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

Malaria Journal

Research

Fred Binka¹  Alexander N Dodoo²  Dan K Kajungu³  Rashid Khatib⁴  Amuri Baraka⁴  Majige Selemani⁴  Mustafa Njozi⁴  Irene Masanja⁴  Umberto D’Alessandro⁵  Jean Macq⁶  Niko Speybroeck⁶ 

[1] INDEPTH Network, P.O Box KD 213, Accra, Kanda, Ghana;INDEPTH Network, P.O Box KD 213, Accra, Kanda, Ghana;Centre for Tropical clinical Pharmacology #38; Therapeutics, University of Ghana Medical School, P.O Box KB4236, Accra, Ghana;INDEPTH Network, P.O Box KD 213, Accra, Kanda, Ghana;Université Catholique de Louvain, Belgium, Clos Chapelle-aux Champs, 1200, Bruxelles, Belgium;Ifakara Health Institute, PO Box 78373, Dar es Salaam, Tanzania;Medical Research Council Unit, The Gambia, The Gambia and Institute of Tropical Medicine, P.O Box 273, BanjulAntwerp, Belgium;Université Catholique de Louvain, Belgium, Clos Chapelle-aux Champs, 1200, Bruxelles, Belgium;

关键词: Polypharmacy;    Co-prescription;    Anti-malarials;    Classification trees;    Data mining;    Tanzania;   

DOI  :  10.1186/1475-2875-11-311

 received in 2012-05-25, accepted in 2012-08-28,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundDrug 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.MethodsA 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.ResultsThis 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 tandard 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.

【 授权许可】

CC BY   
© Kajungu et al.; licensee BioMed Central Ltd. 2012