Cohort profile: the Johns Hopkins COVID Long Study (JHCLS), a United States Nationwide Prospective Cohort Study
Wentz, E.; Ni, Z.; Yenokyan, K.; Vergara, C.; Pahwa, J.; Kammerling, T.; Xiao, P.; Duggal, P.; Lau, B.; Mehta, S. H.
Show abstract
PurposeCOVID-19 disease continues to affect millions of individuals worldwide, both in the short and long term. The post-acute complications of SARS-CoV-2 infection, referred to as long COVID, result in diverse symptoms affecting multiple organ systems. Little is known regarding how the symptoms associated with long COVID progress and resolve over time. The Johns Hopkins COVID Long Study aims to prospectively examine the short- and long-term consequences of COVID-19 disease in individuals both with and without a history of SARS-CoV-2 infection using self-reported data collected in an online survey. ParticipantsSixteen thousand, seven hundred sixty-four adults with a history of SARS-CoV-2 infection and 799 adults without a history of SARS-CoV-2 infection who completed an online baseline survey. Findings to dateThis cohort profile describes the baseline characteristics of the Johns Hopkins COVID Long Study. Among 16,764 participants with a history of SARS-CoV-2 infection and defined long COVID status, 75% reported a good or excellent health status prior to infection, 99% reported experiencing at least one COVID-19 symptom during the acute phase of infection, 9.9% reported a hospitalization, and 63% were defined as having long COVID using the WHO definition. Future plansAnalysis of longitudinal data will be used to investigate the progression and resolution of long COVID symptoms over time.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Cohort Profile: A national prospective cohort study of SARS-CoV-2 pandemic outcomes in the U.S. - The CHASING COVID Cohort Study 97%
- The Basel Long COVID Cohort Study (BALCoS): protocol of a prospective cohort study 96%
- Prevalence and determinants of persistent symptoms after infection with SARS-CoV-2: Protocol for an observational cohort study (LongCOVID-study) 95%
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 97%
- The association between prolonged SARS-CoV-2 symptoms and work outcomes 95%
- The Role of Testing Availability on Intentions to Isolate during the COVID-19 Pandemic: A Randomized Trial 95%
Similar papers in this journal
- Population-Based Sero-Epidemiological Study Protocol For The Impact Of Smoking On SARS-COV-2 Infection And COVID-19 Outcomes – The Troina Study 94%
- Exposure to and engagement with digital psychoeducational content and community related to maternal mental health by perinatal persons and mothers: design of an online survey with optional follow-up and participant characteristics 94%
- A protocol for a pilot randomized controlled trial of guided self-help Behavioral Activation intervention for geriatric depression 92%
Similar papers in this journal
- How is the COVID-19 pandemic impacting our life, mental health, and well-being? Design and preliminary findings of the pan-Canadian longitudinal COHESION Study 94%
- Factors Associated with COVID-19 Testing among People who Inject Drugs: Missed Opportunities for Reaching those Most at Risk 94%
- Cumulative Social Disadvantage and Health-Related Quality of Life: National Health Interview Survey 2013-2017 93%
Similar papers in this journal
- Factors Associated with Long Covid Symptoms in an Online Cohort Study 95%
- 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 95%
- Demographic disparities in clinical outcomes of COVID-19: data from a statewide cohort in South Carolina 94%
"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.