Local topographic wetness indices predict household malaria risk better than land-use and land-cover in the western Kenya highlands

55Citations
Citations of this article
165Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Background: Identification of high-risk malaria foci can help enhance surveillance or control activities in regions where they are most needed. Associations between malaria risk and land-use/land-cover are well-recognized, but these environmental characteristics are closely interrelated with the land's topography (e.g., hills, valleys, elevation), which also influences malaria risk strongly. Parsing the individual contributions of land-cover/land-use variables to malaria risk requires examining these associations in the context of their topographic landscape. This study examined whether environmental factors like land-cover, land-use, and urban density improved malaria risk prediction based solely on the topographically-determined context, as measured by the topographic wetness index. Methods. The topographic wetness index, an estimate of predicted water accumulation in a defined area, was generated from a digital terrain model of the landscape surrounding households in two neighbouring western Kenyan highland communities. Variables determined to best encompass the variance in this topographic wetness surface were calculated at a household level. Land-cover/land-use information was extracted from a high-resolution satellite image using an object-based classification method. Topographic and land-cover variables were used individually and in combination to predict household-level malaria in the communities through an iterative split-sample model fitting and testing procedure. Models with only topographic variables were compared to those with additional predictive factors related to land-cover/land-use to investigate whether these environmental factors improved prediction of malaria based on the shape of the land alone. Results. Variables related to topographic wetness proved most useful in predicting the households of individuals contracting malaria in this region of rugged terrain. Other variables related to human modification of the environment also demonstrated clear associations with household malaria. However, these land-cover/land-use variables failed to produce unambiguous improvements in statistical predictive models controlling for important topographic factors, with none improving prediction of household-level malaria more than 75% of the time. Conclusions. Topographic wetness values in this region of highly varied terrain more accurately predicted houses at greater risk of malaria than did consideration of land-cover/land-use characteristics. As such, those planning control or local elimination strategies in similar highland regions may use topographic and geographic characteristics to effectively identify high-receptivity regions that may require enhanced vigilance. © 2010 Cohen et al; licensee BioMed Central Ltd.

References Powered by Scopus

A new method for the determination of flow directions and upslope areas in grid digital elevation models

1856Citations
N/AReaders
Get full text

Cross-validation of regression models

1247Citations
N/AReaders
Get full text

The economic burden of malaria

796Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Eco-Bio-Social Determinants for House Infestation by Non-domiciliated Triatoma dimidiata in the Yucatan Peninsula, Mexico

69Citations
N/AReaders
Get full text

Modeling the distribution of the West Nile and Rift Valley Fever vector Culex pipiens in arid and semi-arid regions of the Middle East and North Africa

61Citations
N/AReaders
Get full text

Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland

61Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Cohen, J. M., Ernst, K. C., Lindblade, K. A., Vulule, J. M., John, C. C., & Wilson, M. L. (2010). Local topographic wetness indices predict household malaria risk better than land-use and land-cover in the western Kenya highlands. Malaria Journal, 9(1). https://doi.org/10.1186/1475-2875-9-328

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 73

64%

Researcher 23

20%

Professor / Associate Prof. 10

9%

Lecturer / Post doc 8

7%

Readers' Discipline

Tooltip

Agricultural and Biological Sciences 30

35%

Environmental Science 26

30%

Medicine and Dentistry 17

20%

Earth and Planetary Sciences 13

15%

Save time finding and organizing research with Mendeley

Sign up for free