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The accuracy of human population maps for public health application

Identifieur interne : 001F07 ( Pmc/Corpus ); précédent : 001F06; suivant : 001F08

The accuracy of human population maps for public health application

Auteurs : S. I. Hay ; A. M. Noor ; A. Nelson ; A. J. Tatem

Source :

Tropical medicine & international health : TM & IH [ 1360-2276 ] ; 2005.

RBID : PMC:3173846

Abstract

SummaryOBJECTIVES

Human population totals are used for generating burden of disease estimates at global, continental and national scales to help guide priority setting in international health financing. These exercises should be aware of the accuracy of the demographic information used.

METHODS

The analysis presented in this paper tests the accuracy of five large-area, public-domain human population distribution data maps against high spatial resolution population census data enumerated in Kenya in 1999. We illustrate the epidemiological significance, by assessing the impact of using these different human population surfaces in determining populations at risk of various levels of climate suitability for malaria transmission. We also describe how areal weighting, pycnophylactic interpolation and accessibility potential interpolation techniques can be used to generate novel human population distribution surfaces from local census information and evaluate to what accuracy this can be achieved.

RESULTS

We demonstrate which human population distribution surface performed best and which population interpolation techniques generated the most accurate bespoke distributions. Despite various levels of modelling complexity, the accuracy achieved by the different surfaces was primarily determined by the spatial resolution of the input population data. The simplest technique of areal weighting performed best.

CONCLUSIONS

Differences in estimates of populations at risk of malaria in Kenya of over 1 million persons can be generated by the choice of surface, highlighting the importance of these considerations in deriving per capita health metrics in public health. Despite focussing on Kenya the results of these analyses have general application and are discussed in this wider context.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3173846

DOI: 10.1111/j.1365-3156.2005.01487.x
PubMed: 16185243
PubMed Central: 3173846

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PMC:3173846

Le document en format XML

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<author><name sortKey="Hay, S I" sort="Hay, S I" uniqKey="Hay S" first="S. I." last="Hay">S. I. Hay</name>
<affiliation><nlm:aff id="A1">TALA Research Group, Department of Zoology, University of Oxford, Oxford, UK</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="A2">Malaria Public Health and Epidemiology Group, KEMRI, Nairobi, Kenya</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Noor, A M" sort="Noor, A M" uniqKey="Noor A" first="A. M." last="Noor">A. M. Noor</name>
<affiliation><nlm:aff id="A2">Malaria Public Health and Epidemiology Group, KEMRI, Nairobi, Kenya</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Nelson, A" sort="Nelson, A" uniqKey="Nelson A" first="A." last="Nelson">A. Nelson</name>
<affiliation><nlm:aff id="A3">Centre for Computational Geography, School of Geography, University of Leeds, Leeds, UK</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="A4">Global Vegetation Monitoring Unit, JRC (Joint Research Centre of the European Commission), ISPRA (VA), Italy</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Tatem, A J" sort="Tatem, A J" uniqKey="Tatem A" first="A. J." last="Tatem">A. J. Tatem</name>
<affiliation><nlm:aff id="A1">TALA Research Group, Department of Zoology, University of Oxford, Oxford, UK</nlm:aff>
</affiliation>
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<idno type="doi">10.1111/j.1365-3156.2005.01487.x</idno>
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<sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">The accuracy of human population maps for public health application</title>
<author><name sortKey="Hay, S I" sort="Hay, S I" uniqKey="Hay S" first="S. I." last="Hay">S. I. Hay</name>
<affiliation><nlm:aff id="A1">TALA Research Group, Department of Zoology, University of Oxford, Oxford, UK</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="A2">Malaria Public Health and Epidemiology Group, KEMRI, Nairobi, Kenya</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Noor, A M" sort="Noor, A M" uniqKey="Noor A" first="A. M." last="Noor">A. M. Noor</name>
<affiliation><nlm:aff id="A2">Malaria Public Health and Epidemiology Group, KEMRI, Nairobi, Kenya</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Nelson, A" sort="Nelson, A" uniqKey="Nelson A" first="A." last="Nelson">A. Nelson</name>
<affiliation><nlm:aff id="A3">Centre for Computational Geography, School of Geography, University of Leeds, Leeds, UK</nlm:aff>
</affiliation>
<affiliation><nlm:aff id="A4">Global Vegetation Monitoring Unit, JRC (Joint Research Centre of the European Commission), ISPRA (VA), Italy</nlm:aff>
</affiliation>
</author>
<author><name sortKey="Tatem, A J" sort="Tatem, A J" uniqKey="Tatem A" first="A. J." last="Tatem">A. J. Tatem</name>
<affiliation><nlm:aff id="A1">TALA Research Group, Department of Zoology, University of Oxford, Oxford, UK</nlm:aff>
</affiliation>
</author>
</analytic>
<series><title level="j">Tropical medicine & international health : TM & IH</title>
<idno type="ISSN">1360-2276</idno>
<idno type="eISSN">1365-3156</idno>
<imprint><date when="2005">2005</date>
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<front><div type="abstract" xml:lang="en"><title>Summary</title>
<sec id="S1"><title>OBJECTIVES</title>
<p id="P4">Human population totals are used for generating burden of disease estimates at global, continental and national scales to help guide priority setting in international health financing. These exercises should be aware of the accuracy of the demographic information used.</p>
</sec>
<sec id="S2"><title>METHODS</title>
<p id="P5">The analysis presented in this paper tests the accuracy of five large-area, public-domain human population distribution data maps against high spatial resolution population census data enumerated in Kenya in 1999. We illustrate the epidemiological significance, by assessing the impact of using these different human population surfaces in determining populations at risk of various levels of climate suitability for malaria transmission. We also describe how areal weighting, pycnophylactic interpolation and accessibility potential interpolation techniques can be used to generate novel human population distribution surfaces from local census information and evaluate to what accuracy this can be achieved.</p>
</sec>
<sec id="S3"><title>RESULTS</title>
<p id="P6">We demonstrate which human population distribution surface performed best and which population interpolation techniques generated the most accurate bespoke distributions. Despite various levels of modelling complexity, the accuracy achieved by the different surfaces was primarily determined by the spatial resolution of the input population data. The simplest technique of areal weighting performed best.</p>
</sec>
<sec id="S4"><title>CONCLUSIONS</title>
<p id="P7">Differences in estimates of populations at risk of malaria in Kenya of over 1 million persons can be generated by the choice of surface, highlighting the importance of these considerations in deriving <italic>per capita</italic>
 health metrics in public health. Despite focussing on Kenya the results of these analyses have general application and are discussed in this wider context.</p>
</sec>
</div>
</front>
</TEI>
<pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
  <pmc-dir>properties manuscript</pmc-dir>
  <front><journal-meta><journal-id journal-id-type="nlm-journal-id">9610576</journal-id>
<journal-id journal-id-type="pubmed-jr-id">20323</journal-id>
<journal-id journal-id-type="nlm-ta">Trop Med Int Health</journal-id>
<journal-id journal-id-type="iso-abbrev">Trop. Med. Int. Health</journal-id>
<journal-title-group><journal-title>Tropical medicine & international health : TM & IH</journal-title>
</journal-title-group>
<issn pub-type="ppub">1360-2276</issn>
<issn pub-type="epub">1365-3156</issn>
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<article-meta><article-id pub-id-type="pmid">16185243</article-id>
<article-id pub-id-type="pmc">3173846</article-id>
<article-id pub-id-type="doi">10.1111/j.1365-3156.2005.01487.x</article-id>
<article-id pub-id-type="manuscript">UKMS3940</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>The accuracy of human population maps for public health application</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Hay</surname>
<given-names>S. I.</given-names>
</name>
<xref ref-type="aff" rid="A1">1</xref>
<xref ref-type="aff" rid="A2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Noor</surname>
<given-names>A. M.</given-names>
</name>
<xref ref-type="aff" rid="A2">2</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Nelson</surname>
<given-names>A.</given-names>
</name>
<xref ref-type="aff" rid="A3">3</xref>
<xref ref-type="aff" rid="A4">4</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Tatem</surname>
<given-names>A. J.</given-names>
</name>
<xref ref-type="aff" rid="A1">1</xref>
</contrib>
</contrib-group>
<aff id="A1"><label>1</label>
TALA Research Group, Department of Zoology, University of Oxford, Oxford, UK</aff>
<aff id="A2"><label>2</label>
Malaria Public Health and Epidemiology Group, KEMRI, Nairobi, Kenya</aff>
<aff id="A3"><label>3</label>
Centre for Computational Geography, School of Geography, University of Leeds, Leeds, UK</aff>
<aff id="A4"><label>4</label>
Global Vegetation Monitoring Unit, JRC (Joint Research Centre of the European Commission), ISPRA (VA), Italy</aff>
<author-notes><fn id="FN1"><p id="P1"><bold>Simon I. Hay</bold>
 (corresponding author) and <bold>A. J. Tatem</bold>
, TALA Research Group, Tinbergen Building, Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS, UK. Tel./Fax: 44 (0)1865 271243; <email>simon.hay@zoo.ox.ac.uk</email>
, <email>andy.tatem@zoo.ox.ac.uk</email>
</p>
<p id="P2"><bold>A. M. Noor</bold>
, Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI, PO Box 43640, 00100 Nairobi GPO, Kenya. <email>anoor@wtnairobi.mimcom.net</email>
</p>
<p id="P3"><bold>A. Nelson</bold>
, Centre for Computational Geography, School of Geography, University of Leeds, Leeds, LS2 9JT, UK and now Global Vegetation Monitoring Unit, JRC (Joint Research Centre of the European Commission), ISPRA (VA), Italy. <email>andrew.nelson@jrc.it</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="nihms-submitted"><day>1</day>
<month>9</month>
<year>2011</year>
</pub-date>
<pub-date pub-type="ppub"><month>10</month>
<year>2005</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>15</day>
<month>9</month>
<year>2011</year>
</pub-date>
<volume>10</volume>
<issue>10</issue>
<fpage>1073</fpage>
<lpage>1086</lpage>
<permissions><copyright-statement>© 2005 Blackwell Publishing Ltd</copyright-statement>
<copyright-year>2005</copyright-year>
</permissions>
<abstract><title>Summary</title>
<sec id="S1"><title>OBJECTIVES</title>
<p id="P4">Human population totals are used for generating burden of disease estimates at global, continental and national scales to help guide priority setting in international health financing. These exercises should be aware of the accuracy of the demographic information used.</p>
</sec>
<sec id="S2"><title>METHODS</title>
<p id="P5">The analysis presented in this paper tests the accuracy of five large-area, public-domain human population distribution data maps against high spatial resolution population census data enumerated in Kenya in 1999. We illustrate the epidemiological significance, by assessing the impact of using these different human population surfaces in determining populations at risk of various levels of climate suitability for malaria transmission. We also describe how areal weighting, pycnophylactic interpolation and accessibility potential interpolation techniques can be used to generate novel human population distribution surfaces from local census information and evaluate to what accuracy this can be achieved.</p>
</sec>
<sec id="S3"><title>RESULTS</title>
<p id="P6">We demonstrate which human population distribution surface performed best and which population interpolation techniques generated the most accurate bespoke distributions. Despite various levels of modelling complexity, the accuracy achieved by the different surfaces was primarily determined by the spatial resolution of the input population data. The simplest technique of areal weighting performed best.</p>
</sec>
<sec id="S4"><title>CONCLUSIONS</title>
<p id="P7">Differences in estimates of populations at risk of malaria in Kenya of over 1 million persons can be generated by the choice of surface, highlighting the importance of these considerations in deriving <italic>per capita</italic>
 health metrics in public health. Despite focussing on Kenya the results of these analyses have general application and are discussed in this wider context.</p>
</sec>
</abstract>
<kwd-group><kwd>Kenya</kwd>
<kwd>demography</kwd>
<kwd>census</kwd>
<kwd>areal weighting</kwd>
<kwd>pycnophylactic interpolation</kwd>
<kwd>dasymetric mapping</kwd>
<kwd>smart interpolation</kwd>
</kwd-group>
<funding-group><award-group><funding-source country="United Kingdom">Wellcome Trust : </funding-source>
<award-id>081829 || WT</award-id>
</award-group>
<award-group><funding-source country="United Kingdom">Wellcome Trust : </funding-source>
<award-id>069045 || WT</award-id>
</award-group>
</funding-group>
</article-meta>
</front>
</pmc>
</record>

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The accuracy of human population maps for public health application

The accuracy of human population maps for public health application

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