Association between regional deprivation and type 2 diabetes incidence in Germany

Metabolic syndrome and spatial disparities: The role of socioeconomic deprivation and medical resource availability in the Cijin district, Taiwan

Metabolic syndrome (MetS) is a global health concern with spatial disparities, especially in disadvantaged neighborhoods. This study aimed to examine the association between area-level socioeconomic deprivation, the availability of medical resources in disadvantaged areas such as the Cijin district, and the prevalence of MetS in Taiwan. We used two representative secondary cross-sectional datasets, including physical examinations and lifestyle surveys from 2016 to 2020, sourced from the Taiwan Biobank and the Cijin District Adult Lifestyle and Health Survey. Our findings indicate that residing in the Cijin district, characterized by socioeconomic deprivation and limited medical resources, is associated with significantly higher odds of MetS (aOR = 1.45, 95% CI = 1.28-1.64, p <0.001). Additionally, living in areas with medium (aOR = 1.12, 95% CI = 1.07-1.17, p <0.001) and high area-level socioeconomic deprivation indexes (aOR = 1.13, 95% CI = 1.01-1.25, p <0.001) is linked to a higher likelihood of MetS. Conversely, residing in high medical resource availability index areas is associated with a lower risk of MetS (aOR = 0.92, 95% CI = 0.86-0.99, p = 0.026). We found a link between socioeconomic deprivation and limited medical resources, especially in disadvantaged areas such as the Cijin district, contributing to a higher MetS risk. Residents in these areas often struggle to access healthcare and preventive care. Addressing these disparities requires comprehensive public health initiatives and targeted policy interventions to improve residents' well-being.

Mendelian randomization analysis revealed that albuminuria is the key factor affecting socioeconomic status in CKD patients

Previous studies indicate a strong correlation between the incidence of chronic kidney disease (CKD) and lower economic status. However, these studies often struggle to delineate a clear cause-effect relationship, leaving healthcare providers uncertain about how to manage kidney disease in a way that improves patients' financial outcomes. Our study aimed to explore and establish a causal relationship between CKD and socioeconomic status, identifying critical influencing factors. We utilized summary meta-analysis data from the CKDGen Consortium and UK Biobank. Genetic variants identified from these sources served as instrumental variables (IVs) to estimate the association between CKD and socioeconomic status. The presence or absence of CKD, estimated glomerular filtration rate (eGFR), and albuminuria were used as exposures, while income and regional deprivation were analyzed as outcomes. We employed the R packages 'TwoSampleMR' and 'Mendelianrandomization' to conduct both univariable and multivariable Mendelian randomization (MR) analyses, assessing for potential pleiotropy and heterogeneity. Our univariable MR analysis revealed a significant causal relationship between high levels of albuminuria and lower income (OR = 0.84, 95% CI: 0.73-0.96, p = 0.013), with no significant pleiotropy detected. In the multivariable MR analysis, both CKD (OR = 0.867, 95% CI: 0.786-0.957, p = 0.0045) and eGFR (OR = 0.065, 95% CI: 0.010-0.437, p = 0.0049) exhibited significant effects on income. This study underscores that higher albuminuria levels in CKD patients are associated with decreased income and emphasizes the importance of effective management and treatment of albuminuria in CKD patients to mitigate both social and personal economic burdens.

Latent classes of learners in people with type 2 diabetes, stratified by educational status: A cross-sectional study

OBJECTIVE

Research suggests that people with type 2 diabetes (PWT2D) exhibit different approaches to learning about disease-management. This study's aims to identify distinct learner groups among PWT2D and stratify them by educational status (ES).

METHODS

Cross-sectional data from 227 PWT2D, collected through 46 Likert-scale questions on learning behaviors, preferences, and attitudes, were analyzed using latent class analysis, to identify learner groups. Participants were recruited via healthcare practices in central Germany and a countrywide online survey. Group membership was displayed according to low, medium, and high ES, defined by years of schooling.

RESULTS

Four learner groups were identified: casual, versatile, insecure, and theorist learners. Insecure learners accounted for almost half of all respondents in the low ES group (46 %), casual learners were most prevalent among PWT2D with a medium (27 %), versatile (34 %) and theorist (29 %) learners among those with a high ES.

CONCLUSION

This study sheds light on learner groups among PWT2D, which differ by ES, suggesting social disparities in diabetes care. Further research is needed to validate these findings.

PRACTICE IMPLICATIONS

Understanding individual learning preferences and motivations is crucial for developing effective diabetes self-management trainings, which may involve providing additional background material for theorists and practical applications for insecure learners.

Health disparities and inequalities in prevalence of diabetes in the Kingdom of Saudi Arabia

BACKGROUND

Over time, global health systems have witnessed significant improvements in the delivery and coverage of healthcare services. Nevertheless, the increasing prominence of non-communicable diseases remains a persistent challenge. Diabetes is one such non-communicable chronic disease that poses a threat with respect to both mortality and morbidity. This study investigated the socio-economic determinants and inequalities in the prevalence of diabetes in the Kingdom of Saudi Arabia according to data collected from the 2018 Saudi Family Health Survey conducted by the General Authority for Statistics.

METHODS

The analysis was limited to a sample of 11,528 respondents aged ≥ 18 years, selected across all 13 regions of Saudi Arabia, with complete responses for all variables of interest. Socio-economic determinants in diabetes prevalence were explored with univariate, bivariate, and multivariate logistic regression analyses. Furthermore, inequalities were visualised and quantitatively estimated according to construction of a concentration curve and calculation of the concentration index.

RESULTS

The prevalence of diabetes among the 11,528 respondents was 11.20%. Age, education, income, and residence area were significant determinants of diabetes prevalence, with a greater risk of diabetes found in older participants (odds ratio [OR]: 12.262, 95% confidence interval [CI]: 9.820-15.313, p < 0.01) compared to younger participants. Inequality analysis showed a negative education-based concentration index (-0.235, p < 0.01), indicating that diabetes prevalence is concentrated among people with relatively less formal education. For males, the income-based concentration index was significantly positive, whereas the education-based concentration index was significantly negative, indicating a greater concentration of diabetes among Saudi men with higher incomes and less education.

CONCLUSION

These findings emphasize the need to prioritize policies and strategies for diabetes prevention and control with considerations of the socio-economic inequalities in prevalence. Key areas of focus should include improving education levels across all regions, raising awareness about diabetes and implementing nutritional interventions.

A Scoping Review of Possible Solutions for Decreasing Socioeconomic Inequalities in Type 2 Diabetes Mellitus

Background

As socioeconomic inequalities are key factors in access and utilization of type 2 diabetes (T2D) services, the purpose of this scoping review was to identify solutions for decreasing socioeconomic inequalities in T2D.

Methods

A scoping review of scientific articles from 2000 and later was conducted using PubMed, Web of Science (WOS), Scopus, Embase, and ProQuest databases. Using the Arksey and O'Malley framework for scoping review, articles were extracted, meticulously read, and thematically analyzed.

Results

A total of 7204 articles were identified from the reviewed databases. After removing duplicate and nonrelevant articles, 117 articles were finally included and analyzed. A number of solutions and passways were extracted from the final articles. Solutions for decreasing socioeconomic inequalities in T2D were categorized into 12 main solutions and 63 passways.

Conclusions

Applying identified solutions in diabetes policies and interventions would be recommended for decreasing socioeconomic inequalities in T2D. Also, the passways could be addressed as entry points to help better implementation of diabetic policies.

Incidence of type 1 and type 2 diabetes before and during the COVID-19 pandemic in Germany: analysis of routine data from 2015 to 2021

BACKGROUND

To date, there is no data available depicting the trend of the incidence of type 1 and type 2 diabetes across all age groups for the COVID-19 pandemic years in Germany.

METHODS

Based on anonymized routine data from nine million persons covered by statutory health insurance, newly diagnosed diabetes cases (ICD diagnosis E10.- to E14.-) in inpatient or (confirmed in two quarters) outpatient setting were estimated for 2015 to 2021, differentiating between type 1 and type 2 diabetes. The data were linked to the German Index of Socioeconomic Deprivation. The results are age-standardised (population as of 31 Dec. 2021).

RESULTS

Between 2015 and 2021, the incidence of type 1 diabetes increased from 9.5 to 11.6 per 100,000 persons (from 7,007 to 8,699 new cases per year). In contrast, the incidence of type 2 diabetes tended to decline between 2015 and 2019. It continued to drop initially in 2020 during the pandemic, and then rose to 740 per 100,000 persons in 2021 (556,318 new cases per year). The diabetes type-specific seasonal pattern of previous years has changed during the pandemic years. The incidence of both type 1 and type 2 diabetes was observed to be higher in regions of high socioeconomic deprivation as compared to regions characterised by low socioeconomic deprivation.

CONCLUSIONS

The increase in the incidence of type 1 and type 2 diabetes in 2021 may possibly be related to the COVID-19 pandemic. The high incidence and the differences by regional socioeconomic deprivation indicate that there is a need for targeted prevention strategies.

Cumulative Risk Evaluation of Phthalates Under TSCA

The U.S. Environmental Protection Agency (EPA) is currently conducting separate Toxic Substances Control Act (TSCA) risk evaluations for seven phthalates: dibutyl phthalate (DBP), butyl benzyl phthalate (BBP), di(2-ethylhexyl) phthalate (DEHP), diisobutyl phthalate (DIBP), dicyclohexyl phthalate (DCHP), di-isodecyl phthalate (DIDP), and diisononyl phthalate (DINP). Phthalates are highly abundant plastic additives used primarily to soften materials and make them flexible, and biomonitoring shows widespread human exposure to a mixture of phthalates. Evidence supports biological additivity of phthalate mixture exposures, including the enhancement of toxicity affecting common biological targets. Risk estimates based on individual phthalate exposure may not be protective of public health. Thus, a cumulative risk approach is warranted. While EPA initially did not signal that it would incorporate cumulative risk assessment (CRA) as part of its current risk evaluation for the seven phthalates, the agency recently announced that it is reconsidering if CRA for phthalates would be appropriate. Based on our review of existing chemical mixtures risk assessment guidance, current TSCA scoping documents for the seven phthalates, and pertinent peer-reviewed literature, we delineate a CRA approach that EPA can easily implement for phthalates. The strategy for using CRA to inform TSCA risk evaluation for existing chemicals is based upon integrative physiology and a common adverse health outcome algorithm for identifying and grouping relevant nonchemical and chemical stressors. We recommend adjustments for how hazard indices (HIs) or margins of exposure (MOEs) based on CRA are interpreted for determining "unreasonable risk" under TSCA.

Prediction of diabetes prescription volumes of various geographies using regression techniques

Background: Increasing diabetes prevalence is a major public health concern. In this study we ask whether linked open data can be used to predict prescription volumes of drugs used in the treatment of diabetes across small geographies of England. Methods: We propose and demonstrate a methodology of utilising publicly available open data to infer the geo-spatial distribution of prescribed drugs for diabetes, at the lower layer super output area level. Multiple datasets are acquired, processed, and linked together, enabling a more in-depth analysis. Combining these linked datasets with published deprivation factors of geographies across England, we build highly predictive regression models. Results: Regression models were trained and are capable of accurately predicting diabetes prescribing volumes based on deprivation indicators of various geographies across England. Models built with data covering the city of Bradford, England, produced a predicted against actual correlation value of R2 = 0.672 using multiple linear regression and 0.775 using Least Absolute Shrinkage and Selection Operator (LASSO). Median age and air quality factors proved to be significant markers for diabetes prescribing. Conclusions: The results of this study suggest our methodology is robust and accurate. Such predictive models are useful to health authorities in light of increasing costs and increasing prevalence of diabetes. While using publicly available open data negates any issues of data privacy.

Lifestyle and socioeconomic determinants of diabetes: Evidence from country-level data

Objective

The objectives of the study is to investigate the global socioeconomic risk factors associated with diabetes prevalence using evidence from available country-level data.

Design

A cross-sectional study based on (2010 & 2019) countrywide Health Nutrition and Population Statistics data.

Population

People ages 20–79 who have diabetes.

Setting

One hundred and thirty-two countries or territories in the world.

Primary outcome measure

Diabetes prevalence rates were determined from (2010 & 2019) countrywide Health Nutrition and Population Statistics (Health Stats, World Bank Group).

Results

In 2010, a 1% increase in per capita income and total tobacco consumption is associated with a 0.92% (95% CI 0.64% to 1.19%) and 0.02% (95% CI 0.006% to 0.047%) increase in diabetes prevalence respectively; and a 1% increase in alcohol consumption is associated with a -0.85% (95% CI -1.17% to -0.53%) decrease in diabetes prevalence. Statistically significant socioeconomic and lifestyle indices positively associated with diabetes prevalence included gross national income; overweight prevalence (BMI>25 kg/m²); and tobacco consumption. Statistically significant inverse associations with global diabetes prevalence included total population size; unemployment and alcohol consumption. The 2019 data was removed due to sparsity of data.

Conclusion

Statistically significant global lifestyle and socioeconomic determinants of diabetes prevalence include alcohol consumption; tobacco consumption; overweight prevalence; per capita income; total population and unemployment rates. Determinants of diabetes include modifiable risk factors which are consistent at both the micro and macro level and include tobacco consumption and overweight prevalence. Factors which are non-modifiable and warrant further investigation include total population and unemployment rates, which were inversely associated with diabetes prevalence and are a product of other underlying factors. Other determinants such as alcohol consumption was also inversely associated with diabetes prevalence, but has been observed to have both negative and positive associations with diabetes at the micro-level. These associations were dependent upon the amount of alcohol consumed. Global cut-off point of alcohol consumption is critical to establish global policies to reduce diabetes prevalence. Overall, the use of cross-sectional based study for country level aggregate data is a critical tool that should be considered when making global joint strategies or policies against diabetes in both data analysis and decision making.

Diabetogene Umweltfaktoren

Das Risiko, an einem Diabetes mellitus Typ 2 zu erkranken, wird maßgeblich von Merkmalen unserer physischen und sozialen Umwelt beeinflusst. Diese Umweltfaktoren wirken sich insbesondere auf das Ausmaß individueller körperlicher Aktivität sowie das Ernährungsverhalten aus. Weiterhin zählen die Exposition gegenüber Tabakrauch sowie Luftverschmutzung zu den etablierten Risikofaktoren. In den letzten Jahren rückten darüber hinaus weitere mögliche diabetogene Umweltaspekte in den Fokus der Forschung, darunter Umgebungslärm und andere stressfördernde Einflüsse sowie Chemikalien mit endokriner Wirkung. Im folgenden Beitrag wird der Forschungsstand zur Rolle von Umweltfaktoren bei der Entstehung eines Typ-2-Diabetes vorgestellt. Zudem wird diskutiert, wie diese Umweltfaktoren günstig beeinflusst werden können und was DiabetologInnen und andere Diabetesfachkräfte sowie PatientInnen mit Diabetes hierzu beitragen können.

Associations between socioeconomic deprivation and pharmaceutical prescribing in primary care in England

BACKGROUND

Socioeconomic deprivation is associated with health inequality. Previous studies have described associations between primary care prescribing rates and deprivation for individual drugs or drug classes. We explore the correlation between socioeconomic deprivation and the rate of prescribing of individual pharmaceutical drugs, and drug classes, in primary care in England, to identify prescribing inequalities that would require further investigation.

METHODS

In this cross-sectional study, national primary care prescribing data, by primary care practice, were retrieved for the calendar year 2019 in England. Socioeconomic deprivation was quantified using the Index of Multiple Deprivation (IMD) score. Correlations were calculated using Spearman's rank correlation coefficient (ρ), adjusting for practice list size and demographics, with a Bonferroni-corrected p value threshold of 5×10^-5.

RESULTS

We included 1.05 billion prescription items dispensed from 6896 England practices. 142/206 (69%) drug classes and 505/774 (65%) drugs were significantly correlated with IMD score (p<5×10^-5). Of the 774 included drugs, 31 (4%) were moderately positively associated with IMD score (ρ>0.4). Only one was moderately negatively correlated with IMD score (ρ<-0.4), suggesting higher prescribing rates in more affluent areas. The drug classes most strongly associated with IMD score included opioid and non-opioid analgesics, antipsychotics and reflux medications. Drug classes most strongly associated with affluence included epinephrine, combined oral contraceptives and hormone replacement therapy.

CONCLUSION

We identify novel associations of prescribing with deprivation. Further work is required to identify the underlying reasons for these associations so that appropriate interventions can be formulated to address drivers of inequality.