**TL;DR:** The proposal is to use CIEL:856 with a threshold of 1000 copies / mL to calculate the numerator of TX_PVLS and all CIEL:856 + CIEL:1305 to calculate the denominator. For disaggregates for this project we will only calculate sex and age breakdowns using the PEPFAR MER version of these.

Our work on patient-level indicator reporting (Notice D) is supposed to be based on generating data to calculate a single PEPFAR indicator, the TX_PVLS indicator. This indicator counts the “Percentage of ART patients with a suppressed viral load (VL) result (<1000 copies/ml) documented in the medical or laboratory records/laboratory information systems (LIS) within the past 12 months”. The guidance from PEPFAR adds that this should only consider “patients who have been on ART for at least 3 months” and only “the most recent result” if more than one result exists for the last 12 months.

I’ve been putting some thought into what concepts we need to use to store this data, but would welcome thoughts from the community, particularly any implementors who actually calculate this metric.

**Viral Loads**

Identifying viral loads seems to be fairly straight-forward, at least basing this on CIEL: **856** is the concept for viral loads with a numeric value; **1305** is the concept for qualitative viral loads. Obviously, the indicator itself is easiest to calculate using 856 (since we have a defined numeric threshold, though PEPFAR does allow variations to account for local standards).

**Some Implementations**

KenyaEMR appears to use 1305 (qualitative VL) as part of the calculation for both the denominator (all patients with a VL result in the last 12 months) *and* the numerator (at least if the result is “Not Detected” (CIEL:1302). They also use CIEL:856 with the 1000 copies / mL threshold.

UgandaEMR uses the same two concepts, but in a slightly different way. Here they count as suppressed those viral loads where the result is coded as CIEL:1306 (BEYOND DETECTABLE LIMIT) or the numeric viral load is below the 1000 copies / mL threshold.

AMPATH’s code I find a bit hard to follow (in terms of what is still actually in use), but as far as I can tell, viral loads in AMPATH are consistently represented with only CIEL:856 using a threshold of 1000 copies / mL.

**Proposal**

For the purposes of our PLIR, I think it’s sufficient to use values from CIEL:856 with a threshold of < 1000 copies / mL as the standard for viral load suppression. Since observations have a date, identifying the “latest” result should be fairly trivial. This will constitute the numerator for the TX_PVLS indicator.

In order to calculate the denominator for TX_PVLS, we need the total number of viral loads recorded in the system. For these purposes, I think we should take into consideration any patients with a relevant CIEL:1305 result but only if the patient does not have a CIEL:856 result. This means we only count qualitative results if there are no numeric results. Obviously, the denominator is the count of all patients with any kind of viral load result.

**Disaggregates**

So far this is pretty simple, but, of course, the real difficulty comes in identifying and calculating all the necessary disaggregates. In particular, disaggregates are to be classified by whether the VL gathered was *routine* or *targetted* (routine and targetted are apparently new to the 2020 reporting requirements). The indicator also asks for disaggregations by age and sex at birth, disaggregations by those who are pregnant or breastfeeding and the following key populations: “People who inject drugs (PWID); Men who have sex with men (MSM); Transgender people (TG); Female sex workers (FSW); or People in prison and other closed settings”.

Unless I’m missing something, we don’t really have an easy way to identify all the disaggregates PEPFAR asks for. In particular, determining whether a viral load was ordered as part of a routine checkup or not probably requires an additional observation, as we would need for PWID, MSM, TG, FSW, and prisoners. For the purposes of our Notice D work, I would suggest that we focus on the disaggregates for sex and age as these are likely to be there for any data set we use.

**Qualified Patients**

The last thing we need to determine is whether patients have been on ARTs for at least 3 months in our sample data set. How we go about doing this is highly dependent on the sample data set we are working with, as it’s likely represented in different ways in different data sets. I don’t have a concrete proposal for what we do here, but would welcome any suggestions.

@akanter Are there relevant CIEL concepts that I might’ve missed?

I’d also value input from any PEPFAR implementors as to whether or not any of the proposals I have made here will over-simplify things too much to make this a valid test of calculating the TX_PVLS indicator.