Hierarchical Modelling of COVID-19 Death Risk in India in the Early Phase of the Pandemic

Abstract

We improve upon the modelling of India’s pandemic vulnerability. Our model is multidisciplinary and recognises the nested levels of the epidemic. We create a model of the risk of severe COVID-19 and death, instead of a model of transmission. Our model allows for socio-demographic-group differentials in risk, obesity and underweight people, morbidity status and other conditioning regional and lifestyle factors. We build a hierarchical multilevel model of severe COVID-19 cases, using three different data sources: the National Family Health Survey for 2015/16, Census data for 2011 and data for COVID-19 deaths obtained cumulatively until June 2020. We provide results for 11 states of India, enabling best-yet targeting of policy actions. COVID-19 deaths in north and central India were higher in areas with older and overweight populations, and were more common among people with pre-existing health conditions, or who smoke, or who live in urban areas. Policy experts may both want to ‘follow World Health Organisation advice’ and yet also use disaggregated and spatially specific data to improve wellbeing outcomes during the pandemic. The future uses of our innovative data-combining model are numerous.

Severity of the COVID‐19 pandemic in India

“…17 We have also found some positive association between CSR or DCSR and multi-morbidity for monthly data, but not weekly data. The estimated coefficient varies across different models, but, for instance, based on Column 4 on CSR, we observe that a 1% increase in urbanization is associated with a 0.36% increase in CSR as consistent with Das et al (2021) and Olsen et al (2020). As expected, we find that the share of those among the elderly with multi-morbidity conditions is positively associated with DCSR or CSR (e.g., a 1% increase in the share is correlated with a 0.54%-0.67% increase in CSR, Columns 3 and 5).…”

“…The authors found that among the eight states that have contributed to over 80% of the confirmed COVID-19 cases in India, as of June 17, 2020, five states had a high vulnerability index value and the remaining three had medium vulnerability (e.g., Maharashtra with 33% of the total COVID cases and the vulnerability index 0.829, the seventh from the bottom). Although Acharya and Porwal have not estimated the vulnerability index using the actual COVID-19 data, their analysis implied the importance of socioeconomic factors, which is consistent with Olsen et al (2020).…”

“…It is evident that socioeconomic factors influence the COVID-19 pandemic and infections, but virtually no studies have considered them in India, 7 particularly at the national level. An important exception is Olsen et al (2020) who have estimated a hierarchical and multilevel model to estimate the correlates of the risk of death because of COVID-19 in 11 states of India, considering the factors at both individual and district levels. The authors combined the National Family Health Survey for 2015/16, Census data for 2011, and estimates of COVID-19 deaths cumulatively up to June 2020 from How India Lives.…”

New Metrics for Assessing the State Performance in Combating the COVID‐19 Pandemic

“…Olsen et al. (2020) estimated the pandemic vulnerability in the early phase in India using a hierarchical model accounting for social/demographic/group differentials among impact factors. Chen et al.…”