When India locked down on the midnight of March 24, the country had reported just over 500 cases of Covid-19, with 10 dead from the disease. A month into what has been the world’s largest and arguably the most stringent lockdown, the number of cases has breached the 20,000 mark, with 686 people dead by Thursday.

This rate of increase in cases is significantly gentler than most Western nations. Yet, a country-wide growth rate may provide little insight into what might lie ahead for India once the lockdown lifts and inter-state migration resumes, thanks to the great disparities in several key indicators across states.

After all, testing rates in states of comparable sizes and populations widely vary.

Consider Madhya Pradesh and Tamil Nadu, two states with almost the same number of people. While the two states are neck and neck as far as the absolute numbers of cases go, their testing rates are widely divergent. Till April 22, Madhya Pradesh had tested around 350 samples per million people, while the corresponding number for Tamil Nadu was more than double at above 700. One obvious outcome of that is that the positivity rate – the number of samples tested per positive case – in Madhya Pradesh is twice that of Tamil Nadu.

The two states differ on another key yardstick: the mortality rate. Madhya Pradesh’s mortality rate is more than 5%, which is among the highest in the country. Tamil Nadu, on the other hand, has a mortality rate of just over 1%.

What do these numbers mean? Does testing rate have a bearing on mortality? Is there a connection between testing rate and positivity rate? Scroll.in spoke to several experts to answer some of these questions.

Making sense of testing and positivity rates

Tarun Bhatnagar is a scientist at the National Institute of Epidemiology, a unit of the Indian Council of Medical Research that is spearheading India’s response to the Covid-19 crisis. According to Bhatnagar, who is part of the ICMR’s Covid-19 rapid response team, a high testing rate, in general, would mean that a state’s contact-tracing program was working. “The labs are working, the surveillance programme is working pretty well,” he said.

However, along with its testing rate, if a state’s positivity rate kept going up – that is, fewer samples tested per positive case – it meant that the state was not being able to contain the outbreak. “Then transmission is still continuing – that there are still people who are infecting people,” said Bhatnagar. “However, the good thing is that there is testing happening and people are being detected.”

Credit: Nithya Subramanian

Examples of such states would be Delhi and Maharashtra. Both these states have positivity rates much higher than the national average. At the same time, though, they were testing significantly more samples per million population than other states and the nation as a whole.

However, the best-case scenario is for the positivity rate to decline as testing increases – as it has in Kerala. “The transmission has slowed down in such a state,” explained Bhatnagar. “All those who were positives have been isolated. Containment measures are working well.”

So what differentiates Delhi or Maharashtra from Kerala? According to T Jacob John, one of India’s leading virologists and emeritus professor at Vellore’s Christian Medical College, the answer is simple. “It is all about how effective your public health interventions are,” he said. “Kerala started early and hit hard with aggressive testing, unlike Maharashtra and Delhi.”

He added: “The fact that Delhi missed the Tablighi gathering in March tells you something.”

Credit: Nithya Subramanian

At the opposite end of the spectrum from Kerala are Uttar Pradesh and West Bengal, states with a below average testing rate and a high positivity rate. The two factors together, experts say, is an almost sure-shot indicator that several people are going undetected in the state. “If your testing rate is not that good – good and bad are, of course, relative – and if you are getting a high positivity rate, there could be a selection bias,” said Bhatnagar.

Gujarat could also be clubbed in this bracket, say observers. Its testing rate is significantly higher than Uttar Pradesh and Bengal, but considerably lower than states with comparable case density like Maharashtra and Rajasthan. The state also has the highest doubling rate of cases, as a recent analysis in the The Hindu illustrates. “Peaking when the country is stabilising means surveillance was bad all along in Gujarat,” said an epidemiologist tracking the state.

The epidemiologist requested not to be identified, fearing conflict with the state authorities.

While officials in West Bengal denied allegations that the state was not doing adequate contact tracing, health officials in both Gujarat and Uttar Pradesh did not respond to calls and messages seeking comment.

Then there are states which have low and middle-of-the-table testing rates, but low positivity rates. Examples of such states would be Bihar, Chhattisgarh and Orissa.

The trajectory of these states could be difficult to predict, say scientists. While it is possible that the virus simply has not reached these places yet, it could also be because they were not testing the right people. “It depends upon whom you are testing,” said John. “If you increase the testing and it is done intelligently you will find the real picture.”

Giridhar R Babu, professor and head of life course epidemiology at the Public Health Foundation of India, seemed to agree on the need for more focused testing. “If you go age-group wise and take a more syndromic approach, you are likely to catch more positives,” he said. Syndromic approach, which was adopted to identify HIV cases, for instance, is based on identification of consistent groups of symptoms and easily recognised signs.

In the absence of in-bound foreign travellers, India is now largely sticking to testing contacts of laboratory confirmed cases (including healthcare workers), and patients hospitalised with pneumonia-like symptoms.

The first category (asymptomatic contacts of confirmed cases and healthcare workers tending to them) was being tested for what John called “public health” reasons. “The purpose is to stop further spread,” he reasoned. “This testing is not interested in your health – you are tested so that you can be put in quarantine and you don’t spread.”

In contrast, when a hospitalised patient is tested for Covid-19, that is for “diagnosis”, said John. “You have to do a mix of both,” said John. “Only then you will get an accurate picture as in Delhi.”

Babu agreed: “In Delhi, both surveillance and contact tracing seem to be working.”

A man is tested for Covid-19 in Tamil Nadu. Credit: PTI

Are some states then missing out patients of the second category because they are spending all their resources on testing contacts of confirmed cases? Besides, are patients with mild symptoms not being able to go to hospitals because of the lockdown in remote areas? There is simply not enough data to confirm or negate that, said experts. “The noise should be around making SARI [severe acute respiratory infections like pneumonia] data available,” said Babu. “If districts are not reporting, but SARI is showing a high positivity rate in those districts, what measures are we taking?”

Babu was making a case for a closer scrutiny of districts where a large proportion of Covid-19 patients had come in with pneumonia-like symptoms.

Bhatnagar agreed that there was a need to look into the breakdown of the tested samples more minutely. “Among those who are tested, we have to look at who exactly is being tested,” he said. “Data is in each of the testing labs – we can only comment on it when we take a look at that.”

Making sense of mortality rates

Finally, the all-important (and somewhat controversial) confirmed mortality rate defined as the ratio of the number of deaths to the number of confirmed cases. India’s overall mortality rate of 3.15% is significantly less than the global rate, which stands at nearly 7%.

Yet, like most other Covid-19 related indicators, the death rate varies significantly within the country. Among states with more than 100 confirmed cases, Punjab leads the pack with a death rate of 6.37 %. Madhya Pradesh follows with a death rate of over 5 %. Yet again, Kerala stands out. Its mortality rate is a mere 0.7%.

What explains these differences? Mortality rate, Bhatnagar said, was contingent on a range of factors.

One of the most important being the profile of the patient – the age group and existing comorbidities. This is something that a state would have little control over.

Credit: Nithya Subramanian

Anurag Agarwal, who heads Punjab’s health department, blamed the state’s high fatality rate on comorbidities among patients. “All the patients barring two-three had very high comorbidity,” he said. “Someone was bed ridden, someone was HIV positive.”

Besides, he insisted that data being “not very large to put in a trend” (as of April 22, 16 of the 251 people who had tested positive had died).

The other two factors which affect mortality, according to Bhatnagar, are external: the stage at which the disease is detected and, eventually, state preparedness in terms of hospital capacity.

Late detection of cases is largely an outcome of lackadaisical testing, said observers. Both Punjab and Madhya Pradesh have testing rates in the lower end of the table. While Madhya Pradesh did not respond to queries, Punjab said its low testing rate was a result of inadequate infrastructure in the initial days of the outbreak. Anurag Agarwal of the Punjab health department said there was only one testing facility when the epidemic hit the state. “Later on we added more facilities and ramped up our capacity,” he said, adding that the testing numbers would “really go up” in the subsequent weeks.

Even Maharashtra, which closely follows Madhya Pradesh in terms of mortality rate, started to test aggressively only last week of March onwards. While the state now has one of the highest testing rates in the country, the late start may have resulted in cases being detected late resulting in higher mortality numbers.

But there are outliers here too. Uttar Pradesh, for instance, has a mortality rate of less than 1.5 % despite having tested much less than, say, neighbouring Madhya Pradesh. In terms of state capacity, the two states are fairly similar too. What gives?

“When you do not report your cases well, you do not report your deaths well too,” said Babu. “There is something called survivor bias: only those who survive are reported.”

While Bhatnagar agreed that Uttar Pradesh and Madhya Pradesh were quite comparable in terms of “baseline health” indicators, the political turmoil that roiled the latter during the initial stages of the pandemic could have marred its response, he said. “I don’t know if that also led to differences,” he said.

On March 20, days before the country went into a lockdown, the incumbent Congress government in the state collapsed as several legislators of the party defected to the Bharatiya Janata Party.

Virologist John, though, had a bleaker thesis. “We are in the early stages of an epidemic, the numbers will even out soon,” he said. “When you watch a movie, the first ten minutes of the movie will not tell you the whole story.”