Impact of the COVID-19 pandemic on health services utilisation and mortality in Ontario, Canada: an interrupted time series analysis

Abstract

Background

This study explores changing patterns of healthcare utilisation for chronic diseases during the COVID-19 pandemic in Ontario, Canada. It compares prepandemic and pandemic morbidity and mortality, focusing on physician and emergency department visits, hospitalisations for anxiety, depression and chronic diseases, as well as all-cause mortality rates.

Methods

We constructed a cohort of 2 950 384 adults (18+ years), using administrative health databases, who were living in Ontario, Canada, between the period of January 2017 and March 2023 and recorded the number of visits each individual had in the follow-up period related to chronic conditions. The data were then analysed using an interrupted time-series design to observe changes from before compared with during the pandemic in (1) monthly physician or emergency visits and hospitalisations and (2) monthly all-cause deaths. The exposure in this study was the onset of the COVID-19 pandemic in Ontario, Canada.

Results

In the prepandemic period, mean monthly PCR-tested visits in Ontario were 364 880, with a steady increase of 1210 visits per month. During the initial phase of the COVID-19 pandemic, there was a decline in physician visits and hospitalisations for chronic diseases. This trend changed, leading to a significant rise in visits that peaked in March 2021, increasing by 1690 visits monthly. From 2022 onwards, visits saw a notable decline, decreasing by 6830 per month (p<0.05), reflecting reduced healthcare utilisation in the later pandemic phases.

Conclusions

The COVID-19 pandemic caused significant fluctuations in healthcare utilisation in Ontario. These changes suggest increased risks of missed diagnoses and delayed care, impacting morbidity and mortality. The results emphasise the importance of adaptable healthcare systems and strong pandemic preparedness to maintain care continuity, especially for chronic disease management, during resource-limited periods.

WHAT IS ALREADY KNOWN ON THIS TOPIC

• There is substantial evidence on how the COVID-19 pandemic impacted healthcare utilisation, both in terms of short-term and long-term effects.

WHAT THIS STUDY ADDS

• Our study adds insight into the fluctuating patterns of healthcare utilisation for chronic diseases and mental health during different phases of the COVID-19 pandemic in Ontario, Canada.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

• Our study emphasises the need for healthcare systems to enhance pandemic preparedness, ensuring continuity of care for non-COVID-19 conditions. Policy-makers should allocate resources to prevent delays in diagnosis and treatment, which can lead to worse outcomes and higher morbidity and mortality rates.

Background

On 11 March 2020, the WHO declared COVID-19 a global pandemic.1 Canada was one of the countries affected early by the COVID-19 pandemic, with the province of Ontario recording the first confirmed case on 25 January 2020, in Toronto. According to recent data, Ontario has reported the highest number of confirmed COVID-19 cases in Canada.2

To prevent healthcare infrastructure from being overwhelmed with new cases, Canadians were asked to avoid social contact starting from mid-March 2020.3 The aim was to ‘flatten the curve’ by slowing down the number of infected people, ensuring that healthcare facilities did not run out of space and equipment. A directive to ramp down elective surgeries and non-emergent health services was issued on 15 March 2020, in Ontario. Healthcare providers and organisations were also directed to stop or reduce all non-essential or elective services until further notice.4 The Ontario government introduced an online self-assessment tool and implemented drive-through centres for COVID-19 testing in March 2020.3 Telehealth and virtual medicine were also used to assess patients remotely. In early April 2020, the Public Health Agency of Canada released initial interim guidelines for the clinical management of individuals diagnosed with COVID-19.5 Various alternatives were put into place to ensure that individuals could receive care while staying at home. The public health measures implemented to limit the spread of COVID-19 affected all segments of the population with an impact on the processes of normal comprehensive care for chronic patients, due to disruption in delivery of care.6 The government restrictions, increased fear of infection, and a shift in emphasis on COVID-19 treatment led to the discontinuation of many in-person physical face-to-face consultations. Moreover, community-based care and supports were also reduced.6 7 The pandemic restricted laboratory testing, diagnostic imaging and physical examinations, negatively affecting healthcare services and outcomes worldwide.8 9

The emergence of new variants of the virus, such as the alpha, beta, gamma and delta variants, posed challenges to controlling the spread of COVID-19 and led to surges in cases and hospitalisations.10 By 2023, a significant proportion of the Canadian population had been vaccinated against COVID-19, leading to a reduction in severe illness, hospitalisations and deaths.11 In terms of morbidity and mortality, the impact of COVID-19 extended beyond the direct effects of the virus itself.

The Public Health Agency of Canada received reports of nearly 4.6 million confirmed infections identified through PCR testing.12 While a minority of cases required hospitalisation, it is crucial to thoroughly examine the characteristics and outcomes of these cases due to their substantial morbidity, significant cost and the strain they put on healthcare systems.13 14 In this study, we aim to compare the morbidity and mortality from before to during the COVID-19 pandemic in the Ontario population in terms of physician or emergency department (ED) visits as well as hospitalisation related to mental health issues of anxiety or depression and chronic diseases, and mortality in terms of all-cause deaths.

Methodology

Data source

This study used secondary data from administrative health databases accessed through the Ontario Health Database Platform. The platform permitted access to patient-level linked population-based health administrative data held at the Institute for Clinical Evaluation Sciences (ICES). ICES is an independent, non-profit research institute that houses routinely collected health data from Ontario’s publicly funded healthcare system. These datasets were linked using unique encoded identifiers and analysed at ICES. The details about data dictionaries used and study cohort derivation are described in online supplemental material. All those who had used healthcare services, either physician or emergency visits, as well as hospitalisation for chronic conditions of interest in this study and had a PCR test done during the COVID-19 pandemic period, were included. We constructed a cohort of 2 950 384 adults (18+ years), using ICES health administrative databases, who were living in Ontario, Canada, between the period of January 2017 and March 2023 and recorded the number of visits each patient had in the follow-up period. Due to changes in COVID-19 testing guidelines in Ontario, which shifted focus to testing only high-risk populations, our study specifically included individuals who had undergone COVID-19 testing up until December 2021. This decision was made to maintain a focus on the general population and ensure that our findings accurately reflect the broader impact of the pandemic on Ontario residents.

Study settings

This study was conducted in Ontario, Canada, focusing on the adult population aged 18 and above who used healthcare services between January 2017 and March 2023.

Study design

We used a cohort study design to examine the impact of the COVID-19 pandemic on healthcare utilisation. The study period was divided into two phases: the prepandemic period (January 2017 to December 2019) and the pandemic period (January 2020 to March 2023). The intervention in this study was the start of the COVID-19 pandemic in Canada on 25 January 2020.

Cohort derivation

The cohort was derived using an algorithm to identify all Ontario residents aged 18 and above who were alive on 1 January 2017 and used healthcare services for chronic diseases (asthma, chronic obstructive pulmonary disease (COPD), diabetes, heart failure and hypertension) or mental health issues (anxiety and depression) during the study period. We excluded individuals who were not Ontario residents, under 18 years of age or had visits outside the study period. The primary outcome was morbidity, defined as any physician visit, emergency visit or hospitalisation related to chronic diseases or mental health issues, identified using International Classification of Diseases (ICD); ICD-9, ICD-10, and Ontario Health Insurance Plan (OHIP) codes. The secondary outcome was all-cause mortality during the study period.

Primary outcome

The primary outcome of interest in this study was ‘morbidity’ which was defined as ‘either a physician visit or emergency visit or hospitalisation to a medical hospital bed for reasons related to either chronic diseases or mental health issues of anxiety and depression’ during the study period. We used ICD-9, ICD-10 codes and OHIP diagnostic codes to identify physician visits, emergency visits, ambulatory care visits and hospitalisation, either elective or through emergency, related to either chronic diseases or mental health issues during the study period. The secondary outcome of this study was all-cause mortality during the study period.

Variables

Clinical variables

COVID-19 status was based on standard lab-based PCR test, while the prevalence of chronic conditions was identified using ICES-derived cohorts. Details of each chronic condition and algorithm used are described in online supplemental tables S1–S3.

Service utilisation variables

We used an algorithm15 16 to identify cases of health service utilisation among the Ontario population aged ≥18 years within the ICES data holdings, defined as (1) one hospitalisation in the Ontario Mental Health Reporting System) or Discharge Abstract Database with a primary discharge diagnosis of either ‘chronic illnesses of interest, anxiety, depression, and ‘anxiety or depressive episode’ otherwise not specified (NOS) based on Diagnostic Statistic Manual of Mental Disorder -V (DSM-V) criteria and ICD-10 codes or (2) at least one physician billing and/or ED visits in the OHIP claims database or National Ambulatory Care Reporting System with a diagnostic code for either ‘chronic illnesses of interest, anxiety, depression and anxiety or depressive episode’ otherwise not specified (NOS) based on DSM-V criteria and ICD-10 codes. A complete list of the codes used to define the study cohort and the study variables is available in online supplemental materials.

Statistical analysis

Descriptive statistics (eg, mean, median, range and SD) were calculated for the study sample. Frequencies for demographic and service-use variables were examined with cross-tabulations across both pre-COVID-19 period and COVID-19 pandemic period. An interrupted time series study design was adopted, using segmented regression models17 18 to assess the monthly impact of COVID-19 on overall morbidity defined as ‘monthly healthcare services utilisation’ and mortality in the Ontario population. For every assessment period (defined as month), we determined the total number of morbidity episodes and mortality per month for each year. The time period from January 2017 to December 2019 was considered the ‘prepandemic’ period, and the time period from January 2020 to March 2023 was the ‘pandemic’ period. The single point intervention was the month corresponding to the time of first case diagnosis of COVID-19 in Ontario (January 2020).

The primary outcome of ‘morbidity’ was assessed based on physician or ED visits or hospitalisation at two time points; the first after January 2020 and the second point was January 2022. To determine both the immediate and lasting impacts of the COVID-19 pandemic, three periods of the sample study are considered: pre-COVID-19 pandemic (January 2017–December 2019), first COVID-19 pandemic period (January 2020–January 2022) and second COVID-19 pandemic period (January 2022–March 2023). The break between prepandemic and pandemic was chosen to be January 2020. The break between the two pandemic periods was chosen as January 2022.

Autoregressive error segmented regression models were run to assess the change in mean monthly count (slope) pre-COVID-19, the change in the level of mean monthly counts when COVID-19 hit and the difference between slopes (change in mean monthly number), correcting for autocorrelation.

The general model equations17–19 used were:

$${\displaystyle Y_t={\beta }_0+{\beta }_{1}t+{\beta }_2{X}_t+{\beta }_3{Z}_{t,1}+{\beta }_4{Z}_{t,2}+{\epsilon }_t,}$$

where $Y_t$ represents the outcome of interest, that is, the number of healthcare visits or deaths per month $t$ ; $t$ is the time in months since the study period start on January 2017; X_t is the dummy variable marking the period before (0) and after (1) COVID-19 onset in months as the period indicator; $Z_{t,1}$ is the time in time in months for the first pandemic period since pandemic onset; $Z_{t,2}$ is the time in months for the second pandemic period and ${\epsilon }_t$ is the error in month $t$ , which is adjusted for autocorrelation. Consequently, the coefficient ${\beta }_0$ is the baseline monthly estimate; ${\beta }_1$ is the monthly change in outcome (slope) prepandemic; ${\beta }_2$ is the change in the outcome level due to the pandemic; ${\beta }_3$ is the difference in the slope for the first pandemic period compared with prepandemic; and ${\beta }_4$ is the difference in the slope for the two postpandemic periods and $\epsilon ,$ model error term adjusted for autocorrelation.

Estimated monthly count and estimated mean monthly count (trend) values from the maximum likelihood fitted autocorrelation-corrected regression models were also generated with the AUTOREG procedure using SAS Enterprise Guide V.7.1 (SAS Institute).19 20 Autocorrelation was assessed with Durbin-Watson tests. Statistical significance was defined as p<0.05.

Results

Overall healthcare services utilisation

All Ontario adults who had used healthcare services at least once during the study period—including physician visits, emergency visits and hospitalisations—and had a PCR test done during the COVID-19 pandemic period were included in this analysis, shown in figure 1A. Additionally, table 1 gives the coefficients and related interval estimates and statistical significance for the model variables connected to each outcome investigated.

Figure 1.

Monthly health services utilisation and all-cause deaths in Ontario Population (January 2017– March 2023).

Table 1.

Segmented regression analysis of change in health service utilisation and mortality following the onset of the COVID-19 pandemic in Ontario

Baseline monthly visits pre COVID-19		Change in monthly visits pre-COVID-19		Change in monthly visits for first pandemic period		Change in monthly visits for second pandemic period
Estimate	P value (95% CI)	Estimate	P value (95% CI)	Estimate	P value (95% CI)	Estimate	P value (95% CI)
Overall health services utilisation (100 000)
3.6488	<0.0001 (3.43, 3.85)	0.0121	0.0110 (0.00, 0.02)	0.0169	0.1067 (−0.00, 0.03)	−0.0683	<0.0001 (−0.10, –0.03)
Health services utilisation for chronic diseases (100 000)
1.9588	<0.0001 (1.84, 2.07)	0.009136	0.0006 (0.00, 0.01)	0.0109	0.0597 (−0.00, 0.02)	−0.0181	0.0547 (−0.03, 0.00)
Health services utilisation for mental health (100 000)
1.6901	<0.0001 (1.58, 1.79)	0.002950	0.2022 (−0.00, 0.00)	0.005973	0.2467 (−0.00, 0.01)	−0.0502	<0.0001 (−0.06, –0.03)
All-cause deaths (100 000)
0.828	<0.0001 (0.07, 0.08)	0.0000935	0.3985 (−0.00, 0.00)	−0.000069	0.7786 (−0.00, 0.00)	−0.000121	0.7625 (−0.00, 0.00)

The overall monthly services utilisation depicts the overall monthly visits all inclusive (physician/ED visits/hospitalisation), in all those who had PCR tests done either with positive OR negative results and comprise of all the various types of conditions considered (ie, chronic diseases as well as mental health issues).

ED, emergency department.

For the overall PCR-tested population at baseline in 2017, the mean monthly visit number was 364 880. This number increased by an average of 1210 visits per month during the prepandemic period. After the onset of COVID-19 in Ontario, the estimated mean monthly visit number increased steadily, reaching the highest level by March 2021. The difference in rate of change in mean monthly visit counts for the first pandemic period was 1690 per month. For the second pandemic period, there was a statistically significant decline in mean monthly counts by 6830 (p<0.05). To estimate the change in monthly visits in the COVID-19 negative and COVID-19 positive population, the data were stratified based on COVID-19 PCR test result, that is, COVID-19 negative and COVID-19 positive population (table 2 and figure 2). The stratified results based on COVID-19 status were similar to the overall sample for overall health services utilisation. During the prepandemic period, the monthly change in visits was 1700 for the COVID-19 negative group and 141.1 for the COVID-19 positive group. Following the emergence of COVID-19 in Ontario, a comparable pattern of monthly visit number increase was noted among both the COVID-19 negative and positive groups, reaching at highest level in March 2021 during the first period of the pandemic. Subsequently, both groups experienced a statistically significant decrease in mean monthly counts (p<0.05) during the second pandemic period.

Table 2.

Segmented regression analysis of change in health service utilisation following the COVID-19 pandemic onset in Ontario stratified by COVID-19 disease status

	Baseline monthly visits pre-COVID-19		Change in monthly visits Pre-COVID-19		Change in monthly visits for first pandemic period		Change in monthly visits for second pandemic period
	Estimate	P value (95% CI)	Estimate	P value (95% CI)	Estimate	P value (95% CI)	Estimate	P value (95% CI)
Overall health services utilisation (per 100 000)
COVID-19 negative population	3.2774	<0.0001 (3.08, 3.46)	0.017	0.0124 (0.00, 0.01)	0.0146	0.1202 (−0.00, 0.03)	−0.0601	0.0002 (−0.09, 0.02)
COVID-19 positive population	0.3715	<0.0001 (0.34, 0.39)	0.001411	0.0046 (0.00, 0.00)	0.002270	0.0381 (0.00, 0.00)	−0.008189	<0.0001 (−0.01, 0.00)
Health services utilisation for chronic diseases (per 100 000)
COVID-19 negative population	1.7371	<0.0001 (1.63, 1.84)	0.008087	0.0007 (0.00, 0.01)	0.009505	0.0661 (−0.00, 0.01)	−0.0152	0.0711 (−0.03, 0.00)
COVID-19 positive population	0.2216	<0.0001 (0.20, 0.23)	0.001050	0.0004 (0.00, 0.00)	0.001396	0.0297 (0.00, 0.00)	−0.002922	0.0056 (−0.00, 0.00)
Health services utilisation for mental health (per 100 000)
COVID-19 negative population	1.5402	<0.0001 (1.44, 1.63)	0.002589	0.2168 (−0.00, 0.00)	0.005099	0.2751 (−0.00, 0.01)	−0.0450	<0.0001 (−0.06, 0.02)
COVID-19 positive population	0.1498	<0.0001 (0.13, 0.15)	0.000361	0.1041 (−0.00, 0.00)	0.000874	0.0785 (−0.00, 0.00)	−0.005267	<0.0001 (−0.00, 0.00)

The overall monthly services utilisation depicts the overall monthly visits all inclusive (physician/ED/hospitalisation), in all those who had PCR tests done either with positive OR negative results and comprise all kinds of conditions (chronic diseases as well as mental health issues).

ED, emergency department.

Figure 2.

Monthly health services utilisation stratified by COVID-19 disease status (January 2017– March 2023).

Health services utilisation for chronic diseases

A stratified analysis was conducted to analyse the change in monthly visits related only to the five chronic conditions included in this study (figure 1B). For the overall PCR tested population, the baseline mean monthly visit number was 195 880. A non-statistically significant drop was seen at the onset of COVID-19, followed by a statistically significant monthly increase afterwards. The estimated mean monthly visit number increased in the months after pandemic onset in January 2020, reaching the highest level by March 2021. The difference in rate of change in mean monthly visit counts for the first pandemic period was 1090 per month. For the second pandemic period, there was a statistically significant (p<0.05) decline in mean monthly visits by 1810 (table 1). The stratified results based on COVID-19 test result were similar to the overall sample (table 2 and figure 2).

Health services utilisation for mental health issues

Another analysis was conducted to analyse the change in monthly visits number related to mental health issues of anxiety and depression (figure 1C). For the overall PCR tested population, the baseline monthly visit number related to mental health was 169 010. The estimated mean monthly visit number declined initially after COVID-19 onset in January 2020 but increased in later months after pandemic onset in January 2020, reaching the highest level by March 2021. The difference in rate of change in mean monthly visit counts for the first pandemic period was 597.3 per month. For the second pandemic period, there was a statistically significant (p<0.05) decline in mean monthly visits by 5020 (table 1). Similar results were observed after stratification based on COVID-19 PCR result (table 2 and figure 2).

All-cause deaths

We also examined the monthly all-cause deaths as presented in figure 1D. The estimated baseline monthly mortality was 8280 deaths per month. The pre-COVID-19 monthly change as well as the baseline change due to COVID-19 was found to be insignificant. Moreover, the monthly changes for all-cause deaths were not significant during the postpandemic periods (table 1). We also considered the models after stratification into three broad age groups: 18–45 years (young adults), 46–65 years (middle-aged adults) and 66 years and above (older adults). All three groups had a drop in the number of deaths reported at the start of COVID-19. Only the youngest group, 18–45 years, was found to report higher monthly all-cause deaths post-COVID-19 (online supplemental figure S1 and table S4).

Discussion

Summary of findings

This study investigated trends in morbidity and mortality through healthcare utilisation and death records in Ontario during the period from January 2020 to March 2023 related to chronic diseases and mental health issues of anxiety and depression. Our study noted drops in physician visits and hospitalisations in Ontario during the initial COVID-19 wave, likely due to both healthcare system adjustments and fears of virus exposure. Over time, there was a partial recovery in healthcare visits as primary care shifted significantly to virtual visits, disrupting patient appointments in Ontario and elsewhere. However, we did not observe drops in mental health utilisation. This difference aligns with other studies, which showed individuals with mental healthcare needs experienced the smallest decrease in total visits.21 Our observations also revealed a varying pattern to healthcare utilisation during the COVID-19 pandemic. It is also important to note that the period during COVID-19 spanning from mid-March to the end of July 2020 was characterised by varying degrees of restrictions being gradually lifted in different regions during the period from early May to mid-July.22 Trends showed increasing visits over this time period, with greater increases in June and July, along with decreases in virtual care.23 24 Despite the cyclic pattern, the results exhibited a clear upward trend until March 2021, followed by a gradual decline afterwards, which can be attributed to Canada’s extensive vaccination campaign, seasonal variations and natural immunity in individuals who had already contracted the COVID-19 virus, which led to a significant reduction in severe illness, hospitalisations and deaths by 2023.11

We also observed increased mortality, particularly among younger groups following the onset of COVID-19. Likewise, some studies have found higher mortality rates and increased burden of disease.25 In Canada, the number of deaths remained higher than expected from spring 2020 to winter 2021, with excess deaths peaking alongside pandemic waves.26 We observed similar patterns for mortality during the study period. Excess deaths include both direct and indirect pandemic-related causes, such as delayed or missed care, hesitancy to seek care, mental health disorders, substance use and other health determinants affected by the pandemic.27 28

Comparison with existing literature

The outbreak of the COVID-19 pandemic has had a significant impact on healthcare utilisation, leading to changes in healthcare-seeking behaviour globally.25 29 In Ontario, Canada, the pandemic led to fluctuations in healthcare utilisation since January 2020, as healthcare systems have had to adapt to the demands of the pandemic while continuing to provide necessary care for other medical issues.30 31

Physician services experienced a significant decline in three Canadian provinces during March and April 2020, followed by subsequent increases in May and June 2020, while a similar partial recovery was observed in the USA after a period of decline.32 33

As the pandemic continued, there were spikes in physician visits and hospitalisations, likely due to ongoing virus spread, emergence of new variants, and worsening case severity. Studies indicate that 1 year after the pandemic began, physician visits and hospitalisations rose sharply by 60% and 40%, respectively, compared with 2019.21 34 Moreover, in December 2020, hospitalisations for COVID-19 reached their highest levels in Ontario, which put pressure on the healthcare system and may have led to a decrease in non-COVID-19 related hospitalisations.14 35 Following the issuance of stay-at-home orders in January 2021, different regions gradually phased them out between February and early March 2021. The highest healthcare utilisation was seen in March 2021. This aligns with the emergence of the B.1.1.7 (Alpha) variant of concern in Ontario. Moreover, the exponential rise in healthcare utilisation we observed 1-year post-pandemic onset may be attributed to the relaxation of public health measures and the emergence of new virus variants. During the third wave of the pandemic in Ontario, which started in March 2021, hospitalisation rates for COVID-19 reached their highest levels to that point. Visits due to chronic diseases also showed large increases post-COVID-19 onset. The COVID-19 pandemic made managing chronic diseases more difficult due to health service closures, diversion of resources to COVID-19 care and patients’ fear of exposure to the virus. In-person specialist visits for chronic disease management had significantly decreased during the pandemic, with up to 94% fewer visits in April 2020 compared with April 2019.25 The Canadian population’s mental health and coping abilities deteriorated against the backdrop of the pandemic,36 and the situation was even worse for those already suffering from mental illnesses.37 Virtual care services increased, but virtual visits are not always a good substitute for in-person care, and many patients still struggled to access chronic disease care during the pandemic.38 The limitations on accessing healthcare services resulted in a rise in medical complications and emergencies, particularly for elective procedures and care delays. However, it is not clear whether the delays were due to patients delaying seeking medical attention and/or due to the barriers to accessing services.

Implications for clinical practice and policy

The findings from this study highlight the urgent need for healthcare systems to be better prepared for future pandemics, with a focus on maintaining care for chronic diseases and mental health issues. The disruptions observed in healthcare utilisation, including reduced in-person visits and delayed treatments, emphasise the importance of ensuring continuous access to care, especially for vulnerable populations. Healthcare policies should promote a balanced approach between virtual and in-person care to address access challenges and ensure timely treatment. Additionally, addressing the long-term mental health impacts of the pandemic and reducing delays in diagnosis and care are crucial. Policy-makers must also prioritise strategies to reduce healthcare disparities and strengthen the healthcare system’s ability to respond to future public health emergencies.

Strengths and limitations

This study benefited from a large, population-based longitudinal administrative database, offering enhanced statistical power and relative generalisability. However, it focused on individuals with five chronic conditions, potentially limiting its applicability to broader populations. Selection bias may also affect the findings, as the data might exclude those without healthcare access or specific insurance coverage. Despite these limitations, the study provides valuable insights, especially concerning emerging variants and the removal of public health interventions.

Conclusions

This study highlights the significant impact of the COVID-19 pandemic on physician visits and hospitalisations, revealing increased demand for medical care alongside strains on healthcare systems in Canada. It highlights the necessity for adaptable healthcare systems to manage public health emergencies while ensuring access to essential services. Changes in healthcare utilisation during the pandemic may have resulted in missed diagnoses and delayed treatments for non-COVID-19 medical issues, potentially exacerbating morbidity and mortality. The surge in ED visits and hospitalisations underscores the importance of healthcare system preparedness and resource allocation. Addressing barriers to care is crucial to ensure access to necessary medical services, particularly for individuals with chronic conditions, during pandemics to mitigate adverse outcomes and mortality.

Data availability statement

Data may be obtained from a third party and are not publicly available. The data set from this study is held securely in coded form at ICES. While data sharing agreements prohibit ICES from making the data set publicly available, access may be granted to those who meet pre-specified criteria for confidential access, available at https://www.ices.on.ca/DAS. The full data set creation plan and underlying analytical code are available from the authors on request, understanding that the computer programs may rely on coding templates or macros that are unique to ICES and are, therefore, either inaccessible or may require modification.

Ethics statements

Patient consent for publication

Not applicable.

Ethics approval

This study was approved by the University of Waterloo research ethics board (protocol reference no. 43910), and data use was authorised under section 45 of Ontario’s Personal Health Information Protection Act. ICES is a prescribed entity under section 45 of Ontario’s Personal Health Information Protection Act, which authorises ICES to collect personal health information, without consent, for the purpose of health system evaluation.