Forecast Health Mental Health Platform
Cost-effectiveness modelling for mental health interventions
About This Platform
The Forecast Health Mental Health Platform is a cost-effectiveness modelling tool for evaluating mental health interventions. It allows health economists and policymakers to compare baseline and intervention scenarios, inspect key assumptions, and estimate health impacts, resource needs, costs, and cost-effectiveness in a country context.
This tool models intervention coverage, effectiveness, and costs based on epidemiological data and published evidence. It generates estimates of health outcomes (DALYs averted), resource needs (workforce, medications, visits), and cost-effectiveness ratios to inform priority-setting and planning.
It does not replace local validation of assumptions, costs, and service-delivery patterns.
This is a modelling tool, not definitive policy guidance. Results depend on model assumptions and input parameters. Users should interpret outputs in context and consult local evidence and expertise when making decisions.
How to Use This Tool
On each workflow page, use the navigation button in the top right to move forward, and use the menu there to save or load a configuration or open the help panel.
- Configuration: choose the condition, country, start year, and final year.
- Baseline: describe current care and current coverage.
- Intervention: describe what changes under the proposed approach.
- Assumptions: review or edit the background epidemiology and model inputs if needed.
- Resource Pathways: inspect how the model turns people reached into visits, staff time, medicines, and costs.
- Service Inputs: adjust how care is delivered, such as visits, duration, and shares receiving each step.
- Unit Costs: review and update prices, staff rates, medicine costs, and inpatient costs.
- Results: run and compare the health, cost, resource, and cost-effectiveness outputs.
Disclaimer: This is a decision-support tool for cost-effectiveness analysis. Model outputs are estimates based on available data and assumptions. They are not definitive recommendations or medical advice. Users should validate inputs for their context and interpret results with local expertise.