Vision Trend Analysis Engine

python
# Vision Trend Forecasting Framework
vision_service_types = {
    'exam': {'base_cost': 45, 'frequency_annual': 0.52},
    'single_vision': {'base_cost': 85, 'upgrade_rate': 0.35},
    'progressive': {'base_cost': 220, 'upgrade_rate': 0.25},
    'premium_progressive': {'base_cost': 380, 'upgrade_rate': 0.08},
    'contacts': {'base_cost': 220, 'utilization_rate': 0.12},
    'frames': {'allowance': 150, 'out_of_pocket_avg': 45}
}

def calculate_vision_pmpm_by_age(population_demographics, benefit_design):
    """
    Forecast vision PMPM based on age distribution and material choices
    """
    total_cost = 0
    total_members = 0
    
    for age_band, member_count in population_demographics.items():
        # Age-based utilization rates
        if age_band == '18-25':
            exam_util = 0.35
            materials_util = 0.28
            progressive_pct = 0.02
        elif age_band == '26-35':
            exam_util = 0.42
            materials_util = 0.35
            progressive_pct = 0.08
        elif age_band == '36-45':
            exam_util = 0.58
            materials_util = 0.48
            progressive_pct = 0.35
        elif age_band == '46-55':
            exam_util = 0.68
            materials_util = 0.62
            progressive_pct = 0.75
        else:  # 56+
            exam_util = 0.72
            materials_util = 0.68
            progressive_pct = 0.85
        
        # Calculate costs for this age band
        exam_cost = member_count * exam_util * vision_service_types['exam']['base_cost']
        
        # Materials mix
        materials_users = member_count * materials_util
        progressive_users = materials_users * progressive_pct
        single_vision_users = materials_users * (1 - progressive_pct)
        
        materials_cost = (
            single_vision_users * vision_service_types['single_vision']['base_cost'] +
            progressive_users * vision_service_types['progressive']['base_cost']
        )
        
        # Contacts (if benefit includes)
        if benefit_design['contacts_allowance'] > 0:
            contacts_cost = member_count * vision_service_types['contacts']['utilization_rate'] *                           vision_service_types['contacts']['base_cost']
        else:
            contacts_cost = 0
        
        total_cost += exam_cost + materials_cost + contacts_cost
        total_members += member_count
    
    pmpm = total_cost / (total_members * 12)
    
    return pmpm

def forecast_benefit_change_impact(baseline_pmpm, change_type):
    """
    Model impact of vision benefit design changes
    """
    impacts = {
        'add_contact_lens_allowance': 1.32,  # +32% ($220 allowance, 12% utilization)
        'increase_frame_allowance_150_to_200': 1.08,  # +8% (members upgrade frames)
        'add_lasik_discount': 1.00,  # 0% (discount program, no plan cost)
        'increase_exam_frequency_24mo_to_12mo': 1.45,  # +45% (doubles eligible exams)
        'progressive_upgrade_incentive': 0.95  # -5% (negotiate better progressive pricing)
    }
    
    new_pmpm = baseline_pmpm * impacts[change_type]
    
    return {
        'baseline': baseline_pmpm,
        'change': change_type,
        'projected_pmpm': new_pmpm,
        'impact_pct': (impacts[change_type] - 1) * 100
    }

# Example: Workforce Aging Impact
# 2023 Demographics:
#   - 18-35: 45% of workforce
#   - 36-55: 40%
#   - 56+: 15%
#   - PMPM: $6.20
#
# 2028 Demographics (5 years later):
#   - 18-35: 35% (lower hiring)
#   - 36-55: 45%
#   - 56+: 20%
#   - Progressive lens usage: 35% → 52%
#   - Projected PMPM: $8.40 (+35% from demographic shift alone)