<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></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.

Administrative Data Quality: Investigating Record-Level Address Accuracy in the Northern Ireland Health Register

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Many national statistical institutes (NSIs) are seeking to supplement or replace their traditional population census with a methodology underpinned by...

Administrative Data Quality: Investigating Record-Level Address Accuracy in the Northern Ireland Health Register

Publié en ligne: 01 mars 2018

Pages: 55 - 81

Reçu: 01 avr. 2016

Accepté: 01 oct. 2017

DOI: https://doi.org/10.1515/jos-2018-0004

Mots clés
Address data quality, census, population statistics, longitudinal data

© by Brian Foley

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Administrative Data Quality: Investigating Record-Level Address Accuracy in the Northern Ireland Health Register

Publié en ligne: 01 mars 2018

Pages: 55 - 81

Reçu: 01 avr. 2016

Accepté: 01 oct. 2017

DOI: https://doi.org/10.1515/jos-2018-0004

Mots clésAddress data quality, census, population statistics, longitudinal data

© by Brian Foley

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
Address data quality, census, population statistics, longitudinal data