EACS2023: 249 Statutory health insurance-covered pre-exposure prophylaxis in Germany: changing trends in nationwide tenofovir disoproxil/emtricitabine prescriptions during the COVID-19 pandemic H. Prins , A. Dörre , D. Schmidt Robert Koch Institute, Berlin, Germany General data Abstract category: Access and delivery of care Abstract body Purpose: In 2019, Germany introduced a law to reimburse high-incidence populations for pre-exposure prophylaxis (PrEP), prescribed as tenofovir-disoproxil/emtricitabine (TDF/FTC), via statutory-health-insurance (SHI). We studied changes in TDF/FTC-prescriptions after the implementation of this law and during the COVID-19 pandemic. Method: We performed an interrupted time series analysis with monthly prescriptions per defined time period as the outcome. We considered the introduction of SHI-covered PrEP (09/2019) as an interruption, and four COVID-19 waves and two national lockdowns (2020-2021) as explanatory variables. We extrapolated prescriptions had the lockdowns not occurred, and compared this to the actual prescriptions during the same period of time. We performed sub-analyses based on stratification by five federal states with the highest proportion of PrEP users (Berlin-Brandenburg combined, Bavaria, Hamburg, Hesse, North Rhine-Westphalia). We assessed the models’ goodness-of-fit based on the adjusted R-squared using RStudio. Results: The best fitting linear regression model included SHI-covered PrEP and the first COVID-19 lockdown (04/2020) (Table 1). The decrease in prescriptions during the first lockdown was significant nationally (Figure 1), and in the five federal states for single-month prescriptions (Figure 2). The first lockdown resulted in reductions of 57.7% (95% prediction interval (PI): 23.0–92.4%) for single-month prescriptions, while 17.4% (95% PI: 0.28–34.5%) nationally, and 13.9% (95% PI: -3.67– 31.5%) for three-month prescriptions. Table 1. Results of two linear regression models with the best fit to the actual data, based on the estimate and p-value per variable, and adjusted R-squared Variable in linear regression model Estimate Adjusted R-squared First wave First lockdown First wave First lockdown Intercept 8473.0*** 8473.0*** 0.9661 0.9668 Shift directly after SHI coverage of PrEP 7101.3*** 6972.3*** Monthly change before SHI PrEP -72.1 -72.1 Monthly change after SHI PrEP 401.0*** 406.5*** Monthly change after first wave/first lockdown -1345.8 -2496.5* Significance codes: ***: 0–0.001; **: 0.001–0.01; *: 0.01–0.05. PrEP: pre-exposure prophylaxis; SHI: statutory health insurance. Conclusions: The introduction of SHI-covered PrEP resulted in a doubling of TDF/FTC-prescriptions nationwide in the first month alone. A drop in prescriptions was most apparent after the first lockdown, and particularly affected PrEP initiations, possibly due to reduced healthcare access and behavioural changes. Ongoing monitoring of TDF/FTC-prescriptions is needed to safeguard access to preventative care such as PrEP and particularly PrEP initiation during public health crises like COVID19. General conditions 1. I confirm that I previewed this abstract and that all information is correct. I accept that the content of this abstract cannot be modified or corrected after the submission deadline and I am aware that it will be published exactly as submitted.: Yes 2. I confirm that the submission of the abstract constitutes my consent to publication (e.g. conference website, programmes, other promotions, etc.). : Yes 3. I herewith confirm that the contact details saved in this system are those of the corresponding author, who will be notified about the status of the abstract. The corresponding author is responsible for informing the other authors about the status of the abstract.: Yes 4. I agree that all data provided may be used (saved, stored, processed, transmitted and deleted) in compliance with the privacy policy to provide the services described.: Yes 1 1 1 1 13
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