Evidence map›Paper›PMID 37669561›Full record

ArticleArchivos de cardiologia de Mexico2023

[Social, demographic and morbimortality characteristics of the cases treated for COVID-19 at the Ignacio Chávez National Institute of Cardiology. A descriptive cross-sectional study].

Maite Vallejo, Guadalupe Gutiérrez-Esparza, Lucía Ríos-Núñez, Rosalinda Altamira-Mendoza, Lucero E Groves-Miralrio, Enrique Hernández-Lemus, Mireya Martínez-García

Open access · diamondAbstract readEnglish Abstract
In one paragraph

Article in Archivos de cardiologia de Mexico, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 49% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 4 countries.

Maite VallejoUnidad de Investigación Sociomédica, Instituto Nacional de Cardiología Ignacio Chávez, Ciudad de México, México.
Guadalupe Gutiérrez-EsparzaCátedras CONACYT, Consejo Nacional de Ciencia y Tecnología, Ciudad de México, México.
Lucía Ríos-NúñezSubdirección de Planeación, Instituto Nacional de Cardiología Ignacio Chávez, Ciudad de México, México.
Rosalinda Altamira-MendozaSubdirección de Planeación, Instituto Nacional de Cardiología Ignacio Chávez, Ciudad de México, México.
Lucero E Groves-MiralrioUnidad de Investigación Sociomédica, Instituto Nacional de Cardiología Ignacio Chávez, Ciudad de México, México.
Enrique Hernández-LemusDivisión de Genómica Computacional, Instituto Nacional de Medicina Genómica, Ciudad de México, México.
Mireya Martínez-GarcíaUnidad de Investigación Sociomédica, Instituto Nacional de Cardiología Ignacio Chávez, Ciudad de México, México.
Instituto Nacional de Cardiologia · BRConsejo Nacional de Ciencia y Tecnología · GTNational Institute of Genomic Medicine · MX

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The COVID-19 pandemic brought with it a large number of adverse consequences for public health with serious socioeconomic repercussions. In this study we characterize the social, demographic, morbidity and mortality conditions of individuals treated for COVID-19 in one of the SARS-CoV-2 reference hospitals in Mexico City. Method: A descriptive cross-sectional study was carried out in 259 patients discharged from the Instituto Nacional de Cardiología Ignacio Chávez, between April 11, 2020 and March 14, 2021. A multivariate logistic regression model was used to identify the association between sociodemographic and clinical variables. An optimization was performed using maximum likelihood calculations to choose the best model compatible with the data. The maximum likelihood model was evaluated using ROC curves, goodnessof-fit estimators, and multicollinearity analysis. Statistically significant patterns of comorbidities were inferred by evaluating a hypergeometric test over the frequencies of co-occurrence of pairs of conditions. A network analysis was implemented to determine connectivity patterns based on degree centrality, between comorbidities and outcome variables. Results: The main social disadvantages of the studied population are related to the lack of social security (96.5%) and the lag in housing conditions (81%). Variables associated with the probability of survival were being younger (p < 0.0001), having more durable material goods (p = 0.0034) and avoiding: pneumonia (p = 0.0072), septic shock (p < 0.0001) and acute respiratory failure (p < 0.0001); (AUROC: 91.5%). The comorbidity network for survival cases has a high degree of connectivity between conditions such as cardiac arrhythmias and essential arterial hypertension (Degree Centrality = 90 and 78, respectively). Conclusions: Given that among the factors associated with survival to COVID-19 there are clinical, sociodemographic and social determinants of health variables, in addition to age; It is imperative to consider the various factors that may affect or modify the health status of a population, especially when addressing emerging epidemic phenomena such as the current COVID-19 pandemic.

Indexed as

CardiologyCOVID-19Cross-Sectional StudiesDemographyHumansMexicoPandemicsSARS-CoV-2ComorbiditiesCOVID-19NetworktheorySocial determinants of healthSocial development indexSocioeconomic factors

Identifiers

PMID37669561
PMCPMC12169363
OpenAlexW4386315181

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.