Category: Statistics

Psychooncology. 2021 Jun 11. doi: 10.1002/pon.5749. Online ahead of print.

ABSTRACT

OBJECTIVE: Mindfulness-based interventions (MBIs) are increasingly being encouraged for managing treatment-related symptoms but much less is known about the extent to which mindfulness is effective in relieving the psychosocial distress experienced by men with prostate cancer. A systematic review was conducted to synthesise the literature on MBIs for psychological wellbeing and quality of life improvement in men with prostate cancer.

METHODS: Embase, CINAHL, MEDLINE, PsycINFO, PsycArticles and Web of Science were searched up to 7th November 2020. Included studies were assessed for quality using appropriate appraisal tools. Homogeneous study results were pooled in a meta-analysis while narrative synthesis was used to summarise the findings from heterogeneous results. Effect size was expressed as Cohen’s d (95% confidence intervals) and statistical significance (p-value) was set at 0.05.

RESULTS: Four studies comprising three randomised trials and one non-randomised study met the inclusion criteria. MBIs for men with prostate cancer showed small to moderate effect for improving psychological outcomes. The pooled result of quality of life and post-traumatic growth in study participants showed moderate (d = -0.29 [-1.29, 0.71], p = 0.57) and large (d = 0.77 [-0.33,1.88], p = 0.000) effects, respectively.

CONCLUSION: MBI is potentially promising for psychological outcomes, quality of life and post-traumatic growth symptoms improvement in men with prostate cancer but recommendations cannot be made based on current evidence due to limited research and inadequate methodological rigour of published literature. Robust research is needed to draw a reliable conclusion about its sustained effect in men with advanced disease.

PMID:34139035 | DOI:10.1002/pon.5749

Intern Med J. 2021 Jun 17. doi: 10.1111/imj.15432. Online ahead of print.

ABSTRACT

BACKGROUND: COVID-19 is known to cause an acute respiratory illness, although clinical manifestations outside of the respiratory tract may occur. Early reports have identified SARS-CoV-2 as a cause of subacute thyroiditis (SAT).

METHODS: A systematic review was conducted in accordance with the PRISMA guidelines. MEDLINE, Web of Science and PubMed databases were queried in February 2021 for studies from December 2019 to February 2021. MeSH search terms “COVID-19”, “SARS-CoV-2” and “coronavirus” along with search terms “thyroiditis”, “thyrotoxicosis”, “thyroid” were used. Descriptive statistics for continuous variables and proportions for categorical variables were calculated.

RESULTS: 15 publications reporting on 17 individual cases of COVID-19 induced SAT were identified. Age ranged from 18 to 69 years old. The majority of the cases were female (14 of 17, 82%). The delay between onset of respiratory symptoms and diagnosis of SAT ranged from 5 to 49 days (mean, 26.5). Systemic inflammatory response syndrome (SIRS) related to viral infection was uncommonly reported at the time of SAT diagnosis. Thyroid ultrasonography frequently reported an enlarged hypoechoic thyroid with decreased vascularity and heterogenous echotexture. Elevated CRP was common at the time of SAT diagnosis, with results ranging from 4.5 to 176 mg/L (mean, 41 mg/L). Anti-thyroid antibodies were frequently negative. SAT specific treatment included corticosteroids for 12/17 (70.5%) patients. Most return to normal thyroid status.

CONCLUSION: COVID-19 associated SAT may be difficult to identify in a timely manner due to potential absence of classic symptoms, as well as cross-over of common clinical features between COVID-19 and thyrotoxicosis. This article is protected by copyright. All rights reserved.

PMID:34139048 | DOI:10.1111/imj.15432

J Gerontol B Psychol Sci Soc Sci. 2021 Jun 17:gbab109. doi: 10.1093/geronb/gbab109. Online ahead of print.

ABSTRACT

OBJECTIVES: Intergenerational models of adult health contend that children’s educational attainments influence the health and well-being of their parents. However, it is unclear how much of this association is confounded by background characteristics that predict both children’s educational attainments and parents’ subsequent health, particularly in the U.S.

METHODS: Data from the National Longitudinal Study of Adolescent to Adult Health Parent Study are used to examine how having no children who completed college influences parents’ self-rated health and depressive symptoms. We rely on propensity score methods to more squarely assess this relationship net of potential confounding bias and to test for heterogeneity in the consequences associated with having no children who completed college.

RESULTS: Having no children who completed college is negatively associated with parents’ self-rated health and positively associated with depressive symptoms. After statistically balancing differences in background characteristics between groups, these associations remain, though the magnitude of the coefficients is attenuated. Supplemental matching analyses suggest that while the association between children’s education and self-rated health might be spurious, the association with depressive symptoms is more robust. Additionally, among parents with the highest propensity for having no children who complete college, the consequences on depressive symptoms are greatest.

DISCUSSION: This study pays particular attention to selection-related concerns surrounding the association between offspring educational attainment and parent well-being in the United States. These findings are important given the call for investments in children’s educational opportunities as promoting both the well-being of adult children and their parents.

PMID:34139008 | DOI:10.1093/geronb/gbab109

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