The significant impact of education, poverty, and race on Internet-based research participant engagement

PURPOSE: Internet-based technologies are increasingly being used for research studies. However, it is not known whether Internet-based approaches will effectively engage participants from diverse racial and socioeconomic backgrounds. METHODS: A total of 967 participants were recruited and offered genetic ancestry results. We evaluated viewing Internet-based genetic ancestry results among participants who expressed high interest in obtaining the results. RESULTS: Of the participants, 64% stated that they were very or extremely interested in their genetic ancestry results. Among interested participants, individuals with a high school diploma (n = 473) viewed their results 19% of the time relative to 4% of the 145 participants without a diploma (P < 0.0001). Similarly, 22% of participants with household income above the federal poverty level (n = 286) viewed their results relative to 10% of the 314 participants living below the federal poverty level (P < 0.0001). Among interested participants both with a high school degree and living above the poverty level, self-identified Caucasians were more likely to view results than self-identified African Americans (P < 0.0001), and females were more likely to view results than males (P = 0.0007). CONCLUSION: In an underserved population, engagement in Internet-based research was low despite high reported interest. This suggests that explicit strategies should be developed to increase diversity in Internet-based research. Genet Med 19 2, 240–243

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

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

Associations between polygenic risk for psychiatric disorders and substance involvement

Despite evidence of substantial comorbidity between psychiatric disorders and substance involvement, the extent to which common genetic factors contribute to their co-occurrence remains understudied. In the current study, we tested for associations between polygenic risk for psychiatric disorders and substance involvement (i.e., ranging from ever-use to severe dependence) among 2573 non-Hispanic European-American participants from the Study of Addiction: Genetics and Environment. Polygenic risk scores (PRS) for cross-disorder psychopathology (CROSS) were generated based on the Psychiatric Genomics Consortium’s Cross-Disorder meta-analysis and then tested for associations with a factor representing general liability to alcohol, cannabis, cocaine, nicotine, and opioid involvement (GENSUB). Follow-up analyses evaluated specific associations between each of the 5 psychiatric disorders which comprised CROSS—attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (AUT), bipolar disorder (BIP), major depressive disorder (MDD), and schizophrenia (SCZ)—and involvement with each component substance included in GENSUB. CROSS PRS explained 1.10% of variance in GENSUB in our sample (p<0.001). After correction for multiple testing in our follow-up analyses of polygenic risk for each individual disorder predicting involvement with each component substance, associations remained between: A) MDD PRS and non-problem cannabis use, B) MDD PRS and severe cocaine dependence, C) SCZ PRS and non-problem cannabis use and severe cannabis dependence, and D) SCZ PRS and severe cocaine dependence. These results suggest that shared covariance from common genetic variation contributes to psychiatric and substance involvement comorbidity

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 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

A New Statistic to Evaluate Imputation Reliability

As the amount of data from genome wide association studies grows dramatically, many interesting scientific questions require imputation to combine or expand datasets. However, there are two situations for which imputation has been problematic: (1) polymorphisms with low minor allele frequency (MAF), and (2) datasets where subjects are genotyped on different platforms. Traditional measures of imputation cannot effectively address these problems.We introduce a new statistic, the imputation quality score (IQS). In order to differentiate between well-imputed and poorly-imputed single nucleotide polymorphisms (SNPs), IQS adjusts the concordance between imputed and genotyped SNPs for chance. We first evaluated IQS in relation to minor allele frequency. Using a sample of subjects genotyped on the Illumina 1 M array, we extracted those SNPs that were also on the Illumina 550 K array and imputed them to the full set of the 1 M SNPs. As expected, the average IQS value drops dramatically with a decrease in minor allele frequency, indicating that IQS appropriately adjusts for minor allele frequency. We then evaluated whether IQS can filter poorly-imputed SNPs in situations where cases and controls are genotyped on different platforms. Randomly dividing the data into "cases" and "controls", we extracted the Illumina 550 K SNPs from the cases and imputed the remaining Illumina 1 M SNPs. The initial Q-Q plot for the test of association between cases and controls was grossly distorted (lambda = 1.15) and had 4016 false positives, reflecting imputation error. After filtering out SNPs with IQS<0.9, the Q-Q plot was acceptable and there were no longer false positives. We then evaluated the robustness of IQS computed independently on the two halves of the data. In both European Americans and African Americans the correlation was >0.99 demonstrating that a database of IQS values from common imputations could be used as an effective filter to combine data genotyped on different platforms.IQS effectively differentiates well-imputed and poorly-imputed SNPs. It is particularly useful for SNPs with low minor allele frequency and when datasets are genotyped on different platforms

Genome-wide analysis of self-reported risk-taking behaviour and cross-disorder genetic correlations in the UK Biobank cohort

Risk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use, and diet. As a phenotype, risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby identifying genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome-wide association study in 116,255 UK Biobank participants who responded yes/no to the question “Would you consider yourself a risk taker?” Risk takers (compared with controls) were more likely to be men, smokers, and have a history of psychiatric disorder. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behaviour shared significant genetic risk with schizophrenia, bipolar disorder, attention-deficit hyperactivity disorder, and post-traumatic stress disorder, as well as with smoking and total obesity. Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behaviour, a trait that has a major impact on a range of common physical and mental health disorders

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 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

Comorbidity of Severe Psychotic Disorders With Measures of Substance Use

Although early mortality in severe psychiatric illness is linked to smoking and alcohol, no studies have comprehensively characterized substance use behavior in severe psychotic illness. In particular, recent assessments of substance use in individuals with mental illness are based on population surveys that do not include individuals with severe psychotic illness