Empirical sample-specific approaches to define HPV16 and HPV18 seropositivity in unvaccinated, young, sexually active women

ABSTRACT Given low seroconversion rates following human papillomavirus (HPV) infection, fixed external cutoffs may lead to errors in estimating HPV seroprevalence. We evaluated finite mixture modeling (FMM) and group-based trajectory modeling (GBTM) among unvaccinated, sexually active, HPV-exposed women to determine study-specific HPV16 and HPV18 seropositivity thresholds. We included 399 women (aged 18–24 years) enrolled in the HPV Infection and Transmission Among Couples Through Heterosexual Activity (HITCH) cohort study between 2005 and 2011 in Montreal, Canada. Participants’ blood samples from up to six visits spanning 2 years were tested by multiplex serology for antibodies [median fluorescence intensity (MFI)] specific to bacterially expressed HPV16 and HPV18 L1 glutathione S-transferase fusion proteins. We applied FMM and GBTM to baseline and longitudinal antibody titer measurements, respectively, to define HPV type-specific seronegative and seropositive distributions. Study-specific thresholds were generated as five standard deviations above the mean seronegative antibody titers, mimicking cutoffs (HPV16: 422 MFI; HPV18: 394 MFI) derived from an external population of sexually inactive, HPV DNA-negative Korean women (aged 15–29 years). Agreement (kappa) of study-specific thresholds was evaluated against external cutoffs. Seroprevalence estimates using FMM (HPV16: 27.5%–43.2%; HPV18: 21.7%–49.5%) and GBTM (HPV16: 11.8%–11.8%; HPV18: 9.9%–13.4%) thresholds exceeded those of external cutoffs (HPV: 10.2%; HPV18: 9.7%). FMM thresholds showed slight-to-moderate agreement with external cutoffs (HPV16: 0.26%–0.46%; HPV18: 0.20%–0.56%), while GBTM thresholds exhibited high agreement (HPV16: 0.92%–0.92%; HPV18: 0.82%–0.99%). Kappa values suggest that GBTM, used for longitudinal serological data, and otherwise FMM, for cross-sectional data, are robust methods for determining the HPV serostatus without prior classification rules.IMPORTANCEWhile human papillomavirus (HPV) seropositivity has been employed as an epidemiologic determinant of the natural history of genital HPV infections, only a fraction of women incidentally infected with HPV respond by developing significant antibody levels. HPV seropositivity is often determined by a dichotomous fixed cutoff based on the seroreactivity of an external population of women presumed as seronegative, given the lack of evidence of HPV exposure. However, considering the variable nature of seroreactivity upon HPV infection, which arguably varies across populations, such externally defined cutoffs may lack specificity to the population of interest, causing inappropriate assessment of HPV seroprevalence and related epidemiologic uses of that information. This study demonstrates that finite mixture modeling (FMM) and group-based trajectory modeling (GBTM) can be used to independently estimate seroprevalence or serve as the basis for defining study-specific seropositivity thresholds without requiring prior subjective assumptions, consequently providing a more apt internally valid discrimination of seropositive from seronegative individuals.