COVID-19 illness, SARS-CoV2 infection, and subsequent suicidal ideation in the French nationwide population-based EpiCov cohort : a propensity score analysis of more than 50,000 individuals
Bibliographic record
Abstract
Abstract Symptomatic COVID-19 appears to be associated with suicidal ideation but longitudinal evidence is still scarce. SARS-CoV2-induced neurological damages might underline this association, but findings are inconsistent. We therefore investigated the association between COVID-19 disease and subsequent suicidal ideation in the general population, using both self-reported symptoms and serology as well as inverse probability weighting to draw as near as possible to the direct association. Using data from the nationwide French EpiCov cohort, COVID-19 disease was assessed through 1) COVID-19 illness (self-reported symptoms of sudden loss of taste/smell or fever alongside cough, shortness of breath or chest oppression, between February and November 2020), and 2) SARS-CoV2 infection (Spike protein ELISA test screening in dried-blood-spot samples). Suicidal ideation was self-reported between December 2020 and July 2021. Inverse probability weighting with propensity scores was used as an adjustment strategy, leading to balanced sociodemographic and health-related factors between the exposed and non-exposed groups of both COVID-19 disease measures. Then, modified Poisson regression models were used to investigate the association of COVID-19 illness and SARS-CoV2 infection with subsequent suicidal ideation. Among 52,050 participants from the EpiCov cohort, 1.68% [1.54% - 1.82%] reported suicidal ideation in the first half of 2021, 9.57% [9.24% – 9.90%] had a SARS-CoV2 infection in 2020 and 13.23% [12.86% – 13.61%] reported COVID-19 symptoms in 2020. COVID-19 illness in 2020 was associated with higher risks of subsequent suicidal ideation in the first half of 2021 (Relative Risk ipw [CI95%]= 1.43 [1.20 – 1.69]) while SARS-CoV2 infection in 2020 was not (RR ipw = 0.88 [0.69 – 1.12]). If COVID-19 illness was associated with subsequent suicidal ideation, the exact role of SARS-CoV2 infection with respect to suicide risk has yet to be clarified. Psychological support should be offered to persons recovering from symptomatic COVID-19 in order to minimize suicidal ideation risk. Moreover, if such psychological support is to be implemented, serology status alone does not seem a relevant criterion to define persons who suffered from COVID-19 to prioritize.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".