Objectives To evaluate the prevalence and determinants of anxiety and depression and to assess their impact on glycaemic control in participants with type 2 diabetes mellitus. Design A cross-sectional study. Setting Community-based investigation in Xuzhou, China. Participants 893 Chinese men and women aged 18–84 years who fulfilled the inclusion criteria. Methods People with type 2 diabetes completed the Pittsburgh Sleep Quality Index and the Zung Self-Rating Anxiety and Depression Scales. Demographic and physiological characteristics were recorded. Multiple logistic regression was used to evaluate the combined effect of factors associated with anxiety and depression and to assess the effects of anxiety and depression on glycaemic control. Results The prevalence of depressive symptoms and anxiety symptoms was 56.1% and 43.6%, respectively. Multivariate logistic regression analysis indicated that anxiety symptoms were associated with being woman, low income, chronic disease, depressive symptoms and poor sleep quality. Depressive symptoms were associated with being woman, older age, low education level, being single, diabetes complications, anxiety symptoms and poor sleep quality. Glycaemic control was not related to anxiety symptoms (OR=1.31, 95% CIs 0.94 to 1.67) or depressive symptoms (OR=1.23, 95% CI 0.85 to 1.63). A combination of depressive symptoms and anxiety symptoms was associated with poor glycaemic control (relative excess risk due to interaction: 4.93, 95% CI 2.09 to 7.87; attributable proportion due to interaction: 0.27, 95% CI 0.12 to 0.45). Conclusions There was a high prevalence of depressive and anxiety symptoms in this Chinese sample of participants, although depression and anxiety were not singly associated with glycaemic control. However, a combination of depressive and anxiety symptoms was negatively correlated with glycaemic control in participants with type 2 diabetes.

The increasing prevalence of colorectal cancer (CRC) in China and the paucity of information about relevant expenditure highlight the necessity of better understanding the financial burden and effect of CRC diagnosis and treatment. We performed a survey to quantify the direct medical and non-medical expenditure as well as the resulting financial burden of CRC patients in China. We conducted a multicenter, cross-sectional survey in 37 tertiary hospitals in 13 provinces across China between 2012 and 2014. Each enrolled patient was interviewed using a structured questionnaire. All expenditure data were inflated to the 2014 Chinese Yuan (CNY; 1 CNY = 0.163 USD). We quantified the overall expenditure and financial burden and by subgroup (hospital type, age at diagnosis, sex, education, occupation, insurance type, household income, clinical stage, pathologic type, and therapeutic regimen). We then performed generalized linear modeling to determine the factors associated with overall expenditure. A total of 2356 patients with a mean age of 57.4 years were included, 57.1% of whom were men; 13.9% of patients had stage I cancer; and the average previous-year household income was 54,525 CNY. The overall average direct expenditure per patient was estimated to be 67,408 CNY, and the expenditures for stage I, II, III, and IV disease were 56,099 CNY, 59,952 CNY, 67,292 CNY, and 82,729 CNY, respectively. Non-medical expenditure accounted for 8.3% of the overall expenditure. The 1-year out-of-pocket expenditure of a newly diagnosed patient was 32,649 CNY, which accounted for 59.9% of their previous-year household income and caused 75.0% of families to suffer an unmanageable financial burden. Univariate analysis showed that financial burden and overall expenditure differed in almost all subgroups (P < 0.05), except for sex. Multivariate analysis showed that patients who were treated in specialized hospitals and those who were diagnosed with adenocarcinoma or diagnosed at a later stage were likely to spend more, whereas those with a lower household income and those who underwent surgery spent less (all P < 0.05). For patients in China, direct expenditure for the diagnosis and treatment of CRC seemed catastrophic, and non-medical expenditure was non-ignorable. The financial burden varied among subgroups, especially among patients with different clinical stages of disease, which suggests that, in China, CRC screening might be cost-effective.

Poor sleep quality and depression negatively impact the health-related quality of life of patients with type 2 diabetes, but the combined effect of the two factors is unknown. This study aimed to assess the interactive effects of poor sleep quality and depression on the quality of life in patients with type 2 diabetes. Patients with type 2 diabetes (n = 944) completed the Diabetes Specificity Quality of Life scale (DSQL) and questionnaires on sleep quality and depression. The products of poor sleep quality and depression were added to the logistic regression model to evaluate their multiplicative interactions, which were expressed as the relative excess risk of interaction (RERI), the attributable proportion (AP) of interaction, and the synergy index (S). Poor sleep quality and depressive symptoms both increased DSQL scores. The co-presence of poor sleep quality and depressive symptoms significantly reduced DSQL scores by a factor of 3.96 on biological interaction measures. The relative excess risk of interaction was 1.08. The combined effect of poor sleep quality and depressive symptoms was observed only in women. Patients with both depressive symptoms and poor sleep quality are at an increased risk of reduction in diabetes-related quality of life, and this risk is particularly high for women due to the interaction effect. Clinicians should screen for and treat sleep difficulties and depressive symptoms in patients with type 2 diabetes.

Objectives To explore the interactions of sleep quality and sleep duration and their effects on impaired fasting glucose (IFG) in Chinese adults. Design Cross-sectional survey. Setting Community-based investigation in Xuzhou, China. Participants 15 145 Chinese men and women aged 18–75 years old who fulfilled the inclusion criteria. Primary and secondary outcome measures The Pittsburgh Sleep Quality Index was used to produce sleep quality categories of good, common and poor. Fasting blood glucose levels were assessed for IFG. Sleep duration was measured by average hours of sleep per night, with categories of 8 h. The products of sleep and family history of diabetes, obesity and age were added to the logistic regression model to evaluate the addictive interaction and relative excess risk of interaction (RERI) on IFG. The attributable proportion (AP) of the interaction and the synergy index (S) were applied to evaluate the additive interaction of two factors. Bootstrap measures were used to calculate 95% CI of RERI, AP and S. Results The prevalence of IFG was greatest in those with poor sleep quality and short sleep duration (OR 6.37, 95% CI 4.66 to 8.67; p<0.001) compared with those who had good sleep quality and 6–8 h sleep duration, after adjusting for confounders. After adjusting for potential confounders RERI, AP and S values (and their 95% CI) were 1.69 (0.31 to 3.76), 0.42 (0.15 to 0.61) and 2.85 (2.14 to 3.92), respectively, for the interaction between poor sleep quality and short sleep duration, and 0.78 (0.12 to 1.43), 0.61 (0.26 to 0.87) and −65 (−0.94 to −0.27) for the interaction between good sleep quality and long sleep duration. Conclusions The results suggest that there are additive interactions between poor sleep quality and short sleep duration.