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.

[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.

[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.

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.

[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.