Hi Guys,
Various countries want to analyse the data cumulatively. for example compare BCG dose given (data element) for last 12 (or /6/3) month with graph showing monthly data and cumulative value.
Hi Guys,
Various countries want to analyse the data cumulatively. for example compare BCG dose given (data element) for last 12 (or /6/3) month with graph showing monthly data and cumulative value.
Hi Guys,
Various countries want to analyse the data cumulatively. for example compare BCG dose given (data element) for last 12 (or /6/3) month with graph showing monthly data and cumulative value.
Hi Guys,
Various countries want to analyse the data cumulatively. for example compare BCG dose given (data element) for last 12 (or /6/3) month with graph showing monthly data and cumulative value.
One potentially tricky thing to keep in mind here is the problem of missing data, i.e. if the facility does not report for a month, or they report a zero and the data element is zero insignificant. Normally in time-series data, the “last observation would be carried forward” (LOCF). The spec does not really mention this, but in all likelihood, this would need to be part of the implementation.
Hi Guys,
Various countries want to analyse the data cumulatively. for example compare BCG dose given (data element) for last 12 (or /6/3) month with graph showing monthly data and cumulative value.
I'm not sure if auto-replicating the previous month's value for a
missing month is such a good idea. It's conceptually very similar to
the ability in DHIS 1.x to use regression analysis to insert missing
values, BUT we always blocked that for recent months - the idea was
always that using regression analysis to insert missing values was a
last resort which only could/should be available after several months
(i.e. after all other avenues to get the missing data are exhausted).
regards
Calle
···
On 21/01/2015, Jason Pickering <jason.p.pickering@gmail.com> wrote:
One potentially tricky thing to keep in mind here is the problem of missing
data, i.e. if the facility does not report for a month, or they report a
zero and the data element is zero insignificant. Normally in time-series
data, the "last observation would be carried forward" (LOCF). The spec does
not really mention this, but in all likelihood, this would need to be part
of the implementation.
Regards,
Jason
On Wed, Jan 21, 2015 at 10:32 AM, John Lewis <johnlewis.hisp@gmail.com> > wrote:
Hi Lars,
Example in Excel. We had this chart in Dhis1 series.
John
On Wed, Jan 21, 2015 at 4:18 PM, John Lewis <johnlewis.hisp@gmail.com> >> wrote:
On Mon, Jan 19, 2015 at 5:11 PM, Lars Helge Øverland >>> <larshelge@gmail.com >>> > wrote:
Hi John,
yes that would be possible to implement. Feel free to write a blueprint
with a bit more detail and we can schedule it on the road map.
regards,
Lars
On Fri, Jan 16, 2015 at 3:00 AM, John Lewis <johnlewis.hisp@gmail.com> >>>> wrote:
Hi Guys,
Various countries want to analyse the data cumulatively. for example
compare BCG dose given (data element) for last 12 (or /6/3) month with
graph showing monthly data and cumulative value.
Is this possible to include it in analysis
Regards,
John
Hi Calle, it may not be good, but it is usually always necessary with the type of data which DHIS 2 normally stores. Take logistics data as an example. The data is usually reported each day, but for which one may not have recorded zeros for particular activities, such as when a given commodity is not dispensed. It is not really efficient to record all these zeros, so normally, they would simply not be recorded. When it comes to calculating a daily stock level, one will have missing observations, combined with a requirement to have a daily stock level. So, in this case, you must use the “last observation carried forward” approach, unless you want to record a zero for each day, for potentially thousands of commodities. Not really efficient.
Same could be said when aggregating from facilities to national level. If any data is missing. It would needed to be imputed, either by using the LOCF approach, or other statistical methods (means for instance). We just need to take this into account when dealing with cumulative time data, otherwise, any sorts of calculations are usually simply not possible.