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.

*(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).

📁 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.

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.

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.

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;

Context

The Heart Attack Risk Prediction Dataset serves as a valuable resource for delving into the intricate dynamics of heart health and its predictors. Heart attacks, or myocardial infarctions, continue to be a significant global health issue, necessitating a deeper comprehension of their precursors and potential mitigating factors. This dataset encapsulates a diverse range of attributes including age, cholesterol levels, blood pressure, smoking habits, exercise patterns, dietary preferences, and more, aiming to elucidate the complex interplay of these variables in determining the likelihood of a heart attack. By employing predictive analytics and machine learning on this dataset, researchers and healthcare professionals can work towards proactive strategies for heart disease prevention and management. The dataset stands as a testament to collective efforts to enhance our understanding of cardiovascular health and pave the way for a healthier future.

Content

This synthetic dataset provides a comprehensive array of features relevant to heart health and lifestyle choices, encompassing patient-specific details such as age, gender, cholesterol levels, blood pressure, heart rate, and indicators like diabetes, family history, smoking habits, obesity, and alcohol consumption. Additionally, lifestyle factors like exercise hours, dietary habits, stress levels, and sedentary hours are included. Medical aspects comprising previous heart problems, medication usage, and triglyceride levels are considered. Socioeconomic aspects such as income and geographical attributes like country, continent, and hemisphere are incorporated. The dataset, consisting of 8763 records from patients around the globe, culminates in a crucial binary classification feature denoting the presence or absence of a heart attack risk, providing a comprehensive resource for predictive analysis and research in cardiovascular health.

Dataset Glossary (Column-wise)

• Patient ID - Unique identifier for each patient
• Age - Age of the patient
• Sex - Gender of the patient (Male/Female)
• Cholesterol - Cholesterol levels of the patient
• Blood Pressure - Blood pressure of the patient (systolic/diastolic)
• Heart Rate - Heart rate of the patient
• Diabetes - Whether the patient has diabetes (Yes/No)
• Family History - Family history of heart-related problems (1: Yes, 0: No)
• Smoking - Smoking status of the patient (1: Smoker, 0: Non-smoker)
• Obesity - Obesity status of the patient (1: Obese, 0: Not obese)
• Alcohol Consumption - Level of alcohol consumption by the patient (None/Light/Moderate/Heavy)
• Exercise Hours Per Week - Number of exercise hours per week
• Diet - Dietary habits of the patient (Healthy/Average/Unhealthy)
• Previous Heart Problems - Previous heart problems of the patient (1: Yes, 0: No)
• Medication Use - Medication usage by the patient (1: Yes, 0: No)
• Stress Level - Stress level reported by the patient (1-10)
• Sedentary Hours Per Day - Hours of sedentary activity per day
• Income - Income level of the patient
• BMI - Body Mass Index (BMI) of the patient
• Triglycerides - Triglyceride levels of the patient
• Physical Activity Days Per Week - Days of physical activity per week
• Sleep Hours Per Day - Hours of sleep per day
• Country - Country of the patient
• Continent - Continent where the patient resides
• Hemisphere - Hemisphere where the patient resides
• Heart Attack Risk - Presence of heart attack risk (1: Yes, 0: No)

Structure of the Dataset

https://i.imgur.com/5cTusqA.png" alt="">

Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world scenarios. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation.

Cover Photo by: brgfx on Freepik

Thumbnail by: vectorjuice on Freepik

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: 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

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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/

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;

Global Suicide, Mental Health, Substance Use Disorders Trends

Analyzing the Impact Across Countries

By [source]

About this dataset

This dataset contains comprehensive data on global suicide, mental health, substance use disorders, and economic trends from 1990 to 2017. Using this data, researchers can delve deep into the effects of these trends across countries and ultimately uncover important insights about the state of global health. The dataset contains information about suicide rates (per 100,000 people), mental disorder prevalence (as a percentage of population size in 2017), population share with substance use disorders (as a percentage from 1990-2016), GDP per capita by purchasing power parity (in terms of current US$ for 1990-2017) and net national income per capita adjusted for inflation effects(in current US$, as in 2016). Additionally it tracks unemployment rate among populations over time(populaton%, 1991-2017). All this will help us to better understand how issues such as suicide, mental health and substance use disorders are affecting the lives of people globally

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How to use the dataset

This dataset offers insights into how mental health, substance use disorders, and economic status can impact global suicide trends. To get the most out of this data set, it is important to note the various columns available and their purpose as outlined above.

To analyze global suicide rates, look at the column “Probability (%) of dying between age 30 and exact age 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease” for a summary of estimated suicide rates for different countries over time. Additionally the columns “Entity” and “Code” provide useful information on which country is being discussed in each row.

The column “Prevalence- Alcohol and Substance Use Disorders” provides an overview of substance use disorders across different countries while the year column indicates when these trends are taking place.

For economic indicators related to mental health there is data available on national income per capita (current US$, 2016) as well as unemployment rate (population % 1991-2017). Together these metrics give a detailed picture into how economics can be interlinked with mental health and potentially suicide rates.

Finally this dataset also allows you to investigate varying trends overtime between different countries by looking at any common metrics but only in one specific year using appropriate filters when exploring the data set in more detail

Research Ideas

• Analyzing the correlation between mental health and economic indicators.
• Identifying countries with the highest prevalence of substance use disorders and developing targeted interventions for those populations.
• Examining the impact of global suicide rates over time to increase awareness and reduce stigma surrounding mental health issues in different countries

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: share-with-alcohol-and-substance-use-disorders 1990-2016.csv | Column name | Description | |:-----------------------------------------------------|:-----------------------------------------------------------------------------------| | Entity | The name of the country. (String) | | Code | The ISO code of the country. (String) | | Year | The year of the data. (Integer) | | Prevalence - Alcohol and substance use disorders | The percentage of the population with alcohol and substance use disorders. (Float) | | **Prevalence ** | Both (age-standardized percent) (%) |

**File: crude suicide rate...

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.

Introduction. The analysis looks at mental and physical health data from 2000-2019 from various sources the main one being the World Health Organization (WHO).

Task: Analyze health data to gain insights into current consumers health patterns globally and in Kenya to be utilized to make data driven decisions.

Stakeholders: -Company founders and C-suite teams. -Human Resource and Mental Health Professionals. -Government policy makers.

Analysis Objectives: -What is the trend in global and local consumer mental and physical health? -How can these trends influence public and corporate strategies?

ROCCC of Data: A good data source is ROCCC which stands for Reliable, Original, Comprehensive, Current, and Cited.

-Reliablity — High — The data comes from global population sample data sources.

-Originality — LOW — Third party provider (WHO).

-Comprehensive — HIGH — There are several variables summarized into between 1,700-10,980 observations for a period of over 15 years which was fairly comprehensive.

-Current — MID — Data is 3 years old and may not be as relevant as there is no covid data updated to it.

-Cited — HIGH — Data collected from a reliable third party that comprehensively reports its data collection process publicly.

Overall, the dataset is good quality data however its recommended that an updated analysis be done on the health trends during and post-covid.

Key Insights

-There is a higher average suicide rate in men than women both globally and also in Kenya.

-Kenya has a higher average suicide rate for both genders compared to the global average as at 2019.

-The average probability of death between the age of 30 to 70 from from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease in Kenya has been decreasing since 2008 however an increase has been observed since 2016.

-There has been a significant increase in the prevalence of alcohol and substance use disorder in Kenya, moreover, the prevalence in the country increases as the prevalence of anxiety disorders, eating disorders and schizophrenia increases according to the Kenyan correlation heat map.

-As evident on the correlation heat map the prevalence various mental health issues have an impact on each other.

-The global probability of dying between age 30 and 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease has been falling significantly since the 2000s, however, its only been steadily decreasing in Kenya. Men are also at a higher risk of death from these diseases compared to women both globally and locally in Kenya.

-The probability of dying between age 30 and 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease in Kenya has been observed to be significantly inversely proportional to the prevalence of alcohol, substance use anxiety and eating disorders.

-Suicide rates have been observed to not have a significant direct relationship with any mental health disorders both globally and locally however the most significant correlation is the probability of dying between age 30 and 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease in the global analysis.

-Globally a significant inverse relationship between road traffic death rate and eating disorders has been observed however there is a slightly significant relationship between depressive disorders and road traffic death which should be an indicator for further research.

-In Kenya, its been observed that road traffic deaths are inversely proportional to the probability of dying between age 30 and 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease but directly proportional to eating, anxiety, alcohol and substance use disorders.

-Depressive disorders is the most significant variable that has an impact on suicide rates in Kenya therefore further study can look into the impact of depression on attempted and reported suicide cases and other factors that may influence suicide as it has been on the rise in Kenya.

-Road traffic accidents have a significant impact of the mental health of several Kenyans.

Recommendations.

-There should be more education regarding suicide prevention for NGOs.

-Corporate firms should look into providing observed health insurance and mental health days off in addition to more sick days for the affected.

-The government can implement policies and programs that provide more efficient facilities for the handling of observed health issues.

-Insurance companies can restructure their products around the knowledge that mental health issues in Kenya have a significant direct relationship to each other and also that the prevalence of alcohol and substance use critically impacts the road traffic death rate in Kenya.

-The government should critically look at the increase in the prevalence of alcohol...

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

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.

Chile CL: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 10.800 % in 2021. This records an increase from the previous number of 9.500 % for 2011. Chile CL: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 10.150 % from Dec 2011 (Median) to 2021, with 2 observations. The data reached an all-time high of 10.800 % in 2021 and a record low of 9.500 % in 2011. Chile CL: 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 Chile – Table CL.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 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.

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.

Argentina AR: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 5.400 % in 2021. This records a decrease from the previous number of 5.500 % for 2011. Argentina AR: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 5.450 % from Dec 2011 (Median) to 2021, with 2 observations. The data reached an all-time high of 5.500 % in 2011 and a record low of 5.400 % in 2021. Argentina AR: 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 Argentina – Table AR.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;