IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v38y2018i1_supplp24s-31s.html
   My bibliography  Save this article

Contribution of Breast Cancer to Overall Mortality for US Women

Author

Listed:
  • Ronald E. Gangnon

    (Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
    Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA
    Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI)

  • Natasha K. Stout

    (Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA)

  • Oguzhan Alagoz

    (Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA
    Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI
    Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA)

  • John M. Hampton

    (Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA
    Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI)

  • Brian L. Sprague

    (Department of Surgery and University of Vermont Cancer Center, Burlington, VT, USA)

  • Amy Trentham-Dietz

    (Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA
    Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI)

Abstract

Objective. Breast cancer simulation models must take changing mortality rates into account to evaluate the potential impact of cancer control interventions. We estimated mortality rates due to breast cancer and all other causes combined to determine their impact on overall mortality by year, age, and birth cohort. Methods. Based on mortality rates from publicly available datasets, an age-period-cohort model was used to estimate the proportion of deaths due to breast cancer for US women aged 0 to 119 years, with birth years 1900 to 2000. Breast cancer mortality was calculated as all-cause mortality multiplied by the proportion of deaths due to breast cancer; other-cause mortality was the difference between all-cause and breast cancer mortality. Results. Breast cancer and other-cause mortality rates were higher for older ages and birth cohorts. The percent of deaths due to breast cancer increased across birth cohorts from 1900 to 1940 then decreased. Among 50-year-old women, in the 1920 birth cohort, 52 (9.9%) of 100,000 deaths (95% CI, 9.8% to 10.1%) were attributed to breast cancer whereas 476 of 100,000 were due to other causes; in the 1960 birth cohort, 22 (8.5%) of 100,000 deaths (95% CI, 8.3% to 8.7%) were attributed to breast cancer with 242 of 100,000 deaths due to other causes. The percentage of all deaths due to breast cancer was highest (4.1% to 12.9%) for women in their 40s and 50s for all birth cohorts. Conclusions. This study offers evidence that advances in breast cancer screening and treatment have reduced breast cancer mortality for women across the age spectrum, and provides estimates of age-, year- and birth cohort-specific competing mortality rates for simulation models. Other-cause mortality estimates are important in these models because most women die from causes other than breast cancer.

Suggested Citation

  • Ronald E. Gangnon & Natasha K. Stout & Oguzhan Alagoz & John M. Hampton & Brian L. Sprague & Amy Trentham-Dietz, 2018. "Contribution of Breast Cancer to Overall Mortality for US Women," Medical Decision Making, , vol. 38(1_suppl), pages 24-31, April.
    • Handle: RePEc:sae:medema:v:38:y:2018:i:1_suppl:p:24s-31s
    DOI: 10.1177/0272989X17717981
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X17717981
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X17717981?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    2. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rodrigo Chávez-Penha & Maria Teresa Bustamante-Teixeira & Mário Círio Nogueira, 2023. "Age-Period-Cohort Study of Breast Cancer Mortality in Brazil in State Capitals and in Non-Capital Municipalities from 1980 to 2019," IJERPH, MDPI, vol. 20(15), pages 1-16, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Economic Modelling, Elsevier, vol. 37(C), pages 395-407.
    2. Roberto Basile & Luigi Benfratello & Davide Castellani, 2012. "Geoadditive models for regional count data: an application to industrial location," ERSA conference papers ersa12p83, European Regional Science Association.
    3. E. Zanini & E. Eastoe & M. J. Jones & D. Randell & P. Jonathan, 2020. "Flexible covariate representations for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    4. Marra, Giampiero & Wood, Simon N., 2011. "Practical variable selection for generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2372-2387, July.
    5. Basile, Roberto & Durbán, María & Mínguez, Román & María Montero, Jose & Mur, Jesús, 2014. "Modeling regional economic dynamics: Spatial dependence, spatial heterogeneity and nonlinearities," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 229-245.
    6. Feuillet, Thierry & Bulteau, Julie & Dantan, Sophie, 2021. "Modelling context-specific relationships between neighbourhood socioeconomic disadvantage and private car use," Journal of Transport Geography, Elsevier, vol. 93(C).
    7. Shailendra Gurjar & Usha Ananthakumar, 2023. "The economics of art: price determinants and returns on investment in Indian paintings," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 50(6), pages 839-859, January.
    8. Gressani, Oswaldo & Lambert, Philippe, 2021. "Laplace approximations for fast Bayesian inference in generalized additive models based on P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    9. Drew A. Scott & Kathryn D. Eckhoff & Nicola Lorenz & Richard Dick & Rebecca M. Swab, 2021. "Diversity Is Not Everything," Land, MDPI, vol. 10(10), pages 1-20, October.
    10. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
    11. Jennifer F. Bobb & Maricela F. Cruz & Stephen J. Mooney & Adam Drewnowski & David Arterburn & Andrea J. Cook, 2022. "Accounting for spatial confounding in epidemiological studies with individual‐level exposures: An exposure‐penalized spline approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1271-1293, July.
    12. Musolesi Antonio & Mazzanti Massimiliano, 2014. "Nonlinearity, heterogeneity and unobserved effects in the carbon dioxide emissions-economic development relation for advanced countries," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(5), pages 1-21, December.
    13. David L. Miller & Richard Glennie & Andrew E. Seaton, 2020. "Understanding the Stochastic Partial Differential Equation Approach to Smoothing," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(1), pages 1-16, March.
    14. Cornelius Fritz & Göran Kauermann, 2022. "On the interplay of regional mobility, social connectedness and the spread of COVID‐19 in Germany," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 400-424, January.
    15. Scott Tainsky & Brian M. Mills & Jason A. Winfree, 2015. "Further Examination of Potential Discrimination Among MLB Umpires," Journal of Sports Economics, , vol. 16(4), pages 353-374, May.
    16. Giampiero Marra & Rosalba Radice & Till Bärnighausen & Simon N. Wood & Mark E. McGovern, 2017. "A Simultaneous Equation Approach to Estimating HIV Prevalence With Nonignorable Missing Responses," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 484-496, April.
    17. Stefan Sperlich & Raoul Theler, 2015. "Modeling heterogeneity: a praise for varying-coefficient models in causal analysis," Computational Statistics, Springer, vol. 30(3), pages 693-718, September.
    18. Emma M. V. Blomgren & Mohsen Banaei & Razgar Ebrahimy & Olof Samuelsson & Francesco D’Ettorre & Henrik Madsen, 2023. "Intensive Data-Driven Model for Real-Time Observability in Low-Voltage Radial DSO Grids," Energies, MDPI, vol. 16(11), pages 1-22, May.
    19. Feuillet, T. & Valette, J.F. & Charreire, H. & Kesse-Guyot, E. & Julia, C. & Vernez-Moudon, A. & Hercberg, S. & Touvier, M. & Oppert, J.M., 2020. "Influence of the urban context on the relationship between neighbourhood deprivation and obesity," Social Science & Medicine, Elsevier, vol. 265(C).
    20. Brian M. Mills, 2017. "Policy Changes In Major League Baseball: Improved Agent Behavior And Ancillary Productivity Outcomes," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 1104-1118, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:38:y:2018:i:1_suppl:p:24s-31s. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.