Aggregation In the context of DHIS2, aggregation refers to how data elements are combined within a particular hierarchical relationship. As an example, all the health facilities in a particular district would contribute to the total value for the particular district in question. Different aggregation operators are supported within DHIS2, such as SUM, AVERAGE, and COUNT.
Analytics Analytics refers to the process which processes and prepares data which has been entered into DHIS2 into a format which is more suitable for retrieving indicators and aggregated data. When data is entered into DHIS2, it is stored in a format which is optimized for writing the data. However, when data needs to be processed into indicators or aggregated (e.g from months to quarters), it is more efficient to transform and store this data in a different format which is optimized for read-only operations. The analytics system of DHIS2 is used extensively by the analytics apps (GIS, Pivot Table, Event reports, etc.).
It is important to keep in mind that because the data which has been entered into DHIS2 must be processed into the analytics format, the data which appears in the analytics apps only represents the data which was present in the system the last time analytics was run. If data has been entered after that, analytics will need to be run again for this data to appear in the analytics apps.
Aggregate data In the context of DHIS2, aggregate data refers to either data elements or indicators that have been derived from other hierarchical data sources. For instance, aggregate facility data would result from the aggregate totals of all patients that have attended that facility for a particular service. Aggregate district data would result from the aggregate totals of all facilities contained with a particular district.
Application programming interface An application programming interface is a specification of how different software components should interact with each other. The DHIS2 API (or WebAPI) can be used to interface DHIS2 with other software, to build reports or custom data entry forms.
Approvals Approvals can be used to control the visibility and editibility of data. When data is submitted from the lowest reporting level, it can be approved by the next higher level. This approval has two effects:
Data is no longer able to be edited in the data entry screens at the lower level.
Depending on the system settings which have been enabled, the data will become visible at the approval level.
As an example, data is entered at the facility level, and the submitted for approval. Once the data has been approved at the district level, the data will become locked in the data entry screens for the facility level. It will also become visible in the analytics apps to district users.
Category Categories are groups of category options. The are used in combinations to disaggregate data elements. Categories are typically a single type of concept, such as “Age” or “Gender”.
Category combinations Category combinations are used to disaggregate data elements. As an example, the data element “Number of confirmed cases of malaria” could be disaggregated subdivided into to categories: “Age” and “Gender”. In turn each of these categories, would consist of several category options, such as “Male” and “Female” for the gender category. Category combinations may consist of one or several categories.
Category combination options Category combination options are dynamically composed of all of the different combinations of category options which compose a category combination. As an example, two categories “Gender” and “Age”, might have options such as “Male”/“Female” and “<5 years”/“>5 years”. The category combination options would then consist of:
Category option Category options are atomic elements that are grouped into categories.
Comma separated values Comma separated values are series of tabular data stored in a plain-text format. They are commonly used with DHIS2 to export and import data values.
Data dictionary A collection of data elements and indicators, which can be exchanged with other DHIS2 systems. Typically used to define a set of data elements and indicators when setting up the DHIS2 system.
Data exchange format In the context of DHIS2, the “data exchange format” refers to a XML schema that enables the transportation of data and meta-data between disconnected DHIS2 instances, as well as between different applications that support the DXF schema.
Datamart A set of database tables in DHIS2 that contains processed data elements and indicator values that is generated based on aggregation rules and calculated data element and indicator formulae. Datamart tables are used for analysis and report production. Typically, users should not work directly with unaggregated data values, but rather with values that have resulted from a datamart export for analysis.
Data element A data element is the fundamental building block of DHIS2. It is an atomic unit of data with well-defined meaning. Essentially it is a data value that has been actually observed or recorded which is further characterized by a number of dimensions. As an example the data element “Number of fully immunized children” would refer to the number of children that received this particular service. Data elements are always linked to a period as well as an organizational unit. They optionally may be linked to other dimensions.
Data element group Data element groups are used to categorize multiple data elements according to a common theme, such as “Immunization” or “ART”. Typically, they are used during reporting and analysis to allow related data elements to be analysed together.
Data element group sets Data element groups are used to categorize multiple data element groups into a common theme.
Dimension A dimension is used to categorize data elements during analysis. Dimensions provide a mechanism to group and filter data based on common characteristics. Typically, related data elements may be aggregated or filtered during analysis with the use of dimensions. Dimensions may be a member of a hierarchy. For instance the “Period” dimension may be broken down into “Day->Month->Quarter->Year”.
Indicator The divisor of an indicator. Can be composed of multiple data elements with the use of an indicator formula.
This is obviously a very generalized example. The numerator and indicator themselves can be composed of various data elements, factors, and the four basic operands (addition, multiplication, division and subtraction).
Organisational unit An organisational unit is usually a geographical unit, which exists within a hierarchy. As an example, in the United States, “Georgia” would be considered an organisational unit with in the orgunit level of “State”. Organizational units can also be used to specify an administrative unit, such as a ward within a hospital. The organisational unit dimension specifies essentially “where” a particular data value occurs.