Knowledge, attitudes and practices of patients with chronic pharyngitis toward laryngopharyngeal reflux in Suzhou, China

Zhang, Qiumin; Huang, Haiping; Li, Jiachen; Niu, Yuyu; Sun, Peng; Cheng, Fuwei

doi:10.1186/s12889-023-17463-0

Cited by 1 publication

(1 citation statement)

References 24 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interestingly, the phenomenon where higher income offers protection against dysglycemia in rural residents, while middle income is associated with an increased likelihood of dysglycemia, could be attributed to lifestyle habits. Higher-income enables better access to healthcare, nutritious foods, and wellness resources, alongside more opportunities for PA and health screenings [ 60 , 61 ]. Conversely, middle-income individuals might afford diets high in meat and processed foods, increasing the likelihood of dysglycemia, unlike lower-income counterparts who may consume more plant-based, high-fiber diets due to financial constraints [ 62 , 63 ].…”

Section: Discussionmentioning

confidence: 99%

Key influences on dysglycemia across Fujian’s urban-rural divide

Lin,

Hu,

Liu

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Background Screening and treatment of dysglycemia (prediabetes and diabetes) represent significant challenges in advancing the Healthy China initiative. Identifying the crucial factors contributing to dysglycemia in urban-rural areas is essential for the implementation of targeted, precise interventions. Methods Data for 26,157 adults in Fujian Province, China, were collected using the Social Factors Special Survey Form through a multi-stage random sampling method, wherein 18 variables contributing to dysglycemia were analyzed with logistic regression and the random forest model. Objective Investigating urban-rural differences and critical factors in dysglycemia prevalence in Fujian, China, with the simultaneous development of separate predictive models for urban and rural areas. Result The detection rate of dysglycemia among adults was 35.26%, with rates of 34.1% in urban areas and 35.8% in rural areas. Common factors influencing dysglycemia included education, age, BMI, hypertension, and dyslipidemia. For rural residents, higher income (OR = 0.80, 95% CI [0.74, 0.87]), average sleep quality (OR = 0.89, 95% CI [0.80, 0.99]), good sleep quality (OR = 0.89, 95% CI [0.80, 1.00]), and high physical activity (PA) (OR = 0.87, 95% CI [0.79, 0.96]) emerged as protective factors. Conversely, a daily sleep duration over 8 hours (OR = 1.46, 95% CI [1.03, 1.28]) and middle income (OR = 1.12, 95% CI [1.03, 1.22]) were specific risk factors. In urban areas, being male (OR = 1.14, 95% CI [1.02, 1.26]), cohabitation (OR = 1.18, 95% CI [1.02, 1.37]), and central obesity (OR = 1.35, 95% CI [1.19, 1.53]) were identified as unique risk factors. Using logistic regression outcomes, a random forest model was developed to predict dysglycemia, achieving accuracies of 75.35% (rural) and 76.95% (urban) with ROC areas of 0.77 (rural) and 0.75 (urban). Conclusion This study identifies key factors affecting dysglycemia in urban and rural Fujian residents, including common factors such as education, age, BMI, hypertension, and dyslipidemia. Notably, rural-specific protective factors are higher income and good sleep quality, while urban-specific risk factors include being male and central obesity. These findings support the development of targeted prevention and intervention strategies for dysglycemia, tailored to the unique characteristics of urban and rural populations.

show abstract