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Projections of Alzheimer's disease in the United States and the public health impact of delaying disease onset.
Open accessAbstract
OBJECTIVES: The goal of this study was to project the future prevalence and incidence of Alzheimer's disease in the United States and the potential impact of interventions to delay disease onset. METHODS: The numbers of individuals in the United States with Alzheimer's disease and the numbers of newly diagnosed cases that can be expected over the next 50 years were estimated from a model that used age-specific incidence rates summarized from several epidemiological studies, US mortality rates, and US Bureau of the Census projections. RESULTS: in 1997, the prevalence of Alzheimer's disease in the United States was 2.32 million (range: 1.09 to 4.58 million); of these individuals, 68% were female. It is projected that the prevalence will nearly quadruple in the next 50 years, by which time approximately 1 in 45 Americans will be afflicted with the disease. Currently, the annual number of new incident cases in 360,000. If interventions could delay onset of the disease by 2 years, after 50 years there would be nearly 2 million fewer cases than projected; if onset could be delayed by 1 year, there would be nearly 800,000 fewer prevalent cases. CONCLUSIONS: As the US population ages, Alzheimer's disease will become an enormous public health problem. interventions that could delay disease onset even modestly would have a major public health impact.
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Metadata
- Authors
- Brookmeyer, R., Gray, S., & Kawas, C.
- Venue
- American Journal of Public Health
- Publisher
- American Public Health Association
- Published
- 1998-09
- Volume
- 88
- Issue
- 9
- Pages
- 1337-1342
- ISSN
- 0090-0036 (ISSN-L)
- 1541-0048
- OpenAlex
- W2125993125
- Cited by
- 1,787
- References
- 7
- FWCI
- 23.84
- Type
- journal article
- Language
- en
- Open access
- bronze
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.