Ubiquitous Pragmatic Trial Impact Analysis: How to Prevent a Year of Death and Suffering for 84 Cents

Abstract

Of 9.5 million combinations (90% CI: 6.68 million combinations-12.8 million combinations) plausible drug-disease pairings, only 0.342% (90% CI: 0%-1%) have been clinically tested. At the current discovery rate of 15 diseases/year (95% CI: 8 diseases/year-30 diseases/year), clearing this backlog would take ~443 years (90% CI: 255 years-841 years). An open protocol integrating pragmatic clinical trials into standard healthcare at $929 (95% CI: $97-$3,000)/patient (vs. $41,000 (95% CI: $20,000-$120,000) traditional) increases trial capacity 12.3x (90% CI: 4.92x-50.8x), reducing backlog clearance to 36 years (90% CI: 8.15 years-106 years). Combined with eliminating the 8.2 years (90% CI: 4.84 years-11.5 years) post-safety efficacy delay through opt-in trial participation after Phase I, treatments arrive 212 years (90% CI: 124 years-398 years) earlier on average. This timeline shift saves 10.7 billion deaths (90% CI: 6.24 billion deaths-20.3 billion deaths), averts 565 billion DALYs (90% CI: 309 billion DALYs-1.08 trillion DALYs), and eliminates 1.93 quadrillion hours (90% CI: 1.04 quadrillion hours-3.75 quadrillion hours) of suffering (YLD portion of 565 billion DALYs (90% CI: 309 billion DALYs-1.08 trillion DALYs) converted to hours) at $0.842 (90% CI: $0.264-$1.49)/DALY, competitive with bed nets ($89 (95% CI: $78-$100)/DALY) at vastly greater scale. Using standard health economic valuation ($150,000 (90% CI: $100,384-$198,679)/DALY, the US cost-effectiveness threshold; conservative relative to EPA/DOT Value of Statistical Life estimates), full impact yields $84.8 quadrillion (90% CI: $42.9 quadrillion-$172 quadrillion) in cumulative value (565 billion DALYs (90% CI: 309 billion DALYs-1.08 trillion DALYs) cumulative DALYs over the 212 years (90% CI: 124 years-398 years) timeline shift, not annual; 178 thousand (90% CI: 92 thousand-575 thousand):1 ROI).

Keywords

war-on-disease, 1-percent-treaty, medical-research, public-health, peace-dividend, decentralized-trials, dfda, dih, victory-bonds, health-economics, cost-benefit-analysis, clinical-trials, drug-development, regulatory-reform, military-spending, peace-economics, decentralized-governance, wishocracy, blockchain-governance, impact-investing

Executive Summary

There are 9,500 compounds (95% CI: 7,000 compounds-12,000 compounds) that have already been proven safe for human consumption. There are 6,650 diseases (90% CI: 5,700 diseases-8,232 diseases) with zero approved treatments. The number of possible combinations between those safe compounds and those untreated diseases is 9.5 million combinations (90% CI: 6.68 million combinations-12.8 million combinations). The fraction that has actually been tested is 0.342% (90% CI: 0%-1%). At the current rate of 15 per year, testing the rest would take ~443 years (90% CI: 255 years-841 years). Your species is sitting on a combinatorial goldmine of potential cures and has chosen to explore it with a teaspoon.

Of 2.4 billion people (95% CI: 2 billion people-2.8 billion people) with chronic disease, only 1.9 million participate in trials annually (0.06%).

The Solution: An open protocol for embedded pragmatic trials¹³⁵: the learning-health-system model that PCORnet already runs at small scale (ADAPTABLE was a PCORnet trial¹³⁶), given a common standard that existing DCT platforms, EHRs, and health apps adopt, like HTTP or FHIR rather than a platform. The protocol enables:

Subsidized Patient Participation: Patients receive subsidies that offset the costs of taking part (time, travel, devices)
Universal Trial Access: Any patient can join trials from home via their phone or computer - no travel to research centers required
Real-World Data Aggregation: Outcomes from all participants are aggregated into a unified database
Treatment Rankings: Standardized effectiveness rankings for every treatment-condition pair, updated continuously with real-world evidence
Outcome Labels: “Nutrition facts for drugs” showing exactly what happened to real patients who tried each treatment

The Receipts

Metric	Value	Context
Cost-Effectiveness	$0.842 (90% CI: $0.264-$1.49)/DALY	Competitive with bed nets ($89 (95% CI: $78-$100)/DALY) at vastly greater scale
Lives Saved	10.7 billion	One-time benefit from 212 years (90% CI: 124 years-398 years) timeline shift
DALYs Averted	565 billion	Captures both mortality and morbidity
Suffering Eliminated	1.93 quadrillion hours (90% CI: 1.04 quadrillion hours-3.75 quadrillion hours)	YLD portion of DALYs (39%) x 8,760 hrs/yr over timeline shift
Total Economic Value	$84.8 quadrillion (90% CI: $42.9 quadrillion-$172 quadrillion)	Cumulative DALYs x $150K/DALY (WHO threshold) over 212 years (90% CI: 124 years-398 years) shift
Efficacy Lag Eliminated	8.2 years (90% CI: 4.84 years-11.5 years)	Post-Phase I access via trial participation
ROI (R&D Savings)	439 (90% CI: 321-600):1	44.1x (90% CI: 12.8x-210x) cheaper trials
Annual R&D Savings	$40.5 billion (90% CI: $31.4 billion-$51.5 billion)	From 97.7% (90% CI: 92%-100%) cost reduction
Trial Capacity Increase	12.3x (90% CI: 4.92x-50.8x)	Enabling parallel therapeutic space exploration

Why These Numbers Are Large

The economic value figure ($84.8 quadrillion (90% CI: $42.9 quadrillion-$172 quadrillion)) exceeds global GDP ($115 trillion). This is expected, not an error. Three points of context:

1. Standard methodology, applied at scale. The $150,000 (90% CI: $100,384-$198,679)/DALY valuation is the US cost-effectiveness threshold (ICER). It is conservative relative to EPA and DOT Value of Statistical Life estimates, which imply higher per-DALY values when converted (~$300K-$600K/DALY). We did not invent this number. We multiplied it by the number of sick people.

2. GDP measures transactions, not the value of being alive. GDP does not count the value of not being dead, not being in pain, or not watching your children die of treatable diseases. Health economists have measured these values for decades. The global burden of disease (2.88 billion DALYs/year (90% CI: 2.63 billion DALYs/year-3.12 billion DALYs/year)) valued at $150,000 (90% CI: $100,384-$198,679)/DALY produces $400 trillion (90% CI: $252 trillion-$544 trillion)/year in health losses, roughly 3.5x global GDP. This is consistent with the established finding that the value of health substantially exceeds market output^137,138.

3. The figure is cumulative over 212 years (90% CI: 124 years-398 years), not annual. This is the total value of permanently accelerating medical progress, the same methodology used to value smallpox eradication ($300M program -> millions of future lives saved) and climate infrastructure (multi-trillion dollar damage estimates that exceed annual GDP). The only debatable input is whether the timeline shift is really ~212 years; see The Discovery Capacity Model for that derivation.

Key Metric Derivations

Lives Saved:

\[ \begin{gathered} Lives_{max} \\ = Deaths_{disease,daily} \times T_{accel,max} \times 338 \\ = 150{,}000 \times 212 \times 338 \\ = 10.7B \end{gathered} \]
where:
\[ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \]
where:
\[ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} \]
where:
\[ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} \]
where:
\[ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} \]
where:
\[ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} \]
where:
\[ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} \]
where:
\[ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} \]
where:
\[ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} \]
where:
\[ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} \]

Suffering Hours Eliminated:

\[ \begin{gathered} Hours_{suffer,max} \\ = DALYs_{max} \times Pct_{YLD} \times 8760 \\ = 565B \times 0.39 \times 8760 \\ = 1930T \end{gathered} \]
where:
\[ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} \]
where:
\[ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \]
where:
\[ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} \]
where:
\[ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} \]
where:
\[ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} \]
where:
\[ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} \]
where:
\[ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} \]
where:
\[ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} \]
where:
\[ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} \]
where:
\[ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} \]

Cost per DALY:

\[ \begin{gathered} Cost_{direct,DALY} \\ = \frac{NPV_{direct}}{DALYs_{max}} \\ = \frac{\$476B}{565B} \\ = \$0.842 \end{gathered} \]
where:
\[ \begin{gathered} NPV_{direct} \\ = \frac{T_{queue,trial}}{Funding_{trial,ref} \times r_{discount}} \\ = \frac{36}{\$21.8B \times 3\%} \\ = \$476B \end{gathered} \]
where:
\[ \begin{gathered} T_{queue,trial} \\ = \frac{T_{queue,SQ}}{k_{capacity}} \\ = \frac{443}{12.3} \\ = 36 \end{gathered} \]
where:
\[ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} \]
where:
\[ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} \]
where:
\[ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} \]
where:
\[ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} \]
where:
\[ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} \]
where:
\[ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} \]
where:
\[ \begin{gathered} DALYs_{max} \\ = DALYs_{global,ann} \times Pct_{avoid,DALY} \times T_{accel,max} \\ = 2.88B \times 92.6\% \times 212 \\ = 565B \end{gathered} \]
where:
\[ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \]
where:
\[ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} \]
where:
\[ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} \]

The Discovery Capacity Model

The 212 years (90% CI: 124 years-398 years) figure comes from a discovery capacity model of medical research. Think of the therapeutic search space as the set of all untested drug-disease combinations, with trial capacity determining how fast we can explore it.

Parameter	Status Quo	Proposed	Impact
Untreated diseases	6,650	6,650	Same backlog
Discovery rate (first treatments/year)	15 diseases/year (95% CI: 8 diseases/year-30 diseases/year)	185 diseases/year (90% CI: 63.8 diseases/year-816 diseases/year)	12.3x (90% CI: 4.92x-50.8x) faster
Time to explore search space	443 years (90% CI: 255 years-841 years)	36 years (90% CI: 8.15 years-106 years)	Centuries saved
Expected time to first treatment	~443/2	~36/2	204 years (90% CI: 116 years-390 years) earlier

Why treatments arriving sooner saves lives:

A disease that would receive its first effective treatment in year 200 under the status quo might receive it in year 16 with the protocol. During those 184 years, people die from that disease who could have been saved. The 10.7 billion figure captures the cumulative lives saved across all diseases during their acceleration periods.

The two components:

Discovery acceleration (204 years (90% CI: 116 years-390 years)): Higher discovery rate explores the therapeutic space faster, moving treatments forward
Efficacy lag elimination (8.2 years (90% CI: 4.84 years-11.5 years)): Once discovered, treatments reach patients immediately instead of waiting for Phase II/III

Total timeline shift: 212 years (90% CI: 124 years-398 years) = 204 years (90% CI: 116 years-390 years) + 8.2 years (90% CI: 4.84 years-11.5 years)

How the 12.3x (90% CI: 4.92x-50.8x) capacity increase works: With $21.8 billion/year in trial funding at $929 (95% CI: $97-$3,000)/patient (based on ADAPTABLE trial), the protocol enables 23.4 million annual trial participants vs. current 1.9 million patients/year (95% CI: 1.5 million patients/year-2.3 million patients/year), increasing trial completion rate from 15 diseases/year (95% CI: 8 diseases/year-30 diseases/year) to 185 diseases/year (90% CI: 63.8 diseases/year-816 diseases/year). Currently less than 0.06% of willing patients can access trials, and over 9,500 compounds (95% CI: 7,000 compounds-12,000 compounds) proven-safe (FDA-approved drugs + GRAS substances) remain untested for most conditions they could improve.

Capabilities

Core Model: An Open Coordination Protocol

The protocol enables existing systems (DCT platforms, EHRs, health apps) to:

For Treatment Providers (via compliant platforms):

Register treatments through any protocol-compliant system
Access aggregated effectiveness data across all participating platforms
Receive liability coverage through the protocol’s pooled insurance
Benefit from standardized outcome reporting across the network

For Patients (via participating apps and platforms):

Search any condition, see treatments ranked by real-world effectiveness
Join trials through their preferred platform or health app
Receive subsidies to offset participation costs
Report outcomes through any protocol-compliant interface
Access “Outcome Labels” showing what happened to similar patients

The Result: A self-sustaining research network where participating platforms collect outcome data using standardized formats, and the protocol aggregates this into continuously-updated treatment rankings available to all participants.

A map of how sick people might meet doctors, if everyone agreed to use the same computer system. They won’t.

Scenario and Assumptions

Why “Eventually Avoidable” Matters

A critical assumption in this analysis is that 92.6% (95% CI: 50%-98%) of disease deaths are “eventually avoidable” - meaning they could be prevented with sufficient biomedical research over time.

Why this assumption is conservative:

Historical trend: In 1900, life expectancy was ~47 years. Today it’s ~79. Most of that gain came from preventing deaths that were once considered inevitable (infectious disease, childhood mortality, cardiovascular disease).
Known mechanisms exist: For most major disease categories, we understand enough biology to know that interventions are theoretically possible. Cancer is caused by specific mutations. Heart disease has identifiable risk factors. The question is finding the right treatments, not whether treatments can exist.
Already-discovered treatments prove the space: 30% of approved drugs gain new indications, demonstrating that effective treatments exist but haven’t been found yet.

What if this assumption is wrong?

The health impact figures scale linearly with this assumption: at 50% avoidability (the low end of the confidence interval), lives saved and DALYs averted roughly halve and cost per DALY roughly doubles, still far below the bed nets benchmark. The R&D savings case (439 (90% CI: 321-600):1 ROI) does not depend on it at all.

Trial Funding Scenario

This analysis models a scenario with $21.8 billion/year allocated to pragmatic clinical trials. At $929 (95% CI: $97-$3,000)/patient, this funds approximately 23.4 million patient-years annually.

On the Funding Assumption

This analysis demonstrates what becomes possible when the funding constraint is removed. The $21.8 billion/year figure is achievable through multiple mechanisms:

Philanthropic mega-donors: A single Gates Foundation-scale commitment could fund the protocol build, though not the trial budget
Sovereign wealth funds: Norway’s $1.4T fund or similar could view this as humanity-scale infrastructure
WHO/multilateral coordination: Comparable to GAVI or the Global Fund
Military reallocation: Less than 1% of global military spending ($2.72 trillion/year)
Industry consortium: Pharma collectively spends $60 billion (95% CI: $50 billion-$75 billion)/year on trials; redirecting just over a third of it would cover this budget

Protocol Costs (ROM Estimates)

The protocol is open standards and APIs that existing clinical trial systems adopt, the way HTTP lets any browser reach any website and FHIR lets health records move between systems. Like HTTP and FHIR, it needs a steward: an open standards body, modeled on HL7, that publishes the specification, runs conformance testing, and administers the pooled liability insurance. The steward holds no patient data (records stay in the source EHRs and apps and are queried in place) and runs no trials. The technical specification¹³⁵ itemizes the build; the rough-order-of-magnitude totals:

Upfront protocol build (reference implementation, conformance suite, compliance and legal setup, ~2.5 years): $37.5M - $46 million
Annual protocol operations (hosting, maintenance, security audits, participant support): $11M - $26.5M/year. At 5 million participants that is $2.20 - $5.30 per participant per year; each additional participant costs pennies, since nearly all of the cost is fixed.
Integration adoption fund (one-time, to help EHR and DCT platforms connect): $20M - $50M

These figures cover the protocol, not the trials. Trial participation is funded separately (the $21.8 billion/year scenario above); the protocol coordinates information and does not move the money.

Who Participates

The protocol connects existing clinical trial infrastructure:

Participant	Current Investment	How They Integrate
DCT Platforms	$1B+ collectively in VC funding	Adopt outcome reporting standards
Major EHR Systems	Billions in infrastructure	Enable federated queries
Pharma Sponsors	$60 billion (95% CI: $50 billion-$75 billion)/year on trials	Submit trials via compliant systems
Academic Medical Centers	Research infrastructure	Contribute federated data nodes
Consumer Health Apps	Consumer health data	Report patient outcomes to protocol

DCT platforms and major EHR systems have already built the infrastructure for patient recruitment, data collection, and trial management. The protocol doesn’t replicate this work; it makes their existing investments more valuable by letting data flow across systems. Each has a reason to connect: DCT platforms and academic centers gain larger patient pools and trial funding, EHR vendors gain a market for research queries, and sponsors gain cheaper trials and faster enrollment.

Broader Initiative Costs (Scenario Estimates)

Beyond the core protocol, global integration, legal harmonization, and rollout costs depend on how much automation and vendor cooperation actually materialize:

Component	Best Case (Upfront / Annual)	Medium Case (Upfront / Annual)	Worst Case (Upfront / Annual)	Key Assumptions & Variables Driving Range
Global Data Integration	$2M / ~$0	$125M / $10M	$1.5B / $150M	Success of AI/automation, standards adoption, #systems, vendor cooperation.
Bounty & Prize Program	$1M (Prizes) / ~$0	$15M (Bounties) / $2M	$50M (Major Bounties) / $10M	Whether plugin developers show up on their own vs. need bounties to build critical tools.
Legal/Regulatory Harmonization	$1.5M / ~$0	$60M / $3M	$300M / $30M	Effectiveness of AI legal tools, political will, complexity of global law.
Global Rollout & Adoption	~$0 / ~$0	$12M / $3M	$125M / $30M	Need for training/support beyond protocol adoption, user interface complexity.
Governance Operations	~$0 / ~$0	~$1M / $0.3M	~$6M / $1M	Automation level, need for audits, grants, core support staff.
— TOTAL —	~$4.5M / ~$0	~$213M / ~$18.3M	~$1.98B+ / ~$221M+	Total initiative cost excluding core protocol build/ops.

The medium case puts the full initiative in the low hundreds of millions upfront and the low tens of millions per year. The worst case, dominated by integration and legal costs if automation and vendor cooperation fall short, reaches ~$2B upfront and ~$220M/year. The core protocol costs tens of millions; the broader initiative is where the large numbers live.

Benefit Analysis - Quantifying the Savings

This section quantifies the potential societal benefits of the protocol, focusing primarily on R&D cost savings and health outcome improvements.

Market Size and Impact

Annual global spending on clinical trials is approximately $60 billion (95% CI: $50 billion-$75 billion). Much of this spending could be made far more efficient through protocol standardization.

Current Average Costs: Estimates suggest $2.6 billion (95% CI: $1.5 billion-$4 billion) to bring a new drug from discovery through FDA approval, spread across ~10 years.
Per-Patient Phase III Costs: Often $41,000 (95% CI: $20,000-$120,000) (site fees, overhead, staff, monitoring, data management).

Decentralized Trial Costs Modeled on Pragmatic Trials

Oxford RECOVERY: Achieved ~$500 (95% CI: $400-$2,500). Key strategies included:
1. Embedding trial protocols within routine hospital care.
2. Minimizing overhead by using existing staff, resources, and electronic data capture.
3. Focused, pragmatic trial designs.
Systematic Review Evidence: A systematic review of 64 embedded pragmatic clinical trials found a median cost per patient of $97 (95% CI: $19-$478)⁹⁴. This confirms that low-cost execution is a replicable property of the pragmatic design, not an anomaly of any single trial.
ADAPTABLE Trial (PCORnet): The US-based ADAPTABLE trial¹³⁶ ($14 million (95% CI: $14 million-$20 million) / 15,076 patients = $929 (95% CI: $929-$1,400)/patient) provides a more representative benchmark for pragmatic trial costs in typical healthcare settings without emergency conditions.
Protocol Cost Projection: Our projections use $929 (95% CI: $97-$3,000)/patient based on ADAPTABLE. Confidence interval ($500-$3,000) captures range from RECOVERY-like efficiency to complex chronic disease trials.

Input: Pragmatic Trial Cost Distribution

Probability Distribution: Pragmatic Trial Cost per Patient

This chart shows the assumed probability distribution for this parameter. The shaded region represents the 95% confidence interval where we expect the true value to fall.

Extrapolation to New System:
- A well-integrated global protocol could achieve $929 (95% CI: $97-$3,000) in many cases, especially for pragmatic or observational designs.
- Up to ~44.1x (90% CI: 12.8x-210x) cost reduction is achievable by comparing pragmatic trial costs ($929 (95% CI: $97-$3,000)) against traditional costs of $41,000 (95% CI: $20,000-$120,000).
The cost reduction factor:

\[ \begin{gathered} k_{reduce} \\ = \frac{Cost_{P3,pt}}{Cost_{pragmatic,pt}} \\ = \frac{\$41K}{\$929} \\ = 44.1 \end{gathered} \]

The percentage reduction:

\[ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} \]

Scope of Cost Reduction

The reduction applies to Phase II/III efficacy trials, which are 69% (90% CI: 61%-77%) of global trial spending. Phase I first-in-human trials keep their current design and cost, and Phase IV is excluded from the savings to stay conservative. Within efficacy trials, per-patient cost varies with the endpoints: simple comparative studies approach RECOVERY’s $500 (95% CI: $400-$2,500) while trials needing imaging or biopsies run closer to $3,000. The $929 (95% CI: $97-$3,000) central estimate and its confidence interval already carry that spread, so the 97.7% (90% CI: 92%-100%) figure is not discounted a second time for trial complexity.

Gross R&D Savings from the Protocol

Parameter: Percentage reduction in addressable clinical trial costs due to the protocol.
Central Estimate: 97.7% (90% CI: 92%-100%) (44.1x (90% CI: 12.8x-210x))
Source/Rationale:
- Decentralized Clinical Trials (DCTs) demonstrate significant cost reductions¹³⁹ through reduced site management, travel, and streamlined data collection.

The annual gross R&D savings can be calculated as:

\[ S_{\text{annual}} = \alpha \cdot \phi \cdot R_d \]

Where:

$\alpha \in [0,1]$ is the cost reduction percentage (as decimal)
$\phi$ = 69% (90% CI: 61%-77%), the Phase II/III share of trial spending
$R_d$ = $60 billion (95% CI: $50 billion-$75 billion) global clinical trial spending

Base Case Calculation:

Using 97.7% (90% CI: 92%-100%) cost reduction (pragmatic trial costs of $929 (95% CI: $97-$3,000) vs traditional $41,000 (95% CI: $20,000-$120,000)) on the Phase II/III share of spending:

\[ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Pct_{P2+P3} \times Reduce_{pct} \\ = \$60B \times 69\% \times 97.7\% \\ = \$40.5B \end{gathered} \]
where:
\[ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} \]

Uncertainty Analysis - R&D Savings:

Monte Carlo Distribution: Annual R&D Savings from Pragmatic Trials (10,000 simulations)

Simulation Results Summary: Annual R&D Savings from Pragmatic Trials

Statistic	Value
Baseline (deterministic)	$40.5 billion
Mean (expected value)	$40.3 billion
Median (50th percentile)	$39.6 billion
Standard Deviation	$6.21 billion
90% Range (5th-95th percentile)	[$31.4 billion, $51.5 billion]

The histogram shows 1,000 of the 10,000 Monte Carlo draws for Annual R&D Savings from Pragmatic Trials; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.

Post-Safety Efficacy Lag Elimination

The smaller of the two timeline-shift components. Once a treatment has passed Phase I safety testing (2.3 years, unchanged), patients can take it through trial participation rather than waiting the 8.2 years (90% CI: 4.84 years-11.5 years) that Phase II/III currently add before approval, with efficacy measured from the trial data as it accumulates. Removing that wait contributes 8.2 years (90% CI: 4.84 years-11.5 years) of the 212 years (90% CI: 124 years-398 years) total shift (8.77 billion DALYs (90% CI: 4.88 billion DALYs-13.2 billion DALYs), 416 million deaths (90% CI: 244 million deaths-587 million deaths)); discovery acceleration contributes the other 204 years (90% CI: 116 years-390 years), over 10× more. Methodology for the lag estimate: invisible-graveyard.warondisease.org.

Safety and Risk Management

Common concern: Won’t faster trials with lower costs compromise safety?

Proposed System Safety Advantages

Preserved Phase I Safety Testing: Rigorous Phase I safety testing (~2.3 years) is maintained. What changes is eliminating the 8.2 years (90% CI: 4.84 years-11.5 years) efficacy delay after safety is verified.

The old way makes you wait 8 years to find out if your medicine works. The new way tells you immediately and keeps checking. Revolutionary.

Continuous Population-Scale Monitoring: Pragmatic trials with 10,000-100,000+ participants monitored continuously through EHR integration detect safety problems faster than small, time-limited traditional trials. The RECOVERY trial identified an effective treatment (dexamethasone) and an ineffective one (hydroxychloroquine) within about 100 days of launch, and went on to enroll 47,000 patients.
Universal Data Collection: The system automatically collects and publishes outcome data on all treatments, eliminating the publication bias that currently hides negative results.
Faster Adverse Event Detection: Automated EHR pharmacovigilance detects safety signals in months rather than the years required by voluntary reporting systems.
Immediate Mass Notification: When safety signals are detected, all patients taking the drug receive automated alerts through patient portals, enabling immediate clinical review.

Comparative Safety Surveillance

Safety Dimension	Traditional Trials	Pragmatic Trials + EHR Monitoring
Sample size	300-3,000 patients	10,000-100,000+ patients
Patient selection	86.1% excluded	All volunteers (real-world populations)
Monitoring duration	3-12 months (then stops)	Continuous via EHR (indefinite)
Publication rate	~50% unpublished	100% automatically published
Adverse event detection	Voluntary reporting (1-10% capture)	Automated EHR surveillance of every enrolled patient

Pooled Liability Insurance

The protocol includes pooled liability coverage for sponsors, reducing individual company risk while ensuring patient compensation for adverse events. This removes a major barrier to trial participation for smaller sponsors while maintaining accountability.

ROI Analysis

Monte Carlo Distributions

Monte Carlo Distribution: ROI from Pragmatic Trial R&D Savings Only (10,000 simulations)

Simulation Results Summary: ROI from Pragmatic Trial R&D Savings Only

Statistic	Value
Baseline (deterministic)	439:1
Mean (expected value)	445:1
Median (50th percentile)	436:1
Standard Deviation	85.7:1
90% Range (5th-95th percentile)	[321:1, 600:1]

The histogram shows 1,000 of the 10,000 Monte Carlo draws for ROI from Pragmatic Trial R&D Savings Only; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.

Monte Carlo Distribution: NPV Net Benefit (R&D Only) (10,000 simulations)

Simulation Results Summary: NPV Net Benefit (R&D Only)

Statistic	Value
Baseline (deterministic)	$268 billion
Mean (expected value)	$267 billion
Median (50th percentile)	$263 billion
Standard Deviation	$41.3 billion
90% Range (5th-95th percentile)	[$208 billion, $341 billion]

The histogram shows 1,000 of the 10,000 Monte Carlo draws for NPV Net Benefit (R&D Only); the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.

Monte Carlo Distribution: Pragmatic Trial Platform Total NPV Cost (10,000 simulations)

Simulation Results Summary: Pragmatic Trial Platform Total NPV Cost

Statistic	Value
Baseline (deterministic)	$611 million
Mean (expected value)	$609 million
Median (50th percentile)	$606 million
Standard Deviation	$69.3 million
90% Range (5th-95th percentile)	[$499 million, $729 million]

The histogram shows 1,000 of the 10,000 Monte Carlo draws for Pragmatic Trial Platform Total NPV Cost; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.

Probability of Exceeding Threshold: ROI from Pragmatic Trial R&D Savings Only

This exceedance probability chart shows the likelihood that ROI from Pragmatic Trial R&D Savings Only will exceed any given threshold. The higher the curve at a threshold, the more likely the value exceeds it.

Research Acceleration Mechanism

The 12.3x (90% CI: 4.92x-50.8x) research acceleration transforms our ability to explore a therapeutic space that is almost entirely untested.

The Unexplored Therapeutic Frontier

The fundamental problem isn’t that cures are hard to discover. It’s that we’re barely looking:

9.5 million combinations (90% CI: 6.68 million combinations-12.8 million combinations) plausible drug-disease pairings exist (9,500 compounds (95% CI: 7,000 compounds-12,000 compounds) safe × 1,000 diseases (95% CI: 800 diseases-1,200 diseases))
Only 0.342% (90% CI: 0%-1%) of these combinations have been tested - 99.7% (90% CI: 99%-100%) remains unexplored
Only 12% of the human interactome has ever been targeted by drugs
30% of approved drugs gain new indications, proving undiscovered uses exist

\[ \begin{gathered} Ratio_{explore} \\ = \frac{N_{tested}}{N_{combos}} \\ = \frac{32{,}500}{9.5M} \\ = 0.342\% \end{gathered} \]
where:
\[ \begin{gathered} N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} \]

Some of those treatments are probably sitting among compounds already proven safe. Nobody has looked. See The Discovery Capacity Model for how 12.3x (90% CI: 4.92x-50.8x) trial capacity produces the 212 years (90% CI: 124 years-398 years) timeline shift.

Addressing the Returns Question: Diminishing, Linear, or Compounding?

A common objection is that “more trials won’t produce proportionally more cures” - the diminishing returns hypothesis. This deserves serious consideration, but the evidence suggests the opposite may be true.

Why Diminishing Returns Is Unlikely (We Haven’t Started Looking)

The diminishing returns objection assumes we’ve exhausted low-hanging fruit. But we’ve barely begun:

Single compounds alone: 9.5 million combinations (90% CI: 6.68 million combinations-12.8 million combinations) possible combinations of known safe compounds × diseases. At current trial capacity, systematically testing these would take 2,879 years (90% CI: 1,976 years-4,041 years). We won’t finish until the year 5000+.

All the medicine we’ve discovered so far, next to all the medicine we haven’t bothered looking for yet. It’s mostly the second one.

\[ \begin{gathered} T_{explore,safe} \\ = \frac{N_{combos}}{Trials_{ann,curr}} \\ = \frac{9.5M}{3{,}300} \\ = 2{,}880 \end{gathered} \]
where:
\[ \begin{gathered} N_{combos} \\ = N_{safe} \times N_{diseases,trial} \\ = 9{,}500 \times 1{,}000 \\ = 9.5M \end{gathered} \]

Combination therapies expand the space: Modern medicine relies on multi-drug regimens (oncology, HIV, cardiology). Pairwise combinations of safe compounds create 45.1 billion combinations (90% CI: 25 billion combinations-72.6 billion combinations) possibilities, requiring 13.7 million years (90% CI: 7.45 million years-22.6 million years) at current pace - longer than Homo sapiens has existed.
Repurposing success proves cures exist: 30% of approved drugs gain new indications, demonstrating the unexplored space contains discoveries.
Most biology is untargeted: Only 12% of the human interactome has been targeted. We’re ignoring 88% of our own biology.
RECOVERY found treatments in months: The Oxford trial discovered multiple effective COVID treatments rapidly because it looked systematically.

Mathematical Framework: When Would Diminishing Returns Dominate?

We can formalize the competing models to identify when diminishing returns would actually matter.

Model 1: Linear (Baseline)

\[ T_{discovered} = k_0 \cdot N_{trials} \]

Where $k_0$ is the constant discovery rate (effective treatments per trial). This assumes the therapeutic space is sampled uniformly at random.

Model 2: Diminishing Returns (Pessimistic)

As we exhaust the therapeutic space, the hit rate decreases:

\[ k_{dim}(s) = k_0 \cdot (1 - s) \]

Where $s = S_{explored}/S_{total}$ is the fraction of therapeutic space already tested. At current exploration ($s < 0.01$), this gives $k_{dim} \approx 0.99 \cdot k_0$, virtually identical to linear.

Model 3: Learning/Compounding (Optimistic)

Each trial improves our biological models, increasing future hit rates:

\[ k_{learn}(n) = k_0 \cdot \left(1 + \alpha \cdot \ln(1 + n)\right) \]

Where $\alpha$ is the learning coefficient and $n$ is cumulative trials completed. Even modest learning ($\alpha = 0.1$) with 100,000 trials yields $k_{learn} \approx 2.15 \cdot k_0$.

Model 4: Combined (Realistic)

Both effects operate simultaneously:

\[ k_{combined}(s, n) = k_0 \cdot (1 - s) \cdot \left(1 + \alpha \cdot \ln(1 + n)\right) \]

The Crossover Point: When Does Depletion Dominate Learning?

Diminishing returns dominates when the depletion factor exceeds the learning factor. Solving for the critical exploration fraction:

\[ s_{crossover} = 1 - \frac{1}{1 + \alpha \cdot \ln(1 + n)} \]

Learning Coefficient ($\alpha$)	Trials Completed ($n$)	Crossover Exploration ($s_{crossover}$)
0.05 (weak)	100,000	37%
0.10 (modest)	100,000	53%
0.15 (strong)	100,000	63%

Interpretation: Even with weak learning effects, diminishing returns only dominates after exploring 37%+ of therapeutic space. With modest learning, the crossover occurs at 53%+ exploration.

Timeline to Crossover:

At current exploration of 0.342% (90% CI: 0%-1%) (<1%), reaching the 53% crossover would require ~1,500 years at current pace or ~125 years with the protocol. For combination therapies (45.1 billion combinations (90% CI: 25 billion combinations-72.6 billion combinations)), reaching 53% exploration would take millions of years.

Conclusion: For any plausible planning horizon, learning effects dominate. Diminishing returns is a theoretical concern for civilizations operating on multi-century timescales, not a practical constraint for the next 100+ years of medical research.

The Conservative Default: Linear Assumption

Given genuine uncertainty about whether returns are diminishing or compounding, our analysis assumes a linear relationship between trial capacity and treatment discoveries. This is the conservative choice because:

It’s the neutral prior: Without strong evidence for either diminishing or compounding returns, linearity is the least assumptive model
It may underestimate benefits: If platform technologies and learning effects produce compounding returns, our projections are conservative
It’s empirically defensible: The RECOVERY trial’s success (multiple treatments found with increased search) is consistent with linear or better returns

Bottom line: Even under the conservative linear assumption, 12.3x (90% CI: 4.92x-50.8x) more trials produces 12.3x (90% CI: 4.92x-50.8x) more discoveries from a space that is 99%+ unexplored.

Three guesses about whether cures get harder to find over time, easier to find, or stay the same. We picked the boring one.

Funding Level vs. Cost-Effectiveness

While the analysis above addresses whether trials produce proportionally more cures, a separate question is how funding level affects cost per DALY averted. The acceleration formula $T_{accel} = T_{baseline} \times (1 - 1/k)$, where $k$ is the trial capacity multiplier, produces natural diminishing returns: each additional dollar buys less acceleration as $k$ grows. Figure 110.1 shows cost per DALY rising with funding, while Figure 110.2 shows total DALYs approaching an asymptotic ceiling.

Figure 110.1: Cost per DALY averted as a function of annual funding level. Uses undiscounted total cost (upfront platform build + annual funding over queue clearance period) because annual government appropriations are not a discountable capital allocation. The curve is monotonically decreasing: more funding always improves efficiency, but with strongly diminishing returns.


Verification at proposed funding ($21.8B/yr):
  Trial capacity multiplier: 12.3x
  Queue clearance: 36.0 years
  Treatment acceleration: 203.7 years
  Total timeline shift: 211.9 years
  DALYs averted: 565.2B
  Upfront cost: $270M
  Total undiscounted cost: $784.2B
  Cost per DALY: $1.39
  Asymptotic floor: $1.28/DALY (total queue cost: $783B)

Figure 110.2: Total DALYs averted as a function of annual funding level. The curve flattens as funding approaches the asymptotic ceiling where all avoidable diseases receive accelerated treatment. Efficacy lag elimination (8.2 years) provides a fixed baseline benefit independent of funding scale.


Verification at proposed funding ($21.8B/yr):
  Trial capacity multiplier: 12.3x
  Queue clearance: 36.0 years
  Treatment acceleration: 203.7 years
  Total timeline shift: 211.9 years
  DALYs averted: 565B
  Ceiling DALYs: 613B
  Utilization: 92.2% of ceiling
  Efficacy lag baseline: 22B DALYs

The proposed funding level ($21.8 billion/year) sits in the steep part of the curve, where cost-effectiveness is strongest. Even at much higher funding levels, the cost per DALY remains far below the bed nets benchmark ($89 (95% CI: $78-$100)/DALY).

Data Sources and Methodological Notes

Cost of Current Drug Development:
- Tufts Center for the Study of Drug Development often cited for $1.0 - $2.6 billion/drug.
- Journal articles and industry reports (IQVIA, Deloitte) also highlight $2+ billion figures.
ROI Calculation Method:
- Simplified approach comparing aggregated R&D spending to potential savings.
- Does not account for intangible factors (opportunity costs, IP complexities, time-value of money) beyond a basic Net Present Value (NPV) perspective.
Scale & Adoption Rates:
- The largest uncertainties revolve around uptake speed, regulatory harmonization, and participant willingness.
- Projections assume widespread adoption by major pharmaceutical companies and global health authorities.

Conclusion

The protocol reduces clinical trial costs by a factor of 44.1x (90% CI: 12.8x-210x), brings treatments to patients sooner, and extends trials to diseases no sponsor would otherwise fund. The 10-year NPV total cost is $611 million (90% CI: $499 million-$729 million) (upfront plus discounted annual operations), generating $268 billion (90% CI: $208 billion-$341 billion) in net R&D savings. Given that the pharmaceutical industry collectively spends $60 billion (95% CI: $50 billion-$75 billion) annually on clinical trials, 69% (90% CI: 61%-77%) of it on Phase II/III efficacy testing, a 97.7% (90% CI: 92%-100%) reduction on that share yields an ROI of 439 (90% CI: 321-600):1 at scale.

Beyond direct savings, the effects on medical progress are substantial: expanded therapeutic exploration, real-time treatment effectiveness rankings, and research on off-patent treatments that currently lack commercial incentives. With appropriate privacy protections and international coordination, the protocol enables evidence-based personalized medicine at global scale.

Disclaimer

All figures in this document are estimates based on publicly available information, industry benchmarks, and simplifying assumptions. Real-world costs, savings, and ROI will vary greatly depending on the scope of implementation, the speed of adoption, regulatory cooperation, and numerous other factors. Nonetheless, this high-level exercise illustrates the substantial potential gains from a global, continuously learning clinical trial system.

Verification: Complete Derivation Chains

For economist verification, this section provides complete derivation chains for all headline figures. Each metric traces back to primary data sources.

Trial Capacity Multiplier Derivation

Result: 12.3x (90% CI: 4.92x-50.8x)

Step 1: Current trial capacity

Current trial participants: 1.9 million
Current first treatments: 15 diseases/year (95% CI: 8 diseases/year-30 diseases/year)

Step 2: Capacity with $21.8 billion/year

Cost per patient: $929 (95% CI: $97-$3,000)
Fundable patients: 23.4 million

Step 3: Calculate multiplier

\[ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} \]
where:
\[ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} \]
where:
\[ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} \]
where:
\[ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} \]

Timeline Shift Derivation

Result: 212 years (90% CI: 124 years-398 years)

Components:

Component	Value	Source
Efficacy Lag Elimination	8.2 years (90% CI: 4.84 years-11.5 years)	FDA drug approval timeline data
Discovery Acceleration	204 years (90% CI: 116 years-390 years)	Capacity vs. backlog model
Combined Total	212 years (90% CI: 124 years-398 years)	Sum of components

\[ T_{accel,max} = T_{accel} + T_{lag} = 204 + 8.2 = 212 \]
where:
\[ \begin{gathered} T_{accel} \\ = T_{first,SQ} \times \left(1 - \frac{1}{k_{capacity}}\right) \\ = 222 \times \left(1 - \frac{1}{12.3}\right) \\ = 204 \end{gathered} \]
where:
\[ \begin{gathered} T_{first,SQ} \\ = T_{queue,SQ} \times 0.5 \\ = 443 \times 0.5 \\ = 222 \end{gathered} \]
where:
\[ \begin{gathered} T_{queue,SQ} \\ = \frac{N_{untreated}}{Treatments_{new,ann}} \\ = \frac{6{,}650}{15} \\ = 443 \end{gathered} \]
where:
\[ \begin{gathered} N_{untreated} \\ = N_{rare} \times 0.95 \\ = 7{,}000 \times 0.95 \\ = 6{,}650 \end{gathered} \]
where:
\[ \begin{gathered} k_{capacity} \\ = \frac{N_{fundable,ref}}{Slots_{curr}} \\ = \frac{23.4M}{1.9M} \\ = 12.3 \end{gathered} \]
where:
\[ \begin{gathered} N_{fundable,ref} \\ = \frac{Subsidies_{trial,ref}}{Cost_{pragmatic,pt}} \\ = \frac{\$21.8B}{\$929} \\ = 23.4M \end{gathered} \]
where:
\[ \begin{gathered} Subsidies_{trial,ref} \\ = Funding_{trial,ref} - OPEX_{trial} \\ = \$21.8B - \$40M \\ = \$21.8B \end{gathered} \]
where:
\[ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} \]

Lives Saved Derivation

Result: 10.7 billion deaths (90% CI: 6.24 billion deaths-20.3 billion deaths)

Step 1: Daily mortality from eventually avoidable causes

Global disease deaths: 150,000/day¹⁴
Eventually avoidable percentage: 92.6% (95% CI: 50%-98%)

Step 2: Timeline shift period

Total shift: 212 years (90% CI: 124 years-398 years)

Step 3: Calculate lives saved

Cost per DALY Derivation

Result: $0.842 (90% CI: $0.264-$1.49)

Step 1: Total cost (NPV of $21.8 billion/year trial funding over the 36 years (90% CI: 8.15 years-106 years) clearance period, discounted at 3%)

The protocol infrastructure itself ($611 million (90% CI: $499 million-$729 million) 10-year NPV) is small next to the trial funding, which is what actually buys the DALYs, so the trial funding is the numerator.

Step 2: DALYs averted

Total DALYs: 565 billion

Step 3: Calculate cost per DALY

Comparison: Malaria bed nets cost $89 (95% CI: $78-$100)/DALY. The protocol operates at vastly greater scale while achieving competitive cost-effectiveness.

ROI Derivation

Result: 439 (90% CI: 321-600):1

Step 1: Calculate benefits

Annual R&D savings: $40.5 billion (90% CI: $31.4 billion-$51.5 billion)
10-year NPV of savings: $269 billion (90% CI: $208 billion-$342 billion)

Step 2: Calculate costs

10-year NPV total cost: $611 million (90% CI: $499 million-$729 million)

Step 3: Calculate ROI

\[ \begin{gathered} ROI_{RD} \\ = \frac{NPV_{RD}}{Cost_{platform,total}} \\ = \frac{\$269B}{\$611M} \\ = 439 \end{gathered} \]
where:
\[ \begin{gathered} NPV_{RD} \\ = \sum_{t=1}^{10} \frac{Savings_{RD,ann} \cdot \frac{\min(t,5)}{5}}{(1+r)^t} \end{gathered} \]
where:
\[ \begin{gathered} Savings_{RD,ann} \\ = Benefit_{RD,ann} - OPEX_{trial} \\ = \$40.5B - \$40M \\ = \$40.4B \end{gathered} \]
where:
\[ \begin{gathered} Benefit_{RD,ann} \\ = Spending_{trials} \times Pct_{P2+P3} \times Reduce_{pct} \\ = \$60B \times 69\% \times 97.7\% \\ = \$40.5B \end{gathered} \]
where:
\[ \begin{gathered} Reduce_{pct} \\ = 1 - \frac{Cost_{pragmatic,pt}}{Cost_{P3,pt}} \\ = 1 - \frac{\$929}{\$41K} \\ = 97.7\% \end{gathered} \]
where:
\[ \begin{gathered} OPEX_{trial} \\ = Cost_{platform} + Cost_{staff} + Cost_{infra} \\ + Cost_{regulatory} + Cost_{community} \\ = \$15M + \$10M + \$8M + \$5M + \$2M \\ = \$40M \end{gathered} \]
where:
\[ \begin{gathered} Cost_{platform,total} \\ = PV_{OPEX} + Cost_{upfront,total} \\ = \$342M + \$270M \\ = \$611M \end{gathered} \]
where:
\[ \begin{gathered} PV_{OPEX} \\ = \frac{T_{horizon}}{OPEX_{total} \times r_{discount}} \\ = \frac{10}{\$40M \times 3\%} \\ = \$342M \end{gathered} \]
where:
\[ \begin{gathered} OPEX_{total} \\ = OPEX_{ann} + OPEX_{DIH,ann} \\ = \$18.9M + \$21.1M \\ = \$40M \end{gathered} \]
where:
\[ \begin{gathered} Cost_{upfront,total} \\ = Cost_{upfront} + Cost_{DIH,init} \\ = \$40M + \$230M \\ = \$270M \end{gathered} \]

Verification Summary

Metric	Value	Primary Inputs	Data Sources
Trial Capacity	12.3x (90% CI: 4.92x-50.8x)	Funding, trial costs	ADAPTABLE trial, ClinicalTrials.gov
Timeline Shift	212 years (90% CI: 124 years-398 years)	Efficacy lag, backlog model	FDA approval data, disease registry
Lives Saved	10.7 billion	Mortality rates, timeline	WHO GBD, mortality statistics
Cost/DALY	$0.842 (90% CI: $0.264-$1.49)	Trial funding NPV, DALYs	Funding scenario, WHO GBD
ROI	439 (90% CI: 321-600):1	Costs, savings	NPV analysis with 5-year ramp

All parameters, confidence intervals, and Monte Carlo distributions are documented in Parameters and Calculations.

Key Analytical Assumptions

This analysis rests on several core assumptions that should be made explicit for academic transparency:

Linear Scaling Assumption

Assumption: Each additional dollar of trial funding produces proportional additional discoveries.

Justification: This is actually conservative - network effects in data aggregation and platform economics often produce increasing returns. We assume linear to avoid overstating benefits.

Sensitivity: If returns are sublinear (diminishing), health impact estimates would be reduced. However, as documented in Addressing the Returns Question, diminishing returns are unlikely when <1% of therapeutic space has been explored.

Adoption Rate Assumptions

Assumption: Protocol adoption follows a 5-year ramp (20%, 40%, 60%, 80%, 100%) before reaching full capacity.

Justification: Based on historical technology adoption curves in healthcare (EHR adoption, telemedicine during COVID). The ramp is built into NPV calculations.

Sensitivity: Slower adoption delays benefits but doesn’t change eventual steady-state impact. NPV is reduced with slower adoption due to discounting.

Cost Reduction Assumptions

Assumption: Pragmatic trials cost $929 (95% CI: $97-$3,000)/patient versus $41,000 (95% CI: $20,000-$120,000)/patient for traditional trials.

Justification: Based on ADAPTABLE trial ($929 (95% CI: $929-$1,400)/patient) and systematic review of 64 pragmatic trials (median $97 (95% CI: $19-$478)/patient). RECOVERY achieved $500 (95% CI: $400-$2,500)/patient under exceptional NHS/COVID conditions.

Sensitivity: The tornado diagrams show ROI remains strongly positive even at 30% cost reduction (vs. baseline 97.7% (90% CI: 92%-100%)).

Eventually Avoidable Mortality Assumption

Assumption: 92.6% (95% CI: 50%-98%) of disease deaths are eventually avoidable with sufficient biomedical research.

Justification: Historical trend shows ~70% reduction in age-adjusted mortality since 1900. Most major disease categories have known biological mechanisms amenable to intervention. See Why “Eventually Avoidable” Matters.

Sensitivity: Health impact scales linearly with this assumption. At 50% avoidability (the low end of the confidence interval), health benefits fall by roughly half. R&D savings are unaffected.

Counterfactual Baseline Specification

This cost-effectiveness analysis uses the status quo as the baseline counterfactual: current clinical trial infrastructure continues operating at current efficiency ($41,000 (95% CI: $20,000-$120,000)/patient) and capacity (1.9 million participants/year). Under this baseline, the $21.8 billion/year allocated to pragmatic trials would not exist.

Why status quo is the appropriate baseline:

No comparable interventions exist at scale: PCORnet and RECOVERY demonstrate the per-patient cost; no competing proposal funds pragmatic trials at this volume
Historical trend supports it: Trial costs have increased, not decreased, over the past 50 years (105x (90% CI: 72.8x-149x) since 1962)
Incremental improvements are marginal: Ongoing digitization efforts (DCT platforms, EHR integration) produce 10-20% efficiency gains, not the 97.7% (90% CI: 92%-100%) reduction from pragmatic trial design

Alternative counterfactual scenarios:

Organic efficiency improvement: Clinical trial costs decrease 2-3% annually through technology adoption. Under this scenario, the marginal impact of the protocol is reduced by the amount of improvement that would occur anyway. At 3%/year organic improvement over 10 years, approximately 26% of the cost reduction would occur regardless, reducing the protocol’s attributable benefit to ~74% of projections.
Alternative government priorities: Funds are allocated to other health investments (NIH grants, hospital infrastructure, insurance subsidies). Each alternative use would require separate cost-benefit analysis. However, none of these alternatives address the core trial cost problem; they operate within the existing high-cost system.
Return to taxpayers: Funds are returned via tax cuts, enabling private consumption and investment. Under this scenario, the opportunity cost equals the weighted average return on private capital (approximately 3% annually). The protocol ROI of 439 (90% CI: 321-600):1 substantially exceeds this threshold.

Methodological note: The analysis uses the neutral status quo baseline to avoid biasing results in either direction. Sensitivity analysis (tornado diagrams) demonstrates robustness across baseline assumptions.

Methodology Validation Against Accepted Benchmarks

This analysis uses standard health economics methodology identical to that employed by EPA, DOT, GiveWell, NICE, WHO-CHOICE, and CBO:

Our Method	Equivalent Standard	Institution Using It
Value of Statistical Life ($10 million (95% CI: $5 million-$15 million))	VSL for regulatory impact	EPA, DOT, FDA
Cost per DALY ($0.842 (90% CI: $0.264-$1.49)/DALY)	ICER thresholds	GiveWell, NICE, WHO-CHOICE
Monte Carlo uncertainty propagation	Probabilistic sensitivity analysis	ICER, Cochrane, HTA agencies
NPV with discount rate	Standard cost-benefit analysis	CBO, OMB Circular A-94
Long-horizon cumulative impact	Social cost of carbon	EPA, IPCC, Stern Review

Our 90% confidence intervals on the headline figures span a factor of three or more.

Appendix Calculation Frameworks and Detailed Analysis

Calculation Framework - NPV Methodology

Uses 10-year NPV horizon (standard business practice). See Verification: Complete Derivation Chains for full methodology.

Health Impact Uncertainty Analysis

These Monte Carlo distributions show the range of health impact estimates across 10,000 simulations, accounting for uncertainty in timeline shift, mortality rates, and avoidable percentages:

Lives Saved Distribution:

Monte Carlo Distribution: Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (10,000 simulations)

Simulation Results Summary: Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Statistic	Value
Baseline (deterministic)	10.7 billion
Mean (expected value)	12.1 billion
Median (50th percentile)	11.5 billion
Standard Deviation	4.28 billion
90% Range (5th-95th percentile)	[6.24 billion, 20.3 billion]

The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Lives Saved from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.

Economic Value Distribution:

Monte Carlo Distribution: Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput (10,000 simulations)

Simulation Results Summary: Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Statistic	Value
Baseline (deterministic)	$84.8 quadrillion
Mean (expected value)	$95 quadrillion
Median (50th percentile)	$88 quadrillion
Standard Deviation	$40.2 quadrillion
90% Range (5th-95th percentile)	[$42.9 quadrillion, $172 quadrillion]

The histogram shows 1,000 of the 10,000 Monte Carlo draws for Total Economic Benefit from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput; the summary statistics use all 10,000. The exceedance curve (right) shows the probability of the outcome exceeding any given value.

Cost-Utility Framework

We present a cost-utility analysis using the quality-adjusted life years (QALYs) and disability-adjusted life years (DALYs) metrics. This approach is the US and global standard for evaluating the value of health interventions¹⁴¹.

QALY: One year of life in perfect health. Gains are calculated as:

\[ \text{QALYs Gained} = (Q_1 \times T_1) - (Q_0 \times T_0) \]

Where $Q_0$/$Q_1$ = quality of life (0-1) before/after, $T_0$/$T_1$ = years of life before/after.
Cost-Effectiveness: The protocol achieves cost-effectiveness through dual pathways:
1. R&D Savings: $40.5 billion (90% CI: $31.4 billion-$51.5 billion)+ annual savings from 97.7% (90% CI: 92%-100%) trial cost reduction
2. Health Gains: 565 billion DALYs (90% CI: 309 billion DALYs-1.08 trillion DALYs) averted from the full timeline shift (~212 years (90% CI: 124 years-398 years) from 12.3x (90% CI: 4.92x-50.8x) trial capacity + efficacy lag elimination)
This combination creates a dominant intervention: simultaneously saves money and improves health outcomes.
US Willingness-to-Pay Threshold: Typically $100,000–$150,000 per QALY for interventions that add costs. Dominant interventions that both save money and improve health are favorable regardless of this threshold.
Sources for Context:
- QALY methodology and standards¹⁴¹: “The quality-adjusted life year (QALY) is the academic standard for measuring how well all different kinds of medical treatments lengthen and/or improve patients’ lives…”
- Health economic evaluation¹⁰⁶: Standard health economic analysis considers cost-effectiveness across intervention types.

QALY Benefit Streams Breakdown

Three mechanisms contribute to the 212 years (90% CI: 124 years-398 years) timeline shift behind the 565 billion DALYs (90% CI: 309 billion DALYs-1.08 trillion DALYs) figure, with different evidence strength. The model does not apportion DALYs among them; the derivation above treats them together.

A. Faster development of pipeline drugs. Higher trial capacity shortens development time for treatments already in the pipeline. Confidence: high; each month of delay in cancer treatment raises mortality risk by roughly 10%^142,143.
B. Better prevention from population-scale outcome data. Comprehensive outcome data identifies at-risk populations and measures real-world effectiveness. Confidence: medium; the value of prevention is established, the gain from more data is not.
C. Trials for diseases no sponsor will fund. 7,000+ rare diseases, 95% without an approved treatment, become feasible to study at $929 (95% CI: $97-$3,000) per patient instead of $41,000 (95% CI: $20,000-$120,000). Confidence: lower; sound in theory, unproven at scale.

The R&D savings case (439 (90% CI: 321-600):1 ROI) stands on its own if all three health mechanisms are set to zero.

DALY Sensitivity Analysis

The following sensitivity analyses show how cost-effectiveness varies based on uncertainty in input parameters. These use Monte Carlo simulation with uncertainty propagation from parameter distributions.

Key DALY Outcomes:

Sensitivity Indices for Total DALYs from Elimination of Efficacy Lag Plus Earlier Treatment Discovery from Higher Trial Throughput

Regression-based sensitivity showing which inputs explain the most variance in the output.

Input Parameter	Sensitivity Coefficient	Interpretation
Average Total Treatment Timeline Shift (years)	0.9320	Strong driver
Eventually Avoidable DALY Percentage (percentage)	0.3151	Moderate driver
Global Annual DALY Burden (DALYs/year)	0.1348	Weak driver

Interpretation: Standardized coefficients show the change in output (in SD units) per 1 SD change in input. Values near ±1 indicate strong influence; values exceeding ±1 may occur with correlated inputs.

Comparative Cost-Effectiveness vs Other Interventions

For context, the table compares the protocol’s cost-effectiveness against other well-understood public health programs, measured as Quality-Adjusted Life Years (QALYs) Gained per $1 Million of Spending. A higher number signifies greater cost-effectiveness. The comparison is not quite like for like: smallpox eradication and childhood vaccination each targeted one disease, while this funds the discovery process itself, the difference between fixing one pothole and repaving the road.

For standard interventions, this value is calculated as $1,000,000 / ICER, where the ICER (Incremental Cost-Effectiveness Ratio) is the cost to gain one QALY. For dominant interventions that are both more effective and less expensive, the ICER is negative, and this metric isn’t strictly applicable. For these cases, an illustrative range is used to represent their high value.

Intervention	QALYs Gained per $1M Spending¹	Typical ICER Range (Cost per QALY Gained)	Classification	Source / Evidence
This protocol	Dominant	Cost-Saving + Health Gain	Dominant	This analysis’s Sensitivity Analysis. Based on $18.9 million (95% CI: $11 million-$26.5 million)-$40 million (90% CI: $31.1 million-$49.6 million) annual costs generating 565 billion DALYs (90% CI: 309 billion DALYs-1.08 trillion DALYs) from ~212 years (90% CI: 124 years-398 years) timeline shift.
Smallpox Eradication	100,000+³	Dominant (Cost-Saving)	Dominant	The $300M program (1967-1980) prevents 5M annual deaths. Benefit-cost ratio exceeds 100:1. Standard ICER calculation is impractical due to its uncommon scale. (WHO, 2010¹⁴⁴)
Childhood Vaccinations	22+³	Often Dominant to ~$100,000	Dominant / Highly Cost-Effective	CDC estimates routine childhood vaccinations prevent 32M hospitalizations and 1.1M deaths among 1994-2023 US birth cohorts, with $2.9T in societal cost savings. (CDC, 2023¹⁴⁵)
Clean Water Programs	100	~$1,000 - $10,000	Highly Cost-Effective	WHO estimates household water treatment costs $100-$500/DALY averted. Community water supply improvements cost $200/DALY. (WHO, 2004¹⁴⁶)
Hypertension Screening	30 - 50	~$20,000 - $33,000	Highly Cost-Effective	Recent US studies show pharmacist-led hypertension management has ICERs in the $20,000-$33,000 range per QALY gained, falling within standard willingness-to-pay thresholds. (JAMA Netw Open, 2023¹⁴⁷)
Generic Drug Substitution	+³	Dominant (Cost-Saving)	Dominant	By definition cost-saving when therapeutic equivalence is maintained, with typical savings of 30-80% versus brand-name drugs. (WHO, 2015¹⁴⁸)
Statins / Polypill	67+³	Cost-Saving to ~$15,000	Dominant / Highly Cost-Effective	Cost-saving in high-risk populations. ICERs range from dominant to $15k/QALY in lower-risk groups. (eClinicalMedicine, 2022¹⁴⁹)
Pragmatic Trials (RECOVERY model)	~250,000	$4 (90% CI: $1.91-$9.8)/QALY	Highly Cost-Effective	UK RECOVERY trial: $20 million (95% CI: $15 million-$25 million) spent, saving 1 million lives (95% CI: 500 thousand lives-2 million lives) globally via dexamethasone discovery. 44.1x (90% CI: 12.8x-210x) cheaper per patient than traditional Phase 3 trials. (Note: RECOVERY’s $500 (95% CI: $400-$2,500)/patient benefited from NHS infrastructure; ADAPTABLE achieved $929 (95% CI: $929-$1,400)/patient in US settings.)
NIH Standard Research Portfolio	~20	$50,000 (95% CI: $20,000-$100,000)/QALY	Inefficient Baseline	Standard NIH-funded research. Represents current status quo efficiency.⁸⁴

Methodology Notes

¹ QALYs per $1M Calculation:

For the protocol: (Annual QALYs Gained) / (Annual Cost in Millions)
Ranges reflect conservative to optimistic scenarios accounting for parameter uncertainties

³ Dominant Interventions:

For cost-saving (dominant) interventions, standard QALY/$1M calculations are not applicable
Values shown are illustrative to demonstrate relative cost-effectiveness
Upper bounds represent the exceptional value of these interventions

Data Limitations

Historical interventions (e.g., smallpox) use retrospective analyses
Direct comparisons between interventions should consider contextual differences
All costs are in 2023 USD, adjusted using appropriate health inflation indices
QALY calculations use standard health state utility weights where available

Comparison to Other Major Public Investments

To provide context for the estimated costs of a global pragmatic trial system, it is useful to compare them to other significant U.S. government investments in health and technology. The projected ‘Lean Ecosystem’ cost for the protocol of approximately $40 million (90% CI: $31.1 million-$49.6 million) per year (core protocol operations plus the medium-case broader initiative) is modest in comparison to other major federal projects.

Initiative / Project	Approximate Cost / Budget (Annualized)	Comparison to Protocol’s Annual Cost	Source / Note
This protocol (Lean Ecosystem)	~$40 million (90% CI: $31.1 million-$49.6 million) / year	1x (Baseline)	This analysis
Cancer Moonshot Initiative	~$257 Million / year¹⁵⁰ ($1.8B over 7 years)	~6.4x	21st Century Cures Act¹⁵¹
NIH “All of Us” Research Program	~$500M / year (FY23 Approx. Budget)	~12.5x	NIH Budget⁵⁹
HealthCare.gov (Initial Build)	~$1.7 - $2.1 Billion¹⁵² (Total Upfront Cost)	~42x - 52x (of one year’s cost)	GAO Reports / Public Reporting¹⁵²
National Cancer Institute (NCI)	~$7.2 Billion / year¹⁵³ (FY25 Budget)	~180x	NCI Budget Data¹⁵³

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The NIH Pragmatic Trials Collaboratory funds trials at $500K for planning phase, $1M/year for implementation-a tiny fraction of NIH’s budget. The ADAPTABLE trial cost $14 million for 15,076 patients (= $929/patient) versus $420 million for a similar traditional RCT (30x cheaper), yet pragmatic trials remain severely underfunded. PCORnet infrastructure enables real-world trials embedded in healthcare systems, but receives minimal support compared to basic research funding. Additional sources: https://commonfund.nih.gov/hcscollaboratory | https://pcornet.org/wp-content/uploads/2025/08/ADAPTABLE_Lay_Summary_21JUL2025.pdf | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604499/

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37.

Anthis, J. R. US factory farming estimates. (2024).

38.

Rosado, P. More than 80 billion land animals are slaughtered for meat every year. (2024).

39.

University of Oxford, LEAP. Reducing food’s environmental impacts. (2018).

40.

FDA. FDA-approved prescription drug products (20,000+). FDA https://www.fda.gov/media/143704/download

There are over 20,000 prescription drug products approved for marketing. Additional sources: https://www.fda.gov/media/143704/download

41.

FDA. FDA GRAS list count ( 570-700). FDA https://www.fda.gov/food/generally-recognized-safe-gras/gras-notice-inventory

The FDA GRAS (Generally Recognized as Safe) list contains approximately 570–700 substances. Additional sources: https://www.fda.gov/food/generally-recognized-safe-gras/gras-notice-inventory

42.

Ritchie, H., Rosado, P. & Roser, M. Environmental impacts of food production. (2022).

43.

Food and Land Use Coalition. Growing Better: Ten Critical Transitions to Transform Food and Land Use. https://www.foodandlandusecoalition.org/wp-content/uploads/2019/09/FOLU-GrowingBetter-GlobalReport-SummaryReport.pdf (2019).

44.

Gallup. (2013).

45.

Institute for Health Metrics and Evaluation (IHME). IHME global burden of disease 2021 (2.88B DALYs, 1.13B YLD). Institute for Health Metrics and Evaluation (IHME) https://vizhub.healthdata.org/gbd-results/ (2024)

In 2021, global DALYs totaled approximately 2.88 billion, comprising 1.75 billion Years of Life Lost (YLL) and 1.13 billion Years Lived with Disability (YLD). This represents a 13% increase from 2019 (2.55B DALYs), largely attributable to COVID-19 deaths and aging populations. YLD accounts for approximately 39% of total DALYs, reflecting the substantial burden of non-fatal chronic conditions. Additional sources: https://vizhub.healthdata.org/gbd-results/ | https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00757-8/fulltext | https://www.healthdata.org/research-analysis/about-gbd

46.

Costs of War Project, Brown University Watson Institute. Environmental cost of war ($100B annually). Brown Watson Costs of War: Environmental Cost https://watson.brown.edu/costsofwar/costs/social/environment

War on Terror emissions: 1.2B metric tons GHG (equivalent to 257M cars/year) Military: 5.5% of global GHG emissions (2X aviation + shipping combined) US DoD: World’s single largest institutional oil consumer, 47th largest emitter if nation Cleanup costs: $500B+ for military contaminated sites Gaza war environmental damage: $56.4B; landmine clearance: $34.6B expected Climate finance gap: Rich nations spend 30X more on military than climate finance Note: Military activities cause massive environmental damage through GHG emissions, toxic contamination, and long-term cleanup costs far exceeding current climate finance commitments Additional sources: https://watson.brown.edu/costsofwar/costs/social/environment | https://earth.org/environmental-costs-of-wars/ | https://transformdefence.org/transformdefence/stats/

47.

SIPRI. 36:1 disparity ratio of spending on weapons over cures. SIPRI: Military Spending https://www.sipri.org/commentary/blog/2016/opportunity-cost-world-military-spending (2016)

Global military spending: $2.7 trillion (2024, SIPRI) Global government medical research: $68 billion (2024) Actual ratio: 39.7:1 in favor of weapons over medical research Military R&D alone: $85B (2004 data, 10% of global R&D) Military spending increases crowd out health: 1% ↑ military = 0.62% ↓ health spending Note: Ratio actually worse than 36:1. Each 1% increase in military spending reduces health spending by 0.62%, with effect more intense in poorer countries (0.962% reduction) Additional sources: https://www.sipri.org/commentary/blog/2016/opportunity-cost-world-military-spending | https://pmc.ncbi.nlm.nih.gov/articles/PMC9174441/ | https://www.congress.gov/crs-product/R45403

48.

Think by Numbers. Lost human capital due to war ($270B annually). Think by Numbers https://thinkbynumbers.org/military/war/the-economic-case-for-peace-a-comprehensive-financial-analysis/ (2021)

Lost human capital from war: $300B annually (economic impact of losing skilled/productive individuals to conflict) Broader conflict/violence cost: $14T/year globally 1.4M violent deaths/year; conflict holds back economic development, causes instability, widens inequality, erodes human capital 2002: 48.4M DALYs lost from 1.6M violence deaths = $151B economic value (2000 USD) Economic toll includes: commodity prices, inflation, supply chain disruption, declining output, lost human capital Additional sources: https://thinkbynumbers.org/military/war/the-economic-case-for-peace-a-comprehensive-financial-analysis/ | https://www.weforum.org/stories/2021/02/war-violence-costs-each-human-5-a-day/ | https://pubmed.ncbi.nlm.nih.gov/19115548/

49.

PubMed. Psychological impact of war cost ($100B annually). PubMed: Economic Burden of PTSD https://pubmed.ncbi.nlm.nih.gov/35485933/

PTSD economic burden (2018 U.S.): $232.2B total ($189.5B civilian, $42.7B military) Civilian costs driven by: Direct healthcare ($66B), unemployment ($42.7B) Military costs driven by: Disability ($17.8B), direct healthcare ($10.1B) Exceeds costs of other mental health conditions (anxiety, depression) War-exposed populations: 2-3X higher rates of anxiety, depression, PTSD; women and children most vulnerable Note: Actual burden $232B, significantly higher than "$100B" claimed Additional sources: https://pubmed.ncbi.nlm.nih.gov/35485933/ | https://news.va.gov/103611/study-national-economic-burden-of-ptsd-staggering/ | https://pmc.ncbi.nlm.nih.gov/articles/PMC9957523/

50.

CGDev. UNHCR average refugee support cost. CGDev https://www.cgdev.org/blog/costs-hosting-refugees-oecd-countries-and-why-uk-outlier (2024)

The average cost of supporting a refugee is $1,384 per year. This represents total host country costs (housing, healthcare, education, security). OECD countries average $6,100 per refugee (mean 2022-2023), with developing countries spending $700-1,000. Global weighted average of $1,384 is reasonable given that 75-85% of refugees are in low/middle-income countries. Additional sources: https://www.cgdev.org/blog/costs-hosting-refugees-oecd-countries-and-why-uk-outlier | https://www.unhcr.org/sites/default/files/2024-11/UNHCR-WB-global-cost-of-refugee-inclusion-in-host-country-health-systems.pdf

51.

VA. Veteran healthcare cost projections. VA https://department.va.gov/wp-content/uploads/2025/06/2026-Budget-in-Brief.pdf (2026)

VA budget: $441.3B requested for FY 2026 (10% increase). Disability compensation: $165.6B in FY 2024 for 6.7M veterans. PACT Act projected to increase spending by $300B between 2022-2031. Costs under Toxic Exposures Fund: $20B (2024), $30.4B (2025), $52.6B (2026). Additional sources: https://department.va.gov/wp-content/uploads/2025/06/2026-Budget-in-Brief.pdf | https://www.cbo.gov/publication/45615 | https://www.legion.org/information-center/news/veterans-healthcare/2025/june/va-budget-tops-400-billion-for-2025-from-higher-spending-on-mandated-benefits-medical-care

52.

Sinn, M. P. Private industry clinical trial spending estimate. (2025)

Estimated private pharmaceutical and biotech clinical trial spending is approximately $75-90 billion annually, representing roughly 90% of global clinical trial spending.

53.

Cybersecurity Ventures. Cybercrime economy projected to reach $10.5 trillion. Cybersecurity Ventures: $10.5T Cybercrime https://cybersecurityventures.com/hackerpocalypse-cybercrime-report-2016/ (2016)

Global cybercrime costs: $3T (2015) → $6T (2021) → $10.5T (2025 projected) 15% annual growth rate If measured as country, would be 3rd largest economy after US and China Greatest transfer of economic wealth in history Note: More profitable than global trade of all major illegal drugs combined. Includes data theft, productivity loss, IP theft, fraud Additional sources: <https://cybersecurityventures.com/hackerpocalypse-cybercrime-report-2016/> | https://www.boisestate.edu/cybersecurity/2022/06/16/cybercrime-to-cost-the-world-10-5-trillion-annually-by-2025/

54.

Sinn, M. P. The Political Dysfunction Tax. https://manual.warondisease.org/knowledge/appendix/political-dysfunction-tax.html (2025) doi:10.5281/zenodo.18603840

Quantifying the gap between current global governance and theoretical maximum welfare, estimating a 31-53% efficiency score and $97 trillion in annual opportunity costs.

55.

Bolt, J. & Zanden, J. L. van. Maddison project database 2020. (2020)

Historical GDP per capita estimates from year 1 to present. Global GDP per capita in 1900: approximately 1,260 in 1990 international dollars (roughly 3,150 in 2024 USD after PPP and inflation adjustment). Standard reference for long-run comparative economic history.

56.

Applied Clinical Trials. Global government spending on interventional clinical trials: $3-6 billion/year. Applied Clinical Trials https://www.appliedclinicaltrialsonline.com/view/sizing-clinical-research-market

Estimated range based on NIH ( $0.8-5.6B), NIHR ($1.6B total budget), and EU funding ( $1.3B/year). Roughly 5-10% of global market. Additional sources: https://www.appliedclinicaltrialsonline.com/view/sizing-clinical-research-market | https://www.thelancet.com/journals/langlo/article/PIIS2214-109X(20)30357-0/fulltext

57.

UBS. Credit suisse global wealth report 2023. Credit Suisse/UBS https://www.ubs.com/global/en/family-office-uhnw/reports/global-wealth-report-2023.html (2023)

Total global household wealth: USD 454.4 trillion (2022) Wealth declined by USD 11.3 trillion (-2.4%) in 2022, first decline since 2008 Wealth per adult: USD 84,718 Additional sources: https://www.ubs.com/global/en/family-office-uhnw/reports/global-wealth-report-2023.html

58.

World Health Organization. Life expectancy at age 60 (years): Global health observatory. (2021).

59.

Component country budgets. Global government medical research spending ($67.5B, 2023–2024). See component country budgets: NIH Budget https://www.nih.gov/about-nih/what-we-do/budget.

60.

United Nations Department of Economic and Social Affairs, Population Division. World population prospects 2024: Summary of results. (2024)

The 2024 Revision of the World Population Prospects provides population estimates and projections for 237 countries or areas. Global median age approximately 30.5 years in 2024, reflecting population-weighted average across all regions.

61.

SIPRI. Global military spending ($2.72T, 2024). SIPRI https://www.sipri.org/publications/2025/sipri-fact-sheets/trends-world-military-expenditure-2024 (2025).

62.

Stockholm International Peace Research Institute. Trends in world military expenditure, 2024. (2025).

63.

UN. Global population reaches 8 billion. UN: World Population 8 Billion Nov 15 2022 https://www.un.org/en/desa/world-population-reach-8-billion-15-november-2022 (2022)

Milestone: November 15, 2022 (UN World Population Prospects 2022) Day of Eight Billion" designated by UN Added 1 billion people in just 11 years (2011-2022) Growth rate: Slowest since 1950; fell under 1% in 2020 Future: 15 years to reach 9B (2037); projected peak 10.4B in 2080s Projections: 8.5B (2030), 9.7B (2050), 10.4B (2080-2100 plateau) Note: Milestone reached Nov 2022. Population growth slowing; will take longer to add next billion (15 years vs 11 years) Additional sources: https://www.un.org/en/desa/world-population-reach-8-billion-15-november-2022 | https://www.un.org/en/dayof8billion | https://en.wikipedia.org/wiki/Day_of_Eight_Billion

64.

Harvard Kennedy School. 3.5% participation tipping point. Harvard Kennedy School https://www.hks.harvard.edu/centers/carr/publications/35-rule-how-small-minority-can-change-world (2020)

The research found that nonviolent campaigns were twice as likely to succeed as violent ones, and once 3.5% of the population were involved, they were always successful. Chenoweth and Maria Stephan studied the success rates of civil resistance efforts from 1900 to 2006, finding that nonviolent movements attracted, on average, four times as many participants as violent movements and were more likely to succeed. Key finding: Every campaign that mobilized at least 3.5% of the population in sustained protest was successful (in their 1900-2006 dataset) Note: The 3.5% figure is a descriptive statistic from historical analysis, not a guaranteed threshold. One exception (Bahrain 2011-2014 with 6%+ participation) has been identified. The rule applies to regime change, not policy change in democracies. Additional sources: https://www.hks.harvard.edu/centers/carr/publications/35-rule-how-small-minority-can-change-world | https://www.hks.harvard.edu/sites/default/files/2024-05/Erica%20Chenoweth_2020-005.pdf | https://www.bbc.com/future/article/20190513-it-only-takes-35-of-people-to-change-the-world | https://en.wikipedia.org/wiki/3.5%25_rule

65.

International IDEA. International IDEA voter turnout database world export. (2026)

Best current register-based estimate of global registered voters. Sum of the latest available country-level Registration counts in International IDEA’s world export on 2026-04-22 = 4,128,142,495 registered voters across 199 countries and political entities. Methodology notes that Registration is the number of names on the voters’ register as reported by electoral management bodies, and comparability is imperfect because voter rolls and registration systems differ across countries. Additional sources: https://www.idea.int/data-tools/data/voter-turnout-database | https://www.idea.int/data-tools/export?type=region_only&themeId=293&world=all&loc=home

66.

Federation of American Scientists. World nuclear forces. Federation of American Scientists https://fas.org/issues/nuclear-weapons/status-world-nuclear-forces/ (2024)

As of early 2025, we estimate that the world’s nine nuclear-armed states possess a combined total of approximately 12,241 nuclear warheads. Additional sources: https://fas.org/issues/nuclear-weapons/status-world-nuclear-forces/

67.

PMC. Only 12% of human interactome targeted. PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC10749231/ (2023)

Mapping 350,000+ clinical trials showed that only 12% of the human interactome has ever been targeted by drugs. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10749231/

68.

Congressional Research Service. Internal Revenue Service Appropriations, FY2025 (IF12647). https://www.congress.gov/crs_external_products/IF/PDF/IF12647/IF12647.2.pdf (2024).

69.

McFarland, M. J., Hauer, M. E. & Reuben, A. Half of US population exposed to adverse lead levels in early childhood. Proceedings of the National Academy of Sciences 119, e2118631119 (2022)

Leaded gasoline, used in the US from 1923 until its on-road ban in 1996, exposed more than half of the 2015 US population to adverse blood-lead levels in early childhood. The authors estimate childhood lead exposure cost the population a cumulative 824 million IQ points, an average of 2.6 points per person, rising to 5.9 points for the most-exposed 1966-1970 birth cohort.

70.

Ritchie, H. & Roser, M. Half of the world’s habitable land is used for agriculture. (2019).

71.

Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360, 987–992 (2018).

72.

OpenSecrets. Lobbyist statistics for washington d.c. OpenSecrets: Lobbying in US https://en.wikipedia.org/wiki/Lobbying_in_the_United_States

Registered lobbyists: Over 12,000 (some estimates); 12,281 registered (2013) Former government employees as lobbyists: 2,200+ former federal employees (1998-2004), including 273 former White House staffers, 250 former Congress members & agency heads Congressional revolving door: 43% (86 of 198) lawmakers who left 1998-2004 became lobbyists; currently 59% leaving to private sector work for lobbying/consulting firms/trade groups Executive branch: 8% were registered lobbyists at some point before/after government service Additional sources: https://en.wikipedia.org/wiki/Lobbying_in_the_United_States | https://www.opensecrets.org/revolving-door | https://www.citizen.org/article/revolving-congress/ | https://www.propublica.org/article/we-found-a-staggering-281-lobbyists-whove-worked-in-the-trump-administration

73.

Gosse, M. E. Assessing cost-effectiveness in healthcare: History of the $50,000 per QALY threshold. Sustainability Impact Metrics https://ecocostsvalue.com/EVR/img/references%20others/Gosse%202008%20QALY%20threshold%20financial.pdf (2008).

74.

National Institutes of Health BRAIN Initiative. BRAIN 2025: A scientific vision. (2014).

75.

Congressional Research Service. Advanced Gene Editing: CRISPR-Cas9. https://www.congress.gov/crs_external_products/R/PDF/R44824/R44824.7.pdf (2018).

76.

NHGRI. Human genome project and CRISPR discovery. NHGRI https://www.genome.gov/11006929/2003-release-international-consortium-completes-hgp (2003)

Your DNA is 3 billion base pairs Read the entire code (Human Genome Project, completed 2003) Learned to edit it (CRISPR, discovered 2012) Additional sources: https://www.genome.gov/11006929/2003-release-international-consortium-completes-hgp | https://www.nobelprize.org/prizes/chemistry/2020/press-release/

77.

U.S. Government Accountability Office. Electronic Health Records: First Year of CMS’s Incentive Programs Shows Opportunities to Improve Processes to Verify Providers Met Requirements. https://www.gao.gov/products/gao-12-481 (2012).

78.

U.S. Government Accountability Office. Operation Warp Speed Vaccine Candidate Awards Potential Value. https://www.gao.gov/assets/gao-21-207.pdf (2020).

79.

PCORnet. PCORnet quarterly progress report, Q4 2025. (2026).

80.

Jones, G. & Schneider, W. J. Intelligence, human capital, and economic growth: A Bayesian averaging of classical estimates (BACE) approach. Journal of Economic Growth 11, 71–93 (2006)

IQ was significant at the 95% level in 99.8% of 1,330 BACE growth regressions. A 1 point increase in a nation’s average IQ is associated with a persistent 0.11% annual increase in GDP per capita.

81.

Calculated from Orphanet Journal of Rare Diseases (2024). Diseases getting first effective treatment each year. Calculated from Orphanet Journal of Rare Diseases (2024) https://ojrd.biomedcentral.com/articles/10.1186/s13023-024-03398-1 (2024)

Under the current system, approximately 10-15 diseases per year receive their FIRST effective treatment. Calculation: 5% of 7,000 rare diseases ( 350) have FDA-approved treatment, accumulated over 40 years of the Orphan Drug Act = 9 rare diseases/year. Adding 5-10 non-rare diseases that get first treatments yields 10-20 total. FDA approves 50 drugs/year, but many are for diseases that already have treatments (me-too drugs, second-line therapies). Only 15 represent truly FIRST treatments for previously untreatable conditions.

82.

NIH. NIH budget (FY 2025). NIH https://www.nih.gov/about-nih/organization/budget (2024)

The budget total of $47.7 billion also includes $1.412 billion derived from PHS Evaluation financing... Additional sources: https://www.nih.gov/about-nih/organization/budget | https://officeofbudget.od.nih.gov/

83.

Bentley et al. NIH spending on clinical trials: 3.3%. Bentley et al. https://pmc.ncbi.nlm.nih.gov/articles/PMC10349341/ (2023)

NIH spent $8.1 billion on clinical trials for approved drugs (2010-2019), representing 3.3% of relevant NIH spending. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10349341/ | https://catalyst.harvard.edu/news/article/nih-spent-8-1b-for-phased-clinical-trials-of-drugs-approved-2010-19-10-of-reported-industry-spending/

84.

PMC. Standard medical research ROI ($20k-$100k/QALY). PMC: Cost-effectiveness Thresholds Used by Study Authors https://pmc.ncbi.nlm.nih.gov/articles/PMC10114019/ (1990)

Typical cost-effectiveness thresholds for medical interventions in rich countries range from $50,000 to $150,000 per QALY. The Institute for Clinical and Economic Review (ICER) uses a $100,000-$150,000/QALY threshold for value-based pricing. Between 1990-2021, authors increasingly cited $100,000 (47% by 2020-21) or $150,000 (24% by 2020-21) per QALY as benchmarks for cost-effectiveness. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10114019/ | https://icer.org/our-approach/methods-process/cost-effectiveness-the-qaly-and-the-evlyg/

85.

Manhattan Institute. RECOVERY trial 82× cost reduction. Manhattan Institute: Slow Costly Trials https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs

RECOVERY trial: $500 per patient ($20M for 48,000 patients = $417/patient) Typical clinical trial: $41,000 median per-patient cost Cost reduction: 80-82× cheaper ($41,000 ÷ $500 ≈ 82×) Efficiency: $50 per patient per answer (10 therapeutics tested, 4 effective) Dexamethasone estimated to save >630,000 lives Additional sources: https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs | https://pmc.ncbi.nlm.nih.gov/articles/PMC9293394/

86.

Trials. Patient willingness to participate in clinical trials. Trials: Patients’ Willingness Survey https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-015-1105-3

Recent surveys: 49-51% willingness (2020-2022) - dramatic drop from 85% (2019) during COVID-19 pandemic Cancer patients when approached: 88% consented to trials (Royal Marsden Hospital) Study type variation: 44.8% willing for drug trial, 76.2% for diagnostic study Top motivation: "Learning more about my health/medical condition" (67.4%) Top barrier: "Worry about experiencing side effects" (52.6%) Additional sources: https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-015-1105-3 | https://www.appliedclinicaltrialsonline.com/view/industry-forced-to-rethink-patient-participation-in-trials | https://pmc.ncbi.nlm.nih.gov/articles/PMC7183682/

87.

The Commune. Pentagon audit failures ($2.46T unaccounted). The Commune https://thecommunemag.com/the-pentagon-misplaced-2-46-trillion-an-in-depth-look-at-the-financial-audit-failures (2024)

In the most recent audit, the Department of Defense (DoD) could not account for approximately 60% of its $4.1 trillion in assets, amounting to$2.46 trillion unaccounted for. Alternative title: Pentagon unsupported accounting adjustments ($6.5T, single year, US Army) In 2015, the Department of Defense's Inspector General reported that the Army could not adequately support$6.5 trillion in year-end adjustments, indicating severe accounting discrepancies. Additional sources: https://thecommunemag.com/the-pentagon-misplaced-2-46-trillion-an-in-depth-look-at-the-financial-audit-failures | https://accmag.com/audit-pentagon-cannot-account-for-6-5-trillion-dollars-is-taxpayer-money/

88.

Tufts CSDD. Cost of drug development.

Various estimates suggest $1.0 - $2.5 billion to bring a new drug from discovery through FDA approval, spread across 10 years. Tufts Center for the Study of Drug Development often cited for $1.0 - $2.6 billion/drug. Industry reports (IQVIA, Deloitte) also highlight $2+ billion figures.

89.

Value in Health. Average lifetime revenue per successful drug. Value in Health: Sales Revenues for New Therapeutic Agents https://www.sciencedirect.com/science/article/pii/S1098301524027542

Study of 361 FDA-approved drugs from 1995-2014 (median follow-up 13.2 years): Mean lifetime revenue: $15.2 billion per drug Median lifetime revenue: $6.7 billion per drug Revenue after 5 years: $3.2 billion (mean) Revenue after 10 years: $9.5 billion (mean) Revenue after 15 years: $19.2 billion (mean) Distribution highly skewed: top 25 drugs (7%) accounted for 38% of total revenue ($2.1T of $5.5T) Additional sources: https://www.sciencedirect.com/science/article/pii/S1098301524027542

90.

Lichtenberg, F. R. How many life-years have new drugs saved? A three-way fixed-effects analysis of 66 diseases in 27 countries, 2000-2013. International Health 11, 403–416 (2019)

Using 3-way fixed-effects methodology (disease-country-year) across 66 diseases in 22 countries, this study estimates that drugs launched after 1981 saved 148.7 million life-years in 2013 alone. The regression coefficients for drug launches 0-11 years prior (beta=-0.031, SE=0.008) and 12+ years prior (beta=-0.057, SE=0.013) on years of life lost are highly significant (p<0.0001). Confidence interval for life-years saved: 79.4M-239.8M (95 percent CI) based on propagated standard errors from Table 2.

91.

Deloitte. Pharmaceutical r&d return on investment (ROI). Deloitte: Measuring Pharmaceutical Innovation 2025 https://www.deloitte.com/ch/en/Industries/life-sciences-health-care/research/measuring-return-from-pharmaceutical-innovation.html (2025)

Deloitte’s annual study of top 20 pharma companies by R&D spend (2010-2024): 2024 ROI: 5.9% (second year of growth after decade of decline) 2023 ROI: 4.3% (estimated from trend) 2022 ROI: 1.2% (historic low since study began, 13-year low) 2021 ROI: 6.8% (record high, inflated by COVID-19 vaccines/treatments) Long-term trend: Declining for over a decade before 2023 recovery Average R&D cost per asset: $2.3B (2022), $2.23B (2024) These returns (1.2-5.9% range) fall far below typical corporate ROI targets (15-20%) Additional sources: https://www.deloitte.com/ch/en/Industries/life-sciences-health-care/research/measuring-return-from-pharmaceutical-innovation.html | https://www.prnewswire.com/news-releases/deloittes-13th-annual-pharmaceutical-innovation-report-pharma-rd-return-on-investment-falls-in-post-pandemic-market-301738807.html | https://hitconsultant.net/2023/02/16/pharma-rd-roi-falls-to-lowest-level-in-13-years/

92.

Nature Reviews Drug Discovery. Drug trial success rate from phase i to approval. Nature Reviews Drug Discovery: Clinical Success Rates https://www.nature.com/articles/nrd.2016.136 (2016)

Overall Phase I to approval: 10-12.8% (conventional wisdom 10%, studies show 12.8%) Recent decline: Average LOA now 6.7% for Phase I (2014-2023 data) Leading pharma companies: 14.3% average LOA (range 8-23%) Varies by therapeutic area: Oncology 3.4%, CNS/cardiovascular lowest at Phase III Phase-specific success: Phase I 47-54%, Phase II 28-34%, Phase III 55-70% Note: 12% figure accurate for historical average. Recent data shows decline to 6.7%, with Phase II as primary attrition point (28% success) Additional sources: https://www.nature.com/articles/nrd.2016.136 | https://pmc.ncbi.nlm.nih.gov/articles/PMC6409418/ | https://academic.oup.com/biostatistics/article/20/2/273/4817524

93.

SofproMed. Phase 3 cost per trial range. SofproMed https://www.sofpromed.com/how-much-does-a-clinical-trial-cost

Phase 3 clinical trials cost between $20 million and $282 million per trial, with significant variation by therapeutic area and trial complexity. Additional sources: https://www.sofpromed.com/how-much-does-a-clinical-trial-cost | https://www.cbo.gov/publication/57126

94.

Ramsberg, J. & Platt, R. Pragmatic trial cost per patient (median $97). Learning Health Systems https://pmc.ncbi.nlm.nih.gov/articles/PMC6508852/ (2018)

Meta-analysis of 108 embedded pragmatic clinical trials (2006-2016). The median cost per patient was $97 (IQR $19–$478), based on 2015 dollars. 25% of trials cost <$19/patient; 10 trials exceeded $1,000/patient. U.S. studies median $187 vs non-U.S. median $27. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC6508852/

95.

ICRC. International campaign to ban landmines (ICBL) - ottawa treaty (1997). ICRC https://www.icrc.org/en/doc/resources/documents/article/other/57jpjn.htm (1997)

ICBL: Founded 1992 by 6 NGOs (Handicap International, Human Rights Watch, Medico International, Mines Advisory Group, Physicians for Human Rights, Vietnam Veterans of America Foundation) Started with ONE staff member: Jody Williams as founding coordinator Grew to 1,000+ organizations in 60 countries by 1997 Ottawa Process: 14 months (October 1996 - December 1997) Convention signed by 122 states on December 3, 1997; entered into force March 1, 1999 Achievement: Nobel Peace Prize 1997 (shared by ICBL and Jody Williams) Government funding context: Canada established $100M CAD Canadian Landmine Fund over 10 years (1997); International donors provided $169M in 1997 for mine action (up from $100M in 1996) Additional sources: https://www.icrc.org/en/doc/resources/documents/article/other/57jpjn.htm | https://en.wikipedia.org/wiki/International_Campaign_to_Ban_Landmines | https://www.nobelprize.org/prizes/peace/1997/summary/ | https://un.org/press/en/1999/19990520.MINES.BRF.html | https://www.the-monitor.org/en-gb/reports/2003/landmine-monitor-2003/mine-action-funding.aspx

96.

OpenSecrets. Revolving door: Former members of congress. (2024)

388 former members of Congress are registered as lobbyists. Nearly 5,400 former congressional staffers have left Capitol Hill to become federal lobbyists in the past 10 years. Additional sources: https://www.opensecrets.org/revolving-door

97.

Kinch, M. S. & Griesenauer, R. H. Lost medicines: A longer view of the pharmaceutical industry with the potential to reinvigorate discovery. Drug Discovery Today 24, 875–880 (2019)

Research identified 1,600+ medicines available in 1962. The 1950s represented industry high-water mark with >30 new products in five of ten years; this rate would not be replicated until late 1990s. More than half (880) of these medicines were lost following implementation of Kefauver-Harris Amendment. The peak of 1962 would not be seen again until early 21st century. By 2016 number of organizations actively involved in R&D at level not seen since 1914.

98.

Baily, M. N. Pre-1962 drug development costs (baily 1972). Baily (1972) https://samizdathealth.org/wp-content/uploads/2020/12/hlthaff.1.2.6.pdf (1972)

Pre-1962: Average cost per new chemical entity (NCE) was $6.5 million (1980 dollars) Inflation-adjusted to 2024 dollars: $6.5M (1980) ≈ $22.5M (2024), using CPI multiplier of 3.46× Real cost increase (inflation-adjusted): $22.5M (pre-1962) → $2,600M (2024) = 116× increase Note: This represents the most comprehensive academic estimate of pre-1962 drug development costs based on empirical industry data Additional sources: https://samizdathealth.org/wp-content/uploads/2020/12/hlthaff.1.2.6.pdf

99.

Think by Numbers. Pre-1962 physician-led clinical trials. Think by Numbers: How Many Lives Does FDA Save? https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ (1966)

Pre-1962: Physicians could report real-world evidence directly 1962 Drug Amendments replaced "premarket notification" with "premarket approval", requiring extensive efficacy testing Impact: New regulatory clampdown reduced new treatment production by 70%; lifespan growth declined from 4 years/decade to 2 years/decade Drug Efficacy Study Implementation (DESI): NAS/NRC evaluated 3,400+ drugs approved 1938-1962 for safety only; reviewed >3,000 products, >16,000 therapeutic claims FDA has had authority to accept real-world evidence since 1962, clarified by 21st Century Cures Act (2016) Note: Specific "144,000 physicians" figure not verified in sources Additional sources: https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ | https://www.fda.gov/drugs/enforcement-activities-fda/drug-efficacy-study-implementation-desi | http://www.nasonline.org/about-nas/history/archives/collections/des-1966-1969-1.html

100.

GAO. 95% of diseases have 0 FDA-approved treatments. GAO https://www.gao.gov/products/gao-25-106774 (2025)

95% of diseases have no treatment Additional sources: https://www.gao.gov/products/gao-25-106774 | https://globalgenes.org/rare-disease-facts/

101.

Oren Cass, Manhattan Institute. RECOVERY trial cost per patient. Oren Cass https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs (2023)

The RECOVERY trial, for example, cost only about $500 per patient... By contrast, the median per-patient cost of a pivotal trial for a new therapeutic is around $41,000. Additional sources: https://manhattan.institute/article/slow-costly-clinical-trials-drag-down-biomedical-breakthroughs

102.

NHS England; Águas et al. RECOVERY trial global lives saved ( 1 million). NHS England: 1 Million Lives Saved https://www.england.nhs.uk/2021/03/covid-treatment-developed-in-the-nhs-saves-a-million-lives/ (2021)

Dexamethasone saved 1 million lives worldwide (NHS England estimate, March 2021, 9 months after discovery). UK alone: 22,000 lives saved. Methodology: Águas et al. Nature Communications 2021 estimated 650,000 lives (range: 240,000-1,400,000) for July-December 2020 alone, based on RECOVERY trial mortality reductions (36% for ventilated, 18% for oxygen-only patients) applied to global COVID hospitalizations. June 2020 announcement: Dexamethasone reduced deaths by up to 1/3 (ventilated patients), 1/5 (oxygen patients). Impact immediate: Adopted into standard care globally within hours of announcement. Additional sources: https://www.england.nhs.uk/2021/03/covid-treatment-developed-in-the-nhs-saves-a-million-lives/ | https://www.nature.com/articles/s41467-021-21134-2 | https://pharmaceutical-journal.com/article/news/steroid-has-saved-the-lives-of-one-million-covid-19-patients-worldwide-figures-show | https://www.recoverytrial.net/news/recovery-trial-celebrates-two-year-anniversary-of-life-saving-dexamethasone-result

103.

World Bank. Market capitalization of listed domestic companies (current US$). (2026).

104.

National September 11 Memorial & Museum. September 11 attack facts. (2024)

2,977 people were killed in the September 11, 2001 attacks: 2,753 at the World Trade Center, 184 at the Pentagon, and 40 passengers and crew on United Flight 93 in Shanksville, Pennsylvania.

105.

CSIS. Smallpox eradication ROI. CSIS https://www.csis.org/analysis/smallpox-eradication-model-global-cooperation.

106.

ICER. Value per QALY (standard economic value). ICER https://icer.org/wp-content/uploads/2024/02/Reference-Case-4.3.25.pdf (2024)

Standard economic value per QALY: $100,000–$150,000. This is the US and global standard willingness-to-pay threshold for interventions that add costs. Dominant interventions (those that save money while improving health) are favorable regardless of this threshold. Additional sources: https://icer.org/wp-content/uploads/2024/02/Reference-Case-4.3.25.pdf

107.

GAO. Annual cost of u.s. Sugar subsidies. GAO: Sugar Program https://www.gao.gov/products/gao-24-106144

Consumer costs: $2.5-3.5 billion per year (GAO estimate) Net economic cost: $1 billion per year 2022: US consumers paid 2X world price for sugar Program costs $3-4 billion/year but no federal budget impact (costs passed directly to consumers via higher prices) Employment impact: 10,000-20,000 manufacturing jobs lost annually in sugar-reliant industries (confectionery, etc.) Multiple studies confirm: Sweetener Users Association ($2.9-3.5B), AEI ($2.4B consumer cost), Beghin & Elobeid ($2.9-3.5B consumer surplus) Additional sources: https://www.gao.gov/products/gao-24-106144 | https://www.heritage.org/agriculture/report/the-us-sugar-program-bad-consumers-bad-agriculture-and-bad-america | https://www.aei.org/articles/the-u-s-spends-4-billion-a-year-subsidizing-stalinist-style-domestic-sugar-production/

108.

World Bank. Swiss military budget as percentage of GDP. World Bank: Military Expenditure https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS?locations=CH

2023: 0.70272% of GDP (World Bank) 2024: CHF 5.95 billion official military spending When including militia system costs: 1% GDP (CHF 8.75B) Comparison: Near bottom in Europe; only Ireland, Malta, Moldova spend less (excluding microstates with no armies) Additional sources: https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS?locations=CH | https://www.avenir-suisse.ch/en/blog-defence-spending-switzerland-is-in-better-shape-than-it-seems/ | https://tradingeconomics.com/switzerland/military-expenditure-percent-of-gdp-wb-data.html

109.

World Bank. Switzerland vs. US GDP per capita comparison. World Bank: Switzerland GDP Per Capita https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=CH

2024 GDP per capita (PPP-adjusted): Switzerland $93,819 vs United States $75,492 Switzerland’s GDP per capita 24% higher than US when adjusted for purchasing power parity Nominal 2024: Switzerland $103,670 vs US $85,810 Additional sources: https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?locations=CH | https://tradingeconomics.com/switzerland/gdp-per-capita-ppp | https://www.theglobaleconomy.com/USA/gdp_per_capita_ppp/

110.

FDA Study via NCBI. Trial costs, FDA study. FDA Study via NCBI https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248200/

Overall, the 138 clinical trials had an estimated median (IQR) cost of $19.0 million ($12.2 million-$33.1 million)... The clinical trials cost a median (IQR) of $41,117 ($31,802-$82,362) per patient. Additional sources: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6248200/

111.

WHO. Annual global economic burden of alzheimer’s and other dementias. WHO: Dementia Fact Sheet https://www.who.int/news-room/fact-sheets/detail/dementia (2019)

Global cost: $1.3 trillion (2019 WHO-commissioned study) 50% from informal caregivers (family/friends, 5 hrs/day) 74% of costs in high-income countries despite 61% of patients in LMICs $818B (2010) → $1T (2018) → $1.3T (2019) - rapid growth Note: Costs increased 35% from 2010-2015 alone. Informal care represents massive hidden economic burden Additional sources: https://www.who.int/news-room/fact-sheets/detail/dementia | https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/alz.12901

112.

JAMA Oncology. Annual global economic burden of cancer. JAMA Oncology: Global Cost 2020-2050 https://jamanetwork.com/journals/jamaoncology/fullarticle/2801798 (2020)

2020-2050 projection: $25.2 trillion total ($840B/year average) 2010 annual cost: $1.16 trillion (direct costs only) Recent estimate: $3 trillion/year (all costs included) Top 5 cancers: lung (15.4%), colon/rectum (10.9%), breast (7.7%), liver (6.5%), leukemia (6.3%) Note: China/US account for 45% of global burden; 75% of deaths in LMICs but only 50.0% of economic cost Additional sources: https://jamanetwork.com/journals/jamaoncology/fullarticle/2801798 | https://www.nature.com/articles/d41586-023-00634-9

113.

CDC. U.s. Chronic disease healthcare spending. CDC https://www.cdc.gov/chronic-disease/data-research/facts-stats/index.html

Chronic diseases account for 90% of U.S. healthcare spending ( $3.7T/year). Additional sources: https://www.cdc.gov/chronic-disease/data-research/facts-stats/index.html

114.

Diabetes Care. Annual global economic burden of diabetes. Diabetes Care: Global Economic Burden https://diabetesjournals.org/care/article/41/5/963/36522/Global-Economic-Burden-of-Diabetes-in-Adults

2015: $1.3 trillion (1.8% of global GDP) 2030 projections: $2.1T-2.5T depending on scenario IDF health expenditure: $760B (2019) → $845B (2045 projected) 2/3 direct medical costs ($857B), 1/3 indirect costs (lost productivity) Note: Costs growing rapidly; expected to exceed $2T by 2030 Additional sources: https://diabetesjournals.org/care/article/41/5/963/36522/Global-Economic-Burden-of-Diabetes-in-Adults | https://doi.org/10.1016/S2213-8587(17)30097-9

115.

CBO. The 2024 Long-Term Budget Outlook. https://www.cbo.gov/publication/60039 (2024).

116.

Environmental Working Group. US farm subsidy database and analysis. Environmental Working Group https://farm.ewg.org/ (2024)

US agricultural subsidies total approximately $30 billion annually, but create much larger economic distortions. Top 10% of farms receive 78% of subsidies, benefits concentrated in commodity crops (corn, soy, wheat, cotton), environmental damage from monoculture incentivized, and overall deadweight loss estimated at $50-120 billion annually. Additional sources: https://farm.ewg.org/ | https://www.ers.usda.gov/topics/farm-economy/farm-sector-income-finances/government-payments-the-safety-net/

117.

Drug Policy Alliance. The drug war by the numbers. (2021)

Since 1971, the war on drugs has cost the United States an estimated $1 trillion in enforcement. The federal drug control budget was $41 billion in 2022. Mass incarceration costs the U.S. at least $182 billion every year, with over $450 billion spent to incarcerate individuals on drug charges in federal prisons.

118.

International Monetary Fund. IMF fossil fuel subsidies data: 2023 update. (2023)

Globally, fossil fuel subsidies were $7 trillion in 2022 or 7.1 percent of GDP. The United States subsidies totaled $649 billion. Underpricing for local air pollution costs and climate damages are the largest contributor, accounting for about 30 percent each.

119.

Papanicolas, Irene et al. Health care spending in the united states and other high-income countries. Papanicolas et al. https://jamanetwork.com/journals/jama/article-abstract/2674671 (2018)

The US spent approximately twice as much as other high-income countries on medical care (mean per capita: $9,892 vs $5,289), with similar utilization but much higher prices. Administrative costs accounted for 8% of US spending vs 1-3% in other countries. US spending on pharmaceuticals was $1,443 per capita vs $749 elsewhere. Despite spending more, US health outcomes are not better. Additional sources: https://jamanetwork.com/journals/jama/article-abstract/2674671

120.

Hsieh, C.-T. & Moretti, E. Housing constraints and spatial misallocation. American Economic Journal: Macroeconomics https://www.aeaweb.org/articles?id=10.1257/mac.20170388 (2019)

We quantify the amount of spatial misallocation of labor across US cities and its aggregate costs. Tight land-use restrictions in high-productivity cities like New York, San Francisco, and Boston lowered aggregate US growth by 36% from 1964 to 2009. Local constraints on housing supply have had enormous effects on the national economy. Additional sources: https://www.aeaweb.org/articles?id=10.1257/mac.20170388

121.

Yale Budget Lab. The fiscal, economic, and distributional effects of all u.s. tariffs. (2025)

Accounting for all the 2025 US tariffs and retaliation implemented to date, the level of real GDP is persistently -0.6% smaller in the long run, the equivalent of $160 billion 2024$ annually.

122.

Tax Foundation. Tax compliance costs the US economy $546 billion annually. https://taxfoundation.org/data/all/federal/irs-tax-compliance-costs/ (2024)

Americans will spend over 7.9 billion hours complying with IRS tax filing and reporting requirements in 2024. This costs the economy roughly $413 billion in lost productivity. In addition, the IRS estimates that Americans spend roughly $133 billion annually in out-of-pocket costs, bringing the total compliance costs to $546 billion, or nearly 2 percent of GDP.

123.

Cook, C., Cole, G., Asaria, P., Jabbour, R. & Francis, D. P. Annual global economic burden of heart disease. International Journal of Cardiology https://www.internationaljournalofcardiology.com/article/S0167-5273(13)02238-9/abstract (2014)

Heart failure alone: $108 billion/year (2012 global analysis, 197 countries) US CVD: $555B (2016) → projected $1.8T by 2050 LMICs total CVD loss: $3.7T cumulative (2011-2015, 5-year period) CVD is costliest disease category in most developed nations Note: No single $2.1T global figure found; estimates vary widely by scope and year Additional sources: https://www.ahajournals.org/doi/10.1161/CIR.0000000000001258

124.

Manuel, D. U.s. Defense spending history: 100 years of military budgets. DaveManuel.com https://www.davemanuel.com/us-defense-spending-history-military-budget-data.php (2025)

US military spending in constant 2024 dollars: 1939 $29B (pre-WW2 baseline), 1940 $37B, 1944 $1,383B, 1945 $1,420B (peak), 1946 $674B, 1947 $176B, 1948 $117B, 2024 $886B. The post-WW2 demobilization cut spending 88% in two years (1945-1947). Current peacetime spending ($886B) is 30x the pre-WW2 baseline and 62% of peak WW2 spending, in inflation-adjusted dollars.

125.

Statista. US military budget as percentage of GDP. Statista https://www.statista.com/statistics/262742/countries-with-the-highest-military-spending/ (2024)

U.S. military spending amounted to 3.5% of GDP in 2024. In 2024, the U.S. spent nearly $1 trillion on its military budget, equal to 3.4% of GDP. Additional sources: https://www.statista.com/statistics/262742/countries-with-the-highest-military-spending/ | https://www.sipri.org/sites/default/files/2025-04/2504_fs_milex_2024.pdf

126.

U.S. Senate. Treaties. U.S. Senate https://www.senate.gov/about/powers-procedures/treaties.htm

The Constitution provides that the president ’shall have Power, by and with the Advice and Consent of the Senate, to make Treaties, provided two-thirds of the Senators present concur’ (Article II, section 2). Treaties are formal agreements with foreign nations that require two-thirds Senate approval. 67 senators (two-thirds of 100) must vote to ratify a treaty for it to take effect. Additional sources: https://www.senate.gov/about/powers-procedures/treaties.htm

127.

Mexico News Daily. Survey finds mexican migrants pay average of US $7,000 to ’coyotes’. (2023).

128.

Federal Election Commission. Statistical summary of 24-month campaign activity of the 2023-2024 election cycle. (2023)

Presidential candidates raised $2 billion; House and Senate candidates raised $3.8 billion and spent $3.7 billion; PACs raised $15.7 billion and spent $15.5 billion. Total federal campaign spending approximately $20 billion. Additional sources: https://www.fec.gov/updates/statistical-summary-of-24-month-campaign-activity-of-the-2023-2024-election-cycle/

129.

OpenSecrets. Federal lobbying hit record $4.4 billion in 2024. (2024)

Total federal lobbying reached record $4.4 billion in 2024. The $150 million increase in lobbying continues an upward trend that began in 2016. Additional sources: https://www.opensecrets.org/news/2025/02/federal-lobbying-set-new-record-in-2024/

130.

DOT. DOT value of statistical life ($13.6M). DOT: VSL Guidance 2024 https://www.transportation.gov/office-policy/transportation-policy/revised-departmental-guidance-on-valuation-of-a-statistical-life-in-economic-analysis (2024)

Current VSL (2024): $13.7 million (updated from $13.6M) Used in cost-benefit analyses for transportation regulations and infrastructure Methodology updated in 2013 guidance, adjusted annually for inflation and real income VSL represents aggregate willingness to pay for safety improvements that reduce fatalities by one Note: DOT has published VSL guidance periodically since 1993. Current $13.7M reflects 2024 inflation/income adjustments Additional sources: https://www.transportation.gov/office-policy/transportation-policy/revised-departmental-guidance-on-valuation-of-a-statistical-life-in-economic-analysis | https://www.transportation.gov/regulations/economic-values-used-in-analysis

131.

PLOS ONE. Cost per DALY for vitamin a supplementation. PLOS ONE: Cost-effectiveness of "Golden Mustard" for Treating Vitamin A Deficiency in India (2010) https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012046 (2010)

India: $23-$50 per DALY averted (least costly intervention, $1,000-$6,100 per death averted) Sub-Saharan Africa (2022): $220-$860 per DALY (Burkina Faso: $220, Kenya: $550, Nigeria: $860) WHO estimates for Africa: $40 per DALY for fortification, $255 for supplementation Uganda fortification: $18-$82 per DALY (oil: $18, sugar: $82) Note: Wide variation reflects differences in baseline VAD prevalence, coverage levels, and whether intervention is supplementation or fortification Additional sources: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012046 | https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0266495

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133.

UN News. Clean water & sanitation (LMICs) ROI. UN News https://news.un.org/en/story/2014/11/484032 (2014).

134.

PMC. Cost-effectiveness threshold ($50,000/QALY). PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC5193154/

The $50,000/QALY threshold is widely used in US health economics literature, originating from dialysis cost benchmarks in the 1980s. In US cost-utility analyses, 77.5% of authors use either $50,000 or $100,000 per QALY as reference points. Most successful health programs cost $3,000-10,000 per QALY. WHO-CHOICE uses GDP per capita multiples (1× GDP/capita = "very cost-effective", 3× GDP/capita = "cost-effective"), which for the US ( $70,000 GDP/capita) translates to $70,000-$210,000/QALY thresholds. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC5193154/ | https://pmc.ncbi.nlm.nih.gov/articles/PMC9278384/

135.

Sinn, M. P. The Continuous Evidence Generation Protocol: Two-Stage Validation (RWE → Pragmatic Trials). https://dfda-protocol.acceleratedmedicine.org (2025) doi:10.5281/zenodo.18203375

We present the Predictor Impact Score (PIS), a novel composite metric operationalizing Bradford Hill causality criteria for automated signal detection from aggregated N-of-1 observational studies. Combined with pragmatic trial confirmation (based on evidence from 108+ embedded trials), this two-stage framework would generate validated outcome labels at 44.1x lower cost than traditional Phase III trials. This enables continuous, population-scale pharmacovigilance and precision dosing recommendations.

136.

PCORnet. ADAPTABLE: Aspirin dosing — a patient-centric trial assessing benefits and long-term effectiveness. (2021).

137.

Murphy, K. M. & Topel, R. H. The value of health and longevity. Journal of Political Economy 114, 871–904 (2006).

138.

Nordhaus, W. D. The Health of Nations: The Contribution of Improved Health to Living Standards. https://www.nber.org/papers/w8818 (2002) doi:10.3386/w8818.

139.

Rogers et al. Decentralized clinical trials (DCT) cost reduction evidence. Rogers et al. https://discovery.dundee.ac.uk/ws/files/72718478/Brit_J_Clinical_Pharma_2022_Rogers_A_systematic_review_of_methods_used_to_conduct_decentralised_clinical_trials.pdf (2022)

DCTs are developing rapidly. However, there is insufficient evidence to confirm which methods are most effective in trial recruitment, retention, or overall cost." Despite this, DCTs have demonstrated potential for significant cost reductions (20-50.0% or more) through reduced site management, travel, and streamlined data collection. DCTs are considered cost-saving by reducing the number of onsite patient visits and decreasing the costs related to time for study nurses and clinicians. Additional sources: https://discovery.dundee.ac.uk/ws/files/72718478/Brit_J_Clinical_Pharma_2022_Rogers_A_systematic_review_of_methods_used_to_conduct_decentralised_clinical_trials.pdf | https://www.nature.com/articles/s41746-024-01214-5

140.

U.S. Food and Drug Administration Sentinel Initiative. Information for providers: The sentinel initiative. (2025)

FDA now routinely uses RWD available through the Sentinel System to generate evidence about drug safety, drawing on data from claims, hospital stays, outpatient visits, and pharmaceutical dispensing data from more than 100 million individuals.

141.

ICER. ICER QALY methodology and standards. ICER https://icer.org/our-approach/methods-process/cost-effectiveness-the-qaly-and-the-evlyg/ (2024)

The quality-adjusted life year (QALY) is the academic standard for measuring how well all different kinds of medical treatments lengthen and/or improve patients’ lives, and therefore the metric has served as a fundamental component of cost-effectiveness analyses in the US and around the world for more than 30 years. ICER’s health benefit price benchmark (HBPB) will continue to be reported using the standard range from $100,000 to $150,000 per QALY and per evLYG. Additional sources: https://icer.org/our-approach/methods-process/cost-effectiveness-the-qaly-and-the-evlyg/ | https://icer.org/wp-content/uploads/2024/02/Reference-Case-4.3.25.pdf

142.

Hanna, T. P. et al. Mortality due to cancer treatment delay: Systematic review and meta-analysis. BMJ https://doi.org/10.1136/bmj.m4087 (2020) doi:10.1136/bmj.m4087.

143.

London School of Hygiene & Tropical Medicine. Every month delayed in cancer treatment can raise risk of death by around 10%. (2020).

144.

WHO. WHO smallpox eradication commemoration (2010). WHO https://www.who.int/news/item/17-05-2010-statue-commemorates-smallpox-eradication (2010)

On May 17, 2010, WHO unveiled a commemorative statue marking the 30th anniversary of smallpox eradication. The $300 million global campaign (1967-1980) prevented an estimated 5 million deaths annually and demonstrated unprecedented international cooperation in public health. The benefit-cost ratio has exceeded 100:1. Additional sources: https://www.who.int/news/item/17-05-2010-statue-commemorates-smallpox-eradication

145.

CDC. Childhood vaccinations prevent deaths and hospitalizations. CDC https://www.cdc.gov/mmwr/volumes/73/wr/mm7331a2.htm (2023)

Routine childhood vaccinations among 1994–2023 US birth cohorts will prevent 1.1 million deaths, 32 million hospitalizations, and save $2.9 trillion in societal costs. Among children born during 2018–2023, vaccinations will prevent 508,000 premature deaths and 13.5 million hospitalizations over their lifetimes. Additional sources: https://www.cdc.gov/mmwr/volumes/73/wr/mm7331a2.htm

146.

WHO. WHO clean water and sanitation DALY costs. WHO https://iris.who.int/bitstream/handle/10665/68568/WHO_SDE_WSH_04.04.pdf (2004)

Household water treatment and safe storage interventions cost approximately $20-$500 per disability-adjusted life year (DALY) averted. Community water supply improvements cost $200-$2,000 per DALY averted. These are highly cost-effective interventions by WHO standards. Additional sources: https://iris.who.int/bitstream/handle/10665/68568/WHO_SDE_WSH_04.04.pdf

147.

JAMA Network Open. Pharmacist-led hypertension management cost-effectiveness. JAMA Network Open https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2811317 (2023)

Recent US studies show pharmacist-led hypertension management programs have incremental cost-effectiveness ratios (ICERs) under $50,000 per QALY, with most interventions falling in the $20,000-$33,000 per QALY range. These interventions are considered highly cost-effective compared to standard care. Additional sources: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2811317

148.

WHO. WHO generic drug substitution policy. WHO https://apps.who.int/gb/ebwha/pdf_files/WHA58/WHA58_33-en.pdf (2015)

Generic medicines are by definition cost-saving when therapeutic equivalence is maintained, with typical savings of 30-80% versus brand-name drugs. WHO promotes generic drug use as a key strategy for improving access to essential medicines and reducing healthcare costs globally. Additional sources: https://apps.who.int/gb/ebwha/pdf_files/WHA58/WHA58_33-en.pdf

149.

eClinicalMedicine. Statins and polypill cost-effectiveness. eClinicalMedicine https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(22)00155-2/fulltext)00155-2/fulltext (2022)

Polypill strategy was cost-effective compared to other strategies for most sub-groups, ranging from dominance (cost-saving) to £18,811 per QALY. Standard statin was cost-effective across all categories with incremental cost per QALY from £280 to £8,530. In high-risk populations, polypill and statin interventions are often cost-saving. Additional sources: https://www.thelancet.com/journals/eclinm/article/PIIS2589-5370(22)00155-2/fulltext)00155-2/fulltext

150.

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Additional sources: https://www.congress.gov/bill/114th-congress/house-bill/34 | https://www.fda.gov/regulatory-information/selected-amendments-fdc-act/21st-century-cures-act

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GAO Reports and Public Reporting. HealthCare.gov initial build costs. GAO Reports and Public Reporting https://www.gao.gov/assets/gao-07-49.pdf

The HealthCare.gov website and federal health insurance exchange infrastructure had initial development and deployment costs estimated at $1.7 to $2.1 billion during the 2010-2014 period, though exact figures remain uncertain due to complexity of federal contracting and multiple agencies involved. Additional sources: https://www.gao.gov/assets/gao-07-49.pdf

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AIP. NIH institute and center budgets, FY2024. AIP: FY2024 NIH Budget https://www.aip.org/fyi/fy2024-national-institutes-of-health (2024)

Total NIH FY2024: $48.6B appropriation National Cancer Institute (NCI): $7.22B (-1.3% from FY2023) National Institute of Allergy & Infectious Diseases (NIAID): $6.56B (flat from FY2023) National Institute on Aging (NIA): $4.5B+ ($90M for Alzheimer’s/dementia research) Additional sources: https://www.aip.org/fyi/fy2024-national-institutes-of-health | https://crsreports.congress.gov/product/pdf/R/R43341 | https://www.cancer.gov/grants-training/nci-bottom-line-blog/2024/nci-fy-2024-appropriation-brings-clarity-and-difficult-choices