came over and issue today regarding the calculation of indicators based on organisation unit groups. Not sure if there are any easy ways to solve it, but though it was worth discussing.
The problem is ownership reports with indicators using population. Population data is often entered for districts (of similar). These have “government” ownership, thus when comparing performance based on ownership/population, figures will be wrong.
One way to solve this would be to have catchement population for all facilities, giving a population figure that acutally have an ownership. But in cases with several aggregation levels for population (facility + district), the same problem would probably occur in a district or regional reports?
Another option would be to avoid the ownership of population data, either by (somehow) not having an ownership for non-facilities, or by computing such indicators “cumulatively” for org unit groups. This is probably best explained through an example:
A district has a population of children < 1 years of 100. 60 children are given BCG at government facilities, 30 at private facilities. An indicator showing BCG coverage for that district would currently (from how I understand it) show a coverage of 60/100 = 60% for government and 30/0 = 0% for private. Maybe it would make more sense to show this as 60/100 = 60% for government and 30/100 = 30 % for private, giving the the total (correct) coverage of 90%? Without knowing the actual catchment population for the private and government facilities, it’s impossible to calculate the real coverages, but as a whole I believe this method could be better on average?
your observation is correct… org unit group aggregation for indicators does not work well when facilities do not have denominator data. Currently in those situations we use it mostly for data element analysis for comparison of utilization through stacked column charts or pie charts (e.g. how many was immunized at public facilities out of total immunized).
One could come up with a method for improving this like you suggest. To me it becomes a bit too complex to create a general rule for it - one would assume that members of org unit groups are on the same level, then say something like “look at the level above for this data element for denominator data”… And the aggregation rules will become different from when denominator data exists for facilities.
This also raises some interesting questions, like do private facilities really have a catchment area? Should one use geographical bordes or adjust for attendance and migration? Anyway I think the only way to solve this is to apply a reasonable catchment population to facilities. Sorry for not being more helpful.
Hi,
came over and issue today regarding the calculation of indicators based on organisation unit groups. Not sure if there are any easy ways to solve it, but though it was worth discussing.
The problem is ownership reports with indicators using population. Population data is often entered for districts (of similar). These have “government” ownership, thus when comparing performance based on ownership/population, figures will be wrong.
One way to solve this would be to have catchement population for all facilities, giving a population figure that acutally have an ownership. But in cases with several aggregation levels for population (facility + district), the same problem would probably occur in a district or regional reports?
Another option would be to avoid the ownership of population data, either by (somehow) not having an ownership for non-facilities, or by computing such indicators “cumulatively” for org unit groups. This is probably best explained through an example:
A district has a population of children < 1 years of 100. 60 children are given BCG at government facilities, 30 at private facilities. An indicator showing BCG coverage for that district would currently (from how I understand it) show a coverage of 60/100 = 60% for government and 30/0 = 0% for private. Maybe it would make more sense to show this as 60/100 = 60% for government and 30/100 = 30 % for private, giving the the total (correct) coverage of 90%? Without knowing the actual catchment population for the private and government facilities, it’s impossible to calculate the real coverages, but as a whole I believe this method could be better on average?