Comparing Mid-Upper Arm Circumference With Body Mass Index for Assessing Nutritional Status in Indian Adults: Evidence From the National Family Health Survey 2015-16 (NFHS-4).

Background The surge in the twin burden of malnutrition - undernutrition and overweight/obesity - poses a severe threat worldwide including India. The adult group, primarily considered as an economic pillar of the society, suffered significant health problems, yet their nutritional issues are often neglected. Screening of nutritional status through anthropometric measurements is widely accepted. Body mass index (BMI) is commonly used but has certain limitations. Mid-upper arm circumference (MUAC), another simpler tool, is universally accepted in children, but its use in adults is debatable. The current research aims to determine the MUAC cutoffs and their predictive accuracies corresponding to BMI cutoffs for adult men and nonpregnant women. Subject and methods A cross-sectional analysis was conducted of the anthropometric data of Indian adult men and nonpregnant women collected in 2015-16 via the National Family Health Survey (NFHS-4). The receiver operating characteristic (ROC) curve analysis was performed to derive the MUAC cutoffs against BMI cutoffs. Results A significant moderate correlation for both men (r=0.56) and women (r=0.68) was observed. In relation to ROC analysis, the MUAC cutoffs against the BMI cutoffs of 18.5, 23, 25, and 30 kg/m2 were approximated to be 25, 26, 28, and 30 cm for men and 23, 25, 27, and 28 cm for women, respectively. These MUAC cutoffs showed good predictive accuracy with a high range of sensitivity and specificity for both men and women. Conclusions The non-invasive MUAC method correlates very well with BMI and offers several advantages, including accuracy, ease of measurement, and minimal logistical support and training, and can assess the nutritional status even in geographically remote areas. Therefore, it can be an important tool in public health, especially in resource-limited settings, for identifying populations at risk of malnutrition.

Incidence of SARS-CoV-2 Co-infections During the Second Wave in Sub-Himalayan Region, India.

Introduction The second wave of the coronavirus disease 2019 (COVID-19) pandemic in India, which started from April 2021, has been more severe and deadly than the first wave. The aim of this prospective study was to determine the possibility of other respiratory pathogens contributing towards the severity and hospitalization in the current second wave. Materials and methods Nasopharyngeal and oropharyngeal swab samples were collected and processed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by reverse transcription polymerase chain reaction (RT-PCR). These samples were further processed for detection of co-infection in SARS CoV-2 patients by BioFire® Filmarray® 2.0 (bioMérieux, USA). Results We screened 77 COVID-19-positive patients admitted to All India Institute of Medical Sciences (AIIMS), Rishikesh and found cases of co-infections in five (6.49 %) patients. Conclusion Our finding suggests that co-infections had no or minimal role in augmenting the second wave of the COVID-19 pandemic in India, and the emergence of new variants may be the probable cause.

Trend of viral load during the first, second, and third wave of COVID-19 in the Indian Himalayan region: an observational study of the Uttarakhand state.

India had faced three waves throughout the Coronavirus disease 2019 (COVID-19) pandemic, which had already impacted economic lives and affected the healthcare setting and infrastructure. The widespread impacts have inspired researchers to look for clinical indicators of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection prognosis. Cyclic threshold values have been used to correlate the viral load in COVID-19 patients and for viral transmission. In light of this correlation, a retrospective study was conducted to assess the trend of viral load in clinical and demographic profiles across the three waves. Data of a total of 11,125 COVID-19-positive patients were obtained, which had a Ct value of <35. We stratified Ct values as follows: under 25 (high viral load), 25-30 (moderate viral load), and over 30 (low viral load). We found a significantly high proportion of patients with high viral load during the second wave. A significantly high viral load across the symptomatic and vaccinated populations was found in all three waves, whereas a significantly high viral load across age groups was found only in the first wave. With the widespread availability of real-time PCR and the limited use of genomic surveillance, the Ct value and viral load could be a suitable tool for population-level monitoring and forecasting.

Interference of Antinuclear Antibody (ANA) in Indirect Immunofluorescence Assay (IIFA)-Based Perinuclear Antineutrophil Cytoplasmic Antibody (pANCA) Interpretation.

Background: Indirect immunofluorescence assay (IIFA) based on antineutrophil cytoplasmic antibody (ANCA) testing is a commonly employed test for diagnosing autoimmune vasculitis. Antinuclear antibody (ANA) can give rise to a false interpretation of perinuclear-ANCA (pANCA) in ethanol-fixed granulocyte substrates. Analytical interference could frequently occur in setups where ethanol-fixed substrates are used alone. Here, we intend to investigate this ANA interference in pANCA interpretation. Methods: In this retrospective study, we studied anti-MPO-negative but ANA-positive and pANCA (IIFA based) samples. We also correlated immunoblot results (where data were available) and checked the association between grades of blot positivity (an indicator of the concentration of ANA) and frequency of pANCA interpretation. Data were analyzed by appropriate statistical techniques (Chi-square and kappa statistics). Results: About 19.2% of ANA blot (ENA-blot) positive samples displayed a pANCA positive pattern in the ethanol-fixed substrate, while this positivity in ENA-blot negatives was 6.5%. In positive ANA-IIFA samples, about 14.7% yielded pANCA patterns (on ethanol fixed substrates). Out of this, nuclear homogenous pattern yielding samples gave the highest frequency pANCA, that is, in 31.5% followed by speckled (11.1%), DFS (10.3%), and centromere (6.7%).The association of the nuclear homogenous pattern was statistically significant. Conclusions: ANA-positive results may interfere with the interpretation of pANCA as observed in ANA-IIFA and ENA-blot positive samples. ANA-IIFA patterns like nuclear homogenous may strongly associate this pANCA interpretation. This can help laboratories perform ANCA testing more effectively, ruling out ANA interference in ANCA screening.