Evidence map›Paper›PMID 28580275›Full record

ArticleJournal of the economics of ageing2016

Forecasting Trends in Disability in a Super-Aging Society: Adapting the Future Elderly Model to Japan.

Brian K Chen, Hawre Jalal, Hideki Hashimoto, Sze-Chuan Suen, Karen Eggleston, Michael Hurley, Lena Schoemaker, Jay Bhattacharya

Abstract read
In one paragraph

Article in Journal of the economics of ageing, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–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

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Health Risk and the Value of Life.Journal of public economics · 2025
    Article
  6. The urban-rural gap in older Americans' healthy life expectancy.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2025
    Article
  7. Initial outcomes and surgical techniques of prostatic urethral lift for benign prostatic hyperplasia in Japan.International journal of urology : official journal of the Japanese Urological Association · 2024
    Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. 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

8 authors.

Brian K ChenDepartment of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, 915 Greene Street Suite 354, Columbia, SC 29208, USA.
Hawre JalalDepartment of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, 130 DeSoto Street Room 703, Pittsburgh, PA 15261, USA.
Hideki HashimotoUniversity of Tokyo School of Public Health, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
Sze-Chuan SuenEpstein Department of Industrial & Systems Engineering, Viterbi School of Engineering, University of Southern California, 3715 McClintock Ave, Los Angeles, CA 90089, USA.
Karen EgglestonStanford University FSI Shorenstein Asia Pacific Research Center, Encina Hall E301, Stanford, CA 94305, USA.
Michael HurleyInternal Medicine Residency Program, University of California at San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143-0119 USA.
Lena SchoemakerCenter for Health Policy/Center for Primary Care and Outcomes Research, Stanford University, Encina Hall, 616 Serra St • Stanford, CA 94305, USA.
Jay BhattacharyaStanford School of Medicine, 117 Encina Commons, Stanford, CA 94305, USA.

Funding

Understanding and Improving Healthcare Decision-Making and Outcomes for People Living with Alzheimer's Disease and Related DementiasP01AG005842 · NIA · NATIONAL BUREAU OF ECONOMIC RESEARCH · PI KOSALI ILAYPERUMA SIMON · 1986 to 2026
$56.5M
CORE--PROGRAM DEVELOPMENTP30AG017253 · NIA · STANFORD UNIVERSITY · PI BHATTACHARYA, JAY · 1999 to 2019
$5.8M
Estimating the Potential Medicare Savings from Comparative Effectiveness ResearchR37AG036791 · NIA · STANFORD UNIVERSITY · PI GARBER, ALAN MICHAEL · 2011 to 2016
$2.4M
NIA NIH HHS P01 AG005842NIA NIH HHS P30 AG017253NIA NIH HHS R37 AG036791
6 · The paper itself

Abstract

Japan has experienced pronounced population aging, and now has the highest proportion of elderly adults in the world. Yet few projections of Japan's future demography go beyond estimating population by age and sex to forecast the complex evolution of the health and functioning of the future elderly. This study estimates a new state-transition microsimulation model - the Japanese Future Elderly Model (FEM) - for Japan. We use the model to forecast disability and health for Japan's future elderly. Our simulation suggests that by 2040, over 27 percent of Japan's elderly will exhibit 3 or more limitations in IADLs and social functioning; almost one in 4 will experience difficulties with 3 or more ADLs; and approximately one in 5 will suffer limitations in cognitive or intellectual functioning. Since the majority of the increase in disability arises from the aging of the Japanese population, prevention efforts that reduce age-specific morbidity can help reduce the burden of disability but may have only a limited impact on reducing the overall prevalence of disability among Japanese elderly. While both age and morbidity contribute to a predicted increase in disability burden among elderly Japanese in the future, our simulation results suggest that the impact of population aging exceeds the effect of age-specific morbidity on increasing disability in Japan's future.

Indexed as

AgingComorbiditiesFuture Elderly ModelJapanLong-term CareMicrosimulation

Identifiers

PMID28580275
PMCPMC5451156

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.