Abstract A134: Completeness of social determinants of health data collection for patients with breast cancer at a tertiary care cancer center
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".