Amplias variaciones sistemáticas en hospitalizaciones potencialmente evitables en pacientes crónicos: estudio ecológico sobre zonas básicas de salud y áreas sanitarias

Antecedentes y objetivo Las hospitalizaciones potencialmente evitables (HPE) por condiciones crónicas constituyen un problema sanitario que puede ser reflejo de una atención sanitaria de insuficiente calidad. En este trabajo se describen las variaciones sistemáticas en HPE para el conjunto de proveedores del Sistema Nacional de Salud. Materiales y métodos Estudio ecológico sobre datos administrativos en el que se analiza la variación sistemática en las HPE por 6 condiciones crónicas en el período 2013-2015. Para la estimación de la variación se realiza análisis de área pequeña utilizando metodología bayesiana. Resultados Entre 2013 y 2015 se registraron 439.878 ingresos por HPE en el Sistema Nacional de Salud. La variación de tasas de HPE entre zonas básicas de salud (ZBS) extremas fue de hasta 4 veces, con diferencias muy variables dependiendo de la condición analizada El 40% de las ZBS presentó un riesgo de HPE por encima de la esperado. Más allá de la variación sistemática observada entre ZBS, las áreas sanitarias de residencia de los pacientes explicaron un 33% de la variación en las HPE. Sobre estos resultados generales, se observaron diferencias específicas en función de la condición clínica, edad y sexo. Conclusiones La amplia variación sistemática en HPE indica la existencia de un problema de calidad en la atención prestada a pacientes crónicos por el conjunto de proveedores de las áreas sanitarias. La identificación y análisis de aquellas zonas y áreas sanitarias con mejores resultados podría servir de referencia para la mejora de los cuidados en otros proveedores con peor desempeño. Background and objective: Potentially avoidable hospitalisations (PAHs) due to chronic conditions are a healthcare problem that could reflect healthcare of insufficient quality. This study reports the systematic variations in PAHs for the collection of providers of the Spanish National Health System. Materials and methods: We conducted an ecological study on government data, analysing the systematic variation in PAHs for 6 chronic conditions during 2013–2015. To determine the variation, we performed a small area analysis using Bayesian methodology. Results: Between 2013 and 2015, 439, 878 admissions for PAHs were recorded in the Spanish National Health System. There was an up to 4-fold difference in PAH rates between certain basic health areas (BHA), with highly variable differences depending on the analysed condition. Forty percent of the BHAs showed a greater than expected risk of PAH. Beyond the systematic variation observed between BHAs, the healthcare areas of the patients’ residence explained 33% of the variation in PAHs. We observed specific differences in these general results according to clinical condition, age and sex. Conclusions: The wide systematic variation in PAHs suggests a problem of quality in the care provided to chronically ill patients by the providers of healthcare areas in Spain. Identifying and analysing these areas and other healthcare areas with better results could provide a reference for improving the care of other suppliers with poorer performance

Factors associated with hospitalisations in chronic conditions deemed avoidable: Ecological study in the Spanish healthcare system

Objectives: Potentially avoidable hospitalisations have been used as a proxy for primary care quality. We aimed to analyse the ecological association between contextual and systemic factors featured in the Spanish healthcare system and the variation in potentially avoidable hospitalisations for a number of chronic conditions. Methods: A cross-section ecological study based on the linkage of administrative data sources from virtually all healthcare areas (n=202) and autonomous communities (n=16) composing the Spanish National Health System was performed. Potentially avoidable hospitalisations in chronic conditions were defined using the Spanish validation of the Agency for Health Research and Quality (AHRQ) preventable quality indicators. Using 2012 data, the ecological association between potentially avoidable hospitalisations and factors featuring healthcare areas and autonomous communities was tested using multilevel negative binomial regression. Results: In 2012, 151 468 admissions were flagged as potentially avoidable in Spain. After adjusting for differences in age, sex and burden of disease, the only variable associated with the outcome was hospitalisation intensity for any cause in previous years (incidence risk ratio 1.19 (95% CI 1.13 to 1.26)). The autonomous community of residence explained a negligible part of the residual unexplained variation (variance 0.01 (SE 0.008)). Primary care supply and activity did not show any association. Conclusions: The findings suggest that the variation in potentially avoidable hospitalisations in chronic conditions at the healthcare area level is a reflection of how intensively hospitals are used in a healthcare area for any cause, rather than of primary care characteristics. Whether other non-studied features at the healthcare area level or primary care level could explain the observed variation remains uncertain

Federated causal inference based on real-world observational data sources:Application to a SARS-CoV-2 vaccine effectiveness assessment

Introduction Causal inference helps researchers and policy-makers to evaluate public health interventions. When comparing interventions or public health programs by leveraging observational sensitive individual-level data from populations crossing jurisdictional borders, a federated approach (as opposed to a pooling data approach) can be used. Approaching causal inference by re-using routinely collected observational data across different regions in a federated manner, is challenging and guidance is currently lacking. With the aim of filling this gap and allowing a rapid response in the case of a next pandemic, a methodological framework to develop studies attempting causal inference using federated cross-national sensitive observational data, is described and showcased within the European BeYond-COVID&nbsp;project. Methods A framework for approaching federated causal inference by re-using routinely collected observational data across different regions, based on principles of legal, organizational, semantic and technical interoperability, is proposed. The framework includes step-by-step guidance, from defining a research question, to establishing a causal model, identifying and specifying data requirements in a common data model, generating synthetic data, and developing an interoperable and reproducible analytical pipeline for distributed deployment. The conceptual and instrumental phase of the framework was demonstrated and an analytical pipeline implementing federated causal inference was prototyped using open-source software in preparation for the assessment of real-world effectiveness of SARS-CoV-2 primary vaccination in preventing infection in populations spanning different countries, integrating a data quality assessment, imputation of missing values, matching of exposed to unexposed individuals based on confounders identified in the causal model and a survival analysis within the matched&nbsp;population. Results The conceptual and instrumental phase of the proposed methodological framework was successfully demonstrated within the BY-COVID project. Different Findable, Accessible, Interoperable and Reusable (FAIR) research objects were produced, such as a study protocol, a data management plan, a common data model, a synthetic dataset and an interoperable analytical&nbsp;pipeline. Conclusions The framework provides a systematic approach to address federated cross-national policy-relevant causal research questions based on sensitive population, health and care data in a privacy-preserving and interoperable way. The methodology and derived research objects can be re-used and contribute to pandemic&nbsp;preparedness.</p

Timing of surgery for hip fracture and in-hospital mortality: a retrospective population-based cohort study in the Spanish National Health System

<p>Abstract</p> <p>Background</p> <p>While the benefits or otherwise of early hip fracture repair is a long-running controversy with studies showing contradictory results, this practice is being adopted as a quality indicator in several health care organizations. The aim of this study is to analyze the association between early hip fracture repair and in-hospital mortality in elderly people attending public hospitals in the Spanish National Health System and, additionally, to explore factors associated with the decision to perform early hip fracture repair.</p> <p>Methods</p> <p>A cohort of 56,500 patients of 60-years-old and over, hospitalized for hip fracture during the period 2002 to 2005 in all the public hospitals in 8 Spanish regions, were followed up using administrative databases to identify the time to surgical repair and in-hospital mortality. We used a multivariate logistic regression model to analyze the relationship between the timing of surgery (< 2 days from admission) and in-hospital mortality, controlling for several confounding factors.</p> <p>Results</p> <p>Early surgery was performed on 25% of the patients. In the unadjusted analysis early surgery showed an absolute difference in risk of mortality of 0.57 (from 4.42% to 3.85%). However, patients undergoing delayed surgery were older and had higher comorbidity and severity of illness. Timeliness for surgery was not found to be related to in-hospital mortality once confounding factors such as age, sex, chronic comorbidities as well as the severity of illness were controlled for in the multivariate analysis.</p> <p>Conclusions</p> <p>Older age, male gender, higher chronic comorbidity and higher severity measured by the Risk Mortality Index were associated with higher mortality, but the time to surgery was not.</p