Evidence map›Paper›PMID 35055486›Full record

ArticleInternational journal of environmental research and public health2022

Territorial Strategy of Medical Units for Addressing the First Wave of the COVID-19 Pandemic in the Metropolitan Area of Mexico City: Analysis of Mobility, Accessibility and Marginalization.

Mateo Carlos Galindo-Pérez, Manuel Suárez, Ana Rosa Rosales-Tapia, José Sifuentes-Osornio, Ofelia Angulo-Guerrero, Héctor Benítez-Pérez, Guillermo de Anda-Jauregui, Juan Luis Díaz-de-León-Santiago, Enrique Hernández-Lemus, Luis Alonso Herrera and 5 more

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Mateo Carlos Galindo-PérezCentro Regional de Investigaciones Multidisciplinarias, Universidad Nacional Autónoma de México, Cuernavaca 62209, Mexico.
Manuel SuárezInstituto de Geografía, Universidad Nacional Autónoma de México, Ciudad de Mexico 04510, Mexico.ORCID 0000-0002-9441-3716
Ana Rosa Rosales-TapiaInstituto de Geografía, Universidad Nacional Autónoma de México, Ciudad de Mexico 04510, Mexico.ORCID 0000-0002-4146-9193
José Sifuentes-OsornioInstituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Ciudad de Mexico 14080, Mexico.
Ofelia Angulo-GuerreroSecretaría de Educación, Ciencia, Tecnología e Innovación, Gogobierno de la Ciudad de México, Ciudad de Mexico 06010, Mexico.
Héctor Benítez-PérezDirección General de Cómputo y de Tecnologías de Información y Comunicación, Universidad Nacional Autónoma de México, Ciudad de Mexico 04510, Mexico.ORCID 0000-0002-0355-0337
Guillermo de Anda-JaureguiDepartamento de Genómica Computacional, Instituto Nacional de Medicina Genómica, Ciudad de Mexico 14610, Mexico.
Juan Luis Díaz-de-León-SantiagoSecretaría de Educación, Ciencia, Tecnología e Innovación, Gogobierno de la Ciudad de México, Ciudad de Mexico 06010, Mexico.
Enrique Hernández-LemusDepartamento de Genómica Computacional, Instituto Nacional de Medicina Genómica, Ciudad de Mexico 14610, Mexico.ORCID 0000-0002-1872-1397
Luis Alonso HerreraInstituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Ciudad de Mexico 04510, Mexico.ORCID 0000-0003-3998-9306
Oliva López-ArellanoSecretaría de Salud de la Ciudad de México, Ciudad de Mexico 06900, Mexico.
Arturo Revuelta-HerreraSecretaría de Salud de la Ciudad de México, Ciudad de Mexico 06900, Mexico.
Rosaura Ruiz-GutiérrezSecretaría de Educación, Ciencia, Tecnología e Innovación, Gogobierno de la Ciudad de México, Ciudad de Mexico 06010, Mexico.
Claudia Sheinbaum-PardoGobierno de la Ciudad de México, Ciudad de Mexico 06000, Mexico.
David Kershenobich-StalnikowitzInstituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Ciudad de Mexico 14080, Mexico.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has caused an exponential increase in the demand for medical care worldwide. In Mexico, the COVID Medical Units (CMUs) conversion strategy was implemented.

objectiveTo evaluate the CMU coverage strategy in the Mexico City Metropolitan Area (MCMA) by territory. MATERIALS: The CMU directory was used, as were COVID-19 infection and mobility statistics and Mexican 2020 census information at the urban geographic area scale. The degree of urban marginalization by geographic area was also considered.

methodUsing descriptive statistics and the calculation of a CMU accessibility index, population aggregates were counted based on coverage radii. In addition, two regression models are proposed to explain (1) the territorial and temporal trend of COVID-19 infections in the MCMA and (2) the mobility of the COVID-infected population visiting medical units.

resultsThe findings of the evaluation of the CMU strategy were (1) in the MCMA, COVID-19 followed a pattern of contagion from the urban center to the periphery; (2) given the growth in the number of cases and the overload of medical units, the population traveled greater distances to seek medical care; (3) after the CMU strategy was evaluated at the territory level, it was found that 9 out of 10 inhabitants had a CMU located approximately 7 km away; and (4) at the metropolitan level, the lowest level of accessibility to the CMU was recorded for the population with the highest levels of marginalization, i.e., those residing in the urban periphery.

Indexed as

COVID-19CitiesHumansMexicoPandemicsSARS-CoV-2accessibilitycontagionCOVID hospitalsMexico City Metropolitan Areamobility

Identifiers

PMID35055486
PMCPMC8776096

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.