State-level disparities in cervical cancer prevention and outcomes in the U.S.: A modeling study.

Fernando Alarid-Escudero, Valeria Gracia, Marina Wolf, Ran Zhao, Caleb W Easterly, Jane J Kim, Karen Canfell, Inge M C M de Kok, Ruanne V Barnabas, Shalini Kulasingam
Author Information
  1. Fernando Alarid-Escudero: Department of Health Policy, School of Medicine, and Stanford Health Policy, Freeman-Spogli Institute for International Studies, Stanford University, Stanford, CA, USA. ORCID
  2. Valeria Gracia: Department of Health Policy, School of Medicine, Stanford University, Stanford, CA, USA. ORCID
  3. Marina Wolf: University of Minnesota Medical School, Minneapolis, MN, USA. ORCID
  4. Ran Zhao: Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA.
  5. Caleb W Easterly: MD/PhD Program, University of North Carolina at Chapel Hill, Chapel Hill, NC. ORCID
  6. Jane J Kim: Center for Health Decision Science, Department of Health Policy and Management, Harvard TH Chan School of Public Health, Boston, MA, USA.
  7. Karen Canfell: The Daffodil Centre, University of Sydney, a joint venture with Cancer Council NSW, Sydney, NSW. ORCID
  8. Inge M C M de Kok: Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands. ORCID
  9. Ruanne V Barnabas: Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. ORCID
  10. Shalini Kulasingam: Division of Epidemiology & Community Health, University of Minnesota School of Public Health, Minneapolis, MN, USA. ORCID

Abstract

Background: Despite HPV vaccines' availability for over a decade, coverage across the US varies. While 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 US 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 <4 per 100k women.
Results: Increasing vaccination coverage to 80% in Texas over ten 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 (Crl:0.66-1.53) per 100,000 women, and California a 24.4% (CrI:20.0-30.0%) reduction, from 1.01 (Crl:0.66-1.44) to 0.76 (Crl:0.50-1.09) per 100,000 women. Achieving 80% coverage in five years will provide slightly larger and sooner reductions. If the vaccination coverage levels in 2019 continue, cervical cancer elimination could occur nationally by 2051 (Crl: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.

References

  1. MMWR Morb Mortal Wkly Rep. 2022 Sep 02;71(35):1101-1108 [PMID: 36048724]
  2. J Natl Cancer Inst. 2020 Sep 1;112(9):955-963 [PMID: 31821501]
  3. JNCI Cancer Spectr. 2022 Jul 1;6(4): [PMID: 35900184]
  4. Lancet Public Health. 2016 Nov;1(1):e8-e17 [PMID: 29253379]
  5. Int J Cancer. 2013 Jan 1;132(1):198-207 [PMID: 22532127]
  6. Vaccine. 2021 May 12;39(20):2731-2735 [PMID: 33875269]
  7. MMWR Morb Mortal Wkly Rep. 2023 Aug 25;72(34):912-919 [PMID: 37616185]
  8. Lancet. 2007 Sep 8;370(9590):890-907 [PMID: 17826171]
  9. JAMA Netw Open. 2021 Sep 1;4(9):e2124502 [PMID: 34533574]
  10. Pediatrics. 2021 Jun;147(6): [PMID: 33941585]
  11. Vaccine. 2018 Sep 5;36(37):5572-5579 [PMID: 30093290]
  12. Vaccine. 2022 Jan 31;40(5):706-713 [PMID: 35012776]
  13. Epidemiology. 2020 Nov 1;31(6):e47-e49 [PMID: 33560638]
  14. Med Decis Making. 2020 May;40(4):474-482 [PMID: 32486894]
  15. Pediatr Infect Dis J. 2024 Jan 1;43(1):84-87 [PMID: 37963272]
  16. Med Decis Making. 2017 Oct;37(7):735-746 [PMID: 28061043]
  17. J Natl Cancer Inst. 2015 Apr 29;107(6):djv086 [PMID: 25925419]
  18. Curr Med Res Opin. 2021 Dec;37(12):2077-2087 [PMID: 34538163]
  19. MMWR Morb Mortal Wkly Rep. 2021 Sep 03;70(35):1183-1190 [PMID: 34473682]
  20. Front Physiol. 2022 May 09;13:780917 [PMID: 35615677]
  21. Lancet Public Health. 2020 Apr;5(4):e213-e222 [PMID: 32057315]
  22. JAMA Intern Med. 2019 Jul 1;179(7):867-878 [PMID: 31081851]
  23. PLoS Med. 2021 Mar 11;18(3):e1003534 [PMID: 33705382]
  24. Proc Natl Acad Sci U S A. 2016 May 3;113(18):5107-12 [PMID: 27091978]
  25. Hum Vaccin Immunother. 2019;15(1):146-155 [PMID: 30148974]
  26. Cancer Epidemiol Biomarkers Prev. 2021 Oct;30(10):1895-1903 [PMID: 34503948]

Grants

  1. U01 CA253912/NCI NIH HHS

Word Cloud

Created with Highcharts 10.0.0coveragevaccinationcervicalcancerHPV80%perwomen1nationalincidence100000Crl:0USstatesanalysesstatelevelsCaliforniaNewYorkTexasmathematicalmodelstate-level2030outcomeseliminationyears090CrI:0reduction66-1willBackground:Despitevaccines'availabilitydecadeacrossvariestriedincreasemodel-basedfocusimpactsevaluatedhypotheticalchangesusingMethods:developednewtransmissioncreatingmodelsincorporatingcountry-state-specificmortalityquantifiedimpactincreasing2025timedefined<4100kResults:Increasingtenreduce509%95%credibleinterval[CrI]:466-561%210058CrI:119-27857-102Similarlysee273%CrI:239-315%4393-2070453244%CrI:200-300%01447650-1Achievingfiveprovideslightlylargersoonerreductions2019continueoccurnationally2051Crl:2034-2064timelinesmayvarydecadesConclusion:TargetingdisproportionatelybenefitlowhigherGeographicallyfocusedcanbetterinformprioritiesState-leveldisparitiespreventionUS:modelingstudy

Similar Articles

Cited By

No available data.