# Interpretation of rapid MUAC asessment

This question was posted the Assessment and Surveillance forum area and has 9 replies.

### Tamsin Walters

en-net moderator

Forum moderator

5 Jan 2011, 20:36

*From Caroline Muthiga:*We conducted a rapid assessment recently using MUAC and measured 120 children. We found 13 children with a MUAC of <11cm, 35 between 11.0- 11.9, 31 with a MUAC of 12-12.5 while 41 had a MUAC of > 12.5. Is there a way to get a proxy on the probable GAM and SAM rates with this information. Any formula?

### Pascale Delchevalerie

Nutrition Advisor MSF Belgium

Normal user

6 Jan 2011, 09:34

### Mark Myatt

Epidemiologist at Brixton Health

Frequent user

6 Jan 2011, 09:56

### Jennifer

Nutrition Coordinator

Normal user

14 Nov 2013, 09:51

### Mark Myatt

Epidemiologist at Brixton Health

Frequent user

15 Nov 2013, 10:50

```
SD = Interquartile-range / 1.34989
```

other robust estimators can be used but these work pretty well.
Having got your estimate of the mean and SD you can calculate the probability that a child picked at random will (e.g.) have a MUAC < 125 mm. This is the same as prevalence of MUAc < 125 mm. You can do this by standardising and using a set of statistical table or using a computer. Most stats packages and spreadsheets will be able to do this. In OpenOffice Calc (e.g.) we can ask the question "What is the prevalence estimate for MUAC < 125 mm given mean = 145mm and SD = 12 mm" with:
```
=NORMDIST(125;145;12)
```

which returns:
```
0.0477903523
```

which is about 4.8%.
Doing this in Excel is similar - just use "," instead of ";" in the cell formula.
Post back here if you need more detail or need to do more.
I hope this helps.### Jennifer

Nutrition Coordinator

Normal user

27 Nov 2013, 06:51

### Fazal

Normal user

27 Nov 2013, 07:58

### Mark Myatt

Epidemiologist at Brixton Health

Frequent user

27 Nov 2013, 11:32

**all**eligible people in the screened communities. In some settings central location screening will not find all cases as they may not attend due to stigma or shame. Also, sick kids tend to be both lethargic and irritable and carers may be reluctant to attend. If you get this wrong then you will bias your prevalence estimate downwards. If the opposite is the case and only sick children get brought for screening then you will bias you prevalence estimate upwards. You can avoid this. Either go house-to-house for screening or "mop-up" after a central location screening using something like active and adaptive case-finding (avoiding double-counting). In some settings people are reluctant to move outside their immediate neighbourhoods. If you use central location screening then make sure you use several "central" locations. (2) Make sure that your sample of communities is

**not**a convenience sample (e.g. villages close to centres or roads). This would also introduce a bias (probably downwards). I would use a spatially representative sample so the sample comes from all over your program area. (3) You need to have a fair number of communities to be sure that you have a representative sample of communities. The general rule is "more is better" but there is little point in going above about 24 communities. I think 12 or 16 should be sufficient. If you do this every month or so in a small sample of communities (changing the sample each round) then you would have the makings of a nutritional surveillance system. (4) Data analysis should treat the sample as a stratified sample and a weighted analysis should be performed. In the case of mass-screening that meets (1) above the community-specific weights would be the number of children screened in each community. If you get this right (and it is not too hard) then you will have no need for a SMART survey. The SQUEAC / SLEAC Technical reference has material on spatial sampling (pages 93, 94, 95, 96, 100, 101, 102) and analysis of data from stratified samples (pages 127, 128, 129). I hope this is useful.

### Jennifer

Nutrition Coordinator

Normal user

2 Dec 2013, 09:08

### Mark Myatt

Epidemiologist at Brixton Health

Frequent user

2 Dec 2013, 10:15

**alone**you will bias the sample towards finding cases and any prevalence estimate will be an overestimate. Another way of describing these types of method is "optimally biased". I assume that you mean that you will do mass-screening at one or more "central locations" and then do active and adaptive case finding ti "mop up" cases that may have been "hidden". If you use

**only**active and adaptive case-finding to find cases than you estimate prevalence if you have a good estimate of the populations of the sample villages. WRT sample size ... there are two approaches to estimating prevalence. (1) The classic approach is to recode MUAC data to binary variables (e.g. GAM not GAM, MAM not MAM. SAM not SAM) and then estimate by dividing the number of cases by the sample size. To get useful precision you often need a large sample size because (a) you need quite fine precision and (b) you will have a design effect because of the nature of your sample. This needs a sample size of about n = 500 or more. You can use the sample size calculator in the ENA software. The main problem with this approach is that the SAM estimate will lack precision. (2) The PROBIT approach makes more use of the MUAC data and does not need such a large sample size. A sample size of n = 192 is usually sufficient to give similar precision to a classic approach with a sample size 3 or 4 times larger for GAM. The estimates for SAM are more precise than a classic estimator with a sample size 3 or 4 times larger. To illustrate ... a classic estimator with n = 544 (largest sample size in the SMART manual) has relative precisions of about 27% and 65% for GAM and SAM respectively whereas a PROBIT estimate with n = 192 has relative precisions of about 24% and 34% respectively (see here and here for more details). I do not think sample size will be an issue as you will be screening all children in your sampled communities. You need to be sure that you keep track of the number of children in the sampled communities. I hope this helps.

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