MODELING QUALITY-ADJUSTED LIFE EXPECTANCY LOSS RESULTING FROM TOBACCO USE IN THE UNITED STATES*

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1 Social Indicators Research (2006) Ó Springer 2006 DOI /s y ROBERT M. KAPLAN, JOHN P. ANDERSON and CAMERON M. KAPLAN MODELING QUALITY-ADJUSTED LIFE EXPECTANCY LOSS RESULTING FROM TOBACCO USE IN THE UNITED STATES* (Accepted 6 March 2006) ABSTRACT. Purpose: To describe the development of a model for estimating the effects of tobacco use upon Quality Adjusted Life Years (QALYs) and to estimate the impact of tobacco use on health outcomes for the United States (US) population using the model. Method: We obtained estimates of tobacco consumption from 6 years of the National Health Interview Survey (NHIS). In addition, NHIS data were used to impute the Quality of Well- Being (QWB) Scale using a new methodology known as QWBX1. The QWB places health status on a continuum ranging from death (0.0) to full functioning without symptoms (1.0). The method allows the adjustment of life expectancy for reduced quality of life associated with health conditions. NHIS data were matched to the National Death Index for 14,464 deaths occurring by December 31, The analysis is limited to adults between the ages of 18 and 70 years. Results: Quality of Well-Being scores were broken down by age and for six smoking categories: (1) non-smokers, (2) those who smoke 1 10 cigarettes per day, (3) cigarettes per day, (4) cigarettes per day, and (5) cigarettes per day, and (6) 40 or greater cigarettes per day. There was a systematic relationship between current tobacco use and health-related quality of life at each point along the age spectrum and there was a clear and systematic separation of quality-adjusted life expectancy by number of cigarettes smoked per day. Teenagers who continue to smoke loose 3.5 QALYs between ages 18 and 70 in comparison to non-smokers. A greater portion in the loss in QALE is attributable to quality of life than to shorten life expectancy. Conclusions: The overall goal of Healthy People 2010 is to increase Years of Healthy Life (or QALE) in the United States. Each year, tobacco use results in hundreds of thousands of quality-adjusted life years lost. Combined models of morbidity and mortality incorporating a range of tobacco consumption levels are required to best represent the impact of tobacco use. KEY WORDS: Tobacco, quality of life, quality-adjusted life expectancy, years of healthy life In 1996, the U.S. Department of Health and Human Services released guidelines for the evaluation of programs in medicine and health care (Gold, 1996). The report suggested that most activities in health care have the common objective of improving health. The DHHS report noted that * Supported by a Grant 11RT-0243 from the Californian Tobacco Related Disease Research Program (TRDRP)

2 ROBERT M. KAPLAN ET AL. common metrics for health benefit are achievable. In particular, they focused on a combination of life expectancy and life quality known as the Quality- Adjusted Life Year (QALY). Many different activities in health care and in public health policy might be represented in terms of the number of QALYs they produce. Each of these activities is also associated with an expense. The cost/qaly ratio was recommended as a common quantitative index to compare the societal value of very different investments in health care. To date, the literature on cost/qaly in tobacco control has typically used expert judgments of health benefits rather than measured health outcomes. Most previous models fail to combine morbidity and mortality effects. Further, most models include only broad categories, such as smokers versus nonsmokers, failing to recognize that smokers often reduce consumption rather than quit. To improve upon earlier studies, we are building a more complex model of health outcome. In this paper, we describe the development of a model to estimate the effects of tobacco use upon QALYs. We then use the model to estimate the impact of tobacco use on health outcomes for the segment of the United States (US) population between 18 and 70 years of age Data Sources 1. METHOD National Health Interview Survey. Data for the study come from the National Health Interview Survey (NHIS) completed in 1987, and 1990 through We focus on these years because quality of life imputations are available for these data files (see below). NHIS estimates health status for U.S. civilian non-institutionalized population. The study uses a probability sample of American households. Personal interviews are conducted by trained interviewers from the U.S. Bureau of the Census. Samples are drawn weekly and, depending on the year of the survey, the total sample includes 36,000 47,000 households, including 92, ,000 persons. Information is obtained on the number of restricted-activity days, bed days, work- or school-loss days, and all physician visits occurring during the 2-week period prior to the week of the interview. The NHIS questionnaire also asks about acute and chronic conditions that were responsible for reported disabilities, limitations, or health care visits. The NHIS includes sample weights to allow generalization of survey results to the non-institutionalized US population. Typically, there are 686- ethnicity unique cells and adjustments are made in order to properly weight data for representation in the population. These weights are available in

3 TOBACCO AND QALYS public use tapes and the procedures describing them have been presented in various NHIS documents (Kovar, 1989; Adams and Benson, 1990). The sample has been shown to be representative of the US population in terms of age, race, sex, and income. In this analysis, we concentrate on members of the sample who were between 19 and 70 years of age. In order to be included in our analysis, cases needed to have complete information for health status and smoking. Across years, 220,327 unweighted cases were available for analysis. The Ns by year were 1987 (44,123), 1990 (53,888), 1991 (58,554), 1992 (22,996), 1993 (21,028), 1994 (19,738) QWB Scale. Over the past 35 years, a group of investigators at the University of California, San Diego (UCSD) has planned, developed and implemented methods for measuring Quality-Adjusted Life Years. In particular, the Quality of Well-being Scale is a widely applied measure in health outcomes research. It combines preference-weighted measures of functioning with symptoms and problems to generate a point-in-time measure of Well-being (W), on a metric running from 1.0 (for asymptomatic full function) to 0.0 (for death). Elements of the QWB include the Symptom/ Problem Complexes (CPX) and their calculating weights, and Function Scale steps and calculating weights. For our purposes, W is a Health-Related Quality of Life measure composed of CPX and function limitations. The QWB methodology has been described or applied in more than 200 published papers (Anderson et al., 1986, 1989; Anderson and Harris, 2001; Holbrook et al., 2001; R. Kaplan et al., 2001; R. M. Kaplan et al., 1976, 1998; R. M. Kaplan and Erickson, 2000). Underlying the QWB is the concept that illnesses, conditions, and injuries are thought to cause Symptoms and Problems, which may lead to dysfunctions (DYS) here meaning limitations in functioning. For purposes of this study, the various words commonly used to describe health-related effects such as disability, restricted activity days, bed days etc., may be described as sub-sets of dysfunction. Dysfunction is defined as being below the top step (Not Limited) on the Mobility (MOB), Physical Activity (PAC) and/or Social Activity (SAC) Scales without reference to duration. On a particular day, an individual s QWB score is therefore composed of one (or No) Symptom weight, and 0, 1, 2, or 3 dysfunction steps. Preference weights empirically locate how desirable or undesirable a particular situation is in QWB along the continuum between death (0.0) and full functioning without symptoms(r. M. Kaplan et al., 1976, 1979, 1998). Preference weights (for symptoms and for dysfunction) provide the metric underlying all QWB scores. The Quality-Adjusted Life Expectancy (QALE)

4 ROBERT M. KAPLAN ET AL. integrates a mortality component with W. In this paper, we report QWB scores with and without adjustment for mortality. Both are reported on a scale ranging from 0 to Mortality-adjusted QWB is calculated by multiplying the mean QWB score at each age level by the proportion of the birth cohort still alive. In this analysis, mortality-adjusted QWB was estimated by adjusting the mean QWB score for each age group by the number of people in the birth cohort who died prior to Although, the QWB methodology has been applied widely, this paper describes a new methodology for estimating the QWB from NHIS data. Health effects of any NHIS condition may be analytically traced to the symptom weights and Dysfunction weights associated with it, and then used to create the QWB score. QWB scores are preference weights for combinations of symptoms and levels of functioning. For this study, NHIS data were used to create a new QWBX1 index. The QWB Social Activity Scale was matched to items on activity limitations in the NIHS. Questions on both measures are very similar. The physical activity component for the QWB was matched to NHIS items on 2-week work loss days and school loss days. The self-care component of the QWB Social Activity Scale was estimated from NHIS items on self-care, which were added in One of the most difficult challenges was estimating the symptoms and problems needed to calculate the QWBX1. The QWB asks about symptoms but not about medical conditions. Conversely, the NHIS asks about conditions, but not about symptoms. The QWBX1 method requires that symptoms and problems be estimated from conditions on the basis of expert medical judgment. NHIS allows respondents to report multiple conditions. In the QWBX1, the number of specific conditions was limited to the first six reported. These constituted over 99% of the people reporting conditions. Physicians estimated the likely QWB symptoms or problems associated with each NHIS condition. This allowed estimation of each QWB component for each respondent. These components were then incorporated on the 0.0 to 1.0 continuum using standardized weights and an algorithm selecting the single Symptom/Problem with the highest standardized preference weight. The original purpose of the NHIS-QWB estimation project was development of QALY information on persons in the NHIS having illness, meaning those reporting one or more NHIS Conditions. One of the issues in developing these estimates is that QWB is generally more sensitive than the NHIS because it emphasizes symptoms rather than medical conditions. NHIS reports 40% of the population as having a

5 TOBACCO AND QALYS Condition, while QWB probability samples report 75%-80% of the population to have one or more Symptom/Problem Complexes(R. M. Kaplan et al., 1976, 1996). To fill out what would be the normally expected CPX among those without conditions or any function limitations, a hotdecking randomization procedure was employed. CPX were assigned to persons who reported no health conditions, based on an age-related, observed frequencies basis, drawn from among probability samples. The purpose of these procedures was to adjust the overall yearly QWB total population average score to empirically reasonable levels National Death Index (NDI) Our analysis links NHIS responses in the years 1986 to 1994 to deaths up to December 31, To accomplish this linkage, we used the National Health Interview Survey Multiple Cause of Death Public Use Data File for analysis. Information on this resource is available at products. This system links NHIS response to the National Death Index (NDI). The NDI is a computerized system that creates a national index of death records on the basis of state vital statistics reports. The National Center for Health Statistics (NCHS) established the resource for use in epidemiologic studies. The system began in Each year death records are added to the Death Index approximately 12 months after the calendar year ends. The National Death Index includes a set of identifying codes and can be matched by name or social security number. The population was initially divided into two analytic groups: Group (1) never smokers; Group (2), current and former smokers. The current smoker and former smoker groups were combined and further subdivided by categories for number of cigarettes smoked per day. The categories were never smoker, 1 10, 11 20, 21-30, 31 40, and over 40 cigarettes per day. Outcome scores were calculated two ways. First, we estimated Mean QWBX1 separately for each smoking group. A second estimate used a crude estimate of mortality-adjusted QWB. This analysis substituted a score of 0.00 for any respondent who died prior to January 1, For both QWBX1 and mortality-adjusted QWB means by age were estimated and polynomial cures were used to estimate the relationship between smoking level and age and outcome. With sample sizes over 200,000 virtually all observed differences are statistically significant. Therefore, we do not report significance tests.

6 ROBERT M. KAPLAN ET AL. 2. RESULTS Overall 26.6% of the samples were current smokers, 22.9% were former smokers, and 50.5% were never smokers. The participants tended to be female (59.5%) and ranged in age from 1 to 99 years (mean 44.93, SD=18.23). Women had higher mean mortality-adjusted QWB scores (0.732) than men (0.715) Among study participants had died by QWBX1 scores at initial assessment were significantly higher for respondents assumed alive in 1997 in comparison to those who had died by the end of 1997 (Table I). Figure 1 shows both raw and curves fitted with second degree polynomials for mortality-adjusted QWB by age for current smokers, in comparison to former smokers and never smokers. In comparison to never smokers, smokers and former smokers experience reduced qualityadjusted life expectancy at each point along the age continuum. A category for never smokers and five categories combining current and former smokers were created. The smoking categories were 1 10 cigarettes per day, and 11 20, 21 30, 31 40, and greater than 40 cigarettes per day. Using polynomial curve fitting methods, we estimated the functions for quality of life and mortality-adjusted QWB as function of age. A separate function was estimated for never-smokers and for those in the five categories of smoking. These calculations were done separately for men and women. The functions are given in Table II. Figure 2 shows this relationship graphically for men and Figure 3 shows the same relationship for women. The relationship between number of cigarettes smoked per day and mortality-adjusted QWB was stronger for males than for females. Number of pack years was also systematically related to both the QWB and mortality-adjusted QWB estimates. Number of pack years is related to age because younger smokers have not lived long enough to accumulate large numbers of pack years. However, the relationship between pack years and outcomes remains after age adjustment (Figure 4). TABLE I QWBX1 Scores for Those Assumed Dead or Alive in 1997 Vital Status in 1997 N Mean Std. Deviation Std. Error Mean Alive Dead

7 TOBACCO AND QALYS Former = x x R 2 = Current = x x R 2 = Never= x x R 2 = Current Former Never Poly. (Current) Poly. (Former) Poly. (Never) Age Fig. 1. Mortality-adjusted QWB (times 100) by smoking status. Quality-adjusted life expectancy (QALE) loss is estimated by summing the mortality adjusted QWBX1 for the 52 years between ages 18 and 70. QALE is made up of two components: changes in life expectancy and changes in quality of life. Both of these components systematically decline with increasing tobacco use (Figure 5). During the interval between ages 18 and 70, we estimate that non-smokers live QALYs. In contrast, TABLE II Second Degree Polynomial Fit of Smoking and mortality-adjusted QWB for Males and Females Smoking Category Male Equation R 2 Female Equation R 2 Never y=)0.0617x x y=)0.0964x x y=)0.1056x x y=)0.1479x x y=)0.1415x x y=)0.1291x x y=)0.1691x x y=)0.1762x x y=)0.2063x x y=)0.1797x x

8 ROBERT M. KAPLAN ET AL Never Age Fig. 2. QALE (times 100) by age for males Never AGE Fig. 3. QALE (times 100) by age for females.

9 TOBACCO AND QALYS QALE X < Pack-Years Smoked Fig. 4. QALE (times 100) by pack-years smoked. smokers consuming between 21 and 30 cigarettes per day live QALYs during this interval. By age 70, the difference between two packs per day and never smokers is substantial representing a difference of about 3.5 quality adjusted life years. In other words, quality adjusted life expectancy decreases quite substantially with about 1.2 years (34%) attributable to shortened life QWB QALY Mortality-adjusted QWB or QALY Non Smoker 1 to to to to Cigarettes Per Day Fig. 5. Mortality-adjusted QWB and QALY by smoking category.

10 ROBERT M. KAPLAN ET AL. expectancy and another 2.3 years (66%) attributable to reductions in health-related quality of life. 3. DISCUSSION Tobacco use has significant effects on both life expectancy and healthrelated quality of life. We created models that represent the impact of tobacco use using expressions of Quality-Adjusted Life Years. The study was based on a large representative sample of the non-institutionalized U.S. population. The analyses show a systematic relationship between tobacco use and poor health outcomes. Not only does tobacco use cause substantial loss in life years, but these losses are augmented by systematic reductions in health-related quality of life in the years prior to death. Previous models that used only life years lost seriously underestimate the public health consequences of tobacco use. One purpose for constructing these models is to estimate the impact of tobacco control programs. Several studies suggest that tobacco control interventions should be good uses of resources. In one analysis Taylor, Pass, Shepard and Komaroff compared three different approaches to the management of heart disease: cholesterol reduction, blood pressure reduction, and smoking prevention (Taylor et al., 1987). They found that both cholesterol reduction and blood pressure treatment, two well-established secondary prevention approaches, produce relatively little benefit in comparison to tobacco prevention programs. Cholesterol reduction has very little impact on life expectancy, particularly if it begins at age 60. In fact, cholesterol reduction programs may add only about two months of life for a 60-year-old man. The tobacco prevention programs may produce a full 5 years of life for those individuals prevented from smoking by age 20. The Food and Drug Administration (FDA) has evaluated the costeffectiveness of restricting tobacco sales to minors. The study assumed that the restrictive policies might reduce tobacco use among minors by 25%. The analysis considered the cost of such a program to tobacco manufacturers, retailers, consumers, and federal agencies. Since reduced tobacco use would have substantial public health benefits, the analysis estimated that the cost/ QALY would be less than $1,000 (Graham et al., 1998). Kaplan, Ake, Emory, and Navarro evaluated the impact of tobacco tax and found that tax increases result in significant increases in both QALYs and tax revenues (R. Kaplan et al., 2001). In an analysis of the potential for smoking cessation programs, the Agency for Health Care Policy and Research (AHCPR) considered the

11 TOBACCO AND QALYS impact of applying their Smoking Cessation Clinical Practice Guidelines for the US population. The report identified 15 different smoking cessation guidelines, ranging from minimal counseling to intensive counseling. Each intervention is considered with or without concomitant use of nicotine replacement in the form of gum or nicotine patches. The analysis assumed that the interventions would be appropriate for 75 percent of adult smokers, which corresponds to the proportion that made a previous quit attempt. The model assumes that the program would yield 1.7 million new quitters, of whom 40 percent would have quit on their own and 60 percent may have been influenced in some way by the program to quit. Further, the model assumed that 8.8 percent of smokers would quit with no intervention, 10.7 would quit with minimal counseling, 12.1 percent would quit with brief counseling and 18.7 percent would quit with counseling lasting more than 10 minutes. Use of nicotine replacement would boost these effects further. The program would cost an estimated $6.3 billion, or about $32 per smoker. Cost per QALY was estimated at $1,915. Considering a variety of assumptions, the cost/qaly ranged from $1,108 to $4,542, placing it well below most programs that have been analyzed (Cromwell et al., 1997). Our work has a significant number of limitations. First, and most important, our estimates of the effects of tobacco on mortality-adjusted QWB and QALE are almost certainly underestimates. We are confident that our estimates are low because we studied only those respondents between the ages of 18 and 70 years. It has been estimated that only one third of premature tobacco deaths occur prior to age 70 (see sammec). Thus, the majority of premature tobacco deaths may have been excluded from our analysis. Other analyses of mortality show substantial numbers of late life, but premature deaths among smokers ( Impact of smoking on life expectancy and disability, 2001; Malarcher et al., 2000; Thun et al., 2000) A second reason why our model is conservative is that our mortality estimates included only those who were alive in 1987 and dead by the end of This is a very short interval for estimating the mortality consequences of tobacco use. A third concern is that participants in the NHIS are likely to have been healthy volunteers. Further, survey effects can be attenuated when respondents are forgetful about ever smoking or being exposed to second hand smoke (Heller et al., 1998). As a result, our data on smoking exposure are likely to be underestimates of true exposures. Even using this very conservative method, the analysis shows that the health consequences of tobacco use are enormous. Thus, we feel confident that our conclusions are in the correct direction.

12 ROBERT M. KAPLAN ET AL. Another methodological limitation is that estimates of quality of life come from an imputation. The National Health Interview Survey does not directly ask questions required to compute the Quality of Well-being Scale. However, we have reason to believe that the imputations provide relatively good estimates of QWB scores (Anderson, 2001). Caution must also be exercised in interpreting our estimates. Modeling requires many assumptions and the validity of these assumptions may require additional study. Another limitation of the study is the underlying assumption that the difference in self-reported health status between smokers and non-smokers is due to smoking. This analysis did not adjust for other health habits and it is possible that smokers are more like to be risk takers who die from accidents or other causes unrelated to smoking. SES differences between smokers and non-smokers may also account for some of the effects. Although this argument is compelling, Peto argues that it is reasonable to attribute differences between smokers and non-smokers to tobacco (Peto et al., 1994). A final limitation of our model is that the QALE estimates combine current quality of life with future mortality. We are working on more advanced models that combine current smoking with future probabilities of death. Despite these limitations, we believe our model will be informative in evaluating decisions about tobacco control programs. In the future we hope to present estimates of the impact of various tobacco control programs using this methodology. REFERENCES Adams, P.F. and V. Benson: 1990, ÔCurrent estimates from the National Health interview survey, 1989Õ, Vital Health Statistics 10(176), pp Anderson, J.P: 2001, ÔActivity limitations reported in the National Health Interview Survey: An anomaly and its effect on estimates of national well-beingõ, American Journal of Public Health 91(7), pp Anderson, J.P., J.W. Bush, M. Chen and D. Dolenc: 1986, ÔPolicy space areas and properties of benefit-cost/utility analysisõ, Journal of the American Medical Association 255(6), pp Anderson, J.P. and J.P. Harris: 2001, ÔImpact of Meniere s disease on quality of lifeõ, Otology and Neurotology 22(6), pp Anderson, J.P., R.M. Kaplan, C.C. Berry, J.W. Bush and R.G. Rumbaut: 1989, ÔInterday reliability of function assessment for a health status measure. The Quality of Well-Being scaleõ, Medical Care 27(11), pp Cromwell, J., W.J. Bartosch, M.C. Fiore, V. Hasselblad and T. Baker: 1997, ÔCost-effectiveness of the clinical practice recommendations in the AHCPR guideline for smoking cessation. Agency for Health Care Policy and Research [see comments]õ, Journal of the American Medical Association 278(21), pp

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14 ROBERT M. KAPLAN ET AL. University of California San Diego University of California Santa Barbara R.M. Kaplan J.P. Anderson C.M. Kaplan