Background: Generalized anxiety disorder (GAD) is a debilitating mental health illness that affects approximately 3.1% of U.S. adults and can be treated with cognitive behavioral therapy (CBT). With the emergence of digital health technologies, mobile CBT may be a cost-effective way to deliver care. We developed an analysis framework to quantify the cost-effectiveness of internet-based CBT for individuals with GAD. As a case study, we examined the potential value of a new mobile-delivered CBT program for GAD.

Methods: We developed a Markov model of GAD health states combined with a detailed economic analysis for a cohort of adults with GAD in the U.S. In our case study, we used pilot program efficacy data to evaluate a mobile CBT program as either prevention or treatment only and compared the strategies to traditional CBT and no CBT. Traditional CBT efficacy was estimated from clinical trial results. We calculated discounted incremental costs and quality-adjusted life-years (QALYs) over the cohort lifetime.

Mobile CBT may lead to improved health outcomes at lower costs than traditional CBT or no
intervention and may be effective as either prevention or treatment.

Case study results: In the base case, for a cohort of 100,000 persons with GAD, we found that mobile CBT is cost-saving. It leads to a gain of 34,108 QALYs and 81,492 QALYs and a cost reduction of $2.23 billion and $4.54 billion when compared to traditional CBT and no CBT respectively. Results were insensitive to most model inputs and mobile CBT remained cost-saving in almost all scenarios. The case study was conducted for illustrative purposes and used mobile CBT efficacy data from a small pilot program; the analysis should be re-conducted once robust efficacy data is available. The model was limited in its ability to measure the effectiveness of CBT in combination with pharmacotherapy.

Source: PLoS ONE

Further reading

Aug 08 2019

Chan R, Jankovic F, Marinsek N, Foschini L, Kourtis L, Signorini A, Pugh M, Shen J, Yaari R, Maljkovic V, Sunga M, Hee Song H, Joon Jung H, Tseng B, Trister A

Source: KDD 2019

Sep 11 2018

Kumar S, Tran JL, Moseson H, Tai C, Glenn JM, Madero EN, Krebs C, Bott N, Juusola JL

Source: JMIR Aging