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Background Expansion of the testing capacities for severe acute respiratory syndrome-coronavirus-2 is an important issue in the face of ever-increasing case load. So, there is need of point-of-care diagnostic tests in the existing laboratory capacities for early treatment, isolation, and clinical decision making, especially in resource limited settings. Materials and Methods This prospective cohort study was conducted at Jai Prakash Narayan Apex Trauma Center, All India Institute of Medical Sciences, New Delhi. Nasopharyngeal samples and blood samples were collected for antigen and antibody testing. Rapid antigen test was performed as per the kit's instructions. The performance of the kit was compared with the gold standard reverse transcription polymerase chain reaction (RT-PCR) testing. Results Eighty-eight out of 110 patients tested positive by RT-PCR for coronavirus disease 2019 in last 48 to 72 hours were included in the study. Overall, the sensitivity of combined antibody test was 52%, antigen test 26%, and combined sensitivity of both antigen and antibody was 72.7%, respectively. Conclusion The combo kit needs to be used with caution in low prevalence settings, where cases may be missed.

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Background The objective of this study was to find the distinct risk subsets or clusters identified by the combination of factors and important factors to classify under five mortality (U5M) in high focused Indian states. Methods Using population-based cross-sectional data from the National Family Health Survey (NFHS, 2015-2016) on 1, 40, 427 live births of five years preceding the survey occurred to 99,205 women of high focused Indian states with U5M rate above the national level, a recursive partitioning approach based two classification tree models, one without considering missing values and other with missing together approach, were fitted using binary outcome of U5M and independent factors comprising of socioeconomic, demographic, maternal and biological, nutritional and environmental factors. Results There were nine and sixteen sub-groups in model-1 and model-2, respectively. In model-1, breastfeeding = no & birth in past 5 years = (2, 3+ births) and in model-2, breastfeeding = no & birth weight = (<2.5kg, not known) & birth in past 5 years = (2, 3 or more births) were found to be maximum mortality risk sub-groups. In terms of variable importance to predict U5M, model-1 identified birth in past 5 years, breastfeeding, birth order, wealth index, mother‘s age at birth. Model-2 additionally identified delivery complications, birth weight, state, sanitation facility, birth interval, caste, education. Overall correct classification rate was higher for model-1 (66%) than model-2 (64%). Conclusions The main observed risk cluster was combination of two factors like breastfeeding and number of births in past 5 years, which for most people are easily modifiable with appropriate strategies and policies. Finally, to combat U5M in high focused states, identifying risk subsets or clusters is important for targeting and intervening purposes, as the intensity and type of policies and programs may differ according to clusters. This method is suitable to identify complex natural interactions between predictors, important variables and hypothesis generation to inform policy maker on intervention strategies, which may be difficult or impossible to uncover using traditional multivariable techniques.

Poster session 2, September 22, 2022, 12:30 PM - 1:30 PM Background Candida is responsible for roughly 96% of all opportunistic mycoses and is a major cause of bloodstream infections (BSIs). The potential for nosocomial spread of Candidemia infections is a new concern concurrent with the rapid expansion of intensive care facilities for COVID-19 patients. With the pandemic of COVID-19 now moving into 2022, it is understood that critically ill COVID-infected patients in the ICUs are commonly infected with highly resistant bacterial and fungal infections. Objective To estimate the incidence rates and compare the epidemiology of candidemia in COVID infected and non-infected patients requiring ICU care. Methodology In this 2-year retrospective multicentric study, we present the findings on candidemia from the Healthcare-Associated Infections (HAI) surveillance network which includes 40 hospitals across India and with special emphasis on differences in the epidemiology of Candidemia in COVID infected and non-infected patients in the pre-COVID (April 2019 to April 2020) and COVID times (April 2020 to April 2021) across this network. We compared the incidence of candidemia between COVID infected and non-infected patients using Poisson regression analysis. Chi-squared (χ2) test was used to test for differences in variables such as gender and 14-day mortality between the patients and Wilcoxon rank-sum (Mann-Whitney) test was used to compare median between the patients. Results A total of 628 patients with candidemia were screened from HAI Surveillance Database where 68 patients are COVID infected and 560 non-infected patients from both pre-COVID and COVID periods. Incidence of Candida-associated BSI increased significantly from 1.47 (95% CI, 1.35-1.60) to 3.08 (95% CI, 2.38-3.92) in non-infected and COVID-infected patients respectively, while in CLABSI the rates increased from 2.62 (95% CI, 2.34-2.92) in non-infected to 5.99 (95% CI, 4.30-8.12) in COVID-infected patients. COVID infected patients in the age group (&gt;60 years) were significantly more prone to candidemia compared to non-infected patients. During the COVID period, the maximum time for candidemia to develop (from the time of ICU admission) in COVID-infected patients was shorter (&lt;65 days) than in non-infected patients (&gt;90 days). Conclusion We observed an increased incidence of candidemia in hospitalized patients during the COVID period compared with the same during the pre-COVID period.

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We evaluated the appropriateness of antibiotic prescriptions at discharge from a tertiary-care hospital in India. Of the 790 adult patients included, 84.4% received antibiotics. Microbiological specimens were taken from 67.3% of these patients, and pathogens were identified in 28.8% of cases. Overuse of antimicrobials at hospital discharge should be curtailed.