Objectives:The objective of the study was to understand the role of self-monitoring of blood glucose (SMBG) for better management of glycemic fluctuations, reducing the risk of complications, and the associated cost benefits for diabetes patients in India.Materials and Methods:An Excel-based Cost Impact Model was developed to analyze the impact of SMBG by calculating the savings over a 10-year time period. A literature review was undertaken to model the impact of SMBG on the risk of complications and cardiovascular morbidities. The model was developed based on inputs from previous studies.Results:In the base case, SMBG cohort was associated with a 10-year discounted cost of INR 718,340, resulting in an estimated saving of INR 120,173 compared to no SMBG cohort. Implementation of a once-daily SMBG protocol, for a decade, can reduce the complication-related costs. More frequent SMBG and tri-monthly hemoglobin A1c tests along with lifestyle changes can significantly reduce the financial burden on the patient over the lifespan.Conclusion:Our study has shown that proactive management of diabetes with SMBG can improve treatment outcomes and reduce morbidity and mortality associated with this disease. Near-normal blood glucose levels can bring in cost savings in the form of reduced long-term complications and avoidance of repeated hospitalization for the management of such complications, along with an improved quality of life.

Objectives: To examine whether lung function is a significant predictor for incident diabetes, asthma, myocardial infarction, hypertension and all-cause mortality, using a competing risks model. Methods: The data source used was the National Health and Nutrition Examination Survey (Epidemiological follow-up). For this cohort study, participants were prospectively followed from baseline (1970) until the end of the epidemiological follow-up (1984). We measured incident events based on self-reports or hospital visit codes during the study time conditional on no prior history of the same event. Lung function was measured as forced expiratory volume in 1 second (FEV1). The covariates adjusted for the association were: gender, age, body mass index, smoking status, inactivity, marital status, alcohol consumption, chronic obstructive pulmonary disease (COPD), cancer, and Charslon's modified comorbidity score. We used the Fine-Gray subdistribution and cause-specific hazard models to quantify the hazard ratios for the association of FEV1 and the primary events of interest. Results: The final cohort included 3,018 participants (mean FEV1 value: 3.00 liter (standard deviation (SD):0.84); mean age: 45.22(SD:13.49)). We found that for one unit increase in FEV1 (liters), the subdistribution hazad ratio was 0.37(0.22,0.61), 0.20(0.10,0.40), 0.81(0.53,1.26), 0.84(0.71,0.99) and 0.86(0.62,1.18) for diabetes, asthma, heart attack, hypertension and all-cause mortality, respectively. The corresponding estimates for the cause-specific hazard ratios were 0.45(0.33,0.60), 0.43(0.31,0.60), 0.56(0.40,0.77), 0.73(0.63,0.86) and 0.66(0.51, 0.85). Conclusions: While the cause-specific hazard model indicates the instantaneous hazard ratios for the primary events of interest amongst all event-free individuals, the subdistribution hazard model provides insight into cumulative incidence function by adding to the denominator people who may have had experienced competing events. Amongst event-free individuals, FEV1 was statistically significantly associated with an increased instantaneous hazard for the primary events. Lower FEV1 values were also showed to increase the incidence of diabetes, asthma, and hypertension. Therefore, modifying lung function can potentially mitigate the risks of subsequent events.

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OBJECTIVES:To describe characteristics of T2DM patient subgroups who were more likely to achieve HbA1c goal Ͻ 7% with combination treatment of DPP4i with PIO or with MET using a predictive model. METHODS: Stepwise logistic regression was applied to MarketScan claims data to develop a predictive model that estimated the probabilities of HbA1c goal achievement in patients receiving DPP4i combinations. Sample selection criteria included: 1) T2DM diagnosis; 2) treatment of DPP4i with PIO or with MET; 3) baseline HbA1c Ն 7%; and 4) with one-year continuous enrollment. Patients were ranked by the probabilityof achieving HbA1cϽ7% and grouped into cumulative percentiles; baseline characteristics of the optimal subgroups identified as the first 20 th and 80 th percentiles were reported. RESULTS: A total of 328 patients were included. The predictive model showed that patients who had neuropathy, cerebrovascular conditions, or higher total medication use at baseline were less likely to achieve goal on DPP4i combinations while patients with self monitoring blood glucose use at baseline were more likely to achieve goal (PϽ 0.05). The 80 th percentile subgroup (nϭ270) had a goal reaching rate of 57.0%, mean age of 50.3 years old, 44.3% female, 38.5% on MET, 13.8% on thiazolidinedione (TZD), and HbA1c ϭ 9.13% at baseline. The 20 th percentile subgroup (nϭ83), achieved goal at the rate of 72.3%, mean age of 50.6 years old, 46.1% female, 53.9% on MET, 25.7% on TZD, and HbA1c ϭ 8.96% at baseline.