Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Biosupercapacitors for Implantable Bioelectronics & Portable Microfluidic Devices for Prostate Cancer Biomarker Detection and DNA Damage Screening

Cardiovascular diseases and cancer are the top two leading causes of death in the United States according to the U.S. department of health and human services. Fast growing technologies are being developed to early diagnose and/or treat both heart diseases and cancers. One of the most successful cardiovascular devices is the cardiac pacemaker. Although cardiac pacemakers have been used by millions of patients worldwide, these pacemakers still suffer from several limitations because of the power source. Like other electronic devices that rely on batteries for their power, cardiac pacemakers must be replaced when the battery is drained. In addition, batteries electrode materials and electrolytes are toxic which raise serious safety concerns if leakage happen inside the patient’s body. In addition to their toxicity, these batteries represent more than 50-70 % of the size of implantable pacemakers which limits further miniaturization. In addition, portable electrochemical biosensors require durable portable power source to drive their electrochemical reaction and obtain the detection signal. In this thesis, biosupercapacitors were first developed as thin, safe, light-weight, low-cost, and durable power sources for the next generation of miniaturized implantable biomedical devices and portable disease biosensors. Moreover, additive manufacturing techniques such as 3-D printing, and screen printing were used to fabricate the different components of the portable electrochemical biosensors designed for cancer biomarker detection as well as DNA damage screening assays. Finally, novel triboelectric nanogenerator devices were developed and used as a sensor/energy harvester systems for biomedical, mechanical, and soft robotics applications

Microfluidic array for simultaneous detection of dna oxidation and dna-adduct damage

Exposure to chemical pollutants and pharmaceuticals may cause health issues caused by metabolite-related toxicity. This paper reports a new microfluidic electrochemical sensor array with the ability to simultaneously detect common types of DNA damage including oxidation and nucleobase adduct formation. Sensors in the 8-electrode screen-printed carbon array were coated with thin films of metallopolymers osmium or ruthenium bipyridyl-poly( vinylpyridine) chloride (OsPVP, RuPVP) along with DNA and metabolic enzymes by layer-by-layer electrostatic assembly. After a reaction step in which test chemicals and other necessary reagents flow over the array, OsPVP selectively detects oxidized guanines on the DNA strands, and RuPVP detects DNA adduction by metabolites on nucleobases. We demonstrate array performance for test chemicals including 17 beta-estradiol (E-2), its metabolites 4-hydroxyestradiol (4-OHE2), 2-hydroxyestradiol (2-OHE2), catechol, 2-nitrosotoluene (2-NO-T), 4-(methylnitrosamino)-1-(3-pyridyl)1-butanone (NNK), and 2-acetylaminofluorene (2-AAF). Results revealed DNA-adduct and oxidation damage in a single run to provide a metabolic-genotoxic chemistry screen. The array measures damage directly in unhydrolyzed DNA, and is less expensive, faster, and simpler than conventional methods to detect DNA damage. The detection limit for oxidation is 672 8-oxodG per 10(6) bases. Each sensor requires only 22 ng of DNA, so the mass detection limit is 15 pg (similar to 10 pmol) 8-oxodG

Microfluidic array for simultaneous detection of dna oxidation and dna-adduct damage

Exposure to chemical pollutants and pharmaceuticals may cause health issues caused by metabolite-related toxicity. This paper reports a new microfluidic electrochemical sensor array with the ability to simultaneously detect common types of DNA damage including oxidation and nucleobase adduct formation. Sensors in the 8-electrode screen-printed carbon array were coated with thin films of metallopolymers osmium or ruthenium bipyridyl-poly( vinylpyridine) chloride (OsPVP, RuPVP) along with DNA and metabolic enzymes by layer-by-layer electrostatic assembly. After a reaction step in which test chemicals and other necessary reagents flow over the array, OsPVP selectively detects oxidized guanines on the DNA strands, and RuPVP detects DNA adduction by metabolites on nucleobases. We demonstrate array performance for test chemicals including 17 beta-estradiol (E-2), its metabolites 4-hydroxyestradiol (4-OHE2), 2-hydroxyestradiol (2-OHE2), catechol, 2-nitrosotoluene (2-NO-T), 4-(methylnitrosamino)-1-(3-pyridyl)1-butanone (NNK), and 2-acetylaminofluorene (2-AAF). Results revealed DNA-adduct and oxidation damage in a single run to provide a metabolic-genotoxic chemistry screen. The array measures damage directly in unhydrolyzed DNA, and is less expensive, faster, and simpler than conventional methods to detect DNA damage. The detection limit for oxidation is 672 8-oxodG per 10(6) bases. Each sensor requires only 22 ng of DNA, so the mass detection limit is 15 pg (similar to 10 pmol) 8-oxodG

Microfluidic array for simultaneous detection of dna oxidation and dna-adduct damage

Exposure to chemical pollutants and pharmaceuticals may cause health issues caused by metabolite-related toxicity. This paper reports a new microfluidic electrochemical sensor array with the ability to simultaneously detect common types of DNA damage including oxidation and nucleobase adduct formation. Sensors in the 8-electrode screen-printed carbon array were coated with thin films of metallopolymers osmium or ruthenium bipyridyl-poly( vinylpyridine) chloride (OsPVP, RuPVP) along with DNA and metabolic enzymes by layer-by-layer electrostatic assembly. After a reaction step in which test chemicals and other necessary reagents flow over the array, OsPVP selectively detects oxidized guanines on the DNA strands, and RuPVP detects DNA adduction by metabolites on nucleobases. We demonstrate array performance for test chemicals including 17 beta-estradiol (E-2), its metabolites 4-hydroxyestradiol (4-OHE2), 2-hydroxyestradiol (2-OHE2), catechol, 2-nitrosotoluene (2-NO-T), 4-(methylnitrosamino)-1-(3-pyridyl)1-butanone (NNK), and 2-acetylaminofluorene (2-AAF). Results revealed DNA-adduct and oxidation damage in a single run to provide a metabolic-genotoxic chemistry screen. The array measures damage directly in unhydrolyzed DNA, and is less expensive, faster, and simpler than conventional methods to detect DNA damage. The detection limit for oxidation is 672 8-oxodG per 10(6) bases. Each sensor requires only 22 ng of DNA, so the mass detection limit is 15 pg (similar to 10 pmol) 8-oxodG

A comprehensive review on sustainable coastal zone management in Bangladesh: Present status and the way forward

Bangladesh, a coastal developing nation with a diverse sustainable biodiversity of natural resources is currently focused upon by international communities as a result of its high potential of the coastal zone (CZ) with natural gas. Sustainable Coastal Zone Management (SCZM) is key to its national development. SCZM refers to the management of coastal resources in order to provide secure and alternative livelihoods, as well as to manage all types of coastal hazards and social and cultural well-being in order to ensure long-term productivity and minimize environmental impact. This paper aims to delineate the current initiatives and status of coastal management in Bangladesh, highlighting key issues such as climate changes, sea level rise, tropical cyclones, coastal and marine pollution, coastal erosions, saltwater intrusions, and mangrove degradations as well as the future trend in Bangladesh which will facilitate sustainable development by emphasizing the social, ecological, and economic pillars of sustainability. Unsustainable coastal development practices in Bangladesh are going to damage the coastal ecosystems, particularly mangrove forests and coral reefs, which provide protection against tropical cyclones caused by global climate change and coastal erosions. The paper concludes by outlining a roadmap toward achieving SCZM in Bangladesh. The road to achieving SCZM requires collaboration, integration of scientific research, policy frameworks, community engagement, capacity building, and long-term commitment from all stakeholders involved. So, it is required to address all kinds of coastal issues and reframes all existing coastal management practices to ensure a healthy productive ecosystem to achieve SCZM as well as the sustainable development of the country

A Smartphone-Based Decision Support Tool for Predicting Patients at Risk of Chemotherapy-Induced Nausea and Vomiting: Retrospective Study on App Development Using Decision Tree Induction

BackgroundChemotherapy-induced nausea and vomiting (CINV) are the two most frightful and unpleasant side effects of chemotherapy. CINV is accountable for poor treatment outcomes, treatment failure, or even death. It can affect patients' overall quality of life, leading to many social, economic, and clinical consequences. ObjectiveThis study compared the performances of different data mining models for predicting the risk of CINV among the patients and developed a smartphone app for clinical decision support to recommend the risk of CINV at the point of care. MethodsData were collected by retrospective record review from the electronic medical records used at the University of Missouri Ellis Fischel Cancer Center. Patients who received chemotherapy and standard antiemetics at the oncology outpatient service from June 1, 2010, to July 31, 2012, were included in the study. There were six independent data sets of patients based on emetogenicity (low, moderate, and high) and two phases of CINV (acute and delayed). A total of 14 risk factors of CINV were chosen for data mining. For our study, we used five popular data mining algorithms: (1) naive Bayes algorithm, (2) logistic regression classifier, (3) neural network, (4) support vector machine (using sequential minimal optimization), and (5) decision tree. Performance measures, such as accuracy, sensitivity, and specificity with 10-fold cross-validation, were used for model comparisons. A smartphone app called CINV Risk Prediction Application was developed using the ResearchKit in iOS utilizing the decision tree algorithm, which conforms to the criteria of explainable, usable, and actionable artificial intelligence. The app was created using both the bulk questionnaire approach and the adaptive approach. ResultsThe decision tree performed well in both phases of high emetogenic chemotherapies, with a significant margin compared to the other algorithms. The accuracy measure for the six patient groups ranged from 79.3% to 94.8%. The app was developed using the results from the decision tree because of its consistent performance and simple, explainable nature. The bulk questionnaire approach asks 14 questions in the smartphone app, while the adaptive approach can determine questions based on the previous questions' answers. The adaptive approach saves time and can be beneficial when used at the point of care. ConclusionsThis study solved a real clinical problem, and the solution can be used for personalized and precise evidence-based CINV management, leading to a better life quality for patients and reduced health care costs

Electrochemiluminescent array to detect oxidative damage in ds-dna using [os(bpy)2(phen-benz-cooh)]2+/nafion/graphene films

Reactive oxygen species (ROS) oxidize guanosines in DNA to form 8-oxo-7,8-dihydro-2-deoxyguanosine (8-oxodG), a biomarker for oxidative stress. Herein we describe a novel 64-microwell electrochemiluminescent (ECL) array enabling sensitive multiplexed detection of 8-oxodG in dsDNA without hydrolysis. Films of Nafion and reduced graphene oxide containing ECL dye [Os(bpy)(2)(phen-benz-COOH)](2+) (OsNG, {bpy= 2,2\u27-bipyridine and phen-benz-COOH = (4-(1,10-phenanthrolin-6-yl)benzoic acid)}) were assembled into microwells on a pyrolytic graphite wafer to detect 8-oxodG in oligonucleotides by electrochemiluminescence (ECL). DNA oxidation by Fenton\u27s reagent or by ROS formation during redox cycles involving NADPH, Cu-II, and model metabolites was monitored. UPLC-MS/MS of oxidized DNA samples were used for calibration. Detection limit for the fluidic arrays was one 8-oxodG per 670 intact nucleobases, or 0.15%. The method is sensitive enough to evaluate DNA oxidation from biologically relevant ROS-generating reactions of Cull, NADPH, and model metabolites

Automated 3D-Printed Microfluidic Array for Rapid Nanomaterial-Enhanced Detection of Multiple Proteins

© 2018 American Chemical Society. We report here the fabrication and validation of a novel 3D-printed, automated immunoarray to detect multiple proteins with ultralow detection limits. This low cost, miniature immunoarray employs electrochemiluminescent (ECL) detection measured with a CCD camera and employs touch-screen control of a micropump to facilitate automated use. The miniaturized array features prefilled reservoirs to deliver sample and reagents to a paper-thin pyrolytic graphite microwell detection chip to complete sandwich immunoassays. The detection chip achieves high sensitivity by using single-wall carbon nanotube-antibody conjugates in the microwells and employing massively labeled antibody-decorated RuBPY-silica nanoparticles to generate ECL. The total cost of an array is 0.65, and an eight-protein assay can be done in duplicate for 0.14 per protein with limits of detection (LOD) as low as 78-110 fg mL-1 in diluted serum. The electronic control system costs 210 in components. Utility of the automated immunoarray was demonstrated by detecting an eight-protein prostate cancer biomarker panel in human serum samples in 25 min. The system is well suited to future clinical and point-of-care diagnostic testing and could be used in resource-limited environments