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

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

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

Core Data Set Options: Data sets in Research Proposal – Agreement Census Health Card Registrations (CHI) Issues: 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%)

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

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

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

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

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

The 3 LSs NILS E&W LS SLS Number Dates 104 4 20 Size 500,000 (28%) 500,000 (1%) 300,000 (5%) Start Date 2001 1971 1991 Number of Censuses 1 2 Source Health Card Registration Census + GRO Births + NHSCR Immigrants Includes Enumerated, Imputed and List Inflation – can look at enumerated only Enumerated only

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

List Inflation 83,206 Imputed Records Imputed Records 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 Imputed Records 81,626 4.6% Enumerated 2001 census population (29th April 2001) 1,603,641 Enumerated 2001 census population (29th April 2001) 1,603,641 90.7%

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

The very young Age All Patients Published Census Enumeration Imputation Inflation 16321 21683 19542 2141 -5362 1 22170 22363 20860 1503 -193 2 23389 23264 21757 1507 125 3 23800 23584 22142 1442 216

Female vs Male

All People

Imputation

List Inflation

Geographical Area Small geographies Urban – Rural Administrative Areas Settlements Deprivation

Urban/Rural

Urban/Rural

Assembly Areas / Parliamentary Constituencies

MDM - Deciles DECILE Enumerated Imputed List Inflation % LI, Imp   Least Deprived 157,974 5,710 2,525 5% 156,166 5,375 -4,652 0% 158,522 6,122 -273 4% 162,774 6,546 1,150 162,128 8,177 2,133 6% 166,973 8,897 3,644 7% 162,667 7,946 10,109 10% 162,913 9,324 11,422 11% 159,247 10,095 23,688 18% most Deprived 154,277 13,434 33,458 23%

List Inflation Census Imputation Age, gender, geographical area All Census characteristics

Imputation level – Marital Status

Imputation level – Economic Activity

Imputation level – Community Background

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

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

Estimate for NILS 28% sample 4.7% Health Card Registrations with NILS Date Central Health Index All Live Patients (4th May 2001) 508,279 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

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 4.6% Imputation 23,460 Imputation 23,460 90.7% Enumerated 460,904 Unmatched 13,447 Matched 447,457 88% Match Rate 97% Match Rate Adjusted

What % can we expect to match? Core NILS – Census LI, Imp 91% Core NILS – Births LI 95% Core NILS – Births – Census Core NILS – Deaths Core NILS – Deaths – Census NIMS – Deaths – Census Imp

Hypothetical Example General Fertility Rate – 8,000 births 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

Dental Registrations - Males

Dental Registrations - Females

Dental Registrations – Relative differences

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

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

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

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