Dasha Cherepanov

US norms for six generic health-related quality-of-life indexes from the National Health Measurement study.

Background:A number of indexes measuring self-reported generic health-related quality-of-life (HRQoL) using preference-weighted scoring are used widely in population surveys and clinical studies in the United States. Objective:To obtain age-by-gender norms for older adults on 6 generic HRQoL indexes in a cross-sectional US population survey and compare age-related trends in HRQoL. Methods:The EuroQol EQ-5D, Health Utilities Index Mark 2, Health Utilities Index Mark 3, SF-36v2™ (used to compute SF-6D), Quality of Well-being Scale self-administered form, and Health and Activities Limitations index were administered via telephone interview to each respondent in a national survey sample of 3844 noninstitutionalized adults age 35–89. Persons age 65–89 and telephone exchanges with high percentages of African Americans were oversampled. Age-by-gender means were computed using sampling and poststratification weights to adjust results to the US adult population. Results:The 6 indexes exhibit similar patterns of age-related HRQoL by gender; however, means differ significantly across indexes. Females report slightly lower HRQoL than do males across all age groups. HRQoL seems somewhat higher for persons age 65–74 compared with people in the next younger age decade, as measured by all indexes. Conclusions:Six HRQoL measures show similar but not identical trends in population norms for older US adults. Results reported here provide reference values for 6 self-reported HRQoL indexes.

Gender differences in health-related quality-of-life are partly explained by sociodemographic and socioeconomic variation between adult men and women in the US: evidence from four US nationally representative data sets

PurposeThe purpose of this study was to describe gender differences in self-reported health-related quality-of-life (HRQoL) and to examine whether differences are explained by sociodemographic and socioeconomic status (SES) differentials between men and women.MethodsData were from four US nationally representative surveys: US Valuation of the EuroQol EQ-5D Health States Survey (USVEQ), Medical Expenditure Panel Survey (MEPS), National Health Measurement Study (NHMS) and Joint Canada/US Survey of Health (JCUSH). Gender differences were estimated with and without adjustment for sociodemographic and SES indicators using regression within and across data sets with SF-6D, EQ-5D, HUI2, HUI3 and QWB-SA scores as outcomes.ResultsWomen have lower HRQoL scores than men on all indexes prior to adjustment. Adjusting for age, race, marital status, education and income reduced but did not remove the gender differences, except with HUI3. Adjusting for marital status or income had the largest impact on estimated gender differences.ConclusionsThere are clear gender differences in HRQoL in the United States. These differences are partly explained by sociodemographic and SES differentials.

Comparison of 5 Health-Related Quality-of-Life Indexes Using Item Response Theory Analysis:

Background. Five health-related quality-of-life (HRQoL) indexes—EQ-5D, HUI2, HUI3, QWB-SA, and SF-6D—are each used to assign community-based utility scores to health states, although these scores differ. Objective. The authors transform these indexes to a common scale to understand their interrelationships. Methods. Data were from the National Health Measurement Study, a telephone survey of 3844 US adults. The 5 indexes were analyzed using item response theory analysis to estimate scores on an underlying construct of summary health, θ. Unidimensionality was evaluated using nonlinear principal components analysis. Index scores were plotted against the estimated scores on the common underlying construct. In addition, scores on the Health and Activities Limitation Index (HALex), the Centers for Disease Control and Prevention Healthy Days questions, and self-rated health on a 5-category scale ranging from excellent to poor were plotted. Results. SF-6D and QWB-SA are nearly linear across the range of θ but with a shallow slope; EQ-5D, HUI2, and HUI3 are linear with a steep slope from low θ (poor health) into midrange of θ, then approximately linear with a less steep slope for higher θ (health just below to well above average), although the inflection points differ by index. Conclusion. Simple linear functions may serve as crosswalks among these indexes only for lower health states, albeit with low precision. Ceiling effects make crosswalks among most of the indexes ill specified above a certain level of health. Although each index measures generic health on a utility scale, these indexes are not identical but are relatively simply, if imprecisely, related.

Gender differences in multiple underlying dimensions of health-related quality of life are associated with sociodemographic and socioeconomic status.

Purpose:The purpose of the study was to examine whether gender differences in summary health-related quality of life (HRQoL) are due to differences in specific dimensions of health, and whether they are explained by sociodemographic and socioeconomic (SES) variation. Methods:The National Health Measurement Study collected cross-sectional data on a national sample of 3648 black and white noninstitutionalized adults ages 35 to 89 years. Data included the Short Form 36-Item survey, which yielded separate Mental and Physical Component Summary scores (MCS and PCS, respectively), and five HRQoL indexes: Short Form 6 dimension, EuroQol 5 dimension, the Health Utilities Indexes Mark 2 and 3, and the Quality of Well-Being Scale Self-Administered form. Structural equation models were used to explore gender differences in physical, psychosocial, and pain latent dimensions of the 5 indexes, adjusting for sociodemographic and SES indicators. Observed MCS and PCS scores were examined in regression models to judge robustness of latent results. Results:Men had better estimated physical and psychosocial health and less pain than women with similar trends on the MCS and PCS scores. Adjustments for marital status or income reduced gender differences more than did other indicators. Adjusting results for partial factorial invariance of HRQoL attributes supported the presence of gender differentials, but also indicated that these differences are impacted by dimensions being related to some HRQoL attributes differently by gender. Conclusions:Men have better estimated health on 3 latent dimensions of HRQoL—physical, psychosocial, and pain—comparable to gender differences on the observed MCS and PCS scores. Gender differences are partly explained by sociodemographic and SES factors, highlighting the role of socioeconomic inequalities in perpetuating gender differences in health outcomes across multiple domains. These results also emphasize the importance of accounting for measurement invariance for meaningful comparison of group differences in estimated means of self-reported measures of health.

Standard Error of Measurement of 5 Health Utility Indexes across the Range of Health for Use in Estimating Reliability and Responsiveness

Background. Standard errors of measurement (SEMs) of health-related quality of life (HRQoL) indexes are not well characterized. SEM is needed to estimate responsiveness statistics, and is a component of reliability. Purpose. To estimate the SEM of 5 HRQoL indexes. Design. The National Health Measurement Study (NHMS) was a population-based survey. The Clinical Outcomes and Measurement of Health Study (COMHS) provided repeated measures. Subjects. A total of 3844 randomly selected adults from the noninstitutionalized population aged 35 to 89 y in the contiguous United States and 265 cataract patients. Measurements. The SF6-36v2™, QWB-SA, EQ-5D, HUI2, and HUI3 were included. An item-response theory approach captured joint variation in indexes into a composite construct of health (theta). The authors estimated 1) the test-retest standard deviation (SEM-TR) from COMHS, 2) the structural standard deviation (SEM-S) around theta from NHMS, and 3) reliability coefficients. Results. SEM-TR was 0.068 (SF-6D), 0.087 (QWB-SA), 0.093 (EQ-5D), 0.100 (HUI2), and 0.134 (HUI3), whereas SEM-S was 0.071, 0.094, 0.084, 0.074, and 0.117, respectively. These yield reliability coefficients 0.66 (COMHS) and 0.71 (NHMS) for SF-6D, 0.59 and 0.64 for QWB-SA, 0.61 and 0.70 for EQ-5D, 0.64 and 0.80 for HUI2, and 0.75 and 0.77 for HUI3, respectively. The SEM varied across levels of health, especially for HUI2, HUI3, and EQ-5D, and was influenced by ceiling effects. Limitations. Repeated measures were 5 mo apart, and estimated theta contained measurement error. Conclusions. The 2 types of SEM are similar and substantial for all the indexes and vary across health.

Health Condition Impacts in a Nationally Representative Cross-Sectional Survey Vary Substantially by Preference-Based Health Index.

Importance: Many cost-utility analyses rely on generic utility measures for estimates of disease impact. Commonly used generic preference-based indexes may generate different absolute estimates of disease burden despite sharing anchors of dead at 0 and full health at 1.0. Objective: We compare the impact of 16 prevalent chronic health conditions using 6 utility-based indexes of health and a visual analog scale. Design: Data were from the National Health Measurement Study (NHMS), a cross-sectional telephone survey of 3844 adults aged 35 to 89 years in the United States. Main Outcome Measures: The NHMS included the EuroQol-5D-3L, Health and Activities Limitation Index (HALex), Health Utilities Index Mark 2 (HUI2) and Mark 3 (HUI3), preference-based scoring for the SF-36v2 (SF-6D), Quality of Well-Being Scale, and visual analog scale. Respondents self-reported 16 chronic conditions. Survey-weighted regression analyses for each index with all health conditions, age, and sex were used to estimate health condition impact estimates in terms of quality-adjusted life years (QALYs) lost over 10 years. All analyses were stratified by ages 35 to 69 and 70 to 89 years. Results: There were significant differences between the indexes for estimates of the absolute impact of most conditions. On average, condition impacts were the smallest with the SF-6D and EQ-5D-3L and the largest with the HALex and HUI3. Likewise, the estimated loss of QALYs varied across indexes. Condition impact estimates for EQ-5D-3L, HUI2, HUI3, and SF-6D generally had strong Spearman correlations across conditions (i.e., >0.69). Limitations: This analysis uses cross-sectional data and lacks health condition severity information. Conclusions: Health condition impact estimates vary substantially across the indexes. These results imply that it is difficult to standardize results across cost-utility analyses that use different utility measures.

Erratum to: Interpersonal discrimination and health-related quality of life among black and white men and women in the United States

ObjectiveWe assessed associations between discrimination and health-related quality of life among black and white men and women in the United States.MethodsWe examined data from the National Health Measurement Study, a nationally representative sample of 3,648 adults aged 35–89 in the non-institutionalized US population. These data include self-reported lifetime and everyday discrimination as well as several health utility indexes (EQ-5D, HUI3, and SF-6D). Multiple regression was used to compute mean health utility scores adjusted for age, income, education, and chronic diseases for each race-by-gender subgroup.ResultsBlack men and women reported more discrimination than white men and women. Health utility tended to be worse as reported discrimination increased. With a few exceptions, differences between mean health utility scores in the lowest and highest discrimination groups exceeded the 0.03 difference generally considered to be a clinically significant difference.ConclusionsPersons who experienced discrimination tended to score lower on health utility measures. The study also revealed a complex relationship between experiences of discrimination and race and gender. Because of these differential social and demographic relationships caution is urged when interpreting self-rated health measures in research, clinical, and policy settings.

Real-world cardiovascular disease burden in patients with atherosclerotic cardiovascular disease: a comprehensive systematic literature review

Abstract Objective: Based on randomized controlled trials (RCTs), non-fatal myocardial infarction (MI) rates range between 9 and 15 events per 1000 person-years, ischemic stroke between 4 and 6 per 1000 person-years, CHD death rates between 5 and 7 events per 1000 person-years, and any major vascular event between 28 and 53 per 1000 person-years in patients with atherosclerotic cardiovascular disease (ASCVD). We reviewed global literature on the topic to determine whether the real-world burden of secondary major adverse cardiovascular events (MACEs) is higher among ASCVD patients. Methods: We searched PubMed and Embase using MeSH/keywords including cardiovascular disease, secondary prevention and observational studies. Studies published in the last 5 years, in English, with ≥50 subjects with elevated low-density lipoprotein cholesterol (LDL-C) or on statins, and reporting secondary MACEs were included. The Newcastle–Ottawa Scale (NOS) was used to assess the quality of each included study. Results: Of 4663 identified articles, 14 studies that reported MACE incidence rates per 1000 person-years were included in the review (NOS grades ranged from 8 to 9; 2 were prospective and 12 were retrospective studies). Reported incidence rates per 1000 person-years had a range (median) of 12.01–39.9 (26.8) for MI, 13.8–57.2 (41.5) for ischemic stroke, 1.0–94.5 (21.1) for CV-related mortality and 9.7–486 (52.6) for all-cause mortality. Rates were 25.8–211 (81.1) for composite of MACEs. Multiple event rates had a range (median) of 60–391 (183) events per 1000 person-years. Conclusions: Our review indicates that MACE rates observed in real-world studies are substantially higher than those reported in RCTs, suggesting that the secondary MACE burden and potential benefits of effective CVD management in ASCVD patients may be underestimated if real-world data are not taken into consideration.

Protecting the Gains: What Changes Are Needed to Prevent a Reversal of the Downward CVD Mortality Trend?

Cardiovascular disease (CVD) mortality has decreased over 60% over the past 50 years in the United States; however, emerging data indicate CVD incidence may be rising because of shifting demographics, increasing risk factor prevalence, and competing needs for limited resources. We projected CVD mortality from 2015 to 2040 given varying informed assumptions regarding changes in risk factor prevalence, uptake of current therapeutic options, and future innovations.