Healthcare Effectiveness Data and Information Set (HEDIS) measures of alcohol and drug treatment initiation and engagement among people living with the human immunodeficiency virus (HIV) and patients without an HIV diagnosis

Background:

Problematic use of alcohol and other drugs (AOD) is highly prevalent among people living with the human immunodeficiency virus (PLWH), and untreated AOD use disorders have particularly detrimental effects on human immunodeficiency virus (HIV) outcomes. The Healthcare Effectiveness Data and Information Set (HEDIS) measures of treatment initiation and engagement are important benchmarks for access to AOD use disorder treatment. To inform improved patient care, we compared HEDIS measures of AOD use disorder treatment initiation and engagement and health care utilization among PLWH and patients without an HIV diagnosis.

Methods:

Patients with a new AOD use disorder diagnosis documented between October 1, 2014, and August 15, 2015, were identified using electronic health records (EHR) and insurance claims data from 7 health care systems in the United States. Demographic characteristics, clinical diagnoses, and health care utilization data were also obtained. AOD use disorder treatment initiation and engagement rates were calculated using HEDIS measure criteria. Factors associated with treatment initiation and engagement were examined using multivariable logistic regression models.

Results:

There were 469 PLWH (93% male) and 86,096 patients without an HIV diagnosis (60% male) in the study cohort. AOD use disorder treatment initiation was similar in PLWH and patients without an HIV diagnosis (10% vs. 11%, respectively). Among those who initiated treatment, few engaged in treatment in both groups (9% PLWH vs. 12% patients without an HIV diagnosis). In multivariable analysis, HIV status was not significantly associated with either AOD use disorder treatment initiation or engagement.

Conclusions:

AOD use disorder treatment initiation and engagement rates were low in both PLWH and patients without an HIV diagnosis. Future studies need to focus on developing strategies to efficiently integrate AOD use disorder treatment with medical care for HIV.

Development and evaluation of a standardized research definition for opioid overdose outcomes

Background: Increasing epidemiologic and intervention research is being conducted on opioid overdose, a serious and potentially fatal outcome. However, there is little consensus on how to verify opioid overdose outcomes for research purposes. To ensure reproducibility, minimize misclassification, and permit data harmonization across studies, standardized and consistent overdose definitions are needed. The aims were to develop a case criteria classification scheme based on information commonly available in medical records and to compare it with reviewing physician clinical impression and simple encounter documentation. Methods: In 2 large health systems, we developed a case criteria classification scheme for opioid overdose based on prior literature, expert opinion, and pilot testing with sample medical records. We then identified emergency department and hospital encounters (n = 259) with at least 1 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code representing a broad range of opioid and non-opioid related poisonings. Physicians conducted structured medical record reviews to identify the proposed case criteria and generate a clinical impression, and trained abstractors verified documentation. We then compared the case criteria classification scheme with clinical impression and encounter documentation. Results: We developed a quantitative opioid overdose case criteria classification scheme that included 3 sets of major criteria and 9 minor criteria (supporting documentation). For the encounters identified using poisoning codes, the proportion verified as opioid overdoses using the case criteria classification scheme, clinical impression, and encounter documentation ranged from 50.4% to 52.7% at one site and 55.5% to 67.2% at the second site. Discrepancies across approaches and sites related to differences in available records and documentation of clinical signs of overdose. Conclusions: We propose a novel case criteria classification scheme for opioid overdose that could be used to rigorously and consistently define overdose across multiple research settings. However, prior to widespread use, further refinement and validation are needed.

Patient characteristics associated with treatment initiation and engagement among individuals diagnosed with alcohol and other drug use disorders in emergency department and primary care settings

Background: Treatment initiation and engagement rates for alcohol and other drug (AOD) use disorders differ depending on where the AOD use disorder was identified. Emergency department (ED) and primary care (PC) are 2 common settings where patients are identified; however, it is unknown whether characteristics of patients who initiate and engage in treatment differ between these settings. Methods: Patients identified with an AOD disorder in ED or PC settings were drawn from a larger study that examined Healthcare Effectiveness Data and Information Set (HEDIS) AOD treatment initiation and engagement measures across 7 health systems using electronic health record data (n = 54,321). Multivariable generalized linear models, with a logit link, clustered on health system, were used to model patient factors associated with initiation and engagement in treatment, between and within each setting. Results: Patients identified in the ED had higher odds of initiating treatment than those identified in PC (adjusted odds ratio [aOR] = 1.89, 95% confidence interval [CI] = 1.73-2.07), with no difference in engagement between the settings. Among those identified in the ED, compared with patients aged 18-29, older patients had higher odds of treatment initiation (age 30-49: aOR = 1.25, 95% CI = 1.12-1.40; age 50-64: aOR = 1.42, 95% CI = 1.26-1.60; age 65+: aOR = 1.27, 95% CI = 1.08-1.49). However, among those identified in PC, compared with patients aged 18-29, older patients were less likely to initiate (age 30-49: aOR = 0.81, 95% CI = 0.71-0.94; age 50-64: aOR = 0.68, 95% CI = 0.58-0.78; age 65+: aOR = 0.47, 95% CI = 0.40-0.56). Women identified in ED had lower odds of initiating treatment (aOR = 0.80, 95% CI = 0.72-0.88), whereas sex was not associated with treatment initiation in PC. In both settings, patients aged 65+ had lower odds of engaging compared with patients aged 18-29 (ED: aOR = 0.61, 95% CI = 0.38-0.98; PC: aOR = 0.42, 95% CI = 0.26-0.68). Conclusion: Initiation and engagement in treatment differed by sex and age depending on identification setting. This information could inform tailoring of future AOD interventions.

Psychiatric comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) measures of alcohol and other drug treatment initiation and engagement across 7 health care systems

Background: Psychiatric comorbidity is common among patients with alcohol and other drug (AOD) use disorders. To better understand how psychiatric comorbidity influences AOD treatment access in health care systems, the present study examined treatment initiation and engagement among a large, diverse sample of patients with comorbid psychiatric and AOD use disorders. Methods: This study utilized data from a multisite observational study examining Healthcare Effectiveness Data and Information Set (HEDIS) measures of initiation and engagement in treatment (IET) among patients with AOD use disorders from 7 health care systems. Participants were aged 18 or older with at least 1 AOD index diagnosis between October 1, 2014, and August 15, 2015. Data elements extracted from electronic health records and insurance claims data included patient demographic characteristics, ICD-9 (International Classification of Diseases, Ninth Revision) diagnostic codes, and procedure codes. Descriptive analyses and multivariate logistic regression models were used to examine the relationship between patient-level factors and IET measures. Results: Across health care systems, out of a total of 86,565 patients who had at least 1 AOD index diagnosis during the study period, 66.2% (n = 57,335) patients also had a comorbid psychiatric disorder. Among patients with a comorbid psychiatric disorder, 34.9% (n = 19,998) initiated AOD treatment, and of those, 10.3% (n = 2,060) engaged in treatment. After adjusting for age, sex, and race/ethnicity, patients with comorbid psychiatric disorders were more likely to initiate (odds ratio [OR] = 3.20, 95% confidence interval [CI] = 3.08, 3.32) but no more likely to engage (OR = 0.56, 95% CI = 0.51, 0.61) in AOD treatment, compared with those without a comorbid psychiatric disorder. Conclusions: Findings suggest that identification of comorbid psychiatric disorders may increase initiation in AOD treatment. However, innovative efforts are needed to enhance treatment engagement both generally and especially for individuals without diagnosed psychiatric conditions.

Predictors of Healthcare Effectiveness Data and Information Set (HEDIS) treatment initiation and engagement among patients with opioid use disorder across 7 health systems

Background:

The prevalence of opioid use disorder (OUD) has increased rapidly in the United States and improving treatment access is critical. Among patients with OUD, we examined factors associated with the Healthcare Effectiveness Data and Information Set (HEDIS) performance measures of alcohol and other drug (AOD) treatment initiation and engagement.

Methods:

Electronic health record and claims data between October 1, 2014, and August 15, 2015, from 7 health systems were used to identify patients (n = 11,490) with a new index OUD diagnosis (no AOD diagnosis prior <60 days) based on International Classification of Diseases (ICD)-9 codes. Multivariable generalized linear models with a logit link clustered on health system were used to examine the associations of patient demographic and clinical characteristics, and department of index diagnosis, with HEDIS measures of treatment initiation and engagement.

Results:

The prevalence of OUD among all AOD diagnoses varied across health systems, as did rates of AOD initiation (5.7%–21.6%) and engagement (7.6%–24.6%). Those diagnosed in the emergency department (adjusted odds ratio [aOR] = 1.58, 95% confidence interval [CI] = 1.27,1.97) or psychiatry/AOD treatment (aOR = 2.92, 95% CI = 2.47,3.46) were more likely to initiate treatment compared with primary care. Older patients were less likely to initiate (age 50–64 vs. age 18–29: aOR = 0.42, 95% CI = 0.35, 0.51; age 65+ vs. age 18–29: aOR = 0.34, 95% CI = 0.26, 0.43), as were women (aOR = 0.72, 95% CI = 0.62, 0.85). Patients diagnosed in psychiatry/AOD treatment (aOR = 2.67, 95% CI = 1.98, 3.60) compared with primary care were more likely to engage in treatment. Those identified in an inpatient setting (aOR = 0.19, 95% CI = 0.14, 0.27 vs. primary care), those with medical comorbidity (aOR = 0.70, 95% CI = 0.52, 0.95), and older patients (age 50–64 vs. 18–29: aOR = 0.64, 95% CI = 0.46, 0.88; age 65+ vs. 18–29: aOR = 0.36, 95% CI = 0.22, 0.57) were less likely to engage in treatment.

Conclusions:

Rates of initiation and engagement for OUD patients vary widely with noticeable room for improvement, particularly in this critical time of the opioid crisis. Targeting patient and system factors may improve health system performance, which is key to improving patient outcomes.

Factors associated with Healthcare Effectiveness Data and Information Set (HEDIS) alcohol and other drug measure performance in 2014-2015

Background: Only 10% of patients with alcohol and other drug (AOD) disorders receive treatment. The AOD Initiation and Engagement in Treatment (AOD-IET) measure was added to the national Healthcare Effectiveness Data and Information Set (HEDIS) to improve access to care. This study identifies factors related to improving AOD-IET rates. Methods: We include data from 7 health systems with differing geographic, patient demographic, and organizational characteristics; all used a common Virtual Data Warehouse containing electronic health records and insurance claims data. Multilevel logistic regression models examined AOD-IET among adults (18+). Results: A total of 86,565 patients had an AOD diagnosis qualifying for the HEDIS denominator. The overall initiation rate was 27.9% with wide variation; the overall engagement rate was 11.5% and varied from 4.5% to 17.9%. Women versus men (odds ratio [OR] = 0.81, 95% confidence interval [CI] = 0.76-0.86); Hispanics (OR = 0.85, 95% CI = 0.79-0.91), black/African Americans (OR = 0.82, 95% CI = 0.75-0.90), and Asian Americans (OR = 0.83, 95% CI = 0.72-0.95) versus whites; and patients aged 65+ versus 18-29 (OR = 0.82, 95% CI = 0.74-0.90) had lower odds of initiation. Patients aged 30-49 versus 18-29 (OR = 1.11, 95% CI = 1.04-1.19) and those with prior psychiatric (OR = 1.26, 95% CI = 1.18-1.35) and medical (OR = 1.18, 95% CI = 1.10-1.26) conditions had higher odds of initiation. Identification in primary care versus other departments was related to lower odds of initiation (emergency department [ED]: OR = 1.55, 95% CI = 1.45-1.66; psychiatry/AOD treatment: OR = 3.58, 95% CI = 3.33-3.84; other outpatient: OR = 1.19, 95% CI = 1.06-1.32). Patients aged 30-49 versus 18-29 had higher odds of engagement (OR = 1.26, 95% CI = 1.10-1.43). Patients aged 65+ versus 18-29 (OR = 0.51, 95% CI = 0.43-0.62) and black/African Americans versus whites (OR = 0.64, 95% CI = 0.53-0.77) had lower odds. Those initiating treatment in psychiatry/AOD treatment versus primary care (OR = 7.02, 95% CI = 5.93-8.31) had higher odds of engagement; those in inpatient (OR = 0.40, 95% CI = 0.32-0.50) or other outpatient (OR = 0.73, 95% CI = 0.59-0.91) settings had lower odds. Discussion: Rates of initiation and engagement varied but were low. Findings identified age, race/ethnicity, co-occurring conditions, and department of identification as key factors associated with AOD-IET. Focusing on these could help programs develop interventions that facilitate AOD-IET for those less likely to receive care.

The association between medical comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) measures of treatment initiation and engagement for alcohol and other drug use disorders

Background: Medical comorbidity may influence treatment initiation and engagement for alcohol and other drug (AOD) use disorders. We examined the association between medical comorbidity and Healthcare Effectiveness Data and Information Set (HEDIS) treatment initiation and engagement measures.Methods: We used electronic health record and insurance claims data from 7 US health care systems to identify patients with AOD use disorders between October 1, 2014, and August 15, 2015 (N = 86,565). Among patients identified with AOD use disorders in outpatient and emergency department (ED) settings, we examined how Charlson/Deyo comorbidity index scores and medical complications of AOD use were associated with treatment initiation. Among those who initiated treatment in inpatient and outpatient/ED settings, we also examined how comorbidity and AOD use-related medical complications were associated with treatment engagement. Analyses were conducted using generalized estimating equation logistic regression modeling.Results: Among patients identified as having an AOD diagnosis in outpatient and ED settings (n = 69,965), Charlson/Deyo comorbidity index scores of 2 or more were independently associated with reduced likelihood of initiation (risk ratio [RR] = 0.80, 95% confidence interval [CI] = 0.74, 0.86; reference score = 0), whereas prior-year diagnoses of cirrhosis (RR = 1.25, 95% CI = 1.12, 1.35) and pancreatic disease (RR = 1.34, 95% CI = 1.15, 1.56) were associated with greater likelihood of initiation. Among those who were identified in outpatient/ED settings and initiated, higher comorbidity scores were associated with lower likelihood of engagement (score 1: RR = 0.85, 95% CI = 0.76, 0.94; score 2+: RR = 0.61, 95% CI = 0.53, 0.71).Conclusion: Medical comorbidity was associated with lower likelihood of initiating or engaging in AOD treatment, but cirrhosis and pancreatic disease were associated with greater likelihood of initiation. Interventions to improve AOD treatment initiation and engagement for patients with comorbidities are needed, such as integrating medical and AOD treatment.

The prevalence of Healthcare Effectiveness Data and Information Set (HEDIS) initiation and engagement in treatment among patients with cannabis use disorders in 7 US health systems

Background: Cannabis use disorders (CUDs) have increased with more individuals using cannabis, yet few receive treatment. Health systems have adopted the Healthcare Effectiveness Data and Information Set (HEDIS) quality measures of initiation and engagement in alcohol and other drug (AOD) dependence treatment, but little is known about the performance of these among patients with CUDs. Methods: This cohort study utilized electronic health records and claims data from 7 health care systems to identify patients with documentation of a new index CUD diagnosis (no AOD diagnosis ≤60 days prior) from International Classification of Diseases, Ninth revision, codes (October 1, 2014, to August 31, 2015). The adjusted prevalence of each outcome (initiation, engagement, and a composite of both) was estimated from generalized linear regression models, across index identification settings (inpatient, emergency department, primary care, addiction treatment, and mental health/psychiatry), AOD comorbidity (patients with CUD only and CUD plus other AOD diagnoses), and patient characteristics. Results: Among 15,202 patients with an index CUD diagnosis, 30.0% (95% confidence interval [CI]: 29.2-30.7%) initiated, 6.9% (95% CI: 6.2-7.7%) engaged among initiated, and 2.1% (95% CI: 1.9-2.3%) overall both initiated and engaged in treatment. The adjusted prevalence of outcomes varied across index identification settings and was highest among patients diagnosed in addiction treatment, with 25.0% (95% CI: 22.5-27.6%) initiated, 40.9% (95% CI: 34.8-47.0%) engaged, and 12.5% (95% CI: 10.0-15.1%) initiated and engaged. The adjusted prevalence of each outcome was generally highest among patients with CUD plus other AOD diagnosis at index diagnosis compared with those with CUD only, overall and across index identification settings, and was lowest among uninsured and older patients. Conclusion: Among patients with a new CUD diagnosis, the proportion meeting HEDIS criteria for initiation and/or engagement in AOD treatment was low and demonstrated variation across index diagnosis settings, AOD comorbidity, and patient characteristics, pointing to opportunities for improvement.

Novel Opioid Safety Clinic Initiative to Deliver Guideline-Concordant Chronic Opioid Therapy in Primary Care

Objective

To develop and evaluate a novel Opioid Safety Clinic (OSC) initiative to enhance adherence to guidelines on the assessment and monitoring of patients prescribed chronic opioid therapy (COT).

Patients and Methods

The OSC was developed at an urban Federally Qualified Health Center to provide guideline-concordant care for COT, standardize workflows, and efficiently use clinic staff. We evaluated the OSC using a matched cohort study. Five hundred thirty-nine patients participated in the clinic between July 1, 2014, and March 31, 2016. Of these, 472 clinic participants were matched to 472 nonparticipants by sex and age on the date of the OSC visit. The OSC was evaluated by its completion rates of standardized pain assessments, urine toxicology, and naloxone dispensings. We conducted logistic regression comparing OSC participants to OSC nonparticipants.

Results

A total of 539 patients attended an OSC visit, representing approximately 53% of patients in the chronic opioid registry. The OSC participants were more likely than nonparticipants to have completed a pain assessment (adjusted odds ratio [aOR], 169.8; 95% CI, 98.3-293.5), completed a urine toxicology (aOR, 46.1; 95% CI, 30.4-69.9), or had naloxone dispensed (aOR, 2.8; 95% CI, 1.9-4.3) over 12 months of follow-up.

Conclusion

The OSC model improved adherence to guideline-concordant COT in primary care. Future research is needed to assess the impact of these interventions on pain, quality of life, and adverse events from opioid analgesics.

Facility-level changes in receipt of pharmacotherapy for opioid use disorder: Implications for implementation science

BACKGROUND

The U.S. is facing an opioid epidemic, but despite mandates for pharmacotherapy for opioid use disorder to be available at Veterans Health Administration (VHA) facilities, the majority of veterans with opioid use disorder do not receive these medications. In implementation research, facilities are often targeted for qualitative inquiry or quality improvement efforts based on quality measure performance during a one-year period. However, sites that experience quality performance changes from one year to the next may be highly informative because mechanisms that impact facility change may be more discoverable. The current study examined changes in receipt of pharmacotherapy for opioid use disorder in a national healthcare system to determine the extent to which sites fluctuated in performance over a two-year period and illustrate how changes in quality measures over time may be useful for implementation research and healthcare surveillance of quality measures.

METHODS

Using national VHA data from Fiscal Years (FY) 2016 and 2017, we calculated quality measure performance as the number of patients who received pharmacotherapy for opioid use disorder (i.e., methadone, buprenorphine, and naltrexone) divided by the number of patients with a current non-remitted opioid use disorder diagnosis for each FY at each facility (n = 129) and examined change from FY16 to FY17.

RESULTS

The mean rate of receipt of pharmacotherapy for opioid use disorder was 38% (facility range = 3% to 74%) in FY16 and 41% (facility range = 2% to 76%) in FY17. The average facility-level change in performance was 3% and ranged from -19% to 26%. There were 32 facilities that decreased in provision of pharmacotherapy, 12 facilities with no change, and 85 facilities that increased.

CONCLUSIONS

For facilities with average or high performance, it was difficult to maintain their performance over time. Identifying and learning from facilities with recent fluctuations may be more informative to guide the design of future quality improvement efforts than studying facilities with stable high or low performance.

Medications for Alcohol and Opioid Use Disorders and Risk of Suicidal Behavior, Accidental Overdoses, and Crime

OBJECTIVE

The authors examined associations between medications for alcohol and opioid use disorders (acamprosate, naltrexone, methadone, and buprenorphine) and suicidal behavior, accidental overdoses, and crime.

METHOD

In this total population cohort study, 21,281 individuals who received treatment with at least one of the four medications between 2005 and 2013 were identified. Data on medication use and outcomes were collected from Swedish population-based registers. A within-individual design (using stratified Cox proportional hazards regression models) was used to compare rates of suicidal behavior, accidental overdoses, and crime for the same individuals during the period when they were receiving the medication compared with the period when they were not.

RESULTS

No significant associations with any of the primary outcomes were found for acamprosate. For naltrexone, there was a reduction in the hazard ratio for accidental overdoses during periods when individuals received treatment compared with periods when they did not (hazard ratio=0.82, 95% CI=0.70, 0.96). Buprenorphine was associated with reduced arrest rates for all crime categories (i.e., violent, nonviolent, and substance-related) as well as reduction in accidental overdoses (hazard ratio=0.75, 95% CI=0.60, 0.93). For methadone, there were significant reductions in the rate of suicidal behaviors (hazard ratio=0.60, 95% CI=0.40-0.88) as well as reductions in all crime categories. However, there was an increased risk for accidental overdoses among individuals taking methadone (hazard ratio=1.25, 95% CI=1.13, 1.38).

CONCLUSIONS

Medications currently used to treat alcohol and opioid use disorders also appear to reduce suicidality and crime during treatment.

Prediction Model for Two-Year Risk of Opioid Overdose Among Patients Prescribed Chronic Opioid Therapy

BACKGROUND

Naloxone is a life-saving opioid antagonist. Chronic pain guidelines recommend that physicians co-prescribe naloxone to patients at high risk for opioid overdose. However, clinical tools to efficiently identify patients who could benefit from naloxone are lacking.

OBJECTIVE

To develop and validate an overdose predictive model which could be used in primary care settings to assess the need for naloxone.

DESIGN

Retrospective cohort.

SETTING

Derivation site was an integrated health system in Colorado; validation site was a safety-net health system in Colorado.

PARTICIPANTS

We developed a predictive model in a cohort of 42,828 patients taking chronic opioid therapy and externally validated the model in 10,708 patients.

MAIN MEASURES

Potential predictors and outcomes (nonfatal pharmaceutical and heroin overdoses) were extracted from electronic health records. Fatal overdose outcomes were identified from state vital records. To match the approximate shelf-life of naloxone, we used Cox proportional hazards regression to model the 2-year risk of overdose. Calibration and discrimination were assessed.

KEY RESULTS

A five-variable predictive model showed good calibration and discrimination (bootstrap-corrected c-statistic = 0.73, 95% confidence interval [CI] 0.69-0.78) in the derivation site, with sensitivity of 66.1% and specificity of 66.6%. In the validation site, the model showed good discrimination (c-statistic = 0.75, 95% CI 0.70-0.80) and less than ideal calibration, with sensitivity and specificity of 82.2% and 49.5%, respectively.

CONCLUSIONS

Among patients on chronic opioid therapy, the predictive model identified 66-82% of all subsequent opioid overdoses. This model is an efficient screening tool to identify patients who could benefit from naloxone to prevent overdose deaths. Population differences across the two sites limited calibration in the validation site.

Health, Polysubstance Use, and Criminal Justice Involvement Among Adults With Varying Levels of Opioid Use

IMPORTANCE

Health profiles and patterns of involvement in the criminal justice system among people with various levels of opioid use are poorly defined. Data are needed to inform a public health approach to the opioid epidemic.

OBJECTIVE

To examine the association between various levels of opioid use in the past year and physical and mental health, co-occurring substance use, and involvement in the criminal justice system.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective, cross-sectional analysis used the 2015-2016 National Survey on Drug Use and Health to assess the independent association of intensity of opioid use with health, co-occurring substance use, and involvement in the criminal justice system among US adults aged 18 to 64 years using multivariable logistic regression.

EXPOSURES

No opioid use vs prescription opioid use, misuse, or use disorder or heroin use.

MAIN OUTCOMES AND MEASURES

Self-reported physical and mental health, disability, co-occurring substance use, and past year and lifetime involvement in the criminal justice system.

RESULTS

The sample consisted of 78 976 respondents (42 495 women and 36 481 men), representative of 196 280 447 US adults. In the weighted sample, 124 026 842 adults reported no opioid use in the past year (63.2%; 95% CI, 62.6%-63.7%), 61 462 897 reported prescription opioid use in the past year (31.3%; 95% CI, 30.8%-31.8%), 8 439 889 reported prescription opioid misuse in the past year (4.3%; 95% CI, 4.1%-4.5%), 1 475 433 reported prescription opioid use disorder in the past year (0.8%; 95% CI, 0.7%-0.8%), and 875 386 reported heroin use in the past year (0.4%; 95% CI, 0.4%-0.5%). Individuals who reported any level of opioid use were significantly more likely than individuals who reported no opioid use to be white, have a low income, and report a chronic condition, disability, severe mental illness, or co-occurring drug use. History of involvement in the criminal justice system increased as intensity of opioid use increased (no use, 15.9% [19 562 158 of 123 319 911]; 95% CI, 15.4%-16.4%; prescription opioid use, 22.4% [13 712 162 of 61 204 541]; 95% CI, 21.7%-23.1%; prescription opioid misuse, 33.2% [2 793 391 of 8 410 638]; 95% CI, 30.9%-35.6%; prescription opioid use disorder, 51.7% [762 189 of 1 473 552]; 95% CI, 45.4%-58.0%; and heroin use, 76.8% [668 453 of 870 250]; 95% CI, 70.6%-82.1%). In adjusted models, any level of opioid use was associated with involvement in the criminal justice system in the past year compared with no opioid use.

CONCLUSIONS AND RELEVANCE

Individuals who use opioids have complicated health profiles and high levels of involvement in the criminal justice system. Combating the opioid epidemic will require public health interventions that involve criminal justice systems, as well as policies that reduce involvement in the criminal justice system among individuals with substance use disorders.

Preventing Opioid Overdose in the Clinic and Hospital: Analgesia and Opioid Antagonists

Drawing from existing opioid prescribing guidelines, this article describes how medical providers can reduce the risk of overdose. Through primary prevention, providers can prevent initial exposure and associated risks by educating patients, using risk stratification, minimizing opioid dose and duration, and avoiding coprescribing with sedatives. Secondary prevention efforts include monitoring patients with urine toxicology and prescription monitoring programs, and screening for opioid use disorders. Tertiary prevention includes treating opioid use disorders and providing naloxone to prevent overdose death. Specific preventive strategies may be required for those with psychiatric disorders or substance use disorders, adolescents, the elderly, and pregnant women.

Prediction of Future Chronic Opioid Use Among Hospitalized Patients

BACKGROUND

Opioids are commonly prescribed in the hospital; yet, little is known about which patients will progress to chronic opioid therapy (COT) following discharge. We defined COT as receipt of ≥ 90-day supply of opioids with < 30-day gap in supply over a 180-day period or receipt of ≥ 10 opioid prescriptions over 1 year. Predictive tools to identify hospitalized patients at risk for future chronic opioid use could have clinical utility to improve pain management strategies and patient education during hospitalization and discharge.

OBJECTIVE

The objective of this study was to identify a parsimonious statistical model for predicting future COT among hospitalized patients not on COT before hospitalization.

DESIGN

Retrospective analysis electronic health record (EHR) data from 2008 to 2014 using logistic regression.

PATIENTS

Hospitalized patients at an urban, safety net hospital.

MAIN MEASUREMENTS

Independent variables included medical and mental health diagnoses, substance and tobacco use disorder, chronic or acute pain, surgical intervention during hospitalization, past year receipt of opioid or non-opioid analgesics or benzodiazepines, opioid receipt at hospital discharge, milligrams of morphine equivalents prescribed per hospital day, and others.

KEY RESULTS

Model prediction performance was estimated using area under the receiver operator curve, accuracy, sensitivity, and specificity. A model with 13 covariates was chosen using stepwise logistic regression on a randomly down-sampled subset of the data. Sensitivity and specificity were optimized using the Youden's index. This model predicted correctly COT in 79% of the patients and no COT correctly in 78% of the patients.

CONCLUSIONS

Our model accessed EHR data to predict 79% of the future COT among hospitalized patients. Application of such a predictive model within the EHR could identify patients at high risk for future chronic opioid use to allow clinicians to provide early patient education about pain management strategies and, when able, to wean opioids prior to discharge while incorporating alternative therapies for pain into discharge planning.

A Qualitative Study of Hospitalists' Perceptions of Patient Satisfaction Metrics on Pain Management

Hospital initiatives to promote pain management may unintentionally contribute to excessive opioid prescribing. To better understand hospitalists' perceptions of satisfaction metrics on pain management, the authors conducted 25 interviews with hospitalists. Transcribed interviews were systematically analyzed to identify emergent themes. Hospitalists felt institutional pressure to earn high satisfaction scores for pain, which they perceived influenced practices toward opioid prescribing. They felt tying compensation to satisfaction scores commoditized pain. Hospitalists believed satisfaction would improve with increased time spent at the bedside. Focusing on methods to improve patient-physician communication, while maintaining efficiency in clinical practice, may promote both patient-centered pain management and satisfaction.

Why are some people who have received overdose education and naloxone reticent to call Emergency Medical Services in the event of overdose?

BACKGROUND

Overdose Education and Naloxone Distribution (OEND) training for persons who inject drugs (PWID) underlines the importance of summoning emergency medical services (EMS). To encourage PWID to do so, Colorado enacted a Good Samaritan law providing limited immunity from prosecution for possession of a controlled substance and/or drug paraphernalia to the overdose victim and the witnesses who in good faith provide emergency assistance. This paper examines the law's influence by describing OEND trained PWIDs' experience reversing overdoses and their decision about calling for EMS support.

METHODS

Findings from two complementary studies, a qualitative study based on semi-structured interviews with OEND trained PWID who had reversed one or more overdoses, and an on-going fieldwork-based project examining PWIDs' self-identified health concerns were triangulated to describe and explain participants' decision to call for EMS.

RESULTS

In most overdose reversals described, no EMS call was made. Participants reported several reasons for not doing so. Most frequent was the fear that despite the Good Samaritan law, a police response would result in arrest of the victim and/or witness for outstanding warrants, or sentence violations. Fears were based on individual and collective experience, and reinforced by the city of Denver's aggressive approach to managing homelessness through increased enforcement of misdemeanors and the imposition of more recent ordinances, including a camping ban, to control space. The city's homeless crisis was reflected as well in the concern expressed by housed PWID that an EMS intervention would jeopardize their public housing.

CONCLUSION

Results suggest that the immunity provided by the Good Samaritan law does not address PWIDs' fear that their current legal status as well as the victim's will result in arrest and incarceration. As currently conceived, the Good Samaritan law does not provide immunity for PWIDs' already enmeshed in the criminal justice system, or PWID fearful of losing their housing.

Health Conditions and Racial Differences Among Justice-Involved Adolescents, 2009 to 2014

OBJECTIVE

Providers can optimize care for high-risk adolescents by understanding the health risks among the 1 million US adolescents who interact with the justice system each year. We compared the prevalence of physical health, substance use, and mood disorders among adolescents with and without recent justice involvement and analyzed differences according to race/ethnicity.

METHODS

Cross-sectional analysis using the 2009 to 2014 National Survey on Drug Use and Health. Prevalence data were adjusted for sociodemographic differences between adolescents with and without justice involvement. Justice-involved adolescents had a history of past year arrest, parole/probation, or juvenile detention.

RESULTS

Our sample consisted of adolescents aged 12 to 17 years with (n = 5149) and without (n = 97,976) past year justice involvement. In adjusted analyses, adolescents involved at any level of the justice system had a significantly higher prevalence of substance use disorders (P < .001), mood disorders (P < .001), and sexually transmitted infections (P < .01). Adolescents on parole/probation or in juvenile detention in the past year had a higher prevalence of asthma (P < .05) and hypertension (P < .05) compared with adolescents without justice involvement. Among justice-involved adolescents, African American adolescents were significantly less likely to have a substance use disorder (P < .001) or mood disorder (P < .01) compared with white or Hispanic adolescents, but had significantly higher prevalence of physical health disorders (P < .01).

CONCLUSIONS

Adolescents involved at all levels of the justice system have high-risk health profiles compared with the general adolescent population, although these risks differ across racial/ethnic groups. Policymakers and health care providers should ensure access to coordinated, high-quality health care for adolescents involved at all levels of the justice system.

Commentary on Hsu et al. (2017): A systems approach to improving health services for overdose in the hospital and across the continuum of care-an unmet need

Rising opioid overdose hospitalization rates and costs, stagnant in-hospital mortality rates and poor care transitions highlight the need to improve health services for people who use opioids in the hospital and across the continuum of care. A systems approach may identify opportunities to prevent overdoses and improve outcomes from overdose.

Epidemiology of Infectious Disease-Related Death After Release from Prison, Washington State, United States, and Queensland, Australia: A Cohort Study

OBJECTIVES

People in prison may be at high risk for infectious diseases and have an elevated risk of death immediately after release compared with later; their risk of death is elevated for at least a decade after release. We compared rates, characteristics, and prison-related risk factors for infectious disease-related mortality among people released from prisons in Queensland, Australia, and Washington State, United States, regions with analogous available data.

METHODS

We analyzed data from retrospective cohort studies of people released from prison in Queensland (1997-2007, n=37,180) and Washington State (1999-2009, n=76,208) and linked identifiers from each cohort to its respective national death index. We estimated infectious disease-related mortality rates (deaths per person-years in community) and examined associations using Cox proportional hazard models.

RESULTS

The most frequent infectious disease-related underlying cause of death after release from prison was pneumonia (43%, 23/54 deaths) in the Australian cohort and viral hepatitis (40%, 69/171 deaths) in the U.S. cohort. The infectious disease-related mortality rate was significantly higher in the U.S. cohort than in the Australian cohort (51.2 vs. 26.5 deaths per 100,000 person-years; incidence rate ratio = 1.93, 95% confidence interval 1.42, 2.62). In both cohorts, increasing age was strongly associated with mortality from infectious diseases.

CONCLUSION

Differences in the epidemiology of infectious disease-related mortality among people released from prison may reflect differences in patterns of community health service delivery in each region. These findings highlight the importance of preventing and treating hepatitis C and other infectious diseases during the transition from prison to the community.

Attitudes Toward Naloxone Prescribing in Clinical Settings: A Qualitative Study of Patients Prescribed High Dose Opioids for Chronic Non-Cancer Pain

BACKGROUND

Over the last 2 decades, medical providers have increasingly prescribed pharmaceutical opioids for chronic non-cancer pain, while opioid overdose death rates have quadrupled. Naloxone, an opioid antagonist, can be prescribed to patients with chronic pain to reverse an opioid overdose, yet little is known about how patients perceive this emerging practice.

OBJECTIVE

This study assessed the knowledge and attitudes toward naloxone prescribing among non-cancer patients prescribed opioids in primary care.

DESIGN

Qualitative study design using semi-structured interviews.

PARTICIPANTS

Adults (N = 24) prescribed high-dose (≥100 morphine mg equivalent daily dose) chronic opioid therapy in eight primary care internal medicine, family medicine and HIV practices in three large Colorado health systems.

APPROACH

Inductive and deductive methods were used to analyze interview transcripts.

KEY RESULTS

Themes emerged related to knowledge of and benefits, barriers and facilitators to naloxone in primary care. Patients reported receiving limited education about opioid medication risks from providers and limited knowledge of naloxone. When provided with a description of naloxone, patients recognized its ability to reverse overdoses. In addition to pragmatic barriers, such as medication cost, barriers to naloxone acceptance included the perception that overdose risk stems from medication misuse and that providers might infer that they were misusing their opioid medication if they accepted a naloxone prescription, prompting an opioid taper. Facilitators to the acceptance of naloxone included medical providers' using empowering, non-judgmental communication practices, framing naloxone for use in "worst case scenarios" and providing education and training about opioids and naloxone.

CONCLUSIONS

While patients recognized the utility of naloxone prescribing, we identified important barriers to patient acceptance of naloxone prescribing. To improve the naloxone prescribing acceptability in primary care practice, medical providers and health systems may need to enhance patient education, employ empowering, non-judgmental communication styles and adequately frame discussions about naloxone to address patients' fears.

Development and initial validation of primary care provider mental illness management and team-based care self-efficacy scales

OBJECTIVE

Develop and validate self-efficacy scales for primary care provider (PCP) mental illness management and team-based care participation.

STUDY DESIGN AND SETTING

We developed three self-efficacy scales: team-based care (TBC), mental illness management (MIM), and chronic medical illness (CMI). We developed the scales using Bandura's Social Cognitive Theory as a guide. The survey instrument included items from previously validated scales on team-based care and mental illness management. We administered a mail survey to 900 randomly selected Colorado physicians. We conducted exploratory principal factor analysis with oblique rotation. We constructed self-efficacy scales and calculated standardized Cronbach's alpha coefficients to test internal consistency. We calculated correlation coefficients between the MIM and TBC scales and previously validated measures related to each scale to evaluate convergent validity. We tested correlations between the TBC and the measures expected to correlate with the MIM scale and vice versa to evaluate discriminant validity.

RESULTS

PCPs (n=402, response rate=49%) from diverse practice settings completed surveys. Items grouped into factors as expected. Cronbach's alphas were 0.94, 0.88, and 0.83 for TBC, MIM, and CMI scales respectively. In convergent validity testing, the TBC scale was correlated as predicted with scales assessing communications strategies, attitudes toward teams, and other teamwork indicators (r=0.25 to 0.40, all statistically significant). Likewise, the MIM scale was significantly correlated with several items about knowledge and experience managing mental illness (r=0.24 to 41, all statistically significant). As expected in discriminant validity testing, the TBC scale had only very weak correlations with the mental illness knowledge and experience managing mental illness items (r=0.03 to 0.12). Likewise, the MIM scale was only weakly correlated with measures of team-based care (r=0.09 to.17).

CONCLUSION

This validation study of MIM and TBC self-efficacy scales showed high internal validity and good construct validity.

The hospitalist perspective on opioid prescribing: A qualitative analysis

BACKGROUND

Pain is a frequent symptom among patients in the hospital. Pain management is a key quality indicator for hospitals, and hospitalists are encouraged to frequently assess and treat pain. Optimal opioid prescribing, described as safe, patient-centered, and informed opioid prescribing, may be at odds with the priorities of current hospital care, which focuses on patient-reported pain control rather than the potential long-term consequences of opioid use.

OBJECTIVE

We aimed to understand physicians' attitudes, beliefs, and practices toward opioid prescribing during hospitalization and discharge.

DESIGN

In-depth, semistructured interviews.

SETTING

Two university hospitals, a safety-net hospital, a Veterans Affairs hospital, and a private hospital located in Denver, Colorado or Charleston, South Carolina.

PARTICIPANTS

Hospitalists (N = 25).

MEASUREMENTS

We systematically analyzed transcribed interviews and identified emerging themes using a team-based mixed inductive and deductive approach.

RESULTS

Although hospitalists felt confident in their ability to control acute pain using opioid medications, they perceived limited success and satisfaction when managing acute exacerbations of chronic pain with opioids. Hospitalists recounted negative sentinel events that altered opioid prescribing practices in both the hospital setting and at the time of hospital discharge. Hospitalists described prescribing opioids as a pragmatic tool to facilitate hospital discharges or prevent readmissions. At times, this left them feeling conflicted about how this practice could impact the patient over the long term.

CONCLUSIONS

Strategies to provide adequate pain relief to hospitalized patients, which allow hospitalists to safely and optimally prescribe opioids while maintaining current standards of efficiency, are urgently needed. Journal of Hospital Medicine 2016;11:536-542. © 2016 Society of Hospital Medicine.