PrEP indicators

Rationale for reporting

Estimates of ‘PrEP coverage’ provide insights into the extent to which a PrEP programme has reached a target population for PrEP, and conversely, how many people who could benefit from PrEP are currently not accessing it (‘unmet need’).

Low PrEP coverage may signal potential issues that warrant further investigation, ranging from low PrEP awareness and/or willingness to use PrEP, to more structural barriers to access (e.g. financial or geographical barriers).

Related indicators: United States Centres for Disease Control and Prevention: Core indicators for monitoring the Ending the HIV Epidemic initiative: Preexposure Prophylaxis (PrEP) Coverage [12].

Data collection methodology

For the numerator, data on PrEP use can be derived from prescription and claims databases, and/or collected continuously in PrEP facility registries to be aggregated periodically (see ‘Current PrEP users: data collection methodology’).

For the denominator, the size of the population in need of PrEP can only be estimated by combining routine surveillance data with research efforts (e.g. surveys based on local criteria for eligibility).

For each relevant key population for PrEP, one can multiply the following components: (i) the estimated number of people who belong to a specific key population, and (ii) the estimated proportion of people from that key population who can be considered in need of PrEP.

Component (i) can be estimated based on surveys held among the general populations (e.g. census data). Component (ii) can be estimated based on surveys held among specific key populations (e.g. as in sheets 1.2 and 1.3).

Disaggregation

It is highly recommended to disaggregate coverage data by the relevant key populations for PrEP (see sheet 4.1).

Limitations and anticipated challenges

Limitation: This indicator does not represent a true proportion of all those in need of PrEP who are currently using it, since the same (cross-sectional) size estimation of the eligible population is used repeatedly over time as denominator. In reality, the size of this population fluctuates continuously, as HIV risk (and hence, PrEP eligibility) is a fluid concept.

Challenge: Estimating the size of the population in need of PrEP based on large-scale surveys requires extensive financial and human resources.

Possible mitigation strategy: Countries could consider integrating questions to elicit PrEP eligibility (according to local guidelines) within existing surveys (see also sheets 1.2 and 1.3). In addition, national estimates of PrEP eligibility for certain key populations could be derived from existing (international) survey data (e.g. EMIS-2017 data for MSM; see also [10]).

Challenge: Previous experiences suggest that different approaches to conduct size-estimations can yield different outcomes to define the population in need of PrEP.

Possible mitigation strategy: Triangulating different data sources (e.g. by conducting different surveys to estimate the proportion of PrEP-eligible people among a key population) may help mediate the biases of individual data sources.

Data collection methodology

For the numerator, data on PrEP use can be derived from prescription and claims databases, and/or collected continuously in PrEP facility registries to be aggregated periodically (see ‘Current PrEP users – data collection methodology’).

For the denominator, data on new HIV diagnoses can be obtained from (national) HIV-surveillance databases.

For examples, see also [13] and [14].

Limitations and anticipated challenges

Limitation: This indicator is not a true measure of coverage (i.e. it is not a proportion), but compares PrEP use to the ‘epidemic need’ for PrEP, based on the number of new HIV diagnoses. No thresholds have been currently established that indicate whether a specific PNR could be considered as acceptable or favourable.

Nevertheless, this metric may prove particularly useful to reveal trends over time, and to allow for some comparison across populations and/or geographical areas.

Limitation: The number of new HIV diagnoses does not equate HIV incidence, particularly in settings or populations with high rates of immigration and/or a large number of people with a late HIV diagnosis. The proposed denominator may therefore be an imperfect proxy measure for the actual need of PrEP.

Possible mitigation strategy: Consider only including people newly diagnosed with HIV in the denominator, and not people who received a positive HIV test upon, or shortly after, migration to the current country of residence (e.g. a new HIV diagnosis among people with a low CD4 count or people who were already on antiretrovirals and virally suppressed at the time of their latest HIV test).

Rationale for reporting

Effective PrEP use is not necessarily defined by uninterrupted longitudinal use, given that individuals may use PrEP on-demand and/or ‘cycle’ in and out of periods of substantial risk of HIV.

In the light of this challenge, the ECDC expert panel did not find consensus on a meaningful timepoint up until which to assess PrEP continuation rates in order to evaluate the performance of PrEP programmes.

Yet, it was agreed that the time of PrEP initiation provides a useful starting point, since it gives a baseline indication of ‘PrEP need’, ideally based on a judgement of HIV risk as part of the PrEP eligibility screening process.

Given that HIV risk is unlikely to change on the short-term for a large group of people, focusing on sustained PrEP use after initiation might reveal potential shortcomings of a PrEP programme to sufficiently support clients into using PrEP when they need it, or to access follow-up care.

When this indicator is disaggregated by user characteristics (e.g. ‘key populations’ for PrEP), it may reflect whether certain population groups might disproportionately experience barriers to continuous engagement with PrEP (see sheet 4.1).

It should be noted that experience with this indicator is currently too low to interpret low continuation rates as ‘PrEP programme failures’, as users may discontinue PrEP for many different, valid reasons. Countries are encouraged to pilot this indicator, if feasible, and report relevant experiences with its use.

Furthermore, we stress the synergistic nature of using this indicator alongside additional evaluation and implementation science, to gain additional insights into the potential reasons of why PrEP users do not continue to use it after initiation, and to judge the ultimate relevance of this indicator on the long term.

Related indicators:

• WHO: Implementation tool for pre-exposure prophylaxis (PrEP) of HIV infection, Module 5: Monitoring and Evaluation – Early PrEP Continuation [6].
• PEPFAR: Monitoring, Evaluation, and Reporting Indicator Reference Guide. MER 2.6 [17].

Data collection methodology

The indicator is generated by counting the number of people who initiated PrEP in the previous reporting period, and who received a PrEP refill or returned for a PrEP follow-up visit in the subsequent 12 months.

A ‘PrEP follow-up visit’ is defined as any routine contact between the PrEP user and the provider for the purpose of clinical PrEP guidance. It may consist of an in-person visit, online appointment or phone call. The following databases could be used to report on this indicator:

• Prescription data: The data collection process should make a distinction between ‘written’ prescriptions (by healthcare providers) and ‘filled’ prescriptions (dispensed by pharmacies). The latter is a closer proxy to the actual use of PrEP. These have the benefit of using available data at a population level.
  The potential challenges for collecting data based on prescriptions are as follows:
  • The need for an algorithm to distinguish PrEP from other uses of TDF (e.g. hepatitis treatment).
  • This process does not take into account PrEP obtained outside the official prescription system.
  • There is a limited opportunity to collect client-level data (e.g. on membership of key population); and a need for unique identifiers to track the prior use of PrEP.
  Moreover, the prescription database should be able to provide information on the date the PrEP prescription was issued or filled in, in order to measure refills obtained within 12 months after initiation.
• Claims data: This data collection process uses data from a health insurer (private or public) on ‘filled’ PrEP prescriptions. The benefits include: close proxy to the actual use of PrEP, and the use of available data at a population level.
  The potential challenges for collecting data based on claims are as follows:
  • The need for an algorithm to distinguish PrEP from other uses of TDF (e.g. hepatitis treatment).
  • This process does not take into account PrEP obtained outside the health insurance system.
  • There is a limited opportunity to collect client-level data (e.g. on membership of key population); and a need for unique identifiers to track the prior use of PrEP.
  Moreover, the prescription database should be able to give information on the date the PrEP prescription was issued or filled, in order to measure refills obtained within 12 months after initiation.
• Facility registries: These have the benefit of collecting client-level data on a continuous basis. This allows collecting data related to facility visits, possibly including data on self-reported PrEP use, which represents a very close proxy to actual use. In addition, other client-level data could be registered and reported (e.g. on membership of key population). A major challenge relates to the additional administrative burden on local staff tasked with the collection of this data.

Disaggregation

It is highly recommended to disaggregate this indicator by the following user characteristics:

Assigned sex at birth and gender identity, age, key populations for PrEP (see Sheet 4.1).

Limitations and anticipated challenges

Limitation: Population-level databases (e.g. prescription and claims data) have limited ability to distinguish infrequent and periodic PrEP use, from continuous and daily PrEP use. These databases provide information on the volume of PrEP distributed at a certain time point, and not on whether or when PrEP was actually used, nor on how it was intended to be used by the recipient.

Challenge: PrEP use does not necessarily align with ‘PrEP need’. Moreover, PrEP use can have a ‘cyclical’ nature (according to fluctuating HIV risk), so PrEP discontinuation does not necessarily imply sub-optimal use.

Possible mitigation strategy: Perform further investigation into real-world ‘patterns of PrEP use’ and into the reasons for PrEP discontinuation (see ‘optional additional data collection’ below).