{"id":171585,"date":"2025-11-21T15:41:04","date_gmt":"2025-11-21T21:41:04","guid":{"rendered":"https:\/\/tecscience.tec.mx\/en\/?post_type=sciencecommunication&p=171585"},"modified":"2025-11-26T15:43:16","modified_gmt":"2025-11-26T21:43:16","slug":"hospital-inequity","status":"publish","type":"sciencecommunication","link":"https:\/\/tecscience.tec.mx\/en\/science-communication\/hospital-inequity\/","title":{"rendered":"Hospital Inequity: When Location Determines Access to Timely Care"},"content":{"rendered":"\n

By Mar\u00eda Carolina Quintana Noriega<\/a><\/p>\n\n\n\n

Disparities in public health infrastructure across Mexico City reveal a stark reality: hospitals and clinics are unevenly distributed, disproportionately affecting residents who live on the city\u2019s outskirts. This article examines the issue and provides an analysis to understand what is happening\u2014and how these inequalities can be addressed.<\/p>\n\n\n\n

The study \u201cImplementation of Spatial Correlations and Kernel Densities to Identify Deficiencies in the Coverage of Public Health Infrastructure\u201d<\/a><\/em> analyzed the concentration patterns of active COVID-19 cases in areas with lower hospital coverage.<\/p>\n\n\n\n

The findings show that within the first urban containment perimeter\u2014the city\u2019s consolidated central zone\u201457.29% of public health facilities are located. The second perimeter holds 39.58%, while the third, on the periphery, has just 3.23%<\/p>\n\n\n\n

When these data are compared with poverty indicators, a clear correlation emerges between the placement of health facilities and areas with higher levels of urban poverty. Peripheral zones lack not only health services, but also other basic urban amenities, further worsening living conditions for their residents.<\/p>\n\n\n\n

\r\n \r\n \"\"\r\n <\/picture>
Healthcare facilities by Urban Containment Perimeter (PCU), Mexico City. About 57.29% of all units are located in the city\u2019s core, 39.58% in expanding zones, and just 3.23% on the outskirts.
<\/em><\/figcaption><\/figure>\n\n\n\n

The pandemic as a mirror of inequality<\/h3>\n\n\n\n

The analysis reveals a linear correlation between saturated hospitals and COVID-19 cases in the central and northern parts of the city. Heat maps\u2014generated according to hospital capacity\u2014highlighted critical areas of saturation and available capacity.<\/p>\n\n\n\n

\r\n \r\n \"\"\r\n <\/picture>
Urban population living in poverty by Basic Geo-Statistical Area (AGEB) and healthcare facilities located within Urban Containment Perimeters 2 and 3. Areas with higher levels of urban marginalization overlap with a lower presence of healthcare services and basic infrastructure.<\/em><\/figcaption><\/figure>\n\n\n\n

Using daily active-case data by neighborhood from the Respiratory Disease Surveillance System (SINAVE) of the Ministry of Health, researchers identified clusters of neighborhoods with a significantly higher proportion of active cases.<\/p>\n\n\n\n

Because SARS-CoV-2 spreads directly among people, an analysis based on active cases is especially relevant: it allows researchers to pinpoint the most significant clusters of urban areas experiencing infections\u2014not simply transmission events.<\/p>\n\n\n\n

This same correlation-based approach can help pinpoint priority areas for healthcare, not just during a pandemic but also for long-term urban planning of health infrastructure.
<\/p>\n\n\n\n

\r\n \r\n \"\"\r\n <\/picture>
Active cases by neighborhood as of August 15, 2020, and clustering of neighborhoods based on patterns of active cases.<\/em><\/figcaption><\/figure>\n\n\n\n

After identifying these clustering patterns, the study located public health facilities and examined the capacity of their intensive care units.<\/p>\n\n\n\n

This mapping exercise showed a linear correlation between hospitals whose ICUs were saturated and neighborhoods with high numbers of active COVID-19 cases. To evaluate the severity of this saturation, researchers created heat maps based on ICU capacity.<\/p>\n\n\n\n

Visualizing this data pinpoints areas with saturated hospital capacity, areas with available capacity, and clusters of health facilities that\u2014despite operating at critical levels\u2014handle the majority of care across the city.<\/p>\n\n\n\n

When this territorial trend is compared to the distribution of neighborhood-level active-case correlations, the two patterns appear to be almost negatively correlated.<\/p>\n\n\n\n

\r\n \r\n \"\"\r\n <\/picture>
Intensive Care Unit (ICU) capacity in healthcare facilities and weighted clustering by ICU occupancy (heat map).<\/em><\/figcaption><\/figure>\n\n\n\n

The geography behind health inequity<\/h3>\n\n\n\n

To verify this observation, researchers created density maps using the centroid\u2014the geographic center\u2014of daily active COVID-19 cases by neighborhood in Mexico City. The geospatial analysis reveals clear territorial segregation in areas with the highest concentration of cases.<\/p>\n\n\n\n

This cartographic representation shows that the reference points with the highest numbers of active infections do not form a territorial cluster; instead, they reflect segregation.<\/p>\n\n\n\n

This geographic pattern\u2014where active cases are segregated not only from areas with fewer cases but also from the city\u2019s main health-care hubs\u2014suggests a real lack of coverage and limited accessibility to health services in the affected areas.<\/p>\n\n\n\n

\r\n \r\n \"\"\r\n <\/picture>
Reference points in neighborhoods with active cases and kernel density estimates for active cases.<\/em><\/figcaption><\/figure>\n\n\n\n

A Map That Exposes Inequality<\/strong><\/h2>\n\n\n\n

The study concludes that the perceived shortage of health facilities in Mexico City\u2019s peripheral areas is not just a perception\u2014it is a measurable, verified reality.<\/p>\n\n\n\n

The methodology demonstrates the importance of integrating this type of analysis into urban planning to promote more inclusive settlements and ensure that health services are accessible to all. Policies informed by these findings could significantly improve living conditions in Latin American cities.<\/p>\n\n\n\n

It is also important to note that applying geographic information systems to urban planning makes the invisible visible\u2014and improves what can be measured.<\/p>\n\n\n\n

Ultimately, the study confirms that the perceived lack of health facilities in the city\u2019s outskirts, particularly in irregular settlement areas, stems from a real deficiency in where these facilities are located.<\/p>\n\n\n\n

\r\n \r\n \"\"\r\n <\/picture>
Cartographic representation of results (data correlation).<\/em><\/figcaption><\/figure>\n\n\n\n
Reference<\/h5>\n\n\n\n

Quintana Noriega, M.C. (2025). Implementation of Spatial Correlations and Kernel Densities to Identify Deficiencies in the Coverage of Public Health Infrastructure<\/em><\/a>. In: Mata-Rivera, M.F., Zagal-Flores, R., Elisabeth Ballari, D., Le\u00f3n-Borges, J.A. (eds) Geographical Information Systems. GIS-LATAM 2024. Communications in Computer and Information Science, vol 2298. Springer, Cham<\/p>\n\n\n\n

Author<\/strong><\/h5>\n\n\n\n

Mar\u00eda Carolina Quintana Noriega. <\/strong>Assistant research professor at the School of Architecture, Art, and Design at Tecnol\u00f3gico de Monterrey<\/a>. Her work focuses on geo-statistical analysis with an emphasis on land-use planning from a socio-spatial perspective, as well as research on poverty and socio-territorial inequalities, patterns of urban space occupation by ethno-religious groups, and the distribution of ecosystem resources across territories.

<\/p>\n\n\n\n

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