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01.12.2012 | Research | Ausgabe 1/2012 Open Access

Malaria Journal 1/2012

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

Zeitschrift:
Malaria Journal > Ausgabe 1/2012
Autoren:
Dan K Kajungu, Majige Selemani, Irene Masanja, Amuri Baraka, Mustafa Njozi, Rashid Khatib, Alexander N Dodoo, Fred Binka, Jean Macq, Umberto D’Alessandro, Niko Speybroeck
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1475-2875-11-311) contains supplementary material, which is available to authorized users.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

DK drafted the manuscript in consultation with NS; IM, BA, RK, AD, FB and DK contributed to study coordination and review of subsequent manuscript; MS, MN supervised data entry, cleaning, preliminary analysis and review of subsequent manuscript. DK conducted the data mining analyses in consultation with NS; DK, NS, JM, UDA and AD reviewed the manuscript. All authors read and approved the final manuscript.

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

S 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.
Zusatzmaterial
Authors’ original file for figure 1
12936_2012_2505_MOESM1_ESM.tiff
Authors’ original file for figure 2
12936_2012_2505_MOESM2_ESM.tiff
Literatur
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