CMN Cube Looking LogoCoverage Monitoring Network


Improving nutrition programmes through the promotion of quality coverage assessment tools, capacity building and information sharing.

Estimating coverage classifications


The SLEAC methodology uses a two-standard, three-class (low, moderate, high) classifier to classify the coverage in each Health District. This gives a clear spatial representation of coverage in a region or country.

Analysis of data using the simplified LQAS classification technique involves examining the number of cases found in the survey sample (n) and the number of covered cases found. If the number of covered cases found exceeds or does not exceed a threshold value (d) then coverage is classified accordingly.


  1. Information
  2. Making Coverage Classifications
1. Calculating Threshold Values

The threshold values (d) depends on the number of cases found (n) and the standard (p) against which coverage is being evaluated. A specific combination of n and d is called a sampling plan.The following rule-of-thumb formula may be used to calculate a suitable threshold value (d) for any coverage proportion (p) and any sample size (n).

Three classes are sufficient for most SLEAC applications. A three-tier classification method is particularly useful for identifying very high coverage service delivery units and very low coverage service delivery units for inclusion in subsequent SQUEAC investigations when using the SLEAC/SQUEAC strategy illustrated.

Three-tier classifications require two sampling plans/decision rules. For three-tier classifications there are two coverage proportions:

p1 :The upper limit of the ‘low coverage’ tier or class

p2 :The lower limit of the ‘high coverage’ tier or class

The ‘moderate coverage’ class runs from p1 to p2 as shown below:


Two classification thresholds (d1 and d2) are used and are calculated as:


2. Making Coverage Classifications:

Classifications are made using the algorithm illustrated below:


Data should be organised in the following format:


As such, data can then be represented spatially to give an understanding of the geographical spread of coverage, as demonstrated by the image below;