A state level analyses of suicide and the COVID-19 pandemic in Mexico
Bibliographic record
Abstract
BACKGROUND: While suicide rates in high- and middle-income countries appeared stable in the early stages of the pandemic, we know little about within-country variations. We sought to investigate the impact of COVID-19 on suicide in Mexico's 32 states and to identify factors that may have contributed to observed variations between states. METHODS: Interrupted time-series analysis to model the trend in monthly suicides before COVID-19 (from Jan 1, 2010, to March 31, 2020), comparing the expected number of suicides derived from the model with the observed number for the remainder of the year (April 1 to December 31, 2020) for each of Mexico's 32 states. Next, we modeled state-level trends using linear regression to study likely contributing factors at ecological level. RESULTS: Suicide increased slightly across Mexico during the first nine months of the pandemic (RR 1.03; 95%CI 1.01-1.05). Suicides remained stable in 19 states, increase in seven states (RR range: 1.12-2.04) and a decrease in six states (RR range: 0.46-0.88). Suicide RR at the state level was positively associated with population density in 2020 and state level suicide death rate in 2019. CONCLUSIONS: The COVID-19 pandemic had a differential effect on suicide death within the 32 states of Mexico. Higher population density and higher suicide rates in 2019 were associated with increased suicide. As the country struggles to cope with the ongoing pandemic, efforts to improve access to primary care and mental health care services (including suicide crisis intervention services) in these settings should be given priority.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".