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The Invisible Graveyard: Quantifying the Mortality Cost of FDA Efficacy Lag, 1962-2024

The 1962 efficacy requirement adds 8.2 years (95% CI: 4.85 years-11.5 years) to drug approval after safety is proven. This study quantifies two distinct mortality costs: (1) 102M deaths (95% CI: 36.9M deaths-214M deaths) historical deaths during actual drug approval delays (1962-2024), and (2) 416M deaths (95% CI: 225M deaths-630M deaths) future deaths from pushing the disease eradication timeline back. Type II errors exceed Type I benefits by 3.07k:1 (95% CI: 2.88k:1-3.12k:1).
Author
Affiliation

Mike P. Sinn

Decentralized Institutes of Health (DIH)

Keywords

fda-regulation, drug-lag, kefauver-harris-amendments, regulatory-delay, type-ii-errors, mortality-analysis, cost-benefit-analysis, pharmaceutical-regulation, dalys, efficacy-requirements, peltzman, regulatory-economics

NoteThe Short Version

The 1962 Kefauver-Harris Amendments require a 8.2 years (95% CI: 4.85 years-11.5 years) efficacy delay after drugs are proven safe. This creates two distinct mortality costs:

  1. Historical deaths (1962-2024): 102M deaths (95% CI: 36.9M deaths-214M deaths) people died waiting for approved drugs during their approval process - a lower bound excluding drugs never developed due to cost barriers
  2. Future timeline shift (under cascade assumption): 416M deaths (95% CI: 225M deaths-630M deaths) additional deaths will occur because the entire disease eradication timeline is pushed back by 8.2 years (95% CI: 4.85 years-11.5 years)

The ratio: Type II errors (blocking effective drugs) cost 3.07k:1 (95% CI: 2.88k:1-3.12k:1) more lives than Type I errors (approving dangerous drugs) prevent.

1 Abstract

This study quantifies the cumulative mortality and morbidity costs associated with the Unitary Pre-Market Approval (UPMA) model mandated by the 1962 Kefauver-Harris Amendments. By enforcing efficacy testing prior to market entry, the current regulatory framework imposes an average “Efficacy Lag” of 8.2 years (95% CI: 4.85 years-11.5 years) post-safety verification.

Using data from the Tufts Center for the Study of Drug Development (CSDD) and the WHO Global Burden of Disease (GBD) database, we estimate two distinct mortality costs:

  1. Historical mortality (1962-2024): Approximately 102M deaths (95% CI: 36.9M deaths-214M deaths) died waiting for approved drugs during their 8.2 years (95% CI: 4.85 years-11.5 years) approval delays. This is a lower bound - it excludes drugs never developed due to cost barriers.

  2. Future timeline shift (under cascade assumption): An additional 416M deaths (95% CI: 225M deaths-630M deaths) will eventually die because the entire disease eradication timeline has been pushed back by 8.2 years (95% CI: 4.85 years-11.5 years). When cures finally arrive, they arrive 8.2 years (95% CI: 4.85 years-11.5 years) later than they would have without efficacy requirements. During that delay, people die.

Historical Deaths Calculation:

\[ \begin{gathered} Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \\[0.5em] \text{where } Lives_{saved,annual} = \frac{LY_{saved,annual}}{T_{ext}} = \frac{149M}{12} = 12.4M \end{gathered} \]

Monte Carlo Distribution: Total Deaths from Historical Progress Delays (10,000 simulations)

Monte Carlo Distribution: Total Deaths from Historical Progress Delays (10,000 simulations)

Simulation Results Summary: Total Deaths from Historical Progress Delays

Statistic Value
Baseline (deterministic) 102M
Mean (expected value) 107M
Median (50th percentile) 97.3M
Standard Deviation 53.0M
90% Confidence Interval [36.9M, 214M]

The histogram shows the distribution of Total Deaths from Historical Progress Delays across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Combined, these represent 7.94B DALYs (95% CI: 4.43B DALYs-12.1B DALYs) Disability-Adjusted Life Years (DALYs) when adjusted for morbidity. All estimates include Monte Carlo confidence intervals.

Valuing these lost years at a conservative global Value of a Statistical Life Year (VSLY), we find a cumulative economic deadweight loss of approximately $1.19 quadrillion (95% CI: $443T-$2.41 quadrillion) (2024 USD). The study concludes that the societal cost of Type II Regulatory Errors (delayed access to effective therapies) exceeds the averted cost of Type I Regulatory Errors (market access for ineffective therapies) by a factor of 3.07k:1 (95% CI: 2.88k:1-3.12k:1).

2 Scale

9/11: 2.98k people dead. We spent $8 trillion in response.

Holocaust: 6 million dead.

Efficacy lag: 102M deaths (95% CI: 36.9M deaths-214M deaths) dead. That’s 34.1k 9/11s (95% CI: 12.4k 9/11s-71.8k 9/11s), or 17 Holocausts.

We paid $4.84T (95% CI: $3.42T-$6.62T) (lower bound - Phase 2/3 costs only) to cause 34.1k 9/11s (95% CI: 12.4k 9/11s-71.8k 9/11s).

Monte Carlo Distribution: Cumulative Efficacy Testing Cost (1962-2024) (10,000 simulations)

Monte Carlo Distribution: Cumulative Efficacy Testing Cost (1962-2024) (10,000 simulations)

Simulation Results Summary: Cumulative Efficacy Testing Cost (1962-2024)

Statistic Value
Baseline (deterministic) $4.84T
Mean (expected value) $4.88T
Median (50th percentile) $4.81T
Standard Deviation $977B
90% Confidence Interval [$3.42T, $6.62T]

The histogram shows the distribution of Cumulative Efficacy Testing Cost (1962-2024) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Bar chart with ghostly imagery comparing 9/11 deaths (2,977), Holocaust deaths (6 million), and FDA efficacy lag deaths (102 million, 95% CI: 36.9M-214M) with error bars

Bar chart with ghostly imagery comparing 9/11 deaths (2,977), Holocaust deaths (6 million), and FDA efficacy lag deaths (102 million, 95% CI: 36.9M-214M) with error bars

That’s $1.56B (95% CI: $1.23B-$1.89B) per drug for Phase 2/3 efficacy trials, paid by patients through higher drug prices. Before 1962, the AMA’s 144k physicians tracked patient outcomes and JAMA published the results. We replaced that with tiny trials on handpicked patients.

Without mandatory pre-market trials, the market wouldn’t be blind. Knowing whether drugs work is one of the highest consumer demands imaginable. Organizations like Consumer Reports, JAMA, and independent research institutes would compete to provide rigorous, large-scale efficacy data - with no pharma conflicts of interest, across real-world populations, with ongoing monitoring instead of a pre-approval snapshot.

These are underestimates. They only count delays to drugs that got developed. The $2.60B (95% CI: $1.50B-$4B) approval cost killed other drugs before they started. We can’t count deaths prevented by cures that don’t exist.

\[ \begin{gathered} N_{9/11,equiv} = \frac{Deaths_{lag,total}}{N_{9/11}} = \frac{102M}{2{,}980} = 34{,}100 \\[0.5em] \text{where } Deaths_{lag,total} \\ = Lives_{saved,annual} \times T_{lag} \\ = 12.4M \times 8.2 \\ = 102M \\[0.5em] \text{where } Lives_{saved,annual} = \frac{LY_{saved,annual}}{T_{ext}} = \frac{149M}{12} = 12.4M \end{gathered} \]

Monte Carlo Distribution: Efficacy Lag Deaths (9/11 Equivalents) (10,000 simulations)

Monte Carlo Distribution: Efficacy Lag Deaths (9/11 Equivalents) (10,000 simulations)

Simulation Results Summary: Efficacy Lag Deaths (9/11 Equivalents)

Statistic Value
Baseline (deterministic) 34.1k
Mean (expected value) 36.0k
Median (50th percentile) 32.7k
Standard Deviation 17.8k
90% Confidence Interval [12.4k, 71.8k]

The histogram shows the distribution of Efficacy Lag Deaths (9/11 Equivalents) across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Sensitivity Indices for Efficacy Lag Deaths (9/11 Equivalents)

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

Input Parameter Sensitivity Coefficient Interpretation
Existing Drugs Efficacy Lag Deaths Total 1.0000 Strong 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.

3 Introduction

The modern pharmaceutical regulatory paradigm relies on a binary licensure model: a drug is either “safe and effective” (approved) or “unsafe/ineffective” (prohibited). While Phase I trials typically establish safety within 2.3 years, the requirement to prove statistical efficacy (Phase II/III) extends the pre-market timeline by an additional 8.2 years (95% CI: 4.85 years-11.5 years) on average.

This study evaluates the Bifurcated Regulatory Model (BRM), defined as “Safety-First / Efficacy-Later”, to measure the “Invisible Graveyard”: the population that dies during the regulatory latency period between safety verification and final approval.

Timeline diagram showing Phase I safety (2.3 years, necessary) vs Phase II/III efficacy lag (8.2 years, 95% CI: 4.85-11.5 years) where patients die waiting

Timeline diagram showing Phase I safety (2.3 years, necessary) vs Phase II/III efficacy lag (8.2 years, 95% CI: 4.85-11.5 years) where patients die waiting

4 Literature Review: The Drug Lag Debate

4.1 Foundational Economic Analysis

The regulatory cost of FDA efficacy requirements was first rigorously quantified by Peltzman142, who estimated that the 1962 Kefauver-Harris Amendments reduced the flow of new drugs by 50-60%. His analysis concluded that the costs of reduced pharmaceutical innovation substantially exceeded any benefits from keeping ineffective drugs off the market, resulting in net welfare losses to society.

Wardell143 documented the emerging “drug lag” between US and UK drug approvals, finding that the UK had access to significantly more new therapeutic agents. His estimate that beta-blockers alone could save 10,000 American lives annually if approved became a landmark finding in regulatory economics.

Gieringer144 synthesized these estimates, calculating 21,000-120,000 lives lost per decade from FDA delay. His work documented specific drug delays: propranolol (approved in the US 3 years after Europe for cardiac use, 10 years later for hypertension), interleukin-2 (7-year gap), and numerous other therapeutics.

4.2 The Current Debate

Contemporary research continues to find significant regulatory costs. The Tufts Center for the Study of Drug Development documents development timelines of 9.1 years (95% CI: 6 years-12 years) and costs of $2.60B (95% CI: $1.50B-$4B) per approved drug. BIO’s clinical development success rates show only 10% of drugs entering Phase I ultimately reach patients.

Critics argue that faster approval pathways (breakthrough therapy designation, accelerated approval) have addressed these concerns. However, these pathways actually support our argument:

FDA’s Expedited Pathways Prove Speed is Possible Without Catastrophe:

  1. Breakthrough Therapy Designation (2012): ~200+ designations annually by 2020s, median approval time reduced by 2-3 years for qualifying drugs
  2. Accelerated Approval (1992): Born from AIDS activism; allows approval based on surrogate endpoints
  3. Fast Track (1997): Intensive FDA guidance and rolling review
  4. Priority Review: 6-month review vs. standard 10-month

Key observations: - These pathways have NOT produced Thalidomide-scale disasters, validating that speed ≠ danger - They remain exceptional rather than default: ~30% of approvals use expedited pathways; 70% face full regulatory burden - Their existence is an implicit admission that the baseline system is too slow for serious diseases - If expedited pathways are safe for cancer and rare diseases, why are they unsafe for other conditions?

The FDA’s partial reforms prove the system recognizes Type II costs exist. The question is why the recognition is limited to a subset of diseases rather than systematically applied.

4.3 Empirical Case Studies: Demonstrating the Causal Mechanism

The theoretical claim that regulatory delay causes mortality requires empirical grounding. Three case studies demonstrate the mechanism operates in practice:

1. Beta-Blockers (1964-1976): The Classic Drug Lag

Propranolol, the first beta-blocker for treating angina and hypertension, was approved in the UK in 1964. US approval came in 1967 for minor uses, but not until 1973 (angina) and 1976 (hypertension) for cardiovascular indications. Wardell estimated approximately 10,000 Americans died annually during this delay, as the FDA’s doors were “essentially closed to cardiovascular drugs for an entire decade”143. This single drug’s regulatory lag may have caused more American deaths than all other drug-related deaths in that century.

2. HIV/AIDS (1987-1996): Regulatory Reform Under Crisis

The AIDS epidemic demonstrated that regulatory speed is a policy choice. AZT was approved in March 1987 in a record 20 months, without a Phase 3 trial, after Phase 2 showed 19 placebo deaths vs. 1 treatment death145. This proves expedited approval is technically feasible. However, from 1987-1993, no other AIDS drugs were approved, despite 257,000 diagnoses in 1993-1995 alone. ACT UP activism forced regulatory reforms (Parallel Track, Accelerated Approval), proving that the FDA’s pace reflects institutional priorities, not immutable scientific requirements.

3. Hepatitis C (2013-2014): Breakthrough Designation Success

Sovaldi (sofosbuvir) received FDA Breakthrough Therapy designation and was approved December 2013, with Harvoni following in October 2014. These drugs cure HCV in 12 weeks with >95% efficacy. In 2013, HCV caused 19,368 US deaths. Critically, despite rapid approval, no Thalidomide-scale disaster occurred. The drugs’ side effect profile was actually better than prior interferon-based treatments. This demonstrates that fast approval of transformative drugs is both possible and safe.

Implications for Causal Inference:

These cases establish that: - Regulatory delays have measurable mortality costs (beta-blockers: if Wardell’s 10,000/year estimate holds, the 3-year US delay implies ~30,000 excess deaths) - Fast approval is technically feasible when institutional will exists (AZT: 20 months; Sovaldi: Breakthrough pathway) - Fast approval does not inevitably produce catastrophe (Sovaldi: excellent safety profile)

The counterfactual is not purely speculative: we observe the mechanism operating in discrete cases where data is available.

5 Methodology & Data

We define the Total Mortality Cost (\(D_{total}\)) as the sum of two distinct variables:

\[ D_{total} = D_{lag} + D_{void} \]

5.1 Variable Definitions

  • \(D_{lag}\) (Delay Mortality): Deaths occurring while existing, working drugs are in Phase II/III trials.
  • \(D_{void}\) (Innovation Loss): Deaths occurring because high regulatory costs prevented the development of potential cures (The “Innovation Tax”).

5.2 Theoretical Upper Bound: What’s Eventually Preventable?

Before calculating regulatory delay costs, we must establish what percentage of deaths are theoretically preventable with sufficient biomedical advancement. This sets the upper bound for any intervention.

ImportantMethodological Note: Distinguishing Current vs. Theoretical Preventability

The “Max Potential” column represents theoretical upper bounds based on biological precedent and mechanistic understanding, not current medical capability. These estimates extrapolate from:

  1. Demonstrated biological plasticity (organisms that don’t age, mammalian aging reversal)
  2. Identified root causes (90-95% of cancers have environmental/lifestyle roots)
  3. Emerging technologies (gene therapy, regenerative medicine, AI drug discovery)

Current preventability is typically 30-50% lower than theoretical maximum. The gap represents the research opportunity.

5.2.1 Disease Burden by Category

Using WHO Global Burden of Disease146 data, we categorize annual deaths:

Category % of Deaths Current Max Potential Source for Max Estimate
Cardiovascular 26.0% 50% 95% WHO: 80-90% preventable147
Cancer 18.9% 69% 95% 90-95% environmental/lifestyle roots148
Aging-related 23.2% 5% 99% Mammalian aging reversal demonstrated149
Accidents 8.0% 30% 60% WHO: largely preventable150
Metabolic 6.3% 70% 98% Diabetes reversal via gene therapy1511
Respiratory 4.3% 60% 90% WHO: 80% of COPD preventable2
Neurodegenerative 3.6% 10% 80% Stem cell therapy potential152
Infectious 1.9% 95% 99% Vaccines + antimicrobials153
Other 7.7% 50% 95% Weighted average of above categories3

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

5.2.2 Why This Upper Bound? The Biological and Epidemiological Evidence

The “max potential” estimates above are grounded in peer-reviewed research:

  1. Aging has been reversed in mammals. Yamanaka factor therapy extended remaining lifespan by 109% in aged mice149 and reversed epigenetic age in human skin cells by 30 years. The mechanisms are understood; we lack only the engineering to apply them safely in humans.

  2. Cardiovascular disease is 80-90% preventable. WHO and Cleveland Clinic data147 show that addressing lifestyle and environmental risk factors prevents the vast majority of heart attacks and strokes. With gene therapy addressing genetic predisposition, 95% is achievable.

  3. Cancer is 90-95% environmental/lifestyle-driven. Only 5-10% of cancers are purely genetic148; the remainder have modifiable causes (tobacco, diet, infections, pollutants). Perfect prevention + early AI detection + immunotherapy approaches 95%.

  4. Neurodegenerative diseases have regenerative potential. Stem cell therapy shows promise152 for Alzheimer’s, Parkinson’s, and ALS. The 80% max reflects early intervention before irreversible damage.

  5. Accidents remain the hard floor. WHO recognizes most injuries as preventable150, but ~40% of accidental deaths involve instantaneous trauma (explosions, severe falls) beyond any medical intervention. This accounts for the 7.37% unavoidable baseline.

5.2.3 The 7.37% Floor

The remaining deaths are fundamentally unavoidable even with perfect biotechnology:

  • Instantaneous traumatic death (e.g., explosions, severe falls)
  • Drowning beyond rescue window
  • Violence/homicide
  • Certain catastrophic accidents

These represent the hard physical limits of medicine. Everything else, including “natural death from old age,” is an engineering problem with engineering solutions.

5.3 Data Sources & Parameterization

  1. Development Timelines: Biotechnology Innovation Organization (BIO) Clinical Development Success Rates 2011–2020.
  2. Pharmaceutical Impact (Life-Years Saved): Primary source: Lichtenberg (2019)84.
    NoteLife-Years vs. Lives

    Lichtenberg measured life-years saved, not lives. Converting to “lives” requires assuming average life extension per beneficiary (12 years (95% CI: 8 years-18 years)). Life-years is the more rigorous metric; lives is used for intuitive communication. The uncertainty in the conversion is reflected in the confidence intervals.

    Supporting evidence (approximate, for context):
    • Vaccines: ~4.5M lives/year (WHO estimates 154M lives saved over 50 years)153
    • Cardiovascular: ~3.3M lives/year (Resolve to Save Lives / GBD Data)
    • Oncology: ~1.5M lives/year (NBER longevity studies)
  3. Economic Valuation: Standard QALY Valuation.

5.4 Uncertainty Quantification Methodology

This analysis employs Probabilistic Sensitivity Analysis (PSA) via Monte Carlo simulation to propagate parameter uncertainty through all calculations.

Distribution Selection:

  • Normal: Symmetric uncertainty around point estimates (e.g., trial duration)
  • Lognormal: Right-skewed, strictly positive values (costs, relative risks)
  • Beta: Bounded probabilities [0,1] (success rates, adoption rates)
  • Triangular: When only min/mode/max available from literature

Propagation Method:

  1. Sample N=10,000 draws from each input parameter’s distribution
  2. Recompute all derived parameters for each Monte Carlo draw
  3. Report median and 95% credible intervals (2.5th-97.5th percentiles)

Sensitivity Analysis:

Tornado charts identify which input parameters drive outcome uncertainty by varying each parameter ±1 standard deviation while holding others at baseline. Standardized regression coefficients (β*) enable comparison across parameters with different units.

See Parameters & Calculations Appendix for complete parameter distributions, formulas, and sensitivity analyses for each calculated value.

6 Results: The Mortality Burden

6.1 Primary Estimate

Important Clarification: Throughout this analysis, “regulatory delay” refers specifically to the post-safety efficacy testing delay - the period AFTER safety has been established but BEFORE efficacy approval is granted under current FDA/EMA requirements. This is distinct from safety testing (Phase I), which we consider necessary and effective (as demonstrated by the thalidomide case where safety testing prevented thousands of U.S. deaths).

WarningMethodological Caveat: Cascade Assumption

The primary estimate assumes that the 8.2 years (95% CI: 4.85 years-11.5 years) regulatory delay cascades fully through the biomedical research timeline - i.e., that delaying Drug A by 8.2 years (95% CI: 4.85 years-11.5 years) also delays all downstream research that builds on Drug A’s findings by approximately the same amount. This “full cascade” assumption represents a theoretical upper bound. In practice, parallel research tracks, international approvals, and adaptive innovation may partially mitigate cascade effects.

The assumption is not empirically validated at the aggregate level, though individual case studies (beta-blockers, HIV/AIDS, Hepatitis C) demonstrate the mechanism operates in specific instances. The Type II/Type I ratio remains robust even under substantially reduced cascade assumptions (see sensitivity analysis showing the conclusion holds at 10% regulatory attribution).

Metric Estimate Methodology
Total Deaths 416M deaths (95% CI: 225M deaths-630M deaths) Regulatory delay shifts disease eradication timeline by 8.2 years (95% CI: 4.85 years-11.5 years). Uses WHO global disease mortality rate (150k deaths/day (95% CI: 137k deaths/day-162k deaths/day)/day).

Finding: The disease eradication delay model estimates 416M deaths (95% CI: 225M deaths-630M deaths) total eventually avoidable deaths, with 150k deaths/day (95% CI: 137k deaths/day-162k deaths/day) per day - greater than the combined casualties of World War I and World War II over the 62-year period.

\[ \begin{gathered} Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

6.1.1 Sensitivity Analysis

Sensitivity Indices for Total Deaths from Disease Eradication Delay

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

Input Parameter Sensitivity Coefficient Interpretation
Efficacy Lag Years 1.1404 Strong driver
Global Disease Deaths Daily -0.1422 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.

7 Morbidity Analysis: DALYs and QALYs

Mortality counts fail to capture the suffering of patients living with untreated disabilities during the delay period. We calculated Disability-Adjusted Life Years (DALYs) using the formula \(DALY = YLL + YLD\).

7.1 Years of Life Lost (YLL)

\[ \begin{gathered} YLL_{lag} \\ = Deaths_{lag} \times (LE_{global} - Age_{death,delay}) \\ = 416M \times (79 - 62) \\ = 7.07B \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

7.2 Years Lived with Disability (YLD)

\[ \begin{gathered} YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

7.3 Cumulative DALY Burden

\[ \begin{gathered} DALYs_{lag} = YLL_{lag} + YLD_{lag} = 7.07B + 873M = 7.94B \\[0.5em] \text{where } YLL_{lag} \\ = Deaths_{lag} \times (LE_{global} - Age_{death,delay}) \\ = 416M \times (79 - 62) \\ = 7.07B \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \\[0.5em] \text{where } YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

Monte Carlo Distribution: Total DALYs Lost from Disease Eradication Delay (10,000 simulations)

Monte Carlo Distribution: Total DALYs Lost from Disease Eradication Delay (10,000 simulations)

Simulation Results Summary: Total DALYs Lost from Disease Eradication Delay

Statistic Value
Baseline (deterministic) 7.94B
Mean (expected value) 8.05B
Median (50th percentile) 7.89B
Standard Deviation 2.31B
90% Confidence Interval [4.43B, 12.1B]

The histogram shows the distribution of Total DALYs Lost from Disease Eradication Delay across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

Interpretation: The regulatory framework has effectively deleted 7.94B DALYs (95% CI: 4.43B DALYs-12.1B DALYs) billion years of healthy human life.

7.4 Years Lived with Disability - Treatment Beneficiaries

The YLD calculation above captures suffering before death for those who ultimately died from delayed treatments. However, a much larger population - the 982M people (95% CI: 827M people-1.15B people) people annually who receive chronic disease treatment - also suffered during the 8.2 years (95% CI: 4.85 years-11.5 years) delay before their treatments became available.

NoteDistinction: Mortality vs. Morbidity Burden

The “12.4M deaths (95% CI: 7.60M deaths-18.6M deaths) lives saved annually” from Lichtenberg’s analysis captures mortality - people who would have died without post-1962 drugs. But pharmaceutical treatments primarily improve quality of life for people with non-terminal chronic conditions: diabetes, hypertension, depression, COPD, arthritis, and cardiovascular disease.

Treatment beneficiaries vastly exceed mortality beneficiaries.

Data source: IQVIA reports that global pharmaceutical use reached 1.8 trillion days of therapy in 2019, with 71% for chronic conditions (diabetes, CVD, respiratory, cancer)57. From this, we estimate approximately 982M people (95% CI: 827M people-1.15B people) unique patients receive chronic disease treatment annually.

Treatment beneficiary YLD calculation:

\[ \begin{gathered} YLD_{treat\_delay} \\ = N_{treated} \times T_{lag} \times \Delta DW_{treat} \\ = 982M \times 8.2 \times 0.25 \\ = 2.01B \\[0.5em] \text{where } N_{treated} \\ = DOT_{chronic} \times 0.000767 \\ = 1.28T \times 0.000767 \\ = 982M \end{gathered} \]

Interpretation: Each year, patients receiving treatment for chronic conditions would have collectively avoided 2.01B DALYs (95% CI: 661M DALYs-4.41B DALYs) of disability if those treatments had been available 8.2 years (95% CI: 4.85 years-11.5 years) earlier.

Table 1: Comparison of mortality vs. morbidity burden from regulatory delay
Metric Annual Burden Source
Lives saved (mortality)

12.4M deaths (95% CI: 7.60M deaths-18.6M deaths)

 Lichtenberg 2019
Treatment beneficiaries (morbidity)

982M people (95% CI: 827M people-1.15B people)

 IQVIA 2024
Ratio ~80:1 Morbidity >> mortality

The treatment beneficiary population is approximately 80 times larger than the mortality-focused “lives saved” figure, demonstrating that the morbidity cost of regulatory delay vastly exceeds the mortality cost.

8 Economic Valuation

To quantify the Deadweight Loss (DWL) to the global economy, we apply the Value of a Statistical Life Year (VSLY).

\[ DWL = \sum (DALY_{loss} \times VSLY) \]

Using a conservative global VSLY of $150K (95% CI: $100K-$199K):

\[ \begin{gathered} Value_{lag} \\ = DALYs_{lag} \times Value_{QALY} \\ = 7.94B \times \$150K \\ = \$1190T \\[0.5em] \text{where } DALYs_{lag} = YLL_{lag} + YLD_{lag} = 7.07B + 873M = 7.94B \\[0.5em] \text{where } YLL_{lag} \\ = Deaths_{lag} \times (LE_{global} - Age_{death,delay}) \\ = 416M \times (79 - 62) \\ = 7.07B \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \\[0.5em] \text{where } YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \end{gathered} \]

8.1 Contextualizing the Loss

9 Risk Analysis: The Type I vs. Type II Ratio

A critical counter-argument is that the FDA protects society from dangerous or ineffective drugs (Type I Errors). We modeled the maximum potential damage of a “Deregulation Scenario” to generate an Efficiency Ratio.

NoteMethodological Note: Steelmanning the FDA’s Position

To ensure this analysis is maximally fair to proponents of current FDA regulation, we deliberately assume the worst possible case for Type I errors (harm from approving bad drugs). This “steelman” approach means that even if our assumptions are completely wrong in favor of FDA defenders, the conclusion holds.

Specifically, we assume a Thalidomide-scale catastrophe every single year in the counterfactual scenario. This is an extraordinarily extreme overestimate for three reasons:

  1. Thalidomide was a once-in-a-century event - no comparable disaster has occurred since
  2. We propose retaining Phase I safety testing - our critique is of efficacy requirements (Phase II/III), not safety requirements
  3. Thalidomide was caught by 1938 safety requirements, NOT 1962 efficacy requirements - FDA’s Dr. Frances Kelsey blocked thalidomide approval based on safety concerns about nerve damage, using authority from the 1938 Food, Drug, and Cosmetic Act. The 1962 efficacy amendments hadn’t yet passed. Under our proposal, thalidomide would STILL have been blocked.

This means we’re giving FDA credit for preventing disasters that our proposed changes wouldn’t affect. We’re assuming annual occurrences of an event that (a) has happened once in 60+ years, and (b) wouldn’t be enabled by removing efficacy requirements anyway. This is the maximum possible benefit of the doubt.

  • The Cost of Protection (Type II): 7.94B DALYs (95% CI: 4.43B DALYs-12.1B DALYs) lost.
  • The Benefit of Protection (Type I): Even assuming a “Thalidomide Event” occurs every single year under a deregulated model (a deliberate extreme overestimate to steelman the FDA’s position), the total DALYs saved by the FDA is ~2.59M DALYs (95% CI: 1.54M DALYs-4.16M DALYs).
    • Adjusted for “Snake Oil” (Financial Loss): Even valuing financial fraud at DALY equivalents, the benefit caps at ~0.6 Billion DALYs.

Type I Benefit Calculation (Steelman):

\[ \begin{gathered} DALY_{TypeI} = DALY_{thal} \times 62 = 41{,}800 \times 62 = 2.59M \\[0.5em] \text{where } DALY_{thal} \\ = YLD_{thal} + YLL_{thal} \\ = 13{,}000 + 28{,}800 \\ = 41{,}800 \\[0.5em] \text{where } YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \\[0.5em] \text{where } N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \\[0.5em] \text{where } YLL_{thal} = Deaths_{thal} \times 80 = 360 \times 80 = 28{,}800 \\[0.5em] \text{where } Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

9.1 The Risk Trade-off Ratio

\[ \begin{gathered} Ratio_{TypeII} = \frac{DALYs_{lag}}{DALY_{TypeI}} = \frac{7.94B}{2.59M} = 3{,}070 \\[0.5em] \text{where } DALYs_{lag} = YLL_{lag} + YLD_{lag} = 7.07B + 873M = 7.94B \\[0.5em] \text{where } YLL_{lag} \\ = Deaths_{lag} \times (LE_{global} - Age_{death,delay}) \\ = 416M \times (79 - 62) \\ = 7.07B \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \\[0.5em] \text{where } YLD_{lag} \\ = Deaths_{lag} \times T_{suffering} \times DW_{chronic} \\ = 416M \times 6 \times 0.35 \\ = 873M \\[0.5em] \text{where } Deaths_{lag} \\ = T_{lag} \times Deaths_{disease,daily} \times 338 \\ = 8.2 \times 150{,}000 \times 338 \\ = 416M \\[0.5em] \text{where } DALY_{TypeI} = DALY_{thal} \times 62 = 41{,}800 \times 62 = 2.59M \\[0.5em] \text{where } DALY_{thal} \\ = YLD_{thal} + YLL_{thal} \\ = 13{,}000 + 28{,}800 \\ = 41{,}800 \\[0.5em] \text{where } YLD_{thal} \\ = DW_{thal} \times N_{thal,survive} \times LE_{thal} \\ = 0.4 \times 540 \times 60 \\ = 13{,}000 \\[0.5em] \text{where } N_{thal,survive} \\ = N_{thal,US,prevent} \times (1 - Rate_{thal,mort}) \\ = 900 \times (1 - 40\%) \\ = 540 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \\[0.5em] \text{where } YLL_{thal} = Deaths_{thal} \times 80 = 360 \times 80 = 28{,}800 \\[0.5em] \text{where } Deaths_{thal} \\ = Rate_{thal,mort} \times N_{thal,US,prevent} \\ = 40\% \times 900 \\ = 360 \\[0.5em] \text{where } N_{thal,US,prevent} \\ = N_{thal,global} \times Pct_{US,1960} \\ = 15{,}000 \times 6\% \\ = 900 \end{gathered} \]

Conclusion: For every 1 unit of harm the FDA prevents (Type I errors: approving dangerous/ineffective drugs), it generates 3.07k:1 (95% CI: 2.88k:1-3.12k:1) units of harm through delay (Type II errors: blocking effective drugs). This ratio is conservative - it assumes a Thalidomide-scale disaster every single year, dramatically overstating FDA benefits. With realistic Type I estimates, the ratio would be far higher.

Chart showing Type II errors (7.94B DALYs from regulatory delay) vs Type I errors (2.59M DALYs from approving bad drugs) - a 3,070:1 ratio with error bars

Chart showing Type II errors (7.94B DALYs from regulatory delay) vs Type I errors (2.59M DALYs from approving bad drugs) - a 3,070:1 ratio with error bars

Monte Carlo Distribution: Ratio of Type Ii Error Cost to Type I Error Benefit (10,000 simulations)

Monte Carlo Distribution: Ratio of Type Ii Error Cost to Type I Error Benefit (10,000 simulations)

Simulation Results Summary: Ratio of Type Ii Error Cost to Type I Error Benefit

Statistic Value
Baseline (deterministic) 3.07k:1
Mean (expected value) 3.05k:1
Median (50th percentile) 3.09k:1
Standard Deviation 101:1
90% Confidence Interval [2.88k:1, 3.12k:1]

The histogram shows the distribution of Ratio of Type Ii Error Cost to Type I Error Benefit across 10,000 Monte Carlo simulations. The CDF (right) shows the probability of the outcome exceeding any given value, which is useful for risk assessment.

9.2 Acknowledging the Efficacy-as-Safety Argument

A legitimate concern deserves direct engagement: efficacy requirements may function as indirect safety measures. A drug that doesn’t work exposes patients to adverse effects without therapeutic benefit. The risk-benefit ratio becomes infinite when benefit is zero.

Counter-arguments:

  1. Real-world evidence detects inefficacy faster than small RCTs with selected populations
  2. Adaptive trials can withdraw ineffective arms mid-study without full Phase III completion
  3. The 1938-1962 system had physician-reported efficacy assessment without pre-market mandates, and higher approval rates
  4. Post-market surveillance with active monitoring catches ineffective drugs while allowing patient access

9.3 Drugs Appropriately Caught by Phase II/III Trials

This analysis acknowledges that Phase II/III trials do catch some drugs that would have caused harm. Three notable examples:

  1. Torcetrapib (2006): Phase III trial of this CETP inhibitor for cardiovascular disease was terminated early after 82 deaths in the treatment arm vs. 51 in placebo (HR 1.58). The trial caught cardiovascular harm that would have affected millions of patients post-approval154.

  2. Semagacestat (2010): Phase III trial for Alzheimer’s disease found patients on treatment had worse cognitive outcomes than placebo, plus increased skin cancers and infections. The trial prevented approval of a drug that would have accelerated cognitive decline155.

  3. Drisapersen (2016): FDA rejected this Duchenne muscular dystrophy drug after Phase III showed no clinical benefit (P=0.415) alongside serious adverse events including thrombocytopenia and kidney damage in significant fractions of patients156.

However, three critical caveats apply:

  1. Denominator problem: We observe drugs caught by trials but cannot observe the counterfactual harm avoided. FDA does not publish systematic data on rejected drugs and their potential harm.

  2. Detection limits: Trials with 3,000 patients cannot reliably detect adverse events rarer than ~1-in-1,000. Vioxx (38,000-55,000 American deaths) passed Phase III because its cardiovascular risk required millions of patient-years to surface157.

  3. Our Type I estimate is conservative: We assume Thalidomide-scale disasters every year, an extreme upper bound that still yields the 3.07k:1 (95% CI: 2.88k:1-3.12k:1) ratio.

10 Model Assumptions and Limitations

10.1 Key Assumptions

  1. Linear Adoption Model: Assumes drug uptake follows a predictable pattern post-approval
  2. Constant VSLY: Uses global average of $150K (95% CI: $100K-$199K)/year
  3. No Regulatory Learning: Assumes FDA efficiency remained constant 1962-2024
  4. Independence: Treats each drug approval as independent (may underestimate synergies)

10.2 Sensitivity Analysis

The model was tested across multiple scenarios:

  • Discount Rates: 3% (base case)
  • Innovation Elasticity: 0.3–0.8 (base case: 0.5)
  • “Snake Oil” Rate: 10%–40% (base case: 20%)
  • VSLY: $150K (95% CI: $100K-$199K)

Results remain robust across all reasonable parameter ranges, with lower bound estimates exceeding 100M deaths in all scenarios.

10.3 Limitations

  1. Counterfactual Uncertainty: Cannot directly observe what would have happened without 1962 amendments
  2. Confounding Factors: Other policy changes occurred simultaneously (Medicare, NIH funding)
  3. Attribution Challenge: Difficult to separate FDA effects from broader trends
  4. Data Quality: Early period (1960s-1970s) relies on retrospective estimates

Despite these limitations, the plausible mechanism (70% drop in approvals, 13.4:1 (95% CI: 11.9:1-14.7:1) cost increase) provides strong inferential evidence that regulatory changes significantly impacted drug development.

11 Policy Implications

11.1 The False Trade-off

The current debate frames drug approval as a choice between:

  1. Safety (slow, expensive approval) vs.
  2. Speed (fast, dangerous approval)

This is a false dichotomy. The evidence suggests:

  • Phase I safety testing works (Thalidomide prevented in US)
  • Phase II/III efficacy mandates fail (70% fewer approvals, worse real-world outcomes)

11.2 The Bifurcated Alternative

A superior framework would:

  1. Maintain rigorous Phase I safety testing (2.3 years)
  2. Allow provisional approval post-safety with real-world evidence collection
  3. Continuous monitoring via distributed systems (see: decentralized framework for drug assessment)
  4. Outcome-based validation rather than pre-market prediction

This approach would reduce the efficacy lag from 8.2 years (95% CI: 4.85 years-11.5 years) to near-zero while maintaining safety standards.

11.3 Expected Impact

If implemented today, the bifurcated model would:

See 1% treaty impact analysis for full quantified cost-benefit analysis.

11.4 International Regulatory Comparison

Several countries have implemented alternative regulatory models that provide natural experiments:

Country Approval System Avg. Timeline Key Features
USA (FDA) Full Phase III required

9.1 years (95% CI: 6 years-12 years)

 Baseline for comparison
Japan (PMDA) Conditional approval after Phase II 2-3 years Regenerative Medicine Act (2014); real-world monitoring158
EU (EMA) Adaptive Pathways available ~10 years Similar to FDA; conditional marketing authorization option
Canada Priority Review pathway ~12 months (priority) Limited data on outcomes
Australia (TGA) Provisional approval pathway Variable Similar conditional pathways
ImportantCritical Distinction: Efficacy Assessment Reordered, Not Eliminated

Japan’s conditional approval does NOT eliminate efficacy assessment. It REORDERS it from pre-market (Phase III trials) to post-market (real-world monitoring with revocation authority). This is a different regulatory architecture, not deregulation. The HeartSheet withdrawal proves the system still enforces efficacy standards, just through different mechanisms.

Key finding: Japan’s conditional approval system has an 89% success rate (8/9 products) and demonstrated that post-market monitoring CAN catch ineffective treatments:

  • Faster access: 9 products received conditional early approval (2014-2024), reaching patients years earlier than traditional pathways
  • Success cases: STEMIRAC (spinal cord injury) showed 12/13 patients (92%) achieved neurological improvement, with 2 of 5 completely paralyzed patients regaining motor function159. Five CAR-T therapies (Kymriah, Yescarta, Breyanzi, Abecma, Carvykti) are treating cancer patients under national insurance coverage160.
  • The system caught inefficacy: HeartSheet was conditionally approved in 2015 with the requirement to prove efficacy through post-market data. In 2024, after collecting real-world evidence, MHLW determined it hadn’t demonstrated efficacy. The manufacturer voluntarily withdrew the next day. This is the system working as designed - conditional approval was conditional, and the condition wasn’t met.
  • Contrast with FDA: Vioxx killed 38,000-55,000 Americans before withdrawal because the 6% voluntary reporting system failed to detect the signal. Japan’s active monitoring caught HeartSheet’s lack of efficacy with zero reported deaths.

The real question for HeartSheet: During those 9 years, did heart failure patients (who have few alternatives) benefit from access to an unproven treatment? The safety profile was acceptable - efficacy was the issue. This is a genuine tradeoff that merits cost-benefit analysis, not automatic condemnation.

2024 reforms strengthen, not abandon, conditional approval: Japan’s June 2024 amendments to the Regenerative Medicine Act add a formal revocation provision that was previously missing. The old system had no legal mechanism to force withdrawal if efficacy wasn’t proven - HeartSheet was voluntary. The reforms close this gap while expanding coverage to in vivo gene therapy. Japan is refining conditional approval based on experience, not abandoning it.

Pharmacovigilance infrastructure exists: The FDA launched the Sentinel Initiative in 2008 to monitor safety using electronic health records. In 2024, FDA eliminated major barriers to using real-world data. The technology for active surveillance exists - the barrier is institutional inertia, not technical impossibility.

12 Addressing Common Critiques

This analysis will face predictable objections. We address them here not defensively, but to demonstrate that the core conclusion, that regulatory delay costs vastly exceed regulatory benefits, remains robust even under unfavorable assumptions.

12.1 “The PRIMARY Estimate Is Too Speculative”

Critique: The PRIMARY estimate (416M deaths (95% CI: 225M deaths-630M deaths)) assumes we would have eradicated diseases by now without regulations. This is unproven and overly optimistic.

Response:

This critique misunderstands the methodology. The PRIMARY scenario does not assume disease eradication would be complete by 2024. It assumes the entire biomedical research timeline shifts backward by 8.2 years (95% CI: 4.85 years-11.5 years) due to regulatory delay.

The mechanism:

  1. Every drug takes 8.2 years (95% CI: 4.85 years-11.5 years) longer to reach patients (BIO data, Section 2.3)
  2. Downstream research depends on upstream results (Drug B builds on Drug A’s findings)
  3. Capital allocation: $2.60B (95% CI: $1.50B-$4B) cost limits parallel research tracks (97.7% (95% CI: 97.5%-98.9%) reduction enables proportionally more simultaneous trials)
  4. Knowledge accumulation delays compound across the entire field

Robustness test:

Even if you adjust the primary estimate significantly:

12.2 “The ‘Eventually Preventable’ Estimate Is Theoretical”

Critique: The claim that 92.6% (95% CI: 50%-98%) of deaths are eventually preventable is based on theoretical biological potential, not demonstrated medical capability.

Response:

Correct. That’s what “eventually” means.

The document explicitly distinguishes “Current” from “Max Potential” in the disease burden table (Section 2.2). The 92.6% (95% CI: 50%-98%) represents the theoretical upper bound based on:

  1. Aging reversed in mammals: Yamanaka factors extended remaining lifespan by 109% in aged mice149
  2. Cardiovascular disease 80-90% preventable NOW: WHO data147 with current interventions
  3. Cancer 90-95% environmental: Only 5-10% purely genetic148, remainder has modifiable causes

The relevant question isn’t “Can we achieve this upper bound?”

The question is: “When do we achieve it?”

If regulations delay progress by 8.2 years (95% CI: 4.85 years-11.5 years), everyone who dies during that window dies because of the delay.

Note: The PRIMARY estimate uses global disease mortality rates, not the 92.6% (95% CI: 50%-98%) ceiling. This upper bound provides context for the theoretical maximum scenario.

12.3 “Counterfactual Uncertainty - We Can’t Know What Would Have Happened”

Critique: The analysis depends on an unknowable counterfactual: what would have happened without the 1962 amendments.

Response:

Counterfactuals are never directly observable. That’s why science uses natural experiments and inferential evidence. We have both.

12.3.1 Natural Experiments

Alternative Regulatory Models:

  • Japan’s Regenerative Medicine Act (2014): Conditional approval after Phase II safety data, with 2-3 year timelines vs. 9.1 years (95% CI: 6 years-12 years). Critics note quality concerns; proponents note faster access for terminal patients with no alternatives.
  • EU Compassionate Use: Terminal patients access experimental drugs before approval
  • Medical tourism: Americans travel abroad for treatments unavailable in the US, demonstrating revealed preference for faster access

12.3.2 The Standard for Causal Inference

The same standard used in all clinical research:

\[ \text{Causation} = \text{Temporal Correlation} + \text{Mechanism} + \text{Lack of Alternative Explanations} \]

We have:

  1. Temporal correlation: Drug approvals dropped 70% immediately after 1962
  2. Mechanism: Costs increased 13.4:1 (95% CI: 11.9:1-14.7:1), real-world trials banned, efficacy requirements added 8.2 years (95% CI: 4.85 years-11.5 years) to development
  3. Alternative explanations: Other factors exist (complexity, standards, etc.), but the timing and magnitude strongly suggest regulatory latency is a major contributor

If you reject this inferential method, you must also reject the methodology of clinical trials, which use the identical logical structure.

12.4 “Confounding Factors - Other Changes in 1962”

Critique: Medicare (1965), NIH funding changes, Vietnam War, and other 1960s policy shifts confound the analysis. How can we isolate the 1962 amendments’ effect?

Response:

Confounders work against the hypothesis, making the observed effect more remarkable.

Medicare (1965): Expanded healthcare access → should have increased drug demand and development → Yet approvals dropped 70%

NIH Funding: Grew dramatically 1960s-1980s → should have accelerated drug development → Yet approvals dropped 70%

Vietnam War (1965-1973): Primarily affected young males, minimal impact on overall drug development patterns

The temporal precision matters: Drug approval rates dropped 70% in 1962, not 1965 (Medicare) or 1964 (Gulf of Tonkin). The break coincides exactly with the Kefauver-Harris Amendments, not with other major policy changes.

Quantitative test:

If confounders explained the effect, we would expect:

  • Gradual change over the 1960s (as various policies took effect)
  • Recovery after confounders resolved (e.g., Vietnam War ended 1973)

Instead, we observe:

The hypothesis that fits the data is: structural change in drug approval requirements permanently reduced the rate of biomedical progress.

12.4.1 Sensitivity Analysis: What if Regulation Explains Only Part of the Decline?

Even if we concede that non-regulatory factors (complexity, pharmacological saturation, etc.) explain a substantial portion of the approval decline, the conclusion remains robust:

Regulatory Attribution Type II Estimate Type I Estimate Ratio Conclusion
100% (baseline)

7.94B DALYs (95% CI: 4.43B DALYs-12.1B DALYs)

 ~2.59M DALYs (95% CI: 1.54M DALYs-4.16M DALYs)

3.07k:1 (95% CI: 2.88k:1-3.12k:1)

 Type II dominates
75% ~75% of baseline ~2.59M DALYs (95% CI: 1.54M DALYs-4.16M DALYs) ~2,300:1 Type II dominates
50% ~50% of baseline ~2.59M DALYs (95% CI: 1.54M DALYs-4.16M DALYs) ~1,500:1 Type II dominates
25% ~25% of baseline ~2.59M DALYs (95% CI: 1.54M DALYs-4.16M DALYs) ~770:1 Type II dominates
10% ~10% of baseline ~2.59M DALYs (95% CI: 1.54M DALYs-4.16M DALYs) ~300:1 Type II still dominates

Key insight: The Type II/Type I ratio would need to drop below 1:1 for the FDA’s approach to be justified on net mortality grounds. Even at 10% regulatory attribution, the ratio remains ~300:1. The conclusion is robust across a wide range of assumptions about confounding.

12.5 “This Ignores Safety - Deregulation Would Flood Markets with Dangerous Drugs”

Critique: Without efficacy requirements, pharmaceutical companies will sell snake oil and dangerous drugs. Type I errors (approving bad drugs) will explode.

Response:

The analysis explicitly models this in Section 6: Risk Analysis.

What the model assumes:

  • Thalidomide-scale disaster every single year under deregulation (extreme overestimate)
  • 20% of approved drugs are “snake oil” (financially harmful but not dangerous)
  • Financial fraud valued at DALY equivalents

Result: Type I harm caps at ~2.59M DALYs (95% CI: 1.54M DALYs-4.16M DALYs)

What the proposal actually includes:

  1. Phase I safety testing remains (proven effective: prevented thalidomide in US while Europe had thousands of deaths)
  2. Real-world evidence collection (catches problems faster than current passive reporting)
  3. Continuous monitoring via distributed systems (see decentralized framework for drug assessment)

Historical evidence:

The pre-1962 system (1938-1962) included:

  • Phase I safety testing (mandated by 1938 Food, Drug, and Cosmetic Act)
  • Decentralized efficacy assessment by practicing physicians (~229,000 in US by 1960)1614
  • Third-party review via AMA Council on Pharmacy provided independent evaluation
  • Result: Higher approval rates with safety maintained by mandatory Phase I testing

Current system failures:

  • Vioxx: 38,000-55,000 American deaths157 from cardiovascular events that Phase II/III trials (N≈3,000) were statistically underpowered to detect. The 1-in-1,000 risk required millions of patient-years to surface.
  • Statistical reality: Trials with 3,000 patients cannot reliably detect adverse events rarer than ~1-in-1,000

The detection paradox: Pre-market trials on 3,000 selected patients, followed by 6% voluntary post-market reporting, is far more dangerous than active surveillance of millions of real-world patients. The current system catches common problems early but misses rare-but-deadly risks until thousands have died.

13 Conclusion

The quantitative evidence demonstrates that the 1962 Kefauver-Harris efficacy requirements have generated catastrophic human costs:

The 3.07k:1 (95% CI: 2.88k:1-3.12k:1) ratio demonstrates that these costs dwarf the benefits. The regulatory framework optimizes for bureaucratic risk minimization (avoiding blame for approvals) rather than population health maximization (saving lives).

The path forward is clear: maintain safety testing, eliminate efficacy delay, and deploy distributed real-world evidence systems.

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13.
Marron Institute, N. Transit costs project - why US infrastructure costs so much. NYU Transit Costs Project https://transitcosts.com/ (2024)
The United States builds transit infrastructure at dramatically higher costs than peer countries. New York’s Second Avenue Subway cost $2.5 billion per kilometer vs. $200-500 million in European cities. US highway construction similarly costs 2-5x more than comparable projects abroad. Causes include: excessive environmental review, litigation risk, lack of in-house expertise, fragmented project management, and inflated soft costs. Additional sources: https://transitcosts.com/ | https://www.brookings.edu/articles/why-does-infrastructure-cost-so-much/
.
14.
Clemens, M. A. Economics and emigration: Trillion-dollar bills on the sidewalk? Journal of Economic Perspectives 25, 83–106 (2011)
Free global labor mobility would increase gross world product by somewhere in the range of 67-147%... The gains to eliminating migration barriers amount to large fractions of world GDP—one or two orders of magnitude larger than the gains from dropping all remaining restrictions on international flows of goods and capital.
15.
Kleiner, M. M. & Krueger, A. B. Analyzing the extent and influence of occupational licensing on the labor market. Journal of Labor Economics 31, S173–S202 (2013)
Occupational licensing affects 29% of US workers and creates labor market distortions costing 2-3% of GDP.
16.
Lab, Y. B. 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.
17.
Foundation, T. 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.
18.
Organization, W. H. WHO global health estimates 2024. World Health Organization https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates (2024)
Comprehensive mortality and morbidity data by cause, age, sex, country, and year Global mortality:  55-60 million deaths annually Lives saved by modern medicine (vaccines, cardiovascular drugs, oncology):  12M annually (conservative aggregate) Leading causes of death: Cardiovascular disease (17.9M), Cancer (10.3M), Respiratory disease (4.0M) Note: Baseline data for regulatory mortality analysis. Conservative estimate of pharmaceutical impact based on WHO immunization data (4.5M/year from vaccines) + cardiovascular interventions (3.3M/year) + oncology (1.5M/year) + other therapies. Additional sources: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates
.
19.
GiveWell. GiveWell cost per life saved for top charities (2024). GiveWell: Top Charities https://www.givewell.org/charities/top-charities
General range: $3,000-$5,500 per life saved (GiveWell top charities) Helen Keller International (Vitamin A): $3,500 average (2022-2024); varies $1,000-$8,500 by country Against Malaria Foundation: $5,500 per life saved New Incentives (vaccination incentives): $4,500 per life saved Malaria Consortium (seasonal malaria chemoprevention):  $3,500 per life saved VAS program details:  $2 to provide vitamin A supplements to child for one year Note: Figures accurate for 2024. Helen Keller VAS program has wide country variation ($1K-$8.5K) but $3,500 is accurate average. Among most cost-effective interventions globally Additional sources: https://www.givewell.org/charities/top-charities | https://www.givewell.org/charities/helen-keller-international | https://ourworldindata.org/cost-effectiveness
.
20.
literature, E. psychology. Average reading speed.
Adults:  250 words/minute (silent reading)
.
21.
AARP. Unpaid caregiver hours and economic value. AARP 2023 https://www.aarp.org/caregiving/financial-legal/info-2023/unpaid-caregivers-provide-billions-in-care.html (2023)
Average family caregiver: 25-26 hours per week (100-104 hours per month) 38 million caregivers providing 36 billion hours of care annually Economic value: $16.59 per hour = $600 billion total annual value (2021) 28% of people provided eldercare on a given day, averaging 3.9 hours when providing care Caregivers living with care recipient: 37.4 hours per week Caregivers not living with recipient: 23.7 hours per week Note: Disease-related caregiving is subset of total; includes elderly care, disability care, and child care Additional sources: https://www.aarp.org/caregiving/financial-legal/info-2023/unpaid-caregivers-provide-billions-in-care.html | https://www.bls.gov/news.release/elcare.nr0.htm | https://www.caregiver.org/resource/caregiver-statistics-demographics/
.
22.
MMWR, C. Childhood vaccination economic benefits. CDC MMWR https://www.cdc.gov/mmwr/volumes/73/wr/mm7331a2.htm (1994)
US programs (1994-2023): $540B direct savings, $2.7T societal savings ( $18B/year direct,  $90B/year societal) Global (2001-2020): $820B value for 10 diseases in 73 countries ( $41B/year) ROI: $11 return per $1 invested Measles vaccination alone saved 93.7M lives (61% of 154M total) over 50 years (1974-2024) Additional sources: https://www.cdc.gov/mmwr/volumes/73/wr/mm7331a2.htm | https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24
.
23.
CDC. Childhood vaccination (US) ROI. CDC https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6316a4.htm (2017).
24.
Labor Statistics, U. S. B. of. CPI inflation calculator. (2024)
CPI-U (1980): 82.4 CPI-U (2024): 313.5 Inflation multiplier (1980-2024): 3.80× Cumulative inflation: 280.48% Average annual inflation rate: 3.08% Note: Official U.S. government inflation data using Consumer Price Index for All Urban Consumers (CPI-U). Additional sources: https://www.bls.gov/data/inflation_calculator.htm
.
25.
Del Rosal, I. The empirical measurement of rent-seeking costs. Journal of Economic Surveys https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-6419.2009.00621.x (2011)
A comprehensive survey of empirical estimates finds rent-seeking costs range from 0.2% to 23.7% of GDP across different methodologies and countries. Laband & Sophocleus (1988) estimated up to 45% for the US.
26.
via, D. analysis. ClinicalTrials.gov cumulative enrollment data (2025). Direct analysis via ClinicalTrials.gov API v2 https://clinicaltrials.gov/data-api/api
Analysis of 100,000 active/recruiting/completed trials on ClinicalTrials.gov (November 2025) shows cumulative enrollment of 12.2 million participants: Phase 1 (722k), Phase 2 (2.2M), Phase 3 (6.5M), Phase 4 (2.7M). Median participants per trial: Phase 1 (33), Phase 2 (60), Phase 3 (237), Phase 4 (90). Additional sources: https://clinicaltrials.gov/data-api/api
.
27.
CAN, A. Clinical trial patient participation rate. ACS CAN: Barriers to Clinical Trial Enrollment https://www.fightcancer.org/policy-resources/barriers-patient-enrollment-therapeutic-clinical-trials-cancer
Only 3-5% of adult cancer patients in US receive treatment within clinical trials About 5% of American adults have ever participated in any clinical trial Oncology: 2-3% of all oncology patients participate Contrast: 50-60% enrollment for pediatric cancer trials (<15 years old) Note:  20% of cancer trials fail due to insufficient enrollment; 11% of research sites enroll zero patients Additional sources: https://www.fightcancer.org/policy-resources/barriers-patient-enrollment-therapeutic-clinical-trials-cancer | https://hints.cancer.gov/docs/Briefs/HINTS_Brief_48.pdf
.
28.
ScienceDaily. Global prevalence of chronic disease. ScienceDaily: GBD 2015 Study https://www.sciencedaily.com/releases/2015/06/150608081753.htm (2015)
2.3 billion individuals had more than five ailments (2013) Chronic conditions caused 74% of all deaths worldwide (2019), up from 67% (2010) Approximately 1 in 3 adults suffer from multiple chronic conditions (MCCs) Risk factor exposures: 2B exposed to biomass fuel, 1B to air pollution, 1B smokers Projected economic cost: $47 trillion by 2030 Note: 2.3B with 5+ ailments is more accurate than "2B with chronic disease." One-third of all adults globally have multiple chronic conditions Additional sources: https://www.sciencedaily.com/releases/2015/06/150608081753.htm | https://pmc.ncbi.nlm.nih.gov/articles/PMC10830426/ | https://pmc.ncbi.nlm.nih.gov/articles/PMC6214883/
.
29.
C&EN. Annual number of new drugs approved globally:  50. C&EN https://cen.acs.org/pharmaceuticals/50-new-drugs-received-FDA/103/i2 (2025)
50 new drugs approved annually Additional sources: https://cen.acs.org/pharmaceuticals/50-new-drugs-received-FDA/103/i2 | https://www.fda.gov/drugs/development-approval-process-drugs/novel-drug-approvals-fda
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30.
estimates, I. Clinical trial abandonment.
Average:  10% abandoned before completion
.
31.
Report, I. Global trial capacity. IQVIA Report: Clinical Trial Subjects Number Drops Due to Decline in COVID-19 Enrollment https://gmdpacademy.org/news/iqvia-report-clinical-trial-subjects-number-drops-due-to-decline-in-covid-19-enrollment/
1.9M participants annually (2022, post-COVID normalization from 4M peak in 2021) Additional sources: https://gmdpacademy.org/news/iqvia-report-clinical-trial-subjects-number-drops-due-to-decline-in-covid-19-enrollment/
.
32.
Research & Markets. Global clinical trials market 2024. Research and Markets https://www.globenewswire.com/news-release/2024/04/19/2866012/0/en/Global-Clinical-Trials-Market-Research-Report-2024-An-83-16-Billion-Market-by-2030-AI-Machine-Learning-and-Blockchain-will-Transform-the-Clinical-Trials-Landscape.html (2024)
Global clinical trials market valued at approximately $83 billion in 2024, with projections to reach $83-132 billion by 2030. Additional sources: https://www.globenewswire.com/news-release/2024/04/19/2866012/0/en/Global-Clinical-Trials-Market-Research-Report-2024-An-83-16-Billion-Market-by-2030-AI-Machine-Learning-and-Blockchain-will-Transform-the-Clinical-Trials-Landscape.html | https://www.precedenceresearch.com/clinical-trials-market
.
33.
OpenSecrets. Lobbying spend (defense). OpenSecrets https://www.opensecrets.org/federal-lobbying/industries/summary?cycle=2024\&id=D (2024).
34.
GiveWell. Cost per DALY for deworming programs. https://www.givewell.org/international/technical/programs/deworming/cost-effectiveness
Schistosomiasis treatment: $28.19-$70.48 per DALY (using arithmetic means with varying disability weights) Soil-transmitted helminths (STH) treatment: $82.54 per DALY (midpoint estimate) Note: GiveWell explicitly states this 2011 analysis is "out of date" and their current methodology focuses on long-term income effects rather than short-term health DALYs Additional sources: https://www.givewell.org/international/technical/programs/deworming/cost-effectiveness
.
35.
Numbers, T. by. Pre-1962 drug development costs and timeline (think by numbers). Think by Numbers: How Many Lives Does FDA Save? https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ (1962)
Historical estimates (1970-1985): USD $226M fully capitalized (2011 prices) 1980s drugs:  $65M after-tax R&D (1990 dollars),  $194M compounded to approval (1990 dollars) Modern comparison: $2-3B costs, 7-12 years (dramatic increase from pre-1962) Context: 1962 regulatory clampdown reduced new treatment production by 70%, dramatically increasing development timelines and costs Note: Secondary source; less reliable than Congressional testimony Additional sources: https://thinkbynumbers.org/health/how-many-net-lives-does-the-fda-save/ | https://en.wikipedia.org/wiki/Cost_of_drug_development | https://www.statnews.com/2018/10/01/changing-1962-law-slash-drug-prices/
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36.
Medicine, N. Drug repurposing rate ( 30%). Nature Medicine https://www.nature.com/articles/s41591-024-03233-x (2024)
Approximately 30% of drugs gain at least one new indication after initial approval. Additional sources: https://www.nature.com/articles/s41591-024-03233-x
.
37.
EPI. Education investment economic multiplier (2.1). EPI: Public Investments Outside Core Infrastructure https://www.epi.org/publication/bp348-public-investments-outside-core-infrastructure/
Early childhood education: Benefits 12X outlays by 2050; $8.70 per dollar over lifetime Educational facilities: $1 spent → $1.50 economic returns Energy efficiency comparison: 2-to-1 benefit-to-cost ratio (McKinsey) Private return to schooling:  9% per additional year (World Bank meta-analysis) Note: 2.1 multiplier aligns with benefit-to-cost ratios for educational infrastructure/energy efficiency. Early childhood education shows much higher returns (12X by 2050) Additional sources: https://www.epi.org/publication/bp348-public-investments-outside-core-infrastructure/ | https://documents1.worldbank.org/curated/en/442521523465644318/pdf/WPS8402.pdf | https://freopp.org/whitepapers/establishing-a-practical-return-on-investment-framework-for-education-and-skills-development-to-expand-economic-opportunity/
.
38.
PMC. Healthcare investment economic multiplier (1.8). PMC: California Universal Health Care https://pmc.ncbi.nlm.nih.gov/articles/PMC5954824/ (2022)
Healthcare fiscal multiplier: 4.3 (95% CI: 2.5-6.1) during pre-recession period (1995-2007) Overall government spending multiplier: 1.61 (95% CI: 1.37-1.86) Why healthcare has high multipliers: No effect on trade deficits (spending stays domestic); improves productivity & competitiveness; enhances long-run potential output Gender-sensitive fiscal spending (health & care economy) produces substantial positive growth impacts Note: "1.8" appears to be conservative estimate; research shows healthcare multipliers of 4.3 Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC5954824/ | https://cepr.org/voxeu/columns/government-investment-and-fiscal-stimulus | https://ncbi.nlm.nih.gov/pmc/articles/PMC3849102/ | https://set.odi.org/wp-content/uploads/2022/01/Fiscal-multipliers-review.pdf
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39.
Bank, W. Infrastructure investment economic multiplier (1.6). World Bank: Infrastructure Investment as Stimulus https://blogs.worldbank.org/en/ppps/effectiveness-infrastructure-investment-fiscal-stimulus-what-weve-learned (2022)
Infrastructure fiscal multiplier:  1.6 during contractionary phase of economic cycle Average across all economic states:  1.5 (meaning $1 of public investment → $1.50 of economic activity) Time horizon: 0.8 within 1 year,  1.5 within 2-5 years Range of estimates: 1.5-2.0 (following 2008 financial crisis & American Recovery Act) Italian public construction: 1.5-1.9 multiplier US ARRA: 0.4-2.2 range (differential impacts by program type) Economic Policy Institute: Uses 1.6 for infrastructure spending (middle range of estimates) Note: Public investment less likely to crowd out private activity during recessions; particularly effective when monetary policy loose with near-zero rates Additional sources: https://blogs.worldbank.org/en/ppps/effectiveness-infrastructure-investment-fiscal-stimulus-what-weve-learned | https://www.gihub.org/infrastructure-monitor/insights/fiscal-multiplier-effect-of-infrastructure-investment/ | https://cepr.org/voxeu/columns/government-investment-and-fiscal-stimulus | https://www.richmondfed.org/publications/research/economic_brief/2022/eb_22-04
.
40.
Mercatus. Military spending economic multiplier (0.6). Mercatus: Defense Spending and Economy https://www.mercatus.org/research/research-papers/defense-spending-and-economy
Ramey (2011):  0.6 short-run multiplier Barro (1981): 0.6 multiplier for WWII spending (war spending crowded out  40¢ private economic activity per federal dollar) Barro & Redlick (2011): 0.4 within current year, 0.6 over two years; increased govt spending reduces private-sector GDP portions General finding: $1 increase in deficit-financed federal military spending = less than $1 increase in GDP Variation by context: Central/Eastern European NATO: 0.6 on impact, 1.5-1.6 in years 2-3, gradual fall to zero Ramey & Zubairy (2018): Cumulative 1% GDP increase in military expenditure raises GDP by  0.7% Additional sources: https://www.mercatus.org/research/research-papers/defense-spending-and-economy | https://cepr.org/voxeu/columns/world-war-ii-america-spending-deficits-multipliers-and-sacrifice | https://www.rand.org/content/dam/rand/pubs/research_reports/RRA700/RRA739-2/RAND_RRA739-2.pdf
.
41.
(BIO), B. I. O. BIO clinical development success rates 2011-2020. Biotechnology Innovation Organization (BIO) https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf (2021)
Phase I duration: 2.3 years average Total time to market (Phase I-III + approval): 10.5 years average Phase transition success rates: Phase I→II: 63.2%, Phase II→III: 30.7%, Phase III→Approval: 58.1% Overall probability of approval from Phase I: 12% Note: Largest publicly available study of clinical trial success rates. Efficacy lag = 10.5 - 2.3 = 8.2 years post-safety verification. Additional sources: https://go.bio.org/rs/490-EHZ-999/images/ClinicalDevelopmentSuccessRates2011_2020.pdf
.
42.
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
.
43.
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
.
44.
Drugs.com. FDA drug approval timeline. Drugs.com: FDA Drug Approval Process https://www.drugs.com/fda-approval-process.html
Full timeline (preclinical to market): 12-15 years average (10-15 years common range) Preclinical phase: 3-7 years Clinical development + NDA review:  9 years NDA review alone: 10 months average (standard); 6 months (priority review) Historical (pre-PDUFA): 21-29 months for NDA review Note: "10 years" is accurate for total development timeline (10-15 year range). Modern FDA review is faster (10 months) thanks to PDUFA, but overall timeline remains 12-15 years Additional sources: https://www.drugs.com/fda-approval-process.html | https://www.fdareview.org/issues/the-drug-development-and-approval-process/ | https://pmc.ncbi.nlm.nih.gov/articles/PMC6113340/
.
45.
ACLED. Active combat deaths annually. ACLED: Global Conflict Surged 2024 https://acleddata.com/2024/12/12/data-shows-global-conflict-surged-in-2024-the-washington-post/ (2024)
2024: 233,597 deaths (30% increase from 179,099 in 2023) Deadliest conflicts: Ukraine (67,000), Palestine (35,000) Nearly 200,000 acts of violence (25% higher than 2023, double from 5 years ago) One in six people globally live in conflict-affected areas Additional sources: https://acleddata.com/2024/12/12/data-shows-global-conflict-surged-in-2024-the-washington-post/ | https://acleddata.com/media-citation/data-shows-global-conflict-surged-2024-washington-post | https://acleddata.com/conflict-index/index-january-2024/
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46.
UCDP. State violence deaths annually. UCDP: Uppsala Conflict Data Program https://ucdp.uu.se/
Uppsala Conflict Data Program (UCDP): Tracks one-sided violence (organized actors attacking unarmed civilians) UCDP definition: Conflicts causing at least 25 battle-related deaths in calendar year 2023 total organized violence: 154,000 deaths; Non-state conflicts: 20,900 deaths UCDP collects data on state-based conflicts, non-state conflicts, and one-sided violence Specific "2,700 annually" figure for state violence not found in recent UCDP data; actual figures vary annually Additional sources: https://ucdp.uu.se/ | https://en.wikipedia.org/wiki/Uppsala_Conflict_Data_Program | https://ourworldindata.org/grapher/deaths-in-armed-conflicts-by-region
.
47.
Data, O. W. in. Terror attack deaths (8,300 annually). Our World in Data: Terrorism https://ourworldindata.org/terrorism (2024)
2023: 8,352 deaths (22% increase from 2022, highest since 2017) 2023: 3,350 terrorist incidents (22% decrease), but 56% increase in avg deaths per attack Global Terrorism Database (GTD): 200,000+ terrorist attacks recorded (2021 version) Maintained by: National Consortium for Study of Terrorism & Responses to Terrorism (START), U. of Maryland Geographic shift: Epicenter moved from Middle East to Central Sahel (sub-Saharan Africa) - now >50% of all deaths Additional sources: https://ourworldindata.org/terrorism | https://reliefweb.int/report/world/global-terrorism-index-2024 | https://www.start.umd.edu/gtd/ | https://ourworldindata.org/grapher/fatalities-from-terrorism
.
48.
Health Metrics, I. for & (IHME), E. 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 | https://www.healthdata.org/research-analysis/about-gbd
.
49.
War, B. W. C. of. 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/
.
50.
ScienceDaily. Medical research lives saved annually (4.2 million). ScienceDaily: Physical Activity Prevents 4M Deaths https://www.sciencedaily.com/releases/2020/06/200617194510.htm (2020)
Physical activity: 3.9M early deaths averted annually worldwide (15% lower premature deaths than without) COVID vaccines (2020-2024): 2.533M deaths averted, 14.8M life-years preserved; first year alone: 14.4M deaths prevented Cardiovascular prevention: 3 interventions could delay 94.3M deaths over 25 years (antihypertensives alone: 39.4M) Pandemic research response: Millions of deaths averted through rapid vaccine/drug development Additional sources: https://www.sciencedaily.com/releases/2020/06/200617194510.htm | https://pmc.ncbi.nlm.nih.gov/articles/PMC9537923/ | https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.118.038160 | https://pmc.ncbi.nlm.nih.gov/articles/PMC9464102/
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51.
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
.
52.
Numbers, T. by. Lost human capital due to war ($270B annually). Think by Numbers: War Costs $74 <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/
.
53.
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/
.
54.
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
.
55.
Bank, W. World bank trade disruption cost from conflict. World Bank https://www.worldbank.org/en/topic/trade/publication/trading-away-from-conflict
Estimated $616B annual cost from conflict-related trade disruption. World Bank research shows civil war costs an average developing country 30 years of GDP growth, with 20 years needed for trade to return to pre-war levels. Trade disputes analysis shows tariff escalation could reduce global exports by up to $674 billion. Additional sources: https://www.worldbank.org/en/topic/trade/publication/trading-away-from-conflict | https://www.nber.org/papers/w11565 | http://blogs.worldbank.org/en/trade/impacts-global-trade-and-income-current-trade-disputes
.
56.
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
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57.
IQVIA Institute for Human Data Science. The global use of medicines 2024: Outlook to 2028. IQVIA Institute Report https://www.iqvia.com/insights/the-iqvia-institute/reports-and-publications/reports/the-global-use-of-medicines-2024-outlook-to-2028 (2024)
Global days of therapy reached 1.8 trillion in 2019 (234 defined daily doses per person). Diabetes, respiratory, CVD, and cancer account for 71 percent of medicine use. Projected to reach 3.8 trillion DDDs by 2028.
58.
size, D. from global market & ratios, public/private funding. Private industry clinical trial spending.
Private pharmaceutical and biotech industry spends approximately $75-90 billion annually on clinical trials, representing roughly 90% of global clinical trial spending.
59.
IHME Global Burden of Disease (2.55B DALYs), C. from & GDP per capita valuation, global. $109 trillion annual global disease burden.
The global economic burden of disease, including direct healthcare costs (\(8.2 trillion) and lost productivity (\)100.9 trillion from 2.55 billion DALYs × \(39,570 per DALY), totals approximately\)109.1 trillion annually.
60.
Trials, A. C. 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
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61.
Suisse/UBS, C. 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
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62.
budgets:, S. component country. Global government medical research spending ($67.5B, 2023–2024). See component country budgets: NIH Budget #nih-budget-fy2025.
63.
SIPRI. Global military spending ($2.72T, 2024). SIPRI https://www.sipri.org/publications/2025/sipri-fact-sheets/trends-world-military-expenditure-2024 (2025).
64.
budgets, E. from major foundation & activities. Nonprofit clinical trial funding estimate.
Nonprofit foundations spend an estimated $2-5 billion annually on clinical trials globally, representing approximately 2-5% of total clinical trial spending.
65.
IQVIA, I. reports: Global pharmaceutical r&d spending.
Total global pharmaceutical R&D spending is approximately $300 billion annually. Clinical trials represent 15-20% of this total ($45-60B), with the remainder going to drug discovery, preclinical research, regulatory affairs, and manufacturing development.
66.
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
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67.
School, H. K. 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
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68.
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/
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69.
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/
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70.
WHO. ICD-10 code count ( 14,000). WHO https://icd.who.int/browse10/2019/en (2019)
The ICD-10 classification contains approximately 14,000 codes for diseases, signs and symptoms. Additional sources: https://icd.who.int/browse10/2019/en
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71.
Wikipedia. Longevity escape velocity (LEV) - maximum human life extension potential. Wikipedia: Longevity Escape Velocity https://en.wikipedia.org/wiki/Longevity_escape_velocity
Longevity escape velocity: Hypothetical point where medical advances extend life expectancy faster than time passes Term coined by Aubrey de Grey (biogerontologist) in 2004 paper; concept from David Gobel (Methuselah Foundation) Current progress: Science adds  3 months to lifespan per year; LEV requires adding >1 year per year Sinclair (Harvard): "There is no biological upper limit to age" - first person to live to 150 may already be born De Grey: 50% chance of reaching LEV by mid-to-late 2030s; SENS approach = damage repair rather than slowing damage Kurzweil (2024): LEV by 2029-2035, AI will simulate biological processes to accelerate solutions George Church: LEV "in a decade or two" via age-reversal clinical trials Natural lifespan cap:  120-150 years (Jeanne Calment record: 122); engineering approach could bypass via damage repair Key mechanisms: Epigenetic reprogramming, senolytic drugs, stem cell therapy, gene therapy, AI-driven drug discovery Current record: Jeanne Calment (122 years, 164 days) - record unbroken since 1997 Note: LEV is theoretical but increasingly plausible given demonstrated age reversal in mice (109% lifespan extension) and human cells (30-year epigenetic age reversal) Additional sources: https://en.wikipedia.org/wiki/Longevity_escape_velocity | https://pmc.ncbi.nlm.nih.gov/articles/PMC423155/ | https://www.popularmechanics.com/science/a36712084/can-science-cure-death-longevity/ | https://www.diamandis.com/blog/longevity-escape-velocity
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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
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73.
Vaccines, M. Measles vaccination ROI. MDPI Vaccines https://www.mdpi.com/2076-393X/12/11/1210 (2024)
Single measles vaccination: 167:1 benefit-cost ratio. MMR (measles-mumps-rubella) vaccination: 14:1 ROI. Historical US elimination efforts (1966-1974): benefit-cost ratio of 10.3:1 with net benefits exceeding USD 1.1 billion (1972 dollars, or USD 8.0 billion in 2023 dollars). 2-dose MMR programs show direct benefit/cost ratio of 14.2 with net savings of $5.3 billion, and 26.0 from societal perspectives with net savings of $11.6 billion. Additional sources: https://www.mdpi.com/2076-393X/12/11/1210 | https://www.tandfonline.com/doi/full/10.1080/14760584.2024.2367451
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74.
Organization, W. H. Mental health global burden. World Health Organization https://www.who.int/news/item/28-09-2001-the-world-health-report-2001-mental-disorders-affect-one-in-four-people (2022)
One in four people in the world will be affected by mental or neurological disorders at some point in their lives, representing [approximately] 30% of the global burden of disease. Additional sources: https://www.who.int/news/item/28-09-2001-the-world-health-report-2001-mental-disorders-affect-one-in-four-people
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75.
Institute, S. I. P. R. Trends in world military expenditure, 2023. (2024).
76.
Orphanet Journal of Rare Diseases (2024), C. from. 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.
77.
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/
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78.
al., B. et. NIH spending on clinical trials:  3.3%. Bentley et al. https://www.fiercebiotech.com/biotech/nih-spending-clinical-trials-reached-81b-over-decade (2023)
NIH spent $8.1 billion on clinical trials for approved drugs (2010-2019), representing 3.3% of relevant NIH spending. Additional sources: https://www.fiercebiotech.com/biotech/nih-spending-clinical-trials-reached-81b-over-decade | https://www.fiercebiotech.com/biotech/nih-spending-clinical-trials-reached-81b-over-decade
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79.
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/
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80.
Institute, M. 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/
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81.
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/
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82.
CSDD, T. 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.
83.
Health, V. in. Average lifetime revenue per successful drug. Value in Health: Sales Revenues for New Therapeutic Agents02754-2/fulltext) https://www.valueinhealthjournal.com/article/S1098-3015(24
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.valueinhealthjournal.com/article/S1098-3015(24 | https://www.sciencedirect.com/science/article/pii/S1098301524027542
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84.
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.
85.
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/
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86.
Discovery, N. R. D. 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
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87.
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
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88.
PMC. Pragmatic trial cost per patient (median $97). PMC: Costs of Pragmatic Clinical Trials https://pmc.ncbi.nlm.nih.gov/articles/PMC6508852/
The median cost per participant was $97 (IQR $19–$478), based on 2015 dollars. Systematic review of 64 embedded pragmatic clinical trials. 25% of trials cost <$19/patient; 10 trials exceeded $1,000/patient. U.S. studies median $187 vs non-U.S. median $27. Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC6508852/
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89.
WHO. Polio vaccination ROI. WHO https://www.who.int/news-room/feature-stories/detail/sustaining-polio-investments-offers-a-high-return (2019)
For every dollar spent, the return on investment is nearly US$ 39." Total investment cost of US$ 7.5 billion generates projected economic and social benefits of US$ 289.2 billion from sustaining polio assets and integrating them into expanded immunization, surveillance and emergency response programmes across 8 priority countries (Afghanistan, Iraq, Libya, Pakistan, Somalia, Sudan, Syria, Yemen). Additional sources: https://www.who.int/news-room/feature-stories/detail/sustaining-polio-investments-offers-a-high-return
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90.
Olson, M. Big bills left on the sidewalk: Why some nations are rich, and others poor. Journal of Economic Perspectives 10, 3–24 (1996)
Differences between rich and poor countries are primarily due to institutions and policies, not factors of production.
91.
Hayek, F. A. The use of knowledge in society. American Economic Review 35, 519–530 (1945)
The knowledge of the circumstances which we must make use of never exists in concentrated or integrated form but solely as dispersed bits of incomplete and frequently contradictory knowledge which all the separate individuals possess.
92.
Kydland, F. E. & Prescott, E. C. Rules rather than discretion: The inconsistency of optimal plans. Journal of Political Economy 85, 473–492 (1977)
Time-inconsistency describes situations where, with the passing of time, policies that were determined to be optimal yesterday are no longer perceived to be optimal today and are not implemented... This insight shifted the focus of policy analysis from the study of individual policy decisions to the design of institutions that mitigate the time consistency problem.
93.
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
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94.
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
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95.
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.
96.
Wikipedia. US military spending reduction after WWII. Wikipedia https://en.wikipedia.org/wiki/Demobilization_of_United_States_Armed_Forces_after_World_War_II (2020)
Peaking at over $81 billion in 1945, the U.S. military budget plummeted to approximately $13 billion by 1948, representing an 84% decrease. The number of personnel was reduced almost 90%, from more than 12 million to about 1.5 million between mid-1945 and mid-1947. Defense spending exceeded 41 percent of GDP in 1945. After World War II, the US reduced military spending to 7.2 percent of GDP by 1948. Defense spending doubled from the 1948 low to 15 percent at the height of the Korean War in 1953. Additional sources: https://en.wikipedia.org/wiki/Demobilization_of_United_States_Armed_Forces_after_World_War_II | https://www.americanprogress.org/article/a-historical-perspective-on-military-budgets/ | https://www.stlouisfed.org/on-the-economy/2020/february/war-highest-military-spending-measured | https://www.usgovernmentspending.com/defense_spending_history
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97.
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
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98.
Numbers, T. by. 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
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99.
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/
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100.
Oren Cass, M. I. 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
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101.
al., N. E. Á. et. 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
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102.
Museum, N. S. 11. M. &. 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.
103.
Bank, W. World bank singapore economic data. World Bank https://data.worldbank.org/country/singapore (2024)
Singapore GDP per capita (2023): $82,000 - among highest in the world Government spending: 15% of GDP (vs US 38%) Life expectancy: 84.1 years (vs US 77.5 years) Singapore demonstrates that low government spending can coexist with excellent outcomes Additional sources: https://data.worldbank.org/country/singapore
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104.
Fund, I. M. IMF singapore government spending data. (2024)
Singapore government spending is approximately 15% of GDP This is 23 percentage points lower than the United States (38%) Despite lower spending, Singapore achieves excellent outcomes: - Life expectancy: 84.1 years (vs US 77.5) - Low crime, world-class infrastructure, AAA credit rating Additional sources: https://www.imf.org/en/Countries/SGP
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105.
Organization, W. H. WHO life expectancy data by country. (2024)
Life expectancy at birth varies significantly among developed nations: Switzerland: 84.0 years (2023) Singapore: 84.1 years (2023) Japan: 84.3 years (2023) United States: 77.5 years (2023) - 6.5 years below Switzerland, Singapore Global average:  73 years Note: US spends more per capita on healthcare than any other nation, yet achieves lower life expectancy Additional sources: https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-life-expectancy-and-healthy-life-expectancy
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106.
CSIS. Smallpox eradication ROI. CSIS https://www.csis.org/analysis/smallpox-eradication-model-global-cooperation.
107.
PMC. Contribution of smoking reduction to life expectancy gains. PMC: Benefits Smoking Cessation Longevity https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447499/ (2012)
Population-level: Up to 14% (9% men, 14% women) of total life expectancy gain since 1960 due to tobacco control efforts Individual cessation benefits: Quitting at age 35 adds 6.9-8.5 years (men), 6.1-7.7 years (women) vs continuing smokers By cessation age: Age 25-34 = 10 years gained; age 35-44 = 9 years; age 45-54 = 6 years; age 65 = 2.0 years (men), 3.7 years (women) Cessation before age 40: Reduces death risk by  90% Long-term cessation: 10+ years yields survival comparable to never smokers, averts  10 years of life lost Recent cessation: <3 years averts  5 years of life lost Additional sources: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1447499/ | https://www.cdc.gov/pcd/issues/2012/11_0295.htm | https://www.ajpmonline.org/article/S0749-3797(24 | https://www.nejm.org/doi/full/10.1056/NEJMsa1211128
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108.
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
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109.
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/
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110.
Bank, W. 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
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111.
Bank, W. 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/
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112.
Economic Co-operation, O. for & Development. OECD government spending as percentage of GDP. (2024)
OECD government spending data shows significant variation among developed nations: United States: 38.0% of GDP (2023) Switzerland: 35.0% of GDP - 3 percentage points lower than US Singapore: 15.0% of GDP - 23 percentage points lower than US (per IMF data) OECD average: approximately 40% of GDP Additional sources: https://data.oecd.org/gga/general-government-spending.htm
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113.
Economic Co-operation, O. for & Development. OECD median household income comparison. (2024)
Median household disposable income varies significantly across OECD nations: United States: $77,500 (2023) Switzerland: $55,000 PPP-adjusted (lower nominal but comparable purchasing power) Singapore: $75,000 PPP-adjusted Additional sources: https://data.oecd.org/hha/household-disposable-income.htm
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114.
Institute, C. Chance of dying from terrorism statistic. Cato Institute: Terrorism and Immigration Risk Analysis https://www.cato.org/policy-analysis/terrorism-immigration-risk-analysis
Chance of American dying in foreign-born terrorist attack: 1 in 3.6 million per year (1975-2015) Including 9/11 deaths; annual murder rate is 253x higher than terrorism death rate More likely to die from lightning strike than foreign terrorism Note: Comprehensive 41-year study shows terrorism risk is extremely low compared to everyday dangers Additional sources: https://www.cato.org/policy-analysis/terrorism-immigration-risk-analysis | https://www.nbcnews.com/news/us-news/you-re-more-likely-die-choking-be-killed-foreign-terrorists-n715141
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115.
Wikipedia. Thalidomide scandal: Worldwide cases and mortality. Wikipedia https://en.wikipedia.org/wiki/Thalidomide_scandal
The total number of embryos affected by the use of thalidomide during pregnancy is estimated at 10,000, of whom about 40% died around the time of birth. More than 10,000 children in 46 countries were born with deformities such as phocomelia. Additional sources: https://en.wikipedia.org/wiki/Thalidomide_scandal
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116.
One, P. Health and quality of life of thalidomide survivors as they age. PLOS One https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210222 (2019)
Study of thalidomide survivors documenting ongoing disability impacts, quality of life, and long-term health outcomes. Survivors (now in their 60s) continue to experience significant disability from limb deformities, organ damage, and other effects. Additional sources: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210222
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117.
Bureau, U. C. Historical world population estimates. US Census Bureau https://www.census.gov/data/tables/time-series/demo/international-programs/historical-est-worldpop.html
US Census Bureau historical estimates of world population by country and region (1950-2050). US population in 1960:  180 million of  3 billion worldwide (6%). Additional sources: https://www.census.gov/data/tables/time-series/demo/international-programs/historical-est-worldpop.html
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118.
NCBI, F. S. via. 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/
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119.
GBD 2019 Diseases and Injuries Collaborators. Global burden of disease study 2019: Disability weights. The Lancet 396, 1204–1222 (2020)
Disability weights for 235 health states used in Global Burden of Disease calculations. Weights range from 0 (perfect health) to 1 (death equivalent). Chronic conditions like diabetes (0.05-0.35), COPD (0.04-0.41), depression (0.15-0.66), and cardiovascular disease (0.04-0.57) show substantial variation by severity. Treatment typically reduces disability weights by 50-80 percent for manageable chronic conditions.
120.
EPI. CEO compensation. EPI https://www.epi.org/blog/ceo-pay-increased-in-2024-and-is-now-281-times-that-of-the-typical-worker-new-epi-landing-page-has-all-the-details/ (2024)
S&P 500 average: $18.9M (2024) |  $9,087/hour | 285:1 CEO-to-worker ratio Additional sources: https://www.epi.org/blog/ceo-pay-increased-in-2024-and-is-now-281-times-that-of-the-typical-worker-new-epi-landing-page-has-all-the-details/
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121.
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
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122.
Oncology, J. 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
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123.
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
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124.
Care, D. 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://www.thelancet.com/journals/landia/article/PIIS2213-8587(17
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125.
World Bank, B. of E. A. US GDP 2024 ($28.78 trillion). World Bank https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=US (2024)
US GDP reached $28.78 trillion in 2024, representing approximately 26% of global GDP. Additional sources: https://data.worldbank.org/indicator/NY.GDP.MKTP.CD?locations=US | https://www.bea.gov/news/2024/gross-domestic-product-fourth-quarter-and-year-2024-advance-estimate
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126.
Cardiology, I. J. of. Annual global economic burden of heart disease. Int’l Journal of Cardiology: Global Heart Failure Burden02238-9/abstract) https://www.internationaljournalofcardiology.com/article/S0167-5273(13 (2050)
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.internationaljournalofcardiology.com/article/S0167-5273(13 | https://www.ahajournals.org/doi/10.1161/CIR.0000000000001258
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127.
CSV, S. U. L. E. F. B. 1543-2019. US life expectancy growth 1880-1960: 3.82 years per decade. (2019)
Pre-1962: 3.82 years/decade Post-1962: 1.54 years/decade Reduction: 60% decline in life expectancy growth rate Additional sources: https://ourworldindata.org/life-expectancy | https://www.mortality.org/ | https://www.cdc.gov/nchs/nvss/mortality_tables.htm
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128.
CSV, S. U. L. E. F. B. 1543-2019. Post-1962 slowdown in life expectancy gains. (2019)
Pre-1962 (1880-1960): 3.82 years/decade Post-1962 (1962-2019): 1.54 years/decade Reduction: 60% decline Temporal correlation: Slowdown occurred immediately after 1962 Kefauver-Harris Amendment See detailed calculation: [life-expectancy-increase-pre-1962](#life-expectancy-increase-pre-1962) Additional sources: https://ourworldindata.org/life-expectancy | https://www.mortality.org/ | https://www.cdc.gov/nchs/nvss/mortality_tables.htm
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129.
Disease Control, C. for & Prevention. US life expectancy 2023. (2024)
US life expectancy at birth was 77.5 years in 2023 Male life expectancy: 74.8 years Female life expectancy: 80.2 years This is 6-7 years lower than peer developed nations despite higher healthcare spending Additional sources: https://www.cdc.gov/nchs/fastats/life-expectancy.htm
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130.
Bureau, U. C. US median household income 2023. (2024)
US median household income was $77,500 in 2023 Real median household income declined 0.8% from 2022 Gini index: 0.467 (income inequality measure) Additional sources: https://www.census.gov/library/publications/2024/demo/p60-282.html
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131.
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
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132.
Bureau, U. C. Number of registered or eligible voters in the u.s. US Census Bureau https://www.census.gov/newsroom/press-releases/2025/2024-presidential-election-voting-registration-tables.html (2024)
73.6% (or 174 million people) of the citizen voting-age population was registered to vote in 2024 (Census Bureau). More than 211 million citizens were active registered voters (86.6% of citizen voting age population) according to the Election Assistance Commission. Additional sources: https://www.census.gov/newsroom/press-releases/2025/2024-presidential-election-voting-registration-tables.html | https://www.eac.gov/news/2025/06/30/us-election-assistance-commission-releases-2024-election-administration-and-voting
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133.
Senate, U. S. 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
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134.
Commission, F. E. 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/
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135.
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/
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136.
Kirk (2011), H. &. Valley of death in drug development. (2011)
The overall failure rate of drugs that passed into Phase 1 trials to final approval is 90%. This lack of translation from promising preclinical findings to success in human trials is known as the "valley of death." Estimated 30-50% of promising compounds never proceed to Phase 2/3 trials primarily due to funding barriers rather than scientific failure. The late-stage attrition rate for oncology drugs is as high as 70% in Phase II and 59% in Phase III trials.
137.
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
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138.
ONE, P. 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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139.
News, U. Clean water & sanitation (LMICs) ROI. UN News https://news.un.org/en/story/2014/11/484032 (2014).
140.
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/
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141.
Institute, I. B. Chronic illness workforce productivity loss. Integrated Benefits Institute 2024 https://www.ibiweb.org/resources/chronic-conditions-in-the-us-workforce-prevalence-trends-and-productivity-impacts (2024)
78.4% of U.S. employees have at least one chronic condition (7% increase since 2021) 58% of employees report physical chronic health conditions 28% of all employees experience productivity loss due to chronic conditions Average productivity loss: $4,798 per employee per year Employees with 3+ chronic conditions miss 7.8 days annually vs 2.2 days for those without Note: 28% productivity loss translates to roughly 11 hours per week (28% of 40-hour workweek) Additional sources: https://www.ibiweb.org/resources/chronic-conditions-in-the-us-workforce-prevalence-trends-and-productivity-impacts | https://www.onemedical.com/mediacenter/study-finds-more-than-half-of-employees-are-living-with-chronic-conditions-including-1-in-3-gen-z-and-millennial-employees/ | https://debeaumont.org/news/2025/poll-the-toll-of-chronic-health-conditions-on-employees-and-workplaces/
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142.
Peltzman, S. An evaluation of consumer protection legislation: The 1962 drug amendments. Journal of Political Economy 81, 1049–1091 (1973)
Foundational study quantifying the costs of the 1962 Kefauver-Harris Amendments. Peltzman estimated that the efficacy requirements reduced the flow of new drugs by 50-60% and that the costs of reduced innovation substantially exceeded the benefits of keeping ineffective drugs off the market. Concluded that the 1962 amendments resulted in net welfare losses.
143.
Wardell, W. M. Therapeutic implications of the drug lag. Clinical Pharmacology and Therapeutics 15, 73–96 (1974)
Documented the "drug lag" between US and UK drug approvals. Found that the UK had access to significantly more new drugs than the US, and that many effective drugs available in Europe were unavailable to American patients. Estimated beta-blockers alone could save 10,000 lives annually if approved in the US. Foundational work demonstrating the mortality cost of regulatory delay.
144.
FEE. Patients and doctors vs FDA. FEE: Patients and Doctors vs FDA https://fee.org/articles/patients-and-doctors-vs-the-fda
Dale Gieringer (1985): 21,000-120,000 lives lost per decade from FDA delay Gieringer: "Loss of life from delay alone in the hundreds of thousands" (not millions) Beta-blockers alone: William Wardell estimated "10,000 lives/year" if allowed; FDA delay 1965-1976 Sam Peltzman: Post-1962 death toll from regulatory delay "easily number in thousands per year Practolol (beta-blocker): "Could save 10,000 lives/year" (Wardell estimate) FDA allowed propranolol 1968 (3 years after Europe); for hypertension/angina not until 1978 Note: "4-10 million" figure not found in sources. Gieringer’s estimates: 21K-120K deaths per decade, "hundreds of thousands" total (not millions). Specific drug delays (beta-blockers):  100K deaths estimated Additional sources: https://fee.org/articles/patients-and-doctors-vs-the-fda | https://www.econlib.org/library/Enc/DrugLag.html | https://www.fdareview.org/issues/theory-evidence-and-examples-of-fda-harm/
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145.
Fischl, M. A. et al. The efficacy of azidothymidine (AZT) in the treatment of patients with AIDS and AIDS-related complex: A double-blind, placebo-controlled trial. New England Journal of Medicine 317, 185–191 (1987)
Landmark Phase 2 trial of AZT in 282 AIDS patients. 19 placebo recipients died vs. 1 AZT recipient (P < 0.001). Trial stopped early due to overwhelming efficacy. Led to FDA approval in record 107 days.
146.
Health Metrics, I. for & (IHME), E. IHME global burden of disease (2.55B DALYs, 2019). Institute for Health Metrics and Evaluation (IHME) https://vizhub.healthdata.org/gbd-compare/ (2020)
Globally, in 2019, the total number of DALYs from all causes was 2.55 billion. Additional sources: https://vizhub.healthdata.org/gbd-compare/ | https://www.healthdata.org/research-analysis/about-gbd | https://pubmed.ncbi.nlm.nih.gov/33069326/
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147.
WHO. 80% of cardiovascular disease is preventable. WHO: Cardiovascular diseases (CVDs)) https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds (2021)
As many as 80% of all heart attacks and strokes are preventable" through addressing lifestyle risk factors WHO: Most cardiovascular diseases can be prevented by addressing behavioral and environmental risk factors such as tobacco use, unhealthy diet, obesity, physical inactivity, harmful use of alcohol, and air pollution Cleveland Clinic: "90 percent of heart disease is preventable through healthier diet, regular exercise, and not smoking In 2 large cohort studies, a reduction of CVD risk of >80% and diabetes >90% were demonstrated in individuals who followed healthy lifestyle practices Only 5% of individuals follow all lifestyle factors for "ideal" cardiovascular health (American Heart Association) Additional sources: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds | https://world-heart-federation.org/what-we-do/prevention/ | https://newsroom.clevelandclinic.org/2021/09/29/90-percent-of-heart-disease-is-preventable-through-healthier-diet-regular-exercise-and-not-smoking | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC162259/
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148.
PMC. 30-50% of cancer is currently preventable; 90-95% has environmental/lifestyle roots. PMC: Cancer is a Preventable Disease https://pmc.ncbi.nlm.nih.gov/articles/PMC2515569/
IARC/WHO: Environmental and lifestyle factors contribute to about 50% of the global cancer burden; expected to contribute to remaining 50% for which causes currently unknown WHO: 35% of cancer deaths worldwide are due to potentially preventable or modifiable risk factors PMC: "Only 5-10% of all cancer cases can be attributed to genetic defects, whereas the remaining 90-95% have their roots in the environment and lifestyle Breakdown: 25-30% due to tobacco, 30-35% linked to diet, 15-20% due to infections, remainder from radiation, stress, physical activity, pollutants US: 4 out of 10 cancer cases associated with preventable risk factors (tobacco/alcohol use, physical inactivity) Note: Current preventability ( 35-40%) vs. theoretical max (90-95% if all environmental/lifestyle factors addressed) Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC2515569/ | https://www.iarc.who.int/branches-env/ | https://cancerprogressreport.aacr.org/progress/cpr22-contents/cpr22-preventing-cancer-identifying-risk-factors/
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149.
PMC. Aging reversal demonstrated in mammals using yamanaka factors. PMC: Chemically Induced Reprogramming to Reverse Aging https://pmc.ncbi.nlm.nih.gov/articles/PMC10373966/ (2023)
Harvard/Sinclair: Loss of epigenetic information causes aging; restoring epigenome integrity reverses aging signs in mice OSK therapy (Oct4, Sox2, Klf4): Ectopic induction can restore youthful DNA methylation patterns, transcript profiles, and tissue function without erasing cellular identity Results in mice: Systemically delivered adeno-associated viruses encoding inducible OSK in 124-week-old mice extended median remaining lifespan by 109% over wild-type controls Vision restored in glaucoma mice - first successful reversal (not just halting progression) Cyclic partial reprogramming (2 days on, 5 days off) showed improvements after just 6 weeks including reduced age-related spinal curvature Human cells: Babraham Institute showed cellular reprogramming reverses epigenetic age of human skin cells by 30 years Chemical alternatives: Six chemical cocktails identified that restore youthful genome-wide transcript profile in less than a week without compromising cellular identity Note: Demonstrates biological aging is reversible, not inevitable; safety testing ongoing before human application Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC10373966/ | https://www.nature.com/articles/s41467-024-46020-5 | https://www.liebertpub.com/doi/10.1089/cell.2023.0072 | https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-021-01158-7
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150.
WHO. Injuries are largely preventable events. WHO: Injuries and Violence Fact Sheet https://www.who.int/news-room/fact-sheets/detail/injuries-and-violence
WHO: "Injuries have traditionally been regarded as random, unavoidable ’accidents’. Today both unintentional and intentional injuries are viewed as largely preventable events. 4.4 million injury-related deaths annually: 3.16M unintentional, 1.25M violence-related Injuries are preventable by changing environment, individual behavior, products, social norms, legislation, and governmental/institutional policies When standardized per 100,000 population, death rate is nearly double in low/middle-income vs high-income countries (65 vs 35 per 100,000) 90% of injury-related deaths occur in low- and middle-income countries Note:  60% of accidental deaths theoretically preventable through policy, technology, and behavior change; 40% involve instantaneous trauma beyond medical intervention Additional sources: https://www.who.int/news-room/fact-sheets/detail/injuries-and-violence | https://pmc.ncbi.nlm.nih.gov/articles/PMC2912603/ | https://www.who.int/publications-detail-redirect/the-injury-chart-book-a-graphical-overview-of-the-global-burden-of-injuries
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151.
Furuyama, K. et al. Endogenous reprogramming of alpha cells into beta cells, induced by viral gene therapy, reverses autoimmune diabetes. Cell Stem Cell 24, 87–100 (2019)
Used AAV-mediated gene therapy to deliver Pdx1 and MafA to mouse pancreas, reprogramming alpha cells into functional insulin-producing beta cells. Reversed autoimmune diabetes in mice, demonstrating gene therapy potential for T1D reversal.
152.
PMC. Stem cell therapy shows promise for neurodegenerative diseases. PMC: Regenerative Stem Cell Therapy for Neurodegenerative Diseases https://pmc.ncbi.nlm.nih.gov/articles/PMC7926761/ (2023)
R3 paradigm: Rejuvenation (restoring cell function), Regeneration (stimulating repair), Replacement (substituting lost cells) Alzheimer’s: Stanford study showed stem cell transplants reduced brain abnormalities in mice; Neural Stem Cell Therapy shown to improve cognitive function and reduce amyloid plaques Parkinson’s: Clinical trial found Neural Stem Cell Therapy significantly improved motor function and was well-tolerated ALS/Huntington’s: MSC (mesenchymal stem cell) therapy effectiveness confirmed; slows ALS progression Challenge: Clinical trials often enroll patients at advanced stages; many preclinically promising drugs ineffective in late-stage human trials Current status: Largely experimental and early clinical trial stages; researchers working to overcome delivery challenges, safety concerns, and targeting widespread neuronal damage Note: 80% theoretical max for neurodegenerative reflects early-stage intervention potential; current treatments limited ( 10% effective) Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC7926761/ | https://med.stanford.edu/news/all-news/2023/09/stem-cell-alzheimers.html | https://stemcellres.biomedcentral.com/articles/10.1186/s13287-025-04285-7
.
153.
AAF. Return on investment for vaccines. AAF https://www.americanactionforum.org/research/vaccine-protection-and-productivity-the-economic-value-of-vaccines/ (2011)
Every $1 spent on childhood immunizations results in approximately $11 in savings (700% ROI). For low/middle-income countries: $26.1-$51.0 ROI using cost-of-illness approach, $52.2 ROI using value-of-statistical-life approach. US childhood vaccines 1994-2023 saved $540B in direct costs, $2.7T in total societal savings. Additional sources: https://www.americanactionforum.org/research/vaccine-protection-and-productivity-the-economic-value-of-vaccines/ | https://www.healthaffairs.org/doi/10.1377/hlthaff.2020.00103 | https://immunizationevidence.org/featured_issues/the-value-of-vaccines-investments-in-immunization-yield-high-returns/
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154.
Barter, P. J. et al. Torcetrapib and cardiovascular events: The ILLUMINATE trial. New England Journal of Medicine 357, 2109–2122 (2007)
Phase III trial of CETP inhibitor torcetrapib terminated early due to excess mortality. Of 15,067 participants, 82 deaths occurred in the torcetrapib arm vs. 51 in placebo (HR 1.58). Cardiovascular events were also significantly higher. Pfizer spent over $800 million on development before termination. Example of Phase III trial catching serious safety issues before broader approval.
155.
Doody, R. S. et al. A phase 3 trial of semagacestat for treatment of alzheimer’s disease. New England Journal of Medicine 369, 341–350 (2013)
Phase III trial of gamma-secretase inhibitor semagacestat for Alzheimer’s disease was terminated early by Eli Lilly. Patients on treatment showed significantly WORSE cognitive outcomes than placebo, plus increased skin cancers and infections. 1,537 patients randomized. Example of Phase III trial preventing approval of a drug that would have harmed patients.
156.
McDonald, C. M. et al. A randomized placebo-controlled phase 3 trial of drisapersen in duchenne muscular dystrophy. Neuromuscular Disorders 27, 1021–1029 (2017)
Phase III trial of drisapersen for Duchenne muscular dystrophy failed to show clinical benefit (P=0.415). FDA rejected approval in 2016 due to safety concerns including thrombocytopenia in 2% of patients, renal toxicity, and injection site reactions in 79% of patients. BioMarin discontinued development. Example of regulatory system preventing approval of ineffective drug with serious adverse effects.
157.
Graham, D. (FDA). |. L. Vioxx cardiovascular deaths (rofecoxib). PMC: FDA incapable of protecting against another Vioxx https://pmc.ncbi.nlm.nih.gov/articles/PMC534432/ (2007)
Graham testimony (2004): 88,000-139,000 U.S. heart attacks/strokes from Vioxx; up to 55,000 deaths (40% fatality rate) Lancet study estimate: 88,000 Americans had heart attacks from Vioxx; 38,000 died FDA memo (2004): Vioxx contributed to  27,785 heart attacks and sudden cardiac deaths (1999-2003) High-dose Vioxx: Tripled risk of heart attacks and sudden cardiac death Prescriptions: 92.8 million U.S. prescriptions 1999-2003 Withdrawn: September 30, 2004 after APPROVE trial showed cardiovascular risks Note: Vioxx case demonstrates failure of passive post-market surveillance (FAERS) to detect safety signals in time. Voluntary reporting missed cardiovascular risks for years despite millions of prescriptions Additional sources: https://pmc.ncbi.nlm.nih.gov/articles/PMC534432/ | https://www.npr.org/2007/11/10/5470430/timeline-the-rise-and-fall-of-vioxx | https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(05
.
158.
FDLI. Japan’s regenerative medicine act and conditional approval pathway. FDLI: Japan’s Regen Med Pathways https://www.fdli.org/2019/02/global-focus-japans-regenerative-medicine-regulatory-pathways-encouraging-innovation-and-patient-access/ (2019)
Act on Safety of Regenerative Medicine (RM Act) + amended Pharmaceuticals and Medical Devices Act (PMD Act): passed Nov 2013, effective Nov 2014 Conditional and time-limited approval pathway: Obtain approval after exploratory trials demonstrate probable benefit and proven safety 7-year conditional approval period to confirm clinical benefit (e.g., using surrogate endpoints) SAKIGAKE designation (April 2015): Expedited pathway for innovative products targeting serious/life-threatening diseases without effective treatment Benefits: Prioritized consultation, accelerated review, extended re-examination period, premium pricing Examples: Terumo’s HeartSheet and Stemirac obtained conditional approval; Stemirac also SAKIGAKE-designated Additional sources: https://www.fdli.org/2019/02/global-focus-japans-regenerative-medicine-regulatory-pathways-encouraging-innovation-and-patient-access/ | https://www.insights.bio/cell-and-gene-therapy-insights/journal/article/310/Experiences-from-Japan-SAKIGAKE-Designation-System-for-Regenerative-Medical-Products | https://pmc.ncbi.nlm.nih.gov/articles/PMC6696404/
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159.
Honmou, O. et al. Intravenous infusion of auto serum-expanded autologous mesenchymal stem cells in spinal cord injury patients: 13 case series. Clinical Neurology and Neurosurgery 203, 106565 (2021)
In this investigator-initiated clinical trial (2014-2017), 12 of 13 patients (92%) with cervical spinal cord injury who received MSC transplantation achieved at least one level of improvement on American Spinal Injury Association Impairment Scale. Five of six AIS A patients improved to AIS B (3/6) or AIS C (2/6). No severe side effects. Results led to MHLW conditional approval of STEMIRAC in December 2018.
160.
Yong, C. S. M., Deng, J., Tan, E. W., et al. Challenges in global access to CAR-t cells: An asian perspective. International Journal of Hematology https://doi.org/10.1007/s12185-024-03726-6 (2024) doi:10.1007/s12185-024-03726-6
Reviews CAR-T cell therapy access across Asia. Japan has approved five CAR-T products under National Health Insurance: Kymriah, Yescarta, Breyanzi, Abecma, and Carvykti. All priced at 32,647,761 JPY. Japan’s high-cost medical care reimbursement system limits patient out-of-pocket costs.
161.
Medicine (US) Committee on the U. S. Physician Supply, I. of. U.s. Physician supply and requirements: Match or mismatch? The Nation’s Physician Workforce: Options for Balancing Supply and Requirements https://www.ncbi.nlm.nih.gov/books/NBK232541/ (1996)
Historical physician workforce data: 126.6 active nonfederal M.D. physicians per 100,000 population in 1950; 127.4 in 1960; 137.4 in 1970 (DHHS 1993). At 1960 US population of  180M, this corresponds to approximately 229,000 active physicians.

Footnotes

  1. Furuyama et al. (2019) used AAV gene therapy to reprogram alpha cells into insulin-producing beta cells, reversing autoimmune diabetes in mice. Max potential extrapolates from root cause addressability.↩︎

  2. WHO estimates 80% of COPD cases preventable through tobacco control and air quality improvements (see WHO COPD Fact Sheet). The 90% max potential conservatively assumes emerging regenerative medicine may address some remaining cases.↩︎

  3. Calculated as weighted average of “Max Potential” estimates for categories with similar biological mechanisms.↩︎

  4. Institute of Medicine data shows 127.4 active physicians per 100,000 population in 1960. At US population of ~180M, this equals approximately 229,000 active physicians.↩︎

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