Abstract
The COVID-19 pandemic has renewed attention to complex chronic health conditions that challenge conventional biomedical paradigms. Syndromes such as postural orthostatic tachycardia syndrome and myalgic encephalomyelitis/chronic fatigue syndrome have gained broader visibility through the lens of Long COVID. As global vaccination campaigns expanded, a subset of individuals began reporting similarly persistent, multisystem symptoms following COVID-19 immunization—informally referred to as post-COVID-19 vaccination syndrome. These presentations, which include dysautonomia, neuropathic pain, post-exertional malaise, and cognitive dysfunction, resemble post-infectious syndromes and may involve shared immune-related mechanisms. Although no causal relationship to vaccination has been established, these cases—together with comparable reports following other vaccines—highlight limitations in current vaccine safety systems for detecting and evaluating complex chronic outcomes. This article introduces the concept of complex chronic adverse events following immunization (CC-AEFIs) as a pragmatic, surveillance-oriented framework to support the systematic identification and investigation of such cases. CC-AEFIs are not syndromic diagnoses but a higher-order category encompassing persistent, multifactorial conditions that may follow immunization yet challenge existing pharmacovigilance definitions and tools. These conditions often involve multiple organ systems, delayed onset, fluctuating trajectories, diagnostic ambiguity, and symptom heterogeneity. Drawing on the author’s lived experience as an affected patient and integrating clinical, regulatory, and experiential evidence, the analysis examines structural and epistemic limitations across the pharmacovigilance continuum—from underrecognition in clinical settings to analytic exclusion and constrained governance. It concludes by proposing reforms to strengthen safety-system responsiveness, including enhanced diagnostic training, longitudinal surveillance, patient-reported outcome integration, and analytic transparency. Addressing these limitations is essential to sustain public trust, ensure equitable care, and uphold the scientific integrity of immunization programs.
Plain language summary
- Though vaccines are exceptionally safe and critical for public health, a small number of individuals develop long-lasting and complex health problems after vaccination that are difficult to diagnose and poorly understood.
- These problems often involve symptoms such as fatigue, nerve pain, dizziness, heart rhythm issues, or brain fog. They may begin days or weeks after vaccination, affect multiple body systems, and change over time. Because they don’t fit standard medical categories, many people are dismissed or misdiagnosed.
- Similar symptoms have also been observed after infections like COVID-19 (known as Long COVID), suggesting that shared immune-related processes may be involved. While a direct causal link to vaccination is not always clear, emerging evidence shows the importance of systematically studying these cases.
- Current vaccine safety systems work well to detect short-term or clearly defined side effects like fever or allergic reactions. However, they often miss longer-term, more complex, or delayed conditions that do not follow expected patterns.
- This paper introduces a new framework—Complex Chronic Adverse Events Following Immunization (CC-AEFIs)—to help describe, investigate, and monitor these cases more effectively.
- It also identifies challenges across the vaccine safety system—from diagnosis to reporting, data analysis, and governance:
- Recognition: These conditions are often dismissed due to lack of clear tests (biomarkers) and a tendency to label symptoms as psychological.
- Reporting: There are widespread underreporting issues, especially for complex or delayed symptoms.
- Analysis: Current systems lack the long-term tracking and comparison data needed to detect rare or complex patterns.
- Governance: Oversight processes are often opaque, influenced by vaccine manufacturers, and rarely include affected individuals.
- The paper proposes concrete reforms to make vaccine safety systems more transparent, inclusive, and responsive.
Keywords
Introduction
The COVID-19 pandemic disrupted long-standing biomedical assumptions about post-infectious illness. Conditions historically marginalized by medicine—such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and postural orthostatic tachycardia syndrome (POTS)1–5—were cast into sharp focus by the sheer scale of post-acute sequelae of SARS-CoV-2 infection (Long COVID), where ME/CFS- and POTS-like patterns have been widely documented. 6 As growing numbers of individuals developed chronic, disabling, and multisystem symptoms in the wake of SARS-CoV-2 infection—often in the absence of definitive diagnostic markers or localized pathology—it became increasingly untenable to dismiss these conditions as psychosomatic or functional.7,8 Their complexity, relapsing trajectories, and diagnostic ambiguity now demand serious clinical and scientific engagement.
Yet as millions were infected, millions more were also vaccinated—often within overlapping timelines. While the benefits of immunization were substantial and well-documented, reports also began to emerge of individuals experiencing similarly complex, multisystem symptoms following vaccination.9,10 These included autonomic dysregulation, sensory hypersensitivity, cognitive dysfunction, post-exertional malaise, small fiber neuropathy (SFN), and other physiological disturbances—clusters resembling complex infection-associated chronic syndromes11–13 such as POTS and ME/CFS.14–21 Patients and clinicians have increasingly referred to this emerging post-vaccine constellation as post-COVID-19 vaccination syndrome (PCVS)—a term not codified in formal diagnostic nosology, but increasingly invoked in case reports, observational studies, and preliminary mechanistic research.16–18,22–31
While early hypotheses posited that PCVS cases might reflect undetected SARS-CoV-2 infections, 32 this interpretation has been challenged by reports of comparable symptom profiles in individuals who test negative for anti-nucleocapsid antibodies 31 —a marker of natural infection, but not vaccination—thereby weakening the plausibility of prior, unrecognized COVID-19 illness. Moreover, similar symptom constellations have been documented following other vaccines.33–39 These precedents lend plausibility to the hypothesis that vaccination—while protective for the vast majority—may, in rare cases, be associated with the onset of complex, multisystem syndromes that mirror those seen after natural infection. The overlap in symptomatology across vaccine platforms, and between infection- and vaccine-associated conditions, may point to shared or convergent immune-mediated pathophysiological processes. More fundamentally, even if vaccination acts as a trigger for the expression of a latent infection or the unmasking of an underlying subclinical condition, such occurrences remain classified as adverse events following immunization (AEFI) according to the World Health Organization—defined as any event temporally associated with immunization, regardless of causal attribution or predisposition. 40
While no causal relationship between PCVS, POTS, or ME/CFS and vaccination has been established, this absence of evidence must be interpreted in light of the fact that most vaccine–adverse event evaluations do not yield definitive conclusions regarding causality. For example, the 2024 US National Academies of Sciences, Engineering, and Medicine’s Evidence Review of the Adverse Effects of COVID-19 Vaccination examined 85 vaccine–adverse event pairs—including POTS, which was added to the review following public consultations—and found that for 65 of these pairs, the evidence was “inadequate to accept or reject” a causal relationship, even amid unprecedented datasets and targeted monitoring.
As Chandler has argued,39,41 existing pharmacovigilance frameworks are optimized to detect acute, temporally proximate, and organ-specific adverse events—an architecture poorly suited to syndromes such as POTS, ME/CFS, and PCVS. These conditions often evolve gradually, involve multiple organ systems, and resist standardized diagnostic categorization, making them difficult to capture through conventional surveillance methods. Such structural limitations become particularly consequential in large-scale immunization programs, where even rare or poorly defined harms can carry meaningful implications for public trust. As Salmon et al. argued in The New England Journal of Medicine (2024), “rare but serious adverse reactions. . . no longer seem rare when vaccines are given to millions or billions of people.” Yet the scientific infrastructure responsible for evaluating these adverse events remains underdeveloped. As the authors contend, after decades of underinvestment, pharmacovigilance systems often lack the capacity to move beyond signal detection toward the kind of mechanistic and causal understanding that the public now reasonably expects. 42 Taken together, these critiques point to a safety enterprise still calibrated for simplicity—designed to log discrete events on short timelines—yet now confronted with conditions that may be chronic, multisystemic, and, in the context of mass vaccination, statistically expected.
When complex presentations once considered vanishingly rare or diagnostically obscure appear secondary to vaccination, the obligation to recognize, investigate, and respond is not diminished by their rarity; if anything, rigorous attention to rare harms is key to sustaining public trust and confidence in vaccination. 43 For although vaccination—credited with preventing millions of deaths annually—remains among the most transformative achievements in modern public health,44,45 the confidence underpinning this achievement is increasingly fragile, as rising hesitancy threatens decades of progress. 46 While misinformation is often cast as the central driver, public trust also hinges on the perceived transparency, responsiveness, and credibility of vaccine safety systems.47–49 In this context, failure to adequately recognize or investigate complex adverse events becomes a public health liability.
When systems fail to adequately recognize, investigate, or support individuals experiencing complex adverse events, the burden shifts to affected individuals—who are left to secure specialized care often unavailable through standard healthcare pathways, to navigate opaque and fragmented reporting systems, and to shoulder an evidentiary burden of causality they cannot reasonably meet. 43 Yet, the lived experience of managing a serious AEFI is rarely included in academic or policy discourse29,50–52 leaving a critical dimension of these system-level gaps undocumented.
This absence of patient perspectives is consequential, because lived experience reveals system-level failures that formal evaluations seldom capture. These issues are well known to the author because I have lived them. As a previously healthy scientist, I experienced a sudden and unexplained collapse in health within days of receiving a COVID-19 vaccine: severe chest pain, tachycardia, debilitating lightheadedness, a “concussion-like” sensation, and other neurological disturbances that defied familiar classification yet left me functionally incapacitated—a complex symptom profile consistent with dysautonomia and PCVS. In my case, a probable link to vaccination was ultimately suspected by specialist clinicians. Yet countless early encounters through standard care pathways were marked by dismissal, psychologization, inaction, and a lack of coordinated follow-up. No clinicians filed an adverse event report; no mechanism for case management was triggered. My own attempts to initiate reporting yielded only redirected calls, unanswered emails, and bureaucratic impasse. Months elapsed before the case was formally registered—an outcome that neither improved access to care nor prompted meaningful investigation, and one that most patients, lacking institutional knowledge or persistence, would likely never achieve. My case is far from exceptional—it reflects recurrent patterns reported by many patients and observed anecdotally across clinical settings, even though these experiences remain largely undocumented in the formal literature.
The purpose of this paper is to discuss the performance of (post-authorization) vaccine pharmacovigilance systems when confronted with complex, chronic, and multisystem adverse events, and to outline reforms that could strengthen their capacity to detect and assess them. The aim is not to establish or refute causality, but to elucidate the structural and epistemic constraints that limit the ability of existing systems to evaluate potential vaccine–event relationships. This analysis is grounded in a critical synthesis of peer-reviewed literature and regulatory evidence, and informed by my lived experience as an affected patient, which provides insight into dimensions of pharmacovigilance that are rarely visible in formal evaluations. Whereas the clinical and mechanistic aspects of these presentations have been discussed elsewhere, 15 the present paper concentrates specifically on the systemic features of vaccine safety infrastructure that shape recognition, reporting, and assessment.
Pharmacovigilance system performance for complex chronic AEFI
The limitations outlined above—spanning recognition, reporting, and analytic capacity—become especially evident when safety systems encounter complex, chronic, and multisystem presentations. To help delineate this problem space, I introduce the analytic construct of complex chronic adverse events following immunization (CC-AEFI). CC-AEFI refer to a class of post-immunization conditions characterized by several recurring features – namely, multisystem involvement (e.g., neurological, cardiovascular, immunological), diagnostic ambiguity (e.g., absence of a unifying biomarker or standardized case definition), fluctuating or relapsing trajectories, symptom heterogeneity, and an evolving clinical course in which the full syndrome may only become apparent weeks or months post-vaccination. These features distinguish CC-AEFI from adverse events that are acute, temporally bounded, and organ-specific—the categories to which existing pharmacovigilance architectures are best suited.
The construct is intended to encompass—but not be limited to—conditions such as POTS, ME/CFS, Complex Regional Pain Syndrome (CRPS), SFN, PCVS, post-HPV vaccine syndromes. While several of these conditions have historically been framed as “contested illnesses” 53 or categorized under the umbrella of medically unexplained symptoms, 54 recent advances in immunological and post-infectious research have prompted a shift toward recognizing them as “complex chronic conditions” or “infection-associated syndromes”.11–13,55 However, when these conditions arise secondary to vaccination, they are often reinscribed within the contested illness frame – obscuring the possibility of shared immunological or neuroimmune mechanisms with post-infectious analogues.
The CC-AEFI construct is not intended as a syndromic diagnostic label, but rather as a descriptive category to support more systematic identification, investigation, and recognition of a class of conditions whose multisystem, heterogeneous, and often delayed or evolving presentations fall outside the acute, organ-specific, and temporally bounded profiles that pharmacovigilance systems excel at detecting. Importantly, the CC-AEFI construct carries no implication of causality in relation to vaccination. It is consistent with the WHO definition of an AEFI as “any untoward medical occurrence following immunization,” irrespective of causal attribution. 40 Nevertheless, several Bradford Hill considerations for causal inference 56 —consistency (e.g., recurring symptom patterns across vaccines and populations), biological plausibility (e.g., immune-mediated mechanisms), temporality (e.g., onset aligned with immune response windows), and analogy (e.g., similar syndromes after infection)—support the systematic consideration of such cases in vaccine safety assessment.
Available data suggest that CC-AEFI are uncommon to rare in pharmacovigilance data, though true incidence is challenging to determine. For instance, the incidence of POTS following mRNA COVID-19 vaccination has been estimated at 26.8 per 10,000, with an excess incidence of 9.2 per 10,000 compared to the pre-vaccination baseline. 14 Other outcomes—such as PCVS following COVID-19 vaccination 24 and CRPS following HPV vaccination have been documented at rates below 1 per 10,000. 57 Such figures likely underestimate the true burden, given long diagnostic timelines, absent biomarkers, and the lack of structured longitudinal follow-up in most systems.
As summarized in Table 1 and elaborated in the sections that follow, CC-AEFI are vulnerable to attrition at every stage of the pharmacovigilance continuum: from clinical recognition to formal reporting, through signal analysis, and into the governance architectures that define admissible evidence and manage uncertainty. At each stage, technical barriers—such as narrow reporting windows, limited diagnostic codes, and procedural thresholds—intersect with epistemic filters such as institutional norms that cast atypical or heterogeneous presentations as implausible, coincidental, or insufficiently credible without a high evidentiary burden.
Key challenges in recognizing, reporting, and analyzing CC-AEFI.
CC-AEFI, complex chronic adverse events following immunization; GBS, Guillain-Barré syndrome; MAH, marketing authorization holder; ME/CFS, myalgic encephalomyelitis/chronic fatigue syndrome; POTS, postural orthostatic tachycardia syndrome.
Diagnostic challenges
Before a CC-AEFI can be reported, it must first be clinically recognized—and that threshold is uncommonly crossed. As outlined in Table 1, clinical recognition is often hindered by the absence of objective physical signs (e.g., rash, fever, swelling), the lack of confirmatory biomarkers, the diffuse and multisystem nature of symptoms and the high degree of heterogeneity between patients, the expressive burden of articulating complex illness experience, and semantic or social processes that distort or erase meaning (e.g., psychologization, gender bias, diagnostic overshadowing) challenge clinical recognition.
Clinical invisibility and diagnostic inertia
CC-AEFI often manifest with heterogeneous, multisystem symptoms that evolve episodically and resist easy classification. One patient may report insidious onset of orthostatic intolerance and sensory changes; another may present with acute cognitive dysfunction, relapsing joint pain, or post-exertional collapse. These atypical trajectories frequently exceed the bounds of conventional diagnostic frameworks, resulting in premature closure, or outright dismissal—outcomes that, as discussed later, are often accompanied by psychological or functional labeling.58,59
Although emerging research has begun to identify plausible pathophysiological mechanisms—such as cytokine dysregulation, autoantibodies, SFN, brainstem inflammation, and microvascular injury15,17,36,60—these remain largely confined to research settings. While they may hold promise as future biomarkers, they are not yet validated or integrated into clinical diagnostic pathways.
Even when diagnostic tools do exist—such as tilt-table testing for POTS, skin biopsies for SFN, or PET-MR imaging for neuroinflammation—they are typically limited to tertiary care settings and out of reach for many patients.61,62 Referral pathways are often slow and inconsistent, with delays measured in months or years, and access is frequently patterned along geographic, socioeconomic, gendered, racial, and other intersecting lines.
Crucially, most clinicians receive little to no training in complex neuroimmune conditions and may not even be aware that such diagnostic tools exist—let alone when or how to pursue referral. As Smyth and Blitshteyn emphasize, diagnostic delay in complex post-infectious conditions is rarely attributable to absent tools alone. 55 More often, it stems from a lack of clinical training and, more fundamentally, a breakdown in epistemic trust—a failure of clinicians to engage with patient narratives that fall outside familiar diagnostic templates.
The expressive burden of subjective illness
In the absence of objective anchors, the recognition of CC-AEFI continues to rely heavily on subjective symptom reports and interpretive clinical judgment. 3 This places a disproportionate expressive burden on patients, who must translate relapsing, multisystem dysfunction into a coherent narrative legible to biomedical reasoning—an effort that, as the next section explores, can expose them to misinterpretation, bias, or psychologization.
As scholars of illness narrative and phenomenology have long emphasized, the work of narrating illness is both epistemic—requiring patients to make sense of their own symptoms—and embodied, shaped by the physiological disruptions they are trying to describe.53,63–66 Chronic conditions that lack visible signs, offer no clearly recognized causal explanation, or unfold in fluctuating and unpredictable ways often strain a patient’s capacity both to comprehend their symptoms and to articulate them. This expressive burden is further compounded by the posited physiological mechanisms themselves—such as immune-mediated autonomic dysfunction, brainstem irritation, microvascular compromise, cerebrospinal fluid imbalance, and epithelial barrier disruption15,17—which are not easily sensed, localized, or described in everyday language. These disruptions challenge both the body’s internal self-perception and the patient’s ability to narrate their illness in ways that feel intelligible or persuasive 53 ; patients struggle to find words for sensations that elude familiar categories like pain, pressure, or swelling. They are often left grasping for metaphors to render their experience sensible. 67
Moreover, the very multiplicity of symptoms that may signal systemic dysregulation 68 can paradoxically undermine clinical credibility. 4 Patients who report numerous or diffuse symptoms are more likely to be perceived as exaggerating, psychogenic, or difficult. 69 This pattern—where complexity is mistaken for implausibility—is reinforced by psychologization, somatization bias, and gendered expectations about how illness should appear.55,70–74 In some settings, “one problem per visit” policies 75 further risk fragmenting care by discouraging patients from disclosing the full scope of their condition, thereby compounding diagnostic delay and underrecognition. As with other complex illnesses, the expressive burden of CC-AEFI includes not only articulating multifacted symptomatology but continually asserting the legitimacy of one’s condition in the face of diagnostic ambiguity and social doubt 53 —a dynamic that erodes trust and increases risk of dismissal.
Interprative bias and semantic flattening
Even when patients succeed in articulating their symptoms, clinical recognition is often challenged by predictable cognitive shortcuts and structural features of healthcare that bias how unexplained symptoms are interpreted. As Blease et al. argue, when patients lack the conceptual tools to frame their illness—and when clinicians fail to treat patient accounts as credible sources of medical information—this creates a form of hermeneutical injustice (from hermēneuein, “to interpret”), 1 a gap in the interpretive resources required for their symptoms to be understood. This gap delays recognition, forecloses biomedical investigation, and undermines the legitimacy of the patient’s illness experience.
Under time pressure and with limited familiarity, even well-intentioned providers may default to narrow diagnostic scripts.76–78 The translation of rich, metaphorical symptom descriptions into clinical shorthand can erode nuance and meaning. Post-exertional collapse becomes “fatigue”; near-syncope becomes “dizziness”; neuropathic burning becomes “tingling.” What is severe, disabling, and clinically unexplained is recast as a set of ordinary, nonspecific complaints. This process of semantic flattening not only strips away the severity, patterning, and multisystem coherence of the illness but also situates it within a frame of everyday symptoms—making a complex condition appear clinically unremarkable and reducing the likelihood of further biomedical investigation.
Psychologization and disbelief
Beyond semantic flattening, patients with complex chronic conditions often encounter epistemic disbelief: a tendency to doubt the veracity or severity of their symptoms. When an illness narrative does not fit expected biomedical templates, it is frequently met with skepticism rather than curiosity. Smyth and Blitshteyn observe that this erosion of trust often stems from institutionalized language habits. Patients frequently report being told, “there’s nothing wrong with you” or “you don’t look sick”—statements that, while seemingly neutral or even intended to reassure, function as rhetorical dismissals. 55 The cumulative effect, they argue, is not just individual distress but the broader erosion of diagnostic legitimacy for entire categories of illness. 55
In these contexts, epistemic disbelief has concrete clinical consequences. When a patient’s account is treated as implausible or exaggerated, the absence of objective findings too often functions as a rationale for doubt, shifting the burden of proof onto the patient. This can result in what patients describe as gaslighting—an erosion of trust not just in diagnosis, but in the legitimacy of their own perceptions.70,79 This disbelief frequently sets the stage for psychologization—the premature attribution of unexplained symptoms to psychological causes.29,80 This structural reflex is reinforced when symptoms resemble anxiety or depression, or when the patient already carries a psychiatric label. In such cases, clinicians are more likely to default to mental health framings, even in the absence of corroborating evidence. This phenomenon, known as diagnostic overshadowing, allows psychological assumptions to eclipse physiological investigation. 81 While psychiatric explanations may at times be appropriate, they must be subjected to the same level of diagnostic rigor and evidentiary scrutiny as physiological diagnoses. The absence of a confirmed physiological finding does not, in itself, justify defaulting to a psychiatric or functional explanation. Psychiatric and functional diagnoses require their own specialized assessment, criteria, and investigative process—not inference by exclusion.
This pattern is well-documented in POTS, ME/CFS, and Long COVID, where the lack of definitive tests routinely leads to dismissal or misdiagnosis.4,55,82,83 In the context of vaccination, these dynamics may be even more pronounced. 43 A recent study found that over 90% of individuals with PCVS reported that their symptoms had been psychologized, most often by physicians, with such dismissal closely associated with increased psychological distress, including anxiety, depression, and diminished trust in the medical system. 29 Similarly, in a longitudinal study of women referred to Danish HPV vaccine clinics, many participants reported profound functional impairment alongside feelings of dismissal, stigmatization, and disbelief from both clinicians and public health authorities. 52 Even patients with recognized, physiologically confirmed harms—such as vaccine-induced immune thrombotic thrombocytopenia (VITT)—describe repeated misdiagnosis and feeling “gaslit” by clinicians and media, despite their official and objective diagnoses. 50 Many experienced lasting disability and existential distress, illustrating how even “legible” adverse events can be rendered socially invisible.
Equity considerations
The tendency to disbelieve, psychologize, or minimize unexplained symptoms often reflects deeper patterns of structural inequity. These responses follow lines of social power, disproportionately affecting those already marginalized by gender, race, geography, or socioeconomic status. 84 A frequently cited example comes from El Carmen de Bolívar, a rural town in northern Colombia, where hundreds of adolescent girls developed acute and chronic, multisystem symptoms following a school-based HPV vaccination campaign.85,86 A national epidemiological investigation found no organic association with vaccination and classified the episode as a “mass psychogenic illness”, a framing even echoed in media as “contagious psychogenic reactions.” 87 Yet subsequent analyses highlight that these psychogenic attributions emerged in a context marked by structural vulnerability. Narrative and anthropological studies emphasize that age, gender, rural marginalization, and socioeconomic precarity shaped how the girls’ symptoms were interpreted, communicated, and ultimately dismissed—what Baltar-Moreno et al. describe as “cascading marginality”. Importantly, while public health authorities ruled out contamination or infectious causes, no published reports describe systematic evaluation for autonomic or immune dysregulation, and the phenomenon remains etiologically unresolved in the scientific literature. In settings where diagnostic credibility is already structurally undermined—such as among historically marginalized populations—psychological attributions require especially rigorous evidentiary justification.
Reporting challenges
While clinical recognition is a necessary step in bringing an adverse event into view, it is the act of reporting that determines whether it enters the evidentiary stream.
Most post-marketing surveillance relies on passive (or spontaneous) reporting systems—such as the Vaccine Adverse Event Reporting System (VAERS) in the United States and the Canadian Adverse Events Following Immunization Surveillance System (CAEFI). These systems vary in what constitutes a reportable event, who can submit it, and how it is processed. Some jurisdictions have implemented active surveillance mechanisms using linked health records or sentinel networks, but these remain geographically limited and are typically restricted to predefined, acute outcomes.
Any structural limitations of these systems are especially consequential for CC-AEFIs given the constraints of pre-authorization clinical trials, which are not designed to detect rare, delayed, or diagnostically complex events.41,88 Such trials are typically powered to identify common, acute, and temporally proximate effects within narrowly defined post-vaccination windows. Participants with comorbidities or atypical risk profiles are often excluded, and follow-up durations are limited. As a result, post-marketing surveillance systems bear the primary responsibility for identifying chronic or evolving adverse events.
Yet in practice, each step in the reporting process—deciding whether an event is “worth” reporting, navigating time-consuming submission portals, and translating complex symptom constellations into reductive standardized diagnostic codes—introduces opportunities for signal attrition.
Clinician discretion and structural barriers to reporting
Across pharmacovigilance more broadly, it is well established that only a small fraction of adverse drug reactions are formally documented, with estimated reporting rates ranging from 6% to 10%.89–91 While vaccine-specific estimates are less frequently reported, available data reflect similar limitations. For example, Tadrous estimated that only 5%–10% of vaccine-related adverse events are submitted, 92 while Lazarus et al. suggested as few as 1% of AEFI may be reported. 93 Even for acute, clinically distinct outcomes such as anaphylaxis or Guillain-Barré syndrome (GBS), reporting sensitivity within VAERS has been estimated at just 13%–76% and 12%–64%, respectively. 94 That such gaps exist even for severe and temporally proximate outcomes suggests that detection rates for diagnostically ambiguous, delayed onset, or chronic events may be substantially lower.
Most AEFI systems rely on clinician-initiated or clinician-validated reports. Although reporting is often legally mandated or professionally encouraged, enforcement is rare in practice, leaving the decision to report largely at the discretion of frontline providers. Many clinicians receive little or no formal training in pharmacovigilance, remain uncertain about what qualifies as an AEFI, or mistakenly believe that only severe or causally established events should be reported.95,96 As a result, considerable gatekeeping power is conferred at the point of care, where informal filters—applied consciously or unconsciously—shape whether an event is documented. Symptoms perceived as vague, mild, coincidental, or diagnostically ambiguous are frequently dismissed. In politically sensitive contexts, professional risk perception may further dampen reporting. Submitting a report may be construed as fueling vaccine hesistancy, or even an implicit admission of medical error or institutional liability—even when no causal judgment is intended. This perception can deter reporting, particularly when clinicians fear reputational consequences or worry that acknowledging adverse events might undermine public trust in immunization programs.95,97,98
Even when clinicians are willing to report, structural barriers within passive systems often render the process cumbersome and unrewarding. Many reporting platforms are poorly integrated into electronic medical records, rely on outdated or unintuitive interfaces, and require significant time to complete. For example, completing a single VAERS report can take 20–30 minutes—a burden that many providers cannot accommodate within routine workflows. Time constraints, unfamiliarity with appropriate coding, and the absence of clinical feedback mechanisms all contribute to low submission rates and incomplete or low-quality reports. Critical clinical details—such as diagnostic uncertainty, fluctuating symptom trajectories, or multisystem presentations—are often omitted or unavailable at the time of reporting, limiting the value of these submissions. Moreover, follow-up processes are typically absent or opaque, reinforcing the perception that reporting is both burdensome and futile.89,91,94,99
While underreporting remains a pervasive limitation, the converse phenomenon—stimulated or overreporting—also warrants attention. In open-access systems such as VAERS, submission spikes frequently follow periods of heightened media coverage, legal action, or social media amplification.100,101 These surges may reflect increased awareness of genuine harms that would otherwise remain undocumented, but they can also include duplicate entries, temporally—but not causally—associated events, and, intentionally fabricated reports. While addressing underreporting remains essential, oversaturation also introduces its own risks of overwhelming analytic capacity, obscuring legitimate safety signals, and delaying regulatory evaluation.
Although some systems allow patients, caregivers, or members of the public to submit reports directly, these often require validation by a clinician or supporting medical documentation—reintroducing many of the same procedural bottlenecks.
Timing constraints and diagnostic latency
Most vaccine safety surveillance systems define specific post-vaccination timeframes within which adverse events are expected to occur and be considered reportable. These windows typically range from 7 to 42 days, depending on the vaccine product, the nature of the adverse event, and applicable regulatory guidelines. While such parameters are well-suited to identifying acute, temporally proximate reactions, they are poorly aligned with conditions that manifest gradually or require protracted diagnostic evaluation.
For many complex chronic conditions, delayed onset and prolonged diagnostic timelines are well-documented features of the clinical course. In the case of POTS, studies have consistently documented diagnostic delays of multiple years, with median time to diagnosis often exceeding 2 years, and some patients reporting diagnostic odysseys spanning over a decade.102,103 Similarly, ME/CFS is characterized by a minimum duration of 6 months of persistent symptoms before a diagnosis can even be considered—placing it outside the detection windows of many pharmacovigilance systems. 104 These conditions will thus remain structurally invisible to surveillance infrastructures that define and enforce narrow adverse event windows.
Relatedly, the longer the interval between vaccination and symptom onset—or between symptom onset and eventual diagnosis—the less likely any association will be suspected, reported, or retained in the evidentiary stream. Patients may not initially associate delayed or evolving symptoms with prior vaccination, especially in the absence of an acute or distinctive trigger. Providers, too, may view temporally distant presentations as coincidental or attribute them to unrelated causes. Over time, the perceived plausibility of a potential vaccine-event link declines, further eroded by diagnostic uncertainty, social stigma, and cognitive biases such as recall decay, anchoring, or confirmation bias.
Analytic challenges
As outlined above substantial attrition occurs at the stages of clinical recognition and reporting. Yet even when an adverse event is clinically recognized and successfully reported, it may still fail to be accurately represented or meaningfully interpreted within pharmacovigilance systems. After submission, reports undergo data cleaning, coding, triage, and categorization—steps essential for comparability and quality assurance but which also determine which cases remain in the analytic dataset and how they are ultimately classified, aggregated, and understood.
Quality control, follow-up constraints, and severity-based prioritization
Before analysis can begin, adverse event reports must be transformed from their initial submissions—often a mixture of structured fields and free-text narratives—into standardized datasets suitable for aggregate analysis. Passive surveillance systems have long struggled with inconsistent data entry, missing documentation, and limited interoperability across networks, including the absence of standardized case definitions for many CC-AEFI. 107
To promote analytic consistency, pharmacovigilance programs implement internal quality-control and follow-up procedures. For events classified as serious—death, life-threatening illness, hospitalization, or significant disability—systems may request hospital records, autopsy findings, laboratory data, or specialist assessments, which are then reviewed by medical officers or expert committees. In practice, however, pharmacovigilance programs rarely have the resources to pursue missing information at scale—particularly in the context of mass vaccination campaigns, where clinical capacity is already overstretched. Outreach to clinicians requires time, coordination, and cooperation, all of which are scarce in systems designed for passive intake rather than active case curation. As documented in a 2023 BMJ investigation, the unprecedented surge of COVID-19 vaccine reports overwhelmed VAERS’s infrastructure, leading to substantial lapses in follow-up and case validation. 108 Despite protocols requiring expedited review of serious reports, many clinicians and medical examiners reported receiving no contact from Centers for Disease Control and Prevention (CDC) reviewers—sometimes for months, or not at all. 109
Because comprehensive follow-up is rarely attainable, systems must triage which cases receive additional scrutiny. Severity-based prioritization—efficient for detecting acute catastrophic events—is often poorly aligned with the clinical profile of CC-AEFI. Most regulators use the International Council for Harmonisation (ICH) definitions of serious adverse events, which prioritize hospitalization, life-threatening illness, and death for rapid evaluation. Although “persistent or significant disability” is formally included, its application is inconsistent in practice, especially for chronic, function-limiting conditions. Functional losses that severely disrupt daily life—such as inability to work, study, or maintain basic self-care—rarely elicit the same urgency when hospitalization is absent, leading to potential underestimation of the burden of conditions such as ME/CFS or POTS.
These analytic processes also operate within a broader governance architecture that determines what information is ultimately available for independent review. Public-facing pharmacovigilance platforms often display only the initial submission, while clinically important updates—diagnostic clarification, hospitalization records, or death verification—remain in restricted back-end systems. 108 While confidentiality protections are important, limited visibility into the final analytic dataset restricts independent scrutiny—an issue discussed in the final subsection below.
Coding practices and definitional constraints
Once in the surveillance system, reports are encoded into structured formats for integration into analytic databases. While narrative descriptions of the patient’s experience may be captured at the point of entry, the fields that typically drive downstream analysis are largely restricted to categorical inputs. These include reporter type (e.g., patient, physician), patient demographics, vaccine product and lot number, onset interval, outcome status, and clinical features selected from standardized vocabularies—most notably the Medical Dictionary for Regulatory Activities (MedDRA).
MedDRA offers a standardized, hierarchical vocabulary for coding adverse events, with Preferred Terms (PTs) functioning as the main unit used in pharmacovigilance analyses. Although syndromic diagnoses such as POTS, CRPS, and ME/CFS technically exist as PTs, they are rarely selected in practice. Instead, chronic or multisystem presentations are typically coded under isolated symptom terms—such as dizziness, palpitations, fatigue, or pain. This symptom-level coding disperses what may be a coherent syndrome across seemingly unrelated categories, fragmenting the underlying clinical pattern and reducing analytic visibility within surveillance datasets. As Hsu et al. note, when a condition does not conform to prevailing classificatory norms, it becomes less likely to be recognized, reported, or studied within formal surveillance systems. 13
These challenges are further compounded by the absence—or poor integration—of formal case definitions for many CC-AEFI. To date, the Brighton Collaboration—the primary international body responsible for issuing standardized AEFI definitions— has not published dedicated AEFI case definitions for POTS, CRPS, or ME/CFS. Although validated clinical frameworks do exist—such as the Canadian Consensus and IOM criteria for ME/CFS, the Heart Rhythm Society and Raj criteria for POTS, and the Budapest criteria for CRPS—they are seldom incorporated into pharmacovigilance workflows. These diagnostic frameworks require specialist evaluation, longitudinal assessment, and confirmatory testing (e.g., tilt-table testing, autonomic evaluation, exclusionary laboratory work), all of which are rarely available in primary care or captured in spontaneous reporting systems. As a result, even when relevant symptoms are recorded, pharmacovigilance databases typically lack the structured clinical detail necessary to apply these case definitions, leading to inconsistent case capture and diagnostic fragmentation.
Platschek and Boege illustrate how, in the absence of standardized case definitions, pharmacovigilance systems can register relevant symptoms yet lack any mechanism to interrogate whether they may represent a broader chronic syndrome for affected individuals. 23 Examining European Medicines Agency (EMA) product information for COVID-19 vaccines, they found that many hallmark PCVS symptoms—paresthesia, dizziness, tachycardia, palpitations, profound fatigue—were already listed as adverse events. Yet these were uniformly framed as transient reactogenicity or anxiety-related responses, with no apparent mechanism to interrogate clustering or persistence. Similarly, persistent symptoms spanning neurological, cutaneous, ocular, metabolic, inflammatory, and joint-related domains have been reported in post-vaccination cohorts, typically in the range of 3%–16%.105,106 These, too, are usually catalogued as discrete items, and it remains unclear whether they have been examined for syndromic clustering within individuals.
Compounding the matter, most pharmacovigilance systems reduce clinical trajectories to simplified endpoints—such as “recovered,” “recovering,” or “not recovered.” While “recovering” appears dynamic, it implicitly assumes a single arc toward improvement or resolution, rather than the variability, plateau, or relapse that characterize many CC-AEFI. For syndromes such as ME/CFS or PCVS—which may plateau at disabling baselines or fluctuate unpredictably—such outcome categories can impose an administrative illusion of recovery, even in the face of substantial functional impairment. Despite their growing prominence in clinical research, patient-reported outcomes (PROs)—such as measures of symptom burden, quality of life, or activity limitation—remain largely absent from vaccine pharmacovigilance systems, further obscuring the lived course of chronic post-vaccination conditions.
Safety signal detection
Pharmacovigilance systems employ multiple methods for identifying safety signals. For vaccines, however, regulatory assessments have largely centred on observed-versus-expected (O/E) analyses—comparisons of reported events against background incidence. Given their central role in evaluations of POTS, CRPS, and PCVSs, O/E approaches warrant particular scrutiny here.
Both sides of the O/E equation are shaped by deep uncertainty and discretionary assumptions.
On the observed side, multiple sources of attrition reduce the number of cases that ever enter surveillance systems. But even after an event is successfully reported, the counts used in safety signal analyses may still misrepresent the underlying occurrence of cases. First, for emerging or contested conditions such as PCVS, there is often no standardized case definition—and in some settings, no formally recognized clinical entity at all. In this vacuum, regulators, manufacturers, or data managers must decide what will “count” as a case: which MedDRA terms will be searched, which combinations of symptoms qualify, and which reports are included or excluded. These discretionary choices about case construction—how events are defined, aggregated, or disaggregated—can substantially alter apparent case counts and therefore influence downstream signal detection. Second, when individual symptoms are not aggregated into broader syndromic patterns, they are instead recorded as generic descriptors such as “fatigue,” “dizziness,” or “palpitations.” These are among the most common complaints in the general population, with fatigue alone affecting up to 20% of adults at any given time, usually in transient contexts such as stress, infection, or short-lived vaccine reactogenicity. 109 Once separated from a broader constellation of neurological, autonomic, and systemic dysfunction, they are benchmarked against this high background prevalence and interpreted as nonspecific or benign. This dispersal of syndromic presentations across multiple isolated symptom codes reduces visibility and limits the ability of O/E analyses to detect meaningful patterns.
On the expected side, O/E analyses depend on reliable incidence estimates to calculate the number of cases that would occur by chance in the absence of vaccination. For syndromic or variably diagnosed conditions such as POTS or ME/CFS, however, robust incidence data are rarely available. Population-level surveillance systems do not routinely capture new cases, diagnostic latency is common, and underdiagnosis varies substantially across regions. Regulators therefore often rely on point estimates derived from single studies or specialty-clinic samples, or substitute prevalence-based measures when incidence is unknown—sometimes generalizing these values across countries or demographics with limited acknowledgement of underlying uncertainty. These background rates are also seldom stratified by sex or age despite clear demographic clustering. For example, the prevalence of POTS has been estimated at 0.2%–1% in developed countries, with as many as 80% of patients female and most diagnosed between the ages of 15 and 25.110–112 Prevalence estimates for ME/CFS similarly range from ~0.2% to 0.9% in meta-analyses, with women affected about 1.5–2 times more often than men. 113 When expected rates are averaged across whole populations, any true excess risk within vulnerable subgroups may be statistically diluted. The myocarditis signal following mRNA vaccination, for instance, only became discernible once analyses were stratified by age and sex, revealing a sharp excess among young men—an effect initially obscured in aggregate population-level analyses. 114 A further complication is that different diagnostic frameworks produce markedly different baseline estimates, even before considering population variation. For example, prevalence estimates for ME/CFS differ nearly tenfold depending on which case definition is applied, illustrating how sensitive baseline rates are to definitional choices. 113 These differences directly influence the denominator of O/E analyses and can materially shift whether a safety signal appears present or absent. A separate challenge concerns the temporal stability of background rates. Even if an appropriate baseline were identified, it may not apply in the context of a widespread infectious event such as the COVID-19 pandemic. Multiple studies have documented increased diagnoses of POTS and ME/CFS following SARS-CoV-2 infection,115–117 indicating that pre-pandemic baselines may no longer represent the true background rate. In such settings, expected-case estimates derived from historical data risk inflating or obscuring O/E ratios, and the phenotypic overlap between infection-associated and vaccination-associated syndromes further complicates attribution.
Taken together, these uncertainties on both the observed and expected sides make O/E analyses highly sensitive to underlying assumptions. Even modest changes in reporting sensitivity, case-definition choices, or baseline estimates can determine whether a condition is flagged for further scrutiny or dismissed as unremarkable. In their sensitivity analysis of CRPS cases following HPV vaccination in Japan, Huygen et al. demonstrated that the observed incidence would exceed expected rates if fewer than 60% of cases were reported (using US baselines) or fewer than 10% (using Dutch baselines). 57 Both scenarios are plausible given known variability in reporting sensitivity and the absence of robust epidemiological data on CRPS incidence. Yet what qualifies as “plausible” is rarely defined and remains subject to the discretionary judgments of analysts and regulators. This analytical and interpretative latitude allows identical datasets to yield either apparent signals or null conclusions, highlighting how analytic outcomes in pharmacovigilance hinge as much on methodological assumptions as governance and transparency.
Gouvernance and transparency
Public-sector capacity to conduct independent post-marketing safety evaluations is often limited.108,122 As a result, much of the responsibility for ongoing safety evaluation is delegated to marketing authorization holders (MAHs). While MAHs possess the expertise, infrastructure, and analytic capacity needed for large-scale surveillance, 118 this arrangement embeds a structural conflict of interest wherein the proponent of a product is also tasked with evaluating its safety.119–122 At the same time, opportunities for independent verification remain restricted by limited access to raw data, case-level information, and analytic decisions. This governance architecture places considerable analytic discretion in the hands of manufacturers, while leaving regulators and independent investigators with only partial visibility into the evidentiary base. Regulators frequently emphasize that multiple layers of oversight are in place, yet the structure of pharmacovigilance affords only limited external visibility. Consequently, the public and independent researchers must rely on assurances of due diligence without the ability to independently examine the underlying evidence.
Recommendations for strengthening pharmacovigilance
Barriers to detecting CC-AEFI accumulate across every stage of the pharmacovigilance chain—from early clinical recognition through reporting, coding, analysis, and governance. Diagnostic uncertainty constrains what is recognized; procedural and discretionary judgments erode what is reported; coding practices, analytic filters, and severity-based triage shape what is rendered visible and analyzed; and governance arrangements determine whose data, assumptions, and perspectives are prioritized, as well as the extent and form of independent verification or participatory oversight.
Importantly, these structural limitations are not a sign that current systems are ineffective, nor a suggestion that CC-AEFI are common, nor that vaccines are unsafe. Post-marketing pharmacovigilance systems have repeatedly demonstrated their capacity to identify and evaluate genuine safety concerns—ranging from GBS after influenza vaccination 123 to myocarditis after mRNA vaccination 107 —and remain an indispensable safeguard for public health. The point, rather, is that these systems have been optimized to detect acute, well-characterized events, and are far less well equipped to interrogate rare, heterogeneous, or chronic conditions. CC-AEFI are expected to be extremely uncommon, yet—as we have learned from post-infectious chronic conditions like long-COVID—they are extraordinarily difficult to classify, diagnose, and understand. Their burden for affected individuals is nonetheless profound, and they are too often psychologized or dismissed. Precisely for that reason, pharmacovigilance systems must be explicitly designed with these features in mind: to detect, investigate, and transparently evaluate low-frequency, complex outcomes, rather than defaulting to frameworks suited primarily for acute or common events.
Table 2 summarizes key recommendations to foster more inclusive, transparent, and responsive monitoring of CC-AEFI.
Summary of key reforms to strengthen pharmacovigilance systems for CC-AEFI.
CC-AEFI, complex chronic adverse events following immunization; EHR, electronic health record; POTS, postural orthostatic tachycardia syndrome; PRO, patient-reported outcome.
Clinical system reform
Improving the early recognition of CC-AEFIs begins with strengthening clinical capacity. These conditions require a diagnostic posture grounded in longitudinal and cross-system pattern recognition—the slow, interpretive work of assembling dispersed symptoms into a coherent picture.124–126 Such reasoning is acquired cumulatively through repeated exposure to diverse phenotypes and institutional support for revisiting diagnostic assumptions, rather than through a single training module. Foundational training in medical education should emphasize symptom recognition, referral pathways, and the principles of multisystem disorder assessment. While some nocebo effects are inevitable, 127 clinical reforms must also avoid premature psychogenic attribution, ensuring that patients are evaluated for conditions such as dysautonomia or SFN in hte context of immunization, infections, or exposures before their experiences are dismissed as anxiety or stress-related.
Specialized clinics
Generalist practitioners cannot be expected to diagnose and manage complex, multisystem conditions in isolation. While foundational medical education should emphasize early symptom recognition, referral pathways, and the principles of multisystem disorder assessment, these efforts must be complemented by the establishment—or reinforcement—of specialized clinics and centralized referral systems. Such centers consolidate expertise, reduce diagnostic inertia, and ensure that complex cases are systematically assessed and integrated into pharmacovigilance frameworks rather than dispersed across isolated encounters.
By design, these clinics will attract concentrated case series of rare or diagnostically complex syndromes that are unlikely to be detected in generalist practice. Yet such clustering is too often framed as evidence of bias or unrepresentativeness, rather than recognized as a predictable outcome of referral dynamics. Danish autonomic specialists, for instance, faced criticism for reporting clusters of POTS following HPV vaccination, their patients dismissed as a “highly selected group.”119,128,129 Pharmacovigilance systems should therefore establish formal liaison mechanisms with specialist centers, treating concentrated case reports not as anomalies but as vital opportunities for systematic evaluation and signal detection.
Harmonized but adaptive diagnostic frameworks for chronic AEFI
Improving case capture also requires harmonized but adaptive diagnostic frameworks for CC-AEFI. Global regulatory collaboration—through initiatives such as the Brighton Collaboration—should develop standardized definitions flexible enough to reflect the heterogeneity and evolving nature of these conditions. Tiered classifications (e.g., “definite,” “probable,” “possible”) and syndrome-like descriptors (e.g., “POTS-like syndrome,” “ME/CFS spectrum disorder”) would enable systematic case aggregation without prematurely excluding atypical or early-stage presentations. Precedents for such graded diagnostic frameworks already exist in other infection-associated chronic illnesses. 130 Harmonization across jurisdictions would also facilitate more consistent data collection and enhance cross-border signal detection.
Patient-centered surveillance
Improving the detection of complex chronic vitally requires a fundemental shift whose perspectives are considered legitimate sources of safety knowledge. Individuals living with persistent, multisystem conditions often notice subtle patterns, relapsing trajectories, or novel constellations of symptoms long before these are recognized through formal reporting channels. As Hsu et al. 13 observe, patients with complex chronic illnesses routinely act as de facto knowledge producers. Their insights—whether shared through informal networks, online communities, or clinical consultations—offer critical intelligence about the conditions themselves: how they manifest, evolve, and persist. Integrating patient insight enhances the coherence and completeness of safety surveillance—filling gaps that clinician-centered approaches alone cannot address.
To that end, structured, longitudinal PROs should be embedded within vaccine safety monitoring frameworks.131,132 Unlike clinician-reported data, PROs capture symptom onset, severity, progression, and functional impacts directly from patients—unfiltered by interpretation or institutional triage. These tools must be carefully designed to reflect the heterogeneity of lived experience and allow for updates over time, rather than relying on static, one-time assessments. When developed with patient input and deployed consistently, PROs can significantly enhance both signal sensitivity and clinical relevance.
Digital health technologies offer new opportunities to operationalize such approaches. Mobile applications, wearable devices, and online portals—coupled with AI-enabled analytics—can enable real-time symptom tracking and physiological validation at scale. However, the use of digital tools must be ethically grounded, with strict protections for privacy, data governance, and informed consent. Equitable participation requires alternative reporting pathways for digitally underserved groups, including telephone interviews, paper-based systems, or clinician-facilitated reporting in primary care contexts.
Finally, building a patient-centered surveillance system demands governance reform. Formal mechanisms—such as independent patient advisory panels, participatory priority-setting processes, and lived-experience review committees—should be institutionalized across regulatory and pharmacovigilance bodies. These structures would help ensure that patient perspectives are integral to hypothesis generation, signal detection, and policy formation. Absent such governance innovation, efforts to integrate PROs or digital tools may remain technically impressive but structurally marginal, failing to shift the underlying epistemology of vaccine safety science.
Longitudinal monitoring
Longitudinal monitoring should be operationalized through the extension of follow-up windows in existing passive and active surveillance systems, the linkage of electronic health records (EHRs) and administrative datasets, and the integration of structured PROs. This approach would ensure that evolving, delayed onset, or relapsing conditions are systematically captured without requiring the creation of new, stand-alone infrastructures.
To remain sustainable, longitudinal strategies must be designed to minimize burden for regulators, clinicians, and patients. For regulators, leveraging existing infrastructures—such as vaccine registries, EHR linkages, and established sentinel networks—would enable extended follow-up without duplicating effort. For patients, structured PROs should be brief, periodic, and accessible through multiple modalities (e.g., mobile apps, web portals, telephone check-ins, or clinician-facilitated reporting), ensuring inclusivity while avoiding excessive demands on those already living with chronic illness. For clinicians, data capture should be embedded into existing workflows through automated extraction and streamlined reporting tools, rather than requiring additional manual entry.
In complement to these system-level measures, more focused approaches—such as nested cohorts, sentinel clinics, and case–control or cohort studies—would provide the analytic depth required for detailed phenotyping, mechanistic inquiry, and hypothesis testing. When aligned with broader infrastructures, these designs would ensure both population-level comparability and the capacity to interrogate rare, complex clinical trajectories that otherwise risk remaining invisible.
Independent oversight and transparent safety governance
Improving the detection and evaluation of CC-AEFI requires governance systems that are transparent, inclusive, and capable of managing scientific uncertainty. Current pharmacovigilance frameworks often lack the mechanisms needed to reconcile heterogeneous forms of evidence—particularly the disjuncture between individual-level causal inferences and population-based statistical evaluations. They also struggle to mitigate institutional and commercial conflicts of interest, and to integrate the knowledge of those most directly affected. Ultimately, governance reform must make structural space for methodological and knowledge pluralism, transparency, and accountability.
This begins with full public access to anonymized pharmacovigilance data, along with transparent disclosure of the analytic methods, assumptions, and interpretive thresholds used to evaluate safety signals. Sensitivity analyses, for example, are widely used to assess how conclusions vary under different incidence or reporting rate scenarios. Yet these exercises—and the underlying assumptions that shape them—are rarely made public.
Governance systems must also widen the scope of legitimate epistemic contributors. Clinicians treating complex, multisystem syndromes often notice novel symptom patterns long before these are reflected in aggregate datasets. Their insights are critical for refining hypotheses and contextualizing emerging evidence, yet their expertise may not be meaningfully integrated in formal signal adjudication. Even more critically, affected individuals must be meaningfully included in the earliest stages of signal detection and governance. The decision by Colombian health authorities to exclude patient testimony in their investigation of HPV-related syndromes—on the grounds of “memory bias” and “anecdotal elements”—illustrates the limitations of narrow evidentiary filters.85,86 When lived experience is systematically excluded, whole categories of harm risk being rendered invisible.
Conflicts of interest must be actively mitigated—not only those involving manufacturers and market authorization holders, but also the more systemic pressures and institutional priorities that can subtly shape how safety signals are interpreted, investigated, or downplayed (e.g., political or reputational risk to public health regulators, academic researchers, or advisory bodies). It is important to acknowledge that the epistemic openness invited by this paper may carry some risk of opportunistic misuse of pharmacovigilance or compensation frameworks by individuals with legal, financial, or ideological motives. These concerns are legitimate. However, the alternative—systematically neglecting affected individuals, however rare—entails far greater ethical and public health costs, for those suffering, and for vaccine hesitancy.
In this context, patient voices should not be disqualified solely on the basis of legal claims or engagement with compensation processes. Seeking redress for harm does not constitute a conflict of interest; it is a legitimate exercise of accountability within a functioning safety system. Without critical attention to whose accounts are trusted, even improved frameworks risk reproducing familiar patterns of disbelief, dismissal, and exclusion. To counter this, formal structures such as independent patient advisory panels, participatory priority-setting processes, and clinician–patient review committees should be institutionalized within pharmacovigilance systems—not appended to them. Without such reforms, safety governance will continue to reproduce epistemic hierarchies that risk obscuring complex harms, hinder scientific progress, and alienate those most affected.
Discussion
This article introduced the concept of CC-AEFIs as a surveillance-oriented construct for identifying a class of persistent, multisystem, and diagnostically complex conditions that remain poorly captured by existing pharmacovigilance systems. While the narrative arc, structure, and analysis are shaped by the author’s lived experience as an affected patient, the arguments are grounded in a synthesis of peer-reviewed literature, regulatory reviews, and case-based evidence. This perspective has enabled the discussion of structural and epistemic limitations that warrant urgent attention. Nevertheless, as this is not a systematic review nor a formal epidemiological analysis with a reproducible, transparent methodology, I recognize the inherent limitations of this approach—as well as the potential for interpretive bias and blind spots that may accompany any patient perspective. I have therefore aimed to engage these issues with conceptual clarity, analytical discipline, and a consistent commitment to evidence-based critique.
Importantly and as has been mentioned previously, this discussion is not intended to cast doubt on the overwhelming public health consensus that vaccines are safe, effective, and vital to population health, nor to imply any causal relationship between vaccination and the complex conditions described. At the population level, the benefit–risk calculus remains unequivocally clear. Yet when an individual falls into the long tail of rare but serious adverse outcomes, our surveillance and support systems must be equally capable of responding with rigor, scrutiny, diligence, and humility. A safety architecture that acknowledges uncertainty and complexity, and that listens to affected individuals without presumption or delay, is not a threat to public confidence—it is a prerequisite for sustaining it.
While CC-AEFIs may be rare, their impact on affected individuals—and on the legitimacy of safety governance—can be profound. When patients encounter systems that are dismissive, opaque, incapable of meaningful investigation, or default to population-level assurances of safety, it erodes both scientific integrity and moral credibility.
The reforms proposed in this manuscript—enhancing clinical recognition, expanding reporting mechanisms, modernizing analytic tools, embedding PROs, and ensuring independent, transparent governance—aim to build a more inclusive and responsive safety system. One that not only identifies adverse events more effectively, but also responds to them with the clinical nuance, scientific seriousness, and human empathy they deserve.
Limitations
This article represents a conceptual and critical synthesis rather than a systematic review or epidemiological analysis. It draws on peer-reviewed literature, regulatory documents, and lived experience to discuss structural and epistemic limitations in vaccine pharmacovigilance. As a single-author, patient-perspective piece, it may reflect interpretive bias and selective emphasis despite efforts toward analytical rigor and balance. The analysis aims to open a line of inquiry rather than to delimit it, calling for continued reflection on how systems of safety can better meet the realities of those they are meant to protect.
Footnotes
Acknowledgements
The author would like to thank colleagues, clinicians, and patient partners whose insights have shaped the broader research questions on post-vaccination syndromes and pharmacovigilance system reform. These collective conversations have helped inform the conceptual framing and critique offered in this article.
