Using classification tree modelling to investigate drug prescription practices at health facilities in rural Tanzania

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dc.contributor.authorDan, Kajungu
dc.contributor.authorSelemani, Majige
dc.contributor.authorMasanja, Irene M.
dc.contributor.authorAmuri, Mbaraka
dc.contributor.authorNjozi, Mustafa
dc.contributor.authorKhatib, Rashid A.
dc.contributor.authorDodoo, Alexander
dc.contributor.authorBinka, Fred
dc.contributor.authorMacq, Jean
dc.contributor.authorAlessandro, Umberto D.
dc.contributor.authorSpeybroeck, Niko
dc.date.accessioned2016-07-08T12:20:16Z
dc.date.available2016-07-08T12:20:16Z
dc.date.issued2012-09
dc.description.abstractBackground 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. Conclusion 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.en_US
dc.identifier.citationKajungu, D.K., Selemani, M., Masanja, I., Baraka, A., Njozi, M., Khatib, R., Dodoo, A.N., Binka, F., Macq, J., D’Alessandro, U. and Speybroeck, N., 2012. Using classification tree modelling to investigate drug prescription practices at health facilities in rural Tanzania. Malaria journal, 11(1), p.1.en_US
dc.identifier.doi10.1186/1475-2875-11-311 · Source: PubMed
dc.identifier.urihttp://hdl.handle.net/20.500.11810/2914
dc.language.isoenen_US
dc.subjectPolypharmacyen_US
dc.subjectCo-prescriptionen_US
dc.subjectAnti-malarialsen_US
dc.subjectClassification treesen_US
dc.subjectData miningen_US
dc.titleUsing classification tree modelling to investigate drug prescription practices at health facilities in rural Tanzaniaen_US
dc.typeJournal Article, Peer Revieweden_US
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