Multilevel modelling and malaria: a new method for an old disease.
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.


CONCLUSIONS
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.
Mauny
F
F
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
Viel
J F
JF
Handschumacher
P
P
Sellin
B
B
D000293:Adolescent; D000328:Adult; D000531:Altitude; D000977:Antiparasitic Agents; D002648:Child; D003430:Cross-Sectional Studies; D003634:DDT; D004196:Disease Outbreaks; D006801:Humans; D008270:Madagascar; D008288:Malaria / Q000635:transmission*; D008875:Middle Aged; D015233:Models, Statistical; D015980:Public Health Practice; D012621:Seasons
D000977:Antiparasitic Agents; D003634:DDT