A saúde coletiva: vivenciando a teoria na prática, como um relato de experiência

RESUMO Introdução: Este trabalho é um relato de experiência da disciplina saúde coletiva, do curso de graduação em Enfermagem, de uma faculdade privada de São Luís/MA, realizado nas Unidades de Estratégia Saúde da Família (UESFs). Objetivo: Dialogar sobre a experiência vivenciada pelos acadêmicos do 5º período e reforçar a importância da contextualização das aulas teóricas de saúde coletiva, contribuindo para a aprendizagem e sensibilização dos graduandos para a real situação da atenção básica. Metodologia: Relato de experiência, de visitas técnicas, dos alunos aos futuros campos de estágio, com escolha de cada grupo por um programa de saúde pública já implantado; ao final da visita cada grupo elaborou um relatório especificando: dados da estrutura de atendimento em saúde e da realidade social que o cerca, para tanto foi fornecido um roteiro de observações. Resultados: Foi possível identificar as potencialidades dos programas de saúde, suas lacunas, o perfil epidemiológico da população adscrita e contribuiu para a aproximação do alunado com a real situação de saúde no Brasil, desmistificando assuntos que pareciam estar longe da realidade dos acadêmicos. ABSTRACT Introduction: This work is an experience report of the collective health discipline, of the undergraduate Nursing course, of a private college in São Luís/MA, carried out in the Family Health Strategy Units (UESFs). Objective: Dialogue on the experience lived by students in the 5th period and reinforce the importance of contextualizing the theoretical classes of collective health, contributing to the learning and awareness of undergraduates about the real situation of primary care. Methodology: Experience report, technical visits, by students to future internship fields, with each group being chosen by an already implemented public health program; at the end of the visit, each group prepared a report specifying: data from the health care structure and the social reality that surrounds them, for which a script of observations was provided. Results: It was possible to identify the potential of health programs, their gaps, the epidemiological profile of the enrolled population and contributed to bringing students closer to the real health situation in Brazil, demystifying issues that seemed to be far from the reality of academics.

Thoracic Electric Bio-Impedance Artifacts Removal by Block Related Adaptive Eliminators for Remote Health Care

Study of thoracic-electrical- bio-impedance (TEB) simplifies heart attack volume during abrupt cardiac detention. Here in this paper we presented various effective and arithmetically reduced flexible techniques to show high quality TEB element. In scientific circumstances, TEB wave accounts several natural and un-natural events that veil the small things which are needed in finding the depth of the heart attack. In addition to that arithmetical convolution is the significant factor in a present-day wearable health based detecting device. Thus we used a novel signal processing method for TEB improvement in distant health-care structures. To do this we selected higherorder adaptive-filter as an essential component in designing TEB. To boost purifying capacity, merging velocity, to decrease mathematical difficulty of signal processing method, we used information normalization and cutting the data-regressor. The designed applications were checked on original TEB signals and the executed outcome established that the designed regressor eliminated the normalized high order purifier which is apt for a pragmatic health-care structure.

Australia’s Two-Tier Health Care System United Against COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has had and continues to have an unprecedented impact on health care systems worldwide. The Australian system has yet to be truly tested by the pandemic, as rapid implementation of public health measures has curbed infection rates. Australia’s 2-tier health care has allowed sufficient staffing, equipment, and beds to continue providing acute health care in the face of an exceptional and extreme demand. No health system is perfect and, although Australia’s has some wonderful attributes that make it the envy of many other countries, it faces a number of important challenges. This paper describes how Australia’s health care structure has adapted to respond to the COVID-19 crisis, examines the challenges involved and the lessons learned, and explores how this environmental pressure could lead to systemic adaptations.

Improving Management of Infantile Spasms by Adopting Implementation Science

Over the last several decades, significant progress has been made in the discovery of appropriate therapy in the management of infantile spasms (IS). Based on several well-controlled studies, the American Academy of Neurology and the Child Neurology Society have published the current best practice parameters for the treatment of IS. However, dissemination and implementation of evidence-based guidelines remain a significant challenge. Though the number of well-performed controlled trials and systematic reviews is increasing exponentially, the proportion of valuable new information subsequently embedding into the routine clinical care is significantly lower. Planned and systematic implementation of evidence-based interventions in a given health care structure may outstrip the benefits of discovering a new insight, procedure, or drug in another controlled setting. Implementation problems can be broad-ranging to hinder effective, efficient, safe, timely, and patient-centered care without significant variation. The first part of this review article provides a detailed summary of some crucial comparative treatment studies of IS available in the literature. In the second part, practical challenges to mitigate the gap between knowledge and practice to improve outcomes in the management of IS has been explored, and a consolidated framework approach for systematic implementation research methodology has been discussed to implement evidence-based guidelines for the management of IS. Although large multicenter controlled studies will help gather quality evidence in the treatment of IS, a more comprehensive range of scientific methodologies, including qualitative research and mixed research methodologies, will hold the more considerable promise for implementing evidence-based practices in the health care system.

PALOMA Centre of Expertise for refugee mental health – from research to action

Issue Previous migrant population studies have shown that immigrants experience higher level of psychological load than the overall population. This is especially prevalent in those with refugee background. The PALOMA project (Developing National Mental Health Policies for Refugees) was launched in 2016 to tackle this wellbeing cap. One outcome of the project was a PALOMA handbook, which includes recommendations as well as methods and tools for decision-makers, supervisors and professionals for promoting refugees' mental health and preventing, identifying and treating the problems. During the PALOMA project, the need for national coordination, establishing regional Centre of Expertises and support on refugees mental health work was recognized. Results To fulfill this need, the PALOMA2 project (National support system for refugee mental health work and the knowhow dissemination) started in February 2019. The PALOMA2-project establishes a Centre of Expertise for mental health work among refugees. The project personnel includes five regional experts, that each know the specific strengths and challenges of their region, and an expert from the 3rd sector, that ensures that the target group's and the 3rd sector's voice is heard throughout the project. Each of these six experts also pilot two tools from the PALOMA handbook. Lessons As a result of the PALOMA work, the mental health and mental health work with the refugees will be improved. The PALOMA Centre of Expertise will be a place of consultation, influencing, education, networking, developing and work guidance, and it will help all professionals that work in the field. Key messages The PALOMA Centre of Expertise is involved in developing different services in ways that improve refugee’s mental health and equal access for the developed services. The PALOMA Centre of Expertise will be a permanent part of the Finnish social and health care structure.

Covid-19 spread prediction and its correlation with social distancing, available health facilities using GIS mapping data models in Lahore, Pakistan

Virus spreading and its mitigation is an important safety issue that has drawn wide attention of many countries and people. For researchers in this area, it is an interesting work to study virus spreading with safety theories and methods. In this paper, we worked on the spatial extent of SIR model, which considers the known facts of Covid-19 behavior i.e. its spreading extent with time, the total population of area concerned and dedicated health facilities. Also, a special relationship between Covid-19 cases and NLDI data driven by night-time satellite imagery is being discussed. Results predicted a huge gap between predicted and presently available facilities for number of hospitals, beds, and ventilators. Findings suggest that developing countries like our study area Lahore District, Pakistan needs to follow social distancing at immense level, which not only helps in reducing the numbers of infections and fatalities but also the time duration of the whole epidemic. Maps based on NLDI vales, predicted cases, hospitals and ventilators needs could be greatly helpful for policymakers to analyze situation and concentrate on areas which needs immediate attention. Dealing with the pandemic requires a pre-planned command and control structure that could make quick and informed decisions in the whole city. We recommend that the use of proper model prediction at Union Council level can help local government in policymaking related social distancing and healthcare systems. The decision of social distancing should be on time and like what percent of social distancing is needed, which tackle with the already available health care structure.

ENDOCRINOLOGY IN THE TIME OF COVID-19: Management of Cushing’s syndrome

Clinical evaluation should guide those needing immediate investigation. Strict adherence to COVID-19 protection measures is necessary. Alternative ways of consultations (telephone, video) should be used. Early discussion with regional/national experts about investigation and management of potential and existing patients is strongly encouraged. Patients with moderate or severe clinical features need urgent investigation and management. Patients with active Cushing’s syndrome, especially when severe, are immunocompromised and vigorous adherence to the principles of social isolation is recommended. In patients with mild features or in whom a diagnosis is less likely, clinical re-evaluation should be repeated at 3 and 6 months or deferred until the prevalence of SARS-CoV-2 has significantly decreased; however, those individuals should be encouraged to maintain social distancing. Diagnostic pathways may need to be very different from usual recommendations in order to reduce possible exposure to SARS-CoV-2. When extensive differential diagnostic testing and/or surgery is not feasible, it should be deferred and medical treatment should be initiated. Transsphenoidal pituitary surgery should be delayed during high SARS-CoV-2 viral prevalence. Medical management rather than surgery will be the used for most patients, since the short- to mid-term prognosis depends in most cases on hypercortisolism rather than its cause; it should be initiated promptly to minimize the risk of infection in these immunosuppressed patients. The risk/benefit ratio of these recommendations will need re-evaluation every 2–3 months from April 2020 in each country (and possibly local areas) and will depend on the local health care structure and phase of pandemic.

Hemogram data as a tool for decision-making in COVID-19 management: applications to resource scarcity scenarios

Background COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. Purpose This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. Methods Hemogram exams data from 510 symptomatic patients (73 positives and 437 negatives) were used to model and predict qRT-PCR results through Naïve-Bayes algorithms. Different scarcity scenarios were simulated, including symptomatic essential workforce management and absence of diagnostic tests. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. Results Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity, yielding a 100% sensitivity and 22.6% specificity with a prior of 0.9999; 76.7% for both sensitivity and specificity with a prior of 0.2933; and 0% sensitivity and 100% specificity with a prior of 0.001. Regarding background scarcity context, resources allocation can be significantly improved when model-based patient selection is observed, compared to random choice. Conclusions Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency.

Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios

Background: COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. A critical element involves essential workforce management since current protocols recommend release from duty for symptomatic individuals, including essential personnel. Testing capacity is also problematic in several countries, where diagnosis demand outnumbers available local testing capacity. Purpose: This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients and how they can be used to predict qRT-PCR test results. Methods: A Naïve-Bayes model for machine learning is proposed for handling different scarcity scenarios, including managing symptomatic essential workforce and absence of diagnostic tests. Hemogram result data was used to predict qRT-PCR results in situations where the latter was not performed, or results are not yet available. Adjusts in assumed prior probabilities allow fine-tuning of the model, according to actual prediction context. Results: Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity. Data assessment can be performed in an individual or simultaneous basis, according to desired outcome. Based on hemogram data and background scarcity context, resource distribution is significantly optimized when model-based patient selection is observed, compared to random choice. The model can help manage testing deficiency and other critical circumstances. Conclusions: Machine learning models can be derived from widely available, quick, and inexpensive exam data in order to predict qRT-PCR results used in COVID-19 diagnosis. These models can be used to assist strategic decision-making in resource scarcity scenarios, including personnel shortage, lack of medical resources, and testing insufficiency.