Complex chronic adverse events following immunization: a systemic critique and reform proposal for vaccine pharmacovigilance, 2025, Tiff-Annie Kenny

SNT Gatchaman

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Complex chronic adverse events following immunization: a systemic critique and reform proposal for vaccine pharmacovigilance
Tiff-Annie Kenny

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.

Web | DOI | PDF | Therapeutic Advances in Drug Safety | Open Access
 
Competing interests
The author is an affected patient and has lived experience of a serious adverse event following immunization. This dual role—as both researcher and patient—informs the perspective and analysis offered in this article. The author declares no financial or other non-financial conflicts of interest.

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) —were cast into sharp focus by the sheer scale of post-acute sequelae of SARSCoV-2 infection (Long COVID), where ME/ CFS- and POTS-like patterns have been widely documented. 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. Their complexity, relapsing trajectories, and diagnostic ambiguity now demand serious clinical and scientific engagement.
 
In the absence of objective anchors, the recognition of CC-AEFI continues to rely heavily on subjective symptom reports and interpretive clinical judgment. 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.

Moreover, the very multiplicity of symptoms that may signal systemic dysregulation can paradoxically undermine clinical credibility. Patients who report numerous or diffuse symptoms are more likely to be perceived as exaggerating, psychogenic, or difficult. This pattern—where complexity is mistaken for implausibility—is reinforced by psychologization, somatization bias, and gendered expectations about how illness should appear. In some settings, “one problem per visit” policies further risk fragmenting care by discouraging patients from disclosing the full scope of their condition, thereby compounding diagnostic delay and underrecognition.

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”), 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. 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.

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. The cumulative effect, they argue, is not just individual distress but the broader erosion of diagnostic legitimacy for entire categories of illness.

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. This disbelief frequently sets the stage for psychologization—the premature attribution of unexplained symptoms to psychological causes. 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.

This pattern is well-documented in POTS, ME/CFS, and Long COVID, where the lack of definitive tests routinely leads to dismissal or misdiagnosis. In the context of vaccination, these dynamics may be even more pronounced.
 
I think there is a need for requiring long term tracking of large cohorts with regular testing for the relevant infection in order to track post-acute events, but apart from that the proposed system seems to revolve around just fixing all of healthcare.

I wouldn’t mind that happening, but I wonder if more would be gained by proposing something realistic.

I also don’t understand the focus on just chronic adverse events. Wouldn’t you also want to identify long term risk of one-off events, and can’t both be identified through the same cohort tracking?
 
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