Predictors Of Presumptive Treatment Of Uncomplicated Malaria Among Children In Private Retail Outlets In Kenya: Mixed Effects Logistic Regression Modelling

Abstract

Background: The health seeking behavior in Kenya raises concerns in malaria case management at the private sector. Adherence to the national guidelines for the diagnosis, treatment and prevention of malaria is key in management of malaria. Presumptive treatment remains to be a major challenge in Kenya especially in the private sector with major gaps in literature identified on predictors of this treatment. Taking into account county clustering is key in modelling the predictors of presumptive treatment to strengthen interventions. Mixed-effects regression modelling takes into account county clustering, is more accurate in prediction and more efficient and flexible. Objective: The study modeled predictors of presumptive treatment of uncomplicated malaria among children in the private retail outlets in Kenya using the mixed effects logistic regression model. Methodology: The study design was a cross-sectional, nationally representative, retail outlet survey secondary data analysis. The study populations included the health care providers in the retail outlets sampled randomly in both the rural and urban settings in Kenya. The primary outcome of interest was the proportion of health care providers who treated patients presumptively. Descriptive statistics formed the basis of analysis for the selected indicators through frequencies and percentages. Bivariate analysis taking onto account county clustering was conducted to measure the factors associated with presumptive treatment of uncomplicated malaria. Finally, multivariable analysis was conducted for the significant variables adjusting for clustering at the county level to determine the predictors of presumptive treatment. The best fitting model was examined using the Akaike Information Criterion (AIC). Analysis was conducted using STATA and R software. Findings: Out of the 333 health care providers who treated the patient’s 190 (57 percent) treated patients presumptively. The factors that were significantly associated with presumptive treatment adjusting for county clustering at 95% CI were health care providers who asked signs or symptoms, presented with results and access to national guidelines for malaria treatment. All these predictors were negatively associated at (OR=0.24; P-value=<0.001; 95% CI= (0.13 - 0.45), (OR=0.08; P-value = <0.001; 95% CI = (0.03 - 0.20) and (OR = 0.49; P-value = 0.038; 95% CI = (0.25 - 0.96) respectively. Finally, the predictors of presumptive treatment of uncomplicated malaria were case management training (AOR = 0.44; 95% CI = (0.18 – 1.09)), asked signs or symptoms (AOR = 0.19; 95% CI = (0.10 - 0.37)) and results presented (AOR = 0.08 95% CI = (0.03 - 0.19)). Conclusion: Presumptive treatment of uncomplicated malaria remains to be a challenge in the private retail sector. However, case management training and health care providers asking of signs and symptoms and results presented predicts presumptive treatment. Specifically a health care provider who has gone through all the three factors has a lower probability of treating a patient presumptively compared to the other case scenarios