Mixture models for undiagnosed prevalent disease and interval-censored incident disease: applications to a cohort assembled from electronic health records.

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early time points and overestimates later risks. We propose a general family of mixture models for undiagnosed prevalent disease and interval-censored incident disease that we call prevalence-incidence models. Parameters for parametric prevalence-incidence models, such as the logistic regression and Weibull survival (logistic-Weibull) model, are estimated by direct likelihood maximization or by EM algorithm. Non-parametric methods are proposed to calculate cumulative risks for cases without covariates. We compare naive Kaplan-Meier, logistic-Weibull, and non-parametric estimates of cumulative risk in the cervical cancer screening program at Kaiser Permanente Northern California. Kaplan-Meier provided poor estimates while the logistic-Weibull model was a close fit to the non-parametric. Our findings support our use of logistic-Weibull models to develop the risk estimates that underlie current US risk-based cervical cancer screening guidelines. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Assessment of a New Lower-Cost Real-Time PCR Assay for Detection of High-Risk Human Papillomavirus: Useful for Cervical Screening in Limited-Resource Settings?

Inexpensive and easy-to-perform human papillomavirus (HPV) tests are needed for primary cervical cancer screening in lower-resource regions. In a convenience sample of 516 residual exfoliative cervical specimens from the Kaiser Permanente Northern California and U.S. National Cancer Institute Persistence and Progression Study, we assessed the agreement and clinical performance of a simple, inexpensive real-time PCR assay for the detection of 13 carcinogenic HPV types (the H13 assay; Hybribio, Hong Kong) that is marketed in limited-resource settings compared to previous testing by the Hybrid Capture 2 assay (HC2; Qiagen, Germantown, MD) and the Onclarity assay (BD Diagnostics, Sparks, MD). The test set was chosen to include many HPV-positive specimens. The reference standard was a combination of HC2 and Onclarity results for HPV detection and histologic diagnosis of controls (less than cervical intraepithelial neoplasia grade 2 [

Prevalence of HPV types in cervical specimens from an integrated healthcare delivery system: baseline assessment to measure HPV vaccine impact.

PURPOSE: Two human papillomavirus (HPV) vaccines are available to prevent cervical cancer. One early measure of HPV vaccine impact would be a reduction in vaccine-related HPV types (HPV 6, 11, 16, or 18, or HPV 16, 18) in cervical samples from young women. We aimed to assess feasibility of specimen collection and baseline HPV prevalence in an integrated healthcare delivery system. METHODS: Residual cervical specimens collected during routine cervical cancer screening (2006-2008) were retained consecutively from eligible females aged 11-29 years, stratified by age group. Specimens were evaluated for 37 HPV genotypes using the Roche Linear Array assay. RESULTS: Of 10,124 specimens submitted, 10,103 (99 %) were adequate for HPV testing. Prevalence of HPV 6, 11, 16, or 18 genotype was 11.4 % overall and was the highest in the youngest age group (18.1 % in the 11-19-year-olds, 12.5 % in the 20-24-year-olds, and 7.0 % in the 25-29-year-olds). CONCLUSIONS: HPV types 6, 11, 16, or 18 prevalence could be measured over time to assess early HPV vaccine impact using residual specimens from an integrated healthcare delivery system, particularly if sampling focused on young women.