Health, United States is an annual report on trends in health statistics, find more information at http://www.cdc.gov/nchs/hus.htm.

Population-based county-level estimates for prevalence of DC were obtained from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (16). DC prevalence rate was defined as the propor-tion of people within a county who had previously been diagnosed with diabetes (high fasting plasma glu-cose 126 mg/dL, hemoglobin A1c (HbA1c) of 6.5%, or diabetes diagnosis) but do not currently have high fasting plasma glucose or HbA1c for the period 2004-2012. DC prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or HbA1C levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (16). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or HbA1C status for each BRFSS respondent (16). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict county-level prevalence of diabetes-related outcomes, including DC (16). The EQI was constructed for 2006-2010 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). Results are reported as prevalence rate differences (PRD) with 95% confidence intervals (CIs) comparing the highest quintile/worst environmental quality to the lowest quintile/best environmental quality expo-sure metrics. PRDs are representative of the entire period of interest, 2004-2012. Due to availability of DC data and covariate data, not all counties were captured, however, the majority, 3134 of 3142 were utilized in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, K. Price, D. Lobdell, and R. Sargis. Diabetes control is associated with environmental quality in the USA. Endocrine Connections. BioScientifica Ltd., Bristol, UK, 10(9): 1018-1026, (2021).

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.

The dataset consists of 19 variables on 403 subjects from 1046 subjects who were interviewed in a study to understand the prevalence of obesity, diabetes, and other cardiovascular risk factors in central Virginia for African Americans.

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;

New: Diabetes prevalence data is from the Canadian Chronic Disease Surveillance System (CCDSS). Diabetes crude prevalence in Nova Scotia. Includes the following data fields: Management Zone, Sex, Age Group, Population, Diabetes Count, Crude Prevalence Rate %

The dataset includes age-standardized estimates of the prevalence of diabetes mellitus (DM) and its associated risk factors. The data is derived from the Non-Communicable Disease Risk Factor Collaboration (NCD-RisC), a global network of health scientists and practitioners that aims to provide reliable and up-to-date information on the prevalence of non-communicable diseases and their risk factors.

The dataset includes information from 200 countries and territories and covers the period from 1980 to 2014. The data is presented in both male and female categories, and estimates are given for different age groups ranging from 20-79 years old. The data is standardized to account for differences in age distributions across countries and over time.

These datasets provide de-identified insurance data for diabetes. The data is provided by three managed care organizations in Allegheny County (Gateway Health Plan, Highmark Health, and UPMC) and represents their insured population for the 2015 and calendar years.

Disclaimer: Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time, as data provided were collected for purposes other than surveillance. Limitations of these data include but are not limited to: misclassification, duplicate individuals, exclusion of individuals who did not seek care in past two years and those who are: uninsured, enrolled in plans not represented in the dataset, or were not enrolled in one of the represented plans for at least 90 days.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

This dataset provides diabetes prevalence estimates by county and sex for the prevalence of diagnosed, undiagnosed, and total diabetes, as well as rates of diagnosis and effective treatment for 1999-2012. The dataset contains estimates for all states and counties, the District of Columbia, and the United States as a whole.

This dataset presents information on age-standardized prevalence of diabetes for Alberta, for selected geographic areas , expressed as per 100,000 population.

Singapore SG: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 10.990 % in 2017. Singapore SG: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 10.990 % from Dec 2017 (Median) to 2017, with 1 observations. Singapore SG: 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 Singapore – Table SG.World Bank.WDI: 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;

This data package contains dataset on prevalence rates of health conditions and diseases like obesity, diabetes and hearing loss and health risk factors for diseases like tobacco, alcohol and drug use.

South Africa ZA: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 5.520 % in 2017. South Africa ZA: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 5.520 % from Dec 2017 (Median) to 2017, with 1 observations. South Africa ZA: 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 South Africa – Table ZA.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;

This dataset is designed to support researchers, data scientists, and healthcare professionals in predicting and analyzing diabetes prevalence and risk factors among the Indian population. It incorporates a diverse range of demographic, lifestyle, and clinical attributes to ensure a holistic representation of potential diabetes determinants. The dataset's features include:

Demographics: Age, gender, urban/rural residence, and pregnancies (specific to women). Lifestyle Factors: Physical activity, diet type, smoking status, alcohol intake, and stress levels. Medical History: Family history of diabetes, hypertension, thyroid conditions, and regular checkups. Clinical Metrics: BMI, cholesterol levels, fasting and postprandial blood sugar, HBA1C, vitamin D levels, and more. Target Variable: Binary diabetes status (Yes/No).

This dataset presents information on age-sex specific prevalence rates of diabetes by First Nations status for Alberta and five Alberta Health Services (AHS) continuum zones, expressed as a percentage.

The percentage of patients aged 17 or over with diabetes mellitus, as recorded on practice disease registers.

Note on ward level data This data is GP practice level data taken from Fingertips and converted to wards using our Fingertips GP to Ward Lookup Matrix for Birmingham and Solihull dataset. This dataset uses the GP census to allocate an approximate percentage of their patients to each ward based on the citizens home address.

Rationale Diabetes mellitus is one of the common endocrine diseases affecting all age groups, with over three million people in the UK having the condition. Effective control and monitoring can reduce mortality and morbidity. Much of the management and monitoring of diabetic patients, particularly patients with Type 2 diabetes, is undertaken by the GP and members of the primary care team.

Definition of numerator Patients aged 17+ years with diabetes mellitus.

Definition of denominator Total number of patients aged 17+ years registered with the practice.

Caveats None

India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data was reported at 19.800 NA in 2016. This records a decrease from the previous number of 20.000 NA for 2015. India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data is updated yearly, averaging 21.200 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 23.400 NA in 2000 and a record low of 19.800 NA in 2016. India IN: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Female data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.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;

This dataset contains number and percentage of diabetes patients in the US during 2013 grouped by ZIP code. The prevalence and incidence of diabetes have increased in the United States in recent decades, no studies have systematically examined long-term, national trends in the prevalence and incidence of diagnosed diabetes. The prevalence of diabetes increased substantially between 2000 and 2007, mainly because there are more patients with a new diagnosis each year than those who die. The increase observed by 2007 almost reached the World Health Organization prediction for 2030.

This dataset contains 5,288 patient records, covering 14 independent attributes related to demographics, clinical parameters, and medical history. Key features include age, gender, pulse rate, blood pressure (systolic and diastolic), glucose level, BMI, and family history of diabetes, hypertension, and cardiovascular disease. Each patient entry is labeled with a binary diabetes status (diabetic or non-diabetic), making it suitable for predictive modeling and risk assessment. Designed to facilitate machine learning applications, this dataset supports the development of diabetes detection models, risk stratification, and personalized management strategies. The comprehensive feature set enables researchers to explore patterns in diabetes prevalence and related health factors, thereby contributing to improved early diagnosis and targeted interventions.

1. Source of information: National Health Administration national nutrition and health survey.2. Definition of hyperglycemia/diabetes: Fasting blood sugar level 126mg/dL for 8 hours or more, or taking hypoglycemic medications in the past month.