Assessment of factors affecting diabetes management in the City Changing Diabetes (CCD) study in Tianjin.

OBJECTIVE: This study aimed to identify the local levels of vulnerability among patients with Type-II diabetes (T2DM) in Tianjin. The study was aimed at curbing the rise of T2DM in cities. METHODS: 229 participants living with T2DM were purposively sampled from hospitals in Tianjin. Collected data were coded and analysed following well-established thematic analysis principles. RESULTS: Twelve themes involving 29 factors were associated with diabetes patients' vulnerability: 1. Financial constraints (Low Income, Unemployment, No Medical Insurance/Low ratio reimbursement); 2. Severity of disease (Appearance of symptoms, complications, co-morbidities, high BMI, poor disease control); 3. Health literacy (No/Low/Wrong knowledge of health literacy); 4. Health beliefs (Perceived diabetes indifferently, Passively Acquire Health Knowledge, Distrust of primary health services); 5. Medical environment (Needs not met by Medical Services); 6. Life restrictions (Daily Life, Occupational Restriction); 7. Lifestyle change (Adhering to traditional or unhealthy diet, Lack of exercise, Low-quality sleep); 8. Time poverty (Healthcare-seeking behaviours were limited by work, Healthcare-seeking behaviours were limited by family issues); 9. Mental Condition (Negative emotions towards diabetes, Negative emotions towards life); 10. Levels of Support (Lack of community support, Lack of support from Friends and Family, Lack of Social Support); 11. Social integration (Low Degree of Integration, Belief in Suffering Alone); 12. Experience of transitions (Diet, Dwelling Environment). CONCLUSION: Based on our findings, specific interventions targeting individual patients, family, community and society are needed to improve diabetes control, as well as patients' mental health care and general living conditions.

Related factors of quality of life of type 2 diabetes patients: a systematic review and meta-analysis.

BACKGROUND: Diabetes is a chronic disease, and it could affect both health and quality of life (QOL). A lot of studies have reported some predictors of QOL of type 2 diabetes patients. While their results were not completely consistent. So the aim of our study was finding out the related factors (including characteristics related to the disease, life styles and mental health factors) of QOL of type 2 diabetes patients. METHODS: We searched Cochrane library, EmBase, PubMed and CNKI databases for published studies that evaluated the related factors of QOL of type 2 diabetes patients by using a proper statistic method and had effect sizes (OR or ß) and 95% confidence intervals from January 1st 2000 to May 31st 2016. Any study types were acceptable, and we excluded the reviews, letters, editorials and pooled analyses. The data were analyzed using STATA software (Version 12.0; Stata Corporation). Effect sizes and 95% confidence intervals were calculated to evaluate the relationship between these factors and QOL. RESULTS: Eighteen studies were included into our systematic review and meta-analysis, totaling 57,109 type 2 diabetes patients. Do more physical exercises (The pooled ORs ranged from 0.635 to 0.825 for different scales, less than 1.00), glucose check more frequently [pooled OR (95%CI): 0.175 (0.041, 0.756)] were associated with a better QOL. Presence of complications (The pooled ORs ranged from 1.462 to 3.038 for different scales, more than 1.00), presence of hypertension [pooled OR (95%CI): 1.389 (1.173, 1644)], longer duration of diabetes [pooled OR (95%CI): 1.865 (1.088, 3.197)], diet with more red meat [pooled OR (95%CI): 2.085 (1.063, 4.089)] and depression (The pooled ORs ranged from 3.003 to 11.473 for different scales, higher than 1.00) were associated with a worse QOL. CONCLUSION: The results of this study show that physical exercise, glucose check frequently, complications, hypertension, duration of diabetes, diet with more red meat, and depression were associated with the QOL of type 2 diabetes patients.

Identifying Patterns of Lifestyle Behaviors among People with Type 2 Diabetes in Tianjin, China: A Latent Class Analysis.

INTRODUCTION: Lifestyle behaviors are essential elements of diabetes care. The aims of this study were to identify distinct subgroups of people with type 2 diabetes based on personal levels of lifestyle behaviors and explore the different characteristics across these subgroups. METHODS: In 2015 and 2016, 1504 outpatients with a diagnosis of type 2 diabetes were selected via two-stage simple random sampling from 10 municipal district hospitals in Tianjin. Participants accepted an invitation by experienced physicians to complete a questionnaire containing demographic and lifestyle content. Clinical data were collected by reviewing medical records. Latent class analysis was applied to identify patterns of lifestyle behaviors. Multinomial logistic regression was used to investigate the characteristics of the subgroups. RESULTS: The final model yielded a four-class solution: the healthy behavioral group, unhealthy diet and less activity group, smoking and drinking group, and sedentary and extremely inactive group. Further analysis found that variables, including age, sex, general/central obesity, treatment modalities, glycemic control, diabetes duration, and diabetes-related complications and comorbidities, were disproportionately distributed across the four latent classes (P < 0.05). Participants in the unhealthy diet and less activity group were more likely to have a longer duration of diabetes, poor glycemic control and more diabetes-related diseases relative to the other three latent classes. CONCLUSIONS: Identification and characterization of subgroups based on lifestyle behaviors in individuals with type 2 diabetes can help health care providers to shift to targeted intervention strategies.

Latent class analysis suggests four classes of persons with type 2 diabetes mellitus based on complications and comorbidities in Tianjin, China: a cross-sectional analysis.

The aim of this study was to explore a new classification way in persons with type 2 diabetes mellitus based on complications and comorbidities using Latent Class Analysis, moreover, finding out the factors associated with different latent classes and making specific suggestions. In this study, 5,500 patients with type 2 diabetes mellitus from ten hospitals in Tianjin, China were selected, and the response rate was 96.2%. Latent Class Analysis was used to cluster patients. After compared the baseline characteristics, multinomial logistic regression was applied. Patients with type 2 diabetes mellitus were classified into four classes. In the univariate analysis, all variables were significant (p<0.05). According to multinomial logistic regression, we found longer duration of type 2 diabetes mellitus, family history of diabetes, older age, obesity and central obesity, female menopause, living in a suburb, having a higher 2hPG at diagnosis, smoking and drinking were associated with the prevalence of complications and comorbidities. In conclusion, LCA was shown to be an effective method for grouping patients with T2DM, which presented a nuanced approach to data reduction. Further research using LCA may be especially useful to investigate causal relationships between complications and the significant factors identified in our study.