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Economic Evaluation of a Tai Ji Quan Intervention to Reduce Falls in People With Parkinson Disease, Oregon, 2008-2011.

Prev Chronic Dis · 2015-07-30

DOI: 10.5888/pcd12.140413PMID: 26226067

Open accessresearch-article; Randomized Controlled Trial; Journal Article; Research Support, N.I.H., ExtramuralOriginal source ↗

Research overview

The text below is the source article’s original abstract under a verified reuse license. cc by

IntroductionExercise is effective in reducing falls in people with Parkinson disease. However, information on the cost effectiveness of this approach is lacking. We conducted a cost-effectiveness analysis of Tai Ji Quan for reducing falls among patients with mild-to-moderate Parkinson disease.MethodsWe used data from a previous intervention trial to analyze resource use costs related to intervention delivery and number of falls observed during a 9-month study period. Cost effectiveness was estimated via incremental cost-effectiveness ratio (ICER) in which Tai Ji Quan was compared with 2 alternative interventions (Resistance training and Stretching) on the primary outcome of per fall prevented and the secondary outcome of per participant quality-adjusted life years (QALY) gained. We also conducted subgroup and sensitivity analyses.ResultsTai Ji Quan was more effective than either Resistance training or Stretching; it had the lowest cost and was the most effective in improving primary and secondary outcomes. Compared with Stretching, Tai Ji Quan cost an average of $175 less for each additional fall prevented and produced a substantial improvement in QALY gained at a lower cost. Results from subgroup and sensitivity analyses showed no variation in cost-effectiveness estimates. However, sensitivity analyses demonstrated a much lower ICER ($27) when only intervention costs were considered.ConclusionTai Ji Quan represents a cost-effective strategy for optimizing spending to prevent falls and maximize health gains in people with Parkinson disease. While these results are promising, they warrant further validation.

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