tetano
Editor, Senior Moderator
BMC Public Health
. 2022 Feb 15;22(1):321.
doi: 10.1186/s12889-022-12716-w.
Geographic disparities and predictors of COVID-19 hospitalization risks in the St. Louis Area, Missouri (USA)
Morganne Igoe[SUP] 1 [/SUP], Praachi Das[SUP] 2 [/SUP], Suzanne Lenhart[SUP] 1 [/SUP], Alun L Lloyd[SUP] 2 [/SUP], Lan Luong[SUP] 3 [/SUP], Dajun Tian[SUP] 3 [/SUP], Cristina Lanzas[SUP] 4 [/SUP], Agricola Odoi[SUP] 5 [/SUP]
Affiliations
Abstract
Background: There is evidence of geographic disparities in COVID-19 hospitalization risks that, if identified, could guide control efforts. Therefore, the objective of this study was to investigate Zip Code Tabulation Area (ZCTA)-level geographic disparities and identify predictors of COVID-19 hospitalization risks in the St. Louis area.
Methods: Hospitalization data for COVID-19 and several chronic diseases were obtained from the Missouri Hospital Association. ZCTA-level data on socioeconomic and demographic factors were obtained from the American Community Survey. Geographic disparities in distribution of COVID-19 age-adjusted hospitalization risks, socioeconomic and demographic factors as well as chronic disease risks were investigated using choropleth maps. Predictors of ZCTA-level COVID-19 hospitalization risks were investigated using global negative binomial and local geographically weighted negative binomial models.
Results: COVID-19 hospitalization risks were significantly higher in ZCTAs with high diabetes hospitalization risks (p < 0.0001), COVID-19 risks (p < 0.0001), black population (p = 0.0416), and populations with some college education (p = 0.0005). The associations between COVID-19 hospitalization risks and the first three predictors varied by geographic location.
Conclusions: There is evidence of geographic disparities in COVID-19 hospitalization risks that are driven by differences in socioeconomic, demographic and health-related factors. The impacts of these factors vary by geographical location implying that a 'one-size-fits-all' approach may not be appropriate for management and control. Using both global and local models leads to a better understanding of geographic disparities. These findings are useful for informing health planning to identify geographic areas likely to have high numbers of individuals needing hospitalization as well as guiding vaccination efforts.
Keywords: COVID-19; Coronavirus Disease 2019; Disparities; Hospitalization Risks; Epidemiology; Geographically Weighted Regression Models; Missouri; Negative Binomial Models; Predictors; SARS-CoV-2; Severe Acute Respiratory Syndrome Coronavirus 2.
. 2022 Feb 15;22(1):321.
doi: 10.1186/s12889-022-12716-w.
Geographic disparities and predictors of COVID-19 hospitalization risks in the St. Louis Area, Missouri (USA)
Morganne Igoe[SUP] 1 [/SUP], Praachi Das[SUP] 2 [/SUP], Suzanne Lenhart[SUP] 1 [/SUP], Alun L Lloyd[SUP] 2 [/SUP], Lan Luong[SUP] 3 [/SUP], Dajun Tian[SUP] 3 [/SUP], Cristina Lanzas[SUP] 4 [/SUP], Agricola Odoi[SUP] 5 [/SUP]
Affiliations
- PMID: 35168588
- DOI: 10.1186/s12889-022-12716-w
Abstract
Background: There is evidence of geographic disparities in COVID-19 hospitalization risks that, if identified, could guide control efforts. Therefore, the objective of this study was to investigate Zip Code Tabulation Area (ZCTA)-level geographic disparities and identify predictors of COVID-19 hospitalization risks in the St. Louis area.
Methods: Hospitalization data for COVID-19 and several chronic diseases were obtained from the Missouri Hospital Association. ZCTA-level data on socioeconomic and demographic factors were obtained from the American Community Survey. Geographic disparities in distribution of COVID-19 age-adjusted hospitalization risks, socioeconomic and demographic factors as well as chronic disease risks were investigated using choropleth maps. Predictors of ZCTA-level COVID-19 hospitalization risks were investigated using global negative binomial and local geographically weighted negative binomial models.
Results: COVID-19 hospitalization risks were significantly higher in ZCTAs with high diabetes hospitalization risks (p < 0.0001), COVID-19 risks (p < 0.0001), black population (p = 0.0416), and populations with some college education (p = 0.0005). The associations between COVID-19 hospitalization risks and the first three predictors varied by geographic location.
Conclusions: There is evidence of geographic disparities in COVID-19 hospitalization risks that are driven by differences in socioeconomic, demographic and health-related factors. The impacts of these factors vary by geographical location implying that a 'one-size-fits-all' approach may not be appropriate for management and control. Using both global and local models leads to a better understanding of geographic disparities. These findings are useful for informing health planning to identify geographic areas likely to have high numbers of individuals needing hospitalization as well as guiding vaccination efforts.
Keywords: COVID-19; Coronavirus Disease 2019; Disparities; Hospitalization Risks; Epidemiology; Geographically Weighted Regression Models; Missouri; Negative Binomial Models; Predictors; SARS-CoV-2; Severe Acute Respiratory Syndrome Coronavirus 2.