Demographic differences in the cascade of care for unhealthy alcohol use: A cross-sectional analysis of data from the 2015-2019 National Survey on Drug Use and Health.

BACKGROUND: The screening, brief intervention, and referral to treatment (SBIRT) model is recommended by the U.S. Preventive Services Task Force to improve recognition of and intervention for unhealthy alcohol use. How SBIRT implementation differs by demographic characteristics is poorly understood. METHODS: We analyzed data from the 2015-2019 National Survey on Drug Use and Health from respondents ≥18 years old who used an outpatient clinic and had at least one alcoholic drink within the past year. Respondents were grouped into one of three mutually exclusive groups: "no binge drinking or alcohol use disorder (AUD)," "binge drinking without AUD," or "AUD." Outcome variables were likelihood of screening, brief intervention (BI), referral to treatment (RT), and AUD treatment. The demographic predictors on which outcomes were regressed included gender, age, race and ethnicity, sexual orientation, insurance status, and history of military involvement. Consistent with SBIRT guidelines, the entire sample was included in the screening model; screened persons with either binge drinking without AUD or with AUD were included in the BI model; screened persons with AUD were included in the RT model, and persons referred to treatment with AUD were included in the AUD treatment model. RESULTS: Analyses included 120,804 respondents. Women were more likely than men to be screened, but less likely to receive BI or RT. When referred to treatment, women were more likely than men to receive it. Persons aged ≥50 were least likely to be screened about alcohol, but most likely to receive BI, while persons aged 18-25 were least likely to receive BI or AUD treatment. Racial and ethnic minorities were less likely than White persons to be screened; Asians were less likely to receive RT, and Black persons were less likely to receive treatment than White persons. Persons identifying as gay, lesbian, or bisexual were equally as likely or more likely to receive SBIRT or AUD treatment as those identifying as heterosexual. Persons without insurance were less likely to be screened than those with insurance. Persons with a history of military involvement were more likely to be screened and receive BI and RT than persons who had not served in the military. CONCLUSIONS: Demographic disparities in SBIRT implementation exist. Addressing the sources of these disparities and minimizing attrition from care could improve outcomes for persons with unhealthy alcohol use.

Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features.

Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.

Investigation of convergent and divergent genetic influences underlying schizophrenia and alcohol use disorder.

BACKGROUND: Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders. METHODS: We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific. RESULTS: We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001). CONCLUSIONS: Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD.

A flexible modeling approach for biomarker-based computation of absolute risk of Alzheimer's disease dementia.

INTRODUCTION: As Alzheimer's disease (AD) biomarkers rapidly develop, tools are needed that accurately and effectively communicate risk of AD dementia. METHODS: We analyzed longitudinal data from >10,000 cognitively unimpaired older adults. Five-year risk of AD dementia was modeled using survival analysis. RESULTS: A demographic model was developed and validated on independent data with area under the receiver operating characteristic curve (AUC) for 5-year prediction of AD dementia of 0.79. Clinical and cognitive variables (AUC = 0.79), and apolipoprotein E genotype (AUC = 0.76) were added to the demographic model. We then incorporated the risk computed from the demographic model with hazard ratios computed from independent data for amyloid positron emission tomography status and magnetic resonance imaging hippocampal volume (AUC = 0.84), and for plasma amyloid beta (Aß)42/Aß40 (AUC = 0.82). DISCUSSION: An adaptive tool was developed and validated to compute absolute risks of AD dementia. This approach allows for improved accuracy and communication of AD risk among cognitively unimpaired older adults.

Association between recent overdose and chronic pain among individuals in treatment for opioid use disorder.

Chronic pain increases risk for opioid overdose among individuals with opioid use disorder. The purpose of this study is to evaluate the relationship between recent overdose and whether or not chronic pain is active. 3,577 individuals in treatment for opioid use disorder in 2017 or 2018 were surveyed regarding recent overdoses and chronic pain. Demographics from the 2017 Treatment Episode Data Set, which includes all U.S. facilities licensed or certified to provide substance use care, were used to evaluate the generalizability of the sample. χ2 tests and logistic regression models were used to compare associations between recent overdoses and chronic pain. Specifically, active chronic pain was associated with opioid overdose among people in treatment for opioid use disorder. Individuals with active chronic pain were more likely to have had a past month opioid overdose than those with no history chronic pain (adjusted OR = 1.55, 95% CI 1.16-2.08, p = 0.0003). In contrast, individuals with prior chronic pain, but no symptoms in the past 30 days, had a risk of past month opioid overdose similar to those with no history of chronic pain (adjusted OR = 0.88, 95% CI 0.66-1.17, p = 0.38). This suggests that the incorporation of treatment for chronic pain into treatment for opioid use disorder may reduce opioid overdoses.

Analysis of Stimulant Prescriptions and Drug-Related Poisoning Risk Among Persons Receiving Buprenorphine Treatment for Opioid Use Disorder.

Importance: Stimulant medication use is common among individuals receiving buprenorphine for opioid use disorder (OUD). Associations between prescription stimulant use and treatment outcomes in this population have been understudied. Objectives: To investigate whether use of prescription stimulants was associated with (1) drug-related poisoning and (2) buprenorphine treatment retention. Design, Setting, and Participants: This retrospective, recurrent-event cohort study with a case-crossover design used a secondary analysis of administrative claims data from IBM MarketScan Commercial and Multi-State Medicaid databases from January 1, 2006, to December 31, 2016. Primary analyses were conducted from March 1 through August 31, 2021. Individuals aged 12 to 64 years with an OUD diagnosis and prescribed buprenorphine who experienced at least 1 drug-related poisoning were included in the analysis. Unit of observation was the person-day. Exposures: Days of active stimulant prescriptions. Main Outcomes and Measures: Primary outcomes were drug-related poisoning and buprenorphine treatment retention. Drug-related poisonings were defined using International Classification of Diseases, Ninth Revision, and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, codes; treatment retention was defined by continuous treatment claims until a 45-day gap was observed. Results: There were 13 778 567 person-days of observation time among 22 946 individuals (mean [SD] age, 32.8 [11.8] years; 50.3% men) who experienced a drug-related poisoning. Stimulant treatment days were associated with 19% increased odds of drug-related poisoning (odds ratio [OR], 1.19 [95% CI, 1.06-1.34]) compared with nontreatment days; buprenorphine treatment days were associated with 38% decreased odds of poisoning (OR, 0.62 [95% CI, 0.59-0.65]). There were no significant interaction effects between use of stimulants and buprenorphine. Stimulant treatment days were associated with decreased odds of attrition from buprenorphine treatment (OR, 0.64 [95% CI, 0.59-0.70]), indicating that stimulants were associated with 36% longer mean exposure to buprenorphine and its concomitant protection. Conclusions and Relevance: Among persons with OUD, use of prescription stimulants was associated with a modest increase in per-day risk of drug-related poisoning, but this risk was offset by the association between stimulant use and improved retention to buprenorphine treatment, which is associated with protection against overdose.

Association Between Workplace Absenteeism and Alcohol Use Disorder From the National Survey on Drug Use and Health, 2015-2019.

Importance: Alcohol use disorder (AUD) is common and associated with increased morbidity. The degree to which AUD currently factors into workplace absenteeism needs further characterization in the US. Objective: To examine the association between AUD and workplace absenteeism in a nationally representative sample. Design, Setting, and Participants: This cross-sectional study used data from a nationally representative sample of noninstitutionalized US residents from the 2015-2019 National Survey on Drug Use and Health to examine the association of AUD with workplace absenteeism. Eligible respondents were aged 18 years and older who reported full-time employment. Data were analyzed from March to September 2021. Main Outcomes and Measures: Primary outcomes were markers of workplace absenteeism as defined by the number of days missed from work because of illness or injury and days skipped from work in the last 30 days. Descriptive statistics, prevalence ratios, and logistic regression analyses were performed to assess the association between AUD and absenteeism. Results: A total of 110 701 adults aged 18 years and older reported current full-time employment (58 948 [53.2%] men, 51 753 [46.8%] women; 12 776 [11.5%] Black, 18 096 [16.3%] Hispanic, and 69 506 [62.8%] White respondents). Weighted prevalence of AUD in this sample of working adults was 9.3% (95% CI, 9.0%-9.5%); 6.2% (95% CI, 6.0%-6.4%) of respondents met criteria for mild AUD, 1.9% (95% CI, 1.7%-2.0%) for moderate AUD, and 1.2% (95% CI, 1.1%-1.3%) for severe AUD. Mean days missed from work annually increased in a stepwise fashion with increasing AUD severity (no AUD, 13.0 days; 95% CI, 12.7-13.2 days; mild AUD, 17.7 days; 95% CI, 16.4-19.1 days; moderate AUD, 23.6 days; 95% CI, 21.5-25.7 days; severe AUD, 32.3 days; 95% CI, 27.5-37.0 days). People with AUD represented 9.3% of the full-time workforce and contributed to 14.1% of total reported workplace absences. Conclusions and Relevance: In this cross-sectional study, AUD was disproportionately associated with an increased prevalence of workplace absenteeism, with individuals with AUD contributing over 232 million missed workdays annually. These results provide economic incentive for increased investment in AUD prevention and treatment, both for employers and policy makers.

An examination between treatment type and treatment retention in persons with opioid and co-occurring alcohol use disorders.

BACKGROUND AND AIMS: Persons with opioid use disorder (OUD) and co-occurring alcohol use disorder (AUD) are understudied. We identified whether co-occurring AUD was associated with OUD treatment type, compared associations between treatment type and six-month treatment retention and determined whether co-occurring AUD moderated these relationships. METHODS: We used an observational cohort study design to analyze insurance claims data from 2011 to 2016 from persons aged 12-64 with an opioid abuse or opioid dependence diagnosis and OUD treatment claim. Our unit of analysis was the treatment episode; we used logistic regression for analyses. RESULTS: Of 211,047 treatment episodes analyzed, 14 % had co-occurring alcohol abuse or dependence diagnoses. Among persons with opioid dependence, persons with co-occurring alcohol dependence were 25 % less likely to receive medication treatment relative to those without AUD. Further, alcohol dependence was associated with decreased likelihood of treatment with buprenorphine (AOR 0.47, 95 % CI 0.44-0.49) or methadone (AOR 0.31, 95 % CI 0.28-0.35) and increased likelihood of treatment with extended-release (AOR 1.36, 95 % CI 1.21-1.54) or oral (AOR 1.73, 95 % CI 1.57-1.90) naltrexone relative to psychosocial treatment. Buprenorphine and methadone were associated with highest retention prevalence regardless of OUD or AUD severity. Co-occurring alcohol abuse or dependence did not meaningfully change retention prevalence associated with buprenorphine or methadone. Co-occurring AUD was not associated with improved retention among persons receiving either formulation of naltrexone. CONCLUSIONS: Buprenorphine and methadone are associated with relatively high likelihood of treatment retention among persons opioid and alcohol dependence, but are disproportionately under-prescribed.

A cascade of care for alcohol use disorder: Using 2015-2019 National Survey on Drug Use and Health data to identify gaps in past 12-month care.

BACKGROUND: Although effective treatments exist, alcohol use disorder (AUD) is undertreated. We used a cascade of care framework to understand gaps in care for persons with AUD. METHODS: Using 2015-2019 National Survey on Drug Use and Health data, we evaluated the following steps in the cascade of care: (1) adult prevalence of AUD; (2) proportion of adults with AUD who utilized health care in the past 12 months; (3) proportion with AUD screened about their alcohol use; (4) proportion with AUD who received a brief intervention about their alcohol misuse; (5) proportion with AUD who received information about treatment for alcohol misuse; and (6) proportion with AUD who received treatment. Analyses were stratified by AUD severity. RESULTS: Of the 214,505 persons included in the sample, the weighted prevalence of AUD was 7.8% (95% CI 7.6-8.0%). Cascades of care showed the majority of individuals with AUD utilized health care in the past 12 months [81.4% (95% CI 80.7-82.1%)] and were screened about alcohol use [69.9% (95% CI 68.9-70.8%)]. However, only a minority of individuals received subsequent steps of care, including 11.6% (95% CI 11.0-12.2%) who reported receiving a brief intervention, 5.1% (95% CI 4.6-5.6%) who were referred to treatment, and 5.8% (95% CI 5.4-6.3%) who received treatment. Similar patterns were observed when cascades of care were stratified by AUD severity. CONCLUSIONS: Persons with AUD commonly utilize health care and are often screened about alcohol use, but few receive treatment. Healthcare settings-particularly primary care settings-represent a prime opportunity to implement AUD treatment to improve outcomes in this high-risk population.

Studying the Utility of Using Genetics to Predict Smoking-Related Outcomes in a Population-Based Study and a Selected Cohort.

INTRODUCTION: The purpose of this study is to examine the predictive utility of polygenic risk scores (PRSs) for smoking behaviors. AIMS AND METHODS: Using summary statistics from the Sequencing Consortium of Alcohol and Nicotine use consortium, we generated PRSs of ever smoking, age of smoking initiation, cigarettes smoked per day, and smoking cessation for participants in the population-based Atherosclerosis Risk in Communities (ARIC) study (N = 8638), and the Collaborative Genetic Study of Nicotine Dependence (COGEND) (N = 1935). The outcomes were ever smoking, age of smoking initiation, heaviness of smoking, and smoking cessation. RESULTS: In the European ancestry cohorts, each PRS was significantly associated with the corresponding smoking behavior outcome. In the ARIC cohort, the PRS z-score for ever smoking predicted smoking (odds ratio [OR]: 1.37; 95% confidence interval [CI]: 1.31, 1.43); the PRS z-score for age of smoking initiation was associated with age of smoking initiation (OR: 0.87; 95% CI: 0.82, 0.92); the PRS z-score for cigarettes per day was associated with heavier smoking (OR: 1.17; 95% CI: 1.11, 1.25); and the PRS z-score for smoking cessation predicted successful cessation (OR: 1.24; 95% CI: 1.17, 1.32). In the African ancestry cohort, the PRSs did not predict smoking behaviors. CONCLUSIONS: Smoking-related PRSs were associated with smoking-related behaviors in European ancestry populations. This improvement in prediction is greatest in the lowest and highest genetic risk categories. The lack of prediction in African ancestry populations highlights the urgent need to increase diversity in research so that scientific advances can be applied to populations other than those of European ancestry. IMPLICATIONS: This study shows that including both genetic ancestry and PRSs in a single model increases the ability to predict smoking behaviors compared with the model including only demographic characteristics. This finding is observed for every smoking-related outcome. Even though adding genetics is more predictive, the demographics alone confer substantial and meaningful predictive power. However, with increasing work in PRSs, the predictive ability will continue to improve.

Association Between Benzodiazepine or Z-Drug Prescriptions and Drug-Related Poisonings Among Patients Receiving Buprenorphine Maintenance: A Case-Crossover Analysis.

OBJECTIVE: Persons with opioid use disorder who take benzodiazepines are at high risk for overdose. The objective of this study was to evaluate the association of benzodiazepine and Z-drug use with drug-related poisonings among patients receiving buprenorphine maintenance treatment. METHODS: A case-crossover study design was used to analyze prescription claims among persons ages 12-64 with opioid use disorder who had buprenorphine prescriptions and had claims data in the IBM MarketScan databases (2006-2016), encompassing 14,213,075 person-days of observation time for 23,036 individuals who experienced drug-related poisoning. The exposures were buprenorphine prescriptions and benzodiazepine or Z-drug prescriptions, standardized as daily diazepam-equivalent milligram doses and separated by pharmacologic properties (short-acting or long-acting benzodiazepines, Z-drugs). The outcome of interest was nonfatal drug-related poisoning. Conditional logistic regression was used to evaluate variation in benzodiazepine or Z-drug and buprenorphine use between poisoning and nonpoisoning days. RESULTS: Buprenorphine treatment days were associated with a nearly 40% reduction in the risk of poisoning events (odds ratio=0.63, 95% CI=0.60, 0.66) compared with nontreatment days, whereas benzodiazepine or Z-drug treatment days were associated with an 88% increase in the risk of such events (95% CI=1.78, 1.98). In stratified analyses by dose, we observed a 78% (95% CI=1.67, 1.88) and 122% (95% CI=2.03, 2.43) increase in poisonings associated with low-dose and high-dose benzodiazepine or Z-drug treatment days, respectively. High-dose, but not low-dose, benzodiazepine or Z-drug treatment was associated with increased poisonings in combination with buprenorphine cotreatment (odds ratio=1.64, 95% CI=1.39, 1.93), but this was lower than the odds risk associated with benzodiazepine or Z-drug treatment in the absence of buprenorphine (low-dose: odds ratio=1.69, 95% CI=1.60, 1.79; high-dose: odds ratio=2.23, 95% CI=2.04, 2.45). CONCLUSIONS: Increased risk of nonfatal drug-related poisoning is associated with benzodiazepine or Z-drug treatment in patients with opioid use disorder, but this risk is partially mitigated by buprenorphine treatment. Dose reduction of benzodiazepines or Z-drugs while maintaining buprenorphine treatment may provide the advantage of lowering drug-related poisoning risk.

Communicating 5-Year Risk of Alzheimer's Disease Dementia: Development and Evaluation of Materials that Incorporate Multiple Genetic and Biomarker Research Results.

BACKGROUND: Cognitively normal (CN) older adults participating in Alzheimer's disease (AD) research increasingly ask for their research results-including genetic and neuroimaging findings-to understand their risk of developing AD dementia. AD research results are typically not returned for multiple reasons, including possible psychosocial harms of knowing one is at risk of a highly feared and untreatable disease. OBJECTIVE: We developed materials that convey information about 5-year absolute risk of developing AD dementia based on research results. METHODS: 20 CN older adults who received a research brain MRI result were interviewed regarding their wishes for research results to inform material development (Pilot 1). Following material development, 17 CN older adults evaluated the materials for clarity and acceptability (Pilot 2). All participants were community-dwelling older adults participating in longitudinal studies of aging at a single site. RESULTS: Participants want information on their risk of developing AD dementia to better understand their own health, satisfy curiosity, inform family, and future planning. Some articulated concerns, but the majority wanted to know their risk despite the limitations of information. Participants found the educational materials and results report clear and acceptable, and the majority would want to know their research results after reviewing them. CONCLUSION: These materials will be used in a clinical study examining the psychosocial and cognitive effects of offering research results to a cohort of CN older adults. Future AD research may incorporate the return of complex risk information to CN older adults, and materials are needed to communicate this information.

Prolactin and Estrogen Levels in Postmenopausal Women Receiving Aripiprazole Augmentation Treatment for Depression.

BACKGROUND: Antipsychotic drugs are well established to alter serum prolactin levels, often resulting in adverse effects including amenorrhea, galactorrhea, osteoporosis, and loss of libido. There is growing preclinical evidence that prolactin-elevating drugs can instigate the progression of precancerous lesions to breast cancer and that genes activated by prolactin are associated with the development and proliferation of breast cancer. Current guides advise a cautious approach (weighing risks and benefits) to the administration of prolactin-elevating antipsychotic drugs in women with a previously detected breast cancer. Aripiprazole is known to be a prolactin-sparing antipsychotic; however, data regarding its effects on prolactin and estrogens in postmenopausal women are lacking. METHODS: We examined serum hormone levels in n = 66 women who participated in a randomized, double-blind, placebo-controlled, multicenter trial of aripiprazole (high and low doses) added to an antidepressant in adults older than 60 years. Aripiprazole or placebo tablets were administered for 12 weeks as an augmentation strategy in venlafaxine-treated women. The primary outcomes were the difference in prolactin and estrogen levels. RESULTS: There was no significant effect of aripiprazole treatment on prolactin or estrogen levels, including in models that divided groups into low and high doses: prolactin (P = 0.075), estrone (P = 0.67), and estradiol (P = 0.96). CONCLUSIONS: Aripiprazole addition to an antidepressant did not alter serum estrogens or prolactin. These findings may be relevant in the treatment of some postmenopausal women with depression.

Association Between Benzodiazepine Use With or Without Opioid Use and All-Cause Mortality in the United States, 1999-2015.

Importance: Although overall rates of opioid use have been plateauing, coprescriptions of benzodiazepines and opioids have increased greatly in recent years. It is unknown whether this combination is an independent risk factor for all-cause mortality as opposed to being more frequently used by persons with a baseline elevated risk of death. Objective: To evaluate whether benzodiazepine use, with or without opioid use, is associated with increased all-cause mortality relative to the use of low-risk antidepressants. Design, Setting, and Participants: This retrospective cohort study used a large, nationally representative US data set (the National Health and Nutrition Examination Surveys [NHANES]) from 1999 to 2015. Eight cycles of NHANES data were used, spanning 37 610 person-years of follow-up time among 5212 individuals. Statistical analysis was performed from August 24, 2019, through May 23, 2020. Exposures: The primary exposure variable was benzodiazepine and opioid coprescriptions. Individuals taking selective serotonin reuptake inhibitors (SSRIs) served as an active comparator reference group. Main Outcomes and Measures: All-cause mortality was obtained via linkage of NHANES to the National Death Index. Propensity scores were calculated from covariates associated with sociodemographic factors, comorbidities, and medication use for more than 1000 prescription types. Propensity score-weighted mortality hazards were calculated from Cox proportional hazards regression models. Results: Of 5212 participants aged 20 years or older (1993 men [38.2%]; mean [SD] age, 54.8 [16.9] years) followed up for a median of 6.7 years (range, 0.2-16.8 years), 101 deaths (33.0 per 1000 person-years) occurred among those receiving cotreatment, 236 deaths (26.5 per 1000 person-years) occurred among those receiving only benzodiazepines, and 227 deaths (20.2 per 1000 person-years) occurred among SSRI recipients taking neither opioids nor benzodiazepines. After propensity score weighting, a significant increase in all-cause mortality was associated with benzodiazepine and opioid cotreatment (hazard ratio, 2.04 [95% CI, 1.65-2.52]) and benzodiazepines without opioids (hazard ratio, 1.60 [95% CI, 1.33-1.92]). Subgroup analyses revealed an increased risk of mortality for individuals receiving cotreatment who were 65 years or younger but not for those older than 65 years; similar findings were observed for those receiving benzodiazepines without opioids. Conclusions and Relevance: This study found a significant increase in all-cause mortality associated with benzodiazepine use with or without opioid use in comparison with SSRI use. Benzodiazepine and opioid cotreatment, in particular, was associated with a 2-fold increase in all-cause mortality even after taking into account medical comorbidities and polypharmacy burden.

A Cascade of Care for Alcohol Use Disorder: Using 2015-2018 National Survey on Drug Use and Health Data to Identify Gaps in Care.

BACKGROUND: Although effective treatments exist, alcohol use disorder (AUD) is undertreated. We used a cascade of care framework to understand gaps in care between diagnosis and treatment for persons with AUD. METHODS: Using 2015-2018 National Survey on Drug Use and Health data, we evaluated the following steps in the cascade of care: 1) prevalence of adults with AUD; 2) proportion of adults who utilized health care in the past 12 months; 3) were screened about alcohol use; 4) received a brief intervention about alcohol misuse; 5) received information about treatment for alcohol misuse; and 6) proportion of persons with AUD who received treatment. Analyses were stratified by AUD severity. RESULTS: Of the 171,766 persons included in the sample, weighted prevalence of AUD was 7.9% (95% CI 7.7-8.0%). Persons with AUD utilized health care settings at similar rates as those without AUD. Cascades of care showed the majority of individuals with AUD utilized health care and were screened about alcohol use, but the percent who received the subsequent steps of care decreased substantially. For those with severe AUD, 83.5% (CI: 78.3%-88.7%) utilized health care in the past 12 months, 73.5% (CI: 68.1%-78.9%) were screened for alcohol use, 22.7% (CI: 19.4%-26.0%) received a brief intervention, 12.4% (CI: 10%-14.7%) received information about treatment, and 20.5% (CI: 18%-23.1%) were treated for AUD. The greatest decrease in the care continuum occurred from screening to brief intervention and referral to treatment. More persons with severe AUD received treatment than were referred, indicating other pathways to treatment outside of the healthcare system. CONCLUSIONS: Persons with AUD utilize health care at high rates and are frequently screened about alcohol use, but few receive treatment. Health care settings-particularly primary care settings-represent a prime opportunity to implement pharmacologic treatment for AUD to improve outcomes in this high-risk population.

A large-scale genome-wide association study meta-analysis of cannabis use disorder.

BACKGROUND: Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50-70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder. METHODS: To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations. FINDINGS: We identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07-1·15, p=1·84 × 10-9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86-0·93, p=6·46 × 10-9). Cannabis use disorder and cannabis use were genetically correlated (rg 0·50, p=1·50 × 10-21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia. INTERPRETATION: These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder. FUNDING: National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.

Dissecting the genetic overlap of smoking behaviors, lung cancer, and chronic obstructive pulmonary disease: A focus on nicotinic receptors and nicotine metabolizing enzyme.

Smoking is a major contributor to lung cancer and chronic obstructive pulmonary disease (COPD). Two of the strongest genetic associations of smoking-related phenotypes are the chromosomal regions 15q25.1, encompassing the nicotinic acetylcholine receptor subunit genes CHRNA5-CHRNA3-CHRNB4, and 19q13.2, encompassing the nicotine metabolizing gene CYP2A6. In this study, we examined genetic relations between cigarettes smoked per day, smoking cessation, lung cancer, and COPD. Data consisted of genome-wide association study summary results. Genetic correlations were estimated using linkage disequilibrium score regression software. For each pair of outcomes, z-score-z-score (ZZ) plots were generated. Overall, heavier smoking and decreased smoking cessation showed positive genetic associations with increased lung cancer and COPD risk. The chromosomal region 19q13.2, however, showed a different correlational pattern. For example, the effect allele-C of the sentinel SNP (rs56113850) within CYP2A6 was associated with an increased risk of heavier smoking (z-score = 19.2; p = 1.10 × 10-81 ), lung cancer (z-score = 8.91; p = 5.02 × 10-19 ), and COPD (z-score = 4.04; p = 5.40 × 10-5 ). Surprisingly, this allele-C (rs56113850) was associated with increased smoking cessation (z-score = -8.17; p = 2.52 × 10-26 ). This inverse relationship highlights the need for additional investigation to determine how CYP2A6 variation could increase smoking cessation while also increasing the risk of lung cancer and COPD likely through increased cigarettes smoked per day.

Extreme Overvalued Beliefs.

An extreme overvalued belief is shared by others in a person's cultural, religious, or subcultural group. The belief is often relished, amplified, and defended by the possessor of the belief and should be differentiated from a delusion or obsession. Over time, the belief grows more dominant, more refined, and more resistant to challenge. The individual has an intense emotional commitment to the belief and may carry out violent behavior in its service. Study participants (n = 109 forensic psychiatrists) were asked to select among three definitions (i.e., obsession, delusion, and extreme overvalued belief) as the motive for the criminal behavior seen in 12 randomized fictional vignettes. Strong interrater agreement (kappa = 0.91 [95% CI 0.83-0.98]) was seen for vignettes representing extreme overvalued belief. Vignettes representing delusion and obsession also had strong reliability (kappa = 0.99 for delusion and 0.98 for obsession). This preliminary report suggests that forensic psychiatrists, given proper definitions, possess a substantial ability to identify delusion, obsession, and extreme overvalued belief. The rich historical foundation of extreme overvalued belief and this small survey study highlight the benefit of inclusion of "extreme overvalued belief" in future glossaries of the Diagnostic and Statistical Manual.

Identifying blood pressure loci whose effects are modulated by multiple lifestyle exposures.

Although multiple lifestyle exposures simultaneously impact blood pressure (BP) and cardiovascular health, most analysis so far has considered each single lifestyle exposure (e.g., smoking) at a time. Here, we exploit gene-multiple lifestyle exposure interactions to find novel BP loci. For each of 6,254 Framingham Heart Study participants, we computed lifestyle risk score (LRS) value by aggregating the risk of four lifestyle exposures (smoking, alcohol, education, and physical activity) on BP. Using the LRS, we performed genome-wide gene-environment interaction analysis in systolic and diastolic BP using the joint 2 degree of freedom (DF) and 1 DF interaction tests. We identified one genome-wide significant (p < 5 × 10-8 ) and 11 suggestive (p < 1 × 10-6 ) loci. Gene-environment analysis using single lifestyle exposures identified only one of the 12 loci. Nine of the 12 BP loci detected were novel. Loci detected by the LRS were located within or nearby genes with biologically plausible roles in the pathophysiology of hypertension, including KALRN, VIPR2, SNX1, and DAPK2. Our results suggest that simultaneous consideration of multiple lifestyle exposures in gene-environment interaction analysis can identify additional loci missed by single lifestyle approaches.

Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium.

To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = -5.39, p = 7.2 × 10-8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10-8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10-6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10-8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10-7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10-5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10-5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10-5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.