Changes in the severity and lethality of age-related health deficit accumulation in the USA between 1999 and 2018: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: With an ageing population, the number of people with frailty is increasing. Despite this trend, the extent to which the severity and lethality of frailty have changed over time is not well understood. We aimed to investigate how frailty severity and lethality have changed over an 18-year period in the USA. METHODS: In this population-based observational study, we used data from the National Health and Nutrition Examination Survey (NHANES) to identify community-dwelling individuals (aged ≥20 years) in the USA between 1999 and 2018. We analysed data from a series of ten 2-year, nationally representative, cross-sectional, prospective studies (from 1999-2000 to 2017-18) from the NHANES. Frailty was measured by use of the deficit accumulation approach (ie, a 46-item frailty index). The proportion of individuals categorised as non-frail, or living with very mild frailty, mild frailty, moderate frailty, and severe frailty were compared across cohorts. Random-effects models were used to examine the association between frailty index score and sex, age, and cohort. Mortality status as of Dec 31, 2015, was ascertained by use of National Death Index data, and 5-year mortality was available in the first six cohorts (1999-2010). Cox regression models and Kaplan-Meier curves were used to estimate the association between frailty index scores and mortality. FINDINGS: In total, 49 004 individuals were included in our study. Associations were mainly non-linear (quadratic), with frailty increasing at a faster rate in more recent cohorts. Between 1999 and 2018, the proportion of non-frail individuals decreased by 10·4% (from 2747 [63·8%; 95% CI 61·9-65·6] of 4307 to 2884 [53·4%; 51·3-55·5] of 5399), whereas the proportion of individuals with very mild frailty increased by 2·4% (from 987 [22·9%; 21·3-24·6] to 1365 [25·3%; 23·5-27·2]), by 2·7% (from 370 [8·6%; 7·7-9·6] to 609 [11·3%; 10·1-12·5]) in those with mild frailty, by 3·1% (from 140 [3·3%; 2·7-3·9] to 347 [6·4%; 5·6-7·4]) in those with moderate frailty, and by 2·1% (from 63 [1·5%; 1·1-1·9] to 195 [3·6%; 3·0-4·3]) in those with severe frailty. Being a woman, older, and from a more recent cohort were associated with higher frailty index scores (all p<0·0001). In more recent cohorts, mean frailty index scores increased more quickly with age (p<0·0001), and sex differences in mean frailty index scores decreased (p<0·0001). In men of all ages and in women aged 35 years or older, mean frailty index scores were higher in more recent cohorts, with larger increases in frailty in older age groups. In 28 692 individuals from the first six cohorts (1999-2000 to 2009-10) with linked mortality data, frailty index scores were significantly associated with mortality (hazard ratio 1·053 [95% CI 1·050-1·057] per 0·01 increase in frailty index score). The absence of an interaction between cohort and frailty index score (p=0·58) suggested that the association between frailty and mortality was similar for all cohorts. INTERPRETATION: Increasing frailty levels in more recent cohorts of middle-aged and older adults combined with stable frailty lethality between 1999 and 2018, suggest a challenge to healthy longevity, with the proportion of individuals with a high degree of frailty continuing to increase. FUNDING: Supported in part by the Canadian Institutes of Health Research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".