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Enregistrement W2914910089 · doi:10.1111/jgs.15793

Elderly People With Disabilities in China

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

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2019
Typeletter
Langueen
DomaineSocial Sciences
ThématiqueIntergenerational Family Dynamics and Caregiving
Établissements canadiensnon disponible
Organismes subventionnairesDivision of Graduate EducationEducation Department of Guangxi Zhuang Autonomous RegionNational Natural Science Foundation of China
Mots-clésMedicinePopulationGerontologyCensusConfidence intervalDemographyChinaStratified samplingCluster samplingMainland ChinaEnvironmental health

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,097
Score d'incertitude au seuil0,711

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,007
Tête enseignante GPT0,249
Écart entre enseignants0,242 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations23
Publié2019
Routes d'admission1
Résumé présentoui

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