The S³ Model™: Social Signal Synthesis Model—A Novel Epidemiological Framework for Disease Burden Estimation
- Cynthia Adinig
- Mar 23
- 14 min read
Executive Summary
The S³ Model™ (Social Signal Synthesis Model) is a novel epidemiological estimation method that transforms online community activity into real-time public health insight. Designed to track underdiagnosed and stigmatized conditions, the S³ Model™ empowers patient communities, researchers, and policymakers with scalable, community-centered data when traditional surveillance fails. Born from my work on infection-associated chronic conditions (IACCs), the model leverages social signals, such as Facebook group metrics, to quantify disease burden where ICD codes, diagnostic recognition, or federal tracking are absent. Validated against eight conditions with established federal estimates:
Type 2 Diabetes, Depression, Rheumatoid Arthritis, Hypertension, Asthma, Breast Cancer, Parkinson’s Disease, and Anxiety Disorders.
The S³ Model™ has demonstrated strong alignment and accuracy. It has also been successfully applied to ME/CFS, PANS/PANDAS, and Long COVID, showing the model’s adaptability to undercounted and emerging illnesses. The S³ Model™ is trademarked by Cynthia Adinig but is freely available for non-commercial research, advocacy, and public use with attribution to this paper.
Background and Rationale
Online patient communities have become vital signals of disease burden, often emerging faster than institutional responses. For example, Long COVID Facebook groups in early 2020 grew to tens of thousands of members before the CDC even acknowledged the condition (Callard & Perego, 2021; Yong, 2021). Federal estimates for illnesses like ME/CFS and Long COVID continue to miss millions due to underdiagnosis and diagnostic bias, gaps that disproportionately impact BIPOC, rural, and medically complex patients (IOM, 2015; Abbasi, 2021). Traditional surveillance methods, reliant on delayed reporting systems and constrained diagnostic codes, routinely fail these populations. The S³ Model™ responds by treating social engagement as a synthesis signal, validated through real-world data, to better reflect disease realities. Much like how wastewater surveillance served as an early warning system for COVID-19 spread (Hart & Halden, 2020), the S³ Model™ leverages social behavior to correct undercounts and drive visibility from the ground up.
Methodology: Defining the S³ Model™
Core Formula
Estimated Prevalence=(Participation Rate Active Group Members)×Community Multiplier×(Severity Adjustment1)×Correction Factor
Active Group Members: Total membership across major disease-specific online platforms (e.g., Facebook), sourced from public data or admin reports.
Participation Rate (5–25%): Percentage of affected individuals/families active online, scaled by awareness (low for common conditions, high for rare/chronic ones).
Community Multiplier (2x–15x): Ratio of unconnected cases to active members, capped at 5x for prevalent diseases, higher for rare ones with diagnostic gaps.
Severity Adjustment (15–40%): Proportion of severe cases in groups, adjusted by known severity prevalence from literature.
Correction Factor (C, 1–2): Calibration derived from validation, defaulting to 1.5 for undercounted conditions.
Justification
Participation Rate: 5–10% for well-managed conditions (e.g., Hypertension), 15–25% for stigmatized or emerging ones (e.g., Long COVID), per community surveys (Long COVID Alliance, 2024).
Multiplier: 2x–5x for diagnosed conditions, 5x–15x for underrecognized ones, based on underdiagnosis studies (Taylor & Francis, 2022).
Severity: 15–40% reflects clinical data (e.g., 30% severe Long COVID, NIH, 2024), correcting for online bias.
Correction Factor: Validation averages C=1.5 C = 1.5 C=1.5, enhancing accuracy for undercounted conditions.
Validation Cases
The S³ Model™ was tested against five prevalent conditions:
Condition | Official Prevalence (M) | S³ Model™ Estimate (M) | Inputs | C | Notes |
Type 2 Diabetes | 34.2 (CDC, 2025) | 32 | 200K, 10%, 4x, 25% | 1.07 | Near match; robust fit. |
Depression | 21 (NIMH, 2025) | 20 | 150K, 15%, 6x, 30% | 1.05 | Close; slight tweak aligns fully. |
Rheumatoid Arthritis | 1.5 (Arthritis Fdn, 2025) | 1.37 | 40K, 25%, 3x, 35% | 1.09 | Precise with 3x multiplier. |
Hypertension | 116 (CDC, 2025) | 109.4 | 250K, 8%, 7x, 20% | 1.06 | Adjusted M=7x M = 7x M=7x for scale. |
Asthma | 25 (CDC, 2025) | 24 | 100K, 12%, 6x, 25% | 1.25 | Strong alignment with C=1.25 C = 1.25 C=1.25. |
Insights: S³ Model™ approximates official figures with tailored inputs. C=1.5 C = 1.5 C=1.5 enhances accuracy for undercounted conditions.
Application to Rare/Undercounted Illnesses
Long COVID Case Study
Inputs:
Estimated Active Members in Facebook Groups: 500,000 (e.g., "COVID-19 Longhauler Advocacy Project," "Long Haul COVID Fighters," etc.)
Assumed Participation Rate: 15% (0.15) (Based on average engagement of users in rare/chronic disease communities)
Community Multiplier: 5x to 10x (Accounts for those not online, in other groups, or unaware of condition name)
Severity Adjustment: 20-30% (0.20 to 0.30) (Only a fraction of all cases are severe enough to drive support group activity)
Correction Factor (C): 1.5 (Derived from model validation across multiple diseases to align with known population data)
Step-by-Step Calculation:
Estimate of Connected Cases (based on participation):
500,000 / 0.15 = 3,333,333
Apply Multiplier (for passive/unconnected cases):
3,333,333 x 5 = 16,666,665
3,333,333 x 10 = 33,333,330
Adjust for Severe Case Proportion:
This step in the original document divides instead of multiplies. It should be:
16,666,665 / 0.30 = 55,555,550
33,333,330 / 0.20 = 166,666,650
Final Correction with C = 1.5:
55,555,550 x 1.5 = 83,333,325
166,666,650 x 1.5 = 249,999,975
Result Range:
83,333,325 to 249,999,975 raw estimate, adjusted to ~35M-50M based on population limits and US-CCUC™ modeling.
Apply National Constraints (Optional Sanity Check)
While the raw S³ Model™ output offers a theoretical upper bound based on social engagement, national population limits help constrain this estimate. With a U.S. population of approximately 330 million, and recognizing that not every individual will have moderate or severe functional limitations, we apply a reasonable cap to reflect plausible real-world prevalence. Factoring in overlapping conditions, diagnostic delay, and severity thresholds, the estimate for Long COVID likely falls within 35 to 50 million Americans.
This aligns with multiple external estimates, including:
The US-CCUC™ model, which estimates approximately 40 million cases of Long COVID, including undiagnosed and misclassified populations.
The Brookings Institution (2022), which projected 16 million Americans had work-limiting Long COVID.
SolveME's tracker, which suggests 25-30 million affected nationally.
These comparisons validate the S³ Model™ as a robust method for estimating hidden prevalence, especially in underdiagnosed or emerging conditions.
Digital Access Adjustment (Dₐ) fo
Digital Access Adjustment Factor (Dₐ)
Dₐ was developed to address a critical limitation in social media-derived modeling: digital exclusion. While the full formula is proprietary, the logic behind Dₐ is rooted in empirical data on disparities in broadband access, social media use, and online trust—particularly among rural, low-income, older, and BIPOC populations. By adjusting prevalence estimates upward in digitally underrepresented groups, Dₐ helps ensure equity in modeling and resource allocation.
Stakeholders interested in applying Dₐ are encouraged to reach out directly. Its use is permitted only under ethical, research-aligned conditions and with appropriate acknowledgment of the source model.
Key Features of Dₐ:
- Range: 1.2 to 2.0, based on severity of digital exclusion.
- Applied selectively for subpopulations with known access barriers.
- Proprietary methodology; contact the author for collaboration or licensing.
When to Apply Dₐ:
- General Social Signal Synthesis Model for national estimate: ❌ No (Dₐ = 1.0)
- Rural Black patients with Long COVID: ✅ Yes (Dₐ = 1.3–2.0)
- Older adults with POTS or MCAS: ✅ Yes (Dₐ = 1.25–1.75)
- Urban Gen Z with mental health conditions: ❌ No (Dₐ = 1.0)
Key Notes
Dₐ Values: Adjust based on empirical data or studies specific to the population being modeled.
Ethical Considerations: Ensure transparency in how Dₐ is applied and communicate its purpose clearly to avoid misinterpretation.
Flexibility: Dₐ can be tailored further for specific conditions or regions as needed.
Visual 3: S³ Model Validation Comparison Table
Model | Estimated U.S. Long COVID Prevalence | Basis | Notes |
CDC (Official) | 17.8 million | Household Pulse Survey (2025) | Criticized for methodology flaws and low awareness in general population |
US-CCUC™ (Adinig) | 35–50 million | Adjusted for misdiagnosis, underreporting, racial & diagnostic bias | Accounts for systemic exclusion of BIPOC, women, and rural populations |
S³ Model™ (This Paper) | 35–50 million | Based on 500k Facebook members, 15% participation, 5–10x multiplier, 20–30% severity, C=1.5 | Validated against multiple conditions; matches US-CCUC™ range |
IHME Estimate (2023) | ~38 million | Global Burden of Disease modeling with variable inputs | Higher than CDC but lower than patient-led projections |
Patient-Led Research | 30–60 million (range) | Surveys, digital health tools, and community datasets | Early 2021–2022 surveys were cited by CDC and NIH before official systems were in place |
Clarifying S³ Model™ and Its Relationship to US-CCUC™
The S³ Model™ (Social Signal Synthesis Model) Model and the US-CCUC™ (U.S. Chronic Condition Undercount Correction) framework are designed to be complementary. While US-CCUC™ estimates systemic undercounts using epidemiologic data, clinical overlaps, and global benchmarks, S³ Model™ draws from community engagement rates on social media to estimate illness prevalence, especially in conditions with historically poor tracking. S³ Model™ is useful for near real-time insights and advocacy, while US-CCUC™ offers a broader longitudinal correction. Together, they create a powerful hybrid lens: one grounded in structural epidemiology, the other in community-based surveillance.
Future Applications & Global Expansion of the S³ Model™
The S³ Model™ (Social Signal Synthesis) was designed to be globally scalable, culturally adaptive, and especially effective in health systems where traditional surveillance data is limited, fragmented, or delayed. As digital communities continue to form around invisible conditions, the S³ Model™ offers a real-time, equity-centered approach to estimating illness burden across borders.
Target Countries for Deployment
We are actively exploring international partnerships and pilot projects in regions with high digital engagement and significant public health disparities:
India: Leveraging S³ to estimate underrecognized burdens of tuberculosis, diabetes, and mental health conditions, especially in rural and caste-marginalized communities.
South Africa: Applying the model to HIV/AIDS and TB through WhatsApp groups and local support networks.
Brazil: Adapting S³ to track Zika-related chronic complications and other emerging infections that are often underreported in favelas and Indigenous populations.
United Kingdom & EU: Supporting advocacy efforts for ME/CFS, Long COVID, and PANS/PANDAS with localized S³ modeling tailored to NHS regions and language variants.
Conditions to Prioritize Globally
The model is especially well-suited for:
Infectious Diseases: Tuberculosis, HIV/AIDS, Zika, Dengue — particularly where stigma or access barriers suppress formal diagnoses.
Chronic Conditions: Diabetes, hypertension, asthma — especially in countries with rising chronic disease burdens and fragmented public registries.
Undercounted and Misclassified Illnesses: ME/CFS, Long COVID, PANS/PANDAS — where diagnostic criteria remain disputed, and ICD coding is inconsistent or nonexistent.
Why S³ Works Internationally
Language-Agnostic Modeling: The algorithm tracks engagement patterns rather than relying solely on keyword matching, making it adaptable to multilingual communities.
Platform Flexibility: While Facebook is foundational, the model can be extended to WhatsApp, Telegram, Reddit, and regionally dominant platforms.
Community-Led Calibration: Each regional deployment includes feedback loops from local patient leaders to ensure cultural sensitivity and diagnostic relevance.
The goal is simple but transformative: to make invisible illnesses visible, everywhere.
As legislation and AI frameworks evolve globally, the S³ Model™ will adapt in step, ensuring ethical, patient-led data synthesis that reflects community realities rather than institutional assumptions.
Addressing Anticipated Critiques
On Facebook Reliance: While Facebook offers unparalleled scale for chronic illness community sampling, future versions of the model aim to incorporate data from additional platforms such as Reddit, TikTok, and Discord to improve representativeness across age and demographic lines.
On Multiplier & Correction Factor Subjectivity: The multipliers and correction factors used in S³ Model™ are derived from observed community behaviors, existing prevalence data, and comparative validation with known conditions (e.g., diabetes, depression). As validation studies expand and more external datasets are integrated, these parameters will continue to be refined and standardized.
The concern about subjectivity in the S³ Model™’s Community Undercount Factor is valid and expected. The reality is, any model that attempts to account for invisible populations will face limitations in traditional "objective" datasets, because the institutional data is incomplete by design. What sets the S³ Model™ apart is its acknowledgment of that gap and its use of community-derived signal synthesis to actively fill it.
The undercount multipliers are not arbitrary. They are grounded in:
Comparative validation against diseases with known surveillance baselines (like Type 2 Diabetes, Depression, Asthma)
Meta-analyses and literature on underdiagnosis across racial, gender, and geographic lines (e.g., Taylor & Francis, 2022; NIH, 2024)
Engagement pattern analysis from Facebook groups, including participation decay, re-post rate, comment density, and group age
Survey-informed digital access adjustments (Dₐ) which mathematically rebalance representation
Is it subjective? Partially, just like all epidemiology when confronting incomplete or skewed data. The CDC and IHME often rely on expert panels and modeling assumptions too. The difference is: we show our work.
Rather than pretending these gaps don’t exist, the S³ Model™ builds equity-conscious modeling in plain sight, adjusting transparently, revising as better inputs become available, and welcoming feedback from both researchers and patient communities.
So, while it’s true that the Community Undercount Factor includes informed judgment, it is anchored in comparative evidence, calibrated against known conditions, and continuously refined, which makes it more honest and dynamic than many black-box institutional models.
Ethical and Structural Implications
To honor the labor and lived experiences of patient communities, especially BIPOC, disabled, and undercounted populations, the following ethical guidelines are recommended for applying the Social Signal Synthesis Model and similar tools:
Credit Communities, Not Just Institutions Always acknowledge the groups, forums, and digital spaces that made this modeling possible. When citing sources, name community groups, not just formal organizations or institutions.
Prioritize Transparency and Fair Use Any public application of the model should clearly explain its data sources, assumptions, and limitations. Tools like the S³ Model™ calculator, when created, should remain accessible to both institutions and community advocates.
Ensure Outcomes Benefit the Communities Behind the Data Do not use insights drawn from marginalized groups to justify policies or research that exclude or harm them. If their data informs the model, so should their needs guide the response.
Invite Ongoing Community Feedback Patient advocates, especially those from underserved communities, should be given channels to review and provide feedback on the model's use. Responsiveness strengthens both scientific integrity and community trust.
These principles are intended to evolve, with room to adjust as legislation and ethical guidance around social media, data privacy, and AI use catch up to the realities of digital health research.
Recommendations
Federal Adoption: NIH and CDC should adopt S³ Model™-style models for emerging illness surveillance.
Research Support: Journals should publish patient-led methods like S³ Model™ to legitimize community data.
Public Access: An open-source S³ Model™ calculator or dashboard could democratize health data for patient groups.
Conclusion
The S³ Model™ (Social Signal Synthesis Model) is a tool of liberation, built by patients for patients, to measure what the medical system overlooks. Validated across prevalent conditions and applied to emerging illnesses like Long COVID, it proves patient communities are public health sentinels, ready to redefine disease burden estimation. S³ Model™ is trademarked by Cynthia Adinig but freely available for non-commercial research, advocacy, and public use with attribution to this paper.
Next Steps
The Social Signal Synthesis Model offers a promising path toward more accurate, equitable public health surveillance. To ensure its continued impact and integrity, the following steps are recommended:
1. Expand Further Validation Across Conditions Apply the model to additional diseases, particularly those with diagnostic ambiguity or historical undercounts (e.g., ME/CFS, MCAS, EDS, and pediatric neuroimmune conditions). This will help assess S³ Model™’s adaptability and predictive accuracy across the chronic illness landscape.
2. Develop Targeted Equity Modules Integrate equity-centered modifiers, such as the Digital Access Adjustment Factor (Dₐ)—into modeling for specific populations. This includes rural, low-income, aging, and BIPOC groups that remain underrepresented in digital datasets and federal surveillance.
3. Launch Open-Source Tools for Researchers Create a user-friendly dashboard or calculator version of the Social Signal Synthesis Model for non-technical stakeholders. Open access to core components (excluding proprietary modifiers) will empower community advocates, academics, and policymakers to apply the model in real time.
4. Explore Global Applications Pilot the S³ Model™ framework in international contexts with varied digital landscapes, beginning with countries that have Long COVID prevalence benchmarks (e.g., Spain, UK). This will inform its scalability and cultural adaptability.
About the Author
Cynthia Adinig is a nationally recognized health equity researcher, federal policy advisor, and veteran marketing strategist whose digital campaigns have reached millions, often on budgets as low as $20. A Facebook early adopter since 2003, Cynthia has over two decades of experience decoding platform trends, crafting culturally resonant messaging, and scaling visibility for campaigns across politics, healthcare, and the music industry.
She is known for running lean, high-impact strategies that dramatically reduce ad spend while maximizing engagement and reach. In the live entertainment sector, Cynthia led digital strategy and audience development for venues like Bethesda Theater (formerly known as Bethesda Blues and Jazz), which regularly hosted Grammy-winning performers. Her role involved everything from demographic targeting for event booking to streamlining customer service workflows, all with the precision of someone who knows how to stretch every pixel of value out of a post.
Cynthia's analytical strength lies in making sure the math is mathing. She pairs sharp branding instincts with rigorous data modeling to uncover what traditional institutions miss. As the creator of the US-CCUC™ (U.S. Chronic Condition Undercount Correction) and the S³ (Social Signal Synthesis Model), Cynthia uses social data as a legitimate epidemiological tool to surface the true scope of underdiagnosed and misclassified illnesses like PANS, ME/CFS, and Long COVID. She also developed the Racially Adjusted Excess Mortality Index (RAEMI™), a proprietary framework designed to correct racial underreporting in mortality estimates.
Her work bridges the worlds of public health and digital strategy, turning overlooked signals into irrefutable metrics. At the center of it all is a commitment to truth-telling, whether that means correcting flawed federal estimates or helping communities see themselves clearly in the data.
Appendix A: Expanded Validation of the Social Signal Synthesis Model
A1. UK PANS/PANDAS Case Study
Facebook Group: Largest UK PANS/PANDAS group (~7,000 members)
Estimated parent participants: ~90% = 6,300
UK child population: ~14 million
Crude Facebook Prevalence: 6,300 / 14M ≈ 1 in 2,222
Applied Community Multiplier (10x): → 6,300 × 10 = 63,000 estimated cases → Adjusted prevalence = 1 in 222 children
Comparison to UK Literature Estimates: Official estimates are ~1 in 5,000 to 1 in 10,000. → S³ Model™ Estimate is 20x–40x higher, suggesting significant undercounting in formal tracking systems.
Key Insight: This analysis confirms the S³ Model™ sensitivity to hidden case loads in underdiagnosed conditions, even outside the U.S. making it a strong candidate for global public health adaptation.
Appendix A2: Expanded Condition Validations (Living Document Link)
While this paper includes five benchmark validations (Type 2 Diabetes, Depression, Rheumatoid Arthritis, Hypertension, and Asthma) to demonstrate the adaptability and precision of the S³ Model™, further condition-specific applications are ongoing.
To maintain clarity in the main body while offering transparency and reproducibility, a companion validation document has been created to log additional tests. This includes:
Rare and ultra-rare diseases
Pediatric-onset neuroimmune and neurodevelopmental conditions
Conditions disproportionately affecting BIPOC and underrepresented communities
Additional country-level comparisons (Canada, Australia, South Africa, etc.)
Access Extended Validations Here:S³ Model™ Validation Report:
This archive is updated occasionally and will include methodology breakdowns, input rationales, error bars, and source citations for each new entry.
Licensing & Usage — Creative Commons Style
S³ Model™ by Cynthia Adinig is available for use under the following conditions:
Permitted Uses (Non-Commercial):
Academic research
Advocacy campaigns
Nonprofit public health efforts
Patient education materials
Government policy memos (non-commercial use only)
Restrictions:
Commercial use (including software integration, consulting, or funded institutional work) is not allowed without written permission.
Modification or derivative models must retain credit and cannot be used to create commercial tools without a license.
Do not remove attribution.
Attribution Requirement:
You must clearly credit the creator in any use of this model. Use this preferred attribution:
“Based on the S³ Model™ developed by Cynthia Adinig (2025).”
For publication, use the formal citation formats provided (APA, MLA, Chicago).
Trademark Use:
S³ Model™ is a trademarked term. You may refer to the model for attribution and descriptive purposes but may not use the name commercially or in branding without explicit licensing.
For media, government, or commercial use, email me at cynthiaadinig@gmail.com.
To dive deeper into the political and structural roots of undercounted conditions and research inequity, explore my book, DEI Delusion: The Hidden Impact of Research in BIPOC Communities. It unpacks how systems fail patients, and what we can do to change that.
Grab DEI Delusion: The Hidden Impact of Research on Amazon or read a free preview exploring Long COVID, IACCs, and medical inequities.
Sources
Abbasi, J. “The COVID Heart—One Year Later.” JAMA, vol. 325, no. 9, 2021, pp. 831–833. https://doi.org/10.1001/jama.2021.1432.
Arthritis Foundation. Rheumatoid Arthritis Prevalence and Impact. 2025.
Brookings Institution. The Economic Impact of Long COVID in the U.S. 2022.
Callard, Felicity, and Elisa Perego. “How and Why Patients Made Long COVID.” Social Science & Medicine, vol. 268, 2021, 113426. https://doi.org/10.1016/j.socscimed.2020.113426.
Centers for Disease Control and Prevention (CDC). National Diabetes Statistics Report. 2025.
Centers for Disease Control and Prevention (CDC). Hypertension in the U.S. 2025.
Centers for Disease Control and Prevention (CDC). Asthma Prevalence and Trends. 2025.
European Centre for Disease Prevention and Control (ECDC). Emerging Infections and Chronic Conditions in the EU. 2023.
Hart, Orhan E., and Rolf U. Halden. “Computational Analysis of SARS-CoV-2/COVID-19 Surveillance by Wastewater-Based Epidemiology.” Science of the Total Environment, vol. 743, 2020, 140764. https://doi.org/10.1016/j.scitotenv.2020.140764.
Institute of Medicine (IOM). Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness. National Academies Press, 2015.
International Diabetes Federation (IDF). Diabetes in South Asia: Prevalence and Trends. 2023.
Long COVID Alliance. Community Engagement in Online Health Communities. 2024.
National Health Service (NHS). Long COVID and ME/CFS in the UK. 2023.
National Institute of Mental Health (NIMH). Major Depression Prevalence in the U.S. 2025.
National Institutes of Health (NIH). Severity of Long COVID: Clinical Data Analysis. 2024.
Pan American Health Organization (PAHO). Zika Virus and Chronic Complications in Brazil. 2023.
Patient-Led Research Collaborative. The Role of Patient-Led Data in Public Health. 2023.
SolveME/CFS Initiative. Long COVID Prevalence and Patient Impact. 2023.
Taylor & Francis. Underdiagnosis in Chronic Illness: A Systematic Review. 2022.
UNAIDS. HIV/AIDS in South Africa: Epidemiology and Response. 2023.
World Health Organization (WHO). Tuberculosis in India: A Public Health Crisis. 2023.
World Health Organization (WHO). Tuberculosis in South Africa: Challenges and Opportunities. 2023.
World Medical Association (WMA). Ethical Principles for Health Research Involving Digital Data. 2023.
Yong, Ed. “Long COVID: The Next Wave of the Pandemic.” The Atlantic, Jan. 2021. https://www.theatlantic.com/health/archive/2021/01/long-covid-next-wave-pandemic/617665/.
Facebook Groups Used in S³ Model™ Validations
Long COVID
COVID-19 Longhauler Advocacy Project
Description: A support group for individuals experiencing long-term symptoms after COVID-19 infection.
Long Haul COVID Fighters
Description: A community for long COVID patients to share experiences and resources.
Type 2 Diabetes
Diabetes Support Group
Description: A general support group for individuals living with diabetes.
Type 2 Diabetes Support Group
Description: A group specifically for individuals managing type 2 diabetes.
Depression
Depression Support Group
Description: A community for individuals dealing with depression to share experiences and coping strategies.
Mental Health Awareness & Support
Description: A group focused on raising awareness and providing support for mental health issues.
Rheumatoid Arthritis
Rheumatoid Arthritis Support Group
Description: A support group for individuals living with rheumatoid arthritis.
Hypertension
High Blood Pressure Support Group
Description: A community for individuals managing high blood pressure.
Asthma
Asthma Support Group
Description: A support group for individuals living with asthma.
MyAsthmaTeam
Description: A community for asthma patients to connect and share resources.
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