I tried to think over it, but I don't think the current Indicator implementation does not accommodate this requirement.
Wastage rate is of course a strange indicator (they could have created an indicator called usage rate). But somewhere somebody may come up with another strange indicator like Infant survival rate which may be calculated as: 1000 - (Number of <1 dead / Number of live births) Please check again because my calculations could not show me a viable path. If your formula works, it will work for all the 'strange' indicators as well. Seid On Thu, Aug 27, 2015 at 2:28 PM, selam <[email protected]> wrote: > Thanks Seid for checking it out. I will correct it in the evening. > > > Best regards, > Selamawit M. Mekonnen > Tlf:+4741374246 > > > > > ------------------------------ > *From:* Seid Hussein <[email protected]> > *To:* selam <[email protected]> > *Cc:* Abyot Gizaw <[email protected]>; DHIS 2 Users list < > [email protected]>; Sundeep Sahay <[email protected]>; > Jørn Braa <[email protected]>; John Lewis <[email protected]> > *Sent:* Thursday, 27 August 2015, 13:25 > > *Subject:* Re: Conversion factors and their implications > > Hi Selam Abyot, > > I think the formula you wrote is not correct. I replaced the formula with > numerators and denominators and see what happens. > > Numerator ==> a (Doses used) > Denominator ==> b (Doses opened) > > ((100 * b) - a)/b > > Let's assume a (doses given) = 35 and b (doses opened) = 60 > > ((100 * 60) - 35) / 60 > > (6000 - 35) / 60 > > 5965 / 60 > > You get a figure of 9942% > > > Please check again before defining them. > > > > On Wed, Aug 26, 2015 at 11:59 AM, selam <[email protected]> wrote: > > Hi Seid, > > This is the summary of our discussions regarding the conversion factors > and other issues > 1) Regarding the conversion factor, we take the first option you suggested. > > - Capturing the population and calculating each data elements for each > facility by multiplying it with its respective region's factor (hence > coming up with at least 49 data elements) > > > 2) For the two level indicators of vaccine wastage rate, we use the > expression ((100*Dose opened)-Dose given)/Dose opened > 3) The indicators should be revised. most of the denominators are defined > because of lack population data and estimates > 4) Seid please contact those who are working with Phem (IDSR) regarding > how often they collect data. If weekly, is that including Pagume. We should > also ask if they are using the International or Ethiopian calendar when the > weekly data collection. Because as IDSR is an international program, there > is a possibility that they are using the International Calendar to compare > data across countries > 5) Seid please contact Solomon from gate foundation and respond to the > emails of Dykki > 6) Write your technical problems directly to the mailing list > > Keep on the hard work Seido. > > Best regards, > Selamawit M. Mekonnen > Tlf:+4741374246 > > > > > ------------------------------ > *From:* Abyot Gizaw <[email protected]> > *To:* Seid Hussein <[email protected]>; DHIS 2 Users list < > [email protected]> > *Cc:* Selamawit Molla <[email protected]>; Sundeep Sahay < > [email protected]>; Jørn Braa <[email protected]>; John Lewis < > [email protected]> > *Sent:* Wednesday, 26 August 2015, 9:21 > *Subject:* Re: Conversion factors and their implications > > Dear all, > > Please see the forwarded mail if you can help Seid. He is asking how to > provinces can apply conversion factors on national level data. > > Seid can provide more details if necessary. > > --- > Thank you, > Abyot. > > > > On Wed, Aug 26, 2015 at 8:54 AM, Seid Hussein <[email protected]> wrote: > > Hi all, > > I think you are better positioned to comment on this on what approach we > should use. In the file attached, you can see that there are 49 different > conversion factors to come up with approximations of different data > elements like expected pregnancies and infant population. > > If these factors were the same for all regions, we could have added them > as constants and used them to define the denominators for the indicators. > However, as you can see each region has different conversion factors and > there's a big difference among them which makes defining indicators very > difficult. > > I see two options here: > > > - Capturing the population and calculating each data elements for each > facility by multiplying it with its respective region's factor (hence > coming up with at least 49 data elements) > - Defining different indicators for different regions using different > constants as conversion factors (which I think may complicate things) > > In my view the first option is the best option for us because once we > capture the total population, it is easier to generate data for the other > data elements using these conversion factors. Of course, as I stated in my > mail yesterday, there are two sets of population data used in the structure > (MoH uses the official data from Central Statistical Authority > dis-aggregated by Wereda while the regions use data collected from the > ground. The data the regions use may be the most accurate but we have to > accomodate both options I think because the two parties use their own > population data to come up with the population figures, effectively having > two different population data (administrative population and facility > catchment population) > > If we are using two sets of population data, we may have to define the > same for all the 49 other data elements with the factors as well. > > Would you please deliberate over it and suggest? Once we have a concrete > plan, we can discuss with M&E people here on how to proceed. > > Regards, > > > Seid, > > > > > > > >
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