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).

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

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

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).

Dataset Summary

The Pubmed Diabetes dataset consists of 19,717 scientific publications from the PubMed database pertaining to diabetes, classified into one of three classes. The classes are as follows:

Experimental Diabetes Type 1 Diabetes Type 2 Diabetes

  Dataset Structure





  Data Fields

paper_id: The PubMed ID. title: The PubMed paper title. abstract: The PubMed paper abstract. label: The class label assigned to the paper. predicted_ranked_labels: The most… See the full description on the dataset page: https://huggingface.co/datasets/devanshamin/PubMedDiabetes-LLM-Predictions.

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

Brief Summary: The Type 1 Diabetes Exercise Initiave (T1-DEXI) was an observational study of adults living with type 1 diabetes in the U.S., conducted remotely outside of clinics, designed to develop a better understanding of the effects of different levels of exercise intensity and duration on glycemic control during and after exercise across a wide range of patient characteristics. This dataset incorporates aggregated data around exercise events including pertinent diabetes management information (insulin and continuous glucose monitoring data), self-reported and objectively measured physical activity levels (Polar H10 sensor and Verily Study Watch), self-reported stress levels and life-event data such as the timing and composition of meals (Remote Food Photography Method). Genotyping, completed for a subset of participants, may help researchers understand how variations in DNA may be associated with exercise, diabetes, and glycemic response to exercise.

Basic Explanation

It is important to know if a patient will be readmitted in some hospital. The reason is that you can change the treatment, in order to avoid a readmission.

In this database, you have 3 different outputs:

1. No readmission;
2. A readmission in less than 30 days (this situation is not good, because maybe your treatment was not appropriate);
3. A readmission in more than 30 days (this one is not so good as well the last one, however, the reason can be the state of the patient.

In this context, you can see different objective functions for the problem. You can try to figure out situations where the patient will not be readmitted, or if their are going to be readmitted in less than 30 days (because the problem can the the treatment), etc... Make your choice and let's help them creating new approaches for the problem.

Content

"The data set represents 10 years (1999-2008) of clinical care at 130 US hospitals and integrated delivery networks. It includes over 50 features representing patient and hospital outcomes. Information was extracted from the database for encounters that satisfied the following criteria.

1. It is an inpatient encounter (a hospital admission).
2. It is a diabetic encounter, that is, one during which any kind of diabetes was entered to the system as a diagnosis.
3. The length of stay was at least 1 day and at most 14 days.
4. Laboratory tests were performed during the encounter.
5. Medications were administered during the encounter.

The data contains such attributes as patient number, race, gender, age, admission type, time in hospital, medical specialty of admitting physician, number of lab test performed, HbA1c test result, diagnosis, number of medication, diabetic medications, number of outpatient, inpatient, and emergency visits in the year before the hospitalization, etc."

• https://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008

Source

The data are submitted on behalf of the Center for Clinical and Translational Research, Virginia Commonwealth University, a recipient of NIH CTSA grant UL1 TR00058 and a recipient of the CERNER data. John Clore (jclore '@' vcu.edu), Krzysztof J. Cios (kcios '@' vcu.edu), Jon DeShazo (jpdeshazo '@' vcu.edu), and Beata Strack (strackb '@' vcu.edu). This data is a de-identified abstract of the Health Facts database (Cerner Corporation, Kansas City, MO).

Original source of the data set

https://archive.ics.uci.edu/ml/datasets/Diabetes+130-US+hospitals+for+years+1999-2008

Background: Type 2 diabetes rates in the general population have risen with the growing obesity epidemic. Knowledge of temporal patterns and factors associated with comorbid diabetes among stroke patients may enable health practitioners and policy makers to develop interventions aimed at reducing diabetes rates, which may consequently lead to declines in stroke incidence and improvements in stroke outcomes. Methods: Using the Nationwide Inpatient Sample (NIS), a nationally representative data set of US hospital admissions, we assessed trends in the proportion of acute ischemic stroke (AIS) patients with comorbid diabetes from 1997 to 2006. Independent factors associated with comorbid diabetes were evaluated using multivariable logistic regression. Results: Over the study period, the absolute number of AIS hospitalizations declined by 17% (from 489,766 in 1997 to 408,378 in 2006); however, the absolute number of AIS hospitalizations with comorbid type 2 diabetes rose by 27% [from 97,577 (20%) in 1997 to 124,244 (30%) in 2006, p < 0.001]. The rise in comorbid diabetes over time was more pronounced in patients who were relatively younger, Black or ‘other’ race, on Medicaid, or admitted to hospitals located in the South. Factors independently associated with higher odds of diabetes in AIS patients were Black or ‘other’ versus White race, congestive heart failure, peripheral vascular disease, history of myocardial infarction, renal disease and hypertension. Conclusions: Although hospitalizations for AIS in the US decreased from 1997 to 2006, there was a steep rise in the proportion with comorbid diabetes (from 1 in 5 to almost 1 in 3). Specific patient populations may be potential targets for mitigating this trend.

Pima Indians Diabetes Dataset Split

This directory contains split datasets of Pima Indians Diabetes Database. For each splits, we have

Mock data: The mock data is a smaller dataset (10 rows for both train and test) that is used to test the model and data processing code. Private data: Each private data contains 123-125 rows for training, and 32-33 rows for testing.

BackgroundCancer and diabetes are among the leading causes of morbidity and mortality worldwide. Several studies have reported diabetes as a risk factor for developing cancer, a relationship that may be explained by associated factors shared with both diseases such as age, sex, body weight, smoking, and alcohol consumption. Social factors referred to as social determinants of health (SDOH) were shown to be associated with the risk of developing cancer and diabetes. Despite that diabetes and social factors were identified as significant determinants of cancer, no studies examined their combined effect on the risk of developing cancer. In this study, we aim at filling this gap in the literature by triangulating the association between diabetes, indices of SDOH, and the risk of developing cancer.MethodsWe have conducted a quantitative study using data from the Behavioral Risk Factor Surveillance System (BRFSS), whereby information was collected nationally from residents in the United States (US) with respect to their health-related risk behaviors, chronic health conditions, and the use of preventive services. Data analysis using weighted regressions was conducted on 389,158 study participants.ResultsOur findings indicated that diabetes is a risk factor that increases the likelihood of cancer by 13% (OR 1.13; 95%CI: 1.05–1.21). People of White race had higher odds for cancer compared to African Americans (OR 0.44; 95%CI: 0.39–0.49), Asians (OR 0.27; 95%CI: 0.20–0.38), and other races (OR 0.56; 95%CI: 0.46–0.69). The indices of SDOH that were positively associated with having cancer encompassed unemployment (OR 1.78; 95%CI: 1.59–1.99), retirement (OR 1.54; 95%CI: 1.43–1.67), higher income levels with ORs ranging between 1.16–1.38, college education (OR 1.10; 95%CI: 1.02–1.18), college graduates (OR 1.31; 95%CI: 1.21–1.40), and healthcare coverage (OR 1.44; 95%CI: 1.22–1.71). On the other hand, the indices of SDOH that were protective against having cancer were comprised of renting a home (OR 0.86; 95%CI: 0.79–0.93) and never married (OR 0.73; 95%CI: 0.65–0.81).ConclusionThis study offers a novel social dimension for the association between diabetes and cancer that could guide setting strategies for addressing social inequities in disease prevention and access to healthcare.

Medicare Diabetes Prevention Program

  Description

The Medicare Diabetes Prevention Program dataset contains information about suppliers from which eligible Medicare beneficiaries may be furnished associated services. The information in this dataset can include organization name, location, contact information, National Provider Identifier (NPI) among other data points. Location data populates the "Map of MDPP Suppliers furnishing MDPP Services" map.

  Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/medicare-diabetes-prevention-program.

The Diabetes Prevention Program (DPP) is a clinical trial that investigated whether modest weight loss through dietary changes and increased physical activity or treatment with the oral diabetes drug metformin (Glucophage) could prevent or delay the onset of type 2 diabetes in high risk individuals with prediabetes.

The study enrolled overweight persons with elevated fasting and post-load plasma glucose concentrations. Participants were randomized to placebo, metformin (850 mg twice daily), or a lifestyle-modification program with the goals of at least a 7 percent weight loss and at least 150 minutes of physical activity per week. The primary outcome measure was development of diabetes, diagnosed on the basis of an annual oral glucose-tolerance test or a semiannual fasting plasma glucose test, according to the 1997 criteria of the American Diabetes Association: a value for plasma glucose of 126 mg per deciliter (7.0 mmol per liter) or higher in the fasting state, or 200 mg per deciliter (11.1 mmol per liter) or higher two hours after a 75-g oral glucose load. Participation in DPP continued after a diagnosis of diabetes was made, although study medication was discontinued and participants were sent to their local primary care provider for treatment of diabetes once fasting glucose was > 140 mg/dl.

Results showed that both lifestyle changes and treatment with metformin reduced the incidence of diabetes in persons at high risk compared with placebo. Furthermore, the lifestyle intervention was more effective than metformin in preventing the onset of diabetes.

Supplemental measurements were collected using biospecimens that were obtained during the original DPP clinical trial. These measurements included antibodies, biomarkers, hormones, and vitamin D levels to assess the relationships between sex hormones, diabetes risk factors, and the progression to diabetes. The supplemental data showed that sex hormones were associated with diabetes risk in men, but these associations were not found in women. Furthermore, obesity and glycemia were more important predictors of diabetes risk than sex hormones.

The dataset contains information on medical history, residential information and demographic information on CATHGEN participants as well as modeled PM2.5 values at participants' residence. This dataset is not publicly accessible because: The data are human subjects data containing potential identifiable information (PII) and therefore access is restricted to the study investigators. Because base data are owned by other entities, these data need to be requested directly from Duke University. It can be accessed through the following means: These data can accessed upon request to the CATHGEN steering committee at Duke University. Format: Data are stored as SAS files on secure EPA drives.

This dataset is associated with the following publication: Weaver, A., L. McGuinn, L. Neas, J. Mirowsky, R. Devlin, R. Dhingra, C. Ward-Caviness, W. Cascio, W. Kraus, E. Hauser, Q. Di, J. Schwartz, and D. Diaz-Sanchez. Neighborhood sociodemographic effects on the associations between long-term PM2.5 exposure and cardiovascular outcomes and diabetes. Journal of Exposure Science and Environmental Epidemiology. Nature Publishing Group, London, UK, 3(1): e038, (2019).

Nonalcoholic fatty liver disease (NAFLD) is a spectrum of liver conditions associated with fat accumulation that range from benign, non-progressive liver fat accumulation to severe liver injury, cirrhosis, and liver failure. NAFLD is highly prevalent within the United States and is most common in adults who are overweight or have diabetes, insulin resistance, or hyperlipidemia. However, the disease also occurs in children and in persons who are not obese or diabetic. The Nonalcoholic Steatohepatitis Clinical Research Network (NASH CRN) was initiated by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) in 2002 to conduct multicenter, collaborative studies on the etiology, contributing factors, natural history, complications and treatment of NASH.

The NAFLD Pediatric Database 2 was a multicenter, prospective follow-up study of patients with NAFLD or nonalcoholic steatohepatitis (NASH) which aimed to investigate the etiology, pathogenesis, natural history, diagnosis, treatment, and prevention of NAFLD and NASH. The study included longitudinal follow-up of participants enrolled in earlier NASH CRN studies and recruited new participants. The study population included pediatric patients 2- 17 years old at the time of enrollment with histologically confirmed NAFLD or NASH located in the United States. Comprehensive data, including demographics, medical history, symptoms, medication use, alcohol use and routine laboratory studies was collected on all participants at entry and at follow-up visits every 48 weeks from enrollment. A standard of care liver biopsy was collected at baseline if not previously collected, and specimens were collected every 48 weeks during follow-up.

www.insight.hdrhub.org/about-us

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/

abdelhakimDZ/diabetes_QA_dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Defects in pancreatic islets and the progression of multi-tissue insulin resistance in combination with environmental factors are the main causes of type 2 diabetes (T2D). Mass spectrometry-based proteomics of five key-metabolic tissues on a cohort of 42 multi-organ donors provided deep coverage of the proteomes of pancreatic islets, visceral adipose tissue (VAT), liver, skeletal muscle and serum. Enrichment analysis of gene ontology (GO) terms built a tissue-specific map of the chronological order of altered biological processes across healthy controls (CTRL), pre-diabetes (PD) and T2D subjects. This unique dataset allowed us to explore alterations of entire biological pathways and individual proteins in multiple tissues. We confirmed the significant decrease of the citric acid cycle and the respiratory electron transport in VAT and muscle of T2D and we provided a thorough visual representation of the complete set of downregulated proteins. Importantly, we found widespread novel alterations in relevant biological pathways including the increase in hemostasis in pancreatic islets of PD, the increase in the complement cascade in liver and pancreatic islets of PD and the elevation in cholesterol biosynthesis in liver of T2D. Overall, our findings suggest inflammatory, immune and vascular impairments in pancreatic islets as potentially causal factors of insufficient insulin production and increased glucagon levels in the early stages of T2D. In contrast alterations in lipid metabolism and mitochondrial function in the liver and VAT/muscle, respectively, became evident later in manifest T2D. This first multi-tissue proteomic map indicates the temporal order of tissue-specific metabolic dysregulation in T2D development.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

BackgroundDiabetes stigma is recognized to negatively impact health-related outcomes for people living with type 2 diabetes (T2D), but there is a dearth of evidence among U.S. Latino adults with T2D. Our aim was to develop a Spanish-language translation of the Type 2 Diabetes Stigma Assessment Scale (DSAS-2) and examine its psychometric properties among U.S. Latino adults with T2D.MethodsThe translation was developed through a multi-step process, including a focus group with community health workers (n=5) and cognitive debriefing interviews with Latino adults with T2D (n=8). It was field-tested in an online survey of U.S. Latino adults with T2D, recruited via Facebook (October 2018 to June 2019). Exploratory factor analysis examined structural validity. Convergent and divergent validity were assessed by testing hypothesized correlations with measures of general chronic illness stigma, diabetes distress, depressive and anxiety symptoms, loneliness, and self-esteem.ResultsAmong 817 U.S. Latino adults with T2D who participated in the online survey, 517 completed the Spanish-language DSAS-2 (DSAS Spa-US) and were eligible for the study (mean age 54 ± 10 years, and 72% female). Exploratory factor analysis supported a single-factor solution (eigenvalue=8.20), accounting for 82% of shared variance among the 19 items, all loading ≥ 0.5. Internal consistency reliability was high (α=0.93). As expected, strong, positive correlations were observed between diabetes stigma and general chronic illness stigma (rs=0.65) and diabetes distress (rs=0.57); medium, positive correlations, between diabetes stigma and depressive (rs=0.45) and anxiety (rs=0.43) symptoms, and loneliness (rs=0.41); and a moderate negative correlation between diabetes stigma and self-esteem (rs=-0.50). There was no relationship between diabetes stigma and diabetes duration (rs=0.07, ns).ConclusionThe DSAS-2 Spa-US is a version of the DSAS-2, translated into Spanish, that has good psychometric properties for assessing diabetes stigma in U.S. Latino adults with T2D.