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...

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.

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).

Context

Diabetes is a chronic health condition that affects how your body turns food into energy. There are three main types of diabetes: type 1, type 2, and gestational diabetes.

• Type 1 diabetes is an autoimmune disease that causes your body to attack the cells in your pancreas that produce insulin. Insulin is a hormone that helps your body use glucose for energy.

• Type 2 diabetes is the most common type of diabetes. It occurs when your body doesn't respond normally to insulin, or when your body doesn't produce enough insulin.

• Gestational diabetes is a type of diabetes that develops during pregnancy. It usually goes away after the baby is born.

Prevalence of Diabetes

According to the CDC BRFSS 2021, 34.1 million adults in the United States have diabetes, or 10.5% of the adult population. This number has been increasing over time. In 2010, 29.1 million adults in the United States had diabetes, or 9.3% of the adult population.

Content

The Behavioral Risk Factor Surveillance System (BRFSS) is an ongoing, state-based telephone survey that collects data about health-related risk behaviors, chronic health conditions, and the use of preventive services among adults aged 18 years and older residing in the United States. Conducted annually by the Centers for Disease Control and Prevention (CDC), the BRFSS has been providing valuable insights into the health status and behaviors of U.S. adults since its inception in 1984.

For this dataset, a csv of the 2021 BRFSS dataset available on Kaggle was used. The original dataset contains responses from 438,693 individuals and has 303 features. These features are either questions directly asked of participants, or calculated variables based on individual participant responses.

This dataset contains 3 files:

• diabetes_012_health_indicators_BRFSS2021.csv is a clean dataset of 236,378 survey responses to the CDC's BRFSS2021. The target variable Diabetes_012 has 3 classes. 0 is for no diabetes or only during pregnancy, 1 is for prediabetes, and 2 is for diabetes. There is class imbalance in this dataset. This dataset has 21 feature variables.

• diabetes_binary_5050split_health_indicators_BRFSS2021.csv is a clean dataset of 67,136 survey responses to the CDC's BRFSS2021. It has an equal 50-50 split of respondents with no diabetes and with either prediabetes or diabetes. The target variable Diabetes_binary has 2 classes. 0 is for no diabetes, and 1 is for prediabetes or diabetes. This dataset has 21 feature variables and is balanced.

• diabetes_binary_health_indicators_BRFSS2021.csv is a clean dataset of 236,378 survey responses to the CDC's BRFSS2021. The target variable Diabetes_binary has 2 classes. 0 is for no diabetes, and 1 is for prediabetes or diabetes. This dataset has 21 feature variables and is not balanced.

Acknowledgements

It it important to reiterate that I did not create this dataset, it is just a cleaned and consolidated dataset created from the BRFSS 2021 dataset already on Kaggle. That dataset can be found here and the notebook I used for the data cleaning can be found here.

Inspiration

Alex Teboul for Cleaning the dataset for Machine Learning use by using the 2015 BRFSS was the inspiration for creating this dataset and exploring the BRFSS in general.

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

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.

About this file Information about dataset attributes - Pregnancies: To express the Number of pregnancies Glucose: To express the Glucose level in blood BloodPressure: To express the Blood pressure measurement SkinThickness: To express the thickness of the skin Insulin: To express the Insulin level in blood BMI: To express the Body mass index Age: To express the age Outcome: To express the final result 1 is Yes and 0 is No

Null data

The objective of this study was to compare the effect of diabetes and pathologies potentially related to diabetes on the risk of infection and death from COVID-19 among people from Highly-Developed-Country (HDC), including Italians, and immigrants from the High-Migratory-Pressure-Countries (HMPC). Among the population with diabetes, whose prevalence is known to be higher among immigrants, we compared the effect of body mass index among HDC and HMPC populations. A population-based cohort study was conducted, using population registries and routinely collected surveillance data. The population was stratified into HDC and HMPC, according to the place of birth; moreover, a focus was set on the South Asiatic population. Analyses restricted to the population with type-2 diabetes were performed. We reported incidence (IRR) and mortality rate ratios (MRR) and hazard ratios (HR) with 95% confidence interval (CI) to estimate the effect of diabetes on SARS-CoV-2 infection and COVID-19 mortality. Overall, IRR of infection and MRR from COVID-19 comparing HMPC with HDC group were 0.84 (95% CI 0.82–0.87) and 0.67 (95% CI 0.46–0.99), respectively. The effect of diabetes on the risk of infection and death from COVID-19 was slightly higher in the HMPC population than in the HDC population (HRs for infection: 1.37 95% CI 1.22–1.53 vs. 1.20 95% CI 1.14–1.25; HRs for mortality: 3.96 95% CI 1.82–8.60 vs. 1.71 95% CI 1.50–1.95, respectively). No substantial difference in the strength of the association was observed between obesity or other comorbidities and SARS-CoV-2 infection. Similarly for COVID-19 mortality, HRs for obesity (HRs: 18.92 95% CI 4.48–79.87 vs. 3.91 95% CI 2.69–5.69) were larger in HMPC than in the HDC population, but differences could be due to chance. Among the population with diabetes, the HMPC group showed similar incidence (IRR: 0.99 95% CI: 0.88–1.12) and mortality (MRR: 0.89 95% CI: 0.49–1.61) to that of HDC individuals. The effect of obesity on incidence was similar in both HDC and HMPC populations (HRs: 1.73 95% CI 1.41–2.11 among HDC vs. 1.41 95% CI 0.63–3.17 among HMPC), although the estimates were very imprecise. Despite a higher prevalence of diabetes and a stronger effect of diabetes on COVID-19 mortality in HMPC than in the HDC population, our cohort did not show an overall excess risk of COVID-19 mortality in immigrants.

Death rate of a population adjusted to a standard age distribution. As most causes of death vary significantly with people's age and sex, the use of standardised death rates improves comparability over time and between countries, as they aim at measuring death rates independently of different age structures of populations. The standardised death rates used here are calculated on the basis of a standard European population (defined by the World Health Organization). Detailed data for 65 causes of death are available in the database (under the heading 'Data').

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.

Background.

Diabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. Each year more than 1 million people with diabetes are acutely admitted to hospital due to complications of their illness. This includes Diabetic emergencies such as Diabetic Comas, Hypoglycaemia, Diabetic ketoacidosis, and Diabetic Hyperosmolar Hyperglycaemic State. Diabetic emergency management is often not compliant with national guidelines, and there is a pressing need to improve patient care. This dataset includes 65,506 people and 168,706 spells, designed to support research which improves diabetic emergency and unplanned care.

Other causes for admission include diabetic ulcers, neuropathies, kidney disease and associated co-morbidities such as infection, cerebrovascular disease and cardiovascular disease. This dataset includes acute all diabetic admissions to University Hospitals Birmingham NHS Trust from 2000 onwards refreshed to include new admissions as they occur.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

EHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All patients admitted to hospital from year 2002 and onwards, curated to focus on Diabetes. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards and triage). Along with presenting complaints, outpatients admissions, microbiology results, referrals, procedures, therapies, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations and others), all blood results(urea, albumin, platelets, white blood cells and others). Includes all prescribed & administered treatments and all outcomes. Linked images are also available (radiographs, CT scans, MRI).

Available supplementary data: Matched controls; ambulance, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

BackgroundDiabetes is on the rise in the western world, but data from Scandinavia are inconsistent with indications of stable or even reverse trends. To shed new light on this issue, we investigated diabetes trends in Stockholm County 1990–2010, taking into account trends in risk factors and mortality.MethodsWe used data from a large population-based quadrennial public health survey conducted between 1990 and 2010 in Stockholm County (∼2.1 million inhabitants), supplemented with data from national registers. The age-standardized prevalence and incidence rates of diabetes and related risk factors 1990–2010 were estimated in adult inhabitants. We also modelled the influence of potential risk factors on the diabetes trends and estimated the life time risk of diabetes.ResultsThe prevalence of diabetes was 4.6% (95% confidence interval (CI); 4.5–4.8%) in 2010 compared to 2.8% (95% CI; 2.3–3.5%) in 1990. Between 1990 and 2002 the prevalence rose annually by 3.8% (95% CI; 2.1–5.5). Incidence rates showed a similar pattern and rose by 3.0% (95% CI; 0.3–5.7%) annually 1990–2002. The rising incidence was mainly attributable to increasing prevalence of overweight/obesity, which rose by 46% during the observation period. In 2010, the lifetime risk of adult onset diabetes was 28% (95% CI; 26–31%) in men and 19% (95% CI; 17–21%) in women.ConclusionsDiabetes rates have been increasing in Stockholm over the last decades, both in terms of incidence and prevalence, and this development is most likely the result of increasing overweight and obesity in the population.

Up to 30% of older adults (aged 65 years and older) have been diagnosed with Diabetes mellitus. Older diabetics are more likely to experience complications like heart disease, stroke, kidney problems, and nerve damage, leading to higher hospital admission rates.  When managing older diabetic patients, healthcare professionals need to consider factors like frailty, polypharmacy (multiple medications), and potential cognitive impairments. 

This dataset includes 83,303 people and 366,035 spells, designed to support research which improves diabetic emergency and unplanned care in older adults. It includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process, presenting complaints, admissions, microbiology results, referrals, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations and others), all blood results (urea, albumin, platelets, white blood cells and others). Includes all prescribed & administered treatments and all outcomes. Linked images are also available (radiographs, CT scans, MRI).

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of obesity, inactivity and inactivity/obesity-related illnesses. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.The analysis incorporates data relating to the following:Obesity/inactivity-related illnesses (asthma, cancer, chronic kidney disease, coronary heart disease, depression, diabetes mellitus, hypertension, stroke and transient ischaemic attack)Excess weight in children and obesity in adults (combined)Inactivity in children and adults (combined)The analysis was designed with the intention that this dataset could be used to identify locations where investment could encourage greater levels of activity. In particular, it is hoped the dataset will be used to identify locations where the creation or improvement of accessible green/blue spaces and public engagement programmes could encourage greater levels of outdoor activity within the target population, and reduce the health issues associated with obesity and inactivity.ANALYSIS METHODOLOGY1. Obesity/inactivity-related illnessesThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Depression (in adults aged 18+)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (in persons of all ages)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.For each of the above illnesses, the percentage of each MSOA’s population with that illness 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 patients registered with each GP that have that illness The estimated percentage of each MSOA’s population with each illness 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 each illness, within the relevant age range.For each illness, 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 that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn 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 predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 8 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.2. Excess weight in children and obesity in adults (combined)For each MSOA, the number and percentage of children in Reception and Year 6 with excess weight was combined with population data (up to age 17) to estimate the total number of children with excess weight.The first part of the analysis detailed in section 1 was used to estimate the number of adults with obesity in each MSOA, based on GP-level statistics.The percentage of each MSOA’s adult population (aged 18+) with obesity was estimated, using GP-level data (see section 1 above). 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 adult patients registered with each GP that are obeseThe estimated percentage of each MSOA’s adult population with obesity was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of adults in each MSOA with obesity.The estimated number of children with excess weight and adults with obesity were combined with population data, to give the total number and percentage of the population with excess weight.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 excess weight/obesityB) the NUMBER of people within that MSOA who are estimated to have excess weight/obesityAn 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 predicted to have excess weight/obesity, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from excess weight/obesity, and where those people make up a large percentage of the population, indicating there is a real issue with that excess weight/obesity within the population and the investment of resources to address that issue could have the greatest benefits.3. Inactivity in children and adultsFor each administrative district, the number of children and adults who are inactive was combined with population data to estimate the total number and percentage of the population that are inactive.Each district was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that district who are estimated to be inactiveB) the NUMBER of people within that district who are estimated to be inactiveAn 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 district predicted to be inactive, compared to other districts. In other words, those are areas where a large number of people are predicted to be inactive, and where those people make up a large percentage of the population, indicating there is a real issue with that inactivity within the population and the investment of resources to address that issue could have the greatest benefits.Summary datasetAn average of the scores calculated in sections 1-3 was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer the score to 1, the greater the number and percentage of people suffering from obesity, inactivity and associated illnesses. I.e. these are areas where there are a large number of people (both children and adults) who are obese, inactive and suffer from obesity/inactivity-related illnesses, and where those people make up a large percentage of the local population. These are the locations where interventions could have the greatest health and wellbeing benefits for the local population.LIMITATIONS1. For data recorded at the GP practice level, 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 ‘Levels of obesity, inactivity and associated illnesses: Summary (England). Areas with data missing’ 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, 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

BackgroundIdentifying the metabolite profile of individuals with prediabetes who turned to type 2 diabetes (T2D) may give novel insights into early T2D interception. The purpose of this study was to identify metabolic markers that predict the development of T2D from prediabetes in a Chinese population.MethodsWe used an untargeted metabolomics approach to investigate the associations between serum metabolites and risk of prediabetes who turned to overt T2D (n=153, mean follow up 5 years) in a Chinese population (REACTION study). Results were compared with matched controls who had prediabetes at baseline [age: 56 ± 7 years old, body mass index (BMI): 24.2 ± 2.8 kg/m2] and at a 5-year follow-up [age: 61 ± 7 years old, BMI: 24.5 ± 3.1 kg/m2]. Confounding factors were adjusted and the associations between metabolites and diabetes risk were evaluated with multivariate logistic regression analysis. A 10-fold cross-validation random forest classification (RFC) model was used to select the optimal metabolites panels for predicting the development of diabetes, and to internally validate the discriminatory capability of the selected metabolites beyond conventional clinical risk factors.FindingsMetabolic alterations, including those associated with amino acid and lipid metabolism, were associated with an increased risk of prediabetes progressing to diabetes. The most important metabolites were inosine [odds ratio (OR) = 19.00; 95% confidence interval (CI): 4.23-85.37] and carvacrol (OR = 17.63; 95% CI: 4.98-62.34). Thirteen metabolites were found to improve T2D risk prediction beyond eight conventional T2D risk factors [area under the curve (AUC) was 0.98 for risk factors + metabolites vs 0.72 for risk factors, P < 0.05].InterpretationsUse of the metabolites identified in this study may help determine patients with prediabetes who are at highest risk of progressing to diabetes.

About this dataset

Diabetes is one of the fastest growing chronic life threatening diseases that have already affected 422 million people worldwide according to the report of World Health Organization (WHO), in 2018. Due to the presence of a relatively long asymptomatic phase, early detection of diabetes is always desired for a clinically meaningful outcome. Around 50% of all people suffering from diabetes are undiagnosed because of its long-term asymptomatic phase.

This dataset contains 520 observations with 17 characteristics, collected using direct questionnaires and diagnosis results from the patients in the Sylhet Diabetes Hospital in Sylhet, Bangladesh.

How to use this dataset

• Create a classification model to predict diabetes;
• Explore the most common features associated with diabetic risk.

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Acknowledgements

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

Citation

Islam M.M.F., Ferdousi R., Rahman S., Bushra H.Y. (2020) Likelihood Prediction of Diabetes at Early Stage Using Data Mining Techniques. In: Gupta M., Konar D., Bhattacharyya S., Biswas S. (eds) Computer Vision and Machine Intelligence in Medical Image Analysis. Advances in Intelligent Systems and Computing, vol 992. Springer, Singapore. https://doi.org/10.1007/978-981-13-8798-2_12

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BackgroundRates of diabetes in pregnancy are disproportionately higher among Aboriginal than non-Aboriginal women in Australia. Additional challenges are posed by the context of Aboriginal health including remoteness and disadvantage. A clinical register was established in 2011 to improve care coordination, and as an epidemiological and quality assurance tool. This paper presents results from a process evaluation identifying what worked well, persisting challenges and opportunities for improvement.MethodsClinical register data were compared to the Northern Territory Midwives Data Collection. A cross-sectional survey of 113 health professionals across the region was also conducted in 2016 to assess use and value of the register; and five focus groups (49 healthcare professionals) documented improvements to models of care.ResultsFrom January 2012 to December 2015, 1,410 women were referred to the register, 48% of whom were Aboriginal. In 2014, women on the register represented 75% of those on the Midwives Data Collection for Aboriginal women with gestational diabetes and 100% for Aboriginal women with pre-existing diabetes. Since commencement of the register, an 80% increase in reported prevalence of gestational diabetes among Aboriginal women in the Midwives Data Collection occurred (2011–2013), prior to adoption of new diagnostic criteria (2014). As most women met both diagnostic criteria (81% in 2012 and 74% in 2015) it is unlikely that the changes in criteria contributed to this increase. Over half (57%) of survey respondents reported improvement in knowledge of the epidemiology of diabetes in pregnancy since establishment of the register. However, only 32% of survey respondents thought that the register improved care-coordination. The need for improved integration and awareness to increase use was also highlighted.ConclusionAlthough the register has not been reported to improve care coordination, it has contributed to increased reported prevalence of gestational diabetes among high risk Aboriginal women, in a routinely collected jurisdiction-wide pregnancy dataset. It has therefore contributed to an improved understanding of epidemiology and disease burden and may in future contribute to improved management and outcomes. Regions with similar challenges in context and high risk populations for diabetes in pregnancy may benefit from this experience of implementing a register.

This dataset presents the percentage of individuals with type 2 diabetes who have successfully achieved all three key treatment targets recommended by the National Institute for Health and Care Excellence (NICE). These targets include maintaining an HbA1c level of 58 mmol/mol or lower, a blood pressure level of 140/80 mmHg or lower, and, for those at high cardiovascular risk, being prescribed a statin. The dataset provides a valuable measure of effective diabetes management and supports analysis across different population groups and healthcare settings.

Rationale

Achieving all three treatment targets is associated with better health outcomes and reduced risk of diabetes-related complications. This indicator helps assess the quality of diabetes care and supports efforts to improve clinical management and patient outcomes for people living with type 2 diabetes.

Numerator

The numerator includes the number of individuals with type 2 diabetes who have met all three NICE-recommended treatment targets: HbA1c ≤ 58 mmol/mol, blood pressure ≤ 140/80 mmHg, and statin prescription for those at high cardiovascular risk. Data is sourced from the National Diabetes Audit (NDA) and NHS England.

Denominator

The denominator includes all individuals aged 12 and over who are registered with type 2 diabetes at GP practices participating in the National Diabetes Audit. This ensures a consistent and comprehensive population base for calculating the indicator.

Caveats

Data is collected over a 15-month period, from January 1st of the first year to March 31st of the following year. This extended reporting window may affect comparability with other datasets that use different timeframes.

External references

For more information, visit the Public Health England Fingertips Diabetes Profile.

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.

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Background: Diabetes mellitus affects over 3.9 million people in the United Kingdom (UK), with over 2.6 million people in England alone. More than 1 million people living with diabetes are acutely admitted to hospital due to complications of their illness every year. Cardiovascuar disease is the most prevalent cause of morbidity and mortality in people with diabetes. Diabetic retinopathy (DR) is a common microvascular complication of type 1 and type 2 diabetes and remains a major cause of vision loss and blindness in those of working age. This dataset includes the national screening diabetic grade category (seven categories from R0M0 to R3M1) from the Birmingham, Solihull and Black Country DR screening program (a member of the National Health Service (NHS) Diabetic Eye Screening Programme) and the University Hospitals Birmingham NHS Trust cardiac outcome data.

Geography: The West Midlands has a population of 5.9 million. The region includes a diverse ethnic, and socio-economic mix, with a higher than UK average of minority ethnic groups. It has a large number of elderly residents but is the youngest population in the UK. There are particularly high rates of diabetes, physical inactivity, obesity, and smoking.

Data sources:
1. The Birmingham, Solihull and Black Country Data Set, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom. They manage over 200,000 diabetic patients, with longitudinal follow-up up to 15 years, making this the largest urban diabetic eye screening scheme in Europe. 2. The Electronic Health Records held at University Hospitals Birmingham NHS Foundation Trust is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds and 100 ITU beds. UHB runs a fully electronic healthcare record for systemic disease.

Scope: All Birmingham, Solihull and Black Country diabetic eye screened participants who have been admitted to UHB with a cardiac related health concern from 2006 onwards. Longitudinal and individually linked with their diabetic eye care from primary screening data and secondary care hospital cardiac outcome data including • Demographic information (including age, sex and ethnicity) • Diabetes status • Diabetes type • Length of time since diagnosis of diabetes • Visual acuity • The national screening diabetic screening grade category (seven categories from R0M0 to R3M1) • Diabetic eye clinical features • Reason for sight and severe sight impairment • ICD-10 and SNOMED-CT codes pertaining to cardiac disease • Outcome

Website: https://www.retinalscreening.co.uk/

Abstract Introduction With an aging population also come disabling chronic diseases, the diabetes mellitus (DM) is among them. The aim was to evaluate the prevalence of DM in the elderly of the Family Health Program in Porto Alegre, according to the sociodemographic and health variables, and describe the treatment used and medication adherence. Methods Cross-sectional study conducted in individuals above 60 years old. Data were collected by a questionnaire (sociodemographic, health status, lifestyle and drug information). Morisky scale was assessed for the medication adherence. Results We analyzed 763 older adults with a mean age of 69.1 ± 7.5 years, 63.7% were female. The prevalence of DM was 23.5% and was higher in women (27.2%), in people aged 60-79 years (24.6%), in the widowed (28.4%) and those who reported having caregiver (27.6%). On the health variables, elderly with higher BMI and those reporting heart disease presented a higher prevalence of DM. Metformin was the mostly used hypoglycemic drug (76.5%). Conclusion The elderly care has increased in ESF diabetic patients’ and this study will help in developing strategies to better care for this population.

This dataset shows the percentage of patients with non-diabetic hyperglycaemia who took up an offer to participate in the NHS Diabetes Prevention Programme (DPP). The indicator reflects engagement with preventative services aimed at reducing the risk of developing type 2 diabetes. Data is sourced from the National Diabetes Audit (NDA) and includes patients registered at participating GP practices.

Rationale

The NHS Diabetes Prevention Programme is a key initiative to reduce the incidence of type 2 diabetes by supporting individuals at high risk through lifestyle interventions. Monitoring the uptake of DPP courses helps assess the reach and effectiveness of the programme and supports efforts to improve early intervention and reduce long-term health complications.

Numerator

The numerator is the number of patients with non-diabetic hyperglycaemia who were offered and did not decline a DPP course, as recorded by GP practices participating in the National Diabetes Audit.

Denominator

The denominator includes all patients with non-diabetic hyperglycaemia registered at GP practices that participated in the National Diabetes Audit.

Caveats

Some individuals with diabetes may be excluded from the dataset if they were not registered with a GP practice at the time of data collection. This may affect the completeness of the data and the accuracy of the reported uptake rate.

External References

More information is available from the following source:

National Diabetes Audit - NDH & DPP

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.

BackgroundDiabetic sensory neuropathy has rarely been studied in the Asian populations. This study investigated the prevalence and risk factors of sensory symptoms (SS) in the Taiwanese diabetes patients.MethodsA total of 1,400 diabetes patients received a health examination together with a structured questionnaire interview for three categories of abnormal sensation of numbness or tingling pain, electric shock, and skin thickness sensation on seven anatomical sites on upper limbs and six sites on lower limbs. Prevalence of SS was defined using nine different criteria, with the least stringent criterion of “any positive symptom on at least 1 site” and the most stringent criterion of “any positive symptom on at least bilateral and symmetrical 2 sites involving the lower limb.” Logistic regression was used to estimate the odds ratios and their 95% confidence interval for SS by the different definitions. Fasting plasma glucose and hemoglobin A1c were entered in separate models to avoid hypercollinearity.ResultsThe prevalence of SS was 14.4 and 54.0% when using the most stringent and least stringent criterion, respectively. Women consistently had a significantly higher prevalence than men did. Among the three categories of symptoms, numbness or tingling pain was the most common, and fingers and toes were the most commonly involved anatomical sites. For any symptoms, 37.1% of the patients had any symptoms on the upper limbs and 41.7% had any symptoms on the lower limbs. Female sex, diabetes duration, hemoglobin A1c, and hypertension were associated with SS in all models.ConclusionsTaiwanese diabetes patients may have a high prevalence of SS if a structured questionnaire is used for screening. Female sex, diabetes duration, hemoglobin A1c, and hypertension are associated with SS.