Abstract P4-10-21: Disparities in the risk of mortality in breast cancer based on health insurance status
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».