Excess deaths are a statistical measure used to determine the total mortality impact of a crisis. It is not a direct count of deaths from a specific cause, but a comparison between the total number of deaths that occurred and the number that were expected to happen during that same period. This calculation provides a comprehensive view of a crisis’s impact on mortality, capturing consequences beyond the primary cause.
How Excess Deaths Are Measured
The calculation of excess deaths requires establishing a baseline of expected deaths. This baseline represents the number of fatalities that would have occurred without a specific event like a pandemic or natural disaster. Statisticians and epidemiologists create this baseline by averaging the total number of deaths from the previous five years for a given period. The exact years used can vary between organizations to avoid skewing the data with prior anomalies.
Once the baseline is established, it is compared against the observed number of deaths from all causes for the current period. The difference between the observed and expected deaths is the excess mortality figure. Organizations like the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) compile this data, making adjustments for factors like population growth or changes in age structure to ensure accuracy.
The data can be presented as a raw number, showing the total scale of the loss, or as a percentage increase over the baseline. The percentage, sometimes called a P-score, is useful for comparing the impact of a crisis between countries or regions with different population sizes. For instance, a P-score of 50% means a region experienced 50% more deaths than expected for that period.
Drivers of Excess Mortality
The causes of excess deaths are complex and can be broken down into direct and indirect drivers. Direct drivers are deaths immediately attributable to the crisis event itself. During a pandemic, this includes fatalities from the infectious disease, while in a heatwave, it would be deaths from heatstroke or related complications.
A significant portion of excess deaths comes from indirect causes, which are deaths that occur due to the secondary effects of a crisis. These effects include disruptions to healthcare systems. For example, during a pandemic, people with chronic conditions like heart disease or diabetes may not receive needed care because hospitals are overwhelmed or they are afraid to seek medical attention.
Other indirect drivers can include delayed or missed cancer screenings, resulting in more advanced and less treatable cancers later on. Mental health crises, exacerbated by social isolation and economic uncertainty, can also contribute to deaths from suicide or substance abuse. These indirect impacts highlight how a major public health event can have far-reaching consequences.
The Role of Excess Deaths Data in Public Health
Tracking excess deaths provides public health officials with a comprehensive understanding of a crisis’s impact. The data reveals the scale of the event beyond officially counted deaths, capturing fatalities that may have been misdiagnosed or were an indirect result of the crisis. This helps in assessing the severity of a public health emergency and identifying vulnerabilities within the healthcare system.
This information allows officials to evaluate the effectiveness of their response strategies. By monitoring excess mortality trends, authorities can determine if interventions like lockdowns or resource allocations are mitigating the overall impact on mortality. If excess deaths remain high, it can signal that healthcare systems are overwhelmed or that certain communities are disproportionately affected, allowing for a targeted allocation of support.
Analyzing excess mortality data after a crisis helps in planning for future events. It informs the development of more resilient public health infrastructure and emergency preparedness plans. By understanding the drivers of mortality from past events, governments can better anticipate the effects of future crises and implement more effective protective measures.
Analyzing Excess Deaths in Major Crises
Using excess deaths to understand the full scope of a public health crisis is not new. Epidemiologists have used it for over a century to gain insights into the mortality impacts of events like the 1918 influenza pandemic. This historical application demonstrates its value in capturing deaths beyond those officially attributed to the primary cause, offering a clearer picture of a pandemic’s societal toll.
Excess mortality data was extensively used during the COVID-19 pandemic. The data revealed that the total number of deaths was substantially higher than the official count of confirmed COVID-19 fatalities in many countries. This discrepancy highlighted the pandemic’s broader consequences, including deaths among people who could not access care for other serious health conditions.
The analysis of this data showed how the pandemic strained healthcare systems and caused widespread societal disruption. By comparing observed deaths to the expected baseline, researchers could quantify the hidden toll of the pandemic. This provided a more complete and sobering assessment of the global health crisis.