Journal of the National Cancer Institute Advance Access originally published online on November 11, 2008
JNCI Journal of the National Cancer Institute 2008 100(22):1654-1655; doi:10.1093/jnci/djn369
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© The Author 2008. Published by Oxford University Press.
CORRESPONDENCE |
Response
Affiliations of authors: Department of Medicine, Center for Health Policy and Primary Care and Outcomes Research, Stanford University School of Medicine, Stanford, CA (JDG-F); Department of Health Policy and Management, Program in Health Decision Science, Harvard School of Public Health, Boston, MA (SG)
Correspondence to: Jeremy D. Goldhaber-Fiebert, PhD, Department of Medicine, Center for Health Policy and Primary Care and Outcomes Research, Stanford University School of Medicine, 117 Encina Commons, Stanford, CA 94305 (e-mail: jeremygf{at}stanford.edu).
Our recently published decision-analytic study (1) that compared alternative cervical cancer prevention strategies was intended to provide policy makers with estimates of long-term impacts needed for near-term decision making. Although it used the best data available at the time, a necessary part of a model-based approach is the iterative reassessment of internal consistency and face validity.
Rossi and Zappa raise two important points: the responsibility to incorporate newly available data and the potentially valuable insights gained from model-based analyses. Our stochastic model simulates large groups of individual females in the United States, following them from age 9 years over their lifetimes, and tracking human papillomavirus (HPV) infections and cervical carcinogenesis (2). They correctly note the feasibility of estimating the proportions of cervical cancer that occur in screened, unscreened, HPV-vaccinated, and unvaccinated women. Generating such outputs with our model facilitates the further assessment of its consistency with empiric data, providing greater confidence in its estimates of the benefits of different targeted approaches to cervical cancer prevention.
As requested by Rossi and Zappa, using the model to simulate current US screening patterns, we estimated the proportion of invasive cervical cancer occurring in unscreened women at 56% (95% confidence interval [CI] = 51% to 60%), approximating results of two US studies with estimates of 53% (3) and 56% (4). Previous work corroborates our conclusions—specifically, that increased coverage has greater potential to reduce cancer risk than shortening screening intervals, increasing age ranges, or using tests with higher sensitivities (5).
Rossi and Zappa highlight data from other countries that are also relevant for model assessment. Our model is empirically calibrated to country-specific data (eg, type- and age-specific HPV, cervical intraepithelial neoplasia [CIN], and cancer), and we have adapted it to multiple settings, especially developing countries where screening has not been possible (6). We also recently simulated a randomized Swedish screening study (7) that compared combined HPV DNA testing and cervical cytology (Pap) with Pap screening alone. The study reported that with combined testing, women were more likely to have CIN2 or worse detected at baseline (relative rate = 1.51, 95% CI = 1.13 to 2.02; our modeled estimates were 1.21, 95% CI = 1.19 to 1.24). As Rossi and Zappa astutely suggest, estimates of population benefit should be based on adapting the model to simulate country-specific screening patterns. A full report of such an analysis, although beyond the scope of this response, would be a valuable undertaking.
Because no data exist on long-term outcomes for vaccinated girls, we can only use the model to explore such outcomes with short-term results and the implied influence of different uptake patterns. We are cautious in overstating the implications of hypothetical scenarios and, therefore, emphasize their purpose: providing insight and generating dialogue. Aside from assumptions about long-term vaccine efficacy, impacts on other HPV types, and achievable coverage in young adolescent girls, a further assumption required in models of vaccination and screening is whether vaccinated adolescent girls are more or less likely to be screened in the future. We modeled a scenario in which the likelihood of vaccination and screening in an individual girl was unrelated; in this case, approximately 30% of girls who were not vaccinated would account for 40% (95% CI = 30% to 45%) of invasive cervical cancers. However, if unvaccinated girls were less likely to be screened later in their lives, then 69% (95% CI = 60% to 76%) of invasive cervical cancers occurred in unvaccinated women. This finding highlights the importance of achieving high and equitable coverage.
REFERENCES
1. Goldhaber-Fiebert JD, Stout NK, Salomon JA, Kuntz KM, Goldie SJ. Cost-effectiveness of cervical cancer screening with human papillomavirus DNA testing and HPV-16,18 vaccination. J Natl Cancer Inst (2008) 100(5):308–320.
2. Goldhaber-Fiebert JD, Stout NK, Ortendahl J, Kuntz KM, Goldie SJ, Salamon JA. Modeling human papillomavirus and cervical cancer in the United States for analyses of screening and vaccination. Popul Health Metr (2007) 5:11.[CrossRef][Medline]
3. Sung HY, Kearney KA, Miller M, Kinney W, Sawaya GF, Hiatt RA. Papanicolaou smear history and diagnosis of invasive cervical carcinoma among members of a large prepaid health plan. Cancer (2000) 88(10):2283–2289.[CrossRef][Web of Science][Medline]
4. Leyden WA, Manos MM, Geiger AM, et al. Cervical cancer in women with comprehensive health care access: attributable factors in the screening process. J Natl Cancer Inst (2005) 97(9):675–683.
5. Goldie SJ, Kim JJ, Myers E. Chapter 19: Cost-effectiveness of cervical cancer screening. Vaccine (2006) 24((suppl 3)):S164–S170.[CrossRef][Medline]
6. Goldie SJ, O’Shea M, Campos NG, Diaz M, Sweet S, Kim SY. Health and economic outcomes of HPV 16,18 vaccination in 72 GAVI-eligible countries. Vaccine (2008) 26(32):4080–4093.[CrossRef][Web of Science][Medline]
7. Naucler P, Ryd W, Törnberg S, et al. Human papillomavirus and Papanicolaou tests to screen for cervical cancer. N Engl J Med (2007) 357(16):1589–1597.
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