MétaCan
Menu
Back to cohort
Record W4405820469 · doi:10.2196/67567

Cost Utility Analysis of Internet-Based Cognitive Behavioral Therapy for Major Depressive Disorder: Randomized Controlled Trial

2024· article· en· W4405820469 on OpenAlexaff
Wenjing Zhou, Yan Chen, Herui Wu, Hao Zhao, Yanzhi Li, Guangduoji Shi, Wanxin Wang, Yifeng Liu, Yuhua Liao, Huimin Zhang, Caihong Gao, Jiejing Hao, Gia Han Le, Roger S. McIntyre, Xue Han, Ciyong Lu

Bibliographic record

VenueJournal of Medical Internet Research · 2024
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of TorontoBrain and Cognition Discovery FoundationUniversity Health Network
FundersSun Yat-sen UniversityNational Natural Science Foundation of China
KeywordsPreprintRandomized controlled trialCognitive behavioral therapyThe InternetCognitive therapyCognitionChinaMedicinePsychiatryPsychologyClinical psychologyComputer scienceWorld Wide WebSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Unguided internet-based cognitive behavioral therapy (ICBT) has been proven effective for major depressive disorder (MDD). However, few studies have examined its cost-effectiveness in low-resource countries and under nonspecialist routine care. OBJECTIVE: This study aimed to evaluate the short- and long-term cost utility of unguided ICBT compared to a waitlist control for persons with MDD from the perspectives of society and the health care system. METHODS: This analysis was implemented alongside an 8-week 2-arm randomized controlled trial with a 12-month follow-up period conducted in Shenzhen, China. Outcomes including cost and health utility were collected at the pretreatment and posttreatment time points and 3, 6, and 12 months after the intervention. Direct medical costs and indirect costs were prospectively collected using the hospital information system and the Sheehan Disability Scale. Health outcomes were measured using the Chinese version of the Short-Form Six-Dimension health index. The primary outcome was incremental cost utility ratio (ICUR) expressed as the difference in costs between 2 therapies by the difference in quality-adjusted life years (QALYs). The seemingly unrelated regression model and the bootstrap method were performed to estimate adjusted ICURs. Cost-effectiveness planes and cost-effectiveness acceptability curves were used to demonstrate uncertainty. A series of scenario analyses were conducted to verify the robustness of base-case results. RESULTS: In total, 244 participants with MDD were randomly allocated to the ICBT (n=122, 50%) or waitlist control (n=122, 50%) groups. At the pretreatment time point, no statistically significant difference was observed in direct medical cost (P=.41), indirect cost (P=.10), or health utility (P=.11) between the 2 groups. In the base-case analysis, the ICBT group reported higher direct medical costs and better quality of life but lower total costs at the posttreatment time point. The adjusted ICURs at the posttreatment time point were CN ¥-194,720.38 (US $-26,551.50; 95% CI CN ¥-198,766.78 to CN ¥-190,673.98 [US $-27,103.20 to US $-25,999.70]) and CN ¥49,700.33 (US $6776.99; 95% CI CN ¥46,626.34-CN ¥52,774.31 [US $6357.83-$7196.15]) per QALY from the societal and health care system perspectives, respectively, with a probability of unguided ICBT being cost-effective of 75.93% and 54.4%, respectively, if the willingness to pay was set at 1 time the per-capita gross domestic product. In the scenario analyses, the probabilities increased to 76.85% and 77.61%, respectively, indicating the potential of ICBT to be cost-effective over the long term. CONCLUSIONS: Unguided ICBT is a cost-effective treatment for MDD. This intervention not only helps patients with MDD improve clinically but also generates societal savings. These findings provide health economic evidence for a potential scalable MDD treatment method in low- and middle-income countries. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR) ChiCTR2100046425; https://tinyurl.com/bdcrj4zv.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.162
GPT teacher head0.561
Teacher spread0.399 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Medical Internet ResearchSame topicDigital Mental Health InterventionsFrench-language works237,207