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Multilevel modelling and malaria: a new method for an old disease
F Mauny1, J F Viel, P Handschumacher
1Department of Public Health, Biostatistics and Epidemiology Unit, Faculty of Medicine, 2, place Saint Jacques, 25030 Besançon, France. frederic.mauny@ufc-chu.univ.fcomte.fr
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.
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