Jane M. Liebschutz

The relationship between sexual and physical abuse and substance abuse consequences

This study examines the relationship between a history of physical and sexual abuse (PhySexAbuse) and drug and alcohol related consequences. We performed a cross-sectional analysis of data from 359 male and 111 female subjects recruited from an inpatient detoxification unit. The Inventory of Drug Use Consequences (InDUC), measured negative life consequences of substance use. Eighty-one percent of women and 69% of men report past PhySexAbuse, starting at a median age of 13 and 11, respectively. In bivariate and multivariable analyses, PhySexAbuse was significantly associated with more substance abuse consequences (p < 0.001). For men, age < or =17 years at first PhySexAbuse was significantly associated with more substance abuse consequences than an older age at first abuse, or no abuse (p = 0.048). For women, the association of PhySexAbuse with substance use consequences was similar across all ages (p = 0.59). Future research should develop interventions to lessen the substance abuse consequences of physical and sexual abuse.

Racial differences in the prevalence of atrial fibrillation among males.

BACKGROUND Despite being the most common cardiac arrhythmia, little is known about racial differences in atrial fibrillation (AF) prevalence and whether differences persist after accounting for known risk factors. METHODS We identified male respondents to the 1999 Large Health Survey of Veteran Enrollees who had an AF diagnosis in the VA administrative database during the preceding two years. RESULTS Of 664,754 male respondents, 5.3% had AF. By race, age-adjusted prevalence was 5.7% in whites, 3.4% in blacks, 3.0% in Hispanics, 5.4% in native Americans/Alaskans, 3.6% in Asians and 5.2% in Pacific Islanders (p<0.001). Of predisposing conditions, whites were more likely to have valvular heart disease, coronary artery disease and congestive heart failure, blacks had the highest hypertension prevalence; Hispanics had the highest diabetes prevalence. Racial differences remained after adjustment for age, body mass index and these comorbidities. White males were significantly more likely to have AF compared to all races but Pacific Islanders [versus blacks, OR=1.84 (95% CI: 1.71-1.98); versus Hispanics, OR=1.77 (1.60-1.97); vs Native Americans, OR 1.15 [1.04-1.27]; versus Asians, OR=1.41 (1.12-1.77) versus Pacific Islanders, OR=1.16 (0.88-1.53)]. CONCLUSIONS AF prevalence varies by race. White males have the highest AF burden even after adjustment for known risk factors. Recognition of the high AF prevalence, especially among whites, as well as native Americans and Pacific Islanders, should help guide provider practices for screening among older male patients. Further research is necessary to verify and establish reasons for these racial differences.

Survivors of Intimate Partner Violence Speak Out: Trust in the Patient-provider Relationship

OBJECTIVE: To identify characteristics that facilitate trust in the patient-provider relationship among survivors of intimate partner violence (IPV). DESIGN: Semistructured, open-ended interviews were conducted to elicit participants’ beliefs and attitudes about trust in interactions with health care providers. Using grounded theory methods, the transcripts were analyed for common themes. A community advisory group, composed of advocates, counselors and IPV survivors, helped interpret themes and interview exerpts. Together, key components of trust were identified. SETTING: Eastern Massachusetts. PARTICIPANTS: Twenty-seven female survivors of IPV recruited from community-based IPV organizations. MAIN RESULTS: Participants’ ages ranged from 18 to 56 years, 36% were African American, 32% Hispanic, and 18% white. We identified 5 dimensions of provider behavior that were uniquely important to the development of trust for these IPV survivors: 1) communication about abuse: provider was willing to openly discuss abuse; 2) professional competency: provider asked about abuse when appropriate and was familiar with medical and social histories; 3) practice style: provider was consistently accessible, respected confidentiality, and shared decision making; 4) caring: provider demonstrated personal concern beyond biomedical role through nonjudgmental and compassionate gestures, empowering statements, and persistent, committed behaviors; 5) emotional equality: provider shared personal information and feelings and was perceived by the participant as a friend. CONCLUSIONS: These IPV survivors identified dimensions of provider behavior that facilitate trust in their clinical relationship. Strengthening these provider behaviors may increase trust with patients and thus improve disclosure of and referral for IPV.

Identifying prescription opioid use disorder in primary care: diagnostic characteristics of the Current Opioid Misuse Measure (COMM).

&NA; The Current Opioid Misuse Measure (COMM), a self‐report assessment of past‐month aberrant medication‐related behaviors, has been validated in specialty pain management patients. The performance characteristics of the COMM were evaluated in primary care (PC) patients with chronic pain. It was hypothesized that the COMM could identify patients with prescription drug use disorder (PDD). English‐speaking adults awaiting PC visits at an urban, safety‐net hospital, who had chronic pain and had received any opioid analgesic prescription in the past year, were administered the COMM. The Composite International Diagnostic Interview served as the “gold standard,” using DSM‐IV criteria for PDD and other substance use disorders (SUDs). A receiver operating characteristic (ROC) curve demonstrated the COMM’s diagnostic test characteristics. Of the 238 participants, 27 (11%) met DSM‐IV PDD criteria, whereas 17 (7%) had other SUDs, and 194 (82%) had no disorder. The mean COMM score was higher in those with PDD than among all others (ie, those with other SUDs or no disorder, mean 20.4 [SD 10.8] vs 8.4 [SD 7.5], P < .0001). A COMM score of ⩾13 had a sensitivity of 77% and a specificity of 77% for identifying patients with PDD. The area under the ROC curve was 0.84. For chronic pain patients prescribed opioids, the development of PDD is an undesirable complication. Among PC patients with chronic pain‐prescribed prescription opioids, the COMM is a promising tool for identifying those with PDD. Among primary care patients with chronic pain‐prescribed opioids, the validated Current Opioid Misuse Measure (COMM) is a promising tool for identifying patients with prescription opioid use disorder.

Clinical factors associated with prescription drug use disorder in urban primary care patients with chronic pain.

UNLABELLED This study examined characteristics associated with prescription drug use disorder (PDUD) in primary-care patients with chronic pain from a cross-sectional survey conducted at an urban academically affiliated safety-net hospital. Participants were 18 to 60 years old, had pain for ≥ 3 months, took prescription or nonprescription analgesics, and spoke English. Measurements included the Composite International Diagnostic Interview (PDUD, other substance use disorders (SUD), Posttraumatic Stress Disorder [PTSD]); Graded Chronic Pain Scale, smoking status; family history of SUD; and time spent in jail. Of 597 patients (41% male, 61% black, mean age 46 years), 110 (18.4%) had PDUD of whom 99 (90%) had another SUD. In adjusted analyses, those with PDUD were more likely than those without any current or past SUD to report jail time (OR 5.1, 95% CI 2.8-9.3), family history of SUD (OR 3.4, 1.9-6), greater pain-related limitations (OR 3.8, 1.2-11.7), cigarette smoking (OR 3.6, 2-6.2), or to be white (OR 3.2, 1.7-6), male (OR 1.9, 1.1-3.5) or have PTSD (OR 1.9, 1.1-3.4). PDUD appears increased among those with easily identifiable characteristics. The challenge is to determine who, among those with risk factors, can avoid, with proper management, developing the increasingly common diagnosis of PDUD. PERSPECTIVE This article examines risk factors for prescription drug use disorder (PDUD) among a sample of primary-care patients with chronic pain at an urban, academic, safety-net hospital. The findings may help clinicians identify those most at risk for developing PDUD when developing appropriate treatment plans.

Opioid prescribing after nonfatal overdose and association with repeated overdose: a cohort study

Context Hospitalization or presentation to an emergency department for nonfatal opioid overdose represents an opportunity to identify and refer patients with opioid abuse for substance abuse treatment. Contribution Data from a large U.S. health insurer were used to identify patients presenting to a hospital or an emergency department with a nonfatal opioid overdose between 2000 and 2012. Investigators examined whether the patients continued to receive opioid prescriptions and the time to repeated overdose. Caution Out-of-hospital overdoses and opioid overdoserelated deaths were excluded. Implication Nearly all patients having a nonfatal opioid overdose continued to receive prescription opioids, and 7% had a repeated overdose. Treatment of chronic noncancer pain with prescription opioids has increased dramatically in recent decades (13). Opioid misuse and overdose have increased in parallel, and deaths due to prescription opioids quadrupled to 16651 between 1999 and 2010 (4, 5). However, overdose deaths alone do not capture the full morbidity from prescription opioids. More than 300000 patients visited an emergency department in 2008 due to nonmedical use of prescription opioidsmore than double the total from 5 years earlier (6). Presentation to an emergency department or hospital with a nonfatal opioid overdose is an opportunity to identify and refer patients who may be misusing opioids. Prescribing guidelines specify that misuse of opioids and related adverse events are indications to discontinue long-term therapy (7, 8). However, patterns of treatment, including rates of continued prescribing, after an opioid overdose are unknown. Research suggests that opioid overdose is associated with substance use disorders or high opioid dosages (9, 10), but the association between opioid analgesia treatment after an overdose and subsequent overdose is unknown. In this study, we sought to characterize opioid use after an overdose among patients receiving long-term opioid therapy for noncancer pain. We also aimed to determine whether patients who continued to receive prescription opioids after the index overdose switched providers and whether opioid dosage after an overdose was associated with risk for a subsequent overdose. Methods Study Design and Data Source We did a retrospective cohort study of persons having a nonfatal opioid overdose during an episode of long-term opioid use. We used the Optum database (Optum), comprising complete inpatient, outpatient, and pharmacy claims for patients from a large U.S. health insurer with members in all 50 states. We drew our cohort from 50 million commercially enrolled patients between May 2000 and December 2012 with a median follow-up of 15 months. We obtained study approval through the Harvard Pilgrim Health Care Institutional Review Board. Patient Selection We identified 14725 patients aged 18 to 64 years who had an index opioid overdose, defined as the first emergency department or inpatient claim with an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), diagnosis code of overdose due to a prescription opioid (965.00, 965.02, 965.09, E850.1, or E850.2) or heroin (965.01 or E850.0). This coding classification is consistent with past studies using administrative data and consensus recommendations from the Injury Surveillance Workgroup (1113). We combined emergency department or inpatient episodes separated by fewer than 2 days into a single episode. We excluded 1711 patients without 90 days of continuous enrollment before the start date of the index overdose to ensure sufficient data to identify prescription opioid dispensing patterns and patient characteristics at baseline (Appendix Figure 1). We limited the cohort to 3379 patients (26% of the remaining 13014) with evidence of long-term opioid therapy at the time of the index overdose. We defined opioid episodes as consecutive dispensings with 60 or fewer days between the run-out date (fill date plus days supplied) and the fill date of the next dispensing (Figure 1, A). We defined opioid episodes as long-term if they consisted of 3 or more dispensings at least 21 days apart and lasted at least 84 days (12 weeks) with at least 84 days supplied (Figure 1, B). We used First Databank (San Francisco, California) drug summary tables to identify National Drug Codes for opiate agonists using American Hospital Formulary Service classification 28080800. We included the following opioids: codeine, dihydrocodeine, meperidine, morphine, oxycodone, hydrocodone, hydromorphone, fentanyl, oxymorphone, propoxyphene, methadone, tramadol, and levorphanol. Appendix Figure 1. Study flow diagram. Figure 1. Definition of long-term opioid episodes and daily opioid dosage attribution. MED = morphine-equivalent dose. We excluded 479 patients with a diagnosis of cancer (except nonmelanoma skin cancer) during their enrollment based on ICD-9-CM diagnosis codes (140.X-208.X, 209.0-209.3, or V10.X [except V10.83]). We excluded 50 patients (2% of the 2900 remaining) for whom we could not confirm that the overdose was nonfatal by proxy of continued health plan enrollment or a subsequent pharmacy or medical claim. We excluded 2 patients with missing demographic variables, yielding a final cohort of 2848 patients (Appendix Figure 1). Patients were followed from 90 days before the index overdose until 1 of the following 5 stopping criteria was met: a second inpatient or emergency department claim for opioid overdose; disenrollment from the health plan; age of 65 years (Medicare eligible); 730 days (2 years) after the index overdose; or December 2012, the end of the study period. Outcomes The primary outcome measure was daily opioid dosage. We calculated the morphine-equivalent dosage (MED) for each opioid dispensing using established conversion tables (14). We calculated a daily MED by distributing the total MED for each dispensing over the days supplied and summing the total of overlapping dispensings on each day (Figure 1, C). For dispensings that spanned the index overdose, we stopped attribution of opioid dosage on the day of the overdose. We set daily dosage to 0 from the first day after overdose until the date of the first opioid dispensing after the overdose, if any (Figure 1, D). We believe that this attribution scheme best reflects prescriber intent before the overdose and modifiable prescriber behavior after the overdose. We assessed whether the primary opioid prescriber changed between the 90-day periods before and after the index overdose. We used standard provider identifiers to determine who prescribed each opioid dispensing. We identified the patients primary prescriber before and after the index overdose as the provider associated with the most opioid dispensings in both periods. Provider identifiers were available for 95% of the prescriptions. We excluded patients whose primary prescriber could not be determined due to missing data for 1 or more opioid dispensings. We also assessed daily availability of benzodiazepines and buprenorphine after the overdose based on the date of dispensing and days supplied. Benzodiazepines are commonly involved in fatal opioid overdoses and are associated with an increased risk for overdose when combined with opioids (4, 9). Buprenorphine has demonstrated efficacy as an office-based treatment of opioid dependence (15). We examined time to repeated opioid overdose; patients were censored through use of the criteria identified previously. We sought to determine whether changes in opioid dosage (in particular, opioid discontinuation) after the index overdose led to opioid withdrawal visits. We examined time to a medical claim for drug withdrawal, which was identified with ICD-9-CM diagnosis code 292.0. We examined characteristics of patients and their treatments in the 90 days before the index opioid overdose. We identified those who had 1 or more claims with a diagnosis of substance use disorder (ICD-9-CM codes 303.X-305.X), mental health disorder (schizophrenic disorder, 295.X; mood disorder, 296.X, 311.X; or anxiety disorder, 300.X), or dispensing for a benzodiazepine (American Hospital Formulary Service classification 28240800). We identified whether patients received immediate-release opioids, extended-release/long-acting opioids (Appendix Table 1), or both. We created clinically relevant categories of pain indications through review of ICD-9-CM diagnosis codes for medical claims in the 90 days before the overdose (Appendix Table 2). Appendix Table 1. Opioid Formulations Classified as Extended-Release/Long-Acting Appendix Table 2. All ICD-9-CM Diagnosis Codes From 90 d Before the Index Overdose That Were Reviewed by 2 Physicians* and Categorized Into Clinically Relevant Groupings Statistical Analysis We depicted opioid dosage before and after the index overdose in 3 ways. First, we calculated the mean dosage for the cohort on each day of the study period. Second, we examined the percentages of patients in the cohort with daily opioid dosages of none (0 mg MED), low (>0 to <50 mg MED), moderate (50 to <100 mg MED), or large (100 mg MED), consistent with previous studies demonstrating an association between increasing opioid dosage and overdose risk (9, 10, 15). Finally, to examine patterns of change in dosage in individual patients, we examined the category of average daily dosage for the following 3 periods: 60 days before the overdose, days 31 to 90 after the overdose, and days 91 to 365 after the overdose. We then identified the proportion of patients in each dosage category who moved to a different dosage category in the next period. We excluded the first 30 days before and after the index overdose because we could not correctly classify the dosage before the first prescription in each respective period. We compared benzodiazepine and buprenorphine use daily after the overdose, stratified by whether patients had an active opioid dispensing. To assess the association

Disclosing intimate partner violence to health care clinicians - what a difference the setting makes: a qualitative study.

BackgroundDespite endorsement by national organizations, the impact of screening for intimate partner violence (IPV) is understudied, particularly as it occurs in different clinical settings. We analyzed interviews of IPV survivors to understand the risks and benefits of disclosing IPV to clinicians across specialties.MethodsParticipants were English-speaking female IPV survivors recruited through IPV programs in Massachusetts. In-depth interviews describing medical encounters related to abuse were analyzed for common themes using Grounded Theory qualitative research methods. Encounters with health care clinicians were categorized by outcome (IPV disclosure by patient, discovery evidenced by discussion of IPV by clinician without patient disclosure, or non-disclosure), attribute (beneficial, unhelpful, harmful), and specialty (emergency department (ED), primary care (PC), obstetrics/gynecology (OB/GYN)).ResultsOf 27 participants aged 18–56, 5 were white, 10 Latina, and 12 black. Of 59 relevant health care encounters, 23 were in ED, 17 in OB/GYN, and 19 in PC. Seven of 9 ED disclosures were characterized as unhelpful; the majority of disclosures in PC and OB/GYN were characterized as beneficial. There were no harmful disclosures in any setting. Unhelpful disclosures resulted in emotional distress and alienation from health care. Regardless of whether disclosure occurred, beneficial encounters were characterized by familiarity with the clinician, acknowledgement of the abuse, respect and relevant referrals.ConclusionWhile no harms resulted from IPV disclosure, survivor satisfaction with disclosure is shaped by the setting of the encounter. Clinicians should aim to build a therapeutic relationship with IPV survivors that empowers and educates patients and does not demand disclosure.

The Impact of Trauma Exposure and Post-Traumatic Stress Disorder on Healthcare Utilization Among Primary Care Patients

Background:Trauma exposure and post-traumatic stress disorder (PTSD) increase healthcare utilization in veterans, but their impact on utilization in other populations is uncertain. Objectives:To examine the association of trauma exposure and PTSD with healthcare utilization, in civilian primary care patients. Research Design:Cross-sectional study. Subjects:English speaking patients at an academic, urban primary care clinic. Measures:Trauma exposure and current PTSD diagnoses were obtained from the Composite International Diagnostic Interview. Outcomes were nonmental health outpatient and emergency department visits, hospitalizations, and mental health outpatient visits in the prior year from an electronic medical record. Analyses included bivariate unadjusted and multivariable Poisson regressions adjusted for age, gender, income, substance dependence, depression, and comorbidities. Results:Among 592 subjects, 80% had ≥1 trauma exposure and 22% had current PTSD. In adjusted regressions, subjects with trauma exposure had more mental health visits [incidence rate ratio (IRR), 3.9; 95% confidence interval (CI), 1.1–14.1] but no other increased utilization. After adjusting for PTSD, this effect of trauma exposure was attenuated (IRR, 3.2; 95% CI, 0.9–11.7). Subjects with PTSD had more hospitalizations (IRR, 2.2; 95% CI, 1.4–3.7), more hospital nights (IRR, 2.6; 95% CI, 1.4–5.0), and more mental health visits (IRR, 2.2; 95% CI, 1.1–4.1) but no increase in outpatient and emergency department visits. Conclusions:PTSD is associated with more hospitalizations, longer hospitalizations, and greater mental healthcare utilization in urban primary care patients. Although trauma exposure is independently associated with greater mental healthcare utilization, PTSD mediates a portion of this association.

Buprenorphine Treatment for Hospitalized, Opioid-Dependent Patients: A Randomized Clinical Trial

IMPORTANCE Buprenorphine opioid agonist treatment (OAT) has established efficacy for treating opioid dependency among persons seeking addiction treatment. However, effectiveness for out-of-treatment, hospitalized patients is not known. OBJECTIVE To determine whether buprenorphine administration during medical hospitalization and linkage to office-based buprenorphine OAT after discharge increase entry into office-based OAT, increase sustained engagement in OAT, and decrease illicit opioid use at 6 months after hospitalization. DESIGN, SETTING, AND PARTICIPANTS From August 1, 2009, through October 31, 2012, a total of 663 hospitalized, opioid-dependent patients in a general medical hospital were identified. Of these, 369 did not meet eligibility criteria. A total of 145 eligible patients consented to participation in the randomized clinical trial. Of these, 139 completed the baseline interview and were assigned to the detoxification (n = 67) or linkage (n = 72) group. INTERVENTIONS Five-day buprenorphine detoxification protocol or buprenorphine induction, intrahospital dose stabilization, and postdischarge transition to maintenance buprenorphine OAT affiliated with the hospitals primary care clinic (linkage). MAIN OUTCOMES AND MEASURES Entry and sustained engagement with buprenorphine OAT at 1, 3, and 6 months (medical record verified) and prior 30-day use of illicit opioids (self-report). RESULTS During follow-up, linkage participants were more likely to enter buprenorphine OAT than those in the detoxification group (52 [72.2%] vs 8 [11.9%], P < .001). At 6 months, 12 linkage participants (16.7%) and 2 detoxification participants (3.0%) were receiving buprenorphine OAT (P = .007). Compared with those in the detoxification group, participants randomized to the linkage group reported less illicit opioid use in the 30 days before the 6-month interview (incidence rate ratio, 0.60; 95% CI, 0.46-0.73; P < .01) in an intent-to-treat analysis. CONCLUSIONS AND RELEVANCE Compared with an inpatient detoxification protocol, initiation of and linkage to buprenorphine treatment is an effective means for engaging medically hospitalized patients who are not seeking addiction treatment and reduces illicit opioid use 6 months after hospitalization. However, maintaining engagement in treatment remains a challenge. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT00987961.

Physician Burnout: Coaching a Way Out

ABSTRACTTwenty-five to sixty percent of physicians report burnout across all specialties. Changes in the healthcare environment have created marked and growing external pressures. In addition, physicians are predisposed to burnout due to internal traits such as compulsiveness, guilt, and self-denial, and a medical culture that emphasizes perfectionism, denial of personal vulnerability, and delayed gratification. Professional coaching, long utilized in the business world, provides a results-oriented and stigma-free method to address burnout, primarily by increasing one’s internal locus of control. Coaching enhances self-awareness, drawing on individual strengths, questioning self-defeating thoughts and beliefs, examining new perspectives, and aligning personal values with professional duties. Coaching utilizes established techniques to increase one’s sense of accomplishment, purpose, and engagement, all critical in ameliorating burnout. Coaching presumes that the client already possesses strengths and skills to handle life’s challenges, but is not accessing them maximally. Although an evidence base is not yet established, the theoretical basis of coaching’s efficacy derives from the fields of positive psychology, mindfulness, and self-determination theory. Using a case example, this article demonstrates the potential of professional coaching to address physician burnout.