Forecasting the Future of Cardiovascular Disease in the United States: A Policy Statement From the American Heart Association
Open accessAbstract
Background—: Cardiovascular disease (CVD) is the leading cause of death in the United States and is responsible for 17% of national health expenditures. As the population ages, these costs are expected to increase substantially. Methods and Results—: To prepare for future cardiovascular care needs, the American Heart Association developed methodology to project future costs of care for hypertension, coronary heart disease, heart failure, stroke, and all other CVD from 2010 to 2030. This methodology avoided double counting of costs for patients with multiple cardiovascular conditions. By 2030, 40.5% of the US population is projected to have some form of CVD. Between 2010 and 2030, real (2008$) total direct medical costs of CVD are projected to triple, from $273 billion to $818 billion. Real indirect costs (due to lost productivity) for all CVD are estimated to increase from $172 billion in 2010 to $276 billion in 2030, an increase of 61%. Conclusions—: These findings indicate CVD prevalence and costs are projected to increase substantially. Effective prevention strategies are needed if we are to limit the growing burden of CVD.
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Hypertension and diabetes mellitus are major risk factors for cardiac diseases, stroke, and kidney diseases.
“Hypertension (HTN) and diabetes mellitus are major risk factors for cardiac diseases, stroke, and kidney diseases [1-5].”
From Effectiveness of Digital Medicines to Improve Clinical Outcomes in Patients with Uncontrolled Hypertension and Type 2 Diabetes: Prospective, Open-Label, Cluster-Randomized Pilot Clinical Trial (2017) · cites this paper as [5]
In “Potential Impact of CVD Prevention” (page unavailable), the paper calls hypertension a major risk factor for stroke, coronary heart disease, and heart failure. In “Commentary” (page unavailable), it identifies diabetes mellitus as a risk factor for cardiovascular disease. The cited passages do not establish the claim that both conditions are major risk factors for kidney disease, or that diabetes is a major risk factor for each listed cardiac and stroke outcome. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe higher the level of care based on guidelines and performance measures, the greater the impact on prevention and recurrent events.
“The higher the level of care based on the guidelines and performance measures, the better the impact on prevention and recurrent events.82,437”
From European Guidelines on cardiovascular disease prevention in clinical practice (version 2012) (2012) · cites this paper as [437]
The approach of the cardiologist reviewing discharge recommendations and helping the patient comply has a significant impact on mid-term prognosis.
“This approach has a significant impact on mid-term prognosis.250,437”
From European Guidelines on cardiovascular disease prevention in clinical practice (version 2012) (2012) · cites this paper as [437]
A link exists between accuracy in recordings and both quality of care and adherence to guidelines.
“A link exists between accuracy in recordings and both quality of care and adherence to guidelines.437”
From European Guidelines on cardiovascular disease prevention in clinical practice (version 2012) (2012) · cites this paper as [437]
The socio-economic gap in cardiovascular health has not narrowed despite major improvements in cardiovascular disease treatment in recent decades.
“Targeting lower levels in the health impact pyramid will also address the socio-economic divide in CV health, which has not diminished despite major improvements in the treatment of CVD in recent decades. 9,10”
From 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias (2016) · cites this paper as [9]
Targeting lower levels of the health impact pyramid would address the socio-economic divide in cardiovascular health, which has not diminished despite major improvements in cardiovascular disease treatment.
“Targeting lower levels in the health impact pyramid will also address the socio-economic divide in CV health, which has not diminished despite major improvements in the treatment of CVD in recent decades.9,10”
From 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias (2016) · cites this paper as [9]
The high prevalence of poor-quality diet, physical inactivity, and overweight/obesity presages a worsening of the current distribution of health biomarkers.
“However, the prevalence of ideal cardiovascular health is very low in the US population, and the prevalence of poor-quality diet, physical inactivity, and overweight/obesity is alarmingly high, presaging a worsening of the current distribution of health biomarkers.1–8”
From Better Population Health Through Behavior Change in Adults: A Call to Action (2013) · cites this paper as [2]
The prevalence of poor-quality diet, physical inactivity, and overweight/obesity is alarmingly high in the US population.
“However, the prevalence of ideal cardiovascular health is very low in the US population, and the prevalence of poor-quality diet, physical inactivity, and overweight/obesity is alarmingly high, presaging a worsening of the current distribution of health biomarkers.1–8”
From Better Population Health Through Behavior Change in Adults: A Call to Action (2013) · cites this paper as [2]
The prevalence of ideal cardiovascular health is very low in the US population.
“However, the prevalence of ideal cardiovascular health is very low in the US population, and the prevalence of poor-quality diet, physical inactivity, and overweight/obesity is alarmingly high, presaging a worsening of the current distribution of health biomarkers.1–8”
From Better Population Health Through Behavior Change in Adults: A Call to Action (2013) · cites this paper as [2]
Direct annual cardiovascular disease-related costs are projected to triple from $272 billion in 2010 to $818 billion in 2030.
“with direct annual cardiovascular disease–related costs projected to triple, from $272 billion in 2010 to $818 billion in 2030,2”
From Better Population Health Through Behavior Change in Adults: A Call to Action (2013) · cites this paper as [2]
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The modern epidemiology of heart valve disease
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- Authors
- Heidenreich, P. A., Trogdon, J. G., Khavjou, O. A., Butler, J., Dracup, K., Ezekowitz, M. D., Finkelstein, E. A., Hong, Y., Johnston, S. C., Khera, A., Lloyd-Jones, D. M., Nelson, S. A., Nichol, G., Orenstein, D., Wilson, P. W. F., & Woo, Y. J.
- Affiliations
- American Heart Association (US)
- Neurology, Inc (US)
- ORCID
- Venue
- Circulation
- Published
- 2011-03
- Volume
- 123
- Issue
- 8
- Pages
- 933-944
- ISSN
- 0009-7322 (ISSN-L)
- 1524-4539
- OpenAlex
- W2130483595
- Cited by
- 3,208
- References
- 76
- Citations by year
- 2026: 43 · 2025: 100 · 2024: 108 · 2023: 124 · 2022: 159 · 2021: 170 · 2020: 210 · 2019: 207 · 2018: 232 · 2017: 291 · 2016: 283 · 2015: 337 · 2014: 366 · 2013: 318 · 2012: 190
- FWCI
- 206.31
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Circulation (publishedVersion)
- PubMed (publishedVersion)
- DukeSpace (Duke University) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
- Health Sciences › Medicine › Epidemiology › Healthcare Systems and Public Health
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
- Keywords
- cardiovascular disease, cost projections, direct medical costs, indirect costs, productivity loss, hypertension, coronary artery disease, heart failure, stroke, cardiovascular disease prevalence, health care costs, cardiovascular disease prevention, United States
- MeSH terms
- American Heart Association, Cardiovascular Diseases, Coronary Disease, Forecasting, Heart Failure, Humans, Hypertension, United States, Health Care Costs, Stroke, Policy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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