Identify the best method to show the life expectancy of an individual within a population.

The measures of health-related quality-of-life described in the previous section may be used to derive QALY compatible estimates. A QALY is a summary measure of health—based on subjective quantification of illness—that includes both morbidity and mortality. A year in perfect health is equal to 1.0 QALY. The value of a year in ill health is discounted to reflect the relative utility of the ill state versus perfect health; for example, a year bedridden may be valued at 0.5 QALY. In cost-effectiveness analysis of health care interventions, QALYs are now the standard metric for health impacts (Gold, 1996). These impacts are calculated for both individuals getting new treatments and populations with some changes in their health inputs.

A second acronym—QALE (for quality-adjusted life expectancy)—is used in the population health literature as a summary measure of current health status. QALE is life expectancy adjusted for the quality of surviving years and so is measured in QALYs. QALE is by definition less than life expectancy computed in unadjusted years. The discrepancy between QALE and unadjusted life expectancy reflects the relative perceived desirability people place on living a given length of time with morbidity versus living that time in perfect health. In a life-table representation of population health, QALEs are reported undiscounted for time (i.e., just as life expectancies in a population actuarial table are undiscounted).

QALEs and the methods described below to compute them have several good properties. First, they are independent of the age composition of the population. Other measures, such as crude death rates, disease prevalence, or restricted activity days, are highly dependent on age and must be stratified or standardized for many comparisons. Life-table methods are a natural method of standardization that do not require any particular population (such as the U.S. population in 2000) to be chosen. Second, with no additional work, the tables that compute QALE at birth can be used to compute QALE and nonquality-adjusted life expectancy for any age group (e.g., 65-year-old life expectancy as recommended by status of health indicators 2008). Ignoring the health adjustments, these methods compute classic measures of population health, such as life expectancy at birth, to compare with historical data from the United States and other countries. However, using this type of actuarial QALE as a descriptive summary of population health requires cross-sectional surveys of HRQoL, as discussed in the next section.

Medical care may affect life expectancy, HRQoL, or both. For the purpose of cost-effectiveness analysis of medical interventions, these two are generally combined into one QALY measure (Gold, 1996). At the individual person level, generic health over time may be represented as a function of HRQoL over time, q(t). Once one has observed the individual’s HRQoL over time from t0 to t1, one can compute the QALYs accrued by the individual between time t0 and t1 as the integral ∫t0t1q(x)dx, where the function q is empirically defined by the observations.

More often, empirically defined QALYs are computed by weighting time intervals lived, such as 1-year intervals, by an observed or estimated HRQoL average for the interval, then summing products of HRQoL and interval length across intervals. In this fashion, consider qa to be the average HRQoL for a person in the year interval from a to a + 1. Let tpx be the probability that a person age x will survive to age x + t. The empirically QALE conditioned on current age being a, will be QALEa=∑t=1∞(qa+t-1)(pta). This computation is a variation on standard life-table calculation of age-specific life expectancy where each year of life is weighted by age-specific HRQoL. This method is widely attributed to Sullivan (1971).

Rosenberg, Fryback, and Lawrence (1999) demonstrate this technique using qa values measured with the QWB index estimated in a community population and combine these with life-table survival probabilities to compute QALEa for males and females ages 55 and 65. Others have used a binary-valued 0,1 HRQoL function giving disability an HRQoL of 0 to calculate disability-free life expectancy (Molla, Wagener, and Madans, 2001). A similar method that allows for a few states between life and death is multistate life tables (Cai et al., 2003).

These techniques can estimate QALE for a health account. As with ordinary life expectancy, QALE measures do not predict future health, but instead summarize health in the current year. Two inputs are needed: (1) a life table describing mortality experience in the population and (2) average HRQoL at each year of age in a population. Age-specific death rates from NCHS would be needed as the first input. Data from population surveys using any of the HRQoL indexes described above would suffice for the second. In the next section we list existing surveys using one or more of these measures. Table 5-2 shows how the necessary life-table calculations would be structured.

Data for column 1 come from NCHS vital statistics. They calculate the age-specific death rates from data from the decennial census on midyear population of each age, together with their collected deaths by age. Column 2 shows how many people of each age remain alive with these death rates at each age, assuming an initial hypothetical cohort of 1,000 births. Column 3 is new: it would require a population survey of HRQoL. Columns 4–9 are computed quantities using standard life-table techniques augmented for HRQoL weighting, as described above. Column 4 is the product of population × (1 – death rate × fraction of year lost to death). The fraction of years lost to death is usually very close to one-half except for infants because, on average, people die half way through the year. The row corresponding to 100, the largest age in the table, is special, as it covers more than 1 year: the death rate is 100 percent and years lived means expected future years for those exactly 100 years old. Column 5 is the years lived at that age × average HRQoL at that age, so it equals the QALYs lived by the remaining hypothetical population at that age. Columns 6 and 7 are added from the bottom to get cumulative health in years and in QALYs from each age to death in the hypothetical cohort. Finally, columns 8 and 9 divide the remaining years by column 2, the number of people of that age alive, to get life expectancy and QALE.

These tables also can be used to calculate other generic measures that have been collected for years in many countries, such as infant mortality and (nonquality-adjusted) life expectancy from birth and at other ages, such as 21 and 65. The national health account would need to do so to facilitate historical and cross-country comparisons, although we expect other Western countries to begin calculating and reporting QALE also.

Although restricted activity presumably is reflected in HRQoL, there might be some interest in these numbers and trends as well. One might use life-table methods to standardize other age-dependent measures, such as restricted activity days, calculating the expected lifetime-restricted activity days for a period cohort with death rates and restricted activity days by age as in the current year, but it seems more natural just to report the actual number of restricted activity days, perhaps stratified into large age groups such as children and adults over and under age 65.

Several cross-sectional surveys of HRQoL currently exist; Box 5-1 is a list of data sources. In Table 5-2, new data in column 3 would be the result of periodic HRQoL surveys of the population. Several one-time national data sets and at least two continuing periodic surveys collect one or more of the HRQoL indexes described earlier. However, without augmentation, none of these is entirely sufficient for an ongoing and detailed health account computation of QALE. Three one-time surveys have collected systematic HRQoL data. Although these studies can be used to estimate age-specific HRQoL of community living adults in the United States, they all miss persons younger than age 18, institutionalized persons, and persons living in the community but unable to respond to a survey for physical or cognitive reasons. The Joint Canada/United States Survey of Health was conducted in English, French, and Spanish. The National Health Measurement Study was conducted in English, and the U.S. Valuation of the EQ-5D was conducted in English and Spanish. Hanmer, Hays, and Fryback (2007) discuss similarities and differences of these surveys and implications for HRQoL estimates.

Cross-Sectional Surveys of Health-Related Quality of Life. One-Time Surveys The Joint Canada/United States Survey of Health was conducted in 2002–2003 by the U.S. National Center for Health Statistics and Statistics Canada. Approximately 3,500 (more...)

Two ongoing surveys of the U.S. adult population collect information for one or more of the HRQoL indexes. Other studies of note that have included HRQoL indexes include the Health and Retirement Study (HRS, see http://hrsonline.isr.umich.edu/), which administered HUI3 in 2000 as a module for approximately one-twelfth of the full HRS sample, or about 1,600 individuals. The Centers for Medicare & Medicaid Services are required by law to survey a sample of 1,000 Medicare recipients from each participating Medicare Advantage plan. The resulting sample is nearly 100,000 persons per year and has been ongoing since 1998. This survey was formerly known as the Health of Seniors study, but in 1999 was renamed the Medicare Health Outcomes Survey (HOS, see http://www.cms.hhs.gov/hos/ and http://www.hosonline.org/). HOS included the SF-36 version 1 questionnaire through 2007; this questionnaire serves for calculating SF-6D HRQoL scores. In 2008, the HOS plans to change the questionnaire to a version of SF-36 developed for the U.S. Department of Veterans Affairs (Kazis et al., 2004a, 2004b, 2004c). Whether SF-6D scores can be calculated after this change is not known at this time.

None of the surveys mentioned above consistently includes actual medical examination of participants but, beginning two waves ago, the HRS has been collecting blood and some anthropometric measures. The ongoing National Health and Nutrition Examination Survey (NHANES, see http://www.cdc.gov/nchs/nhanes.htm) does include a medical examination and testing of participants. To assist in modeling relationships of proven medical conditions (versus self-reported ones) for HRQoL, it would be highly desirable for NHANES to include at least one of the HRQoL indexes in its protocol. More broadly, given that multiple surveys (each with a somewhat different population scope) are required to adequately measure health across the population broadly, there should be some effort by the statistical agencies to pick a common quality of life instrument to use in the different surveys.

Recommendation 5.1: A committee of members from agencies responsible for collecting population health data (Agency for Healthcare Research and Quality, National Center for Health Statistics, Census Bureau, etc.) should be charged with identifying and putting in place a single standard population health measurement tool (or set of tools) to use in a wide range of surveys. The best instrument, which is situation specific, may simply be the one that can be added to enough surveys collected over time so that most of the population is covered.

Ideally, agencies would collaborate and choose one instrument that can be followed over time, for the purpose of having at least one comparable measure for different years, but others could be considered as well. For example, it would be very useful if a generic quality of life measure were added to the Medicare Current Beneficiary Survey (MCBS). The MEPS and MCBS should pick at least one instrument in common that both will consistently use over time. Although the choice of instrument should be left to the agencies, the system should strive for consistency of instrument use over time. The main reason for standardization is that chaining from one instrument to another is problematic. If a change does occur, an overlap period is needed for the transition.

In addition to generic measures of health, the national health accounts should also contain information on a set of specific diseases. WHO, for example, collects data on the incidence, prevalence, patterns of treatment, and mortality from tuberculosis for almost all countries in the world, and these are reported annually (World Health Organization, 2008). In the United States, separate data collection efforts are ongoing for cancer, HIV/AIDS, end-stage renal disease, and other diseases, the results of which can be used by researchers and policy makers. The databases may include information on stage at diagnosis, treatments, disease severity, and mortality. Is it reasonable to try to include such data on all major diseases in the health accounts?

Disease-specific data are considered by many to be the cornerstones of health research as they can provide the basis for learning, for example, if the incidence or prevalence of Alzheimer’s, HIV/AIDS, or lung cancer is going up or down; how limiting is arthritis or asthma; or how many people are dying from food poisoning or heart attacks. Disease-specific data may fit more closely with the intermediate outcome of expenditures organized by disease-specific episodes of treatment that are used to estimate improvement per expenditure. They are vital in planning policies and research on these diseases and in evaluating current programs.

However, as a general population-wide measure of health, disease-specific data have several problems. To be comprehensive, an enormous number of diseases would need to be covered, so the price tag would be high. Aggregation into a single or a few measures seems very difficult. How would one add up cases of asthma and breast cancer, or even cases of breast cancers of different severity? How would one treat cases of cancer in remission or aggregate cases with different stages? Also, each disease-specific health unit is on its own scale. Researchers are now in the early stages of trying to estimate the QALY impact for specific diseases on future health and survival. In one of these initial studies, Eggelston et al. (2009) measured the net economic value of improvements in health status (to an admittedly homogenous population), using a QALY metric defined in terms of 10-year cardiovascular risk, from spending on care for patients with type 2 diabetes.3

Another issue is the overlap of diseases, which raises the problem of allocating the total health decrement to each that is present. It is even difficult to split up deaths in this way—given that multiple possible causes of death are common-place, information is currently not precise enough to confidently estimate death trends by disease. It is also useful when these measures include cognitive impairment. This is a large problem for coverage of the elderly and also for populations outside the United States—for example, deaths of children in developing countries from malnutrition.

Recommendation 5.2: Recognizing the difficulties in estimating the incidence and prevalence of disease, the National Center for Health Statistics should commission research on selecting and specifying a set of important acute and chronic diseases and feasible methods for estimating acute incidence and chronic prevalence that might be part of a national health data system. These counts would be a supplement to the systematic data systems to measure health-related quality of life using measures that transcend single diseases (Recommendation 5.1).

Such estimates should prove useful for looking at the impact of trends in screening, expenditures per case, and deaths.

A possible alternative to the QALY approach for measuring population health stock would be to base it instead on the disability-adjusted life year (DALY) methodology (Murray, 2002). These measures were developed to help WHO and others working in the developing world understand the burden of (generally infectious) diseases, plan and evaluate policies to reduce the impact of those diseases, and assess health using data that might be available in those countries.

The DALY approach to measuring health is based on disease incidence and prevalence—a fixed burden per person is computed based on the methodology, and then total burden is counted by multiplying this times the number of people affected. Early versions of the methodology used expert opinion to set the burden per person of morbidity, but the DALY weights and methods overall have been fluid in the past several years in response to criticisms, and they are now much closer to a QALY. One minor difference is that the DALY method assesses a health gap: the difference between a person’s life and a life at good health to some maximal age, whereas QALY methods typically say how much life is achieved. If the methods for assessing HRQoL were identical, there would be the identity that the QALYs (at less than maximal age) + DALY gap = maximal life span. The developing world’s focus on DALYs may be partly driven by the enormous data gaps that exist, and it makes sense conceptually for those trying to maximize the reductions in disease-based disability and death (burden of disease) for a given cost outlay. However, they are not sufficient for that purpose, because estimates of the effect of any program on each DALY are also necessary.4

The QALY method is much more widely used than are DALY methods in the United States and among the developed countries in the Organisation for Economic Co-operation and Development. In these countries, the major health problems are no longer childhood infectious diseases but the chronic conditions that people accumulate with age and need to manage to preserve their health. Also, the QALY methods have been the subject of extensive scientific investigation and were, in particular, developed for the health questions typically asked on the ongoing surveys. Economic accounts are in the business of organizing data for measuring the value of goods and services generated in the economy, so it makes sense to think in terms of QALYs (the good that is being generated from medical care). And for a U.S. application, one would want data of sufficient quality to estimate QALE using QALY methods.

All things considered, for the purpose of measuring population health for a national health account, QALY is the most appropriate metric currently available.

Recommendation 5.3: Initially the national health account should focus on quality-adjusted life expectancy measured in qualify-adjusted life years as the best summary measure of health in each year.

Data on important risk factors that impact future health should be collected, but more research is needed before useful calculations can be made of the future life expectancy of the current population.5 Expected future QALYs will need to be revised as the effect of treatments become known—this is similar to other parts of the NIPAs.