Abstract P4-10-21: Disparities in the risk of mortality in breast cancer based on health insurance status
Bibliographic record
Abstract
Abstract In 2007 Foley et al showed that low-income Canadian residents had a survival advantage over low-income US residents, which was attributed to the equal access to medical care in Canada's universal health care system. Niu et al. found that uninsured and Medicaid insured patients with breast, cervical, colorectal, head and neck, lung, prostate or uterine cancer have higher mortality rates compared to patients with private insurance or Medicare. As substantial proportions of the US population are uninsured or enrolled in Medicaid, we examined the association between in-hospital mortality and different primary payers in patients with breast cancer. Methods Adult female admissions (adm) with a primary diagnosis of breast cancer between 1999 and 2014 were extracted from the National Inpatient Sample database using the ICD-9 code 174.9 (N=98631, for a weighted N=484859). The sample was weighted to approximate the full inpatient population of the US over the time period. To minimize the effect of changes in mortality rates based on insurance status over the time interval studied, we grouped the adm into three categories: Group 1 for adm from 1999 to 2003, Group 2 2004 to 2008, and Group 3 2009 to 2014. Chi-Square analysis was done to determine the in-hospital mortality rates by payer group and Cox Proportional Hazard regression was used to determine the hazard of death (HR) within 30 days of adm by payer. Results In-hospital Mortality (%) by primary payerYear GroupMedicareMedicaidPrivate InsuranceSelfpay/UninsuredP value13.836.773.4310.140.000124.587.554.9590.000132.843.463.0210.260.0001 Hazard ratio of death within the hospitalizationYear GroupMedicaid vs MedicareP valueUninsured vs MedicareP value12.03 (1.49, 2.76)0.00013.2 (2.49, 4.22)0.000121.56 (1.19, 2.46)0.00012.9 (2.20, 3.80)0.000132.55 (1.99, 3.27)0.00017.76 (1.99, 3.27)0.0001 The number of adm with Medicare or Private insurance were higher than those with Medicaid or Selfpay/uninsured. The in-hospital mortality was highest for Selfpay/uninsured, followed by admissions with Medicaid. After controlling for age, race, median income and comorbidities, the HR was significantly higher in the Medicaid and selfpay/uninsured admissions compared to Medicare admissions. In Group 1, compared to Medicare adm the HR was 103% higher for Medicaid and 220% higher for uninsured. In Group 2, the HR was 56% higher for Medicaid and 196% higher for uninsured. In Group 3, it was 155% higher for Medicaid and 676% higher in uninsured. Conclusion Even after controlling for other factors which are implicated in the mortality, the HR is significantly higher in Medicaid and uninsured admissions when compared with Medicare enrolled admissions with breast cancer. Equitable distribution of health was one of the “Aims for Improvement” in the Institute of Medicine's 2001 report, but our results suggests that this has not yet been achieved. Insurance status still appears to play a crucial role in patient outcomes and should be considered as a metric of equitable care. More scientific research is needed in the area of differential receipt of standard therapy in cancer patients considering the limitations of our study. Citation Format: Perimbeti S, Chakunta H, Liu L, Ward K, Jain M, Styler M. Disparities in the risk of mortality in breast cancer based on health insurance status [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-10-21.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".