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Noppert GA, Clarke P, Hoover A, Kubale J, Melendez R, Duchowny K, Hegde ST. State variation in neighborhood COVID-19 burden across the United States. COMMUNICATIONS MEDICINE 2024; 4:36. [PMID: 38429552 PMCID: PMC10907669 DOI: 10.1038/s43856-024-00459-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 02/12/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 infection burden has been distributed across neighborhoods, a key determinant of both risk and resilience. Without more spatially resolute data, efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities will remain difficult to quantify and intervene on. METHODS We leveraged spatially-referenced data from 21 states collated through the COVID Neighborhood Project to examine the distribution of COVID-19 cases across neighborhoods and states in the U.S. We also linked the COVID-19 case data with data on the neighborhood social environment from the National Neighborhood Data Archive. We then estimated correlations between neighborhood COVID-19 burden and features of the neighborhood social environment. RESULTS We find that the distribution of COVID-19 at the neighborhood-level varies within and between states. The median case count per neighborhood (coefficient of variation (CV)) in Wisconsin is 3078.52 (0.17) per 10,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (CV) is 810.98 (0.84) per 10,000 population. We also find that correlations between features of the neighborhood social environment and burden vary in magnitude and direction by state. CONCLUSIONS Our findings underscore the importance that local contexts may play when addressing the long-term social and economic fallout communities will face from COVID-19.
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Affiliation(s)
- Grace A Noppert
- Institute for Social Research, University of Michigan, Ann Arbor, USA.
| | - Philippa Clarke
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - Andrew Hoover
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - John Kubale
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - Robert Melendez
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - Kate Duchowny
- Institute for Social Research, University of Michigan, Ann Arbor, USA
| | - Sonia T Hegde
- Department of Epidemiology, Johns Hopkins University, Baltimore, USA
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Rakeman-Cagno JL, Persing DH, Loeffelholz MJ. Maintaining point of care testing capacity and pandemic preparedness in the post-COVID-19 era. Expert Rev Mol Diagn 2024; 24:147-151. [PMID: 37724428 DOI: 10.1080/14737159.2023.2260743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/15/2023] [Indexed: 09/20/2023]
Abstract
INTRODUCTION Testing at the point of care (we also refer to the 'point of need'), with rapid, actionable results reported to the patient and provider within hours can impact the individual as well as public health. Faster testing is good for patients and public health outcomes during 'peace time' (outside of the pandemic setting). AREAS COVERED Testing at the point of need was important during the COVID-19 pandemic to meet testing capacity demands, providing actionable results, and for providing testing within communities to increase access for all populations. Resources were acquired and built up dramatically during the pandemic as part of the response. With the end of the COVID-19 public health emergency and transition back to 'peace time' some testing sites have successfully shifted to using this capacity for testing for other critical needs, like sexually transmitted infection (STI) testing, and response to other seasonal diseases and for outbreak response. EXPERT OPINION The increased testing capacity added to handle unprecedented testing volume during the COVID-19 pandemic can be repurposed for other critical infectious diseases during 'peace time' (post-COVID-19 pandemic). This maintains testing capacity for the next pandemic.
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Affiliation(s)
| | - David H Persing
- Department Pathology, Stanford University, Palo Alto, CA, USA
- Scientific Affairs, Cepheid, Sunnyvale, CA, USA
| | - Michael J Loeffelholz
- Department Pathology, University of Texas Medical Branch, Galveston, TX, USA
- Scientific Affairs, Cepheid, Sunnyvale, CA, USA
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Noppert GA, Clarke P, Hoover A, Kubale J, Melendez R, Duchowny K, Hegde ST. State Variation in Neighborhood COVID-19 Burden: Findings from the COVID Neighborhood Project. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.19.23290222. [PMID: 37293100 PMCID: PMC10246150 DOI: 10.1101/2023.05.19.23290222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 burden has been distributed across neighborhoods, a known geographic unit of both risk and resilience, and is hampering efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities. Using spatially-referenced data from 21 states at the ZIP code or census tract level, we documented how the distribution of COVID-19 at the neighborhood-level varies significantly within and between states. The median case count per neighborhood (IQR) in Oregon was 3,608 (2,487) per 100,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (IQR) was 8,142 (11,031) per 100,000. We also found that the association between features of the neighborhood social environment and burden varied in magnitude and direction by state. Our findings underscore the importance of local contexts when addressing the long-term social and economic fallout communities will face from COVID-19.
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Affiliation(s)
| | | | - Andrew Hoover
- Institute for Social Research, University of Michigan
| | - John Kubale
- Institute for Social Research, University of Michigan
| | | | - Kate Duchowny
- Institute for Social Research, University of Michigan
| | - Sonia T Hegde
- Department of Epidemiology, Johns Hopkins University
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Hong K, Yum S, Kim J, Yoo D, Chun BC. Epidemiology and Regional Predictors of COVID-19 Clusters: A Bayesian Spatial Analysis Through a Nationwide Contact Tracing Data. Front Med (Lausanne) 2021; 8:753428. [PMID: 34746188 PMCID: PMC8563697 DOI: 10.3389/fmed.2021.753428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 09/21/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: Revealing the clustering risks of COVID-19 and prediction is essential for effective quarantine policies, since clusters can lead to rapid transmission and high mortality in a short period. This study aimed to present which regional and social characteristics make COVID-19 cluster with high risk. Methods: By analyzing the data of all confirmed cases (14,423) in Korea between January 10 and August 3, 2020, provided by the Korea Disease Control and Prevention Agency, we manually linked each case and discovered clusters. After classifying the cases into clusters as nine types, we compared the duration and size of clusters by types to reveal high-risk cluster types. Also, we estimated odds for the risk factors for COVID-19 clustering by a spatial autoregressive model using the Bayesian approach. Results: Regarding the classified clusters (n = 539), the mean size was 19.21, and the mean duration was 9.24 days. The number of clusters was high in medical facilities, workplaces, and nursing homes. However, multilevel marketing, religious facilities, and restaurants/business-related clusters tended to be larger and longer when an outbreak occurred. According to the spatial analysis in COVID-19 clusters of more than 20 cases, the global Moran's I statistics value was 0.14 (p < 0.01). After adjusting for population size, the risks of COVID-19 clusters were related to male gender (OR = 1.29) and low influenza vaccination rate (OR = 0.87). After the spatial modeling, the predicted probability of forming clusters was visualized and compared with the actual incidence and local Moran's I statistics 2 months after the study period. Conclusions: COVID-19 makes different sizes of clusters in various contact settings; thus, precise epidemic control measures are needed. Also, when detecting and screening for COVID-19 clusters, regional risks such as vaccination rate should be considered for predicting risk to control the pandemic cost-effectively.
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Affiliation(s)
- Kwan Hong
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, South Korea
| | - Sujin Yum
- Graduate School of Public Health, Korea University, Seoul, South Korea
| | - Jeehyun Kim
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, South Korea.,Transdisciplinary Major in Learning Health Systems, Department of Healthcare Sciences, Graduate School, Korea University, Seoul, South Korea
| | - Daesung Yoo
- Transdisciplinary Major in Learning Health Systems, Department of Healthcare Sciences, Graduate School, Korea University, Seoul, South Korea.,Veterinary Epidemiology Division, Animal and Plant Quarantine Agency, Gimcheon, South Korea
| | - Byung Chul Chun
- Department of Preventive Medicine, Korea University College of Medicine, Seoul, South Korea.,Graduate School of Public Health, Korea University, Seoul, South Korea.,Transdisciplinary Major in Learning Health Systems, Department of Healthcare Sciences, Graduate School, Korea University, Seoul, South Korea
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