Analysis of HIV pre-exposure prophylaxis (PrEP) needs and PrEP use in Germany among men who have sex with men

To estimate the total size of the four PrEP needs groups in the MSM population in Germany, we made assumptions about self-selection biases for these groups in EMIS-2017. Based on the PrEP user estimates at different time points, we determined that 30–35% of all PrEP users at these time points in Germany participated in EMIS-2017 and the first two rounds of the PrApp-Survey surveys, suggesting very high self-selection biases for actual PrEP users (see Table 1). For comparison, 20,000 male participants not diagnosed with HIV took part in EMIS-2017, which corresponds to almost 6% of the estimated non-HIV-diagnosed population of gay men living in Germany (N = 350,000, 1.5% of adult males aged 15–64).

We assume that the intention to use PrEP is associated with a self-selection bias that lies in-between the self-selection bias for PrEP users (30-35%) and the average self-selection bias for survey participants (6%). We also assume that the size of the bias is associated with factors such as PrEP use intention, partner numbers, self-perceived risk, and PrEP awareness — the key factors we identified in a multivariable regression analysis (see Table S5). We chose weights of a magnitude of 2–2.5 as a combined effect of higher partner numbers and PrEP use intention, which is slightly lower than the empirically derived weight of 3 for EMIS-2017 respondents with a syphilis diagnosis in the previous 12 months. The distribution of partner numbers, PrEP awareness, and recency of HIV testing that we consider a surrogate for self-perceived risk in this context in the four groups is shown in Fig. S1.

Table S6 provides absolute data for the size of the four PrEP need groups in EMIS-2017.

Based on these assumptions we provide range estimates for the sizes of all four PrEP need groups in Table 3. The minimum size for the PrEP need groups would reflect a self-selection bias similar to actual PrEP users, the maximum size would reflect no specific self-selection bias for PrEP use intention. The two intermediate variants represent two different — arbitrary — assumptions of intermediate self-selection biases. The estimated PrEP need ranges between 49,500 and 109,000 men, the unsatisfied PrEP need between 33,500 and 93,000 men for scenario 1 as of June 2020, and between 27,500 and 87,000 for scenario 2, assuming no change in PrEP needs between end of 2017 and June 2020, and using a total population size estimate of 350,000 adult gay men not diagnosed with HIV living in Germany (16).

In the next step, we compare how this estimated PrEP need from 2017 compares to the number of estimated PrEP users in 2020. To calculate a regional PrEP need distribution in Table 4, we used the totals from variant 1 shown in Table 3 and apply it to the scenario 1 total of current PrEP users (N = 15,600) and the scenario 2 total of current PrEP users (N = 21,600). The variant 1 estimate assumes that PrEP use intention and the number of sex partners in the last 12 months are the main determinants for the selection bias.

Table 4 shows proportions of satisfied intention to use PrEP and unmet needs with no intention to use PrEP by federal state by June 2020. Figure 3a shows the almost perfect correlation between the regional distribution of intention to use PrEP in November 2017 and the distribution of the gay population in Germany, while Fig. 3b shows how intention to use PrEP correlates with actual PrEP use by mid-2020, suggesting an unequal regional distribution of unmet needs, regardless which of the two scenarios is used.

There were a total of 246 licensed HIV specialists listed on the DAGNÄ website in early 2021. We use this information as proxy for the number of licensed PrEP prescribers in Germany. Since this number differs regionally by federal state, it might explain the unequal distribution of unsatisfied PrEP needs. Thus, we analysed the correlation between satisfied PrEP needs and number of HIV specialists per 10,000 gay men practicing in the respective federal state, and found a strong correlation between these two parameters (Fig. 4).

We focus now on the validity of the assumptions used for the two scenarios to estimate the total number of PrEP users in Germany:

For both scenarios we would like to discuss primarily the validity of the (PrApp-Survey-derived) proportions of on-demand/intermittent use and the validity of the regional distribution, particularly the proportion of PrEP users from Berlin. To estimate proportions of on-demand/intermittent use and regional distribution of PrEP users, we used data on PrEP use from three consecutive online surveys among MSM in Germany. In general, health and sexual behaviour online surveys among MSM such as EMIS-2017 provide reliable data on proportional regional and age distribution (Marcus et al. 2009; Scholz et al. 2019). However, mono-thematic online surveys (such as the PrApp-Surveys) may be affected by more unpredictable participation biases, determined by (for example) unmet needs. Robust evidence for a large online survey participation bias are the very similar proportions of EMIS-2017 respondents and PlanetRomeo user profiles indicating PrEP use in November 2017 and June 2020, despite the more than tenfold increase of PrEP users (see supplemental Table S4). We tried to calibrate online survey data with PrEP use and prescription data from other sources. From these calibrations, we can conclude that self-selection biases for PrEP users to participate in online surveys can be very high and can vary considerably between surveys, depending probably on various factors such as framing of the survey, recruitment methods and advertising of the survey, perceived relevance to individuals, community mobilisation around PrEP access, and competing social and health issues. In our experience, the proportion of estimated PrEP users in Germany participating in online surveys varied between 37% in 2017 and 6% of all MSM actually using PrEP in 2020 (see Table 1). The low participation rate in spring 2020 is probably explained by a reduced intensity of recruitment activities, the coincidence with Coronavirus lockdowns and the overwhelming dominance of the Coronavirus pandemic in public perception and discourse. It is thus conceivable that participation biases could also affect the proportions of on-demand/intermittent PrEP use reported, and differ between regions with high satisfied demand and easy access to PrEP such as Berlin, and regions with higher access barriers. This could result in disproportional lower participation of PrEP users from Berlin in the PrApp-Surveys, as well as disproportional lower participation of on-demand/intermittent PrEP users in the consecutive PrApp-Survey rounds.

The participation bias in Berlin would reduce the discrepancy between the PrApp-Survey-derived PrEP user proportion and the prescription-derived distribution, and would thus be more consistent with the alternative explanations for these discrepancies.

To assess the plausibility of scenario 2, we have to discuss primarily the probability that the total number of PrEP users exceeds the estimate of scenario 1 as suggested by the calculated number of PrEP user profiles on PlanetRomeo. While the number of PlanetRomeo profiles indicating PrEP use is almost identical to the estimate of scenario 1, this coincidence fails to consider that — as outlined above — a proportion of 30% of PrEP users does not include PrEP use information in their online profiles. However, we must consider that a relevant proportion of PrEP profiles might belong to men already diagnosed with HIV who prefer to state in their profile that they take antiretroviral drugs for prevention rather than for treatment. Taking these two pieces of information into account, the PrEP profile number on PlanetRomeo supports that there are approximately 17,400 current PrEP users among MSM in Germany, a number that falls between our scenario 1 and 2 estimates. This number could be even higher if we consider that not all PrEP users may have a profile on PlanetRomeo. Furthermore, abandoned profiles, people creating multiple profiles, and former PrEP users not updating their profile information could lead to an overestimation of PrEP use based on the number of online profiles.

Our regional distribution estimates reveal a distinct discrepancy with regard to the number and proportion of PrEP users in Berlin between different data sources, most pronounced between PrApp-Survey data and statutory health insurance data on prescriptions.

In the prescription data, prescriptions are allocated to the geographical area of the prescribing care provider. This results in well-known geographical biases for HIV drug prescription data, since people living in rural and underserved areas usually attend HIV specialists in urban centres for HIV treatment or PrEP prescription. This bias is particularly pronounced in the prescription data for Berlin and Hamburg where the number of prescriptions is thus higher, and is reversed in the surrounding federal states of Schleswig-Holstein, Lower Saxony, Mecklenburg-Western Pomerania, Brandenburg, Saxony-Anhalt, and Thuringia. However, if we assume that the discrepancy between the proportion of PrEP users in the PrApp-Survey and the prescription data is fully explained by this bias, we would have to assume large shifts from other federal states to Berlin to obtain prescriptions there. This is not impossible, but, given its magnitude, appears implausible. We find it more plausible to account for this difference between the PrApp-Survey and prescription data-derived regional distribution for Berlin by a combination of drug prescription shift to Berlin and a disproportional self-selection bias of PrEP users from Berlin in the PrApp-Survey. This could be the case if we assume that the comparatively high levels of satisfied PrEP need reached in Berlin would negatively impact the participation rate in a mono-thematic PrEP-focussed survey.

Thus, we believe it is likely that neither prescription data nor the PrApp-survey data represent the proportion of PrEP users living in Berlin correctly. The proportions observed in the PlanetRomeo PrEP profiles might be closer to the real distribution than PrApp-Survey and drug prescription data, as long as we can safely assume that PrEP profiles of men already diagnosed with HIV do not introduce a specific regional bias. PrEPuser profiles on GayRomeo represent a larger fraction of PrEP users in Germany than PrApp survey samples; the limitations inherent in the distribution of regional prescription data have been discussed. If any, one could assume that reporting PrEP use instead of treatment might be preferred in areas with higher levels of HIV-associated stigma, i.e., outside large cities, thus further increasing — rather than reducing — the regional inequalities that we identified.

Another possible reason for unreliability of regional distribution of drug prescription data is the inability to distinguish between prescriptions for HIV treatment and prescriptions for PrEP use. As described above, we designated excess prescriptions of TDF/FTC from the third quarter 2019 onwards — assuming no relevant changes in the amount of TDF/FTC used for HIV treatment — as PrEP prescriptions. If TDF/FTC use in HIV treatment developed differently in some regions, this would affect the regional distribution.

If our assumptions on self-selection biases are correct, different self-selection biases in countries with different social and political contexts must be considered for multi-country surveys such as EMIS-2017, which would have consequences for comparability of estimates for PrEP use and PrEP needs across countries (Hayes et al. 2019).

It is also difficult to estimate the self-selection biases for survey participants intending to use PrEP or “in need of PrEP” based on number of reported sex or CAI partners in the last 12 months. There are indications that sexual activity is an important factor contributing not only to perceived PrEP need, but also to self-selection for survey participation, suggesting that higher sexual activity is associated with a higher probability for survey participation. One aspect of this self-selection is the probability of seeing a survey recruitment message on a dating app. One of the apps we used for recruitment broadcasted each survey advertisement only for a period of 24 h. That means that frequent app users would have had a higher likelihood to see this message than infrequent users. This needs to be considered when extrapolating absolute numbers of PrEP users, intentional PrEP users, and men “in need of PrEP” based on their sexual risks, to the total MSM population of a country from proportions found in online surveys. In addition, other observations on the EMIS data suggest self-selection biases with increased sexual activity: e.g., the comparison of self-reported syphilis diagnoses in the last 12 months with syphilis cases among MSM reported to the German infectious disease surveillance system suggests a threefold higher probability that men diagnosed with syphilis had participated in the survey compared to a random distribution [personal communication by Ulrich Marcus and Susi Schink]. In the absence of an established method to estimate survey participation biases, the estimates based on sexual activity we chose are arbitrary, yet plausible. For this reason, we reported ranges with a minimum and a maximum estimated number.

A sizeable fraction of MSM indicated an intention to use PrEP if PrEP were available and affordable in a country, but did not report sexual risks defined as two or more non-steady CAI partners in the previous 12 months. These may be men who have been able to effectively manage risks, e.g., by using condoms, but who would consider switching to PrEP use as an alternative or additional risk management strategy if PrEP became available to them. Due to this group of men who would consider using PrEP, but who have also been able to manage their HIV risks effectively by using condoms, it is inherently difficult to estimate PrEP needs. We can show that this group of men with PrEP use intention and low risk reported in the past is not very different in terms of sexual partner numbers and HIV test uptake from the large group of men with no PrEP use intention and low reported risk (see Fig. S1). The groups with intention to use PrEP differ from the groups not intending to use PrEP by a slightly higher awareness of PrEP (see Table S4). How many of the men will use PrEP will likely depend on circumstances such as how convenient access to and monitoring of PrEP is, how PrEP use is advertised, how PrEP use is perceived by the gay community, how people assess risks and adverse effects of PrEP use for themselves, how satisfied these men are with the quantity and quality of the sex they have, and their expectations as to how this would change if they take PrEP. In addition, the size of this group may increase as PrEP knowledge and awareness is increasing in the MSM population.

With our approach to estimate PrEP needs in Germany, we arrive at estimates of approximately 52,000 (variant 2) to 66,000 (variant 1) MSM currently in need of PrEP, of which approximately 16,000 (scenario 1) to 22,000 (scenario 2) were taking PrEP as of June 2020. The estimated number of PrEP need is close to the actual estimated number of MSM diagnosed with HIV in Germany (an der Heiden et al. 2019) and would be equivalent to 7.5% of the estimated total MSM population aged 18–65 years old, or 15% of the openly gay population aged 18–65 years old.

The proportion of MSM with intention to use PrEP in 2017 that is using PrEP in 2020 may vary between approximately 63% and 88% in Berlin (or even up to 104% if we use the regional distribution of PlanetRomeo PrEP profiles, which would mean that a larger proportion of people took PrEP in Berlin in June 2020 than people indicating PrEP use intention in 2017) and is below or around 20% in the federal states with the lowest coverage. This suggests marked regional differences and persistent barriers to access PrEP for people who intend to use PrEP.

One possible barrier after costs were covered by health insurance would be limited access to health-care providers licensed to prescribe PrEP covered by health insurance, either due to geographic distance, limited number of prescribers, or inconvenience associated with scheduling an appointment, or due to individual barriers such as fear to discuss safer sex or sex in general with a health-care provider, or lack of perceived social support for taking PrEP (Mayer et al. 2020). The strong correlation between satisfied PrEP needs and number of HIV specialists per 10,000 gay men practicing in the respective federal state suggests that easy access to PrEP prescribers has indeed a large impact on meeting the needs of potential PrEP users.

For MSM in Germany, taking up PrEP requires outing oneself towards a health-care provider, just as for other medical interventions such as HIV testing. Thus, factors identified as barriers to uptake of regular HIV testing among MSM in Germany such as problems identifying and making appointments with a gay-friendly health-care service, not feeling comfortable with being out about their own sexual orientation towards friends, family members, and co-workers, or not feeling at risk of HIV infection due to perceived familiarity with sex partners will likely play a similar role for PrEP uptake (Marcus et al. 2016). In particular, the problem of identifying and making appointments with a gay-friendly health-care service is likely to play a major role, since the number of services providing PrEP prescriptions is much smaller than the number of services providing HIV testing. This assumption is supported by the strong correlation between satisfied PrEP needs and number of HIV specialists at federal state level. This correlation is exacerbated by the practical difficulties of getting PrEP prescriptions that are experienced by men not living in or near the largest cities where most of the HIV specialists practice. While the skewed geographical distribution of HIV specialists appears to have no major impact on access to treatment for HIV, this skewed distribution still appears to be a major barrier for accessing drugs for prophylaxis. This assumption regarding PrEP uptake in Germany is supported by a similar analysis of the French EMIS-2017 dataset which concludes: “(PrEP-)Eligible MSM who are not using PrEP are mostly younger, […] living in small cities, […] and more distant from preventive health care and information than PrEP users. Despite free PrEP availability in France, results suggest that PrEP is not fully accessible and that there is a need to increase PrEP demand and decentralize PrEP delivery” (Annequin et al. 2020).

If we consider MSM who might benefit from but are currently not taking PrEP, a proportion of almost 50% of them may not have had an intention to use PrEP when asked in 2017. For informed decision-making, these men may need balanced and targeted information on PrEP. They may benefit from being offered PrEP proactively and from opportunities to discuss advantages and disadvantages of taking PrEP. This would require a proactive approach including sexual history taking by health-care providers and HIV test counsellors to actively provide this information, and might encourage demand by MSM who might probably benefit from PrEP.