Impact of Chronic Disease Self-Management Program on the Self-Perceived Health of People in Areas of Social Vulnerability in Asturias, Spain.

The Chronic Disease Self-Management Program (CDSMP) focuses on a health promotion perspective with a salutogenic approach, reinforcing the pillars of self-efficacy. The aim of this study was to assess the impact of the CDSMP on Self-perceived Health (SPH) in disadvantaged areas of Asturias, España. The study included vulnerable adults with experience of chronic diseases for over six months, along with their caregivers. The intervention consisted of a six-session workshop led by two trained peers. SPH was evaluated by administering the initial item of the SF-12 questionnaire at both baseline and six months post-intervention. To evaluate the variable "Change in SPH" [improvement; remained well; worsening/no improvement (reference category)], global and disaggregated by sex multivariate multinomial logistic regression models were applied. There were 332 participants (mean = 60.5 years; 33.6% were at risk of social vulnerability; 66.8% had low incomes). Among the participants, 22.9% reported an improvement in their SPH, without statistically significant sex-based differences, while 38.9% remained in good health. The global model showed age was linked to decreased "improvement" probability (RRRa = 0.96), and the "remaining well" likelihood drops with social risk (RRRa = 0.42). In men, the probability of "remaining well" decreased by having secondary/higher education (RRRa = 0.25) and increased by cohabitation (RRRa = 5.11). Women at social risk were less likely to report "remaining well" (RRRa = 0.36). In conclusion, six months after the intervention, 22.9% of the participants had improved SPH. Age consistently decreased the improvement in the different models.

A New Hierarchy of Research Evidence for Tumor Pathology: A Delphi Study to Define Levels of Evidence in Tumor Pathology.

The hierarchy of evidence is a fundamental concept in evidence-based medicine, but existing models can be challenging to apply in laboratory-based health care disciplines, such as pathology, where the types of evidence and contexts are significantly different from interventional medicine. This project aimed to define a comprehensive and complementary framework of new levels of evidence for evaluating research in tumor pathology-introducing a novel Hierarchy of Research Evidence for Tumor Pathology collaboratively designed by pathologists with help from epidemiologists, public health professionals, oncologists, and scientists, specifically tailored for use by pathologists-and to aid in the production of the World Health Organization Classification of Tumors (WCT) evidence gap maps. To achieve this, we adopted a modified Delphi approach, encompassing iterative online surveys, expert oversight, and external peer review, to establish the criteria for evidence in tumor pathology, determine the optimal structure for the new hierarchy, and ascertain the levels of confidence for each type of evidence. Over a span of 4 months and 3 survey rounds, we collected 1104 survey responses, culminating in a 3-day hybrid meeting in 2023, where a new hierarchy was unanimously agreed upon. The hierarchy is organized into 5 research theme groupings closely aligned with the subheadings of the WCT, and it consists of 5 levels of evidence-level P1 representing evidence types that merit the greatest level of confidence and level P5 reflecting the greatest risk of bias. For the first time, an international collaboration of pathology experts, supported by the International Agency for Research on Cancer, has successfully united to establish a standardized approach for evaluating evidence in tumor pathology. We intend to implement this novel Hierarchy of Research Evidence for Tumor Pathology to map the available evidence, thereby enriching and informing the WCT effectively.

Evaluación de una actividad formativa en acción comunitaria realizada por estudiantes pregrado / Evaluation of a training activity on community action conducted by undergraduate students

Objetivo: evaluar el aprendizaje y la metodología de un trabajo académico sobre el diseño de una actividad comunitaria local.Método: estudio evaluativo mixto realizado en estudiantes de Enfermería. Tras una introducción teórica a 144 estudiantes sobre acción comunitaria se formaron equipos que, durante dos meses, realizaron un análisis de población parcialmente real, mapeo de activos, búsqueda bibliográfica para conocer los estilos de vida, búsqueda y análisis crítico de experiencias comunitarias anteriores y diseño de una propuesta de actividad. El profesorado la calificó mediante rúbrica (0 a 10 puntos) y se ofreció al alumnado evaluarla mediante cuatro preguntas abiertas y dos cerradas (interés suscitado y necesidad de mantener la actividad formativa -escalas Likert de 1 mín. a 5 máx.-). Se llevaron a cabo índices estadísticos descriptivos y análisis de contenido.Resultados: realizaron la actividad 27 grupos que obtuvieron una calificación mediana de 8,17 puntos. La evaluaron 96 estudiantes que consideraron que era interesante (x–= 4,29) y debía mantenerse (x–= 4,17). Identificaron como “necesarios” e importantes los proyectos de promoción de la salud en la comunidad local y el trabajo compartido con ella. Reflejaron haber descubierto otro campo de acción profesional. Apenas reflejaron visión salutogénica y encontraron dificultades para enfocar la tarea, en la búsqueda de bibliografía y de experiencias previas.Conclusión: la actividad fue resuelta con éxito por los estudiantes y logró en el corto plazo la familiarización con el trabajo comunitario que buscaba. El alumnado encontró dificultades para desarrollar la metodología, pero percibieron la actividad como interesante y relevante. (AU)

Objective: to evaluate the level of learning and the methodology of an academic paper on the design of a local community activity.Method: a mixed evaluative study conducted on Nursing students. After a theoretical introduction on community action, 144 students formed teams and, during two months, conducted a partially real population analysis, article mapping and bibliographic search in order to understand the lifestyles, search and critical analysis of previous community experiences, and design a proposal of activity. Professors graded it through signature (0 to 10 scores), and students were offered an evaluation through four open questions and two closed-ended questions (interest generated and the need to maintain the training activity, Likert scales with min. 1 to max. 5). Descriptive statistical analyses were conducted, as well as content analysis.Results: the activity was conducted by 27 groups, who achieved a median score of 8.17. It was then evaluated by 96 students, who considered that it was interesting (x–= 4.29) and should be maintained (x–= 4.17). They identified as “necessary” and important the health promotion projects in the local community and the work shared with it. They showed that they had discovered another area of professional action. They scarcely showed salutogenic vision and had difficulties to focus the task on a search for bibliography and previous experiences.Conclusion: the activity was successfully conducted by students, and the desired familiarity with the community work was achieved at short term. Students met difficulties to develop the methodology but perceived the activity as interesting and relevant. (AU)

Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease).

AIM: The aim of this study is to compare the utility of several supervised machine learning (ML) algorithms for predicting clinical events in terms of their internal validity and accuracy. The results, which were obtained using two statistical software platforms, were also compared. MATERIALS AND METHODS: The data used in this research come from the open database of the Framingham Heart Study, which originated in 1948 in Framingham, Massachusetts as a prospective study of risk factors for cardiovascular disease. Through data mining processes, three data models were elaborated and a comparative methodological study between the different ML algorithms - decision tree, random forest, support vector machines, neural networks, and logistic regression - was carried out. The global selection criterium for choosing the right set of hyperparameters and the type of data manipulation was the area under a curve (AUC). The software tools used to analyze the data were R-Studio® and RapidMiner®. RESULTS: The Framingham study open database contains 4240 observations. The algorithm that yielded the greatest AUC when analyzing the data in R-Studio was neural network applied to a model that excluded all observations in which there was at least one missing value (AUC = 0.71); when analyzing the data in RapidMiner and applying the same model, the best algorithm was support vector machines (AUC = 0.75). CONCLUSIONS: ML algorithms can reinforce the diagnostic and prognostic capacity of traditional regression techniques. Differences between the applicability of those algorithms and the results obtained with them were a function of the software platforms used in the data analysis.

Entorno construido en función del nivel de desarrollo de los distritos de la ciudad de Madrid / Built Environment according to the level of development of districts in the city of Madrid

Objetivo: describir los elementos del entorno construido (EC) de Madrid en relación con el nivel de desarrollo de los distritos de la ciudad. Método: estudio descriptivo transversal. Los datos del EC de la ciudad (demográficos, de renta, superficie, acceso a recreación y bienes básicos, estética, zonas verdes, transporte público y ambiente de los barrios) se extrajeron de fuentes documentales: páginas web, sistemas de información geográfica, organismos oficiales. Se consideraron los 21 distritos de la ciudad estratificándolos en cuatro niveles de desarrollo en función de su resultado en un índice combinado de salud, conocimiento y renta. Se realizó un análisis estadístico univariante y bivariante (Chi cuadrado y ANOVA); se calcularon intervalos de confianza al 95% de seguridad (IC95%). Resultados: se encontraron diferencias estadísticamente significativas (p≤0,05) en la dotación de escuelas infantiles y centros de mayores (superior en distritos de desarrollo bajo); los centros deportivos y áreas infantiles para menores de 14 años (mayor en distritos de desarrollo medio alto); la renta media neta anual de los hogares, el comercio, las líneas de metro y bus, los elementos disuasorios del uso de automóvil y las concentraciones de población y vivienda (mayores en los distritos de desarrollo alto). Conclusiones: los distritos más desarrollados obtuvieron más indicadores en primera posición. Es necesario avanzar en relación a qué elementos son los que más mejoran el EC de los barrios (AU)

Objective: to describe the elements of the Built Environment (BE) according to the level of development in the city districts. Method: a descriptive transversal study. Data on the city BE (demographical, income, surface, access to recreation and basic commodities, appearance, green areas, public transport, and neighbourhood environment) were extracted from documentary sources: web-pages, geographical information systems, and official organizations. The 21 city districts were stratified into four levels of development based on their outcomes in a combined index for health, knowledge and income. A univariate and bivariate analysis was conducted (Square CHI and ANOVA); confidence intervals were calculated at 95% (CI95%). Results: statistically significant differences (p≤0.05) were found in funding for children’s schools and centres for the elderly (higher in low-development districts); sports centres and areas for <14-year-old children (higher in districts with medium-high development): the mean net annual income of homes, commerce, underground and bus lines, elements to discourage the use of cars, and concentration of population and housing (higher in those districts with high development). Conclusions: districts with higher development obtained more indicators in the first position. It is necessary to move forward regarding which elements improve the BE in neighbourhoods (AU)