Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 479

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study

Open access
Fan, Junning, Yu, Canqing, Guo, Yu, Bian, Zheng, Sun, Zhijia, Yang, Ling, Chen, Yiping, Du, Huaidong, Li, Zhongxiao, Lei, Yulong, Sun, Dianjianyi, Clarke, Robert, Chen, Junshi, Chen, Zhengming, Lv, Jun, Li, Liming·2020·The Lancet Public Health

Abstract

BackgroundThe fraily index is a useful proxy measure of accelerated biological ageing and in estimating all-cause and cause-specific mortality in older individuals in European and US populations. However, the predictive value of the frailty index in other populations outside of Europe and the USA and in adults younger than 50 years is unknown. We aimed to examine the association between the frailty index and mortality in a population of Chinese adults.MethodsIn this prospective cohort study, we used data from the China Kadoorie Biobank. We included adults aged 30–79 years from ten areas (five urban areas and five rural areas) of China who had no missing values for the items that made up the frailty index. We did not exclude participants on the basis of baseline morbidity status. We calculated the follow-up person-years from the baseline date to either the date of death, loss to follow-up, or Dec 31, 2017, whichever came first, through linkage with the registries of China's Disease Surveillance Points system and local residential records. Active follow-up visits to local communities were done annually for participants who were not linked to any established registries. Causes of death from official death certificates were supplemented, if necessary, by reviewing medical records or doing standard verbal autopsy procedures. The frailty index was calculated using 28 baseline variables, all of which were health status deficits measured by use of questionnaires and physical examination. We defined three categories of frailty status: robust (frailty index ≤0·10), prefrail (frailty index >0·10 to <0·25), and frail (frailty index ≥0·25). The primary outcomes were all-cause mortality and cause-specific mortality in Chinese adults aged 30–79 years. We used a Cox proportional hazards model to estimate the associations between the frailty index and all-cause and cause-specific mortality, adjusting for chronological age, education, and lifestyle factors.Findings512 723 participants, recruited between June 25, 2004, and July 15, 2008, were followed up for a median of 10·8 years (IQR 10·2–13·1; total follow-up 5 551 974 person-years). 291 954 (56·9%) people were categorised as robust, 205 075 (40·0%) people were categorised as prefrail, and 15 694 (3·1%) people were categorised as frail. Women aged between 45 years and 79 years had a higher mean frailty index and a higher prevalence of frailty than did men. During follow-up, 49 371 deaths were recorded. After adjustment for established and potential risk factors for death, each 0·1 increment in the frailty index was associated with a higher risk of all-cause mortality (hazard ratio [HR] 1·68, 95% CI 1·66–1·71). Such associations were stronger among younger adults than among older adults (pinteraction<0·0001), with HRs per 0·1 increment of the frailty index of 1·95 (95% CI 1·87–2·03) for those younger than 50 years, 1·80 (1·76–1·83) for those aged 50–64 years, and 1·56 (1·53–1·59) for those 65 years and older. After adjustments, there was no difference between the sexes in the association between the frailty index and all-cause mortality (pinteraction=0·75). For each 0·1 increment of the frailty index, the corresponding HRs for risk of death were 1·89 (95% CI 1·83–1·94) from ischaemic heart disease, 1·84 (1·79–1·89) from cerebrovascular disease, 1·19 (1·16–1·22) from cancer, 2·54 (2·45–2·63) from respiratory disease, 1·78 (1·59–2·00) from infection, and 1·78 (1·73–1·83) from all other causes.InterpretationThe frailty index is associated with all-cause and cause-specific mortality independent of chronological age in younger and older Chinese adults. The identification of younger adults with accelerated ageing by use of surrogate measures could be useful for the prevention of premature death and the extension of healthy active life expectancy.FundingThe National Natural Science Foundation of China, the National Key R&D Program of China, the Chinese Ministry of Science and Technology, the Kadoorie Charitable Foundation, and the Wellcome Trust.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/s2468-2667(20)30113-4

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Metadata

Authors
Fan, J., Yu, C., Guo, Y., Bian, Z., Sun, Z., Yang, L., Chen, Y., Du, H., Li, Z., Lei, Y., Sun, D., Clarke, R., Chen, J., Chen, Z., Lv, J., & Li, L.
Venue
The Lancet Public Health
Publisher
Elsevier BV
Published
2020-12
Volume
5
Issue
12
Pages
e650-e660
ISSN
2468-2667 (ISSN-L)
OpenAlex
W3110280394
Cited by
479
References
3
FWCI
13.31
Type
journal article
Language
en
Open access
gold

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study. https://app.cochat.ai/papers/10.1016/s2468-2667(20)30113-4
The paper
Fan, J., Yu, C., Guo, Y., Bian, Z., Sun, Z., Yang, L., Chen, Y., Du, H., Li, Z., Lei, Y., Sun, D., Clarke, R., Chen, J., Chen, Z., Lv, J., & Li, L. (2020). Frailty index and all-cause and cause-specific mortality in Chinese adults: a prospective cohort study. The Lancet Public Health. https://doi.org/10.1016/s2468-2667(20)30113-4

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.