Background: Cancer survivors are a growing population, due in large part to the aging of the baby boomer generation and the related “silver tsunami” facing the U.S. health care system. Understanding the impact of a graying nation on cancer prevalence and comorbidity burden is critical in informing efforts to design and implement quality cancer care for this population.

Methods: Incidence and survival data from 1975 to 2011 were obtained from the Surveillance, Epidemiology, and End Results (SEER) Program to estimate current cancer prevalence. SEER-Medicare claims data were used to estimate comorbidity burden. Prevalence projections were made using U.S. Census Bureau data and the Prevalence Incidence Approach Model, assuming constant future incidence and survival trends but dynamic projections of the U.S. population.

Results: In 2016, there were an estimated 15.5 million cancer survivors living in the United States, 62% of whom were 65 years or older. The prevalent population is projected to grow to 26.1 million by 2040, and include 73% of survivors who are 65 years and older. Comorbidity burden was highest in the oldest survivors (those ≥85 years) and worst among lung cancer survivors.

Conclusions: Older adults, who often present with complex health needs, now constitute the majority of cancer survivors and will continue to dominate the survivor population over the next 24 years.

Impact: The oldest adults (i.e., those >75 years) should be priority populations in a pressing cancer control and prevention research agenda that includes expanding criteria for clinical trials to recruit more elderly participants and developing relevant supportive care interventions. Cancer Epidemiol Biomarkers Prev; 25(7); 1029–36. ©2016 AACR.

This article is featured in Highlights of This Issue, p. 1007

Cancer survivors are a rapidly growing population, fueled in large part by the aging U.S. population (1). Current prevalence figures already indicate that more than half of all cancer survivors are older than 65 years of age, and that the graying of the baby boomer generation will cause the numbers of older survivors to continue to rise dramatically (2, 3). This demographic shift in the composition of the survivor population will pose challenges to the design and delivery of quality cancer care (4). Older adults commonly have preexisting comorbid conditions that can be exacerbated by cancer-related toxicities that may persist after completion of primary treatment or appear years later (1, 5). One large population-based study estimated that older adults experienced an average of five comorbid conditions, two of which were reported to have developed after a cancer diagnosis (6). It is increasingly recognized that older survivors have complex healthcare needs, but these are poorly understood (7). Importantly, little is known about the burden of cancer and comorbidity among the oldest adults (i.e., especially those aged 85 years and older) compared with other older adults, and how this burden may change over time (8).

In assessing the needs of older survivors, it is important to consider the increasingly common experience of cancer as a chronic disease (9). With advances in early detection and the effectiveness of cancer treatments, long-term survivorship is becoming a reality for most of those diagnosed with cancer. Currently, an estimated 64% of all survivors have lived 5 years beyond diagnosis and 40% have lived 10 years beyond diagnosis (3, 10). This combination of attributes in the cancer survivor profile (ie, advanced age, longevity, and multiple health conditions) may foreshadow a “silver tsunami” (11) of cancer survivors whose health needs we are unprepared to meet (5, 12).

The primary purpose of this report is to provide current estimates of comorbidity prevalence at diagnosis and projections of future cancer prevalence that reflect the aging of the U.S. population under the assumptions of current cancer control efforts. This report will focus on cancer survivors 65 years and older to inform research and care targeting this growing, heterogeneous, and often medically complex population. The specific aims are to: (i) assess the effect of an aging population on cancer prevalence among older adults from 1975 to present, and provide prevalence projections from 2016 to 2040; (ii) update current prevalence of cancer distributed by gender and age; and (iii) compare current comorbidity burden among older adults with and without a cancer history across age-related subgroups using medical claims linked to cancer registry data.

Prevalence projections: Method overview

We projected future U.S. cancer prevalence from 2016 to 2040 using the Prevalence Incidence Approach Model (PIAMOD; refs. 13–15). This statistical method provides estimates and projections of cancer prevalence from cancer incidence, cancer survival, and mortality for all causes of death. The method uses a mathematical equation that relates prevalence to incidence and mortality. The input data required by PIAMOD are the estimated number of U.S. cancer incidence cases, U.S. population, U.S. deaths (all causes), and modeled cancer survival. The PIAMOD method has been used previously to provide projections of cancer prevalence through 2020 (14).

Data sources for prevalence projections

Population estimates from 1975 through 2013 were obtained from the U.S. Census Bureau. We used a demographic method to obtain smoothed single age population estimates from age-related subgroups (85–89, 90–94, and 95–99 years) by calendar year, sex, and race. U.S. population projections from 2013 through 2040 were obtained from the 2014 National Projections (16). These projections are based on the 2010 Census and on assumptions about future births, deaths, and net international migration and are available for individuals ages 1 to 99 years and 100 years and older.

SEER incidence

The number of U.S. incidence cancer cases from 1975 through 2012 (17) is estimated using SEER data from the nine initial registries that include the states of Connecticut, Hawaii, Iowa, New Mexico, Utah, and four metropolitan areas: Atlanta, Detroit, San Francisco-Oakland, and Seattle, representing approximately 11% of the U.S. population (18). Only the first malignant cancer is included in incidence rates so that individual observations are unique (i.e., counted only once) in prevalence estimates.

U.S. incidence

The number of U.S. cancer cases were calculated by applying the yearly SEER-9 rates to the respective US population by sex, race (white, black, and other), year (1975–2012), and age (0 to 99). Projections of U.S. cancer cases from 2013 through 2040 were calculated by applying the (2008–2012) average SEER-9 5-year cancer incidence rates to the U.S. population projections by sex, year, and age, thus assuming flat incidence (i.e., no changes) from 2013 through 2040.

Cancer survival

Relative survival for patients diagnosed with malignant cancer between 1973 and 2011 in SEER-9 was calculated from SEER*Stat software (19) by sex, period of diagnosis (1973–1975, 1976–1978…2009–2011), and age at diagnosis (15–44, 45–54, 55–64, 65–74, 75–84, and 85–99). A parametric mixture cure model was fit to the SEER relative survival (15, 20). The model was stratified by age group with a trend parameter for year of diagnosis (15). Because of data variability and difficulties in modeling relative survival for ages 85 and older (models did not converge or provided unrealistic estimates), we assumed that cancer survival for patients diagnosed at ages 85 to 99 years was the same as for patients diagnosed at ages 75 to 84 years. To project prevalence, we assumed that cancer survival for patients diagnosed in 2012 or later was the same as the modeled survival for patients diagnosed in 2009–2011.

The number of U.S. deaths (all causes) were provided by the National Center for Health Statistics from SEER*Stat software (19) using sex, age, and year at death from 1975 to 2012. Projections of the number of U.S. deaths from 2013 through 2040 were calculated by applying the 5-year average U.S. mortality rates (2008–2012) to U.S. population projections by sex, year, and age groups.

Prevalence projection method and assumptions

The cancer prevalence projections for years 2013 and beyond are based on dynamic projections of the U.S. population and flat incidence and survival, as observed in the last years of available data (2008–2012). These projections can be interpreted as the effect of the growth and aging of the U.S. population based on current cancer control technologies. The PIAMOD method provided prevalence estimates for ages 0 to 99. To include cancer survivors ages 100 years and older, we assumed that cancer prevalence of people ages 100 and older was the same as for people ages 95 to 99. We estimated the number of cancer survivors 100 years and older by multiplying the proportion of survivors between ages 95 and 99 and applying to estimates and projections of the population of adults 100 years and older.

Procedures for calculating comorbidity burden

Claims from the November 2014 SEER-Medicare linkage data and from a 5% random sample of Medicare beneficiaries residing in the SEER areas free of cancer were used to describe comorbidity burden (18). The cancer cohort consisted of Medicare beneficiaries 66 to 90 years of age residing in the SEER 11 cancer registry areas diagnosed with cancer between 2007 and 2011. Noncancer controls were randomly selected from the 5% sample of cancer-free beneficiaries in the SEER areas for each calendar year and were frequency matched to cancer cases by sex and age. Comorbid conditions for cancer survivors were identified in the year before the date of cancer diagnosis. For individuals without cancer, their comorbid conditions were identified in the year before their birthday in the matched calendar year. Cases and controls were required to have continuous Part A and Part B fee-for-service Medicare coverage during the whole year. The 5% cohort of cancer-free Medicare beneficiaries consisted of multiple records per individual.

We ascertained 16 comorbid conditions described by Charlson and colleagues (21) using similar procedures developed by Klabunde and colleagues (22, 23). The conditions included: acute myocardial infarction (MI), myocardial infarction history, acquired immune deficiency syndrome (AIDS), cerebrovascular disease (CVD), chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF), dementia, diabetes, diabetes with complications, liver disease (mild), liver disease (moderate/severe), paralysis (hemiplegia or paraplegia), peripheral vascular disease, renal disease, rheumatologic disease, and ulcer. Similar conditions, including liver disease (mild) and liver disease (moderate/severe) as well as diabetes and diabetes with complications, were grouped together. Because the focus was on studying comorbid conditions in cancer patients, solid cancers, leukemias, and lymphomas were excluded. Conditions were identified from physician and hospital claims using the International Classification of Diseases, Ninth Revision, Clinical Modification and fourth edition of Common Procedural Terminology codes. Conditions in the physician claims, but not in hospital claims, were required to appear more than once in a period greater than 30 days.

Instead of using a simple count of conditions to estimate burden, weights for each condition were estimated using a Cox proportional hazard model to represent the impact of the condition on noncancer survival (24). For each individual, a comorbidity score was calculated as the weighted sum of their identified comorbid conditions by the condition weight.

The severity of comorbidity burden was considered by defining three groups based on comorbidity weights and clinical implications (24, 25). The groups were: (i) no reported comorbidities; (ii) moderate comorbidity, which included conditions that usually do not require physicians to adjust cancer treatment (such as acute MI, history of MI, ulcers, rheumatologic disease, diabetes, peripheral vascular disease, cerebrovascular disease and paralysis); and (iii) severe comorbidity burden, which refers to severe illnesses that frequently lead to organ failure or systemic dysfunction and always require adjustment of cancer treatment. The severe conditions included COPD, liver dysfunction, chronic renal failure, dementia, CHF, and AIDS as well as people with multiple comorbidities who may require special considerations for cancer treatment. It should be noted that common conditions such as hypertension or arthritis have not been included in this study, which is consistent with Charlson index.

Projected cancer prevalence

Between 2016 and 2040, the projected prevalence of cancer survivors will increase from an estimated 15.5 million (total population 324 million) to 26.1 million survivors (total population 380 million) across all age groups. In the prevalent population, it is expected that 73% of survivors will be age 65 and older in 2040, compared with 18% of survivors 50 to 64 years and just 8% of survivors who will be less than 50 years old at that time. This dramatic increase mirrors the pattern of increased incidence driven by an aging population, such that in 2040, the most prevalent groups of survivors will be those from older age groups. Specifically, in 2040, survivors ages 65–74 years will account for 24% of all survivors, those 75–84 years will account for 31% of all survivors, and those 85 years and older will represent 18% of survivors (Fig. 1).

Figure 1.

Estimated cancer prevalence by age in the U.S. population from 1975 (216 M) to 2040 (380 M).

Figure 1.

Estimated cancer prevalence by age in the U.S. population from 1975 (216 M) to 2040 (380 M).

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Pairing these projections with historical trends since 1975, we observed a steep overall incline in prevalence, especially for older survivors. Among survivors 50 to 64 years, we estimated an almost 4-fold increase in projected prevalence from 1975 to 2040. However, the projected increase in prevalence was substantially larger in older age groups. Comparing 1975 prevalence estimates to projected prevalence estimates in 2040, we noted a 6-fold increase for those 65 to 74 years; a 10-fold increase for those ages 75 to 84 years, and a 17-fold increase for those 85 years and older (Fig. 1). These data foretell a steady rise in cancer prevalence among older age groups over the next 24 years.

Updated prevalence of cancer distributed by age and gender

Examining prevalence across the U.S. population (estimated at 323,995,680 in 2016), we observed that overall cancer prevalence was similar between men and women of all ages, but that cancer prevalence was higher among men than women in older age groups (Fig. 2). In younger age groups, including those ages 55–59 and 60–64 years, the percentage of male and female cancer survivors is similar, with men trailing slightly behind women (6%–7% and 9%–10%, respectively), but this shifts in adults older than age 65 years, wherein 14% of the nearly 8 million men ages 65–69 years had been diagnosed with cancer, compared with 12% of 8.9 million women of the same age group. This differential becomes larger in older age groups, such that there is a higher percentage of male than female survivors for: 70–74 year olds (5% more males than females); 75–79 year olds (8% more males than females); and 80–84 and 85–89 year olds (11% more male than female survivors). The greatest difference for cancer survivors by gender is for those 90 years and older, wherein 37% of the 765,660 men ages 90 years and older are cancer survivors, 12% more than females of the same age group, which included 1.7 million women (Fig. 2).

Figure 2.

Percentage of cancer survivors in the U.S. population (324 M) by gender and age, 2016.

Figure 2.

Percentage of cancer survivors in the U.S. population (324 M) by gender and age, 2016.

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Comparison of comorbidity burden of older adults with and without a cancer history

In our assessment of survivors of all cancer sites across age groups, we noted some unexpected results (Table 1). For example, approximately 50% of survivors 70–74 years experienced no comorbidities (based on our procedures) before diagnosis. However, an increase in history of severe comorbidity was observed with increasing age, rising from 27% in survivors 66–69 years (all cancer sites) to 47% in adults 85 years and older (all cancer sites).

Table 1.

Comorbidity burdena by age and cancer site

NoncancerAll sitesBladderCRCFemale breastLeukemiaLungOralProstate
Total 100,000 457,659 26,045 48,138 52,179 12,447 76,365 8,043 80,087 
66–69 y 20,180 92,362 4,036 7,361 11,407 1,893 14,558 1,918 22,196 
None 66% 56% 52% 56% 65% 56% 39% 56% 66% 
Moderate 17% 18% 20% 20% 17% 19% 12% 16% 18% 
Severe 16% 26% 28% 24% 17% 24% 48% 27% 16% 
70–74 y 24,699 113,029 5,740 10,036 12,870 2,512 18,982 1,998 25,315 
None 61% 50% 47% 51% 59% 51% 34% 52% 61% 
Moderate 19% 19% 21% 20% 20% 20% 13% 16% 19% 
Severe 20% 31% 32% 29% 21% 30% 52% 32% 19% 
75–79 y 21,555 98,644 5,848 10,077 10,951 2,623 17,781 1,592 17,185 
None 54% 45% 43% 46% 54% 45% 32% 46% 55% 
Moderate 19% 19% 20% 19% 20% 20% 13% 18% 20% 
Severe 26% 36% 37% 35% 26% 35% 55% 35% 24% 
80–84 y 17,409 79,676 5,339 9,630 8,824 2,528 14,180 1,241 9,362 
None 48% 40% 38% 42% 49% 42% 30% 43% 49% 
Moderate 18% 18% 19% 19% 21% 17% 14% 17% 19% 
Severe 33% 41% 42% 40% 30% 41% 56% 40% 32% 
85+ y 16,157 73,948 5,082 11,034 8,127 2,891 10,864 1,294 6,029 
None 42% 37% 36% 37% 44% 36% 29% 39% 43% 
Moderate 17% 17% 17% 17% 19% 15% 13% 17% 16% 
Severe 42% 47% 47% 46% 37% 49% 57% 44% 41% 
NoncancerAll sitesBladderCRCFemale breastLeukemiaLungOralProstate
Total 100,000 457,659 26,045 48,138 52,179 12,447 76,365 8,043 80,087 
66–69 y 20,180 92,362 4,036 7,361 11,407 1,893 14,558 1,918 22,196 
None 66% 56% 52% 56% 65% 56% 39% 56% 66% 
Moderate 17% 18% 20% 20% 17% 19% 12% 16% 18% 
Severe 16% 26% 28% 24% 17% 24% 48% 27% 16% 
70–74 y 24,699 113,029 5,740 10,036 12,870 2,512 18,982 1,998 25,315 
None 61% 50% 47% 51% 59% 51% 34% 52% 61% 
Moderate 19% 19% 21% 20% 20% 20% 13% 16% 19% 
Severe 20% 31% 32% 29% 21% 30% 52% 32% 19% 
75–79 y 21,555 98,644 5,848 10,077 10,951 2,623 17,781 1,592 17,185 
None 54% 45% 43% 46% 54% 45% 32% 46% 55% 
Moderate 19% 19% 20% 19% 20% 20% 13% 18% 20% 
Severe 26% 36% 37% 35% 26% 35% 55% 35% 24% 
80–84 y 17,409 79,676 5,339 9,630 8,824 2,528 14,180 1,241 9,362 
None 48% 40% 38% 42% 49% 42% 30% 43% 49% 
Moderate 18% 18% 19% 19% 21% 17% 14% 17% 19% 
Severe 33% 41% 42% 40% 30% 41% 56% 40% 32% 
85+ y 16,157 73,948 5,082 11,034 8,127 2,891 10,864 1,294 6,029 
None 42% 37% 36% 37% 44% 36% 29% 39% 43% 
Moderate 17% 17% 17% 17% 19% 15% 13% 17% 16% 
Severe 42% 47% 47% 46% 37% 49% 57% 44% 41% 

aCategories included comorbidities described in the Charlson Index and weighted for effect on noncancer mortality. None implies a “0” score for comorbidities, though this may not reflect common conditions that are not currently included in the Index. Moderate comorbidity included conditions that usually do not require physicians to adjust cancer treatment. Severe comorbidity burden refers to severe illnesses that frequently lead to organ failure or systemic dysfunction and always require adjustment of cancer treatment for survivors. See Materials and Methods for more detail.

We noted more similarity in comorbidity burden related to age than cancer status, which has also been observed in other studies (26, 27). In the noncancer cohort, an estimated 42% of those 85 years and older had serious comorbidity burden, which was only slightly lower than the estimated prevalence of serious comorbidity burden in survivors of all cancer sites (47%). The groups with the least burden were those 66–74 years, where up to 66% of adults without a cancer history scored zero for comorbid conditions. But, up to 56% of cancer survivors 66–74 years also fell into this category, again suggesting the role of younger age in mitigating comorbidity burden.

Focusing on specific cancer sites, we noted that lung cancer survivors consistently had the worst comorbidity burden of any cancer site across age groups, with nearly 50% of lung cancer survivors ages 65–69 years experiencing severe comorbidity burden and 57% of survivors 85 years or older experiencing severe comorbidity burden. In comparison, breast and prostate cancer survivors had consistently lower comorbidity burden than survivors of other cancer sites, which was generally more similar to the noncancer cohort, an association that persisted in older age groups. For example, breast and prostate cancer survivors ages 65–69 years had lower burden of severe comorbidity (17% and 16%, respectively) compared with 16% for noncancer controls. This burden rose to 37% (breast cancer survivors) and 41% (prostate cancer survivors) in survivors 85 years and older, still lower than the estimates for noncancer controls (42%) of that age group. These estimates were also notably lower than the observed comorbidity burden among similarly aged survivors of other cancer sites, including colorectal, oral, bladder, and leukemia cancers (Table 1).

Among cancer survivors, the three most prevalent comorbid conditions were CHF, COPD, and diabetes (Table 2). Colorectal cancer survivors had the highest overall prevalence of CHF compared with other sites, with the oldest survivors having the highest burden (22% of those 85 years and older). Lung cancer survivors had the highest prevalence of COPD, more than 40% of lung cancer survivors 65–84 years experiencing this condition. Comparatively, female breast cancer survivors had the lowest prevalence of COPD, with 13% or less experiencing this condition in any age group. Diabetes was consistently the most common of the three conditions for survivors of all cancer sites, across all age groups. The range of those experiencing diabetes was 22% to 29% of survivors, with little variation between age groups (Table 2).

Table 2.

Percentage of common comorbidities in noncancer controls and cancer survivors (by age and cancer site)a

Noncancer (n = 100,000)All sites (n = 457,659)BladderCRCFemale breastLeukemiaLungOralProstate
Age% with condition(% without)% with condition(% without)% with condition% with condition% with condition% with condition% with condition% with condition% with condition
CHF 
 66–69 y 4% (96%) 6% (94%) 6% 7% 3% 7% 10% 6% 4% 
 70–74 y 6% (94%) 8% (92%) 9% 10% 5% 9% 12% 7% 5% 
 75–79 y 8% (92%) 11% (89%) 11% 12% 7% 13% 15% 10% 7% 
 80–84 y 11% (89%) 15% (85%) 14% 16% 10% 17% 17% 13% 11% 
 85+ y 17% (83%) 20% (80%) 19% 22% 16% 23% 23% 19% 17% 
COPD 
 66–69 y 9% (91%) 17% (83%) 17% 13% 11% 14% 40% 18% 8% 
 70–74 y 10% (90%) 18% (82%) 18% 15% 12% 15% 43% 20% 10% 
 75–79 y 12% (88%) 21% (79%) 20% 18% 13% 18% 43% 20% 11% 
 80–84 y 14% (86%) 21% (79%) 20% 18% 13% 19% 41% 19% 14% 
 85+ y 14% (86%) 19% (81%) 18% 17% 12% 18% 36% 18% 14% 
Diabetes 
 66–69 y 20% (80%) 25% (75%) 27% 28% 21% 26% 24% 20% 21% 
 70–74 y 23% (77%) 27% (73%) 28% 30% 24% 26% 26% 23% 23% 
 75–79 y 24% (76%) 28% (72%) 29% 29% 25% 28% 26% 26% 24% 
 80–84 y 23% (77%) 27% (73%) 28% 28% 24% 25% 25% 24% 24% 
 85+ y 20% (80%) 23% (77%) 23% 23% 21% 21% 22% 22% 21% 
Noncancer (n = 100,000)All sites (n = 457,659)BladderCRCFemale breastLeukemiaLungOralProstate
Age% with condition(% without)% with condition(% without)% with condition% with condition% with condition% with condition% with condition% with condition% with condition
CHF 
 66–69 y 4% (96%) 6% (94%) 6% 7% 3% 7% 10% 6% 4% 
 70–74 y 6% (94%) 8% (92%) 9% 10% 5% 9% 12% 7% 5% 
 75–79 y 8% (92%) 11% (89%) 11% 12% 7% 13% 15% 10% 7% 
 80–84 y 11% (89%) 15% (85%) 14% 16% 10% 17% 17% 13% 11% 
 85+ y 17% (83%) 20% (80%) 19% 22% 16% 23% 23% 19% 17% 
COPD 
 66–69 y 9% (91%) 17% (83%) 17% 13% 11% 14% 40% 18% 8% 
 70–74 y 10% (90%) 18% (82%) 18% 15% 12% 15% 43% 20% 10% 
 75–79 y 12% (88%) 21% (79%) 20% 18% 13% 18% 43% 20% 11% 
 80–84 y 14% (86%) 21% (79%) 20% 18% 13% 19% 41% 19% 14% 
 85+ y 14% (86%) 19% (81%) 18% 17% 12% 18% 36% 18% 14% 
Diabetes 
 66–69 y 20% (80%) 25% (75%) 27% 28% 21% 26% 24% 20% 21% 
 70–74 y 23% (77%) 27% (73%) 28% 30% 24% 26% 26% 23% 23% 
 75–79 y 24% (76%) 28% (72%) 29% 29% 25% 28% 26% 26% 24% 
 80–84 y 23% (77%) 27% (73%) 28% 28% 24% 25% 25% 24% 24% 
 85+ y 20% (80%) 23% (77%) 23% 23% 21% 21% 22% 22% 21% 

Abbreviations: CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; CRC, colorectal cancer.

aFor cancer survivors, comorbidities were identified prior to receiving a cancer diagnosis. See Materials and Methods for details.

Compared with noncancer controls of similar age groups, a perceptibly larger proportion of survivors experienced CHF, COPD, and diabetes. For example, among adults 66–84 years without cancer, 9% to 14% experienced COPD, compared with 17% to 21% of survivors of all sites ages 66–84 years. Although the difference between survivors and noncancer controls was less pronounced when assessing presence of CHF and diabetes, a general pattern across age groups was still present that suggested slightly higher prevalence of these conditions among survivors of all sites compared with other adults without cancer history of similar ages (Table 2). However, it should be noted that breast/prostate cancer survivors were the exception to this pattern, presenting similar prevalence of CHF, COPD, and diabetes as noncancer controls.

Although the aging of the U.S. population has long been recognized as a major contributor to the cancer burden (28, 29), this report provides a discrete and previously undescribed view of the cancer prevalence exclusively among age-related subgroups of older adults projected to 2040. Current U.S. demographics will literally change the face of the survivor population in the decades to come. In a prior prevalence report (3), the population of U.S. cancer survivors was estimated at 13.7 million (2012). In 2016, we estimate that there are 15.5 million U.S. cancer survivors, a number that will continue to rise. Currently, we estimate that almost 40% of men age 90 years and older are cancer survivors, out of a total population of 765,660 American men in that age group. Similarly, we estimate that 25% of women age 90 years and older, out of a total population of 1,739,100, are cancer survivors. It can be inferred that higher cancer prevalence in elderly men is due to prostate cancer, which is typically diagnosed in men at older ages. Furthermore, our projections portend a steady increase in cancer prevalence by age, wherein the oldest adults (i.e., 85 years and older) are expected to have the sharpest increase in prevalence looking from 1975 forward to 2040, compared with other age groups. Similar, but slightly less dramatic projected increases, will also occur among adults between 65–74 and 74–85 years, underscoring the need to focus on adults who are (or are soon to become) Medicare-eligible as priority populations for cancer prevention and control research.

Despite their numbers, older adults continue to be understudied, a point emphasized by the Institute of Medicine in addressing challenges in delivery quality cancer care (4). Cancer patients older than age 65 have been largely excluded from clinical trials (30), precluding opportunities to develop greater insight about the unique needs of older as well as long-term survivors. Recent studies have demonstrated variability of physical and psychosocial conditions for older adults as they exceed age 65, differences which may be amplified in cancer survivors (31–33). Clinicians are increasingly recognizing the benefits of a geriatric assessment framework, including opportunities for personalized medicine in geriatric oncology (8, 34). Greater participation by elderly survivors in clinical trials could provide more clarity in optimal dosing of chemotherapeutic agents, potentially reducing treatment-related toxicity for elderly cancer patients among other benefits for health and well-being into long-term survivorship (34).

On the basis of our data, more than 60% of survivors ages 85 and older had at least one comorbid condition before a cancer diagnosis and between 37% and 49% had more severe comorbidity burden, based on weighted scores. However, because we only included comorbidities identified by Charlson and colleagues, common conditions that affect the elderly (such as hypertension, arthritis, atrial fibrillation, chronic ischemic coronary artery disease, and thromboembolic disease) are not reflected and our report of comorbidity prevalence may in fact be an underestimate of actual comorbidity burden in older survivors. Clinicians may also not code for all comorbidities an older patient may experience, further increasing the likelihood of an underestimate of actual comorbidity burden. Importantly, we did not see a large difference between comorbidity burden in older adults of similar age groups when stratified on cancer history. However, we did observe that survivorship experiences with comorbidity varied by cancer site, such that lung cancer survivors had the highest comorbidity burden before diagnosis across age groups and breast/prostate cancer survivors had some of the lowest comorbidity burden before diagnosis across age groups. We also observed small, but notable differences in CHF, COPD, and diabetes, suggesting slightly higher prevalence of these conditions in cancer survivors of all sites compared with noncancer controls, with the exception of breast/prostate cancer survivors, who were similar to noncancer controls. Longitudinal assessment of comorbidity burden (including differences by age and cancer site) would be informative in future research studies.

These findings have several implications. First, cancer survivors, like adults in the general population, are at risk for diseases associated with aging and lifestyle. Lifestyle counseling, including physical activity, may be important to reduce cancer-related symptoms, as symptom burden may impede an active lifestyle after treatment (35, 36). In addition, evidence suggests that comorbidities identified before a cancer diagnosis are important predictors of function and health status in the posttreatment period, potentially offering an opportunity to focus on rehabilitation to reduce these negative sequelae (26, 37). Furthermore, our findings relative to comorbidity patterns point to the need to consider more systematic screening for functional risk and broader delivery of supportive interventions, including rehabilitation, to vulnerable subgroups (such as lung cancer survivors) to help increase their ability to live through and beyond cancer treatment (38, 39). However, among the challenges in addressing the needs of the growing survivor population is the looming question of cost of care and caregiving capacity.

With anticipated shortages of oncology providers (4), and the complex health needs of older patients, finding efficient and effective ways to meet the medical surveillance needs of older survivors will become increasingly important (5, 7, 40, 41). The role of primary care providers may become even more critical in recognizing and managing late and long-term effects of treatment (42) as well as broader morbidity and mortality risk (43). At the same time, determining which survivors could be appropriately served in a primary care setting, versus a more oncology-driven model, are questions of particular interest (44, 45). Moving forward, research and education will be needed to facilitate care coordination between oncology and primary care providers.

As part of caregiving considerations, understanding the impact on family and other informal caregivers will also be required. Efforts to address the health and functional needs of informal caregivers (sometimes viewed as ‘secondary’ survivors) are critically needed as their numbers and burden of care (including financial burden) will likely increase in step with the prevalent cancer population (46, 47). In addition to increasing burden of care, informal caregivers may also become progressively more involved with medical decisions. In a recent report, the National Alliance on Caregiving estimated that 73% of caregivers over the age of 75 years communicate with health care professionals on behalf of their care recipient, making them an important part of the medical team (48). Efforts will be needed to address the capacity of informal cancer caregivers to support care recipients as well as mitigate the potential adverse effects of caregiving demands on their own health and function (47, 49).

Although this report provides timely insights on burden of cancer in older adults, we acknowledge several limitations of our analysis. Data from SEER cancer registries and Medicare claims only include data from specific regions that participate in the registry, and thus are not nationally representative. Furthermore, to generate our prevalence estimates, we held incidence and survival constant. In reality, incidence has been declining slowly and steadily for most cancers, and survival, in turn, has increased. It is possible that these trends may balance each other (i.e., fewer cancer cases, but longer survival). A shift in either of these trends over the long term could result in our estimates being an over- or underestimation of the true figures. However, the U.S. Census Bureau provides the best available population projections to estimate the future number of cancer survivors in the United States.

Because it is difficult to anticipate future developments in cancer control technologies and their impact on survival and incidence trends, we produced future prevalence estimates based on projections assuming flat incidence and survival. These projections represent the effect of the growth and aging of the U.S. population based on current cancer control strategies. Overall cancer incidence decreased for men 1.8% annually from 2007 to 2011 and incidence was stable for women from 1998 to 2011 (21). Cancer survival has been increasing for most cancer sites in the most recent years (5-year relative survival for patients diagnosed in 2002–2004 with any cancer was 67% compared with 69% for patients diagnosed in 2005–2011; ref. 17). Compared with previous reports (1, 3), prevalence projections incorporating recent trends in survival and incidence are similar, assuming flat trends. In addition, comorbidity measures were based on a claims algorithm and Medicare data and do not include information about the role of comorbidity on functional status, which may be helpful in interpreting the burden of illness and quality of life. Finally, although we were able to estimate current prevalence of comorbidity in adult cancer survivors using data from the year before cancer diagnosis, few data are available to examine comorbidity burden in long-term cancer survivors. This represents a significant gap in our current knowledge base.

This report clearly demonstrates a rising tide of cancer prevalence, stimulated by the maturation of the baby boomers, as well as marked heterogeneity in comorbidity burden that is more pronounced in the oldest age groups. The overall message continues to be that cancer survivors are a diverse population, with needs that vary by age as well as cancer site and other factors. We hope, however, that the data presented here will lend urgency to previous calls to action (4, 50), particularly for the complex needs of older survivors. Although a number of interventions have been developed to help survivors cope with cancer and thrive in the face of cancer-related symptoms, few have targeted older adults and this remains an area of critical need in survivorship science (51). Importantly, effective strategies must be implemented now to address the tsunami of aging cancer survivors that is rapidly approaching.

No potential conflicts of interest were disclosed.

Conception and design: S.M. Bluethmann, A.B. Mariotto, J.H. Rowland

Development of methodology: S.M. Bluethmann, A.B. Mariotto

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): S.M. Bluethmann, A.B. Mariotto, J.H. Rowland

Writing, review, and/or revision of the manuscript: S.M. Bluethmann, A.B. Mariotto, J.H. Rowland

Study supervision: A.B. Mariotto

The authors thank William Klein, Janet de Moor, and other staff in the NCI Division of Cancer Control and Population Sciences for their assistance in the preparation of this report.

1.
Parry
C
,
Kent
EE
,
Mariotto
AB
,
Alfano
CM
,
Rowland
JH
. 
Cancer survivors: a booming population
.
Cancer Epidemiol Biomarkers Prev
2011
;
20
:
1996
2005
.
2.
Frey
WH
. 
Baby boomers and the new demographics of america's seniors
.
Generations
2010
;
34
:
28
37
.
3.
de Moor
JS
,
Mariotto
AB
,
Parry
C
,
Alfano
CM
,
Padgett
L
,
Kent
EE
, et al
Cancer survivors in the United States: prevalence across the survivorship trajectory and implications for care
.
Cancer Epidemiol Biomarkers Prev
2013
;
22
:
561
70
.
4.
Levit
L
,
Balogh
E
,
Nass
S
,
Ganz
PA
.
Delivering high-quality cancer care: charting a new course for a system in crisis
.
Washington, DC
:
Institute of Medicine
; 
2013
.
5.
Rowland
JH
,
Bellizzi
KM
. 
Cancer survivorship issues: life after treatment and implications for an aging population
.
J Clin Oncol
2014
;
32
:
2662
8
.
6.
Leach
CR
,
Weaver
KE
,
Aziz
NM
,
Alfano
CM
,
Bellizzi
KM
,
Kent
EE
, et al
The complex health profile of long-term cancer survivors: prevalence and predictors of comorbid conditions
.
J Cancer Surviv
2014
;
9
:
239
51
.
7.
Bellizzi
KM
,
Aziz
NM
,
Rowland
JH
,
Weaver
K
,
Arora
NK
,
Hamilton
AS
, et al
Double jeopardy? age, race, and HRQOL in older adults with cancer
.
J Cancer Epidemiol
2012
;
2012
:
478642
.
8.
Hurria
A
,
Levit
LA
,
Dale
W
,
Mohile
SG
,
Muss
HB
,
Fehrenbacher
L
, et al
Improving the evidence base for treating older adults with cancer: American society of clinical oncology statement
.
J Clin Oncol
2015
;
33
:
3826
33
.
9.
Hewitt
M
,
Rowland
JH
,
Yancik
R
. 
Cancer survivors in the united states: age, health, and disability
.
J Gerontol A Biol Sci Med Sci
2003
;
58
:
M82
91
.
10.
Rowland
JH
,
Yancik
R
. 
Cancer survivorship: the interface of aging, comorbidity, and quality care
.
J Natl Cancer Inst
2006
;
98
:
504
5
.
11.
Bartels
SJ
,
Naslund
JA
. 
The underside of the silver tsunami — older adults and mental health care
.
N Engl J Med
2013
;
368
:
493
6
.
12.
Smith
BD
,
Smith
GL
,
Hurria
A
,
Hortobagyi
GN
,
Buchholz
TA
. 
Future of cancer incidence in the United States: burdens upon an aging, changing nation
.
J Clin Oncol
2009
;
27
:
2758
65
.
13.
Verdecchia
A
,
Angelis
GD
,
Capocaccia
R
. 
Estimation and projections of cancer prevalence from cancer registry data
.
Stat Med
2002
;
21
:
3511
26
.
14.
Mariotto
AB
,
Yabroff
KR
,
Feuer
EJ
,
De Angelis
R
,
Brown
M
. 
Projecting the number of patients with colorectal carcinoma by phases of care in the US: 2000–2020
.
Cancer Causes Control
2006
;
17
:
1215
26
.
15.
Mariotto
AB
,
Yabroff
KR
,
Shao
Y
,
Feuer
EJ
,
Brown
ML
. 
Projections of the cost of cancer care in the united states: 2010–2020
.
J Natl Cancer Inst
2011
;
103
:
117
28
.
16.
Colby
SL
,
Ortman
JM
(
U.S. Census Bureau, Washington, DC
).
Washington, DC
:
US Census Bureau
; 
2014
Mar. Report No.: P25-1143
.
17.
Howlader
N
,
Noone
A
,
Krapcho
M
.
SEER Cancer Statistics Review, 1975–2012
,
National Cancer Institute
; 
2015
.
Available from
: http://seer.cancer.gov/csr/1975_2013/.
18.
Warren
JL
,
Klabunde
CN
,
Schrag
D
,
Bach
PB
,
Riley
GF
. 
Overview of the SEER-medicare data: Content, research applications, and generalizability to the United States elderly population
.
Med Care
2002
;
40
:
IV3
IV18
.
19.
Surveillance Research Program, National Cancer Institute SEER*Stat software version 8.2.1. Avaialble from
: http://www.seer.cancer.gov/seerstat.
20.
Yu
B
,
Tiwari
RC
,
Cronin
KA
,
Feuer
EJ
. 
Cure fraction estimation from the mixture cure models for grouped survival data
.
Stat Med
2004
;
23
:
1733
47
.
21.
Charlson
ME
,
Pompei
P
,
Ales
KL
,
MacKenzie
CR
. 
A new method of classifying prognostic comorbidity in longitudinal studies: development and validation
.
J Chronic Dis
1987
;
40
:
373
83
.
22.
Klabunde
CN
,
Potosky
AL
,
Legler
JM
,
Warren
JL
. 
Development of a comorbidity index using physician claims data
.
J Clin Epidemiol
2000
;
53
:
1258
67
.
23.
Klabunde
CN
,
Legler
JM
,
Warren
JL
,
Baldwin
L
,
Schrag
D
. 
A refined comorbidity measurement algorithm for claims-based studies of breast, prostate, colorectal, and lung cancer patients
.
Ann Epidemiol
2007
;
17
:
584
90
.
24.
Mariotto
AB
,
Wang
Z
,
Klabunde
CN
,
Cho
H
,
Das
B
,
Feuer
EJ
. 
Life tables adjusted for comorbidity more accurately estimate noncancer survival for recently diagnosed cancer patients
.
J Clin Epidemiol
2013
;
66
:
1376
85
.
25.
Cho
H
,
Klabunde
CN
,
Yabroff
KR
,
Wang
Z
,
Meekins
A
,
Lansdorp-Vogelaar
I
, et al
Comorbidity-adjusted life expectancy: a new tool to inform recommendations for optimal screening strategies
.
Ann Intern Med
2013
;
159
:
667
76
.
26.
Garman
KS
,
Pieper
CF
,
Seo
P
,
Cohen
HJ
. 
Function in elderly cancer survivors depends on comorbidities
.
J Gerontol A Biol Sci Med Sci
2003
;
58
:
M1119
24
.
27.
Cho
H
,
Mariotto
AB
,
Mann
BS
,
Klabunde
CN
,
Feuer
EJ
. 
Assessing non-cancer-related health status of US cancer patients: other-cause survival and comorbidity prevalence
.
Am J Epidemiol
2013
;
178
:
339
49
.
28.
Edwards
BK
,
Howe
HL
,
Ries
LAG
,
Thun
MJ
,
Rosenberg
HM
,
Yancik
R
, et al
Annual report to the nation on the status of cancer, 1973–1999, featuring implications of age and aging on U.S. cancer burden
.
Cancer
2002
;
94
:
2766
92
.
29.
Yancik
R
. 
Population aging and cancer: a cross-national concern
.
Cancer J
2005
;
11
:
437
41
.
30.
Townsley
CA
,
Selby
R
,
Siu
LL
. 
Systematic review of barriers to the recruitment of older patients with cancer onto clinical trials
.
J Clin Oncol
2005
;
23
:
3112
24
.
31.
Pal
SK
,
Katheria
V
,
Hurria
A
. 
Evaluating the older patient with cancer: understanding frailty and the geriatric assessment
.
CA Cancer J Clin
2010
;
60
:
120
32
.
32.
Puts
MTE
,
Santos
B
,
Hardt
J
,
Monette
J
,
Girre
V
,
Atenafu
EG
, et al
An update on a systematic review of the use of geriatric assessment for older adults in oncology
.
Ann Oncol
2014
;
25
:
307
15
.
33.
Wildiers
H
,
Heeren
P
,
Puts
M
,
Topinkova
E
,
Janssen-Heijnen
MLG
,
Extermann
M
, et al
International society of geriatric oncology consensus on geriatric assessment in older patients with cancer
.
J Clin Oncol
2014
;
32
:
2595
603
.
34.
Walko
CM
,
McLeod
HL
. 
Personalizing medicine in geriatric oncology
.
J Clin Oncol
2014
;
32
:
2581
6
.
35.
Sprod
LK
,
Mohile
SG
,
Demark-Wahnefried
W
,
Janelsins
MC
,
Peppone
LJ
,
Morrow
GR
, et al
Exercise and cancer treatment symptoms in 408 newly diagnosed older cancer patients
.
J Geriatr Oncology
2012
;
3
:
90
7
.
36.
Bluethmann
SM
,
Basen-Engquist
K
,
Vernon
SW
,
Cox
M
,
Gabriel
KP
,
Stansberry
SA
, et al
Grasping the ‘teachable moment’: time since diagnosis, symptom burden and health behaviors in breast, colorectal and prostate cancer survivors
.
Psychooncology
2015
;
24
:
1250
7
.
37.
Hurria
A
. 
Management of elderly patients with cancer
.
J Natl Compr Canc Netw
2013
;
11
:
698
701
.
38.
Naeim
A
,
Aapro
M
,
Subbarao
R
,
Balducci
L
. 
Supportive care considerations for older adults with cancer
.
J Clin Oncol
2014
;
32
:
2627
34
.
39.
Cohen
HJ
. 
Functional assessment and the cancer survivor: something old, something new
.
J Natl Cancer Inst
2010
;
102
:
1450
1
.
40.
Hurria
A
,
Mohile
SG
,
Dale
W
. 
Research priorities in geriatric oncology: addressing the needs of an aging population
.
J Natl Compr Canc Netw
2012
;
10
:
286
8
.
41.
Hubbard
JM
,
Cohen
HJ
,
Muss
HB
. 
Incorporating biomarkers into cancer and aging research
.
J Clin Oncol
2014
;
32
:
2611
6
.
42.
Nekhlyudov
L
,
Aziz
NM
,
Lerro
C
,
Virgo
KS
. 
Oncologists' and primary care physicians' awareness of late and long-term effects of chemotherapy: implications for care of the growing population of survivors
.
J Oncol Pract
2014
;
10
:
e29
36
.
43.
Howlader
N
,
Mariotto
AB
,
Woloshin
S
,
Schwartz
LM
. 
Providing clinicians and patients with actual prognosis: cancer in the context of competing causes of death
.
J Natl Cancer Inst Monogr
2014
;
2014
:
255
64
.
44.
Chubak
J
,
Tuzzio
L
,
Hsu
C
,
Alfano
CM
,
Rabin
BA
,
Hornbrook
MC
, et al
Providing care for cancer survivors in integrated health care delivery systems: practices, challenges, and research opportunities
.
J Oncol Pract
2012
;
8
:
184
9
.
45.
Rubin
G
,
Berendsen
A
,
Crawford
SM
,
Dommett
R
,
Earle
C
,
Emery
J
, et al
The expanding role of primary care in cancer control
.
Lancet Oncol
2015
;
16
:
1231
72
.
46.
Yabroff
KR
,
Kim
Y
. 
Time costs associated with informal caregiving for cancer survivors
.
Cancer
2009
;
115
:
4362
73
.
47.
Romito
F
,
Goldzweig
G
,
Cormio
C
,
Hagedoorn
M
,
Andersen
BL
. 
Informal caregiving for cancer patients
.
Cancer
2013
;
119
:
2160
9
.
48.
National Caregiving Alliance and American Association of Retired Persons
.
Caregiving in the U.S. Bethesda, MD
:
National Caregiving Alliance
; 
2015
.
49.
Kent
EE
,
Rowland
JH
,
Northouse
L
,
Litzelman
K
,
Chou
WS
,
Shelburne
N
, et al
Caring for caregivers and patients: research and clinical priorities for informal cancer caregiving
.
Cancer
2016
Mar 17. [Epub ahead of print]
.
50.
Moy
B
,
Flaig
TW
,
Muss
HB
,
Clark
B
,
Tse
W
,
Windham
TC
. 
Geriatric oncology for the 21st century: a call for action
.
J Oncol Pract
2014
;
10
:
241
3
.
51.
Stanton
AL
,
Ganz
PA
,
Rowland
JH
,
Meyerowitz
BE
,
Krupnick
JL
,
Sears
SR
. 
Promoting adjustment after treatment for cancer
.
Cancer
2005
;
104
:
2608
13
.