Longitudinal study of COVID-19 stay-at-home orders’ impact on deaths of despair in the United States, January 2019 to December 2020
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic led to increase in mental health problems and substance misuse. Yet, little is known about its impact on rates of deaths of despair (death by suicide and drug overdose). Our objective was to determine the impact of COVID-19 stay-at-home orders on deaths of despair using population-level data. We hypothesized that the longer duration of stay-at-home orders would increase rates of deaths of despair. METHODS: Utilizing quarterly suicide and drug-overdose mortality data from the National Center for Health Statistics from January 2019 through December 2020, we estimated fixed-effects models to examine the effects of the duration of stay-at-home orders as differentially implemented in 51 jurisdictions in the United States on each outcome. RESULTS: Controlling for seasonal patterns, the duration of jurisdictional-level stay-at-home order was positively associated with drug-overdose death rates. The duration of stay-at-home orders was not associated with suicide rates when adjusting for calendar quarter. CONCLUSIONS: Findings suggest an increase in age-adjusted drug-overdose death rates in the United States from 2019 to 2020 possibly attributable to the duration of jurisdictional COVID-19 stay-at-home orders. This effect may have operated through various mechanisms, including increases in economic distress and reduced access to treatment programs when stay-at-home orders were in effect.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".