State-level disparities in cervical cancer prevention and outcomes in the United States: a modeling study

Abstract

Background: Despite human papillomavirus (HPV) vaccines' availability for over a decade, coverage across the United States varies. Although some states have tried to increase HPV vaccination coverage, most model-based analyses focus on national impacts. We evaluated hypothetical changes in HPV vaccination coverage at the national and state levels for California, New York, and Texas using a mathematical model.

Methods: We developed a new mathematical model of HPV transmission and cervical cancer, creating national- and state-level models, incorporating country- and state-specific vaccination coverage and cervical cancer incidence and mortality. We quantified the national- and state-level impact of increasing HPV vaccination coverage to 80% by 2025 or 2030 on cervical cancer outcomes and the time to elimination defined as less than 4 per 100 000 women.

Results: Increasing vaccination coverage to 80% in Texas over 10 years could reduce cervical cancer incidence by 50.9% (95% credible interval [CrI] = 46.6%-56.1%) by 2100, from 1.58 (CrI = 1.19-2.09) to 0.78 (CrI = 0.57-1.02) per 100 000 women. Similarly, New York could see a 27.3% (CrI = 23.9%-31.5%) reduction from 1.43 (CrI = 0.93-2.07) to 1.04 (CrI = 0.66-1.53) per 100 000 women, and California a 24.4% (CrI = 20.0%-30.0%) reduction from 1.01 (CrI = 0.66-1.44) to 0.76 (CrI = 0.50-1.09) per 100 000 women. Achieving 80% coverage in 5 years will provide slightly larger and sooner reductions. If the vaccination coverage levels in 2019 continue, cervical cancer elimination could occur nationally by 2051 (CrI = 2034-2064), but state timelines may vary by decades.

Conclusion: Targeting an HPV vaccination coverage of 80% by 2030 will disproportionately benefit states with low coverage and higher cervical cancer incidence. Geographically focused analyses can better inform priorities.

Figure 1.

Cervical cancer incidence and mortality over time for the United States, California, New York, and Texas, assuming human papillomavirus vaccination coverage in 2019. The vertical dashed line denotes the US Food and Drug Administration approval of the human papillomavirus vaccine for women. The vertical dotted line denotes the beginning of the predicted outcomes. The horizontal dashed line in cervical cancer incidence at 4 per 100 000 represents the cervical cancer elimination goal. The shaded area shows the 95% posterior model-predictive credible interval (CrI) of the outcomes. The solid lines show the posterior model-predicted mean based on 1000 simulations using samples from the posterior distribution.

Figure 2.

Percentage difference in cervical cancer incidence over time for different human papillomavirus vaccination coverage scenarios (ie, 25% reduction in vaccination coverage for 5 years [backslide] or reaching 80% vaccination coverage [Healthy People] in 5 or 10 years) relative to continuing with coverage in 2019 for the United States and 3 states. The shaded area shows the 95% posterior model-predictive credible interval of the outcomes. The solid lines show the posterior model-predicted mean based on 1000 simulations using samples from the posterior distribution.

Figure 3.

Percentage difference in cervical cancer mortality over time for different human papillomavirus vaccination coverage scenarios (ie, 25% reduction in vaccination coverage for 5 years [backslide] or reaching 80% vaccination coverage [Healthy People] in 5 or 10 years) relative to continuing with coverage in 2019 for the United States and 3 states. The shaded area shows the 95% posterior model-predictive credible interval of the outcomes. The solid lines show the posterior model-predicted mean based on 1000 simulations using samples from the posterior distribution.

Figure 4.

Hexamaps visualizing percentage differences in cervical cancer incidence for each scenario compared with continuing with 2019 coverage as a function of age, calendar year, and birth year. The vertical lines represent calendar years, the horizontal lines represent ages, and the diagonal lines represent the aging of each birth cohort with calendar years from the bottom left to the top right. In each scenario, the solid diagonal line represents the birth cohort that became eligible to be vaccinated from the start of the simulation. Cohorts born after 1994 are represented by less heated colors at the bottom right of the red line.