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Characteristics associated with household transmission of SARS-CoV-2 in Ontario, Canada

Our take —

The objective of this study, available as a preprint and thus not yet peer reviewed, was to identify the rate of secondary transmission within households in Ontario, Canada from January to July 2020. Overall, the study identified 26,152 cases, with 7,993 (30.6%) from households with secondary transmission. Of these, 4,926 were cases of secondary transmission. Among their index cases that resulted in secondary transmission, there was a median 1 additional case. The study used machine learning techniques to address-match cases to a household, and there may be misspecification based on this algorithm. They also were limited to only laboratory-confirmed cases reported to the public health authority and thus asymptomatic cases may have been missed; nevertheless, this represents the largest study of household transmission to date.

Study design

Prospective Cohort

Study population and setting

The study objective was to describe the rates of secondary transmission within households for all laboratory-confirmed COVID-19 cases in Ontario, Canada among 21,226 private households using data from Public Health Ontario from January to July 2020. Using natural language processing, the study attempted to address-match cases living in households, apartment buildings, and multi-unit dwellings, but not those that address-matched to congregate facilities such as prisons or nursing homes. Individuals living in apartments or multi-unit dwellings without a specific apartment number were also excluded. Secondary transmission in a household was defined as address-matched cases within 1 to 14 days after the index case was identified. Households with multiple cases on the index date were also excluded, given it was not possible to determine if secondary transmission occurred within the index cluster. The study collected individual-level data such as employment status, high-risk status based on age and pre-existing comorbidities, and prior association with a known COVID-19 outbreak (such as at a workplace, or a long-term care facility), case month, case’s age, sex, and region of residence. They also assessed delay metrics: (1) delay between symptom onset and testing; (2) delay between specimen collection and receipt of test results; and (3) delay between the test report and entry into the disease reporting system. Finally, neighborhood characteristics from the 2016 Canada census were considered, including average family size, proportion of households with multiple persons per room, proportion of multi-family households, and urbanicity.

Summary of Main Findings

The study identified 38,984 confirmed COVID-19 cases in Ontario. After exclusion criteria based on index clustering and address-matching, there were 26,152 cases residing in private households, with 18,169 cases (69.5%) from households without secondary transmission and 7,993 (30.6%) with secondary transmission. Of these, there were 3,067 index cases, with a median of 1 case of secondary transmission per index case. The study found adults 20 to 59 years old and considered low-risk (based on comorbidities and age) were more likely to acquire and transmit infection within households. Index cases without secondary transmission were more likely to be healthcare workers (OR: 0.56, 95% CI: 0.50 – 0.62) or to have been associated with an outbreak outside the home (OR: 0.61, 95% CI: 0.55 – 0.68). Individuals without any symptoms flagged in the reportable disease system also had reduced odds of household transmission (OR: 0.48, 95% CI: 0.38 – 0.61). Living in a neighborhood with larger than average family size was also associated with increased transmission of nearly 1.88 odds per one-person increase (95% CI: 1.70 – 2.09).

Study Strengths

The study used the reportable disease system to identify a large number of cases in Ontario, which likely reduced selection bias to have a more representative population. They also were able to link this data using address-matching, which made it easier to automatically identify people within households than if they had required individuals to report one another. They also used a comprehensive set of covariates that examined not only individual- or neighborhood-level risk factors, but also health system-level with their delay on testing.


They primary limitation was that the study did not have the total number of individuals living in a given household, and therefore had to estimate using the neighborhood-level characteristics and other individual-level characteristics. They also were limited to the individuals with laboratory-confirmed testing, as opposed to COVID-19 diagnosis which would capture cases that did not get tested or universal testing strategies that would better identify asymptomatic cases in the population. Therefore, there may be some misclassification of cases. Additionally, during this time, schools were largely closed and did not reopen until the fall, after the study period had ended. Therefore, the lack of transmission among children may not be generalizable to other time periods. Finally, because the address-matching was through machine learning techniques, there may be some misspecification based on the processor and some individuals may be mis-matched.

Value added

This study is the largest of private households reported to date, as others most often used standard contact tracing methods which limited their sample.

This review was posted on: 22 January 2021