Good agreements between self and clinician-collected specimens for the detection of human papillomavirus in Brazilian patients
Mem. Inst. Oswaldo Cruz
;
109(3): 352-355, 06/2014. tab
Article
in English
| LILACS
| ID: lil-711737
ABSTRACT
Women infected with human papillomavirus (HPV) are at a higher risk of developing cervical lesions. In the current study, self and clinician-collected vaginal and cervical samples from women were processed to detect HPV DNA using polymerase chain reaction (PCR) with PGMY09/11 primers. HPV genotypes were determined using type-specific PCR. HPV DNA detection showed good concordance between self and clinician-collected samples (84.6%; kappa = 0.72). HPV infection was found in 30% women and genotyping was more concordant among high-risk HPV (HR-HPV) than low-risk HPV (HR-HPV). HPV16 was the most frequently detected among the HR-HPV types. LR-HPV was detected at a higher frequency in self-collected; however, HR-HPV types were more frequently identified in clinician-collected samples than in self-collected samples. HPV infections of multiple types were detected in 20.5% of clinician-collected samples and 15.5% of self-collected samples. In this study, we demonstrated that the HPV DNA detection rate in self-collected samples has good agreement with that of clinician-collected samples. Self-collected sampling, as a primary prevention strategy in countries with few resources, could be effective for identifying cases of HR-HPV, being more acceptable. The use of this method would enhance the coverage of screening programs for cervical cancer.
Full text:
Available
Index:
LILACS (Americas)
Main subject:
Papillomaviridae
/
Specimen Handling
/
Cervix Uteri
/
Papillomavirus Infections
Type of study:
Diagnostic study
/
Prognostic study
Limits:
Adult
/
Aged
/
Female
/
Humans
Country/Region as subject:
South America
/
Brazil
Language:
English
Journal:
Mem. Inst. Oswaldo Cruz
Journal subject:
Tropical Medicine
/
Parasitology
Year:
2014
Type:
Article
Affiliation country:
Brazil
Similar
MEDLINE
...
LILACS
LIS