1. Choosing Core NILS data and its impact on Research Rónán Adams Máire Brolly NILS User Forum 11 th 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 1 st – 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 Central Health Index GRO Births GRO Deaths 2001 Census Records

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

7. NILS/NIMS 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

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

10. NILSE&W LSSLS Number Dates104420 Size500,000 (28%)500,000 (1%)300,000 (5%) Start Date200119711991 Number of Censuses 142 SourceHealth Card Registration Census + GRO Births + NHSCR Immigrants Includes Enumerated, Imputed and List Inflation – can look at enumerated only Enumerated only 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 (4 th May 2001) 1,768,473 Published 2001 census population One Number (29 th April 2001) 1,685,267 Enumerated 2001 census population (29 th April 2001) 1,603,641 Imputed Records 81,626 Imputed Records 81,626 Enumerated 2001 census population (29 th April 2001) 1,603,641 List Inflation 83,206 4.6% 90.7% 4.7%

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

15. AgeAll Patients Published CensusEnumerationImputationInflation 01632121683195422141-5362 12217022363208601503-193 22338923264217571507125 32380023584221421442216 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

23. Assembly Areas / Parliamentary Constituencies

24. DECILEEnumeratedImputed List Inflation% LI, Imp Least Deprived 157,974 5,710 2,5255% 156,166 5,375-4,6520% 158,522 6,122-2734% 162,774 6,546 1,1505% 162,128 8,177 2,1336% 166,973 8,897 3,6447% 162,667 7,946 10,10910% 162,913 9,324 11,42211% 159,247 10,095 23,68818% most Deprived 154,277 13,434 33,45823% 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. ‘real’ people on census 9.3% of health card registrations with no census return Don’t know who exactly they are Some will be people who didn’t fill out form Some will be people who shouldn’t be on list

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

33. 4.6% 90.7% 4.7% Health Card Registrations with NILS Date Central Health Index All Live Patients (4 th May 2001) 508,279 Imputation 23,460 Enumerated 460,904 List Inflation 23,914 Imputation 23,460 Matched 447,457 List Inflation 23,914 Unmatched 13,447 88% Match Rate 97% Match Rate Adjusted NILS Match Rate (MCR-Census)

34. Core NILS – CensusLI, Imp91% Core NILS – BirthsLI95% Core NILS – Births – CensusLI, Imp91% Core NILS – DeathsLI95% Core NILS – Deaths – CensusLI, Imp91% NIMS – Deaths – CensusImp95% What % can we expect to match?

35. Impacts Simple RatesTOO LOW –% people who move –Crude death rate –Birth Rate, TPFR Rates (census)TOO HIGH –Standardised rates –Models Relative differences?

36. 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

37. Dental Registrations Registration Rate for NILS members (all) Registration Rate for NILS with Census link Registration Rate for NILS with no Census link

38. Dental Registrations - Males

39. Dental Registrations - Females

40. Dental Registrations – Relative differences

41. 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

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

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

44. All People

45. Males

46. Females

47. Assembly Areas

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