Madhya Pradesh and Bihar are the States that are most vulnerable to Covid-19, while Sikkim and Arunachal Pradesh are the safest when it comes to the pandemic that has affected more than a million Indians so far, according to a study published in The Lancet .

Two population council researchers – Rajib Acharya and Akash Porwal – who mapped out community-level vulnerability to Covid-19 for the first time in the country, also marked out 20 most vulnerable districts in the country with Darbangha in Bihar being the worst. On the other hand, three districts in Sikkim – South, North and West are the safest.

To compute the composite index of Covid-19 vulnerability, the researchers used 15 indicators across five domains – socio-economic, demographic, housing and hygiene, epidemiological, and health system. While the State or district with 0 index is the least vulnerable, those with an index score of 1 are the most vulnerable.

The study found that districts with high vulnerability (with an index of greater than 0.75) are there in every region in the country, barring the north-east, but the maximum number of them are from nine large States – Bihar, Madhya Pradesh, Telangana, Jharkahand, Uttar Pradesh, Maharashtra, Odisha and Gujarat. The researchers said these States also had an index score of 0.75 and thus highly vulnerable among the Indian States.

Although our intention was not to predict the risk of infection for a district or State, we observed similarities between vulnerability and the current concentration of Covid-19 cases at the State level. However, this relationship was not clear at the district level,” they said in the paper.

While the districts from north-eastern States crowded the top-20 least vulnerable districts, five districts from Himachal Pradesh and one district each from Haryana, Uttarakhand and Jammu and Kashmir also found mention among these. Among the most vulnerable districts were eight from Bihar, six from Uttar Pradesh, and four from Madhya Pradesh.

While the Scheduled Caste and Schedule Tribe households, education level of population, and poor households in the State determined its socio-economic index, the share of elderly population, urbanisation and population density used for counting demographic vulnerability. The researchers used parameters such as people per room, households with no toilet facility, and households with no hand-hygiene facility determined housing and hygiene vulnerability condition.

For measuring epidemiological vulnerability, they used data regarding the number of men and women with chronic morbidity and the number of men who smoke in the district or State population. The weakness or strength of the public healthcare system was yet another parameter used by the researchers for arriving at the composite index.