Prevalence and risk factors of kidney stone disease in population aged 40–70 years old in Kharameh cohort study: a cross-sectional population-based study in southern Iran
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
BACKGROUND: Kidney stone is the major cause of morbidity, and its prevalence is increasing in the world. This study aimed to assess the prevalence and risk factors of kidney stone in the adult population of southern Iran based on the data of the Kharameh Cohort Study. METHODS: This cross-sectional study was conducted on 10,663 individuals aged 40-70 years old, using the baseline data of Kharamah cohort study, which started in 2014. Among all participants, 2251 individuals had a history of kidney stone. The participants' demographic characteristics, behavioral habits, and the history of underlying diseases were investigated. The crude and Age Standardized Prevalence Rate of kidney stones was calculated. Also, logistic regression was used to identify the predictors of kidney stone. To check the goodness of fit index of the model, we used the Hosmer-Lemeshow test. All analyses were performed in STATA software. RESULTS: The prevalence of kidney stone was estimated 21.11%. Also, the Age Standardized Prevalence Rate in men and women was calculated 24.3% and 18.7%, respectively. The mean age of the participants was 52.15 years. Higher prevalence of kidney stone was seen in women aged 40-50 years (40.47%, p = 0.0001) and moderate level of social economic status (31.47%, p = 0.03), men with overweight (44.69%, p < 0.0001) and those in a very high level of social economic status (35.75%, p = 0.001). The results of multiple logistic regression showed that the chance of having kidney stone was 1.17 times higher in diabetic individuals, 1.43 times higher in hypertensive individuals, 2.21 times higher in individuals with fatty liver, and 1.35 times higher in individuals with overweight. The level of socio economic status, male sex, and age were the other factors related to kidney stone. CONCLUSION: In this study, underlying diseases such as fatty liver, diabetes, and hypertension as well as age, male sex, overweight, and high social economic status were identified as important risk factors for kidney stone. Therefore, identifying individuals at risk of kidney stone and providing the necessary training can greatly help to reduce this disease. However, health policymakers should prepare preventive strategies to reduce the occurrence of kidney stone.
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.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 découle