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Choosing Core NILS data and its impact on Research
Rónán AdamsMáire Brolly
NILS User Forum11th 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 SetOptions:
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%)
Existing Links between Data Sources
NICR Data
Central HealthIndex
GRO Births
GRO Deaths
2001 CensusRecords
Proposed Links between Data Sources
NICR Data
Central HealthIndex
GRO Births
GRO Deaths
2001 CensusRecords
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
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 Mothers
Births to Fathers
Stillbirths & Infant Deaths
Migration (Immigrants, Emigrants,
Re-Entrants & Within NI Movers)
VLA/Rating DataPOINTER
Address Database
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 4 2
Source Health Card Registration
Census + GRO Births + NHSCR
Immigrants
Census + GRO Births + NHSCR
Immigrants
Includes Enumerated,
Imputed and List Inflation – can look at enumerated only
Enumerated only Enumerated only
The 3 LSs
SG Discussion
• LS and SLS reps included on NILS SG
• Recognition and Agreement between 3 LSs
• NILS methodology would allow future ‘UK’ analyses
Health Card Registrations
Central Health Index
All Live Patients
(4th May 2001)
1,768,473
Published 2001
census population
One Number
(29th April 2001)
1,685,267
Enumerated 2001
census population
(29th April 2001)
1,603,641
Imputed Records81,626
Imputed Records81,626
Enumerated 2001
census population
(29th April 2001)
1,603,641
List Inflation83,206
4.6%
90.7%
4.7%
Imputation and List Inflation: different profiles by age, gender,
geography and other characteristics
NOT 4.6% & 4.7% across all groupings
Age All PatientsPublished Census Enumeration Imputation Inflation
0 16321 21683 19542 2141 -5362
1 22170 22363 20860 1503 -193
2 23389 23264 21757 1507 125
3 23800 23584 22142 1442 216
The very young
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
DECILE Enumerated ImputedList
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 5%
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%
MDM - Deciles
• List Inflation– Age, gender, geographical area
• Census Imputation– 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
• 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
(4th May 2001)
508,279
Imputation23,460
Enumerated
460,904
List Inflation23,914
4.6%
90.7%
4.7%
Health Card Registrations
with NILS Date
Central Health Index
All Live Patients
(4th May 2001)
508,279
Imputation23,460
Enumerated
460,904
List Inflation23,914
Imputation23,460
Matched
447,457
List Inflation23,914
Unmatched13,447
88%MatchRate
97%Match RateAdjusted
NILS Match Rate (MCR-Census)
Core NILS – Census LI, Imp 91%
Core NILS – Births LI 95%
Core NILS – Births – Census LI, Imp 91%
Core NILS – Deaths LI 95%
Core NILS – Deaths – Census LI, Imp 91%
NIMS – Deaths – Census Imp 95%
What % can we expect to match?
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
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
