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Visualizing Europe’s demographic scars with coplots and contour plots

Identifieur interne : 000508 ( Pmc/Checkpoint ); précédent : 000507; suivant : 000509

Visualizing Europe’s demographic scars with coplots and contour plots

Auteurs : Jonathan Minton [Royaume-Uni] ; Laura Vanderbloemen [Royaume-Uni] ; Danny Dorling [Royaume-Uni]

Source :

RBID : PMC:3781004

Abstract

We present two enhancements to existing methods for visualizing vital statistics data. Data from the Human Mortality Database were used and vital statistics from England and Wales are used for illustration. The simpler of these methods involves coplotting mean age of death with its variance, and the more complex of these methods is to present data as a contour plot. The coplot method shows the effect of the 20th century’s epidemiological transitions. The contour plot method allows more complex and subtle age, period and cohort effects to be seen.

The contour plot shows the effects of broad improvements in public health over the 20th century, including vast reductions in rates of childhood mortality, reduced baseline mortality risks during adulthood and the postponement of higher mortality risks to older ages. They also show the effects of the two world wars and the 1918 influenza pandemic on men of fighting age, women and children. The contour plots also show a cohort effect for people born around 1918, suggesting a possible epigenetic effect of parental exposure to the pandemic which shortened the cohort’s lifespan and which has so far received little attention.

Although this article focuses on data from England and Wales, the associated online appendices contain equivalent visualizations for almost 50 series of data available on the Human Mortality Database. We expect that further analyses of these visualizations will reveal further insights into global public health.


Url:
DOI: 10.1093/ije/dyt115
PubMed: 24062300
PubMed Central: 3781004


Affiliations:


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PMC:3781004

Le document en format XML

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<p>Although this article focuses on data from England and Wales, the associated online appendices contain equivalent visualizations for almost 50 series of data available on the Human Mortality Database. We expect that further analyses of these visualizations will reveal further insights into global public health.</p>
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<journal-id journal-id-type="iso-abbrev">Int J Epidemiol</journal-id>
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<article-title>Visualizing Europe’s demographic scars with coplots and contour plots</article-title>
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<name>
<surname>Minton</surname>
<given-names>Jonathan</given-names>
</name>
<xref ref-type="aff" rid="dyt115-AFF1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="dyt115-COR1">*</xref>
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<name>
<surname>Vanderbloemen</surname>
<given-names>Laura</given-names>
</name>
<xref ref-type="aff" rid="dyt115-AFF1">
<sup>2</sup>
</xref>
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<name>
<surname>Dorling</surname>
<given-names>Danny</given-names>
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<xref ref-type="aff" rid="dyt115-AFF1">
<sup>3</sup>
</xref>
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<aff id="dyt115-AFF1">
<sup>1</sup>
Department of Urban Studies, University of Glasgow, Glasgow, UK,
<sup>2</sup>
Department of Health Science, University of York, York, UK and
<sup>3</sup>
School of Geography and the Environment, University of Oxford, Oxford, UK</aff>
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<corresp id="dyt115-COR1">*Corresponding author. Department of Urban Studies, University of Glasgow, 25 Bute Gardens, Glasgow, G12 8RS. E-mail:
<email>nate.minton@gmail.com</email>
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<pub-date pub-type="ppub">
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<month>8</month>
<year>2013</year>
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<month>8</month>
<year>2013</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on the . </pmc-comment>
<volume>42</volume>
<issue>4</issue>
<fpage>1164</fpage>
<lpage>1176</lpage>
<history>
<date date-type="accepted">
<day>21</day>
<month>5</month>
<year>2013</year>
</date>
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<permissions>
<copyright-statement>Published by Oxford University Press on behalf of the International Epidemiological Association © The Author 2013</copyright-statement>
<copyright-year>2013</copyright-year>
<license license-type="creative-commons" xlink:href="http://creativecommons.org/licenses/by-nc/3.0">
<license-p>
<pmc-comment>CREATIVE COMMONS</pmc-comment>
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by-nc/3.0/">http://creativecommons.org/licenses/by-nc/3.0/</ext-link>
), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com</license-p>
</license>
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<abstract>
<p>We present two enhancements to existing methods for visualizing vital statistics data. Data from the Human Mortality Database were used and vital statistics from England and Wales are used for illustration. The simpler of these methods involves coplotting mean age of death with its variance, and the more complex of these methods is to present data as a contour plot. The coplot method shows the effect of the 20th century’s epidemiological transitions. The contour plot method allows more complex and subtle age, period and cohort effects to be seen.</p>
<p>The contour plot shows the effects of broad improvements in public health over the 20th century, including vast reductions in rates of childhood mortality, reduced baseline mortality risks during adulthood and the postponement of higher mortality risks to older ages. They also show the effects of the two world wars and the 1918 influenza pandemic on men of fighting age, women and children. The contour plots also show a cohort effect for people born around 1918, suggesting a possible epigenetic effect of parental exposure to the pandemic which shortened the cohort’s lifespan and which has so far received little attention.</p>
<p>Although this article focuses on data from England and Wales, the associated online appendices contain equivalent visualizations for almost 50 series of data available on the Human Mortality Database. We expect that further analyses of these visualizations will reveal further insights into global public health.</p>
</abstract>
<kwd-group>
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<li>Glasgow</li>
<li>Oxford</li>
</settlement>
<orgName>
<li>Université d'Oxford</li>
<li>Université de Glasgow</li>
</orgName>
</list>
<tree>
<country name="Royaume-Uni">
<region name="Écosse">
<name sortKey="Minton, Jonathan" sort="Minton, Jonathan" uniqKey="Minton J" first="Jonathan" last="Minton">Jonathan Minton</name>
</region>
<name sortKey="Dorling, Danny" sort="Dorling, Danny" uniqKey="Dorling D" first="Danny" last="Dorling">Danny Dorling</name>
<name sortKey="Vanderbloemen, Laura" sort="Vanderbloemen, Laura" uniqKey="Vanderbloemen L" first="Laura" last="Vanderbloemen">Laura Vanderbloemen</name>
</country>
</tree>
</affiliations>
</record>

Pour manipuler ce document sous Unix (Dilib)

EXPLOR_STEP=$WICRI_ROOT/Sante/explor/PandemieGrippaleV1/Data/Pmc/Checkpoint
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000508 | SxmlIndent | more

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Pour mettre un lien sur cette page dans le réseau Wicri

{{Explor lien
   |wiki=    Sante
   |area=    PandemieGrippaleV1
   |flux=    Pmc
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   |type=    RBID
   |clé=     PMC:3781004
   |texte=   Visualizing Europe’s demographic scars with coplots and contour plots
}}

Pour générer des pages wiki

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       | NlmPubMed2Wicri -a PandemieGrippaleV1 

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Data generation: Wed Jun 10 11:04:28 2020. Site generation: Sun Mar 28 09:10:28 2021