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Lopez AD, Mathers CD, Ezzati M, et al., editors. Global Burden of Disease and Risk Factors. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; 2006. Co-published by Oxford University Press, New York.

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Global Burden of Disease and Risk Factors.

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Chapter 4Comparative Quantification of Mortality and Burden of Disease Attributable to Selected Risk Factors

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Detailed descriptions of the level and distribution of diseases and injuries and their causes are important inputs into strategies for improving population health. A substantial body of work has focused on quantifying causes of mortality and, more recently, the burden of disease (Murray and Lopez 1997; Preston, 1976; see also chapter 3 in this volume). Data on disease or injury outcomes alone, such as death or hospitalization, tend to focus on the need for curative or palliative services. Reliable and comparable analyses of risks to health are critical for preventing disease and injury. Investigators have frequently analyzed morbidity and mortality due to risk factors in the context of methodological traditions of individual risk factors and for selected populations (Kunzli and others 2000; Leigh and others 1999; McGinnis and Foege 1993; Peto and others 1992; Single and others 1999; Smith 2000; Smith, Corvalan, and Kjellstrom 1999; Willet 2002). As a result, most estimates have been affected by the following shortcomings, which limit comparability:

  • The causal attribution of morbidity and mortality due to risk factors has been estimated relative to arbitrary exposure levels without standardizing baseline exposure across risk factors. For example, the implicit baseline for the burden of injuries attributable to occupational factors has been "no work," because estimates have been based on occupational registries intended to register all injuries, regardless of whether they are avoidable (Leigh and others 1999).
  • The intermediate stages and interactions in the causal process have not been considered when calculating the disease burden attributable to risk factors. As a result, attributable burden could only be calculated for those risk factor and disease combinations for which epidemiological studies had been conducted.
  • The outcomes of analyses have been morbidity or mortality from specific diseases without conversion to a standard unit, making comparisons among different diseases and/or risk factors difficult.

To permit the assessment of risk factors in a unified framework while acknowledging characteristics specific to individual risk factors, the Comparative Risk Assessment (CRA) project initiated a systematic evaluation of the changes in population health that would result from modifying the population distribution of exposure to a risk factor or to a group of risk factors (Murray and others 2003; Murray and Lopez 1999; Ezzati and others 2004). In particular, the CRA framework

  • compares the burden of disease due to the observed distribution of exposure in a population with the burden from an alternative distribution consistently defined across risk factors;
  • considers multiple stages in the causal network of multiple risk factors and disease outcomes to allow inferences about combinations of risk factors for which epidemiological studies have not been conducted, including the joint effects of changes in multiple risk factors;
  • converts the burden of disease and injury into a summary measure of population health that permits comparing fatal and nonfatal outcomes while also taking severity and duration into account (the summary measure used in this chapter is the disability-adjusted life year [DALY], whose definition and calculation are described in chapter 3).

Therefore, even though CRA is similar to other risk assessment exercises in the sense that it applies knowledge about the hazardous effects of risk factors from epidemiological research to data on exposure in the broader population, it creates conceptual and methodological consistency in measuring the impacts of various risk factors on population health. Furthermore, we have attempted to use consistent and comparable criteria for evaluating the scientific evidence on prevalence, causality, and magnitude of hazardous effects across risk factors. As a result, the unified framework for describing population exposure to risk factors and their consequences for population health is an important step in linking the growing interest in the causal determinants of health across a variety of disciplines from natural, physical, and medical sciences to the social sciences and humanities.

We note that risk assessment as defined here is distinct from intervention analysis, whose purpose is to estimate the benefits of a given intervention or group of interventions in a specific population at a particular time. Rather, risk assessment aims at mapping alternative population health scenarios that arise from changes in the distribution of exposure to risk factors, irrespective of whether exposure change is achievable using existing interventions. The alternative visions of population health in turn contribute to identifying those risk factors for which effective or cost-effective interventions should be implemented or new interventions should be developed.

Burden of Disease Attributable to Risk Factors

Mathers and others (2002) describe two traditions for the causal attribution of health outcomes or states: categorical attribution and counterfactual analysis. In categorical attribution, an event such as death is attributed to a single cause, such as a disease or a risk factor, or to a group of causes, according to a defined set of rules such as the International Classification of Diseases (ICD) system (WHO 1992). In counterfactual analysis, the effects of one or a group of diseases or risk factors is estimated by comparing the current or future disease burden with the levels that would be expected under some alternative hypothetical scenario, referred to as the counterfactual, including the absence of or reduction in the diseases or risk factors of interest (see Maldonado and Greenland 2002 for a discussion of the conceptual and methodological issues involved in the use of counterfactuals). In theory, causal attribution of the burden of disease to risk factors can be done using both categorical and counterfactual approaches. For example, researchers have used categorical attribution for attributing diseases and injuries to occupational risk factors in occupational health registries (Leigh and others 1999) and motor vehicle accidents to alcohol use. However, categorical attribution to risk factors overlooks that many diseases have multiple causes (Rothman 1976).

The CRA estimates of the burden of disease and injuries due to risk factors are based on a counterfactual exposure distribution that would result in the lowest population risk, irrespective of whether currently attainable in practice, referred to as the theoretical-minimum-risk exposure distribution (Murray and Lopez 1999). Using the theoretical-minimum-risk exposure distribution as the counterfactual has the advantage of providing an indication of potential gains in population health from reducing the risk from all levels of suboptimal exposure in a consistent way across risk factors.

Risk Factor Selection

The CRA project included a selected group of risk factors, presented in table 4.1. The criteria for selecting risk factors included the following:

  • they were likely to be among the leading causes of the disease burden globally or regionally;
  • they were not too specific, for example, every one of the hundreds of air pollutants or fruits and vegetables, or too broad, such as the environment or diet taken as a single exposure;
  • the likelihood of causality was high based on collective scientific knowledge;
  • reasonably complete data on exposure and risk levels were available or sufficient data were available to extrapolate information when necessary;
  • they were potentially modifiable.

Table 4.1. CRA Risk Factors, Exposure Variables, Theoretical-Minimum-Risk Exposure Distributions, and Disease Outcomes.

Table 4.1

CRA Risk Factors, Exposure Variables, Theoretical-Minimum-Risk Exposure Distributions, and Disease Outcomes.

The risks to health examined in the CRA project cover many of the important hazards to health addressed in various fields of scientific inquiry. Arguably, hundreds of risk exposures are harmful to health. We selected only a relatively small number of exposures for quantification, largely determined by the availability of data and scientific research about their level and health effects in different parts of the world.

We also had to make choices about the definition of each risk factor. Given the close interrelationships among diet, exercise, and physiological risks on the one hand, or among water, sanitation, and personal hygiene on the other, the exact definition of what a risk factor is requires careful attention. The absence of a particular risk factor like dietary fat intake from table 4.1 does not imply that it is of limited relevance. Similarly, the assessment of unsafe sex separately from that of non-use and use of ineffective methods of contraception does not override their close linkages. Rather, we focused the analysis on risk factors for which we were likely to be able to satisfactorily quantify their population exposure distributions and health effects using existing scientific evidence and available data and for which intervention strategies are available or might be envisioned.

Estimating Population Attributable Fractions

The contribution of a risk factor to disease or mortality is expressed as the fraction of disease or death attributable to the risk factor in a population and is referred to as the population attributable fraction (PAF), and is given by the generalized potential impact fraction in equation 4.1 (Eide and Heuch 2001; Walter 1980).

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where RR(x) is the relative risk at exposure level x, P(x) is the population distribution of exposure, P′ (x) is the counterfactual distribution of exposure, and m is the maximum exposure level.

The corresponding relationship when exposure is described as a discrete variable with n levels is given by

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PAFs obtained in this way estimate the proportional reduction in disease or death that would occur if exposure to the risk factor were reduced to the counterfactual distribution. The alternative (counterfactual) scenario used is the exposure distribution that would result in the lowest population risk, referred to as the theoretical-minimum-risk exposure distribution (Ezzati and others 2002; Murray and Lopez 1999). For risk factors for which the assumption of constant relative risk was not appropriate, we estimated PAFs by accounting for the determinants of hazard heterogeneity. For example, the PAFs for injuries as a result of alcohol use accounted for alcohol drinking patterns (moderate versus binge).

Because most diseases are caused by multiple risk factors, PAFs for individual risk factors for the same disease overlap and can add to more than 100 percent (Murray and Lopez 1999; Rothman 1976). For example, some deaths from childhood pneumonia may have been avoided by preventing exposure to indoor smoke from household use of solid fuels, childhood underweight, and zinc deficiency (which itself affects weight-for-age); and some cardiovascular disease events may be due to a combination of smoking, physical inactivity, and low fruit and vegetable intake. Such cases would be attributed to all these risk factors.

Attributable Mortality and Burden of Disease

For each risk factor and disease pair, we calculated PAFs for each age and sex group, and in each region, using the relationships in equations 4.1a and 4.1b, separately for mortality (PAF M ) and incidence (PAF I ) when the relative risks for mortality and incidence were different. For each of these age, sex, and region groups, we obtained estimates of mortality (AM ij ) and the burden of disease (AB ij ) from disease j attributable to risk factor i as follows:

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where YLL denotes years of life lost because of premature mortality and YLD denotes years of healthy life lost as a result of disability.

Data on Exposure and Hazard

Between 1999 and 2002,for each risk factor, an expert working group conducted a comprehensive review of the published literature and other sources (government reports, international databases, and so on) to obtain data on the prevalence of risk factor exposure and hazard size (relative risk or absolute hazard size when appropriate, such as the effects of lead on blood pressure) (Ezzati and others 2004). The work included collecting primary data and undertaking a number of reanalyses of original data, systematic reviews, and meta-analyses. To increase comparability while acknowledging the fundamental differences in exposure and hazard quantification across risk factors, the criteria for using the scientific evidence included consistency of exposure variables used in exposure data sources with those used in epidemiological studies on hazard, population representativeness of exposure data, and study design for estimating the magnitude of hazardous effects (including minimizing the effects of confounders).

Data were initially presented separately for males and females and broken down into eight age groups (0–4, 5–14, 15–29, 30–44, 45–59, 60–69, 70–79, and 80 years old and older) and the 14 epidemiological subregions of the Global Burden of Disease (GBD) study (see chapter 3), which are based on a combination of World Health Organization regions and child and adult mortality levels, as described in the annexes of the annual World Health Report 2002 (WHO 2002). Data sources, models, and assumptions used to extrapolate exposure or relative risk across countries or regions are described in detail in chapters devoted to individual risk factors elsewhere (Ezzati and others 2004). External reviewers anonymously peer reviewed each risk factor chapter, including conducting re-reviews as appropriate.

In this reanalysis, estimates of mortality and disease burden attributable to risk factors were needed in World Bank regions (see map 1 inside the front cover). For six risk factors (childhood underweight, high blood pressure, high cholesterol, overweight and obesity, smoking, and indoor smoke from household use of solid fuels), country-level data were available and allowed reestimating exposure directly for World Bank regions. In such cases, we used newly available data sources on exposure to update CRA project estimations. For seven risk factors (unsafe water, sanitation, and hygiene; zinc deficiency; vitamin A deficiency; iron deficiency anemia; physical inactivity; low fruit and vegetable intake; and child sexual abuse), we estimated exposure in World Bank regions from the 14 GBD subregions using population-weighted averages. For another five risk factors (unsafe sex, urban air pollution, illicit drug use, non-use and use of ineffective methods of contraception, and contaminated injections in health care settings), where both exposure and hazards change across populations, we converted PAFs from GBD subregions to World Bank regions, with PAFs weighted by age-, sex-, and disease-specific mortality rates. The prevalence of alcohol use was converted from GBD subregions to World Bank regions and was used to estimate exposure and PAFs in World Bank regions for most disease outcomes, because relative risks did not vary across populations. For all injury outcomes, ischemic heart disease, depression, stroke, and diabetes, whose hazards varied across regions, PAFs were converted from GBD subregions to World Bank regions using mortality weighting.

Theoretical-Minimum-Risk Exposure Distributions

The theoretical-minimum-risk exposure distribution was zero for risk factors for which zero exposure could be defined and reflected minimum risk, such as no smoking. For some risk factors, zero exposure was an inappropriate choice, either because it is physiologically impossible, as in the case of body mass index (BMI) or high cholesterol, or because physical lower limits to exposure reduction exist, as for concentrations of ambient particulate matter. For the latter risk factors, we used the lowest levels observed in specific populations and epidemiological studies to choose the theoretical-minimum-risk exposure distribution. For example, counterfactual exposure distributions of 115 mmHg for systolic blood pressure and 3.8 mmol/L for total cholesterol, each with a small standard deviation, are the lowest levels at which meta-analyses of cohort studies have characterized dose-response relationships (Chen and others 1991; Eastern Stroke and Coronary Heart Disease Collaborative Research Group 1998; Law, Wald, and Thompson 1994).

Alcohol has benefits as well as causing harm for different diseases depending on the disease and on patterns of alcohol consumption (Corrao and others 2000; Puddey and others 1999). Rehm and others (2004) chose a counterfactual of zero for alcohol use. This was because despite its benefits for cardiovascular diseases in some populations, the global and regional burden of disease due to alcohol use was dominated by its impacts on neuropsychiatric diseases and injuries that are considerably larger than these benefits.

Finally, for factors with protective effects, namely, fruit and vegetable intake and physical activity, we chose a counterfactual exposure distribution based on a combination of levels observed in high-intake populations and the level to which the benefits may continue given current scientific evidence. Table 4.1 reports the theoretical-minimum-risk exposure distributions for the risk factors.

Burden of Disease Attributable to Individual Risk Factors

Detailed results by risk factor, disease outcome, age, sex, and region are provided in annex 4A. Figure 4.1 shows the contributions of the leading global risk factors to all-cause mortality and burden of disease. The different ordering of risk factors in their contributions to mortality and to the disease burden expressed in DALYs reflects the age profile of mortality, such as the higher contribution to the disease burden from mortality among children as a result of underweight, and of nonfatal outcomes, such as neuropsychiatric diseases caused by alcohol use.

Figure 4.1

Figure 4.1

Mortality and the Burden of Disease Attributable to Leading Global Risk Factors, by World Bank Region

The leading causes of mortality and the disease burden include risk factors for communicable, maternal, perinatal, and nutritional conditions (Group I as defined in chapter 3), such as undernutrition; indoor smoke from household use of solid fuels; unsafe water, sanitation, and hygiene, whose burden is primarily concentrated in low-income regions of South Asia and Sub-Saharan Africa; and unsafe sex. They also include risk factors for noncommunicable diseases and injuries (Groups II and III as defined in chapter 3), such as high blood pressure and cholesterol, smoking, alcohol use, and overweight and obesity, which affect most regions.

Undernutrition is the single leading global cause of health loss, as it was in 1990 (the 2001 results disaggregate undernutrition into underweight and micronutrient deficiencies). Even though the prevalence of underweight has decreased in most regions in the past decade, it has increased in Sub-Saharan Africa (de Onis, Frongilla, and Blossner 2000; de Onis and others 2004), where its effects are disproportionately large because of simultaneous exposure to other risk factors for childhood disease. Three-quarters of the burden of disease attributable to unsafe sex is also in Sub-Saharan Africa, primarily as a result of HIV/AIDS, followed by South Asia (13 percent). The burden of disease attributable to unsafe water, sanitation, and hygiene has declined since 1990, mostly because of a worldwide decline in mortality from diarrheal disease, which is partly a result of improved case management interventions, particularly oral rehydration therapy. The increase in the global burden of disease attributable to smoking since 1990 mostly reflects the increased accumulated hazards of this risk, which is most noticeable in developing countries, but the increase is also partially due to methodological changes based on new evidence on the magnitude of the hazard after correction for confounding (Ezzati, Henley, Lopez, and others 2005; Ezzati, Henley, Thun, and others 2005; Ezzati and Lopez 2003; Thun, Apicella, and Henley 2000). The large increase in the burden of disease due to high blood pressure is likely to be an outcome of major methodological improvements, that is, relative risks that account for regression dilution bias and choice of theoretical-minimum-risk exposure distribution based on epidemiological evidence versus clinical definitions.

Table 4.2 shows the distributions of mortality and the disease burden attributable to the risk factors by age and sex. The disease burden attributable to underweight and micronutrient deficiencies in children was equally distributed among males and females, but the total all-age disease burden from iron and vitamin A deficiencies was slightly greater among females because of the effects on maternal mortality and morbidity conditions. Other diet-related risks, physical inactivity, environmental risks, and unsafe sex contributed almost equally to the disease burden in males and females. Approximately 77 to 86 percent of the disease burden from addictive substances occured among men, reflecting the social and economic forces that have so far made addictive substances more widely used by men, especially in developing countries. Women suffered an estimated two-thirds of the disease burden from child sexual abuse and all of the burden caused by non-use and use of ineffective methods of contraception.

Table 4.2. Distribution of Risk Factor-Attributable Mortality and Burden of Disease, by Age and Sex.

Table 4.2

Distribution of Risk Factor-Attributable Mortality and Burden of Disease, by Age and Sex.

The estimated disease burdens from childhood undernutrition and unsafe water, sanitation, and hygiene were almost exclusively among children under five years of age. For these risks, more than 90 percent of the total attributable burden occurred in this age group, with the exception of iron deficiency, where adults bore more than 40 percent of burden, especially women of childbearing age. The disease burdens attributable to overweight and obesity and smoking were almost equally distributed among adults below and above the age of 60 years. The disease burdens attributable to other diet-related risks and physical inactivity were higher among those older than 60 (see also chapter 5).

More than 90 percent of the disease burden attributable to non-use and use of ineffective methods of contraception, illicit drug use, and child sexual abuse and more than 75 percent of the disease burden attributable to alcohol use and unsafe sex occurred in adults younger than 60. Most of the risks whose burden is concentrated among younger adults are those with outcomes that include HIV/AIDS, maternal conditions, neuropsychiatric diseases, and injuries. This illustrates the large, and at times neglected, disease burden from risks that affect young adults, especially in low-and-middle-income countries, with important consequences for economic development.

Only a small fraction of the disease burden from the risk factors considered was among those aged 5 to 14 years. This was because some of the leading conditions that affect this age group, such as motor vehicle accidents and other injuries and depression, have complex and heterogeneous causes that could not easily be included in the risk-based framework used. For other leading diseases of this group, such as diarrhea and lower respiratory infections, most epidemiological studies have focused on children younger than five and do not provide estimates of hazardous effects for older children.

Figure 4.2 presents the burden of disease due to the 10 leading risk factors for low- and middle-income countries and for high-income countries by disease or disease group. Leading causes of the burden of disease in low- and middle-income countries include the risk factors affecting the poor and associated with communicable, maternal, perinatal, and nutritional conditions (Group I)—such as childhood underweight (8.7 percent); unsafe water, sanitation, and hygiene (3.7 percent); indoor smoke from household use of solid fuels (3.0 percent); and unsafe sex (5.8 percent)—along with risk factors for noncommunicable diseases (Group II), including addictive substances, nutrition related risks, and physical inactivity.

Figure 4.2

Figure 4.2

Burden of Disease Attributable to 10 Leading Regional Risk Factors, by Disease Type

The relative contribution of unsafe sex was disproportionately larger in Sub-Saharan Africa (17.8 percent) than in all other regions, because HIV/AIDS prevalence and mortality are higher in Sub-Saharan Africa than anywhere else. This makes unsafe sex a leading cause of the burden of disease in this region together with childhood underweight (17.1 percent). The outcomes of these two risk factors were mostly communicable, maternal, perinatal, and nutritional conditions, which dominate the disease burden in high-mortality developing regions.

In addition to their relative magnitude, the absolute loss of healthy life years attributed to risk factors in low- and middle-income regions is enormous. In these regions, which account for 85 percent of the global population, childhood underweight and unsafe sex alone contributed more to the loss of healthy life (200 million DALYs[3,0]) than all diseases and injuries in high-income countries (149 million DALYs[3,0]). In high-income countries, smoking (12.9 percent), high blood pressure (9.3 percent), overweight and obesity (7.2 percent), high cholesterol (6.3 percent), and alcohol use (4.4 percent) were the leading causes of loss of healthy life, contributing mainly to noncommunicable diseases and injuries (groups II and III).

Joint Effects of Multiple Risk Factors

Many users of risk assessment, who may be familiar with categorical attribution systems such as the ICD, desire information characterized by additive decomposition. In other words, they would like to know what fraction of the disease burden is related to a particular risk factor or group of risk factors independent of changes in other risk factors. As Mathers and others (2002) discuss, additive decomposition is not generally a property of counterfactual attribution, because many diseases are caused by the interaction of multiple determinants acting simultaneously (Rothman 1976; Rothman and Greenland 1998; Walter 1980; Yerushalmy and Palmer 1959). Indeed, the sum of PAFs for a single disease due to multiple risk factors is theoretically unbounded.

Although epidemiologically unavoidable and conceptually acceptable, the lack of additivity adds to policy complexity and implies the need for great care when interpreting and communicating estimates of PAF and attributable burden. With multiple attribution, a reduction in one risk factor would seem to make other, equally important, risk factors potentially irrelevant from a perspective with limited scope in relation to interpreting quantitative results. It also necessitates the development of methods to quantify the effects of joint counterfactual distributions for multiple risk factors. Estimating the joint effects of multiple distal and proximal risks is particularly important, because many factors act through other intermediate factors (Murray and Lopez 1999; Yerushalmy and Palmer 1959) or in combination with other factors. For example, education, occupation, and income may affect smoking, physical activity, and diet, which are risk factors for cardiovascular diseases, both directly and through further layers of such intermediate factors as BMI, blood pressure, and high cholesterol. Multicausality also means that a range of interventions can be used for disease prevention, with the specific choices determined by factors such as cost, technology availability, infrastructure, and preferences.

In equations 4.1a and 4.1b, RR, P, and P′ may represent joint relative risks and exposure distributions for multiple risk factors, that is, x may be a vector of risk factors, with RR for each risk factor estimated at the appropriate level of the remaining ones (Eide and Heuch 2001). While such data have been gathered for a small number of risk factor combinations, for example, alcohol and smoking for oral cancer (Rothman and Keller 1972) and some cardiovascular risks (Neaton and Wentworth 1992; Yusuf and others 2004), they are generally rare in epidemiological studies. Alternatively, for n biologically independent and uncorrelated risk factors, the joint PAF is given by equation 4.3 (Miettinen 1974; Walter 1976):

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where PAF i shows the PAFs of individual risk factors.

If risk factors are independent and uncorrelated, the proportion of the remaining disease that is attributed to the ith additional risk factor equals PAF i , and hence (1 − PAF i ) is not attributable to this factor. Therefore, the second term in the right-hand-side of equation 4.3, that is, the product of all (1 − PAF i ) terms, is the fraction of disease not attributable to any of the n risk factors. One minus this term is the fraction attributable to the combined effects of the n risk factors.

Estimating the joint effects of multiple risk factors is, in practice, complex and does not follow the simple, independent, and uncorrelated relationship of equation 4.3 for several reasons. First, some of the effects of the more distal factors, such as physical inactivity, are mediated through intermediate factors. For instance, a proportion of the hazards of physical inactivity is mediated through overweight and obesity, which is itself mediated through elevated blood pressure (figure 4.3). Estimating the joint effects of distal and intermediate factors requires knowledge of independent hazards of the distal ones (versus individual risk factor effects, which are based on total hazard). Second, the hazard due to a risk factor may depend on the presence of other risk factors (Koopman 1981; Rothman and Greenland 1998) (effect modification).1 Third, correlation may exist between exposures to multiple risk factors because they are affected by the same distal factors and policies. For example, under-nutrition, unsafe water and sanitation, and use of solid fuels are more common among poor rural households in developing countries and smokers generally have higher and more harmful patterns of alcohol consumption and worse diets than nonsmokers.

Figure 4.3

Figure 4.3

Mediated and Direct Effects of a Risk Factor

The epidemiological literature refers to the first and second issues as biological interaction and the third issue as statistical interaction (Miettinen 1974; Rothman and Greenland 1998; Rothman, Greenland, and Walker 1980). This distinction is, however, somewhat arbitrary, and the three scenarios may occur simultaneously. For example, zinc deficiency affects mortality from diarrhea directly as well as by reducing growth (first issue) (Brown and others 2002; Zinc Investigators' Collaborative Group 1999), and may also be correlated with underweight, other micronutrient deficiencies, and unsafe water and sanitation (third issue). Similarly, alcohol and smoking may not only be correlated (third issue), but also affect each other's hazard for some diseases (second issue) (Rothman and Keller 1972).

Data Sources for Mediated Effects and Effect Modification

Despite the emphasis on removing or minimizing the effects of confounding in epidemiological research, mediated and stratified hazards have received disproportionately little empirical attention. We therefore reviewed the literature and reanalyzed cohort data to strengthen the empirical basis for considering interactions. The sensitivity of estimates to these assumptions were negligible as described in detail elsewhere (Ezzati, Vander Hoorn, and others 2004; Ezzati and others 2003).

Joint Hazards of Cardiovascular Disease Risk Factors

Epidemiological studies of the effects of overweight and obesity, physical inactivity, and low fruit and vegetable intake on cardiovascular diseases have illustrated some attenuation of the effects after adjustment for intermediate factors such as blood pressure or cholesterol (Berlin and Colditz 1990; Blair, Cheng, and Holder 2001; Eaton 1992; Gaziano and others 1995; Jarrett, Shipley, and Rose 1982; Jousilahti and others 1999; Khaw and Barrett-Connor 1987; Liu and others 2000, 2001; Manson and others 1990, 2002; Rosengren, Wedel, and Wilhelmsen 1999; Tate, Manfreda, and Cuddy 1998). This attenuation confirms that some of the hazard of the more distal factors is mediated through the intermediate ones (figure 4.3). The extent of attenuation has varied from study to study, but has consistently been less than half of the excess risk of the distal factors. We used an estimate of 50 percent as the proportion of the excess risk from these risk factors mediated through intermediate factors that are themselves among the selected risks. To include effect modification, we used deviations from the multiplicative model of 10 percent for ischemic heart disease and 30 percent for ischemic stroke based on existing studies, both submultiplicative (Eastern Stroke and Coronary Heart Disease Collaborative Research Group 1998; Neaton and Wentworth 1992).

Joint Hazards of Smoking and Other Risk Factors

Liu and others (1998, figures 4 and 6) find that in China, the relative risks of mortality from lung and other cancers, respiratory diseases, and vascular diseases are approximately constant in different cities where mortality rates for these diseases among nonsmokers varied by a factor of 4 to 10. Studies that stratified hazards of smoking on serum cholesterol have confirmed this finding (Jee and others 1999).

Joint Hazards of Childhood Undernutrition for Infectious Diseases

Zinc affects growth in children (Brown and others 2002), and some of its effects on infectious diseases may be mediated through reducing growth. Because no published source for such mediated effects existed, data from some of the available zinc trials were reanalyzed (Zinc Investigators' Collaborative Group 1999). We used an upper bound of 50 percent on the proportion of zinc deficiency risk mediated through underweight.

Investigators have found that vitamin A deficiency, which affects some of the same diseases as underweight and zinc deficiency, does not change the hazard size for the other two risk factors based on stratified results from clinical trials and recent reviews of the literature on micronutrient deficiencies (Christian and West 1998; Ramakrishnan, Latham, and Abel 1995; Ramakrishnan and Martorell 1998; West and others 1991).

Joint Hazards of Undernutrition and Environmental Risk Factors in Childhood Diseases

Anthropometric (growth) indicators of childhood nutrition, such as weight-for-age, are aggregate measures of multiple factors that include nutrition and previous infection (Pelletier, Frongillo, and Habicht 1993; Scrimshaw, Taylor, and Gordon 1968; UNICEF 1990). Therefore, some of the risks from indoor smoke from household use of solid fuels and unsafe water, sanitation, and hygiene, which result in lower respiratory infections and diarrhea respectively, may be mediated through underweight. In a review of the literature, Briend (1990) concludes that attempts to disentangle direct and mediated contributions, especially over the long periods needed to affect population-level anthropometry, have not established diarrhea as a significant cause of underweight. Other works, however, have found evidence that infection, especially diarrhea, could reduce growth and increase the prevalence of underweight (Black 1991; Guerrant and others 1992; Lutter and others 1989, 1992; Martorell, Habicht and others 1975; Martorell, Yarbrough, and others 1975; Stephensen 1999). To account for potential mediated effects, we considered an upper bound of 50 percent on the proportion of the excess risks from indoor smoke from household use of solid fuels and unsafe water, sanitation, and hygiene mediated through underweight in regions where underweight was present.

Risk Factor Correlation

To estimate the joint effects of risk factors with a continuous exposure variable, for instance, blood pressure and cholesterol, each integral in the PAF relationship may be replaced with

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where subscripts 1 and 2 denote the two risk factors and P is the joint distribution of the two exposures. If joint RR were a linear function of exposure levels (x1 and x2), then correlation between the two risk factors would not affect total hazard. Because individual RRs are nonlinear functions of exposure, for example, in a Cox proportional hazard model, and joint RRs are the product of such nonlinear terms, positive correlation between risk factors would, in general, imply a larger PAF than zero correlation,2 which in turn would be larger than negative correlation. Similarly, for categorical risk factors, positive correlation would in general result in a larger PAF (see also Greenland 1984). For the range of exposures and relative risks in the CRA, this secondary effect of risk factor correlation would be considerably smaller than the joint attributable fraction, as described in detail elsewhere (Ezzati and others 2003).

Burden of Disease Attributable to Multiple Risk Factors

This section presents the disease burden attributable to the joint hazards of the risk factors in table 4.1.

All Selected Risk Factors

Table 4.3 shows the joint contributions of all the risk factors shown in table 4.1 to the total mortality and disease burden in different regions. Globally, an estimated 45 percent of mortality and 36 percent of the disease burden were attributable to the joint effects of the 19 selected risk factors. Sub-Saharan Africa (49 percent of the disease burden) and Europe and Central Asia (46 percent of the disease burden) had the largest regional PAFs, and the Middle East and North Africa (25 percent of the disease burden) and East Asia and the Pacific (27 percent of the disease burden) had the smallest. The regions with large joint PAFs are those where a relatively small number of diseases and their risk factors are responsible for large losses of life, for example, HIV/AIDS and childhood disease risk factors in Sub-Saharan Africa and cardiovascular risks, smoking, and alcohol consumption in Europe and Central Asia. Those with smaller joint PAFs are regions where the causes of health loss are distributed among a larger number of diseases and their risk factors.

Table 4.3. Joint Contributions (PAFs) of the Leading Risk Factors to Mortality and Burden of Disease, by Region.

Table 4.3

Joint Contributions (PAFs) of the Leading Risk Factors to Mortality and Burden of Disease, by Region.

Table 4.4 shows the individual and joint contributions of the selected risk factors to the 10 leading diseases in the world and in low- and middle-income and high-income countries. As the table shows, for most diseases the joint effects of these risk factors were substantially less than the crude sum of their individual effects. For example, globally four separate risk factors were each responsible for 88, 50, 20, and 11 percent of the diarrheal disease burden, but with a joint PAF of 92 percent; or seven separate risk factors were each responsible for 45, 46, 18, 28, 21, 17, and 17 percent of ischemic heart disease, but with a joint PAF of 80 percent. This confirms that the joint actions of more than one of these risk factors acting simultaneously or through other factors cause a large proportion of disease.

Table 4.4. Individual and Joint Contributions of Risk Factors to 10 Leading Diseases and Total Burden of Disease.

Table 4.4

Individual and Joint Contributions of Risk Factors to 10 Leading Diseases and Total Burden of Disease.

Globally, large fractions of the burden of HIV/AIDS (96 percent), diarrhea (92 percent), ischemic heart disease (80 percent), lung cancer (74 percent), stroke (65 percent), chronic obstructive pulmonary disease (64 percent), and lower respiratory infections (53 percent) were attributable to the joint effects of the 19 risk factors considered here. The joint PAFs for a number of other important diseases and injuries, such as perinatal and maternal conditions, certain other cancers, and intentional and unintentional injuries, which have more diverse risk factors, were smaller but nonnegligible. Even though the fraction of the total malaria burden attributable to childhood undernutrition was relatively large (59 percent), this was because of the contribution of mortality at younger ages to the malaria burden. No adult malaria was attributed to the risk factors in table 4.1, because the epidemiological literature has focused on quantifying increased risk of malaria as a result of childhood undernutrition only. Finally, with the exception of alcohol and drug dependence, which were fully attributable to their namesake risk factors, small or zero fractions of neuropsychiatric conditions, tuberculosis, congenital anomalies, and a number of other diseases were attributed to the risk factors considered here.

An important finding of this analysis is the key role of nutrition in health worldwide. Approximately 11 percent of the global disease burden was attributable to the joint effects of underweight or micronutrient deficiencies. In addition, almost 16 percent of the burden (28 percent for those aged 30 years and older) can be attributed to risk factors that have substantial dietary determinants (high blood pressure, high cholesterol, overweight and obesity, and low fruit and vegetable intake) and to physical inactivity. These patterns are not uniform within regions, however, and the transition has been healthier in some countries than in others (Lee, Popkin, and Kim 2000; Popkin 2002a; Popkin 2002b; Popkin and others 2001). Furthermore, the major nutritional and related risk factors show interregional heterogeneity, for instance, the relative contributions of blood pressure, cholesterol, and BMI differed across regions.

At the same time, the joint contributions of these risk factors left an important part of the global disease burden unexplained, because only a small fraction of some important diseases was attributable to the risk factors considered here. These include diseases whose determinants (a) are diffuse among environmental and behavioral factors, for example, some cancers, perinatal conditions, and neuropsychiatric diseases; (b) have more complex, multifactor etiology and often heterogeneous determinants in different populations, and are therefore difficult to quantify without data on a small scale, such as tuberculosis and injuries; (c) involve long delays between risk factor exposure and disease outcome; or (d) have limited quantitative research at the population level, for instance, neuropsychiatric diseases, often as a result of the previous three factors as well as difficulties in measuring exposure or outcome (Evans 1976, 1978). The mitigation of many such conditions, including malaria, tuberculosis, and injuries, may be better guided by analyses of the effects of interventions tailored to individual settings than by risk factor analysis.

Risk Factor Clusters

In addition to estimating the joint contributions of all the risk factors in table 4.1 to the all-cause mortality and disease burden, we also examined the role of selected clusters of risks that may be of particular interest to disease prevention policies and programs. The risk factor clusters were those affecting cancers (alcohol use, smoking, low fruit and vegetable intake, indoor smoke from household use of solid fuels, urban air pollution, overweight and obesity, physical inactivity, contaminated injections in health care settings, and unsafe sex), cardiovascular diseases (high blood pressure, high cholesterol, smoking, overweight and obesity, alcohol use, physical inactivity, low fruit and vegetable intake, and urban air pollution), and child mortality (childhood underweight; vitamin A deficiency; zinc deficiency; iron deficiency anemia; unsafe water, sanitation, and hygiene; and indoor smoke from household use of solid fuels). Tables 4.5 through 4.7 show the individual and joint contributions of these risk factors to mortality and to the burden of disease for specific diseases within each cluster.

Table 4.5. Individual and Joint Contributions of Risk Factors to Mortality and Burden of Disease from Site-Specific Cancers.

Table 4.5

Individual and Joint Contributions of Risk Factors to Mortality and Burden of Disease from Site-Specific Cancers.

Table 4.7. Individual and Joint Contributions of Risk Factors to Mortality and Burden of Disease from Major Diseases of Children.

Table 4.7

Individual and Joint Contributions of Risk Factors to Mortality and Burden of Disease from Major Diseases of Children.

Globally, the cancers with the largest mortality fraction attributable to the risk factors in table 4.1 were cervix uteri cancer (100 percent); trachea, bronchus, and lung cancers (74 percent); and esophagus cancer (62 percent), and those with the smallest joint PAFs were colon and rectum cancers (13 percent) and leukemia (9 percent) (table 4.5). The largest number of deaths attributable to the joint effects of the risk factors was from trachea, bronchus, and lung cancer (930,000 deaths) and liver cancer (283,000 deaths), which reflects both the relatively large joint PAF and the total number of deaths from these cancers. Except for cervix uteri cancer, which was by definition fully attributable to the risk factor unsafe sex, joint PAFs were larger in high-income countries than in low- and middle-income countries for all cancer sites, mostly because of the higher contribution of smoking and alcohol use. The joint PAFs for all cancers combined, however, were similar for the two groups of countries (34 percent versus 37 percent for the disease burden), because of the distributions of total mortality from various site-specific cancers.

Almost two-thirds of all cardiovascular deaths were attributable to eight of the selected risk factors that affect these outcomes (table 4.6). The joint effects of these risk factors were much lower than the crude sum of individual effects (64 percent versus 126 percent for the disease burden), pointing to the extensive overlap in their hazards for cardiovascular diseases compared with cancers. The overlap is partly because the hazardous effects of some risks are mediated through others and partly because multiple risk factors act in combination. The joint PAF differed little between low- and middle-income and high-income countries, reflecting the high levels of multiple cardiovascular risk factors in many middle-income nations (Ezzati and others 2005). Coupled with substantially more cardiovascular deaths and a larger disease burden in low- and middle-income countries, these risk factors result in a much larger loss of healthy life in these nations.

Table 4.6. Individual and Joint Contributions of Risk Factors to Mortality and Burden of Disease from Cardiovascular Diseases.

Table 4.6

Individual and Joint Contributions of Risk Factors to Mortality and Burden of Disease from Cardiovascular Diseases.

Worldwide, approximately half of the mortality among children under five years of age (about 5 million deaths) was attributable to six major risk factors, with childhood underweight alone accounting for more than a quarter of all child deaths. Practically all the mortality and disease burden from childhood diseases attributable to major risk factors occurred in low- and middle-income countries (table 4.7). The reasons for this large disparity in the disease burden attributable to risk factors are higher risk factor exposure coupled with lower access to case management, which affects child mortality together with risk factor exposure.

Directions for Future Research

Health research has at times focused on topics that, while scientifically intriguing, have not always taken population health consequences into account when shaping specific research questions (Editorial 2001; Gross, Anserson, and Powe 1999; Horton 2003). The collation of evidence on exposure and hazard for different risks and the existing data gaps revealed the areas where data and monitoring need to be improved for better quantification of important risks and for more effective intervention. This includes the need for more detailed and higher quality data on exposure to most risks using exposure variables that capture the full distribution of hazards in the population. Important examples include detailed data on alcohol consumption volumes and patterns, dietary and biological markers for micronutrients, physical activity, and indoor smoke from household use of solid fuels, all of which were quantified using indirect measures with limited resolution. Furthermore, assumptions and extrapolations were needed in quantifying the relationships between risk factors and disease because of research gaps for some important global risk factors. Examples include limited quantitative assessments of the hazards of specific sexual behaviors for HIV/AIDS and other sexually transmitted diseases (UNAIDS 2001), of alcohol drinking patterns (Puddey and others 1999), or of exposure to indoor smoke from household use of solid fuels (Ezzati and Kammen 2002). Equally important are detailed exposure data for risks that have traditionally been studied in developed countries, but have global importance and require more detailed data and hazard quantification in developing regions, including smoking, body mass index, blood pressure, and cholesterol (Yusuf and others 2004).

The limited evidence on the effects of multiple risk factors and risk factor interactions also points to important gaps in research on multirisk and stratified hazards. Including multiple layers of causality in epidemiological research and risk assessment would allow investigators to estimate the benefits of reducing combinations of distal and proximal exposures using multiple interventions. Examples of such integrated strategies include using education and economic tools to promote physical activity and a healthier diet coupled with screening and lowering cholesterol, and addressing the overall childhood nutrition and physical environment instead of focusing on individual components. In such research, risk factor groups should be selected based on both biological relationships and socioeconomic factors that affect multiple diseases. Examples include those risk factors that are affected by the same policies and distal socioeconomic factors, such as malnutrition; unsafe water, sanitation, and hygiene; indoor smoke from household use of solid fuels; and rural development policies, or affect the same group of diseases, for instance, the previous example for childhood infectious diseases and smoking, diet, physical activity, and blood pressure for vascular diseases. Once risk factors are selected, the emphasis on reducing confounding should be matched by equally important inquiry into independent and mediated hazard sizes that are stratified based on the levels of other risks.

Finally, to inform interventions and policies, similar analyses should take place at smaller scales than global or regional levels, for example, rural and urban areas or different geographical regions of individual countries, and should include micro-level data and possibly a more comprehensive list of both distal and proximal risk factors, such as adverse life events and stress, risk factors for injuries, salt and fat intake, and blood glucose.

Discussion

Despite inherent uncertainties in population health risk assessment, described in chapter 5 and in chapters devoted to individual risk factors elsewhere (Ezzati and others 2004), the quantification of the burden of disease attributable to the individual and joint hazards of selected risk factors illustrates that those risk factors that affect the poorest regions and populations, such as undernutrition; unsafe water, sanitation, and hygiene; and indoor smoke from household use of solid fuels, continue to dominate the loss of health worldwide. These are coupled with hazards such as alcohol use, smoking, high blood pressure, high cholesterol, and overweight and obesity that are globally widespread and have large health effects.

The large remaining burden due to childhood mortality risks such as undernutrition; unsafe water, sanitation, and hygiene; and indoor smoke from household use of solid fuels indicates the persistent need for developing and delivering effective interventions, including lowering the costs of pertinent technological interventions. At the same time, four of the five leading causes of lost healthy life affect adults: high blood pressure, unsafe sex, smoking, and alcohol use (figure 4.1). Risk factors for both adult communicable and noncommunicable diseases already make substantial contributions to the disease burden even in regions with low income and high infant mortality. Therefore, the public health community should continually reassess the need for interventions addressing both childhood disease risk factors and those that affect adult health. Dynamic and systematic policy responses can, to a large extent, mitigate the spread of such risk factors and their more distal causes throughout the development process, for example, through cleaner environmental or healthier nutritional transitions (Arrow and others 1995; Lee, Popkin, and Kim 2000). In addition, as illustrated by the persistence of diseases such as malaria or the large increase in the disease burden due to HIV/AIDS and its risk factors since 1990, as well as the potential for generalized HIV/AIDS epidemics in some Eastern European countries (MacLehose, McKee, and Weinberg 2002) and China (Kaufman and Jing 2002), risk factors for important communicable diseases also require dynamic monitoring and policy responses.

Risk factors that were not among the leading global causes of the disease burden should not be neglected for a number of reasons. First, the analysis could be expanded with other risk factors that are both prevalent and hazardous. Second, although smaller than other risk factors, many make non-negligible contributions to the burden of disease in various populations. For example, in the low- and middle-income countries of East Asia and the Pacific, which is dominated by China in terms of population, urban air pollution from transportation and industrial and household energy use based on coal has health effects comparable to those of micronutrient deficiencies. Similarly, non-use and use of ineffective methods of contraception was associated with a larger disease burden than most chronic disease risk factors among females in South Asia and Sub-Saharan Africa. Third, for other risk factors, such as child sexual abuse, ethical considerations may outweigh direct contributions to the disease burden in policy debate. Finally, while the burden of disease due to a risk factor may be comparatively small, effective or cost-effective interventions may be known. Examples include reducing the number of unnecessary injections at health facilities coupled with the use of sterile syringes and the reduction in exposure to urban air pollution in industrial countries in the second half of the 20th century, which often also led to benefits such as energy savings.

A small number of risks account for large contributions to the global loss of healthy life. Furthermore, several are relatively prominent in regions at all stages of development. While reducing all the risks discussed to their theoretical minimums may not be possible using current interventions, the results illustrate that preventing disease by addressing known distal and proximal risk factors can provide substantial and underutilized public health gains. Treating established disease will always have a role in public health, especially in the case of diseases such as tuberculosis, where treatment contributes to prevention. At the same time, the current devotion of a disproportionately small share of resources to prevention by reducing major known risk factors through personal and nonpersonal interventions should be reconsidered in a more systematic way in light of the evidence presented here.

The estimates of the joint contributions of 19 selected global risk factors showed that these risks together contributed to a considerable loss of healthy life in different regions of the world. In particular, for some of the leading global diseases, such as lower respiratory infections, diarrhea, HIV/AIDS, lung cancer, ischemic heart disease, and stroke, substantial proportions were attributable to these selected risk factors. This concentration of the disease burden further emphasizes the contribution of leading risks such as undernutrition, unsafe sex, high blood pressure, and smoking and alcohol use to the loss of healthy life globally. The results further emphasize that for more effective and affordable implementation of a prevention paradigm, policies, programs, and scientific research should acknowledge and take advantage of the interactive and correlated role of major risks to health, across and within causality layers.

ANNEX 4A: Population Attributable Fractions, Attributable Deaths, Years of Life Lost Because of Premature Mortality (YLL), and Disability-Adjusted Life Years (DALYs) by Risk Factor, Disease Outcome, Age, Sex, and Region

Table 4A.1. Risk factor: Childhood underweight Disease: Diarrheal diseases

Table 4A.2. Risk factor: Childhood underweight Disease: Measles

Table 4A.3. Risk factor: Childhood underweight Disease: Malaria

Table 4A.4. Risk factor: Childhood underweight Disease: Lower respiratory infections

Table 4A.5. Risk factor: Childhood underweight Disease: Protein-energy malnutrition

Table 4A.6. Risk factor: Childhood underweight Disease: Selected other Group I diseases

Table 4A.7. Risk factor: Childhood underweight Disease: All causes

Table 4A.8. Risk factor: Iron-deficiency anemia Disease: Maternal conditions

Table 4A.9. Risk factor: Iron-deficiency anemia Disease: Perinatal conditions

Table 4A.10. Risk factor: Iron-deficiency anemia Disease: Iron-deficiency anemia

Table 4A.11. Risk factor: Iron-deficiency anemia Disease: All causes

Table 4A.12. Risk factor: Vitamin A deficiency Disease: Diarrheal diseases

Table 4A.13. Risk factor: Vitamin A deficiency Disease: Measles

Table 4A.14. Risk factor: Vitamin A deficiency Disease: Malaria

Table 4A.15. Risk factor: Vitamin A deficiency Disease: Other infectious diseases

Table 4A.16. Risk factor: Vitamin A deficiency Disease: Selected maternal conditions

Table 4A.17. Risk factor: Vitamin A deficiency Disease: Vitamin A deficiency

Table 4A.18. Risk factor: Vitamin A deficiency Disease: All causes

Table 4A.19. Risk factor: Zinc deficiency Disease: Diarrheal diseases

Table 4A.20. Risk factor: Zinc deficiency Disease: Malaria

Table 4A.21. Risk factor: Zinc deficiency Disease: Lower respiratory infections

Table 4A.22. Risk factor: Zinc deficiency Disease: All causes

Table 4A.23. Risk factor: High blood pressure Disease: Hypertensive heart disease

Table 4A.24. Risk factor: High blood pressure Disease: Ischemic heart disease

Table 4A.25. Risk factor: High blood pressure Disease: Cerebrovascular disease

Table 4A.26. Risk factor: High blood pressure Disease: Selected other cardiovascular diseases

Table 4A.27. Risk factor: High blood pressure Disease: All causes

Table 4A.28. Risk factor: High cholesterol Disease: Ischemic heart disease

Table 4A.29. Risk factor: High cholesterol Disease: Cerebrovascular disease

Table 4A.30. Risk factor: High cholesterol Disease: All causes

Table 4A.31. Risk factor: Overweight and obesity Disease: Colon and rectal cancers

Table 4A.32. Risk factor: Overweight and obesity Disease: Breast cancer

Table 4A.33. Risk factor: Overweight and obesity Disease: Corpus uteri cancer

Table 4A.34. Risk factor: Overweight and obesity Disease: Diabetes mellitus

Table 4A.35. Risk factor: Overweight and obesity Disease: Hypertensive heart disease

Table 4A.36. Risk factor: Overweight and obesity Disease: Ischemic heart disease

Table 4A.37. Risk factor: Overweight and obesity Disease: Cerebrovascular disease

Table 4A.38. Risk factor: Overweight and obesity Disease: Osteoarthritis

Table 4A.39. Risk factor: Overweight and obesity Disease: All causes

Table 4A.40. Risk factor: Low fruit and vegetable intake Disease: Esophageal cancer

Table 4A.41. Risk factor: Low fruit and vegetable intake Disease: Stomach cancer

Table 4A.42. Risk factor: Low fruit and vegetable intake Disease: Colon and rectal cancers

Table 4A.43. Risk factor: Low fruit and vegetable intake Disease: Trachea, bronchus, and lung cancers

Table 4A.44. Risk factor: Low fruit and vegetable intake Disease: Ischemic heart disease

Table 4A.45. Risk factor: Low fruit and vegetable intake Disease: Cerebrovascular disease

Table 4A.46. Risk factor: Low fruit and vegetable intake Disease: All causes

Table 4A.47. Risk factor: Physical inactivity Disease: Colon and rectal cancers

Table 4A.48. Risk factor: Physical inactivity Disease: Breast cancer

Table 4A.49. Risk factor: Physical inactivity Disease: Diabetes mellitus

Table 4A.50. Risk factor: Physical inactivity Disease: Ischemic heart disease

Table 4A.51. Risk factor Physical inactivity Disease: Cerebrovascular disease

Table 4A.52. Risk factor: Physical inactivity Disease: All causes

Table 4A.53. Risk factor: Unsafe sex Disease: Sexually transmitted diseases excluding HIV/AIDS

Table 4A.54. Risk factor: Unsafe sex Disease: HIV/AIDS

Table 4A.55. Risk factor: Unsafe sex Disease: Cervix uteri cancer

Table 4A.56. Risk factor: Unsafe sex Disease: All causes

Table 4A.57. Risk factor: Alcohol use Disease: Low birthweight

Table 4A.58. Risk factor: Alcohol use Disease: Mouth and oropharynx cancers

Table 4A.59. Risk factor: Alcohol use Disease: Esophageal cancer

Table 4A.60. Risk factor: Alcohol use Disease: Liver cancer

Table 4A.61. Risk factor: Alcohol use Disease: Breast cancer

Table 4A.62. Risk factor: Alcohol use Disease: Selected other neoplasms

Table 4A.63. Risk factor: Alcohol use Disease: Diabetes mellitus

Table 4A.64. Risk factor: Alcohol use Disease: Unipolar depressive disorders

Table 4A.65. Risk factor: Alcohol use Disease: Epilepsy

Table 4A.66. Risk factor: Alcohol use Disease: Alcohol use disorders

Table 4A.67. Risk factor: Alcohol use Disease: Hypertensive heart disease

Table 4A.68. Risk factor: Alcohol use Disease: Ischemic heart disease

Table 4A.69. Risk factor: Alcohol use Disease: Cerebrovascular disease

Table 4A.70. Risk factor: Alcohol use Disease: Cirrhosis of the liver

Table 4A.71. Risk factor: Alcohol use Disease: Road traffic accidents

Table 4A.72. Risk factor: Alcohol use Disease: Poisonings

Table 4A.73. Risk factor: Alcohol use Disease: Falls

Table 4A.74. Risk factor: Alcohol use Disease: Drownings

Table 4A.75. Risk factor: Alcohol use Disease: Other unintentional injuries

Table 4A.76. Risk factor: Alcohol use Disease: Self-inflicted injuries

Table 4A.77. Risk factor: Alcohol use Disease: Violence

Table 4A.78. Risk factor: Alcohol use Disease: Other intentional injuries

Table 4A.79. Risk factor: Alcohol use Disease: All causes

Table 4A.80. Risk factor: Illicit drug use Disease: HIV/AIDS

Table 4A.81. Risk factor: Illicit drug use Disease: Drug use disorders

Table 4A.82. Risk factor: Illicit drug use Disease: Unintentional injuries

Table 4A.83. Risk factor: Illicit drug use Disease: Self-inflicted injuries

Table 4A.84. Risk factor: Illicit drug use Disease: All causes

Table 4A.85. Risk factor: Unsafe water, sanitation, and hygiene Disease: Diarrheal diseases

Table 4A.86. Risk factor: Unsafe water, sanitation, and hygiene Disease: All causes

Table 4A.87. Risk factor: Child sexual abuse Disease: Unipolar depressive disorders

Table 4A.88. Risk factor: Child sexual abuse Disease: Alcohol use disorders

Table 4A.89. Risk factor Child sexual abuse Disease: Drug use disorders

Table 4A.90. Risk factor: Child sexual abuse Disease: Post-traumatic stress disorder

Table 4A.91. Risk factor: Child sexual abuse Disease: Panic disorder

Table 4A.92. Risk factor: Child sexual abuse Disease: Self-inflicted injuries

Table 4A.93. Risk factor: Child sexual abuse Disease: All causes

Table 4A.94. Risk factor: Indoor smoke from household use of solid fuels Disease: Lower respiratory infections

Table 4A.95. Risk factor: Indoor smoke from household use of solid fuels Disease: Trachea, bronchus, and lung cancers

Table 4A.96. Risk factor: Indoor smoke from household use of solid fuels Disease: Chronic obstructive pulmonary disease

Table 4A.97. Risk factor: Indoor smoke from household use of solid fuels Disease: All causes

Table 4A.98. Risk factor: Contaminated injections in health care setting Disease: HIV/AIDS

Table 4A.99. Risk factor: Contaminated injections in health care setting Disease: Hepatitis B

Table 4A.100. Risk factor: Contaminated injections in health care setting Disease: Hepatitis C

Table 4A.101. Risk factor: Contaminated injections in health care setting Disease: Liver cancer

Table 4A.102. Risk factor: Contaminated injections in health care setting Disease: Cirrhosis of the liver

Table 4A.103. Risk factor: Contaminated injections in health care setting Disease: All causes

Table 4A.104. Risk factor: Urban air pollution Disease: Respiratory infections

Table 4A.105. Risk factor: Urban air pollution Disease: Trachea, bronchus, and lung cancers

Table 4A.106. Risk factor: Urban air pollution Disease: Selected cardiopulmonary causes

Table 4A.107. Risk factor: Urban air pollution Disease: All causes

Table 4A.108. Risk factor: Smoking Disease: Chronic obstructive pulmonary disease

Table 4A.109. Risk factor: Smoking Disease: Trachea, bronchus, and lung cancers

Table 4A.110. Risk factor: Smoking Disease: Liver cancer

Table 4A.111. Risk factor: Smoking Disease: Cervix uteri cancer

Table 4A.112. Risk factor: Smoking Disease: Bladder cancer

Table 4A.113. Risk factor: Smoking Disease: Pancreas cancer

Table 4A.114. Risk factor: Smoking Disease: Stomach cancer

Table 4A.115. Risk factor: Smoking Disease: Upper aerodigestive cancer

Table 4A.116. Risk factor: Smoking Disease: Leukemia

Table 4A.117. Risk factor: Smoking Disease: Ischemic heart disease

Table 4A.118. Risk factor: Smoking Disease: Selected other cardiovascular diseases

Table 4A.119. Risk factor: Smoking Disease: Cerebrovascular disease

Table 4A.120. Risk factor: Smoking Disease: Selected respiratory diseases

Table 4A.121. Risk factor: Smoking Disease: Selected medical conditions

Table 4A.122. Risk factor: Smoking Disease: All causes

Table 4A.123. Risk factor: Non-use and use of ineffective methods of contraception Disease: Abortion

Table 4A.124. Risk factor: Non-use and use of ineffective methods of contraception Disease: Maternal causes other than abortion

Table 4A.125. Risk factor: Non-use and use of ineffective methods of contraception Disease: All causes

References

  1. Arrow K., Bolin B., Costanza R., Dasgupta P., Folke C., Holling C. S., Jansson B.-O., Levin S., Maler K.-G., Perrings C., Pimente D. Economic Growth, Carrying Capacity, and the Environment. Science. 1995;168(2):520–21. [PubMed: 17756719]
  2. Berlin J. A., Colditz G. A. A Meta-analysis of Physical Activity in the Prevention of Coronary Heart Disease. American Journal of Epidemiology. 1990;132(4):612–28. [PubMed: 2144946]
  3. Black R. E. Would Control of Childhood Infectious Diseases Reduce Malnutrition? Acta Paediatrica Scandandinavica Supplement. 1991;374:133–40. [PubMed: 1957617]
  4. Blair S. N., Cheng Y., Holder J. S. Is Physical Activity or Physical Fitness More Important in Defining Health Benefits? Medicine and Science in Sports and Exercise. 2001;33(6 Suppl):S379–S399. [PubMed: 11427763]
  5. Briend A. Is Diarrhoea a Major Cause of Malnutrition among the Under-Fives in Developing Countries? A Review of Available Evidence. European Journal of Clinical Nutrition. 1990;44(9):611–28. [PubMed: 2261894]
  6. Brown K. H., Peerson J. M., Rivera J., Allen L. H. Effect of Supplemental Zinc on the Growth and Serum Zinc Concentrations of Prepubertal Children: A Meta-analysis of Randomized Controlled Trials. American Journal of Clinical Nutrition. 2002;75(6):1062–71. [PubMed: 12036814]
  7. Chen Z., Peto R., Collins R., MacMahon S., Lu J., Li W. Serum Cholesterol Concentration and Coronary Heart Disease in Population with Low Cholesterol Concentrations. British Medical Journal. 1991;303(6797):276–82. [PMC free article: PMC1670480] [PubMed: 1888927]
  8. Christian P., West, Jr K. P. Interactions between Zinc and Vitamin A: An Update. American Journal of Clinical Nutrition. 1998;68(2 Suppl):435S–441S. [PubMed: 9701158]
  9. Corrao G., Rubbiati L., Bagnardi V., Zambon A., Poikolainen K. Alcohol and Coronary Heart Disease: A Meta-analysis. Addiction. 2000;95(10):1505–23. [PubMed: 11070527]
  10. Curtis V., Cairncross S., Yonli R. Domestic Hygiene and Diarrhoea: Pinpointing the Problem. Tropical Medicine and International Health. 2000;5(1):22–32. [PubMed: 10672202]
  11. de Onis M., Blossner M., Borghi E., Frongillo E. A., Morris R. Estimates of Global Prevalence of Childhood Underweight in 1990 and 2015. Journal of the American Medical Association. 2004;291(21):2600–6. [PubMed: 15173151]
  12. de Onis M., Frongillo E., Blossner M. Is Malnutrition Declining? An Analysis of Changes in Levels of Child Malnutrition since 1980. Bulletin of the World Health Organization. 2000;78(10):1222–33. [PMC free article: PMC2560621] [PubMed: 11100617]
  13. Eastern Stroke and Coronary Heart Disease Collaborative Research Group. Blood Pressure, Cholesterol, and Stroke in Eastern Asia. Lancet. 1998;352(9143):1801–7. [PubMed: 9851379]
  14. Eaton C. B. Relation of Physical Activity and Cardiovascular Fitness to Coronary Heart Disease, Part I: A Meta-analysis of the Independent Relation of Physical Activity and Coronary Heart Disease. Journal of the American Board of Family Practice. 1992;5(1):31–42. [PubMed: 1532879]
  15. Editorial The Human Genome, in Proportion. Lancet. 2001;357(9255):489. [PubMed: 11229660]
  16. Eide G. E., Heuch I. Attributable Fractions: Fundamental Concepts and Their Visualization. Statistical Methods in Medical Research. 2001;10(3):159–93. [PubMed: 11446147]
  17. Esrey S. A. Water, Waste, and Well-Being: A Multicountry Study. American Journal of Epidemiology. 1996;143(6):608–23. [PubMed: 8610678]
  18. Evans A. S. Causation and Disease: The Henle-Koch Postulates Revisited. Yale Journal of Biology and Medicine. 1976;49(2):175–95. [PMC free article: PMC2595276] [PubMed: 782050]
  19. ———1978Causation and Disease: A Chronological Journey American Journal of Epidemiology 108(4):249–58. [PubMed: 727194]
  20. Ezzati M., Kammen D. M. The Health Impacts of Exposure to Indoor Air Pollution from Solid Fuels in Developing Countries: Knowledge, Gaps, and Data Needs. Environmental Health Perspectives. 2002;110(11):1057–68. [PMC free article: PMC1241060] [PubMed: 12417475]
  21. Ezzati M., Lopez A. D. Estimates of Global Mortality Attributable to Smoking in 2000. Lancet. 2003;362(9387):847–52. [PubMed: 13678970]
  22. ———. 2004. "Smoking and Oral Tobacco Use." In Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors, ed. M. Ezzati, A. D. Lopez, A. Rodgers, and C. J. L. Murray, 883–956. Geneva: World Health Organization.
  23. Ezzati M., Henley S. J., Lopez A. D., Thun M. J. The Role of Smoking in Global and Regional Cancer Epidemiology: Current Patterns and Research Needs. International Journal of Cancer. 2005;116(6):963–71. [PubMed: 15880414]
  24. Ezzati M., Henley S. J., Thun M. J., Lopez A. D. The Role of Smoking in Global and Regional Cardiovascular Mortality. Circulation. 2005;112(4):489–97. [PubMed: 16027251]
  25. Ezzati, M., A. D. Lopez, A. Rodgers, and C. J. L. Murray. 2004. Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors. Geneva: World Health Organization.
  26. Ezzati M., Lopez A. D., Rodgers A., Vander Hoorn S., Murray C. J. L. the Comparative Risk Assessment Collaborative Group. Selected Major Risk Factors and Global and Regional Burden of Disease. Lancet. 2002;360(9343):1347–60. [PubMed: 12423980]
  27. Ezzati, M., S. Vander Hoorn, A. Rodgers, A. D. Lopez, C. D. Mathers, and C. J. L. Murray. 2004. "Potential Health Gains from Reducing Multiple Risk Factors." In Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors, ed. M. Ezzati, A. D. Lopez, A. Rodgers, and C. J. L. Murray, 2167–90. Geneva: World Health Organization.
  28. Ezzati M., Vander Hoorn S., Rodgers A., Lopez A. D., Mathers C. D., Murray C. J. L. the Comparative Risk Assessment Collaborative Group. Estimates of Global and Regional Potential Health Gains from Reducing Multiple Major Risk Factors. Lancet. 2003;362(9380):271–80. [PubMed: 12892956]
  29. Ezzati M., Vander Hoorn S., Lawes C. M. M., Leach R., James W. P. T., Lopez A. D., Rodgers A., Murray C. J. L. Rethinking the `Diseases of Affluence' Paradigm: Global Patterns of Nutritional Risks in Relation to Economic Development. PLoS Medicine. 2005;2(5):e133. [PMC free article: PMC1088287] [PubMed: 15916467]
  30. Gaziano J. M., Manson J. E., Branch L. G., Colditz G. A., Willett W. C., Buring J. E. A Prospective Study of Consumption of Carotenoids in Fruits and Vegetables and Decreased Cardiovascular Mortality in the Elderly. Annals of Epidemiology. 1995;5(4):255–60. [PubMed: 8520706]
  31. Greenland S. Bias in Methods for Deriving Standardized Morbidity Ratio and Attributable Fraction Estimates. Statistics in Medicine. 1984;3:131–41. [PubMed: 6463450]
  32. ———1987Quantitative Methods in the Review of Epidemiologic Literature Epidemiologic Reviews 91–30. [PubMed: 3678409]
  33. Gross C. P., Anderson G. F., Powe N. R. The Relation between Funding by the National Institutes of Health and the Burden of Disease. New England Journal of Medicine. 1999;340(24):1881–7. [PubMed: 10369852]
  34. Guerrant R. L., Schorling J. B., McAuliffe J. F., de Souza M. A. Diarrhea as a Cause and an Effect of Malnutrition: Diarrhea Prevents Catch-Up Growth and Malnutrition Increases Diarrhea Frequency and Duration. American Journal of Tropical Medicine and Hygiene. 1992;47(1 pt 2):28–35. [PubMed: 1632474]
  35. Horton R. Medical Journals: Evidence of Bias against the Diseases of Poverty. Lancet. 2003;361(9359):712–3. [PubMed: 12620731]
  36. Jarrett R. J., Shipley M. J., Rose G. Weight and Mortality in the Whitehall Study. British Medical Journal. 1982;285(6341):535–7. [PMC free article: PMC1499061] [PubMed: 6809160]
  37. Jee S. H., Suh I., Kim I. S., Appel L. J. Smoking and Atherosclerotic Cardiovascular Disease in Men with Low Levels of Serum Cholesterol: The Korea Medical Insurance Corporation Study. Journal of the American Medical Association. 1999;282(22):2149–55. [PubMed: 10591337]
  38. Jousilahti P., Vartiainen E., Tuomilehto J., Puska P. Sex, Age, Cardiovascular Risk Factors, and Coronary Heart Disease: A Prospective Follow-Up Study of 14,786 Middle-Aged Men and Women in Finland. Circulation. 1999;99(9):1165–72. [PubMed: 10069784]
  39. Kaufman J., Jing J. China and AIDS: The Time to Act Is Now. Science. 2002;296(5577):2339–40. [PubMed: 12089428]
  40. Khaw K. T., Barrett-Connor E. Dietary Fiber and Reduced Ischemic Heart Disease Mortality Rates in Men and Women: A 12-Year Prospective Study. American Journal of Epidemiology. 1987;126(6):1093–102. [PubMed: 2825519]
  41. Koopman J. S. Interaction between Discrete Causes. American Journal of Epidemiology. 1981;113(6):716–24. [PubMed: 7234861]
  42. Kunzli N., Kaiser R., Medina S., Studnicka M., Chanel O., Filliger P., Herry M., Horak F., Puybonnieux-Texier V., Quenel P., Schneider J., Seethaler R., Vergnaud J. C., Sommer H. Public-Health Impact of Outdoor and Traffic-Related Air Pollution: A European Assessment. Lancet. 2000;356(9232):795–801. [PubMed: 11022926]
  43. Law M. R., Wald N. J., Thompson S. G. By How Much and How Quickly Does Reduction in Serum Cholesterol Concentration Lower Risk of Ischaemic Heart Disease? British Medical Journal. 1994;308(6925):367–73. [PMC free article: PMC2539460] [PubMed: 8043072]
  44. Lee M.-J., Popkin B. M., Kim S. The Unique Aspects of the Nutrition Transition in South Korea: The Retention of Healthful Elements in Their Traditional Diet. Public Health Nutrition. 2000;5(14):197–203. [PubMed: 12027285]
  45. Leigh J., Macaskill P., Kuosma E., Mandryk J. Global Burden of Disease and Injury Due to Occupational Factors. Epidemiology. 1999;10(5):626–31. [PubMed: 10468442]
  46. Liu B. Q., Peto R., Chen Z. M., Boreham J., Wu Y. P., Li J. Y., Campbell T. C., Chen J. S. Emerging Tobacco Hazards in China: 1. Retrospective Proportional Mortality Study of One Million Deaths. British Medical Journal. 1998;317(7170):1411–22. [PMC free article: PMC28719] [PubMed: 9822393]
  47. Liu S., Lee I. M., Ajani U., Cole S. R., Buring J. E., Manson J. E. Intake of Vegetables Rich in Carotenoids and Risk of Coronary Heart Disease in Men: The Physicians' Health Study. International Journal of Epidemiology. 2001;30(1):130–5. [PubMed: 11171873]
  48. Liu S., Manson J. E., Lee I. M., Cole S. R., Hennekens C. H., Willett W. C., Buring J. E. Fruit and Vegetable Intake and Risk of Cardiovascular Disease: The Women's Health Study. American Journal of Clinical Nutrition. 2000;72(4):922–8. [PubMed: 11010932]
  49. Lutter C. K., Habicht J. P., Rivera J. A., Martorell R. The Relationship between Energy Intake and Diarrheal Disease in Their Effects on Child Growth: Biological Model, Evidence, and Implications for Public Health Policy. Food and Nutrition Bulletin. 1992;14:36–42.
  50. Lutter C. K., Mora J. O., Habicht J. P., Rasmussen K. M., Robson D. S., Sellers S. G., Perri M. G., Sheps D. S., Pettinger M. B., Siscovick D. S. Nutritional Supplementation: Effects on Child Stunting because of Diarrhea. American Journal of Clinical Nutrition. 1989;50(1):1–8. [PubMed: 2750681]
  51. MacLehose L., McKee M., Weinberg J. Responding to the Challenge of Communicable Disease in Europe. Science. 2002;295(5562):2047–50. [PubMed: 11896269]
  52. Maldonado G., Greenland S. Estimating Causal Effects. International Journal of Epidemiology. 2002;31(2):422–9. [PubMed: 11980807]
  53. Manson J. E., Colditz G. A., Stampfer M. J., Willett W. C., Rosner B., Monson R. R., Speizer F. E., Hennekens C. H. A Prospective Study of Obesity and Risk of Coronary Heart Disease in Women. New England Journal of Medicine. 1990;322(13):882–9. [PubMed: 2314422]
  54. Manson J. E., Greenland P., LaCroix A. Z., Stefanick M. L., Mouton C. P., Oberman A., Perri M. G., Sheps D. S., Pettinger M. B., Siscovick D. S. Walking Compared with Vigorous Exercise for the Prevention of Cardiovascular Events in Women. New England Journal of Medicine. 2002;347(10):755–56. [PubMed: 12213942]
  55. Martorell R., Habicht J. P., Yarbrough C., Lechtig A., Klein R. E., Western K. A. Acute Morbidity and Physical Growth in Rural Guatemalan Children. American Journal of Diseases of Children. 1975;129(11):1296–301. [PubMed: 1190161]
  56. Martorell R., Yarbrough C., Lechtig A., Habicht J. P., Klein R. E. Diarrheal Diseases and Growth Retardation in Preschool Guatemalan Children. American Journal of Physical Anthropology. 1975;43(3):341–6. [PubMed: 1211430]
  57. Mathers, C. D., M. Ezzati, A. D. Lopez, C. J. L. Murray, and A. Rodgers. 2002. "Causal Decomposition of Summary Measures of Population Health." In Summary Measures of Population Health: Concepts, Ethics, Measurement, and Applications, ed. C. J. L. Murray, J. Salomon, C. D. Mathers, and A. D. Lopez. 273–290. Geneva: World Health Organization.
  58. McGinnis J. M., Foege W. H. Actual Causes of Death in the United States. Journal of American Medical Association. 1993;270(18):2207–12. [PubMed: 8411605]
  59. Miettinen O. S. Proportion of Disease Caused or Prevented by a Given Exposure, Trait, or Intervention. American Journal of Epidemiology. 1974;99(5):325–32. [PubMed: 4825599]
  60. Murray C. J. L., Ezzati M., Lopez A. D., Rodgers A., Vander Hoorn S. Comparative Quantification of Health Risks: Conceptual Framework and Methodological Issues. Population Health Metrics. 2003;1(1):1. [PMC free article: PMC156894] [PubMed: 12780936]
  61. Murray C. J. L., Lopez A. D. Global Mortality, Disability, and the Contribution of Risk Factors: Global Burden of Disease Study. Lancet. 1997;349(9063):1436–42. [PubMed: 9164317]
  62. ———1999On the Comparable Quantification of Health Risks: Lessons from the Global Burden of Disease Epidemiology 10(5):594–605. [PubMed: 10468439]
  63. Neaton J. D., Wentworth D. Serum Cholesterol, Blood Pressure, Cigarette Smoking, and Death from Coronary Heart Disease. Overall Findings and Differences by Age for 316,099 White Men. Multiple Risk Factor Intervention Trial Research Group. Archives of Internal Medicine. 1992;152(1):56–64. [PubMed: 1728930]
  64. Pelletier D. L., Frongillo, Jr E. A., Habicht J. P. Epidemiologic Evidence for a Potentiating Effect of Malnutrition on Child Mortality. American Journal of Public Health. 1993;83(8):1130–3. [PMC free article: PMC1695164] [PubMed: 8342721]
  65. Peto R., Lopez A. D., Boreham J., Thun M., Heath, Jr C. Mortality from Tobacco in Developed Countries. Lancet. 1992;339(8804):1268–78. [PubMed: 1349675]
  66. Popkin B. M. An Overview on the Nutrition Transition and Its Health Implications: The Bellagio Meeting. Public Health Nutrition. 2002a;5(1A):93–103. [PubMed: 12027297]
  67. ———2002bThe Shift in Stages of the Nutrition Transition in the Developing World Differs from Past Experiences Public Health Nutrition 51A205–14. [PubMed: 12027286]
  68. Popkin B. M., Horton S., Kim S., Mahal A., Shuigao J. Trends in Diet, Nutritional Status and Diet-Related Noncommunicable Diseases in China and India: The Economic Costs of the Nutrition Transition. Nutrition Reviews. 2001;59(12):379–90. [PubMed: 11766908]
  69. Preston, S. H. 1976. Mortality Patterns in National Populations: With Special Reference to Recorded Causes of Death. New York: Academic Press.
  70. Puddey I. B., Rakic V., Dimmitt S. B., Beilin L. J. Influence of Pattern of Drinking on Cardiovascular Disease and Cardiovascular Risk Factors: A Review. Addiction. 1999;94(5):649–63. [PubMed: 10563030]
  71. Ramakrishnan U., Martorell R. The Role of Vitamin A in Reducing Child Mortality and Morbidity and Improving Growth. Salud Publica de Mexico. 1998;40(2):189–198. [PubMed: 9617200]
  72. Ramakrishnan U., Latham M. C., Abel R. Vitamin A Supplementation Does Not Improve Growth of Preschool Children: A Randomized, Double-Blind Field Trial in South India. Journal of Nutrition. 1995;125(2):202–11. [PubMed: 7861247]
  73. Rehm, J., R. Room, M. Monteiro, G. Gmel, K. Graham, N. Rehn, C. T. Sempas, V. Frick, and D. Jerrigan. 2004. "Alcohol Use." In Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors, ed. M. Ezzati, A. D. Lopez, C. J. Murray, and A. Rogers, 959–1108. Geneva: World Health Organization.
  74. Rosengren A., Wedel H., Wilhelmsen L. Body Weight and Weight Gain during Adult Life in Men in Relation to Coronary Heart Disease and Mortality: A Prospective Population. European Health Journal. 1999;20(4):269–77. [PubMed: 10099921]
  75. Rothman K. J. Causes. American Journal of Epidemiology. 1976;104(6):587–92. [PubMed: 998606]
  76. Rothman, K. J., and S. Greenland. 1998. Modern Epidemiology. Philadelphia: Lippincott-Raven.
  77. Rothman K. J., Keller A. The Effect of Joint Exposure to Alcohol and Tobacco on the Risk of Cancer of the Mouth and Pharynx. Journal of Chronic Disease. 1972;25(12):711–6. [PubMed: 4648515]
  78. Rothman K. J., Greenland S., Walker A. M. Concepts of Interaction. American Journal of Epidemiology. 1980;112(4):467–70. [PubMed: 7424895]
  79. Scrimshaw, N. S., C. E. Taylor, and J. E. Gordon. 1968. Interactions of Nutrition and Infection. World Health Organization Monograph Series 57. Geneva: World Health Organization. [PubMed: 4976616]
  80. Single E., Robson L., Rehm J., Xie X. Morbidity and Mortality Attributable to Alcohol, Tobacco, and Illicit Drug Use in Canada. American Journal of Public Health. 1999;89(3):385–90. [PMC free article: PMC1508614] [PubMed: 10076491]
  81. Slaymaker, E., N. Walker, B. Zaba, and M. Collumbien. 2004. "Unsafe Sex." In Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors, ed. M. Ezzati, A. D. Lopez, A. Rodgers, and C. J. L. Murray, 1177–254. Geneva: World Health Organization.
  82. Smith K. R. The National Burden of Disease from Indoor Air Pollution in India. Proceedings of the National Academy of Sciences. 2000;97(24):13286–93. [PMC free article: PMC27217] [PubMed: 11087870]
  83. Smith K. R., Corvalan C. F., Kjellstrom T. How Much Global Ill Health Is Attributable to Environmental Factors. Epidemiology. 1999;10(5):573–84. [PubMed: 10468437]
  84. Smith, K. R., S. Mehta, and M. Maeusezahl-Feuz. 2004. "Indoor Air Pollution from Household Use of Solid Fuels." In Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors, ed. M. Ezzati, A. D. Lopez, C. J. Murray, and A. Rogers, 1435–94. Geneva: World Health Organization.
  85. Stephensen C. B. Burden of Infection on Growth Failure. Journal of Nutrition. 1999;129(25 Suppl):534S–538S. [PubMed: 10064326]
  86. Tate R. B., Manfreda J., Cuddy T. E. The Effect of Age on Risk Factors for Ischemic Heart Disease: The Manitoba Follow-up Study, 1948–1993. Annals of Epidemiology. 1998;8(7):415–21. [PubMed: 9738687]
  87. Thun M. J., Apicella L. F., Henley S. J. Smoking vs. Other Risk Factors as the Cause of Smoking-Attributable Mortality: Confounding in the Courtroom. Journal of the American Medical Association. 2000;284(6):706–12. [PubMed: 10927778]
  88. UNAIDS (Joint United Nations Programme on HIV/AIDS). 2001. Together We Can: Leadership in a World of AIDS. Geneva: UNAIDS. http://www​.unaids.org​/UNGASS/leadership/English/leader_en​.pdf.
  89. UNICEF (United Nations Children's Fund). 1990. Strategy to Improve Nutrition of Children and Women in Developing Countries: A UNICEF Policy Review. New York: UNICEF.
  90. Walter S. D. The Estimation and Interpretation of Attributable Risk in Health Research. Biometrics. 1976;32(4):829–49. [PubMed: 1009228]
  91. ———1980Prevention of Multifactorial Disease American Journal of Epidemiology 112(3):409–16. [PubMed: 7424889]
  92. West K. P. Jr,, Pokhrel R. P., Katz J., LeClerq S. C., Khatry S. K., Shrestha S. R., Pradhan E. K., Tielsch J. M., Pandey M. R., Sommer A. Efficacy of Vitamin A in Reducing Preschool Child Mortality in Nepal. Lancet. 1991;338(8759):67–71. [PubMed: 1676467]
  93. WHO (World Health Organization). 1992. International Statistical Classification of Disease and Related Health Problems, 10th ed. Geneva: WHO.
  94. WHO (World Health Organization). 2002. World Health Report 2002: Reducing Risks, Promoting Healthy Life. Geneva: WHO. [PubMed: 14741909]
  95. Willet W. C. Balancing Life-Style and Genomics Research for Disease Prevention. Science. 2002;296(5568):695–8. [PubMed: 11976443]
  96. Yerushalmy J., Palmer C. E. On the Methodology of Investigations of Etiologic Factors in Chronic Diseases. Journal of Chronic Disease. 1959;108(1):27–40. [PubMed: 13664755]
  97. Yusuf S., Hawken S., Ounpuu S., Dans T., Avezum A., Lanas F., McQueen M., Budaj A., Pais P., Varigos J., Lisheng L. the INTER-HEART Study Investigators. Effect of Potentially Modifiable Risk Factors Associated with Myocardial Infarction in 52 Countries (the INTERHEART Study): Case-Control Study. Lancet. 2004;364(9438):937–52. [PubMed: 15364185]
  98. Zinc Investigators' Collaborative Group. Prevention of Diarrhea and Pneumonia by Zinc Supplementation in Children in Developing Countries: Pooled Analysis of Randomized Controlled Trials. Journal of Pediatrics. 1999;135(6):689–97. [PubMed: 10586170]

Footnotes

1

Some special cases of effect modification can be identified through the terminology of "sufficient" and "component" causes (Rothman 1976; Rothman and Greenland 1998) with implications for the assessment of joint interventions as follows:

  • If two risk factors are sufficient causes for a disease and a fraction of the population is affected by both sufficient causes, then the burden avoidable by reductions in both risk factors is larger than the sum of the burdens avoidable by reduction of each individual risk factor. This is because for those affected by the two risks, removal of both risks is needed to avoid disease (and hence the hazard as measured by the avoidable fraction of disease depends on the presence of the other risk). Consider, for example, the role of clean water and sanitary latrines as risk factors for diarrheal diseases. Improving water quality alone may not have much effect on the prevalence of disease without the introduction of sanitation or hygienic behavior, because fecal-oral transmission may take place through routes other than drinking water (Curtis, Cairncross, and Yonli 2000; Esrey 1996). However, the introduction of both clean water sources and sanitary latrines may reduce disease levels substantially. In the extreme, where every exposed person is affected by both sufficient causes, a change in exposure to a risk factor may result in no change in disease outcome under some circumstances. This phenomenon is known as saturation.
  • If two risk factors are component causes of the same sufficient cause, then the burden avoidable by reductions in both risk factors is smaller than the sum of the burdens attributable to each individual risk factor. This is a case of synergy or positive interaction between risk factors, in which the existence of both risk factors has an effect larger than the sum of the effects from the existence of each (Rothman 1976). Synergistic interactions may be complete or partial depending on whether the risk factors are components of a single or multiple sufficient causes. Rothman (1976) uses the inheritance of the phenylketonuria gene and phenylalanine in the diet as an example of synergy.

2

Submultiplicative effect modification could result in a slightly smaller PAF even with positive correlation for some RR values.

Copyright © 2006, The International Bank for Reconstruction and Development/The World Bank Group.
Bookshelf ID: NBK11813PMID: 21250375

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