Ciencia perdida: un debate latinoamericano

La presente contribución analiza las formas en que se construye la ciencia en América Latina y el Caribe. Su objetivo principal es destacar las particularidades de esta región y cómo estas diferencias afectan el desarrollo científico en comparación con otras áreas del mundo, desde la perspectiva de la ciencia perdida e invisible. Se argumenta que la ciencia en América Latina y el Caribe se ha desarrollado principalmente a través de la colaboración y la cooperación entre países, pero sus revistas científicas se encuentran en desventaja a partir de la ciencia globalizada que limita el acceso a temáticas locales. Este enfoque se ha impulsado por la necesidad de superar las limitaciones económicas y tecnológicas presentes en la región. Como resultado, se han creado iniciativas propias que destacan la importancia de las aportaciones y el conocimiento local en la construcción de la ciencia. Tener una perspectiva propia y contextualizada es fundamental para abordar los desafíos y necesidades específicas de la región. Esto se refleja en la diversidad de temas de investigación y enfoques científicos. El artículo también menciona algunos de los desafíos y obstáculos que enfrenta la ciencia en América Latina y el Caribe, a pesar los cuales se destaca la resiliencia y creatividad de los científicos latinoamericanos y caribeños, quienes han logrado hacer contribuciones significativas a nivel mundial.

ABTRASCT This contribution analyzes the ways in which science is constructed in Latin America and the Caribbean. Its main objective is to highlight the particularities of this region and how these differences affect scientific development compared to other areas of the world, from the lost and invisible science's perspective. It is argued that science in Latin America and the Caribbean has developed mainly through collaboration and cooperation between countries, but its scientific journals are at a disadvantage due to globalized science that limits access to local topics. This approach has been driven by the need to overcome the economic and technological limitations present in the region. As a result, own initiatives have been created that highlight the importance of contributions and local knowledge in the construction of science. Having its own and contextualized perspective is essential to address the specific challenges and needs of the region. This is reflected in the diversity of research topics and scientific approaches. The article also mentions some of the challenges and obstacles faced by science in Latin America and the Caribbean, despite which the resilience and creativity of Latin American and Caribbean scientists stands out, who have managed to make significant contributions worldwide.

Evaluation of a prototype machine learning tool to semi-automate data extraction for systematic literature reviews.

BACKGROUND: Evidence-based medicine requires synthesis of research through rigorous and time-intensive systematic literature reviews (SLRs), with significant resource expenditure for data extraction from scientific publications. Machine learning may enable the timely completion of SLRs and reduce errors by automating data identification and extraction. METHODS: We evaluated the use of machine learning to extract data from publications related to SLRs in oncology (SLR 1) and Fabry disease (SLR 2). SLR 1 predominantly contained interventional studies and SLR 2 observational studies. Predefined key terms and data were manually annotated to train and test bidirectional encoder representations from transformers (BERT) and bidirectional long-short-term memory machine learning models. Using human annotation as a reference, we assessed the ability of the models to identify biomedical terms of interest (entities) and their relations. We also pretrained BERT on a corpus of 100,000 open access clinical publications and/or enhanced context-dependent entity classification with a conditional random field (CRF) model. Performance was measured using the F1 score, a metric that combines precision and recall. We defined successful matches as partial overlap of entities of the same type. RESULTS: For entity recognition, the pretrained BERT+CRF model had the best performance, with an F1 score of 73% in SLR 1 and 70% in SLR 2. Entity types identified with the highest accuracy were metrics for progression-free survival (SLR 1, F1 score 88%) or for patient age (SLR 2, F1 score 82%). Treatment arm dosage was identified less successfully (F1 scores 60% [SLR 1] and 49% [SLR 2]). The best-performing model for relation extraction, pretrained BERT relation classification, exhibited F1 scores higher than 90% in cases with at least 80 relation examples for a pair of related entity types. CONCLUSIONS: The performance of BERT is enhanced by pretraining with biomedical literature and by combining with a CRF model. With refinement, machine learning may assist with manual data extraction for SLRs.

Understanding the spatiotemporal evolution of opioid overdose events using a regionalized sequence alignment analysis.

BACKGROUND: Opioid overdose events and deaths have become a serious public health crisis in the United States, and understanding the spatiotemporal evolution of the disease occurrences is crucial for developing effective prevention strategies, informing health systems policy and planning, and guiding local responses. However, current research lacks the capability to observe the dynamics of the opioid crisis at a fine spatial-temporal resolution over a long period, leading to ineffective policies and interventions at the local level. METHODS: This paper proposes a novel regionalized sequential alignment analysis using opioid overdose events data to assess the spatiotemporal similarity of opioid overdose evolutionary trajectories within regions that share similar socioeconomic status. The model synthesizes the shape and correlation of space-time trajectories to assist space-time pattern mining in different neighborhoods, identifying trajectories that exhibit similar spatiotemporal characteristics for further analysis. RESULTS: By adopting this methodology, we can better understand the spatiotemporal evolution of opioid overdose events and identify regions with similar patterns of evolution. This enables policymakers and health researchers to develop effective interventions and policies to address the opioid crisis at the local level. CONCLUSIONS: The proposed methodology provides a new framework for understanding the spatiotemporal evolution of opioid overdose events, enabling policymakers and health researchers to develop effective interventions and policies to address this growing public health crisis.

Alfabetización informacional crítica para la equidad y la inclusión. El caso de la violencia obstétrica en Brasil

Este artículo aborda las contribuciones de la Teoría Crítica como una forma específica de investigar y promover la formación en alfabetización informacional crítica. Se presentan algunas implicaciones cuando se transpone el modelo de la Teoría Crítica al estudio de los procesos informacionales y en particular se acota el significado que esta teoría puede tener en el desarrollo de la alfabetización informacional crítica. Desde el punto de vista metodológico, se parte de las contribuciones de Horkheimer y otros autores de esta línea de pensamiento, para mostrar, desde diversas perspectivas, por qué la alfabetización informacional crítica debe ser considerada por los profesionales de la Ciencia de la Información. Se defiende la necesidad de adquirir una competencia informacional crítica que se oponga a la actitud positivista o cientificista y reconozca las contradicciones inherentes a su propio objeto. El análisis del caso de la violencia obstétrica, ejemplo de la nula o deficiente alfabetización informacional crítica, muestra cómo la amplia y compleja actuación del racismo institucional y cultural, su forma de organización y desarrollo, a través de estructuras, prácticas, normas, procesos y políticas institucionales, genera comportamientos interpersonales y, de manera sutil, naturaliza las desigualdades producidas en el cuidado de las parturientas. Como medida de prevención se propone la formación en alfabetización informacional crítica de todos los colectivos profesionales que atienden a las puérperas, tarea que deberían asumir los profesionales de la Ciencia de la Información, además de extender su área de docencia e investigación a las de especialización médica como la Obstetricia.

This article addresses the contributions of Critical Theory as a specific way to investigate and promote training in critical information literacy. Some implications are presented when the Critical Theory model is transposed to the study of information processes and, in particular, the meaning that this theory can have in the development of critical information literacy is delimited. From the methodological point of view, it is based on the contributions of Horkheimer and other authors of this line of thought, to show, from different perspectives, why critical information literacy should be considered by Information Science professionals. The need to acquire critical informational competence is defended to oppose the positivist or scientific attitude and recognizes the contradictions inherent to its own object. The analysis of the case of obstetric violence, an example of null or deficient critical information literacy, shows how the broad and complex performance of institutional and cultural racism, its form of organization and development, through structures, practices, norms, processes and institutional policies, generates interpersonal behaviors and, in a subtle way, naturalizes the inequalities produced in the care of parturients. As a preventive measure, the training in critical information literacy of all professional groups that care for puerperal women is proposed, a task that Information Science professionals should assume, in addition to extending their area of teaching and research to those of medical specialization. like obstetrics.

COVID-19 Variant Surveillance and Social Determinants in Central Massachusetts: Development Study.

BACKGROUND: Public health scientists have used spatial tools such as web-based Geographical Information System (GIS) applications to monitor and forecast the progression of the COVID-19 pandemic and track the impact of their interventions. The ability to track SARS-CoV-2 variants and incorporate the social determinants of health with street-level granularity can facilitate the identification of local outbreaks, highlight variant-specific geospatial epidemiology, and inform effective interventions. We developed a novel dashboard, the University of Massachusetts' Graphical user interface for Geographic Information (MAGGI) variant tracking system that combines GIS, health-associated sociodemographic data, and viral genomic data to visualize the spatiotemporal incidence of SARS-CoV-2 variants with street-level resolution while safeguarding protected health information. The specificity and richness of the dashboard enhance the local understanding of variant introductions and transmissions so that appropriate public health strategies can be devised and evaluated. OBJECTIVE: We developed a web-based dashboard that simultaneously visualizes the geographic distribution of SARS-CoV-2 variants in Central Massachusetts, the social determinants of health, and vaccination data to support public health efforts to locally mitigate the impact of the COVID-19 pandemic. METHODS: MAGGI uses a server-client model-based system, enabling users to access data and visualizations via an encrypted web browser, thus securing patient health information. We integrated data from electronic medical records, SARS-CoV-2 genomic analysis, and public health resources. We developed the following functionalities into MAGGI: spatial and temporal selection capability by zip codes of interest, the detection of variant clusters, and a tool to display variant distribution by the social determinants of health. MAGGI was built on the Environmental Systems Research Institute ecosystem and is readily adaptable to monitor other infectious diseases and their variants in real-time. RESULTS: We created a geo-referenced database and added sociodemographic and viral genomic data to the ArcGIS dashboard that interactively displays Central Massachusetts' spatiotemporal variants distribution. Genomic epidemiologists and public health officials use MAGGI to show the occurrence of SARS-CoV-2 genomic variants at high geographic resolution and refine the display by selecting a combination of data features such as variant subtype, subject zip codes, or date of COVID-19-positive sample collection. Furthermore, they use it to scale time and space to visualize association patterns between socioeconomics, social vulnerability based on the Centers for Disease Control and Prevention's social vulnerability index, and vaccination rates. We launched the system at the University of Massachusetts Chan Medical School to support internal research projects starting in March 2021. CONCLUSIONS: We developed a COVID-19 variant surveillance dashboard to advance our geospatial technologies to study SARS-CoV-2 variants transmission dynamics. This real-time, GIS-based tool exemplifies how spatial informatics can support public health officials, genomics epidemiologists, infectious disease specialists, and other researchers to track and study the spread patterns of SARS-CoV-2 variants in our communities.

A data driven learning approach for the assessment of data quality.

BACKGROUND: Data quality assessment is important but complex and task dependent. Identifying suitable measurement methods and reference ranges for assessing their results is challenging. Manually inspecting the measurement results and current data driven approaches for learning which results indicate data quality issues have considerable limitations, e.g. to identify task dependent thresholds for measurement results that indicate data quality issues. OBJECTIVES: To explore the applicability and potential benefits of a data driven approach to learn task dependent knowledge about suitable measurement methods and assessment of their results. Such knowledge could be useful for others to determine whether a local data stock is suitable for a given task. METHODS: We started by creating artificial data with previously defined data quality issues and applied a set of generic measurement methods on this data (e.g. a method to count the number of values in a certain variable or the mean value of the values). We trained decision trees on exported measurement methods' results and corresponding outcome data (data that indicated the data's suitability for a use case). For evaluation, we derived rules for potential measurement methods and reference values from the decision trees and compared these regarding their coverage of the true data quality issues artificially created in the dataset. Three researchers independently derived these rules. One with knowledge about present data quality issues and two without. RESULTS: Our self-trained decision trees were able to indicate rules for 12 of 19 previously defined data quality issues. Learned knowledge about measurement methods and their assessment was complementary to manual interpretation of measurement methods' results. CONCLUSIONS: Our data driven approach derives sensible knowledge for task dependent data quality assessment and complements other current approaches. Based on labeled measurement methods' results as training data, our approach successfully suggested applicable rules for checking data quality characteristics that determine whether a dataset is suitable for a given task.

Development of a data utility framework to support effective health data curation.

OBJECTIVES: The value of healthcare data is being increasingly recognised, including the need to improve health dataset utility. There is no established mechanism for evaluating healthcare dataset utility making it difficult to evaluate the effectiveness of activities improving the data. To describe the method for generating and involving the user community in developing a proposed framework for evaluation and communication of healthcare dataset utility for given research areas. METHODS: Aninitial version of a matrix to review datasets across a range of dimensions wasdeveloped based on previous published findings regarding healthcare data. Thiswas used to initiate a design process through interviews and surveys with datausers representing a broad range of user types and use cases, to help develop afocused framework for characterising datasets. RESULTS: Following 21 interviews, 31 survey responses and testing on 43 datasets, five major categories and 13 subcategories were identified as useful for a dataset, including Data Model, Completeness and Linkage. Each sub-category was graded to facilitate rapid and reproducible evaluation of dataset utility for specific use-cases. Testing of applicability to >40 existing datasets demonstrated potential usefulness for subsequent evaluation in real-world practice. DISCUSSION: Theresearch has developed an evidenced-based initial approach for a framework tounderstand the utility of a healthcare dataset. It likely to require further refinementfollowing wider application and additional categories may be required. CONCLUSION: The process has resulted in a user-centred designed framework for objectively evaluating the likely utility of specific healthcare datasets, and therefore, should be of value both for potential users of health data, and for data custodians to identify the areas to provide the optimal value for data curation investment.

A method for interoperable knowledge-based data quality assessment.

BACKGROUND: Assessing the quality of healthcare data is a complex task including the selection of suitable measurement methods (MM) and adequately assessing their results. OBJECTIVES: To present an interoperable data quality (DQ) assessment method that formalizes MMs based on standardized data definitions and intends to support collaborative governance of DQ-assessment knowledge, e.g. which MMs to apply and how to assess their results in different situations. METHODS: We describe and explain central concepts of our method using the example of its first real world application in a study on predictive biomarkers for rejection and other injuries of kidney transplants. We applied our open source tool-openCQA-that implements our method utilizing the openEHR specifications. Means to support collaborative governance of DQ-assessment knowledge are the version-control system git and openEHR clinical information models. RESULTS: Applying the method on the study's dataset showed satisfactory practicability of the described concepts and produced useful results for DQ-assessment. CONCLUSIONS: The main contribution of our work is to provide applicable concepts and a tested exemplary open source implementation for interoperable and knowledge-based DQ-assessment in healthcare that considers the need for flexible task and domain specific requirements.

Integrating evidence-based medicine skills into a medical school curriculum: a quantitative outcomes assessment.

OBJECTIVES: This research project aims to determine the potential differential impact of two curricular approaches to teaching evidence-based medicine (EBM) on student performance on an EBM assignment administered during the first year of clerkship. A meaningful result would be any statistically significant difference in scores on the assignment given to measure student performance. DESIGN: In order to assess and compare student learning under the different curricula, the principal investigator and a team of five faculty members blinded to assignment date and other possibly identifying details used a modified version of the previously validated Fresno rubric to retrospectively grade 3 years' worth of EBM assignments given to students in clerkship rotations 1-3 (n=481) during the Internal Medicine clerkship. Specifically, EBM performance in three separate student cohorts was examined. SETTING: The study took place at a large Midwestern medical school with nine campuses across the state of Indiana. PARTICIPANTS: Study participants were 481 students who attended the medical school and completed the Internal Medicine clerkship between 2017 and 2019. INTERVENTIONS: Prior to the inception of this study, our institution had been teaching EBM within a discrete 2-month time period during medical students' first year. During a large-scale curricular overhaul, the approach to teaching EBM was changed to a more scaffolded, integrated approach with sessions being taught over the course of 2 years. In this study, we assess the differential impact of these two approaches to teaching EBM in the first 2 years of medical school. MAIN OUTCOME MEASURES: We used clerkship-level EBM assignment grades to determine whether there was a difference in performance between those students who experienced the old versus the new instructional model. Clerkship EBM assignments given to the students used identical questions each year in order to have a valid basis for comparison. Additionally, we analysed average student grades across the school on the EBM portion of step 1. RESULTS: Four hundred and eighty-one assignments were graded. Mean scores were compared for individual questions and cumulative scores using a one-way Welch Analysis of Variance test. Overall, students performed 0.99 of a point better on the assignment from year 1 (Y1), prior to EBM curriculum integration, to year 3 (Y3), subsequent to EBM integration (p≤0.001). Statistically significant improvement was seen on questions measuring students' ability to formulate a clinical question and critically appraise medical evidence. Additionally, on the United States Medical Licensing Examination (USMLE) step 1, we found that student scores on the EBM portion of the examination improved from Y1 to Y3. CONCLUSIONS: Results of this study suggest that taking a scaffolded, curriculum-integrated approach to EBM instruction during the preclinical years increases, or at the very least does not lessen, student retention of and ability to apply EBM concepts to patient care. Although it is difficult to fully attribute students' retention and application of EBM concepts to the adoption of a curricular model focused on scaffolding and integration, the results of this study show that there are value-added educational effects to teaching EBM in this new format. Overall, this study provides a foundation for new research and practice seeking to improve EBM instruction. TRIAL REGISTRATION NUMBER: IRB approval (Protocol number 1907054875) was obtained for this study.

Using Geospatial Analysis to Evaluate Access to Lung Cancer Screening in the United States.

BACKGROUND: Screening current and former heavy smokers 55 to 80 years of age for lung cancer (LC) with low-dose chest CT scanning has been recommended by the United States Preventive Services Task Force since 2013. Although the number of screening facilities in the United States has increased, screening uptake has been slow. RESEARCH QUESTION: To what extent is geographic access to screening facilities a barrier for screening uptake nationally? STUDY DESIGN AND METHODS: Screening facilities were defined as American College of Radiology (ACR) Lung Cancer Screening Registry (LCSR) facilities. Analysis was performed at different geographic levels using a road network to calculate travel distances for the recommended age groups. Full access to screening was defined as the entire 55- to 79-year-old population being within 40 miles of an ACR LCSR facility. No access was defined as lack of access by the entire target population. Partial access was expressed in intervening quartiles. A geospatial approach then was used to integrate accessibility with smoking prevalence and LC mortality rates to identify potential focus areas visually. RESULTS: Screening facilities addresses were geocoded to identify 3,592 unique locations. Analysis of census tracts and aggregation to counties revealed that among 3,142 counties, adults 55 to 79 years of age have full access to an LC screening registry facility in 1,988 (63%) counties, partial access in 587 (19%) counties, and no access in 567 (18%) counties. Overall, less than 6% of those 55 to 79 years of age do not have access to registry screening facilities. Variation in screening facility access was noted across the United States, between states, and within some states. INTERPRETATION: It is recommended to calculate accessibility using subcounty geographies and to examine variation regionally and within states. A foundation geographic accessibility layer can be integrated with other variables to identify geographic disparities in access to screening and to focus on areas for interventions. Identifying areas of greatest need can inform state and local officials and healthcare organizations when planning and implementing LC screening programs.

A Bayesian approach to investment in innovation projects with the presence of fake innovators.

The paper proposes a game-theoretic model of interaction between investors and innovators, taking into account the existence of so-called "fake" innovators offering knowingly unprofitable projects. The model is a Bayesian non-cooperative, repetitive game with recalculated payments and partly unobservable ex interim player types. It allows quantifying the parameters of the strategy for all player types to find equilibrium solutions. The model describes rational modes for screening "fake" innovators based on adjusting players' probabilistic estimates.

Economic stimulus for COVID-19 pandemic and its determinants: evidence from cross-country analysis.

Countries around the world are announcing stimulus packages in response to the COVID 19 pandemic. This research attempts to measure the extent and progress of stimulus packages by proposing a multidimensional index that standardizes governments' economic responses and allows us to examine the differences in economic policies from country to country. We apply the Euclidean distance formula to develop the new index and then identify the determinants of the economic stimulation of COVID-19 through beta-regression. The results show that Chile, Switzerland, Croatia, Sweden and the Netherlands responded more strongly to the COVID-19 pandemic, while the remaining countries responded slightly to the pandemic. Empirical results also indicate that most countries increased COVID-19 economic support, although not significantly. Finally, the results of the beta regression show that the median age of the population, the number of hospitals, beds per capita, the number of total COVID-19 cases, GDP, health care expenditure and the index of the severity of the government's response is significantly related to the level of the countries' stimulus packages.

Information specialist collaboration in Europe: collaborative methods, processes, and infrastructure through EUnetHTA.

The history of European health technology assessment (HTA) goes back more than 30 years. Almost as old as HTA agencies themselves is the desire to achieve European collaboration. This gained further impetus with the establishment of the European Network of Health Technology Assessment (EUnetHTA) in 2006. In this context, the field of information management faced specific challenges. Although these services are an integral part of HTA and information specialists play a key role here, this field is often not adequately represented in the HTA agencies within EUnetHTA. Furthermore, the organization of HTA production, including the types of HTAs produced, as well as funding, varies considerably. In order to meet these different conditions, information specialists have created various products and defined processes. With the EUnetHTA guideline, a common methodological understanding for the production of rapid Relative Effectiveness Assessments now exists. Furthermore, the Standard Operating Procedures map the complex information retrieval processes within EUnetHTA in a hands-on manner. The newly established Information Specialist Network (ISN) will in future ensure that information specialists are involved in all EUnetHTA assessments and that the methods are applied consistently in all assessments. In addition, the steering committee of the ISN manages enquiries and can be contacted to discuss methodological issues. Major barriers such as heterogeneity in the daily work of the EUnetHTA members can only be overcome through more collaboration and training.

Combating Health Care Fraud and Abuse: Conceptualization and Prototyping Study of a Blockchain Antifraud Framework.

BACKGROUND: An estimated US $2.6 billion loss is attributed to health care fraud and abuse. With traditional health care claims verification and reimbursement, the health care provider submits a claim after rendering services to a patient, which is then verified and reimbursed by the payer. However, this process leaves out a critical stakeholder: the patient for whom the services are actually rendered. This lack of patient participation introduces a risk of fraud and abuse. Blockchain technology enables secure data management with transparency, which could mitigate this risk of health care fraud and abuse. OBJECTIVE: The aim of this study is to develop a framework using blockchain to record claims data and transactions in an immutable format and to enable the patient to act as a validating node to help detect and prevent health care fraud and abuse. METHODS: We developed a health care fraud and abuse blockchain technical framework and prototype using key blockchain tools and application layers including consensus algorithms, smart contracts, tokens, and governance based on digital identity on the Ethereum platform (Ethereum Foundation). RESULTS: Our technical framework maps to the claims adjudication process and focuses on Medicare claims, with the US Centers for Medicare and Medicaid Services (CMS) as the central authority. A prototype of the framework system was developed using the blockchain platform Ethereum (Ethereum Foundation), with its design features, workflow, smart contract functions, system architecture, and software implementation outlined. The software stack used to build the system consisted of a front-end user interface framework, a back-end processing server, and a blockchain network. React was used for the user interface framework, and NodeJS and an Express server were used for the back-end processing server; Solidity was the smart contract language used to interact with a local Ethereum blockchain network. CONCLUSIONS: The proposed framework and the initial prototype have the potential to improve the health care claims process by using blockchain technology for secure data storage and consensus mechanisms, which make the claims adjudication process more patient-centric for the purposes of identifying and preventing health care fraud and abuse. Future work will focus on the use of synthetic or historic CMS claims data to assess the real-world viability of the framework.

A Socio-technical Approach to Evaluating an Electronic Medical Record System implemented in the Public Health Services of Aguascalientes / Una Aproximación Socio-técnica a la Evaluación de un Expediente Médico Electrónico implementado en Servicios de Salud Públicos de Aguascalientes

Abstract The objective of this study was to explore a strategy for evaluating an Electronic Medical Record (EMR) system implemented in the public health services of Aguascalientes, Mexico. A questionnaire based on DeLone and McLean's Model of Information Systems Success (MISS) was adapted to Spanish and applied with 62 primary care physicians working in health centers of the Instituto de Servicios de Salud del Estado de Aguascalientes (ISSEA or the State of Aguascalientes Institute for Health Services). Opportunities for improving EMR systems were also explored from the informants' perspectives. Additionally, the relationships between MISS components were analyzed using Structural Equations Modeling (SEM). Some MISS components and particular items (service quality and overall satisfaction) presented low averages, reflecting opportunities for improving the development and implementation of EMR, such as the need to continuously update information pertaining to diagnostic and medicine catalogs and develop systems that are interoperable between the second and third levels of care. In conclusion, the present study contributes generating evidence on the use of the MISS to evaluating EMR systems in public health services of Mexico. More evidence should be generated in this field in order to promote the continuous improvement of these information systems.

Resumen El objetivo de este estudio fue explorar una estrategia para la evaluación de un Expediente Clínico Electrónico (ECE) implementado en servicios de salud públicos de Aguascalientes, México. Se adaptó al español un cuestionario basado en el Modelo de Éxito de Sistemas de Información (MISS) de DeLone y McLean y se aplicó a 62 médicos de atención primaria que trabajan en centros de salud del Instituto de Servicios de Salud del Estado de Aguascalientes (ISSEA). Se exploraron también las oportunidades de mejora del ECE desde la perspectiva de los informantes. Además, se analizaron las relaciones entre los componentes del MISS mediante el modelado de ecuaciones estructurales (SEM). Algunos componentes del MISS e items particulares mostraron promedios bajos (p.ej., calidad del servicio y satisfacción) que reflejan algunas oportunidades de mejora en el desarrollo e implementación del ECE, como la necesidad de una actualización continua de la información sobre diagnósticos y catálogos de medicamentos; y el desarrollo de sistemas de interoperabilidad con el segundo y tercer nivel de atención. En conclusión, el presente estudio contribuye en la generación de evidencia sobre el uso del MISS para evaluar los sistemas de EMR en servicios de salud públicos de México. Se debe generar más evidencia en este campo para promover la mejora continua de estos sistemas de información.

Examining the external-factors-led growth hypothesis for the South African economy.

Reducing unemployment rate and achieving a sustainable economic growth underscore the Sustainable Development Goal 8. Our study investigates a new model that specifies the external-factors-led growth hypothesis for the South African economy. The independent variables include trade openness, external debt, FDI and exchange rate against GDP as the targeted variable. The ARDL approach was adopted after achieving a mixed order of integration from the stationarity test using traditional unit root tests. All external factors were found to exert a positive influence on economic expansion. Trade openness and exchange rate specifically, exert significant influence on economic growth, which means that an improvement in these factors will proportionately favour economic expansion. In essence, a 1% improvement in trade openness and exchange rate will generate an equivalent of 0.30% and 0.19% increase in GDP in the long-run. On average, trade openness, exchange rate and external loan are beneficial to the economy of South Africa. Thus, recommend the need for the authority concern to open more line of bilateral trade to enable the economy to fully tap from the benefits accrued from indulging in economic openness.

The Female Empowerment Index (FEMI): spatial and temporal variation in women's empowerment in Nigeria.

Improving female empowerment is an important human rights and development goal that needs better monitoring. A number of indices have been developed to track female empowerment at the national level, but these are incomplete and may obscure important sub-national variation. We developed the Female Empowerment Index (FEMI) to track multiple domains of women's empowerment at the sub-national level. The index is based on six categories of empowerment: violence against women, employment, education, reproductive healthcare, decision making, and access to contraceptives. The FEMI has a range of zero to one (low to high empowerment), and it is calculated as the mean proportion of positive outcomes in the six categories. To provide a proof of concept, we computed the FEMI for Nigeria and its 36 states from five Demographic and Health Surveys between the years of 1990 and 2013, using questions asked to 98,542 women between 15 and 49 years old. At the national level, the FEMI increased from 0.34 to 0.48. However, there was substantial sub-national variation, with state-level values ranging from 0.16-0.60 in 1990 to 0.19-0.73 in 2013. Our findings thus illustrate the importance of considering sub-national variation in female empowerment. The FEMI can be readily computed for other countries, and its ability to track spatial and temporal variation in woman's empowerment across a broad set of categories may make it more useful than existing approaches.

A Vendor-Independent Mobile Health Monitoring Platform for Digital Health Studies: Development and Usability Study.

BACKGROUND: Medical smartphone apps and mobile health devices are rapidly entering mainstream use because of the rising number of smartphone users. Consequently, a large amount of consumer-generated data is being collected. Technological advances in innovative sensory systems have enabled data connectivity and aggregation to become cornerstones in developing workable solutions for remote monitoring systems in clinical practice. However, few systems are currently available to handle such data, especially for clinical use. OBJECTIVE: The aim of this study was to develop and implement the digital health research platform for mobile health (DHARMA) that combines data saved in different formats from a variety of sources into a single integrated digital platform suitable for mobile remote monitoring studies. METHODS: DHARMA comprises a smartphone app, a Web-based platform, and custom middleware and has been developed to collect, store, process, and visualize data from different vendor-specific sensors. The middleware is a component-based system with independent building blocks for user authentication, study and patient administration, data handling, questionnaire management, patient files, and reporting. RESULTS: A prototype version of the research platform has been tested and deployed in multiple clinical studies. In this study, we used the platform for the follow-up of pregnant women at risk of developing pre-eclampsia. The patients' blood pressure, weight, and activity were semi-automatically captured at home using different devices. DHARMA automatically collected and stored data from each source and enabled data processing for the end users in terms of study-specific parameters, thresholds, and visualization. CONCLUSIONS: The increasing use of mobile health apps and connected medical devices is leading to a large amount of data for collection. There has been limited investment in handling and aggregating data from different sources for use in academic and clinical research focusing on remote monitoring studies. In this study, we created a modular mobile health research platform to collect and integrate data from a variety of third-party devices in several patient populations. The functionality of the platform was demonstrated in a real-life setting among women with high-risk pregnancies.

Cost-Effectiveness of Digital Wound Care Education in a Healthcare Organization.

Advantages of digitalization are understood, but implementation to healthcare is slow. Cost savings and quality improvements are needed in healthcare. Continuous education of healthcare professionals is essential for quality, and digital education (DE) enables that cost-efficiently. The aim was to evaluate the cost-effectiveness of a DE for wound care by comparing it to lecture education (LE). DE enabled a slightly better learning outcome than LE. However, combination resulted in superior outcome. DE provided best cost-effectiveness.