Exploring 4 years of HIV pre-exposure prophylaxis as a statutory health insurance benefit in Germany—a longitudinal claims data analysis
Bibliographic record
Abstract
Background: Introduction of HIV pre-exposure prophylaxis (PrEP) into German statutory health insurance in September 2019 reduced barriers to an effective tool for HIV prevention. Objective: This study aims to provide a longitudinal analysis of HIV-PrEP care in a real-world setting in Germany between 09/2019-08/2023 using claims data from the insurance provider BARMER. Methods: We assessed anonymized claims data to analyze PrEP care and user characteristics. PrEP courses were defined and analyzed based on initiations, discontinuations and reinitiations, as well as proportion of days covered (PDC) as metric of adherence, post-exposure prophylaxis (PEP) and incident HIV infections. Results: A total of 5,208 PrEP users with a median and average observation time of 1.3 and 1.7 years were identified, totaling to 8,822 person-years. By the end of observation 58% were active PrEP users and 43% were still on their first PrEP course. Median age was 34 years (IQR 28-43) and 98.6% were male. Median PDC was 91.3% (IQR 78.9-98.6%) for all observed courses. PrEP initiations increased again in 2022 after a decrease in 2020 and 2021. Of 267 PEP courses, 73.8% took place before PrEP initiation. HIV incidence rate was 0.07/100 person-years. Out of the six detected HIV infections five occurred after PrEP discontinuation. Conclusion: Overall PrEP pill coverage was high and PrEP proved to be highly effective in preventing HIV. No stagnation or decline in PrEP uptake was observed, suggesting that PrEP needs in Germany are not yet fully covered. In 4 years of observation we saw a considerable number of PrEP discontinuations and reinitiations, which indicates that PrEP is utilized in accordance with specific user needs. Most PEP occurred before PrEP initiation, indicating that HIV risk was recognized which eventually led to PrEP prescription. We found a negative association between individuals' total PDC and length of PrEP use, suggesting need for interventions to increase adherence among long-term PrEP users. This study supports previous findings of high PrEP demand in men, assumingly mostly men who have sex with men and suggests that barriers to accessing PrEP and gaps in provision still exist, at least among non-males who would benefit from PrEP.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".