COVID Excess Mortality Rate and Lockdowns and Vaccinations Compared

Excess mortality, as a concept, emerged with the pandemic. It refers to the difference between expected and observed mortality in a given period. The aim was to eliminate biases caused by different testing and cause-of-death assignment. In May 2020, Chief Medical Officer Cecília Müller claimed that “we cannot speak of excess mortality during the pandemic.” The statement that was supported by the data at the time.

Then, the numbers got worse. They got worse, and the government media continued to assert that all was fine. Now, four years have passed since the outbreak. It was time to analyse the data, so we sat down with biostatistician Tamás Ferenci.

If there was already a latent distrust and paranoia in Hungary prior to the pandemic, Covid-19 provoked and exacerbated it. An already semi-transparent government became shrouded in secrecy. Factions became near-tribal – PestiSrácok, for example, found Átlátszó’s compilation and demonstration of the statistics provided by Cecília Müller’s team through graphs and maps on a site called Koronamonitor highly suspicious and hostile for reasons not entirely clear yet.

Data was concealed and management strategies were obfuscated, which did little to improve public trust in institutions. Metrics changed overnight with no announcement, which meant effective pandemic responses were difficult to measure. Success became a fluid concept – concerning, particularly because the stakes were lives.

To combat the obfuscation and instrumental vagueness, the concept of excess mortality was introduced. The government eagerly invoked this index. So did the opposition.

“It is widespread fact that we have always accounted for excess mortality during influenza epidemics,” said Müller on May 27, 2020. “This cannot be the case during the Covid pandemic, but even so – including the deaths attributable to the pandemic – the mortality data for the first three months of this year is lower in Hungary than it was last year.”

This was true. We discussed with biostatistician Tamás Ferenci exactly what the government indices mean when they are looked at in conjunction with excess mortality. In addition, we discuss:

• Is it worthwhile to compare excess mortality cross-nationally?
• What were the real impacts of the Chinese vaccine numerically?

We also examine how the Epidemiological and Surveillance Centre of Semmelweis University responded to our inquiry.

What did the lockdowns accomplish, and how can we measure their effectiveness?

Ferenci’s study demonstrated the impact of a lockdown using a Czech case study. After the lockdown, the number of infections continued to rise for two to three weeks, and deaths increased over an even longer period due to the effects of incubation time and the delay from infection to death.

“This is the inertia of epidemic management, and it holds a crucial lesson: in epidemic management, it is a very dangerous tactic to wait until the situation deteriorates and only then act,” he wrote. “It risks us following outdated trends in infections and deaths. If the trend is rapidly increasing, this can cause huge problems.”

We created a chart that depicts the steps of domestic epidemic management alongside the excess mortality statistics:

According to Ferenci, however, the chart raises more questions than it answers.

“The curve is shaped by various variables,” he analysed, “like the start and end to measures, the presence or absence of mass gatherings, the use or non-use of masks, lockdowns and openings, behavioural changes, changes in the virus itself, vaccination administration, immunity, re-vaccination, other viruses, seasonal effects – and on and on and on.”

Focusing on 15 factors out of a million suggests that those 15 are the ones that need to be taken into account when reading the chart, he noted. The other variables aren’t included. What if the ignored variables impact the relationship? What if they are confounding variables? Failing to account for confounding variables can lead to distorted or spurious relationships.

“It’s why I find these sorts of graphs very misleading or unfortunate,” said Ferenci.

The biostatistician believes that no conclusions can be drawn from excess mortality through this kind of statistical analysis and visualization. He emphasizes in his own study that the Czech example works only because it examines a very short, homogeneous period with a single, impactful, and temporally concentrated measure.

A death campaign faces off against excess mortality rates – which, if either, is backed by the data?

Data reveals that, in Hungary, the number of Covid deaths and the excess mortality measure closely align. If we were to rank countries based solely on excess mortality during the pandemic, Hungary would place ninth from the bottom within the EU.

“EU data refutes the leftist death campaign,” stated the then-deputy president of the KSH (Hungarian Central Statistical Office) László Windisch, currently the president of the State Audit Office, according to government media from April 2021. By that time, the first wave had passed. A nighttime curfew had been implemented, and the first Pfizer vaccines and eastern vaccines had been administered.

Windisch was correct in many respects when he noted that “an examination of excess mortality data provides a more accurate picture than the statistics on COVID-19-related deaths provided by individual countries.” The latter statistic is influenced by both the rules surrounding cause-of-death classification and the intensity of testing, both of which varied across countries.

In March 2023, two years later, mandiner.hu reinforced the government narrative. “The opposition’s claims during the pandemic were not true,” they wrote. “Statistics prove that the claim that Hungary had the highest excess mortality in Europe was false.”

It should be noted that no such general claim was ever made, only regarding specific moments during the pandemic. An overall assessment ranked Hungary in the bottom third of European countries. Whether this is a success or not is up to interpretation.

We examined another study with Ferenci, titled “Differences in Excess Mortality Among Countries with Varying Vulnerabilities from 2020 to 2023,” published in the journal PNAS. The research showed that the virus predominantly caused deaths among those over 64 years of age in Hungary, as it did in Italy and Germany.

Chile, according to the study, had one of the worst excess mortality rates. Chile administered the Chinese vaccine, which was also popularly given in Hungary. Ferenci warns, however, against jumping to any conclusions.

“If you write that Israel vaccinated with Pfizer and had few deaths, while Chile used the Chinese vaccine and had many, it implies that the type of vaccine used is significant,” he explained. “Why didn’t you say that in Israel, where a lot of testing was done, there were fewer deaths, while in Chile, where less testing was done, there were many? Or that in Israel, there are more nurses per hundred thousand people, leading to fewer deaths, whereas in Chile, with fewer nurses, there were more deaths? That in Chile, where more people smoke, there were more deaths, while in Israel, where fewer smoke, there were fewer? And so on, and so forth. The list can go on indefinitely,” he said.

“Countries differ in countless factors beyond just the type of vaccine used,” he continued. “Therefore, even if we find a difference in mortality rates, we cannot definitively say it is due to the vaccine, or other factors, or possibly some combination of these factors,” the expert pointed out. Interpreting excess mortality is not straightforward – arbitrarily picking one variable and ignoring the others can distort the science.

Despite the vaccination of five million Hungarians, excess mortality continued to rise at the end of 2021.

“We could argue that despite five million people being vaccinated, the level of excess mortality was almost the same as in the previous period when there was no vaccination. Does this mean the vaccine was ineffective? Here comes the second issue: we lack the counterfactual,” said Ferenci.

“What happened at the end of 2021? Winter arrived, increasing the virus’s activity, and the Delta variant emerged, which was more severe. The strict measures of the previous year were gone. The argument could be made that the vaccine didn’t work because the curve went up anyway,” Ferenci notes, “but we have no idea what would have happened without the vaccination! If, for instance, due to the aforementioned factors, the curve would have climbed even higher, then the vaccination indeed worked.

“I should add that the reverse is also true: mortality could have decreased even more without the vaccine, which would make the vaccine harmful. We run into the same problems when comparing different periods within the same country as we do in cross-country comparisons,” he said.

The comparative effectiveness of the Chinese vaccine

Ferenci stressed that it is almost futile to compare countries as a whole because individuals’ health statuses range too widely, as do their access to hospital care and the nurse-to-patient ratio. These factors, both individually and collectively, can impact mortality rates, which would later reflect in excess mortality numbers.

We know that the Chinese vaccine used in Hungary was not the most effective option, although this was not evident in the excess mortality statistics.

According to findings from the HUN-VE 3 study, “the adjusted vaccine effectiveness against hospitalization due to COVID-19 in the primary immunized population aged 65-100 years was 76.6% for Pfizer-BioNTech, 83.8% for Moderna, 78.3% for Sputnik V, 73.8% for AstraZeneca, 45.7% for Sinopharm, and 26.4% for Janssen within 14-120 days after the second dose.”

The authors of this document included Chief Medical Officer Cecília Müller and Miklós Kásler, who was responsible for healthcare at that time.

Ferenci notes that this study used unique data and controlled comprehensively for confounding factors. The official bodies, however, did not communicate its results to the public.

Excess mortality is useful, according to the biostatistician, but not conclusive on its own.

The Semmelweis University Epidemiological and Surveillance Centre Mission

“The Semmelweis University Epidemiological and Surveillance Centre (ESK) was established in October 2020 to contribute to the fight against infectious diseases and non-communicable diseases in Hungary through scientific research methods,” the institution’s website writes.

Additionally, they note: “Through our projects, we play an important role in the collaboration of partners across various sectors. A good example of this is the COVID-19 pandemic, during which we supported stakeholders in managing the pandemic and future epidemic risks by providing better access to data, deeper analytical competencies, and more effective tools and information needed for decision-making.”

They used EU support to create a website that allows for the analysis of territorial inequalities in mortality at the county and district levels between 2007 and 2021, categorized by primary cause of death, gender and three age groups (0-X years, 25-64 years, 65-X years). The same territorial inequalities in mortality due to Covid-19 can also be analysed for the years 2020 to 2021 at the same levels for the same three age groups. ESK also publishes data.

ESK, then, could provide information on the correlation between government measures and excess mortality. We requested an interview with the institute, but the university declined.

“We will not be able to assist with the interview,” they stated, “but the articles uploaded by the Epidemiological and Surveillance Centre are available to the public.”