HPV Infection Clinical Trial
— HPVOfficial title:
A Pilot Study on HPV and Cervical Cancer Screening in Mumbai
The Study goals are to test feasibility and acceptability of point of care HPV testing with 227 women already accessing an existing cancer screening program in Mumbai, India. Describe HPV infection types in women screened. Compare if the quality of HPV clinician-collected and self-collected samples are equally efficient to detect HPV and cervical cancer precursor lesions. Compare the agreement between HPV GeneXpert and HPV HC2 test on the clinician-collected sample
Status | Not yet recruiting |
Enrollment | 227 |
Est. completion date | May 2018 |
Est. primary completion date | April 2018 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | Female |
Age group | 30 Years to 55 Years |
Eligibility |
Inclusion Criteria: - Females aged 30 to 55 years (the age when screening for HPV and cervical cancer is recommended) who participate in the TMH cancer screening outreach program. Exclusion Criteria: - Women with a known diagnosis of cancer, any other terminal diagnosis, or who are pregnant |
Country | Name | City | State |
---|---|---|---|
India | Tata Memorial Hospital | Mumbai | Mharashtra |
Lead Sponsor | Collaborator |
---|---|
Tata Memorial Hospital | University of Arizona |
India,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | The feasibility of HPV testing as a single visit approach will be summarized using the appropriate descriptive statistics. | This will be predominantly for counts and percentages for categorical variables (e.g., number of women screened, number of women with incomplete self-collected specimens). In addition to the numbers of participants, the notes from the providers' debriefing regarding culturally appropriateness of HPV testing will be considered. | 3 months | |
Secondary | Overall agreement between clinician-collected versus self-collected samples will be compared using pairwise t tests | For all pairwise comparisons, the agreement will be estimated with the kappa statistic and corresponding 95% confidence intervals. McNemar's test shall be used to detect significant differences in the discordant results (p<0.05), indicating systematic differences by assay or sample type in the detection of HPV. | 3 months | |
Secondary | The focus group data will be analyzed using constant comparative data analysis to identify thematic components and sub-themes. | The research team will conduct a content analysis taking the following approach set forth by Huberman and Miles. | 3 months | |
Secondary | The HPV GeneXpert by health care provider and HPV HC2 samples by health care provider will be compared to study the agreement rates. | For all pairwise comparisons, the agreement will be estimated with the kappa statistic and corresponding 95% confidence intervals. McNemar's test shall be used to detect significant differences in the discordant results (p<0.05), indicating systematic differences by assay or sample type in the detection of HPV. | 3 months | |
Secondary | Documentation of six different genotypes/ genotype families of HPV from cervical tissue samples, which will inform the development of HPV vaccinations specific to the strains of HPV in India. | The prevalence and distribution of HPV genotypes in Indian women with cervical cancer and the trends in HPV infections will be summarized using the appropriate descriptive statistics. | 3 months | |
Secondary | Probe for perceptions about self-collection of vaginal samples to describe acceptability of this process. | Mainly descriptive statistics in percentage, numbers and description | 3 months | |
Secondary | Fit a predictive model of HPV infection(s). | This will be predicted by sexual behavior, sociodemographic and clinical characteristics of the women screened, controlling for the two screening sites. A positive intra-class correlation (ICC) is expected among participants at the same screening site due to commonalities in selection, exposure, mutual interaction, or a combination of those factors. We will fit Linear Mixed Models (LMMs) and Generalized Linear Mixed Models (GLMMs) to account for various levels of correlation among participants (also known as a "mixed-model ANCOVA" (Brown, 2009). We will fit these models using SAS PROC MIXED and GLIMMIX, Version 9.3. | 3 months |
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