Publication Date: 2015
Malaysian Journal Of Computer Science (01279084)28(1)pp. 46-58
Image reconstruction is an important part of computed tomography imaging systems, which converts the measured data into images. Because of high computational cost and slow convergence of iterative reconstruction algorithms, these methods are not widely used in practice. In this paper, we propose a hybrid iterative algorithm by combining multigrid method,Tikhonov regularization and Simultaneous Iterative Reconstruction Technique (SIRT) for reconstruction of the computed tomography image that reduces this drawback. To do so, we reduce the time and the volume of computations considerably by finding astable and appropriate starting point. The experimental results indicate that the proposed iterative algorithm has more rapid convergence and reconstructs high quality images in short computational time than the classical ones.
This paper presents a novel image security system based on the replacement of the pixel values using recursive Cellular automata (CA) substitution. The advantages of our proposed method are that it is computationally efficient and it is reasonably passing sensibility analysis tests. The proposed method is carried out by using one half of image data to encrypt the other half of the image mutually. Our algorithm can encrypt image in parallel and be also applied to color image encryption. In this proposed method, size of key is dynamic and by changing a bit of security key the image cannot retrieve because our method is key sensitive. Simulation results obtained using color; white-black and gray-level images demonstrate the good performance of the proposed image security system. © 2011 IEEE.
Alamatsaz, N.,
Tabatabaei, L.,
Yazdchi, M.,
Payan, H.,
Alamatsaz, N.,
Nasimi, F. Publication Date: 2024
Biomedical Signal Processing and Control (17468108)90
Objective: Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for monitoring heart electrical signals and evaluating its functionality. The human heart can suffer from a variety of diseases, including cardiac arrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can lead to stroke and can be diagnosed via ECG recordings. Since early detection of cardiac arrhythmias is of great importance, computerized and automated classification and identification of these abnormal heart signals have received much attention for the past decades. Methods: This paper introduces a light Deep Learning (DL) approach for high accuracy detection of 8 different cardiac arrhythmias and normal rhythms. To employ DL techniques, the ECG signals were preprocessed using resampling and baseline wander removal techniques. The classification was performed using an 11-layer network employing a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Results: In order to evaluate the proposed technique, ECG signals are chosen from the two physionet databases, the MIT-BIH arrhythmia database and the long-term AF database. The proposed DL framework based on the combination of CNN and LSTM showed promising results than most of the state-of-the-art methods. The proposed method reaches the mean diagnostic accuracy of 98.24%. Conclusion: A trained model for arrhythmia classification using diverse ECG signals were successfully developed and tested. Significance: This study presents a lightweight classification technique with high diagnostic accuracy compared to other notable methods, making it a potential candidate for implementation in Holter monitor devices for arrhythmia detection. Finally, we used SHapley Additive exPlanations (SHAP), the most popular Explainable Artificial Intelligence (XAI) method to understand how our model make predictions. The results indicate that those features (ECG samples) that have contributed the most to a prediction are consonant with clinicians’ decisions. Therefore, the use of interpretable models increases the trust of clinicians in AI and thus leads to decreasing the number of misdiagnoses of cardiovascular diseases. © 2023 Elsevier Ltd
Publication Date: 2022
PLoS ONE (19326203)17(2 February)
Differentiating between shockable and non-shockable Electrocardiogram (ECG) signals would increase the success of resuscitation by the Automated External Defibrillators (AED). In this study, a Deep Neural Network (DNN) algorithm is used to distinguish 1.4-second segment shockable signals from non-shockable signals promptly. The proposed technique is frequency-independent and is trained with signals from diverse patients extracted from MIT-BIH, MIT-BIH Malignant Ventricular Ectopy Database (VFDB), and a database for ventricular tachyarrhythmia signals from Creighton University (CUDB) resulting, in an accuracy of 99.1%. Finally, the raspberry pi minicomputer is used to load the optimized version of the model on it. Testing the implemented model on the processor by unseen ECG signals resulted in an average latency of 0.845 seconds meeting the IEC 60601-2-4 requirements. According to the evaluated results, the proposed technique could be used by AED’s. Copyright: © 2022 Nasimi, Yazdchi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Publication Date: 2022
Biomedical Signal Processing and Control (17468108)78
Proposing a practical method for high-performance emotion recognition could facilitate human–computer interaction. Among existing methods, deep learning techniques have improved the performance of emotion recognition systems. In this work, a new multimodal neural design is presented wherein audio and visual data are combined as the input to a hybrid network comprised of a bidirectional long short term memory (BiLSTM) network and two convolutional neural networks (CNNs). The spatial and temporal features extracted from video frames are fused with Mel-Frequency Cepstral Coefficients (MFCCs) and energy features extracted from audio signals and BiLSTM network outputs. Finally, a Softmax classifier is used to classify inputs into the set of target categories. The proposed model is evaluated on Surrey Audio–Visual Expressed Emotion (SAVEE), Ryerson Audio–Visual Database of Emotional Speech and Song (RAVDESS), and Ryerson Multimedia research Lab (RML) databases. Experimental results on these datasets prove the effectiveness of the proposed model where it achieves the accuracy of 99.75%, 94.99%, and 99.23% for the SAVEE, RAVDESS, and RML databases, respectively. Our experimental study reveals that the suggested method is more effective than existing algorithms in adapting to emotion recognition in these datasets. © 2022 Elsevier Ltd
Publication Date: 2022
PLoS ONE (19326203)17(2 February)
The demand for long-term continuous care has led healthcare experts to focus on development challenges. On-chip energy consumption as a key challenge can be addressed by data reduction techniques. In this paper, the pseudo periodic nature of ElectroCardioGram (ECG) signals has been used to completely remove redundancy from frames. Compressing aligned QRS complexes by Compressed Sensing (CS), result in highly redundant measurement vectors. By removing this redundancy, a high cluster of near zero samples is gained. The efficiency of the proposed algorithm is assessed using the standard MIT-BIH database. The results indicate that by aligning ECG frames, the proposed technique can achieve superior reconstruction quality compared to state-of-the-art techniques for all compression ratios. This study proves that by aligning ECG frames with a 0.05% unaligned frame rate(R-peak detection error), more compression could be gained for PRD > 5% when 5-bit non-uniform quantizer is used. Furthermore, analysis done on power consumption of the proposed technique, indicates that a very good recovery performance can be gained by only consuming 4.9μW more energy per frame compared to traditional CS. Copyright: © 2022 Nasimi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Publication Date: 2020
Biomedical Signal Processing and Control (17468108)60
Background and objectives: Data compression techniques have been used in order to reduce power consumption when transmitting electrocardiogram (ECG) signals in wireless body area networks (WBAN). Among these techniques, compressed sensing allows sparse or compressible signals to be encoded with only a small number of measurements. Although ECG signals are not sparse, they can be made sparse in another domain. Numerous sparsifying techniques are available, but when signal quality and energy consumption are important, existing techniques leave room for improvements. Methods: To leverage compressed sensing, we increased the sparsity of an ECG frame by removing the redundancy in a normal frame. In this study, by framing a signal according to the detected QRS complex (R peaks), consecutive frames of the signal become highly similar. This helps remove redundancy and consequently makes each frame sparse. In order to increase detection performance, different frames that symptomize a cardiovascular disease are sent uncompressed. Results: For evaluating and comparing our proposed technique with different state-of-the-art techniques two datasets that contained normal and abnormal ECG: MIT-BIH Arrhythmia Database and MIT-BIH Long Term Database were used. For performance evaluation, we performed heart rate variability (HRV) analysis as well as energy-based distortion analysis. The proposed method reaches an accuracy of 99.9%, for a compression ratio of 25. For MIT-BIH Long Term Database, the average percentage root-mean squared difference (PRD) is less than 10 for all compression ratios. Conclusion: Removing the redundancy between successive similar frames and exact transmission of dissimilar frames, the proposed method proves to be appropriate for heart rate variability analysis and abnormality detection. © 2020
Publication Date: 2021
Computer Networks (13891286)196
In the present article, we propose a virtual machine placement (VMP) algorithm for reducing power consumption in heterogeneous cloud data centers. We propose a novel model for the estimation of power consumption of datacenter's network. The proposed model is employed to estimate power consumption of a Fat-Tree network. It calculates the traffic of each network layer and uses the results to estimate the average power consumption of each switch in the network, which is used for network power calculation. Further, we employ the chemical reaction optimization (CRO) algorithm as a meta-heuristic algorithm to obtain a power-efficient mapping of virtual machines (VMs) to physical machines (PMs). Moreover, two kinds of solution encoding schemes, namely permutation-based and grouping-based encoding schemes, were utilized for representing individuals in CRO. For each encoding scheme, we designed proper operators required by the CRO for manipulating the molecules in search of more optimal solution candidates. Additionally, we modeled VMs with east–west and north–south communications, and PMs with constrained CPU, memory, and bandwidth capacity. Our network power model is integrated into the CRO algorithms to enable the estimation of both PMs and network power consumption. We compared our proposed methods with a number of similar methods. The evaluation results indicate that the proposed methods perform well and the CRO algorithm with the grouping-based encoding outperforms the rest of the methods in terms of power consumption. The evaluation results also show the significance of network power consumption. © 2021 Elsevier B.V.
Publication Date: 2021
Journal of Supercomputing (15730484)77(5)pp. 5120-5147
Graphics processing units (GPUs) are powerful in performing data-parallel applications. Such applications most often rely on the GPU’s memory hierarchy to deliver high performance. Designing efficient memory hierarchy for GPUs is a challenging task because of its wide architectural space. To moderate this challenge, this paper proposes a framework, called stack distance-analytic modeling (SDAM), to estimate memory performance of the GPU in terms of memory cycle counts. Providing the input data to the model is crucial in terms of the accuracy of the input data, and the time spent to obtain them. SDAM employs the stack distance analysis method and analytical modeling to obtain the required input accurately and swiftly. Further, it employs a detailed analytical model to estimate memory cycles. SDAM is validated against real GPU executions. Further, it is compared with a cycle accurate simulator. The experimental evaluations, performed on a set of memory-intensive benchmarks, prove that SDAM is faster and more accurate than cycle-accurate simulation, thus it can facilitate the GPU cache design-space exploration. For a selection of data-intensive benchmarks, SDAM showed a 32% average error in estimating memory data transfer cycles in a modern GPU, which outperforms cycle-accurate simulation, while it is an order of magnitude faster than the cycle-accurate simulation. Finally, the applicability of SDAM in exploring cache design-space in GPUs is demonstrated through experimenting with various cache designs. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2019
Journal of Circuits, Systems and Computers (17936454)28(14)
Modern GPUs can execute multiple kernels concurrently to keep the hardware resources busy and to boost the overall performance. This approach is called simultaneous multiple kernel execution (MKE). MKE is a promising approach for improving GPU hardware utilization. Although modern GPUs allow MKE, the effects of different MKE scenarios have not adequately studied by the researchers. Since cache memories have significant effects on the overall GPU performance, the effects of MKE on cache performance should be investigated properly. The present study proposes a framework, called RDMKE (short for Reuse Distance-based profiling in MKEs), to provide a method for analyzing GPU cache memory performance in MKE scenarios. The raw memory access information of a kernel is first extracted and then RDMKE enforces a proper ordering to the memory accesses so that it represents a given MKE scenario. Afterward, RDMKE employs reuse distance analysis (RDA) to generate cache-related performance metrics, including hit ratios, transaction counts, cache sets and Miss Status Holding Register reservation fails. In addition, RDMKE provides the user with the RD profiles as a useful locality metric. The simulation results of single kernel executions show a fair correlation between the generated results by RDMKE and GPU performance counters. Further, the simulation results of 28 two-kernel executions indicate that RDMKE can properly capture the nonlinear cache behaviors in MKE scenarios. © 2019 World Scientific Publishing Company.
Publication Date: 2019
Simulation Modelling Practice and Theory (1569190X)91pp. 102-122
Memory footprint is a metric for quantifying data reuse in memory trace. It can also be used to approximate cache performance, especially in shared cache systems. Memory footprint is acquired through memory footprint analysis (FPA). However, its main limitation is that, for a memory trace of n accesses, the all-window FPA algorithm requires O(n3) time. Therefore, in this paper, we propose an analytical algorithm for FPA, whereby the average footprints are calculated in O(n2). The proposed algorithm can also be employed for window distribution analysis. Moreover, we propose a framework to enable the application of FPA to GPU kernels and model the performance of L1 cache memories. The results of experimental evaluations indicate that our proposed framework functions 1.55X slower than the Xiang's formula, as a fast average FPA method, while it can also be utilized for window distribution analysis. In the context of FPA-based cache performance estimation, the experimental results indicate a fair correlation between the estimated L1 miss rates and those of the native GPU executions. On average, the proposed framework has 23.8% error in the estimation of L1 cache miss rates. Further, our algorithm runs 125X slower than the reuse distance analysis (RDA) when analyzing a single kernel. However, the proposed method outperforms RDA in modeling shared caches and multiple kernel executions in GPUs. © 2018 Elsevier B.V.
Publication Date: 2019
ACM Transactions on Architecture and Code Optimization (15443973)15(4)
Reuse distance analysis (RDA) is a popular method for calculating locality profiles and modeling cache performance. The present article proposes a framework to apply the RDA algorithm to obtain reuse distance profiles in graphics processing unit (GPU) kernels. To study the implications of hardware-related parameters in RDA, two RDA algorithms were employed, including a high-level cache-independent RDA algorithm, called HLRDA, and a detailed RDA algorithm, called DRDA. DRDA models the effects of reservation fails in cache blocks and miss status holding registers to provide accurate cache-related performance metrics. In this case, the reuse profiles are cache-specific. In a selection of GPU kernels, DRDA obtained the L1 miss-rate breakdowns with an average error of 3.86% and outperformed the state-of-the-art RDA in terms of accuracy. In terms of performance, DRDA is 246,000×slower than the real GPU executions and 11×faster than GPGPUSim. HLRDA ignores the cache-related parameters and its obtained reuse profiles are general, which can be used to calculate miss rates in all cache sizes. Moreover, the average error incurred by HLRDA was 16.9%. © 2018 Association for Computing Machinery.
Publication Date: 2019
Computing and Informatics (25858807)38(2)pp. 421-453
In the present paper, we propose RDGC, a reuse distance-based performance analysis approach for GPU cache hierarchy. RDGC models the thread-level parallelism in GPUs to generate appropriate cache reference sequence. Further, reuse distance analysis is extended to model the multi-partition/multi-port parallel caches and employed by RDGC to analyze GPU cache memories. RDGC can be utilized for architectural space exploration and parallel application development through providing hit ratios and transaction counts. The results of the present study demonstrate that the proposed model has an average error of 3.72 % and 4.5 % (for L1 and L2 hit ratios, respectively). The results also indicate that the slowdown of RDGC is equal to 47 000 times compared to hardware execution, while it is 59 times faster than GPGPU-Sim simulator. © 2019 Slovak Academy of Sciences. All rights reserved.
Performance modeling plays an important role for optimal hardware design and optimized application implementation. This paper presents a very low overhead performance model, called VLAG, to approximate the data localities exploited by GPU kernels. VLAG receives source code-level information to estimate per memory-access instruction, per data array, and per kernel localities within GPU kernels. VLAG is only applicable to kernels with regular memory access patterns. VLAG was experimentally evaluated using an NVIDiA Maxwell GPU. For two different Matrix Multiplication kernels, the average errors of 7.68% and 6.29%, was resulted, respectively. The slowdown of VLAG for MM was measured 1.4X which, comparing with other approaches such as trace-driven simulation, is negligible. © 2017 IEEE.
Modern GPUs employ simultaneous kernel executions (SKE), an equivalent to multitasking in CPUs, to maximize the hardware utilization and enhance the resulted performance. SKE paradigm is not yet fully explored by the research community. In this study, a reuse-distance (RD) based analysis approach, called SKERD, is proposed to analyze the effect of SKE scenarios on the kernel data reuse and GPU cache memories performance. Only two simultaneous kernels were considered in this work. Moreover, Three types of coarse-grained SM (streaming multiprocessor) partitioning schemes were investigated including an even SM to kernel partitioning and two SM partitioning schemes that assign the SMs to the kernels based on the kernel workloads. The simulation results show that none of the mentioned partitioning schemes always functions better than the others. Further, for some memory intensive kernels, SKE resulted in cache contentions and hit ratio degradation. Consequently, the effects of SKE on cache memories should be carefully considered. © 2017 IEEE.
Publication Date: 2025
BMC Public Health (14712458)25(1)
Background: There are different opinions about looting after disasters. Many believe that post-disaster chaos is the best chance for antisocial behavior. Aim: The purpose of this systematic review is to explore the literature regarding looting after disasters, its different dimensions, and to examine coping strategies. Methods: This study is a systematic review of publications about disaster-related looting and antisocial behavior, and the study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Web of Science, Scopus, ProQuest, PubMed, ScienceDirect, and Google Scholar were the primary databases used for the search of literature. From the 2,467 records identified through database searching in the early stage; after investigating and analyzing the characteristics and content analysis, 8 articles were included in the final stage of this review study to answer the study questions. Results: The findings of this systematic review that emerged from the content analysis of included studies are summarized in four themes: socioeconomic status (SES), social capital, media, and looting prevention. Conclusions: To reduce looting, governments should incorporate looting into disaster planning, take help from community capacities and non-governmental organizations (NGOs), and try to increase social capital in the long term. Also, correct information transmission by the media is an important part of post-disaster looting management. © The Author(s) 2025.
Publication Date: 2025
BMC Public Health (14712458)25(1)
Background: The development of COVID-19 vaccines was progressing rapidly, but vaccination acceptance posed many challenges in different communities. This study systematically reviewed the impact of religious leaders on the acceptance of COVID-19 vaccinations. It also examined religious leaders’ role in shaping their followers’ vaccination decisions and explored the strategies religious organizations use to promote vaccination against COVID-19. Method: The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The primary databases used to search the literature were PubMed, Web of Science (WOS), Scopus, ProQuest, ScienceDirect, and Google Scholar. To identify relevant published literature, the title of this systematic review was divided into two key components: keywords related to COVID-19 vaccination and religious leaders, along with their synonyms. Results: This review analyzed seven articles using content analysis to explore the diverse roles of religious leaders in COVID-19 vaccination acceptance. The analysis identified two key themes: the positive contributions of religious leaders in promoting vaccination and their negative or neutral roles, highlighting differing perspectives on their influence during the pandemic. Conclusion: Engaging religious leaders in disseminating and adopting national and global health initiatives, such as capacity building, training, trust building, collaboration with health providers, and dialogue with the community about the COVID-19 vaccination program, is a powerful strategy to advance the World Health Organization (WHO) goals. © The Author(s) 2025.
Heidari, M.,
Heidari, M.,
Zuniga, Y.M.H.,
Zumla, A.,
Zuhlke, L.J.,
Zoladl, M.,
Ziaeian, B.,
Zhong, C.,
Zhao, X.G.,
Zhang, Z.,
Zhang, J.,
Zepro, N.B. Publication Date: 2025
Nature Communications (20411723)16(1)
Chronic care manages long-term, progressive conditions, while acute care addresses short-term conditions. Chronic conditions increasingly strain health systems, which are often unprepared for these demands. This study examines the burden of conditions requiring acute versus chronic care, including sequelae. Conditions and sequelae from the Global Burden of Diseases Study 2019 were classified into acute or chronic care categories. Data were analysed by age, sex, and socio-demographic index, presenting total numbers and contributions to burden metrics such as Disability-Adjusted Life Years (DALYs), Years Lived with Disability (YLD), and Years of Life Lost (YLL). Approximately 68% of DALYs were attributed to chronic care, while 27% were due to acute care. Chronic care needs increased with age, representing 86% of YLDs and 71% of YLLs, and accounting for 93% of YLDs from sequelae. These findings highlight that chronic care needs far exceed acute care needs globally, necessitating health systems to adapt accordingly. © The Author(s) 2025.
Heidari, M.,
Heidari, M.,
Oh, J.,
Kim, S.,
Kim, M.S.,
Abate, Y.H.,
Abd elhafeez, S.,
Abdelkader, A.,
Abdi, P.,
Abdulah, D.M.,
Aboagye, R.G.,
Abolhassani, H. Publication Date: 2025
The Lancet Respiratory Medicine (2213-2600)13(5)pp. 425-446
Background: Asthma and atopic dermatitis are common allergic conditions that contribute to substantial health loss, economic burden, and pain across individuals of all ages worldwide. Therefore, as a component of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, we present updated estimates of the prevalence, disability-adjusted life-years (DALYs), incidence, and deaths due to asthma and atopic dermatitis and the burden attributable to modifiable risk factors, with forecasted prevalence up to 2050. Methods: Asthma and atopic dermatitis prevalence, incidence, DALYs, and mortality, with corresponding 95% uncertainty intervals (UIs), were estimated for 204 countries and territories from 1990 to 2021. A systematic review identified data from 389 sources for asthma and 316 for atopic dermatitis, which were further pooled using the Bayesian meta-regression tool. We also described the age-standardised DALY rates of asthma attributable to four modifiable risk factors: high BMI, occupational asthmagens, smoking, and nitrogen dioxide pollution. Furthermore, as a secondary analysis, prevalence was forecasted to 2050 using the Socio-demographic Index (SDI), air pollution, and smoking as predictors for asthma and atopic dermatitis. To assess trends in the burden of asthma and atopic dermatitis before (2010–19) and during (2019–21) the COVID-19 pandemic, we compared their average annual percentage changes (AAPCs). Findings: In 2021, there were an estimated 260 million (95% UI 227–298) individuals with asthma and 129 million (124–134) individuals with atopic dermatitis worldwide. Asthma cases declined from 287 million (250–331) in 1990 to 238 million (209–272) in 2005 but increased to 260 million in 2021. Atopic dermatitis cases consistently rose from 107 million (103–112) in 1990 to 129 million (124–134) in 2021. However, age-standardised prevalence rates decreased—by 40·0% (from 5568·3 per 100 000 to 3340·1 per 100 000) for asthma and 8·3% (from 1885·4 per 100 000 to 1728·5 per 100 000) for atopic dermatitis. In 2021, there were substantial variations in the burden of asthma and atopic dermatitis across different SDI groups, with the highest age-standardised DALY rate found in south Asia for asthma (465·0 [357·2–648·9] per 100 000) and the high-income super-region for atopic dermatitis (3552·5 [3407·2–3706·1] per 100 000). During the COVID-19 pandemic, the decline in asthma prevalence had stagnated (AAPC pre-pandemic –1·39% [–2·07 to –0·71] and during the pandemic 0·47% [–1·86 to 2·79]; p=0·020); however, there was no significant difference in atopic dermatitis prevalence in the same period (pre-pandemic –0·28% [–0·33 to –0·22] and during the pandemic –0·35% [–0·78 to 0·08]; p=0·20). Modifiable risk factors were responsible for 29·9% of the global asthma DALY burden; among them, high BMI was the greatest contributor (39·4 [19·6–60·2] per 100 000), followed by occupational asthmagens (20·8 [16·7–26·5] per 100 000) across all regions. The age-standardised DALY rate of asthma attributable to high BMI was highest in high-SDI settings, whereas the contribution of occupational asthmagens was highest in low-SDI settings. According to our forecasting models, we expect 275 million (224–330) asthma cases and 148 million (140–158) atopic dermatitis cases in 2050, with population growth driving this increase. However, age-standardised prevalence rates are expected to remain stable (–23·2% [–44·4 to 5·3] for asthma and –1·4% [–9·1 to 7·0] for atopic dermatitis) from 2021 to 2050. Interpretation: Although the increases in the total number of asthma and atopic dermatitis cases will probably continue until 2050, age-standardised prevalence rates are expected to remain stable. A considerable portion of the global burden could be managed through efforts to address modifiable risk factors. Additionally, the contribution of risk factors to the burden substantially varied by SDI, which suggests the need for tailored initiatives for specific SDI settings. The growing number of individuals expected to be affected by asthma and atopic dermatitis in the future suggests that it is essential to improve our understanding of risk factors for asthma and atopic dermatitis and collect disease prevalence data that are globally generalisable. Funding: Gates Foundation. © 2025 Elsevier Ltd
Heidari, M.,
Heidari, M.,
Bernabe, E.,
Marcenes, W.,
Abdulkader, R.S.,
Abreu, L.G.,
Afzal, S.,
Alhalaiqa, F.N.,
Al-maweri, S.,
Alsharif, U.,
Anyasodor, A.E.,
Arora, A. Publication Date: 2025
The Lancet (0140-6736)405(10482)pp. 897-910
Background: The WHO Global Oral Health Action Plan has set an overarching global target of achieving a 10% reduction in the prevalence of oral conditions by 2030. Robust and up-to-date information on the global burden of oral conditions is paramount to monitor progress towards this target. The aim of this systematic data analysis was to produce global, WHO region, and country-level estimates of the prevalence of, and disability-adjusted life-years (DALYs) attributed to, untreated caries, severe periodontitis, edentulism, other oral disorders, lip and oral cavity cancer, and orofacial clefts from 1990 to 2021. Methods: This report is based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. Input data were extracted from epidemiological surveys, population-based registries, and vital statistics. Data were modelled with DisMod-MR 2.1, a Bayesian meta-regression modelling tool, to ensure consistency between prevalence, incidence, remission, and mortality estimates for oral conditions. DALYs were estimated as the aggregation of the years of life lost (YLLs) due to premature mortality and years lived with disability (YLDs). YLDs were calculated by multiplying prevalence estimates, the severity of the oral condition's sequelae (disability weight) and duration of the sequelae. Although all oral conditions lead to YLDs, only lip and oral cavity cancer and orofacial clefts lead to YLLs as well. 95% uncertainty intervals (UIs) were generated for every metric with the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: The combined global age-standardised prevalence of the main oral conditions (untreated caries, severe periodontitis, edentulism, and other oral disorders) was 45 900 (95% UI 42 300 to 49 800) per 100 000 population in 2021, with 3·69 billion (3·40 to 4·00) people affected globally. Untreated dental caries of permanent teeth and severe periodontitis were the most common oral conditions, with a global age-standardised prevalence of 27 500 (24 000 to 32 000) per 100 000 population and 12 500 (10 500 to 14 500) per 100 000 population, respectively. Edentulism, severe periodontitis, and lip and oral cavity cancer caused the highest burden as demonstrated by their counts of DALYs and age-standardised DALY rates. Existing trends for 1990–2021 reveal relatively small changes (upward or downward) in prevalence and burden. Increasing counts of prevalent cases and DALYs were noted for all oral conditions but untreated caries of deciduous teeth (no percentage change in prevalence or DALYs) and orofacial clefts (–68·3% [–79·3 to –46·5] decrease in DALYs). There were decreases in both age-standardised prevalence and DALY rate for untreated caries of permanent teeth and edentulism, no change in both for untreated caries of deciduous teeth and severe periodontitis, an increase in the prevalence but no change in the DALY rate for lip and oral cavity cancer, and no change in the prevalence but a decrease in the DALY rate for orofacial clefts. By WHO region, the African and Eastern Mediterranean regions showed the largest increases in prevalent cases and DALYs for most oral conditions, while the European region showed the smallest increases or no change. The European region was the only region with decreasing age-standardised prevalence of untreated caries in both deciduous (–9·88%; –12·6 to –6·71) and permanent teeth (–5·94% (–8·38 to –3·62). The prevalence and DALY rate of severe periodontitis decreased in the African region, while the prevalence and DALY rate of edentulism decreased in the African region, South-East Asia region, and Western Pacific region. Furthermore, DALY rates of lip and oral cavity cancer decreased in the European region and the region of the Americas, while DALY rates of orofacial clefts decreased in all regions. Interpretation: The minor changes in the burden of oral conditions over the past 30 years demonstrate that past and current efforts to control oral conditions have not been successful and that different approaches are needed. Many countries now face the double challenge of controlling the occurrence of new cases of oral conditions and addressing the huge unmet need for oral health care. Funding: Bill & Melinda Gates Foundation. © 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
Sheikhi, R.A.,
Heidari, M.,
Noorbakhsh, S.,
Sarpiri, M.R. Publication Date: 2025
Florence Nightingale Journal of Nursing (26876442)33(1)
AIM: This systematic review examines the tele-nursing methods used during the coronavirus disease-2019 outbreak to manage the increase in patient numbers and investigates strategies for reducing hospital bed occupancy. METHOD: The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The primary databases used to search the literature were PubMed, Web of Science, Scopus, ProQuest, ScienceDirect, and Google Scholar. One hundred sixty eight articles have been reviewed. The keywords for this review included “Coronavirus Disease 2019,” “tele-nursing,” and “bed occupancy.” Equivalent terms were derived from Medical Subject Headings and expert opinions and extracted from related articles. RESULTS: Out of the 168 records identified through the initial database search, seven articles were ultimately included in the final stage of this review after a thorough analysis of their features and content to address the study questions. The results of this systematic review, based on the content analysis of the selected studies, reveal various approaches used worldwide to manage the influx of patients in hospitals due to COVID-19 infection. The findings also highlight strategies employed to reduce bed occupancy, along with the challenges faced in implementing telenursing. The results are summarized into three main themes: current care models, challenges in establishing telenursing, and strategies to decrease bed occupancy. CONCLUSION: Tele-nursing and virtual care are crucial for reducing bed occupancy during disasters like coronavirus disease 2019. Creating communication infrastructure, developing distance education through virtual space, licensing the private sector to run tele-nursing, clarifying the medical and legal responsibilities of telehealth, developing protocols of care, community education, and using new technology for remote consultation are ways to facilitate tele-nursing and reduce hospital bed occupancy. © 2025, Istanbul University-Cerrahpasa, Florence Nightingale Faculty of Nursing. All rights reserved.
Heidari, M.,
Heidari, M.,
Weaver, N.D.,
Bertolacci, G.J.,
Rosenblad, E.,
Ghoba, S.,
Cunningham, M.,
Ikuta, K.S.,
Moberg, M.E.,
Mougin, V.,
Han, C.,
Wool, E.E. Publication Date: 2025
The Lancet Public Health (24682667)10(3)
Background: Deaths from suicide are a tragic yet preventable cause of mortality. Quantifying the burden of suicide to understand its geographical distribution, temporal trends, and variation by age and sex is an essential step in suicide prevention. We aimed to present a comprehensive set of global, regional, and national estimates of suicide burden. Methods: We produced estimates of the number of deaths and age-standardised mortality rates of suicide globally, regionally, and for 204 countries and territories from 1990 to 2021, and disaggregated these results by age and sex. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 estimates of deaths attributable to suicide were broken down into two comprehensive categories: those by firearms and those by other specified means. For this analysis, we also produced estimates of mean age at the time of death from suicide, incidence of suicide attempts compared with deaths, and age-standardised rates of suicide by firearm. We acquired data from vital registration, verbal autopsy, and mortality surveillance that included 23 782 study-location-years of data from GBD 2021. Point estimates were calculated from the average of 1000 randomly selected possible values of deaths from suicide by age, sex, and geographical location. 95% uncertainty intervals (UIs) were derived from the 2·5th and 97·5th percentiles from a 1000-draw distribution. Findings: Globally, 746 000 deaths (95% UI 692 000-800 000) from suicide occurred in 2021, including 519 000 deaths (485 000-556 000) among males and 227 000 (200 000-255 000) among females. The age-standardised mortality rate has declined over time, from 14·9 deaths (12·8-15·7) per 100 000 population in 1990 to 9·0 (8·3-9·6) per 100 000 in 2021. Regionally, mortality rates due to suicide were highest in eastern Europe (19·2 [17·5-20·8] per 100 000), southern sub-Saharan Africa (16·1 [14·0-18·3] per 100 000), and central sub-Saharan Africa (14·4 [11·0-19·1] per 100 000). The mean age at which individuals died from suicide progressively increased during the study period. For males, the mean age at death by suicide in 1990 was 43·0 years (38·0-45·8), increasing to 47·0 years (43·5-50·6) in 2021. For females, it was 41·9 years (30·9-46·7) in 1990 and 46·9 years (41·2-52·8) in 2021. The incidence of suicide attempts requiring medical care was consistently higher at the regional level for females than for males. The number of deaths by suicide using firearms was higher for males than for females, and substantially varied by country and region. The countries with the highest age-standardised rate of suicides attributable to firearms in 2021 were the USA, Uruguay, and Venezuela. Interpretation: Deaths from suicide remain variable by age and sex and across geographical locations, although population mortality rates have continued to improve globally since the 1990s. This study presents, for the first time in GBD, a quantification of the mean age at the time of suicide death, alongside comprehensive estimates of the burden of suicide throughout the world. These analyses will help guide future approaches to reduce suicide mortality that consider a public health framework for prevention. Funding: Bill & Melinda Gates Foundation. © 2025 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
Sheikhi, R.A.,
Heidari, M.,
Jafari, H.,
Heydarpoor, S.,
Yadollahi, S. Publication Date: 2025
Health In Emergencies And Disasters Quarterly (23454210)10(4)pp. 279-290
Background: Due to the nature of emergency medical services (EMS) and the rush to provide emergency services, ambulance crashes (ACs) occur more frequently and more severely than crashes related to vehicles with similar size and weight characteristics, disrupting the relief process. This research was done to explain the experiences of emergency medical technicians (EMTs) in ACs and provide solutions to prevent and mitigate these accidents. Materials and Methods: This qualitative study used a framework analysis approach and a purposeful sampling method. It involved conducting semi-structured interviews with 18 EMTs who had experiences with ACs. The study utilized the Haddon matrix framework as the basic framework. Data analysis and code extraction were carried out using MAXQDA software, version 10. The codes were extracted using both deductive and inductive methods. Results: According to the Haddon matrix framework, in the host part, factors include personnel health, lack of skills, a staffing shortage, stress and fear, burnout and feeling unsupported. In the agent part, factors include worn-out ambulances, a shortage of them, speed, lights-andsiren use that stabilizes the vehicle, and delays in EMS. In the environment part, factors include public expectations for response times, unsafe roads, unfamiliarity with the roads, inadequate emergency service coverage and root cause analysis. Conclusion: Generally, working in an ambulance can be hazardous. Implementing educational, operational, and engineering strategies can significantly reduce the risk of harm to EMS providers, patients and the public. © 2025 The Author(s).
Publication Date: 2024
BMC Emergency Medicine (1471227X)24(1)
Background: Although unplanned deliveries in ambulances are uncommon, Emergency Medical Services (EMS) providers may encounter this situation before reaching the hospital. This research aims to gather insights from Emergency Medical Technicians (EMTs), midwives, and expectant mothers to examine the causes of giving birth in ambulances and the challenges EMTs, pregnant women, and midwives face during delivery. Methods: A qualitative study was conducted, and 28 EMTs, midwives, and pregnant women who had experience with pre-hospital births in the ambulance were interviewed. Data were analyzed using thematic content analysis. The MAXQDA/10 software was employed for data analysis and code extraction. Results: The analysis of the interviews revealed two main categories: factors that cause delivery in the ambulance and its challenges. The factors include cultural problems, weak management, and inaccessibility to facilities. The challenges consist of fear and anxiety, native culture, and lack of resources. Conclusions: Several approaches should be implemented to reduce the number of births in ambulances and Pre-hospital Emergency Medical Services (PEMS). These include long-term community cultural activities, public education, awareness campaigns, education and follow-up for pregnant women, and improved accessibility to health facilities. Additionally, EMTS need to receive proper education and training for ambulance deliveries. Enhancing ambulance services and supporting EMTs in dealing with litigation claims are also critical. © The Author(s) 2024.
Heidari, M.,
Heidari, M.,
Ghalichi, L.,
Shariat, S.V.,
Naserbakht, M.,
Taban, M.,
Abbasi-kangevari, M.,
Afrashteh, F.,
Ajami, M.,
Akbarialiabad, H.,
Amiri, S.,
Arabloo, J. Publication Date: 2024
The Lancet Global Health (2572-116X)12(12)
Background: Mental and behavioural disorders account for a large proportion of the burden of diseases in Iran. Identifying the pattern of change can help in policy making and provision of mental health services. We aimed to analyse the burden of mental disorders (excluding substance use disorders) in Iran at national and subnational levels with data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019. Methods: We used data from 1990 to 2019 on anxiety disorders, attention-deficit hyperactivity disorder, autism spectrum disorders, bipolar disorder, conduct disorder, depressive disorders, eating disorders, idiopathic developmental intellectual disability, schizophrenia, and other mental disorders in Iran and its 31 provinces. We calculated total disability-adjusted life-years (DALYs), age-standardised DALYs, and prevalence rates in 1990 and 2019, as well as the percentage change between these time periods. Findings: Mental disorders accounted for 1 159 410 (4·6%) of 25 007 732 all-cause DALYs in Iran in 1990 and 2 053 871 (10·3%) of 19 828 721 in 2019. Although total DALYs for mental disorders increased by 77·1% (95% uncertainty interval 76·7 to 77·6%) during this period, age-standardised DALY rate increased by 1·8% (–4·1 to 7·7%). The overall patterns of change were similar at the subnational level as the national level, although the rates differed between provinces with a highest-to-lowest ratio of 1·22 for age-standardised DALY rates in 2019. Interpretation: The increase in the burden of mental disorders in Iran is higher than the general trend in the world. The slight change in age-standardised DALYs suggests that the increase is mainly attributable to changes in the size and structure of the population. Considering the absolute and relative increase in the burden of mental disorders during the past 30 years at national and provincial levels, there is an urgent need to address the determinants of mental health and upgrade mental health services across all levels of care in Iran. Funding: Bill & Melinda Gates Foundation. © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license
Heidari, M.,
Heidari, M.,
Malekpour, M.,
Rezaii, N.,
Azadnajafabad, S.,
Khanali, J.,
Azangou-khyavy, M.,
Moghaddam, S.S.,
Heidari-foroozan, M.,
Rezazadeh-khadem, S.,
Ghamari, S.,
Abbasi-kangevari, M. Publication Date: 2024
Public Health (00333506)237pp. 212-231
Objectives: In this study, the trends and current situation of the injury burden as well as attributable burden to injury risk factors at global, regional, and national levels based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 are presented. Study design: To assess the attributable burden of injury risk factors, the data of interest on data sources were retrieved from the Global Health Data Exchange (GHDx) and analyzed. Methods: Cause-specific death from injuries was estimated using the Cause of Death Ensemble model in the GBD 2019. The burden attributable to each injury risk factor was incorporated in the population attributable fraction to estimate the total attributable deaths and disability-adjusted life years. The Socio-demographic Index (SDI) was used to evaluate countries’ developmental status. Results: Globally, there were 713.9 million (95% uncertainty interval [UI]: 663.8 to 766.9) injuries incidence and 4.3 million (UI: 3.9 to 4.6) deaths caused by injuries in 2019. There was an inverse relationship between age-standardized disability-adjusted life year rate and SDI quintiles in 2019. Overall, low bone mineral density was the leading risk factor of injury deaths in 2019, with a contribution of 10.5% (UI: 9.0 to 11.6) of total injuries and age-standardized deaths, followed by occupational risks (7.0% [UI: 6.3–7.9]) and alcohol use (6.8% [UI: 5.2 to 8.5]). Conclusion: Various risks were responsible for the imposed burden of injuries. This study highlighted the small but persistent share of injuries in the global burden of diseases and injuries to provide beneficial data to produce proper policies to reach an effective global injury prevention plan. © 2024
