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. 2024 Mar 15;81(11):7556–7570. doi: 10.1111/jan.16136

The impact of the COVID‐19 pandemic on longitudinal trends of surgical mortality and inpatient quality of care in Ontario, Canada

Steven Habbous 1,2,✉, Maggie Ford 3, Stacey Bar‐Ziv 3, Terri Donovan 3, Erik Hellsten 1
PMCID: PMC12535327  PMID: 38491720

Abstract

Aims

Previous studies have shown the COVID‐19 pandemic was associated with reductions in volume across a spectrum of non‐SARS‐CoV‐2 hospitalizations. In the present study, we examine the impact of the pandemic on patient safety and quality of care.

Design

This is a retrospective population‐based study of discharge abstracts.

Methods

We applied a set of nationally validated indicators for measuring the quality of inpatient care to hospitalizations in Ontario, Canada between January 2010 and December 2022. We measured 90‐day mortality after selected types of higher risk admissions (such as cancer surgery and cardiovascular emergency) and the rate of patient harm events (such as delirium, pressure injuries and hospital‐acquired infections) occurring during the hospital stay.

Results

A total 13,876,377 hospitalization episodes were captured. Compared with the pre‐pandemic period, and independent of SARS‐CoV‐2 infection, the pandemic period was associated with higher rates of mortality after bladder cancer resection (adjusted risk ratio [aRR] 1.20 (1.07–1.34)) and open repair for abdominal aortic aneurysm (aRR 1.45 (1.06–1.99)). The pandemic was also associated with higher rates of delirium (adjusted odds ratio [aOR] 1.04 (1.02–1.06)), venous thromboembolism (aOR 1.10 (1.06–1.13)), pressure injuries (aOR 1.28 (1.24–1.33)), aspiration pneumonitis (aOR 1.15 (1.12–1.18)), urinary tract infections (aOR 1.02 (1.01–1.04)), Clostridiodes difficile infection (aOR 1.05 (1.02–1.09)), pneumothorax (aOR 1.08 (1.03–1.13)), and use of restraints (aOR 1.12 (1.10–1.14)), but was associated with lower rates of viral gastroenteritis (aOR 0.22 (0.18–0.28)). During the pandemic, SARS‐CoV‐2‐positive admissions were associated with a higher likelihood of various harm events.

Conclusion

The COVID‐19 pandemic was associated with higher rates of patient harm for a wide range of non‐SARS‐CoV‐2 inpatient populations.

Impact

Understanding which quality measures are improving or deteriorating can help health systems prioritize quality improvement initiatives.

Patient or Public Contribution

No patient or public contribution.

Keywords: admissions, COVID‐19, infection, mortality, nurse, nursing‐sensitive conditions, patient safety, quality indicators

1. INTRODUCTION

Over the course of the COVID‐19 pandemic, there were substantial reductions observed in hospitalization volumes for a wide range of non‐SARS‐CoV‐2 diagnostic groups (Cassell et al., 2022; Habbous, Lambrinos, Petersen, et al., 2023; Saunders et al., 2021). Periodic surges in SARS‐CoV‐2 admissions put pressure on the capacity of hospitals and health workforce to continue to provide care for all patients, in addition to patients admitted with SARS‐CoV‐2 infection. Notably, increased absence rates, job vacancies and elevated staffing demand forced many hospitals to implement alternate staffing strategies, including redeployment of health care staff to other areas of care (most frequently nurses), increase use of private agency staff, increased overtime and longer work hours for existing staff (Blackwell, 2023; Galanis et al., 2021; Maltezou et al., 2022; Pirdal et al., 2022; Troisi et al., 2022). A report recently released by the Canadian Institute for Health Information (CIHI) highlighted similar staffing challenges during the pandemic, demonstrating a 17% increase in reported sick time and a 50% increase in overtime hours comparing the 2021/22 fiscal year compared with the previous one (Canadian Institute for Health Information, 2023).

A large body of research has emerged in recent years focusing on the impacts of the pandemic on volumes and wait times. While these are important indicators for monitoring the state of the healthcare system, there has been less work examining the implications of the pandemic on quality of care, and relatively few studies examining these impacts from a system‐wide perspective. Through literature review and stakeholder surveys, the Agency for Healthcare Research and Quality (AHRQ) identified a set of procedures and diagnoses for which patient management is complex and regional variation in mortality exists (Farquhar, 2008). The CIHI in collaboration with Healthcare Excellence Canada (HEC, previously the Canadian Patient Safety Institute) in consultation with leading patient safety experts, identified and refined a comprehensive set of in‐hospital harm indicators for continuous reporting and quality improvement using a modified Delphi (Fekri et al., 2017). In this study, we examined longitudinal trends across a range of hospital inpatient quality and patient safety indicators (PSIs) in Ontario, Canada, and assessed how pre‐pandemic trends in these indicators were impacted by the pandemic period.

2. METHODS

This is a retrospective population‐based study of hospitalization episodes in Ontario, Canada. Healthcare is funded by a third‐party payer (universal health care system).

2.1. Data sources

Hospitalization episodes were derived from the CIHI Discharge Abstract Database (CIHI‐DAD). All hospitals in Ontario are mandated to report all admission records (discharge abstracts) to the CIHI‐DAD. Coding is performed by trained nosologists and routine data quality checks are performed (Canadian Institute for Health Information, 2022). A single discharge abstract includes up to 25 diagnostic codes (International Classification of Diseases, 10th edition; ICD‐10), each of which is associated with a diagnosis type (e.g., most responsible or principal diagnosis, post‐admission complication) and a diagnosis cluster (a character that links two or more diagnoses together). The post‐admission complication diagnosis type enables the measurement of in‐hospital harm indicators such as nosocomial infections or in‐hospital accidents (e.g., falls resulting in fractures). Each abstract also allows for reporting of up to 20 intervention codes (these are procedural codes as per the Canadian Classification of Interventions), each with an associated attribute code (this study excluded procedures that were abandoned) or had an out‐of‐hospital flag (this study required procedures to be performed in‐hospital). Deaths were obtained from the Registered Persons Database and supplemented using the date of discharge if the patients had a discharge disposition 72, 73, or 74. Patients were linked between databases using a unique encoded identifier. The data for this study were extracted on 17 April 2023 and included inpatient discharges occurring from 1 January 2010 until 31 December 2022.

2.2. Scope and selection of key performance indicators

We adapted a series of previously validated inpatient quality indicators (IQI) and PSI published by the AHRQ to Ontario hospital data (Agency for Healthcare Research and Quality, 2022). For measures of harm, we used the CIHI Hospital Harm indicator set, developed specifically for Canadian acute inpatient data and a systematic review of nursing‐sensitive quality indicators (Canadian Institute for Health Information, 2021; Oner et al., 2021).

2.2.1. Outcome indicators I: Mortality

The first set of IQIs estimated mortality within 90 days of admission following major events. The first category included surgical resection (e.g., esophagectomy and pancreatectomy, primarily for cancer) (Agency for Healthcare Research and Quality, 2022). We extended this list to other common or high‐risk oncologic resections, including colorectal, stomach, liver, brain and bladder using ICD‐10 diagnostic codes and intervention codes (Ministry of Health and Long‐Term Care, 2019). The second category included selected cardiovascular admissions: abdominal aortic aneurysm (AAA) repair (Salata et al., 2018), coronary artery bypass graft (CABG) (CIHI, 2023), acute stroke (McCormick et al., 2015), heart failure (HF) (Schultz et al., 2013), acute myocardial infarction (AMI) (Manuel et al., 2007), percutaneous coronary intervention (PCI) (Lee et al., 2013; Sun et al., 2021), carotid endarterectomy (CEA) (Table S1). The third category included high‐risk non‐cardiovascular admissions: gastric bypass, gastrointestinal haemorrhage, hip fracture, hysterectomy, lower limb amputation.

2.2.2. Outcome indicators II: Harm

The CIHI Hospital Harm indicators capture unintended occurrences of harm during a hospital stay, and are categorized into four domains (Canadian Institute for Health Information, 2021). The first domain (healthcare/medication‐associated conditions), included the rate of hemorrhagic events, delirium, venous thromboembolism, hypoglycemia, pressure injuries, electrolyte and fluid imbalance, medication incidents, infusion/transfusion/intravenous‐related complications and aspiration pneumonitis. The second domain (healthcare‐associated infections) included catheter‐associated urinary tract infections (CA‐UTI), post‐procedural infections, viral gastroenteritis, sepsis and infections due to Clostridioides difficile (C. diff), methicillin‐resistant Staphylococcus aureus (MRSA), and vancomycin‐resistant enterococci (VRE). We omitted pneumonia from this domain due to potential confounding with diagnoses related to SARS‐CoV‐2. The third domain (patient accidents) included patient trauma occurring in‐hospital unrelated to medical or surgical procedures such as fractures resulting from a fall. The fourth domain (procedure‐associated conditions) included hemorrhagic events, patient trauma, device failure, lacerations or punctures, pneumothorax, wound disruption, foreign objects unintentionally left in a patient after a surgical or medical procedure, shock and other selected serious events.

2.3. Indicator measurement

Each quality indicator was defined at the level of the discharge abstract. To avoid double‐counting events in hospitalization episodes involving multiple discharge records (e.g., in cases involving patient transfers or planned readmissions), related hospitalization records were resolved into episodes of care as previously described (Habbous, Lambrinos, Petersen, et al., 2023). For indicators that required a minimum length of stay (e.g., nosocomial infections), length of stay was determined using the duration of the discharge with the relevant diagnosis.

2.4. Covariates

Age at admission was derived using patients' date of birth from the Registered Persons Database. Using patients' postal codes from the Registered Persons Database, we linked to the 2016 Canada Census to ascertain rurality (urban vs. rural) and area‐level socioeconomic characteristics (material deprivation and ethnic diversity) from the Ontario Marginalization Index (Matheson et al., 2022). The material deprivation index includes factors related to income, educational attainment, employment, lone‐parent households, homes in disrepair; the ethnic diversity index factors the proportion of the region comprised of visible minorities and recent immigrants (Matheson et al., 2022). Comorbidity was measured using the Charlson comorbidity score with a 3‐year look‐back window from the date of the admission episode (Table S2) (Dobbins et al., 2015). The major clinical category (MCC) partition categorizes an admission as diagnostic or interventional, and the MCC code further categorizes admissions into 21 categories using the most responsible diagnosis (Canadian Institute for Health Information, 2008). The MCC partition and MCC code associated with the first admission record for the episode was used, keeping only unique values.

2.5. Sensitivity analysis by COVID‐19 status

Hospitalization episodes were categorized as SARS‐CoV‐2‐positive if there was either a positive PCR test from the Ontario Laboratory Information System (OLIS) or an ICD‐10 diagnostic code of U071 in DAD or NACRS reported during the hospitalization episode or within 14 days prior to the index admission (Habbous, Lambrinos, Ming, et al., 202; Lix et al., 2023). The small number of hospitalization episodes starting before 2020 and classified as COVID‐19 positive were omitted in this sensitivity analysis (due to data quality issues or unusually protracted hospital stays).

2.6. Statistical methods

We present the 90‐day mortality indicators as a count and percent of relevant hospitalizations. For harm events, we present the incidence as the number of events per 100,000 hospitalizations. In sensitivity analysis, we indirectly adjust these crude incidence rates by sex and age strata (1 to <5, 5 to <18, 18 to <40, 40 to <50, 50 to <60, 60 to <70, 70 to <80 and 80+), using the 2019 hospitalized Ontario population as the reference. The <1‐year age group was omitted due to a high prevalence of null strata or zero‐event counts. For mortality indicators, we used modified Poisson regression to estimate the relative risk of death and logistic regression to estimate the relative odds of a harm event occurring in the COVID‐19 era (March 2020 to December 2022) compared with the “pre‐COVID‐19 era” (January 2016 to February 2020). The modified Poisson regression employs a robust standard error estimation to enable risk ratio estimation (Zou, 2004). We also compared the pre‐COVID‐19 era with earlier admissions between January 2010 and December 2015 to assess historical trends. For 90‐day mortality, models were adjusted for age (continuous), sex, comorbidity (continuous), rurality and neighbourhood quintiles of material deprivation and ethnic diversity (categorical). For measures of harm, we also adjusted for the MCC partition and MCC category. Results were presented using adjusted risk ratios (aRR) or odds ratios (aOR) with 95% confidence intervals (CI). In a second adjusted model, the analysis was restricted to only SARS‐CoV‐2‐negative hospitalizations.

2.7. Software and privacy

All analyses were performed using Statistical Analysis Software v9.3 (SAS Institute, Cary, NC). Research ethics was not required as this work was done for the purpose of quality improvement and no identifying information was obtained: this study was compliant with section 45(1) of PHIPA (Ontario Health is a prescribed entity): ethics review was not required. All linkage was done using the patients' health card number, a unique identifier for Ontario residents. Data access and analyses were performed using a secure digital environment established through a virtual private network.

3. RESULTS

A total 6,820,095 patients (13,876,377 hospitalization episodes) were included (cohort selection in Table S3).

3.1. Mortality events

3.1.1. Surgical resections

Ninety‐day post‐operative mortality following resection of the lung steadily improved since 2010; this improving trend continued through the COVID‐19 era (4.6% in 2010–2015; 3.6% in 2016 to Feb 2020; and 2.7% in the COVID‐19 era) (Table 1 columns 2–4; Figure 1a). By contrast, 90‐day mortality following bladder resection was similar in the periods from 2010 to 2015 and 2016 to 2020 (5.1% and 5.4%, respectively; aRR 1.06 (0.96–1.16)), but worsened during the pandemic period (6.8%; aRR 1.23 (1.10–1.38); Table 1 columns 5–6). There were no changes over time in 90‐day post‐operative mortality following oesophageal, pancreatic, kidney, colorectal, brain, liver, or stomach resections (Figure 1a‐b). Hospitalizations related to SARS‐CoV‐2 did not influence these trends (Table 1 column 7 vs. 6).

TABLE 1.

Indicators of 90‐day mortality.

Indicator [1] Early era (March 2010–October 2015) [2] Middle era (March 2016–October 2019) [3] COVID‐19 era (March 2020–October, 2022) [4] aRR (95% CI) a
Middle versus early era[5] COVID‐19 versus middle era [6] COVID‐19 versus middle era b [7]
Deaths per episode Deaths per episode Deaths per episode aRR (95% CI) aRR (95% CI) aRR (95% CI)
Major cancer surgeries
Bladder resection 1158/22916 (5.05%) 756/13962 (5.41%) 515/7596 (6.78%) 1.06 (0.96–1.16) 1.23 (1.10–1.38) 1.20 (1.07–1.35)
Brain resection 1423/14087 (10.1%) 991/10004 (9.91%) 707/7067 (10%) 0.97 (0.90–1.05) 0.98 (0.90–1.08) 0.97 (0.89–1.06)
Colorectal resection 4607/62498 (7.37%) 3101/40987 (7.57%) 2318/29839 (7.77%) 1.03 (0.99–1.08) 1.02 (0.97–1.08) 1.00 (0.95–1.06)
Esophagectomy 120/1810 (6.63%) 72/1312 (5.49%) 61/932 (6.55%) 0.82 (0.62–1.09) 1.16 (0.84–1.61) 1.14 (0.82–1.59)
Liver resection 236/4587 (5.14%) 169/3421 (4.94%) 116/2547 (4.55%) 0.97 (0.80–1.18) 0.93 (0.74–1.17) 0.86 (0.68–1.09)
Lung resection 769/16701 (4.6%) 444/12320 (3.6%) 214/7829 (2.73%) 0.82 (0.73–0.92) 0.77 (0.65–0.90) 0.74 (0.63–0.88)
Nephrectomy 233/11364 (2.05%) 128/8592 (1.49%) 101/5482 (1.84%) 0.72 (0.58–0.90) 1.20 (0.93–1.56) 1.19 (0.92–1.54)
Pancreatectomy 101/2336 (4.32%) 74/1842 (4.02%) 68/1478 (4.6%) 0.89 (0.67–1.19) 1.15 (0.83–1.58) 1.12 (0.81–1.55)
Stomach resection 306/3763 (8.13%) 222/3048 (7.28%) 174/2082 (8.36%) 0.90 (0.77–1.07) 1.13 (0.94–1.37) 1.09 (0.90–1.33)
Abdominal aortic aneurysm
Open repair (elective) 151/3568 (4.23%) 88/2051 (4.29%) 65/1057 (6.15%) 1.06 (0.82–1.37) 1.37 (1.01–1.86) 1.36 (1.00–1.85)
Open repair (ruptured) 250/703 (35.56%) 126/393 (32.06%) 112/230 (48.7%) 0.94 (0.79–1.12) 1.47 (1.21–1.78) 1.50 (1.24–1.82)
Endovascular repair (elective) 90/4313 (2.09%) 67/2983 (2.25%) 42/1606 (2.62%) 1.05 (0.77–1.43) 1.22 (0.84–1.78) 1.21 (0.83–1.78)
Endovascular repair (ruptured) 60/251 (23.9%) 37/165 (22.42%) 22/98 (22.45%) 0.93 (0.65–1.33) 1.01 (0.64–1.60) 1.06 (0.67–1.67)
Acute stroke
Intracerebral haemorrhage 4882/15062 (32.41%) 3343/10622 (31.47%) 2339/7749 (30.18%) 0.96 (0.93–1.00) 0.98 (0.93–1.02) 0.98 (0.94–1.03)
Ischemic stroke 10,120/58239 (17.38%) 7875/49654 (15.86%) 6019/36838 (16.34%) 0.91 (0.88–0.93) 1.06 (1.03–1.10) 1.06 (1.03–1.09)
Subarachnoid haemorrhage 1103/4698 (23.48%) 690/3113 (22.17%) 474/2251 (21.06%) 0.94 (0.87–1.02) 0.93 (0.84–1.03) 0.93 (0.84–1.04)
Cardiac
Acute myocardial infarction 14,530/121458 (11.96%) 8376/80125 (10.45%) 4938/50900 (9.7%) 0.92 (0.90–0.95) 1.01 (0.98–1.04) 1.01 (0.97–1.04)
Coronary artery bypass graft 1499/45008 (3.33%) 989/29802 (3.32%) 647/17946 (3.61%) 1.05 (0.97–1.13) 1.11 (1.01–1.22) 1.09 (0.99–1.20)
Carotid endarterectomy 102/6407 (1.59%) 81/4272 (1.9%) 67/2921 (2.29%) 1.13 (0.84–1.52) 1.16 (0.84–1.60) 1.13 (0.82–1.56)
Heart failure 26,067/124098 (21.01%) 20,334/96094 (21.16%) 14,339/65358 (21.94%) 1.00 (0.98–1.02) 1.06 (1.04–1.08) 1.05 (1.03–1.07)
Percutaneous coronary intervention 3427/82650 (4.15%) 2774/55101 (5.03%) 1907/31522 (6.05%) 1.14 (1.08–1.19) 1.17 (1.10–1.23) 1.15 (1.08–1.22)
Other
Gastric bypass 47/15002 (0.31%) 13/13866 (0.09%) 15/8749 (0.17%) 0.37 (0.20–0.69) 1.98 (0.92–4.27) 1.99 (0.92–4.30)
Gastrointestinal haemorrhage 9816/64013 (15.33%) 7225/44183 (16.35%) 5565/30140 (18.46%) 1.03 (1.00–1.06) 1.14 (1.11–1.18) 1.12 (1.08–1.16)
Hip fracture 8438/62373 (13.53%) 5463/43179 (12.65%) 4110/31308 (13.13%) 0.93 (0.90–0.96) 1.07 (1.03–1.11) 1.06 (1.02–1.10)
Hysterectomy 218/85784 (0.25%) 132/50610 (0.26%) 96/24239 (0.4%) 0.90 (0.73–1.12) 1.19 (0.91–1.55) 1.18 (0.90–1.54)
Lower limb amputation 1439/9510 (15.13%) 923/6594 (14%) 623/4501 (13.84%) 0.99 (0.92–1.08) 1.05 (0.96–1.16) 1.02 (0.92–1.12)
Restraint use c n/a 256/18076 (1.42%) 1170/31111 (3.76%) n/a 1.27 (1.12–1.44) 1.24 (1.09–1.41)
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Abbreviations: aRR, adjusted risk ratio; CI, confidence interval; n/a, not available.

a

Adjusted for age, sex, rurality, comorbidity score, and neighbourhood quintiles of material deprivation and ethnic diversity.

b

Restricted to SARS‐CoV‐2 negative admission episodes.

c

Restricted to admission episodes starting 1 April 2018.

FIGURE 1.

FIGURE 1

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90‐day mortality over time following oncologic resections (a‐b); abdominal aortic aneurysm repairs (c); cerebrovascular events (d); other cardiovascular events (e); and other events or procedures (f).

3.1.2. Cardiovascular events

AAAs: compared with the pre‐COVID‐19 era, 90‐day mortality worsened during the pandemic for open surgical repair of ruptured (49% vs. 32%; aRR 1.47 (1.21–1.78)) and elective AAA repair (6.1% vs. 4.3%; aRR 1.37 (1.01–1.86)) (Figure 1c). By contrast, no change was observed in mortality following endovascular repair for either ruptured or elective AAA.

Cerebrovascular events: 90‐day mortality following admission for ischemic stroke improved over time before the COVID‐19 pandemic (aRR 0.91 (0.88–0.93)). This improvement reversed during the pandemic (aRR 1.06 (1.03–1.10)), potentially driven by a transitory increase in mortality in 2020 (Figure 1d).

Other cardiac events: 90‐day mortality following CABG and HF worsened slightly during the pandemic era (aRR 1.11 (1.01–1.22) and 1.06 (1.04–1.08), respectively), while mortality rates for other cardiovascular events largely continued their pre‐pandemic trends during the pandemic era (Figure 1e).

3.1.3. Non‐cardiovascular events

Ninety‐day mortality rates following admission for gastrointestinal haemorrhage, hysterectomy, gastric bypass, and lower limb amputation were unaffected by the pandemic (Figure 1f). In contrast, an improving trend in mortality following admission for hip fracture between the middle‐vs.‐early eras (aRR 0.93 (0.90–0.96)) worsened during the pandemic era (aRR 1.07 (1.03–1.11) vs. the middle era). Although pre‐pandemic data on use of restraints are sparse, mortality following an admission requiring some restraint was worse during the pandemic (aRR 1.27 (1.12–1.44)) than the pre‐pandemic era.

3.2. Patient safety indicators

3.2.1. COVID‐19‐related admissions and harm events

During the pandemic period, SARS‐CoV‐2 infection was associated with a higher risk of all healthcare or medication‐associated conditions, healthcare‐associated infections and patient accident harm indicators, with odds ratios ranging from aOR 1.52 (1.24–1.86) for infusion, transfusion, and injection complications to 4.37 (2.48–7.72) for viral gastroenteritis (Figure 2; full model in Table S4). SARS‐CoV‐2 positivity was associated with a higher risk for all procedure‐associated conditions, except pneumothorax (aOR 0.67 (0.58–0.77)).

FIGURE 2.

FIGURE 2

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Effect of SARS‐CoV‐2 infection on the risk of individual harm events.

3.2.2. Trends over time

Healthcare or medication‐associated conditions

The incidence of delirium increased from 500 per 100,000 hospitalization episodes in 2010, potentially plateauing by 2019 (at 1071.1 per 100,000 admission episodes), followed by a rise in 2020 (1205 per 100,000 admission episodes) (Figure 3a; Table S5). Similar trends were observed when reporting standardized rates (Figure S1). Compared with the pre‐pandemic period, the incidence of delirium increased during the pandemic (aOR 1.13 (1.11–1.15)), and this was only partially accounted for by SARS‐CoV‐2 infection (aOR 1.05 (1.03–1.06) excluding SARS‐CoV‐2‐related admissions) (Table 2; Figure S2). The incidence of delirium among SARS‐CoV‐2‐positive patients was 4‐fold higher at the start of the pandemic (4.33% in 2020), which subsequently decline to 3.50% in 2021 and 2.68% in 2022 (Figure S2).

FIGURE 3.

FIGURE 3

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Crude incidence of harm indicators over time for healthcare or medication‐associated conditions (a); healthcare‐associated infections (b); and patient accidents or procedure‐associated conditions (c).

TABLE 2.

Safety and quality indicators.

Harm event [1] Early era (March 2010–October 2015) [2] Middle era (March 2016–October 2019) [3] COVID‐19 era (March 2020–October, 2022) [4] Adjusted odds ratio (95% CI) a
Middle versus early era [5] COVID‐19 versus middle era [6] COVID‐19 versus middle era b [7]
Rate per 100 K Rate per 100 K Rate per 100 K OR (95% CI) OR (95% CI) OR (95% CI)
Category A: Healthcare‐ or medication‐associated conditions
A01: Anaemia (haemorrhage) not associated with a procedure 91.2 93.1 99.5 0.99 (0.95–1.04) 1.07 (1.02–1.13) 0.99 (0.93–1.04)
A05: Delirium a 743.9 1042.2 1187.5 1.34 (1.32–1.36) 1.13 (1.11–1.15) 1.05 (1.03–1.06)
A06: Venous thromboembolism a 247.8 231.6 296 0.90 (0.88–0.93) 1.30 (1.26–1.34) 1.11 (1.08–1.15)
A07: Hypoglycemia a 140.5 140.8 127.6 0.98 (0.94–1.01) 0.92 (0.88–0.96) 0.86 (0.82–0.90)
A08: Pressure injury a 162.6 169.1 260.4 0.97 (0.94–1.00) 1.52 (1.47–1.57) 1.28 (1.24–1.33)
A09: Electrolyte and fluid imbalance a 147.5 168 169 1.08 (1.05–1.12) 1.00 (0.96–1.03) 0.93 (0.89–0.96)
A10: Medication incidents a 26.9 20.2 17.2 0.73 (0.67–0.79) 0.85 (0.76–0.95) 0.82 (0.73–0.92)
A11: Infusion, transfusion, and injection complications a 48 63.1 55.1 1.30 (1.24–1.37) 0.87 (0.82–0.93) 0.85 (0.80–0.91)
A12: Aspiration pneumonitis a 340 381.6 473.6 1.04 (1.02–1.06) 1.20 (1.18–1.23) 1.14 (1.11–1.16)
Category B: Healthcare‐associated infections
B13: Catheter‐associated urinary tract infection 1046.2 838.2 946.8 0.76 (0.75–0.77) 1.11 (1.09–1.13) 1.00 (0.98–1.02)
B14: Post‐procedural infections 532.7 512.3 501.1 0.95 (0.94–0.97) 1.00 (0.98–1.02) 0.92 (0.90–0.94)
B15: Viral gastroenteritis 14.2 13.1 3.4 0.90 (0.81–1.00) 0.26 (0.21–0.32) 0.23 (0.18–0.29)
B17: Sepsis 436.2 422.5 462.9 0.91 (0.89–0.93) 1.07 (1.04–1.09) 0.92 (0.90–0.94)
B18: Infection
C. diff infection 284 214.7 243.9 0.72 (0.70–0.74) 1.12 (1.09–1.16) 1.05 (1.02–1.09)
MRSA infection 248.6 283.7 299.3 1.08 (1.05–1.11) 1.06 (1.03–1.09) 1.03 (1.00–1.07)
VRE infection 8.3 13.6 13.5 1.59 (1.40–1.81) 0.98 (0.86–1.13) 0.88 (0.76–1.02)
Category C: Patient accidents
C19: Patient trauma unrelated to a medical/surgical procedure 190.6 235.8 289 1.16 (1.13–1.19) 1.19 (1.15–1.22) 1.11 (1.08–1.15)
Use of restraints c n/a 1024.9 1159.5 n/a 1.11 (1.08–1.13) 1.08 (1.06–1.11)
Category D: Procedure‐associated conditions
D01: Anaemia–haemorrhage 518.9 359.2 341.9 0.68 (0.67–0.70) 1.00 (0.97–1.03) 0.98 (0.95–1.01)
D19: Patient trauma, related to a medical/surgical procedure 188.1 359.1 357.1 1.91 (1.86–1.95) 1.06 (1.03–1.08) 1.05 (1.03–1.08)
D20: Device failure 159 194.4 178.1 1.19 (1.16–1.23) 0.93 (0.90–0.96) 0.90 (0.86–0.93)
D21: Laceration/puncture during medical/surgical procedure 304 320.2 297.1 1.07 (1.04–1.09) 0.98 (0.96–1.01) 0.98 (0.95–1.01)
D22: Pneumothorax associated with a medical/surgical procedure 91.1 94 103.9 1.00 (0.96–1.05) 1.12 (1.06–1.17) 1.15 (1.10–1.21)
D23: Wound disruption 145.6 221.7 234.6 1.51 (1.47–1.55) 1.08 (1.05–1.12) 1.06 (1.03–1.10)
D24: Retained foreign body 5 6 5.1 1.16 (0.98–1.37) 0.90 (0.73–1.10) 0.85 (0.69–1.05)
D25: Procedure‐associated shock 29.2 45.9 74.2 1.56 (1.46–1.67) 1.67 (1.57–1.77) 1.61 (1.52–1.72)
D26: Selected serious events 0 0 0 n/a n/a n/a
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Abbreviations: C. diff, Clostridioides difficile; CI, confidence interval; MRSA, methicillin‐resistant Staphylococcus aureus; n/a, not available; OR, odds ratio; VRE, vancomycin‐resistant enterococci.

a

Adjusted for age, sex, comorbidity score, major clinical category admission type (interventional vs. diagnostic) and major clinical category rurality, and neighbourhood quintiles of material deprivation and ethnic diversity.

b

Restricted to SARS‐CoV‐2 negative admission episodes.

c

Restricted to admission episodes starting 1 April 2018.

There was a similar uptick in incidence during the COVID‐19 pandemic (compared with the pre‐pandemic era) for aspiration pneumonitis (473.6 vs. 381.6 events per 100,000 admissions), venous thromboembolism (296.0 vs. 231.6 events per 100,000 admissions), and pressure injury (260.4 vs. 161.1 events per 100,000 admissions) (Table 2; Figure 3a). Compared with the pre‐pandemic period, the risk of these events all increased: (aOR 1.20 (1.18–1.23) for aspiration pneumonitis; aOR 1.30 (1.26–1.34) for venous thromboembolism; and aOR 1.52 (1.47–1.57) for pressure injury), and these increases were only partially accounted for by SARS‐CoV‐2 infection (aOR reduced but still >1 on restricting to SARS‐CoV‐2‐negative hospitalization episodes; Figure S2; Table 2).

Healthcare‐associated infections

The incidence of CA‐UTI decreased steadily from 1098 per 100,000 hospitalization episodes in 2010 to 782.5 per 100,000 hospitalizations episodes in 2019, but abruptly rose to 907 per 100,000 episodes by 2020 and remained elevated throughout the COVID‐19 era (Figure 3b). A similar trend was observed for C. diff infections. MRSA infection was also elevated during the pandemic, but the increase began in 2017 and plateaued after 2019. Conversely, the incidence of viral gastroenteritis, although uncommon, decreased at the onset of the pandemic.

The increase in sepsis during the pandemic period (aOR 1.07 (1.04–1.09)) was completely accounted for by SARS‐CoV‐2 infection: the pre‐pandemic improving trend (aOR 0.91 (0.89–0.93)) continued during the pandemic among SARS‐CoV‐2‐negative admissions (aOR 0.92 (0.90–0.94)) (Figure S2). In contrast, the increase in CA‐UTI during the pandemic period (aOR 1.11 (1.09–1.13)) was completely accounted for by SARS‐CoV‐2‐related admissions (aOR 1.00 (0.98–1.02) among SARS‐CoV‐2‐negative admissions), but pre‐pandemic reductions (aOR 0.76 (0.75–0.77)) did not persist. Similar findings were observed for C. diff infection. Conversely, the incidence of viral gastroenteritis decreased slightly during the pre‐pandemic period (aOR 0.90 (0.81–1.00)), but decreased markedly during the pandemic period (aOR 0.26 (0.21–0.32)) independent of SARS‐CoV‐2 status at admission.

Patient accidents, procedure‐associated conditions, and other indicators

The use of restraints increased in the pandemic period (aOR 1.11 (1.08–1.13)). The change in incidence for other healthcare/medication‐associated conditions was unremarkable throughout the study period. Year‐over‐year changes were observed across multiple procedure‐associated conditions and patient traumas unrelated to a procedure during the early and pre‐pandemic periods; but the start of the COVID‐19 pandemic was not associated with deviations from the pre‐pandemic trends for these conditions (Figure 3c). Furthermore, admissions related to SARS‐CoV‐2 infection did not alter these trends (Table 2; Figure S2). For example, the increasing incidence of procedure‐associated shock began before the pandemic (aOR 1.56 (1.46–1.67)) but continued through December 2022 (aOR 1.67 (1.57–1.77)).

4. DISCUSSION

This study examined longitudinal trends for a broad range of healthcare quality indicators over a 12‐year period, with a focus on the pandemic era; we found evidence that certain inpatient quality and PSIs were affected by the pandemic, including higher mortality following bladder tumour resection and open surgical repair of AAA, and a higher incidence of delirium, aspiration pneumonitis, pressure injury, venous thromboembolism, CA‐UTI and C. difficile infections.

While we did not attempt an exhaustive evaluation of each indicator to uncover the root causes behind the trends, we hypothesize some potential contributing factors. Through the reduction in services and cancellation of surgeries, there was a decrease in hospitalizations observed for many patient populations during the COVID‐19 pandemic, the patients that were admitted during this period were more likely to present with advanced illness (e.g., later stage cancer) or worse prognosis (e.g., older, more frail, greater comorbidity) (Ganesh et al., 2022; Guven et al., 2021; Ilagan‐Ying et al., 2022). Although we attempted to adjust for case‐mix at the time of admission by including sociodemographic characteristics and MCC associated with the admission, this is unlikely to fully account for changing acuity during the pandemic period. Moreover, due to restricted visitation policies, more vulnerable patients may have experienced worse outcomes due to the absence of family or friends to advocate for them (Hugelius et al., 2021; Nguyen et al., 2023). Importantly, the impact of overtime work, COVID‐19‐related stressors, and a resultant uptake of agency nurses may have affected the quality of care. The impact of agency nurses on healthcare quality does, however, remain a matter of debate and warrants further study. For example, supplemental nurses may enable nurse‐to‐patient ratios and therefore improve quality of care, but conversely, may lack familiarity with the hospital environment that may result in compromised care (Aiken et al., 2013).

Changing incidence patterns for particular types of disease during the COVID‐19 pandemic may have also contributed to some of the trends observed here. For example, the pandemic was associated with a higher rate of admissions related to mental health and addictions, which may partially explain the higher incidence of restraints use (Habbous, Lambrinos, Petersen, et al., 2023; Jones et al., 2022). Conversely, non‐pharmacologic interventions against SARS‐CoV‐2 such as masking and social distancing may have curbed the spread of norovirus—the leading cause of acute gastroenteritis—in hospitals as well as in the community (Eigner et al., 2021). Similarly, the increase in delirium observed during the COVID‐19 pandemic appears to be mostly, but not entirely, driven by SARS‐CoV‐2 infection rather than compromised quality of care. This may be due to both the direct impact of SARS‐CoV‐2 infection or the indirect impacts of pandemic‐era hospital policies on patients leading to increased physical and social isolation (Tyson et al., 2022; Wilke et al., 2022; Woolley, 2021). The rise of in‐hospital infections observed during the pandemic is somewhat surprising given hospitals' added focus on infection control.

Some of our Ontario findings echo those found in studies of pandemic‐era trends in other jurisdictions, while other findings diverge from those in previous studies. Similar to our results, one study from the United States observed a rise in central‐line associated blood stream infections, catheter‐associated CA‐UTI, and MRSA bacteremia across 148 hospitals during the first 6 months of the pandemic (Baker et al., 2022). Conversely, other studies reported decreases in MRSA and VRE, contrasting with our findings (O'Toole, 2021). Other studies have reported that decreased C. difficile infection incidence early in the pandemic may have been driven by improved infection control protocols, but could also have been driven by increased broad‐spectrum antibiotic use, higher hospital occupancy levels and reduced testing during the pandemic (Hazel et al., 2022; Spigaglia, 2022).

Our study found a trend of increasing year‐over‐year 90‐day mortality following admission with GI haemorrhage that continued through the pandemic. Other studies focusing on the early pandemic period did not find differences in mortality despite a reduction in the use of endoscopy (Khan et al., 2022; Pausawasdi et al., 2022). The trend of increasing 90‐day mortality following PCI that pre‐dated COVID‐19 did not appear to significantly worsen during the pandemic. In contrast, we observed significantly higher 90‐day mortality following open surgical repair of AAA during the pandemic than in previous periods. Our findings of higher rates of pandemic‐era mortality following admission for acute ischemic stroke in Ontario echo similar findings observed for ischemic stroke mortality in Alberta and Germany (Ganesh et al., 2022; Richter et al., 2022).

4.1. Limitations

There are a number of limitations associated with some of the specific indicators and populations studied here: first, the prevalence of pressure injury reported in this study is limited to acute inpatients and underestimates the true burden of pressure injury in the province, which has been found to be closer to ~12% when considering additional high‐risk settings such as long‐term‐care (Borojeny et al., 2020; Liu et al., 2020; Woo et al., 2017). Second, there is the possibility of reverse causality in cases with SARS‐CoV‐2 infection: while it is possible that harm events resulted in a prolonged hospital length of stay that resulted in SARS‐CoV‐2 infection, the opposite may be true as well, with patients admitted with SARS‐CoV‐2 infection being at higher risk for a range of hospital harms. Imperfect capture of SARS‐CoV‐2 infection may still account for some increase of harm events during the pandemic. Third, there may be data quality concerns for some quality indicators. For some, previous validation studies verify the accuracy of the codes used to define a condition (e.g., surgical resections, AAAs, Salata et al., 2018), while others have limited evidence (e.g., the restraints data element was only introduced in April 2018) or even evidence of suboptimal accuracy (e.g., delirium, Chuen et al., 2022). Fourth, a general limitation in interpreting the results for these indicators is the absence of benchmarks. Although a zero‐event rate is ideal for mortality and harm indicators, we were unable to distinguish truly attainable never‐events from those where the event is unavoidable (Laeeque et al., 2020). Fifth, this study focused purposefully on a broad range of outcome measures (Chalmers et al., 2018). We did not consider process indicators, and hence cannot attribute changes in outcomes observed in this analysis to the presence or absence of specific factors in the care delivery process. Sixth, it is possible that the increasing rate of some harm indicator (e.g., delirium) over time may have been due to better reporting or changing definitions over time rather than increasing incidence. Deeper dives on specific measures of harm are needed with subject matter expertise to more fully understand these trends. For illnesses with a clinical diagnosis, nursing clinic notes may be an important component of improving physician documentation and hospital administrators to code abstracts accurately (Agency for Healthcare Research and Quality, 2023). Finally, it is unclear whether these results would generalize to other jurisdictions.

5. CONCLUSION

This broad‐scale examination of a multi‐domain set of quality indicators revealed that the COVID‐19 pandemic era was associated with changes in the observed quality of inpatient care for a number of patient groups, including patients receiving surgery for bladder cancer, patients receiving open AAA repair, and patients at risk for hospital‐acquired infections, delirium, and pressure injuries. Focused analyses within these affected populations are required to fully understand the trends observed in this study and identify areas for quality improvement.

AUTHOR CONTRIBUTIONS

Made substantial contributions to conception and design, and interpretation of data: all authors. Made substantial contributions to acquisition of data or analysis: SH. Involved in drafting the manuscript or revising it critically for important intellectual content: all authors. Given final approval of the version to be published. Each author should have participated sufficiently in the work to take public responsibility for appropriate portions of the content: All authors. Agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: SH and EH.

FUNDING INFORMATION

No specific sources of funding were used.

CONFLICT OF INTEREST STATEMENT

The authors have no conflicts of interest to disclose.

PEER REVIEW

The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer‐review/10.1111/jan.16136.

DATA SOURCE DISCLOSURES

Parts of this material are based on data and information compiled and provided by CIHI. However, the analyses, conclusions, opinions and statements expressed herein are those of the author, and not necessarily those of CIHI. Parts of this publication are based on data provided by ICES. However, the views expressed in this publication are those of the researcher and do not necessarily represent those of ICES. This report was produced with the support of the Ontario Ministry of Health. However, the views expressed herein are those of the author, and not necessarily those of the Ontario Ministry of Health or the Government of Ontario.

Supporting information

Appendix S1.

JAN-81-7556-s001.zip (296.8KB, zip)

Habbous, S. , Ford, M. , Bar‐Ziv, S. , Donovan, T. , & Hellsten, E. (2025). The impact of the COVID‐19 pandemic on longitudinal trends of surgical mortality and inpatient quality of care in Ontario, Canada. Journal of Advanced Nursing, 81, 7556–7570. 10.1111/jan.16136

DATA AVAILABILITY STATEMENT

Ontario Health is prohibited from making the data used in this research publicly accessible if it includes potentially identifiable personal health information and/or personal information as defined in Ontario law, specifically the Personal Health Information Protection Act (PHIPA) and the Freedom of Information and Protection of Privacy Act (FIPPA). Due to these legal and ethical restrictions, data will not be made publicly available. However, upon request, data de‐identified to a level suitable for public release may be provided.

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Associated Data

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

Supplementary Materials

Appendix S1.

JAN-81-7556-s001.zip (296.8KB, zip)

Data Availability Statement

Ontario Health is prohibited from making the data used in this research publicly accessible if it includes potentially identifiable personal health information and/or personal information as defined in Ontario law, specifically the Personal Health Information Protection Act (PHIPA) and the Freedom of Information and Protection of Privacy Act (FIPPA). Due to these legal and ethical restrictions, data will not be made publicly available. However, upon request, data de‐identified to a level suitable for public release may be provided.

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