Aims: Many new mobile technologies are available to assist people in managing chronic conditions, but data on the association between the use of these technologies and medical spending remains limited. As the available digital technology offerings to aid in diabetes management increase, it is important to understand their impact on medical spending. The aim of this study was to investigate the financial impact of a remote digital diabetes management program using medical claims and real-time blood glucose data. Materials and methods: A retrospective analysis of multivariate difference-indifference and instrumental variables regression modeling was performed using data collected from a remote digital diabetes management program. All employees with diabetes were invited, in a phased introduction, to join the program. Data included blood glucose (BG) values captured remotely from members via connected BG meters and medical spending claims. Participants included members (those who accepted the invitation, n ¼ 2,261) and non-members (n ¼ 8,741) who received health insurance benefits from three self-insured employers. Medical spending was compared between people with well-controlled (BG 154 mg/dL) and poorly controlled (BG > 154 mg/dL) diabetes. Results: Program access was associated with a 21.9% (p < 0.01) decrease in medical spending, which translates into a $88 saving per member per month at 1 year. Compared to non-members, members experienced a 10.7% (p < 0.01) reduction in diabetes-related medical spending and a 24.6% (p < 0.01) reduction in spending on office-based services. Well-controlled BG values were associated with 21.4% (p ¼ 0.03) lower medical spending. Limitations and conclusions: Remote digital diabetes management is associated with decreased medical spending at 1 year. Reductions in spending increased with active utilization. It will be beneficial for future studies to analyze the long-term effects of the remote diabetes management program and assess impacts on patient health and well-being.

Precision medicine is an emerging scientific topic for disease treatment and prevention that takes into account individual patient characteristics. It is an important direction for clinical research, and many statistical methods have been proposed recently. One of the primary goals of precision medicine is to obtain an optimal individual treatment rule (ITR), which can help make decisions on treatment selection according to each patient's specific characteristics. Recently, outcome weighted learning (OWL) has been proposed to estimate such an optimal ITR in a binary treatment setting by maximizing the expected clinical outcome. However, for ordinal treatment settings, such as individualized dose finding, it is unclear how to use OWL. In this article, we propose a new technique for estimating ITR with ordinal treatments. In particular, we propose a data duplication technique with a piecewise convex loss function. We establish Fisher consistency for the resulting estimated ITR under certain conditions, and obtain the convergence and risk bound properties. Simulated examples and an application to a dataset from a type 2 diabetes mellitus observational study demonstrate the highly competitive performance of the proposed method compared to existing alternatives.

No abstract

No abstract

IntroductionMany patients with diabetes may require high-dose insulin treatment to achieve target HbA1c level, but the prevalence, disease burden, and patient characteristics of the population remain unclear. We therefore investigated people with insulin-treated diabetes in the UK from 2009 to 2013, who were prescribed high daily doses (> 200 units/day).MethodsA retrospective analysis was conducted using the UK primary care electronic dataset from the Clinical Practice Research Datalink (CPRD). Trends of demographics, insulin dose, clinical characteristics, and annualized incidence rate of the insulin initiators were analyzed. Patients with type 1 (T1D) or type 2 diabetes (T2D) and who were prescribed insulin between 2009 and 2013 were categorized into either a low- or high-dose insulin user group. Two-sample t test and chi-square test were used for comparison of continuous variables and categorical variables, respectively. A multivariable negative binomial regression analysis with (log) person time as an offset was used to assess the impact of different covariates on incidence of high-dose insulin initiation.ResultsBetween 2009 and 2013, 19,631 patients with diabetes were treated with insulin (T1DM, 7620; T2DM, 12,011). In 2013, 415 high-dose insulin initiators were identified (T1DM, N = 170; T2DM, N = 245). More than half were male (T1DM/T2DM, 62.4%/56.3%) and 94.1%/83.7% of T1DM/T2DM patients were prescribed an insulin analogue at high-dose insulin initiation. At 6 months, 43.6% of T1DM and 42.6% of T2DM remained to have suboptimal HbA1c level of ≥ 8% (64 mmol/mol). Overall, 15.9%/63.3% of high-dose insulin initiators (HDII) with T1DM/T2DM took oral antidiabetics. From 2009 to 2013, the estimated glomerular filtration rate worsened in both T1DM and T2DM HDII.ConclusionDespite a number of patients requiring high doses of insulin in the UK, achievement of optimal HbA1c levels remains poor. Early identification of HDII is important in order to plan for alternative/adjuvant antidiabetic and lifestyle strategies to achieve optimal glycemic targets in this patient group.FundingEli Lilly and Company.