Tag: pubmed

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):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

PLoS One. 2021 Jun 17;16(6):e0253221. doi: 10.1371/journal.pone.0253221. eCollection 2021.

ABSTRACT

INTRODUCTION: Anemia remains a major public health problem for children in sub-Saharan Africa (SSA). Iron-rich foods consumption has a determinant role on the anemia status. Hence, this study aimed to determine the prevalence of good consumption of iron-rich foods and its associated factors among children aged 6-23 months in SSA.

MATERIALS AND METHODS: The recent Demographic and Health Survey data sets of thirty-five SSA countries were used. Data were analyzed using STATA/MP version 16.0 and all statistical analyses were done after weighting the data. A generalized linear mixed model using Poisson regression with robust error variance was used to determine factors associated with good consumption of iron-rich food. Association of variables was declared at a p-value of ≤0.05 and adjusted prevalence ratio (aPR) ratio with its 95% confidence interval (CI) was calculated for each variable.

RESULTS: The total weighted samples of 77,001 children aged 6-23 months were included. The prevalence of consumption of iron rich foods was 42.1% (95% CI: 41.78-42.48). Children with age of 12-17 (adjusted prevalence ratio (aPR) = 1.96, 95% CI: 1.89-2.04) and 18-23 months (aPR = 2.05, 95% CI: 1.97-2.14), who took drugs for intestinal parasites (aPR = 1.30, 95% CI: 1.26-1.34), with postnatal check within 2 months (aPR = 1.09, 95% CI: 1.06-1.13), and children from women with ANC visit of 1-3 (aPR = 1.31, 95% CI: 1.24-1.37) and ≥4 (aPR = 1.41, 95% CI: 1.34-1.48) had higher prevalence of good consumption of iron rich foods. Moreover, the prevalence of consumptions of iron rich foods was higher among children from; family with rich (aPR = 1.36, 95%CI: 1.30-1.42) and middle (aPR = 1.14 95% CI: 1.09-1.19) wealth index, and mother with media exposure (aPR = 1.26, 95%CI: 1.22-1.31).

CONCLUSION: The prevalence of good consumption of iron-rich foods among children aged 6-23 months in SSA countries is low. Child factors, family factors, and community-level factors were significantly associated with consumption of iron rich foods. Strategies to increase the consumption of iron-rich foods during this critical stage of growth and development should be designed in SSA.

PMID:34138916 | DOI:10.1371/journal.pone.0253221

PLoS One. 2021 Jun 17;16(6):e0252146. doi: 10.1371/journal.pone.0252146. eCollection 2021.

ABSTRACT

Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter 10 (PM10,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran’s I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO2 (lag 2), SO2 (lag 1), PM10 (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R2 value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.

PMID:34138899 | DOI:10.1371/journal.pone.0252146

J Ayub Med Coll Abbottabad. 2021 Apr-Jun;33(2):293-298.

ABSTRACT

BACKGROUND: Melasma is an acquired cutaneous disorder characterized by hyperpigmentation of the face predominantly affecting the areas exposed to direct sun light. The triple combination cream, i.e., a mid-potency corticosteroid (Fluocinolone acetonide 0.01%), a retinoid (Tretinoin 0.05%), and Hydroquinone 4% is one of the widely used topical medicament for melasma treatment world over. Tranexamic acid is another agent found to be effective in melasma treatment when used topically, intra-lesionally or orally. This study has been conducted to compare mean decrease in Melasma Area Severity Index (MASI) score when tranexamic acid is combined with triple combination cream versus triple combination cream alone for melasma treatment.

METHODS: A randomized controlled trial was conducted in a tertiary care hospital of Pakistan. Sixty-three patients of melasma who met the inclusion criteria and gave written informed consent for the study were enrolled. These patients were randomly divided into 2 treatment groups. Group A was given triple combination cream and oral tranxemic acid while Group B was given triple combination cream for duration of 8 weeks. Severity of melasma was assessed by MASI, which was calculated at baseline and at the end of week 8. Mean decrease in MASI score was calculated in both groups and statistically analysed employing SPSS 20.

RESULTS: Sixty patients, 30 in both groups, completed the study. Study participants were predominantly female (81.67%), with mean age of 30.46±6.24 years in group A while 31.90±4.53 in group B. No statistically significant difference was noted in both treatment groups for mean decrease in the MASI score (6.4933±4.38358 in group A compared to 5.7833±5.04251 in the group B; p-value 0.56).

CONCLUSIONS: The addition of oral tranexamic acid did not contribute significantly in decrease in MASI score when used in combination with topical triple regimen. It may have a role as an adjuvant to topical triple combination cream.

PMID:34137548