Prevalence of Self-medication with Antibiotics amongst Clients Referred to Outpatient University Dental Clinics in Iranian Population: A Questionnaire-based Study

Introduction: Self-medication with antibiotics may increase the risk of inappropriate use and development of antibiotic-resistant bacteria. The aim of this study was to determine the prevalence of self-medication with antibiotics amongst dental outpatients in Iranian population.  Methods and Materials: One thousand and two hundred of dentistry patients, who were referred to dental school clinics in ten major provinces of Iran, participated in this study. A valid self-administered questionnaire regarding self-medication with antibiotics in case of dental pain was used to collect data. Data were analysed using descriptive statistics and Logistic regression analysis. Results: In our study population, the prevalence of self-medication was 42.6%. Amongst the Iranian cities, the highest prevalence of self-medication with antibiotics belonged to the city of Bandar Abbas (64%) and the lowest was seen in the city of Kerman (27.3%). Men were more likely to take antibiotics. Amoxicillin was the mostly used antibiotic. Severe pain, previous self-medications and high costs of dental visits were the most common reasons for self-medication with antibiotics in the investigated population. In addition, the present study showed that marriage, acceptable financial status and high level of education could decrease self-medication with antibiotics. Conclusions: In the current investigation, an alarming fact was that self-medication for dental problems seemed very common amongst the studied population. One of its most important consequences was bacterial resistance. Therefore, there should be plans to promote and prioritize public health awareness and encourage general public’s motivation to reduce the practice of self-medication.Keywords: Antibiotics; Dental Clinics; Prevalence; Self-medicatio

Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features

In this paper, we develop four malware detection methods using Hamming distance to find similarity between samples which are first nearest neighbors (FNN), all nearest neighbors (ANN), weighted all nearest neighbors (WANN), and k-medoid based nearest neighbors (KMNN). In our proposed methods, we can trigger the alarm if we detect an Android app is malicious. Hence, our solutions help us to avoid the spread of detected malware on a broader scale. We provide a detailed description of the proposed detection methods and related algorithms. We include an extensive analysis to asses the suitability of our proposed similarity-based detection methods. In this way, we perform our experiments on three datasets, including benign and malware Android apps like Drebin, Contagio, and Genome. Thus, to corroborate the actual effectiveness of our classifier, we carry out performance comparisons with some state-of-the-art classification and malware detection algorithms, namely Mixed and Separated solutions, the program dissimilarity measure based on entropy (PDME) and the FalDroid algorithms. We test our experiments in a different type of features: API, intent, and permission features on these three datasets. The results confirm that accuracy rates of proposed algorithms are more than 90% and in some cases (i.e., considering API features) are more than 99%, and are comparable with existing state-of-the-art solutions.Comment: 20 pages, 8 figures, 11 tables, FGCS Elsevier journa

Efficiency of Respiratory Index in Determining Short-Term Prognosis of Multiple Trauma Patients: A Cross-Sectional Study

Background Being aware of trauma patients conditions and predicting their outcome has always been of a great interest. To determine the state and prognosis of these patients, we should find ways to enable the timely identification of those with poor health and allow the physicians to treat them before the situation gets out of hand. Objectives The present study aimed at evaluating the efficiency of respiratory index (RI) in determining the short-term prognosis of multiple trauma patients in comparison with revised trauma score (RTS). Methods In this cross-sectional study, all multiple trauma patients who were admitted to emergency department (ED) of Shahid Rajaee hospital, Shiraz, Iran, during September and October 2013 were included. Demographic data and data regarding vital signs (blood pressure, heart rate, respiratory rate, GCS, and oxygen saturation), respiratory tract status, trauma type, blood gases, procedures performed in resuscitation room, and final outcome of the patients (discharge, disposition to general unit, intensive care unit, or operating room, and dying) were recorded using a predesigned checklist. Based on the collected data, RTS and RI were calculated for each patient and their correlation and the final outcome were evaluated. Results Evaluating 187 multiple trauma patients showed that 131 (70) patients had head injury, 78 (42) chest injury, 66 (35) abdominal injury, 49 (26) extremity injury, 27 (14) neck injury, and 4 (2) vascular injury. A significant correlation was seen between RI and RTS (P = 0.024). RTS differentiated patients with good and poor health (P < 0.05), while RI showed no significant correlation with patients short-term final outcome. Conclusions Based on the findings of this study, RI cannot properly estimate short-term prognosis of multiple trauma patients, but it can be used as an independent factor in evaluating the severity of injury

The Prevalence of Pain and the Role of Analgesic Drugs in Pain Management in Patients with Trauma in Emergency Department

Background: Pain could potentially affect all aspects of patient admission course and outcome in emergency&nbsp;department (ED) when left undertreated. The alleviation of acute pain remains simply affordable but is usually,&nbsp;and sometimes purposefully, left untreated in patients with trauma. This study challenged the conventional&nbsp;emergency department policies in reducing the intensity of acute pain considering the pharmacological treatments.Methods: In this case-control study, the prevalence and intensity of pain in 200 patients were evaluated on&nbsp;admission (T1) and 24 hours later (T2) based on the valid, standardized 10-point numeric rating scale (NRS 0-10) for pain intensity. A group of patients received analgesic drugs and others did not. Changes in pain&nbsp;patterns regarding different aspects of trauma injuries in these two groups were compared.Results: The pain prevalence was high both on admission and 24 hours later. 51.5% of the study population&nbsp;received analgesics and 77.6% of them reported a decrease in the intensity of their pain. Only half of the&nbsp;patients, who did not receive any medication, reported a decrease in their pain intensity after 24 hours. The&nbsp;most beneficial policy to manage the acute pain was a combination therapy of the injury treatment and a&nbsp;supplementary pharmacological intervention.Conclusions: Pharmacological management of pain in patients with trauma is shown to be significantly&nbsp;beneficial for patients as it eases getting along with the pain, and still seems not to affect the diagnostic aspects&nbsp;of the trauma. Pain management protocols or algorithms could potentially minimize the barriers in current pain&nbsp;management of patients with trauma

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

CiteScores of cardiology and cardiovascular journals indexed in Scopus in 2019: A bibliometric analysis

Background: Citations are considered a measure of the scientific impact of research articles. CiteScore is a standard metric, based on the Scopus database, of the number of times articles in a given journal were cited during a given period relative to the number of articles published by that journal during that period.Objectives: To investigate the factors associated with CiteScores of journals on cardiology and cardiovascular diseases and indexed in Scopus in 2019.Methods: This cross-sectional, descriptive-analytical study examined 338 journals to analyse the correlation between CiteScore and such other variables and parameters as coverage by indexing services (databases), type of access, language, type of published articles, age of the journal (year of establishment), H-Index, Scimago Journal Rank, and the quartile of the journal.Results: CiteScore of a journal was positively correlated to the following variables or parameters: coverage by PubMed, Web  of Science, and EMBASE (p < 0.001), articles    in English (p < 0.001), age of the journal (p = 0.001), publishing review articles (p =  0.23), H-Index (p < 0.001), and Scimago Journal Rank (p < 0.001).Conclusion: Coverage of a journal in international databases, especially in PubMed, Web of Science, and EMBASE, is critical to increasing its visibility. Publishing review articles, which tend to be cited more often because they serve as comprehensive sources of information, can increase the CiteScore of a journal. Also, publishing more articles in English contributes to the number of times articles in a journal are cited.

Application of multilevel zero-inflated Poisson regression for assessing the risk factors of excess hospitalization among patients undergoing abdominal surgeries in Shiraz, Iran

Background and Objective: Prolonged hospitalization lead to considerable financial burden for patients as well as health care system. This study aimed to identifying important factors resulting in excess hospitalization days in patients undergoing abdominal surgeries using the multilevel zero-inflated Poisson regression model. Methods: In this descriptive - analytic study, 485 patients from five teaching and private hospitals in Shiraz (southern Iran) were selected based on convince sampling method. Multilevel zero-inflated Poisson regression model was used to determine the risk factors of excess hospitalization day. Maximum likelihood method was used to estimate parameters of the model. Moreover, Akaike Information Criterion (AIC) and Bayes Information Criterion (BIC) indices were applied to assess the goodness of fit of the model. Results: The primary analysis of data showed that 81.2% of the patients did not undergo excess hospitalization days. Based on findings, age, respiration rate, blood infusion, fever, smoking and drug abuse did not affect excess hospitalization days. In contrast, gender, renal diseases, operation history, laparoscopic gallbladder removal, prostate surgery and ileus significantly led to excess hospitalization days (P<0.05). Laparoscopic gallbladder removal, prostate surgery increased the chance of excess of hospitalization days to 4.64 and 9 times, respectively (P<0.05). Conclusion: Geder, renal diseases, operation history, laparoscopic gallbladder removal, prostate surgery and ileus significantly led to excess hospitalization days