BACKGROUND: Type 2 diabetes mellitus (T2DM) results in a substantial economic burden on patients, health care systems, and society. Most literature assessing the cost of T2DM focuses on the long-term complications of the disease, the association between glucose control and cost, and patient characteristics resulting in poor and costly outcomes. However, it is likely that attributes specific to diabetes therapy can affect the use of costly resources. OBJECTIVE: To estimate the effect of diabetes treatment-related attributes, such as improved efficacy, adherence, and reduced risk for hypoglycemia, on costs to T2DM patients. METHODS: An observational, retrospective study was conducted using the Optum Clinformatics Database, which links medical and pharmacy claims to laboratory results. Patients aged ≥ 18 years with T2DM who had ≥ 1 antidiabetic medication claim; ≥ 1 hemoglobin A1c (A1c) test result; continuous enrollment in the health plan from April 1, 2010, to March 31, 2011; and at least 1 follow-up day were included. Nondiabetes specific total, inpatient, outpatient, emergency room, and other costs (along with antidiabetes medication costs) were defined for each patient. Generalized linear models with logarithm link were used to predict the 1-year and cumulative 3-year costs. Demographic factors and comorbidities were included as covariates in addition to the diabetes treatment-related attributes. RESULTS: In the entire analysis cohort, the average 3-year cost per patient was $74,862. The percentage effect on cost of diabetes treatment-related variables ranged from -18% to 429%. Drug adherence was associated with lower inpatient, outpatient, and emergency room costs and higher drug costs. Hypoglycemia was associated with higher inpatient, outpatient, emergency room, and other direct costs (except antidiabetic drug costs). Compared with A1c values ≤ 7%, patients with higher levels were associated with higher total and drug costs. CONCLUSIONS: Study results demonstrate the association between diabetes treatment-related attributes and costs, including inpatient, outpatient, drug, and total costs. This association raises the question: what would the effect of a new diabetes therapy, with high efficacy, high adherence, and reduced risk of hypoglycemia have on economic outcomes? DISCLOSURES: Funding from Sanofi supported this study. Tong was an employee of ProUnlimited, under contract with Sanofi during the time of the study. Kitio-Dschassi was a Sanofi employee at time of the analysis. Meng, Casciano, Stern, and Gultyaev are employees of LASER Analytica, which received research funds from Sanofi to conduct this database analysis. Lee was an employee at LASER Analytica at the time of the analysis and has received grants from Sanofi. This manuscript was presented as a poster at the American Diabetes Association, 76th Scientific Sessions; New Orleans, Louisiana; June 10-14, 2016. Study concept and design were contributed by Meng, Casciano, Gultyaev, and Kitio-Dschassi. Meng and Stern collected the data, and data interpretation was performed by Casciano, Lee, Tong, and Kitio-Dschassi. The manuscript was written primarily by Lee, along with Meng and Stern, and revised by Stern, Meng, Tong, Kitio-Dschassi, and Lee.
BACKGROUND: Type 2 diabetes mellitus (T2DM) results in a substantial economic burden on patients, health care systems, and society. Most literature assessing the cost of T2DM focuses on the long-term complications of the disease, the association between glucose control and cost, and patient characteristics resulting in poor and costly outcomes. However, it is likely that attributes specific to diabetes therapy can affect the use of costly resources. OBJECTIVE: To estimate the effect of diabetes treatment-related attributes, such as improved efficacy, adherence, and reduced risk for hypoglycemia, on costs to T2DM patients. METHODS: An observational, retrospective study was conducted using the Optum Clinformatics Database, which links medical and pharmacy claims to laboratory results. Patients aged ≥ 18 years with T2DM who had ≥ 1 antidiabetic medication claim; ≥ 1 hemoglobin A1c (A1c) test result; continuous enrollment in the health plan from April 1, 2010, to March 31, 2011; and at least 1 follow-up day were included. Nondiabetes specific total, inpatient, outpatient, emergency room, and other costs (along with antidiabetes medication costs) were defined for each patient. Generalized linear models with logarithm link were used to predict the 1-year and cumulative 3-year costs. Demographic factors and comorbidities were included as covariates in addition to the diabetes treatment-related attributes. RESULTS: In the entire analysis cohort, the average 3-year cost per patient was $74,862. The percentage effect on cost of diabetes treatment-related variables ranged from -18% to 429%. Drug adherence was associated with lower inpatient, outpatient, and emergency room costs and higher drug costs. Hypoglycemia was associated with higher inpatient, outpatient, emergency room, and other direct costs (except antidiabetic drug costs). Compared with A1c values ≤ 7%, patients with higher levels were associated with higher total and drug costs. CONCLUSIONS: Study results demonstrate the association between diabetes treatment-related attributes and costs, including inpatient, outpatient, drug, and total costs. This association raises the question: what would the effect of a new diabetes therapy, with high efficacy, high adherence, and reduced risk of hypoglycemia have on economic outcomes? DISCLOSURES: Funding from Sanofi supported this study. Tong was an employee of ProUnlimited, under contract with Sanofi during the time of the study. Kitio-Dschassi was a Sanofi employee at time of the analysis. Meng, Casciano, Stern, and Gultyaev are employees of LASER Analytica, which received research funds from Sanofi to conduct this database analysis. Lee was an employee at LASER Analytica at the time of the analysis and has received grants from Sanofi. This manuscript was presented as a poster at the American Diabetes Association, 76th Scientific Sessions; New Orleans, Louisiana; June 10-14, 2016. Study concept and design were contributed by Meng, Casciano, Gultyaev, and Kitio-Dschassi. Meng and Stern collected the data, and data interpretation was performed by Casciano, Lee, Tong, and Kitio-Dschassi. The manuscript was written primarily by Lee, along with Meng and Stern, and revised by Stern, Meng, Tong, Kitio-Dschassi, and Lee.
Authors: Kelvin Lingjet Tran; Young In Park; Shalin Pandya; Navin John Muliyil; Brandon David Jensen; Kovin Huynh; Quang T Nguyen Journal: Am Health Drug Benefits Date: 2017-06
Authors: Salah Abusnana; Salem A Beshyah; Nawal Al-Mutawa; Rima Tahhan; Mahir Jallo; Ravi Arora; Hazem Aly; Sagar Singhal Journal: Sultan Qaboos Univ Med J Date: 2019-03-28
Authors: Chuo Yew Ting; Shahren Ahmad Zaidi Adruce; Mohamed Azmi Hassali; Hiram Ting; Chien Joo Lim; Rachel Sing-Kiat Ting; Abu Hassan Alshaari Abd Jabar; Nor Anizah Osman; Izzul Syazwan Shuib; Shing Chyi Loo; Sui Theng Sim; Su Ee Lim; Donald E Morisky Journal: Trials Date: 2018-06-05 Impact factor: 2.279