Between 2023 and 2024, over sixteen percent of all those registered with type 2 diabetes in England were Asian or Asian British. This statistic displays the share of individuals registered with diabetes in England in 2023/24, by ethnicity.

United Kingdom UK: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 4.280 % in 2017. United Kingdom UK: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 4.280 % from Dec 2017 (Median) to 2017, with 1 observations. United Kingdom UK: 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 United Kingdom – Table UK.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;

Between 2022 and 2023, there were 32,276 young people with type 1 diabetes and 1,245 with type 2 across England and Wales. The most affected were the ones with white ethnicity. This statistic shows the share of young people under the age of 24, with type 1 and 2 diabetes in England and Wales from 2022 to 2023, by ethnicity.

Type 2 diabetes is a condition that affects the amount of sugar in a person’s bloodstream and causes it to become too high. This type of diabetes can be caused by risk factors such as obesity and inactivity and, as shown in this figure, type 2 diabetes is more common among older individuals. Between 2023 and 2024, of those registered with type 2 diabetes in England, **** percent were aged between 40 and 64 years. Increasing prevalence of diabetes in England Between 2023 and 2024, there were over *** million people in England who were registered as diabetics. The number of individuals registered with diabetes in England has increased year-on-year since 2008. The vast majority of those registered with diabetes in England are diagnosed with type 2 diabetes, with over *** million, while approximately *** thousand living with type 1 diabetes. Diabetes medication By far the most used drug in England for the treatment of diabetes was metformin hydrochloride with over ** million items used in 2022 alone. Additionally, already in 2018 approximately **** percent of all prescribed drugs in primary care in England were for the treatment of diabetes. The share of prescribed diabetes drugs has steadily increased since 2005.

This is an overview of the treatment and demographics of 227,435 adults with type 1 diabetes. From 2019 to 2022 glucose control in people with type 1 diabetes in England and Wales improved while blood pressure control deteriorated. Use of diabetes technology (wearable glucose monitoring devices in England and insulin pumps in England and Wales) was associated with lower glucose levels. Diabetes technology was used less by those in the most deprived groups and in ethnic minorities. 30% of people with type 1 diabetes did not attend specialist care in 2021-22 and were less likely to receive annual checks or achieve treatment targets as recommended by the National Institute for Health and Care Excellence (NICE).

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.

The prevalence of QOF-recorded diabetes (in adults aged 17+) in the population Source: The Information Centre for health and social care (IC) Publisher: Association of Public Health Observatories (APHO) Geographies: Local Authority District (LAD), County/Unitary Authority, Government Office Region (GOR), National Geographic coverage: England Time coverage: 2008 Type of data: Administrative data

This statistic shows the mean HbA1c measurement of children and young people in England and Wales from 2022 to 2023, by ethnicity. During this period, the mean HbA1c measurement of children from a black ethnic background was 69.9 mmol/mol.

The Health Survey for England is an annual survey of the health of the population. It has an annually repeating core accompanied by different topic modules each year. The focus of the 2004 report is on the health of minority ethnic groups with an emphasis on cardiovascular disease (CVD). The report also covers the behavioural risk factors associated with CVD such as drinking, smoking and eating habits and health status risk factors such as diabetes, blood pressure, and cholesterol. For children the emphasis is on respiratory health.

BackgroundSocio-economic position (SEP) and ethnicity influence type 2 diabetes mellitus (T2DM) risk in adults. However, the influence of SEP on emerging T2DM risks in different ethnic groups and the contribution of SEP to ethnic differences in T2DM risk in young people have been little studied. We examined the relationships between SEP and T2DM risk factors in UK children of South Asian, black African-Caribbean and white European origin, using the official UK National Statistics Socio-economic Classification (NS-SEC) and assessed the extent to which NS-SEC explained ethnic differences in T2DM risk factors. Methods and FindingsCross-sectional school-based study of 4,804 UK children aged 9–10 years, including anthropometry and fasting blood analytes (response rates 70%, 68% and 58% for schools, individuals and blood measurements). Assessment of SEP was based on parental occupation defined using NS-SEC and ethnicity on parental self-report. Associations between NS-SEC and adiposity, insulin resistance (IR) and triglyceride differed between ethnic groups. In white Europeans, lower NS-SEC status was related to higher ponderal index (PI), fat mass index, IR and triglyceride (increases per NS-SEC decrement [95%CI] were 1.71% [0.75, 2.68], 4.32% [1.24, 7.48], 5.69% [2.01, 9.51] and 3.17% [0.96, 5.42], respectively). In black African-Caribbeans, lower NS-SEC was associated with lower PI (−1.12%; [−2.01, −0.21]), IR and triglyceride, while in South Asians there were no consistent associations between NS-SEC and T2DM risk factors. Adjustment for NS-SEC did not appear to explain ethnic differences in T2DM risk factors, which were particularly marked in high NS-SEC groups. ConclusionsSEP is associated with T2DM risk factors in children but patterns of association differ by ethnic groups. Consequently, ethnic differences (which tend to be largest in affluent socio-economic groups) are not explained by NS-SEC. This suggests that strategies aimed at reducing social inequalities in T2DM risk are unlikely to reduce emerging ethnic differences in T2DM risk.

In 2024, around 16 percent of adults between the ages of 20 and 79 had diabetes in Turkey. Other selected countries with a high prevalence of diabetes that year included Mexico, the United States, and Portugal. Diabetes is a metabolic disease that causes high blood sugar levels. Diabetes worldwide In 2024, an estimated 11 percent of the global adult population had diabetes. In concrete numbers, there were about 589 million diabetic adults (20-79 years) worldwide in 2024, and this total is predicted to grow to approximately 852.5 million by the year 2050. Spending per patient The country that spent the most on patients with diabetes in 2024 was Switzerland. At that time, providing for a diabetic patient in Switzerland cost an average of over 12 thousand U.S. dollars. The United States stood in second place, spending about 10,500 U.S. dollars per patient. In the same year, the ten countries by lowest average spending per person with diabetes were all African and Asian countries. Bangladesh had the lowest annual diabetes-related health expenditures per person, with just 74 U.S. dollars.

The Type 1 Diabetes Genetics Consortium (T1DGC) was an international, multicenter program organized to promote research to identify genes and alleles that determine an individual's risk for type 1 diabetes. The program had two primary goals: (1) to identify genomic regions and candidate genes whose variants modify an individual’s risk of type 1 diabetes and help explain the clustering of the disease in families and (2) to make research data available to and establish resources that can be used by the research community. The T1DGC assembled a resource of affected sib-pair families, parent-child trios, and case-control collections with banks of DNA, serum, plasma, and EBV-transformed cell lines. In addition to T1DGC-recruited ASP families, the T1DGC recruited trio families from ethnic groups with lower prevalence of type 1 diabetes. The T1DGC also welcomed the inclusion of earlier ascertained case-control collections (from the UK, Denmark, etc.). Research with T1DGC data has included genome-wide linkage scans, evaluation of the human major histocompatibility complexes, examination of published candidate genes for type 1 diabetes, and examination of autoimmune disease genes and those affecting β-cell function in type 2 diabetes.

In 2007, the T1DGC incorporated over 7,000 cases from the UK (the JDRF/WT case series, aka GRID). GRID samples are available here, and data from dbGaP, the European Genome-phenome Archive (EGA) and data and documentation at the JDRF/WT DIL.

The National Diabetes Audit (NDA) continues to provide a comprehensive view of Diabetes Care in England and Wales and measures the effectiveness of diabetes healthcare against NICE Clinical Guidelines and NICE Quality Standards, in England and Wales. This national report presents the key findings and recommendations on care processes and treatment target achievement rates from 2015-2016 in all age groups in England and Wales along with information on offers and attendance for structured education places. This year, for the first time information is reported on the number of people with diabetes who also have a learning disability and completion of care processes and treatment target achievement. A separate national report presents the key findings and recommendations; The Learning Disability - Supplementary Information report has also been developed as a power point presentation. As with last year's publication the main report contains information on the national key findings and recommendations and has also been developed as a power point presentation, along with slides highlighting the national findings there is also space to allow the incorporation of locally produced slides using the tables and charts from the interactive spreadsheets. We hope that users will find this beneficial to help disseminate the results of the audit locally. Supplementary data for England and Wales are contained in the excel spreadsheets. There are 6 excel spreadsheets; two spreadsheets contains the tables and charts in the national report and learning disability report along with some supplementary national figures, a further spreadsheet provides all 8 care process completion and all 3 treatment target achievement for CCGs/LHBs by age group. There are also 3 interactive excel spreadsheets which allow users to select the CCG/GP practice (England only), Local Health Board (Wales only) or Secondary Care Service (England only) of choice, information for the chosen site is then displayed in tables and charts. Please note that the interactive excel spreadsheets are large files (approximately 12MB) and may take some time to open. This report was updated on 09/02/17. The following amendments have been made to the report: The CCG/GP spreadsheet was updated as some of the CCGs/general practices were not available in the interactive aspect. We have also added a reference table for practice codes and names. All the data for care processes and treatment targets was correct in the supporting data tables. The spreadsheet report for Wales and LHBs has been amended. A practice wrongly appeared in a LHB, this practice has now been assigned to the correct LHB which has changed the results for LHB 7A2 and 7A3. The specialist service spreadsheet has been updated as the interactive aspect was not working for all hospitals. This does not change the results for specialist services. Both the CCG/GP and LHB spreadsheets have been updated for structured education offered and attendance. This has changed the results for individual CCGs/Practices and LHBs but not the national results. We have updated the methodology documentation for structured education to explain more fully how we have analysed and reported on the structured education data for the 2015-16 audit report. We have also added a link, which can be found below in resources, to our interactive dashboard for the 2015-16 report. This dashboard provides CCGs, LHBs and GPs (England only) with an alternative way to view their data for completion of all 8 care process and achievement of all 3 treatment targets as well their data on registrations by age, sex, deprivation and ethnicity.

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.

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Type 2 Diabetes Market Size 2025-2029

The type 2 diabetes market size is forecast to increase by USD 53.8 billion, at a CAGR of 12.2% between 2024 and 2029.

The market is experiencing significant growth due to the rising prevalence of diabetes, fueled by an increasing geriatric population. This demographic trend, coupled with the underdiagnosis of the condition, presents both opportunities and challenges for market participants. The large, untapped population of undiagnosed individuals represents a significant market opportunity for innovative diagnostic solutions. However, the low diagnosis rate also poses a challenge, as untreated diabetes can lead to severe health complications and increased healthcare costs. The geriatric population, with its higher susceptibility to diabetes, further exacerbates this challenge.
To capitalize on the market opportunities and navigate these challenges effectively, companies must focus on developing innovative diagnostic tools and effective patient education programs. By addressing the unmet needs in diabetes diagnosis and management, market participants can differentiate themselves and capture a larger share of this growing market.

What will be the Size of the Type 2 Diabetes Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by advancements in diabetes management, public health, and weight management. Pancreatic beta-cell destruction and the subsequent need for insulin therapy remain at the forefront of market dynamics. Diabetes burden is a significant concern, with diabetic nephropathy, diabetic retinopathy, and diabetic neuropathy posing significant health risks. SGLT2 inhibitors have emerged as a promising treatment option, addressing both glycemic control and cardiovascular disease. Healthcare access and access to care are crucial factors shaping the market. Global health initiatives and healthcare costs are influencing the development of diabetes technology, including insulin delivery devices, glucose sensors, and remote monitoring.

Precision medicine, data analytics, and machine learning are transforming diabetes education and patient engagement. Lifestyle modifications, such as dietary changes and physical activity, remain essential components of diabetes management. Insulin resistance, insulin therapy, and HbA1c testing continue to be key areas of focus in the medical device industry. Ongoing research in areas like Genetic Testing, Stem Cell Therapy, and primary care physician engagement is further shaping the market. Health disparities, early detection, and glycemic control are critical public health concerns. Diabetes prevention and healthcare provider collaboration are essential to improving health outcomes. The market is continually unfolding, with ongoing drug development, clinical trials, and advancements in diabetes technology driving innovation.

How is this Type 2 Diabetes Industry segmented?

The type 2 diabetes industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Drug Class

  Insulin
  DPP-4 inhibitor
  GLP-1 receptor agonists
  SGLT-2 inhibitors
  Others


Distribution Channel

  Retail pharmacies
  Hospital pharmacies
  Online pharmacies


Delivery Mode

  Oral
  Injectable
  Continuous glucose monitoring (CGM)


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

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By Drug Class Insights

The insulin segment is estimated to witness significant growth during the forecast period.

Type 2 diabetes is a complex condition characterized by insulin resistance and pancreatic beta-cell dysfunction, leading to elevated blood glucose levels. Metabolic syndrome, an autoimmune disease, and a precursor to type 2 diabetes, affects millions worldwide. The medical device industry is responding with innovative solutions, such as artificial pancreas systems and insulin delivery devices, to improve glycemic control and enhance quality of life. Artificial intelligence and machine learning are revolutionizing diabetes management by facilitating early detection, personalized treatment plans, and remote monitoring. Diabetes burden is significant, with complications including diabetic foot ulcers, diabetic retinopathy, diabetic neuropathy, and diabetic nephropathy.

Prevention and management involve lifestyle modifications, such as dietary changes and physical activity, as well as pharmacological interventions, including oral antidiabetic medications, insulin therapy, and newer classes like GLP-1 re

The characteristics of the type 2 diabetes mellitus adult population diagnosed before 2012 and with follow-up for the 5 years (n = 49,380).

。英国：糖尿病患病率：20-79岁人口百分比数据按年更新，12-01-2017至12-01-2017期间平均值为4.280%，共1份观测结果。CEIC提供的英国：糖尿病患病率：20-79岁人口百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的英国 – 表 UK.世行.WDI：卫生统计。