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Medicina (Buenos Aires)

versión impresa ISSN 0025-7680versión On-line ISSN 1669-9106

Resumen

BORRACCI, Raúl A; SEGAL, Silvia V  y  MENDEZ, José H. Epidemiological dynamic modeling of human papillomavirus-related diseases to assess vaccination strategies in Argentina. Medicina (B. Aires) [online]. 2018, vol.78, n.5, pp.315-328. ISSN 0025-7680.

Our objective was to develop and test a dynamic simulation model of human papillomavirus (HPV)- related diseases to assess rational vaccination strategies in Argentina. A dynamic stochastic transmission model for hetero- and homosexual transmission of HPV oncogenic and low-risk oncogenic types among females and males was developed. The model included HPV transmission and vaccination, the natural history of HPV-related diseases, disease outcomes, and cervical cancer screening. Considering all cervical cancers, covered or not by the current quadrivalent vaccine, the existing coverage rate would lead to 60% reduction in the global incidence of cervical cancer at 25 years, and to 79% at 50 years. Isolated current female vaccination without a screening program would need around 100 years to eliminate cervical cancer from the local population. Current coverage rate would lead to 59% reduction of vulvar cancer, 76% of vaginal cancer, 85% of anal cancer, and 87% of oropharyngeal cancer, estimated over a 25-year time prospect. Female HPV vaccination within the context of current cervical cancer screening should reach a minimum long-term mean coverage of 60% of girls, receiving at least a two-dose vaccine schedule, to significantly reduce or virtually eliminate cervical cancer at 50 years. Including vaccination to boys to improve herd immunity did not influence the incidence of cervical cancer over time, as long as female coverage did not fall below 50%. Regarding vulvar, vaginal, anal, penile, and some oropharyngeal cancers, current girls-only based vaccination could virtually eliminate these cancer types after 35-40 years, both in women and men.

Palabras clave : Human papillomavirus; Vaccines; Cancer; Epidemiology; Mathematical model.

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