Description of tool

Admissions-over-time is the most important item of routine program data. Trends in admissions-over-time give program managers a quick insight as to whether the program is reaching the target population. It also acts as a gauge by which to assess the effects of changes or reforms made in the program (e.g. opening of new sites, recruitment of volunteers, initiation of outreach activities) or of events such as public holidays, harvest time or RUTF stock-outs.

Data requirements

Every CMAM program is expected to be monitoring the entry, progress of treatment and exit of individual children with severe acute malnutrition (SAM) using an outpatient care treatment card which are then compiled and recorded into a computerised database (see template database). A tutorial on how to use an electronic database for CMAM program monitoring can be found here. It is from this electronic database that data for admissions can be obtained. If this database is not available, data for admissions should be compiled and recorded from the outpatient care treatment cards.

Analysis of data

Data on admissions 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 create a line graph using a spreadsheet can be found here and a template spreadsheet can be found here. An example of a computer-generated line graph of admissions over time is shown in Figure 1 using data from a CMAM program in Somalia.

Figure 1: Admissions-over-time

_{Data courtesy of SAACID and Oxfam Novib} In order to show the trend in admissions over time, some method of smoothing of the data is needed. In Figure 1, the solid gray line represents smoothed data using the M3A3 approach (i.e. running median-of-three or M3 and then running average-of-three or A3) method described in the FANTA SQUEAC / SLEAC technical reference¹. A template spreadsheet that performs the M3A3 smoothing and plots the admissions-over-time line graph can be found here. For the most part, moving averages over time spans of 3 such as the M3A3 discussed above and in the tutorial to smooth time-series data such as admissions-over-time is enough to bring out the trend component of the time-series. However, program admissions data for longer periods of time (i.e., 2 years or more) may require moving averages over longer time spans (i.e., time spans covering an entire seasonal cycle) in order to reveal the trend in the time-series. 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.

Interpretation

The smoothed line graph allows for easier visualisation of the increasing or decreasing trend in admissions over time. The program context needs to be considered when interpreting this trend in relation to program coverage. There is a distinctive pattern in the plot of admissions over time in an emergency-response program with a reasonable coverage. Figure 2 illustrates this pattern in which initially the number of admissions increases rapidly then falls slightly before stabilising, and finally drops away as the emergency abates and the program is scaled down and approaches closure².

Figure 2: Pattern of admissions over time over an entire program cycle for an emergency-response CMAM program

_{From FANTA technical reference page 15} Figure 3 on the other hand shows the plot of admissions over time of an emergency-response program that has not prioritised community mobilisation efforts and outreach activities at program initiation. There is the same characteristic rapid initial peak in admissions seen in Figure 2 but soon after a rapid decline until community mobilisation is started and an acceptable pattern of admissions established.

Figure 3: Admissions over time in an emergency-response CMAM program with initially poor community mobilisation

  _{From FANTA technical reference page 15} In a non-emergency setting, the interpretation of trend of admissions over time requires additional information on the probable or expected incidence of SAM. This additional information can be gathered from seasonal calendars of childhood illnesses associated with SAM such as diarrhoea, fever and acute respiratory infection and seasonal pattern of food availability. Health and nutrition or food-security assessments would usually be able to provide this information. If not available, this should be collected at the start of the program or during the SQUEAC investigation (see section on seasonal calendar). Figure 4 is an example of a seasonal calendar for Somalia available from the Famine Early Warning Systems Network or FEWS NET³.

Figure 4: Seasonal calendar for a typical year in Somalia

    Click here for admissions-over-time with seasonal calendar combined.   Information on probable or expected SAM incidence contextualises the trend of admissions over time observed and helps determine whether the program is responsive to need, that is the program has increasing admissions at times of expected high incidence of SAM. Based on information from Figure 4, higher incidence of SAM can be expected sometime after February onwards during the lean season and during the rainy season when higher incidence of diarrhoea is expected and should start to decrease by July to August when harvest begins. Looking back at Figure 2, the pattern of admissions over time show an increasing trend during the period when we expect SAM incidence to be high. This pattern of admissions over time is compatible with a program that is responsive to need and is potentially a program with reasonable coverage.

Endnotes

¹ Myatt, M. et al., 2012. Semi-Quantitative Evaluation of Access and Coverage (SQUEAC)/ Simplified Lot Quality Assurance Sampling Evaluation of Access and Coverage (SLEAC) Technical Reference, Washington, DC: FHI 360/FANTA pages 203 to 206. ² ibid, page 12. ³ http://www.fews.net/east-africa/Somalia