Search

Objectives: Few panel studies have investigated how different types of mental health (MH) and socio-emotional wellbeing (SEW) outcomes have changed during the pandemic and if their burden has been equally distributed at the population-level. We aimed to examine temporal changes in these outcomes and their socio-ecological predictors using panel data., Study Design: Longitudinal population-based survey with overlapping panels., Methods: Analyses were carried out using four measurements of data from the Health and Social Survey (April 2020 to April 2021). Participants included Andalusian (Spanish) residents aged 16 years or older who participated in all four measurements (n = 1223). Seven dichotomous MH and SEW outcomes, as well as several socio-ecological predictors informed by a conceptual model, were examined in descriptive and multivariate analyses., Results: Unadjusted odds of regular/bad perceived mental health (vs. excellent/very good/good), low socio-emotional wellbeing (vs. regular), low happiness (vs. regular), and feeling anxious (vs. not feeling anxious) decreased significantly from the first to the second measurement; however, in the fourth, low socio-emotional wellbeing significantly increased while low optimism decreased. Considering varying coefficients, objectively measured COVID-19 status and self-reported severity levels of the infection were statistically significant. Health status, social support, and household financial difficulty predicted higher adjusted odds in most of the seven assessed outcomes., Conclusions: Significant temporal variations in MH and SEW outcomes, along with their predictors, were observed during the first year of the pandemic. Some of these outcomes worsened as the pandemic progressed, whereas others improved. Findings also suggest that some individuals such as those experiencing poor health, limited social support, and low socioeconomic status are disproportionately impacted., (© 2024. The Author(s).)

Background: The reallocation of health care services during the coronavirus disease 2019 (COVID-19) pandemic disrupted the continuity of primary care. This study examines the repercussions of the COVID-19 pandemic on clinical indicators within the Catalan population, emphasising individuals with chronic conditions. It provides insights into mortality and transfer rates considering intersectional perspectives., Methods: We designed a retrospective, observational population-based cohort study based on routinely collected data from January 2015 to June 2021 for all individuals available in the Information System for Research in Primary Care (Sistema d'Informació per al Desenvolupament de la Investigació en Atenció Primària (SIDIAP)), the largest public primary care database in Catalonia, Spain. We included 6 301 095 individuals, constituting 81.6% of Catalonia's population in 2020. To perform a repeated measurements analysis of the indicators, we focussed on individuals who had one or more indicators in both the pre-pandemic (January 2015 to March 2020) and pandemic periods (March 2020 to June 2021), and those diagnosed with type 2 diabetes mellitus (T2D), high blood pressure, and heart failure. We selected key clinical indicators for analysis, including systolic and diastolic blood pressure, body mass index (BMI), cholesterol (total, high, and low-density lipoprotein), triglycerides, glycosylated haemoglobin, the Barthel index, and cardiovascular risk (Registre Gironí del cor (REGICOR) index)., Results: Mortality and transfer rates increased during the pandemic, contributing to a decline in the active population in the public health system. We also observed a reduction in pandemic period prevalence of patients with chronic conditions: -26.7% for heart failure, -15.1% for high blood pressure, and -14.6% for T2D. In both pre-pandemic and pandemic periods, 1 632 013 subjects had at least one clinical indicator record. Clinical indicators worsened in patients diagnosed with chronic conditions during the pandemic. Most indicators worsened, with differences between men and women (+9.4% vs +3.7% for the REGICOR index and -14.1% vs -16.6% for the Barthel index in men and in women, respectively), and to a similar extent (or greater in some cases) in individuals without these conditions., Conclusions: We used longitudinal data to assess the repercussions of the COVID-19 pandemic on population health while considering a wide range of clinical indicators and socioeconomic determinants. Our analysis shows a deterioration in clinical indicators during the pandemic, particularly in cardiometabolic factors, underscoring the importance of continuous primary care for individuals with chronic conditions., Competing Interests: Disclosure of interest: The authors completed the ICMJE Disclosure of Interest Form (available upon request from the corresponding author) and disclose no relevant interests., (Copyright © 2024 by the Journal of Global Health. All rights reserved.)

Background: Although many studies have assessed the socioeconomic inequalities caused by COVID-19 in several health outcomes, there are numerous issues that have been poorly addressed. For instance, have socioeconomic inequalities in mortality from COVID-19 increased? What impact has the pandemic had on inequalities in specific causes of mortality other than COVID-19? Are the inequalities in COVID-19 mortality different from other causes? In this paper we have attempted to answer these questions for the case of Spain., Methods: We used a mixed longitudinal ecological design in which we observed mortality from 2005 to 2020 in the 54 provinces into which Spain is divided. We considered mortality from all causes, not excluding, and excluding mortality from COVID-19; and cause-specific mortality. We were interested in analysing the trend of the outcome variables according to inequality, controlling for both observed and unobserved confounders., Results: Our main finding was that the increased risk of dying in 2020 was greater in the Spanish provinces with greater inequality. In addition, we have found that: (i) the pandemic has exacerbated socioeconomic inequalities in mortality, (ii) COVID-19 has led to gender differences in the variations in risk of dying (higher in the case of women) and (iii) only in cardiovascular diseases and Alzheimer did the increased risk of dying differ between the most and least unequal provinces. The increase in the risk of dying was different by gender (greater in women) for cardiovascular diseases and cancer., Conclusion: Our results can be used to help health authorities know where and in which population groups future pandemics will have the greatest impact and, therefore, be able to take appropriate measures to prevent such effects., (© 2023. The Author(s).)

The World Health Organization has proposed that a search be made for alternatives to vaccines for the prevention and treatment of COVID-19, with one such alternative being selective serotonin reuptake inhibitors (SSRIs). This study thus sought to assess: the impact of previous treatment with SSRI antidepressants on the severity of COVID-19 (risk of hospitalisation, admission to an intensive care unit [ICU], and mortality), its influence on susceptibility to SARS-CoV-2 and progression to severe COVID-19. We conducted a population-based multiple case-control study in a region in the north-west of Spain. Data were sourced from electronic health records. Adjusted odds ratios (aORs) and 95%CIs were calculated using multilevel logistic regression. We collected data from a total of 86,602 subjects: 3060 cases PCR+, 26,757 non-hospitalised cases PCR+ and 56,785 controls (without PCR+). Citalopram displayed a statistically significant decrease in the risk of hospitalisation (aOR=0.70; 95% CI 0.49-0.99, p = 0.049) and progression to severe COVID-19 (aOR=0.64; 95% CI 0.43-0.96, p = 0.032). Paroxetine was associated with a statistically significant decrease in risk of mortality (aOR=0.34; 95% CI 0.12 - 0.94, p = 0.039). No class effect was observed for SSRIs overall, nor was any other effect found for the remaining SSRIs. The results of this large-scale, real-world data study indicate that, citalopram, could be a candidate drug for being repurposed as preventive treatment aimed at reducing COVID-19 patients' risk of progressing to severe stages of the disease., Competing Interests: Conflicts of Interest The authors declare that they have no conflicts of interest., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Colchicine is one of the most widely studied and best-known anti-inflammatory treatments. This study aimed to assess the effect of colchicine on risk of hospitalization due to COVID-19; and its effect on susceptibility to and severity of the virus in patients with COVID-19. We carried out a population-based case-control study. The following groups were applied: (1) to assess risk of hospitalization, cases were patients with a positive PCR who were hospitalized due to COVID-19, and controls without a positive PCR; (2) to assess susceptibility to COVID-19, cases were patients with a positive PCR (hospitalized and non-hospitalized), and the same controls; (3) to determine potential severity, cases were subjects with COVID-19 hospitalized, and controls patients with COVID-19 nonhospitalised. Different electronic, linked, administrative health and clinical databases were used to extract data on sociodemographic variables, comorbidities, and medications dispensed. The study covered 3060 subjects with a positive PCR who were hospitalized, 26 757 with a positive PCR who were not hospitalized, and 56 785 healthy controls. After adjustment for sociodemographic variables, comorbidities and other treatments, colchicine did not modify risk of hospitalization due to COVID-19 (adjusted odd ratio [OR] 1.08 [95% confidence interval (CI) 0.76-1.53]), patients' susceptibility to contracting the disease (adjusted OR 1.12 (95% CI 0.91-1.37)) or the severity of the infection (adjusted OR 1.03 [95% CI 0.67-1.59]). Our results would neither support the prophylactic use of colchicine for prevention of the infection or hospitalization in any type of patient, nor justify the withdrawal of colchicine treatment due to a higher risk of contracting COVID-19., (© 2023 The Authors. Journal of Medical Virology published by Wiley Periodicals LLC.)

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.

The COVID-19 pandemic has had major impacts on population health not only through COVID-positive cases, but also via the disruption of healthcare services, which in turn has impacted the diagnosis and treatment of all other diseases during this time. We study changes in all new registered diagnoses in ICD-10 groups during 2020 with respect to a 2019 baseline. We compare new diagnoses in 2019 and 2020 based on administrative records of the public primary health system in Central Catalonia, Spain, which cover over 400,000 patients and 3 million patient visits. We study the ratio of new diagnoses between 2019 and 2020 and find an average decline of 31.1% in new diagnoses, with substantial drops in April (61.1%), May (55.6%), and November (52%). Neoplasms experience the largest decline (49.7%), with heterogeneity in the magnitudes of the declines across different types of cancer diagnoses. While we find evidence of temporal variation in new diagnoses, reductions in diagnoses early in the year are not recouped by the year end. The observed decline in new diagnoses across all diagnosis groups suggest a large number of untreated and undetected cases across conditions. Our findings provide a year-end summary of the impact of the pandemic on healthcare activities and can help guide health authorities to design evidence-based plans to target under-diagnosed conditions in 2021.

The heterogenous distribution of both COVID-19 incidence and mortality in Catalonia (Spain) during the firsts moths of the pandemic suggests that differences in baseline risk factors across regions might play a relevant role in modulating the outcome of the pandemic. This paper investigates the associations between both COVID-19 incidence and mortality and air pollutant concentration levels, and screens the potential effect of the type of agri-food industry and the overall land use and cover (LULC) at area level. We used a main model with demographic, socioeconomic and comorbidity covariates highlighted in previous research as important predictors. This allowed us to take a glimpse of the independent effect of the explanatory variables when controlled for the main model covariates. Our findings are aligned with previous research showing that the baseline features of the regions in terms of general health status, pollutant concentration levels (here NO 2 and PM 10 ), type of agri-food industry, and type of land use and land cover have modulated the impact of COVID-19 at a regional scale. This study is among the first to explore the associations between COVID-19 and the type of agri-food industry and LULC data using a population-based approach. The results of this paper might serve as the basis to develop new research hypotheses using a more comprehensive approach, highlighting the inequalities of regions in terms of risk factors and their response to COVID-19, as well as fostering public policies towards more resilient and safer environments.

Objectives: To assess the impact of prior chronic treatment with angiotensin-converting enzyme inhibitors (ACEIs)/ angiotensin-receptor blockers (ARBs), both as a group and by active ingredient, on severity (risk of hospitalization and mortality), progression of and susceptibility to COVID-19. Methods: We conducted a multiple population-based case–control study in Galicia (north-west Spain). The study data were sourced from medical, administrative and clinical databases. We assessed: (1) risk of hospitalization, by selecting all patients hospitalized due to COVID-19 with PCR + as cases, and a random sample of subjects without a PCR + as controls; (2) COVID-19 mortality risk; (3) risk of disease progression; and (4) susceptibility to SARS-CoV-2, considering all patients with PCR + as cases, and the same subjects used in the previous model as controls. Adjusted odds ratios (aORs) were calculated. Results: ACEIs and ARBs were shown to decrease the risk of hospitalization (aOR = 0.78 [95%CI 0.69–0.89] and aOR = 0.80 [95%CI 0.72–0.90] respectively), risk of mortality (aOR = 0.71 [95%CI 0.52–0.98] and aOR = 0.69 [95%CI 0.52–0.91] respectively), and susceptibility to the virus (aOR = 0.88 [95%CI 0.82–0.94] and aOR = 0.92 [95%CI 0.86–0.97] respectively). By active ingredient: use of enalapril was associated with a significantly lower risk of hospitalization (aOR = 0.72 [95%CI 0.61–0.85]), mortality (aOR = 0.59 [95%CI 0.38–0.92]) and susceptibility to COVID-19 (aOR = 0.86 [95%CI 0.79–0.94]); and use of candesartan was associated with a decreased risk of hospitalization (aOR = 0.76 [95%CI 0.60–0.95]), mortality (aOR = 0.36 [95%CI 0.17–0.75]) and disease progression (aOR = 0.73 [95%CI 0.56–0.95]). Conclusion: This large-scale real-world data study suggest that enalapril and candesartan are associated with a considerable reduction in risk of severe COVID19 outcomes. [ABSTRACT FROM AUTHOR]

The risk of infection and death by COVID-19 could be associated with a heterogeneous distribution at a small area level of environmental, socioeconomic and demographic factors. Our objective was to investigate, at a small area level, whether long-term exposure to air pollutants increased the risk of COVID-19 incidence and death in Catalonia, Spain, controlling for socioeconomic and demographic factors. We used a mixed longitudinal ecological design with the study population consisting of small areas in Catalonia for the period February 25 to May 16, 2020. We estimated Generalized Linear Mixed models in which we controlled for a wide range of observed and unobserved confounders as well as spatial and temporal dependence. We have found that long-term exposure to nitrogen dioxide (NO 2) and, to a lesser extent, to coarse particles (PM 10) have been independent predictors of the spatial spread of COVID-19. For every 1 μm/m3 above the mean the risk of a positive test case increased by 2.7% (95% credibility interval, ICr: 0.8%, 4.7%) for NO 2 and 3.0% (95% ICr: -1.4%,7.44%) for PM 10. Regions with levels of NO 2 exposure in the third and fourth quartile had 28.8% and 35.7% greater risk of a death, respectively, than regions located in the first two quartiles. Although it is possible that there are biological mechanisms that explain, at least partially, the association between long-term exposure to air pollutants and COVID-19, we hypothesize that the spatial spread of COVID-19 in Catalonia is attributed to the different ease with which some people, the hosts of the virus, have infected others. That facility depends on the heterogeneous distribution at a small area level of variables such as population density, poor housing and the mobility of its residents, for which exposure to pollutants has been a surrogate. Number of positive PCR test stratified by quartiles of NO 2. Number of daily deaths stratified by quartiles of NO 2. Image 1 • Long-term exposure to NO 2 and to PM 10 are predictors of the spread of COVID-19. • Air pollutants could have actually been surrogates of the mobility of people. • High mobility implied a greater possibility of contact. • More contact could imply a higher probability of infection. [ABSTRACT FROM AUTHOR]

After the cases of COVID-19 skyrocketed, showing that it was no longer possible to contain the spread of the disease, the governments of many countries launched mitigation strategies, trying to slow the spread of the epidemic and flatten its curve. The Spanish Government adopted physical distancing measures on March 14; 13 days after the epidemic outbreak started its exponential growth. Our objective in this paper was to evaluate ex-ante (before the flattening of the curve) the effectiveness of the measures adopted by the Spanish Government to mitigate the COVID-19 epidemic. Our hypothesis was that the behavior of the epidemic curve is very similar in all countries. We employed a time series design, using information from January 17 to April 5, 2020 on the new daily COVID-19 cases from Spain, China and Italy. We specified two generalized linear mixed models (GLMM) with variable response from the Gaussian family (i.e. linear mixed models): one to explain the shape of the epidemic curve of accumulated cases and the other to estimate the effect of the intervention. Just one day after implementing the measures, the variation rate of accumulated cases decreased daily, on average, by 3.059 percentage points, (95% credibility interval: −5.371, −0.879). This reduction will be greater as time passes. The reduction in the variation rate of the accumulated cases, on the last day for which we have data, has reached 5.11 percentage points. The measures taken by the Spanish Government on March 14, 2020 to mitigate the epidemic curve of COVID-19 managed to flatten the curve and although they have not (yet) managed to enter the decrease phase, they are on the way to do so. Unlabelled Image • After the COVID-19 cases skyrocketed, the Spanish government launched physical distancing. • The measures taken to mitigate the COVID-19 epidemic managed to flatten the curve. • The daily variation rate of accumulated cases decreased by 3.059 percentage points. • This reduction will be greater as time passes. [ABSTRACT FROM AUTHOR]