Tag: NIH

Long COVID Disability Burden in US Adults: YLDs and NIH Funding Relative to Other Conditions

Abstract:

Background Long COVID (LC) is novel, debilitating and likely chronic. Yet, scant data exist about its disability burden to guide scientific research and public health planning. We estimated Long COVID’s non-fatal disease burden in US adults and its FY2024 actual: burden-commensurate research funding from the National Institutes of Health (NIH) relative to other conditions, and biological sex.

Methods We present YLDs/100,000 for 70 NIH Research, Condition, and Disease Categories (RCDCs). Prevalence of disabling Long COVID was obtained from cross sectional surveys of representative samples of US adults, from September 2022 to August 2023. Disabling Long COVID was defined as incident symptoms persisting more than 3 months post-COVID, that significantly compromise daily activities. We calculated burden-commensurate funding for the top YLD conditions and for female vs. male dominant conditions.

Findings Disabling Long COVID was reported by 1.5% (n= 10,401) of n=757,580 respondents: Compared to the overall sample, those with disabling LC disproportionately identify as female (64.4% vs. 51.4%) and experiencing disability (80.8% vs. 52.9%) anxiety (57.5% vs. 23.8%) and depression (51.3% vs.18.5%). It ranked in the top 25% of YLDs at 320/100,000, between Alzheimer’s (279.4/100,000) and asthma (355.7/100,000) but received just 10% of its actual: YLD-commensurate funding. Only 5 conditions received less actual: burden: commensurate funding, including Myalgic Encephalitis/Chronic Fatigue Syndrome (<1%), another post-viral, female-dominant condition.

Interpretation LC has debilitated 3.8 million (weighted frequency) US adults. Research funding for it, like other female dominant conditions, lags behind its disability burden.

Research in Context Evidence before this study – We analyzed Long-COVID’s (LC) non-fatal disease burden in the US–represented by YLD (years lived with disability= prevalence x disability weight) — and National Institutes of Health (NIH) research 2024 funding relative to other conditions. We searched PubMed through 11/28/2023 for Long COVID prevalence (US), and Long COVID disability and disease burden (not US-specific). The keywords “years lived with disability” + “COVID” yielded n= 38 articles (11/29/23); but most referenced “disability-adjusted life years” (DALYs) in other countries. Similarly, “disease burden” + Long COVID yielded 23 papers, but no US YLD data. See Supplement 1 for meta-analyses, systematic reviews and US studies of Long COVID prevalence and impact.

We instead sourced YLD data from the US Census Bureau’s Household Pulse Survey (HPS) and the Institute for Health Metrics and Evaluation (IHME) /Global Burden of Disease (GBD) Long COVID Study Group. The HPS queries adults about Long COVID-related symptoms and their impact on daily activities. We applied the IHME/GBD’s estimated Long COVID disability weight of 0.21 and harmonized it with our LC case definition from the HPS data in consultation with IHME/GBD researchers. To harmonize IHME/GBD disability weights for non-LC diseases/conditions with the NIH’s terminology, we consulted with NIH staff. LC definition and measurement affects prevalence and burden estimates; our use of high-quality data sources and transparency in reporting how they were applied reduces the risk of biased assumptions.

Added value of this study- Long COVID is a chronic debilitating condition. While there is ample research on COVID’s acute illness and loss of life, there are no population-based data on its disability burden. We provide that data. To guide scientific research and public health planning, we report YLDs associated with disabling Long COVID (i.e., symptoms significantly limit activity), and; compare it to other conditions’ YLDs, NIH funding, and female-vs. male-dominance. It ranked in the top 25% of YLDs at 320/100,000, between Alzheimer’s (279.4/100,000) and asthma (355.7/100,000) but received just 10% of its YLD-commensurate funding. Only 5 conditions received less burden-commensurate funding; 3/5 were female-dominant, including Myalgic Encephalitis/Chronic Fatigue Syndrome (ME/CFS) at <1%, another post-viral condition that shares significant overlap with Long COVID. Overall, median funding/YLD was >= 5 times greater for male-vs. female-dominant conditions.

Implications of all the available evidence-Nearly 4 million US adults (weighted frequency) live with disabling Long COVID. They disproportionately identify as female and as having a disability, anxiety and depression. Yet NIH funding for diagnostic and treatment research for Long COVID hasn’t kept pace with its disability burden.

Source: Karen Bonuck, Qi Gao, Seth Congdon, Ryung Kim. Long COVID Disability Burden in US Adults: YLDs and NIH Funding Relative to Other Conditions. medRxiv 2024.01.09.24301057; doi: https://doi.org/10.1101/2024.01.09.24301057 https://www.medrxiv.org/content/10.1101/2024.01.09.24301057v1.full-text (Full text)

The importance of patient-partnered research in addressing long COVID: Takeaways for biomedical research study design from the RECOVER Initiative’s Mechanistic Pathways taskforce

Abstract:

The NIH-funded RECOVER study is collecting clinical data on patients who experience a SARS-CoV-2 infection. As patient representatives of the RECOVER Initiative’s Mechanistic Pathways task force, we offer our perspectives on patient motivations for partnering with researchers to obtain results from mechanistic studies. We emphasize the challenges of balancing urgency with scientific rigor. We recognize the importance of such partnerships in addressing post-acute sequelae of SARS-CoV-2 infection (PASC), which includes ‘long COVID,’ through contrasting objective and subjective narratives.

Long COVID’s prevalence served as a call to action for patients like us to become actively involved in efforts to understand our condition. Patient-centered and patient-partnered research informs the balance between urgency and robust mechanistic research. Results from collaborating on protocol design, diverse patient inclusion, and awareness of community concerns establish a new precedent in biomedical research study design. With a public health matter as pressing as the long-term complications that can emerge after SARS-CoV-2 infection, considerate and equitable stakeholder involvement is essential to guiding seminal research. Discussions in the RECOVER Mechanistic Pathways task force gave rise to this commentary as well as other review articles on the current scientific understanding of PASC mechanisms.

Source: Kim C, Chen B, Mohandas S, Rehman J, Sherif ZA, Coombs K; RECOVER Mechanistic Pathways Task Force; RECOVER Initiative. The importance of patient-partnered research in addressing long COVID: Takeaways for biomedical research study design from the RECOVER Initiative’s Mechanistic Pathways taskforce. Elife. 2023 Sep 22;12:e86043. doi: 10.7554/eLife.86043. PMID: 37737716; PMCID: PMC10516599. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516599/ (Full text)

De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository

Abstract:

Machine learning (ML)-driven computable phenotypes are among the most challenging to share and reproduce. Despite this difficulty, the urgent public health considerations around Long COVID make it especially important to ensure the rigor and reproducibility of Long COVID phenotyping algorithms such that they can be made available to a broad audience of researchers. As part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, researchers with the National COVID Cohort Collaborative (N3C) devised and trained an ML-based phenotype to identify patients highly probable to have Long COVID. Supported by RECOVER, N3C and NIH’s All of Us study partnered to reproduce the output of N3C’s trained model in the All of Us data enclave, demonstrating model extensibility in multiple environments. This case study in ML-based phenotype reuse illustrates how open-source software best practices and cross-site collaboration can de-black-box phenotyping algorithms, prevent unnecessary rework, and promote open science in informatics.

Source: Pfaff ER, Girvin AT, Crosskey M, Gangireddy S, Master H, Wei WQ, Kerchberger VE, Weiner M, Harris PA, Basford M, Lunt C, Chute CG, Moffitt RA, Haendel M; N3C and RECOVER Consortia. De-black-boxing health AI: demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository. J Am Med Inform Assoc. 2023 May 22:ocad077. doi: 10.1093/jamia/ocad077. Epub ahead of print. PMID: 37218289. https://pubmed.ncbi.nlm.nih.gov/37218289/

A Mixed Methods System for the Assessment of Post Exertional Malaise in Encephalomyelitis/Chronic Fatigue Syndrome

Abstract:

Background A central feature of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is post exertional malaise (PEM), which is an acute worsening of symptoms after a physical, emotional and/or mental exertion. PEM is also a feature of Long COVID. Dynamic measures of PEM have historically included scaled questionnaires which have not been validated in ME/CFS. To enhance our understanding of PEM and how best to measure it, we conducted semi-structured qualitative interviews (QIs) at the same intervals as Visual Analog Scale (VAS) measures after a Cardiopulmonary Exercise Test (CPET).

Methods Ten ME/CFS and nine healthy volunteers participated in a CPET. For each participant, PEM symptom VAS (7 symptoms) and semi-structured QIs were administered at six timepoints over 72 hours before and after a single CPET. QI data were used to plot the severity of PEM at each time point and identify the self-described most bothersome symptom for each patient. QI data were used to determine the symptom trajectory and peak of PEM. Performance of QI and VAS data were compared to each other using Spearman correlations.

Results QIs documented that each ME/CFS volunteer had a unique PEM experience, with differences noted in the onset, severity, trajectory over time, and most bothersome symptom. No healthy volunteers experienced PEM. Scaled QI data were able to identify PEM peaks and trajectories, even when VAS scales were unable to do so due to known ceiling and floor effects. QI and VAS fatigue data corresponded well prior to exercise (baseline, r=0.7) but poorly at peak PEM (r=0.28) and with the change from baseline to peak (r=0.20). When the most bothersome symptom identified from QIs was used, these correlations improved (r=.0.77, 0.42. and 0.54 respectively) and reduced the observed VAS scale ceiling and floor effects.

Conclusion QIs were able to capture changes in PEM severity and symptom quality over time in all the ME/CFS volunteers, even when VAS scales failed to do so. Information collected from QIs also improved the performance of VAS. Measurement of PEM can be improved by using a quantitative-qualitative mixed model approach.

Disclaimer This research/work/investigator was supported (in part) by the Division of Intramural Research of the National Institutes of Health, NINDS. The content is solely the responsibility of the author(s) and does not necessarily represent the official views of the National Institutes of Health.

Source: Barbara Stussman, Brice Calco, Gina Norato, Angelique Gavin, Snigdha Chigurupati, Avindra Nath, Brian Walitt. A Mixed Methods System for the Assessment of Post Exertional Malaise in Encephalomyelitis/Chronic Fatigue Syndrome. medRxiv 2023.04.24.23288821;  doi: https://doi.org/10.1101/2023.04.24.23288821 https://www.medrxiv.org/content/10.1101/2023.04.24.23288821v1.full-text (Full text)

Transcript: NIH ME/CFS Advocacy Call – March 28, 2022

Transcript:

Ms. Barbara McMakin: Good afternoon everyone and thank you for standing by. My name is Barbara McMakin and I’m from the NINDS Office of Neurosciece Communications and Engagement. On behalf of the NIH, I would like to welcome you to this afternoon’s call and to thank you for your interest in participating in this discussion with us today.

Today’s call is being recorded. If you have any objections please disconnect at this time. Dr. Vicky Whittemore, Program Director at NINDS, will introduce the speakers, each of whom will make some remarks, after which we will answer your questions. If you have a question for our speakers, we invite you to submit it through the Q and A box at the bottom of the Zoom screen. We will try to make our remarks brief so that we can answer as many questions as possible in the time available to us this afternoon.

I also wanted to mention that we are exploring different formats for these telebriefings going forward. For our next telebriefing we plan to include live oral questions during the question and answer session. That telebriefing has not yet been scheduled, but once we have those details we will send out a message to the listserv and post the call information on the ME/CFS website. Now, I would like to hand the call over to Dr. Whittemore.

Read the rest of this transcript HERE.

Bridging Knowledge Gaps in the Diagnosis and Management of Neuropsychiatric Sequelae of COVID-19

Abstract:

Importance: Neuropsychiatric symptoms have been reported as a prominent feature of postacute sequelae of COVID-19 (PASC), with common symptoms that include cognitive impairment, sleep difficulties, depression, posttraumatic stress, and substance use disorders. A primary challenge of parsing PASC epidemiology and pathophysiology is the lack of a standard definition of the syndrome, and little is known regarding mechanisms of neuropsychiatric PASC.

Observations: Rates of symptom prevalence vary, but at least 1 PASC neuropsychiatric symptom has been reported in as many as 90% of patients 6 months after COVID-19 hospitalization and in approximately 25% of nonhospitalized adults with COVID-19. Mechanisms of neuropsychiatric sequelae of COVID-19 are still being elucidated. They may include static brain injury accrued during acute COVID-19, neurodegeneration triggered by secondary effects of acute COVID-19, autoimmune mechanisms with chronic inflammation, viral persistence in tissue reservoirs, or reactivation of other latent viruses. Despite rapidly emerging data, many gaps in knowledge persist related to the variable definitions of PASC, lack of standardized phenotyping or biomarkers, variability in virus genotypes, ascertainment biases, and limited accounting for social determinants of health and pandemic-related stressors.

Conclusions and relevance: Growing data support a high prevalence of PASC neuropsychiatric symptoms, but the current literature is heterogeneous with variable assessments of critical epidemiological factors. By enrolling large patient samples and conducting state-of-the-art assessments, the Researching COVID to Enhance Recovery (RECOVER), a multicenter research initiative funded by the National Institutes of Health, will help clarify PASC epidemiology, pathophysiology, and mechanisms of injury, as well as identify targets for therapeutic intervention.

Source: Frontera JA, Simon NM. Bridging Knowledge Gaps in the Diagnosis and Management of Neuropsychiatric Sequelae of COVID-19. JAMA Psychiatry. 2022 Jun 29. doi: 10.1001/jamapsychiatry.2022.1616. Epub ahead of print. PMID: 35767287.  https://jamanetwork.com/journals/jamapsychiatry/fullarticle/2793903 (Full text)

Research Update: The Relation Between ME/CFS Disease Burden and Research Funding in the USA

Abstract:

Background: Myalgic Encephalomyelitis / Chronic Fatigue Syndrome (ME/CFS) is a debilitating, chronic, multisystem disease that affects an estimated 1 to 2.5 million Americans. It has no widely accepted biomarkers and no FDA-approved treatment. ME/CFS has traditionally been one of the lowest funded diseases by the United States National Institutes of Health (NIH).

Objectives: We provide here an update to our 2016 article, which estimated the disease burden of ME/CFS in the United States in 2013 and its relation to NIH’s 2015 analysis of research funding and disease burden. This update incorporates more recent burden data from 2015 and funding data from 2017.

Methods: We perform a regression analysis on funding versus disease burden to determine 2017 funding levels that would be commensurate with burden. Burden figures for 2017 are estimated using population-based extrapolations of earlier data.

Results: We find the disease burden of ME/CFS is double that of HIV/AIDS and over half that of breast cancer. We also find that ME/CFS is more underfunded with respect to burden than any disease in NIH’s analysis of funding and disease burden, with ME/CFS receiving roughly 7% of that commensurate with disease burden.

Conclusions: To be commensurate with disease burden, NIH funding would need to increase roughly 14-fold.

Source: Mirin AA, Dimmock ME, Jason LA. Research update: The relation between ME/CFS disease burden and research funding in the USA [published online ahead of print, 2020 Jun 16]. Work. 2020;10.3233/WOR-203173. doi:10.3233/WOR-203173 https://pubmed.ncbi.nlm.nih.gov/32568148/

NIH announces centers for myalgic encephalomyelitis/chronic fatigue syndrome research

Press Release: NIH, September 27, 2017. The National Institutes of Health (NIH) will award four grants to establish a coordinated scientific research effort on myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The total cost of the projects for fiscal year 2017 will be over $7 million, with support from multiple NIH Institutes and Centers that are part of the Trans-NIH ME/CFS Working Group.

The grants will support the creation of a consortium made up of three Collaborative Research Centers (CRC) and a Data Management Coordinating Center (DMCC). The CRCs will each conduct independent research but will also collaborate on several projects, forming a network to help advance knowledge on ME/CFS. The data will be managed by the DMCC and will be shared among researchers within the CRCs and more broadly with the research community.

“These important grants will provide a strong foundation for expanding research in ME/CFS, and lead to knowledge about the causes and ways to treat people affected by this mysterious, heartbreaking, and debilitating disease,” said NIH Director Francis S. Collins, M.D., Ph.D.

ME/CFS, which affects more than 1 million Americans, is characterized by profound fatigue that does not improve with rest, and may include problems with thinking and memory, pain and a range of other symptoms that negatively impact everyday life. A key feature of the disease is post-exertional malaise, which is a worsening of symptoms following mental or physical activity. The disease can last for years or decades, with those most severely impacted ending up house- or bed-bound. It is unknown what causes the disease and there are no proven treatments.

“These grants will use innovative technologies and research methods to unravel this devastating disease, which we know so little about,” said Walter Koroshetz, M.D., director of NIH’s National Institute of Neurological Disorders and Stroke (NINDS) and chair of the Trans-NIH ME/CFS Working Group.

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NIH conference. Chronic fatigue syndrome research. Definition and medical outcome assessment

Abstract:

A workshop was held 18 to 19 March 1991 at the National Institutes of Health to address critical issues in research concerning the chronic fatigue syndrome (CFS). Case definition, confounding diagnoses, and medical outcome assessment by laboratory and other means were considered from the perspectives of key medical specialties involved in CFS research.

It was recommended that published Centers for Disease Control (CDC) case-definition criteria be modified to exclude fewer patients from analysis because of a history of psychiatric disorder. Specific recommendations were made concerning the inclusion or exclusion of other major confounding diagnoses, and a standard panel of laboratory tests was specified for initial patient evaluation.

The workshop emphasized the importance of recognizing other conditions that could explain the patient’s symptoms and that may be treatable. It was viewed as essential for the investigator to screen for psychiatric disorder using a combination of self-report instruments followed by at least one structured interview to identify patients who should be excluded from studies or considered as a separate subgroup in data analysis.

Because CFS is not a homogeneous abnormality and because there is no single pathogenic mechanism, research progress may depend upon delineation of these and other patient subgroups for separate data analysis. Despite preliminary data, no physical finding or laboratory test was deemed confirmatory of the diagnosis of CFS.

For assessment of clinical status, investigators must rely on the use of standardized instruments for patient self-reporting of fatigue, mood disturbance, functional status, sleep disorder, global well-being, and pain. Further research is needed to develop better instruments for quantifying these domains in patients with CFS.

 

Source: Schluederberg A, Straus SE, Peterson P, Blumenthal S, Komaroff AL, Spring SB, Landay A, Buchwald D. NIH conference. Chronic fatigue syndrome research. Definition and medical outcome assessment. Ann Intern Med. 1992 Aug 15;117(4):325-31. http://www.ncbi.nlm.nih.gov/pubmed/1322076