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dc.contributor.authorGomez-Barroso, Diana 
dc.contributor.authorGarcía-Carrasco, Emely
dc.contributor.authorHerrador, Zaida 
dc.contributor.authorNcogo, Policarpo
dc.contributor.authorRomay-Barja, Maria 
dc.contributor.authorOndo Mangue, Martín Eka
dc.contributor.authorNseng, Gloria
dc.contributor.authorRiloha, Matilde
dc.contributor.authorSantana, Maria Angeles
dc.contributor.authorValladares, Basilio
dc.contributor.authorAparicio, Pilar 
dc.contributor.authorBenito, Agustin 
dc.date.accessioned2018-03-19T11:47:46Z
dc.date.available2018-03-19T11:47:46Z
dc.date.issued2017-04-12
dc.identifier.citationMalar J. 2017; 16(1):146es_ES
dc.identifier.issn1475-2875es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/5747
dc.description.abstractBACKGROUND: The transmission of malaria is intense in the majority of the countries of sub-Saharan Africa, particularly in those that are located along the Equatorial strip. The present study aimed to describe the current distribution of malaria prevalence among children and its environment-related factors as well as to detect malaria spatial clusters in the district of Bata, in Equatorial Guinea. METHODS: From June to August 2013 a representative cross-sectional survey using a multistage, stratified, cluster-selected sample was carried out of children in urban and rural areas of Bata District. All children were tested for malaria using rapid diagnostic tests (RDTs). Results were linked to each household by global position system data. Two cluster analysis methods were used: hot spot analysis using the Getis-Ord Gi statistic, and the SaTScan™ spatial statistic estimates, based on the assumption of a Poisson distribution to detect spatial clusters. In addition, univariate associations and Poisson regression model were used to explore the association between malaria prevalence at household level with different environmental factors. RESULTS: A total of 1416 children aged 2 months to 15 years living in 417 households were included in this study. Malaria prevalence by RDTs was 47.53%, being highest in the age group 6-15 years (63.24%, p < 0.001). Those children living in rural areas were there malaria risk is greater (65.81%) (p < 0.001). Malaria prevalence was higher in those houses located <1 km from a river and <3 km to a forest (IRR: 1.31; 95% CI 1.13-1.51 and IRR: 1.44; 95% CI 1.25-1.66, respectively). Poisson regression analysis also showed a decrease in malaria prevalence with altitude (IRR: 0.73; 95% CI 0.62-0.86). A significant cluster inland of the district, in rural areas has been found. CONCLUSIONS: This study reveals a high prevalence of RDT-based malaria among children in Bata district. Those households situated in inland rural areas, near to a river, a green area and/or at low altitude were a risk factor for malaria. Spatial tools can help policy makers to promote new recommendations for malaria control.es_ES
dc.description.sponsorshipSpecial thanks to the Spanish Agency for International Development Cooperation (AECID) and the Network Biomedical Research on Tropical Diseases (RICET in Spanish RD12/0018/0001) for the financial support that enabled us to carry out this study.es_ES
dc.language.isoenges_ES
dc.publisherBioMed Central (BMC) es_ES
dc.type.hasVersionVoRes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectChildrenes_ES
dc.subjectEquatorial Guineaes_ES
dc.subjectMalariaes_ES
dc.subjectRapid diagnostic testses_ES
dc.subjectSpatiales_ES
dc.subjectAdolescentes_ES
dc.subjectAnimalses_ES
dc.subjectChildes_ES
dc.subjectChild, Preschooles_ES
dc.subjectCross-Sectional Studieses_ES
dc.subjectEnvironmentes_ES
dc.subjectFemalees_ES
dc.subjectHumanses_ES
dc.subjectImmunochromatographyes_ES
dc.subjectInfantes_ES
dc.subjectMalees_ES
dc.subjectPrevalencees_ES
dc.subjectRisk Factorses_ES
dc.subjectRural Populationes_ES
dc.subjectSpatial Analysises_ES
dc.subjectTopography, Medicales_ES
dc.subjectUrban Populationes_ES
dc.subjectCluster Analysises_ES
dc.titleSpatial clustering and risk factors of malaria infections in Bata district, Equatorial Guineaes_ES
dc.typejournal articlees_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.identifier.pubmedID28403879es_ES
dc.format.volume16es_ES
dc.format.number1es_ES
dc.format.page146es_ES
dc.identifier.doi10.1186/s12936-017-1794-zes_ES
dc.contributor.funderAgencia Española de Cooperación Internacional para el Desarrollo 
dc.contributor.funderRETICS-Investigación colaborativa en Enfermedades Tropicales (RICET-ISCIII) (España) 
dc.description.peerreviewedes_ES
dc.identifier.e-issn1475-2875es_ES
dc.relation.publisherversionhttps://doi.org/10.1186/s12936-017-1794-zes_ES
dc.identifier.journalMalaria Journales_ES
dc.repisalud.centroISCIII::Centro Nacional de Medicina Tropicales_ES
dc.repisalud.centroISCIII::Centro Nacional de Epidemiologíaes_ES
dc.repisalud.institucionISCIIIes_ES
dc.rights.accessRightsopen accesses_ES


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Atribución 4.0 Internacional
Este Item está sujeto a una licencia Creative Commons: Atribución 4.0 Internacional