Cost-Effectiveness of Community-Based Diet and Exercise for Patients with Knee Osteoarthritis and Obesity or Overweight.

OBJECTIVE: Obesity exacerbates pain and functional limitation in persons with knee osteoarthritis (OA). In the Weight Loss and Exercise for Communities with Arthritis in North Carolina (WE-CAN) study, a community-based diet and exercise (D + E) intervention led to an additional 6 kg weight loss and 20% greater pain relief in persons with knee OA and body mass index (BMI) >27 kg/m2 relative to a group-based health education (HE) intervention. We sought to determine the incremental cost-effectiveness of the usual care (UC), UC + HE, and UC + (D + E) programs, comparing each strategy with the "next-best" strategy ranked by increasing lifetime cost. METHODS: We used the Osteoarthritis Policy Model to project long-term clinical and economic benefits of the WE-CAN interventions. We considered three strategies: UC, UC + HE, and UC + (D + E). We derived cohort characteristics, weight, and pain reduction from the WE-CAN trial. Our outcomes included quality-adjusted life years (QALYs), cost, and incremental cost-effectiveness ratios (ICERs). RESULTS: In a cohort with mean age 65 years, BMI 37 kg/m2, and Western Ontario and McMaster Universities Osteoarthritis Index pain score 38 (scale 0-100, 100 = worst), UC leads to 9.36 QALYs/person, compared with 9.44 QALYs for UC + HE and 9.49 QALYS for UC + (D + E). The corresponding lifetime costs are $147,102, $148,139, and $151,478. From the societal perspective, UC + HE leads to an ICER of $12,700/QALY; adding D + E to UC leads to an ICER of $61,700/QALY. CONCLUSION: The community-based D + E program for persons with knee OA and BMI >27kg/m2 could be cost-effective for willingness-to-pay thresholds greater than $62,000/QALY. These findings suggest that incorporation of community-based D + E programs into OA care may be beneficial for public health.

Administrative Data Use in National Registry Efforts: Blessing or Curse?

ABSTRACT: "Big data" refers to a growing field of large database research. Administrative data, a subset of big data, includes information from insurance claims, electronic medical records, and registries that can be useful for investigating novel research questions. While its use provides salient advantages, potential researchers relying on big data would benefit from knowing about how these databases are coded, common errors they may encounter, and how to best use large data to address various research questions. In the first section of this paper, Dr. Nicholas A. Bedard addresses the four major pitfalls to avoid with diagnosis and procedure codes in administrative data. In the next section, Dr. Jeffrey N. Katz et al. focus on the strengths and limitations of administrative data, suggesting methods to mitigate these limitations. Lastly, Dr. Elena Losina et al. review the uses and misuses of large databases for cost-effectiveness research, detailing methods for careful economic evaluations.