OBJECTIVE: To examine the economic impact of patients with anemia in selected diseases. METHODS: A retrospective cohort design was used to estimate the differences in costs between anemic and nonanemic patients. The analysis used administrative claims data (1999-2001) from a US population to assess direct costs and disability and productivity data (1997-2001) to estimate indirect costs. Adult patients with a diagnosis of rheumatoid arthritis (RA), inflammatory bowel disease (IBD), chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), cancer, or congestive heart failure (CHF) were identified. Costs were estimated using a generalized linear model, adjusting for age, sex, comorbidities, and disease severity. The adjustment variables for disease severity were based on ICD-9, HCPCS, or pharmacy codes. These costs were projected to a 1-million-member, similar population. RESULTS: The percentage of anemia patients varied among conditions (6.9-26.1%); the CKD population had the highest prevalence. CKD anemic patients incurred the greatest average annual direct costs ($78,209), followed by CHF ($72,078) and cancer ($60,447). After adjusting for baseline characteristics including severity, the difference in direct costs between anemic and nonanemic patients decreased for all diseases; CHF patients incurred the greatest adjusted cost difference between anemic and nonanemic ($29,511), followed by CKD ($20,529) and cancer ($18,418). Unmeasured severity and coding bias may account for a portion of the differences in the adjusted cost. CONCLUSION: Anemia may substantially increase health-care costs at a level that is economically very relevant, despite the fact that these patients may comprise only one tenth of the overall anemic population.
OBJECTIVE: To examine the economic impact of patients with anemia in selected diseases. METHODS: A retrospective cohort design was used to estimate the differences in costs between anemic and nonanemic patients. The analysis used administrative claims data (1999-2001) from a US population to assess direct costs and disability and productivity data (1997-2001) to estimate indirect costs. Adult patients with a diagnosis of rheumatoid arthritis (RA), inflammatory bowel disease (IBD), chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), cancer, or congestive heart failure (CHF) were identified. Costs were estimated using a generalized linear model, adjusting for age, sex, comorbidities, and disease severity. The adjustment variables for disease severity were based on ICD-9, HCPCS, or pharmacy codes. These costs were projected to a 1-million-member, similar population. RESULTS: The percentage of anemiapatients varied among conditions (6.9-26.1%); the CKD population had the highest prevalence. CKD anemicpatients incurred the greatest average annual direct costs ($78,209), followed by CHF ($72,078) and cancer ($60,447). After adjusting for baseline characteristics including severity, the difference in direct costs between anemic and nonanemic patients decreased for all diseases; CHFpatients incurred the greatest adjusted cost difference between anemic and nonanemic ($29,511), followed by CKD ($20,529) and cancer ($18,418). Unmeasured severity and coding bias may account for a portion of the differences in the adjusted cost. CONCLUSION:Anemia may substantially increase health-care costs at a level that is economically very relevant, despite the fact that these patients may comprise only one tenth of the overall anemic population.
Authors: Mauro Tettamanti; Ugo Lucca; Francesca Gandini; Angela Recchia; Paola Mosconi; Giovanni Apolone; Alessandro Nobili; Maria Vittoria Tallone; Paolo Detoma; Adriano Giacomin; Mario Clerico; Patrizia Tempia; Luigi Savoia; Gilberto Fasolo; Luisa Ponchio; Matteo G Della Porta; Emma Riva Journal: Haematologica Date: 2010-06-09 Impact factor: 9.941
Authors: Ioannis E Koutroubakis; Claudia Ramos-Rivers; Miguel Regueiro; Efstratios Koutroumpakis; Benjamin Click; Marc Schwartz; Jason Swoger; Leonard Baidoo; Jana G Hashash; Arthur Barrie; Michael A Dunn; David G Binion Journal: J Clin Gastroenterol Date: 2016-09 Impact factor: 3.062
Authors: Shelby D Reed; Yanhong Li; Stephen J Ellis; John J Isitt; Sunfa Cheng; Kevin A Schulman; David J Whellan Journal: J Card Fail Date: 2012-10 Impact factor: 5.712