1. Introduction

1. Analysing long-term trends in sickness and health: further evidence from the Hampshire Friendly Society Bernard Harris, Martin Gorsky , Aravinda Guntupalli and Andrew Hinde Longitudinal data from historical sources University of Guelph, 24/5/10

2. 1. Introduction • Hampshire (and General) Friendly Society • History of morbidity

3. 2. The Hampshire Friendly Society and its members • ‘County’ or patronised society • Founded in Winchester in 1824 • Based in Hampshire in southern England • Majority of members were rural working-class males (no women admitted to the Assurance Section after 1877)

4. 3. Research aims • Identify members on joining • Use sickness benefits to identify sickness episodes • Trace these from joining to departure/death • 10% sample of all members joining the assurance section (but note that not all the members who joined the assurance section were men who were eligible for sickness benefit)

5. 4. Sources

8. 5. Research questions • Seasonality of sickness claims • Age-of-onset of ‘chronic’ morbidity • Changes in age-specific morbidity over time • Causes of sickness claims • Changing pattern of morbidity • Cause-specific morbidity and age-specific morbidity

9. 6. Seasonal patterns

10. 7. Morbidity and age

11. 9. Three gauges of morbidity • Incidence = no. of sickness episodes • Duration = length of episodes • Prevalence = no. of ‘sick-years’ per head of population exposed to risk

12. 9. Three gauges of morbidity, cont. • Problems in measuring the incidence of sickness episodes • Cannot identify separate episodes within the same quarter • Cannot definitely identify episodes which extend across quarters • Assume that all the sick-days within a given quarter formed part of a single episode within that quarter • Assume that sickness-periods in consecutive quarters formed part of a single episode unless there is clear evidence to the contrary

13. 9. Three gauges of morbidity, cont. • If we can estimate incidence, we can calculate duration: • So our method of estimating incidence yields a lower estimate of the total number of episodes (incidence) and a higher estimate of sickness duration • But this is only a problem if we think that the extent to which we may have underestimated the incidence of sickness episodes changed over time

14. 10. Age-specific sickness rates

15. 10. Age-specific sickness rates, cont.

16. 10. Age-specific sickness rates, cont.

17. 11. The proximate causes of sickness claims • Problems associated with the use of epidemiological descriptions and categories • Interpretation of handwriting • Diagnostic changes • Numbers of categories • Use of ICD chapters • Age problems • Focus on 40-64 year olds

18. 11. The proximate causes of sickness claims, cont.

19. 11. The proximate causes of sickness claims, cont.

20. 11. The proximate causes of sickness claims, cont.

21. 12. Sickness-cause and sickness-age

22. 12. Sickness-cause and sickness-age, cont.

23. 13. What else can we do with these data? • The determinants of sickness episodes • Factors associated with the initiation of sickness episodes • Factors associated with the duration of sickness episodes • Links with other HFS data • Date and place of birth • Occupation on joining • Benefit entitlements • HFS administrative data • HFS financial data

24. 13. What else can we do these data, cont. • Links with other data sources • Wage rates • Meteorological data • Unemployment data • Local mortality data • Individual death certificates • Medical registers • Other research questions • Migration • Welfare provision