Illicit drug overdose deaths resulting from income assistance payments: Analysis of the ‘check effect’ using daily mortality data
Bibliographic record
Abstract
BACKGROUND: Although income assistance is an important source of support for low income individuals, there is evidence that adverse outcomes may increase when payments are disbursed on the same day for all recipients. The objective of this study was to assess the temporal patterns and causal relation between population-level illicit drug overdose deaths and income assistance payments using daily mortality data for British Columbia over a period of five years. METHODS: Retrospective data on daily mortality due to illicit drug overdose, 2009-2013, were provided by the BC Coroners Service. These data were analyzed using regression models and time series tests for causality in relation to dates of income assistance payments. RESULTS: 1343 deaths due to illicit drug overdose were reported in BC during 2009-2013; 394 occurred during cheque weeks (n=60) and 949 occurred during non-cheque weeks (n=202). Average weekly mortality due to illicit drug overdose was 40% higher during weeks of income assistance payments compared to weeks without payments (P<0.001). Consistent increases in mortality appeared the day after cheque disbursement and were significantly higher for two days, and marginally higher after 3 days, even when controlling for other temporal trends. Granger causality testing suggests the timing of cheque issue was causally linked to increased drug overdose mortality (P<0.001). CONCLUSIONS: Our findings clarify the temporal relation and causal impact of income assistance payments on illicit drug deaths. We estimate 77 avoidable deaths were attributable to the synchronized disbursement of income assistance cheques over the five year period. An important consideration is whether varying the timing of payments among recipients could reduce this excess mortality and the related demands on health and social services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".