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Introduction:Fifty-three percent of Indian under-5 deaths occur during the neonatal age group. Recognizing that there is a lack of illustrated district-level data on neonatal mortality in India, we mapped this to visually highlight districts where neonatal health issues require the most attention.Methods:District-level estimates of 596 Indian districts were used to generate maps and to illustrate neonatal mortality rates (NMRs), absolute numbers of neonatal deaths; the best and worst performing districts (positive and negative deviants) in each Indian state; the neonatal female/male death ratio; and district lag in NMR reductions.Results:The NMR ranged from 4.3 (Kannur, Kerala) to 65.1 (Datia, Madhya Pradesh), with the mean NMR being 29.8. Almost two-thirds of the districts (n = 380, 63.7%) had NMRs between 20 and 40. The top third of neonatal deaths could be accounted for by just 71 districts of a total of 596.Conclusion:There is an urgent need for up-to-date data on district-level neonatal mortality in India.

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Bland-Altman analysis is a very commonly used method in the biomedical research. This method is used to study the agreement between two measurements that are in continuous scale. Although this method is commonly used in medical research, the statistical software packages do not have the menu-driven operation for the Bland-Altman analysis. Hence this paper intends to provide an R function (BA.plot) for Bland-Altman analysis.

A bstract Background Coronavirus disease-2019 (COVID-19) pandemic has exposed healthcare workers (HCWs) to a unique set of challenges and stressors. Our frontline workers are under tremendous psychological pressure because of the ever-rising crisis. This study was done to assess the magnitude of the psychological impact of the COVID-19 pandemic on clinical and nonclinical HCWs in India. Materials and methods It was a cross-sectional, online survey that was done from June 1, 2020, to July 4, 2020. A total of 313 clinical and nonclinical HCWs, who were directly or indirectly involved in patient care, participated in the study. The psychological impact was assessed in terms of four variables: insomnia, anxiety, depression, and stress. Insomnia was assessed by the Insomnia Severity Index (ISI). Anxiety and depression were assessed via the Patient Health Questionnaire-4 (PHQ-4), which included a 2-item anxiety scale and a 2-item depression scale (PHQ-2). Stress was assessed via the Perceived Stress Scale (PSS). We also compared the psychological impact of this pandemic between clinical and nonclinical HCWs. Results 7.3% of HCWs were having moderate insomnia, 3.8% had severe insomnia, and 20.8% were having subthreshold insomnia. Severe anxiety and depression were found in 6.7% of respondents. 8.0 and 32.3% of the respondents had moderate and mild anxiety–depression, respectively. 6.4% had high perceived stress. 47.6 and 46.0% of the respondents had moderate and low stress, respectively. There was a statistically significant difference in severe insomnia between clinical and nonclinical HCWs, whereas no significant difference in anxiety, depression, and stress between clinical and nonclinical HCWs. Conclusion This study suggests that psychological morbidity is prevalent among both clinical and nonclinical HCWs and both males and females. Early intervention may be beneficial to prevent this issue. How to cite this article Sunil R, Bhatt MT, Bhumika TV, Thomas N, Puranik A, Chaudhuri S, et al. Weathering the Storm: Psychological Impact of COVID-19 Pandemic on Clinical and Nonclinical Healthcare Workers in India. Indian J Crit Care Med 2021;25(1):16–20.

Background: Hot-spot detection of Maternal Mortality Ratio (MMR) can assist in identifying the exact geographic location of regions that need urgent attention. Aims &Objectives: To detect hot-spots of MMR at district level in the selected nine states of India and the observed pattern was further correlated with hot-spots of certain known risk factors of MMR in the same region. Material &Methods: Data on MMR was obtained from Annual Health Survey 2012-13. Moran’s I was computed for MMR to quantify spatial autocorrelation. The hot-spot analysis of MMR and its potential risk factors were performed using Getis-Ord Gi* statistic, a measure of local indicators of spatial autocorrelation (LISA). The spatial analysis was based on queen’s contiguity weight matrix and analyses were done using ArcGIS 10.3. Results: The Moran’s I value of MMR was found to be 0.69 indicating a positive spatial autocorrelation. Districts with MMR hot-spotting was largely observed in Uttar Pradesh and Madhya Pradesh, followed by Assam, Bihar and Jharkhand. The hot-spot analysis unveiled an inverse relation of MMR with female literacy rate, mothers who received any antenatal check-up (%), mothers who utilized Janani Suraksha Yojana (%), safe delivery (%) and urbanization (%). Marriages among females below 18 years (%), total fertility rate and women with unmet need for spacing (%) had a direct relation with MMR. Conclusion: Information on hot-spots as depicted in this study can help locate the regions vulnerable to MMR and the potential risk factors, which in turn could aid in implementing targeted intervention programs.

The 2015/2016 National Family Health Survey (NFHS-4) revealed that the prevalence of anemia among children under 5 years is 58% in India. Lack of nutritional supplementation and lack of health care facilities are found to be important influential factors of anemia among children. We aimed to examine district-level spatial heterogeneity and clustering of associated factors with childhood anemia in India. Geographically weighted regression was applied on the NFHS-5 data for 335 districts. Factors such as prevalence of nutritional supplementation in children and mothers, birth order, antenatal care, diarrhea in children, and stunting were found to be significantly associated. Spatial scan statistics technique identified 3 significant local spatial clusters of anemia. This study provides findings based on the latest available data which can further assist in the design and execution of tailor-made policies.

Lead is a ubiquitous heavy metal toxin of significant public health concern. Every individual varies in their response to lead’s toxic effects due to underlying genetic variations in lead metabolizing enzymes or proteins distributed in the population. Earlier studies, including our lab, have attributed the influence of ALAD (δ-Aminolevulinate dehydratase) polymorphism on blood lead retention and ALAD activity. The present study aimed to investigate the influence of VDR (Vitamin D receptor) and HFE (Hemochromatosis) polymorphisms in modulating blood lead levels (BLLs) of occupationally exposed workers. 164 lead-exposed subjects involved in lead alloy manufacturing and battery breaking and recycling processes and 160 unexposed controls with BLLs below 10 µg/dL recruited in the study. Blood lead levels, along with a battery of biochemical assays and genotyping, were performed. Regression analysis revealed a negative influence of BLLs on ALAD activity ( p < 0.0001) and a positive influence on smokeless tobacco use ( p < 0.001) in lead-exposed subjects. A predicted haplotype of the three VDR polymorphisms computed from genotyping data revealed that T-A-A haplotype increased the BLLs by 0.93 units ( p ≤ 0.05) and C-C-A haplotype decreased the BLLs by 7.25 units ( p ≤ 0.05). Further analysis revealed that the wild-type CC genotype of HFE H63D presented a higher median BLL, indicating that variant C allele may have a role in increasing the concentration of lead. Hence, the polymorphism of genes associated with lead metabolism might aid in predicting genetic predisposition to lead and its associated effects.