Diabetes Surveillance Research Articles

1455-P: Developing Computable Phenotypes (CPs) for Identifying Children, Adolescents, and Young Adults with Diabetes Using Electronic Health Records in the DiCAYA Network

The DiCAYA Network, which is funded by the CDC and NIDDK, is working to create a nationwide electronic health record-based diabetes surveillance system. The feasibility and performance of existing diabetes computable phenotypes in this system were unknown, as they had only been tested in individual health systems. This study aimed to develop a DiCAYA-wide EHR-based computable phenotype (CP) to identify prevalent cases of diabetes by type. We conducted network-wide chart reviews for 1830 youth (age < 18y) and 1830 young adults (age 18 - <45y) among individuals with possible diabetes, identified as those with any potential indication of diabetes in the EHR (any diabetes-related diagnosis, medication, or abnormal lab test). We assessed the performance of the DiCAYA CP (two outpatient diagnoses or one inpatient diagnosis by type) for identifying diabetes and diabetes type in children and young adults in the reviewed sample, using chart review as the gold standard. (Table 1). We developed a highly accurate EHR-based CP for diabetes in children and young adults based on EHR data from the DiCAYA network. Our results support the feasibility of deploying a unified CP across a nationwide EHR network for diabetes surveillance, with consistently high accuracy across the participating sites. Disclosure H.Shao: Consultant; Lilly Diabetes. B.E.Dixon: Consultant; Merck & Co., Inc. E.Lustigova: None. M.Rosenman: None. V.W.Zhong: None. S.Conderino: None. J.Divers: None. L.Thorpe: None. A.D.Liese: None. C.Rudisill: Stock/Shareholder; Pfizer Inc., Viatris Inc., Teladoc Health, Catalent. J.S.Obeid: None. Y.Guo: None. M.E.Pavkov: None. G.Imperatore: None. J.A.Ewing: None. J.Bian: None. P.Li: None. D.Dabelea: None. R.B.Conway: None. T.L.Crume: None. B.S.Schwartz: None. A.G.Hirsch: None. Funding Centers for Disease Control and Prevention (U18DP006512)

629-P: Social Vulnerability Index and Recognition Status with the National Diabetes Prevention Program (National DPP)

Individuals in low socioeconomic status (SES) areas are disproportionately affected by diabetes and have poor lifestyle change outcomes. Organizations delivering the CDC National DPP lifestyle change program receive pending, preliminary, or full recognition status based on specific program criteria and outcomes. Full recognition is critical for organizations to sustain the program and receive reimbursements to cover costs, but organizations in low SES areas may face barriers to receiving it. A multinomial logit model was used to examine the association between the CDC social vulnerability index (SVI) and in-person full recognition organizations at the county level, adjusting for confounders (n=843). Using the National DPP registry, Census data, SVI, and CDC Diabetes Surveillance System, a three-level categorical dependent variable was created: counties without full recognition organizations, counties with at least one non-full recognition organization, and counties with all organizations with full recognition (reference group). The CDC created a four-level categorical SVI (Low, Low-Medium, Medium-High, High) using 16 Census variables to rank counties by social vulnerability: SES, Household Composition and Disability (e.g., age), Minority Status and Language, and Housing Type and Transportation (e.g., vehicle). Of the studied counties, 38.4% had no organizations with full recognition, 22.6% had at least one organization without full recognition, and 39.0% had all organizations with full recognition. Counties with higher SVI were more likely to be in counties without full recognition organizations (High vs. Low; OR=2.71, P <0.001). Counties with high SVI lack fully recognized programs, indicating barriers for organizations in high SVI areas to attain full recognition. This study highlighted the need to develop strategies to ensure organizations in high SVI areas can reach full recognition status to better serve at-risk populations. Disclosure T.Formagini: None. A.Rezwan: None. D.Rodriguez: None. B.Ng: None.

Highly tunable and ultrasensitive hybrid plasmonic platform for multi-spectral sensing: Non-invasive diabetes surveillance and global warming directives

The development of tunable and extremely sensitive devices, as well as the emergence of metamaterials, has tremendously aided the evolution of plasmonic sensing technologies. However, the natural property of metamaterials severely restricts the scalability and application potential of mono-functional sensor systems. To address this shortcoming, we have investigated an ultranarrow five-band plasmonic sensor with structural tunability and high sensing behaviors in compatibility with multi-purpose application scenarios, using the finite-difference time-domain approach rigorously. Meanwhile, we theoretically formulated a hybridization model for the mutual induction of Fabry–Pérot resonances and localized surface plasmon polariton resonance, providing a theoretical basis for the utilization and conception of the highly-sensitive tunable plasmonic sensor. Most notably, we discovered and interpreted far-field phase extinction inducing separation of polarization resonance peaks in physical mechanisms. The tunability of its mutual inductance effect at the intersection distances from two-intersecting silver nanogroove was adequately exploited to further validate and effectively attain five ultra-narrow absorptivities of 96.70%, 93.93%, 99.73%, 100.00%, and 89.72% by top-level optimization. In addition, the redundant proximity between nearby peaks is compensated, extending the operating band and enhancing the practicability in multi-purpose scenarios. As a reliable source for future optical sensing, it performs well in the visible to near-infrared region, enabling detection response to non-invasive diabetes mellitus, global climate warming, and unrestricted temperature and pressure measurement. Meanwhile, minimum FWHM = 4.806 nm and maximum FOM = 115.13 RIU−1 are obtained at 293.15 K, with an associated highest sensitivity of 557.93 nm/RIU. Moreover, the fabrication technologies development of electron beam lithography and focused ion beam etching technologies enables large-scale mass production at low cost and long-term stability.

Intraoperative techniques to prevent deep incisional or organ-space surgical site infection after emergency surgery for nonappendiceal perforation peritonitis: a prospective two-center observational study.

The rate of surgical site infection (SSI) after surgery for secondary peritonitis is very high. This study investigated the relationship between intraoperative procedures of emergency surgery for nonappendiceal perforation peritonitis and deep incisional or organ-space SSI. This prospective, two-center observational study included patients aged ≥ 20years who underwent emergency surgery for perforation peritonitis between April 2017 and March 2020. We compared patients with deep incisional or organ-space SSI (Group S) to patients without SSIs or with superficial incisional SSIs (Group C). Thereafter, we evaluated the association between intraoperative technical variables and deep incisional or organ-space SSI using a multivariate logistic regression model. All multivariate analyses were adjusted for potentially relevant risk factors (e.g., age, body mass index, diabetes, smoking habit, and National Nosocomial Infection Surveillance risk index). Of the 75 participants, 14 were in Group S and 61 were in Group C. The use of a wound protector device was significantly associated with decreased odds of deep incisional or organ-space SSI (adjusted odds ratios [AOR], 0.017; 95% confidence intervals [CI] 0.0014-0.19, p = 0.0011). A 1000ml increase in intra-abdominal lavage with normal saline was significantly associated with increased odds of deep incisional or organ-space SSI (AOR: 1.28, 95% CI 1.02-1.61, p = 0.033). Wound protector devices should be used in emergency surgery for nonappendiceal perforation peritonitis. Excessive intra-abdominal lavage with normal saline for peritonitis may have unsatisfactory benefits and increases the incidence of deep incisional or organ-space SSI.

Genetic study of type 2 diabetes risk in diverse populations of survivors of childhood cancer: A report from the St. Jude Lifetime Cohort (SJLIFE) and the Childhood Cancer Survivor Study (CCSS).

10029 Background: Type 2 diabetes (T2D) is an established late effect of cancer treatment among long-term survivors of childhood cancer. Genetic factors underpinning T2D in diverse populations of survivors have not been well studied. Methods: We conducted a multi-ancestry genome-wide association study (GWAS) for clinically ascertained T2D among survivors of European (EUR; N = 3,102 with 261 cases) and African (AFR; N = 574 with 43 cases) ancestry from SJLIFE. Replication analyses were performed between ancestries in SJLIFE and in EUR survivors in CCSS (N = 5,965; 270 self-reported cases). Two published T2D polygenic risk scores (PRSs) were assessed in survivors: a 338-variant multi-ancestry PRS (N~1.4 million; 49% non-EUR descent) and a ~6.9 million-variant EUR-only PRS (N~160K; DIAGRAM Consortium and UK Biobank Study). Treatment-related T2D risk effect modification was evaluated for abdominal irradiation and alkylating agents. Results: In SJLIFE AFR survivors, one novel locus with suggestive significance was identified (5p15.2: OR = 10.19, P = 5.1x10-7), replicating in both SJLIFE EUR (P = 0.011) and CCSS EUR survivors (P = 0.021). A EUR-specific genome-wide significant association at 8q11.21 ( SNTG1 intronic variant; OR = 1.99; P = 4.4x10-8) was replicated in CCSS (P = 8.1x10-3). Two other loci with suggestive associations (P < 5x10-6) in SJLIFE EUR survivors replicated in SJLIFE AFR survivors (P < 0.05), achieving genome-wide significance in multi-ancestry meta-analysis (2p25.3: OR = 2.05, P = 4.5x10-8; 19p12: OR = 2.43 P = 5.7x10-9). Each of the three novel trans-ancestral loci overlapped putative Polycomb-repressed regions, i.e., chromatin-based gene regulation elements, in pancreatic cells and displayed treatment-related effect heterogeneity across ancestry groups. Notably, AFR survivors with risk alleles experienced disproportionately greater T2D risk if treated with alkylating agents (2p25.3: OR = 3.95; 19p12: OR = 5.74; 5p15.2: OR = 17.81). Increases in the T2D odds per multi-ancestry PRS standard deviation were consistent in survivors across ancestries (SJLIFE EUR: OR = 1.84, P = 1.1x10-16; SJLIFE AFR: OR = 1.80, P = 2.8x10-3, CCSS EUR: OR = 1.60, P = 8.4x10-13). However, T2D risk association with the EUR-only PRS was absent in AFR survivors (OR = 0.97, P = 0.95). Conclusions: Multi-ancestry genetic analyses revealed three novel T2D risk alleles associated with disproportionately greater alkylating agent-related risk among African-ancestry survivors. Furthermore, an external multi-ancestry T2D PRS was associated with increased risk in diverse ancestry survivors, whereas a EUR-only PRS was not useful for African-ancestry survivors. This study supports precision diabetes surveillance and survivorship care for all childhood cancer survivors, including those in minority ancestry groups.

Development and Internal Validation of Case Definitions for Prevalence Estimation of Microvascular Complications of Diabetes in Routine Data

Surveillance of diabetes requires up-to-date information on the prevalence of diabetes and its complications over time. For this purpose, statutory health insurance (SHI) data is being increasingly used, as the data is available in a timely fashion and case numbers enable detailed estimates also of diabetes complications. The aim of the present study was the development and internal validation of case definitions for the prevalence estimation of diabetic retinopathy (DRP), diabetic polyneuropathy (DPN) and diabetic foot syndrome (DFS). Persons with diabetes differentiated by type 1, type 2, and other diabetes in an age- and sex-stratified sample of persons insured by Barmer SHI in 2018 (n=72,744) comprised the study popuation. Based on the central ICD codes for microvascular complications (DRP: H36.0; DPN: G63.2; DFS: E1X.74/.75), case definitions were developed including additional ICD codes for complications without direct diabetes reference. Subsequently, the case definitions were internally validated. For the validation, coding in the inpatient setting (m1S) or repeatedly in the outpatient setting (m2Q) as well as coding of specific procedures (EBM, OPS) and drug prescriptions or by relevant specialists were considered. Additionally, we analysed the documentation of the diagnoses in the previous years. In 2018, the prevalence of the central ICD codes was 8.4% for DRP (H36.0), 18.9% for DPN (G63.2) and 13.4% for DFS (E1X.74/.75). After inclusion of additional ICD codes in the case definition, prevalence increased significantly for DRP (9.6%) and DPN (20.7%), and barely for DFS (13.5%). Internal validation confirmed the majority of diagnoses (DRP: 96.7%; DPN: 96.5% DFS: 95.8%) and m2Q represented the most relevant criterion. When up to four previous years were considered, prevalences were up to 30% higher for DPN and DFS and up to 64% higher for DRP. The inclusion of additional ICD codes in the case definition of microvascular complications of diabetes appears meaningful, as this increases the sensitivity of the prevalence estimate. Internal validation suggests that the documented diagnoses are plausible. However, not all diagnoses are documented annually, leading to an underestimation of the prevalence using a cross-sectional study design of one year.

Assessing racial bias in type 2 diabetes risk prediction algorithms.

Risk prediction models for type 2 diabetes can be useful for the early detection of individuals at high risk. However, models may also bias clinical decision-making processes, for instance by differential risk miscalibration across racial groups. We investigated whether the Prediabetes Risk Test (PRT) issued by the National Diabetes Prevention Program, and two prognostic models, the Framingham Offspring Risk Score, and the ARIC Model, demonstrate racial bias between non-Hispanic Whites and non-Hispanic Blacks. We used National Health and Nutrition Examination Survey (NHANES) data, sampled in six independent two-year batches between 1999 and 2010. A total of 9,987 adults without a prior diagnosis of diabetes and with fasting blood samples available were included. We calculated race- and year-specific average predicted risks of type 2 diabetes according to the risk models. We compared the predicted risks with observed ones extracted from the US Diabetes Surveillance System across racial groups (summary calibration). All investigated models were found to be miscalibrated with regard to race, consistently across the survey years. The Framingham Offspring Risk Score overestimated type 2 diabetes risk for non-Hispanic Whites and underestimated risk for non-Hispanic Blacks. The PRT and the ARIC models overestimated risk for both races, but more so for non-Hispanic Whites. These landmark models overestimated the risk of type 2 diabetes for non-Hispanic Whites more severely than for non-Hispanic Blacks. This may result in a larger proportion of non-Hispanic Whites being prioritized for preventive interventions, but it also increases the risk of overdiagnosis and overtreatment in this group. On the other hand, a larger proportion of non-Hispanic Blacks may be potentially underprioritized and undertreated.

Young-onset diabetes in women with Polycystic Ovary Syndrome: A territory-wide retrospective analysis in Hong Kong

AimTo ascertain the risk of progression to diabetes among Chinese women with PCOS. MethodsWomen with PCOS (n = 3978) were identified from the Hong Kong Diabetes Surveillance Database based on the ICD-9 code for PCOS diagnosis and women without PCOS served as controls (n = 39780), matched 1:10 by age. Result(s)The mean follow-up was 6.28 ± 4.20 and 6.95 ± 4.33 years in women with PCOS and controls, respectively. The crude incidence rate of diabetes was 14.25/1000 person-years in women with PCOS compared with 3.45 in controls. The crude hazard ratio of diabetes in women with PCOS was 4.23 (95 % CI: 3.73–4.80, p < 0.001). Further stratified by age group, the risk of developing diabetes decreased with increasing age but it remained significantly higher in women with PCOS across all age groups. It also suggested that the incidence rate of diabetes in women with PCOS aged 20–29 is highly comparable to that in healthy women aged ≥ 40. More than half of the incident diabetes captured during the follow-up in women with PCOS cohort were young-onset diabetes. ConclusionWomen diagnosed with PCOS at a younger age have the highest relative risk of developing diabetes, suggesting frequent glycemic status screening is required to detect diabetes at an early stage.

Effect of Accessible Nonmydriatic Retinal Imaging on Diabetic Retinopathy Surveillance Rates.

Purpose: To evaluate the impact on surveillance rates for diabetic retinopathy (DR) by providing nonmydriatic retinal imaging as part of comprehensive diabetes care at no cost to patients or insurers. Methods: A retrospective comparative cohort study was designed. Patients were imaged from April 1, 2016 to March 31, 2017 at a tertiary diabetes-specific academic medical center. Retinal imaging was provided without additional cost beginning October 16, 2016. Images were evaluated for DR and diabetic macular edema using standard protocol at a centralized reading center. Diabetes surveillance rates before and after no-cost imaging were compared. Results: A total of 759 and 2,080 patients respectively were imaged before and after offering no-cost retinal imaging. The difference represents a 274% increase in the number of patients screened. Furthermore, there was a 292% and 261% increase in the number of eyes with mild DR and referable DR, respectively. In the comparative 6-month period, 92 additional cases of proliferative DR were identified, estimated to prevent 6.7 cases of severe visual loss with annual cost savings of $180,230 (estimated yearly cost of severe vision loss per person: $26,900). In patients with referable DR, self-awareness was low, with no significant difference in the before and after groups (39.4% vs. 43.8%, p = 0.3725). Conclusions: Providing retinal imaging as part of comprehensive diabetes care substantially increased the number of patients identified by nearly threefold. The data suggest that the removal of out-of-pocket costs substantially increased patient surveillance rates, which may translate to improved long-term patient outcomes.

Identifying type 1 / type 2 diabetes in medico-administrative database to improve health surveillance, medical research and prevention in diabetes: Algorithm development and application

IntroductionBig data sources represent an opportunity for diabetes research. One example is the French national health data system (SNDS), gathering information on medical claims of out-of-hospital health care and hospitalizations for the entire French population (66 million). Currently, a validated algorithm based on antidiabetic drug reimbursement is able to identify people with pharmacologically-treated diabetes in the SNDS. But it cannot distinguish type 1 from type 2 diabetes. Differentiating type 1 and type 2 diabetes is crucial in diabetes surveillance, because they carry differences in their prevention, populations at risk, disease natural history, pathophysiology, management and risk of complications.This article investigates the development of a type 1/type 2 diabetes classification algorithm using artificial intelligence and its application to estimate the prevalence of type 1 and type 2 diabetes in France. MethodsThe final data set comprised all diabetes cases from the CONSTANCES cohort (n = 951). A supervised machine learning method based on eight steps was used: final data set selection, target definition (type 1), coding features, final data set splitting into training and testing data sets, feature selection and training and validation and selection of algorithms. The selected algorithm was applied to SNDS data to estimate the type 1 and type 2 diabetes prevalence among adults 18–70 years of age. ResultsAmong the 3481 SNDS features, 14 were selected to train the different algorithms. The final algorithm was a linear discriminant analysis model based on the number of reimbursements for fast-acting insulin, long-acting insulin and biguanides over the previous year (specificity 97% and sensitivity 100%). In 2016, after adjusting for algorithm performance, type 1 and type 2 diabetes prevalence in France was estimated to be 0.3% and 4.4%, respectively. ConclusionOur type 1/type 2 classification algorithm was found to perform well and to be applicable to any prescription or medical claims database from other countries. Artificial intelligence opens new possibilities for research and diabetes prevention.

Patterns of Postpartum Primary Care Follow-up and Diabetes-Related Care After Diagnosis of Gestational Diabetes

Gestational diabetes (GD) affects up to 10% of pregnancies and increases lifetime risk of type 2 diabetes 10-fold; postpartum diabetes evaluation and primary care follow-up are critical in preventing and detecting type 2 diabetes. Despite clinical guidelines recommending universal follow-up, little remains known about how often individuals with GD access primary care and type 2 diabetes screening. To describe patterns of primary care follow-up and diabetes-related care among individuals with and without GD in the first year post partum. This cohort study used a private insurance claims database to compare follow-up in the first year post partum between individuals with GD, type 2 diabetes, and no diabetes diagnosis. Participants included postpartum individuals aged 15 to 51 years who delivered between 2015 and 2018 and had continuous enrollment from 180 days before to 366 days after the delivery date. Data were analyzed September through October 2021 and reanalyzed November 2022. Primary care follow-up visits and diabetes-related care (blood glucose testing and diabetes-associated visit diagnoses) were determined by evaluation and management, Current Procedural Terminology, and International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes, respectively. A total of 280 131 individuals were identified between 2015 and 2018 (mean age: 31 years; 95% CI, 27-34 years); 12 242 (4.4%) had preexisting type 2 diabetes and 18 432 (6.6%) had GD. A total of 50.9% (95% CI, 49.9%-52.0%) of individuals with GD had primary care follow-up, compared with 67.2% (95% CI, 66.2%-68.2%) of individuals with preexisting type 2 diabetes. A total of 36.2% (95% CI, 35.1%-37.4%) of individuals with GD had diabetes-related care compared with 56.9% (95% CI, 55.7%-58.0%) of individuals with preexisting diabetes. Only 36.0% (95% CI, 34.4%-37.6%) of individuals with GD connected with primary care received clinical guideline concordant care with blood glucose testing 12 weeks post partum. In this cohort study of postpartum individuals, individuals with GD had lower rates of primary care and diabetes-related care compared with those with preexisting type 2 diabetes, and only 36% of those with GD received guideline-recommended blood glucose testing in the first 12 weeks post partum. This illustrates a missed opportunity for early intervention in diabetes surveillance and prevention and demonstrates the need to develop a multidisciplinary approach for postpartum follow-up.

Baseline and longitudinal trajectories of body-mass index and all-cause mortality among patients with type 2 diabetes

AimTo investigate the associations of baseline body mass index (BMI) and longitudinal BMI trajectories with all-cause mortality among patients with type 2 diabetes mellitus (T2DM). Methods,We used data from the diabetes surveillance system of Yinzhou Health Information System with T2DM patients registered from 2010 to 2015. Participants aged ≥ 40 years were included and were followed up until September 30, 2021. The latent class growth mixture model was used to identify different changing patterns in BMI for 5 years from registration. Cox proportional hazards models were used to examine the associations of baseline BMI and 5-year BMI trajectories with all-cause mortality. ResultsWe observed a nonlinear association between baseline BMI and all-cause mortality (P for nonlinearity < 0.001), with an increased risk of death for low but not high BMI. However, compared with participants with medium-stable BMI for 5 years from baseline, individuals with increasing BMI had higher mortality, with adjusted hazard ratios (95% confidence intervals) 1.21 (1.02;1.43) for early-increasing and 1.47 (1.19;1.80) for late-sharp increasing groups. ConclusionThese findings suggest that while obesity itself may not be associated with an increased risk for mortality, weight gain, and in particular rapid weight gain, is a risk factor for mortality among patients with T2DM.

Social Determinants and Health Disparities Pertaining to Diabetes in Appalachia.

Diabetes poses a significant threat to public health in the United States, with an estimated total prevalence of 37.3 million individuals in 2019, of which 28.7 million were diagnosed and 8.5 million remained undiagnosed. The high prevalence of diabetes imposes a considerable economic burden on the U.S. healthcare system. Appalachian Ohio is disproportionately impacted with southeastern Ohio exhibiting a diabetes prevalence that exceeds the national average by more than twofold and a critical shortage of healthcare providers. To quantify the associations between diabetes prevalence and incidence in Ohio counties, considering various factors that impact health and quality-of-life outcomes. The data used in this study were obtained from the United States Diabetes Surveillance System (USDSS) on the 88 counties in Ohio. Pearson correlation tests were employed to investigate the relationship between diabetes (prevalence and incidence) and social determinants of health. A t-test and multivariate analysis of variance (MANOVA) test were performed to analyze the disparities in diabetes and social determinants between Appalachian and non-Appalachian counties. The results of this study demonstrate notable disparities in diabetes prevalence between Appalachian and non-Appalachian counties (P < .001, α = .05). Furthermore, the MANOVA test revealed significant differences between these 2 groups regarding social determinants of health (P < .05). These findings suggest that Appalachian counties may face a disproportionate impact from health-related factors and experience limited access to healthcare services. The data highlight the need for focused efforts to address the specific challenges faced by Appalachian counties. Improving access to healthcare services in the Appalachian region is paramount to ensure equitable healthcare and enhance the overall health outcomes of affected communities.

Monitoring glucose levels in urine using FTIR spectroscopy combined with univariate and multivariate statistical methods

The development of novel platforms for non-invasive continuous glucose monitoring applied in the screening and monitoring of diabetes is crucial to improve diabetes surveillance systems. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy of urine can be an alternative as a sustainable, label-free, fast, non-invasive, and highly sensitive analysis to detect changes in urine promoted by diabetes and insulin treatment. In this study, we used ATR-FTIR to evaluate the urinary components of non-diabetic (ND), diabetic (D), and diabetic insulin-treated (D + I) rats. As expected, insulin treatment was capable to revert changes in glycemia, 24-h urine collection volume, urine creatinine, urea, and glucose excretion promoted by diabetes. Several differences in the urine spectra of ND, D, and D + I were observed, with urea, creatinine, and glucose analytes being related to these changes. Principal components analysis (PCA) scores plots allowed for the discrimination of ND and D + I from D with an accuracy of ∼ 99 %. The PCA loadings associated with PC1 confirmed the importance of urea and glucose vibrational modes for this discrimination. Univariate analysis of second derivative spectra showed a high correlation (r: 0.865, p < 0.0001) between the height of 1074 cm-1 vibrational mode with urinary glucose concentration. In order to estimate the amount of glucose present in the infrared spectra from urine, multivariate curve resolution-alternating least square (MCR-ALS) was applied and a higher predicted concentration of glucose in the urine was observed with a correlation of 78.9 % compared to urinary glucose concentration assessed using enzyme assays. In summary, ATR-FTIR combined with univariate and multivariate chemometric analyses provides an innovative, non-invasive, and sustainable approach to diabetes surveillance.

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.

Smartphone Apps for Surveillance of Gestational Diabetes: Scoping Review.

Developments and evolutions in the information and communication technology sector have provided a solid foundation for the emergence of mobile health (mHealth) in recent years. The cornerstone to management of gestational diabetes mellitus (GDM) is the self-management of glycemic indices, dietary intake, and lifestyle adaptations. Given this, it is readily adaptable to incorporation of remote monitoring strategies involving mHealth solutions. We sought to examine and assess the available smartphone apps which enable self-monitoring and remote surveillance of GDM with a particular emphasis on the generation of individualized patient feedback. Five databases were searched systematically for any studies evaluating mHealth-supported smartphone solutions for GDM management from study inception until January 2022. The studies were screened and assessed for eligibility of inclusion by 2 independent reviewers. Ultimately, 17 studies were included involving 1871 patients across 11 different countries. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) conceptual framework was adhered to for data extraction and categorization purposes. All studies analyzed as part of this review facilitated direct uploading of data from the handheld glucometer to the downloaded patient-facing smartphone app. Glycemic data were captured by all studies and were reassuringly found to be either improved or noninferior to extant models of hospital-based care. Feedback was delivered in either an automated fashion through in-app communication from the health care team or facilitated through bidirectional communication with the app and hospital portal. Although resource utilization and cost-effective analyses were reported in some studies, the results were disparate and require more robust analysis. Where patient and staff satisfaction levels were evaluated, the response was overwhelmingly positive for mHealth smartphone-delivered care strategies. Emergency cesarean section rates were reduced; however, elective cesarean sections were comparatively increased among studies where the mode of delivery was assessed. Most reviewed studies did not identify any differences in maternal, perinatal, or neonatal health when app-based care was compared with usual in-person review. This comprehensive scoping review highlights the feasibility, reliability, and acceptability of app-assisted health care for the management of GDM. Although further exploration of the economic benefit is required prior to implementation in a real-world clinical setting, the prospect of smartphone-assisted health care for GDM is hugely promising.

Comparing health insurance and survey data in estimating prevalence of chronic diseases

Abstract Background Population prevalence of chronic conditions can be estimated from national health surveys and from administrative data sources such as insurance records. This study evaluated the agreement between the Belgian Health Interview Survey (BHIS) and the Belgian compulsory health insurance data (BCHI) in ascertaining chronic hypertension, hypercholesterolemia and diabetes in Belgium. Methods The most recent cycle of BHIS (2018) provided the self-reported prevalence of diabetes, hypertension, and hypercholesterolemia among a representative sample of Belgian adults. For BCHI, the chronic conditions were attributed for every individual in the BHIS reviewing the medication prescription records identified using the ATC/DDD system. These two data sources were linked through unique identifiers by STATBEL. Disease prevalence, measures of agreement, and measures of concordance were estimated. Logistic regression was performed to determine the factors affecting agreement between BHIS and BCHI’s disease classifications. Results Data linkage was done for 9,753 individuals aged 15 years and older. From the sample, BHIS and BCHI respectively identified 5.9% and 5.6% diabetes cases, 18% and 24% of hypertension cases, and 18% and 16% of hypercholesterolemia cases. The kappa coefficient between BCHI and self-reported diabetes, hypertension, and hypercholesterolemia was 0.79, 0.59, and 0.49, respectively. Gender, age, and subjective health significantly affected the agreement in chronic condition classification between BHIS and BCHI. Conclusions Data on reimbursed drugs is a potential alternative method in the surveillance of chronic diabetes. This procedure could be used in estimating disease prevalence but further validation is needed to evaluate its applicability and bias in other chronic conditions. Key messages • BCHI is a possible alternative data source for the surveillance of diabetes in the population. • BCHI overestimated hypertension and underestimated hypercholesterolemia prevalence.

The role of age on the risk relationship between prediabetes and major morbidities and mortality: analysis of the Hong Kong diabetes surveillance database of 2 million Chinese adults

Intensive lifestyle modification showed variable success in the prevention of major clinical events and mortality among people with prediabetes. We propose that age may partly explain the heterogeneity and that health hazards related to prediabetes are age-specific. We conducted a retrospective analysis of a territory-wide diabetes surveillance dataset from the Hong Kong Hospital Authority between 2000 and 2019. Prediabetes was defined according to the American Diabetes Association criteria. Proportional Cox regression was performed, stratified by baseline age categories (20-39, 40-59, 60-79 and ≥80 years). 1,630,942 individuals were included in the analysis. Compared with normoglycaemia, prediabetes was associated with greater hazards for cardiovascular disease (CVD) and all-cause mortality in most age groups but the effect size attenuated with ascending age (p value for trend <0·05). In the youngest and in the oldest age categories, the respective hazard ratios (95% confidence interval) of prediabetes vs normoglycaemia were 1·79 (1·59, 2·01) and 1·00 (0·95, 1·05) for CVD, and 1·36 (1·20, 1·55) and 0·99 (0·97, 1·02) for all-cause mortality. Similar associations were found for chronic kidney disease, end-stage kidney disease, all-site cancer, all-site infection, subtypes of CVD, and cause-specific mortality. The associations became attenuated but remained after excluding people who later developed diabetes and adjusting for metabolic factors. Similar associations were observed in prediabetes defined by impaired fasting glucose, but not HbA1c. Prediabetes is associated with higher risk of major clinical events, even excluding subsequent development of diabetes and adjusting for metabolic factors. The risk relationships are stronger in young than older people. This study did not receive any specific funding.

Medical sharps in Portugal: a cross-sectional survey of disposal practices among the diabetic population

ObjectiveWe aim to determine the disposal site for biohazardous materials resulting from diabetes surveillance and therapy.DesignCross-sectional study.SettingFive Portuguese primary care facilities.ParticipantsWe randomly sampled diabetic patients representative of five primary care...

Effects of RAS inhibitors on all-site cancers and mortality in the Hong Kong diabetes surveillance database (2002-2019).

SummaryBackgroundCancer is replacing cardiovascular-disease as a leading cause of death in type 2 diabetes (T2D). The association of RAS-inhibitors (RASi) and cancer, including differences between angiotensin-converting-enzyme-inhibitor (ACEi) and angiotensin-receptor-blocker (ARBs) as well as their associations independent of blood pressure lowering, remains inconclusive in T2D.MethodsWe conducted a cohort study with new-user design in 253,491 patients in the Hong-Kong-Diabetes-Surveillance-Database (HKDSD) in 2002-2019. We evaluated the associations of time-varying RASi use (ACEi and ARBs) with all-site cancer, diabetes-related cancers, and cancer-specific mortality including comparison with new-users of calcium-channel-blockers (CCBs) as an active-comparator group.FindingsOf 253,491, 133,730 (52.8%) were new-RASi and 119,761 (47.2%) were non-RASi users with a median follow-up period of 6.3 (interquartile ragne: 3.4-9.2) years (1,678,719 patient-years). After propensity-score weighting and adjustment for time-varying covariables, RASi use was associated with lower risk of all-site cancer (HR=0.76, 95%CI: 0.74-0.79), diabetes-related cancer (HR=0.79, 95%CI: 0.75-0.84), cancer-specific mortality (HR=0.50, 95%CI: 0.47-0.53), and diabetes-related cancer mortality (HR=0.49, 95%CI: 0.45-0.54) versus non-RASi. Amongst RASi users, ARBs use was associated with lower risk of cancer-specific mortality versus ACEi (HR=0.77, 95%CI: 0.66-0.91). Use of RASi was associated with an estimated-prevention of 2.6 (95%CI: 2.3-3.0) all-site cancer per-1000-person-years and 2.2 (95%CI: 2.0-2.5) cancer-related mortality per-1000-person-years. Lower risk of cancer-specific mortality was similarly observed in new-RASi compared with new-CCBs users.InterpretationRASi use was independently associated with lower cancer risk in T2D with stronger associations in users of ARBs than ACEi. The benefits of RASi in patients with diabetes might go beyond cardiovascular-renal protection if confirmed by other real-world studies and trials.FundingDr. Aimin Yang was supported by a CUHK Impact-Research-Fellowship Scheme.