Where you live matters: a spatial analysis of COVID-19 mortality

01/11/2021
by   Behzad Javaheri, et al.
17

The COVID-19 pandemic has caused   2 million fatalities. Significant progress has been made in advancing our understanding of the disease process, one of the unanswered questions, however, is the anomaly in the case/mortality ratio with Mexico as a clear example. Herein, this anomaly is explored by spatial analysis and whether mortality varies locally according to local factors. To address this, hexagonal cartogram maps (hexbin) used to spatially map COVID-19 mortality and visualise association with patient-level data on demographics and pre-existing health conditions. This was further interrogated at local Mexico City level by choropleth mapping. Our data show that the use of hexagonal cartograms is a better approach for spatial mapping of COVID-19 data in Mexico as it addresses bias in area size and population. We report sex/age-related spatial relationship with mortality amongst the Mexican states and a trend between health conditions and mortality at the state level. Within Mexico City, there is a clear south, north divide with higher mortality in the northern municipalities. Deceased patients in these northern municipalities have the highest pre-existing health conditions. Taken together, this study provides an improved presentation of COVID-19 mapping in Mexico and demonstrates spatial divergence of the mortality in Mexico.

READ FULL TEXT

page 4

page 5

page 6

research
12/21/2020

The COVID-19 pandemic: socioeconomic and health disparities

Disadvantaged groups around the world have suffered and endured higher m...
research
06/25/2021

On assessing excess mortality in Germany during the COVID-19 pandemic

Coronavirus disease 2019 (COVID-19) is associated with a very high numbe...
research
03/20/2023

A Two-stage Bayesian Model for Assessing the Geography of Racialized Economic Segregation and Premature Mortality Across US Counties

Racialized economic segregation, a key metric that simultaneously accoun...
research
02/16/2023

Using Explainable AI to Cross-Validate Socio-economic Disparities Among Covid-19 Patient Mortality

This paper applies eXplainable Artificial Intelligence (XAI) methods to ...
research
04/11/2020

Bayesian modelling for spatially misaligned health areal data: a multiple membership approach

Diabetes prevalence is on the rise in the UK, and for public health stra...
research
09/09/2022

Impacts of Census Differential Privacy for Small-Area Disease Mapping to Monitor Health Inequities

US Census Bureau (USCB) has implemented a new privacy-preserving disclos...

Please sign up or login with your details

Forgot password? Click here to reset