Shrinking a large dataset to identify variables associated with increased risk of <i>Plasmodium falciparum</i> infection in Western Kenya

Tremblay, M, Dahm, JS, Wamae, CN, De Glanville, WA, Fevre, EM ORCID: 0000-0001-8931-4986 and Doepfer, D (2015) Shrinking a large dataset to identify variables associated with increased risk of <i>Plasmodium falciparum</i> infection in Western Kenya. EPIDEMIOLOGY AND INFECTION, 143 (16). pp. 3538-3545.

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Official URL: http://dx.doi.org/10.1017/s0950268815000710

Abstract

Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization.

Item Type:	Article
Uncontrolled Keywords:	Cattle, data mining, Kenya, malaria, zoonotic diseases
Subjects:	?? Q1 ?? ?? R1 ??
Depositing User:	Symplectic Admin
Date Deposited:	17 Apr 2015 08:45
Last Modified:	05 Oct 2023 09:26
DOI:	10.1017/S0950268815000710
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/2010119