Chapter 11. Data Elements and Custom Dimensions

Table of Contents

11.1. Data elements
11.2. Categories and custom dimensions
11.3. Data element groups

This chapter first discusses an important building block of the system: the data element. Second it discusses the category model and how it can be used to achieve highly customized meta-data structure for storage of data.

11.1. Data elements

The data element is together with the organisation unit the most important building block of a DHIS2 database. It represents the what dimension and explains what is being collected or analysed. In some contexts this is referred to an indicator, however in DHIS2 this meta-data element of data collection and analysis is referred to as a data element. The data element often represents a count of some event and its name describes what is being counted, e.g. "BCG doses given" or "Malaria cases". When data is collected, validated, analysed or presented it is the data elements or expressions built with data elements that describe what phenomenon, event or case the data is registered for. Hence the data elements become important for all aspects of the system and decide not only how data is collected, but more importantly how the data is represented in the database and how data can be analysed and presented.

An important principle behind designing data elements is to think of data elements as a self-contained description of an phenomenon or event and not as a field in a data entry form. Each data element lives on its own in the database, completely detached and independent from the collection form. It is important to consider that data elements are used directly in reports, charts and other tools for data analysis, in which the context in any given data entry form is not accessible nor relevant. In other words, it must be possible to clearly identify what event a data element represents by only looking at its name. Based on this one can derive a rule of thumb saying that the name of the data element must be able to stand on its own and describe the data value also outside the context of its collection form.

For instance, a data element called “Malaria” might be concise when seen in a data entry form capturing immunization data, in a form capturing vaccination stocks as well as in a form for out-patient data. When viewed in a report, however, outside the context of the data entry form, it is impossible to decide what event this data element represents. If the data element had been called “Malaria cases”, “Malaria stock doses received” or “Malaria doses given” it would have been clear from a user perspective what the report is trying to express. In this case we are dealing with three different data elements with completely different semantics.