The accuracy of human population maps for public health application

Authors


Authors
Simon I. Hay (corresponding author) and A. J. Tatem, TALA Research Group, Tinbergen Building, Department of Zoology, University of Oxford, South Parks Road, Oxford, OX1 3PS, UK. Tel./Fax: 44 (0)1865 271243; E-mail: simon.hay@zoo.ox.ac.uk, andy.tatem@zoo.ox.ac.uk
AM Noor, Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI, PO Box 43640, 00100 Nairobi GPO, Kenya. E-mail: anoor@wtnairobi.mimcom.net
A Nelson, 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. E-mail: andrew.nelson@jrc.it

Summary

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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