Association of Influenza Vaccination With SARS-CoV-2 Infection and Associated Hospitalization and Mortality Among Patients Aged 66 Years or Older

This cohort study assesses whether influenza vaccination is associated with SARS-CoV-2 infection and related outcomes such as hospitalization or mortality in individuals aged 66 years or older.


Introduction
Methods Study design 4 Present key elements of study design early in the paper.

Setting 5
Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection.

Methods
Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up. (b) For matched studies, give matching criteria and number of exposed and unexposed.
(6.1) The methods of study population selection (such as codes or algorithms used to identify subjects) should be listed in detail. If this is not possible, an explanation should be provided. (6.2) Any validation studies of the codes or algorithms used to select the population should be referenced. If validation was conducted for this study and not published elsewhere, detailed methods and results should be provided. (6.3) If the study involved linkage of databases, consider use of a flow diagram or other graphical display to demonstrate the data linkage process, including the number of individuals with linked data at each stage.

Methods, Appendix
Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable.
(7.1) A complete list of codes and algorithms used to classify exposures, outcomes, confounders, and effect modifiers should be provided. If these cannot be reported, an explanation should be provided.

Results
Other analyses 17 Report other analyses done (e.g. analyses of subgroups and interactions, and sensitivity analyses).

Key results 18
Summarize key results with reference to study objectives.

Limitations 19
Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias.
(19.1) Discuss the implications of using data that were not created or collected to answer the specific research question(s). Include discussion of misclassification bias, unmeasured confounding, missing data, and changing eligibility over time, as they pertain to the study being reported.

Discussion
Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence.

Discussion
Generalizability 21 Discuss the generalizability (external validity) of the study results.

Discussion
Other information Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based.

Acknowledgments & Funding
Accessibility of protocol, raw data, and programming code N/A (22.1) Authors should provide information on how to access any supplemental information such as the study protocol, raw data, or programming code. We used ICES-specific asthma database to identify patients with asthma based on 2 or more ambulatory care visits and/or 1 or more hospital admissions. This variable was included a priori considering its relationship to severe COVID-19 outcomes. [2] OHIP OHIP diagnostic code: 493 DAD ICD-9 diagnostic code: 493 ICD-10 diagnostic codes: J45, J46 Chronic obstructive pulmonary disease (COPD) We used ICES-specific COPD database to identify patients with COPD based on 1 or more ambulatory care visits and/or 1 or more hospital admissions. This variable was included a priori considering its relationship to severe COVID-19 outcomes. We used ICES-specific hypertension database to identify patients with hypertension, based on 1 or more DAD diagnoses or 2 or more OHIP diagnoses in a two-year period; or 1 OHIP diagnosis followed by an OHIP/DAD diagnosis within two years. This variable was included a priori as hypothesized to be directly related to severe COVID-19. [ We used ICES-derived CHF database to identify patients with CHF, based on 1 NACRS, DAD, SDS, or OHIP claim and a second claim (from either) in 1 year. The CHF database is limited to those aged 40 years or older. This variable was included a priori as hypothesized to be directly related to severe COVID-19. [ We used Hospital Frailty Risk Score based on an algorithm already derived using all DAD hospitalizations in the 5 years before index. This risk score also includes frailty-related conditions such as dementia and other chronic conditions that are separately reported.

Variable Definition Cancer
Ontario Cancer Registry was used to identify patients with underlying cancer diagnosed in the 5 years prior to index, except for non-melanoma skin cancer (ICD-O-3 Topography = C44 and Morphology = 87xx3). This variable was included a priori as hypothesized to be directly related to COVID-19 infection risk and severity of illness. [7] Chronic kidney disease (CKD) This variable was included a priori as hypothesized to be directly related to COVID-19 infection risk [8]. We defined this variable as having a CKD diagnosis code in DAD, NACRS, OHIP in the past 5 years, or: o At least 1 dialysis code in each of the 3 months prior to index o Diagnosis and procedure codes found in concept dictionary OHIP OHIP diagnostic codes: 403, 585 NACRS, DAD ICD-10 diagnostic codes: E102, E112, E132, E142, I12, I13, N08, N18, N19 Patients who were on chronic dialysis in the year before index date were identified as those with at least 2 of any of the following codes in OHIP, DAD, or SDS separated by at least 90 days, but less than 150 days. OHIP OHIP service codes: R849, G323, G325, G326, G860, G862, G865 G863, G866, G330, G331, G332, G333, G861, G082, G083, G085, G090, G091, G092, G093, G094, G095, G096, G294, G295, G864, H540, H740 DAD, SDS CCI procedure codes: 5195, 6698 CCP procedure code: 1PZ21 Immunocompromised (HIV, transplant, immunosuppressive therapy) We included immunosuppressive conditions a priori as hypothesized to be directly related to COVID-19 infection risk [9]. We included advanced liver disease a priori as hypothesized to be directly related to COVID-19 infection risk [10]. Defined using the Cirrhosis Algorithm 9:Two or more physician visits (diagnosis code 571), or one or more hospital diagnosis of cirrhosis, using the following diagnostic codes: ICD-9 : 456.1, 571.2, 571.5 ICD-10: I85.9, I98.2, K70.3,K71.7, K74.6 Defined using the Decompensated Cirrhosis Algorithm 5 (from above reference): One or more physician visits with diagnosis code 571 and (one or more hospital diagnosis or one or more procedure), using the following diagnostic codes: Acute ischemic stroke or transient ischemic attack were included a priori as hypothesized to be directly related to COVID-19 infection risk [12]. Transient Ischemic Attack: DAD and NACRS were used to identify patients with a history of a transient ischemic attack, based on at least 1 hospitalization or ED visit with a diagnosis coded with one of the following codes: ICD-9 diagnostic codes: 435, 3623 ICD-10 diagnostic codes: G450, G451, G452, G453, G458, G459, H340 Acute Ischemic Stroke: DAD was used to identify patients with a history of acute ischemic stroke, based on at least 1 hospitalization with a main diagnosis coded with one of the following codes: ICD-9 diagnostic codes: 43301, 43311 43321 43331 43381 43391 434, 436 ICD-10 diagnostic codes: I63, I64, H34.1 ADG quintiles ADGs are clusters of similar diagnoses, defined based on their estimated impact on health services resource consumption. Membership in an ADG was calculated by the Johns Hopkins Adjusted Clinical Groups (ACG) software, ACG® System Version 10, based on DAD and OHIP records in the past 2 years. Individuals were ranked by quintile based on the number of ADGs they belonged to. Number of ADGs assigned by Johns Hopkins Adjusted Clinical Groups (ACG) System Version 10 software, using DAD and OHIP records in the 2 years pre-index. We ranked individuals in the province by number of ADGs into 5 categories (quintiles), each containing approximately 20% of the overall Ontario population. There are a total of 32 ADGs that can be defined by the software: "Time Limited: Minor", "Time Limited: Minor-Primary Infections", "Time Limited: Major", "Time Limited: Major-Primary Infections", "Allergies", "Asthma", "Likely to Recur: Discrete", "Likely to Recur: Discrete-Infections", "Chronic Medical: Stable", "Chronic Medical: Unstable", "Chronic Speciality: Stable-Orthopedic", "Chronic Specialty: Stable-Ear, Nose, Throat", "Chronic Specialty: Stable-Eye", "Chronic Specialty: Unstable-Orthopedic", "Chronic Specialty: Unstable-Ear, Nose, Throat", "Chronic Specialty: Unstable-Eye", "Dermatologic", "Injuries/Adverse Effects: Minor", "Injuries/Adverse Effects: Major", "Psychosocial: Time Limited, Minor", "Psychosocial: Recurrent or Persistent, Stable", "Psychosocial: Recurrent or Persistent, Unstable", "Signs/Symptoms: Minor", "Signs/Symptoms: Uncertain", "Signs/Symptoms: Major", "Discretionary", "See and Reassure", "Prevention/Administrative", "Malignancy", "Pregnancy", and "Dental".