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Examining the immediate and ongoing impact of the COVID-19 pandemic on population-based estimates of dementia: a population-based time series analysis in Ontario, Canada
OBJECTIVES: Population-based chronic disease surveillance systems were likely disrupted by the COVID-19 pandemic. The objective of this study was to examine the immediate and ongoing impact of the COVID-19 pandemic on the claims-based incidence of dementia. METHODS: We conducted a population-based t...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9842601/ https://www.ncbi.nlm.nih.gov/pubmed/36639204 http://dx.doi.org/10.1136/bmjopen-2022-067689 |
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author | Jones, Aaron Bronskill, Susan E Maclagan, Laura C Jaakkimainen, Liisa Kirkwood, David Mayhew, Alexandra Costa, Andrew P Griffith, Lauren E |
author_facet | Jones, Aaron Bronskill, Susan E Maclagan, Laura C Jaakkimainen, Liisa Kirkwood, David Mayhew, Alexandra Costa, Andrew P Griffith, Lauren E |
author_sort | Jones, Aaron |
collection | PubMed |
description | OBJECTIVES: Population-based chronic disease surveillance systems were likely disrupted by the COVID-19 pandemic. The objective of this study was to examine the immediate and ongoing impact of the COVID-19 pandemic on the claims-based incidence of dementia. METHODS: We conducted a population-based time series analysis from January 2015 to December 2021 in Ontario, Canada. We calculated the monthly claims-based incidence of dementia using a validated case ascertainment algorithm drawing from routinely collected health administrative data. We used autoregressive linear models to compare the claims-based incidence of dementia during the COVID-19 period (2020–2021) to the expected incidence had the pandemic not occurred, controlling for seasonality and secular trends. We examined incidence by source of ascertainment and across strata of sex, age, community size and number of health conditions. RESULTS: The monthly claims-based incidence of dementia dropped from a 2019 average of 11.9 per 10 000 to 8.5 per 10 000 in April 2020 (32.6% lower than expected). The incidence returned to expected levels by late 2020. Across the COVID-19 period there were a cumulative 2990 (95% CI 2109 to 3704) fewer cases of dementia observed than expected, equivalent to 1.05 months of new cases. Despite the overall recovery, ascertainment rates continued to be lower than expected among individuals aged 65–74 years and in large urban areas. Ascertainment rates were higher than expected in hospital and among individuals with 11 or more health conditions. CONCLUSIONS: The claims-based incidence of dementia recovered to expected levels by late 2020, suggesting minimal long-term changes to population-based dementia surveillance. Continued monitoring of claims-based incidence is necessary to determine whether the lower than expected incidence among individuals aged 65–74 and in large urban areas, and higher than expected incidence among individuals with 11 or more health conditions, is transitory. |
format | Online Article Text |
id | pubmed-9842601 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-98426012023-01-17 Examining the immediate and ongoing impact of the COVID-19 pandemic on population-based estimates of dementia: a population-based time series analysis in Ontario, Canada Jones, Aaron Bronskill, Susan E Maclagan, Laura C Jaakkimainen, Liisa Kirkwood, David Mayhew, Alexandra Costa, Andrew P Griffith, Lauren E BMJ Open Health Services Research OBJECTIVES: Population-based chronic disease surveillance systems were likely disrupted by the COVID-19 pandemic. The objective of this study was to examine the immediate and ongoing impact of the COVID-19 pandemic on the claims-based incidence of dementia. METHODS: We conducted a population-based time series analysis from January 2015 to December 2021 in Ontario, Canada. We calculated the monthly claims-based incidence of dementia using a validated case ascertainment algorithm drawing from routinely collected health administrative data. We used autoregressive linear models to compare the claims-based incidence of dementia during the COVID-19 period (2020–2021) to the expected incidence had the pandemic not occurred, controlling for seasonality and secular trends. We examined incidence by source of ascertainment and across strata of sex, age, community size and number of health conditions. RESULTS: The monthly claims-based incidence of dementia dropped from a 2019 average of 11.9 per 10 000 to 8.5 per 10 000 in April 2020 (32.6% lower than expected). The incidence returned to expected levels by late 2020. Across the COVID-19 period there were a cumulative 2990 (95% CI 2109 to 3704) fewer cases of dementia observed than expected, equivalent to 1.05 months of new cases. Despite the overall recovery, ascertainment rates continued to be lower than expected among individuals aged 65–74 years and in large urban areas. Ascertainment rates were higher than expected in hospital and among individuals with 11 or more health conditions. CONCLUSIONS: The claims-based incidence of dementia recovered to expected levels by late 2020, suggesting minimal long-term changes to population-based dementia surveillance. Continued monitoring of claims-based incidence is necessary to determine whether the lower than expected incidence among individuals aged 65–74 and in large urban areas, and higher than expected incidence among individuals with 11 or more health conditions, is transitory. BMJ Publishing Group 2023-01-13 /pmc/articles/PMC9842601/ /pubmed/36639204 http://dx.doi.org/10.1136/bmjopen-2022-067689 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Health Services Research Jones, Aaron Bronskill, Susan E Maclagan, Laura C Jaakkimainen, Liisa Kirkwood, David Mayhew, Alexandra Costa, Andrew P Griffith, Lauren E Examining the immediate and ongoing impact of the COVID-19 pandemic on population-based estimates of dementia: a population-based time series analysis in Ontario, Canada |
title | Examining the immediate and ongoing impact of the COVID-19 pandemic on population-based estimates of dementia: a population-based time series analysis in Ontario, Canada |
title_full | Examining the immediate and ongoing impact of the COVID-19 pandemic on population-based estimates of dementia: a population-based time series analysis in Ontario, Canada |
title_fullStr | Examining the immediate and ongoing impact of the COVID-19 pandemic on population-based estimates of dementia: a population-based time series analysis in Ontario, Canada |
title_full_unstemmed | Examining the immediate and ongoing impact of the COVID-19 pandemic on population-based estimates of dementia: a population-based time series analysis in Ontario, Canada |
title_short | Examining the immediate and ongoing impact of the COVID-19 pandemic on population-based estimates of dementia: a population-based time series analysis in Ontario, Canada |
title_sort | examining the immediate and ongoing impact of the covid-19 pandemic on population-based estimates of dementia: a population-based time series analysis in ontario, canada |
topic | Health Services Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9842601/ https://www.ncbi.nlm.nih.gov/pubmed/36639204 http://dx.doi.org/10.1136/bmjopen-2022-067689 |
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