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Monitoring with MUAC on admission

This question was posted the Prevention and treatment of severe acute malnutrition forum area and has 11 replies. You can also reply via email – be sure to leave the subject unchanged.

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Casie Tesfai

Senior Technical Advisor for Nutrition (IRC)

Normal user

21 Feb 2018, 17:53

We're considering monitoring MUAC on admission but are having some issues in how to interpret it.

1) What would be the MUAC cut-off of early vs. late admissions? We've considered looking at setting it at a critical cut-off (e.g. 9.0 cm), but if this is always zero or close to zero, it hides other low MUACs and might mean that we really do nothing at all.

2) I get that the graph is useful, but how can you interpret this as an indicator? For example, if you said something like proportion of children with MUAC on admission less than 10.7 cm, what would it mean if this were 20% or 30%? There is no threshold and we're having difficulty interpreting this. Likewise for mean/median MUAC on admission, which as one agency shared with me is prone to digit preference, is there a threshold to interpret this?

thanks

Mark Myatt

Consultant Epidemiologist, Brixton Health

Frequent user

22 Feb 2018, 12:02

Guidance on this is included in the FANTA SQUEAC / SLEAC Technical Reference on pages 18-21.

If you want to use a summary statistic then you could use the median MUAC at admission. This should be close to the admission criteria. If (e.g.) you admit on MUAC < 115 mm then the median MUAC at admission should be 114 or 113 mm as in Figure 14, 15, and 16A in the FANTA SQUEAC / SLEAC Technical Reference. Another approach would be to pick a threshold (e.g. 110 mm) and report the proportion of admissions with (e.g.) MUAC >= 110 mm. This should be high (e.g. >= 75% or >= 80%).

I have found the "distribution" approach (as in the FANTA SQUEAC / SLEAC Technical Reference) most useful.

Lio

CMAM Advisor

Frequent user

22 Feb 2018, 12:08

Dear Casie,

in coverage surveys, we usually calculate MUAC median at admission to estimate early/late seeking behaviour/timely identification. The following cut-off generally used are:
11.0 – 11.4 early
10.5 – 10.9 medium
<10.5 late
I personally consider "critical cases" when MUAC is <10.0
Research data indicates that mortality risk significantly increases from MUAC <11.0. However, the decision to use 0.5 cm between each category is more a question of "agreement" between implementation partners and a way of monitoring the progress of a programme. When a CMAM program is set, it is expected that median is low but with the uptake of the activities, including active case finding and self-referral, the median should increase and children should be identified very quick. I am sure Mark Myatt can give you more information about the cut-off but, as far as I know, it is more a programmatic monitoring cut off decision. When MUAC is <10.0, the mortality risk is very high and the reason should be investigated

Paul

Technical expert

22 Feb 2018, 13:53

Hi Casie,
Just to add to what Mark and Lio said.

In Malawi we found that children with SAM with a median MUAC on admission of less than 10cm took longer than 3 months to recover to MUAC >12.5cm. Typically we might have an 'action protocol' something like: "a child that does not recover in 3 months should be referred to inpatient care" so less than 10cm would be very late. Children with MUAC less than 9cm on admission seem to have a high mortality rate in outpatient treatment. Lio's suggestion for 10cm as a "critical cut off" seems reasonable.
It would be interesting to look at your data and see if you think there is a MUAC cut off below which we should be recommending referral to inpatient even if there is good appetite and no apparent complications.

In terms of indicators you could base this on the classes suggested by Lio using the percentages suggested by Mark. For example, for an individual treatment site > 75% admissions should be 'early', <25% should be 'medium' and none should be late.

If resources are available, late admissions should be mapped and critical admissions should have a full case history taken as a 'critical incident' (as we might in SQUEAC for example). Mapping late admissions should reveal if the problem with case finding is localised or systematic (and subsequently the necessary corrective action).

For summary medians across the programme you might ask for example that > 75% of sites should report summary 'median MUAC on admission' for all cases in the 'early' category (11.0 cm or greater) etc. If sites are reporting a greater proportion of early admissions then they should also see a reduction in the reported median length of stay. If the median LOS doesn't improve this may indicate a problem with attendance or poor protocol implementation.

The digit preference for median MOA probably results from tendencies to round up the MUAC readings when recording on the OTP card. Charting the distribution of MUAC on admission usually shows up the 'stacking' quite clearly so individual sites doing this could be easily identified. I often see this around 11.4cm and 11.0cm, less frequently for lower measurements. Individual practitioners should be reminded to record accurately since obviously rounding up to 11cm when readings are lower would give a false impression of the proportion of 'early' admissions when using Lio's categories.

Cheers.

Mark Myatt

Consultant Epidemiologist, Brixton Health

Frequent user

26 Feb 2018, 09:42

It has just occurred to me ... Another reason to monitor MUAC at admission particularly in innovative programs (e.g. IRC's CMAM by community health workers with poor letter and number skills) is that it is a key component in estimating expected mortality in the patient cohort. This is essential for cost-effectiveness analysis using DALYs. As CMAM program models proliferate (e.g. NGO CMAM, integrated CMAM, COMPAS, CAM/Surge, CHW delivered CMAM, IRC's community delivered CMAM) it will be important to have some idea of what works and at what cost.

MUAC monitoring is essential but we do need a simple and standard way of doing CEA fro CMAM programs. UNICEF Nigeria and Concern Worldwide are working on this.

I hope this is of some use.

Casie Tesfai

Senior Technical Advisor for Nutrition (IRC)

Normal user

13 Mar 2018, 19:49

Thanks all for your feedback.

To follow-up, does anyone know what setting a critical cut-off at MUAC 10.0 is based on? Is there a MUAC cut-off that has a high association with inpatient care or mortality for example that might justify an automatic referral by CHWs? In our study in South Sudan of CHW low-literacy SAM treatment (results published soon), we set an automatic referral at anything at MUAC 9.0 cm or lower (and included a dark red color on the MUAC to signal a danger sign). We saw from our OTP data that those children generally were sent to inpatient care, and since ICCM is about reducing the amount of decision making, we felt it was important to set an automatic referral for critical cases. If anyone could help us understand what the critical cut-off is based on, we could reconsider our automatic referral threshold.

thanks!
Casie

Mark Myatt

Consultant Epidemiologist, Brixton Health

Frequent user

14 Mar 2018, 10:36

I don't know if the threshold has a detailed evidence base. It has an evidence base (i.e. low MUAC = high severity = high mortality). A review of the original CTC literature might show the reasoning. My guess is that it comes from accumulated clinical experience. I know (e.g.) that Lio and Paul have extensive CMAM experience.

I have a few OTP datasets to hand. Just looking at MUAC in the two groups in one program:

    MUAC in died or transfered to inpatient care
       Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      80.00   91.00   96.00   97.91  105.50  112.00 
    -----------------------------------------------
    MUAC in others
       Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
       82.0   105.0   110.0   108.4   114.0   115.0 

The difference is significant (p < 0.0001) even though the number of deaths and transfers to stabilisation is small.

It should be possible to collect several OTP datasets (so we have a good number of the events of interest) and analyse the collected data using ROC analysis. This would not be a very difficult analysis. Most of the work will be in assembling the study database.

I have data from a handful of programs that I can seek permissions for such an analysis. Is anyone out there interested in providing data for such an analysis or doing the analysis or writing up the analysis?

FRANCK ALE

epidemiologiste

Normal user

14 Mar 2018, 13:04


Dear Mark

I have some data that I can use or share. I can also do the analysis.

i hope i can help
thank

Mark Myatt

Consultant Epidemiologist, Brixton Health

Frequent user

14 Mar 2018, 13:47

Franck,

Thanks for volunteering to do the analysis / share data. I have some data too.

Let us wait for a few days to see if anyone else wants to join the fun.

Taye Girma

Normal user

14 Mar 2018, 14:38

Dear all I learned a lot about MUAC on admission:
 Monitoring MUAC on admission.
 'Median MUAC on admission' for all cases in the 'early' category (11.0 cm or greater) etc
 MUAC at admission particularly in innovative programs (e.g. IRC's CMAM by community health workers with poor letter and number skills) is that it is a key component in estimating expected mortality in the patient cohort. This is essential for cost-effectiveness analysis using DALYs. As CMAM program models proliferate (e.g. NGO CMAM, integrated CMAM, COMPAS, CAM/Surge, CHW delivered CMAM, IRC's community delivered CMAM) it will be important to have some idea of what works and at what cost.
 To follow-up, does anyone know what setting a critical cut-off at MUAC 10.0 is based on? Is there a MUAC cut-off that has a high association with inpatient care or mortality for example that might justify an automatic referral by CHWs?

Best regards

Taye Girma, Community Mobilization Supervisor @ AAH-Ethiopia

Jay Berkley

KEMRI/Wellcome Trust Research Programme, Kenya

Frequent user

16 Mar 2018, 04:53

Hi Mark and everyone

Do you have data on any symptoms and clinical signs (including those that would not qualify the child as ‘complicated’? The reason for asking is that we have started looking at our ‘complicated’ SAM databases and looking at signs to see just how complicated children are, a work in progress, but we are seeing a very strong relationship with mortality. Children at lowest level of being ‘complicated’ have a low mortality risk during the next 6 months, around 1-2% as might be seen at an OTP.

Cheers

Jay

Mark Myatt

Consultant Epidemiologist, Brixton Health

Frequent user

16 Mar 2018, 10:38

Jay,

From my experience ...

Most OTP datasets will be limited to routine program data (i.e. admissions and exits by type over time). In some settings this might be expanded to include admission criteria. Data collected by SQUEAC coverage assessments will usually include MUAC / WHZ at admission in addition to routine programming data. Some NGOs collect wider datasets but these are usually limited to anthropometry at admission and throughout the treatment episode.

This means that we are limited to research datasets. I think the richest datasets will be from the VALID's CTC research program. They collected full clinical data for all cases. I guess that ACF may have similar data from their alternative to CTC (similar to CTC/CMAM but with a short inpatient stay for all children). VALID Nutrition have tested quite a number of RUTF formulations over the past decade or so and may also have useful data.

Maybe you know all this.

I have data from a study in Malawi which records week number, date, weight, height, MUAC, oedema, TSF (2 measurements), diarrhoea, fever, vomiting, cough, RUTF ration, and outcome for each visit.

I also have data from Nigeria which was a records pull, which records routine admission medications give, admission weight, admission height, admission MUAC, admission oedema, diarrhoea at admission, fever at admission, vomiting at admission, cough at admission, diarrhoea after, fever after admission, vomiting after admission, cough after admission, and some other data. This has the usual issues with pulled records but the sample size is > 100,000. The sample size brings false discovery issues.

I think it should be possible to share these datasets with you. Let me know if they are of any interest.

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