Outcome variable
The outcome variable for the study was appropriate treatment-defined as
a drug seller prescribing and dispensing age appropriate medication to
children less than five years old with symptoms of pneumonia,
uncomplicated malaria and non-bloody diarrhoea in the right dose,
frequency and duration for the right indication as per the standard iCCM
treatment guidelines 27.
2.5 Data analysis
To assess the effectiveness of peer supervision among drug sellers on
appropriate treatment of children less than five years of age with
pneumonia symptoms, uncomplicated malaria and non-bloody diarrhoea,
interrupted time series for multiple group analysis was
conducted29. The ITSA
method was the preferred choice for this this study because it was not
possible to randomise peer supervision (the
intervention)30. In
addition we wanted to cater for policy shifts if they occurred during
the study period-a major strength of using interrupted time
series31. Consideration
was made in using interrupted time series since the study had six data
points before and six data points after introduction of peer supervision
which are considered a minimum for reliable
results32,33.
The mean with the corresponding standard deviation and median number of
visits by DDIs and peer supervisors was computed. Then, the percentage
of appropriately treated children by drug seller by month by district
was calculated by dividing the total number of appropriately treated
children for a given childhood illness by the total number of children
presenting with symptoms of that particular illness multiplied by 100.
Results were then aggregated at district level by month covering both
the pre-intervention and intervention period and used to show the trend
of appropriate treatment in both districts for both periods.
This was followed by fitting the multiple group ITSA model (Equation 1).
In the final step, the Cumby-Huizinga test was used to test for
autocorrelation. The model was re-run by specifying the lag order
accounting for autocorrelation. Data from the sick child registers was
entered in Epi Data (www.epidata.dk),
exported to excel for cleaning and coding. Data analysis was done using
STATA version 15 (Stata Corp, College Station, TX, 2015).The ITSA model
is as follows;
\(y_{t}=\ \beta_{0}+\beta_{1}T_{t}+\beta_{2}X_{t}+\beta_{3}X_{t}T_{t}+\ \beta_{4}Z+\beta_{5}ZT_{t}+\ \beta_{6}ZX_{t}+\ \beta_{7}ZX_{t}T_{t}+\ \epsilon_{t}\)..(Equation
1)
The names and definitions of all variables used in this model are listed
in table 1.
The ITSA uses ordinary least squares (OLS) approaches to estimate the
effects of the intervention (peer supervision) on appropriate treatment
for multiple treatment (intervention) periods. The ITSA model accounts
for auto correlation among equally spaced observation data by comparison
for the lag order for which auto correlation is assumed to be present.
The estimated coefficients from the model together with their standard
errors are reported.