Excess Deaths In Tamil Nadu: Wave-2 Deaths Higher Than Wave-1, But Nowhere Near Some Scary Projections
It’s too early to analyse the second wave. Remember that if headlined excess death estimates are erroneous at state-level, headlined pan-India numbers are likely to be even more so.
During first Covid wave, Tamil Nadu’s death registrations were in line with historic trend, pointing to reliable Covid reporting. During second wave, the state witnessed 40,000 above-trend deaths till end-May, which is high but well below headlined estimates.
June data is required for more comprehensive analysis.
Trend Analysis For Tamil Nadu
In the first part of this series of essays (read here) I had outlined an approach to estimating ‘excess deaths’, by viewing Covid-period data against relevant long-run trends.
This essay illustrates that process using data from a non-controversial state. Since most Indians can’t tell MDMK from DMDK, it’s easier to make this about analytics, not politics. All we need is one graph.
Pre-Covid data, in blue, is from the Civil Registration System (CRS) for 2019.
Covid-period data, in red, is from media reports. Note that the two right-most points pertain to four months and one month, while the rest pertain to a year. Just eyeball the data, maybe hold up a ruler against the series of dots.
It’s easy to see that, except for one outlier month (likely to become two), Covid-period deaths are in line with prior trend. This tells us that:
1. First-wave deaths (2020, early-2021) are in line with trend. If excess deaths are the road to undercount, this is a dead-end.
2. This is not so in the second wave. May 2021 witnessed around 40,000 deaths above trend (I deliberately use round numbers for estimates as a reminder that all this is inherently imprecise).
Since June could also be above-trend, we’ll await more data before making any profound inference.
If above-trend equates to undercount, corollary requires us to equate below-trend (or in-line) to no undercount. But I won’t go that far. Some undercount is inevitable everywhere.
Even the West, with better systems, estimates at least a 1.5x undercount.
Let’s just say, outside of inherently unavoidable undercount, Tamil Nadu’s Covid reporting seems reasonable in the first-wave. At the least, excess death analysis doesn’t offer a path to undercount.
One picture, few words, no math. That didn’t seem hard. But, that’s not how it’s typically done.
Different Approach And Inconsistent Data Lead To Inflated Estimates
How do other estimates compare to 40,000? One report has it at 161,581. Another at 129,215 (admittedly, with 13 days of June included). A part of this difference is because both reports directly compare 2020-21 data to the 2018-19 average, without incorporating trend.
To illustrate problems with this approach, had you compared India’s 2019 deaths to the 2016-17 average, the headline would read “1.2 million excess deaths in 2019”. Infinite undercount too.
The second reason for the difference is that the reports use 2018-19 data that neither match each other nor match published CRS-2019 data. There are other inconsistencies.
In one report, Tamil Nadu deaths rose faster in 2019 than 2020, making it odd to claim excess deaths in 2020. While one report provides month-wise data for January-May 2021, the other provides aggregate data till 13 June 2021, mentioning that the state doesn’t provide month-wise data.
So, 2020’s 6.44 lakh deaths is from detailed data uploaded by a media analyst in XL format. With CRS-2019 showing 6.34 lakh deaths in 2019, the 1.6 per cent growth in 2020 is below-trend compared to decadal 3.5 per cent CAGR.
Since no trend is perfectly smooth, it is best to view small deviations as inevitable fluctuations rather than quantify every blip into under/over counts. Unless a deviation from trend is material, we end up analysing noise, not signal.
I mention these quirks so that you appreciate the messiness of it all, not to doubt anyone. Across multiple states, analysts have done a remarkable job of shifting discourse from anecdotes to statistics by meticulously extracting systemic data. I respect them and their work.
What is seen here is inherent unreliability of mid-period data from an evolving death registration system. Getting into inner workings has revealed inconsistencies in every report I have seen (particularly severe in a recent Uttar Pradesh estimation, but that’s for another essay).
When data is patchy and the method is inappropriate, headline numbers are best ignored unless independently corroborated.
What Am I Hiding?
Nothing really, but this is a good question to ask since what’s not said is often more important than what’s said. Let’s make a list.
1. I use averages, not month-wise data. This was okay for the first wave since it was spread over most of 2020. In 2021, did I hide an April-spike? No, since April was 58,000 versus the four-month average of 56,000.
2. Seasonality. Gotcha. What if the January-May baseline is lower than the full year? As it turns out, it’s actually higher in Tamil Nadu, based on available data. More generally, month-wise data is noisier than yearly data, seasonality is often inconsistent and extracting decadal baselines for each month is impossible. I’d rather consistently use full-year averages as trend-data. Soon, we’ll just be looking back at all of 2020 and 2021 without nit-picking over specific months.
3. What about the first 13 days of June? Yes, June seems above trend so far. However, given above inconsistencies, I ignore part-month data that only one of two reports mentioned. I’ll get to June once full-month data is out.
4. No comments on way above-trend deaths in second-wave. I’ll address this and close.
Second-Wave Analysis And Extrapolation
The bulk of Covid deaths in the second-wave happened in April and May, although Tamil Nadu’s delayed second-wave could impact June too. Given the three to four-week lag in death registrations, these will be mostly recorded over May and June.
The registration spike seen in May corresponds to a combination of April and May deaths. Covid death reporting is generally more timely than death registrations.
However, part of it is delayed, as states retrospectively recognise prior-period deaths. Mismatched timeframes for mortality and Covid reporting means we shouldn’t over-analyse short durations (eg, just May). It also doesn’t make sense to start too early, since data is incomplete.
A reasonable approach is to view April-May-June as a single-unit, as soon as data permits. Same analysis should then be repeated for 2021 as a whole, since excess deaths tend to be mean-reverting and full-year numbers might even be lower than half-year numbers.
In summary, it’s too early to (over) analyse the second wave. When others do so, remember that if headlined excess death estimates are erroneous at state-level (as seen with Tamil Nadu), headlined pan-India numbers are likely to be even more so.
I started with a picture. Data embedded in it pertains to a single state. Method embedded in it is universally applicable. It entails framing recent data against long history rather than arbitrarily subtracting one recent data point from another.
Luckily, all required history is available in one place. In fact, on one page — ‘Statement 9’ on Page 28 of CRS-2019 report. Simply framing Covid-period data from media reports against prior data from this page leads to a better inference than what others want you to believe.
Post-script: Apart from data sources mentioned above, data is also available from Tamil Nadu’s CRS system. Unfortunately, that data does not tally with either the national CRS-2019 report (for 2018, 2019) or with data uploaded as part of one of the media reports referred to above. According to TN-CRS, registered deaths grew at a very high 16 percent in 2019 (pre-covid) and 8 percent in 2019. Depending on data source used, TN’s growth in registered deaths varies between 2 percent and 8 percent, compared to trend growth of 4 percent. At the upper end of this range, it is possible that TN recorded around 20,000 above-trend deaths in 2020.
However, with 12,000 reported covid deaths over the same period and not all excess deaths being attributable to covid, this article’s inference of no first-wave undercount (above an inherently unavoidable level) holds. As does its inference of TN’s excess deaths being elevated mainly during May-June 2021, coinciding with second-wave. Per TN-CRS, TN could witness over 100,000 above-trend deaths across May-June 2021, since June appears more elevated than May. However, as this data has been inconsistent with centrally published CRS data in the past, it is best to treat these numbers as provisional.
Anand Sridharan is an investment professional. Views are personal. This article was originally published here. Reprinted in Swarajya with permission from the author. The first part of this series is published here.
As you are no doubt aware, Swarajya is a media product that is directly dependent on support from its readers in the form of subscriptions. We do not have the muscle and backing of a large media conglomerate nor are we playing for the large advertisement sweep-stake.
Our business model is you and your subscription. And in challenging times like these, we need your support now more than ever.
We deliver over 10 - 15 high quality articles with expert insights and views. From 7AM in the morning to 10PM late night we operate to ensure you, the reader, get to see what is just right.
Becoming a Patron or a subscriber for as little as Rs 1200/year is the best way you can support our efforts.