MétaCan
Menu
Back to cohort
Record W4391645822 · doi:10.1038/s41514-024-00136-6

Cost of care for Alzheimer’s disease and related dementias in the United States: 2016 to 2060

2024· article· en· W4391645822 on OpenAlexaff
Arindam Nandi, Nathaniel Z. Counts, Janina Bröker, Simiao Chen, Rachael Han, Jessica Klusty, Benjamin Seligman, Daniel L. Tortorice, Daniel E. Vigo, David E. Bloom

Bibliographic record

Venuenpj Aging · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPer capitaHealth careBusinessConfidence intervalEconomic costMedicineDemographic economicsActuarial scienceEconomicsEnvironmental healthEconomic growthPopulation

Abstract

fetched live from OpenAlex

Medical and long-term care for Alzheimer's disease and related dementias (ADRDs) can impose a large economic burden on individuals and societies. We estimated the per capita cost of ADRDs care in the in the United States in 2016 and projected future aggregate care costs during 2020-2060. Based on a previously published methodology, we used U.S. Health and Retirement Survey (2010-2016) longitudinal data to estimate formal and informal care costs. In 2016, the estimated per patient cost of formal care was $28,078 (95% confidence interval [CI]: $25,893-$30,433), and informal care cost valued in terms of replacement cost and forgone wages was $36,667 ($34,025-$39,473) and $15,792 ($12,980-$18,713), respectively. Aggregate formal care cost and formal plus informal care cost using replacement cost and forgone wage methods were $196 billion (95% uncertainty range [UR]: $179-$213 billion), $450 billion ($424-$478 billion), and $305 billion ($278-$333 billion), respectively, in 2020. These were projected to increase to $1.4 trillion ($837 billion-$2.2 trillion), $3.3 trillion ($1.9-$5.1 trillion), and $2.2 trillion ($1.3-$3.5 trillion), respectively, in 2060.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.339
GPT teacher head0.452
Teacher spread0.114 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations147
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuenpj AgingSame topicHealth Systems, Economic Evaluations, Quality of LifeFrench-language works237,207