Mapping prescription drug monitoring program data to self-report measures of non-medical prescription opioid use in community pharmacy settings.

BACKGROUND: Community pharmacists are well-positioned to identify patients engaged in non-medical prescription opioid use (NMPOU) through Prescription Drug Monitoring Program (PDMP) databases. Integrating patient-reported outcomes with PDMP data may improve the interpretability of PDMP information to support clinical decision-making. OBJECTIVE: This study linked patient-reported clinical measures of substance use with PDMP data to examine relationships between average daily opioid dose in morphine milligram equivalents (MME) and visits to multiple pharmacies/prescribers with self-reported NMPOU. METHODS: Data from a cross-sectional health assessment given to patients aged ≥18 years filling opioid prescriptions were linked to PDMP records. NMPOU in the past three months was assessed on a continuous scale (range 0-39) using an adapted version of the Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST). PDMP measures included average daily MME and number of distinct pharmacies/prescribers visited in the past 180 days. Univariable and multivariable zero-inflated negative binomial models estimated associations between PDMP measures and any NMPOU and severity of use. RESULTS: The sample included 1421 participants. In multivariable models adjusted for sociodemographic, mental health, and physical health characteristics, any NMPOU was associated with higher average daily MME (adjusted OR = 1.22, 95% CI = 1.05-1.39) and number of distinct prescribers visited (adjusted OR = 1.15, 95% CI = 1.01-1.30). Higher average daily MME (adjusted mean ratio (MR) = 1.12, 95% CI = 1.08-1.15), number of distinct pharmacies visited (adjusted MR = 1.11, 95% CI = 1.04-1.18), and number of distinct prescribers visited (adjusted MR = 1.07, 95% CI = 1.02-1.11) were associated with increased NMPOU severity. CONCLUSIONS: We observed significant, positive associations between average daily MME and visits to multiple pharmacies/prescribers with any NMPOU and severity of use. This study demonstrates self-report clinical measures of substance use can be cross-walked to PDMP data and translated into clinically interpretable information.

Associations between elevated depressive symptoms and substance use, prescription opioid misuse, overdose history, pain, and general health among community pharmacy patients prescribed opioids.

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.

Pain Severity and Interference and Substance Use Among Community Pharmacy Patients Prescribed Opioids: A Secondary Analysis of the PHARMSCREEN Study.

This secondary analysis examined relationships between pain severity and interference and substance use among patients filling opioid prescriptions in Indiana and Ohio community pharmacies (n = 1,461). We likewise sought to explore the moderating role of gender in pain-substance use relations. We used patient-reported data from a cross-sectional health survey linked with controlled substance dispensing data from statewide prescription drug monitoring programs. Multivariable logistic regression estimated associations between pain severity and interference and various indices of risky prescription opioid use and non-opioid substance use. Exploratory analyses examined whether gender moderated associations. Increased pain severity was associated with increased odds of moderate- to high-risk opioid use (OR: 1.23; 95% CI: 1.16-1.31) and opioid-benzodiazepine co-use (OR: 1.20; 95% CI: 1.03-1.40). Increased pain interference was associated with greater odds of receiving opioids from multiple pharmacies or providers (OR: 1.15; 95% CI: 1.01-1.31). Increased pain severity and interference were associated with higher odds of any tobacco use (severity: OR: 1.13; 95% CI: 1.06-1.21; interference: OR: 1.07; 95% CI: 1.01-1.12) and weekly to daily sedative use (severity: OR: 1.13; 95% CI: 1.03-1.25; interference: OR: 1.13; 95% CI: 1.04-1.22). Increased pain severity was associated with decreased odds of any alcohol use (OR: 0.93; 95% CI: 0.88-0.99). Gender was a significant effect modifier in associations between pain and alcohol, tobacco, and cannabis use. The study was registered in the database of clinicaltrials.gov (register number NCT03936985). Perspective: This study suggests that pain severity and interference are associated with increased use of non-medical prescription opioids, sedatives, and tobacco and decreased use of alcohol, in ways that are different between women and men. Findings may guide the development of gender-sensitive evidence-based strategies to ameliorate or prevent substance misuse among patients living with pain.

Risky alcohol use among patients dispensed opioid medications: A clinical community pharmacy study.

BACKGROUND: Included among the significant risk factors for opioid overdose is concomitant use of other central nervous system depressants, particularly alcohol. Given the continued expansion of community pharmacy in the continuum of care, it is imperative to characterize alcohol use among pharmacy patients dispensed opioids in order to establish a foundation for identification and intervention in these settings. METHODS: This secondary analysis utilized data from a one-time, cross-sectional health assessment conducted among patients dispensed opioid medications in 19 community pharmacies in Indiana and Ohio. Adult, English speaking, patients not receiving cancer care who were dispensed opioid medications were asked to self-report alcohol and substance use, behavioral and physical health, and demographic information. Descriptive and logistic regression analyses were employed to characterize alcohol use/risky alcohol use and patient characteristics associated therewith. RESULTS: The analytical sample included 1494 individuals. Participants were on average 49 years of age (Standard Deviation=14.9)-with 6% being persons of color (n = 89). Weekly drinking was reported by 18.1% (n = 204) and daily drinking was reported by 6.8% (n = 77) of the study sample, with a total of 143 (9.6%) participants reporting moderate/high risk drinking. Males (Adjusted Odds Ratio [AOR]=1.94, 95% CI=1.3,2.9), those with higher pain interference (AOR=1.44, 95% CI=1.0,2.0), overdose history (AOR=1.93, 95% CI=1.1,3.5), sedative use (AOR=2.11, 95% CI=1.3,3.5), and tobacco use (AOR=2.41, 95% CI=1.6,3.7) had increased likelihood of moderate/high risk alcohol use (all p < 0.05). CONCLUSIONS: Medication labeling and clinical guidelines clearly indicate that patients should abstain from concomitant use of opioids and alcohol. This study has identified rates and associated risk factors of risky alcohol use among a clinical sample of community pharmacy patients dispensed opioid medications. Continuing this line of research and potential clinical service development has the ability to improve patient safety through addressing a significant gap within the current opioid epidemic.

Validation of the tobacco, alcohol, prescription medication, and other substance use (TAPS) tool with the WHO alcohol, smoking, and substance Involvement screening test (ASSIST).

INTRODUCTION: Community pharmacies are emerging as a valuable setting to identify patients with substance use. Few tools have been specially validated to screen patients in these settings, particularly among those prescribed opioid medications. The goal of this study was to validate the performance of the Tobacco, Alcohol, Prescription medication, and other Substance use (TAPS) tool in community pharmacy settings compared to a reference-standard substance use assessment. METHODS: Participants were recruited while receiving opioid medications (not solely buprenorphine) from 19 pharmacies from a large national chain in Ohio and Indiana. Adults who were not involved in the criminal justice system or receiving cancer treatment were invited to participate in a one-time, cross-sectional, self-administered, health survey which included the TAPS tool. Substance use risks calculated from the TAPS tool were compared with the reference standard, World Health Organization Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) using confusion matrices. We calculated Areas Under the Curve (AUC) of Receiver Operating Characteristics Curves (ROC) to evaluate the TAPS tool's validity. RESULTS: The TAPS tool showed fair or better discrimination between moderate-risk use and high-risk use for tobacco, alcohol, and prescription opioids (AUCs: 0.75-0.97 and fair or better discrimination between low-risk and moderate-risk use in five of eight subscales, including tobacco, alcohol, marijuana, stimulants, and heroin (AUCs: 0.70-0.92). CONCLUSION: The TAPS tool detected clinically relevant problem substance use in several drug classes and likely would be a valuable assessment for screening illicit drug use among community pharmacy patients prescribed opioid medications.

Validation and threshold identification of a prescription drug monitoring program clinical opioid risk metric with the WHO alcohol, smoking, and substance involvement screening test.

BACKGROUND: Prescription drug monitoring programs (PDMPs) are critical for pharmacists to identify risky opioid medication use. We performed an independent evaluation of the PDMP-based Narcotic Score (NS) metric. METHODS: This study was a one-time, cross-sectional health assessment within 19 pharmacies from a national chain among adults picking-up opioid medications. The NS metric is a 3-digit composite indicator. The WHO Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) was the gold-standard to which the NS metric was compared. Machine learning determined optimal risk thresholds; Receiver Operating Characteristic curves and Spearman (P) and Kappa (K) coefficients analyzed concurrent validity. Regression analyses evaluated participant characteristics associated with misclassification. RESULTS: The NS metric showed fair concurrent validity (area under the curve≥0.70; K=0.35; P = 0.37, p < 0.001). The ASSIST and NS metric categorized 37% of participants as low-risk (i.e., not needing screening/intervention) and 32.3% as moderate/high-risk (i.e., needing screening/intervention). Further, 17.2% were categorized as low ASSIST risk but moderate/high NS metric risk, termed false positives. These reported disability (OR=3.12), poor general health (OR=0.66), and/or greater pain severity/interference (OR=1.12/1.09; all p < 0.05; i.e., needing unmanaged-pain screening/intervention). A total of 13.4% were categorized as moderate/high ASSIST risk but low NS metric risk, termed false negatives. These reported greater overdose history (OR=1.24) and/or substance use (OR=1.81-12.66; all p < 0.05). CONCLUSIONS: The NS metric could serve as a useful initial universal prescription opioid-risk screener given its: 1) low-burden (i.e., no direct assessment); 2) high accuracy (86.5%) of actionable data identifying low-risk patients and those needing opioid use/unmanaged pain screening/intervention; and 3) broad availability.

Concomitant Cannabis Misuse and Associations with Depression, Pain and Substance Misuse among Patients Prescribed Opioids.

BACKGROUND: Cannabis use is common among individuals with pain who are prescribed opioids, occurring in approximately 10% of this population. This study aims to explore the relationship between non-medical cannabis use and other health risks among individuals filling opioids at community pharmacies. METHODS: This study was an exploratory secondary data analysis of a National Drug Abuse Treatment Clinical Trials Network (CTN)-sponsored study, Validation of a Community Pharmacy-Based Prescription Drug Monitoring Program Risk Screening, examining the relationship between risky cannabis use and depressive symptoms, pain, overdose, and other substance misuse among individuals filling opioid prescriptions in community pharmacies (N = 1440). RESULTS: Participants reporting moderate- to high-risk compared to low-risk cannabis use were more likely to report depressive symptoms (adjusted OR = 1.67, 95% CI = 1.11-2.56), history of overdose (adjusted OR = 2.15, 95% CI = 1.34-3.44), and moderate- to high-risk use of alcohol (adjusted OR = 2.10, 95% CI = 1.28-3.45), opioids (adjusted OR = 2.50, 95% CI = 1.67-3.76), sedatives (adjusted OR = 2.58, 95% CI = 1.72-3.86), stimulants (adjusted OR = 4.79, 95% CI = 2.83-8.01), and tobacco (adjusted OR = 3.60, 95% CI = 2.47-5.24). CONCLUSIONS: Community pharmacies may be valuable sites for identifying, studying, and intervening with substance use problems.

Pain Appraisals in Patients with Physical Injury: Assessing the Role of Distress Tolerance in the Relationship between Depression and Pain Catastrophizing.

Pain is a complex construct contributing to significant impairment, particularly among physically injured patients seeking treatment in trauma and orthopedic surgery settings in which social workers are an integral component of care. The biopsychosocial theory, fear-avoidance, and cognitive mediation models of pain suggest that psychological factors (for example, depression) affect one's ability to tolerate distress, leading to negative pain appraisals, such as catastrophizing. This study examined whether distress tolerance serves as a mechanism by which depression is associated with pain catastrophizing. We administered a health survey to outpatient trauma and orthopedic surgery clinic patients who were using opioid medications; 84 patients were included in the final analysis; 39.3 percent screened positive for depression. A multilevel mediation model using structural equation modeling revealed a significant direct effect from depression to pain catastrophizing (ß = .31, z = 3.96, p < .001) and a significant indirect effect by distress tolerance (Δß = .27, z = 3.84, p < .001). These results, which suggest that distress tolerance partially mediated the path from depression to pain catastrophizing, can inform social workers and other members of the multidisciplinary team about both the critical role of psychosocial factors after injury and interventions to improve postinjury recovery.

Using machine learning to predict risk of incident opioid use disorder among fee-for-service Medicare beneficiaries: A prognostic study.

OBJECTIVE: To develop and validate a machine-learning algorithm to improve prediction of incident OUD diagnosis among Medicare beneficiaries with ≥1 opioid prescriptions. METHODS: This prognostic study included 361,527 fee-for-service Medicare beneficiaries, without cancer, filling ≥1 opioid prescriptions from 2011-2016. We randomly divided beneficiaries into training, testing, and validation samples. We measured 269 potential predictors including socio-demographics, health status, patterns of opioid use, and provider-level and regional-level factors in 3-month periods, starting from three months before initiating opioids until development of OUD, loss of follow-up or end of 2016. The primary outcome was a recorded OUD diagnosis or initiating methadone or buprenorphine for OUD as proxy of incident OUD. We applied elastic net, random forests, gradient boosting machine, and deep neural network to predict OUD in the subsequent three months. We assessed prediction performance using C-statistics and other metrics (e.g., number needed to evaluate to identify an individual with OUD [NNE]). Beneficiaries were stratified into subgroups by risk-score decile. RESULTS: The training (n = 120,474), testing (n = 120,556), and validation (n = 120,497) samples had similar characteristics (age ≥65 years = 81.1%; female = 61.3%; white = 83.5%; with disability eligibility = 25.5%; 1.5% had incident OUD). In the validation sample, the four approaches had similar prediction performances (C-statistic ranged from 0.874 to 0.882); elastic net required the fewest predictors (n = 48). Using the elastic net algorithm, individuals in the top decile of risk (15.8% [n = 19,047] of validation cohort) had a positive predictive value of 0.96%, negative predictive value of 99.7%, and NNE of 104. Nearly 70% of individuals with incident OUD were in the top two deciles (n = 37,078), having highest incident OUD (36 to 301 per 10,000 beneficiaries). Individuals in the bottom eight deciles (n = 83,419) had minimal incident OUD (3 to 28 per 10,000). CONCLUSIONS: Machine-learning algorithms improve risk prediction and risk stratification of incident OUD in Medicare beneficiaries.

Opioid use patterns and risk characteristics among injured patients.

Background: Injured patients are at risk for prolonged opioid use after discharge from care. Limited evidence exists regarding how continued opioid use may be related to opioid medication misuse and opioid use disorder (OUD) following injury. This pilot study characterized opioid consumption patterns, health characteristics, and substance use among patients with active prescriptions for opioid medications following injury care. Methods: This study was a cross-sectional screening survey combined with medical record review from February 2017 to March 2018 conducted among outpatient trauma and orthopedic surgery clinic patients. Eligible patients were 18-64 years of age, admitted/discharged for an injury or trauma-related orthopedic surgery, returning for clinic follow-up ≤6 months post hospital discharge after the index injury, prescribed opioid pain medication at discharge, and currently taking an opioid medication (from discharge or a separate prescription post discharge). Data collected included demographic, substance use, mental health, and physical health information. Descriptive and univariate statistics were calculated to characterize the population and opioid-related risks. Results: Seventy-one participants completed the survey (92% response). Most individuals (≥75%) who screened positive for misuse or OUD reported no nonmedical/illicit opioid use in the year before the index injury. A positive depression screen was associated with a 3.88 times increased likelihood for misuse or OUD (95% confidence interval [CI] = 1.1-13.5). Nonopioid illicit drug use (odds ratio [OR] = 1.89, 95% CI = 1.1-3.4) and opioid craving (OR = 1.29, 95% CI = 1.1-1.5) were also associated with increased likelihood for misuse or OUD. Number of emergency department visits in the 3 years previous to the index injury was associated with a 22% likelihood of being misuse or OUD positive (95% CI = 1.0-1.5). Conclusions: Patients with behavioral health concerns and greater emergency department utilization may have heightened risk for experiencing adverse opioid-related outcomes. Future research must further establish these findings and possibly develop protocols to identify patients at risk prior to pain management planning.

Psychiatric comorbidity and co-occurring opioid misuse: Depression mediates the relationship between post-traumatic stress disorder and opioid misuse in community pharmacy settings.

AbstractsBackground: Individuals who misuse opioids frequently have comorbid psychiatric issues, including post-traumatic stress disorder (PTSD) and depression. However, little is known about the mechanisms by which these disorders are associated with opioid misuse and specifically in community pharmacy settings. The current study examined whether depression mediated the relationship between PTSD and opioid misuse in patients filling opioid prescriptions. Methods: We administered a health survey in four community pharmacies among patients filling opioid medications in southwestern Pennsylvania. Univariate statistics were used to assess relationships among demographic and clinical characteristics of PTSD, depression, and opioid misuse behaviors. We then examined whether depression mediated the relationship between PTSD and opioid misuse using ordinary least squares path analysis with bootstrapping. Results: A total of 333 participants completed the health survey. Opioid misuse was reported among 15.9% of all participants, 33.3% among those with a positive PTSD screen, and 29.3% of those who screened positive for depression. Depression significantly mediated the relationship between PTSD and opioid misuse. Specifically, there was a statistically significant indirect effect (ab) of PTSD on opioid misuse through a pathway mediated by depression (ab = .06, SEab = .02, 95% CI = .02-.10). The direct effect (c') of PTSD on opioid misuse was also significant (c' = .12, SEc' = .05, P = .01, 95% CI = .03-.22), suggesting partial mediation. Conclusions: Results suggest an indirect pathway by which clinical intervention may help ameliorate outcomes in patients with PTSD. Further, there is an increased need for screening, assessment, and intervention protocols for this patient population in which community pharmacy is a novel setting to expand future efforts within the patient population.

Opioid Prescribing Safety Measures in Medicaid Enrollees With and Without Cancer.

INTRODUCTION: Opioid prescribing safety among individuals with cancer is poorly understood. This study estimates the prevalence of Pharmacy Quality Alliance opioid measures among individuals with cancer undergoing or not undergoing active treatment versus those without cancer. METHODS: Pennsylvania Medicaid data (2016) were analyzed in 2018 to identify adults aged 18-64 years with and without cancer diagnoses who had 2 or more opioid prescriptions. Active cancer treatment, defined as having chemotherapy, radiotherapy, cancer surgery, or hospitalization with a primary diagnosis of cancer, was evaluated from October 2015 to December 2016 allowing a ≥3-month look-back period for cancer diagnoses observed in the first quarter of 2016. Opioid dosages (>120 morphine milligram equivalents for ≥90 consecutive days), multiple providers (4 or more prescribers and 4 or more pharmacies), and opioid and benzodiazepines overlapping ≥30 days were evaluated. RESULTS: The sample with opioid prescriptions included 111,491 enrollees without cancer diagnoses and 12,819 with cancer, 58.8% of whom were not in active cancer treatment. Among enrollees undergoing cancer treatment, with cancer but not in active treatment, and without cancer, the prevalence of high morphine milligram equivalents was 7.1%, 6.0%, and 4.7% (p<0.001), respectively. The corresponding prevalence of multiple providers was 6.7%, 4.1%, and 3.4% (p<0.001). Concurrent opioid and benzodiazepine prescriptions occurred in 28.6%, 30.5%, and 26.8% (p<0.001), respectively. CONCLUSIONS: Individuals with cancer, regardless of treatment status, had higher-risk opioid use based on Pharmacy Quality Alliance measures versus those without cancer. Their systematic exclusion from opioid quality surveillance could create missed opportunities to identify patients at high risk of adverse opioid-related outcomes.

Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid Prescriptions.

Importance: Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk. Objective: To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription. Design, Setting, and Participants: A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples. Exposures: Potential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation. Main Outcomes and Measures: Opioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator-type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity. Results: Beneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2% [142 180] of the cohort), medium-risk (18.6% [34 579] of the cohort), and high-risk (5.2% [9747] of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome. Conclusions and Relevance: Machine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose.

Excessive Opioid Prescribing After Major Urologic Procedures.

OBJECTIVE: To examine the use of prescription opioids in patients undergoing major prostate and kidney operations. METHODS: This is a prospective observational study that includes opioid naïve patients who underwent a major prostate or kidney operation from January 2017-May 2017. A telephone survey was conducted 3-4 weeks postoperatively. The survey assessed the number of 5 mg oxycodone-equivalents prescribed, opioid use, and disposal. RESULTS: A total of 155 patients were included in our analysis. Most patients were male (86%), most were married (74%), the median was age 64 (interquartile range 59-70), and the majority were Caucasian (84%). Most patients reported social alcohol use (56%), but most denied current tobacco use (77%) or current and/or previous drug use (76%). Opioid prescribing exceeded use from 1.9- to 6.8-fold for all procedural categories. Overall, a total of 4065 oxycodone-equivalents were prescribed during this study and 60% of pills prescribed went unused. This resulted in 2622 excess pills in the community. CONCLUSION: Opioids are prescribed far in excess of need following major open and minimally invasive urologic procedures. Overall, 60% of prescribed opioids were unused. These data provide initial benchmarks for appropriate opioid prescribing after major prostate and kidney procedures. Future work to validate this initial guideline and improve patient counseling regarding appropriate perioperative opioid use and disposal is needed.

Psychosocial predictors in the transition from acute to chronic pain: a systematic review.

Chronic non-cancer pain (CNCP) is a major health problem which psychosocial factors have significant implications in. There is a gap in regards to evidence for the prevention of chronicity specifically addressing psychological and social domains. Four databases were searched with terms related to "psychosocial", "acute pain", and "chronic pain". A total of 1,389 studies were identified in which titles, abstracts, and full texts were assessed for inclusion criteria. A data template was used to capture pertinent details, and overall themes and patterns were organized according to type of pain examined and psychosocial variables measured. Of the 18 articles that met inclusion criteria, fifteen (83%) of the articles reported an association between psychosocial factors and chronicity. A total of 5 of the studies (29%) demonstrated that depression was a possible predictor and 6 (35%) of the studies found fear-avoidance to be associated with chronicity. This review provides evidence that psychosocial factors are associated with chronicity within CNCP. These results suggest a need for targeting psychosocial predictors in prevention and early intervention through clinical guidelines and a national strategy to support a cultural change in pain care.

Frequent Experience of LGBQ Microaggression on Campus Associated With Smoking Among Sexual Minority College Students.

Introduction: Lesbian, gay, bisexual, or queer/questioning (LGBQ) microaggressions refer to often-unintentional insults, assaults, and invalidations that denigrate sexual minorities. While experiencing hostile discrimination and violence has previously been associated with elevated rates of smoking cigarettes for LGBQ college students, the relationship between LGBQ microaggressions and smoking is unknown. Methods: Data from a national anonymous online survey of sexual and gender minority college students were used to examine the relationship between past month cigarette smoking and interpersonal LGBQ microaggressions. Multivariable logistic regression assessed the relationship between smoking and frequent (chronic) experiences of microaggressions, using a hierarchical procedure to control for demographics, predictors of smoking, and academic factors. Results: Past year frequent LGBQ microaggression was reported by 48% of respondents and was more common among students who smoked in past 30 days. Experiencing past year physical violence was reported by 15% and did not differ by smoking status. Past year frequent experience of microaggressions was associated with increased odds of 1.72 (95% CI 1.03-2.87) for past 30-day smoking after adjusting for age, gender, race, socioeconomic indicators, alcohol misuse, physical violence, as well as academic stress and engagement. Conclusions: The results indicate that frequently experiencing LGBQ microaggressions is a risk factor for LGBQ college students smoking cigarettes. The mechanisms underlying this relationship require additional research, as does identifying positive coping strategies and institutional strategies to address LGBQ microaggressions on campuses. Tobacco control efforts should consider the impact of microaggression on the social environment for the prevention and treatment of tobacco use among LGBQ individuals. Implications: Microaggressions related to sexual minority identity include subtle forms of discrimination experienced during daily interactions that can create hostile and stressful social environments for a socially disadvantaged group. Prior research has demonstrated that LGBQ college students have higher rates of cigarette smoking, and that experiencing identity-based violence is a risk factor. This study revealed that experiencing frequent LGBQ microaggressions on college campus is associated with increased likelihood of current smoking among LGBQ college students.

Using Bioinformatics to Treat Hospitalized Smokers: Successes and Challenges of a Tobacco Treatment Service.

BACKGROUND: Hospitals face increasing regulations to provide and document inpatient tobacco treatment, yet few blueprint data exist to implement a tobacco treatment service (TTS). METHODS: A hospitalwide, opt-out TTS with three full-time certified counselors was developed in a large tertiary care hospital to proactively treat smokers according to Chronic Care Model principles and national treatment guidelines. A bioinformatics platform facilitated integration into the electronic health record to meet evolving Centers for Medicare & Medicaid Services meaningful use and Joint Commission standards. TTS counselors visited smokers at the bedside and offered counseling, recommended smoking cessation medication to be ordered by the primary clinical service, and arranged for postdischarge resources. RESULTS: During a 3.5-year span, 21,229 smokers (31,778 admissions) were identified; TTS specialists reached 37.4% (7,943), and 33.3% (5,888) of daily smokers received a smoking cessation medication order. Adjusted odds ratios (AORs) of receiving a chart order for smoking cessation medication during the hospital stay and at discharge were higher among patients the TTS counseled > 3 minutes and recommended medication: inpatient AOR = 7.15 (95% confidence interval [CI] = 6.59-7.75); discharge AOR = 5.3 (95% CI = 4.71-5.97). As implementation progressed, TTS counseling reach and medication orders increased. To assess smoking status ≤ 1 month postdischarge, three methods were piloted, all of which were limited by low follow-up rates (4.5%-28.6%). CONCLUSION: The TTS counseled approximately 3,000 patients annually, with increases over time for reach and implementation. Remaining challenges include the development of strategies to engage inpatient care teams to follow TTS recommendations, and patients postdischarge in order to optimize postdischarge smoking cessation.

Postdischarge smoking cessation in subgroups of hospitalized smokers: A latent class analysis.

BACKGROUND: Hospitalization presents a window of opportunity to treat smoking, and hospital-initiated smoking treatment has demonstrated effectiveness. Despite effective interventions, not all smokers will discontinue use, highlighting the need to better understand which patients achieve cessation. Traditional regression methods may not capture the complexity of inpatient smoker subgroups. METHODS: Latent class analysis (LCA) was conducted with data from 397 hospitalized adult cigarette smokers enrolled in a randomized trial. Six categorical indicator variables known to impact cessation were selected to estimate subgroups: health conditions (smoking-related disease [SRD], depressive symptoms, positive screen for alcohol problems) and smoking-related variables (time to first cigarette, cigarettes/day, smoking indoors). The probability of achieving biologically verified 7-day tobacco cessation 6 months after discharged was estimated. RESULTS: A 3-class model best fit the trial data: a Light Smokers subgroup had lower probability for most indicators; a High Health Burden subgroup had high smoking behavior probabilities and similar health problems to the Light Smokers subgroup; and a Heavy Smoking Drinking Depressed subgroup had high nicotine dependence, depressive symptoms, and alcohol misuse probabilities. Probability of biologically verified cessation conditional on class membership was significantly higher (P < .001) for the High Health Burden and the Light Smokers subgroups compared with the Heavy Smoking Drinking Depressed subgroup. CONCLUSION: Results suggest that subgroups with lower probabilities of alcohol misuse and depression and higher probability of SRD had higher probability of successful cessation after hospital discharge. Hospitalized patients with nicotine dependence combined with behavioral and mental health problems have additional cessation barriers that may require intervention focus.

Risk Factors for Symptoms of Prescription Opioid Misuse: Do Older Adults Differ from Younger Adult Patients?

Limited research is available regarding the health risks associated with opioid medication misuse among middle age (50-64 years) and older adults (65 and older). Understanding the misuse symptom risk profiles of these populations has potential to advance the national opioid epidemic response. A survey was conducted in four community pharmacies in southwestern Pennsylvania among adult, non-cancer patients filling opioid medications (N = 318) regarding opioid medication misuse symptoms and misuse risk factors. Descriptive and multivariate statistical analyses compared respondent characteristics, misuse symptoms, and misuse risks among those 65 and older, 50 to 64, and those less than 50 years old. Those 65 and older (Incidence Rate Ratio [IRR] = 2.4, 95% CI = 1.46-3.95) and those 50 to 64 years (IRR = 1.26, 95% CI = 1.03-1.54) who reported illicit drug use had increased rates of misuse symptoms. Those 50-64 with posttraumatic stress disorder also had an increased rate of misuse symptoms (IRR = 1.29, 95% CI = 1.08-1.54). Misuse behaviors of those 65 and older and 50-64 involved shortening time between dosages (≥65 = 11.4%, 50-64 = 27.6%), taking higher dosages than prescribed (≥65 = 11.4%, 50-64 = 13.6%), and early refills (≥11.8%, 50-64 = 12.7%). Gerontological social workers must continue to work to understand and identify individuals engaged in misuse in order to develop and provide age appropriate care.