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Enregistrement W3025945067 · doi:10.1002/wps.20767

Preventing suicide in the context of the <scp>COVID</scp>‐19 pandemic

2020· letter· en· W3025945067 sur OpenAlexaff
Roger S. McIntyre, Yena Lee

Notice bibliographique

RevueWorld Psychiatry · 2020
Typeletter
Langueen
DomaineHealth Professions
ThématiqueEmployment and Welfare Studies
Établissements canadiensUniversity of TorontoBrain and Cognition Discovery FoundationUniversity Health Network
Organismes subventionnairesNatural Science Foundation of Shanxi Province
Mots-clésUnemploymentRecessionContext (archaeology)PandemicMedicineDepression (economics)DemographyGovernment (linguistics)Coronavirus disease 2019 (COVID-19)Suicide ratesDemographic economicsSuicide preventionEconomicsPoison controlEconomic growthEnvironmental healthGeographyInternal medicine

Résumé

récupéré en direct d'OpenAlex

The impact of the COVID-19 pandemic on the labour market, as well as the government’s response to mitigate risk via social isolation and quarantine, has resulted in the greatest and most rapid change in the employment sector ever recorded in the US. Notwithstanding emergency government financial response, it is anticipated that a significant percentage of the labour market will contract1. Moreover, the predicted increase in unemployment is expected to approximate, and perhaps exceed, that reported during the Great Depression lasting from 1929 to 1939 (i.e., 24.9%)2. The foregoing rapid rise in unemployment and associated economic insecurity is likely to significantly increase the risk for suicide. In fact, during the most recent economic recession, a 1% rise in unemployment was associated with a rise in the suicide rate of 0.99% in the US (95% CI: 0.60-1.38, p<0.0001)3. Similarly, each percentage point increase in unemployment was accompanied by a 0.79% rise in suicide (95% CI: 0.16-1.42, p=0.016) in individuals 65 years of age or younger in Europe (e.g., Spain, Greece)4. During the 1997-1998 Asian economic recession, unemployment was a critical determinant mediating the increase in suicides in Japan, Hong Kong, and South Korea5. We used time-trend regression models to assess and forecast excess suicides attributable to the economic downturn following the COVID-19 pandemic. Suicide mortality was estimated for three possible scenarios: a) no significant change in unemployment rate (i.e., 3.6% for 2020, 3.7% for 2021); b) moderate increase in projected unemployment rate (i.e., 5.8% for 2020, 9.3% for 2021), mirroring unemployment rates in 2008-2009; and c) extreme increase in projected unemployment rate (i.e., 24% for 2020, 18% for 2021). The annual suicide mortality rate accelerated in the US by 1.85% (95% CI: 1.70-2.00, p<0.0001) between 1999 and 2018. We found that a percentage point increase in unemployment was associated with an increase in suicide rates of 1.00% (95% CI: 1.02-1.06, p<0.0001) between 1999 and 2018. The suicide rate was 14.8 per 100,000 in 2018 (N=48,432). In the first above-mentioned scenario (i.e., unemployment rate remains relatively consistent), the predicted suicide rates per 100,000 are 15.7 (95% CI: 15.3-16.1) in 2020 and 16.2 (95% CI: 15.7-16.8) in 2021. The foregoing suicide rates would result in 51,657 suicides in 2020 and 53,480 in 2021 (assuming 2019 pop­ulation size of 329,158,518). In the second scenario (i.e., moderate increase in projected unemployment rate), suicide rates per 100,000 will increase to 16.9 in 2020 (95% CI: 16.4-17.5; N=52,728) and 17.5 in 2021 (95% CI: 16.8-18.2; N=55,644). This second scenario would result in a total of 3,235 excess suicides over the 2020-2021 period, representing a 3.3% increase in suicides per year (when compared to the 2018 rate of 48,432). In the third scenario (i.e., extreme increase in projected unemployment rate), suicide rates per 100,000 are projected to increase to 17.0 in 2020 (95% CI: 16.6-17.5; N=56,052) and 17.4 in 2021 (95% CI: 16.8-18.0; N=57,249). This rise in suicide rate would result in 8,164 excess suicides over the two-year period, representing an 8.4% increase in suicides (when compared to the 2018 rate of 48,432). What is especially concerning about our projections is the genuine uncertainty with respect to the labour market post-COVID-19, as well as the tremendous financial uncertainty and decrease in consumer sentiment, all of which are independent and additional contributors to suicide6. Moreover, social isolation and quarantine, which are critical viral transmission risk mitigation strategies, are recommended nation-wide. Social isolation is well established as a significant risk factor for suicidality7. Multiple studies have reported that government policy response can significantly mitigate the increased risk of suicide due to economic hardship and unfavourable labour market dynamics. For example, in Japan, a 1% per capita increase in local government expenditures was associated with a 0.2% decrease in suicide in the years following the 2008 recession8. The Japanese experience was replicated in Europe, wherein government spending, especially on social programs intended to mitigate suicide risk, significantly reduced projected suicides in Denmark9. Preventing suicide in the context of the COVID-19-related unemployment and financial insecurity is a critical public health priority. In addition to financial provisions (e.g., tax deferral, wage subsidy), investing in labour market programs that intend to retrain workers is warranted. Furthermore, government support for employers is critical to reduce the massive increase in unemployment and contraction of the labour market. Proactive public-private partnerships that aim to provide psychological first-aid and psychiatric emergency services to persons at imminent risk of suicide are essential. Individual resilience enhancement strategies should be implemented (e.g., exercise, sleep hygiene, structured daily schedule, better diet). Approximately half of suicides in the US are committed with a gun; recommendations surrounding appropriate gun and ammunition storage are warranted. For persons with clinically significant depressive/anxiety symptoms or persons experiencing features of post-traumatic stress disorder or drug/alcohol abuse, timely access to comprehensive treatment should be part of the COVID-19 management strategy.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,018
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,043
Score d'incertitude au seuil0,086

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,018
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,080
Tête enseignante GPT0,389
Écart entre enseignants0,308 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations167
Publié2020
Routes d'admission1
Résumé présentoui

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