The Impact of Neighbourhood-Level Marginalization on risk of Opioid Overdose and Opioid Use Disorder
Bibliographic record
Abstract
Background: Canada is in the midst of an opioid crisis, characterized by a dramatic increase in opioid-related poisonings, deaths, and dependency over the course of several years. Neighbourhood-level marginalization, defined as the degree to which individuals living within a neighbourhood are peripheralized and isolated from society, has been proposed as a risk factor for opioid-related harm, but the relationship has not been well-documented. Objectives: To investigate the relationship between three neighbourhood-level dimensions of marginalization and risk of opioid overdose (OD) (fatal and non-fatal), fatal OD, and opioid use disorder (OUD). Methods: Data were sourced from administrative datasets housed at ICES. Non-fatal OD was sourced using ICD-10 codes from inpatient, community health, and hospital discharge datasets. Fatal OD was sourced using information from the Chief Coroner’s office. Incident OUD diagnoses were sourced using ICD-10, DSM-V, OHIP fee codes, and DINs. Covariates included previous mental illness or substance use disorder diagnoses, income, rurality, age, and sex. Results: Using simple random sampling from a larger cohort of 10,806,807 Ontarians, a total of 1,886,385 people were included in the cohort for the first manuscript and a total of 217,820 were included in the second. Households and dwellings were significantly associated with all three forms of opioid-related harm. Material resources impacted all quintiles for overdose, some for opioid use disorder, and only the most marginalized quintile for fatal overdose. Age and labour force influenced all quintiles of overdose, but very weakly, only impacted the most marginalized quintile for opioid use disorder, and did not significantly impact fatal overdose at all. Conclusion: Neighbourhood-level marginalization significantly increased risk of opioid-related harm, but varied in magnitude in different quintiles and for different outcomes. These results were seen even after adjusting for relevant clinical and demographic factors, including income. Future research should further investigate these dimensions and interventions should focus on highly marginalized areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".