*(Age-standardised incidence rates per 100,000 individuals per year with 95% confidence intervals. † For cells labeled as NA, 95% CIs could not be estimated as there was only 1 data point).

The objective of this study was to compare the effect of diabetes and pathologies potentially related to diabetes on the risk of infection and death from COVID-19 among people from Highly-Developed-Country (HDC), including Italians, and immigrants from the High-Migratory-Pressure-Countries (HMPC). Among the population with diabetes, whose prevalence is known to be higher among immigrants, we compared the effect of body mass index among HDC and HMPC populations. A population-based cohort study was conducted, using population registries and routinely collected surveillance data. The population was stratified into HDC and HMPC, according to the place of birth; moreover, a focus was set on the South Asiatic population. Analyses restricted to the population with type-2 diabetes were performed. We reported incidence (IRR) and mortality rate ratios (MRR) and hazard ratios (HR) with 95% confidence interval (CI) to estimate the effect of diabetes on SARS-CoV-2 infection and COVID-19 mortality. Overall, IRR of infection and MRR from COVID-19 comparing HMPC with HDC group were 0.84 (95% CI 0.82–0.87) and 0.67 (95% CI 0.46–0.99), respectively. The effect of diabetes on the risk of infection and death from COVID-19 was slightly higher in the HMPC population than in the HDC population (HRs for infection: 1.37 95% CI 1.22–1.53 vs. 1.20 95% CI 1.14–1.25; HRs for mortality: 3.96 95% CI 1.82–8.60 vs. 1.71 95% CI 1.50–1.95, respectively). No substantial difference in the strength of the association was observed between obesity or other comorbidities and SARS-CoV-2 infection. Similarly for COVID-19 mortality, HRs for obesity (HRs: 18.92 95% CI 4.48–79.87 vs. 3.91 95% CI 2.69–5.69) were larger in HMPC than in the HDC population, but differences could be due to chance. Among the population with diabetes, the HMPC group showed similar incidence (IRR: 0.99 95% CI: 0.88–1.12) and mortality (MRR: 0.89 95% CI: 0.49–1.61) to that of HDC individuals. The effect of obesity on incidence was similar in both HDC and HMPC populations (HRs: 1.73 95% CI 1.41–2.11 among HDC vs. 1.41 95% CI 0.63–3.17 among HMPC), although the estimates were very imprecise. Despite a higher prevalence of diabetes and a stronger effect of diabetes on COVID-19 mortality in HMPC than in the HDC population, our cohort did not show an overall excess risk of COVID-19 mortality in immigrants.

This dataset provides a comprehensive exploration of global sugar consumption patterns over six decades, synthesizing economic, agricultural, and public health data to uncover the drivers and consequences of rising sugar intake. Spanning 1960–2023, country-year entries for 200+ nations, with 25+ variables such as:

Economic Indicators: GDP per capita, urbanization rates, and retail sugar prices.

Agricultural Data: Sugarcane/beet production yields, climate suitability, and trade dynamics (imports/exports).

Health Metrics: Diabetes prevalence, obesity rates, and daily sugar intake.

Policy Interventions: Sugar taxes, subsidies, and national education campaigns.

Key Features: Temporal & Geographic Depth: Track regional shifts (e.g., Southeast Asia’s sugarcane boom, North America’s HFCS dominance) and long-term trends (e.g., 300% rise in per capita intake in developing nations post-2000).

Health-Economic Correlations: Model relationships between sugar consumption, GDP growth, and non-communicable diseases (e.g., +1kg/year sugar intake → +0.5% obesity rate).

Policy Impact Analysis: Evaluate the effectiveness of interventions like sugar taxes (5–15% consumption decline in adopters) or subsidies.

Synthetic Realism: Data mimics real-world sources (e.g., FAO, WHO) with logical constraints (e.g., Total_Sugar_Consumption = Population × Per_Capita).

Applications: Public Health: Identify nations at risk for diabetes/obesity epidemics.

Economic Policy: Analyze trade dependencies or subsidy impacts.

Agricultural Planning: Optimize crop yields based on climate trends.

Machine Learning: Predict future consumption or simulate policy outcomes.

📁 Dataset Overview: Diabetes Risk Factors Across African Communities This dataset is part of a broader healthcare analytics initiative focused on understanding and mitigating diabetes risk factors in various African regions. The data was generated and modeled based on real-world healthcare indicators to simulate key risk components, offering deep insights for public health monitoring, predictive modeling, and healthcare strategy development.

📊 Dataset Features The dataset captures individual- and community-level risk factors for diabetes, with the following key variables:

Column Name Description Community_ID Unique identifier for each community or region Country Country name (e.g., Nigeria, Kenya, Ghana) Region Specific region or city (e.g., Jos West, Accra East) Average_BMI Average Body Mass Index for the community Daily_Sugar_Intake Estimated average daily sugar consumption (grams) Weekly_Physical_Activity Total hours of physical activity per week Family_History_Diabetes Binary indicator (Yes/No) for genetic predisposition Population_At_High_Risk Proportion of the population classified as high risk (0–1 scale) High_Risk_Community Binary (1/0) flag identifying high-risk zones Diabetes_Risk_Score Composite score calculated from BMI, sugar intake, activity, and family history

🔬 Purpose & Application This dataset is intended to:

Support data storytelling and visual analytics in platforms like Power BI or Tableau.

Serve as a training dataset for machine learning models predicting diabetes risk.

Enable public health researchers and data analysts to simulate and explore intervention strategies across regions.

Encourage policy design by identifying communities at the highest risk for diabetes.

📈 Dashboard Use Case The dataset powers a Power BI dashboard (Project 8 of the “Emperor Healthcare Analytics Series”) with:

Region-specific heat maps

Risk categorization

KPI summaries (BMI, Risk Score, Activity, etc.)

Family history and behavior-based risk modeling

📌 Notes All values are synthetically generated and do not represent real patient data.

The dataset is designed for educational, research, and data product prototyping purposes.

📣 Creator Fatolu Peter (Emperor Data Analytics) A passionate data analyst using the power of visualization and machine learning to advance preventive healthcare across Africa.

Germany DE: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 6.900 % in 2021. This records an increase from the previous number of 5.300 % for 2011. Germany DE: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 6.100 % from Dec 2011 (Median) to 2021, with 2 observations. The data reached an all-time high of 6.900 % in 2021 and a record low of 5.300 % in 2011. Germany DE: Diabetes Prevalence: % of Population Aged 20-79 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Germany – Table DE.World Bank.WDI: Social: Health Statistics. Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes. It is calculated by adjusting to a standard population age-structure.;International Diabetes Federation, Diabetes Atlas.;Weighted average;

BackgroundHigh body mass index (BMI) plays a critical role in the initiation and development of type 2 diabetes (T2D). Up to now, far too little attention has been paid to the global burden of T2D attributable to high BMI. This study aims to report the deaths and disability-adjusted life years (DALYs) of T2D related to high BMI in 204 countries and territories from 1990 to 2019.MethodsData on T2D burden attributable to high BMI were retrieved from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019. The global cases, age-standardized rates of mortality (ASMR), and disability-adjusted life years (ASDR) attributable to high BMI were estimated by age, sex, geographical location, and socio-demographic index (SDI). The estimated annual percentage change (EAPC) was calculated to quantify the trends of ASMR and ASDR during the period 1990–2019.ResultsGlobally, there were 619,494.8 deaths and 34,422,224.8 DALYs of T2D attributed to high BMI in 2019, more than triple in 1990. Moreover, the pace of increase in ASMR and ASDR accelerated during 1990–2019, with EAPC of 1.36 (95% CI: 1.27 to 1.45) and 2.13 (95% CI: 2.10 to 2.17) separately, especially in men, South Asia, and low-middle SDI regions. Oceania was the high-risk area of standardized T2D deaths and DALYs attributable to high BMI in 2019, among which Fiji was the country with the heaviest burden. In terms of SDI, middle SDI regions had the biggest T2D-related ASMR and ASDR in 2019.ConclusionThe global deaths and DALYs of T2D attributable to high BMI substantially increased from 1990 to 2019. High BMI as a major public health problem needs to be tackled properly and timely in patients with T2D.

Diabetes is a chronic disease that affects millions of people worldwide. It is characterized by high blood glucose levels due to insufficient insulin production or action. Early detection and treatment of diabetes can prevent or delay serious complications, such as cardiovascular disease, kidney failure, nerve damage, and blindness. This dataset contains medical and demographic data of 768 patients, along with their diabetes status. It can be used to build and evaluate machine learning models for diabetes diagnosis, based on various features, such as glucose level, blood pressure, body mass index, age, etc.”

BackgroundTaxing sugar-sweetened beverages (SSBs) has been proposed in high-income countries to reduce obesity and type 2 diabetes. We sought to estimate the potential health effects of such a fiscal strategy in the middle-income country of India, where there is heterogeneity in SSB consumption, patterns of substitution between SSBs and other beverages after tax increases, and vast differences in chronic disease risk within the population.Methods and FindingsUsing consumption and price variations data from a nationally representative survey of 100,855 Indian households, we first calculated how changes in SSB price alter per capita consumption of SSBs and substitution with other beverages. We then incorporated SSB sales trends, body mass index (BMI), and diabetes incidence data stratified by age, sex, income, and urban/rural residence into a validated microsimulation of caloric consumption, glycemic load, overweight/obesity prevalence, and type 2 diabetes incidence among Indian subpopulations facing a 20% SSB excise tax. The 20% SSB tax was anticipated to reduce overweight and obesity prevalence by 3.0% (95% CI 1.6%–5.9%) and type 2 diabetes incidence by 1.6% (95% CI 1.2%–1.9%) among various Indian subpopulations over the period 2014–2023, if SSB consumption continued to increase linearly in accordance with secular trends. However, acceleration in SSB consumption trends consistent with industry marketing models would be expected to increase the impact efficacy of taxation, averting 4.2% of prevalent overweight/obesity (95% CI 2.5–10.0%) and 2.5% (95% CI 1.0–2.8%) of incident type 2 diabetes from 2014–2023. Given current consumption and BMI distributions, our results suggest the largest relative effect would be expected among young rural men, refuting our a priori hypothesis that urban populations would be isolated beneficiaries of SSB taxation. Key limitations of this estimation approach include the assumption that consumer expenditure behavior from prior years, captured in price elasticities, will reflect future behavior among consumers, and potential underreporting of consumption in dietary recall data used to inform our calculations.ConclusionSustained SSB taxation at a high tax rate could mitigate rising obesity and type 2 diabetes in India among both urban and rural subpopulations.Please see later in the article for the Editors' Summary

BackgroundHealth literacy (HL) skills are essential to enable self-management and shared decision-making in patients with type 2 diabetes mellitus (T2DM). Limited HL in these patients is associated with poorer outcomes. It is not clear what the burden of limited HL in patients with T2DM across countries and what factors influence it.MethodsA systematic review was conducted according to the PRISMA guidelines. The study protocol was registered with PROSPERO (CRD42017056150). We searched MEDLINE, EMBASE, PsycINFO, CINAHL and ERIC for articles published up to January 2017. Articles that measured HL levels in adult patients with T2DM; that used validated HL tools; and that were reported in English were included. Two reviewers assessed studies for eligibility and quality, and extracted the data. Prevalence of limited HL is calculated from the number of patients with less than adequate HL over the total number of patients with T2DM in the study. Meta-analysis and meta-regression analysis were conducted using the Open Meta-analyst software.ResultsTwenty-nine studies involving 13,457 patients with T2DM from seven countries were included. In total, seven different HL measurement tools were used. The prevalence of limited HL ranged from 7.3% to 82%, lowest in Switzerland and the highest in Taiwan. Meta-regression analysis of all included studies showed the country of study (p<0.001), HL tool used (p = 0.002), and the country’s region (p<0.001) contributed to the variation findings. Thirteen studies in the USA measured functional HL. The pooled prevalence of inadequate functional HL among patients with T2DM in the USA was 28.9% (95% CI: 20.4–37.3), with high heterogeneity (I2 = 97.9%, p <0.001). Studies were done in the community as opposed to a hospital or primary care (p = 0.005) and populations with education level lower than high school education (p = 0.009) reported a higher prevalence of limited HL.ConclusionThe prevalence of limited HL in patients with T2DM varied widely between countries, HL tools used and the country’s region. Pooled prevalence showed nearly one in three patients with T2DM in the USA had limited functional HL. Interactions with healthcare providers and educational attainment were associated with reported of prevalence in the USA.

Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 1.900 % in 2015. This records an increase from the previous number of 1.000 % for 2010. Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 2.100 % from Dec 1987 (Median) to 2015, with 6 observations. The data reached an all-time high of 4.700 % in 2006 and a record low of 0.500 % in 1987. Mali ML: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.World Bank: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

Background: Diabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. More than 1 million people living with diabetes are acutely admitted to hospital due to complications of their illness every year. Complications include Diabetic emergencies such as Diabetic Comas, Hypoglycaemia, Diabetic ketoacidosis and Diabetic Hyperosmolar Hyperglycaemic State. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. This dataset includes acute all diabetic admissions to University Hospitals Birmingham NHS Trust from 2000 onwards with linked eye data including the national screening diabetic grade category (seven categories from R0M0 to R3M1) from the Birmingham, Solihull and Black Country DR screening program (a member of the National Health Service (NHS) Diabetic Eye Screening Programme) and the University Hospitals Birmingham NHS Trust Ophthalmology clinic at Queen Elizabeth Hospital, Birmingham .

Geography: The West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.

Data sources:
1. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 diabetic patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic eye screening scheme in Europe. 2. The Electronic Health Records held at University Hospitals Birmingham NHS Foundation Trust is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. UHB runs a fully electronic healthcare record both for systemic disease as well as the Ophthalmology records.

Scope: All hospitalised patients admitted to UHB with a diabetes related health concern from 2000 onwards. Longitudinal and individually linked with their diabetic eye care from primary screening data and secondary care ophthalmology data including • Demographic information (including age, sex and ethnicity) • Diabetes status • Diabetes type • Length of time since diagnosis of diabetes • Visual acuity • The national screening diabetic screening grade category (seven categories from R0M0 to R3M1) • Diabetic eye clinical features • Reason for sight and severe sight impairment • ICD-10 and SNOMED-CT codes pertaining to diabetes • Diagnosis for the acute/emergency admission • Co-morbid conditions • Medications • Outcome

www.insight.hdrhub.org/about-us

Background: Diabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. More than 1 million people living with diabetes are acutely admitted to hospital due to complications of their illness every year. Cardiovascuar disease is the most prevalent cause of morbidity and mortality in people with diabetes. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. This dataset includes the national screening diabetic grade category (seven categories from R0M0 to R3M1) from the Birmingham, Solihull and Black Country DR screening program (a member of the National Health Service (NHS) Diabetic Eye Screening Programme) and the University Hospitals Birmingham NHS Trust cardiac outcome data.

Geography: The West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.

Data sources:
1. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 diabetic patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic eye screening scheme in Europe. 2. The Electronic Health Records held at University Hospitals Birmingham NHS Foundation Trust is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. UHB runs a fully electronic healthcare record for systemic disease.

Scope: All Birmingham, Solihull and Black Country diabetic eye screened participants who have been admitted to UHB with a cardiac related health concern from 2006 onwards. Longitudinal and individually linked with their diabetic eye care from primary screening data and secondary care hospital cardiac outcome data including • Demographic information (including age, sex and ethnicity) • Diabetes status • Diabetes type • Length of time since diagnosis of diabetes • Visual acuity • The national screening diabetic screening grade category (seven categories from R0M0 to R3M1) • Diabetic eye clinical features • Reason for sight and severe sight impairment • ICD-10 and SNOMED-CT codes pertaining to cardiac disease • Outcome

Website: https://www.retinalscreening.co.uk/

CF: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 1.500 % in 2019. This records an increase from the previous number of 0.700 % for 2018. CF: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 1.850 % from Dec 1994 (Median) to 2019, with 8 observations. The data reached an all-time high of 11.000 % in 2000 and a record low of 0.700 % in 2018. CF: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Central African Republic – Table CF.World Bank.WDI: Social: Health Statistics. Prevalence of overweight, female, is the percentage of girls under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;;Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.

Background: Diabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. This dataset includes the national screening diabetic grade category (seven categories from R0M0 to R3M1) from the Birmingham, Solihull and Black Country DR screening program (a member of the National Health Service (NHS) Diabetic Eye Screening Programme) and the Queen Elizabeth Hospital, University Hospitals Birmingham NHS Trust ophthalmology treatment and visual outcome data.

Geography: The West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.

Data sources:
1. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 diabetic patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic eye screening scheme in Europe. 2. The Electronic Health Records from the Ophthalmology clinic at Queen. Elizabeth Hospital, University Hospitals Birmingham NHS Foundation.

Scope: All Birmingham, Solihull and Black Country diabetic eye screened participants who have been see in ophthalmology outpatients at University Hospitals Birmingham NHS Foundation from 2006 onwards. Longitudinal and individually linked with their diabetic eye care from primary screening data and secondary hospital eye care including • Demographic information (including age, sex and ethnicity) • Diabetes status • Diabetes type • Length of time since diagnosis of diabetes • Visual acuity • The national screening diabetic screening grade category (seven categories from R0M0 to R3M1) • Diabetic eye clinical features • Reason for sight and severe sight impairment • Ocular treatment including laser treatment and surgical treatment • Visual Outcome

There are two data sets of eye scans available. The first of these is a set fundus images of which the are c. 7.0 million. The other is a set of OCT scans of which there are c. 440, 000.

This dataset contains routine clinical ophthalmology data for every patient who have been seen at Queen Elizabeth Hospital and the Birmingham, Solihull and Black Country Diabetic Retinopathy screening program at University Hospitals Birmingham NHS Foundation Trust, with longitudinal follow-up for 15 years. Key data included are: • Total number of patients. • Demographic information (including age, sex and ethnicity) • Past ocular history • Intravitreal injections • Length of time since eye diagnosis • Visual acuity • The national screening diabetic grade category (seven categories from R0M0 to R3M1) • Reason for sight and severe sight impairment

Geography University Hospitals Birmingham is set within the West Midlands and it has a catchment population of circa 5.9million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.

Data source: Ophthalmology department at Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic screening scheme in Europe.

Pathway: The routine secondary care follow-up in the hospital eye services for all ophthalmic diseases at Queen Elizabeth Hospital. The Birmingham, Solihull and Black Country dataset is representative of the patient pathway for community screening and grading of diabetic eye disease.

BackgroundThe prevalence of diabetes is increasing rapidly in low- and middle-income countries (LMICs), urgently requiring detailed evidence to guide the response of health systems to this epidemic. In an effort to understand at what step in the diabetes care continuum individuals are lost to care, and how this varies between countries and population groups, this study examined health system performance for diabetes among adults in 28 LMICs using a cascade of care approach.Methods and findingsWe pooled individual participant data from nationally representative surveys done between 2008 and 2016 in 28 LMICs. Diabetes was defined as fasting plasma glucose ≥ 7.0 mmol/l (126 mg/dl), random plasma glucose ≥ 11.1 mmol/l (200 mg/dl), HbA1c ≥ 6.5%, or reporting to be taking medication for diabetes. Stages of the care cascade were as follows: tested, diagnosed, lifestyle advice and/or medication given (“treated”), and controlled (HbA1c < 8.0% or equivalent). We stratified cascades of care by country, geographic region, World Bank income group, and individual-level characteristics (age, sex, educational attainment, household wealth quintile, and body mass index [BMI]). We then used logistic regression models with country-level fixed effects to evaluate predictors of (1) testing, (2) treatment, and (3) control. The final sample included 847,413 adults in 28 LMICs (8 low income, 9 lower-middle income, 11 upper-middle income). Survey sample size ranged from 824 in Guyana to 750,451 in India. The prevalence of diabetes was 8.8% (95% CI: 8.2%–9.5%), and the prevalence of undiagnosed diabetes was 4.8% (95% CI: 4.5%–5.2%). Health system performance for management of diabetes showed large losses to care at the stage of being tested, and low rates of diabetes control. Total unmet need for diabetes care (defined as the sum of those not tested, tested but undiagnosed, diagnosed but untreated, and treated but with diabetes not controlled) was 77.0% (95% CI: 74.9%–78.9%). Performance along the care cascade was significantly better in upper-middle income countries, but across all World Bank income groups, only half of participants with diabetes who were tested achieved diabetes control. Greater age, educational attainment, and BMI were associated with higher odds of being tested, being treated, and achieving control. The limitations of this study included the use of a single glucose measurement to assess diabetes, differences in the approach to wealth measurement across surveys, and variation in the date of the surveys.ConclusionsThe study uncovered poor management of diabetes along the care cascade, indicating large unmet need for diabetes care across 28 LMICs. Performance across the care cascade varied by World Bank income group and individual-level characteristics, particularly age, educational attainment, and BMI. This policy-relevant analysis can inform country-specific interventions and offers a baseline by which future progress can be measured.

Background: With increased awareness of type 2 diabetes (T2D) in children and adolescents, an overview of country-specific differences in epidemiology data is needed to develop a global picture of the disease development. Summary: This study examined country-specific prevalence and incidence data of youth-onset T2D published between 2008 and 2019, and searched for national guidelines to expand the understanding of country-specific similarities and differences. Of the 1,190 articles and 17 congress abstracts identified, 58 were included in this review. Our search found the highest reported prevalence rates of youth-onset T2D in China (520 cases/100,000 people) and the USA (212 cases/100,000) and lowest in Denmark (0.6 cases/100,000) and Ireland (1.2 cases/100,000). However, the highest incidence rates were reported in Taiwan (63 cases/100,000) and the UK (33.2 cases/100,000), with the lowest in Fiji (0.43 cases/100,000) and Austria (0.6 cases/100,000). These differences in epidemiology data may be partly explained by variations in the diagnostic criteria used within studies, screening recommendations within national guidelines and race/ethnicity within countries. Key Messages: Our study suggests that published country-specific epidemiology data for youth-onset T2D are varied and scant, and often with reporting inconsistencies. Finding optimal diagnostic criteria and screening strategies for this disease should be of high interest to every country. Trial Registration: Not applicable.

Aim: Diabetic peripheral neuropathy (DPN) is a common, severe microvascular complication of diabetes. Our study was to assess prevalence and risk factors for DPN in subjects with type 2 diabetes from 14 different countries.Methods: A total of 2,733 subjects with type 2 diabetes aged 18–65 years (45.3% men, mean duration of diabetes = 8.8 years) were included to perform this International Prevalence and Treatment of Diabetes and Depression (INTERPRET-DD) study in 14 countries. After a structured questionnaire was used in face-to-face interviews to collect sociodemographic characteristics and medical records of the participating subjects, laboratory tests were carried out for clinical measurement. Depressive symptoms were diagnosed and measured using the Patient Health Questionnaire-9. The potential risk factors for DPN were determined by multilevel mixed-effects logistic regression, accounting for clustering of participants within the country. Robustness of the estimates was assessed by sensitivity analysis.Results: The overall prevalence of DPN across different countries was 26.71%, whereas country-specific prevalences showed considerable variation. Multivariate analysis revealed that duration of diabetes (OR: 1.08 per 1-year increase, 95% CI: 1.06–1.09), poor glycemic control (OR: 1.11 per 1% increase in HbA1c, 95% CI: 1.05–1.18), and history of hypertension (OR: 1.58, 95% CI: 1.18–2.12), cardiovascular disease (OR: 2.07, 95% CI: 1.55–2.78) and depressive symptoms (OR: 1.92, 95% CI: 1.43–2.58) were independently and positively associated with the risk of DPN. Sensitivity analyses including or excluding patients from countries with extreme low or high prevalence of DPN yielded similar estimates in terms of trend and magnitude.Conclusions: This international study illustrates that more than a quarter of individuals with type 2 diabetes developed DPN. The prevalence was positively associated with the duration of diabetes, poor glycemic control, and history of hypertension, cardiovascular disease and depressive symptoms.

Cambodia KH: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 7.300 % in 2021. This records an increase from the previous number of 2.900 % for 2011. Cambodia KH: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 5.100 % from Dec 2011 (Median) to 2021, with 2 observations. The data reached an all-time high of 7.300 % in 2021 and a record low of 2.900 % in 2011. Cambodia KH: Diabetes Prevalence: % of Population Aged 20-79 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cambodia – Table KH.World Bank.WDI: Social: Health Statistics. Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes. It is calculated by adjusting to a standard population age-structure.;International Diabetes Federation, Diabetes Atlas.;Weighted average;

IntroductionSecondhand smoke (SHS) exposure represents an underappreciated global health risk for type 2 diabetes mellitus (T2DM), with complex epidemiological implications.MethodsLeveraging the comprehensive Global Burden of Disease (GBD) 2021 dataset, we systematically evaluated the worldwide burden of type 2 diabetes mellitus attributable to secondhand smoke (T2DM-SHS) across 204 countries. The analysis encompassed both death and disability-adjusted life years (DALYs) across various genders, age groups, and 204 nations over the period from 1990 to 2021. We examined trends and socioeconomic impacts by analyzing age-standardized DALYs rates and estimated annual percentage changes, stratified by socio-demographic Index (SDI) quintiles.ResultsThe following changes occurred between 1990 and 2021: while age-standardized mortality rates decreased by 8.903% (95% UI: -16.824% to -1.399%), DALYs increased by 17.049% (95% UI: 9.065% to 25.557%). Age-stratified analysis revealed peak death in the 70–74 years group, with females experiencing highest DALYs in the 75–79 years group and males in the 90–94 years group. An inverted U-shaped relationship between SDI and disease burden emerged, with peak rates at moderate SDI levels.DiscussionDespite lowest burdens in high-income countries, disease dynamics were most complex in middle-range SDI countries, indicating that economic development does not linearly correlate with health outcomes. This comprehensive analysis unveils the multifaceted global landscape of T2DM-SHS, exposing critical disparities across gender, age, and socioeconomic contexts. The findings urgently call for targeted, context-specific public health interventions, particularly in low- and middle-income countries, to mitigate the escalating T2DM-SHS burden.