tetano
Editor, Senior Moderator
PLoS One
. 2021 Nov 19;16(11):e0259601.
doi: 10.1371/journal.pone.0259601. eCollection 2021.
Immunosuppression and COVID-19 infection in British Columbia: Protocol for a linkage study of population-based administrative and self-reported survey data
Ana Michelle Avina-Galindo[SUP] 1 [/SUP], Zahra A Fazal[SUP] 1 2 [/SUP], Shelby Marozoff[SUP] 1 [/SUP], Jessie Kwan[SUP] 1 3 [/SUP], Na Lu[SUP] 1 [/SUP], Alison M Hoens[SUP] 1 4 [/SUP], Jacek Kopec[SUP] 1 5 [/SUP], Diane Lacaille[SUP] 1 6 [/SUP], Hui Xie[SUP] 1 7 [/SUP], Jonathan M Loree[SUP] 8 9 [/SUP], J Antonio Avina-Zubieta[SUP] 1 6 [/SUP], SCOUT team
Affiliations
Abstract
Introduction: Cases of the novel coronavirus disease (COVID-19) continue to spread around the world even one year after the declaration of a global pandemic. Those with weakened immune systems, due to immunosuppressive medications or disease, may be at higher risk of COVID-19. This includes individuals with autoimmune diseases, cancer, transplants, and dialysis patients. Assessing the risk and outcomes of COVID-19 in this population has been challenging. While administrative databases provide data with minimal selection and recall bias, clinical and behavioral data is lacking. To address this, we are collecting self-reported survey data from a randomly selected subsample with and without COVID-19, which will be linked to administrative health data, to better quantify the risk of COVID-19 infection associated with immunosuppression.
Methods and analysis: Using administrative and laboratory data from British Columbia (BC), Canada, we established a population-based case-control study of all individuals who tested positive for SARS-CoV-2. Each case was matched to 40 randomly selected individuals from two control groups: individuals who tested negative for SARS-CoV-2 (i.e., negative controls) and untested individuals from the general population (i.e., untested controls). We will contact 1000 individuals from each group to complete a survey co-designed with patient partners. A conditional logistic regression model will adjust for potential confounders and effect modifiers. We will examine the odds of COVID-19 infection according to immunosuppressive medication or disease type. To adjust for relevant confounders and effect modifiers not available in administrative data, the survey will include questions on behavioural variables that influence probability of being tested, acquiring COVID-19, and experiencing severe outcomes.
. 2021 Nov 19;16(11):e0259601.
doi: 10.1371/journal.pone.0259601. eCollection 2021.
Immunosuppression and COVID-19 infection in British Columbia: Protocol for a linkage study of population-based administrative and self-reported survey data
Ana Michelle Avina-Galindo[SUP] 1 [/SUP], Zahra A Fazal[SUP] 1 2 [/SUP], Shelby Marozoff[SUP] 1 [/SUP], Jessie Kwan[SUP] 1 3 [/SUP], Na Lu[SUP] 1 [/SUP], Alison M Hoens[SUP] 1 4 [/SUP], Jacek Kopec[SUP] 1 5 [/SUP], Diane Lacaille[SUP] 1 6 [/SUP], Hui Xie[SUP] 1 7 [/SUP], Jonathan M Loree[SUP] 8 9 [/SUP], J Antonio Avina-Zubieta[SUP] 1 6 [/SUP], SCOUT team
Affiliations
- PMID: 34797824
- DOI: 10.1371/journal.pone.0259601
Abstract
Introduction: Cases of the novel coronavirus disease (COVID-19) continue to spread around the world even one year after the declaration of a global pandemic. Those with weakened immune systems, due to immunosuppressive medications or disease, may be at higher risk of COVID-19. This includes individuals with autoimmune diseases, cancer, transplants, and dialysis patients. Assessing the risk and outcomes of COVID-19 in this population has been challenging. While administrative databases provide data with minimal selection and recall bias, clinical and behavioral data is lacking. To address this, we are collecting self-reported survey data from a randomly selected subsample with and without COVID-19, which will be linked to administrative health data, to better quantify the risk of COVID-19 infection associated with immunosuppression.
Methods and analysis: Using administrative and laboratory data from British Columbia (BC), Canada, we established a population-based case-control study of all individuals who tested positive for SARS-CoV-2. Each case was matched to 40 randomly selected individuals from two control groups: individuals who tested negative for SARS-CoV-2 (i.e., negative controls) and untested individuals from the general population (i.e., untested controls). We will contact 1000 individuals from each group to complete a survey co-designed with patient partners. A conditional logistic regression model will adjust for potential confounders and effect modifiers. We will examine the odds of COVID-19 infection according to immunosuppressive medication or disease type. To adjust for relevant confounders and effect modifiers not available in administrative data, the survey will include questions on behavioural variables that influence probability of being tested, acquiring COVID-19, and experiencing severe outcomes.