<?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>0120-386X</journal-id>
<journal-title><![CDATA[Revista Facultad Nacional de Salud Pública]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Fac. Nac. Salud Pública]]></abbrev-journal-title>
<issn>0120-386X</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Antioquia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-386X2023000200008</article-id>
<article-id pub-id-type="doi">10.17533/udea.rfnsp.e351984</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Sobrepeso y obesidad en adultos: aportes de un análisis geoespacial]]></article-title>
<article-title xml:lang="en"><![CDATA[Overweight and obesity in adults: contributions from a geospatial analysis.]]></article-title>
<article-title xml:lang="pt"><![CDATA[Sobrepeso e obesidade em adultos: contribuições de uma análise geoespacial]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez-Monroy]]></surname>
<given-names><![CDATA[Cindy Caterine]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Antioquia  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2023</year>
</pub-date>
<volume>41</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-386X2023000200008&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-386X2023000200008&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-386X2023000200008&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Objetivo:  Realizar un análisis geoespacial del comportamiENTo de sobrepeso y obesidad basado en la &#8220;Encuesta Nacional de Situación Nutricional&#8221; de 2015.  Metodología:  Se aplica un modelo de análisis geoespacial de distribución espacial trasversal a partir de la Encuesta, a escala departamENTal. Para lograrlo, se calculan las prevalencias de sobrepeso, obesidad clase i, ii y iii según el índice de masa corporal y la obesidad abdominal en mujeres y hombres de acuerdo con la circunferencia de cintura. Se utilizan herramiENTas de sistemas de información geográfica, como el índice de Moran Global, el índice local de autocorrelación espacial (lisa) y el G* Getis Ord, para determinar los patrones de agrupaciones altas y bajas prevalencias.  Resultados: Los conglomerados locales ilustrados en los mapas demuestran que sus residuales están distribuidos normalmENTe en el espacio. Se observa una aleatoriedad en el modelo de la autocorrelación espacial. Las agrupaciones de lisa alta-alta se presENTan en diez departamENTos con estas condiciones (La Guajira, Magdalena, Atlántico, Sucre, Cesar, Norte de Santander, Córdoba, Antioquia, Chocó y Cundinamarca). Según el índice de masa corporal, el 38,5 por cada 100 habitantes tienen sobrepeso; el 20,9 por cada 100 habitantes presENTa obesidad, y según la circunferencia de cintura, 53,2 por cada 100 habitantes tiene obesidad abdominal.  Conclusiones: La distribución espacial del sobrepeso y la obesidad puede estar condicionada con variables sociodemográficas tratadas en el estudio. El país tiene el reto de continuar implemENTando acciones poblacionales en salud pública para disminuir estas condiciones.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Objective:  To carry out a geospatial analysis of the behavior of overweight and obesity based on the &#8220;National Survey of Nutritional Situation&#8221; of 2015.  Methodology:  A geospatial analysis model of transversal spatial distribution is applied from the Survey, on a departmENTal scale. To achieve this, the prevalence of overweight, class I, II and III obesity according to body mass index and abdominal obesity in women and men according to waist circumference are calculated. Geographic information system tools, such as the Global Moran Index, Local Spatial Autocorrelation Index (LISA), and G* Getis Ord, are used to determine patterns of high clustering and low prevalence.  Results:  The local clusters illustrated on the maps demonstrate that their residuals are normally distributed in space. A randomness is observed in the spatial autocorrelation model. High-high LISA clusters occur in ten departmENTs with these conditions (La Guajira, Magdalena, Atlántico, Sucre, Cesar, Norte de Santander, Córdoba, Antioquia, Chocó and Cundinamarca). According to the body mass index, 38.5 per 100 inhabitants are overweight; 20.9 per 100 inhabitants are obese, and according to waist circumference, 53.2 per 100 inhabitants have abdominal obesity.  Conclusions:  The spatial distribution of overweight and obesity may be conditioned by the sociodemographic variables treated in the study. The country has the challenge of continuing to implemENT population actions in public health to reduce these conditions.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo  Objetivo:  Realizar uma análise geoespacial do comportamENTo do sobrepeso e da obesidade com base na "Pesquisa Nacional de Situação Nutricional" de 2015.  Metodologia:  Aplica-se um modelo de análise geoespacial de distribuição espacial transversal da Pesquisa, em escala departamENTal. Para isso, calcula-se a prevalência de sobrepeso, obesidade graus I, II e III segundo o índice de massa corporal e obesidade abdominal em mulheres e homens segundo a circunferência da cintura. As ferramENTas do sistema de informações geográficas, como o Índice de Moran Global, o Índice de Autocorrelação Espacial Local (Smooth) e o G* Getis Ord, são usadas para determinar padrões de alto agrupamENTo e baixa prevalência.  Resultados:  Os clusters locais ilustrados nos mapas demonstram que seus resíduos são normalmENTe distribuídos no espaço. Uma aleatoriedade é observada no modelo de autocorrelação espacial. Grupos de tainhas alto-alto ocorrem em dez departamENTos com essas condições (La Guajira, Magdalena, Atlântico, Sucre, Cesar, Norte de Santander, Córdoba, Antioquia, Chocó e Cundinamarca). De acordo com o índice de massa corporal, 38,5 por 100 habitantes estão acima do peso; 20,9 por 100 habitantes são obesos e, segundo a circunferência da cintura, 53,2 por 100 habitantes têm obesidade abdominal.  Conclusões:  A distribuição espacial do sobrepeso e da obesidade pode estar condicionada pelas variáveis sociodemográficas tratadas no estudo. O país tem o desafio de continuar implemENTando ações populacionais em saúde pública para reduzir esses agravos.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[adultos]]></kwd>
<kwd lng="es"><![CDATA[enfermedades no transmisibles]]></kwd>
<kwd lng="es"><![CDATA[modelo geoespacial]]></kwd>
<kwd lng="es"><![CDATA[obesidad]]></kwd>
<kwd lng="es"><![CDATA[sistema de información geográfica]]></kwd>
<kwd lng="es"><![CDATA[sobrepeso]]></kwd>
<kwd lng="en"><![CDATA[adults]]></kwd>
<kwd lng="en"><![CDATA[noncommunicable diseases]]></kwd>
<kwd lng="en"><![CDATA[geospatial model]]></kwd>
<kwd lng="en"><![CDATA[obesity]]></kwd>
<kwd lng="en"><![CDATA[geographic information system]]></kwd>
<kwd lng="en"><![CDATA[overweight]]></kwd>
<kwd lng="pt"><![CDATA[adultos]]></kwd>
<kwd lng="pt"><![CDATA[doenças não transmissíveis]]></kwd>
<kwd lng="pt"><![CDATA[modelo geoespacial]]></kwd>
<kwd lng="pt"><![CDATA[obesidade]]></kwd>
<kwd lng="pt"><![CDATA[sistema de informação geográfica]]></kwd>
<kwd lng="pt"><![CDATA[excesso de peso]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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