Antonio Gimeno-Miguel

Dr. Antonio Gimeno-Miguel

Aragon Institute of Health Sciences (IACS)—Foundation IIS Aragón
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Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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BPBeatriz Poblador-Plou
Beatriz Poblador-Plou
JCJonás Carmona-Pírez
Jonás Carmona-Pírez
Ignatios Ioakeim-SkoufaIgnatios Ioakeim-Skoufa
Ignatios Ioakeim-Skoufa
APAntonio Poncel-Falcó
Antonio Poncel-Falcó
KBKevin Biek-Bueno
Kevin Biek-Bueno
EGEpiChron Group
EpiChron Group
We aimed to analyze baseline socio-demographic and clinical factors associated with an increased likelihood of mortality in men and women with coronavirus disease (COVID-19). We conducted a retrospective cohort study (PRECOVID Study) on all 4412 individuals with laboratory-confirmed COVID-19 in Aragon, Spain, and followed them for at least 30 days from cohort entry. We described the socio-demographic and clinical characteristics of all patients of the cohort. Age-adjusted logistic regressions models were performed to analyze the likelihood of mortality based on demographic and clinical variables. All analyses were stratified by sex. Old age, specific diseases such as diabetes, acute myocardial infarction, or congestive heart failure, and dispensation of drugs like vasodilators, antipsychotics, and potassium-sparing agents were associated with an increased likelihood of mortality. Our findings suggest that specific comorbidities, mainly of cardiovascular nature, and medications at the time of infection could explain around one quarter of the mortality in COVID-19 disease, and that women and men probably share similar but not identical risk factors. Nonetheless, the great part of mortality seems to be explained by other patient- and/or health-system-related factors. More research is needed in this field to provide the necessary evidence for the development of early identification strategies for patients at higher risk of adverse outcomes.

16 January 2018 in International Journal of Epidemiology
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APAlexandra Prados-Torres
Alexandra Prados-Torres
BPBeatriz Poblador-Plou
Beatriz Poblador-Plou
Antonio Gimeno-MiguelAntonio Gimeno-Miguel
Antonio Gimeno-Miguel
ACA Calderón-Larrañaga
A Calderón-Larrañaga
APA Poncel-Falcó
A Poncel-Falcó
JAJ M Abad-Díez
J M Abad-Díez
Cohort profile in a nutshellEpiChron is a health register-based population cohort study investigating the clinical epidemiology, inappropriate health service us

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Yolanda GilaberteYolanda Gilaberte
Yolanda Gilaberte
JPJuan Blas Pérez-Gilaberte
Juan Blas Pérez-Gilaberte
BPBeatriz Poblador-Plou
Beatriz Poblador-Plou
KBKevin Biek-Bueno
Kevin Biek-Bueno
Antonio Gimeno-MiguelAntonio Gimeno-Miguel
Antonio Gimeno-Miguel
APAlexandra Prados-Torres
Alexandra Prados-Torres
This study aimed at exploring atopic dermatitis (AD) prevalence in children and exhaustively analyzing their comorbidity. We conducted a descriptive analysis of their socio-demographic and comorbidity characteristics in the EpiChron Cohort (Aragón, Spain). Adjusted odds ratios (OR) were calculated for each comorbidity using logistic regression models. In total, 33,591 children had a diagnosis of AD, resulting in an overall prevalence of 15.5%. AD prevalence was higher in girls compared to boys, in 3–9-year-olds compared to children of other ages, and in Spanish children compared to those of other nationalities. Multimorbidity was present in 43% of children, with the most frequent chronic comorbidities being asthma (13.1%), psychosocial disorders (7.9%), and visual impairment (7.8%). Many diseases were, regardless of their prevalence, statistically associated with AD. The strongest associations (odds ratio (OR) (95% confidence interval (CI))) were found in asthma (2.10 (2.02–2.17)), allergic rhinitis (2.00 (1.91–2.10)), and irritable bowel syndrome (1.90 (1.56–2.31)). A better understanding of the array of comorbidities associated with AD in children might help improve their clinical management. Future longitudinal studies are encouraged to shed light on the potential underlying pathophysiological mechanisms involved in the identified associations.

11 November 2020 in Scientific Reports
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IAInmaculada Guerrero Fernández de Alba
Inmaculada Guerrero Fernández de Alba
Antonio Gimeno-MiguelAntonio Gimeno-Miguel
Antonio Gimeno-Miguel
BPBeatriz Poblador-Plou
Beatriz Poblador-Plou
LGLuis Andrés Gimeno-Feliu
Luis Andrés Gimeno-Feliu
Ignatios Ioakeim-SkoufaIgnatios Ioakeim-Skoufa
Ignatios Ioakeim-Skoufa
APAlexandra Prados-Torres
Alexandra Prados-Torres
Type 2 diabetes mellitus (T2D) is often accompanied by chronic diseases, including mental health problems. We aimed at studying mental health comorbidity prevalence in T2D patients and its association with T2D outcomes through a retrospective, observational study of individuals of the EpiChron Cohort (Aragón, Spain) with prevalent T2D in 2011 (n = 63,365). Participants were categorized as having or not mental health comorbidity (i.e., depression, anxiety, schizophrenia, and/or substance use disorder). We performed logistic regression models, controlled for age, sex and comorbidities, to analyse the likelihood of 4-year mortality, 1-year all-cause hospitalization, T2D-hospitalization, and emergency room visit. Mental health comorbidity was observed in 19% of patients. Depression was the most frequent condition, especially in women (20.7% vs. 7.57%). Mortality risk was higher in patients with mental health comorbidity (odds ratio 1.24; 95% confidence interval 1.16–1.31), especially in those with substance use disorder (2.18; 1.84–2.57) and schizophrenia (1.82; 1.50–2.21). Mental health comorbidity also increased the likelihood of all-cause hospitalization (1.16; 1.10–1.23), T2D-hospitalization (1.51; 1.18–1.93) and emergency room visit (1.26; 1.21–1.32). These results suggest that T2D healthcare management should include specific strategies for the early detection and treatment of mental health problems to reduce its impact on health outcomes.

1 October 2018 in Patient Preference and Adherence
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EMEnrica Menditto
Enrica Menditto
CCCaitriona Cahir
Caitriona Cahir
MAMercedes Aza-Pascual-Salcedo
Mercedes Aza-Pascual-Salcedo
DBDario Bruzzese
Dario Bruzzese
BPBeatriz Poblador-Plou
Beatriz Poblador-Plou
APAlexandra Prados-Torres
Alexandra Prados-Torres
Purpose: The purpose of this study was to evaluate and compare medication adherence to chronic therapies in older populations across different regions in Europe. Methods: This explorative study applied a harmonized method of data extraction and analysis from pharmacy claims databases of three European countries to compare medication adherence at a cross-country level. Data were obtained for the period between January 1, 2010, and December 31, 2011. Patients (aged ≥65 years) who newly initiated to oral antidiabetics, antihyperlipidemics, or antiosteoporotics were identified and followed for over a 12-month period. Main outcome measures were medication adherence (medication possession ratio, [MPR]; implementation) and persistence on index treatment. All country-specific data sets were prepared by employing a common data input model. Outcome measures were calculated for each country and pooled using random effect models. Results: In total, 39,186 new users were analyzed. In pooled data from the three countries, suboptimal implementation (MPR 50% of older people non-adherent to antihyperlipidemics and antiosteoporotics in the three European cohorts. However, the degree of variability in adherence rates among the three countries was high. A harmonized method of data extraction and analysis across health-related database in Europe is useful to compare medication-taking behavior at a cross-country level.

23 December 2019 in BMJ Open
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Antonio Gimeno-MiguelAntonio Gimeno-Miguel
Antonio Gimeno-Miguel
AGAnyuli Gracia Gutiérrez
Anyuli Gracia Gutiérrez
BPBeatriz Poblador-Plou
Beatriz Poblador-Plou
CCCarlos Coscollar-Santaliestra
Carlos Coscollar-Santaliestra
JPJ Ignacio Pérez-Calvo
J Ignacio Pérez-Calvo
FRFernando J Ruiz-Laiglesia
Fernando J Ruiz-Laiglesia
Objectives To characterise the comorbidities of heart failure (HF) in men and women, to explore their clustering into multimorbidity patterns, and to measure the impact of such patterns on the risk of hospitalisation and mortality. Design Observational retrospective population study based on electronic health records. Setting EpiChron Cohort (Aragón, Spain). Participants All the primary and hospital care patients of the EpiChron Cohort with a diagnosis of HF on 1 January 2011 (ie, 8488 women and 6182 men). We analysed all the chronic diseases registered in patients’ electronic health records until 31 December 2011. Primary outcome We performed an exploratory factor analysis to identify the multimorbidity patterns in men and women, and logistic and Cox proportional-hazards regressions to investigate the association between the patterns and the risk of hospitalisation in 2012, and of 3-year mortality. Results Almost all HF patients (98%) had multimorbidity, with an average of 7.8 chronic diseases per patient. We identified six different multimorbidity patterns, named cardiovascular, neurovascular, coronary, metabolic, degenerative and respiratory. The most prevalent were the degenerative (64.0%) and cardiovascular (29.9%) patterns in women, and the metabolic (49.3%) and cardiovascular (43.2%) patterns in men. Every pattern was associated with higher hospitalisation risks; and the cardiovascular, neurovascular and respiratory patterns significantly increased the likelihood of 3-year mortality. Conclusions Multimorbidity is the norm rather than the exception in patients with heart failure, whose comorbidities tend to cluster together beyond simple chance in the form of multimorbidity patterns that have different impact on health outcomes. This knowledge could be useful to better understand common pathophysiological pathways underlying this condition and its comorbidities, and the factors influencing the prognosis of men and women with HF. Further large scale longitudinal studies are encouraged to confirm the existence of these patterns as well as their differential impact on health outcomes.