Of course ... MUAC is a far better indicator than WHZ. The problem is that a lot of people want prevalence by WHZ even though key programs (e.g. CMAM) tend not to use WHZ. The case definitions in common use are: SAM : MUAC < 115 mm or OEDEMA MAM : 115 <= MUAC < 125 GAM : MUAC < 125 or OEDEMA An "at risk" category: 125 <= MUAC < 135 is commonly used.

From Blessing Mureverwi: You can only present GAM if the sampling was indeed exhaustive,and therefore every child was reached.However,if this was conducted in a central place,rather than from household to household,representativeness may be challenged.To compute GAM in this case you need to be able to estimate the sampling error and therefore come up with confidence intervals.This has become common practice.Did your survey fulfill the above?

In fact the sampling was supposed to be exhaustive say, of 7400 children 6800 children were screened and this was done in a central place, where all caretakers were asked to bring their children.

I apologize for my lack of knowledge- I do not know GAM- what it stands for and the criteria

There is a potential for bias if the kids that were not measured were more (or less) likely than the kids that were measured to be SAM or MAM cases. It is good practice to test case-finding exhaustivity using a capture recapture study. You do not have this. What you do have is a very large sampling fraction: SF = 6800 / 7400 = 91.9% If you found (e.g.) 680 GAM cases then you would estimate GAM in the usual way (i.e. 10%). You could then put forward some "what-if" scenarios ... with (e.g.) the same prevalence as the sample, a much higher prevalence (20%) than the sample, and a much lower prevalence (5%) than the sample in the missing 600 children and estimate with these "what-if" numbers. The the same prevalence we would have 680 + 0.1 * 600 = 740. Our estimate would be: data: 740 and 7400 95 percent confidence interval: 0.09325574 0.10706118 sample estimates: probability of success 0.1 With the much higher prevalence scenario we have 680 + 0.2 * 600 = 800 cases. Our estimate would now be: data: 800 and 7400 95 percent confidence interval: 0.1011211 0.1154055 sample estimates: probability of success 0.1081081 With the much lower prevalence scenario we have 680 + 0.05 * 600 = 710 cases. Our estimate would now be: data: 710 and 7400 95 percent confidence interval: 0.08932819 0.10288391 sample estimates: probability of success 0.09594595 We can present the average for the estimate: estimate = (0.1 + 0.1081081 + 0.09594595) / 3 = 0.1013514 = 10.14% and the smallest and largest confidence limits found in the what-if scenarios giving 10.14% (95% CI = 8.93% - 11.54%). When you report this estimate you should also report on what you did and why you chose the two what-if scenarios. Is this any help?

Hie Mark, This looks very logical.Many thanks.

You are most welcome.