Visualizing Europe's demographic scars with coplots and contour plots.
Identifieur interne : 000877 ( PubMed/Checkpoint ); précédent : 000876; suivant : 000878Visualizing Europe's demographic scars with coplots and contour plots.
Auteurs : Jonathan Minton [Royaume-Uni] ; Laura Vanderbloemen ; Danny DorlingSource :
- International journal of epidemiology [ 1464-3685 ] ; 2013.
Descripteurs français
- KwdFr :
- Adolescent, Adulte, Adulte d'âge moyen, Angleterre (épidémiologie), Démographie (), Enfant, Enfant d'âge préscolaire, Europe (épidémiologie), Facteurs de l'âge, Humains, Jeune adulte, Mortalité (tendances), Mâle, Nourrisson, Nouveau-né, Pays de Galles (épidémiologie), Première Guerre mondiale, Seconde Guerre mondiale, Sujet âgé, Sujet âgé de 80 ans ou plus, Taux de survie.
- MESH :
- tendances : Mortalité.
- épidémiologie : Angleterre, Europe, Pays de Galles.
- Adolescent, Adulte, Adulte d'âge moyen, Démographie, Enfant, Enfant d'âge préscolaire, Facteurs de l'âge, Humains, Jeune adulte, Mâle, Nourrisson, Nouveau-né, Première Guerre mondiale, Seconde Guerre mondiale, Sujet âgé, Sujet âgé de 80 ans ou plus, Taux de survie.
English descriptors
- KwdEn :
- Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Child, Child, Preschool, Demography (statistics & numerical data), England (epidemiology), Europe (epidemiology), Humans, Infant, Infant, Newborn, Male, Middle Aged, Mortality (trends), Survival Rate, Wales (epidemiology), World War I, World War II, Young Adult.
- MESH :
- geographic , epidemiology : England, Europe, Wales.
- statistics & numerical data : Demography.
- trends : Mortality.
- Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Child, Child, Preschool, Humans, Infant, Infant, Newborn, Male, Middle Aged, Survival Rate, World War I, World War II, Young Adult.
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.
DOI: 10.1093/ije/dyt115
PubMed: 24062300
Affiliations:
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xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Démographie</term><term>Enfant</term><term>Enfant d'âge préscolaire</term><term>Facteurs de l'âge</term><term>Humains</term><term>Jeune adulte</term><term>Mâle</term><term>Nourrisson</term><term>Nouveau-né</term><term>Première Guerre mondiale</term><term>Seconde Guerre mondiale</term><term>Sujet âgé</term><term>Sujet âgé de 80 ans ou plus</term><term>Taux de survie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24062300</PMID><DateCompleted><Year>2014</Year><Month>05</Month><Day>07</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1464-3685</ISSN><JournalIssue CitedMedium="Internet"><Volume>42</Volume><Issue>4</Issue><PubDate><Year>2013</Year><Month>Aug</Month></PubDate></JournalIssue><Title>International journal of epidemiology</Title><ISOAbbreviation>Int J Epidemiol</ISOAbbreviation></Journal><ArticleTitle>Visualizing Europe's demographic scars with coplots and contour plots.</ArticleTitle><Pagination><MedlinePgn>1164-76</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/ije/dyt115</ELocationID><Abstract><AbstractText>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. 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IdType="pubmed">21807641</ArticleId></ArticleIdList></Reference><Reference><Citation>Philos Trans R Soc Lond B Biol Sci. 2015 Apr 19;370(1666):null</Citation><ArticleIdList><ArticleId IdType="pubmed">25750242</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Epidemiol. 2001 Aug;30(4):696-703</Citation><ArticleIdList><ArticleId IdType="pubmed">11511585</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Epidemiol. 2001 Aug;30(4):678-83</Citation><ArticleIdList><ArticleId IdType="pubmed">11511582</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Nutr Soc. 2007 May;66(2):216-36</Citation><ArticleIdList><ArticleId IdType="pubmed">17466104</ArticleId></ArticleIdList></Reference><Reference><Citation>Heart. 2000 Dec;84(6):595-8</Citation><ArticleIdList><ArticleId IdType="pubmed">11083734</ArticleId></ArticleIdList></Reference><Reference><Citation>Science. 2002 May 10;296(5570):1029-31</Citation><ArticleIdList><ArticleId IdType="pubmed">12004104</ArticleId></ArticleIdList></Reference><Reference><Citation>Explor Econ Hist. 2001;38(3):339-65</Citation><ArticleIdList><ArticleId IdType="pubmed">18524044</ArticleId></ArticleIdList></Reference><Reference><Citation>Paediatr Perinat Epidemiol. 2001 Jul;15(3):220-5</Citation><ArticleIdList><ArticleId IdType="pubmed">11489148</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Epidemiol. 2011 Apr;40(2):271-7</Citation><ArticleIdList><ArticleId IdType="pubmed">21415000</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Epidemiol. 2012 Jun;41(3):579-88</Citation><ArticleIdList><ArticleId IdType="pubmed">22798690</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Epidemiol. 2001 Aug;30(4):688-96</Citation><ArticleIdList><ArticleId IdType="pubmed">11511584</ArticleId></ArticleIdList></Reference><Reference><Citation>Lancet. 1991 Mar 2;337(8740):530-4</Citation><ArticleIdList><ArticleId IdType="pubmed">1671899</ArticleId></ArticleIdList></Reference><Reference><Citation>Daedalus. 1994 Fall;123(4):61-77</Citation><ArticleIdList><ArticleId IdType="pubmed">11639364</ArticleId></ArticleIdList></Reference><Reference><Citation>Milbank Q. 2005;83(4):731-57</Citation><ArticleIdList><ArticleId IdType="pubmed">16279965</ArticleId></ArticleIdList></Reference><Reference><Citation>Reprod Toxicol. 2005 Sep-Oct;20(3):345-52</Citation><ArticleIdList><ArticleId IdType="pubmed">15893910</ArticleId></ArticleIdList></Reference><Reference><Citation>Br J Cancer. 2011 Apr 26;104(9):1500-4</Citation><ArticleIdList><ArticleId IdType="pubmed">21487403</ArticleId></ArticleIdList></Reference><Reference><Citation>J Natl Cancer Inst. 2004 Apr 7;96(7):539-46</Citation><ArticleIdList><ArticleId IdType="pubmed">15069116</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Epidemiol. 2011 Apr;40(2):363-74</Citation><ArticleIdList><ArticleId IdType="pubmed">21149280</ArticleId></ArticleIdList></Reference><Reference><Citation>J Am Coll Nutr. 2004 Dec;23(6 Suppl):588S-595S</Citation><ArticleIdList><ArticleId IdType="pubmed">15640511</ArticleId></ArticleIdList></Reference><Reference><Citation>Science. 2001 Feb 23;291(5508):1491-2</Citation><ArticleIdList><ArticleId IdType="pubmed">11234076</ArticleId></ArticleIdList></Reference><Reference><Citation>Nat Neurosci. 2009 Mar;12(3):241-3</Citation><ArticleIdList><ArticleId IdType="pubmed">19238182</ArticleId></ArticleIdList></Reference><Reference><Citation>Int J Epidemiol. 2012 Feb;41(1):62-74</Citation><ArticleIdList><ArticleId IdType="pubmed">22422449</ArticleId></ArticleIdList></Reference><Reference><Citation>Epigenetics. 2011 Jul;6(7):798-803</Citation><ArticleIdList><ArticleId IdType="pubmed">21597338</ArticleId></ArticleIdList></Reference><Reference><Citation>J Epidemiol Community Health. 2007 Feb;61(2):159-64</Citation><ArticleIdList><ArticleId IdType="pubmed">17234877</ArticleId></ArticleIdList></Reference><Reference><Citation>Ann Nutr Metab. 1991;35 Suppl 1:53-63</Citation><ArticleIdList><ArticleId IdType="pubmed">1888128</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Royaume-Uni</li></country><region><li>Angleterre</li><li>Oxfordshire</li></region><settlement><li>Oxford</li></settlement><orgName><li>Université de Glasgow</li></orgName></list><tree><noCountry><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></noCountry><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Minton, Jonathan" sort="Minton, Jonathan" uniqKey="Minton J" first="Jonathan" last="Minton">Jonathan 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