Multimorbidity Patterns and Their Association with Social Determinants, Mental and Physical Health during the COVID-19 Pandemic.

BACKGROUND: The challenge posed by multimorbidity makes it necessary to look at new forms of prevention, a fact that has become heightened in the context of the pandemic. We designed a questionnaire to detect multimorbidity patterns in people over 50 and to associate these patterns with mental and physical health, COVID-19, and possible social inequalities. METHODS: This was an observational study conducted through a telephone interview. The sample size was 1592 individuals with multimorbidity. We use Latent Class Analysis to detect patterns and SF-12 scale to measure mental and physical quality-of-life health. We introduced the two dimensions of health and other social determinants in a multinomial regression model. RESULTS: We obtained a model with five patterns (entropy = 0.727): 'Relative Healthy', 'Cardiometabolic', 'Musculoskeletal', 'Musculoskeletal and Mental', and 'Complex Multimorbidity'. We found some differences in mental and physical health among patterns and COVID-19 diagnoses, and some social determinants were significant in the multinomial regression. CONCLUSIONS: We identified that prevention requires the location of certain inequalities associated with the multimorbidity patterns and how physical and mental health have been affected not only by the patterns but also by COVID-19. These findings may be critical in future interventions by health services and governments.

Spanish students' use of the Internet for mental health information and support seeking.

The Internet is a growing source of information for health in general, with university student being online almost daily. Evaluating their use of the Internet for mental health information and support can help understanding if online tools and websites should be used for mental health promotion and, to some extent, care. A survey was conducted with more than 600 students of Law, Nursing and Computer Science of the University of Cadiz in Spain to determine their general use of the Internet and their perceptions and trust in using this medium for mental health information. Data were collected using a 25-item-questionnaire and findings indicated that students had a strong distrust in online mental health information, notwithstanding their daily use of the Internet. The frequency and methods of their research on the Internet correlated with their health status, their medical consultations and with certain socio-demographic characteristics.

Development and spatial representation of synthetic indexes of outpatient mental health care in Andalusia (Spain).

INTRODUCTION: There is a need to develop composite indicators to monitor mental health care in countries such as Spain, where there is wide variability of care systems in 17 different regions. The aim of this study is to generate and to test the usability of synthetic indexes in Andalusia (Southern Spain). METHOD: Seven mental health indicators were selected by expert opinion from a previous list of simple indicators used to compare mental health care systems across Spain (Psicost-74). A Geographical Information Systems (GIS) was used to delineate 71 sectors based on the catchment areas of the mental health centers in Andalusia. Synthetic indexes were obtained through linear combinations of simple indicators via Principal Components Analysis (PCA), using activity data from the Mental Health Information System of Andalusia (SISMA). Maps of these indexes were drawn for 71 catchment areas. RESULTS: Two synthetic indexes were obtained and showed high consistency in the PCA. The Care Load Index (component 1) related to population size and total outpatient care provided within the area. The Case Load Index (component 2) related to assisted morbidity in relation to the population size. The care load index was higher in populated urban areas, whereas the case load was higher in rural areas. DISCUSSION: Care and case load indexes show a different pattern in urban and rural areas. This may be related to a different underlying model of care related to the degree of urbanisation. Geographical Information Systems (GIS) improved recognition and assessment of the spatial phenomena related to the mental health care system, and support policy decision making process in mental health.

Índices multivariantes de gestión para el Hospital Clínico de la Universidad de Chile / Multivariant indices of Hospital management for the Clinical Hospital of the University of Chile

El objetivo del presente trabajo es obtener índices útiles para la gestión hospitalaria mediante la utilización de técnicas estadísticas multivariantes descriptivas. Para ello durante el primer trimestre del año 2004 se analizó la información del Hospital Clínico de la Universidad de Chile correspondiente a los egresos hospitalarios del año 2003. Se estudiaron las variables monitorizadas por la Gerencia de Operaciones del Hospital Universitario: número de egresos, tasa de letalidad, tasa de reingresos, número de consultas externas, número de días de camas ocupadas e índice de complejidad quirúrgica. Las variables se midieron para un total de 24.345 egresos. Aplicamos la técnica de análisis de componentes principales (ACP), y se utilizó la matriz de correlaciones R. Se seleccionaron las dos primeras componentes, con un porcentaje acumulado de variabilidad de 75,92 %: 47,02 % la primera y 28,90 % la segunda. La primera componente puede ser asimilada a un nuevo índice que tiene que ver con la dificultad de los casos atendidos; la hemos llamado "complejidad asistencial". La segunda explicaría la cuantía de personas atendidas, y la hemos llamado "demanda asistencial". Ambos índices permiten dar una clasificación de los servicios hospitalarios

This study aims to obtain useful indices for hospital management, based on descriptive multivariate techniques. Data on the hospital admissions in 2003 was analyzed during the first trimester of 2004 in the Clinical Hospital of the University of Chile. The variables monitored by the Operations Management Department in the University Hospital were studied: number of discharges, lethality rate, re-admissions rate, number of outpatient consultations, number of stays and surgical complexity index. The variables were considered for a total of 24,345 discharges. The Principal Components Analysis (PCA) technique was applied and the R correlation matrix was used. The first two principal components were selected, with an accumulated variability percentage of 75.92 %: 47.02 % for the first and 28.90 % for the second one. The first component may be assimilated to a new index representing the difficulty of the attended cases, which we have termed "Case Complexity". The second principal component would explain the number of attended persons, which we have termed "Case Load". These two indices allow us to classify hospital services