Is Mental Health and Psychosocial Support (MHPSS) cost effective? An economic evaluation among Congolese refugees in the Kyaka Ⅱ community in Uganda
Bibliographic record
Abstract
Background: Mental health is a global public health issue. Mental health disorders can develop in anyone, but the prevalence is higher among refugees. Mental Health and Psychosocial Support (MHPSS), aims to improve mental health and is commonly applied among persons from conflict-afflicted settings such as, refugees. There are several studies demonstrating the effectiveness of MHPSS but there are limited studies on its cost-effectiveness. Purpose: The purpose of this study is to determine if a MHPSS intervention among Congolese refugees and some Ugandans living in and around the Kyaka Ⅱ refugee camp in Uganda, is cost- effective compared to a ‘do-nothing approach’ and to determine if this effect differs over sex, age, refugee status, MHPSS type, and project funder. Methods: A Cost-Effectiveness Analysis (CEA) will be conducted from the societal perspective. The main outcome of interest is mental health measured via the Self-Report Questionnaire-20 (SRQ-20), measured pre and post intervention. The cost analysis is performed by analyzing financial expenditure reports and conducting interviews. The effectiveness is determined by the mean raw score change in the SRQ-20, the number of recovered participants, and Hedges G effect size. A multivariable regression at a 95% confidence level, is used to determine differences in effect based on sex, age, refuge status, MHPSS type and project funder. Findings: The total cost of the intervention is $704,038 USD and the cost per participant is $281. The SRQ-20 score decreased by 12.7 points and 88 percent of participants recovered. The cost per unit reduction in the SRQ-20 is $22 per participant and at a cost of $331 one participant will recover. Only sex had a statistically significant effect on the outcome, but the ICER differs minimally between females and males. Conclusions: Based on WHO’s CHOICE framework for arguing cost-effectiveness and comparative literature, the intervention is cost-effective at an ICER of 22 USD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".