Temporal patterns of cancer burden in Asia, 1990–2019: a systematic examination for the Global Burden of Disease 2019 study

BackgroundCancers represent a challenging public health threat in Asia. This study examines the temporal patterns of incidence, mortality, disability and risk factors of 29 cancers in Asia in the last three decades. MethodsThe age, sex and year-wise estimates of incidence, mortality, and disability-adjusted life years (DALYs) of 29 cancers for 49 Asian countries from 1990 through 2019 were generated as a part of the Global Burden of Disease, Injuries and Risk Factors 2019 study. Besides incidence, mortality and DALYs, we also examined the cancer burden measured in terms of DALYs and deaths attributable to risk factors, which had evidence of causation with different cancers. The development status of countries was measured using the socio-demographic index. Decomposition analysis was performed to gauge the change in cancer incidence between 1990 and 2019 due to population growth, aging and age-specific incidence rates. FindingsAll cancers combined claimed an estimated 5.6 million [95% uncertainty interval, 5.1–6.0 million] lives in Asia with 9.4 million [8.6–10.2 million] incident cases and 144.7 million [132.7–156.5 million] DALYs in 2019. The age-standardized incidence rate (ASIR) of all cancers combined in Asia was 197.6/100,000 [181.0–214.4] in 2019, varying from 99.2/100,000 [76.1–126.0] in Bangladesh to 330.5/100,000 [298.5–365.8] in Cyprus. The age-standardized mortality rate (ASMR) was 120.6/100,000 [110.1–130.7] in 2019, varying 4-folds across countries from 71.0/100,000 [59.9–83.5] in Kuwait to 284.2/100,000 [229.2–352.3] in Mongolia. The age-standardized DALYs rate was 2970.5/100,000 [2722.6–3206.5] in 2019, varying from 1578.0/100,000 [1341.2–1847.0] in Kuwait to 6574.4/100,000 [5141.7–8333.0] in Mongolia. Between 1990 and 2019, deaths due to 17 of the 29 cancers either doubled or more, and 20 of the 29 cancers underwent an increase of 150% or more in terms of new cases. Tracheal, bronchus, and lung cancer (both sexes), breast cancer (among females), colon and rectum cancer (both sexes), stomach cancer (both sexes) and prostate cancer (among males) were among top-5 cancers in most Asian countries in terms of ASIR and ASMR in 2019 and cancers of liver, stomach, hodgkin lymphoma and esophageal cancer posted the most significant decreases in age-standardized rates between 1990 and 2019. Among the modifiable risk factors, smoking, alcohol use, ambient particulate matter (PM) pollution and unsafe sex remained the dominant risk factors between 1990 and 2019. Cancer DALYs due to ambient PM pollution, high body mass index and fasting plasma glucose has increased most notably between 1990 and 2019. InterpretationWith growing incidence, cancer has become more significant public health threat in Asia, demanding urgent policy attention and guidance. Its heightened risk calls for increased cancer awareness, preventive measures, affordable early-stage detection, and cost-effective therapeutics in Asia. The current study can serve as a useful resource for policymakers and researchers in Asia for devising interventions for cancer management and control. FundingThe GBD study is funded by the Bill and Melinda Gates Foundation.This work is supported by: - University Grants Commission - Chandigarh University - National Science and Technology Council - grant no. [112-2410-H-003-031] - Bill and Melinda Gates Foundation - grant no. [OPP1152504] - Fundamental Research Funds for the Central Universities - grant no. [30923011101] - Social Science Foundation of Jiangsu Province - grant no. [21GLD008] - National Natural Science Foundation of China - grant no. [72204112

Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

Background The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic; characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic; and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure Database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95$7·3 trillion (95% UI 7·2–7·4) in 2019; 293·7 times the $24·8 billion (95$43·1 billion in development assistance was provided to maintain or improve health. The pandemic led to an unprecedented increase in development assistance targeted towards health; in 2020 and 2021, $1·8 billion in DAH contributions was provided towards pandemic preparedness in LMICs, and$37·8 billion was provided for the health-related COVID-19 response. Although the support for pandemic preparedness is 12·2% of the recommended target by the High-Level Independent Panel (HLIP), the support provided for the health-related COVID-19 response is 252·2% of the recommended target. Additionally, projected spending estimates suggest that between 2022 and 2026, governments in 17 (95% UI 11–21) of the 137 LMICs will observe an increase in national government health spending equivalent to an addition of 1% of GDP, as recommended by the HLIP. Interpretation There was an unprecedented scale-up in DAH in 2020 and 2021. We have a unique opportunity at this time to sustain funding for crucial global health functions, including pandemic preparedness. However, historical patterns of underfunding of pandemic preparedness suggest that deliberate effort must be made to ensure funding is maintained

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Epidemiological analysis of Lung cancer in Erbil province of Iraqi Kurdistan: Incidence, Survival, Relative Risk Ratio, and Treatment Regimes in males and females

Abstract This study compares survival function, relative risk, incident rates and treatment regime between genders. A total of 590 cases of Lung Cancer admitted to Nanakali hospital, Erbil province of Iraqi Kurdistan, were collected for 5 years period between 1st January 2013 to 31st of December 2017. The follow-up of the cases continued till the 1st of April 2018 to complete the record. Chi-square, correlation, relative risks and basic exploratory data analysis were carried out. Simple linear regression was carries out for number of lung cancer among males and females. A multivariate Cox-regression model was used to determine the prognostic factors for lung cancer patients. Pearson’s Correlation Coefficient (r) for total cases of Lung cancer (Males and Females) was equal to (0.875 ± 0.033 with P= 0.044) and R-square (Precision) of 0.766. The Prediction Regression equation that Female Lung Cancer (F) = 11.79 + 0.6714 Age group. This means any age group can be selected to predict for expected incidence. The prediction equation is that Male Lung Cancer cases = - 2.857 + 0.7690 Age group. The Regression coefficient is + 0.769 per 10 years of age and it is highly significant (P=0001).The result of multivariable cox regression model indicates that gender had no influence on survival outcome (HR ~= 0.81, 95%CI: 0.56 to 1.16, p=0.0.247). However, taking surgery and immune system are statistically significant prognostic factors for lung cancer patients. The model indicated that the risk of mortality increases by 92% if lung cancer patients do not take surgery (HR ~= 1.92, 95%CI: 0.31 to 0.97, p=0.039). Furthermore, the risk of mortality is reduced by 44% among those patients who took immune system. The study concludes that female patients survive longer than males and median survival probability in female is greater than male lung cancer patients. Taking surgery and immune system are statistically significant prognostic factors for lung cancer patients.</jats:p

A Wavelet Shrinkage Mixed with a Single-level 2D Discrete Wavelet Transform for Image Denoising

The single-level 2D discrete wavelet transform method is a powerful technique for effectively removing Gaussian noise from natural images. Its effectiveness is attributed to its ability to capture a signal's energy at low energy conversion values, allowing for efficient noise reduction while preserving essential image details. The wavelet noise reduction method mitigates the noise present in the waveform coefficients produced by the discrete wavelet transform. In this study, three different wavelet families—Daubechies (db7), Coiflets (coif5), and Fejér-Korovkin (fk4)—were evaluated for their noise removal capabilities using the Bayes shrink method. This approach was applied to a set of images, and the performance was analyzed using the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics. Our results demonstrated that among the wavelet families tested, the Fejér-Korovkin (fk4) wavelet consistently outperformed the others. The fk4 wavelet family yielded the lowest MSE values, indicating minimal reconstruction error, and the highest PSNR values, reflecting superior noise suppression and better image quality across all tested images. These findings suggest that the fk4 wavelet family, when combined with the Bayes shrink method, provides a robust framework for Gaussian noise reduction in natural images. The comparative analysis highlights the importance of selecting appropriate wavelet families to optimize noise reduction performance, paving the way for further research and potential improvements in image denoising techniques

Bayesian machine learning analysis with Markov Chain Monte Carlo techniques for assessing characteristics and risk factors of Covid-19 in Erbil City-Iraq 2020–2021

The study aims to showcase machine learning techniques in the application of medical datasets for improving identification of correlations and relationships between variables, which will lead to more informed decision-making. Unlike other studies, intensive statistical modelling is used to understand and find the effective of variables cause to lead death due to Covid-19. Due to large dataset, not common approaches derive us to ideal conclusion. Furthermore, Bayesian technique is applied to generate predictive posterior distributions of the unknown parameters in the model in neural network as well as logistic regression, which helps us to avoid overfitting in machine learning applications and have additional measurements in assessing fitted model performance. According to the results extracted from the statistical analysis, the Bayesian neural network demonstrated superior performance in terms of classification measurements such as AUC (84.66%), F1 (87.11%), Precision (88.29%), and Recall (85.96%). The Bayesian logistic regression also performed well, but with slightly lower scores, achieving AUC (83.07%), F1 (85.59%), Precision (84.55%), and Recall (85.59%). In contrast, logistic regression (MLE) technique had the worst performance with very low scores (AUC = 52.38%, F1 = 57.55%, Precision = 57.01%, Recall = 58.10%). Regarding the variables' association with mortality, stepwise forward selection helped to identify seven significant variables. Age was found to be the most significant variable in predicting the probability of dying, with patients in the age group of (18–44) having 12 times higher odds, patients in the age group of (45–64) having 123 more odds, and patients above 65 years old having 436 times more chance to die compared to patients below 18 years old. Severe coughing was also significant with 7.26 odds, and patients suffering from diabetes had 2.82 times more chance to die. Moreover, SpO2 contributed to a decrease of 20% in the relative risk of dying from Covid-19 disease. Gender and Smoking did not show a significant association with mortality. Finally, the Bayesian approach showed higher sensitivity and specificity than the classic neural network

Relation of angiography to hematological, hormonal and some biochemical variables in coronary artery bypass graft patients

Abstract The present study was designed to investigate the relation between severity of atherosclerosis via angiography and alteration of some important biochemical, hormonal and hematological variables in patients underwent Coronary Artery Bypass Graft (CABG) surgery. Eighty adult patients underwent coronary angiography were included in this study, and a standardized case-control study of acute myocardial infarction was established through taking 20 healthy individuals. Diagnostic coronary angiography was performed by a team of expert cardiologists. The patients were grouped according the number of major epicardial coronary arteries into one vessel disease (1VD), two vessels disease (2VD) or three vessels disease (3VD). The evaluation of biochemical tests were performed. The results of association of measurements with the severity of disease showed the priority of cholesterol and its related indexes (especially LDL) rather than TG indicating the severity of atherosclerosis. While, blood glucose and HbA1c were not apparently related to the degree of atherosclerosis. Significant reduction of T3 hormone and platelets and elevation in MPV were recorded in patients suffering from three vessels occlusion. This finding suggested strong association between severity of atherosclerosis and LDL, MPV and T3 in CABG patients.</jats:p

Investigating surgical risk in coronary artery disease: Utilizing ordinal regression for predictive modeling

In this study, an ordinal regression model with a Negative Log-log link function was used to predict the likelihood of having multiple blocked arteries based on various clinical and lifestyle factors. This analysis is essential for identifying individuals at higher risk of severe cardiovascular conditions, enabling earlier and more effective interventions. The model showed a good fit with the observed data, as evidenced by a Pearson Chi-Square value of 244.927 (p = 0.224) and a Deviance value of 167.846 (p = 0.999). Additionally, the model yielded high Pseudo R-square values: Cox and Snell = 0.710 and Nagelkerke = 0.723, indicating a strong explanatory power for the outcome variable. Significant predictors identified include blood urea, low-density lipoprotein (LDL) cholesterol levels, hemoglobin (HGB), red cell distribution width (RDW), and smoking status (smoker: yes). Notably, smoking increases the odds of having multiple blocked arteries by a factor of 3.27 (OR = 3.2704, p = 0.028), while higher RDW is also associated with increased risk (OR = 1.5945, p = 0.0048). In contrast, higher blood urea levels are linked to a reduced risk (OR = 0.9594, p = 0.0045). These findings highlight the value of statistical modeling in identifying cardiovascular risk factors, helping healthcare providers make more informed decisions regarding patient treatment and preventive care