The Well-Being of Long-Term Cancer Survivors

This study compares the well-being of long-term cancer survivors with that of US residents of similar age and demographic characteristics, patients recently diagnosed with cancer, and individuals with chronic illness.


Objectives: To compare the well-being of long-term cancer survivors with that of US residents of similar age and demographic characteristics, patients recently diagnosed with cancer, and individuals with chronic illness.

Study Design: Retrospective observational study.

Methods: Using the Health and Retirement Study, a survey of US residents older than 50 years, we defined 4 cohorts: long-term cancer survivors (>4 years post diagnosis), individuals recently diagnosed with cancer (≤4 years post diagnosis), individuals with chronic illness, and US residents older than 50 years (“nationally representative cohort”). Well-being measures included self-reported health, utility, happiness, medical utilization and spending, employment, and earnings, and these measures were compared across cohorts, adjusting for survey year, demographic characteristics, smoking, and number of comorbidities. We imputed medical spending using the Medical Expenditure Panel Survey and the Medicare Current Beneficiary Survey.

Results: Long-term cancer survivors fared significantly better than those recently diagnosed with cancer, those with chronic illness, and individuals in the nationally representative cohort in the majority of well-being measures (P <.05), including fewer doctor visits, hospitalizations, and hospital nights; better utility and self-reported health; and greater likelihood of employment. Long-term cancer survivors had lower healthcare spending than those recently diagnosed with cancer (P <.01) and significantly greater happiness than the nationally representative cohort and those with chronic illness (P <.05).

Conclusions: Although patients with cancer experience diminished well-being in the short term across a variety of measures, in the long term, cancer survivors do as well as or better than US residents of similar age and demographic characteristics. This finding is striking given that one might expect long-term cancer survivors to do worse than similar individuals without a history of cancer.

Am J Manag Care. 2018;24(4):188-195Takeaway Points

  • Although patients with cancer experience diminished well-being in the short term across a variety of measures, in the long term, cancer survivors do as well as or better than US residents of similar age and demographic characteristics.
  • We found that long-term cancer survival has an effect on well-being comparable with that of many chronic conditions. This is notable given that 70% of US residents older than 50 years have at least 1 comorbidity.
  • Advances in cancer care that offer a chance of long-term survival may enable patients to experience a high quality of life.

Cancer care is changing rapidly. Over the past 60 years in the United States, population aging and declining mortality from heart disease have led to an increase in the total number of deaths from cancer.1 However, this increase in the total number of deaths masks substantial improvement in the cancer mortality risk faced by individuals. Adjusting for population aging, cancer mortality rates in the United States have declined by 23% from 1990 to 2012, from 214.95 to 166.40 per 100,000 individuals.2 Reasons for this decline include advances in early detection and treatment and decreased smoking.3 Although the debate continues about the efficacy of the “war on cancer” and further progress is needed,4-7 the trend of improving mortality is clear. Studies examining many tumor types have found that advances in care have led to significant mortality declines in recent decades.8-10 These reductions in mortality have increased the number of cancer survivors.

However, surviving cancer does not guarantee a high quality of life. Adverse effects of cancer treatment are well documented. Cardiac toxicities are a known complication of specific chemotherapeutics used in breast and other solid tumors, leaving many cancer survivors with degrees of heart failure.11Radiotherapy in patients with breast cancer has been linked to an increased risk of coronary events.12 Impairment of renal function is a complication of certain cancer treatment regimens, often resulting in acute damage and chronic kidney disease.13-16

Patients with cancer also face an increased risk of second primary malignancies.17-22 For example, a meta-analysis found that patients treated for Hodgkin lymphoma are at increased risk of developing second primary lung cancer.17 A study of testicular cancer survivors found that, compared with surgery only, radiotherapy below the diaphragm significantly increased the risk of second primary cancers or cardiovascular diseases.18

Because long-term cancer survivors face the risk of second primary malignancies and may live with complications from treatment toxicities, one might expect that they would experience a low quality of life. However, research to date has tended to focus on clinical and short-term outcomes, while comparatively little is known about the well-being of long-term cancer survivors. As improvements in treatment have made long-term survival a possibility for some patients with advanced cancers, understanding the well-being of long-term survivors is of increasing importance.

In this study, we measured the well-being of long-term cancer survivors and compared their well-being with that of individuals recently diagnosed with cancer, individuals with chronic illness, and US residents of similar age and demographic characteristics. According to the CDC, “There is no consensus around a single definition of well-being.”23 Therefore, we studied a variety of well-being measures encompassing patients’ health, life satisfaction, and productivity.


This study compared well-being outcomes across 4 cohorts: 1) long-term cancer survivors, 2) patients recently diagnosed with cancer, 3) individuals with at least 1 nonterminal chronic illness, and 4) a nationally representative sample of US residents older than 50 years. Regression analysis was used to compare outcomes across the cohorts, adjusting for survey year, demographic characteristics, smoking, and number of comorbidities. A study schematic is presented in Figure 1.

Cohort Selection and Data Sources

We used the Health and Retirement Study (HRS), a nationally representative biannual survey of US residents older than 50 years, to construct the 4 cohorts and follow their well-being outcomes over time. The HRS was designed to illuminate various aspects of US residents’ lives, including health, finances, and employment, as they approach and move into retirement.24

The common definition of long-term survival in individuals who have had a cancer diagnosis is 5 years post diagnosis.25-31 Because the HRS data are only collected every 2 years, we chose 4 years as the threshold for separating short- from long-term cancer survivors (ie, long-term survivors were diagnosed at least 4 years ago, whereas short-term survivors were diagnosed less than 4 years ago). This definition enabled us to focus on long-term survivors beyond the acute treatment phase. Taking a long-term perspective was important because research suggests that the transition from patient to long-term survivor can be challenging,32 and we sought to understand survivors’ well-being after that transition has been undertaken.

The chronic illness cohort included individuals with a diagnosis of diabetes, hypertension, heart disease, lung disease, or prior stroke, but with no prior cancer diagnosis. The nationally representative cohort consisted of all individuals older than 50 years in the 2010 HRS data, as this was the most recent nationally representative wave in the HRS. The cohorts were analyzed over 2004 through 2012 at the person-year level, although we used HRS data from as far back as 1992 to identify whether patients were short-term or long-term cancer survivors. A patient could appear in multiple cohorts.

Because most outcomes in the HRS data are self-reported, these data are not ideally suited for tracking medical expenditures. Therefore, we developed an algorithm to impute total medical expenditures, including medical and pharmacy costs, in the HRS based on 2 data sets: the Medicare Current Beneficiary Survey (MCBS) for those older than 65 years and the Medical Expenditure Panel Survey (MEPS) for those between ages 50 and 64 years.

Specifically, we used the MCBS Cost and Use files, which combine results of a nationally representative survey of Medicare beneficiaries with administrative data to obtain accurate estimates of Medicare program spending.33 Health conditions were identified using the Health Status and Functioning (Community) data file from the Cost and Use files. We used the MCBS and the HRS from 1992 to 2010 to develop this imputation and applied it to our 2004-2012 HRS sample.

To estimate the medical expenditures of individuals aged between 50 and 64 years, we used the MEPS years 2000 through 2010. Although MEPS is based on survey data, it is designed to measure healthcare cost and utilization.34 In addition, the MEPS data were used to estimate utility for everyone older than 50 years based on the EQ-5D-3L measure, a utility measure introduced by the EuroQol Group in 1990. All health conditions were identified via self-reports from the MEPS condition files and household consolidated files.


Because there is no single consensus definition of well-being,23 we selected a wide range of well-being outcomes, including medical service use, healthcare spending, employment status, earnings, self-reported health, utility, and happiness.

Medical service use measures included the numbers of doctor visits, hospital admissions, and hospital nights over the 2 years prior to the survey. Healthcare spending (medical and pharmacy) was measured annually in the year of the survey and inflated to 2014 dollars using the medical Consumer Price Index (CPI). We constructed a binary variable for whether an individual was working at the time of the survey. We identified individuals as working if they were working full-time, they were working part-time, or they were workingpart-time in retirement. Earnings were measured annually in the year of the survey, conditional on the individual working, and inflated using the CPI for all urban consumers. Earnings summarized the market value of an individual’s effort in the labor force and served as a proxy for productivity.

For self-reported health, we constructed a binary variable: poor or fair health versus good, very good, or excellent health. Self-reported health was measured at the time of the survey.

Utility was measured using a regression of EQ-5D-3L score on health, demographics, and functional status over the year prior to the survey.35 For happiness, we constructed a binary variable: not at all, a little, or moderately happy versus quite a bit or very much happy. Happiness was measured over the 30 days prior to the survey.

We looked at the differences in these well-being outcomes across the 4 cohorts controlling for the year of the survey (2004-2012), age group (51-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, ≥85 years), gender, race (white/Caucasian, black/African American, other), ethnicity (Hispanic, non-Hispanic), education (less than high school, general educational development certificate, high school graduate, some college, college and above), health insurance (insured, not insured), smoking history (ever smoked, never smoked), and comorbidity count. The latter included any prior diagnoses of cancer, diabetes, heart disease, hypertension, lung disease, or stroke. Prior diagnosis was determined by whether an individual indicated ever being diagnosed with any of the mentioned diseases, as determined in the year of eligibility for the cohort in question.

Statistical Analysis

Descriptive analysis. We performed a descriptive analysis on the HRS data using standard weights to compare outcomes across cohorts at the person-year level. Simple 2-way ttests were conducted to assess the significance of the differences in means across cohorts.

Multivariable analysis. We ran multivariable analyses for all outcomes to compare the well-being of long-term cancer survivors with that of the other 3 cohorts, adjusting for survey year, demographic characteristics, smoking, and number of comorbidities. Because cancer could plausibly affect the subsequent development of comorbidities,11-16,36-38 we also ran an alternative specification omitting number of comorbidities as a covariate. Models were selected to be appropriate for the given outcome. We used a Poisson model for medical service use, an ordinary least squares model for annual earnings conditional on being employed, a Tobit model for utility, and a generalized least squares model with a gamma distribution and log link for healthcare spending. Additionally, we used logistic models for the binary outcomes: self-reported health, happiness, and employment status. After conducting the analyses, we estimated the predicted values of all outcomes using the mean values of the covariates for the nationally representative cohort in 2010.


In 2010, there were 1184 long-term cancer survivors; 676 individuals recently diagnosed with cancer; 12,583 individuals with chronic illness; and 22,034 US residents older than 50 years (“the nationally representative cohort”). Over the biennial survey waves from 2004 to 2012, there were 8817; 3374; 57,108; and 22,034 person-years in the cohorts, respectively. Cohort characteristics are shown in Table 1. Long-term cancer survivors were, on average, older compared with the other cohorts and more likely to be female, white, and non-Hispanic and to have health insurance. Table 2 presents the results of the descriptive analysis. The means of medical service use, employment, and medical spending for individuals recently diagnosed with cancer, the chronic illness cohort, and the nationally representative cohort were all significantly different from those of long-term cancer survivors (P <.01). The means of utility and earnings conditional on being employed for the chronic illness and nationally representative cohorts were significantly different from those of long-term cancer survivors (P <.01), whereas those of the recently diagnosed cohort were not statistically different from those of long-term cancer survivors. Grouping self-reported health as excellent, very good, or good versus fair or poor, the recently diagnosed cohort had a significantly lowerlikelihood of being excellent/very good/good,and the nationally representative cohort had a significantly higher likelihood, than long-term cancer survivors (P <.01). Grouping happiness as very much or quite a bit versus moderately, a little, or not at all, the chronic illness cohort was significantly less likely to be very much/quite a bit than long-term cancer survivors (P <.01).

The results of the multivariable analyses are presented in Table 3, which shows the predicted values of the different well-being measures in each cohort. Long-term cancer survivors fared better than the recently diagnosed cohort in terms of healthcare utilization, utility, healthcare spending, self-reported health, and employment (all P <.01). Differences in other outcomes were not significant. Similarly, long-term cancer survivors fared better than individuals with chronic illness in terms of healthcare utilization, utility, self-reported health (all P <.01), happiness, and employment status (both P <.05). Differences in other outcomes were not significant. Compared with the nationally representative sample, long-term cancer survivors fared better in terms of healthcare utilization, utility, employment status (all P <.01), self-reported health, and happiness (both P <.05). Other outcomes were not significantly different.

The multivariable results are presented graphically in Figure 2, which shows how the well-being measures compared across the 4 cohorts. To display all outcomes on a common scale, well-being measures were normalized so that the value of the long-term cancer survivors cohort is 100%.

The results of the alternative specification in which number of comorbidities was omitted as a covariate are presented in the eAppendix (available at As in the base case, long-term cancer survivors fared better than the recently diagnosed cohort in the majority of well-being outcomes, with the exceptions of utility, happiness, and earnings, which were not significantly different. Compared with individuals with chronic illness, long-term cancer survivors had moderately higher healthcare utilization and spending and lower self-reported health but greater utility. There were no significant differences in hospital nights, happiness, employment, and earnings between long-term cancer survivors and individuals with chronic illness. Compared with the nationally representative cohort, long-term cancer survivors had greater healthcare utilization and spending and lower self-reported health and employment. Differences between long-term cancer survivors and the nationally representative cohort in terms of utility, happiness, and earnings were not significant.


Our study results show a marked improvement in cancer survivors’ well-being in the long term compared with the first 4 years after diagnosis. Moreover, long-term cancer survivors fared at least as well as average US residents across a variety of well-being measures, controlling for demographics and the number of comorbidities. When comorbidities were not controlled for, long-term cancer survivors had modestly higher healthcare utilization and spending and modestly lower self-reported health and employment compared with average US residents. Taken together, these results suggest that when long-term cancer survivors fare worse than individuals without cancer, it is due to their comorbidity burden. These differences tended to be modest in size (eg, 1 extra doctor visit over 2 years). Moreover, even without controlling for comorbidity burden, there were no differences in happiness, utility, or earnings between long-term cancer survivors and average US residents.

Our analysis indicated that 70% of US residents older than 50 years have at least 1 comorbidity. Therefore, the fact that long-term cancer survivors fare comparably with others their age, many of whom are also dealing with health conditions, suggests that in the long term, cancer has an effect on well-being comparable with that of many chronic conditions.

Previous literature has compared the well-being of cancer survivors with that of individuals with chronic illness.39,40 Steel et al studied the difference in health-related quality of life (HRQoL) of individuals with hepatocellular carcinoma (HCC), individuals with chronic liver disease (CLD), and the general population.39 Those with HCC had statistically significantly poorer HRQoL than those with CLD and the general population. Those with HCC and CLD had better social and family well-being than the general population.

In a similar study, Elliot et al compared the well-being of cancer survivors without other chronic conditions, cancer survivors with chronic conditions, and individuals with chronic conditions other than cancer with that of healthy individuals.40 They found that, generally, the well-being outcomes of cancer survivors without chronic conditions did not statistically differ from those of individuals with 1 chronic condition.

Other studies have compared the well-being of cancer survivors with that of the general population. Using data from the American Cancer Society’s Study of Cancer Survivors, Zhou et al showed that ovarian cancer survivors had an HRQoL similar to that of the general population 1 year post diagnosis.41 Kunitake et al found that long-term colorectal cancer survivors had better overall physical and mental health compared with the general population.30 Similarly, Greenwald et al found that cervical cancer does not reduce the quality of life of long-term survivors,42 and Schmidt et al found that breast cancer survivors may experience long-term quality of life comparable with that of the general population.43 Finally, Thong et al found that although disease progression reduces cancer survivors’ quality of life, the effect diminishes over time.31

Our study echoes the existing literature in finding that long-term cancer survivors fared as well as or sometimes better than the general population. The main contribution of our study is that it investigates short- and long-term cancer survivors’ well-being across a variety of measures in a large, nationally representative sample.

Our findings are important given that recent breakthroughs in cancer treatment have raised the prospect of long-term survival for increased numbers of cancer patients. For example, new immuno-oncology agents, including ipilimumab, pembrolizumab, and nivolumab, have offered significant survival gains to patients,44-48 and preliminary data suggest the prospect of long-term survival for some metastatic cancer patients.49


Our study does have limitations. First, the HRS data do not distinguish between cancer types and, therefore, we were unable to perform separate analyses by tumor type. As such, our results represent the experience of the “average” cancer survivor and will overrepresent those with particularly common or less deadly cancers.

Second, the most recent nationally representative cohort in the HRS was in 2010, which was a recession year. Because the nationally representative cohort was constructed from the 2010 survey year and all other cohorts were constructed from the 2004-2012 survey years, secular trends could affect the well-being outcomes. However, we controlled for this by including survey year indicators in our analysis.

Third, in studying the well-being of cancer survivors, we necessarily limited the analysis to those who survived. Thus, we cannot definitively determine whether the observed changes in cancer survivors’ well-being between the short- and long-term time horizons are due to cancer survivorship itself or to characteristics potentially common to individuals more likely to survive cancer.

Finally, we focused mainly on objective measures of well-being rather than subjective measures. However, focusing on objective measures does come with the advantage that, in doing so, we avoided any biases that can result from the way that subjective well-being outcomes are measured, as these outcomes can be subject to self-reporting or measurement bias.50


Our study results are striking, given concerns that patients with cancer may experience a low quality of life.51 Although we do find that quality of life is reduced in the short term, we find that it solidly rebounds in the years after diagnosis, becoming comparable with or even better than that of others of similar age and demographic characteristics. The high quality of life experienced by long-term cancer survivors is relevant in the midst of debate about the cost and value of cancer care. Although cost and other up-front considerations are important, it is also important to keep the patient’s perspective in mind. Prior research has shown that patients value therapies that give them a chance of long-term survival.52 Our study shows that the well-being of long-term cancer survivors eventually recovers, becoming similar to or even better than that of the general population. To the extent that long-term survivors enjoy a high quality of life, it makes interventions offering patients with cancer a chance of long-term survival more valuable.Author Affiliations: Precision Health Economics (JS, JTS, EvE), Los Angeles, CA; Bristol-Myers Squibb (TO), Plainsboro, NJ; Wake Forest Baptist Medical Center (KB), Winston-Salem, NC; Georgetown University (TD), Washington, DC.

Source of Funding: Financial support for this research was provided by Bristol-Myers Squibb (BMS).

Author Disclosures: Mr Sullivan, Dr Thornton Snider, and Ms van Eijndhoven are employed at Precision Health Economics, which receives consulting fees from life sciences companies, including BMS. Dr Okoro was formerly employed as senior manager of health economics and outcomes research at BMS, has attended conferences on behalf of BMS, and formerly owned stock in BMS. Drs Batt and DeLeire have worked as consultants for Precision Health Economics.

Authorship Information: Concept and design (JS, JTS, EvE, TO, KB, TD); acquisition of data (JS); analysis and interpretation of data (JS, JTS, EvE, TO, KB, TD); drafting of the manuscript (JTS, EvE, KB); critical revision of the manuscript for important intellectual content (JS, JTS, EvE, TO, KB, TD); statistical analysis (JS, JTS, EvE); provision of patients or study materials (EvE, JS); obtaining funding (TO); administrative, technical, or logistic support (JS, JTS, EvE); and supervision (JS, KB).

Address Correspondence to: Julia Thornton Snider, PhD, Precision Health Economics, 11100 Santa Monica Blvd, Ste 500, Los Angeles, CA 90025. Email:

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