1. Demographic Analysis and Evaluation
Workshop on
Demographic Analysis and Evaluation 1

2. Mortality: Constructing Life Tables from Fragmented Data Using Model Life Tables

3. Constructing a Life Table from Fragmentary Data
Mortality indicators of good quality tend to be limited
to certain age groups.
A census may provide reported deaths for all ages in a population while a demographic survey may only provide child survivorship data supporting estimation of mortality.
However, survey data available for a few ages may reflect mortality levels more accurately than census data.
And such “fragmentary” data can be used to develop life tables that measure mortality for all age groups. 3

4. Recall from “Model Life Tables” Lecture…
Model life table functions have been constructed so that, if a value for one life table function is available, the rest of the life table can be estimated.
For example, if an estimate of e(x) or q(x) is available for one age, q(x) values for other ages can be interpolated using model life tables.
Similarly, if mortality rates for an actual population are severely distorted because of errors in the data, these rates can be smoothed using model life tables. 4

5. Constructing a Life Table from Fragmentary Data
The United Nations MORTPAK program MATCH utilizes an index life table indicator together with an empirical or model pattern of dying across all ages to generate abridged life tables.
For selected sex and age, MATCH can utilize:
a central death rate, m(x)
the probability of dying, q(x)
survivors to an exact age, l(x)
life expectancy, e(x) 5

6. Constructing a Life Table from Fragmentary Data
Spreadsheet:
MATCH.xls or MATCH_BS.xls 6

7. Synthesizing Information on Mortality
to Select a Pattern
If the age distribution of mortality is limited, on what basis can we make assumptions about the age pattern of mortality and, in turn, incorporate what we do have into MATCH?
By comparing the mortality across ages to model life tables, we can make assumptions about the distribution of mortality among ages with missing data. 7

8. United Nations and Coale-Demeny West MLT Patterns Compared, Females at e(0) = 60 & 70 Years8

9. United Nations and Coale-Demeny West MLT Patterns Compared, Females at e(0) = 60 & 70 Years9

10. United Nations and Coale-Demeny West MLT Patterns Compared, Females at e(0) = 60 & 70 Years10

11. Synthesizing Information on Mortality
to Select a Pattern
Both COMPAR and DHSQCOM calculate life expectancies at birth corresponding to reported age-specific death rates or probabilities of dying for each of the five United Nations models and four Coale-Demeny models.
The smaller the variability in the corresponding life expectancies at birth across age groups, the closer the empirical measures are to a specific regional model.
Both programs provide indices measuring the dispersion of e(0) values across age groups 11

12. Indices
The median e(0) is calculated and an index formed as the sum of the absolute values of age-specific e(0) deviations from the median. The index with the smallest value indicates the life table most closely matching the empirical data.
COMPAR provides indices for all age groups, the broad age group under-10 and the broad age group over-10. It also provides the difference between the under-10 and 10+ scores.
DHSQCOM provides one index measuring the difference between the e(0) for age 0 and the e(0) for age group 1-4. DHSQCOM also ranks the models on that difference. 12