Decomposition of Heart Failure Prevalence and Mortality Among Older Adults in the United States: Medicare-Based Partitioning Analysis
Bibliographic record
Abstract
Background: Heart failure (HF) is a challenging clinical and public health problem characterized by high prevalence and mortality among US older adults, along with a recent decline in HF prevalence and increase in mortality. The changes of prevalence can be decomposed into pre-existing disease prevalence, disease incidence, and respective survival, while the changes of mortality can be decomposed into mortality in the general population independent from HF, pre-existing HF prevalence, incidence, and respective survival. These epidemiological components may contribute differently to the changes in prevalence and mortality. Objective: We aimed to investigate and compare the relative contributions of epidemiologic determinants in HF prevalence and mortality trends. Methods: This study was a secondary data analysis of 5% of Medicare claims data for 1992-2017 in the United States. Medicare is a federal health insurance program for older adults aged 65+ years as well as people with specific disabilities and end-stage renal disease. Age-adjusted prevalence and incidence-based mortality (IBM; all-cause mortality that occurred in patients with HF) were partitioned into their respective epidemiologic determinants using the partitioning analysis approach. Results: The age-adjusted HF prevalence (1/100 person-years) increased from 11 in 1994 to 14.6 in 2005, followed by a decline to 12.6 in 2017, and the age-adjusted HF IBM (1/100,000) increased from 2220.8 in 1994 to 2563.7 in 2000, then declined to 2075.9 in 2016, followed by an increase to 2094.7 in 2017. The HF incidence (1/1000 person-years) declined from 29.4 in 1992 to 19.9 in 2017. The 1-, 3-, and 5-year survival trend showed declines in recent years. Partitioning of HF prevalence showed three phases: (1) decelerated increasing prevalence (1994-2006), (2) accelerated declining prevalence (2007-2014), and (3) decelerated declining prevalence (2015-2017). During the whole period, the decreasing HF incidence contributed to the declines in prevalence, overpowering prevalence increases contributed from survival. Likewise, partitioning of HF IBM showed three phases: (1) decelerated increasing mortality (1994-2001), (2) accelerated declining mortality (2002-2012), and (3) decelerated declining mortality (2013-2017). The decreasing HF incidence in 1994-2017 and increasing survival in 2002-2006 contributed to the declines in mortality, while the decreasing survival in 2007-2017 contributed to the mortality increase. Conclusions: Decade-long declines in HF prevalence and mortality mainly reflected decreasing incidence, while the most recent increase of mortality was predominantly due to the declining survival. If current trends persist, HF prevalence and mortality are forecasted to grow substantially in the next decade. Prevention strategies should continue the prevention of HF risk factors as well as improvement of treatment and management of HF after diagnosis.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".