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Record W2914910089 · doi:10.1111/jgs.15793

Elderly People With Disabilities in China

2019· letter· en· W2914910089 on OpenAlexaboutno aff
Wei Ling, Yi Huang, Zhao Hai‐Lu

Bibliographic record

VenueJournal of the American Geriatrics Society · 2019
Typeletter
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsnot available
FundersDivision of Graduate EducationEducation Department of Guangxi Zhuang Autonomous RegionNational Natural Science Foundation of China
KeywordsMedicinePopulationGerontologyCensusConfidence intervalDemographyChinaStratified samplingCluster samplingMainland ChinaEnvironmental health

Abstract

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Over a billion people, comprising 15% of the world's population, have some form of disability.1 The number of disabilities is continuing to grow due to population aging and increased accidents and chronic disorders.2 China has the largest population of elderly people3; however, studies on disabilities in Chinese elderly people have rarely been reported. Here, we report nationwide disabilities of Chinese elderly people. We obtained data from the 2006 National Survey on Disability. The survey used multistage, stratified, random cluster sampling of the noninstitutionalized 2.5 million population of mainland China, with probability proportional to size, to derive nationally representative samples.4 The sample cases in each age group matched with the population structure based on the 2005 estimation. Disabilities were determined by trained interviewers who used the International Classification of Functioning, Disability and Health5 to inquire about visual, hearing, and speech disability, physical or intellectual disability, and mental disability, as previously described by Zheng et al.4 Those who have two or more kind of disabilities were defined as multiple disability. Total number and prevalence of disabilities in elderly people aged 60 years or older in 2010 were standardized using the general rates of the 2010 National Population Census.6 Disabilities were identified in 85,260 elderly individuals (40,321 men, 47.3%) among the 354,859 sample elderly population (171,903 men, 48.4%) surveyed, indicating prevalence of 240 per 1000 elderly individuals and significantly higher prevalence in women (24.6% vs 23.5% in men; P < .001; 95% confidence interval = 1.03-1.06). Overall, the prevalence of disabilities increased from 12.4% among elderly individuals aged 60 to 64 years to 55.9% among elderly individuals aged 85 years or older (all P < .001). Table 1 shows the number and prevalence of the elderly individuals included in this study. Among the disabilities in elderly individuals, the most prevalent disabilities were hearing loss of 8.3%, physical disability of 6.1%, visual disability of 4.6%, followed by multiple disabilities of 3.9%, mental disability of 0.7%, intellectual disability of 0.3%, and speech disability of 0.1%. Elderly women showed higher prevalence of visual disability, mental disability, and multiple disability, while elderly men had higher prevalence of hearing loss and speech disability (Table 1). Predominant risk factors were presbycusis (72.5%) and tympanitis (9.4%) for hearing loss, cataracts (68.4%) and retinopathy and pigment choroidopathy (12.9%) for visual loss, cerebrovascular disease (31.5%) and osteoarthritis (27.0%) for physical disability, cerebral infarction (40.1%) for speech disability, brain disease (57.4%) for intellectual disability, and schizophrenia (35.0%) and dementia (34.5%) for mental disability. The 2010 National Census disclosed 177 million (13.3%) people aged 60 years or older, including 118 million (8.9%) people aged 65 years or older, in mainland China. Accordingly, elderly people with disabilities were an estimated 42.7 million, including 14.8 million with hearing loss, 10.7 million with physical disability, 8.2 million with visual disability, 7.0 million with multiple disability, 1.2 million with mental disability, half million with intellectual disability, and 300,000 with speech disability. In this study, we demonstrate that over half (51.5%) of the disabled population (82.96 million) in mainland China were people older than 60 years. This situation may become more serious in the future due to population aging. Typically, the prevalence of disability will grow due to aging; the World Health Organization estimated that 46.1% of the world populations older than 60 years are affected by moderate or severe disability.1 It is known that China has the largest elderly population than any other countries; according to the China 2010 census,6 the number of people aged 60 years and older was 177 million, accounting for 13.3% of the whole population. Old people with disabilities required more healthcare and clinical needs than those without disabilities. Given the graduated escalation of aging population worldwide, the burden of disabilities in global elderly individuals will be more severe in the coming years. For the first time, we report an overview of disabled elderly individuals in mainland China, albeit underestimation might be likely since the prevalence of disability in 2010 was derived by the general rate in 2006. Nevertheless, this survey had been conducted by trained medical staffs using standardized questionnaires to obtain disabled conditions, such as presbycusis, to avoid any potential bias deliberately inherited by self-reporting approaches. The report of this national survey with the large sample size is valuable for disability care in elderly individuals. We are grateful to Dr. Ray Wiss, Professor in the Department of Emergency Medicine, Northern Ontario School of Medicine, for his critical comments and corrections. Financial Disclosure: This study was supported by the National Natural Science Foundation of China (81471054) and the Innovation Project of Guangxi Graduate Education (JGY2015128). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Conflicts of Interest: The authors have no conflicts of interest to report. Author Contributions: All three authors made substantial contributions to the manuscript in terms of design; acquisition, analysis, and interpretation of data; drafting the article; and approval of the final version. Sponsor's Role: None.

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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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations23
Published2019
Admission routes1
Has abstractyes

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