EXAMINING DISPARITIES IN THE TREATMENT AND COSTS OF KNEE OSTEOARTHRITIS USING AN INTERACTIVE MARKOV MODEL

Purpose:

This study aims to quantify the potential differences in lifetime costs and benefits associated with disparities in recommended treatment to different patient populations with knee osteoarthritis.

Methods:

We constructed a Markov model in Excel to demonstrate the progression of knee osteoarthritis and calculate lifetime costs and benefits of twenty unique treatment sequences (Table 1). Using NHIS and BRFSS survey data, we calculated the cross-sectional incidence and transition probabilities for populations determined by age, gender, race, obesity status and comorbid conditions. Treatment effects and the impact of comorbid conditions were obtained from the literature.

Direct medical costs were obtained from various sources and represent public, private and uninsured populations. Indirect costs associated with loss of earnings were modeled using the MEPS database and were stratified by education level. We obtained utilities for model health states from the literature. Finally, using the @Risk add-in software in Excel, we ran a series of probabilistic sensitivity analyses to assess the robustness of the results. The model's interactive capabilities in Excel are intended to allow users to make multiple comparisons on populations defined by various characteristics and incorporate this information into the medical decision making process.

Results:

In populations with more comorbid conditions, treatment sequences that included many years of less-invasive treatments, such as lifestyle modifications, were cost-saving – yielding more benefit at a lower cost. Further, the differences were more pronounced for minority patients and obese individuals. All comparisons indicated initiating treatment earlier, rather than waiting, provided more benefit to patients, regardless of the cost. Patients still in the workforce yielded more benefit from less aggressive pathways than retired individuals due to the indirect costs of lost wages. Sensitivity analyses indicated that results were most influenced by variation in costs of treatment, impact on progression of knee osteoarthritis by comorbid conditions, and the effect of the treatment.

Conclusion:

Conclusions about treatment decisions should be made in the context of willingness-to-pay thresholds for patients, insurers and health systems overall. This is the first interactive model developed to demonstrate the disparities in how patient populations experience knee osteoarthritis and benefit from various treatment options. Further, the comparison of both surgical and non-surgical treatments is an important step in filling a gap in the current economic evaluation literature surrounding knee osteoarthritis.