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The Collapse of Infectious Disease Diagnoses Commonly Due to Communicable Respiratory Pathogens During the Coronavirus Disease 2019 Pandemic: A Time Series and Hierarchical Clustering Analysis
BACKGROUND: Nonpharmaceutical interventions such as physical distancing and mandatory masking were adopted in many jurisdictions during the coronavirus disease 2019 pandemic to decrease spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We determined the effects of these interve...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047204/ https://www.ncbi.nlm.nih.gov/pubmed/35791356 http://dx.doi.org/10.1093/ofid/ofac205 |
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author | Zhang, Ali Surette, Matthew D Schwartz, Kevin L Brooks, James I Bowdish, Dawn M E Mahdavi, Roshanak Manuel, Douglas G Talarico, Robert Daneman, Nick Shurgold, Jayson MacFadden, Derek |
author_facet | Zhang, Ali Surette, Matthew D Schwartz, Kevin L Brooks, James I Bowdish, Dawn M E Mahdavi, Roshanak Manuel, Douglas G Talarico, Robert Daneman, Nick Shurgold, Jayson MacFadden, Derek |
author_sort | Zhang, Ali |
collection | PubMed |
description | BACKGROUND: Nonpharmaceutical interventions such as physical distancing and mandatory masking were adopted in many jurisdictions during the coronavirus disease 2019 pandemic to decrease spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We determined the effects of these interventions on incidence of healthcare utilization for other infectious diseases. METHODS: Using a healthcare administrative dataset, we employed an interrupted time series analysis to measure changes in healthcare visits for various infectious diseases across the province of Ontario, Canada, from January 2017 to December 2020. We used a hierarchical clustering algorithm to group diagnoses that demonstrated similar patterns of change through the pandemic months. RESULTS: We found that visits for infectious diseases commonly caused by communicable respiratory pathogens (eg, acute bronchitis, acute sinusitis) formed distinct clusters from diagnoses that often originate from pathogens derived from the patient’s own flora (eg, urinary tract infection, cellulitis). Moreover, infectious diagnoses commonly arising from communicable respiratory pathogens (hierarchical cluster 1: highly impacted diagnoses) were significantly decreased, with a rate ratio (RR) of 0.35 (95% confidence interval [CI], .30–.40; P < .001) after the introduction of public health interventions in April–December 2020, whereas infections typically arising from the patient’s own flora (hierarchical cluster 3: minimally impacted diagnoses) did not demonstrate a sustained change in incidence (RR, 0.95 [95% CI, .90–1.01]; P = .085). CONCLUSIONS: Public health measures to curtail the incidence of SARS-CoV-2 were widely effective against other communicable respiratory infectious diseases with similar modes of transmission but had little effect on infectious diseases not strongly dependent on person-to-person transmission. |
format | Online Article Text |
id | pubmed-9047204 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-90472042022-04-28 The Collapse of Infectious Disease Diagnoses Commonly Due to Communicable Respiratory Pathogens During the Coronavirus Disease 2019 Pandemic: A Time Series and Hierarchical Clustering Analysis Zhang, Ali Surette, Matthew D Schwartz, Kevin L Brooks, James I Bowdish, Dawn M E Mahdavi, Roshanak Manuel, Douglas G Talarico, Robert Daneman, Nick Shurgold, Jayson MacFadden, Derek Open Forum Infect Dis Major Article BACKGROUND: Nonpharmaceutical interventions such as physical distancing and mandatory masking were adopted in many jurisdictions during the coronavirus disease 2019 pandemic to decrease spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We determined the effects of these interventions on incidence of healthcare utilization for other infectious diseases. METHODS: Using a healthcare administrative dataset, we employed an interrupted time series analysis to measure changes in healthcare visits for various infectious diseases across the province of Ontario, Canada, from January 2017 to December 2020. We used a hierarchical clustering algorithm to group diagnoses that demonstrated similar patterns of change through the pandemic months. RESULTS: We found that visits for infectious diseases commonly caused by communicable respiratory pathogens (eg, acute bronchitis, acute sinusitis) formed distinct clusters from diagnoses that often originate from pathogens derived from the patient’s own flora (eg, urinary tract infection, cellulitis). Moreover, infectious diagnoses commonly arising from communicable respiratory pathogens (hierarchical cluster 1: highly impacted diagnoses) were significantly decreased, with a rate ratio (RR) of 0.35 (95% confidence interval [CI], .30–.40; P < .001) after the introduction of public health interventions in April–December 2020, whereas infections typically arising from the patient’s own flora (hierarchical cluster 3: minimally impacted diagnoses) did not demonstrate a sustained change in incidence (RR, 0.95 [95% CI, .90–1.01]; P = .085). CONCLUSIONS: Public health measures to curtail the incidence of SARS-CoV-2 were widely effective against other communicable respiratory infectious diseases with similar modes of transmission but had little effect on infectious diseases not strongly dependent on person-to-person transmission. Oxford University Press 2022-04-19 /pmc/articles/PMC9047204/ /pubmed/35791356 http://dx.doi.org/10.1093/ofid/ofac205 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Major Article Zhang, Ali Surette, Matthew D Schwartz, Kevin L Brooks, James I Bowdish, Dawn M E Mahdavi, Roshanak Manuel, Douglas G Talarico, Robert Daneman, Nick Shurgold, Jayson MacFadden, Derek The Collapse of Infectious Disease Diagnoses Commonly Due to Communicable Respiratory Pathogens During the Coronavirus Disease 2019 Pandemic: A Time Series and Hierarchical Clustering Analysis |
title | The Collapse of Infectious Disease Diagnoses Commonly Due to Communicable Respiratory Pathogens During the Coronavirus Disease 2019 Pandemic: A Time Series and Hierarchical Clustering Analysis |
title_full | The Collapse of Infectious Disease Diagnoses Commonly Due to Communicable Respiratory Pathogens During the Coronavirus Disease 2019 Pandemic: A Time Series and Hierarchical Clustering Analysis |
title_fullStr | The Collapse of Infectious Disease Diagnoses Commonly Due to Communicable Respiratory Pathogens During the Coronavirus Disease 2019 Pandemic: A Time Series and Hierarchical Clustering Analysis |
title_full_unstemmed | The Collapse of Infectious Disease Diagnoses Commonly Due to Communicable Respiratory Pathogens During the Coronavirus Disease 2019 Pandemic: A Time Series and Hierarchical Clustering Analysis |
title_short | The Collapse of Infectious Disease Diagnoses Commonly Due to Communicable Respiratory Pathogens During the Coronavirus Disease 2019 Pandemic: A Time Series and Hierarchical Clustering Analysis |
title_sort | collapse of infectious disease diagnoses commonly due to communicable respiratory pathogens during the coronavirus disease 2019 pandemic: a time series and hierarchical clustering analysis |
topic | Major Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9047204/ https://www.ncbi.nlm.nih.gov/pubmed/35791356 http://dx.doi.org/10.1093/ofid/ofac205 |
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