title: Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya.
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.
authors: Departments of Medicine and Pathobiological Sciences,School of Veterinary Medicine,University of Wisconsin-Madison,Madison,WI,USA.
authors: M
authors: M
authors: Tremblay
authors: Departments of Medicine and Pathobiological Sciences,School of Veterinary Medicine,University of Wisconsin-Madison,Madison,WI,USA.
authors: J S
authors: JS
authors: Dahm
authors: Center for Microbiology Research,Kenya Medical Research Institute (KEMRI),Nairobi,Kenya.
authors: C N
authors: CN
authors: Wamae
authors: Centre for Immunity,Infection and Evolution,Institute for Immunology and Infection Research,School of Biological Sciences,University of Edinburgh,Ashworth Laboratories,Edinburgh,UK.
authors: W A
authors: WA
authors: De Glanville
authors: International Livestock Research Institute,Nairobi,Kenya.
authors: E M
authors: EM
authors: Fèvre
authors: Departments of Medicine and Pathobiological Sciences,School of Veterinary Medicine,University of Wisconsin-Madison,Madison,WI,USA.
authors: D
authors: D
authors: Döpfer
mesh_terms: D000818:Animals; D056808:Biostatistics / Q000379:methods*; D002417:Cattle; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D007630:Kenya / Q000453:epidemiology; D016778:Malaria, Falciparum / Q000453:epidemiology*; D008297:Male; D018570:Risk Assessment
keywords: Cattle; Kenya; data mining; malaria; zoonotic diseases