ESTIMATING THE CONTRIBUTIONS OF LIFESTYLE-RELATED FACTORS TO PREVENTABLE DEATH—A WORKSHOP SUMMARY

The Institute of Medicine (IOM) of the National Academy of Science held a workshop, December 13–14, 2004, to estimate the contributions of lifestyle-related factors to preventable death. The workshop’s statement of task included these specific questions:

  • What are the best available methods for estimating the number of preventable deaths among the leading causes of death in the United States?

  • Can scientists estimate the relative contribution of lifestyle-related factors as causes of preventable deaths with an acceptable level of accuracy?

  • What are the best measures of the public health burden of these preventable deaths: the number of preventable deaths, years of life lost, reduction in quality of years lived, disabilities caused by lifestyle factors, or the economic costs of death and disability?

  • What types of estimates provide the most scientifically sound basis for public policies that aim to reduce preventable deaths from lifestyle-related factors?

The workshop was sponsored by the Centers for Disease Control and Prevention.

Dr. Harvey Fineberg, President of the Institute of Medicine moderated the workshop, which included presentations from experts in statistical design, epidemiology, quality-of-life measures, communication, and public policy and discussions among the participants. Panels of experts addressed the following topics: methodological issues when estimating the public health burden of lifestyle factors; estimating “attributable risk” in practice; alternative ways of measuring the health burden; public policy issues. Dr. Michael Stoto, workshop rapporteur was charged with summarizing the highlights of the presentations and discussions from the two days and presenting them to the audience. At the end of the second day, Dr. Fineberg asked each participant to provide observations on lesson learned from the workshop and ideas for possible next steps.

This report summarizes the workshop presentations and discussions. Neither the workshop nor the summary is designed to draw conclusions or offer collective recommendations. In particular, the section on lessons learned and next steps should be understood as observations made by participants. Appendix A provided the workshop agenda, Appendix B contains speaker biosketches, and Appendix C provides a list of the individuals who attended the workshop.

Please note that in the summary of a number of discussions the report uses the term “obesity” or “poor diet and physical inactivity”. The concepts are different, as several presenters explain, and the terms used reflect the choice of the speakers.



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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary ESTIMATING THE CONTRIBUTIONS OF LIFESTYLE-RELATED FACTORS TO PREVENTABLE DEATH—A WORKSHOP SUMMARY The Institute of Medicine (IOM) of the National Academy of Science held a workshop, December 13–14, 2004, to estimate the contributions of lifestyle-related factors to preventable death. The workshop’s statement of task included these specific questions: What are the best available methods for estimating the number of preventable deaths among the leading causes of death in the United States? Can scientists estimate the relative contribution of lifestyle-related factors as causes of preventable deaths with an acceptable level of accuracy? What are the best measures of the public health burden of these preventable deaths: the number of preventable deaths, years of life lost, reduction in quality of years lived, disabilities caused by lifestyle factors, or the economic costs of death and disability? What types of estimates provide the most scientifically sound basis for public policies that aim to reduce preventable deaths from lifestyle-related factors? The workshop was sponsored by the Centers for Disease Control and Prevention. Dr. Harvey Fineberg, President of the Institute of Medicine moderated the workshop, which included presentations from experts in statistical design, epidemiology, quality-of-life measures, communication, and public policy and discussions among the participants. Panels of experts addressed the following topics: methodological issues when estimating the public health burden of lifestyle factors; estimating “attributable risk” in practice; alternative ways of measuring the health burden; public policy issues. Dr. Michael Stoto, workshop rapporteur was charged with summarizing the highlights of the presentations and discussions from the two days and presenting them to the audience. At the end of the second day, Dr. Fineberg asked each participant to provide observations on lesson learned from the workshop and ideas for possible next steps. This report summarizes the workshop presentations and discussions. Neither the workshop nor the summary is designed to draw conclusions or offer collective recommendations. In particular, the section on lessons learned and next steps should be understood as observations made by participants. Appendix A provided the workshop agenda, Appendix B contains speaker biosketches, and Appendix C provides a list of the individuals who attended the workshop. Please note that in the summary of a number of discussions the report uses the term “obesity” or “poor diet and physical inactivity”. The concepts are different, as several presenters explain, and the terms used reflect the choice of the speakers.

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary INTRODUCTION Moderator: Dr. Harvey Fineberg This workshop is designed to enrich understanding of the contribution of lifestyle-related factors to preventable death and guide public policy designed to combat such death and related disease. While most of the presentations will address measurement and interpretation, the workshop’s broader purpose is to raise questions about the role of preventable death as a driving force in public health. The term “preventable death” is somewhat of a misnomer, for no death is truly preventable. The real questions concern death’s timing and cause. The answers tell us whether death occurs prematurely—and, if so, what can be done to prolong life through behavior change or public policy. The topic of preventable death poses questions that are partly philosophical, partly logical, partly methodological, and partly epistemological. Experts assembled here need to bear in mind the topic’s complexity when considering how to measure the impacts on public health of such factors and interpret research findings. The implications of efforts to extend life and improve its quality are far-reaching: they shape the actions of individuals, communities, and decision makers at local, national, and international levels. Measuring the Health Impact of Lifestyles: Scientific Challenges Presenter: Dr. Julie Gerberding The IOM offers a unique setting for scientists to discuss dispassionately efforts by the Centers for Disease Control and Prevention (CDC) to quantify and interpret lifestyle contributions to preventable death. CDC can benefit by listening to, and learning from, experts who have come together to explore the topic, discuss controversial and emerging scientific issues, and move the field forward. The workshop aims to address the methodology of a recent CDC study of the causes of preventable death, as well as the broader issues it raises (Mokdad et al. 2004). Appearing in the Journal of the American Medical Association (JAMA), the CDC study updated another study published a decade ago (McGinnis and Foege 1993). That earlier study broke new ground by estimating the contribution of several modifiable lifestyle factors—including tobacco use, alcohol use, and poor diet and physical inactivity—to death. The study set the stage for years of research, analysis, and public health policy. Yet while attempting to refine the earlier study’s estimates, the 2004 study created controversy over its methodology. CDC also discovered, after publication, a computer-related computational error that slightly overestimated the contribution of diet and physical activity as causes of preventable death. CDC submitted an erratum to the same journal correcting the computation, and launched a review of its internal mechanisms of peer review. The corrected figure is 365,000 deaths, instead of 400,000, from poor diet and physical inactivity (Mokdad et al. 2005).

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary CDC’s main objective for this workshop is to improve the study’s methodology. The process of developing and publishing the study has brought to the fore several broad scientific challenges. Developing a Health Protection Research Agenda The biggest challenge is simple: there is not enough research to estimate with the precision that we would like ultimately to achieve the contributions of lifestyle factors to mortality, and to reduce their impact. Though much is known that can serve as the basis for public health action, gaps remain concerning how optimally to protect the public’s health by measuring the burden of disease, determinants of risky behavior, interventions to change lifestyle, assessing the preventable fraction of deaths from these factors, the cost-effectiveness of interventions, and communications to maximize diffusion of effective interventions. Recognizing that CDC previously gave insufficient priority to research on preventable death, the agency is planning—through its health protection research agenda—to focus on two major research gaps: measuring the preventable fraction, and evaluating the cost-effectiveness of interventions to reduce morbidity and mortality. Closing the Knowledge Gap CDC and the public health field have been working for four decades to weigh tobacco’s impact on mortality and morbidity. Despite this longstanding focus, the science is not perfect, particularly regarding multiple risk factors interacting in various populations and at various stages of life. Perfection is obviously unattainable, but we have ample, unequivocal evidence for public health action. Greater uncertainty surrounds the impact of diet and physical fitness on mortality and morbidity. Research has focused on these factors for less than a decade, and views diverge on methods for estimating the impact of diet and physical fitness, the effects of co-factors and interacting risk factors. Scientists need to think through what is being measured and the utility of the measures for the public and policymakers. One lesson CDC has learned is humility: there is no room for scientific arrogance and overconfidence in an emerging area of knowledge with no right answer. We also will often need to act (as we do in other areas of public policy) based on the preponderance of evidence together with other considerations (such as the costs of not acting) rather than wait for absolute scientific certainty. Bringing Together Diverse Disciplinary Threads The need for collaboration across disciplines, life stages, advocacy groups, and funding lines is great. The public health community must work together to transcend these divides to focus on the real people whose health needs protection. People are more than a collection of body parts and risk behaviors, such as tobacco use and lack of physical fitness. They and their families often face more than one health issue, and live in communities confronting more than one health threat. Scientific collaboration helps ensure a more holistic approach to protecting health. This recognition has propelled CDC to restructure itself to create new processes for scientific collaboration from the outset of research rather than at the tail end.

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary Balancing Scientific Diversity and Scientific Consensus Science is a quest for new knowledge that thrives on healthy expression of differences, competing hypotheses, peer review, and self-correction. However, the public often misinterprets these processes, which work so well within the field, as lack of knowledge, uncertainty, or incompetence. Given scientific debate surrounding lifestyle risks, researchers must press for the best possible science and avoid “group think” and premature consensus, while also striving to communicate uncertainty to the public without appearing inept. Communicating to Policy Makers and the Public Scientists are often so cautious about the caveats and limitations of their findings that the public cannot make sense of what they are saying. The public health community needs to achieve the right balance between scientific language and information that non-scientists can interpret. This is not a new problem, but the issues surrounding diet and physical exercise illustrate very well why communication with the public is so challenging. CDC wishes to improve its methods and approaches—and, especially, to advance its research agenda—to provide the most accurate estimates of the health burden of various behavioral factors. CDC also would like to do a much better job of communicating science both internally and externally. Its overt goal is to overcome these challenges while creating an environment where efforts to advance one health issue do not detract from the importance of others. Attributing Risks in Preventable Deaths: What Metrics Best Inform Health Policy? Presenter: Dr. George A. Mensah The challenges facing public health in the twenty-first century are remarkably different from past challenges. Whereas infectious diseases were once the leading causes of mortality in the United States, today chronic diseases have taken their place. More than 1.7 million Americans die annually of chronic diseases. Four of those diseases—heart disease, cancer, stroke, and diabetes—cause almost two-thirds of all deaths (see Figure 1). One key question for health policymakers is whether death is the best measure of the societal burden of chronic disease. What other outcome measures might give policymakers a sensitive and reliable gauge of the public health impact of chronic diseases? Options include life expectancy, mortality from all lifestyle-related causes or specific causes, preventable deaths (premature mortality), disability, healthy days (quality of life), direct or indirect costs, cost-benefits, and return on investment. Policymakers could focus their attention on health programs and interventions that yield the most beneficial impact on the selected outcome measure. Health impact could be measured in a variety of ways, including lower mortality, better access to quality health services, healthier environments, expanded wellness programs, or reduced health disparities.

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary FIGURE 1. The 10 Leading Causes of Death in the United States, 2002 SOURCE: Anderson et al. 2002. For example, decision makers might choose preventable death as the key measure to inform policy, considering that some 33 percent of all U.S. deaths can be attributed to three behaviors: tobacco use, physical inactivity, and poor eating habits. Or policymakers could select cost as their key measure and target resources to preventing the costliest conditions, which include heart disease, cancer, trauma, and mental disorders. As another alternative, policymakers or their counterparts in the business or insurance industry could focus on programs that generate the biggest return on investment, such as worksite programs to promote health. One study found that every dollar spent on Citibank’s worksite health promotion program saved nearly $5 in medical expenditures (Ozminkowski et al. 1999). Several other studies have found similarly high returns on investment for worksite health programs (Ozminkowski et al. 2002), including one study that reviewed 13 health promotion programs (Aldana 2001). Other useful indices for informing policy include objective measures of morbidity such as hospitalizations. Policymakers interested in reducing the burden of heart attacks, for example, might adopt smoke-free ordinances for public places and worksites. The impact of this intervention can can be assessed using changes in hospital admissions. for myocardial infarction. After Helena, Montana, passed an ordinance in 2002 banning smoking in public places, hospital admissions for acute myocardial infarction decreased significantly (from an average of 40 admissions during the same months in the years before the law was in effect, to a total of 24 admissions during the six months the law was enforced). After a court order suspended the law several months later, the hospital admissions increased to the previous years’ average. (see Figure 2) (Sargent et al. 2004). The purpose of this workshop is to promote discussion on which metrics or combination of metrics will best inform policymakers. The agency is also seeking the best ways to communicate to the public and policymakers the nature of the scientific evidence, especially in complex issues such as obesity and health. Informing policymakers requires not only the best scientific measure(s) but also clear, concise, and consistent messages about the practical health implications of observed changes in these metrics.

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary FIGURE 2 Admissions for Acute Myocardial Infarction During 6-Month Periods June–November Before (1998-2001), During (2002), and After (2003) the Smoke-Free Ordinance SOURCE: Sargent et al. 2004. SETTING THE STAGE FOR DISCUSSION Causality Presenter: Dr. Richard Scheines The essential philosophical problem underlying this workshop is estimating the effects of an intervention regarding lifestyle factors and mortality from statistical associations among passively observed variables. For instance, scientists may know the “unmanipulated” probability [natural state] that a person will survive to age 80, given one hour of exercise a day and many other factors. However, we also want to know the “manipulated” probability [probability after imposing an intervention on an otherwise unmodified natural state]—the probability that a person will survive to age 80 given he or she is forced to do exactly one hour of exercise a day. In other words, the challenge is to use non-experimental data to estimate the effects of intervention. In a typical clinical trial, a randomization procedure determines which subjects receive a placebo and which receive treatment. The randomization procedure determines the distribution of who takes the drug and who does not, and replaces the factors that naturally might influence taking a drug. We can model this with causal graphs, and given the pre-manipulation joint distribution of all the variables and a random assignment of treatment, we can calculate the post-manipulation joint distribution. We cannot do this as easily when, instead of randomizing treatment, we observe things passively. Why? Because unless we know important features of the causal structure, we cannot use non-experimental associations to estimate the associations following an intervention.

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary Analysts have developed a number of approaches to calculate, estimate, or search for the causal structure from non-experimental data. Most of this work hinges on the connection between causal structure and probability in terms of independent relationships called the causal Markov condition. The Markov condition is the assumption that every variable is independent of its non-effects and is conditional on its direct causes. With this assumption, we can start with a causal graph and compute what independence relations are predicted to be true in a distribution generated by that graph. We then use regression, logistic regression, contingency tables, and other analyses to determine what independence relations actually hold in the data to see if the predictions made by the causal graph holds in the data. The difficulty in proceeding from data to graph is that many causal explanations are consistent with the same set of associations or independencies. To sort these out, scientists rely on discovery algorithms to use any available background knowledge and statistical work to determine which models are causally consistent with—or explain—those data. While this work typically occurs informally, quite a few algorithms have been developed for moving from statistical data to causal equivalence classes. For instance, Spirtes, Glymour and Scheines (2000) have developed algorithms that are provably correct for computing the set of equivalent models given a set of observed associations. One problem with the causal graph approach is that the interventions that policymakers are interested in are rarely ideal, so modeling them from data is very difficult. An ideal intervention on X would target X directly, be exogenous to the system and completely determine P(X). Another problem is that when scientists estimate the effect of manipulating something, we often assume that the marginal difference we predict from the population we observe will be the same in another target population, no matter how different. Moreover, even though the idea of intervening and setting the value of a variable is the foundation on which this approach rests, what actually happens given an intervention is sometimes ambiguous. Serum cholesterol can be modified, for instance, by changing either its high-density or low-density components, or both. Depending on which of these components change, the risk of heart disease can rise or fall. So understanding how aggregate variables such as total serum cholesterol supervene on more finely grained variables that combine to form cholesterol is crucial in estimating the effect of interventions on preventable death. In summary, what has been presented here are some of the challenges faced when trying to calculate or estimate causal structure from data that is in non-experimental contexts, and some techniques that have been used to improve inferring causal claims from data. Attributable Risk in Epidemiology: Interpreting and Calculating Population Attributable Fractions Presenter: Dr. Steven Goodman The epidemiological concept of “attributable risk” is central to this workshop’s focus on lifestyle and preventable deaths. However, textbooks and courses for public health professionals rarely cover attributable risk and related epidemiological concepts in depth. Major issues concern

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary definition, terminology, properties, and interpretation, raising questions about the usefulness of the concept for evaluating the impact of an intervention on a population Terminology Attributable risk for a given factor in an individual is defined as the excess risk incurred by exposure to that risk factor, i.e. the component of overall risk “attributable” to exposure. It is measured by calculating the difference in risk between exposed individuals and unexposed individuals. The implication is that removing the exposure would reduce an exposed individual’s risk to that of an unexposed individual. For public policy purposes, a more important epidemiological concept is the “population attributable fraction” (PAF). Unlike attributable risk, population attributable fraction applies to a population rather than to an individual, and it is not a measure of “risk”. PAF is the fraction of disease cases in a population associated with an exposure. “Attributable” is somewhat misleading because it implies causality, i.e. that removal of that exposure would in fact eliminate that fraction of cases. We will see that is typically not true, one reason being that complex causal connections, such as that between obesity and mortality, are not fully understood. Still, this term is preferable to its synonyms (which include population attributable risk and population attributable risk percent) because it avoids the term “risk.” Population attributable fraction should not be confused with similar concepts (such as etiologic fraction,1 incidence density fraction, and preventable fraction). Perhaps the best term would be population associated fraction (which would maintain the same acronym), but for the purposes of consistency with current terminology, I will retain the term “attributable”. Population attributable fraction is the probability of the disease in the overall population (the average risk in both unexposed and exposed people) minus the probability of disease in the unexposed population. Re-expressing the probability of disease as conditional upon exposure: This general formula is very important to keep in mind because it makes clear that PAF is based on contrast of risks on an additive scale. In the 1950s, Levin (Levin, 1953) developed a simple way of calculating this ratio based on a multiplicative measure, the relative risk: 1   The proportion of cases in which the exposure played an etiologic role.

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary We will see later that this use of a multiplicative measure to calculate an additive contrast only applies in certain very simple situations, situations that rarely occur in modern epidemiologic analyses, and is the source of much confusion today. Use and Interpretation Proper use and interpretation of PAF requires a nuanced understanding of Levin’s formula, how each component is derived, and what types of outcome measures the formula requires. This formula and conception requires that the outcome be all-or-none, i.e. that if not for the exposure, the outcome would never have occurred within a defined period (e.g., birth defects, rare cancers, infections, injuries). PAF is not a good measure of population impact if the timing of the outcome is relevant. Time-related outcomes are those that would have occurred eventually (e.g. all-cause mortality) or almost certainly (e.g. highly prevalent age-related outcomes), and the exposure serves mainly to hasten occurrence. For these outcomes, other measures such as adjusted life years lost, may be more desirable than PAF for measuring the population impact of an intervention. Calculating PAFs with Levin’s formula requires actual measures of relative risk. Odds ratios, generated by logistic regressions, do not accurately estimate the relative risk except when the risk is rare (<10 percent). In addition, Levin’s formula uses the ratio of cumulative risks, not the incidence rate ratio, the latter being measured by two other popular regression approaches – proportional hazards and Poisson models. So most standard multivariate approaches to epidemiologic analyses do not produce the quantity that is used in the Levin formula, although they sometimes come close. PAFs are commonly misinterpreted as being additive, i.e. summing to 100 percent. In fact, PAFs are not additive when multiplicative (e.g. logistic) models of data analysis are used to generate the relative risk inputs, models which are standard in epidemiologic analyses. PAFs are also non-additive when causes are multifactorial, when individual lifestyle factors require each other to exert their effect, or when one factor is in the causal pathway of the other (cholesterol elevation and obesity, for example). The major implication of non-additivity is that it is incorrect to say that if 30 percent of deaths are attributable to one lifestyle factor (e.g., poor diet), then 70 percent are due to the other factors (e.g., tobacco, alcohol, firearms, sexual behavior). As with PKU disease, it can be completely correct to say that a case of disease is 100 percent attributable to an environmental factor (phenylalanine exposure) and is also 100 percent genetic (the phenylketonuria [PKU] gene). Another caveat is that interpreting PAFs depends on properly adjusting for the impact of confounders (other factors that affect the risk of the outcome being studied). This adjustment is quite different than the adjustment that occurs in a standard multiplicative regression model, since even if the relative risk of an exposure is constant at different ages, the PAF associated with that same exposure could be quite different in populations with different age structures. This occurs for the same reason that a constant relative risk produces very different absolute risk differences as the underlying risk changes. So one cannot take a RR “adjusted for age” and then ignore the age structure of the population for whom the PAF is being calculated. The proper equations for calculating population-attributable fraction (PAF) take this into account, but it is important that we recognize that our intuition about multiplicative “adjustment” doesn’t apply. Interpreting PAFs also depends on understanding whether interactions occur between lifestyle factors and confounders

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary (obesity could interact with age to influence the risk of death, for example). Thus PAFs are not easily predictable from standard multiplicative measures and models. In addition to requiring an all-or-none outcome, PAFs (using the Levin formula) also require an all-or-none exposure. In the case of an exposure measured on a continuum, e.g. weight, physical activity, etc., we must be very careful about how we define the baseline state, i.e. the state that corresponds to an “elimination” of the exposure. While we can eliminate smoking, we cannot eliminate BMI. It is more meaningful to measure the impact of a shift in the exposure distribution, e.g. everyone losing 10 lbs, rather than everyone attaining an “ideal” BMI of less than 25. PAFs can be calculated for situations when exposures shift, but not with simple formulae. PAFs do not measure the proportion of cases for which a given factor plays a causal role. That measure is the etiologic fraction. Nor do PAFs, by themselves, indicate the impact of any given intervention on modifying risk, for many reasons. An intervention might not eliminate a given exposure, it could have adverse effects that offset its benefits, it may have effects on other factors that augment its benefits, and it may affect the size of the population at risk by modifying competing risks. Finally, and perhaps most important, we often don’t actually know what the causal risk effect is of changing a person’s exposure. That is, we may know the mortality risk of persons with a BMI of 25, and those with a BMI of 30, but this does not necessarily tell us what the risk change will be for a person with a BMI of 30 who drops to a BMI of 25. That person will almost certainly not attain the same risk of someone who is naturally at the lower level, and it may depend on how exactly that BMI alteration occurred, e.g. by severe calorie restriction, by diet and exercise, or by surgical means. This again underscores the importance of specifying the intervention designed to change a risk factor. Thus, a significant and serious problem of calculating and interpreting PAFs is that they confuse numbers associated with risk factors with the effects of interventions. For both policy and scientific purposes, it is the impact of an intervention that we are interested in, not the impact of changing a single risk factor in an equation; those numbers can be profoundly different. If we change our language and conceptualization from mathematical manipulations of isolated variables to assessing the effects of achievable interventions then many of the problems discussed previously disappear. Predicting the effects of interventions in the absence of randomized trials still remains a challenge, but the intervention perspective keeps us focused on the proper concepts, measures and actions. In Levin’s era, the exposure that motivated him (smoking) and the intervention (smoking cessation) were closely related, and the causal effect of a successful intervention was virtually identical to the effect predicted by the variable in equations, so these distinctions were not critical. But as we apply the concept he developed in much more complex settings, we must appreciate the nuances of its interpretation and calculation, and be careful to distinguish between the mathematical effects of variable changes with the health effects of interventions.

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary METHODOLOGICAL ISSUES WHEN ESTIMATING LIFESTYLE FACTORS Partial Adjustment Presenter: Dr. Katherine Flegal Estimating the impact of lifestyle factors on mortality can be accomplished by calculating the population attributable fraction (PAF). Levin’s formula for PAF uses only two parameters: the prevalence of exposure to a lifestyle factor (such as obesity), and the unadjusted relative risk of mortality associated with that factor. However, Levin’s formula can be biased when there is confounding of the exposure-outcome relationship. Those circumstances require a different approach. The “weighted sum” method is one way to calculate PAF without bias when there is confounding. In a simple example, consider a population in which there are two subgroups and subgroup member is a confounding factor, because the prevalence of the exposure and the probability of deaths both differ by subgroup, but the relative risks are the same in the two subgroups. The weighted sum method calculates the number of excess deaths in each subgroup using Levin’s formula and then sums them to get an estimate for the entire population. To use the method, analysts have to know the number of deaths within each subgroup (such as the number of deaths among smokers and the number of deaths among never-smokers)—information usually not available for the U.S. population. The “partially adjusted” method (Mokdad et al.2004)does not require knowing the number of deaths in each subgroup. Instead, the method calculates the relative risk adjusted for subgroup membership and then applies that adjusted relative risk to the prevalence of exposure in the entire population, using Levin’s formula for unadjusted relative risks. This method may be referred to as “partially adjusted” because the relative risk is adjusted but the attributable fraction formula itself is not adjusted. However, this use of the formula is biased, and the magnitude of bias depends on the degree of confounding. In a 1998 review article, Beverly Rockhill maintained that the use of adjusted relative risk in a formula only appropriate for unadjusted relative risks is probably the most common error in PAF calculations (Rockhill 1998). To characterize the magnitude of the bias in the partially adjusted method applied to the obesity-mortality association, Flegal and colleagues (2004) constructed hypothetical examples that are plausible approximations of reality based on U.S. data. These scientists looked at confounding by age and sex, because those are strong confounders of the obesity-mortality relationship; older people have higher mortality rates and a lower prevalence of obesity. In this case, because this is a hypothetical example, the “correct” number of deaths attributable to obesity is fixed at 195,000. However, the partially adjusted method yields an estimate of 230,000—a 17 percent overestimate. Another issue in the relationship between obesity and mortality is “effect modification”: the relative risk of mortality associated with obesity declines with age. Typically, the relative risks for a PAF calculation come from a derivation cohort such as the Framingham Heart Study, and are applied to a target population such as the entire United States. The prevalence of exposures can be

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary burden of disease, social criticism is sure to ensue. The focus on methodological problems has also obscured fundamental questions about the roles, rights, and responsibilities of individuals versus governments. We also need to consider how to extend the precautionary principle to lifestyle interventions. This principle generally holds that scientific uncertainty should not be an excuse to postpone preventive measures for serious or irreversible public health threats. Failure to apply the precautionary principle has come back to haunt us with clearly identifiable diseases such as HIV and Creutzfeldt-Jakob disease. Applying the precautionary principle to lifestyle interventions to combat problems such as smoking and obesity is much more difficult. The harm is indirect—it's harder to see, name, and count individuals, and the social redress for dealing with those failures is very different from that for failing to screen blood, for example. Legal remedies are also very different. We also need to invest in data and data systems. We can't answer the kinds of questions we wish to answer without continuously better data, more research and development, and better methods of analysis. Better data systems will also enable states to tackle public health problems within their borders. Efforts to translate and communicate scientific findings are also challenging. The meaning and public health implications of concepts related to populations as opposed to individuals, such as QALYs, DALYs, are hard to grasp. We also need to clarify the business case for interventions—the return on investment. Return on investment can accrue to individuals, the healthcare system, or the broader society. Failure to take action also manifests itself in many ways, particularly in terms of Medicare and Medicaid expenditures. The timeframe in which return on investment accrues is also important. We need to be thinking 30 years ahead, when Medicare costs will be so high that we will be unable to pay for our children’s education, even though such a timeframe poses a tremendous problem of accountability. Yet generating short-term improvements through lifestyle interventions that are cost-effective today is also essential. State Policy Perspective Presenter: Dr. George Benjamin This presentation will offer the perspective of a former state health official regarding lifestyle and cause of death, address the value of data, and provide a real-life example. Policymakers and the public do not fully understand the concept of lifestyle factors and mortality. The preventable components of disease are very complex. The public health establishment has failed to make the case for lifestyle causes of death in terms of measurable public health burden. We have also failed to give the public a sense of scale, comparing the national response to the handful of deaths caused by the anthrax attacks with the poor response to the

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary thousands killed each year by influenza virus. Attaching understandable numbers to a public health problem is essential to give the public a sense of its size and scope. We have also failed to convey consistent messages. By emphasizing subtle differences in data and analysis (such as how obesity is measured), we confuse the public and make ourselves look less credible. We also make coherent discussions of the science and its implications for policy far more difficult. Death is the ultimate discriminator: people understand what it means. Using mortality as the endpoint in PAF analyses is therefore valuable. The public does not understand complex measures such as years of productive life lost. We should generally choose a measure that is more explainable to the public. My experience with Maryland’s Department of Public Health illustrates the benefits of using PAFs to quantify lifestyle factors. A state or local health department is where the rubber meets the road. When I served in government, Maryland ranked second among states in cancer mortality, and the governor decided to take on tobacco and cancer. The state had received $4.5 billion from the tobacco settlement fund, and the governor decided to allocate $100 million annually to the health department over a 10-year period. We had to prioritize which cancers to target with this new funding, to ensure that our anti-cancer and anti-tobacco programs would address the top three or four major causes of death. CDC’s analysis of PAFs provided the rationale for targeting certain tobacco-related cancers rather than other cancers. We also used the CDC study to support our anti-tobacco media campaigns, which the legislature would not otherwise have supported through state funding. Discussion of Public Policy and State Policy Discussants voiced the following points: Targeting our analysis to policymakers, particularly at the state level, is essential. Focusing on that intended audience will suggest what methodological tradeoffs epidemiologists need to make in calculating PAFs. For example, we need to supply state-specific prevalence estimates even if they are less precise than national estimates. We should also emphasize that different risk factors—such as obesity, alcohol, and tobacco—vary according to life stages, suggesting the need for age-specific interventions. Promulgating the message that health policymaking should seek to reduce costs may be ill-advised because most efforts to improve health, in fact, increase costs. The critical message is to spend resources in ways that obtain maximal value. That is why cost-effectiveness modeling is so useful: it considers all interventions—treatment and prevention alike—within a similar metric. Ethical Issues Presenter: Dr. Daniel Wikler Efforts to estimate the impact of lifestyle on morbidity and mortality raise several ethical issues. If we employ health measures that seek to incorporate social or ethical values, the extent of

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary lifestyle’s impact on health may be measured differently according to which values affect the weighting. Responsibility for health might be an example of these values. Weighting outcomes: As an aid to health resource allocation, the QALY measure includes more information than life years, since quality of life is factored in. This, in turn, could be further adjusted in an attempt to reflect ethical values. For example, the World Health Organization not long ago counted health benefits or burdens befalling young adults as greater than those affecting the very young or old. The basis for this age weighting was a survey in which members of the public assigned greater importance to the health of young adults than to others (probably because they tend to have young dependents). The most natural interpretation of this practice is that the resulting measure (the DALY—disability-adjusted life year) represented both the burden that the symptom or disability placed on the individual and also the ethical or social importance of that person’s burden. It was a “moralized” summary measure of health. Similarly, Alan Williams, the health economist, believes that quality-adjusted life-years for those who have yet to live a normal lifespan should count more (the “fair innings” argument, which he attributes to the philosopher John Harris). In addition, the goal of narrowing disparities in healthy life expectancy might be served by increasing the weight given to health outcomes for those at the low end of the social health gradient. Any estimate of the impact of lifestyle on morbidity that is denominated in moralized QALYs is likely to reflect the weights that have been assigned to incorporate these and other values—as in a society in which the prevalence obesity is greater among the poor. Responsibility for health: A question in “moralizing QALYs” is whether to adjust for personal responsibility. Should we count QALYs the same regardless of the role of the individual’s personal choices in bringing about their health deficit? Consider, for example, cosmetic surgery: A recent survey from Britain found that most people were willing to pay for removing birthmarks but not tattoos, even when they were equally disfiguring. Another study found that some Americans believe that alcoholics should be given lower priority for liver transplants. Lifestyle decisions resulting in excess morbidity involve some combination of choice (responsibility) and circumstance (fate), and there is in most cases no objective way to estimate the ratio. John Roemer, an economist, suggests that the population be partitioned according to the sources of health behavior that are reckoned within that society to be beyond the individual’s control. Then, within each resulting group, those whose unhealthy behavior exceeds the median for the group should be held responsible for their choices to that extent. If steelworkers smoke more than mathematicians, for example, and if this reflects circumstances beyond the steelworkers’ control, it will still be true that some steelworkers smoke much more than others and thus, in Roemer’s view, can be held accountable for the excess. Attributions of personal responsibility, however, are subject to highly tendentious arguments on behalf of special interests. Roger Scruton, a highly-regarded conservative British economist, published an essay (WHO, What, and Why?, 2000) that maintained that the World Health Organization had strayed from its mandate when it sought to curb the promotion of tobacco. For Scruton, an individual’s decision that the pleasures of tobacco use outweigh its threat to health is no business of WHO, which ought instead to focus its attention on communicable diseases that currently overwhelm the world’s poor. This argument, in effect, discounts health deficits stemming from tobacco use, on the grounds that the putative role of voluntary individual choice removes any resulting burden from the public agenda. Scruton’s credibility was shattered when it emerged that he was secretly on retainer to a tobacco company when he wrote this and other essays that pressed

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary this kind of argument. But the fact remains that his argument was calculated to minimize the significance attached to a category of “lifestyle”-connected health problems—evidence that this kind of claim has resonance among the public. Those concerned with the health impact of “lifestyle choices” cannot avoid the need to contend with its appeal and with the consequent possibility that some of our most pressing health problems will be drained of any sense of urgency. An adequate response requires both evidence on the mechanisms of unhealthy choices and a measure of moral argument that contests their significance for priorities in health policy. Communication Challenges Presenter: Dr. Katherine E. Rowan Numerous communication challenges are inherent in media coverage of the debate over preventable causes of death. This presentation describes a few of these challenges and offers evidence-based steps for addressing them. Major challenges associated with sharing science news through mass media include explaining scientific uncertainty to lay audiences; dealing with headlines written to emphasize controversy; earning trust from top science and medical reporters, and developing effective ways of explaining commonly misunderstood concepts such as risk factors, uncertainty, and obesity. There are no magic words to address these challenges, but there is research-based guidance. The “CAUSE” model summarizes some of this research and gives practical tips on earning Confidence, creating Awareness, deepening Understanding, gaining Satisfaction, and motivating Enactment or behavior change when communicating about science and risk (e.g., Rowan et al., 2003). The model’s analysis suggests that to increase the likelihood of careful news coverage concerning new scientific findings, scientists should identify journalists whose work they respect and invite coverage from these individuals. Communication officers at scientific institutions can facilitate this process. Additionally, in media interviews scientists should state their own values, emphasizing their concern for the public’s health and their respect for journalists’ abilities to increase attention to important topics. It is useful in such contexts to understand that scientific uncertainty may be read as incompetence by a lay audience. To deepen lay understanding of complexities, scientists should be alert for key terms being used in media coverage of an issue that may not be understood as scientists intend them. For example, in the debate over preventable causes of death, the public should understand that experts in this debate are wrestling with the meaning of the term “obesity” and whether or not “obesity” or “poor dietary practices” are the root cause of preventable death. Another way to have a forum for explaining complexities is to volunteer to write “Sunday pieces” in major circulation newspapers. Sunday pieces are lengthy letters or columns written by experts on timely and important topics such as recent research on causes of preventable death. If coverage of important issues seems consistently poor, scientists can turn for assistance to groups that check the accuracy of reporting such as the Center for Media and Public Affairs. The Center for Media and Public Affairs conducts rapid quantitative assessments or content analyses of mass media news coverage on controversial topics.

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary In summary, there are no magic words to make communicating science through the mass media a simple process, but there are better and worse ways to proceed. Research on science and risk communication offers additional information on this topic. I am providing some additional references that may be helpful to you. References and Additional Sources for Further Information Bibliography on health risk communication available at http://www.nih.gov/pubs/cbm/health_risk_communication.html Babrow, A. (2001). Uncertainty, value, communication, and problematic integration. Journal of Communication, 51, 553-573. Booth-Butterfield, M. (2003). Embedded health behaviors from adolescence to adulthood: The impact of tobacco. Health Communication, 15, 171-184. Brashers, D. E. (2001). Communication and uncertainty management. Journal of Communication, 51, 477-497. Friedman, S. M., Dunwoody, S., & Rogers, C. L. (1986). Scientists and journalists: Reporting science as news. New York: The Free Press. Friedman, S. M., Dunwoody, S., & Rogers, C. L. (1999). Communicating new and uncertain science. Mahwah, NJ: Erlbaum. Rimal, R. N. (2000). Closing the knowledge-behavior gap in health promotion: The mediating role of self-efficacy. Health Communication, 12, 219-237. Rowan, K. E. (1999). Effective explanation of uncertain and complex science. In S. M. Friedman, S. Dunwoody, & C. L. Rogers (eds.). Communicating new and uncertain science (pp. 201-233). Mahwah, NJ: Erlbaum. Rowan, K. E., Bethea, L. S., Pecchioni, L., & Villagran, M. (2003). A research-based-guide for physicians communicating cancer risk. Health Communication, 15, 239-252. Witte, K., Meyer, G., & Martell, D. (2001). Effective health risk messages: A step-by-step guide. Thousand Oaks, CA: Sage. Rapporteur’s Report Rapporteur: Dr. Michael Stoto In thinking about the highlights of the presentations and discussion we need to keep in mind the goals of this workshop and the motivations behind CDC’s analysis of causes of death. Dr. McGinnis reminded us that the analysis of “actual causes of death” in 1990 (McGinnis and Foege 1993) aimed to raise awareness of the importance of prevention, quantify the impact of distinct lifestyle factors so policymakers and the public could compare them, and enable scientists to track progress in reducing the impact of lifestyle factors. Drs. Gerberding and Stroup suggested that the 2000 analysis (Mokdad et al. 2004) aimed to update the earlier analysis, develop methods that would enable individual states to replicate the calculations, and quantify the impact of modifiable behavioral risk factors on mortality. These two analyses seem to have served well their main purposes of raising the profile of prevention and enabling both state and national governments to track progress in addressing

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary behavioral risk factors. But controversy arises when scientists try to quantify the impact of individual factors, presumably because the results can or should be used to set priorities for social investment. Quantification highlights the mismatch between PAF and related calculations, and between those measures and the questions policymakers and the public want answered or think the calculations answer. In simple terms, the problem arises because “attributable to X” in PAF morphs to “due to X,” “caused by X,” “would not have occurred if X were eliminated,” and eventually “will not occur if X is eliminated.” The fundamental issue is what “cause” and related words mean. Panelists have identified three possible solutions to that problem: to develop better data and methods to answer the question that policymakers and the public think PAF is addressing; to better explain what PAF calculations mean; and to reformulate the problem to be more policy relevant and answerable. The first alternative—developing better data and methods—has much potential but is very difficult, as these discussions illustrate. To understand why, consider the examples in Table 3. Scientists can calculate the impact of a well-defined acute disease such as influenza simply by counting the number of cases in a certain period. Whether the cases have been verified by laboratory analysis—and whether the patients have underlying conditions that might predispose them to die if infected—present only slight complications. The fundamental point is that during an outbreak, determining whether any particular death is “due to” influenza is relatively easy. In the case of a chronic disease such as coronary heart disease, co-morbidity and multiple causes make assigning a single “cause” to any death difficult. Compositional and substitution effects lead to further complications—basically, people who do not die of coronary heart disease will die of something else—so efforts to calculate the impact of such diseases require demographic and statistical methods. TABLE 3 Examples of “Attributable” Deaths   Case counting Statistical estimation Disease Influenza Lab verification Predisposing conditions Coronary heart disease Definition of coronary heart disease Co-morbidity and multiple causes Compositional and substitution effects Modifiable risk factor Drunken driving Blood alcohol cutoff Road conditions, other drivers, etc. Obesity—all of the above, plus PAF methods (partial adjustment, stratify by age and sex, regression simulations, etc.) Continuous vs. dichotomous scale Bias in RR estimates, especially due to extrapolation outside observed distribution Cross-sectional Δ Δ over time Observed Δ Manipulated Δ Causal paths Δ in other risk factors and outcomes Estimating the impact of a modifiable risk factor such as drunken driving takes us back to case counting, but also entails the problem of assigning a single cause. How high does someone’s blood alcohol concentration have to be before a death is attributed to drunken driving, for example? How do analysts factor in road conditions, the actions of other drivers, and so on?

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary Efforts to estimate the impact of risk factors such as obesity present all those problems plus additional ones. As speakers have noted, the various statistical methods for calculating attributable deaths make different assumptions. There are also issues such as the use of a continuous versus a dichotomous scale for the risk factor, and bias in estimates of relative risk, especially when extrapolating outside the observed distribution. Other issues include cross-sectional differences interpreted as differences over time, differences in risk factors seen in observational studies versus those intentionally manipulated, complex causal paths, and changes in risk factors and outcomes other than the subject of the calculations. Faced with such difficulties, we can reasonably ask whether we should focus on better explaining what PAF calculations mean rather than simply trying to improve the calculation of PAF. As speakers have indicated, this entails more than just being careful about what we say and saying it clearly. In particular, speakers noted that we need to find ways to represent uncertainty (including but not limited to confidence intervals), and to present the results of sensitivity analyses. DISCUSSION OF LESSONS LEARNED AND NEXT STEPS Edited by Miriam Davis In closing the workshop, Dr. Harvey Fineberg asked attendees to cite the most important take-home lessons and possible action steps. Comments fell into the following categories: reframing the dialogue, improving methodology, developing an action plan, and guiding public policy and creating messages for the public. These comments are not to be interpreted as consensus comments or recommendations. Discussants Voiced the Following Take Home Lessons and Next Steps Reframing the Debate Focus on lifestyle-related risks as a collective problem, as government intervenes on collective risks over which individuals have little or no control. Reframe the debate to focus on the impact of proposed interventions rather than risk factors. Understanding risk factors merely tells us where and how we might pursue interventions. Such a shift would provide estimates most relevant to policymakers, who invest in programs, not risk factors. Conceptual and practical problems remain in assessing the impact of interventions, as observational data are weak or absent. That deficit points to the need for more research and better methodology. Avoid a list of individual attributes and misleading terms like obesity, and do not rush to judgment about the growing prevalence of obesity.

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary Improving Methodology Bring measures and analyses of the impact of diet, physical activity, and obesity to the same level of sophistication as tobacco-related analyses. IOM and NIH could take the lead in identifying critical gaps in data, methods, and estimates. Develop an annual summary of progress in reducing diet- and activity-related risks, as occurs for tobacco, to drive both research and public health. Ensure that scientific methods are rigorous and defensible. Use several different techniques for measuring lifestyle-related risks and disease burden to analyze identical data, and systematically compare results to determine the best approach. In gathering and analyzing data, remember that the perfect should not be the enemy of the good. There is no perfect method for estimating PAFs. In the face of uncertainty, be conservative. Measure inactivity and nutrition and separate them conceptually from obesity. Yet recognize that while important, inactivity and nutrition will be difficult for states to track. Focus on identifying measures that are easy to communicate to policymakers and the public Use causal models to broaden the range of sensitivity analyses applied to PAF calculations. Stratify by age in computing population attributable deaths from lifestyle-related risks. Failing to do so inflates the estimated number of deaths by 30–50 percent or more. Raise the profile of policy-relevant methods of measuring risk and disease burden as legitimate scientific pursuits for epidemiologists, and ensure that they take them as seriously as more traditional research methods. Developing an Action Plan Create a coordinated action plan to improve research methods, communicate findings, and develop interventions that would exert an impact on public health. We cannot afford to wait another 10 years to address the role of lifestyle factors in preventable death. Develop a research agenda that offers the strong justification needed to persuade policymakers of the public health importance of reducing the impact of preventable lifestyle-related risks. Ensure that estimates of risk and disease burden are credible and specific enough to suggest cost-effective interventions. Through IOM or the National Institutes of Health (NIH) or other venue, periodically convene a multidisciplinary group of epidemiologists, other scientists, and public health professionals to clarify the questions that need analyzing, determine the appropriate measures to answer each question, and interpret results. Include real-world decision makers in the group… Develop training strategies and improve teaching of PAFs and related concepts.

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Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary Guiding Public Policy and Creating Messages for the Public Make public health messages simpler and clearer yet also more explicit regarding the uncertainty underlying estimates of the impact of lifestyle-related risks. Motivate the public to demand policy intervention around preventable illness. Avoid creating a horse race among risk factors such as diet, physical activity, tobacco, and alcohol. We know the importance of a basic nonsmoking, active lifestyle in which alcohol consumption is moderate. Portray lifestyle-related risks as a public health concern rather than an individual problem. Highlight the social costs of under funding the public health surveillance systems that could answer questions about lifestyle-related risks and enable society to use trillions of healthcare dollars more effectively. Demonstrate the economic payoff of interventions on lifestyle-related risks to the business community. That effort is important because that community wields enormous political influence, and because companies can improve their own productivity by focusing on lifestyle interventions. REFERENCES Aldana SG. McGinnis JM, Foege WH. 2001. Actual causes of death in the United States. JAMA. 270(18):2207-12. Aldana SG. 1998.Financial impact of health promotion programs: a comprehensive review of the literature. Am J Health Promot. 15(5):296-320. Allison DB, Zannolli R, Narayan KM. 1999.The direct health care costs of obesity in the United States. Am J Public Health. 89(8):1194-9. Allison DB, Zannolli R, Faith MS, Heo M, Pietrobelli A, VanItallie TB, Pi-Sunyer FX, Heymsfield SB. 1999. Weight loss increases and fat loss decreases all-cause mortality rate: results from two independent cohort studies. Int J Obes Relat Metab Disord. 23(6):603-11. Allison DB, Faith MS, Heo M, Townsend-Butterworth D, Williamson DF. 1999. Meta-analysis of the effect of excluding early deaths on the estimated relationship between body mass index and mortality. Obes Res. 7(4):342-54. Allison DB, Heo M, Flanders DW, Faith MS, Williamson DF. 1997. Examination of "early mortality exclusion" as an approach to control for confounding by occult disease in Epidemiologic studies of mortality risk factors. Am J Epidemiol. 146(8):672-80. [No authors listed] 1999. Effect of smoking on the body mass index-mortality relation: empirical evidence from 15 studies. BMI in Diverse Populations Collaborative Group. Am J Epidemiol. 150(12):1297-308. Andres R. 1985. Mortality and obesity: The rationale for age-specific height-weight tables. In: Andres R, Bierman EL, & Hazzard WR, eds. Principles of Geriatric Medicine. New York: McGraw-Hill Book Co. pp: 311-318. Anderson RN. 2002. Death: Leading causes for 2000. National Vital Statistics Reports. Hyattsville, Maryland: National Center for Health Statistics 50:16

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