Choosing Core NILS data and its impact on Research

1. Choosing Core NILS data and its impact on Research Rónán Adams Máire Brolly NILS User Forum 11th December 2009

2. Aims: • Understand the structure of the NILS • Understand the level/impact of Census Imputation and List Inflation • Understand the research implications

3. Identifying the Core NILS data • requirement to have a core data source • coverage of the data source • complete information on dates of birth • existing linkages between the data sources

4. Core Data Set Options: Data sets in Research Proposal – Agreement • Census • Health Card Registrations (CHI) Issues: • Census • Census Office only require Age • Any missing day/month Imputed to 1st • 1st too high, all other dates too low • Person imputation (95%) • Health Card registrations • List Inflation (105%)

5. Existing Links between Data Sources NICR Data GRO Deaths Central Health Index GRO Births 2001 Census Records

6. Proposed Links between Data Sources NICR Data GRO Deaths Central Health Index GRO Births 2001 Census Records

7. NILS c28% of population Sample members from Health Card Registrations List Inflation an issue Census Imputation NIMS 100% of deaths Census members linked to deaths Only enumerated people can be linked NILS/NIMS

8. Sample Selection in NI • 104 dates – 100 NI, 4 E&W • For each download (6 monthly) • Is the DDMM of the DOB a NILS date? If so then they are in the sample • NILS sample – a person who has ever been in one of the 6-monthly downloads

9. Deaths 2001 Census Database Births to Mothers 1991 Census Births to Fathers Stillbirths & Infant Deaths Births Data (baby linked to Birth Registration) 1997 births onwards Migration (Immigrants, Emigrants, Re-Entrants & Within NI Movers) POINTER Address Database VLA/Rating Data Contextual Data NILS Core Data Events Health Card Registrations Key demographic information on NILS members (514,000 live) @ Census Date New members (c40,000)

10. The 3 LSs

11. SG Discussion • LS and SLS reps included on NILS SG • Recognition and Agreement between 3 LSs • NILS methodology would allow future ‘UK’ analyses

13. Health Card Registrations Central Health Index All Live Patients (4th May 2001) 1,768,473 List Inflation 83,206 4.7% Published 2001 census population One Number (29th April 2001) 1,685,267 Imputed Records 81,626 4.6% Imputed Records 81,626 Enumerated 2001 census population (29th April 2001) 1,603,641 Enumerated 2001 census population (29th April 2001) 1,603,641 90.7%

14. Imputation and List Inflation: different profiles by age, gender, geography and other characteristics NOT 4.6% & 4.7% across all groupings

15. The very young

16. Female vs Male

17. All People

18. Imputation

19. List Inflation

20. Geographical Area • Small geographies • Urban – Rural • Administrative Areas • Settlements • Deprivation

21. Urban/Rural

22. Urban/Rural

23. Assembly Areas / Parliamentary Constituencies

24. MDM - Deciles

25. List Inflation • Age, gender, geographical area • Census Imputation • All Census characteristics

26. Imputation level – Marital Status

27. Imputation level – Economic Activity

28. Imputation level – Community Background

29. Summary • Characteristics of List Inflation & Imputation are different from Enumerated • Highest in deprived, urban areas • Affects males more than females • Affects 17-35 year olds most • Unemployed, students, living alone

30. Impact on NILS • Imputed people can’t be linked – no names, DOBs etc. • List inflation people unlikely to be on other administrative data (births, deaths, …) • Can only expect to link a proportion of population

31. Estimate for NILS 28% sample Health Card Registrations with NILS Date Central Health Index All Live Patients (4th May 2001) 508,279 4.7% List Inflation 23,914 • Don’t know who is ‘list inflation’ • Don’t know who is ‘imputed’ • Assume 28% is representative 4.6% Imputation 23,460 90.7% Enumerated 460,904

32. NILS Match Rate (MCR-Census) 4.7% Health Card Registrations with NILS Date Central Health Index All Live Patients (4th May 2001) 508,279 List Inflation 23,914 List Inflation 23,914 Imputation 23,460 4.6% Imputation 23,460 Unmatched 13,447 90.7% Enumerated 460,904 Matched 447,457 88% Match Rate 97% Match Rate Adjusted

33. What % can we expect to match?

34. Hypothetical Example • General Fertility Rate – • number of births per 1,000 women aged 16-44 • 8,000 births • NILS members (16-44) • 130,000 • GFR = 62.1 per 1,000 • NILS members with Census link (16-44) • 110,000 • GFR = 72.7 per 1,000 • ‘true’ estimate • 120,000 • GFR = 67.2 per 1,000

35. Dental Registrations - Males

36. Dental Registrations - Females

37. Dental Registrations – Relative differences

38. Summary • List inflation & imputation are issues • Imputation can be measured – lots of information • LI cannot be easily measured – limited information • Match rates cannot be easily determined • Characteristics of imputed and list inflation different from ‘normal’ population • Need to consider impact on your research

39. Aim: • Understand the structure of the NILS • Understand the level/impact of Census Imputation and List Inflation • Understand the research implications

40. Choosing Core NILS data and its impact on Research Rónán Adams Máire Brolly

41. CSA • FPS • CHI • NHAIS • BSO • H&C, CHIN HEALTH CARD REGISTRATIONS