Geographic Normalization Engine

python
# Geographic Cost Adjustment Framework
geographic_indices = {
    'NY-Newark-Jersey City': {
        'cbsa_code': '35620',
        'medical_index': 1.32,
        'pharmacy_index': 1.08,
        'mental_health_index': 1.18
    },
    'Birmingham-Hoover, AL': {
        'cbsa_code': '13820',
        'medical_index': 0.78,
        'pharmacy_index': 0.92,
        'mental_health_index': 0.85
    },
    'San Francisco-Oakland-Berkeley': {
        'cbsa_code': '41860',
        'medical_index': 1.45,
        'pharmacy_index': 1.12,
        'mental_health_index': 1.28
    },
    'National Average': {
        'medical_index': 1.00,
        'pharmacy_index': 1.00,
        'mental_health_index': 1.00
    }
}

def normalize_costs_by_geography(member_costs, member_locations):
    normalized_costs = []
    
    for member in member_costs:
        cbsa = member_locations[member.id].cbsa_code
        region_index = geographic_indices[cbsa]
        
        # Normalize Each Cost Component
        normalized_medical = member.medical_costs / region_index['medical_index']
        normalized_rx = member.rx_costs / region_index['pharmacy_index']
        normalized_mh = member.mental_health_costs / region_index['mental_health_index']
        
        normalized_total = normalized_medical + normalized_rx + normalized_mh
        
        normalized_costs.append({
            'member_id': member.id,
            'actual_costs': member.total_costs,
            'normalized_costs': normalized_total,
            'cbsa': cbsa,
            'adjustment_factor': member.total_costs / normalized_total
        })
    
    return normalized_costs

# Example: Multi-State Employer Comparison
# Location 1: New York (1.32x index)
#   - Actual PMPY: $15,000
#   - Normalized PMPY: $11,364 ($15K / 1.32)
# 
# Location 2: Birmingham (0.78x index)
#   - Actual PMPY: $8,000
#   - Normalized PMPY: $10,256 ($8K / 0.78)
# 
# Conclusion: Birmingham is actually LESS efficient than NYC
# when adjusted for regional cost differences.