Between- and within-person associations between opioid overdose risk and depression, suicidal ideation, pain severity, and pain interference

Association of medications for opioid use disorder with reduced risk of repeat opioid overdose in Medicaid: A cohort study

INTRODUCTION

Following a nonfatal opioid overdose, patients are at high risk for repeat overdose. The objective of this study was to examine the association of MOUD after nonfatal opioid overdose with risk of repeat overdose in the following year.

METHODS

This retrospective cohort study analyzed Missouri Medicaid claims from July 2012 to December 2021. The study identified opioid overdoses occurring between 2013 and 2020 using diagnosis codes for opioid poisoning in an inpatient or emergency department setting. The study implemented Cox models with a time-varying covariate for post-overdose receipt of MOUD.

RESULTS

During the study period, MOUD receipt after overdose more than tripled, from 4.8 % to 18.9 %. Overall, only 12.1 % of patients received MOUD in the year after index. MOUD during follow-up was associated with significantly lower risk of repeat overdose (HR = 0.34, 95 % CI = 0.14-0.82). Out of 3017 individuals meeting inclusion criteria, 13.6 % had a repeat opioid overdose within 1 year. Repeat overdose risk was higher for those whose index overdose involved heroin or synthetic opioids (HR = 1.71, 95 % CI = 1.35-2.15), but MOUD was associated with significantly reduced risk in this group (HR = 0.34, 95 % CI = 0.13-0.92).

CONCLUSIONS

MOUD receipt was associated with reduced risk of repeat overdose. Those whose index overdoses involved heroin or synthetic opioids were at greater risk of repeat overdose, but MOUD was associated with reduced risk in this group.

A pilot study to identify pharmacogenomic and clinical risk factors associated with opioid related falls and adverse effects in older adults

Given the high prevalence of pain in older adults and current trends in opioid prescribing, inclusion of genetic information in risk prediction tools may improve opioid risk assessment. Our objectives were to (1) determine the feasibility of recruiting socioeconomically disadvantaged and racially diverse middle aged and older adult populations for a study seeking to identify risk factors for opioid-related falls and other serious adverse effects and (2) explore potential associations between the Risk Index for Overdose or Serious Opioid-induced Respiratory Depression (CIP-RIOSORD) risk class and other patient factors with falls and serious opioid adverse effects. This was an observational study of 44 participants discharged home from the emergency department with an opioid prescription for acute pain and followed for 30 days. We found pain interference may predict opioid-related falls or serious adverse effects within older, opioid-treated patients. If validated, pain interference may prove to be a beneficial marker for risk stratification of older adults initiated on opioids for acute pain.

Identifying Prescription-Opioid-Related Risks Using Prescription Drug Monitoring Programs' Algorithms and Clinical Screening Tools

BACKGROUND

Pharmacists adopt various approaches to identifying prescription-opioid-related risks and harms, including prescription drug monitoring programs (PDMPs) and clinical screening tools. This study aims to compare 'at-risk' patients according to the published Australian PDMP algorithms with the validated Routine Opioid Outcome Monitoring (ROOM) clinical screening tool.

METHODS

Data were used from an implementation study amongst people who had been prescribed regular opioids. We examined the results from ROOM and the patients' dispensing history over the previous 90 days. A chi-squared test was used to examine the association between risk according to (i) a PDMP alert and a clinical risk per ROOM; (ii) a PDMP alert and positive screening for opioid use disorder; and (iii) a PDMP 'high-dose' alert (average of >100 mg OME/day in the past 90 days) and any ROOM-validated risk.

RESULTS

No significant associations were found between being 'at-risk' according to any of the PDMP alerts and clinical risk as identified via the ROOM tool (x2 = 0.094, p = 0.759). There was only minimal overlap between those identified as 'at-risk' via PDMP alerts and those meeting the clinical risk indicators; most patients who were 'at-risk' of clinical opioid-related risk factors were not identified as 'at-risk' based on PDMP alerts.

CONCLUSIONS

PDMP alerts were not predictive of clinical risk (as per the ROOM tool), as many people with well-established clinical risks would not receive a PDMP alert. Pharmacists should be aware that PDMPs are limited to identifying medication-related risks which are derived using algorithms; therefore, augmenting PDMP information with clinical screening tools can help create a more detailed narrative of patients' opioid-related risks.

High suicidality predicts overdose events among people with substance use disorder: A latent class analysis

Introduction

Suicide is the tenth leading cause of death in the United States and continues to be a major public health concern. Suicide risk is highly prevalent among individuals with co-occurring substance use disorders (SUD) and mental health disorders, making them more prone to adverse substance use related outcomes including overdose. Identifying individuals with SUD who are suicidal, and therefore potentially most at risk of overdose, is an important step to address the synergistic epidemics of suicides and overdose fatalities in the United States. The current study assesses whether patterns of suicidality endorsement can indicate risk for substance use and overdose.

Methods

Latent class analysis (LCA) was used to assess patterns of item level responses to the Concise Health Risk Tracking Self-Report (CHRT-SR), which measures thoughts and feelings associated with suicidal propensity. We used data from 2,541 participants with SUD who were enrolled across 8 randomized clinical trials in the National Drug Abuse Treatment Clinical Trials Network from 2012 to 2021. Characteristics of individuals in each class were assessed, and multivariable logistic regression was performed to examine class membership as a predictor of overdose. LCA was also used to analyze predictors of substance use days.

Results

Three classes were identified and discussed: Class (1) Minimal Suicidality, with low probabilities of endorsing each CHRT-SR construct; Class (2) Moderate Suicidality, with high probabilities of endorsing pessimism, helplessness, and lack of social support, but minimal endorsement of despair or suicidal thoughts; and Class (3) High Suicidality with high probabilities of endorsing all constructs. Individuals in the High Suicidality class comprise the highest proportions of males, Black/African American individuals, and those with a psychiatric history and baseline depression, as compared with the other two classes. Regression analysis revealed that those in the High Suicidality class are more likely to overdose as compared to those in the Minimal Suicidality class (p = 0.04).

Conclusion

Suicidality is an essential factor to consider when building strategies to screen, identify, and address individuals at risk for overdose. The integration of detailed suicide assessment and suicide risk reduction is a potential solution to help prevent suicide and overdose among people with SUD.

U.S. Military veterans and the opioid overdose crisis: a review of risk factors and prevention efforts

U.S. military veterans have been heavily impacted by the opioid overdose crisis, with drug overdose mortality rates increasing by 53% from 2010-2019. Risk for overdose among veterans is complex and influenced by ongoing interaction among physiological/biological, psychological, and socio-structural factors. A thorough understanding of opioid-related overdose among veterans, one that goes beyond simple pharmacological determinism, must examine the interplay of pain, pain treatment, and stress, as well as psychological and social experiences-before, during, and after military service. Comprehensive efforts to tackle the overdose crisis among veterans require interventions that address each of these dimensions. Promising interventions include widespread naloxone distribution and increased provision of low-threshold wrap-around services, including medications for opioid use disorder (MOUD) and holistic/complementary approaches. Interventions that are delivered by peers - individuals who share key experiential or sociodemographic characteristics with the population being served - may be ideally suited to address many of the barriers to opioid-related risk mitigation common among veterans. Community care models could be beneficial for the large proportion of veterans who are not connected to the Veterans Health Administration and for veterans who, for various reasons including mental health problems and the avoidance of stigma, are socially isolated or reluctant to use traditional substance use services. Interventions need to be tailored in such a way that they reach those more socially isolated veterans who may not have access to naloxone or the social support to help them in overdose situations. It is important to incorporate the perspectives and voices of veterans with lived experience of substance use into the design and implementation of new overdose prevention resources and strategies to meet the needs of this population. Key messagesU.S. military veterans have been heavily impacted by the opioid overdose crisis, with drug overdose mortality rates increasing by 53% from 2010-2019.The risks for overdose that veterans face need to be understood as resulting from an ongoing interaction among biological/physiological, psychological, and social/structural factors.Addressing drug overdose in the veteran population requires accessible and non-judgemental, low threshold, wraparound, and holistic solutions that recognise the complex aetiology of overdose risk for veterans.

Fentanyl preference among people who use opioids in New York City

BACKGROUND

Several studies have pointed to a sub-sample of people who use drugs (PWUD) who prefer the use of non-pharmaceutical fentanyl (NPF) and, as such, pose a unique challenge for public health initiatives amidst the continued rise in overdose mortality. However, matters of drug preference and autonomy of choice remain under-studied and often misunderstood. This paper examined the experiences of PWUD reporting a preference for NPF or an NPF-heroin mixture, specifically how they navigate the perceived benefits of NPF and its established risks.

METHODS

22 in-depth interviews were conducted in New York City between March 2018 and August 2019 with PWUD who self-reported a preference for NPF or an NPF-heroin mix. Interviews were audio-recorded, and the resulting transcripts analyzed using a thematic approach.

RESULTS

Participants highlighted various factors that contributed to expressed preference for NPF or an NPF-heroin mix, including a desire to feel good, financial resources, drug availability, decreased consumer autonomy, and physiological demand. Participants reported practicing several risk reduction strategies; however, they highlighted that many, particularly carrying naloxone and always using with someone else, were difficult to implement in the context of illicit drug use.

CONCLUSIONS

Our results demonstrate participants' decreased consumer agency and greater exposure to systemic factors in the illicit markets, highlighting the need for expansion of various services, including drug checking resources and systems of outreach for PWUD who do not use intravenously. To promote tailored interventions, continued efforts in overdose prevention ought to more thoughtfully consider the context, perceptions, preferences, and behaviors of PWUD.

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.

Profiles of Patients with Opioid Use Disorders Presenting a History of Suicidal Ideations and Attempts

Suicide rates are higher for people with an opioid use disorder, compared to the general population. This study aims to characterize opioid agonist treatment entrants who present a history of suicidal ideations or suicide attempts, according to concurrent comorbidity profiles, in an opioid use disorder treatment facility. A chart review design was used. Data was collected from 202 patient files. Bivariate and multivariate analyses were conducted. In multivariate analysis, patients with a diagnosis or symptoms of a mood disorder were 2.48 [1.01 - 6.11] times more likely to report suicidal ideations and 2.64 [1.05 - 6.62] times more likely to report suicide attempts. Those with a diagnosis or symptoms of an anxiety disorder were 2.41 [1.01 - 5.81] times more likely to report suicidal ideations. Patients who report chronic pain were 2.59 [1.06 - 6.35] times more likely to report suicidal ideations as well. The probability to report suicide attempts was 5.09 [1.16 - 22.4] times higher for those with a confirmed or suspected personality disorder. Clinicians should bear in mind the high suicide rates in people with opioid use disorder, as well as the importance of addressing suicidal risk and providing easy access to mental health and chronic pain treatment as part of the service offer in opioid agonist treatment. Future research should focus on evaluating the effectiveness of treatments aimed at addressing the needs of opioid agonist treatment patients with interrelated mental health and pain comorbidity profiles to reduce risks associated with suicide.

Naloxone protection, social support, network characteristics, and overdose experiences among a cohort of people who use illicit opioids in New York City

BACKGROUND

Despite increased availability of take-home naloxone, many people who use opioids do so in unprotected contexts, with no other person who might administer naloxone present, increasing the likelihood that an overdose will result in death. Thus, there is a social nature to being "protected" from overdose mortality, which highlights the importance of identifying background factors that promote access to protective social networks among people who use opioids.

METHODS

We used respondent-driven sampling to recruit adults residing in New York City who reported recent (past 3-day) nonmedical opioid use (n = 575). Participants completed a baseline assessment that included past 30-day measures of substance use, overdose experiences, and number of "protected" opioid use events, defined as involving naloxone and the presence of another person who could administer it, as well as measures of network characteristics and social support. We used modified Poisson regression with robust variance to estimate unadjusted and adjusted prevalence ratios (PRs) and 95% confidence intervals (CIs).

RESULTS

66% of participants had ever been trained to administer naloxone, 18% had used it in the past three months, and 32% had experienced a recent overdose (past 30 days). During recent opioid use events, 64% reported never having naloxone and a person to administer present. This was more common among those: aged ≥ 50 years (PR: 1.18 (CI 1.03, 1.34); who identified as non-Hispanic Black (PR: 1.27 (CI 1.05, 1.53); experienced higher levels of stigma consciousness (PR: 1.13 (CI 1.00, 1.28); and with small social networks (< 5 persons) (APR: 1.14 (CI 0.98, 1.31). Having a recent overdose experience was associated with severe opioid use disorder (PR: 2.45 (CI 1.49, 4.04), suicidality (PR: 1.72 (CI 1.19, 2.49), depression (PR: 1.54 (CI 1.20, 1.98) and positive urinalysis result for benzodiazepines (PR: 1.56 (CI 1.23, 1.96), but not with network size.

CONCLUSIONS

Results show considerable gaps in naloxone protection among people who use opioids, with more vulnerable and historically disadvantaged subpopulations less likely to be protected. Larger social networks of people who use opioids may be an important resource to curtail overdose mortality, but more effort is needed to harness the protective aspects of social networks.

The leading neighborhood-level predictors of drug overdose: A mixed machine learning and spatial approach

BACKGROUND

Drug overdose is a leading cause of unintentional death in the United States and has contributed significantly to a decline in life expectancy during recent years. To combat this health issue, this study aims to identify the leading neighborhood-level predictors of drug overdose and develop a model to predict areas at the highest risk of drug overdose using geographic information systems and machine learning (ML) techniques.

METHOD

Neighborhood-level (block group) predictors were grouped into three domains: socio-demographic factors, drug use variables, and protective resources. We explored different ML algorithms, accounting for spatial dependency, to identify leading predictors in each domain. Using geographically weighted regression and the best-performing ML algorithm, we combined the output prediction of three domains to produce a final ensemble model. The model performance was validated using classification evaluation metrics, spatial cross-validation, and spatial autocorrelation testing.

RESULTS

The variables contributing most to the predictive model included the proportion of households with food stamps, households with an annual income below $35,000, opioid prescription rate, smoking accessories expenditures, and accessibility to opioid treatment programs and hospitals. Compared to the error estimated from normal cross-validation, the generalized error of the model did not increase considerably in spatial cross-validation. The ensemble model using ML outperformed the GWR method.

CONCLUSION

This study identified strong neighborhood-level predictors that place a community at risk of experiencing drug overdoses, as well as protective factors. Our findings may shed light on several specific avenues for targeted intervention in neighborhoods at risk for high drug overdose burdens.

The effect of depressive symptoms on pain in a substance-using population with persistent pain: a cross-sectional cohort study

BACKGROUND

In light of the ongoing opioid overdose crisis, there is an urgent need for research on the impacts of mental health among people presenting with concurrent pain and substance use. This study examined the effect of depressive symptoms on pain severity and functional interference among people who use drugs (PWUD) during a community-wide overdose crisis.

METHODS

From December 1st 2016 to December 31st 2018, 288 participants in two cohort studies of PWUD in Vancouver, Canada completed interviewer-administered questionnaires that included the Brief Pain Inventory and PROMIS Emotional Distress-Depression instruments. Generalized linear regression modelling (GLM) was used to examine the cross-sectional effect of depressive symptoms and other confounding factors on pain severity and interference.

RESULTS

Moderate to severe depressive symptoms were significantly associated with greater pain-related functional interference (adjusted β = 1.24, 95% confidence interval [CI] = 0.33-2.15), but not significantly associated with greater average pain severity (adjusted β = 0.22, 95% CI = - 0.3 - 0.82), when controlling for confounding variables. Reported daily heroin use (adjusted β = 1.26, 95% CI = 0.47-2.05) and non-fatal overdose (adjusted β = 1.02, 95% CI = 0.08-1.96) were also significantly associated with greater pain-related functional interference.

CONCLUSIONS

In a substance-using population, greater pain-related functional interference was positively associated with depressive symptoms as well as overdose and daily heroin use. These findings emphasize the need to address the functional impact of pain, mental health comorbidity, and high-risk substance use that may contribute to overdose and other harms.

Validation of the Opioid Overdose Risk Behavior Scale, version 2 (ORBS-2)

OBJECTIVE

To examine the factor structure of a revised and expanded opioid overdose risk behavior scale and assess its associations with known overdose indicators and other clinical constructs.

BACKGROUND

Opioid-related overdose remains high in the U.S. We lack strong instrumentation for assessing behavioral risk factors. We revised and expanded the opioid overdose risk behavior scale (ORBS-1) for use among a broader range of people who use opioids.

SETTING & SAMPLING FRAME

Using respondent-driven sampling we recruited adults (18+) reporting current unprescribed opioid use and New York City residence.

METHOD

Participants (N = 575) completed the ORBS-1, ORBS-2, and a variety of clinical measures and then completed the ORBS-2 and overdose risk outcomes across monthly follow-up assessments over a 13-month period.

RESULTS

Principal components analysis was used to identify six ORBS-2 subscales, Prescription Opioid Misuse, Risky Non-Injection Use, Injection Drug Use, Concurrent Opioid and Benzodiazepine Use, Concurrent Opioid and Alcohol Use, and Multiple-Drug Polysubstance Use. All subscales showed moderate non-parametric correlations with the ORBS-1 and with corresponding clinical constructs. Five of the subscales were significantly (p < .01) positively associated with self-reported non-fatal overdose. Of note, the Risky Non-Injection Use subscale was the most strongly associated with past-month overdose indicators.

CONCLUSIONS

Psychometrics for the opioid overdose risk behavior subscales identified suggest the ongoing utility of risk behavioral instrumentation for epidemiological research and clinical practice focused on risk communication and minimization. Use of the entire ORBS-2 measure can provide insight into the proximal/behavioral factors of greatest concern to reduce overdose mortality.

Opioid overdose in the age of fentanyl: Risk factor differences among subpopulations of overdose survivors

BACKGROUND

Although fentanyl is the drug most frequently implicated in overdose deaths, the association between overdose risk and attitudes and behaviors surrounding fentanyl in opioid-using communities has remained understudied. Possible subpopulation differences in fentanyl-related overdose risk remain equally unexamined. This paper addresses these gaps by exploring the association between overdose and fentanyl-related attitudes/behaviors in three subpopulations of overdose survivors.

METHODS

In this cross-sectional study, we sampled 432 individuals who currently or recently used opioids from New Jersey methadone and acute residential detoxification programs. Using multinomial regression analysis, we compared overdose risk factors, including fentanyl-related attitudes/behaviors, of those who never overdosed with three subgroups of overdose survivors who experienced: 1. recent overdoses occurring after, but not before, fentanyl expansion; 2. past overdoses occurring before, but not after, fentanyl expansion; 3. persistent overdoses occurring before and after fentanyl expansion.

RESULTS

Forty percent of respondents had knowingly used fentanyl and 38% deliberately sought overdose-implicated drugs. Respondents with persistent overdoses represented under 10% of the sample but accounted for 44% of all lifetime overdoses (x̅ =8.03 vs. 1.71 for the full sample). This was also the only subgroup for whom PTSD (AOR=3.84; 95%CI=1.45-10.16; p=.01) and fentanyl-seeking (AOR=1.50; 95% CI=1.16-1.94; p=.01) were significant overdose risk factors. Those with recent overdoses engaged in frequent drug combining (AOR=2.28; 95% CI=1.19-6.98; p=.05), which could have led to inadvertent fentanyl use. Those with past overdoses were not at overdose risk from fentanyl-seeking or drug combining and had rates of methadone treatment comparable to rates of those with no overdoses.

CONCLUSION

Harm reduction strategies will need to address consumers' evolving drug preferences as fentanyl continues to saturate local drug markets. Targeting comprehensive interventions, including mental health treatment, to the small group of opioid users with longstanding overdose histories may reduce the burden of overdose in opioid-using communities.