MétaCan
Menu
Retour à la cohorte
Enregistrement W4402840988 · doi:10.1016/s2589-7500(24)00152-3

Impact of the COVID-19 pandemic on health-care use among patients with cancer in England, UK: a comprehensive phase-by-phase time-series analysis across attendance types for 38 cancers

2024· article· en· W4402840988 sur OpenAlexaff
Yen Yi Tan, Wai Hoong Chang, Michail Katsoulis, Spiros Denaxas, Kayla C. King, Murray P. Cox, Charles Davie, François Balloux, Alvina G. Lai

Notice bibliographique

RevueThe Lancet Digital Health · 2024
Typearticle
Langueen
DomaineMedicine
ThématiqueCOVID-19 and healthcare impacts
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesNIHR Great Ormond Street Hospital Biomedical Research CentreWellcome TrustAcademy of Medical SciencesUniversity College London Hospitals Biomedical Research CentreUCLH Biomedical Research CentreMedical Research CouncilNational Institute for Health and Care ResearchUniversity College London
Mots-clésAttendancePandemicCoronavirus disease 2019 (COVID-19)Phase (matter)Series (stratigraphy)CancerMedicineNew englandFamily medicinePolitical scienceInternal medicineDiseaseBiology

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: The COVID-19 pandemic resulted in the widespread disruption of cancer health provision services across the entirety of the cancer care pathway in the UK, from screening to treatment. The potential long-term health implications, including increased mortality for individuals who missed diagnoses or appointments, are concerning. However, the precise impact of lockdown policies on national cancer health service provision across diagnostic groups is understudied. We aimed to systematically evaluate changes in patterns of attendance for groups of individuals diagnosed with cancer, including the changes in attendance volume and consultation rates, stratified by both time-based exposures and by patient-based exposures and to better understand the impact of such changes on cancer-specific mortality. METHODS: In this retrospective, cross-sectional, phase-by-phase time-series analysis, by using primary care records linked to hospitals and the death registry from Jan 1, 1998, to June 17, 2021, we conducted descriptive analyses to quantify attendance changes for groups stratified by patient-based exposures (Index of Multiple Deprivation, ethnicity, age, comorbidity count, practice region, diagnosis time, and cancer subtype) across different phases of the COVID-19 pandemic in England, UK. In this study, we defined the phases of the COVID-19 pandemic as: pre-pandemic period (Jan 1, 2018, to March 22, 2020), lockdown 1 (March 23 to June 21, 2020), minimal restrictions (June 22 to Sept 20, 2020), lockdown 2 (Sept 21, 2020, to Jan 3, 2021), lockdown 3 (Jan 4 to March 21, 2021), and lockdown restrictions lifted (March 22 to March 31, 2021). In the analyses we examined changes in both attendance volume and consultation rate. We further compared changes in attendance trends to cancer-specific mortality trends. Finally, we conducted an interrupted time-series analysis with the lockdown on March 23, 2020, as the intervention point using an autoregressive integrated moving average model. FINDINGS: From 561 611 eligible individuals, 7 964 685 attendances were recorded. During the first lockdown, the median attendance volume decreased (-35·30% [IQR -36·10 to -34·25]) compared with the preceding pre-pandemic period, followed by a median change of 4·38% (2·66 to 5·15) during minimal restrictions. More drastic reductions in attendance volume were seen in the second (-48·71% [-49·54 to -48·26]) and third (-71·62% [-72·23 to -70·97]) lockdowns. These reductions were followed by a 4·48% (3·45 to 7·10) increase in attendance when lockdown restrictions were lifted. The median consultation rate change during the first lockdown was 31·32% (25·10 to 33·60), followed by a median change of -0·25% (-1·38 to 1·68) during minimal restrictions. The median consultation rate decreased in the second (-33·89% [-34·64 to -33·18]) and third (-4·98% [-5·71 to -4·00]) lockdowns, followed by a 416·16% increase (409·77 to 429·77) upon lifting of lockdown restrictions. Notably, across many weeks, a year-over-year decrease in weekly attendances corresponded with a year-over-year increase in cancer-specific mortality. Overall, the pandemic period revealed a statistically significant reduction in attendances for patients with cancer (lockdown 1 -24 070·19 attendances, p<0·0001; minimal restrictions -19 194·89 attendances, p<0·0001; lockdown 2 -31 311·28 attendances, p<0·0001; lockdown 3 -43 843·38 attendances, p<0·0001; and lockdown restrictions lifted -56 260·50 attendances, p<0·0001) compared with before the pandemic. INTERPRETATION: The UK's COVID-19 pandemic lockdown affected cancer health service access negatively. Many groups of individuals with cancer had declines in attendance volume and consultation rate across the phases of the pandemic. A decrease in attendances might lead to delays in cancer diagnoses, treatment, and follow-up, putting such groups of individuals at higher risk of negative health outcomes, such as cancer-specific mortality. We discuss the factors potentially responsible for explaining changes in service provision trends and provide insight to help inform clinical follow-up for groups of individuals at risk, alongside potential future policy changes in the care of such patients. FUNDING: Wellcome Trust, National Institute for Health Research University College London Hospitals Biomedical Research Centre, National Institute for Health Research Great Ormond Street Hospital Biomedical Research Centre, Academy of Medical Sciences, and the University College London Overseas Research Scholarship.

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)0,000
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: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,031
Score d'incertitude au seuil0,978

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
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)0,0000,000

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 Opus0,076
Tête enseignante GPT0,457
Écart entre enseignants0,381 · 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'étudeObservationnel
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

Citations21
Publié2024
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueThe Lancet Digital HealthMême sujetCOVID-19 and healthcare impactsTravaux en français237 207