# Measures of Life Expectancy & Mortality: Terms & Tools Video

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• 00:00 Life and Death
• 00:30 Crude Death Rate
• 1:17 Age- and Sex-Specific Rates
• 2:57 Infant Mortality Rate
• 4:00 Life…
• 6:05 Lesson Summary
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Lesson Transcript
Instructor: Christopher Muscato

Chris has a master's degree in history and teaches at the University of Northern Colorado.

How long will you live? Calculating mortality rates and life expectancy is one of the important uses of statistics. Explore how these are calculated and test your understanding with a brief quiz.

## Life and Death

How long will you live? I can't tell you, but maybe someone else can. No, I'm not talking about a fortune teller or the person who writes the daily horoscope. I mean a statistician. Yeah, believe it or not, statisticians have ways to predict how long you'll live. That's your life expectancy, the estimated number of years someone born in a specific year will live. Yes, statisticians can predict your lifespan. And they don't even need to see your palm.

## Crude Death Rate

To start predicting life expectancy, we need a few things. No, not a crystal ball, tarot cards, or astrology charts. We need some statistics.

Let's start with something called crude death rates, or the average rate of death within a population. The crude death rate tells you, simply enough, the average number of deaths per 1,000 people in a population in a single calendar year. This is pretty easy to calculate. Say that you have a population of 75,000, and 1,200 of those people died in the same year. Divide 1,200 by 75,000, then multiply by 1,000 and voila, there you have it! The crude death rate in this population is 20 per every 1,000 people.

## Age- and Sex-Specific Rates

Now, crude death rates can be misleading. They don't take into account differences in sex and age, which are really important when trying to determine someone's life expectancy. Age- and sex-specific rates calculate mortality within a specific population. For determining life expectancy, age-specific rates are actually really important.

Take a look at this chart.

So, for example, if you look at the 60-64 age group, the number to the right of that is how many people lived in that area during a three-year period. The next column shows that there were 992 deaths in that age group in that time, and then that number was divided by the number in the second column to get the number in the far-right column, 0.013. So the death rate for 60- to 64-year olds in that time frame was 13 per 1,000 people.

When we calculate mortality rates for the entire population in five-year increments, we are creating what is called a life table. This is just one kind of life table; we'll show you another one in a bit.

## Infant Mortality Rate

Age-specific rates are important for figuring out life expectancy, but there is one group in particular that often gets special attention. Infants, defined by statistics as children under one year old, are a very vulnerable segment of the population. Infants are very susceptible to diseases and injuries, and so their mortality rates are often studied separately.

Infant mortality rates are becoming more and more often studied since researchers have discovered some interesting correlations between this and other factors. For example, it has been noted that infant mortality rates are high in areas with governments that are about to collapse and low in areas with high levels of social equality. Basically, because infants are at such high risk, pretty much any change in society can affect their mortality rates.

Infant mortality is used to help determine quality of life, the effectiveness of a nation's healthcare, and even a nation's level of reliance on industrial technologies. How's that for a fancy bit of fortune telling?

## Life Expectancy/Expectation of Life

All right, so these are some of the important statistics we need. How do we use those to predict life expectancy? Well, this can be a really complex process. Some formulas, for example, have ways of adjusting for infant mortality rates since this segment of the population may skew results. But let's just start with a simple calculation.

First, we need that life table from earlier. There we go. Next, we use these age-specific mortality rates and calculate the probability of dying in between each segment of the population. This is what that looks like.

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