Cost-Effectiveness of a Culturally Adapted Manual-Assisted Brief Psychological Intervention for Self-Harm in Pakistan: A Secondary Analysis of the Culturally Adapted Manual-Assisted Problem-Solving Training Randomized Controlled Trial.

Value in health regional issues(2021)

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摘要
OBJECTIVES:Self-harm is a serious public health problem. A culturally adapted manual-assisted problem-solving training (C-MAP) intervention improved and sustained the reduction in suicidal ideation, hopelessness, and depression compared with treatment as usual (TAU) alone. Here, we evaluate its cost-effectiveness. METHODS:Patients admitted after an episode of self-harm were randomized individually to either C-MAP plus TAU or TAU alone in Karachi. Improvement in health-related quality-adjusted life years (QALYs) was measured using the Euro Qol-5D-3L instrument at baseline and at 3 months and 6 months after randomization. The primary economic outcome was health service cost per QALY gained as the incremental cost-effectiveness ratio, based on 2019 US dollars and a 6-month time horizon. Nonparametric bootstrapping was used to assess uncertainties, and sensitivity analysis to examine the impact of hospitalization costs. RESULTS:A total of 108 and 113 participants were enrolled among the intervention and standard arms, respectively. The intervention resulted in 0.04 more QALYs (95% confidence interval [CI] 0.00-0.08) 6 months after enrolment. The mean cost per participant in the intervention arm was US $1001 (95% CI 968-1031), resulting in an incremental cost of the intervention of US $640 (95% CI 595-679). The incremental cost-effectiveness ratio for the C-MAP intervention versus TAU was US $16 254 (95% CI 7116-99 057) per QALY gained. The probability that C-MAP is cost-effective was between 66% and 83% for cost-effective thresholds between US $20 000 and US $30 000. Cost-effectiveness results remained robust to sensitivity analyses. CONCLUSIONS:C-MAP may be a valuable self-harm intervention. Further studies with longer follow-up and larger sample sizes are needed to draw reliable conclusions.
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