COVID-19 second wave: District level study of concentration of confirmed cases and fatality in India

COVID-19 Vaccine Coverage in India: A District-Level Analysis

India implemented the largest COVID-19 vaccination drive in the world, through which it vaccinated the majority of its population. Lessons from the Indian COVID-19 vaccination experience can be invaluable for other LMICs as well as for preparedness for future outbreaks. Our study is designed to explore the factors associated with COVID-19 vaccination coverage in India at the district level. We used data from COVID-19 vaccination in India combined with several other administrative data to create a unique data set that facilitated a spatio-temporal exploratory analysis by uncovering the factors associated with vaccination rates across different vaccination phases and districts. We found evidence that past reported infection rates were positively correlated with COVID-19 vaccination outcomes. Past cumulative COVID-19 deaths as a proportion of district populations were associated with lower COVID-19 vaccination, but the percentage of past reported infection was positively correlated with first-dose COVID-19 vaccination, which might indicate a positive role of higher awareness created by a higher reported infection rate. Districts that on average had a higher population burden per health centre were likely to have lower COVID-19 vaccination rates. Vaccination rates were lower in rural areas relative to urban areas, whereas the association with literacy rate was positive. Districts with a higher percentage of children with complete immunisation were associated with higher COVID-19 vaccination, whereas low vaccination was observed in districts that had higher percentages of wasted children. COVID-19 vaccination was lower among pregnant and lactating women. Higher vaccination was observed among populations with higher blood pressure and hypertension (which were a few of the co-morbidities associated with COVID-19 infection).

Association of greenness with COVID-19 deaths in India: An ecological study at district level

BACKGROUND

The world has witnessed a colossal death toll due to the novel coronavirus disease-2019 (COVID-19). A few environmental epidemiology studies have identified association of environmental factors (air pollution, greenness, temperature, etc.) with COVID-19 incidence and mortality, particularly in developed countries. India, being one of the most severely affected countries by the pandemic, still has a dearth of research exploring the linkages of environment and COVID-19 pandemic.

OBJECTIVES

We evaluate whether district-level greenness exposure is associated with a reduced risk of COVID-19 deaths in India.

METHODS

We used average normalized difference vegetation index (NDVI) from January to March 2019, derived by Oceansat-2 satellite, to represent district-level greenness exposure. COVID-19 death counts were obtained through May 1, 2021 (around the peak of the second wave) from an open portal: covid19india.org. We used hierarchical generalized negative binomial regressions to check the associations of greenness with COVID-19 death counts. Analyses were adjusted for air pollution (PM_2.5), temperature, rainfall, population density, proportion of older adults (50 years and above), sex ratio over age 50, proportions of rural population, household overcrowding, materially deprived households, health facilities, and secondary school education.

RESULTS

Our analyses found a significant association between greenness and reduced risk of COVID-19 deaths. Compared to the districts with the lowest NDVI (quintile 1), districts within quintiles 3, 4, and 5 have respectively, around 32% [MRR = 0.68 (95% CI: 0.51, 0.88)], 39% [MRR = 0.61 (95% CI: 0.46, 0.80)], and 47% [MRR = 0.53 (95% CI: 0.40, 0.71)] reduced risk of COVID-19 deaths. The association remains consistent for analyses restricted to districts with a rather good overall death registration (>80%).

CONCLUSION

Though cause-of-death statistics are limited, we confirm that exposure to greenness was associated with reduced district-level COVID-19 deaths in India. However, material deprivation and air pollution modify this association.

The continued effects of COVID-19 on the lives and livelihoods of vegetable farmers in India

India experienced a rapid rise in COVID-19 infections from March 2021. States imposed varying levels of lockdowns and curfews to curb the spread of the disease. These restrictions severely affected the functioning of food systems. The objective of this study was to analyze how COVID-19 continues to affect agricultural production, food security and household diets of vegetable farmers. A phone-based survey was conducted with 595 vegetable farmers in the states of Andhra Pradesh, Assam, Jharkhand, Karnataka and Odisha, 60% of whom had been interviewed a year earlier. Overall, 60% of farmers experienced decreased vegetable production; over 80% reported a reduction in consumption of at least one food group; and 45% reported some level of food insecurity between May 2020 and May 2021. Farmers who reported decreased staples production, difficulty accessing seeds/seedlings, or reduced their household spending were more likely to report decreased vegetable production. Vegetable consumption was positively associated with receipt of COVID-19 relief benefits, borrowing money, or having home gardens. Farmers who received public agricultural assistance, or had reduced expenses, were more likely to have lower vegetable consumption. Greater severity of food insecurity was associated with farmers belonging to underprivileged social groups, non-Hindus, or those who experienced decrease in livestock production, weather related disruptions or received COVID-19 assistance. This is one of few studies that have conducted a longitudinal assessment of the impacts across multiple waves of COVID-19. COVID-19 is seen to be one among several shocks experienced by farm households, and exacerbated existing issues within agriculture and food security. There is a need for public policy support to strengthen both production and consumption of vegetables.

Mapping of space-time patterns of infectious disease using spatial statistical models: a case study of COVID-19 in India

INTRODUCTION

Mapping of infectious diseases like COVID-19 is the foremost importance for diseases control and prevention. This study attempts to identify the spatio-temporal pattern and evolution trend of COVID-19 at the district level in India using spatial statistical models.

MATERIALS AND METHODS

Active cases of eleven time-stamps (30 March-2 December, 2020) with an approximately 20-day interval are considered. The study reveals applications of spatial statistical tools, i.e. optimised hotspot and outlier analysis (which follow Gi* and Moran I statistics) and emerging hotspot with the base of space time cube, are effective for the spatio-temporal evolution of disease clusters.

RESULTS

The result shows the overall increasing trend of COVID-19 infection with a Mann-Kendall trend score of 2.95 (p = 0.0031). The spatial clusters of high infection (hotspots) and low infection (coldspots) change their location over time but are limited to the districts of the south-western states (Kerala, Karnataka, Andhra Pradesh, Maharashtra, Gujarat) and the north-eastern states (West Bengal, Jharkhand, Assam, Tripura, Manipur, etc.) respectively.

CONCLUSIONS

A total of eight types of patterns are identified, but the most concerning types are consecutive (7.24% of districts), intensifying (15.13% districts) and persistent (24.34% of districts) which will help health policy makers and the government to prioritize-based resource allocation and control measures.

Spatial variation of covid-19 morbidity status and identification of risk zone in Tamil Nadu (India) during first wave

COVID-19 is a pandemic disease caused by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). The study area, Tamilnadu, is a southern state in India. The present study demonstrates the significance of mapping in identifying the risk zones of COVID-19 disease by taking a study of COVID-19 cases in Tamilnadu state. The main objective of the study was to identify the risk zone of COVID-19 disease in the first wave of Tamilnadu through geospatial mapping. Data on COVID-19 cases collected during March 2020 to March 2021 month wise in all 37 districts (unit area) of Tamilnadu in two-month interval wise was analysed. The study is based on secondary sources from respective office in Tamilnadu. Z-score techniques were used for standardizing data. Correlation analysis provided a measure of correlation between COVID-19 and working population. Location Quotient analysis aided the identification of COVID-19 risk zones in Tamilnadu. Present study indicates tertiary workers are more vulnerable to COVID-19 disease.

Epidemiological Characteristics of the COVID-19 Pandemic During the First and Second Waves in Chhattisgarh, Central India: A Comparative Analysis

Background

There is a scarcity of reports of comparative analysis between the first and second waves of the pandemic from any part of India. This article aims to comprehensively investigate the epidemiology of coronavirus disease 2019 (COVID-19) during the course of the pandemic in the state of Chhattisgarh, central India.

Methodology

A comparative epidemiological analysis of the first and second waves of COVID-19 across Chhattisgarh was conducted on the vital parameters of total tests performed, cases diagnosed, age and gender distribution, case fatality ratio (CFR), and mitigation strategy reported by the state and central government health agencies using the data from Indian Council of Medical Research and National Informatics Centre portals.

Results

The second wave was shorter than the first wave but the absolute number of cases increased by 2.4 times and deaths by 2.7 times. There was a significant increase in cases per million, deaths per million, and test positivity rate. The hospitalization rate and test per case ratio dropped in the second wave from 33 to 20 and from 12.6 to 7.2, respectively. Both infection and deaths were higher among males in both the waves (p < 0.001). CFR increased from 1.2% in the first wave to 1.4% in the second wave (p < 0.001; odds ratio = 1.14 (1.1-1.19)). Increased mortality was seen in all ages except the young (≤20 years) and the old (>60 years).

Conclusions

The significantly high number of cases and deaths during the second wave provides evidence to undertake preparedness measures for mitigating any future waves. Regular surveillance, monitoring, and analysis of epidemiological data are pertinent for continued situational awareness.