The period dimension becomes an important factor when analysing data over time e.g. when looking at cumulative data, when creating quarterly or annual aggregated reports, or when doing analysis that combines data with different characteristics like monthly routine data, annual census/population data or six-monthly staff data.
In DHIS2, periods are organised according to a set of fixed period types described below. The following list is for the default ISO 8601 calendar type.
Weekly: The system supports various weekly period types, with Monday, Wednesday, Thursday, Saturday and Sunday as the first day of the week. You collect data through data sets configured to use the desired weekly period type. The analytics engine will attribute weekly data to the month which contains four days or more of the week.
Bi-weekly: Two week periods beginning with the first week of the year.
Monthly: Refers to standard calendar months.
BiMonthly: Two-month periods beginning in January.
Quarterly: Standard ISO quarters, beginning in January.
SixMonthly: Six-month periods beginning in January
Yearly: This refers to a calendar year.
Financial April: Financial year period beginning on April 1st and ending on March 31st of the calendar next year
Financial July: Financial year period beginning on July 1st and ending on June 31st of the calendar next year
Financial Oct: Financial year period beginning on October 1st and ending on September 31st of the calendar next year
Six-monthly April: Six-month periods beginning on April 1st with a duration of six calendar months.
As a general rule, all organisation units should collect the same data using the same frequency or periodicity. A data entry form therefore is associated with a single period type to make sure data is always collected according to the correct and same periodicity across the country.
It is possible however to collect the same data elements using different period types by assigning the same data elements to multiple data sets with different period types, however then it becomes crucial to make sure no organisation unit is collecting data using both data sets/period types as that would create overlap and duplication of data values. If configured correctly the aggregation service in DHIS2 will aggregate the data together, e.g. the monthly data from one part of the country with quarterly data from another part of the country into a national quarterly report. For simplicity and to avoid data duplication it is advised to use the same period type for all organisation units for the same data elements when possible.
In addition to the fixed period types described in the previous section, DHIS2 also support relative periods for use in the analysis modules.
When creating analytical resources within DHIS2 it is possible to make use of the relative periods functionality. The simplest scenario is when you want to design a monthly report that can be reused every month without having to make changes to the report template to accommodate for the changes in period. The relative period called “Last month” allows for this, and the user can at the time of report generation through a report parameter select the month to use in the report.
A slightly more advanced use case is when you want to make a monthly summary report for immunisation and want to look at the data from the current (reporting) month together with a cumulative value for the year so far. The relative period called “This year” provides such a cumulative value relative to the reporting month selecting when running the report. Other relative periods are the last 3,6, or 12 months periods which are cumulative values calculated back from the selected reporting month. If you want to create a report with data aggregated by quarters (the ones that have passed so far in the year) you can select “Last four quarters”. Other relative periods are described under the reporting table section of the manual.
|Organisation Unit||Data Element||Reporting month||So far this year||Reporting month name|
|Gerehun CHC||Measles doses given||15||167||Oct-09|
|Tugbebu CHP||Measles doses given||17||155||Oct-09|
While data needs to be collected on a given frequency to standardise data collection and management, this does not put limitations on the period types that can be used in data analysis and reports. Just like data gets aggregated up the organisational hierarchy, data is also aggregated according to a period hierarchy, so you can create quarterly and annual reports based on data that is being collected on a Monthly basis. The defined period type for a data entry form (data set) defines the lowest level of period detail possible in a report.
When aggregating data on the period dimension there are two options for how the calculation is done, namely sum or average. This option is specified on a per data element in DHIS2 through the use of the ‘aggregation operator’ attribute in the Add/Edit Data Elements dialog.
Most of the data collected on a routinely basis should be aggregated by summing up the months or weeks, for instance to create a quarterly report on Measles immunisation one would sum up the three monthly values for “Measles doses given”.
Other types of data that are more permanently valid over time like “Number of staff in the PHU” or an annual population estimate of “Population under 1 year” need to be aggregated differently. These values are static for all months as long as there are valid data. For example, the “Estimated population under 1”, calculated from the census data ,is the same for all months of a given year, or the number of nurses working in a given facility is the same for every month in the 6 months period the number is reported for.
This difference becomes important when calculating an annual value for the indicator morbidity service burden for a facility. The monthly head-counts are summed up for the 12 months to get the annual headcount, while the number of staff for the PHU is calculated as the average of the two 6-monthly values reported through the 6-monthly staff report. So in this example the data element “OPD headcount” would have the aggregation operator “SUM” and the data element “Number of staff” would have it set to “AVERAGE”.
Another important feature of average data elements is the validity period concept. Average data values are standing values for any period type within the borders of the period they are registered for. For example, an annual population estimate following the calendar year, will have the same value for any period that falls within that year no matter what the period type. If the population under 1 for a given facility is 250 for the year of 2015 that means that the value will be 250 for Jan-15, for Q3-15, for Week 12 of 2015 and for any period within 2015. This has implications for how coverage indicators are calculated, as the full annual population will be used as denominator value even when doing monthly reports. If you want to look at an estimated annual coverage value for a given month, then you will have the option of setting the indicator to “Annualised” which means that a monthly coverage value will be multiplied by a factor of 12, a quarterly value by 4, in order to generate an effective yearly total. The annualised indicator feature can therefore be used to mimic the use of monthly population estimates.