{"id":20498,"date":"2024-10-07T15:06:54","date_gmt":"2024-10-07T19:06:54","guid":{"rendered":"https:\/\/www.ices.on.ca\/?post_type=journal_article&p=20498"},"modified":"2024-10-11T15:17:57","modified_gmt":"2024-10-11T19:17:57","slug":"quantifying-the-magnitude-of-the-general-contextual-effect-in-a-multilevel-study-of-sars-cov-2-infection-in-ontario-canada-application-of-the-median-rate-ratio-in-population-health-research","status":"publish","type":"journal_article","link":"https:\/\/www.ices.on.ca\/fr\/publications\/journal-articles\/quantifying-the-magnitude-of-the-general-contextual-effect-in-a-multilevel-study-of-sars-cov-2-infection-in-ontario-canada-application-of-the-median-rate-ratio-in-population-health-research\/","title":{"rendered":"Quantifying the magnitude of the general contextual effect in a multilevel study of SARS-CoV-2 infection in Ontario, Canada: application of the median rate ratio in population health research"},"content":{"rendered":"

Background <\/strong>\u2014\u00a0Regional variations in SARS-CoV-2 infection were observed in Canada and other countries. Studies have used multilevel analyses to examine how a context, such as a neighbourhood, can affect the SARS-CoV-2 infection rates of the people within it. However, few multilevel studies have quantified the magnitude of the general contextual effect (GCE) in SARS-CoV-2 infection rates and assessed how it may be associated with individual- and area-level characteristics. To address this gap, we will illustrate the application of the median rate ratio (MRR) in a multilevel Poisson analysis for quantifying the GCE in SARS-CoV-2 infection rates in Ontario, Canada.<\/p>\n

Methods<\/strong> \u2014\u00a0We conducted a population-based, two-level multilevel observational study where individuals were nested into regions (i.e., forward sortation areas [FSAs]). The study population included community-dwelling adults in Ontario, Canada, between March 1, 2020, and May 1, 2021. The model included seven individual-level variables (age, sex, asthma, diabetes, hypertension, congestive heart failure, and chronic obstructive pulmonary disease) and four FSA census-based variables (household size, household income, employment, and driving to work). The MRR is a median value of the rate ratios comparing two patients with identical characteristics randomly selected from two different regions ordered by rate. We examined the attenuation of the MRR after including individual-level and FSA census-based variables to assess their role in explaining the variation in rates between regions.<\/p>\n

Results<\/strong> \u2014\u00a0Of the 11 789 128 Ontario adult community-dwelling residents, 343 787 had at least one SARS-CoV-2 infection during the study period. After adjusting for individual-level and FSA census-based variables, the MRR was attenuated to 1.67 (39% reduction from unadjusted MRR). The strongest FSA census-based associations were household size (RR\u2009=\u20091.88, 95% CI: 1.71\u20131.97) and driving to work (RR\u2009=\u20090.68, 95% CI: 0.65\u20130.71).<\/p>\n

Conclusions<\/strong> \u2014\u00a0The individual- and area-level characteristics in our study accounted for approximately 40% of the between-region variation in SARS-CoV-2 infection rates measured by MRR in Ontario, Canada. These findings suggest that population-based policies to address social determinants of health that attenuate the MRR may reduce the observed between-region heterogeneity in SARS-CoV-2 infection rates.<\/p>\n","protected":false},"excerpt":{"rendered":"

Background \u2014\u00a0Regional variations in SARS-CoV-2 infection were observed in Canada and other countries. Studies have used multilevel analyses to examine how a context, such as a neighbourhood, can affect the SARS-CoV-2 infection rates of the people within it. However, few multilevel studies have quantified the magnitude of the general contextual effect (GCE) in SARS-CoV-2 infection […]<\/p>\n","protected":false},"template":"","migration-helper-automated":[],"migration-manual":[],"topic":[59,39,35],"migration-helper-qa-sample-set":[],"class_list":["post-20498","journal_article","type-journal_article","status-publish","hentry","topic-data-science","topic-infectious-diseases","topic-public-health"],"acf":{"citation":"Watson T, Kwong JC, Kornas K, Mishra S, Rosella LC. Popul Health Metr<\/em>. 2024; 22(1):27.","source_url":"https:\/\/doi.org\/10.1186\/s12963-024-00348-8","ices_scientist":[1149,1320,1339],"site":[6735],"research_program":[6745],"news_release":"","journal_article":"","atlas":"","research_report":"","infographic":"","video":"","downloads":null,"links":null,"sitecore_item_id":"","sitecore_item_name":"","sitecore_field_values":"","previous_url":""},"yoast_head":"\nICES | Quantifying the magnitude of the general contextual effect in a multilevel study of SARS-CoV-2 infection in Ontario, Canada: application of the median rate ratio in population health research<\/title>\n<meta name=\"description\" content=\"Background \u2014\u00a0Regional variations in SARS-CoV-2 infection were observed in Canada and other countries. 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