Apala Saha

Bio: Apala Saha is an academic researcher from Banaras Hindu University. The author has contributed to research in topics: Population & Bengali. The author has an hindex of 2, co-authored 5 publications receiving 9 citations. Previous affiliations of Apala Saha include Jawaharlal Nehru University.

District level correlates of COVID-19 pandemic in India

Abstract: Background The number of patients with coronavirus infection (COVID-19) has amplified in India. Understanding the district level correlates of the COVID-19 infection ratio (IR) is therefore essential for formulating policies and intervention. Objectives The present study examines the association between socio-economic and demographic characteristics of India’s population and the COVID-19 infection ratio at district level… Data and Methods Using crowdsourced data on the COVID-19 prevalence rate, we analyzed state and district level variation in India from March 14 to July 31 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out a regression analysis to highlight the district level demographic, socio-economic, infrastructure, and health-related correlates of the COVID-19 infection ratio. Results The results showed that the IR is 42.38 per one hundred thousand population in India. The highest IR was observed in Andhra Pradesh (145.0), followed by Maharashtra (123.6), and was the lowest in Chhattisgarh (10.1). About 80 per cent of infected cases, and 90 per cent of deaths were observed in nine Indian states (Tamil Nadu, Andhra Pradesh, Telangana, Karnataka, Maharashtra, Delhi, Uttar Pradesh, West Bengal, and Gujarat). Moreover, we observed COVID-19 cold-spots in central, northern, western, and north-eastern regions of India. Out of 736 districts, six metropolitan cities (Mumbai, Chennai, Thane, Pune, Bengaluru, and Hyderabad) emerged as the major hotspots in India, containing around 30 per cent of confirmed total COVID-19 cases in the country. Simultaneously, parts of the Konkan coast in Maharashtra, parts of Delhi, the southern part of Tamil Nadu, the northern part of Jammu & Kashmir were identified as hotspots of COVID-19 infection. Moran’s-I value of 0.333showed a positive spatial clusteringlevel in the COVID-19 IR case over neighboring districts. Our regression analysis found that district-level population density (β: 0.05, CI:004-0.06), the percent of urban population (β:3.08, CI: 1.05-5.11), percent of Scheduled Caste Population (β: 3.92, CI: 0.12-7.72),and district-level testing ratio (β: 0.03, CI: 0.01-0.04) are positively associated with the prevalence of COVID-19. Conclusion COVID-19 cases were heavily concentrated in 9 states of India. Several demographic, socio-economic, and health-related variables are correlated with COVID-19 prevalence rate. However, after adjusting the role of socio-economic and health-related factors, the COVID-19 infection rate was found to be more rampant in districts with a higher population density, a higher percentage of the urban population, and a higher percentage of deprived castes and with a higher level of testing ratio. The identified hotspots and correlates in this study give crucial information for policy discourse.

District level correlates of COVID-19 pandemic in India during March-October 2020.

Abstract: Background COVID-19 is affecting the entire population of India. Understanding district level correlates of the COVID-19’s infection ratio (IR) is essential for formulating policies and interventions. Objective The present study aims to investigate the district level variation in COVID-19 during March-October 2020. The present study also examines the association between India’s socioeconomic and demographic characteristics and the COVID-19 infection ratio at the district level. Data and methods We used publicly available crowdsourced district-level data on COVID-19 from March 14, 2020, to October 31, 2020. We identified hotspot and cold spot districts for COVID-19 cases and infection ratio. We have also carried out two sets of regression analysis to highlight the district level demographic, socioeconomic, household infrastructure facilities, and health-related correlates of the COVID-19 infection ratio. Results The results showed on October 31, 2020, the IR in India was 42.85 per hundred thousand population, with the highest in Kerala (259.63) and the lowest in Bihar (6.58). About 80 percent infected cases and 61 percent deaths were observed in nine states (Delhi, Gujarat, West Bengal, Uttar Pradesh, Andhra Pradesh, Maharashtra, Karnataka, Tamil Nadu, and Telangana). Moran’s- I showed a positive yet poor spatial clustering in the COVID-19 IR over neighboring districts. Our regression analysis demonstrated that percent of 15–59 aged population, district population density, percent of the urban population, district-level testing ratio, and percent of stunted children were significantly and positively associated with the COVID-19 infection ratio. We also found that, with an increasing percentage of literacy, there is a lower infection ratio in Indian districts. Conclusion The COVID-19 infection ratio was found to be more rampant in districts with a higher working-age population, higher population density, a higher urban population, a higher testing ratio, and a higher level of stunted children. The study findings provide crucial information for policy discourse, emphasizing the vulnerability of the highly urbanized and densely populated areas.

Actual and ideal fertility differential among natives, immigrants, and descendants of immigrants in a northeastern state of India

Abstract: Little research has been conducted on the native immigrant fertility differential in low income settings. The objective of our paper is to examine the actual and ideal fertility differential of native and immigrant families in Assam. We used the data from a primary quantitative survey carried out in 52 villages in five districts of Assam during 2014–2015. We performed bivariate analysis and used a multilevel mixed effects linear regression model to analyse the actual and ideal fertility differential by type of village. The average number of children ever born is the lowest in native villages in contrast to the highest average number of children ever born in immigrant villages. The likelihood of having more children is also the highest among women in immigrant villages. However, the effect of religion surpasses the effect of the type of village the women reside in.

India’s Child Brides and Youngest Mothers: Beyond Popular Perceptions:

Abstract: Over one million girls in India are married off before they turn 15 years of age, and more than four hundred thousand among them bear children. This article aims to identify and locate regionally t...

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

A Field of One's Own: Gender and Land Rights in South Asia.

Abstract: Preface l. Land rights for women: making the case 2. Conceptualizing gender relations 3. Customary rights and associated practices 4. Erosion and disinheritance: traditionally matrilineal and bilateral communities today 5. Contemporary law: contestation and content 6. Whose share? Who claims? The gap between law and practice 7. Whose land? Who commands? The gap between ownership and control 8. Tracing cross-regional diversities 9. Struggles over resources, struggles over meanings l0. The long march ahead.

Child Marriage Among Girls in India: Prevalence, Trends and Socio-Economic Correlates:

Abstract: This article examines prevalence of child marriage among girls in India, its trends and socio-economic correlates. It is based on data extracted from the National Family Health Survey (NFHS). This ...

Under-Five Child Growth and Nutrition Status:Spatial Clustering of Indian Districts. VID Working Paper 03/2019

Abstract: Variation in human growth and the genetic and environmental factors that are influencing it have been described worldwide. The objective of this study is to assess the geographical variance of under-five children nutritional status and its related covariates across Indian districts. We use the most recent fourth round of the Indian National Family Health Survey conducted in 2015-2016, which for the first time offers district level information. We employ principal component analysis (PCA) on the demographic and socio-economic determinants of childhood morbidity and conduct hierarchical clustering analysis to identify geographical patterns in nutritional status among children of age under five at the district level. Our results reveal strong geographical clustering among the districts of India. Throughout most of Southern India, children are provided with relatively better conditions for growth and improved nutritional status, as compared to districts in the central, particularly rural parts of India. Looking at average weight, as well as the proportion of children that suffer from underweight and wasting, northeastern Indian districts seem to be offering living conditions more conducive to healthy child development. The geographical clustering of malnutrition, as well as below-average child height and weight coincides with high poverty, low female education, lower BMI among mothers, higher prevalence of both parity 4+ and teenage pregnancies. The present study highlights the importance of combining PCA and cluster analysis methods in studying variation in under-five child growth and nutrition at the district level. We identify the geographical areas, where children are under severe risk of undernutrition, stunting and wasting and contribute to formulating policies to improve child nutrition in India.