Uptake of screening mammogram in West Central Illinois during the COVID-19 pandemic: lessons learned
Notice bibliographique
Résumé
Objectives. The study aims to reveal the trend of mammogram uptake in seventeen rural counties in Illinois to understand how the COVID-19 pandemic influenced breast cancer screening in the area. Material and methods. Aggregated data on mammography screening for West Central Illinois was provided by the Illinois Hospital Association. Data for 2018 and 2019 was used to determine the typical monthly and annual screenings for the two years before the onset of COVID-19. Then, the two years’ data was compared to the 2020 data. The monthly mean values for the aggregated 2018 and 2019 data were generated as the base “year” to compare with the monthly value for 2020. Paired t-test analysis was used to find if there were any statistically significant differences between the years and between the base year and 2020. Results. January 2020 revealed an uptick to 2921, which is more than the uptake for January 2018 (2700) and January 2019 (2488), and 13% greater than the mean value of 2594 for the previous two years. This was followed by a gradual decrease in uptake in February 2020 by 4% compared to previous years at a mean of 2518 and a further decline in March (44%), with a drastic fall (98%) by April 2020 at 56 screening mammograms in all 17 counties. The lowest uptake in any three months occurred from March through May 2020. Compared to previous years, an increase in uptake was noted across the region in 2020 June (8%) and July (4%) after the pandemic restrictions were relaxed. Overall, the total uptake in 2020 was 15% less than the average annual uptake for 2018–2019 with a deficit of 5537. There was no statistically significant difference in mammogram uptake across the three years. Conclusions. The findings reveal that there was a significant reduction in uptake during the pandemic restriction period. However, increased uptake during the rest of the year effectively mitigated this reduction to such an extent that there was no statistically significant downturn compared to each of the previous two years. A rising trend in total annual uptake noted in preceding years could have continued without the COVID-19 event.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».