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Alphonso SR, Andrews MR, Regan SD, Shishkov A, Cantor JH, Powell-Wiley TM, Tamura K. Geospatially clustered low COVID-19 vaccine rates among adolescents in socially vulnerable US counties. Prev Med Rep 2024; 37:102545. [PMID: 38186659 PMCID: PMC10767486 DOI: 10.1016/j.pmedr.2023.102545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 12/05/2023] [Accepted: 12/07/2023] [Indexed: 01/09/2024] Open
Abstract
COVID-19 vaccinations are widely available across the United States (U.S.), yet little is known about the spatial clustering of COVID-19 vaccinations. This study aimed to test for geospatial clustering of COVID-19 vaccine rates among adolescents aged 12-17 across the U.S. counties and to compare these clustering patterns by sociodemographic characteristics. County-level data on COVID-19 vaccinations and sociodemographic characteristics were obtained from the COVID-19 Community Profile Report up to April 14, 2022. A total of 3,108 counties were included in the analysis. Global Moran's I statistic and Anselin Local Moran's analysis were used, and clustering patterns were compared to sociodemographic variables using t-tests. Counties with low COVID-19 vaccinated clusters were more likely, when compared to unclustered counties, to have higher numbers of individuals in poverty and uninsured individuals, and higher values of Social Vulnerability Index (SVI) and COVID-19 Community Vulnerability Index (CCVI). While high COVID-19 vaccinated clusters, compared to neighboring counties, had lower numbers of Black population, individuals in poverty, and uninsured individuals, and lower values of SVI and CCVI, but a higher number of Hispanic population. This study emphasizes the importance of addressing systemic barriers, such as poverty and lack of health insurance, which were found to be associated with low COVID-19 vaccination coverage.
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Affiliation(s)
- Sophie R. Alphonso
- Socio-Spatial Determinants of Health (SSDH) Laboratory, Population and Community Health Sciences Branch, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Marcus R. Andrews
- Department of Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Seann D. Regan
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
| | - Alyssa Shishkov
- Socio-Spatial Determinants of Health (SSDH) Laboratory, Population and Community Health Sciences Branch, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | | | - Tiffany M. Powell-Wiley
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Intramural Research Program, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
| | - Kosuke Tamura
- Socio-Spatial Determinants of Health (SSDH) Laboratory, Population and Community Health Sciences Branch, Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, USA
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Shaweno D, Trauer JM, Doan TN, Denholm JT, McBryde ES. Geospatial clustering and modelling provide policy guidance to distribute funding for active TB case finding in Ethiopia. Epidemics 2021; 36:100470. [PMID: 34052666 DOI: 10.1016/j.epidem.2021.100470] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 01/27/2020] [Accepted: 05/17/2021] [Indexed: 10/21/2022] Open
Abstract
Tuberculosis (TB) exhibits considerable spatial heterogeneity, occurring in clusters that may act as hubs of community transmission. We evaluated the impact of an intervention targeting spatial TB hotspots in a rural region of Ethiopia. To evaluate the impact of targeted active case finding (ACF), we used a spatially structured mathematical model that has previously been described. From model equilibrium, we simulated the impact of a hotspot-targeted strategy (HTS) on TB incidence ten years from intervention commencement and the associated cost-effectiveness. HTS was also compared with an untargeted strategy (UTS). We used logistic cost-coverage analysis to estimate cost-effectiveness of interventions. At a community screening coverage level of 95 % in a hotspot region, which corresponds to screening 20 % of the total population, HTS would reduce overall TB incidence by 52 % compared with baseline. For UTS to achieve an equivalent effect, it would be necessary to screen more than 80 % of the total population. Compared to the existing passive case detection strategy, the HTS at a CDR of 75 percent in hotspot regions is expected to avert 1,023 new TB cases over ten years saving USD 170 per averted case. Similarly, at the same CDR, the UTS will detect 1316 cases over the same period saving USD 3 per averted TB case. The incremental-cost effectiveness-ratio (ICER) of UTS compared with HTS is USD 582 per averted case corresponding to 293 more TB cases averted at an additional cost of USD 170,700. Where regional TB program spending was capped at current levels, maximum gains in incidence reduction were seen when the regional budget was shared between hotspots and non-hotspot regions in the ratio of 40% : 60%. Our analysis suggests that a spatially targeted strategy is efficient and cost-saving, with the potential for significant reduction in overall TB burden.
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Affiliation(s)
- Debebe Shaweno
- Department of Medicine, University of Melbourne, 300 Grattan Street, Melbourne, Victoria, 3050, Australia; Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, 792 Elizabeth Street, Melbourne, 3000, Victoria, Australia; Department of Health Economics and Decision Science, School of Health and Related Research, The University of Sheffield, 30 Regent Street, Sheffield, S1 4DA, United Kingdom.
| | - James M Trauer
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, 792 Elizabeth Street, Melbourne, 3000, Victoria, Australia; School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd, Melbourne, Victoria, 3004, Australia
| | - Tan N Doan
- Department of Medicine, University of Melbourne, 300 Grattan Street, Melbourne, Victoria, 3050, Australia; Australian Institute of Tropical Health and Medicine, James Cook University, Douglas, Townsville, QLD, 4814, Australia
| | - Justin T Denholm
- Victorian Tuberculosis Program at the Peter Doherty Institute for Infection and Immunity, 792 Elizabeth Street, Melbourne, 3000, Victoria, Australia; Department of Microbiology and Immunology, University of Melbourne792 Elizabeth Street, Melbourne, 3000, Victoria, Australia
| | - Emma S McBryde
- Department of Medicine, University of Melbourne, 300 Grattan Street, Melbourne, Victoria, 3050, Australia; Australian Institute of Tropical Health and Medicine, James Cook University, Douglas, Townsville, QLD, 4814, Australia
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Tamura K, Duncan DT, Athens JK, Bragg MA, Rienti M, Aldstadt J, Scott MA, Elbel B. Geospatial clustering in sugar-sweetened beverage consumption among Boston youth. Int J Food Sci Nutr 2017; 68:719-725. [PMID: 28095725 PMCID: PMC10809269 DOI: 10.1080/09637486.2016.1276519] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Revised: 12/18/2016] [Accepted: 12/19/2016] [Indexed: 01/26/2023]
Abstract
The objective was to detect geospatial clustering of sugar-sweetened beverage (SSB) intake in Boston adolescents (age = 16.3 ± 1.3 years [range: 13-19]; female = 56.1%; White = 10.4%, Black = 42.6%, Hispanics = 32.4%, and others = 14.6%) using spatial scan statistics. We used data on self-reported SSB intake from the 2008 Boston Youth Survey Geospatial Dataset (n = 1292). Two binary variables were created: consumption of SSB (never versus any) on (1) soda and (2) other sugary drinks (e.g., lemonade). A Bernoulli spatial scan statistic was used to identify geospatial clusters of soda and other sugary drinks in unadjusted models and models adjusted for age, gender, and race/ethnicity. There was no statistically significant clustering of soda consumption in the unadjusted model. In contrast, a cluster of non-soda SSB consumption emerged in the middle of Boston (relative risk = 1.20, p = .005), indicating that adolescents within the cluster had a 20% higher probability of reporting non-soda SSB intake than outside the cluster. The cluster was no longer significant in the adjusted model, suggesting spatial variation in non-soda SSB drink intake correlates with the geographic distribution of students by race/ethnicity, age, and gender.
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Affiliation(s)
- Kosuke Tamura
- Department of Population Health, School of Medicine, New York University, New York, NY
| | - Dustin T. Duncan
- Department of Population Health, School of Medicine, New York University, New York, NY
- College of Global Public Health, New York University, New York, NY
| | - Jessica K. Athens
- Department of Population Health, School of Medicine, New York University, New York, NY
| | - Marie A. Bragg
- Department of Population Health, School of Medicine, New York University, New York, NY
- College of Global Public Health, New York University, New York, NY
| | - Michael Rienti
- Department of Geography, University at Buffalo, State University of New York, Buffalo, NY
| | - Jared Aldstadt
- Department of Geography, University at Buffalo, State University of New York, Buffalo, NY
| | - Marc A. Scott
- College of Global Public Health, New York University, New York, NY
- PRIISM Applied Statistics Center, New York University, New York, NY
| | - Brian Elbel
- Department of Population Health, School of Medicine, New York University, New York, NY
- Wagner Graduate School of Public Service, New York University, New York, NY
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