1. Multilevel modelling and malaria: a new method for an old disease
- Author
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Jean-François Viel, B. Sellin, Frédéric Mauny, Pascal Handschumacher, Laboratoire Chrono-environnement - CNRS - UBFC (UMR 6249) (LCE), Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Laboratoire Chrono-environnement ( LCE ), and Université Bourgogne Franche-Comté ( UBFC ) -Centre National de la Recherche Scientifique ( CNRS ) -Université de Franche-Comté ( UFC )
- Subjects
Multivariate analysis ,Epidemiology ,MESH: DDT ,MESH : Malaria ,Disease Outbreaks ,MESH: Madagascar ,0302 clinical medicine ,MESH : Cross-Sectional Studies ,MESH : Child ,MESH: Child ,Econometrics ,MESH: Disease Outbreaks ,Child ,MESH: Antiparasitic Agents ,MESH: Middle Aged ,Antiparasitic Agents ,Altitude ,Multilevel model ,MESH : Models, Statistical ,[ SDV.SPEE ] Life Sciences [q-bio]/Santé publique et épidémiologie ,General Medicine ,MESH : Adult ,Middle Aged ,Random effects model ,3. Good health ,Geography ,Seasons ,0305 other medical science ,Adult ,medicine.medical_specialty ,Adolescent ,MESH : Madagascar ,030231 tropical medicine ,MESH: Malaria ,MESH : Public Health Practice ,DDT ,03 medical and health sciences ,MESH: Cross-Sectional Studies ,MESH : Adolescent ,parasitic diseases ,medicine ,Madagascar ,Hierarchical organization ,Humans ,MESH : Middle Aged ,MESH : Disease Outbreaks ,MESH : Antiparasitic Agents ,MESH: Adolescent ,030505 public health ,Models, Statistical ,MESH: Humans ,MESH : Seasons ,Public health ,MESH : DDT ,MESH : Humans ,Statistical model ,MESH: Adult ,medicine.disease ,MESH: Altitude ,Malaria ,Data set ,Cross-Sectional Studies ,MESH: Public Health Practice ,Public Health Practice ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,MESH: Seasons ,MESH: Models, Statistical ,MESH : Altitude - Abstract
International audience; 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.
- Published
- 2004
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