Piecing together the puzzle of disparities in adolescents and young adults
Notice bibliographique
Résumé
As we sit poised to witness the evolution of the delivery of health care in the United States, we are hard pressed not to consider the impact of the health care delivery model on patient outcome.To assess what impact, if any, we have made on patient outcome, it is our duty to evaluate each piece of this model both before it is changed and, then, after it has evolved.This is especially important when we consider vulnerable populations; because, in many instances, these are the patients that reform is aiming to protect, whose health promotion and disease treatment we are aiming to improve.1,2 The National Cancer Institute has deemed adolescents and young adults (ages 15-39 years) (AYAs) with cancer to be a vulnerable population, because, over time, these AYAs have not experienced the same improvements in survival outcome as have children or adults aged 40 years.3 Young adults represent the largest group of uninsured in the United States both before and after implementation of the Affordable Care Act (ACA) 4 ; however, this age group has experienced the steepest increase in coverage in the immediate post-ACA period.5 The multiple facets of the ACA are voluminous; those most applicable to the AYA population include regulations aimed at: 1) improving the general health of the population by requiring new health plans to offer at least the minimum health benefits, 2) limiting gaps in coverage by outlawing pre-exisiting condition exclusions along with annual or lifetime limits and allowing young adults to remain on parents' plans until age 26 years, 3) making health insurance more affordable by creating a marketplace exchange, 4) minimizing out-of-pocket costs with the establishment of a temporary highrisk pool along with Medicaid eligibility expansion, 5) containing cost, and 6) increasing access for cancer patients, including mandated coverage for clinical trials and concurrent hospice/therapeutic care for children.6 Through these mechanisms, higher proportions of AYAs with cancer probably are covered; however, with the changing patterns of benefits and coverage of plans themselves, it is unclear whether other elements in the delivery of health care in AYA oncology are changing as well.Thus, as we design our before and after evaluations of the US health care delivery model, studies like that by Rosenberg and colleagues in the current issue of Cancer are crucial.7 Dr. Rosenberg et al report a population-level analysis of the impact of insurance status on patient outcomes in AYAs with cancer.With the objective of distinguishing associations between insurance status and both advanced-stage cancer and cancer-specific mortality, they interrogate data from the Surveillance, Epidemiology, and End Results (SEER) Program to evaluate common malignancies in AYAs ages 15 to 39 years who were diagnosed within the 3 years before implementation of the early parts of the ACA.All diagnoses included were consistently staged using American Joint Committee on Cancer (AJCC) criteria: thyroid cancer, breast cancer, Hodgkin and non-Hodgkin lymphomas, female genitourinary cancers (including cervical cancer), male genitourinary cancers (including testicular germ cell tumors), melanoma, colon cancer, bone/soft-tissue sarcomas (excluding Kaposi sarcoma), upper gastrointestinal cancers, lung cancer, hepatic tumors, renal tumors, and nonpelvic germ cell tumors.Unfortunately, central nervous system tumors and leukemias, both of which are common malignancies in the AYA population, were excluded because they are not staged with AJCC criteria and, thus, could not be analyzed similarly.In this study, nearly 58,000 eligible patients were identified who had available data: 54,765 patients (ages 20-39 years) were included in the analysis of disease stage, and 48,816 patients (ages 25-39 years) were included in the analysis of survival.The authors draw a line to evaluate patients aged <25 years versus older patients (ie, ages 20-24 years vs ages 25-39 years), a choice that is supported by their use of likelihood ratio testing; this would have been more beneficial as a policy-level analysis if, instead, the age cutoff had been 26 years, because the ACA expanded coverage in 2010 to allow young adults to remain on their parents' policies until age 26 years.
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 | 0,015 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,006 | 0,008 |
| Communication savante | 0,007 | 0,014 |
| Science ouverte | 0,002 | 0,012 |
| Intégrité de la recherche | 0,006 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».