Administrative data quality: Investigating record-level address accuracy in the Northern Ireland Health Register
Administrative data quality: Investigating record-level address accuracy in the Northern Ireland Health Register
Many national statistical institutes (NSIs) are seeking to supplement or replace their traditional population census with a methodology underpinned by administrative sources. Health service register data are key in this regard owing to their high population coverage; it is therefore important to improve understanding of data quality in this administrative source. This study investigated the factors associated with record-level address data mismatch between the Northern Ireland (NI) Health Card Registration System (HCRS) and the 2011 Census, using the NI Longitudinal Study (NILS). Address information in the form of anonymised Unique Property Reference Number (XUPRN) was available for circa 334,000 NILS members with census returns in 2001 and 2011, which provided a benchmark to assess XUPRN accuracy in their linked HCRS record for comparable time points. Multinomial logistic regression revealed a significantly greater likelihood of address mismatch in the HCRS for: males; young adults; individuals with no limiting long-term illness; migrants in the year prior to each census; and residents of communal establishments. Identification of population groups affected by poor quality address information in administrative sources can assist NSIs with the development and implementation of methodological improvements to ensure that official population statistics generated from these sources are fit for purpose.
Address data quality, census, longitudinal data, population statistics
55-81
Foley, Brian
c1c1666b-81a5-47c2-bfa0-a5d54f4b211c
Shuttleworth, Ian
a9fde039-0085-4046-8630-528fe79ab14e
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Foley, Brian
c1c1666b-81a5-47c2-bfa0-a5d54f4b211c
Shuttleworth, Ian
a9fde039-0085-4046-8630-528fe79ab14e
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Foley, Brian, Shuttleworth, Ian and Martin, David
(2018)
Administrative data quality: Investigating record-level address accuracy in the Northern Ireland Health Register.
Journal of Official Statistics, 34 (1), .
(doi:10.1515/jos-2018-0004).
Abstract
Many national statistical institutes (NSIs) are seeking to supplement or replace their traditional population census with a methodology underpinned by administrative sources. Health service register data are key in this regard owing to their high population coverage; it is therefore important to improve understanding of data quality in this administrative source. This study investigated the factors associated with record-level address data mismatch between the Northern Ireland (NI) Health Card Registration System (HCRS) and the 2011 Census, using the NI Longitudinal Study (NILS). Address information in the form of anonymised Unique Property Reference Number (XUPRN) was available for circa 334,000 NILS members with census returns in 2001 and 2011, which provided a benchmark to assess XUPRN accuracy in their linked HCRS record for comparable time points. Multinomial logistic regression revealed a significantly greater likelihood of address mismatch in the HCRS for: males; young adults; individuals with no limiting long-term illness; migrants in the year prior to each census; and residents of communal establishments. Identification of population groups affected by poor quality address information in administrative sources can assist NSIs with the development and implementation of methodological improvements to ensure that official population statistics generated from these sources are fit for purpose.
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[Journal of Official Statistics] Administrative Data Quality Investigating Record-Level Address Accuracy in the Northern Ireland Health Register
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Accepted/In Press date: 10 August 2017
e-pub ahead of print date: 1 March 2018
Keywords:
Address data quality, census, longitudinal data, population statistics
Identifiers
Local EPrints ID: 414153
URI: http://eprints.soton.ac.uk/id/eprint/414153
ISSN: 0282-423X
PURE UUID: 78284e17-ae05-4cd1-908e-89d8f6e6bcfa
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Date deposited: 15 Sep 2017 16:30
Last modified: 16 Mar 2024 02:44
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Author:
Brian Foley
Author:
Ian Shuttleworth
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