tetano
Editor, Senior Moderator
JMIR Public Health Surveill
. 2023 Feb 22.
doi: 10.2196/34163. Online ahead of print.
Demographic Determinants and Geographical Variability of COVID-19 Vaccine Hesitancy in Underserved Communities
Jennifer L Matas[SUP] 1 [/SUP], Latrice G Landry[SUP] 2 3 4 [/SUP], LaTasha Lee[SUP] 5 [/SUP], Shantoy Hansel[SUP] 1 [/SUP], Makella S Coudray[SUP] 1 [/SUP], Lina V Mata-McMurry[SUP] 1 [/SUP], Nishanth Chalasani[SUP] 6 [/SUP], Liou Xu[SUP] 1 [/SUP], Taylor Stair[SUP] 1 [/SUP], Christina Edwards[SUP] 1 [/SUP], Gary Puckrein[SUP] 1 [/SUP], William Meyer[SUP] 7 [/SUP], Gary Wiltz[SUP] 8 [/SUP], Marian Sampson[SUP] 9 [/SUP], Paul Gregerson[SUP] 10 [/SUP], Charles Barron[SUP] 11 [/SUP], Jeffrey Marable[SUP] 12 [/SUP], Ola Akinboboye[SUP] 13 [/SUP], Dora Il'yasova[SUP] 14 15 [/SUP]
Affiliations
Abstract
Background: COVID-19 hospitalizations and deaths disproportionately affect underserved and minority populations, emphasizing that vaccine hesitancy can be an especially important public health risk factor in these populations.
Objective: To characterize COVID-19 vaccine hesitancy in underserved diverse populations.
Methods: The Minority and Rural Coronavirus Insights Study recruited a convenience sample of adults (ages ≥18, n=3,735) from Federally Qualified Health Centers in California, Midwest (Illinois/Ohio), Florida and Louisiana and collected baseline data in November 2020-April 2021. Vaccine hesitancy status was defined as responses "no" or "undecided" to the question "Would you get a coronavirus vaccine, if it was available?" ("yes" categorized as not hesitant). The cross-sectional descriptive analyses and logistic regression models examined vaccine hesitancy prevalence by age, gender, race/ethnicity, and geography. The expected vaccine hesitancy estimates for the general population were calculated for the study counties using published county-level data. Crude associations with demographic characteristics within each region were assessed by the chi-squared test. The main effect model included age, gender, race/ethnicity, and geographical region to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Interactions between geography and each demographic characteristic were evaluated in separate models.
Results: The strongest vaccine hesitancy variability was by geographic region: in California 28.3% (26.5-31.1), the Midwest 36.1 % (32.1-40.2), Louisiana 59.1% (56.0-62.1), Florida 67.9% (65.0-70.8). The expected estimates for the general population were lower: 9.7% (California), 15.2 % (Midwest), 18.2% (Florida), and 27.0% (Louisiana). The demographic patterns also varied by geography. An inverted U-shape age pattern was found, with the highest prevalence among ages 25-34 in the Midwest (39.3%), Florida (79.5%,) and Louisiana (79.4%) (p <0.05). Females were more hesitant than males in the Midwest (36.5% vs 23.9%), Florida (71.6% vs 59.4%), and Louisiana (66.5% vs. 46.4%) (p<0.05). Racial/ethnic differences were found in California with the highest prevalence among non-Hispanic Black (45.8%) and in Florida with the highest among Hispanic (69.3%) participants (p<0.05) but not in the Midwest and Louisiana. The main effect model confirmed the U-shape association with age: strongest association with age 25-34, OR=2.28 (1.74, 2.99). Statistical interactions of gender and race/ethnicity with the region were significant, following the pattern found by the crude analysis. The associations with the female gender were strongest in Florida and Louisiana: ORs were 7.83 (5.94, 10.33) and 6.04 (4.52, 8.06) compared to males in California, respectively. Compared to non-Hispanic White participants in California, the strongest associations were found with being Hispanic in Florida and Black in Louisiana: ORs were 11.18 (7.01, 17.85) and 8.94 (5.53, 14.47), respectively. However, the strongest race/ethnicity variability was observed within California and Florida: ORs varied 4.7- and 2-fold between racial/ethnic groups in these regions, respectively.
Conclusions: These findings highlight the role of local contextual factors in driving vaccine hesitancy and its demographic patterns.
. 2023 Feb 22.
doi: 10.2196/34163. Online ahead of print.
Demographic Determinants and Geographical Variability of COVID-19 Vaccine Hesitancy in Underserved Communities
Jennifer L Matas[SUP] 1 [/SUP], Latrice G Landry[SUP] 2 3 4 [/SUP], LaTasha Lee[SUP] 5 [/SUP], Shantoy Hansel[SUP] 1 [/SUP], Makella S Coudray[SUP] 1 [/SUP], Lina V Mata-McMurry[SUP] 1 [/SUP], Nishanth Chalasani[SUP] 6 [/SUP], Liou Xu[SUP] 1 [/SUP], Taylor Stair[SUP] 1 [/SUP], Christina Edwards[SUP] 1 [/SUP], Gary Puckrein[SUP] 1 [/SUP], William Meyer[SUP] 7 [/SUP], Gary Wiltz[SUP] 8 [/SUP], Marian Sampson[SUP] 9 [/SUP], Paul Gregerson[SUP] 10 [/SUP], Charles Barron[SUP] 11 [/SUP], Jeffrey Marable[SUP] 12 [/SUP], Ola Akinboboye[SUP] 13 [/SUP], Dora Il'yasova[SUP] 14 15 [/SUP]
Affiliations
- PMID: 36811869
- DOI: 10.2196/34163
Abstract
Background: COVID-19 hospitalizations and deaths disproportionately affect underserved and minority populations, emphasizing that vaccine hesitancy can be an especially important public health risk factor in these populations.
Objective: To characterize COVID-19 vaccine hesitancy in underserved diverse populations.
Methods: The Minority and Rural Coronavirus Insights Study recruited a convenience sample of adults (ages ≥18, n=3,735) from Federally Qualified Health Centers in California, Midwest (Illinois/Ohio), Florida and Louisiana and collected baseline data in November 2020-April 2021. Vaccine hesitancy status was defined as responses "no" or "undecided" to the question "Would you get a coronavirus vaccine, if it was available?" ("yes" categorized as not hesitant). The cross-sectional descriptive analyses and logistic regression models examined vaccine hesitancy prevalence by age, gender, race/ethnicity, and geography. The expected vaccine hesitancy estimates for the general population were calculated for the study counties using published county-level data. Crude associations with demographic characteristics within each region were assessed by the chi-squared test. The main effect model included age, gender, race/ethnicity, and geographical region to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Interactions between geography and each demographic characteristic were evaluated in separate models.
Results: The strongest vaccine hesitancy variability was by geographic region: in California 28.3% (26.5-31.1), the Midwest 36.1 % (32.1-40.2), Louisiana 59.1% (56.0-62.1), Florida 67.9% (65.0-70.8). The expected estimates for the general population were lower: 9.7% (California), 15.2 % (Midwest), 18.2% (Florida), and 27.0% (Louisiana). The demographic patterns also varied by geography. An inverted U-shape age pattern was found, with the highest prevalence among ages 25-34 in the Midwest (39.3%), Florida (79.5%,) and Louisiana (79.4%) (p <0.05). Females were more hesitant than males in the Midwest (36.5% vs 23.9%), Florida (71.6% vs 59.4%), and Louisiana (66.5% vs. 46.4%) (p<0.05). Racial/ethnic differences were found in California with the highest prevalence among non-Hispanic Black (45.8%) and in Florida with the highest among Hispanic (69.3%) participants (p<0.05) but not in the Midwest and Louisiana. The main effect model confirmed the U-shape association with age: strongest association with age 25-34, OR=2.28 (1.74, 2.99). Statistical interactions of gender and race/ethnicity with the region were significant, following the pattern found by the crude analysis. The associations with the female gender were strongest in Florida and Louisiana: ORs were 7.83 (5.94, 10.33) and 6.04 (4.52, 8.06) compared to males in California, respectively. Compared to non-Hispanic White participants in California, the strongest associations were found with being Hispanic in Florida and Black in Louisiana: ORs were 11.18 (7.01, 17.85) and 8.94 (5.53, 14.47), respectively. However, the strongest race/ethnicity variability was observed within California and Florida: ORs varied 4.7- and 2-fold between racial/ethnic groups in these regions, respectively.
Conclusions: These findings highlight the role of local contextual factors in driving vaccine hesitancy and its demographic patterns.