Graphical abstract

Introduction

The coronavirus disease 2019 (COVID-19) pandemic has had a catastrophic impact on the global economy, human health, and all sociological aspects. The world was caught unprepared [1–3]. The pandemic has resulted in massive numbers of infections and deaths across various countries. Despite the implementation of lockdowns by most countries, the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus led to increasing infection and death rates. Every country has focused on tracking these 2 critical statistics: infection and death rates. To monitor the real-time status of COVID-19 and understand its spread and mortality, several web-based dashboards have been developed [4]. The World Health Organization (WHO) has also established a dashboard to project the numbers of infections and deaths. As of March 31, 2024, there have been approximately 774.9 million cumulative infection cases and 7.04 million cumulative death cases recorded. Researchers have been investigating the life years lost due to COVID-19. Most epidemiological studies have aimed to assess infection and mortality rates to better understand the disease processes associated with COVID-19 [5]. Various models have been created using COVID-19 mortality data to explore different factors related to mortality. Pourhomayoun and Shakibi [6] analyzed various machine learning algorithms, including decision trees, networks, random forest, artificial neural logistic regression, K-nearest neighbor, and support vector machine, to predict the mortality rate among COVID-19 patients. Aslam and Biswas [7] developed a method using machine learning techniques for real-time forecasting of COVID-19 death rates.

Along with the mortality during COVID-19, researchers sought to understand excess mortality. Excess mortality refers to the difference between the total number of deaths during an emergency and the number of deaths under normal conditions. It reflects increased mortality over what would be expected based on historical trends and may include both direct and indirect deaths. Analyzing excess mortality provides insight into the augmented global mortality rate and can indicate the severity of a global health crisis, aiding in the formulation of new policies [8,9]. Researchers aimed to explore the trends in excess mortality throughout this pandemic. Therefore, they examined excess mortality during various time frames, either in specific locations or globally. COVID-19 Excess Mortality Collaborators evaluated the excess deaths in 191 countries and territories, as well as several subnational locations, during the COVID-19 pandemic from 2020 to 2021. They reported approximately 18.2 million deaths, with an all-age rate of excess mortality of 120.3 deaths per 100,000 population during this period [9]. Msemburi et al. [10] estimated excess deaths using a consistent and comprehensive approach based on WHO estimates for 2020 and 2021. They calculated a global excess mortality of about 14.83 million during these years, which was 2.74 times higher than the reported deaths (5.42 million) during the COVID-19 period. Karlinsky and Kobak analyzed excess mortality across countries during the COVID-19 pandemic. They collected all-cause mortality (ACM) data (weekly, monthly, quarterly) from 103 countries using the world mortality database. They identified Peru, Ecuador, Bolivia, and Mexico as the countries most severely affected, with excess deaths exceeding 50% of the expected annual deaths. Bulgaria, Peru, North Macedonia, and Serbia reported more than 400 excess deaths per 0.1 million people. Conversely, countries like Australia and New Zealand experienced mortality rates lower than usual during the pandemic. They also noted that several countries, including Uzbekistan, Russia, and Nicaragua, underreported their COVID-19 mortality [8]. In addition to global analyses, researchers also estimated excess mortality in specific locations. Cevallos-Valdiviezo et al. [11] assessed excess mortality in Ecuador from March 17, 2020, to October 22, 2020. They found that all-cause excess mortality amounted to 36,922 during the analysis period. Similarly, Chen et al. [12] investigated excess mortality among Californians aged 18–65, considering their occupation and sector. The study recorded 11,628 more deaths than expected among working-age adults, equivalent to 46 excess deaths and a 22% relative excess per 0.1 million individuals. The highest per-capita and relative excess deaths occurred in the agriculture/food, logistics/transportation, manufacturing, and facilities sectors. In the agriculture/food sector, there were 75 excess deaths per 100,000 people (39% relative excess). The study also analyzed the highest per-capita and relative excess mortality across ethnic and racial groups, with Latino working-age Californians experiencing the highest relative excess mortality (37%). However, there is an urgent need to understand excess mortality among older adults. Modig et al. [13]. estimated excess mortality for individuals over the age of 70 in Sweden during the first wave of the COVID-19 pandemic (2020). They divided this age group into 3 categories: independently living, those supported with home care, and those residing in care homes. They observed the highest excess mortality among those in care homes, with rates in April, May, and June ranging from 75%–100%, 25%–50%, and 0%–25%, respectively, depending on age. Understanding excess mortality in older adults globally requires further analysis across different age groups.

In this study, we approached our research from 2 perspectives. First, we analyzed the cumulative excess mortality across all age groups globally and specifically in the top 20 countries affected during the COVID-19 pandemic. Second, we conducted a detailed evaluation of excess deaths among older adults. For this demographic, we categorized the 2020 and 2021 data into age groups: 65–74, 75–84, and over 85, using information from those 20 countries. We employed 2 statistical models in our analysis: the binomial distribution and the correlation coefficient model. The findings from our analysis of excess mortality will be instrumental in shaping healthcare policies for older adults.

Materials and Methods

Overview

We summarized the estimates of excess mortality, providing detailed descriptions of the data used and the methodology applied in this study. Initially, we retrieved the cumulative mortality data for 234 countries from the WHO’s official COVID-19 dashboard (https://covid19.who.int/info). Subsequently, we ranked the country-wise mortality data and identified the top 20 countries. For these countries, we estimated the excess mortality in 2 specific categories: (1) overall estimated excess mortality and (2) excess mortality among older adults.

In this study, we conducted a detailed analysis of excess mortality among older adults. We categorized the data into age groups: 65–74, 75–84, and over 85. Additionally, we examined excess mortality in older adults from January 2020 to December 2021.

Literature Survey

We conducted an extensive literature review using databases such as PubMed, Web of Science, and Google Scholar. The search employed various keywords including “excess mortality,” “pandemic,” “COVID-19,” “elderly adults,” and “global mortality,” either individually or in combination. Our search yielded a single paper focused on excess mortality in Sweden during the COVID-19 pandemic in 2020 [13]. This indicates a need for further research to understand excess deaths among older adults.

Dataset

We utilized data from the WHO, specifically the official COVID-19 dashboard, to assess cumulative mortality on a global scale. Subsequently, we identified the top 20 countries based on mortality data. In these countries, we focused on estimating excess mortality among older adults. Additionally, we employed the WHO dataset for older adults to analyze the estimated excess mortality.

For overall excess mortality, we used Our World in Data (https://ourworldindata.org/). In this study, we initially retrieved the cumulative mortality rates for 234 countries from the WHO’s official COVID-19 dashboard. Subsequently, we ranked these countries based on their mortality data and identified the top 20. We then analyzed the cumulative excess mortality globally and for these top 20 countries across all age groups during the COVID-19 pandemic.

Procedure of Analysis

All-ages cumulative excess mortality analysis throughout the world and in the top 20 nations with the highest mortality rates during the pandemic period

We analyzed the cumulative excess mortality for all ages worldwide, utilizing data from Our World in Data.

In this study, we conducted a comprehensive analysis of cumulative excess mortality across all age groups, focusing on the top 20 countries with the highest mortality rates. For this analysis, we also utilized data from Our World in Data.

Analysis of excess mortality in older adults in the top 20 countries with the highest mortality rates during the pandemic period

We analyzed excess mortality in the top 20 countries with the highest mortality rates using WHO data [14]. The data for older adults were categorized into 3 age groups: 65–74, 75–84, and over 85. We included data from both men and women in our analysis, which covered the period from January 2020 to December 2021. Our methodology adhered to WHO’s established methods for data analysis. We examined ACM, expected deaths, excess deaths, and negative excess deaths, providing extensive definitions for each term in Supplementary Material 1.

Statistical Analysis

Statistical models were developed periodically. For the analysis of excess mortality among older adults, we employed models based on the binomial distribution and correlation coefficients. The correlation coefficient, usually represented by “r,” quantifies the strength and direction of a linear relationship between 2 variables. This coefficient can vary between -1 and 1, with each value providing insights into the nature of the relationship between the variables. We provide a detailed description of the correlation coefficient model in Supplementary Material 1.

The range of values is as follows: r=1 indicates a perfect positive linear correlation, where 1 variable increases at a constant rate as the other does. r=–1 signifies a perfect negative linear correlation, characterized by 1 variable decreasing at a constant rate as the other increases. r=0 denotes no linear correlation, meaning there is no apparent linear relationship between the 2 variables, although they may still have a nonlinear relationship. The magnitude of the correlation is categorized as follows: a correlation coefficient (|r|) ranging from 0 to 0.3 indicates a weak correlation, suggesting little to no linear relationship between the 2 variables. A coefficient from 0.3 to 0.7 signifies a moderate correlation, where a noticeable linear relationship exists, though it is not perfect. A coefficient from 0.7 to 1 denotes a strong correlation, indicating a clear linear relationship, either positive or negative [15,16]. We utilized the open-source software package R to develop the statistical models for all data.

Justification of the Statistical Methods

When selecting specific models, such as the binomial distribution or logistic regression, for analyzing data across various age groups in studies related to COVID-19 or other health issues, it is crucial to base the choice on the nature of the data, the research objectives, and the way these models manage different types of relationships.

The binomial distribution is suitable when each outcome in a population is binary, categorized as either success or failure. It is used to model the probability of achieving a certain number of successes within a predetermined set of trials. This distribution is particularly appropriate for age-specific data when the main focus is on modeling counts or proportions associated with events such as infection, hospitalization, or death.

Logistic regression enhances a binomial model by analyzing the relationships between 1 or more predictor variables and a binary outcome, thereby increasing its flexibility. It models the log odds of the outcome as a function of various predictors, such as age, health status, and exposure levels.

We employ the binomial distribution when our primary interest lies in binary outcomes, aiming to model the probability of success within distinct age groups. Conversely, logistic regression is utilized when analyzing more complex relationships that involve multiple predictors, interactions, or nonlinear effects, such as assessing the impact of various demographic and health factors on age-specific outcomes.

This approach ensures that the selected model aligns with the nature of the data and the research questions, ultimately leading to more accurate and interpretable results.

Lastly, a flowchart was created to explain the overall methodology of the study (Figure 1).

Results

Estimation of Global All-Ages Cumulative Excess Mortality

We estimated the cumulative excess mortality rate for all ages per 1,000,000 people worldwide during COVID-19. The central estimate globally is 358.89, with an upper bound of 442.98 and a lower bound of 232.72. The confirmed global COVID-19 death rate stands at 88.10 per 100,000 people at this time (Figure 2A).

Using data from Our World in Data, a world map illustrates the cumulative excess mortality per 1,000,000 people of all ages during COVID-19 (Figure 2B).

We estimated the global cumulative excess mortality for all ages over the past 12 months per 1,000,000 people during the COVID-19 pandemic. The central estimate worldwide is 28.88, with an upper limit of 77.45 and a lower limit of 13.63. During this period, the confirmed COVID-19 death rate worldwide was 1.49 per 100,000 people (Figure 2C). A world map illustrates the cumulative excess mortality per 1,000,000 people over the last 12 months, based on our global data (Figure 2D).

All-Ages Cumulative Excess Mortality in the Top 20 Countries

We estimated cumulative excess mortality in all ages the top 20 countries with the highest excess mortality rates during COVID-19, which were the United States of America (USA; Figure S1A, Supplementary Material 1), Brazil (Figure S1B, Supplementary Material 1), India (Figure S1C, Supplementary Material 1), Russian Federation (Figure S1D, Supplementary Material 1), Mexico (Figure S1E, Supplementary Material 1), United Kingdom (UK; Figure S1F, Supplementary Material 1), Peru (Figure S1G, Supplementary Material 1), Italy (Figure S1H, Supplementary Material 1), Germany (Figure S1I, Supplementary Material 1), France (Figure S1J, Supplementary Material 1), Indonesia (Figure S2A, Supplementary Material 1), Iran (Figure S2B), Colombia (Figure S2C, Supplementary Material 1), Argentina (Figure S2D, Supplementary Material 1), China (Figure S2E, Supplementary Material 1), Spain (Figure S2F, Supplementary Material 1), Poland (Figure S2G, Supplementary Material 1), Ukraine (Figure S2H, Supplementary Material 1), South Africa (Figure S2I, Supplementary Material 1), and Japan (Figure S2J, Supplementary Material 1).

The central estimate for COVID-19 deaths in the USA was 406.14 per 100,000 people, with an upper bound of 446.79 and a lower bound of 481.78. The confirmed COVID-19 death rate in the USA was 344.18 per 100,000 people at this point. In Brazil, the central estimate stood at 440.12 per 100,000 people, with an upper bound of 448.78 and a lower bound of 432.84. The confirmed COVID-19 death rate in Brazil was 326.9 per 100,000 people at this point. Similarly, India’s central estimate was 506.54 per 100,000 people, with an upper bound of 726.17 and a lower bound of 195.07. The confirmed COVID-19 death rate in India was 37.64 per 100,000 people during this period. Russia’s central estimate stands at 1,125.13, with an upper bound of 1,318.91 and a lower bound of 861.34. During this period, Russia’s confirmed COVID-19 death rate was 277.63 per 100,000 people. Similarly, Japan’s central estimate was 1,125.13, with an upper bound of 1,318.91 and a lower bound of 861.34.

Excess Mortality in Older Adults in the Top 20 Countries

Excess mortality in older adults during 2020

Excess mortality among older men in 2020 was recorded in the top 20 countries with the highest mortality rates, specifically within the age groups of 65–74, 75–84, and >85 (Table S1). We observed the highest mean excess mortality in India for the age groups of 65–74 and >85, with figures of 176,308 and 117,279, respectively. Conversely, the lowest mean excess mortality was noted in Japan for the age group >85, recorded at –450.

In this study, we observed the highest ACM (age-specific mortality count) of excess mortality in China within the age groups of 65–74 and 75–84, with counts of 1,538,960 and 1,530,890, respectively. Conversely, the lowest ACM mean of excess mortality was recorded in South Africa among individuals aged over 85, totaling 15,952.

We observed the highest mean expected deaths from excess mortality in China within the age groups of 65–74 and 75–84, with figures reaching 1,531,352 and 1,529,186, respectively. Conversely, the lowest mean expected deaths from excess mortality were recorded in South Africa for the age group of >85, totaling 12,074.

Excess mortality among older women in 2020 was documented in the top 20 countries with the highest mortality rates, where the average expected deaths, ACM, and average excess deaths were calculated for the age groups 65–74, 75–84, and >85 (Table S2). We observed the highest average excess deaths in America within the >85 age group, totaling 105,646. Conversely, the lowest average excess deaths were recorded in China and Japan for the same age group, with figures of –54,696 and –47,796, respectively.

In this study, we observed the highest average ACM of excess mortality in China within the age groups of 65–74 and 75–84, with figures of 1,228,542 and 1,172,953, respectively. Meanwhile, the lowest average ACM of excess mortality was recorded in South Africa among individuals aged over 85, totaling 32,695. We observed the highest mean expected deaths from excess mortality in China within the age groups of 75–84 and >85, with values of 1,266,743 and 1,227,662, respectively. Conversely, the lowest mean expected deaths from excess mortality were recorded in Peru within the 65–74 age group, totaling 15,557.

However, we observed a significantly higher excess mortality in women over the age of 85 among older adults in 2020.

Excess mortality in older adults during 2021

In 2021, excess mortality among older men was recorded in the top 20 countries with the highest mortality rates, specifically within the age groups of 65–74, 75–84, and over 85, as shown in Table S3. Our analysis revealed that India had the highest mean excess deaths in the 65–74 age group, totaling 588,931. Conversely, China reported the lowest mean excess deaths in the age groups of 75–84 and 65–74, with figures of –100,092 and –32,014, respectively.

In this study, we observed the highest average ACM of excess mortality in China within the 65–74 age group, totaling 1,733,563. Conversely, the lowest average ACM of excess mortality was recorded in South Africa among individuals aged over 85, with a total of 16,801.

Similarly, we found the highest expected deaths mean for excess mortality in China in the age groups of 65–74 and 75–84, which were 1,578,937 and 1,546,334. Similarly, we found the lowest expected deaths mean for excess mortality in South Africa in the age groups of >85, which was 11,722.

We observed the highest mean expected deaths from excess mortality in China within the age groups of 65–74 and 75–84, with figures reaching 1,578,937 and 1,546,334, respectively. Conversely, the lowest mean expected deaths from excess mortality were recorded in South Africa for the age group of >85, totaling 11,722.

Excess mortality among older women in 2021 was documented in the top 20 mortality countries, where the mean expected deaths, ACM, and mean excess deaths were calculated for the age groups 65–74, 75–84, and >85 (Table S4). We observed the highest mean excess deaths in India within the age groups 65–74 and 75–84, totaling 557,140 and 401,395, respectively. Conversely, the lowest mean excess deaths were recorded in China and Japan. Specifically, in China, the figures for the age groups 65–74 and 75–84 were –26,055 and -78,783, respectively. In Japan, the age group >85 showed a result of –29,934.

We observed the highest average ACM of excess mortality in India and China. In India, the age groups 65–74 and 75–84 had excess mortality figures of 1,515,705 and 1,321,884, respectively. In China, the corresponding figures for the age groups 75–84 and >85 were 1,195,811 and 1,399,316. Conversely, the lowest average ACM of excess mortality was recorded in Spain within the 65–74 age group, totaling 21,167.

We observed the highest mean expected deaths from excess mortality in China within the age groups of 75–84 and >85, with values of 1,263,073 and 1,258,550, respectively. Conversely, the lowest mean expected deaths from excess mortality were recorded in Peru for the age group of 65–74, totaling 16,092.

We also observed a significantly higher excess mortality in women over the age of 85 among older adults in 2021.

Binomial distribution of the mean value of excess mortality in older adults during 2020

The binomial distribution is commonly utilized in biological systems. This model, given a set of parameters, calculates the probability of 1 of 2 possible outcomes and is particularly useful in analyzing population distributions. In our study, we developed various binomial distribution models to represent the average number of excess deaths among older men in 3 age groups: 65–74 (Figure 3A), 75–84 (Figure 3B), and over 85 (Figure 3C) during the year 2020. These models are depicted through histograms that display the distribution of the average excess deaths. Our analysis using the binomial distribution models revealed that the distributions approximated a normal curve for the average excess deaths among these groups of older men. Specifically, we created a model for the 65–74 age group, showing that the histograms were negatively skewed (Figure 3A). A similar approach was taken for the 75–84 age group, which also demonstrated negatively skewed histograms (Figure 3B). The model for the oldest group (over 85) indicated a similar negative skew in the histograms (Figure 3C).

Similarly, we developed binomial distribution models for the mean excess deaths from excess mortality among older women aged 65–74. These models showed that most histograms were negatively stacked (Figure 3D). Similarly, we depicted the binomial distribution models for the mean excess deaths among women aged 75–84, which also showed histograms that were predominantly negatively stacked, although 1 histogram bar was positively stacked (Figure 3E).

The third age group of older adults (those over 85) demonstrated both positive and negative attributes (Figure 3F).

In the context of a binomial distribution, smoothing the lines of a discrete probability distribution can enhance visualization, especially when dealing with large sample sizes. The binomial distribution is, by nature, discrete. It uses a Bernoulli distribution to model the outcomes, representing a random variable with 2 possible results. Each independent experiment in a series may result in 2 possible outcomes, which remain consistent across events. This series of experiments is referred to as Bernoulli's trials [17,18]. Depending on the sample size and the probabilities involved, various methods can be used to smooth lines in a binomial distribution. These include normal approximation, kernel density estimation, or Poisson approximation. For large samples, the most commonly used method is the normal approximation with continuity correction. This approach provides a continuous, smooth curve that closely approximates the underlying binomial distribution.

Binomial distribution of the mean value of excess mortality in older adults during 2021

We employed a binomial distribution model to analyze the mean excess deaths among older men across 3 age groups: 65–74 (Figure 4A), 75–84 (Figure 4B), and over 85 (Figure 4C) during the year 2021. Initially, the model for the 65–74 age group revealed that, except for 1 positively stacked bar, all histogram bars were negatively stacked (Figure 4A). Similarly, the model for the 75–84 age group showed that all histogram bars were negatively stacked (Figure 4B). For the oldest age group, those over 85, the analysis again indicated that all histogram bars were negatively stacked (Figure 4C).

Similarly, we developed binomial distribution models for the mean excess deaths from excess mortality among older women aged 65–74. This model showed that all histogram bars were negatively stacked (Figure 4D). We also conducted binomial distribution models for the mean excess deaths among women aged 75–84. All histogram bars were negatively stacked, except for 2 bars that were positively stacked (Figure 4E).

The third age group of older adults (over 85) was predominantly represented by negative values, except for 1 bar that was positively stacked (Figure 4F).

The binomial distribution models consistently revealed 2 distinct classes of data.

Correlation coefficient estimation for excess mortality in older adults during 2020

The study analyzed the correlation coefficient of the mean excess deaths from excess mortality among older adult men and women. We calculated the correlation coefficients for men aged 65–74 (Figure 5A), 75–84 (Figure 5B), and over 85 (Figure 5C) during 2020. For men aged 65–74, the correlation coefficient of the mean excess deaths was 0.7628 (Figure 5A). For the 75–84 age group, the correlation coefficient was 0.8916 (Figure 5B). Similarly, for men aged over 85, the correlation coefficient was 0.7791 (Figure 5C).

Using the mean excess deaths as a measure of excess mortality among older women, we estimated the correlation coefficients for the age groups 65–74 (Figure 5D), 75–84 (Figure 5E), and >85 (Figure 5F) during 2020. The mean excess deaths for women aged 65–74 indicate a correlation coefficient of 0.8842 (Figure 5D). Similarly, for women aged 75–84, the correlation coefficient is 0.8736 (Figure 5E). For the age group >85, the correlation coefficient is 0.9204 (Figure 5F).

The estimation of the correlation coefficients indicated a positive linear correlation among all variables.

Figure 5 illustrates the correlation coefficient of the mean excess deaths from excess mortality in older adults (both male and female categories) during 2020. This study examines the relationship between age groups and mortality, indicating that a strong positive correlation would imply an increase in mortality as age increases, which could inform preventative healthcare strategies.

The correlation coefficient is fundamental for identifying and quantifying linear relationships between 2 variables. However, it requires careful interpretation, especially in recognizing its limitations to linear relationships and the potential for other variables or outliers to skew the perceived strength of a relationship. Understanding the direction, strength, and context of the correlation enables researchers and decision-makers to draw meaningful conclusions. Consequently, our correlation coefficient model indicates that excess mortality in older adults (Figure 5) is significant.

Correlation coefficient estimation for excess mortality in older adults during 2021

The study analyzed the correlation coefficient of the mean excess deaths from excess mortality among older men and women during 2021. We calculated the correlation coefficients for the mean excess deaths from excess mortality among older men in the age groups of 65–74 (Figure 6A), 75–84 (Figure 6B), and over 85 (Figure 6C) during 2021. For men aged 65–74, the correlation coefficient of the mean excess deaths from excess mortality was 0.6376 (Figure 6A). For the 75–84 age group, the correlation coefficient was 0.7066 (Figure 6B). Similarly, for men aged over 85, the correlation coefficient was 0.6611 (Figure 6C).

Using the mean excess deaths as a measure of excess mortality among older women, we estimated the correlation coefficients for the age groups 65–74 (Figure 6D), 75–84 (Figure 6E), and >85 (Figure 6F) during 2021. The mean excess deaths for women aged 65–74 yielded a correlation coefficient of 0.621 (Figure 6D). For women aged 75–84, the correlation coefficient was 0.7237 (Figure 6E). Similarly, for women aged over 85, the correlation coefficient was 0.8396 (Figure 6F).

The estimation of the correlation coefficient indicated a positive linear correlation among all variables.

Like Figure 5, Figure 6 illustrates the correlation coefficient of excess deaths, indicating excess mortality among older adults (both male and female) during 2021. Thus, our correlation coefficient model for excess deaths, representing excess mortality in older adults in 2021, is also significant (Figure 6).

Discussion

Our study is the first, to our knowledge, to evaluate the excess mortality of older adults worldwide during the pandemic period. We divided the older population into 3 age groups: 65–74, 75–84, and above 85, to assess their excess mortality. Previous research has explored excess mortality in Sweden during the first wave of the pandemic by level of care [13]. In contrast, our study dealt with global excess mortality. We examined cumulative mortality data from 234 countries using the WHO’s official COVID-19 dashboard. We then categorized the mortality data by country and identified the top 20 countries with the highest rates. In these countries, we estimated the excess mortality among older adults. Our analysis covered 2 complete years of the pandemic, from early 2020 to the end of 2021, focusing on 3 key parameters: average ACM, expected deaths, and excess deaths. Additionally, we developed 2 statistical models using the mean excess deaths: a binomial distribution model and a correlation coefficient model. Concurrently, we analyzed all-age excess mortality globally, specifically in these top 20 countries. Several researchers have studied excess deaths during the pandemic, focusing on specific counties or regions [19–24]. Our study, however, estimates excess mortality among older adults in 3 age groups and all-age excess mortality during the COVID-19 pandemic in the top 20 countries with the highest mortality rates worldwide. Thus, our research provides a comprehensive overview of the impact of the pandemic on global mortality rates.

Excess mortality accounts for all deaths and includes factors both directly and indirectly related to it. Directly associated factors encompass the impact of the virus, while indirect factors include disruptions to travel and essential health services, among others. In this context, WHO data on excess mortality encompasses ACM. However, the absence of ACM data for many countries complicates the straightforward evaluation of excess mortality globally. The WHO typically collects mortality data annually or over longer intervals. In our analysis of mortality data across 234 countries, it was noted that WHO does not provide excess mortality data for Turkey, which ranks 20th. Therefore, we included Japan, which ranks 21st, in our datasheet [14]. Msemburi et al. [10] faced similar challenges due to the unavailability of ACM data in many countries. They devised 3 categories of models: countries with complete ACM data for the entire period analyzed (such as the USA), countries with mixed ACM data (such as India), and countries lacking ACM data (such as Pakistan and Ethiopia). In our study, we have divided the ACM excess mortality of older adults into 3 age groups: 65–74, 75–84, and above 85 years, with ACM recorded in all instances (Tables S1–S3).

Figures 2A and B illustrate global excess mortality, as presented by Our World in Data for the years 2020 to 2024. Concurrently, Figures 2B and C focus on the world’s excess mortality over the past 12 months. It has been observed that, with the exception of the last 12 months, excess mortality during the pandemic was more pronounced. However, excess mortality worldwide in the last 12 months has been lower. Figure 2B highlights that significant excess mortality occurred in Russia and moderate excess mortality was observed in Mexico, India, Peru, and other countries. The COVID-19 Excess Mortality Collaborators have estimated excess deaths at various national and regional levels due to COVID-19. At the country level, the most cumulative excess deaths were recorded in the USA (1.13 million), Russia (1.07 million), India (4.07 million), Indonesia (736,000), Brazil (792,000), Pakistan (664,000), and Mexico (798,000). Regionally, South Asia, the Middle East, Eastern Europe, and North Africa experienced notably higher excess deaths due to the pandemic.

Our study on excess deaths among older adults during the pandemic made several key observations: First, the research demonstrated that COVID-19 has led to significant excess mortality among older adults across 20 countries. We analyzed excess deaths in these countries by examining 3 components: excess mean, ACM mean, and expected mean. In most cases, these components showed positive values, although negative values were observed in a few instances. It was observed that the impacts of COVID-19 led to notable excess mortality in older adults across these 3 categories in the 20 regions studied. Second, the study revealed higher excess mortality among women over the age of 85, suggesting that the impact of the SARS-CoV-2 virus is particularly severe in older women. This finding aligns with the observations made by Kontopantelis et al. [25], who also reported increased excess mortality in this demographic. However, their analysis during the first wave was limited to overall age groups in Wales and England. Third, our models indicate trends of excess mortality in these 20 countries, highlighting both the direct and indirect effects of COVID-19 on the health of older adults. Several studies have attempted to quantify the excess mortality attributable to the COVID-19 pandemic [9,25,26], with some specifically focusing on older adults and identifying underlying causes [13,27,28]. Our findings are consistent with these global trends, with only a few exceptions.

One study analyzed the age profiles (single year of age) for England, Wales, and the USA, concluding that each COVID-19 variant exhibits a distinct mortality age profile [29].

The age profile of COVID-19 mortality varies with different virus variants, influenced by factors such as immune response, comorbidities, and vaccination status. The study highlights a crucial observation: each COVID-19 variant distinctly impacts various age groups, particularly in terms of mortality rates in countries like England, Wales, and the USA.

2020: Mortality due to Wuhan+Alpha Variants

In 2020, the initial Wuhan strain and the Alpha variant accounted for the majority of COVID-19-related deaths [30]. During this time, the highest mortality rates were observed in older adults, particularly those aged 65 and older. This age group exhibited a weaker immune response, and the presence of pre-existing conditions such as cardiovascular disease, diabetes, and respiratory issues increased their vulnerability [31]. In England and Wales, the older population, especially those residing in care homes or suffering from multiple comorbidities, faced significant mortality rates. This was prior to the implementation of extensive vaccination programs, leaving older adults particularly prone to severe outcomes [25].

2021: Mortality due to Alpha+Delta Variants

In 2021, while the Alpha variant was still circulating, the Delta variant emerged as a more transmissible and aggressive strain. Delta notably had a more pronounced impact on younger populations compared to earlier variants. Concurrently, there was a slight shift in the age profile for mortality, with an increase in deaths among individuals under 65, although older adults continued to be at high risk.

Delta disproportionately affected younger populations because initial vaccination campaigns prioritized older and more vulnerable groups [32]. By the time Delta emerged as a prevalent variant, many younger adults remained unvaccinated, increasing their susceptibility. Furthermore, Delta’s heightened transmissibility and its potential to cause severe illness resulted in increased hospitalization rates among middle-aged and younger adults [33,34].

The unique age profiles associated with illness or mortality for each variant can be explained by considering the characteristics of the variant, the immune responses across different age groups, and the initial focus of vaccine rollouts on protecting older adults and high-risk individuals.

We have analyzed excess mortality by examining ACM, expected deaths, excess deaths, and adverse excess deaths in accordance with WHO guidelines. Other researchers have also attempted to analyze excess mortality from this perspective [1,35–37]. Consequently, our analysis holds significant importance. Some researchers present excess mortality as a percent increase over the number of deaths in 2019. Calculating excess mortality as a percentage of 2019 deaths, however, offers a more nuanced perspective, providing a clearer understanding of the pandemic’s relative impact. This approach accounts for demographic variations and can alter the rankings, frequently placing countries with younger populations or less robust healthcare systems higher on the list [38]. Additionally, it underscores how specific variants, such as Delta, can disproportionately impact younger demographics, leading to significant increases in mortality that may not be as apparent when only absolute death counts are considered.

Measuring excess mortality as a percentage increase over the number of deaths in 2019 significantly alters the ranking of countries in terms of the pandemic’s impact, particularly when compared to using absolute numbers of excess deaths. This approach accounts for variations in population size, age demographics, and baseline mortality rates, facilitating a more uniform comparison [9,39]. For instance, countries like the USA may exhibit a relatively moderate percentage increase in excess mortality, partly because the older, more vulnerable segments of the population received vaccinations early [40]. Our study, however, calculates excess mortality by analyzing ACM, expected deaths, and adverse excess deaths in accordance with WHO guidelines. This method is considered 1 of the standard approaches, which is why we adhere to the WHO procedure.

The age distribution of COVID-19 fatalities is critical in shaping public health strategies, including lockdowns and vaccination drives. By focusing on the age groups most at risk of severe outcomes, public health officials can use resources more efficiently, minimize disruptions to society, and potentially save more lives. At the onset of the pandemic, older adults were significantly more likely to suffer from severe illness and death. This disparity led to age-specific lockdown measures, imposing stricter restrictions on older adults while allowing younger, healthier individuals more freedom due to their lower risk [41]. Such measures, including targeted lockdowns or isolation for those at higher risk, were particularly relevant in countries like Italy, which have substantial older populations and experienced considerable losses in care homes. Strategically isolating older adults while granting younger people more liberties to sustain economic activities could help reduce both mortality and broader societal damage [42].

Young adults and children, while less susceptible to fatal outcomes, still contributed to the spread of the disease. Consequently, age-specific restrictions might have differed based on the national context, enabling younger individuals to resume work and educational activities with appropriate precautions. For instance, some countries considered protecting older adults and other vulnerable groups while permitting younger, working-age adults to continue economic activities. However, this approach was not broadly adopted due to uncertainties regarding the transmission dynamics at the time [43,44].

It has been observed that there is a correlation between the extent of COVID-19 testing and the number of reported deaths. Research indicates that the number of tests conducted per population is directly related to the reported death counts [45–47]. Notably, regions conducting fewer tests tend to report fewer deaths.

Exploration of Gender-Specific Vulnerabilities

The findings indicate a disproportionately high mortality rate among older women due to the COVID-19 pandemic, particularly in those aged 85 and older. This observation is crucial, yet further investigation into the underlying causes of this trend is necessary. Exploring potential gender-specific vulnerabilities, such as biological factors, social conditions, or differences in access to healthcare, could offer more profound insights into why this particular demographic is more severely impacted by the pandemic. Therefore, our study is essential for informing public health decisions. The insights gained will assist policymakers in developing targeted strategies for the care and protection of older adults.

Strengths of This Study

This study may represent the first investigation into excess mortality among older adults. It offers a comprehensive overview of excess mortality in this demographic, utilizing WHO data and Our World in Data for 20 countries. The findings depict the state of patient care during the COVID-19 pandemic in these countries and identify patterns of excess mortality among the most vulnerable groups. The study reveals that the pandemic poses a significant threat to older adults, potentially overwhelming healthcare systems in certain countries. It clearly demonstrates the increased risk of mortality for older adults, which has escalated over the course of 2 years in the pandemic.

Limitations

The study has several limitations. First, it was conducted using comprehensive data analysis, which means our estimates are dependent on the scope and availability of the data. Second, due to the reliance on available data, the research cannot determine the causes of increased mortality among women over 85 years old. Third, although various modeling strategies are closely related, we utilized data from WHO and Our World in Data. Consequently, our calculations of global excess mortality and the excess mortality in these 20 countries for all age groups are based on Our World in Data, while we rely on WHO data to calculate different components of excess mortality, such as excess mean, ACM mean, and the expected mean for older adults. Fourth, our analysis focused on 20 countries, examining excess mortality during the pandemic period without extending the analysis to other countries.

Issues regarding methodology have occasionally been raised during the calculation of excess mortality. Concurrently, conflicts in this area have also been reported [48–51]. However, in this analysis, we utilize the WHO data and the methodologies outlined by Our World in Data for calculating excess mortality.

Future Directions

Further research is required using the excess mortality data of older adults from around the world. Our future studies will focus on this data. Additionally, it is necessary to estimate the excess mortality among older adults throughout the pandemic period, utilizing the complete data sets from 2020, 2021, 2022, and 2023. We will concentrate on analyzing these data for the specified years. Furthermore, we aim to develop predictive models for forecasting excess mortality among older adults in any forthcoming pandemic.

Conclusion

Our estimates of excess mortality during this crisis period suggest that the impact on mortality has been particularly severe. It is crucial for the global community to monitor excess mortality among older adults using reliable sources to discern patterns. In this study, we analyze excess mortality data from the WHO, which may reveal trends in the excess mortality of older adults during a pandemic. Understanding these patterns from our comprehensive analysis will aid in the development of public healthcare policies tailored for older adults, potentially mitigating the effects of future pandemics. This understanding will also inform the design of post-pandemic healthcare policies and facilitate a gap analysis of emergency medical care resources related to ACM. Ultimately, our study will contribute to the enhancement of emergency medical care resources.

HIGHLIGHTS

• Excess mortality of aging adults was higher during COVID-19.

• The study calculated excess mortality in elderly (3 age groups: 65–74, 75–84, and >85) in 20 higher mortality countries.

• The study observed a higher excess mortality mean in the >85 age group female.

• It helps to formulate the health care strategy for aging adults.

Supplementary Material

Article information

Ethics Approval

Not applicable.

Conflicts of Interest

The authors have no conflicts of interest to declare.

Funding

None.

Availability of Data

Data are already available in the manuscript.

Authors’ Contributions

Conceptualization: CC; Data curation: CC; Formal analysis: MB, SSL; Investigation: CC; Methodology: CC; Project administration: CC; Resources: CC, MB; Software: CC, MB; Supervision: CC; Validation: MB, SSL; Visualization: MB, SSL; Writing–original draft: CC; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Acknowledgements

The authors are thankful to the respective universities/institutes.

Figure 1.

A flowchart illustrating the overall methodology of the study. WHO, World Health Organization.

Figure 2.

Plot and map showing worldwide cumulative excess mortality for all ages: (A) the plot depicting cumulative excess mortality from March 2020 to March 2024; (B) the map illustrating cumulative excess mortality from March 2020 to March 2024; (C) the plot depicting cumulative excess mortality over the last 12 months; (D) the map illustrating cumulative excess mortality over the last 12 months.

Figure 3.

The binomial distribution of the mean excess mortality in older adults during 2020, categorized by age and sex: (A) men aged 65–74, (B) men aged 75–84, (C) men aged 85 or older, (D) women aged 65–74, (E) women aged 75–84, and (F) women aged 85 or older.

Figure 4.

The binomial distribution of the mean excess mortality in older adults during 2021, categorized by age and sex: (A) men aged 65–74, (B) men aged 75–84, (C) men aged 85 and older, (D) women aged 65–74, (E) women aged 75–84, and (F) women aged 85 and older.

Figure 5.

Correlation coefficients for the mean excess mortality among older adults in 2020, categorized by age and sex: (A) men aged 65–74, (B) men aged 75–84, (C) men aged 85 and older, (D) women aged 65–74, (E) women aged 75–84, and (F) women aged 85 and older.

Figure 6.

Correlation coefficient of the mean excess deaths in older adults during 2021, categorized by age and sex: (A) men aged 65–74, (B) men aged 75–84, (C) men aged 85 or older, (D) women aged 65–74, (E) women aged 75–84, and (F) women aged 85 or older.

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