Estimating vulnerability to COVID-19 in India
Authors: Laxmi Kant Dwivedi, Anjali Bansal
Journal: The Lancet Global Health (2020)

In their ecological study, Rajib Acharya and Akash Porwal1Acharya R Porwal A A vulnerability index for the management of and response to the COVID-19 epidemic in India: an ecological study.Lancet Glob Health. 2020; 8: e1142-e1151Summary Full Text Full Text PDF PubMed Scopus (122) Google Scholar measure the vulnerability of India to COVID-19 in five main areas: socioeconomic, demographic, housing and hygiene, availability of health care, and epidemiological conditions. The authors examined the five dimensions and 15 indicators of vulnerability for all the districts and states of India separately, which can contribute to mitigating the ongoing pandemic. However, the study has some shortcomings. The proportion belonging to a scheduled caste or tribe might be a weak indicator of socioeconomic vulnerability because, in the northeastern states, most of the population are part of scheduled tribes and their socioeconomic status is entirely different from the rest of India. Further, the categorisation of households as non-poor on the basis of having a bicycle is very weak because about 16% of households in India have only a bicycle out of the full list of household commodities, of which about 90% are in the lowest two wealth quintiles.2International Institute for Population SciencesNational family health survey (NFHS-4), 2015–16. International Institute for Population Sciences, MumbaiDecember, 2017https://ruralindiaonline.org/library/resource/national-family-health-survey-nfhs-4-2015-16-india/Date accessed: July 20, 2020Google Scholar Acharya and Porwal reflect that the residents of Bihar are less socioeconomically vulnerable than those in Madhya Pradesh, Rajasthan, and Tripura and are equally vulnerable as the residents of Dadra and Nagar Haveli, but Bihar is in fact less socioeconomically developed than these states and Union Territory. Therefore, demographic vulnerability indicators should have been derived using the 2011 census for the year 2020 via an appropriate projection method or using an available population projection report. Most of the indicators in Acharya and Porwal's availability of health-care domain are derived using perception-based questions in the National Family Health Survey 2015–16 (NFHS-4),2International Institute for Population SciencesNational family health survey (NFHS-4), 2015–16. International Institute for Population Sciences, MumbaiDecember, 2017https://ruralindiaonline.org/library/resource/national-family-health-survey-nfhs-4-2015-16-india/Date accessed: July 20, 2020Google Scholar and these should have been given lower weighting in the vulnerability index than other indicators of health care (eg, the availability of public health hospitals and beds). Also, the variable related to health insurance underestimates the current scenario, because most of the health insurance schemes such as Ayushman Bharat Yojana were launched after the survey. Acharya and Porwal exclude women older than 49 years and men older than 54 years. However, the onset of most chronic morbidities in both men and women starts after the age of 40 years,3Qiao Q Hu G Tuomilehto J et al.Age-and sex-specific prevalence of diabetes and impaired glucose regulation in 11 Asian cohorts.Diabetes Care. 2003; 26: 1770-1780Crossref PubMed Scopus (310) Google Scholar hence the results are not aligned with the part of the lifespan in which these diseases manifest. Furthermore, the burdens of diabetes, cancer, and cardiovascular diseases might not truly estimate the district prevalence in the total adult population because this information was collected to provide state-level estimates in NFHS-4. So, considering other surveys such as the National Sample Survey and Longitudinal Ageing Study in India in the computation of epidemiological vulnerability might provide better results. The authors should also have calculated rural–urban differentials. Acharya and Porwal have used few variables to calculate vulnerability. It would also be important to consider access to mobile phones, because mobile phones have been used as a vital strategy to raise awareness about COVID-19;4Manjunath BS Covid-19: 8 ways in which technology helps pandemic management.The Economic Times. April 14, 2020; https://cio.economictimes.indiatimes.com/news/next-gen-technologies/covid-19-8-ways-in-which-technology-helps-pandemic-management/75139759/Date accessed: July 18, 2020Google Scholar adult nutritional status, since it has an effect on immunity; and the flow of migration.5Rai D As migrants return, Covid-19 cases spike in home states, shows study. India Today.https://www.indiatoday.in/diu/story/as-migrants-return-covid-19-casesspike-in-home-states-showsstudy-1682238-2020-05-26Date: May 26, 2020Date accessed: July 20, 2020Google Scholar Finally, the authors should have provided standard errors or confidence intervals for each indicator or domain of vulnerability. We declare no competing interests. Estimating vulnerability to COVID-19 in IndiaRajib Acharya and Akash Porwal1 calculate a composite COVID-19 community vulnerability index at the state and district level in India. The index is based on a set of 15 indicators across five domains that contribute to vulnerability or the consequences of COVID-19 disease in the population. Full-Text PDF Open AccessEstimating vulnerability to COVID-19 in India – Authors' replyWe express our sincere thanks to several colleagues who took interest in our Article on a vulnerability index for management of COVID-19 in India1 and provided valuable feedback. We will address the comments in a collective way, not individually. Full-Text PDF Open AccessA vulnerability index for the management of and response to the COVID-19 epidemic in India: an ecological studyThe vulnerability index presented in this paper identified a number of vulnerable districts in India, which currently do not have large numbers of COVID-19 cases but could be strongly impacted by the epidemic. Our index aims to help planners and policy makers effectively prioritise regions for resource allocation and adopt risk mitigation strategies for better preparedness and responses to the COVID-19 epidemic. Full-Text PDF Open AccessEstimating vulnerability to COVID-19 in IndiaIn the ongoing COVID-19 pandemic, timely and best available data surveillance is crucial to guide policy decisions. Rajib Acharya and Akash Porwal (September, 2020)1 exemplify this by developing a vulnerability index based on socioeconomic, demographic, housing and hygiene, epidemiological, and health system characteristics across states and districts in India. Such exercises can help decision makers identify priority areas for the purpose of resource allocation and designing containment and mitigation strategies. Full-Text PDF Open Access