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.

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;

Population-based county-level estimates for diagnosed (DDP), undiagnosed (UDP), and total diabetes prevalence (TDP) were acquired from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (Evaluation 2017). 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 hemoglobin A1C (HbA1C) levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (Dwyer-Lindgren, Mackenbach et al. 2016). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or A1C status for each BRFSS respondent (Dwyer-Lindgren, Mackenbach et al. 2016). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict the county-level prevalence of each of the diabetes-related outcomes (Dwyer-Lindgren, Mackenbach et al. 2016). Diagnosed diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis, represented as an age-standardized prevalence percentage. Undiagnosed diabetes was defined as proportion of adults (age 20+ years) who have a high FPG or HbA1C but did not report a previous diagnosis of diabetes. Total diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis and/or had a high FPG/HbA1C. The age-standardized diabetes prevalence (%) was used as the outcome. The EQI was constructed for 2000-2005 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). 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, S. Shaikh, D. Lobdell, and R. Sargis. Association between environmental quality and diabetes in the U.S.A.. Journal of Diabetes Investigation. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(2): 315-324, (2020).

Ireland: Diabetes prevalence, percent of population ages 20-79: The latest value from 2021 is 3 percent, a decline from 5.2 percent in 2011. In comparison, the world average is 8.60 percent, based on data from 195 countries. Historically, the average for Ireland from 2011 to 2021 is 4.1 percent. The minimum value, 3 percent, was reached in 2021 while the maximum of 5.2 percent was recorded in 2011.

Nigeria NG: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 2.420 % in 2017. Nigeria NG: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 2.420 % from Dec 2017 (Median) to 2017, with 1 observations. Nigeria NG: 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 Nigeria – Table NG.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;

These data represent the predicted (modeled) prevalence of Diabetes among adults (Age 18+) for each census tract in Colorado. Diabetes is defined as ever being diagnosed with Diabetes by a doctor, nurse, or other health professional, and this definition does not include gestational, borderline, or pre-diabetes.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

Food components are expressed in kilocalories/person/day, such that each row displays the impact on diabetes prevalence of a 1 kilocalorie/person/day increase in the availability of the given food category (e.g., a 1 kilocalorie/person/day rise in sugar relates to a 0.0072% rise in diabetes prevalence). Urbanization refers to the percentage of the population living in urban areas. Aging is the percentage of the population 65 years of age and older. Obesity is the percentage of the population with BMI at least 30 kg/m2.Robust standard errors in parentheses.*p < 0.05, ** p < 0.01, *** p < 0.001

The share of the population with overweight in the United States was forecast to continuously increase between 2024 and 2029 by in total 1.6 percentage points. After the fifteenth consecutive increasing year, the overweight population share is estimated to reach 77.43 percent and therefore a new peak in 2029. Notably, the share of the population with overweight of was continuously increasing over the past years.Overweight is defined as a body mass index (BMI) of more than 25.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the share of the population with overweight in countries like Canada and Mexico.

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 dataset presents information on age-sex specific incidence rates of diabetes by First Nations status for Alberta, expressed as per 100,000 population.

Adult respondents ages 18+ who were ever diagnosed with diabetes by a doctor. Years covered are from 2013-2014 by zip code. Data taken from the California Health Interview Survey Neighborhood Edition (AskCHIS NE) (http://askchisne.ucla.edu/), downloaded February 2018. AskCHIS Neighborhood Edition is an online data dissemination and visualization platform that provides health estimates at sub-county geographic regions. Estimates are powered by data from The California Health Interview Survey (CHIS). CHIS is conducted by The UCLA Center for Health Policy Research, an affiliate of UCLA Fielding School of Public Health.Health estimates available in AskCHIS NE (Neighborhood Edition) are model-based small area estimates (SAEs).SAEs are not direct estimates (estimates produced directly from survey data, such as those provided through AskCHIS).CHIS data and analytic results are used extensively in California in policy development, service planning and research, and is recognized and valued nationally as a model population-based health survey.Before using estimates from AskCHIS NE, it is recommended that you read more about the methodology and data limitations at: http://healthpolicy.ucla.edu/Lists/AskCHIS%20NE%20Page%20Content/AllItems.aspx. You can go to http://askchisne.ucla.edu/ to create your own account.Produced by The California Health Interview Survey and The UCLA Center for Health Policy Research and compiled by the Los Angeles County Department of Public Health. "Field Name = Field Definition"Zipcode" = postal zip code in the City of Los Angeles “Percent” = estimated percentage of adult respondents ages 18+ who were ever diagnosed with diabetes by a doctor"LowerCL" = the lower 95% confidence limit represents the lower margin of error that occurs with statistical sampling"UpperCL" = the upper 95% confidence limit represents the upper margin of error that occurs in statistical sampling "Population" = estimated population 18 and older (denominator) residing in the zip code Notes: 1) Zip codes are based on the Los Angeles Housing Department Zip Codes Within the City of Los Angeles map (https://media.metro.net/about_us/pla/images/lazipcodes.pdf).2) Zip codes that did not have data available (i.e., null values) are not included in the dataset; there are additional zip codes that fall within the City of Los Angeles.3) Zip code boundaries do not align with political boundaries. These data are best viewed with a City of Los Angeles political boundary file (i.e., City of Los Angeles jurisdiction boundary, City Council boundary, etc.) FAQS: 1. Which cycle of CHIS does AskCHIS Neighborhood Edition provide estimates for?All health estimates in this version of AskCHIS Neighborhood Edition are based on data from the 2013-2014 California Health Interview Survey. 2. Why do your population estimates differ from other sources like ACS? The population estimates in AskCHIS NE represent the CHIS 2013-2014 population sample, which excludes Californians living in group quarters (such as prisons, nursing homes, and dormitories). 3. Why isn't there data available for all ZIP codes in Los Angeles?While AskCHIS NE has data on all ZCTAs (Zip Code Tabulation Areas), two factors may influence our ability to display the estimates:A small population (under 15,000): currently, the application only shows estimates for geographic entities with populations above 15,000. If your ZCTA has a population below this threshold, the easiest way to obtain data is to combine it with a neighboring ZCTA and obtain a pooled estimate.A high coefficient of variation: high coefficients of variation denote statistical instability.

Romania RO: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 9.740 % in 2017. Romania RO: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 9.740 % from Dec 2017 (Median) to 2017, with 1 observations. Romania RO: 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 Romania – Table RO.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;

Spain ES: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 7.170 % in 2017. Spain ES: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 7.170 % from Dec 2017 (Median) to 2017, with 1 observations. Spain ES: 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 Spain – Table ES.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;

Netherlands NL: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 5.290 % in 2017. Netherlands NL: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 5.290 % from Dec 2017 (Median) to 2017, with 1 observations. Netherlands NL: 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 Netherlands – Table NL.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;

Malaysia Diabetes Prevalence: % of Population Aged 20-79 data was reported at 16.740 % in 2017. Malaysia Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 16.740 % from Dec 2017 (Median) to 2017, with 1 observations. Malaysia 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 Malaysia – Table MY.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 is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. Adult smoking prevalence in California, males and females aged 18+, starting in 2012. Caution must be used when comparing the percentages of smokers over time as the definition of ‘current smoker’ was broadened in 1996, and the survey methods were changed in 2012. Current cigarette smoking is defined as having smoked at least 100 cigarettes in lifetime and now smoking every day or some days. Due to the methodology change in 2012, the Centers for Disease Control and Prevention (CDC) recommend not conducting analyses where estimates from 1984 – 2011 are compared with analyses using the new methodology, beginning in 2012. This includes analyses examining trends and changes over time. (For more information, please see the narrative description.) The California Behavioral Risk Factor Surveillance System (BRFSS) is an on-going telephone survey of randomly selected adults, which collects information on a wide variety of health-related behaviors and preventive health practices related to the leading causes of death and disability such as cardiovascular disease, cancer, diabetes and injuries. Data are collected monthly from a random sample of the California population aged 18 years and older. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. The survey has been conducted since 1984 by the California Department of Public Health in collaboration with the Centers for Disease Control and Prevention (CDC). In 2012, the survey methodology of the California BRFSS changed significantly so that the survey would be more representative of the general population. Several changes were implemented: 1) the survey became dual-frame, with both cell and landline random-digit dial components, 2) residents of college housing were eligible to complete the BRFSS, and 3) raking or iterative proportional fitting was used to calculate the survey weights. Due to these changes, estimates from 1984 – 2011 are not comparable to estimates from 2012 and beyond. Center for Disease Control and Policy (CDC) and recommend not conducting analyses where estimates from 1984 – 2011 are compared with analyses using the new methodology, beginning in 2012. This includes analyses examining trends and changes over time.Current cigarette smoking was defined as having smoked at least 100 cigarettes in lifetime and now smoking every day or some days. Prior to 1996, the definition of current cigarettes smoking was having smoked at least 100 cigarettes in lifetime and smoking now.

The objective of SDPP is to study the importance of hereditary, individual and environmental determinants of impaired glucose tolerance, diabetes and related morbidity like obesity and high blood pressure as well as consequences. Stockholm Diabetes Prevention Programme is a population based survey in which 3128 men and 4821 women (between the ages 35-56) from five municipalities of Stockholm County Council were screened between the years 1992-1998. A follow up study was conducted about 10 years later where 76,2 percentage of the men and 69,1 percentage of women responded. 2014 about 20 years after the baseline a new follow up study started and all participants from the baseline were invited. For the participants a screening occasion includes an oral glucose tolerance test and an extensive questionnaire. The study is focusing on diabetes heredity and therefore includes all individuals with at least one first, or two second grade relatives with diabetes. Those without any diabetes in the family are also over-sampled.

Adult respondents ages 18+ who were ever diagnosed with diabetes by a doctor. Years covered are from 2011 to 2012 by zip code. Data taken from the California Health Interview Survey Neighborhood Edition (AskCHIS (http://askchisne.ucla.edu/), downloaded January 2016. "Field" = "Definition""ZIPCODE" = postal zip code in LA County "Zip_code" = postal zip code in LA County "PAdDiab" = 'fraction of projected 18 and older population with residing in Zip Code'"PAdDiab2" = percentage of projected 18 and older population with Diabetes conditions residing in Zip Code"NAdDiab" = number of projected 18 and older population with Diabetes conditions residing in Zip Code"Pop_18olde" = projected 18 and older population total residing in Zip CodeHealth estimates available in AskCHIS NE are model-based small area estimates (SAEs).SAEs are not direct estimates (estimates produced directly from survey data, such as those provided through AskCHIS).CHIS data and analytic results are used extensively in California in policy development, service planning and research, and is recognized and valued nationally as a model population-based health surveyFAQ: 1. Which cycle of CHIS does AskCHIS Neighborhood Edition provide estimates for?All health estimates in this version of AskCHIS Neighborhood Edition are based on data from the 2011- 2012 California Health Interview Survey. Socio-demographic indicators come from the 2008-2012 American Community Survey (ACS) 5-year summary tables. 2. Why do your population estimates differ from other sources like ACS? The population estimates in AskCHIS NE represent the CHIS 2011-2012 population sample, which excludes Californians living in group quarters (such as prisons, nursing homes, and dormitories). 3. Why isn't there data available for all ZIP codes / cities in Los Angeles?While AskCHIS NE has data on all ZCTAs (Zip Code Tabulation Areas), two factors may influence our ability to display the estimates:A small population (under 15,000): currently, the application only shows estimates for geographic entities with populations above 15,000. If your ZCTA has a population below this threshold, the easiest way to obtain data is to combine it with a neighboring ZCTA and obtain a pooled estimate. A high coefficient of variation: high coefficients of variation denote statistical instability.

Switzerland Diabetes Prevalence: % of Population Aged 20-79 data was reported at 5.590 % in 2017. Switzerland Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 5.590 % from Dec 2017 (Median) to 2017, with 1 observations. Switzerland 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 Switzerland – Table CH.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;

Type-2 Diabetes mellitus (T2DM) increases both the patient risk of cardiovascular disease (CVD) and renal outcomes, such as chronic kidney disease (CKD). Recent clinical trials of the glucose-lowering drug-class of sodium-glucose co-transporter-2 inhibitors (SGLT2is) have shown benefits in preventing CVD events and progression of CKD, leading to an update of the Dutch T2DM treatment guideline for patients at risk. The aim of this study is to assess the health and economic impact of the guideline-recommended utilization of SGLT2is in the Netherlands. The patient population at risk was determined by multiplying Dutch T2DM prevalence rates with the total numbers of inhabitants of the Netherlands in 2020. Subsequently, two analyses, comparing a treatment setting before and after implementation of the new guideline for SGLT2is, were conducted. Clinical and adverse event rates in both settings as well as direct healthcare costs were sourced from the literature. Total costs were calculated by multiplying disease prevalence, event rates and costs associated to outcomes. One-time disutilities per event were included to estimate the health impact. The potential health and economic impact of implementing the updated guideline was calculated. Using a 5-year time horizon, the guideline-suggested utilization of SGLT2is resulted in a health impact equal to 4835 quality adjusted life years gained (0.0031 per patient per year) and €461 million cost-savings. The costs of treatment with SGLT2is were €813 million. Hence the net budget impact was €352 million for the total Dutch T2DM population, which translated to €0,57 per patient per day. SGLT2is offer an option to reduce the number of CVD and CKD related events and associated healthcare costs and health losses in the Netherlands. Further research is needed to include the benefits of improved T2DM management options from a broader societal perspective.HighlightsThe glucose-lowering drug-class of sodium-glucose co-transporter-2 inhibitors (SGLT2is) has shown benefits in preventing cardiovascular events and progression of kidney disease in patients with type-2 diabetes leading to a revision of the respective Dutch treatment guideline.The 5-year budget impact of the adoption of SGLT2is in the new treatment guideline was equal to €352 million or €0.57 per patient per day, with a total of 4385 quality adjusted life years gained.The introduction of SGLT2is for Dutch type-2 diabetes patients has the potential to substantially reduce the number of cardiovascular as well as renal disease events and related healthcare costs while also delivering a health benefit. The glucose-lowering drug-class of sodium-glucose co-transporter-2 inhibitors (SGLT2is) has shown benefits in preventing cardiovascular events and progression of kidney disease in patients with type-2 diabetes leading to a revision of the respective Dutch treatment guideline. The 5-year budget impact of the adoption of SGLT2is in the new treatment guideline was equal to €352 million or €0.57 per patient per day, with a total of 4385 quality adjusted life years gained. The introduction of SGLT2is for Dutch type-2 diabetes patients has the potential to substantially reduce the number of cardiovascular as well as renal disease events and related healthcare costs while also delivering a health benefit.