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Enregistrement W4313555895 · doi:10.1101/2023.01.05.23284220

Direct Economic Burden of Mental Health Disorders Associated with Polycystic Ovary Syndrome: Systematic Review and Meta-analysis

2023· preprint· en· W4313555895 sur OpenAlexaboutno aff
Surabhi Yadav, Olivia Delau, Adam Bonner, Daniela Markovic, William Patterson, Sasha Ottey, Richard P. Buyalos, Ricardo Azziz

Notice bibliographique

RevuemedRxiv · 2023
Typepreprint
Langueen
DomaineMedicine
ThématiqueOvarian function and disorders
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPolycystic ovaryEating disordersMeta-analysisMedicineAnxietyPsycINFODepression (economics)PsychiatryMEDLINESystematic reviewPostpartum depressionMental healthObesityInternal medicineInsulin resistancePregnancy

Résumé

récupéré en direct d'OpenAlex

ABSTRACT BACKGROUND Polycystic ovary syndrome (PCOS) is the most common hormone disorder affecting about one in seven reproductive-aged women worldwide and approximately 6 million women in the United States (U.S.). PCOS can be a significant burden to those affected and is associated with an increased prevalence of mental health (MH) disorders such as depression, anxiety, eating disorders, and postpartum depression. We undertook this study to determine the excess economic burden associated with MH disorders in women with PCOS, in order to allow for a more accurate prioritization of the disorder as a public health priority. METHODS Followed PRISMA reporting guidelines for systematic review, we searched PubMed, Web of Science, EBSCO, Medline, Scopus, and PsycINFO through July 16, 2021, for studies on MH disorders in PCOS. Excluded were studies not in humans, without controls, without original data, or not peer reviewed. As anxiety, depression, eating disorders, and postpartum depression were by far the most common MH disorders assessed by the studies, we performed our meta-analysis on these disorders. Meta-analyses were performed using the DerSimonian-Laird random-effects model to compute pooled estimates of prevalence ratios (PR) for the associations between PCOS and these MH disorders, and then calculated the excess direct costs of related to these disorders in U.S. dollars (USD) for women suffering from PCOS in the U.S. alone. The quality of selected studies was assessed using the Newcastle-Ottawa Scale. RESULTS We screened 78 articles by title/abstract, assessed 43 articles in full-text, and included 25 articles. Pooled PRs were 1.42 (95% CI: 1.32-1.52) for anxiety, 1.65 (95% CI: 1.44-1.89;) for depression, 1.48 (95% CI: PR: 1.06-2.05) for eating disorders, and 1.20 (95% CI: 0.96-1.50) for postpartum depression, for PCOS relative to controls. In the U.S, the additional direct healthcare costs associated with anxiety, depression and eating disorders in PCOS were estimated to be $1.939 billion/yr., 1.678 billion/yr., and $0.644 billion/yr. in 2021 USD, respectively. Postpartum depression was excluded from the cost analyses due to the non-significant meta-analysis result. Taken together, the additional direct healthcare costs associated with anxiety, depression and eating disorders in PCOS was estimated to be $4.261 billion/yr. in 2021 USD. CONCLUSIONS Overall, the direct healthcare annual costs for the most common MH disorders in PCOS, namely anxiety, depression, and eating disorders exceeds $4 billion in 2021 USD for the U.S. population alone. Taken together with our prior work, these data suggest that the healthcare-related economic burden of PCOS exceeds $15 billion yearly, considering the costs of PCOS diagnosis, and cost related to PCOS-associated MH, reproductive, vascular, and metabolic disorders. As PCOS has much the same prevalence across the world, the excess economic burden attributable to PCOS globally is enormous, mandating that the scientific and policy community increase its focus on this important disorder. FUNDING The study was supported, in part, by PCOS Challenge: The National Polycystic Ovary Syndrome Association and by the Foundation for Research and Education Excellence CLINICAL TRIAL NUMBER N/A

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 candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: Méta-analyse
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,876
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0050,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,038
Tête enseignante GPT0,292
Écart entre enseignants0,253 · 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.

Devis d'étudeMéta-analyse
Domainenon disponible
GenreEmpirique

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

En bref

Citations4
Publié2023
Routes d'admission1
Résumé présentoui

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