The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update.

The University of Wisconsin Breast Cancer Epidemiology Simulation Model (UWBCS), also referred to as Model W, is a discrete-event microsimulation model that uses a systems engineering approach to replicate breast cancer epidemiology in the US over time. This population-based model simulates the lifetimes of individual women through 4 main model components: breast cancer natural history, detection, treatment, and mortality. A key feature of the UWBCS is that, in addition to specifying a population distribution in tumor growth rates, the model allows for heterogeneity in tumor behavior, with some tumors having limited malignant potential (i.e., would never become fatal in a woman's lifetime if left untreated) and some tumors being very aggressive based on metastatic spread early in their onset. The model is calibrated to Surveillance, Epidemiology, and End Results (SEER) breast cancer incidence and mortality data from 1975 to 2010, and cross-validated against data from the Wisconsin cancer reporting system. The UWBCS model generates detailed outputs including underlying disease states and observed clinical outcomes by age and calendar year, as well as costs, resource usage, and quality of life associated with screening and treatment. The UWBCS has been recently updated to account for differences in breast cancer detection, treatment, and survival by molecular subtypes (defined by ER/HER2 status), to reflect the recent advances in screening and treatment, and to consider a range of breast cancer risk factors, including breast density, race, body-mass-index, and the use of postmenopausal hormone therapy. Therefore, the model can evaluate novel screening strategies, such as risk-based screening, and can assess breast cancer outcomes by breast cancer molecular subtype. In this article, we describe the most up-to-date version of the UWBCS.

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.

Responding to vaccine safety signals during pandemic influenza: a modeling study.

BACKGROUND: Managing emerging vaccine safety signals during an influenza pandemic is challenging. Federal regulators must balance vaccine risks against benefits while maintaining public confidence in the public health system. METHODS: We developed a multi-criteria decision analysis model to explore regulatory decision-making in the context of emerging vaccine safety signals during a pandemic. We simulated vaccine safety surveillance system capabilities and used an age-structured compartmental model to develop potential pandemic scenarios. We used an expert-derived multi-attribute utility function to evaluate potential regulatory responses by combining four outcome measures into a single measure of interest: 1) expected vaccination benefit from averted influenza; 2) expected vaccination risk from vaccine-associated febrile seizures; 3) expected vaccination risk from vaccine-associated Guillain-Barre Syndrome; and 4) expected change in vaccine-seeking behavior in future influenza seasons. RESULTS: Over multiple scenarios, risk communication, with or without suspension of vaccination of high-risk persons, were the consistently preferred regulatory responses over no action or general suspension when safety signals were detected during a pandemic influenza. On average, the expert panel valued near-term vaccine-related outcomes relative to long-term projected outcomes by 3:1. However, when decision-makers had minimal ability to influence near-term outcomes, the response was selected primarily by projected impacts on future vaccine-seeking behavior. CONCLUSIONS: The selected regulatory response depends on how quickly a vaccine safety signal is identified relative to the peak of the pandemic and the initiation of vaccination. Our analysis suggested two areas for future investment: efforts to improve the size and timeliness of the surveillance system and behavioral research to understand changes in vaccine-seeking behavior.

Consequences of false-positive screening mammograms.

IMPORTANCE: False-positive mammograms, a common occurrence in breast cancer screening programs, represent a potential screening harm that is currently being evaluated by the US Preventive Services Task Force. OBJECTIVE: To measure the effect of false-positive mammograms on quality of life by measuring personal anxiety, health utility, and attitudes toward future screening. DESIGN, SETTING, AND PARTICIPANTS: The Digital Mammographic Imaging Screening Trial (DMIST) quality-of-life substudy telephone survey was performed shortly after screening and 1 year later at 22 DMIST sites and included randomly selected DMIST participants with positive and negative mammograms. EXPOSURE: Mammogram requiring follow-up testing or referral without a cancer diagnosis. MAIN OUTCOMES AND MEASURES: The 6-question short form of the Spielberger State-Trait Anxiety Inventory state scale (STAI-6) and the EuroQol EQ-5D instrument with US scoring. Attitudes toward future screening as measured by women's self-report of future intention to undergo mammographic screening and willingness to travel and stay overnight to undergo a hypothetical new type of mammography that would identify as many cancers with half the false-positive results. RESULTS: Among 1450 eligible women invited to participate, 1226 (84.6%) were enrolled, with follow-up interviews obtained in 1028 (83.8%). Anxiety was significantly higher for women with false-positive mammograms (STAI-6, 35.2 vs 32.7), but health utility scores did not differ and there were no significant differences between groups at 1 year. Future screening intentions differed by group (25.7% vs 14.2% more likely in false-positive vs negative groups); willingness to travel and stay overnight did not (9.9% vs 10.5% in false-positive vs negative groups). Future screening intention was significantly increased among women with false-positive mammograms (odds ratio, 2.12; 95% CI, 1.54-2.93), younger age (2.78; 1.5-5.0), and poorer health (1.63; 1.09-2.43). Women's anticipated high-level anxiety regarding future false-positive mammograms was associated with willingness to travel overnight (odds ratio, 1.94; 95% CI, 1.28-2.95). CONCLUSIONS AND RELEVANCE: False-positive mammograms were associated with increased short-term anxiety but not long-term anxiety, and there was no measurable health utility decrement. False-positive mammograms increased women's intention to undergo future breast cancer screening and did not increase their stated willingness to travel to avoid a false-positive result. Our finding of time-limited harm after false-positive screening mammograms is relevant for clinicians who counsel women on mammographic screening and for screening guideline development groups.

Variation in tumor natural history contributes to racial disparities in breast cancer stage at diagnosis.

Black women tend to be diagnosed with breast cancer at a more advanced stage than whites and subsequently experience elevated breast cancer mortality. We sought to determine whether there are racial differences in tumor natural history that contribute to these disparities. We used the University of Wisconsin Breast Cancer Simulation Model, a validated member of the National Cancer Institute's Cancer Intervention and Surveillance Modeling Network, to evaluate the contribution of racial differences in tumor natural history to observed disparities in breast cancer incidence. We fit eight natural history parameters in race-specific models by calibrating to the observed race- and stage-specific 1975-2000 U.S. incidence rates, while accounting for known racial variation in population structure, underlying risk of breast cancer, screening mammography utilization, and mortality from other causes. The best fit models indicated that a number of natural history parameters must vary between blacks and whites to reproduce the observed stage-specific incidence patterns. The mean of the tumor growth rate parameter was 63.6 % higher for blacks than whites (0.18, SE 0.04 vs. 0.11, SE 0.02). The fraction of tumors considered highly aggressive based on their tendency to metastasize at a small size was 2.2 times greater among blacks than whites (0.41, SE 0.009 vs. 0.019, SE 0.008). Based on our simulation model, breast tumors in blacks grow faster and are more likely to metastasize earlier than tumors in whites. These differences suggest that targeted prevention and detection strategies that go beyond equalizing access to mammography may be needed to eliminate breast cancer disparities.

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

OBJECTIVE: We assessed associations between discrimination and health-related quality of life among black and white men and women in the United States. METHODS: We 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. RESULTS: Black 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. CONCLUSIONS: Persons 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.

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

OBJECTIVE: We assessed associations between discrimination and health-related quality of life among black and white men and women in the United States. METHODS: We 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. RESULTS: Black men and women reported more discrimination compared to 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. CONCLUSIONS: Persons 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.

Agreement about identifying patients who change over time: cautionary results in cataract and heart failure patients.

BACKGROUND: Preference-based measures of health-related quality of life all use the same dead = 0.00 to perfect health = 1.00 scale, but there are substantial differences among measures. OBJECTIVE: The objective was to examine agreement in classifying patients as better, stable, or worse. METHODS: The EQ-5D, Health Utilities Index Mark 2 and Mark 3, Quality of Well-Being-Self-Administered scale, Short-Form 36 (Short-Form 6D), and disease-targeted measures were administered prospectively in 2 clinical cohorts. The study was conducted at academic medical centers: University of California, Los Angeles; University of California, San Diego; University of Wisconsin-Madison; and University of Southern California. Patients undergoing cataract extraction surgery with lens replacement completed the 25-item National Eye Institute Visual Function Questionnaire (NEI-VFQ-25). Patients newly referred to congestive heart failure specialty clinics completed the Minnesota Living with Heart Failure Questionnaire (MLHF). In both cohorts, subjects completed surveys at baseline and at 1 and 6 months. The NEI-VFQ-25 and MLHF were used as gold standards to assign patients to categories of change. Agreement was assessed using κ. RESULTS: There were 376 cataract patients recruited. Complete data for baseline and the 1-month follow-up were available on all measures for 210 cases. Using criteria specified by Altman, agreement was poor for 6 of 9 pairs of comparisons and fair for 3 pairs. There were 160 heart failure patients recruited. Complete data for baseline and the 6-month follow-up were available for 86 cases. Agreement was negligible for 5 pairs and fair for 1. The study was conducted on selected patients at a few academic medical centers. CONCLUSIONS: The results underscore the lack of interchangeability among different preference-based measures.

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.

Race and gender associations between obesity and nine health-related quality-of-life measures.

PURPOSE: To assess how health-related quality of life (HRQoL) varies by body mass index (BMI) category among gender and racial subgroups using nine HRQoL measures. METHODS: Among 3,710 US adults, we evaluated self-reported height, weight, and HRQoL that was measured by six indexes (EQ-5D; HUI2; HUI3; SF-6D; QWB-SA; HALex) and three summary measures (theta; PCS; MCS). Mean HRQoL was estimated by weighted regression for normal, overweight, and obese subgroups (BMI: 18.5-24.9 kg/m(2); 25-29.9; and 30-50). RESULTS: HRQoL was significantly lower (P < 0.0001) with increasing BMI category except for MCS. Obese individuals were 5.3 units lower on PCS (1-100 scale) and 0.05-0.11 lower on the HRQoL indexes (0-1 scale) than those with normal weight. MCS scores were significantly lower for obese than normal-weight among women (P = 0.04) but not men (P = 0.11). Overweight blacks had higher HRQoL than blacks in other BMI categories (P = 0.033). CONCLUSIONS: Six commonly used HRQoL indexes and two of three health status summary measures indicated lower HRQoL with obesity and overweight than with normal BMI, but the degree of decrement varied by index. The association appeared driven primarily by physical health, although mental health also played a role among women. Counter to hypotheses, blacks may have highest HRQoL when overweight.

Health-related quality of life in adults reporting arthritis: analysis from the National Health Measurement Study.

BACKGROUND: Arthritis is the leading cause of disability in the United States. We assess the generic health-related quality-of-life (HRQOL) among a nationally representative sample of U.S. adults with and without self-reported arthritis. METHODS: The NHMS, a cross-sectional survey of 3,844 adults (35-89 years) administered EuroQol-5D (EQ-5D), Health Utilities Index Mark 2 (HUI2) and 3 (HUI3), SF-36v2™, Quality of Well-being Scale self-administered form (QWB-SA), and the Health and Activities Limitations index (HALex) to each respondent via a telephone interview. Weighted multiple linear regression was used to generate age-gender-arthritis-stratified unadjusted HRQOL means and means adjusted for sociodemographic, socioeconomic covariates and comorbidities by arthritis-age category. RESULTS: The estimated population prevalence of self-reported arthritis was 31%. People with arthritis were more likely to be woman, older, of lower socioeconomic status, and had more self-reported comorbidities than were those not reporting arthritis. Adults with arthritis had lower HRQOL on six different indexes compared with adults without arthritis, with overall differences ranging from 0.03 (QWB-SA, age-group 65-74) to 0.17 (HUI3, age-group 35-44; all P-value < .05). CONCLUSION: Arthritis in adults is associated with poorer HRQOL. We provide age-related reference values for six generic HRQOL measures in people with arthritis.

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.

Predicting the EuroQol Group's EQ-5D index from CDC's "Healthy Days" in a US sample.

BACKGROUND: Obtaining reliable preference-based scores from the widely used Healthy Days measures would enable calculation of quality-adjusted life years (QALYs) and cost-utility analyses in many US community populations and over time. Previous studies translating the Healthy Days to the EQ-5D, a preference-based measure, relied on an indirect method because of a lack of population-based survey data that asked both sets of questions of the same respondents. METHOD: Data from the 2005-2006 National Health Measurement Study (NHMS; n = 3844 adults 35 years old or older) were used to develop regression-based models to estimate EQ-5D index scores from self-reported age, self-rated general health, and numbers of unhealthy days. RESULTS: The models explained up to 52% of the variance in the EQ-5D. Estimated EQ-5D scores matched well to the observed EQ-5D scores in mean scores overall and by age, gender, race/ethnicity, income, education, body mass index, smoking, and disease categories. The average absolute differences were 0.005 to 0.006 on a health utility scale. After estimating mean EQ-5D index scores overall and for various subgroups in a large representative US sample of Healthy Days respondents, the authors found that these mean scores also closely matched the corresponding mean scores of EQ-5D respondents obtained from another large US representative sample with an average absolute difference of 0.013 points. CONCLUSIONS: This study yielded a mapping algorithm to estimate EQ-5D index scores from the Healthy Days measures for populations of adults 35 years old and older. Such analysis confirms it is feasible to estimate mean EQ-5D index scores with acceptable validity for use in calculating QALYs and cost-utility analyses based on the overall model fit and relatively small differences between the observed and the estimated mean scores.

Race and preference-based health-related quality of life measures in the United States.

BACKGROUND: Health-related quality of life instruments (HRQoL) are widely used to produce measures that summarize population health and to inform decision-making and health policy. Although the literature about the relationship between health and race in the United States is quite extensive, there is a lack of studies that comprehensively examine the relationship between race and preference-based HRQoL. Given the widespread use of these measures, it becomes important to understand the extent of the race differences in HRQoL scores and factors associated with any such differences. METHODS: We examined the differences in HRQoL, between blacks and whites and associated factors, using the summary scores of the SF-6D, EQ-5D, QWB-SA, HUI2, HUI3, administered by telephone to a nationally representative sample of 3,578 black and white US adults between the ages of 35 and 89 in the National Health Measurement Study (NHMS). RESULTS: Black women had substantially lower HRQoL than white women. The difference was largely explained by sociodemographic and socioeconomic variables. Black men did not differ significantly from white men, except for the EQ-5D. HRQoL among black men was higher at higher income levels, while the HRQoL of black women was especially low compared to other groups at high income levels.

Five preference-based indexes in cataract and heart failure patients were not equally responsive to change.

OBJECTIVE: To compare the responsiveness to clinical change of five widely used preference-based health-related quality-of-life indexes in two longitudinal cohorts. STUDY DESIGN AND SETTING: Five generic instruments were simultaneously administered to 376 adults undergoing cataract surgery and 160 adults in heart failure management programs. Patients were assessed at baseline and reevaluated after 1 and 6 months. The measures were the Short Form (SF)-6D (based on responses scored from SF-36v2), Self-Administered Quality of Well-being Scale (QWB-SA), the EuroQol-5D developed by the EuroQol Group, the Health Utilities Indexes Mark 2 (HUI2) and Mark 3 (HUI3). Cataract patients completed the National Eye Institute Visual Functioning Questionnaire-25, and heart failure patients completed the Minnesota Living with Heart Failure Questionnaire. Responsiveness was estimated by the standardized response mean. RESULTS: For cataract patients, mean changes between baseline and 1-month follow-up for the generic indices ranged from 0.00 (SF-6D) to 0.052 (HUI3) and were statistically significant for all indexes except the SF-6D. For heart failure patients, only the SF-6D showed significant change from baseline to 1 month, whereas only the QWB-SA change was significant between 1 and 6 months. CONCLUSIONS: Preference-based methods for measuring health outcomes are not equally responsive to change.

Underlying dimensions of the five health-related quality-of-life measures used in utility assessment: evidence from the National Health Measurement Study.

BACKGROUND: Preference-weighted health-related quality-of-life (HRQoL) indexes produce a summary score from discrete health states determined by questions falling into several attributes, such as pain and mobility. Values of HRQoL are used alongside other health outcomes to monitor the health of populations. OBJECTIVE: The purpose of this study was to examine among US adults, the underlying factor structure of HRQoL attribute scores across 5 indexes of HRQoL: EuroQol-5 Dimension, Health Utilities Index Mark 2, Health Utilities Index Mark 3, Short Form-6 Dimension, and Quality of Well-Being Scale Self-Administered form. METHODS: The National Health Measurement Study surveyed a nationally representative sample of 3844 noninstitutionalized adults aged 35 to 89 years residing in the continental US. Simultaneous data on all 5 indexes were collected cross-sectionally from June 2005 to August 2006. Exploration of underlying dimensions of HRQoL was done by categorical exploratory factor analysis of HRQoL indexes' attribute scores. Item response theory was applied to explore the amount of information HRQoL attributes contribute to the underlying latent dimensions. RESULTS: Three main dimensions of HRQoL emerged: physical, psychosocial, and pain. Most HRQoL index attributes contributed to the physical or psychosocial dimension. The 3 dimensions were correlated: 0.47 (physical and psychosocial), 0.57 (physical and pain), 0.46 (psychosocial and pain). Some HRQoL index attributes displayed relatively more unique variance: HUI3 hearing, speech, and vision, and some contributed to more than 1 dimension The identified factor structure fit the HRQoL data well (Comparative Fit Index = 0.98, Tucker-Lewis Index = 0.98, and Root Mean Square Error of Approximation = 0.042). CONCLUSIONS: The attributes of 5 commonly used HRQoL indexes share 3 underlying latent dimensions of HRQoL, physical, psychosocial, and pain.

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.

PURPOSE: The 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. METHODS: Data 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. RESULTS: Women 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. CONCLUSIONS: There 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, theta. 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 but with a shallow slope; EQ-5D, HUI2, and HUI3 are linear with a steep slope from low (poor health) into midrange of , then approximately linear with a less steep slope for higher (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.

Three methods tested to model SF-6D health utilities for health states involving comorbidity/co-occurring conditions.

OBJECTIVES: Compare three commonly used methods to combine the impacts of multiple health conditions on SF-6D health utility scores. STUDY DESIGN AND SETTING: We used data from the 1998-2004 Medicare Health Outcomes Survey to compare three commonly suggested models of multiple health conditions' impacts on health-related quality of life: additive, minimum, and multiplicative. We modeled SF-6D scores using information about 15 health conditions, both unadjusted and adjusted for age, sex, education, and income. Model performance was assessed using mean squared error, mean predictive error by number of health conditions, and mean predictive error for groups with specific combinations of health conditions. RESULTS: Ninety-five thousand one hundred ninety-five observations were used for model estimation, and 94,794 observations were used for model testing. The adjusted models always had better performance than the unadjusted models. The multiplicative model showed smaller mean predictive error than the other models in both those younger than 65 years and those 65 years and older. Mean predictive error for the multiplicative model was generally within the minimally important difference of the SF-6D. CONCLUSION: All tested models are imperfect in these Medicare data, but the multiplicative model performed best.

Measuring health-related quality of life in population-based studies of coronary heart disease: comparing six generic indexes and a disease-specific proxy score.

PURPOSE: To compare HRQoL differences with CHD in generic indexes and a proxy CVD-specific score in a nationally representative sample of U.S. adults. METHODS: The National Health Measurement Study, a cross-sectional random-digit-dialed telephone survey of adults aged 35-89, administered the EQ-5D, QWB-SA, HUI2, HUI3, SF-36v2 (yielding PCS, MCS, and SF-6D), and HALex. Analyses compared 3,350 without CHD (group 1), 265 with CHD not taking chest pain medication (group 2), and 218 with CHD currently taking chest pain medication (group 3), with and without adjustment for demographic variables and comorbidities. Data on 154 patients from heart failure clinics were used to construct a proxy score utilizing generic items probing CVD symptoms. RESULTS: Mean scores differed between CHD groups for all indexes with and without adjustment (P < 0.0001 for all except MCS P = 0.018). Unadjusted group 3 versus 1 differences were about three times larger than for group 2 versus 1. Standardized differences for the proxy score were similar to those for generic indexes, and were about 1.0 for all except MCS for group 3 versus 1. CONCLUSIONS: Generic indexes capture differences in HRQoL in population-based studies of CHD similarly to a score constructed from questions probing CVD-specific symptoms.