JNCI | Articles 1763
Cancer is the second leading cause of death and a major cause of
lost productivity in adults in the United States ( 1 , 2 ). Progress
toward reducing mortality from cancer has been steady for many
sites, including lung, breast, non-Hodgkin lymphoma, colon and
rectum, and cervix. Overall, cancer mortality rates have decreased
by 1.1% per year from 1993 through 2002 ( 3 ).
Decreased cancer mortality is attributable to many health ini-
tiatives, including prevention, early detection, and effective treat-
ments. However, the economic gain from these investments is
highly variable. A model that predicts the economic benefi t of
reduced cancer mortality provides critical information for allocat-
ing scarce resources to interventions with the greatest benefi ts.
The value of productivity lost from premature mortality is a key
element in such a model because these costs refl ect substantial
losses to society.
We developed a model based on the human capital method ( 4 )
to estimate and project the value of lost productivity attributable to
death from cancer for the years 2000 – 2020. We estimated the
value of lost productivity for all cancers combined and for the 19
most prevalent sites of cancer (urinary bladder, female breast,
brain and other nervous system, cervix uteri, colorectal, corpus
and uterus, stomach, head and neck, Hodgkin lymphoma, kidney
and renal pelvis, leukemia, liver and intrahepatic bile duct, lung
and bronchus, melanoma, non-Hodgkin lymphoma, ovary, pan-
creas, prostate, and testis). We also estimated the value of lost
earnings per year, by sex and 5-year age groups, from paid employ-
ment as well as non-paid caregiving and housekeeping activities.
Data Sources and Methods
The human capital method has a long history in economic and
health services research as a means to calculate the expected life-
time earnings that would have been realized had the disease or
Affiliations of authors: Department of Health Administration, Massey Cancer
Center (CJB) and Department of Internal Medicine, Division of Quality Health
Care (BD), Virginia Commonwealth University, Richmond, VA; Health
Services and Economics Branch, Applied Research Program (KRY, MLB) and
Statistical Research and Applications Branch, Surveillance Research Program
(EJF, AM), National Cancer Institute, Bethesda, MD .
Correspondence to: Cathy J. Bradley, PhD, Department of Health Adminis-
tration, Massey Cancer Center, Virginia Commonwealth University, 1008 E. Clay
Street, P.O. Box 980203, Richmond, VA 23298 (e-mail: email@example.com ).
See “Funding” and “Notes” following “References.”
© The Author 2008. Published by Oxford University Press. All rights reserved.
For Permissions, please e-mail: firstname.lastname@example.org.
Productivity Costs of Cancer Mortality in the United
States: 2000 – 2020
Cathy J. Bradley , K. Robin Yabroff , Bassam Dahman , Eric J. Feuer , Angela Mariotto , Martin L. Brown
Background A model that predicts the economic benefit of reduced cancer mortality provides critical information for
allocating scarce resources to the interventions with the greatest benefits.
Methods We developed models using the human capital approach, which relies on earnings as a measure of pro-
ductivity, to estimate the value of productivity lost as a result of cancer mortality. The base model aggre-
gated age- and sex-specific data from four primary sources: 1) the US Bureau of the Census, 2) US death
certificate data for 1999 – 2003, 3) cohort life tables from the Berkeley Mortality Database for 1900 – 2000,
and 4) the Bureau of Labor Statistics Current Population Survey. In a model that included costs of caregiv-
ing and household work, data from the National Human Activity Pattern Survey and the Caregiving in the
U.S. study were used. Sensitivity analyses were performed using six types of cancer assuming a 1%
decline in cancer mortality rates. The values of forgone earnings for employed individuals and imputed
forgone earnings for informal caregiving were then estimated for the years 2000 – 2020.
Results The annual productivity cost from cancer mortality in the base model was approximately $115.8 billion in
2000; the projected value was $147.6 billion for 2020. Death from lung cancer accounted for more than
27% of productivity costs. A 1% annual reduction in lung, colorectal, breast, leukemia, pancreatic, and
brain cancer mortality lowered productivity costs by $814 million per year. Including imputed earnings
lost due to caregiving and household activity increased the base model total productivity cost to $232.4
billion in 2000 and to $308 billion in 2020.
Conclusions Investments in programs that target the cancers with high incidence and/or cancers that occur in younger,
working-age individuals are likely to yield the greatest reductions in productivity losses to society.
J Natl Cancer Inst 2008;100: 1763 – 1770
1764 Articles | JNCI Vol. 100, Issue 24 | December 17, 2008
death been avoided. This method assumes that earnings reflect
underlying productivity. Because it relies on earnings as the basis
for its cost estimations, this method gives greater weight to work-
ing-age men compared with women, the young, racial and ethnic
minorities, and the elderly ( 5 ). Furthermore, the human capital
method does not measure the value of a life, but instead, it mea-
sures only the value of labor, using earnings or imputed earnings
as a proxy measure.
We used the human capital method with an incidence-based
approach to estimate the costs of cancer deaths that occurred and are
predicted to occur between 2000 and 2020. The base model refl ects
employment and income transitions over the life cycle by summing
the expected earnings in each year of forgone life over a given life
expectancy, accounting for changes in the probability of employ-
ment and wages that occur from year to year and from age group to
age group. For example, life expectancy for a man aged 35 in 2000
was an additional 42.2 years ( 6 ). Using assumptions in our model, a
man who died at age 35 years in 2000 had a .93 probability of being
employed, and his average annual full-time earnings plus the value
of fringe benefi ts would be $56 519. Had he lived, his probability of
employment would have decreased to 0.87 at age 50, but his annual
average earnings would have increased to $87 706 (including fringe
benefi ts) in the year 2015. His probability of employment would
have further decreased at age 65 in the year 2030 and continued to
decline for his remaining life span. We accounted for such year-by-
year transitions in employment probabilities and expected earnings
throughout the expected lifetime of the individuals who would have
otherwise lived in the absence of cancer.
Because our society relies on individuals to provide essential
household and caregiving functions that would otherwise be fi lled
by other potentially more expensive providers and because these
activities may also impact providers ’ participation in the labor force
( 7 ), we added the estimated value of informal caregiving and
household activities to the base model. We estimated the value of
informal caregiving and housekeeping separately by imputing a
wage for the hours spent engaged in these activities.
We report the present value of lifetime earnings (PVLE) as the
sum of productivity costs and the sum of the imputed value of
caregiving and household activities. Thus, the PVLE takes into
account life expectancy for different sex and age groups, the per-
centage of people in the labor force, and/or those who are engaged
in caregiving and household activities, the current pattern of earn-
ings at successive ages, an imputed value of caregiving and house-
hold activities, and the discount rate ( 8 ). A discount rate (3%) is
applied to convert future dollars to their present value.
The base model used aggregate age- and sex-specific data from
four sources. First, the US Bureau of the Census provided the
National Interim Projections of the US population from 2000
through 2020 ( 9 ). Second, US death certificate data covering 1999
through 2003 were used to estimate age-adjusted cancer site-
specific mortality rates. Third, cohort life tables from the Berkeley
Mortality Database for birth years 1900 – 2000 were used to esti-
mate and project sex-specific life expectancy in the years 2000 –
2020. The Berkeley Mortality Database, which was developed
from historical series of national vital statistics (ie, births, deaths,
and census populations), is part of the Human Mortality Database
project, whose aim is to construct high-quality national cohort life
tables. Projections incorporate observed trends in life expectancy
in the past century. Because these life tables only contain years of
birth through 2000, we assumed that individuals born after 2000
(ie, 2001 – 2020) would have the same life expectancy as those born
in 2000. These cohort life table data and related documentation
are available at http://www.demog.berkeley.edu/ ~ bmd/states.html
( 10 ). Fourth, all estimates of wages, employment rates, and full-
and part-time employment rates were from the Current Population
Survey (CPS). The CPS is a monthly survey of households that is
conducted by the Bureau of the Census for the Bureau of Labor
Statistics (BLS); it is the primary source of information on labor
force characteristics and behavior of the US population ( 11 ).
Fringe benefits constitute approximately 27.4% of compensation
( 12 ). These benefits include vacation pay, health insurance, retire-
ment benefits, and annual and personal leave. It has been argued
( 12 ) that paid leave should not be used to adjust annual earnings.
Therefore, Grosse ( 12 ) suggests that annual earnings should be
adjusted upward by 22.4% instead of 27.4% to reflect the absence
of paid leave and to compensate for worker categories (eg, agricul-
tural workers) that do not have generous benefits ( 12 ). Following
the example set by Grosse, we used a rate of 22.4% to upwardly
adjust annual earnings for full-time workers and a rate of 10.3%
to upwardly adjust part-time workers ’ annual earnings.
CONTEXT AND CAVEATS
A model to estimate the economic benefit of reduced cancer mor-
tality would provide information regarding which interventions
would have the greatest economic impact.
Models to estimate the value of productivity lost due to premature
death due to cancer during 2000 – 2020 were developed using the
human capital approach, which uses earnings to measure produc-
tivity. A model that included caregiving and household work was
The annual productivity cost of cancer mortality was $115.8 billion
in 2000 and was projected to be $147.6 billion in 2020. Including
caregiving and household activity increased these values to $232.4
billion for 2000 and $308 billion for 2020. A 1% annual reduction in
death from lung, colorectal, breast, pancreatic, and brain cancer
and leukemia reduced costs by $814 million per year.
The most useful targets for cancer control programs, from an eco-
nomic perspective of cost in terms of productivity, are those that
have high incidence or occur at younger ages.
The costs may be underestimated because factors such as produc-
tivity costs due to morbidity and disability of the cancer and/or its
treatment were not included in the calculations, life expectancy
was used rather than survivorship, and those who died from can-
cer before age 20 were not included.
From the Editors
JNCI | Articles 1765
Two additional data sources were used to estimate the number
of caregivers and housekeepers in the population. First, we use
estimates from Grosse ( 12 ) of the number of individuals who were
engaged in both housekeeping and caregiving. These estimates are
based on responses to the National Human Activity Pattern Survey
(NHAPS) administered by the US Environmental Protection
Agency. This survey collected information on household produc-
tion (housework, food cooking and cleanup, taking care of plants
and animals, home and auto maintenance, and obtaining goods and
services) and providing care (childcare, child guidance, playing
with children, transporting children, helping and caring for adults,
helping adults with other personal activities, and personal care
travel). Grosse ( 12 ) used these estimates to determine the preva-
lence of individuals living in households and time spent on various
caregiving and household activities and then applied a wage rate,
derived from the CPS, corresponding to the proportion of time
spent doing various activities. Imputed housekeeping and caregiv-
ing wages were then adjusted up by a fringe benefi t rate ranging
from 10.3% to 14.1% ( 12 ). The second source was the Caregiving
in the U.S. study ( 13 ), which was conducted by the National
Alliance for Caregiving and the American Association of Retired
Persons. This national survey identifi ed 1247 caregivers primarily
through the random digit dial technique and collected precise
information on hours spent caregiving and the type of care pro-
vided. The estimates for caregiving and household activities refl ect
the value of unpaid activities in which individuals would have been
engaged if they had not died from cancer. We used this study to
estimate the percentage of the US population who were engaged
in caregiving and, among those individuals, the percentage who
provided round-the-clock care.
We estimated the number of deaths, person-years of life lost
(PYLL), and average person-years of life lost. Individual years of
life lost estimates were summed into 5-year age groups starting
with ages 20 – 24 and ending with a single group for persons aged
85 and older.
We used mean weekly wages by sex for all races and occupa-
tions combined for the years 2000 – 2006, available from the BLS
on request. Wages were reported for the 5-year age groups of
interest but were combined for all aged 70 and older. We used the
wages published in Grosse ( 12 ) to determine the imputed value of
caregiving and household activities. Different wage rates were
imputed for caregivers and housekeepers; these wages were then
weighted by the time spent engaged in each type of activity. For
example, a typical single individual without children who is
employed full-time spends less time engaged in caregiving and
household activities than an unemployed individual with children.
To estimate wages for future years, we adjusted wages beyond
2006 for infl ation using the consumer price index (CPI) ( 14 ).
Annual infl ation conversion factors were 2.1% in 2007 and 2.2%
in 2008 – 2020. The CPI-infl ated wages were used as a proxy for
real future wages.
We incorporated estimates of full- and part-time employment
from the BLS for the years 2000 through 2007 for individuals who
were age 18 – 79 years. Because comparable estimates were not
available for individuals who were age 80 years and older, we
applied the rates for the 75- to 79-year age group to those who
were age 80 and older. We used the average employment rates
from 2000 through 2007 to project future employment rates.
According to the Caregiving in the U.S. survey, approximately
21% of the US population older than 18 years is engaged in care
giving activities, which includes housekeeping chores for another
individual who is also 18 years or older. Approximately 10% of these
caregivers provide more than 40 hours per week of care and assist
another individual with two or more activities of daily living (ADLs).
Therefore, we assigned an annual earning equivalent as the annual
charge for nursing home care, which was $74 000 in 2005, and
adjusted it using the CPI and a projection of the CPI for future years
( 15 ). This level of care was projected to last for 2.4 years, which is
the average length of time patients reside in a nursing home ( 16 ).
Because a 20-year-old individual in 2020 was expected to live 62
additional years, all estimates of wages, employment, and caregiver
and housekeeping rates were projected to 2082 to account for the
maximum number of years this cohort of individuals could have
lived. The number of deaths, PYLL, employment and caregiver
and housekeeper rates, wages by sex and age, and the average
PVLE for the year 2000 are reported ( Table 1 ).
We conducted a sensitivity analysis to determine the degree of
infl uence certain assumptions had on the outcome of the overall
model. We chose three assumptions for which uncertainty exists.
First, we relaxed our assumption that individuals aged 80 and older
were employed at the same rate as individuals aged 74 – 79 years
because the BLS does not report the earnings and employment
rate of persons aged 80 and older. To test the sensitivity of our
results to this assumption, we assumed that no one over the age
of 79 was engaged in paid work. Second, we removed the cost of
nursing home care from the model. Finally, we reduced the preva-
lence of caregiving and housekeeping in the model.
The model was developed using Microsoft Excel 2003. The
model included the ability to conduct sensitivity analyses on key
In 2000, the total PVLE lost due to cancer deaths was approxi-
mately $115.8 billion, which steadily increased to approximately
$147.6 billion in 2020 ( Figure 1 ). The productivity costs were
higher for men than women ($75.9 billion vs $39.9 billion in
2000), which is a function of the higher death rate among men
(17 647 more men than women died in the year 2000), their higher
labor force participation, and their higher wages.
The total PVLE lost and PVLE lost per death by cancer site for
the year 2010 were estimated ( Table 2 ). Lung cancer deaths
accounted for more than 27% of the total costs ($39.0 billion). The
next most expensive cancers, in terms of productivity costs, were
colon and rectal cancers ($12.8 billion) and female breast cancer
($10.9 billion), which accounted for approximately 9% and 8% of
the total PVLE lost, respectively.
The most costly cancer per death in 2010 was testicular cancer
($1.3 million). Although there were few testicular cancer deaths
relative to deaths from other sites of cancer, and therefore the
total productivity cost was relatively low, the majority of deaths
1766 Articles | JNCI Vol. 100, Issue 24 | December 17, 2008
Table 1 . Model inputs and average PVLE for the year 2000 *
of life lost
of life lost
including caregiving and
household wages in $US
20 – 24
1 338 188
2 230 023
25 – 29
1 284 081
2 142 912
30 – 34
1 167 549
1 972 799
35 – 39
1 019 631
1 767 713
40 – 44
1 551 323
45 – 49
1 309 721
50 – 54
1 050 032
55 – 59
60 – 64
65 – 69
70 – 74
75 – 79
80 – 84
20 – 24
2 158 521
2 692 877
25 – 29
2 100 893
2 623 380
30 – 34
1 944 784
2 440 182
35 – 39
1 721 472
2 187 183
40 – 44
1 464 748
1 893 059
45 – 49
1 195 154
1 579 902
50 – 54
1 250 273
55 – 59
60 – 64
65 – 69
70 – 74
75 – 79
80 – 84
* PVLE = present value of lifetime earnings. Annual earnings are adjusted up by 22.4% to include the value of fringe benefits. Part-time earnings are adjusted by 10.3% to include the value of fringe benefits. Persons
living in households and the upwardly adjusted caregiving and household wages are from Grosse ( 12 ). We assume that 2.1% of the population provides care for an individual that would otherwise be institutionalized.
For these individuals, this care is provided for 2.4 years, which is the average nursing home length of stay, and estimated at a value of $74 000 in 2005 and adjusted using the CPI in all other years. Estimates of persons living in households and their adjusted wages are from Grosse ( 12 ).
JNCI | Articles 1767
occur in younger working-age men. Likewise, the second most
costly cancer per death was Hodgkin lymphoma ($544 118) fol-
lowed by brain ($392 853) and cervical ($387 440) cancers because
these deaths occur in working-age individuals. In contrast, pros-
tate cancer was the least costly cancer per death ($93 540), due to
its prevalence in older men who are no longer in the workforce.
The total PVLE lost for the most costly cancers was estimated
among women and men by age groups ( Figure 2 ). For women
younger than age 55, breast cancer resulted in the greatest produc-
tivity loss. For women age 55 and older, the mortality rate from
lung cancer increased and it became the most costly cancer for
women. The next most costly cancers for women were those of
the colon and rectum, followed by ovarian and pancreatic cancer.
The cost of lung cancer deaths dominated the cost of all other
cancers for men aged 35 and older. Among men younger than age
35, death from brain cancer resulted in the greatest producti-
Recent trends suggest that overall cancer mortality has declined
by approximately 1% per year. Reducing annual mortality rates by
another 1%, starting in 2010, would reduce productivity costs of
six costly cancers — lung, colon and rectum, female breast, pan-
creas, leukemia, and brain — by approximately $814 million per
year ( Figure 3 ). Clearly, a reduction in lung cancer mortality
would offer the greatest reduction in productivity costs, of approx-
imately $390 million in 2010 and $416 million in 2020.
Caregiving and Household Activity
When the value of caregiving and household activities was
included in the model, costs increased dramatically. In 2000, for
example, the total cost was $232.4 billion (relative to $115.8 bil-
lion) and in 2020, the cost rose to $308 billion (relative to 147.6
billion). In 2010, the caregiving costs for men were 42% ($58.5
billion) of the total costs ($139.7 billion) and the caregiving costs
for women were 64% ($78.3 billion) of the total costs ($122 bil-
Table 2 . Site-specific present value of lifetime earnings (PVLE) among adults 20 and older in 2010
Cancer sitePVLE, $US Percentage of total cost DeathsPVLE/death, $US
Total (all cancers)
Lung and bronchus
Colon and rectum
Brain and other nervous system
Liver and intrahepatic bile duct
Kidney and renal pelvis
Head and neck
Melanoma of the skin
Corpus and uterus
All other sites
142 373 887 175
38 953 476 028
12 802 283 437
10 878 840 020
7 058 015 604
5 879 620 378
5 851 151 373
5 755 042 326
4 638 204 280
2 944 996 275
3 632 633 377
3 630 391 776
3 537 601 571
3 453 510 837
3 298 014 331
1 976 965 144
1 807 797 110
1 101 322 676
828 691 758
471 622 615
23 873 706 259
1 267 803
lion). The percentage of total costs that were attributable to care-
giving remained about the same in 2020 ( Figure 4 ). The cost of
nursing home care was only 1% in 2010 and 2% in 2020.
We conducted a sensitivity analysis that was based on modifying
three assumptions. First, we relaxed our assumption that individu-
als aged 80 and older were employed. Second, we removed the cost
of nursing home care from the model. Finally, we reduced the
prevalence of caregiving and housekeeping in our model.
The results were not sensitive to employment and earning rates
for individuals who were age 80 and older or to the inclusion of
nursing home costs in the model. In the base model, the exclusion
of employment and earnings from paid work for individuals aged
80 and older reduced total costs by only 2.9%.
The inclusion of caregiving and housekeeping greatly increased
mortality costs. However, nursing home costs were only 1% of
costs in 2010 and 2% of costs in 2020. Caregiving and housekeep-
ing services were 65% and 43% of the total cost in 2010 for
Figure 1 . Present value of lifetime earnings due to cancer mortality,
adults age 20 and older, years 2000 – 2020. Hatched bar , males; solid
bar , females.
1768 Articles | JNCI Vol. 100, Issue 24 | December 17, 2008
women and men, respectively. The Caregiving in the U.S. survey to
individuals age 18 and older ( 13 ) estimates that 21% of Americans
provide caregiving services but does not provide the prevalence of
caregiving by sex and age. Nevertheless, when we used the overall
estimate of prevalence as 21%, the total productivity cost from cancer
mortality was $153 billion in 2010 and rose to $164 billion in 2020.
The productivity costs from premature cancer mortality are sub-
stantial. In the base model, we estimated that the total productivity
costs in 2000 were approximately $115.8 billion and that, with cur-
rent mortality rates, these costs would increase to $147.6 billion in
2020. A fixed cancer mortality rate based on the most recent avail-
able data was used for the projections. Therefore, the increases in
cost over time strictly reflect expected growth and aging in the
population. To put the productivity costs from cancer deaths into
perspective, the annual cost amounts to approximately 1% of the
US gross domestic product (GDP; $13.84 trillion) in 2007 ( 17 ).
Other reports estimate the cost due to both morbidity and pre-
mature mortality from cancer as $139.9 billion in fi scal year 2005
( 18 , 19 ). Mortality cost alone has been estimated as $116.1 billion
in 2007 by the National Heart, Lung, and Blood Institute
(NHLBI) ( 19 ). This mortality cost estimate was obtained by
fi rst multiplying the number of deaths in 2004 in each age- and
Figure 3 . Reduction in productivity costs with an additional 1% annual
reduction (starting in the year 2010) in cancer mortality in selected sites,
years 2010 and 2020. From left to right: lung cancer, colorectal cancer,
breast cancer, pancreatic cancer, leukemia, and brain cancer.
Figure 4 . Composition of total productivity costs by sex, years 2010 and
2020. Left diagonal , full-time employment; horizontal , part-time employ-
ment; solid , caregiver time; open , nursing home cost.
Figure 2 . Cancers with highest present
value of lifetime earnings, adults age 20
and older in the year 2010. A ) Women.
From left to right: female breast cancer,
lung cancer, colorectal cancer, ovarian
cancer, and pancreatic cancer. B ) Men.
From left to right: lung cancer, colorectal
cancer, pancreatic cancer, brain cancer,
JNCI | Articles 1769
sex-specifi c group by the 2003 value of lifetime earnings dis-
counted at 3%; summing these estimates for each diagnostic
group; and multiplying the estimates by a 2003 – 2008 infl ation fac-
tor (1.14) that was based on change in mean earnings. These esti-
mates are similar to our reported productivity costs. The difference
is due primarily to our use of more detailed methods and the inclu-
sion of the dollar value of fringe benefi ts in addition to earnings,
which increases full-time wages by 22.4% and part-time wages by
10.3%. Without the inclusion of fringe benefi ts and caregiving in
the model, our estimates for 2007 would decrease to $115.3 bil-
lion. The remaining difference between the NHLBI estimates and
our estimates may refl ect more pessimistic employment data in our
model, more optimistic mortality data, the exclusion of the cost of
cancer deaths in individuals under age 20 in our model, and/or the
differences in the methods used to estimate cost.
Death from lung cancer was the most costly — alone it accounted
for more than a quarter of the total costs ($39 billion in 2010).
Death from colon and rectum cancer was the second most costly
($12.8 billion in 2010), and death from female breast cancer was
the third most costly ($10.9 billion in 2010). In addition to consid-
ering total costs, we reported costs per death by cancer site in
5-year age groups. These estimates highlight the impact of deaths
in working-age individuals. For example, death from testicular
cancer (approximately $1.3 million per death in 2010) was the most
costly among men of working age, followed by death from
Hodgkin lymphoma ($544 118 per death in 2010) among men and
women of working age.
These estimates provide an order of magnitude assessment of
the mortality costs of cancer and can be used by policymakers to
decide how funds should be allocated among health care programs
and between programs that focus on specifi c sites of cancer.
Relative to many other diseases, the productivity cost due to cancer
mortality is high. For example, the annual lost earnings due to
premature infl uenza deaths in the United States are approximately
$10.1 billion, and the annual cost of lost earnings due to deaths
from diabetes is approximately $26.9 billion ( 20 , 21 ).
When we included the value of caregiving and household
activities in the model, the costs increased to $232.4 billion in 2000
and to $308 billion in 2020. The inclusion of some or all of these
costs is subject to debate. The argument unfolds as follows. Many
Americans provide care to young children and disabled family
members. Few of these caregivers are paid for their work ( 7 , 22 )
and the value of their services is not included in the GDP, which
suggests that they should not be included in estimates of produc-
tivity loss. Nevertheless, without the services caregivers provide,
these services would have to be purchased using paid labor. These
services range from housekeeping, meal preparation, and transpor-
tation to complex medical tasks, medication administration, and
assistance with ADLs ( 7 , 22 ), which are valuable to the US society.
We note, however, that the estimates we use are from the NHAPS,
which does not distinguish between activities performed as part
of caregiving and activities performed for oneself. Activities per-
formed as part of self-care may be considered “consumption”
rather than production. For these reasons, we reported costs from
paid wages in the base model and added imputed caregiving and
housekeeping wages separately. We also included a more modest
estimate of caregiving prevalence in the sensitivity analysis.
We focused our estimations on productivity costs, which are
heavily infl uenced by working-age individuals and earnings.
Estimates that use a value of life (estimated to be approximately
$150 000 per year) as opposed to earnings report that costs from
cancer mortality in 2000 were approximately $1031 billion (nearly
nine times the value of productivity loss) ( 23 ). This method values
each year of life lost equally ($150 000 per year) — without regard
to age, employment probability, caregiving or housekeeping activ-
ity, or earnings.
Several limitations of this study are noteworthy. First, we used
all-cause mortality to approximate other-cause mortality in esti-
mating PYLL. Because all-cause mortality includes cancer deaths,
the hazards of death are overstated, and as a result, the PYLL esti-
mates are understated. The understatement is the greatest when
using all-cause mortality to approximate other-cause mortality for
all cancers. Second, we used life expectancy to estimate the years
of life lost rather than using the conditional probability of living an
additional year given survivorship to a particular age. However, the
two methods yield estimates that differ by less than 5% (data not
shown). Third, we did not include employment and earnings from
the underground economy (eg, unreported income). Fourth, we
did not include the productivity costs of those who died before the
age of 20 and we did not include childhood cancers in our model.
Finally, we did not include morbidity costs of cancer in our esti-
mates. Patients are often disabled by cancer, especially in the fi nal
phases of life. In addition, one-third or more of cancer survivors
leave the workforce altogether during the 6 months immediately
following diagnosis ( 2 ), and, if they return, they often report work-
related disabilities that prohibit them from performing their jobs
at their pre-diagnosis capacity ( 24 ). Absenteeism among patients
who remain employed can add up to several months or more, and
in one study ( 25 ) cancer survivors had the greatest absenteeism rate
relative to patients with other chronic diseases ( 26 ). Taken
together, these limitations suggest that our estimations refl ect the
lower end of the range of the true productivity costs of cancer.
Estimates such as the ones provided here support the Institute of
Medicine’s recommendation that the National Institutes of Health
strengthen its use of data that estimate the burden and cost of disease
in setting its research priorities ( 27 ). Methods for reducing cancer
mortality include primary prevention (eg, vaccines, risk factor modi-
fi cation), early detection of cancers for which successful treatment is
most likely, and delivery of effective treatments. Decision makers can
use the information we provide as a basis to assess the costs of inter-
ventions relative to their benefi ts to determine how to best allocate
resources among these strategies. From a productivity loss perspec-
tive, investments in programs that reduce lung, breast, colorectal,
leukemia, and/or pancreatic cancer mortality are likely to yield the
largest annual reduction in productivity costs for US society.
1. National Center for Health Statistics . Deaths — Leading Causes . http://
www.cdc.gov/nchs/fastats/lcod.htm . Accessed August 2, 2007 .
2. Bradley C , Neumark D , Luo Z , Schenk M . Employment and cancer: fi nd-
ings from a longitudinal study of breast and prostate cancer survivors .
Cancer Invest . 2007 ; 25 ( 1 ): 47 – 54 .
3. Edwards B , Brown M , Wingo P , et al . Annual report to the nation on the
status of cancer, 1975 – 2002, featuring population-based trends in cancer
treatment . J Natl Cancer Inst . 2005 ; 97 ( 19 ): 1407 – 1427 .
1770 Articles | JNCI Vol. 100, Issue 24 | December 17, 2008 Download full-text
4. Hodgson T , Meiners M . Cost-of-illness methodology: a guide to current
practices and procedures . Milbank Q . 1982 ; 60 ( 3 ): 429 – 462 .
5. Cooper B , Rice D . The economic cost of illness revisited . Soc Secur Bull.
1976 ; 39 ( 2 ): 21 – 36 .
6. Arias E . Natl Vital Stat Rep . 2002 ; 51 ( 3 ): 1 – 38 .
7. Donelan K , Hill CA , Hoffman C , et al . Challenged to care: informal
caregivers in a changing health system . Health Aff. 2002 ; 21 ( 4 ): 222 – 231 .
8. Max W , Rice DP , Sung H , Michel M . Valuing Human Life: Estimating the
Present Value of Lifetime Earnings, 2000 . Center for Tobacco Control
Research and Education. Economic Studies and Related Methods. 2004 .
http://repositories.cdlib.org/ctcre/esarm/PVLE2000 . Accessed January
21, 2008 .
9. US Bureau of the Census . National Interim Projections . http://www.census.
gov/ipc/www/usinterimproj/ . Accessed June 19, 2007 .
10. Berkeley Mortality Database . http://www.demog.berkeley.edu/ ~ bmd/states.
html . Accessed June 3, 2005 .
11. U.S. Department of Labor, Bureau of Statistics and U.S. Department of
Commerce, Economics and Statistics Administration, U.S. Census Bureau .
Current Population Survey, Design and Methodology . Technical Paper TP63RV.
http://www.bls.census.gov/cps/tp/tp63.htm . Accessed August 2, 2007 .
12. Grosse S . Appendix I: productivity loss tables . In: Haddix A , Teutsch S ,
Corso P , eds. Prevention Effectiveness: A Guide to Decision Analysis and
Economic Evaluation . 2nd ed . New York : Oxford University Press ; 2003 ,
pp. 245 – 259 .
13. National Alliance for Caregiving and the American Association of Retired
Persons . Caregiving in the U.S. 2004 . http://www.caregiving.org/
data04execsum.pdf. Accessed October 9, 2008 .
14. U.S. Department of Labor, Bureau of Statistics . Consumer Price Index .
http://stats.bls.gov/cpi/home.htm # data . Accessed December 18, 2006 .
15. Metlife Mature Market Institute. Metlife Market Survey of Nursing Home
and Home Care Costs, 2005 . http://www.metlife.com/WPSAssets/41453139
pdf. Accessed October 9, 2008 .
16. National Center for Health Statistics . National Nursing Home Survey .
Atlanta, GA : Centers for Disease Control . http://www.cdc.gov/nchs/
about/major/nnhsd/ResidentTables_Estimates.htm # PaymentSource .
Accessed October 9, 2008 .
17. U.S. Department of Commerce, Bureau of Economic Analysis . National
Income and Products Accounts Table. http://www.bea.gov/national/
nipaweb/TableView.asp?SelectedTable = 5&ViewSeries = NO&Java = no&
Request3Place = N&3Place = N&FromView = YES&Freq = Year&First
Year = 2007&LastYear = 2008&3Place = N&Update = Update&JavaBox =
no # Mid . Accessed June 19, 2008 .
18. National Cancer Institute . National Cancer Institute (NCI) Fact Book, 2007 .
http://obf.cancer.gov/fi nancial/attachments/07Factbk.pdf . Accessed June
19, 2008 .
19. National Institutes of Health . National Heart, Lung, Blood Institute Fact
Book, 2007 . http://www.nhlbi.nih.gov/about/factbook/FactBookFinal.pdf .
Accessed June 19, 2008 .
20. Molinari NM , Ortega-Sanchez IR , Messonnier JL , et al . The annual impact
of seasonal infl uenza in the US: measuring disease burden and costs . Vaccine .
2007 ; 25 ( 27 ): 5086 – 5096 .
21. American Diabetes Association . Economic cost of diabetes in the U.S. in
2007 . Diabetes Care . 2008 ; 31 ( 3 ): 596 – 615 .
22. Johnson R , Schaner S . Many Older Americans Engage in Caregiving
Activities. Perspectives on Productive Aging . The Urban Institute. http://www.
urban.org/publications/311203.html . Accessed July 14, 2008 .
23. Yabroff KR , Bradley CJ , Mariotto AB , Brown ML , Feuer EJ . Value of
life lost due to early death from cancer . J Natl Cancer Inst . 2008 ; 100 ( 24 ):
1755 – 1762 .
24. Short PF , Vasey JJ , Belue R . Work disability associated with cancer sur-
vivorship and other chronic conditions . Psychooncology . 2008 ; 17 ( 1 ): 91 – 97 .
25. Bradley C , Oberst K , Schenk MJ . Absenteeism from work: experiences of
employed breast and prostate cancer patients 6 months following diagno-
sis . Psychooncology . 2005 ; 15 ( 8 ): 739 – 747 .
26. Kessler RC , Greenberg PE , Mickelson KD , et al . The effect of chronic
medical conditions on work loss and work cutback . J Occup Environ Med .
2001 ; 43 ( 3 ): 218 – 225 .
27. Institute of Medicine . Scientifi c Opportunities and Public Needs: Improving
Priority Setting and Public Input at the National Institutes of Health .
Washington, DC : National Academy Press ; 1998 .
This research was supported by National Cancer Institute contract with C. J.
We thank Danielle Melbert and Martin Krapcho of Information Management
Services, Inc for assistance in calculating and projecting cancer mortality rates,
other causes of death, and PYLL.
The authors take full responsibility for the study design, data collection,
analysis and interpretation of the data, the decision to submit the manuscript
for publication, and the writing of the manuscript.
Manuscript received December 21 , 2007 ; revised May 21 , 2008 ; accepted
May 27 , 2008 .