Economic burden of patients with anemia in selected diseases.
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.


CONCLUSIONS
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.
Ershler
William B
WB
Institute for Advanced Studies in Aging and Geriatric Medicine, Washington, DC, USA.
Chen
Kristina
K
Reyes
Eileen B
EB
Dubois
Robert
R
D000328:Adult; D000740:Anemia / Q000150:complications / Q000191:economics*; D001172:Arthritis, Rheumatoid / Q000150:complications / Q000191:economics; D015897:Comorbidity; D017281:Cost of Illness; D003430:Cross-Sectional Studies; D019992:Databases as Topic; D004526:Efficiency; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data*; D005102:Health Expenditures / Q000706:statistics & numerical data; D006333:Heart Failure / Q000150:complications / Q000191:economics; D006801:Humans; D015212:Inflammatory Bowel Diseases / Q000150:complications / Q000191:economics; D007676:Kidney Failure, Chronic / Q000150:complications / Q000191:economics; D008297:Male; D008329:Managed Care Programs / Q000191:economics*; D008875:Middle Aged; D017059:Models, Econometric; D009369:Neoplasms / Q000150:complications / Q000191:economics; D029424:Pulmonary Disease, Chronic Obstructive / Q000150:complications / Q000191:economics; D012189:Retrospective Studies; D014481:United States