Substance abuse最新文献

Background: Individuals with pain prescribed opioids experience high rates of comorbid depression. The aim of this study was to characterize pain, substance use, and health status as a function of depressive symptom level in individuals filling an opioid prescription at a community pharmacy. Methods: Participants (N = 1268) filling an opioid prescription enrolled in a study validating a prescription drug monitoring metric completed an online survey assessing sociodemographics, depressive symptoms, substance use, prescription opioid misuse, overdose history, general health, and pain severity and interference. Results: Approximately one-fifth (19.3%) had a positive depression screen result. In covariate-adjusted logistic regression analyses, individuals with a positive depression screen result were more likely to have moderate/high substance use risk scores for prescription opioids (adjusted odds ratio [AOR] = 2.06; 95% confidence interval [CI], 1.51-2.79); street opioids (AOR = 7.18; 95% CI, 2.57-20.01); cannabis (AOR = 2.00; 95% CI, 1.34-3.00); cocaine (AOR = 3.46; 95% CI, 1.46-8.22); tobacco (AOR = 1.59; 95% CI, 1.18-2.15); methamphetamine (AOR = 7.59; 95% CI, 2.58-22.35); prescription stimulants (AOR = 2.95; 95% CI, 1.59-5.49); and sedatives (AOR = 3.41; 95% CI, 2.43-4.79). Individuals with a positive depression screen were more likely to misuse prescription opioids (AOR = 3.46; 95% CI, 2.33-5.15), experience a prior overdose (AOR = 2.69; 95% CI, 1.76-4.11), report poorer general health (AOR = 0.25, 95% CI, 0.18-0.35), and report moderate/severe pain severity (AOR = 4.36, 95% CI, 2.80-6.77) and interference (AOR = 6.47, 95% CI, 4.08-10.26). Conclusions: Individuals prescribed opioids with heightened depression were more likely to report other substance use, prescription opioid misuse, prior overdose, greater pain, and poorer health.

Background: Women with alcohol disorders have more severe problems related to their drinking than men. They have higher mortality from alcohol-related accidents and enter treatment with more serious medical, psychiatric, and social consequences.

Objective: This study assessed the effects of Intensive Motivational Interviewing (IMI), a new, 9-session counseling intervention for women with drinking problems.

Methods: A randomized clinical trial was conducted with 215 women. Most were white (83%), college educated (61%), and older (mean age 51). Half received IMI and half a standard single session of MI (SMI) along with an attention control (nutritional education).

Results: Generalized estimating equations models showed women who were heavy drinkers at baseline in the IMI condition reduced heavy drinking more than those in the SMI condition at 2-, 6-, and 12-month follow-up. Analyses of disaggregated subgroups showed IMI was most effective for women with low psychiatric severity, more severe physical and impulse control consequences associated with drinking, and higher motivation. However, formal 3-way interaction models (condition by moderator by time) showed significant effects primarily at 2 months.

Conclusions: Improvements associated with IMI were limited to heavy drinking and varied among subgroups of women. Studies of women with more diverse characteristics are needed.

Background: The opioid epidemic continues to erode communities across Pennsylvania (PA). Federal and PA state programs developed grants to establish Hub and Spoke programs for the expansion of medications for opioid use disorders (MOUD). Employing the telementoring platform Project ECHO (Extension for Community Health Outcomes), Penn State Health engaged the other seven grant awardees in a Collaborative Health Systems (CHS) ECHO. We conducted key informant interviews to better understand impact of the CHS ECHO on health systems collaboration and opioid crisis efforts. Methods: For eight one-hour sessions, each awardee presented their unique strategies, challenges, and opportunities. Using REDCap, program characteristics, such as number of waivered prescribers and number of patients served were collected at baseline. After completion of the sessions, key informant interviews were conducted to assess the impact of CHS ECHO on awardee's programs. Results: Analysis of key informant interviews revealed important themes to address opioid crisis efforts, including the need for strategic and proactive program reevaluation and the convenience of collaborative peer learning networks. Participants expressed benefits of the CHS ECHO including allowing space for discussion of challenges and best practices and facilitating conversation on collaborative targeted advocacy and systems-level improvements. Participants further reported bolstered motivation and confidence. Conclusions: Utilizing Project ECHO provided a bidirectional platform of learning and support that created important connections between institutions working to combat the opioid epidemic. CHS ECHO was a unique opportunity for productive and convenient peer learning across external partners. Open dialogue developed during CHS ECHO can continue to direct systems-levels improvements that benefit individual and population outcomes.

Background: Adherence to clinical practice guidelines for alcohol and drug screening, brief intervention, and referral to treatment (SBIRT) is often inadequate. Mobile apps developed as clinical translation tools could improve the delivery of high fidelity SBIRT.

Methods: This study tested the effectiveness of an SBIRT mobile app conceptually aligned with the Theory of Planned Behavior (TPB) to support SBIRT delivery by health care trainees (nursing, social work, internal medicine, psychiatry, and psychology) working in clinical settings (N = 101). Bivariate analyses examined the rate of SBIRT delivery between trainees assigned to the experimental (app) and control (no app) study conditions; as well as the relationship between TPB-based constructs, intention to deliver SBIRT, and screening rates.

Results: No significant differences were identified between the study conditions in SBIRT delivery. Significant correlations were found between intent to screen and TPB variables including attitudes/behavioral beliefs concerning substance use treatment (r = .49, p = .01); confidence in clinical skills (r = .36, p = .01); subjective norms (r = .54, p = .01) and perceived behavioral control over appointment time constraints (r = .42, p = .01). Also significant were correlations between percent of patients screened and confidence (r = .24, p = .05); subjective norms (r = .22, p = .05) and perceived behavioral control (r = .28, p = .01).

Conclusions: The negative results of the study condition comparisons indicate the need for further investigation of strategies to optimize mobile app utilization, engagement, and effectiveness as a clinical translation tool. Findings of significant correlations between substance use screening rates and both norms and confidence support the potential value of the TPB model in explaining behavior of health care learners in SBIRT delivery.

Background: Accurate prevalence estimates are critical to epidemiological research but discordant responses on self-report surveys can lead to over- or underestimation of drug use. We sought to examine the extent and nature of underreported cannabis use (among those later reporting blunt use) from a national drug survey in the US. Methods: We used data from the 2015-2019 National Survey on Drug Use and Health (N = 281,650), a nationally representative probability sample of non-institutionalized populations in the US. We compared self-reported prevalence of past-year cannabis use and blunt use and delineated correlates of underreporting cannabis use, defined as reporting blunt use but not overall cannabis use. Results: An estimated 4.8% (95% CI: 4.4-5.2) of people reported blunt use but not cannabis use. Although corrected prevalence, cannabis use recoded as use only increased from 15.2% (95% CI: 15.0-15.4) to 15.5% (95% CI: 15.3-15.7), individuals who are aged ≥50 (aOR = 1.81, 95% CI: 1.06-3.08), female (aOR = 1.35, 95% CI: 1.12-1.62), Non-Hispanic Black (aOR = 1.43, 95% CI: 1.16-1.76), or report lower English proficiency (aOR = 3.32, 95% CI: 1.40-7.83) are at increased odds for providing such a discordant response. Individuals with a college degree (aOR = 0.57, 95% CI: 0.39-0.84) and those reporting past-year use of tobacco (aOR = 0.75, 95% CI: 0.62-0.91), alcohol (aOR = 0.42, 95% CI: 0.33-0.54), cocaine (aOR = 0.50, 95% CI: 0.34-0.73), or LSD (aOR = 0.52, 95% CI: 0.31-0.87) were at lower odds of providing a discordant response. Conclusion: Although changes in prevalence are small when correcting for discordant responses, results provide insight into subgroups that may be more likely to underreport use on surveys.

Background: As the drug-related overdose crisis and COVID-19 pandemic continue, communities need increased access to medications for opioid use disorder (MOUD) (i.e., buprenorphine and methadone). Disparities in the type of MOUD prescribed or administered by racial and ethnic categories are well described in the outpatient clinical environment. It is unknown, however, if these disparities persist when MOUD is provided in acute care hospitals. Methods: This study assessed differences in the delivery of buprenorphine versus methadone during acute medical or surgical hospitalizations for veterans with opioid use disorder (OUD) by racial categories (Black Non-Hispanic or Latino vs. White Non-Hispanic or Latino). Data were obtained retrospectively from the Veterans Health Administration (VHA) for federal fiscal year 2017. We built logistic regression models, adjusted for individual and hospital-related covariates, and calculated the predicted probabilities of MOUD delivery by racial categories. Results: The study cohort (n = 1,313 unique patients; N = 107 VHA hospitals) had a mean age of 57 (range 23 to 87 years), was predominantly male (96%), and composed entirely of Black (29%) or White (71%) patients. White patients were 11% more likely than Black patients to receive buprenorphine than methadone during hospitalization (p = 0.010; 95% CI: 2.7%, 20.0%). Among patients on MOUD prior to hospitalization, White patients were 21% more likely than Black patients to receive buprenorphine (p = 0.000; 95% CI: 9.8%, 31.5%). Among patients newly initiated on MOUD during hospitalization, there were no differences by racial categories. Conclusion: We observed disparities in the delivery of buprenorphine versus methadone during hospitalization by racial categories. The observed differences in hospital-based MOUD delivery may be influenced by MOUD received prior to hospitalization within the racialized outpatient addiction treatment system. The VHA and health systems more broadly must address all aspects of racism that contribute to inequitable MOUD access throughout all clinical contexts.

Background: COVID-19 is disproportionately affecting disadvantaged populations, including persons with opioid use disorder (OUD). Persons with OUD are at greater risk for direct (e.g., COVID-19 diagnosis, severe symptoms) and indirect consequences (relapse, overdose) of COVID-19. Given stay at home orders, at-home testing options may serve as a viable tool to curb the increase in COVID-19 transmission and adverse effects of COVID-19. Methods: From May to September 2020, we surveyed 110 persons in treatment for OUD about their willingness to use various self-administered at-home COVID-19 testing measures. Participants were stratified by age, gender, and racial-ethnic identity to observe differences in willingness to use COVID-19 tests. The three COVID-19 tests included a throat swab sample, a saliva-based sample, and a blood prick test. Results: A high willingness to use at-home COVID-19 tests was observed in patients in treatment for OUD. A greater proportion of females were willing to use a saliva-based test; greater proportions of White participants were willing to use a saliva based COVID-19 test compared to racial-ethnic minorities. Older aged participants (≥45 years old) reported greater rates of willingness to use a self-administered at home blood prick test for COVID-19. Conclusions: At-home testing measures for COVID-19 appear highly acceptable among people with OUD.

Medication treatment for opioid use disorder (MOUD) is an effective evidence-based therapy for decreasing opioid-related adverse outcomes. Effective strategies for retaining persons on MOUD, an essential step to improving outcomes, are needed as roughly half of all persons initiating MOUD discontinue within a year. Data science may be valuable and promising for improving MOUD retention by using "big data" (e.g., electronic health record data, claims data mobile/sensor data, social media data) and specific machine learning techniques (e.g., predictive modeling, natural language processing, reinforcement learning) to individualize patient care. Maximizing the utility of data science to improve MOUD retention requires a three-pronged approach: (1) increasing funding for data science research for OUD, (2) integrating data from multiple sources including treatment for OUD and general medical care as well as data not specific to medical care (e.g., mobile, sensor, and social media data), and (3) applying multiple data science approaches with integrated big data to provide insights and optimize advances in the OUD and overall addiction fields.

Background: Screening for unhealthy drug use is now recommended for adult primary care patients, but primary care providers (PCPs) generally lack the time and knowledge required to screen and deliver an intervention during the medical visit. To address these barriers, we developed a tablet computer-based 'Substance Use Screening and Intervention Tool (SUSIT)'. Using the SUSIT, patients self-administer screening questionnaires prior to the medical visit, and results are presented to the PCP at the point of care, paired with clinical decision support (CDS) that guides them in providing a brief intervention (BI) for unhealthy drug use. Methods: PCPs and their patients with moderate-risk drug use were recruited from primary care and HIV clinics. A pre-post design compared a control 'screening only' (SO) period to an intervention 'SUSIT' period. Unique patients were enrolled in each period. In both conditions, patients completed screening and identified their drug of most concern (DOMC) before the visit, and completed a questionnaire about BI delivery by the PCP after the visit. In the SUSIT condition only, PCPs received the tablet with the patient's screening results and CDS. Multilevel models with random intercepts and patients nested within PCPs examined the effect of the SUSIT intervention on PCP delivery of BI. Results: 20 PCPs and 79 patients (42 SO, 37 SUSIT) participated. Most patients had moderate-risk marijuana use (92.4%), and selected marijuana as the DOMC (68.4%). Moderate-risk use of drugs other than marijuana included cocaine (15.2%), hallucinogens (12.7%), and sedatives (12.7%). Compared to the SO condition, patients in SUSIT had higher odds of receiving any BI for drug use, with an adjusted odds ratio of 11.59 (95% confidence interval: 3.39, 39.25), and received more elements of BI for drug use. Conclusions: The SUSIT significantly increased delivery of BI for drug use by PCPs during routine primary care encounters.

Background: Our study explored whether latent classes adequately represented the social capital recovery indicators at the resident level and whether latent class membership predicted subsequent exits from the recovery homes. Method: Our sample included about 600 residents in 42 recovery homes. Over a 2-year period of time, every 4 months, data were collected on eight elements of recovery capital. Results: We found 5 latent classes were optimal for representing 8 elements of recovery capital. Representing 79% of the sample, 3 of the 5 latent class profiles of the means of the 8 recovery indicators were roughly parallel and differed only in level, but the remaining 2 latent class profiles, representing 21% of the sample, were not parallel to the first 3, suggesting that a single quantitative dimension of perceived recovery may capture most but not all of the important details of the recovery process. Next, using longitudinal data from homes, the distal outcomes of resident eviction and voluntary exit were found to be related to latent class membership. Resident level pre-existing predictors (e.g., employment status, educational attainment, gender, Latinx ethnicity) and house level pre-existing predictors (e.g., financial health, poverty level of typical population served, new resident acceptance rate) significantly discriminated the classes. In a model that combined both pre-existing predictors and distal outcomes, latent class membership was still the strongest predictor of evictions controlling for the pre-existing predictors. Conclusions: These classes help to clarify the different aspects of the recovery latent score, and point to classes that have different ethnic and gender characteristics as well as outcomes in the recovery homes. For example, the high levels of self-confidence found in class 3 suggest that Latinx might be at higher risk for having some difficulties within these recovery communities.