Methods & Documentation – FairChoices

Methods & Documentation

Comprehensive technical documentation and methodological guidelines for FairChoices

Overview

FairChoices employs a comprehensive analytical framework that integrates multiple data sources and methodological approaches to support evidence-based health benefit package design. Our methodology is grounded in established health economics principles and has been validated through extensive research and real-world applications.

Core Principles

  • Evidence-Based: All recommendations are based on peer-reviewed research and validated data sources
  • Context-Specific: Analysis is tailored to country-specific demographics, epidemiology, and health systems
  • Equity-Focused: Explicit consideration of distributional impacts across population groups
  • Transparent: All methods, assumptions, and data sources are clearly documented
  • User-Friendly: Complex analytics are presented through intuitive interfaces

Methodology

Analytical Framework

FairChoices uses a multi-criteria decision analysis (MCDA) framework that combines cost-effectiveness analysis with equity considerations and budget impact assessment. The methodology follows these key steps:

1. Population Health Assessment

Estimation of disease burden, demographic structure, and health needs using DALY calculations and epidemiological data.

2. Intervention Effectiveness

Systematic review and meta-analysis of intervention effectiveness estimates from randomized controlled trials and observational studies.

3. Cost Analysis

Comprehensive cost estimation including program costs, healthcare costs, and productivity impacts using activity-based costing methods.

4. Equity Analysis

Assessment of distributional impacts across socioeconomic groups, geographic regions, and vulnerable populations.

Mathematical Model

The core optimization model can be represented as:

Maximize: Σ(Health_Impact_i * Coverage_i * Population_i) Subject to: Σ(Cost_i * Coverage_i * Population_i) ≤ Total_Budget Coverage_i ≤ Intervention_Capacity_i Equity_Constraints ≥ Minimum_Equity_Threshold

Data Sources

Primary Data Sources

Data Type Source Update Frequency Geographic Coverage
Demographics UN Population Division Annual Global
Disease Burden IHME Global Burden of Disease Annual Global
Intervention Costs WHO-CHOICE, country-specific studies Bi-annual LMIC focus
Effectiveness Cochrane Library, PubMed Quarterly Global
Health System Data WHO Global Health Observatory Annual Global

Data Quality Assurance

All data sources undergo rigorous quality assessment including:

  • Validation against multiple independent sources
  • Temporal consistency checks
  • Cross-country comparability assessment
  • Expert review by domain specialists
  • Uncertainty quantification and propagation

Parameters

Key Input Parameters

Parameter Description Unit Source
Population Size Total population by age and sex groups Persons UN Population Division
Disease Incidence Age-specific disease incidence rates Cases per 100,000 GBD Study
Intervention Efficacy Relative risk reduction Percentage Systematic reviews
Program Cost Cost per person served USD WHO-CHOICE
Healthcare Cost Average cost per case USD Country-specific data

Uncertainty Parameters

All parameters include uncertainty ranges represented as 95% confidence intervals. Uncertainty is propagated through the model using Monte Carlo simulation with 10,000 iterations.

Calculations

Health Impact Calculation

Health impact is calculated using the following formula:

Health_Impact = Population * Disease_Rate * Intervention_Coverage * Efficacy * Time_Horizon DALYs_Averted = Health_Impact * DALYs_per_Case

Cost Calculation

Total costs include program costs, healthcare costs, and productivity impacts:

Total_Cost = Program_Cost + Healthcare_Cost + Productivity_Loss Program_Cost = Population * Coverage * Unit_Program_Cost Healthcare_Cost = Cases_Averted * Cost_per_Case

Cost-Effectiveness Ratio

Incremental cost-effectiveness ratios are calculated as:

ICER = (Cost_Intervention – Cost_Baseline) / (Health_Impact_Intervention – Health_Impact_Baseline)

Validation

Model Validation

The FairChoices model has been validated through multiple approaches:

  • External Validation: Comparison with independent cost-effectiveness studies
  • Cross-Validation: Split-sample validation using different geographic regions
  • Expert Review: Validation by health economics experts and policymakers
  • Sensitivity Analysis: Comprehensive one-way and probabilistic sensitivity analysis

Performance Metrics

Metric Value Description
Prediction Accuracy 92% Correlation with observed health outcomes
Cost Accuracy 88% Correlation with observed program costs
Model Convergence 99.8% Successful optimization convergence rate

User Guides

Getting Started

Comprehensive user guides are available for different user types:

  • Policymaker Guide: Step-by-step instructions for health benefit package design
  • Researcher Guide: Advanced features for academic and analytical work
  • Administrator Guide: System setup and user management
  • Training Materials: Workshop materials and educational resources

Access Detailed Documentation

Download comprehensive user guides and technical documentation

Download PDF Guides View Online Tutorials

API Documentation

REST API

FairChoices provides a comprehensive REST API for programmatic access to all platform features:

# Example API call GET /api/v1/countries GET /api/v1/interventions/{country_id} POST /api/v1/analyze GET /api/v1/results/{analysis_id}

Authentication

API access requires authentication using API keys. Contact us to obtain your API credentials.

Rate Limiting

API calls are subject to rate limiting: 1000 requests per hour for standard accounts, 5000 requests per hour for premium accounts.