Space-time clustering of childhood malaria at the household level: a dynamic cohort in a Mali village.
BACKGROUND
Spatial and temporal heterogeneities in the risk of malaria have led the WHO to recommend fine-scale stratification of the epidemiological situation, making it possible to set up actions and clinical or basic researches targeting high-risk zones. Before initiating such studies it is necessary to define local patterns of malaria transmission and infection (in time and in space) in order to facilitate selection of the appropriate study population and the intervention allocation. The aim of this study was to identify, spatially and temporally, high-risk zones of malaria, at the household level (resolution of 1 to 3 m).


METHODS
This study took place in a Malian village with hyperendemic seasonal transmission as part of Mali-Tulane Tropical Medicine Research Center (NIAID/NIH). The study design was a dynamic cohort (22 surveys, from June 1996 to June 2001) on about 1300 children (<12 years) distributed between 173 households localized by GPS. We used the computed parasitological data to analyzed levels of Plasmodium falciparum, P. malariae and P. ovale infection and P. falciparum gametocyte carriage by means of time series and Kulldorff's scan statistic for space-time cluster detection.


RESULTS
The time series analysis determined that malaria parasitemia (primarily P. falciparum) was persistently present throughout the population with the expected seasonal variability pattern and a downward temporal trend. We identified six high-risk clusters of P. falciparum infection, some of which persisted despite an overall tendency towards a decrease in risk. The first high-risk cluster of P. falciparum infection (rate ratio = 14.161) was detected from September 1996 to October 1996, in the north of the village.


CONCLUSIONS
This study showed that, although infection proportions tended to decrease, high-risk zones persisted in the village particularly near temporal backwaters. Analysis of this heterogeneity at the household scale by GIS methods lead to target preventive actions more accurately on the high-risk zones identified. This mapping of malaria risk makes it possible to orient control programs, treating the high-risk zones identified as a matter of priority, and to improve the planning of intervention trials or research studies on malaria.
Gaudart
Jean
J
Medical Statistics and Informatics Research Team, LIF -UMR 6166- CNRS/Aix-Marseille University, Faculty of Medicine, 27 Bd Jean Moulin 13385 Marseille Cedex 05, France. jean.gaudart@univmed.fr
Poudiougou
Belco
B
Dicko
Alassane
A
Ranque
Stéphane
S
Toure
Ousmane
O
Sagara
Issaka
I
Diallo
Mouctar
M
Diawara
Sory
S
Ouattara
Amed
A
Diakite
Mahamadou
M
Doumbo
Ogobara K
OK
D000818:Animals; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D003657:Decision Making; D018450:Disease Progression; D005191:Family Characteristics; D006801:Humans; D008288:Malaria / Q000453:epidemiology* / Q000469:parasitology; D008302:Mali / Q000453:epidemiology; D010963:Plasmodium falciparum / Q000302:isolation & purification / Q000469:parasitology; D010965:Plasmodium malariae / Q000302:isolation & purification / Q000469:parasitology; D041122:Plasmodium ovale / Q000302:isolation & purification / Q000469:parasitology; D016012:Poisson Distribution; D012111:Residence Characteristics; D018570:Risk Assessment; D012307:Risk Factors; D012423:Rural Health / Q000706:statistics & numerical data*; D012621:Seasons; D011795:Surveys and Questionnaires; D014330:Tropical Medicine