Medical resource use data and responses to the EuroQol 5-Dimension (EQ-5D) instrument were collected at baseline and throughout the trial. Medical resources and medications were assigned values by using U.S. Medicare payments and wholesale acquisition costs, respectively. Secondary analyses used English costs.

Patients were followed for an average of 3.3 years, during which time those randomized to EQW experienced 0.41 fewer inpatient days (7.05 vs. 7.46 days; relative rate ratio 0.91; P = 0.05). Rates of outpatient medical visits were similar, as were total inpatient and outpatient costs. Mean costs for nonstudy diabetes medications over the study period were ~$1,600 lower with EQW than with placebo (P = 0.01). Total within-study costs, excluding study medication, were lower in the EQW arm than in the placebo arm ($28,907 vs. $30,914; P ≤ 0.01). When including the estimated cost of EQW, total mean costs were significantly higher in the EQW group than in the placebo group ($42,697 vs. $30,914; P < 0.01). With English costs applied, mean total costs, including exenatide costs, were £1,670 higher in the EQW group than the placebo group (£10,874 vs. £9,204; P < 0.01). There were no significant differences in EQ-5D health utilities between arms over time.

Medical costs were lower in the EQW arm than the placebo arm, but total costs were significantly higher once the cost of branded exenatide was incorporated.

We analyzed admissions data listing DKA or HHS at two academic hospitals. We determined 1) the frequency distributions of HHS, DKA, and combined DKA-HHS (DKA criteria plus elevated effective osmolality); 2) the relationship of markers of severity of illness and clinical comorbidities with 30-day all-cause mortality; and 3) the relationship of hospital complications associated with insulin therapy (hypoglycemia and hypokalemia) with mortality.

There were 1,211 patients who had a first admission with confirmed hyperglycemic crises criteria, 465 (38%) who had isolated DKA, 421 (35%) who had isolated HHS, and 325 (27%) who had combined features of DKA-HHS. After adjustment for age, sex, BMI, race, and Charlson Comorbidity Index score, subjects with combined DKA-HHS had higher in-hospital mortality compared with subjects with isolated hyperglycemic crises (adjusted odds ratio [aOR] 2.7; 95% CI 1.4, 4.9; P = 0.0019). In all groups, hypoglycemia (<40 mg/dL) during treatment was associated with a 4.8-fold increase in mortality (aOR 4.8; 95% CI 1.4, 16.8). Hypokalemia ≤3.5 mEq/L was frequent (55%). Severe hypokalemia (≤2.5 mEq/L) was associated with increased inpatient mortality (aOR 4.9; 95% CI 1.3, 18.8; P = 0.02).

Combined DKA-HHS is associated with higher mortality compared with isolated DKA or HHS. Severe hypokalemia and severe hypoglycemia are associated with higher hospital mortality in patients with hyperglycemic crises.

The Environmental Determinants of Diabetes in the Young (TEDDY) study followed 7,522 genetically high-risk children in Sweden, Finland, Germany, and the U.S. from birth for a median of 9.0 years (interquartile range 5.7–10.6) with available growth data. Of these, 761 (10.1%) children developed IA and 290 (3.9%) children were diagnosed with T1D. Bayesian two-phase piecewise linear mixed models with a random change point were used to estimate children’s individual growth trajectories. Cox proportional hazards models were used to assess the effects of associated growth parameters on the risks of IA and progression to T1D.

A higher rate of weight gain in infancy was associated with increased IA risk (hazard ratio [HR] 1.09 [95% CI 1.02, 1.17] per 1 kg/year). A height growth pattern with a lower rate in infancy (HR 0.79 [95% CI 0.70, 0.90] per 1 cm/year), higher rate in early childhood (HR 1.48 [95% CI 1.22, 1.79] per 1 cm/year), and younger age at the phase transition (HR 0.76 [95% CI 0.58, 0.99] per 1 month) was associated with increased risk of progression from IA to T1D. A higher rate of weight gain in early childhood was associated with increased risk of progression from IA to T1D (HR 2.57 [95% CI 1.34, 4.91] per 1 kg/year) in children with first-appearing GAD autoantibody only.

Growth patterns in early life better clarify how specific growth phases are associated with the development of T1D.

We examined the association between SH and cognitive function in 718 older adults with T1D from the Study of Longevity in Diabetes (SOLID). Subjects self-reported recent SH (previous 12 months) and lifetime history of SH resulting in inpatient/emergency department utilization. Global and domain-specific cognition (language, executive function, episodic memory, and simple attention) were assessed. The associations of SH with cognitive function and impaired cognition were evaluated via linear and logistic regression models, respectively.

Thirty-two percent of participants (mean age 67.2 years) reported recent SH and 50% reported lifetime SH. Compared with those with no SH, subjects with a recent SH history had significantly lower global cognition scores. Domain-specific analyses revealed significantly lower scores on language, executive function, and episodic memory with recent SH exposure and significantly lower executive function with lifetime SH exposure. Recent SH was associated with impaired global cognition (odds ratio [OR] 3.22, 95% CI 1.30, 7.94) and cognitive impairment on the language domain (OR 3.15, 95% CI 1.19, 8.29).

Among older adults with T1D, recent SH and lifetime SH were associated with worse cognition. Recent SH was associated with impaired global cognition. These findings suggest a deleterious role of SH on the brain health of older patients with T1D and highlight the importance of SH prevention.

Data from 2,662 individuals with self-reported diabetes who participated in the Health and Retirement Study (HRS) were used. Participants were followed from 2006 through 2014. Financial hardship, psychosocial, and neighborhood-level social determinant factors were based on validated surveys from the biennial core interview and RAND data sets. All social determinant factors and measurements of HbA_1c from the time period were used and treated as time varying in analyses. SAS PROC GLIMMIX was used to fit a series of hierarchical linear mixed models. Models controlled for nonindependence among the repeated observations using a random intercept and treating each individual participant as a random factor. Survey methods were used to apply HRS weighting.

Before adjustment for demographics, difficulty paying bills (β = 0.18 [95% CI 0.02, 0.24]) and medication cost nonadherence (0.15 [0.01, 0.29]) were independently associated with increasing HbA_1c over time, and social cohesion (–0.05 [–0.10, –0.001]) was independently associated with decreasing HbA_1c over time. After adjusting for both demographics and comorbidity count, difficulty paying bills (0.13 [0.03, 0.24]) and religiosity (0.04 [0.001, 0.08]) were independently associated with increasing HbA_1c over time.

Using a longitudinal cohort of older adults with diabetes, this study found that financial hardship factors, such as difficulty paying bills, were more consistently associated with worsening glycemic control over time than psychosocial and neighborhood factors.

Data on health care resource utilization from 100,391 people with type 2 diabetes were extracted from the electronic database used at the Catalan Health Institute. Multivariate regression models were carried out to test the impact of glycemic control (HbA_1c) on total health care, hospital admission, and medication costs; model 1 adjusted for a variety of covariates, and model 2 also included micro- and macrovascular complications. Glycemic control was classified as good for HbA_1c <7%, fair for ≥7% to <8%, poor for ≥8% to <10%, and very poor for ≥10%.

Mean per patient annual direct medical costs were 3,039 ± SD 6,581. Worse glycemic control was associated with higher total health care costs: compared with good glycemic control, health care costs increased by 18% (509.82) and 23% (661.35) in patients with very poor and poor glycemic control, respectively, when unadjusted and by 428.3 and 395.1, respectively, in model 2. Medication costs increased by 12% in patients with fair control and by 28% in those with very poor control (model 2). Patients with poor control had a higher probability of hospitalization than those with good control (5% in model 2) and a greater average cost when hospitalization occurred (811).

Poor glycemic control was directly related to higher total health care, hospitalization, and medication costs. Preventive strategies and good glycemic control in people with type 2 diabetes could reduce the economic impact associated with this disease.

We measured inpatient (n = 816) health care utilization for Bronx A1C participants using an administrative data set containing all hospital discharges for New York State from 2006 to 2014. Multilevel mixed modeling was used to assess changes in health care utilization and costs between the telephonic diabetes intervention (Tele/Pr) arm and print-only (PrO) control arm.

During follow-up, excess relative reductions in all-cause hospitalizations for the Tele/Pr arm compared with PrO arm were statistically significant for odds of hospital use (odds ratio [OR] 0.89; 95% CI 0.82, 0.97; P < 0.01), number of hospital stays (rate ratio [RR] 0.90; 95% CI 0.81, 0.99; P = 0.04), and hospital costs (RR 0.90; 95% CI 0.84, 0.98; P = 0.01). Reductions in hospital use and costs were even stronger for diabetes-related hospitalizations. These outcomes were not significantly related to changes observed in hemoglobin A_1c during individuals’ participation in the 1-year intervention.

These results indicate that the impact of the Bronx A1C intervention was not just on short-term improvements in glycemic control but also on long-term health care utilization. This finding is important because it suggests the benefits of the intervention were long-lasting with the potential to not only reduce hospitalizations but also to lower hospital-associated costs.

We analyzed data from a large representative sample of adults with T1D (N = 5,558). We assessed the presence of symptomatic neuropathy using the dichotomized (≥4) Michigan Neuropathy Screening Instrument Patient Questionnaire score. Logistic regression models were used to investigate associations between DPN and risk factors, as well as with other complications.

The burden of DPN is substantial with 13% prevalence overall. Adjusting for attained age, diabetes duration, and sex, the odds of DPN increased mainly with waist-to-hip ratio, lipids, poor glycemic control (odds ratio 1.51 [95% CI 1.21–1.89] for levels of 75 vs. 53 mmol/mol), ever versus never smoking (1.67 [1.37–2.03]), and worse renal function (1.96 [1.03–3.74] for estimated glomerular filtration rate levels <30 vs. ≥90 mL/min/1.73 m²). The odds significantly decreased with higher HDL cholesterol (0.77 [0.66–0.89] per mmol/L). Living in more deprived areas was associated with DPN (2.17 [1.78–2.65]) for more versus less deprived areas adjusted for other risk factors. Finally, individuals with prevalent DPN were much more likely than others to have other diabetes complications.

Diabetic neuropathy remains substantial, particularly affecting those in the most socioeconomically deprived groups. Those with clinically manifest neuropathy also have a higher burden of other complications and elevated levels of modifiable risk factors. These data suggest that there is considerable scope to reduce neuropathy rates and narrow the socioeconomic differential by better risk factor control.