Apgar is a scoring system,

…a simple and repeatable method to quickly and summarily assess the health of newborn children immediately after childbirth. (…) The Apgar score is determined by evaluating the newborn baby on five simple criteria on a scale from zero to two and summing up the five values thus obtained. The resulting Apgar score ranges from zero to 10.

One of the criteria is the skin color, which can be blue all over, blue at extremities and normal. This is an *ordinal* variable, which means that the variable does not have number values, but named and ordered levels. Blue at extremities is worse than normal, blue all over is worse than blue at extremities. By transition, blue all over is worse than normal.

Apgar score is meant to provide a *single* number as an outcome. To achieve that, five ordinal criteria need to be aggregated. Unfortunately, there is no way to directly aggregate skin color with pulse, for example. However, numbers are easy to aggregate, by means of addition. Hence the idea of transforming levels to numbers and aggregating them.

This is somewhat dangerous approach. The main purpose of Apgar is:

…to determine quickly whether a newborn needs immediate medical care.

However, having the Apgar outcome in form of *numbers*, people might be quick to calculate mean value and standard deviation. Looking for “mean apgar” in scholar.google.com reveals some 400 documents. It’s not a majority, because ther are 71 thousands documents with word “apgar”, so those 400 are only 0.5%.

Calculating mean and standard deviance of Apgar values wasn’t something that Apgar creator had in mind. Its purpose was to quickly assess if a newborn needs medical care.

Apgar score values are not numbers. They are summed identifiers of five ordinal variables. In order to calculate statistics, the original data (criteria values) should be used, as there are dedicated statistical methods to analyze ordinal variables. These methods, as the reader may already have guessed, are not transforming ordinal variable values into numbers in order to perform calculations on them.

When designing a survey for statistical analysis, Apgar score must not be used. The five original criteria must be included in the survey instead.

All the things that apply to Apgar, apply also to the Aristotle Score, which I have already criticized. Height and weight are numbers. Generally, things that are measured, are numbers. Things that are assessed subjectively, like newborn skin color, are ordinal and do not have values. Aristotle Score values are seemingly numbers. However, it’s important to bear in mind that they are not! Therefore, one must not calculate mean or standard deviation of Aristotle Score.

Current Basic Score reports are based on mean Basic Score values, which is an abuse of a scoring system. I suggest finding another method of quality of care evaluation.