Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions. Funding: Bill & Melinda Gates Foundation

Color evaluation of carbon monoxide treated porcine blood

The stability of liquid porcine blood, treated with carbon monoxide (CO) at different pH values (7.40, 6.70, and 6.00) up to its complete saturation, was studied. Lowering the pH from 7.40 to 6.70 resulted in a decrease in the amount of CO necessary to obtain 100% carboxyhemoglobin. Further pH lowering to 6.00 did not result in additional reduction in the amount of gas. During 4 days of refrigerated storage CO treated liquid blood maintained, at every pH, a more stable and attractive red color than fresh blood, which was a result of an increase (P0.05) on L* (lightness) value. Hue (h*) and chroma (C*) decreased in the untreated blood but not in the CO-treated blood. The results indicate that blood saturation with CO yields a product having greater potential for use in meat products without compromising its visual appearance

Composition and color stability of carbon monoxide treated dried porcine blood

Color stability of swine blood was studied over 12 weeks of storage in plastic bags, after pH (7.40, 6.70, or 6.00) adjustment, saturation with carbon monoxide (CO) and spray-drying. CO-treated dried blood presented a redder color and higher reflectance between 610 and 700 nm, compared to a brownish-red color and lower reflectance of untreated samples. As indicated by reflectance spectra, blood pH adjustment did not influence (P > 0.05) the initial color of dried blood but influenced (P < 0.05) its color stability (browning index). During storage, CO-treated blood showed a reduction in reflectance percentages as well as in CIE L∗ and a∗ values, which was more pronounced in polyethylene (OTR = 4130 cm3/m2/day/atm) packaged samples. After 12 weeks of storage, CO-treated samples packaged in high OTR bags presented color indexes similar to those of the untreated dried samples. CO-treated samples packaged in nylon-polyethylene (OTR = 30–60 cm3/m2/day/atm) bags showed a smaller rate of discoloration and color difference (ΔE∗) between the CO-treated and untreated samples. Even with some darkening, packaging CO-treated dry blood in low OTR bags still gives an acceptable reddish color after 12 weeks of storage while untreated dry blood has a brownish color just after drying

Evolutionary Design Of Neurofuzzy Networks For Pattern Classification

We consider a neural network based fuzzy system model whose basic processing unit consists of two types of generic logic (OR and AND) neurons. The net is structured into a multilayer topology and trained by a competitive learning algorithm, together with a genetic algorithm approach to select the most suitable triangular norms and co-norms that model the logic neurons. The main features of the system include: automatic rule generation and selection, learning capability, processing time independent of the input space partition, and automatic selection of the t-norms and s-norms that model the basic logic operators (OR, AND) encountered in the theory of fuzzy sets. Four benchmark problems are considered to compare the performance of the proposed method with those produced by alternative strategies. © 1999 IEEE.212371244Caminhas, W.M., Tavares, H.M.F., Gomide, F.A.C., Competitive learning of fuzzy, logical neural networks (1995) Proc. of the Sixth International Fuzzy Systems Association World Congress, 2, pp. 639-642. , São Paulo, BrazilCaminhas, W.M., Tavares, H.M.F., Gomide, F.A.C., A neural fuzzy approach for fault diagnosis in dynamic systems (1996) Proc. Information Processing and Management of Uncertainty on Knowledge-based Systems - IPMU'96, 1, pp. 175-180. , Granada, SpainFischer, R.A., The use of multiple measurements in taxonomic problems (1936) Annals of Eugenics, 7, pp. 170-188Hirota, K., Pedrycz, W., OR/AND neuron in modeling fuzzy set connectives (1994) IEEE Trans, on Fuzzy Systems, 2 (2), pp. 151-161Kohonen, T., Self-organized formation of topologically correct feature maps (1982) Biological Cybernetics, 43, pp. 59-69Kohonen, T., Improved versions of learning vector quantization (1990) International Joint Conf. on Neural Networks, pp. 545-550. , San Diego, CAMichalewicz, Z., (1996) Genetic Algorithms + Data Structures = Evolution Programs, , 3rd edition, SpringerMitchell, M., (1996) An Introduction to Genetic Algorithms, , MIT Press, Cambridge MassachussetsNozaki, K., Ischibuchi, H., Tanaka, H., Adaptive fuzzy rule-based classification systems (1996) IEEE Trans, on Fuzzy Systems, 4 (3), pp. 238-250Pedrycz, W., Fuzzy sets in pattern recognition: Methodology and methods (1990) Pattern Recognition, 23, pp. 121-146Pedrycz, W., Gomide, F., (1998) An Introduction to Fuzzy Sets: Analysis and Design, , MIT Press, Cambridge, Massachusset