Sociodemographic factors associated with knowledge of type 2 diabetes in rural Tamil Nadu, India
Notice bibliographique
Résumé
INTRODUCTION: This study aimed to investigate awareness of type 2 diabetes and how sociodemographic factors influence diabetes knowledge in a rural population of Tamil Nadu, India. Previous research has identified poor awareness of diabetes in several low and middle-income countries, which can lead to a high prevalence of undiagnosed diabetes. India having the second highest prevalence of diabetes globally, it is increasingly important to assess how diabetes can be addressed in rural Indian populations. METHODS: Systematic random sampling was used to gather study participants in 17 villages within the Krishnagiri district of Tamil Nadu, India. Data on diabetes knowledge was collected using a validated questionnaire. Knowledge score range was 0-8; a score of zero was designated as 'low knowledge', scores 1-4 as 'moderate knowledge', and scores 5-8 as 'good knowledge'. Associations between sociodemographic factors and composite diabetes knowledge score were assessed using a multinomial logistic GLLAMM model in Stata. RESULTS: A total of 753 individuals participated in the study. The average age of participants was 47 years and 55% were women. Overall awareness of diabetes was low, with 66% of individuals having no knowledge of diabetes. Only 16% and 17% achieved a moderate and a good knowledge score, respectively. Achieving a moderate knowledge score was significantly positively associated with education, wealth, participation in the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), and business ownership as a source of income. Achieving a good knowledge score was significantly positively associated with education, wealth, rurality, participation in MGNREGA, business ownership as a source of income, and frequency of healthcare utilization. Rurality was significantly negatively associated (relative risk ratio (95% confidence interval)) with both moderate knowledge score (0.34 (0.19-0.59)), and good knowledge score (0.43 (0.24-0.74)). The strongest predictor of having a good knowledge score was having a high-school graduate or post-secondary education (11.07 (4.44-27.61)). Enrolment in MGNREGA employment was the strongest predictor for having a moderate knowledge score (3.27 (1.93-5.54)), as well as strongly associated with having a good knowledge score (2.39 (1.31-4.36)). CONCLUSION: The low awareness of diabetes among participants of this study raises serious concerns for public health in India. Public health efforts must prioritize health equity to lessen the impacts of diabetes in rural populations, where individuals face systemic barriers to receiving prevention and treatment for conditions such as diabetes.
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Comment cette classification a été obtenuedéplier
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».