COVID-19 prevention is shaped by polysocial risk: A cross-sectional study of vaccination and testing disparities in underserved populations.
Understanding disparities in COVID-19 preventive efforts among underserved populations requires a holistic approach that considers multiple social determinants of health (SDOH). While disparities in individual COVID-19 risk factors are well-documented, the cumulative impact of these factors on vacci...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0328779 |
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| author | David R Brown Derek D Cyr Lisa Wruck Troy A Stefano Nader Mehri Zoran Bursac Richard Munoz Marianna K Baum Eileen Fluney Prasad Bhoite Nana Aisha Garba Frederick W Anderson Haley R Fonseca Sara Assaf Krista M Perreira |
| author_facet | David R Brown Derek D Cyr Lisa Wruck Troy A Stefano Nader Mehri Zoran Bursac Richard Munoz Marianna K Baum Eileen Fluney Prasad Bhoite Nana Aisha Garba Frederick W Anderson Haley R Fonseca Sara Assaf Krista M Perreira |
| author_sort | David R Brown |
| collection | DOAJ |
| description | Understanding disparities in COVID-19 preventive efforts among underserved populations requires a holistic approach that considers multiple social determinants of health (SDOH). While disparities in individual COVID-19 risk factors are well-documented, the cumulative impact of these factors on vaccine uptake and testing remains insufficiently quantified. This study applies a polysocial risk framework to assess the combined influence of geo-demographic, economic, and health-related factors on COVID-19 vaccination and testing. Using cross-sectional data from 9,758 participants enrolled in the NIH Rapid Acceleration of Diagnostics - Underserved Populations (RADx-UP) program (February 2020-April 2023), we analyzed associations between polysocial risk and preventive behaviors using multivariable generalized estimating equations (GEE). Overall, 72.5% of participants reported COVID-19 vaccination, and 82.1% reported testing. However, disparities were evident across polysocial risk profiles. Individuals experiencing intersecting geo-demographic (Non-Hispanic Black, age 45, Southern residence), economic (low education, unemployment, financial hardship), and health-related risk factors (substance use, low CVD risk, no flu vaccination) were 43-48 percentage points less likely to be vaccinated compared to groups with higher adoption (p < 0.001). Testing disparities were narrower but remained significant, with differences ranging from 2 to 27 percentage points depending on the specific polysocial risk profiles. The findings underscore the utility of polysocial risk modeling as a predictive tool for identifying populations at highest risk of disengagement from preventive care, informing targeted precision public health interventions. Beyond COVID-19, this approach has broader applicability for understanding disparities in chronic disease prevention, cancer screening, maternal and child health, and health-related social needs (HRSN) interventions. Integrating polysocial risk assessments into clinical and public health settings can enhance data-driven strategies to improve population health outcomes. |
| format | Article |
| id | doaj-art-66a4235404d14d4698bbbcd7e290e591 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-66a4235404d14d4698bbbcd7e290e5912025-08-20T03:28:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032877910.1371/journal.pone.0328779COVID-19 prevention is shaped by polysocial risk: A cross-sectional study of vaccination and testing disparities in underserved populations.David R BrownDerek D CyrLisa WruckTroy A StefanoNader MehriZoran BursacRichard MunozMarianna K BaumEileen FluneyPrasad BhoiteNana Aisha GarbaFrederick W AndersonHaley R FonsecaSara AssafKrista M PerreiraUnderstanding disparities in COVID-19 preventive efforts among underserved populations requires a holistic approach that considers multiple social determinants of health (SDOH). While disparities in individual COVID-19 risk factors are well-documented, the cumulative impact of these factors on vaccine uptake and testing remains insufficiently quantified. This study applies a polysocial risk framework to assess the combined influence of geo-demographic, economic, and health-related factors on COVID-19 vaccination and testing. Using cross-sectional data from 9,758 participants enrolled in the NIH Rapid Acceleration of Diagnostics - Underserved Populations (RADx-UP) program (February 2020-April 2023), we analyzed associations between polysocial risk and preventive behaviors using multivariable generalized estimating equations (GEE). Overall, 72.5% of participants reported COVID-19 vaccination, and 82.1% reported testing. However, disparities were evident across polysocial risk profiles. Individuals experiencing intersecting geo-demographic (Non-Hispanic Black, age 45, Southern residence), economic (low education, unemployment, financial hardship), and health-related risk factors (substance use, low CVD risk, no flu vaccination) were 43-48 percentage points less likely to be vaccinated compared to groups with higher adoption (p < 0.001). Testing disparities were narrower but remained significant, with differences ranging from 2 to 27 percentage points depending on the specific polysocial risk profiles. The findings underscore the utility of polysocial risk modeling as a predictive tool for identifying populations at highest risk of disengagement from preventive care, informing targeted precision public health interventions. Beyond COVID-19, this approach has broader applicability for understanding disparities in chronic disease prevention, cancer screening, maternal and child health, and health-related social needs (HRSN) interventions. Integrating polysocial risk assessments into clinical and public health settings can enhance data-driven strategies to improve population health outcomes.https://doi.org/10.1371/journal.pone.0328779 |
| spellingShingle | David R Brown Derek D Cyr Lisa Wruck Troy A Stefano Nader Mehri Zoran Bursac Richard Munoz Marianna K Baum Eileen Fluney Prasad Bhoite Nana Aisha Garba Frederick W Anderson Haley R Fonseca Sara Assaf Krista M Perreira COVID-19 prevention is shaped by polysocial risk: A cross-sectional study of vaccination and testing disparities in underserved populations. PLoS ONE |
| title | COVID-19 prevention is shaped by polysocial risk: A cross-sectional study of vaccination and testing disparities in underserved populations. |
| title_full | COVID-19 prevention is shaped by polysocial risk: A cross-sectional study of vaccination and testing disparities in underserved populations. |
| title_fullStr | COVID-19 prevention is shaped by polysocial risk: A cross-sectional study of vaccination and testing disparities in underserved populations. |
| title_full_unstemmed | COVID-19 prevention is shaped by polysocial risk: A cross-sectional study of vaccination and testing disparities in underserved populations. |
| title_short | COVID-19 prevention is shaped by polysocial risk: A cross-sectional study of vaccination and testing disparities in underserved populations. |
| title_sort | covid 19 prevention is shaped by polysocial risk a cross sectional study of vaccination and testing disparities in underserved populations |
| url | https://doi.org/10.1371/journal.pone.0328779 |
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