The accuracy of human population maps for public health application
The accuracy of human population maps for public health application
Objectives
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.
climate, demography, endemic diseases, humans, kenya, epidemiology, malaria, malria epidemiology, malaria transmission, population density, public health, risk factors, topography, medical methods, medical standards
1073-1086
Hay, S. I.
067a664b-0123-405d-85e8-e9c99d168054
Noor, A. M.
f74f1788-1653-4b64-8410-94d7258efa47
Nelson, A.
6207f97d-f330-408b-8e2b-b33d1e13e9ae
Tatem, A. J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
2005
Hay, S. I.
067a664b-0123-405d-85e8-e9c99d168054
Noor, A. M.
f74f1788-1653-4b64-8410-94d7258efa47
Nelson, A.
6207f97d-f330-408b-8e2b-b33d1e13e9ae
Tatem, A. J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Abstract
Objectives
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.
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More information
Published date: 2005
Additional Information:
Funded by Wellcome Trust: Mapping human population distribution in relation to malaria risk (069045/Z/02/Z)
Keywords:
climate, demography, endemic diseases, humans, kenya, epidemiology, malaria, malria epidemiology, malaria transmission, population density, public health, risk factors, topography, medical methods, medical standards
Organisations:
Geography & Environment, PHEW – P (Population Health), PHEW – S (Spatial analysis and modelling)
Identifiers
Local EPrints ID: 344424
URI: http://eprints.soton.ac.uk/id/eprint/344424
ISSN: 1360-2276
PURE UUID: a4c6cfb3-db84-427e-b19a-b18234f70045
Catalogue record
Date deposited: 11 Feb 2013 12:34
Last modified: 15 Mar 2024 03:43
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Contributors
Author:
S. I. Hay
Author:
A. M. Noor
Author:
A. Nelson
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