Search

Although regression models play a central role in the analysis of medical research projects, there still exist many misconceptions on various aspects of modeling leading to faulty analyses. Indeed, the rapidly developing statistical methodology and its recent advances in regression modeling do not seem to be adequately reflected in many medical publications. This problem of knowledge transfer from statistical research to application was identified by some medical journals, which have published series of statistical tutorials and (shorter) papers mainly addressing medical researchers. The aim of this review was to assess the current level of knowledge with regard to regression modeling contained in such statistical papers. We searched for target series by a request to international statistical experts. We identified 23 series including 57 topic-relevant articles. Within each article, two independent raters analyzed the content by investigating 44 predefined aspects on regression modeling. We assessed to what extent the aspects were explained and if examples, software advices, and recommendations for or against specific methods were given. Most series (21/23) included at least one article on multivariable regression. Logistic regression was the most frequently described regression type (19/23), followed by linear regression (18/23), Cox regression and survival models (12/23) and Poisson regression (3/23). Most general aspects on regression modeling, e.g. model assumptions, reporting and interpretation of regression results, were covered. We did not find many misconceptions or misleading recommendations, but we identified relevant gaps, in particular with respect to addressing nonlinear effects of continuous predictors, model specification and variable selection. Specific recommendations on software were rarely given. Statistical guidance should be developed for nonlinear effects, model specification and variable selection to better support medical researchers who perform or interpret regression analyses.

Before organizing mixed-mode data collection for the self-administered questionnaire of the Belgian Health Interview Survey, measurement effects between the paper-and-pencil and the web-based questionnaire were evaluated. A two-period cross-over study was organized with a sample of 149 employees of two Belgian research institutes (age range 22-62 years, 72% female). Measurement agreement was assessed for a diverse range of health indicators related to general health, mental and psychosocial health, health behaviors and prevention with kappa coefficients and intraclass correlation (ICC). The quality of the data collected by both modes was evaluated by quantifying the missing, 'don't know' and inconsistent values and data entry mistakes. Good to very good agreement was found for all categorical indicators with kappa coefficients superior to 0.60, except for two mental and psychosocial health indicators namely the presence of a sleeping disorder and of a depressive disorder (kappa≥0.50). For the continuous indicators high to acceptable agreement was observed with ICC superior to 0.70. Inconsistent answers and data-entry mistakes were only occurring in the paper-and-pencil mode. There were no less missing values in the web-based mode compared to the paper-and-pencil mode. The study supports the idea that web-based modes provide, in general, equal responses to paper-and-pencil modes. However, health indicators based upon factual and objective items tend to have higher measurement agreement than indicators requiring an assessment of personal subjective feelings. A web-based mode greatly facilitates the data-entry process and guides the completing of a questionnaire. However, item non-response was not positively affected. ispartof: PLOS ONE vol:13 issue:5 ispartof: location:United States status: published

Objective To survey the knowledge, attitude, and practices of Vietnamese pharmacists regarding the COVID-19 pandemic. Method This cross-sectional, paper-based study was conducted from June to August 2020. A validated questionnaire (Cronbach’s alpha = 0.84) was used to interview 1,023 pharmacists in nine provinces of Vietnam. Analysis of covariance was employed to identify factors associated with the knowledge of pharmacists. The best model was chosen by using the Bayesian Model Averaging method in R software version 4.0.4. Results The mean knowledge score was 12.02 ± 1.64 (range: 6–15), which indicated that 93.4% of pharmacists had good knowledge of COVID-19. There was no difference in the average score between males and females (p > 0.05). The multivariate linear regression model revealed that the knowledge was significantly associated with pharmacists’ age, education level, and residence (p < 0.001). About attitude and practices, pharmacists daily sought and updated information on the COVID-19 pandemic through mass media and the internet (social network and online newspapers). Nearly 48% of them conceded that they communicated with customers when at least one person did not wear a face mask at the time of the COVID-19 outbreak. At medicine outlets, many measures were applied to protect pharmacists and customers, such as equipping pharmacists with face masks and hand sanitizers (95.0%), using glass shields (83.0%), and maintaining at least one-meter distance between two people (85.2%). Conclusion The pharmacists’ knowledge of COVID-19 transmission, symptoms, and prevention was good. Many useful measures against the spread of this perilous virus were applied in medicine outlets. However, pharmacists should restrict forgetting to wear face masks in communication with medicine purchasers. The government and health agencies should have practical remedies to reduce the significant differences in the COVID-19 knowledge of pharmacists among provinces and education-level groups.

We propose to use Twitter data as social-spatial sensors. This study deals with the question whether research papers on certain diseases are perceived by people in regions (worldwide) that are especially concerned by these diseases. Since (some) Twitter data contain location information, it is possible to spatially map the activity of Twitter users referring to certain papers (e.g., dealing with tuberculosis). The resulting maps reveal whether heavy activity on Twitter is correlated with large numbers of people having certain diseases. In this study, we focus on tuberculosis, human immunodeficiency virus (HIV), and malaria, since the World Health Organization ranks these diseases as the top three causes of death worldwide by a single infectious agent. The results of the social-spatial Twitter maps (and additionally performed regression models) reveal the usefulness of the proposed sensor approach. One receives an impression of how research papers on the diseases have been perceived by people in regions that are especially concerned by these diseases. Our study demonstrates a promising approach for using Twitter data for research evaluation purposes beyond simple counting of tweets.

Background Adiponectin is an adipocyte-derived hormone that acts as a marker of insulin sensitivity. Bloodspot sampling by fingerstick onto filter paper may increase the feasibility of large-scale studies of the determinants of insulin sensitivity. We first describe the validation of an enzyme-linked immunoassay (ELISA) for quantifying adiponectin from dried blood spots and then demonstrate its application in a large trial (PROBIT). Methods We quantified adiponectin from 3-mm diameter discs (≈3 µL of blood) punched from dried blood spots obtained from: i) whole blood standards (validation); and ii) PROBIT trial samples (application) in which paediatricians collected blood spots from 13,879 children aged 11.5 years from 31 sites across Belarus. We examined the distribution of bloodspot adiponectin by demographic and anthropometric factors, fasting insulin and glucose. Results In the validation study, mean intra-assay coefficients of variation (n = 162) were 15%, 13% and 10% for ‘low’ (6.78 µg/ml), ‘medium’ (18.18 µg/ml) and 'high’ (33.13 µg/ml) internal quality control (IQC) samples, respectively; the respective inter-assay values (n = 40) were 23%, 21% and 14%. The correlation coefficient between 50 paired whole bloodspot versus plasma samples, collected simultaneously, was 0.87 (95% CI: 0.78 to 0.93). Recovery of known quantities of adiponectin (between 4.5 to 36 µg/ml) was 100.3–133%. Bloodspot adiponectin was stable for at least 30 months at −80°C. In PROBIT, we successfully quantified fasting adiponectin from dried blood spots in 13,329 of 13,879 (96%) children. Mean adiponectin (standard deviation) concentrations were 17.34 µg/ml (7.54) in boys and 18.41 µg/ml (7.92) in girls and were inversely associated with body mass index, fat mass, triceps and subscapular skin-fold thickness, waist circumference, height and fasting glucose. Conclusions Bloodspot ELISA is suitable for measuring adiponectin in very small volumes of blood collected on filter paper and can be applied to large-scale studies.

Informed consent is the cornerstone of human research subject protection. Many subjects sign consent documents without understanding the study purpose, procedures, risks, benefits, and their rights. Proof of comprehension is not required and rarely obtained. Understanding might improve by using an interactive system with multiple options for hearing, viewing and reading about the study and the consent form at the subject’s own pace with testing and immediate feedback. This prospective randomized study compared the IRB-approved paper ICF for an actual clinical research study with an interactive presentation of the same study and its associated consent form using an iPad device in two populations: clinical research professionals, and patients drawn from a variety of outpatient practice settings. Of the 90 participants, 69 completed the online test and survey questions the day after the session (maximum 36 hours post-session). Among research professionals (n = 14), there was a trend (p = .07) in the direction of iPad subjects testing better on the online test (mean correct = 77%) compared with paper subjects (mean correct = 57%). Among patients (n = 55), iPad subjects had significantly higher test scores than standard paper consent subjects (mean correct = 75% vs 58%, p < .001). For all subjects, the total time spent reviewing the paper consent was 13.2 minutes, significantly less than the average of 22.7 minutes total on the three components to be reviewed using the iPad (introductory video, consent form, interactive quiz). Overall satisfaction and overall enjoyment slightly favored the interactive iPad presentation. This study demonstrates that combining an introductory video, standard consent language, and an interactive quiz on a tablet-based system improves comprehension of research study procedures and risks.

Extensive research shows that residential segregation has severe health consequences for racial and ethnic minorities. Most research to date has operationalized segregation in terms of either poverty or race/ethnicity rather than a synergy of these factors. A novel version of the Index of Concentration at the Extremes (ICERace-Income) specifically assesses racialized economic segregation in terms of spatial concentrations of racial and economic privilege (e.g., wealthy white people) versus disadvantage (e.g., poor Black people) within a given area. This multidimensional measure advances a more comprehensive understanding of residential segregation and its consequences for racial and ethnic minorities. The aim of this paper is to critically review the evidence on the association between ICERace-Income and health outcomes. We implemented the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct a rigorous search of academic databases for papers linking ICERace-Income with health. Twenty articles were included in the review. Studies focused on the association of ICERace-Income with adverse birth outcomes, cancer, premature and all-cause mortality, and communicable diseases. Most of the evidence indicates a strong association between ICERace-Income and each health outcome, underscoring income as a key mechanism by which segregation produces health inequality along racial and ethnic lines. Two of the reviewed studies examined racial disparities in comorbidities and health care access as potential explanatory factors underlying this relationship. We discuss our findings in the context of the extant literature on segregation and health and propose new directions for future research and applications of the ICERace-Income measure.

Background & methods National protests in the summer of 2020 drew attention to the significant presence of police in marginalized communities. Recent social movements have called for substantial police reforms, including “defunding the police,” a phrase originating from a larger, historical abolition movement advocating that public investments be redirected away from the criminal justice system and into social services and health care. Although research has demonstrated the expansive role of police to respond a broad range of social problems and health emergencies, existing research has yet to fully explore the capacity for health insurance policy to influence rates of arrest in the population. To fill this gap, we examine the potential effect of Medicaid expansion under the Affordable Care Act (ACA) on arrests in 3,035 U.S. counties. We compare county-level arrests using FBI Uniform Crime Reporting (UCR) Program Data before and after Medicaid expansion in 2014–2016, relative to counties in non-expansion states. We use difference-in-differences (DID) models to estimate the change in arrests following Medicaid expansion for overall arrests, and violent, drug, and low-level arrests. Results Police arrests significantly declined following the expansion of Medicaid under the ACA. Medicaid expansion produced a 20–32% negative difference in overall arrests rates in the first three years. We observe the largest negative differences for drug arrests: we find a 25–41% negative difference in drug arrests in the three years following Medicaid expansion, compared to non-expansion counties. We observe a 19–29% negative difference in arrests for violence in the three years after Medicaid expansion, and a decrease in low-level arrests between 24–28% in expansion counties compared to non-expansion counties. Our main results for drug arrests are robust to multiple sensitivity analyses, including a state-level model. Conclusions Evidence in this paper suggests that expanded Medicaid insurance reduced police arrests, particularly drug-related arrests. Combined with research showing the harmful health consequences of chronic policing in disadvantaged communities, greater insurance coverage creates new avenues for individuals to seek care, receive treatment, and avoid criminalization. As police reform is high on the agenda at the local, state, and federal level, our paper supports the perspective that broad health policy reforms can meaningfully reduce contact with the criminal justice system under historic conditions of mass criminalization.

Background Ward-Based Outreach Teams’ (WBOTs) use of mobile technologies can promote better quality and improved health services for populations in low- and middle-income countries. However, the implementation of such systems is fraught with threats to sustainability. Purpose The purpose of this study was to gain a deeper understanding of users’ views and experiences of the implementation of mHealth in a selected sub-district in North West Province, South Africa. Methods The study was qualitative, exploratory and descriptive. Data were collected from community health workers (CHWs) (n = 24) and outreach team leaders (OTLs) (n = 4) who used mobile devices loaded with the Mobenzi application through semi-structured focus group interviews, using an interview guide. Creswell’s stages of thematic analysis were used for data analysis, and codes, categories and themes were validated with the participants. Results Three themes emerged from the findings, namely the transition from a paper-based system to an electronic system; the use of the application; and reverting to paper-based documentation. The findings revealed that WBOTs considered the mHealth application as useful, easy to use, and supportive to their workflow. They experienced some technical challenges and lamented the inaccessibility of technicians. The mobile initiative’s termination caused distress among the participants, especially CHWs who had to revert to paper-based documentation to capture community health data. OTLs were concerned about the quality of health data being captured in the absence of the application. Conclusions The study concluded that community-based health care programmes that use WBOTs and mHealth technologies are essential in strengthening the health care system. WBOTs are facilitators for improving access to health care. Policy-makers and district managers will gain useful insights if they engage application users in discussions regarding future mobile health interventions for household and community-based care.

This study establishes a new method for estimating the monthly Average Population Present (APP) in Australian regions. Conventional population statistics, which enumerate people where they usually live, ignore the significant spatial mobility driving short term shifts in population numbers. Estimates of the temporary or ambient population of a region have several important applications including the provision of goods and services, emergency preparedness and serve as more appropriate denominators for a range of social statistics. This paper develops a flexible modelling framework to generate APP estimates from an integrated suite of conventional and novel data sources. The resultant APP estimates reveal the considerable seasonality in small area populations across Australia’s regions alongside the contribution of domestic and international visitors as well as absent residents to the observed monthly variations. The modelling framework developed in the paper is conceived in a manner such that it can be adapted and re-deployed both for use with alternative data sources as well as other situational contexts for the estimation of temporary populations.

Taking the Guangdong-Hong Kong-Macao Greater Bay Area as the research area, this paper used OD cluster analysis based on Baidu migration data from January 11 to January 25 (before the sealing-off of Wuhan) and concluded that there is a significant correlation 1the migration level from Wuhan to the GBA and the epidemic severity index. This paper also analyzed the migration levels and diffusivity of the outer and inner cities of the GBA. Lastly, four evaluation indexes were selected to research the possibility of work resumption and the rating of epidemic prevention and control through kernel density estimation. According to the study, the amount of migration depends on the geographical proximity, relationship and economic development of the source region, and the severity of the epidemic depends mainly on the migration volume and the severity of the epidemic in the source region. The epidemic risk is related not only to the severity of the epidemic in the source region but also to the degree of urban traffic development and the degree of urban openness. After the resumption of work, the pressure of epidemic prevention and control has been concentrated mainly in Shenzhen and Canton; the further away a region is from the core cities, the lower the pressure in that region. The mass migration of the population makes it difficult to control the epidemic effectively. The study of the relationship between migration volume, epidemic severity and epidemic risk is helpful to further analyze transmission types and predict the trends of the epidemic.

This paper uses event study based on the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model to study the impact of the COVID-19 outbreak on China’s financial market. It finds that the pandemic had an overall significant and negative impact on the stock prices of firms listed on SSE, SZSE and ChiNext. However, such impact appeared to be heterogeneous across industries, affecting listed firms in industries such as pharmaceutical and telecommunications positively, but those in services industries such as accommodation, catering, and commercial services negatively. Apparently, a crisis for some had been an opportunity for others. In addition, this paper seeks to understand the micro mechanism behind the heterogeneity of pandemic shock from the perspective of firms’ financial position. It finds that listed firms with higher debt level were hit harder, whereas those with more net cash flow had displayed higher resilience against the blow of the pandemic. However, the opposite pattern is found among those listed on ChiNext and in industries severely devastated by the pandemic. These findings have policy implications in terms of preventing systemic financial risks and facilitating recovery during pandemic-induced economic downturns. It also helps investor adjust investment strategies, hedge against risks, and secure gains when the market conditions in general are unfavorable.

The skill of analyzing and interpreting research data is central to the scientific process, yet it is one of the hardest skills for students to master. While instructors can coach students through the analysis of data that they have either generated themselves or obtained from published articles, the burgeoning availability of preprint articles provides a new potential pedagogical tool. We developed a new method in which students use a cognitive apprenticeship model to uncover how experts analyzed a paper and compare the professional’s cognitive approach to their own. Specifically, students first critique research data themselves and then identify changes between the preprint and final versions of the paper that were likely the results of peer review. From this activity, students reported diverse insights into the processes of data presentation, peer review, and scientific publishing. Analysis of preprint articles is therefore a valuable new tool to strengthen students’ information literacy and understanding of the process of science.

COVID-19 leads small and medium-sized enterprises (SMEs) to survive very hard. The development difficulties of SMEs lead to weak employment and GDP growth in various countries. In the process of COVID-19’s continuous spread, what is the major reason for the difficulties of SMEs? This paper hopes to answer this question by studying SMEs in Beijing. On this basis, this paper uses structural equation model (SEM) to study the relatively fast recovery of SMEs in Beijing, China, to explore the factors affecting SMEs in the pandemic. After detailed desk research and interviews with relevant entrepreneurs, this paper collects 234 valid questionnaires from SMEs in various industries in Beijing with the help of Federation of Industry and Commerce and Chamber of Commerce in Beijing. Then the data is analyzed with the SEM, which shows the relationship between cash flow from financing activities, markets, employees, costs, government policies and the impact of the pandemic. Finally, an impact model of the pandemic on SMEs is established. The result of the model indicates that the direct effect of the pandemic on the market is the most prominent, and government policies can significantly reduce the negative impact of the pandemic on SMEs indirectly. Based on this, this paper puts forward some policy suggestions, such as the targeted issuance of consumption vouchers and the reduction of administrative barriers. This will enable megacities in various countries to improve policy support for SMEs and promote the recovery and development of SMEs.

Since the outbreak of the global COVID-19 pandemic, social distancing has been known to everyone and recommended almost everywhere everyday. Social distancing has been and will be one of the most effective measures and sometimes, the only available one for fighting epidemics and saving lives. However, it has not been so clear how social distancing should be practiced or managed, especially when it comes to regulating everyone’s otherwise normal social activities. The debate on how to implement social distancing often leads to a heated political argument, while research on the subject is lacking. This paper is to provide a theoretical basis for the understanding of the scientific nature of social distancing by considering it as a social dilemma game played by every individual against his/her population. From this perspective, every individual needs to make a decision on how to engage in social distancing, or risk being trapped into a dilemma either exposing to deadly diseases or getting no access to necessary social activities. As the players of the game, the individual’s decisions depend on the population’s actions and vice versa, and an optimal strategy can be found when the game reaches an equilibrium. The paper shows how an optimal strategy can be determined for a population with either closely related or completely separated social activities and with either single or multiple social groups, and how the collective behaviors of social distancing can be simulated by following every individual’s actions as the distancing game progresses. The simulation results for populations of varying sizes and complexities are presented, which not only justify the choices of the strategies based on the theoretical analysis, but also demonstrate the convergence of the individual actions to an optimal distancing strategy in silico and possibly in natura as well, if every individual makes rational distancing decisions.

Background A pandemic of coronavirus disease 2019 (COVID-19) emerged and affected most of the world in early 2020. To inform effective public health measures we conducted a knowledge, attitude and practice (KAP) survey among a Hausa Muslim society in Nigeria in March 2020. Methods The study is an analytic cross-sectional survey with questionnaires administered to the general population including Health Care Workers (HCW) in Kano, Nigeria. Participants were recruited by convenience sampling following informed consent. The percentage of KAP scores were categorized as good and poor. Independent predictors of good knowledge of COVID 19 were ascertained using a binary logistic regression model. Results The questionnaire was administered among urban 32.8%, peri-urban dwellers 32.4%, and to online participants 34.8%. The peri-urban and urban participants were given paper questionnaires. There were 886 study participants with mean age 28.58yrs [SD:10.25] (Interquartile range [IQR]:22yrs–32yrs), males 55.4% with 57.3% having had or were in tertiary education. Most participants were students 40% and civil servants 20%. The overall mean [standard deviation (SD)] for knowledge, attitude and practice scores expressed in percentage was 65.38%[SD15.90], 71.45% [SD14.10], and 65.04% [SD17.02] respectively. Out of the respondents, 270(30.47%) had good knowledge (GK), 158(17.8%) had good attitude (GA), and 230(25.96%) had good practice (GP) using cut-off scores of 75%, 86.5%, and 75% respectively. Over 48% did not agree COVID-19 originated from animals while 60% perceived the pandemic to be due to God’s punishment. Also, 36% thought it was a man-made virus. When rating fear, most respondents [63.5%] had marked fear i.e. ≥ 7 out of 10 and 56% admitted to modifying their habits recently in fear of contracting the virus. As regards attitude to religious norms, 77.77% agreed on cancellation of the lesser pilgrimage as a measure to curb the spread of the disease while 23.64% admitted that greater pilgrimage (Hajj) should proceed despite the persistence of the ongoing pandemic. About 50% of the respondents insisted on attending Friday congregational prayers despite social distancing. One in four people still harbored stigma towards a person who has recovered from the virus. 28% felt some races are more at risk of the disease though 66% mentioned always practicing social distancing from persons coughing or sneezing. Almost 70% of respondents said they were willing to accept a vaccine with 39% saying they would be willing to pay for it if not publicly funded. In univariate analysis increasing age and having been ever married were associated with GK while tertiary education was associated with GA [Odds Ratio; 95% Confidence Interval] 2.66(1.79–3.95). Independent positive predictors of GK were those who were or had ever been married, those who had marked fear of COVID-19, and had modified their habits in the last three months. Those who had non-tertiary education and had the questionnaire administered as paper rather than online version had GK but age was not a predictor. Conclusion Knowledge of transmission and preventive measures should be improved in the general population cognizant of cultural norms and Islamic practices. The study highlights the importance of considering belief systems and perception in developing control measures against COVID-19.

At present, people are demanding better indoor air quality during the COVID-19 pandemic. In addition to maintaining the basic functions, new air-conditioning should also add air purification functions to improve indoor air quality and reduce the possibility of virus transmission. Nowadays, there is lack of research results on the innovation of air-conditioning. The aim of this study is to present a two-stage mathematical model for identifying critical manufacturing factors in the innovation process of air conditioning. In this paper, Kano and quality function deployment (QFD) are used to analyze the critical factors affecting air-conditioning innovation. Some studies have proposed using Kano-QFD model to analyze product innovation, but the study only studies one stage, which loses the analysis of the subsequent stages of product innovation. Based on this, this paper studies the priority method of two-stage critical factors for air-conditioning innovation. Firstly, the questionnaire survey and fuzzy sets are used to collect demand information of multi-agent (customers and professional technicians). Secondly, the Kano model is used to classify and calculate satisfaction of multi-agent. Then, QFD is used to transform multi-agent demands into engineering property indexes (first stage) and technical property indexes (second stage) and calculate the weight of each index. Finally, the applicability and superiority of this method is illustrated by taking the central air-conditioning as an example.

Background and objectives With the increase in the number of COVID-19 infections, the global health apparatus is facing insufficient resources. The main objective of the current study is to provide additional data regarding the clinical characteristics of the patients diagnosed with COVID-19, and in particular to analyze the factors associated with disease severity, lack of improvement, and mortality. Methods 102 studies were included in the present meta-analysis, all of which were published before September 24, 2020. The studies were found by searching a number of databases, including Scopus, MEDLINE, Web of Science, and Embase. We performed a thorough search from early February until September 24. The selected papers were evaluated and analyzed using Stata software application version 14. Results Ultimately, 102 papers were selected for this meta- analysis, covering 121,437 infected patients. The mean age of the patients was 58.42 years. The results indicate a prevalence of 79.26% for fever (95% CI: 74.98–83.26; I2 = 97.35%), 60.70% for cough (95% CI: 56.91–64.43; I2 = 94.98%), 33.21% for fatigue or myalgia (95% CI: 28.86–37.70; I2 = 96.12%), 31.30% for dyspnea (95% CI: 26.14–36.69; I2 = 97.67%), and 10.65% for diarrhea (95% CI: 8.26–13.27; I2 = 94.20%). The prevalence for the most common comorbidities was 28.30% for hypertension (95% CI: 23.66–33.18; I2 = 99.58%), 14.29% for diabetes (95% CI: 11.88–16.87; I2 = 99.10%), 12.30% for cardiovascular diseases (95% CI: 9.59–15.27; I2 = 99.33%), and 5.19% for chronic kidney disease (95% CI: 3.95–6.58; I2 = 96.42%). Conclusions We evaluated the prevalence of some of the most important comorbidities in COVID-19 patients, indicating that some underlying disorders, including hypertension, diabetes, cardiovascular diseases, and chronic kidney disease, can be considered as risk factors for patients with COVID-19 infection. Furthermore, the results show that an elderly male with underlying diseases is more likely to have severe COVID-19.

Background As the world’s largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. Methods Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. Results The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. Conclusions The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

Replication is an important tool to promote high quality research and ensure policy makers can rely on studies in making guidelines or funding programs. By ensuring influential studies are replicable we provide assurance that the policies based on these studies are well-founded and the conclusions and recommendations are robust-to different estimation models or different choices. In this paper, we argue that replication is not only useful but necessary to ensure that an author's choice in how to analyse data is not the only factor that determines whether an intervention is effective or not. We also show that while most research is done well and provides robust results, small differences can lead to different interpretations and these differences need to be acknowledged. This special issue highlights 5 such replication studies, which are replications of influential studies on biomedical, social, behavioural and structural interventions for HIV prevention and treatment. We reflect on their findings. Four out of five studies, which conduct push button replication and pure replication, were able to reproduce the results of the original studies with minor differences, mainly due to minor typographical errors or rounding differences. The analysis of the measurement and estimation analyses conducted in these five studies reveals that the original results are not very robust to alternative analytical approaches, especially when these results rely on a small number of observations. In these cases, the original results are weakened. Furthermore, in contrast to the original papers, two of the five included replication studies conducted a theory of change analysis-to explore how or why the interventions work (or do not) not just whether the intervention works or not. These two analyses indicate that the estimated impacts of the interventions are drawn from few mediators. In addition, they demonstrate that, in some cases, a lack of effect may be related to lack of adequate exposure to the intervention rather than inefficacy of the intervention per se. However, overall, the included replication studies show that the results presented in the original papers are trustworthy and robust, especially when based on larger sample sizes. Replication studies can not only verify the results of a study, they can also provide additional insights on the published results, such as how and why an intervention was effective or less effective than expected. They can thus be a tool to inform the research community and/ or policymakers about whether and how interventions could be adopted, which need to be tested further, and which should be discontinued because of their ineffectiveness. Thus, publishing these replication studies in peer-reviewed journals makes the work public and publicized. The work advances knowledge, and publication should be encouraged, as it is for other types of research.

This paper analyzes the dynamics of the labor market in Latin America during the COVID-19 pandemic. After a decade of a virtuous circle of growth with the creation of formal jobs, the pandemic has had an considerable impact on the region’s labor market, generating an unparalleled increase in the proportion of the inactive population, considerable reductions in informality, and, in contrast, smaller fluctuations in formal jobs. In this context, the formal sector, given its lower flexibility, became a "social safety net" that preserved the stability of employment and wages. Based on the findings presented in this paper, it is projected that, starting in 2021, informality will grow to levels higher than those of the pre-COVID-19 era–with 7.56 million additional informal jobs–as a result of the population returning to the labor market to compensate for the declines in incomes. According to the simulations presented, postponing or forgiving income tax payments and social security contributions conditional on the generation of formal jobs could reduce the growth of informality by 50 to 75 percent. Achieving educational improvements has the potential to reduce it by 50 percent.

An unbiased, widely accepted estimate of the rate of occurrence of new cases of autism over time would facilitate progress in understanding the causes of autism. The same may also apply to other disorders. While incidence is a widely used measure of occurrence, birth prevalence—the proportion of each birth year cohort with the disorder—is the appropriate measure for disorders and diseases of early childhood. Studies of autism epidemiology commonly speculate that estimates showing strong increases in rate of autism cases result from an increase in diagnosis rates rather than a true increase in cases. Unfortunately, current methods are not sufficient to provide a definitive resolution to this controversy. Prominent experts have written that it is virtually impossible to solve.This paper presents a novel method, time-to-event birth prevalence estimation (TTEPE), to provide accurate estimates of birth prevalence properly adjusted for changing diagnostic factors. It addresses the shortcomings of prior methods. TTEPE is based on well-known time-to-event (survival) analysis techniques. A discrete survival process models the rates of incident diagnoses by birth year and age. Diagnostic factors drive the probability of diagnosis as a function of the year of diagnosis. TTEPE models changes in diagnostic criteria, which can modify the effective birth prevalence when new criteria take effect. TTEPE incorporates the development of diagnosable symptoms with age. General-purpose optimization software estimates all parameters, forming a non-linear regression. The paper specifies all assumptions underlying the analysis and explores potential deviations from assumptions and optional additional analyses.A simulation study shows that TTEPE produces accurate parameter estimates, including trends in both birth prevalence and the probability of diagnosis in the presence of sampling effects from finite populations. TTEPE provides high power to resolve small differences in parameter values by utilizing all available data points.

The paper examines the interest of the commercial banks' stakeholders in Pillar 3 disclosures and their behaviour during the timing of serious market turbulence. The aim is to discover to which extent current banking regulation supports stakeholders' interest in the information required by regulators to be disclosed. The examined data consists of log files that were pre-processed using web mining techniques and from which were extracted frequent item sets by quarters and evaluated in terms of quantity. The authors have proposed a methodology to evaluate frequent item sets of web parts over a dedicated time. Based on the verification of applied methodology on two commercial banks, the results show that stakeholders' interest in disclosures is highest in the first quarter at each year and after turbulent times in 2009 their interests decreased. Moreover, the results suggest that stakeholders expressed higher interest than in regulatory required Pillar 3 information in the following group of information: Pillar3 related information, Annual reports, Information on Group. Following our results, the paper contributes to cover the gap in the research by analysing Pillar 3 disclosures and their compliance with regulatory requirements, which also increase the interest of the relevant stakeholders to conduce them as an effective market discipline tool. Článek zkoumá zájem zainteresovaných stran komerčních bank o zveřejňování informací podle 3. pilíře a jejich chování v době vážných tržních turbulencí. Cílem je zjistit, do jaké míry současná bankovní regulace podporuje zájem zainteresovaných stran o informace, jejichž zveřejnění regulátoři vyžadují. Zkoumaná data se skládají z logů, které byly předem zpracovány pomocí technik web miningu a z nichž byly extrahovány soubory častých položek podle čtvrtletí a vyhodnoceny z hlediska kvantity. Autoři navrhli metodiku vyhodnocování sad častých položek webových částí za vyhrazený čas. Na základě ověření aplikované metodiky na dvou komerčních bankách výsledky ukazují, že zájem zainteresovaných stran o zveřejňované informace je v každém roce nejvyšší v prvním čtvrtletí a po turbulentním období v roce 2009 jejich zájem poklesl. Výsledky dále naznačují, že zainteresované strany projevily vyšší zájem než o regulatorně požadované informace 3. pilíře o následující skupinu informací: Informace související s pilířem 3, Výroční zprávy, Informace o skupině. V návaznosti na naše výsledky přispívá tento článek k pokrytí mezery ve výzkumu analýzou informací zveřejňovaných v rámci Pilíře 3 a jejich souladu s regulatorními požadavky, což také zvyšuje zájem příslušných zainteresovaných stran o jejich vedení jako účinného nástroje tržní disciplíny.

Selecting a target journal is a universal decision faced by authors of scientific papers. Components of the decision, including expected turnaround time, journal acceptance rate, and journal impact factor, vary in terms of accessibility. In this study, I collated recent turnaround times and impact factors for 82 journals that publish papers in the field of fisheries sciences. In addition, I gathered acceptance rates for the same journals when possible. Findings indicated clear among-journal differences in turnaround time, with median times-to-publication ranging from 79 to 323 days. There was no clear correlation between turnaround time and acceptance rate nor between turnaround time and impact factor; however, acceptance rate and impact factor were negatively correlated. I found no field-wide differences in turnaround time since the beginning of the COVID-19 pandemic, though some individual journals took significantly longer or significantly shorter to publish during the pandemic. Depending on their priorities, authors choosing a target journal should use the results of this study as guidance toward a more informed decision.

Drowning is a global public health problem, but accurately estimating drowning risk remains a challenge. Coastal drowning comprises a significant proportion of the drowning burden in Australia and is influenced by a range of behavioural factors (e.g. risk perception, knowledge, attitudes and behaviours) that are poorly understood. These factors, along with those that impact exposure (e.g. coastal visitation and activity participation) all impact on drowning risk. While excellent mortality and morbidity data exists in Australia, a lack of coastal participation data presents challenges to identifying high-risk groups or activities and prioritising prevention efforts. This methods paper describes the development and evolution of an ongoing, annual, nationally representative online survey as an effective tool used to capture valuable data about the Australian population’s relationship with the coast. This paper explores how the survey is structured (12–14 sections spanning multiple topics and themes), the different question types used (including open text, 4-digit responses and categorical questions), the sample size (1400–1600 respondents), sampling strategy (using demographic quota sampling which can then be post-weighted to the population if required) and how topics and themes have changed over time to enhance the quality of data collected (i.e., wording changes to enhance participant comprehension or data usability and changing issue-specific ‘feature’ topics of interest such as campaign evaluation). How the survey is implemented online is described, both practically through to third-party recruitment processes and ethically to maximise anonymity of respondents and ensure data quality. Interim analyses indicate the impact of considering exposure when calculating fatal drowning rates, especially by activity (e.g., crude boating drowning rate 0.12 per 100,000 population vs 0.95 per 100,000 exposed population [relative risk = 8.01; 95% confidence interval: 4.55–14.10]). This study highlights lessons learned in the process of conducting a nationally representative coastal participation survey as well as the strengths and limitations of adopting this approach. Data collected will provide more detailed information on the skills, behaviours, knowledge and attitudes of coastal activity participants. Analyses of this unique dataset will inform research that will underpin development and evaluation of coastal drowning prevention initiatives prioritising those most at risk. It is hoped that the methods detailed within this study may be useful for other countries to develop similar approaches to understanding their own population.

In this paper, we propose a hierarchical statistical model for a single repairable system subject to several failure modes (competing risks). The paper describes how complex engineered systems may be modelled hierarchically by use of Bayesian methods. It is also assumed that repairs are minimal and each failure mode has a power-law intensity. Our proposed model generalizes another one already presented in the literature and continues the study initiated by us in another published paper. Some properties of the new model are discussed. We conduct statistical inference under an objective Bayesian framework. A simulation study is carried out to investigate the efficiency of the proposed methods. Finally, our methodology is illustrated by two practical situations currently addressed in a project under development arising from a partnership between Petrobras and six research institutes.

Since the financial system has illustrated an increasingly prominent characteristic of inextricable connections, information theory is gradually utilized to study the financial system. By collecting the daily data of industry index (2005-2020) and region index (2012-2020) listed in China as samples, this paper applies an innovative measure named partial mutual information on mixed embedding to generate directed networks. Based on the analysis of nonlinear relationships among sectors, this paper realizes the accurate construction of “time-varying” financial network from the perspective of risk spillover. The results are presented as follow: (1) interactions can be better understood through the nonlinear networks among distinct sectors, and sectors in the networks could be classified into different types according to their topological properties connected to risk spillover; (2) in the rising stage, information is transmitted rapidly in the network, so the risk is fast diffused and absorbed; (3) in the declining stage, the network topology is more complex and panic sentiments have long term impact leading to more connections; (4) The US market, Japan market and Hongkong market have significant affect on China’s market. The results suggest that this nonlinear measure is an effective approach to develop financial networks and explore the mechanism of risk spillover.

The SARS-CoV-2 pandemic has emerged as one of the most dramatic health crises of recent decades. This paper treats mainstream news about the current pandemic as a valuable entry point for analyzing the relationship between science and politics in the public sphere, where the outbreak must be both understood and confronted through appropriate public-health policy decisions. In doing so, the paper aims to examine which actors, institutions, and experts dominate the SARS-CoV-2 media narratives, with particular attention to the roles of political, medical, and scientific actors and institutions within the pandemic crisis. The study relies on a large dataset consisting of all SARS-CoV-2 articles published by eight major Italian national newspapers between January 1, 2020 and June 15, 2020. These articles underwent a quantitative analysis based on a topic modeling technique. The topic modeling outputs were further analyzed by innovatively combining ad-hoc metrics and a classifier based on the stacking ensemble method (combining regularized logistic regression and linear stochastic gradient descent) for quantifying scientific salience. This enabled the identification of relevant topics and the analysis of the roles that different actors and institutions engaged in making sense of the pandemic. The results show how the health emergency has been addressed primarily in terms of political regulation and concerns and only marginally as a scientific matter. Hence, science has been overwhelmed by politics, which, in media narratives, exerts a moral as well as regulatory authority. Media narratives exclude neither scientific issues nor scientific experts; rather, they configure them as a subsidiary body of knowledge and expertise to be mobilized as an ancillary, impersonal institution useful for legitimizing the expansion of political jurisdiction over the governance of the emergency.

During the Covid-19 pandemic, the Vietnamese government has actively implemented various policies to achieve dual objectives: (i) to minimize the loss of life due to the infection; and (ii) to support economic growth. This paper is conducted to examine the effect of the government’s containment and closure policy on the stock market quality in Vietnam. Unlike other papers, we focus exclusively on market quality during the pandemic. We find that the policies appear to positively affect the market quality, except for closing-school policy (negative effect) and international travel (no effect). We argue that the government should sustain the policies until the wide availability of the vaccine to support the stock market quality in the near future.

BackgroundThis study examines the relationship between experiencing intimate partner violence (IPV), exposure to prior childhood adversity, lifetime adverse experiences, drug-related relationship dependencies with intimate partners and overdose, hospitalization for drug use, friends and family members who overdosed and witnessing overdose.MethodologyThis paper included a sample of 201 women who use drugs in heterosexual relationships with criminal justice-involved men in New York City. We included measures of experiencing overdose, hospitalization for drug use, witnessing overdose, and having friends and family who overdosed. Intimate partner violence consisted of either 1) none/verbal only, 2) moderate and 3) severe abuse. Dichotomous indicators of drug-related relationship dependencies included financial support, drug procurement, splitting and pooling drugs. A scale measured cumulative exposure to childhood adversity and lifetime exposures to adverse events. This paper hypothesized that experiencing moderate and severe IPV, drug-related dependencies and exposure to prior childhood and lifetime adversity would be associated with a greater risk of experiencing overdose, hospitalization for drug use, witnessing overdose and having friends and family members who overdosed. Generalized linear modeling with robust variance estimated relative risk ratios that accounted for potential bias in confidence intervals and adjusted for race, ethnicity, education and marital status.ResultsWe found experiencing moderate or severe IPV was associated with ever being hospitalized for drug use and having a family member who experienced overdose. Experiencing moderate IPV was associated with increased risk of witnessing overdose, Partner drug dependencies were associated with overdose, ever being hospitalized for drug use, witnessing overdose, and having a family member or friend who experienced overdose. Childhood and lifetime adversity exposures were significantly associated with increased risk of overdose, ever being hospitalized for drug use, ever witnessing overdose and having a friend and family member who overdosed.ConclusionFindings underscore the intersection of experiencing IPV and drug-related relationship dependencies, childhood adversity and lifetime adversity in shaping experiences of and witnessing overdose among women who use drugs. They highlight the urgent need to address IPV, adversity experiences and drug-related relationship dependencies in overdose prevention for women who use drugs.

Personal motivation is a key factor in the service of foster care, impending both on the welfare of the child and on the satisfaction of the carer. This paper explores the benefits, challenges and dilemmas involved in the job of professional (i.e. state-supported) foster carer in Romania-a country where the issue of child protection has drawn a great deal of international attention over the last thirty years. The principal hypothesis concerns whether the benefits, challenges and dilemmas identified by foster carers are influenced by the factors that led to their taking up this kind of work. Quantitative research was conducted using a questionnaire as the main tool. The paper takes a descriptive, cross-sectional and multifactorial approach. Sampling was carried out by self-selecting method, and the study involved 51 participants. The research project identified a statistically significant variation in the challenges and dilemmas reported by foster carers. Thus, the results of the study show that the majority of the carers indicate a primarily intrinsic motivation for their work. By way of conclusion, it is argued that social workers, operating in collaboration with multidisciplinary teams, can offer carers support in managing more difficult periods in the child-carer relationship, thus enhancing the sustainability of the foster care service.

The number of COVID-19 infections is key for accurately monitoring the pandemics. However, due to differential testing policies, asymptomatic individuals and limited large-scale testing availability, it is challenging to detect all cases. Seroprevalence studies aim to address this gap by retrospectively assessing the number of infections, but they can be expensive and time-intensive, limiting their use to specific population subgroups. In this paper, we propose a complementary approach that combines estimated (1) infection fatality rates (IFR) using a Bayesian melding SEIR model with (2) reported case-fatality rates (CFR) in order to indirectly estimate the fraction of people ever infected (from the total population) and detected (from the ever infected). We apply the technique to the U.S. due to their remarkable regional diversity and because they count with almost a quarter of all global confirmed cases and deaths. We obtain that the IFR varies from 1.25% (0.39–2.16%, 90% CI) in Florida, the most aged population, to 0.69% in Utah (0.21–1.30%, 90% CI), the youngest population. By September 8, 2020, we estimate that at least five states have already a fraction of people ever infected between 10% and 20% (New Jersey, New York, Massachussets, Connecticut, and District of Columbia). The state with the highest estimated fraction of people ever infected is New Jersey with 17.3% (10.0, 55.8, 90% CI). Moreover, our results indicate that with a probability of 90 percent the fraction of detected people among the ever infected since the beginning of the epidemic has been less than 50% in 15 out of the 20 states analyzed in this paper. Our approach can be a valuable tool that complements seroprevalence studies and indicates how efficient have testing policies been since the beginning of the outbreak.

Sexual violence against women and girls is a major public health problem globally and in South Africa. Although young men have been identified as an important risk group for prevention interventions, scant attention have been given to this age cohort in low and middle-income countries. There is strong evidence that perpetration starts early and increasing attention is being drawn to Higher Education Institutions (HEIs) as settings for prevention interventions. The main objective of this study was to adapt the One Man Can Intervention for use with male university students in residences and develop materials for implementation. This paper presents the qualitative findings of the adaptation process of the One Man Can Intervention with 15 young male student leaders at a HEI in South Africa. The same participants who started in the study, participated throughout. Only five of the 15 participants were located and participated in the interviews six months post intervention. The results show the emergence of a six-hour session adapted intervention that addresses key drivers of violence against women and girls (VAWG). Critical engagement and dialogue on sexual violence is shown to shift key norms on gender equality, on being a man and reflection on their role in preventing sexual violence. This paper contributes to the field where much learning, refining and improvement of prevention interventions for VAWG are ongoing.

BackgroundThe 2013–15 Ebola epidemic in West Africa was the largest so far recorded, and mainly affected three adjacent countries, Guinea, Liberia and Sierra Leone. The worst affected country (in terms of confirmed cases) was Sierra Leone. The present paper looks at the epidemic in Sierra Leone. The epidemic in this country was a concatenation of local outbreaks. These local outbreaks are not well characterized through analysis using standard numerical techniques. In part, this reflects difficulties in record collection at the height of the epidemic. This paper offers a different approach, based on application of field-based techniques of social investigation that provide a richer understanding of the epidemic.MethodsIn a post-epidemic study (2016–18) of two districts (Bo and Moyamba) we use ethnographic data to reconstruct local infection pathways from evidence provided by affected communities, cross-referenced to records of the epidemic retained by the National Ebola Response Commission, now lodged in the Ebola Museum and Archive at Njala University. Our study documents and discusses local social and contextual factors largely missing from previously published studies.ResultsOur major finding is that the epidemic in Sierra Leone was a series of local outbreaks, some of which were better contained than others. In those that were not well contained, a number of contingent factors helps explain loss of control. Several numerical studies have drawn attention to the importance of local heterogeneities in the Sierra Leone Ebola epidemic. Our qualitative study throws specific light on a number of elements that explain these heterogeneities: the role of externalities, health system deficiencies, cultural considerations and local coping capacities.ConclusionsSocial issues and local contingencies explain the spread of Ebola in Sierra Leone and are key to understanding heterogeneities in epidemiological data. Integrating ethnographic research into epidemic-response is critical to properly understand the patterns of spread and the opportunities to intervene. This conclusion has significant implications for future interdisciplinary research and interpretation of standard numerical data, and consequently for control of epidemic outbreaks.

Objective This paper reflected on the prevalence of hypertension and diabetes in Bangladesh, which is spreading rapidly in low-income countries. The rationale of constructing more health centers for addressing NCDs was assessed in this paper by determining the relationship between prevalence of NCDs, particularly hypertension and diabetes, and distance to health facilities. Methods From BDHS (Bangladesh Health and Demographic Survey) 2011 data set, 7544 samples were analyzed to demonstrate association between Non-communicable diseases (NCD) and distance from respondents’ home to health facilities like hospitals, community clinics, pharmacies or doctors’ chambers, and community facilities like market, post office or cinema hall. Bivariate analysis was conducted between accessibility to health facilities and prevalence of the diseases. The causal relationship between the spatial effects and the prevalence of the diseases were analyzed by applying Generalized Linear Mixed Model (GLMM) was fitted. Results Fitting linear mixed effect models, we found that hypertension and diabetes react differently with various spatial effects. Distance from home to hospital had significant effect (P < 0.001) on hypertension showing people living further from the facilities or town centers seemed to be less hypertensive, whereas diabetes showed no such affiliation. Conclusion Higher prevalence of diabetes (40.9%) over hypertension (26.5%) in people aging 35 or higher, have appeared to have caused the difference, which concluded that each non-communicable disease should be dealt to its own merit for policy making instead considering as a group of diseases.

ObjectivesLittle is known about the gender and age differences associated with the effects of economic fluctuations on hospitalization for cardiovascular diseases. This paper investigates the impact of economic fluctuations on hospitalization for ischemic heart disease (IHD), stroke, and hypertension by age and gender between January 1996 and December 2012 in Taiwan.MethodsWe adopted a multiple-input time series analysis to examine the strength of the immediate and latent effects of the 17-year quarterly unemployment rates (UR), air pollution exposure (APE), gross domestic product (GDP), per capita consumption expenditure in cigarette and alcohol (ECA), and per capita healthcare expenditure (HE) on the adjusted quarterly incidence rate of hospitalization. The data used in this paper were retrieved from the National Health Insurance Research Database and the website of the Directorate-General of Budget, Accounting and Statistics (DGBAS), Executive Yuan.ResultsOur findings indicate that higher UR increased IHD hospitalization in young men and women and middle-aged women but reduced stroke hospitalization in young men. Higher APE increased IHD hospitalization in young men but reduced it for young women, increased stroke hospitalization in old men and middle-aged women but reduced it for young men, and increased hypertension hospitalization in middle-aged men and young women. Higher ECA reduced IHD hospitalization in middle-aged men, increased stoke hospitalization in middle-aged and old men and middle-aged women. Higher HE reduced IHD hospitalization in old men, young and old women, reduced stroke hospitalization in old women, and reduced hypertension hospitalization in young and middle-aged women.ConclusionsOverall, we found that the economic fluctuations caused increased harmful effects in certain population subgroups but also brought some soothing effects to some groups.

Introduction Stigma and discrimination (SAD) related to HIV compromise access and adherence to treatment and support programs among people living with HIV (PLHIV). The ambitious goal of ending the epidemic of HIV by 2030 set by the United Nations Joint Program of HIV/AIDS (UNAIDS) will thus only be achieved if HIV-related stigma and discrimination are reduced. The objective of this review was to locate, appraise and describe international literature reporting on interventions that addressed HIV-related SAD in healthcare settings. Methods The databases searched were: Cumulative Index to Nursing and Allied Health (CINAHL), Excerpta Medica Database from Elsevier (EMBASE), PubMed and Psychological Information (PsycINFO) database. Two individuals independently appraised the quality of the papers using appraisal instruments from the Joanna Briggs Institute (JBI). Data were extracted from papers included in the review using the standardized data extraction tool from JBI. Quality of evidence for major outcomes was assessed using Grading of Recommendations, Assessment, Development and Evaluation (GRADE). Results We retained 14 records reporting on eight studies. Five categories of SAD reduction (information-based, skills building, structural, contact-based and biomedical interventions) were identified. Training popular opinion leaders (POLs) resulted in significantly lower mean avoidance intent scores (MD = -1.87 [95% CI -2.05 to -1.69]), mean prejudicial attitude scores (MD = -3.77 [95% CI -5.4 to -2.09]) and significantly higher scores in mean compliance to universal precaution (MD = 1.65 [95% CI 1.41 to 1.89]) when compared to usual care (moderate quality evidence). The Summary of Findings table (SOF) is shown in Table 1. Conclusions Evidence of moderate quality indicates that training popular opinion leaders is effective in reducing avoidance intent and prejudicial attitude and improving compliance to universal precaution. Very low quality evidence indicates that professionally-assisted peer group interventions, modular interactive training, participatory self-guided assessment and intervention, contact strategy combined with information giving and empowerment are effective in reducing HIV-related stigma.Further Randomized Controlled Trials (RCTs) are needed. Future trials need to use up-to-date and validated instruments to measure stigma and discrimination.

One of the main strategies to control the spread of infectious animal diseases is the implementation of movement restrictions. This paper shows a loss in efficiency of the movement restriction policy (MRP) when behavioral responses of farmers are taken into account. Incorporating the strategic behavior of farmers in an epidemiologic model reveals that the MRP can trigger premature animal sales by farms at high risk of becoming infected that significantly reduce the efficacy of the policy. The results are validated in a parameterized network via Monte Carlo simulations and measures to mitigate the loss of efficiency of the MRP are discussed. Financial aid to farmers can be justified by public health concerns, not only for equity. This paper contributes to developing an interdisciplinary analytical framework regarding the expansion of infectious diseases combining economic and epidemiologic dimensions.

Background The report of the Joint United Nations Programme on HIV/AIDS (UNAIDS) for World AIDS Day 2014 highlighted a Fast-Track Strategy that sets ambitious treatment and prevention targets to reduce global HIV incidence to manageable levels by 2020 and end the AIDS epidemic by 2030. The 90-90-90 treatment targets for 2020 call for 90% of people living with HIV to know their HIV status, 90% of people who know their status to receive treatment, and 90% of people on HIV treatment to be virally suppressed. This paper examines how scale-up of voluntary medical male circumcision (VMMC) services in four priority countries in sub-Saharan Africa could contribute to ending the AIDS epidemic by 2030 in the context of concerted efforts to close the treatment gap, and what the impact of VMMC scale-up would be if the 90-90-90 treatment targets were not completely met. Methods Using the Goals module of the Spectrum suite of models, this analysis modified ART (antiretroviral treatment) scale-up coverage from base scenarios to reflect the 90-90-90 treatment targets in four countries (Lesotho, Malawi, South Africa, and Uganda). In addition, a second scenario was created to reflect viral suppression levels of 75% instead of 90%, and a third scenario was created in which the 90-90-90 treatment targets are reached in women, with men reaching more moderate coverage levels. Regarding male circumcision (MC) coverage, the analysis examined both a scenario in which VMMCs were assumed to stop after 2015, and one in which MC coverage was scaled up to 90% by 2020 and maintained at 90% thereafter. Results Across all four countries, scaling up VMMC is projected to provide further HIV incidence reductions in addition to those achieved by reaching the 90-90-90 treatment targets. If viral suppression levels only reach 75%, scaling up VMMC leads to HIV incidence reduction to nearly the same levels as those achieved with 90-90-90 without VMMC scale-up. If only women reach the 90-90-90 targets, scaling up VMMC brings HIV incidence down to near the levels projected with 90-90-90 without VMMC scale-up. Regarding cost, scaling up VMMC increases the annual costs during the scale-up phase, but leads to lower annual costs after the MC coverage target is achieved. Conclusions The scenarios modeled in this paper show that the highly durable and effective male circumcision intervention increases epidemic impact levels over those of treatment-only strategies, including the case if universal levels of viral suppression in men and women are not achieved by 2020. In the context of 90-90-90, prioritizing continued successful scale-up of VMMC increases the possibility that future generations will be free not only of AIDS but also of HIV.

Voluntary medical male circumcision is an integral part of the South African government’s response to the HIV and AIDS epidemic. Following circumcision, it is recommended that patients abstain from sexual activity for six weeks, as sex may increase the risk of female-to-male HIV transmission and prolong the healing period. This paper investigates the resumption of sexual activity during the healing period among a cohort of school-going males in the KwaZulu-Natal province of South Africa. The analysis for this paper compares two groups of sexually active school-going males: the first group reported having sex during the healing period (n = 40) and the second group (n = 98) reported no sex during the healing period (mean age: 17.7, SD: 1.7).The results show that 29% (n = 40) of young males (mean age: 17.9, SD: 1.8) who were previously sexually active, resumed sexual activity during the healing period, had on average two partners and used condoms inconsistently. In addition, those males that engage in sexual activity during the healing period were less inclined to practice safe sex in the future (AOR = 0.055, p = 0.002) than the group of males who reported no sex during the healing period. These findings suggest that a significant proportion of young males may currently and in the future, subject themselves to high levels of risk for contracting HIV post circumcision. Education, as part of a VMMC campaign, must emphasize the high risk of HIV transmission for both the males their partners during the healing period.

BackgroundOutbreaks of hand-foot-mouth disease (HFMD) have been reported for many times in Asia during the last decades. This emerging disease has drawn worldwide attention and vigilance. Nowadays, the prevention and control of HFMD has become an imperative issue in China. Early detection and response will be helpful before it happening, using modern information technology during the epidemic.MethodIn this paper, a hybrid model combining seasonal auto-regressive integrated moving average (ARIMA) model and nonlinear auto-regressive neural network (NARNN) is proposed to predict the expected incidence cases from December 2012 to May 2013, using the retrospective observations obtained from China Information System for Disease Control and Prevention from January 2008 to November 2012.ResultsThe best-fitted hybrid model was combined with seasonal ARIMA [Formula: see text] and NARNN with 15 hidden units and 5 delays. The hybrid model makes the good forecasting performance and estimates the expected incidence cases from December 2012 to May 2013, which are respectively -965.03, -1879.58, 4138.26, 1858.17, 4061.86 and 6163.16 with an obviously increasing trend.ConclusionThe model proposed in this paper can predict the incidence trend of HFMD effectively, which could be helpful to policy makers. The usefulness of expected cases of HFMD perform not only in detecting outbreaks or providing probability statements, but also in providing decision makers with a probable trend of the variability of future observations that contains both historical and recent information.

Brucellosis is a bacterial disease caused by brucella; mainly spread by direct contact transmission through the brucella carriers, or indirect contact transmission by the environment containing large quantities of bacteria discharged by the infected individuals. At the beginning of 21st century, the epidemic among dairy cows in Zhejiang province, began to come back and has become a localized prevalent epidemic. Combining the pathology of brucellosis, the reported positive data characteristics, and the feeding method in Zhejiang province, this paper establishes an SEIV dynamic model to excavate the internal transmission dynamics, fit the real disease situation, predict brucellosis tendency and assess control measures in dairy cows. By careful analysis, we give some quantitative results as follows. (1) The external input of dairy cows from northern areas may lead to high fluctuation of the number of the infectious cows in Zhejiang province that can reach several hundreds. In this case, the disease cannot be controlled and the infection situation cannot easily be predicted. Thus, this paper encourages cows farms to insist on self-supplying production of the dairy cows. (2) The effect of transmission rate of brucella in environment to dairy cattle on brucellosis spreading is greater than transmission rate of the infectious dairy cattle to susceptible cattle. The prevalence of the epidemic is mainly aroused by environment transmission. (3) Under certain circumstances, the epidemic will become a periodic phenomenon. (4) For Zhejiang province, besides measures that have already been adopted, sterilization times of the infected regions is suggested as twice a week, and should be combined with management of the birth rate of dairy cows to control brucellosis spread.

BackgroundClimate change is likely to be one of the most important threats to public health in the coming years. Yet despite the large number of papers considering the health impact of climate change, few have considered what public health interventions may be of most value in reducing the disease burden. We aimed to evaluate the effectiveness of public health interventions to reduce the disease burden of high priority climate sensitive diseases.Methods and findingsFor each disease, we performed a systematic search with no restriction on date or language of publication on Medline, Web of Knowledge, Cochrane CENTRAL and SCOPUS up to December 2010 to identify systematic reviews of public health interventions. We retrieved some 3176 records of which 85 full papers were assessed and 33 included in the review. The included papers investigated the effect of public health interventions on various outcome measures. All interventions were GRADE assessed to determine the strength of evidence. In addition we developed a systematic review quality score. The interventions included environmental interventions to control vectors, chemoprophylaxis, immunization, household and community water treatment, greening cities and community advice. For most reviews, GRADE showed low quality of evidence because of poor study design and high heterogeneity. Also for some key areas such as floods, droughts and other weather extremes, there are no adequate systematic reviews of potential public health interventions.ConclusionIn conclusion, we found the evidence base to be mostly weak for environmental interventions that could have the most value in a warmer world. Nevertheless, such interventions should not be dismissed. Future research on public health interventions for climate change adaptation needs to be concerned about quality in study design and should address the gap for floods, droughts and other extreme weather events that pose a risk to health.

BackgroundThe potential role of DSS in CVD prevention remains unclear as only a few studies report on patient outcomes for cardiovascular disease.Methods and ResultsA systematic review and meta-analysis of randomised controlled trials and observational studies was done using Medline, Embase, Cochrane Library, PubMed, Amed, CINAHL, Web of Science, Scopus databases; reference lists of relevant studies to 30 July 2011; and email contact with experts. The primary outcome was prevention of cardiovascular disorders (myocardial infarction, stroke, coronary heart disease, peripheral vascular disorders and heart failure) and management of hypertension owing to decision support systems, clinical decision supports systems, computerized decision support systems, clinical decision making tools and medical decision making (interventions). From 4116 references ten studies met our inclusion criteria (including 16,312 participants). Five papers reported outcomes on blood pressure management, one paper on heart failure, two papers each on stroke, and coronary heart disease. The pooled estimate for CDSS versus control group differences in SBP (mm of Hg) was - 0.99 (95% CI −3.02 to 1.04 mm of Hg; I2 = 0; p = 0.851).ConclusionsDSS show an insignificant benefit in the management and control of hypertension (insignificant reduction of SBP). The paucity of well-designed studies on patient related outcomes is a major hindrance that restricts interpretation for evaluating the role of DSS in secondary prevention. Future studies on DSS should (1) evaluate both physician performance and patient outcome measures (2) integrate into the routine clinical workflow with a provision for decision support at the point of care.

BackgroundOverweight/obesity is a serious public health problem that affects a large part of the world population across all age and racial/ethnic groups. However, there has not been a meta-analysis of the prevalence of childhood and adolescent overweight/obesity in China during the past 30 years.MethodsThe China National Knowledge Infrastructure and Wanfang DATA, MEDLINE, EMBASE and Cumulative Index to Nursing and Allied Health Literature were searched for relevant studies published between January 1970 and June 2012. The prevalence of overweight/obesity over time was pooled using Stata/SE, version 9. Summary statistics (odds ratios, ORs) were used to compare sex-specific and urban-rural preponderance of overweight/obesity using Review Manager.ResultsAfter screening 1326 papers, we included 35 papers (41 studies), most of medium quality. The prevalence of overweight/obesity increased from 1.8% (95% confidence interval [CI], 0.4%-3.1%) and 0.4% (95% CI, -0.1% to -0.8%) respectively in 1981-1985 to 13.1% (95% CI, 11.2%-15.0%) and 7.5% (95% CI, 6.6%-8.4%) respectively in 2006-2010. The average annual increase was 8.3% and 12.4% respectively. Boys were more likely to be overweight/obese than girls (OR, 1.36; 95% CI, 1.24-1.49 and OR, 1.68; 95% CI, 1.52-1.86 respectively). The prevalence of overweight/obesity was higher in urban areas than in rural areas (OR, 1.66; 95% CI, 1.54-1.79 and OR, 1.97; 95% CI, 1.68-2.30 respectively). For age-specific subgroup analyses, both overweight and obesity increased more rapidly in the toddler stage than in other developmental stages. Sensitivity analyses showed that sample-size differences, study quality, overweight/obesity criteria and geographical distribution affected overweight/obesity prevalence.ConclusionsToddlers and urban boys were at particularly high risk; the prevalence in these groups increased more rapidly than in their counterparts. Public health prevention strategies are urgently needed to modify health behaviors of children and adolescents and control overweight/obesity in China.

Background In 2007, the World Health Organization endorsed male circumcision as an effective HIV prevention strategy. In 2008, the Government of Kenya (GoK) launched the national voluntary medical male circumcision (VMMC) program in Nyanza Province, the geographic home to the Luo, the largest non-circumcising ethnic group in Kenya. Currently, several other African countries are in the early stages of implementing this intervention. Methods and Results This paper uses data from a health facility needs assessment (n = 81 facilities) and a study to evaluate the implementation of VMMC services in 16 GoK facilities (n = 2,675 VMMC clients) to describe Kenya's experience in implementing the national program. The needs assessment revealed that no health facility was prepared to offer the minimum package of services as outlined by the national guidelines, and partner organizations were called upon to fill this gap. The findings concerning human resource shortages facilitated the GoK's decision to endorse trained nurses to provide VMMCs, enabling more facilities to offer the service. Findings from the evaluation study resulted in replacing voluntary counseling and testing (VCT) with provider-initiated testing and counseling (PITC) and subsequently doubling the proportion of VMMC clients tested for HIV. Conclusions This paper outlines how certain challenges, like human resource shortages and low HIV test rates, were addressed through national policy changes, while other challenges, like large fluctuations in demand, were addressed locally. Currently, the program requires significant support from partner organizations, but a strategic plan is under development to continue to build capacity in GoK staff and facilities. Coordination between all parties was essential and was facilitated through the formation of national, provincial, and district VMMC task forces. The lessons learned from Kenya's VMMC implementation experience are likely generalizable to other African countries.

Psychotic disorders are among the most complex medical conditions. Longitudinal cohort studies may offer further insight into determinants of functional outcome after a psychotic episode. This paper describes the Psychosis Recent Onset in GRoningen Survey (PROGR-S) that currently contains data on 1076 early-episode patients with psychosis, including symptoms, personality, cognition, life events and other outcome determinants. Our goal in this report is to give an overview of PROGR-S, as a point of reference for future publications on the effect of cognition, personality and psychosocial functioning on outcomes. PROGR-S contains an extensive, diagnostic battery including anamnesis, biography, socio-demographic characteristics, clinical status, drug use, neuropsychological assessment, personality questionnaires, and physical status tests. Extensive follow-up data is available on psychopathology, physical condition, medication use, and care consumption. Sample characteristics were determined and related to existing literature. PROGR-S (period 1997-2009, n = 718) included the majority of the expected referrals in the catchment area. The average age was 27 (SD = 8.6) and two-thirds were male. The average IQ was lower than that in the healthy control group. The majority had been diagnosed with a psychotic spectrum disorder. A substantial number of the patients had depressive symptoms (479/718, 78%) and current cannabis or alcohol use (465/718, 75%). The level of community functioning was moderate, i.e. most patients were not in a relationship and were unemployed. The PROGR-S database contains a valuable cohort to study a range of aspects related to symptomatic and functional outcomes of recent onset psychosis, which may play a role in the treatment of this complex and disabling disorder. Results reported here show interesting starting points for future research. Thus, we aim to investigate long-term outcomes on the basis of cognition, personality, negative symptoms and physical health. Ultimately, we hope that this paper will contribute improving the health of patients with psychotic disorders.

INTRODUCTION: The field of diagnostics for active tuberculosis (TB) is rapidly developing. TB diagnostic modeling can help to inform policy makers and support complicated decisions on diagnostic strategy, with important budgetary implications. Demand for TB diagnostic modeling is likely to increase, and an evaluation of current practice is important. We aimed to systematically review all studies employing mathematical modeling to evaluate cost-effectiveness or epidemiological impact of novel diagnostic strategies for active TB. METHODS: Pubmed, personal libraries and reference lists were searched to identify eligible papers. We extracted data on a wide variety of model structure, parameter choices, sensitivity analyses and study conclusions, which were discussed during a meeting of content experts. RESULTS & DISCUSSION: From 5619 records a total of 36 papers were included in the analysis. Sixteen papers included population impact/transmission modeling, 5 were health systems models, and 24 included estimates of cost-effectiveness. Transmission and health systems models included specific structure to explore the importance of the diagnostic pathway (n = 4), key determinants of diagnostic delay (n = 5), operational context (n = 5), and the pre-diagnostic infectious period (n = 1). The majority of models implemented sensitivity analysis, although only 18 studies described multi-way sensitivity analysis of more than 2 parameters simultaneously. Among the models used to make cost-effectiveness estimates, most frequent diagnostic assays studied included Xpert MTB/RIF (n = 7), and alternative nucleic acid amplification tests (NAATs) (n = 4). Most (n = 16) of the cost-effectiveness models compared new assays to an existing baseline and generated an incremental cost-effectiveness ratio (ICER). CONCLUSION: Although models have addressed a small number of important issues, many decisions regarding implementation of TB diagnostics are being made without the full benefits of insight from mathematical models. Further models are needed that address a wider array of diagnostic and epidemiological settings, that explore the inherent uncertainty of models and that include additional epidemiological data on transmission implications of false-negative diagnosis and the pre-diagnostic period.

Anthropometry is a Greek word that consists of the two words “Anthropo” meaning human species and “metery” meaning measurement. It is a science that deals with the size of the body including the dimensions of different parts, the field of motion and the strength of the muscles of the body. Specific individual dimensions such as heights, widths, depths, distances, environments and curvatures are usually measured. In this article, we investigate the anthropometric characteristics of patients with chronic diseases (diabetes, hypertension, cardiovascular disease, heart attacks and strokes) and find the factors affecting these diseases and the extent of the impact of each to make the necessary planning. We have focused on cohort studies for 10047 qualified participants from Ravansar County. Machine learning provides opportunities to improve discrimination through the analysis of complex interactions between broad variables. Among the chronic diseases in this cohort study, we have used three deep neural network models for diagnosis and prognosis of the risk of type 2 diabetes mellitus (T2DM) as a case study. Usually in Artificial Intelligence for medicine tasks, Imbalanced data is an important issue in learning and ignoring that leads to false evaluation results. Also, the accuracy evaluation criterion was not appropriate for this task, because a simple model that is labeling all samples negatively has high accuracy. So, the evaluation criteria of precession, recall, AUC, and AUPRC were considered. Then, the importance of variables in general was examined to determine which features are more important in the risk of T2DM. Finally, personality feature was added, in which individual feature importance was examined. Performing by Shapley Values, the model is tuned for each patient so that it can be used for prognosis of T2DM risk for that patient. In this paper, we have focused and implemented a full pipeline of Data Creation, Data Preprocessing, Handling Imbalanced Data, Deep Learning model, true Evaluation method, Feature Importance and Individual Feature Importance. Through the results, the pipeline demonstrated competence in improving the Diagnosis and Prognosis the risk of T2DM with personalization capability.

High Flow Nasal Oxygen (HFNO) therapy offers a proven means of delivering respiratory support to critically ill patients suffering from viral illness such as COVID-19. However, the therapy has the potential to modify aerosol generation and dispersion patterns during exhalation and thereby put healthcare workers at increased risk of disease transmission. Fundamentally, a gap exists in the literature with regards to the effect of the therapy on the fluid dynamics of the exhalation jet which is essential in understanding the dispersion of aerosols and hence quantifying the disease transmission risk posed by the therapy. In this paper, a multi-faceted approach was taken to studying the aerosol-laden exhalation jet. Schlieren imaging was used to visualise the flow field for a range of expiratory activities for three healthy human volunteers receiving HFNO therapy at flow rates of 0—60 L/min. A RANS turbulence model was implemented using the CFD software OpenFOAM and used to perform a parametric study on the influence of exhalation velocity and duration on the dispersion patterns of non-evaporating droplets in a room environment. A dramatic increase in the turbulence of the exhalation jet was observed when HFNO was applied. Quantitative analysis indicated that the mean exhalation velocity was increased by 2.2—3.9 and 2.3—3 times that for unassisted breathing and coughing, respectively. A 1—2 second increase was found in the exhalation duration. The CFD model showed that small droplets (10—40 μm) were most greatly affected, where a 1 m/s increase in velocity and 1 s increase in duration caused an 80% increase in axial travel distance.