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Background: Cardiovascular diseases (CVDs) are estimated to be the leading cause of global death. Air pollution is the biggest environmental threat to public health worldwide. It is considered a potentially modifiable environmental risk factor for CVDs because it can be prevented by adopting the right national and international policies. The present study was conducted to synthesize the results of existing studies on the burden of CVDs attributed to air pollution, namely prevalence, hospitalization, disability, mortality, and cost characteristics. Methods: A systematic search was performed in the Scopus, PubMed, and Web of Science databases to identify studies, without time limitations, up to June 13, 2023. Exclusion criteria included prenatal exposure, exposure to indoor air pollution, review studies, conferences, books, letters to editors, and animal and laboratory studies. The quality of the articles was evaluated based on the Agency for Healthcare Research and Quality Assessment Form, the Newcastle–Ottawa Scale, and Drummond Criteria using a self-established scale. The articles that achieved categories A and B were included in the study. Results: Of the 566 studies obtained, based on the inclusion/exclusion criteria, 92 studies were defined as eligible in the present systematic review. The results of these investigations supported that chronic exposure to various concentrations of air pollutants, increased the prevalence, hospitalization, disability, mortality, and costs of CVDs attributed to air pollution, even at relatively low levels. According to the results, the main pollutant investigated closely associated with hypertension was PM2.5. Furthermore, the global DALY related to stroke during 2016–2019 has increased by 1.8 times and hospitalization related to CVDs in 2023 has increased by 8.5 times compared to 2014. Conclusion: Ambient air pollution is an underestimated but significant and modifiable contributor to CVDs burden and public health costs. This should not only be considered an environmental problem but also as an important risk factor for a significant increase in CVD cases and mortality. The findings of the systematic review highlighted the opportunity to apply more preventive measures in the public health sector to reduce the footprint of CVDs in human society. [ABSTRACT FROM AUTHOR]

Background and objectives: Sepsis is accompanied by a considerably high risk of mortality in the short term, despite the availability of recommended mortality risk assessment tools. However, these risk assessment tools seem to have limited predictive value. With the gradual integration of machine learning into clinical practice, some researchers have attempted to employ machine learning for early mortality risk prediction in sepsis patients. Nevertheless, there is a lack of comprehensive understanding regarding the construction of predictive variables using machine learning and the value of various machine learning methods. Thus, we carried out this systematic review and meta-analysis to explore the predictive value of machine learning for sepsis-related death at different time points. Methods: PubMed, Embase, Cochrane, and Web of Science databases were searched until August 9th, 2022. The risk of bias in predictive models was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). We also performed subgroup analysis according to time of death and type of model and summarized current predictive variables used to construct models for sepsis death prediction. Results: Fifty original studies were included, covering 104 models. The combined Concordance index (C-index), sensitivity, and specificity of machine learning models were 0.799, 0.81, and 0.80 in the training set, and 0.774, 0.71, and 0.68 in the validation set, respectively. Machine learning outperformed conventional clinical scoring tools and showed excellent C-index, sensitivity, and specificity in different subgroups. Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) are the preferred machine learning models because they showed more favorable accuracy with similar modeling variables. This study found that lactate was the most frequent predictor but was seriously ignored by current clinical scoring tools. Conclusion: Machine learning methods demonstrate relatively favorable accuracy in predicting the mortality risk in sepsis patients. Given the limitations in accuracy and applicability of existing prediction scoring systems, there is an opportunity to explore updates based on existing machine learning approaches. Specifically, it is essential to develop or update more suitable mortality risk assessment tools based on the specific contexts of use, such as emergency departments, general wards, and intensive care units. [ABSTRACT FROM AUTHOR]

Background: Hepatitis E can potentially progress to HEV-related acute liver failure (HEV-ALF). East and South Asia bear a substantial burden of HEV infection, with Bangladesh, China, and India facing the most severe threat in this region. Therefore, we conducted a systematic review and meta-analysis to evaluate the burden of HEV-ALF in these three high-risk countries. Methods: A systematic literature search was performed utilizing PubMed, the Cochrane Library, Medline, Embase, and Web of Science databases. Studies in English or Chinese that reported data on the burden of HEV-ALF in Bangladesh, China and India were included. Outcomes were pooled with meta-analysis utilizing R software. Estimates were calculated with random-effects models, and subgroup analysis and sensitivity analysis were conducted to address heterogeneity. Egger's test and Begg's test were performed to assess publication bias. Results: A total of 20 eligible studies were included in this study. The pooled HEV-attributable proportion of viral-related acute liver failure was estimated to be 40.0% (95% CI: 0.28–0.52), 30.0% (95% CI: 0.18–0.44), and 61.0% (95% CI: 0.49–0.72) among non-pregnant individuals in India, China and Bangladesh, while in Indian pregnant females, it was 71.0% (95% CI: 0.62–0.79). The combined prevalence among non-pregnant HEV-infected participants was 28.0% (95% CI: 0.20–0.37) and 10.0% (95% CI: 0.01–0.28) in India and China, and it was 34.0% (95% CI: 0.27–0.42) in Indian pregnant females with HEV infection. The overall mortality of HEV-ALF was estimated to be 32.0% (95% CI: 0.23–0.42) and 64.0% (95% CI: 0.50–0.77) among the non-pregnant and the pregnant participants in India, and it was 23.0% (95% CI: 0.14–0.34) in Chinese non-pregnant participants. Conclusions: The burden of HEV-ALF in Bangladesh, China, and India is non-negligible despite geographic and population heterogeneity. The prevention of HEV infection and early recognition of HEV-ALF are of great significance, especially in high-risk countries and populations. Registration: PROSPERO registration ID is CRD42022382101. [ABSTRACT FROM AUTHOR]

Background: Care during pregnancy, childbirth and puerperium are fundamental to avoid pathologies for the mother and her baby. However, health issues can occur during this period, causing misfortunes, such as the death of the fetus or neonate. Predictive models of fetal and infant deaths are important technological tools that can help to reduce mortality indexes. The main goal of this work is to present a systematic review of literature focused on computational models to predict mortality, covering stillbirth, perinatal, neonatal, and infant deaths, highlighting their methodology and the description of the proposed computational models. Methods: We conducted a systematic review of literature, limiting the search to the last 10 years of publications considering the five main scientific databases as source. Results: From 671 works, 18 of them were selected as primary studies for further analysis. We found that most of works are focused on prediction of neonatal deaths, using machine learning models (more specifically Random Forest). The top five most common features used to train models are birth weight, gestational age, sex of the child, Apgar score and mother's age. Having predictive models for preventing mortality during and post-pregnancy not only improve the mother's quality of life, as well as it can be a powerful and low-cost tool to decrease mortality ratios. Conclusion: Based on the results of this SRL, we can state that scientific efforts have been done in this area, but there are many open research opportunities to be developed by the community. [ABSTRACT FROM AUTHOR]

Background: Current guidelines for healthcare of community-dwelling older people advocate screening for frailty to predict adverse health outcomes, but there is no consensus on the optimum instrument to use in such settings. The objective of this systematic review of population studies was to compare the ability of the frailty index (FI) and frailty phenotype (FP) instruments to predict all-cause mortality in older people. Methods: Studies published before 27 July 2022 were identified using Ovid MEDLINE, Embase, Scopus, Web of Science and CINAHL databases. The eligibility criteria were population-based prospective studies of community-dwelling older adults (aged 65 years or older) and evaluation of both the FI and FP for prediction of all-cause mortality. The Scottish Intercollegiate Guidelines Network's Methodology checklist was used to assess study quality. The areas under the receiver operator characteristic curves (AUC) were compared, and the proportions of included studies that achieved acceptable discriminatory power (AUC>0.7) were calculated for each frailty instrument. The results were stratified by the use of continuous or categorical formats of each instrument. The review was reported in accordance with the PRISMA and SWiM guidelines. Results: Among 8 studies (range: 909 to 7713 participants), both FI and FP had comparable predictive power for all-cause mortality. The AUC values ranged from 0.66 to 0.84 for FI continuous, 0.60 to 0.80 for FI categorical, 0.63 to 0.80 for FP continuous and 0.57 to 0.79 for FP categorical. The proportion of studies achieving acceptable discriminatory power were 75%, 50%, 63%, and 50%, respectively. The predictive ability of each frailty instrument was unaltered by the number of included items. Conclusions: Despite differences in their content, both the FI and FP instruments had modest but comparable ability to predict all-cause mortality. The use of continuous rather than categorical formats in either instrument enhanced their ability to predict all-cause mortality. [ABSTRACT FROM AUTHOR]

Background: Glycemic patterns have been reported to be prognostic factors for stroke; however, this remains to be further evaluated. This meta-analysis aimed to evaluate the usefulness of glycemic patterns such as persistent hyperglycemia (PH) including short duration and long duration PH (SPH; LPH), admission hyperglycemia (AH), short-duration hyperglycemia (SH), and persistent normoglycemia (PN) in predicting stroke prognosis using published results.Methods: Major scientific databases including but are not limited to PubMed, EMBASE, Web of Science, Ovid, CNKI (Chinese National Knowledge Infrastructure), and Clinicaltrials.gov were searched till 1st March 2021 for clinical trials on the correlation between glycemic patterns and stroke outcomes. The primary outcome was defined as short-term (1- or 3-month) post-stroke mortality, and the secondary outcome was post-stroke hemorrhage at 6 months.Results: Ten studies involving 3584 individuals were included in the final analysis. In subgroup analyses, PH patients with no history of diabetes had increased post-stroke mortality (odds ratio [OR]: 4.80, 95% CI: 3.06-7.54) than patients with no PH; and patients with glucose levels > 140 mg/dl had greater mortality (OR: 5.12, 95% CI: 3.21-8.18) than those with glucose levels < 140 mg/dl; compared with AH patients, PH patients had increased short-term mortality (OR: 0.31, 95% CI: 0.16-0.60). In the prediction of stroke mortality among patients without diabetes, SPH (OR: 0.28, 95%CI: 0.12-0.69) seemed to be more related to increased mortality than LPH (OR: 0.35, 95% CI: 0.14--0.90).Conclusions: PH, especially SPH, could predict increased post-stroke mortality in non-diabetic patients. The rank of individual glycemic patterns in predicting stroke mortality in non-diabetic patients was SPH > LPH > AH > PN. [ABSTRACT FROM AUTHOR]

Background: Globally, low back pain (LBP) is a major public health problem affecting mainly adults of the working class and is the leading cause of disability. The estimated lifetime prevalence of LBP is 50 to 80%. From 1990 to 2015, the years lived with disability caused by LBP have scaled up by 54% with the greatest increase observed in low-middle-income countries (LMICs). LBP poses a significant socio-economic burden to the society regardless of all the technological advancement in diagnosis and intervention approaches in recent years. Despite an increase in the literature of LBP in LMICs, chronic low back pain (CLBP) is poorly investigated yet it is responsible for the largest amount of burden. The purpose of this scoping review is to map the existing evidence on the prevalence, incidence, mortality, risk factors, and cost associated with CLBP among adults in Sub-Saharan Africa (SSA). Methods: We will conduct a scoping review to explore, describe, and map literature on the prevalence, incidence, mortality, risk factors, and costs associated with CLBP among adults in SSA. The search will be performed using the EBSCOhost platform by searching the following databases within the platform: Academic search complete, health source: nursing/academic edition, CINAHL with full text, Embase, PubMed, MEDLINE, Science Direct databases, Google Scholar, and the World Health Organization library databases. The search will include peer-reviewed, review articles, and gray literature. The first (title and abstract) and the second (full text) screening phases will be performed by two independent reviewers, with the third reviewer employed to adjudicate discrepancies. The reference list of all included articles will also be searched for eligible articles. This scoping review will be reported in accordance to the MOOSE and PRISMA-ScR guidelines. The NVivo 12 data analysis software will be used to generate themes, and a thematic content analysis will be used to give the narrative account of the review. Discussion: The study anticipates finding relevant literature on the prevalence, incidence, risk factors, mortality, and cost associated with CLBP among adults in SSA. The study outcomes will aid in identifying research gaps, planning, informing policy, commissioning of future research, and funding prioritization. [ABSTRACT FROM AUTHOR]

Background: Numerous studies have reported contradicting results on the relationship between cancer mortality and schizophrenia. Our aim is to quantify the mortality rate of common site-specific cancers among patients with schizophrenia and to synthesize the available research evidence. Methods: We performed a systemic search of the PubMed, EMBASE and Web of Science databases. Studies reporting the mortality rate of different cancer in patients with schizophrenia were included. A random-effects model was applied to calculate the pooled relative risks (RRs) with 95% confidence intervals (95%CIs). Results: Seven studies consisting of 1,162,971 participants with schizophrenia were included in this meta-analysis. Data regarding mortality risk of breast, colon, lung and prostate cancer among schizophrenia patients were subjected to quantitative analysis. Pooled results showed significant increases in mortality risk of breast cancer (RR = 1.97, 95%CI 1.38–2.83), lung cancer (RR = 1.93, 95%CI 1.46–2.54) and colon cancer (RR = 1.69, 95%CI 1.60–1.80) in patients with schizophrenia compared with those in the general population or control group. The mortality risk of prostate cancer increased in male patients, although no significant difference was detected (RR = 1.58, 95% CI 0.79–3.15). Increased risks of mortality from lung and colon cancer were observed in female patients (RR = 2.49, 95%CI 2.40–2.59 and RR = 2.42, 95%CI 1.39–4.22, respectively) and elevated risks of mortality from lung and colon cancer in male patients (RR = 2.40, 95%CI 2.30–2.50 and RR = 1.90, 95%CI 1.71–2.11, respectively) were detected. Conclusions: Individuals with schizophrenia have a significantly high risk of mortality from breast, colon, and lung cancer. [ABSTRACT FROM AUTHOR]