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Message #07937
Re: Conversion factors and their implications
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 <selam_molla@xxxxxxxxx> wrote:
> Thanks Seid for checking it out. I will correct it in the evening.
>
>
> Best regards,
> Selamawit M. Mekonnen
> Tlf:+4741374246
>
>
>
>
> 
> *From:* Seid Hussein <seid.hisp@xxxxxxxxx>
> *To:* selam <selam_molla@xxxxxxxxx>
> *Cc:* Abyot Gizaw <abyota@xxxxxxxxx>; DHIS 2 Users list <
> dhis2users@xxxxxxxxxxxxxxxxxxx>; Sundeep Sahay <sundeep.sahay@xxxxxxxxx>;
> Jørn Braa <jornbraa@xxxxxxxxx>; John Lewis <johnlewis.hisp@xxxxxxxxx>
> *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 <selam_molla@xxxxxxxxx> 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 <abyota@xxxxxxxxx>
> *To:* Seid Hussein <seid.hisp@xxxxxxxxx>; DHIS 2 Users list <
> dhis2users@xxxxxxxxxxxxxxxxxxx>
> *Cc:* Selamawit Molla <selam_molla@xxxxxxxxx>; Sundeep Sahay <
> sundeep.sahay@xxxxxxxxx>; Jørn Braa <jornbraa@xxxxxxxxx>; John Lewis <
> johnlewis.hisp@xxxxxxxxx>
> *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 <seid.hisp@xxxxxxxxx> 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
> disaggregated 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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