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Background: Few prospective studies of Long COVID risk factors have been conducted. The purpose of this study was to determine whether sociodemographic factors, lifestyle, or medical history preceding COVID-19 or characteristics of acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are associated with Long COVID. Methods: In March 26, 2020, the COVID-19 Citizen Science study, an online cohort study, began enrolling participants with longitudinal assessment of symptoms before, during, and after SARS-CoV-2 infection. Adult participants who reported a positive SARS-CoV-2 test result before April 4, 2022 were surveyed for Long COVID symptoms. The primary outcome was at least 1 prevalent Long COVID symptom greater than 1 month after acute infection. Exposures of interest included age, sex, race/ethnicity, education, employment, socioeconomic status/financial insecurity, self-reported medical history, vaccination status, variant wave, number of acute symptoms, pre-COVID depression, anxiety, alcohol and drug use, sleep, and exercise. Results: Of 13 305 participants who reported a SARS-CoV-2 positive test, 1480 (11.1%) responded. Respondents' mean age was 53 and 1017 (69%) were female. Four hundred seventy-six (32.2%) participants reported Long COVID symptoms at a median 360 days after infection. In multivariable models, number of acute symptoms (odds ratio [OR], 1.30 per symptom; 95% confidence interval [CI], 1.20-1.40), lower socioeconomic status/financial insecurity (OR, 1.62; 95% CI, 1.02-2.63), preinfection depression (OR, 1.08; 95% CI, 1.01-1.16), and earlier variants (OR = 0.37 for Omicron compared with ancestral strain; 95% CI, 0.15-0.90) were associated with Long COVID symptoms. Conclusions: Variant wave, severity of acute infection, lower socioeconomic status, and pre-existing depression are associated with Long COVID symptoms.
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Importance: Prolonged symptoms following SARS-CoV-2 infection, or Long COVID, is common, but few prospective studies of Long COVID risk factors have been conducted. Objective: To determine whether sociodemographic factors, lifestyle, or medical history preceding COVID-19 or characteristics of acute SARS-CoV-2 infection are associated with Long COVID. Design: Cohort study with longitudinal assessment of symptoms before, during, and after SARS-CoV-2 infection, and cross-sectional assessment of Long COVID symptoms using data from the COVID-19 Citizen Science (CCS) study. Setting: CCS is an online cohort study that began enrolling March 26, 2020. We included data collected between March 26, 2020, and May 18, 2022. Participants: Adult CCS participants who reported a positive SARS-CoV-2 test result (PCR, Antigen, or Antibody) more than 30 days prior to May 4, 2022, were surveyed. Exposures: Age, sex, race/ethnicity, education, employment, socioeconomic status/financial insecurity, self-reported medical history, vaccination status, time of infection (variant wave), number of acute symptoms, pre-COVID depression, anxiety, alcohol and drug use, sleep, exercise. Main Outcome: Presence of at least 1 Long COVID symptom greater than 1 month after acute infection. Sensitivity analyses were performed considering only symptoms beyond 3 months and only severe symptoms. Results: 13,305 participants reported a SARS-CoV-2 positive test more than 30 days prior, 1480 (11.1% of eligible) responded to a survey about Long COVID symptoms, and 476 (32.2% of respondents) reported Long COVID symptoms (median 360 days after infection).Respondents' mean age was 53 and 1017 (69%) were female. Common Long COVID symptoms included fatigue, reported by 230/476 (48.3%), shortness of breath (109, 22.9%), confusion/brain fog (108, 22.7%), headache (103, 21.6%), and altered taste or smell (98, 20.6%). In multivariable models, number of acute COVID-19 symptoms (OR 1.30 per symptom, 95%CI 1.20-1.40), lower socioeconomic status/financial insecurity (OR 1.62, 95%CI 1.02-2.63), pre-infection depression (OR 1.08, 95%CI 1.01-1.16), and earlier variants (OR 0.37 for Omicron compared to ancestral strain, 95%CI 0.15-0.90) were associated with Long COVID symptoms. Conclusions and Relevance: Variant wave, severity of acute infection, lower socioeconomic status and pre-existing depression are associated with Long COVID symptoms. Key Points: Question: What are the patterns of symptoms and risk factors for Long COVID among SARS-CoV-2 infected individuals?Findings: Persistent symptoms were highly prevalent, especially fatigue, shortness of breath, headache, brain fog/confusion, and altered taste/smell, which persisted beyond 1 year among 56% of participants with symptoms; a minority of participants reported severe Long COVID symptoms. Number of acute symptoms during acute SARS-CoV-2 infection, financial insecurity, pre-existing depression, and infection with earlier variants are associated with prevalent Long COVID symptoms independent of vaccination, medical history, and other factors.Meaning: Severity of acute infection, SARS-CoV-2 variant, and financial insecurity and depression are associated with Long COVID symptoms.
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BACKGROUND: HIV infection is a concern in the army troupes because of the risk behaviour of the military population. In order to allow regular access to CD4+ T cell enumeration of military personnel as well as their dependents and civilians living with HIV, the Senegalese Army AIDS program is implementing PIMATM Alere technology in urban and semi-urban military medical centres. Validation such device is therefore required prior their wide implementation. The purpose of this study was to compare CD4+ T cell count measurements between the PIMATM Alere to the BD FACSCountTM. METHODOLOGY: We selected a total of 200 subjects including 50 patients with CD4+ T-cells below 200/mm3, 50 between 200 and 350/mm3, 50 between 351 and 500/mm3, and 50 above 500/mm3. CD4+ T-cell count was performed on venous blood using the BD FASCountTM as reference method and the PIMATM Point of Care technology. The mean biases and limits of agreement between the PIMATM Alere and BD FACSCountTM were assessed with the Bland-Altman analysis, the linear regression performed using the Passing-Bablok regression analysis, and the percent similarity calculated using the Scott method. RESULTS: Our data have shown a mean difference of 22.3 cells/mm3 [95%CI:9.1-35.5] between the BD FACSCountTM and PIMATM Alere CD4 measurements. However, the mean differences of the two methods was not significantly different to zero when CD4+ T-cell count was below 350/mm3 (P = 0.76). The Passing-Bablok regression in categorized CD4 counts has also showed concordance correlation coefficient of 0.89 for CD4+ T cell counts below 350/mm3 whilst it was 0.5 when CD4 was above 350/mm3. CONCLUSION: Overall, our data have shown that for low CD4 counts, the results from the PIMATM Alere provided accurate CD4+ T cell counts with a good agreement compared to the FACSCountTM.
