Utility of the Social Vulnerability Index in Addressing Breast Cancer Disparities: A Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the utility of the Social Vulnerability Index (SVI) in understanding disparities in breast cancer screening, incidence, and mortality. BACKGROUND: Despite major advances in breast cancer detection and treatment, significant disparities persist-particularly among socioeconomically and geographically vulnerable populations. The SVI, developed by the CDC, is a composite index that captures community-level vulnerability across multiple social domains and may serve as a tool to identify and address inequities in cancer care. METHODS: This systematic review and meta-analysis were conducted in accordance with PRISMA guidelines and registered in PROSPERO (CRD42024616874). PubMed, Scopus, and Embase were searched for studies examining associations between SVI and breast cancer outcomes. Studies were evaluated using the Newcastle-Ottawa Scale or appropriate Cochrane tools. Meta-analyses were performed where applicable. RESULTS: Fifteen studies were included. Seven studies examined screening; a pooled meta-analysis (n = 3) showed reduced screening in high-SVI areas (pooled OR: 0.55, 95% CI: 0.24-1.26; I² = 99%). Four studies reported reduced incidence in high-SVI populations, likely reflecting underdiagnosis. Five studies demonstrated increased mortality in high-SVI populations, with ORs ranging from 1.09 to 2.84. Other studies addressed comorbidities, access to care, and disease subtypes. CONCLUSION: The SVI is a valuable, multidimensional tool for characterizing and addressing disparities in breast cancer outcomes, with implications for public health interventions and policy.
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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 | 0.038 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.075 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".