Government Interventions Linked to Higher Excess Mortality - Vaccines Show No Positive Effect on All-Cause Mortality

New analysis of 2020-2023 all-cause mortality data suggests that stringent lockdowns, rising poverty, and pre-existing health issues were the main drivers of increased excess deaths.

Note: A previously published version of this article contained inaccurate data, which was kindly brought to my attention by a reader, Henjin. The data has since been corrected, and the analysis has been expanded to include new insights that emerged as a result. Thank you for your vigilance!

Background

In 2024, many countries have reported detailed all-cause mortality data by age, allowing for a comprehensive retrospective analysis of the impact of lockdowns and vaccinations. By utilizing Age-Standardized Mortality Rates (ASMR), we can better assess how government interventions, such as lockdowns and vaccination campaigns, correlate with mortality outcomes during the COVID-19 pandemic, independent of age—a major confounding factor. This approach provides clearer insights into how these measures influenced public health across different populations.

Data & Methods

For the mortality data, I used age-standardized values provided by Mortality.Watch and applied a simple 3-year pre-pandemic average. While there are various methods to estimate excess mortality, as Levitt et al. have argued, there is no universal standard for extrapolating mortality rates from trends. Using trends, as often done, may “result in generating an unrealistically low number of expected deaths.”

To address this, I applied a straightforward 3-year average of the 2017-2019 age-standardized mortality rate, following the cautious, conservative methodology used by Levitt et al.

The government response was measured using the Oxford Government Stringency Index, which quantifies the strength of government interventions on a daily scale from 0% to 100%. For this analysis, I aggregated this data annually.

COVID-19 vaccination was measured as the percentage of the population that received at least one dose. While further analysis based on the number of doses is possible, it is important to acknowledge the influence of confounding factors, such as the “healthy user” and placebo effects. Individuals who are more health-conscious and confident in vaccines may be likely to receive more doses, while those who are skeptical or in poorer health may receive fewer or none. If the vaccine’s effectiveness is as high as reported (e.g., “95%”), we should observe a significant impact even after a single dose. Notably, the difference in coverage & efficacy between one and two doses was often marginal.

Possible confounding variables were included from the Our World in Data (OWID) dataset & the CIA World Factbook.

Results

Government Stringency

When analyzing the correlation between government stringency and excess mortality, a significant relationship is observed in 2020 and 2022. However, this correlation disappears in 2021, likely due to the higher overall stringency levels during that year.

When grouping the data by levels of government intervention for 2020, we find that the group with ‘low’ intervention group also experienced the lowest excess mortality.

This suggests a potential relationship where higher levels of government intervention were associated with higher excess mortality.

An often-cited argument—that governments responded because COVID-19 levels were higher—is flawed. A novel pathogen, alleged to cause COVID-19, should theoretically spread equally across all populations.

COVID-19 Vaccination

The correlation plots between vaccination coverage and excess mortality reveal a distinct pattern in 2021 - it appears as if the vaccine significantly reduces excess mortality. However, this correlation disappears in 2022 and 2023. But let’s take a closer look…

Naturally, I remained skeptical and suspected potential confounding, as previously highlighted by Ulf Lorre, given that this is high-level population data. To address this, I included 17 additional variables and plotted them against excess mortality.

Highlighted are all Pearson correlations above (or below) ±0.4, indicating at least a moderate relationship. The year 2021 is heavily influenced by at least six confounding variables: Tobacco/Smoking, Life Expectancy, HDI, GDP, Poverty, and Cardiovascular Deaths.​

Then I adjusted the excess mortality rates to account for the impact of the confounders. Below is an overview of the adjusted excess mortality rates for the past three years, considering six moderate or higher confounders.

And voila, the correlation observed in 2021 completely disappears, revealing itself as a spurious relationship!

This is how the adjusted chart looks like when we just adjust by Extreme Poverty:

And here adjusted by Cardiovascular Deaths:

Conveniently OWID has a map of Cardiovascular deaths, which confirms there’s a steep slope between rich and developing countries.

OWID summarizes what causes these kinds of deaths: “Cardiovascular diseases encompass all conditions affecting the heart and blood vessels, including heart attacks, strokes, atherosclerosis, ischemic heart disease, hypertension, cardiomyopathy, rheumatic heart disease, and more. These conditions typically develop gradually with age, particularly in individuals with risk factors like high blood pressure, smoking, alcohol use, poor diet, and air pollution.”

This suggests that excess mortality was largely driven by the impacts of lockdowns, which exacerbated existing health & environmental issues, such as cardiovascular diseases linked to unhealthy lifestyles, drugs & alcohol, and possibly pollution.

Summary

The analysis of government stringency and excess mortality reveals significant correlations in 2020 and 2022, which weaken in 2021. This change is likely linked to widespread lockdowns, which led to increased excess mortality across countries. Grouping the data by levels of government intervention shows that countries with the lowest levels of intervention experienced the least excess mortality, suggesting a potential association where stricter interventions corresponded with higher excess mortality.

When examining the relationship between COVID-19 vaccination and excess mortality, a noticeable correlation appears in 2021 but fades in 2022 and 2023. To account for potential confounding, 17 variables were included in the analysis, such as tobacco use, life expectancy, GDP, extreme poverty, and cardiovascular death rates. After adjusting for these strongest confounders, the previously observed correlation in 2021 disappears completely, indicating that the initial relationship may have been driven by these confounding factors.

Contrary to prior assumptions, stringent government interventions may have contributed to higher excess mortality, and vaccination did not show the anticipated reduction in all-cause excess mortality. There remains a possibility that vaccines may have increased mortality due to known side effects, including heart and neurological issues. Countries with minimal interventions appeared to have lower excess mortality from 2020 to 2023 compared to those with stricter measures. Vaccination did not show a clear positive impact on all-cause mortality.

Code & Data

• Github: 1, 2, 3, 4

• OWID