Sample size for rapid nutritional assessment of U5 using MUAC

The RAM (Rapid Assessment Method) and similar methods in Ethiopia, Sierra Leone, and Sudan use that n = 192 collected using 16 clusters of 12 children. The first stage sample is taken using a stratified spatial sample or CSAS. The within-cluster sample is collected as 4 clusters of 3 children using a map/segment/sample method or QTR + EPI5. A PROBIT estimator is used. This yields useful precision.

I suppose that you will have to ask "NGO's and the donor community". Here is some information that might help them decide ... It is still early days for RAM. Work is proceeding. We have experienced few technical setbacks. RAM is a cluster-sampled method and has all the limitations of such a method WRT clustered phenomena. For example, it will have quite poor precision WRT some WASH indicators in rural settings. It is not a "magic bullet" for all that is weak in current approaches. It does provide some sample size savings. It also does not require population data in advance of sampling. Work on testing the PROBIT method (still ongoing as we try to improve accuracy and precision) used computer-based simulations based on a survey area population of 100,000 total persons with 17% aged between 6 and 59 months (i.e. 17,000 children). This was felt to be typical of areas in which RAM might be applied and is not so different, in terms of sampling, to the 25,000 you mention above. Results show the method yields similar precision to (e.g.) a SMART survey with c. 3 times the n = 192 sample size. The gain in precision in field-use is likely to be somewhat higher than this because the within-cluster sampling methods employed reduce variance loss (and DEFF) compared to the proximity sampling commonly used in SMART surveys. SMART surveys could, of course, adopt methods such as MSS or QTR + EPI5 for within cluster sampling which should result in improved precision. There is an issue of accuracy (or bias). The classical estimator is generally unbiassed. The PROBIT estimator is not unbiassed. The level of bias is, however, small. Here is an example of relative precision and bias for GAM prevalence estimates with variants of the PROBIT estimator and the classical estimator using computer-based simulation: PERFORMANCE OF RAM/PROBIT CANDIDATE ESTIMATORS FOR GAM PREVALENCE Method Location Dispersion Error (%) Rel. Prec. (%) ------- ------------------ ---------------- --------- -------------- PROBIT Mean SD 0.8667 23.99 Mean (transformed) SD (transformed) 0.7321 24.05 Median MAD * 1.42860 0.1852 24.58 Median IQR / 1.34898 0.0670 24.66 Tukey's Trimean IQR / 1.34898 0.1059 24.62 Mid-hinge IQR / 1.34898 0.1947 24.58 ------- ------------------ ---------------- --------- -------------- CLASSIC NA NA -0.0006 27.22 ------- ------------------ ---------------- --------- -------------- The classical method is unbiased. PROBIT with (e.g.) median and IQR is slightly more precise (i.e. the 95% CI will be about 10% narrower) than the classical method at the tested sample sizes (i.e. n = 192 for PROBIT and n = 544 for CLASSIC). The bias for this PROBIT variant is 0.067% (i.e. almost zero). This shows the method to underestimate prevalence slightly. I think the work outlined above shows that RAM can do as well as SMART with GAM (it does much better with SAM in terms of precision) with smaller sample sizes. If SMART is good enough "NGO's and the donor community" then RAM/PROBIT should also be good enough. I do not want to make too strong a claim for RAM. More testing is required and the method remains a very promising approach. Here are some notes WRT indicators ... PROBIT makes very full use of the data compared to the classical estimator. This can account for the improved performance. This is an example of indicator redesign. The approach here as been to reverse the frequentist formula of: probability = proportion so that: proportion = probability This approach can be applied to other indicators. For example, estimating proportions using survival probabilities for continuing breastfeeding is about eight times more efficient (i.e. in terms of sample size requirements) than using the proportion-based approach of the current IYCF indicators. It is important to note that the survival-based indicator has problems that limit its utility for frequent M&E but the general approach works. Another approach is to use whole-sample indicators. IYCF indicators are plagued with issues of sample size because the denominators become vanishingly small for some indicators. For example, the indicator for continuing breastfeeding at 12 months (CBF12M) would have a sample size of n = 50 from a survey sample of n = 900 (large for SMART). If CBF12M is clustered then n = 50 might become an effective sample size of n = 25 or fewer. It is, however, possible to rethink the IYCF indicator set to have indicators that apply to the entire sample but still provide useful information. We have been using this approach in a couple of countries with some success. I am not sure about the issue of population density. RAM is a general purpose survey method employing a spatial sample in the first stage. This has allowed the method to be used in urban settings which are (by definition) areas of high population density. I hope this helps.

BTW ... forgot to say that EPI uses n = 210 (as 30 clusters of 7). EPI coverage is often quite patchy so we end up doing M&E on our most important and effective child-survival program using surveys with an effective sample size of n = 100 or fewer. The main differences between EPI and RAM are that RAM uses a (smaller) spatial sample in stage 1 and a more representative sample in stage 2.

Much literature has been produced in co-operation with partners (governments, UNOs, NGOs) and I am not free to distribute that without first seeking permissions. There is the original RAM proposal, the first development update (with new PROBIT estimators tested), and the Sierra Leone M&E manual (material on sampling). These cover some aspects of RAM. Data analysis is by a blocked and weighted bootstrap (BWB) estimation procedure (this will be described in brief by HelpAge in their upcoming report from CHAD). The BWB procedure takes into account the sample design (i.e. blocking for the cluster-sample design and weighting by a "roulette wheel" algorithm for posterior weighting). If you need a detailed technical briefing on RAM then you should contact VALID or me directly.

Here is the second RAM development update. This has results of testing a few more variants of the PROBIT indicator. All results are for GAM and SAM by MUAC (< 125 mm and < 115 mm). There is also some material describing IYCF indicators in RAM type surveys.

Glad to have been of use. I am a little confused by the methodology you describe. Best if you send me the methodology. My email address is: mark@twinketoes.cinderella.brixtonhealth.com without the "twinkletoes.cinderella" (this is just to hinder spam).

Telephone surveys can be difficult to get right as all sorts of biases can be introduced and there are quite a few difficulties that need to be addressed.

SAGE have a volume "How to Conduct Telephone Surveys" as part of their "survey toolkit" series that you may find useful ... see:

    https://dx.doi.org/10.4135/9781412984423

It is cheap from Amazon.

Sample sizes needs are not that different from any other survey. We really need to know, for each key indicator, what value you expect and how precise you want the estimate to be. The simplest calculation is:

    n = (p * (1 - p)) / (e / 1.96)^2

where:

    n = required sample size
    p = expected proportion
    e = half-width for 95% confidence interval

If (e.g.) you are collecting data on dietary diversity and expect 25% of children to be consuming the appropriate number of food groups and you want precision of +/- 5% then:

    n = (0.25 * (1 - 0.25)) / (0.05 / 1.96)^2 = 288

This is the sample size needed for a simle random sample.

I hope this is useful.