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SMART Vis-à-vis EPI info 6 on definition of Flags

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Anonymous 81

Public Health Nutritionist

Normal user

28 May 2009, 07:54

Why SMART is customized very tight set within range mean -3 to +3? EPI Info 6 is within -5 to +5. SMART excludes a lot of children being considered as out of range. this is creating misunderstanding/confusion on the interpretation of data.

The other point is about plausibility checking of SMART software. When the plausibility check report shows that the overall score (sex ratio, age distribution, digit preference etc) of a given survey is poor, does it mean the data is totally useless?

Mark Myatt

Consultant Epideomiologist

Frequent user

28 May 2009, 10:51

I cannot speak for the SMART Initiative and have little experience of their software but I will attempt to answer this question.

I am not sure what you mean by "range mean -3 to +3". If this is the range outside of which a calculated WHZ is deemed to be unreliable then it is too restrictive since WHZ < -3 is a case-defining threshold for SAM and the software would only use cases of bilateral pitting oedema when estimating the prevalence of SAM. If this is a mean WHZ for the entire survey dataset then this is sensible (for standard survey work - see below) as a mean WHZ of -3 would correspond to a 50% prevalence of SAM.

The EpiNut module of EpiInfo v6.xx uses these thresholds to identify individual cases that may have erroneous data. This makes sense since this thresholds are probably incompatible with life.

I think you should check what SMART means by "range mean -3 to +3".

If cases are just flagged then there are no consequences to this. If they are automatically excluded from reports then it will stop you using SMART software to perform such analyses as investigating the range of WHZ of children admitted to a therapeutic feeding program since the mean WHZ in this application will almost always be below -3.

I think that you should be careful when interpreting automatic plausibility checks. They are useful but can mislead. As an example I will look at the age distribution. Plausibility checks on this data are a little limited because after about two years ages are often reported as whole years. The standard approach is to recode data into year-centred age groups and then check for a deviation from uniformity in the distribution. This is a reasonable approach but if there was a serious shock in the recent past that caused a great deal of infant mortality then the age distribution will show a gap where the dead children would have been had they not died. The data may be good but the plausibility check will suggest that they are not. A similar situation could arise if infant mortality differs between sexes or (e.g.) female children are preferentially aborted as has been reported from China and India. The checks may also provide unreliable results if there are older (stunted) children in the data causing the test to be based on small numbers in some strata or, if older children are treated as 5 year olds, reporting an excess of older children.

There is nothing wrong with plausibility checks and I would encourage you to use them but you do have to treat them with caution and check whether there are alternative (i.e. to poor data quality) explanations for the "suspect" patterns in the data.

The issue of digit preference has been discussed on a previous thread on this forum.

As for "totally useless" ... data would have to be of very poor quality for it to be "totally useless". I think that digit preference is far less serious than a poor sample.

Kevin Sullivan

Normal user

28 May 2009, 14:38

The FLAG field in Epi Info 6 is used to identify records where there are missing data or a strong likelihood that the data are incorrect based on extreme Z-scores. Based on the previous WHO reference (CDC/NCHS reference), the following are flagged:

Index Minimum Maximum
HAZ -6.0 +6.0
WHZ -4.0 +6.0
WAZ -6.0 +6.0

Two other criteria are:

HAZ>+3.09 and WHZ<-3.09 (very tall and skinny)
HAZ<-3.09 and WHZ>+3.09 (very short and heavy)

All the z values are provided in Epi Info and it is up to the user to decide whether to accept Epi Info's approach to flagging extreme values or use a different method. Users must write their own Epi Info code to remove extreme values and example code is provided that can be used in Epi Info's ANALYSIS module (amiss1.pgm and amiss2.pgm).

In my own experience I have occasionally found children who were flagged but the measurements were correct - usually children with either a severe chronic disease and/or severe malnutrition.

This is not an exact science - this a delicate balance to remove children with inaccurate measurements while trying to retain children with correct but extreme measurements.

With properly trained personnel using good measurement equipment and careful recording of results there should be few incorrect measurements.

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