Hui Wang1, Ran Xu2, Shijin Qu3, Marlene Schwartz4, Aaron Adams5, Xiang Chen6. 1. Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, ID 83844, USA. Electronic address: huiwang@uidaho.edu. 2. Department of Allied Health Sciences, University of Connecticut, Storrs, CT 06269, USA; Institute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut, Storrs, CT 06269, USA. Electronic address: ran.2.xu@uconn.edu. 3. Department of Land Resources Management, China University of Geosciences, Wuhan, Hubei 430074, China. Electronic address: qusj@cug.edu.cn. 4. Department of Human Development and Family Sciences and Rudd Center for Food Policy & Obesity, University of Connecticut, Hartford, CT 06103, USA. Electronic address: marlene.schwartz@uconn.edu. 5. Department of Geography, University of Connecticut, Storrs, CT 06269, USA. Electronic address: aaron.adams@uconn.edu. 6. Institute for Collaboration on Health, Intervention, and Policy (InCHIP), University of Connecticut, Storrs, CT 06269, USA; Department of Geography, University of Connecticut, Storrs, CT 06269, USA. Electronic address: xiang.chen@uconn.edu.
Abstract
BACKGROUND: In the United States, distribution plans for the COVID-19 vaccination were established at the state level. However, some states, such as Connecticut, followed an age-based strategy without considering occupations or co-morbid conditions due to its simplicity in implementation. This strategy raised concerns about exacerbating health inequities because it did not prioritize vulnerable communities, specifically, minorities and low-income groups. The study aims to examine the vaccination inequities among different population groups for people aged 65+. METHODS: A cross-sectional analysis of quantile-based independent sample t-test was employed to examine the relationship between eight social vulnerability indices (SVIs, i.e., below poverty, unemployed, without high school diploma, disability, minority, speaks English less than well, no vehicle, and mobile homes) and vaccination rates at the town level in Connecticut during the second phase of the vaccine distribution plan when individuals aged 65 and over were eligible. Negative binomial regressions were employed to further justify the relationships between SVIs and vaccination rates. RESULTS: The report shows that the differences in vaccination rates were statistically significant between the most vulnerable and the least vulnerable towns with respect to six SVIs (i.e., below poverty, without high school diploma, disability, minority, speaks English less than well, and no vehicle). The vaccination gap was greater for people aged 75+ than people aged 65-74. Among the selected SVIs, below poverty was negatively correlated with the vaccination rate for 75+, and without high school diploma was negatively correlated with both rates. CONCLUSIONS: This report reveals the significant health inequities in COVID-19 vaccination among the elderly population at the early vaccination phase. It can shed insights into health policy initiatives to improve vaccination coverage in the elderly communities, such as promoting onsite scheduling and increasing at-home vaccination services.
BACKGROUND: In the United States, distribution plans for the COVID-19 vaccination were established at the state level. However, some states, such as Connecticut, followed an age-based strategy without considering occupations or co-morbid conditions due to its simplicity in implementation. This strategy raised concerns about exacerbating health inequities because it did not prioritize vulnerable communities, specifically, minorities and low-income groups. The study aims to examine the vaccination inequities among different population groups for people aged 65+. METHODS: A cross-sectional analysis of quantile-based independent sample t-test was employed to examine the relationship between eight social vulnerability indices (SVIs, i.e., below poverty, unemployed, without high school diploma, disability, minority, speaks English less than well, no vehicle, and mobile homes) and vaccination rates at the town level in Connecticut during the second phase of the vaccine distribution plan when individuals aged 65 and over were eligible. Negative binomial regressions were employed to further justify the relationships between SVIs and vaccination rates. RESULTS: The report shows that the differences in vaccination rates were statistically significant between the most vulnerable and the least vulnerable towns with respect to six SVIs (i.e., below poverty, without high school diploma, disability, minority, speaks English less than well, and no vehicle). The vaccination gap was greater for people aged 75+ than people aged 65-74. Among the selected SVIs, below poverty was negatively correlated with the vaccination rate for 75+, and without high school diploma was negatively correlated with both rates. CONCLUSIONS: This report reveals the significant health inequities in COVID-19 vaccination among the elderly population at the early vaccination phase. It can shed insights into health policy initiatives to improve vaccination coverage in the elderly communities, such as promoting onsite scheduling and increasing at-home vaccination services.