Geographic disparities and determinants of COVID-19 incidence risk in the greater St. Louis Area, Missouri (United States)
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Abstract
Evidence seems to suggest that the risk of Coronavirus Disease 2019 (COVID-19) might vary across communities due to differences in population characteristics and movement patterns. However, little is known about these differences in the greater St Louis Area of Missouri and yet this information is useful for targeting control efforts. Therefore, the objectives of this study were to investigate (a) geographic disparities of COVID-19 risk and (b) associations between COVID-19 risk and socioeconomic, demographic, movement and chronic disease factors in the Greater St. Louis Area of Missouri, USA.
Methods
Data on COVID-19 incidence and chronic disease hospitalizations were obtained from the Department of Health and Missouri Hospital Association, respectively. Socioeconomic and demographic data were obtained from the 2018 American Community Survey while population mobility data were obtained from the SafeGraph website. Choropleth maps were used to identify geographic disparities of COVID-19 risk and several sociodemographic and chronic disease factors at the ZIP Code Tabulation Area (ZCTA) spatial scale. Global negative binomial and local geographically weighted negative binomial models were used to investigate associations between ZCTA-level COVID-19 risk and socioeconomic, demographic and chronic disease factors.
Results
There were geographic disparities found in COVID-19 risk. Risks tended to be higher in ZCTAs with high percentages of the population with a bachelor’s degree (p<0.0001) and obesity hospitalizations (p<0.0001). Conversely, risks tended to be lower in ZCTAs with high percentages of the population working in agriculture (p<0.0001). However, the association between agricultural occupation and COVID-19 risk was modified by per capita between ZCTA visits. Areas that had both high per capita between ZCTA visits and high percentages of the population employed in agriculture had high COVID-19 risks. The strength of association between agricultural occupation and COVID-19 risk varied by geographic location.
Conclusions
Geographic disparities of COVID-19 risk exist in the St. Louis area and are associated with sociodemographic factors, population movements, and obesity hospitalization risks. The latter is particularly concerning due to the growing prevalence of obesity and the known immunological impairments among obese individuals. Therefore, future studies need to focus on improving our understanding of the relationships between COVID-19 vaccination efficacy, obesity and waning of immunity among obese individuals so as to better guide vaccination regimens and reduce disparities.
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SciScore for 10.1101/2021.12.18.21268027: (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 Sentences Resources Descriptive Analysis and identification of geographic disparities in COVID-19 Risk: Descriptive analyses were performed in R version 4.1.0 [17] and implemented in RStudio version 1.4.1103 [18]. RStudiosuggested: (RStudio, RRID:SCR_000432)Goodness-of-fit of the final multivariable negative binomial model was assessed using Pearson and Deviance chi-square goodness-of-fit tests in STATA 16.1 [18]. STATAsuggested: (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 …
SciScore for 10.1101/2021.12.18.21268027: (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 Sentences Resources Descriptive Analysis and identification of geographic disparities in COVID-19 Risk: Descriptive analyses were performed in R version 4.1.0 [17] and implemented in RStudio version 1.4.1103 [18]. RStudiosuggested: (RStudio, RRID:SCR_000432)Goodness-of-fit of the final multivariable negative binomial model was assessed using Pearson and Deviance chi-square goodness-of-fit tests in STATA 16.1 [18]. STATAsuggested: (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:Strengths and limitations: There is currently lack of standardized data collection approaches across ZCTAs potentially resulting in differences in case ascertainment across ZCTAs. Moreover, hospitalization data can be influenced by differences in access to healthcare and may not always be representative of the population at large. A limitation of the safe SafeGraph data is that it may not be representative of all the segments of the population. For instance, it uses data collected by applications on only smartphones and thus does not include data on individuals who do not use smartphones. Some smartphone users may also opt-out from the service that collects mobility data and hence their data may not be collected. Additionally, for privacy reasons, SafeGraph data from ZCTAs with less than 5 devices were not included in the analysis. Therefore, SafeGraph data may not be representative of the true population mobility and hence the findings should be interpreted with this limitation in mind. The primary strength of this study is that it used both global and local models to identify predictors of COVID-19 risk. This approach allows identification of geographically varying regression coefficients and better descriptions of the associations between predictors and outcomes so as to better guide geographically targeted local control and prevention efforts. Another strength of the study is the use of SafeGraph population mobility data. The relevance of population movements to the COVID...
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.
Results from scite Reference Check: We found no unreliable references.
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