<?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>2594-2166</journal-id>
<journal-title><![CDATA[Medicina y ética]]></journal-title>
<abbrev-journal-title><![CDATA[Med. ética]]></abbrev-journal-title>
<issn>2594-2166</issn>
<publisher>
<publisher-name><![CDATA[Universidad Anáhuac México, Facultad de Bioética]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2594-21662021000300759</article-id>
<article-id pub-id-type="doi">10.36105/mye.2021v32n3.04</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Los confinamientos de la población por el Covid-19 pueden empeorar las desigualdades socioeconómicas que impactan de forma desproporcionada en las minorías raciales: rentabilidad aumentada por aprendizaje automático y análisis ético computacional con contrato social personalista]]></article-title>
<article-title xml:lang="en"><![CDATA[Covid-19 population lockdowns may worsen socioeconomic inequities disproportionately impacting racial minorities: Machine learning-augmented cost effectiveness and computational ethical analysis with personalist social contract]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Monlezun]]></surname>
<given-names><![CDATA[Dominique J.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sotomayor]]></surname>
<given-names><![CDATA[Claudia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Peters]]></surname>
<given-names><![CDATA[Nathan J.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gallagher]]></surname>
<given-names><![CDATA[Colleen M.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[García]]></surname>
<given-names><![CDATA[Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Iliescu]]></surname>
<given-names><![CDATA[Cezar]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Texas Centro Oncológico MD Anderson ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>USA</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Italia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Italia</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad de Texas Centro Oncológico MD Anderson ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Estados Unidos</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Ateneo Pontificio Regina Apostolorum  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Italy</country>
</aff>
<aff id="Af6">
<institution><![CDATA[,Universidad de Texas Centro Oncológico MD Anderson División de Medicina Interna]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>USA</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2021</year>
</pub-date>
<volume>32</volume>
<numero>3</numero>
<fpage>759</fpage>
<lpage>800</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2594-21662021000300759&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2594-21662021000300759&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2594-21662021000300759&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La nueva enfermedad del coronavirus de 2019 (Covid-19), producida por el coronavirus del Síndrome Respiratorio Agudo Severo 2 (SARS-CoV-2), es una pandemia que está creando una creciente crisis sanitaria mundial, dada su novedad, su alcance y sus inicialmente limitadas opciones de tratamiento eficaz. En efecto, se sabe poco acerca de las intervenciones no farmacéuticas óptimas para evitar la morbilidad y mortalidad causadas por él, y también se conoce poco sobre la rentabilidad y aspectos éticos de dichas intervenciones. Por lo tanto, éste es el primer análisis ético y de rentabilidad de las medidas de contención del Covid-19 que se conoce (así como de las medidas de cuarentena de la población para cualquier pandemia en particular), con el fin de ayudar a los sistemas de salud y a los gobiernos a garantizar la atención clínica más rentable que pueda proporcionarse equitativamente al mayor número posible de pacientes, durante esta pandemia y en las futuras crisis mundiales que sean similares. Este análisis utilizó la metodología adoptada por los Centros para el Control y la Prevención de Enfermedades (CDC), cifras comúnmente aceptadas como entradas, y los supuestos mínimos tanto extremos como del mundo real para proporcionar los resultados más sólidos y confiables posibles. El análisis de costos indicó que en los escenarios extremos, o en el mejor de los casos para las medidas de contención del Covid-19 durante la cuarentena de la población, hay una relación de costo-efectividad de $154.86 millones de dólares gastados por cada muerte evitada, y un costo neto de $1.92 billones a nivel mundial. En el mundo real, esta intervención tiene una proporción de 2,520 millones de dólares gastados por cada muerte evitada y un costo neto de 1,99 billones de dólares. La ética del contrato social personalista, tal y como se articula en el popular sistema ético de derechos y deberes de las Naciones Unidas, pone de manifiesto la especial preocupación por el hecho de que estos confinamientos pueden derivar en injusticias poco éticas perpetuadas por los Estados, de manera que socavan las vidas y las libertades individuales, al tiempo que afectan de manera desproporcionada a las comunidades de menores ingresos, especialmente a las minorías raciales. Este estudio sugiere, por tanto, que la práctica prevalente de la cuarentena entre la población, en comparación con las precauciones de tipo estándar y las intervenciones más específicas, puede proporcionar un beneficio neto inadecuado por su costo financiero y ético. En un momento de escasez mundial, que pone a prueba la capacidad de los sistemas sanitarios para detectar y atender adecuadamente a los pacientes con Covid-19, especialmente en los países en desarrollo y en las comunidades desatendidas, este estudio respalda que se dediquen menos recursos a intervenciones con una relación costobeneficio poco clara, y más recursos a intervenciones más asequibles, éticas y equitativas, con mayores pruebas de sus beneficios netos para salvar vidas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The novel coronavirus disease of 2019 (Covid-19) produced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a pandemic creating a growing global health crisis given its novelty, scope, and initially limited efficacious treatment options. Yet little is known about optimal non-pharmaceutical interventions to improve its morbidity and mortality, particularly their cost effectiveness and ethical aspects. This is thus the first known machine learning-augmented cost effectiveness and ethical analysis of Covid-19 containment measures (and of population quarantine measures in particular for any pandemic) to assist health systems and governments in ensuring the most clinically and cost-effective care that can be equitably provided to as many patients as possible during this pandemic and future similar global crises. This analysis utilized the methodology adopted by the Centers for Disease Control and Prevention (CDC), commonly accepted figures as inputs, and both extreme and real-world minimal assumptions to provide the most robust and reliable results possible. Cost analysis indicated that in the extreme or best-case scenarios for the Covid-19 containment measure of population quarantine, there is a cost-effectiveness ratio of $154.86 million spent per averted death and a net cost of $1.92 trillion globally. In real-world scenarios, this intervention has a ratio of $2.52 billion spent per averted death for a net cost of $1.99 trillion. Personalist social contract ethics as articulated by the United Nations&#8217; popular ethical system of rights and duties highlights the particular concerns that such lockdowns may be unethical injustices perpetuated by states in a manner that undermine individual lives and liberties while disproportionately negatively impacting lower income communities particularly racial minorities. This study thus suggests that the prevalent practice of population quarantine compared to standard precautions and more targeted interventions may provide inadequate net benefit for its financial and ethical cost. At a time of global shortages straining health system capacities to adequately detect and care for Covid-19 patients particularly in developing nations and underserved communities, this study supports focusing resources less on interventions with unclear benefit-cost trade-off and more on better affordable, ethical, and equitable interventions with greater evidence for their life-saving net benefits.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Covid-19]]></kwd>
<kwd lng="es"><![CDATA[equidad sanitaria]]></kwd>
<kwd lng="es"><![CDATA[rentabilidad]]></kwd>
<kwd lng="es"><![CDATA[bioética global]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="en"><![CDATA[Covid-19]]></kwd>
<kwd lng="en"><![CDATA[health equities]]></kwd>
<kwd lng="en"><![CDATA[cost effectiveness]]></kwd>
<kwd lng="en"><![CDATA[global bioethics]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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