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Record W4401611812 · doi:10.2196/50617

Investigating Frailty, Polypharmacy, Malnutrition, Chronic Conditions, and Quality of Life in Older Adults: Large Population-Based Study

2024· article· en· W4401611812 on OpenAlexvenueno aff
Yunmei Liu, Lei Huang, Fei Hu, Xiuwen Zhang

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsPolypharmacyPreprintMalnutritionEnvironmental healthQuality of life (healthcare)MedicineGerontologyIntensive care medicineComputer scienceWorld Wide WebNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Aging, a significant public health issue, is associated with multiple concurrent chronic diseases and aging-related conditions (geriatric syndromes). OBJECTIVE: This study aims to investigate the impact of age and chronic conditions on geriatric syndromes and the intercorrelations between multiple geriatric syndromes and quality of life (QoL) in older adults (aged ≥65 years) at the population level. METHODS: A large representative sample was randomly selected from a county in China, Feidong, with 17 towns and 811,867 residents. Multiple chronic conditions, geriatric syndromes (frailty, polypharmacy, and malnutrition), and QoL were assessed and compared. Associations of demographic information and chronic conditions with geriatric conditions and QoL in older adults were assessed using multivariable-adjusted logistic regression. Intercorrelations between age, multiple geriatric syndromes, and QoL were investigated using both correlation analysis and restricted cubic splines-based multivariable-adjusted dose-response analysis. RESULTS: Older adults comprised 43.42% (3668/8447) of the entire study population. The prevalence of frailty, premalnutrition or malnutrition, polypharmacy, and impaired QoL (median age 73, IQR 69-78 years; 1871/3668, 51% men) was 8.26% (303/3668), 15.59% (572/3668), 3.22% (118/3668), and 10.8% (396/3668), respectively. Different age and sex subgroups mostly had similar prevalence of geriatric syndromes (except that frailty occurred more often with older age). Premalnutrition or malnutrition were associated with a lower frequency of obesity and a higher frequency of constipation, polypharmacy with a higher frequency of diabetes and constipation, frailty with a higher frequency of constipation and hernia, and impaired QoL with a higher frequency of hypertension, diabetes, physical disability, and constipation. Mini Nutritional Assessment-Short Form, Groningen Frailty Indicator, and EQ-5D-5L scores, as well as the number of medications used, mostly predicted each other and QoL. Impaired QoL was associated with a higher frequency of frailty, premalnutrition or malnutrition, and polypharmacy, and frailty with a higher frequency of premalnutrition or malnutrition and polypharmacy. At a 1.5-year follow-up, impaired QoL was linked to polypharmacy and frailty at baseline, premalnutrition or malnutrition and polypharmacy were associated with frailty at baseline, and frailty was linked to both premalnutrition or malnutrition and polypharmacy at baseline. Causal mediation analyses showed that frailty mediated the link between polypharmacy and worse QoL and that polypharmacy mediated the link between frailty and worse QoL. CONCLUSIONS: In this large population-based study of older adults, multiple chronic conditions were associated with ≥1 of the investigated geriatric syndromes. Geriatric syndromes were mostly intercorrelated with, and well predictive of, each other and QoL; and causal relationships existed between geriatric syndromes and QoL, with other geriatric syndromes being mediators. The findings might be biased by residual confounding factors. It is important to perform personalized geriatric syndrome assessments stratified by chronic condition; active prevention of, or intervention for, any syndrome might help to reduce the others and improve QoL.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.384
Teacher spread0.333 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations22
Published2024
Admission routes1
Has abstractyes

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