PREreviews of “Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee” | PREreview

Skip to preprint details Skip to PREreviews

Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee

by Lukasz S. Wylezinski, Coleman R. Harris, Cody N. Heiser, Jamieson D. Gray, and Charles F. Spurlock III

Posted: July 30, 2021
Server: medRxiv
DOI: 10.1101/2021.07.28.21260814

Abstract

The SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the United States, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities.

We combined county-level COVID-19 testing data, COVID-19 vaccination rates, and SDOH information in Tennessee. Between February-May 2021, we trained machine learning models on a semi-monthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance.

Our results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race, and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased.

Incorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policymakers with additional data resources to improve health equity and resilience to future public health emergencies.

Read the preprint

7 Rapid PREreviews

by Robin Elise Champieux, Lime Crow, Cole Brashaw, Yellowgreen Otter, Erfan Pourshahri, Lime Crow, and Daniela Saderi

Where can I fill out a Rapid PREreview?

We’ve recently replaced Rapid PREreviews with Structured PREreviews.

You can write a Structured PREreview now.

We’ll be updating older Rapid PREreviews like these soon.

Aggregated Rapid PREreviews
Question	Yes	Unsure	N/A	No
Are the findings novel?	86%	14%	0%	0%
Are the results likely to lead to future research?	100%	0%	0%	0%
Is sufficient detail provided to allow reproduction of the study?	0%	0%	0%	100%
Are the methods and statistics appropriate for the analysis?	29%	57%	0%	14%
Are the principal conclusions supported by the data and analysis?	86%	14%	0%	0%
Does the manuscript discuss limitations?	14%	0%	0%	86%
Have the authors adequately discussed ethical concerns?	29%	0%	0%	71%
Does the manuscript include new data?	29%	0%	0%	71%
Are the data used in the manuscript available?	57%	29%	14%	0%
Is the code used in the manuscript available?	0%	14%	0%	86%
Would you recommend this manuscript to others?	86%	0%	14%	0%
Do you recommend this manuscript for peer review?	100%	0%	0%	0%

1 PREreview

Write a PREreview Request a PREreview

PREreview by Daniela Saderi et al.

Authored by Daniela Saderi, Katrina Murphy, Leire Abalde-Atristain, Cole Brashaw, Robin Elise Champieux, and Yellowgreen Otter

This review is the result of a virtual, live-streamed preprint journal club organized and hosted by PREreview and OHSU’s BioData Club. The discussion was joined by 9 people, including OHSU researchers and the event organizing team.
Wylezinski et al. investigated the impact of clinical and social …

Read the PREreview by Daniela Saderi et al.