Why do suicide rates increase in the spring and decrease in the fall? Insights from an empirical evaluation of Stengel's social hypothesis on suicide
Bibliographic record
Abstract
In 1952, the psychiatrist Erwin Stengel hypothesized that suicide becomes rarer in times when the value of life within a society is lower, when death is more common. In Canada, suicide rates and death rates present with consistent seasonality between years, fluctuating inversely with one another. However, the typical pre-pandemic seasonality seen in death rates was broken during the first year of the COVID-19 pandemic, as a consequence of inordinate deaths occurring at an odd time of year. This offers a unique opportunity to observe whether suicide rates were inversely disrupted. The present study models weekly Canadian suicide rates using death rates, from time series data spanning 2010 to 2019. Results indicate that suicide rates decrease on average by 0.27 standard deviations per 1 standard deviation increase in the death rate. This model is then used to predict 2020 suicide rates, using 2020 death rates. Predictions account for 11.6% of the variation in actual 2020 suicide rates. Furthermore, actual 2020 suicide rates display a negative disruption at the same time as the 2020 death rate positive disruption, and this suicide rate disruption is mimicked by our model predictions. Overall, results are interpreted as being favorable towards Stengel’s theory. As alternative interpretation, we suggest that each non-suicide-related death removes a potential suicide from the population. Overall analysis suggests that elevated death rates during colder seasons are driven by increased communicable disease transmission relative to warmer seasons, resulting in suppressed suicide rates.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".