Malaria investigation and treatment of children admitted to county hospitals in western Kenya

This was a descriptive cross sectional study. A subset of the data from five of the 14 hospitals participating in the CIN collected during a 24-month period (March, 2014 to February, 2016) was used. All the five hospitals are located in the western Kenya, a high malaria prevalence region and serve predominantly poor populations.

All children aged between 2 and 59 months admitted to the paediatric wards in the participating hospitals with a diagnosis of malaria were eligible for inclusion in the study.

Since its establishment in October 2013 CIN has supported data capture from paediatric medical records that include a standardised admission record form using the non-proprietary Research Electronic Data Capture (REDCap) tool in all participating hospitals [17]. Support provided to hospitals participating in the network includes an additional post for a clerical assistant in each hospital and provision of one basic computer. Data collection is done on a daily basis by the clerk from medical records of all admitted patients in paediatric wards using detailed standard operating procedures under the joint supervision of the hospital medical records department and the CIN team. Data quality is promoted through the use of range and quality checks in the electronic data tool and error checking procedures. The details of data management have been previously published [17]. In brief, a process for validating the data collected at each site is conducted bimonthly through conducting double entry for selected records. During the validation at least 140 records previously entered by CIN data clerks in the 2-month period prior to the validation are re-entered by an independent trained data collection team. Ten records are selected at random for re-entry at each hospital (giving a total of 140 records across the network). Analysis is then done to validate data using R scripts that conduct field by field comparison for all fields in the database. A total concordance score is then calculated for each record and feedback is immediately given to data clerks in areas where there is discordance between the two sets of entries. During the most recent round of data validation the overall concordance was 85 %.

No formal sample size calculations were undertaken, but data on over 13,000 children aged between 2 and 59 months with an admission diagnosis of malaria were considered adequate to provide reasonably precise estimates around malaria case management practices. Descriptive analysis of the malaria admissions pooled across all hospitals and for each hospital included calculating frequencies, relative frequencies and medians (IQR) for categorical and continuous variables, respectively. Indicators of good practice for five key areas of inpatient malaria case management spanning clinical assessment, laboratory investigation and treatment are defined in Table 1 and proportions of patients managed according to the current malaria guidelines are reported. The proportion of children for whom six specific features of malaria were assessed and documented in medical records was calculated. The level of adherence to parasitological testing recommendations was determined by calculating the percentage of malaria admissions with parasitological confirmation at the time of initiating anti-malarial drugs. Finally, for parenteral anti-malarials the proportion with the correct dosages based on body weight (drug dose per kg body weight), allowing for the dosage to be within 20 % of recommended dosage (guideline dosage ± 20 %) were calculated. No attempts were made to impute data where these data were missing for calculating specific indicators (for example if the child’s weight was missing correctness of drug dosage could not be estimated). In addition, the following indicators were used to assess the routine practice of using laboratory testing to guide management; proportion with laboratory requests of haemoglobin level, blood chemistry and glucose level. To identify evidence that clinicians considered other causes of febrile illnesses the proportion of malaria patients with the following tests requested: HIV status, blood culture/lumbar culture, x-rays and urine tests and the proportions prescribed antibiotics were calculated. All analyses were conducted using R statistical software (version 3.2.2) [19].