Usage of purchased self-tests for HIV and sexually transmitted infections in Amsterdam, the Netherlands: results of population-based and serial cross-sectional studies among the general population and sexual risk groups
Bibliographic record
Abstract
Objectives There are limited data on the usage of commercially bought self-tests for HIV and other sexually transmitted infections (STIs). Therefore, we studied HIV/STI self-test usage and its determinants among the general population and sexual risk groups between 2007 and 2015 in Amsterdam, the Netherlands. Setting Data were collected in four different studies among the general population (S1–2) and sexual risk groups (S3–4). Participants S1–Amsterdam residents participating in representative population-based surveys (2008 and 2012; n=6044) drawn from the municipality register; S2–Participants of a population-based study stratified by ethnicity drawn from the municipality register of Amsterdam (2011–2015; n=17 603); S3–Men having sex with men (MSM) participating in an HIV observational cohort study (2008 and 2013; n=597) and S4–STI clinic clients participating in a cross-sectional survey (2007–2012; n=5655). Primary and secondary outcome measures Prevalence of HIV/STI self-test usage and its determinants. Results The prevalence of HIV/STI self-test usage in the preceding 6–12 months varied between 1% and 2% across studies. Chlamydia self-tests were most commonly used, except among MSM in S3. Chlamydia and syphilis self-test usage increased over time among the representative sample of Amsterdam residents (S1) and chlamydia self-test usage increased over time among STI clinic clients (S4). Self-test usage was associated with African Surinamese or Ghanaian ethnic origin (S2), being woman or MSM (S1 and 4) and having had a higher number of sexual partners (S1–2). Among those in the general population who tested for HIV/STI in the preceding 12 months, 5–9% used a self-test. Conclusions Despite low HIV/STI self-test usage, we observed increases over time in chlamydia and syphilis self-test usage. Furthermore, self-test usage was higher among high-risk individuals in the general population. It is important to continue monitoring self-test usage and informing the public about the unknown quality of available self-tests in the Netherlands and about the pros and cons of self-testing.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".