This is an interesting report. Most of the experience using MUAC for CMAM admissions has been with the more extreme 110 mm threshold and we need more reports form programs using the 115 mm threshold. The first point to make is that we have to be very careful when we use terms such as gender bias. Just because an indicator selects more girls than boys does not mean that it is "gender biased". If girls are more at risk of an adverse outcome than boys then the indicator should select more girls than boys. MUAC is used in CMAM programs because it is the best practical predictor of near term mortality. If, in your setting, girls have a bad deal compared to boys when it comes to nutrition, infection, access to health services, &c. and this puts them at risk of morbidity and mortality then you would expect MUAC to select more girls than boys. This is not a gender bias (it is the opposite of a gender bias). In such a setting, an indicator that did not select more girls than boys would be gender biased. You have to ask yourself (and consult census and epidemiological reports) whether, in your setting, girls probably get a worse deal than boys. They tend do in many settings than I have worked in. The second point to make is that comparing WHZ and MUAC is not useful as they measure different things and the predictive power (i.e. for near term mortality) of WHZ is usually the weakest of all practical indicators. It is possible that, in your setting, WHZ is exhibiting a gender bias in favour of boys. Looking at your data ... The observed sex ration (boys : girls) is 349 / 666 = 0.5240. I have compares this with the sex ratios observed in 560 nutritional anthropometry survey datasets from 39 countries and find: Complete database (458951, 15014 cases with MUAC < 115 mm) : Number of boys with MUAC < 115 mm := 6852
Number of girls with MUAC < 115 mm := 8162
Sex ratio (boys : girls) := 0.8395 I calculated the sex ratio in 87 datasets with at least 50 cases (so as not to confuse myself with very low prevalence datasets in which (e.g) 1 boy and 2 girls gives me a sex ratio of 0.5) and got: Minimum := 0.3182
Q1 : = 0.6937
Median := 0.8571
Mean := 0.8725
Q3 := 1.0170
Max := 1.5530 This confirms your finding of more girls than boys being selected. Your sex ratio is quite extreme (i.e in the bottom 3.5% of the distributions of sex ratios observed in the 87 datasets above). It may be that girls in Nepal get a really bad deal compared to boys but ... we have to be very careful about assumptions here ... your data are workload data not population data. It could be that, somehow, your program has a bias against boys. I would definitely check out case-finding and recruitment activities as well as trying to get some idea of cultural attitudes to the program and to malnutrition (e.g. it might be very shameful to have a thin boy but not shameful to have a thin girl and this might effect attendance at the program). NOTE : I am not ruling out the possibility that MUAC may have a gender bias. If you look at the WHO MUAC/A reference table: http://www.who.int/childgrowth/standards/second_set/acfa_girls_3_5_zscores.pdf http://www.who.int/childgrowth/standards/second_set/acfa_boys_3_5_zscores.pdf You will see that, at younger ages and under conditions ideal for growth, girls tend to have slightly lower MUACs than boys. This means that a fixed MUAC threshold will tend to select slightly more girls than boys (we have to be wary of how we use reference data since we seldom work in settings where conditions are ideal for growth). I don't think this difference alone can account for your data though.
I hope this helps.
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