No credit card sexy online free chat - Updating the natural history of hpv and anogenital cancer

Baseline model input parameters were primarily derived from large, prospective cohort studies [17–19], supplemented by data from the published literature and expert opinion, and involved extensive model-fitting to observed data.

For the parameters with high uncertainty and variability, we relied on a multiparameter calibration process [10, 20] to maximize correspondence between model outputs and empirical targets from the United States, such as age- and genotype- specific HPV prevalence and HPV genotype distribution in CIN3 and cervical cancer [21, 22].

updating the natural history of hpv and anogenital cancer-33updating the natural history of hpv and anogenital cancer-31

We employed an individual-based cervical cancer natural history model that integrates empirical data from the largest prospective and clinical studies of HPV-induced cervical carcinogenesis into a single analytic framework [10].

This model, which is continually updated and refined using emerging empirical data, is well published and has informed cervical cancer prevention policy worldwide [11–16].

Our model projected that among all cervical cancers, 50% and 75% of women acquired their causal HPV infection by ages 20.6 (range: 20.1–21.1) and 30.6 (range: 29.6–31.6) years, respectively. Assuming 95% efficacy against HPV16 and HPV18 infections, the direct reduction in lifetime risk of cervical cancer varied from 55% (53–56%) among women vaccinated at age 9 years to 6% (range: 6–7%) among women vaccinated at age 45 years.

Similar patterns were observed for the second-generation vaccine.

Individual women enter the model at age 9 years with a healthy cervix and face monthly probabilities of acquiring HPV (HPV16, 18, 31, 33, 45, 52, and 58, other grouped high-risk types, and grouped low-risk types) and transitioning between HPV-related health states (e.g., normal, HPV infection, cervical intraepithelial neoplasia, grades 2 (CIN2) and 3 (CIN3) and cervical cancer) until death, either from background causes or cervical cancer after its onset.

Transitions may be a function of duration (i.e., time since HPV infection or precancer development), HPV genotype, age, and history of HPV infections.

The risk of acquiring cervical human papillomavirus (HPV) infections, causally linked to several cancers and genital warts, peaks shortly after sexual initiation, subsequently declining with age in most women [1].

Following acquisition, high-risk HPV infections may persist and progress to a precancerous lesion, a proportion of which will become invasive cancer over time [2] if not detected and treated in a timely fashion.

In settings without existing screening programs, the impact of HPV vaccination at older ages on long-term outcomes will inform whether secondary prevention approaches, such as HPV testing, should also be coupled with an HPV vaccination policy.

The optimal choice of intervention(s) will be determined by the cumulative proportion of causal infections that have already been acquired at the time of vaccination, and thereby the resulting cancer could only be prevented through screening, diagnosis, and treatment of the antecedent cervical precancer, compared with those that are yet to be acquired and could be prevented via HPV vaccination [8].

The development and calibration process of our US individual-based natural history model of cervical carcinogenesis has been previously described [10, 20], validated, and used to analyze US screening and vaccination policy (e.g., Kim et al 2015 [14] and Kim et al 2016 [13]).

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