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The American Journal of Managed Care April 2018
Delivering on the Value Proposition of Precision Medicine: The View From Healthcare Payers
Jane Null Kogan, PhD; Philip Empey, PharmD, PhD; Justin Kanter, MA; Donna J. Keyser, PhD, MBA; and William H. Shrank, MD, MSHS
Care Coordination for Children With Special Needs in Medicaid: Lessons From Medicare
Kate A. Stewart, PhD, MS; Katharine W.V. Bradley, PhD, MBA; Joseph S. Zickafoose, MD, MS; Rachel Hildrich, BS; Henry T. Ireys, PhD; and Randall S. Brown, PhD
Cost Sharing and Branded Antidepressant Initiation Among Patients Treated With Generics
Jason D. Buxbaum, MHSA; Michael E. Chernew, PhD; Machaon Bonafede, PhD; Anna Vlahiotis, MA; Deborah Walter, MPA; Lisa Mucha, PhD; and A. Mark Fendrick, MD
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The Well-Being of Long-Term Cancer Survivors
Jeffrey Sullivan, MS; Julia Thornton Snider, PhD; Emma van Eijndhoven, MS, MA; Tony Okoro, PharmD, MPH; Katharine Batt, MD, MSc; and Thomas DeLeire, PhD
Financial Burden of Healthcare Utilization in Consumer-Directed Health Plans
Xinke Zhang, PhD; Erin Trish, PhD; and Neeraj Sood, PhD
Progress of Diabetes Severity Associated With Severe Hypoglycemia in Taiwan
Edy Kornelius, MD; Yi-Sun Yang, MD; Shih-Chang Lo, MD; Chiung-Huei Peng, DDS, PhD; Yung-Rung Lai, PharmD; Jeng-Yuan Chiou, PhD; and Chien-Ning Huang, MD, PhD
Physician and Patient Tools to Improve Chronic Kidney Disease Care
Thomas D. Sequist, MD, MPH; Alison M. Holliday, MPH; E. John Orav, PhD; David W. Bates, MD, MSc; and Bradley M. Denker, MD
Limited Distribution Networks Stifle Competition in the Generic and Biosimilar Drug Industries
Laura Karas, MD, MPH; Kenneth M. Shermock, PharmD, PhD; Celia Proctor, PharmD, MBA; Mariana Socal, MD, PhD; and Gerard F. Anderson, PhD
Provider and Patient Burdens of Obtaining Oral Anticancer Medications
Daniel M. Geynisman, MD; Caitlin R. Meeker, MPH; Jamie L. Doyle, MPH; Elizabeth A. Handorf, PhD; Marijo Bilusic, MD, PhD; Elizabeth R. Plimack, MD, MS; and Yu-Ning Wong, MD, MSCE

The Well-Being of Long-Term Cancer Survivors

Jeffrey Sullivan, MS; Julia Thornton Snider, PhD; Emma van Eijndhoven, MS, MA; Tony Okoro, PharmD, MPH; Katharine Batt, MD, MSc; and Thomas DeLeire, PhD
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.
Variables

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 analysisWe 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. 

RESULTS

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 (<.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 (<.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 (<.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 (<.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 <.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 <.01), happiness, and employment status (both <.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 <.01), self-reported health, and happiness (both <.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 ajmc.com). 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.


 
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