Inpatient Opioid Use Disorder and Social Determinants of Health: A Nationwide Analysis of the National Inpatient Sample (2012-2014 and 2016-2017)

Demographic, Social, Behavioral, and Clinical Characteristics Associated with Long-Term Opioid Therapy and Any Opioid Prescription in High-Risk VA Patients

BACKGROUND

Social risks (individual social and economic conditions) have been implicated as playing a major role in the opioid epidemic and may be more prevalent in the most medically vulnerable patients. However, the extent to which specific social risks and other patient factors are associated with opioid use among high-risk patients has not been comprehensively assessed.

OBJECTIVE

To identify patient-reported and electronic health record (EHR)-derived demographic, social, behavioral/psychological, and clinical characteristics associated with opioid use in Veterans Affairs (VA) patients at high risk for hospitalization or death.

DESIGN

We used generalized estimating equations to calculate the probability of long-term opioid therapy (LTOT) and the probability of filling any opioid prescription (regardless of duration) over five intervals during a 4-year period (12/2016-12/2020).

PARTICIPANTS

Prospective cohort of 4121 medically high-risk VA patients not receiving palliative or end-of-life care, and who responded to a survey mailed to a nationally representative sample of 10,000 high-risk VA patients.

MAIN MEASURES

Patient-reported demographic, social risk, behavioral/psychological, and clinical measures, and linked EHR-derived data.

KEY RESULTS

The average age was 69.8 years, 6.7% were female, and 17.5% were Non-Hispanic Black race/ethnicity. The majority had diagnosed chronic pain (76.1%). LTOT and any opioid prescription were positively associated with the following: younger age, non-Hispanic White race/ethnicity (compared to non-Hispanic Black race/ethnicity), male sex assigned at birth (LTOT only), not being currently employed, current tobacco use, no alcohol use, higher grit (any opioid prescription only), functional limitations, diagnosed chronic pain, lower comorbidity burden (LTOT only), obesity class I or class II/III (any opioid prescription only), undergoing surgery (any opioid prescription only), and diagnosed cancer (any opioid prescription only).

CONCLUSIONS

Multifactor screening could help identify individuals at elevated risk for adverse opioid-related outcomes and augment current multifaceted initiatives, as several social risks and patient characteristics were predictors of LTOT and any opioid prescription.

Emergency Department-Based Medication for Opioid Use Disorder Program: Addressing Gaps in Linkages to Care

Background Emergency department (ED)-based medication for opioid use disorder (MOUD) has been shown to be effective in providing ease of access and successful treatment rates for patients with opioid use disorder (OUD). This study examined the social determinants of health (SDOH) of patients entering an ED-based MOUD program through individual and focus group surveys. SDOH may impact treatment retention for current and future patients. Methods A survey of all patients entering our MOUD program at two hospital-based EDs and two free-standing EDs was conducted from January to March 2022. Addiction care coordinators (ACCs) used standardized screening tools to enroll patients into the MOUD program, and trained research coordinators used a standardized form, using previously validated survey questions, to examine the role of SDOH. Focused group surveys were also collected. The survey measured patients' perspectives of the program and solicited feedback on SDOH and program barriers. Results Of the 60 OUD patients inducted into the ED-based MOUD program during our survey period, 19 (32%) participated in an individual or focus group interview. Of these, 16 patients (27%) completed all survey questions. The mean age was 42 years old, 94% identified as Caucasian, and 65% were males. Over 94% of subjects found the ACCs helpful in providing follow-up care. Nearly 40% experienced transportation and financial issues. The vast majority found the MOUD program beneficial in coping with withdrawal symptoms, dealing with their addiction, and supporting recovery. Conclusion OUD patients found the ACCs and the MOUD program helpful for their transition to the treatment stage. The MOUD program can improve some patients' reluctance to engage with a healthcare system by addressing barriers related to transportation to appointments and financial issues.

Development and validation of a community-level social determinants of health index for drug overdose deaths in the HEALing Communities Study

INTRODUCTION

Social determinants of health (SDoH), such as socioeconomic status, education level, and food insecurity, are believed to influence the opioid crisis. While global SDoH indices such as the CDC's Social Vulnerability Index (SVI) and Area Deprivation Index (ADI) combine the explanatory power of multiple social factors for understanding health outcomes, they may be less applicable to the specific challenges of opioid misuse and associated outcomes. This study develops a novel index tailored to opioid misuse outcomes, tests the efficacy of this index in predicting drug overdose deaths across contexts, and compares the explanatory power of this index to other SDoH indices.

METHODS

Focusing on four HEALing Communities Study (HCS) states (Kentucky, Massachusetts, New York and Ohio; encompassing 4269 ZIP codes), we identified multilevel SDoH potentially associated with opioid misuse and aggregated publicly available data for each measure. We then leveraged a random forest model to develop a composite measure that predicts age-adjusted drug overdose mortality rates based on SDoH. We used this composite measure to understand HCS and non-HCS communities in terms of overdose risk across areas of varying racial composition. Finally, we compared variance in drug overdose deaths explained by this index to variance explained by the SVI and ADI.

RESULTS

Our composite measure included 28 SDoH measures and explained approximately 89 % percent of variance in age-adjusted drug overdose mortality across HCS states. Health care measures, including emergency department visits and primary care provider availability, were top predictors within the index. Index accuracy was robust within and outside of HCS communities and states. This measure identified high levels of overdose mortality risk in segregated communities.

CONCLUSIONS

Existing SDoH indices fail to explain much variation in area-level overdose mortality rates. Having tailored composite indices can help us to identify places in which residents are at highest risk based on their composite contexts. A comprehensive index can also help to develop effective community interventions for programs such as HCS by considering the context in which people live.

Community-level determinants of stakeholder perceptions of community stigma toward people with opioid use disorders, harm reduction services and treatment in the HEALing Communities Study

BACKGROUND

Community stigma toward people with opioid use disorder (OUD) can impede access to harm reduction services and treatment with medications for opioid use disorder (MOUD). Such community OUD stigma is partially rooted in community-level social and economic conditions, yet there remains a paucity of large-scale quantitative data examining community-level factors associated with OUD stigma. We examined whether rurality, social inequity, and racialized segregation across communities from four states in the HEALing Communities Study (HCS) were associated with 1) greater perceived community stigma toward people treated for OUD, 2) greater perceived intervention stigma toward MOUD, and 3) greater perceived intervention stigma toward naloxone by community stakeholders in the HCS.

METHODS

From November 2019-January 2020, a cross-sectional survey about community OUD stigma was administered to 801 members of opioid overdose prevention coalitions across 66 communities in four states prior to the start of HCS intervention activities. Bivariate analyses assessed pairwise associations between community rural/urban status and each of the three stigma variables, using linear mixed effect modeling to account for response clustering within communities, state, and respondent sociodemographic characteristics. We conducted similar bivariate analyses to assess pairwise associations between racialized segregation and social inequity.

RESULTS

On average, the perceived community OUD stigma scale score of stakeholders from rural communities was 4% higher (β=1.57, SE=0.7, p≤0.05), stigma toward MOUD was 6% higher (β=0.28, SE=0.1, p≤0.05), and stigma toward naloxone was 10% higher (β=0.46, SE=0.1, p≤0.01) than among stakeholders from urban communities. No significant differences in the three stigma variables were found among communities based on racialized segregation or social inequity.

CONCLUSION

Perceived community stigma toward people treated for OUD, MOUD, and naloxone was higher among stakeholders in rural communities than in urban communities. Findings suggest that interventions and policies to reduce community-level stigma, particularly in rural areas, are warranted.

Treatment Initiation, Substance Use Trajectories, and the Social Determinants of Health in Persons Living With HIV Seeking Medication for Opioid Use Disorder

BACKGROUND

People living with HIV and opioid use disorder (OUD) are disproportionally affected by adverse socio-structural exposures negatively affecting health, which have shown inconsistent associations with uptake of medications for OUD (MOUD). This study aimed to determine whether social determinants of health (SDOH) were associated with MOUD uptake and trajectories of substance use in a clinical trial of people seeking treatment.

METHODS

Data are from a 2018 to 2019 randomized trial comparing the effectiveness of different MOUD to achieve viral suppression among people living with HIV and OUD. SDOH were defined by variables mapping to Healthy People 2030 domains: education (Education Access and Quality), income (Economic Stability), homelessness (Neighborhood and Built Environment), criminal justice involvement (Social and Community Context), and recent SUD care (Health Care Access and Quality). Associations between SDOH and MOUD initiation were assessed with Cox proportional hazards models, and SDOH and substance use over time with generalized estimating equation models.

RESULTS

Participants (N = 114) averaged 47 years old, 63% were male, 56% were Black, and 12% Hispanic. Participants reported an average of 2.3 out of 5 positive SDOH indicators (SD = 1.2). Stable housing was the most commonly reported SDOH (61%), followed by no recent criminal justice involvement (59%), having a high-school level education or greater (56%), income stability (45%), and recent SUD care (13%). Each additional favorable SDOH was associated with a 25% increase in the likelihood of MOUD initiation during the study period [adjusted HR = 1.25, 95% CI = (1.01, 1.55), P = .044]. Positive SDOH were also associated with a decrease in the odds of baseline opioid use and a greater reduction in opioid use during subsequent weeks of the study (P < .001 for a joint test of baseline and slope differences).

CONCLUSIONS

Positive social determinants of health, in aggregate, may increase the likelihood of MOUD treatment initiation among people living with HIV and OUD.

Social determinants of health derived from people with opioid use disorder: Improving data collection, integration and use with cross-domain collaboration and reproducible, data-centric, notebook-style workflows

Deriving social determinants of health from underserved populations is an important step in the process of improving the well-being of these populations and in driving policy improvements to facilitate positive change in health outcomes. Collection, integration, and effective use of clinical data for this purpose presents a variety of specific challenges. We assert that combining expertise from three distinct domains, specifically, medical, statistical, and computer and data science can be applied along with provenance-aware, self-documenting workflow tools. This combination permits data integration and facilitates the creation of reproducible workflows and usable (reproducible) results from the sensitive and disparate sources of clinical data that exist for underserved populations.

Sex-Differences in Pain and Opioid Use Disorder Management: A Cross-Sectional Real-World Study

(1) Background: It is essential to focus attention on sex-specific factors which are clinically relevant in pain management, especially with regards to opioid use disorder (OUD) risk. The aim of this study was to explore potential sex-differences in chronic non-cancer pain (CNCP) outpatients. (2) Methods: An observational cross-sectional study was conducted under CNCP outpatients with long-term prescribed opioids (n = 806), wherein 137 patients had an OUD diagnosis (cases, 64% females) and 669 did not (controls, 66% females). Socio-demographic, clinical, and pharmacological outcomes were analyzed. (3) Results: Female controls presented an older age and less intensive pain therapy but higher psychotropic prescriptions and emergency department visits compared to male controls. Meanwhile, cases demonstrated a younger age, higher work disability, double morphine equivalent daily dose, and benzodiazepine use compared with controls. Here, female cases showed an 8% greater substance use disorder (OR 2.04 [1.11–3.76]) and 24% lower tramadol use, while male cases presented a 22% higher fentanyl use (OR 2.97 [1.52–5.81]) and reported the highest number of adverse drug reactions (24%, OR 2.40 [1.12–5.16]) compared with controls. (4) Conclusions: An OUD individual risk profile was evidenced with sex-differences to take into consideration to design equal prevention programs.

Trend Analysis of Substance Use Disorder During Pregnancy

Objectives: This study aims to analyze the trends in substance use among pregnant women in the United States.

Methodology: In this retrospective study, we utilized the National Inpatient Sample (NIS) dataset sponsored by the Agency for Healthcare Research and Quality (AHRQ) under the Healthcare Cost and Utilization Project (HCUP). Major Diagnostic Category (MDC) 14 (Pregnancy, Childbirth, and the Puerperium) and International Classification of Disease (ICD 10) codes were used to identify pregnancy-related diagnoses and presentations with any of the substance use disorder (SUD) indicators that met the inclusion criteria among the birthing population in the NIS dataset (2016-2018). We analyzed the demographic and regional characteristics between 2015 and 2018.

Results: Among the population, a total of 23,475 (2.7%) had a primary or secondary diagnosis of SUD, and 851,428 (97.3%) did not. In the study group of 332,275 (2.8%) that met the inclusion criteria, 12,750 (0.1%) use alcohol, 108,960 (0.9%) had opioid use disorder (OUD), 171,490 (1.4%) use cannabis, 6,375 (0.1%) use sedatives, 28,075 (0.2%) use cocaine, 48,765 (0.4%) use other stimulants, 1,155 (0%) use hallucinogens, 115 (0%) use inhalants, and 23,950 (0.2%) had other psychoactive diagnosis. Further analysis comparing the risk of severity and mortality at presentation, procedure type, delivery method, and cost of care shows statistically significant differences (p < 0.005) between the study and control groups.

Conclusion: The current trends necessitate a further assessment and implementation of comprehensive community-based treatment programs tailored to the most frequent regional SUD presentations, which could aid in mitigating drug use during pregnancy.