in autoimmune diseases. Fingolimod/Gilenya (FTY720), an oral medication for relapsing-remitting multiple sclerosis, targets S1P 1 receptors on immune and neural cells to suppress neuroinfl ammation. However, suppression of endothelial S1P 1 receptors is associated with cardiac and vascular adverse effects. Here we report the genetic variations of the S1P 1 coding region from exon sequencing of >12,000 individuals and their functional consequences. We conducted functional analyses of 14 nonsynonymous single nucleotide polymorphisms (SNPs) of the S1PR1 gene . One SNP mutant (Arg 120 to Pro) failed to transmit sphingosine 1-phosphate (S1P)-induced intracellular signals such as calcium increase and activation of p44/42 MAPK and Akt. Two other mutants (Ile 45 to Thr and Gly 305 to Cys) showed normal intracellular signals but impaired S1P-induced endocytosis, which made the receptor resistant to FTY720-induced degradation. Another SNP mutant (Arg 13 to Gly) demonstrated protection from coronary artery disease in a high cardiovascular risk population. Individuals with this mutation showed a signifi cantly lower percentage of multi-vessel coronary obstruction in a risk factor-matched case-control study. This study suggests that individual genetic variations of S1P 1 can infl uence receptor function and, therefore, infer differential disease risks and interaction with S1P 1 -targeted therapeutics. -Obinata, H., S. Gutkind, J. Stitham, T. Okuno, T. Yokomizo, J. Hwa, and T. Hla. Individual variation of human S1P 1 coding sequence leads to heterogeneity in receptor function and drug interactions. J. Lipid Res. 2014. 55: 2665-2675.

Objective The Department of Veterans Affairs’ (VA) electronic health records (EHR) offer a rich source of big data to study medical and health care questions, but patient eligibility and preferences may limit generalizability of findings. We therefore examined the representativeness of VA veterans by comparing veterans using VA healthcare services to those who do not. Methods We analyzed data on 3051 veteran participants age ≥ 18 years in the 2019 National Health Interview Survey. Weighted logistic regression was used to model participant characteristics, health conditions, pain, and self-reported health by past year VA healthcare use and generate predicted marginal prevalences, which were used to calculate Cohen’s d of group differences in absolute risk by past-year VA healthcare use. Results Among veterans, 30.4% had past-year VA healthcare use. Veterans with lower income and members of racial/ethnic minority groups were more likely to report past-year VA healthcare use. Health conditions overrepresented in past-year VA healthcare users included chronic medical conditions (80.6% vs. 69.4%, d = 0.36), pain (78.9% vs. 65.9%; d = 0.35), mental distress (11.6% vs. 5.9%; d = 0.47), anxiety (10.8% vs. 4.1%; d = 0.67), and fair/poor self-reported health (27.9% vs. 18.0%; d = 0.40). Conclusions Heterogeneity in veteran sociodemographic and health characteristics was observed by past-year VA healthcare use. Researchers working with VA EHR data should consider how the patient selection process may relate to the exposures and outcomes under study. Statistical reweighting may be needed to generalize risk estimates from the VA EHR data to the overall veteran population.