Home >Opinion >Views >Opinion | Our youth may not shield us from high covid mortality

As India and several other countries contemplate opening up their economies, one argument that supports the strategy of a gradual release from lockdown is that the country’s young demographic profile will act as a protective shield. Covid-19 has had a more severe impact on populations of older adults across countries. In Italy, the likelihood of dying on account of the disease was estimated at 20% among those over age 80, but less than 0.5% among those below 50. The corresponding figures for South Korea are 13% and 0.5%. Areas where older demographic groups form a higher share of the total population are thus likely to see higher deaths and a higher fatality rate overall.

India’s median age is 27 years, compared with South Korea’s 41 and Italy’s 47. There is, therefore, reason to believe that fatality rates may be lower in India. However, certain factors point in the opposite direction. In general, Indians at a given age are likely to have worse health than Italians and South Koreans. The prevalence of lung infections due to exposure to heavy air pollution or of co-morbidities may make the young much more vulnerable here. Healthcare quality also varies and many individuals may not have access to treatment.

As of now, nobody knows whether the protective advantage of India’s youth will outweigh the disadvantage of its health dispositions and health system. We examined the data so far to see if there was a sign that places with younger populations were experiencing lower fatality rates. If they are, then local age distributions might be a useful factor to consider in deciding which places to reopen.

The data challenges, however, are significant. The fatality rate reported in most countries is the case fatality rate (CFR), calculated as the number of deaths due to covid-19 divided by the number of its infections. In India, there are two major challenges to calculating the CFR. First, due to insufficient testing, we don’t know the true number of deaths caused by covid-19. Second, for the same reason, we don’t know the true number of infections. As a result, we are somewhat in the dark, and will be so until the country can dramatically scale up its testing.

Working with the data we have, we first predicted what mortality rates would be if Indians in each age group had the same CFR as that observed in Italy. Italy has an aggregate CFR of around 9%. If Indians had the same fatality rate at every age as Italians, then our aggregate CFR would be only 1%. This shows the potential protective effect of a young population.

However, India’s CFR so far, at 3.3%, is considerably higher. We don’t know if this is because we are failing to count a large number of infections (thus driving up the measured CFR), or because the factors noted earlier give India a much higher age-specific fatality rate than Italy.

One worrisome fact is that in Maharashtra (the only major state to report death rates by age), the CFR begins to rise at a much younger age. In Italy, mortality starts to spike after age 60; the CFR is 1% for 50–59 year olds and a disconcerting 12.8% for 70–79 year olds. In Maharashtra, young people also have low fatality rates, but the CFR starts to rise at much younger ages— it is 4% at age 40–49 and 8% at age 50–59. We don’t know if these numbers reflect the true infection fatality rate—but if they do, then it’s clear that even the youth in Maharashtra have a relatively high probability of death from covid-19.

How much does variation in age affect which states will be hard hit? Using state-level age distributions to predict mortality in each state, we find that the estimated fatality rates do not vary all that much. Since there is little variation in the age profiles of states, one of the lowest risk states (namely, Jharkhand) has just half the predicted mortality of one of the country’s highest risk states (Kerala). All Indian states are fairly young; the share of their population under the age of 30 varies from 47% in Kerala to 65% in Bihar.

On the other hand, there is a wide variation in actual CFRs calculated using the reported number of deaths and cases across Indian states. If we consider all states that have more than 20 reported cases and at least one reported death, the CFR varies from 0.4% in Kerala to 6.1% in Jharkhand. This is the reverse of what the age distribution predicted. It also suggests that relative poverty may be a bigger predictor of death in this country than age.

In all these cases, however, we are merely guessing at numbers in the dark at the moment. Without a large-scale testing regime, it may be several months before we know the true fatality rate and how it varies across the country.

That is why it is so important to scale up a population-based testing regime. At a fairly low cost, we could calculate covid-19 mortality rates more accurately, and use the findings to understand which places are going to be the hardest hit in terms of mortality. Investing in getting basic facts about the mortality rate in India should be considered an essential step in planning a re-opening that does not cause more harm than necessary.

Anup Malani, professor at the University of Chicago Law School and Pritzker School of Medicine, Sahil Gandhi of University of Southern California and Brookings India, and Sam Asher, assistant professor of international economics at Johns Hopkins SAIS, also contributed to this article.

Vaidehi Tandel and Paul Novosad are, respectively, junior fellow at IDFC Institute and assistant professor of economics at Dartmouth University

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