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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.v10i4.3013</article-id>
      <article-id pub-id-type="publisher-id">10:3:008</article-id>
      <title-group>
        <article-title>Assessing the spatial pattern and temporal stability of violence in Scotland.
          A data linkage study using ambulance data</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Morales</surname>
            <given-names initials="A">Ana</given-names>
          </name>
          <xref ref-type="aff" rid="affil-1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>McVie</surname>
            <given-names initials="S">Susan</given-names>
          </name>
          <xref ref-type="aff" rid="affil-1">1</xref>
        </contrib>
      </contrib-group>
      <aff id="affil-1"><label>1</label><institution>University of Edinburgh, Edinburgh, United Kingdom</institution></aff>
      <pub-date date-type="pub" publication-format="electronic">
        <day>01</day>
        <month>06</month>
        <year>2025</year>
      </pub-date>
      <pub-date date-type="collection" publication-format="electronic">
        <year>2025</year>
      </pub-date>
      <volume>8</volume>
      <issue>4</issue>
      <elocation-id>3013</elocation-id>
      <permissions>
        <license license-type="open-access"
          xlink:href="https://creativecommons.org/licences/by/4.0/">
          <license-p>This work is licenced under a Creative Commons Attribution 4.0 International
            License.</license-p>
        </license>
      </permissions>
      <self-uri xlink:href="https://ijpds.org/article/view/3013">This article is available from the
        IJPDS website at: https://ijpds.org/article/view/3013</self-uri>
    </article-meta>
  </front>
  <body>
    <sec>
      <title>Introduction</title>
      <p>This work programme uses linked Census and administrative datasets to quantify the impact
        of various health conditions, and treatments for those conditions, on labour market
        variables such as employment, pay and benefit receipts. This new, powerful evidence base
        will inform UK government policies and spending decisions aiming to increase labour market
        participation and economic growth.</p>
    </sec>
    <sec>
      <title>Methods</title>
      <p>The Office for National Statistics (ONS) has created a data asset comprising National
        Health Service (NHS) health records, HM Revenue and Customs (HMRC) pay-as-you-earn records,
        Department for Work and Pensions (DWP) benefit receipts, ONS death registrations and ONS
        Census data. The datasets are linked at the individual level, cover almost the entire
        working-age population of England, and provide nearly a decade of longitudinal observations.
        We are applying quasi-experimental techniques, such as pre-post fixed effects regression
        modelling with inverse-probability weighting or matching where relevant, to estimate the
        relationship between health conditions/interventions and labour market outcomes whilst
        controlling for relevant time-varying confounders. We are also exploring heterogeneity in
        these relationships by socio-demographic and clinical characteristics.</p>
    </sec>
    <sec>
      <title>Results</title>
      <p>To date, our analysis has demonstrated that: people from ethnic minority groups and
        deprived areas were disproportionately more likely to start receiving employment-related
        benefits during the COVID-19 pandemic; people who undergo bariatric (weight loss) surgery
        have a sustained improvement in the likelihood of employment up to five years after the
        surgery compared with pre-surgery; and talking therapies services are likely to be effective
        at helping unemployed people with common mental health disorders re-enter the workforce and
        increase their earnings potential up to seven years after treatment.</p>
    </sec>
    <sec>
      <title>Conclusion</title>
      <p>The new linked data asset created by ONS is already being used to generate valuable
        insights that have the potential to improve the livelihoods of individuals and the health of
        the macroeconomy. ONS will continue to publish outputs in this area throughout 2025,
        including on the labour market effects of diabetes prevention programmes, musculoskeletal
        surgery, cardiovascular disease, endometriosis, adverse pregnancy events, and extended NHS
        waiting lists.</p>
    </sec>
  </body>
</article>