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Enregistrement W4399326644 · doi:10.1007/s12325-024-02893-y

Direct and Indirect Costs of Breast Cancer and Associated Implications: A Systematic Review

2024· review· en· W4399326644 sur OpenAlexaboutno aff
Matthew Franklin, Daniel Pollard, Janvi Sah, Annabel Rayner, Yuxiao Sun, France Dube, Anthea Sutton, Lei Qin

Notice bibliographique

RevueAdvances in Therapy · 2024
Typereview
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueEconomic and Financial Impacts of Cancer
Établissements canadiensnon disponible
Organismes subventionnairesAstraZeneca
Mots-clésMedicineBreast cancerEconLitIndirect costsHealth careHealth economicsMEDLINEDisease burdenEpidemiologyEnvironmental healthFamily medicineCancerPublic healthDemographyEconomic growthPopulationPathologyAccountingInternal medicineBusiness

Résumé

récupéré en direct d'OpenAlex

INTRODUCTION: Breast cancer is currently the leading cause of global cancer incidence. Breast cancer has negative consequences for society and economies internationally due to the high burden of disease which includes adverse epidemiological and economic implications. Our aim is to systematically review the estimated economic burden of breast cancer in the United States (US), Canada, Australia, and Western Europe (United Kingdom, France, Germany, Spain, Italy, Norway, Sweden, Denmark, Netherlands, and Switzerland), with an objective of discussing the policy and practice implications of our results. METHODS: We included English-language published studies with cost as a focal point using a primary data source to inform resource usage of women with breast cancer. We focussed on studies published since 2017, but with reported costs since 2012. A systematic search conducted on 25 January 2023 identified studies relating to the economic burden of breast cancer in the countries of interest. MEDLINE, Embase, and EconLit databases were searched via Ovid. Study quality was assessed based on three aspects: (1) validity of cost findings; (2) completeness of direct cost findings; and (3) completeness of indirect cost findings. We grouped costs based on country, cancer stage (early compared to metastatic), and four resource categories: healthcare/medical, pharmaceutical drugs, diagnosis, and indirect costs. Costs were standardized to the year 2022 in US (US$2022) and International (Int$2022) dollars. RESULTS: Fifty-three studies were included. Studies in the US (n = 19) and Canada (n = 9) were the majority (53%), followed by Western European countries (42%). Healthcare/medical costs were the focus for the majority (89%), followed by pharmaceutical drugs (25%), then diagnosis (17%) and indirect (17%) costs. Thirty-six (68%) included early-stage cancer costs, 17 (32%) included metastatic cancer costs, with 23% reporting costs across these cancer stages. No identified study explicitly compared costs across countries. Across cost categories, cost ranges tended to be higher in the US than any other country. Metastatic breast cancer was associated with higher costs than earlier-stage cancer. When indirect costs were accounted for, particularly in terms of productivity loss, they tended to be higher than any other estimated direct cost (e.g., diagnosis, drug, and other medical costs). CONCLUSION: There was substantial heterogeneity both within and across countries for the identified studies' designs and estimated costs. Despite this, current empirical literature suggests that costs associated with early initiation of treatment could be offset against potentially avoiding or reducing the overall economic burden of later-stage and more severe breast cancer. Larger scale, national, economic burden studies are needed, to be updated regularly to ensure there is an ongoing and evolving perspective of the economic burden of conditions such as breast cancer to inform policy and practice.

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: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,489
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)0,0040,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,040
Tête enseignante GPT0,334
Écart entre enseignants0,294 · 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'étudeRevue systématique
Domainenon disponible
GenreSynthèse

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

En bref

Citations20
Publié2024
Routes d'admission1
Résumé présentoui

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