Dataset Information

Multiscale Dimensions of Spatial Process: COVID-19 Fully Vaccinated Rates in U.S. Counties

ABSTRACT:

Introduction

To examine the heterogeneity of the associations between social determinants and COVID-19 fully vaccinated rate.

Methods

This study proposes three multiscale dimensions of spatial process, including “level of influence” (the percentage of population affected by a certain determinant across the entire area), “scalability” (the spatial process of a determinant into global, regional, and local process), and “specificity” (the determinant that has the strongest association with the fully vaccinated rate). The multiscale geographically weighted regression was applied to the COVID-19 fully vaccinated rates in U.S. counties (N=3,106) as of October 26, 2021 and the analyses were conducted in May 2022.

Results

The results suggest that: (1) percent of Republican votes in 2020 presidential election is a primary influencer as 84% of the U.S. population lived in counties where this determinant is found the most dominant; (2) demographic compositions (e.g., percentages of racial/ethnic minorities) play a larger role than socioeconomic conditions (e.g., unemployment) in shaping fully vaccinated rates; (3) the spatial process underlying fully vaccinated rates is largely local.

Conclusions

The findings challenge the one-size-fits-all approach to designing interventions promoting COVID-19 vaccination and highlight the importance of a place-based perspective in ecological health research.

SUBMITTER: Yang T

PROVIDER: S-EPMC9259504 | biostudies-literature |

REPOSITORIES: biostudies-literature

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