Overview and Preparation for Analysis of MER 2.0 Data

Overview and Preparation for Analysis of MER 2.0 Data
2017 PEPFAR Data and Systems Applied Learning Summit Overview and Preparation for Analysis of MER 2.0 Data September 18, 2017

Course Logistics Part 1 Notes
This section of the document is intended to capture information about the profile of the trainer who should deliver this course (at the Summit, and as teams consider who would be best suited to deliver the training to others) and logistics information for the delivery of this course.

Logistics Needs for this Course
Element Notes Room setup/ configuration (Theatre style, semi-circle, classroom, etc.) Classroom, or groups of tables centered around a projector Computer needs for learners (certain programs) Yes, learners should bring computers to this course Clicker? Yes White board?/ flip chart/ markers? Flip chart/markers needed to keep track of relevant questions Specific materials for interactive exercise? (i.e. post-its, pens, markers, tape, etc.) N/A Notes This slide is intended to capture logistics information for the delivery of this course.

Software/Program/Technical Needs for this Course
Element Notes Software (what program (s) should learners have downloaded on their computers for this course? I.e. GIS Software, Excel, etc.) None Access (what logins to PEPFAR supported systems, data sets, etc., should learners have access to for this course?) Panorama DATIM What additional technical requirements should learners have in order to participate in this course? Notes This slide is intended to capture software/program/technical information for the delivery of this course.

Trainer Profile Trainer Profile Criteria Notes
Background: This slide is intended to capture information about the skillset a person would need to deliver this training in the future. Trainer Profile Criteria Notes Level of familiarity with subject mater Describe what types of tasks or areas of knowledge the trainer should be comfortable with in order to deliver this course Trainer should have experience using the data and visualizations in Panorama. Trainers should also be comfortable using DATIM Group Sets and understand the differences between Group Sets and DATIM data elements Recommended years of experience with PEPFAR Program 1-3 Recommended certifications What certifications should the trainer have to deliver this course? N/A Specific skills required Does the trainer require a certain level of proficiency with PEPFAR systems or other applications? If so, describe the types of skills/ tasks the trainer should be able to perform in the system Locate and interpret relevant Panorama visualizations, including age/sex disaggregates and differences between MER age/sex disaggregates Use DATIM Group Sets to pull down indicators such as TX_NEW and HTS_TST_POS Access needs to PEPFAR systems To teach this course, does the trainer require a certain type of account or level of access to a PEPFAR information system? Panorama DATIM Notes This slide is intended to capture information about the skillset a person would need to deliver this training in the future.

Welcome & Introductions

Agenda Topic Estimated Time 1. Review of MER 2.0 and Data Elements 5
2. Analyzing MER data using DATIM Group Sets Follow-along in DATIM with presenter 60 Break 3. Introduction to Analyzing MER data in Panorama Follow along in Panorama with presenter or do exercise at own pace 30 4. Exercises with DATIM Group Sets Follow along in DATIM with presenter or do exercise at own pace 45 This session begins with a brief overview of what you have already been taught about data entry and data elements. From there, we will first cover MER data in Panorama, focusing on target vs result achievement for MER indicators across different levels. The next section will cover DATIM: a quick review of what you already know, followed by an introduction to DATIM group sets—what they are, how to use them, and common missteps to avoid. We will conclude with DATIM exercises that you can walk through on your own or following along on the screen.

Session Learning Objectives
By the end of this session participants will be able to: Understand and use both data elements and DATIM group sets in DATIM for data extraction, analysis and interpretation Identify key analyses in Panorama, with a focus on MER 2.0 program performance for FY17 and trends over time

I. Review of MER 2.0 and Data Element Structure
At some point over the next 5 minutes, please log into Panorama!

Participant Knowledge We Assume
Understanding of: How to make a basic pivot table in DATIM using data elements Data entry in DATIM, including the organizational hierarchy Data element structure, including: Total numerators compared to disaggregates Time period reporting requirements DSD vs TA Basic MER 1.0  MER 2.0 changes In our session, though we’re going to start with Panorama, take a look through the things you should know how to do in DATIM before we get started. If any of this looks unfamiliar to you, great news! That’s exactly what the Level I folks will be covering in their session. If, on the other hand, you looked at our agenda and are already familiar with the topics we’re proposing, the Level III group is also just starting now. We’re going to move into a quick review of some of these topics for just a few minutes.

Review: MER 2.0 Indicator Structure
Data entered within DATIM at different level Facility, Community or OU level Data associated with specific time periods Quarterly, Semi-annual, Annual Snap shot (TX_CURR) vs Cumulative (HTS_TST) For each data indicator there are three important categories of data elements Numerators/Denominators vs Disaggregates Results vs Targets DSD vs TA First, MER 2.0! It may be Q3 now, but the MER 1.0 – 2.0 changes were broad and may have taken some time to fully digest. In MER 2.0, data is entered at the facility, community, or OU level. Does anybody know what data is entered at the OU level? Data also continues to be uploaded in coordination with the data entry calendar—some of it quarterly, some semi-annual, and some annual. Some indicators are analyzed by summing quarters to date (cumulative indicators, like HTS_TST) whereas some of them are associated with a single time period (like TX_CURR) In each data element, there are three broad categories that make up data elements. The first is total numerators or denominators vs disaggregates. Does anybody know what it means when we refer to a “total numerator.” Answer: The total numerator means the total number of individuals who received PEPFAR services, not disaggregated by age/sex, or other. This is a direct number that is entered into DATIM. Indicator are also split by DSD/TA and results/targets.

Data Flow Review: Facility, Community, OU, and Military Operating Unit
(OU) Total Data Flow Province (Military) District This is an overview of the bottom-up pyramid reporting structure. Community site vs. facility site (HTC_TST community, HTC_TST facility) are reported at different levels, and that Military data are reported differently - SNU (SNU 1) Health Facility Community Site

TX_NEW Example Data Entry: MER 2.0
TX_NEW (N, DSD): New on ART TX_NEW (N, DSD, Preg/Feeding): New on ART TX_NEW (N, DSD, TB Diagnosis): New on ART Red text: Data entered in each box is translated into a data element that you will see in DATIM or in a data export from DATIM For many indicators, the numerator and disaggregates are entered separately. For some indicators, the disaggregates are entered first and will sum automatically to create the numerator Each of these disaggs is entered separately and although they SHOULD align both with the numerator and each other, they may not. The analyses you can perform are based on which disaggs are entered. Certain disaggs are REQUIRED by HQ. The default requirement is finer age/sex disagg reporting Key Populations Type TX_NEW (N, DSD, KeyPop): New on ART

TX_NEW Example Data Entry: MER 2.0
Fine Age/Sex Detail: TX_NEW (N, DSD, AgeAboveTen/Sex): New on ART 20-24, Male TX_NEW (N, DSD, Age/Sex): New on ART Coarse Age/Sex TX_NEW (N, DSD, Aggregated Age/Sex) v2: New on ART

MER Definitions to DATIM Data Elements
MER Definition – New on Treatment DATIM Data elements Let’s look at a MER definition, for TX_NEW, and break it down to briefly review how each section of a MER indicator becomes a data element in DATIM. First, the Numerator becomes the description behind the indicator title in DATIM. Next, you’ll see the disaggregate type (if applicable) Finally, you’ll see the disagg itself. So, if the type is “age,” the disagg would be “10-15,” for example.

II. Analyzing MER 2.0 in DATIM: Group Set Analysis

Group Sets: DATIM Pivot Tables Only
If you are doing the group set analysis, you are only going to be working in the DATIM Pivot Table App. You could try to open a table made with Group Sets in the Visualizer, but you should not. Group Sets have not been tested or optimized in other DATIM apps. There are several ways to view data in DATIM. We will focus on the Pivot Table app. This is the only one that works for Group Sets!

An Overview of new DATIM Group Sets
What are group sets? Analytic improvements to DATIM Data Dimensions that facilitate easier review of data for clinical cascade indicators in MER 2.0.

Why Use Group Sets? New Analytic Capabilities
Easier access to MER 2.0 enhancements including: Clinical cascade age bands HTS Modalities KP Groupings This provides the ability to: Select technical areas without selecting individual data elements Display results and targets in one pivot table Display DSD/TA without selecting individual data elements

Why Analyze MER 2.0 Data in DATIM?
We have many systems, so why use DATIM? DATIM is currently the only system that allows you to see data live, within one day of entering it. Other systems, such as Panorama, only populate with your data one week after the DATIM data entry deadline. This means you can see your data much faster in DATIM than in Panorama or other systems.

DATIM Analytic Improvements
Now Expansion of MER data analytic capabilities, implemented iteratively with ongoing user feedback. Before

New Data Dimensions Meaning of new dimension
N/A—CANNOT be combined with group sets No change: Time period No change: Organization Units No change: Coarse ages as entered All age bands used in clinical cascade indicators Sex categorization used in MER 2.0 No change: De-dup Disagg category for all indicators Priority category at facility level No change: Agency No change: Mechanism Modalities used in HTS_TST MER 2.0

New Data Dimensions No change: Partner
KP disaggs along clinical cascade indicators Active mechanisms for each OU Numerator and Denominator specifications Priority SNU type No change: Sex as entered in DATIM DSD/TA Target or Result Short-name indicator No change: Testing results (default) Numerator or Denominator No change: Community/Facility/Other Org Unit

Data Elements: Each Part is a Group Set
Numerator/ Denominator Targets/ Results Cascade Age TX_NEW (N, TA, Age/Sex) Target: New on ART Technical Area Support Type Cascade Sex Description (not a group set)

What is Technical Area? TX_NEW Technical Area: Contains all data elements under the TX_NEW bucket, including the following and disaggregates.

With New Power Comes New Responsibilities
With the addition of these new dimensions, there are more opportunities for missteps. Think of using these analytic dimensions like filters on an Excel-based pivot table: if you do not filter elements out, they will be automatically summed. Note that the Group Sets currently work for clinical cascade indicators only. Make sure that you check your data dimension selections carefully before using the data in analyses. With the increased number of dimensions, and using new dimensions instead of data elements, there are more opportunities for a misstep that could cause the pivot table to show values that are too high or blank. We’ll go into some examples of common missteps and how to avoid them after we walk through this example.

For more information see the Group Set Cheat Sheet
This is available for you on PEPFAR SharePoint.

Exercise 1: TX_NEW Using Group Sets
We are handing out an exercise sheet. We will be going through these shortly on the screen, but please feel free to move at your own pace. If you do finish early, please find a facilitator who will come around and hand you a linkage exercise. If you’re interested in practicing linkages, even if you don’t finish early, see a facilitator!

Please check the following: Can you log on to DATIM?
Before We Begin… Please check the following: Can you log on to DATIM? If you do not have an account, please see the Help Desk outside this room. Do you have an agency account? If so, please use it for this exercise. Are you here from a MoH team? If so, please follow along with a neighbor Use an agency account if you can for this exercise. Note that if you are an interagency user, you may not have access to the unapproved data, as Q3 is in the middle of the data cleaning process. If you are in this situation, please see a facilitator.

Remember Your DATIM User settings/Privileges define the datasets you can see Close all applications, browsers ( etc.) Clear your Google Chrome Browser Cache Clear your DATIM cache Wifi and DATIM support right outside

Your pivot table might not populate with data if…
Why Might I Not See Data? Your pivot table might not populate with data if… You work with an agency that does not report TX_NEW data You work in the interagency space. During data cleaning, your pivot table may be blank or show all negative numbers Your partners are doing large cleaning. If so, your data may look abnormally small or large If any of this applies to you, please follow along with a neighbor for this exercise! Use an agency account if you can for this exercise. Note that if you are an interagency user, you may not have access to the unapproved data, as Q3 is in the middle of the data cleaning process. If you are in this situation, please see a facilitator.

DATIM Group Sets: TX_NEW Exercise
In your OU of interest, how many individuals have been newly placed on treatment in FY17 so far? Try this using DATIM Group Sets!

Before you begin, login into DATIM and open the “Pivot Table” app. The next slides will walk through how to use the new analytic dimensions in DATIM to create a pivot table for TX_NEW by targets and results.

Step 0: Do Not Select Data Elements Important: Do not select any data element. Leave this tab blank

Step 1: Select the time periods of interest for FY 17 For TX_NEW, we will pull Q1 (Oct.- Dec. 2016), Q2 (Jan.-March 2017), and Q3 (April – June 2017) Note: Do not forget to de-select “Last Financial Year”

Step 2: Select your geographical unit from Organizational Units

Step 3: Select Results from Targets/ Results

Step 4: Select TX_NEW from Technical Area

Step 5: Select Top Level Numerator from Top Level

What is Top Level Numerator?
TX_NEW (N, DSD): New on ART Note: The “Top Line Numerator” value equals the value that was directly entered into the Numerator box in DATIM. The Numerator is independent of the disaggregate boxes. The disaggregates are entered separately for indicators that are not auto-summed. Though the sum of disaggregates may match, it usually may not. The “top level” indication refers only to the directly entered numerator value.

Step 6: Select “Update” and view your pivot table! Note: Interagency users cannot yet see approved Q3 cleaned data. This screen shot does not reflect Tanzania’s actual Q3 data entry at this time.

Step 7: Remove subtotals and totals from Pivot Table, so others do not misinterpret them.

How Do I Log a DATIM Help Desk Ticket?
Log on to DATIM Support Select “Submit a Request” Document your issue Use an agency account if you can for this exercise. Note that if you are an interagency user, you may not have access to the unapproved data, as Q3 is in the middle of the data cleaning process. If you are in this situation, please see a facilitator.

How Can I Stay Updated? DATIM is changing rapidly to meet the needs of country teams. To know what changes were made since your last visit, you can find that information in the Release Notes.

How Can I Provide Feedback on DATIM?
Submit a Help Desk ticket and copy your SI Advisor.

5 minute break Log into Panorama!

Quick Reminder Close all applications, browsers (email etc.)
Clear your Google Chrome Browser Cache

Panorama: Access and Exercises
Know Your Data, Know Your Program

Session Goals Use Panorama to review MER 2.0 data and answer key analytic questions about the data, with a focus on: Identifying high and low performing SNUs and IPs Understanding Age/Sex Disaggs: Differences Between MER 1.0 and MER 2.0 in Panorama Identifying additional disaggregates We also provide a few slides at the end to show existing data visualizations in Panorama, but we will cover those in more detail during Day 2.

What is Panorama? What is the Panorama tool? What do you use it for?
Web-based visualizer tool used for quarterly MER analytics What do you use it for? Can easily compare MER and SIMS indicators and view the data by: Standard data views meaningful to multiple audiences/stakeholders Easily access existing visualizations What does it not do? (yet!) No EA data Does not show historical data prior to FY15 OU SNU DSD/TA Year/Quarter Agency Mechanism Partner Age Sex Panorama is an online tool that is used primarily for POART calls and MER data analysis. Panorama breaks down data in many levels, including geographically as well as partner/agency/mechanism and disaggregate. Panorama has the ability to display a wide range of data at once, without needing to go to multiple tools. However, it does not yet contain EA data or MER data prior to 2015.

1 2 3 4 & 5 6 Panorama Quarterly Data Flow
Submission of preliminary data into DATIM 2 Preliminary data available in Panorama 3 POART Review 4 & 5 POART Call & 3-week cleaning of preliminary data 6 Panorama and Public Dashboard refreshed with cleaned data You should all be relatively familiar with this process flow for when the data is entered compared to when you see it in Panorama. First, data is entered into DATIM. After the end of data entry close, about a week later, data is available in Panorama. Then, POART reviews take place. Following those reviews, data is cleaned, and about a week after the cleaning period closes, Panorama is refreshed with the cleaned data. It is important to remember that this timeline means that you are not looking at “live” data that you are currently entering into DATIM when you look at Panorama. You are always looking at data from the most recently closed data period. For right now, that means we’ll be looking at the Q3 preliminary submission MER data. Note: Q4 will have two cleaning periods: World AIDS Day and normal cleaning

Panorama Orientation I am going to go through just a few high-level orientation points about the screens you are seeing now. Next, we will go through a guided demonstration with exercises where you can follow along with facilitator help to see the different functions of some of the Target vs Result data pages. This home page should look like each of your screens. This page which shows a general map of your OU, along with some key indicators for You also see the high-level areas that we’ll be exploring along the top. HOME: This shows you high-level information for each indicator. You can also click here to see the “All PEPFAR Ous (Global) Home” that shows some Panorama data summed to the global level. OU & SNU Analysis: This is where many of your analyses or POART calls may start, showing you targets against results by geography. Partner & Mech Analysis: This shows you the same information, but split by Agency, Partner, and Mechanism for further analysis. Map Viewer: This newer feature shows you basic MER data on a map and allows you to filter for geographic areas like DREAMS SNUs Disaggregates & Narratives: These pages show your MER data at a more detailed level, accounting for differences between MER 1 and 2. Site Summary & SIMS: We won’t get to this tab in the training—you’ll get here during the SIMS session—but please check it out on your own time, as there are a lot of new features here. We’ll focus on the following tabs today: OU & SNU Analysis, Partner & Mechanism, and Disaggregates. However, there are many more for you to explore!

OU Level Results and Targets

OU Level Analysis: Targets and Results
We are going to start by navigating to our OU Level Targets and Results page. We’ll get there by selecting the “OU & SNU Analysis” dropdown menu, and choosing the “OU Level” tab.

OU and SNU Analysis: Targets and Results
Once we’re on the OU/SNU analysis tab there are a few different components to get us oriented. Starting on the top left, we see that we have an option to select an indicator bundle. These are based primarily off of the indicator bundles: Knowing HIV Status, On ART, and Prevention. The third “90,” viral load retention, will be included in Q4. We also have a bundle for “other,” which includes some disaggregates. You may select to view these one at a time, or all together by selecting “(All).” Note that the default view is to show Knowing HIV Status, so don’t be confused if you don’t see all of your Q3 indicators here right away! Next we see a large table 1. At the top, DSD+TA is highlighted, telling us we are looking at combined values. The first column shows the indicator name, while the next column looks at the total results for that indicator in FY15 as a comparison point for the next few columns that show quarterly progress of results through Q3 of FY17. Next, we can see the FY17 cumulative results (meaning that indicators other than TX_CURR are summed), and the FY17 target that was set during COP. This is the default settings. You can click “Select Filters” to see additional quarters, including quarterly results and targets for FY16 and 15. The last colored column shows us % achievement. That is a term we throw around a lot, but who can tell us how that is defined here? You can find that information by clicking on the legend. Since we are looking at Q3 data, the judgment is that you should be approximately 75% of the way towards the target. Is that a reasonable threshold for measuring against your target? Is it realistic? What are the assumptions that are being made here? (linear progress, etc.)

Exercise 1: OU Level Results
What is your OU’s percent achievement for FY17 cumulative results (as of Q3) compared to the FY17 target for TX_NEW?

What is your OU’s percent achievement for FY17 cumulative results (as of Q3) compared to the FY17 target for TX_NEW? Since we are looking at Q3 data, the judgment is that you should be approximately 75% of the way towards the target. Is that a reasonable threshold for measuring against your target? Is it realistic? What are the assumptions that are being made here? (linear progress, etc.) How is % achievement calculated? How can we interpret these findings? What else might you want to know to judge progress?

A few things to keep in mind when looking at OU results….. Context matters Where can we find qualitative information about policies, implementation, or data quality challenges affecting performance? …Narrative data! Narratives are accessible both in DATIM as well as in Panorama!

Narratives: Find them on Panorama!
If you remember from the Data Streams Plenary this morning, HQ is being encouraged to pull the narratives more frequently. Including them in Panorama shows that we want everyone to be looking at these so that everyone can be on the same page when looking at the full picture of your data.

SNU Level Results and Targets

Exercise 2: SNU Level Results
Which SNU level 1 for your country is farthest from reaching its target for TX_NEW for FY17 as of Q3?

To solve this exercise, we’ll click back up on the “OU & SNU Analysis” tab and navigate to the “SNU Level” page.

The filters work just like Excel: you can hover on your screen to see a small arrow with a dropdown, and select to Sort Ascending or Sort Descending depending on your interest. TIP: Make sure that you’ve selected your indicator of interest from the left-hand side of the screen. Remember that you may need to change Indicator Bundles to select your indicator.

Exercise 2: Example Answer
Which SNU level 1 for your country is farthest from reaching its target for TX_NEW for FY17 as of Q3? Now the SNUs on the left side are sorted by % Achievement, with those farthest from reaching their targets on the top and those closest to reaching targets on the bottom.

Partner/Mechanism Analysis

Partner/Mechanism Analysis in Panorama
Once you know the SNUs in your country that are farthest from achieving their targets, you may want to know which implementing partner(s) work in those SNUs. Does anybody know which tab in Panorama you could select to view Agency, IP, and Mechanism data at the SNU level?

Mech by SNU Analysis Page
The Mechanism by SNU page shows the same data in the same format—but this time, it includes the data split by Agency, Partner, and mechanism.

Mech by SNU: Filtering by SNU
How can I drill down to the SNUs that were the farthest from reaching their targets? In the previous exercise, we found that the SNU North Eastern was farthest from reaching TX_NEW targets at Q3. This page functions the same way that the SNU page does—meaning that you can identify the highest and lowest-performing partners in each SNU by sorting the column by ascending or descending. You can also filter for the SNUs you identified in the previous exercise to drill down on SNUs that are farthest from reaching their targets.

Mech by SNU: Filtering by SNU
To get to this page, I scrolled down to Table 2, and selected the “Select Filters” dropdown. This pulls up a dialogue box with several options. We’ll start by looking at SNU1, as that was the SNU level that we identified in the previous exercise. However, you’ll note that you can drill all the way down to SNU6, which is facility level for many Ous. Briefly, you can also see here that you can filter by DREAMS SNUS only, by SNU Prioritization Level, by Agency, Partner, MechanismID, or Mechanism Name.

Mech by SNU Exercise: Partner % Achievement
Using the FY17 % Achievement, how can we find the Partners/Mechanisms that are farthest from reaching targets? By filtering for the SNU in question, and then sorting the % Achievement column by Ascending, we can identify the Partners/Mechanisms that might be farthest from reaching targets in that SNU.

Mech by SNU Page: Deduplicated Values?
Discussion: Agency and IM-level data must be counted on their own, rather than undergoing deduplication. This might mean the grand totals do not match the same ones you might see on the OU level page. Why might these values not be deduplicated? What might this mean for analysis?

Age/Sex Disaggregates Page: Focus on MER 1.0- 2.0

Age/Sex Disaggregates
When you arrive on the Age/Sex Disaggregates page, you may not initially see the age/sex options. That’s because the default is set to the non-disaggregated values. You have to select the “Disaggregated by Age” selector at the top to get to the age/sex options. The reason why both disaggregated and non-disaggregated are both shown on this page is because, when looking at age/sex disaggregates, it’s often helpful to compare them against the totals for each indicator. It’s easier to flip back and forth on this page than to re-load the OU/SNU analysis page to get to your totals.

Age/Sex Disaggregates
When you get to the “Disaggregated by Age” page, you’ll see that each indicator takes up much more room, because each age/sex disaggregate option is its own row. It is critical to check your selections on this page. Note the filters at the top of the page. Starting on the left-hand side, you’ll see that we’re currently filtered to “Fine MER 2.0.” However, MER 2.0 started in FY17. So why do we see 2016 data here? In MER 2.0, many age bands changed. Can anyone give an example of age bands that changed in MER 2.0 for HTS_TST? One example may be that 1-4 and 5-9 are now consolidated to show 1-9 in MER 2.0. Let’s look through that example. In MER 1.0, there were age bands for 1-4 and 5-9. You can sum those two age bands to get to the MER 2.0 age band of 1-9. Therefore, for FY16, the 1-9 age band is actually the sum of the 1-4 and 5-9 age bands. The “Fine MER 2.0” label means that we are viewing the disaggregates through the lens of the ones that were reportable in MER 2.0. Where that is possible, it’s been normalized with MER 1.0. However, that is not always possible. In our next view, we’ll shift to the MER 1.0 view to show what this example looks like in the reverse.

Age/Sex Disaggregates: MER 1.0 View
In this example, you can see the 1-4 and 5-9 age bands, because those were reportable in MER 1.0. For 2016, you see data populated for those age bands. However, in the FY17 column, that data is not available. That’s because, though we can sum the 1-4 and 5-9 age bands to reach the 1-9 age band, we cannot split the 1-9 age band to find 1-4 and 5-9. That age band is not analogous, so it is left blank in this row for FY17.

Exercise: Fine Age Bands
In your country, How did TX_NEW Results for 1-9 year olds differ between 2016 and 2017? Would you go to “Fine MER 2.0” or “Fine MER 1.0” view to find this answer? Write on the white board: MER 1.0 = 1-4 and 5-9, MER 2.0 = 1-9

Exercise: Fine Age Bands: MER 1.0 View
This is the Fine MER 1.0 age bands section. You’ll see that the age bands you were familiar with in MER 1.0 are here: <1, 1-4, 5-9 and so on. Where would we find the 1-9 age band?

Exercise: Fine Age Bands: MER 2.0 View
This is the MER 2.0 view: The 1-9 age band is here, fully populated for both MER 1.0 and 2.0. Play around with indicators in the program area of your choice. You’ll see that many are compatible between the two MER types (the majority of these age bands across the clinical cascade align between the two years, but that many do not work.

Age/Sex Disaggs: Male and Female
Until now, we’ve been focusing on the differences between age bands, while not filtering for male/female. However, in MER 2.0, there is a big difference in pediatrics especially between Male, Female, and showing both. Does anybody know the big difference for <10 that occurred in MER 2.0 for sex disaggregates? Answer: There are no more male/female distinctions for <1 and 1-9. However, let’s see what happens when you click for Females only.

MER 2.0 Disaggs Filtered by Female
You’ll notice a few things in this view: Data populates for 2016 for the Female only view for the <1 and 1-9 age bands. However, then it stops populating for 2017 (you may expect a few lingering data points, but you’ll switch back to “all” sex to view the data for these bands for 2017). The “Unknown” category shows up and is populated for 2017 only, to reflect the addition of the “unknown age band” category in MER 2.0.

How is this different from the coarse disagg group?
MER 2.0: What is Most Complete Coarse? Most Complete Coarse is comprised of either the coarse or the fine disaggs, depending on completeness. Can anybody explain % completeness with age/sex disaggs compared to the numerator? MCAD is a script that is run that identifies the age/sex disagg type (coarse or fine) that is closer to the numerator, and sums those values to achieve a “more complete” <15 or 15+. How is this different from the coarse disagg group?

Additional Disaggregates

Additional Disaggregates
You may have noticed that the previous pages were only for the age/sex disaggregates. But what if your program area/indicator(s) of interest contain additional disaggregates, like Modality or Circumcision Technique? On this page, you can view all disaggregates that are not age/sex disaggregates. The page is sorted by Prevention, HIV Testing, and HIV Treatment. In Q4, this page will also include Retention on Treatment, TX_PVLS, and other systems indicators reported in Q4.

You will learn about many of these in Day 2 MER Analytics!
Panorama Analyses What visualizations or tools have already been done for you in Panorama? In Panorama, you can find…. Full Clinical Cascade Cascade by AgeSex Cascade by Key Populations Net New Analysis Testing Visuals, drawn from the ICPI CHIPS Tool TB/HIV Visuals OVC Visuals PMTCT Cascade Visuals VMMC Visuals You will learn about many of these in Day 2 MER Analytics! There are additional slides at the end of this deck that will orient you to these analyses. During the Day 2 MER Analytics, we’ll dive into many of these tools. However, there are simultaneous breakout sessions and you may not be able to take the time to test your knowledge in each area. Feel free to explore these slides on your own, and we’ll get into more detail tomorrow!

DATIM Group Set Analysis: HTS Exercise

Review: HTS Facility Modalities
PITC Inpatient PITC Pediatric PITC Malnutrition PITC TB PICT PMTCT (ANC Only) VMMC Other PITC VCT Index Testing

Review: Age/Sex by Modality

KP: Not a Modality NOTE: Key Populations is NOT a Testing Modality… …so it is not included in the list of HTS Testing Modalities Group Set.

Question 1: Pulling HTS_TST_POS by Age, Sex, and Modality
We are handing out an exercise sheet. We will be going through these shortly on the screen, but please feel free to move at your own pace. If you do finish early, please find a facilitator who will come around and hand you a linkage exercise. If you’re interested in practicing linkages, even if you don’t finish early, see a facilitator!

DATIM Group Sets: HTS_TST Exercise 1
In your OU of interest, how many individuals were tested in Q3? By Positive and Negative Test Results? Try this using DATIM Group Sets! Note that if you are an interagency user, you may not have access to the unapproved data, as Q3 is in the middle of the data cleaning process. If you are in this situation, please see a facilitator.

DATIM Group Sets: HTS Exercise 1
Before you begin, login into DATIM and open the “Pivot Table” app. The next slides will walk through how to use the new analytic dimensions in DATIM to create a series of pivot tables for HTS_TST by Positive and Negative test results.

Step 0: Do Not Select Data Elements Important: Do not select any data element. Leave this tab blank

Step 1: Select the time period of interest [April – June 2017] from Periods data dimension Note: Do not forget to de-select “Last Financial Year”

Step 2: Select OU of interest from Organization Units data dimension
DATIM Group Sets: HTS Exercise 1 Step 2: Select OU of interest from Organization Units data dimension

Step 3: Select “Result” from Targets/Results data dimension.
DATIM Group Sets: HTS Exercise 1 Step 3: Select “Result” from Targets/Results data dimension.

Step 4: Select “HTS_TST” from Technical Area data dimension
DATIM Group Sets: HTS Exercise 1 Step 4: Select “HTS_TST” from Technical Area data dimension

Step 5: Select all modalities from the HTS Modality data dimension Note: KP is no longer included here as a disaggregate. It is not a modality. If you do not select modalities using the HTS Modality group set, you may double-count KP HTS results since the KP disagg results are included in the HTS_TST technical area group set.

Step 6: Select both “Positive” and “Negative” from Test Results
DATIM Group Sets: HTS Exercise 1 Step 6: Select both “Positive” and “Negative” from Test Results

Step 7: Organize the table layout in the Layout dropdown Note: If you want to review total HTS results without modality specific results, you can shift “HTS Modality” to report filter.

Step 8: Select “Update” at top of screen.
DATIM Group Sets: HTS Exercise 1 Step 8: Select “Update” at top of screen. Note: Subtotals can be removed in the Options dropdown.

Exercise 2: Pulling HTS_TST for FY 17

In your OU of interest, what were the cumulative HTS_TST and HTS_POS results in FY17? Try this using DATIM Group Sets!

Step 1: Add the following to the Periods data dimension and select Update. Q1 [Oct to Dec 2016] Q2 [Jan to Mar 2017] Q3 [April – June 2017] Note: The layout of the table can be modified based on analytic needs and/or personal preference. In the example screenshot above, Periods was shifted to the row dimensions and OU was shifted to the filter. Cumulative results can also be shown by moving Periods to the filter selection and OU to the row section as shown below.

Note: The layout of the table can be modified based on analytic needs and/or personal preference.

Exercise 3: Pulling HTS_TST Modalities

In your OU of interest, what is the breakdown of Q3 tests and results by modality? How many tests were reported from Inpatient and what was the Inpatient yield?

Step 1: Adjust Table Layout to show HTS Modality results. Select Update.

DATIM Exercises Part II

Knowledge Check When selecting cascade sex, which HTS modality results will be excluded from the table? Which HTS modality results are excluded when you select cascade age bands? Answer to number 1: ANC & VMMC, since the corresponding data is not automatically associated with the Female or Male category in DATIM Answer to number 2: Pediatric and Malnutrition. The <5 age bands do not conform to the cascade age bands in DATIM.

Exercise 3 part a: Pulling HTS_TST by Age/Sex/Modality

DATIM Group Sets: HTS_TST Exercise 3a
Step 1: Adjust HTS Modality data dimension. Remove Malnutrition, PMTCT ANC, Pediatric, and VMMC modalities Note: Malnutrition, PMTCT ANC, Pediatric, and VMMC modalities are removed since related results will not populate if cascade age and sex are also selected.

Step 2: Select all age bands in the Cascade Age Band data dimension
DATIM Group Sets: HTS_TST Exercise 3a Step 2: Select all age bands in the Cascade Age Band data dimension

Step 3: Select all sex options in the Cascade Sex data dimension
DATIM Group Sets: HTS_TST Exercise 3a Step 3: Select all sex options in the Cascade Sex data dimension

Step 4: Using the layout button, arrange the pivot table to fit analytic needs and then click “Update” Note: By arranging your age, sex, results, and modalities into rows, your data will be in a good format to pull into an excel pivot table.

Step 5: Download the table.
DATIM Group Sets: HTS_TST Exercise 3a Step 5: Download the table. Reminder: This must be merged with two additional tables to complete the HTS Modality x Age x Sex x Result dataset.

Step 6: De-select Sex options in the Cascade Sex data dimension
DATIM Group Sets: HTS_TST Exercise 3a Step 6: De-select Sex options in the Cascade Sex data dimension

Step 7: Select only VMMC and PMTCT_ANC Modalities from the HTS Modality data dimension.

Step 8: Review your table layout and adjust to align with your previous table layout (to facilitate an easier merge). Select Update.

Step 10: Select only Pediatric and Malnutrition modalities in the HTS Modality data dimension

Step 11: De-select all age bands in the Cascade Age bands data dimension Note: Because these results are entered in a unique age band, <5, they will not show up if other MER age bands are selected

Step 12: Adjust your table layout to align with previous HTS modality result pulls (to facilitate an easier merge). Select Update.

Step 14: When integrating PMTCT_ANC and VMMC data into the broader data set, you will need to leave a space to manually add sex (Male for VMMC and Female for PMTCT_ANC). Step 15: When integrating Malnutrition and Pediatric (<5 clinic) results, you will need to leave two column spaces to manually add the age category (<5) and sex (unspecified gender). By doing this, your data fields will align across the three exports and allow for correct analyses. Note: There are a variety of ways to export and merge these three data sets. The important thing is to remember how to correctly pull all of the necessary data for this analysis.

Additional exercise on Linkages available upon request!

Summary

Summary During today’s session we discussed how to:
Understand and use both data elements and DATIM group sets in DATIM for data extraction, analysis and interpretation Identify key analyses in Panorama, with a focus on MER 2.0 program performance for FY17 and trends over time

Questions?

Key Contacts Presenter Email Kristin Roha RohaKM@state.gov Jess Brown
Leigh Tally John Aberle-Grasse Jasmine Buttolph Jason Roffenbender Pooja Vinayak Shaylee Mehta Kristin Krebs

Thank You! Speaker Notes
Don’t forget to that everyone for their time, participation, and attention during the session! Leave on a high note!

Additional Slides: Visual Data Analyses in Panorama

Where to Find the MER Clinical Cascade

Panorama: Clinical Cascade Age/Sex Disaggs
This looks very similar on the onset, but note that the values you see depend on the filters you select. To make sure you’re selecting filters that make sense, look at the Disaggregate Completeness as Percentage of Numerator table. Notice that you have to select HTS and TX separately to find whether fine or coarse disaggs make more sense for the cascade as a whole.

Clinical Cascade Age/Sex Disaggs Graph

Age/Sex Disaggs Graph: Selecting <15 Female
Note that you will have to select Age and Sex separately. Notice these are the age bands that are available under MER 2.0, so you’ll see a few items, including: You’ll have to select <01 in addition to <05. This is because these apply to different modalities, so for HTS_TST, you’ll have to make sure to select both to get a full and accurate count for <15.

Age/Sex Disaggs Graph: Selecting <15 Female
Now, select the sex band you are interested in looking at. Note that if you do not select between males and females, the dashboard will automatically apply both selections. In this way, if you are interested in a specific age band across both males and females, simply do not apply a selection to the “Sex” filter. Why did I select “unknown” in addition to female? Remember that <1 and 1-9 age bands are not collected by sex in MER 2.0 for clinical cascade indicators. Panorama has attached an sex titled “Unknown” to these disaggs, so to include your <1 and 1-9 age bands, you must also select “Unknown.”

Clinical Cascade Age/Sex Disaggs Graph
Note that the page looks almost exactly the same. The only difference is the numbers in the tables and on the axes. Therefore, you always have to be careful about knowing what filters you have applied to the graphs at any given time.

Clinical Cascade Coarse Age/Sex Disaggs
Note that, when you select the coarse option, you may only select age/sex combos. You’ll also notice that <15 is only available for Unknown Sex. This dashboard was consolidated in this way because, for clinical cascade indicators, ages less than ten years were not collected by sex. However, ages were collected by sex. Therefore, when many users selected <15 and also selected either males or females, the dashboards would only populate with the age bands under 15 that did contain a sex attribute (that is, it only pulled over the age band and did not pull the <1 and 1-9 age bands). This made the <15 female or male selections look deceivingly small. Therefore, the only valid age/sex combination to look at the complete <15 age band is “Unknown Sex.” This dashboard only lets you select valid combinations.

TX_NET_NEW Analysis Visual
We navigated all the way back up away from the “clinical cascade” visuals to discuss another important part of the clinical cascade: the number of patients put net new on treatment.

Panorama: Testing Visuals
A critical component of the clinical cascade—and the basis of the first 90—is testing. In Panorama, we have a dashboard specific to testing that gets into much more detail about the HTS_TST indicator than the cascade in our previous exercises (the “Treatment Continuum & Net New Visuals” dashboard). In the next few minutes, we’ll briefly cover what you can find on these pages. We will get into more detail on the testing component in our breakouts later this session.

Panorama: Testing by Service Modality
Note that some of the modalities say [added], some say [removed], and others have no indication. This refers back to the changes between MER 1.0 and MER 2.0. In MER 2.0, some modalities from MER 1.0 were removed. Other new ones were added. The modalities without an indication listed are consistent between MER 1.0 and 2.0. Note on this page that you can select to view the visual annually or by quarter.

Panorama: Testing Positive and Yield
This page shows you how many positives and negatives were found by each different type of testing modality. It also shows you the Yield—note the y-axis on the right side of the chart.

Panorama: Testing Trend Analysis
Note that this visual can be filtered by POS and YIELD. For the trend analysis, note that you can only see the trend going back to That is because data prior to that time is not in the PEPFAR Data Hub, from which Panorama pulls. You’ll see that there are several additional tabs on this page, including looking at testing by age/sex, total volume, and a sex breakdown by modality. Come to the Clinical Cascade MER Analytics session tomorrow to learn more about how to use these tabs to understand your testing program. In the meantime, we just want to point out that this is where you can come to learn about testing data.

TB/HIV in Panorama

TB/HIV in Panorama What do you notice that’s different about this cascade? Answer: It starts in the middle and stretches out to the right and to the left to tell you different things: one about screening positive and one about screening negative with prevention We will be doing a deep dive into this cascade as part of the TB/HIV mini-breakout.

OVC Visuals in Panorama

OVC Visuals In this view, you can see OVC results by Participation Status. Click through the different tabs to view OVC_SERV by age/sex visualized, and the OVC_HIVSTAT numerators and denominators.

PMTCT in Panorama

PMTCT in Panorama This is a different kind of cascade than the example we just viewed. Based on your understanding of a cascade, how do you think these data points in the visual relate to each other?

PMTCT in Panorama: Program Coverage
In this visual, we look not just at % Achievement as results divided by targets, but we look at percentages as numerators over denominators for these indicators. In the PMTCT mini-breakout, we’ll take a closer look at what this means for us programmatically. Note: May need to introduce concept of denominators and how they relate to numerators and other indicators as part of a cascade

VMMC Visuals

VMMC Visuals In this visual, you can view OU, SNU, and IM Analysis for VMMC_CIRC. You can also see a summary of all the VMMC countries across the bottom of the page. Click through additional tabs to see priority age bands for VMMC_CIRC, and a visualization of testing status disaggs.

Thank You!

Overview and Preparation for Analysis of MER 2.0 Data

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Overview and Preparation for Analysis of MER 2.0 Data

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