This chart shows the rate of hospitalizations for short- term complications of diabetes for the most recent data year by age range and county. It also shows the 2017 objective by age range. This chart is based on one of three datasets related to the Prevention Agenda Tracking Indicators county level data posted on this site. Each dataset consists of county level data for 68 health tracking indicators and sub-indicators for the Prevention Agenda 2013-2017: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. Each dataset includes tracking indicators for the five Priority Areas of the Prevention Agenda 2013-2017. The most recent year dataset includes the most recent county level data for all indicators. The trend dataset includes the most recent county level data and historical data, where available. Each dataset also includes the Prevention Agenda 2017 state targets for the indicators. Sub-indicators are included in these datasets to measure health disparities among socioeconomic groups. For more information, check out: http://www.health.ny.gov/prevention/prevention_agenda/2013-2017/ and https://www.health.ny.gov/PreventionAgendaDashboard. The "About" tab contains additional details concerning this dataset.

Rate: Age-adjusted death rate, number of deaths due to diabetes, per 100,000 population.

Definition: Deaths with diabetes as the underlying cause of death (ICD-10 codes: E10-E14).

Data Sources:

(1) Death Certificate Database, Office of Vital Statistics and Registry, New Jersey Department of Health

(2) Population Estimates, State Data Center, New Jersey Department of Labor and Workforce Development

Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).

This is a source dataset for a Let's Get Healthy California indicator at "https://letsgethealthy.ca.gov/. This table displays the prevalence of diabetes in California. It contains data for California only. The data are from the California Behavioral Risk Factor Surveillance Survey (BRFSS). The California BRFSS is an annual cross-sectional health-related telephone survey that collects data about California residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. This prevalence rate does not include pre-diabetes, or gestational diabetes. This is based on the question: "Has a doctor, or nurse or other health professional ever told you that you have diabetes?" The sample size for 2014 was 8,832. NOTE: Denominator data and weighting was taken from the California Department of Finance, not U.S. Census. Values may therefore differ from what has been published in the national BRFSS data tables by the Centers for Disease Control and Prevention (CDC) or other federal agencies.

Data Series: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease, by sex Indicator: III.11 - Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease, by sex Source year: 2022 This dataset is part of the Minimum Gender Dataset compiled by the United Nations Statistics Division. Domain: Health and related services

Diabetes Health Indicators Dataset

Overview

This dataset contains 100,000 patient records designed for diabetes risk prediction, analysis, and machine learning applications. The dataset is clean, preprocessed, and ready for use in classification, regression, feature engineering, statistical analysis, and data visualization.

• Rows: 100,000
• Columns: 35+
• File: diabetes_dataset.csv

Dataset Description

The dataset includes patient profiles with features based on demographics, lifestyle habits, family history, and clinical measurements that are well-established indicators of diabetes risk. All data is generated using statistical distributions inspired by real-world medical research, ensuring privacy preservation while reflecting realistic health patterns.

Features

Data Quality

• ✅ Complete: No missing values or duplicates
• ✅ Clean: All values fall within medically realistic ranges
• ✅ Balanced Features: Distribution matches realistic population health patterns
• ✅ Target Distribution: ~20–25% diagnosed cases (balanced for ML classification)

Use Cases

• 🩺 Binary Classification → Predict diagnosed_diabetes (Yes/No)
• 🧮 Multiclass Classification → Predict diabetes_stage
• 📊 Regression → Predict glucose_fasting, hba1c, or diabetes_risk_score
• 🔍 EDA & Visualization → Explore lifestyle and clinical health patterns
• 🧠 Machine Learning → Train ML/DL models for healthcare prediction tasks
• 📈 Statistical Testing → Hypothesis testing on health indicators

Key Indicators of Heart Disease

2022 annual CDC survey data of 400k+ adults related to their health status

What subject does the dataset cover?

According to the CDC, heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect "patterns" in the data that can predict a patient's condition.

Where did the data set come from and what treatments has it undergone?

The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the CDC: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.

What can you do with this data set?

As described above, the original dataset of nearly 300 variables was reduced to 40variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable "HadHeartAttack" as binary ("Yes" - respondent had heart disease; "No" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?

What steps did you use to convert the dataset?

Check out this notebook in my GitHub repository: https://github.com/kamilpytlak/data-science-projects/blob/main/heart-disease-prediction/2022/notebooks/data_processing.ipynb

Diabetes is a chronic disease that affects the way the body processes blood sugar, also known as glucose. Glucose is an important source of energy for the body's cells, and insulin, a hormone produced by the pancreas, helps to regulate glucose levels in the blood.

In people with diabetes, the body either doesn't produce enough insulin, or it can't effectively use the insulin it produces. This causes glucose to build up in the blood, leading to a range of health problems over time.

There are two main types of diabetes: type 1 and type 2. Type 1 diabetes, also known as juvenile diabetes, is usually diagnosed in children and young adults. It occurs when the body's immune system attacks and destroys the cells in the pancreas that produce insulin. People with type 1 diabetes need to take insulin injections or use an insulin pump to manage their blood sugar levels.

Type 2 diabetes is the most common form of diabetes, accounting for around 90% of all cases. It usually develops in adults, but can also occur in children and teenagers. In type 2 diabetes, the body becomes resistant to the effects of insulin, and the pancreas may not produce enough insulin to keep blood sugar levels in check. Lifestyle changes, such as a healthy diet and regular exercise, can help manage type 2 diabetes, and some people may also need medication or insulin therapy.

Both types of diabetes can lead to serious health complications over time, including heart disease, stroke, kidney disease, nerve damage, and eye problems. It's important for people with diabetes to work closely with their healthcare team to manage their condition and prevent these complications.

دیابت بیماری مزمنی است که نحوه پردازش قند خون را در بدن تحت تأثیر قرار می‌دهد. قند یک منبع مهم انرژی برای سلول‌های بدن است و انسولین، یک هورمون توسط پانکراس تولید شده، به کنترل سطح قند خون در بدن کمک می‌کند

در افراد دیابتی، بدن یا انسولین کافی تولید نمی‌کند یا نمی‌تواند به طور موثر از انسولینی که تولید می‌شود، استفاده کند. این باعث می‌شود که قند در خون تجمع پیدا کند که به مشکلات سلامتی در طول زمان منجر می‌شود

دو نوع اصلی دیابت وجود دارد: نوع ۱ و نوع ۲. دیابت نوع ۱ یا دیابت جوانان، معمولاً در کودکان و جوانان بزرگسال تشخیص داده می‌شود. این بیماری زمانی رخ می‌دهد که سیستم ایمنی بدن سلول‌های پانکراسی را که انسولین تولید می‌کنند، حمله می‌کند و از بین می‌برد. افراد دیابتی نوع ۱ باید تزریقات انسولین یا استفاده از پمپ انسولین برای کنترل سطح قند خون خود استفاده کنند

دیابت نوع ۲ شایع‌ترین نوع دیابت است که حدود ۹۰٪ از کل موارد را شامل می‌شود. این نوع بیماری معمولاً در بزرگسالان ایجاد می‌شود، اما ممکن است در کودکان و نوجوانان نیز رخ دهد. در دیابت نوع ۲، بدن به اثرات انسولین مقاومت پیدا می‌کند و پانکراس ممکن است انسولین کافی برای کنترل سطح قند خون تولید نکند. تغییرات سبک زندگی مانند رژیم غذایی سالم و ورزش منظم می‌تواند به مدیریت دیابت نوع ۲ کمک کند و برخی افراد ممکن است نیاز به دارو یا درمان انسولین داشته باشند

هر دو نوع دیابت می‌تواند منجر به مشکلات سلامتی جدی در طول زمان شود، از جمله بیماری قلبی، سکته مغزی، بیماری کلیه، آسیب عصبی و مشکلات چشمی. برای افراد دارای دیابت، مهم است که به همراه تیم مراقبت از سلامتی خود همکاری کرده و برای جلوگیری از این مشکلات تلاش کنند

Diabetes ist eine chronische Krankheit, die die Art und Weise beeinflusst, wie der Körper Blutzucker, auch als Glukose, verarbeitet. Glukose ist eine wichtige Energiequelle für die Zellen des Körpers, und Insulin, ein Hormon, das von der Bauchspeicheldrüse produziert wird, hilft bei der Regulierung des Glukosespiegels im Blut.

Bei Menschen mit Diabetes produziert der Körper entweder nicht genug Insulin oder kann das Insulin, das er produziert, nicht effektiv nutzen. Dies führt dazu, dass sich Glukose im Blut ansammelt, was im Laufe der Zeit zu einer Reihe von Gesundheitsproblemen führen kann.

Es gibt zwei Haupttypen von Diabetes: Typ 1 und Typ 2. Diabetes Typ 1, auch als juveniler Diabetes bekannt, wird in der Regel bei Kindern und jungen Erwachsenen diagnostiziert. Es tritt auf, wenn das Immunsystem des Körpers die Zellen in der Bauchspeicheldrüse angreift und zerstört, die Insulin produzieren. Menschen mit Diabetes Typ 1 müssen Insulininjektionen oder eine Insulinpumpe verwenden, um ihren Blutzuckerspiegel zu kontrollieren.

Diabetes Typ 2 ist die häufigste Form von Diabetes und macht etwa 90% aller Fälle aus. Es entwickelt sich in der Regel bei Erwachsenen, kann aber auch bei Kindern und Jugendlichen auftreten. Bei Diabetes Typ 2 wird der Körper gegenüber den Wirkungen von Insulin resistent, und die Ba...

This Obesity and Diabetes Related Indicators dataset provides a subset of data (40 indicators) for the two topics: Obesity and Diabetes. The dataset includes percentage or rate for Cirrhosis/Diabetes and Obesity and Related Indicators, where available, for all counties, regions and state.
New York State Community Health Indicator Reports (CHIRS) were developed in 2012, and annually updated to provide data for over 300 health indicators, organized by 15 health topic and data for all counties, regions and state are presented in table format with links to trend graphs and maps (http://www.health.ny.gov/statistics/chac/indicators/). Most recent county and state level data are provided. Multiple year combined data offers stable estimates for the burden and risk factors for these two health topics. For more information, check out: http://www.health.ny.gov/statistics/chac/indicators/ or go to the “About” tab.

United States US: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 10.790 % in 2017. United States US: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 10.790 % from Dec 2017 (Median) to 2017, with 1 observations. United States US: 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 USA – Table US.World Bank: Health Statistics. Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes.; ; International Diabetes Federation, Diabetes Atlas.; Weighted average;

Nigeria NG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data was reported at 20.900 NA in 2016. This records an increase from the previous number of 20.800 NA for 2015. Nigeria NG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data is updated yearly, averaging 21.000 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 22.600 NA in 2000 and a record low of 20.800 NA in 2015. Nigeria NG: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Diabetes is a chronic disease that has a serious impact on a person's life, with a persistent increase in morbidity worldwide in recent decades. Primary cases are the number of new cases, or first cases of the disease, in the population during some period that is reported as a multiplier, for example per 100,000 inhabitants. The number of cases of diabetes is calculated. The dataset shows the diabetes incidence rate by diagnosis (ICD-10), sex, age group residence county and year.

Sex-specific mortality rates and mortality rate ratios for men versus women in different strata of attained age and in all ages at baseline, and the proportion distribution of age at death in men, women and both sexes, respectively

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of diabetes mellitus in persons (aged 17+). Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to diabetes mellitus in persons (aged 17+).This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.The percentage of each MSOA’s population (aged 17+) with diabetes mellitus was estimated. This was achieved by calculating a weighted average based on:The percentage of the MSOA area that was covered by each GP practice’s catchment areaOf the GPs that covered part of that MSOA: the percentage of registered patients that have that illness The estimated percentage of each MSOA’s population with diabetes mellitus was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with depression, within the relevant age range.Each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have diabetes mellitusB) the NUMBER of people within that MSOA who are estimated to have diabetes mellitusAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA that are estimated to have diabetes mellitus, compared to other MSOAs. In other words, those are areas where it’s estimated a large number of people suffer from diabetes mellitus, and where those people make up a large percentage of the population, indicating there is a real issue with diabetes mellitus within the population and the investment of resources to address that issue could have the greatest benefits.LIMITATIONS1. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).2. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.3. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of diabetes mellitus, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of diabetes mellitus.TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:Health and wellbeing statistics (GP-level, England): Missing data and potential outliersLevels of obesity, inactivity and associated illnesses (England): Missing dataDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

Chronic kidney disease has become an increasingly significant clinical and public health issue, accounting for 1.1 million deaths worldwide. Information on the epidemiology of chronic kidney disease and associated risk factors is limited in the United Arab Emirates. Therefore, this study aimed to evaluate the incidence and causes of chronic kidney disease stages 3–5 in adult United Arab Emirates nationals with or at high risk of cardiovascular disease. This retrospective study included 491 adults with or at high risk of cardiovascular disease (diabetes mellitus or associated clinical disease) who attended outpatient clinics at a tertiary care hospital in Al-Ain, United Arab Emirates. Estimated glomerular filtration rate was assessed every 3 months from baseline to June 30, 2017. Chronic kidney disease stages 3–5 were defined as an estimated glomerular filtration rate < 60 mL/min/1.73 m2 for ≥ 3 months. Multivariable Cox's proportional hazards analysis was used to determine the independent risk factors associated with developing chronic kidney disease stages 3–5. The cumulative incidence of chronic kidney disease stages 3–5 over a 9-year period was 11.4% (95% confidence interval 8.6, 14.0). The incidence rate of these disease stages was 164.8 (95% confidence interval 121.6, 207.9) per 10,000 person-years. The independent risk factors for developing chronic kidney disease stages 3–5 were older age, history of coronary heart disease, history of diabetes mellitus, and history of smoking. These data may be useful to develop effective strategies to prevent chronic kidney disease development in high-risk United Arab Emirates nationals.

This dataset presents the relative risk of mortality from diabetic complications. It compares the observed number of deaths among people with diabetes due to specific complications—such as angina, myocardial infarction, heart failure, or stroke—with the expected number of such deaths in the diabetic population. The data is derived from ONS death registrations and modelled estimates from the National Diabetes Audit (NDA).

Rationale

People with diabetes are at increased risk of developing serious cardiovascular complications, which can lead to premature mortality. Monitoring mortality from these complications helps identify disparities in care and outcomes, and supports efforts to improve diabetes management and reduce preventable deaths. This indicator provides a benchmark for evaluating the effectiveness of interventions aimed at reducing cardiovascular risk in people with diabetes.

Numerator

The numerator is the number of people with diabetes, as recorded on their death certificate, who died from complications such as angina, myocardial infarction, heart failure, or stroke.

Denominator

The denominator is the modelled number of people with diabetes who would be expected to die from these complications, based on data from the National Diabetes Audit.

Caveats

No specific caveats are noted for this indicator. However, as with all modelled data, assumptions and estimation methods may influence the accuracy of the expected mortality figures.

External References

More information is available from the following source:

National Diabetes Audit - NHS Digital

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

Diabetes is a chronic disease that has a serious impact on a person's life, with a persistent increase in morbidity worldwide in recent decades. Primary cases are the number of new cases, or first cases of the disease, in the population during some period that is reported as a multiplier, for example per 100,000 inhabitants. The number of cases of diabetes is calculated. The dataset shows the incidence rate of diabetes by diagnosis (ICD-10), sex, age group and year.

BackgroundRandomized controlled trials have shown the importance of tight glucose control in type 1 diabetes (T1DM), but few recent studies have evaluated the risk of cardiovascular disease (CVD) and all-cause mortality among adults with T1DM. We evaluated these risks in adults with T1DM compared with the non-diabetic population in a nationwide study from Scotland and examined control of CVD risk factors in those with T1DM. Methods and FindingsThe Scottish Care Information-Diabetes Collaboration database was used to identify all people registered with T1DM and aged ≥20 years in 2005–2007 and to provide risk factor data. Major CVD events and deaths were obtained from the national hospital admissions database and death register. The age-adjusted incidence rate ratio (IRR) for CVD and mortality in T1DM (n = 21,789) versus the non-diabetic population (3.96 million) was estimated using Poisson regression. The age-adjusted IRR for first CVD event associated with T1DM versus the non-diabetic population was higher in women (3.0: 95% CI 2.4–3.8, p<0.001) than men (2.3: 2.0–2.7, p<0.001) while the IRR for all-cause mortality associated with T1DM was comparable at 2.6 (2.2–3.0, p<0.001) in men and 2.7 (2.2–3.4, p<0.001) in women. Between 2005–2007, among individuals with T1DM, 34 of 123 deaths among 10,173 who were <40 years and 37 of 907 deaths among 12,739 who were ≥40 years had an underlying cause of death of coma or diabetic ketoacidosis. Among individuals 60–69 years, approximately three extra deaths per 100 per year occurred among men with T1DM (28.51/1,000 person years at risk), and two per 100 per year for women (17.99/1,000 person years at risk). 28% of those with T1DM were current smokers, 13% achieved target HbA1c of <7% and 37% had very poor (≥9%) glycaemic control. Among those aged ≥40, 37% had blood pressures above even conservative targets (≥140/90 mmHg) and 39% of those ≥40 years were not on a statin. Although many of these risk factors were comparable to those previously reported in other developed countries, CVD and mortality rates may not be generalizable to other countries. Limitations included lack of information on the specific insulin therapy used. ConclusionsAlthough the relative risks for CVD and total mortality associated with T1DM in this population have declined relative to earlier studies, T1DM continues to be associated with higher CVD and death rates than the non-diabetic population. Risk factor management should be improved to further reduce risk but better treatment approaches for achieving good glycaemic control are badly needed. Please see later in the article for the Editors' Summary

Series Name: Mortality rate attributed to cardiovascular disease cancer diabetes or chronic respiratory disease (probability)Series Code: SH_DTH_NCOMRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory diseaseTarget 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-beingGoal 3: Ensure healthy lives and promote well-being for all at all agesFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Time series data for the statistic Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%) and country Chad. Indicator Definition:Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).The indicator "Mortality from CVD, cancer, diabetes or CRD between exact ages 30 and 70 (%)" stands at 23.40 as of 12/31/2021, the lowest value at least since 12/31/2001, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -4.49 percent compared to the value the year prior.The 1 year change in percent is -4.49.The 3 year change in percent is -2.90.The 5 year change in percent is -2.90.The 10 year change in percent is -7.51.The Serie's long term average value is 24.96. It's latest available value, on 12/31/2021, is 6.25 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2021, to it's latest available value, on 12/31/2021, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/2002, to it's latest available value, on 12/31/2021, is -9.30%.