Municipality-Level Predictors of COVID-19 Mortality in Mexico: A Cautionary Tale

  1. Alejandra Contreras-Manzano
  2. Carlos M. Guerrero-López
  3. Mercedes Aguerrebere
  4. Ana Cristina Sedas
  5. Héctor Lamadrid-Figueroa

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Abstract

Objective:

Local characteristics of populations have been associated with coronavirus disease 2019 (COVID-19) outcomes. We analyze the municipality-level factors associated with a high COVID-19 mortality rate (MR) of in Mexico.

Methods:

We retrieved information from cumulative confirmed symptomatic cases and deaths from COVID-19 as of June 20, 2020, and data from most recent census and surveys of Mexico. A negative binomial regression model was adjusted, the dependent variable was the number of COVID-19 deaths, and the independent variables were the quintiles of the distribution of sociodemographic and health characteristics among the 2457 municipalities of Mexico.

Results:

Factors associated with high MRs from COVID-19, relative to quintile 1, were diabetes and obesity prevalence, diabetes mortality rate, indigenous population, economically active population, density of economic units that operate essential activities, and population density. Among factors inversely associated with lower MRs from COVID-19 were high hypertension prevalence and houses without sewage drainage. We identified 1351 municipalities without confirmed COVID-19 deaths, of which, 202 had high and 82 very high expected COVID-19 mortality (mean = 8 and 13.8 deaths per 100,000, respectively).

Conclusion:

This study identified municipalities of Mexico that could lead to a high mortality scenario later in the epidemic and warns against premature easing of mobility restrictions and to reinforce strategies of prevention and control of outbreaks in communities vulnerable to COVID-19.

Article activity feed

  1. Version published to 10.1017/dmp.2020.485
  2. ScreenIT

    SciScore for 10.1101/2020.07.11.20151522: (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

    Software and Algorithms
    SentencesResources
    The analysis and maps were developed using the statistical program STATA 14
    STATA
    suggested: (Stata, RRID:SCR_012763)
    College Station, TX: StataCorp LP), findings at p<0.05 were considered significant.
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    As per the limitations within our study, que precision of the estimations depends on the quality of the databases for example; the aging of our data for some variables is up to 10 years, which may not reflect heterogeneous changes within municipalities by 2020. Nevertheless, data from previous years, still holds relation to the following years and they were useful to find expected associations. For obesity, diabetes and hypertension prevalence, the information from the ENSANUT-2018 are representatively at State level reducing the variability within Municipalities. However, for the rest of the variables, such as mortality rates of diabetes and hypertension, information was disaggregated at the municipality. Finally, the ecological design of our study prevented us to from establishing causal associations. To our knowledge this is the first study that estimated mortality rate of COVID-19 by using the burden of related comorbidities and sociodemographic characteristics, in order to identify municipalities at risk of high mortality rates of coronavirus in Mexico. Our findings could contribute to the national strategic preparedness and response plans towards a “new normality”30 by informing at a Municipality-level level factors ought to consider in the decision-making process and public health interventions to minimize the negative impact of COVID-19 on the health and livelihoods of the most at-risk communities. Based on our results, we considered this is a good moment to modify th...

    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 10.1101/2020.07.11.20151522v1 on medRxiv