PREreview of Impact of prescription-free access to sexually transmitted infection screening tests in medical-biological laboratories: cross-sectional analysis of data from clinical laboratories in France

This preprint evaluates France’s nationwide prescription-free access (PFA) pathway for sexually transmitted infection (STI) screening by comparing it with the traditional prescription-based access (PBA) model. Under the new prescription-free access model, individuals can go directly to a laboratory for STI testing without first obtaining a doctor’s referral, whereas the traditional prescription-based pathway requires consultation with a healthcare provider before testing is authorized. The authors used over one million laboratory records collected between 2025 and 2026 to examine who uses each pathway and how testing access relates to STI detection across infections such as chlamydia, gonorrhea, HIV, hepatitis B, and syphilis.

The authors claim PFA substantially broadened STI screening reach, particularly among men and individuals aged 20-39, who were significantly more likely to use the prescription-free pathway than the prescription-based one (47.4% male vs. 36.3% under PBA; 34.0% aged 20–29 vs. 27.4%). This conclusion should be interpreted with caution, as the study does not compare screening rates before and after implementation of the prescription-free policy. While the findings demonstrate demographic differences between individuals using PFA and PBA, they do not definitively establish that PFA increased overall screening uptake or expanded reach relative to prior testing patterns.

In addition, they found that PFA was associated with higher detection of bacterial STIs, particularly Chlamydia (4.6% vs. 3.6%), especially among young women, while viral and chronic infections (syphilis by TSI, HBV, HIV) were predominantly detected through the PBA pathway. This suggests a complementary effect of the two pathways, rather than suggesting that the PBA model should be fully replaced.

Finally, the study found no significant difference in co-infection proportions between the PFA and PBA groups. Instead, age remained the strongest predictor of co-infection burden, with adolescents aged 15-19 demonstrating the highest rates regardless of testing pathway.

Strengths 

• The inclusion of 1.3 million laboratory records provides substantial statistical power and allows for detailed subgroup analyses across sex, age, and infection type. The scale of the dataset strengthens the reliability of descriptive trends and regression findings.

• This study evaluates a recently implemented national healthcare policy with direct implications for STI prevention and healthcare access, making the findings highly relevant for policymakers considering similar systems.

• Unlike pilot studies or controlled intervention settings, this analysis reflects routine clinical practice, which improves the practical applicability of the findings.

• Rather than focusing on a single infection, the study evaluates several bacterial and viral STIs.

• The study uses a robust analytical approach that combines descriptive statistics, bivariate analyses, and multivariable logistic regression. By stratifying regression models by sex and adjusting for age and region, the authors account for potential demographic confounders.

Potential Limitations Not Addressed by the Authors

• Although one of the stated aims of the study was to characterize the behavioral profile of individuals undergoing STI testing, the analysis does not actually include behavioral variables such as sexual practices, number of partners, condom use, prior STI history, or healthcare-seeking behaviors. Instead, the study primarily provides demographic comparisons between testing pathways. Without behavioral data, it is difficult to fully assess the populations being reached through prescription-free access or understand the underlying factors driving differences in STI positivity rates.

• While the findings demonstrate demographic differences between individuals using PFA and PBA, they do not definitively establish that PFA increased overall screening uptake or expanded reach relative to prior testing patterns, as they do not compare screening rates before and after policy implementation.

  • Similarly, the study begins six months post-implementation and has no pre-September 2024 data, making it impossible to quantify net new testers versus pathway switchers. The true incremental reach of PFA cannot be determined.

• All of the authors are associated with the laboratory network/healthcare network Cerballiance, which is where the study data was sourced from. This has the potential to be a conflict of interest if the laboratory network benefits in any way from this research.

• The study only includes data from one laboratory network, Cerballiance, which may not perfectly represent the entire French population despite its regional coverage. Individuals without access to formal laboratory services or those facing socioeconomic barriers may remain underrepresented.

• The PFA/PBA categorization was recorded by reception staff and may not be uniformly applied across all laboratory sites, potentially introducing non-differential misclassification that could bias findings toward the null.

• The study evaluates testing and diagnosis but does not assess downstream outcomes such as treatment initiation, partner notification, or retention in care. Therefore, the broader public health impact of prescription-free testing remains uncertain.

• Although the study evaluates France’s prescription-free STI screening policy, the manuscript provides limited detail about how the policy actually functions in practice, particularly regarding cost and reimbursement. Important contextual information, such as the fact that STI testing is fully reimbursed for individuals under 26 and may be partially or fully covered for older individuals with French social security coverage, is not described. This omission makes it difficult to interpret the true accessibility of the prescription-free pathway and whether financial barriers may still influence who utilizes testing services. A more detailed explanation of the policy’s reimbursement structure would strengthen the public health interpretation of the findings.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.