Estimating the Contributions of Lifestyle-Related Factors to Preventable Death A Workshop Summary (2005) / Chapter Skim
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Workshop Summary
Workshop Summary
Pages 1-54
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- What are the best measures of the public health burden of these preventable deaths: the number of preventable deaths, years of life lost, reduction m 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 soumd basis for public policies that aim to reduce preventable deaths from lifestyle-related factors?
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. That earlier study broke new ground by estirnatrng the contribution of several modifiable lifestyle factors including tobacco use, alcohol use, and poor diet and physical inactivity to death.
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Despite this longstanding focus, the science is not perfect, particularly regarding multiple risk factors interacting m various populations and at various stages of life. Perfection is obviously unattainable, but we have ample, unequivocal evidence for public health action.
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What other outcome measures might give policyrnakers 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)
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deaths can be attributed to three behaviors: tobacco use, physical inactivity, and poor eating habits. Or policyrnakers could select cost as their key measure and target resources to preventing the costliest conditions, which include heart disease, cancer, trauma, and mental disorders.
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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]
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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 m 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.
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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,' incidence density fraction, and preventable fraction)
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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)
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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.
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Dr. Katherine Flegal 11 Estimating the impact of lifestyle factors on mortality can be accomplished by calculating the population attributable fraction (PAF)
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of risk factors and confounders; (2)
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What distinguishes this calculation from other approaches is that it uses the information on prevalence from the general population as a basis for calculating attributable risk, rather than usmg a representative sample of cases to determine the prevalence of combinations of joint risk factors. National survey data can provide representative data not only for relative risks but also for the prevalence of the joint risk factors.
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In addition, the covariates adjusted for m determining attributable risk must also correspond to those adjustments made for the component relative risks. The problems raised above relate to bias (the tendency of an estimate to be systematically too high or too low)
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, and to study relationships among risk factors. It allows simulation of interventions to change smoking, blood pressure, total cholesterol, physical activity, body mass index, and chronic conditions such as diabetes.
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Phenylketonuria (PKU) illustrates that when two risk factors interact (e.g., genes and phenylalanine exposure)
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The greatest impact on public health will occur if analysts examme their hidden assumptions about where it is feasible to intervene or manipulate a risk factor's effect. ATTRIBUTABLE RISK IN PRACTICE: EXAMPLES FROM TIIE FIELD Overview of Actual Causes of Death, 1993 Presenter.
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(est.) 60,000 35,000 30,000 25,000 20,000 1,060,000 2,150,000 Our initial intention was to separate the impact of diet on mortality from that of physical activity.
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Part of that coverage focused on competing risk factors, which this workshop can address. The article also spurred letters from some scientists about our methods and assumptions.
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Furthermore, the natural result of conflating explanation with cause is that PAFs are commonly misinterpreted as the proportion of cases who actually have the risk factors. But a PAF has no meanung for individuals.
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The fact that we know that a certain number of deaths m the United States will be due to inactivity, obesity, or breast cancer risk factors tells us nothing about which individuals will die as a result of those exposures. To an individual, the attributable fraction has no meanung, but it does have meaning for those working on prevention policy.
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One of the largest limitations m estimating the public health impact of excessive drinking relates to prevalence: the Behavioral Risk Factor Surveillance System and other surveys substantially underreport alcohol use. Another problem is that the risk estimates used m ARDI were calculated by usmg average daily alcohol consumption levels that begin at levels greater than those typically used to define excessive drinking m the United States.
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The relative risk of all-cause mortality varies by alcohol consumption, age, and cardiovascular risk, with the findings resembling a J-shaped curve (see Figure 4, drawn from the American Cancer Society cohort) .3 Thus age, consumption, and cardiovascular risk influence the relative risk estimates used to estimate the population-attributable fraction.
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Because the relative risks of coronary heart disease and cerebrovascular disease decline with age, we stratify those risks Into two groups: 35 to 64 years of age, and age 65 and above. We do not stratify by age for cancers and chronic obstructive pulmonary disease because age does not markedly affect relative risk.
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and lit a fire, the deaths are more likely due to the fact that smokers engage m other high-risk behaviors such as drinking and driving, and not to smoking per se. Some causes of deaths have complicated confounders, which makes it difficult to parse out the number of deaths attributed to smoking from those attributable to other risk factors (e.g., alcohol use, tobacco use, and liver cancer)
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The Canadian Center on Substance Abuse using meta-analysis to identify relative risks from tobacco and illicit drug use obtained lower relative risk estimates for lung cancer, chronic obstructive pulmonary disease, and ischemic heart disease. The overall number of smoking-related deaths m the Canadian study was 15 percent lower than that estimated by CPS-II.
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Because illness makes people lose weight and also makes people die earlier, the confounding effects of illness could explain the apparent relationship between low BMI and mortality to some extent. But there is also some biological plausibility for thinness as a risk factor.
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5 Allison Do, Faith MS, Heo M, Kotler DP. Hypothesis concerning the U-sbaped relation between body mass index and mortality.
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. We have found that fat loss conditional on weight loss among non-severely obese people is associated with reduced mortality rate, whereas weight loss conditional on fat loss is associated with greater mortality rate (Allison et al.
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Physical activity and diet should be measured directly to calculate attributable risk fractions. Using obesity as a surrogate for the behaviors of physical activity and diet confuses the issue.
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This ideal experiment does not require assumptions, but it does require an impossibly large sample size to consider a reasonable number of risk factors and possible interventions. Even if a trial could precisely estimate the impact of an intervention on U.S.
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was the primary outcome variable, and the study measured and examined the effect of a variety of common risk factors, although information on dietary fat intake was not available. A Monte Carlo simulation estimated the proportion of people who would develop CHD under each intervention wherein the joint distribution of CHD and the risk factors roughly equals the joint distribution implied by the parametric g-formula.7 The results obtained are only valid under the assumptions of correct model specification and of no unmeasured confounding factors.
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· We need a theory about why the relative risk relationship changes with age, to determine the best methodology to apply to age stratification. One example concerns cholesterol and age: the importance of cholesterol as a risk factor declines with age.
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HRQOL has two components: a description of the health state or its associated health status, and an assessment of preference for that health state. HRQOL can be assessed usmg preference-based methods (m which preferences for health states are elicited directly)
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launched a comparative risk assessment of 25 leading risk factors worldwide. In a series of analyses, the Comparative Risk Assessment Collaborating Group measured disease burden and mortality attributable to the 25 risk factors.
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What actually did make a difference was analyzing the impact of death versus years of life lost, and incorporating nonfatal health outcomes. The attributable disease burden of 20 risk factors measured as percent of global DALYs shows that the leading risk factor m the developing world is underweight (see Figure 9)
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The region includes Canada, United States, and Cuba; ffie US population represents 85% of tbe region.
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Marthe Gold This presentation explores the benefits of usmg health-adjusted life years (HALYs) to promote understanding of disease burden for public health policymaking.
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The most important recommendations remaining include examining how well particular measures serve different local, national, and international purposes; linking information on population health to risk factors to generate epidemiological insights; and testing measures to develop an empirical base on the distributive implications of different measures for reducing health disparities. A measurement system that integrates the outputs of public health efforts and medical care would be ideal, as would linking health measures to specific risks and interventions, as has been done by Michael WolLson and colleagues at Statistics Canada.
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Two approaches to estimating medical and other costs attributable to select risk factors are common: the epidemiologic approach, and the econometric approach. This presentation compares their advantages and disadvantages.
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. The econometric approach further avoids double-counting costs across risk factors.
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Smoking cessation programs are the next most effective use of health resources. Tetanus boosters every 10 years are a relatively ineffective use of resources, yielding 4 life-years per $ 1 million.
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However, policy analysts do not seem to have examined the cost-effectiveness of interventions for obesity, and people do not think about costs very soberly. PAFs are better than other measures of disease burden, such as years of life lost and QALYs, because analysts have produced them with much rigor and attention to confounding for many years.
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Policyrnakers and the public do not fully understand the concept of lifestyle factors and mortality. The preventable components of disease are very complex.
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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 illadvised because most efforts to improve health, m fact, Increase costs.
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. in addition, the goal of narrowing disparities m 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.
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Major challenges associated with sharing science news through mass media include explaining scientific uncertainty to lay audiences; dealing with headlines written to emphasize controversy; earnmg 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.
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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.
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Continuous vs. dichotomous scale Bias in RR estimates, especially due to extrapolation outside observed distribution Cross-sectional ~ ~ ~ over time Observed ~ ~ Manipulated Causal paths 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.
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There are also issues such as the use of a continuous versus a dichotomous scale for the risk factor, and bias m estimates of relative risk, especially when extrapolating outside the observed distribution. Other issues include cross-sectional differences interpreted as differences over time, differences m risk factors seen in observational studies versus those intentionally manipulated, complex causal paths, and changes m risk factors and outcomes other than the subject of the calculations.
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- Raise the profile of policy-relevant methods of measurmg risk and disease burden as legitiTnate 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.
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. - Avoid creating a horse race among risk factors such as diet, physical activity, tobacco, and alcohol.
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2002. Selected major risk factors and global and regional burden of disease.
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1998. Population attributable fraction estimation for established breast cancer risk factors: considering the issues of high prevalence and unmodifiability.
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