Cancer Screening Disparities Before and After the COVID-19 Pandemic
Notice bibliographique
Résumé
Importance: Breast, cervical, and colorectal cancer-screening disparities existed prior to the COVID-19 pandemic, and it is unclear whether those have changed since the pandemic. Objective: To assess whether changes in screening from before the pandemic to after the pandemic varied for immigrants and for people with limited income. Design, Setting, and Participants: This population-based, cross-sectional study, using data from March 31, 2019, and March 31, 2022, included adults in Ontario, Canada, the country's most populous province, with more than 14 million people, almost 30% of whom are immigrants. At both dates, the screening-eligible population for each cancer type was assessed. Exposures: Neighborhood income quintile, immigrant status, and primary care model type. Main Outcomes and Measures: For each cancer screening type, the main outcome was whether the screening-eligible population was up to date on screening (a binary outcome) on March 31, 2019, and March 31, 2022. Up to date on screening was defined as having had a mammogram in the previous 2 years, a Papanicolaou test in the previous 3 years, and a fecal test in the previous 2 years or a flexible sigmoidoscopy or colonoscopy in the previous 10 years. Results: The overall cohort on March 31, 2019, included 1 666 943 women (100%) eligible for breast screening (mean [SD] age, 59.9 [5.1] years), 3 918 225 women (100%) eligible for cervical screening (mean [SD] age, 45.5 [13.2] years), and 3 886 345 people eligible for colorectal screening (51.4% female; mean [SD] age, 61.8 [6.4] years). The proportion of people up to date on screening in Ontario decreased for breast, cervical, and colorectal cancers, with the largest decrease for breast screening (from 61.1% before the pandemic to 51.7% [difference, -9.4 percentage points]) and the smallest decrease for colorectal screening (from 65.9% to 62.0% [difference, -3.9 percentage points]). Preexisting disparities in screening for people living in low-income neighborhoods and for immigrants widened for breast screening and colorectal screening. For breast screening, compared with income quintile 5 (highest), the β estimate for income quintile 1 (lowest) was -1.16 (95% CI, -1.56 to -0.77); for immigrant vs nonimmigrant, the β estimate was -1.51 (95% CI, -1.84 to -1.18). For colorectal screening, compared with income quintile 5, the β estimate for quntile 1 was -1.29 (95% CI, 16 -1.53 to -1.06); for immigrant vs nonimmigrant, the β estimate was -1.41 (95% CI, -1.61 to -1.21). The lowest screening rates both before and after the COVID-19 pandemic were for people who had no identifiable family physician (eg, moving from 11.3% in 2019 to 9.6% in 2022 up to date for breast cancer). In addition, patients of interprofessional, team-based primary care models had significantly smaller reductions in β estimates for breast (2.14 [95% CI, 1.79 to 2.49]), cervical (1.72 [95% CI, 1.46 to 1.98]), and colorectal (2.15 [95% CI, 1.95 to 2.36]) postpandemic screening and higher uptake of screening in general compared with patients of other primary care models. Conclusions and Relevance: In this cross-sectional study in Ontario that included 2 time points, widening disparities before compared with after the COVID-19 pandemic were found for breast cancer and colorectal cancer screening based on income and immigrant status, but smaller declines in disparities were found among patients of interprofessional, team-based primary care models than among their counterparts. Policy makers should investigate the value of prioritizing and investing in improving access to team-based primary care for people who are immigrants and/or with limited income.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».