Life & death questions in the Covid pandemic

Economic revival in both rich and poor states is key to preventing more deaths

Haruki Murakami observed in Norwegian Wood in 1987 in a philosophical vein, “Death is not the opposite of life but an innate part of it.” Indeed, but it has become more so with the corona pandemic. More importantly, however, TV images of corpses covered in white sheets with patients on either side in public hospitals are heart-wrenching. Long queues at cremation grounds becoming longer by the day, with many not able to afford the escalating costs of cremation abandoning the dead at these places, are deeply disturbing too. As the pandemic continues to surge, such images and worse will proliferate and become more agonising.

Following a recent World Bank study (Covid-19 Mortality in Rich and Poor Countries: A Tale of Two Pandemics?, June, 2020), we construct two measures of severity of Covid-19: One is cumulative severity ratio in which fatalities from the date of first death to the cut-off date (June 21, 2020) are divided by all-cause deaths during a period of same duration in a pre-Covid year; another is daily severity ratio in which Covid fatalities on a particular date are divided by the daily deaths in a pre-Covid year. The cumulative severity measure indicates a pressure point on the health system which may have adapted to deaths in a normal year. The daily ratio instead focusses on the progression of the Covid pandemic: whether it has intensified, or weakened or remained unchanged.

 We have analysed official data on Covid-19 cases and fatalities in selected states of India during March 13 (date of first death in India) to June 21, 2020. The period for each state, however, varies depending on when the first Covid fatality occurred.

We have selected two rich states, Delhi and Maharashtra, and two poor states, Bihar and Madhya Pradesh.

Each state displays a somewhat unique pattern in the rise of the cumulative severity. Briefly, Delhi shows a slow rise until May 13 and thereafter a sudden spike from June 9 to June 17, followed by a third spike until June 21, with the cumulative severity rising to 5.6 per cent. Maharashtra, by contrast, shows a steady rise with a sudden spike between June 17 and June 21, peaking at 3.48 per cent. The severity ratio of Delhi crossed that of Maharashtra on June 3, and since then, the gap has been widening.

Madhya Pradesh shows a surge in the cumulative severity during the first two lockdown phases and thereafter a slower rise. Bihar shows a U-shaped pattern with a sharp fall until May 3, and then a gradual rise. The mean cumulative ratio for Madhya Pradesh (0.36 per cent) is much higher than for Bihar (0.02 per cent) between March 25 and June 21.

Delhi shows a flat but low daily severity ratio in the first three lockdown phases, followed by sharp spikes thereafter. The mean daily ratio spiked from 5.5 per cent in the fourth lockdown phase (ending May 31) to 21.7 per cent in the first three weeks of June. In sharp contrast, Maharashtra’s slightly lower mean of 4.28 per cent more than doubled to 10.17 per cent.

In yet another striking contrast, the two poor states, Madhya Pradesh and Bihar, show not only considerably lower means but also slight increases. In Madhya Pradesh, the daily ratio rose from 0.70 per cent to 0.74 per cent, and, in Bihar, from 0.13 to 0.21 per cent between the fourth lockdown phase and the first three weeks of June.

From a policy perspective, the cumulative severity is of much greater interest than the daily severity, as it points to additional pressure on a fragile and creaky health system.

Our findings suggest that the higher the (proportionate) increase in (lagged) Covid cases, there is a less than (proportionate) increase in the cumulative severity ratio. While income induces the highest increase, the effect diminishes with higher increases in income. So growth revival in both rich and poor states is likely to increase the cumulative severity but at a diminishing rate. Specifically, if rich states grow at a slower rate than poor states, the severity is likely to converge to a lower rate. Urban population density — as a proxy for social distancing — is associated with a more-than-proportionate increase in the severity ratio. So as of now, the higher incidence of cumulative severity is only partly driven by any likely “contagion” effects.

A daunting challenge is discovery of an effective coronavirus vaccine and its availability in the next six-nine months in India. Among other policy imperatives, economic revival in both rich and poor states is key to preventing deaths and ensuring healthy lives.

Kaicker is assistant professor of management, Ambedkar University, Delhi; Gaiha is research affiliate, Population Studies Centre, University of Pennsylvania, USA, and (Honorary) Professorial Research Fellow, Global Development Institute, University of Manchester, England