Estimated Prevalence Of Diabetes Research Articles

Dialysis More Available Than Patient Education in Counties With High Diabetes Prevalence.

Poorly controlled diabetes is a principal cause of end stage renal disease (ESRD), generating an estimated 44% of new cases. Diabetes self-management education and support (DSMES) has been documented to reduce adverse outcomes such as ESRD. Helping patients better manage their condition could ultimately reduce ESRD prevalence. We compared the county-level availability of DSMES and dialysis as of November 2022 sorted by the estimated prevalence of diabetes among residents aged 18 years or older. The locations of DSMES programs and ESRD dialysis facilities were obtained from 2 professional organizations and the Centers for Medicare & Medicade Services. Estimated diabetes prevalence was obtained from the Centers for Disease Control and Prevention's PLACES data set. Counties were considered to have high diabetes prevalence if they fell into the top quartile for diabetes prevalence in 2019 (≥14.4% of adults). Analyses were conducted in 2023. DSMES was available in 41.0% of counties but in only 20.7% of counties with high diabetes prevalence versus 47.9% of low prevalence counties. Dialysis facilities were present in 59.2% of all counties, in 52.8% of all high diabetes prevalence counties, and in 61.4% of other counties. DSMES availability was linked to the presence of a hospital in the county, with only 6.3% of counties without a hospital offering the service. DSMES could play a role in reducing the prevalence of ESRD. Public health professionals need to be aware of the differing levels of local availability of this service and work to develop partnerships to provide DSMES in high-prevalence areas not currently served.

Prevalence and risk of new-onset diabetes mellitus after COVID-19: a systematic review and meta-analysis.

After the acute phase of SARS-CoV-2 infection, the onset of glycemic impairment and diabetes have been reported. Nevertheless, the exact burden of glycemic impairment and diabetes after COVID-19 has not been clearly described. Electronic search was run in Pubmed (MEDLINE), Web of Science, Scopus, and ClinicalTrial.org for reports published from database inception to September 2022. We included observational studies reporting quantitative data on diabetes prevalence or its onset in subjects with a history of SARS-CoV-2 infection from at least 60 days. Risk of bias was assessed by the JBI's critical appraisal checklist. Random effect model was used to calculate pooled data. The review protocol was registered on PROSPERO (CRD42022310722). Among 1,630 records screened, 20 studies were included in the analysis. The mean or median age of participants ranged from ~ 35 to 64 years, with a percentage of males ranging from 28% to 80%. Only two studies were considered at low risk of bias. The estimate of diabetes prevalence, calculated on a total of 320,948 participants pooled with 38,731 cases, was 16% (95%CI: 11-22%). The estimate of proportion of incident cases of diabetes was 1.6% (95%CI: 0.8-2.7%). Subgroup analysis showed that previous hospitalization increased the prevalence of diabetes and the proportion of incident cases. Diabetes is common in individuals who have experienced SARS-CoV-2 infection, especially if they required hospitalization. This data may be helpful to screen for diabetes and manage its complications in individuals who experienced COVID-19. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022310722, identifier CRD42022310722.

Neighborhood-Level Risk Factors for Severe Hyperglycemia among Emergency Department Patients without a Prior Diabetes Diagnosis.

A person's place of residence is a strong risk factor for important diagnosed chronic diseases such as diabetes. It is unclear whether neighborhood-level risk factors also predict the probability of undiagnosed disease. The objective of this study was to identify neighborhood-level variables associated with severe hyperglycemia among emergency department (ED) patients without a history of diabetes. We analyzed patients without previously diagnosed diabetes for whom a random serum glucose value was obtained in the ED. We defined random glucose values ≥ 200mg/dL as severe hyperglycemia, indicating probable undiagnosed diabetes. Patient addresses were geocoded and matched with neighborhood-level socioeconomic measures from the American Community Survey and claims-based surveillance estimates of diabetes prevalence. Neighborhood-level exposure variables were standardized based on z-scores, and a series of logistic regression models were used to assess the association of selected exposures and hyperglycemia adjusting for biological and social individual-level risk factors for diabetes. Of 77,882 ED patients without a history of diabetes presenting in 2021, 1,715 (2.2%) had severe hyperglycemia. Many geospatial exposures were associated with uncontrolled hyperglycemia, even after controlling for individual-level risk factors. The most strongly associated neighborhood-level variables included lower markers of educational attainment, higher percentage of households where limited English is spoken, lower rates of white-collar employment, and higher rates of Medicaid insurance. Including these geospatial factors in risk assessment models may help identify important subgroups of patients with undiagnosed disease.

Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative risk assessment framework to estimate the risk-attributable type 2 diabetes burden for 16 risk factors falling under risk categories including environmental and occupational factors, tobacco use, high alcohol use, high body-mass index (BMI), dietary factors, and low physical activity. Using a regression framework, we forecast type 1 and type 2 diabetes prevalence through 2050 with Socio-demographic Index (SDI) and high BMI as predictors, respectively. In 2021, there were 529 million (95% uncertainty interval [UI] 500-564) people living with diabetes worldwide, and the global age-standardised total diabetes prevalence was 6·1% (5·8-6·5). At the super-region level, the highest age-standardised rates were observed in north Africa and the Middle East (9·3% [8·7-9·9]) and, at the regional level, in Oceania (12·3% [11·5-13·0]). Nationally, Qatar had the world's highest age-specific prevalence of diabetes, at 76·1% (73·1-79·5) in individuals aged 75-79 years. Total diabetes prevalence-especially among older adults-primarily reflects type 2 diabetes, which in 2021 accounted for 96·0% (95·1-96·8) of diabetes cases and 95·4% (94·9-95·9) of diabetes DALYs worldwide. In 2021, 52·2% (25·5-71·8) of global type 2 diabetes DALYs were attributable to high BMI. The contribution of high BMI to type 2 diabetes DALYs rose by 24·3% (18·5-30·4) worldwide between 1990 and 2021. By 2050, more than 1·31 billion (1·22-1·39) people are projected to have diabetes, with expected age-standardised total diabetes prevalence rates greater than 10% in two super-regions: 16·8% (16·1-17·6) in north Africa and the Middle East and 11·3% (10·8-11·9) in Latin America and Caribbean. By 2050, 89 (43·6%) of 204 countries and territories will have an age-standardised rate greater than 10%. Diabetes remains a substantial public health issue. Type 2 diabetes, which makes up the bulk of diabetes cases, is largely preventable and, in some cases, potentially reversible if identified and managed early in the disease course. However, all evidence indicates that diabetes prevalence is increasing worldwide, primarily due to a rise in obesity caused by multiple factors. Preventing and controlling type 2 diabetes remains an ongoing challenge. It is essential to better understand disparities in risk factor profiles and diabetes burden across populations, to inform strategies to successfully control diabetes risk factors within the context of multiple and complex drivers. Bill & Melinda Gates Foundation.

1383-P: Prevalence of Municipal Policies to Help Reduce Type 2 Diabetes Risk by Providing Environments That Facilitate Healthy Eating and Breastfeeding

Introduction: Communities with high diabetes burden may benefit from local policies that facilitate healthy eating and breastfeeding. Methods: Data from the 2021 National Survey of Community-Based Policy and Environmental Supports for Healthy Eating and Active Living (CBS HEAL; n=1,927 municipalities) were linked to 2019 county level small area estimates of adult diabetes prevalence (the most recent available) from the Centers for Disease Control and Prevention. We estimated the percentage of US municipalities reporting policies to support healthier food access in supermarkets, convenience stores, and farmers markets and policies to support breastfeeding for municipal employees. Chi-Square tests were used to determine if the presence of policies differed by community diabetes burden. Results: Among municipalities with the highest diabetes burden (≥10.1%), 33.8% had a policy for supermarkets, 9.3% had a policy for convenience stores, and 62.9% had a policy for farmers markets and the percent of municipalities reporting these policies did not differ significantly by diabetes burden. The percentage of municipalities that provided breastfeeding employees time and space to pump breast milk differed by diabetes burden (p=0.006) and ranged from 47.7% among those with the highest diabetes burden to 69.3% among municipalities with the lowest burden (≤6.5%). Among municipalities with the highest diabetes burden, 38.2% provided any paid maternity leave, and 19.9% provided ≥8 weeks of maternity leave. Frequency of maternity leave policies did not differ significantly by diabetes burden. Conclusion: Among US municipalities healthy food retail policies did not differ by diabetes burden. Less than half of municipalities with the greatest burden reported policies to support breastfeeding for municipal employees. Opportunities exist to improve support for healthy eating and breastfeeding among communities with high burden of diabetes. Disclosure S.J.Onufrak: None. D.Galuska: None. S.L.Pierce: None. C.A.Macgowan: None. L.Moore: None.

307 Machine learning identification of diabetic foot ulcer severity to reduce amputation risk

OBJECTIVES/GOALS: Target: Computationally identify the markers of ulcer severity and risk of amputation from datasets that include demographics data, clinical, laboratory data, and medical history over 6000 patients. METHODS/STUDY POPULATION: In this study we will use tables of demographics such as age, gender, and ethnicity/race. Inspired by previous research we’ll include wound age (duration in days), wound size, number of concurrent wounds of any etiology, evidence of bioburden/infection, Wagner grade, being non ambulatory, renal dialysis, renal transplant, peripheral vascular disease, and patient hospitalization. Another table will include laboratory vital signs to include physiological variables such as height, weight, body mass index, pulse rate, blood pressure, respiratory rate, and temperature. We’ll include also social data like smoking status, socio-economic status, housing condition. RESULTS/ANTICIPATED RESULTS: Our project aligns with previous efforts to identify high risk Diabetic Foot Ulcer individuals but also takes a different perspective by collecting and marking clinical data from a subset of patients (e.g., severity, Hispanic versus non-Hispanic) and computationally process these data to provide a tool that can identify DFU severity and high-risk patients. We will obtain samples from Hispanics and non-Hispanics because these two groups are likely to have significant differences in the progression of ulcer severity. The rationale is that by comparing these two groups, we will assess and study the factors that are differentially present. It is our expectation that the proposed project will provide an easy-to-use tool for DFU progression and risk of amputation and contribute to identify high-risk individuals. DISCUSSION/SIGNIFICANCE: Diabetes prevalence estimates in Bexar County, TX exceeds national estimates (15.5% vs. 11.3%) and diagnosed cases are higher among Hispanic adults (13.4%) compared to their non-Hispanic white counterparts (9.5%). Late identification of severe foot ulcers minimizes the likelihood of reducing amputation risk.

Socioeconomic inequality in awareness, treatment and control of diabetes among adults in India: Evidence from National Family Health Survey of India (NFHS), 2019–2021

Diabetes is a growing epidemic and a major threat to most of the households in India. Yet, there is little evidence on the extent of awareness, treatment, and control (ATC) among adults in the country. In this study, we estimate the prevalence and ATC of diabetes among adults across various sociodemographic groups and states of India. We used data on 2,078,315 individuals aged 15 years and over from the recent fifth round, the most recent one, of the National Family Health Survey (NFHS-5), 2019–2021, that was carried out across all the states of India. Diabetic individuals were identified as those who had random blood glucose above 140 mg/dL or were taking diabetes medication or has doctor-diagnosed diabetes. Diabetic individuals who reported diagnosis were labelled as aware, those who reported taking medication for controlling blood glucose levels were labelled as treated and those whose blood glucose levels were < 140 mg/dL were labelled as controlled. The estimates of prevalence of diabetes, and ATC were age-sex adjusted and disaggregated by household wealth quintile, education, age, sex, urban–rural residence, caste, religion, marital status, household size, and state. Concentration index was used to quantify socioeconomic inequalities and multivariable logistic regression was used to estimate the adjusted differences in those outcomes. We estimated diabetes prevalence to be 16.1% (15.9–16.1%). Among those with diabetes, 27.5% (27.1–27.9%) were aware, 21.5% (21.1–21.7%) were taking treatment and 7% (6.8–7.1%) had their diabetes under control. Across the states of India, the adjusted rates of awareness varied from 14.4% (12.1–16.8%) to 54.4% (40.3–68.4%), of treatment from 9.3% (7.5–11.1%) to 41.2% (39.9–42.6%), and of control from 2.7% (1.6–3.7%) to 11.9% (9.7–14.0%). The age-sex adjusted rates were lower (p < 0.001) among the poorer and less educated individuals as well as among males, residents of rural areas, and those from the socially backward groups Among individuals with diabetes, the richest fifth were respectively 12.4 percentage points (pp) (11.3–13.4; p < 0.001), 10.5 pp (9.7–11.4; p < 0.001), and 2.3 pp (1.6–3.0; p < 0.001) more likely to be aware, getting treated, and having diabetes under control, than the poorest fifth. The concentration indices of ATC were 0.089 (0.085–0.092), 0.083 (0.079–0.085) and 0.017 (0.015–0.018) respectively. Overall, the ATC of diabetes is low in India. It is especially low the poorer and the less educated individuals. Targeted interventions and management can reduce the diabetes burden in India.

Socioeconomic disparities in diabetes prevalence and management among the adult population in Bangladesh.

Diabetes, one of the major metabolic disorders, is rising in Bangladesh. Studies indicate there is inequality in prevalence and care-seeking behavior, which requires further exploration to understand the socioeconomic disparities in the pathophysiology of diabetes. This study examined the latest nationally representative estimates of diabetes prevalence, awareness, and management among adults aged 18 years and above in Bangladesh and its association with socioeconomic status in 2017-18. We used the 2017-18 Bangladesh Demographic and Health Survey data. Diabetic status of 12,092 adults aged 18 years and above was measured in the survey using fasting plasma glucose levels. We applied multivariate logistic regressions to examine the role of socioeconomic status on diabetes prevalence, awareness, and management, after controlling for relevant covariates. Overall, 10% of adults had diabetes in Bangladesh in 2017-18, with the highest prevalence of 16% in the age group 55-64 years. Our analyses found statistically significant disparities by socioeconomic status in the prevalence of diabetes as well as the person's awareness of his/her diabetic condition. However, the effect of socioeconomic status on receiving anti-diabetic medication only approached significance (p = 0.07), and we found no significant association between socioeconomic status and control of diabetes. We expect to see an 'accumulation' of the number of people with diabetes to continue in the coming years. The rising prevalence of diabetes is only the tip of an iceberg; a large number of people with uncontrolled diabetes and a lack of awareness of their condition will lead to increased morbidity and mortality, and that could be the real threat. Immediate measures to increase screening coverage and exploration of poor control of diabetes are required to mitigate the situation.

State-level metabolic comorbidity prevalence and control among adults age 50-plus with diabetes: estimates from electronic health records and survey data in five states

BackgroundAlthough treatment and control of diabetes can prevent complications and reduce morbidity, few data sources exist at the state level for surveillance of diabetes comorbidities and control. Surveys and electronic health records (EHRs) offer different strengths and weaknesses for surveillance of diabetes and major metabolic comorbidities. Data from self-report surveys suffer from cognitive and recall biases, and generally cannot be used for surveillance of undiagnosed cases. EHR data are becoming more readily available, but pose particular challenges for population estimation since patients are not randomly selected, not everyone has the relevant biomarker measurements, and those included tend to cluster geographically.MethodsWe analyzed data from the National Health and Nutritional Examination Survey, the Health and Retirement Study, and EHR data from the DARTNet Institute to create state-level adjusted estimates of the prevalence and control of diabetes, and the prevalence and control of hypertension and high cholesterol in the diabetes population, age 50 and over for five states: Alabama, California, Florida, Louisiana, and Massachusetts.ResultsThe estimates from the two surveys generally aligned well. The EHR data were consistent with the surveys for many measures, but yielded consistently lower estimates of undiagnosed diabetes prevalence, and identified somewhat fewer comorbidities in most states.ConclusionsDespite these limitations, EHRs may be a promising source for diabetes surveillance and assessment of control as the datasets are large and created during the routine delivery of health care.Trial Registration: Not applicable.

Latent growth trajectories of county-level diabetes prevalence in the United States, 2004–2017, and associations with overall environmental quality

Background:The prevalence of type 2 diabetes (T2D) has increased in the United States, and recent studies suggest that environmental factors contribute to T2D risk. We sought to understand if environmental factors were associated with the rate and magnitude of increase in diabetes prevalence at the county level.Methods:We obtained age-adjusted diabetes prevalence estimates from the CDC for 3,137 US counties from 2004 to 2017. We applied latent growth mixture models to these data to identify classes of counties with similar trends in diabetes prevalence over time, stratified by Rural Urban Continuum Codes (RUCC). We then compared mean values of the US EPA Environmental Quality Index (EQI) 2006–2010, overall and for each of the five domain indices (air, water, land, sociodemographic, and built), with RUCC-specific latent class to examine associations of environmental factors and class of diabetes prevalence trajectory.Results:Overall diabetes prevalence trends between 2004 and 2017 were similar across all RUCC strata. We identified two classes among metropolitan urbanized (RUCC 1) counties; four classes among non-metro urbanized (RUCC 2) counties; and three classes among less urbanized (RUCC 3) and thinly populated (RUCC 4) counties. Associations with overall EQI values and class of diabetes prevalence trends differed by RUCC strata, with the clearest association between poor air EQI and steeper increases in diabetes prevalence among rural counties (RUCC 3 and 4).Conclusions:Similarities in county-level diabetes prevalence trends between 2004 and 2017 were identified for each RUCC strata, although associations with environmental factors varied by rurality.

Estimating Disease Prevalence in Administrative Data.

Disease prevalence estimates from population-based administrative databases are often biased due to measurement (misclassification) errors. The purpose of this article is to review the methodology for estimating disease prevalence in administrative data, with a focus on bias correction. Several approaches to bias correction in administrative data were reviewed and application of these methods was demonstrated using an example from the literature: physician claims and hospitalization data were employed to estimate diabetes prevalence in Ontario, Canada. Misclassification bias in prevalence estimates from administrative data can be reduced by developing and selecting an optimal algorithm for case identification, applying a bias correction formula, or using statistical modelling. An algorithm for which sensitivity equals positive predictive value provides an unbiased estimate of prevalence. Bias reduction methods generally require information about the measurement properties of the algorithm, such as sensitivity, specificity, or predictive value. These properties depend on disease type, prevalence, algorithm definition (including the observation window), and may vary by population and time. Prevalence estimates can be improved by applying multivariable disease prediction models. Frequency of a positive case identification algorithm in administrative data is generally not equivalent to disease prevalence. Although prevalence estimates can be corrected for bias using known measurement properties of the algorithm, these properties may be difficult to estimate accurately; therefore, disease prevalence estimates based on administrative data must be treated with caution.

Prevalence of, and risk factors for, diabetes and prediabetes in Bangladesh: Evidence from the national survey using a multilevel Poisson regression model with a robust variance.

To estimate the age-standardized prevalence of diabetes and prediabetes and identify factors associated with these conditions at individual, household, and community levels. Data from 11952 Bangladeshi adults aged 18-95 years available from the most recent Bangladesh Demographic and Health Survey 2017-18 were used. Anthropometric measurements and fasting blood glucose samples were taken as part of the survey. Prevalence estimates of diabetes and prediabetes were age-standardized with direct standardization, and risk factors were identified using multilevel mix-effects Poisson regression models with robust variance. The overall age-standardised prevalence of diabetes was 9.2% (95%CI 8.7-9.7) (men: 8.8%, women: 9.6%), and prediabetes was 13.3% (95%CI 12.7-13.9) (men: 13.0%, women: 13.6%). Among people with diabetes, 61.5% were unaware that they had the condition. 35.2% took treatment regularly, and only 30.4% of them had controlled diabetes. Factors associated with an increased prevalence of having diabetes were increasing age, male, overweight/obesity, hypertension, being in the highest wealth quintile, and living in the Dhaka division. People currently employed and living in the Rangpur division were less likely to have diabetes than those currently not employed and living in the Barishal division. Diabetes and prediabetes affect a substantial proportion (over one-quarter) of the Bangladeshi adult population. Continuing surveillance and effective prevention and control measures, focusing on obesity reduction and hypertension management, are urgently needed.

1215-P: Evaluating Alternate Bias Correction Methods in Estimating Diabetes Prevalence with Electronic Health Records

Background: The Assessing the Burden of Diabetes by Type in Children, Adolescents and Young Adults (DiCAYA) Network aims to provide timely estimates of diabetes prevalence and incidence using large scale electronic health record (EHR) data. However, patients who self-select into health systems are likely to differ demographically and by health status than the overall source population of interest, which could limit the generalizability of surveillance estimates. Methods: Using simulations, we evaluated the potential of established bias correction methods (inverse probability weighting, post stratification, raking and multilevel regression with post stratification (MRP)) to mitigate selection biases within EHR data. The EHR sample was assumed to have overrepresentation of younger, higher socioeconomic status (SES) and white individuals than the source. Simulations varied the selection processes (e.g. dependent on diabetes status) and misspecification in the adjustment methods. The performance of each method was assessed as the absolute difference between the estimated prevalence in the sample and true prevalence in the simulated source. Results: When selection was dependent on demographics alone, the mean bias in the crude prevalence estimate was -0.7 percentage points (pp) . Each method performed well in removing this bias when demographics that affected selection were adequately accounted for. Biases remained when SES was misclassified in the adjustment process, with best performance seen for MRP (-0.5 pp) . When selection was dependent on diabetes status, the mean bias in the crude prevalence estimate was 1.3 pp and each method similarly worsened this bias (2.3 pp) . Conclusions: These methods are easy to implement and can provide reliable surveillance estimates if factors that are associated with selection into the EHR can be accounted for. However, novel methods may be needed when selection is dependent on diabetes status or other unmeasured factors. Disclosure S.Conderino: None. M.Rosenman: None. V.W.Zhong: None. K.Reynolds: Research Support; Amgen Inc., Merck &amp; Co., Inc., Novartis Pharmaceuticals Corporation. S.Park: None. L.H.Utidjian: None. J.Divers: None. R.Anthopolos: None. L.Thorpe: None. B.Cai: None. H.Shao: Board Member; BRAVO4HEALTH, LLC. T.C.Ong: None. T.L.Crume: None. B.S.Schwartz: None. H.Kirchner: None. Funding Centers for Disease Control and Prevention (1 U18DP006633-01-00)

Diabetes Screening by Race and Ethnicity in the United States: Equivalent Body Mass Index and Age Thresholds.

Racial/ethnic minority populations in the United States have increased rates of diabetes compared with White populations. The 2021 guidelines from the U.S. Preventive Services Task Force recommend diabetes screening for adults aged 35 to 70 years with a body mass index (BMI) of 25 kg/m2 or greater. To determine the BMI threshold for diabetes screening in major racial/ethnic minority populations with benefits and harms equivalent to those of the current diabetes screening threshold in White adults. Cross-sectional study. NHANES (National Health and Nutrition Examination Survey), 2011 to 2018. Nonpregnant U.S. adults aged 18 to 70 years (n= 19335). A logistic regression model was used to estimate diabetes prevalence at various BMIs for White, Asian, Black, and Hispanic Americans. For each racial/ethnic minority group, the equivalent BMI threshold was defined as the BMI at which the prevalence of diabetes in 35-year-old persons in that group is equal to that in 35-year-old White adults at a BMI of 25 kg/m2. Ranges were estimated to account for the uncertainty in prevalence estimates for White and racial/ethnic minority populations. Among adults aged 35 years with a BMI of 25 kg/m2, the prevalence of diabetes in Asian Americans (3.8% [95% CI, 2.8% to 5.1%]), Black Americans (3.5% [CI, 2.7% to 4.7%]), and Hispanic Americans (3.0% [CI, 2.1% to 4.2%]) was significantly higher than that in White Americans (1.4% [CI, 1.0% to 2.0%]). Compared with a BMI threshold of 25 kg/m2 in White Americans, the equivalent BMI thresholds for diabetes prevalence were 20 kg/m2 (range, <18.5 to 23 kg/m2) for Asian Americans, less than 18.5 kg/m2 (range, <18.5 to 23 kg/m2) for Black Americans, and 18.5 kg/m2 (range, <18.5 to 24 kg/m2) for Hispanic Americans. Sample size limitations precluded assessment of heterogeneity within racial/ethnic groups. Among U.S. adults aged 35 years or older, offering diabetes screening to Black Americans and Hispanic Americans with a BMI of 18.5 kg/m2 or greater and Asian Americans with a BMI of 20 kg/m2 or greater would be equivalent to screening White adults with a BMI of 25 kg/m2 or greater. Using screening thresholds specific to race/ethnicity has the potential to reduce disparities in diabetes diagnosis. Richard A. and Susan F. Smith Center for Outcomes Research in Cardiology.

DEVELOPMENT OF TELEMEDICINE APPLICATION BASED ON ARTIFICIAL INTELLIGENCE IN THE HANDLING OF DIABET FOOT

DEVELOPMENT OF TELEMEDICINE APPLICATION BASED ON ARTIFICIAL INTELLIGENCE IN THE HANDLING OF DIABET FOOT

Diabetes in Pakistan: addressing the crisis

Population-based estimates for both type 1 and type 2 diabetes in low-income and middle-income countries are imprecise because of the paucity of registration and surveillance systems in these settings. Current estimates for diabetes prevalence are largely derived from the Global Burden of Disease study, 1 GBD 2019 Diabetes Mortality CollaboratorsDiabetes mortality and trends before 25 years of age: an analysis of the Global Burden of Disease Study 2019. Lancet Diabetes Endocrinol. 2022; 10: 177-192 Summary Full Text Full Text PDF PubMed Scopus (5) Google Scholar often complemented with more targeted surveys and compilations by the International Diabetes Federation (IDF). The most recent IDF Atlas 2 Sun H Saeedi P Karuranga S et al. IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022; 183109119 Summary Full Text Full Text PDF Scopus (191) Google Scholar estimated that there are 33 million people living with type 2 diabetes in Pakistan—the third largest diabetes population globally. An additional 11 million adults in Pakistan have impaired glucose tolerance, while approximately 8·9 million people with diabetes remain undiagnosed. Data on long-term complications among people with diabetes in Pakistan are limited.

In vitro and in silico Screening of Bioactive Compounds from Jasminum subtriplinerve Blume as α-glucosidase Inhibitor

In vitro and in silico Screening of Bioactive Compounds from Jasminum subtriplinerve Blume as α-glucosidase Inhibitor

Genetic Epidemiological Studies of Diabetes Mellitus in Hospital Population of District Bahawalpur

Diabetes is considered a disease that is characterized by high glucose levels in the blood. Diabetes has become a global epidemic disease which is most common in developing countries. The aims are to provide an estimate of diabetes prevalence in the hospital population of district Bahawalpur. The study was carried out on 1000 Diabetic patients, Data was collected by a well-designed Questionnaire. In the statistical analysis, the percentage (%), Standard Error (S.E), mean (M), and Chi-Square tests were used. Out of 1000 Diabetic patients, 39.6% (396) were females and 60.4% (604) were males. The mean age was 51.57 ± 0.50 years in females and for the males, it was 47.99 ± 0.53. A greater number of patients belonged to the urban areas with 70.5% (705/1000) and Minimum belonged to Rural areas with 29.5% (295/1000). BMI recorded in the maximum range of 23-26.9 with 39.9% (399/1000) of the patients while it is recorded minimum the range of ≥27 with 10.1% (101/1000). By family history, much of the diabetic patients belonged to the consanguinity group with 35.7% (357/1000), while it was minimum in the out of cast 7.8 (78/1000). Type II Diabetes mellitus was more common with 64.2% (642/1000) and less common with 35.8% (358/1000). A more fetal onset of Diabetes occurs in the patients with Diabetic family history.

Prevalence of undiagnosed diabetes and the relative importance of its risk factors among adults in Bangladesh: Findings from a nationwide survey

AimTo estimate the prevalence of undiagnosed diabetes, and to identify the relative importance of risk factors for undiagnosed diabetes among Bangladeshi adults. MethodData from 11, 421 Bangladeshi adults aged 18 years and older available from the most recent nationally representative Bangladesh Demographic and Health Survey 2017–18 were used. Anthropometric measurements and fasting blood glucose samples were taken as part of the survey. Prevalence estimates of undiagnosed diabetes was age-standardised with direct standarisation, and risk factors were identified using multilevel mix-effects Poisson regression models with robust variance. ResultsThe overall age-standardised prevalence of undiagnosed diabetes was 6.0% (95 %CI, 5.5–6.4%) (men: 6.1%, women: 5.9%). Risk factors associated with undiagnosed diabetes were older age, elevated body mass index (BMI), highest wealth quintile, hypertension, and being male. The top two modifiable risk factors contributing over 50% to undiagnosed diabetes were BMI and wealth quintiles. ConclusionUndiagnosed diabetes affects a substantial proportion of Bangladeshi adults. Since elevated BMI and the highest wealth quintile are strong risk factors, these offer an opportunity for early detection and screening to reduce undiagnosed diabetes in Bangladesh. In addition, wide-reaching awareness campaigns among the general public, clinicians, and policymakers are needed.

Prevalence and Treatment of Diabetes in China, 2013-2018

Recent data on prevalence, awareness, treatment, and risk factors of diabetes in China is necessary for interventional efforts. To estimate trends in prevalence, awareness, treatment, and risk factors of diabetes in China based on national data. Cross-sectional nationally representative survey data collected in adults aged 18 years or older in mainland China from 170 287 participants in the 2013-2014 years and 173 642 participants in the 2018-2019 years. Fasting plasma glucose and hemoglobin A1c levels were measured for all participants. A 2-hour oral glucose tolerance test was conducted for all participants without diagnosed diabetes. Primary outcomes were diabetes and prediabetes defined according to American Diabetes Association criteria. Secondary outcomes were awareness, treatment, and control of diabetes and prevalence of risk factors. A hemoglobin A1c level of less than 7.0% (53 mmol/mol) among treated patients with diabetes was considered adequate glycemic control. In 2013, the median age was 55.8 years (IQR, 46.4-65.2 years) and the weighted proportion of women was 50.0%; in 2018, the median age was 51.3 years (IQR, 42.1-61.6 years), and the weighted proportion of women was 49.5%. The estimated prevalence of diabetes increased from 10.9% (95% CI, 10.4%-11.5%) in 2013 to 12.4% (95% CI, 11.8%-13.0%) in 2018 (P < .001). The estimated prevalence of prediabetes was 35.7% (95% CI, 34.2%-37.3%) in 2013 and 38.1% (95% CI, 36.4%-39.7%) in 2018 (P = .07). In 2018, among adults with diabetes, 36.7% (95% CI, 34.7%-38.6%) reported being aware of their condition, and 32.9% (95% CI, 30.9%-34.8%) reported being treated; 50.1% (95% CI, 47.5%-52.6%) of patients receiving treatment were controlled adequately. These rates did not change significantly from 2013. From 2013 to 2018, low physical activity, high intake of red meat, overweight, and obesity significantly increased in prevalence. In this survey study, the estimated diabetes prevalence was high and increased from 2013 to 2018. There was no significant improvement in the estimated prevalence of adequate treatment.