Characteristics and predictors of Long COVID among diagnosed cases of COVID-19.

BackgroundLong COVID or long-term symptoms after COVID-19 has the ability to affect health and quality of life. Knowledge about the burden and predictors could aid in their prevention and management. Most of the studies are from high-income countries and focus on severe acute COVID-19 cases. We did this study to estimate the incidence and identify the characteristics and predictors of Long COVID among our patients.MethodologyWe recruited adult (≥18 years) patients who were diagnosed as Reverse Transcription Polymerase Chain Reaction (RTPCR) confirmed SARS-COV-2 infection and were either hospitalized or tested on outpatient basis. Eligible participants were followed up telephonically after four weeks and six months of diagnosis of SARS-COV-2 infection to collect data on sociodemographic, clinical history, vaccination history, Cycle threshold (Ct) values during diagnosis and other variables. Characteristics of Long COVID were elicited, and multivariable logistic regression was done to find the predictors of Long COVID.ResultsWe have analyzed 487 and 371 individual data with a median follow-up of 44 days (Inter quartile range (IQR): 39,47) and 223 days (IQR:195,251), respectively. Overall, Long COVID was reported by 29.2% (95% Confidence interval (CI): 25.3%,33.4%) and 9.4% (95% CI: 6.7%,12.9%) of participants at four weeks and six months of follow-up, respectively. Incidence of Long COVID among patients with mild/moderate disease (n = 415) was 23.4% (95% CI: 19.5%,27.7%) as compared to 62.5% (95% CI: 50.7%,73%) in severe/critical cases(n = 72) at four weeks of follow-up. At six months, the incidence among mild/moderate (n = 319) was 7.2% (95% CI:4.6%,10.6%) as compared to 23.1% (95% CI:12.5%,36.8%) in severe/critical (n = 52). The most common Long COVID symptom was fatigue. Statistically significant predictors of Long COVID at four weeks of follow-up were-Pre-existing medical conditions (Adjusted Odds ratio (aOR) = 2.00, 95% CI: 1.16,3.44), having a higher number of symptoms during acute phase of COVID-19 disease (aOR = 11.24, 95% CI: 4.00,31.51), two doses of COVID-19 vaccination (aOR = 2.32, 95% CI: 1.17,4.58), the severity of illness (aOR = 5.71, 95% CI: 3.00,10.89) and being admitted to hospital (Odds ratio (OR) = 3.89, 95% CI: 2.49,6.08).ConclusionA considerable proportion of COVID-19 cases reported Long COVID symptoms. More research is needed in Long COVID to objectively assess the symptoms and find the biological and radiological markers

Percentage of self-reported Long COVID symptoms at four weeks and six months of follow-up.

Percentage of self-reported Long COVID symptoms at four weeks and six months of follow-up.</p

STROBE statement—Checklist of items that should be included in reports of <i>cross-sectional studies</i>.

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Predictors of self-reported Long COVID symptoms at four weeks follow-up.

Predictors of self-reported Long COVID symptoms at four weeks follow-up.</p

Clinical features and management of acute illness of COVID-19 among participants (n = 487).

Clinical features and management of acute illness of COVID-19 among participants (n = 487).</p

Hospital-Based Contact Tracing of Patients With COVID-19 and Health Care Workers During the COVID-19 Pandemic in Eastern India: Cross-sectional Study

BackgroundThe contact tracing and subsequent quarantining of health care workers (HCWs) are essential to minimizing the further transmission of SARS-CoV-2 infection and mitigating the shortage of HCWs during the COVID-19 pandemic situation. ObjectiveThis study aimed to assess the yield of contact tracing for COVID-19 cases and the risk stratification of HCWs who are exposed to these cases. MethodsThis was an analysis of routine data that were collected for the contact tracing of COVID-19 cases at the All India Institute of Medical Sciences, Bhubaneswar, in Odisha, India. Data from March 19 to August 31, 2020, were considered for this study. COVID-19 cases were admitted patients, outpatients, or HCWs in the hospital. HCWs who were exposed to COVID-19 cases were categorized, per the risk stratification guidelines, as high-risk contacts or low-risk contacts ResultsDuring contact tracing, 3411 HCWs were identified as those who were exposed to 360 COVID-19 cases. Of these 360 cases, 269 (74.7%) were either admitted patients or outpatients, and 91 (25.3%) were HCWs. After the risk stratification of the 3411 HCWs, 890 (26.1%) were categorized as high-risk contacts, and 2521 (73.9%) were categorized as low-risk contacts. The COVID-19 test positivity rates of high-risk contacts and low-risk contacts were 3.8% (34/890) and 1.9% (48/2521), respectively. The average number of high-risk contacts was significantly higher when the COVID-19 case was an admitted patient (number of contacts: mean 6.6) rather than when the COVID-19 case was an HCW (number of contacts: mean 4.0) or outpatient (number of contacts: mean 0.2; P=.009). Similarly, the average number of high-risk contacts was higher when the COVID-19 case was admitted in a non–COVID-19 area (number of contacts: mean 15.8) rather than when such cases were admitted in a COVID-19 area (number of contacts: mean 0.27; P<.001). There was a significant decline in the mean number of high-risk contacts over the study period (P=.003). ConclusionsContact tracing and risk stratification were effective and helped to reduce the number of HCWs requiring quarantine. There was also a decline in the number of high-risk contacts during the study period. This indicates the role of the implementation of hospital-based, COVID-19–related infection control strategies. The contact tracing and risk stratification approaches that were designed in this study can also be implemented in other health care settings

Congenital rubella syndrome surveillance in India, 2016–21: Analysis of five years surveillance data

Background: In India, facility-based surveillance for congenital rubella syndrome (CRS) was initiated in 2016 to estimate the burden and monitor the progress made in rubella control. We analyzed the surveillance data for 2016–2021 from 14 sentinel sites to describe the epidemiology of CRS. Method: We analyzed the surveillance data to describe the distribution of suspected and laboratory confirmed CRS patients by time, place and person characteristics. We compared clinical signs of laboratory confirmed CRS and discarded case-patients to find independent predictors of CRS using logistic regression analysis and developed a risk prediction model. Results: During 2016–21, surveillance sites enrolled 3940 suspected CRS case-patients (Age 3.5 months, SD: 3.5). About one-fifth (n = 813, 20.6%) were enrolled during newborn examination. Of the suspected CRS patients, 493 (12.5%) had laboratory evidence of rubella infection. The proportion of laboratory confirmed CRS cases declined from 26% in 2017 to 8.7% in 2021. Laboratory confirmed patients had higher odds of having hearing impairment (Odds ratio [OR] = 9.5, 95% confidence interval [CI]: 5.6–16.2), cataract (OR = 7.8, 95% CI: 5.4–11.2), pigmentary retinopathy (OR = 6.7, 95 CI: 3.3–13.6), structural heart defect with hearing impairment (OR = 3.8, 95% CI: 1.2–12.2) and glaucoma (OR = 3.1, 95% CI: 1.2–8.1). Nomogram, along with a web version, was developed. Conclusions: Rubella continues to be a significant public health issue in India. The declining trend of test positivity among suspected CRS case-patients needs to be monitored through continued surveillance in these sentinel sites