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1.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-21268536

RESUMO

BackgroundLong COVID or long-term complication 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 cases. We did this study to estimate the prevalence 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 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 individual data with a median follow-up of 44 days (Inter quartile range (IQR): 39,47). Overall, Long COVID was reported by 29.2% (95% Confidence interval (CI): 25.3%,33.4%) participants. Prevalence 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). The most common Long COVID symptom was fatigue (64.8%) followed by cough (32.4%). Statistically significant predictors of Long COVID were - Pre-existing medical conditions (Adjusted Odds ratio (aOR)=2.00, 95% CI: 1.16,3.44), having a more significant 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.

2.
Preprint em Inglês | medRxiv | ID: ppmedrxiv-20220475

RESUMO

IntroductionContact tracing and subsequent quarantining of Health Care Workers (HCWs) is essential to minimize further transmission of COVID-19 infection. In this study, we have reported the yield of Contact Tracing of COVID-19 Patients and HCWs and risk stratification of exposed HCWs. MethodologyThis is a secondary analysis of routine data collected for contact tracing from 19th March to 31st August 2020 at All India Institute of Medical Sciences, Bhubaneswar, Odisha, India. HCWs exposed to COVID-19 infections were categorized as per the risk stratification guidelines and the high-risk contacts were quarantined for 14 days and tested on 7th day from last day of exposure. The low risk contacts were encouraged to closely monitor their symptoms while continuing to work. ResultsOut of 3411 HCWs exposed to COVID 19 patients (n=269) and HCWs (n=91), 890 (26.1%) were high risk contacts and 2521 (73.9%) were low risk contacts. The test positivity rate of high-risk contact was 3.82% and for low risk contact was 1.90%. Average number of high-risk contacts was significantly higher; for admitted patients (6.6) as compared to HCWs (4.0) and outpatients (0.2), p value = 0.009; for patients admitted in non-COVID areas (15.8) as compared to COVID areas (0.27), p value < 0.001; and when clustering of cases was present (14.3) as compared to isolated cases (8.2); p value < 0.001. Trend analysis (15 days block period) showed a significant decline in number of mean numbers of high-risk contacts during the study period. ConclusionContact tracing and risk stratification was effective and helped in reducing the number of HCWs going for quarantine. There was also a decline in high-risk contacts during study period suggesting role of implementation of hospital based COVID related infection control strategies. This contact tracing and risk stratification approach designed in the current study can also be implemented in other healthcare settings.

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