Multimorbidity Profiles and Infection Severity in COVID-19 Population Using Network Analysis in the Andalusian Health Population Database.

Identifying the population at risk of COVID-19 infection severity is a priority for clinicians and health systems. Most studies to date have only focused on the effect of specific disorders on infection severity, without considering that patients usually present multiple chronic diseases and that these conditions tend to group together in the form of multimorbidity patterns. In this large-scale epidemiological study, including primary and hospital care information of 166,242 patients with confirmed COVID-19 infection from the Spanish region of Andalusia, we applied network analysis to identify multimorbidity profiles and analyze their impact on the risk of hospitalization and mortality. Our results showed that multimorbidity was a risk factor for COVID-19 severity and that this risk increased with the morbidity burden. Individuals with advanced cardio-metabolic profiles frequently presented the highest infection severity risk in both sexes. The pattern with the highest severity associated in men was present in almost 28.7% of those aged ≥ 80 years and included associations between cardiovascular, respiratory, and metabolic diseases; age-adjusted odds ratio (OR) 95% confidence interval (1.71 (1.44-2.02)). In women, similar patterns were also associated the most with infection severity, in 7% of 65-79-year-olds (1.44 (1.34-1.54)) and in 29% of ≥80-year-olds (1.35 (1.18-1.53)). Patients with mental health patterns also showed one of the highest risks of COVID-19 severity, especially in women. These findings strongly recommend the implementation of personalized approaches to patients with multimorbidity and SARS-CoV-2 infection, especially in the population with high morbidity burden.

La evaluación de impacto en protección de datos en los proyectos de investigación / Impact assessment on data protection in research projects

Los recientes cambios en la normativa europea de protección de datos de carácter personal siguen permitiendo el uso de los datos sanitarios con fines de investigación, pero establecen la evaluación de impacto en protección de datos como instrumento de reflexión y análisis de riesgos en el proceso de tratamiento de datos. La publicación de una guía facilita la realización de esta evaluación de impacto, aunque no es de aplicación directa para los proyectos de investigación. Se detalla la experiencia en un proyecto concreto, y se muestra cómo el contexto del tratamiento toma relevancia respecto a las características de los datos. La realización de una evaluación de impacto es una oportunidad para asegurar el cumplimiento de los principios de la protección de datos en un entorno cada vez más complejo y con mayores desafíos éticos

Recent changes in European regulations for personal data protection still allow the use of health data for research purposes, but they have set the Impact Assessment on Data Protection as an instrument for reflection and risk analysis in the process of data processing. The publication of a guide for facilitates this impact assessment, although it is not directly applicable to research projects. Experience in a specific project is detailed, showing how the context of the treatment becomes relevant with respect to the data characteristics. Carrying out an impact assessment is an opportunity to ensure compliance with the principles of data protection in an increasingly complex environment with greater ethical challenges

Conexión de registros sanitarios: base poblacional de salud de Andalucía / Health record linkage: Andalusian health population database

OBJETIVO: Describir el desarrollo de un sistema de información que conecta datos procedentes de múltiples registros, sanitarios y otros, para su uso con fines asistenciales, de administración, gestión, evaluación, inspección, investigación y salud pública. MÉTODO: Conexión determinística de datos pseudonimizados de una población de 8,5 millones de habitantes, procedentes de Base de datos de usuarios, Historia clínica electrónica DIRAYA, Conjunto mínimo básico de datos (hospitalización, cirugía mayor ambulatoria, urgencias hospitalarias y hospital de día médico) y sistemas de información de salud mental, pruebas de imagen, pruebas analíticas, vacunas, pacientes renales y farmacia. Se utilizó un codificador automático para los diagnósticos clínicos y se definieron 80 enfermedades crónicas para su seguimiento. La arquitectura del sistema de información constó de tres capas: datos (base de datos Oracle 11g), aplicaciones (MicroStrategy BI) y presentación (MicroStrategy Web, librerías JavaScript, HTML 5 y hojas de estilo CSS). Se implantaron medidas para la gobernanza del sistema. RESULTADOS: Se incluyeron datos de 12,5 millones de personas que fueron usuarias entre los años 2001 y 2017, con 435,5 millones de diagnósticos. El 88,7% de estos diagnósticos fueron generados por el codificador automático. Los datos se presentan mediante informes predefinidos o consultas dinámicas, ambos exportables a ficheros CSV para su tratamiento fuera del sistema. Analistas expertos pueden acceder directamente a las bases de datos y realizar extracciones mediante SQL o tratar directamente los datos con herramientas externas. CONCLUSIÓN: El trabajo ha mostrado cómo la conexión de registros sanitarios abre nuevas posibilidades en el análisis de datos

OBJECTIVE: To describe the development of an information system that connects data from multiple health records to improve assistance to patients, health services administration, management, evaluation, and inspection, as well as public health and research. METHOD: Deterministic connection of pseudonymized data from a population of 8.5 million inhabitants provided by: a users database, DIRAYA electronic medical records, minimum basic data sets (inpatients, outpatient mayor surgery, hospital emergencies and medical day hospital), mental health information systems, analytical and image tests, vaccines, renal patients, and pharmacy. An automatic coder was used to code clinical diagnoses and 80 chronic pathologies were identified to follow-up. The architecture of the information system consisted of three layers: data (Oracle Database 11g), applications (MicroStrategy BI) and presentation (MicroStrategy Web, JavaScript libraries, HTML 5 and CSS style sheets). Measures for the governance of the system were implemented. RESULTS: Data from 12.5 million health system users between 2001 and 2017 were gathered, including 435.5 million diagnoses, 88.7% of which were generated by the automatic coder. Data can be accessed through predefined reports or dynamic queries, both exportable to CSV files for processing outside the system. Expert analysts can directly access the databases and perform queries using SQL or directly treat the data with external tools. CONCLUSION: The work has shown that the connection of health records opens new possibilities for data analysis

[Impact assessment on data protection in research projects]. / La evaluación de impacto en protección de datos en los proyectos de investigación.

Recent changes in European regulations for personal data protection still allow the use of health data for research purposes, but they have set the Impact Assessment on Data Protection as an instrument for reflection and risk analysis in the process of data processing. The publication of a guide for facilitates this impact assessment, although it is not directly applicable to research projects. Experience in a specific project is detailed, showing how the context of the treatment becomes relevant with respect to the data characteristics. Carrying out an impact assessment is an opportunity to ensure compliance with the principles of data protection in an increasingly complex environment with greater ethical challenges.

[Health record linkage: Andalusian health population database]. / Conexión de registros sanitarios: base poblacional de salud de Andalucía.

OBJECTIVE: To describe the development of an information system that connects data from multiple health records to improve assistance to patients, health services administration, management, evaluation, and inspection, as well as public health and research. METHOD: Deterministic connection of pseudonymized data from a population of 8.5 million inhabitants provided by: a users database, DIRAYA electronic medical records, minimum basic data sets (inpatients, outpatient mayor surgery, hospital emergencies and medical day hospital), mental health information systems, analytical and image tests, vaccines, renal patients, and pharmacy. An automatic coder was used to code clinical diagnoses and 80 chronic pathologies were identified to follow-up. The architecture of the information system consisted of three layers: data (Oracle Database 11g), applications (MicroStrategy BI) and presentation (MicroStrategy Web, JavaScript libraries, HTML 5 and CSS style sheets). Measures for the governance of the system were implemented. RESULTS: Data from 12.5 million health system users between 2001 and 2017 were gathered, including 435.5 million diagnoses, 88.7% of which were generated by the automatic coder. Data can be accessed through predefined reports or dynamic queries, both exportable to CSV files for processing outside the system. Expert analysts can directly access the databases and perform queries using SQL or directly treat the data with external tools. CONCLUSION: The work has shown that the connection of health records opens new possibilities for data analysis.

Concordance between the Clinical Definition of Polypathological Patient versus Automated Detection by Means of Combined Identification through ICD-9-CM Codes.

It is unknown whether the digital application of automated ICD-9-CM codes recorded in the medical history are useful for a first screening in the detection of polypathological patients. In this study, the objective was to identify the degree of intra- and inter-observer concordance in the identification of in-patient polypathological patients between the standard clinical identification method and a new automatic method, using the basic minimum data set of ICD-9-CM codes in the digital medical history. For this, a cross-sectional multicenter study with 1518 administratively discharged patients from Andalusian hospitals during the period of 2013-2014 has been carried out. For the concordance between the clinical definition of a polypathological patient and the polypathological patient classification according to ICD-9-CM coding, a 0.661 kappa was obtained (95% confidence interval (CI); 0.622-0.701) with p < 0.0001. The intraclass correlation coefficient between both methods for the number of polypathological patient categories was 0.745 (95% CI; 0.721-0.768; p < 0.0001). The values of sensitivity, specificity, positive-, and negative predictive values of the automated detection using ICD-9-CM coding were 78%, 88%, 78%, and 88%, respectively. As conclusion, the automatic identification of polypathological patients by detecting ICD-9-CM codes is useful as a screening method for in-hospital patients.

Características de las urgencias hospitalarias del Servicio Andalus de Salud durante 2012. A partir del Conjunto Mínimo Básico de Datos de urgencias / Case-mix of hospital emergencies in the Andalusian Health Service based on the 2012 Minimum Data Set. Spain

Fundamentos: La implantación de Diraya-Urgencias en los hospitales del Servicio Andaluz de Salud (SAS) y el desarrollo de un codificador automático propio ha permitido instaurar el Conjunto Mínimo Básico de Datos de Urgencias (CMBD-U). El objetivo de este artículo es describir la casuística de los servicios de urgencias hospitalarios utilizando las distintas dimensiones contenidas en el CMBD-U. Métodos: Utilizando el CMBD-U, se clasificaron 3.235.600 registros de urgencias hospitalarias de 2012 en categorías clínicas mediante el código CIE-9-MC proporcionado por el codificador automático. Se definieron reglas de validez para la explotación de los tiempos. Se realizó un análisis descriptivo obteniendo indicadores demográficos, cronológicos, tasas de hospitalización, retorno y exitus y tiempos asistenciales y de permanencia en urgencias. Resultados: Las mujeres generaron el 54,26% de las urgencias. Su edad media (39,98 años) superó a la de los hombres (37,61). El 21,49% fueron urgencias pediátricas. La máxima afluencia horaria fue de 10:00 a 13:00 y de 16:00 a 17:00. Los pacientes que no pasaron por observación (92,67%) permanecieron en urgencias 153 minutos de media. Más del 50% de las urgencias fueron generadas por lesiones e intoxicaciones, enfermedades respiratorias, osteomusculares y síntomas y signos. Entre los procesos asistenciales integrados se identificaron 79.191 casos de dolor torácico, 28.741 de insuficiencia cardiaca y 27.989 infecciones graves. Conclusiones: El CMBD-U permite analizar sistemáticamente las urgencias hospitalarias identificando la actividad desarrollada, la casuística atendida, los tiempos de respuesta asistencial y permanencia en urgencias y la calidad asistencial (AU)

Background: The implementation of digital health records in emergency departments (ED) in hospitals in theAndalusian Health Service and the development of an automatic encoder for this area have allowed us to establish a Minimum Data Set for Emergencies (MDS-ED). The aim of this article is to describe the casemix of hospital EDs using various dimensions contained in the MDS-ED. Methods: 3.235.600 hospital emergency records in 2012 were classified in clinical categories from the ICD-9-CM codes generated by the automatic encoder. Operating rules to obtain response time and length of stay were defined. A descriptive analysis was carried out to obtain demographic and chronological indicators as well as hospitalization, return and death rates and response time and length of stay in the EDs. Results:Women generated 54,26%of all occurrences and their average age (39,98 years) was higher than men’s (37,61). Paediatric emergencies accounted for 21,49% of the total. The peak hours were from 10:00 to 13:00 and from 16:00 to 17:00. Patients who did not undergo observation (92,67%) remained in the ED an average of 153 minutes. Injuries and poisoning, respiratory diseases, musculoskeletal diseases and symptoms and signs generated over 50% of all visits. 79.191 cases of chest pain, 28.741 episodes of heart failure and 27.989 episodes of serious infections were identified among the most relevant disorders. Conclusions: The MDS-ED makes it possible to address systematically the analysis of hospital emergencies by identifying the activity developed, the case-mix attended, the response times, the time spent in ED and the quality of the care (AU)

[Case-Mix of hospital emergencies in the Andalusian Health Service based on the 2012 Minimum Data Set. Spain]. / Características de las urgencias hospitalarias del Servicio Andaluz de Salud durante 2012 a partir del Conjunto Mínimo Básico de Datos de Urgencias.

BACKGROUND: The implementation of digital health records in emergency departments (ED) in hospitals in the Andalusian Health Service and the development of an automatic encoder for this area have allowed us to establish a Minimum Data Set for Emergencies (MDS-ED). The aim of this article is to describe the case mix of hospital EDs using various dimensions contained in the MDS-ED. METHODS: 3.235.600 hospital emergency records in 2012 were classified in clinical categories from the ICD-9-CM codes generated by the automatic encoder. Operating rules to obtain response time and length of stay were defined. A descriptive analysis was carried out to obtain demographic and chronological indicators as well as hospitalization, return and death rates and response time and length of stay in the Eds. RESULTS: Women generated 54,26% of all occurrences and their average age (39,98 years) was higher than men's (37,61). Paediatric emergencies accounted for 21,49% of the total. The peak hours were from 10:00 to 13:00 and from 16:00 to 17:00. Patients who did not undergo observation (92,67%) remained in the ED an average of 153 minutes. Injuries and poisoning, respiratory diseases, musculoskeletal diseases and symptoms and signs generated over 50% of all visits. 79.191 cases of chest pain, 28.741 episodes of heart failure and 27.989 episodes of serious infections were identified among the most relevant disorders. CONCLUSIONS: The MDS-ED makes it possible to address systematically the analysis of hospital emergencies by identifying the activity developed, the case-mix attended, the response times, the time spent in ED and the quality of the care.

Características de las urgencias hospitalarias del Servicio Andaluz de Salud durante 2012 a partir del Conjunto Mínimo Básico de Datos de Urgencias / Case-mix of hospital emergencies in the Andalusian Health Service based on the 2012 Minimum Data Set. Spain

Fundamentos: La implantación de Diraya-Urgencias en los hospitales del Servicio Andaluz de Salud (SAS) y el desarrollo de un codificador automático propio ha permitido instaurar el Conjunto Mínimo Básico de Datos de Urgencias (CMBD-U). El objetivo de este artículo es describir la casuística de los servicios de urgencias hospitalarios utilizando las distintas dimensiones contenidas en el CMBD-U. Métodos: Utilizando el CMBD-U, se clasificaron 3.235.600 registros de urgencias hospitalarias de 2012 en categorías clínicas mediante el código CIE-9-MC proporcionado por el codificador automático. Se definieron reglas de validez para la explotación de los tiempos. Se realizó un análisis descriptivo obteniendo indicadores demográficos, cronológicos, tasas de hospitalización, retorno y exitus y tiempos asistenciales y de permanencia en urgencias. Resultados: Las mujeres generaron el 54,26% de las urgencias. Su edad media (39,98 años) superó a la de los hombres (37,61). El 21,49% fueron urgencias pediátricas. La máxima afluencia horaria fue de 10:00 a 13:00 y de 16:00 a 17:00. Los pacientes que no pasaron por observación (92,67%) permanecieron en urgencias 153 minutos de media. Más del 50% de las urgencias fueron generadas por lesiones e intoxicaciones, enfermedades respiratorias, osteomusculares y síntomas y signos. Entre los procesos asistenciales integrados se identificaron 79.191 casos de dolor torácico, 28.741 de insuficiencia cardiaca y 27.989 infecciones graves. Conclusiones: El CMBD-U permite analizar sistemáticamente las urgencias hospitalarias identificando la actividad desarrollada, la casuística atendida, los tiempos de respuesta asistencial y permanencia en urgencias y la calidad asistencial (AU)

Background: The implementation of digital health records in emergency departments (ED) in hospitals in the Andalusian Health Service and the development of an automatic encoder for this area have allowed us to establish a Minimum Data Set for Emergencies (MDS-ED). The aim of this article is to describe the case mix of hospital EDs using various dimensions contained in the MDS-ED. Methods: 3.235.600 hospital emergency records in 2012 were classified in clinical categories from the ICD-9-CM codes generated by the automatic encoder. Operating rules to obtain response time and length of stay were defined. A descriptive analysis was carried out to obtain demographic and chronological indicators as well as hospitalization, return and death rates and response time and length of stay in the Eds. Results: Women generated 54,26% of all occurrences and their average age (39,98 years) was higher than men's (37,61). Paediatric emergencies accounted for 21,49% of the total. The peak hours were from 10:00 to 13:00 and from 16:00 to 17:00. Patients who did not undergo observation (92,67%) remained in the ED an average of 153 minutes. Injuries and poisoning, respiratory diseases, musculoskeletal diseases and symptoms and signs generated over 50% of all visits. 79.191 cases of chest pain, 28.741 episodes of heart failure and 27.989 episodes of serious infections were identified among the most relevant disorders. Conclusions: The MDS-ED makes it possible to address systematically the analysis of hospital emergencies by identifying the activity developed, the case-mix attended, the response times, the time spent in ED and the quality of the care(AU)