W62/6828/2017
University
of Nairobi
ABSTRACT
Introduction: Malaria is a global health problem and
the World Health Organization (WHO) estimates that nearly a half (3.4 billion)
of the world’s population is at risk of the disease. WHO African region
continues to have a disproportionately global malaria burden. According to the
2018 WHO malaria report, the estimated morbidity and mortality reported
currently is 92% and 93% respectively which is the malaria burden in Africa.
Although
the burden of Malaria is high in the Sub-Saharan Africa, the Ministry of Health
through the National Malaria Control Program has implemented comprehensive
evidence-based strategies and policies to fight this disease in Kenya. About
25% of the population in Kenya seeks medical care form the private retail
sector. To attain the Sustainable Development Goals (SDGs), Universal Health
Coverage is key in ensuring quality of care for all malaria patients.
The WHO
recommends that all suspected malaria cases should be tested with RDTs or
microscopy before treatment. However, inappropriate treatment practices
(presumptive treatment) of uncomplicated malaria among children has been a
major challenge in Kenya. Several
studies have been conducted on presumptive treatment of malaria among children
with a major gap in literature identified on predictors of this treatment. The
study results will strengthen interventions in malaria management in the
private retail sector.
Objectives: The objectives of this study are to
determine the proportion of health care providers who treat uncomplicated
malaria presumptively; factors associated with presumptive treatment of
uncomplicated malaria and predictors of presumptive treatment of uncomplicated
malaria among children in private retail outlets in Kenya.
Methodology: The study design will be a secondary
data analysis from a cross-sectional, nationally representative, private retail
outlet survey. The study populations will include the health care providers in
the retail outlets sampled randomly in both the rural and urban settings in
Kenya. Descriptive statistics will form the basis of analysis for the selected
indicators through frequencies and percentages. Bivariate analysis will be
conducted using the chi-square test to determine the factors associated with
presumptive treatment of uncomplicated malaria among children. Mixed effects
logistic regression modelling to adjust for clustering at the county level will
be done to determine the predictors of presumptive treatment of uncomplicated
malaria among children. The best fitting model will be examined using the
Akaike Information Criterion (AIC).
Data will be cleaned, coded and analyzed using the R software and
presented in tables.
No comments:
Post a Comment