COVID-19 Infections and Mortality in Long-term Care Facilities During the First Wave

NCT ID: NCT04782427

Last Updated: 2021-07-28

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 1197 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-01-01
• Study Completion Date: 2021-05-13

Brief Summary

The medical charts of all COVID-19 cases (n=1200) from 17 long-term care facilities in Montreal, Canada will be reviewed, to compare patients who survived to patients who did not survive. Through multilevel logistic regression, the risk of death will be estimated for institutional predictors of mortality, while controlling for individual risk factors.

Individual covariates include clinical features (age, sex, Charlston comorbidity index, SMAF autonomy score, severity criteria) and medical treatments (IV fluids, anticoagulation, oxygen, regular opiates, corticosteroids). Aggregate covariates include epidemiological data (attack rates, timing of outbreak) and institutional characteristics (number of beds, air exchange per hour, presence of a dedicated COVID-19 unit at the time of outbreak, staff compliance to infection control measures, staff infection rates, understaffing, proportion of semi-private rooms, proportion of wandering wards and other special units).

Detailed Description

A lot has been written about individual risk factors for COVID-19 death, mostly in the hospitalized population. However, even though most deaths around the world have occurred among the frail and elderly, little is known about the risk factors specific to the long-term care population.

In this retrospective cohort study, the investigators will review the medical charts of all COVID-19 cases (n=1200) from 17 long-term care facilities in Montreal, Canada, to compare patients who survived to patients who did not survive. Through multilevel logistic regression, the risk of death will be estimated for institutional predictors of mortality, while controlling for individual risk factors. The objective is to influence local and national policies in long-term care facilities, in the hopes of avoiding the tragic spring 2020 outcomes during subsequent waves of COVID-19 or future pandemics.

Covariates in the models will be drawn from a review of the medical literature and known risk factors for COVID-19 death. Individual-level covariates include clinical features (age, sex, Charlston comorbidity index, SMAF autonomy score, severity criteria) as well as medical treatments (IV fluids, anticoagulation, oxygen, regular opiates, corticosteroids). Aggregate-level covariates include epidemiological data (attack rates, timing of outbreak) and institutional characteristics (number of beds, air exchange per hour, presence of a dedicated COVID-19 unit at the time of outbreak, staff compliance to infection control measures, staff infection rates, understaffing, proportion of semi-private rooms, proportion of wandering wards and other special units).

Conditions

Covid19; Death; Infection Viral; Infections, Respiratory; Infection, Coronavirus; Elderly Infection; Old Age; Dementia; Epidemic Disease

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Interventions

COVID-19 infection

Case of nosocomial infection within the long-term care facility

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Resident of a long-term care facility within the CIUSSS Centre-Sud-de-l'Île-de-Montréal
• Nosocomial COVID-19 infection diagnosis between March 23rd and July 11th 2020

Exclusion Criteria

• Admission to the long-term care facility after July 11th 2020
• COVID-19 infection which was not acquired within the long-term care facility

• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Université de Montréal

OTHER

Sponsor Role: collaborator

Centre integre universitaire de sante et de services sociaux du Centre-Sud-de-l'Île-de-Montréal

OTHER_GOV

Sponsor Role: lead

Responsible Party

Sophie Zhang

Head physician of long-term care facilities and researcher

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Sophie Zhang, M.D.

Role: PRINCIPAL_INVESTIGATOR

Université de Montréal

Locations

CIUSSS Centre-Sud-de-l'Île-de-Montréal

Montreal, Quebec, Canada

Countries

Canada

Other Identifiers

CentreSuddeMontréal

Identifier Type: -

Identifier Source: org_study_id