Public health utility of cause of death data : applying empirical algorithms to improve data quality

Background: Accurate, comprehensive, cause-specific mortality estimates are crucial for informing public health decision making worldwide. Incorrectly or vaguely assigned deaths, defined as garbage-coded deaths, mask the true cause distribution. The Global Burden of Disease (GBD) study has developed methods to create comparable, timely, cause-specific mortality estimates; an impactful data processing method is the reallocation of garbage-coded deaths to a plausible underlying cause of death. We identify the pattern of garbage-coded deaths in the world and present the methods used to determine their redistribution to generate more plausible cause of death assignments. Methods: We describe the methods developed for the GBD 2019 study and subsequent iterations to redistribute garbage-coded deaths in vital registration data to plausible underlying causes. These methods include analysis of multiple cause data, negative correlation, impairment, and proportional redistribution. We classify garbage codes into classes according to the level of specificity of the reported cause of death (CoD) and capture trends in the global pattern of proportion of garbage-coded deaths, disaggregated by these classes, and the relationship between this proportion and the Socio-Demographic Index. We examine the relative importance of the top four garbage codes by age and sex and demonstrate the impact of redistribution on the annual GBD CoD rankings. Results: The proportion of least-specific (class 1 and 2) garbage-coded deaths ranged from 3.7% of all vital registration deaths to 67.3% in 2015, and the age-standardized proportion had an overall negative association with the Socio Demographic Index. When broken down by age and sex, the category for unspecified lower respiratory infections was responsible for nearly 30% of garbage-coded deaths in those under 1 year of age for both sexes, representing the largest proportion of garbage codes for that age group. We show how the cause distribution by number of deaths changes before and after redistribution for four countries: Brazil, the United States, Japan, and France, highlighting the necessity of accounting for garbage-coded deaths in the GBD

The implementation of public health and economic measures during the first wave of COVID-19 by different countries with respect to time, infection rate and death rate

Since its first confirmed case in December 2019, the COVID-19 outbreak has continued to spread across countries at an alarming rate and resulted in governments worldwide implementing various public health and economic measures to contain the spread. This research studies the context of 227 countries concerning their implementation of ten public health and seven economic measures during the first wave of COVID-19 and reflects on the discrepancy in adopting these measures with respect to time, infection rate, death rate, and test rate. The results reveal that public health measures have been adopted more often and earlier than economic measures. The implementation of measures was mostly influenced by the infection rate. The analysis also finds considerable variances in adopting the measures across countries. Potentially such variances explain the large difference in COVID-19 related causalities across nations worldwide. This is further reflected in this article by considering the top ten countries that experienced a higher death toll. The article also explores Australia s notable success in controlling the spread and fatalities of COVID-19 during the first wave, and how it fared against the world regarding its implementation of various measures. Overall, this research highlights the high uncertainties governments encounter when facing a new pandemic and the need for global cooperation during such uncertainties. (Article 16

The implementation of public health and economic measures during the first wave of COVID-19 by different countries with respect to time, infection rate and death rate

Since its first confirmed case in December 2019, the COVID-19 outbreak has continued to spread across countries at an alarming rate and resulted in governments worldwide implementing various public health and economic measures to contain the spread. This research studies the context of 227 countries concerning their implementation of ten public health and seven economic measures during the first wave of COVID-19 and reflects on the discrepancy in adopting these measures with respect to time, infection rate, death rate, and test rate. The results reveal that public health measures have been adopted more often and earlier than economic measures. The implementation of measures was mostly influenced by the infection rate. The analysis also finds considerable variances in adopting the measures across countries. Potentially such variances explain the large difference in COVID-19 related causalities across nations worldwide. This is further reflected in this article by considering the top ten countries that experienced a higher death toll. The article also explores Australia s notable success in controlling the spread and fatalities of COVID-19 during the first wave, and how it fared against the world regarding its implementation of various measures. Overall, this research highlights the high uncertainties governments encounter when facing a new pandemic and the need for global cooperation during such uncertainties. (Article 16

How did socio-demographic status and personal attributes influence compliance to COVID-19 preventive behaviours during the early outbreak in Japan? Lessons for pandemic management

This study focuses on how socio-demographic status and personal attributes influence self-protective behaviours during a pandemic, with protection behaviours being assessed through three perspectives – social distancing, personal protection behaviour and social responsibility awareness. The research considers a publicly available and recently collected dataset on Japanese citizens during the COVID-19 early outbreak and utilises a data analysis framework combining Classification and Regression Tree (CART), a data mining approach, and regression analysis to gain deep insights. The analysis reveals Socio-demographic attributes – sex, marital family status and having children – as having played an influential role in Japanese citizens' abiding by the COVID-19 protection behaviours. Especially women with children are noted as more conscious than their male counterparts. Work status also appears to have some impact concerning social distancing. Trust in government also appears as a significant factor. The analysis further identifies smoking behaviour as a factor characterising subjective prevention actions with non-smokers or less-frequent smokers being more compliant to the protection behaviours. Overall, the findings imply the need of public policy campaigning to account for variations in protection behaviour due to socio-demographic and personal attributes during pandemics and national emergencies

How did socio-demographic status and personal attributes influence compliance to COVID-19 preventive behaviours during the early outbreak in Japan? Lessons for pandemic management

This study focuses on how socio-demographic status and personal attributes influence self-protective behaviours during a pandemic, with protection behaviours being assessed through three perspectives – social distancing, personal protection behaviour and social responsibility awareness. The research considers a publicly available and recently collected dataset on Japanese citizens during the COVID-19 early outbreak and utilises a data analysis framework combining Classification and Regression Tree (CART), a data mining approach, and regression analysis to gain deep insights. The analysis reveals Socio-demographic attributes – sex, marital family status and having children – as having played an influential role in Japanese citizens' abiding by the COVID-19 protection behaviours. Especially women with children are noted as more conscious than their male counterparts. Work status also appears to have some impact concerning social distancing. Trust in government also appears as a significant factor. The analysis further identifies smoking behaviour as a factor characterising subjective prevention actions with non-smokers or less-frequent smokers being more compliant to the protection behaviours. Overall, the findings imply the need of public policy campaigning to account for variations in protection behaviour due to socio-demographic and personal attributes during pandemics and national emergencies

A comparative analysis of active learning for biomedical text mining

An enormous amount of clinical free-text information, such as pathology reports, progress reports, clinical notes and discharge summaries have been collected at hospitals and medical care clinics. These data provide an opportunity of developing many useful machine learning applications if the data could be transferred into a learn-able structure with appropriate labels for supervised learning. The annotation of this data has to be performed by qualified clinical experts, hence, limiting the use of this data due to the high cost of annotation. An underutilised technique of machine learning that can label new data called active learning (AL) is a promising candidate to address the high cost of the label the data. AL has been successfully applied to labelling speech recognition and text classification, however, there is a lack of literature investigating its use for clinical purposes. We performed a comparative investigation of various AL techniques using ML and deep learning (DL)-based strategies on three unique biomedical datasets. We investigated random sampling (RS), least confidence (LC), informative diversity and density (IDD), margin and maximum representativeness-diversity (MRD) AL query strategies. Our experiments show that AL has the potential to significantly reducing the cost of manual labelling. Furthermore, pre-labelling performed using AL expediates the labelling process by reducing the time required for labelling

iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides

Antimalarial peptides (AMAPs) varying in length, amino acid composition, charge, conformational structure, hydrophobicity, and amphipathicity reflect their diversity in antimalarial mechanisms. Due to the worldwide major health problem concerning antimicrobial resistance, these peptides possess great therapeutic value owing to their low incidences of drug resistance as compared to conventional antibiotics. Although well-known experimental methods are able to precisely determine the antimalarial activity of peptides, these methods are still time-consuming and costly. Thus, machine learning (ML)-based methods that are capable of identifying AMAPs rapidly by using only sequence information would be beneficial for the high-throughput identification of AMAPs. In this study, we propose the first computational model (termed iAMAP-SCM) for the large-scale identification and characterization of peptides with antimalarial activity by using only sequence information. Specifically, we employed an interpretable scoring card method (SCM) to develop iAMAP-SCM and estimate propensities of 20 amino acids and 400 dipeptides to be AMAPs in a supervised manner. Experimental results showed that iAMAP-SCM could achieve a maximum accuracy and Matthew’s coefficient correlation of 0.957 and 0.834, respectively, on the independent test dataset. In addition, SCM-derived propensities of 20 amino acids and selected physicochemical properties were used to provide an understanding of the functional mechanisms of AMAPs. Finally, a user-friendly online computational platform of iAMAP-SCM is publicly available at http://pmlabstack.pythonanywhere.com/iAMAP-SCM. The iAMAP-SCM predictor is anticipated to assist experimental scientists in the high-throughput identification of potential AMAP candidates for the treatment of malaria and other clinical applications

SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides

Staphylococcus aureus is deemed to be one of the major causes of hospital and community-acquired infections, especially in methicillin-resistant S. aureus (MRSA) strains. Because antimicrobial peptides have captured attention as novel drug candidates due to their rapid and broad-spectrum antimicrobial activity, anti-MRSA peptides have emerged as potential therapeutics for the treatment of bacterial infections. Although experimental approaches can precisely identify anti-MRSA peptides, they are usually cost-ineffective and labor-intensive. Therefore, computational approaches that are able to identify and characterize anti-MRSA peptides by using sequence information are highly desirable. In this study, we present the first computational approach (termed SCMRSA) for identifying and characterizing anti-MRSA peptides by using sequence information without the use of 3D structural information. In SCMRSA, we employed an interpretable scoring card method (SCM) coupled with the estimated propensity scores of 400 dipeptides. Comparative experiments indicated that SCMRSA was more effective and could outperform several machine learning-based classifiers with an accuracy of 0.960 and Matthews correlation coefficient of 0.848 on the independent test data set. In addition, we employed the SCMRSA-derived propensity scores to provide a more in-depth explanation regarding the functional mechanisms of anti-MRSA peptides. Finally, in order to serve community-wide use of the proposed SCMRSA, we established a user-friendly webserver which can be accessed online at http://pmlabstack.pythonanywhere.com/SCMRSA. SCMRSA is anticipated to be an open-source and useful tool for screening and identifying novel anti-MRSA peptides for follow-up experimental studies

Masturbation Experience: A Case Study of Undergraduate Students in Bangladesh

In Bangladesh, masturbation is considered an impious activity. It has been widely documented that free access to internet porn has led to high incidences of masturbation, especially among the youth. This study attempts to understand the prevalence and practice of masturbation among university students in Bangladesh. The methodology adopted was semi-structured interviews with 299 students from a private university in Khulna, west Bangladesh. The sample was selected using stratified sampling techniques from different academic departments (strata) of the university. Chi-square test and binary logistic regression were performed to examine the association between masturbation and access to online pornography.The prevalence of masturbation among the students was 33.00% and it was found to be significantly higher among male students (42.20%). Students who watched pornography at least once a week or once a month were more likely to masturbate, with OR 161.43 (OR: 161.43, CI=38.64-674.39) and 112.3 (OR: 112.30, CI=22.80-553.22).The study provides the foundation for understanding the practice of masturbation among students in Bangladesh, with the aim of normalizing this activity.</p

Masturbation Experience: A Case Study of Undergraduate Students in Bangladesh

In Bangladesh, masturbation is considered an impious activity. It has been widely documented that free access to internet porn has led to high incidences of masturbation, especially among the youth. This study attempts to understand the prevalence and practice of masturbation among university students in Bangladesh. The methodology adopted was semi-structured interviews with 299 students from a private university in Khulna, west Bangladesh. The sample was selected using stratified sampling techniques from different academic departments (strata) of the university. Chi-square test and binary logistic regression were performed to examine the association between masturbation and access to online pornography.The prevalence of masturbation among the students was 33.00% and it was found to be significantly higher among male students (42.20%). Students who watched pornography at least once a week or once a month were more likely to masturbate, with OR 161.43 (OR: 161.43, CI=38.64-674.39) and 112.3 (OR: 112.30, CI=22.80-553.22).The study provides the foundation for understanding the practice of masturbation among students in Bangladesh, with the aim of normalizing this activity.</p