Quality of Life Post Heart Failure Diagnosis: Population-Level Trends in the U.S

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Individuals with heart failure (HF) experience various symptoms making both diagnosis and disease burden estimates challenging. While HF-specific patient-reported outcome measures (PROMs) are widely used, their focus on clinical cohorts limits their generalizability. Preference-based measures like the EQ-5D enable standardized health-related quality of life (QoL) comparisons across conditions, supporting resource allocation decisions. The CDC’s Healthy Days (HD) Survey—a simple two-question tool that can be mapped to the EQ-5D—offers a broader approach to tracking QoL but remains underutilized in HF populations. Methods. Using a nationally representative U.S. sample, we mapped HD Survey responses to EQ-5D utility scores to compare QoL between individuals with and without HF and examined changes in QoL over time. We assessed whether HD-derived scores align with HF-specific utility measures to support population-level health monitoring. Results. Individuals with HF report significantly more physically unhealthy days (8.46 vs. 3.42) and mentally unhealthy days (5.42 vs. 3.86) compared to those without HF. HF respondents are, on average, 20 years older than those without HF, consistent with HF's prevalence in older adults. The likelihood of an HF diagnosis is similar for men and women but higher among non-Hispanic whites and blacks than Hispanics and other races. Those with HF are more likely to have health insurance. Adjusting for age, sex, race, and insurance, mean EQ-5D utility scores for individuals with and without HF are 0.785 (95% CI: 0.714–0.825) and 0.840 (95% CI: 0.827–0.851), respectively. Utility scores for HF patients remain significantly lower than those without HF up to 10 years post-diagnosis. Conclusion. HF reduces QoL by 6.55%, surpassing the clinically significant threshold of a 1–2% decrement. These findings highlight the potential of the HD Survey to inform public health monitoring and underscore the need for tailored interventions to address QoL deficits in HF populations. Heart failure Quality of life (QoL) Healthy Days (HD) Survey EQ-5D utility scores Health-related quality of life (HRQoL) Figures Figure 1 Background Heart failure (HF) has diverse and variable symptoms such as dyspnea, fatigue, edema, fluid accumulation, irregular heartbeats, cognitive impairment, and chest pain (1). These symptoms complicate diagnosis and make assessing the disease burden challenging. Research on health-related quality of life (QoL) in HF patients and how this differs from that of the general population is limited and often outdated. Most studies focus on HF-specific patient-reported outcome measures (PROMs) collected from clinical cohorts which often do not fully generalize to the broader population with the disease (i.e., they skew younger, healthier, have more specialized care and have higher socio-economics statuses), restricting comparisons with other populations (2). Furthermore, the research available is predominantly international, with minimal representation of U.S.-based populations (3–8). The two most common assessments for heart failure are The Kansas City Cardiomyopathy Questionnaire (KCCQ) (9) and the Minnesota Living with Heart Failure Questionnaire (MLHFQ) (2,10). Both assessment tools have been designed to measure the effects of heart failure on patients' daily lives and have been validated, showing strong sensitivity to changes in health status over time (9,11) and predicting clinical outcomes such as hospitalizations and mortality (12–14). While both instruments are valuable tools for assessing the impact of HF, both the KCCQ and the MLHFQ need algorithms that map the questionnaire responses to utility scores obtained from preference-based measures such as the EuroQol five-dimensional (EQ-5D) to produce utility scores suitable for broader comparisons and cost-effectiveness analyses (15,16). Preference-based measures are preferable to HF-specific PROMs because they provide a standardized way of assessing health-related quality of life across different health conditions, allowing for comparability between studies and facilitating decision-making in healthcare resource allocation. Mapping to the EQ-5D or other preference-based algorithms that generate utility scores allow disease-specific questionnaires that focus narrowly on aspects relevant to a specific condition to be eventually transformed into single utility values. Preference-based measures linked to more general QoL questionnaires, like the EQ-5D, use information representative of the broader population to elicit preferences for different health states through valuation exercises, such as time trade-offs or standard gambling methods (17). This approach produces values that reflect societal preferences regarding the desirability of different health outcomes and can be used to calculate quality-adjusted life years (QALYs), which are needed in cost-effectiveness analyses. The Healthy Days (HD) Survey is an instrument developed by the Centers for Disease Control and Prevention (CDC) to assess the general population's self-reported health-related quality of life, rather than HF-specific QoL. To our knowledge, the Healthy Days survey has not yet been used to assess QOL in patients with HF. The HD survey consists of two questions and can be found in CDC-sponsored surveys, such as the Behavioral Risk Factor Surveillance System (BRFSS) (18) and the National Health and Nutrition Examination Survey (NHANES) (19), which are openly available to the public. Participants are asked to report the number of days in the past month when they experienced poor physical and mental health (referred to as "unhealthy days"). Jia & Lubetkin (2008) proposed a mapping for this survey to obtain preference-based values for the EQ-5D utility score (20). As such, the HD survey could be more widely used in public health research and surveillance to monitor population health and track trends over time. We use the HD instrument to compare QoL between people with and without heart failure and for tracking the change in QoL over time for people diagnosed with the condition in a US representative population. Because the Healthy Days survey is considerably shorter and, therefore, easier to implement than disease specific questionnaires, we ask whether, at a population level, it can produce similar QoL values for people with and without HF by comparing our results to those in the literature using a common instrument (in our case the Eq5d mapped from the KCCQ) to elicit population-based preferences. Data and Methods The HD questionnaire in the publicly available NHANES is available for the years 2001 through 2012, covering six two-year survey cohorts. From a total of 61,951 individuals interviewed across these survey years, 32,766 (52.89%) responded to both the physically and mentally unhealthy days questions. The HD questionnaire was administered to respondents aged 12 years and older (21). We restrict our sample to individuals older than 18 years to align with the focus on adult health outcomes. Detailed sample sizes by cohort and variables used in the analysis are provided in Additional File 1, Appendices A and B. According to U.S. Department of Health and Human Services regulations (45 CFR 46.102) and institutional guidelines, studies using publicly available datasets do not constitute 'human subjects research' and are therefore exempt from Institutional Review Board (IRB) review. Since this dataset is explicitly designed and released for public use, ethics approval was not required for this study. NHANES employs a stratified multi-stage, unequal probability cluster sampling design to ensure that the sample is representative of the total civilian non-institutionalized population of the United States. Each participant is assigned a sample weight reflecting the number of people represented by that individual, accounting for survey non-response, over-sampling, post-stratification, and sampling error. These weights were incorporated into all analyses using the svyset and svy commands in Stata/SE 18 Stata (College Station, TX, USA: StataCorp LP). Population preference-based QoL is a weighted measure on a scale from 0 to 1, where 0 represents death and 1 represents perfect health. We computed QoL using NHANES survey data and Jia and Lubetkin’s (2008) mapping to obtain preference-based values for the EQ-5D questionnaire index, based on respondents’ HD questions. This allow us to estimate average patient utility levels for persons with HF and without HF. Additional Files 1, Appendix C shows Jia and Lubetkin’s (2008) algorithm producing a single utility score that ranges from 0 to 1 where 1 represents perfect health and 0 represents death. To create a baseline QOL, we define: HD = 30- minimum (30, PUD + MUD), where physically unhealthy days (PUD) and mentally unhealthy days (MUD) were combined, capped at a maximum of 30 days. The resulting measure was then transformed using the Jia and Lubetkin algorithm to produce a single utility score ranging from 0 to 1. Analytical Approach To examine the association between HF and QoL, we conducted two main analyses: Longitudinal Impact of HF : We assessed QoL for individuals with HF at different time points (years since HF diagnosis) to evaluate disease progression and its impact on health status. Years since diagnosis were calculated by subtracting the reported age of HF diagnosis from the age at the time of the survey. We estimated mean EQ-5D utility scores for individuals with and without HF after controlling for the mean age at diagnosis and estimated annual average marginal EQ-5D utility score decrement (dy/dx) following the year of diagnosis. By design, pre-HF utility scores are not available. To address this limitation, we compared the utility scores of individuals with HF from age 48 onward to those without HF at the same age. Comparison of HF and Non-HF Groups : We compared QoL between individuals with and without HF, controlling for confounders, including age, sex, race, and insurance status. We employed the post-double lasso (PDL) method to select covariates and estimate causal effects for robustness. In the first stage, separate lasso regressions were performed: one to identify predictors of QoL and another to identify confounders associated with HF. Variables selected in these lasso regressions were included in a second-stage ordinary least squares (OLS) regression to estimate the effect of HF on QoL while minimizing bias (22,23) . We used t-tests to compare utility scores between HF and non-HF groups. Since six cohorts were combined, weighted averages were calculated to account for differences in sample size and population representation across cycles (24). All statistical analyses were conducted using Stata/SE 18. The NHANES survey design and weights were incorporated into all analyses using the svy commands to ensure valid population estimates and variance calculations. Our findings were compared against previously published results using the EQ-5D instrument. Literature comparisons were based on criteria such as methodology, time since diagnosis, and confounding adjustments to contextualize and validate our results within the broader body of evidence on HF and QoL. Results Table 1 presents the demographic and health-related characteristics of individuals with and without HF. Respondents with HF reported significantly more physically and mentally unhealthy days compared to those without HF. Specifically, the mean number of physically unhealthy days was 8.46 among those with HF, compared to 3.42 among those without HF. Similarly, the mean number of mentally unhealthy days was 5.42 for individuals with HF, compared to 3.86 for those without HF. Missing responses to the HD questionnaire were comparable between the two groups, with 14% missing in the HF group and 13% in the non-HF group. Individuals with HF were significantly older than those without HF, with a mean age of 66.72 years compared to 46.06 years, reflecting the higher prevalence of HF among older adults. The average age at HF diagnosis was approximately 57.71 years. The distribution of HF diagnoses was similar between men and women, with 50.80% of individuals with HF identifying as male compared to 47.95% in the non-HF group. However, racial and ethnic disparities were evident. Non-Hispanic White (73.87%) and Black (14.52%) individuals were more likely to report a diagnosis of HF compared to Hispanic (6.47%) and other racial/ethnic groups (5.12%). Insurance coverage was significantly higher among individuals with HF, with 92.36% reporting any form of insurance compared to 80.29% among those without HF. Medicare and Medicaid coverage were notably more prevalent in the HF group (66.42% and 14.61%, respectively) compared to the non-HF group (15.65% and 5.37%, respectively). Conversely, private insurance was more common among individuals without HF (64.03%) than those with HF (44.39%). These results highlight the greater physical and mental health burden, older age profile, and distinct demographic and insurance patterns among individuals with HF compared to those without. Table 1 Demographic and Health Characteristics of Individuals with and without Heart Failure Characteristics HF (N = 1,142) No HF (N = 31,879) Unhealthy days*—Physical (mean, SD) 8.46 3.42 Unhealthy days*—Mental (mean, SD) 5.42 3.86 Missing** 159 (14%) 4,219 (13%) Age (mean, SD) 66.72 46.06 Age at diagnosis (mean, SD) 57.71 N/A Male (%) 50.80 47.95 Race (%) White 73.87 69.64 Black 14.52 11.22 Hispanic 6.47 13.01 Other/non-available 5.12 6.12 Insurance (any, yes = 1) (%) 92.36 80.29 Private 44.39 64.03 Medicare 66.42 15.65 Medicaid 14.61 5.37 *coded as a number between 0 and 30. ** Not all survey participants respond to the HD questionnaire Figure 1 shows the quality of life (QoL) scores and their annual rate of change (dy/dx) for individuals with and without HF, starting at a baseline age of 48 years. At age 48, individuals with HF have a significantly lower baseline QoL score (0.74; 95% CI: 0.67–0.80) compared to those without HF (0.853; 95% CI: 0.849–0.856). For individuals with HF, QoL declines annually at an estimated rate of -0.0056 (95% CI: -0.0071 to 0.0001). This indicates a slightly faster decline in QoL over time, though the confidence interval includes zero, suggesting large variations across patients. For individuals without HF, QoL is more consistent, and confidence intervals are narrower. The results show that quality of life is significantly and substantially lower for individuals with HF compared to their non-HF counterparts, and these differences persist over time following diagnosis. Table 2 presents the coefficients and 95% confidence intervals (CIs) from two regression models comparing quality of life (QoL) scores between individuals with and without heart failure (HF). Model 1 excludes covariates, while Model 2 includes adjustments for sex, age, race, and insurance status. Both models indicate statistically significant differences in QoL between individuals with and without HF. Model 1 estimates a QoL reduction of -0.107 (95% CI: -0.123, -0.091) for individuals with HF compared to those without. After adjusting for covariates, Model 2 shows that approximately half of this decrement (-0.055; 95% CI: -0.071, -0.040) can be attributed to differences in demographic and socioeconomic factors. It is important to note that statistical significance does not necessarily equate to clinical significance. On the 0–1 utility scale, decrements exceeding 10% are often associated with substantial changes in patient-reported outcomes, potentially necessitating adjustments to treatment protocols ( 25 ). The literature on minimum clinically important differences in QoL in patients with chronic disease indicates that these differences typically correspond to half a standard deviation ( 26 ), equivalent to a 1–2% deviation from the mean in most samples. Table 2 Regression analysis (HF vs. No HF) with (model 2) and without covariates (model 1) Dependent variable: utility scores Coefficient (model 1) 95% CI Lower; Upper Coefficient (model 2) 95% CI Lower; Upper HF(= 1) -0.107 -0.123; -0.091 -0.055 -0.071; -0.040 Current Age-48 -0.002 -0.002; -0.001 Male(= 1) 0.032 0.028; 0.037 Hispanic 0.000 -0.011; 0.012 NH White -0.012 -0.023; -0.001 NH Black -0.007 -0.018; 0.005 Private insurance 0.025 0.019; 0.031 Medicare -0.017 -0.026; -0.007 Medicaid -0.053 -0.064; -0.042 constant 0.853 0.849; 0.856 .840 .827; .851 Comparison group for insurance status: other insurance or no insurance. Comparison group for race: Other race. Comparison group for male: female. We put into perspective our results by comparing the decrement found here with that of other studies. Based on the most recent meta-analyses published at the time of writing ( 27 ) only two studies are US based, all other studies represent samples from Germany, Japan, Scandinavia and Canada. In the US studies the decrement in utility from having HF compared to not having HF is -0.064 and − 0.042 ( 28 , 29 ), respectively. Our estimate of -0.055 aligns closely with these findings. The decrement in quality of life over time among patients diagnosed with HF appears to be considerably larger than those found in this study, albeit the follow up in these studies is less than one year ( 3 , 30 ). Discussion The Healthy Days survey provides a unique opportunity to derive preference-based QoL measures from just two simple questions, offering a practical tool for evaluating population health. To our knowledge, this survey has not been previously used to compare QoL between individuals with and without (HF or to track changes in QoL among HF populations over time. By mapping these data to preference-based indicators, we gain insights into utilities that, when combined with life expectancy, can generate QALYs. QALYs are essential for translating health gains into standardized metrics, facilitating comparisons across conditions and enabling the calculation of incremental cost-effectiveness ratios (ICERs) at optimal thresholds. Nationally representative surveys like NHANES are particularly valuable for tracking QoL in diverse populations, as they provide robust and generalizable data. Unfortunately, the Healthy Days survey is no longer administered, leaving a critical gap in the ability to track QoL at a national level with a tool that can be easily mapped to utility scores. Given its simplicity and utility, our findings indicate the need for the reintroduction of the HD survey in national health datasets to monitor and address the burden of chronic diseases like HF. One limitation of this analysis is the inability to stratify QoL by HF severity, as individuals with varying degrees of disease progression are pooled together. Notably, we are unable to account for decrements during acute events that characterize HF (i.e. hospitalizations and the refractory period of QoL recovery after an acute decompensation). This aggregation could mask nuanced differences in QoL among subgroups. Additionally, response bias might skew the results if healthier individuals are more likely to participate in the survey. However, our findings indicate that non-response rates for the HF questionnaire are nearly identical between those with HF (14%) and those without (13%), suggesting that response bias may not be a significant concern (Table 1 ). Conclusion Our findings underscore the profound and ongoing impact of HF on health-related quality of life. People with HF not only exhibit significantly lower QoL compared to those without HF but also experience a faster rate of decline in QoL over time compared to those without HF. These results highlight the importance of addressing the long-term burden of HF and prioritizing interventions that improve QoL in affected populations. While the heterogeneity of HF patients, ranging from mildly symptomatic to critically ill, presents a limitation to direct application to specific subgroups, our study provides valuable insights at a population level. The analysis remains useful for understanding the steady state of HF QoL. The methods employed here, including the use of the Healthy Days survey, offer a robust framework for assessing QoL in potentially any population where such measures are collected. Reintroducing these straightforward but impactful survey questions into national datasets would be a critical step in capturing the societal burden of chronic conditions like HF. Such data would enhance our ability to monitor trends, inform targeted interventions, and ultimately improve the lived experience of individuals coping with HF at the population level. Abbreviations HF = Heart Failure PROMs = Patient reported outcome measures QoL = quality of life HD = Healthy Days KCCQ = Kansas City Cardiomyopathy Questionnaire MLHFQ = Minnesota Living with Heart Failure Questionnaire EQ-5D = EuroQol five-dimensional CDC = Centers for Disease Control and Prevention BRFSS = Behavioral Risk Factor Surveillance System NHANES = National Health and Nutrition Examination Survey PUD = Physically unhealthy days MUD = Mentally unhealthy days PDL = post-double lasso OLS = ordinary least squares CIs = Confidence Intervals QALYs = Quality-adjusted life years ICERs = Incremental cost-effectiveness ratios Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed in the study are available from the corresponding author on reasonable request. Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationship that could be contrived as a potential competing interest. Funding This work was supported in part by Lexicon Pharmaceuticals. Authors’ Contributions Full access to all of the study’s data and is responsible for its integrity and accuracy: M.A. Concept and designs: M.A., S.D., S.S., P.K., K.A., Z.Z., W.W Acquisition and analysis of data: M.A. Drafting of the manuscript: M.A. Read, edited and approved the final manuscript: M.A., S.D., S.S., P.K., K.A., Z.Z., W.W References Albert N, Trochelman K, Li J, Lin S. Signs and Symptoms of Heart Failure: Are You Asking the Right Questions? Am J Crit Care. 2010;19(5):443–52. Mendes JL, Dos Santos CM, Sousa-Pinto B. Assessment of patient-reported outcomes measures in heart failure: a systematic review. 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Supplementary Files AdditionalFile1QualityofLife.docx Cite Share Download PDF Status: Published Journal Publication published 30 May, 2025 Read the published version in Health and Quality of Life Outcomes → Version 1 posted Editorial decision: Revision requested 22 Feb, 2025 Reviews received at journal 22 Feb, 2025 Reviewers agreed at journal 17 Feb, 2025 Reviews received at journal 21 Jan, 2025 Reviewers agreed at journal 21 Jan, 2025 Reviewers invited by journal 21 Jan, 2025 Editor assigned by journal 19 Dec, 2024 Submission checks completed at journal 19 Dec, 2024 First submitted to journal 18 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"","lastName":"Andrade","suffix":""},{"id":392398792,"identity":"06bfc059-bde5-4c55-8e50-0f01185015ff","order_by":5,"name":"Zugui Zhang","email":"","orcid":"","institution":"Christiana Care Health System","correspondingAuthor":false,"prefix":"","firstName":"Zugui","middleName":"","lastName":"Zhang","suffix":""},{"id":392398793,"identity":"52573373-1ded-4f3f-9ad4-2ddba69d1aa3","order_by":6,"name":"William Weintraub","email":"","orcid":"","institution":"Georgetown University","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Weintraub","suffix":""}],"badges":[],"createdAt":"2024-12-18 21:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5672161/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5672161/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12955-025-02372-0","type":"published","date":"2025-05-30T15:57:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72337403,"identity":"b2bd2946-8f24-4d63-955e-62d3d2ebb42d","added_by":"auto","created_at":"2024-12-25 16:03:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65350,"visible":true,"origin":"","legend":"\u003cp\u003eQoL and Annual Rate of Change (dy/dx) for individuals with and without HF starting at age=48\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5672161/v1/e5573ac902d3b373e6f45c53.png"},{"id":83783641,"identity":"7d11d240-602e-453c-a385-e08ab3d8d3be","added_by":"auto","created_at":"2025-06-02 16:12:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":631071,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5672161/v1/f26b21c2-56e8-46ad-9344-b59fa2f8be6f.pdf"},{"id":72338133,"identity":"7c23bd3a-3fcd-465a-b33b-3f0b1ca26161","added_by":"auto","created_at":"2024-12-25 16:11:18","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15549,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1QualityofLife.docx","url":"https://assets-eu.researchsquare.com/files/rs-5672161/v1/27fa845a9573576875a0ea68.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quality of Life Post Heart Failure Diagnosis: Population-Level Trends in the U.S","fulltext":[{"header":"Background","content":"\u003cp\u003eHeart failure (HF) has diverse and variable symptoms such as dyspnea, fatigue, edema, fluid accumulation, irregular heartbeats, cognitive impairment, and chest pain\u0026nbsp;(1). These symptoms complicate diagnosis and make assessing the disease burden challenging. Research on health-related quality of life (QoL) in HF patients and how this differs from that of the general population is limited and often outdated. Most studies focus on HF-specific patient-reported outcome measures (PROMs) collected from clinical cohorts which often do not fully generalize to the broader population with the disease (i.e., they skew younger, healthier, have more specialized care and have higher socio-economics statuses), restricting comparisons with other populations\u0026nbsp;(2). Furthermore, the research available is predominantly international, with minimal representation of U.S.-based populations\u0026nbsp;(3–8).\u003c/p\u003e\n\u003cp\u003eThe two most common assessments for heart failure are The Kansas City Cardiomyopathy Questionnaire (KCCQ)\u0026nbsp;(9)\u0026nbsp;and the Minnesota Living with Heart Failure Questionnaire (MLHFQ)\u0026nbsp;(2,10). Both assessment tools have been designed to measure the effects of heart failure on patients' daily lives and have been validated, showing strong sensitivity to changes in health status over time\u0026nbsp;(9,11)\u0026nbsp;and predicting clinical outcomes such as hospitalizations and mortality\u0026nbsp;(12–14).\u0026nbsp;While both instruments are valuable tools for assessing the impact of HF, both the KCCQ and the MLHFQ need algorithms that map the questionnaire responses to utility scores obtained from preference-based measures such as the EuroQol five-dimensional (EQ-5D) to produce utility scores suitable for broader comparisons and cost-effectiveness analyses (15,16).\u003c/p\u003e\n\u003cp\u003ePreference-based measures are preferable to HF-specific PROMs because they provide a standardized way of assessing health-related quality of life across different health conditions, allowing for comparability between studies and facilitating decision-making in healthcare resource allocation.\u0026nbsp;Mapping to the EQ-5D or other preference-based algorithms that generate utility scores allow disease-specific questionnaires that focus narrowly on aspects relevant to a specific condition to be eventually transformed into single utility values. Preference-based measures linked to more general QoL questionnaires, like the EQ-5D, use information representative of the broader population to elicit preferences for different health states through valuation exercises, such as time trade-offs or standard gambling methods (17). This approach produces\u0026nbsp;values that reflect societal preferences regarding the desirability of different health outcomes and can be used to calculate quality-adjusted life years (QALYs), which are needed in cost-effectiveness analyses.\u003c/p\u003e\n\u003cp\u003eThe Healthy Days (HD) Survey is an instrument developed by the Centers for Disease Control and Prevention (CDC) to assess the general population's self-reported health-related quality of life, rather than HF-specific QoL. To our knowledge, the Healthy Days survey has not yet been used to assess QOL in patients with HF. The HD survey consists of two questions and can be found in CDC-sponsored surveys, such as the Behavioral Risk Factor Surveillance System (BRFSS) (18) and the National Health and Nutrition Examination Survey (NHANES) (19), which are openly available to the public. Participants are asked to report the number of days in the past month when they experienced poor physical and mental health (referred to as \"unhealthy days\"). Jia \u0026amp; Lubetkin (2008) proposed a mapping for this survey to obtain preference-based values for the EQ-5D utility score (20).\u0026nbsp;As such, the HD survey could be more widely used in public health research and surveillance to monitor population health and track trends over time. \u0026nbsp;We use the HD instrument to compare QoL between people with and without heart failure and for tracking the change in QoL over time for people diagnosed with the condition in a US representative population.\u003c/p\u003e\n\u003cp\u003eBecause the Healthy Days survey is considerably shorter and, therefore, easier to implement than disease specific questionnaires, we ask whether, at a population level, it can produce similar QoL values for people with and without HF by comparing our results to those in the literature using a common instrument (in our case the Eq5d mapped from the KCCQ) to elicit population-based preferences.\u003c/p\u003e"},{"header":"Data and Methods","content":"\u003cp\u003eThe HD questionnaire in the publicly available NHANES is available for the years 2001 through 2012, covering six two-year survey cohorts. From a total of 61,951 individuals interviewed across these survey years, 32,766 (52.89%) responded to both the physically and mentally unhealthy days questions. The HD questionnaire was administered to respondents aged 12 years and older (21). We restrict our sample to individuals older than 18 years to align with the focus on adult health outcomes. Detailed sample sizes by cohort and variables used in the analysis are provided in Additional File 1, Appendices A and B. According to U.S. Department of Health and Human Services regulations (45 CFR 46.102) and institutional guidelines, studies using publicly available datasets do not constitute \u0026apos;human subjects research\u0026apos; and are therefore exempt from Institutional Review Board (IRB) review. Since this dataset is explicitly designed and released for public use, ethics approval was not required for this study.\u003c/p\u003e\n\u003cp\u003eNHANES employs a stratified multi-stage, unequal probability cluster sampling design to ensure that the sample is representative of the total civilian non-institutionalized population of the United States. Each participant is assigned a sample weight reflecting the number of people represented by that individual, accounting for survey non-response, over-sampling, post-stratification, and sampling error. These weights were incorporated into all analyses using the svyset and svy commands in Stata/SE 18 Stata (College Station, TX, USA: StataCorp LP).\u003c/p\u003e\n\u003cp\u003ePopulation preference-based QoL is a weighted measure on a scale from 0 to 1, where 0 represents death and 1 represents perfect health. We computed QoL using NHANES survey data and Jia and Lubetkin\u0026rsquo;s (2008) mapping to obtain preference-based values for the EQ-5D questionnaire index, based on respondents\u0026rsquo; HD questions. This allow us to estimate average patient utility levels for persons with HF and without HF. Additional Files 1, Appendix C shows Jia and Lubetkin\u0026rsquo;s (2008) algorithm producing a single utility score that ranges from 0 to 1 where 1 represents perfect health and 0 represents death.\u003c/p\u003e\n\u003cp\u003eTo create a baseline QOL, we define:\u003c/p\u003e\n\u003cp\u003eHD = 30- minimum (30, PUD + MUD),\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ewhere physically unhealthy days (PUD) and mentally unhealthy days (MUD) were combined, capped at a maximum of 30 days. The resulting measure was then transformed using the Jia and Lubetkin algorithm to produce a single utility score ranging from 0 to 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalytical Approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the association between HF and QoL, we conducted two main analyses:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eLongitudinal Impact of HF\u003c/em\u003e\u003c/strong\u003e: We assessed QoL for individuals with HF at different time points (years since HF diagnosis) to evaluate disease progression and its impact on health status. Years since diagnosis were calculated by subtracting the reported age of HF diagnosis from the age at the time of the survey. We estimated mean EQ-5D utility scores for individuals with and without HF after controlling for the mean age at diagnosis and estimated annual average marginal EQ-5D utility score decrement (dy/dx) following the year of diagnosis. By design, pre-HF utility scores are not available. To address this limitation, we compared the utility scores of individuals with HF from age 48 onward to those without HF at the same age.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003e\u003cem\u003eComparison of HF and Non-HF Groups\u003c/em\u003e\u003c/strong\u003e: We compared QoL between individuals with and without HF, controlling for confounders, including age, sex, race, and insurance status.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe employed the post-double lasso (PDL) method to select covariates and estimate causal effects for robustness. In the first stage, separate lasso regressions were performed: one to identify predictors of QoL and another to identify confounders associated with HF. Variables selected in these lasso regressions were included in a second-stage ordinary least squares (OLS) regression to estimate the effect of HF on QoL while minimizing bias (22,23) .\u003c/p\u003e\n\u003cp\u003eWe used t-tests to compare utility scores between HF and non-HF groups. Since six cohorts were combined, weighted averages were calculated to account for differences in sample size and population representation across cycles (24).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using Stata/SE 18. The NHANES survey design and weights were incorporated into all analyses using the svy commands to ensure valid population estimates and variance calculations.\u003c/p\u003e\n\u003cp\u003eOur findings were compared against previously published results using the EQ-5D instrument. Literature comparisons were based on criteria such as methodology, time since diagnosis, and confounding adjustments to contextualize and validate our results within the broader body of evidence on HF and QoL.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic and health-related characteristics of individuals with and without HF. Respondents with HF reported significantly more physically and mentally unhealthy days compared to those without HF. Specifically, the mean number of physically unhealthy days was 8.46 among those with HF, compared to 3.42 among those without HF. Similarly, the mean number of mentally unhealthy days was 5.42 for individuals with HF, compared to 3.86 for those without HF. Missing responses to the HD questionnaire were comparable between the two groups, with 14% missing in the HF group and 13% in the non-HF group.\u003c/p\u003e \u003cp\u003eIndividuals with HF were significantly older than those without HF, with a mean age of 66.72 years compared to 46.06 years, reflecting the higher prevalence of HF among older adults. The average age at HF diagnosis was approximately 57.71 years.\u003c/p\u003e \u003cp\u003eThe distribution of HF diagnoses was similar between men and women, with 50.80% of individuals with HF identifying as male compared to 47.95% in the non-HF group. However, racial and ethnic disparities were evident. Non-Hispanic White (73.87%) and Black (14.52%) individuals were more likely to report a diagnosis of HF compared to Hispanic (6.47%) and other racial/ethnic groups (5.12%).\u003c/p\u003e \u003cp\u003eInsurance coverage was significantly higher among individuals with HF, with 92.36% reporting any form of insurance compared to 80.29% among those without HF. Medicare and Medicaid coverage were notably more prevalent in the HF group (66.42% and 14.61%, respectively) compared to the non-HF group (15.65% and 5.37%, respectively). Conversely, private insurance was more common among individuals without HF (64.03%) than those with HF (44.39%).\u003c/p\u003e \u003cp\u003eThese results highlight the greater physical and mental health burden, older age profile, and distinct demographic and insurance patterns among individuals with HF compared to those without.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and Health Characteristics of Individuals with and without Heart Failure\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHF (N\u0026thinsp;=\u0026thinsp;1,142)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo HF (N\u0026thinsp;=\u0026thinsp;31,879)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnhealthy days*\u0026mdash;Physical (mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnhealthy days*\u0026mdash;Mental (mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,219 (13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis (mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther/non-available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsurance (any, yes\u0026thinsp;=\u0026thinsp;1) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*coded as a number between 0 and 30. ** Not all survey participants respond to the HD questionnaire\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the quality of life (QoL) scores and their annual rate of change (dy/dx) for individuals with and without HF, starting at a baseline age of 48 years. At age 48, individuals with HF have a significantly lower baseline QoL score (0.74; 95% CI: 0.67\u0026ndash;0.80) compared to those without HF (0.853; 95% CI: 0.849\u0026ndash;0.856). For individuals with HF, QoL declines annually at an estimated rate of -0.0056 (95% CI: -0.0071 to 0.0001). This indicates a slightly faster decline in QoL over time, though the confidence interval includes zero, suggesting large variations across patients. For individuals without HF, QoL is more consistent, and confidence intervals are narrower. The results show that quality of life is significantly and substantially lower for individuals with HF compared to their non-HF counterparts, and these differences persist over time following diagnosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the coefficients and 95% confidence intervals (CIs) from two regression models comparing quality of life (QoL) scores between individuals with and without heart failure (HF). Model 1 excludes covariates, while Model 2 includes adjustments for sex, age, race, and insurance status. Both models indicate statistically significant differences in QoL between individuals with and without HF. Model 1 estimates a QoL reduction of -0.107 (95% CI: -0.123, -0.091) for individuals with HF compared to those without. After adjusting for covariates, Model 2 shows that approximately half of this decrement (-0.055; 95% CI: -0.071, -0.040) can be attributed to differences in demographic and socioeconomic factors.\u003c/p\u003e \u003cp\u003eIt is important to note that statistical significance does not necessarily equate to clinical significance. On the 0\u0026ndash;1 utility scale, decrements exceeding 10% are often associated with substantial changes in patient-reported outcomes, potentially necessitating adjustments to treatment protocols (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The literature on minimum clinically important differences in QoL in patients with chronic disease indicates that these differences typically correspond to half a standard deviation (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), equivalent to a 1\u0026ndash;2% deviation from the mean in most samples.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression analysis (HF vs. No HF) with (model 2) and without covariates (model 1)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable: utility scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003cp\u003e(model 1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI Lower; Upper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient (model 2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI Lower; Upper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHF(=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.123; -0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.071; -0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Age-48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.002; -0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale(=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.028; 0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.011; 0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.023; -0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNH Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.018; 0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019; 0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.026; -0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.064; -0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.849; 0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.827; .851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eComparison group for insurance status: other insurance or no insurance. Comparison group for race: Other race. Comparison group for male: female.\u003c/p\u003e \u003cp\u003eWe put into perspective our results by comparing the decrement found here with that of other studies. Based on the most recent meta-analyses published at the time of writing (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) only two studies are US based, all other studies represent samples from Germany, Japan, Scandinavia and Canada. In the US studies the decrement in utility from having HF compared to not having HF is -0.064 and \u0026minus;\u0026thinsp;0.042 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), respectively. Our estimate of -0.055 aligns closely with these findings. The decrement in quality of life over time among patients diagnosed with HF appears to be considerably larger than those found in this study, albeit the follow up in these studies is less than one year (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe Healthy Days survey provides a unique opportunity to derive preference-based QoL measures from just two simple questions, offering a practical tool for evaluating population health. To our knowledge, this survey has not been previously used to compare QoL between individuals with and without (HF or to track changes in QoL among HF populations over time. By mapping these data to preference-based indicators, we gain insights into utilities that, when combined with life expectancy, can generate QALYs. QALYs are essential for translating health gains into standardized metrics, facilitating comparisons across conditions and enabling the calculation of incremental cost-effectiveness ratios (ICERs) at optimal thresholds.\u003c/p\u003e \u003cp\u003eNationally representative surveys like NHANES are particularly valuable for tracking QoL in diverse populations, as they provide robust and generalizable data. Unfortunately, the Healthy Days survey is no longer administered, leaving a critical gap in the ability to track QoL at a national level with a tool that can be easily mapped to utility scores. Given its simplicity and utility, our findings indicate the need for the reintroduction of the HD survey in national health datasets to monitor and address the burden of chronic diseases like HF.\u003c/p\u003e \u003cp\u003eOne limitation of this analysis is the inability to stratify QoL by HF severity, as individuals with varying degrees of disease progression are pooled together. Notably, we are unable to account for decrements during acute events that characterize HF (i.e. hospitalizations and the refractory period of QoL recovery after an acute decompensation). This aggregation could mask nuanced differences in QoL among subgroups. Additionally, response bias might skew the results if healthier individuals are more likely to participate in the survey. However, our findings indicate that non-response rates for the HF questionnaire are nearly identical between those with HF (14%) and those without (13%), suggesting that response bias may not be a significant concern (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings underscore the profound and ongoing impact of HF on health-related quality of life. People with HF not only exhibit significantly lower QoL compared to those without HF but also experience a faster rate of decline in QoL over time compared to those without HF. These results highlight the importance of addressing the long-term burden of HF and prioritizing interventions that improve QoL in affected populations. While the heterogeneity of HF patients, ranging from mildly symptomatic to critically ill, presents a limitation to direct application to specific subgroups, our study provides valuable insights at a population level. The analysis remains useful for understanding the steady state of HF QoL. The methods employed here, including the use of the Healthy Days survey, offer a robust framework for assessing QoL in potentially any population where such measures are collected. Reintroducing these straightforward but impactful survey questions into national datasets would be a critical step in capturing the societal burden of chronic conditions like HF. Such data would enhance our ability to monitor trends, inform targeted interventions, and ultimately improve the lived experience of individuals coping with HF at the population level.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHF = Heart Failure\u003c/p\u003e\n\u003cp\u003ePROMs = Patient reported outcome measures\u003c/p\u003e\n\u003cp\u003eQoL = quality of life\u003c/p\u003e\n\u003cp\u003eHD = Healthy Days\u003c/p\u003e\n\u003cp\u003eKCCQ = Kansas City Cardiomyopathy Questionnaire\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMLHFQ = Minnesota Living with Heart Failure Questionnaire\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEQ-5D = EuroQol five-dimensional\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCDC = Centers for Disease Control and Prevention\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBRFSS = Behavioral Risk Factor Surveillance System\u003c/p\u003e\n\u003cp\u003eNHANES = National Health and Nutrition Examination Survey\u003c/p\u003e\n\u003cp\u003ePUD = Physically unhealthy days\u003c/p\u003e\n\u003cp\u003eMUD = Mentally unhealthy days\u003c/p\u003e\n\u003cp\u003ePDL = post-double lasso\u003c/p\u003e\n\u003cp\u003eOLS = ordinary least squares\u003c/p\u003e\n\u003cp\u003eCIs = Confidence Intervals\u003c/p\u003e\n\u003cp\u003eQALYs = Quality-adjusted life years\u003c/p\u003e\n\u003cp\u003eICERs = Incremental cost-effectiveness ratios\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed in the study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationship that could be contrived as a potential competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by Lexicon Pharmaceuticals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFull access to all of the study\u0026rsquo;s data and is responsible for its integrity and accuracy: M.A.\u003c/p\u003e\n\u003cp\u003eConcept and designs: M.A., S.D., S.S., P.K., K.A., Z.Z., W.W\u003c/p\u003e\n\u003cp\u003eAcquisition and analysis of data: M.A.\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: M.A.\u003c/p\u003e\n\u003cp\u003eRead, edited and approved the final manuscript: M.A., S.D., S.S., P.K., K.A., Z.Z., W.W\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlbert N, Trochelman K, Li J, Lin S. Signs and Symptoms of Heart Failure: Are You Asking the Right Questions? Am J Crit Care. 2010;19(5):443\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendes JL, Dos Santos CM, Sousa-Pinto B. Assessment of patient-reported outcomes measures in heart failure: a systematic review. 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Eur J Heart Fail. 2016;18(3):306\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJorge AJL, Rosa MLG, Correia DMDS, Martins WDA, Ceron DMM, Coelho LCF et al. Evaluation of Quality of Life in Patients with and without Heart Failure in Primary Care. Arquivos Brasileiros de Cardiologia [Internet]. 2017 [cited 2024 Jun 5]; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.scielo.br/scielo.php?script=sci_arttext\u0026amp;pid=S0066-782X2017000900248\u003c/span\u003e\u003cspan address=\"https://www.scielo.br/scielo.php?script=sci_arttext\u0026amp;pid=S0066-782X2017000900248\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeo S, Moser DK, Lennie TA, Zambroski CH, Chung ML. A comparison of health-related quality of life between older adults with heart failure and healthy older adults. 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Int J Cardiol. 2016;202:676\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreen CP, Porter CB, Bresnahan DR, Spertus JA. Development and evaluation of the Kansas City Cardiomyopathy Questionnaire: a new health status measure for heart failure. J Am Coll Cardiol. 2000;35(5):1245\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRector T, Kubo S, Cohn J. Patients\u0026rsquo; self-assessment of their congestive heart failure. Part 2: Content, reliability and validity of a new measure, the Minnesota Living with Heart Failure questionnaire. Heart Fail. 1987;198\u0026ndash;209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRector TS, Cohn JN. Assessment of patient outcome with the Minnesota Living with Heart Failure questionnaire: Reliability and validity during a randomized, double-blind, placebo-controlled trial of pimobendan. 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Assessing responsiveness of generic and specific health related quality of life measures in heart failure. Health Qual Life Outcomes. 2006;4:89.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"health-and-quality-of-life-outcomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hqlo","sideBox":"Learn more about [Health and Quality of Life Outcomes](http://hqlo.biomedcentral.com)","snPcode":"12955","submissionUrl":"https://submission.nature.com/new-submission/12955/3","title":"Health and Quality of Life Outcomes","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Heart failure, Quality of life (QoL), Healthy Days (HD) Survey, EQ-5D utility scores, Health-related quality of life (HRQoL)","lastPublishedDoi":"10.21203/rs.3.rs-5672161/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5672161/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground.\u003c/b\u003e Individuals with heart failure (HF) experience various symptoms making both diagnosis and disease burden estimates challenging. While HF-specific patient-reported outcome measures (PROMs) are widely used, their focus on clinical cohorts limits their generalizability. Preference-based measures like the EQ-5D enable standardized health-related quality of life (QoL) comparisons across conditions, supporting resource allocation decisions. The CDC\u0026rsquo;s Healthy Days (HD) Survey\u0026mdash;a simple two-question tool that can be mapped to the EQ-5D\u0026mdash;offers a broader approach to tracking QoL but remains underutilized in HF populations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods.\u003c/b\u003e Using a nationally representative U.S. sample, we mapped HD Survey responses to EQ-5D utility scores to compare QoL between individuals with and without HF and examined changes in QoL over time. We assessed whether HD-derived scores align with HF-specific utility measures to support population-level health monitoring.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults.\u003c/b\u003e Individuals with HF report significantly more physically unhealthy days (8.46 vs. 3.42) and mentally unhealthy days (5.42 vs. 3.86) compared to those without HF. HF respondents are, on average, 20 years older than those without HF, consistent with HF's prevalence in older adults. The likelihood of an HF diagnosis is similar for men and women but higher among non-Hispanic whites and blacks than Hispanics and other races. Those with HF are more likely to have health insurance.\u003c/p\u003e \u003cp\u003eAdjusting for age, sex, race, and insurance, mean EQ-5D utility scores for individuals with and without HF are 0.785 (95% CI: 0.714\u0026ndash;0.825) and 0.840 (95% CI: 0.827\u0026ndash;0.851), respectively. Utility scores for HF patients remain significantly lower than those without HF up to 10 years post-diagnosis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion.\u003c/b\u003e HF reduces QoL by 6.55%, surpassing the clinically significant threshold of a 1\u0026ndash;2% decrement. These findings highlight the potential of the HD Survey to inform public health monitoring and underscore the need for tailored interventions to address QoL deficits in HF populations.\u003c/p\u003e","manuscriptTitle":"Quality of Life Post Heart Failure Diagnosis: Population-Level Trends in the U.S","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-25 16:03:13","doi":"10.21203/rs.3.rs-5672161/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-22T18:31:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-22T17:00:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32059153635420373941015676184813399964","date":"2025-02-17T14:37:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-22T02:33:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202835623725379876642171450481747356593","date":"2025-01-22T00:43:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-21T14:22:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-19T13:30:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-19T13:28:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Health and Quality of Life Outcomes","date":"2024-12-18T21:08:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"health-and-quality-of-life-outcomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hqlo","sideBox":"Learn more about [Health and Quality of Life Outcomes](http://hqlo.biomedcentral.com)","snPcode":"12955","submissionUrl":"https://submission.nature.com/new-submission/12955/3","title":"Health and Quality of Life Outcomes","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c8c65740-e23c-47cd-a8b7-4aa64af8da6b","owner":[],"postedDate":"December 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T16:11:02+00:00","versionOfRecord":{"articleIdentity":"rs-5672161","link":"https://doi.org/10.1186/s12955-025-02372-0","journal":{"identity":"health-and-quality-of-life-outcomes","isVorOnly":false,"title":"Health and Quality of Life Outcomes"},"publishedOn":"2025-05-30 15:57:25","publishedOnDateReadable":"May 30th, 2025"},"versionCreatedAt":"2024-12-25 16:03:13","video":"","vorDoi":"10.1186/s12955-025-02372-0","vorDoiUrl":"https://doi.org/10.1186/s12955-025-02372-0","workflowStages":[]},"version":"v1","identity":"rs-5672161","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5672161","identity":"rs-5672161","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}