Inequities in Genetic Testing for Hereditary Breast Cancer: Implications for Public Health Practice
Bibliographic record
Abstract
Background and context: The Ontario Breast Screening Program for women with a genetic predisposition to breast cancer is 1 of the first international models of a government-funded public health service that offers systematic genetic screening to women at a high-risk of breast cancer. However, since the implementation of the program in 2011, enrolment rates have been lower than anticipated. While there may be several reasons for this to happen, it does call into consideration the 'inverse equity law', whereby the more advantaged in society are the first to participate and benefit from universal health services. An outcome of this phenomenon is an increase in the health divide between those that are at a social advantage vs those that are not. Aim: Using an intersectionality lens this review illuminates the role of the social determinants of health and social identity in creating possible barriers in the access to genetic screening for hereditary breast cancer, and the implications for public health practice in recognizing and ameliorating these differences. Strategy/Tactics: Although it remains too early to understand the exact cause for underenrolment in the OBSP high-risk screening program, this review serves to illuminate how screening programs that are used as targeted interventions to improve health outcomes must take into consideration the complexities associated with utilization and need across the entire population. A failure to do so may further disenfranchise socially disadvantaged individuals and widen the health equity gap that currently exists between population groups based on social location. Program/Policy process: The Ontario Breast Screening Program (OBSP) for High Risk Women is funded by the government; therefore, financial barriers in terms of access to care do not exist for individuals seeking screening. Despite this, the program has had low levels of enrolment based on their population targets (Cancer Care Ontario, 2012). Outcomes: While access to health care services is an important social determinant of health (Whitehead, 1992), the structure and design of health services can render them structurally unavailable and socially inacceptable to certain population groups (Gilson et al., 2007). Indeed, recent studies clearly demonstrate how socially disadvantaged individuals, such as those with lower levels of education, and those from ethnic minority groups consistently underuse health services despite the lack of a financial barrier to care (Maddison, 2011). What was learned: The way in which genetic testing is both accessed and used follow similar trends, such that higher levels of both income and education correlate with an increased awareness of genetic testing, a greater likely hood of receiving referrals for genetic testing, appropriateness of genetic counseling and the final decision to proceed with genetic testing.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".