Diabetes in Shenzhen, China: epidemiological investigation and health care challenges

Yan, Xiaoxiao; Xia, Hui; Li, Haitao; Deng, Xiaojuan; Yang, Lizhen; Zhao, Shaojuan; Zou, Jianfeng; Luo, Yi; Cao, Sijing

doi:10.7189/jogh.07.011102

Cited by 10 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the variables of predisposing factors from the Andersen model, age was the only significant variable associated with diabetes diagnosis, in particular, old seniors aged ≥ 75 increased risk odds of undiagnosed diabetes. Although previous studies have shown that prevalence of diabetes [4,12,13] including undiagnosed diabetes [13,39,40] increased with age, the effect of age on health service utilization was ambiguous [41], what may hinder the elderly in accessing health service utilization and diagnosis is worth further exploring. Some evidence showed that gender, education level and marriage status were related to health service utilization [41], but not in this study.…”

Section: Discussionmentioning

confidence: 98%

“…In China, the prevalence of diabetes presented a noticeable rise as well [9][10][11]. The results from a national survey [4] indicated that diabetes had already reached epidemic proportions in the general adult population, in particular, the prevalence of undiagnosed diabetes and pre-diabetes were underestimated and would become a major threat to public health in the near future if no measure was taken [12]. Therefore, studies of undiagnosed diabetes are of importance to early diagnosis and the reduction in complications of diabetes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predictors of Undiagnosed Diabetes among Middle-Aged and Seniors in China: Application of Andersen’s Behavioral Model

Mou

Lyu

2021

IJERPH

View full text Add to dashboard Cite

Undiagnosed diabetes is a threat to public health. This study aims to identify potential variables related to undiagnosed diabetes using Andersen’s behavioral model. Baseline data including blood test data from the China Health and Retirement Longitudinal Study (CHARLS) were adopted. First, we constructed health service related variables based on Andersen model. Second, univariate analysis and multiple logistic regression were used to analyze the relations of variables to undiagnosed diabetes. The strength of relationships was presented by odds ratios (ORs) and 95% confidence intervals (CIs). Finally, the prediction of multiple logistic regression model was assessed using the Receiver Operating Characteristic (ROC) curve and the area under the ROC curve (AUC). According to diagnosis standards, 1234 respondents had diabetes, among which 560 were undiagnosed and 674 were previously diagnosed. Further analysis showed that the following variables were significantly associated with undiagnosed diabetes: age as the predisposing factor; medical insurance, residential places and geographical regions as enabling factors; having other chronic diseases and self-perceived health status as need factors. Moreover, the prediction of regression model was assessed well in the form of ROC and AUC. Andersen model provided a theoretical framework for detecting variables of health service utilization, which may not only explain the undiagnosed reasons but also provide clues for policy-makers to balance health services among diverse social groups in China.

show abstract

Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Predictors of Undiagnosed Diabetes among Middle-Aged and Seniors in China: Application of Andersen’s Behavioral Model

Mou

Lyu

2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Currently, there are an estimated 114 million people with diabetes in China ( 1 ). According to statistics in 2013, about a quarter of diabetes-related deaths worldwide occurred in China ( 2 ). China has experienced one of the largest increases in the prevalence of diabetes globally and the significant increase in the prevalence of diabetes is largely attributable to type 2 diabetes (T2D) ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

Glycemic control and its influencing factors in type 2 diabetes patients in Anhui, China

Xing

Wang²,

Fang³

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

ObjectiveTo investigate the status of glycemic control and analyze its influencing factors in patients with type 2 diabetes (T2D) in Anhui, China.Methods1,715 T2D patients aged 18–75 years old were selected from 4 counties or districts in Anhui Province in 2018, using a convenience sampling method. All patients have undergone a questionnaire survey, physical examination, and a glycosylated hemoglobin (HbA1c) test. According to the 2022 American Diabetes Association criteria, HbA1c was used to evaluate the glycemic control status of patients, and HbA1c < 7.0% was defined as good glycemic control. The influencing factors of glycemic control were analyzed by multivariate unconditional logistic regression.ResultsThe prevalence of good glycemic control among people with T2D in the Anhui Province was low (22.97%). On univariate analysis, gender, education level, occupation, region, smoking, drinking, waist circumference and disease duration (all P < 0.05) were significantly associated with glycemic control. The factors associated with pool glycemic control were female gender [OR = 0.67, 95%CI (0.52, 0.86), P = 0.001], higher level of education [OR = 0.47, 95%CI (0.27, 0.83), P = 0.001], living in rural areas [OR = 1.77, 95%CI (1.39, 2.26), P < 0.001], central obesity [OR = 1.58, 95%CI (1.19, 2.09), P = 0.001] and longer duration of disease [OR = 2.66, 95%CI (1.91, 3.69), P < 0.001].ConclusionsThe prevalence of good glycemic control in people with T2D in Anhui Province was relatively low, and gender, region, education level, central obesity and course of the disease were influencing factors. The publicity and education on the importance of glycemic control should be further strengthened in T2D patients, and targeted intervention measures should be carried out for risk groups.

show abstract

“…China has experienced a rapid increase in obesity rates in the past three decades, with more than one-fifth of school-age children being overweight or obese [ 14 ]. Shenzhen, a city in southern China, has experienced very rapid economic development since the middle of the 20th century and is now a national economic center, representing one of the most developed regions in China [ 15 ]. In Shenzhen, the prevalence of overweight and obesity among school-aged children (mean age 10.3 years) was estimated at 20.2% during 2016–2017 [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of Exposure to Paternal Smoking on Overweight and Obesity in Children: Findings from the Children Lifeway Cohort in Shenzhen, Southern China

You¹,

Liu²,

Hua³

et al. 2022

Obes Facts

View full text Add to dashboard Cite

Introduction: Paternal smoking associated with childhood overweight and obesity has been a concern, but studies have not investigated smoking exposure and smoking details. We investigated the association of exposures from paternal smoking as well as smoking details on offspring overweight/obesity. Methods: A total of 4513 children (aged 7-8 years) in Shenzhen were enrolled. Four different exposures from paternal smoking as well as smoking quantity, duration of smoking and age of starting smoking details were the exposure variables and demographic characteristics, circumstances of birth, dietary intake, lifestyle and nonpaternal smoking exposure were covariates in logistic regression analysis to determine the effect of paternal smoking on childhood overweight/obesity, estimating odds ratios (ORs) and 95% confidence intervals (CIs). Results: Paternal smoking was positively associated with childhood overweight/obesity (p<0.05). Moreover, only pre-conception exposure, and both pre- and post-conception exposure were significantly associated with childhood overweight/obesity (OR 1.54 (95% CI 1.14-2.08) and OR 1.73 (95% CI 1.14-2.61) respectively), restricted to boys but not girls. Furthermore, for children with only pre-conception paternal-smoking exposure, the dose–response relation was positive between smoking quantity, duration of smoking, age at starting and overweight/obesity for boy offspring (P trend <0.001). We did not find any significant association between only post-natal exposure to paternal smoking and childhood overweight/obesity (P>0.05). Conclusions: Our findings suggest that paternal smoking is associated with boys’ overweight/obesity, and this association may due to the paternal smoking exposure before conception rather than the post-natal exposure to paternal smoking. Reducing paternal smoking exposure before conception might help reduce overweight/obesity in boys.

show abstract

Diabetes in Shenzhen, China: epidemiological investigation and health care challenges

Cited by 10 publications

References 21 publications

Predictors of Undiagnosed Diabetes among Middle-Aged and Seniors in China: Application of Andersen’s Behavioral Model

Predictors of Undiagnosed Diabetes among Middle-Aged and Seniors in China: Application of Andersen’s Behavioral Model

Glycemic control and its influencing factors in type 2 diabetes patients in Anhui, China

Effect of Exposure to Paternal Smoking on Overweight and Obesity in Children: Findings from the Children Lifeway Cohort in Shenzhen, Southern China

Contact Info

Product

Resources

About