The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors
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{"title"=>"The preventable causes of death in the United States: Comparative risk assessment of dietary, lifestyle, and metabolic risk factors", "type"=>"journal", "authors"=>[{"first_name"=>"Goodarz", "last_name"=>"Danaei", "scopus_author_id"=>"15730459500"}, {"first_name"=>"Eric L.", "last_name"=>"Ding", "scopus_author_id"=>"54413849800"}, {"first_name"=>"Dariush", "last_name"=>"Mozaffarian", "scopus_author_id"=>"56751007100"}, {"first_name"=>"Ben", "last_name"=>"Taylor", "scopus_author_id"=>"9233057100"}, {"first_name"=>"Jürgen", "last_name"=>"Rehm", "scopus_author_id"=>"7102718648"}, {"first_name"=>"Christopher J.L.", "last_name"=>"Murray", "scopus_author_id"=>"55481130700"}, {"first_name"=>"Majid", "last_name"=>"Ezzati", "scopus_author_id"=>"6701418638"}], "year"=>2009, "source"=>"PLoS Medicine", "identifiers"=>{"issn"=>"15491277", "arxiv"=>"10.1371/journal.pmed.1000058", "scopus"=>"2-s2.0-66149137211", "sgr"=>"66149137211", "pui"=>"354637185", "isbn"=>"1549-1676 (Electronic)\\r1549-1277 (Linking)", "pmid"=>"19399161", "doi"=>"10.1371/journal.pmed.1000058"}, "id"=>"700103ac-d78c-3813-938f-7904d52040f8", "abstract"=>"BACKGROUND Knowledge of the number of deaths caused by risk factors is needed for health policy and priority setting. Our aim was to estimate the mortality effects of the following 12 modifiable dietary, lifestyle, and metabolic risk factors in the United States (US) using consistent and comparable methods: high blood glucose, low-density lipoprotein (LDL) cholesterol, and blood pressure; overweight-obesity; high dietary trans fatty acids and salt; low dietary polyunsaturated fatty acids, omega-3 fatty acids (seafood), and fruits and vegetables; physical inactivity; alcohol use; and tobacco smoking. METHODS AND FINDINGS We used data on risk factor exposures in the US population from nationally representative health surveys and disease-specific mortality statistics from the National Center for Health Statistics. We obtained the etiological effects of risk factors on disease-specific mortality, by age, from systematic reviews and meta-analyses of epidemiological studies that had adjusted (i) for major potential confounders, and (ii) where possible for regression dilution bias. We estimated the number of disease-specific deaths attributable to all non-optimal levels of each risk factor exposure, by age and sex. In 2005, tobacco smoking and high blood pressure were responsible for an estimated 467,000 (95% confidence interval [CI] 436,000-500,000) and 395,000 (372,000-414,000) deaths, accounting for about one in five or six deaths in US adults. Overweight-obesity (216,000; 188,000-237,000) and physical inactivity (191,000; 164,000-222,000) were each responsible for nearly 1 in 10 deaths. High dietary salt (102,000; 97,000-107,000), low dietary omega-3 fatty acids (84,000; 72,000-96,000), and high dietary trans fatty acids (82,000; 63,000-97,000) were the dietary risks with the largest mortality effects. Although 26,000 (23,000-40,000) deaths from ischemic heart disease, ischemic stroke, and diabetes were averted by current alcohol use, they were outweighed by 90,000 (88,000-94,000) deaths from other cardiovascular diseases, cancers, liver cirrhosis, pancreatitis, alcohol use disorders, road traffic and other injuries, and violence. CONCLUSIONS Smoking and high blood pressure, which both have effective interventions, are responsible for the largest number of deaths in the US. Other dietary, lifestyle, and metabolic risk factors for chronic diseases also cause a substantial number of deaths in the US.", "link"=>"http://www.mendeley.com/research/preventable-causes-death-united-states-comparative-risk-assessment-dietary-lifestyle-metabolic-risk-15", "reader_count"=>667, "reader_count_by_academic_status"=>{"Unspecified"=>17, "Professor > Associate Professor"=>29, "Librarian"=>3, "Researcher"=>98, "Student > Doctoral Student"=>42, "Student > Ph. D. 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  • {"month"=>"9", "year"=>"2018", "pdf_views"=>"91", "xml_views"=>"5", "html_views"=>"360"}
  • {"month"=>"10", "year"=>"2018", "pdf_views"=>"99", "xml_views"=>"1", "html_views"=>"404"}
  • {"month"=>"11", "year"=>"2018", "pdf_views"=>"106", "xml_views"=>"3", "html_views"=>"405"}
  • {"month"=>"12", "year"=>"2018", "pdf_views"=>"82", "xml_views"=>"5", "html_views"=>"367"}
  • {"month"=>"1", "year"=>"2019", "pdf_views"=>"112", "xml_views"=>"1", "html_views"=>"440"}
  • {"month"=>"2", "year"=>"2019", "pdf_views"=>"88", "xml_views"=>"3", "html_views"=>"259"}
  • {"month"=>"3", "year"=>"2019", "pdf_views"=>"109", "xml_views"=>"5", "html_views"=>"185"}
  • {"month"=>"4", "year"=>"2019", "pdf_views"=>"93", "xml_views"=>"5", "html_views"=>"170"}
  • {"month"=>"5", "year"=>"2019", "pdf_views"=>"99", "xml_views"=>"4", "html_views"=>"248"}
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  • {"month"=>"7", "year"=>"2019", "pdf_views"=>"112", "xml_views"=>"16", "html_views"=>"249"}
  • {"month"=>"8", "year"=>"2019", "pdf_views"=>"119", "xml_views"=>"9", "html_views"=>"277"}
  • {"month"=>"9", "year"=>"2019", "pdf_views"=>"98", "xml_views"=>"7", "html_views"=>"363"}
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  • {"month"=>"12", "year"=>"2019", "pdf_views"=>"85", "xml_views"=>"6", "html_views"=>"196"}
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  • {"month"=>"10", "year"=>"2020", "pdf_views"=>"51", "xml_views"=>"2", "html_views"=>"131"}

Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/900317"], "description"=>"<p>The proportion of population and mortality effects in different exposure categories. We have not included dietary risks other than dietary salt in this table primarily because current guidelines do not recommend a specific level of intake.</p>a<p>Deaths assigned to diabetes mellitus in the vital statistics and deaths attributable to renal failure are included in the ≥7 mmol/l category because all individuals whose deaths are assigned to diabetes or diabetic renal failure would, by definition, have been diagnosed with diabetes disease, and hence have FPG ≥7 mmol/l.</p>", "links"=>[], "tags"=>["attributable", "deaths", "ranges", "categories", "defined", "thresholds"], "article_id"=>570768, "categories"=>["Biotechnology", "Medicine", "Information And Computing Sciences"], "users"=>["Goodarz Danaei", "Eric L. Ding", "Dariush Mozaffarian", "Ben Taylor", "Jürgen Rehm", "Christopher J. L. Murray", "Majid Ezzati"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000058.t010", "stats"=>{"downloads"=>5, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_risk_factor_exposure_and_attributable_deaths_by_ranges_or_categories_of_exposure_defined_using_common_clinical_and_public_health_thresholds_and_guidelines_/570768", "title"=>"Distribution of risk factor exposure and attributable deaths by ranges or categories of exposure defined using common clinical and public health thresholds and guidelines.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-28 00:12:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/900136"], "description"=>"a<p>We used ACS CPS-II as the source of RRs because the Smoking Impact Ratio (SIR), which was used as the exposure metric for tobacco smoking in the main analysis, is calculated using ACS CPS-II cohort and because the study provided separate RRs for different cancers and cardiovascular diseases by age. The CPS-II RRs were also adjusted for multiple potential confounders.</p>b<p>For these disease outcomes, RRs in the source were reported for all ages combined. We used median age at event and the age pattern of excess risk from IHD to estimate RRs for each age category.</p>c<p>This category includes lower respiratory tract infections and asthma.</p>", "links"=>[], "tags"=>["magnitudes", "risks", "smoking", "disease-specific"], "article_id"=>570587, "categories"=>["Biotechnology", "Medicine", "Information And Computing Sciences"], "users"=>["Goodarz Danaei", "Eric L. Ding", "Dariush Mozaffarian", "Ben Taylor", "Jürgen Rehm", "Christopher J. L. Murray", "Majid Ezzati"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000058.t006", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sources_and_magnitudes_of_relative_risks_for_the_effects_of_tobacco_smoking_on_disease_specific_mortality_/570587", "title"=>"Sources and magnitudes of relative risks for the effects of tobacco smoking on disease-specific mortality.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-28 00:09:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/900058"], "description"=>"a<p>Exposure categories were: Abstainer, a person not having had a drink containing alcohol within the last year; DI 0–19.99 g of pure alcohol daily (females) and 0–39.99 g (males); DII, 20–39.99 g (females) and 40–59.99 g (males); and DIII, >40 g (females) and >60 g (males). Binge drinking was defined as having at least one occasion of five or more drinks in the last month. For IHD, the categories refer to non-binge drinkers.</p>b<p>For these risk factor–disease pairs, RRs in the source were reported for all ages combined. We used median age at event and the age pattern of excess risk from smoking and the same disease to estimate RRs for each age category.</p>c<p>This category includes ICD-9 codes 210–239.</p>d<p>These odds ratios were used to estimate PAF as described in the <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#s2\" target=\"_blank\">Methods</a> section.</p>e<p>Used to estimated PAF for having drunk alcohol in the last 6 h before injury.</p>", "links"=>[], "tags"=>["magnitudes", "risks", "disease-specific"], "article_id"=>570508, "categories"=>["Biotechnology", "Medicine", "Information And Computing Sciences"], "users"=>["Goodarz Danaei", "Eric L. Ding", "Dariush Mozaffarian", "Ben Taylor", "Jürgen Rehm", "Christopher J. L. Murray", "Majid Ezzati"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000058.t004", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sources_and_magnitudes_of_relative_risks_for_the_effects_of_alcohol_use_on_disease_specific_mortality_/570508", "title"=>"Sources and magnitudes of relative risks for the effects of alcohol use on disease-specific mortality.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-28 00:08:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/900264"], "description"=>"<p>Numbers show percent in each age group or in each sex and the corresponding 95% confidence intervals of sampling uncertainty.</p>a<p>There is no sampling uncertainty for this outcome because all the deaths due to diabetes are by definition attributable to high blood glucose.</p>b<p>The negative proportions for alcohol use and cardiovascular diseases in older ages and in females occur because the protective effects are larger than the hazardous effects.</p>c<p>This category includes liver cirrhosis, acute and chronic pancreatitis, and alcohol use disorders.</p>d<p>We did not estimate sampling uncertainty for injury outcomes because the attributable deaths used data sources that did not report sampling uncertainty.</p>e<p>This category includes lower respiratory tract infections, asthma, and tuberculosis.</p>", "links"=>[], "tags"=>["cause-specific", "all-cause", "deaths", "attributable", "factors"], "article_id"=>570714, "categories"=>["Biotechnology", "Medicine", "Information And Computing Sciences"], "users"=>["Goodarz Danaei", "Eric L. Ding", "Dariush Mozaffarian", "Ben Taylor", "Jürgen Rehm", "Christopher J. L. Murray", "Majid Ezzati"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000058.t009", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_cause_specific_and_all_cause_deaths_attributable_to_risk_factors_by_age_group_and_by_sex_/570714", "title"=>"Distribution of cause-specific and all-cause deaths attributable to risk factors by age group and by sex.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-28 00:11:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/900021"], "description"=>"a<p>Omega-3 intake categories in the analysis were (1) 0 to <62.5; (2) 62.5 to <125; (3) 125 to <187.5; (4) 187.5 to <250; and (5) ≥250 mg/d of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA).</p>b<p>For each disease outcome, RRs in the source were reported for all ages combined. We used median age at event and the age pattern of excess risk for serum total cholesterol and the same disease to estimate RRs for each age category.</p>c<p>RRs were summarized via meta-regression across intake levels <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Greenland3\" target=\"_blank\">[79]</a>. When RRs were reported for fish intake, we converted the units to omega 3 intake using the average omega-3 content of one serving of fish estimated using NHANES 2003–2004.</p>", "links"=>[], "tags"=>["magnitudes", "risks", "categorical", "dietary", "factors", "disease-specific"], "article_id"=>570470, "categories"=>["Biotechnology", "Medicine", "Information And Computing Sciences"], "users"=>["Goodarz Danaei", "Eric L. Ding", "Dariush Mozaffarian", "Ben Taylor", "Jürgen Rehm", "Christopher J. L. Murray", "Majid Ezzati"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000058.t003", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sources_and_magnitudes_of_relative_risks_RRs_for_the_effects_of_categorical_dietary_risk_factors_on_disease_specific_mortality_/570470", "title"=>"Sources and magnitudes of relative risks (RRs) for the effects of categorical dietary risk factors on disease-specific mortality.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-28 00:07:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/900184"], "description"=>"a<p>See Danaei et al. <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Danaei1\" target=\"_blank\">[61]</a> for sensitivity to using RRs from systematic reviews of other epidemiological studies.</p>b<p>For these risk factor–disease pairs, RRs in the source were reported for all ages combined. We used median age at event and the age pattern of excess risk from another risk factor and the same disease (e.g., age pattern of total serum cholesterol and ischemic stroke was applied to LDL and ischemic stroke) or from the same risk factor and another disease (e.g., age pattern of excess risk for SBP and all cardiovascular diseases was applied to SBP and hypertensive disease) to estimate RRs for each age category.</p>c<p>We used a null association in those 70-y-old and older because RRs in two large meta-analyses of prospective studies <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Lewington2\" target=\"_blank\">[95]</a>, <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Lawes3\" target=\"_blank\">[97]</a> were not statistically significant from null, and did not show consistent benefits for lower total cholesterol in these ages. There is some evidence from clinical trials that statins reduce the risk of stroke in older ages <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Collins2\" target=\"_blank\">[98]</a>. However, statins may reduce stroke mortality through other, non-cholesterol mechanisms such as stabilization of atherosclerotic plaques <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Libby1\" target=\"_blank\">[99]</a>. In the sensitivity analysis for high LDL cholesterol and ischemic stroke, we used an RR of 1.12 in these age groups.</p>d<p>This category includes rheumatic heart disease, acute and subacute endocarditis, cardiomyopathy, other inflammatory cardiac diseases, valvular disorders, aortic aneurysm, pulmonary embolism, conduction disorders, peripheral vascular disorders, and other ill-defined cardiovascular diseases.</p>e<p>We used meta-analyses of studies with measured weight and height because using self-reported weight and height can lead to bias in estimated RRs. The correlation between self-reported and measured weight, as found in selected studies <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Willett2\" target=\"_blank\">[100]</a>, <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Manson2\" target=\"_blank\">[101]</a>, does not remove the possibility of bias because even with perfect correlation, the absolute bias in self-reported weight and height may be a function of its true value.</p>f<p>The RRs reported for Asian and Australia–New Zealand populations were not significantly different in this meta-analysis providing empirical evidence on absence of significant effect modification in the multiplicative scale by ethnicity. A meta-analysis of studies in Europe and North America included studies <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Whitlock1\" target=\"_blank\">[102]</a> with self-reported height and weight and was thus not used in this analysis. The RRs reported in that meta-analysis ranged from 1.02 to 1.26 and the average RR weighted by number of cases was 1.07 per kg/m<sup>2</sup> which is almost equal to the RR for 60- to 69-y-olds in this analysis.</p><p>APCSC, Asia-Pacific Cohorts Studies Collaboration; PSC, Prospective Studies Collaboration.</p>", "links"=>[], "tags"=>["magnitudes", "risks", "metabolic", "factors", "disease-specific"], "article_id"=>570627, "categories"=>["Biotechnology", "Medicine", "Information And Computing Sciences"], "users"=>["Goodarz Danaei", "Eric L. Ding", "Dariush Mozaffarian", "Ben Taylor", "Jürgen Rehm", "Christopher J. L. Murray", "Majid Ezzati"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000058.t007", "stats"=>{"downloads"=>3, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sources_and_magnitudes_of_relative_risks_for_the_effects_of_metabolic_risk_factors_on_disease_specific_mortality_/570627", "title"=>"Sources and magnitudes of relative risks for the effects of metabolic risk factors on disease-specific mortality.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-28 00:10:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/899722"], "description"=>"<p>Data are shown for both sexes combined (upper graph); men (middle graph); and women (lower graph). See <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed-1000058-t008\" target=\"_blank\">Table 8</a> for 95% CIs. Notes: We used RRs for blood pressure, LDL cholesterol, and FPG that were adjusted for regression dilution bias using studies that had repeated exposure measurement <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Lawes1\" target=\"_blank\">[7]</a>,<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Lewington1\" target=\"_blank\">[11]</a>,<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Law2\" target=\"_blank\">[12]</a>; for blood pressure and LDL cholesterol, the adjusted magnitude is supported by effect sizes from randomized studies <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Law3\" target=\"_blank\">[13]</a>,<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Collins1\" target=\"_blank\">[14]</a>. Evidence from a large prospective study using multiple measurements of weight and height showed that regression dilution bias did not affect the RRs for BMI, possibly because there is less variability <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Reeves1\" target=\"_blank\">[15]</a>. RRs for dietary salt and PUFA were from intervention studies, and hence unlikely to be affected by regression dilution bias. RRs for dietary trans fatty acids were primarily from studies that had used cumulative averaging of repeated measurements <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Mozaffarian1\" target=\"_blank\">[16]</a> that reduces but may not fully correct for regression dilution bias. RRs for physical inactivity, alcohol use, smoking, and dietary omega-3 fatty acids and fruits and vegetables were not corrected for regression dilution bias due to insufficient current information from epidemiological studies on exposure measurement error and variability, which is especially important when error and variability of self-reported exposure may themselves differ across studies. Regression dilution bias often, although not always, underestimates RRs in multivariate analysis <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Clarke1\" target=\"_blank\">[48]</a>. <sup>a</sup>The figures show deaths attributable to the total effects of each individual risk. There is overlap between the effects of risk factors because of multicausality and because the effects of some risk factors are partly mediated through other risks. Therefore, the number of deaths attributable to individual risks cannot be added. <sup>b</sup>The effect of high dietary salt on cardiovascular diseases was estimated through its measured effects on systolic blood pressure. <sup>c</sup>The protective effects of alcohol use on cardiovascular diseases are its net effects. Regular moderate alcohol use is protective for IHD, ischemic stroke, and diabetes, but any use is hazardous for hypertensive disease, hemorrhagic stroke, cardiac arrhythmias, and other cardiovascular diseases. NCD, noncommunicable diseases.</p>", "links"=>[], "tags"=>["attributable"], "article_id"=>570178, "categories"=>["Biotechnology", "Medicine", "Information And Computing Sciences"], "users"=>["Goodarz Danaei", "Eric L. Ding", "Dariush Mozaffarian", "Ben Taylor", "Jürgen Rehm", "Christopher J. L. Murray", "Majid Ezzati"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000058.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Deaths_attributable_to_total_effects_of_individual_risk_factors_by_disease_/570178", "title"=>"Deaths attributable to total effects of individual risk factors, by disease.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-28 00:02:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/900223"], "description"=>"a<p>Excludes uncertainty in intentional and unintentional injury outcomes because the attributable deaths used data sources that did not report sampling uncertainty.</p>", "links"=>[], "tags"=>["causes", "attributable", "factors", "intervals", "sampling"], "article_id"=>570672, "categories"=>["Biotechnology", "Medicine", "Information And Computing Sciences"], "users"=>["Goodarz Danaei", "Eric L. Ding", "Dariush Mozaffarian", "Ben Taylor", "Jürgen Rehm", "Christopher J. L. Murray", "Majid Ezzati"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000058.t008", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Deaths_from_all_causes_thousands_of_deaths_attributable_to_risk_factors_and_the_95_confidence_intervals_of_their_sampling_uncertainty_/570672", "title"=>"Deaths from all causes (thousands of deaths) attributable to risk factors and the 95% confidence intervals of their sampling uncertainty.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-28 00:11:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/445256", "https://ndownloader.figshare.com/files/445344"], "description"=>"<div><h3>Background</h3><p>Knowledge of the number of deaths caused by risk factors is needed for health policy and priority setting. Our aim was to estimate the mortality effects of the following 12 modifiable dietary, lifestyle, and metabolic risk factors in the United States (US) using consistent and comparable methods: high blood glucose, low-density lipoprotein (LDL) cholesterol, and blood pressure; overweight–obesity; high dietary trans fatty acids and salt; low dietary polyunsaturated fatty acids, omega-3 fatty acids (seafood), and fruits and vegetables; physical inactivity; alcohol use; and tobacco smoking.</p><h3>Methods and Findings</h3><p>We used data on risk factor exposures in the US population from nationally representative health surveys and disease-specific mortality statistics from the National Center for Health Statistics. We obtained the etiological effects of risk factors on disease-specific mortality, by age, from systematic reviews and meta-analyses of epidemiological studies that had adjusted (i) for major potential confounders, and (ii) where possible for regression dilution bias. We estimated the number of disease-specific deaths attributable to all non-optimal levels of each risk factor exposure, by age and sex. In 2005, tobacco smoking and high blood pressure were responsible for an estimated 467,000 (95% confidence interval [CI] 436,000–500,000) and 395,000 (372,000–414,000) deaths, accounting for about one in five or six deaths in US adults. Overweight–obesity (216,000; 188,000–237,000) and physical inactivity (191,000; 164,000–222,000) were each responsible for nearly 1 in 10 deaths. High dietary salt (102,000; 97,000–107,000), low dietary omega-3 fatty acids (84,000; 72,000–96,000), and high dietary trans fatty acids (82,000; 63,000–97,000) were the dietary risks with the largest mortality effects. Although 26,000 (23,000–40,000) deaths from ischemic heart disease, ischemic stroke, and diabetes were averted by current alcohol use, they were outweighed by 90,000 (88,000–94,000) deaths from other cardiovascular diseases, cancers, liver cirrhosis, pancreatitis, alcohol use disorders, road traffic and other injuries, and violence.</p><h3>Conclusions</h3><p>Smoking and high blood pressure, which both have effective interventions, are responsible for the largest number of deaths in the US. Other dietary, lifestyle, and metabolic risk factors for chronic diseases also cause a substantial number of deaths in the US.</p><h3></h3><p><em>Please see later in the article for Editors' Summary</em></p></div>", "links"=>[], "tags"=>["preventable", "causes", "united", "comparative", "metabolic", "factors"], "article_id"=>147758, "categories"=>["Biotechnology", "Medicine", "Information And Computing Sciences"], "users"=>["Goodarz Danaei", "Eric L. Ding", "Dariush Mozaffarian", "Ben Taylor", "Jürgen Rehm", "Christopher J. L. Murray", "Majid Ezzati"], "doi"=>["https://dx.doi.org/10.1371/journal.pmed.1000058.s001", "https://dx.doi.org/10.1371/journal.pmed.1000058.s002"], "stats"=>{"downloads"=>8, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_Preventable_Causes_of_Death_in_the_United_States_Comparative_Risk_Assessment_of_Dietary_Lifestyle_and_Metabolic_Risk_Factors/147758", "title"=>"The Preventable Causes of Death in the United States: Comparative Risk Assessment of Dietary, Lifestyle, and Metabolic Risk Factors", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-04-28 02:09:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/899977"], "description"=>"a<p>For these risk factor–disease pairs, RRs in the source were reported for all ages combined. We used median age at event and the age pattern of excess risk for serum total cholesterol and IHD to estimate RRs for each age category.</p>b<p>The interventions studies replaced dietary SFA with PUFA, hence the RRs measure the effect of replacement. Effects of replacing PUFA for other macronutrients have not been evaluated in randomized interventions studies. However, evidence from cohort studies suggests that replacement of PUFA for carbohydrates, but not carbohydrates for SFA, would produce similar benefits <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Jakobsen1\" target=\"_blank\">[78]</a>, indicating that the measured benefits are due to PUFA.</p>", "links"=>[], "tags"=>["magnitudes", "risks", "dietary", "factors", "disease-specific"], "article_id"=>570427, "categories"=>["Biotechnology", "Medicine", "Information And Computing Sciences"], "users"=>["Goodarz Danaei", "Eric L. Ding", "Dariush Mozaffarian", "Ben Taylor", "Jürgen Rehm", "Christopher J. L. Murray", "Majid Ezzati"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000058.t002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sources_and_magnitudes_of_relative_risks_for_the_effects_of_continuous_dietary_risk_factors_on_disease_specific_mortality_/570427", "title"=>"Sources and magnitudes of relative risks for the effects of continuous dietary risk factors on disease-specific mortality.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-28 00:07:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/899840"], "description"=>"<p>Data are shown for both sexes combined (upper graph); men (middle graph); and women (lower graph). See <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed-1000058-g001\" target=\"_blank\">Figure 1</a> notes.</p>", "links"=>[], "tags"=>["attributable", "70", "years"], "article_id"=>570295, "categories"=>["Biotechnology", "Medicine", "Information And Computing Sciences"], "users"=>["Goodarz Danaei", "Eric L. Ding", "Dariush Mozaffarian", "Ben Taylor", "Jürgen Rehm", "Christopher J. L. Murray", "Majid Ezzati"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000058.g002", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Deaths_attributable_to_total_effects_of_individual_risk_factors_by_disease_in_those_below_70_years_of_age_/570295", "title"=>"Deaths attributable to total effects of individual risk factors, by disease in those below 70 years of age.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-04-28 00:04:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/900100"], "description"=>"<p>Categories of physical activity were defined as below using responses to questions regarding physical activity during the past 30 d: inactive, no moderate or vigorous physical activity; low-active, <2.5 h/wk of moderate activity or <600 met·min/wk; moderately active: either ≥2.5 h/wk of moderate activity or ≥1 h of vigorous activity and ≥600 met·min/wk; highly active: ≥1 h/wk of vigorous activity and ≥1,600 met·min/wk.</p>a<p>The meta-analysis of RRs for physical inactivity used three categories: inactive, insufficiently active, and recommended-level active. For this analysis, we re-scaled the RRs to set the highly active group as the reference category. The ratio of excess risk from recommended-level active to high-active was from Manson et al. for IHD <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Manson1\" target=\"_blank\">[69]</a>, Hu et al. for ischemic stroke <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Hu2\" target=\"_blank\">[70]</a>, Patel et al. 2003 for breast cancer <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Patel2\" target=\"_blank\">[71]</a>, and Chao et al. for colon cancer <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Chao1\" target=\"_blank\">[72]</a>.</p>", "links"=>[], "tags"=>["magnitudes", "risks", "inactivity", "disease-specific"], "article_id"=>570552, "categories"=>["Biotechnology", "Medicine", "Information And Computing Sciences"], "users"=>["Goodarz Danaei", "Eric L. Ding", "Dariush Mozaffarian", "Ben Taylor", "Jürgen Rehm", "Christopher J. L. Murray", "Majid Ezzati"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000058.t005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sources_and_magnitudes_of_relative_risks_for_the_effects_of_physical_inactivity_on_disease_specific_mortality_/570552", "title"=>"Sources and magnitudes of relative risks for the effects of physical inactivity on disease-specific mortality.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-28 00:09:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/899930"], "description"=>"a<p>Outcomes in italics are those for which the effects were not quantified in the main analysis due to weaker evidence on causality (e.g. tobacco smoking and colorectal cancer or high blood glucose and cancers) or because there were very few deaths from the disease (e.g. high BMI and gallbladder cancer).</p>b<p>We evaluated sensitivity to the choice of exposure metric by using total cholesterol instead of LDL-cholesterol (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058.s001\" target=\"_blank\">Table S1</a>).</p>c<p>Two alternative TMREDs for LDL cholesterol with means of 1.6 mmol/l and 2.3 mmol/l were examined in sensitivity analysis (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058.s001\" target=\"_blank\">Table S1</a>).</p>d<p>This category includes rheumatic heart disease, acute and subacute endocarditis, cardiomyopathy, other inflammatory cardiac diseases, valvular disorders, aortic aneurysm, pulmonary embolism, conduction disorders, peripheral vascular disorders, and other ill-defined cardiovascular diseases.</p>e<p>We did not include some of the cancers that were found to have significant association with BMI in a recent meta-analysis <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Renehan1\" target=\"_blank\">[17]</a> either because there were very few deaths in the US (adenocarcinoma of esophagus and gallbladder cancer) or because there was not strong evidence on a causal effect from other studies (leukemia and multiple myeloma). We included non-Hodgkin lymphoma in a sensitivity analysis (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058.s001\" target=\"_blank\">Table S1</a>).</p>f<p>The NHANES rounds in 2003–2006 include a 2-d dietary intake survey and could be used to estimate dietary trans fatty acids. However, a reliable source for the trans fat content of each food item was not available to us. We have used the intake estimates in the Continuing Survey of Food Intakes by Individuals (CSFII) 1989–1991 <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Allison1\" target=\"_blank\">[68]</a> in our analysis.</p>g<p>Omega-3 intake categories in the analysis were: 0 to <62.5; 62.5 to <125; 125 to <187.5; 187.5 to <250; and ≥250 mg/d of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA).</p>h<p>The effect of reduction in salt intake on SBP and the effect of subsequent decline in SBP on the relevant disease outcomes were estimated at the individual level to account for possible correlation between salt intake and SBP.</p>i<p>We evaluated sensitivity to the assumption of normal distribution for fruit and vegetable intake (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058.s001\" target=\"_blank\">Table S1</a>).</p>j<p>Exposure categories were: Abstainer, a person not having had a drink containing alcohol within the last year; DI, 0–19.99 g of pure alcohol daily (females) and 0–39.99 g (males); DII, 20–39.99 g (females) and 40–59.99 g (males); and DIII, >40 g (females) and >60 g (males). Binge drinking was defined as having at least one occasion of five or more drinks in the last month.</p>k<p>An alternative TMRED for alcohol use as regular drinking of small amounts of alcohol is considered in sensitivity analysis (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058.s001\" target=\"_blank\">Table S1</a>).</p>l<p>This category includes ICD-9 codes 210–239.</p>m<p>This category includes ICD-9 codes 291, 303, and 305.0.</p>n<p>Categories of physical activity were defined as below using responses to questions regarding physical activity during the past 30 d: inactive, no moderate or vigorous physical activity; low-active, <2.5 h/wk of moderate activity or <600 met·min/wk; moderately active, either ≥2.5 h/wk of moderate activity or ≥1 h of vigorous activity and ≥600 met·min/wk; highly active, ≥1 h/wk of vigorous activity and ≥1,600 met·min/wk.</p>o<p>This TMRED is based on multiple prospective studies that report beneficial effects of physical activity continuing above the current recommended levels <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Manson1\" target=\"_blank\">[69]</a>–<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-Chao1\" target=\"_blank\">[72]</a>.</p>p<p>We also calculated the mortality effects of tobacco smoking using the prevalence of current and former smokers, as used by Smoking-Attributable Mortality, Morbidity, and Economic Costs (SAMMEC; <a href=\"http://apps.nccd.cdc.gov/sammec\" target=\"_blank\">http://apps.nccd.cdc.gov/sammec</a>) <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-US1\" target=\"_blank\">[73]</a>, in a sensitivity analysis (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058.s001\" target=\"_blank\">Table S1</a>).</p>q<p>This category includes lower respiratory tract infections and asthma.</p>r<p>Evidence of a causal association between tobacco smoking and colorectal cancer was classified as suggestive in the 2004 Report of the US Surgeon General <a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058-US1\" target=\"_blank\">[73]</a>. The 2004 report also excluded hypertensive disease from the outcomes considered in smoking-attributable mortality. Therefore, colorectal cancer and hypertensive disease were not included in the main analysis, but were included in sensitivity analysis (<a href=\"http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1000058#pmed.1000058.s001\" target=\"_blank\">Table S1</a>).</p>", "links"=>[], "tags"=>["factors", "theoretical-minimum-risk", "sources"], "article_id"=>570380, "categories"=>["Biotechnology", "Medicine", "Information And Computing Sciences"], "users"=>["Goodarz Danaei", "Eric L. Ding", "Dariush Mozaffarian", "Ben Taylor", "Jürgen Rehm", "Christopher J. L. Murray", "Majid Ezzati"], "doi"=>"https://dx.doi.org/10.1371/journal.pmed.1000058.t001", "stats"=>{"downloads"=>6, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Risk_factors_in_this_analysis_their_exposure_variables_theoretical_minimum_risk_exposure_distributions_disease_outcomes_and_data_sources_for_exposure_/570380", "title"=>"Risk factors in this analysis, their exposure variables, theoretical-minimum-risk exposure distributions, disease outcomes, and data sources for exposure.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-04-28 00:06:20"}

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Relative Metric

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