Underdiagnosis of myalgic encephalomyelitis/chronic fatigue syndrome-like illness in a large integrated healthcare system -- Kaiser Permanente Northern California, 2022-2023
Wood, M.; Halmer, N.; Bertolli, J.; Amsden, L. B.; Nugent, J. R.; Lin, J.-M. S.; Rothrock, G.; Nadle, J.; Chai, S. J.; Champsi, J. H.; Yang, J.; Unger, E. R.; Skarbinski, J.
Show abstract
BackgroundSurveillance of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), a chronic, debilitating multisystem illness, is challenging because ME/CFS can be under-recognized in healthcare settings. MethodsUsing a population-based panel study of 9,820 adult members of Kaiser Permanente Northern California (KPNC), a large, integrated healthcare system, we compared survey-defined ME/CFS-like illness with presence of an ME/CFS diagnosis in the electronic health record (EHR) to evaluate ME/CFS underdiagnosis. ResultsOf those with survey-defined ME/CFS-like illness, an estimated 97.8% (95% confidence interval [CI] 97.1%-98.4%) did not have an ME/CFS diagnosis in the EHR. The group without EHR diagnosis was younger, less likely to identify as white non-Hispanic, and more likely to have developed fatigue in the past three years than the EHR diagnosed group. Both diagnosed and undiagnosed ME/CFS-like illness groups had significantly impaired physical, cognitive, and social functioning, and significantly worse mental health and anxiety than those without ME/CFS-like illness. ConclusionME/CFS is underdiagnosed in the Kaiser Permanente Northern California healthcare system. Enhanced syndromic surveillance that characterizes patients with ME/CFS who have not been diagnosed has the potential to increase timely recognition of ME/CFS.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- High proportion of post-acute sequelae of SARS-CoV-2 infection in individuals 1-6 months after illness and association with disease severity in an outpatient telemedicine population 93%
- Post-acute sequelae of SARS-CoV-2 (PASC) impact quality of life at 6, 12 and 18 months post-infection 93%
- Clinical symptoms among ambulatory patients tested for SARS-CoV-2 92%
Similar papers in this journal
- Study protocol for the Innovative Support for Patients with SARS-COV-2 Infections Registry (INSPIRE): a longitudinal study of the medium and long-term sequelae of SARS-CoV-2 infection 94%
- Incidence of Lyme disease in the United Kingdom and association with fatigue: a population-based, historical cohort study 93%
- The association between prolonged SARS-CoV-2 symptoms and work outcomes 92%
Similar papers in this journal
- The Validity of the Parsley Symptom Index: an e-PROM designed for Telehealth 88%
- Development of the NeuroFlow Severity Score and Comparison With Validated Measures for Depression and Anxiety 88%
- Performance of a Computational Phenotyping Algorithm for Sarcoidosis Using Diagnostic Codes in Electronic Medical Records: A Pilot Study from Two Veterans Affairs Medical Centers 87%
Similar papers in this journal
- Population-based estimates of post-acute sequelae of SARS-CoV-2 infection (PASC) prevalence and characteristics: A cross-sectional study 92%
- Risk Factors For Infection And Health Impacts Of The COVID-19 Pandemic In People With Autoimmune Diseases 92%
- Development and validation of the long covid symptom and impact tools, a set of patient-reported instruments constructed from patients’ lived experience 90%
Similar papers in this journal
- A patient-centered view of symptoms, functional impact, and priorities in post-COVID-19 syndrome: Cross-sectional results from the Québec Action Post-COVID cohort 93%
- Association between SARS-CoV-2 Infection and Select Symptoms and Conditions 31 to 150 Days After Testing among Children and Adults 93%
- Efficacy of Nirmatrelvir/ritonavir in reducing the risk of severe outcome in patients with SARS-CoV-2 infection: a real-life full-matched case-control study (SAVALO Study) 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.