Page 35 - The Importance of Common Metrics for Advacing Social Science Theory and Research

The Importance of Common Metrics for Advancing Social Science Theory and Research: A Workshop Summary
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MEASUREMENT IN THE SOCIAL SCIENCES 23

elicit utility weights (preferences) for health states, with average preference
weights from a community sample of people. He acknowledged that defin-
ing perfect health can be a problem.
Fryback returned to the two areas of concern as health outcomes—
morbidity and mortality. Morbidity is how people feel, how health prob-
lems affect them, abilities, disabilities, functional capacity, independence,
and other aspects of health and well-being. Mortality is how long people
live. Health care and health interventions affect both of these aspects of
health.
According to Fryback, one summary measure, HRQoL, combines all
the aspects of morbidity. A second summary measure, quality-adjusted life
expectancy (QALE), combines HRQoL and mortality into a single num-
ber. QALE would be the expected number of quality-adjusted life years
(QALYs) experienced by a cohort of the same starting age and quality of
life. It is perhaps the best estimate of future health-adjusted life years for a
random member of that cohort.
Fryback shared other uses of QALYs. Canada follows HRQoL over
time with a large longitudinal panel data as well as with successive cross-
sectional population surveys. The U.S. Panel on Cost-Effectiveness in Health
and Medicine tried to standardize cost-effectiveness analyses (CEA), calling
for something like QALYs as the generic outcome measure for meaning-
ful analysis. Fryback considered CEA to be more prominent in the United
Kingdom and Great Britain, where the National Institute for Clinical Ex-
cellence uses QALYs as a basis for policy on what gets into the National
Health Service, particularly for drug therapies.
Fryback described how cross-sectional samples of individuals’ HRQoL
at a point in time can be used for meaningful population health measures.
Community averages of HRQoL summarize health at a point in time.
Cross-sectional HRQoL data can be combined with mortality data, and life
table techniques can be used to weight life expectancy computations (Molla
et al., 2001). To illustrate this, he presented data on women in the United
States from the 2000 census and the National Health Interview Survey
(NHIS). The life expectancy for women ages 55 to 59 at that time was 27.1
years, but the QALE was 20.5 years, about a 25 percent difference. For
women 10 years older, ages 65 to 69 at that time, the QALE was 13.8 years,
which means that for the cohort between ages 55 and 65, the expected
QALY at that time was about 6.7 years (or 20.5 less 13.8 years). It would
have been 10 years had the quality of life not degraded during this period.
According to Fryback, the key to making meaningful comparisons over
time and across populations is the systematic collection of standardized
measures with sufficient sample sizes. To date in the United States, only a
few data sets have suitable measures, and only one has committed to longi-
tudinal data collection. He argued that the population data system should

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