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Economic implications of comorbid conditions among Medicaid beneficiaries with COPD
Pei-Jung Lin1, Fadia T Shaya, Steven M Scharf
1Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA 02111, USA. plin@tuftsmedicalcenter.org
Objectives:
To characterize a comprehensive comorbidity profile and to explore the economic implications of comorbidity among patients with chronic obstructive pulmonary disease (COPD).
Methods:
This retrospective cohort study analyzed medical claims from the Maryland Medicaid database. We employed a 1:2 case-control design to select COPD patients (n=1388) and demographically matched controls (n=2776) aged 40 to 64 years with 24 months of continuous enrollment. Odds ratios were employed to compare comorbidity differences, including 17 conditions defined by the Charlson Comorbidity Index (CCI) and 6 additional conditions commonly observed in COPD patients. We estimated the incremental medical utilization and medical cost by specific condition.
Results:
Compared with the controls, Medicaid COPD patients had higher comorbidity burden and were more likely to have myocardial infarction, congestive heart failure, cerebrovascular disease, peptic ulcer, mild liver disease, hypertension, sleep apnea, tobacco use, and edema. COPD patients on average had 24% more medical claims (81.4 vs. 65.4, p<0.001) and were 33% more expensive than controls ($7603 vs. $5732, p<0.001). Ten conditions defined by the CCI as well as hypertension, tobacco use, and edema were associated with incremental medical utilization and cost in COPD patients; depression was associated with incremental medical utilization but not cost.
Conclusions:
The high burden of comorbidity in COPD patients translates into additional medical utilization and cost. Effective disease management and treatment protocols are needed to reduce comorbidity burden. The development of a COPD-specific comorbidity measure may be used to identify high-risk subgroups and to predict utilization and cost.
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