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.

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.

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.

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.

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.

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.

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.

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 US 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.

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.

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.

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.

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.

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.

Effects of mode and order of administration on generic health-related quality of life scores

OBJECTIVE

We evaluate the effects of mode and order of administration on health-related quality of life (HRQOL) scores.

METHOD

We analyzed HRQOL data from the Clinical Outcomes and Measurement of Health Study (COMHS). In COMHS, we enrolled patients with heart failure or cataracts at three sites (University of California, San Diego, University of California, Los Angeles, and University of Wisconsin). Patients completed self-administered HRQOL instruments at baseline and months 1 and 6 post-baseline, including the EuroQol (EQ-5D), Health Utilities Index (HUI), Quality of Well-Being Scale--self-administered (QWB-SA), and the Short Form (SF)-36v2. At the 6 months follow-up, individuals were randomized to mail or telephone administration first, followed by the other mode of administration. We used repeated measures mixed effects models, adjusting for site, patient age, education, gender, and race.

RESULTS

Included were 121 individuals entering a heart failure program and 326 individuals scheduled for cataract surgery who completed the survey by mail or phone at the 6-month follow-up. The majority of the sample was female (53%) and white (86%). About a quarter of the sample had high school education or less (26%). The average age was 66 (36-91 range). HRQOL scores were higher (more positive) for phone administration following mail administration. The largest differences in scores between phone and mail responses occurred for comparisons of telephone responses for those who were randomized to a mail survey first compared with mail responses for those randomized to a telephone survey first (i.e., mode effects for responses that were given on the second administration of the HRQOL measures). The QWB-SA was the only measure that did not display the pattern of mode effects. The biggest differences between modes were 4 points on the SF-36v2 physical health and mental health component summary scores, 0.06 on the SF-6D, 0.03 on the QWB-SA, 0.08 on the EQ-5D, 0.04 on the HUI2, and 0.10 on the HUI3.

CONCLUSIONS

Telephone administration yields significantly more positive HRQOL scores for all of the generic HRQOL measures except for the QWB-SA. The magnitude of effects was clearly important, with some differences as large as a half-standard deviation. These findings confirm the importance of considering mode of administration when interpreting HRQOL scores.

Effects of mode and order of administration on generic health-related quality of life scores

OBJECTIVE

We evaluate the effects of mode and order of administration on health-related quality of life (HRQOL) scores.

METHOD

We analyzed HRQOL data from the Clinical Outcomes and Measurement of Health Study (COMHS). In COMHS, we enrolled patients with heart failure or cataracts at three sites (University of California, San Diego, University of California, Los Angeles, and University of Wisconsin). Patients completed self-administered HRQOL instruments at baseline and months 1 and 6 post-baseline, including the EuroQol (EQ-5D), Health Utilities Index (HUI), Quality of Well-Being Scale--self-administered (QWB-SA), and the Short Form (SF)-36v2. At the 6 months follow-up, individuals were randomized to mail or telephone administration first, followed by the other mode of administration. We used repeated measures mixed effects models, adjusting for site, patient age, education, gender, and race.

RESULTS

Included were 121 individuals entering a heart failure program and 326 individuals scheduled for cataract surgery who completed the survey by mail or phone at the 6-month follow-up. The majority of the sample was female (53%) and white (86%). About a quarter of the sample had high school education or less (26%). The average age was 66 (36-91 range). HRQOL scores were higher (more positive) for phone administration following mail administration. The largest differences in scores between phone and mail responses occurred for comparisons of telephone responses for those who were randomized to a mail survey first compared with mail responses for those randomized to a telephone survey first (i.e., mode effects for responses that were given on the second administration of the HRQOL measures). The QWB-SA was the only measure that did not display the pattern of mode effects. The biggest differences between modes were 4 points on the SF-36v2 physical health and mental health component summary scores, 0.06 on the SF-6D, 0.03 on the QWB-SA, 0.08 on the EQ-5D, 0.04 on the HUI2, and 0.10 on the HUI3.

CONCLUSIONS

Telephone administration yields significantly more positive HRQOL scores for all of the generic HRQOL measures except for the QWB-SA. The magnitude of effects was clearly important, with some differences as large as a half-standard deviation. These findings confirm the importance of considering mode of administration when interpreting HRQOL scores.

Socioeconomic status and age variations in health-related quality of life: results from the national health measurement study

OBJECTIVES

We examine whether multiple health-related quality of life (HRQoL) measures are stratified by socioeconomic status (SES) and age in the United States.

METHODS

Data are from the 2005/2006 National Health Measurement Study, a telephone survey of a nationally representative sample of U.S. adults. We plot mean HRQoL scores by SES within age groups. Regression analyses test whether education, income, and assets each have independent associations with three "preference-based" HRQoL measures and self-rated health (SRH). We test whether these associations vary by age.

RESULTS

There are SES disparities in HRQoL and SRH among adults in the United States at all age groups. Income differentials in HRQoL are strong across current adult age cohorts, except the 75-89 age cohort. Education and assets have statistically significant but weaker associations with HRQoL. All three SES measures are associated with SRH (net of each other) at every age group. Those in the lowest income and education groups in the 35-44 age cohort have worse HRQoL and SRH than those in higher SES groups in the 65+ age cohort.

DISCUSSION

Significant improvements in HRQoL at the population level will only be possible if we improve the HRQoL of people at the lowest end of the socioeconomic distribution.

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.

Cost-effectiveness of digital mammography breast cancer screening

BACKGROUND

The DMIST (Digital Mammography Imaging Screening Trial) reported improved breast cancer detection with digital mammography compared with film mammography in selected population subgroups, but it did not assess the economic value of digital relative to film mammography screening.

OBJECTIVE

To evaluate the cost-effectiveness of digital mammography screening for breast cancer.

DESIGN

Validated, discrete-event simulation model.

DATA SOURCES

Data from DMIST and publicly available U.S. data.

TARGET POPULATION

U.S. women age 40 years or older.

TIME HORIZON

Lifetime.

PERSPECTIVE

Societal and Medicare.

INTERVENTION

All-film mammography screening; all-digital mammography screening; and targeted digital mammography screening, which is age-targeted digital mammography (for women <50 years of age) and age- and density-targeted digital mammography (for women <50 years of age or women > or =50 years of age with dense breasts).

OUTCOME MEASURES

Cost per quality-adjusted life-year (QALY) gained.

RESULTS OF BASE-CASE ANALYSIS

All-digital mammography screening cost $331,000 (95% CI, $268,000 to $403,000) per QALY gained relative to all-film mammography screening but was more costly and less effective than targeted digital mammography screening. Targeted digital mammography screening resulted in more screen-detected cases of cancer and fewer deaths from cancer than either all-film or all-digital mammography screening, with cost-effectiveness estimates ranging from $26,500 (CI, $21,000 to $33,000) per QALY gained for age-targeted digital mammography to $84,500 (CI, $75,000 to $93,000) per QALY gained for age- and density-targeted digital mammography. In the Medicare population, the cost-effectiveness of density-targeted digital mammography screening varied from a base-case estimate of $97,000 (CI, $77,000 to $131,000) to $257,000 per QALY gained (CI, $91,000 to $536,000) in the alternative-case analyses, in which the sensitivity of film mammography was increased and the sensitivity of digital mammography in women with nondense breasts was decreased.

RESULTS OF SENSITIVITY ANALYSIS

Results were sensitive to the cost of digital mammography and to the prevalence of dense breasts.

LIMITATIONS

Results were dependent on model assumptions and DMIST findings.

CONCLUSION

Relative to film mammography, screening for breast cancer by using all-digital mammography is not cost-effective. Age-targeted screening with digital mammography seems cost-effective, whereas density-targeted screening strategies are more costly and of uncertain value, particularly among women age 65 years or older.

Health-related quality of life before and after a breast cancer diagnosis

While many reports describe health-related quality of life (QOL) among breast cancer survivors, few compare QOL before and after diagnosis and whether changes in QOL substantially differ from changes experienced by all women during aging. QOL was examined in a cohort of female residents of Beaver Dam, Wisconsin, aged 43-86 years at the time of a 1988-1990 baseline examination (N = 2,762; 83% of eligible). Participants were re-contacted four times through 2002 to ascertain QOL using the 36-item Short Form Health Survey (SF-36). QOL data for 114 incident breast cancer cases identified by data linkage with the statewide cancer registry were compared with data for 2,527 women without breast cancer. Women with breast cancer averaged 4.5 (95% CI: 1.6, 7.3) points lower than control women on the SF-36 Physical Component Summary (PCS) scale, regardless of time since diagnosis (up to 13 years). Women with breast cancer also reported lower scores on the SF-36 Mental Component Summary (MCS) scale within two years after diagnosis, but not at more distant times. In longitudinal analyses, 26 women who completed the SF-36 before and after breast cancer diagnosis experienced larger declines than age-matched controls in seven of the eight SF-36 health domains (all but role-emotional) and reported relative declines of -7.0 (95% CI: -11.5, -2.6) and -2.9 (95% CI: -6.3, 0.6) on the PCS and MCS scales, respectively. These results suggest that breast cancer survivors experience relative declines in health-related QOL across a broad spectrum of domains, even many years after diagnosis.

Health perceptions in patients who undergo screening and workup for prostate cancer

OBJECTIVES

False-positive screening tests may induce persistent psychological distress. This study was designed to determine whether a positive screening test with negative biopsy findings for prostate cancer is associated with worsened mental health during short-term follow-up.

METHODS

We conducted a cross-sectional telephone survey of two groups of men approximately 2 months after testing: group 1, 109 men with an abnormal prostate-specific antigen level or digital rectal examination findings but with negative biopsy findings for prostate cancer; and group 2, 101 age-matched primary care patients with PSA screening levels in the reference range (less than 4 ng/mL). Primary outcomes included state anxiety and prostate cancer-related worry. Secondary outcomes included Medical Outcomes Study Short Form 36-item Health Survey subscales and sexual function items. Multivariate regression techniques were used to adjust for differences in baseline covariates.

RESULTS

Group 1 patients were more worried than group 2 patients about getting prostate cancer (mean worry 3.9 versus 4.5, P = 0.0001, using a 5-point scale, with 1 indicating extreme worry and 5 no worry). Group 1 patients also perceived their risk of prostate cancer to be significantly greater than that of controls (P = 0.001). No significant differences were found across state anxiety or Medical Outcomes Study Short Form 36-item Health Survey subscales. Sexual bother was greater for group 1 patients, with 19% reporting that sexual function was a moderate to big problem compared with 10% of group 2 patients (P = 0.0001).

CONCLUSIONS

Men with abnormal prostate cancer screening tests report increased cancer-related worry and more problems with sexual function, despite having a negative biopsy result. Effective counseling interventions are needed before prostate cancer screening and during follow-up.

Report of nationally representative values for the noninstitutionalized US adult population for 7 health-related quality-of-life scores

BACKGROUND

Despite widespread use of generic health-related quality-of-life (HRQoL) scores, few have publicly published nationally representative US values.

PURPOSE

To create current nationally representative values for 7 of the most common HRQoL scores, stratified by age and sex.

METHODS

The authors used data from the 2001 Medical Expenditures Panel Survey (MEPS) and the 2001 National Health Interview Survey (NHIS), nationally representative surveys of the US noninstitutionalized civilian population: The MEPS was used to calculate 6 HRQoL scores: categorical self-rated health, EuroQoL-5D with US scoring, EuroQoL-5D with UK scoring, EuroQol Visual Analog Scale, mental and physical component summaries from the SF-12, and the SF-6D. The authors estimated Quality of Well-being scale scores from the NHIS.

RESULTS

They included 22,523 subjects from MEPS 2001 and 32,472 subjects from NHIS 2001. Most age and sex categories had instrument completion rates above 85%. Females reported lower scores than males across all ages and instruments. In general, those in older age groups reported lower scores than younger age groups, with the exception of the mental component summary from the SF-12.

CONCLUSION

This is one of the first sets of publicly available, nationally representative US values for any standardized HRQoL measure. These values are important for use in both generalized comparisons of health status and in cost-effectiveness analyses.

Chapter 7: The Wisconsin Breast Cancer Epidemiology Simulation Model

The Wisconsin Breast Cancer Epidemiology Simulation Model is a discrete-event, stochastic simulation model using a systems-science modeling approach to replicate breast cancer incidence and mortality in the U.S. population from 1975 to 2000. Four interacting processes are modeled over time: (1) natural history of breast cancer, (2) breast cancer detection, (3) breast cancer treatment, and (4) competing cause mortality. These components form a complex interacting system simulating the lives of 2.95 million women (approximately 1/50 the U.S. population) from 1950 to 2000 in 6-month cycles. After a "burn in" of 25 years to stabilize prevalent occult cancers, the model outputs age-specific incidence rates by stage and age-specific mortality rates from 1975 to 2000. The model simulates occult as well as detected disease at the individual level and can be used to address "What if?" questions about effectiveness of screening and treatment protocols, as well as to estimate benefits to women of specific ages and screening histories.

Retrospective cost-effectiveness analysis of screening mammography

BACKGROUND

Many guidelines recommend screening mammography every 1-2 years for women older than 40 years; more than 70% of women now participate in routine screening. No studies have examined the societal impact of screening practices over the past decade in the United States on costs and quality-adjusted life-years (QALYs). We performed a retrospective cost-effectiveness analysis comparing actual and alternative screening mammography scenarios.

METHODS

We used a discrete-event simulation model of breast cancer epidemiology to estimate the costs and the number of QALYs that were associated with observed screening mammography patterns in the United States from 1990 to 2000 for women aged 40 years or older. We also estimated costs and QALYS for no screening and for 64 alternative screening scenarios. Incremental cost-effectiveness ratios were computed. Sensitivity analyses were performed on key parameters.

RESULTS

Actual U.S. screening patterns from 1990 to 2000 accrued 947.5 million QALYs and cost $166 billion over the lifetimes of the screened women, resulting in a gain of 1.7 million QALYs for an additional cost of $62.5 billion compared with no screening. Among those polices that were not dominated--i.e., for which no alternative existed that produced more QALYs for lower costs--screening all women aged 40-80 years annually per some U.S. guidelines was the most expensive option, costing $58,000 per additional QALY gained compared with the next most costly alternative, screening all women aged 45-80 years annually. Many alternative screening scenarios generated more QALYs for less cost (with savings up to $6 billion) than actual screening patterns over the study period. Sensitivity analysis showed that conclusions about the cost-effectiveness of screening mammography policies were highly sensitive to small, short-term detrimental effects on quality of life from the screening test itself.

CONCLUSIONS

Choosing among the efficient policies to guide current screening recommendations requires consideration of costs to promote participation in screening and measurement of acute quality-of-life effects of mammography.

Relative disutilities of 47 risk factors and conditions assessed with seven preference-based health status measures in a national U.S. sample: toward consistency in cost-effectiveness analyses

BACKGROUND

Preference-based health measures yield summary scores that are compatible with cost-effectiveness analyses. There is limited comparative information, however, about how different measures weight health conditions in the U.S. population.

METHODS

We examined data from 11,421 adults in the 2000 Medical Expenditure Panel Survey, a nationally representative sample of the U.S. general population, using information on sociodemographics (age, gender, race/ethnicity, income, and education), health status (EQ-5D, EQ-VAS, and SF-12), 4 risk factors (smoking, overweight, obesity, and lacking health insurance), and 43 conditions. From the EQ-5D, we derived summary scores using U.K. [EQ(UK)] and U.S. weights. From the SF-12 we derived SF-6D, and regression-predicted EQ-5D (U.S. and U.K. weights) and Health Utility Index scores. Each of the 7 preference measures was regressed on each of the 47 problems (risk factors and conditions) to determine the disutility associated with the problem, adjusting for socio-demographics.

RESULTS

The adjusted disutilities averaged across the 47 problems for the 7 preference measures ranged from 0.059 for the SF-6D to 0.104 for the EQ(UK). Correlations between each of the measures of the adjusted disutilities ranged from 0.85-1.0. Standardization, using linear regression, attenuated between measure differences in disutilities.

CONCLUSIONS

Absolute incremental cost-effectiveness analyses of a given problem would likely vary depending on the measure used, whereas the relative ordering of incremental cost-effectiveness analyses of a series of problems would likely be similar regardless of the measure chosen, as long as the same measure is used in each series of analyses. Absolute consistency across measures may be enhanced by standardization.

Effect of screening and adjuvant therapy on mortality from breast cancer

BACKGROUND

We used modeling techniques to assess the relative and absolute contributions of screening mammography and adjuvant treatment to the reduction in breast-cancer mortality in the United States from 1975 to 2000.

METHODS

A consortium of investigators developed seven independent statistical models of breast-cancer incidence and mortality. All seven groups used the same sources to obtain data on the use of screening mammography, adjuvant treatment, and benefits of treatment with respect to the rate of death from breast cancer.

RESULTS

The proportion of the total reduction in the rate of death from breast cancer attributed to screening varied in the seven models from 28 to 65 percent (median, 46 percent), with adjuvant treatment contributing the rest. The variability across models in the absolute contribution of screening was larger than it was for treatment, reflecting the greater uncertainty associated with estimating the benefit of screening.

CONCLUSIONS

Seven statistical models showed that both screening mammography and treatment have helped reduce the rate of death from breast cancer in the United States.

American College of Radiology Imaging Network digital mammographic imaging screening trial: objectives and methodology

This study was approved by the Institutional Review Board (IRB) of the American College of Radiology Imaging Network (ACRIN) and each participating site and by the IRB and the Cancer Therapy Evaluation Program at the National Cancer Institute. The study was monitored by an independent Data Safety and Monitoring Board, which received interim analyses of data to ensure that the study would be terminated early if indicated by trends in the outcomes. The ACRIN, which is funded by the National Cancer Institute, conducted the Digital Mammographic Imaging Screening Trial (DMIST) primarily to compare the diagnostic accuracy of digital and screen-film mammography in asymptomatic women presenting for screening for breast cancer. Over the 25.5 months of enrollment, a total of 49 528 women were included at the 33 participating sites, which used five different types of digital mammography equipment. All participants underwent both screen-film and digital mammography. The digital and screen-film mammograms of each subject were independently interpreted by two radiologists. If findings of either examination were interpreted as abnormal, subsequent work-up occurred according to the recommendations of the interpreting radiologist. Breast cancer status was determined at biopsy or follow-up mammography 11-15 months after study entry. In addition to the measurement of diagnostic accuracy by using the interpretations of mammograms at the study sites, DMIST included evaluations of the relative cost-effectiveness and quality-of-life effects of digital versus screen-film mammography. Six separate reader studies using the de-identified archived DMIST mammograms will also assess the diagnostic accuracy of each of the individual digital mammography machines versus screen-film mammography machines, the effect of breast density on diagnostic accuracy of digital and screen-film mammography, and the effect of different rates of breast cancer on the diagnostic accuracy in a reader study.

Measuring Economic Outcomes of Cancer

Economic outcomes are increasingly important in cancer research. This article provides an introduction to the nature and measurement of economic outcomes in cancer and gives the reader entry points to a considerable literature on measuring economic endpoints in health and medicine. Economic outcomes data are defined here as measures of resources consumed in prevention, detection, and treatment of cancer and its sequellae. Part I of this essay discusses the questions addressed by economic outcomes. Part II presents a typology of and introduction to measurement of economic outcomes. Part III discusses important measurement issues and calls for development and validation of standardized protocols and questionnaires to measure economic outcomes, especially at the patient level.

Understanding differences between self-ratings and population ratings for health in the EuroQOL

OBJECTIVE

To examine the source and magnitude of differences between self-ratings for health and ratings of corresponding health state profiles by the general population in the EuroQOL.

DATA AND METHODS

EuroQOL data were analysed from the 1993 measurement and valuation of health study (MVH), a sample of 2997 members of the UK adult population, nationally representative by age, gender and social class. Multivariate regression analyses were used to examine the source of differences in visual analogue scores (VAS) between self-ratings and general population ratings. The source of observed differences were investigated with respect to four hypothesized factors: (1) Socio-demographics (age, gender, education, social class); (2) The level of respondent difficulty in completing the rating task; (3) Values for particular EQ-5D health profile attributes; and (4) Differences in the scope of health attributes and levels considered in the rating task (e.g., self-ratings may reflect preferences for attributes not captured by EQ-5D profiles).

RESULTS

Overall, mildly ill individuals provided lower self-ratings (3-4 points), and moderately ill individuals higher self-ratings (7 points), than ratings for these states provided by the general population. Socio-demographic characteristics and difficulties in rating task completion did not explain differences between self and general population VAS ratings, contributing differences of 1 point or less in all 15 rating comparisons examined. Rating differences related more closely to a lack of correspondence between health state descriptions and self-raters' actual health experiences (differences in scope) than differences in values for health profile attributes between self-raters and the general population.

CONCLUSIONS

EQ-5D health state descriptions may be too sparse to comprehensively describe certain health states. Adding new health state levels or dimensions, or changing the nature and tone of health state descriptions, may be useful steps for improvement.

HALYS and QALYS and DALYS, Oh My: similarities and differences in summary measures of population Health

Health-adjusted life years (HALYs) are population health measures permitting morbidity and mortality to be simultaneously described within a single number. They are useful for overall estimates of burden of disease, comparisons of the relative impact of specific illnesses and conditions on communities, and in economic analyses. Quality-adjusted life years (QALYs) and disability-adjusted life years (DALYs) are types of HALYs whose original purposes were at variance. Their growing importance and the varied uptake of the methodology by different U.S. and international entities makes it useful to understand their differences as well as their similarities. A brief history of both measures is presented and methods for calculating them are reviewed. Methodological and ethical issues that have been raised in association with HALYs more generally are presented. Finally, we raise concerns about the practice of using different types of HALYs within different decision-making contexts and urge action that builds and clarifies this useful measurement field.

An elementary introduction to Bayesian computing using WinBUGS

Bayesian statistics provides effective techniques for analyzing data and translating the results to inform decision making. This paper provides an elementary tutorial overview of the WinBUGS software for performing Bayesian statistical analysis. Background information on the computational methods used by the software is provided. Two examples drawn from the field of medical decision making are presented to illustrate the features and functionality of the software.

Bayesian cost-effectiveness analysis. An example using the GUSTO trial

A desirable element of cost-effectiveness analysis (CEA) modeling is a systematic way to relate uncertainty about input parameters to uncertainty in the computational results of the CEA model. Use of Bayesian statistical estimation and Monte Carlo simulation provides a natural way to compute a posterior probability distribution for each CEA result. We demonstrate this approach by reanalyzing a previously published CEA evaluating the incremental cost-effectiveness of tissue plasminogen activator compared to streptokinase for thrombolysis in acute myocardial infarction patients using data from the GUSTO trial and other auxiliary data sources. We illustrate Bayesian estimation for proportions, mean costs, and mean quality-of-life weights. The computations are performed using the Bayesian analysis software WinBUGS, distributed by the MRC Biostatistics Unit, Cambridge, England.

The first positive: computing positive predictive value at the extremes

Computing the positive predictive value (PPV) of a wellknown test for a relatively common disease is a straight-forward exercise. However, in the case of a new test for a rare disorder; the extreme numbers involved-the very low prevalence of the disorder and the lack of previous false-positive results--make it difficult to compute the PPV. As new genetic tests become available in the next decade, more and more clinicians will have to answer questions about PPVs in cases with extreme prevalence, sensitivity, and specificity. This paper presents some tools for thinking about these calculations. First, a standard PPV calculation with rough estimates of the prevalence, sensitivity, and specificity is reviewed. The "zero numerator" problem posed by not having seen any false-positive results is then discussed, and a Bayesian approach to this problem is described. The Bayesian approach requires specification of a prior distribution that describes the initial uncertainty about the false-positive rate. This prior distribution is updated as new evidence is obtained, and the updated expected false-positive rate is used to calculate PPVs. The Bayesian approach provides appropriate and defensible PPVs and can be used to estimate failure rates for other rare events as well.

A time-tradeoff method for cost-effectiveness models applied to radiology

PURPOSE

The wait tradeoff (WTO) is a simple time-tradeoff method designed for temporary health states that uses a realistic and intuitive interface for the patient/subject. This method was tested by assessing patients' preferences for magnetic resonance angiography (MRA) versus x-ray angiography (XRA).

MATERIALS AND METHODS

The WTO was tested by telephone interview in 38 patients with atherosclerotic peripheral vascular disease, all having previously undergone both MRA and XRA. At indifference point, patients were ambivalent about having MRA or XRA and immediate treatment, versus having a waiting period for test results and treatment after a hypothetical "ideal test" that entailed no pain or risk.

RESULTS

The patients were willing to wait a mean of 42.1 days after the ideal test for results and treatment, as opposed to XRA. They were willing to wait only 16.1 days as opposed to MRA. This difference in waiting times was significant (p = 0.0001) and indicates a clear preference for MRA, in agreement with known literature.

CONCLUSION

The WTO method assesses preferences for these radiologic tests in an intuitive fashion that does not invoke artificial or irrelevant health states. This approach may also prove useful for other testing situations or short-term treatments being evaluated for cost-effectiveness.

Is the societal perspective in cost-effectiveness analysis useful for decision makers?

BACKGROUND

The U.S. Public Health Service's Panel on Cost-Effectiveness in Health and Medicine recommends that cost-effectiveness analyses (CEAs) intended to help allocate health resources in the public interest include a reference case analysis, conducted from the perspective of society as a whole. This perspective requires that an analysis measure all health effects and changes in resource use caused by an intervention. VALUE OF SOCIETAL CEAs: Tensions unavoidably arise among the parties to medical decisions--patients, their families and friends, clinicians, and third-party payers. One common approach to handling these tensions is to ignore some of them, to "solve" them by pretending they do not or should not exist. Patients do this when they demand the best care for themselves without regard to the cost to others, payers when they make coverage decisions that shift costs ot others. But by estimating all gains and losses, calculations that reflect the safety, effectiveness, and side effects of an intervention as well as its costs, societal CEAs can help resolve those tensions and provide the basis for decisions that are fair to all parties, an agenda for negotiating such decisions, and information essential for designing compensation and incentives to support them. MAKING BETTER USE OF SOCIETAL CEAs: Use of the societal perspective asks that all parties be aware of and consider the interests of others. Some process or procedure needs to be developed for presenting CEA information to the parties most likely to be affected by decisions, soliciting their views, and negotiating an acceptable decision. This process could be used by government decision makers or by managed care organizations, professional societies, or payers.

A Bayesian approach to modelling the natural history of a chronic condition from observations with intervention

To assess the costs and benefits of screening and treatment strategies, it is important to know what would have happened had there been no intervention. In today's ethical climate, however, it is almost impossible to observe this directly and therefore must be inferred from observations with intervention. In this paper, we illustrate a Bayesian approach to this situation when the observations are at separated and unequally spaced time points and the time of intervention is interval censored. We develop a discrete-time Markov model which combines a non-homogeneous Markov chain, used to model the natural progression, with mechanisms that describe the possibility of both treatment intervention and death. We apply this approach to a subpopulation of the Wisconsin Epidemiologic Study of Diabetic Retinopathy, a population-based cohort study to investigate prevalence, incidence, and progression of diabetic retinopathy. In addition, posterior predictive distributions are discussed as a prognostic tool to assist researchers in evaluating costs and benefits of treatment protocols. While we focus this approach on diabetic retinopathy cohort data, we believe this methodology can have wide application.

Treatment choice and quality of life in patients with choroidal melanoma

OBJECTIVE

To determine if quality of life differs between patients with choroidal melanoma treated with enucleation and those treated with radiation therapy.

MATERIALS AND METHODS

Patients treated for choroidal melanoma at 5 Midwest centers were asked to participate. There were 65 participants treated with enucleation and 82 treated with radiation therapy. Quality of life was assessed using the Medical Outcome Study Short Form 36 and the National Eye Institute Visual Function Questionnaire and by the Time-Tradeoff interview method.

RESULTS

The average length of follow-up was 4.9 years for the group treated with radiation therapy and 6.3 years for the group treated with enucleation (P = .05). After adjusting for age, sex, years of follow-up, and the number of chronic conditions, there were few differences in any of the quality-of-life measures by treatment status. Participants in the group treated with radiation therapy were more likely to have higher (better) scores on the Vitality and Mental Component subscales of the Medical Outcome Study Short Form 36 than participants treated with enucleation. There were no differences on the National Eye Institute Visual Function Questionnaire or the Time-Tradeoff measures of quality of life.

CONCLUSION

Choice of treatment for choroidal melanoma does not seem to be associated with large differences in quality of life in long-term follow-up.

Computing population-based estimates of health-adjusted life expectancy

Observed health-adjusted life expectancy (HALE) is an indicator of population health. There are a number of ways to compute HALE for a community. The authors surveyed several methods and demonstrate resulting variation in the estimates of HALE. Quality of well-being (QWB) measures from 1,430 participants in the Beaver Dam Health Outcomes Study are taken as weights. Actuarial life-table methods using community mortality data, State of Wisconsin census data from two time frames, and U.S. census data are used with the QWB to estimate HALE. Measurement of community population health using HALE computations can be completed with national, regional, or local data. Community-level estimates may not be well approximated using large-scale mortality experience. A Bayesian method is developed combining the local data with regional data. The Bayesian method creates a smooth set of rates, retains the local flavor of the community, and gives a measure of variability of the estimated HALE.

Sleep-disordered breathing and self-reported general health status in the Wisconsin Sleep Cohort Study

OBJECTIVE

To determine the relationship between sleep-disordered breathing and self-reported general health status. breathing status assessed by overnight in-laboratory polysomnography.

SETTING

General Community.

SUBJECTS

Employed men (n=421) and women (n=316), ages 30-60 years, enrolled in the Wisconsin Sleep Cohort Study.

INTERVENTIONS

None.

OUTCOME MEASURE

Self-reported general health profile and life satisfaction measured by the Medical Outcomes Survey Short Form-36 and obtained by interview.

RESULTS

Sleep-disordered breathing was associated with lower general health status before and after adjustment for age, sex, body mass index, smoking status, alcohol usage, and a history of cardiovascular conditions. Even mild sleep-disordered breathing (apnea-hypopnea index = 5) was associated with decrements in the Medical Outcomes Short Form 36 Survey health constructs comparable to the magnitude of decrements linked to other chronic conditions such as arthritis, angina, hypertension, diabetes, and back problems.

CONCLUSIONS

Sleep-disordered breathing is independently related to lower general health status, and this relationship is of clinical significance. Given the growing emphasis of the importance of patients' perceptions of health, these findings are relevant to estimating the overall impact of sleep-disordered breathing.

Disease progression and cost of insulin dependent diabetes mellitus: development and application of a simulation model

This paper presents the development and application of a Markov model that simulates the onset and progression of insulin dependent diabetes mellitus (IDDM) and its sequelae. The model estimates direct medical costs resulting from different patterns of health states during the course of the disease. The model is user-friendly and allows for changes in the input variables to be specified, thereby providing a frame-work for sensitivity analysis. The Markov process moves a cohort of individuals through six different health states: Healthy, IDDM without chronic microvascular complications, IDDM with retinopathy alone, IDDM with neuropathy alone or with retinopathy, IDDM with nephropathy, and death. Epidemiologic data were obtained from governmental sources as well as the medical literature. Transition probabilities were estimated using data from the Wisconsin Epidemiologic Study of Diabetic Retinopathy (WESDR). Direct medical costs were estimated from actual charges from a local health maintenance organization in Wisconsin. The validity of the model was tested in several ways including a comparison with an independent cost estimate made by the Wisconsin Department of Health and Family Services. The model appears to be useful in estimating the progression and associated costs of IDDM for any large population over any period of time and in allowing changes in the inputs to evaluate their impact thus providing pertinent information to healthcare decision-makers and health planners.