Abstract A134: Completeness of social determinants of health data collection for patients with breast cancer at a tertiary care cancer center
Notice bibliographique
Résumé
Abstract Introduction Disparities in breast cancer outcomes are well-documented and influenced by race, ethnicity and social determinants of health (SDOH), including poverty, social isolation, and healthcare access. Higher breast cancer mortality has been documented in marginalized groups, underscoring the critical need for equity-focused interventions. Routine collection of demographic data is required to measure need and progress over time, as well as to identify those who may benefit from focused services. In Canada, collection of this data is not mandated. At our large university-affiliated tertiary cancer center, race, ethnicity and SDOH data can be entered by providers via the electronic health record (EHR) or by patients via their online health portal, but no standardized workflow or assigned responsibility currently exists for data entry. This study aimed to measure completeness of the race, ethnicity and SDOH fields within the EHR of all new patients with breast cancer seen at a single institution. We hypothesized that these data fields would be incomplete in a significant proportion of the EHR. Methods A review of EHRs was conducted for all patients (>18 years of age) with a new breast cancer diagnosis presenting for an outpatient visit at a single institution in Ontario, Canada between January 1, 2022 and December 31, 2023. Descriptive statistics were used to report completion rates of individual race, ethnicity and SDOH variables. Fields were categorized as frequently, moderately or rarely filled if they were completed for ≥75%, 74-2% or ≤1% of patients, respectively. Results The study cohort included 1892 breast cancer patients (mean age at diagnosis: 62±13 years). Stage I was the most common cancer stage (34%), and invasive ductal carcinoma was the most prevalent breast cancer subtype (68%). Frequently completed SDOH fields included “Preferred Language” [99%; English 96% (1784/1865)], “Need Interpreter” [97%; No 98% (1800/1839)], and “Religion” [75%; Christianity 43% (613/1425)]. Moderately completed fields included “Ethnicity” [24%; Canadian 43% (222/521)], “Race” [27%; White 80% (424/531)], “Sexual Orientation” [26%; Straight/Heterosexual 89% (444/498)], “Gender Identity” [31%; Female 98% (577/589)], and “Financial Resource Strain: Difficulty Paying Daily Living Expenses” [19%; Not hard at all 38% (132/351)]. Fields that were rarely or never completed included “Hard of Hearing” (0.2%), “Low Vision” (0.1%), “Transportation Needs” (0.1%), “Housing Stability” (0.1%), “Food Insecurity” (0%), and “Social Connections” (0%). Conclusion Race, ethnicity and SDOH fields in the EHR were infrequently completed for breast cancer patients at this single institution in Canada. When entered, data primarily reflected patients from socially and economically advantaged backgrounds. This incomplete and unrepresentative data hinders efforts to identify and address breast cancer outcome disparities across diverse populations. Standardization of collection and reporting of this important data is needed. Citation Format: Anjali Chauhan, Maisa Saddik, Elena Parvez. Completeness of social determinants of health data collection for patients with breast cancer at a tertiary care cancer center [abstract]. In: Proceedings of the 18th AACR Conference on the Science of Cancer Health Disparities; 2025 Sep 18-21; Baltimore, MD. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(9 Suppl):Abstract nr A134.
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 | 0,017 | 0,075 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,008 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».