Piecing together the puzzle of disparities in adolescents and young adults
Bibliographic record
Abstract
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.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 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".