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;

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.

Armenia AM: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 5.600 % in 2021. This records a decrease from the previous number of 8.500 % for 2011. Armenia AM: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 7.050 % from Dec 2011 (Median) to 2021, with 2 observations. The data reached an all-time high of 8.500 % in 2011 and a record low of 5.600 % in 2021. Armenia AM: 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 Armenia – Table AM.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;

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.

Diabetes mellitus affects over 3.9 million people in the 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. The National Institute for Health and Care Excellence recommendations are for annual screening using digital retinal photography for all patients with diabetes aged 12 years and over until such time as specialist surveillance or referral to Hospital Eye Services (HES) is required.

Birmingham, Solihull and Black Country DR screening program is a member of the National Health Service (NHS) Diabetic Eye Screening Programme. This dataset contains routine community annual longitudinal screening patient results of over 200000 patients with screening results per patient ranging from 1 year to 15 years. Key data included are: • Total number of patients screened and graded over a 15 year period. • 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 • Screening Outcome (digital surveillance and time; referral to HES)

Geography Birmingham, Solihull and Black Country is set within the West Midlands and has a 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: 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.

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

Pathway: The Birmingham, Solihull and Black Country dataset is representative of the patient pathway for community screening and grading of diabetic eye disease. It covers standard UK Public Health England Diabetic Eye Screening requirements and will include patients receiving screening through the standard model, routine diabetic screening, surveillance and slit lamp examination.

AimTo report the point prevalence, deaths and disability-adjusted-life-years (DALYs) due to type 2 diabetes and its attributable risk factors in 204 countries and territories during the period 1990-2019.MethodsWe used the data of the Global Burden of Disease (GBD) Study 2019 to report number and age-standardised rates per 100 000 population of type 2 diabetes. Estimates were reported with 95% uncertainty intervals (UIs).ResultsIn 2019, the global age-standardised point prevalence and death rates for type 2 diabetes were 5282.9 and 18.5 per 100 000, an increase of 49% and 10.8%, respectively, since 1990. Moreover, the global age-standardised DALY rate in 2019 was 801.5 per 100 000, an increase of 27.6% since 1990. In 2019, the global point prevalence of type 2 diabetes was slightly higher in males and increased with age up to the 75-79 age group, decreasing across the remaining age groups. American Samoa [19876.8] had the highest age-standardised point prevalence rates of type 2 diabetes in 2019. Generally, the burden of type 2 diabetes decreased with increasing SDI (Socio-demographic Index). Globally, high body mass index [51.9%], ambient particulate matter pollution [13.6%] and smoking [9.9%] had the three highest proportions of attributable DALYs.ConclusionLow and middle-income countries have the highest burden and greater investment in type 2 diabetes prevention is needed. In addition, accurate data on type 2 diabetes needs to be collected by the health systems of all countries to allow better monitoring and evaluation of population-level interventions.

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

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. The National Institute for Health and Care Excellence recommendations are for annual screening using digital retinal photography for all patients with diabetes aged 12 years and over until such time as specialist surveillance or referral to Hospital Eye Services (HES) is required.

Birmingham, Solihull and Black Country DR screening program is a member of the National Health Service (NHS) Diabetic Eye Screening Programme. This dataset contains routine community annual longitudinal screening patient results of over 200000 patients with screening results per patient ranging from 1 year to 15 years. Key data included in this imaging dataset are: • Fundal photographs • The national screening diabetic grade category (seven categories from R0M0 to R3M1) • Screening Outcome (digital surveillance and time; referral to HES)

Geography Birmingham, Solihull and Black Country is set within the West Midlands and has a 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: 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.

Intro

This open dataset aggregates all known cause of deaths (diseases and accidents). It contains sex and age group-specific deaths. The novelty lies in the entity relations using weighted biological causations and not only correlations. Data for France are curated and ready. Data for the USA in work (see Tasks). To help review, curate or correct any information from the dataset, please see Tasks and/or use the Kaggle discussion section of this dataset. Thank you.

Novelties/Differentiations

1)causality relations between all diseases/causes and detailed data (m/f, age group) in a single dataset 2)dataset focuses on causality/biology mechanisms and excludes correlations without causation (correlation does not mean causation) 3)add dimensions and causations between concepts: root -> indirect(s) -> direct e.g. "infection by VIH" -> "AIDS" -> "infection" -> "pneumonia" -> "low oxygen in blood"

How to cite/reuse this work?

Mentioned this source as ""Multi-Level causality relations of underlying of deaths with age, sex and country stratifications, Schicklin, C., Version [version number]. https://doi.org/10.34740/kaggle/dsv/2161283 Retrieved [month] [day], [year]"". Before publishing any rework, reuse, commercially or non-commercially, please send the info to the contact contributor https://www.kaggle.com/cedricschicklin "Contact User". This is a non-blocking step for information and data tracking only. For reuse in entity relation analysis, it is recommended to use the JSON parsable "predecessor_array".

Quality Charter

Data sources for each total deaths country-specific are available in sources_total_deaths_[COUNTRY] or computed via causality. Data sources for each causality mechanisms are available in sources_total_deaths_[COUNTRY]. Weights in the predecessor arrays are computed using total deaths for a country and are not recomputed per sub-group. "Unknown"-like concepts in the dataset means that the concept has not discovered yet as per the best knowledge of contributors from available scientific information. quality columns indicate if the source is extensive, and among others, level on knowledge. Please see the column header description for more information. @contributors: only mention allowed data sources (check if copyrighted, and at minimum reusable for non-commercial use), if any, add copyright specific information in the source column after the source data.

Format

CSV with JSON arrays. The decimal sign is the point. The comma is only allowed in source CSV quoted text e.g. [{"predecessor_id":"ischemia","impact":"0.601"},{"predecessor_id":"kidney cancer","impact":"0.142"},{"predecessor_id":"diabetes","impact":"0.256"}]

Disclaimer

This compilation of data is intended for data scientists only and not for patients. Please use this project for health statistical reporting and analysis only. For health issues, please consult a medical doctor. This compilation is a collaborative work distributed without any warranty. Clause de non-responsabilité: les autheurs et les évaluateurs ne sont pas responsable de l'usage qui pourrait être fait des informations données ci-après.

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

The size of the Germany Continuous Glucose Monitoring Industry market was valued at USD 1.69 Million in 2023 and is projected to reach USD 2.80 Million by 2032, with an expected CAGR of 7.47% during the forecast period. The business of continuous glucose monitoring in Germany is a fast-developing entity that plays a significant role in diabetes management. Continuous glucose monitoring systems are a non-invasive method of measuring glucose levels continuously, utilizing glucose present in the interstitial fluid and delivering real-time data to the patient and the healthcare provider. It has managed to put an innovative aspect into diabetes management by giving the patient complete leverage over the decisions regarding insulin dosage, diet, and exercise patterns. Germany is the country that has experienced the highest adoption of CGM use with a well-functioning healthcare system and over one million individuals diagnosed with diabetes. Germany has a good structure in terms of diabetes clinics and experts who can utilize it properly. In addition, the German government took many initiatives that promoted the application of CGM, such as reimbursement programs and educational campaigns. The use of CGM in diabetic patients has several benefits in Germany. This enables patients to be in control of their own situation by monitoring their glucose consistently, upon which they could derive trends and patterns. Based on such information, they might alter their insulin dosage, diet, and exercise for optimal glucose regulation. Furthermore, CGM can prevent hypoglycemia, or low blood sugar, which is dangerous and hyperglycemia, or high blood sugar, which is equally as dangerous. For healthcare providers, CGM provides critical information about diabetes management. Doctors can question CGM data relating to treatment plan appropriateness, anticipate potential issues, and make adjustments concerning medication therapy. Lifestyle interventions, such as diet and exercise, can also be monitored in the context of glucose levels. German CGM is becoming increasingly diverse in terms of the composition of players with major multinational corporations and local manufacturers to innovative start-ups competing with each other. They are continuously working on innovations to develop new technologies and improve the existing CGM systems for the betterment of patient experience and control over diabetes management. The competitive landscape is, therefore, encouraging innovation and, consequently driving more advanced CGM solutions for Germany. It is therefore that far the German continuous glucose monitoring industry concludes as a booming sector with significantly high contributions in terms of management of diabetes. In general, the common CGM in Germany has spread empowerment among the patients toward managing their condition, and healthcare providers have good tools to better diabetes care. The German industry of continuous glucose monitoring is forward towards taking a leading role in forming the future of diabetes management. Recent developments include: January 2023: LifeScan announced that the peer-reviewed Journal of Diabetes Science and Technology published Improved Glycemic Control Using a Bluetooth Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence From Over 144,000 People With Diabetes, detailing results from a retrospective analysis of real-world data from over 144,000 people with diabetes - one of the largest combined blood glucose meter and mobile diabetes app datasets ever published., January 2022: Roche launched its new point-of-care blood glucose monitor designed for hospital professionals, with a companion device shaped like a touchscreen smartphone that will run its own apps. The hand-held Cobas pulse includes an automated glucose test strip reader as well as a camera and touchscreen for logging other diagnostic results. It's designed to be used with patients of all ages, including neonates and people in intensive care.. Key drivers for this market are: Rising Prevalence of Cancer Worldwide, Technological Advancements in Diagnostic Testing; Increasing Demand for Point-of-care Treatment. Potential restraints include: High Cost of Molecular Diagnostic Tests, Lack of Skilled Workforce and Stringent Regulatory Framework. Notable trends are: Rising diabetes prevalence is likely to boost the market studied over the forecast period..

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

Algeria DZ: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 12.800 % in 2019. This records an increase from the previous number of 12.400 % for 2012. Algeria DZ: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 12.900 % from Dec 1992 (Median) to 2019, with 7 observations. The data reached an all-time high of 15.100 % in 2002 and a record low of 8.700 % in 1992. Algeria DZ: 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 Algeria – Table DZ.World Bank.WDI: Social: 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 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.;See SH.STA.OWGH.ME.ZS for aggregation;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.

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/

The Synthetic Colorectal Cancer Global Dataset is a fully anonymised, high-dimensional synthetic dataset designed for global cancer research, predictive modelling, and educational use. It encompasses demographic, clinical, lifestyle, genetic, and healthcare access factors relevant to colorectal cancer incidence, outcomes, and survivability.

Dataset Features

• Patient_ID: Unique identifier for each patient.
• Country: Patient's country of residence.
• Age: Age at diagnosis (in years).
• Gender: Biological sex of the patient (Male/Female/Other).
• Cancer_Stage: Stage of colorectal cancer at diagnosis (e.g., Stage I–IV).
• Tumor_Size_mm: Size of the tumor in millimeters.
• Family_History: Presence of colorectal cancer in family history (True/False).
• Smoking_History: Smoking behavior or history (e.g., Current, Former, Never).
• Alcohol_Consumption: Level of alcohol consumption (e.g., High, Moderate, None).
• Obesity_BMI: BMI classification related to obesity.
• Diet_Risk: Diet-related cancer risk (e.g., High Fat, Low Fiber).
• Physical_Activity: Level of physical activity (e.g., Sedentary, Active).
• Diabetes: Diabetes diagnosis (True/False).
• Inflammatory_Bowel_Disease: Presence of IBD (True/False).
• Genetic_Mutation: Genetic mutations relevant to colorectal cancer (e.g., APC, KRAS).
• Screening_History: History of cancer screenings (True/False).
• Early_Detection: Whether cancer was detected early (True/False).
• Treatment_Type: Primary treatment type (e.g., Surgery, Chemotherapy, Radiation).
• Survival_5_years: 5-year survival status (True/False).
• Mortality: Mortality outcome (Alive/Deceased).
• Healthcare_Costs: Estimated treatment costs (in USD).
• Incidence_Rate_per_100K: Country-level incidence rate per 100,000 people.
• Mortality_Rate_per_100K: Country-level mortality rate per 100,000 people.
• Urban_or_Rural: Patient's living area (Urban/Rural).
• Economic_Classification: Country's economic level (e.g., Low, Middle, High income).
• Healthcare_Access: Access level to healthcare services (e.g., Good, Limited).
• Insurance_Status: Insurance coverage status (Insured/Uninsured).
• Survival_Prediction: Model-derived survival prediction (probability or binary).

Distribution

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Usage

This dataset can be used for:

• Global Cancer Research: Analyze how clinical, lifestyle, and socioeconomic factors affect colorectal cancer outcomes worldwide.
• Predictive Modeling: Develop models to estimate survival probability or treatment outcomes.
• Healthcare Policy Analysis: Study disparities in healthcare access and outcomes across countries.
• Educational Use: Support training in epidemiology, oncology, public health, and machine learning.

Coverage

The dataset includes 100% synthetic yet clinically plausible records from diverse countries and demographic groups. It is anonymized and modeled to reflect real-world variability in risk factors, diagnosis stages, treatment, and survival without compromising patient privacy.

License

CC0 (Public Domain)

Who Can Use It

• Epidemiologists and Medical Researchers: To explore global patterns in colorectal cancer.
• Public Health Experts and Policymakers: For assessing equity in healthcare access and cancer outcomes.
• Data Scientists and Educators: As a rich dataset for teaching data analysis, classification, regression, and health informatics.

The European blood glucose monitoring market, valued at €3.14 billion in 2025, is projected to experience robust growth, driven by the rising prevalence of diabetes across the region. A compound annual growth rate (CAGR) of 6.38% from 2025 to 2033 indicates a significant market expansion, fueled by several key factors. Increased awareness of diabetes management, coupled with advancements in technology leading to more accurate and user-friendly glucometers, test strips, and lancets, are primary drivers. The aging population in Europe, a significant risk factor for diabetes, further contributes to market growth. Furthermore, the increasing adoption of continuous glucose monitoring (CGM) systems, offering real-time data and improved patient management, is expected to significantly impact market segmentation and growth trajectory over the forecast period. However, high costs associated with CGM technology and the availability of less expensive alternative monitoring methods might act as market restraints to some extent. The market is segmented by component into glucometer devices, test strips, and lancets, with significant variations in market share among these components. Leading players like Abbott Diabetes Care, Roche Diabetes Care, and LifeScan, hold substantial market shares, leveraging their established brand recognition and extensive distribution networks. The market demonstrates a strong regional disparity within Europe, with countries like Germany, France, and the UK likely contributing the largest shares, driven by higher diabetes prevalence and better healthcare infrastructure compared to other regions. The competitive landscape is characterized by both established multinational companies and smaller regional players. Strategic alliances, mergers and acquisitions, and technological innovation are prominent strategies employed by market players to gain a competitive edge. The forecast period from 2025 to 2033 promises sustained growth, propelled by the continued rise in diabetes prevalence and the ongoing development of innovative blood glucose monitoring technologies. While pricing pressures and regulatory hurdles may present challenges, the overall outlook for the European blood glucose monitoring market remains positive, promising significant opportunities for market participants. Further analysis of specific regional markets (France, Germany, Italy, Spain, UK, Russia, and Rest of Europe) would provide granular insights into market dynamics and inform targeted strategies. Recent developments include: January 2023: LifeScan announced that the peer-reviewed Journal of Diabetes Science and Technology published improved glycemic control using a Bluetooth-connected blood glucose meter and a mobile diabetes app: Real-World Evidence from over 144,000 people with diabetes, detailing results from a retrospective analysis of real-world data from over 144,000 people with diabetes-one of the largest combined blood glucose meter and mobile diabetes app datasets ever published., January 2022: Roche launched its new point-of-care blood glucose monitor designed for hospital professionals, with a companion device shaped like a touchscreen smartphone that will run its own apps. The hand-held Cobas pulse includes an automated glucose test strip reader as well as a camera and touchscreen for logging other diagnostic results. It's designed to be used with patients of all ages, including neonates and people in intensive care.. Notable trends are: Increasing Diabetes Prevalence is Driving the Europe self-monitoring Blood Glucose Devices Market in forecast period.

Azerbaijan Diabetes Prevalence: % of Population Aged 20-79 data was reported at 5.600 % in 2021. This records an increase from the previous number of 2.800 % for 2011. Azerbaijan Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 4.200 % from Dec 2011 (Median) to 2021, with 2 observations. The data reached an all-time high of 5.600 % in 2021 and a record low of 2.800 % in 2011. Azerbaijan 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 Azerbaijan – Table AZ.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;

Chad TD: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 5.800 % in 2021. This records an increase from the previous number of 3.900 % for 2011. Chad TD: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 4.850 % from Dec 2011 (Median) to 2021, with 2 observations. The data reached an all-time high of 5.800 % in 2021 and a record low of 3.900 % in 2011. Chad TD: 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 Chad – Table TD.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;

Monaco MC: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 6.200 % in 2021. This records an increase from the previous number of 5.600 % for 2011. Monaco MC: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 5.900 % from Dec 2011 (Median) to 2021, with 2 observations. The data reached an all-time high of 6.200 % in 2021 and a record low of 5.600 % in 2011. Monaco MC: 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 Monaco – Table MC.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;

The size of the Saudi Arabia Self-Monitoring Blood Glucose Devices Market was valued at USD 117 Million in 2023 and is projected to reach USD 180.86 Million by 2032, with an expected CAGR of 6.42% during the forecast period. Growth in the Saudi Arabia Self-Monitoring Blood Glucose Devices market can be attributed to the increasing prevalence of diabetes and increasing awareness of self-care among patients. Self-Monitoring Blood Glucose Devices are medical devices that enable patients to measure their blood sugar levels in the comfort of home. It is an important part of diabetic management as it enables the tracking of a patient's glucose levels and monitoring of the insulin dosage required; this information will drive the decisions regarding diet and exercise. The SMBGs are highly in use throughout the country among the diabetic population in Saudi Arabia. These machines give the patients much control over their health condition, thus preventing complications of hypoglycemia and hyperglycemia. Through frequent checking and adjustment of their blood glucose levels, patients prevent inappropriate aggravation of the disease processes. SMBG allows individuals to be preventive of their diabetes, meaning they are able to enjoy a better quality of life while being cost-effective as well as effective in management. Recent developments include: January 2023: LifeScan announced that the peer-reviewed Journal of Diabetes Science and Technology published Improved Glycemic Control Using a Bluetooth-Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence From Over 144,000 People With Diabetes, detailing results from a retrospective analysis of real-world data from over 144,000 people with diabetes-one of the largest combined blood glucose meter and mobile diabetes app datasets ever published., January 2022: Roche launched its new point-of-care blood glucose monitor designed for hospital professionals, with a companion device shaped like a touchscreen smartphone that will run its own apps. The hand-held Cobas Pulse included an automated glucose test strip reader as well as a camera and touchscreen for logging other diagnostic results. It was designed to be used with patients of all ages, including neonates and people in intensive care.. Key drivers for this market are: Rising Prevalence of Cancer Worldwide, Technological Advancements in Diagnostic Testing; Increasing Demand for Point-of-care Treatment. Potential restraints include: High Cost of Molecular Diagnostic Tests, Lack of Skilled Workforce and Stringent Regulatory Framework. Notable trends are: Rising Diabetes Prevalence in Saudi Arabia.