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DOI: https://doi.org/10.56712/latam.v6i5.4699

Modelamiento de enfermedades respiratorias en función de ga= ses efecto invernadero

Modeling respiratory diseases based on greenhouse gases

 

Araceli Cruz Reyes[1]<= /b>

cr232543@uaeh.edu.mx

https://orcid.org/0000-0002-7697-9687

Universidad Autónoma del Estado de Hidalgo

Pachuca de Soto – México

 

Erik Gerardo Jiménez Proa

erik_jimenez10371@uaeh.edu.mx

https://orcid.org/0009-0009-9164-2167

Universidad Autónoma del Estado de Hidalgo

Pachuca de Soto – México

 

Juan Bacilio Guerrero Escamilla

juan_guerrero9464@uaeh.edu.mx

https://orcid.org/0000-00002-0971-7564

Universidad Autónoma del Estado de Hidalgo

Pachuca de Soto – México

 

Artíc= ulo recibido: 03 de julio de 2025. Aceptado para publicación: 24 de octu= bre de 2025.

Conflictos de Interés: Ninguno que declarar.

 

Resumen<= /o:p>

Desde organismos internacionales se ha señalado el efecto en= las enfermedades respiratorias del ser humano que tiene la excesiva emisi&oacut= e;n de gases efecto invernadero de origen antropogénico, por lo que se ha priorizado en la agenda pública como un problema de atención prioritaria. De acuerdo a la Organización Mundial de la Salud, las enfermedades pulmonares obstructivas crónicas, las infecciones aguda= s en vías respiratorias bajas y los cánceres de pulmón se encuentran entre las principales causas de muertes prematuras relacionadas = a la contaminación ambiental. Mediante la presente investigación se demuestra la incidencia que tienen los gases efecto invernadero en las enfermedades respiratorias clasificadas como infecciones respiratorias agud= as (Y1), Covid-19 (Y2) y otras enfermedades respiratorias= (Y3). Para el análisis, las variables correspondientes a los gases han sido transformadas en dimensiones y se ha realizado mediante el escalamiento multidimensional y la construcció= n de un modelo matemático de correlación canónica, basado e= n la metodología de la investigación de operaciones. Las relaciones canónicas muestran una correspondencia significativa entre el increm= ento en la emisión de los gases y el registro de enfermedades respiratori= as en la zona de estudio, impactando principalmente en la incidencia de infecciones respiratorias agudas.

Palabras clave: regresió= ;n canónica, enfermedades respiratorias, gases efecto invernadero<= /o:p>

 

Abstract

International organizations have indicated the impact on human respiratory diseases caused by excessive anthropogenic greenhouse gas emissions, which is why it has been prioritized on the public agenda as a priority issue. According to the World Health Organization, chronic obstruc= tive pulmonary disease, acute lower respiratory tract infections, and lung cancer are among the leading causes of premature death related to environmental pollution. This research demonstrates the impact of greenhouse gases on respiratory diseases classified as acute respiratory infections (Y1), Covid-19 (Y2), and other respiratory diseases (<= span class=3DSpellE>Y3). For the analysis, the variables corresponding to= the gases have been transformed into dimensions and carried out through multidimensio= nal scaling enfermedades respi= ratorias agudasand the construction of a canonical corre= lation mathematical model, based on the methodology of operations research. Canoni= cal relationships show a significant correlation between the increase in gas emissions and the incidence of respiratory diseases in the study area, primarily impacting the incidence of acute respiratory infections.

Keywords: canonical regresión, respiratory diseases, greenhouses g= ases

 

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Todo el contenido de LATAM Revista Latinoamerica= na de Ciencias Sociales y Humanidades, publicado en este sitio está disponibles bajo Licencia Creative Commons.=

C= ómo citar:  Cruz Reyes, A., Jiménez Proa, E. G., & Guerrero Escamilla, J. B. (2025). Modelamiento de enfermedades respiratorias en función de gases efecto invernadero. LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades 6 (5), 1699 – 1723. https://doi.org/10.56712/latam.v6i5.4699

 

INTRODUCCIÓN

Desde el contex= to internacional

Las enfermedades respiratorias son tambi&eacut= e;n consecuencia de los gases efecto invernadero (GEI), desde organismos internacionales se ha visibilizado al cambio climát= ico como una emergencia global pues afecta el bienestar de las personas, genera= ndo una amenaza a la que debe responderse de forma progresiva y eficaz, desde l= os mejores conocimientos científicos disponibles, bajo este contexto, el Acuerdo de París pretende mantener el aumento de la temperatura media mundial por debajo de los 2°C con respecto = a los niveles preindustriales, además de aumentar la capacidad de adaptación a los efectos del cambio climático a través= de la resiliencia al clima, acompañado de un desarrollo con bajas emisi= ones de GEI y centrando los esfuerzos en reducir al máximo las emisiones antropogénicas  (Naciones Unidas, 2015).  Los GEI son causantes de afecciones en la salud humana de forma directa, en el que la s= alud respiratoria se ve afectada, debido a las morbilidades que en particular ca= da compuesto causa y agrava, sin embargo, su efecto también es de forma indirecta pues generan variaciones extremas en la temperatura y de acuerdo = a la literatura existente estas variaciones llegan tener influencia en el comportamiento de ciertas afecciones, como son las enfermedades respiratori= as. En este sentido se encuentra un estudio realizado en S= eoul, en el que analizando el impacto de la exposición a la temperatura en= las visitas a urgencias por enfermedades respiratorias, se subraya el impacto significativo que tiene la exposición tanto al frío como al calor  en diferente intensidad= y perfiles de riesgo en los grupos poblacionales (Lee y = Yoon, 2024), en el mismo tenor, desde instancias especializadas en el tema se refuerza este hecho, pues de acuerdo al grupo de expertos del Panel Intergubernamental sobre Cambio Climático (IPCC, por sus siglas en inglés) (2023), el aumento de los episodios de calor extremo ha caus= ado en todas las regiones mortalidad y morbilidad, además de que se ha observado el impacto adverso del cambio climático sobre la salud en = las áreas urbanas. La contaminación del ambiente puede modificar = las características naturales de la atmósfera, crear posibles consecuencias negativas sobre el calentamiento global y la salud; entre los= contaminantes que mayormente impactan a la salud pública son las partículas, monóxido de carbono, dióxido de nitrógeno y dió= xido de azufre, a pesar de que todo el mundo está expuesto a ellas, su exposición varía de forma significativa entre poblaciones, condiciones ambientales y zonas geográficas (Organización Panamericana de la Salud, s.f.).

El problema generado por los GEI y su presencia en la atmósfera traducido en contaminación ambiental ha generado un gran impacto no solo en la morbilidad, tambi&eacut= e;n en las tasas de mortandad pues de acuerdo a la Organización Mundial = de la Salud (OMS) (2024), la contaminación del aire ambiente en ciudade= s y zonas rurales es causante de muertes prematuras, por enfermedades cardiovasculares, respiratorias y cánceres a causa de la exposici&oa= cute;n de material particulado fino, según estimaciones de 2019, el 99% de = la población radica en zonas que superan las directrices de calidad del aire recomendadas por la OMS, el 14% de las muertes prematuras relacionadas= a la contaminación ambiental se debieron a causa de enfermedades pulmonares obstructivas crónicas, y 14% a causa de infecciones agudas en  vías respiratorias = bajas, en tanto el 4% fue a causa de cánceres de pulmón, impactando = en mayor medida a poblaciones de países de ingreso bajo y mediano. Aunq= ue la emisión de GEI se da en forma natural= a la atmósfera, es la actividad antropogénica la que ha disparado = la emisión de los mismos. Durante 2019, las principales fuentes de emisión de GEI correspondieron a la generación de energía eléctrica con el 23% del total, = autotransporte con 18.5% y de la fermentación entérica y manejo de excretas = de bovinos con un 13.2%&n= bsp; (Instituto Nacional de Ecología y Cambio Climát= ico , 2022).

Relevancia=

El impacto generado por los GEI en la salud respiratoria debe tomarse como un tema prioritario en las agend= as públicas en todos los órdenes de gobierno. Son consideradas c= omo enfermedades respiratorias aquellas que afectan las vías respiratori= as en lo que se incluye fosas nasales, bronquios y pulmones, se contemplan den= tro de la clasificación a infecciones respiratorias agudas, enfermedades respiratorias crónicas, enfermedad pulmonar obstructiva cróni= ca y cáncer de pulmón, la carga de enfermedad se agrava debido a determinante como la exposición intensa a la contaminación atmosférica en interiores y exteriores y factores socioeconóm= icos intervienen en el aumento de su prevalencia y gravedad  (World= Health Organization, 2025= ), tras pasar por una emergencia sanitaria pandémica el Covid-19 es consider= ado para los efectos de este estudio, que plantea la necesidad de establecer la relación que guarda la emisión de GEI con los registros de enfermedades respiratorias en un área de estudi= o en específico.

El análisis de correlación canónica (CCA)  es un m= odelo estadístico multivariante que tiene por objeto correlacionar simultáneamente varias variables dependientes métricas y vari= as variables métricas independientes, a diferencia de la regresió= ;n múltiple, la correlación canónica implica múlti= ples variables dependientes y se desarrolla bajo el principio de desarrollar una combinación lineal de cada conjunto de variables para maximizar la correlación entre ambos conjuntos (variables dependiente y variables independientes), es decir,  bu= sca la correlación única máxima entre los conjuntos de variab= les; referente a los datos con los que se trabaja, la correlación canónica establece el menor número de restricciones (Hair, Anderson, Tatham y Black , 1999). El resultado = de la relación canónica oscila entre 0 y 1,  por lo que un valor cercano a 0 in= dica la imposibilidad de explicar la existencia de ambos grupos mediante las variables discriminantes seleccionadas, mientras que un valor cercano a 1 refiere una fuerte relación entre los grupos de pertenencia y los puntajes hallados por la función discriminante (De la Garza, Morales= y González , 2013), para el desarrollo del estudio se han tomado como variables independientes seis compuestos considerados como gases efecto invernadero y tres variables independientes en las que se agrupan diversas enfermedades respiratorias. El área de estudio corresponde a la Zona Metropolitana de Tula (ZMT), ubicada al suroest= e del estado de Hidalgo, considerando datos correspondientes al año 2022.<= o:p>

Definició= ;n espacial del problema

La Zona Metropolitana de Tula fue reconocida d= esde 2001 hasta 2023 como la conurbanidad integrada = por los municipios de Atitalaquia, Atotonilco de Tula, Tlaxcoapan, Tlahuelilpan= y Tula de Allende, debido a su ubicación estratégica y diversas vías de comunicación, además ha tenido un desarrollo e= conómico de suma importancia para la región centro del país. Sus principales actividades económicas pertenecen al sector primario y secundario: el 7.70% de su población ocupada realiza actividades del sector primario que contempla agricultura, ganadería, silvicultura, = pesca y caza, en tanto el 35.77% de la población ocupada en la ZMT se desempeña en el sector secundario que comprende la extracción de petróleo y gas, industria manufacturera, electricidad, agua y construcción (Instituto Nacional= de Estadística y Geografía, 2017).  En la ZMT confluyen cuerpos de aguas negras, se desarrollan actividades agríco= las y ganaderas, además de las industrias asentadas en la región entre las que se encuentran industrias mineras, cementeras, de plást= icos y hules, farmacéuticas, químicas y agroquímicas, metalúrgicas y cementeras, además de tener asentada una refinería perteneciente a la paraestatal PEMEX quien también cuenta con una central eléctrica en su interior y una central termoeléctrica perteneciente a la Comisión Federal de Electri= cidad, sin dejar atrás el desarrollo de la actividad de servicios; el desarrollo económico de la ZMT es respon= sable en gran parte de la emisión excesiva de GEI a la atmósfera. De acuerdo a datos oficiales, durante mayo de 2025, la refinería procesó 216.156 miles de barriles diarios (Petróleos Mexicanos [PEMEX], 2025), por su parte, la central termoeléctrica Francisco Pérez Ríos identificada como = del municipio de Tula de Allende, que trabaja con tecnología termoel&eac= ute;ctrica convencional,  y la central eléctrica del sector privado de Refinería Miguel Hidalgo con tecnología termoeléctrica convencional/cogeneración a = 2022 representaron una capacidad instalada de 1606 MW (megavatios) termoeléctrica convencional producida en el estado de Hidalgo   (Secretaría de Energ&= iacute;a, 2023). Desde el ámbito periodístico, académico y socia= l se ha señalado constantemente la excesiva emisión de contaminant= es en la ZMT y la preocupación por sus efec= tos en la salud de los habitantes de la región, particularmente por la mala calidad del aire que se respira en la zona.

METODOLOGÍA

En este estudio se trabaja con las variables “gases efecto invernadero”, “sector económico̶= 1; y “número de habitantes”, las cuales son variables que impactan en enfermedades respiratorias agudas, Covid-19 y otras enfermedade= s. Se realizaron dos modelos, el primero una técnica de Escalamiento Multidimensional y el segundo una Regresión Canónica.

La técnica de análisis de correlación canónica, se trata de una técnica que anal= iza la relación entre múltiples variables dependientes y varias variables independientes, ambas de naturaleza métrica, su objetivo primordial es utilizar las variables independientes (cuyos valores son conocidos) para predecir las variables criterio (dependientes) (Pérez , 2004)

Las técnicas de escalado multidimension= al se presentan como una generalización de la idea de componentes principales, en el momento en que en lugar de disponer de una matriz de observaciones por variables se dispone de una matriz (D) cuadrada n x n de distancias entre los n elementos   (Cuadras, 2014).=

Para el análisis se han considerado dat= os sobre emisiones de GEI en los cinco municipios = que integran la ZMT, obtenidos mediante el Laborato= rio de Análisis Territorial, Ambiente y Ciencias de Datos de la Universidad Autónoma del Estado de Hidalgo (UAEH), así mismo se han considerado los registros de enfermedades respirato= rias durante 2022 en los municipios sujetos a estudio

Para la construcción de los modelos probabilísticos se sustenta en la Metodología de Investigación de Operaciones, la cual debe cumplir cinco etapas (Thrierauf, 1982; Taha, 2012):

Primera etapa – definición y delimitación del problema: se definen la población, la muestra y el instrumento, con la finalidad de obtener = la información necesaria para construir los modelos.<= /p>

Segunda etapa – construcción o formulación del modelo: en esta sección se proponen matemáticamente las variables que participarán en la construcción de los modelos; con base en ciertas condiciones, se selecciona el modelo que logre un mejor ajuste.

Tercera etapa – solución o estimación del modelo: una vez identificado el modelo, se realiza la estimación de parámetro= s.

Cuarta etapa – validación del modelo: después de seleccionar el model= o, se lleva a cabo su validación mediante el cumplimiento de ciertas condiciones de inferencia estadística.

Quinta etapa – implementación y análisis de resultados: en esta última sección, se interpretan los parámetros obtenido= s.

Con la construcción de estos modelos, se implementan los elementos necesarios para prever el comportamiento del fenómeno a partir de la relación lineal entre la variable dependiente y las variables independientes (Xi, i =3D 1, 2, 3… k). Lo= que permitirá a partir del análisis de los parámetros obtenidos atender el problema desde una perspectiva multidisciplinaria.

Delimitaci&oacu= te;n del problema

La Zona Metropolitana de Tula, es generadora de grandes cantidades de contaminantes ambientales, la emisión de PM, <= span class=3DSpellE>SO2, CO2, Nox, CH4 y N2O ha llevad= o a mermar la calidad del aire de forma recurrente en la zona, aunado, en los municipios que integran la Zona, los problemas de salud de la poblaci&oacut= e;n han percibido un aumento, particularmente en vías respiratorias (tab= la 1).

Tabla 1

Enfermedades respiratorias asociadas a los Gases Efecto Invernadero

Gas contamina= nte

Enfermedades respiratorias

Material particulado

(PM2.5 y10)

 Asma, bronquitis crónica, enfermedad pulmonar obstructiva crónica (EPOC), cáncer de pulmón, infecciones respiratorias agudas. Puede incidir en la severidad y el impacto del COVID-19.

Dióxido de azufre

(SO₂)

Irritación de vías respiratorias, bronquitis aguda, crisis asmáticas, disminución de la función pulmonar. Puede incidir en la severidad y el impacto del COVID-19.

Dióxido de carbono

(CO₂)

En concentraciones ambientales normales no causa enfermedad respiratoria directa, pero niveles elevados pueden causar hipercapnia, dificultad respiratoria y agravar enfermedades preexistentes.

Óxidos de nitrógeno

(NOₓ)

 Inflamación de vías respiratorias, asma, bronquitis crónica, reducción de la función pulmonar, aumento de infecciones respiratorias.

Metano

(CH₄)

No es tóxico directamente a concentraciones bajas, pero en ambientes cerrados puede desplazar el oxígeno causando hipoxia; la hipoxia p= uede provocar dificultad respiratoria.

Óxido nitroso

(N₂O)

A concentraciones altas puede causar depresión del sistema nervioso y dificultad respiratoria; uso crónico asociado a daño pulmon= ar en entornos laborales. Puede incidir en la severidad y el impacto del COVID-19.

 

Fuente: (Organización Mundial de la Salud, 2018; Agencia de Protección Ambiental de Estados Unidos, 2023).

De acuerdo a la literatura científica, = se han encontrado relaciones significativas entre los niveles de contaminaci&o= acute;n ambiental y las enfermedades del sistema respiratorio considerando enfermed= ades respiratorias, enfermedad COVID y otras enfermedades respiratorias, notando= una particular vulnerabilidad en grupos poblacionales específicos (infan= tes, adultos mayores y personas con enfermedades crónicas), tambié= n, diversos autores han referido mayor incidencia de afectaciones en poblacion= es cercanas a fuentes constantes de emisión de GEI= .

La igualdad algebraica del objeto de investigación:

Y1 + Y2+ Y3 = =3D X1 + X2 + X3 + X4 + X5 + X6 X7 + X8+ X9

Donde:

Y1 &= nbsp;           &nbs= p;           son las Infecciones respiratorias agudas

Y2  =             &nb= sp;          Covid-19

Y3 &= nbsp;           &nbs= p;           Otras enfermedades respiratorias

X1 &= nbsp;           &nbs= p;          Sector económico (Primario, Secundario y Terciario)

X2 &= nbsp;           &nbs= p;          Municipio

X3 &= nbsp;           &nbs= p;          Poblaci&oac= ute;n

X4 &= nbsp;           &nbs= p;          Material particulado (PM)

X5 &= nbsp;           &nbs= p;          Dioxido de azufre (SO2)

X6 &= nbsp;           &nbs= p;          Dióx= ido de carbono (CO2)

X7 &= nbsp;           &nbs= p;          Óxid= os de nitrógeno (NOx)

X8 &= nbsp;           &nbs= p;          Metano (CH4)

X9 &= nbsp;           &nbs= p;          Óxido nitroso (N2O)

Gráfico = 1


Matriz correlación

 

Fuente: elaboración propia

El gráfico 1 muestra el análisis realizado en donde se expresa que existe una correlación entre las variables, por lo que en medida en que alguna de ellas cambie en su valor, probablemente las otras variables tenderán a cambiar también.= Las correlaciones no significativas han sido de Municipio (X2) con el Sector económico (X1) y con las variables dependientes IRA’s (Y1), Covid-19 (Y2) y con Otras enfermedades respirato= rias (Y3). También se mantiene como no significativa= la relación entre Material Particulado (G1)= e IRA’s (Y1).

Formulaci&oacut= e;n del modelo de escalamiento multidimensional

Las técnicas de escalado multidimension= al se presentan como una generalización de la idea de componentes principales, en el momento en que en lugar de disponer de una matriz de observaciones por variables se dispone de una matriz (D) cuadrada n x n de distancias entre los n elementos  (Cuadras, 2014). Aunque no siempre es posible encontrar p variables que exactamente representen las distancias iniciales, es común encontrar variables que representen l= as distancias iniciales en un aproximado.

Ω=3D {1, 2, …= ., n}

(1)

De forma = que:

Ω=3D un conjunto finito=

n=3D elementos diferentes

Sea δ ij  =3D = 48; (i,j) una distancia o disimilarid= ad entre los elementos i,j de Ω

Se habla de distancia métrica cuando se cumplen tres condiciones:

Δ (i,i) =3D 0   para todo i.=

δ (i,j)= =3D δ (j,i) ≥ 0   para todo i, j.

δ (i,j)= ≤ δ (i,k) + δ (j,= k)    para todo i, j, k ( desigualdad triangular).

En caso de cumplirse solo las dos primeras condiciones, se establece que δ (i,j) es= una disimilaridad. El objetico de la técnica es e= ncontrar la X más adecuada, partiendo de la matriz de distancias ∆.

Representación de la matriz de disimilaridades (distancias):

(2)

 

Donde:

∆ =3D (δij) representa= una matriz de distancias euclídeas si existen n puntos X1, . . . Xn Ꞓ Rp<= /span>

Siendo:

  . , ,       &nbs= p; =

(3)

 

Tal que:


Fuente: elaboración propia.

Gráfico = 2


Dendograma de gases

 

Fuente: elaboración propia.

El gráfico 2 muestra el agrupamiento de= las variables, jerarquizando sus valores de acuerdo a los valores de la matriz correlación. Con ello se establecen tres nuevas dimensiones.

Nombramiento de= las dimensiones

Dimensión 1-> Indicador de Gasas de Quema de Combustibles Fósiles =3D IGQF

Dimensió= n 2 -> Indicador de Gases de Producción Química =3D IGPQ=3D

Dimensió= n 3 -> Indicador de Gases de Concentración de C02 =3D IGCT

De tal manera, que IGQF + IGPQ + IGCT son dimensiones que producen efectos negativos; es decir, la forma en que estos aumenten, las infecciones respiratorias graves aumentaran.

En la Figura 3 se observa el graso de peso que tienen las dimensiones creadas, el Indicador de Gasas de Quema de Combustib= les Fósiles se presenta como el de mayor peso.

Gráfico = 3


Grado de peso de las dimensiones

 

Fuente: elaboración propia.

Formulaci&oacut= e;n del modelo de correlación canónica

En esta sección, ya creados las tres dimensiones de gases (IGQF, IGPQ, IGCT), a partir de la técnica multivaria= ble escalamiento multidimensional. Se parte para aplicar una correlación canónica, para relacionar los indicadores IGQF<= /span>, IGPQ e IGCT y el se= ctor económico con tres tipos de enfermedades:

Variables dependientes

Y1 =   Infecciones respiratorias agudas

Y2   Covid-19

Y3 =   Otras enfermedades respiratorias

Variables independientes

X1 Sector económico

IGQF (Gases por quema de combustibles fósiles)

IGPQ (Gases de producción química)

La expresión general de la correlación canónica se presenta como:

Y1<= /m:sSub>=3Df(IGQF, IGPQ, X1<= /m:sSub>)<= /m:oMathPara>Y2<= /m:sSub>=3Df(IGQF, IGPQ, X1<= /m:sSub>)<= /m:oMathPara>Y3<= /m:sSub>=3Df(IGQF, IGPQ, X1<= /m:sSub>)<= /m:oMathPara>El análi= sis de correlación canónica es un tipo de análisis estadístico lineal de múltiples variables. Analiza la relación multidimensional entre múltiples variables independi= entes y múltiples variables dependientes (Pérez= , 2004)

Cada función canónica se basa en= la correlación entre dos valores teóricos canónicos, un v= alor teórico para las variables dependientes y otro para las variables independientes.

Los objetivos del método son:

<= span style=3D'mso-list:Ignore'>●<= span style=3D'mso-list:Ignore'>●<= span style=3D'mso-list:Ignore'>●Gy<= m:sub>1,<= m:sSubPr>y<= m:sub>2,…,<= m:sSub>y<= m:sub>n=3DF<= /i>x<= m:sub>1,<= m:sSubPr>x<= m:sub>2,…,<= m:sSub>x<= m:sub>nW1<= /m:sSub>=3D<= m:sSubPr>a11= X1<= /m:sSub>+a12= X2<= /m:sSub>+…+a1p<= /i>Xp<= /m:sSub>V1<= /m:sSub>=3D<= m:sSubPr>b11= Y1<= /m:sSub>+b12= Y2<= /m:sSub>+…+b1q<= /i>Yq<= /m:sSub>a11,…, a1p= =   y =

(11)

 

Tal que  es máximo<= /p>

Donde:

y  son llamadas variables canón= icas y  es la correlación entre ella= s, llamada correlación canónica.

En la figura 6, se observa que las variables independientes Y1 (Infecciones Respiratorias Ag= udas “IRA”, Y2 (COVID-19), y Y3 (Otras enfermedades respiratorias “OER”), = tienen una correlación significativa entre ellas; esto significa que, aumen= tan o disminuyen al mismo tiempo, esto puede ser debido a la calidad del aire y= al tipo de sector económico de la región. Del mismo modo, las en= fermedades respiratorias están moderadamente correlacionadas con los indicadore= s IGQF (Gases por quema de combustibles fósiles = e IGPQ (Gases de producción química), pue= s en la Zona Metropolitana de Tula, es una región que tiene asentado en su territorio una refinería, una termoeléctrica, industrias cementeras, manufactureras, y actividad agroindustrial, e inciden en la aparición de enfermedades respiratorias. La dimensión IGCT (Gases relacionados con concentración de = Monoxico de Carbono), no está significativamen= te relacionada con las enfermedades respiratorias, como lo está las otr= as dimensiones IGQF e IGPQ. Asimismo, las variables dependientes Y1, Y2 y <= span class=3DSpellE>Y3, están relacionadas con la variable dependi= ente X1(Sector económico “Primario, Secundari= o y Terciario”), en el caso de la zona metropolitana de Tula predomina el sector secundario pues es industrial, y la población está expuesta a la calidad del aire.

Gráfico = 4


Matriz de correlación

 

Fuente: elaboración propia.

El gráfico 4 muestra el nivel de correlación entre las variables dependientes y las variables independientes, entre las que se encuentran las nuevas dimensiones creadas.=

RESULTADOS

La salida de R, de la variable dependiente Y1 (Infecciones respiratorias agudas) es explicada po= r las variables independientes: IGQF, IGPQ y X1, se interpreta lo siguiente (Figura 5):

Los tres coeficientes son significativos, pues= son menores a (p < 0.001) (Kutner, Nachtsheim, Neter, & Li, Applied linear statistical models<= /span>, 2004).

Se puede ver que la variable IGQF tiene un coeficiente[2]<= ![endif]> de 131.34, es decir, por cada unidad que aumente IGQF<= /span>, Y1 aumentará en promedio 131.34 unidades, manteniendo IGPQ y X1 constantes.

La variable IGPQ t= iene un coeficiente de 319.22, por cada unidad que aumenta = IGPQ, Y1 aumenta en promedio 319.22 unidades, manteni= endo IGQF y X1 constantes.

La variable X1 tie= ne un coeficiente de 186.67, por cada unidad que aumenta X1<= /span>, Y1 aumenta en promedio 186.67 unidades, manteni= endo IGQF e IGPQ constantes. <= o:p>

Los p-value son estadísticamente significativos, ya que son menores a (< 0.05): <= span class=3DSpellE>IGQF (4.66e-07), IGPQ (1.31e-08) y X1 (1.70e-10).=

El modelo tiene una variabilidad del 48.2% en = Y1, se explica por las variables independientes. Por = tanto, el modelo es explicado en un 48.2% por la variable correspondiente a Infecc= iones Respiratorias Agudas (Y1) y las variables Indic= ador de gases por quema de combustibles fósiles (IGQ= F), Indicador de gases por producción química (IGPQ) y el sector económico (X1).

IGPQ tiene más alto el coeficiente; = es decir, es la variable con mayor efecto en Y1 continuado por la variab= le IGQF y X1.

Figura 1

Corrida en Y1


Fuente: elaboración propia.

En la Figura 1 = se observa que, la variable dependiente Y2 (Covid-19), es explicada por las variables independientes: IGQF, IGPQ y X1, se interpreta lo siguiente:

Los tres coeficientes son significativos, pues= son menores a (p < 0.001) (Kutner, Nachtsheim, Neter, & Li, Applied linear statistical models<= /span>, 2004).

Se puede ver que la variable IGQF tiene un coeficiente[3]<= ![endif]> de 11.455, es decir, por cada unidad que aumente IGQF<= /span>, Y1 aumentará en promedio 11.455 unidades, manteniendo IGPQ y X1 constantes.

La variable IGPQ t= iene un coeficiente de 24.220, por cada unidad que aumenta = IGPQ, Y 2 aumenta en promedio 24.220 unidades, manteniendo I= GQF y X1 constantes.

La variable X1 tie= ne un coeficiente de 11.736, por cada unidad que aumenta X1<= /span>, Y1 aumenta en promedio 11.736 unidades, manteni= endo IGQF e IGPQ constantes. <= o:p>

Los p-value son estadísticamente significativos, ya que son menores a (< 0.05): <= span class=3DSpellE>IGQF (8.40e-13), IGPQ (1.10e-12) y X1 (1.13e-11).

El modelo tiene una variabilidad del 59.7% en = Y2, se explica por las variables independientes. A efecto, el modelo es explica= do en un 59.7% por la variable correspondiente a Covid-19 (Y1) y las variables Indicador de gases por quema de combustibles fósiles= (IGQF), Indicador de gases por producción química (IGPQ) y el sector económ= ico (X1).

IGPQ tiene más alto el coeficiente; = es decir, es la variable con mayor efecto en Y2 continuado por la variable X1 y posterior IGQF<= /span>.

Figura 2

Corrida en Y2


Fuente: elaboración propia.

En la figura 2 se muestra que, la variable dependiente Y 3 (Otras enfermedades), es explicada por las variables independientes: IGQF, IGPQ= y X1, se interpreta lo siguiente:

Los tres coeficientes son significativos, pues= son menores a (p < 0.001) (Kutner, Nachtsheim, Neter, & Li, Applied linear statistical models<= /span>, 2004).

Se puede ver que la variable IGQF tiene un coeficiente[4]<= ![endif]> de 5.4875, es decir, por cada unidad que aumente IGQF<= /span>, Y1 aumentará en promedio 5.4875 unidades, manteniendo IGPQ y X1 constantes.

La variable IGPQ t= iene un coeficiente de 13.0195, por cada unidad que aumenta IGPQ, Y1 aumenta en promedio 13.0195 unidades, manten= iendo IGQF y X1 constantes.

La variable X1 tie= ne un coeficiente de 5.9363, por cada unidad que aumenta X1<= /span>, Y1 aumenta en promedio 5.9363 unidades, manteni= endo IGQF e IGPQ constantes. <= o:p>

Los p-value son estadísticamente significativos, ya que son menores a (< 0.05): <= span class=3DSpellE>IGQF (6.77e-11), IGPQ (8.11e-13) y X1 (9.03e-11).

El modelo tiene una variabilidad del 57.1% en = Y3, se explica por las variables independientes. A ef= ecto, el modelo es explicado en un 57.1% por la variable correspondiente a Otras enfermedades respiratorias (Y3), en y las varia= bles Indicador de gases por quema de combustibles fósiles (IGQF), Indicador de gases por producción química (IGPQ) y el sector económ= ico (X1).

IGPQ tiene más alto el coeficiente; = es decir, es la variable con mayor efecto en Y2 continuado por la variable X1 y posterior IGQF<= /span>.

 

Figura 3

Corrida en Y3

 

Fuente: elaboración propia.

DISCUSIÓN

Los resultado muestran que al mantener los val= ores constantes de las variables IGPQ y X1, por cada unidad (aumentó de 1) que aumente= el Indicador de gases por quema de combustibles fósiles (IGQF) habrá en promedio 131.34 nuevos casos registrados de personas con Infecciones respiratorias agudas IRA’s, mientras que al mantener constantes los valores del IGQF y de X1, por cada unidad que aumente el Indicador de gases por producción química (IGPQ) se registran en promedio 319.22  nuevos casos de IRA’s (Y1) y  entre mayor cambie el sector económico al que pertenece la localidad, (de primario a secundario o= de secundario a terciario) aumentarán en promedio 186.67 los registros = de enfermos por IRA’s (= Y1), manteniendo constantes los niveles de los Indicador IG= QF y de IGPQ.

En cuanto a la incidencia en el registro de Covid-19 (Y2), al mantener los valores constantes del Indicador IGPQ y de X1, por cada un= idad que aumente el Indicador de gases por quema de combustibles fósiles (IGQF) habrá en promedio 11.455 nuevos registro= s de personas enfermas, mientras que al mantener constantes los valores del Indicador IGQF y de X1, por cada unidad que aumente el Indicador de gases por producción química (IGPQ) se registran en promedio = 24.220 nuevos casos de Covid-19 (Y2). Y entre mayor cambie el sector económ= ico al que pertenece la localidad, (de primario a secundario o de secundario a terciario) aumentarán en promedio 11.736 los registros de enfermos p= or Covid-19 (Y2), siempre que se mantengan constantes los niveles de los indicadores IGQF e IGPQ.

En el impacto que tiene el aumento de los GEI en otras enfermedades respiratorias se observa qu= e al mantener los valores constantes del IGPQ y de <= span class=3DSpellE>X1, por cada unidad que aumente el Indicador de gases= por quema de combustibles fósiles (IGQF) hab= rá en promedio 5.4875 nuevos registros de personas enfermas por Otras enfermed= ades respiratorias (Y3). Al mantener constantes los valores del Indicador IGQF y X1, por cada unidad que aumente el Indicador de gases por producción química (IGPQ) se registran en promedio 13.0195 nuevos casos de Otras enfermedades respiratorias (Y3) en la zona de estudio. También se muestra que entre mayor cambie el sector económico al que pertenece la localidad, (de primario a secundario o de secundario a terciario) aumentarán en promedio 5.9363 los registros de enfermos por Otras enfermedades respiratorias (Y3), manteniendo constantes los niveles de los indica= dores IGQF e IGPQ.

Los resultados implican la base científ= ica para atender el problema público con un enfoque multidisciplinario.<= o:p>

El estudio se puede complementar a futuro al delimitar a zonas específicas en donde se asienten algunas de las principales fuentes de emisiones de gases de efecto invernadero, como es el caso de industrias, tomando en consideración datos de la poblaci&oac= ute;n que habita en un área a la redonda, incluso con trabajadores de plan= ta del sector industrial.

CONCLUSIÓN

La construcción de un modelo mediante la combinación del escalamiento multidimensional y el análisis de correlación canónica ha permitido establecer la relació= ;n que guarda la emisión de gases efecto invernadero en la incidencia de enfermedades en los municipios que conforman la Zona Metropolitana de Tula,= Hgo., la determinación de las relaciones canónicas requirió de forma previa la transformación de los datos mediante la técnica de escalonamiento para homogeneizar los datos y crear las nuevas variables independientes denominadas indicadores, = lo que ha permitido determinar la relación canónica. Los indicad= ores construidos tienen una incidencia en el registro de infecciones respiratori= as agudas (Y1), de Covid-19 (Y2) y de otras enferm= edades respiratorias (Y3), la predicción del mo= delo permite pronosticar un aumento en las enfermedades respiratorias en la población de la Zona, a medida que aumenta la emisión de gases efecto invernadero. La afectación de mayor impacto se observa median= te la relación que guarda el Indicador de Gases de Producción Química (IGPQ), seguido del Indicador de= Gases por Quema de Combustibles Fósiles (IGQF)= , en la incidencia de Infecciones respiratorias agudas. Con ello se evidencia de forma científica el problema de salud respiratoria que se presenta e= n la Zona Metropolitana de Tula, Hgo, debido a la ex= cesiva emisión de GEI y cimienta la necesidad de atender el problema público desde diversas disciplinas.

REFERENCIAS

Agencia de Protección Ambiental de Esta= dos Unidos. (2023). Agencia de Protección Ambiental de Estados Unidos.

Cuadras, C. M. (2014). Nuevos Métodos de Análisis Multivariante.

De la Garza, G, Morales, S y González, = C. (2013). Análisis estadístico multivariante, un enfoque teórico y práctico. Mc Graw Hill.

Hair, J, Anderson, R, Tatham, R y Black, W. (1999). Análisis Multivariante, 5a ed. Prentice Hall.=

Lee, H., Yoon, = HY. (2024). Impact of ambient temperature on respiratory disease: a case-crosso= ver study in Seoul. Respiratory Research,&= nbsp;25, 73 (2024). https://doi.org/10.1186/s12931-024-0= 2699-0

Instituto Nacional de Ecología y Cambio Climático. (2022). Inventario Nacional de Emisiones de Gases y Compuestos de Efecto Invernadero (INEGYCEI) de México 1990 - 2019. Instituto Nacional de Ecología y Cambio Climático. https://www.gob.mx/inecc/articulos/inventario-nacional-de-emisiones-de-gase= s-y-compuestos-de-efecto-invernadero-inegycei-de-mexico-1990-2019

Instituto Nacional de Estadística y Geografía. (2017). Anuario Estadístico y Geográfico de Hidalgo 2017. INEGI. Recuperado de https://www.inegi.org.mx/contenido/productos/prod_serv/contenidos/espanol/b= vinegi/productos/nueva_estruc/anuarios_2017/702825095093.pdf

Kutner, M., Nachtsheim= , C., Neter, J., & Li, W. (2004). Applied lin= ear statistical models. McGraw-Hill/Irwin.

Naciones Unidas. (2015). Acuerdo de Parí= ;s. París: Naciones Unidas. Recuperado de https://unfccc.int/sites/defau= lt/files/spanish_paris_agreement.pdf

Organización Mundial de la Salud. (2018= ). COP24 special report: health and climate = change Organización Mundial de la Salud. https://www.who.int/publications/i/item/cop24-special-report-health-climate= -change

Organización Mundial de la Salud. (2024= ). Contaminación del aire ambiente (exterior) y salud. Organizaci&oacut= e;n Mundial de la Salud. https://www.who.int/es/news-room/fact-sheets/detail/ambient-(outdoor)-air-q= uality-and-health

Organización Panamericana de la Salud. (s.f.). Calidad del aire. Organizaion Panameric= ana de la Salud. https://www.paho.org/es/temas/calidad= -aire

Panel Intergubernamental sobre Cambio Climático. (2023). Cambio Climático 2023, Informe de síntesis. Resumen para los formuladores de políticas. Ginebra: IPCC. Recuperado de https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf

Petroleos Mexicanos. (2025). Pemex Transformación Industrial. PEMEX Base de Datos Institucional. Recuperado de: https://ebdi.pemex.com/bdi/bdiController.do?a= ction=3Dcuadro&cvecua=3DRPROREF

Pérez, C. (2004). Técnicas de Análisis Multivariante de Datos. Aplicaciones con SPSS. Pearson Educación.

Secretaría de Energía. (2023). Programa de Desarrollo del Sistema Eléctrico Nacional 2023-2037. SEN= ER. Recuperado de https://www.gob.mx/sener/articulos/programa-de-desarrollo-del-sistema-elect= rico-nacional-2023-2037

Taha, H. A. (2012). Investigación de Operaciones. Arkansas: Pearson.

Thierauf= , R. J. (1982).= An introductory approach to operations research. R. E. Krieger Publishing Comp= any.

World Health Organization. (2025). Enfermedades del tracto <= span class=3DSpellE>respiratorio. Woorl Health Organization, Eastern Mediterranean Region. https://www.emro.who.int/health-topics/respiratory-tract-diseases/index.htm= l

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Todo el contenido de LATAM Revista Latinoamericana de Cien= cias Sociales y Humanidades, publicados en este sitio está disponible= s bajo Licencia Creative = Commons 3D"https://revistacientifica.uamericana.edu.py/public/site/images/aduar=.

ANEXOS

Código

######-----Modelamiento-----####

library(psych)

library(corrplot)

library(tseries= )

library(lmtest)=

library(car)

library(nortest= )

library(= MASS)

library(faraway)

library(forecast)

library(moments)

library(readxl)=

library(dplyr)<= o:p>

library(ggplot2= )

library(= GGally)

library(= Hmisc)

library(corrplot)

library<= /span>(PerformanceAnalytics)

 

###---Variables---###

#Y1 =3D Infecciosa= s

#Y2 =3D Covid=

#Y3 =3D Otras enfe= rmedades

#G1 =3D Particulas por millón

#G2 =3D Dió= xido de azufre

#G3 =3D Dió= xido de carbono

#G4 =3D Óxi= do nitrico y Dióxido nitrógeno<= /span>

#G5 =3D Metano

#G6 =3D Óxi= do de nitrógeno

#X1 =3D Sector (1 = =3D Primerio, 2 =3D Secundario, 3 =3D Terciario)

#X2 =3D Municipio = (1 =3D ATI, 2 =3D ATO, 3 =3D TLAH, 4 =3D TLAX, 5 =3D TULA)

 

####Trasformado la base a matriz######

Y1 =3D base$Y1

Y2 =3D base$Y2

Y3 =3D base$Y3

G1 =3D log(1+(base$G= 1))

G2 =3D sqrt(1+(base$G2))

G3 =3D log(1+(base$G3))

G4 =3D sqrt(1+(base$G4))

G5 =3D log(1+(base$G5))

G6 =3D sqrt(1+(base$G6))

X1 =3D base$X1

X2 =3D base$X2

 

matriz=3Dcbind(Y1,Y2,Y3,G1,G2,G3,G4,G5,G6,X1,X2<= /span>)

matriz

 

#Matriz Correlación

chart.Correlati= on(matriz, histogram =3D F, pch =3D = 19)

 

###---ESCALAMIENTO MULTID= IMENCIONAL

library(magrittr)<= o:p>

library(dplyr)

library(ggpubr)=

 

matriz1<= /span>=3Dcbind(G1,G2,G3,G4,G5,G6)

matriz1

 

#--Estaradizando l= os datos

provMap_s=3Dscale(matriz1)

provMap_s

 

#--Creando grupos de variables

print(cor(matriz1),2)

X=3D(= as.dist(1-cor(matriz1),2))

X

par(mfcol=3Dc(1,1))

plot(hclust(as.dist(1-cor(matriz1))),main=3D"Dendogra= ma de gases")#dendograma

 D=3D (1-cor(matriz1))

D

corrplot= (D, method=3D"number", type=3D"upper= ")

 

#--Obteniendo <= span class=3DSpellE>distancias

provMap_d=3Ddist(provMap_s)

provMap_d

provMap_r=3Dcmdscale(provMap_d,eig=3DTRUE, k=3D3)

provMap_r

 

#--coordenadas

A3=3DprovMap_r$point= s

A3

 

#--Cumplimiento de supuestos=

summary(A3) #Valor esperado

C=3Dcor(A3)

corrplot= (C, cex=3D1.2 , main =3D"= ;Matriz de correlación")

V=3Dvar(A3)

V

corPlot<= /span>(V, cex =3D 1.2, main =3D &qu= ot;Matriz de varianza y covarianzas")

 

#---Nombrando las dimensiones

IGQF=3DA3[,1] #--Indicador de Gasas de Quema de Combistibles fósiles

IGPQ=3DA3[,2] #--Indicador de Gases de Producción Quimica

IGCT=3DA3[,3] #--Indicador de Gases de Concentración de C02

 

Matriz2 =3D cbind(= Y1,Y2,Y3,IGQF,IGPQ,IGCT,X1,X2)

Matriz2

 

#Matriz Correlación

chart.Correlati= on(Matriz2, histogram =3D F,= pch =3D 19)

 

# --- Modelo de infecciones: Y1 =3D f(IGQF, IGPQ, X1)

# --- Modelo de covid: Y2 =3D f(IGQF, IGPQ, X1)

# --- Modelo de otras enfermedades: Y3 =3D f(IGQF, IGPQ, X1)

 

##--CORRELACIÓN CA= NONICA

library<= /span>(CCA)=

library<= /span>(MVN)

library<= /span>(CCP)

library(candisc)

 

Matriz3 =3D cbind(= Y1,Y2,Y3,IGQF,IGPQ,X1)

Matriz3

 

#-explorando datos

head(Matriz3<= /span>)

 

#--separando los conjuntos X e Y

X =3D Matriz3[,c("IGQF","= ;IGPQ","X1"= )]

Y =3D Matriz3[,c("Y1","Y= 2","Y3")]

 

#--correlación can= onica

matcor(X,Y)$XYcor

 

#--normalidad multivariada para X

mvn(data =3D as.data.frame(X), univariateTest= =3D "SW")$univariateNormality

mvn(data =3D as.data.frame= (X), mvnTest =3D "hz"= ;)$multivariateNormality

 

#--normalidad multivariada para Y

mvn(data =3D as.data.frame(Y), univariateTest= =3D "SW")$univariateNormality

mvn(data =3D as.data.frame= (Y), mvnTest =3D "hz"= ;)$multivariateNormality

 

#--construcción de parametros

cca1 =3D cancor= (X,Y)

cca1

cca1$cor

 

# Evaluar significancia de las correlaciones canónicas

p.asym(cca1$cor, nrow(X), ncol(X), ncol(Y))

 

#--visualización de la relación = canonica

data =3D cbind(X, Y)

data

modelo =3D lm(cbind(Y1, Y2, Y3) ~ 0+IGQF+IGPQ+X1, data =3D as.data.frame(data))

summary(modelo)

candisc_result =3D candisc(modelo)

plot(candisc_result, which =3D 1)


[1] Autora de corresponde= ncia.

[2] Los coeficientes repre= sentan el cambio esperado en Y1 por unidad = de aumento en la variable correspondiente, manteniendo las otras constantes (<= span class=3DSpellE>Kutner, Nachtsheim, Neter, & Li, 2004).

[3] Los coeficientes repre= sentan el cambio esperado en Y2 por unidad de aumento en la variable correspondiente, manteniendo las otras constantes (Kut= ner, Nachtsheim, Neter, = & Li, 2004).

[4] Los coeficientes repre= sentan el cambio esperado en Y3 por unidad = de aumento en la variable correspondiente, manteniendo las otras constantes (<= span class=3DSpellE>Kutner, Nachtsheim, Neter, & Li, 2004).

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LATAM Revista Latinoamericana de Cien= cias Sociales y Humanidades, Asunción, Paraguay.

ISSN en línea: 2789-3855, octubre, 2025, Volumen VI, Número 5 p 1692.

 

 

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