Evidence map›Paper›PMID 39621935›Full record

ArticleJMIR public health and surveillance2024

Decomposition of Heart Failure Prevalence and Mortality Among Older Adults in the United States: Medicare-Based Partitioning Analysis.

Bin Yu, Julia Kravchenko, Arseniy Yashkin, Igor Akushevich

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Bin YuDepartment of Epidemiology and Health Statistics, School of Public Health, Wuhan University, 115 Donghu Road, Wuchang District, Wuhan, 430071, China, 86 13797095040.ORCID 0000-0001-6941-8083
Julia KravchenkoDepartment of Surgery, School of Medicine, Duke University, Durham, NC, United States.ORCID 0000-0002-6436-1548
Arseniy YashkinSocial Science Research Institute, Duke University, Durham, NC, United States.ORCID 0000-0002-1185-148X
Igor AkushevichSocial Science Research Institute, Duke University, Durham, NC, United States.ORCID 0000-0003-3471-7846

Funding

Racial and Geographic Disparities in Risk and Survival of Alzheimer's Disease and Related DementiasR01AG066133 · NIA · DUKE UNIVERSITY · PI AKUSHEVICH, IGOR · 2019 to 2023
$3.7M
Determinants of geographic disparities in mortality and multimorbidity in the U.S.R01AG057801 · NIA · DUKE UNIVERSITY · PI AKUSHEVICH, IGOR, KRAUCHANKA, YULIYA · 2019 to 2023
$2.1M
NIA NIH HHS R01 AG057801NIA NIH HHS R01 AG066133
6 · The paper itself

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.

Indexed as

Heart FailureMedicareAgedAged, 80 and overFemaleHumansIncidenceMaleMortalityPrevalenceUnited Statesepidemiologic determinantsheart failuremortalitypartitioningprevalencetime trends

Identifiers

PMID39621935
PMCPMC11611790

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.