We invite you to a webinar scheduled to take place on Tuesday 16 January, 14:00 - 15:00 (CET) to explore how DHIS2 can be used to analyse and strengthen data quality, improving the ability of the system to support evidence-based decision-making. DHIS2 experts will explain key functionalities, implementation considerations and lessons learned from countries.
Whether you are a Ministry representative, a country stakeholder, a donor or a global partner, register now for this free event here and you will receive a link to the webinar before the event takes place.
This webinar is organized by HISP-UiO with support from GAVI.
Dear all, thank you for attending todayâs webinar. The recording of the webinar is available on the DHIS2 YouTube channel and the presentation can be found here.
Is the min/max functionality a manual process or is it set based on the values entered?
Min-Max values need to be set, they are not automatically available. The same is true of outlier values, you need to calculate them,
For now, we suggest setting min-max values outside of DHIS2 (and have provided some tools for doing so); however outliers can be set directly within DHIS2.
Is there any parameter based on the completeness of the dataset or the variables themselves?
I am not sure I understand this fully, as you can have both data set completeness and/or data element completeness configured for routine review.
Data dimension - the WHO app only has 3 programmatic areas. Can other programmes be added?
It is up to you to configure the program areas that you want to review. The only constraint here is the system itself and the data it collects which will vary by implementation.
Does the app consider the zeros as true missing values?
If you are recording and storing zeros, then zeros are not considered missing values. Missing values would only be considered if there is NO value for a particular organisation unit and period.
Re: question 2 (âIs there any parameter based on the completeness of the dataset or the variables themselves?â)- I think a good example that could be checked out to better understand how to set up routine checks on variablesâ completeness is present in the Malaria data quality dashboard available in the HMIS demo - screenshot below. The same principle can of course be set up for all the health programmes and tailored to the local implementations.
