Derivation of background mortality by smoking and obesity in cancer simulation models

Y. Claire Wang, Barry I. Graubard, Marjorie A. Rosenberg, Karen M. Kuntz, Ann G. Zauber, Lisa Kahle, Clyde B. Schechter, Eric J. Feuer

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Background. Simulation models designed to evaluate cancer prevention strategies make assumptions on background mortality - the competing risk of death from causes other than the cancer being studied. Researchers often use the U.S. life tables and assume homogeneous other-cause mortality rates. However, this can lead to bias because common risk factors such as smoking and obesity also predispose individuals for deaths from other causes such as cardiovascular disease. Methods. We obtained calendar year-, age-, and sex-specific other-cause mortality rates by removing deaths due to a specific cancer from U.S. all-cause life tables. Prevalence across 12 risk factor groups (3 smoking [never, past, and current smoker] and 4 body mass index [BMI] categories [<25, 25-30, 30-35, 35+ kg/m2]) were estimated from national surveys (National Health and Nutrition Examination Surveys [NHANES] 1971-2004). Using NHANES linked mortality data, we estimated hazard ratios for death by BMI/smoking using a Poisson regression model. Finally, we combined these results to create 12 sets of BMI and smoking-specific other-cause life tables for U.S. adults aged 40 years and older that can be used in simulation models of lung, colorectal, or breast cancer. Results. We found substantial differences in background mortality when accounting for BMI and smoking. Ignoring the heterogeneity in background mortality in cancer simulation models can lead to underestimation of competing risk of deaths for higher-risk individuals (e.g., male, 60-year old, white obese smokers) by as high as 45%. Conclusion. Not properly accounting for competing risks of death may introduce bias when using simulation modeling to evaluate population health strategies for prevention, screening, or treatment. Further research is warranted on how these biases may affect cancer-screening strategies targeted at high-risk individuals.

Original languageEnglish (US)
Pages (from-to)176-197
Number of pages22
JournalMedical Decision Making
Volume33
Issue number2
DOIs
StatePublished - Feb 2013

Fingerprint

Obesity
Smoking
Life Tables
Mortality
Body Mass Index
Neoplasms
Nutrition Surveys
Cause of Death
Early Detection of Cancer
Colorectal Neoplasms
Lung Neoplasms
Cardiovascular Diseases
Research Personnel
Breast Neoplasms
Health
Research
Population

Keywords

  • background mortality
  • cancer simulation
  • life tables

ASJC Scopus subject areas

  • Health Policy
  • Medicine(all)

Cite this

Wang, Y. C., Graubard, B. I., Rosenberg, M. A., Kuntz, K. M., Zauber, A. G., Kahle, L., ... Feuer, E. J. (2013). Derivation of background mortality by smoking and obesity in cancer simulation models. Medical Decision Making, 33(2), 176-197. https://doi.org/10.1177/0272989X12458725

Derivation of background mortality by smoking and obesity in cancer simulation models. / Wang, Y. Claire; Graubard, Barry I.; Rosenberg, Marjorie A.; Kuntz, Karen M.; Zauber, Ann G.; Kahle, Lisa; Schechter, Clyde B.; Feuer, Eric J.

In: Medical Decision Making, Vol. 33, No. 2, 02.2013, p. 176-197.

Research output: Contribution to journalArticle

Wang, YC, Graubard, BI, Rosenberg, MA, Kuntz, KM, Zauber, AG, Kahle, L, Schechter, CB & Feuer, EJ 2013, 'Derivation of background mortality by smoking and obesity in cancer simulation models', Medical Decision Making, vol. 33, no. 2, pp. 176-197. https://doi.org/10.1177/0272989X12458725
Wang YC, Graubard BI, Rosenberg MA, Kuntz KM, Zauber AG, Kahle L et al. Derivation of background mortality by smoking and obesity in cancer simulation models. Medical Decision Making. 2013 Feb;33(2):176-197. https://doi.org/10.1177/0272989X12458725
Wang, Y. Claire ; Graubard, Barry I. ; Rosenberg, Marjorie A. ; Kuntz, Karen M. ; Zauber, Ann G. ; Kahle, Lisa ; Schechter, Clyde B. ; Feuer, Eric J. / Derivation of background mortality by smoking and obesity in cancer simulation models. In: Medical Decision Making. 2013 ; Vol. 33, No. 2. pp. 176-197.
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abstract = "Background. Simulation models designed to evaluate cancer prevention strategies make assumptions on background mortality - the competing risk of death from causes other than the cancer being studied. Researchers often use the U.S. life tables and assume homogeneous other-cause mortality rates. However, this can lead to bias because common risk factors such as smoking and obesity also predispose individuals for deaths from other causes such as cardiovascular disease. Methods. We obtained calendar year-, age-, and sex-specific other-cause mortality rates by removing deaths due to a specific cancer from U.S. all-cause life tables. Prevalence across 12 risk factor groups (3 smoking [never, past, and current smoker] and 4 body mass index [BMI] categories [<25, 25-30, 30-35, 35+ kg/m2]) were estimated from national surveys (National Health and Nutrition Examination Surveys [NHANES] 1971-2004). Using NHANES linked mortality data, we estimated hazard ratios for death by BMI/smoking using a Poisson regression model. Finally, we combined these results to create 12 sets of BMI and smoking-specific other-cause life tables for U.S. adults aged 40 years and older that can be used in simulation models of lung, colorectal, or breast cancer. Results. We found substantial differences in background mortality when accounting for BMI and smoking. Ignoring the heterogeneity in background mortality in cancer simulation models can lead to underestimation of competing risk of deaths for higher-risk individuals (e.g., male, 60-year old, white obese smokers) by as high as 45{\%}. Conclusion. Not properly accounting for competing risks of death may introduce bias when using simulation modeling to evaluate population health strategies for prevention, screening, or treatment. Further research is warranted on how these biases may affect cancer-screening strategies targeted at high-risk individuals.",
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