What is the best measure (cases, hospitalizations or deaths) for measuring the level of infection in a community?
What is the best measure (cases, hospitalizations or deaths) for measuring the level of infection in a community?
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Ultimately, no single statistic or measurement can accurately indicate the state of a disease within a population. To best understand the level of infection in a community, all these numbers need to be looked at together. Until there is more routine testing to identify all infected patients (with or without symptoms), the risk of infection is likely to remain unclear. In order to attempt to measure the level of infection in a community, we can look at the number of hospitalizations, the proportion of the population who has the disease at any given moment (period prevalence), or the number of new cases of disease over a given time interval (incidence rate). The number of COVID-19 deaths during a given period can provide an important snapshot to understand the impact of the virus, but it is not a very good measure of a population's risk of contracting the virus. Tracking incidence rate is a more useful measure, because it helps us understand what proportion of an initially disease-free population develops the disease over a specified time period. This is a far more accurate measure of how likely a person in a population is to get infected compared to the number of deaths within a population. Additionally, in trying to understand how the number of deaths vary between populations, it's best to compare the mortality rate (number of deaths in relation to the overall population), because simply looking at the number of deaths does not account for differences in the size of populations.
Ultimately, no single statistic or measurement can accurately indicate the state of a disease within a population. To best understand the level of infection in a community, all these numbers need to be looked at together. Until there is more routine testing to identify all infected patients (with or without symptoms), the risk of infection is likely to remain unclear. In order to attempt to measure the level of infection in a community, we can look at the number of hospitalizations, the proportion of the population who has the disease at any given moment (period prevalence), or the number of new cases of disease over a given time interval (incidence rate). The number of COVID-19 deaths during a given period can provide an important snapshot to understand the impact of the virus, but it is not a very good measure of a population's risk of contracting the virus. Tracking incidence rate is a more useful measure, because it helps us understand what proportion of an initially disease-free population develops the disease over a specified time period. This is a far more accurate measure of how likely a person in a population is to get infected compared to the number of deaths within a population. Additionally, in trying to understand how the number of deaths vary between populations, it's best to compare the mortality rate (number of deaths in relation to the overall population), because simply looking at the number of deaths does not account for differences in the size of populations.
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What our experts say
Ultimately, no single statistic or measurement can accurately indicate the state of a disease within a population. To best understand the level of infection in a community, all these numbers need to be looked at together. Until there is more routine testing to identify all infected patients (with or without symptoms), the risk of infection is likely to remain unclear.
In order to attempt to measure the level of infection in a community, we can look at the number of hospitalizations, the proportion of the population who has the disease at any given moment (period prevalence), or the number of new cases of disease over a given time interval (incidence rate). The number of COVID-19 deaths during a given period can provide an important snapshot to understand the impact of the virus, but it is not a very good measure of a population's risk of contracting the virus.
Tracking incidence rate is a more useful measure, because it helps us understand what proportion of an initially disease-free population develops the disease over a specified time period. This is a far more accurate measure of how likely a person in a population is to get infected compared to the number of deaths within a population.
Additionally, in trying to understand how the number of deaths vary between populations, it's best to compare the mortality rate (number of deaths in relation to the overall population), because simply looking at the number of deaths does not account for differences in the size of populations.
Ultimately, no single statistic or measurement can accurately indicate the state of a disease within a population. To best understand the level of infection in a community, all these numbers need to be looked at together. Until there is more routine testing to identify all infected patients (with or without symptoms), the risk of infection is likely to remain unclear.
In order to attempt to measure the level of infection in a community, we can look at the number of hospitalizations, the proportion of the population who has the disease at any given moment (period prevalence), or the number of new cases of disease over a given time interval (incidence rate). The number of COVID-19 deaths during a given period can provide an important snapshot to understand the impact of the virus, but it is not a very good measure of a population's risk of contracting the virus.
Tracking incidence rate is a more useful measure, because it helps us understand what proportion of an initially disease-free population develops the disease over a specified time period. This is a far more accurate measure of how likely a person in a population is to get infected compared to the number of deaths within a population.
Additionally, in trying to understand how the number of deaths vary between populations, it's best to compare the mortality rate (number of deaths in relation to the overall population), because simply looking at the number of deaths does not account for differences in the size of populations.
Context and background
There are many numbers and statistical values that are commonly in the news to show how well countries are doing at managing the COVID-19 pandemic. Some countries are reporting very high numbers of infected people (cases) and some are reporting very high numbers of deaths.
If we were to compare two groups (Group A and Group B) that each had 1,000 deaths, at a glance, the numbers may appear similar. If we were to find out, however, that Group A had 10,000 people with the virus and 1,000 died, that would mean that 10% of those infected in the population had died. If Group B had 100,000 people with the virus and 1,000 died, that would mean that 1% of those infected in the population had died. The difference between Group A and Group B is much more significant when viewed this way; the patients in Group A were far more likely to die than those in Group B.
Though there is scientific evidence that suggests that older people or those with existing medical conditions like diabetes, obesity, and high blood pressure are at higher risk of becoming infected and seriously ill from COVID-19, genetics, dietary habits, and other factors may play an important role. True susceptibility to the virus is challenging to measure. Not everyone who is exposed to the virus becomes infected, and not everyone who is infected develops symptoms.
There are many numbers and statistical values that are commonly in the news to show how well countries are doing at managing the COVID-19 pandemic. Some countries are reporting very high numbers of infected people (cases) and some are reporting very high numbers of deaths.
If we were to compare two groups (Group A and Group B) that each had 1,000 deaths, at a glance, the numbers may appear similar. If we were to find out, however, that Group A had 10,000 people with the virus and 1,000 died, that would mean that 10% of those infected in the population had died. If Group B had 100,000 people with the virus and 1,000 died, that would mean that 1% of those infected in the population had died. The difference between Group A and Group B is much more significant when viewed this way; the patients in Group A were far more likely to die than those in Group B.
Though there is scientific evidence that suggests that older people or those with existing medical conditions like diabetes, obesity, and high blood pressure are at higher risk of becoming infected and seriously ill from COVID-19, genetics, dietary habits, and other factors may play an important role. True susceptibility to the virus is challenging to measure. Not everyone who is exposed to the virus becomes infected, and not everyone who is infected develops symptoms.
Resources
- Principles of Epidemiology (U.S. CDC)
- Coronavirus Disease 2019 Case Surveillance — United States, January 22–May 30, 2020 (U.S. CDC)
- Age-dependent effects in the transmission and control of COVID-19 epidemics, 2020 (Nature Medicine)
- Do Your Genes Predispose You to COVID-19? 2020 (Scientific American)
- Assessing risk factors for COVID-19, 2020 (U.S. CDC)
- COVID-19 in Racial and Ethnic Minority Groups, 2020 (U.S. CDC)
- Principles of Epidemiology (U.S. CDC)
- Coronavirus Disease 2019 Case Surveillance — United States, January 22–May 30, 2020 (U.S. CDC)
- Age-dependent effects in the transmission and control of COVID-19 epidemics, 2020 (Nature Medicine)
- Do Your Genes Predispose You to COVID-19? 2020 (Scientific American)
- Assessing risk factors for COVID-19, 2020 (U.S. CDC)
- COVID-19 in Racial and Ethnic Minority Groups, 2020 (U.S. CDC)
