Intention to Use eSanjeevani-OPD App: An Empirical Investigation Using Health Belief Model

Digital health interventions have become an essential component of every public health system since the COVID-19 pandemic. 'eSanjeevani OPD - Stay at Home OPD' is a telemedicine system that connects doctors and patients launched as part of the Ayushman Bharat project of the Indian government. This study analyses various factors affecting the intention to use eSanjeevani. A theoretical model integrating the health belief model and the theory of reasoned action was framed and empirically tested. Responses were collected using a survey questionnaire(n=248). A partial least square-structural equation modeling was used to analyze the linkages between the constructs. Perceived susceptibility and benefits were found to be the most contributing variables. Attitude has a significant mediating effect on the intention to use eSanjeevani. © 2023 IEEE.

Prioritization of public health financing, organization, and workforce transformation: a Delphi study in Canada.

BACKGROUND: The increased scrutiny on public health brought upon by the ongoing COVID-19 pandemic provides a strong impetus for a renewal of public health systems. This paper seeks to understand priorities of public health decision-makers for reforms to public health financing, organization, interventions, and workforce. METHODS: We used an online 3-round real-time Delphi method of reaching consensus on priorities for public health systems reform. Participants were recruited among individuals holding senior roles in Canadian public health institutions, ministries of health and regional health authorities. In Round 1, participants were asked to rate 9 propositions related to public health financing, organization, workforce, and interventions. Participants were also asked to contribute up to three further ideas in relation to these topics in open-ended format. In Rounds 2 and 3, participants re-appraised their ratings in the view of the group's ratings in the previous round. RESULTS: Eighty-six public health senior decision-makers from various public health organizations across Canada were invited to participate. Of these, 25/86 completed Round 1 (29% response rate), 19/25 completed Round 2 (76% retention rate) and 18/19 completed Round 3 (95% retention rate). Consensus (defined as more than 70% of importance rating) was achieved for 6 out of 9 propositions at the end of the third round. In only one case, the consensus was that the proposition was not important. Proposition rated consensually important relate to targeted public health budget, time frame for spending this budget, and the specialization of public health structures. Both interventions related and not related to the COVID-19 pandemic were judged important. Open-ended comments further highlighted priorities for renewal in public health governance and public health information management systems. CONCLUSION: Consensus emerged rapidly among Canadian public health decision-makers on prioritizing public health budget and time frame for spending. Ensuring that public health services beyond COVID-19 and communicable disease are maintained and enhanced is also of central importance. Future research shall explore potential trade-offs between these priorities.

Centralization and integration of public health systems: Perspectives of public health leaders on factors facilitating and impeding COVID-19 responses in three Canadian provinces.

The extent to which power, resources, and responsibilities for public health are centralized or decentralized within a jurisdiction and how public health functions are integrated or coordinated with health care services may shape pandemic responses. However, little is known about the impacts of centralization and integration on public health system responses to the COVID-19 pandemic. We examine how public health leaders perceive centralization and integration facilitated and impeded effective COVID-19 responses in three Canadian provinces. We conducted a comparative case study involving semi-structured interviews with 58 public health system leaders in three Canadian provinces with varying degrees of centralization and integration. Greater public health system centralization and integration was seen by public health leaders to facilitate more rapidly initiated and well-coordinated provincial COVID-19 responses. Decentralization may have enabled locally tailored responses in the context of limited provincial leadership. Opacity in provincial decision-making processes, jurisdictional ambiguity impacting Indigenous communities, and ineffectual public health investments were impediments across jurisdictions and thus appear to be less impacted by centralization and integration. Our study generates novel insights about potential structural facilitators and impediments of effective COVID-19 pandemic responses during the second year of the pandemic. Findings highlight key areas for future research to inform system design that support leaders to manage large-scale public health emergencies.

Policy implementation challenges and the ritualization of public health emergency plans: An investigation of urban communities in Jiangsu Province, China.

Introduction: The COVID-19 pandemic has been a global public health emergency, and countries worldwide have responded to it through a vast array of pre-planned, adaptively devised and ad-hoc measures. In China, public health emergency plans - the plans expected to drive the response to epidemics or pandemics - demonstrated a concerning tendency towards "ritualization." "Ritualization" denotes the practice of public health emergency plans to be reliably developed so that a formal requirement is met, while being implemented selectively or not at all in the emergency response. Methods: This study explored the phenomenon of ritualization by analyzing data from 1485 questionnaires, 60 in-depth interviews and 85 actual public health emergency plans. It used the Smith Policy-Implementation-Processing pattern as its conceptual framework. Results: The study found that the infeasibility of plans, their ineffective implementation by emergency management agencies, the obstructive behaviors of community residents, and the lack of an appropriate policy environment all contributed to the practice of ritualization. Discussion: As China seeks to better respond to COVID-19 and accelerate the recovery of its health system, it is essential to ensure that its public health emergency plans are effectively developed and implemented.

Impact of Migration on COVID-19 Epidemic Process in Romania: A Simulation Study

The Russian war in Ukraine, which escalated on February 24, 2022, caused massive destruction and the death of thousands of people. In addition, the Russian invasion has affected the public health system and the spread of infectious diseases. Millions of Ukrainians fled from the war, which caused a pan-European migration crisis. This study is devoted to testing the hypothesis of the impact of population migration caused by the Russian war in Ukraine on the dynamics of the spread of COVID-19 in Romania. For this, a machine learning model was developed based on the polynomial regression method. The model showed high accuracy. However, the formulated hypothesis was not confirmed fully. The results of the experimental study showed that population migration have not impacted the fatality caused by COVID-19, but has the impact on COVID-19 new cases. The further investigation is needed to find out the exact factors which influenced the epidemic process. © 2022 IEEE.

"We need to talk to each other": Crossing traditional boundaries between public health and occupational health to address COVID-19.

Introduction: This study examined how public health (PH) and occupational health (OH) sectors worked together and separately, in four different Canadian provinces to address COVID-19 as it affected at-risk workers. In-depth interviews were conducted with 18 OH and PH experts between June to December 2021. Responses about how PH and OH worked across disciplines to protect workers were analyzed. Methods: We conducted a qualitative analysis to identify Strengths, Weakness, Opportunities and Threats (SWOT) in multisectoral collaboration, and implications for prevention approaches. Results: We found strengths in the new ways the PH and OH worked together in several instances; and identified weaknesses in the boundaries that constrain PH and OH sectors and relate to communication with the public. Threats to worker protections were revealed in policy gaps. Opportunities existed to enhance multisectoral PH and OH collaboration and the response to the risk of COVID-19 and potentially other infectious diseases to better protect the health of workers. Discussion: Multisectoral collaboration and mutual learning may offer ways to overcome challenges that threaten and constrain cooperation between PH and OH. A more synchronized approach to addressing workers' occupational determinants of health could better protect workers and the public from infectious diseases.

An Approach for Collaboration Between Different Stakeholders to Strengthen the Public Health System

Nowadays, the healthcare problem is one of the major crises in many parts of the world, especially the COVID-19 pandemic has exacerbated this to a greater extent. Many developing countries with inadequate healthcare systems are suffering greatly from this crisis to provide proper medical services. The reasons are the insufficient number of healthcare providers, costs of medical tests and equipment, lack of accessible points of care and data analysis, and lack of sufficient online healthcare facilities. However, research on the benefits of establishing e-health platforms to strengthen the conventional public-health system is limited—most of the research targets patients in specific disease groups. This paper focuses on an approach for designing a healthcare social media platform for services provisioning, consuming, enabling patients to find an alternate source of healthcare advice, and then building a collaborative health community for all kinds of people. Its usability and applicability have been experimented with as a prototype on Android-based smartphone devices. The results show six features and benefits that are distinct from existing approaches in the literature. In addition, the approach will be considered an affordable alternative to conventional healthcare in case of emergency treatment. © 2022 Slovene Society Informatika. All rights reserved.

PERCEPTIONS OF THE PEOPLE RECRUITED ABOUT THE STRATEGIC ACTION “O BRASIL CONTA COMIGO” (BRAZIL COUNTS ON ME)

Objective: to analyze the perceptions of academics and supervisors about the strategic action “O Brasil Conta Comigo”(Brazil counts on me). Method: cross-sectional study conducted in Mato Grosso do Sul-Brazil, from December 2020 to March 2021, in two stages: 1) Exploratory (quantitative), via analysis of reports of the 33 supervisors and 90 academics convened, application of an online questionnaire;2) In-depth (qualitative), using the remote focus group technique, with convenience sampling. Descriptive and content analysis was performed. Results: the reasons for joining and remaining with the action (scholarship, internship hours, and social responsibility), the pedagogical aspects (training, evaluation, and support from educational institutions, and management), and the contributions of the action (assistance and professional training) were identified. Conclusion: the general perceptions of the recruits are positive. Pedagogical aspects related to the evaluation of academics and support from higher education institutions should be reviewed, for more assertive actions in times of health crisis. © 2022, Universidade Federal do Parana. All rights reserved.

Machine Learning Techniques For Public Health System: A Scientometric Review

Technology development plays a vital role in every aspect of life, whether it is in the medical domain or any other era. In the present scenario, scientists, researchers, and healthcare experts are still looking for a new technology against Air-Borne Disease(ABD) over the world. The use of Artificial Intelligence (AI) has encouraged researchers by providing a modern approach for combating Air-Borne diseases. The current study reviews approximately 145 papers for the literature review that insights AI-based application technology to prevent ABD. For this purpose, the current study discusses ideal epidemiology study, early disease diagnosis, and its progression concerning ABD. Although the reviewed AI application-based methods for ABD control are promising, this review suggests research that integrates ML advances should be done. ML techniques may lead to success in controlling the ABD if these suggestions are implemented. Finally, this study addresses the obstacles and opportunities for future research. © 2022 IEEE.

Enumeration of the Public Health Workforce in New York State: Workforce Changes in the Wake of COVID-19.

The governmental public health workforce in the United States has faced staffing shortages for over a decade that have been exacerbated by the COVID-19 pandemic. To assess this critical issue, the Region 2 Public Health Training Center collaborated with the New York State Association of County Health Officials to enumerate the city and county public health workforce in New York State. The organizations used an online survey to: (1) count employees and full-time equivalent (FTEs) staff in local health departments in 2021; (2) assess workforce trends since the COVID-19 pandemic; and, (3) identify challenges local health departments encounter in recruiting and retaining qualified public health workers. To assess trends, findings were compared with secondary data from 2019. Despite playing a central role in COVID-19 mitigation, local health departments experienced no overall increase in staffing in 2021 compared to 2019, with many health departments experiencing large increases in vacant positions. Recruitment challenges include noncompetitive salaries, difficulties finding qualified candidates, and lengthy hiring processes. This study complements accumulating evidence indicating that long-term investment in local public health infrastructure is needed to bolster the workforce and ensure that communities are protected from current and future health threats.

COVID-19 Vaccine Administration: Phase 2 of an in Progress Review in New York State Local Health Departments.

Since the onset of the COVID-19 pandemic in New York State (NYS), local health departments (LHDs) have worked to mitigate the highly infectious disease. As lead public health experts in their communities, LHDs are responsible for providing communicable disease control, emergency response, and establishing immunization programs, including leading large-scale vaccine distribution efforts. The aim of this qualitative study was to understand the processes used by LHDs in NYS to administer COVID-19 vaccines, as well as identify successes and challenges, and highlight lessons learned to improve future mass vaccination campaigns. Data were collected in two phases: (1) extant data collection of public communications; and (2) discussion groups with public health leaders across the state. Notable themes from both phases include: partnerships, programmatic elements, communication, role of LHD, State-LHD coordination, and human and physical resources. Analysis of both public and internal communications from LHDs across NYS revealed several core challenges LHDs faced during COVID-19 vaccine rollout and identified innovative solutions that LHDs used to facilitate vaccine access, administration, and uptake in their communities. Findings from this multi-phase qualitative analysis support the need to bolster the capacity and training of the local public health workforce to ensure preparedness for future public health emergencies.

The future of public health

The disinvestment in public health agencies and their workforces over the last several decades, and in particular following the 2008 financial crisis, has been well-described. Now, more than 2years into the public health response to an unprecedented modern global pandemic, we also have a clearer picture of the weaknesses in our public health systems that were exposed by COVID-19. The role of social determinants and racism in COVID's inequitable impacts, the burnout of the workforce, and the politicization of the response are three of the major factors that will influence the future of the public health system. As the Omicron variant fueled fourth wave of the pandemic fades, both the public, and many public health leaders, appear ready to relax restrictions and learn to live with COVID-19 as an endemic disease. However, even this phase of the pandemic will take hard and thoughtful work to reimagine our postpandemic world and to build a public health system than can ensure we maintain our ability to respond to future public health emergencies. Investments in public health preparedness after the September 11, 2001, and the subsequent anthrax attacks were not effectively linked to improved outcomes or increased preparedness, which left a public health system that was not ready to respond to the COVID-19 pandemic, public health's greatest challenge in more than a century. Therefore, the question we must answer now is what is the future of public health?

Deep Learning Techniques to Identify and Classify COVID-19 Abnormalities on Chest X-ray Images

The new coronavirus disease (COVID-19) comprises the public health systems around the world. The number of infected people and deaths are escalating day-to-day, which puts enormous pressure on healthcare systems. COVID-19 symptoms include fatigue, cough, and fever. These symptoms are also diagnosed for other pneumonia, which creates complications in identifying COVID-19, especially throughout the influenza season. The rise of the COVID-19 pandemic among individuals has made it essential to improve medical image screening of this pneumonia. Rapid identification is a necessary step to stop the spread of this virus and plays a vital role in early detection. With this as a motivator, we applied deep learning techniques to diagnose the coronavirus using chest X-ray images and to implement a robust AI application to classify COVID-19 pneumonia from non-COVID-19 for the respiratory system in these images. This paper proposes different deep learning algorithms, including classification and segmentation methods. By taking advantage of convolutional neural network models, we exploited different pre-trained deep learning models such as (ResNet50, ResNet101, VGG-19, and U-Net architectures) to extract features from chest X-ray images. Four datasets of chest X-ray images have been employed to assess the performance of the proposed methods. These datasets have been split into 80% for training and 20% for validation of the architectures. The experimental results showed an overall accuracy of 99.42% for the classification and 93% for segmentation approaches. The proposed approaches can help radiologists and medical specialists to identify the insights of infected regions for the respiratory system in the early stages. © 2022 SPIE.

District-Coupled Epidemic Control via Deep Reinforcement Learning

The rapid spread of the Coronavirus (COVID-19) poses an unprecedented threat to the public health system and social economy, with approximately 500 million confirmed cases worldwide. Policymakers confront with high-stakes to make a decision on interventions to prevent the pandemic from further spreading, which is a dilemma between public health and a steady economy. However, the epidemic control problem has vast solution space and its internal dynamic is driven by population mobility, which makes it difficult for policymakers to find the optimal intervention strategy based on rules-of-thumb. In this paper, we propose a Deep Reinforcement Learning enabled Epidemic Control framework (DRL-EC) to make a decision on intervention to effectively alleviate the impacts of the epidemic outbreaks. Specifically, it is driven by reinforcement learning to learn the intervention policy autonomously for the policymaker, which can be adaptive to the various epidemic situation. Furthermore, District-Coupled Susceptible-Exposed-Infected-Recovered (DC-SEIR) model is hired to simulate the pandemic transmission between inter-district, which characterize the spatial and temporal nature of infectious disease transmission simultaneously. Extensive experimental results on a real-world dataset, the Omicron local outbreaks in China, demonstrate the superiority of the DRL-EC compared with the strategy based on rules-of-thumb. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

‘Period doubling’ induced by optimal control in a behavioral SIR epidemic model

We consider a behavioral SIR epidemic model to describe the action of the public health system aimed at enhancing the social distancing during an epidemic outbreak. An optimal control problem is proposed where the control acts in a specific way on the contact rate. We show that the optimal control of social distancing is able to generate a period doubling–like phenomenon. Namely, the ‘period’ of the prevalence is the double of the ‘period’ of the control, and an alternation of small and large peaks of disease prevalence can be observed. © 2022 Elsevier Ltd

Forecasting the COVID-19 Pandemic in Bangladesh Using ARIMA Model

The effects of the coronavirus disease in 2019 are visible in every corner of the globe. The public health system is mostly affected, and the economic and social crises are also increasing day by day. Due to the widespread nature and the unavailability of drugs or vaccines for this pandemic, it is urgent to predict the COVID-19 infected cases to handle the situation more efficiently. Time series prediction is a crucial technique of the machine learning domain to deal with the issue. This research aims to predict the number of daily confirmed COVID-19 cases for a successful time. To forecast COVID-19 instances in Bangladesh, we use the Autoregressive Integrated Moving Average (ARIMA) model. The experimental results show that the estimated best models are: ARIMA(3,1,0) with drift, ARIMA(3,1,2) with drift, ARIMA(5,1,0) perform significant predictions on three different kinds of COVID-19 datasets. © 2021 IEEE.

Variations in end-of-life care practices in older critically ill patients with COVID-19 in Europe.

BACKGROUND: Previous studies reported regional differences in end-of-life care (EoLC) for critically ill patients in Europe. OBJECTIVES: The purpose of this post-hoc analysis of the prospective multicentre COVIP study was to investigate variations in EoLC practices among older patients in intensive care units during the coronavirus disease 2019 pandemic. METHODS: A total of 3105 critically ill patients aged 70 years and older were enrolled in this study (Central Europe: n = 1573; Northern Europe: n = 821; Southern Europe: n = 711). Generalised estimation equations were used to calculate adjusted odds ratios (aORs) to population averages. Data were adjusted for patient-specific variables (demographic, disease-specific) and health economic data (gross domestic product, health expenditure per capita). The primary outcome was any treatment limitation, and 90-day mortality was a secondary outcome. RESULTS: The frequency of the primary endpoint (treatment limitation) was highest in Northern Europe (48%), intermediate in Central Europe (39%) and lowest in Southern Europe (24%). The likelihood for treatment limitations was lower in Southern than in Central Europe (aOR 0.39; 95% confidence interval [CI] 0.21-0.73; p = 0.004), even after multivariable adjustment, whereas no statistically significant differences were observed between Northern and Central Europe (aOR 0.57; 95%CI 0.27-1.22; p = 0.15). After multivariable adjustment, no statistically relevant mortality differences were found between Northern and Central Europe (aOR 1.29; 95%CI 0.80-2.09; p = 0.30) or between Southern and Central Europe (aOR 1.07; 95%CI 0.66-1.73; p = 0.78). CONCLUSION: This study shows a north-to-south gradient in rates of treatment limitation in Europe, highlighting the heterogeneity of EoLC practices across countries. However, mortality rates were not affected by these results.

Multidisciplinary Analysis of International Environments Based on Impacts of Covid-19: State of Art

Drawing lessons from the Covid-19 pandemic according to literature, this contribution aims to show that greening the United Nations System with stronger environmental considerations, can help to shift the global economy from fossil energy to renewable energy with public-health resilient systems. This contribution starts with highlighting the fact that past economic crises and the implementation of the Sustainable Development Global Agenda have not been able to generate strong institutional arrangements for sustainable development including climate resilience building and public health resilient systems. This allows us to apprehend the possibility that the Covid-19 pandemic crisis may face the same incapacity. In response to these statements, this contribution shares the opinion that institutional reforms within the United Nations System may lead to perennial normative provisions and institutional arrangements able to make sustainable development happen with resilient public-health systems. This note highlights the fall of GHG emissions during the Covid-19 pandemic. It shows, however, based on the history of the past crisis, that the huge investment being mobilized to recover from the pandemic can quickly absorb GHG emissions fall. The way out suggested is that both the Global Economy and the Global Public Health agendas can be revisited to be strengthened by stronger environmental considerations. One of our findings is that multilateralism can adopt suitable institutional arrangements in Global Environmental Governance throughout the current global agenda on International Environmental Governance Reform within the United Nations System. © Iran University of Science and Technology 2022

Reimaging a system of care to promote the well-being of children and families: Proceedings of a workshop

This book discusses a virtual workshop, organized by the National Academies of Sciences, Engineering, and Medicine's Forum for Children's Well-Being (the Forum) and focused on building systems to support children and families in the aftermath of the COVID-19 pandemic. The workshop speakers and organizers paid particular attention to how these systems can combat structural racism. Participants engaged in discussions about a broad range of existing tools and resources that could be used to further promote family well-being and health equity in the United States. To highlight the various experiences of youth and families during the pandemic, Forum staff interviewed several teens and parents. A compilation of their responses was shown at the start of the workshop, demonstrating real-life challenges many of them have encountered throughout the year, including instances of racism, mental health struggles, and juggling work and school responsibilities. The workshop is organized into five chapters. Following the introduction, which gives background on the Forum and context for the workshop, the book presents an overview of the need to address structural racism systemically that includes a description of the ways in which it manifests in society. It specifically focuses on the economic system and includes goals and suggestions for reimagining systems for the future. Similarly, it also explores behavioral and public health systems and potential solutions for moving toward an equitable system. Finally, the book highlights the key messages from the presentations and discussions throughout the workshop. This book presents a document that has been prepared by the workshop Rapporteur as a factual summary of what occurred at the workshop. (PsycInfo Database Record (c) 2022 APA, all rights reserved)