<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-9940</journal-id>
<journal-title><![CDATA[Archivos de cardiología de México]]></journal-title>
<abbrev-journal-title><![CDATA[Arch. Cardiol. Méx.]]></abbrev-journal-title>
<issn>1405-9940</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Cardiología Ignacio Chávez]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-99402023001000075</article-id>
<article-id pub-id-type="doi">10.24875/acm.22000095</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Características sociales, demográficas y de morbimortalidad de los casos atendidos por COVID-19 en el Instituto Nacional de Cardiología Ignacio Chávez. Un estudio transversal descriptivo]]></article-title>
<article-title xml:lang="en"><![CDATA[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]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vallejo]]></surname>
<given-names><![CDATA[Maite]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gutiérrez-Esparza]]></surname>
<given-names><![CDATA[Guadalupe]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ríos-Núñez]]></surname>
<given-names><![CDATA[Lucía]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Altamira-Mendoza]]></surname>
<given-names><![CDATA[Rosalinda]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Groves-Miralrio]]></surname>
<given-names><![CDATA[Lucero E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hernández-Lemus]]></surname>
<given-names><![CDATA[Enrique]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martínez-García]]></surname>
<given-names><![CDATA[Mireya]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Nacional de Cardiología Ignacio Chávez Unidad de Investigación Sociomédica ]]></institution>
<addr-line><![CDATA[ Ciudad de México]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Consejo Nacional de Ciencia y Tecnología Cátedras CONACYT ]]></institution>
<addr-line><![CDATA[ Ciudad de México]]></addr-line>
<country>México</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Instituto Nacional de Cardiología Ignacio Chávez Subdirección de Planeación ]]></institution>
<addr-line><![CDATA[ Ciudad de México]]></addr-line>
<country>México</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Instituto Nacional de Medicina Genómica División de Genómica Computacional ]]></institution>
<addr-line><![CDATA[ Ciudad de México]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<volume>93</volume>
<fpage>75</fpage>
<lpage>86</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-99402023001000075&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-99402023001000075&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-99402023001000075&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Antecedentes y Objetivos: La pandemia de enfermedad por coronavirus 2019 (COVID-19) trajo aparejadas una gran cantidad de consecuencias adversas para la salud pública con serias repercusiones socioeconómicas. En este estudio caracterizamos las condiciones sociales, demográficas y de morbimortalidad de los casos atendidos por COVID-19 en uno de los hospitales de referencia de coronavirus 2 del síndrome respiratorio agudo grave (SARS-CoV-2) en la Ciudad de México.  Método: Se llevó a cabo un estudio transversal descriptivo en 259 pacientes egresados del Instituto Nacional de Cardiología Ignacio Chávez, entre el 11 de abril de 2020 y el 14 de marzo de 2021. Se utilizó un modelo de regresión logística multivariante para identificar la asociación entre variables sociodemográficas y clínicas. Se realizó una optimización mediante cálculos de máxima verosimilitud para elegir el mejor modelo compatible con los datos. El modelo de máxima verosimilitud fue evaluado mediante curvas ROC, estimadores de bondad de ajuste y análisis de multicolinealidad. Se infirieron patrones de comorbilidades estadísticamente significativos mediante la evaluación de una prueba hipergeométrica en las frecuencias de coocurrencia de pares de condiciones. Se implementó un análisis de redes para determinar los patrones de conectividad basado en la centralidad de grado, entre algunas comorbilidades y las variables de desenlace.  Resultados: Las principales desventajas sociales de la población estudiada se relacionan con la falta de seguridad social (96.5%) y el rezago en las condiciones de vivienda (81%). Las variables asociadas a la probabilidad de sobrevivir fueron tener una menor edad (p &lt; 0.0001), contar con más bienes materiales durables (p = 0.0034) y evitar: la neumonía (p = 0.0072), el choque séptico (p &lt; 0.0001) y la insuficiencia respiratoria aguda (p &lt; 0.0001); (AUROC: 91.5%). Las red de comorbilidades para los casos de supervivencia tienen un alto grado de conectividad entre padecimientos como las arritmias cardiacas e hipertensión arterial esencial (centralidad de grado: 90 y 78 respectivamente).  Conclusiones: En vista de que entre los factores asociados a supervivencia existen variables clínicas, sociodemográficas y determinantes sociales de la salud, además de la edad, resulta imperativo considerar los diversos factores que puedan incidir o modificar el estado de salud de una población, sobre todo al abordar los fenómenos epidémicos emergentes como es el caso de la actual pandemia de COVID-19.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Background and Objectives: 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, goodness-of-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 &lt; 0.0001), having more durable material goods (p = 0.0034) and avoiding: pneumonia (p = 0.0072), septic shock (p &lt; 0.0001) and acute respiratory failure (p &lt; 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.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="es"><![CDATA[Comorbilidades]]></kwd>
<kwd lng="es"><![CDATA[Determinantes sociales de la salud]]></kwd>
<kwd lng="es"><![CDATA[Factores socioeconómicos]]></kwd>
<kwd lng="es"><![CDATA[Índice de desarrollo social]]></kwd>
<kwd lng="es"><![CDATA[Teoría de redes]]></kwd>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[Comorbidities]]></kwd>
<kwd lng="en"><![CDATA[Social determinants of health]]></kwd>
<kwd lng="en"><![CDATA[Socioeconomic factors]]></kwd>
<kwd lng="en"><![CDATA[Social development index]]></kwd>
<kwd lng="en"><![CDATA[Network theory]]></kwd>
</kwd-group>
</article-meta>
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<given-names><![CDATA[JE]]></given-names>
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