Changes in health inequalities for patients with diabetes among middle-aged and elderly in China from 2011 to 2015

Socioeconomic inequalities in type 2 diabetes mellitus: a study based on a population-based survey in Iran

BACKGROUND

Type 2 diabetes mellitus (T2DM) is the most prevalent form of Diabetes Mellitus (DM), with social and economic determinants significantly influencing its prevalence. This study aimed to analyze the socioeconomic inequalities associated with T2DM in Iran.

METHODS

Data from an observational survey in Iran, titled "Diabetes Care (DiaCare)," were utilized for this study. Socioeconomic inequalities were assessed through variables including Hemoglobin A1C (HbA1c), Fasting Blood Glucose (FBG), and Triple target (HbA1c, blood pressure, LDL-C), using concentration indices (CIs) and a multivariate logistic regression analysis. Individual socioeconomic status (SES) was determined by calculating an asset index using principle component analysis (PCA) based on their properties. Data analysis was conducted using STATA software version 14.

RESULTS

A total of 13,321 participants were included in the study. The CIs were significantly positive for controlled HbA1c (0.0324) and triple target (0.1067), while for controlled FBG, it was 0.0125, although not significant. Among females, the CIs were significantly positive for controlled HbA1c (0.0745), FBG (0.0367), and triple target (0.209). Additionally, in the 45-55 and 65-75 age groups, the CIs were significantly positive for controlled HbA1c (0.0607) and FBG (0.0708), respectively. This index was significant for controlled Triple target in the 35-45 (0.376) and 65-75 (0.124) age groups. The CI for controlled FBG was significant in rural dwellers (-0.044) while the concentration of controlled triple target was significant in urban dwellers (0.0967). Controlled HbA1c showed significant concentration in both urban (0.0306) and rural (-0.0576) dwellers. Furthermore, the CIs were significant for controlled HbA1c in regions with medium prevalence (0.0534) and FBG in regions with low prevalence (-0.0277). This index was significantly positive for controlled triple target in regions with high prevalence (0.124).

CONCLUSIONS

Diabetes care is more concentrated among individuals with higher SES. Policymakers should consider this to mitigate the inequality and alleviate the burden of T2DM.

Trends in the distribution of socioeconomic inequalities in smoking and cessation: evidence among adults aged 18 ~ 59 from China Family Panel Studies data

INTRODUCTION

Cigarette smoking is usually more prevalent among those with a lower socioeconomic status (SES), which can be driven by inequalities in the initiation and cessation of smoking, giving rise to SES disparities in health. This study aimed to gauge the SES inequalities in smoking related behaviours and their evolving trends based on a nationally representative database.

METHOD

Data were extracted from repeated cross-sectional China Family Panel Studies (CFPS) of adults aged ≥18 and <60 years in 2012, 2014, 2016 and 2018. SES was constructed by principal component analysis based on income, education and occupation. Regression-based odds ratios and coefficients as the relative effect index of inequality were applied to quantify the degree of socioeconomic inequality in smoking related behaviours and to adjust for possible confounding factors. Multivariable regressions were utilized to explore the temporal trends in smoking inequalities.

RESULTS

The smoking prevalence among men decreased from 61.16% to 2012 to 57.88% in 2018, cigarette consumption among current smokers declined from 16.71 to 15.49 cigs/per day, and the cessation rate increased from 17.55% to 24.08%. Cigarette consumption for women decreased from 13.39 in 2012 to 11.01 cigs/per day in 2018. Smoking prevalence showed significant SES inequalities among men and women from 2012 to 2018 (men: OR₂₀₁₂ (95%CI)= 0.72 (0.63, 0.83), OR₂₀₁₄ = 0.60 (0.52, 0.69), OR₂₀₁₆ = 0.58 (0.50, 0.67), OR₂₀₁₈ = 0.56 (0.48, 0.66); women: OR₂₀₁₂ = 0.63 (0.41, 0.97), OR₂₀₁₄ = 0.50 (0.32, 0.79), OR₂₀₁₆ = 0.44 (0.26,  0.73), OR₂₀₁₈ = 0.50 (0.30,  0.85)). Cigarette consumption showed significant SES inequalities among men from 2012 to 2018 (β₂₀₁₂=-1.39 (-2.22, -0.57), β₂₀₁₄=-2.37 (-3.23, -1.50), β₂₀₁₆=-2.35 (-3.25, -1.44), β₂₀₁₈=-2.91 (-3.86, -1.97)). In 2018, inequality emerged in smoking cessation rates among men and smoking intensity among women. However, all tests for trends in changes over time were not statistically significant (P varied from 0.072 to 0.602).

CONCLUSION

The smoking prevalence declined between 2012 and 2018 in China. However, SES inequalities in smoking persist, while socioeconomic inequalities in smoking were not alleviated among adults aged 18 ~ 59 in China. Tobacco control measures should be implemented by giving more attention to people with lower SES who are more vulnerable to tobacco use.

Inequalities in the prevalence, diagnosis awareness, treatment coverage and effective control of diabetes: a small area estimation analysis in Iran

OBJECTIVE

This study aims to assess geographic inequalities in the prevalence, awareness of diagnosis, treatment coverage and effective control of diabetes in 429 districts of Iran.

METHODS

A modelling study by the small area estimation method, based on a nationwide cross-sectional survey, Iran STEPwise approach to surveillance (STEPS) 2016, was performed. The modelling estimated the prevalence, awareness of diagnosis, treatment coverage, and effective control of diabetes in all 429 districts of Iran based on data from available districts. The modelling results were provided in different geographical and socio-economic scales to make the comparison possible across the country.

RESULTS

In 2016, the prevalence of diabetes ranged from 3.2 to 19.8% for women and 2.4 to 19.1% for men. The awareness of diagnosis ranged from 51.9 to 95.7% for women and 35.7 to 100% for men. The rate of treatment coverage ranged from 37.2 to 85.6% for women and 24.4 to 80.5% for men. The rate of effective control ranged from 12.1 to 63.6% for women and 12 to 73% for men. The highest treatment coverage rates belonged to Ardebil for women and Shahr-e-kord for men. The highest effective control rates belonged to Sanandaj for women and Nehbandan for men. Across Iran districts, there were considerable differences between the highest and lowest rates of prevalence, diagnosis awareness, treatment coverage, and effective control of diabetes. The concentration indices of diabetes prevalence, awareness of diagnosis, and treatment coverage were positive and significant for both sexes.

CONCLUSION

Findings of this study highlight the existence of inequalities in diagnosis awareness, treatment coverage, and effective control of diabetes in all Iran regions. More suitable population-wide strategies and policies are warranted to handle these inequalities in Iran.

Trends in Socio-economic Inequality in Smoking Among Middle-aged and Older Adults in China: Evidence From the 2011 and 2018 China Health and Retirement Longitudinal Study

INTRODUCTION

Socio-economic inequalities in smoking and related health problems are a public health concern worldwide. To support the development of effective tobacco control policies, this study examines trends in smoking rates according to socio-economic status (SES) in China.

AIMS AND METHODS

We analyzed data from repeated cross-sectional China Health and Retirement Longitudinal Study (CHARLS) on adults aged ≥45 years for the years 2011 and 2018, which involved 16 471 participants in 2011 and 19 367 in 2018. We then estimated the SES of individuals based on four types of wealth-related variables, namely, education, occupation, household characteristics, and durable consumer goods. Principal-component analysis was conducted to measure SES, and the Erreygers normalised concentration index (ECI) was used to calculate socio-economic inequality in current smoking by gender, age, and region.

RESULTS

The overall ECI (95% confidence interval) for women was -0.042 (-0.054 to -0.031) and -0.038 (-0.047 to -0.029) for 2011 and 2018, respectively. The ECI (95% confidence interval) for men was -0.077 (-0.101 to -0.050) and -0.019 (-0.042 to 0.005) for 2011 and 2018, respectively. The inequality in smoking by SES for adults aged < 60 years in the Northeast region increased during 2011-2018, from -0.069 (-0.144 to 0.006) to -0.119 (-0.199 to -0.038) for women and from 0.009 (-0.115 to 0.132) to -0.164 (-0.296 to -0.032) for men.

CONCLUSIONS

smoking inequality by socio-economic among adults aged ≥45 years declined in recent years in China. However, smoking inequality by SES increased in other population groups.

IMPLICATIONS

Our research indicated that socio-economic inequality of current smoking among residents aged 45 years and older declined in 2018 when compared with 2011 numbers, particularly for men aged ≥ 60 years. Women in the Northeast region displayed more significant smoking inequality by SES than women in other regions did. During the study period, there was an increase in inequality in smoking by SES for adults aged < 60 years in the Northeast region. Thus, tobacco control policies and interventions should be targeted at high-risk subpopulations with lower SES, particularly in Northeast China.

Development and validation of the diabetic self-management scale based on information-motivation-behavioral skills theory

Background

Self-management is important for the blood sugar control of middle-aged and elderly Type 2 diabetes mellitus (T2DM) patients, of which diet, exercise, and drug compliance are the most common components. The Information-Motivation-Behavioral Skills Model (IMB) has been widely used in health behavior management and intervention.

Objective

The purpose of this study is to develop and validate the Diabetic Self-Management Scale (DSMS) based on the IMB model.

Methods

Self-report survey data was collected from middle-aged and elderly T2DM patients in Zhongmu City, Henan Province, China in November 2021 using convenience sampling. The original DSMS was developed through a literature review and summary of previous similar scales using an inductive approach. Item modification was finished by a panel of specialists. Exploratory factor analysis and confirmatory factor analysis were used to evaluate the reliability, convergent validity, discriminant validity, and criterion validity of DSMS.

Results

Four hundred and sixty nine T2DM patients completed the questionnaire survey. The final DSMS consists of 22 items with three dimensions, including information (five items), motivation (eight items), and behavior skills (nine items). The results of simple factor analysis showed that the KMO value was 0.839, Bartlett spherical test 2 = 3254.872, P < 0.001. The results of confirmatory factor analysis showed that 2/df = 2.261, RMSEA = 0.073, CFI = 0.937, TLI = 0.930, and SRMR = 0.096. The standardized factor loadings of 22 DSMS items were all above 0.6, and the CR values of 3 dimensions were all higher than 0.9. In addition, DSMS also showed good discriminant and criterion validity.

Conclusion

The 22-item DSMS has good reliability and validity, and can be used to make diabetic self-management assessment regarding diet, physical activity, and medication among middle-aged and elderly Chinese T2DM patients. DSMS is of moderate length and easy to understand. It can be promoted in China in the future to understand the self-management status of middle-aged and elderly T2DM patients in China.

Prevalence and potential risk factors of self-reported diabetes among elderly people in China: A national cross-sectional study of 224,142 adults

Aim

To evaluated the prevalence and potential risk factors of self-reported diabetes among the elderly in China, by demographic data, socioeconomic factors, and psychological factors.

Methods

Descriptive analysis and Chi-square analysis were used to assess the prevalence and variation between self-reported diabetes and non-diabetes by demographic data, living habits, socioeconomic factors and comorbidities. Univariate and multivariate logistic regression were used to describe the odds ratios (OR) of diabetes prevalence in different groups, while stratification analysis was performed to describe prevalence based on gender, age, and urban/rural areas.

Results

215,041 elderly adults (102,692 males and 112,349 females) were eventually included in the analysis. The prevalence of self-reported diabetes among the elderly in China is about 8.7%, with the highest prevalence in Beijing (20.8%) and the lowest prevalence in Xizang (0.9%). Logistic regression analysis showed that urban area (P < 0.001), older age (65-84 years old, P < 0.001), female (P < 0.001), higher income(P < 0.001), poor sleep quality (P = 0.01) and some other factors were potential risk factors for diabetes.

Conclusions

This study illustrates the prevalence and potential risk factors of diabetes among the elderly in China Meanwhile, these results provide information to assist the government in controlling non-communicable diseases in the elderly.

Incidence and risk factors of diabetes mellitus in the Chinese population: a dynamic cohort study

OBJECTIVE

Diabetes mellitus is a common condition often associated with an ageing population. However, only few longitudinal studies in China have investigated the incidence of diabetes and identified its risk factors. Therefore, this study aimed to investigate the incidence and risk factors of diabetes in Chinese people aged ≥45 years using the harmonised China Health and Retirement Longitudinal Study (CHARLS) data.

DESIGN

A dynamic cohort study.

SETTING

The harmonised CHARLS 2011-2018.

PARTICIPANTS

19 988 adults aged ≥45 years.

PRIMARY OUTCOME MEASURE

Incident diabetes from 2011 to 2018.

RESULTS

The harmonised CHARLS is a representative longitudinal survey of people aged ≥45 years. Using data extracted from the harmonised CHARLS, we calculated the incidence of diabetes and used a competing risk model to determine risk factors of diabetes. In 2011-2013, 2013-2015, 2015-2018, the crude incidence of diabetes among middle-aged and older people in China was 1403.21 (1227.09 to 1604.19), 1673.22 (1485.73 to 1883.92) and 3919.83 (3646.01 to 4213.30) per 100 000 person-years, respectively, with a significant increasing trend. There were no geographical variations in the incidence of diabetes. Age, obesity and alcohol consumption were associated with an increased risk of incident diabetes.

CONCLUSION

The incidence of diabetes increased annually, without any geographical differences. Age, obesity and alcohol consumption were found to be risk factors for incident diabetes.

Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China

Diabetes, which is a chronic disease with a high prevalence in people over 45 years old in China, is a public health issue of global concern. In order to explore the spatiotemporal patterns of diabetes among people over 45 years old in China, to find out diabetes risk factors, and to assess its risk, we used spatial autocorrelation, spatiotemporal cluster analysis, binary logistic regression, and a random forest model in this study. The results of the spatial autocorrelation analysis and the spatiotemporal clustering analysis showed that diabetes patients are mainly clustered near the Beijing−Tianjin−Hebei region, and that the prevalence of diabetes clusters is waning. Age, hypertension, dyslipidemia, and smoking history were all diabetes risk factors (p < 0.05), but the spatial heterogeneity of these factors was weak. Compared with the binary logistic regression model, the random forest model showed better accuracy in assessing diabetes risk. According to the assessment risk map generated by the random forest model, the northeast region and the Beijing−Tianjin−Hebei region are high-risk areas for diabetes.

Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective cohort study was conducted using the health screening data of adults older than 65 years in Wuhan, China from 2018 to 2020. With a strict data filtration, 127,031 records from the eligible participants were utilized. Overall, 8298 participants were diagnosed with incident T2DM during the 2-year follow-up (2019–2020). The dataset was randomly split into training set (n = 101,625) and test set (n = 25,406). We developed prediction models based on four ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Using LASSO regression, 21 prediction features were selected. The Random under-sampling (RUS) was applied to address the class imbalance, and the Shapley Additive Explanations (SHAP) was used to calculate and visualize feature importance. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The XGBoost model achieved the best performance (AUC = 0.7805, sensitivity = 0.6452, specificity = 0.7577, accuracy = 0.7503). Fasting plasma glucose (FPG), education, exercise, gender, and waist circumference (WC) were the top five important predictors. This study showed that XGBoost model can be applied to screen individuals at high risk of T2DM in the early phrase, which has the strong potential for intelligent prevention and control of diabetes. The key features could also be useful for developing targeted diabetes prevention interventions.