Fatal drug use in the COVID-19 pandemic response: Changing trends in drug-involved deaths before and after stay-at-home orders in Louisiana
Bibliographic record
Abstract
The effect of disaster events on increasing drug-involved deaths has been clearly shown in previous literature. As the COVID-19 pandemic led to stay-at-home orders throughout the United States, there was a simultaneous spike in drug-involved deaths around the country. The landscape of a preexisting epidemic of drug-involved deaths in the United States is one which is not geographically homogenous. Given this unequal distribution of mortality, state-specific analysis of changing trends in drug use and drug-involved deaths is vital to inform both care for people who use drugs and local policy. An analysis of public health surveillance data from the state of Louisiana, both before and after the initial stay-at-home order of the COVID-19 pandemic, was used to determine the effect the pandemic may have had on the drug-involved deaths within this state. Using the linear regression analysis of total drug-involved deaths, as well as drug-specific subgroups, trends were measured based on quarterly (Qly) deaths. With the initial stay-at-home order as the change point, trends measured through quarter 1 (Q1) of 2020 were compared to trends measured from quarter 2 (Q2) of 2020 through quarter 3 (Q3) of 2021. The significantly increased rate of change in Qly drug-involved deaths, synthetic opioid-involved deaths, stimulant-involved deaths, and psychostimulant-involved deaths indicates a long-term change following the initial response to the COVID-19 pandemic. Changes in the delivery of mental health services, harm reduction services, medication for opioid use disorder (MOUD), treatment services, withdrawal management services, addiction counseling, shelters, housing, and food supplies further limited drug-involved prevention support, all of which were exacerbated by the new stress of living in a pandemic and economic uncertainty.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".