Open Access Research

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

Dan K Kajungu14*, Majige Selemani2, Irene Masanja2, Amuri Baraka2, Mustafa Njozi2, Rashid Khatib2, Alexander N Dodoo13, Fred Binka1, Jean Macq4, Umberto D’Alessandro5 and Niko Speybroeck4

Author Affiliations

1 INDEPTH Network, P.O Box KD 213 Kanda, Accra, Ghana

2 Ifakara Health Institute, PO Box 78373, Dar es Salaam, Tanzania

3 Centre for Tropical clinical Pharmacology #38; Therapeutics, University of Ghana Medical School, P.O Box KB4236, Accra, Ghana

4 Université Catholique de Louvain, Belgium, Clos Chapelle-aux Champs, Bruxelles 1200, Belgium

5 Medical Research Council Unit, The Gambia, P.O Box 273, Banjul, The Gambia and Institute of Tropical Medicine, Antwerp, Belgium

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Malaria Journal 2012, 11:311  doi:10.1186/1475-2875-11-311

Published: 5 September 2012

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.

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.

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