The trend of defaulters over time is one of the measures of a program’s ability to retain SAM children for treatment. This measure gives an indication of specific seasons or periods in which SAM children tend to default and provides insight into the possible seasonal or event-related reasons for defaulting. This information can then be used by the program to design and implement strategies and interventions that would mitigate defaulting. Continuous assessment of defaulting over time would then be able to show whether these strategies or interventions have reduced defaulting during the times when defaulting are expected to be high.

Data requirements

Defaulting data can be obtained from the program’s database (if available) or from the outpatient care treatment cards.

Analysis of data

Data on defaulters over time is analysed graphically. A line graph is created either by hand or using a computer with time (in months) on the x-axis and number of admissions on the y-axis. A tutorial on how to use a spreadsheet in creating a line graph can be found here and a template spreadsheet that creates a line graph for defaulters-over-time can be found here. An example of a computer-generated line graph of defaulters over time is shown in Figure 1 using data from a CMAM program in Somalia.

Figure 1: Defaulters over time

Data courtesy of SAACID and Oxfam Novib

The dashed red line is from the actual defaulters data while the solid gray line is based on smoothed data using the M3A3 approach described in the previous section and in the FANTA SQUEAC / SLEAC technical reference1. A template spreadsheet that calculates the M3A3 smoother and creates the line graph of defaulters-over-time automatically can be found here.

A tutorial on the use of moving averages to smooth time-series data can be found here. This also discusses the use of longer time spans on longer time-series data.


As with admission over time, defaulters over time is ideally analysed alongside a seasonal calendar (see seasonal calendar) that also includes other events (either program- or community-related) that have an impact on attendance (i.e. RUTF stock-outs, episodes of insecurity or violence). Programs with reasonable coverage generally have low levels of defaulters with minor peaks over time.

Any increasing pattern in defaulters should be assessed as to whether they can be explained by coinciding events. For example in Figure 1, there is a significant increase in defaulting starting in August up to September of that program year which were the same months when serious episodes of insecurity occurred in the program area directly affecting one of the program sites. Defaulters over time should also be analysed alongside defaulter rate (see section on discharge outcomes).

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