Estimation of Cancer Deaths Averted From Prevention, Screening, and Treatment Efforts, 1975-2020

Katrina A B Goddard; Eric J Feuer; Jeanne S Mandelblatt; Rafael Meza; Theodore R Holford; Jihyoun Jeon; Iris Lansdorp-Vogelaar; Roman Gulati; Natasha K Stout; Nadia Howlader; Amy B Knudsen; Daniel Miller; Jennifer L Caswell-Jin; Clyde B Schechter; Ruth Etzioni; Amy Trentham-Dietz; Allison W Kurian; Sylvia K Plevritis; John M Hampton; Sarah Stein; Liyang P Sun; Asad Umar; Philip E Castle

doi:10.1001/jamaoncol.2024.5381

. 2024 Dec 5;11(2):162–167. doi: 10.1001/jamaoncol.2024.5381

Estimation of Cancer Deaths Averted From Prevention, Screening, and Treatment Efforts, 1975-2020

Katrina A B Goddard ^1,^✉, Eric J Feuer ¹, Jeanne S Mandelblatt ², Rafael Meza ^3,⁴, Theodore R Holford ⁵, Jihyoun Jeon ⁶, Iris Lansdorp-Vogelaar ⁷, Roman Gulati ⁸, Natasha K Stout ¹, Nadia Howlader ¹, Amy B Knudsen ^9,¹⁰, Daniel Miller ¹¹, Jennifer L Caswell-Jin ¹², Clyde B Schechter ¹³, Ruth Etzioni ⁸, Amy Trentham-Dietz ¹⁴, Allison W Kurian ^12,¹⁵, Sylvia K Plevritis ¹⁶, John M Hampton ¹⁴, Sarah Stein ¹⁷, Liyang P Sun ¹⁵, Asad Umar ¹⁸, Philip E Castle ^18,¹⁹

¹Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland

²Georgetown Lombardi Institute for Cancer and Aging Research and Cancer Prevention and Control Program, Lombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC

³Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, British Columbia, Canada

⁴School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada

⁵Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut

⁶Department of Epidemiology, University of Michigan, Ann Arbor

⁷Department of Public Health, Erasmus University Medical Center, Rotterdam, the Netherlands

⁸Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington

⁹Institute for Technology Assessment, Massachusetts General Hospital, Boston

¹⁰Department of Radiology, Harvard Medical School, Boston, Massachusetts

¹¹Information Management Services Inc, Calverton, Maryland

¹²Department of Medicine, Stanford University School of Medicine, Stanford, California

¹³Department of Family and Social Medicine, Albert Einstein College of Medicine, Bronx, New York

¹⁴Department of Population Health Sciences and Carbone Cancer Center, University of Wisconsin–Madison School of Medicine and Public Health

¹⁵Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California

¹⁶Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California

¹⁷Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts

¹⁸Division of Cancer Prevention, National Cancer Institute, National Institutes of Health, Bethesda, Maryland

¹⁹Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland

Accepted for Publication: August 13, 2024.

Published Online: December 5, 2024. doi:10.1001/jamaoncol.2024.5381

^✉

Corresponding Author: Katrina A. B. Goddard, PhD, Division of Cancer Control and Population Sciences, National Institutes of Health, National Cancer Institute, 9609 Medical Center Dr, MSC 9764, Bethesda, MD 20892 (katrina.goddard@nih.gov).

Author Contributions: Dr Goddard had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Goddard, Feuer, Mandelblatt, Meza, Umar, Castle.

Acquisition, analysis, or interpretation of data: Feuer, Mandelblatt, Meza, Holford, Jeon, Lansdorp-Vogelaar, Gulati, Stout, Howlader, Knudsen, Miller, Caswell-Jin, Schechter, Etzioni, Trentham-Dietz, Kurian, Plevritis, Hampton, Stein, Sun, Castle.

Drafting of the manuscript: Goddard, Feuer, Mandelblatt, Stout, Miller, Hampton, Umar, Castle.

Critical review of the manuscript for important intellectual content: Feuer, Mandelblatt, Meza, Holford, Jeon, Lansdorp-Vogelaar, Gulati, Stout, Howlader, Knudsen, Caswell-Jin, Schechter, Etzioni, Trentham-Dietz, Kurian, Plevritis, Stein, Sun, Umar, Castle.

Statistical analysis: Feuer, Mandelblatt, Meza, Holford, Jeon, Lansdorp-Vogelaar, Gulati, Stout, Howlader, Knudsen, Miller, Schechter, Plevritis, Hampton, Stein, Sun.

Obtained funding: Meza, Holford, Trentham-Dietz, Plevritis.

Administrative, technical, or material support: Feuer, Mandelblatt, Trentham-Dietz, Umar, Castle.

Supervision: Castle.

Conflict of Interest Disclosures: Dr Mandelblatt reported a pending patent (PCT/US2022/028741) filed by Georgetown University, related to “Use of RAGE Inhibitors to Treat Cancer-Related Cognitive Decline,” licensed to Cantex Pharmaceuticals. Dr Mandelblatt has waived her rights to any revenue from this license. Dr Meza, Dr Lansdorp-Vogelaar, Dr Gulati (R50CA221836), Dr Knudsen, Dr Schechter, Dr Trentham-Dietz (U01CA253911, P30CA014520), Dr Hampton, and Dr Sun reported grants from the National Cancer Institute (NCI) during the study. Dr Caswell-Jin reported NCI grants during the study and grants from Effector Therapeutics, Novartis, and QED Therapeutics outside the study. Dr Plevritis reported National Institutes of Health (NIH) grants and serves on Adela Biosciences' advisory board. No other disclosures were reported.

Funding/Support: This work was supported by the NCI at NIH through the CISNET Breast, Cervical, Colorectal, Lung, and Prostate Cancer Working Groups (U01CA253911, U01CA253913, U01CA253858, U01CA253915). Additional support was provided by NCI grants R35CA197289, R35CA283926, P30CA014520, and R50CA221836.

Disclaimer: The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or NCI.

Data Sharing Statement: See Supplement 2.

Additional Contributions: Beyond the included CISNET-affiliated authors, we wish to acknowledge the entire CISNET Breast, Cervical, Colorectal, Lung, and Prostate Cancer Working Groups, whose models, tools, resources, and collegial conversations are the infrastructure that makes these types of efforts possible. The Breast Cancer Surveillance Consortium and the National Comprehensive Cancer Center Network provided data to the CISNET Breast Cancer Working Group.

^✉

Corresponding author.

PMCID: PMC11622066 PMID: 39636625

Key Points

Question

What is the contribution of prevention, screening (to remove precursors [interception] or early detection), and treatment interventions to deaths averted in breast, cervical, colorectal, lung, and prostate cancer from 1975 to 2020?

Findings

In this model-based study using population-level cancer mortality data, an estimated 5.94 million deaths were averted from these 5 cancers combined. Prevention and screening accounted for 8 of every 10 averted deaths, and the contribution varied by cancer site.

Meaning

Cancer prevention and screening were main contributors to reducing mortality from these 5 cancers over the past 45 years; further mortality reductions will require increased use of effective interventions and new discoveries.

Abstract

Importance

Cancer mortality has decreased over time, but the contributions of different interventions across the cancer control continuum to averting cancer deaths have not been systematically evaluated across major cancer sites.

Objective

To quantify the contributions of prevention, screening (to remove precursors [interception] or early detection), and treatment to cumulative number of cancer deaths averted from 1975 to 2020 for breast, cervical, colorectal, lung, and prostate cancers.

Design, Setting, and Participants

In this model-based study using population-level cancer mortality data, outputs from published models developed by the Cancer Intervention and Surveillance Modeling Network were extended to quantify cancer deaths averted through 2020. Model inputs were based on national data on risk factors, cancer incidence, cancer survival, and mortality due to other causes, and dissemination and effects of prevention, screening (for interception and early detection), and treatment. Simulated or modeled data using parameters derived from multiple birth cohorts of the US population were used.

Interventions

Primary prevention via smoking reduction (lung), screening for interception (cervix and colorectal) or early detection (breast, cervix, colorectal, and prostate), and therapy (breast, colorectal, lung, and prostate).

Main Outcomes and Measures

The estimated cumulative number of cancer deaths averted with interventions vs no advances.

Results

An estimated 5.94 million cancer deaths were averted for breast, cervical, colorectal, lung, and prostate cancers combined. Cancer prevention and screening efforts averted 8 of 10 of these deaths (4.75 million averted deaths). The contribution of each intervention varied by cancer site. Screening accounted for 25% of breast cancer deaths averted. Averted cervical cancer deaths were nearly completely averted through screening and removal of cancer precursors as treatment advances were modest during the study period. Averted colorectal cancer deaths were averted because of screening and removal of precancerous polyps or early detection in 79% and treatment advances in 21%. Most lung cancer deaths were avoided by smoking reduction (98%) because screening uptake was low and treatment largely palliative before 2014. Screening contributed to 56% of averted prostate cancer deaths.

Conclusions and Relevance

Over the past 45 years, cancer prevention and screening accounted for most cancer deaths averted for these causes; however, their contribution varied by cancer site according to these models using population-level cancer mortality data. Despite progress, efforts to reduce the US cancer burden will require increased dissemination of effective interventions and new technologies and discoveries.

This model-based study using population-level cancer mortality data evaluates the association of prevention, screening, and treatment interventions with cumulative number of cancer deaths averted from 1975 to 2020 for breast, cervical, colorectal, lung, and prostate cancers.

Introduction

Overall US mortality has declined over time for most major cancer sites because of progress in prevention, screening, and treatment. Nevertheless, the reignited Cancer Moonshot goal to reduce the age-adjusted cancer mortality rate by 50% in the next 25 years will not be achieved without accelerating progress.¹ A comprehensive plan to reduce cancer mortality includes interventions in cancer prevention, detection, diagnosis, treatment, and survivorship care. As we strive to decrease the burden of cancer, we need to understand which intervention strategies are most effective in reducing deaths from cancer. Past analyses have only evaluated contributions to mortality for individual cancer sites.^2,3,4,5,6

We extended models from established Cancer Intervention and Surveillance Modeling Network (CISNET) teams and National Cancer Institute (NCI) collaborators to quantify the relative contributions of prevention, screening (for interception and early detection), and treatment advances to cumulative mortality of 5 cancer sites (breast, cervical, colorectal, lung, and prostate) from 1975 to 2020. Our analysis focused on these 5 cancer sites because they are among the most common causes of cancer deaths, and interventions are available for their prevention, interception and/or early detection, and treatment advances (hereafter referred to collectively as all interventions). The results are intended to provide insights into different approaches needed for varying cancer sites and to highlight gaps and opportunities for future efforts to meet national goals to reduce the US cancer burden.

Methods

In this model-based study using population-level mortality data, we estimated the number of deaths averted, extending prior analyses from CISNET^2,3,4,6 and NCI collaborators using Surveillance, Epidemiology, and End Results Program cancer registry mortality rates^5,7 and used observed US mortality rates from the National Center for Health Statistics (Supplement 1). Prior analyses were extended to a common timeframe through 2020. CISNET models used a similar approach to estimate cancer mortality and details are available elsewhere and in Supplement 1. The CISNET models simulated multiple birth cohorts from the US population in a given year. Age-specific, period-specific, and cohort-specific cancer incidence-based methods and survival trends were simulated assuming standard care in 1975 (breast, cervical, colorectal, and lung) or 1990 (prostate) continued through 2020. The simulations were repeated overlaying interventions as they disseminated into the population over time to generate cancer-specific incidence and mortality trends. Prevention, screening (for interception and early detection), and treatment advances partially or completely interrupt the development and/or progression of disease (Figure). Prevention and interception were assumed to avoid incident cancer. If cancer developed, mortality was reduced through screening and earlier detection, as survival is improved compared with clinical detection. Treatment advances improved survival, and individuals could die of cancer or other causes. Models varied in specific features and interventions by cancer site (Table 1; eFigures 1-6 in Supplement 1). The previously published research that produced additional outputs for this study was deemed exempt from institutional review board approval. Informed consent was not required because we used publicly available secondary data that were deidentified.

Figure. — This schematic of cancer progression shows opportunities for prevention, screening (for interception and early detection), and treatment advances to partially or completely interrupt the development and/or progression of disease that would cause death.

Table 1. Summary of Model Features for Each Cancer Site.

Intervention	Female breast cancer	Cervical cancer	Colorectal cancer	Lung cancer	Prostate cancer
Prevention	Risk factors not explicitly modeled, aside from their impact on cancer incidence	No	No, although risk factors are included in the model, we did not simulate different risk factor scenarios	Age-specific and gender-specific probabilities of smoking cessation by year vs patterns prior to the 1964 US Surgeon General’s report	No
Incidence	Age-specific, period-specific, cohort-specific approach	No	Model was calibrated to match age-specific, sex-specific, race-specific, stage-specific, and location-specific incidence from 1975 to 1979, a period with little to no population screening for colorectal cancer	Individual risk model by age and smoking history; age-specific, period-specific, cohort-specific approach	Individual risk model of longitudinal PSA associated with cancer onset and progression
Interception	No	High-grade intraepithelial neoplasia	Adenomatous polyps, where polyp rates are based on population risk factors	No	No
Screening	Plain film, digital mammography, and tomosynthesis	Pap smear and HPV testing	Colonoscopy, sigmoidoscopy, stool testing	No	PSA
Tumor features	ER/ERBB2 subtypes	No	Adenoma size (1 to <6 mm, 6 to <10 mm, ≥10 mm), cancer stage (TNM)	Histologic characteristics and stage	Grade
Survival	Age-specific, stage-specific, and subtype-specific in the absence of systemic therapy	No	Age-specific, sex-specific, race-specific, location-specific, and stage-specific survival by period of diagnosis	Overall age-specific, gender-specific, stage-specific, and histology-specific survival in time periods	Age-specific, stage-specific, and grade-specific with secular improvement in early PSA era
Treatment	Systemic endocrine, chemotherapy, and ERBB2-targeted therapy; treatment and treatment effects vary over time periods	No	Trends in the dissemination of chemotherapy agents over time by sex, location, and age group; chemotherapy-specific hazard ratios	Treatment and tobacco control were modeled separately using different approaches; targeted therapy and immunotherapy from 2014 to 2020	Radiotherapy, surgery, and endocrine therapy (initial and later courses); treatment and treatment effects vary over time periods

Open in a new tab

Abbreviations: ER, estrogen receptor; ERBB2, human epidermal growth factor receptor 2; HPV, human papillomavirus; PSA, prostate-specific antigen.

Statistical Analysis

The models projected cumulative cancer deaths averted from 1975 to 2020. For breast, colorectal, and prostate cancer, the models examined annual cancer mortality rates under 4 scenarios.

Scenario 1: no prevention, screening (for interception or early detection), or treatment advances;
Scenario 2: prevention and/or screening only;
Scenario 3:treatment advances only; and
Scenario 4: prevention/screening and treatment advances.

For cervical and lung cancer, not all 4 scenarios were modeled, as described in Supplement 1. Because there is a synergy between interventions (eg, the benefit of screening varies depending on the difference in treatment benefit from early to late-stage diagnosis), our primary outcome was the relative contribution of prevention and/or screening in the presence of treatment advances. We computed cancer deaths averted from prevention and/or screening by calculating the difference in the estimated number of cancer deaths between scenario 4 and scenario 3. Similarly, to estimate cancer deaths averted from treatment advances only, we calculated the difference in the estimated number of cancer deaths between scenario 3 and scenario 1. We computed the percentage of cancer deaths averted from total cancer deaths in the absence of all interventions by dividing by the total cancer deaths in scenario 1. Results for a secondary outcome are provided along with details about the models for each cancer site in Supplement 1.

Results

From 1975 to 2020, an estimated 5.94 million cancer deaths were averted from the combination of prevention, screening (for interception and early detection), and treatment advances (Table 2). Across all interventions combined, prevention and/or screening were estimated to account for 80% of cancer deaths averted (4.75 million), with tobacco control for lung cancer alone contributing 3.45 million of the 4.75 million deaths. The contribution of prevention and/or screening to cancer deaths averted varied across cancer sites. Prevention of lung cancer due to tobacco control accounted for 98% of lung cancer deaths averted and screening accounted for 100% of cervical cancer deaths averted. Prevention and/or screening accounted for 79% and 56% of deaths averted for colorectal and prostate cancers, respectively. In contrast, given the development of very effective therapies for breast cancer over this period, screening was estimated to account for a minority, about 25%, of female breast cancer deaths averted and therapies accounted for the remaining deaths averted. Less than half of total cancer deaths in the absence of all interventions (scenario 1) were averted for each cancer site (Table 2).

Table 2. Cancer Deaths Averted From Prevention and/or Screening, and Treatment Advances vs Counterfactual.

Intervention, calendar years, and age range considered	Total cancer deaths averted with all interventions^a	Cancer deaths averted, No. (%)^a^,^b			Cancer deaths averted from total cancer deaths in absence of all interventions, %			Total cancer deaths in absence of all interventions (scenario 1)
Intervention, calendar years, and age range considered	Total cancer deaths averted with all interventions^a	Prevention and/or screening in the presence of treatment advances (scenario 4 − scenario 3)		Treatment advances only (scenario 3 − scenario 1)	Prevention and/or screening in the presence of treatment advances (scenario 4 − scenario 3)/scenario 1	Treatment advances only (scenario 3 − scenario 1)/scenario 1
Female breast cancer^c
Mammography 1975-2020 Persons aged ≥25 y	1 030 000		260 000 (25)	770 000 (75)	10	28	2 710 000
Cervical cancer^d
Pap testing and HPV testing 1975-2020 All ages	160 000	160 000 (100)		Negligible	43	Negligible		370 000
Colorectal cancer^e
Screening modalities in USPSTF guidelines 1975-2020 All ages	940 000	740 000 (79)		200 000 (21)	21	6		3 450 000
Lung cancer^f
Tobacco control 1975-2020 Persons aged 30-84 y	3 450 000	3 390 000 (98)		60 000 (2)	37	1		9 200 000
Prostate cancer^g
PSA testing 1990-2020 Persons aged 50-84 y	360 000	200 000 (56)		170 000 (44)	20	17		1 010 000
Total
All interventions combined	5 940 000	4 750 000 (80)		1 200 000 (20)	NA	NA		NA

Open in a new tab

Abbreviations: HPV, human papillomavirus; NA, not applicable; PSA, prostate-specific antigen; USPSTF, US Preventive Services Task Force.

^{^a}

Total deaths averted included prevention and/or screening and treatment advances as indicated. Total may not be a sum of each intervention group alone due to the negative synergy between each. In cancers with effective therapies, screening effects decrease in the presence vs absence of treatment advances. In cancer sites with less effective treatments, the relative screening impact increases in the present vs absence of treatment advances.

^{^b}

Secondary outcome for the contribution of prevention and/or screening without treatment advances is shown in eTables 4 and 5 in Supplement 1 (percentage of cancer deaths averted from total cancer deaths in the absence of all interventions). Numbers may not sum to totals due to rounding.

^{^c}

Estimates of female breast and lung cancer deaths averted are reported as the average across 3 and 2 cancer simulation models, respectively. The breast models include mammography used in year calendar period (plain film, digital, and tomosynthesis) and treatment advances. See details in eTable 1 in Supplement 1.

^{^d}

Cervical cancer model included screening and assumed no impact of treatment advances since survival was unchanged over the study period.

^{^e}

Colorectal cancer model included colonoscopy, sigmoidoscopy, and stool testing, removal of adenomatous polyps, and chemotherapy. See details in eTable 2 in Supplement 1.

^{^f}

Treatment advances and tobacco control were modeled separately using different approaches. Treatment advances focused on the modern era from 2014 to 2020 and included all ages. Because the treatment analysis uses a projection of observed lung cancer deaths before the treatment-associated decline in deaths starting in 2014 (see eFigures 5 and 6 in Supplement 1), this analysis is implicitly conducted in the presence of the impact of tobacco control.

^{^g}

See details in eTable 3 in Supplement 1.

Discussion

In this model-based study using population-level data, interventions discovered through research on cancer prevention and screening (for interception and early detection) were estimated to account for 8 of every 10 deaths averted from breast, cervical, colorectal, lung, and prostate cancer over the past 45 years. Deaths averted from the impact of tobacco control on lung cancer had the most predominant impact. With the exception of breast cancer, these results indicate relatively fewer deaths will be avoided through treatment advances compared with prevention (via smoking cessation) or screening (for interception or early detection). These 5 cancer sites represent 51% of all patients with cancer, and deaths from these cancers represent 42% of all cancer deaths.⁸ The models did not quantify emerging interventions with low uptake during the study period, suggesting interventions like human papillomavirus (HPV) vaccines, lung cancer screening, and new therapies could further decrease mortality. Reports from Europe provided evidence from clinical practice that HPV vaccination reduces cervical cancer incidence.^9,10,11,12

Limitations

Despite the use of well-established and validated models, several caveats should be considered. First, we did not model several cancers with high mortality rates (eg, liver, pancreatic, and ovarian) because population models for these cancers are less mature and challenging to validate. We did not include rare cancer sites, which, when combined, represent 25% of all cancer-related deaths.¹³ Our results for the overall population may not be generalizable to groups experiencing cancer disparities.¹⁴ We did not consider harms of interventions (eg, false-positive screening) or the available resources/capacity to provide services. Mortality measures fail to consider meaningful outcomes for cancer survivors, such as quality of life. Early diagnosis and prevention may reduce or eliminate treatment, minimize harmful adverse effects of the cancer or treatment, and reduce the financial burden of cancer. Finally, our analysis focused on the burden of cancer in the US, which does not reflect the burden worldwide.

Conclusions

In this model-based study of population-level mortality data, cancer prevention and screening activities were the main contributors to reductions in mortality due to the most common cancers in the US over the past 45 years. Despite progress, efforts to further reduce the US cancer burden will require increased dissemination of effective interventions and development of new technologies and discoveries. Future efforts should continue to invest significantly in prevention and screening strategies as part of a comprehensive plan to accelerate progress and reduce cancer mortality.

Supplement 1.

eFigure 1. US Cervical Cancer Age-Adjusted Mortality Rates: 1975-2020 (Adjusted to US 2000 Standard Population)

eTable 1. Cumulative Breast Cancer Deaths Averted During the Period 1975 through 2020 for Women Aged 25 and Older in the US

eTable 2. Cumulative Colorectal Cancer Deaths Averted during the Period 1975 through 2020 for All Individuals in the US

eFigure 2. US Prostate Cancer Age-Adjusted Mortality Rates and Projections with and without Screening or Changes in Care

eTable 3. Cumulative Prostate Cancer Deaths Averted during the Period 1990 through 2020 for Men Aged 50-84 in the US

eFigure 3. US Age-Specific Smoking Prevalence by Birth Cohort under the Actual Tobacco Control Scenario

eFigure 4. US Age-Specific Smoking Prevalence by Birth Cohort under the No Tobacco Control Scenario

eFigure 5. Total Number of Lung Cancer Deaths Averted from 2014 to 2020 in Men, United States

eFigure 6. Total Number of Lung Cancer Deaths Averted from 2014 to 2020 in Women, United States

eTable 4. Summary of Cumulative Cancer Deaths Averted and Percentage of Deaths Averted from Prevention and/or Screening and Treatment Advances for Five Cancer Types Compared with Counterfactuals of No Interventions Showing Both Primary Outcomes (in the Presence of Treatment Advances) and Secondary Outcomes (in the Absence of Treatment Advances)

eTable 5. Summary of Percentage of Cancer Deaths Averted From All Cancer Deaths in the Absence of Prevention and/or Screening and Treatment Advances for Five Cancer Types

jamaoncol-e245381-s001.pdf^{(1.4MB, pdf)}

Supplement 2.

Data Sharing Statement

jamaoncol-e245381-s002.pdf^{(18.3KB, pdf)}

References

1.Shiels MS, Lipkowitz S, Campos NG, et al. Opportunities for achieving the cancer moonshot goal of a 50% reduction in cancer mortality by 2047. Cancer Discov. 2023;13(5):1084-1099. doi: 10.1158/2159-8290.CD-23-0208 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Caswell-Jin JL, Sun LP, Munoz D, et al. Analysis of breast cancer mortality in the US—1975 to 2019. JAMA. 2024;331(3):233-241. doi: 10.1001/jama.2023.25881 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Edwards BK, Ward E, Kohler BA, et al. Annual report to the nation on the status of cancer, 1975-2006, featuring colorectal cancer trends and impact of interventions (risk factors, screening, and treatment) to reduce future rates. Cancer. 2010;116(3):544-573. doi: 10.1002/cncr.24760 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Etzioni R, Tsodikov A, Mariotto A, et al. Quantifying the role of PSA screening in the US prostate cancer mortality decline. Cancer Causes Control. 2008;19(2):175-181. doi: 10.1007/s10552-007-9083-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Howlader N, Forjaz G, Mooradian MJ, et al. The effect of advances in lung cancer treatment on population mortality. N Engl J Med. 2020;383(7):640-649. doi: 10.1056/NEJMoa1916623 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Moolgavkar SH, Holford TR, Levy DT, et al. Impact of reduced tobacco smoking on lung cancer mortality in the United States during 1975-2000. J Natl Cancer Inst. 2012;104(7):541-548. doi: 10.1093/jnci/djs136 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.National Cancer Institute . Cancer stat facts: cervical cancer. Accessed November 8, 2024. https://seer.cancer.gov/statfacts/html/cervix.html
8.Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12-49. doi: 10.3322/caac.21820 [DOI] [PubMed] [Google Scholar]
9.Falcaro M, Castañon A, Ndlela B, et al. The effects of the national HPV vaccination programme in England, UK, on cervical cancer and grade 3 cervical intraepithelial neoplasia incidence: a register-based observational study. Lancet. 2021;398(10316):2084-2092. doi: 10.1016/S0140-6736(21)02178-4 [DOI] [PubMed] [Google Scholar]
10.Kjaer SK, Dehlendorff C, Belmonte F, Baandrup L. Real-world effectiveness of human papillomavirus vaccination against cervical cancer. J Natl Cancer Inst. 2021;113(10):1329-1335. doi: 10.1093/jnci/djab080 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Lei J, Ploner A, Elfström KM, et al. HPV vaccination and the risk of invasive cervical cancer. N Engl J Med. 2020;383(14):1340-1348. doi: 10.1056/NEJMoa1917338 [DOI] [PubMed] [Google Scholar]
12.Luostarinen T, Apter D, Dillner J, et al. Vaccination protects against invasive HPV-associated cancers. Int J Cancer. 2018;142(10):2186-2187. doi: 10.1002/ijc.31231 [DOI] [PubMed] [Google Scholar]
13.National Cancer Institute . MyPART—My Pediatric and Adult Rare Tumor Network. Accessed November 8, 2024. https://www.cancer.gov/pediatric-adult-rare-tumor/
14.Mandelblatt J, Meza R, Trentham-Dietz A, Heckman-Stoddard B, Feuer E. Using simulation modeling to guide policy to reduce disparities and achieve equity in cancer outcomes: state of the science and a road map for the future. J Natl Cancer Inst Monogr. 2023;2023(62):159-166. doi: 10.1093/jncimonographs/lgad033 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials