<?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>0004-0622</journal-id>
<journal-title><![CDATA[Archivos Latinoamericanos de Nutrición]]></journal-title>
<abbrev-journal-title><![CDATA[Arch Latinoam Nutr]]></abbrev-journal-title>
<issn>0004-0622</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Latinoamericana de Nutrición]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0004-06222023000500345</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[P186/S3-P59 INDIVIDUAL AND HOUSEHOLD CHARACTERISTICS BUT NOT CONTEXTUAL CHARACTERISTICS ARE PREDICTORS OF BODY MASS INDEX IN COLOMBIAN ADULTS]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Poveda-Rey]]></surname>
<given-names><![CDATA[Dra. Natalia Elvira]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martorell]]></surname>
<given-names><![CDATA[Prof. Reynaldo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Patel]]></surname>
<given-names><![CDATA[Dra. Shivani A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramírez-Zea]]></surname>
<given-names><![CDATA[Dr. Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Stein]]></surname>
<given-names><![CDATA[Prof. Aryeh D.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Emory University Rollins School of Public Health Hubert Department of Global Health]]></institution>
<addr-line><![CDATA[Atlanta ]]></addr-line>
<country>USA</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Institute of Nutrition of Central America and Panama (INCAP) INCAP Research Center for the Prevention of Chronic Diseases (CIIPEC), ]]></institution>
<addr-line><![CDATA[Ciudad de Guatemala ]]></addr-line>
<country>Guatemala</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>10</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>10</month>
<year>2023</year>
</pub-date>
<volume>73</volume>
<fpage>345</fpage>
<lpage>345</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_arttext&amp;pid=S0004-06222023000500345&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_abstract&amp;pid=S0004-06222023000500345&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://ve.scielo.org/scielo.php?script=sci_pdf&amp;pid=S0004-06222023000500345&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract:  Introduction:  Colombia is experiencing a rapid nutrition transition and a consequent increase in adult excess weight with varying degrees across regions. The last National Nutrition Survey (ENSIN 2015) showed that the state-level prevalence of excess weight ranged from 48.6% to 72.4%.  Objective:  We conducted a multilevel analysis to study the contribution of individual, household, municipality, and state factors to variations in body mass index (BMI) among Colombian adults.  Methods:  We linked individual and household information from ENSIN 2015, a nationally representative nutrition survey (n=72,750, mean BMI: 26.2 kg/m2), to contextual information from public data repositories and the 2018 Census.  Results:  Of the total adjusted variation in BMI, 81.4% was attributable to between-individual differences, 17.3% to between-household differences, and the remaining 1.1% to differences between regions (municipalities and states). Older individuals, women, afro/other minorities, those with higher schooling attainment, married or living with a partner, and with formal employment had a higher BMI than their counterparts. At the household level, wealth index, income and urbanicity were positively associated with BMI whereas food insecurity and population density showed inverse associations.  Conclusions:  Regional differences in mean BMI resulted from differences in prevalence of sociodemographic characteristics at the individual and household level. There are some sociodemographic groups that have higher BMI across the country, among whom appropriate and targeted nation-wide interventions need to be directed.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[BMI]]></kwd>
<kwd lng="en"><![CDATA[obesity]]></kwd>
<kwd lng="en"><![CDATA[Colombia]]></kwd>
<kwd lng="en"><![CDATA[ENSIN]]></kwd>
<kwd lng="en"><![CDATA[national survey]]></kwd>
<kwd lng="en"><![CDATA[multilevel analysis]]></kwd>
</kwd-group>
</article-meta>
</front>
</article>
