Achieving Program Targets: An HIV Care Cascade Approach

Achieving Program Targets: An HIV Care Cascade Approach
MOLLY: Good morning. Thank you for joining today’s webinar which is focused on achieving ICAP program targets using an HIV Care Cascade approach. My name is Molly McNairy and I will be presenting with Bill Reidy as this approach is a joint effort between the CTU and SI Units. Molly McNairy and Bill Reidy, ICAP-NY March 28, 2013

Webinar Overview Background Examples of low target performance
Dimensions of the problem: M&E & Clinical Introduce a cascade approach A case study Toolkit inventory Molly For today’s webinar, we will start with background on this topic of performance towards targets, then provide some real-life ICAP examples illustrating this is a wide-spread challenge across country programs. We will then approach the problem from two distinct but complementary perspectives: a M&E perspective focused on data and a clinical perspective (focused on clinical or programmatic interventions). We will introduce a cascade approach including a case study and small toolkit for you to use with your teams.

Dimensions of the problem: M&E & Clinical Introduce a cascade approach A case example Toolkit inventory Let’s begin with some background to give this webinar more context.

Background There are many reasons why a program may face challenges reaching key targets Even the highest-functioning program can have low target performance It is important that we address these challenges on an ongoing basis Country teams have various methods for monitoring progress to targets (e.g., ongoing DQA, reports to funders, slide sets, URS) Targets are the way we as programs measure the progress and success of our work—both internally and to funders. There are many reasons why a program may face challenges reaching key targets. Examples may include any number of the following: -the target setting process was flawed, meaning the target was set too high, -there may be issues with the supply chain of equipment, reagents or even drugs -there may be shortages of health care workers or poorly trained workers --there may be issues with data quality, data availability and/or reporting procedures -there also may be more macro issues of declining HIV incidence and/or increased ART coverage in a region Country teams have various methods for monitoring progress to targets including ongoing data quality activities, reports to funders, URS and others. We want to emphasize that even the highest functioning program and hardest-working teams can have low target performance. This webinar is not about eliminating low target performance; rather it is about the importance of recognizing low performance to targets as soon as possible and taking steps to improve performance in a systematic way and on an ongoing basis. Next bill will walk us through an example of how to identify progress to targets using URS

URS Targets Dashboard https://urs2.icap.columbia.edu/#dashboard
Filter by country and time period Bill The URS targets dashboard, shown here in a screenshot, has been added recently and uses data submitted to NY by country teams to show progress towards 10 core targets, including PMTCT, HIV care and treatment, TB/HIV, and HIV testing and counseling. To see this dashboard, the user simply needs to click on the tab labeled [CLICK] “Targets”, on the far right side of the screen. Progress to annual targets is first of all displayed graphically, as shown here. To show country-specific targets, the user would click on [CLICK] “display filters”, and then select the country of interest, and click “submit”. Scrolling down [CLICK} through this targets dashboard shows…[CLICK]

URS Targets Dashboard Export data to Excel sheet Bill
…data on performance targets presented in a table format, with annual target values and, where relevant, quarterly data presented for each target indicator. Again, if the target dashboard is filtered by country, the data presented here will be for the country chosen. Important to note is that this table can be exported to Excel [CLICK] and downloaded by the user. Note that for some countries, not all targets shown in the URS Targets Dashboard apply, and probably for all countries, these targets are not all-inclusive of the full set of performance targets across all program areas and funding sources. So while this dashboard may be quite valuable in quickly providing summaries of progress to-date on key targets in the program areas I mentioned, it will also be crucial that country teams have customized summaries of progress to targets, produced from country databases, for review each quarter. This is one area that the SI Unit within ICAP-NY has been providing support for some country teams; we encourage country teams to reach out to their NY SI Specialist if any additional support might be useful.

Dimensions of the problem: M&E & Clinical Introduce a Cascade approach A case example Toolkit inventory BILL: Before getting into the Cascade approach methods, we wanted to provide a few recent examples of challenging targets we have encountered here in ICAP.

ART Initiation: Swaziland
One example comes from Swaziland, where by the end of Q3 (or Apr-June) in the last project year, we had reached only 50% of the annual target of 11,296 patients initiated on ART. If we were completely on track to reach the annual target, we would have seen something closer to 8,500 (rather than 5,500) patients initiated on ART by the end of Apr-Jun I should note here that this Q3 data helped to set into motion an ICAP HIV Care Cascade effort intended to improve performance towards this target, which we will review as a case study during the last part of this presentation today.

Retention on ART: Mozambique
Target = 85% retained 59% * 50% Another example comes from Mozambique, where in the 2012 ICAP annual progress report, the median crude 12-month retention for adult ART patients was 50% across all supported facilities, which was significantly below the CDC Mozambique target of 85%. This, incidentally, has prompted its own HIV Cascade effort to implement and track performance improvements. As a first step in this process, ICAP assessed data quality through comparing MOH data with re-counted data from patient charts at 22 of the 38 supported health facilities. This data quality assessment revealed substantially higher numbers of patients initiated, and retained, on ART than were reported in the MOH data, showing in a revised crude retention of 59%. *Excludes patients who transferred out

Pediatric TB screening One OPD facility: Tanzania
Target = 100% screened 32% The final example we have on hand is data from a pediatric TB/HIV center of excellence in Tanzania, which has been attempting to introduce universal TB screening of children across the health facility – in other words, with a target of 100% of children screened. The roll-out of the program has been challenging logistically, so the team has been monitoring progress within individual points of service in the facility quite closely. Here, we see the TB screening coverage among children in the OPD, with a very low proportion screened by mid-Jan of this year, and a fairly rapid increase to roughly a third of patients screened by the first week in Feb. The team in TZ continues to monitor progress closely and has been targeting support for points of service based on this type of data. 25% 8%

Dimensions of the problem: M&E & Clinical Introduce a Cascade approach A case example Toolkit inventory One of the key threads we hope to convey throughout this presentation is that performance improvements, especially in relation to progress to targets, will almost inevitably involve efforts that might typically be thought of as “M&E activities” and input and efforts that might be thought of as “clinical”.

Solution = must include both components
Low performance may have multiple and overlapping M&E-Clinical components M&E Clinical Data quality Data availability M&E system issues Structural barriers Staffing issues Health system issues For example, some of the issues listed on this slide, like data quality and the availability of data: These might be generally thought of as M&E issues, but in many cases – such as missing CD4 results, problems keeping appointment dates and defaulter tracing outcomes up-to-date, and cases where charts unable to be found – the M&E issues are just a part of a larger problem of systems and work flow in clinics. Therefore [CLICK] it is crucial that the types of performance improvement efforts we’re discussing involve both clinical and M&E staff, each bringing their respective areas of focus and expertise, to try to diagnose problems and implement solutions. Solution = must include both components

Dimensions of the problem: M&E & Clinical Introduce a cascade approach A case study Toolkit inventory MOLLY: Now, we’d like to introduce an approach for addressing low performance to targets which uses a CASCADE approach.

A Cascade Approach: Why?
A care cascade outlines the multiple steps in a clinical pathway needed to achieve optimal health outcomes. The target of interest is part of a larger cascade of care in which the previous steps affect the target Improving the entire cascade will lead to improvements in the target as well as other targets simultaneously Improving the entire cascade will lead to more sustainable improvements Molly: Now we’d like to introduce an approach for addressing low performance to a target. We call this approach the CASCADE approach. A care cascade refers to the multiple steps in a clinical pathway which are needed to achieve optimal health outcomes. Often, the target of interest which is of concern is part of a larger cascade of care in which the previous steps influence the target. I will show you some examples shortly. Improvements in the entire cascade will lead to improvements in the target of interest as well as improve other targets at the same time– meaning any effort a team towards the target of interests can be leveraged to benefit other targets. Also, improving the entire cascade will likely lead to more sustainable improvements over time.

Steps in the Cascade Approach
Identify steps in the cascade that relate to target Identify baseline data to operationalize the cascade Choose priority sites Choose interventions and prioritize them Use a cohort methodology to monitor progress There are basically 5 steps in the cascade approach. We must start by identifying steps in the cascade that relate to the target of interest. Is the target related to adult care and treatment cascade, HIV-TB cascade, the PMTCT cascade or others? The second step is identifying baseline data to operational the cascade in your context. The third step is choosing prioity sites to begin investigating and remidating the situation– it may be too difficult to do this approach immediatiely in all clinics in a country program. We must prioritize which clinics or geographic areas need to most urgent attention. The forth step is choosing interventions and prioritizing the order of their implementation. Finally, the approach uses a cohort methodology to monitor the impact of interventions over time

1. Identify steps in the cascade that relate to target
The cascade’s steps are specific to the disease (i.e. HIV, TB) and the patient population (i.e. adults, children, pregnant women/infants). The first step: Each cascade’s steps are specific to the clinical pathway of interest and are typically disease specific (for example HIV or TB) and patient population specific (for example adults, pediatrics or pregnant women and their infants). Let’s look at some examples together: (NEXT SLIDE)

Adult Care & Treatment ART Eligible Link McNairy, El-Sadr AIDS 2012
For adult HIV care and treatment, the first step in the cascade is HIV testing. Individuals who test HIV positive must then link to HIV care, often termed pre-ART care. Next they must be assessed for ART-eligibility and if eligible, must promptly initiate treatment. All patients on ART but continue to be retained in care and adhere to medications to achieve VL suppression—which ultimately is responsible for optimal individual health outcomes and decreased HIV transmission on the population level. McNairy, El-Sadr AIDS 2012

Retain, counsel monitor and support Evaluate for TB disease
Tuberculosis TB Suspect TB Disease TB Treatment TB Treatment Success Screen Retain, counsel monitor and support Prevent recurrence, ongoing screening Evaluate for TB disease This is an example of a TB care cascade which begins with the step of TB screening followed by evaluation of TB disease , TB treatment, and ongoing TB screening and surveillance. Fayorsey, Howard 2013

2. Identify Baseline Data to Operationalize Cascade
Where to get baseline data for a cascade? Routinely-reported M&E data, e.g.: Country aggregate databases URS Original data collection from clinics BILL: After a team has identified the steps in the cascade that relate to the performance measure or measures of interest, it will be very useful to gather relevant existing data to better understand the performance to-date. It might be helpful to look at data over time for cascade indicators, or across facilities or regions or provinces – however the team thinks might help to understand what is happening, and how efforts to intervene might be best focused. In general it’s likely that the sources of data available will include routinely-reported M&E data, for example the data kept in country databases and in the URS, and also data sitting in records in facilities – registers, patient files – that can be abstracted and summarized to understand cascade performance at facilities.

What source to use for baseline data?
Routinely-reported M&E data Advantages: historical data is readily available data available for many facilities collection requires no additional efforts Disadvantages: indicators not flexible (may not measure what you need) data may have quality issues Particular danger when target shortfall is in part due to M&E system issues It’s worth recognizing the relative strengths and weaknesses of the routine aggregate data and in abstraction of the original facility data. Routinely-reported M&E data has a few very important advantages, including the fact that a large amount of historical data may be readily available for use; that there may be data available for many, if not all, of the facilities supported by ICAP; and, quite importantly, that collection requires no additional efforts . Routinely reported M&E data also may prove to be quite limited for a couple of key reasons, including that the indicators are of course pre-defined and therefore not flexible (and they may not measure what you need); also, the data may have problems with respect to completeness and/or accuracy. This latter issue poses a particular danger in the use of routine M&E data when a target shortfall is in part due to M&E system issues.

What source to use for baseline data?
Original data collection from clinic Advantages: have access to all data collected high level of flexibility in defining set of indicators can use highest-quality data available may be used to compare to reported M&E data Disadvantages: burden of data collection lack of a large amount of historical data for comparison If at all possible, advisable to collect original data to supplement routine M&E data Another option very much worth considering is abstraction of data from original sources such as registers and patient files in facilities. This option offers us the advantage of abstracting data from the full set of data elements that are collected across all tools in a facility, therefore providing us a great deal of flexibility in selecting the data we will use to assess clinic performance. Another aspect of this flexibility is that we can choose to take data from the original, highest quality data source – for example a patient file rather than from a register or monthly report. Lastly, collection of data from original data sources allows us to assess the accuracy of the reported M&E data, like was done in the example shared from Mozambique. The disadvantages of this approach include the obvious burden of data collection; and the lack of a large amount of historical data for comparison, to assess trends across a broad time period. Our recommendation is that routine M&E data is used to its fullest extent to examine problems, and if at all possible, original data is collected to fill in key gaps, allowing us to identify the factors contributing towards a low performance outcome.

3. Determining & Prioritizing Interventions
Root cause Analysis/Driver Diagram Focusing Matrix MOLLY: Step 3 is determining options for interventions and prioritizing them for implementation. I will present 2 tools . The first is a driver diagram and the 2nd is a focusing matrix. These tools will be on the wiki if you are interested.

Driver Diagram A tool to facilitate root cause analysis An example…
Articulates the aim of the campaign Organizes primary categories for reasons contributing to low performance Subdivides categories into specific reasons Facilitates a specific intervention tied to each reason An example… A driver diagram is a common tool used to facilitate root cause analysis for low performance towards a target. Root cause analysis refers to trying to get to the root cause-or underlying reason- for why there is low performance. There are other tools which can be used but a driver diagram is very intuitive to understand and easy to use. It: Articulates the aim of the campaign Organizes primary categories for reasons contributing to low performance Subdivides categories into specific reasons Facilitates a specific intervention tied to each reason Illustrates relationships between interventions and aims Let’s take a look more closely

Secondary Drivers Interventions Primary Drivers Aim
Here is a blank driver diagram

Driver Diagram Step 1: Aim Target Numerical goal for improvement
Time frame Location (place or # of clinics) You start by listing your aim for improvement. The aim must be specific, stating a target, a numeric goal for improvement, a time period for improvement, and a location (either place, or number of clinics).

Increase ART initiations by at least 30%
Secondary Drivers Interventions Increase ART initiations by at least 30% in 3 months at 15 priority clinics Primary Drivers Aim For example: “improving adult ART initiations” is not an aim. Rather “increasing ART initiations by at least 30% in 3 months at 15 priority clinics” is a great am.

Driver Diagram Step 1: Aim Step 2: Primary Drivers Time frame Location
Make a list of broad categories of factors that must be addressed to achieve aim Next, list the primary drivers of reasons or categories your team feels are most likely responsible for low performance. This is a brainstorming team effort.

Increase ART initiations by at least 30%
Secondary Drivers Interventions Increase ART initiations by at least 30% in 3 months at 15 priority clinics Provider/Patient Supplies (CD4/Lab) Drugs Primary Drivers Aim In this example, a team may list patient/provider reasons, supply issues or drug issues.

Driver Diagram Step 1: Aim Step 2: Primary Drivers
Time frame Location Step 2: Primary Drivers Make a list of factors that must be addressed to achieve aim Step 3: Secondary Drivers Specific problems under each category Step 4: Match specific interventions to each driver Next list specific reasons under each primary driver category that contribute to poor performance and create a specific, concrete intervention to address that driver

Aim Primary Drivers Increase ART initiations by at least 30%
Secondary Drivers Interventions Aim Increase ART initiations by at least 30% in 3 months at 15 priority clinics Provider/Patient Knowledge of WHO staging Staging posted in clinics, train providers Eligible patient but not on ART Outreach, phone calls, home visits Patient refuses ART Assign peer counselor CD4/Lab ART For example, if we look at the category of provider and patient reasons for low performance, we could expand this category to more specific categories of providers not using WHO staging in the absence of CD4 testing to determine ART eligiblity, patients having known eligiblity but not starting ART, and patients who refuse ART for unclear reasons. For each of these reasons, the team could brainstorm a specific intervention to address them. For example, We could train providers with an WHO staging job aide to ensure 100%pts were assessed for ART eligibility. Continue to fill in and complete boxes for all secondary drivers and interventions

Focusing Matrix Tool to aid in prioritizing interventions
Uses both importance and ease of implementation to rank priority An example… The second tool, the focusing matrix, is helpful to prioritize interventions, once they are generated using a driver diagram.

Ease of Implementation
Focusing Matrix IMPORTANCE 1 (Least) 2 3 4 5 (Most) (Hardest) (Easiest) Ease of Implementation Step 1 is to draw this chart and lable the axis with importance of intervention (1 is least impt and 5 most) and ease of implementation (1 is hardest to implement and 5 is easiest)

Ease of Implementation
Focusing Matrix IMPORTANCE 1 (Least) 2 3 4 5 (Most) (Hardest) (Easiest) Ease of Implementation # 2 priority The idea is that once we populate the table with our ranked interventions, we will want to first implement interventions we feel are most impt to improving performance and easiest to implement, followed by those that may be impt but harder to implement or easy to implement and less impt and avoid interventions that are hard to implement and not thought to be important. most important and easiest to implement – #1 priority

Proposed Intervention Ease of Implementation
Prioritizing Interventions Example: Low ART Initiations (adult) Item # Proposed Intervention Importance Ease of Implementation A WHO staging posted in clinics to be reference for providers 3 5 B Identify ART eligible patients who have not yet initiated ART and call them to return C Fix broken CD4 machines 1 D Outreach ART eligible patients at home if no show for appointment E Assign peer counselor to patients who refuse ART IMPORTANCE 1 2 3 4 5 C E D A B EASE of IMPLEMENTATION In this example, we the low-hanging fruit are interventions B&A should be first immediate priority– all can be prioritized over time but first the first several months, let’s focus on the most impt and easiest to impelement. Interventions B and A should be first priority

4. Choosing Priority Sites
Highest Volume Lowest Performance 65% % % 55% % % 20% % % The next step of the cascade approach is to strategically choose sites– you can’t do everything everywhere. What sites or regions are driving low performance. You could take the approach to choosing high volume clinics or lowest performing clinics, or a combination. 40% % %

5. Cohort Methodology to measure change in performance towards target
Goal is to assess impact of approach on relevant target and cascade indicators Impact must be sustainable A cohort methodology: Define cohorts of patients Collect cascade data for cohort from source documents Summarize graphically Review data and revisit intervention plans Repeat process 2-4 periodically (e.g., every month) BILL: With an intervention plan in place, the next essential step is to develop tools and procedures for data collection and routine review of data, using what we will call here a Cohort Methodology. Throughout this step in the process, it is crucial to keep in mind the goal of data collection in the cascade approach, which is to assess the impact of a set of interventions on a cascade of indicators including, presumably, one or more indicators that have not reached their target levels. Also we need to keep in mind that the intervention must be sustainable, so the data collection and review should, if possible, be able to help identify the sustained impact of the set of interventions. Here we will review the elements of our proposed cohort methodology, which is designed to help guide us towards the goal we’ve stated here.

Define Cohorts of Patients
A cohort is a group of people sharing a common trait, usually defined by a point in time (e.g., birth cohort of people born in 1981) For this cascade approach, define cohort as any patient who entered the cascade during a specified time period, e.g.: Patients testing HIV-positive at Kagera Regional Hospital during January 2013 Patients enrolling in HIV care at RFM Hospital during 2011 First, a quick review of terminology. The term cohort for our purposes refers to a group of people sharing a common trait, usually defined by a point in time (a specific example of this might be a birth cohort of people born in 1981). An essential part of the cohort methodology we’re describing here is the definition, by the team conducting the Cascade effort, of cohorts to be included in data collection. Under the cohort methodology we are describing, it is most straightforward to define a cohort as made up of any patient who entered the cascade during a specified time period, for example all Patients testing HIV-positive at Kagera Regional Hospital during January 2013; or all Patients enrolling in HIV care at RFM Hospital during the year 2011.

Collect cascade data for cohort from source documents
Operationalize the steps in relevant cascade # enrolling in HIV care # with ART eligibility assessed via CD4/WHO stage # ART eligible # initiating ART # retained on ART (e.g., at 6 months, 12 months) Specify the best source of data for each step Design simple tools (paper, Excel) for abstracting and summarizing this data Plan for periodic data collection Measuring retrospective improvements Measuring improvements moving foward To plan to data collection and presentation of cohort data, we need to [CLICK] map out the specific data elements that make up the cascade we’re interested in. For example, the set of data elements listed here make up a segment of the HIV care cascade. [CLICK] For each of these data elements, an original data source should be identified, ideally one that provides the highest-quality data available. [CLICK] Simple data collection tools – these might be paper tools or perhaps MS Excel – will need to be designed, as well as a method for plotting out the results of data collected for cohorts onto easy-to-understand graphs. [CLICK] Frequency of data collection should also be specified – keeping in mind that, depending on the focus of the intervention, we may expect changes to happen in cohorts retrospectively, as well as prospectively after the intervention is implemented. Therefore several waves of data collection may need to be planned for individual cohorts, to measure changes in cascade indicators within that cohort over time.

Summarize cohort in a graph
Intervention begins 58% 71% Shown here is illustrative data from a baseline HIV care cascade cohort - a cohort that enrolled in pre-ART care prior to the implementation of an intervention. In this data, we see that only 58% of pre-ART patients in the cohort were assessed for ART eligibility using CD4 and/or WHO stage. Also, only 36% of patients who were determined to be ART eligible had actually started ART. Before moving on to the successive cohorts who enrolled after the intervention began, just for illustration I’d like to show an example of monitoring the impact on a retrospective cohort, like this one enrolled prior to the intervention. Let’s assume that the intervention included an effort to track down patients who were deemed ART eligible, but had not started ART, and that cohort 1 patients were targeted with this part of the intervention. In this case, we would want to plan for follow-up waves of data collection on cohort 1 – for example, reviewing the pre-ART register monthly and re-counting the # starting ART – and update this graph. For sake of illustration, if 10 additional patients were found and ultimately returned to the clinic and started ART, the graph could be updated in some fashion [CLICK] to reflect the impact of the intervention on the baseline cohort. However, of main interest is how the intervention affects performance throughout the cascade over time, after the intervention is implemented. 36% 36% 20

Intervention begins 73% 58% 58% So here is some example data for a second cohort, showing what appears to be improvements in two key parts of the cascade. 36% 36%

Intervention begins 79% 73% 58% 70% 58% With further improvements see in cohort 3…. 36%

Intervention begins 79% 95% 73% 58% 88% 70% 58% and cohort 4… 36%

Intervention begins 99% 79% 95% 73% 58% 91% 88% 70% 58% …with, by the 5th cohort, 99% of new pre-ART patients assessed for ART eligibility and 91% of eligible patients initiating ART. Here with 5 cohorts of data, we can see the proportion who were assessed for ART eligibility increased consistently, from 58% at baseline to nearly 100% by the 5th cohort. Therefore the aspects of the intervention focused on improving ascertainment of ART eligibility status – say, through increased baseline CD4 testing – would seem to have been a success. Similarly, the intervention components aimed at increasing ART initiations among those eligible would appear to have been successful, with only 36% of eligible patients initiating ART at baseline, and 91% initiating ART by the time we reach cohort 5. [CLICK] Note that the combined impact of these 2 parts of the intervention in this example increased the number of ART initiations from 10 among the baseline/Cohort 1 group, to 50 among Cohort 5.

Review data and revisit intervention plan
Review pre- and post-intervention cohort data Identify successes and ongoing challenges Take inventory of factors enabling program improvement Outline likely barriers to improvement Consider revising intervention plan Identify activities to keep in place, those to drop, and any new activities to begin Keep in mind sustainability of activities and improvements Repeat this process as new cohort data becomes available The cohort methodology allows the team to continually review data – at multiple points throughout the addition of new cohorts and with new waves of data – and revisit the chosen interventions based on improvements, or lack of improvements, seen in the cohorts. It cannot be assumed that the selected interventions will be effective and sustainable, and that this can be known definitively to be the case from a short period of data collection and review. Therefore it is essential to think of this methodology as an ONGOING process.

Dimensions of the problem: M&E & Clinical Introduce a cascade approach A case study Toolkit inventory MOLLY: Next, let’s try to use this cascade approach in a real-life situation.

Case Study: ART Initiations
ICAP Swaziland at end of Q3 reported reaching 50% of annual target for ART initiations Dimensions: M&E, Clinical The Cascade approach was implemented with the following steps and results Identify steps in the cascade that relate to target Identify baseline data to operationalize cascade Choose priority sites Choose interventions and prioritize them Use a cohort methodology The swaziland team generously has offered to share their experience with the larger ICAP community. In September 2012 they were confronted with low performance of ART initiations. At the end of Q3, we had only achieved 50% of the annual target—as compared to 75% or more if we were quote On track.” Let’s quickly go through the cascade steps using this example We put together a task team from SD and NY including clinical and M&E point persons to address the situation and we started by consider the overall perspective—was this low performance due to M&E (data quality, avail) issues and/or program issues. Then, we together developed what we now share as the cascade approach.

1. Identify steps in the Cascade
# persons test HIV + (not reliable) # persons enroll in HIV care # persons assessed for ART eligibility (WHO, CD4) # persons eligible for ART # persons initiated ART Step 1: identify relevant steps in the care cascade related to ART initiations: Swaziland decided on these steps

2. Identify baseline data to operationalize cascade
Step 2: identify baseline data-- swaziland used routinely collected data that had been previously collected to create cascade for each clinic. You can see that some clinics are high volume (point to first), some are lower volume (later), some have a large drop in the cascade between enrolled and ART assessed (dark and light blue bar) and some have more of a problem for initiating those already with known eligiblity (dark red and pink).

3. Choose priority sites 10 largest volume clinics in 3 regions = 30 sites Volume was defined as # of patients enrolling in HIV care in the past quarter Step 3: choose priority sites. Swaziland decided to choose the 10 largest volume clinics in each of the 3 regions we work in for a total of 30 sites

Choose interventions and prioritize them
Identify patients with known ART eligibility but no ART initiation and put them in a “expectant” patient box for expert clients to call to return to care Introduce WHO Staging job aid to assist providers to assess patients for ART eligibility given reports of CD4 stock outs Transfer reported CD4 results from lab registers to patient charts Based on driver diagram and focusing matrix, the team prioritized the following 3 interventions for immediate implementation

5. Introduce Cohort Methodology
Identify steps in relevant cascade Specify the best source of data for each step Pre-ART register, patient HIV medical care file Design simple tools (paper, Excel) for abstracting and summarizing this data Excel sheet for data collection/management Graph to display cascade data over time Identify cohort members Cohorts will be defined by month of pre-ART enrollment For this presentation, initial baseline cohort will include 3 months combined Expect to see changes prospectively and retrospectively Plan for periodic data collection BILL: The next step in the process is to introduce a structured process for data collection and summarizing of data collected over time using a cohort methodology. First, identifying the specific data elements that will measure the steps in the relevant cascade, in this case already specified for us by Molly, is necessary in order to understand which data will need to be collected. [CLICK] For each of these data elements in the cascade, the best data source should be identified – in this case, [CLICK] the pre-ART register and HIV medical care file were identified as data sources for various cascade data elements. [CLICK] Simple tools for abstraction, management, and summarizing of this data are required and must be designed. The Swazi team developed [CLICK] an Excel sheet for data collection and plugged this data into a cascade graph like the one we reviewed earlier in this presentation. [CLICK] The definition of cohorts of patients whose data will be abstracted over time was determined [CLICK], specifically to be defined by calendar month of pre-ART enrollment, 1 cohort per month. The one exception was for the baseline cohort, which was made of patients enrolling in pre-ART care across 3 months. In this case example we’re reviewing, the primary task at hand was to improve HIV care and ART program performance from that point into the future – which is to say, prospectively. However there was also an opportunity to intervene with patients from the recent past who it did not make it through the care cascade as they should have – specifically with patients who had been deemed eligible for ART by CD4 and/or WHO stage criteria, but did not, in fact, start ART. The chance for us to intervene with a retrospective cohort from months ago – reaching out to these ART-eligible patients to see if we could start them on ART – prompted us to identify three-months of “baseline” patients who entered the cascade prior to implementation of the intervention. In this 3-month cohort, we would try to intervene on the last step of the cascade, moving patients from this wider time period onto ART. We might think of these a changes within a retrospective cohort. Lastly, the team planned for periodic data collection, including multiple waves of data collection for each cohort, to capture changes in the numbers of patients within an individual cohort initiating ART as the intervention is conducted.

Additional post-intervention cohort data to-be collected
roll-out begins 90% Additional post-intervention cohort data to-be collected 80% 66% At this point we have data only for the 3-month, June-August baseline cohort, which you see is a group of patients who enrolled in care prior to roll-out of the intervention. From the initial wave of data collection for this cohort, we saw that 90% of these patients did in fact have ART eligibility assessed. However, among those patients who were classified as eligible for ART, only two-thirds of them had actually started ART. However, as part of additional waves of data collection on this cohort after the intervention was rolled out, we saw that more than 200 additional patients who were ART eligible had come back to the clinic and started ART – bringing the proportion for the last step in the cascade up to 80% This data reflects the first part of how a cohort can be useful, that is to say, tracking changes within a cohort. We are looking forward to the findings from data collection on the successive cohorts enrolling in pre-ART care after the intervention was implemented in September, to see if even higher proportions of patients are assessed for ART eligibility, and if a greater proportion of ART eligible patients do promptly start ART. 1356

Supplemental M&E Component: Verifying national M&E data
Collection of cascade data from sites allowed us to re-count national reported M&E data Recount of site-level ART initiations showed a substantial, systematic undercount in the national M&E data (generated by MOH database) Have since implemented a system for identifying patients not counted in M&E system, and having their information entered into MOH database Also working towards improving routine M&E processes so all patients are entered into database Discrepancy highlights need for routine conduct of in-depth data quality assessments (DQA) Another part of the Swazi cascade effort was a verification of the national M&E data – in effect, an ongoing data quality assessment. [CLICK] The collection of cascade data from sites allowed the team to compare numbers of patients initiating ART as recorded in the pre-ART registers with the numbers reported through the national M&E system, which we in turn use to report to CDC. [CLICK] This recount of site-level ART initiations showed a substantial, systematic undercount in the national M&E data (which is generated by MOH database) [4 CLICKS] The team has since implemented a semi-automated system for identifying patients not counted in M&E system, and a process for having their information entered into the MOH database. The Swazi team is also working to identify key bottlenecks in the process of transporting files and entering patient data into the database, in an effort to improve routine M&E processes so all patients are entered into database. Import to note here that this discrepancy highlights need for routine conduct of in-depth data quality assessments.

Re-count of ART initiation data
Across the first 3 quarters of the project year, the team found nearly 400 additional patients who had started ART, who were not reported in the national M&E data [BACK]

Summary 1: Results after 3 Months
Recall that ICAP Swaziland had reached only 50% of annual ART initiations target by the end of Q3 Combined M&E and clinical efforts during Q4 allowed team to report reaching 81% of the target by project year end Findings from efforts informed target-setting for current year Lastly we wanted to share with you one of the more immediate outcomes of the efforts in the case study we’ve been describing. Remember that this process began as a result of the team reaching only 50% of the targeted number of patients initiating ART by the end of Q3 of the project year. Partly as a result of the clinic-based and M&E interventions we just described, ICAP in Swaziland was able to report reaching 81% of the target at the end of the project year. While this is less than 100%, which we would have liked to achieve somehow, given the context this was an impressive achievement and one that we think was recognized by CDC/PEPFAR. Also it’s worth noting that the large amount of data collected from sites allowed the team to better understand the true numbers of patients enrolling in care, eligible for ART, and actually starting ART – with or without the clinical interventions – which in turn allowed the ICAP in Swazi team to set a realistic annual target based on projected site-level estimates.

Summary 2: work is an ongoing process
Identify successes and ongoing challenges Lack of SOP for expert clients calling back ART-eligible patients  develop SOP Providers not listing f/u appt in chart or register  investigate frequency and cause in 10 clinics (3 per region) Data still not systematically getting from primary clinics to central clinics  task team with MOH Revisit intervention plan Identify activities to keep in place, those to drop, and any new activities to begin Keep in mind sustainability of activities and improvements MOLLY: We want to emphasize that although the SD team has started using this approach, it will be an ongoing process, as it is designed to be. Some examples of challenges have been that the team realizes that not all expert clients are calling back the pts identified as ART-eligible but have yet to initiate and a SOP needs to be developed. They have decided to do a small investigative exercise to further evaluate why this is happening in 10 of the 30 priority clinics. Additionally, data from rural clinics are not getting entered with reliability in the MOH database and SD to meet with MOH. Additionally, Through the team’s experience with the first several months of this approach, interventions may have to be revised.

Dimensions of the problem: M&E & Clinical Introduce a cascade approach A case study Toolkit inventory

Toolkit Cascade Approach Overview Cohort Methodology Driver Diagram
Focusing Matrix URS Reports DQA SOP

Acknowledgements Country team staff who are conducting cascade approach Especially ICAP in Swaziland who have seen much success

Thank you!

Achieving Program Targets: An HIV Care Cascade Approach

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Achieving Program Targets: An HIV Care Cascade Approach

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