Evidence map›Paper›PMID 42265774›Full record

ArticlePopulation health metrics2026

Why healthy life expectancy estimates diverge: insights from survey-based, administrative, and model-based metrics across Japanese prefectures, 2001-2019: a comparative ecological study.

Shuhei Nomura, Akifumi Eguchi, Daisuke Yoneoka, Hana Tomoi, Lisa Yamasaki, Yuta Tanoue, Takayuki Kawashima, Aoi Kataoka, Yuri Ito, Naoki Kondo

Abstract readComparative Study
In one paragraph

Article in Population health metrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Shuhei NomuraGlobal Health Policy Lab, International Research Institute of Disaster Science (IRIDeS), Tohoku University, 468-1 Aoba, Aramaki, Aoba-ku, Sendai, Miyagi, 980-8572, Japan. shuhei.nomura.c5@tohoku.ac.jp.
Akifumi EguchiCenter for Preventive Medical Sciences, Chiba University, Chiba, Japan.
Daisuke YoneokaInfectious Disease Surveillance Center, National Institute of Infectious Diseases, Japan Institute for Health Security, Tokyo, Japan.
Hana TomoiLondon School of Hygiene and Tropical Medicine, Department of Infectious Disease Epidemiology, Faculty of Epidemiology and Population Health, London, UK.
Lisa YamasakiDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Yuta TanoueFaculty of Marine Technology, Tokyo University of Marine Science and Technology, Tokyo, Japan.
Takayuki KawashimaSchool of Computing, Institute of Science Tokyo, Tokyo, Japan.
Aoi KataokaDepartment of Medical Statistics, Osaka Medical and Pharmaceutical University, Research & Development Center, Osaka, Japan.
Yuri ItoDepartment of Medical Statistics, Osaka Medical and Pharmaceutical University, Research & Development Center, Osaka, Japan.
Naoki KondoDepartment of Social Epidemiology, Graduate School of Medicine and School of Public Health, Kyoto University, Kyoto, Japan.

Funding

Ministry of Health, Labour and Welfare 25FA1005
6 · The paper itself

Abstract

backgroundHealthy life expectancy (HALE) can be measured using different approaches, yet estimates may diverge. Understanding divergence sources could help identify which health dimensions current systems capture or miss. However, systematic subnational comparisons remain scarce.

methodsWe conducted a comparative ecological study across Japan's 47 prefectures from 2001 to 2019. Three Ministry of Health, Labour and Welfare (MHLW) metrics-disability-free life expectancy based on activity limitation (DFLE-AL) and life expectancy in subjective health (LE-SH) derived from national surveys, and disability-free life expectancy based on activities of daily living (DFLE-ADL) derived from long-term care insurance records-were obtained from MHLW. Model-based HALE from the Global Burden of Disease (GBD) 2023 Study was extracted for corresponding years. We assessed cross-sectional concordance and examined associations with disease burden, risk factors, and socioeconomic indicators using fixed-effects panel regression.

resultsOver 2001-2019, DFLE-AL and LE-SH correlated with GBD HALE among males (r = 0.33 to 0.65) but weakly among females (r =  - 0.11 to 0.32); DFLE-ADL showed concordance for both sexes (r = 0.66 to 0.96). GBD HALE exceeded MHLW estimates in females (0.53 years for DFLE-AL, 0.23 for LE-SH) but not males (- 0.18, - 0.14). In fixed-effects analysis of MHLW-minus-GBD differences, mental disorders showed negative associations with DFLE-AL and LE-SH differences among males; high BMI and musculoskeletal disorders showed positive associations with DFLE-AL differences. Among females, population ageing showed positive associations with DFLE-AL, LE-SH, and DFLE-ADL differences.

conclusionsMHLW metrics and GBD HALE capture complementary health dimensions and should not be treated as interchangeable. The consistent sex difference in concordance indicates that divergence between HALE metrics reflects differences in which health dimensions are emphasised-self-reported health states, objectively assessed functional status, or disease-based disability modelling-rather than a single, uniform construct of population health.

Indexed as

Healthy Life ExpectancyLife ExpectancyActivities of Daily LivingAgedAged, 80 and overCross-Sectional StudiesEast Asian PeopleFemaleGlobal Burden of DiseaseHealth StatusHumansJapanMaleSurveys and QuestionnairesGlobal burden of diseaseHealth metricsHealthy life expectancyJapanSubnational analysis

Identifiers

PMID42265774
PMCPMC13474879

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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.