Interplay of regional economic development, income, gender and type 2 diabetes: evidence from half a million Chinese

The effects of economic status on metabolic control in type 2 diabetes mellitus at 10 metabolic management centers in China

OBJECTIVE

This study investigated the association of economic status with metabolic index control in type 2 diabetes mellitus (T2DM) patients.

METHODS

In total, 37 454 T2DM patients from 10 National Metabolic Management Centers in China were recruited and categorized into two groups: a high-gross domestic product (GDP) group (n = 23 993) and a low-GDP group (n = 13 461). Sociodemographic characteristics, medical histories, and lifestyle factors were recorded. Logistic regression and interaction analysis were performed to evaluate the association of economic status and healthy lifestyle with metabolic control.

RESULTS

Compared to the low-GDP group, there were fewer patients with glycated hemoglobin (HbA1c) levels ≥7% in the high-GDP group. Fewer patients with a high GDP had an abnormal metabolic state (HbA1c ≥ 7%, blood pressure [BP] ≥130/80 mm Hg, total cholesterol [TCH] ≥4.5 mmol/L or body mass index [BMI] ≥24 kg/m2 ). The risks of developing HbA1c ≥ 7% (odds ratios [OR] = 0.545 [95% CI: 0.515-0.577], p < .001), BP ≥ 130/80 mm Hg (OR = 0.808 [95% CI: 0.770-0.849], p < .001), BMI ≥ 24 kg/m2 (OR = 0.840 [95% CI: 0.799-0.884], p < .001), and an abnormal metabolic state (OR = 0.533 [95% CI: 0.444-0.636], p < .001) were significantly lower in the high-GDP group even after adjustment for confounding factors. Younger participants; those with a family history of diabetes, normal weight, and a physical activity level up to standard; and those who did not drink alcohol in the high-GDP group were predisposed to better glycemic levels.

CONCLUSIONS

T2DM patients in economically developed regions had better metabolic control, especially glycemic control. A healthy lifestyle had an additive effect on achieving glycemic goals, even among high-GDP patients.

Projected rapid growth in diabetes disease burden and economic burden in China: a spatio-temporal study from 2020 to 2030

Background

This study projects the trend of disease burden and economic burden of diabetes in 33 Chinese provinces and nationally during 2020-2030 and investigates its spatial disparities.

Methods

Time series prediction on the prevalence and disability-adjusted life-year (DALY) rates of diabetes was conducted using a Bayesian modelling approach in 2020-2030. The top-down method and the human capital method were used to predict the direct and indirect costs of diabetes for each Chinese province. Global and local spatial autocorrelation analyses were used to identify geographic clusters of low-or high-burden areas.

Findings

Diabetes prevalence in Chinese adults aged 20-79 years was projected to increase from 8.2% to 9.7% during 2020-2030. During the same period, the total costs of diabetes would increase from $250.2 billion to $460.4 billion, corresponding to an annual growth rate of 6.32%. The total costs of diabetes as a percentage of GDP would increase from 1.58% to 1.69% in China during 2020-2030, suggesting a faster growth in the economic burden of diabetes than China's economic growth. Consistently, the per-capita economic burden of diabetes would increase from $231 to $414 in China during 2020-2030, with an annual growth rate of 6.02%. High disease and economic burden areas were aggregated in Northeast and/or North China.

Interpretation

Our study projects a significant growth of disease and economic burden of diabetes in China during 2020-2030, with strong spatial aggregation in northern Chinese regions. The increase in the economic burden of diabetes will exceed that of GDP.

Funding

National Natural Science Foundation of China, Outstanding Young Scholars Funding.

Influencing Indicators and Spatial Variation of Diabetes Mellitus Prevalence in Shandong, China: A Framework for Using Data-Driven and Spatial Methods

To control and prevent the risk of diabetes, diabetes studies have identified the need to better understand and evaluate the associations between influencing indicators and the prevalence of diabetes. One constraint has been that influencing indicators have been selected mainly based on subjective judgment and tested using traditional statistical modeling methods. We proposed a framework new to diabetes studies using data-driven and spatial methods to identify the most significant influential determinants of diabetes automatically and estimated their relationships. We used data from diabetes mellitus patients' health insurance records in Shandong province, China, and collected influencing indicators of diabetes prevalence at the county level in the sociodemographic, economic, education, and geographical environment domains. We specified a framework to identify automatically the most influential determinants of diabetes, and then established the relationship between these selected influencing indicators and diabetes prevalence. Our autocorrelation results showed that the diabetes prevalence in 12 Shandong cities was significantly clustered (Moran's I = 0.328, p < 0.01). In total, 17 significant influencing indicators were selected by executing binary linear regressions and lasso regressions. The spatial error regressions in different subgroups were subject to different diabetes indicators. Some positive indicators existed significantly like per capita fruit production and other indicators correlated with diabetes prevalence negatively like the proportion of green space. Diabetes prevalence was mainly subjected to the joint effects of influencing indicators. This framework can help public health officials to inform the implementation of improved treatment and policies to attenuate diabetes diseases.

The Association between Socioeconomic Factors and Visual Function among Patients with Age-Related Cataracts

Background

With the development of the economy, socioeconomic factors, such as inequalities in the status of regional economies and the subsequent effects on health systems, have influenced the status of health. We explored the association between age-related cataracts and socioeconomic indicators, including the regional economy, health systems, and energy industries.

Methods

This was a prospective, multicenter, Chinese population-based, cross-sectional study. A total of 830 participants from seven centers were enrolled. Data on the best-corrected visual acuity (BCVA), Lens Opacities Classification System III (LOCS III) score, Visual Function Index-14 (VF-14) score, total and subscale scores of the 25-item National Eye Institute Visual Functioning Questionnaire (NEI-VFQ-25), per capita disposable income (PCDI), medical resource-related indicators, and investments in the energy industry were obtained. Associations among these parameters were analyzed.

Results

The PCDI ranking was correlated with the VF-14 score (R = -0.426, P < 0.01), total score of NEI-VFQ-25 (r = -0.500, P < 0.01), and BCVA (r = 0.278, P < 0.01). The number of health agencies (r1 = 0.267, r2 = -0.303, r3 = -0.291,), practicing or assistant practicing doctors (r1 = -0.283, r2 = 0.427, r3 = 0.502,), registered nurses (r1 = -0.289, r2 = 0.409, r3 = 0.469, P < 0.01), and health technicians (r1 = -0.278, r2 = 0.426, r3 = 0.500, P < 0.01) per 10,000 of the population was each correlated with the BCVA, VF-14 score, and total score of NEI-VFQ-25, respectively. Health expenditure per capita was correlated with the VF-14 score (r = 0.287, P < 0.01) and total score of NEI-VFQ-25 (r = 0.459, P < 0.01). The LOCS III P score was correlated with investments in the energy industry (r = 0.485, P < 0.001).

Conclusions

Patients in higher economic regions with greater medical resources show a greater demand to undergo cataract surgery at a better subjective and objective visual function. The energy industry has a significant effect on cataracts, especially the posterior subcapsular cataract, and thus more attention should be paid to people in regions with abundant energy industries.

The Play of Genes and Non-genetic Factors on Type 2 Diabetes

Diabetes has been a disease of public health concern for a number of decades. It was in the 1930s when scientists made an interesting discovery that the disease is actually divided into two types as some patients were insensitive to insulin treatment then. Type 2 Diabetes which happens to be the non-insulin dependent one is the most common form of the disease and is caused by the interaction between genetic and non-genetic factors. Despite conflicting results, numerous studies have identified genetic and non-genetic factors associated with this common type of diabetes. This review has summarized literature on some genes and non-genetic factors which have been identified to be associated with Type 2 diabetes. It has sourced literature from PubMed, Web of Science and Medline without any limitation to regions, publication types, or languages. The paper has started with the introduction, the play of non-genetic factors, the impact of genes in general, and ended with the interaction between some genes and environmental factors.