1. Summary measures of mortality – Life expectancy and life tables
Data analysis and Report writing workshop for Civil registration and vital statistics data.

2. Why do we construct life tables?
One important method of assessing the health of a population is to ask how long people can expect to live.
‘Life expectancy’ is the most common way to describe population health and reflects the overall mortality level of a population. It measures the average number of years a person is expected to live (based on current age and sex-specific death rates).

3. What is a life table?
A life table shows, for a person at each age, the probability they die before their next birthday. A number of other statistics can be derived:
the probability of surviving any particular year of age
the remaining years someone can expect to live once they have reached a certain age
the proportion of the original birth cohort still alive
Note that you should have separate life tables for males and females

4. The abridged life table
uses deaths rates calculated from groups of ages
most common groupings are <1, 1-4,5-9,…85+
where populations are very small – 10 year groupings are used

5. What does an life table look like?

6. ex -this is the key output you will need
Definition= expected (average) number of years of life left for a person aged x The average number of years a newborn can expect to live – also called life expectancy at birth e0

7. Model Life Tables
Age-specific mortality data are not always available/reliable
i.e. we do not have accurate vital registration data
The life table of a similar population, such as a neighboring country, can be useful
Alternatively, we can use a model life table
Model life tables are sets of hypothetical life tables that let us pick one to use based on different patterns of age- specific mortality (Alison used these for some of the Pacific country population projections because they were more stable/reliable than vital statistics based life tables)

8. Calculate using the excel tool
all you need are your age-sex-specific mortality rates (deaths divided by population)
Paste your values in to the “Data Entry” tab that matches your last age group (e.g. 75+, 80+, 85+). The excel sheet will do the rest!
Does the value for e0 look realistic for your country?
Is female e0 higher than male e0 ?