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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">IJPDS</journal-id>
<journal-title-group>
<journal-title>International Journal of Population Data Science</journal-title>
<abbrev-journal-title>IJPDS</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2399-4908</issn>
<publisher>
<publisher-name>Swansea University</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.23889/ijpds.v11i5.3685</article-id>
<article-id pub-id-type="publisher-id">11:5:3685</article-id>
<article-id pub-id-type="pii">S2399490821036855</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Population Data Science</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Using the UK Census Longitudinal Studies to Investigate Long Term Change</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author"><name><surname>Sizer</surname><given-names initials="A">Alison</given-names></name><xref ref-type="aff" rid="affil-1"><sup>1</sup></xref></contrib>
<contrib contrib-type="author"><name><surname>Lowry</surname><given-names initials="E">Estelle</given-names></name><xref ref-type="aff" rid="affil-2"><sup>2</sup></xref></contrib>
<contrib contrib-type="author"><name><surname>Williamson</surname><given-names initials="L">Lee</given-names></name><xref ref-type="aff" rid="affil-3"><sup>3</sup></xref></contrib>
<aff id="affil-1"><label>1</label><institution>University College London, London, United Kingdom</institution></aff>
<aff id="affil-2"><label>2</label><institution>Queens University Belfast, Belfast, United Kingdom</institution></aff>
<aff id="affil-3"><label>3</label><institution>University of Edinburgh, Edinburgh, United Kingdom</institution></aff>
</contrib-group>
<pub-date date-type="pub" publication-format="electronic"><day></day><month></month><year></year></pub-date>
<pub-date date-type="collection" publication-format="electronic"><year></year></pub-date>
<volume>11</volume>
<issue>5</issue>
<elocation-id>3685</elocation-id>
<permissions>
<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by-nc-nd/4.0/">
<license-p>This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.</license-p>
</license>
</permissions>
<self-uri xlink:href="https://ijpds.org/article/view/3685">This article is available from the IJPDS website at: https://ijpds.org/article/view/3685</self-uri>
<abstract>
<p>This paper introduces the three UK Census Longitudinal Studies (UKLSs), and the data, including availability of linked administrative data. It discusses their size and scope, and linkage of the 2021/22 Census data. Arrangements for accessing the data from the user support units and some key areas for research are also highlighted. Each of the UKLSs takes a sample of the population from Census data and follows it across time, linking administrative data, with capacity to link further data at low level geographies. The UKLSs provide unparalleled coverage and sample sizes which allow research using risk factors and outcomes often unavailable from other sources. The ONS Longitudinal Study has 50 years of follow-up 1971–2021. It follows a 1% sample of the England &amp; Wales population linked to births, deaths and cancer registration data. The Scottish Longitudinal Study has 31 years of follow-up 1991–2022, and linkages include health, education and environmental data (e.g. pollution), births, deaths, marriages. It covers a 5% sample of the Scottish population. The Northern Ireland Longitudinal Study covers 28% of population with 40 years of follow-up 1981–2021 and linkages to datasets including health (e.g., prescribing data), births, deaths, marriages and property data. Linkage of the 2021/22 Census data to each of the studies enables researchers to examine changes taking place in 2011–2021/2 period, which saw Brexit and the Covid-19 pandemic.</p>
</abstract>
</article-meta>
</front>
</article>