tetano
Editor, Senior Moderator
Int J Epidemiol. 2013 Aug;42(4):1164-76. doi: 10.1093/ije/dyt115.
Visualizing Europe's demographic scars with coplots and contour plots.
Minton J, Vanderbloemen L, Dorling D.
Source
Department of Urban Studies, University of Glasgow, Glasgow, UK, Department of Health Science, University of York, York, UK and School of Geography and the Environment, University of Oxford, Oxford, UK.
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.
KEYWORDS:
Demography, epidemiological transition, mortality, public health, visualisation, vital statistics
PMID:
24062300
[PubMed - in process]
PMCID:
PMC3781004
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/24062300
Visualizing Europe's demographic scars with coplots and contour plots.
Minton J, Vanderbloemen L, Dorling D.
Source
Department of Urban Studies, University of Glasgow, Glasgow, UK, Department of Health Science, University of York, York, UK and School of Geography and the Environment, University of Oxford, Oxford, UK.
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.
KEYWORDS:
Demography, epidemiological transition, mortality, public health, visualisation, vital statistics
PMID:
24062300
[PubMed - in process]
PMCID:
PMC3781004
Free full text
http://www.ncbi.nlm.nih.gov/pubmed/24062300