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Cost-Effectiveness of Computer-Assisted Cognitive Behavioral Therapy for Depression Among Adults in Primary Care

2024· article· en· W4404346490 on OpenAlexaff
Shehzad Ali, Feben W. Alemu, Jesse Owen, Tracy D. Eells, Becky F. Antle, John Tayu Lee, Jesse H. Wright

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsWestern University
FundersNational Institute of Mental HealthAgency for Healthcare Research and QualityNational Institutes of HealthOtsuka PharmaceuticalAmerican Psychiatric Publishing
KeywordsMedicinePopulationRandomized controlled trialDepression (economics)Cognitive restructuringCost effectivenessEconomic evaluationSocioeconomic statusQuality of life (healthcare)Cognitive therapyCognitive behavioral therapyHealth careClinical trialCognitionFamily medicinePhysical therapyPsychiatryInternal medicineNursing

Abstract

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Importance: Approximately 1 in 5 adults are diagnosed with depression in their lifetime. However, less than half receive help from a health professional, with the treatment gap being worse for individuals with socioeconomic disadvantage. Computer-assisted cognitive behavioral therapy (CCBT) is an effective and convenient strategy to treat depression; however, its cost-effectiveness in a sociodemographically diverse population remains unknown. Objective: To evaluate the cost-effectiveness of clinician-supported CCBT compared with treatment as usual (TAU) in a primary care population with a substantial number of patients with low income, limited computer or internet access, and lack of college education. Design, Setting, and Participants: This economic evaluation was a randomized clinical trial-based cost-effectiveness analysis. The trial was conducted at the Departments of Family and Geriatric Medicine and Internal Medicine at the University of Louisville. Enrollment occurred from June 24, 2016, to May 13, 2019. Participants had mild to moderate depression and were followed up for 6 months after treatment completion. The last follow-up assessment was conducted on January 30, 2020. Statistical analysis was performed from August 2023 to August 2024. Exposure: CCBT intervention was provided for 12 weeks and included 9 modules ranging from behavioral activation and cognitive restructuring to relapse prevention strategies, supported by telephonic sessions with a clinician, in addition to TAU, which included standard clinical management in primary care. Main Outcomes and Measures: The primary health outcome was quality-adjusted life years (QALYs), estimated using the Short-Form 12 questionnaire (SF-12). The secondary outcome was treatment response, defined as at least 50% improvement in the Patient Health Questionnaire. The intervention cost included sessions with mental health clinicians and the cost of the CCBT software, plus the cost of loaner computer and internet data plan for low-resource households. An incremental cost-effectiveness ratio (ICER) was computed, while adjusting for baseline scores, age, and sex. The cost-effectiveness acceptability curve presented the probability of CCBT being cost-effective for a range of willingness-to-pay values. Results: Among the 175 primary care patients included in this study, 148 (84.5%) were female; 48 (27.4%) were African American, 2 (1.2%) were American Indian or Alaska Native, 4 (2.5%) were Hispanic, 106 (60.5%) were White, and 15 (8.6%) were multiracial; and the mean (SD) age was 47.03 (13.15) years. CCBT was associated with better quality of life and higher chance of treatment response at the posttreatment and 6-month time points, compared with the TAU group. The ICER for CCBT was $37 295 (95% CI, $22 724-$66 546) per QALY, with a probability of 89.4% of being cost-effective at a willingness-to-pay threshold of $50 000/QALY. The ICER per case of treatment response was $3623 (95% CI, $2617-$5377). Conclusions and Relevance: In this trial-based economic evaluation, CCBT was found to be cost-effective, compared with TAU, in primary care patients with depression. As this study included individuals with low income and with limited internet access who are underrepresented in cost-effectiveness studies, it has important policy implications for addressing unmet needs in sociodemographically diverse populations.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.083
GPT teacher head0.439
Teacher spread0.356 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations5
Published2024
Admission routes1
Has abstractyes

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