bluuebunny/crossref_metadata_2025_split huggingface dataset:
Health Care Access, Utilization, and Glycemic Control in Youth and Young Adults with Diabetes in South Carolina
Authors: LIESE ANGELA D. , MA XIAONAN , REID LAUREN , EBERTH JAN M. , BELL BETHANY A. , PROBST JANICE , TURLEY CHRISTINE B. , MAYER-DAVIS ELIZABETH J. 
Published: June 2018

Affordability and spatial accessibility are key dimensions of access to health care and important determinants of health. In populations with diabetes, evidence is largely limited to those with type 2 diabetes and older adults. We evaluated the association of insurance coverage and diabetes care provider characteristics with glycemic control in youth and young adults (YYA) with diabetes aged 6 to 30 years (median 16 years), participating in the SEARCH for Diabetes in Youth study in South Carolina between January 2012 and July 2015. A total of 540 YYAs with diabetes (437 type 1, 103 type 2) provided information on health insurance type, diabetes care provider (type and address); HbA1c was measured in-person. Geographic information systems were used to calculate distance to the reported provider. Multivariate linear regression analyses were utilized to evaluate the association. More than half of YYAs with diabetes utilized state/federal insurance (46%) or were uninsured (18%). Only 79% indicated a diabetes care provider. The majority (62%) were seeing an endocrinologist, traveling 27 miles on average (median 16 miles). Average HbA1c was 9.2%. Compared to those with private insurance, YYAs with state/federal health insurance had 0.78 higher HbA1c levels (p&lt;.001) and those with no insurance had 1.1 higher HbA1c (p&lt;.0001), independent of age, sex, race/ethnicity, urbanicity, diabetes type, duration, and medication regimen. Compared to receiving care by an endocrinologist, reporting care by a primary care provider was associated with a 0.6 higher HbA1c (p=.028) and a 0.8 higher level (p=.007) with no routine diabetes care provider reported. Distance to provider was not associated with glycemic control (p=.18).
In summary, health insurance status and type, and provider type were significantly associated with glycemic control. This study underscores the importance of social determinants of health on diabetes management outcomes in youth and young adults.

Disclosure
A.D. Liese: None. X. Ma: None. L. Reid: None. J.M. Eberth: None. B.A. Bell: None. J. Probst: None. C.B. Turley: Stock/Shareholder; Self; AbbVie Inc., Johnson & Johnson Services, Inc., Pfizer Inc., CVS/Caremark, Amgen Inc., Walgreen Co., UnitedHealth Group, Novartis AG, Abbott. E.J. Mayer-Davis: None.
Delays in Antihyperglycemic Treatment Intensification and Risk of Diabetes Complications
Authors: NICHOLS GREGORY , ROMO-LETOURNEAU VICTORIA J. , VUPPUTURI SUMA , THOMAS SHEILA 
Published: June 2018

Longer delays in anti-hyperglycemic treatment intensification (TI) are associated with poorer glycemic control, but whether these delays increase the risk of poor outcomes is unclear. We combined 2005-2016 data from the Northwest and Mid-Atlantic regions of Kaiser Permanente to identify 3 cohorts of patients who intensified therapy by: 1) initiating metformin (MET) as their 1st diabetes drug (n=18,762); 2) adding a sulfonylurea (SU) to existing MET (n=10,076); 3) initiating insulin after MET and/or SU therapy (n=9,778). We used the TI date to calculate time between A1C ≥7% and TI, pre-TI A1C, and achievement of A1C &gt;7% within 1 year. From the ∼1-year post-TI A1C, we followed patients through December 2016 until they experienced an MI, stroke, or other cardiovascular (CV) event, a heart failure (HF) hospitalization or all-cause mortality, censoring follow-up when the cohort therapy changed. Multivariable cox regression controlled for CV risk factors including age, sex, race/ethnicity, smoking, blood pressure, statin and ACE/ARB use, and prior history of CV or HF. After adjustment for CV risk factors, Pre-TI A1C and time from A1C ≥7% to TI date was significantly associated with the outcomes (Table). Delays in treatment intensification increase CV, HF and mortality risk at all stages of diabetes therapy; intensifying therapy at the first sign of deteriorating glycemic control may be critically important.

Disclosure
G. Nichols: Research Support; Self; Boehringer Ingelheim GmbH, Amarin Corporation, Janssen Pharmaceuticals, Inc., Sanofi. V.J. Romo-LeTourneau: Employee; Self; Sanofi. S. Vupputuri: Research Support; Self; Sanofi US. S. Thomas: Employee; Self; Sanofi. Stock/Shareholder; Self; Sanofi.
Efficacy and Safety of an Expanded Dulaglutide Dose Range—A Phase 2, Placebo-Controlled Trial in T2D Patients on Metformin
Authors: FRIAS JUAN P. , WYNNE ALAN G. , MATYJASZEK-MATUSZEK BEATA , BARTASKOVA DAGMAR , COX DAVID , WOODWARD BRAD , LI GRACE , MILICEVIC ZVONKO 
Published: June 2018

Dulaglutide is approved at two doses (0.75 and 1.5 mg) for treatment of T2D. There has been limited assessment of higher doses. We hypothesized that higher doses of dulaglutide may provide further improvement in glucose and body weight control. In this study, 3 and 4.5 mg doses were evaluated for safety/efficacy after 18 weeks (weeks) of treatment, including a 6 week dose escalation. Patients (N=318) on ≥1500 mg metformin, were randomized (1:1:1:1) to placebo (n=82), dulaglutide 1.5 mg (n=81), dulaglutide 3 mg (n=79), dulaglutide 4.5 mg (n=76). The primary objective was superiority of dulaglutide doses over placebo in HbA1c reduction at 18 weeks. Table 1 presents the primary and selected secondary efficacy data. Reductions in HbA1c and body weight were significant for each dose vs. placebo. Incidence of gastrointestinal events (mostly mild to moderate) were dose-dependent for nausea (placebo, 4.9%; dulaglutide 1.5 mg, 22.2%; dulaglutide 3 mg, 24.1%; dulaglutide 4.5 mg, 30.3%) but not for vomiting (placebo 4.9%; dulaglutide 1.5 mg, 11.1%; dulaglutide 3 mg, 10.1%; dulaglutide 4.5 mg, 13.2%). No patients experienced severe hypoglycemia. The results of this trial show that 3 mg and 4.5 mg doses, compared to the 1.5 mg dose, may provide additional glycemic benefit and weight reduction with an acceptable safety profile in treatment of T2D patients, providing support for further phase 3 development.

Disclosure
J.P. Frias: Research Support; Self; AbbVie Inc., Allergan, Amgen Inc., Boehringer Ingelheim Pharmaceuticals, Inc., Bristol-Myers Squibb Company. Consultant; Self; CeQur Corporation. Research Support; Self; Cirius Therapeutics, AstraZeneca, Calibra Medical, Elcelyx Therapeutics, Inc.. Consultant; Self; Elcelyx Therapeutics, Inc.. Research Support; Self; Eli Lilly and Company, Genentech, Inc., Ionis Pharmaceuticals, Inc., ICON plc., Janssen Pharmaceuticals, Inc.. Consultant; Self; Johnson & Johnson Diabetes Institute, LLC.. Research Support; Self; Lexicon Pharmaceuticals, Inc., Ligand Pharmaceuticals, Inc.. Consultant; Self; Ligand Pharmaceuticals, Inc.. Research Support; Self; Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Novo Nordisk Inc., Pfizer Inc., Sanofi. Consultant; Self; Sanofi. Speaker's Bureau; Self; Sanofi. Research Support; Self; Theracos, Inc.. A.G. Wynne: None. B. Matyjaszek-Matuszek: None. D. Bartaskova: None. D. Cox: Employee; Self; Eli Lilly and Company. Stock/Shareholder; Self; Eli Lilly and Company. B. Woodward: Employee; Self; Eli Lilly and Company. G. Li: Employee; Self; Eli Lilly and Company. Z. Milicevic: Employee; Self; Eli Lilly and Company.
A Model of Good Inpatient Diabetes Care with Consistently Low Level of Medication Errors
Authors: PARSAD MEENAKSHI , LAMBERT KIMBERLEY D. , ARDERN DIANE , MEZZULLO ABIGAIL C. 
Published: June 2018

The National Diabetes Inpatient Audit is a yearly audit aimed at providing a snapshot view on various aspects of diabetes care across hospitals in England. Results are provided for each hospital and are shown in comparison to other hospitals. Since 2011, our 450-bedded hospital has been in the first quartile with regards to medication errors, prescription errors, medicine management errors and insulin errors. In the 2016 audit, we had 11.6% medication errors whereas across England, there were 44.6% medication errors. Our service is exemplary on many fronts. Foundation year doctors get formal training in diabetes care. Information Technology is key in providing the fail-proof electronic prescribing software, access to General Practitioner’s Health records, safe insulin prescribing e-module, and up-to-date online clinical guidelines. We have Pharmacists and Pharmacist Technicians who check electronic prescriptions on a daily basis. We also benefit from the service of a Diabetes Specialist Pharmacist. We provide a twice-weekly specialist diabetes ward round across the whole hospital and patients are usually identified from the Electronic Patients Record software. The Diabetes Specialist Nurses are additionally available for one-to-one education Monday to Friday 0900 to 1700. Finally, we run a comprehensive diabetes education day for nurses and other allied health care professionals twice a year.

Disclosure
M. Parsad: None. K.D. Lambert: None. D. Ardern: None. A.C. Mezzullo: None.
Effects of Ileo-Colonic Delivery of Conjugated Bile Acids on Glucose Metabolism, GLP-1, and Body Weight in Patients with Obesity and Type 2 Diabetes Mellitus—A Randomized Controlled Trial
Authors: MANRIQUE GERARDO CALDERON , DAVIS JUDITH , BONIS ALISON N. , KHEMANI DISHA , GEDULIN BRONISLAVA , VELLA ADRIAN , CAMILLERI MICHAEL , ACOSTA ANDRES 
Published: June 2018

Bile acid secretion increases significantly after Roux-in-Y gastric bypass and may play a role in weight loss after bariatric surgery. The acute intrajejunal-delivery or intra-rectal delivery of bile acids improves glucose metabolism. We aimed to study the effect of conjugated bile acids released on the ileo-colonic region (IC-CBAS) on glucose metabolism and body weight. In a placebo-controlled, double-blinded, randomized, 28-days trial, we studied the effect of IC-CBAS 500 mg BID on glucose metabolism, incretin hormones (GLP-1 and FGF-19), gastric emptying (scintigraphy) and weight loss in obese type 2 diabetic subjects (n=24 white patients, age=57±2 years, BMI=38.6±1.3 kg/m2, HbA1c=8.5±0.2%, fasting glucose 9.9±0.4 mmol/L). Participants were taking stable doses of DPP-4 inhibitors and metformin and were instructed to continue their current diet and exercise routine. Subjects underwent a meal challenge at baseline and at the end of the treatment period. The primary analyses compared treatment groups at the end of 28 days’ treatment using analysis of covariance models incorporating the corresponding baseline study value as a covariate. IC-CBAS significantly increases postprandial GLP-1 (mean p=0.04, AAB p=0.04), postprandial insulin (AAB p=0.04) and postprandial c-peptide (AAB p=0.01); and decreases fasting insulin (p=0.04) when compared to placebo. IC-CBAS also induced a numerically (but not statistically significantly) improvement in fasting glucose (delta -16 mg/dl), postprandial glucose (delta mean -14 mg/dl) and weight (delta -0.45±.4 kg) compared to placebo. There were no reported side effects, or dropouts; and no difference in FGF-19, gastric emptying or number of bowel movements. IC-CBAS 28-day treatment results in significant increase in GLP-1 and postprandial insulin, suggesting a beneficial role in incretins, insulin and glucose homeostasis.

Disclosure
G. Calderon Manrique: None. J. Davis: None. A.N. Bonis: None. D. Khemani: None. B. Gedulin: None. A. Vella: Research Support; Self; Novo Nordisk Inc., XOMA Corporation. Advisory Panel; Self; VTV Therapeutics, Bayer AG. M. Camilleri: None. A. Acosta: None.
Comparing Diabetes Screening Strategies in China—An Agent-Based Modeling Approach
Authors: XIE XIAOLEI , PANG BOWEN , LI YAN 
Published: June 2018

Purpose: There are 114 million people with diabetes in China and more than 60% of them are undiagnosed. People with undiagnosed diabetes have a high risk of developing complications or death. Early detection of diabetes through screening is thus critical in preventing costly complications. This study aims to compare long-term impact of several diabetes screening strategies on Chinese adults.
Methods: We developed an agent-based model to simulate the development of diabetes and its complications (i.e., neuropathy, nephropathy and cardiovascular diseases). Based on data from the China Health and Nutrition Survey and the published literature, we estimated model parameters and validated the model. We compared a set of screening strategies, including screening for 10%, 20%, and 50%, and screening people who smoke, have unhealthy diet or have limited physical activity. We simulated 10,000 people for 10 years and reported the number of diabetes, myocardial infarction (MI), stroke, neuropathy and nephropathy.
Results:Random screening for 50% of the population performs well in reducing all complications. Screening people with unhealthy diet is more effective in reducing cases of MI and neuropathy, and screening people who smoke is more effective in reducing cases of stroke.
Conclusions:Agent-based modeling is a promising tool to evaluate diabetes screening strategies, which can provide decision support to policymakers.

Disclosure
X. Xie: None. B. Pang: None. Y. Li: None.
Individualized Diabetes Risk Prediction in Women with a History of Gestational Diabetes
Authors: MAN BERNICE , SCHWARTZ ALAN , XIA YINGLIN , GERBER BEN S. 
Published: June 2018

Without treatment, prediabetic women with a history of gestational diabetes (GDM) are 71% more likely to progress to type 2 diabetes (DM) compared to parous women without prior GDM. In women with prior GDM, both intensive lifestyle intervention (ILI) and metformin are effective in reducing the progression to DM in the Diabetes Prevention Program (DPP). Evidence suggests heterogeneity in treatment response.
We performed a secondary analysis of DPP data, including 1555 parous women with prediabetes; 19% had prior GDM, 31% were &lt; 45 years old, 44% were of non-Caucasian race/ethnicity and 71% were obese. At three years, 323 women developed diabetes. We examined 16 baseline clinical variables using Cox proportional hazards regression with backward elimination (excluding variables with p&gt;0.05) to predict DM risk. Our final multivariable model includes treatment arm (ILI vs. metformin vs. placebo), BMI category, height, waist-hip ratio, fasting glucose and triglycerides, and hemoglobin A1c (Table). Interactions with treatment arms were explored.
This model differs from others recently developed for the general population at risk for DM. There remains a need for external validation to determine predictive performance. In the future, use of a clinical prediction model to calculate individual 3-year DM risk specifically for women with prior GDM may improve decision making for prediabetes treatment in this high-risk group.

Disclosure
B. Man: None. A. Schwartz: None. Y. Xia: None. B.S. Gerber: None.
Outcomes of Telehealth in the Management of Type 2 Diabetes—A Systematic Review and Meta-analysis of Randomised Controlled Trials
Authors: NANGRANI NISHA , MALABU USMAN , VANGAVETI VENKAT 
Published: June 2018

Background: The outcomes of telehealth in the management of type 2 diabetes (T2DM) have not been evaluated since the publication of recent clinical trials.
Objective: To conduct a systematic review and meta-analysis of recently published randomized controlled trials (RCTs) to assess the biochemical, clinical and psychosocial outcomes of telehealth in subjects with T2DM.
Methods: Electronic databases, MEDLINE, CINAHL, INFORMIT, SCOPUS and the Cochrane Central Register of Controlled Trials and reference lists of existing systematic reviews were searched until August 2017 to identify relevant studies. Study search and selection were performed by two independent reviewers. 4791 articles were retrieved of which 11 RCTs (n=3772) were included. A meta-analysis with random effects model was applied to estimate the pooled results.
Results: Telehealth was associated with a statistically significant and clinically relevant absolute decline in glycosylated haemoglobin (HbA1c) compared to usual care (mean difference -0.17%; 95% CI -0.25 to -0.09%; p&lt;0.0001), especially if participants had a mean baseline HbA1c≥8.0%; were less than sixty years of age or received telehealth for less than one year. There was no clinically significant reduction in LDL-cholesterol (LDL-c), body mass index (BMI), systolic (SBP) or diastolic blood pressure (DBP).
Conclusion: Telehealth interventions were associated with improved glycaemic control (HbA1c) in T2DM diabetic patients. However, no clinically relevant impact was observed on lipid profile, blood pressure, body mass index and psychosocial well-being. Future studies should seek to evaluate the effect of intervention duration on HbA1c, psychosocial outcomes and the effectiveness of telehealth in rural and underserved populations.

Disclosure
N. Nangrani: None. U. Malabu: None. V. Vangaveti: None.
Inpatient Use of Insulin Pens—Eliminating Risk for Inappropriate Use of Pen on More than One Patient
Authors: MACMASTER HEIDEMARIE W. , MARUOKA ANDREW , LUIS CRAIG SAN , GONZALEZ SABINA , RUSHAKOFF ROBERT J. 
Published: June 2018

Use of insulin pens for inpatient insulin administration is accurate and safe. However, if the pen is used on more than one patient, there is potential risk of infection with pathogens including hepatitis viruses and HIV. Rapid-acting insulin (RAI) is a time-critical medication, both for correctional and nutritional doses. Many hospitals using insulin pens choose to dispense them with a patient specific ID barcode from the pharmacy. This workflow ensures the correct patient ID for scanning, yet it may result in dispensing duplicates and may create time-critical delays. At UCSF, automated dispensing machines (ADM) on adult-units house time-critical medications, including RAI. In this workflow, the RN labels the insulin pen with the patient specific 3D barcode generated on the unit. Prior to insulin pen administration, 3 barcodes must be scanned: 1. Patient armband, 2. Patient label on insulin pen. (NEW) 3. Insulin pen manufacturer (confirms an active order for this type of insulin). The EMR will flash a screen size alert with a red stop sign and a warning if the patient’s armband ID and the pen label’s patient ID do not match. Implemented in 2/2017, this workflow and alert system, for the approximately 18,000 insulin pen SQ injections administered per month has triggered 10-25 alerts per month. As of 11/1/2017, all alerts have stopped the process and the correct patient’s insulin pen was retrieved and administered. Each alert is followed by an investigation. Significant factors contributing to the triggering of an alert involve RNs caring for &gt;1 patient with RAI insulin orders, pens returned to the wrong patient specific cassette in centralized medication rooms, carrying &gt; 1 patient’s insulin pen at a time and opening &gt; 1 patient’s EMR in the patient’s room when scanning. We conclude that pen misuse can be avoided with compliance to our triple scanning system, in conjunction with best practices, while preserving the critical timing of rapid-acting insulin.

Disclosure
H.W. Macmaster: None. A. Maruoka: None. C. San Luis: None. S. Gonzalez: None. R.J. Rushakoff: None.
Pharmacist-Led Digital Care Improved Community Diabetes Control in China—An Observational Study in a Large Population Sample
Authors: CAO JIUMEI , LI YINGJIE , WANG WEI , WANG ZHEN , LI DONGXING , ZHANG HONGGUANG , LIU KAI 
Published: June 2018

Objective: To evaluate the effects of digital diabetic care and pilot-test the healthcare ecosystem with retail pharmacists as the care provider.
Methods: We managed to build a digital healthcare ecosystem that engages diabetic patients and pharmacists with a cloud engine that operates 5 different algorithms for diabetic care support. A set of glucose tele-monitor and an App was installed in the drug stores and pharmacists provided personalized interventions to the walk-in patients according to the onsite glucose tests and the supporting App contents generated by the cloud engine in real-time. Between Jun 6th 2015 and Dec 26th 2017, we served a total of 283,938 patients in 4,035 pharmacies in 183 cities, and 14,609 of them were selected in this analysis following 4 inclusion criteria: 1) the baseline fasting blood glucose (FBG) or random blood glucose (RBG) were diabetic, 2) the intervention duration was ≥ 1 month, 3) the number of received diabetic care was ≥ 3 times, and 4) the baseline characteristics were complete. Among them, 11,131 received FBG tests, 4,731 had RBG tests and 1,253 had both.
Results: FBG and RBG were reduced on average by 0.8 mM (baseline: 9.1 ± 2.2 mM, last time: 8.3 ± 2.2 mM, n=11,131, P&lt; 0.001) and 3.2 mM (baseline: 14.5 ± 3.2 mM, last time: 11.3 ± 4.0 mM, n= 4,731, P&lt;0.001), respectively. The diabetes control rates were improved with 3,165 patients of the FBG population (28%) and 2,545 patients of the RBG population (54%) returning to normal. The greatest glycemic control effect took place following the initial intervention with the reduction of FBG by 11% and RBG by 18%. Nonlinear fitting using compound function identified descending glucose reduction to time: FBG, y = 1.122x-1/3 + 7.970; RBG, y = 2.927x-1-0.003x + 11.070.
Conclusion: Retail pharmacists equipped with digital tools can effectively control diabetes and are valuable resources to help streamline the process of community healthcare delivery.

Disclosure
J. Cao: None. Y. Li: None. W. Wang: None. Z. Wang: None. D. Li: None. H. Zhang: None. K. Liu: None.
Cost and Cost-Effectiveness of a Faith-Based Lifestyle Intervention for Diabetes Prevention among African-Americans—A Within-Trial Analysis
Authors: RHODES ELIZABETH C. , CHANDRASEKAR EESHWAR , PATEL SHIVANI , NARAYAN K.M. VENKAT , JOSHUA THOMAS V. , MARION LUCY , WILLIAMS LOVORIA B. , ALI MOHAMMED K. 
Published: June 2018

Background: Estimates of the economic value of investing in real-life diabetes prevention lifestyle interventions are scarce. We assessed the cost of implementation and cost-effectiveness of a church-based lifestyle intervention (Fit Body and Soul [FBAS]), in which African American participants received 18 lifestyle education sessions.
Methods: An incremental cost-effectiveness ratio (ICER) was calculated using data from a cluster randomized controlled trial comparing FBAS with a health education (HE) control group among 604 overweight participants in 20 churches. We calculated the ICER as the difference in costs to deliver FBAS vs. HE over the difference in weight change at one-year follow-up. Costs included those incurred for participant identification (screening at baseline) and program implementation (payment for health advisors to lead sessions, materials, equipment, and incentives to churches and participants). To estimate adjusted difference in weight (kg) change between FBAS and HE, we fit mixed linear models, accounting for clustering of participants within churches and for age, sex, and educational attainment. We repeated this analysis for waist circumference (cm).
Results: The per-person intervention cost of FBAS was $50.39 more than HE (per-person FBAS cost: $442.22; per-person HE cost: $391.83). The adjusted difference in weight change between the FBAS group and the HE group was 1.9 kg (95% CI 1.0, 2.8), and the adjusted difference in waist circumference change was 2.4 cm (95% CI, 1.3 to 3.4). Compared to HE, implementation of FBAS cost an additional $26.52 per kg weight lost and $21.00 per cm reduction in waist circumference.
Conclusion: For a modest increase in cost, FBAS led to greater weight loss and reduction in waist circumference among African Americans compared to standard HE at a church setting.

Disclosure
E.C. Rhodes: None. E. Chandrasekar: None. S. Patel: None. K. Narayan: None. T.V. Joshua: None. L. Marion: None. L.B. Williams: None. M.K. Ali: None.