Determinants of self-rated health among shanghai elders: a cross-sectional study
Notice bibliographique
Résumé
As the most populous nation in the world, China has now becoming an emerging ageing society. Shanghai is the first city facing the challenge of ageing demographics. Against this background, a study that employs self-rated health (SRH) assessment system was designed to explore the health status of Shanghai elders, and learn their attitudes toward health issues; as well as to investigate the determinants of SRH among Shanghai elders. Understanding SRH is crucial for finding appropriate solutions that could effectively tackle the increasing eldercare demand. This study adopted a quantitative research strategy. Using a multistage stratified cluster sampling method, we conducted a questionnaire survey in August 2011 in Shanghai, which collected 2001 valid survey responses. SRH assessments were categorized by five levels: very good, fairly good, average, fairly poor, or poor. The respondents’ functional status was evaluated using the Barthel index of activities for daily living. In the data analysis, we used chi-squared test to determine differences in socio-demographic characteristics among various groups. Along with statistics, several logistic regression models were designed to determine the associations between internal influence factors and SRH. Younger age (χ 2 = 27.5, p < 0.05), male sex (χ 2 = 11.5, p < 0.1), and living in the suburbs (χ 2 = 55.1, p < 0.05) were associated with better SRH scores. Higher SRH scores were also linked with health behaviour of the respondents; namely, do not smoke (χ 2 = 18.0, p < 0.1), do not drink (χ 2 = 18.6, p < 0.1), or engage in regular outdoor activities (χ 2 = 69.3, p < 0.05). The respondents with better social support report higher SRH scores than those without. Respondents’ ability to hear (χ 2 = 38.7, p < 0.05), speak (χ 2 = 16.1, p < 0.05) and see (χ 2 = 78.3, p < 0.05) impacted their SRH scores as well. Meanwhile, chronic illness except asthma was a major influence factor in low SRH score. Applying multiple regression models, a series of determinants were analysed to establish the extent to which they contribute to SRH. The impact of these variables on SRH scores were 6.6% from socio-demographic and health risk behaviours, 2.4% from social support, 8.5% from mental health, 20% from physical conditions, and13% from chronic diseases. This is the first study that examines the determinants of SRH among Shanghai elders. Nearly 40% of our study’s respondents reported their health status as “good”. The main determinants of SRH among elders include living condition, health risk behaviour, social support, health status, and the economic status of the neighbourhood.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,005 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».