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Record W4400733311 · doi:10.2196/57045

Impact of Healthy Lifestyle Factors on Life Expectancy and Lifetime Health Care Expenditure: Nationwide Cohort Study

2024· article· en· W4400733311 on OpenAlexvenueno aff
Wei‐Cheng Lo, Tsuey‐Hwa Hu, Cheng-Yu Shih, Hsien-Ho Lin, Jing‐Shiang Hwang

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsnot available
Fundersnot available
KeywordsLife expectancyEnvironmental healthGerontologyMedicineCohortCohort studyHealth careDemographyPopulationEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: The association between lifestyle risk factors and the risk of mortality and chronic diseases has been established, while limited research has explored the impact of healthy lifestyle factors on lifetime health care expenditure using longitudinal individual data. OBJECTIVE: We aimed to determine the individual and combined effects of 5 healthy lifestyle factors on life expectancy and lifetime health care expenditure in Taiwan. METHODS: Using data from the National Health Interview Survey cohort, 5 healthy lifestyle behaviors were defined and analyzed: nonsmoking, avoiding excessive alcohol consumption, engaging in sufficient physical activity, ensuring sufficient fruit and vegetable intake, and maintaining a normal weight. We used a rolling extrapolation algorithm that incorporated inverse probability of treatment weighting to estimate the life expectancy and lifetime health care expenditure of the study populations with and without healthy lifestyle factors. RESULTS: A total of 19,893 participants aged ≥30 (mean age 48.8, SD 13.4) years were included, with 3815 deaths recorded during a median follow-up period of 15.6 years. The life expectancy and per capita estimated lifetime health care expenditures for the overall study population were 35.32 years and US $58,560, respectively. Multivariable-adjusted hazard ratios for all-cause mortality in participants adhering to all 5 healthy lifestyle factors, compared with those adhering to none, were 0.37 (95% CI 0.27-0.49). We found significant increases in life expectancy for nonsmokers (2.31 years; 95% CI 0.04-5.13; P=.03), those with sufficient physical activity (1.85 years; 95% CI 0.25-4.34; P=.02), and those with adequate fruit and vegetable intake (3.25 years; 95% CI 1.29-6.81; P=.01). In addition, nonsmokers experienced a significant reduction in annual health care expenditure (-9.78%; 95% CI -46.53% to -1.45%; P=.03), as did individuals maintaining optimal body weight (-18.36%; 95% CI -29.66% to -8.57%; P=.01). Overall, participants adhering to all 5 healthy lifestyle behaviors exhibited a life gain of 7.13 years (95% CI 1.33-11.11; P=.02) compared with those adhering to one or none, with a life expectancy of 29.19 years (95% CI 25.45-33.62). Furthermore, individuals adopting all 5 healthy lifestyle factors experienced an average annual health care expenditure reduction of 28.12% (95% CI 4.43%-57.61%; P=.02) compared with those adopting one or none. CONCLUSIONS: Adopting a healthy lifestyle is associated with a longer life expectancy and a reduction of health care expenditure in Taiwanese adults. This contributes to a more comprehensive understanding of the impact of healthy lifestyle factors on the overall health and economic burden.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.437
Teacher spread0.398 · 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.

Study designObservational
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

Citations17
Published2024
Admission routes1
Has abstractyes

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