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Record W3002083585 · doi:10.1101/2019.12.14.19014787

Educational attainment reduces the risk of suicide attempt among individuals with and without psychiatric disorders independent of cognition: a multivariable Mendelian randomization study with more than 815,000 participants

2019· preprint· en· W3002083585 on OpenAlexaff
Daniel B. Rosoff, Zachary Kaminsky, Falk W. Lohoff

Bibliographic record

VenuemedRxiv · 2019
Typepreprint
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsRoyal Ottawa Mental Health CentreUniversity of Ottawa
FundersNational Institute on Alcohol Abuse and AlcoholismNational Institutes of HealthH. Lundbeck A/SLundbeckfondenMedical Research CouncilUniversity of Bristol
KeywordsMendelian randomizationConfoundingOdds ratioSuicide attemptPsychologyPsychiatryObservational studyMedicineDemographyPoison controlClinical psychologySuicide preventionInternal medicineMedical emergency

Abstract

fetched live from OpenAlex

ABSTRACT Background Rates of suicidal ideation, attempts and completions are increasing and identifying causal risk factors continues to be a public health priority. Observational literature has shown that educational attainment (EA) and cognitive performance (CP) can influence suicide attempt risk; however, due to residual confounding and reverse causation, the causal nature of these relationships is unknown. Methods We perform a multivariable two-sample Mendelian randomization (MR) analysis to disentangle the effects of EA and CP on suicide attempt risk. We use summary statistics from recent genome-wide association studies (GWAS) of EA, CP, household income versus suicide attempt risk in individuals with and without mental disorders, with more than 815,000 combined study participants. Results We found evidence that both EA and CP significantly reduced the risk of suicide attempt when considered separately in single variable MR (SVMR) (Model 1 EA odds ratio (OR), 0.524, 95% CI, 0.412-0.666, P = 1.07⨯10 −7 ; CP OR, 0.714, 95% CI, 0.577-0.885, P = 0.002). When simultaneously analyzing EA,CA, and adjusting for household income but not comorbid mental disorders (Model 1), we found evidence that the direct effect of EA, independent of CP, on suicide attempt risk was greater than the total effect estimated by SVMR, with EA, independent of CP, significantly reducing the risk of suicide attempt by almost 66% (95% CI, 43%-79%); however, the effect of CP was no longer significant independent of EA (Model 1 EA OR, 0.342, 95% CI, 0.206-0.568, P = 1.61×10 −4 ; CP OR, 1.182, 95% CI, 0.842-1.659, P = 0.333). Further, when accounting for comorbid mental disorders (Model 2), these results did not significantly change: we found EA significantly reduced the risk of suicide attempt by 55% (35%-68%), a lower point estimate but still within the 95% confidence interval of Model 1; the effect of CP was still not significant (Model 2 EA OR, 0.450, 95% CI, 0.314-0.644, P < 1.00×10 −4 ; CP OR, 1.143, 95% CI, 0.803-1.627, P = 0.475). Conclusions Our results show that even after accounting for comorbid mental disorders and adjusting for household income, EA, but not CP, is a causal risk factor in suicide attempt. These findings could have important implications for health policy and prevention programs aimed at reducing the increasing rates of suicide.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.028
GPT teacher head0.323
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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