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D G Fryback

Publications and source records attributed to D G Fryback.

60 records · Page 4Linked to original sources
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The Beaver Dam Health Outcomes Study: initial catalog of health-state quality factors.

The Beaver Dam Health Outcomes Study (BDHOS) is an ongoing longitudinal cohort study of health status and health-related quality of life for a random sample of adults (age range at interview was 45 to 89 years; mean = 64.1, SD = 10.8) in a community population. In a face-to-face interview lasting approximately an hour, each participant responds to several batteries of questions. Included are a history of chronic medical conditions, current medications, and past surgeries; the SF-36 (a general health-status questionnaire); the Quality of Well-being index; self-rated health status on a five-point scale from "excellent" to "poor"; and evaluation of current health using the method of time tradeoffs. The authors present results from 1,356 interviews on these four principal measures, reporting mean scores by sex, by age, and for persons reporting being affected by various medical conditions. They believe data from the BDHOS will provide researchers and policy makers a reference collection of vital statistics for health-related quality of life. Additionally, the data provide a way to compare results from studies that utilize different indices from among the four principal measures of the BDHOS.

Adult↗
PubMedDate not supplied

Predicting Quality of Well-being scores from the SF-36: results from the Beaver Dam Health Outcomes Study.

BACKGROUND: The SF-36 and the Quality of Well-being index (QWB) both quantify health status, yet have very different methodologic etiologies. The authors sought to develop an empirical equation allowing prediction of the QWB from the SF-36. DATA: They used empirical observations of SF-36 profiles and QWB scores collected in interviews of 1,430 persons during the Beaver Dam Health Outcomes Study, a community-based population study of health status, and 57 persons from a renal dialysis clinic. METHOD: The eight scales of the SF-36, their squares, and all pairwise cross-products, were used as candidate variables in stepwise and best-subsets regressions to predict QWB scores using 1,356 interviews reported in a previous paper. The resulting equation was cross-validated on the remaining 74 cases and using the renal dialysis patients. RESULTS: A six-variable regression equation drawing on five of the SF-36 components predicted 56.9% of the observed QWB variance. The equation achieved an R2 of 49.5% on cross-validation using Beaver Dam participants and an R2 of 58.7% with the renal dialysis patients. An approximation for computing confidence intervals for predicted QWB mean scores is given. CONCLUSION: SF-36 data may be used to predict mean QWB scores for groups of patients, and thus may be useful to modelers who are secondary users of health status profile data. The equation may also be used to provide an overall health utility summary score to represent SF-36 profile data so long as the profiles are not severely limited by floor or ceiling effects of the SF-36 scales. The results of this study provide a quantitative link between two important measures of health status.

Activities of Daily Living↗
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Dollars may not buy as many QALYs as we think: a problem with defining quality-of-life adjustments.

The scale of health state quality that should be used to compute quality-adjusted life years (QALYs) ranges from 0 (death) to 1.0 (excellent health); this is called the "Q" scale. But many cost-utility analyses (CUAs) in the literature use the upper anchor of the scale to denote only the absence of the particular health condition under investigation, and weight the disease state proportional to this endpoint; these are called "q" scales. Computations using q-scale health-state weights ignore the fact that the average patient is still subject to chronic and acute conditions comorbid with the condition being analyzed; the absence of a particular condition is not in general the same as excellent health, i.e., the Q scale is longer than a q scale. CUAs based on q scales yield "qALYs." Incremental $/qALY ratios are generally lower than $/QALY ratios; in the example presented, $/qALY must be inflated by about 15% to yield $/QALY. Other CUAs correctly weight disease states using the Q scale, but erroneously assign a quality weight of 1.0 to absence of the disease in the CUA computations. The results of such analyses are called "NP-QALYs," as the correction factor to compute QALYs is not a simple proportional adjustment. The authors suggest that analysis doing cost-utility analyses without access to primary data from treated patients use average age-specific health-related quality-of-life weights from population-based studies to represent the state of not having a particular disease. Consumers of CUAs should closely examine the nature of the QALYs in any published analyses before making decisions based on their results.

Adult↗
PubMedDate not supplied

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.

Actuarial Analysis↗
PubMedDate not supplied

Long-term survival among men with conservatively treated localized prostate cancer.

OBJECTIVE: To determine age-specific, all-cause mortality, disease-specific mortality, and life expectancy for men aged 65 to 75 years who are treated only with immediate or delayed hormonal therapy for newly diagnosed, clinically localized prostate cancer. DESIGN: A population-based, retrospective cohort study. SETTING: Patient records were abstracted from 37 acute care hospitals and two Veterans Affairs medical centers in Connecticut. Original pathology slides were sent to a referee pathologist who was blinded to case outcomes. SUBJECTS: All men identified by the Connecticut Tumor Registry with clinically localized prostate cancer diagnosed in 1971 to 1976 who were aged 65 to 75 years at the time of diagnosis and were untreated or treated with immediate or delayed hormonal therapy. MAIN OUTCOME MEASURES: Parametric proportional hazards models incorporating tumor histologic findings, comorbidity, and age at the time of diagnosis to compare cohort survival with that of men in the general population. RESULTS: After a mean follow-up of 15.5 years, the age-adjusted survival for men with Gleason score 2 to 4 tumors was not significantly different from that of the general population. Maximum estimated lost life expectancy for men with Gleason score 5 to 7 tumors was 4 to 5 years and for men with Gleason score 8 to 10 tumors was 6 to 8 years. Tumor histologic findings and patient comorbidities were powerful independent predictors of survival. CONCLUSIONS: Compared with the general population, men aged 65 to 75 years with conservatively treated low-grade prostate cancer incur no loss of life expectancy. Men with higher-grade tumors (Gleason scores 5 to 10) experience a progressively increasing loss of life expectancy. Case series reports of survival/mortality experienced by men with clinically localized prostate cancer that fail to control for age, tumor histologic features, and comorbidities risk significant bias.

Aged↗