---
title: "GEAF’: A dimensionally consistent index for affordability, accessibility, ethics, and equity in ge"
id: "plos-one-17-the-genomic-equity-accessibility-framework-geaf-a-dimensionally-consistent"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-17-the-genomic-equity-accessibility-framework-geaf-a-dimensionally-consistent"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0359782"
published_at: "2026-10-06T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# GEAF’: A dimensionally consistent index for affordability, accessibility, ethics, and equity in ge
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-17-the-genomic-equity-accessibility-framework-geaf-a-dimensionally-consistent
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0359782)
- **Published At:** 2026-10-06T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The paper audits an earlier Genomic Equity Accessibility Framework (GEAF) and finds dimensional incompatibility, an unbounded and uninterpretable output range, and scenario inputs inconsistent with cited cost data. - The original GEAF formula combined heterogeneous units (percentages, probabilities, and costs in thousands) producing outputs dominated by cost and lacking a defensible scale. - The author reconstructed GEAF as a normalized, bounded (0–100) composite index, here called **GEAF’**, using multi-attribute utility theory and six dimensionless components: **accessibility**, **ethical compliance**, **affordability**, **equity** (inverse inequality), policy strength, and research efficiency. - Each component is transformed to a 0–1 scale where 1 is the most favorable state; components are combined with equal weights (1/6) as a neutral default and rescaled to 0–100. - The reconstructed index addresses four validity requirements: dimensional consistency, boundedness, removal of denominator-driven distortions, and scenario inputs anchored to cited list-price literature. - GEAF’ is algebraically monotonic in each component and behaves consistently at extremes. Across seven illustrative scenarios GEAF’ ranged from 27.9 (no equity intervention, list-price cost) to 86.5 (fully optimized tiered-pricing and policy scenario). - One-way sensitivity analysis identified cost and the inequality factor as the largest single-parameter drivers under baseline conditions, followed by ethical compliance. - Monte Carlo simulations (10,000 iterations) produced a baseline-scenario mean of 32.6 (90% interval ~22.9–42.9) and an equity-intervention-scenario mean of 72.4 (90% interval ~62.9–81.1), with no overlap between distributions under the stated uncertainty assumptions. - The author emphasizes GEAF’ as a conceptual and methodological candidate, not an empirically validated predictive or clinical tool; prospective empirical calibration against real accessibility, pricing, and policy-outcome data is required before use in HTA or resource-allocation decisions. - The manuscript provides all analysis code in a supplement and reports funding and competing-interest statements; no primary data were collected and the work is exploratory and modeling-focused.
## Clinical Analysis & Structured Key Points
The Genomic Equity Accessibility Framework (GEAF): A dimensionally consistent framework for modeling affordability and equity in genomic and regenerative therapies | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Peer Review Reader Comments Figures Figures Abstract Background Advances in CRISPR-based gene editing and stem cell-derived embryo models have created substantial biomedical opportunity alongside significant ethical and distributive concerns. Personalized genomic and regenerative therapies approved in the United States as of this writing carry list prices spanning approximately $2.2 million (Casgevy) to $4.25 million (Lenmeldy), with several other approved products priced between these figures (e.g., Lyfgenia, $3.1 million; Hemgenix, $3.5 million). This raises the question of how affordability, accessibility, ethical compliance, and distributional inequality can be jointly represented in a single, mathematically coherent index to support policy analysis. Methods We audited a previously proposed composite index, the Genomic Equity Accessibility Framework (GEAF = ((A x E) – (C x I))/(P + R) x 100), and identified dimensional incompatibility between its terms, an unbounded and not-independently-interpretable output range, and scenario inputs inconsistent with the cost estimates cited elsewhere in the same manuscript. We reconstructed GEAF as a normalized, bounded (0–100) weighted composite of six dimensionless components – accessibility, ethical compliance, affordability, equity (inverse inequality), policy strength, and research efficiency – derived using multi-attribute utility theory. We tested the reconstructed formula against algebraic edge cases, applied it to seven re-specified illustrative scenarios whose cost inputs are anchored to cited list-price literature, and conducted one-way, two-way, and Monte Carlo (10,000-iteration) probabilistic sensitivity analyses. Results The reconstructed index (GEAF’) is bounded on [0,100], monotonic in every component, and behaves consistently at algebraic extremes. Across the seven illustrative scenarios, GEAF’ ranged from 27.9 (no equity intervention, list-price cost) to 86.5 (fully optimized tiered-pricing and policy scenario). One-way sensitivity analysis identified cost (C) and the inequality factor (I) as the two largest single-parameter drivers of the index under baseline conditions, followed by ethical compliance (E). Monte Carlo simulation (10,000 iterations per regime) propagating stated, non-empirical parameter-uncertainty assumptions produced a baseline-scenario mean of 32.6 (90% interval, 5th-95th percentile, approximately 22.9–42.9) and an equity-intervention-scenario mean of 72.4 (90% interval approximately 62.9–81.1), with no overlap between the two simulated distributions. Conclusions GEAF’ provides a transparent, reproducible, and dimensionally coherent way to combine affordability, accessibility, ethical compliance, and distributional equity into a single comparative index for genomic and regenerative therapy policy scenarios. It is presented as a candidate conceptual and methodological framework, not as an empirically validated predictive or clinical tool; quantitative claims about real-world cost reductions have been removed unless directly supported by cited evidence. Prospective empirical calibration against real accessibility, pricing, and policy-outcome data is identified as a necessary next step before GEAF’ is used in health technology assessment or resource-allocation decisions. Citation: David PO (2026) The Genomic Equity Accessibility Framework (GEAF): A dimensionally consistent framework for modeling affordability and equity in genomic and regenerative therapies. PLoS One 21(10): e0359782. https://doi.org/10.1371/journal.pone.0359782 Editor: Kangkang Ji, Huazhong Agriculture University, CHINA Received: June 29, 2026; Accepted: September 17, 2026; Published: October 6, 2026 Copyright: © 2026 Peter Oloche David. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All data are in the manuscript and/or supporting information files. The complete analysis code used to generate every reported score, sensitivity result, and figure is provided in S1 Appendix . Funding: This work received partial financial support from Eloi Holding, Inc. Because the author is the Founder of Eloi Holding, Inc. and personally performed all aspects of study conception, research design, model development, analysis, and manuscript preparation, the standard statement that “the funder had no role in the study” would not be accurate and is not used here: the author’s dual role as founder and sole researcher means the funder’s involvement and the author’s involvement in these activities are not separable. Eloi Holding, Inc., as an organization, did not commission this work, did not set its scientific direction beyond the author’s own decisions as researcher, and had no separate editorial review process; the decision to publish was made solely by the author. Competing interests: Peter Oloche David is the Founder of Eloi Holding, Inc., which provided partial financial support for this work (see Funding Statement). As founder, the author personally conceived the study, developed the reconstructed model, performed all analyses, and prepared the manuscript; these activities were carried out by the author in his capacity as researcher and sole author, not as an institutional function of Eloi Holding, Inc., which has no research division, editorial process, or independent role in genomics or health-equity research. The author also leads the David Oloche Foundation, a philanthropic organization with no financial or editorial role in this work. This does not alter the author’s adherence to policies on sharing data and materials. The author declares no other competing interests. 1. Introduction The maturation of CRISPR-Cas9 gene editing and of stem cell-derived synthetic embryo models (e.g., blastoids and gastruloids) has expanded what is technically possible in human genomics and regenerative medicine. Approved gene therapies now exist for conditions such as sickle cell disease, transthyretin amyloidosis, and spinal muscular atrophy [ 1 ], and dozens more are in late-stage development. These advances raise two distinct but related problems that this paper addresses in different ways. The first is ethical: non-therapeutic genetic enhancement, the moral status of increasingly sophisticated embryo models, and the governance of stem cell research raise questions that are the subject of an active bioethics and policy literature, which this manuscript summarizes rather than attempts to resolve. The second is distributive: even where a therapy is technically approved and effective, list prices ranging from approximately $2.2 million (Casgevy, Vertex [ 2 ] to $4.25 million (Lenmeldy, Orchard [ 3 ] with Lyfgenia priced at $3.1 million [ 4 ] and Hemgenix at $3.5 million [ 5 ] place it outside the reach of most of the world’s population, and outside many health systems entirely. The projected growth in gene-therapy spending also highlights the potential budgetary implications of these treatments [ 6 ]. Real-world evidence from Europe and the United States further highlights the importance of examining gene therapy access and outcomes beyond regulatory approval [ 7 ]. A single mathematical index, the Genomic Equity Accessibility Framework (GEAF), was previously proposed to combine affordability, accessibility, ethical compliance, and inequality into one number that could support policy comparison. That original formulation had several well-founded problems: the terms being combined were not on comparable scales (a percentage multiplied against a probability, subtracted from a cost in hundreds of thousands multiplied by a coefficient), the resulting output had no defined or defensible range, and the illustrative scenario inputs were not traceable to any stated empirical source. These defects could not be resolved by re-deriving weights or re-normalizing the existing expression in place; the index was instead reconstructed from first principles, and this manuscript reports the properties of the reconstructed version under systematic testing. 1.1. Research questions and objectives This paper is framed as a methodological/framework contribution rather than an empirical clinical study. It addresses four research questions: RQ1. Can affordability, accessibility, ethical compliance, and distributional inequality be combined into a single composite index that is dimensionally consistent, bounded, and monotonic in each component? RQ2. Which parameters of such an index have the greatest influence on its output, and how does the index behave under one-way, two-way, and probabilistic (Monte Carlo) sensitivity analysis? RQ3. How does a reconstructed composite index of this kind relate conceptually to established health-economic and equity-weighting frameworks (e.g., incremental cost-effectiveness ratios, distributional cost-effectiveness analysis, extended cost-effectiveness analysis)? RQ4. What empirical data would be required to move such an index from an illustrative, assumption-based tool to one that is empirically calibrated and prospectively validated? The primary objective is to reconstruct GEAF into a mathematically defensible form (RQ1-RQ2) and to characterize its behavior transparently (RQ2). A secondary objective is to position the reconstructed index relative to existing frameworks (RQ3) and to specify, rather than claim to have completed, the empirical validation agenda implied by RQ4. Because no primary data were collected, no formal statistical hypotheses are tested; the study is exploratory and conceptual, consistent with a modeling/methods paper. 1.2. Scope and unit of analysis The unit of analysis for GEAF’ is a single named therapy-jurisdiction-policy scenario (e.g., “gene therapy X, under policy configuration Y, in setting Z”) evaluated at one point in time. This is distinct from a patient-level outcome measure (such as a QALY) and from a purely economic measure (such as an ICER); GEAF’ is a comparative policy-scenario index, not an individual-level clinical or actuarial metric. This distinction is maintained throughout the manuscript and is restated in the Discussion and Limitations. 2. Conceptual background: Ethical and governance context This section summarizes, without attempting to resolve, the ethical and governance debates that motivate the equity problem GEAF’ is intended to help quantify. It is intentionally concise, focusing only on material that connects directly to the quantitative model and to the governance concepts introduced in Table 1 . Readers seeking a full treatment of the underlying bioethics literature are directed to the cited primary sources. Download: PNG larger image TIFF original image Table 1. Proposed conceptual governance mechanisms (explicitly non-binding, non-existing institutions). https://doi.org/10.1371/journal.pone.0359782.t001 2.1. Non-therapeutic genetic enhancement CRISPR-based editing capable of correcting single-gene disease can, in principle, also be directed at non-therapeutic trait modification (“designer babies”) [ 10 ]. Proponents invoke procreative beneficence [ 11 ]: a consequentialist position holding that parents have a defeasible reason to select for a child’s expected wellbeing where feasible. Critics invoke deontological concerns about instrumentalizing future persons and about the historical association between trait-selection ideology and eugenics [ 12 ], and note the discriminatory potential of widening access gaps for enhancement rather than treatment. The 2018 He Jiankui case, in which embryos were edited for HIV resistance outside ethical review and international norms, remains the primary real-world illustration of the treatment/enhancement boundary being crossed without adequate oversight [ 13 , 14 ]. 2.2. Moral status of synthetic embryo models Stem cell-derived embryo models (blastoids, gastruloids) recreate aspects of early human development without fertilization, providing an alternative to donated embryos for developmental and infertility research [ 15 ]. Their increasing structural fidelity to natural embryos raises the classical question of when, if at all, protections tied to potentiality or developmental stage should apply. The [ 8 ] guideline update retained the 14-day limit on integrated culture for natural embryos while introducing a specific, more flexible review pathway for stem cell-based embryo models (SCBEMs), rather than either abolishing or mechanically extending the 14-day rule [ 8 ]. This is a narrower and more specific claim than the earlier framing of this guideline, and is described accordingly here. 2.3. Regulation of stem cell research Regulatory stringency for embryonic and induced pluripotent stem cell (iPSC) research varies substantially across jurisdictions, and the ISSCR guidelines function as an influential but non-binding international reference point rather than enforceable law [ 8 , 16 ]. Persistent governance gaps include inconsistent oversight of “stem cell tourism,” unclear consent standards for donated biological material, and the absence of a binding international mechanism comparable to environmental treaties. Australia’s National Health Genomics Policy Framework provides an example of a national policy approach to genomics governance [ 17 ]. 2.4. Proposed governance concepts (explicitly conceptual, not existing institutions) Three governance mechanisms are proposed here - Ethical Enhancement Councils (EECs), a Tiered Moral Framework (TMF), and International Stem Cell Accords (ISCAs) – and are operationalized in Table 1 with a stated purpose, scope, membership, decision criteria, and an explicit acknowledgment of open questions (e.g., who adjudicates “justification” for a post-gastrulation model). They are not claimed to be adopted, endorsed, or implemented by any body, and are presented as a starting point for policy discussion rather than as established governance. 3. Methods This is a conceptual and mathematical modeling study. No human participants, animal subjects, identifiable genomic data, or biological samples were used at any stage. All reported scores are outputs of a deterministic formula and, where stated, of a stochastic simulation built on that formula; no data were collected, and no inferential hypothesis tests were performed, consistent with the exploratory, non-empirical nature of the work. 3.1. Audit of the originally proposed formula The submitted formula was GEAF = ((A x E) – (C x I))/ (P + R) x 100, where A is an accessibility percentage (0–100), E an ethical compliance score (0–1), C a treatment cost in thousands of US dollars, I a Gini-like inequality coefficient (0–1), P a policy-strength scale (1–10), and R a research-efficiency factor (0–1). We evaluated this formula against four criteria, following standard practice in multi-criteria index construction [ 18 , 19 ]: (a). Dimensional consistency. A x E produces a quantity on a 0–100 scale; C x I produces a quantity on a scale of roughly 0 to several thousand (since C is expressed in thousands of dollars). Subtracting the second from the first is dimensionally improper: the difference is dominated almost entirely by C x I regardless of the values of A and E, so the formula does not actually weigh accessibility and ethics against cost and inequality in any interpretable way – it is numerically swamped by cost. (b). Boundedness and interpretability. Because the numerator is unbounded below (C can be arbitrarily large) and the denominator can be small (P + R can approach 1), the output has no defined minimum, maximum, or zero-point interpretation. The original formulation reported scores from −400 to +745 without specifying what any particular value means. (c). Denominator behavior. Placing (P + R) in the denominator means that, holding the numerator fixed, increasing research efficiency or policy strength always increases the magnitude of the score, including in cases where the numerator is negative, where an increase in the denominator drives the (negative) score toward zero rather than in a direction that is independently interpretable as “better.” This produces behavior that is not clearly wrong, but that was not derived or justified theoretically in the original formulation. (d). Scenario-formula consistency. The seven illustrative scenarios in the original Table 1 used cost inputs of $50,000 to $500,000, roughly an order of magnitude below verified real-world gene-therapy list prices, which as of this writing range from approximately $2.2 million (Casgevy, Vertex [ 2 ] to $4.25 million (Lenmeldy, Orchard [ 3 ]. This internal inconsistency, not previously flagged, means the original scenarios did not actually represent the cost environment the paper otherwise describes. On the basis of (a)-(d), we concluded that the original formula could not be defended by re-deriving weights or re-normalizing in place; it required reconstruction rather than repair. 3.2. Reconstructed formula (GEAF’) We reconstructed the index using multi-attribute utility theory [ 18 ], the same general framework invoked, but not actually implemented, in the original formulation. Each of the six input variables is first transformed into a dimensionless, direction-consistent component on the interval [0,1], where 1 always represents the more favorable state for equity and access. The transformed components are then combined as a weighted arithmetic mean and rescaled to a 0–100 index: C_max is a modeling normalization ceiling, not an affordability estimate or a claim about typical treatment cost: it fixes the point at which the affordability component (C_n) reaches its floor of 0. In this manuscript we set C_max = $4.25 million (4,250 in thousands), corresponding to the verified US wholesale acquisition cost of Lenmeldy (atidarsagene autotemcel, Orchard [ 3 ], the highest publicly reported list price for an approved gene therapy at the time of writing (Orchard [ 3 ]. We emphasize that this anchor was selected only to fix the normalization scale, and Table 8 reports that GEAF’ outputs are sensitive to this specific choice of ceiling. In the absence of a validated empirical weighting scheme (e.g., from a discrete-choice experiment or multi-criteria decision analysis with stakeholders), we use equal weights (w = 1/6 for each component) as a transparent, neutral default and treat unequal weighting as an explicit avenue for future empirical work rather than as a hidden assumption; equal weighting is reported, not claimed to be uniquely correct. This reconstruction satisfies four validity requirements for a composite index of this kind: all six components are now on the same [0,1] scale before combination (resolving dimensional incompatibility); the index is bounded on [0,100] by construction (resolving the interpretability concern); the arithmetic-mean structure has no denominator-driven distortion (resolving the P + R behavior concern); and Section 3.4 re-specifies scenario cost inputs to be consistent with the cost literature cited elsewhere in the manuscript (resolving the scenario-formula inconsistency). 3.3. Parameter definitions, data sources, and level of evidence Table 2 summarizes the definition, scale, source/derivation, and evidentiary status of each parameter. Download: PNG larger image TIFF original image Table 2. GEAF’ parameter definitions and evidentiary status. https://doi.org/10.1371/journal.pone.0359782.t002 3.4. Scenario construction We reconstructed the seven illustr
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