Association of pre-existing comorbidities with mortality and disease severity among 167,500 individuals with COVID-19 in Canada: A population-based cohort study

Erjia Ge; Yanhong Li; Shishi Wu; Elisa Candido; Xiaolin Wei

doi:10.1371/journal.pone.0258154

. 2021 Oct 5;16(10):e0258154. doi: 10.1371/journal.pone.0258154

Association of pre-existing comorbidities with mortality and disease severity among 167,500 individuals with COVID-19 in Canada: A population-based cohort study

Erjia Ge ¹, Yanhong Li ¹, Shishi Wu ¹, Elisa Candido ², Xiaolin Wei ^1,^*

Editor: Orvalho Augusto³

PMCID: PMC8491945 PMID: 34610047

Abstract

Background

The novel coronavirus disease 2019 (COVID-19) has infected 1.9% of the world population by May 2, 2021. Since most previous studies that examined risk factors for mortality and severity were based on hospitalized individuals, population-based cohort studies are called for to provide evidence that can be extrapolated to the general population. Therefore, we aimed to examine the associations of comorbidities with mortality and disease severity in individuals with COVID-19 diagnosed in 2020 in Ontario, Canada.

Methods and findings

We conducted a retrospective cohort study of all individuals with COVID-19 in Ontario, Canada diagnosed between January 15 and December 31, 2020. Cases were linked to health administrative databases maintained in the ICES which covers all residents in Ontario. The primary outcome is all-cause 30-day mortality after the first COVID-19 diagnosis, and the secondary outcome is a composite severity index containing death and hospitalization. To examine the risk factors for the outcomes, we employed Cox proportional hazards regression models and logistic regression models to adjust for demographic, socio-economic variables and comorbidities. Results were also stratified by age groups. A total of 167,500 individuals were diagnosed of COVID-19 in 2020 and included in the study. About half (43.8%, n = 73,378) had at least one comorbidity. The median follow-up period were 30 days. The most common comorbidities were hypertension (24%, n = 40,154), asthma (16%, n = 26,814), and diabetes (14.7%, n = 24,662). Individuals with comorbidity had higher risk of mortality compared to those without (HR = 2.80, 95%CI 2.35–3.34; p<0.001), and the risk substantially was elevated from 2.14 (95%CI 1.76–2.60) to 4.81 (95%CI 3.95–5.85) times as the number of comorbidities increased from one to five or more. Significant predictors for mortality included comorbidities such as solid organ transplant (HR = 3.06, 95%CI 2.03–4.63; p<0.001), dementia (HR = 1.46, 95%CI 1.35–1.58; p<0.001), chronic kidney disease (HR = 1.45, 95%CI 1.34–1.57; p<0.001), severe mental illness (HR = 1.42, 95%CI%, 1.12–1.80; p<0.001), cardiovascular disease (CVD) (HR = 1.22, 95%CI, 1.15–1.30), diabetes (HR = 1.19, 95%, 1.12–1.26; p<0.001), chronic obstructive pulmonary disease (COPD) (HR = 1.19, 95%CI 1.12–1.26; p<0.001), cancer (HR = 1.17, 95%CI, 1.09–1.27; p<0.001), hypertension (HR = 1.16, 95%CI, 1.07–1.26; p<0.001). Compared to their effect in older age groups, comorbidities were associated with higher risk of mortality and severity in individuals under 50 years old. Individuals with five or more comorbidities in the below 50 years age group had 395.44 (95%CI, 57.93–2699.44, p<0.001) times higher risk of mortality compared to those without. Limitations include that data were collected during 2020 when the new variants of concern were not predominant, and that the ICES databases do not contain detailed individual-level socioeconomic and racial variables.

Conclusion

We found that solid organ transplant, dementia, chronic kidney disease, severe mental illness, CVD, hypertension, COPD, cancer, diabetes, rheumatoid arthritis, HIV, and asthma were associated with mortality or severity. Our study highlights that the number of comorbidities was a strong risk factor for deaths and severe outcomes among younger individuals with COVID-19. Our findings suggest that in addition of prioritizing by age, vaccination priority groups should also include younger population with multiple comorbidities.

Introduction

Since the first case of the coronavirus disease 2019 (COVID-19) was reported in December 2019, the exponential growth of the pandemic has profoundly changed every aspect of human lives. By May 2, 2021, there were over 151 million reported COVID-19 cases, accounting for 1.9% of the world population, among them over 3 million died [1]. Since October 2020, COVID-19 become the third leading cause of death in the United States for adults aged 45 years or older [2], and is likely to continue to rise over the list in many countries [3].

Existing evidence from a growing body of research on the risk factors for adverse outcomes of COVID-19 revealed that old age is a strong predictor of COVID-19 mortality; in addition, male sex, hypertension, diabetes, cardiovascular disease (CVD), kidney disease, cancer and dementia are risk factors for COVID-19 mortality and hospitalization [4–10]. However, the majority of the previous studies was based on cohorts of hospitalized COVID-19 patients because electronic medical records were more accessible [5, 9, 11]. One limitation of using hospital-based cohorts is that risk factors identified from severe cases may not apply to the general population, since the majority of COVID-19 cases are in the community. Therefore, evidence from population-based cohorts is called for to provide a more comprehensive and robust analysis of risk factors that can be extrapolated to the general population. As of May 2 2021, only four studies employed cohorts containing both outpatients and inpatients in examining risk factors for COVID-19 mortality. The two studies that were conducted in the United States and the South Africa are based on public health insurance records [12, 13], which may miss a large proportion of residents with private insurance. In addition, the study in the United States did not report relative risks [13] and the South African study contained limited information on comorbidities [12]. Another study employed research network databases in the United States which had limited population coverage [10]. The study in South Korea were based on national health insurance data with a full population coverage [14], but the accuracy of diagnoses of asthma and diabetes was as low as 50% due to false claims made for profit reasons [15]. Furthermore, all the previous studies were based on data from the early wave of the pandemic, while more cases among younger population were observed in the following waves [16, 17]. Therefore, there is an urgent need to capture the changing risk factors for COVID-19 outcomes using large and accurate population-based data to provide evidence to inform vaccination roll-out, public health and clinical responses.

The first COVID-19 case in Canada was reported in Toronto, Ontario on January 25, 2020. By May 2, 2021, there were over 1.23 million cases and 24,300 deaths reported in Canada [18]. As the largest province in Canada, Ontario reported the highest number of cases (470,465) and the second highest number of deaths (8,102) by May 2, 2021 [19]. In Ontario, health care services, including physician services, hospital care, and diagnostic testing, are universally-funded for all residents with the provincial government as the single payer. Using population-based health administrative databases, we aim to examine the associations of comorbidities with mortality and disease severity in individuals with COVID-19 diagnosed in 2020 in Ontario, Canada.

Methods

Study population and data sources

We conducted a population-based retrospective cohort study of individuals who had a positive test result of the novel coronavirus (SARS-CoV-2) reported through the Ontario Laboratories Information System (OLIS) from January 15 to December 31, 2020. Individuals who were not eligible for the Ontario Health Insurance Plan (OHIP) and those who were not residents of Ontario at the beginning of the study period were not included in the database. The OLIS collects testing SARS-COV-2 results based on the polymerase chain reaction (PCR) test processed at all provincial public health laboratories, hospital laboratories, and commercial laboratories in Ontario. We treated the first date of sample collection with a following positive SARS-COV-2 result diagnosed in 2020 as the diagnosing date. The study cohort derived from OLIS was linked to population-based provincial health administrative data to ascertain baseline information on socio-demographic characteristics and chronic conditions, as well as outcomes of interest. These datasets were linked using unique encoded identifiers and analyzed at ICES. ICES is an independent, non-profit research institute whose legal status under Ontario’s health information privacy law allows it to collect and analyze health care and demographic data, without consent, for health system evaluation and improvement.

Ethics consideration

The study has received ethical approval from the Office of Research Ethics at the University of Toronto (#39138). The consent form was not obtained as the data is from ICES, which is an independent, non-profit research institute whose legal status under Ontario’s health information privacy law allows it to collect and analyze health care and demographic data, without consent, for health system evaluation and improvement.

Definitions of outcomes and risk factors

The primary outcome of our study is mortality, which is defined as all-cause death within 30 days after the first positive SARS-COV-2 test. Data on individuals’ mortality was retrieved from the Canadian Institute for Health Information’s Discharge Abstract Database and the Registered Persons Database until January 30, 2021.

We adopted a composite severity outcome, which consists of all-cause deaths within 30 days after the first SARS-COV-2 positive test and hospitalization happened between the period of 14 days prior to and 30 days after the first SARS-COV-2 positive test, as our secondary outcome. Data on hospital admission and discharge was retrieved from the National Ambulatory Care Reporting System, and the Ontario Health Insurance Plan databases until January 30, 2021.

We extracted individuals’ demographic and residential information, including age, sex, residence, income quintile, and whether the individual was resident of a long-term care (LTC) facility 90 days prior to the testing date from the database. Age was grouped as below 50, 50–59, 60–69, 70–79, and above 80 years old. Residence was distinguished between rural and urban. The income variable was neighborhood-based and determined using methods developed by Statistics Canada, where income was adjusted for household size and cost of living across the province so that each dissemination area would have 20% of its population in each income quintile. Quintile five indicated the highest income group while quintile one indicated the lowest. Each individual was assigned with the neighborhood income quintile of the dissemination area which was matched with his/her postal code [20]. LTC residence was included as risk factor because high mortality in LTC facilities was reported in several countries, including Canada [21–23]. Individuals’ pre-existing comorbidities (S1 Appendix) were identified using pre-existing ICES chronic disease cohorts and data from the Discharged Abstract Database, National Ambulatory Care Reporting System, Ontario Health Insurance Plan, and the Ontario Drug Benefit Claims Database. ICES chronic disease cohorts have been derived for several chronic conditions using administrative data algorithms. The majority of these have been validated through chart review and demonstrate high sensitivity and specificity [24, 25]. The comorbidities include asthma, chronic pulmonary obstructive disease (COPD), dementias (including Alzheimer’s and delirium), human immunodeficiency virus (HIV), hypertension, diabetes, chronic kidney disease, cancer, CVD (including cardiac ischemic disease, congestive heart failure, acute ischemic stroke, and hemorrhagic stroke), rheumatoid arthritis, inflammatory bowel disease, liver disease, severe mental illness (including other hospitalized mental illness excluding dementia), and solid organ transplant. A list of the case definition of the comorbidities can be found in S1 Appendix.

Statistical analysis

We first conducted descriptive statistical analyses of risk factors, by calculating proportions, means with standard deviation (SD), and medians with inter-quartile ranges (IQRs) of the variables. To compare the differences in risk factors between the deceased and alive groups, we employed statistical tests such as student-t test, Wilcoxon rank-sum test, Chi-Square test, Fisher’s exact test, or Kruskal-Wallis test when appropriate.

To explore risk factors for mortality, we employed Cox proportional hazards regression model to estimate the hazard ratios (HR) and corresponding 95% confidence intervals (CIs). We calculated the follow-up time for each individual from the date of COVID-19 diagnosis until death, 30 days if alive, or the end of the follow-up date of study (30 January 2021). Since many cases were hospitalized before COVID-19 diagnosis in the first wave, we used logistic regression to estimate the risks of the composite severity outcome, which consists of COVID-19 death and hospitalization, in odds ratio (ORs) and 95%CI. In multiple regression analyses employing either Cox or logistic regression models, we controlled demographic, socio-economic variables, LTC contacts, and all comorbidities. We conducted separate analyses for three comorbidity-related variables (whether having any comorbidity, the number of comorbidities, and types of comorbidity) by fitting them in three models to avoid collinearity. To understand the influence of comorbidities under different age groups, we conducted stratified multivariable Cox regression and logistic regression analyses to examine the associations between comorbidities and the two outcomes in five age strata: individuals below 50, between 50–59, between 60–69, between 70–79, and above 80 years old. We also applied the Kaplan-Meier product limit method to graphically examine the relation between comorbidities and time to mortality. Log-rank test was used to compare the differences in survival functions between levels of comorbidities.

All statistical analyses were performed using R version 4.0.3. All the variables had no missing variables except two, residence type and income quintile, having 0.28% missing values, which were randomly imputed based on the distributions of non-missing values of the variables. For all the statistical tests, a p-value less than 0.05 was considered statistically significant. The study is reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S2 Appendix). Analyses were planned prior to accessing the complete data prepared by ICES, but no prospective analysis plan was published.

Results

We identified 3,483,595 individuals who had a SARS-COV-2 tests in 2020 where we excluded 3,226,740 who had all tests as negative, and then excluded 89,335 who had a positive test after December 31, 2020. Therefore, the study included a total of 167,500 (1.1%) confirmed COVID-19 cases that were reported in Ontario, Canada as of December 31, 2020 (Fig 1). Among whom, unfortunately 2.8% (n = 4,747) deceased within 30 days after their first positive test. Of individuals with COVID-19, the average age was 42.7 years old (standard deviation, SD = 21.9), 52% (n = 87,071) were females, 24.6% (n = 41,185) belong to the lowest income quintile, and 5.6% (n = 9,357) lived in LTC facilities. The majority of individuals with COVID-19 (96.2%, n = 161,149) lived in urban areas. Nearly half (43.8%, n = 73,378) of Ontarians diagnosed with COVID-19 had at least one comorbidity. The most common comorbidities were hypertension (24%, n = 40,154), asthma (16%, n = 26,814), and diabetes (14.7%, n = 24,662), while the least were HIV (0.2%, n = 332) and solid organ transplant (0.1%, n = 117, Table 1). In unadjusted analyses almost all comorbidities except HIV and inflammatory bowel disease had significant associations with mortality.

Table 1. Characteristics of individuals with COVID-19 in Ontario, Canada by December 31, 2021.

Characteristics	Total individuals	Alive	Deceased within 30 days after first positive COVID-19 test	P value
Total (%)	167,500 (100)	162,753 (97.2)	4,747 (2.8)
Mean Age (± SD) (yrs)	42.7 (21.9)	41.6 (21.1)	82.2 (11.5)	<0.001
Age group, N (%) (yrs)
< = 49	104716 (62.5)	104660 (64.3)	56 (1.2)	<0.001
50–59	25960 (15.5)	25803 (15.9)	157 (3.3)
60–69	16044 (9.6)	15610 (9.6)	434 (9.1)
70–79	8802 (5.3)	7871 (4.8)	931 (19.6)
80+	11978 (7.2)	8809 (5.4)	3169 (66.8)
Sex, N (%)
Male	80429 (48.0)	78099 (48.0)	2330 (49.1)	0.14
Female	87071 (52.0)	84654 (52.0)	2417 (50.9)
Income quantile, N (%)
1 (lowest)	41185 (24.6)	39807 (24.5)	1378 (29.0)	<0.001
2	36880 (22.0)	35670 (21.9)	1210 (25.5)
3	36002 (21.5)	35080 (21.6)	922 (19.4)
4	29138 (17.4)	28501 (17.5)	637 (13.4)
5 (highest)	23836 (14.2)	23250 (14.3)	586 (12.3)
Missing	459 (0.3)	445 (0.3)	14 (0.3)
Rural, N (%)
No	161149 (96.2)	156563 (96.2)	4586 (96.6)	0.16
Yes	5936 (3.5)	5786 (3.6)	150 (3.2)
Missing	415 (0.2)	404 (0.2)	11 (0.2)
LTC resident, N (%)
No	158143 (94.4)	155966 (95.8)	2177 (45.9)	<0.001
Yes	9357 (5.6)	6787 (4.2)	2570 (54.1)
Comorbidities
Any comorbidity, N (%)
No	94122 (56.2)	93969 (57.7)	153 (3.2)	<0.001
Yes	73378 (43.8)	68784 (42.3)	4594 (96.8)
Comorbidities, N (%)
0	94122 (56.2)	93969 (57.7)	153 (3.2)	<0.001
1	40141 (24.0)	39586 (24.3)	555 (11.7)
2	16363 (9.8)	15334 (9.4)	1029 (21.7)
3	8615 (5.1)	7487 (4.6)	1128 (23.8)
4	4541 (2.7)	3680 (2.3)	861 (18.1)
5+	3718 (2.2)	2697 (1.7)	1021 (21.5)
Asthma, N (%)
No	140686 (84.0)	136768 (84.0)	3918 (82.5)	0.006
Yes	26814 (16.0)	25985 (16.0)	829 (17.5)
COPD, N (%)
No	157784 (94.2)	154440 (94.9)	3344 (70.4)	<0.001
Yes	9716 (5.8)	8313 (5.1)	1403 (29.6)
Dementia, N (%)
No	158890 (94.9)	156516 (96.2)	2374 (50.0)	<0.001
Yes	8610 (5.1)	6237 (3.8)	2373 (50.0)
HIV, N (%)
No	167168 (99.8)	162431 (99.8)	4737 (99.8)	0.98
Yes	332 (0.2)	322 (0.2)	10 (0.2)
Hypertension, N (%)
No	127346 (76.0)	126535 (77.7)	811 (17.1)	<0.001
Yes	40154 (24.0)	36218 (22.3)	3936 (82.9)
Diabetes, N (%)
No	142838 (85.3)	140302 (86.2)	2536 (53.4)	<0.001
Yes	24662 (14.7)	22451 (13.8)	2211 (46.6)
Chronic kidney disease, N (%)
No	161759 (96.6)	158155 (97.2)	3604 (75.9)	<0.001
Yes	5741 (3.4)	4598 (2.8)	1143 (24.1)
Cancer, N (%)
No	160322 (95.7)	156580 (96.2)	3742 (78.8)	<0.001
Yes	7178 (4.3)	6173 (3.8)	1005 (21.2)
Cardiac vascular disease, N (%)
No	159279 (95.1)	156072 (95.9)	3207 (67.6)	<0.001
Yes	8221 (4.9)	6681 (4.1)	1540 (32.4)
Rheumatoid arthritis, N (%)
No	165964 (99.1)	161355 (99.1)	4609 (97.1)	<0.001
Yes	1536 (0.9)	1398 (0.9)	138 (2.9)
Inflammatory bowel disease, N (%)
No	167017 (99.7)	162285 (99.7)	4732 (99.7)	0.82
Yes	483 (0.3)	468 (0.3)	15 (0.3)
Liver disease, N (%)
No	166278 (99.3)	161657 (99.3)	4621 (97.3)	<0.001
Yes	1222 (0.7)	1096 (0.7)	126 (2.7)
Severe mental illness, N (%)
No	166384 (99.3)	161710 (99.4)	4674 (98.5)	<0.001
Yes	1116 (0.7)	1043 (0.6)	73 (1.5)
Solid organ transplant, N (%)
No	167324 (99.9)	162600 (99.9)	4724 (99.5)	<0.001
Yes	176 (0.1)	153 (0.1)	23 (0.5)

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Note: 1. In the row of total the percentages are within the row, whereas for the rest of the table the percentages are in columns.

2. P values were obtained through student-t test, Wilcoxon rank-sum test, Chi-Square test, Fisher’s exact test, or Kruskal-Wallis test when appropriate.

The median follow-up period under the multivariable analysis for mortality was 30 days after their first diagnoses of COVID-19. As shown in Fig 2, male sex was a significant risk factor for mortality (HR = 1.65; 95% CI 1.55–1.75; p<0.001), while older age appeared to be a strong predictor for all-cause COVID-19 mortality. Compared with those between 50–59 years old, individuals in the age groups above 80 years, between 70–79 years, and between 60–69 years had 23.10 (95%CI 19.37–27.56; p<0.001), 10.80 (95%CI 9.06–12.89; p<0.001), and 3.71 (95%CI 3.11–4.42; p<0.001) times higher risk of mortality respectively, while individuals under 50 years old had 90% lower risk of death (HR = 0.10, 95%CI 0.07–0.14, p<0.001). Individuals in higher income quintiles (quintile 4 and 5) had 13% (95%CI 0.79–0.96, p<0.001) and 14% (95%CI 0.78–0.95, p<0.001) lower risk of mortality than those in the lowest income quintile (quintile 1). Individuals who were residents in LTC facilities had 1.86 (95CI% 1.72–2.01; p<0.001) times higher risk of mortality than their counterparts who were not. Individuals with comorbidities had 2.80 times higher risk of death (95%CI 2.35–3.34; p<0.001) compared with those without, and the risk increased with respect to time (Fig 3). Furthermore, the mortality risks substantially elevated from 2.14 (95%CI 1.76–2.60) to 4.81 (95%CI 3.95–5.85) times as the number of comorbidities increased from one to five or more. Additionally, we observed higher risks of mortality in the following comorbidities: solid organ transplant (HR = 3.06, 95%CI 2.03–4.63; p<0.001), dementia (HR = 1.46, 95%CI 1.35–1.58; p<0.001), chronic kidney disease (HR = 1.45, 95%CI 1.34–1.57; p<0.001), severe mental illness (HR = 1.42, 95%CI%, 1.12–1.80; p<0.001), CVD (HR = 1.22, 95%CI, 1.15–1.30), diabetes (HR = 1.19, 95%, 1.12–1.26; p<0.001), COPD (HR = 1.19, 95%CI 1.12–1.26; p<0.001), cancer (HR = 1.17, 95%CI, 1.09–1.27; p<0.001), and hypertension (HR = 1.16, 95%CI, 1.07–1.26; p<0.001). We did not find significant associations between mortality and comorbidities such as asthma, HIV, rheumatoid arthritis, liver disease, and inflammatory bowel disease in the multivariable analysis.

In multivariable logistic models for the composite disease severity indicator (Fig 4), older age remained to be the most substantial predictor. We also observed that higher income quintile was associated with reduced odds of severe outcomes, as individuals in the highest income quintile (quintile 5) had the lowest odds (OR = 0.73, 95%CI 0.69–0.78; p<0.001) compared with individuals in lowest income quintile (quintile 1). Male sex, living in urban areas, and residents of LTC facilities remained significant predictors for disease severity. Compared with individuals without comorbidity, individuals who had comorbidities were two times more likely to experience severe outcomes (OR = 2.16, 95%CI 2.04, 2.29; p<0.001). As the number of comorbidities increased from one to five or more, the odds ratio increased substantially from 1.70 (95%CI, 1.60–1.86, p<0.001) to 6.17 (95%CI, 5.60–6.81, p<0.001). All comorbidities that were significantly associated with mortality were also found to be associated with the composite severity outcome. In addition, asthma (OR = 1.09, 95%CI 1.03–1.16, p<0.001), HIV (OR = 1.67, 95%CI 1.15–2.42, p<0.01), liver disease (OR = 1.43, 95%CI, 1.25–1.64), and rheumatoid arthritis (OR = 1.25, 95%CI 1.09–1.43; p<0.001) became significantly associated with increased odds of disease severity.

Age stratified analysis

We conducted subgroup analyses using the multivariable Cox proportional hazard regression models to explore the effect of comorbidities on mortality in each age group. As shown in Table 2, the number of comorbidities profoundly elevated mortality risks among individuals in lower age groups. On the other hand, the influence of comorbidities on mortality reduced as age increases. For example, compared with individuals of the same age group without comorbidities, the mortality risk among those having five or more comorbidities was 395.44 (95%CI, 57.93–2699.44, p<0.001), 35.87 (95%CI, 18.42–69.85, p<0.001), and 12.30 (95%CI 2.94–51.46, p<0.001) times in the age groups of below 50, between 50–59, and between 60–69 years respectively. However, the associations were not significant among individuals in their 70s and 80s. Additionally, among individuals under 50 years old, elevated mortality risks were observed, including comorbidities such as HIV (HR = 13.07, 95%CI, 3.19–53.58, p<0.001), severe mental illness (HR = 13.33, 95%CI, 5.00–35.52, p<0.001), rheumatoid arthritis (HR = 6.96, 95%CI, 1.63–29.68, p<0.01), CVD (HR = 6.82, 95%CI, 2.46–18.9, p<0.001), cancer (HR = 6.82, 95%CI, 2.61–17.82, p<0.001), liver disease (HR = 6.05, 95%CI, 1.83–20.00, p<0.001), and diabetes (HR = 2.48, 95%CI, 1.25–4.93, p<0.01). Among all age groups, diabetes and cancer remained a significant risk factor for mortality.

Table 2. Age groups based analyses regarding associations of demo-socio-economic characters and comorbidities with all-cause 30-day mortality by Cox proportional hazards regressions.

Risk factor	Hazard ratio (95% CI)
Risk factor	Age < = 49 years	Age 50–59 years	Age 60–69 years	Age 70–79 years	Age 80+ years
Total (%)	104,716 (62.5)	25,960 (15.5)	16,044 (9.6)	8,802 (5.3)	11,978 (7.2)
Age (every 1-year increases)	1.09 (1.05, 1.14) ^***	1.13 (1.06, 1.20) ^***	1.07 (1.03, 1.12) ^***	1.07 (1.05, 1.09) ^***	1.05 (1.05, 1.05) ^***
Sex
Male	2.80 (1.56, 5.04) ^***	1.72 (1.23, 2.39) ^***	1.39 (1.14, 1.69) ^***	1.73 (1.51, 1.99) ^***	1.77 (1.63, 1.91) ^***
Female	reference	reference	reference	reference	reference
Income quantile
1 (lowest)	reference	reference	reference	reference	reference
2	0.91 (0.45, 1.85)	0.77 (0.50, 1.19)	0.84 (0.65, 1.09)	0.89 (0.74, 1.06)	1.06 (0.96, 1.17)
3	0.86 (0.42, 1.78)	0.76 (0.48, 1.22)	0.76 (0.58, 1.00)	0.86 (0.71, 1.05)	1.06 (0.96, 1.17)
4	0.67 (0.29, 1.56)	0.79 (0.48, 1.28)	0.64 (0.47, 0.88) ^**	0.80 (0.65, 1.00)	0.96 (0.85, 1.08)
5 (highest)	0.30 (0.09, 1.04)	0.57 (0.31, 1.05)	0.57 (0.39, 0.83) ^***	0.80 (0.63, 1.02)	0.97 (0.86, 1.09)
Rural
No	reference	reference	reference	reference	reference
Yes	1.17 (0.28, 4.91)	0.78 (0.29, 2.12)	1.21 (0.73, 2.01)	0.82 (0.56, 1.19)	1.06 (0.87, 1.29)
LTC resident
No	reference	reference	reference	reference	reference
Yes	10.80 (1.82, 64.30) ^**	4.71 (2.47, 9.00) ^***	3.60 (2.63, 4.92) ^***	1.97 (1.65, 2.35) ^***	1.49 (1.38, 1.61) ^***
Any comorbidities
No	reference	reference	reference	reference	reference
Yes	3.00 (1.67, 5.41) ^***	2.39 (1.43, 3.97) ^***	2.23 (1.50, 3.29) ^***	1.86 (1.21, 2.86) ^***	1.13 (0.79, 1.60)
Number of comorbidities
0	reference	reference	reference	reference	reference
1	2.46 (1.37, 4.43) ^***	2.18 (1.26, 3.78) ^*	1.28 (0.80, 2.06)	1.11 (0.68, 1.80)	1.06 (0.73, 1.54)
2	2.39 (0.86, 6.61)	1.65 (0.85, 3.21)	2.05 (1.31, 3.22) ^***	1.80 (1.15, 2.83) ^*	1.06 (0.75, 1.51)
3	5.53 (1.35, 22.67) *	4.31 (2.17, 8.55) ^***	2.66 (1.66, 4.26) ^***	2.14 (1.36, 3.36) ^***	1.11 (0.78, 1.57)
4	20.7 (5.05, 84.88) ^***	5.05 (2.13, 11.97) ^***	5.81 (3.63, 9.30) ^***	2.25 (1.40, 3.60) ^***	1.07 (0.75, 1.53)
5+	109.95 (33.92, 356.38) ^***	5.81 (2.27, 14.89) ^***	4.95 (2.98, 8.24) ^***	2.69 (1.68, 4.31) ^***	1.36 (0.96, 1.94)
Asthma
No	reference	reference	reference	reference	reference
Yes	1.12 (0.53, 2.35)	1.42 (0.94, 2.14)	1.08 (0.84, 1.40)	1.02 (0.86, 1.22)	0.96 (0.87, 1.06)
COPD
No	reference	reference	reference	reference	reference
Yes	1.28 (0.30, 5.48)	1.75 (1.14, 2.69) ^**	1.26 (0.99, 1.59)	1.30 (1.13, 1.49) ^***	1.09 (1.01, 1.18) ^*
Dementia
No	reference	reference	reference	reference	reference
Yes	0.77 (0.05, 11.09)	2.56 (1.26, 5.18) ^**	1.82 (1.33, 2.49) ^***	1.68 (1.41, 2.01) ^***	1.28 (1.19, 1.39) ^***
HIV
No	reference	reference	reference	reference	reference
Yes	13.07 (3.19, 53.58) ^***	0.64 (0.09, 4.66)	1.82 (0.58, 5.68)	0.61 (0.15, 2.44)	1.38 (0.34, 5.54)
Hypertension
No	reference	reference	reference	reference	reference
Yes	0.75 (0.33, 1.70)	0.84 (0.59, 1.20)	1.13 (0.89, 1.43)	1.13 (0.95, 1.35)	0.97 (0.86, 1.09)
Diabetes
No	reference	reference	reference	reference	reference
Yes	2.48 (1.25, 4.93) ^**	1.92 (1.35, 2.73) ^***	1.49 (1.20, 1.85) ^***	1.30 (1.13, 1.49) ^***	1.13 (1.04, 1.22) ^***
Chronic kidney disease
No	reference	reference	reference	reference	reference
Yes	1.60 (0.45, 5.72)	3.56 (2.22, 5.70) ^***	2.46 (1.91, 3.17) ^***	1.42 (1.21, 1.66) ^***	1.31 (1.21, 1.42) ^***
Cancer
No	reference	reference	reference	reference	reference
Yes	6.82 (2.61, 17.82) ^***	2.94 (1.84, 4.71) ^***	1.45 (1.10, 1.90) ^**	1.20 (1.02, 1.40) ^*	1.08 (1.00, 1.17)
Cardiovascular disease
No	reference	reference	reference	reference	reference
Yes	6.82 (2.46, 18.90) ^***	1.36 (0.82, 2.27)	1.82 (1.44, 2.31) ^***	1.22 (1.04, 1.43) ^**	1.11 (1.02, 1.20) ^**
Rheumatoid arthritis
No	reference	reference	reference	reference	reference
Yes	6.96 (1.63, 29.68) ^**	1.84 (0.58, 5.85)	1.03 (0.51, 2.09)	1.15 (0.81, 1.64)	1.03 (0.83, 1.28)
Inflammatory bowel disease
No	reference	reference	reference	reference	reference
Yes	3.29 (0.43, 25.24)	NA	2.01 (0.65, 6.28)	1.00 (0.32, 3.12)	0.64 (0.32, 1.27)
Liver disease
No	reference	reference	reference	reference	reference
Yes	6.05 (1.83, 20.00) ^***	1.34 (0.62, 2.87)	1.11 (0.69, 1.77)	1.00 (0.72, 1.40)	0.98 (0.76, 1.26)
Severe mental illness
No	reference	reference	reference	reference	reference
Yes	13.33 (5.00, 35.52) ^***	1.34 (0.47, 3.78)	1.88 (1.11, 3.19) ^*	0.84 (0.52, 1.38)	1.40 (0.99, 2.00)
Solid organ transplant
No	reference	reference	reference	reference	reference
Yes	3.53 (0.38, 32.93)	0.93 (0.22, 3.90)	2.27 (1.14, 4.51) ^*	2.69 (1.47, 4.94) ^***	NA

Open in a new tab

Note: 1. Statistical significance is denoted by asterisk.

* means <0.05,

** means <0.01, and

*** means <0.001.

2. Three separate models that include each of the comorbidity-related variables (i.e., whether having any comorbidity, the number of comorbidities, and types of comorbidity) were created to avoid collinearity, while this table presents the combined results.

Similar patterns were identified in the subgroup analysis for the composite severity outcome that the number of comorbidities were associated with elevated risk to disease severity among younger age groups, while it became non-significant to disease severity among individuals of 80 years or older (Table 3). Among individuals under 50 years old, preexisting conditions such as severe mental illness (OR = 8.00, 95%CI 6.33–10.13, p<0.001), solid organ transplant (OR = 6.69, 95%CI 3.43–13.02, p<0.001), chronic kidney disease (OR = 2.83, 95%I 2.15–3.72, p<0.001), liver disease (OR = 2.59, 95%CI, 1.65–4.06, p<0.001), CVD (OR = 2.51, 95%CI, 1.76–3.57, p<0.001), diabetes (OR = 2.08, 95%CI, 1.81–2.38, p<0.001), cancer (OR = 1.82, 95%CI, 1.36–2.44, p<0.001), hypertension (OR = 1.45, 95%CI 1.26–1.66, p<0.001), and asthma (HR = 1.31, 95%CI, 1.16–1.47, p<0.001) were significant predicators for severe outcomes. COPD, dementia and cancer appeared significantly associated with severe outcomes in most age groups.

Table 3. Age groups based analyses regarding associations of demo-socio-economic characters and comorbidities with the composite severity outcome that includes all-cause 30-day mortality and hospitalization by logistic regression models.

Risk factor	Odds ratio (95% CI)
Risk factor	Age < = 49 years	Age 50–59 years	Age 60–69 years	Age 70–79 years	Age 80+ years
Total (%)	104,716 (62.5)	25,960 (15.5)	16,044 (9.6)	8,802 (5.3)	11,978 (7.2)
Age (every 1-year increases)	1.04 (1.04, 1.04) ^***	1.04 (1.02, 1.06) ^***	1.06 (1.04, 1.08) ^***	1.07 (1.05, 1.09) ^***	1.03 (1.03, 1.03) ^***
Sex
Male	1.01 (0.93, 1.09)	1.57 (1.42, 1.73) ^***	1.38 (1.25, 1.52) ^***	1.52 (1.38, 1.68) ^***	1.86 (1.72, 2.01) ^***
Female	reference	Reference	Reference	Reference	Reference
Income quintile
1 (lowest)	reference	Reference	Reference	Reference	Reference
2	0.81 (0.72, 0.91) ^***	0.86 (0.75, 0.99) ^*	0.78 (0.69, 0.88) ^***	0.85 (0.74, 0.98) ^*	0.92 (0.84, 1.02)
3	0.75 (0.67, 0.84) ^***	0.69 (0.59, 0.81) ^***	0.70 (0.61, 0.81) ^***	0.83 (0.72, 0.95) ^**	0.96 (0.85, 1.08)
4	0.72 (0.63, 0.82) ^***	0.79 (0.67, 0.92) ^***	0.67 (0.58, 0.77) ^***	0.73 (0.62, 0.85) ^***	0.85 (0.76, 0.96) ^**
5 (highest)	0.69 (0.59, 0.81) ^***	0.73 (0.63, 0.86) ^***	0.69 (0.59, 0.81) ^***	0.75 (0.64, 0.88) ^***	0.79 (0.71, 0.89) ^***
Rural
No	reference	Reference	Reference	reference	Reference
Yes	1.06 (0.84, 1.34)	0.79 (0.59, 1.06)	0.96 (0.76, 1.22)	0.65 (0.51, 0.82) ^***	0.84 (0.68, 1.05)
LTC resident
No	reference	Reference	Reference	Reference	Reference
Yes	4.81 (2.52, 9.18) ^***	1.51 (1.02, 2.23) ^*	1.39 (1.12, 1.73) ^***	0.66 (0.57, 0.78) ^***	0.61 (0.57, 0.66) ^***
Any comorbidities
No	reference	reference	reference	reference	reference
Yes	1.80 (1.64, 1.99) ^***	1.63 (1.45, 1.84) ^***	1.60 (1.39, 1.84) ^***	1.72 (1.41, 2.09) ^***	0.99 (0.78, 1.25)
Number of comorbidities
0	reference	reference	reference	reference	reference
1	1.51 (1.37, 1.66) ^***	1.36 (1.19, 1.56) ^***	1.16 (0.99, 1.36)	1.27 (1.02, 1.58) ^*	0.92 (0.72, 1.19)
2	2.64 (2.21, 3.15) ^***	1.62 (1.38, 1.89) ^***	1.62 (1.38, 1.89) ^***	1.62 (1.30, 2.00) ^***	0.95 (0.74, 1.23)
3	4.85 (3.69, 6.39) ^***	2.69 (2.21, 3.27) ^***	2.05 (1.72, 2.45) ^***	1.88 (1.51, 2.33) ^***	1.00 (0.78, 1.29)
4	6.30 (3.93, 10.08) ^***	2.64 (1.93, 3.61) ^***	2.92 (2.40, 3.55) ^***	1.92 (1.51, 2.42) ^***	0.97 (0.75, 1.25)
5+	12.43 (6.14, 25.17) ^***	5.00 (3.59, 6.98) ^***	3.10 (2.45, 3.92) ^***	2.69 (2.13, 3.40) ^***	1.14 (0.88, 1.47)
Asthma
No	Reference	reference	Reference	reference	Reference
Yes	1.31 (1.16, 1.47) ^***	1.28 (1.12, 1.47) ^***	1.17 (1.04, 1.32) ^**	1.01 (0.90, 1.14)	1.07 (0.97, 1.18)
COPD
No	Reference	Reference	reference	reference	Reference
Yes	1.17 (0.84, 1.64)	1.46 (1.23, 1.74) ^***	1.52 (1.35, 1.71) ^***	1.46 (1.30, 1.64) ^***	1.12 (1.03, 1.21) ^**
Dementia
No	Reference	Reference	Reference	Reference	reference
Yes	2.18 (0.82, 5.81)	2.48 (1.58, 3.90) ^***	1.39 (1.08, 1.79) ^**	1.63 (1.40, 1.91) ^***	1.08 (1.00, 1.17)
HIV
No	Reference	Reference	reference	Reference	reference
Yes	3.06 (1.77, 5.31) ^***	0.93 (0.45, 1.93)	0.79 (0.34, 1.88)	0.87 (0.30, 2.51)	2.05 (0.37, 11.53)
Hypertension
No	Reference	Reference	reference	Reference	reference
Yes	1.45 (1.26, 1.66) ^***	1.13 (1.02, 1.24) ^*	1.09 (0.99, 1.21)	1.11 (0.98, 1.24)	0.95 (0.85, 1.07)
Diabetes
No	reference	reference	reference	Reference	reference
Yes	2.08 (1.81, 2.38) ^***	1.49 (1.33, 1.68) ^***	1.52 (1.38, 1.68) ^***	1.35 (1.22, 1.49)^***	1.21 (1.12, 1.31) ^***
Chronic kidney disease
No	Reference	Reference	Reference	Reference	Reference
Yes	2.83 (2.15, 3.72) ^***	2.83 (2.28, 3.51) ^***	2.36 (2.02, 2.76) ^***	1.88 (1.64, 2.15) ^***	1.43 (1.30, 1.58) ^***
Cancer
No	Reference	Reference	Reference	Reference	Reference
Yes	1.82 (1.36, 2.44) ^***	1.73 (1.40, 2.15) ^***	1.25 (1.07, 1.46) ^**	1.20 (1.06, 1.35) ^**	1.04 (0.94, 1.15)
Cardiovascular disease
No	Reference	Reference	reference	reference	Reference
Yes	2.51 (1.76, 3.57) ^***	1.48 (1.19, 1.83) ^***	1.62 (1.41, 1.85) ^***	1.42 (1.26, 1.60) ^***	1.19 (1.10, 1.28) ^***
Rheumatoid arthritis
No	Reference	Reference	Reference	Reference	Reference
Yes	1.55 (0.91, 2.64)	1.19 (0.77, 1.82)	1.34 (0.98, 1.83)	1.38 (1.05, 1.81) *	1.15 (0.93, 1.43)
Inflammatory bowel disease
No	Reference	Reference	Reference	reference	Reference
Yes	1.72 (0.93, 3.15)	1.80 (0.89, 3.65)	0.88 (0.41, 1.89)	1.70 (0.82, 3.51)	0.68 (0.36, 1.31)
Liver disease
No	Reference	Reference	Reference	reference	Reference
Yes	2.59 (1.65, 4.06) ^***	1.86 (1.33, 2.59) ^***	1.26 (0.94, 1.69)	1.19 (0.90, 1.56)	1.04 (0.78, 1.40)
Severe mental illness
No	Reference	Reference	Reference	reference	Reference
Yes	8.00 (6.33, 10.13) ^***	3.35 (2.27, 4.96) ^***	2.61 (1.80, 3.79) ^***	1.09 (0.74, 1.62)	1.75 (1.14, 2.69) ^**
Solid organ transplant
No	Reference	Reference	Reference	reference	reference
Yes	6.69 (3.43, 13.02) ^***	1.16 (0.57, 2.35)	5.99 (2.96, 12.13) ^***	3.35 (1.47, 7.64) ^***	0.88 (0.05, 15.36)

Open in a new tab

Note: 1. Statistical significance is denoted by asterisk.

* means <0.05,

** means <0.01, and

*** means <0.001.

Discussion

In this study, we mobilized a large population-based cohort to identify disease-related risk to 30-day all-cause mortality and severe outcomes. Between January 15 and December 31, 2020, a total of 167,500 COVID-19 cases were diagnosed in Ontario, Canada. Among the comorbidities examined in our study, we found that solid organ transplant, severe mental illness, dementia, chronic kidney disease, CVD, diabetes, hypertension, cancer and COPD were predictors of COVID-19 mortality and severity. We also found that increased number of comorbidities was strongly associated with COVID-19 deaths and severity, while the associations of comorbidity reduced as age increases. Comorbidities, such as HIV, severe mental illness, rheumatoid arthritis, CVD, cancer, and diabetes were associated with higher risk of COVID-19 mortality and severity among individuals under 50 years old, compared to their effect in older age groups. Our study also suggested that old age is the most significant risk factor for mortality and severity after controlling demographic and comorbidity conditions.

Our results were largely consistent with two previous population-based studies that reported CVD, cancer, liver disease, renal disease, dementia, and diabetes as risk factors to mortality [10, 14], while we added solid organ transplant, severe mental illness, hypertension and COPD to the list. Our results were also consistent with two systematic reviews synthesizing facility-based COVID-19 cohorts where CVD, hypertension, diabetes, chronic kidney disease, and cancer were identified as risk factors for COVID-19 mortality [5, 9]. Similar results were reported from a study in the United Kingdom based on hospitalized COVID-19 patients in the first wave of the pandemic [26]. We identified that solid organ transplant and severe mental illness were associated with the highest risks of mortality among all the comorbidities examined in our study. The level of risk identified in our study were similar to studies based on other large cohorts of COVID-19 patients [10, 26, 27], but was lower than the level reported from studies based on small number of patients in the beginning of the pandemic [5, 6, 9]. Similar to Harrison’s study [10], we did not find that HIV was associated with mortality, probably due to the fact that we had a small number of HIV positive patients (n = 332, 0.2%). In a South African study which contained 19% HIV positive patients in its cohort, both HIV and tuberculosis appeared to be significant risk factors for mortality [12].

Our study added to the evidence base by highlighting the number of comorbidities as a strong risk factor for COVID-19 mortality and severity among younger individuals. While a previous study only showed an elevated risk of mortality among individuals with two or more comorbidities [6], we found that as the number of comorbidities increased from one to five or more, the mortality risks increased exponentially among individuals in younger age groups. On the contrary, the number of comorbidities was not a significant risk factor for mortality among individuals above 80 years old. Furthermore, we found that comorbidities had a greater impact on individuals less than 50 years old compared with those in older age groups. For individuals under 50 years old, we identified that HIV, severe mental illness, rheumatoid arthritis, CVD, cancer and diabetes posed substantially high risks of mortality or severity, which is consistent with Harrison’s findings [10]. As vaccines are rolling out to younger populations, our study suggests that decision-makers should take the number of comorbidities into consideration when prioritizing high-risk groups for vaccination among young populations, instead of making decisions based solely on age groups or risk areas.

Strengths and limitations

To our knowledge, it is one of the first studies employed near complete population-based data in examining risk factors for COVID-19 mortality and severity. Because the Government of Ontario is the single payer for all physician consultations and hospitalization under the universal health coverage, we were able to link all individuals diagnosed with COVID-19 with their previous medical history and encounters. As our database recorded exact date of mortality, we were able to report hazard ratio through Cox models, which is a more accurate estimate of risks than odds ratios [28]. As shown in S1 Appendix, the ICES chronic disease databases employed multi-database to catch the chronic disease records of all Ontarians. The majority of the chronic disease were have been validated from previous studies demonstrating high sensitivity and specificity [24, 25], so misclassification of comorbidities is minimal.

Our findings should be considered in light of a number of limitations. First, the study was designed to identify risk factors associated with mortality and severity, and the estimates reported here do not reflect any causal effects. Second, our data was collected until December 31 2020 when the new SARS-COV-2 variants of concern (VOC) including B.1.1.7 were only sporadically reported in Ontario, but the B.1.1.7 variant has become predominant in the United Kingdom and Canada at the time of writing [29]. Variant B.1.1.7 has higher transmissibility and the younger population is more likely to be infected [30]; hence, our knowledge base regarding the risk factors for COVID-19 mortality and severity needs to be updated in light of the prevalence of the new VOCs. Third, the ICES database did not record socio-demographic data regarding ethnicity, education, and individual level income, which were found to be associated with COVID-19 outcomes in several studies. Researchers have identified that people in ethnic minority groups or with low social economic status had high risks of COVID-19 infection and death [10, 31]. Variables of ethnicity and other socio-economic status such as education may also interact with each other [32, 33]. Fourth, since the reasons of death were not available in our database, we used the all-cause 30-day mortality as our COVID-19 mortality outcome, which was also used in the previous studies [27, 34]. However, we may underestimate the risk factors as we excluded a small number of COVID-19 individuals who died more than 30 days following their diagnoses but we may also overestimate as this may include death unrelated with COVID-19. Fifth, we defined disease severity as death or hospitalization. In Ontario, Canada, all COVID-19 patients would reach a physician/ nurse practitioner if symptoms persist and would be hospitalized if the case is severe. All Ontarian residents are covered by a tax-funded universal health coverage that removes any financial barriers to access medical care. We understand this may not be the case in other settings as patients who are hospitalized have to pay or be covered by insurance, so hospitalization may not be an appropriate indicator for severity. Sixth, most of the comorbidity information were updated until March 31 2020 except three comorbidities, cardiac heart failure, inflammatory bowel disease and rheumatoid arthritis were updated until March 31, 2019. We may miss cases diagnosed after the date. The ICES chronic disease databases do not contain further details of specific comorbidities. Seventh, although the study population is close to the entire population in Ontario it excluded a very small number of people who lived in Ontario but were not eligible for OHIP. Last but not the least, though OLIS covered almost all laboratories in Ontario, the system may slightly under report COVID-19 cases, especially at the beginning of the pandemic when laboratory testing for SARS-COV-2 was still ramping up.

In conclusion, by analysing data from a large population-based cohort that includes all COVID-19 individuals identified in 2020 in Ontario Canada, we found that solid organ transplant, severe mental illness, dementia, chronic kidney disease, CVD, diabetes, hypertension, cancer and COPD were predictors of mortality and severity. Our study highlights that the number of comorbidities was a strong risk factor for deaths and severe outcomes among the younger COVID-19 individuals. We also found that the impact of comorbidities was more substantial among individuals under 50 years old. Findings of our study suggests that in addition of prioritizing by age, vaccination priority groups should include younger population with multiple comorbidities.

Supporting information

S1 Appendix. List of definitions of comorbidities.

(DOCX)

Click here for additional data file.^{(37.1KB, docx)}

S2 Appendix. STROBE checklist for items that should be included in reports of cohort studies.

(DOC)

Click here for additional data file.^{(99KB, doc)}

Acknowledgments

We would thank Aasha Gnanalingam and Daniella Barron from the Institute for ICES for their assisting with data access, dataset creation, and technical support. This study was supported by the Ontario Health Data Platform (OHDP), a Province of Ontario initiative to support Ontario’s ongoing response to COVID-19 and its related impacts. This study was also supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). Parts of this material are based on data and information compiled and provided by the Canadian Institute for Health Information (CIHI) and the MOH. The analyses, conclusions, opinions and statements expressed herein are solely those of the authors and do not reflect those of ICES, the funding or data sources; no endorsement is intended or should be inferred. No endorsement by the OHDP, its partners, or the Province of Ontario is intended or should be inferred.

Abbreviations

COVID-19: coronavirus disease 2019
COPD: chronic obstructive pulmonary disease
CVD: cardiovascular disease
CI: confidence interval
HIV: human immunodeficiency virus
HR: hazard ratio
IQR: inter-quartile range
LTC: long-term care
OHIP: Ontario Health Insurance Plan
OLIS: Laboratories Information System
OR: odds ratio
PCR: polymerase chain reaction
SARS-CoV-2: severe acute respiratory syndrome coronavirus 2
SD: standard deviation
VOC: variants of concern

Data Availability

The conditions under which the data were provided do not allow for the data to be made publicly available. The data we used for this paper were third-party data and we didn’t have any special access privileges to it. We acquired the data from the ICES (www.ices.on.ca), an independent, non-profit research institute whose legal status under Ontario’s health information privacy law allows it to collect and analyze health care and demographic data. Release and/or sharing of these data are not covered under our current data use agreement with ICES. Any request to the data should be made directly to the ICES in the same way as described above.

Funding Statement

XW received the funding from Canadian Institute of Health Research (CIHR) https://cihr-irsc.gc.ca/ and International Research and Development Centre (IDRC) https://www.idrc.ca (439835). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS One. doi: 10.1371/journal.pone.0258154.r001

Decision Letter 0

Orvalho Augusto

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

13 Aug 2021

PONE-D-21-19230

Association of pre-existing comorbidities with mortality and disease severity among 167,500 individuals with COVID-19 in Canada: a population-based cohort study

PLOS ONE

Dear Dr. Wei,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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PLOS ONE

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Additional Editor Comments (if provided):

This is an important and well-written report to help set action priorities to mitigate COVID-19 consequences. It is one of the few population based cohort analysis. The authors use multiple health administrative databases covering the Ontario population to identify SARS-CoV-2 positive cases, build a retrospective cohort of SARS-CoV-2 infected patients, and ascertain all-cause mortality and hospitalization within 30 days post-test. The period they cover (January to December 2020) is largely prior to vaccination availability.

Few issues to be addressed:

1. Create a specific ethics consideration subsection

2. The definition of severity here is a composite of hospitalization or death within 30 days. This is problematic as one of the reviewers point out. Please discuss this in the limitations.

3. Statistical analysis subsection: Lines 177 and 178, it is written that no model including the three comorbidity-related variables (whether having any comorbidity, the number of comorbidities, and types of comorbidity) was created due to potential colinearity. However, table 2 includes such kind of model. Can you clarify this?

4. Statistical analysis subsection: line 187. There is confusion between “R” and “R Studio”. Can you please correct to cite R.

5. Line 202 put the age unit.

6. Table 1 - Add a row to put totals rather than putting them on the header. In this row you can, for example, put 167500 (100) which will alert that percentages are in columns

7. Table 2 - Add a row for total participants in the analysis in each column. And make sure the rows are well aligned (for example, the number of comorbidities is quite hard to follow).

8. Figure 2 - please label the X-axis to say “HR” or “Hazard-ratio”

9. Figure 3 - it would be better to use on X-axis multiples of 7 (0, 7, 14, 21, 28) and 30 days because most of the descriptions use weeks. This is a suggestion.

In fact, this figure suggests different hazards pre-day 15 compared to post-day 15. Did you assess for time-varying coefficients in this analysis? And did you assess the proportionality assumption?

10 . Figure 4 - This plot is based on logistic regression, right? So change the “HR” to “OR”. And label the X-axis to mean either “Odds ratio” or “OR”. Also, change the footnote (lines 268 to 270).

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The paper deals with an important topic. In their study, the authors have demonstrated that the risk of death among COVID-19 patients, increases with the number of co-morbidities, at all ages. The findings are not really new, but it’s important that they be documented.

The study design is appropriate and the methodology appears sound.

I believe there should be more discussion on potential biases, particularly information bias on the co-morbidities. These could affect the strength of the findings. Also a discussion of attributable risk of the major co-morbidities could be useful.

Reviewer #2: This is a retrospective cohort study of 167,500 individuals diagnosed of COVID-19 in Ontario throughout the 12 months of 2020 which aimed to examine the associations of comorbidities with mortality and disease severity in individuals with COVID-19. The results obtained are very relevant and consistent with two previous population-based studies and added other four comorbidities (solid organ transplant, severe mental illness, hypertension and COPD) to the list of risk factors.

For a better understanding of the manuscript these comments are formulated

Comments:

1. The study is not really designed to measure the severity of COVID-19. The title of the manuscript should not contain the words “disease severity” since the secondary outcome in the study do not include the usual items of “disease severity” (such as requirement of non-invasive ventilation, mechanical ventilation or UCI admission) and instead it only includes hospitalization which is not a criterion of severity by itself. For the same reason, the terms "disease severity" should not be used throughout the text of the manuscript.

2. Methods. The ICES database did not record socio-demographic data regarding ethnicity, education, and individual level income, which were found to be associated with COVID-19 outcomes in several studies. However, the manuscript says "we employed Cox proportional hazards regression models and logistic regression models to adjust for demographic, socio-economic variables…” This apparent contradiction should be clarified and corrected

3. The definition of comorbidities that are risks factors for mortality should be better specified. For example, how the presence of “severe mental illness” , “human immunodeficiency virus infection” , “cancer” or “rheumatoid arthritis” have been defined. In HIV patients it is very important to know whether or not they are in virological remission and immune recovery. It is also necessary to know whether cancer patients have a disseminated disease or are in partial or complete remission, or whether or not patients with rheumatoid arthritis are receiving corticosteroids or immunosuppressive medications.

4. In the Discussion, lines 338-340, the sentence “Asthma, HIV, and rheumatoid arthritis were significantly associated with severity but not deaths, indicating the three conditions were more related to COVID-19 hospitalization” should be changed .

5. In conclusion paragragh, the sentence: “the number of comorbidities was a strong risk factor for deaths and severe outcomes among the younger COVID-19 individuals” should be changed since severity outcomes were not actually measured

6. Also, in the conclusion paragraph, the sentence “Findings of our study suggests that in addition of prioritizing by age, vaccination priority groups should include younger population with multiple comorbidities” must be modified because it is an interpretation that is not derived directly from the results of the study

**********

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Reviewer #1: Yes: Manfred S. Green

Reviewer #2: No

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PLoS One. 2021 Oct 5;16(10):e0258154. doi: 10.1371/journal.pone.0258154.r002

Author response to Decision Letter 0

7 Sep 2021

1. Create a specific ethics consideration subsection

Answer: Thanks. We have created a new subsection ethics consideration in Methods.

2. The definition of severity here is a composite of hospitalization or death within 30 days. This is problematic as one of the reviewers point out. Please discuss this in the limitations.

Answer: We defined disease severity of COVID-19 based on previous studies on similar topic. As a novel pandemic, there has not been consistent definitions for COVID severity. We employed this definition because in Ontario, Canada, all cases have been reviewed by physicians and only severe cases are hospitalized. In addition, under the Canadian Medicare all Ontario residents are entitled to free consultation and hospitalization, so there are no accessibility issues. We revised in the limitation.

Answer: Thanks for pointing this out. We conducted separate models but combined the results into one table to avoid multi tables. We added one footnote under each of the tables to avoid the confusion.

Three separate models that include each of the comorbidity-related variables (i.e., whether having any comorbidity, the number of comorbidities, and types of comorbidity) were created to avoid collinearity, while this table presents the combined results.

4. Statistical analysis subsection: line 187. There is confusion between “R” and “R Studio”. Can you please correct to cite R.

Answer: Thanks. We changed to R.

5. Line 202 put the age unit.

Answer: Yes, it was years.

6. Table 1 - Add a row to put totals rather than putting them on the header. In this row you can, for example, put 167500 (100) which will alert that percentages are in columns

Answer: Thanks. We revised Table 1.

7. Table 2 - Add a row for total participants in the analysis in each column. And make sure the rows are well aligned (for example, the number of comorbidities is quite hard to follow).

Answer: Thanks. We added the row and also realigned the table.

8. Figure 2 - please label the X-axis to say “HR” or “Hazard-ratio”

Answer: Thanks. We revised.

9. Figure 3 - it would be better to use on X-axis multiples of 7 (0, 7, 14, 21, 28) and 30 days because most of the descriptions use weeks. This is a suggestion.

In fact, this figure suggests different hazards pre-day 15 compared to post-day 15. Did you assess for time-varying coefficients in this analysis? And did you assess the proportionality assumption?

Answer:

Thank you for the suggestion. We used days to describe deaths and other outcomes instead of weeks to conduct the analysis, so it does not specifically give a break by weeks. Using 7 day as a break will make it hard for us to divide the x- axis as the total is 30 days (then will have two days left). Thus, it is better to use 5 days break.

As the data did not have comorbidity diagnosis date variables, and all the risk factors were occurred before the diagnosis of COVID-19, so we did not do time-varying analysis.

For the Cox models, we checked proportional assumption for each age sub-cohort. There was assumption violation in a sub-cohort, but the models were robust to the violation. So, we did not stratify the variables that violated the assumption. The overall proportional assumptions were met in all the other sub-cohorts.

Answer: Thank you so much for letting us know the typo! We corrected it and added X labels “Odds Ratio” under Figure 4.

The study design is appropriate and the methodology appears sound.

Answer: Thanks for the suggestion. We added this in the discussion:

As shown in Appendix 1, the ICES chronic disease databases employed multi-database to catch the chronic disease records of all Ontarians. The majority of the chronic disease has been validated from previous studies demonstrating high sensitivity and specificity [24, 25], so misclassification of comorbidities is minimal.

For the attributable risk of comorbidities, the data shown that, among the total 4747(= 4594+153) death cases, including the study individuals with comorbidities and without comorbidities, more than 92% of them were attributable to comorbidities. However, please keep in mind that, this is just like an univariable analysis as the attributable risk does not being adjusted other confounders, so I think it will be misleading if presenting the result of attributable risk.

For a better understanding of the manuscript these comments are formulated

Comments:

Answer: As a novel pandemic, there has not been consistent definitions for COVID severity. We employed this definition because in Ontario, Canada, all cases have been reviewed by physicians and only severe cases are hospitalized. In addition, under the Canadian Medicare all residents in Ontario are entitled to free consultation and hospitalization, so there is no accessibility issue for medical care. In another word, patients who did not have severe symptoms are not hospitalized in Ontario. We also have information regarding ICU admission in the database but we feel that hospitalization for COVID itself means a lot to patients, that can be justified as a severe outcome. We understand this may not be the case in other settings as patients who are hospitalized have to pay or be covered by insurance, so hospitalization may not be an appropriate indicator for severity. We mentioned this in the limitation.

Answer: The ICES database contains the income level as defined below. Statistics Canada has developed a composite indicator that takes account of the income, household size and cost of living in the province. In this way, we felt that we did control socioeconomic indicators. But we certainly did not have ethnicity and education level. We revised the limitation.

The income variable was neighborhood-based and determined using methods developed by Statistics Canada, where income was adjusted for household size and cost of living across the province so that each dissemination area would have 20% of its population in each income quintile. Quintile five indicated the highest income group while quintile one indicated the lowest. Each individual was assigned with the neighborhood income quintile of the dissemination area which was matched with his/her postal code [20].

Answer: Please see the detailed definition of each comorbidity in Appendix 1. The ICES chronic disease databases do not contain further details. We also add this into the limitation.

Answer: Thanks. We also felt that this statement is probably misleading and deleted it.

Answer: Please see Table 3 where associations with COVID severity by age groups were presented.

Answer: Please see the above. The study did measure associations with both severity and deaths, and presented the results in different tables (Table 3 and Table 4).

Attachment

Submitted filename: PLOS one response to comments Aug 19.docx

Click here for additional data file.^{(199.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0258154.r003

Decision Letter 1

Orvalho Augusto

20 Sep 2021

Association of pre-existing comorbidities with mortality and disease severity among 167,500 individuals with COVID-19 in Canada: a population-based cohort study

PONE-D-21-19230R1

Dear Dr. Wei,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Orvalho Augusto, MD, MPH

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

This is the revised version of a very important report of a population-based cohort study of SARS-CoV-2 positive patients.

Few general comments: Please make sure the abbreviation SARS-CoV-2 is written the same in the document. You do have a mixture of SARS-COV-2 and others SARS-CoV-2.

Abstract: No comments

Author’s summary:

- No limitation is included here. Please add this.

- Line 91 - multivariate regression. I would suggest changing this to “multiple regression” or “multivariable regression”

Background: No comments

Methods:

- A minor issue is that the income variable has the issue the quintile definition is specific to each dissemination area. So two different dissemination areas would be seen the same.

- Line 231 - change the “multivariate analyses” to “multiple regression analyses” or “multivariable regression analyses”.

- Thank you for adding the STROBE statement form.

Results:

- Table 1 - a) please add a footnote to alert the reader that in the rows of totals the percentages are within the row, whereas for the rest of the table the percentages are in columns; b) state how the p-values were computed

- Lines 272, 296, 306, 330 change the “multivariate” to “multivariable”.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: All the comments in the review have been addressed. The paper reads well and the limitations of the study have been clarified.

Reviewer #2: The key points raised have been solved. Therefore the study indicates that the number of comorbidities was a strong risk factor for deaths and severe outcomes among the younger COVID-19 patients

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

PLoS One. doi: 10.1371/journal.pone.0258154.r004

Acceptance letter

Orvalho Augusto

24 Sep 2021

PONE-D-21-19230R1

Association of pre-existing comorbidities with mortality and disease severity among 167,500 individuals with COVID-19 in Canada: a population-based cohort study

Dear Dr. Wei:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Orvalho Augusto

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Appendix. List of definitions of comorbidities.

(DOCX)

Click here for additional data file.^{(37.1KB, docx)}

S2 Appendix. STROBE checklist for items that should be included in reports of cohort studies.

(DOC)

Click here for additional data file.^{(99KB, doc)}

Attachment

Submitted filename: PLOS one response to comments Aug 19.docx

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Data Availability Statement

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PERMALINK

Association of pre-existing comorbidities with mortality and disease severity among 167,500 individuals with COVID-19 in Canada: A population-based cohort study

Erjia Ge

Yanhong Li

Shishi Wu

Elisa Candido

Xiaolin Wei

Roles

Abstract

Background

Methods and findings

Conclusion

Introduction

Methods

Study population and data sources

Ethics consideration

Definitions of outcomes and risk factors

Statistical analysis

Results

Fig 1. Flowchart of 167,500 individuals included in this study.

Table 1. Characteristics of individuals with COVID-19 in Ontario, Canada by December 31, 2021.

Fig 2. Associations of demo-socio-economic characters and comorbidities with 30-day all-cause mortality from Cox proportional hazard regression models.

Fig 3. Cumulative probability of all-cause death within 30 days separated by number of comorbidities.

Fig 4. Associations of demo-socio-economic characters and comorbidities with the compositive disease severity outcome that includes all-cause 30-day mortality and hospitalization from logistic regression models.

Age stratified analysis

Table 2. Age groups based analyses regarding associations of demo-socio-economic characters and comorbidities with all-cause 30-day mortality by Cox proportional hazards regressions.

Table 3. Age groups based analyses regarding associations of demo-socio-economic characters and comorbidities with the composite severity outcome that includes all-cause 30-day mortality and hospitalization by logistic regression models.

Discussion

Strengths and limitations

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Orvalho Augusto

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Orvalho Augusto

Roles

Acceptance letter

Orvalho Augusto

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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