Incidence and Early Outcomes of Heart Failure in Commercially Insured and Medicare Advantage Patients, 2006 to 2014
Bibliographic record
Abstract
H eart failure (HF) affects >5.7 million individuals in the United States 1 with 870 000 individuals newly diagnosed each year. 2 Previous epidemiological studies have demonstrated that the incidence of HF varies by race and ethnicity, with the highest incidence in blacks. [3][4]4][5] However, this association is partially mediated by differences in socioeconomic factors, 3 such as access to care, and whether differences extend to commercially insured populations requires examination.Furthermore, although the incidence of HF is known to increase with age, 6,7 younger individuals with HF remain understudied because several large epidemiological cohorts 8 and claims-based studies 6 are restricted to older individuals.Our goal was to address these gaps in knowledge by leveraging a large US insurance claims database, containing information from >100 million individuals enrolled in private and Medicare Advantage health plans.The objectives of this study were to evaluate the incidence of HF by age, sex, and race/ ethnicity and to examine differences in the rate of hospitalizations and office visits in the year after diagnosis. Methods and Results Data SourceWe conducted a retrospective analysis using the OptumLabs Data Warehouse, a large US commercial insurance database. 9The database comprises medical claims for individuals in all 50 states and of all ages and ethnic and racial groups. 10Medical claims include claims for professional (eg, physician), facility (eg, hospital), and outpatient prescription medication services.Pursuant to the Health Insurance Portability and Accountability Act, the use of de-identified data does not require Institutional Review Board approval. Study PopulationWe included adult enrollees for whom a diagnosis of HF (International Classification of Diseases, Ninth Revision, Clinical Modification codes 428.XX, 402.X1, 404.X1, or 404.X3 6 appeared on a single inpatient claim between January 1, 2006 and April 1, 2014.We also included individuals with an HF diagnosis on at least 3 physician or outpatient claims on different days within 20 consecutive months. 6The incidence date was defined as the earliest discharge date of the qualifying inpatient claim or the latest service date of the qualifying outpatient claim.To ensure that enrollees had newly diagnosed HF, we required them to have at least 2 previous years of continuous medical coverage with no claim listing HF as a diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".