1. Preparing & presenting demographic information: 1
(Session 05)

2. Learning Objectives At the end of this session, you will be able to
interpret and use conventions about age-grouping of demographic data
sensibly choose the types of breakdown of general population samples needed for various demographic purposes
discuss some of the issues involved in choosing how much detail to report
explain the need to think of demographic data in age, period & cohort terms

3. Age-grouping conventions: 1
Quite often data from major sources is presented “by single year of age”, but if
this is overwhelming for users, or
Data don’t justify so much disaggregation, broader age groups are used.
Relatively high mortality amongst the very young means we often use birth up to 1st birthday, 1st birthday to 5th, then 5-year age-groups until a high age e.g. small popn of people 80+ are all put in 1 group.

4. Age-grouping conventions: 2
5-year age-groups easy to follow in decimal system. Very rough categories often used in general population studies:-
0 - <5 pre-school; <15 school age;
15 - <50 female fertile years;
15 - <60 potentially economically active;
60+ retired; 80+ aged.
How do these fit in in your country? Discuss.

5. Age-grouping conventions: 3
In relation to general population studies, note that comparison between studies & data sources is hampered if people use other than 5-year bands.
In specific settings, e.g. of school-related topics, you must fit local framework e.g. if school starts at age 6, 0-<6 is “pre-school” e.g. for attainment measurement might use grades, not ages, as categories.

6. Age-misreporting
Historically there was a tendency for older people, & the educationally-disadvantaged to report ages vaguely. Standard measures e.g. Myers’ index are used to quantify age-heaping. Frequent tendency is to round ages to numbers ending in 0 or 5, and using 5-year age-groups alleviates any distorting effects on population composition.
Do you think five-year age-gps would better correct age-misreporting if centred at 5 and 0, or wd confusion created exceed gains?

7. Disaggregation: 1
Specific rates are those where the population reported is sub-divided e.g. by age &/or sex. Usually a “crude” rate e.g. Crude Birth Rate is not subdivided at all, but of course Age-Specific Death Rates (ASDR) are crude with respect to sex, unless they are separated for M & F [then Age- and Sex-Specific].
Note that all such rates have so far been discussed on a national basis

8. Disaggregation: 2
For many studies internal to one country, it is valuable to sub-divide results by region or district (administrative areas) and/or by other stratification variables e.g. often metropolitan/other urban/rural or e.g. Buddhist/Christian/Hindu/Muslim/other.
In specific settings this needs to fit analysis needs e.g. rainfed/irrigated in rural livelihoods studies.

9. Disaggregation: 3
Note that where data source is a sample survey, not a full census, numbers are soon cut down in disaggregated categories, e.g. with a representative general population sample of N= 5,000 people,
25 to 29-year-old rural males might ≈
1/20 of 1/2 of 1/3 i.e. 1/120 ~ about 40 people.
Results then subject to very large sampling variability. Only report estimates for such gps if accompanied by error estimates &/or warnings!

10. Reporting detailed data: 1
Demography has a bad reputation for generating mountains of detailed data.
You can’t read/assimilate it when presented like this  on screen.
Apologies to UK National Stats Office who never intended “Child Health Statistics” to appear like this. See next slide!

11. Reporting detailed data: 2
Distinguish between (i) presentation tables with very few key numbers, boldly/clearly presented & well-explained for general audience, and (ii) reference tables giving detailed disaggregated numbers for those who need to look up and use.
is a very good downloadable “data” book; easy access to, and summary of, reference data relating to UK child health and contact points to get more on any theme.

12. Reporting detailed data: 3
A very common consideration in demography is the reporting of figures (e.g. disease-specific death rates) repeatedly for each yr.
Time series graphs are useful to display a general trend or compare a few trend lines.
N.B trends meaningful only if data collected very consistently at constant high quality. If definitions (e.g. diagnostic criteria, reporting requirements) change, there is a break in the series. Take care linking data across any breaks.

13. The age-period-cohort problem
Demographic data presentation always faces groupings & approximations. A specialised one is that e.g. the cohort born in 1953 will each be 30 as of their birthdays in 1983.
With age vs time graph, cohort is a diagonal line. The three concepts are always entangled in this way. Example below:-

14. Example: Age-Period-Cohort diagram
Line shows late stages of the life of Mr. X, born 1/11/
became 30 on 1/11/
died aged 31 yrs 7 mths on 1/6/1985

15. Age-Period-Cohort data: 1
Statistics giving male population size by age for mid-1984, corresponding to 1 square in diagram, wd include Mr. X as a 30-yr-old.
The population from the 1953 birth cohort alive in mid-1984 wd. include Mr.X but as part of the black lozenge shape, not a square, in the diagram.

16. Age-Period-Cohort data: 2
Strictly speaking Mr. X’s untimely
death belongs in a single triangle
of the diagram. To have all data
organised so that we can draw out
Age by Period OR Age by Cohort figures, we
need the original data doubly classified in
this triangular way.

17. Practical work follows to ensure learning objectives are achieved…