Search

Abstract 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.

Background:During the last decade, Germany has seen an increased prevalence and a redistribution from undetected to diagnosed diabetes mellitus. Due to this substantial epidemiological development, the number of people with documented type 2 diabetes was 8.7 million in 2022. An estimated two million undiagnosed subjects are to be added. Beyond that, the life expectancy of diabetic subjects is increasing due to more responsive health systems in terms of care. Possible reasons include improved screening of at-risk individuals, the introduction of HbA1c for diagnosis in 2010, and the higher use of risk scores. Additionally, quality aspects of the laboratory methodology should be taken into consideration.Methods:Epidemiology and clinical management of diabetes in Germany are presented in the light of publications retrieved by a selective search of the PubMed database. Additionally, the data from German external quality assessment (EQA) surveys for the measurands glucose in plasma and HbA1c in whole blood, reviewed from 2010 until 2022, were evaluated. Above this, data concerning the analytical performance of near-patient glucometer devices, according to the ISO norm 15197:2013, were analyzed.Results:Two laboratory aspects are in good accordance with the observation of an increase in the diabetes mellitus prevalence when retrospectively reviewing the period 2010 to 2022: First, the analytical performance according to the ISO norm 15197:2013 of the glucometer devices widely used by patients with diabetes for the glucose self-testing, has improved during this period. Secondly, concerning the EQA program of INSTAND, the number of participating laboratories raised significantly in Germany. The spreads of variations of the specified results for plasma glucose remained unchanged between 2010 and 2022, whereas for HbA1c a significant decrease of the result scattering could be observed.Conclusion:These retrospectively established findings testify to an excellent analytical quality of laboratory diagnostics for glucose and HbA1c throughout Germany which may be involved in a better diagnosis and therapy of previously undetected diabetes mellitus.

Healthcare's evolution amid rapid scientific progress demands the efficient management of extensive data. This has given rise to electronic health passports, which play a crucial role in modern healthcare by providing secure access to comprehensive medical histories. This study, exemplified by diabetes, addresses the mounting global prevalence of this disease, which saw cases soar from 108 million in 1980 to 422 million by 2014, resulting in a 5 % rise in diabetes-related premature mortality from 2000 to 2016. Disease prediction based on patient attributes holds potential to optimize healthcare resource allocation. The research aim is to identify new hidden dependencies and parameters in the diagnosis of diabetes, utilizing machine learning techniques. The first stage of this involves a thorough comparison and optimization of various modeling techniques, from Decision Trees (e.g., Random Forest, Adaboost) to advanced Deep Learning models like CNN, RNN, and LSTM networks, aiming to determine the most effective implementation method. Leveraging a subset of the MIMIC-III Critical Care Database, a comprehensive de-identified health-related dataset, this research provides insights into addressing the escalating diabetes challenge. The study revealed that the Gradient Boosting Machine was the most effective for making diagnoses. In the realm of deep learning, the Recurrent Neural Network (RNN) demonstrated the best results. This work aligns with the broader context of utilizing electronic health passports to enhance healthcare efficiency and decision-making in an era characterized by abundant health data. It is essential to emphasize that this work represents the initial phase of a broader research trajectory, and its continuation will be expounded upon in forthcoming publications. Dataset: https://doi.org/10.13026/C2XW26. Dataset license: PhysioNet Credentialed Health Data Use Agreement 1.5.0.

BACKGROUND. The clinical and epidemiological characteristics of diabetes mellitus (DM) and the quality of its therapy are the key prognostic dominant that determines the organizational aspects of the diabetic service. The continuous dynamic monitoring of DM has been carried out in the Russian Federation (RF) since 1996 through the activities of the Federal Register of Diabetes Mellitus (FDR).AIMS. The aim of our study was to analyze the epidemiological characteristics of DM in the RF (prevalence, morbidity, mortality), the prevalence diabetic of complications, the state of carbohydrate metabolism (level of HbA1c) and the dynamics of the structure of glucose-lowering therapy (GLT) according to the FDR.MATERIALS AND METHODS. The database of FRD (https://www.diaregistry.ru/), 85 regions of the RF. The data are presented as of 01.01.2023 and in dynamics for the period 2010–2022.RESULTS. The total number of DM patients in the RF as of 01.01.2023 was 4 962 762 (3.31% of the population), including: Type 1 (T1) — 5.58% (277.1 ths), T2 — 92.33% (4.58 million), other DM types —2.08% (103 ths). The dynamics of prevalence over the 13-year period (2010→2022) was 146.0→191.0/100 ths people with T1, and 2036.2→3158.8/100 ths people with T2; morbidity in T1 12.3→8.2/100 ths population, in T2 260.1→191.4/100 ths population; мortality: T1 2.1→2.4/100 ths population, T2 41.2→86.1/100 ths of the population. The main cause of death was cardiovascular: in T1 38.6% cases, in T2 — 50.9%. Life expectancy (average age of death of patients): T1 was 52.7 years, the dynamics in males 50.9 → 50.7 years, females 62.1→56.0 years; in T2 — 74.2 years, males 69.5→70.4 years, females 74.2→76.1 years. The dynamic of DM duration from onset to the death: in T1 15.4→19.9 years; in T2 11→11.4 10.2→11.8 years. The proportion of patients with laboratory-measured HbA1c

Background Diabetes and its complications represent a significant public health burden in the United States. Some communities have disproportionately high risks of the disease. Identification of these disparities is critical for guiding policy and control efforts to reduce/eliminate the inequities and improve population health. Thus, the objectives of this study were to investigate geographic high-prevalence clusters, temporal changes, and predictors of diabetes prevalence in Florida. Methods Behavioral Risk Factor Surveillance System data for 2013 and 2016 were provided by the Florida Department of Health. Tests for equality of proportions were used to identify counties with significant changes in the prevalence of diabetes between 2013 and 2016. The Simes method was used to adjust for multiple comparisons. Significant spatial clusters of counties with high diabetes prevalence were identified using Tango’s flexible spatial scan statistic. A global multivariable regression model was fit to identify predictors of diabetes prevalence. A geographically weighted regression model was fit to assess for spatial non-stationarity of the regression coefficients and fit a local model. Results There was a small but significant increase in the prevalence of diabetes in Florida (10.1% in 2013 to 10.4% in 2016), and statistically significant increases in prevalence occurred in 61% (41/67) of counties in the state. Significant, high-prevalence clusters of diabetes were identified. Counties with a high burden of the condition tended to have high proportions of the population that were non-Hispanic Black, had limited access to healthy foods, were unemployed, physically inactive, and had arthritis. Significant non-stationarity of regression coefficients was observed for the following variables: proportion of the population physically inactive, proportion with limited access to healthy foods, proportion unemployed, and proportion with arthritis. However, density of fitness and recreational facilities had a confounding effect on the association between diabetes prevalence and levels of unemployment, physical inactivity, and arthritis. Inclusion of this variable decreased the strength of these relationships in the global model, and reduced the number of counties with statistically significant associations in the local model. Conclusions The persistent geographic disparities of diabetes prevalence and temporal increases identified in this study are concerning. There is evidence that the impacts of the determinants on diabetes risk vary by geographical location. This implies that a one-size-fits-all approach to disease control/prevention would be inadequate to curb the problem. Therefore, health programs will need to use evidence-based approaches to guide health programs and resource allocation to reduce disparities and improve population health.

BACKGROUND. One of the priority directions in the development of the health care system is to reduce the medical and social damage caused by the increase in the prevalence of diabetes mellitus (DM). From this point of view, the development of a diabetes register is very important as the main information and analytical platform for clinical and epidemiological monitoring of diabetes in the Russian Federation (RF).AIMS. The aim of our study was to analyze a dynamic (2016–2020) of the epidemiological characteristics of diabetes mellitus in the Russian Federation (prevalence, morbidity, mortality), the prevalence of complications, the level of HbA1c and the dynamics of the structure of glucose-lowering therapy (GLT) according to the Federal Diabetes Register (FDR).MATERIALS AND METHODs. The database of FRD (http://diaregistry.ru) 84 regions of the RF. The data are presented in dynamics 2016→2020.RESULTS. The total number of DM patients in the RF as of 01.01.2021 was 4,799,552 (3.23% of the population), including: Type 1 (T1) — 5.5% (265.4 ths) , T2 — 92.5% (4.43 million), other DM types — 2.0% (99.3 ths). The dynamics of prevalence was 168.7→180.9/100 ths people with T1, and 2709 → 3022/100 ths people with T2; morbidity in T1 10.5→7.7/100 ths population, in T2 219.6→154.2/100 ths population. Age and sex characteristics: the proportion of men in T1 — 54%, in T2 — 30%; the max proportion of patients with T1 at the age of 30–39 years, T2 65–69 years. Mortality: T1 3.0 → 2.7/100 ths population, T2 87.7→93.9/100 ths of the population, the main cause of death was cardiovascular: in T1 38,1% cases, in T2 — 52,0%. Life expectancy (average age of death of patients): T1 was 53.2years, the dynamics in males 50.7 → 50.5years, ­females 58.7→55.2years; in T2 — 73.5 years, males 70.2→70.1years, females 75.7→75.4 years. The dynamic of DM duration until the death: in T1 17.4→19.0 years; in T2 11→11.4 years. The incidence of diabetic complications in T1 and T2 patients: neuropathy 43.3% and 24.4%, nephropathy (CKD) 25.9% and 18.4%, retinopathy 31.7% and 13.5%, respectively. The ­proportion of ­patients with HbA1c

The Hmong, an ethnic group in Laos, immigrated to the United States at the end of the Vietnam War. Minnesota and California have the largest Hmong populations. Hmong were historically farmers and hunter-gatherers with inadequate food supply in Laos. Here in America, with a surplus of food and a sedentary lifestyle, the Hmong are at increased risk for diabetes, hence the Thrifty Gene Hypothesis. This retrospective study compares the prevalence and control of diabetes mellitus-type 2 between the Hmong and other ethnic and racial groups at the Health And Life Organization (HALO) clinic in Sacramento, California. A total of 9,285 charts were reviewed. The Hmong had the highest prevalence but poorest control of diabetes mellitus when compared with the Laotian, Vietnamese, Blacks, Whites, and Hispanics (chi-square p-value

Abstract Background Despite Israel’s universal health coverage, disparities in health services provision may still exist. We aimed to assess socioeconomic disparities in diabetes prevalence and quality of care among Israeli children, and to assess whether these changed over time. Methods We used repeated cross-sectional analyses in the setting of the National Program for Quality Indicators in Community Healthcare. The data were derived from electronic medical records from Israel’s four health maintenance organizations. The study population included all Israeli children aged 2–17 years in 2011–2018 (2018: N = 2,404,856). Socio-economic position (SEP) was measured using Central Bureau of Statistics data further updated by a private company (Points Business Mapping Ltd), and grouped into 4 categories, ranging from 1 (lowest) to 4 (highest). We used logistic regression to assess the association of SEP with diabetes prevalence, diabetes clinic visits, hemoglobin A1C (HbA1C) testing, and poor glycemic control (HbA1c > 9%), and assessed whether these changed over time. Results Diabetes prevalence increased with age and SEP, with a total of 3019 children with diabetes. SEP was positively associated with visiting a specialized diabetes clinic (age and sex adjusted Odds Ratio (aORSEP 4 vs. 1 2.45, 95% Confidence Interval (CI) 1.67–3.69)). Although children in higher SEPs were less likely to undergo HbA1c testing (aORSEP 4 vs. 1 0.54, 95% CI 0.40–0.72), they were also less likely to have poor glycemic control (aORSEP 4 vs. 1 0.25, 95% CI 0.18–0.34). Disparities were especially apparent among children aged 2–9 (6.5% poor glycemic control in SEP 4 vs. 38.2% in SEP 1). Poor glycemic control decreased over time, from 44.0% in 2011 to 34.1% in 2018. Conclusions While poor glycemic control rates among children have improved, they remain high compared to rates in adults. Additionally, substantial socioeconomic gaps remain. It is eminent to study the causes of these disparities and develop policies to improve care provided to children in the lower SEP levels, to promote health equity.

Objectives: Web information systems may serve as a diagnostic tool for the Internet users and they also support the epidemiological work of doctors and health care providers. As part of this study, a system has been created for detecting and calculating cardiovascular risk. The aim of this study has been the comparison of cardiovascular risk factors and calculated fatal cardiovascular risk in 2 periods of time: 2004–2009 and 2010–2015 in Poland, as determined via the Internet. Material and Methods: The “Ryzyko program” (“Risk program”) is available on the website of the Medical University of Gdańsk. To assess the cardiovascular death risk in a 10-year period, the algorithm of the SCORE (Systematic Coronary Risk Evaluation) project was used and 30 402 results of the algorithm have been analyzed. Results: Over 30 402 webpage visitors entered the required data and received the outcome. More than 78% of the Internet users who had entered the data, received a recommendation for medical check-up. Significant differences between the data collected in 2004– 2009 and 2010–2015 were noticed. Hypercholesterolemia prevalence (67.3% vs. 70.8%; p < 0.001), mean total cholesterol concentration in blood (5.60±1.65 mml/l vs. 5.66±1.35 mml/l; p < 0.001), prevalence of hypertension (36.6% vs. 35.3%; p = 0.039), mean systolic blood pressure (131.5±20.3 mm Hg vs. 132.6±18.0 mm Hg; p < 0.001), prevalence of declared smoking (30.7% vs. 26.5%; p < 0.001), declared diabetes mellitus (DM) (6.4% vs. 9.7%; p < 0.001), and declared coronary artery disease (CAD) (7.2% vs. 14.1%; p < 0.001), respectively. Conclusions: The prevalence of cardiovascular risk factors has changed during the observed period of time. Online automatic gathering of new data by “Ryzyko program” provides up-to-date observations. Int J Occup Med Environ Health 2017;30(3):499–510

This paper assesses concordance and inconsistency among three small area estimation methods that are currently providing county-level health indicators in the United States. The three methods are multi-level logistic regression, spatial logistic regression, and spatial Poison regression, all proposed since 2010. Diabetes prevalence is estimated for each county in the continental United States from the 2012 sample of Behavioral Risk Factor Surveillance System. The mapping results show that all three methods displayed elevated diabetes prevalence in the South. While the Pearson correlation coefficients among three model-based estimates were all above 0.60, the highest one was 0.80 between the multilevel and spatial logistic methods. While point estimates are apparently different among the three small area estimate methods, their top and bottom of quintile distributions are fairly consistent based on Bangdiwala’s B-statistic, suggesting that outputs from each method would support consistent policy making in terms of identifying top and bottom percent counties.

Physical inactivity is one of the risk factors of diabetes. In addition, physical inactivity is attributed to urbanization-related factors, such as poverty, which is also one of the risk factors of diabetes. We hypothesized that physical inactivity is a mediator in the association between diabetes and poverty, and that spatial heterogeneity exists in these relationships. This study adopted a spatiotemporal modelling approach to conduct this mediator analysis. From 2004-2011, data were collected at the county level in 48 contiguous states (with a total of 3,109 counties) from the Behavioral Risk Factor Surveillance System (BRFSS) and American Community Survey. Poverty percentage significantly affected physical inactivity prevalence and diabetes prevalence in two separate models. Using a model with both physical inactivity and poverty percentages as independent variables, we verified that physical inactivity prevalence is a significant mediator. In this model, physical inactivity prevalence resulted in a significant positive association with diabetes prevalence, and the influence of poverty percentage on diabetes prevalence was significantly reduced (P=0.0009). An advanced spatiotemporal analysis revealed that 32.65% of counties having a significant positive association between diabetes prevalence and physical inactivity prevalence also had a significant positive association between physical inactivity prevalence and poverty percentage. Those counties were also likely located in the South and Southeast of USA. In summary, the findings of this study demonstrate the mediating effect of physical inactivity between diabetes and poverty. When implementing diabetes prevention in communities with higher poverty, appropriate strategies to reduce the cost burden of physical activity programmes should be considered.

Clinical and epidemiological research has reported a strong association between diabetes and obesity. However, whether increased diabetes prevalence is more likely to appear in areas with increased obesity prevalence has not been thoroughly investigated in the United States (US). The Bayesian structured additive regression model was applied to identify whether counties with higher obesity prevalence are more likely clustered in specific regions in 48 contiguous US states. Prevalence data adopted the small area estimate from the Behavioral Risk Factor Surveillance System. Confounding variables like socioeconomic status adopted data were from the American Community Survey. This study reveals that an increased percentage of relative risk of diabetes was more likely to appear in Southeast, Northeast, Central and South regions. Of counties vulnerable to diabetes, 36.8% had low obesity prevalence, and most of them were located in the Southeast, Central, and South regions. The geographic distribution of counties vulnerable to diabetes expanded to the Southwest, West and Northern regions when obesity prevalence increased. This study also discloses that 7.4% of counties had the largest average in predicted diabetes prevalence compared to the other counties. Their average diabetes prevalence escalated from 8.7% in 2004 to 11.2% in 2011. This study not only identifies counties vulnerable to diabetes due to obesity, but also distinguishes counties in terms of different levels of vulnerability to diabetes. The findings can provide the possibility of establishing targeted surveillance systems to raise awareness of diabetes in those counties.