Pareto Risk-Reduction Targeting Top 25 Rare Diseases · Pearl SCM Framework

In plain wordsThis tool ranks 25 rare diseases by how many lives could be saved for each treatment put into use. It uses brute-force math to find the best small set of treatments. Sickle cell disease ranks highest, driven by new gene therapies. Best-case research estimates, not medical advice.
Per-disease desired risk reduction sliders → minimum-effective intervention set discovered via brute-force Pareto enumeration of 2n intervention subsets, scored on Pearl cross-correlation-adjusted hazard ratio. Longevity Olympics Research Initiative · v1.0 · 26 May 2026
80%
Aggregate lives saved / yr
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At current per-disease targets
Diseases meeting target
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Out of 25 named diseases
Diseases infeasible
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Target exceeds full-bundle ceiling
Total interventions deployed
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Sum across 25 diseases at target
Weighted mean reduction
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Mortality-weighted achieved

Pareto Frontier — Lives Saved vs. Interventions Deployed

Each point = one disease at its chosen target. X = minimum interventions; Y = annual lives saved (log scale). Hover for details. Diseases on the upper-left dominate (high lives-saved for few interventions).

Lives Saved per Intervention Deployed

Cost-effectiveness ranking: lives saved per intervention dose-bundle (mortality-weighted return per intervention). Higher = more impact per care-delivery unit.

Aggregate Pareto Results

Ranked by mortality. Click column headers to sort. n* = minimum effective intervention count; HRadj = Pearl cross-correlation-adjusted bundle hazard ratio at chosen target.

# Disease Deaths/yr Target Achieved n* HRadj E-value Lives saved/yr Status

Per-Disease Targeting Controls

Each card shows the chosen target reduction (slider), the Pareto-optimal minimum intervention set, and a mini Pareto frontier curve. Green chips = selected interventions in the minimum effective set; struck-through chips = excluded from the optimal subset; gray chips = excluded because the chosen criterion did not require them.

Method. For each disease with n interventions, enumerate all 2n subsets. For each subset, compute the Pearl-adjusted bundle HR using the same dose-response and cross-correlation removal as the main dashboard. The Pareto frontier is the set of non-dominated (count, reduction) points. Minimum effective set = smallest subset whose adjusted reduction ≥ target. If no subset reaches target, the disease is flagged infeasible at that target. All HRs are reported at the global adherence slider value; E-values use the VanderWeele & Ding (2017) bound RR + √[RR(RR−1)] where RR = 1/HR.

Caveat. Lives-saved figures are upper bounds assuming universal bundle deployment; realistic figures should be discounted by current access rates (typically 15–60% in HICs, <5% in LMICs). Cross-correlation parameter ρ̄ uses disease-specific values calibrated to registry data where available, else a 0.30 prior.