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Medical expenditures of adult cancer survivors aged <65 years in the United States†
Article first published online: 23 DEC 2010
Copyright © 2010 American Cancer Society
Volume 117, Issue 12, pages 2791–2800, 15 June 2011
How to Cite
Short, P. F., Moran, J. R. and Punekar, R. (2011), Medical expenditures of adult cancer survivors aged <65 years in the United States. Cancer, 117: 2791–2800. doi: 10.1002/cncr.25835
The authors thank Steven B. Cohen and Thomas M. Selden in the Center for Financing, Access, and Cost Trends of the Agency for Healthcare Research and Quality for technical assistance in creating survey weights for these analyses.
- Issue published online: 7 JUN 2011
- Article first published online: 23 DEC 2010
- Manuscript Accepted: 8 NOV 2010
- Manuscript Revised: 13 OCT 2010
- Manuscript Received: 9 AUG 2010
- cancer survivor;
- health expenditures;
- personal expenditures;
- costs of cancer;
- prescription expenditures
To the authors' knowledge, this is the first study to provide national estimates of medical expenditures for all adult cancer survivors aged <65 years. Most studies of expenditures for cancer survivors in this age group have been based on the Medical Expenditure Panel Survey (MEPS) and were limited to “affected survivors.”
MEPS expenditure data for 2001 to 2007 were linked to data identifying all survivors from the National Health Interview Survey (NHIS), which is the MEPS sampling frame. The sample was comprised of adults ages 25 to 64 years. Propensity-score matching was used to estimate the effects of cancer on average total and out-of-pocket expenditures for all services and separately for prescriptions. Probit models were used to estimate effects on the probability of exceeding different expenditure thresholds.
Mean annual expenditures on all services in 2007 were $16,910 ± $3911 for survivors who were newly diagnosed with cancer, $7992 ± $972 for survivors who had been diagnosed in previous years, and $3303 ± $103 for other adults. Fifty-three percent of survivors were not identified in MEPS but only by linking to NHIS. Expenditures for all survivors averaged approximately $9300 compared with $13,600 for “affected survivors.” For previously diagnosed survivors, the increase in mean expenditures attributable to cancer was approximately $4000 to $5000 annually. On average, relatively little of the increase was paid out of pocket, but cancer nearly doubled the risk of high out-of-pocket expenditures.
Previous MEPS analyses overstated average expenditures for all survivors. Nevertheless, the current results indicated that the increase in expenditures attributable to cancer is substantial, even for longer term survivors, and that cancer increases the relative risk of high out-of-pocket expenditures. Cancer 2011;. © 2010 American Cancer Society.
In this report, we provide national estimates of medical expenditures for cancer survivors in the United States in keeping with the definition of “cancer survivor” adopted by the National Cancer Institute (NCI) and others. We also provide estimates of the cancer-related increase in medical expenditures per survivor according to the same definition. According to the NCI, “An individual is considered a cancer survivor from the time of diagnosis through the balance of his or her life.” 1 This definition implies that the population of survivors includes all living individuals ever diagnosed with cancer and corresponds exactly to the epidemiologic concept of prevalence. Cancer prevalence in the United States has increased from 6 million to 12 million in the last 20 years. 2 Now that more individuals are living longer with a history of cancer, clinicians, public health officials, cancer organizations, and survivors are asking more questions about the long-term and late effects of cancer survivorship. 3-5
These questions are all the more urgent because some gains in survival have been accomplished with potentially more damaging treatments that involve higher dosages and combinations of surgical, radiation, chemotherapy, and hormone therapies. 6, 7 Research has demonstrated that cancer treatment can have a variety of long-term health effects, including impaired physical and organ function, changes in appearance, sexual dysfunction, incontinence, lymphedema, hormone imbalances, cognitive difficulties, and fatigue. 3 Cancer survivors are subject to psychological stresses and are at increased risk of mental illness. 8, 9 One of the greatest risks of survivorship is the possibility of recurrence and heightened risk of second cancers.
Given the potential for long-term health effects and the need for heightened vigilance and monitoring, cancer survivors are likely to incur more medical expenses than other individuals. However, because of data limitations, information about this aspect of survivorship is limited. In particular, for the population aged <65 years, to our knowledge, there are no published national estimates of average annual medical expenditures for everyone ever diagnosed with cancer. For the population aged ≥65 years, Medicare claims have been used to estimate annual medical expenditures for all survivors, who are identified by searching for cancer claims over many previous years or by linking to cancer registries. 10, 11 Most estimates of cancer-related expenditures among individuals aged <65 years are based on the Medical Expenditure Panel Survey (MEPS), an ongoing national survey conducted by the Agency for Healthcare Research and Quality (AHRQ). 3, 12-15 Although MEPS has many advantages for studying national medical expenditures, including data for non-Medicare payers, until recently the questionnaire did not systematically identify all prevalent cancer cases.
In the current study, we have focused on medical expenditures for survivors aged <65 years. Because of Medicare's nearly universal coverage after age 65 years, medical care is financed much differently before and after age 65. Considering the differences in financing and the increased healthcare needs and expenditures at older ages, it is more meaningful to analyze expenditures for younger and older survivors separately than together. Given space limitations, the availability of Medicare data for older survivors, and the potential usefulness of expenditure estimates for younger survivors to inform the implementation of national healthcare reforms, we limit attention here to the expenditures of younger survivors.
MATERIALS AND METHODS
The sample for this study was derived from 2 large, nationally representative surveys, the Household Component of the MEPS (MEPS-HC) and the National Health Interview Survey (NHIS). MEPS-HC provides detailed information about annual medical expenditures according to the type of service and the source of payment for each individual in the sample. NHIS collects information about health, access to and use of health services, health insurance, and health behaviors. Because NHIS is the sampling frame for MEPS, MEPS data can be linked to previously collected data on the same individuals in NHIS. In the current study, questions that are asked in the NIHS (but not in the MEPS) to systematically identify all cancer survivors are linked to expenditure data from MEPS to calculate national expenditure estimates for cancer survivors. The Pennsylvania State University's Human Subjects Protection Committee determined that this analysis of public-use data was not human participant research.
The questions that identify cancer survivors are in a section of the NHIS questionnaire that is administered to 1 randomly sampled adult per household. The sequence begins, “Has a physician or other health provider EVER told you that you have a cancer or malignancy of any kind?” Anyone who answered “yes” is shown a flashcard that lists 29 cancer sites and is asked, “What kind of cancer was that?” Respondents can report up to 3 different cancers, and analysts can calculate the time since diagnosis from questions about the respondent's age at each diagnosis.
Until the 2007 panel was fielded, MEPS did not include similar questions to identify all cancer survivors. Consequently, all studies to date of expenditures of cancer survivors based on MEPS have been limited to “affected prevalence,” which means that survey respondents reported cancer-related use or restricted activity days during the survey year. Whenever care is reported in MEPS, respondents are asked, “Was this (visit) for any specific health condition or were any conditions discovered during this visit? …What condition was that?” Similar questions are asked about restricted activity days. For all conditions that are identified in this way, the MEPS condition files provide “clinical classification” codes that were developed by the AHRQ and are assigned from text survey responses coded by trained professionals into codes from the International Classification of Diseases, ninth revision. We used clinical classification codes 11 through 46, excluding code 23 (nonmelanoma skin cancer) and code 44 (unclassified neoplasm), to identify individuals with cancer from the MEPS condition files. Although we mainly identified cancer survivors from NHIS, we also used the MEPS condition files to identify survivors who were newly diagnosed after they were interviewed in the NHIS.
Each year, a new panel of MEPS households is selected from among those that participated in the previous year's NHIS. Then, data are collected from the panel for 2 calendar years. Consequently, there are 2 years of data for most individuals in MEPS (eg, the 2006 MEPS panel has 2006 and 2007 data for a sample that was drawn from the 2005 NHIS). By the same token, there are data from 2 panels for each calendar year (eg, MEPS data for 2007 are from samples that were drawn from the 2005 and 2006 NHIS). 16 In the current study, we pooled annual data from 2001 through 2007 to obtain a large enough sample of cancer survivors to make reliable estimates for survivor subgroups and to characterize entire expenditure distributions for survivors.
Considering the cancer data from NHIS, along with cancers reported in the MEPS condition files, we classified each individual in each survey year as 1) a newly diagnosed cancer survivor (in that calendar year), 2) a previously diagnosed survivor (in a previous calendar year), or 3) not a survivor. All newly diagnosed survivors were identified from cancer-related use or restricted activity days in MEPS. Skin cancers other than melanoma were ignored. Selecting years when patients were aged <65 years yielded a final sample of 361 observations for newly diagnosed survivors, 2119 for previously diagnosed survivors, and 47,690 for adults who were not survivors. There were 2 years of data for most unique individuals in the dataset.
To produce national estimates for the civilian, noninstitutionalized adult population of the United States from the linked sample, we modified the annual individual-level survey weights on MEPS public-use files. The adult population was defined as aged ≥25 years in MEPS. Because college students who live in student housing in NHIS are not followed in MEPS, the college students who are sampled in an alternative way in MEPS are missing NHIS cancer data. 16 The first step in the reweighting procedure was to adjust the original weights for differences in the probability of selection for NHIS-sampled adults in different sized families. For each sampled adult with a positive MEPS weight, an adjustment factor was defined as the inverse of the weighted proportion of household adults represented by the sampled adult. To avoid extreme adjustments for some individuals, the factor was capped at 4.25 (the 99th percentile of unconstrained values).
Next, we used a “raking” procedure to modify the adjusted weights to match population control totals calculated by calendar year from public-use weights for the full MEPS sample by age, sex, race/ethnicity, family income as a percentage of poverty, region, and metropolitan location (yes or no). The new weights for the 7 pooled annual samples, which summed to approximately 7 times the US population, were rescaled to sum to the US population in 2007. Applying the rescaled weights to the pooled, linked adult sample yielded a national estimate of 12.2 ± 0.8 million adult cancer survivors at the start of 2007 and 1.8 ± 0.2 million new cancer cases diagnosed in 2007. The former estimate is consistent with national prevalence estimates based on Surveillance, Epidemiology, and End Results data (11.3 million adults at the beginning of 2006), 2 but the latter estimate is somewhat higher than the American Cancer Society's incidence estimates (1.44 million in 2007). 17 Note that survivors who died in 2007 are included in the MEPS sample, so the combined total of 14 million survivors (12.2 ± 1.8 million) is the number alive at any time in 2007 and not cancer prevalence at the end of the year.
Expenditures for earlier years were inflated to 2007 with the Personal Health Care Expenditure (PHCE) price index, which was developed by the Office of the Actuary, Centers for Medicare and Medicaid Services. 18 Separate components of the PHCE index were applied to hospital, professional, dental, prescription, medical equipment and vision, and home health expenditures.
Descriptive statistics were tabulated by cancer status (newly diagnosed survivor, previously diagnosed survivor, not a cancer survivor) without survey weights. Weighted population estimates, mean expenditures by type of service (eg, inpatient hospital, outpatient hospital, professional visits, prescriptions) and source of payment (out-of-pocket, private insurance, Medicare, Medicaid, other), and expenditure shares were tabulated by survivor status using the modified survey weights. Confidence intervals and Z tests of paired comparisons involving weighted national estimates were constructed from standard errors that were adjusted for the complex survey design using survey estimation procedures in SAS (version 9.1; SAS Institute, Inc., Cary, NC) and Stata (version 11; Stata Corp., College Station, Tex).
The effects of cancer on annual expenditures are quite different for newly diagnosed survivors (most of whom would be in treatment during the calendar year) and for previously diagnosed survivors (most of whom would not be in treatment), so we focused on previously diagnosed survivors to estimate increases in expenditures attributable to cancer. Although the short-term effects of cancer on expenditures are direct and relatively obvious (that is, services provided during primary treatment are likely to be identified by MEPS respondents or by codes on claims as “cancer-related”), the longer term effects on survivors in the years after treatment are less direct and less obvious. Furthermore, if newly diagnosed patients who were undergoing treatment usually reported the expense as cancer-related in MEPS, then estimates of cancer-related expenditures published in earlier MEPS studies 12-13, 15 should be reasonably accurate for recently diagnosed cases. Linking back to the cancer questions in NHIS mainly improves expenditure estimates for longer term survivors, so we focused on them in this report. In addition, the MEPS sample of long-term survivors is much larger.
To estimate increases in mean total and out-of-pocket expenditures both directly and indirectly attributable to cancer for previously diagnosed survivors, we used propensity-score matching. The matching analyses were limited to a subsample ages 40 to 64 years, because the small number of survivors in the group ages 25 to 40 years presented difficulties in constructing a comparable group of young controls. Considering sex differences in service use and cancer types, we performed separate matching analyses for men and women. Four expenditure outcomes were considered: total and out-of-pocket expenditures for all services and total and out-of-pocket expenditures for prescriptions. Physician-administered drugs, such as chemotherapy agents, were not counted as prescription expenses but were included in the total cost for all services.
In the matching analyses, we sought to estimate “the average effect of treatment on the treated (ATT)”: in this case, the average effect of cancer on the expenditures of a population with the characteristics of our sample of cancer survivors. Matching has the advantage of estimating the ATT from averaged differences among matched individuals who have similar observed characteristics without imposing any assumptions about the functional form of expenditures.
In practice, it is rare to match directly on covariates because of the potentially large number of dimensions across which matches would have to be made. Rosenbaum and Rubin demonstrated that the ATT can be estimated consistently by matching on unidimensional propensity scores rather than a multidimensional set of covariates. 19 In this instance, the propensity score was the predicted probability of being a previously diagnosed cancer survivor, which was estimated from a dataset that included all person-years of MEPS data for previously diagnosed survivors and adults with no cancer history. The propensity scores were estimated as Probits. The covariates included indicators for single years of age, race/ethnicity, education, marital status, Census region, and metropolitan location and dummy variables for survey year. One specification also included indicators for specific chronic conditions (arthritis, asthma, diabetes, emphysema, heart disease, hypertension, and stroke). These conditions were excluded from an alternative specification because of the possibility that they sometimes resulted from the individual's cancer history and should have been included in the total cancer effect. For example, anthracycline-based chemotherapy sometimes causes heart problems, premature menopause can alter a woman's health risks in many ways, and chest radiation carries a risk of lung damage. 3 From the MEPS questionnaire, we could not reliably distinguish conditions that were diagnosed before and after an individual patient's cancer.
Propensity scores and ATTs were estimated using PSMATCH2 in Stata 20 without survey weights. Standard errors for ATTs were estimated from 200 bootstrap replications of the entire estimation procedure (propensity scores and matching), with clustering at the individual level to account for multiple observations of each individual. To ensure that only comparable individuals were compared, we imposed a “trimming” condition that removed 2% of the cancer observations with relatively few close matches among controls. We used kernel matching, which estimates the ATT as a distance-weighted mean of all comparison group observations, with weights related inversely to the propensity-score differences between each noncancer observation and the cancer observation. After matching, t tests for equality of means in the cancer and noncancer groups were performed on every covariate and separately for women and men using the PSTEST procedure in PSMATCH2. Rejection rates on these tests (0 of 42 and 2 of 42, respectively) were within the expected range given a Type I error rate (P value) of .05. To estimate counterfactual expenditure means for cancer survivors in the absence of cancer, weighted national estimates of actual mean expenditures for previously diagnosed survivors ages 40 to 64 years were reduced by the ATT from matching.
To characterize the effect of cancer on the distribution of total and out-of-pocket expenditures, we estimated a sequence of Probit models with a survivorship indicator on the right-hand side, as suggested by Angrist. 21 The dependent variables were indicators for annual expenditures that exceeded each of the following: $0, $1000, $5000, $10,000, $20,000, and $40,000 for total expenditures on all services; $0, $500, $1000, $2000, and $5000 for out-of-pocket expenditures on all services and for total and out-of-pocket expenditures on prescriptions. The Probit models used the same samples as the matching estimates and were estimated in Stata using the specially constructed NHIS-MEPS survey weights described above, clustering on the individual identification number to account for multiple observations on each individual. The same covariates were used in the Probit and propensity-score models. Marginal effects of cancer were calculated as the differences between the cancer and comparison groups in the probability of exceeding each expenditure threshold, holding other covariates at their means. To estimate the counterfactual expenditure distribution for cancer survivors in the absence of cancer, weighted national estimates of the actual percentages of survivors that exceeded each level of expenditure were generated for previously diagnosed survivors ages 40 to 64 years; then, the actual percentages were altered by the Probit marginal effects. Actual and counterfactual distributions were graphed in terms of the percentages of survivors that fell into each expenditure interval and were calculated for each interval by differencing the percentages that exceeded its lower and upper limits.
Unweighted sample characteristics are listed according to cancer status in Table 1. The cancer survivors were considerably older than the comparison group. Thirty-eight percent of each survivor subgroup was in the oldest age group considered (ages 55-64 years) compared with 19% of other adults. The survivors were disproportionately women, non-Hispanic whites, unmarried, and publicly insured. The prevalence of various chronic illnesses was significantly greater among cancer survivors, which would be expected in light of the age differences between the cancer and control groups but could reflect health effects of cancer and its treatment. Fifty-three percent of survivors in the group ages 25 to 64 years were not identified in MEPS but were only identified by linking to the cancer questions in NHIS.
|Variable||Newly Diagnosed||Previously Diagnosed||No Cancer|
|No. of survivors||361||2119||47,690|
|Metropolitan Statistical Area (yes)||83||77a||81|
The mean annual expenditure on all services for individuals with newly diagnosed cancer in 2007 was $16,910 ± $3911 (Table 2). The mean was approximately half as large for survivors who were diagnosed in previous years ($7992 ± $972), but it was more than twice that for adults who had no history of cancer ($3303 ± $103). Annual expenditures for survivors >5 years after diagnosis averaged $7383 compared with $9301 for survivors 1 to 5 years after diagnosis, a difference that was not statistically significant. Although new diagnoses accounted for only 15% of the total number of cancer survivors, they accounted for 28% of total survivor spending. Medicare and Medicaid paid the largest share of expenses for previously diagnosed survivors. Medicare's share was larger for previously diagnosed survivors than for newly diagnosed survivors.
|Variable||Newly Diagnosed||Previously Diagnosed||No Cancer|
|Mean total expenditure, $a||16,910a||7992a||3303|
|Source of payment (% of total), $|
|Out-of-pocket||2159 (13)a||1391 (17)a||679 (21)|
|Private health insurance||11,560 (68)a||4325 (54)a||1832 (55)|
|Medicare||548 (3)a||819 (10)a||193 (6)|
|Medicaid||1308 (8)a||763 (10)a||301 (9)|
|Other||1335 (8)a||693 (9)a||298 (9)|
The combined mean annual expenditure for all survivors who were identified from NHIS and MEPS was $9293 ± $1044, which was approximately 66% of the mean for the subgroup identified in MEPS without linking to NHIS ($13,558 ± $2038) (not shown in Table 2). Survivors who were identified only in NHIS averaged $5526 ± $717 per year. Given the higher average annual expenditures of survivors who were identified in MEPS, MEPS captured nearly 70% of aggregate national expenditures that were not covered by the NHIS link ($47 billion of $68 billion) but counted only half as many survivors as the linked surveys (7 million of 14 million survivors).
Inpatient hospital services accounted for 36% of total expenditures for newly diagnosed survivors (Table 3) followed closely by professional services (32% of total expenditures). Although total expenditures for previously diagnosed survivors were much higher than those for adults without cancer, the distributions by type of service were fairly similar. Newly diagnosed survivors paid an average of $2159 out of pocket in 2007 (Table 4), a larger dollar amount but a smaller share of total expenditures (13%) compared with previously diagnosed survivors ($1391 out of pocket; 17% of total expenditures), who, in turn, paid more out of pocket but a smaller share of the total compared with adults who did not have cancer ($679 out of pocket; 21% of total expenditures). Prescriptions accounted for the largest share of out-of-pocket expenses for both previously diagnosed survivors (44%) and adults without cancer (39%). However, the average dollar amount paid out-of-pocket for prescriptions was more than twice as large for survivors ($607 compared with $265).
|Variable||Newly Diagnosed||Previously Diagnosed||No Cancer|
|Mean total expenditure, $a||16,910a||7992||3303|
|Type of service (% of total), $|
|Inpatient hospital||6166 (36)a||1755 (22)a||690 (22)|
|Outpatient hospital||2284 (14)a||1114 (14)a||396 (12)|
|Professional services||5444 (32)a||2743 (34)a||1051 (33)|
|Prescriptions||2347 (14)a||1691 (21)a||754 (24)|
|Dental||406 (2)a||385 (5)a||289 (9)|
|Other||263 (2)a||304 (4)a||123 (4)|
|Variable||Newly Diagnosed||Previously Diagnosed||No Cancer|
|Mean out-of-pocket expenditure, $a||2159a||1391a||679|
|Type of service (% of total), $|
|Inpatient hospital||109 (5)a||67 (5%)||25 (4%)|
|Outpatient hospital||118 (5)a||84 (6%)a||35 (5%)|
|Professional services||850 (39)a||354 (25)a||176 (26)|
|Prescriptions||808 (37)a||607 (44)a||265 (39)|
|Dental||194 (9)a||195 (14)a||130 (19)|
|Other||79 (4)a||84 (6)a||47 (7)|
For previously diagnosed survivors in the group ages 40 to 64 years, propensity score matching revealed that the increase in average annual expenditures for all services attributable to cancer was $4452 ± $1188 for women and $5112 ± $2062 for men (Table 5). Matching also on the presence of specific chronic conditions reduced the estimates to $3631 ± $1083 for women and $3560 ± $1433 for men (not shown in Table 5). The increase in out-of-pocket expenditures attributable to cancer was considerably smaller than the increase in total expenditures (Table 5). Women paid only 9% of the cancer-related increase in total expenditures out of pocket, whereas men paid 16% of the cancer-related increase in total expenditures out of pocket. We estimate that cancer added $832 ± $286 to the average annual expenditure on prescriptions for survivors who were women and $1219 ± $637 for survivors who were men. Survivors of both sexes paid approximately 30% of the cancer-related increase in prescription expenses out of pocket.
|Mean Annual Medical Expenditures, $|
|Women (N=1227): Population=3.5 Million||Men (N=459): Population=1.6 Million|
|Variable||Actual||If No CA||CA Effect±SEa||Actual||If No CA||CA Effect±SEa|
|Out of pocket||1456||1039||417±98||1626||816||811±204|
|Out of pocket||702||442||259±57||640||290||350±99|
Estimates from Probit models of the likelihood of exceeding different expenditure thresholds indicated that previously diagnosed survivors in the group ages 40 to 64 years were about twice as likely to experience unusually high levels of total spending because of their cancer history (Fig. 1). Approximately 9% of cancer survivors of either sex exceeded $20,000 in total annual expenditures for all services (including 4% who spent >$40,000) compared with a projected 5% to 6% in the absence of cancer (including 2%-3% who spent >$40,000). Approximately 20% of cancer survivors spent >$2000 out of pocket (including 5% who spent >$5000 out of pocket) compared with a projected 13% to 15% in the absence of cancer (including 3% who spent >$5000 out of pocket), as illustrated in Figure 2. The projected percentage that spent >$5000 in total prescription expenses in the absence of cancer (Fig. 3) was fairly similar to the actual percentage (8% with cancer vs 6% without cancer among women who were survivors; 7% with cancer vs 5% without cancer among survivors who were men). Cancer added approximately 3 percentage points to the risk of spending >$2000 out of pocket on prescriptions (Fig. 4).
A widely accepted definition equates the population of cancer survivors with the prevalence of cancer and includes everyone who was ever diagnosed with cancer from the time of diagnosis to death. By linking to cancer history from the NHIS for individuals in MEPS, we discovered that approximately half of the cancer survivors who were encompassed by this definition were ignored in previous MEPS studies of cancer expenditures. Furthermore, because the survivors who were missing from earlier studies were those affected least by cancer (ie, they reported no cancer-related use or health effects in MEPS), the average expenditure per survivor with the missing survivors included was approximately 33% lower than the average without them. Thus, the change made in the MEPS questionnaire in 2007—to ask everyone whether they were ever diagnosed with cancer—should greatly improve the picture of cancer survivorship offered by that survey. In the meantime, because the new cancer questions will not support the analysis of expenditures for all survivors until a sufficiently large sample can be accumulated from MEPS public-use files over the next several years, the current study provides a more accurate description of the expenditures of survivors than would be available otherwise. Although the NHIS link will not be needed to estimate expenditures for cancer survivors from MEPS in future years, the link could be used to study trends among cancer survivors that span the questionnaire change and to study prevalent cases of other diseases that are identified systematically in NHIS but not in MEPS.
Although we conclude that estimates from earlier studies overstated average expenditures for the full population of cancer survivors, we still observed that cancer had a considerable effect on annual total medical expenditures of adult survivors, even in the years after diagnosis (roughly $4000-$5000). Our estimates of cancer-related increases in expenditures were somewhat sensitive to matching on comorbidities: Estimates assuming that the entire increase in relative risk of comorbidities in the cancer sample was attributable to cancer (with comorbidities excluded as covariates) differed by $820 for women and $1550 for men from estimates assuming that none of the increased risk was attributable to cancer (with comorbidities included as covariates). These 2 extremes, which bracket the potential contribution of comorbidities to cancer-related expenditures, are well within the confidence intervals surrounding both estimates.
Although the increase in expenditures associated with surviving cancer represents an economic burden on society, only a small share of the aggregate burden falls directly on survivors and their families. However, out-of-pocket expenditures are distributed unevenly among cancer survivors: Approximately 60% of survivors in the group ages 40 to 64 years spent ≤$1000 in 2007, and 5% spent >$5000. The relative risk of spending >$5000 out of pocket in the years after a cancer diagnosis was nearly double the risk without cancer (5% compared with 3%).
Cancer survivors have a considerable stake in the details of prescription benefits that are negotiated as part of the implementation of national health reforms, including formularies and the tiering of copayments for different drugs based on cost. For longer term survivors, prescriptions accounted for 44% of out-of-pocket expenditures, an average of $600 per patient in 2007 compared with approximately $350 for professional services (the next most costly service in terms of out-of-pocket expenses). Depending on sex, from 7% to 8% of previously diagnosed survivors ages 40 to 64 years spent >$2000 out of pocket on prescriptions, including 1% to 2% who spent >$5000 out of pocket. The appropriate level of insurance coverage for costly drugs taken by some cancer survivors remains controversial. 22-23 USA Today reported monthly costs for patented cancer drugs that, 4 years ago, were as much as $2500 to $4500 for Gleevec, Avastin, and Herceptin and soared to $10,000 or more for Erbitux and Rituxan. 24
Although, to our knowledge, this is the first study to provide national estimates of average medical expenditures for all adult cancer survivors aged <65 years in the United States, the estimates have limitations that should be recognized. All of the data identifying individuals who were diagnosed with, treated for, or affected by cancer were self-reported. The sample of survivors in any single year of the survey was too small to analyze reliably; consequently, the data had to be pooled over multiple years with adjustments for time trends in inflation and use. Given sample size constraints and different methods in MEPS and NHIS for determining cancer type, we have presented estimates for all cancers combined and have ignored differences in expenditures by type of cancer. We could not identify recurrences and second cancers despite their effect on expenditures.
Another methodological consideration is the comparative strength of different data sources for analyzing expenditures associated with incident and prevalent cases. Although high expenditures associated with the treatment of incident cases disproportionately affect the overall average for all survivors, confidence intervals on expenditure estimates for incident cases are widened by the small sample in a general population survey like MEPS. Furthermore, our MEPS estimates of calendar year expenditures for newly diagnosed survivors encompassed the earlier part of the year before diagnosis and should not be confused with estimates of the first 12 months of expenditures after a cancer diagnosis. Given these issues of sample size and timing, data drawn from health insurance claims or provider records probably are better suited to studying expenditures of incident cases than MEPS. Conversely, as long as the questionnaire identifies everyone who was ever diagnosed with cancer, a nationally representative and population-based survey like MEPS probably is better at capturing all expenditures (for all services and payers) for all prevalent cases, including those little affected by cancer.
CONFLICT OF INTEREST DISCLOSURES
The authors made no disclosures.
- 1Office of Cancer Survivorship, National Cancer Institute. About cancer survivorship research: survivorship definitions. Bethesda, MD: National Cancer Institute; 2010. http://www.cancercontrol.cancer.gov/ocs/definitions.html. Accessed June 15, 2010.
- 2Office of Cancer Survivorship, National Cancer Institute. Estimated US cancer prevalence counts: who are our cancer survivors in the US?. Bethesda, MD: National Cancer Institute; 2010. http://www.cancercontrol.cancer.gov/ocs/prevalence/index.html. Accessed June 15, 2010.
- 3Hewitt M, Greenfield S, Stoval E, eds. From Cancer Patient to Cancer Survivor: Lost in Transition. Washington, DC: National Academy Press; 2005.
- 4Adler NE, Page AEK, eds. Cancer Care for the Whole Patient: Meeting Psychosocial Health Needs. Washington, DC: National Academy Press; 2008.
- 5Cancer survivorship—United States, 1971-2001. MMWR Weekly. 2004; 53: 526-529.
- 8Hewitt M, Herdman R, Holland J, eds. Meeting Psychosocial Needs of Women with Breast Cancer (report of the National Cancer Policy Board/Institute of Medicine and National Research Council). Washington, DC: National Academy Press; 2004.
- 9Psychosocial adaptation of cancer survivors. In: Holland JC, ed. Psycho-Oncology. New York: Oxford University Press, 1998: 223-241.
- 11Estimating health care costs related to cancer treatment from SEER-Medicare data. Med Care. 2002; 40( suppl): 104-117., , , et al.
- 13Health insurance and spending among cancer patients. Health Aff. 2003; W3: 189-198.,
- 14The Five Most Costly Conditions 2000 and 2004: National Estimates for the Civilian Non-Institutionalized Population. MEPS Statistical Brief No. 167. Rockville MD: Agency for Healthcare Research and Quality; 2007.
- 16Sample Design of the Medical Expenditure Panel Survey Household Component, 1998-2007. Methodology Report No. 22. Rockville, MD: Agency for Healthcare Research and Quality; 2008., ,
- 17American Cancer Society. Cancer Facts and Figures 2007. Atlanta, GA: American Cancer Society; 2007.
- 18Office of the Actuary, Centers for Medicare and Medicaid Services. National Health Expenditures Accounts: Definitions, Sources, and Methods, 2008. Baltimore, MD: Centers for Medicare and Medicaid Services; 2008.
- 20PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. Version 3.0.0. Statistical software components series. Chestnut Hill, MA: Department of Economics, Boston College; 2003. http://www.ideas.repec.org/c/boc/bocode/s432001.html. Accessed December 6, 2010., .
- 23Cancer drugs' cost-effectiveness questioned. Wall Street Journal. July 2, 2009. http://www.online.wsj.com/article/SB10001424052970203872404574258302761872972.html. Accessed June 15, 2010.
- 24Prices soar for cancer drugs. USA Today. July 10, 2006. http://www.usatoday.com/news/health/2006-07-10-cancer-costs_x.htm. Accessed June 15, 2010.