F Mauny1, J F Viel, P Handschumacher, B Sellin. 1. Department of Public Health, Biostatistics and Epidemiology Unit, Faculty of Medicine, 2, place Saint Jacques, 25030 Besançon, France. frederic.mauny@ufc-chu.univ.fcomte.fr
Abstract
BACKGROUND: Malaria is influenced by a web of individual and ecological factors, i.e. factors relating to people and relating to environment. For a long time analysing these factors concurrently has raised statistical problems. Multilevel modelling provides a new attractive solution, which is still uncommon in tropical medicine. METHODS: Using an actual data set of 3864 individuals from 38 villages of the Highland Madagascar, a two-level modelling process is presented. Individual malaria parasitaemia is modelled step by step according to age (individual factor), altitude, and DDT indoor house-spraying status (village factors). RESULTS: The hierarchical organization of a data set in levels, fixed and random effects, and cross-level interactions are considered. Accurate estimations of standard errors, impact of unknown or unmeasured variables quantified and accounted for through random effects, are the highlighted advantages of multilevel modelling. CONCLUSION: While not denying the importance of understanding an aetiological chain, the authors recommend an increased use of multilevel modelling, mainly to identify accurately ecological targets for public health policy.
BACKGROUND:Malaria is influenced by a web of individual and ecological factors, i.e. factors relating to people and relating to environment. For a long time analysing these factors concurrently has raised statistical problems. Multilevel modelling provides a new attractive solution, which is still uncommon in tropical medicine. METHODS: Using an actual data set of 3864 individuals from 38 villages of the Highland Madagascar, a two-level modelling process is presented. Individual malaria parasitaemia is modelled step by step according to age (individual factor), altitude, and DDT indoor house-spraying status (village factors). RESULTS: The hierarchical organization of a data set in levels, fixed and random effects, and cross-level interactions are considered. Accurate estimations of standard errors, impact of unknown or unmeasured variables quantified and accounted for through random effects, are the highlighted advantages of multilevel modelling. CONCLUSION: While not denying the importance of understanding an aetiological chain, the authors recommend an increased use of multilevel modelling, mainly to identify accurately ecological targets for public health policy.
Authors: Jacob Mazalale; Christabel Kambala; Stephan Brenner; Jobiba Chinkhumba; Julia Lohmann; Don P Mathanga; Bjarne Robberstad; Adamson S Muula; Manuela De Allegri Journal: Trop Med Int Health Date: 2015-03-02 Impact factor: 2.622
Authors: Gabriel Carrasco-Escobar; Dionicia Gamboa; Marcia C Castro; Shrikant I Bangdiwala; Hugo Rodriguez; Juan Contreras-Mancilla; Freddy Alava; Niko Speybroeck; Andres G Lescano; Joseph M Vinetz; Angel Rosas-Aguirre; Alejandro Llanos-Cuentas Journal: Sci Rep Date: 2017-08-14 Impact factor: 4.379