title: Semiparametric models for malaria rapid diagnosis test result.
abstract: BACKGROUND
  More than 75% of the total of Ethiopia is malarious. Therefore, malaria is a leading public health problem in Ethiopia. This study aims to identify socio-economic, geographic and demographic factors contributing to the spread of malaria and is based on the results of a malaria Rapid Diagnosis Test survey.
  
  
  METHODS
  The data used in this study originates from the baseline malaria indicator survey, conducted in the Amhara, Oromiya and Southern Nation Nationalities and People (SNNP) regions of Ethiopia from December 2006 to January 2007. The study applies the method of generalized additive mixed model (GAMM) to analyse data. The response variable is the presence or absence of malaria, using the malaria Rapid Diagnosis Test (RDT).
  
  
  RESULTS
  The results provide an improved insight into the distribution of malaria in relation to the age of affected people, the altitude, the total number of rooms, the total number of mosquito nets, family size, and the number of months that their rooms have been sprayed. The results confirm that positive malaria RDT test results are high for children under 15 years and for older persons. Gender, source of drinking water, time needed to fetch water, toilet facilities, main materials used for the construction of walls, floors and roofs, and use of mosquito nets were all found to have a significant impact on the results of the malaria rapid diagnosis test.
  
  
  CONCLUSIONS
  The result of the analysis identifies poor socio-economic conditions as a major contributing factor or determinant for the spread of malaria. With the correct use of mosquito nets, indoor residual spraying with insecticide and other preventative measures, the incidence of malaria could be decreased. In addition, improving housing conditions is a means to reduce the risk of malaria. Other measures such as creating awareness of the use of mosquito nets, indoor residual spraying with insecticide, and malaria transmission, can lead to a further reduction in the number of malaria cases.
authors.affiliation: School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, Private Bag X01, Scottsville 3209, South Africa. EJIGMUL@GMAIL.COM.
authors.forename: Dawit G
authors.initials: DG
authors.lastname: Ayele
authors.forename: Temesgen T
authors.initials: TT
authors.lastname: Zewotir
authors.forename: Henry G
authors.initials: HG
authors.lastname: Mwambi
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000531:Altitude; D002648:Child; D002675:Child, Preschool; D003710:Demography; D003955:Diagnostic Tests, Routine; D005002:Ethiopia / Q000453:epidemiology; D005191:Family Characteristics; D005260:Female; D006798:Housing; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D007306:Insecticides; D008288:Malaria / Q000175:diagnosis / Q000453:epidemiology* / Q000209:etiology / Q000517:prevention & control; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D057147:Mosquito Nets; D011203:Poverty; D012307:Risk Factors; D055815:Young Adult
chemical_list: D007306:Insecticides