Accepted for/Published in: JMIR mHealth and uHealth
Date Submitted: Nov 25, 2022
Date Accepted: May 23, 2023
Economic Evaluation of Digital Therapeutic Care Apps for Unsupervised Treatment of Low Back Pain: Monte Carlo Simulation
ABSTRACT
Background:
Digital therapeutic care (DTC) programs are unsupervised app-based treatments that provide video exercises and educational material to patients with non-specific low back pain (LBP) during episodes of pain and functional disability. German statutory health insurance can reimburse DTC programs since 2019, but evidence on efficacy and reasonable pricing remains scarce. This paper presents a probabilistic sensitivity analysis (PSA) to evaluate efficacy and cost-utility of a DTC app against treatment-as-usual (TAU) in Germany.
Objective:
The aim of this study was to perform a PSA in the form of a Monte Carlo simulation based on the deterministic base case analysis in order to account for model assumptions and parameter uncertainty.
Methods:
The PSA builds upon a state-transition Markov chain with four weeks cycle length over a model time horizon of three years from a recently published deterministic cost-utility-analysis. Monte-Carlo-simulation with 10,000 iterations and cohort size of 10,000 was employed to evaluate the cost-utility from a societal perspective. Quality-adjusted life years (QALYs) were derived from the VR-6D and SF-6D single utility scores.
Results:
The simulation yielded on average €135.97 incremental cost and 0.004 incremental QALYs per person and year for DTC compared to TAU, the ICUR is €34,315.19 per additional QALY. DTC yielded more QALYs in 54.96% of the iterations. DTC dominates TAU in 24.04% of the iterations for QALYs.
Conclusions:
Decision-makers should be cautious when considering the reimbursement of DTC apps since no significant treatment effect was found, and the probability of cost-effectiveness remains below 60% even for an infinite WTP.
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