1. Train the Trainer Manual for Day 1 MER - DRAFT
For Reference and Exercises
PALS 2017

2. Purpose
This file contains supplemental slides and exercises for those who wish to deliver trainings to colleagues when they return to country after PALS.

3. Key Data Dimension: Funding Agency/IM, and IP
Key Data Dimensions
Funding Agency Who?
Funding Mechanism Who?
Implementing Partner Who?
Agency accounts by default see data that represents the individual contribution by the agency, mechanism or partner (duplicated/unadjusted data)
Interagency accounts by default see aggregate total data across agencies (de-duplicated/adjusted data), but can select specific agencies, IMs, or partners as data dimensions (will show duplicated/unadjusted data)
Funding Agency, Funding Mechanism, Implementing Partner
What DATIM user can see depends on their account type access and where data is sitting within the approval workflow (e.g. whether it has been submitted to a certain level). IPs will only see mechanism-level data, most agency users will only see data from their IPs, and interagency users will see data from across IPs and agencies.
You can narrow your options to view results relevant to specific agencies, funding mechanisms (IMs), and implementing partners (IPs), but be aware of duplication/de-duplication.
Duplication may occur if more than one agency/mechanism/partner is reporting results on the same indicator, in the same site, in the same period. For certain reporting purposes, such as aggregating results at a site level upward, if duplication exists we then need to de-duplicate data. During data entry/approval, interagency users with certain permissions use the “de-duplication” app after discussing with relevant points of contact.
Agency user account default view = Duplicated/Unadjusted. Agency accounts by default should see data that represents the individual contribution by the agency, mechanism or partner (duplicated/unadjusted).
De-Duplication note for analysis. Depending on your DATIM account type, the default MER results and targets that you see in DATIM Pivot Table and Visualizer App are either de-duplicated (interagency account) or unadjusted/duplicated (agency account).
Interagency user account default view = De-duplicated/Adjusted. For interagency accounts, the default results shown are a de-duplicated/adjusted aggregate total. You see de-duplicated data, because behind the scenes DATIM runs de-duplication adjustments. DATIM uses a negative number to correct or offset for duplication of activities or people reached when aggregating data across agencies/IMs/partners. However, if you “pick and click” specific agencies, IMs, or partners as data dimensions, you are telling DATIM to show you the DUPLICATED/UNADJUSTED results/targets. In that case, DATIM stops including the de-duplication factors – so the negative de-dup values “come out” of the data set in your table/chart.

4. Understanding MER Indicators
Questions:
What is the indicator definition?
At what level is this indicator reported?
For what time periods/ at what frequency is this indicator reported?
How do you calculate an annual total?
What disaggregates are collected?
KP_PREV
VMMC_CIRC
HTC_TST
PMTCT_STAT
TX_CURR
OVC_SERV

5. DSD/TA Quiz Question (1 of 2)
Read excerpt of TX_CURR narrative from South Africa in FY16Q2 in the handout.
Would you describe this as TX_CURR TA or TX_CURR DSD?

6. DSD/TA Quiz Question (2 of 2)
For DSD, Aurum provided Aurum-funded data capturers to some facilities. They are physically stationed at the facility daily. They are deployed primarily to support TIER.NET, etr.net and DHIS implementation. Data capturers are supported weekly by Aurum Data Monitors and occasionally by Aurum District Data Managers.
In Ekhurhuleni, doctors, NIMART nurses and counsellors were also hired to provide direct service delivery inorder to fast-track the achievement of the targets. The appointment of DSD /ART teams was delayed in Bojanala.

7. Caveats to Using the Pivot Table Data Dimensions
We recommend most users not create pivot tables from scratch (using data elements); instead use pre-existing DATIM favorites
No inherent relationship or grouping between data elements and disaggregations
Aggregate total may not equal sum of disaggregate “slices”
Not all disaggregates will be entered by IP
DATIM cannot “fix” bad or incomplete data entry
Limited data validation rules in DATIM
System not as restrictive as you might expect
Few cross-indicator validations
Choose the wrong data elements for analysis, get the wrong answer
So now that we’ve covered using Favorites to pull up, manipulate, save, and share pivot table views in DATIM, let’s turn to using data elements within the pivot table app to pull up and view/modify/download data

8. Why Are DATIM Favorites Useful?
Quick access to complex, frequently used outputs
i.e. Clinical Cascade, TB Care and Treatment
Group outputs according to reporting or time periods
New Dashboards for each reporting period
Allows tracked and easy sharing for:
Quick consensus building
Resolution of data questions
Pulling up that amazing pivot table you did last week but can’t remember the exact variables now…
TOT manual

9. HIV Index Testing Example: Using “old” dimensions completeness favorite
Individual data elements selected for DSD and TA, for ages <10 and ages 10+
Total tests is auto-summed in pivot table from age/sex/test result data elements
Facilitator go to this favorite:
PEPFAR FY17 Q2 HTC_TST Completeness Review Pivot_PALSDay1MERL1

10. DATIM Pivot Table: Data Dimensions vs. Group Sets
MER indicators in DATIM are a series of data elements and disaggregations (DHIS2 category combinations)
New group sets dimensions (or analytic dimensions) help generate metadata to support particular analyses
Group sets offer a new way to slice or aggregate data, and can make life easier.
However, the method of selecting items in data dimensions panel is DIFFERENT. >>>
With the transition to MER 2.0, we now have improved DATIM analytics functions (data element/dimensions options) on the left-hand side panel in the Pivot Table App. Note that we still have the “old” data dimensions, and we’ve added several new dimensions, which when used together to generate data are called “group sets.”
General Notes about Improved DATIM Analytics
The improved analytic functions are evolving, and we expect to use them soon for more indicators beyond HTS.
Like the traditional data element selection method, with this newer function, you still need to select key, minimal pieces of information in order for any data to be displayed--such as when, where, and what.
The “when” and “where” are the same as the traditional method
The “what” is different: You don’t select data elements.
Instead, you must select a technical area, and then, based on that technical area, carefully select other data dimensions to narrow down to what you wish to view (e.g. age, sex, support type)
Careful! Just selecting “HTS” as the technical area shows all data aggregated together (summing numerators and disaggs), and that is not useful. You must select additional dimensions.
Utility: Data is now able to be separated out and grouped together in more logical and useful ways
Example: can more easily compare DSD vs TA, or sum them together
HOWEVER, it’s not easy to set up one visual to do a completeness check. Currently default numerators and denominators cannot be shown alongside their disaggregates in the same table.

11. Using Group Sets: Common Errors
Example Missteps
There are more opportunities for missteps with addition of new dimensions
Think of using these new analytic dimensions like filters on an Excel-based pivot table: if you do not filter elements out, they will be automatically summed
Make sure that you check your data dimension selections carefully before using the data in analyses

12. HTS Group Sets Quiz
How many modalities were reported at this facility?
Are the finer age/sex/test result disaggregates complete?

13. Saving Data from Group Sets Data Extraction
In DATIM: Control A + Control C
Manipulate table Layout and Options
Download and as Excel document/other format
Save pivot table data layouts as new Favorites
Or copy/paste directly from DATIM into Excel!
Similar to working with Pivot Table data pulled with old data dimensions, you can do the following :
In Excel: Control V

14. Practice with Group Sets
For more practice with Group Sets, access the presentation and exercises from the Day MER Level 2 session.
Exercise 1: PRINT_MERPrepforAnalysis_Level 2_DATIMExercise_FINAL
Exercise 2: PRINT_MERPrepforAnalysis_Level 2_DATIMExercise Advanced_FINAL

15. Group Sets Knowledge Check
When using Group Sets….
Which item in the left-hand Data Dimensions Panel should you NOT select?
Which testing modalities will disappear if you select Cascade Sex?
Which testing modalities will disappear if you select Cascade Age?

16. Logic check: TX_NEW with TX_CURR
We’ve been looking at single indicators in DATIM, but we can also pull multiple indicators.
Logic Check: TX_CURR ≥ TX_NEW
See take-home exercise to complete a logic check for TX_CURR and TX_NEW.