The online version of this article (doi:10.1186/1475-2875-11-311) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
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.
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.
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.
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.
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.
World Health Organization: Report of the conference of Experts. The Rational Use of Drugs. 1987, WHO, Nairobi, 25-29.
World Health Organization: Medicines use in primary care in developing and transitional countries. Fact Book summarizing results from studies reported between 1990 and 2006. 2009, WHO, Geneva, WHO/EMP/MAR/2009.3
McManus P, Hammond ML, Whicker SD, Primrose JG, Mant A, Fairall SR: Antibiotic use in the Australian community. Med J Aust. 1997, 167: 124-127. PubMed
Ministry of Health and Social Welfare URoT: National Guidelines for Diagnosis and Treatment of Malaria control series 11. 2006, National Malaria Control Program, Dar es Salaam
World Health Organization: WHO guidelines for the treatment of malaria. 2006, WHO, Geneva, 1108-/HTM/MAL/2006
Skarbinski J, Ouma PO, Causer LM, Kariuki SK, Barnwell JW, Alaii JA, de Oliveira AM, Zurovac D, Larson BA, Snow RW, Rowe AK, Laserson KF, Akhwale WS, Slutsker L, Hamel MJ: Effect of malaria rapid diagnostic tests on the management of uncomplicated malaria with artemether-lumefantrine in Kenya: a cluster randomized trial. Am J Trop Med Hyg. 2009, 80: 919-926. PubMed
Rufiji DSS: Tanzania Ministry of Health. Tanzania Essential Health Interventions Project. Adult Morbidity and Mortality Project- Indepth Monograph: Vol 1 Part C. http://www.indepth-network.org/dss_site_profiles/rufiji.pdf accessed on 12th Aug, 2012
Ifakara HDSS: Tanzania. Accessed at http://www.indepth-network.org/leadership/IFAKARA%20HDSS.pdf on 12th Aug, 2012
Lauritsen JM, Bruus M: EpiData (version 3). A comprehensive tool for validated entry and documentation of data. 2003, The EpiData Association, Odense, Denmark
StataCorp LP: STATA version 11. 2012, College Station. 2012, STATA Corporation, Texas, USA
IBM SPSS software: SPSS for Windows version 16. 2007, SPSS Inc, Chicago
Speybroeck N: Classification and Regression trees. Int J Publ Heath. 2012, 57: 243-246. 10.1007/s00038-011-0315-z. CrossRef
Breiman L, Friedman JH, Olsen RA, Stone CJ: Classification and regression trees. 1984, The Wadsworth statistics/probability Series, Belmont, California
Speybroeck N, Berkvens D, Mfoukou-Ntsakala A, Aerts M, Hens N, Van Huylebroeck G, Thys E: Classification trees versus multinomial models in the analysis of urban farming systems in Central Africa. Agric Systems. 2004, 80: 133-149. 10.1016/j.agsy.2003.06.006. CrossRef
van Engelsdorp D, Speybroeck N, Jay DE, Nguyen B, Mullin C, Frazier M, Frazier J, Cox-Foster D, Chen Y, Tarpy D, Haubruge E, Pettis J, Saegerman C: Weighing risk factors associated with bee colony collapse disorder by classification and regression tree analysis. J Econ Entomol. 2010, 103: 1517-1523. 10.1603/EC09429. CrossRef
Zhang H, Singer B: Recursive Partitioning in the Health Sciences. 1999, Springer, New York CrossRef
McCarthy FD, Wolf H, Wu Y: The growth costs of malaria. 2000, National Bureau of Economic Research, 2000 Working Paper 7541, Cambridge, MA, USA CrossRef
WHO/UNICEF: Integrated Management of Childhood Illness IMCI Information package. 1999, WHO/CHS/CAH/98
Rennie W, Lugo L, Rosser E, Harvey SA: Willingness to use and pay for a new malaria diagnostic test for children under 5: Results from Benin, Peru, and Tanzania. 2009, Center for Human Services, Bethesda, MD
The Affordable Medicines Facility for Malaria (AMFm). 2012, http://rbm.who.int/psm/amfm.html last accessed on 12th Aug 2012
Dodoo A, Fogg C, Asiimwe A, Nartey E, Kodua A, Tenkorang O, Ofori-Adjei D: Pattern of drug utilization for treatment of uncomplicated malaria in urban Ghana following national treatment policy change to artemisinin-combination therapy. Malar J. 2009, 8: 2-10.1186/1475-2875-8-2. PubMedCentralCrossRefPubMed
Mino-León D, Galván-Plata ME, Doubova SV, Flores-Hernandez S, Reyes-Morales H: A pharmacoepidemiological study of potential drug interactions and their determinant factors in hospitalized patients. Rev Invest Clin. 2011, 63: 170-178. PubMed
World Health Organization: Introduction to drug utilization research. 2003, WHO, Geneva
- Using classification tree modelling to investigate drug prescription practices at health facilities in rural Tanzania
Dan K Kajungu
Alexander N Dodoo
- BioMed Central
Neu im Fachgebiet Innere Medizin
Meistgelesene Bücher aus der Inneren Medizin
e.Med Kampagnen-Visual, Mail Icon II