Implement Intervention Measures Research Articles

Signal detection and analysis of adverse events associated with Genvoya® based the FAERS database.

This study aims to evaluate and understand the safety profile of Genvoya® by mining and analyzing adverse drug event (ADE, adverse drug event) reports from the FDA Adverse Event Reporting System (FAERS, FDA Adverse Event Reporting System) database, thus providing valuable reference information for individuals infected with HIV. Data were obtained from the FAERS database, covering the period from the first quarter of 2015 to the fourth quarter of 2023, focusing on reports where Genvoya® was the primary suspected drug. Data import and extraction were conducted using MySQL 8.0, with adverse events standardized according to the Medical Dictionary for Regulatory Activities (MedDRA, Medical Dictionary for Regulatory Activities) 27.0 terminology. Potential adverse event signals were identified through disproportionality analysis, including the reporting odds ratio (ROR, reporting odds ratio) method and the comprehensive standard by the Medicines and Healthcare products Regulatory Agency (MHRA, Medicines and Healthcare products Regulatory Agency) method. Statistical analyses and graphical representations were performed. A total of 2, 376 adverse drug event reports related to Genvoya® were analyzed. Reports from male patients accounted for 74.33%, while those from female patients accounted for 22.39%. Common adverse events included weight gain, drug interactions, and increased viral load. Additionally, new potential adverse reactions, such as fat redistribution, HIV-associated neurocognitive disorders, and meningoencephalitis, were identified. These reactions were not adequately described in the existing literature and drug labels. Multiple adverse reactions were observed with the use of Genvoya®. Clinicians should closely monitor these reactions and implement necessary preventive and intervention measures based on patient-specific conditions and treatment guidelines. Although this study has limitations, the analysis of FAERS database data has revealed various potential risks associated with Genvoya®, providing important safety references for HIV treatment.

Adverse Childhood Experiences are associated with Mental Health Problems later in life: An Umbrella Review of Systematic Review and Meta-analysis.

Evidence suggested a link between early adversity and mental health problems. However, it is unclear how much adverse childhood experiences (ACEs) contribute to mental health problems because researchers have produced inconsistent findings. Therefore, the objective of this umbrella review was to combine the contradictory data regarding the effect of ACEs on the development of mental health problems later in life in the global context. PubMed, Embase, Scopus, Web of Sciences, Cochrane Database of Systematic Reviews, Scopus, and Google Scholar which reported the effect of ACEs on the development of mental health problems were searched. The quality of the included studies was assessed using the Assessment of Multiple Systematic Reviews (AMSTAR). A weighted inverse variance random-effects model was applied to find the pooled estimates. The subgroup analysis, heterogeneity, publication bias, and sensitivity analysis were also assessed. Forty-three SRM with 14707614 study participants were included. The pooled effect of ACEs on the development of mental health problems later in life in the global context is found to be (AOR=1.66 (1.46, 1.87)). Subgroup analysis based on country revealed (AOR=1.67(1.23, 2.11)) in UK, (AOR=0.61(0.41, 0.81)) in Canada, (AOR=1.55(1.40, 1.69)) in Brazil, (AOR=5.65(4.12, 7.18)) in Ethiopia, (AOR=1.92(1.45, 2.38)) in USA, (AOR=2.30(1.89, 2.72)) in Australia and (AOR=1.66(1.46, 1.87)) in irland. While subgroup analysis based on types of adverse childhood adverse experience: domestic violence ((AOR=4.13(1.96, 6.30)), maltreatment (AOR=1.5(0.79, 2.21)), physical abuse(AOR=1.56(1.43,1.63), sexual abuse (AOR=2.07(1.63, 2.51)), child abuse (AOR=5.66(4.12,7.18)), parental mental health problem (AOR=1.73(1.39,2.08)), bullying (AOR=1.99(1.69, 2.29), neglect(AOR=2.11(1.53,2.69)), and parental divorce (AOR=1.66(1.46,1.87)). Based on the type of mental health problem the pooled effect size is 1.87(1.45, 2.30) for depression and 1.67(1.22, 2.13) for anxiety. This umbrella review revealed that adverse childhood experience is significantly associated (with 66% increased risk) with anxiety and depression later in life in a global context. This association is most noticeable when one is subjected to domestic violence, maltreatment, physical abuse, sexual abuse, child abuse, parental mental health problems, bullying, neglect, and parental divorce. Childhood periods are a critical window of opportunity for reducing the risk of developing mental illness in the future and for implementing intervention measures. Preventing childhood maltreatment and addressing psychiatric risk factors can prevent psychopathology. Longitudinal studies are needed to optimize healthcare responses to ACEs. Increased awareness and public health interventions are needed to prevent childhood adversity and prevent mental problems among these victims. To optimize healthcare responses to unfavorable outcomes of childhood adversities, longitudinal and intervention research findings, more public health initiatives, and awareness are required.

A two-strain COVID-19 co-infection model with strain 1 vaccination

COVID-19 has caused substantial morbidity and mortality worldwide. Previous models of strain 1 vaccination with re-infection when vaccinated, as well as infection with strain 2 did not consider co-infected classes. To fill this gap, a two co-circulating COVID-19 strains model with strain 1 vaccination, and co-infected is formulated and theoretically analyzed. Sufficient conditions for the stability of the disease-free equilibrium and single-strain 1 and -strain 2 endemic equilibria are obtained. Results show as expected that (1) co-infected classes play a role in the transmission dynamics of the disease (2) a high efficacy vaccine could effectively help mitigate the spread of co-infection with both strain 1 and 2 compared to the low-efficacy vaccine. Sensitivity analysis reveals that the main drivers of the effective reproduction number Re are primarily the effective contact rate for strain 2 (β2), the strain 2 recovery rate (τ2), and the vaccine efficacy or infection reduction due to the vaccine (η). Thus, implementing intervention measures to mitigate the spread of COVID-19 should not ignore the co-infected individuals who can potentially spread both strains of the disease.

Burden of non-communicable diseases in China and its provinces, 1990-2021: Results from the Global Burden of Disease Study 2021.

Non-communicable diseases (NCDs) are the primary causes of disability and death. The aim of this study is to analyze the disease burden of NCDs in China from 1990 to 2021. This study used data on NCDs in China and its provinces from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. The study analyzed the disease burden of NCDs in 2021 and its changes from 1990 to 2021 using indicators including deaths and disability-adjusted life years (DALYs). Between 1990 and 2021, the NCD burden in China exhibited an upward trend. In 2021, China had 10.6 (95% uncertainty interval [UI]: 9.0-12.2)million deaths and 349.3 (95% UI: 301.5-401.2)million DALYs due to NCDs, accounting for 91.0% (95% UI: 90.4-91.7%) of all deaths and 86.7% (95% UI: 86.0-87.4%) of all DALYs. NCDs caused a lower disease burden in females than in males. Cardiovascular diseases and neoplasms were the main NCD level 2 causes of deaths and DALYs, resulting in 5.1 (95% UI: 4.3-5.9) and 2.8 (95% UI: 2.3-3.4) million deaths and 100.2 (95% UI: 84.6-116.6) million and 71.2 (95% UI: 59.3-85.2) million DALYs in 2021, respectively. Chronic respiratory diseases were the third leading cause of NCD deaths, while musculoskeletal disorders were the third leading cause of NCD DALYs. Qinghai, Xizang, and Heilongjiang had the highest age-standardized mortality rates and age-standardized DALY rates (per 100,000) for NCDs, while Hong Kong Special Administration Region (SAR), Macao SAR, and Shanghai recorded the lowest age-standardized mortality rates and age-standardized DALY rates. NCDs caused a high disease burden in China and exhibited heterogeneity across sexes and provinces. China needs to focus on addressing key NCDs and implement intervention measures tailored to the disease distribution characteristics to reduce the NCD burden.

Emotional contagion on social media and the simulation of intervention strategies after a disaster event: a modeling study

With the advent of climate change and the 5 G era, online communities are increasingly becoming the main medium for information dissemination after emergencies such as natural disasters. The widespread dissemination of negative online information may generate cyber violence or lead to serious adverse psychological outcomes. This study considered a natural disaster event involving avoidable deaths and child casualties as an example to identify emotional contagion and conduct simulation interventions. Data about the aftermath of the 8·13 flash flood in the Longcaogou Scenic Area, Sichuan Province, China, were derived from the Chinese Sina microblog. We analyzed key parameters and modeled them in a dynamic model. We further evaluated the effects of implementing intervention measures (such as transmission path interruption and changing the number of different emotions) on emotional spread. The overall sentiment of posters after this flood was negative, with three epidemic peaks. Negative emotions were more persistent and contagious than positive emotions. Reducing the number of negative blog posts by half could have led to a 14.97% reduction in negative comments and a 7.17% reduction in positive comments. Simultaneously, reducing the number of negative blog posts and increasing the number of positive posts would have helped reduce the relative ratio of negative to positive comments. The findings have theoretical and practical implications for developing an emotional contagion model and formulating intervention strategies to guide public opinion after an emergency that involves extensive online debate.

Prevalence of burnout among military personnel in the plateau region of China: a cross-sectional survey

ObjectivesThe geographical environment and military activities in the plateau area pose potential work-related stressors for military personnel, leading to burnout which is an external manifestation of internal energy exhaustion caused by stress. Without countermeasures, this can result in serious military problems. This study aims to examine the association between burnout and occupational stressors among military personnel stationed in the plateau area of China.Material and methodsA stratified randomized cluster sampling survey was conducted among 2026 military personnel from 6 different troops stationed in the plateau area of China. The Chinese Maslach Burnout Inventory-General Survey(MBI-GS in Chinese) was administered from March 2022 to December 2023, and data were analyzed using SPSS version 25.ResultsA total of 2026 military personnel participated in the survey. The mean overall burnout score was 3.37 ± 0.73, with emotional exhaustion at 2.69 ± 0.89, depersonalization at 3.58 ± 0.92, and professional achievement at 3.81 ± 0.85 levels respectively reported by participants on average scale scores ranging from zero to six. Severe level of burnout was reported by 43.2% of participants while medium level of burnout was reported by 54 .3%. Age, education level, length of military service, and household income were identified as important factors influencing burnout.ConclusionThis study highlights a relatively high prevalence of burnout among military personnel stationed in plateau areas necessitating attention towards their occupational health particularly focusing on working hours and economic aspects so as to formulate effective policies and implement intervention measures that strengthen career development for soldiers deployed in such regions.

Would it have been cheaper to let them become unemployed? Costs and benefits of First Aid intervention for companies in Slovakia during the COVID-19 pandemic

Research background: The global COVID-19 pandemic, which started in the first quarter of 2020, triggered unprecedented economic challenges, prompting governments worldwide to implement intervention measures to mitigate its impacts on business and employment. Without the state’s financial help, many companies were forced to lay off their employees. Among these measures was the First Aid intervention program introduced in Slovakia in April 2020, aimed at providing financial support to companies facing operational disruptions and potential layoffs of their employees. Purpose of the article: This study assesses the impact of the First Aid intervention program during the COVID-19 pandemic on unemployment in selected sectors, with an emphasis on the financial aspect, emphasising the international relevance and long-term implications of the state intervention in the crisis period. By analysing its effectiveness in preserving jobs and mitigating unemployment in selected sectors, the research seeks to offer valuable insights that can inform crisis response strategies and labour market policies in the country and beyond national borders. Methods: Employing a counterfactual approach, we quantify the financial consequence of the First Aid+ intervention program on the state budget, comparing unemployment costs against the benefits of maintaining employment in targeted sectors. Through this methodological framework, we aim to provide a replicable model for evaluating the efficacy of intervention programs in different socio-economic contexts. Findings & value added: Our analysis reveals not only the immediate impacts of the First Aid+ program on mitigating unemployment during the pandemic, but also its broader implications for policy and crisis management strategies. By elucidating the cost-benefit analysis of intervention measures, the research contributes to the effective labour market policies in times of crisis.

Construction and improvement strategies of an age-friendly evaluation system for public spaces in affordable housing communities: a case study of Shenzhen.

Characterized by early construction periods, as the concentration of low-income populations and a high level of aging, affordable housing communities face prominent challenges such as incongruence between age-friendly construction and the needs of the older adult population. It is urgent to provide pathways and tools for identifying age-friendly issues and optimizing the built environment. The systematic evaluation of age-friendly communities serves as the foundation for implementing intervention measures by developers. Therefore, the construction of a scientifically systematic evaluation system becomes an objective necessity for age-friendly community development. Building upon existing research, this study systematically outlines the subjects, processes, methods, and content involved in constructing an age-friendly community evaluation system. By the methods such as factor analysis and analytical hierarchy process (AHP), the study focuses on the public spaces of affordable housing communities in Shenzhen as a case for constructing an age-friendly evaluation system. The empirical validation of the indicator system is conducted, and the application results are resulted into concrete improvement recommendations and action items, aiming to provide a practical, quantitative tool for community age-friendliness evaluation. The study reveals that adhering to an effective evaluation process, exploring collaborations among multiple stakeholders, determining hierarchical evaluation criteria, and adopting diversified evaluation methods are key to constructing an age-friendly evaluation system for communities. Additionally, the specificity of the evaluation system is influenced by regional demographic structures, policy backgrounds, and the built environment.

Epidemic-Prevention Measures and Health Management in a Nursing Home during the Coronavirus Disease 2019 Pandemic.

This study aimed to investigate the impact of epidemic prevention and isolation policies on residents' health and well-being and assess the effectiveness of implementing intervention measures to maintain their quality of life. This mixed-methods research study involved a retrospective record review of residents' daily life diaries and descriptive statistical analysis. Data were collected between March 2021 and June 2022, and epidemic-prevention measures were implemented using Taiwan's Centers for Disease Control guidelines. Three interventions were developed to address residents' health, social, and rehabilitation needs. Despite an overall infection rate of 10% at various times between 2021 and 2022, there were no reported outbreaks of nosocomial infections. The concept of reablement proved effective in helping residents maintain their independence and physical function, with a maintenance rate of 66.6%, thereby improving their quality of life. By implementing epidemic-prevention measures, we found that proper hand washing and the use of surgical masks were effective in controlling infections. Furthermore, the decline in physical function is a continuous and gradual process for older adults. Even under the restriction of social interaction, it is essential to incorporate rehabilitation plans into residents' daily activities and encourage their active participation, as this promotes improved physical function and enhances their overall quality of life.

Social media multitasking and college students' academic performance: A situation–organism–behavior–consequence perspective

AbstractA growing number of researchers have begun to highlight the potential negative effects of the excessive use of social media on college students. In particular, college students engage in social media multitasking, in which they perform multiple tasks simultaneously. Such action could adversely influence their academic performance. Hence, on the basis of the situation–organism–behavior–consequence paradigm, we developed a research model to explore the etiology of social media multitasking and its impact on college students' academic performance. Using partial least squares–structural equation modeling techniques to analyze the survey data of 523 Chinese college students, we found that the fear of missing out (the situation) is positively associated with social media multitasking (the organism), which in turn is positively related to cognitive distraction (the behavior) and ultimately leads to a decline in academic performance (the consequence). Our study deepens the understanding of the phenomenon of social media multitasking among college students and can provide a reference for educators to implement intervention measures.

Status of animal health biosecurity measures of dairy farms in urban and peri-urban areas of central Ethiopia.

Ethiopian dairy farming has many constraints including disease and lack of appropriate biosecurity measures. With this into consideration, a cross-sectional survey was carried out from November 2021 to April 2022 to determine the animal health biosecurity status of dairy farms and investigate the sociodemographic characteristics of livestock keepers on dairy farm management. A face-to-face questionnaire survey using an online application was used to collect data. The interview involved a total of 380 dairy farms located in six towns in central Ethiopia. The results showed that out of the surveyed farms, 97.6% missed footbaths at their gate points, 87.4% lacked isolation areas for either sick or newly introduced cattle, and 83.4% did not check the health status or quarantine newly introduced cattle. Furthermore, written formal record-keepings on animal health was uncommon, except for a few farms (7.9%). However, nearly all of the respondents (97.9%) gave medical treatments for sick cattle, and 57.1% of them vaccinated their herds regularly during the past 12 months before the survey. Hygienic aspects of the farms showed that 77.4% of the dairy farms appeared to clean the barn on a daily basis. However, 53.2% of respondents did not utilize personal protective equipment while cleaning their farms. A quarter of the dairy farmer (25.8%) avoided mixing their cattle with other herds, and 32.9% of them have implemented isolation of sick animals. In general, the animal health biosecurity assessment of the farms showed that most of the dairy farms (79.5%) earned unacceptable biosecurity levels (score of ≤ 50%), whereas the remaining 20.5% of dairy farms had received a score of >50% ("acceptable level"). The gender of dairy farmers (χ2 value = 7.61; p = 0.006), education level (χ2 value = 12.04; p = 0.007), dairy farm ownership (χ2 value = 41.6; p < 0.001), training on dairy farm management (χ2 value = 37.1; p < 0.001), towns (χ2 value = 31.69; p < 0.001), farm size (χ2 value = 7.7; p = 0.006), and herd size (χ2 value = 28.2; p < 0.001) showed a significant statistical association with biosecurity status. Finally, the study revealed that the level of biosecurity adoption of dairy farms in central Ethiopia is mostly unsatisfactory and calls for designing and implementing intervention measures toward improved animal health in dairy farms and further public health.

Assessment of the Effectiveness of Omicron Transmission Mitigation Strategies for European Universities Using an Agent-Based Network Model.

Returning universities to full on-campus operations while the coronavirus disease 2019 pandemic is ongoing has been a controversial discussion in many countries. The risk of large outbreaks in dense course settings is contrasted by the benefits of in-person teaching. Transmission risk depends on a range of parameters, such as vaccination coverage and efficacy, number of contacts, and adoption of nonpharmaceutical intervention measures. Owing to the generalized academic freedom in Europe, many universities are asked to autonomously decide on and implement intervention measures and regulate on-campus operations. In the context of rapidly changing vaccination coverage and parameters of the virus, universities often lack sufficient scientific insight on which to base these decisions. To address this problem, we analyzed a calibrated, data-driven agent-based simulation of transmission dynamics among 13 284 students and 1482 faculty members in a medium-sized European university. Wed use a colocation network reconstructed from student enrollment data and calibrate transmission risk based on outbreak size distributions in education institutions. We focused on actionable interventions that are part of the already existing decision process of universities to provide guidance for concrete policy decisions. Here we show that, with the Omicron variant of the severe acute respiratory syndrome coronavirus 2, even a reduction to 25% occupancy and universal mask mandates are not enough to prevent large outbreaks, given the vaccination coverage of about 85% reported for students in Austria. Our results show that controlling the spread of the virus with available vaccines in combination with nonpharmaceutical intervention measures is not feasible in the university setting if presence of students and faculty on campus is required.

Risk assessment for precise intervention of COVID-19 epidemic based on available big data and spatio-temporal simulation method: Empirical evidence from different public places in Guangzhou, China

Risk assessment of the intra-city spatio-temporal spreading of COVID-19 is important for providing location-based precise intervention measures, especially when the epidemic occurred in the densely populated and high mobile public places. The individual-based simulation has been proven to be an effective method for the risk assessment. However, the acquisition of individual-level mobility data is limited. This study used publicly available datasets to approximate dynamic intra-city travel flows by a spatio-temporal gravity model. On this basis, an individual-based epidemic model integrating agent-based model with the susceptible-exposed-infectious-removed (SEIR) model was proposed and the intra-city spatio-temporal spreading process of COVID-19 in eleven public places in Guangzhou China were explored. The results indicated that the accuracy of dynamic intra-city travel flows estimated by available big data and gravity model is acceptable. The spatio-temporal simulation method well presented the process of COVID-19 epidemic. Four kinds of spatial-temporal transmission patterns were identified and the pattern was highly dependent on the urban spatial structure and location. It indicated that location-based precise intervention measures should be implemented according to different regions. The approach of this research can be used by policy-makers to make rapid and accurate risk assessments and to implement intervention measures ahead of epidemic outbreaks.

Bite-o-Mat: A device to assess the individual manipulative behaviour of group housed pigs

• Validation of a new device to automatically assess manipulative behaviour in pigs. • Algorithms were developed and validated for the rearing and fattening period. • About 60 % (= sensitivity) of the rope manipulations were detected by an algorithm. • The algorithm reached a specificity and accuracy above 80% for the detection of rope manipulations. • During greater than 65 % of manipulations the manipulating pig was correctly identified. Tail biting is still an important problem in pig husbandry. In addition to addressing the underlying causes, the negative consequences of tail biting can be mitigated by detecting it early e.g. using behavioural changes and implementing intervention measures. Due to the labour intensity of behavioural observations, it would be highly advantageous to detect behavioural changes automatically. This study aimed at developing and validating an automatic device (“Bite-o-Mat”) to assess the individual manipulative behaviour of group-housed pigs. The Bite-o-Mat consisted of a single point load cell (SPLC) that recorded the force with which pigs manipulated a rope, and an UHF-RFID system to individually identify the manipulating pig. Data were recorded in a rearing (8 pigs) and a fattening pen (5 pigs) and validated using twelve hours of video recordings each, distributed across six days. Pigs were observed 596 (rearing) and 277 (fattening) times to manipulate the rope (=manipulation event) in the videos to which the automatically recorded data were compared. Using a linear mixed model, summarising data per manipulation event, it was possible to show that on average stronger forces are exerted on a rope during manipulation events than during times without manipulations and that these forces can be measured by a SPLC (manipulations vs. no manipulations, model estimates in kg; rearing: -0.01 vs. -0.004 (mean), fattening: -0.05 vs. -0.01 (mean)). Based on these results, an algorithm was developed to automatically detect manipulation events using the data of the SPLC. Validation of the algorithm using a second-by-second comparison to the video analysis, showed that it is suitable to detect manipulation events automatically in the rearing and fattening period (sensitivity: 0.60 (rearing and fattening); specificity: 0.87 (rearing), 0.93 (fattening); precision: 0.55 (rearing), 0.58 (fattening); accuracy: 0.81 (rearing), 0.88 (fattening)). In addition, we aimed to identify the manipulating pig with an UHF-RFID antenna. The performance parameters (sensitivity, specificity, accuracy, precision) of the UHF-RFID antenna confirmed that it is sufficiently suitable to detect the pigs within the reading range. Compared with the video recordings, 69% (rearing) and 82% (fattening) of pigs were correctly identified automatically to be the manipulating pigs by the UHF-RFID algorithm. These results indicate that the Bite-o-Mat is not only a promising device to automatically detect manipulative behaviour in group-housed pigs, but also to identify the manipulating animal.

The sexual network and risky sexual behaviours among male migrant workers in China’s gender imbalance context.

In the context of China's gender imbalance and marriage squeeze, this study measured the overall characteristics of egocentric sexual networks, and examined the associations between sexual network characteristics and risky sexual behaviours, including commercial sex, concurrent partners and homosexual sex, among rural-urban male migrant workers. This cross-sectional study was based on data obtained from 713 Chinese men (age ≥28years) with rural household registration and who were migrants (or had rural-urban migration experience) in 2017. Logistic regression models were used to examine the associations between participants' sexual network characteristics and risky sexual behaviours. Adjusting for participants' sociodemographic characteristics, the sexual network variables of partner age, partner marital status, partner type and relationship stability were found to be significantly associated with commercial sex. Partner type and relationship stability were also associated with concurrent partners and homosexual sex. The network characteristics of male migrant workers not only differed by marital status, but the associations between sexual network characteristics and risky sexual behaviours were more pronounced among unmarried male migrant workers. Overall, sexual networks with mixed patterns of partner age, partner marital status, partner type and relationship stability may explain the HIV transmission from male migrants to other populations at the population level. Future research should focus on the determinants of structured sexual networks, and be used to identify key groups at risk for cross-population HIV transmission and implement intervention measures.

The magnitude of hypertension and associated factors among clients on highly active antiretroviral treatment in Southern Ethiopia, 2020: A hospital-based cross-sectional study.

Following the introduction of Highly Active Anti Retro Viral Treatment (HAART), the survival of people living with HIV/AIDS (PLHIV) has improved. However, hypertension remains a major challenge for people living with HIV. Very little effort has been made to examine the magnitude of hypertension and its contributing factors among clients receiving HAART, particularly in southern Ethiopia. Hence, the current study aimed at determining the frequency of Hypertension and associated factors among clients receiving HAART at Wachemo University Nigist Eleni Mohammed Memorial Referral Hospital, southern Ethiopia, 2020. A hospital-based cross-sectional study took place from January 20- March 20, 2020. A systematic sampling technique was employed in the selection of 397 clients. Interviewer administered pretested structured questionnaire was used for data collection. Blood pressure and anthropometric parameters of PLHIV were measured. The data was encoded and entered using Epi Data Version 3.1 and exported to SPSS version 23 for analysis. Then bivariable and multivariable logistic regression analyses were used to identify associated factors. Adjusted Odds Ratio (AOR) with 95% CI was used to present the estimated effect size and declare the presence of statistically significant association respectively. The magnitude of hypertension among clients on HAART was 11.0% 95% CI [7.93, 14.04]. Being on HAART for at least 60 months (AOR: 2.57, 95% CI: 1.24-5.21), being on TDF/3TC/EFV combination (AOR: 4.61, 95% CI: 2.52-8.3), and high alcohol consumption (AOR: 4.31, 95% CI: 1.84-10.02) were identified as significant predictors of hypertension among clients on HAART. The magnitude of hypertension in the study area was in a considerable state to plan and implement intervention measures. For those clients who have received TDF/3TC/EFV and TDF/3TC/NVP and those who have been on HAART for 60 months, a strong emphasis should be placed on planning a strict follow-up. A concerted effort among health care providers is needed through counseling and education to discourage the habit of high alcohol consumption among clients.

Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model.

ABSTRACTEpidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses and hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10 320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. For the data in the Hospital and the ICU we took advantage of the observations at the Nursery Intensive Care Unit of the Consortium University General Hospital, Valencia, Spain (included as author). The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20, 50 and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modeling exercise exemplifies the application of membrane computing for designing appropriate multilateral interventions in epidemic situations.

Agent-Based Simulation Framework for Epidemic Forecasting during Hajj Seasons in Saudi Arabia

The religious pilgrimage of Hajj is one of the largest annual gatherings in the world. Every year approximately three million pilgrims travel from all over the world to perform Hajj in Mecca in Saudi Arabia. The high population density of pilgrims in confined settings throughout the Hajj rituals can facilitate infectious disease transmission among the pilgrims and their contacts. Infected pilgrims may enter Mecca without being detected and potentially transmit the disease to other pilgrims. Upon returning home, infected international pilgrims may introduce the disease into their home countries, causing a further spread of the disease. Computational modeling and simulation of social mixing and disease transmission between pilgrims can enhance the prevention of potential epidemics. Computational epidemic models can help public health authorities predict the risk of disease outbreaks and implement necessary intervention measures before or during the Hajj season. In this study, we proposed a conceptual agent-based simulation framework that integrates agent-based modeling to simulate disease transmission during the Hajj season from the arrival of the international pilgrims to their departure. The epidemic forecasting system provides a simulation of the phases and rituals of Hajj following their actual sequence to capture and assess the impact of each stage in the Hajj on the disease dynamics. The proposed framework can also be used to evaluate the effectiveness of the different public health interventions that can be implemented during the Hajj, including size restriction and screening at entry points.

Simulation and prediction of spread of COVID-19 in The Republic of Serbia by SEAIHRDS model of disease transmission

As a response to the pandemic caused by SARS-Cov-2 virus, on 15 March 2020, the Republic of Serbia introduced comprehensive anti-epidemic measures to curb COVID-19. After a slowdown in the epidemic, on 6 May 2020, the regulatory authorities decided to relax the implemented measures. However, the epidemiological situation soon worsened again. As of 7 February 2021, a total of 406,352 cases of SARSCov-2 infection have been reported in Serbia, 4,112 deaths caused by COVID-19. In order to better understand the epidemic dynamics and predict possible outcomes, we have developed an adaptive mathematical model SEAIHRDS (S-susceptible, E-exposed, A-asymptomatic, I-infected, H-hospitalized, R-recovered, d-dead due to COVID-19 infection, S-susceptible). The model can be used to simulate various scenarios of the implemented intervention measures and calculate possible epidemic outcomes, including the necessary hospital capacities. Considering promising results regarding the development of a vaccine against COVID-19, the model is extended to simulate vaccination among different population strata. The findings from various simulation scenarios have shown that, with implementation of strict measures of contact reduction, it is possible to control COVID-19 and reduce number of deaths. The findings also show that limiting effective contacts within the most susceptible population strata merits a special attention. However, the findings also show that the disease has a potential to remain in the population for a long time, likely with a seasonal pattern. If a vaccine, with efficacy equal or higher than 65%, becomes available it could help to significantly slow down or completely stop circulation of the virus in human population.The effects of vaccination depend primarily on: 1. Efficacy of available vaccine(s), 2. Prioritization of the population categories for vaccination, and 3. Overall vaccination coverage of the population, assuming that the vaccine(s) develop solid immunity in vaccinated individuals. With expected basic reproduction number of Ro=2.46 and vaccine efficacy of 68%, an 87% coverage would be sufficient to stop the virus circulation.

COVID-19 and post-traumatic stress disorder among nurses: a descriptive cross-sectional study in a COVID hospital.

Background:In March 2020, the World Health Organisation (WHO) declared the novel coronavirus (COVID-19) outbreak a global pandemic. Healthcare professionals directly involved in diagnosing, treating and caring for patients with COVID-19 are at risk of developing post-traumatic stress disorder (PTSD). Objective: This study investigated the prevalence of PTSD among nurses working in a COVID hospital and evaluated associated factors.Methods:A descriptive cross-sectional study was conducted at Crema Hospital and the Impact of Event Scale – Revised (IES-R) was administered. Data collection took place from July to September 2020, during which 275 questionnaires were distributed.Results:Of the total sample, 39.88% received a provisional PTSD diagnosis deserving of further analysis. Nurses stated that they were predominantly overwhelmed by intrusive thoughts (M = 1.55). Working in the emergency department during the COVID-19 pandemic (OR=2.40; p=0.02), irregular work shifts (OR=5.41; p=0.01) and coming from a mental health ward (OR=3.80; p=0.02) increased the risk of receiving a provisional PTSD diagnosis. Our findings showed significantly higher IES-R scores among women than among men (p = 0.01). The activities that caused the most distress were related to technical skills required for managing ventilation and intubation devices.Conclusions:The results of the study highlighted the presence of considerable psychological distress in the sample. There is an urgent need to monitor the short- and long-term consequences of the COVID-19 pandemic and implement early intervention measures.