Epidemiology (Cambridge, Mass.) · 2019
Estimating the Human Papillomavirus Genotype Attribution in Screen-detected High-grade Cervical Lesions.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Lissenberg-Witte Birgit I, Bogaards Johannes A, Quint Wim G V, Berkhof Johannes
Аннотация
BACKGROUND: Genotype attribution in high-grade cervical lesions (CIN3+) can be calculated by the hierarchical or proportional method, but these do not account for the genotype distribution in the general population and cannot assess the number of genotype-specific high-grade cervical lesions (CIN3+).
METHODS: We present a statistical method for estimating genotype-specific CIN3+ risks and genotype attribution in CIN3+ from cervical screening samples. A key assumption is that genotype-specific infections in women with multiple infections have independent progression risks. We applied the method to 512 human papillomavirus (HPV)-positive women referred for colposcopy and validated it by laser-capture microscopy-polymerase chain reaction. We also compared performance by simulation.
RESULTS: For endpoint CIN3+, the summed deviation of attributable fractions between the estimated genotype-specific attributable fractions and laser-capture microscopy polymerase chain reaction-based attributable fractions was similar for the three methods: 0.17 for the new method (95% confidence interval [CI] = 0.091, 0.28), 0.19 (95% CI = 0.11, 0.33) for the hierarchical method and 0.15 (95% CI = 0.085, 0.26) for the proportional method. Simulations indicated that the new method outperformed the other methods for endpoint CIN3+ when the number of HPV-positive women was large. Exclusion of HPV16-positive women had only a small effect on the estimated genotype-specific risks, supporting the independence assumption.
CONCLUSIONS: Genotype-specific attribution in CIN3+ can be accurately predicted by a model that assumes independence between genotypes with respect to disease progression. The method can be used to monitor HPV vaccine effectiveness for prevention of genotype-specific CIN3+ and to assess disease risk after vaccination.
METHODS: We present a statistical method for estimating genotype-specific CIN3+ risks and genotype attribution in CIN3+ from cervical screening samples. A key assumption is that genotype-specific infections in women with multiple infections have independent progression risks. We applied the method to 512 human papillomavirus (HPV)-positive women referred for colposcopy and validated it by laser-capture microscopy-polymerase chain reaction. We also compared performance by simulation.
RESULTS: For endpoint CIN3+, the summed deviation of attributable fractions between the estimated genotype-specific attributable fractions and laser-capture microscopy polymerase chain reaction-based attributable fractions was similar for the three methods: 0.17 for the new method (95% confidence interval [CI] = 0.091, 0.28), 0.19 (95% CI = 0.11, 0.33) for the hierarchical method and 0.15 (95% CI = 0.085, 0.26) for the proportional method. Simulations indicated that the new method outperformed the other methods for endpoint CIN3+ when the number of HPV-positive women was large. Exclusion of HPV16-positive women had only a small effect on the estimated genotype-specific risks, supporting the independence assumption.
CONCLUSIONS: Genotype-specific attribution in CIN3+ can be accurately predicted by a model that assumes independence between genotypes with respect to disease progression. The method can be used to monitor HPV vaccine effectiveness for prevention of genotype-specific CIN3+ and to assess disease risk after vaccination.