ABSTRACT
Background: The COVID-19 pandemic has affected almost 100 million people worldwide. Although the disease spectrum has still not been fully understood, there have been the reports of the persistence of symptoms well beyond the acute stage or after discharge from the hospital. Therefore, there is a need to document the persistence of symptoms to identify and provide physical as well as psychosocial support for ensuring the complete recovery of COVID-19 survivors. The present study examines the postacute stage persistence of symptoms in severe acute respiratory syndrome-coronavirus-2 patients. Materials and Methods: A longitudinal follow-up study was conducted on 1170 patients discharged from COVID hospital. All the study participants were contacted after discharge and at 7-day intervals for 42 days, and details of the persistence of symptoms were sought from them. Results: It was found that 43.8% of patients had persistence of symptoms, and 12.4% had symptoms even after 30 days of discharge from the hospital. Among symptoms, the most common persisting symptom was found to be fatigue (26%) followed by respiratory difficulty. The presence of comorbidity (odds ratio 1.61, 95% confidence interval 1.56-2.25, P < 0.01) and moderate/severe disease were found to be independent risk factors for the persistence of COVID-related symptoms. Conclusion: The findings of the study indicate that a large number of COVID-19 survivors continue to suffer from COVID-19 symptoms well after the recovery from the acute stage (discharge from hospital). Therefore, there is a genuine need for instituting measures for the monitoring of patients postdischarge and if required providing treatment to those having persistent symptoms of COVID-19. © 2022 by the Author(s).
ABSTRACT
Background: The change in serological status of community may be used as input for guiding the public health policy. Hence, the present study was conducted to determine change in seroprevalence of COVID-19 among healthcare workers (HCWs). Methods: From the baseline multicentric study sample, a subsample was followed up, and a seroepidemiological study was conducted among them between 6 and 22 weeks after the second dose of the vaccination. Multistage population proportion to size sampling was performed for the selection of subsample of HCWs. The serosurvey was conducted using the enzyme-linked immunosorbent assay-based IgG antibody test (COVID KAVACH). Results: Follow-up serological testing was done in subsample of 1122 participants of original 3253 participants. The mean age of the participants was 34.6 (8.13) years. A total of 300 (26.7%) participants were females. The seroprevalence was 78.52, (95%CI:76-80.1). Among those who were seronegative at initial test, 708 (77.04%) were seroconverted. Those who were not seroconverted (241 (21.5%)) have longer duration from the second dose of the vaccination (93 (31.4) vs. 56 (38.4); p value < 0.001). The COVID-19 infection was significantly associated with seropositive status and being a medical staff was associated with remaining seronegative on follow-up. The higher age (≥50 years) was found to be significantly associated with seroreversion. Conclusion: Four in five HCWs had detectable antibodies. Seroepidemiological studies carry vital information to control the public health response in the course of the pandemic. The study can also further help as a platform to study the seroconversion and effect of vaccination among HCWs for newer variants of SARS-CoV-2.
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Background: The ongoing coronavirus disease 2019 (COVID-19) pandemic has placed an unprecedented strain on Indian healthcare systems, with rapidly increasing demand for life-saving equipment and intensive care unit beds. The present study presents an analysis of average length of stay (LOS) as per different demographic and clinical factors in a dedicated COVID hospital. As the pandemic escalates, average LOS in COVID hospital will form the basis of determining the optimum requirement for healthcare resources (beds, staff, and equipment), which is a key priority for bolstering a strong public health response against COVID-19. Materials and Methods: Using the medical records at a dedicated COVID-19 hospital, the demographic details and select clinical characteristics of 342 admitted patients (from July 13, 2020, to August 30, 2020) were ed. Hospital LOS, calculated from the actual admission and discharge dates, was compared within the categories of demographic and clinical characteristics using Student's test and analysis of variance. SPSS version 20 was used for descriptive as well as inferential statistics. Results: The mean LOS was 9.93 +/- 4.45 days with a range of 3-37 days. LOS increased with increasing age, with maximum being for >61 years (12.69 +/- 7.14) and minimum for the younger age category of <40 years (8.88 +/- 1.95) (P = 0.001). As COVID-19 severity increased, LOS increased, with longest being for severe patients (25.59 +/- 7.30) and shortest being for Mild patients (8.74 +/- 1.80) (P = 0.001). LOS was also longer for patients having multiple comorbidities (13.00 +/- 7.96) and shortest for those with no comorbidities (9.33 +/- 2.96) (P = 0.001). Conclusion: LOS is significantly affected by age, severity, and comorbidities. The actual duration and factors influencing LOS are crucial for health administrators and policymakers to better allocate the already scarce health resources.
ABSTRACT
BACKGROUND: The immune response after SARS-CoV-2 is complex and may be affected by severity of the disease, race, etc. The present study was conducted to assess the serial antibody response among the COVID-19 patients admitted in the hospital. METHODS: The study was conducted between July and October 2020 in a dedicated COVID-19 hospital. All consented patients underwent serial testing of antibodies using a rapid chromatographic immunoassay-based qualitative IgG/IgM kit every third day until their discharge or death. The data about age, sex, severity of disease, length of stay, onset of symptoms, date of molecular testing were also collected. Appropriate statistical tests were used. RESULTS: The mean age of 1000 COVID-19 patients was 47.5 ± 17.9 years. Out of the total, 687 (68.7%) were males. With respect to severity, 682 (68.2%) were asymptomatic/mild, 200 (20%) were moderate and 118 (11.8%) were severe cases. The seroconversion percentage increased from 12.8% to 97.9% and 16.3% to 80.9% for IgG and IgM respectively in 21 days. The median time for seroconversion was 10 days (IQR:6-12 days) for IgG and eight days (IQR: 6-11 days) for IgM. At the time of discharge (median nine days), detectable IgG and IgM antibodies were present in 502 (52.46%) and 414 (43.26%) participants respectively. Seroconversion was associated with days after the symptoms, increasing severity of the disease and the presence of co-morbidity. CONCLUSION: Seroconversion increased during the period of observation. The severe/moderate cases of COVID-19 tend to have an early seroconversion as compared to the asymptomatic/mild cases. Only half of the patients were seroconverted at discharge.
ABSTRACT
BACKGROUND: The World Health Organization on 11 March 2020, declared COVID-19 as a pandemic. India initiated social distancing measures to combat the epidemic of COVID-19. The course of the epidemic of COVID-19 for India was predicted using stochastic probability-based mathematical modeling. METHODS: Data synthesis for the top few countries affected was studied for various factors affecting the epidemic. For projections of infected cases for India, the modified susceptible-exposed-infectious-removed/recovered framework modified for the effect of social distancing (Rho) was used. Simulation was carried out for 10,000 runs using Python. Projections for infected cases and hospitalization requirement were estimated. RESULTS: The epidemic curve will peak in the third week of June in India with 17,525,869 and 2,153,200 infected people with reproduction number of 1.8 and Rho of 0.7 and 0.6, respectively. Compared with the baseline scenario of no social distancing, for transmissibility with R0 = 1.8, the reduction in infections due to social distancing measure is 78% (Rho = 0.7) and 97% (Rho = 0.6). Similarly for R0 = 2.2 and 2.4, the reduction in infected numbers slightly lowers to 62% and 66% with Rho = 0.7 and 92% and 75% with Rho = 0.6, respectively. With R0 = 1.8 and Rho = 0.6, the Intensive Care Unit (ICU) bed requirement is 107,660, whereas if transmissibility is high, the ICU bed requirement would increase to 1,994,682. CONCLUSIONS: The social distancing measures seem to have been working for India in absence of treatment in sight for COVID-19. Although with the government's response strategy of social distancing, the peak of the epidemic is extended giving more months for preparedness to the country; however, the sustainability of these measures is uncertain.