Risk Factors Associated With Nonfatal Opioid Overdose Leading to Intensive Care Unit Admission: A Cross-sectional Study

ODD: A Benchmark Dataset for the Natural Language Processing Based Opioid Related Aberrant Behavior Detection

Opioid related aberrant behaviors (ORABs) present novel risk factors for opioid overdose. This paper introduces a novel biomedical natural language processing benchmark dataset named ODD, for ORAB Detection Dataset. ODD is an expert-annotated dataset designed to identify ORABs from patients' EHR notes and classify them into nine categories; 1) Confirmed Aberrant Behavior, 2) Suggested Aberrant Behavior, 3) Opioids, 4) Indication, 5) Diagnosed opioid dependency, 6) Benzodiazepines, 7) Medication Changes, 8) Central Nervous System-related, and 9) Social Determinants of Health. We explored two state-of-the-art natural language processing models (fine-tuning and prompt-tuning approaches) to identify ORAB. Experimental results show that the prompt-tuning models outperformed the fine-tuning models in most categories and the gains were especially higher among uncommon categories (Suggested Aberrant Behavior, Confirmed Aberrant Behaviors, Diagnosed Opioid Dependence, and Medication Change). Although the best model achieved the highest 88.17% on macro average area under precision recall curve, uncommon classes still have a large room for performance improvement. ODD is publicly available.

Psychosocial factors associated with overdose subsequent to Illicit Drug use: a systematic review and narrative synthesis

BACKGROUND AND AIMS

Psychological and social status, and environmental context, may mediate the likelihood of experiencing overdose subsequent to illicit drug use. The aim of this systematic review was to identify and synthesise psychosocial factors associated with overdose among people who use drugs.

METHODS

This review was registered on Prospero (CRD42021242495). Systematic record searches were undertaken in databases of peer-reviewed literature (Medline, Embase, PsycINFO, and Cinahl) and grey literature sources (Google Scholar) for work published up to and including 14 February 2023. Reference lists of selected full-text papers were searched for additional records. Studies were eligible if they included people who use drugs with a focus on relationships between psychosocial factors and overdose subsequent to illicit drug use. Results were tabulated and narratively synthesised.

RESULTS

Twenty-six studies were included in the review, with 150,625 participants: of those 3,383-4072 (3%) experienced overdose. Twenty-one (81%) studies were conducted in North America and 23 (89%) reported polydrug use. Psychosocial factors associated with risk of overdose (n = 103) were identified and thematically organised into ten groups. These were: income; housing instability; incarceration; traumatic experiences; overdose risk perception and past experience; healthcare experiences; perception of own drug use and injecting skills; injecting setting; conditions with physical environment; and social network traits.

CONCLUSIONS

Global rates of overdose continue to increase, and many guidelines recommend psychosocial interventions for dependent drug use. The factors identified here provide useful targets for practitioners to focus on at the individual level, but many identified will require wider policy changes to affect positive change. Future research should seek to develop and trial interventions targeting factors identified, whilst advocacy for key policy reforms to reduce harm must continue.

Prescription opioid dispensing patterns among patients with schizophrenia or bipolar disorder

BACKGROUND

Patients with schizophrenia (SZ) or bipolar disorder (BD) may have increased risk of complications from prescribed opioids, including opioid-induced respiratory depression. We compared prescription opioid pain medication dispensing for patients with SZ or BD versus controls over 5 years to assess dispensing trends.

METHODS

This retrospective, observational study analysed US claims data from the IBM® MarketScan® Commercial and Multi-State Medicaid databases for individuals aged 18-64 years with prevalent SZ or BD for years 2015-2019 compared with age- and sex-matched controls. Baseline characteristics, comorbidities, and medication use were assessed. Proportions of individuals dispensed prescription opioids chronically (ie, ≥70 days over a 90-day period or ≥ 6 prescriptions annually) or nonchronically (≥1 prescription, chronic definition not met) were assessed.

RESULTS

In 2019, the Commercial and Medicaid databases contained records for 4773 and 30,179 patients with SZ and 52,780 and 63,455 patients with BD, respectively. Patients with SZ or BD had a higher prevalence of comorbidities, including pain, versus controls in each analysis year. From 2015 to 2019, among commercially insured patients with SZ, chronic opioid-dispensing proportions decreased from 6.1% (controls: 2.7%) to 2.3% (controls: 1.2%) and, for patients with BD, from 11.4% (controls: 2.7%) to 6.4% (controls: 1.6%). Chronic opioid dispensing declined in Medicaid-covered patients with SZ from 15.0% (controls: 14.7%) to 6.7% (controls: 6.0%) and, for patients with BD, from 27.4% (controls: 12.0%) to 12.4% (controls: 4.7%). Among commercially insured patients with SZ, nonchronic opioid dispensing decreased from 15.5% (controls: 16.4%) to 10.7% (controls: 11.0%) and, for patients with BD, from 26.1% (controls: 17.5%) to 20.0% (controls: 12.2%). In Medicaid-covered patients with SZ, nonchronic opioid dispensing declined from 22.5% (controls: 24.4%) to 15.1% (controls: 12.7%) and, for patients with BD, from 32.3% (controls: 25.9%) to 24.6% (controls: 13.6%).

CONCLUSIONS

The proportions of individuals dispensed chronic or nonchronic opioid medications each year were similar between commercially and Medicaid-insured patients with SZ versus controls and were higher for patients with BD versus controls. From 2015 to 2019, the proportions of individuals who were dispensed prescription opioids chronically or nonchronically decreased for patients with SZ or BD and controls.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Background

Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality.

Methods

We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes.

Discussion

Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

The association between community social vulnerability and prescription opioid availability with individual opioid overdose

BACKGROUND

This study aims to assess the association of community social vulnerability and community prescription opioid availability with individual non-fatal or fatal opioid overdose.

METHODS

We identified patients 12 years of age or older from the Oregon All Payer Claims database (APCD) linked to other public health datasets. Community-level characteristics were captured in an exposure period (EP) (1/1/2018-12/31/2018) and included: census tract-level social vulnerability domains (socio-economic status, household composition, racial and ethnic minority status, and housing type and transportation), census tract-level prescriptions and community-level opioid use disorder (OUD) diagnoses per 100 capita binned into quartiles or quintiles. We employed Cox models to estimate the risk of fatal and non-fatal opioid overdoses events in the 12 months following the EP.

MAIN FINDINGS

We identified 1,548,252 individuals. Patients were mostly female (54%), White (61%), commercially insured (54%), and lived in metropolitan areas (81%). Of the total sample, 2485 (0.2%) experienced a non-fatal opioid overdose and 297 died of opioid overdose. There was higher hazard for non-fatal overdose in communities with greater OUD per 100 capita. We also found higher non-fatal and fatal hazards for opioid overdose among patients in communities with higher housing type and transportation-related vulnerability compared to the lowest quintile. Conversely, patients were at less risk of opioid overdose when living in communities with greater prevalence of the young or the elderly, the disabled, single parent families or low English proficiency.

CONCLUSION

These findings underscore the importance of the environmental context when considering public health policies to reduce opioid harms.

Enriching Real-world Data with Social Determinants of Health for Health Outcomes and Health Equity: Successes, Challenges, and Opportunities

OBJECTIVE

To summarize the recent methods and applications that leverage real-world data such as electronic health records (EHRs) with social determinants of health (SDoH) for public and population health and health equity and identify successes, challenges, and possible solutions.

METHODS

In this opinion review, grounded on a social-ecological-model-based conceptual framework, we surveyed data sources and recent informatics approaches that enable leveraging SDoH along with real-world data to support public health and clinical health applications including helping design public health intervention, enhancing risk stratification, and enabling the prediction of unmet social needs.

RESULTS

Besides summarizing data sources, we identified gaps in capturing SDoH data in existing EHR systems and opportunities to leverage informatics approaches to collect SDoH information either from structured and unstructured EHR data or through linking with public surveys and environmental data. We also surveyed recently developed ontologies for standardizing SDoH information and approaches that incorporate SDoH for disease risk stratification, public health crisis prediction, and development of tailored interventions.

CONCLUSIONS

To enable effective public health and clinical applications using real-world data with SDoH, it is necessary to develop both non-technical solutions involving incentives, policies, and training as well as technical solutions such as novel social risk management tools that are integrated into clinical workflow. Ultimately, SDoH-powered social risk management, disease risk prediction, and development of SDoH tailored interventions for disease prevention and management have the potential to improve population health, reduce disparities, and improve health equity.

Associations Between Natural Language Processing-Enriched Social Determinants of Health and Suicide Death Among US Veterans

Importance

Social determinants of health (SDOHs) are known to be associated with increased risk of suicidal behaviors, but few studies use SDOHs from unstructured electronic health record notes.

Objective

To investigate associations between veterans' death by suicide and recent SDOHs, identified using structured and unstructured data.

Design, Setting, and Participants

This nested case-control study included veterans who received care under the US Veterans Health Administration from October 1, 2010, to September 30, 2015. A natural language processing (NLP) system was developed to extract SDOHs from unstructured clinical notes. Structured data yielded 6 SDOHs (ie, social or familial problems, employment or financial problems, housing instability, legal problems, violence, and nonspecific psychosocial needs), NLP on unstructured data yielded 8 SDOHs (social isolation, job or financial insecurity, housing instability, legal problems, barriers to care, violence, transition of care, and food insecurity), and combining them yielded 9 SDOHs. Data were analyzed in May 2022.

Exposures

Occurrence of SDOHs over a maximum span of 2 years compared with no occurrence of SDOH.

Main Outcomes and Measures

Cases of suicide death were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. Suicide was ascertained by National Death Index, and patients were followed up for up to 2 years after cohort entry with a study end date of September 30, 2015. Adjusted odds ratios (aORs) and 95% CIs were estimated using conditional logistic regression.

Results

Of 6 122 785 veterans, 8821 committed suicide during 23 725 382 person-years of follow-up (incidence rate 37.18 per 100 000 person-years). These 8821 veterans were matched with 35 284 control participants. The cohort was mostly male (42 540 [96.45%]) and White (34 930 [79.20%]), with 6227 (14.12%) Black veterans. The mean (SD) age was 58.64 (17.41) years. Across the 5 common SDOHs, NLP-extracted SDOH, on average, retained 49.92% of structured SDOHs and covered 80.03% of all SDOH occurrences. SDOHs, obtained by structured data and/or NLP, were significantly associated with increased risk of suicide. The 3 SDOHs with the largest effect sizes were legal problems (aOR, 2.66; 95% CI, 2.46-2.89), violence (aOR, 2.12; 95% CI, 1.98-2.27), and nonspecific psychosocial needs (aOR, 2.07; 95% CI, 1.92-2.23), when obtained by combining structured data and NLP.

Conclusions and Relevance

In this study, NLP-extracted SDOHs, with and without structured SDOHs, were associated with increased risk of suicide among veterans, suggesting the potential utility of NLP in public health studies.

Social determinants of recovery

PURPOSE OF REVIEW

The purpose of this review is to examine evidence describing the influence of social determinants on recovery following hospitalization with critical illness. In addition, it is meant to provide insight into the several mechanisms through which social factors influence recovery as well as illuminate approaches to addressing these factors at various levels in research, clinical care, and policy.

RECENT FINDINGS

Social determinants of health, ranging from individual factors like social support and socioeconomic status to contextual ones like neighborhood deprivation, are associated with disability, cognitive impairment, and mental health after critical illness. Furthermore, many social factors are reciprocally related to recovery wherein the consequences of critical illness such as financial toxicity and caregiver burden can put essential social needs under strain turning them into barriers to recovery.

SUMMARY

Recovery after hospitalization for critical illness may be influenced by many social factors. These factors warrant attention by clinicians, health systems, and policymakers to enhance long-term outcomes of critical illness survivors.