{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:08:47Z","timestamp":1790230127973,"version":"4.1.0"},"reference-count":61,"publisher":"National Academy of Sciences","issue":"18","content-domain":{"domain":["www.pnas.org"],"crossmark-restriction":true},"short-container-title":["Proc. Natl. Acad. Sci. U.S.A."],"published-print":{"date-parts":[[2006,5,2]]},"abstract":"<jats:p>\n                    Multiple-species parasitic infections are pervasive in the developing world, yet resources for their control are scarce. We present an integrated approach for risk profiling and spatial prediction of coinfection with\n                    <jats:italic>Schistosoma mansoni<\/jats:italic>\n                    and hookworm for western C\u00f4te d\u2019Ivoire. Our approach combines demographic, environmental, and socioeconomic data; incorporates them into a geographic information system; and employs spatial statistics. Demographic and socioeconomic data were obtained from education registries and from a questionnaire administered to schoolchildren. Environmental data were derived from remotely sensed satellite images and digitized ground maps. Parasitologic data, obtained from fecal examination by using two different diagnostic approaches, served as the outcome measure. Bayesian variogram models were used to assess risk factors and spatial variation of\n                    <jats:italic>S. mansoni<\/jats:italic>\n                    \u2013hookworm coinfection in relation to demographic, environmental, and socioeconomic variables. Coinfections were found in 680 of 3,578 schoolchildren (19.0%) with complete data records. The prevalence of monoinfections with either hookworm or\n                    <jats:italic>S. mansoni<\/jats:italic>\n                    was 24.3% and 24.1%, respectively. Multinomial Bayesian spatial models showed that age, sex, socioeconomic status, and elevation were good predictors for the spatial distribution of\n                    <jats:italic>S. mansoni<\/jats:italic>\n                    \u2013hookworm coinfection. We conclude that our integrated approach, employing a diversity of data sources, geographic information system and remote sensing technologies, and Bayesian spatial statistics, is a powerful tool for risk profiling and spatial prediction of\n                    <jats:italic>S. mansoni<\/jats:italic>\n                    \u2013hookworm coinfection. More generally, this approach facilitates risk mapping and prediction of other parasite combinations and multiparasitism, and hence can guide integrated disease control programs in resource-constrained settings.\n                  <\/jats:p>","DOI":"10.1073\/pnas.0601559103","type":"journal-article","created":{"date-parts":[[2006,4,21]],"date-time":"2006-04-21T20:37:47Z","timestamp":1145651867000},"page":"6934-6939","update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":103,"title":["An integrated approach for risk profiling and spatial prediction of\n                    <i>Schistosoma mansoni<\/i>\n                    \u2013hookworm coinfection"],"prefix":"10.1073","volume":"103","author":[{"given":"Giovanna","family":"Raso","sequence":"first","affiliation":[{"name":"*Department of Public Health and Epidemiology, Swiss Tropical Institute, P.O. 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