Impact of Education Attainment on Premature Cancer Deaths and Productivity Loss in the US

1. Productivity Loss Due to Premature Cancer Deaths in the United States, 2004-2008: how much does education attainment matter? Hannah Weir, PhD Chunyu Li, MD, PhD Division of Cancer Prevention and Control Centers for Disease Control and Prevention, USA North American Association of Central Cancer Registries Austin, Texas 11 June 2013 National Center for Chronic Disease Prevention and Health Promotion Place Descriptor Here

2. Background • Socioeconomic status is associated with cancer mortality: groups with lower educational attainment tend to have higher cancer death rates • 2011 ACS Cancer Facts and Figures contained a special section on cancer disparities and premature cancer deaths

3. Objective To estimate excess premature cancer deaths and associated productivity loss in populations (25-74 years old) with lower educational attainment, controlling for race, age and gender

4. Methods • Sources of data • 2000 County Attributes (CA) file • 2004-2008 mortality data linked to CA • Population estimates (2000 Census) • 2006 US life table

5. Percentage of the Population Not Graduating High School at the County Level (2000) • High 3.04-15.00% • Med 15.01-25.00% • Low 25.01-65.30% Singh SD, et al. Association of cutaneous melanoma with area-based socioeconomic indicators – United States, 2004-2006 J Am AcadDermatol. 2011 Nov;65(5 Suppl 1):S58-68.

6. Cancer Death Rate (per 100,000) by % High School Graduation at the County Level (Males, All Races)

7. Cancer Death Rate (per 100,000) by % High School Graduation at the County Level (Females, All Races)

8. Total Population (2004-2008) by % High School Graduation at the County Level (All Races)

9. Methods Estimating Excess Cancer Deaths • Calculated race, gender and 5 yr age specific death rates (25-74 yr) of people residing in high education counties • Obtained observed (O) deaths among people residing in medium and low education counties • Estimated expected (E) deaths by applying death rates of people residing in high education counties to population estimates in medium and low education counties • Calculated excess deaths (O-E) by race, gender and age

10. Results Excess Deaths Between 2004 and 2008 • Among males • 59,015 (medium education) • 39,855 (low education) • Among females • 26,523 (medium education) • 13,076 (low education)

11. Excess Cancer Deaths among Lower Education Levels (Males, All Races)

12. Excess Cancer Deaths among Lower Education Levels (Females, All Races)

13. MethodsEstimating Productivity Lost due to Premature Deaths • Used life expectancy method to estimate the years of potential life lost (YPLL) associated with premature cancer deaths • Applied the human capital approach* to estimate the productivity loss due to these YPLL in 2007 US dollars using a 3% discount rate • Sensitivity analyses for 0% and 5% discount rate • * Grosse SD, Krueger KV, Mvundura M. Economic productivity by age and sex: 2007 estimates for the United States. Med Care. 2009 Jul;47(7 Suppl 1):S94-103

14. Present Value of Total Lifetime Production ($) and Excess Cancer Deaths (#) by Age Group (Males 2004-2008)

15. Present Value of Total Lifetime Production ($) and Excess Cancer Deaths (#) by Age Group (Females 2004-2008)

16. Results

17. Conclusion Eliminating educational disparities and Improving socio-economic status could result in approximately 280,000 fewer cancer deaths and an increase in ~ $17 billion in productivity gain in men and women each year

18. Future Work • Results will be updated to include 2010 mortality data • Examine incidence data by stage to estimate costs associated with treating late stage cancer

19. Thank You Hannah K. Weir, PhDDivision of Cancer Prevention and Control Centers for Disease Control and Preventionhbw4@cdc.go770 488-3006The findings and conclusions in this presentation are those of the presenter and do not necessarily representthe official position of theCenters for Disease Control and Prevention.