Anatomic Stage IV Breast Cancer AJCC v8 Clinical Trial
Official title:
The Impact of Social Determinants of Health on Cancer Care Delivery & Outcomes for Metastatic Breast Cancer Patients
NCT number | NCT06206239 |
Other study ID # | 22G.119 |
Secondary ID | |
Status | Completed |
Phase | |
First received | |
Last updated | |
Start date | March 9, 2022 |
Est. completion date | March 31, 2024 |
Verified date | May 2024 |
Source | Thomas Jefferson University |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
This study evaluates disparities and barriers in cancer care delivery and outcomes in women of color by identifying socioeconomic variables that may be related to the inequity. Social determinants of health, or the conditions in which people live, work, and play, have a profound effect on health outcomes. This research is being done to understand whether social determinants of health factors like employment, household income, and home ownership affect access to care services and outcomes for patients with metastatic breast cancer who receive their cancer treatment at Sidney Kimmel Cancer Center at Jefferson Health.
Status | Completed |
Enrollment | 44 |
Est. completion date | March 31, 2024 |
Est. primary completion date | March 31, 2023 |
Accepts healthy volunteers | |
Gender | Female |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: - Female - Aged 18 and older - English-speaking - Diagnosed with metastatic breast cancer within 24 months of study enrollment - Receiving treatment at the SKCC in Center City, New Jersey, or Methodist Hospital - Provide signed and dated informed consent form - Willing to comply with all study procedures and be available for the duration of the study Exclusion Criteria: - Male breast cancer patients - Women with breast cancer that is not metastatic - Women who discontinue treatment for their metastatic disease at SKCC |
Country | Name | City | State |
---|---|---|---|
United States | Thomas Jefferson University | Philadelphia | Pennsylvania |
Lead Sponsor | Collaborator |
---|---|
Thomas Jefferson University | Pfizer |
United States,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Acuity Risk Score Aim 1 | For modeling the acuity risk scores, the 3-level scores will be collapsed into a binary outcome measure (low vs. medium/high) at each assessment. These longitudinal observations collected over time will be modeled by generalized logistic regression using Generalized Estimating Equations (GEE) for robust error variance accounting for the correlations in observations from the same patient. The model will include predictor terms for race, time, and race-by-time interactions. This approach will be capable of characterizing baseline racial disparities in acuity risk, as well as distinguishing which racial groups might benefit the most from the intervention of the social worker in changing acuity risk score. The coefficients for race at baseline and linear combinations of coefficients for race and time to characterize changes in acuity risk will be exponentiated for representation as odds ratios and presented with 95% confidence intervals. | 6 months | |
Primary | Treatment delays and interruption Aim 2 | Will be represented as binary endpoints for modeling. | At end of study (3-6 months after baseline visit) | |
Primary | Patient adherence to recommended cancer treatment Aim 2 | Will be represented as binary endpoints for modeling. | At end of study (3-6 months after baseline visit) | |
Primary | Patient encounter with nurse navigator, palliative care, and/or genetic counselor Aim 2 | Will be represented as binary endpoints for modeling. | At end of study (3-6 months after baseline visit) | |
Primary | Clinical trial participation within first 24 months of metastatic breast cancer diagnosis Aim 2 | Will be represented as binary endpoints for modeling | 24 months | |
Secondary | Social determinants of health (Aim 1) | Secondary analyses will include similar models of other outcomes, including financial toxicity, social needs, barriers, and recommendations. Will explore confounding in these models, as well, by replacing the time and/or race-time interaction term(s) with socioeconomic and demographic variables. To identify the socioeconomic variables within the comprehensive screening tool that may be related to these disparities, will fit and summarize similar longitudinal GEE regression models of the respective socioeconomic component measures to identify which one(s) drive or counteract baseline racial disparities in acuity risk and/or changes in risk associated with social worker intervention. | At baseline, and repeated within 6 months | |
Secondary | Social needs screening (Aim 1) | Via Centers for Medicare and Medicaid Services' Innovation's Accountable Health Communities Health-Related Social Needs Screening Tool Binary scale (yes/no): Living situation, Utilities, Education, Disabilities Likert Scale: Safety, Family and community support Categorical: Employment, Digital literacy | 6 months |
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