 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.

 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. 

 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 (

<.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 (

<.01) and significantly greater happiness than the nationally representative cohort and those with chronic illness (

 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.

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.

 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.

 Reasons for this decline include advances in early detection and treatment and decreased smoking.

 Although the debate continues about the efficacy of the “war on cancer” and further progress is needed,

 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.

 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.

Radiotherapy in patients with breast cancer has been linked to an increased risk of coronary events.

 Impairment of renal function is a complication of certain cancer treatment regimens, often resulting in acute damage and chronic kidney disease.

Patients with cancer also face an increased risk of second primary malignancies.

 For example, a meta-analysis found that patients treated for Hodgkin lymphoma are at increased risk of developing second primary lung cancer.

 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.

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.”

 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 

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.

The common definition of long-term survival in individuals who have had a cancer diagnosis is 5 years post diagnosis.

 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,

 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.

 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.

 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,

 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.

 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. 

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 

tests were conducted to assess the significance of the differences in means across cohorts. 

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,

 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 

. 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. 

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 (

The results of the multivariable analyses are presented in 

, 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 

, 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 

). 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.

 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.

 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.

 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.

 Kunitake et al found that long-term colorectal cancer survivors had better overall physical and mental health compared with the general population.

 Similarly, Greenwald et al found that cervical cancer does not reduce the quality of life of long-term survivors,

 and Schmidt et al found that breast cancer survivors may experience long-term quality of life comparable with that of the general population.

 Finally, Thong et al found that although disease progression reduces cancer survivors’ quality of life, the effect diminishes over time.

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,

 and preliminary data suggest the prospect of long-term survival for some metastatic cancer patients.

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.

Our study results are striking, given concerns that patients with cancer may experience a low quality of life.

 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.

 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.

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.

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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.

The Well-Being of Long-Term Cancer Survivors

 Patients with end-stage renal disease (ESRD) are clinically complex, requiring intensive and costly care. Coordinated care may improve outcomes and reduce costs. The objective of this study was to determine the impact of a payer—provider care partnership on key clinical and economic outcomes in enrolled patients with ESRD.

 Data on patient demographics and clinical outcomes were abstracted from the electronic health records of the dialysis provider. Data on healthcare costs were collected from payer claims. Data were collected for a baseline period prior to initiation of the partnership (July 2011-June 2012) and for two 12-month periods following initiation (April 2013-March 2014 and April 2014-March 2015).

 Among both Medicare Advantage and commercial insurance program members, the rate of central venous catheter use for vascular access was lower following initiation of the partnership compared with the baseline period. Likewise, hospital admission rates, emergency department visit rates, and readmission rates were lower following partnership initiation. Rates of influenza and pneumococcal vaccination were higher than 95% throughout all 3 time periods. Total medical costs were lower for both cohorts of members in the second 12-month period following partnership initiation compared with the baseline period. 

 Promising trends were observed among members participating in this payer—provider care partnership with respect to both clinical and economic outcomes. This suggests that collaborations with shared incentives may be a valuable approach for patients with ESRD.

Payer—provider partnerships have been proposed as a means to improve patient outcomes and reduce healthcare costs among patients requiring complex, coordinated care. Here, we report the outcomes of such a partnership established to care for patients with end-stage renal disease (ESRD). 

In the 2 years following the initiation of the partnership, both Medicare Advantage and commercial insurance patients showed improvements in the proportion of patients dialyzing with a permanent vascular access, hospital and emergency department utilization, and total healthcare expenditures. 

Coordination of care through partnership programs may be of significant benefit to patients with ESRD.

Patients with end-stage renal disease (ESRD) require extensive, complex, and costly medical care. Life-sustaining dialysis treatments, often administered as thrice-weekly hemodialysis, rid the body of the toxins and fluids normally removed from the blood by the kidneys. In 2013, although patients with ESRD represented less than 1% of the US Medicare population, they accounted for approximately 7% of Medicare fee-for-service expenditures, with an annual average cost of $84,550 per patient.

 On average, patients with ESRD were hospitalized 1.69 times per year in 2013, and approximately one-third of those hospitalized were readmitted within 30 days of discharge,

 making hospitalizations a major cost driver in this population.

Intensive patient management by dialysis providers and their partner nephrologists has been shown to improve outcomes among patients with ESRD

 and may simultaneously reduce costs. For example, the method used to access the patient’s circulatory system for dialysis has a strong association with outcomes: arteriovenous grafts (AVGs) or arteriovenous fistulae (AVFs) have a lower rate of infections and other complications compared with central venous catheters (CVCs). Thus, patient management at the time of dialysis initiation

 and initiatives to increase the use of AVFs and minimize use of CVCs

 have been associated with improved outcomes and lower costs. Patient management programs can also enhance adherence to complex medication regimens.

Mechanisms for delivering coordinated care to patients with ESRD include federally funded experimental models such as the CMS ESRD Management Demonstration Project,

 and, most recently, ESRD Seamless Care Organizations. In the private sector, payer—provider partnerships have been proposed as a means to provide high-quality and cost-effective care for patients with chronic illnesses, including ESRD.

Recently, a payer and a provider (a subsidiary of a large dialysis organization) initiated a contractual partnership with the goal of improving care and reducing costs for patients with ESRD who were receiving their dialysis treatments at the provider’s facilities in Pennsylvania. The payer's claims data combined with the provider’s analytic capabilities enabled the use of risk stratification and predictive models to target patients for specific interventions. These included supplemental care pathways and clinical protocols targeting specific objectives, such as transitioning patients from CVCs to AVFs, reducing hospitalizations, and decreasing the frequency of readmissions. Field-based renal nurse care managers supported fluid management, immunizations, nutritional supplementation, and end-of-life counseling. Both Medicare Advantage (MA) and commercial insurance members were eligible for the program, which used a shared savings model to create economic incentives for the partners. Incentive payments were contingent on the provider meeting or exceeding United States Renal Data System quality benchmarks for rates of AVF and CVC use, influenza and pneumococcal pneumonia  vaccination, and hospital readmissions. Savings were calculated by comparing the actual total cost of care with a predetermined, actuarially derived expected total cost of care. Incentive payments constituted a contractually stipulated portion of the calculated savings. Here, we report an observational analysis of outcomes and costs of care during the first 2 years of the partnership. 

We conducted an observational analysis of outcomes and costs among a payer’s plan members receiving dialysis through a specific provider. The payer’s MA and commercial insurance members were analyzed separately. Two consecutive 12-month periods (April 1, 2013-March 31, 2014 and April 1, 2014-March 31, 2015) following initiation of the partnership were compared with a 12-month baseline period (July 1, 2011-June 20, 2012). We excluded patients who 1) were in the top 1% in terms of total annual aggregated nondialysis costs, 2) were missing dialysis/inpatient/skilled nursing facility (SNF) claims in at least 50% of the identified eligible member months, 3) had dialysis/inpatient/SNF claims of less than $1500 (commercial) or $1000 (MA) per member per month (PMPM) in at least 50% of eligible member months, or 4) received a kidney transplant (exclusion began in the month that the transplant was received).

This retrospective, observational, noninterventional analysis of outcomes in patients in the partnership was conducted using existing deidentified patient data; therefore, according to the HHS regulations, 45 CFR Part 46, this study was exempt from institutional review board or ethics committee approval. We adhered to the Declaration of Helsinki and informed consent was not required.

Demographic information and clinical data in each study period were abstracted from the electronic health records of the dialysis provider. Comorbidities were determined from patient utilization within the payer claims database using 

International Classification of Diseases, Ninth Revision

diagnosis codes. Payer claims were used to determine healthcare costs. 

We considered several outcomes that were based on benchmarks set by the National Kidney Foundation, including vascular access type, vaccination rates, and hospital readmission rates. Vascular access type among hemodialysis patients was defined as AVF, AVG, or CVC based on the access type used for the majority of treatments in each month and was expressed as the proportion of patients using each access type in each study period. Rates of pneumococcal vaccination were considered as the proportion of patients who had received the vaccination in the past 5 years or had received 2 doses ever. Influenza vaccination rates were determined as the annual period prevalence of patients (aggregated proportion) who received vaccinations between September 1 and March 31 within each study year. Hospital readmission rate was defined as the proportion of patients readmitted within 30 days of discharge following an inpatient stay.

We further considered healthcare costs, hospitalization and emergency department (ED) visit rates, length of stay, and hospitalized days. PMPM costs were considered, excluding dialysis treatment costs, and a 90-day claims run-out period was used to allow consistent data capture. Nondialysis medical costs included 4 categories: 1) inpatient hospital care costs, excluding inpatient care following kidney transplant; 2) professional medical care costs, defined as costs attributed to physician visits; 3) prescription drug costs; and 4) all other medical care costs, including but not limited to those of laboratory tests, SNFs, EDs, and home health care. Hospitalization and ED visit rates were calculated as the number of events per 1000 patient-years; length of hospital stay (days) and costs per admission were considered as the mean values in each study period. The total number of hospitalized days in the period was also determined. 

All outcomes were considered separately for commercial and MA members; no statistical comparisons were made. 

Payer beneficiaries receiving dialysis at the provider’s facilities in Pennsylvania were eligible for enrollment in the program; approximately 80% to 85% of eligible patients elected to enroll. Patient characteristics by type and year are summarized in the 

). Year to year, member characteristics were similar within each plan type. Overall, compared with commercial plan members, MA members tended to be older, have a greater burden of comorbidity, and have been on dialysis longer. 

Analysis of plan members with respect to the vascular access and vaccination outcomes that were part of the program performance metric is shown in 

. Among both commercial and MA members, AVF and AVG utilization was greater in year 2 than at baseline, whereas CVC utilization was lower. Pneumococcal and influenza vaccination rates were consistently higher than 95% among both commercial and MA members across all study periods. 

Analyses of hospitalizations and ED visits are shown in 

. Among both member cohorts, the hospital admission rate declined over the study period. The length of stay for inpatient admissions also declined, from 6.4 days at baseline to 5.3 days in year 2 for commercial members and from 6.9 to 6.3 days, respectively, for MA members. The reductions in the hospital admission rate and length of stay resulted in a marked decline in the number of hospitalized days for plan members over the study period. 

Hospital readmission rates were moderately lower in year 2 than at baseline. Among commercial plan members, the rate fell from 22% at baseline to 18% and 19% in years 1 and 2, respectively. The readmission rate was 29% at baseline among MA members, declining to 24% and 25% in years 1 and 2, respectively. The ED visit rate among commercial members declined from 1377 to 864 visits per 1000 patient-years from baseline to year 2. For MA members, it declined from 2178 to 1305 visits per 1000 patient-years over the same period. 

Costs of medical care for program members also declined over the 2 years of the program (

). In aggregate, more than $5 million was saved relative to the year 1 and year 2 actuarially determined cost expectations, with $3.04 million derived from the commercial members and $2.01 million from the MA members. Across the primary cost categories and considering both years, commercial members experienced a 48% decline in inpatient care costs, a 38% decline in professional costs, and a 34% decline in other medical expenses. For the MA members, these costs decreased by 19%, 9%, and 12%, respectively. The only exception to this trend toward decreasing costs was prescription drug costs, which increased from baseline through year 2 in both member cohorts (by 32% for commercial members and 30% for MA members). 

Management of patients with ESRD is difficult, requiring frequent and routine access to patients, partnership with nephrologists, specialized care protocols, and customized technology. In an effort to improve outcomes and lower costs, a dialysis provider and a payer created a shared savings incentive program. Members enrolled in the program were provided targeted services aimed at improving specific quality metrics and controlling nondialysis healthcare expenditures. 

As shown here, this payer—provider partnership resulted in improved clinical care quality metrics and reduced medical costs relative to the baseline population. Improvements were observed in vascular access utilization, with an increase in AVF and AVG utilization and a decrease in CVC utilization among both commercial and MA members. Vaccination rates for influenza and pneumococcus were above 95% for both member cohorts across the study period. These vaccination rates are substantially higher than those most recently reported for the Medicare ESRD population generally (30% for pneumococcus

Hospitalizations are a major cost driver among the ESRD population. Notably, the hospital admission rate, number of hospitalized days, ED visit rate, and 30-day readmission rate all decreased following the inception of the payer—provider partnership, and these decreases were accompanied by decreases in inpatient costs and healthcare costs overall. The hospital admission rate for MA program members in year 2 (1.3 admissions/patient-year) compares favorably with the rate for the Medicare hemodialysis population overall, which averaged 1.69 admissions per patient-year in 2013.

 The hospital readmission rate for MA program members in year 2 (25%) was markedly lower than the Medicare average (36.9% in 2013).

 Our findings are consistent with prior evidence demonstrating that care coordination can improve outcomes and may help contain costs for patients with ESRD.

Although overall costs and inpatient costs declined among plan members, our analyses documented incremental increases in overall medication costs during years 1 and 2 compared with the baseline period. This result may be related to improved patient adherence to prescribed medications arising from enhanced clinical management. Alternatively, this observation may have been driven by a small number of patients receiving treatment with extremely expensive medications; further analysis will be required to fully understand this result. 

Several factors may limit the generalizability of our findings to other ESRD populations. First and foremost, this was a retrospective observational analysis that was not designed to address cause-and-effect relationships. The patient population studied was defined by the patients’ choice of payer and provider and their willingness to enroll in the program. These factors, along with annual changes in membership and patient attrition, must be taken into consideration when interpreting our findings. Additionally, geography has been shown to influence healthcare spending,

; our study was limited to patients who received treatment in Pennsylvania.

The promising trends observed among members participating in this payer—provider ESRD population health partnership suggest that collaborations with shared incentives may be a valuable approach for improving ESRD patient outcomes and reducing care costs.&ensp;

The authors acknowledge the medical writing assistance of Dena E. Cohen, PhD. The authors thank Carly Busch, Sarah Falkof, and John Plonka for assistance with data acquisition.

DaVita, Inc (JK, DR, SM), Denver, CO; Highmark, Inc (RW), Pittsburgh, PA.

Manuscript editorial support was provided by DaVita, Inc. There was no source of funding for the study itself.

Mr Kindy, Dr Roer, and Dr McMurray are employed by DaVita, which is party to the agreement and receives shared savings payments on program results. Dr Roer has attended meetings or conferences of the American Society of Nephrology, Renal Physicians Association, and Capability Maturity Model Integration. Dr Wanovich is employed in a full-time management position at Highmark, Inc, the payer discussed in the manuscript. Dr McMurray reports stock ownership in DaVita, Inc. 

Concept and design (JK, DR, RW, SM); acquisition of data (RW); analysis and interpretation of data (JK); drafting of the manuscript (DR, SM); critical revision of the manuscript for important intellectual content (JK, DR, RW, SM); and administrative, technical, or logistic support (RW).

Justin Kindy, FSA, MAAA, DaVita, Inc, 2000 16th St, Denver, CO 80202. Email: justin.kindy@davita.com.

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TRENDS FROM THE FIELD

A report on the clinical and economic outcomes of a new payer–provider partnership serving patients with end-stage renal disease.

A Payer–Provider Partnership for Integrated Care of Patients Receiving Dialysis

 To provide actionable recommendations for improving care coordination programs for children with special healthcare needs (CSHCN) in Medicaid managed care.

 Literature review and interviews with stakeholders and policy experts to adapt lessons learned from Medicare care coordination programs for CSHCN in Medicaid managed care.

 We reviewed syntheses of research on Medicare care coordination programs to identify lessons learned from successful programs. We adapted findings from Medicare to CSHCN in Medicaid based on an environmental scan and discussions with experts. The scan focused on Medicaid financing and eligibility for care coordination and how these intersect with Medicaid managed care. The expert discussions included pediatricians, Medicaid policy experts, Medicaid medical directors, and a former managed care executive, all experienced in care coordination for CSHCN. 

 We found 6 elements that are consistently associated with improved outcomes from Medicare care coordination programs and relevant to CSHCN in Medicaid: 1) identifying and targeting high-risk patients, 2) clearly articulating what outcomes programs are likely to improve, 3) encouraging active engagement between care coordinators and primary care providers, 4) requiring some in-person contact between care coordinators and patients, 5) facilitating information sharing among providers, and 6) supplementing care coordinators’ expertise with that of other clinical experts.

 States and Medicaid managed care organizations have many options for designing effective care coordination programs for CSHCN. Their choices should account for the diversity of conditions among CSHCN, families’ capacity to coordinate care, and social determinants of health.

This study describes 6 attributes of care coordination programs associated with improved outcomes among Medicare beneficiaries with chronic illnesses that are applicable to children with special healthcare needs (CSHCN) in Medicaid managed care. These findings may help managed care decision-makers implement and improve care coordination programs for CSHCN by: 

Identifying children who might benefit from care coordination. 

Developing requirements for care coordinators’ activities while allowing them to draw on their clinical expertise and judgment. 

Identifying other professionals (eg, clinical pharmacologists) who should team with care coordinators.

Care coordination has the potential to improve care and reduce costs for children with special healthcare needs (CSHCN) enrolled in Medicaid.

 CSHCN are defined broadly as children who “have or are at increased risk for a chronic physical, developmental, behavioral, or emotional condition and who also require health and related services of a type or amount beyond that required by children generally.”

 This group includes children with the most intensive and challenging healthcare needs, increasingly referred to as children with medical complexity. In addition to accounting for a large proportion of Medicaid spending, CSHCN are at risk for preventable hospitalizations and emergency care resulting from poor coordination among families, medical care providers, schools, and other community-based programs.

The pediatric community has emphasized the importance of care coordination for CSHCN for decades, but limited infrastructure and accountability have slowed implementation.

 This issue has become even more salient in Medicaid as state policy makers look to use resources more efficiently and shift more Medicaid-enrolled CSHCN into managed care plans. However, states provide Medicaid managed care organizations (MMCOs) with little guidance on how to implement effective care coordination programs for CSHCN.

 Furthermore, despite an extensive literature on care coordination for CSHCN,

 it is challenging to draw conclusions about design features of effective programs because existing studies often lack methodological rigor and vary widely in their underlying conceptual frameworks.

Given the paucity of reliable evidence on children’s programs, how should states and MMCOs design and implement care coordination for CSHCN? Research on care coordination interventions for Medicare beneficiaries is a potential source of insights. Although CSHCN and elderly Medicare beneficiaries may have very different healthcare and nonhealthcare needs, the goals and design elements of care coordination programs may be similar. In contrast to the CSHCN literature, there is a rigorous evidence base on care coordination for Medicare enrollees, including numerous experimental and quasi-experimental analyses of care coordination demonstrations funded by federal agencies and other organizations.

 We sought to identify characteristics of successful Medicare care coordination programs and the extent to which they might be applicable to Medicaid-enrolled CSHCN, with the goal of providing actionable recommendations for state policy makers and MMCOs.

For this study, we reviewed evidence on Medicare care coordination programs to describe the attributes of successful programs and then assessed the applicability of those findings to CSHCN in Medicaid. Regardless of whether these studies used the terms 

, we focused on programs that perform coordination functions, such as organizing or linking multiple services and engaging the patient.

We began our review of the Medicare literature with research syntheses that described findings across multiple federally funded care coordination demonstrations for community-dwelling Medicare fee-for-service beneficiaries. The syntheses were derived from randomized controlled trials and quasi-experimental analyses.

 We supplemented these syntheses with additional studies of care coordination programs targeted to Medicare beneficiaries that had equally strong research designs and were published in the last 10 years. These were primarily identified through summary articles on other care coordination programs targeted to Medicare beneficiaries in primary care practices which were included in a recent National Academy of Medicine workshop, “Models of Care for High-Need Patients.”

 We reviewed the original research cited in the syntheses and summary articles for additional detail on program characteristics and outcomes, as needed. 

Second, we conducted an environmental scan of the Medicaid financing and eligibility mechanisms through which states may provide care coordination to CSHCN, and we examined the extent to which existing programs rely on or interact with Medicaid managed care. We began the scan by reviewing articles that described existing programsand additional materials on the programs contained in those compilations.

 We also performed internet searches for 

because it is not defined in Medicaid regulations and, in practice, exists in Medicaid under multiple programs.

 We also reviewed Medicaid law and federal guidance for clarification of the financing and eligibility options available to states interested in providing care coordination through Medicaid managed care.

Finally, we translated common characteristics of successful Medicare care coordination programs into program design considerations for CSHCN in Medicaid. Adapting evidence from Medicare for pediatric populations requires attention to unique characteristics of children’s health, such as the need for parental support, developmental trajectories, and differences in treatments for serious illnesses compared with such treatments in adults. We then refined our recommendations with the help of experts in children’s healthcare. We gathered feedback from 11 outside experts, including 5 Medicaid medical directors, 3 practicing pediatric clinicians, 1 former managed care executive, and 2 policy professionals well-versed in the research on Medicaid, care coordination, and CSHCN. 

We reviewed 30 publications, including syntheses, summary articles, and individual studies of Medicare care coordination programs. Twenty-four of these publications reported evidence on impacts from rigorous studies and were included in this study. Nearly all of the Medicare care coordination programs used nurses as care coordinators. The goals of the Medicare care coordination programs were to reduce beneficiaries’ need for emergency department (ED) and inpatient care and to lower overall Medicare spending. Several studies also reported effects on mortality,

 We refer readers to summary articles for descriptions of the various care coordination interventions.

We identified 6 design elements common among programs that were successful in reducing healthcare utilization or Medicare spending. Each of these has potential value for care coordination programs for CSHCN in Medicaid, but each requires adaptation to account for differences between the populations, funding sources, and programmatic constraints. In the remainder of this section, we discuss these 6 program design considerations and their relevance to CSHCN in Medicaid, noting particular challenges in adapting evidence from Medicare for pediatric populations. We provide additional detail in the 

1. Identify and target specific subgroups

. Medicare care coordination programs that had positive impacts tended to focus on specific subpopulations. These included beneficiaries with particular chronic conditions and comorbidities and those projected to have high medical care use and spending.

 Even among programs with favorable overall effects, impacts were often concentrated in, or larger for, a higher-risk subpopulation of their enrollees.

 Similarly, among care coordination interventions that did not demonstrate overall impacts, several had positive effects for a subset of higher-risk enrollees.

States and MMCOs may well benefit from strategies similar to those used in many Medicare programs to identify beneficiaries with either high recent utilization of EDs or inpatient care or high predicted future medical care spending, in addition to or instead of condition-based criteria. In the Medicaid context, social determinants of health and behavioral health comorbidities are additional factors that may help states and MMCOs identify children who are at especially high risk. 

Medicaid-specific programmatic and care delivery factors may also drive, or limit, the targeting of care coordination programs for children. Several states combine Medicaid managed care with other Medicaid financing and eligibility pathways to care coordination that inherently target medically complex populations (eg, managed long-term services and supports for children covered by 1915[c] waivers and targeted case management delivered through managed care). States may also limit certain programs, such as targeted case management and home- and community-based benefits, to specific geographic areas, which may preclude targeting to all CSHCN who would benefit from care coordination. Likewise, the settings in which CSHCN receive care may inform targeting decisions. For example, CSHCN who receive care at tertiary care hospital outpatient clinics may be an easily identifiable high-risk target population, although targeting such CSHCN may exclude those who live far away from the selected clinics. In contrast, it may require more effort (eg, examination of diagnoses in encounter data) to identify CSHCN treated by primary care providers in the community who are most likely to benefit from care coordination.

2. Set clear goals for outcomes that are feasible to achieve within the time period examined. 

Few Medicare care coordination programs led to significantly lower Medicare spending,

although some programs’ care coordination costs were offset by reductions in spending on inpatient or ED care.

 More commonly, interventions showed evidence of reduced hospitalizations,

These findings underscore the importance of specifying explicit goals in advance and developing logic models that connect care coordination activities to intended outcomes. States and MMCOs whose goals focus on improved well-being and functional status should not be surprised if their care coordination efforts actually increase net Medicaid spending. For states and MMCOs whose goals focus on reducing utilization and Medicaid spending, care coordination may initially increase healthcare utilization and spending before reducing the use of avoidable expensive services. Moreover, some positive outcomes of care coordination for CSHCN in Medicaid will not accrue to Medicaid programs, such as increases in parents’ labor force participation or children’s eventual success as independent adults. States and MMCOs must be realistic about expected outcomes specific to CSHCN and the time periods necessary to achieve them. 

Our findings also point to the importance of defining the operational parameters of care coordination programs, such as caseloads, specific activities, and interactions with patients and family members. Defining these processes, and potentially specifying them in state Medicaid managed care contracts, provides an opportunity to subsequently measure them. In turn, measurement allows states and MMCOs to identify factors contributing to program outcomes. 

Encourage active engagement between care coordinators and primary care providers. 

As increasing numbers of children with special healthcare needs move into Medicaid managed care, health plans can improve care coordination using evidence from Medicare.

April 2018

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Care Coordination for Children With Special Needs in Medicaid: Lessons From Medicare

Delivering on the Value Proposition of Precision Medicine: The View From Healthcare Payers

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