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What is the minimum rate of reduction in GAM in order to determine if an intervention is worth the investment? In other words, I am trying to get a sense of the appropriate effect size to use for calculating sample size in a control-treatment, nutrition outcome (acute malnutrition) impact evaluation.
We usually measure "worth the investment" in terms of cost per DALY averted. With wasting this is mostly about averting deaths. The problem comes with the definition of GAM. If this is based on W/H then the problem is that the functional outcome of (e.g.) having a WHZ of -2.76 varies from place to place so you may have difficulties calculating the number of deaths that you will avert with a given drop in GAM. If you use MUAC then you are on firmer ground with functional outcomes (since it tends not to vary much from place to place).
The basic approach will be to take survey data and, based on the prevalence and the distribution of your indicator (MUAC or WHZ), calculate an expected number of deaths (and disability ... death is total disability). Based on age and life tables you will then calculate the DALYs at the baseline and the DALYs at the endline. The difference between the two is your impact. Since you know the cost of the program you can then divide cost by the difference in DALYs to give a cost per DALY averted. For low-income counts anything less than about US$100 per DALY averted is considered "worth the investment".
You problem then becomes a "what-if" modelling problem (you could do this in a spreadsheet - I prefer a Monte Carlo simulation approach) to find the difference that will make it all worthwhile. The model can get complicated as you have to factor in coverage and because (if your program is responding to need) costs are not independent of prevalence.
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Donev, D., Zaletel-Kragelj, L., Bjegovic, V., & Burazeri, G. (2010). MEASURING THE BURDEN OF DISEASE: DISABILITY ADJUSTED LIFE YEAR (DALY). WEB, 1–33.
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Rushby, J. F., & Hanson, K. (2001). Calculating and presenting disability adjusted life years (DALYs) in cost-effectiveness analysis. Health policy and planning, 16(3), 326–331.
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The passage contains 154 "i"s.
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