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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.v10i3.3262</article-id>
      <article-id pub-id-type="publisher-id">10:3:227</article-id>
      <title-group>
        <article-title>Linking local authority data to understand and respond to the school
          attendance challenge</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Boldon</surname>
            <given-names initials="R">Ruth</given-names>
          </name>
          <xref ref-type="aff" rid="affil-1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Scott</surname>
            <given-names initials="K">Kyla</given-names>
          </name>
          <xref ref-type="aff" rid="affil-1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Ofomata</surname>
            <given-names initials="C">Chinelo</given-names>
          </name>
          <xref ref-type="aff" rid="affil-1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Gardner</surname>
            <given-names initials="G">Grace</given-names>
          </name>
          <xref ref-type="aff" rid="affil-1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Lidstone-Scott</surname>
            <given-names initials="R">Rebecca</given-names>
          </name>
          <xref ref-type="aff" rid="affil-1">1</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Sheffer</surname>
            <given-names initials="K">Kate</given-names>
          </name>
          <xref ref-type="aff" rid="affil-1">1</xref>
        </contrib>
      </contrib-group>
      <aff id="affil-1"><label>1</label><institution>Newcastle City Council, Newcastle, 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>3262</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/3262">This article is available from the
        IJPDS website at: https://ijpds.org/article/view/3262</self-uri>
    </article-meta>
  </front>
  <body>
    <sec>
      <title>Objectives</title>
      <p>Pupil absence rates in Newcastle are among the highest in the country. Teachers, local
        authority attendance officers and community partners face significant challenges. While
        these professionals possess tacit knowledge about the reasons behind pupil absenteeism,
        supporting data is lacking.</p>
    </sec>
    <sec>
      <title>Method</title>
      <p>We linked data from the school census, child in need census and attendance reports for
        pupils in years 1-11 in Newcastle from 2020/21 to 2022/23. Using multi-level negative
        binomial regression models, we examined how factors like Free School Meal eligibility,
        Special Educational Needs, Child in Need status, and socioeconomic conditions, influence
        likelihood of school absence.</p>
      <p>We estimated the variation in pupil absence across Newcastle attributable to school-level
        factors and identified outlier schools with the highest and lowest population-adjusted
        absence rates for subsequent qualitative research. This research was conducted by
        researchers within &lt;Redacted&gt;, facilitating effective collaboration with local
        stakeholders.</p>
    </sec>
    <sec>
      <title>Results</title>
      <p>Results will be available at the ADR UK Conference 2025. Administrative data will be
        descriptively analysed and graphically displayed to characterise the school attendance
        challenge in Newcastle and identify trends over recent years. We will present adjusted
        associations between pupil factors and absence, as well as estimates of the school
        environment’s influence on pupil absence. The limitations of our analysis will be discussed,
        along with how the research was enriched through focus groups, interviews and surveys with
        children, families and relevant professionals.</p>
    </sec>
    <sec>
      <title>Conclusion</title>
      <p>Missing school can adversely affect mental wellbeing and educational attainment, which can
        impact employment opportunities and health in the long term. By identifying those at higher
        risk, we aim to support local stakeholder discussions and inform the development of early
        interventions to improve outcomes for children and their families.</p>
    </sec>
  </body>
</article>