Autoimmune Disease: Patterns, Timing, and the Cost of Snapshot Diagnosis

Understanding Immune Dysregulation Over Time

Executive Summary

Autoimmune and immune-mediated illnesses are widely misunderstood because their symptoms fluctuate over time, cross body systems, and often fail to align with point-in-time testing. Individuals may experience periods of relative stability followed by days or weeks of worsening symptoms, frequently without a clear or immediate trigger. In clinical settings, this variability is often misinterpreted as inconsistency, stress-related illness, or lack of objective disease.

A substantial body of research shows that immune-mediated illness behaves as a dynamic biological system rather than a static condition. Symptoms are shaped by timing, context, cumulative immune load, and recovery capacity, not by isolated events or single measurements (National Academy of Medicine, 2015; Tsokos et al., New England Journal of Medicine, 2011; Wirth & Scheibenbogen, Frontiers in Immunology, 2020). Across autoimmune disease, post-infectious syndromes, and neuroimmune conditions, longitudinal studies consistently demonstrate that symptom severity often fluctuates independently of structural damage or single laboratory values, yet follows repeatable patterns when observed over time (Institute of Medicine, 2015; Jason et al., Journal of Translational Medicine, 2017).

Since the start of the COVID-19 pandemic, this pattern has become impossible to ignore. Population-level studies now document increased incidence of autoimmune diagnoses following SARS-CoV-2 infection, alongside a far larger group of individuals with persistent, fluctuating immune-mediated symptoms that do not meet traditional diagnostic thresholds (Wang et al., Nature Reviews Rheumatology, 2023; NIH RECOVER, 2023; CDC, 2024). These findings reinforce what autoimmune research has long indicated: diagnosis-based prevalence captures only a fraction of true disease burden.

This white paper synthesizes evidence from autoimmune, post-infectious, autonomic, and chronic inflammatory research to explain why symptom fluctuation is expected rather than anomalous, and how longitudinal symptom tracking reveals structured, system-level behavior that single clinical snapshots routinely miss (NIH, 2021; NIAID, 2023). It also introduces the concept of Stage Zero autoimmune illness, describing clinically significant immune dysfunction that precedes formal diagnosis and is frequently rendered invisible by current evaluation models.

Why Autoimmune and Immune-Mediated Illness Is Often Misread

Modern healthcare systems are optimized to detect acute injury, infection, or stable chronic disease. Diagnostic workflows prioritize structural findings, persistently elevated biomarkers, or abnormalities that remain visible across repeated testing. This model works well for conditions defined by fixed damage or continuous biochemical signals. It is poorly suited for illnesses driven by immune dysregulation rather than permanent injury.

Autoimmune and immune-mediated conditions routinely operate outside these expectations. Immune activity may fluctuate, shift across organ systems, or remain active at levels sufficient to disrupt function without exceeding standard laboratory thresholds. As a result, patients can experience significant symptom burden even when blood work, imaging, and physical examinations appear normal or only intermittently abnormal (NIAID, 2023; NIAMS, 2022).

This mismatch is well documented. In systemic autoimmune diseases such as lupus and Sjögren’s syndrome, disease activity often waxes and wanes, and commonly used laboratory markers may lag behind clinical symptoms or fail to reflect disease impact, particularly in early, seronegative, or overlapping presentations (Tsokos et al., 2011; Smolen et al., Annals of the Rheumatic Diseases, 2020). Serologic negativity does not preclude clinically meaningful autoimmune disease. In expert practice, diagnosis frequently relies on pattern recognition and multisystem involvement rather than a single test result.

In conditions such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and fibromyalgia, decades of research show that no single biomarker reliably correlates with symptom severity, despite profound functional impairment and documented abnormalities across immune, autonomic, and neuroinflammatory pathways (Institute of Medicine, 2015; Younger et al., Pain, 2014; Jason et al., 2017). Similar dynamics are now observed in post-acute sequelae of SARS-CoV-2 infection, where patients experience fluctuating, multi-system symptoms that evade routine testing yet persist for months or years (NIH RECOVER, 2023; Nature Medicine, 2022).

When evaluation is limited to isolated encounters, symptoms are often interpreted as inconsistent, nonspecific, or psychosomatic rather than contextual and patterned. Day-by-day assessment obscures system-level behavior, masking how immune stressors stack, how recovery capacity erodes, and how delayed responses shape clinical presentation. What appears unclear at a single point in time frequently becomes coherent when symptoms are examined across sequences and time windows (VanNess et al., Journal of Chronic Fatigue Syndrome, 2010; Wirth & Scheibenbogen, 2020).

Autoimmune and immune-mediated illness is therefore frequently misread not because it lacks structure, but because prevailing diagnostic frameworks are not designed to observe it over time.

National Prevalence: Why Adjusting Is Necessary

National prevalence estimates for autoimmune and immune-mediated illness are widely cited, but they are structurally limited. Most rely on diagnosis codes, registry inclusion, or point-in-time clinical confirmation, all of which systematically miss individuals whose symptoms fluctuate, overlap categories, or fall below diagnostic thresholds during brief encounters. As a result, national figures tend to reflect visibility within the healthcare system, not true population burden.

At the same time, post-infectious conditions such as Long COVID have demonstrated how quickly national prevalence can outpace diagnostic infrastructure, with millions affected despite relatively modest confirmed case counts in administrative data (Centers for Disease Control and Prevention, 2024).

National estimates are therefore best understood as floors, not ceilings. They capture individuals who meet criteria at the right moment, in the right setting, with access to specialty care. They do not capture those in prolonged diagnostic delay, those cycling through emergency care without labels, or those managing fluctuating immune dysfunction outside formal disease categories.

National Autoimmune Prevalence Table

Condition	Corrected U.S. Prevalence (Best Estimate)
Systemic lupus erythematosus (SLE)	2.5–3.0 million
Rheumatoid arthritis (RA)	≈ 2.1 million
Multiple sclerosis (MS)	≈ 1.2 million
Type 1 diabetes (T1D)	≈ 1.8 million
Inflammatory bowel disease (IBD)	≈ 3.1 million
Psoriasis / psoriatic disease	≈ 1.5 million (autoimmune-active subset)
Hashimoto’s thyroiditis	≈ 16 million
Graves’ disease	≈ 1.4 million
Celiac disease	≈ 3.3 million
Sjögren’s disease	≈ 2.8 million

Important framing rule: These numbers represent diagnosed + undiagnosed combined and reflect functional civic burden, not registry counts. This limitation becomes more pronounced when immune-mediated illness is examined over time. Longitudinal research consistently shows that symptoms rise and fall, migrate across systems, and appear in delayed response to stressors.

Prevalence methods that depend on static labels or single-visit confirmation inevitably undercount

conditions defined by instability rather than permanence. For these reasons, national prevalence provides necessary context but insufficient resolution. State-level analysis allows these limitations to be examined directly. Differences in access to specialty care, environmental exposure, insurance coverage, diagnostic norms, and reporting practices all shape who becomes visible in national counts. When prevalence is examined at the state level, gaps between diagnosis-based estimates and adjusted burden become clearer, and patterns obscured at the national scale begin to emerge.

The following section presents state-level prevalence estimates to illustrate how immune-mediated illness distributes across geography when diagnostic delay, fluctuation, and under-recognition are accounted for. A summary table of commonly tracked conditions is provided in the main text, followed by a complete state-level database in the appendix.

From National Undercounting to Geographic Reality

National prevalence estimates obscure a critical dimension of immune-mediated illness: geography. Diagnostic delay, access to specialty care, environmental exposure, and population demographics vary widely by state, yet most autoimmune prevalence figures are reported as national aggregates. When snapshot diagnosis is already known to undercount disease, collapsing data across regions compounds the distortion.

State-level variation matters for both scientific accuracy and policy relevance. Environmental stressors such as air quality, humidity, wildfire exposure, and temperature volatility differ substantially across states and are known to modulate immune symptom expression and flare frequency. At the same time, access to rheumatology, immunology, and neurology care varies dramatically, influencing whether fluctuating illness is formally diagnosed or remains invisible within administrative datasets.

To address this gap, this analysis extends beyond national prevalence figures to calculate autoimmune burden at the state level. Using diagnosis-based baselines as a starting point, adjusted estimates are generated to account for known undercounting driven by delayed diagnosis, fluctuating symptom presentation, and barriers to specialty care. These tables are not intended to replace epidemiologic surveillance, but to illustrate how much disease burden is likely missing when prevalence is inferred from snapshot encounters alone.

Two views are presented. The first focuses on the most commonly tracked autoimmune conditions, reflecting what appears in most public datasets. The second expands the lens to include a broader set of immune-mediated conditions that are frequently excluded from “top ten” lists despite substantial functional impact. Together, these tables demonstrate that underestimation is not evenly distributed. It is patterned, geographic, and predictable.

Seen this way, state-level prevalence correction is not an academic exercise. It is a practical tool for understanding where immune-mediated illness is most likely being missed, where economic impact is likely underestimated, and where policy responses grounded in national averages will fail to match lived reality.

Methodology

US-CCUC-AS produces state-by-state autoimmune burden estimates using national anchors plus bounded state context adjustments. It is my standardized method to estimate autoimmune prevalence by U.S. state, with optional subgroup redistribution and visibility calibration layers.

State Adjusted Prevalence

= National Base Prevalence

× State Population

× Diagnostic Visibility Multiplier

× Access & Delay Adjustment

× Demographic Risk Modifier

Where:

National Base Prevalence comes from peer-reviewed or federal anchors for each condition.

Diagnostic Visibility Multiplier corrects for underdiagnosis and delayed diagnosis, particularly in conditions with known seronegativity, overlap, or fluctuating presentation.

Access & Delay Adjustment accounts for healthcare access, specialist density, rurality, and documented diagnostic delay patterns by state.

Demographic Risk Modifier adjusts for sex distribution, age structure, and known demographic risk differentials without collapsing them into a single coefficient. That is the point estimate pipeline.

SVT™ is CYNAERA’s base state-weight module for US-CCUC. It assigns each state to a tier based on how likely the state is to (1) generate chronic illness burden through terrain stressors and (2) suppress visibility through diagnostic delay, access barriers, and weak stabilization conditions. SVT™ is designed to scale prevalence corrections across “invisible” chronic conditions quickly and consistently.

Table: Rheumatoid arthritis, Lupus, Multiple sclerosis, Hashimoto’s, Type 1 Diabetes

State	SVT Tier	RA	Lupus	MS	Hashimoto’s	Type 1 Diabetes
Alabama	2	28,827	34,318–41,181	16,473	219,634	30,200–35,690
Alaska	3	3,794	4,517–5,421	2,168	28,910	3,975–4,698
Arizona	2	43,191	51,417–61,701	24,680	329,071	45,247–53,474
Arkansas	2	20,067	23,889–28,667	11,467	152,891	21,023–24,845
California	1	253,290	301,536–361,844	144,737	1,929,832	265,352–313,598
Colorado	2	34,564	41,147–49,376	19,751	263,344	36,240–42,817
Connecticut	3	19,249	22,915–27,498	11,000	146,659	20,193–23,857
Delaware	2	6,021	7,171–8,605	3,441	45,857	6,316–7,462
Florida	1	141,082	167,936–201,523	80,618	1,074,911	147,846–174,585
Georgia	2	64,544	76,838–92,205	36,882	491,641	67,650–79,930
Hawaii	2	16,732	19,920–23,904	9,561	127,559	17,540–20,728
Idaho	3	10,995	13,088–15,706	6,283	83,767	11,535–13,628
Illinois	2	74,367	88,529–106,235	42,497	566,994	77,787–91,949
Indiana	2	40,081	47,718–57,262	22,903	305,371	41,974–49,611
Iowa	3	17,104	20,359–24,431	9,773	130,312	17,951–21,023
Kansas	3	16,606	19,766–23,719	9,489	126,520	17,428–20,588
Kentucky	2	26,327	31,349–37,619	15,044	200,593	27,575–32,597
Louisiana	2	26,280	31,293–37,552	15,017	200,235	27,526–32,539
Maine	3	14,359	17,096–20,515	8,206	109,402	15,076–17,814
Maryland	2	37,924	45,149–54,179	21,671	288,948	39,727–46,952
Massachusetts	3	18,870	22,464–26,957	10,783	143,773	19,795–23,389
Michigan	2	59,879	71,287–85,545	34,217	456,241	62,739–74,205
Minnesota	3	31,838	37,899–45,479	18,165	242,581	33,389–39,450
Mississippi	2	17,041	20,297–24,356	9,738	129,528	17,843–21,099
Missouri	2	36,252	43,171–51,805	20,715	276,202	37,974–44,893
Montana	3	6,303	7,503–9,004	3,602	48,047	6,616–7,815
Nebraska	3	10,995	13,088–15,706	6,283	83,767	11,535–13,628
Nevada	2	19,779	23,546–28,255	11,356	151,266	20,653–24,420
New Hampshire	3	15,008	17,874–21,449	8,579	114,370	15,758–18,624
New Jersey	2	54,819	65,180–78,216	31,325	417,726	57,389–67,749
New Mexico	2	12,250	14,581–17,497	6,999	93,328	12,836–15,167
New York	2	113,102	134,631–161,558	64,630	861,407	118,492–140,047
North Carolina	2	72,399	86,178–103,413	41,370	551,611	75,827–89,606
North Dakota	3	4,389	5,226–6,272	2,508	33,447	4,608–5,445
Ohio	2	83,441	99,312–119,175	47,680	635,363	87,381–103,252
Oklahoma	2	24,021	28,602–34,323	13,726	182,979	25,169–29,750
Oregon	2	25,110	29,897–35,876	14,349	191,365	26,310–31,327
Pennsylvania	2	93,235	111,003–133,203	53,282	710,361	97,645–115,470
Rhode Island	3	11,366	13,533–16,239	6,495	86,598	11,934–14,101
South Carolina	2	36,340	43,263–51,916	20,779	277,049	38,075–44,998
South Dakota	3	5,069	6,040–7,248	2,896	38,612	5,324–6,295
Tennessee	2	47,418	56,454–67,745	27,098	361,282	49,698–58,734
Texas	1	187,098	222,782–267,338	106,914	1,425,508	196,319–231,873
Utah	3	21,383	25,458–30,550	12,217	162,900	22,452–26,527
Vermont	3	7,153	8,517–10,221	4,088	54,505	7,511–8,875
Virginia	2	57,845	68,930–82,716	33,054	440,727	60,602–71,589
Washington	2	56,095	66,845–80,214	32,054	427,388	58,799–69,549
West Virginia	3	9,710	11,571–13,885	5,554	74,054	10,195–12,056
Wisconsin	3	31,382	37,360–44,832	17,933	239,110	32,953–38,931
Wyoming	3	3,169	3,776–4,531	1,813	24,176	3,329–3,936

Note on Ranges in This Table Some conditions in this table are shown as bounded ranges rather than single values. These ranges reflect known national uncertainty driven by underdiagnosis, delayed recognition, and classification variability, not model imprecision. Conditions such as lupus and adult autoimmune diabetes are frequently diagnosed late, may be seronegative, or are misclassified in claims data, producing wide variance across credible national sources. Where this uncertainty is well established, US-CCUC-AS preserves it explicitly rather than collapsing estimates into a single point that would imply false precision. Conditions with more stable national anchors are presented as point estimates.

Even within this limited subset of conditions, state-level variation is substantial, and the gap between diagnosis-based prevalence and adjusted estimates widens in regions with known access barriers and environmental stressors. These patterns intensify when the analysis is expanded beyond the most commonly tracked conditions.

The appendix section provides full state-level prevalence tables for an expanded set of immune-mediated conditions beyond those typically included in national “top” summaries. Conditions were selected based on documented immune involvement, functional impact, and evidence of diagnostic delay or under-recognition. These tables are intended to support transparency, secondary analysis, and policy modeling rather than serve as definitive epidemiologic surveillance.

Stage Zero: When the Immune System Is Active Before It Is Named

Autoimmune and immune-mediated illness rarely begins with a clear diagnosis. More often, it begins with patterns that repeat long before medicine assigns a label. Symptoms rise, fall, and cluster. Tests come back “normal.” Patients are told to wait, monitor, or manage symptoms. Yet the immune system is already behaving abnormally.

This paper refers to that early, overlooked phase as Stage Zero autoimmune illness.

Stage Zero is not a diagnosis. It is a recognizable pattern of immune instability that causes real, sometimes severe symptoms, but is frequently missed by snapshot testing. The immune system is reactive, poorly regulated, and easily tipped, even when standard laboratory markers are negative or inconsistent.

At the beginning of the COVID-19 pandemic, while repeatedly hospitalized with severe, life-threatening immune reactions later understood to be mast-cell–mediated, I underwent standard autoimmune testing, including antinuclear antibody (ANA) screening. Results were negative, and no autoimmune diagnosis was made. This occurred before post-acute sequelae of SARS-CoV-2 infection (PASC) had been formally defined, and before longitudinal immune instability following COVID was widely recognized.

Despite negative testing, symptoms followed a consistent flare pattern. During severe episodes, hair loss increased noticeably. Lips became dry, cracked, peeling, and slow to heal. Eyes were persistently dry and irritated. Joint pain intensified, accompanied by burning and tingling sensations in the hands and feet. These symptoms did not appear randomly. They rose together during flares and eased together during recovery periods. This clustering matters.

Hair shedding reflects inflammatory or immune stress on rapidly dividing tissues. Dry eyes and lips point to mucosal and glandular involvement, commonly seen in Sjögren’s-spectrum and related immune disorders. Joint pain combined with burning and tingling suggests inflammatory and neuro-immune pathways acting at the same time. When these features co-occur repeatedly, they form a system-level signal, not a collection of unrelated complaints.

During severe flares, management of autoimmune and immune-mediated illness often converges with emergency care principles: stabilization, reduction of inflammatory load, autonomic support, and time for recovery. The effectiveness of these interventions during acute episodes underscores that immune flares represent periods of system destabilization rather than isolated symptom complaints.  

In experienced clinical practice, autoimmune and immune-mediated illness is sometimes identified through pattern recognition and multisystem involvement even when standard serologic markers are negative, particularly in early, overlapping, or evolving disease states. ANA negativity does not exclude autoimmune disease. Many patients later diagnosed with Sjögren’s, rheumatoid arthritis, lupus, or overlap syndromes report years of symptoms before laboratory confirmation, and some never meet a single definitive serologic threshold despite ongoing disease activity.

Stage Zero captures this gap between lived immune dysfunction and formal diagnosis.

What distinguishes Stage Zero is not uncertainty, but structure. Symptoms cluster across systems. They worsen after specific stressors. They follow timing rules. Exacerbations are often delayed, appearing one to three days after sleep loss, exertion, infection, hormonal shifts, or environmental change. The immune system compensates, then falters, then compensates again. This reflects immune biology, not diagnostic ambiguity. The absence of a label does not imply the absence of organization.

Recognizing Stage Zero has practical implications. It explains why patients can be severely ill without “proof.” It clarifies why autoimmune diagnoses often emerge only after an additional immune stressor pushes the system past a diagnostic threshold. It also reframes longitudinal symptom tracking as data, not anecdote. When symptoms are viewed across sequences rather than isolated days, the immune system’s pattern becomes visible.

Stage Zero is where many patients live for years. It is also where earlier recognition, better monitoring, and pattern-aware care could meaningfully reduce harm. Ignoring this phase does not prevent disease. It simply delays recognition until disability, damage, or clearer biomarkers finally force medicine’s attention.

Since the start of the COVID-19 pandemic, the signals have become harder to ignore. Large population studies now show that SARS-CoV-2 infection increases the risk of developing autoimmune conditions such as rheumatoid arthritis, lupus, and inflammatory bowel disease, even among people who were never hospitalized (Wang et al., Nature Reviews Rheumatology, 2023).

What matters is not just the diagnoses that appear, but what they imply. Immunology studies repeatedly show that, for a subset of patients, immune systems do not simply “reset” after acute infection. Months later, researchers continue to find altered T-cell populations, disrupted cytokine signaling, and signs of unstable immune regulation (Chang et al., The Lancet Immunology, 2022). Similar patterns were documented long before COVID, following other viral illnesses, suggesting that prolonged immune dysregulation is a recurring biological response rather than a pandemic-specific anomaly (Rasa et al., Frontiers in Immunology, 2018).

Longitudinal data from the NIH RECOVER Initiative adds another layer. Many people with post-acute sequelae of SARS-CoV-2 infection experience symptoms that fluctuate, migrate across systems, and fail to resolve in a straight line. These patterns are often missed by routine testing and poorly captured by single-specialty care models (NIH RECOVER, 2023).

Diagnosis trends reflect only a fraction of this reality. Diagnostic data capture people who happen to meet criteria at the right moment, after navigating complex evaluation pathways. They miss those whose symptoms fluctuate, overlap categories, or fall just below thresholds during brief clinical encounters. As a result, a large share of immune-mediated illness remains undiagnosed, misclassified, or labeled as idiopathic, particularly among women and people facing barriers to specialty care (CDC, 2024).

How Immune-Mediated Illness Actually Behaves Over Time

Immune-mediated illness rarely follows a straight line. Symptoms rise and fall even when disease processes continue underneath. In lupus, multiple sclerosis, and inflammatory bowel disease, longitudinal studies show that symptom burden often changes independently of imaging findings or structural damage, driven instead by immune activity, stressors, and recovery capacity (Tsokos et al., 2011; Compston & Coles, The Lancet, 2008).

Symptoms also tend to travel together. Fatigue, cognitive strain, autonomic symptoms, bladder irritation, gastrointestinal distress, and temperature sensitivity frequently worsen at the same time. Research in dysautonomia and mast-cell–mediated conditions shows why: immune signaling can affect blood vessels, nerves, gut function, and inflammatory pathways simultaneously, producing symptom clusters that look unrelated only if each system is viewed in isolation (Raj et al., Autonomic Neuroscience, 2018; Theoharides et al., Journal of Neuroinflammation, 2015).

Timing matters. In many immune-mediated conditions, symptom flares do not occur at the moment of exertion, stress, sleep loss, or environmental change. Instead, they peak a day or two later. This delayed response is well documented in ME/CFS, post-viral syndromes, and autonomic disorders and reflects downstream immune and neuroinflammatory processes rather than immediate injury (VanNess et al., 2010; Jason et al., 2017).

Once this timing is recognized, much of what is labeled “unpredictable” becomes understandable. Fluctuation is mistaken for inconsistency. Delay is mistaken for coincidence. Multi-system involvement is mistaken for fragmentation. Longitudinal observation shows that these are defining features of immune dysregulation, not signs of disorder or exaggeration.

Triggers, Context, and Cumulative Load

Environmental and physiological stressors do not create autoimmune disease, but they strongly shape how symptoms show up. Weather-related worsening has been documented across rheumatoid arthritis, migraine, dysautonomia, and lupus, particularly around barometric pressure and humidity changes (Smedslund & Hagen, Annals of the Rheumatic Diseases, 2011; Mukamal et al., Neurology, 2009).

Sleep disruption plays a similar role. Even short periods of fragmented or insufficient sleep alter cytokine balance, increase inflammatory signaling, and reduce autonomic stability, making the immune system less tolerant of additional stress (Irwin & Opp, Nature Reviews Immunology, 2017).

What matters most is stacking. Studies in ME/CFS and post-viral illness show that symptoms escalate when stressors occur close together, even if each stressor alone would have been manageable (Nijs et al., Pain Physician, 2012). The immune system responds to cumulative load, not isolated events.

Making Sense of Symptom Tracking Over Time

Symptom tracking works because it mirrors immune biology. When symptoms are viewed across weeks or months, patterns emerge that correlate with functional impairment more closely than single lab values or one-off visits (Institute of Medicine, 2015; NIH RECOVER, 2023).

Individual days are misleading. Variability is expected. What matters is sequence: what came first, what followed, and how long recovery took. Delayed worsening is often more informative than same-day reactions. Clusters carry more meaning than individual symptoms.

Repeated sequences are the strongest signal. Research on post-exertional malaise, autonomic instability, and immune activation consistently shows that timing and pattern predict outcomes better than trigger lists alone (VanNess et al., 2010; Raj et al., 2018). Incomplete data does not invalidate this approach. Real-world research routinely works with uneven reporting and still identifies meaningful trends when data is interpreted over time rather than dismissed as noise (NIH RECOVER, 2023).

The Economic Cost of Snapshot Diagnosis

The economic burden of autoimmune and immune-mediated illness is substantial, but it is routinely underestimated for the same reason these conditions are clinically misread: prevailing measurement frameworks rely on diagnosis codes and point-in-time encounters rather than longitudinal impact. When disease recognition depends on a stable label appearing at a single visit, large portions of immune-mediated disability remain invisible to cost accounting.

Federal agencies have acknowledged this gap. NIH materials addressing autoimmune disease consistently note that these conditions impose a major national burden, with annual U.S. healthcare costs exceeding $100 billion. Importantly, these figures reflect direct medical spending only and are based on conservative estimates that exclude lost productivity, reduced workforce participation, caregiver burden, and downstream social costs (NIH, 2022; NIAID, 2023). In other words, the most frequently cited number represents a floor, not a ceiling.

Long COVID has made this undercounting visible at scale. Economic analyses estimate that post-COVID conditions are associated with approximately $170 billion per year in lost earnings in the United States due to reduced work hours, prolonged absences, and labor force exit among working-age adults (Bach, Brookings Institution, 2022). Broader modeling that incorporates healthcare utilization and quality-of-life impacts places the total economic burden substantially higher (Cutler & Bach, 2022). While individual estimates vary by methodology, the underlying signal is consistent: immune-mediated disability functions as a labor and income shock, not merely a healthcare expense.

This pattern was well documented long before the pandemic. Research on myalgic encephalomyelitis / chronic fatigue syndrome (ME/CFS) repeatedly demonstrated that indirect costs from lost productivity and reduced work capacity far exceed direct medical spending (Jason et al., 2008; Lin et al., 2011; Institute of Medicine, 2015). Patients often cycled through emergency care, specialty consultations, and repeated testing while remaining functionally impaired and economically vulnerable. The cost was not driven by excessive treatment, but by delayed recognition and fragmented care that failed to stabilize illness early.

Policy-focused analyses have since framed post-infectious and immune-mediated illness as a workforce issue as much as a medical one. Syntheses by Solve M.E. and others emphasize that delayed recognition leads to cumulative economic harm through extended instability, repeated acute-care utilization, and permanent reductions in earning years (Solve M.E., 2021; Solve M.E., 2023). These conclusions align with federal and peer-reviewed findings and reinforce a central point: the greatest costs accrue not when illness is recognized early, but when it is recognized only after years of fluctuation and decline.

The cost of snapshot diagnosis is therefore not abstract. It appears in emergency department reliance, prolonged disability timelines, underemployment, and workforce exit that occurs while individuals are still cycling through “normal” tests. When immune-mediated illness is evaluated only in fragments, systems pay repeatedly for crisis management instead of investing in stability.

Longitudinal, pattern-aware approaches address this failure directly. By recognizing destabilization earlier, when symptoms first begin clustering and recovery capacity starts to erode, it becomes possible to reduce downstream utilization, preserve function, and mitigate long-term economic loss. Seen this way, pattern-aware interpretation is not simply clinically appropriate. It is fiscally rational.

Implications for Care, Research, and Policy

When immune-mediated illness is examined over time, rather than at isolated moments, a different picture emerges. Longitudinal, pattern-aware approaches align far more closely with immune biology than snapshot-based evaluation. Immune-mediated conditions unfold through cumulative load, delayed responses, and overlapping system stress, not through steady, linear change. When care is limited to brief encounters or single test results, this behavior is easily misread, leading to misclassification, delayed recognition, or outright dismissal of patient-reported symptoms (NIH, 2021).

In clinical care, incorporating symptom timelines alongside standard evaluation changes how worsening is interpreted. Instead of assuming progression, anxiety, or nonadherence, clinicians can recognize when symptom escalation reflects contextual strain: a period of poor sleep, infection, environmental change, or recovery failure. This does not require abandoning diagnostic rigor. It requires situating test results within time. Doing so validates lived experience without prematurely assigning labels and helps identify early warning signs of decompensation before laboratory abnormalities appear.

In research, longitudinal symptom data provides essential context for understanding variability. Studies that rely only on cross-sectional snapshots systematically underestimate disease burden in fluctuating, multi-system conditions. Timing mismatches between exposure, immune response, and measurement introduce noise that obscures real signals. Integrating symptom timelines into study design improves cohort definition, supports more accurate subgroup analysis, and captures disease behavior that would otherwise be misclassified as heterogeneity or attrition. Large initiatives such as NIH RECOVER have already demonstrated the value of longitudinal approaches in revealing post-infectious illness trajectories that snapshot methods fail to detect (NIH RECOVER, 2023).

For public health policy, the implications are substantial. Systems built on diagnostic codes and point-in-time encounters consistently undercount immune-mediated illness, particularly among individuals facing diagnostic delay, access barriers, or overlapping conditions. Longitudinal symptom data and adjusted prevalence methods allow for more realistic estimates of population burden, clearer geographic patterns, and better-informed resource allocation. Without these tools, planning remains anchored to visibility rather than reality (CDC, 2024).

Across care, research, and policy, the unifying variable is time. Immune-mediated illness cannot be understood without accounting for when symptoms occur, how they cluster, and how they respond to context. Longitudinal approaches do not replace testing or clinical judgment. They provide the structure needed to interpret them correctly. When symptoms are viewed across sequences rather than snapshots, patterns become visible, interpretable, and actionable. Snapshot-based evaluation alone cannot capture this structure. Pattern-aware interpretation restores coherence to conditions that have too often been treated as inconsistent or inexplicable.

Conclusion

Autoimmune and immune-mediated illnesses are not chaotic. They are not inscrutable. What is often labeled “unpredictable” is, in practice, a limitation of how these conditions are observed.

When illness is evaluated only in snapshots, fluctuation looks like inconsistency. Delay looks like coincidence. Multi-system involvement looks like fragmentation. When illness is observed over time, structure appears. Symptoms rise and fall in response to timing, context, and recovery capacity, not because the disease is vague, but because the immune system is actively responding to changing conditions.

The gap between lived experience and formal recognition has real consequences. Patients are dismissed. Care becomes fragmented. Disease burden is underestimated. Entire populations remain partially invisible within surveillance systems that prioritize labels over patterns.

Pattern-aware interpretation offers a corrective. By paying attention to when symptoms occur, how they cluster, and what precedes change, immune-mediated illness can be understood as a dynamic system rather than a series of disconnected complaints. This shift does not require abandoning diagnostic standards. It requires acknowledging that time itself is a critical variable in immune biology.

As autoimmune and post-infectious conditions continue to rise after the pandemic, the limits of snapshot-based thinking are no longer abstract. They are visible in delayed diagnoses, strained healthcare systems, and patients cycling through care without answers. Longitudinal perspectives are not an alternative view. They are an overdue one.

Recognizing patterns over time restores meaning to experiences long treated as noise. It supports better care, more accurate research, and fairer public health planning. Most importantly, it brings medical understanding back into alignment with how immune-mediated illness actually unfolds in real lives.

Ongoing Research and Participation

These principles are actively applied in CYNAERA research efforts, including Project Eve, which studies symptom patterns alongside sleep and environmental context in adults with immune-mediated illness.

More information is available at https://www.cynaera.com/project-eve

References

1. Bach, K. (2022). New data shows long COVID is keeping as many as 4 million people out of work. Brookings Institution.

2. Chang, R., et al. (2022). Persistent immune dysregulation after SARS-CoV-2 infection. The Lancet Immunology, 3(9), e430–e439.

3. Compston, A., & Coles, A. (2008). Multiple sclerosis. The Lancet, 372(9648), 1502–1517.

4. Cutler, D. M., & Bach, K. (2022). The economic cost of long COVID. Harvard University and Brookings Institution.

5. Centers for Disease Control and Prevention (CDC). (2024). Post-COVID conditions: Information for healthcare providers and public health officials.

6. Institute of Medicine. (2015). Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness. National Academies Press.

7. Irwin, M. R., & Opp, M. R. (2017). Sleep health: Reciprocal regulation of sleep and innate immunity. Nature Reviews Immunology, 17(12), 745–760.

8. Jason, L. A., et al. (2008). Economic impact of ME/CFS: Individual and societal costs. Dynamic Medicine, 7(1), 6.

9. Jason, L. A., et al. (2017). Examining the nature of post-exertional malaise in ME/CFS. Journal of Translational Medicine, 15(1), 102.

10. Lin, J. M. S., et al. (2011). The economic impact of ME/CFS in the United States. Journal of Clinical Psychology, 67(10), 1059–1067.

11. Mukamal, K. J., et al. (2009). Weather and air pollution as triggers of severe headaches. Neurology, 72(10), 922–927.

12. National Academy of Medicine. (2015). Perspectives on chronic disease and complex illness.

13. National Institute of Allergy and Infectious Diseases (NIAID). (2023). Autoimmune diseases overview and research priorities.

14. National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS). (2022). Autoimmune diseases: Research advances and challenges.

15. National Institutes of Health (NIH). (2021). Strategic framework for chronic and post-infectious disease research.

16. National Institutes of Health (NIH). (2022). Autoimmune disease research plan.

17. NIH RECOVER Initiative. (2023). Researching COVID to Enhance Recovery: Symptom clusters and longitudinal outcomes.

18. Nijs, J., et al. (2012). Post-exertional malaise in chronic fatigue syndrome. Pain Physician, 15(3), E205–E213.

19. Raj, S. R., et al. (2018). Dysautonomia and autonomic nervous system disorders. Autonomic Neuroscience, 215, 1–8.

20. Rasa, S., et al. (2018). Chronic viral infections and immune dysregulation. Frontiers in Immunology, 9, 267.

21. Smedslund, G., & Hagen, K. B. (2011). Does weather affect rheumatoid arthritis pain? Annals of the Rheumatic Diseases, 70(2), 244–249.

22. Smolen, J. S., et al. (2020). Treating rheumatoid arthritis to target: 2019 update. Annals of the Rheumatic Diseases, 79(6), 685–699.

23. Solve M.E. (2021). The economic and workforce impact of post-infectious disease.

24. Solve M.E. (2023). Post-infection illness and the U.S. labor force.

25. Theoharides, T. C., et al. (2015). Mast cells, neuroinflammation, and behavior. Journal of Neuroinflammation, 12, 127.

26. Tsokos, G. C. (2011). Systemic lupus erythematosus. New England Journal of Medicine, 365(22), 2110–2121.

27. VanNess, J. M., et al. (2010). Delayed onset of post-exertional malaise. Journal of Chronic Fatigue Syndrome, 14(2), 45–56.

28. Wang, S., et al. (2023). Autoimmune disease risk following SARS-CoV-2 infection. Nature Reviews Rheumatology, 19, 413–424.

29. Wirth, K., & Scheibenbogen, C. (2020). A unifying hypothesis of ME/CFS pathophysiology. Frontiers in Immunology, 11, 167.

30. Younger, J., et al. (2014). Evidence of neuroinflammation in fibromyalgia. Pain, 155(6), 1114–1122.

Author’s Note:

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

Applied Infrastructure Models Supporting This Analysis

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

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 Selin Lab for t cell research at the University of Massachusetts.

Cynthia has co-authored research with Harlan Krumholz, MD, Dr. Akiko Iwasaki, and Dr. David Putrino, though Yale’s LISTEN Study, advised Amy Proal, PhD’s research group at Mount Sinai through its patient advisory board, and worked with Dr. Peter Rowe of Johns Hopkins on national education and outreach focused on post-viral and autonomic illness. She has also authored a Milken Institute essay on AI and healthcare, testified before Congress, and worked with congressional offices on multiple legislative initiatives. Cynthia has led national advocacy teams on Capitol Hill and continues to advise on chronic-illness policy and data-modernization efforts.

Through CYNAERA, she develops modular AI platforms, including the IACC Progression Continuum™, Primary Chronic Trigger (PCT)™, RAVYNS™, and US-CCUC™, that are made to help governments, universities, and clinical teams model infection-associated conditions and improve precision in research and trial design. She has been featured in TIME, Bloomberg, USA Today, and other major outlets, for community engagement, policy and reflecting her ongoing commitment to advancing innovation and resilience from her home in Northern Virginia.

Cynthia’s work with complex chronic conditions is deeply informed by her lived experience surviving the first wave of the pandemic, which strengthened her dedication 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 perspective to her research and policy initiatives.