Course · Intermediate · Sector Analysis
Nutrition Analysis: CMAM and SMART
Anthropometry, admission and outcome indicators, and a SMART survey analysed end to end against the WHO growth standards and the Sphere performance thresholds.
What you will be able to do
- Compute weight-for-height, height-for-age and weight-for-age z-scores against the WHO 2006 standards, from raw measurements
- Apply the SAM and MAM case definitions correctly, including the oedema rule that overrides anthropometry
- Say where MUAC and weight-for-height disagree, by how much, and which children each criterion finds
- Produce a SMART plausibility report and decide whether a survey should be accepted
- Estimate GAM and SAM prevalence with a design-adjusted interval and read it against the IPC acute malnutrition phases
- Compute CMAM performance on the denominator Sphere actually specifies, and find the site the district figure is hiding
- Explain why programme admissions over expected caseload is not coverage, and what estimating coverage requires
Standards and methodologies
This is the first course in the programme where the sector content is the hard part. Everything in modules 2 and 3 was method that transferred across sectors. This does not: the WHO growth standards, the SMART plausibility criteria, the Sphere performance thresholds and the IPC phases are specific, published, consequential, and this audience is held to all four.
The course is built on three registers that between them cover the whole programme cycle. A SMART survey of 930 children, shipped as raw weight and height so the z-scores must be computed. A screening register of 4,218 children, which is where cases are found. And a CMAM admission register of 1,100 episodes, which is where they are treated and where performance is judged.
Three findings from that data shape the course, and each is a real number computed from the committed files rather than a claim.
The two admission criteria find different children. Of the children severe by either measure, 468 are severe by weight-for-height alone, 24 by MUAC alone and 142 by both — a concordance of 22.4%. The disagreement is age: the MUAC-only children average 14.3 months, the weight-for-height-only children 31.4.
The denominator decides the cure rate. Computed as Sphere specifies — over children who reached an outcome, excluding transfers — the programme cures 82.3%. Divide the same numerator by all 1,100 admissions and it reads 73.6%.
The district figure hides the site. Defaulting is 13.7% overall, inside the Sphere maximum of 15%, and 20.5% at one site, outside it.
Both Python and R throughout. You should have done module 3, or at least be able to defend a denominator and put an interval on a proportion — this course adds the clinical definitions to both.
Practice
Reading the lessons is not the same as having done the work. Each of these applies the course to a dataset it did not teach on.
- Lab · 180 minThe MUAC sub-sample nobody weightedA household survey measured one child under five per household. Estimating acute malnutrition from it needs a weight nobody applied, a design nobody declared, and an honest statement about which threshold it can be read against.
- Exercise · 60 minThe cure rate that moved nine pointsA donor asks why the draft report said 73.6% and the final one says 82.3%. Nothing in the data changed. Reconstruct all three denominators, say which Sphere means, and write the answer.
Progress
Take it offline
The whole course as a typeset PDF — every lesson, every code example, the data dictionary and the indicator definitions. Generated from the same source as this page.
The LaTeX source ships alongside each PDF, so an organisation can rebrand the handout or fold a lesson into its own training pack.