Linking Community-based Substance Use Disorder Treatment to Health Administrative Data: understanding vulnerable populations.
Bibliographic record
Abstract
IntroductionMost substance use disorder (SUD) treatment occurs in community settings. Community-based SUD treatment information is rarely captured or utilized. The objective of this study was to examine the efficiency of a data linkage of community-based SUD treatment to health administrative data holdings in Ontario, Canada, and to describe sociodemographic and clinical characteristics of individuals accessing SUD services. MethodsData from community-based SUD service providers (>180) from April 2015 to March 2022 were linked to administrative data holdings at ICES. Linkage rates were evaluated. Sociodemographic (age, sex, neighbourhood-level income) and clinical (substance use, physician visits, Emergency Department (ED) visits and hospitalizations) characteristics were evaluated. ResultsThe linkage rate of DATIS ICES data holdings was >92% for all years (2015-2022). Of the 234,501 individuals admitted to a DATIS program, 36.1% were female, 46.2% were 25-44 years old, and 51.6% resided in the two lowest neighbourhood income quintiles. Alcohol (33.4%) was the most common substance identified. For outpatient care occurring within 1 year prior to admission, around 56.0% and 23.3% had Mental Health and Addictions (MHA) related primary care and psychiatrist visits, respectively. For acute health services, 29.8% and 14.2% had a MHA- related ED visit or hospitalization, respectively. DiscussionSUD treatment data from community settings can be successfully linked to other health administrative data. Individuals with SUD have a high rate of acute health care use, and a relatively low access to psychiatrists. SUD treatment data linkage should be used to understand how to optimize access to care for vulnerable individuals.
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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 | 0.026 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.028 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".