Cost Effectiveness of Digital Interventions for Generalised Anxiety Disorder: A Model-Based Analysis

Abstract

Background Digital interventions (DIs) are increasingly being used in mental health care, despite limited evidence regarding their value for money. This study aimed to evaluate the cost effectiveness of DIs for generalised anxiety disorder (GAD), in comparison with alternative care options, from the perspective of the UK health care system. Methods An open-source decision analytic cohort model was used to extrapolate the results of a network meta-analysis over a patient’s lifetime and estimate the costs and outcomes (quality-adjusted life-years) of DIs and their comparators. The net monetary benefit (NMB) and probability of cost effectiveness was estimated for each comparator, and we conducted a Value of Information analysis to evaluate the scale and drivers of uncertainty. Results DIs were associated with lower NMB compared with medication and with group therapy, but greater NMB compared with non-therapeutic controls and with usual care. DIs that were supported by a clinician, an assistant or a lay person had higher delivery costs than purely patient-self-directed DIs, yielding a greater NMB when opportunity cost was above £3000/QALY. There was considerable uncertainty in the findings driven largely by uncertainty in the estimated treatment effects. The value of further research to establish the effectiveness of DIs for GAD was substantial, at least £12.9 billion. Conclusions The high uncertainty about these results does not allow for recommendations based on the cost effectiveness of DIs. However, the analysis highlights areas for future research, and demonstrates that apparent cost savings associated with DIs can be offset by reduced effectiveness. Supplementary Information The online version contains supplementary material available at 10.1007/s41669-021-00318-y.

Digital interventions in mental health: evidence syntheses and economic modelling

“…The consistency plots (Figures 27 and 28) show that there are a few deviant data points for which the inconsistency models lead to higher residual deviance than the consistency models, further supporting the latter. Appendix 9 Health-care costs in scenario analysis P arts of this appendix have been reproduced with permission from Jankovic et al 189 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for non-commercial use, provided the original work is properly cited. See: https://creativecommons.org/licenses/by-nc/4.0/.…”

“…Hazen 2009 161 Hirsch 2018 162 Howell 2018 163 Johansson 2013 37 Total number of comparisons P arts of this appendix have been reproduced with permission from Jankovic et al 189 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for non-commercial use, provided the original work is properly cited. See: https://creativecommons.org/licenses/by-nc/4.0/.…”

“…Mortalitygeneral populationSex and age specific (see Table29) Uncertainty in utilities was derived by fitting a beta distribution to the reported mean and standard error, using the method of moments 192. b Beta distribution applied to the risk of death in each state, then 25,000 random samples used to derive the relative risk.NoteAdapted with permission from Jankovic et al189 This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for non-commercial use, provided the original work is properly cited. See: https://creativecommons.org/by-nc/4.0.…”

Development and delivery cost of digital health technologies for mental health: Application to the Narrative Experiences Online Intervention

“…Economic evaluations of mental health DHIs have been conducted for a range of conditions such as anxiety ( 10 , 11 ), depression ( 10 – 12 ), eating disorders ( 10 , 13 ), and substance abuse disorders ( 10 , 14 ). The quality of economic evaluations has been suggested to be very variable including heterogeneous reporting of costs, outcomes and comparators ( 15 ). Such inconsistencies may be a barrier to the adoption of effective DHIs for mental health within healthcare systems or may lead policymakers to invest in services that are not cost-effective ( 16 – 18 ).…”

“…There is a lack of standardization in how costs are reported and described for economic evaluations of DHIs ( 11 , 15 ). The National Institute of Health and Care Excellence's (NICE) evidence standards framework for DHTs recommends reporting all costs associated with the intervention and costs relevant to a health and social care decision maker ( 19 ).…”