Estimating the Human Papillomavirus Genotype Attribution in Screen-detected High-grade Cervical Lesions

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)590-596
JournalEpidemiology (Cambridge, Mass.)
Volume30
Issue number4
DOIs
Publication statusPublished - 1 Jul 2019

Cite this

@article{57f436380c9641daa2aec946f780f43e,
title = "Estimating the Human Papillomavirus Genotype Attribution in Screen-detected High-grade Cervical Lesions",
abstract = "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.",
author = "Lissenberg-Witte, {Birgit I.} and Bogaards, {Johannes A.} and Quint, {Wim G. V.} and Johannes Berkhof",
year = "2019",
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doi = "10.1097/EDE.0000000000001026",
language = "English",
volume = "30",
pages = "590--596",
journal = "Epidemiology (Cambridge, Mass.)",
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Estimating the Human Papillomavirus Genotype Attribution in Screen-detected High-grade Cervical Lesions. / Lissenberg-Witte, Birgit I.; Bogaards, Johannes A.; Quint, Wim G. V.; Berkhof, Johannes.

In: Epidemiology (Cambridge, Mass.), Vol. 30, No. 4, 01.07.2019, p. 590-596.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Estimating the Human Papillomavirus Genotype Attribution in Screen-detected High-grade Cervical Lesions

AU - Lissenberg-Witte, Birgit I.

AU - Bogaards, Johannes A.

AU - Quint, Wim G. V.

AU - Berkhof, Johannes

PY - 2019/7/1

Y1 - 2019/7/1

N2 - 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.

AB - 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.

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UR - https://www.ncbi.nlm.nih.gov/pubmed/30985528

U2 - 10.1097/EDE.0000000000001026

DO - 10.1097/EDE.0000000000001026

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SP - 590

EP - 596

JO - Epidemiology (Cambridge, Mass.)

JF - Epidemiology (Cambridge, Mass.)

SN - 1531-5487

IS - 4

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