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

Art. 1.1475/ringraziamenti

Abstract. -Tuberculosis (TB) is still a leading cause of death worldwide. Almost a third of the world's population is infected with TB bacilli, and each year approximately 8 million people develop active tuberculosis and 2 million die as a result. However, there are few studies of long-term TB treatment outcomes from Directly Observed Therapy, Shor t-course (DOTS) programs in high-burden settings and particularly settings of high drug resistance. This study is a systematic review to evidence the incidence and prevalence of latent TB infection (LTBI) and disease and to evaluate the impact of various preventive strategies that have been attempted. To identify relevant studies, we searched electronic databases and journals, and contacted experts in the field. This review demonstrates that, various types of tuberculosis have different imaging findings, and typical computed tomography (CT) and magnetic resonance (MG) findings can suggest the diagnosis. Available evidence reinforces the need to design and implement simple, effective, and affordable tuberculosis infection-control programs in health-care facilities in our countries. With the revision of all the data's, we are able to conclude that the controlling of tuberculosis by human beings is yet not achieved. So, there is an urgency to develop awareness amongst the individuals and also a new drugs regimen for the proper treatment of tuberculosis

Quantification of T98G glioma cell growth after treatment with UCN-01 or staurosporine using the ColonyArea.

<p>Colony formation of T98G human glioma cells was studied after treatment with increasing concentrations of the staurosporine derivative UCN-01 or staurosporine (STS). Image data were analyzed using ColonyArea. Its output parameters were then used to generate dose response curves and determine the half maximal inhibitory concentrations (IC₅₀) of the compounds. (<b>A</b>) Examples of dose response curves using the colony area percentage; IC₅₀ = 35.8±4.5 nM (UCN-01) and IC₅₀ = 16.4±1.8 nM (STS). (<b>B</b>) Examples of dose response curves using the colony intensity percentage; IC₅₀ = 37.5±5.7 nM (UCN-01) and IC₅₀ = 16.1±1.4 nM (STS). Dots correspond to averages and error bars to the standard deviations of measurements from four wells. Curves were fitted using <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone.0092444.e005" target="_blank">equation (<b>3</b></a><b>)</b>. Additional independent experimental repeats can be found in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone.0092444.s001" target="_blank">Figures S1</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone.0092444.s002" target="_blank">S2</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone.0092444.s003" target="_blank">S3</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone.0092444.s004" target="_blank">S4</a></b>. (<b>C</b>) Correlation analysis of results obtained using the colony area percentage and results obtained using the colony intensity percentage. Regression lines are drawn and the Pearson product moment correlation coefficients ‘r’ is displayed for each data set.</p

Determination of the background threshold.

<p>(<b>A</b>) Two 8-bit greyscale images of wells showing high (<b>left</b>) and lower (<b>right</b>) intensities of cell staining with similar colony density. (<b>B</b>) For each case, the colony area percentage is plotted as a function of the applied intensity threshold. At this point, the colony area percentage corresponds to the percentage of the well area that is selected based on the criterion that each pixel in the area has an intensity value below a given intensity threshold. (<b>C</b>) First and (<b>D</b>) second derivatives of the colony area percentage function shown in (<b>B</b>), which allow identifying the correct intensity threshold. After the correct threshold has been identified, the colony area parameter gives the percentage of the well area that is occupied by cells. In all plots (<b>B–D</b>), the highlighted region represents the intensity range where only cells are selected. Above that intensity threshold the background starts to be included, which identifies this intensity value as the background threshold.</p

Flow chart of the processing steps in the ColonyArea plugin.

<p>Steps performed by the user are represented by ovals and the grey shapes are those requiring user input. All other shapes represent steps performed by the three macros Colony_area (rounded rectangles), Colony_thresholder (hexagons) and Colony_measurer (stars) that are packaged as one plugin file.</p

Removal of the background.

<p>(<b>A</b>) 8-bit greyscale images of individual wells showing different levels of colony formation of T98G cells treated with indicated concentrations of staurosporine. (<b>B</b>) Same individual wells after thresholding and background removal by the macro “Colony_thresholder”. Color bar represents the intensity scale displayed in the thresholded wells. Zero intensity (white) corresponds to areas where no cells were identified (background).</p

Comparison of the ColonyArea quantification with an absorption method.

<p>The identical wells that were quantified with ColonyArea in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone-0092444-g005" target="_blank"><b>Figure 5</b></a> were analyzed using a method where the absorption of the crystal violet dye that was washed out from labeled cells is measured. (<b>A</b>) Examples of dose response curves using the optical density from the absorbance measurements of the dye; IC₅₀ = 36.1±6.0 nM (UCN-01) and IC₅₀ = 6.2±0.5 nM (STS). Dots correspond to averages and error bars to the standard deviations of the exact same four wells that were analyzed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone-0092444-g005" target="_blank"><b>Figure 5</b></a>. Curves were fitted using <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone.0092444.e005" target="_blank">equation (<b>3</b></a>). (<b>B</b>) Correlation analysis of data from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone-0092444-g005" target="_blank"><b>Figure 5A</b></a> that were obtained using the colony area percentage and those obtained using the absorbance in (<b>A</b>). (<b>C</b>) Correlation analysis of colony intensity percentage from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone-0092444-g005" target="_blank"><b>Figure 5B</b></a> and the corresponding absorbance data. Regression lines are drawn and the Pearson product moment correlation coefficients ‘r’ is displayed for each set of data. Additional correlative analysis of experimental repeats can be found in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone.0092444.s001" target="_blank">Figures S1</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone.0092444.s002" target="_blank">S2</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone.0092444.s003" target="_blank">S3</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092444#pone.0092444.s004" target="_blank">S4</a></b>.</p

Identification of wells and generation of a well image stack.

<p>(<b>A</b>) Scanned image of a 12-well plate showing different levels of colony formation of drug treated T98G human glioblastoma cells. (<b>B</b>) Same image as in (A) after automatic identification of the wells. The image was converted into an 8-bit greyscale and spaces between wells were removed using a mask. (<b>C</b>) Each well image was then concentrically cropped and added to an image stack to allow for the analysis of each well individually.</p