Author + information
- Received July 27, 2017
- Revision received December 12, 2017
- Accepted December 13, 2017
- Published online April 30, 2018.
- Karen E. Van Nuys, PhDa,∗ (, )
- Zhiwen Xie, MAa,
- Bryan Tysinger, MPAa,
- Mark A. Hlatky, MDb and
- Dana P. Goldman, PhDa
- aLeonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, Los Angeles, California
- bStanford University School of Medicine, Stanford, California
- ↵∗Address for correspondence:
Dr. Karen Van Nuys, Leonard D. Schaeffer Center for Health Policy and Economics, University of Southern California, 635 Downey Way, Verna & Peter Dauterive Hall, Suite 210, Los Angeles, California 90089-3333.
Objectives The goal of this study was to illustrate the potential benefit of effective congestive heart failure (CHF) treatment in terms of improved health, greater social value, and reduced health disparities between black and white subpopulations.
Background CHF affects 5.7 million Americans, costing $32 billion annually in treatment expenditures and lost productivity. CHF also contributes to health disparities between black and white Americans: black subjects develop CHF at a younger age and are more likely to be hospitalized and die of this disease. Improved CHF treatment could generate significant health benefits and reduce health disparities.
Methods We adapted an established economic-demographic microsimulation to estimate scenarios in which a hypothetical innovation eliminates the incidence of CHF and, separately, 6 other diseases in patients 51 to 52 years of age in 2016. This cohort was followed up until death. We estimated total life years, quality-adjusted life years, and disability-free life years with and without the innovation, for the population overall and for race- and sex-defined subpopulations.
Results CHF prevalence among 65- to 70-year-olds increased from 4.3% in 2012 to 8.5% in 2030. Diagnosis with CHF coincided with significant increases in disability and medical expenditures, particularly among black subjects. Preventing CHF among those 51 to 52 years of age in 2016 would generate nearly 2.9 million additional life years, 1.1 million disability-free life years, and 2.1 million quality-adjusted life years worth $210 to $420 billion. These gains are greater among black subjects than among white subjects.
Conclusions CHF prevalence will increase substantially over the next 2 decades and will affect black Americans more than white Americans. Improved CHF treatment could generate significant social value and reduce existing health disparities.
There is much concern about the increasing share of national income devoted to health care (1), but spending increases have been accompanied by significant health improvements. Most significantly, age-standardized death rates from all causes have fallen 43% since 1969 (from 1,279 deaths per 100,000 in 1969 to 730 per 100,000 in 2013) (2). Better cardiovascular outcomes have driven much of this improvement, with age-adjusted deaths from heart disease falling from 520 per 100,000 in 1969 to 168.5 per 100,000 in 2015 (2,3). Evidence-based treatment of associated risk factors has been credited with contributing to these declines (4).
However, progress may be slowing and, in some disease areas such as congestive heart failure (CHF), may even be reversing. An estimated 5.7 million American adults experience CHF, and CHF is a contributing factor in 1 in 9 U.S. deaths (4). The Centers for Disease Control and Prevention reports that between 2011 and 2014, age-adjusted death rates from heart failure rose from 16.9 to 18.6 per 100,000 (5).
This trend may also exacerbate existing racial health disparities. African-American subjects develop heart failure earlier than white subjects and are more likely to be admitted to the hospital for this disease (6,7). In addition, the 5-year risk-adjusted all-cause mortality rate for patients with CHF is 34% higher for African-American subjects than for white subjects (8,9). Given these existing racial disparities, the fact that the age-adjusted death rates from CHF are increasing is particularly alarming.
Adding to the personal toll of CHF (premature death, disability, and loss of quality of life), its economic costs are substantial: almost $32 billion annually for U.S. treatment costs and lost productivity (7). Fortunately, recent treatment innovations suggest that the future impact of CHF on patient outcomes, economic productivity, and overall social value could be reduced, perhaps even in a way that mitigates health disparities (10–12).
The goal of the present paper was to model the potential benefits to population health from continued innovation in CHF treatment. Using U.S. population-wide simulations, we estimated trends in CHF prevalence and how much improved CHF treatments could improve overall social value, as well as reduce racial and sex differences in health outcomes.
To illustrate the potential benefits of improved CHF treatment, we adapted the Future Elderly Model (FEM), an established economic-demographic microsimulation that has been used to study a wide variety of health policy questions. The FEM has been developed over time with support from the National Institute on Aging, the Department of Labor, the MacArthur Foundation, and the Centers for Medicare & Medicaid Services to study health care innovation in a wide variety of contexts, including heart disease (13–17).
The FEM simulates health and medical spending for Americans ≥51 years of age. The model uses initial demographic characteristics and health conditions for each individual to project their medical spending, health conditions and behaviors, disability status, and quality of life. A key advantage of the FEM is that it tracks individual-level health trajectories and patient outcomes, which allows us to consider the impact of innovation according to characteristics such as sex and race.
The FEM’s core module uses individuals’ current characteristics to calculate transition probabilities among health states, including mortality, functional status, body mass index (BMI), and 6 disease conditions: diabetes, high blood pressure, heart disease (including CHF), cancer (excluding skin cancer), stroke, and lung disease. The model uses inputs from 3 nationally representative datasets: the Health and Retirement Study (HRS), a biennial survey of the American population ≥51 years of age, which has been conducted since 1992; the Medical Expenditure Panel Survey (MEPS), a set of large-scale surveys of the noninstitutionalized U.S. population; and the Medicare Current Beneficiary Survey, a survey of Medicare beneﬁciaries about their health status, health care use, and insurance coverage. More details on the model and data sources are provided in Goldman et al. (18).
Prevalence and incidence of CHF
To predict which individuals have or will be diagnosed with CHF during the simulation, HRS historical data were used to build a 2-year CHF incidence model based on predictors, including age, sex, education, race, age–race interactions, BMI, smoking behavior, marital status, and the 6 disease conditions modeled. This model uses a first-order Markov process in which time-varying components enter via their status 2 years before. For example, diabetes status in the prior wave of the survey is a predictor of incident CHF in the current wave. All transition models in the simulation have this structure.
CHF status is included as a predictor of other outcomes of interest, including mortality, functional limitations (activities of daily living [ADL]), and instrumental activities of daily living (IADL) limitations. Mortality is estimated as a 2-year probit model, controlling for age, race, sex, education, widowhood, smoking status, the 6 chronic diseases, ADLs, and IADLs. The number of functional limitations is estimated as an ordered probit with 4 categories: none, 1, 2, and ≥3. This ADL model controls for the same set of variables as the mortality model, plus BMI. IADL limitations are also modeled with the same predictors, as an ordered probit with 3 categories: none, 1, and ≥2.
Valuing health benefits
To value health benefits, quality-adjusted life years (QALYs) were predicted by using the EuroQol Five-Dimension Questionnaire (EQ-5D), a widely used health-related quality of life index. The EQ-5D instrument includes 5 questions regarding the extent of problems in mobility, self-care, daily activities, pain, and anxiety/depression, and has been widely used in both Europe and the United States (19,20). Using the 2001 MEPS, a linear model was estimated fitting EQ-5D scores as a function of 6 chronic conditions and functional status (details in Goldman et al. ). A QALY measure was predicted for every person in the simulation in every year based on their simulated health and functional status.
Outcomes were simulated for a representative cohort of 51- and 52-year-olds beginning in 2016 (n = 13,040). This cohort, described in Goldman et al. (18), is based on respondents from the HRS. In each year, the spending module predicts medical expenditures over the next 2 years (the HRS is biennial) based on each individual’s current “state.” The health module is then used to predict who will survive to year 2018, and their obesity status, disease, and functional state, and a predicted QALY for that year. The spending module is then used to predict that period's health care resource use. The simulation iterates in this manner until everyone in the 2016 cohort has died. The simulation was repeated 500 times for each scenario and the average outcomes and resulting confidence intervals (CIs) were reported. Primary outcomes are life expectancy, quality-adjusted life expectancy, and lifetime medical spending. All costs and QALYs were discounted by using a 3% annual discount rate as suggested by Gold et al. (21).
To predict the prevalence of CHF from 2016 to 2030, the population ≥50 years of age was simulated in 2016 and beyond, accounting for projected demographic trends over time. To examine the burden of CHF, the life trajectories of the cohort of individuals 51 to 52 years of age in 2016 were modeled to construct a baseline scenario, and this outcome was compared to a “No CHF” scenario in which no individuals in the cohort develop CHF throughout the simulation, maintaining all other transition dynamics. Although completely preventing CHF might seem unrealistic, it provides an upper bound for the potential social gain from a medical innovation. For comparison, similar analyses were performed that eliminated, in turn, diabetes, high blood pressure, lung disease, cancer, obesity, and stroke. To calculate total QALYs added in each scenario, the number of individuals 51 to 52 years of age in 2016 was calculated, and this number was multiplied by the average QALYs added.
Transition models were estimated by using the RAND HRS version P, using nationally representative waves from 1998 to 2012 (n = 114,489 person-waves). Medical costs were estimated by using MEPS 2007 to 2010 for the non-Medicare population and the Medicare Current Beneficiary Survey 2007 to 2012 for the Medicare population. All estimations were performed with Stata version 14.0 (StataCorp, College Station, Texas). Goldman et al. (18) presents estimates.
Figure 1 shows the prevalence of CHF through 2030: years 1996 through 2010 represent data from the HRS, and years 2012 and beyond reflect simulation estimates. Among 65- to-70-year-olds, the prevalence of CHF is expected to increase from 4.29% in 2010 to 8.45% (95% CI: 8.03% to 8.87%) in 2030.
Analysis of the 2010 to 2012 HRS data among patients with cardiovascular disease found that the age-adjusted incidence of CHF is higher among black subjects than white subjects, and highest for black female subjects (4.8%) (black male subjects: 4.1%, white female subjects: 4.0%; white male subjects: 3.5%).
We also considered the impact of CHF on disability status and how this factor varies with race and sex. Using the 2000 to 2012 HRS data, all patients without CHF in 1 period who went on to develop CHF in the subsequent period were identified, as was their reported ability to perform 5 ADLs: eating, bathing, dressing, walking across a room, and getting in or out of bed. Figure 2 reports the age-adjusted proportion of these patients who report limitations in ≥3 ADLs before and after CHF diagnosis. Immediately before CHF diagnosis, 9.6% of patients reported ≥3 limitations, rising to 17.4% after CHF diagnosis. The onset of significant disability with CHF diagnosis is particularly severe among black men: before diagnosis, 7.4% of black male subjects who will develop CHF reported ≥3 limitations, increasing to 20% immediately after diagnosis. Among black female subjects who developed CHF, the proportion reporting ≥3 limitations was 20.3% before diagnosis and 30.2% afterward. The proportion of the population that did not develop CHF across 2 consecutive waves saw no significant changes in age-adjusted disability.
Medical expenditures follow a similar pattern (Figure 3). In the 2000 to 2012 HRS data, we found that, before diagnosis, patients who will develop CHF are somewhat sicker than the average person of the same age, with medical expenditures 25% to 30% higher than those of people without CHF. After diagnosis, patients with CHF have medical expenditures 50% to 56% higher. The increment is especially large among black female subjects.
Increasing prevalence of a disease such as CHF, with its significant mortality, disability, and expenditure implications for the overall population and differential implications according to race and sex, underscores the potential benefits from improved treatment. We explored these benefits by simulating scenarios in which we eliminated 7 diseases—CHF, cancer, diabetes, high blood pressure, lung disease, obesity, and stroke—and compared the resulting gains in life expectancy, QALYs, and disability-free life years (DFLYs). Affected patients retained all the other characteristics and comorbidities of a patient with the disease in question; they were not returned to “average” health. Figure 4 presents these results. Among patients who otherwise would have developed CHF, eliminating the disease increases average life expectancy by 1.92 years (95% CI: 1.91 to 1.93), increases the average time lived without a disability by 0.78 year (95% CI: 0.78 to 0.79), and increases quality-adjusted life expectancy by 1.43 years (95% CI: 1.42 to 1.44). Only eliminating cancer, lung disease, and diabetes generate greater life expectancy increases for their affected populations. Table 1 presents simulation results, including the lifetime risk of each condition and the impact of eliminating each on the life expectancy for the entire population, which combines the impact on the affected population with prevalence.
If an innovation to eliminate heart failure is applied to the 4.1 million individuals 51 to 52 years of age in 2016, it could generate nearly 2.9 million additional life years, 2.1 million QALYs, and 1.2 million DFLYs. Depending on the value of each additional QALY, the population health benefits of such an innovation range from $210 to $420 billion.
Figure 5A shows that eliminating heart failure increases average life expectancy among those affected by 2.10 years (95% CI: 2.06 to 2.14) for black male subjects, 1.90 years (95% CI: 1.88 to 1.92) for white male subjects, 2.18 years (95% CI: 2.15 to 2.22) for black female subjects, and 1.84 years (95% CI: 1.82 to 1.86) for white female subjects. Figure 5B shows that eliminating CHF adds 0.86 DFLY (95% CI: 0.84 to 0.88) for black male subjects, 0.88 DFLY (95% CI: 0.87 to 0.89) for white male subjects, 0.76 DFLY (95% CI: 0.74 to 0.78) for black female subjects, and 0.72 DFLY (95% CI: 0.71 to 0.73) for white female subjects. Figure 5C displays QALY gains: 1.52 (95% CI: 1.50 to 1.55) for black male subjects versus 1.44 (95% CI: 1.43 to 1.46) for white male subjects, and 1.55 (95% CI: 1.53 to 1.58) for black female subjects compared with 1.37 (95% CI: 1.36 to 1.39) for white female subjects.
CHF prevalence and lifetime risk
Our estimates of the future prevalence of CHF are generally higher than in other studies (22,23). This outcome is due in part because our estimates focused on prevalence among the older population, whereas other estimates report prevalence among the entire U.S. population. However, our simulation also incorporates trends in the risk factors that lead to CHF, which are themselves increasing.
For example, Heidenreich et al. (22) project CHF prevalence increasing from 2.4% in 2012 to 3.0% in 2030. Their estimates are driven by changes in the size of subpopulations (defined according to age, sex, and ethnicity), but they do not allow the prevalence within a subpopulation to change over time. Our subpopulation-specific CHF prevalence estimates incorporate projected trends in comorbidities and other health indicators that accompany CHF, including obesity and hypertension. Thus, increasing obesity over time will increase the prevalence of CHF even within demographic subpopulations, leading to higher CHF prevalence estimates than those using the methods of Heidenreich et al. (22,23).
Our model projects a lifetime risk of CHF incidence of 35% for patients 51 to 52 years of age, similar to other lifetime risk estimates based on large-scale population studies (7). Our findings of disparities between black and white subjects in the risk of CHF in the HRS data mirror results of previous studies. Most notably, the Atherosclerosis Risk in Communities Study found that the lifetime risk of CHF for those 45 to 75 years of age was higher for black subjects than for white subjects, and highest for black female subjects (24% vs. 21% for black male subjects, 19% for white male subjects, and 13% for white female subjects) (7).
Disability and disparities
People with CHF often have other serious medical conditions, such as arthritis (62%) or diabetes (38%); are unable to walk 2 to 3 blocks or walk up 10 steps (57%); need help with ADLs (11%); and take 6.4 prescription medications on average (24). Such factors may affect the ability of patients with CHF to live independently, with needs ranging from help from an informal caregiver to moving to a nursing facility.
We showed (Figure 2) that disability outcomes vary according to race: black patients with CHF diagnoses are much more likely to report limitations in ≥3 ADLs than their white counterparts. Among men diagnosed with CHF, roughly the same fractions of black and white patients report ≥3 ADLs before diagnosis (7.4% vs. 7.1%), but after diagnosis, that fraction increases by >170% for black male subjects and only 62% for white male subjects. Similar trends appear in the expenditure data (Figure 3).
Although the correlation between CHF and disability may be the result of CHF occurring in patients who are already very sick and disabled, our findings suggest that CHF may also play a causal role in patients’ decline. For many, the onset of CHF can be relatively sudden, and preceded by relatively good health, but disability may progress rapidly after diagnosis. If so, then perhaps the appearance of significant disability could be forestalled if a diagnosis of CHF could be delayed or eliminated. In addition, if health disparities are driven by differential disability outcomes according to race and sex, then more effective treatments for CHF could reduce health disparities.
Value of innovation in CHF
Until recently, there has been relatively little innovation in CHF treatment, with standard care involving medications to treat symptoms, including angiotensin-converting enzyme inhibitors, beta-blockers, and diuretic agents (11). New drugs for heart failure have recently been approved that reduced the risk of death or hospitalization from heart failure by 20% in clinical trials (25,26). Although these new treatments do not eliminate CHF, they highlight the potential for significant innovation in this disease area.
Our results show that eliminating CHF, even without changing patients’ underlying health characteristics, would add 1.92 years to each affected patient’s life, which is more than affected patients would gain by eliminating stroke, obesity, or high blood pressure. Improvements in CHF treatment can also enhance patients’ quality of life, with elimination adding 1.43 QALYs or 0.78 DFLY to the average CHF patient’s life. These findings also compare favorably with innovations to eliminate high blood pressure, a condition that receives far more public health attention than CHF.
We estimated that eliminating CHF could narrow the disparity between average life expectancy of black and white subjects. In our baseline scenario, for the subgroup that developed CHF, white male subjects live 5.1 years longer than black male subjects, and white female subjects 3.5 years longer than black female subjects, on average. Curing CHF reduces this gap by 0.1 and 0.3 year, respectively.
First, our results are derived from simulations estimated by using the FEM, which uses simplifications of the dynamic relationships driving outcomes in the real world and parameterizes those relationships by using estimates from the published data. If these simplifications are incomplete, or the parameter estimates are imperfect, model results may not correspond with actual outcomes.
Second, our “No-CHF” scenario assumes an innovation that eliminates CHF, although medical innovation is unlikely to eliminate CHF in the near term. Instead, actual innovations are more likely to reduce CHF incidence or lessen its severity but not eliminate it. This scenario does, however, also help answer the question: what is the social cost of CHF in older populations?
Third, we do not explicitly model the social determinants of health disparities, such as greater poverty, poorer access to care, and lower health literacy among black patients with CHF (27). Our “No-CHF” scenario implicitly assumes that a cure for CHF is applied equally to all patients with CHF, regardless of social determinants, without modeling how that penetration would happen. Nevertheless, understanding the social value that such a cure, uniformly applied, would unlock helps inform what kinds of policies would be most beneficial.
Finally, our model of the relationship between CHF and outcomes (disability, quality-of-life, and mortality) is based on associations observed in cohorts of thousands of older Americans followed up over decades. We have not demonstrated a direct causal link.
Policy programs to improve public health have often focused on interventions for high-prevalence diseases and conditions such as diabetes, obesity, and hypertension. Cancer has also received much policy attention, including the recent Cancer Moonshot, with the goal of hastening cures, despite limited progress (28). Our research suggests that similar emphasis, focus, and investment in finding ways to eliminate CHF could have as much or more impact in terms of adding life years, QALYs, and DFLYs, and potentially reducing racial disparities among the population.
Heart failure is one example of the growing disease burden older Americans bear as they live longer but face growing risks of disability (29). From a societal standpoint, policy makers and other decision-makers must balance competing aims to benefit all people generally and disadvantaged groups specifically to achieve goals of both efficiency and equity. Innovations that improve disease outcomes—not just eliminate them—can improve efficiency by increasing benefits to society through longer, healthier, more productive lives. Some treatment innovations can also improve equity by narrowing longstanding health disparities among minorities and women.
Innovation in CHF deserves scientific and policy attention not simply because it can extend lives and reduce disability and decline in older Americans but also because it could ameliorate some racial and sex disparities in health outcomes associated with cardiovascular disease.
COMPETENCY IN MEDICAL KNOWLEDGE: CHF prevalence will increase substantially over the next 2 decades, affecting black Americans more than white Americans. A CHF diagnosis coincides with significant increases in disability and medical expenditures, particularly among black subjects compared with white subjects. Improving CHF treatment could generate significant social value and reduce existing racial/ethnic health disparities.
TRANSLATIONAL OUTLOOK: Future research to identify more effective treatment and prevention of CHF could both improve the quality and length of life for patients with CHF and reduce disparities among patients affected by CHF.
Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number P30AG043073 and by the Schaeffer Center for Health Policy and Economics at the University of Southern California. Additional support was provided by Novartis, Inc. The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsors. Dr. Goldman is a co-founder of Precision Health Economics and holds equity in its parent company. Dr. Van Nuys has served as a consultant to Precision Health Economics. Dr. Hlatky has received consulting fees from Acumen, Blue Cross Blue Shield Association, and the George Institute; and research grants from HeartFlow, Milestone Pharmaceuticals, Sanofi, and St. Jude Medical. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Abbreviations and Acronyms
- activities of daily living
- body mass index
- congestive heart failure
- confidence interval
- disability-free life year
- EuroQol Five-Dimension Questionnaire
- Future Elderly Model
- Health and Retirement Study
- instrumental activities of daily living
- Medical Expenditure Panel Survey
- quality-adjusted life year
- Received July 27, 2017.
- Revision received December 12, 2017.
- Accepted December 13, 2017.
- 2018 American College of Cardiology Foundation
- ↵National Health Expenditure Data Highlights. Center for Medicare & Medicaid Services Report 2016. Available at: https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/highlights.pdf. Accessed December 1, 2017.
- ↵Center for Disease Control and Prevention – National Center for Health Statistics. “Underlying Causes of Death 1999-2015”. Available at: http://wonder.cdc.gov/ucd-icd10.html. Accessed September 28, 2017.
- Writing Group Members,
- Mozaffarian D.,
- Benjamin E.J.,
- et al.
- Ni H.,
- Xu J.
- Huffman M.D.,
- Berry J.D.,
- Ning H.,
- et al.
- East M.A.,
- Peterson E.D.,
- Shaw L.K.,
- Gattis W.A.,
- O'Connor C.M.
- Durstenfeld M.S.,
- Ogedegbe O.,
- Katz S.D.,
- Park H.,
- Blecker S.
- Fonarow G.C.,
- Hernandez A.F.,
- Solomon S.D.,
- Yancy C.W.
- Yancy C.W.,
- Jessup M.,
- Bozkurt B.,
- et al.
- Goldman D.P.,
- Shang B.,
- Bhattacharya J.,
- et al.
- Goldman D.P.,
- Cutler D.,
- Rowe J.W.,
- et al.
- Goldman D.P.,
- Orszag P.R.
- Gaudette É.,
- Goldman D.P.,
- Messali A.,
- Sood N.
- ↵Goldman DP, Leaf DE, Sullivan J, Tysinger B, Xie Z. Innovation in Heart Failure Treatment: Life Expectancy, Disability, and Health Disparities—Technical Appendix. USC Schaeffer Center, Working Paper, 2017. Available at: https://healthpolicy.box.com/s/6fen3u5ajjswbvxj64grna729li406ao. Accessed December 11, 2017.
- Gold M.,
- Siegel J.,
- Russell L.,
- Weinstein M.
- Heidenreich P.A.,
- Albert N.M.,
- Allen L.A.,
- et al.
- Heidenreich P.A.,
- Trogdon J.G.,
- Khavjou O.A.,
- et al.
- Fala L.
- ↵U.S. Department of Health and Human Services - Agency for Health Care Research and Quality (AHRQ). National Healthcare Disparities Report 2007. Feburary 2008. Available at: https://archive.ahrq.gov/qual/nhdr07/nhdr07.pdf. Accessed on December 11, 2017.
- Gaudette É.,
- Tysinger B.,
- Cassil A.,
- Goldman D.P.