Objective: The objective of this study was to develop an econometric model for the cost of treatment of pediatric 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.

Design: The study was conducted as a health facility based cross sectional survey targeting paediatric patients.

Setting: The 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.

Participants: We enrolled 254 inpatient children (139 males and 115 females) all of whom participated up to the end of this study.

Primary outcome measure: The 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.

Results: A 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.

Conclusion Higher level health facilities in Homa-Bay County, where the more specialized 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..

Study setting

Kenya is classified by the World Bank as a lower middle income country [8] with the under-five mortality rate at 45.6 per 1000 live births (9).  Homa-Bay is a rural County with a predominantly peasant economy with poverty levels above the national average and some of the poorest child health indicators in the Kenya. In 2016, the County under-five mortality was 130 per 1000 live births (10). The Kenya Demographic and Health Survey (KDHS) 2014 also indicated that most residents of Homa-Bay County lie in the lowest wealth quintile with an unfavourable Gini coefficient. These are economic indicators that may demonstrate that the average income of the residents is low. The County is located in the Eastern shores of Lake Victoria hence has climatic conditions that favour malaria endemicity, with pregnant women and children bearing the greatest burden of the disease. The prevalence of malaria in Homa-Bay County, as of 2016, was 58,820 per 100000 persons, which was more than double the national prevalence of 20,252 per 100,000 persons at that time (10). Malaria incidence usually peaks in Kenya around September to October rainy season which coincides with the time the study was conducted.

Study design

The study was conducted as a cross sectional survey. This design was considered appropriate since it was strategic and affordable to collect all the data from the patients in one encounter at the end of the treatment process. Both the exposures and outcome were examined at the same time.

Costing Approach

Costing was conducted from a patient perspective. The total costs were summed up from component expenditures incurred per category in the process of procuring care for the child with malaria as reported by the caregiver. In this study, the costing was computed to include all user fees and non-user fee monetary expenditures incurred by the caregiver in the process of seeking care for the sick child.

Target population

The target population comprised all children below 13 years exiting government health facilities in Homa-Bay County after treatment for malaria during the period of study.  Children below the age of 13 years were targeted because they tend to depend wholly on their parents or guardians for health seeking decisions since they are not yet independent financially or socially.

Sampling

The sample size was based on the Melody study [11] which recommends that for cost outcomes, a sample size of approximately 200 would be required to generate a 95% CI precise to within ±10% of the mean. A multistage sampling approach was adopted. First, the County was divided into eight zones; the eight sub-counties. The health facilities were then stratified based on service provision levels (level II to level V, Table 1). The County had neither a level I nor a VI facility and only one level V facility at the time of data collection. The level five facility was purposively included in the sample. Therefore, actual facility sampling was done only for levels II, III and IV facilities. A total of 13 facilities were sampled as shown in Table 2. From each facility, approximately 20 in-patients were sampled using the systematic random sampling approach where every other paediatric patient exiting after treatment for malaria was approached by the research assistant for recruitment. This process was repeated on daily basis until the targeted sample size of 254 participants was met.

Table 1: Classification of Health facilities based on service level

Source: Ministry of Health, 2017: The Health care system in Kenya and Kenyan healthcare sector: market study report

 

Table 2: Number of health facilities and participants sampled in the study

 

Recruitment of study participants

A research assistant approached a caregiver of an in-patient who had just been treated for malaria at the time of exit. This was followed by self introduction after which the caregiver was requested to read the consent explanation (in a language they best understood- English, Swahili or Luo) and consider consenting.

Data collection

Data on contributors to cost as well as cost of paediatric malaria treatment was collected from August 2016 to November 2016 by use of a structured exit interview guide on consented participants. This period was selected because malaria infections in the region usually peak during rainy seasons of September to October. Both direct costs such as out of pocket payments and cost of medicines purchased as well as indirect costs such as transport, food, waiting time and opportunity cost incurred while taking care of the sick child were measured in Kenya shillings but reported in USD ( 100 K.Shs = 1 USD) .

The interviewers were trained to probe opportunity cost borne by caregivers in the process of seeking health care for the sick child by asking them to state their occupation. From this, the approximate forgone benefit was estimated to triangulate the information gathered from their response to the direct questions posed to the caregiver about their estimated opportunity cost.  

Quality assurance

Data collectors were trained for two days on the survey tool as well as the procedure in a classroom setting and then practised in the pre-test health facility outside the data collection area (Migori County). Data collection instruments were examined by supervisors and other experts to ascertain their quality and validity.

Data analysis

Data was checked for completeness, cleaned, sorted and coded. This was followed by data entry into excel (2016) spreadsheet in readiness for analysis by use of the R statistical software to compute descriptive statistics on cost of treatment.  The cost of treatment was then further modeled on R (Studio) statistical package using a stepwise multiple linear regression approach.

Ethical Considerations

All principles pertaining ethical conduct of research with human subjects were adhered to in this study as embodied in the Declaration of Helsinki. Ethical approval was obtained from the Kenyatta National Hospital/University of Nairobi-Ethics and Review Committee (KNH-UON ERC Ref-P389/05/2016).

Patient and Public Involvement

Given that there was no funding for this study, no funds or time was allocated for patient or public involvement. We were therefore unable to involve patients. However, we plan to invite representatives of the public to help us write a plain language summary for further dissemination of the results.