Skip to main content
Social Sci LibreTexts

3.1: Chapter 8- Measuring Mortality

  • Page ID
    199438
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \) \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)\(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\) \(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\)\(\newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    8

     

    Anya Hageman and Pauline Galoustian

    8-header-1.png

    Chapter-8-summary.png
    The COVID-19 pandemic which began in 2020 was a crisis.  The virus was new to us; we did not know exactly how it spread, how easily it spread, or how sick it could make us.  This crisis caused deaths directly and indirectly.  There were deaths from COVID infections, and there were deaths from other conditions in people weakened by COVID-19. There were deaths because of delays in medical treatment or fear of going to hospital. There were deaths from substance abuse and suicide related to the isolation of lockdowns, economic hardship, and uncertainty caused by the pandemic.  There have been and will be deaths due to the long-term effects of having been infected.

    disease-categorization.png

    Crisis deaths are deaths which are caused by exceptional situations -epidemics or pandemics, natural disasters, war, and severe economic downturns.  By contrast, chronic deaths are deaths we expect due to the prevailing standard of living – the prevailing level of income, nutrition, medical care, sanitation, and safety – and due to the endemic diseases that exist in the population.

    isaac-quesada-fuGPLDhQBo8-unsplash.jpg
    Photo by Isaac Quesada, 2020.  CC BY 2.0/Unsplash License

    Usually, the presence of a crisis and the resulting crisis deaths means that the population will have more deaths than it would have otherwise. There will be excess deaths, that is to say, more deaths than what would have been expected under normal conditions. However, crises could conceivably result in fewer people dying than usual.  COVID-19, which typically did result in excess deaths, nonetheless reduced the number of chronic deaths from ordinary forms of influenza as people washed their hands more frequently, wore masks, and socially distanced themselves.  As people quarantined, air quality improved, and the number of workplace and traffic accidents fell.
    Figure 8-0 shows us that, in the first 17 months of the pandemic, Nova Scotia and Prince Edward Island experienced fewer deaths than usual: excess deaths were negative.  In contrast, British Columbia had more than 150 excess deaths per 100,000 people at midyear, or 1.5 per 1000.

     

    Figure-8-0-header.png

    This bar graph shows that excess deaths were much higher in western provinces than anywhere else in Canada. Quebec had a higher number of deaths from COVID-19 than the (smaller) western provinces, but its excess deaths were about half. Nova Scotia and PEI had negative excess deaths. From the graph it appears that PEI had no deaths from COVID-19 at all.
    Source:  Figure 2 of McGrail (2022).

    To calculate whether a nation experienced excess deaths due to a disaster or pandemic, we need to know the number of deaths that would have occurred without the disaster. How do we do that?

    Nowadays we use statistical regression analysis to predict the number of deaths that will occur in any year. Regression analysis sifts through mounds and mounds of data to tease out the relationship between the the number of deaths in a particular age/sex group and a number of factors, factors like the size of the age/sex group, the number of deaths in that age/sex group in the previous year, GDP, average daily temperature, average daily sugar consumption, etc. Then, to find what deaths for each age/sex group would have been in 2020 without the COVID-19 pandemic, the researcher makes an inference based on what the 2020 values of all the relevant factors would have been without the pandemic.

    ujtbyhrtgvjuyhtgfrghytgrf.jpg

    Hurricane Maria was a Category 5 storm that battered the northeastern Caribbean in September, 2017.  In 2018, the Milken Institute School of Public Health at George Washington University published a study of excess deaths in Puerto Rico due to Hurricane Maria.  To come up with their calculations, they first collected age and gender-specific mortality rates and socioeconomic data for many years prior to the disaster, then used this data to predict what mortality rates would have been in the period September 2017 – March 2018 without the hurricane. They then compared these predicted deaths to the actual deaths during September 2017 – March 2018.

    Morning after Hurricane Maria, 2017. Photo by R. Skerrit, 2017.  P ublic Domain

    The scholars found that, during the six months after Hurricane Maria, Puerto Rico experienced 2,975 more deaths than would have been experienced without the hurricane. These excess deaths made up about 20% of the total deaths during the six-month period. As a comparison, excess deaths due to COVID-19 in 2020 were about 5% of total deaths in Canada and 11% of total deaths in the USA.[1].

    Figure 8-1 below shows the spike in the monthly death rate in Puerto Rico in 2017. The dotted red line shows that the death rate would be even higher if you removed from the denominator all the people who left Puerto Rico during that 6-month period. The denominator in these monthly death rates is the mid-month population.

     

    new-8-1-header.png

    This graph has age-standardized deaths per 10,000 per year on the vertical axis, and time on the horizontal axis. It shows that, since July 2010, the death rate gyrated up and down but was generally trending down until about April 2015 when it began to trend up. There were, however, very large spikes in early 2016, early 2017,and then late 2017-early 2018 when the hurricane. The hurricane spike would be even higher, outlined in red, if the midyear population had been adjusted for people who left after the hurricane hit.
    Source: Milken Institute School of Public Health (2018)

    Figure 8-2 shows that less-privileged people suffered more excess deaths, and that their experience continued to deteriorate for four months, both in an absolute sense and relative to more privileged people.

    This graph has time on the horizontal axis and relative excess mortality (actual deaths relative to predicted deaths) on the vertical axis. It shows how the number of excess deaths relative to chronic deaths changed from the day the hurricane hit until 5 months later. It shows this for three different socioeconomic groups. We see that the poorest people, who have high chronic death rates, experienced fewer excess deaths right after the hurricane hit than the richest people. The excess deaths experienced by the poorest rose steeply before hitting a high point of 60% more more deaths than usual four months after the hurricane. Five months after the hurricane, there were about 50% more deaths than usual for the poorest group. By contrast, for the middle and high income groups, excess mortality peaked about one month after the hurricane hit and then declined for another month. In December relative excess mortality grew again until declining once more, ending up at 20% more deaths than usual after 5 months (middle income) and 18% more for the high income group. Clearly the poorest group suffered much more from the after-effects of the hurricane.
    Source: Milken Institute School of Public Health (2018)

    To understand Figure 8-2, note that the vertical axis shows the ratio of actual deaths to usual deaths. A level of “1” means no excess deaths. A level of “1.2” means that there are twenty per cent more deaths because of the hurricane.

    Figure 8-3 shows that it is not necessarily true that women suffer more during crises. The oldest people of both sexes, and middle-aged male Puerto Ricans, were hardest hit in the month following Hurricane Maria, possibly because of actions they were taking to protect others. All groups recovered to some degree in October. Later, young and middle-aged women began to experience less mortality than usual, while men, especially the older men, continued to suffer excess deaths.

    Like the previous graph, this graph shows excess deaths relative to predicted deaths on the vertical axis, and the passage of time on the horizontal axis. It shows this for women less than forty years old, women between 40 and 64, and women over 65, as well as for men in each of those three age categories. Everyone experienced excess mortality when the hurricane hit, except for the women under forty. The excess mortality for women of every age trended down after the hurricane hit. In fact, young women and middle-aged women were experiencing negative excess deaths three months after the hurricane hit. Middle-aged and older men suffered the most excess deaths (relative to their predicted rates) immediately after the hurricane. Middle aged male excess mortality trended down after that, but senior men continued to experience high rates of excess death which grew during the third and fourth months after the hurricane, ending up at 40% extra deaths after five months. Young men's excess mortality was initially about 10%. It gyrated up and down before ending up at about 10% extra deaths after five months.
    Source: Milken Institute School of Public Health (2018)

    In another study of excess deaths in Puerto Rica due to Hurricane Maria, Cruz-Cano and Mead (2019) determined that out of 1205 excess deaths in September and October 2017:

     “Most excess deaths occurred from heart disease (253 deaths), “other” causes (204 deaths), diabetes (195 deaths), Alzheimer’s disease (122 deaths), and septicemia (81 deaths).”[2]

    The crisis led to a deterioration of living conditions and lack of medical care. In this way crisis deaths included deaths from endemic diseases such as diabetes.
    Picture134-scaled-1.jpg

    Historically, have more people been killed by pandemics, war, famine and other disasters, or by the daily grind of malnutrition, heavy labour, exposure to the elements, domestic violence, crime, endemic diseases and so forth?

    Robert Fogel (1992) tackled this question and concluded that chronic living conditions were the main determinant of death rates in the past.  He presented two pieces of evidence to support this claim.

    La Miseria by Cristóbal Rojas (1886). Rojas was suffering from chronic tuberculosis when he painted this. Here he depicts the social aspect of the disease, and its relation with living conditions at the close of the 19th century. Painting by: Cristóbal Rojas, 1886 (Public Domain)
    La Miseria by Cristóbal Rojas (1886). Rojas was suffering from tuberculosis when he painted this. In this painting we see nineteenth century living conditions that would have influenced disease outcomes. Public Domain.

    First, Fogel examined Wrigley and Schofield’s excess death calculations for England for the period 1541-1871[3]. Fogel found that the excess deaths during the three hundred and thirty year period were a small fraction, less than 5%, of total deaths.

    The second thing Fogel did was collect data on height – which is a measure of the net intake of nutrition from conception through adolescence – and weight – which is a measure of current net nutrition.  Based on how height and weight affect mortality rates today, Fogel found that the height and weight of English, French, and Swedish soldiers prior to 1875 implied mortality rates very similar to the mortality rates they actually experienced. Fogel concluded that most of the mortality 1775-1875, and half the mortality 1875-1975, could be explained by nutrition, at least for the adult age groups he considered.

    French retreat from Russia in 1812, starvation and death of French soldiers. Artist: Illarion Pryanishnikov, (1874) (Public Domain)
    French retreat from Russia in 1812 resulting in mass starvation and deaths of French soldiers. Artist: Illarion Pryanishnikov, 1874. Public Domain.

    Kannisto and his team (1999) found that during famine (1866-1869) and wars (1789, 1808-9), most deaths in Finland were caused by infectious disease spread by armies, veterans, and refugees.  People’s base level of health, nutrition, housing, and sanitation – their chronic living conditions – were key to survival.

    Ordinary living conditions matter. We saw during the COVID-19 pandemic that the people whose health suffered most from COVID-19 were people with pre-existing health conditions, people eating poor diets, and people whose jobs required them to take public transit and to serve in public places. By the same token, some of the most powerful remedies against infection were simple: distancing, masking, and washing of hands. How many of these new habits will become part of our everyday lives? How many of our pre-COVID social norms and traditions developed in response to crises long ago?

     

    Picture135-scaled-1.jpg

    Consider two nations. In one, most of the population lives in poverty and without access to hospitals.  In the other, most of the population enjoys a good diet, clean water, and access to sophisticated medical care.  A newly-evolved flu virus attacks both nations.

    1. Which nation is likely to experience a greater number of crisis deaths per 1000? Why?
    2. Which nation is more likely to have a higher ratio of crisis to chronic deaths? Why?

    1. Statistics Canada's The Daily, April 16, 2021
    2. https://www.nature.com/articles/d41586-019-00442-0
    3. Wrigley and Schofield did not use modern methods to compute excess deaths, so this stream of research should be updated.
     

    3.1: Chapter 8- Measuring Mortality is shared under a CC BY 4.0 license and was authored, remixed, and/or curated by LibreTexts.

    • Was this article helpful?