Differences in age-specific HPV prevalence between self-collected and health personnel collected specimen in a cross-sectional study in Ghana
Bibliographic record
Abstract
BACKGROUND: HPV infections are ubiquitous and particularly common among sexually active young women. However, there are regional and national variations in age-specific HPV prevalence, which have implications for cervical cancer control. Data on age-specific HPV prevalences for Ghana and most sub-Saharan countries are scanty. Therefore, this study primarily sought to determine the age-specific HPV prevalence among women in a Ghanaian community and to determine whether these prevalences determined with health-personnel and self-collected specimens were comparable. METHODS: In this cross-sectional study, conducted between March 2012 and March 2013, cervical specimens were collected by self- and health-personnel collection from 251 women who were between the ages of 15 and 65 years. HPV present in these specimens were genotyped by a nested-multiplex PCR and Luminex fluoro-microspheres based method. Information on the demographic, sexual and reproductive characteristics of the women were also obtained. A Chi-square test of association was employed to determine the association of the distribution of age groups with each categorised sexual and reproductive characteristic and HPV risk type's status. RESULTS: = 0.011) HPV prevalences. The age-specific HPV prevalence pattern for each of the HPV risk types, determined with self-collected specimen, showed three peaks (at 20-24 years; 40-44 years and ≥ 55 years), while those determined with health-personnel collected specimen, showed two peaks (at 20-24 years and ≥ 55 years) for each HPV risk type's prevalence pattern. The high risk HPV prevalences determined with self-collected specimen were often higher than those determined with health-personnel specimen for the age groups between 25 and 45 years, who are mostly targeted for screening by HPV testing. Additionally, there were interesting variations in patterns of age-specific HPV genotype-specific prevalence between the two specimen collection methods. CONCLUSIONS: The usefulness of self-collected specimen for high risk HPV burden determination and the existence of a two peaked and three peaked age-specific HPV prevalences in Ghana have been clearly indicated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".