Sex-Based Differences in Severe Outcomes, Including Cardiovascular Hospitalization, in Adults With COVID-19 in Ontario, Canada.

Background: While men have experienced higher risks of SARS-CoV-2 infection compared to women, an analysis of sex differences by age in severe outcomes during the acute phase of infection is lacking. Objectives: The purpose of this study was to assess heterogeneity in severe outcome risks by age and sex by conducting a retrospective cohort study of community-dwelling adults in Ontario who tested positive for SARS-CoV-2 infection during the first 3 waves. Methods: Adjusted odds ratios were estimated using multilevel multivariable logistic regression models including an interaction term for age and sex. The primary outcome was a composite of severe outcomes (hospitalization for a cardiovascular (CV) event, intensive care unit admission, mechanical ventilation, or death) within 30 days. Results: Among 30,736, 199,132, and 186,131 adults who tested positive during the first 3 waves, 1,908 (6.2%), 5,437 (2.7%), and 5,653 (3.0%) experienced a severe outcome within 30 days. For all outcomes, the sex-specific risk depended on age (all P for interaction <0.05). Men with SARS-CoV-2 infection experienced a higher risk of outcomes than infected women of the same age, except for the risk of all-cause hospitalization being higher for young women than men (ages 18-45 years) during waves 2 and 3. The sex disparity in CV hospitalization across all ages either persisted or increased with each subsequent wave. Conclusions: To mitigate risks in subsequent waves, it is helpful to further understand the factors that contribute to the generally higher risks faced by men across all ages, and the persistent or increasing sex disparity in the risk of CV hospitalization.

Comorbidity-stratified estimates of 30-day mortality risk by age for unvaccinated men and women with COVID-19: a population-based cohort study.

BACKGROUND: The mortality risk following COVID-19 diagnosis in men and women with common comorbidities at different ages has been difficult to communicate to the general public. The purpose of this study was to determine the age at which unvaccinated men and women with common comorbidities have a mortality risk which exceeds that of 75- and 65-year-old individuals in the general population (Phases 1b/1c thresholds of the Centre for Disease Control Vaccine Rollout Recommendations) following COVID-19 infection during the first wave. METHODS: We conducted a population-based retrospective cohort study using linked administrative datasets in Ontario, Canada. We identified all community-dwelling adults diagnosed with COVID-19 between January 1 and October 31st, 2020. Exposures of interest were age (modelled using restricted cubic splines) and the following conditions: major cardiovascular disease (recent myocardial infarction or lifetime history of heart failure); 2) diabetes; 3) hypertension; 4) recent cancer; 5) chronic obstructive pulmonary disease; 6) Stages 4/5 chronic kidney disease (CKD); 7) frailty. Logistic regression in the full cohort was used to estimate the risk of 30-day mortality for 75- and 65-year-old individuals. Analyses were repeated after stratifying by sex and medical condition to determine the age at which 30-day morality risk in strata exceed that of the general population at ages 65 and 75 years. RESULTS: We studied 52,429 individuals (median age 42 years; 52.5% women) of whom 417 (0.8%) died within 30 days. The 30-day mortality risk increased with age, male sex, and comorbidities. The 65- and 75-year-old mortality risks in the general population were exceeded at the youngest age by people with CKD, cancer, and frailty. Conversely, women aged < 65 years who had diabetes or hypertension did not have higher mortality than 65-year-olds in the general population. Most people with medical conditions (except for Stage 4-5 CKD) aged < 45 years had lower predicted mortality than the general population at age 65 years. CONCLUSION: The mortality risk in COVID-19 increases with age and comorbidity but the prognostic implications varied by sex and condition. These observations can support communication efforts and inform vaccine rollout in jurisdictions with limited vaccine supplies.

Relationship of frailty with excess mortality during the COVID-19 pandemic: a population-level study in Ontario, Canada.

BACKGROUND: There is a paucity of the literature on the relationship between frailty and excess mortality due to the COVID-19 pandemic. METHODS: The entire community-dwelling adult population of Ontario, Canada, as of January 1st, 2018, was identified using the Cardiovascular Health in Ambulatory Care Research Team (CANHEART) cohort. Residents of long-term care facilities were excluded. Frailty was categorized through the Johns Hopkins Adjusted Clinical Groups (ACG® System) frailty indicator. Follow-up was until December 31st, 2020, with March 11th, 2020, indicating the beginning of the COVID-19 pandemic. Using multivariable Cox models with patient age as the timescale, we determined the relationship between frailty status and pandemic period on all-cause mortality. We evaluated the modifier effect of frailty using both stratified models as well as incorporating an interaction between frailty and the pandemic period. RESULTS: We identified 11,481,391 persons in our cohort, of whom 3.2% were frail based on the ACG indicator. Crude mortality increased from 0.75 to 0.87% per 100 person years from the pre- to post-pandemic period, translating to ~ 13,800 excess deaths among the community-dwelling adult population of Ontario (HR 1.11 95% CI 1.09-1.11). Frailty was associated with a statistically significant increase in all-cause mortality (HR 3.02, 95% CI 2.99-3.06). However, all-cause mortality increased similarly during the pandemic in frail (aHR 1.13, 95% CI 1.09-1.16) and non-frail (aHR 1.15, 95% CI 1.13-1.17) persons. CONCLUSION: Although frailty was associated with greater mortality, frailty did not modify the excess mortality associated with the pandemic.

Clinical risk, sociodemographic factors, and SARS-CoV-2 infection over time in Ontario, Canada.

We aimed to determine whether early public health interventions in 2020 mitigated the association of sociodemographic and clinical risk factors with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We conducted a population-based cohort study of all adults in Ontario, Canada who underwent testing for SARS-CoV-2 through December 31, 2020. The outcome was laboratory-confirmed SARS-CoV-2 infection, determined by reverse transcription polymerase chain reaction testing. Adjusted odds ratios (ORs) were determined for sociodemographic and clinical risk factors before and after the first-wave peak of the pandemic to assess for changes in effect sizes. Among 3,167,753 community-dwelling individuals, 142,814 (4.5%) tested positive. The association between age and SARS-CoV-2 infection risk varied over time (P-interaction < 0.0001). Prior to the first-wave peak, SARS-CoV-2 infection increased with age whereas this association reversed thereafter. Risk factors that persisted included male sex, residing in lower income neighborhoods, residing in more racially/ethnically diverse communities, immigration to Canada, hypertension, and diabetes. While there was a reduction in infection rates after mid-April 2020, there was less impact in regions with higher racial/ethnic diversity. Immediately following the initial peak, individuals living in the most racially/ethnically diverse communities with 2, 3, or ≥ 4 risk factors had ORs of 1.89, 3.07, and 4.73-fold higher for SARS-CoV-2 infection compared to lower risk individuals in their community (all P < 0.0001). In the latter half of 2020, this disparity persisted with corresponding ORs of 1.66, 2.48, and 3.70-fold higher, respectively. In the least racially/ethnically diverse communities, there was little/no gradient in infection rates across risk strata. Further efforts are necessary to reduce the risk of SARS-CoV-2 infection among the highest risk individuals residing in the most racially/ethnically diverse communities.

The risk of death or unplanned readmission after discharge from a COVID-19 hospitalization in Alberta and Ontario.

BACKGROUND: The frequency of readmissions after COVID-19 hospitalizations is uncertain, as is whether current readmission prediction equations are useful for discharge risk stratification of COVID-19 survivors or for comparing among hospitals. We sought to determine the frequency and predictors of death or unplanned readmission after a COVID-19 hospital discharge. METHODS: We conducted a retrospective cohort study of all adults (≥ 18 yr) who were discharged alive from hospital after a nonpsychiatric, nonobstetric, acute care admission for COVID-19 between Jan. 1, 2020, and Sept. 30, 2021, in Alberta and Ontario. RESULTS: Of 843 737 individuals who tested positive for SARS-CoV-2 by reverse transcription polymerase chain reaction during the study period, 46 412 (5.5%) were adults admitted to hospital within 14 days of their positive test. Of these, 8496 died in hospital and 34 846 were discharged alive (30 336 discharged after an index admission of ≤ 30 d and 4510 discharged after an admission > 30 d). One in 9 discharged patients died or were readmitted within 30 days after discharge (3173 [10.5%] of those with stay ≤ 30 d and 579 [12.8%] of those with stay > 30 d). The LACE score (length of stay, acuity, Charlson Comorbidity Index and number of emergency visits in previous 6 months) for predicting urgent readmission or death within 30 days had a c-statistic of 0.60 in Alberta and 0.61 in Ontario; inclusion of sex, discharge locale, deprivation index and teaching hospital status in the model improved the c-statistic to 0.73. INTERPRETATION: Death or readmission after discharge from a COVID-19 hospitalization is common and had a similar frequency in Alberta and Ontario. Risk stratification and interinstitutional comparisons of outcomes after hospital admission for COVID-19 should include sex, discharge locale and socioeconomic measures, in addition to the LACE variables.

The impact of shifting demographics, variants of concern and vaccination on outcomes during the first 3 COVID-19 waves in Alberta and Ontario: a retrospective cohort study.

BACKGROUND: In Canada, published outcome data for COVID-19 come largely from the first 2 waves of the pandemic. We examined changes in demographics and 30-day outcomes after SARS-CoV-2 infection during the first 3 pandemic waves in Alberta and Ontario; for wave 3, we compared outcomes between those infected with a variant of concern and those infected with the original "wild-type" SARS-CoV-2. METHODS: We conducted a population-based retrospective cohort study using linked health care data sets in Alberta and Ontario. We identified all-cause hospitalizations or deaths within 30 days after a positive result on a reverse transcription polymerase chain reaction test for SARS-CoV-2 in individuals of any age between Mar. 1, 2020, and June 30, 2021, with genomic confirmation of variants of concern. We compared outcomes in wave 3 (February 2021 to June 2021) with outcomes in waves 1 and 2 combined (March 2020 to January 2021) after adjusting for age, sex and Charlson Comorbidity Index score. Using wave 3 data only, we compared outcomes by vaccination status and whether or not the individual was infected with a variant of concern. RESULTS: Compared to those infected with SARS-CoV-2 during waves 1 and 2 (n = 372 070), we found a shift toward a younger and healthier demographic in those infected during wave 3 (n = 359 079). In wave 3, patients were more likely to be hospitalized (adjusted odds ratio [aOR] 1.57, 95% confidence interval [CI] 1.46-1.70) but had a shorter length of hospital stay (median 6 days v. 7 days, p < 0.001) and lower 30-day mortality (aOR 0.73, 95% CI 0.65-0.81). The 217 892 patients in wave 3 who were infected with a variant of concern (83.5% confirmed to have the Alpha variant, 1.7% confirmed to have the Delta variant) had a higher risk of death (Alpha: aOR 1.29, 95% CI 1.16-1.44; Delta: aOR 2.05, 95% CI 1.49-2.82) and hospitalization (Alpha: aOR 1.59, 95% CI 1.53-1.66; Delta: aOR 1.88, 95% CI 1.64-2.15) than those infected with wild-type SARS-CoV-2. INTERPRETATION: We observed a shift among those infected with SARS-CoV-2 toward younger patients with fewer comorbidities, a shorter length of hospital stay and lower mortality risk as the pandemic evolved in Alberta and Ontario; however, infection with a variant of concern was associated with a substantially higher risk of hospitalization or death. As variants of concern emerge, ongoing monitoring of disease expression and outcomes among vaccinated and unvaccinated individuals is important to understand the phenotypes of COVID-19 and the anticipated burdens for the health care system.

Factors associated with SARS-CoV-2 test positivity in long-term care homes: A population-based cohort analysis using machine learning.

BACKGROUND: SARS-Cov-2 infection rates are high among residents of long-term care (LTC) homes. We used machine learning to identify resident and community characteristics predictive of SARS-Cov-2 infection. METHODS: We linked 26 population-based health and administrative databases to identify the population of all LTC residents tested for SARS-Cov-2 infection in Ontario, Canada. Using ensemble-based algorithms, we examined 484 factors, including individual-level demographics, healthcare use, comorbidities, functional status, and laboratory results; and community-level characteristics to identify factors predictive of infection. Analyses were performed separately for January to April (early wave 1) and May to August (late wave 1). FINDINGS: Among 80,784 LTC residents, 64,757 (80.2%) were tested for SARS-Cov-2 (median age 86 (78-91) years, 30.6% male), of whom 10.2% of 33,519 and 5.2% of 31,238 tested positive in early and late wave 1, respectively. In the late phase (when restriction of visitors, closure of communal spaces, and universal masking in LTC were routine), regional-level characteristics comprised 33 of the top 50 factors associated with testing positive, while laboratory values and comorbidities were also predictive. The c-index of the final model was 0.934, and sensitivity was 0.887. In the highest versus lowest risk quartiles, the odds ratio for infection was 114.3 (95% CI 38.6-557.3). LTC-related geographic variations existed in the distribution of observed infection rates and the proportion of residents at highest risk. INTERPRETATION: Machine learning informed evaluation of predicted and observed risks of SARS-CoV-2 infection at the resident and LTC levels, and may inform initiatives to improve care quality in this setting. FUNDING: Funded by a Canadian Institutes of Health Research, COVID-19 Rapid Research Funding Opportunity grant (# VR4 172736) and a Peter Munk Cardiac Centre Innovation Grant. Dr. D. Lee is the Ted Rogers Chair in Heart Function Outcomes, University Health Network, University of Toronto. Dr. Austin is supported by a Mid-Career investigator award from the Heart and Stroke Foundation. Dr. McAlister is supported by an Alberta Health Services Chair in Cardiovascular Outcomes Research. Dr. Kaul is the CIHR Sex and Gender Science Chair and the Heart & Stroke Chair in Cardiovascular Research. Dr. Rochon holds the RTO/ERO Chair in Geriatric Medicine from the University of Toronto. Dr. B. Wang holds a CIFAR AI chair at the Vector Institute.

Statins and SARS-CoV-2 Infection: Results of a Population-Based Prospective Cohort Study of 469 749 Adults From 2 Canadian Provinces.

Background Small observational studies have suggested that statin users have a lower risk of dying with COVID-19. We tested this hypothesis in a large, population-based cohort of adults in 2 of Canada's most populous provinces: Ontario and Alberta. Methods and Results We examined reverse transcriptase-polymerase chain reaction swab positivity rates for SARS-CoV-2 in adults using statins compared with nonusers. In patients with SARS-CoV-2 infection, we compared 30-day risk of all-cause emergency department visit, hospitalization, intensive care unit admission, or death in statin users versus nonusers, adjusting for baseline differences in demographics, clinical comorbidities, and prior health care use, as well as propensity for statin use. Between January and June 2020, 2.4% of 226 142 tested individuals aged 18 to 65 years, 2.7% of 88 387 people aged 66 to 75 years, and 4.1% of 154 950 people older than 75 years had a positive reverse transcriptase-polymerase chain reaction swab for SARS-CoV-2. Compared with 353 878 nonusers, the 115 871 statin users were more likely to test positive for SARS-CoV-2 (3.6% versus 2.8%, P<0.001), but this difference was not significant after adjustment for baseline differences and propensity for statin use in each age stratum (adjusted odds ratio 1.00 [95% CI, 0.88-1.14], 1.00 [0.91-1.09], and 1.06 [0.82-1.38], respectively). In individuals younger than 75 years with SARS-CoV-2 infection, statin users were more likely to visit an emergency department, be hospitalized, be admitted to the intensive care unit, or to die of any cause within 30 days of their positive swab result than nonusers, but none of these associations were significant after multivariable adjustment. In individuals older than 75 years with SARS-CoV-2, statin users were more likely to visit an emergency department (28.2% versus 17.9%, adjusted odds ratio 1.41 [1.23-1.61]) or be hospitalized (32.7% versus 21.9%, adjusted odds ratio 1.19 [1.05-1.36]), but were less likely to die (26.9% versus 31.3%, adjusted odds ratio 0.76 [0.67-0.86]) of any cause within 30 days of their positive swab result than nonusers. Conclusions Compared with statin nonusers, patients taking statins exhibit the same risk of testing positive for SARS-CoV-2 and those younger than 75 years exhibit similar outcomes within 30 days of a positive test. Patients older than 75 years with a positive SARS-CoV-2 test and who were taking statins had more emergency department visits and hospitalizations, but exhibited lower 30-day all-cause mortality risk.

Predictors of mortality among long-term care residents with SARS-CoV-2 infection.

BACKGROUND: While individuals living in long-term care (LTC) homes have experienced adverse outcomes of SARS-CoV-2 infection, few studies have examined a broad range of predictors of 30-day mortality in this population. METHODS: We studied residents living in LTC homes in Ontario, Canada, who underwent PCR testing for SARS-CoV-2 infection from January 1 to August 31, 2020, and examined predictors of all-cause death within 30 days after a positive test for SARS-CoV-2. We examined a broad range of risk factor categories including demographics, comorbidities, functional status, laboratory tests, and characteristics of the LTC facility and surrounding community were examined. In total, 304 potential predictors were evaluated for their association with mortality using machine learning (Random Forest). RESULTS: A total of 64,733 residents of LTC, median age 86 (78, 91) years (31.8% men), underwent SARS-CoV-2 testing, of whom 5029 (7.8%) tested positive. Thirty-day mortality rates were 28.7% (1442 deaths) after a positive test. Of 59,702 residents who tested negative, 2652 (4.4%) died within 30 days of testing. Predictors of mortality after SARS-CoV-2 infection included age, functional status (e.g., activity of daily living score and pressure ulcer risk), male sex, undernutrition, dehydration risk, prior hospital contacts for respiratory illness, and duration of comorbidities (e.g., heart failure, COPD). Lower GFR, hemoglobin concentration, lymphocyte count, and serum albumin were associated with higher mortality. After combining all covariates to generate a risk index, mortality rate in the highest risk quartile was 48.3% compared with 7% in the first quartile (odds ratio 12.42, 95%CI: 6.67, 22.80, p < 0.001). Deaths continued to increase rapidly for 15 days after the positive test. CONCLUSIONS: LTC residents, particularly those with reduced functional status, comorbidities, and abnormalities on routine laboratory tests, are at high risk for mortality after SARS-CoV-2 infection. Recognizing high-risk residents in LTC may enhance institution of appropriate preventative measures.