HERAS GUTIERREZ, D. De Las    CADENA VARGAS, E.. Geografía del cáncer de mama y cervicouterino en la Megalópolis de México. []. , 108, e60538.   12--2022. ISSN 2448-7279.  https://doi.org/10.14350/rig.60538.

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En este trabajo se analiza la distribución espacial de las tasas de mortalidad (TBM) por cáncer de mama (CM) y cuello de útero (CCU) en la Megalópolis del Centro de México (MCM) en 2013-2020 con técnicas de análisis espacial. Se calculó el índice de autocorrelación local de Moran para detectar la presencia de clústeres, así como el índice local bivariado de Moran para analizar la relación entre los patrones espaciales de las enfermedades y seis variables de tipo demográfico, socioeconómico, educativo y cultural. Las TBM por CM y CCU en la MCM presentan en el periodo 2013-2020 una dinámica creciente y estable, respectivamente. Se determinó una distribución espacial no aleatoria de sus TBM y unos patrones desiguales en los dos tipos de neoplasias. Se detectaron conglomerados significativos de valores altos de mortalidad, para CM en torno a los municipios centrales metropolitanos (fundamentalmente en la Ciudad de México) y conurbados, y en el caso del CCU en el sur y este de la MCM, esencialmente en municipios periféricos. Se encontró una asociación significativa positiva entre las TBM de CM y el porcentaje de mujeres mayores y con estudios superiores, y negativa con el índice de marginación, población femenina con rezago educativo y población sin acceso a servicios de salud. Para el CCU únicamente se encontró una asociación significativa positiva con el porcentaje de mujeres mayores, localmente se detectaron clústeres bivariados entre CCU y marginación en el sur de la MCM.

This paper analyzes the spatial distribution of raw mortality rates (RMR) for breast (BC) and cervical cancer (CC) in the Megalopolis of Central Mexico (MCM) in 2013-2020 using spatial analysis techniques.

MCM is a polycentric urban structure stretching across 18 841 km2 and harboring 30 754 024 inhabitants in 2020. It is composed of urban areas around Mexico City, encompassed by the geographic and functional absorption of urban and suburban territories.

The aim of the present work was to determine the spatial distribution of RMRs for BC and CC in MCM for the period 2013-2020 using exploratory spatial data analysis (ESD) techniques. We used cluster analysis and bivariate correlations between spatial patterns of mortality rates with several demographic, socioeconomic, and educational factors. The analysis was performed at the municipal level for the State of Mexico and Mexico City (alcaldìas).

We calculated Moran’s Local Autocorrelation Index (MLA) to detect clusters and Moran’s Bivariate Local Index (MBLI) to analyze the relationship between spatial patterns of the diseases and six demographic, socioeconomic, edu cational, and cultural variables.

In the period 2013-2020, the RMRs for BC and CC in the MCM showed increasing and stable dynamics, respectively. The RMRs for BC were above the national average, 11.15 per hundred thousand (national average: 10.2); those for CC were marginally below the country average, 5.9 per hundred thousand (national average 6.25). Deaths for BC increased from 1 512 in 2013 to 1 995 in 2020; for CC, deaths have remained stable since 2014 at around 950 per year.

We determined the nonrandom spatial distribution of RMRs (Moran’s indices of 0.257 and 0.126), finding uneven patterns for the two types of cancers. CM is distributed in the large metropolitan urban centers and their neighboring municipalities, especially Mexico City and capital cities such as Puebla, Cuernavaca, Pachuca, and Cuautla. On the other hand, CC is concentrated in the south and southeast of the MCM and mainly peripheral municipalities.

Applying Moran’s Bivariate Local Index between RMRs for the two types of cancer and the six variables, a signifi cant spatial correlation was obtained between 5 of the six variables with BM and only 1 of the six variables (with weak significance) for CC.

For BC, demographic aging and the proportion of wo men with higher education are associated with high RMRs. In contrast, a high marginalization level (and less signifi cantly), high percentages of the population with no access to healthcare services, and low education levels are associated with low RMRs. The cultural variable (population speaking an indigenous language) did not yield a significant score.

Mortality for BC was associated with economic, social, and educational development, particularly with higher demographic aging, education level, and lower marginali zation. In contrast, low mortality rates are related to high percentages of the population with no access to healthcare services and indigenous population. Spatially, high RMRs for BC showed a significant positive association with a high percentage of population over 60 years of age and population 18 years of age and older with post-basic education in Mexico City municipalities, and with marginalization, education lag, and population with no public healthcare services in the northeast, east, and southeast outskirts of the MCM

The MBLI yielded non-significant results for CC, except for the female population older than 60 years of age, with a low score (0.13). There is a positive correlation of RMRs for CCU with the marginalization level, education lag, and indigenous population, and a negative correlation with the percentage of women with higher education. A relationship between CC and unfavorable socioeconomic status and education level is demonstrated. There is a significant spatial association between RMRs for CC, which are low in the municipalities located north of Mexico City and in the Toluca Metropolitan Zone, and with a low percentage of women aged 60 years and older.

The study of BC and CC from a geographic perspective allows detecting areas with extreme RMRs, areas that concentrate high values, and the spatial relationship of diseases with demographic, socioeconomic, and cultural factors. This information can be used as a diagnostic tool for decision-making and for applying preventive and palliative measures.

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