RESUMO
Este trabajo tiene como objetivo presentar una mirada global de la aplicabilidad de los modelos de análisis multinivel en el ámbito de la investigación sanitaria. Ofrece información sobre los fundamentos teóricos, metodológicos y estadísticos y, además, menciona los pasos básicos para la construcción de estos modelos, y da ejemplos de su uso, según la estructura jerárquica de los datos. Cabe resaltar que, antes de utilizar estos modelos, se requiere contar con un soporte teórico sobre la necesidad de uso y una valoración estadística que dé cuenta del porcentaje de varianza explicada por el efecto de agrupación de las observaciones. Los requisitos para llevar a cabo este tipo de análisis dependen de condiciones especiales como el tipo de variables, la cantidad de unidades por nivel o el tipo de estructura jerárquica. Se concluye que los modelos de análisis multinivel son una herramienta útil para lograr la integración de información, dadas la complejidad de las relaciones y las interacciones que determinan la mayoría de las condiciones de salud, incluida la pérdida de independencia entre las unidades de observación.
This topic review aims to present a global vision of multilevel analysis models' applicability to health research, explaining its theoretical, methodological, and statistical foundations. We describe the basic steps to build these models and examples of their application according to the data hierarchical structure. It ir worth noticing that before using these models, researchers must have a rationale for needing them, and a statistical evaluation accounting for the variance percentage explained by the observations grouping effect. The requirements to conduct this type of analysis depends on special conditions such as the type of variables, the number of units per level, or the type of hierarchical structure. We conclude that multilevel analysis models are a useful tool to integrate information, considering the complexity of the relationships and interactions involved in most health conditions, including the loss of independence between observation units.
RESUMO
This topic review aims to present a global vision of multilevel analysis models' applicability to health research, explaining its theoretical, methodological, and statistical foundations. We describe the basic steps to build these models and examples of their application according to the data hierarchical structure. It ir worth noticing that before using these models, researchers must have a rationale for needing them, and a statistical evaluation accounting for the variance percentage explained by the observations grouping effect. The requirements to conduct this type of analysis depends on special conditions such as the type of variables, the number of units per level, or the type of hierarchical structure. We conclude that multilevel analysis models are a useful tool to integrate information, considering the complexity of the relationships and interactions involved in most health conditions, including the loss of independence between observation units.
Este trabajo tiene como objetivo presentar una mirada global de la aplicabilidad de los modelos de análisis multinivel en el ámbito de la investigación sanitaria. Ofrece información sobre los fundamentos teóricos, metodológicos y estadísticos y, además, menciona los pasos básicos para la construcción de estos modelos, y da ejemplos de su uso, según la estructura jerárquica de los datos. Cabe resaltar que, antes de utilizar estos modelos, se requiere contar con un soporte teórico sobre la necesidad de uso y una valoración estadística que dé cuenta del porcentaje de varianza explicada por el efecto de agrupación de las observaciones. Los requisitos para llevar a cabo este tipo de análisis dependen de condiciones especiales como el tipo de variables, la cantidad de unidades por nivel o el tipo de estructura jerárquica. Se concluye que los modelos de análisis multinivel son una herramienta útil para lograr la integración de información, dadas la complejidad de las relaciones y las interacciones que determinan la mayoría de las condiciones de salud, incluida la pérdida de independencia entre las unidades de observación.
RESUMO
Introduction: Happiness is understood as the perception of subjective well-being, it can be a quality, a result, or a state characterized by well-being or satisfaction that every person wants to achieve. In older adults, this satisfaction is a sum of lifelong achievements and triumphs; However, some factors influence this ideal. Objective: Analyze demographic, family, social, personal, and health factors associated with the subjective perception of happiness in older adults, using data from a study conducted in five cities in Colombia, in order to make a theoretical contribution in the search for improvement of their physical, mental and social health. Materials and methods: A quantitative, cross-sectional, analytical study was carried out, using primary source information, obtained with 2,506 surveys from voluntary participants aged 60 and over, who had no cognitive impairment, and who reside in urban areas but not in long-term centers. The variable happiness (classified as high or moderate/low) was used for: (1) A univariate explorative characterization of older adult, (2) a bivariate estimation of the relationships with the factors studied, and (3) a multivariate construction of profiles through multiple correspondences. Results: 67.2% reported high happiness levels, with differences by city: Bucaramanga (81.6%), Pereira (74.7%), Santa Marta (67.4), Medellín (64%), and Pereira (48.7%). Happiness was explained by the absence of risk of depression and little hopelessness, strengthened psychological well-being, a perception of high quality of life, and living in a functional family. Conclusion: This study provided an overview of possible factors that can be enhanced and strengthened with public policies (structural determinant), community empowerment, family strengthening (intermediate determinant), and educational programs (proximal determinant). These aspects are included in the essential functions of public health, in favor of mental and social health in older adults.
