Influence of Chronic Disease and Comorbidity on Colorectal Cancer Screening and Diagnostic Testing
Notice bibliographique
Résumé
Colorectal cancer (CRC) is a leading cause of morbidity and mortality. Population-based CRC screening has been recommended by in Canada since 2001 and organized screening programs, which involve coordinated activities from screening through diagnosis, have been implemented in most Canadian jurisdictions. Those who have chronic comorbidities may be less likely to participate in all steps of the cancer preventive pathway due to the competing demands of chronic disease management. As some major chronic conditions are also associated with increased cancer morbidity and mortality, understanding the use of cancer preventive services in these populations is necessary to improve the effectiveness of screening programs.This dissertation aimed to determine whether major chronic medical and mental health comorbidities are associated with lower use CRC screening and follow-up diagnostic testing, focusing on the province of Ontario, Canada. The first study was a systematic review and meta-analysis examining the effect of one “model” chronic condition – diabetes – on screening for CRC and other common cancers for which universal screening has been recommended (breast and cervical cancer). The second and third studies were longitudinal population-based cohorts using Ontario health administrative data, with the second study investigating the influence of diabetes, heart disease, renal failure, chronic obstructive pulmonary disease, and mental health conditions on periodic CRC screening test uptake, and the third study considering the effects of these conditions on follow-up diagnostic testing receipt. The first study found that diabetes was associated with lower likelihood of breast and cervical cancer screening, while the results for CRC screening were mixed. There were significant methodological limitations in the evidence base, including the focus on one-time screening in opportunistic settings and the use of cross-sectional designs and self-report data. The second and third studies found that major chronic conditions were associated with lower rates of periodically becoming up-to-date with CRC screening and with receiving follow-up testing. Having multiple medical conditions or comorbid medical and mental health conditions exacerbated these relationships. The findings of this thesis highlight that organized screening programs may need to consider additional strategies to reduce breakdowns in the cancer preventive pathway and improve appropriateness of screening for people with comorbidities.
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 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 | 0,005 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,007 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 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 ».