Prevalence, Awareness, Treatment, and Control of Type 2 Diabetes in South Korea (1998 to 2022): Nationwide Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: Type 2 diabetes poses an increasing disease burden in South Korea. The development and management of type 2 diabetes are closely related to lifestyle and socioeconomic factors, which have undergone substantial changes over the past few decades, including during the COVID-19 pandemic. OBJECTIVE: This study aimed to investigate long-term trends in type 2 diabetes prevalence, awareness, treatment, and control. It also aimed to determine whether there were substantial alterations in the trends during the pandemic and whether these changes were more pronounced within specific demographic groups. METHODS: This study examined the prevalence, awareness, treatment, and control of type 2 diabetes in a representative sample of 139,786 South Koreans aged >30 years, using data from the National Health and Nutrition Examination Survey and covering the period from 1998 to 2022. Weighted linear regression and binary logistic regression were performed to calculate weighted β coefficients or odds ratios. Stratified analyses were performed based on sex, age, region of residence, obesity status, educational background, household income, and smoking status. β (difference) was calculated to analyze the trend difference between the prepandemic period and the COVID-19 pandemic. To identify groups more susceptible to type 2 diabetes, we estimated interaction terms for each factor and calculated weighted odds ratios. RESULTS: From 1998 to 2022, a consistent increase in the prevalence of type 2 diabetes was observed among South Koreans, with a notable rise to 15.61% (95% CI 14.83-16.38) during the pandemic. Awareness followed a U-shaped curve, bottoming out at 64.37% (95% CI 61.79-66.96) from 2013 to 2015 before increasing to 72.56% (95% CI 70.39-74.72) during the pandemic. Treatment also increased over time, peaking at 68.33% (95% CI 65.95-70.71) during the pandemic. Control among participants with diabetes showed no substantial change, maintaining a rate of 29.14% (95% CI 26.82-31.47) from 2020 to 2022, while control among treated participants improved to 30.68% (95% CI 27.88-33.48). During the pandemic, there was a steepening of the curves for awareness and treatment. However, while the slope of control among participants being treated increased, the slope of control among participants with diabetes showed no substantial change during the pandemic. Older populations and individuals with lower educational level exhibited less improvement in awareness and control trends than younger populations and more educated individuals. People with lower income experienced a deceleration in prevalence during the pandemic. CONCLUSIONS: Over the recent decade, there has been an increase in type 2 diabetes prevalence, awareness, treatment, and control. During the pandemic, a steeper increase in awareness, treatment, and control among participants being treated was observed. However, there were heterogeneous changes across different population groups, underscoring the need for targeted interventions to address disparities and improve diabetes management for susceptible populations.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».