Local protection bubbles: an interpretation of the slowdown in the spread of coronavirus in the city of São Paulo, Brazil, in July 2020.

After four months of fighting the pandemic, the city of São Paulo, Brazil, entered a phase of relaxed social distancing measures in July 2020. Simultaneously, there was a decline in the social distancing rate and a reduction in the number of cases, fatalities, and hospital bed occupancy. To understand the pandemic dynamics in the city of São Paulo, we developed a multi-agent simulation model. Surprisingly, the counter-intuitive results of the model followed the city's reality. We argue that this phenomenon could be attributed to local bubbles of protection that emerged in the absence of contagion networks. These bubbles reduced the transmission rate of the virus, causing short and temporary reductions in the epidemic curve - but manifested as an unstable equilibrium. Our hypothesis aligns with the virus spread dynamics observed thus far, without the need for ad hoc assumptions regarding the natural thresholds of collective immunity or the heterogeneity of the population's transmission rate, which may lead to erroneous predictions. Our model was designed to be user-friendly and does not require any scientific or programming expertise to generate outcomes on virus transmission in a given location. Furthermore, as an input to start our simulation model, we developed the COVID-19 Protection Index as an alternative to the Human Development Index, which measures a given territory vulnerability to the coronavirus and includes characteristics of the health system and socioeconomic development, as well as the infrastructure of the city of São Paulo.

Data Quality in Health Research: Integrative Literature Review.

BACKGROUND: Decision-making and strategies to improve service delivery must be supported by reliable health data to generate consistent evidence on health status. The data quality management process must ensure the reliability of collected data. Consequently, various methodologies to improve the quality of services are applied in the health field. At the same time, scientific research is constantly evolving to improve data quality through better reproducibility and empowerment of researchers and offers patient groups tools for secured data sharing and privacy compliance. OBJECTIVE: Through an integrative literature review, the aim of this work was to identify and evaluate digital health technology interventions designed to support the conducting of health research based on data quality. METHODS: A search was conducted in 6 electronic scientific databases in January 2022: PubMed, SCOPUS, Web of Science, Institute of Electrical and Electronics Engineers Digital Library, Cumulative Index of Nursing and Allied Health Literature, and Latin American and Caribbean Health Sciences Literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist and flowchart were used to visualize the search strategy results in the databases. RESULTS: After analyzing and extracting the outcomes of interest, 33 papers were included in the review. The studies covered the period of 2017-2021 and were conducted in 22 countries. Key findings revealed variability and a lack of consensus in assessing data quality domains and metrics. Data quality factors included the research environment, application time, and development steps. Strategies for improving data quality involved using business intelligence models, statistical analyses, data mining techniques, and qualitative approaches. CONCLUSIONS: The main barriers to health data quality are technical, motivational, economical, political, legal, ethical, organizational, human resources, and methodological. The data quality process and techniques, from precollection to gathering, postcollection, and analysis, are critical for the final result of a study or the quality of processes and decision-making in a health care organization. The findings highlight the need for standardized practices and collaborative efforts to enhance data quality in health research. Finally, context guides decisions regarding data quality strategies and techniques. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2022.05.31.22275804.

Myositis Ossificans of the Breast Mimicking Breast Cancer: A Case Report.

Myositis ossificans (MO) is an uncommon tumor characterized by a rapidly growing mass following a history of local trauma. Few cases of MO affecting the breast have been reported, and some were misdiagnosed as primary osteosarcoma of the breast or metaplastic breast carcinoma. The following case report presents a patient with a growing breast lump whose core biopsy result was suspicious for breast cancer. MO was diagnosed after analysis of the mastectomy specimen. This case highlights the importance of MO as a differential diagnosis of a growing soft-tissue mass after trauma to avoid unnecessary overtreatment. Keywords: Myositis Ossificans, Osteosarcoma, Breast Cancer, Mastectomy, Heterotopic Ossification © RSNA, 2023.

The Minimum Data Set for Rare Diseases: Systematic Review.

BACKGROUND: The minimum data set (MDS) is a collection of data elements to be grouped using a standard approach to allow the use of data for clinical and research purposes. Health data are typically voluminous, complex, and sometimes too ambiguous to generate indicators that can provide knowledge and information on health. This complexity extends further to the rare disease (RD) domain. MDSs are essential for health surveillance as they help provide services and generate recommended population indicators. There is a bottleneck in international literature that reveals a global problem with data collection, recording, and structuring in RD. OBJECTIVE: This study aimed to identify and analyze the MDSs used for RD in health care networks worldwide and compare them with World Health Organization (WHO) guidelines. METHODS: The population, concept, and context methodology proposed by the Joanna Briggs Institute was used to define the research question of this systematic review. A total of 4 databases were reviewed, and all the processes were reported using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. The data elements were analyzed, extracted, and organized into 10 categories according to WHO digital health guidelines. The quality assessment used the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist. RESULTS: We included 20 studies in our review, 70% (n=14) of which focused on a specific health domain and 30% (n=6) of which referred to RD in general. WHO recommends that health systems and networks use standard terminology to exchange data, information, knowledge, and intelligence in health. However, there was a lack of terminological standardization of the concepts in MDSs. Moreover, the selected studies did not follow the same standard structure for classifying the data from their MDSs. All studies presented MDSs with limitations or restrictions because they covered only a specific RD, or their scope of application was restricted to a specific context or geographic region. Data science methods and clinical experience were used to design, structure, and recommend a fundamental global MDS for RD patient records in health care networks. CONCLUSIONS: Our study highlights the difficulties in standardizing and categorizing findings from MDSs for RD because of the varying structures used in different studies. The fundamental RD MDS designed in this study comprehensively covers the data needs in the clinical and management sectors. These results can help public policy makers support other aspects of their policies. We highlight the potential of our results to help strategic decisions related to RD. TRIAL REGISTRATION: PROSPERO CRD42021221593; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221593. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.procs.2021.12.034.

From Raw Data to FAIR Data: The FAIRification Workflow for Brazilian Tuberculosis Research.

Among the main factors that negatively influence the decision-making process, it is possible to highlight the low quality, availability, and integration of population health data. This study aims to highlight the difficulty of research based on tuberculosis data available in Brazil. The FAIR methodology is a solution for standardizing data and sharing information about the disease. All the main actors involved, including those who generate data and administrators of information systems, should be encouraged to know their strengths and weaknesses. Continuously fostering strategies to promote data quality is, therefore, a strong stimulus for strengthening national health information systems and can potentially benefit from recommendations on how to overcome the inherent limitations of these information systems. Data quality management in Brazilian tuberculosis information systems is still not carried out organized and systematically. According to the FAIR principles, the evaluation demonstrates only 37.75% of compliance.

Development of a Mobile Application with Health Guidelines for TB Patients Care.

Health guidelines inform recommendations for different clinical practices or public health policies. They are a simple way to organize and retrieve relevant information that can impact patient care. Although these documents are easy to use, most are not user-friendly because they are difficult to access. Our work aims to present the developing approach for a decision-making tool based on health guidelines to assist health professionals in caring for patients with tuberculosis. This tool is being developed for use on mobile devices and as a web-based system, which will transform a passive and declarative health guideline document into an interactive tool that will provide data, information, and knowledge. User tests with functional prototypes developed for the Android platform show that this application has the potential to be applied in TB healthcare facilities in the future.

A Computational Infrastructure for Analyzing Tuberculosis Research Data in Brazil.

Tuberculosis (TB) is one of the infectious diseases that currently causes the most deaths, with 6.4 million new cases recorded in 2021. Although it is a curable disease, drug-resistant strains emerge due to a lack of hygiene and low-quality or inappropriate medications, among other factors. With this in mind, the World Health Organization initiated the End TB Strategy campaign to improve the health system in the fight against tuberculosis. For this, reliable and high-quality health data is necessary to create effective public policies. However, despite technological advancements such as emerging concepts like Big Data and the Internet of Things, generating health information faces several obstacles. Therefore, the present work aims to describe a pipeline for TB research in Brazil to contribute to obtaining high-quality data.

Availability of Genetic Tests in Public Health Services in Brazil: Data from the Brazilian Rare Diseases Network.

INTRODUCTION: The Brazilian Policy for Comprehensive Care for People with Rare Diseases (BPCCPRD) was published in 2014, accrediting several reference centers and incorporating many genetic tests for the diagnosis of rare diseases (RDs). The Brazilian Network of Rare Diseases (RARAS) comprises more than 40 institutions that offer diagnosis and treatment for RDs in Brazil. This network includes Reference Services for Rare Diseases (RDRS), Reference Services for Newborn Screening (NSRS), and University Hospitals distributed in all Brazilian regions. OBJECTIVE: The aim of the study was to map the availability and distribution of the BPCCPRD diagnostic procedures in the Brazilian Unified Health System through RARAS. METHOD: Data were collected through a questionnaire on the Research Electronic Data Capture platform, with 22 questions regarding the availability of procedures. Thirty-seven coordinators from RARAS participating centers received the questionnaire link for participation by email from August/2020 to March/2021. All participating institutions ethically approved this project. RESULTS: Of the 37 institutions, 23 (62.16%) offered cytogenetic tests, 20 (54.05%) offered molecular procedures, and 22 (59.46%) offered inborn errors of metabolism diagnostic tests. The Southern blot analysis, enzyme assays on cultured tissue and urinary organic acid tests had the highest outsourcing rate. On the other hand, the procedures most frequently performed on-site were bone marrow karyotype and long-term cultured karyotype. It was observed that 10 of the 37 centers (27%) did not provide access to investigated procedures (on-site or outsourced). The North and Midwest regions stood out in terms of the unavailability of such techniques in at least 40% of the evaluated institutions. DISCUSSION AND CONCLUSION: This study reveals large discrepancies in the supply of diagnostic procedures in the Brazilian territory. Moreover, there is a broad collaboration between services through the outsourcing of multiple diagnostic techniques to address this issue. Finally, this work corroborates the importance of mapping services for the diagnosis and treatment of individuals with RDs to propose actions for the better supply and distribution of these procedures.

REDbox: a comprehensive semantic framework for data collection and management in tuberculosis research.

Clinical research outcomes depend on the correct definition of the research protocol, the data collection strategy, and the data management plan. Furthermore, researchers often need to work within challenging contexts, as is the case in tuberculosis services, where human and technological resources for research may be scarce. Electronic Data Capture Systems mitigate such risks and enable a reliable environment to conduct health research and promote result dissemination and data reusability. The proposed solution is based on needs pinpointed by researchers, considering the need for an accommodating solution to conduct research in low-resource environments. The REDbox framework was developed to facilitate data collection, management, sharing, and availability in tuberculosis research and improve the user experience through user-friendly, web-based tools. REDbox combines elements of the REDCap and KoBoToolbox electronic data capture systems and semantics to deliver new valuable tools that meet the needs of tuberculosis researchers in Brazil. The framework was implemented in five cross-institutional, nationwide projects to evaluate the users' perceptions of the system's usefulness and the information and user experience. Seventeen responses (representing 40% of active users) to an anonymous survey distributed to active users indicated that REDbox was perceived to be helpful for the particular audience of researchers and health professionals. The relevance of this article lies in the innovative approach to supporting tuberculosis research by combining existing technologies and tailoring supporting features.

Unsupervised analysis of COVID-19 pandemic evolution in brazilian states: Vaccination Scenario.

Brazil is one of the countries with the worst response against the pandemic scenario of coronavírus. At the beginning we were on average with 4000 deaths in a 24 hours period. In the course of this situation, large amounts of health and medicine datasets were being generated in real time, requiring effective ways to extract information and discover patterns that can help in the fight against this disease. And even more important is to monitor the progress of prophylactic measures and whether they are being effective in reducing the spread of the virus. Thus, the aim of this study is to analyze how the coronavirus has different ways to evolve in each Brazilian state with the influences of the vaccination process. To achieve this goal, the time series Clustering Technique based on a K-Means variation was applied, with the similarity metric Dynamic Time Warping (DTW). We produced this study using the data reported by the Ministry of Health in Brazil, referring to deaths per 100k inhabitants and all vaccination data available. Our results indicate an unevenly occurring vaccination and the need to identify other associated patterns with human development indices and other socio-economic indicators, being this the first analysis developed in the country, under the goals above.

Local protection bubbles: an interpretation of the slowdown in the spread of coronavirus in the city of São Paulo, Brazil, in July 2020 / Bolhas de proteção locais: uma interpretação da diminuição da velocidade de disseminação do coronavírus na cidade de São Paulo, Brasil, em julho de 2020 / Burbujas de protección locales: una interpretación de la reducción de la velocidad de propagación del coronavirus en la ciudad de São Paulo, Brasil, en julio de 2020

Abstract: After four months of fighting the pandemic, the city of São Paulo, Brazil, entered a phase of relaxed social distancing measures in July 2020. Simultaneously, there was a decline in the social distancing rate and a reduction in the number of cases, fatalities, and hospital bed occupancy. To understand the pandemic dynamics in the city of São Paulo, we developed a multi-agent simulation model. Surprisingly, the counter-intuitive results of the model followed the city's reality. We argue that this phenomenon could be attributed to local bubbles of protection that emerged in the absence of contagion networks. These bubbles reduced the transmission rate of the virus, causing short and temporary reductions in the epidemic curve - but manifested as an unstable equilibrium. Our hypothesis aligns with the virus spread dynamics observed thus far, without the need for ad hoc assumptions regarding the natural thresholds of collective immunity or the heterogeneity of the population's transmission rate, which may lead to erroneous predictions. Our model was designed to be user-friendly and does not require any scientific or programming expertise to generate outcomes on virus transmission in a given location. Furthermore, as an input to start our simulation model, we developed the COVID-19 Protection Index as an alternative to the Human Development Index, which measures a given territory vulnerability to the coronavirus and includes characteristics of the health system and socioeconomic development, as well as the infrastructure of the city of São Paulo.

Resumo: Após quatro meses lutando contra a pandemia, a cidade de São Paulo, Brasil, entrou em uma fase de flexibilização das medidas de distanciamento social em julho de 2020. Simultaneamente, houve queda na taxa de distanciamento social e redução no número de casos, mortes e ocupação de leitos hospitalares. Um modelo de simulação multiagente foi desenvolvido para entender a dinâmica da pandemia na cidade de São Paulo. Ao contrário do esperado, os resultados contraintuitivos do modelo acompanharam a realidade da cidade. Argumentamos que este fenômeno pode ser atribuído às bolhas locais de proteção que surgiram na ausência de redes de contágio. Estas bolhas reduziram a taxa de transmissão do vírus, causando reduções curtas e temporárias na curva epidêmica - mas se manifestaram como um equilíbrio instável. Nossa hipótese está alinhada com a dinâmica da propagação do vírus observada até o momento, sem a necessidade de suposições ad hoc sobre limiares de imunidade coletiva natural ou heterogeneidade da taxa de transmissão da população, o que pode levar a previsões errôneas. Nosso modelo foi projetado para ser fácil de usar e não requer nenhum conhecimento científico ou de programação para gerar resultados sobre a transmissão do vírus em um determinado local. Além disso, como insumo para iniciar nosso modelo de simulação, desenvolvemos o Índice de Proteção contra a COVID-19 como alternativa ao Índice de Desenvolvimento Humano, que mede a vulnerabilidade de um determinado território ao coronavírus e inclui características do sistema de saúde e do desenvolvimento socioeconômico, além da infraestrutura da cidade de São Paulo.

Resumen: Tras cuatro meses luchando contra la pandemia, la ciudad de São Paulo, Brasil, empezó una fase de flexibilización de las medidas de alejamiento social en julio de 2020. A la vez, hubo una reducción en la tasa de alejamiento social y en el número de casos, muertes y ocupación de camas en los hospitales. Se desarrolló un modelo de simulación multiagente para entender la dinámica de la pandemia en la ciudad de São Paulo. Diferente de lo esperado, los resultados contradictorios del modelo reflejaron la realidad de la ciudad. Sostenemos que se puede atribuir este fenómeno a las burbujas locales de protección que surgieron durante la ausencia de redes de contagio. Estas burbujas redujeron la tasa de transmisión del virus, reduciendo de forma corta y temporal la curva epidémica -pero se manifestaron como un equilibrio inestable. Nuestra hipótesis se alinea con la dinámica de la propagación del virus observada hasta el momento, sin la necesidad de suposiciones ad hoc sobre umbrales de inmunidad colectiva natural o heterogeneidad de la tasa de transmisión de la población, lo que puede provocar previsiones equivocadas. Nuestro modelo se proyectó para ser fácil de usar y no necesita ningún conocimiento científico o de programación para generar resultados sobre la transmisión del virus en un determinado local. Además, como insumo para iniciar nuestro modelo de simulación, desarrollamos el Índice de Protección contra la COVID-19 como una alternativa al Índice de Desarrollo Humano, que mide la vulnerabilidad de un determinado territorio al coronavirus e incluye características del sistema de salud y del desarrollo socioeconómico, además de la infraestructura de la ciudad de São Paulo.

Mental Health Indicators for the Brazilian Psychosocial Care Network: A proposal. / Indicadores de saúde mental para a Rede de Atenção Psicossocial brasileira: uma proposta.

OBJECTIVE: to propose Mental Health Indicators aimed at management of the Mental Health Care Network, starting with convergence of their use, in countries with public health organization. METHOD: an exploratory analysis of the indicators adopted and used in these countries, from the detailed analysis of their respective normative documents, considering the World Health Organization guidelines. After selection of the indicators, the Mental Health Matrix was adopted as a suggestion for their development and application in the Brazilian Psychosocial Care Network. The matrix was prepared in two dimensions, respecting the inclusion and exclusion criteria for the indicators studied, as follows: geographical (national/regional, local, individual), and time (entry, process and results). RESULTS: the analysis indicates 41 indicators that presented diverse evidence regarding their use. All were allocated in the Mental Health Matrix, contributing as a metric to analyze the purpose of the Mental Health services, in the levels and phases of each dimension. CONCLUSION: the indicators selected, distributed in the different Mental Health Matrix dimensions, are being made available for their use in management and in the clinical practice, as well as for scientific studies and, in the future, to be used as definers of Mental Health policies.

Development and content validation of a mobile application for monitoring latent tuberculosis treatment.

BACKGROUND: Non-compliance with latent tuberculosis infection (LTBI) treatment is a reality. The objective of this study was to develop and validate an mobile device application for monitoring the treatment of LTBI. METHODS: We defined the requirements, elaborated on the application's conceptual map, generated implementation and prototyping alternatives, and validated content. RESULTS: Feedback on the validity of content were: "usefulness, consistency, clarity, objectivity, vocabulary, and precision" from professionals, and "clarity" from patients. CONCLUSIONS: The application proved to be easy to understand, according to the assessment of both professionals and people undergoing treatment for LTBI.

Epidemiology of rare diseases in Brazil: protocol of the Brazilian Rare Diseases Network (RARAS-BRDN).

The Brazilian Policy of Comprehensive Care for People with Rare Diseases (BPCCPRD) was established by the Ministry of Health to reduce morbidity and mortality and improve the quality of life of people with rare diseases (RD). Several laboratory tests, most using molecular genetic technologies, have been incorporated by the Brazilian Public Health System, and 18 specialised centres have so far been established at university hospitals (UH) in the capitals of the Southern, Southeastern and Northeastern regions. However, whether the available human and technological resources in these services are appropriate and sufficient to achieve the goals of care established by the BPCCPRD is unknown. Despite great advances in diagnosis, especially due to new technologies and the recent structuring of clinical assessment of RD in Brazil, epidemiological data are lacking and when available, restricted to specific disorders. This position paper summarises the performance of a nationally representative survey on epidemiology, clinical status, and diagnostic and therapeutic resources employed for individuals with genetic and non-genetic RD in Brazil. The Brazilian Rare Disease Network (BRDN) is under development, comprising 40 institutions, including 18 UH, 17 Rare Diseases Reference Services and five Newborn Screening Reference Services. A retrospective study will be initially conducted, followed by a prospective study. The data collection instrument will use a standard protocol with sociodemographic data and clinical and diagnostic aspects according to international ontology. This great collaborative network is the first initiative of a large epidemiological data collection of RD in Latin America, and the results will increase the knowledge of RD in Brazil and help health managers to improve national public policy on RD in Brazil.

COVID-19 BR: A web portal for COVID-19 information in Brazil.

Brazil is a large developing country that requires attention to regionalized behaviors regarding the dissemination of COVID-19. To deal with this complexity, the COVID-19 Brazil observatory was developed. The Portal aims to monitor and analyze data from different sources. Therefore, with a detailed audit, we centralized this information on the evolution of the disease, allowing for territorial and temporal monitoring. The daily publication of numbers about COVID-19 allowed anyone to follow the current scenario in several Brazilian cities. With about 1,7 million accesses, the Portal offers clarity and an easy understanding of the pandemic data in the country.

Unsupervised analysis of COVID-19 pandemic evolution in brazilian states.

Extracting information and discovering patterns from a massive dataset is a hard task. In an epidemic scenario, this data has to be integrated providing organization, agility, transparency and, above all, it has to be free of any type of censorship or bias. The aim of this paper is to analyze how coronavirus contamination has evolved in Brazil applying unsupervised analysis algorithms to extract information and find characteristics between them. To achieve this goal we describe an implementation that uses data about Covid-19 spread in Brazilian states (26 states and the federal district), applying a Time Series Clustering technique based on a K-Means variation, using Dynamic Time Warping as a similarity metric. We used data reported by the Ministry of Health in Brazil, referring to deaths per 100k inhabitants, during 452 days from the first reported death in each state. Two analyzes were performed, one considering 3 clusters and the other with 6 clusters. Through these analysis, 3 patterns of responses to the pandemic can be observed, ranging from one of greater to lesser control of the pandemic, although in recent months all clusters showed a highly increase in the number of deaths. The identification of these patterns is important to highlight possible actions and events, as well as other characteristics that determine the correct or incorrect public decision-making in combating the Covid-19 pandemic.

Indicadores de saúde mental para a Rede de Atenção Psicossocial brasileira: uma proposta / Mental Health Indicators for the Brazilian Psychosocial Care Network: A proposal / Indicadores de salud mental para la Red Brasileña de Atención Psicosocial: una propuesta

Resumo Objetivo: propor indicadores de saúde mental destinados a gestão da Rede de Atenção em saúde mental, a começar da convergência da sua utilização, em países com organização pública de saúde. Método: análise exploratória dos indicadores, adotados e utilizados nesses países, a partir da análise detalhada dos seus respectivos documentos normativos, considerando as orientações da Organização Mundial de Saúde. Após a seleção dos indicadores, adotou-se a Matriz de Saúde Mental como sugestão para seu desenvolvimento e aplicação na Rede de Atenção Psicossocial brasileira. Respeitando os critérios de inclusão e exclusão dos indicadores estudados, a matriz foi construída, em duas dimensões: geográfica: (nacional/regional, local, individual) e temporal (entrada, processo e resultados). Resultados: a análise aponta 41 indicadores que apresentaram evidências quanto ao seu uso. Todos foram posicionados na Matriz de Saúde Mental, contribuindo como uma métrica para analisar a finalidade dos serviços de saúde mental, nos níveis e fases de cada dimensão. Conclusão: os indicadores selecionados, distribuídos nas diferentes dimensões da Matriz de Saúde Mental, estão sendo disponibilizados para uso, para a gestão e na prática clínica, bem como para estudos científicos e, num horizonte futuro, para uso como definidor de políticas de saúde mental.

Abstract Objective: to propose Mental Health Indicators aimed at management of the Mental Health Care Network, starting with convergence of their use, in countries with public health organization. Method: an exploratory analysis of the indicators adopted and used in these countries, from the detailed analysis of their respective normative documents, considering the World Health Organization guidelines. After selection of the indicators, the Mental Health Matrix was adopted as a suggestion for their development and application in the Brazilian Psychosocial Care Network. The matrix was prepared in two dimensions, respecting the inclusion and exclusion criteria for the indicators studied, as follows: geographical (national/regional, local, individual), and time (entry, process and results). Results: the analysis indicates 41 indicators that presented diverse evidence regarding their use. All were allocated in the Mental Health Matrix, contributing as a metric to analyze the purpose of the Mental Health services, in the levels and phases of each dimension. Conclusion: the indicators selected, distributed in the different Mental Health Matrix dimensions, are being made available for their use in management and in the clinical practice, as well as for scientific studies and, in the future, to be used as definers of Mental Health policies.

Resumen Objetivo: proponer indicadores de salud mental para la gestión de la Red de Atención en Salud Mental, a partir de la convergencia de uso en países con organización pública de salud. Método: análisis exploratorio de los indicadores que adoptan y utilizan estos países, a partir del análisis detallado de sus respectivos documentos normativos, considerando las directrices de la Organización Mundial de la Salud. Después de seleccionar los indicadores, se sugirió adoptar la Matriz de Salud Mental para desarrollarlos y aplicarlos en la Red Brasileña de Atención Psicosocial. Respetando los criterios de inclusión y exclusión de los indicadores estudiados, la matriz fue construida en dos dimensiones: geográfica (nacional/regional, local, individual) y temporal (entrada, proceso y resultados). Resultados: el análisis indica que 41 indicadores presentaron evidencia de uso. Todos fueron posicionados en la Matriz de Salud Mental, y contribuyeron como métrica para analizar la finalidad de los servicios de salud mental, en los niveles y fases de cada dimensión. Conclusión: los indicadores seleccionados, distribuidos en diferentes dimensiones de la Matriz de Salud Mental, están disponibles para ser utilizados tanto en la gestión y en la práctica clínica, como en estudios científicos y, en un horizonte futuro, para definir políticas de salud mental.

Development and content validation of a mobile application for monitoring latent tuberculosis treatment

ABSTRACT Background: Non-compliance with latent tuberculosis infection (LTBI) treatment is a reality. The objective of this study was to develop and validate an mobile device application for monitoring the treatment of LTBI. Methods: We defined the requirements, elaborated on the application's conceptual map, generated implementation and prototyping alternatives, and validated content. Results: Feedback on the validity of content were: "usefulness, consistency, clarity, objectivity, vocabulary, and precision" from professionals, and "clarity" from patients. Conclusions: The application proved to be easy to understand, according to the assessment of both professionals and people undergoing treatment for LTBI.

A Mechanism for Verifying the Integrity and Immutability of Tuberculosis Data Using IOTA Distributed Ledger Technology.

BACKGROUND: Intensified research and innovation and rapid uptake of new tools, interventions, and strategies are crucial to fight Tuberculosis, the world's deadliest infectious disease. The sharing of health data remains a significant challenge. Data consumers must be able to verify the consistency and integrity of data. Solutions based on distributed ledger technologies may be adequate, where each member in a network holds a unique credential and stores an identical copy of the ledger and contributes to the collective process of validating and certifying digital transactions. OBJECTIVES: This work proposes a mechanism and presents a use case in Digital Health to allow the verification of integrity and immutability of TB electronic health records. METHODS: IOTA was selected as a supporting tool due to its data immutability, traceability and tamper-proof characteristics. RESULTS: A mechanism to verify the integrity of data through hash functions and the IOTA network is proposed. Then, a set of TB related information systems was integrated with the network. CONCLUSION: IOTA technology offers performance and flexibility to enable a reliable environment for electronic health records.

The use of indicators for the management of Mental Health Services.

OBJECTIVE: to identify indicators that can be used in the management of Mental Health Services. METHOD: an integrative review in which we adopted the Population, Concept, and Context strategy to formulate the following Guiding Question: "Which indicators can be used for the management of mental health services?". RESULTS: a total of 22 articles were included and divided into two main groups: countries with initial high income (54%) as well as low- and middle-income countries (46%). We identified 5 studies that had experienced the use of indicators, 5 studies that had reported partial implementation, 9 studies that did not report use or implementation, 1 study on the indicator selection process, 1 as an implementation pilot, and a final study with a discussion for implementation. High-income countries also find it difficult to implement mental health indicators. The main difficulties in adopting the use of indicators are lack of basic mental health services, financial resources, legislation, political interest, and guidelines for its management. CONCLUSION: it is unusual to find a descriptive comparison of quality monitoring programs at the system level in the technical-scientific literature related to mental health indicators.