Day 2: Clinical Cascade, HIV Testing Services (HTS) Mini-breakout Exercise Using Panorama or ICPI HITS Tool

Transcription

1 Background: This exercise will help you understand the HTS landscape in a given OU by drilling down to the age/sex disaggregate, site and modality levels Directions: You may choose between: use of Panorama or an Excel-based ICPI tool. Please only select ONE option for this 50 minute mini-breakout Option 1: Panorama 1. Internet access needed 2. Access to Panorama Option 2: HIV Testing at the Site Level (HITS) tool 1. Internet and Excel access needed 2. Download HITS via 1. Navigate to Home > HQ > Interagency Collaborative for Program Improvement (ICPI) > ICPI Data Store > ICPI Approved Tools (most current version) > HITS (HTS and Linkages) Option 1: Panorama Question 1: How do trends in testing volume, identification of positives and yield vary over time from FY16 to FY17 Q3? Remember to compare OU and SNU level results by prioritization level. For ease of comparison, feel free to use the snip tool to take screenshots of your work, and paste into a separate document. Step 1 Day 2: Clinical Cascade, HIV Testing Services (HTS) Mini-breakout Exercise Using Panorama or ICPI HITS Tool 1. Load and login using your credentials. Select an OU of interest 2. Navigate to OU & SNU Analysis, TESTING VISUALS

2 v 3. Click on Trend Analysis (highlighted in blue below). This is the main visual for this analytic question. You can use the Select Filters dropdown to your left (see red box above) to select different fiscal years, PSNUs or Prioritization levels. You can use the radio buttons on the top to select either HTS_TST, HTS_TST_POS or HTS_TST Yield. Step 2 1. How many tests were completed in scale-up saturation, scale-up aggressive, sustained and centrally supported SNUs in FY17 Q1, Q2 and Q3? i. Click on radio button HTS_TST, and you can get this information from the visual above. ii. OR you can click on select filters, Fiscal year, and check 2017, click Apply. Then click on Prioritization, and check Scale up: Satuation, Scale up: Aggressive, Sustained or any other Prioritization levels, and finally click Apply. See below. 2

3 iii. You should get this output to answer the question above! Step 3 1. How many positives were identified in scale-up saturation, scale-up aggressive, sustained and centrally supported SNUs in FY17 Q1, Q2 and Q3? i. Select radio button for HTS_TST_POS at the top 2. What was the yield in scale-up saturation, scale-up aggressive, sustained and centrally supported SNUs in FY17 Q1, Q2 and Q3? i. Select radio button for HTS_TST_Yield 3. What could be reasons for differences in testing, POS and yield amongst and within prioritization levels? Think about SNU and even site level interventions, above site factors, policy considerations etc that may affect rates of identification of positives. Question 2: How does testing volume and identification of positives, by age and sex, differ in scale up saturation SNUs vs sustained SNUs? Do these patterns differ by IP? Step 1 3

4 1. Click on Testing Volume (see below below). This will be the core visual for this analytic question. It shows trends over time for testing volume by fine age group. 2. If you click Select Filters, you can sort by Fiscal Year, Result status, etc. Step 4 3. Click Result Status, and then click the Positive box. Click Apply 4. Click Prioritization, and then click Scale Up Saturation. Click Apply. Take a screenshot of this and save to a separate document 5. Click Prioritization, and then click Sustained. Click Apply. Take a screenshot of this and save to a separate document 4

5 1. Are the patterns of persons identified as positive (HTS_TST_POS) amongst age groups similar or different over time? That is, do you notice any shifts in identification of positives in scale-up SNUs vs Sustained SNUs by age? 2. What could be reasons for these differences? Think about SNU and even site level interventions, demographics of patients, above site factors, policy considerations etc that may affect rates of identification of positives. Question 3: Is there an age or sex differential in use of testing services by modality in a given SNU? Step 1 1. Click on Sex Breakdown by Modality. This is the core visual and table for this exercise. As above, using Filters allows you to select different variables (such as Modality, Age as shown below) 2. Click on Testing Age Pyramid and Fine MER 2.0 to display the age/sex pyramid of HTS_TST Results. If you select Filters, and SNU3, you can see data at lower levels in the Org hierarchy. For the example below, SNU3 for Cameroon displays site level data. This visual shows the number of individuals tested (HTS_TST) by age group, sex and modality. It s useful to compare patterns in male vs female testing by age in the index testing vs. VCT modalities for example. This helps determine whether some modalities may see more males vs females (of a certain age) for example. 5

6 1. Select SNU2 under Select Filters a. Select any SNU of choice 2. Select Modality and Index (or any other modality of choice) 3. Click Apply 4. Within the visual itself, select the HTS_TST_POS radio button to view only POS results 5. Are there any differences between males vs females in the following age groups a yrs b yrs Note: See HITS download instructions on Page 1 Option 2: HITS (HIV Testing at the Site Level) Tool Question 1: Are site level modalities optimized to successfully identify positives? Remember to look at community- and facility- sites. a. At site-level, which modalities reported a high number of positives and highest yield? b. Are sites reporting on all modalities possible or appropriate? Are there missed opportunities for increased testing in certain modalities? c. What important contextual information is needed to understand site-level identification of positives within a given modality? Question 1a. At site-level, which modalities reported a high number of positives and highest yield? Step 1 1. On the landing page, navigate to the Modality FY17 button (see red box below). This will show you site yield by PSNU prevalence and a site list of yield and prevalence. 6

7 2. Scroll down to the second visual: Tested Positive (+) by Yield and Modalities, FY17. In this visual, you can review the number of persons identified as positive (x-axis) against the yield (y-axis). Each dot represents a different modality. You can drill down using the buttons at the top: SNU1, Site Name, Site type, Result Status, FY17 modality, Prime Partner and Implementing Mechanism. 3. Drill down to your SNU of interest, 4. Select either Facility or Community level under Site Type. 5. Select Positive under Result Status 6. Which modalities identified the highest number of positives (x-axis) and highest yield (y-axis)? That is, which modalities are in the top right hand corner/quadrant of the visual? 7. Locate index modality on this visual. Is it high yield and high volume of positives? What are the possible reasons for this result? How could you increase both the yield and volume of positives identified for index modality? That is, how can you push index into the upper right quadrant of this visual? Question 1b. Are sites reporting on all modalities possible or appropriate? Are there missed opportunities for increased testing using certain modalities? Step 1: Stay on the same page or navigate back to Modalities FY17 tab. Scroll down to the visual showing # sites by modality & FY17 Time Periods. This visuals shows the number of unique sites reporting on each modality over time 7

8 (FY17 Q1 Q3). Using your knowledge of sites, this can help determine whether all possible modalities are being used at a given site. 1. As with the above visual, you can drill down using the filters at the top of the page. a. Select an SNU of interest under SNU b. Select Facility under Site Type c. Select Positive under Result Status 2. How many unique sites reported tests in FY17Q3 (see red box)? a. How many unique sites reported modality data under Index for FY17Q3? Are these results increasing from FY17 Q1 to FY17 Q3? b. How many unique sites reported modality data under VCT for FY17 Q3? Are these results increasing from FY17 Q1 to FY17 Q3? c. Are the number of sites roughly the same comparing VCT and Index? Are there opportunities for scale up of index testing, based on these data and your knowledge of sites? 3. Scroll up to the top to the Volume Tested by Modality, FY17 visual. This shows the percentage of sites that are reporting positives from the modalities listed. a. What is the percentage of positive persons identified in index vs VCT in FY17 Q2? b. If the percentage of positives identified using the index modality is lower than VCT, are there opportunities for scale up of index testing based on these data and your knowledge of sites? 8

9 Question 1 c: What important contextual information is needed to understand site-level identification of positives within a given modality? 1. What factors could have influenced site-level identification of positives using the index and VCT modalities? Think about type of patients using these services (Key populations?), extent of scale up and/or adoption of index patient testing strategies at site level, size of VCT clinic/volume of patients at VCT etc Question 2: Are sites successfully identifying positives in accordance with the expected SNU prevalence? Remember to look at community- and facility- sites. a. Do site level yields vary within a given SNU of high or low prevalence over time? b. What important contextual information is needed to understand site-level yield against the benchmark of SNU level prevalence? Question 2a. Do site level yields vary within a given SNU of high or low prevalence over time? Step 1 1. Navigate to the Landing page. Click on the Yield vs Prevalence button. 2. Select an SNU that you know has high prevalence 3. Select FY17 Q3 under Time Period 9

10 This visual shows the yield from each site (x-axis) as a product of its percentage achievement against the SNU level prevalence. In other words, each site s yield is divided by the SNU level prevalence to calculate how close the site level yield is to the SNU level prevalence. If a site, for example, has a yield of 10% in an SNU of 10% prevalence, the % Achievement will be 100%. The site will also touch the purple horizontal line. But if a site yield is 5% in that same SNU of 10% prevalence, the % achievement will be 50%. This statistic helps gauge how site-level yield is performing against the benchmark of SNU level prevalence. Step 2 1. Are any sites achieving 100%? An easy way to check is seeing whether any sites are touching the purple horizontal line? 2. Pick one site that is close to 100% achievement. Change the time period to FY17 Q2 and then FY17Q1. Does the % achievement change over time? 3. Repeat the Step 1 and 2 using a SNU you know has low prevalence. Do you notice any differences? Question 2b. What important contextual information is needed to understand whether site-level yield would be higher or lower than the SNU level prevalence? 1. What are the important site level characteristics that may affect site-level yield? Think about the type of site (referral site, primary care site, PMTCT only, ART only), the volume and type of patients (key populations?), current levels of diagnosed within that SNU etc. List some of those reasons here to understand site-level yields. 10