Spatial epidemiological study of the distribution, clustering, and risk factors associated with early COVID-19 mortality in Mexico

  1. Ricardo Ramírez-Aldana
  2. Juan Carlos Gomez-Verjan
  3. Omar Yaxmehen Bello-Chavolla
  4. Carmen García-Peña

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Abstract

COVID-19 is a respiratory disease caused by SARS-CoV-2, which has significantly impacted economic and public healthcare systems worldwide. SARS-CoV-2 is highly lethal in older adults (>65 years old) and in cases with underlying medical conditions, including chronic respiratory diseases, immunosuppression, and cardio-metabolic diseases, including severe obesity, diabetes, and hypertension. The course of the COVID-19 pandemic in Mexico has led to many fatal cases in younger patients attributable to cardio-metabolic conditions. Thus, in the present study, we aimed to perform an early spatial epidemiological analysis for the COVID-19 outbreak in Mexico. Firstly, to evaluate how mortality risk from COVID-19 among tested individuals (MRt) is geographically distributed and secondly, to analyze the association of spatial predictors of MRt across different states in Mexico, controlling for the severity of the disease. Among health-related variables, diabetes and obesity were positively associated with COVID-19 fatality. When analyzing Mexico as a whole, we identified that both the percentages of external and internal migration had positive associations with early COVID-19 mortality risk with external migration having the second-highest positive association. As an indirect measure of urbanicity, population density, and overcrowding in households, the physicians-to-population ratio has the highest positive association with MRt. In contrast, the percentage of individuals in the age group between 10 to 39 years had a negative association with MRt. Geographically, Quintana Roo, Baja California, Chihuahua, and Tabasco (until April 2020) had higher MRt and standardized mortality ratios, suggesting that risks in these states were above what was nationally expected. Additionally, the strength of the association between some spatial predictors and the COVID-19 fatality risk varied by zone.

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  1. Version published to 10.1371/journal.pone.0254884
  2. ScreenIT

    SciScore for 10.1101/2020.11.26.20239376: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    COVID-19 t-CFRs estimation by state: We obtained quantile maps associated with raw and smoothed t-CFRs of COVID-19 cases.
    We
    suggested: None
    Software and Algorithms
    SentencesResources
    The risk of dying in tested individuals (t-CFRs) was chosen as the dependent variable on the basis of relevant indicators of COVID-19 epidemiology in Mexico; notably, the number of tests per 100,00 individuals is limited compared to other countries, which decreases detection rates and given the likely under-detection of mild SARS-CoV-2 cases in this setting, standardizing deaths by tested cases considers the extent of detection, which could similarly be influenced by structural factors (17).
    Mexico
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Despite these limitations, we were able to identify some spatial predictors of fatality risks associated with COVID-19 at an early stage of the pandemic, likely reflecting factors which could have been addressed to mitigate SARS-CoV-2 spread. In conclusion, metabolic diseases, internal and external migration, physicians-to-population ratio, GDP per capita in states without the biggest cities, and age group between 10 to 39 years old were significantly associated with early COVID-19 fatality risks in Mexico. These predictors likely influence the growth of the pandemic moving forward, but variables as prevalence of metabolic diseases cannot be easily modified in the short-term. However, the identification of important variables in Mexico associated with the risks and in specific geographical areas, could help to decide necessary public policies which could have long-term impacts on future epidemic scenarios. Even though, this is an analysis in an early stage of SARS-CoV-2 spread, it allows us to understand how the pandemic evolved within Mexico and the possible measures that should be addressed for additional waves or similar diseases in Mexico and in specific zones of the country.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  3. Version published to 10.1101/2020.11.26.20239376v1 on medRxiv