Modelling the household cost of paediatric malaria treatment in a rural county in Kenya: do non-user fee payments matter? A partial cost of illness analysis

ObjectiveThe objective of this study was to develop an econometric model for the cost of treatment of paediatric malaria from a patient perspective in a resource scarce rural setting of Homa Bay County, Kenya. We sought to investigate the main contributors as well as the contribution of non-user fee payments to the total household cost of care. Costs were measured from a patient perspective.DesignThe study was conducted as a health facility based cross sectional survey targeting paediatric patients.SettingThe study was conducted in 13 health facilities ranging from level II to level V in Homa Bay County which is in the Eastern shores of Lake Victoria, Kenya. This is a malaria endemic area.ParticipantsWe enrolled 254 inpatient children (139 males and 115 females) all of whom participated up to the end of this study.Primary outcome measureThe primary outcome measure was the cost of pediatric malaria care borne by the patient. This was measured by asking exiting caregivers to estimate the cost of various items contributing to their total expenditure on care seeking.ResultsA total of 254 respondents who consented from 13 public government health facilities were interviewed. Age, number of days spent at the health facility, being treated at a level V facility, medical officer prescribing and seeking initial treatment from a retail shop were found significant predictors of cost.ConclusionHigher level health facilities in Homa Bay County, where the more specialised medical workers are stationed, are more costly hence barring the poorest from obtaining quality paediatric malaria care from here. Waiving user fees alone may not be sufficient to guarantee access to care by patients due to unofficial fees and non-user fees expenditures.

Predicting Paediatric Malaria Occurrence Using Classification Algorithm in Data Mining

This paper gives the current overview of the application of data mining techniques on the haematological and biochemical dataset to predict the occurrence of malaria in children between age zero (0) and five (5). Malaria has been eradicated from the developed countries but still affecting a large part of the world negatively. A larger percentage of malaria is estimated to affect young children in sub-Sahara Africa. In order to reduce mortality from paediatric malaria, there should be an efficient and effective prediction method. In healthcare, data mining is one of the most vital and motivating areas of research with the objective of finding meaningful information from huge data sets and provides an efficient analytical approach for detecting unknown and valuable information in healthcare data. In this study, a model was built to predict the occurrence of malaria in children between age zero (0) and five (5) years, using decision tree classification algorithms on WEKA workbench tool. The classification algorithms used are LMT, REPTree, Hoeffding tree and J48. A J48 algorithm was used for building the decision tree model since it has higher accuracy for performance with least error margin.

Impact of devolution on the trends of paediatric malaria admissions and mortality in Homa-Bay County, Kenya

Background: Malaria is a leading cause of paediatric admissions, morbidity and mortality. Malaria burden is endemic in Homa Bay County in the Lake Region in Kenya. Low social-economic status in Homa Bay County enhances malaria transmission, morbidity and mortality. Paediatric malaria admission and mortality have recently increased in the lake region unlike the rest of Kenya. Literature review did not show studies interrogating health policy correlates of this malaria problem in the region. The policy of the recently devolvement of the government system in Kenya was to bring services closer to the people. Devolved government in which the county governments are now responsible for healthcare delivery may have unique challenges that may influence disease morbidity and mortality. Objective: The aim of this study was to investigate the impact of devolution on paediatric malaria admission and mortality trends in public health facilities in Homa Bay County. Methods: This was a retrospective quasi-experimental study in which paediatrics records of 36 months before and 36 months after the devolvement of government were retrieved and analyzed for malaria incidence and deaths. All records of paediatric malaria cases reported in all 164 public health facilities in Homa-Bay County were examined. Data from the sub-County was obtained from the electronic records at the County Hospital. Hard copy data from health facilities in eight sub-Counties was also inspected at the sub-County level. Analysis of the data was accomplished by use of the Interrupted Time Series (ITS). Permission to conduct the study was obtained from the appropriate authorities. Data coding system was used in order to ensure confidentiality. Results: From January 2013, deaths increased gradually until around the 33rd month when it rose abruptly to nearly 800 then declined to levels below 200 in the 34 th month, which was around the time of devolution. This was followed by a period of stability. Admissions had a similar trend. Conclusions: There was a slight raise in paediatric malaria admissions and in the number of deaths due to malaria morbidity in Homa Bay County after the devolvement of government system in Kenya a factor which could be attributed to teething challenges of devolution. More studies are necessary to assess progress towards universal access to good healthcare services post devolution.