National and State Estimates of Lost Earnings From Cancer Deaths in the United States | Cancer Screening, Prevention, Control | JAMA Oncology | JAMA Network
[Skip to Navigation]
Sign In
Figure 1.  Age-Standardized Lost Earning Rates (Million Dollars per 100 000) Owing to Death From All Cancers Combined and Lung Cancer in Persons Aged 16 to 84 Years by Sex and State, 2015
Age-Standardized Lost Earning Rates (Million Dollars per 100 000) Owing to Death From All Cancers Combined and Lung Cancer in Persons Aged 16 to 84 Years by Sex and State, 2015

Lost earning rates are age-adjusted to the 2000 US standard population.

Figure 2.  Age-Standardized Lost Earning Rates (Million Dollars per 100 000) Owing to Death From Selected Major Cancers in Persons Aged 16 to 84 Years in Both Sexes Combined by State, 2015
Age-Standardized Lost Earning Rates (Million Dollars per 100 000) Owing to Death From Selected Major Cancers in Persons Aged 16 to 84 Years in Both Sexes Combined by State, 2015

Lost earning rates are age-adjusted to the 2000 US standard population; IHBD indicates intrahepatic bile duct.

Table.  Number of Cancer Deaths and Associated PYLL and Lost Earnings in Persons Aged 16 to 84 Years in Both Sexes Combined, United States, 2015
Number of Cancer Deaths and Associated PYLL and Lost Earnings in Persons Aged 16 to 84 Years in Both Sexes Combined, United States, 2015
1.
Siegel  RL, Miller  KD, Jemal  A.  Cancer statistics, 2019.  CA Cancer J Clin. 2019;69(1):7-34. doi:10.3322/caac.21551PubMedGoogle ScholarCrossref
2.
Yabroff  KR, Lund  J, Kepka  D, Mariotto  A.  Economic burden of cancer in the United States: estimates, projections, and future research.  Cancer Epidemiol Biomarkers Prev. 2011;20(10):2006-2014. doi:10.1158/1055-9965.EPI-11-0650PubMedGoogle ScholarCrossref
3.
Ekwueme  DU, Chesson  HW, Zhang  KB, Balamurugan  A.  Years of potential life lost and productivity costs because of cancer mortality and for specific cancer sites where human papillomavirus may be a risk factor for carcinogenesis—United States, 2003.  Cancer. 2008;113(10)(suppl):2936-2945. doi:10.1002/cncr.23761PubMedGoogle ScholarCrossref
4.
Bradley  CJ, Yabroff  KR, Dahman  B, Feuer  EJ, Mariotto  A, Brown  ML.  Productivity costs of cancer mortality in the United States: 2000-2020.  J Natl Cancer Inst. 2008;100(24):1763-1770. doi:10.1093/jnci/djn384PubMedGoogle ScholarCrossref
5.
Hanly  P, Pearce  A, Sharp  L.  The cost of premature cancer-related mortality: a review and assessment of the evidence.  Expert Rev Pharmacoecon Outcomes Res. 2014;14(3):355-377. doi:10.1586/14737167.2014.909287PubMedGoogle ScholarCrossref
6.
Taksler  GB, Rothberg  MB.  Assessing years of life lost versus number of deaths in the United States, 1995-2015.  Am J Public Health. 2017;107(10):1653-1659. doi:10.2105/AJPH.2017.303986PubMedGoogle ScholarCrossref
7.
Yabroff  KR, Bradley  CJ, Mariotto  AB, Brown  ML, Feuer  EJ.  Estimates and projections of value of life lost from cancer deaths in the United States.  J Natl Cancer Inst. 2008;100(24):1755-1762. doi:10.1093/jnci/djn383PubMedGoogle ScholarCrossref
8.
Ekwueme  DU, Guy  GP  Jr, Rim  SH,  et al.  Health and economic impact of breast cancer mortality in young women, 1970-2008.  Am J Prev Med. 2014;46(1):71-79. doi:10.1016/j.amepre.2013.08.016PubMedGoogle ScholarCrossref
9.
Weir  HK, Li  C, Henley  SJ, Joseph  D.  Years of life and productivity loss from potentially avoidable colorectal cancer deaths in US counties with lower educational attainment (2008-2012).  Cancer Epidemiol Biomarkers Prev. 2017;26(5):736-742. doi:10.1158/1055-9965.EPI-16-0702PubMedGoogle ScholarCrossref
10.
National Cancer Institute. SEER*Stat Database: Total US mortality, 1990-2016. Bethesda, MD: National Cancer Institute; 2018. https://seer.cancer.gov/data-software/documentation/seerstat/. Accessed June 5, 2019.
11.
Arias  E, Xu  JQ. United States life tables, 2015. National Vital Statistics Reports; vol 67, no 7. Hyattsville, MD: National Center for Health Statistics; 2018.
12.
United States Department of Labor. Occupational employment statistics. https://www.bls.gov/oes/tables.htm. Accessed January 8, 2019.
13.
Grosse  S. Appendix I: productivity loss tables. In: Haddix  AC, Teutsch  SM, Corso  PS, eds.  Prevention Effectiveness: A Guide to Decision Analysis and Economic Evaluation. New York: Oxford University Press; 2003:245-258.
14.
Greenland  S.  Interval estimation by simulation as an alternative to and extension of confidence intervals.  Int J Epidemiol. 2004;33(6):1389-1397. doi:10.1093/ije/dyh276PubMedGoogle ScholarCrossref
15.
Sanders  GD, Neumann  PJ, Basu  A,  et al.  Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second Panel on Cost-Effectiveness in Health and Medicine.  JAMA. 2016;316(10):1093-1103. doi:10.1001/jama.2016.12195PubMedGoogle ScholarCrossref
16.
Sauer  AG, Siegel  RL, Jemal  A, Fedewa  SA.  Updated review of prevalence of major risk factors and use of screening tests for cancer in the United States.  Cancer Epidemiol Biomarkers Prev. 2017;26(8):1192-1208. doi:10.1158/1055-9965.EPI-17-0219PubMedGoogle ScholarCrossref
17.
Hashibe  M, Kirchhoff  AC, Kepka  D,  et al.  Disparities in cancer survival and incidence by metropolitan versus rural residence in Utah.  Cancer Med. 2018;7(4):1490-1497. doi:10.1002/cam4.1382PubMedGoogle ScholarCrossref
18.
Islami  F, Goding Sauer  A, Miller  KD,  et al.  Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States.  CA Cancer J Clin. 2018;68(1):31-54. doi:10.3322/caac.21440PubMedGoogle ScholarCrossref
19.
Park  ER, Gareen  IF, Japuntich  S,  et al.  Primary care provider–delivered smoking cessation interventions and smoking cessation among participants in the National Lung Screening Trial.  JAMA Intern Med. 2015;175(9):1509-1516. doi:10.1001/jamainternmed.2015.2391PubMedGoogle ScholarCrossref
20.
Curry  SJ, Krist  AH, Owens  DK,  et al; US Preventive Services Task Force.  Behavioral weight loss interventions to prevent obesity-related morbidity and mortality in adults: US Preventive Services Task Force recommendation statement.  JAMA. 2018;320(11):1163-1171. doi:10.1001/jama.2018.13022PubMedGoogle ScholarCrossref
21.
Campaign for Tobacco-Free Kids. State cigarette excise tax rates and rankings. https://www.tobaccofreekids.org/assets/factsheets/0097.pdf. Updated December 21, 2018. Accessed June 13, 2018.
22.
Smith  RA, Andrews  KS, Brooks  D,  et al.  Cancer screening in the United States, 2018: a review of current American Cancer Society guidelines and current issues in cancer screening.  CA Cancer J Clin. 2018;68(4):297-316. doi:10.3322/caac.21446PubMedGoogle ScholarCrossref
23.
Walker  TY, Elam-Evans  LD, Yankey  D,  et al.  National, regional, state, and selected local area vaccination coverage among adolescents aged 13-17 years—United States, 2017.  MMWR Morb Mortal Wkly Rep. 2018;67(33):909-917. doi:10.15585/mmwr.mm6733a1PubMedGoogle ScholarCrossref
24.
Office of Disease Prevention and Health Promotion. Healthy People 2020: immunization and infectious diseases. https://www.healthypeople.gov/2020/topics-objectives/topic/immunization-and-infectious-diseases/objectives. Updated May 24, 2019. Accessed July 2, 2018.
25.
Klabunde  CN, Schenck  AP, Davis  WW.  Barriers to colorectal cancer screening among Medicare consumers.  Am J Prev Med. 2006;30(4):313-319. doi:10.1016/j.amepre.2005.11.006PubMedGoogle ScholarCrossref
26.
White  A, Thompson  TD, White  MC,  et al.  Cancer screening test use—United States, 2015.  MMWR Morb Mortal Wkly Rep. 2017;66(8):201-206. doi:10.15585/mmwr.mm6608a1PubMedGoogle ScholarCrossref
27.
Joseph  DA, King  JB, Richards  TB, Thomas  CC, Richardson  LC.  Use of colorectal cancer screening tests by state.  Prev Chronic Dis. 2018;15:E80. doi:10.5888/pcd15.170535PubMedGoogle ScholarCrossref
28.
Jemal  A, Fedewa  SA.  Lung cancer screening with low-dose computed tomography in the United States—2010 to 2015.  JAMA Oncol. 2017;3(9):1278-1281. doi:10.1001/jamaoncol.2016.6416PubMedGoogle ScholarCrossref
29.
Copeland  G, Lake  A, Firth  R,  et al.  Cancer in North America: 2010-2014. Volume Two: Registry-Specific Cancer Incidence in the United States and Canada. Springfield, IL: North American Association of Central Cancer Registries, Inc; 2017.
30.
Sineshaw  HM, Wu  XC, Flanders  WD, Osarogiagbon  RU, Jemal  A.  Variations in receipt of curative-intent surgery for early-stage non–small cell lung cancer (NSCLC) by state.  J Thorac Oncol. 2016;11(6):880-889. doi:10.1016/j.jtho.2016.03.003PubMedGoogle ScholarCrossref
31.
Polite  BN, Adams-Campbell  LL, Brawley  OW,  et al.  Charting the future of cancer health disparities research: a position statement from the American Association for Cancer Research, the American Cancer Society, the American Society of Clinical Oncology, and the National Cancer Institute.  CA Cancer J Clin. 2017;67(5):353-361. doi:10.3322/caac.21404PubMedGoogle ScholarCrossref
32.
Sineshaw  HM, Ng  K, Flanders  WD, Brawley  OW, Jemal  A.  Factors that contribute to differences in survival of black vs white patients with colorectal cancer.  Gastroenterology. 2018;154(4):906-915.e7. doi:10.1053/j.gastro.2017.11.005PubMedGoogle ScholarCrossref
33.
Fang  P, He  W, Gomez  D,  et al.  Racial disparities in guideline-concordant cancer care and mortality in the United States.  Adv Radiat Oncol. 2018;3(3):221-229. doi:10.1016/j.adro.2018.04.013PubMedGoogle ScholarCrossref
34.
Lin  CC, Bruinooge  SS, Kirkwood  MK,  et al.  Association between geographic access to cancer care, insurance, and receipt of chemotherapy: geographic distribution of oncologists and travel distance.  J Clin Oncol. 2015;33(28):3177-3185. doi:10.1200/JCO.2015.61.1558PubMedGoogle ScholarCrossref
35.
Shalowitz  DI, Vinograd  AM, Giuntoli  RL  II.  Geographic access to gynecologic cancer care in the United States.  Gynecol Oncol. 2015;138(1):115-120. doi:10.1016/j.ygyno.2015.04.025PubMedGoogle ScholarCrossref
36.
American Society of Clinical Oncology.  The state of cancer care in America, 2017: a report by the American Society of Clinical Oncology.  J Oncol Pract. 2017;13(4):e353-e394. doi:10.1200/JOP.2016.020743PubMedGoogle ScholarCrossref
37.
Yabroff  KR, Guy  GP  Jr, Ekwueme  DU,  et al.  Annual patient time costs associated with medical care among cancer survivors in the United States.  Med Care. 2014;52(7):594-601. doi:10.1097/MLR.0000000000000151PubMedGoogle ScholarCrossref
38.
Zheng  Z, Yabroff  KR, Guy  GP  Jr,  et al.  Annual medical expenditure and productivity loss among colorectal, female breast, and prostate cancer survivors in the United States.  J Natl Cancer Inst. 2015;108(5). doi:10.1093/jnci/djv382PubMedGoogle Scholar
39.
Guy  GP  Jr, Yabroff  KR, Ekwueme  DU, Rim  SH, Li  R, Richardson  LC.  Economic burden of chronic conditions among survivors of cancer in the United States.  J Clin Oncol. 2017;35(18):2053-2061. doi:10.1200/JCO.2016.71.9716PubMedGoogle ScholarCrossref
40.
Ryen  L, Svensson  M.  The willingness to pay for a quality adjusted life year: a review of the empirical literature.  Health Econ. 2015;24(10):1289-1301. doi:10.1002/hec.3085PubMedGoogle ScholarCrossref
41.
Pearce  A, Sharp  L, Hanly  P,  et al.  Productivity losses due to premature mortality from cancer in Brazil, Russia, India, China, and South Africa (BRICS): a population-based comparison.  Cancer Epidemiol. 2018;53:27-34. doi:10.1016/j.canep.2017.12.013PubMedGoogle ScholarCrossref
42.
Luengo-Fernandez  R, Leal  J, Gray  A, Sullivan  R.  Economic burden of cancer across the European Union: a population-based cost analysis.  Lancet Oncol. 2013;14(12):1165-1174. doi:10.1016/S1470-2045(13)70442-XPubMedGoogle ScholarCrossref
43.
Albano  JD, Ward  E, Jemal  A,  et al.  Cancer mortality in the United States by education level and race.  J Natl Cancer Inst. 2007;99(18):1384-1394. doi:10.1093/jnci/djm127PubMedGoogle ScholarCrossref
44.
Kinsey  T, Jemal  A, Liff  J, Ward  E, Thun  M.  Secular trends in mortality from common cancers in the United States by educational attainment, 1993-2001.  J Natl Cancer Inst. 2008;100(14):1003-1012. doi:10.1093/jnci/djn207PubMedGoogle ScholarCrossref
45.
Siegel  RL, Jemal  A, Wender  RC, Gansler  T, Ma  J, Brawley  OW.  An assessment of progress in cancer control.  CA Cancer J Clin. 2018;68(5):329-339. doi:10.3322/caac.21460PubMedGoogle ScholarCrossref
46.
Islami  F, Ward  EM, Jacobs  EJ,  et al.  Potentially preventable premature lung cancer deaths in the USA if overall population rates were reduced to those of educated whites in lower-risk states.  Cancer Causes Control. 2015;26(3):409-418. doi:10.1007/s10552-014-0517-9PubMedGoogle ScholarCrossref
Original Investigation
July 3, 2019

National and State Estimates of Lost Earnings From Cancer Deaths in the United States

Author Affiliations
  • 1Surveillance and Health Services Research Program, American Cancer Society, Atlanta, Georgia
JAMA Oncol. 2019;5(9):e191460. doi:10.1001/jamaoncol.2019.1460
Key Points

Question  What is the estimated monetary value of lost earnings due to cancer deaths in the United States nationally and by state?

Findings  In this population-based study, estimated lost earnings due to cancer deaths in persons aged 16 to 84 years in the United States in 2015 were $94.4 billion; age-standardized lost earning rates were the largest in the South, followed by the Midwest. Achieving Utah’s age-specific lost earning rates in all states could reduce lost earnings related to cancer deaths in the United States in 2015 by 29.3% ($27.7 billion), ranging from 13.6% in Colorado to 47.3% in Kentucky and 47.0% in Mississippi.

Meaning  Preventing premature deaths from cancer through delivery of effective cancer prevention, screening, and treatment may have economic benefit for the United States nationally and in all states.

Abstract

Importance  Information on the economic burden of cancer mortality can serve as a tool in setting policies and prioritizing resources for cancer prevention and control. However, contemporary data are lacking for the United States nationally and by state.

Objective  To estimate lost earnings due to death from cancer overall and for the major cancers in the United States nationally and by state.

Design, Setting, and Participants  Person-years of life lost (PYLL) were calculated using numbers of cancer deaths and life expectancy data in individuals aged 16 to 84 years who died from cancer in the United States in 2015. The annual median earnings in the United States were used to assign a monetary value for each PYLL by age and sex. Cancer mortality and life expectancy data were obtained from the National Center for Health Statistics and annual median earnings from the US Census Bureau’s 2016 Current Population Survey’s March Annual Social and Economic Supplement. Data analysis was performed from October 22, 2018, to February 25, 2019.

Main Outcomes and Measures  Lost earnings due to cancer death, represented as estimated future wages in the absence of premature death.

Results  A total of 8 739 939 person-years of life were lost to cancer death in persons aged 16 to 84 years in the United States in 2015, translating to lost earnings of $94.4 billion (95% CI, $91.7 billion-$97.3 billion). For individual cancer sites, lost earnings were highest for lung cancer ($21.3 billion), followed by colorectal ($9.4 billion), female breast ($6.2 billion), and pancreatic ($6.1 billion) cancer. Age-standardized lost earning rates per 100 000 were lowest in the West and highest in the South, ranging from $19.6 million (95% CI, $19.1 million-$20.2 million) in Utah to $35.3 million ($34.4 million-$36.3 million) in Kentucky. Approximately 2.4 million PYLL and $27.7 billion (95% CI, $26.9 billion-$28.5 billion) in lost earnings (29.3% of total that occurred in 2015) would have been avoided in 2015 if all states had the same age-specific PYLL or lost earning rates as Utah.

Conclusions and Relevance  Our findings indicate large state variation in the economic burden of cancer and suggest the potential for substantial financial benefit through delivery of effective cancer prevention, screening, and treatment to minimize premature cancer mortality in all states.

Introduction

Cancer is the second leading cause of death and is projected to cause more than 606 880 deaths in the United States in 2019.1 Cancer deaths impose significant economic burden in the United States because of productivity losses due to premature death.2 A common approach to assess this burden is to estimate the loss of future earnings due to cancer death.3-5 This measure—hereafter termed lost earnings—is based on person-years of life lost (PYLL) and expected earnings during those years. Person-years of life lost incorporate age and residual life expectancy at death to represent the average number of years a person would have lived in the absence of cancer.6 Thus, deaths at younger ages are associated with higher PYLL and, consequently, greater lost earnings.

Several studies have examined the economic burden of cancer death in the United States, but they are based on older data and/or include a limited number of cancer sites.3-5,7 Furthermore, despite substantial geographic variation in cancer mortality,1 little research has estimated this burden at subnational levels.8,9 Contemporary comprehensive information on the economic burden associated with cancer mortality at national and state levels could be used in setting policies and prioritizing resources for cancer prevention and control. In this study of population-based data, we provide contemporary estimates for PYLL due to cancer death and associated lost earnings at national and state levels for all cancers combined and for major cancers in men and women in the United States.

Methods

We obtained data from the National Center for Health Statistics on the number of cancer deaths by single year of age, sex, and the highest attained educational level and life expectancy by age and sex in 2015 in the United States.10,11 National Center for Health Statistics mortality data provide complete state- and national-level coverage of the US population.10 At the national level, we evaluated all cancers combined and the top 15 causes of cancer death in each sex (19 cancer sites in total) (Table). At the state level, we limited our analyses to all cancers combined and 7 cancer sites with adequate numbers of cancer deaths, including lung and bronchus (lung), female breast, colorectum, prostate, pancreas, ovary, and liver and intrahepatic bile duct. This study was based on deidentified publicly available data and did not require institutional review board approval or patient written consent.

We obtained data on annual median earnings of employed primary or sole salary and wage workers in 2015 stratified by age group, sex, educational level, and employment status from the US Census Bureau’s 2016 Current Population Survey’s March Annual Social and Economic Supplement (eMethods in the Supplement).12 National median earnings and life expectancies were used in both national- and state-level calculations to compare lost earnings across states using a common metric. Data analysis was performed from October 22, 2018, to February 25, 2019.

Statistical Analysis

We upwardly adjusted earnings for fringe benefits (eg, health insurance, retirement benefits, and paid leave) because they are compensation provided to employees, using national estimates from previous studies (ie, by 22.4% for full-time workers and 10.3% for part-time workers).4,13 To quantify the results of uncertainty in wage and employment status data and generate 95% CIs for our estimates, we applied a simulation method with 1000 replications for each age group and sex stratum.14

Person-years of life lost were calculated by multiplying the number of cancer deaths in each age (single year from 16 to 84 years) and sex group by corresponding residual life expectancy.6 Age- and sex-specific lost earnings were calculated by multiplying PYLL by the annual median earning in the corresponding group in 2015 US dollars. A person who died because of cancer at a certain age would have had different earnings and probabilities of employment in older ages in the absence of premature death. We accounted for these variations using lost earnings in the corresponding age group in each future year of residual life expectancy. All estimated future lost earnings were adjusted with a 3% annual discount rate consistent with recommendations for converting future dollars to their present values15 and for increases in median wage over time using the average annual change during 2013-2017 in the United States (1.6% increase annually).12 Person-years of life lost and lost earnings were summed over all age groups to obtain total PYLL and lost earnings for each cancer site by sex. To provide comparable results across states, we calculated age-standardized PYLL and lost earning rates using the 2000 US standard population. Rates for sex-specific cancers are per 100 000 persons of the corresponding sex. We also calculated lost earnings that would have been prevented if age-specific lost earning rates (ages 16-19, 20-24, …, 80-84 years) in all states were the same as those in the state with the lowest age-standardized lost earning rate. In an additional sensitivity analysis, we calculated lost earnings based on decedents’ educational level as described in the eMethods in the Supplement. Person-years of life lost and lost earnings were calculated using Stata statistical software, version 13.1 (StataCorp).

Results
National Estimates

A total of 492 146 cancer deaths occurred in persons aged 16 to 84 years in the United States in 2015, translating to a total of 8 739 939 PYLL (Table). Overall lost earnings were $94.4 billion (95% CI, $91.7 billion-$97.3 billion), with a rate of $29.0 million (95% CI, $28.6 million-$30.3 million) per 100 000 persons and mean lost earnings of $191 900 (95% CI, $186 400-$197 600) per cancer death.

For individual cancer sites, the total lost earning was highest for lung cancer ($21.3 billion; 22.5% of total), followed by colorectal ($9.4 billion; 10.0%), female breast ($6.2 billion; 6.5%), and pancreatic ($6.1 billion; 6.5%) cancer (Table). The cancer with the highest PYLL and lost earnings in persons 50 years or older was lung cancer. In those aged 16 to 49 years, PYLL were highest for female breast cancer, but lost earnings were highest for leukemia in ages 16 to 39 years and lung cancer in ages 40 to 49 years (eTable 2 in the Supplement), reflecting lower labor participation rates and wages among women (eTable 1 in the Supplement), which in addition to a higher number of deaths among men (eTable 3 in the Supplement), could also explain higher PYLL and lost earnings for all cancers combined and non–sex-specific cancers in men.

The total lost earnings in additional analysis based on decedents’ educational level ($86.6 billion; 95% CI, $84.4 billion-$88.9 billion) (eTable 4 in the Supplement) was 9% lower compared with estimates described above. By cancer site, lost earnings were generally comparable or slightly lower, with the highest absolute difference for lung cancer ($17.4 billion vs $21.3 billion).

State-Level Estimates

Person-years of life lost and total lost earnings in 2015 in persons aged 16 to 84 years ranged from 13 338 and $139 million in Wyoming to 862 942 and $9512 million in California, respectively (eTable 3 in the Supplement). The overall age-standardized lost earning rate in million dollars per 100 000 ranged from $19.6 million (95% CI, $19.1 million-$20.2 million) in Utah to $35.3 million ($34.4 million-$36.3 million) in Kentucky. States with the highest age-standardized lost earning rates were located in the South, followed by states in the Midwest (Figure 1; eTable 3 in the Supplement). States with the lowest age-standardized lost earning rates were in the West or Northeast and Hawaii. Sex-specific patterns were similar to those of overall findings (Figure 1, eTable 3 in the Supplement).

By cancer site, total lost earnings were higher for lung cancer than any other individual cancer site in all states (eTable 3 in the Supplement). Similar to all cancers combined, states with the highest age-standardized lost earning rates for lung (Figure 1) and female breast, colorectal, prostate, and pancreatic cancer (Figure 2) were mostly located in the South, followed by the Midwest. States in the South (notably along the southern US border) and on the West Coast, the District of Columbia, and Hawaii had the highest age-standardized lost earnings for liver and intrahepatic bile duct cancer. By sex, states with the highest age-standardized lost earning rates for lung cancer among women were mostly located in the Midwest and neighboring states in the South and among men in the South (Figure 1). The distribution of states in terms of age-standardized lost earning rates for other non–sex-specific cancer sites in women and men were generally comparable (eTable 3 in the Supplement).

Approximately 2.4 million PYLL and $27.7 billion (95% CI, $26.9 billion-$28.5 billion) in lost earnings (29.3% of the total) would have been avoided in the United States in 2015 alone if age-specific PYLL and lost earning rates, respectively, in all states were the same as those in Utah (eFigure 1 in the Supplement). The proportion of avoidable lost earnings by state using Utah as the reference ranged from 13.6% in Colorado to 47.3% in Kentucky.

In additional analysis based on decedents’ educational level, total lost earnings were slightly lower (eg, $9.4 billion vs $9.5 billion for all cancers combined in California) but the pattern of age-standardized lost earning rates across states was similar to those described above (eTable 4 in the Supplement), with a wide variation across states in the proportion of avoidable lost earnings if age-specific lost earning rates were the same as those in the state with the lowest age-standardized rate, ranging from 16.4% in California to 42.1% in Mississippi (eFigure 2 in the Supplement).

Discussion

We estimated that, in persons aged 16 to 84 years, more than 8.7 million years of life were lost due to cancer deaths in the United States in 2015, translating to $94.4 billion in lost earnings. We also found considerable variation in age-standardized lost earning rates across states, with the rate in Kentucky approximately 80% higher than in Utah. States with the highest overall age-standardized lost earning rates were in the South, followed by the Midwest. By individual cancer site, the total lost earning was highest for lung cancer in all states. If age-specific lost earning rates in all states were the same as in Utah, approximately 2.4 million PYLL and $27.7 billion in lost earnings would have been avoided in the United States in 2015 alone. By quantifying the economic burden of premature mortality due to cancer, our findings highlight state-level disparities and indicate that preventing premature cancer deaths would have substantial economic benefit nationally and for all states.

Person-years of life lost and lost earnings were high for many cancers associated with modifiable risk factors and effective screening and treatment, suggesting that a substantial proportion of the mortality burden is potentially avoidable. This notion is further supported by our findings: although exposure to modifiable risk factors, cancer screening, and high-quality treatment can be further improved in Utah,16,17 even achieving Utah’s present age-specific lost earning rates by other states could reduce lost earnings from cancer deaths by 29% nationally and by as much as 47% in Kentucky and Mississippi.

According to prior research, considerable proportions of deaths from all cancers combined (45%) and several major cancer types, including lung (86%), colorectal (54%), breast (28%), and pancreas (24%), in the United States are attributable to potentially modifiable risk factors, such as smoking, excess body weight, physical inactivity, and dietary factors, although little is known about the cause of some other cancers (eg, brain cancer).18 Differences in the prevalence of potentially modifiable risk factors could in part explain geographic variations in age-standardized PYLL and lost earning rates. For example, consistent with our results for lung cancer, smoking prevalence among men in the United States is highest in the Southern states and lowest in Utah.16 The prevalence of excess body weight is also higher in the Southern states,16 which may in part explain greater age-standardized lost earning rates for female breast, colorectal, and liver cancers in those states. Clinic-based interventions and/or referrals for smoking cessation and improved diet and physical activity can help patients reduce their exposure to risk factors.19,20

Other proven interventions vary across states and generally are suboptimal.16,18 For example, the state-level tax per cigarette pack as of June 2018 ranged from $0.17 in Missouri to $4.35 in New York and Connecticut; it was less than $1.00 in 14 states and $4.00 or higher in only 3 states.21 Approximately 2% of all cancers (including virtually all cervical cancers) are associated with human papillomavirus,18 and human papillomavirus vaccination could prevent these cancers.22 Yet, the proportion of adolescents aged 13 to 17 years who were up-to-date with human papillomavirus vaccination in 2017 was 48.6% nationally, and by state, with the exception of Rhode Island and the District of Columbia (approximately 78%), the proportion ranged from 28.8% in Mississippi to 65.5% in Massachusetts,23 which is substantially lower than the Healthy People 2020 target (80%).24

Screening for early detection of cervical, colorectal, and breast cancer has been a major contributor to substantial declines in mortality rates of these cancers in the United States,22 and health care professional recommendation for screening is associated with screening receipt.25 However, proportions of eligible individuals who are up-to-date with recommended cancer screening remain below the Healthy People 2020 targets, with variations across states.16,22,26 For example, the proportion of age-eligible adults who are up-to-date with colorectal cancer screening in 2016 was 58.8% in Oklahoma and 75.8% in Connecticut.27 More recently, screening for lung cancer with low-dose computed tomography has been recommended for some current or recent heavy smokers, but the uptake in 2015 was only 3.9%,28 and little is known about state-level variation. Stage of disease at diagnosis, which can reflect use of effective screening and early clinical evaluation of cancer symptoms, also varies widely by state. For example, the proportion of localized-stage colorectal cancer among men 50 years or older in 2010-2014 was considerably lower in Oklahoma (34.0%) than in Utah (41.9%),29 which had the highest and lowest age-standardized lost earning rates for male colorectal cancer in this study, respectively.

Following a cancer diagnosis, patients interact with multiple clinicians, including surgeons, medical oncologists, and radiation oncologists, to make decisions about cancer treatment. However, receipt of evidence-based treatment varies by state. For example, the proportion of patients with early-stage non–small cell lung cancer in 2007-2011 who received curative-intent surgery ranged from 52% to 54% in Louisiana and Wyoming to 75% to 77% in Massachusetts, New Jersey, and Utah.30 Less is known about state-level variation in systemic treatments, including chemotherapy, hormonal therapy, immunotherapy, and targeted agents. Overall, treatment receipt is associated with socioeconomic status and health insurance coverage, which also vary substantially by state.31-36

We used a human capital approach, which assigned more PYLL value for individuals with higher incomes and placed no value on PYLL for individuals not in the workforce, including children, homemakers, and retirees. A number of approaches have been used to value PYLL as well as time lost from work and usual activities.37-39 Other approaches use economic concepts related to willingness-to-pay for each additional year of life (eg, $150 000)7 and have estimated higher economic burden for cancer deaths in the United States than human capital approaches (eg, $960.6 billion7 in 2000). The human capital approach mainly estimates the effect of cancer deaths on the economy, whereas the willingness-to-pay approach provides estimates of the overall value of life lost.7 We chose to estimate lost earnings for which objective data were available, whereas values given to a year of life lost in the willingness-to-pay approach were less clear.40 Even so, the total lost earning in this analysis—$94.4 billion in persons 16 to 84 years—for cancer deaths that occurred in 2015 is substantial.

Several studies have estimated lost earnings due to premature cancer deaths in other countries, in which total lost earnings have been lower than our estimates, largely due to fewer cancer deaths, shorter life expectancy, or lower wages.5,41,42 For many countries, lost earnings are highest for lung cancer with some exceptions, for example, liver cancer in China (largely due to hepatitis B virus infection) and cancer of the lip and oral cavity in India (due to smokeless tobacco use).41

Our estimate for total lost earnings due to cancer death is lower than that of an earlier study ($115.8 billion in 2000).4 Unlike the prior study, we did not include cancer deaths in persons 85 years or older, nor did we generalize employment status in those 75 to 79 years to all older ages in that study, which likely overestimated lost earnings in older age groups. We also estimated lost earnings based on decedents’ educational level in sensitivity analysis because cancer deaths occur more commonly in persons with lower levels of education (often with lower earnings) than higher levels of education owing to higher exposure to known cancer risk factors (eg, smoking) or more limited access to care43-45; thus, estimates based on median earnings for all educational levels combined may overestimate the actual amount of lost earnings. Nevertheless, differences between total lost earnings calculated using these 2 approaches were relatively small, with variation by cancer site. Differences were generally greater for cancers with higher occurrence in lower educational level groups, such as lung and other smoking-related cancers.46 In interpretation of results based on decedents’ educational level, age-standardized lost earning rates reflect both PYLL rate and educational attainment distribution in states. Thus, between 2 states with the same PYLL rate, the state with lower educational attainment would have a lower age-standardized lost earning rate.

Limitations and Strengths

We likely underestimated productivity loss because our estimates do not include lost earnings from lower performance or absenteeism, informal caregiving, and cancer deaths in persons younger than 16 years and 85 years or older because of data limitations. Furthermore, the mean life expectancies were based on life tables for all causes of death, thus underestimating life expectancies in the absence of cancer. Strengths of this study include use of observed nationwide mortality data by single year of age and sex for cancer deaths and detailed reporting of PYLL and lost earnings by sex, cancer site, and state. We also conducted a detailed sensitivity analysis that reflects differing economic conditions across states.

Conclusions

The economic burden of lost earnings from premature cancer deaths in the United States appears to be significant. There is also large variation across states, reflecting disparities in the burden. Previous studies have shown that approximately half of all cancer deaths in the United States and a substantial proportion of deaths from cancer types with the highest economic burden in this study (eg, lung and colorectal cancer) are attributable to potentially modifiable risk factors and that delivery of effective screening and treatment could prevent a number of premature cancer deaths. Implementation of comprehensive cancer prevention interventions and equitable access to high-quality care across all states could reduce the burden of cancer and associated geographic and other differences in the country. Health care professionals can contribute to achieving this goal because they play a central role in the delivery of cancer prevention, screening, and treatment.

Back to top
Article Information

Accepted for Publication: March 18, 2019.

Corresponding Author: Farhad Islami, MD, PhD, Surveillance and Health Services Research Program, American Cancer Society, 250 Williams St, Atlanta, GA 30303 (farhad.islami@cancer.org).

Published Online: July 3, 2019. doi:10.1001/jamaoncol.2019.1460

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

Concept and design: Islami, Zheng, Zhao, Han, Ma, Jemal, Yabroff.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Islami, Yabroff.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Islami.

Administrative, technical, or material support: Miller, Jemal, Yabroff.

Conflict of Interest Disclosures: The authors are employed by the American Cancer Society, which received a grant from Merck Inc for intramural research outside the submitted work; however, their salaries are solely funded through the American Cancer Society. No other disclosures were reported.

Funding/Support: This work was supported by the Intramural Research Department of the American Cancer Society.

Role of the Funder/Sponsor: All authors are employed by the American Cancer Society, but the management of the American Cancer Society played no part in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

References
1.
Siegel  RL, Miller  KD, Jemal  A.  Cancer statistics, 2019.  CA Cancer J Clin. 2019;69(1):7-34. doi:10.3322/caac.21551PubMedGoogle ScholarCrossref
2.
Yabroff  KR, Lund  J, Kepka  D, Mariotto  A.  Economic burden of cancer in the United States: estimates, projections, and future research.  Cancer Epidemiol Biomarkers Prev. 2011;20(10):2006-2014. doi:10.1158/1055-9965.EPI-11-0650PubMedGoogle ScholarCrossref
3.
Ekwueme  DU, Chesson  HW, Zhang  KB, Balamurugan  A.  Years of potential life lost and productivity costs because of cancer mortality and for specific cancer sites where human papillomavirus may be a risk factor for carcinogenesis—United States, 2003.  Cancer. 2008;113(10)(suppl):2936-2945. doi:10.1002/cncr.23761PubMedGoogle ScholarCrossref
4.
Bradley  CJ, Yabroff  KR, Dahman  B, Feuer  EJ, Mariotto  A, Brown  ML.  Productivity costs of cancer mortality in the United States: 2000-2020.  J Natl Cancer Inst. 2008;100(24):1763-1770. doi:10.1093/jnci/djn384PubMedGoogle ScholarCrossref
5.
Hanly  P, Pearce  A, Sharp  L.  The cost of premature cancer-related mortality: a review and assessment of the evidence.  Expert Rev Pharmacoecon Outcomes Res. 2014;14(3):355-377. doi:10.1586/14737167.2014.909287PubMedGoogle ScholarCrossref
6.
Taksler  GB, Rothberg  MB.  Assessing years of life lost versus number of deaths in the United States, 1995-2015.  Am J Public Health. 2017;107(10):1653-1659. doi:10.2105/AJPH.2017.303986PubMedGoogle ScholarCrossref
7.
Yabroff  KR, Bradley  CJ, Mariotto  AB, Brown  ML, Feuer  EJ.  Estimates and projections of value of life lost from cancer deaths in the United States.  J Natl Cancer Inst. 2008;100(24):1755-1762. doi:10.1093/jnci/djn383PubMedGoogle ScholarCrossref
8.
Ekwueme  DU, Guy  GP  Jr, Rim  SH,  et al.  Health and economic impact of breast cancer mortality in young women, 1970-2008.  Am J Prev Med. 2014;46(1):71-79. doi:10.1016/j.amepre.2013.08.016PubMedGoogle ScholarCrossref
9.
Weir  HK, Li  C, Henley  SJ, Joseph  D.  Years of life and productivity loss from potentially avoidable colorectal cancer deaths in US counties with lower educational attainment (2008-2012).  Cancer Epidemiol Biomarkers Prev. 2017;26(5):736-742. doi:10.1158/1055-9965.EPI-16-0702PubMedGoogle ScholarCrossref
10.
National Cancer Institute. SEER*Stat Database: Total US mortality, 1990-2016. Bethesda, MD: National Cancer Institute; 2018. https://seer.cancer.gov/data-software/documentation/seerstat/. Accessed June 5, 2019.
11.
Arias  E, Xu  JQ. United States life tables, 2015. National Vital Statistics Reports; vol 67, no 7. Hyattsville, MD: National Center for Health Statistics; 2018.
12.
United States Department of Labor. Occupational employment statistics. https://www.bls.gov/oes/tables.htm. Accessed January 8, 2019.
13.
Grosse  S. Appendix I: productivity loss tables. In: Haddix  AC, Teutsch  SM, Corso  PS, eds.  Prevention Effectiveness: A Guide to Decision Analysis and Economic Evaluation. New York: Oxford University Press; 2003:245-258.
14.
Greenland  S.  Interval estimation by simulation as an alternative to and extension of confidence intervals.  Int J Epidemiol. 2004;33(6):1389-1397. doi:10.1093/ije/dyh276PubMedGoogle ScholarCrossref
15.
Sanders  GD, Neumann  PJ, Basu  A,  et al.  Recommendations for conduct, methodological practices, and reporting of cost-effectiveness analyses: Second Panel on Cost-Effectiveness in Health and Medicine.  JAMA. 2016;316(10):1093-1103. doi:10.1001/jama.2016.12195PubMedGoogle ScholarCrossref
16.
Sauer  AG, Siegel  RL, Jemal  A, Fedewa  SA.  Updated review of prevalence of major risk factors and use of screening tests for cancer in the United States.  Cancer Epidemiol Biomarkers Prev. 2017;26(8):1192-1208. doi:10.1158/1055-9965.EPI-17-0219PubMedGoogle ScholarCrossref
17.
Hashibe  M, Kirchhoff  AC, Kepka  D,  et al.  Disparities in cancer survival and incidence by metropolitan versus rural residence in Utah.  Cancer Med. 2018;7(4):1490-1497. doi:10.1002/cam4.1382PubMedGoogle ScholarCrossref
18.
Islami  F, Goding Sauer  A, Miller  KD,  et al.  Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States.  CA Cancer J Clin. 2018;68(1):31-54. doi:10.3322/caac.21440PubMedGoogle ScholarCrossref
19.
Park  ER, Gareen  IF, Japuntich  S,  et al.  Primary care provider–delivered smoking cessation interventions and smoking cessation among participants in the National Lung Screening Trial.  JAMA Intern Med. 2015;175(9):1509-1516. doi:10.1001/jamainternmed.2015.2391PubMedGoogle ScholarCrossref
20.
Curry  SJ, Krist  AH, Owens  DK,  et al; US Preventive Services Task Force.  Behavioral weight loss interventions to prevent obesity-related morbidity and mortality in adults: US Preventive Services Task Force recommendation statement.  JAMA. 2018;320(11):1163-1171. doi:10.1001/jama.2018.13022PubMedGoogle ScholarCrossref
21.
Campaign for Tobacco-Free Kids. State cigarette excise tax rates and rankings. https://www.tobaccofreekids.org/assets/factsheets/0097.pdf. Updated December 21, 2018. Accessed June 13, 2018.
22.
Smith  RA, Andrews  KS, Brooks  D,  et al.  Cancer screening in the United States, 2018: a review of current American Cancer Society guidelines and current issues in cancer screening.  CA Cancer J Clin. 2018;68(4):297-316. doi:10.3322/caac.21446PubMedGoogle ScholarCrossref
23.
Walker  TY, Elam-Evans  LD, Yankey  D,  et al.  National, regional, state, and selected local area vaccination coverage among adolescents aged 13-17 years—United States, 2017.  MMWR Morb Mortal Wkly Rep. 2018;67(33):909-917. doi:10.15585/mmwr.mm6733a1PubMedGoogle ScholarCrossref
24.
Office of Disease Prevention and Health Promotion. Healthy People 2020: immunization and infectious diseases. https://www.healthypeople.gov/2020/topics-objectives/topic/immunization-and-infectious-diseases/objectives. Updated May 24, 2019. Accessed July 2, 2018.
25.
Klabunde  CN, Schenck  AP, Davis  WW.  Barriers to colorectal cancer screening among Medicare consumers.  Am J Prev Med. 2006;30(4):313-319. doi:10.1016/j.amepre.2005.11.006PubMedGoogle ScholarCrossref
26.
White  A, Thompson  TD, White  MC,  et al.  Cancer screening test use—United States, 2015.  MMWR Morb Mortal Wkly Rep. 2017;66(8):201-206. doi:10.15585/mmwr.mm6608a1PubMedGoogle ScholarCrossref
27.
Joseph  DA, King  JB, Richards  TB, Thomas  CC, Richardson  LC.  Use of colorectal cancer screening tests by state.  Prev Chronic Dis. 2018;15:E80. doi:10.5888/pcd15.170535PubMedGoogle ScholarCrossref
28.
Jemal  A, Fedewa  SA.  Lung cancer screening with low-dose computed tomography in the United States—2010 to 2015.  JAMA Oncol. 2017;3(9):1278-1281. doi:10.1001/jamaoncol.2016.6416PubMedGoogle ScholarCrossref
29.
Copeland  G, Lake  A, Firth  R,  et al.  Cancer in North America: 2010-2014. Volume Two: Registry-Specific Cancer Incidence in the United States and Canada. Springfield, IL: North American Association of Central Cancer Registries, Inc; 2017.
30.
Sineshaw  HM, Wu  XC, Flanders  WD, Osarogiagbon  RU, Jemal  A.  Variations in receipt of curative-intent surgery for early-stage non–small cell lung cancer (NSCLC) by state.  J Thorac Oncol. 2016;11(6):880-889. doi:10.1016/j.jtho.2016.03.003PubMedGoogle ScholarCrossref
31.
Polite  BN, Adams-Campbell  LL, Brawley  OW,  et al.  Charting the future of cancer health disparities research: a position statement from the American Association for Cancer Research, the American Cancer Society, the American Society of Clinical Oncology, and the National Cancer Institute.  CA Cancer J Clin. 2017;67(5):353-361. doi:10.3322/caac.21404PubMedGoogle ScholarCrossref
32.
Sineshaw  HM, Ng  K, Flanders  WD, Brawley  OW, Jemal  A.  Factors that contribute to differences in survival of black vs white patients with colorectal cancer.  Gastroenterology. 2018;154(4):906-915.e7. doi:10.1053/j.gastro.2017.11.005PubMedGoogle ScholarCrossref
33.
Fang  P, He  W, Gomez  D,  et al.  Racial disparities in guideline-concordant cancer care and mortality in the United States.  Adv Radiat Oncol. 2018;3(3):221-229. doi:10.1016/j.adro.2018.04.013PubMedGoogle ScholarCrossref
34.
Lin  CC, Bruinooge  SS, Kirkwood  MK,  et al.  Association between geographic access to cancer care, insurance, and receipt of chemotherapy: geographic distribution of oncologists and travel distance.  J Clin Oncol. 2015;33(28):3177-3185. doi:10.1200/JCO.2015.61.1558PubMedGoogle ScholarCrossref
35.
Shalowitz  DI, Vinograd  AM, Giuntoli  RL  II.  Geographic access to gynecologic cancer care in the United States.  Gynecol Oncol. 2015;138(1):115-120. doi:10.1016/j.ygyno.2015.04.025PubMedGoogle ScholarCrossref
36.
American Society of Clinical Oncology.  The state of cancer care in America, 2017: a report by the American Society of Clinical Oncology.  J Oncol Pract. 2017;13(4):e353-e394. doi:10.1200/JOP.2016.020743PubMedGoogle ScholarCrossref
37.
Yabroff  KR, Guy  GP  Jr, Ekwueme  DU,  et al.  Annual patient time costs associated with medical care among cancer survivors in the United States.  Med Care. 2014;52(7):594-601. doi:10.1097/MLR.0000000000000151PubMedGoogle ScholarCrossref
38.
Zheng  Z, Yabroff  KR, Guy  GP  Jr,  et al.  Annual medical expenditure and productivity loss among colorectal, female breast, and prostate cancer survivors in the United States.  J Natl Cancer Inst. 2015;108(5). doi:10.1093/jnci/djv382PubMedGoogle Scholar
39.
Guy  GP  Jr, Yabroff  KR, Ekwueme  DU, Rim  SH, Li  R, Richardson  LC.  Economic burden of chronic conditions among survivors of cancer in the United States.  J Clin Oncol. 2017;35(18):2053-2061. doi:10.1200/JCO.2016.71.9716PubMedGoogle ScholarCrossref
40.
Ryen  L, Svensson  M.  The willingness to pay for a quality adjusted life year: a review of the empirical literature.  Health Econ. 2015;24(10):1289-1301. doi:10.1002/hec.3085PubMedGoogle ScholarCrossref
41.
Pearce  A, Sharp  L, Hanly  P,  et al.  Productivity losses due to premature mortality from cancer in Brazil, Russia, India, China, and South Africa (BRICS): a population-based comparison.  Cancer Epidemiol. 2018;53:27-34. doi:10.1016/j.canep.2017.12.013PubMedGoogle ScholarCrossref
42.
Luengo-Fernandez  R, Leal  J, Gray  A, Sullivan  R.  Economic burden of cancer across the European Union: a population-based cost analysis.  Lancet Oncol. 2013;14(12):1165-1174. doi:10.1016/S1470-2045(13)70442-XPubMedGoogle ScholarCrossref
43.
Albano  JD, Ward  E, Jemal  A,  et al.  Cancer mortality in the United States by education level and race.  J Natl Cancer Inst. 2007;99(18):1384-1394. doi:10.1093/jnci/djm127PubMedGoogle ScholarCrossref
44.
Kinsey  T, Jemal  A, Liff  J, Ward  E, Thun  M.  Secular trends in mortality from common cancers in the United States by educational attainment, 1993-2001.  J Natl Cancer Inst. 2008;100(14):1003-1012. doi:10.1093/jnci/djn207PubMedGoogle ScholarCrossref
45.
Siegel  RL, Jemal  A, Wender  RC, Gansler  T, Ma  J, Brawley  OW.  An assessment of progress in cancer control.  CA Cancer J Clin. 2018;68(5):329-339. doi:10.3322/caac.21460PubMedGoogle ScholarCrossref
46.
Islami  F, Ward  EM, Jacobs  EJ,  et al.  Potentially preventable premature lung cancer deaths in the USA if overall population rates were reduced to those of educated whites in lower-risk states.  Cancer Causes Control. 2015;26(3):409-418. doi:10.1007/s10552-014-0517-9PubMedGoogle ScholarCrossref
×