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. 2017 Sep 16;390(10100):1345-1422.
doi: 10.1016/S0140-6736(17)32366-8.

Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016

Collaborators

Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016

GBD 2016 Risk Factors Collaborators. Lancet. .

Erratum in

  • Department of Error.
    [No authors listed] [No authors listed] Lancet. 2017 Oct 14;390(10104):1736. doi: 10.1016/S0140-6736(17)32485-6. Epub 2017 Sep 18. Lancet. 2017. PMID: 28935160 Free PMC article. No abstract available.
  • Department of Error.
    [No authors listed] [No authors listed] Lancet. 2017 Oct 28;390(10106):e38. doi: 10.1016/S0140-6736(17)32649-1. Epub 2017 Oct 13. Lancet. 2017. PMID: 29032996 Free PMC article. No abstract available.

Abstract

Background: The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of risk factor exposure and attributable burden of disease. By providing estimates over a long time series, this study can monitor risk exposure trends critical to health surveillance and inform policy debates on the importance of addressing risks in context.

Methods: We used the comparative risk assessment framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2016. This study included 481 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk (RR) and exposure estimates from 22 717 randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources, according to the GBD 2016 source counting methods. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. Finally, we explored four drivers of trends in attributable burden: population growth, population ageing, trends in risk exposure, and all other factors combined.

Findings: Since 1990, exposure increased significantly for 30 risks, did not change significantly for four risks, and decreased significantly for 31 risks. Among risks that are leading causes of burden of disease, child growth failure and household air pollution showed the most significant declines, while metabolic risks, such as body-mass index and high fasting plasma glucose, showed significant increases. In 2016, at Level 3 of the hierarchy, the three leading risk factors in terms of attributable DALYs at the global level for men were smoking (124·1 million DALYs [95% UI 111·2 million to 137·0 million]), high systolic blood pressure (122·2 million DALYs [110·3 million to 133·3 million], and low birthweight and short gestation (83·0 million DALYs [78·3 million to 87·7 million]), and for women, were high systolic blood pressure (89·9 million DALYs [80·9 million to 98·2 million]), high body-mass index (64·8 million DALYs [44·4 million to 87·6 million]), and high fasting plasma glucose (63·8 million DALYs [53·2 million to 76·3 million]). In 2016 in 113 countries, the leading risk factor in terms of attributable DALYs was a metabolic risk factor. Smoking remained among the leading five risk factors for DALYs for 109 countries, while low birthweight and short gestation was the leading risk factor for DALYs in 38 countries, particularly in sub-Saharan Africa and South Asia. In terms of important drivers of change in trends of burden attributable to risk factors, between 2006 and 2016 exposure to risks explains an 9·3% (6·9-11·6) decline in deaths and a 10·8% (8·3-13·1) decrease in DALYs at the global level, while population ageing accounts for 14·9% (12·7-17·5) of deaths and 6·2% (3·9-8·7) of DALYs, and population growth for 12·4% (10·1-14·9) of deaths and 12·4% (10·1-14·9) of DALYs. The largest contribution of trends in risk exposure to disease burden is seen between ages 1 year and 4 years, where a decline of 27·3% (24·9-29·7) of the change in DALYs between 2006 and 2016 can be attributed to declines in exposure to risks.

Interpretation: Increasingly detailed understanding of the trends in risk exposure and the RRs for each risk-outcome pair provide insights into both the magnitude of health loss attributable to risks and how modification of risk exposure has contributed to health trends. Metabolic risks warrant particular policy attention, due to their large contribution to global disease burden, increasing trends, and variable patterns across countries at the same level of development. GBD 2016 findings show that, while it has huge potential to improve health, risk modification has played a relatively small part in the past decade.

Funding: The Bill & Melinda Gates Foundation, Bloomberg Philanthropies.

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Figures

Figure 1
Figure 1
Relationship between SEVs and SDI for the three metabolic, behavioural, and environmental or occupational risk factors that are responsible for the largest number of attributable DALYs globally Each point corresponds to a country in either 1990 (red) or 2016 (blue). Pearson correlation coefficients have been estimated to summarise the relationship between SEVs and SDI in 1990 and in 2016. SEVs=summary exposure values. SDI=Socio-demographic Index. DALYs=disability-adjusted life-years.
Figure 2
Figure 2
DALYs attributable to all Level 2 risk factors apportioned by Level 2 cause for each risk, both sexes combined, 2016, at the global level (A); for low SDI countries (B); for low-middle SDI countries (C); for middle SDI countries (D); for middle-high SDI countries (E); and for high SDI countries (F) DALYs from causes attributable to each risk factor are shown in different colours. Cutoffs on the SDI scale for the quintiles were selected based on examining the entire distribution of locations between 1980 and 2016. DALYs=disability-adjusted life-years. SDI=Socio-demographic Index.
Figure 3
Figure 3
Leading 30 Level 3 risk factors by attributable DALYs at the global level, 1990, 2006, and 2016, for males (A) and females (B) Risks are connected by lines between time periods. Behavioural risk factors are shown in red, environmental risks in blue, and metabolic risks in green. For the time period of 1990 to 2006 and for 2006–16, three measures of change are shown: percent change in the number of DALYs, percent change in the all-age DALY rate, and percent change in the age-standardised DALY rate. Statistically significant increases or decreases are shown in bold (p<0·05). DALYs=disability-adjusted life-years.
Figure 4
Figure 4
Relationship between attributable DALYs in 2016 for Level 3 risk factors and annualised rate of change in SEV, at the global level, both sexes combined, 1990–2016 DALYs are represented on a logarithmic scale. Risks shown exhibited a statistically significant change in SEV between 1990 and 2016. The following six risks, each of which is responsible for fewer than 100 thousand DALYs, are not shown: occupational exposure to benzene, beryllium, cadmium, chromium, formaldehyde, and trichloroethylene. DALYs=disability-adjusted life-years. SEV=summary exposure value. Ambient PM=ambient particulate matter pollution. Alcohol=alcohol use. Arsenic=occupational exposure to arsenic. Asbestos=occupational exposure to asbestos. Asthmagens=occupational asthmagens. BMD=low bone mineral density. BMI=high body-mass index. Calcium=diet low in calcium. Cholesterol=high total cholesterol. Diesel=occupational exposure to diesel engine exhaust. Disc breast=discontinued breastfeeding. Drugs=drug use. Ergonomics=occupational ergonomic factors. Fibre=diet low in fibre. FPG=high fasting plasma glucose. Fruits=diet low in fruits. Handwashing=no access to handwashing facility. Household air=household air pollution from solid fuels. Impaired kidney=impaired kidney function. IPV=intimate partner violence. Iron=iron deficiency. Lead=lead exposure. Legumes=diet low in legumes. Milk=diet low in milk. Nickel=occupational exposure to nickel. Noise=occupational noise. Nuts and seeds=diet low in nuts and seeds. Occupational SHS=occupational exposure to second-hand smoke. Omega 3=diet low in seafood omega 3 fatty acids. Ozone=ambient ozone pollution. PAH=occupational exposure to polycyclic aromatic hydrocarbons. Part breastfeeding=non-exclusive breastfeeding. Physical activity=low physical activity. PM, gases, and fumes=occupational particulate matter, gases, and fumes. Processed meat=diet high in processed meat. PUFA=diet low in polyunsaturated fatty acids. Radon=residential radon. Red meat=diet high in red meat. Sanitation=unsafe sanitation. SBP=high systolic blood pressure. Sexual abuse=childhood sexual abuse. SHS=second-hand smoke. Silica=occupational exposure to silica. Smokeless=smokeless tobacco. Sodium=diet high in sodium. Stunting=child stunting. Sugar-sweetened beverages=diet high in sugar-sweetened beverages. Sulfuric acid=occupational exposure to sulfuric acid. Transfatty acids=diet high in transfatty acids. Underweight=child underweight. Vegetables=diet low in vegetables. Vitamin A=vitamin A deficiency. Wasting=child wasting. Water=unsafe water source. Whole grains=diet low in whole grains. Zinc=zinc deficiency.
Figure 5
Figure 5
Percent change in deaths (A) and DALYs (B) at the global level, 2006–16, due to population growth, population ageing, trends in exposure to all risks included in GBD 2016, and and all other (risk-deleted or residual) factors Results are shown for all causes combined; communicable, maternal, neonatal, and nutritional diseases; non-communicable diseases; and injuries. DALYs=disability-adjusted life-years.
Figure 6
Figure 6
Percent change in all-cause DALYs, by age, at the global level, 2006-2016, due to the following drivers: population growth, population ageing, trends in exposure to all risks included in GBD 2016, and all other factors DALYs=disability-adjusted life-years.

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