1. Ten things you need to know about death…..longitudinally speaking
de mort O’Reilly

2. Possible census-based linkages & analyses

3. Possible census-based linkages & analyses

4. Death has a lot going for it…
Statutory obligation;
therefore complete and
good quality data
Long experience of use
One of the main reasons for starting ONS-LS
Aim: to highlight some caveats with the data

5. 1. Death is not necessarily certain
There are known unknowns. That is to say, there are things that we know we don't know.
Rumsfeld

6. 1. Death is not necessarily certain
Some deaths are not linked
Who are these immortals?

7. Percentage of death records not linked to a census record, by year of registration of death

8. Age and sex distribution of unlinked death records

9. Aged less than 65 Aged more than 65
Characteristics of those with an unlinked death record (deaths and results of log. regression)
Aged less than 65
Aged more than 65
Sex
Deaths OR
Male
8,130
1.00
25,443
Female
4,941
0.63 ***
31,775
0.92*
Marital status
Married
7,398
19,450
Single
3,549
1.57 ***
8,873
2.83 ***
Widowed
776
1.40 ***
27,758
1.97 ***
Sep/Divorced
1,348
2.52 ***
1,137
3.30 ***
Place of death
Home
6,066
13,378
N/R home
1,009
1.05
12,771
2.00 ***
Hospital
5,996
0.80 ***
31,069
1.28 ***

10. Variations according to area characteristics
Density:
<65: little variation
>65 reduced linkage in more rural areas
Deprivation:
Reduced linkage in most deprived areas
Imputation:
Strong gradient (esp. at younger ages) and reduced linkage where census worst

11. Variation by cause of death
All ages
Under 65 years old
Deaths (%unmatched)
All causes
70,289 (6.9%)
13,071 (11.1%)
I.H.D
13,970 (5.6%)
2,064 (9.4%)
Stroke
7,211 (6.8%)
542 (8.9%)
Respiratory Disease
9,722 (7.0%)
802 (9.9%)
Cancer
18,572 (5.6%)
4,846 (8.1%)
All External causes
2,634 (15.2%)
1,648 (20.3%)
Accidents
1,719 (12.3%)
830 (18.2%)
Suicides
702 (19.9%)
649 (21.4%)
Other Causes
12,840 (8.9%)
2,579 (13.6%)

12. 2. Some people are ghosts even before they are dead!
Problem: Some people were not there in the first place
Imputation and the one-number census
Why is this a problem?
How is this handled in the different LS-s
Solutions: ?sensitivity analyses
Last night upon the kitchen stair, I met a man who wasn’t there, He wasn’t there again today, I wish, I wish he’d go away... (Hughes Mearns)

13. 3. Sometimes there aren’t enough deaths
(death is not what it used to be…)
“Events, dear boy, events” Harold Macmillan

14. General Problem ... falling death rates
Crude death rate in Northern Ireland 1971 – 2014
in people aged less than 75 years
Male
Female

15. 3. Sometimes there aren’t enough deaths: other small number problems …
Population sub-groups
Eg U/E; ethnic minorities; others?
Cause-specific mortality;
Suicides, trauma, specific cancers etc

16. 3. Sometimes there aren’t enough deaths:
Potential solutions …
Increase the length of follow-up;
Aggregate sub-populations;
Increase the cohort size

17. 4. Death is more or less certain; cause of death less so.
Uncertainties in cause of death;
Coding difficulties ICD9-10 changes.
Why are these problems?

18. Some problems with mortality data
Diagnosis may be wrong
Coding may be wrong
Interpretation difficult
Incidence confounded by investigation
Mortality confounded by treatment

22. 5. Migration
(further problems with the un-dead)

23. Possible LS Linkages & Analyses
Census 1
Census 2 a b c

24. 5. Migration Problem 1: (the un-dying)
Two things in life that are not certain, death and taxes… ask a non-dom
Problem 1: (the un-dying)
Selective unrecorded emigration;
who does this affect?
Death rate = (Number of deaths) / (population at risk)
Solution: ?? Better sources of migration

25. 5. Migration Problem 2: (the un-dead)
Non inclusion of migration data; is this a problem and if so what is the effect?
Death rate = (Number of deaths) / (population at risk)
Solution: ??

26. 6. Yes, but you wouldn’t want to start from here.

27. 6. Yes, but you wouldn’t want to start from here.
Problem:
All census-based longitudinal studies start with a cross-sectional study.
U/E ??
Risk of death

28. Possible LS Linkages & Analyses
Start point
Census 1
Census 2 a b c

29. 6. Yes, but you wouldn’t want to start from here.
U/E
Risk of death
Poor health
Health selection effects
Others eg Marital status; Carers; the retired

30. 6. Yes, but you wouldn’t want to start from here.
Possible solutions:
Adjust using LLTI/GH
Drop early deaths
Time dependent variable
Other statistical techniques

31. 7. The non-longitudinal aspects of longitudinal data
Problem:
We assume that most characteristics do not change between censuses.
Is this valid? What is the effect on results?
Solutions:
Are there any alternative ways to update the data?
I ain't what I used to be, but who the hell is? Dizzy Dean

32. 8. There’s never a good time to start thinking about death
Study 1: What is the mortality risk of carers?
deaths
Carer status
2021 Census
2011 Census

33. Study 2: What are the social factors related to mortality risk for alcohol-related causes of death?
deaths
1991 Census
2001 Census
2011 Census
2021 Census

34. 9. It all depends how well adjusted you are
Problem:
Adjust for S.E.S.
Effectiveness, fairness; residual confounding
Urban/rural differences
Difficulties at older ages
Should you adjust for economic activity?
Adjust for Health
How is this done?
Solutions: ???

35. 10. Death is not the only answer; there are always other options.
Good for some research questions
Overall mortality risk
Premature mortality
Avoidable mortality etc
Not so good for others
Confounded by variations in detection and treatment
Eg breast and testicular cancer
Alcohol-related mortality gross underestimation of burden and associations (RTA, suicides etc)

36. Thank you for your patience Any questions?
It's not that I'm afraid to die, I just don't want to be there when it happens.
I don't want to achieve immortality through my work. I want to achieve it through not dying Woody Allen