Hello Dear Collaborator,

We think these Chumla formulas can help you.

The patient should lie on their back or sit with their knee raised and forming a 90 degree angle between the leg and the thigh. The foot also makes a 90 degree angle with the leg.

Formula:

Male size in cm: (2.02 * TG) - (0.04 * age) +64.10

Woman size in cm: (1.83xTG) - (0.24 * age) +84.88.

These formulas require a lot of rigor and calculation.

Dr EGNON, PM Food For Peace Kasai Oriental & Lomami, DR Congo.

Health Nutrition Behaviour Researcher

Thank you Dr Kouakou. Indeed I know that formula and is quite a calculation but easily done in Excel. In a context where the patient might not be able to communicate well and age is not possible to retrieve, do you have an alternative that would work ? Are you convinced that Chumlu's is better than what WHO proposes for here, DRC? Or demi span or half span?  From your reply I understand you think knee height and age is better than involvement of arm?  Maybe you can explain to me why? Do you have any specific data on this as well? Thanks so much for sharing your ideas/experiences. Mija

Dear Mija, 

Without reading and searching i cannot comment on which anthropometric measurement will best predict height in your population - BUT given our experiences in trying to do this in very sick adults in the Philippines - i suggest that the ease of measurement is perhaps more important. Reducing the amount of measurement error is probably more important than small differences in performance of the equations (which will have been tested under "perfect" conditions, and probably not in the exact same population groups as you have anyway).  We found that demi-span was the most practical in bed-bound immobile patients often wiht severe breathing difficulties. 

I have tended to use demi-span to estimate height in older adults before using the estimated height to calculate BMI. You could use knee-height or arm-span. Whichever measurement you use, you will suffer from problems with estimation error because the estimate is squared when calculating BMI and this has the effect of magnifying the error in estimating height.

There is a problem with using "standard formulae" for turning arm-spans (or whatever you pick) into heights. This is because the relationship between limb length (or whatever you use) and height varies between populations ... a formula that works well in one setting may not work as well in another setting. I usually develop context-specific formulae using data from a small cross-sectional survey of people whose height and arm-span can both be measured. With this data you can perform a linear regression:

    height = constant + B1 * arm-spans

and use the results to find height from arm-span. You may want to include age (probably in decades) in the model and fit models for males and females separately.

Sample sizes do not need be very large. I tend to use a corrected "subject to variable ratio" approach to sample size for this application. There are a number of options for this. I prefer that presented in "Thorndike RM, Correlation procedures for research, Gardner, New York, 1978 (p 184)" which is:

    N = 10p + 50

Where "N" is the minimum required sample size and "p" is the number of predictors. In the case of a model such as:

    height = constant + armspan + sex
    
you have two predictors (arm-span and age) so the minimum sample size is:

    N = 10 * 2 + 50 = 70 + 50 = 70
    
This is your absolute minimum sample size. If you can get and afford to measure more than 70 subjects then measure as many as you can. A sample size of < 100 will probably be considered as "small". An alternative rule from "Nunnally JC, Psychometric theory (2nd Ed.), McGraw-Hill, New York, 1978" is:

    N = 40p

which, with two predictors, give:

    N = 40 * 2 = 80

These sample sizes are for finding estimating equations for men only or women only.

The Nunnally (1978) rule is considered safer than the Thorndike (1978) rule if you use stepwise techniques to build a model (see "Tabachnick BG, Fiddel LS, Using multivariate statistics (2nd Ed.), Harper Collins, New York, 1989 (p. 129)"). As usual, large sample sizes are better than smaller sample sizes.

Any sample size > 100 will not be considered "small" and may be easier to justify when it comes to publication.

BTW : Work done in in elderly adults in Africa and Asia found that CAMA, AMA, calf-muscle area, and MUAC were better predictors of function (as measured over several dimensions of function as well as by a validated "activities of daily life" score) than BMI. You may consider, therefore, using MUAC rather than BMI. I am not sure of the value of BMI in care of Ebola cases.

I hope this is of some use.

Dear Mija,

2 years ago, I did some littérature search on anthropometric measurements in sick adults , while writing a protocole for nutrition support in hospitals (surgery and ICU): our conclusion at that time, was that for very sick patients like ICU (or Ebola where nursing care is complicated, with limited time at patient bedside and should be simplified), MUAC would be a better tool than BMI.

I found an interesting article where the researchers tried to estimate BMI from MUAC: I am not at home for now, but I can send you the article later if you are interested.

Cheers,

Pascale

Thanks to you all wonderful colleagues

Sharon, thanks and i do know your work you did in The Philipines, and I understand that there demi-span worked well measuring from suprasternal notch to the root of middle/ring finger. 

Mark, as always useful info with lots of calculations (smile!) but you make a fair point on why not measuring it in a non-affected population creating my won formulae. I thought of that and need to consider this but I might not have the means/time. To be seen.

Pascale, great to hear from you and I know the work of Alice Tang if that is what you were referring to. 

Both Mark and Pascale suggest perhaps rather MUAC. I can do that and using it NOT for diagnosing any form of acute malnutrition maybe (nor as proxy for BMI) but rather as an anthropometric indicator to monitor the loss of muscle/proteins as, correct me if I am wrong, it is fairly insensitive to dehydration which is obviously a huge problem in Ebiola patients Any (other) thoughts?

Thanks again to those that responded, always wonderful to hear and learn from colleagues!

Mija. 

Mija,

Thank you for you kind comments and the cheeky smile. I think people like me to show workings and fin dthem helpful (I may be wrong about this).

The process of creating your own estimating formulae can be quick an simple as you do not need a massive sample size. The stats needed are within what can be done with the most basic of stats packages ... even in Excel if that is all you have.

One problem with BMI is that weight can be strongly influenced by dehydration - this might not be a big issue as it may draw attention to poor hydration which can be treated. MUAC is also less influenced by dehydration and a good measure of muscle mass. It will probably be the easiest option. I'd try using it alongside BMI and report back here.

Let us know how you get on.