title: Using classification tree modelling to investigate drug prescription practices at health facilities in rural Tanzania.
abstract: BACKGROUND
  Drug prescription practices depend on several factors related to the patient, health worker and health facilities. A better understanding of the factors influencing prescription patterns is essential to develop strategies to mitigate the negative consequences associated with poor practices in both the public and private sectors.
  
  
  METHODS
  A cross-sectional study was conducted in rural Tanzania among patients attending health facilities, and health workers. Patients, health workers and health facilities-related factors with the potential to influence drug prescription patterns were used to build a model of key predictors. Standard data mining methodology of classification tree analysis was used to define the importance of the different factors on prescription patterns.
  
  
  RESULTS
  This analysis included 1,470 patients and 71 health workers practicing in 30 health facilities. Patients were mostly treated in dispensaries. Twenty two variables were used to construct two classification tree models: one for polypharmacy (prescription of ≥3 drugs) on a single clinic visit and one for co-prescription of artemether-lumefantrine (AL) with antibiotics. The most important predictor of polypharmacy was the diagnosis of several illnesses. Polypharmacy was also associated with little or no supervision of the health workers, administration of AL and private facilities. Co-prescription of AL with antibiotics was more frequent in children under five years of age and the other important predictors were transmission season, mode of diagnosis and the location of the health facility.
  
  
  CONCLUSIONS
  Standard data mining methodology is an easy-to-implement analytical approach that can be useful for decision-making. Polypharmacy is mainly due to the diagnosis of multiple illnesses.
authors.affiliation: INDEPTH Network, P,O Box KD 213 Kanda, Accra, Ghana. kajungu_dan@yahoo.co.uk
authors.forename: Dan K
authors.initials: DK
authors.lastname: Kajungu
authors.forename: Majige
authors.initials: M
authors.lastname: Selemani
authors.forename: Irene
authors.initials: I
authors.lastname: Masanja
authors.forename: Amuri
authors.initials: A
authors.lastname: Baraka
authors.forename: Mustafa
authors.initials: M
authors.lastname: Njozi
authors.forename: Rashid
authors.initials: R
authors.lastname: Khatib
authors.forename: Alexander N
authors.initials: AN
authors.lastname: Dodoo
authors.forename: Fred
authors.initials: F
authors.lastname: Binka
authors.forename: Jean
authors.initials: J
authors.lastname: Macq
authors.forename: Umberto
authors.initials: U
authors.lastname: D'Alessandro
authors.forename: Niko
authors.initials: N
authors.lastname: Speybroeck
mesh_terms: D000293:Adolescent; D000328:Adult; D001291:Attitude of Health Personnel; D002648:Child; D002675:Child, Preschool; D003430:Cross-Sectional Studies; D011307:Drug Prescriptions / Q000706:statistics & numerical data*; D005260:Female; D006268:Health Facilities; D007722:Health Knowledge, Attitudes, Practice; D006302:Health Services Research; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D015233:Models, Statistical; D012424:Rural Population; D013636:Tanzania; D055815:Young Adult