Tag: pubmed

Trop Med Int Health. 2021 Jun 17. doi: 10.1111/tmi.13642. Online ahead of print.

ABSTRACT

BACKGROUND: Increasing antimicrobial resistance (AMR) raises serious health and financial concerns. However, the main drivers of the emergence, spread and subsequent colonization of resistant bacterial strains between humans, animals and the environment are still poorly understood.

OBJECTIVE: The aim of this review was to identify molecular studies on AMR in One Health settings in Africa and to determine the prevalence of antimicrobial resistance genes in humans, animals and the environment. Due to the very low number of studies including environmental samples, the meta-analysis only includes data obtained from animals and humans.

METHODS: The PubMed, Web of Science and Scopus databases were searched, identifying 10,464 publications on AMR in Africa from January 1^st , 2000 until June 1^st , 2020. Inclusion criteria were: (1) Integrated studies assessing AMR simultaneously in an animal-human, animal-environment, human-environment or animal-human-environment context, (2) Genotypic characterization of AMR and (3) temporal and spatial relationship between samples from humans and animals. Statistical random effects model meta-analysis was performed.

RESULTS: Overall, 18 studies met our eligibility criteria and were included in this review. Six studies investigated E. coli and Salmonella spp. (N = 6). The most prevalent AMR genes in animals included sul1 (36.2%), sul2 (32.0%), tetA (31.5%), strB (30.8%) and blaTEM (30.0%), whereas sul2 (42.4%), tetA (42.0%), strB (34.9%), blaTEM (28.8%) and sul1 (27.8%) were most prevalent in humans. We observed no clear pattern for a higher prevalence in either the animal or the human reservoir.

CONCLUSION: To date, data on AMR in a One Health perspective in Africa are scarce. Prospective and longitudinal studies using an integrated One Health approach assessing the environment, animals and humans at the same time are needed to better understand the main drivers of AMR sharing in Africa.

PMID:34139031 | DOI:10.1111/tmi.13642

PLoS One. 2021 Jun 17;16(6):e0253505. doi: 10.1371/journal.pone.0253505. eCollection 2021.

ABSTRACT

OBJECTIVE: To quantify excess all-cause mortality in Switzerland in 2020, a key indicator for assessing direct and indirect consequences of the COVID-19 pandemic.

METHODS: Using official data on deaths in Switzerland, all-cause mortality in 2020 was compared with that of previous years using directly standardized mortality rates, age- and sex-specific mortality rates, and life expectancy.

RESULTS: The standardized mortality rate was 8.8% higher in 2020 than in 2019, returning to the level observed 5-6 years before, around the year 2015. This increase was greater for men (10.6%) than for women (7.2%) and was statistically significant only for men over 70 years of age, and for women over 75 years of age. The decrease in life expectancy in 2020 compared to 2019 was 0.7%, with a loss of 9.7 months for men and 5.3 months for women.

CONCLUSIONS: There was an excess mortality in Switzerland in 2020, linked to the COVID-19 pandemic. However, as this excess only concerned the elderly, the resulting loss of life expectancy was restricted to a few months, bringing the mortality level back to 2015.

PMID:34138948 | DOI:10.1371/journal.pone.0253505

PLoS One. 2021 Jun 17;16(6):e0253423. doi: 10.1371/journal.pone.0253423. eCollection 2021.

ABSTRACT

The literature on the timeliness of childhood vaccination (i.e. vaccination at the earliest appropriate age) in low-and middle-income countries has important measurement and methodological issues that may limit their usefulness and cross comparison. We aim to conduct a comprehensive scoping review to map the existing literature with a key focus on how the literature on vaccination timeliness has evolved, how it has been defined or measured, and what determinants have been explored in the period spanning the last four decades. This scoping review protocol was developed based on the guidance for scoping reviews from the Joanna Briggs Institute. We will include English and French language peer-reviewed publications and grey literature on the timeliness of routine childhood vaccination in low-and middle-income countries published between January 1978 through to 2021. A three-step search strategy that involves an initial search of two databases to refine the keywords, a full search of all included electronic databases, and screening of references of previous studies for relevant articles missing from our full search will be employed. The search will be conducted in five electronic databases: MEDLINE, EMBASE, Global Health, CINAHL and Web of Science. Google search will also be conducted to identify relevant grey literature on vaccination timeliness. All retrieved titles from the search will be imported into Endnote X9.3.3 (Clarivate Analytics) and deduplicated. Two reviewers will screen the titles, abstracts and full texts of publications for eligibility using Rayyan-the web based application for screening articles for systematic reviews. Using a tailored data extraction template, we will extract relevant information from eligible studies. The study team will analyse the extracted data using descriptive statistical methods and thematic analysis. The results will be presented using tables, while charts and maps will be used to aid the visualisation of the key findings and themes. The proposed review will generate evidence on key methodological gaps in the literature on timeliness of childhood vaccination. Such evidence would shape the direction of future research, and assist immunisation programme managers and country-level stakeholders to address the needs of their national immunisation system.

PMID:34138965 | DOI:10.1371/journal.pone.0253423

PLoS One. 2021 Jun 17;16(6):e0253310. doi: 10.1371/journal.pone.0253310. eCollection 2021.

ABSTRACT

BACKGROUND: Proton pump inhibitors (PPIs) are known to reduce the risk of upper gastrointestinal bleeding in patients on oral anticoagulants, and patients are increasingly on oral anticoagulants and PPI co-therapy. However, evidence is lacking on the safety and effectiveness of oral anticoagulants when co-administered with PPIs.

METHODS: Among patients initiating oral anticoagulants (warfarin and non-vitamin K antagonist oral anticoagulants [NOACs], i.e. rivaroxaban, dabigatran, apixaban, and edoxaban) during 2013-2017, those concomitantly prescribed PPIs were identified (n = 19,851). The primary endpoint was hospitalization for major upper gastrointestinal bleeding, and secondary endpoints were death and ischemic stroke.

RESULTS: During a mean 1.4 years of follow-up, the primary endpoint occurred in 512 (2.58%) patients. Overall, NOACs were associated with lower upper gastrointestinal bleeding risk after adjustment for age, sex, comorbidities and concomitant medications (adjusted hazard ratio 0.78, 95% confidence interval 0.65-0.94), compared to warfarin. There was no significant difference in upper gastrointestinal bleeding risk among the individual NOACs. This trend of reduced risk for upper gastrointestinal bleeding in NOACs compared to warfarin was consistent for both regular and reduced doses, throughout bleeding risk groups, and other subgroup analyses. NOACs were also associated with lower risk of death compared to warfarin. The risk for ischemic stroke was not significantly different among the oral anticoagulants in patients with atrial fibrillation.

CONCLUSION: In patients on oral anticoagulant and PPI co-therapy, NOACs were associated with lower risk of upper gastrointestinal bleeding and mortality compared to warfarin, while there was no difference among the oral anticoagulants for stroke prevention. In patients on PPI therapy, NOACs may preferred over warfarin for decreasing risk of upper gastrointestinal bleeding and mortality.

PMID:34138972 | DOI:10.1371/journal.pone.0253310

PLoS One. 2021 Jun 17;16(6):e0253172. doi: 10.1371/journal.pone.0253172. eCollection 2021.

ABSTRACT

AIMS: Malnutrition is a major health issue among Bangladeshi under-five (U5) children. Children are malnourished if the calories and proteins they take through their diet are not sufficient for their growth and maintenance. The goal of the research was to use machine learning (ML) algorithms to detect the risk factors of malnutrition (stunted, wasted, and underweight) as well as their prediction.

METHODS: This work utilized malnutrition data that was derived from Bangladesh Demographic and Health Survey which was conducted in 2014. The selected dataset consisted of 7079 children with 13 factors. The potential risks of malnutrition have been identified by logistic regression (LR). Moreover, 3 ML classifiers (support vector machine (SVM), random forest (RF), and LR) have been implemented for predicting malnutrition and the performance of these ML algorithms were assessed on the basis of accuracy.

RESULTS: The average prevalence of stunted, wasted, and underweight was 35.4%, 15.4%, and 32.8%, respectively. It was noted that LR identified five risk factors for stunting and underweight, as well as four factors for wasting. Results illustrated that RF can be accurately classified as stunted, wasted, and underweight children and obtained the highest accuracy of 88.3% for stunted, 87.7% for wasted, and 85.7% for underweight.

CONCLUSION: This research focused on the identification and prediction of major risk factors for stunting, wasting, and underweight using ML algorithms which will aid policymakers in reducing malnutrition among Bangladesh’s U5 children.

PMID:34138925 | DOI:10.1371/journal.pone.0253172

PLoS One. 2021 Jun 17;16(6):e0253211. doi: 10.1371/journal.pone.0253211. eCollection 2021.

ABSTRACT

While the localization of radiological sources has traditionally been handled with statistical algorithms, such a task can be augmented with advanced machine learning methodologies. The combination of deep and reinforcement learning has provided learning-based navigation to autonomous, single-detector, mobile systems. However, these approaches lacked the capacity to terminate a surveying/search task without outside influence of an operator or perfect knowledge of source location (defeating the purpose of such a system). Two stopping criteria are investigated in this work for a machine learning navigated system: one based upon Bayesian and maximum likelihood estimation (MLE) strategies commonly used in source localization, and a second providing the navigational machine learning network with a “stop search” action. A convolutional neural network was trained via reinforcement learning in a 10 m × 10 m simulated environment to navigate a randomly placed detector-agent to a randomly placed source of varied strength (stopping with perfect knowledge during training). The network agent could move in one of four directions (up, down, left, right) after taking a 1 s count measurement at the current location. During testing, the stopping criteria for this navigational algorithm was based upon a Bayesian likelihood estimation technique of source presence, updating this likelihood after each step, and terminating once the confidence of the source being in a single location exceeded 0.9. A second network was trained and tested with similar architecture as the previous but which contained a fifth action: for self-stopping. The accuracy and speed of localization with set detector and source initializations were compared over 50 trials of MLE-Bayesian approach and 1000 trials of the CNN with self-stopping. The statistical stopping condition yielded a median localization error of ~1.41 m and median localization speed of 12 steps. The machine learning stopping condition yielded a median localization error of 0 m and median localization speed of 17 steps. This work demonstrated two stopping criteria available to a machine learning guided, source localization system.

PMID:34138929 | DOI:10.1371/journal.pone.0253211

PLoS One. 2021 Jun 17;16(6):e0253179. doi: 10.1371/journal.pone.0253179. eCollection 2021.

ABSTRACT

In the last decade, NBA has grown into a billion-dollar industry where technology and advanced game plans play an essential role. Investors are interested in research examining the factors that can affect the team value. The aim of this research is to investigate the factors that affect the NBA team values. The value of a team can be influenced not only by performance-based variables, but also by macroeconomic indicators and demographic statistics. Data, analyzed in this study, contains of game statistics, economic variables and demographic statistics of the 30 teams in the NBA for the 2013-2020 seasons. Firstly, Pearson correlation test was implemented in order to identify the related variables. NBA teams’ characteristics and similarities were assessed with Machine Learning techniques (K-means and Hierarchical clustering). Secondly, Ordinary linear regression (OLS), fixed effect and random effect models were implemented in the statistical analyses. The models were compared based on Akaike Information Criterion (AIC). Fixed effect model with one lag was found the most effective model and our model produced consistently good results with the R2 statistics of 0.974. In the final model, we found that the significant determinants of team value at the NBA team level are revenue, GDP, championship, population and key player. In contrast, the total number of turnovers has a negative impact on team value. These findings would be beneficial to coaches and managers to improve their strategies to increase their teams’ value.

PMID:34138919 | DOI:10.1371/journal.pone.0253179

PLoS One. 2021 Jun 17;16(6):e0253126. doi: 10.1371/journal.pone.0253126. eCollection 2021.

ABSTRACT

INTRODUCTION: Maternal and neonatal tetanus remains a global public health problem affecting mainly the poorest and most marginalized subpopulations. In spite of the problem, studies conducted on the associated factors of births protected against neonatal tetanus are scarce in Africa. Therefore, this study aimed to identify both individual and community-level factors associated with births protected against neonatal tetanus in the region.

METHODS: The most recent Demographic and Health Survey datasets of five African countries (Ethiopia, Burundi, Comoros, Zimbabwe and Zambia) were used to investigate the associated factors of births protected from neonatal tetanus. STATA Version 14 statistical software was used for the analysis. The data were weighted before doing any statistical analysis and deviance was used for model comparison. Multilevel binary logistic regression was used to identify the associated factors of births protected against neonatal tetanus. Finally, the adjusted odds ratio (AOR) with its 95% confidence interval (CI) was calculated for each potential factors included in the multivariable multilevel logistic regression model.

RESULTS: A total weighted sample of 30897 reproductive age women who had a birth within 5 years preceding the survey were included in the analysis. Those women with age of 20-34 (AOR = 1.32, 95%CI: 1.18-1.48) and 35-49 years (AOR = 1.26, 95% CI: 1.10-1.44), high community level of women education (AOR = 1.13, 95%CI: 1.04-1.23), being from poorer(AOR = 1.23, 95% CI: 1.14-1.33), middle (AOR = 1.31, 95%CI: 1.21-1.43), richer (AOR = 1.21, 95%CI: 1.11-1.32) and richest households (AOR = 1.59, 95%CI: 1.44-1.74), having antenatal care follow up (AOR = 9.62, 95% CI: 8.79-10.54), not perceiving distance to health facility as a big problem (AOR = 1.18, 95% CI: (1.11-1.25) had higher odds of having births protected against neonatal tetanus.

CONCLUSION: Both individual and community level factors were found to be associated with births protected against neonatal tetanus in Africa. This suggests that a variety of factors are affecting births protected against neonatal tetanus in the region. Hence, the impact of these factors should be recognized while developing strategies to reduce neonatal tetanus in the region.

PMID:34138922 | DOI:10.1371/journal.pone.0253126

PLoS One. 2021 Jun 17;16(6):e0253057. doi: 10.1371/journal.pone.0253057. eCollection 2021.

ABSTRACT

Real World Evidence (RWE) and its uses are playing a growing role in medical research and inference. Prominently, the 21st Century Cures Act-approved in 2016 by the US Congress-permits the introduction of RWE for the purpose of risk-benefit assessments of medical interventions. However, appraising the quality of RWE and determining its inferential strength are, more often than not, thorny problems, because evidence production methodologies may suffer from multiple imperfections. The problem arises to aggregate multiple appraised imperfections and perform inference with RWE. In this article, we thus develop an evidence appraisal aggregation algorithm called EA3. Our algorithm employs the softmax function-a generalisation of the logistic function to multiple dimensions-which is popular in several fields: statistics, mathematical physics and artificial intelligence. We prove that EA3 has a number of desirable properties for appraising RWE and we show how the aggregated evidence appraisals computed by EA3 can support causal inferences based on RWE within a Bayesian decision making framework. We also discuss features and limitations of our approach and how to overcome some shortcomings. We conclude with a look ahead at the use of RWE.

PMID:34138908 | DOI:10.1371/journal.pone.0253057

PLoS One. 2021 Jun 17;16(6):e0252306. doi: 10.1371/journal.pone.0252306. eCollection 2021.

ABSTRACT

INTRODUCTION: Evidence that supports the use of COVID-19 convalescent plasma (CCP) for treatment of COVID-19 is increasingly emerging. However, very few African countries have undertaken the collection and processing of CCP. The aim of this study was to assess the feasibility of collecting and processing of CCP, in preparation for a randomized clinical trial of CCP for treatment of COVID-19 in Uganda.

METHODS: In a cross-sectional study, persons with documented evidence of recovery from COVID-19 in Uganda were contacted and screened for blood donation via telephone calls. Those found eligible were asked to come to the blood donation centre for further screening and consent. Whole blood collection was undertaken from which plasma was processed. Plasma was tested for transfusion transmissible infections (TTIs) and anti-SARS CoV-2 antibody titers. SARS-CoV-2 testing was also done on nasopharyngeal swabs from the donors.

RESULTS: 192 participants were contacted of whom 179 (93.2%) were eligible to donate. Of the 179 eligible, 23 (12.8%) were not willing to donate and reasons given included: having no time 7(30.4%), fear of being retained at the COVID-19 treatment center 10 (43.5%), fear of stigma in the community 1 (4.3%), phobia for donating blood 1 (4.3%), religious issues 1 (4.4%), lack of interest 2 (8.7%) and transport challenges 1 (4.3%). The median age was 30 years and females accounted for 3.7% of the donors. A total of 30 (18.5%) donors tested positive for different TTIs. Antibody titer testing demonstrated titers of more than 1:320 for all the 72 samples tested. Age greater than 46 years and female gender were associated with higher titers though not statistically significant.

CONCLUSION: CCP collection and processing is possible in Uganda. However, concerns about stigma and lack of time, interest or transport need to be addressed in order to maximize donations.

PMID:34138909 | DOI:10.1371/journal.pone.0252306