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This article centers around the development and analysis of machine learning (ML) and deep learning models aimed at enhancing diabetes diagnosis. In the swiftly evolving landscape of data technologies, it becomes crucial to explore the applications of these methods for accurate predictions and improved medical decision-making. Our research encompasses diverse datasets, leveraging state-of-the-art algorithms and technologies for model training and testing. The primary emphasis lies in evaluating the accuracy, sensitivity, and specificity of models within the realm of diabetes diagnosis. The study results reveal significant advancements in disease prediction, underscoring the potential of ML and deep learning in medical applications. This work introduces fresh perspectives on the utilization of computational methods in healthcare and serves as a foundation for prospective research in this domain. [ABSTRACT FROM AUTHOR]

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: 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. [ABSTRACT FROM AUTHOR]

This study aims to optimize the performance of the bagged tree in an ensemble classifier for predicting diabetes prevalence in women. The study used a dataset of 1,888 women with six features: age, BMI, glucose level, insulin level, blood pressure, and pregnancy status. The dataset was divided into training and testing sets with a 70:30 ratio. The bagged tree ensemble classifier was used for the analysis, and five-fold cross-validation was applied. The study found that using all features during training resulted in a 92.3% training accuracy and a 99.5% testing accuracy. However, applying optimization techniques such as feature selection, parameter tuning, and a maximum number of splits improved model performance. Feature selection optimized the accuracy performance by 0.2%, while parameter tuning improved the test accuracy by 0.2%. Moreover, decreasing the maximum number of splits from 1322 to 800 or 600 resulted in an optimized model with 0.1% higher validation accuracy. Finally, the optimized bagged tree models were evaluated using various performance metrics, including accuracy, precision, recall, and F1 score. The study found that Model 1, which used 800 maximum number of splits and 50 learners, outperformed Model 2 in terms of recall and F1 score, while Model 2, which used 600 maximum number of splits and 50 learners, had a higher precision score. The study concludes that optimization techniques can significantly improve the performance of the bagged tree in predicting diabetes prevalence in women. [ABSTRACT FROM AUTHOR]

Estimates of glucose concentrations vary among types of blood samples, which impact on the assessment of diabetes prevalence. Guidelines recommend a conversion factor to calculate plasma glucose from measurements of glucose in whole blood. The American Diabetes Association recommends the use of blood drawing tubes containing sodium fluoride (NaF) and citrate, which have not yet been evaluated regarding possible differences in glucose concentration and conversion factors. Thus, we compared glucose measurements in NaF-citrate plasma and venous whole blood and estimated the impact of differences on diabetes and prediabetes prevalence. Glucose differences were calculated by Bland-Altman analysis with pairwise comparison of glucose measurements from whole blood and NaF-citrate plasma (n=578) in clinical studies of the German Diabetes Center. Subsequently, we computed the impact of the glucose difference on diabetes and prediabetes prevalence in the population-based National Health and Nutrition Examination Survey (NHANES). Even upon conversion of whole blood to plasma glucose concentrations using the recommended conversion factor, mean glucose concentration difference remained 4.72 % higher in NaF-citrate plasma. Applying the higher glucose estimates, increases the population-based diabetes and prediabetes prevalence by 13.67 and 33.97 % or more than 7.2 and 13 million people in NHANES, respectively. Additional economic burden could be about 20 $ billion per year due to undiagnosed diabetes. The recommended conversion factor is not valid for NaF-citrate plasma. Systematic bias of glucose measurements due to sampling type leads to clinically relevant higher estimates of diabetes and prediabetes prevalence. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

[Display omitted] • We report higher prevalence of diabetes than previous Hungarian estimates. • 18.3% of women and 23.5% of men with diabetes were undiagnosed. • Men in their forties had a higher risk of being undiagnosed with diabetes. • Employment status and education level were not associated with being undiagnosed. • Universal health care could provide equitable access to diabetes diagnosis. To estimate prevalence of diagnosed (dDM) and undiagnosed diabetes (uDM) in Hungary and investigate determinants of uDM. Data was obtained from the nationally representative H-UNCOVER study. As laboratory measurements were available for 11/19 Hungarian counties, n = 5,974/17,787 people were eligible. After exclusions, 5,673 (representing 4,976,097 people) were included. dDM was defined by self-reporting, while uDM as negative self-reporting and elevated fasting glucose (≥7 mmol/l) and/or HbA1c (≥48 mmol/mol). Logistic regression for complex samples was used to calculate comparisons between dDM and uDM adjusted for age and BMI. Diabetes prevalence was 12.0 %/11.9 % (women/men, 95 %CI:10.7–13.4 %/10.7–13.2 %), while 2.2 %/2.8 % (1.7–2.8 %/2.2–3.6 %) of women/men were uDM. While the proportion of uDM vs. dDM was similar for women ≥ 40, men in their forties had the highest odds for uDM. Neither unemployment (women/men OR:0.58 [0.14–2.45]/0.50 [0.13–1.92]), nor education level (tertiary vs. primary; women/men OR: 1.16 [0.53–2.56]/ 0.53 [0.24–1.18]) were associated with uDM. The risk of uDM was lower in both sexes with chronic morbidities. We report higher prevalence of diabetes and undiagnosed diabetes than previous Hungarian estimates. The finding that socioeconomic factors are not associated to uDM suggests that universal health care could provide equitable access to diabetes diagnosis. [ABSTRACT FROM AUTHOR]

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.

Diabetes mellitus is a disease associated with many complications leading to premature death. The aim of this study was to estimate prevalence of type 2 diabetes (T2D), and the proportion of the population unaware of the condition, in association with modifiable risk factors. Data from the Hellenic National Nutrition and Health Survey were used (n = 3773 adults, 40.8% men) and were obtained by trained personnel. Diabetes mellitus disease status was categorized as per the International Classification of Diseases codes (10th version). A subsample from the two main metropolitan areas was used to assess T2D and impaired fasting glucose (IFG) (n = 990; 38.2% men) from plasma analysis. The prevalence of T2D in the population was 5.2% in total, reaching 13.7% in adults aged >60 years (no sex differences). IFG was observed in 27.3% of adults in the two metropolitan areas, and 40% were unaware of having T2D in this subsample. The likelihood of having T2D significantly increased with age and body weight, whereas it decreased with higher educational level and physical activity (P for all <0.001). The high T2D prevalence in adults, especially among the older age-groups, suggests a major public health problem in Greece. • Impaired fasting glucose was observed in 27.3% of adults in the two metropolitan areas, and 40% were unaware of having T2D. • Odds of T2D significantly increased with age and body weight, and decreased with educational level and physical activity. • The population prevalence of T2D was found 5.2% in total, reaching 13.7% in adults aged >60 years (no sex differences). [ABSTRACT FROM AUTHOR]

Diabetes is a major chronic disease that was the seventh leading cause of death in the United States in 2017. Although diabetes self-management education (DSME) programs have been implemented to promote patients' self-management skills, the disease continues to grow and some regions have a heavier burden of diabetes diagnoses than others. Focusing on the unbalanced diabetes prevalence across counties, this study aims to identify regional distribution of diabetes education programs in Texas counties, and explore whether diabetes prevalence is worse in certain counties that are geographically isolated from DSME programs. Data from the Centers for Disease Control and Prevention, American Diabetes Association, and American Association of Diabetes Educators were analyzed using ESRI ArcGIS software. When the authors geocoded 167 DSME programs in Texas, it was found that 47 programs (28.7%) were concentrated in 1 single county, and only 49 counties (19.3%) of the total counties (n = 254) had at least 1 DSME program. It also was revealed that the 25 counties (10%) with the highest diabetes prevalence had only 4 DSME programs (2.4%), indicating that there are still marginalized areas with no access to diabetes education programs. Considering the distance from each county's center to the nearest DSME program and diabetes prevalence, 3 counties were identified as diabetes education deserts. When designing, implementing, and evaluating strategies to reduce the burden of diabetes, policy makers and health care providers should account for the existing regional disparity in diabetes education and its impact on diabetes prevalence. [ABSTRACT FROM AUTHOR]

PurposeFew models have been developed specifically for the epidemiology of diabetes. Diabetes incidence is critical in predicting diabetes prevalence. However, reliable estimates of disease incidence rates are difficult to obtain. The aim of this study was to propose a mathematical framework for predicting diabetes prevalence using incidence rates estimated within the model using body mass index (BMI) data.MethodsA generic mechanistic model was proposed considering birth, death, migration, aging, and diabetes incidence dynamics. Diabetes incidence rates were determined within the model using their relationships with BMI represented by the Hill equation. The Hill equation parameters were estimated by fitting the model to National Health and Nutrition Examination Survey (NHANES) 1999-2010 data and used to predict diabetes prevalence pertaining to each NHANES survey year. The prevalences were also predicted using diabetes incidence rates calculated from the NHANES data themselves. The model was used to estimate death rate parameters and to quantify sensitivities of prevalence to each population dynamic.ResultsThe model using incidence rate estimates from the Hill equations successfully predicted diabetes prevalence of younger, middle-aged, and older adults (prediction error, 20.0%, 9.64%, and 7.58% respectively). Diabetes prevalence was positively associated with diabetes incidence in every age group, but the associations among younger adults were stronger. In contrast, diabetes prevalence was more sensitive to death rates in older adults than younger adults. Both diabetes incidence and prevalence were strongly sensitive to BMI at younger ages, but sensitivity gradually declined as age progressed. Younger and middle aged adults diagnosed with diabetes had at least a two-fold greater risk of death than their nondiabetic counterparts. Nondiabetic older adults were found to be under slightly higher death risk (0.079) than those diagnosed with diabetes (0.073).ConclusionsThe proposed model predicts diagnosed diabetes incidence and prevalence reasonably well using the link between BMI and diabetes development risk. Ethnic group and gender-specific model parameter estimates could further improve predictions. Model prediction accuracy and applicability need to be comprehensively evaluated with independent data sets.

Purpose Of Review: Latin America is the scenario of great inequalities where about 32 million human beings live with diabetes. Through this review, we aimed at describing the current state of the prevalence, awareness, treatment, and control of diabetes mellitus and completion of selected guidelines of care across Latin America and identify opportunities to advance research that promotes better health outcomes.Recent Findings: The prevalence of diabetes mellitus has been consistently increasing across the region, with some variation: higher prevalence in Mexico, Haiti, and Puerto Rico and lower in Colombia, Ecuador, Dominican Republic, Peru, and Uruguay. Prevalence assessment methods vary, and potentially underestimating the real number of persons with diabetes. Diabetes unawareness varies widely, with up to 50% of persons with diabetes who do not know they may have the disease. Glycemic, blood pressure, and LDL-C control and completion of guidelines to prevent microvascular complications are not consistently assessed across studies, and the achievement of control goals is suboptimal. On the other hand, multiple interventions, point-of-care/rapid assessment tools, and alternative models of health care delivery have been proposed and tested throughout Latin America. The prevalence of diabetes mellitus continues to rise across Latin America, and the number of those with the disease may be underestimated. However, some local governments are embedding more comprehensive diabetes assessments in their local national surveys. Clinicians and public health advocates in the region have proposed and initiated various multi-level interventions to address this enormous challenge in the region. [ABSTRACT FROM AUTHOR]

Epidemiological evidence for the link between long-term exposure to air particulate matter (PM2.5) and occurrence of type 2 diabetes (T2D) is limited such that little is known about the effect of PM2.5 exposure and adult T2D prevalence. Thus, the aim of this ecological study is to evaluate the contribution of ambient PM2.5 exposure to the adult T2D prevalence in the large population of Iran. The study was conducted based on Iran's large-scale cross-sectional surveilling non-communicable diseases (NCDs) risk factors (Timpka et al. 2015b). A total of 31,050 participants were enrolled in three sequential processes of study using cluster random sampling. PM2.5 data in the urban area of 31 provinces of Iran were acquired from Tehran Air Quality Control Company (AQCC) and the Department of Environment (DoE) of Iran during 2012–2016. Moreover, major and minor diabetes risk factors were considered; the Pearson correlation and a stepwise regression model were performed to estimate associations between risk factors and diabetes and prediabetes prevalence. The results showed T2D prevalence was more frequent among women (10.61%) than men (9.35%). A weak positive correlation was observed between PM2.5 level and diabetes prevalence with a correlation coefficient of 0.275, although there was no significant association between PM2.5 value and prediabetes prevalence. Moreover, none of the variables included in the regression model could predict the prevalence of diabetes and prediabetes. According to our study results, it can be suggested that investigating the association between PM2.5 exposure and T2D prevalence at individual level may provide a better understanding of PM2.5 exposure and the risk of T2D prevalence. [ABSTRACT FROM AUTHOR]

Purpose: To develop a nomogram for predicting 5-year incidence of type 2 diabetes (T2D) in Chinese adults. Methods: This is a retrospective cohort study from a prospectively collected database. We included a total 32,766 adults free of T2D at baseline with a median follow-up of 3 years. Univariate and multivariate Cox regression analyses were applied to identify independent predictors. A nomogram was constructed to predict 5-year incident rate of T2D based on the multivariate analysis results. Harrell's C-indexes and calibration plots were used to evaluate the accuracy of the nomogram in both internal and external validations. Results: The overall prevalence of T2D was 2.1%. Participants were randomly divided into a training set (n = 21,844) and a validation set (n = 10,922). After multivariate analysis in the training set, age, sex, BMI, hypertension, dyslipidemia, smoking status, and family history were found as risk predictors and integrated into the nomogram. Harrell's C-indexes were 0.815 (95% CI: 0.797–0.834) and 0.779 (95% CI: 0.747–0.811) in the training and validation sets, respectively. The calibration plots demonstrated good agreement between the estimated probability and the actual observation. Conclusion: Our nomogram could be a simple and reliable tool for predicting 5-year risk of developing T2D in high-risk Chinese. Through the model, early identifying high-risk individuals is helpful for timely intervention to reduce the incidence of T2D. [ABSTRACT FROM AUTHOR]

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

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Luppa, Zeller, Pieper, Kaiser, Weiss, Vierbaum and Freckmann.)

Diabetes is a severe chronic disease that arises when insulin generation is insufficient, or the generated insulin cannot be used in the body, resulting a long-term metabolic disorder. Diabetes affects an estimated 537 million adults worldwide between the age of 20 to 79 (10.5% of all adults in this age range). By 2030, 643 million people will have diabetes globally, increasing to 783 million by 2045. According to the IDF 10th edition, the incidence of diabetes has been rising in South-East Asia (SEA) nations for at least 20 years, and current estimates have outperformed all previous forecasts. This review aims to provide updated estimates and future projections of diabetes prevalence at the national and global levels by using data from the 10th edition of the IDF Diabetes Atlas 2021. For this review, we studied more than 60 previously published related articles from various sources, such as PubMed and Google Scholar, and we extracted 35 studies out of 60. however, we used only 34 studies directly related to diabetes and its prevalence at the global, SEA, and Indian levels. This review article concludes that in 2021 more than 1 in 10 adults worldwide developed diabetes. The estimated prevalence of diabetes in adults (20 to 79 years) has more than tripled since the first edition in 2000, rising from an estimated 151 million (4.6% of the world's population at the time) to 537.5 million (10.5%) of the world's population today. The prevalence rate will be higher than 12.8% by 2045. In addition, this study indicates that the incidence of diabetes in the world, Southeast Asia, and India was 10.5%, 8.8%, and 9.6%, respectively, throughout 2021 and will rise to 12.5%, 11.5%, and 10.9%, respectively by 2045., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Diabetes has become a critical population health issue in the United States. Although researchers have focused on diabetes prevalence at the individual level, few studies address community contexts of diabetes prevalence at the county level. The objective of this study is to explore the association between community characteristics and diabetes prevalence in 254 counties in Texas. Using 3 countywide data sets in 2012, the authors measured county-level community characteristics, including social, environmental, and health behavior factors, and examined how these county-level factors are associated with diabetes prevalence. Multivariate ordinary least squares models weighted with population of county were used for estimation. Rurality, physical inactivity prevalence, and obesity prevalence were positively associated with diabetes prevalence, while the percentage of nonphysician health care professionals was negatively associated with diabetes prevalence. The findings indicate that environmental and health behaviors are significantly associated with a high prevalence of diabetes, but more nonphysician health care professionals, including nurse practitioners, may mitigate diabetes prevalence. This study highlights the significance of community factors in diabetes prevalence and provides insights for diabetes prevention programs with nonphysician health care professionals. [ABSTRACT FROM AUTHOR]

Indigenous populations around the world have a higher health disparity and an increased risk of diabetes. Scientific literature on the prevalence of diabetes in India is not available, and the current work is a pilot study to explore the risk of diabetes in one such indigenous population in India. This is a cross-sectional survey and screening study. The study took place in a remote tribal hamlet of Machuru in South India. A door-to-door survey was conducted in the hamlet with a population of 555. The Indian Diabetes Risk Score (IDRS) questionnaire was completed by 160 individuals older than 25 years. Capillary blood glucose levels were measured to compare the glycaemic status with the predicted IDRS. Of 160 adults who completed the questionnaire, 37 were at high risk (23.13%) as per the IDRS, 52 at medium risk (32.5%) and 71 at low risk (44.38%). None of the respondents knew their family history of diabetes owing to the lack of awareness about the condition. Interestingly, the villagers had a sedentary lifestyle owing to their unique family dynamics but a healthy diet. Five participants were diagnosed with diabetes, and 18 were diagnosed with impaired fasting glucose or prediabetes. The IDRS might not be an accurate measure to understand the risk of diabetes in this particular population owing to their unique family dynamics and a lack of awareness about diabetes. The best possible way to assess the diabetes risk might be through blood examination. • Diabetes risk assessment using the IDRS was difficult in this indigenous population owing to multiple factors. • The particular population has sedentary lifestyle after the age of 30 years. • None of the respondents of the IDRS questionnaire knew their family history of diabetes. • The indigenous population had a reduced prevalence of type 2 diabetes compared with the country's average. • Their healthy diet and sleep patterns might possibly have reduced prevalence of type 2 diabetes. [ABSTRACT FROM AUTHOR]

The 2019 Diabetes Atlas delineated both accomplishments and challenges in diabetes care in Taiwan between 2005 and 2014. The series reported that Taiwan had significantly improved aspects of care quality for patients with diabetes. For example, the mortality rate decreased, the difference between the life expectancies of patients with diabetes and those of the general population decreased, and the rates of hospitalization because of heart diseases, cerebrovascular diseases, chronic kidney diseases, and unsatisfactory glycemic control decreased. However, despite these achievements, the 2019 Diabetes Atlas also reported some substantial challenges that have not been overcome. For example, the incidence of diabetes among women aged <19 years and 20-39 years increased by 27% and 33%, respectively. Furthermore, a high prevalence of macrovascular complications, a continuous increase in the dialysis prevalence rates among men with diabetes of all ages, and a low participation rate (<20%) of patients with diabetes in the pay-for-performance program were observed. The publication of the 2019 Diabetes Atlas is a milestone that demonstrates a strong will in medical societies to improve the quality of diabetes care. We expect this initiative can be reorganized every 5 years to report the results of continuous monitoring and surveillance and update the epidemiological features of diabetes in Taiwan. [ABSTRACT FROM AUTHOR]

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Over the previous 30 years, the status of diabetes has changed from being considered as a mellow issue of the elderly to one of the significant reasons for morbidity and mortality influencing the young and moderately aged individuals. The aim of the study was to estimate the prevalence of type 2 diabetes mellitus (T2DM) and impaired fasting glycemia (IFG) in the adult population, age ≥ 20 years of District Srinagar and determine its sociodemographic and lifestyle correlates. The study was conducted among the adult population of District Srinagar with a sample size of 580. A three-stage cluster random sampling design was used, and study participants were selected using a Kish grid method. Sociodemographic data, anthropometric data, and other data were collected as per the pretested semi-structured proforma. The participants were then subjected to fasting venous blood glucose estimation. The American Diabetes Association (ADA) criteria 2015 were used for classifying the subjects as normal, pre-diabetic, and diabetics. The prevalence of T2DM was 9.8% and that of IFG was 22.2%,the majority of them (9.1%) had known T2DM. The prevalence of IFG and T2DM was statistically associated with increasing age (p < 0.001), marital status (p < 0.001), educational status (p < 0.001), high chocolates and carbonated drinks consumption (p < 0.001), level of physical inactivity (p < 0.05), body mass index (p < 0.001), family history of T2DM (p < 0.001), and smoking behavior and hypertension (p < 0.001). Our analysis suggests an increase in the prevalence of T2DM in District Srinagar. [ABSTRACT FROM AUTHOR]

To develop risk scores for predicting the prevalence of diabetes in the Lao population. This was a cross-sectional study of both men and women (age 30 to 70 years) living in rural villages of the Vientiane municipality in the Lao PDR. Multiple logistic regressions with backward stepwise selection were used; the diabetes risk score was derived from the β-coefficient. Performance of the score was determined by the area under the receiver operating characteristic curve (AUC), the sensitivity, the specificity, and the positive predictive value for the specified cut-off value. The prevalence of undiagnosed diabetes was 7%. The factors included in the predictive in model were 17 (40 to 70 years of age) + 14 (high waist circumference) + 11 (hypertension) + 7 (family history of diabetes). A cut-off point of risk scores of 29.5 out of 49 produced the optimal sum, leading to a sensitivity of 0.75, a specificity of 0.55, a positive predictive value of 17.8%, and an AUC of 0.70. Data suggested that the combination of age, waist circumference, hypertension, and family history of diabetes could be utilized to identify Lao individuals at high risk of undiagnosed diabetes. The generalizability for other Lao population needs further investigation. [ABSTRACT FROM AUTHOR]

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.

Objectives: Estimates of glucose concentrations vary among types of blood samples, which impact on the assessment of diabetes prevalence. Guidelines recommend a conversion factor to calculate plasma glucose from measurements of glucose in whole blood. The American Diabetes Association recommends the use of blood drawing tubes containing sodium fluoride (NaF) and citrate, which have not yet been evaluated regarding possible differences in glucose concentration and conversion factors. Thus, we compared glucose measurements in NaF-citrate plasma and venous whole blood and estimated the impact of differences on diabetes and prediabetes prevalence., Methods: Glucose differences were calculated by Bland-Altman analysis with pairwise comparison of glucose measurements from whole blood and NaF-citrate plasma (n=578) in clinical studies of the German Diabetes Center. Subsequently, we computed the impact of the glucose difference on diabetes and prediabetes prevalence in the population-based National Health and Nutrition Examination Survey (NHANES)., Results: Even upon conversion of whole blood to plasma glucose concentrations using the recommended conversion factor, mean glucose concentration difference remained 4.72 % higher in NaF-citrate plasma. Applying the higher glucose estimates, increases the population-based diabetes and prediabetes prevalence by 13.67 and 33.97 % or more than 7.2 and 13 million people in NHANES, respectively. Additional economic burden could be about 20 $ billion per year due to undiagnosed diabetes., Conclusions: The recommended conversion factor is not valid for NaF-citrate plasma. Systematic bias of glucose measurements due to sampling type leads to clinically relevant higher estimates of diabetes and prediabetes prevalence., (© 2023 the author(s), published by De Gruyter, Berlin/Boston.)

Aims: To find diabetes prevalence of diagnosed cases, stratified by gender, age, and urban/rural municipalities in the Republic of Macedonia, based on data derived from the National eHealth System.Methods: The National eHealth System was searched for all patients with ICD-10 diagnoses E10-E14 in their electronic healthcare records. Data records from the National eHealth System contained patient's gender, date of birth, place of living, and ICD-10 code.Results: Total number of diagnosed diabetes cases was 84,568: 36,119 males (42.7%) and 48,449 females (57.3%). Diabetes prevalence of diagnosed cases in population 20-79 years was 5.0% (n = 78,233; N = 1,562,203), prevalence was higher in females than in males (4.6% vs 3.4%); in rural than in urban municipalities (5.6% vs 3.6%), and was highest in the age group 60-79 years (14.6%).Conclusion: These were the first findings on diabetes prevalence of diagnosed cases in the Republic of Macedonia derived from the National eHealth System, stratified by age, gender, and urban/rural municipalities. These data could enable more precise estimations of the total diabetes prevalence in the country, including both diagnosed and undiagnosed cases, and further analysis of the risk factors leading to higher diabetes prevalence in females and rural municipalities in the Republic of Macedonia. [ABSTRACT FROM AUTHOR]

Introduction: There has been no nationwide health (diabetes) survey in Nigeria since 1992, when a diabetes mellitus (DM) prevalence of 2.2% was reported. We aimed to determine the prevalence of and risk factors for DM in Nigeria by performing a systematic review and meta-analysis.Methods: We searched Medline, EMBASE, PubMed, PapersFirst, the Cochrane Library, Scopus, Bioline, African Journals Online, Institute of Scientific Information, and Google Scholar from the year 1990 to 2017. Using MeSH headings, the terms “diabetes mellitus,” “risk factors,” “prevalence,” and “Nigeria” as well as variations thereof were searched for. The last search was performed on 26 November 2017. We only included studies that utilized the random plasma glucose test, the fasting plasma glucose test, the oral glucose tolerance test (OGTT), or HbA1c to diagnose DM. A total of 23 studies (n = 14,650 persons) were evaluated. A random effects model was used to estimate the pooled prevalence of DM. We estimated the overall pooled prevalence of DM and subgroup-specific DM prevalences while accounting for inter-study and intra-study variability/heterogeneity.Results: The overall pooled prevalence of DM was 5.77% (95% CI 4.3-7.1). The pooled prevalences of DM in the six geopolitical zones of Nigeria were 3.0% (95% CI 1.7-4.3) in the north-west, 5.9% (95% CI 2.4-9.4) in the north-east, 3.8% (95% CI 2.9-4.7) in the north-central zone, 5.5% (95% CI 4.0-7.1) in the south-west, 4.6% (95% CI 3.4-5.9) in the south-east, and 9.8% (95% CI 7.2-12.4) in the south-south zone. Risk factors for the pooled prevalence of DM were a family history of DM (4.6%; 95% CI 3.5-5.6); urban dwelling (6.0%; 95% CI 4.3-7.8); unhealthy dietary habits (8.0%; 95% CI 5.4-10.5); cigarette smoking (4.4%; 95% CI 1.3-10.2); older age (6.6%; 95% CI 4.5-8.7); physical inactivity (4.8%; 95% CI 3.2-6.4); and obesity (5.3%; 95% CI 3.8-6.9).Conclusion: There has been an increase in the prevalence of DM in Nigeria. All regions of the country have been affected, with the highest prevalence seen in the south-south geopolitical zone. Urban dwelling, physical inactivity, advanced age, and unhealthy diet are important risk factors for DM among Nigerians. A national diabetes care and prevention policy is highly recommended. [ABSTRACT FROM AUTHOR]

Aims: To describe the prevalence and management of diabetes among immigrants according to ethnic group and duration of stay, compared to Italian citizens.Methods: Diabetic immigrant and Italian residents aged 20-69 years in the administrative database of the Lombardy Region. Immigrants were classified by region of origin and as long-term residents (LTR) and short-term residents (STR). Age- and sex-adjusted prevalence and indicators of diabetes management were calculated for immigrants by region of origin and by length of stay using Cox proportional models.Results: In 2010 19,992 immigrants (mean age 49.1 ± 10.8, 53.7% males) and 195,049 Italians (mean age 58.7 ± 9.3, 61.1 males) with diabetes were identified. Immigrants had a higher adjusted diabetes prevalence than Italians (OR 1.48; 95% CI 1.45-1.50). STR received significantly fewer recommended cardiovascular drugs (antiplatelets, statins and ACE-inhibitors/ARBs) than Italians, although prescription was higher among LTR from some ethnic groups. Immigrants were less likely to be seen by a diabetologist and to do at least one HbA1c test per year. Although the recommended tests/visits were more often done for the LTR than the STR, in the majority of ethnic groups these indicators were still far from optimal.Conclusion: The prevalence and management of diabetes differ between immigrants and Italians, although some improvement can be seen among LTR. [ABSTRACT FROM AUTHOR]

Introduction: Since the year 2000, IDF has been measuring the prevalence of diabetes nationally, regionally and globally.Aim: To produce estimates of the global burden of diabetes and its impact for 2017 and projections for 2045.Methods: A systematic literature review was conducted to identify published studies on the prevalence of diabetes, impaired glucose tolerance and hyperglycaemia in pregnancy in the period from 1990 to 2016. The highest quality studies on diabetes prevalence were selected for each country. A logistic regression model was used to generate age-specific prevalence estimates or each country. Estimates for countries without data were extrapolated from similar countries.Results: It was estimated that in 2017 there are 451 million (age 18-99 years) people with diabetes worldwide. These figures were expected to increase to 693 million) by 2045. It was estimated that almost half of all people (49.7%) living with diabetes are undiagnosed. Moreover, there was an estimated 374 million people with impaired glucose tolerance (IGT) and it was projected that almost 21.3 million live births to women were affected by some form of hyperglycaemia in pregnancy. In 2017, approximately 5 million deaths worldwide were attributable to diabetes in the 20-99 years age range. The global healthcare expenditure on people with diabetes was estimated to be USD 850 billion in 2017.Conclusion: The new estimates of diabetes prevalence, deaths attributable to diabetes and healthcare expenditure due to diabetes present a large social, financial and health system burden across the world. [ABSTRACT FROM AUTHOR]

Objective Diabetes mellitus affects almost one in 10 individuals in Germany. So far, little is known about the diabetes prevalence in maximum care hospitals. We assessed the diabetes prevalence, proportion of undiagnosed cases, the effectiveness of diabetes screening in a university hospital, the consequences for hospital stay and acquired complications. Research Design and Methods Over a 4 week period we determined HbA1c from 3 733 adult patients which were hospitalized at the university hospital of Tuebingen and had an available blood sample. Diabetes diagnosis was defined as HbA1c ≥ 6.5 % and/or previously documented diabetes diagnosis, prediabetes was defined as HbA1c ≥ 5.7 % and < 6.5 % without history of previous diabetes. Results 23.68 % of the patients had prediabetes and 22.15 % had diabetes with a high variation between the specialised departments (range 5–43 %). The rate of unknown diabetes was 3.7 %, the number needed to screen was 17 in patients older than 50 years. Patients with diabetes had a prolonged hospital stay compared to the mean length of stay for their diagnosis related group (diabetes: 1.47 ± 0.24 days; no diabetes: − 0.18 ± 0.13 days, p = 0.0133). The prevalence of hospital acquired complications was higher in diabetic patients (diabetes: 197 of 630; no diabetes: 447 of 2 459, p < 0.0001). Conclusions Every fourth patient in the university hospital had diabetes and every second had either prediabetes or diabetes. It is also worthwhile to screen for unknown diabetes in patients over the age of 50. The high prevalence and negative consequences of diabetes require screening and intensified specialized diabetes treatment in hospitals. [ABSTRACT FROM AUTHOR]