COVID-19 Vaccination Strategy for Ontario Using Age and Neighbourhood-Based Prioritization

Abstract

on behalf of the Ontario COVID-19 Science Advisory Table Key Message SARS-CoV-2 infection has taken a disproportionate toll on Ontario older adults, and on residents of disadvantaged and racialized urban neighbourhoods throughout the province. Prioritizing and implementing vaccine distribution for Ontarians based on both age and neighbourhood of residence could ensure that those at the highest risk of SARS-CoV-2 infection, and hospitalization, ICU admission or death from COVID-19 will be among the first to receive vaccines. This vaccine strategy will maximize the prevention of deaths and long-term morbidity, and best maintain health care system capacity by reducing hospitalizations and ICU admissions due to COVID-19 as compared with a strategy that prioritizes vaccination based on age alone (Figure 1). The strategy would not interfere with the ongoing and future vaccination of any specific high-risk population, as it is intended to guide the mass distribution of vaccines to the general Ontario population. Figure 1. Projected Number of Prevented COVID-19 Hospitalizations, ICU Admissions and Deaths by Two Strategies for Mass Distribution of Vaccines in Ontario, March 1 to May 31, 2021 Bar graph presenting the projected number of prevented hospitalizations, ICU admissions and deaths due to COVID-19 in Ontario from March 1-May 31, 2021 under two strategies for mass distribution of vaccines: 1) prioritization based on age alone (blue bars) and 2) prioritization based on age and neighbourhood of residence (orange bars). The brackets above the bar graphs report the relative difference (%) in prevented outcomes between the two strategies for mass distribution of vaccines. See Table 1 below for the number of projected events associated with the different distribution strategies.

Effectiveness of mRNA and ChAdOx1 COVID-19 vaccines against symptomatic SARS-CoV-2 infection and severe outcomes with variants of concern in Ontario

“…To do this, we created a logistic regression model of the probability a N501Y-/E484K-case was Delta based on the date of specimen collection and the forward sortation area (geographical unit based on the first three characters of the postal code) ranked on the cumulative incidence of laboratory-confirmed SARS-CoV-2 cases between 23 January 2020 and 28 March 2021 and grouped into deciles. 11 For each decile, we examined the trajectories of the daily counts of N501Y-/E484K-specimens between 1 April 2021 and 30 May 2021 to estimate the probability that a N501Y-/E484K-specimen represented the Delta variant. We classified specimens with >75% probability of being a Delta variant case to be 'probable Delta' variant cases, those with 25-75% probability to be 'possible Delta' variant cases, and those with <25% probability to be 'unlikely Delta' variant cases.…”

“…We classified specimens collected after 1 April 2021 that were negative for both N501Y and E484K (N501Y-/E484K-) mutations as either probable, possible, or unlikely Delta cases based on the predicted probability that it was Delta. To do this, we created a logistic regression model of the probability a N501Y-/E484K-case was Delta based on the date of specimen collection and the forward sortation area (geographical unit based on the first three characters of the postal code) ranked by the cumulative incidence of laboratory-confirmed SARS-CoV-2 cases between 23 January 2020 and 28 March 2021 and grouped into deciles 24 . For each decile, we examined the trajectories of the daily counts of N501Y-/E484K-specimens between 1 April 2021 and 30 May 2021 to estimate the predicted probability that a N501Y-/E484K-specimen represented Delta.…”

Adaptive data-driven age and patch mixing in contact networks with recurrent mobility

“…Following through Eqs. (11)(12), we obtain X h gag ′ a ′ y , representing the total contacts formed within home pools. Then, the total type y contacts formed between populations P ga and P g ′ a ′ across all relevant mixing pools is given by the sum:…”

Early Impact of Ontario’s COVID-19 Vaccine Rollout on Long-Term Care Home Residents and Health Care Workers

“…4 Ontario's LTC homes employ approximately 100,000 clinical and non-clinical staff, who are involved in clinical work, caregiving, administration, housekeeping, food preparation, facilities management, maintenance, and recreation. 5 The majority of personal care for LTC residents is provided by approximately 50,000 personal support workers (PSWs), who represent the largest group of employees in the LTC sector, followed by approximately 25,000 registered practical nurses, registered nurses, and nurse practitioners. Nearly 90% of Ontario's PSWs are women, half are between the ages of 35 and 54 years, and more than 40% are visible minorities.…”

“…Nearly 90% of Ontario's PSWs are women, half are between the ages of 35 and 54 years, and more than 40% are visible minorities. 5 While accounting for only 0.5% of Ontario's population of 15 million, LTC residents across the province have had disproportionately high rates of SARS-COV-2 infections and COVID-19 deaths. As of February 23, 2021, there has been a total of 14,958 cumulative SARS-CoV-2 infections and 3,858 cumulative COVID-19 deaths among LTC residents (55% of Ontario's 6,940 COVID-19 deaths).…”