Prevaccine Human Papillomavirus Status in Invasive and Intraepithelial Lesions of the Vulva in New Zealand Women.

OBJECTIVE: The human papillomavirus (HPV) vaccine, introduced in New Zealand (NZ) in 2008, is predicted to substantially lower the incidence of HPV-associated precancers and cancers. The aim of this study is to estimate the proportion of vulvar intraepithelial neoplasia (VIN) lesions and invasive vulvar squamous cell carcinomas (SCCV) attributable to HPV in NZ women treated by the Auckland Regional Gynecological Oncology Service, covering an estimated 50% of the NZ population. MATERIALS AND METHODS: Polymerase chain reaction and reverse hybridization were used to analyze retrospective histologically proven SCCV from 1990 to 2007 and VIN lesions from 2000 to 2007 for HPV content and genotype in a collaborative study with the Catalan Institute of Oncology. Immunohistochemistry for p16INK4a was performed on SCCV, which were attributed to HPV if both tested positive. RESULTS: Polymerase chain reaction testing for HPV content and genotype was performed on 66 VIN lesions (all high-grade squamous intraepithelial lesions) and 189 SCCV. In addition, p16 immunohistochemistry was performed on 168 of the 189 SCCV (88.9%) tested for HPV-DNA. Overall, 61 SCCV cases (36.3%) were attributed to HPV (HPV+/p16+), and 89 SCCV cases (53%) were considered to have developed independently of HPV (HPV-/p16-). Known high-risk HPV genotypes were present in 96.8% of HPV-DNA-positive vulvar high-grade squamous intraepithelial lesions and 98.4% of HPV-attributable SCCV. Human papillomavirus 16 represented the most common genotype in both. CONCLUSIONS: Overall, the HPV vaccine is likely to substantially alter the profile of SCCV in our region. The results provide a baseline assessment of the HPV status of vulvar neoplasia before the introduction of the HPV vaccine.

autopsych: An R Shiny tool for the reproducible Rasch analysis, differential item functioning, equating, and examination of group effects.

In this paper, we present autopsych, a novel online tool that allows school assessment experts, test developers, and researchers to perform routine psychometric analyses and equating of student test data and to examine the effect of student demographic and group conditions on student test performance. The app extends current open-source software by providing (1) extensive embedded result narration and summaries for written reports, (2) improved handling of partial credit data via customizable item-person Wright maps, (3) customizable item- and person-flagging systems, (4) item-response theory model constraints and controls, (5) many-facets Rasch analysis to examine item bias, (6) Rasch fixed item equating for mapping student ability across test forms, (7) tabbed spreadsheet outputs and immediate options for secondary data analysis, (8) customizable graphical color schemes, (9) extended ANOVA analysis for examining group differences, and (10) inter-rater reliability analyses for the verifying the consistency of rater scoring systems. We present the app's architecture and functionalities and test its performance with simulated and real-world small-, medium-, and large-scale assessment data. Implications and planned future developments are also discussed.