Comprehensive Evaluation of the Impact of Sociodemographic Inequalities on Adverse Outcomes and Excess Mortality During the Coronavirus Disease 2019 (COVID-19) Pandemic in Mexico City
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Abstract
Background
The impact of the coronavirus disease 2019 (COVID-19) pandemic in Mexico City has been sharp, as several social inequalities at all levels coexist. Here we conducted an in-depth evaluation of the impact of individual and municipal-level social inequalities on the COVID-19 pandemic in Mexico City.
Methods
We analyzed suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases, from the Mexico City Epidemiological Surveillance System from 24 February 2020 to 31 March 2021. COVID-19 outcomes included rates of hospitalization, severe COVID-19, invasive mechanical ventilation, and mortality. We evaluated socioeconomic occupation as an individual risk, and social lag, which captures municipal-level social vulnerability, and urban population density as proxies of structural risk factors. Impact of reductions in vehicular mobility on COVID-19 rates and the influence of risk factors were also assessed. Finally, we assessed discrepancies in COVID-19 and non-COVID-19 excess mortality using death certificates from the general civil registry.
Results
We detected vulnerable groups who belonged to economically unfavored sectors and experienced increased risk of COVID-19 outcomes. Cases living in marginalized municipalities with high population density experienced greater risk for COVID-19 outcomes. Additionally, policies to reduce vehicular mobility had differential impacts modified by social lag and urban population density. Finally, we report an under-registry of COVID-19 deaths along with an excess mortality closely related to marginalized and densely populated communities in an ambulatory setting. This could be attributable to a negative impact of modified hospital admission criteria during the pandemic.
Conclusions
Socioeconomic occupation and municipality-wide factors played a significant role in shaping the course of the COVID-19 pandemic in Mexico City.
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SciScore for 10.1101/2021.03.11.21253402: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Nevertheless, some limitations should be acknowledged. All the estimations made in our population analyses were based on projections of the economic population of the first quarter of 2020 and may not capture the dynamics around employment occupation and changes in population structure within the study period. Furthermore, …
SciScore for 10.1101/2021.03.11.21253402: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Nevertheless, some limitations should be acknowledged. All the estimations made in our population analyses were based on projections of the economic population of the first quarter of 2020 and may not capture the dynamics around employment occupation and changes in population structure within the study period. Furthermore, since there are no data on the demographic structure of the working categories, we were unable to perform age-adjustment for this section of the analysis. Second, our categorization by MUPD/DISLI categories was based solely on Mexico City, which may not represent the country’s socio-economic disparities or heterogeneity within smaller communities or individual socioeconomic levels. Third, the NESS may not fully capture patients with mild and asymptomatic SARS-CoV-2 infection, which represents a bias towards cases with moderate and high risk for COVID-19 complications. Finally, excess mortality data does not report disaggregated etiology of non-COVID-19 deaths, which prevents further assessment of areas which may have required further attention during the pandemic to prevent excess deaths. In summary, individual- and municipal-level socioeconomic and structural factors played a significant role in shaping the course of the COVID-19 pandemic in Mexico City. Notably, despite higher incidence of SARS-CoV-2 infection in economically active workers, a higher burden of adverse COVID-19 outcomes was observed in non-specified workers, retired adults, home related, a...
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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