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Enregistrement W3210111098 · doi:10.1016/s2468-2667(21)00229-2

PHQ-8 scores and estimation of depression prevalence

2021· letter· en· W3210111098 sur OpenAlexafffund
Brooke Levis, Felix Fischer, Andrea Benedetti, Brett D. Thombs

Notice bibliographique

RevueThe Lancet Public Health · 2021
Typeletter
Langueen
DomainePsychology
ThématiqueMental Health Treatment and Access
Établissements canadiensJewish General HospitalMcGill UniversityMcGill University Health Centre
Organismes subventionnairesFonds de Recherche du Québec - SantéCanada Excellence Research Chairs, Government of Canada
Mots-clésPatient Health QuestionnaireScopusDepression (economics)MedicinePublic healthPopulationPsychiatryMEDLINEMajor depressive disorderDemographyFamily medicineDepressive symptomsEnvironmental healthPolitical scienceAnxietyPathology

Résumé

récupéré en direct d'OpenAlex

In the Article by Jorge Arias-de la Torre and colleagues,1Arias-de la Torre J Vilagut G Ronaldson A et al.Prevalence and variability of current depressive disorder in 27 European countries: a population-based study.Lancet Public Health. 2021; 6: e729-e738Summary Full Text Full Text PDF PubMed Scopus (17) Google Scholar the authors used data for 258 888 individuals obtained from the second wave of the European Health Interview Survey to estimate depression prevalence in 27 European countries based on scores of 10 or higher on the eight-item Patient Health Questionnaire (PHQ-8). The authors reported an overall prevalence of current depressive disorder of 6·38% (95% CI 6·24–6·52) with substantial heterogeneity across countries.1Arias-de la Torre J Vilagut G Ronaldson A et al.Prevalence and variability of current depressive disorder in 27 European countries: a population-based study.Lancet Public Health. 2021; 6: e729-e738Summary Full Text Full Text PDF PubMed Scopus (17) Google Scholar Depression symptom questionnaires and standard cutoffs, such as PHQ-8 scores of 10 and higher, are not intended to estimate disorder prevalence, but are designed for screening purposes; they are intended to identify a higher number of individuals than would be diagnosed with depression if assessed using validated diagnostic criteria.2Thombs BD Kwakkenbos L Levis AW Benedetti A Addressing overestimating of the prevalence of depression based on self-report screening questionnaires.CMAJ. 2018; 190: e44-e49Crossref PubMed Scopus (69) Google Scholar An individual participant data meta-analysis of 44 studies,3Levis B Benedetti A Ioannidis JPA et al.Patient Health Questionnaire-9 scores do not accurately estimate depression prevalence: individual participant data meta-analysis.J Clin Epidemiol. 2020; 122: 115-128Summary Full Text Full Text PDF Scopus (54) Google Scholar which included 9242 participants (of whom 1389 had Structured Clinical Interview for DSM [SCID] major depression) found that, on average, prevalence based on nine-item Patient Health Questionnaire (PHQ-9) scores of 10 or higher (which perform similarly to scores of 10 or higher on the PHQ-84Wu Y Levis B Riehm KE et al.Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: a systematic review and individual participant data meta-analysis.Psychol Med. 2020; 50: 1368-1380Crossref PubMed Scopus (47) Google Scholar) overestimated SCID-based prevalence by 11·9%. In the 44 studies, the mean ratio of PHQ-9 scores of 10 or higher to SCID-based prevalence was 2·5.3Levis B Benedetti A Ioannidis JPA et al.Patient Health Questionnaire-9 scores do not accurately estimate depression prevalence: individual participant data meta-analysis.J Clin Epidemiol. 2020; 122: 115-128Summary Full Text Full Text PDF Scopus (54) Google Scholar Consistent with evidence that PHQ-9 scores of 10 or higher exaggerate prevalence, although the PHQ-8 assesses symptoms in the previous 2 weeks, in the European Health Interview Survey, the prevalence of current depressive disorder was higher than 12-month European prevalence based on a validated diagnostic interview.5Alonso J Angermeyer MC Bernert S et al.Prevalence of mental disorders in Europe: results from the European Study of the Epidemiology of Mental Disorders (ESEMeD) project.Acta Psychiatr Scand. 2004; 109: 21-27Google Scholar Depression is an important concern. However, using the proportion of individuals with scores above screening cutoffs on self-report questionnaires does not generate valid prevalence estimates, and the estimates reported by Arias-de la Torre and colleagues are not likely to represent the actual prevalence of depression in Europe. There are ways to incorporate self-report questionnaires into methods for estimating prevalence, but simply reporting the proportion of participants with positive screens is not recommended.2Thombs BD Kwakkenbos L Levis AW Benedetti A Addressing overestimating of the prevalence of depression based on self-report screening questionnaires.CMAJ. 2018; 190: e44-e49Crossref PubMed Scopus (69) Google Scholar We declare no competing interests. Prevalence and variability of current depressive disorder in 27 European countries: a population-based studyDepressive disorders, although common across Europe, vary substantially in prevalence between countries. These results could be a baseline for monitoring the prevalence of current depressive disorder both at a country level in Europe and for planning health-care resources and services. Full-Text PDF Open AccessPHQ-8 scores and estimation of depression prevalence – Author's replyWe thank Brooke Levis and colleagues for their interest in our work and for suggesting that we might have overestimated the prevalence of depression by using the eight-item Patient Health Questionnaire (PHQ-8) in our study.1 Although we acknowledged the limitations associated with the use of the PHQ-8, we believe that further discussion is required. Full-Text PDF Open Access

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,167
Score d'incertitude au seuil0,689

Scores Codex et Gemma par catégorie

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

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 Opus0,140
Tête enseignante GPT0,421
Écart entre enseignants0,281 · 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 tête enseignante, 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

Citations12
Publié2021
Routes d'admission2
Résumé présentoui

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