OBJECTIVE: To estimate and compare the annual direct healthcare cost among Type 1 (T1DM) and Type 2 (T2DM) diabetes patients using two cost estimation methods: (1) DM-attributable cost and (2) all cause case-control cost. RESEARCH DESIGN AND METHODS: An administrative claims cohort study using the HealthCore Integrated Research Database (HIRD(R)) identified T1DM and T2DM patients age >or=18 and <65 years between 1/1/2006 - 12/31/2006. DM patients (cases) were matched 1:1 with non-DM patients (controls) by age, gender, state, and commercial plan type (HMO, PPO, POS). All patients had continuous eligibility for calendar years 2006-07. DM-attributable cost was assessed by summing medical claims for DM (ICD-9-CM codes 250.xx) and pharmacy claims for anti-hyperglycemic agents, and all cause health care cost was assessed for cases and controls, for the calendar year 2007. RESULTS: A total of 12,096 T1DM and 256,245 T2DM cases and matched controls were identified. T1DM and T2DM cases had significantly higher average baseline comorbidities and Deyo-Charleson Comorbidity scores than controls (2.17 vs. 0.23 and 1.62 vs. 0.39, respectively, p < 0.0001 for both).While DM attributable cost estimation resulted in a mean annual cost of $6247 for T1DM and $3002 for T2DM in 2007, the mean annual (per patient) all-cause total cost estimation using the case-control method resulted in a difference of $10,837 ($14,060 for cases, vs. $3223 for controls) for T1DM; and $4217 ($8070 for cases, vs. $3853 for controls) for T2DM. CONCLUSIONS: The DM-attributable cost method underestimated costs by 42% for T1DM and 29% for T2DM compared to the case-control method. The difference was smaller but still significant (33% for T1DM and 14% for T2DM) when multivariate technique was used. Patients with DM may use a substantial amount of medical and pharmacy services not directly attributable to DM, and attributable cost method may underestimate the total cost of DM. This study has limitations inherent to the retrospective claims data analysis and generalizability of results is limited to those from similar population.
OBJECTIVE: To estimate and compare the annual direct healthcare cost among Type 1 (T1DM) and Type 2 (T2DM) diabetespatients using two cost estimation methods: (1) DM-attributable cost and (2) all cause case-control cost. RESEARCH DESIGN AND METHODS: An administrative claims cohort study using the HealthCore Integrated Research Database (HIRD(R)) identified T1DM and T2DM patients age >or=18 and <65 years between 1/1/2006 - 12/31/2006. DMpatients (cases) were matched 1:1 with non-DMpatients (controls) by age, gender, state, and commercial plan type (HMO, PPO, POS). All patients had continuous eligibility for calendar years 2006-07. DM-attributable cost was assessed by summing medical claims for DM (ICD-9-CM codes 250.xx) and pharmacy claims for anti-hyperglycemic agents, and all cause health care cost was assessed for cases and controls, for the calendar year 2007. RESULTS: A total of 12,096 T1DM and 256,245 T2DM cases and matched controls were identified. T1DM and T2DM cases had significantly higher average baseline comorbidities and Deyo-Charleson Comorbidity scores than controls (2.17 vs. 0.23 and 1.62 vs. 0.39, respectively, p < 0.0001 for both).While DM attributable cost estimation resulted in a mean annual cost of $6247 for T1DM and $3002 for T2DM in 2007, the mean annual (per patient) all-cause total cost estimation using the case-control method resulted in a difference of $10,837 ($14,060 for cases, vs. $3223 for controls) for T1DM; and $4217 ($8070 for cases, vs. $3853 for controls) for T2DM. CONCLUSIONS: The DM-attributable cost method underestimated costs by 42% for T1DM and 29% for T2DM compared to the case-control method. The difference was smaller but still significant (33% for T1DM and 14% for T2DM) when multivariate technique was used. Patients with DM may use a substantial amount of medical and pharmacy services not directly attributable to DM, and attributable cost method may underestimate the total cost of DM. This study has limitations inherent to the retrospective claims data analysis and generalizability of results is limited to those from similar population.
Authors: R Brett McQueen; Samuel L Ellis; David M Maahs; Heather D Anderson; Kavita V Nair; Anne M Libby; Jonathan D Campbell Journal: Patient Date: 2014 Impact factor: 3.883
Authors: Vincent J Willey; Sheldon Kong; Bingcao Wu; Amit Raval; Todd Hobbs; Andrea Windsheimer; Gaurav Deshpande; Ozgur Tunceli; Brian Sakurada; Jonathan R Bouchard Journal: Am Health Drug Benefits Date: 2018-09
Authors: Taulant Muka; David Imo; Loes Jaspers; Veronica Colpani; Layal Chaker; Sven J van der Lee; Shanthi Mendis; Rajiv Chowdhury; Wichor M Bramer; Abby Falla; Raha Pazoki; Oscar H Franco Journal: Eur J Epidemiol Date: 2015-01-18 Impact factor: 8.082
Authors: Yutian Yin; Weiqing Han; Yuhan Wang; Yue Zhang; Shili Wu; Huiping Zhang; Lingling Jiang; Rui Wang; Peng Zhang; Yaqin Yu; Bo Li Journal: Int J Environ Res Public Health Date: 2015-10-12 Impact factor: 3.390