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Background: The principal objective of this paper is to introduce an online interactive application that helps in real-time monitoring of the COVID-19 pandemic in Catalonia, Spain (PandemonCAT)., Methods: This application is designed as a collection of user-friendly dashboards using open-source R software supported by the Shiny package., Results: PandemonCAT reports accumulated weekly updates of COVID-19 dynamics in a geospatial interactive platform for individual basic health areas (ABSs) of Catalonia. It also shows on a georeferenced map the evolution of vaccination campaigns representing the share of population with either one or two shots of the vaccine, for populations of different age groups. In addition, the application reports information about environmental and socioeconomic variables and also provides an interactive interface to visualize monthly public mobility before, during, and after the lockdown phases. Finally, we report the smoothed standardized COVID-19 infected cases and mortality rates on maps of basic health areas ABSs and regions of Catalonia. These smoothed rates allow the user to explore geographic patterns in incidence and mortality rates. The visualization of the variables that could have some influence on the spatiotemporal dynamics of the pandemic is demonstrated., Conclusions: We believe the addition of these new dimensions, which is the key innovation of our project, will improve the current understanding of the spread and the impact of COVID-19 in the community. This application can be used as an open tool for consultation by the public of Catalonia and Spain in general. It could also have implications in facilitating the visualization of public health data, allowing timely interpretation due to the unpredictable nature of the pandemic.

This manuscript describes the rationale and protocol of a real-world data (RWD) study entitled Health Care and Social Survey (ESSOC, Encuesta Sanitaria y Social). The study's objective is to determine the magnitude, characteristics, and evolution of the COVID-19 impact on overall health as well as the socioeconomic, psychosocial, behavioural, occupational, environmental, and clinical determinants of both the general and more vulnerable population. The study integrates observational data collected through a survey using a probabilistic, overlapping panel design, and data from clinical, epidemiological, demographic, and environmental registries. The data will be analysed using advanced statistical, sampling, and machine learning techniques. The study is based on several measurements obtained from three random samples of the Andalusian (Spain) population: general population aged 16 years and over, residents in disadvantaged areas, and people over the age of 55. Given the current characteristics of this pandemic and its future repercussions, this project will generate relevant information on a regular basis, commencing from the beginning of the State of Alarm. It will also establish institutional alliances of great social value, explore and apply powerful and novel methodologies, and produce large, integrated, high-quality and open-access databases. The information described here will be vital for health systems in order to design tailor-made interventions aimed at improving the health care, health, and quality of life of the populations most affected by the COVID-19 pandemic.

Background: Our main objective was to evaluate the fundamental biases detected in studies assessing the effects the Great Recession had on health for the case of Spain. As secondary objectives we presented methods to control these biases and to discuss the results of the studies in question if they had controlled for them., Methods: We carried out a systematic review of the literature published up to June 2018. We evaluated the biases that could have happened in all the eligible studies., Results: From the review, we finally selected 53 studies. Of the studies we reviewed, 60.38% or 32 out of 53, were evaluated as having a high risk of bias. The main biases our review revealed were problems with evaluation, time bias, lack of control of unobserved confounding, and non-exogeneity when defining the onset of the Great Recession., Conclusions: The results from the studies that controlled the biases were quite consistent. Summing up, the studies reviewed found that the Great Recession increased the risk of declaring poor self-rated health and the deterioration of mental health. Both the mortality rate and the suicide rate may well have increased after the Great Recession, probably after a three- to four-year delay.