Are patent medicine vendors effective agents in malaria control? Using lot quality assurance sampling to assess quality of practice in Jigawa, Nigeria

Abstract

Background: Patent medicine vendors (PMV) provide antimalarial treatment and care throughout Sub-Saharan Africa, and can play an important role in the fight against malaria. Their close-to-client infrastructure could enable lifesaving artemisinin-based combination therapy (ACT) to reach patients in time. However, systematic assessments of drug sellers' performance quality are crucial if their role is to be managed within the health system. Lot quality assurance sampling (LQAS) could be an efficient method to monitor and evaluate PMV practice, but has so far never been used for this purpose.

Methods: In support of the Nigeria Malaria Booster Program we assessed PMV practices in three Senatorial Districts (SDs) of Jigawa, Nigeria. A two-stage LQAS assessed whether at least 80% of PMV stores in SDs used national treatment guidelines. Acceptable sampling errors were set in consultation with government officials (alpha and beta <0.10). The hypergeometric formula determined sample sizes and cut-off values for SDs. A structured assessment tool identified high and low performing SDs for quality of care indicators.

Findings: Drug vendors performed poorly in all SDs of Jigawa for all indicators. For example, all SDs failed for stocking and selling first-line antimalarials. PMV sold no longer recommended antimalarials, such as Chloroquine, Sulfadoxine-Pyrimethamine and oral Artesunate monotherapy. Most PMV were ignorant of and lacked training about new treatment guidelines that had endorsed ACTs as first-line treatment for uncomplicated malaria.

Conclusion: There is urgent need to regularly monitor and improve the availability and quality of malaria treatment provided by medicine sellers in Nigeria; the irrational use of antimalarials in the ACT era revealed in this study bears a high risk of economic loss, death and development of drug resistance. LQAS has been shown to be a suitable method for monitoring malaria-related indicators among PMV, and should be applied in Nigeria and elsewhere to improve service delivery.

Figure 1. Operating Characteristic Curve for health facility assessments in Jigawa, Nigeria.

Operating Characteristic Curve for health facility assessments with sample size n = 7 and a decision rule of d = 5 for the upper cut-off P_U = 80% and the lower cut-off P_L = 50% for Senatorial Districts with a total of N = 9 and 10 health facilities. The area in between the upper and lower cut-off is the “grey area”. Changing d would shift the curve to the left or right; increasing the sample size would result in a sharper gradient of the curve and a smaller grey area. However, no matter how large the sample size there will always be a grey area and a certain level of misclassification error. We selected n and d to reduce the two forms of misclassification errors; it was ensured that the alpha error or the probability of mistakenly failing to accept an SD with acceptable performance (≥80%) did not exceed a value of 0.10, and that similarly the beta error or the probability of mistakenly accepting an SD with unacceptable performance (≤50%) did not exceed 0.10.