RAVYNS™ : Reporting Analysis Via Yielding, Neglect & Sabotage

Quantifying Invisibility in Chronic Illness and Care-Linked Abuse

By Cynthia Adinig

Origin Story: The Data of Deception

RAVYNS™ was not imagined. It was forced into existence. For four years I lived inside a system being used against me. It wasn't until spring of 2024 that I discovered my now ex-husband orchestrated a calculated campaign of medical sabotage by lying to doctors, falsifying context, and framing me as mentally unstable to conceal his ongoing domestic violence. He posed as a devoted caregiver, sometimes even expressing outrage at how I was treated by local hospitals, to friends, family and even high profile, A-List justice centered music artists. Even landing lucrative contracts, built around his advocacy of a disabled wife of color. While behind the scenes he coordinated the very harm he pretended to protest.

He incited medical violence by proxy, convincing physicians to deny me treatment, withhold medication, or document me as delusional. He weaponized empathy to build a false record, turning the very institutions meant to protect me into silent accomplices. Each denial of care looked bureaucratic. Each humiliation appeared procedural. Every attempt to have me institutionalized seemed like concern. It was control and physical violence, sanitized. A more indirect yet equally life threatening domestic violence.

What makes this even more horrifying is that I was warned. Long-time chronic illness advocates told me years ago that this pattern existed, that disabled women were being disbelieved, misdiagnosed, and re-injured by systems too naive to imagine how coercion could move through a medical chart. Still, I could not comprehend the lengths to which he would go, or how invisible those lengths would become once filtered through institutional trust.

Now, in 2025, I have been speaking out across platforms and in media, on this type of intimate partner violence amongst people with disabling chronic conditions. We have the largest disabled population in modern history, yet this documented form of harm has no name in federal health literature. There are no protocols, no data tracking, and no oversight. Hundreds of women are describing identical experiences across social media threads, being denied care by proxy, gaslit through documentation, and nearly killed under the banner of concern. This TIME article was spurned by my speaking out publicly, I have also worked behind the scenes to help women in this situation get care and safety.

RAVYNS™ was built to capture that invisible architecture, to give coordinates to what survivors have been shouting into the void. It translates every manipulation, every falsified note, every erased symptom, and every orchestrated crisis into a measurable signal of systemic failure. I built it because survival should not require becoming your own epidemiologist. Disbelief kills quietly, procedurally, and with plausible deniability. RAVYNS™ is how we end that invisibility, not through outrage alone but through evidence strong enough to indict silence itself.

1. Summary

RAVYNS™ is the first integrated system to transform partner interference and institutional neglect in chronic-illness care into an auditable public-health metric. It estimates how harm becomes invisible through coerced yielding, systemic neglect, and deliberate sabotage, translating survivor intuition into measurable evidence (Uvelli et al., 2023; see also Warshaw et al., 2019, on medicalized control tactics).

Traditional intimate-partner violence (IPV) surveillance captures only visible or self-reported incidents (Black et al., 2011). RAVYNS™ extends this to hidden dynamics, where illness becomes a control mechanism and systems unwittingly silence patients (Langford & Duffy, 2025; Hegarty et al., 2020). It quantifies three invisibility dimensions, yielding, neglect, and sabotage, while exposing bias structures that sustain them (Bagherzadeh et al., 2024; Hoffmann & Tarzian, 2001).

Integrating CYNAERA’s US-CCUC™ prevalence corrections with behavioral analytics, RAVYNS™ reframes invisibility as structural failure, not enigma (Jason et al., 2021; Towle & Godolphin, 2011). Survivor narratives become auditable data for institutional reform (Stark, 2007).

Projected National Total — Medical Sabotage in IACCs

Based on U.S. prevalence data, RAVYNS™ modeling estimates that between 5 and 10 million adults living with the top 4 common infection-associated chronic conditions such as Long COVID, ME/CFS, dysautonomia, and MCAS experience medical sabotage each year. These are the most visible cases within a much larger population of 65–75 million Americans living with infection-associated chronic conditions (IACCs).

When the full IACC population is included, the national total of adults likely experiencing medical sabotage, partner interference, or care-linked neglect rises to approximately 24 million adults. About 6 in 10 are women, but the remainder are men facing similar disbelief and manipulation. Within the Long COVID population alone, the exposure rate is roughly 1 in 7 to 1 in 10 adults, or about 5 million people annually. These cases are not isolated, they represent a systemic blind spot where partner deception, institutional bias, and disbelief intersect to erase accountability.

The difference between the 1 in 7 and 1 in 10 estimates reflects time, not contradiction. Lifetime studies show that up to half of women with chronic or episodic disabilities experience medical sabotage or partner interference at some point. Because Long COVID has only existed for 5 years, cumulative exposure is still catching up to historical patterns. When adjusted for time since onset, the one-in-seven projection mirrors the long-term trajectory already observed in ME/CFS, dysautonomia, and autoimmune conditions. Without intervention, this epidemic of disbelief will reach the same 1 in 2 lifetime exposure seen in other chronic illnesses.

2. Background

Public-health surveillance prioritizes acute, visible violence, wounds, disclosures, police reports (Campbell et al., 2018; García-Moreno et al., 2015). It misses chronic control: autonomy eroded via healthcare dependency, transportation, or digital access (Komaroff & Bateman, 2021; Sweet, 2019).

For millions with Long COVID, ME/CFS, dysautonomia, MCAS, or MCS, medical access hinges on partners who may cancel appointments, withhold portal credentials, or frame patients as unstable (Montoya et al., 2022; Hegarty & Tarzia, 2016). “Help” masks coercion; no police report emerges, only eroded credibility in charts and diagnostic doubt (Wuest et al., 2010).

This mirrors CYNAERA’s US-CCUC™ diagnostic undercount (Jason et al., 2021; see also Valdez & Gutmanis, 2021, on post-viral erasure). Social systems ignore medicalized abuse for the same reason: it hides in paperwork (Ford-Gilboe et al., 2017). RAVYNS™ applies epidemiologic rigor to behavior, locating system failure rather than survivor silence (Koss et al., 1994).

3. Model Overview

RAVYNS™ computes invisibility via visibility, credibility, access, and correction factors:

RAVYNS = (V × C × A × K) × 100

Where: V = visibility score (signal strength across media and research) C = credibility factor (quality and reliability of sources) A = advocacy amplitude (measured community or organizational engagement) K = knowledge retention (persistence of topic awareness over time)

Variable	Meaning	Example Indicators	Range
V (Visibility)	Public & institutional awareness	PubMed papers, NIH funding, news volume, search trends	0–1
C (Credibility)	Believability of patient narrative	Gender bias ratios, psychiatric mislabel rates	0–1
A (Access)	Autonomy within care system	Patient-portal control, transport freedom	0–1
K (Correction)	Structural correction term	Multiplies bias adjustments for undercount, gender, overlap, suppression	1.0–2.5

K: The Correction Engine

K=U×G×O×SK = U \times G \times O \times SK=U×G×O×S

• U (Undercount): Institutional underreporting (1.2–5×; Reed et al., 2015; Breiding et al., 2014).

• G (Gender Bias): CYNAERA’s CGPI adjusts male under-diagnosis, female credibility erosion (Samulowitz et al., 2018; Rowe et al., 2017; Zhang et al., 2021).

• O (Overlap): Prevents double-counting comorbidities (0.7–0.95; Komaroff & Bateman, 2021; Natelson, 2020).

• S (Suppression): Digital/geographic silence (1.1–1.6×; FCC, 2023; ITU, 2023; Prüss-Ustün et al., 2016).

Worked Example – Long COVID V=0.8 V = 0.8 V=0.8, C=0.65 C = 0.65 C=0.65, A=0.70 A = 0.70 A=0.70, K=1.84 K = 1.84 K=1.84 (U 1.5 × G 1.05 × O 0.9 × S 1.3) → RAVYNS ≈ 67: publicly visible yet systemically disbelieved (Davis et al., 2023).

4. Visibility Calibration Framework

V = 0.35(PubMed) + 0.30(News) + 0.20(Search Trends) + 0.15(NIH Funding)

Where: PubMed = volume of peer-reviewed literature News = number of mainstream or science media mentions Search Trends = public interest index from search data NIH Funding = relative proportion of active federal research grants.

Condition	V	CF (100 / V)
Breast Cancer	100	1.00
Long COVID	70	1.43
ME/CFS	12	8.33
Dysautonomia	10	10.00
MCAS	6	16.67

Each 10-point drop raises delayed-diagnosis and misattributed-abuse risk (Proctor et al., 2022). At V = 12 (ME/CFS), risk is eightfold versus cancer (Chu et al., 2021).

Exposure estimate:

IPVₑₓₚₒₛᵤᵣₑ = Baseᵣᵢₛₖ × Partneredₛₕₐᵣₑ × Episodicᵢₙₜₑₙₛᵢₜy × CF

Where: Baseᵣᵢₛₖ = baseline probability of IPV occurrence Partneredₛₕₐᵣₑ = proportion of individuals currently in partnered relationships Episodicᵢₙₜₑₙₛᵢₜ y = multiplier for average episode severity or frequency CF = correction factor accounting for underreporting or sampling bias

Base risk ≈ 8% (Smith et al., 2018), partnered ≈ 70%, intensity ≈ 1.7 → Long COVID ≈ 13–14% annual risk (Wong et al., 2024).

5. Validation Layers

Five normalized streams (0–100) weighted by size, recency, reliability (Cohen et al., 2023; Guyatt et al., 2008):

1. S³ Social Signal Index – Negative framing predicts mislabels (Boyle & Sullivan, 2022; D’Onofrio et al., 2021).

2. US-CCUC™ – Hidden IACC prevalence (Jason et al., 2021; Mirin et al., 2022).

3. Federal Reference – CDC/WHO multipliers (2–6×; Reed et al., 2015; Devries et al., 2013).

4. Global Median – U.S. vs. WHO/NGO baselines (WHO, 2021; Sardinha et al., 2022).

5. Digital Access (D_a) – 1.2–2.0× multiplier (FCC, 2023; ITU, 2023; Bauerly et al., 2019).

Multi-source triangulation mirrors infection-control rigor (Hill, 1965).

6. Gender Correction

Ratios from US-CCUC™ + CGPI correct male undercount, female over-stigma (Samulowitz et al., 2018; Rowe et al., 2017; Jason et al., 2021; see also Epstein et al., 2023).

Condition	Corrected U.S. Prevalence	Female %	Male %
Long COVID	35–50 million	55	45
ME/CFS	15–21.5 million	55	45
Dysautonomia	14–18 million	60	40
MCAS	15–20 million	55	45

Pediatric parity pre-adolescence; post-adolescence male drop signals cultural suppression (Rowe et al., 2017; Brown et al., 2020). CGPI uplifts male prevalence 1.3–1.8× (Dusenbery, 2018). RAVYNS™ weights both erasures.

7. IPV Exposure Model

IPVₑₓₚₒₛᵤᵣₑ = g × p × A × CF

Where: g = 6–10% (Peterman et al., 2022)p = 0.6–0.75A = 1.5–2.0 (dependency cycles; Saftlas et al., 2014)CF = correction factor (context-specific multiplier)

• Long COVID: 0.08 × 0.70 × 1.7 × 1.43 ≈ 13.6% (≈1 in 7).

• ME/CFS: 0.08 × 0.70 × 1.7 × 8.33 ≈ 79% ceiling (lifetime alignment; Campbell et al., 2018; Trevillion et al., 2014).

Scores flag missed harm probability (Stark & Hester, 2019).

Hidden Harm in Plain Sight — Estimated U.S. Annual Burden of top IACCI

(Adults, conservative ranges; uses RAVYNS exposure logic)

Condition | U.S. Population | Female % | Affected | Prevalence Ratio

---------------------------------------------------------------------------------------------------------------------------------

Long COVID 35–50 M 55 3.5 – 7.5 M 1:10 to 1:7

ME/CFS 15–21.5 M 55 2.1 – 4.3 M 1:7 to 1:5

Dysautonomia 14–18 M 60 1.8 – 3.0 M 1:8 to 1:6

MCAS 15–20 M 55 1.7 – 3.3 M 1:9 to 1:6

8. Interpretation

RAVYNS™ reveals system failure, not diagnosis.

• <40: Visible population.

• 40–60: Transitional neglect.

• ≥60: Active invisibility (Towle & Godolphin, 2011; Cusitar, 2020).

Score ≥60 predicts delayed diagnosis, unrecorded coercion, “non-compliance” labels (Wuest et al., 2010). Longitudinal mapping = epidemiologic heat map of institutional blindness (Krieger, 2021).

Ten-point rise → +0.7 ED visits, +2.8 months delay, +$2,000 personal cost (Section 9; see also McCauley et al., 1998). Invisibility shortens remission, drains finances, burns caregivers, an engineered inefficiency (Purtle et al., 2020).

9. Impact Accounting Per Person

Let R R R = score, r=R/100 r = R / 100 r=R/100.

9.1 Core Outcomes (Max at R=100)

Outcome	Value
Diagnosis delay	12 months
Excess ED visits	1.0 / yr
Hospital admissions	0.15 / yr
Remission lost	4 months / yr
QALY loss	0.06 / yr

9.2 Unit Costs (USD)

Item	Value
Outpatient inefficiency / mo	$500
Income loss / mo	$1,200
Caregiver/time loss / mo	$300
ED visit	$1,600
Admission	$14,000
Lost remission / mo	$800
Value per QALY	$100,000

9.3 Equations

Given parameters:

dₐdd = 12r; erₑₓcₑₛₛ = 1.0r; admₑₓcₑₛₛ = 0.15r; remₗₒₛₛ = 4.0r

Impact cost per year:

Impact cost / yr = [dₐdd × (500 + 1200 + 300)] + (erₑₓcₑₛₛ × 1600) + (admₑₓcₑₛₛ × 14000) + (remₗₒₛₛ × 800)

9.4 Example

(R=70) r=0.7 r=0.7 r=0.7 → Delay 8.4 mo, ED 0.7, Adm 0.105, Rem 2.8 mo Impact ≈ $21,630 + $4,200 QALY = $25,830 total.

9.5 Sensitivity

Scenario	R	Impact/yr	+QALY	Total
Low	30	$9,270	$1,800	$11,070
Medium	50	$15,450	$3,000	$18,450
High	90	$27,990	$5,400	$33,390

Ten-point reduction saves ~$2,200/patient (Hacker, 2024; Loeb et al., 2022).

National Economic Impact

Medical sabotage is not only a humanitarian crisis , it is an economic one. When disbelief becomes a systemic reflex, every delayed diagnosis and dismissed symptom carries a measurable price.

Severity Band	Per-Person Annual Loss (USD)	Estimated National Cost (USD)	GDP Share
Low (minor interference)	11,070	265.7 B	0.9 %
Moderate (sustained neglect)	18,450	442.8 B	1.6 %
High (severe sabotage)	33,390	801.4 B	2.9 %

Even the most conservative scenario represents nearly $266 billion in annual losses, equivalent to the entire yearly output of the state of Virginia. Each 10-point reduction in the average RAVYNS score translates to $2,200 in savings per patient per year, or about $52.8 billion nationally.

• Workforce attrition: Long-term disability and early retirement erode productivity in the same sectors already short-staffed since the pandemic.

• Emergency overuse: Patients denied timely care generate repeated ER and hospitalization cycles that inflate public-payer costs.

• Institutional inefficiency: Every instance of disbelief wastes clinical time, reduces provider morale, and compounds liability exposure.

• Macroeconomic drag: The combination of untreated chronic illness, caregiver burnout, and social-safety-net strain suppresses growth across multiple GDP sectors.

Ignoring medical sabotage is not neutral, it is fiscally reckless. The United States cannot afford to lose up to 3 percent of GDP each year to preventable harm hiding in plain sight. By quantifying disbelief as a cost center, RAVYNS™ reframes empathy as efficiency and justice as economic recovery.

10. Why It Matters

RAVYNS™ reframes violence, neglect, and erasure as quantifiable system errors rather than moral gray zones (Stark, 2007). Once invisibility becomes data, inaction becomes a policy choice.

For clinicians, the model exposes where disbelief has quietly replaced diagnosis. It identifies the moment a patient’s lived evidence was filtered through bias rather than science. It translates “we don’t know” into measurable gaps that can be corrected through training, tracking, and system-level reform.

For health systems, RAVYNS™ reveals how partner interference, transportation barriers, and environmental sensitivities evolve into preventable costs. It uncovers the invisible overhead of disbelief, ER visits mislabeled as anxiety, misdiagnosed allergic crises, or dismissed neurological decline, all of which drain public resources and patient trust.

For policymakers, it creates a numerical bridge between public-health access and domestic-violence prevention. RAVYNS™ allows cross-agency auditing: how a missing diagnosis in one system compounds into eviction risk in another, or how a cut in disability transit funding can directly increase hospitalizations and caregiver burnout.

Every uncounted patient represents not just a personal tragedy but an economic failure. Each point of invisibility strips months of productivity, tens of billions in GDP, and immeasurable dignity from people already fighting to stay alive (Purtle et al., 2020).

Invisibility is not an accident. It is a product of institutional processes that fail to measure what they do not value. RAVYNS™ gives those failures coordinates, allowing public agencies to audit bias with the same rigor that FEMA audits infrastructure or the CDC audits outbreaks (CDC, 2022).

At its heart, RAVYNS™ asks a moral question disguised as math: How much is disbelief costing us? The answer, by every metric, is too much.

11. Next Steps

1. EHR API for Narrative Partiality Audits - RAVYNS™ can be embedded within electronic health record systems as an API that scans clinical notes for narrative partiality, instances where subjective impressions (“anxious,” “noncompliant,” “attention-seeking”) replace physiological observations. The audit flags both individual and systemic language patterns, allowing institutions to measure where disbelief occurs in documentation and how it influences patient outcomes. These audits can guide bias-reduction training, credentialing requirements, and equity-linked reimbursement models.

2. Sex-Disaggregated IPV Charts for All IACCs - Chronic illness and intimate partner violence intersect along gendered fault lines, yet data rarely reflect that overlap. Integrating sex-disaggregated IPV statistics into infection-associated chronic condition (IACC) registries will reveal how violence amplifies medical neglect and delayed diagnosis. It will also quantify protective effects of early recognition, transforming IPV data from a social metric into a predictor of health-system strain and recovery potential.

3. RAVYNS-Clinical™ Intake Screen - A clinical screening tool built on the RAVYNS™ logic would allow frontline providers to identify risk signatures in real time. Questions on partner control, access to care, and environmental triggers would feed directly into an automated scoring dashboard, flagging cases that match known invisibility patterns. The result: an early warning system for both medical neglect and coercive control, using the same logic that predicts readmissions or infection spread.

4. Open-Source Calculator - Transparency drives trust. An open-source version of the RAVYNS™ calculator will let researchers, advocates, and policymakers test how different variables, visibility, credibility, advocacy, and knowledge retention, affect the Invisibility Index. This open model invites validation, collaboration, and public accountability while keeping proprietary weighting algorithms protected within institutional versions.

5. Visibility-Parity Dashboard (Media vs. NIH) - Public narrative and research investment rarely move in sync. The RAVYNS™ Visibility-Parity Dashboard compares how much attention an illness or issue receives in media, search trends, and NIH funding. It quantifies the gap between public discourse and institutional action, exposing how underrepresentation drives underfunding. By tracking that gap longitudinally, agencies can monitor whether reforms, advocacy, and funding initiatives are actually closing it.

Special Author’s Note

This work began as my way to stay alive, but it became a blueprint for protecting others. Medical coercion and sabotage are not rare anomalies; they are structural blind spots that have gone unmeasured for decades. Every survivor who documents disbelief is gathering public health data whether anyone recognizes it or not. It is time for institutions to do the same.

RAVYNS™ should not live only as a private model. It belongs in the frameworks of the NIH, HHS, and CDC. It deserves the same legitimacy that other forms of violence surveillance receive. This system proves that harm can be mapped, corrected, and prevented when disbelief itself becomes measurable. The future of chronic illness care, domestic violence prevention, and equitable medicine depends on whether we are brave enough to count what was once uncounted.

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Author’s Note:

All insights, frameworks, and recommendations in this white paper reflect the author's independent analysis and synthesis. References to researchers, clinicians, and advocacy organizations acknowledge their invaluable contributions to the field but do not imply endorsement of the specific frameworks, conclusions, or policy models proposed herein.

Applied Infrastructure Models Supporting This Analysis

Several standardized diagnostic and forecasting models developed through CYNAERA were utilized or referenced in the construction of this white paper. These tools support real-time surveillance, economic forecasting, and symptom stabilization planning for infection-associated chronic conditions (IACCs).

Note: These models were developed to bridge critical infrastructure gaps in early diagnosis, stabilization tracking, and economic impact modeling. Select academic and public health partnerships may access these modules under non-commercial terms to accelerate independent research and system modernization efforts.

Licensing and Customization

Enterprise, institutional, and EHR/API integrations are available through CYNAERA Market for organizations seeking to license, customize, or scale CYNAERA's predictive systems.

Learn More: https://www.cynaera.com/systems

About the Author

Cynthia Adinig is a researcher, health policy advisor, author, and patient advocate. She is the founder of CYNAERA and creator of the patent-pending Bioadaptive Systems Therapeutics (BST)™ platform. She serves as a PCORI Merit Reviewer, Board Member at Solve M.E., and collaborator with the Selin Lab at the University of Massachusetts.

Cynthia has co-authored research with Harlan Krumholz, MD, Dr. Akiko Iwasaki, and Dr. David Putrino; contributed to Yale’s LISTEN Study; advised Amy Proal, PhD through the Mount Sinai Patient Advisory Board; and worked with Dr. Peter Rowe of Johns Hopkins on national outreach for post-viral and autonomic illness. She authored a Milken Institute essay on AI and healthcare, testified before Congress (video), and has worked with congressional offices on multiple Long COVID legislative initiatives. She helped design and teach the patient-and-research advocacy curriculum, and was later appointed to the HHS Long COVID Advisory Committee.

Through CYNAERA she develops modular AI platforms, including the IACC Progression Continuum™, Primary Chronic Trigger (PCT)™, RAVYNS™, and US-CCUC™—that help governments, universities, and clinical teams model infection-associated conditions and improve precision in research and trial design. Her leadership and national Long COVID work have been featured in TIME, USA Today, and other major outlets.

Cynthia’s work is informed by her lived experience surviving the first wave of the COVID-19 pandemic, which deepened her commitment to reforming how chronic conditions are understood, studied, and treated. She is also an advocate for domestic-violence prevention and patient safety, bringing a trauma-informed lens to every project. Even after becoming disabled from Long COVID, she remained a resource for other parents of gifted children, guiding her son Aiden, who joined Mensa International at age five, and continuing to mentor mothers navigating the intersection of disability, gifted education, and resilience.