Bulletin of the World Health Organization
Print version ISSN 0042-9686
BROOKER, Simon et al. Use of remote sensing and a geographical information system in a national helminth control programme in Chad. Bull World Health Organ [online]. 2002, vol.80, n.10, pp. 783-789. ISSN 0042-9686. http://dx.doi.org/10.1590/S0042-96862002001000006.
OBJECTIVE: To design and implement a rapid and valid epidemiological assessment of helminths among schoolchildren in Chad using ecological zones defined by remote sensing satellite sensor data and to investigate the environmental limits of helminth distribution. METHODS: Remote sensing proxy environmental data were used to define seven ecological zones in Chad. These were combined with population data in a geographical information system (GIS) in order to define a sampling protocol. On this basis, 20 schools were surveyed. Multilevel analysis, by means of generalized estimating equations to account for clustering at the school level, was used to investigate the relationship between infection patterns and key environmental variables. FINDINGS: In a sample of 1023 schoolchildren, 22.5% were infected with Schistosoma haematobium and 32.7% with hookworm. None were infected with Ascaris lumbricoides or Trichuris trichiura. The prevalence of S. haematobium and hookworm showed marked geographical heterogeneity and the observed patterns showed a close association with the defined ecological zones and significant relationships with environmental variables. These results contribute towards defining the thermal limits of geohelminth species. Predictions of infection prevalence were made for each school surveyed with the aid of models previously developed for Cameroon. These models correctly predicted that A. lumbricoides and T. trichiura would not occur in Chad but the predictions for S. haematobium were less reliable at the school level. CONCLUSION: GIS and remote sensing can play an important part in the rapid planning of helminth control programmes where little information on disease burden is available. Remote sensing prediction models can indicate patterns of geohelminth infection but can only identify potential areas of high risk for S. haematobium.
Keywords : Helminths [growth and development]; Helminthiasis [epidemiology]; Schistosoma haematobium [growth and development]; Schistosomiasis haematobia [epidemiology]; Ancylostomatoidea [growth and development]; Hookworm infections [epidemiology]; Ascaris lumbricoides [growth and development]; Trichuris [growth and development]; Environmental monitoring; Ecology; Information systems; Epidemiologic studies; Chad.