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Volume 31, Number 4—April 2025
Research

Population-Based Matched Cohort Study of COVID-19 Healthcare Costs, Ontario, Canada

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Author affiliation: ICES, Toronto, Ontario, Canada (B. Sander, S. Mishra, S. Swayze, R. Duchen, K. Quinn, J. Kwong); University of Toronto, Toronto (B. Sander, S. Mishra, K. Quinn, J. Kwong); University Health Network, Toronto (B. Sander, Y. Sahakyan); Public Health Ontario, Toronto (B. Sander, J. Kwong); Unity Health Toronto, Toronto (S. Mishra); British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada (N. Janjua, H. Sbihi); University of British Columbia, Vancouver (H. Sbihi)

Suggested citation for this article

Abstract

Estimates of COVID-19–related healthcare costs are key to health system planning, but attributable cost data remain limited. We characterized healthcare costs attributable to COVID-19 through a population-based matched cohort study in Ontario, Canada, by using health administrative data. We matched SARS-CoV-2–positive persons from 2020 to unexposed historical control persons from 2016–2018. We estimated phase-based and survival-adjusted COVID-19–attributable healthcare costs from the health system perspective. We matched 159,817 persons. Mean (95% CI) attributable 10-day costs per person were $1 ($–4 to $6) preindex, $240 ($231–$249) during acute care, $18 ($14–$21) in postacute phases, $3,928 ($3,471–$4,384) in the terminal phase for early deaths, and $1,781 ($1,182–$2,380) for late deaths. Mean cumulative survival-adjusted cost at 360 days was $2,553 ($2,348–$2,756) per person. SARS-CoV-2 infection is associated with substantial long-term healthcare costs, consistent with understanding of post-COVID condition. Determining phase-specific costs can inform budget and pandemic planning.

Although only recently emerged, SARS-CoV-2 has negatively affected the health system of Canada (1). The health effects of SARS-CoV-2 range from asymptomatic to long-term disability because of the post-COVID condition (PCC), which can lead to substantial long-term costs to health systems (2,3). PCC is characterized by the continuation or occurrence of new symptoms 3 months after the initial SARS-CoV-2 infection, with symptoms persisting for >2 months without any other explanation (4). Surveys report 31% of US adults and 15% of adults from Canada with a history of SARS-CoV-2 infection experienced PCC (5,6). Emerging data suggest an association between PCC and increased health service utilization for patients requiring both outpatient and inpatient care (3,7–9). Over a 7-month period, healthcare costs nearly doubled for patients with PCC compared with patients without PCC, and anticipated cost increased with extended follow-up (10). Because of the high prevalence of SARS-CoV-2 infection (≈83% of adults in Ontario, Canada), evidence of long-term effects of COVID-19, and the effect on health services utilization (11), health system coordinators must understand the costs of caring for patients with PCC at different stages of disease and across different sectors of the health system for effective resource allocation and planning.

Previous studies on COVID-19–related costs largely focused on inpatient costs (12), the acute period (13), or the postacute period (14,15), potentially underestimating the overall financial effect of COVID-19. The importance of studying longer postinfection periods is evident in recent studies documenting the long-term health (2,3) and resource use (7,8) effects of PCC. Our aim was to characterize acute and long-term COVID-19–attributable healthcare costs from the Ontario health system perspective from persons who tested positive for SARS-CoV-2 compared with matched unexposed persons.

Methods

Study Design, Setting, and Population

Our study followed the RECORD statement for observational studies (16). We conducted an incidence-based matched cohort study to measure the COVID-19–attributable healthcare costs among persons registered in the publicly funded Ontario Health Insurance Plan (OHIP). Ontario is Canada’s most populous province, containing 40% of Canada’s population, and OHIP covers most residents. We used population-based health administrative datasets with information on all healthcare encounters covered by OHIP. We linked the datasets (Appendix Table 1) by using unique encoded identifiers and analyzed them at ICES (Toronto, ON, Canada).

We identified persons as exposed if they tested positive for SARS-CoV-2 by PCR during January 1, 2020–December 31, 2020, enabling 1 year of follow-up, and limiting potential biases because of variant emergence and vaccination beginning in December 2020 (17). We defined the index date as the first occurrence of a positive SARS-CoV-2 PCR test on the basis of the earliest of symptom onset date recorded in Ontario’s Case and Contact Management (CCM) system, specimen collection date, observation date or reporting date in the Ontario Laboratories Information System (OLIS), or case report date in CCM. We excluded patients with hospital-acquired SARS-CoV-2 infection, defined as testing positive >8 days after admission or up to 2 days after discharge, given a length of stay of >8 days, or readmission with a positive test within 8 days after discharge from the hospital (18–20).

We identified unexposed persons by using a 50% random sample of the OHIP-registered population from January 1, 2016– December 31, 2018. We chose a historical unexposed cohort because health service use declined by 27%–43% during the pandemic compared with prepandemic years because of system, structural, and behavioral factors (21–23) and to avoid potential contamination bias because not all persons with SARS-CoV-2 infection had a PCR test for a variety of reasons, including being ineligible for PCR testing, lacking access to or desire for testing, or being asymptomatic, which ranged from 20%–62% of persons (22,24; P. Jha, unpub. data, https://europepmc.org/article/PPR/PPR293015). Unexposed persons were assigned a pseudo–index date at random. We excluded persons with death dates before their pseudo–index dates from the study and excluded persons assigned to the historical unexposed cohort from the exposed cohort.

Exclusion criteria for both groups included not residing in Ontario, having invalid or missing gender, birthdate, or income quintile information, being >65 years of age and not having any healthcare system interactions for 3 years before the study start date (January 1, 2020, for the exposed group; January 1, 2016, for the unexposed group), being <65 year of age and not having any healthcare system interactions for 10 years before the study start date, being >110 years of age, and residing in long-term care. We followed all persons for 1 year from their index date.

We used hard and propensity score matching on selected baseline covariates. We matched each exposed person to 1 unexposed person by using nearest-neighbor matching without replacement on index date (day and month) +60 days, sex, age category, resource utilization band with a 2-year look-back window, and the logit of the propensity score by using a caliper distance of 0.2 SD (25,26). The resource utilization bands were derived from the Johns Hopkins adjusted clinical groups (ACG) and categorized comorbidity into 6 groupings from nonutilizer to high complexity of illness (27,28).

The propensity score included the following baseline measures: public health unit (PHU) of residence, rural or urban residence, census tract of residence (that are not contiguous with the boundaries of local PHUs), frailty, high-risk occupation neighborhood concentration quintile, immigrant status, and Ontario Marginalization Index (29) quintiles for age and labor force, material resources, racialized and newcomer populations, and households and dwellings. We defined frailty by using ACG (27). High-risk occupation neighborhood concentration quintile was based on the proportion of residents in a census dissemination area employed as essential workers.

Because healthcare utilization and associated costs tend to increase before death (30), we examined attributable cost before death by rematching exposed persons who died to unexposed persons who also died during the observation period on their death dates (day and month) +30 days, sex, income quintile, recent immigrant, and the logit of the propensity score by using a caliper distance of 0.2 SD (25,26). The propensity score included the following baseline measures: PHU of residence, rural/urban residence, census tract of residence, high-risk occupation neighborhood concentration quintile, and Ontario Marginalization Index quintiles (29) for age and labor force, material resources, racialized and newcomer populations, and households and dwellings. We assessed the balance for all matches by using standardized differences with a threshold of 0.10 (25,26).

Outcomes

We calculated healthcare costs adjusted for survival over a 1-year period after the index date. Costs in 2023 Canadian dollars were calculated from the Ontario health system perspective and included all publicly funded health services: inpatient hospitalizations, outpatient hospital visits (same-day surgeries, outpatient cancer therapies, and dialyses), emergency department visits, publicly funded drugs (for everyone >65 years of age and select younger persons based on means), physician services, rehabilitation services, complex care, homecare, long-term care, and other (e.g., laboratory tests and services, OHIP-covered nonphysician services, assistive devices).

Analyses

To calculate survival-adjusted costs, we first estimated healthcare costs, standardized to 10 days, from index date to the end of follow-up or death, by using ICES person-level cost methods (31). Next, we used the phase-of-care cost approach to assign 10-day costs to phases of care along the natural history of disease trajectory (32). Phase-of-care lengths were determined on the basis of clinician expertise, World Health Organization’s definition of PCC (4), and joinpoint analysis, identifying significant changes in the trend of mean 10-day costs (33). We defined 4 phases of care: prediagnosis (30 days before index date), acute care (80 days after index date), postacute care (time between acute care phase and terminal phase follow-up), and terminal (60 days before death date). We first defined the terminal phase length, followed by acute, postacute, and prediagnosis phases. To determine the terminal phase length, we analyzed 10-day cost trajectories up to 90 days before death among decedents. Joinpoint analysis identified an inflection point at 60 days before death, marking the start of the terminal phase. We further stratified deaths as early if death occurred within 60 days of index date and late if death occurred >60 days following index date. To determine the acute phase length, we analyzed 10-day cost trajectories after the index date up to 360 days, censoring patients 60 days before death. Guided by the World Health Organization’s definition of PCC, which refers to the continuation or occurrence of new symptoms 3 months after the initial SARS-CoV-2 infection (4), and on the basis of joinpoint regression results, we selected 80 days postindex as the acute-phase endpoint. We allocated the remaining costs between the end of the acute phase to end of follow-up or to the start of the terminal phase, whichever occurred first, to the postacute phase. Finally, we analyzed prediagnosis costs up to 360 days before the index date, remaining flat during that period. Guided by expert opinion, we chose 30 days before index as the prediagnosis phase start. We calculated mean attributable phase-of-care-specific costs (standardized to 10 days) and 95% CIs by using a generalized estimating equation model. Last, we combined attributable phase-of-care-specific costs with crude survival data to determine 1-year costs adjusted for survival, as described previously (32).

We stratified analyses by age, sex, income quintile, and resource utilization band quintile. We conducted sensitivity analyses and varied the lengths of the prediagnosis (120 days before index date), acute care (30 days post index date), and postacute care (time between acute care phase and terminal phase follow-up) phases. We used SAS Enterprise Guide 7.15 (SAS Institute, https://www.sas.com) for all statistical analyses.

Results

Study Cohort

During January 1, 2020–December 31, 2020, in Ontario, 181,979 residents tested positive for SARS-CoV-2 (Appendix Figure 1). Among them, 165,838 met eligibility criteria for the exposed cohort; mean age was 40.4 ± 19.7 years, 50.7% were female, and 49.3% were male. There were 6,641,074 unexposed persons residing in Ontario during January 1, 2016–December 31, of whom 2,018 who were eligible for matching; mean age was 40.5 ± 22.8 years, 50.6% were female, and 49.4% were male (Table 1).

Most exposed persons (96.0%) were matched to an unexposed person. The matched cohort consisted of 319,634 persons (159,817 exposed, 159,817 unexposed); mean age was 40.4 ± 19.8 years, 50.7% were female, 49.3% were male, 24.5% lived in neighborhoods with the lowest income levels, 20.7% had high or very high resource utilization, and 2.3% were frail (mean ACG score 4.9) (Table 1). The matched cohort was well-balanced, with no standardized differences above 0.1. Unmatched exposed persons were more likely to live in lower income neighborhoods, were more likely to live in urban areas, had a higher mean ACG score, and were more likely to be frail compared with matched exposed persons (Table 1).

We identified 3,357 exposed persons in the terminal phase and matched 93.0% (n = 3,114) to achieve balanced matches (Appendix Table 2). Within 14 days of the index date, 5.1% of the matched exposed cohort were hospitalized, and 26.5% of those were admitted to an intensive care unit (ICU). During the follow-up, 2% of the matched cohort died, including 20.1% of those who were hospitalized within 14 days of index date without ICU admission and 39.1% of those admitted to an ICU.

COVID-19–Attributable Healthcare Costs

Mean (median) time contributed by matched exposed persons were 30 (30) days for preindex, 77 (79) days for acute, 274 (280) days for postacute, and 35 (30) days for terminal phases. In the preindex phase, the mean (95% CI) 10-day healthcare costs were similar at $107 ($103–$110) for exposed and $106 ($103–$109) for unexposed persons (Appendix Table 3).

Figure 1

Source of COVID-19–attributable healthcare costs (2023 Canadian dollars), standardized to 10 days by phases-of-care, Ontario, Canada. Cost categories are displayed by phase of care. A) Full data; B) data truncated at $300 to improve visualization of the postacute-phase costs. ED, emergency department

Figure 1. Source of COVID-19–attributable healthcare costs (2023 Canadian dollars), standardized to 10 days by phases-of-care, Ontario, Canada. Cost categories are displayed by phase of care. A) Full data; B) data truncated...

During the acute phase, the mean (95% CI) 10-day health costs were $334 ($325–$343) per person for exposed and $94 ($92–$97) for unexposed persons, resulting in $240 ($231–$249) of COVID-19–attributable costs. Most acute phase costs were because of inpatient care (77%) (Figure 1; Appendix Table 3).

During the postacute phase, the mean (95% CI) 10-day health costs were $112 ($109–$115) per person for exposed and $95 ($93–$97) for unexposed persons, resulting in $18 ($14–$21) of COVID-19-attributable costs. Most postacute phase costs were because of inpatient care (42%) (Figure 1; Appendix Table 3).

During the terminal phase, among persons with early deaths, the mean (95% CI) 10-day health costs were $8,724 ($8,328–$9,119) per person for exposed and $4,796 ($4,551–$5,041) for unexposed persons, resulting in $3,928 ($3,471–$4,384) of COVID-19–attributable costs. Among persons with late deaths, the mean (95% CI) 10-day health costs were $6,709 ($6,194–$7,224) per person for exposed and $4,928 ($4,603–$5,253) for unexposed persons, resulting in $1,781 ($1,182–$2,380) of COVID-19-attributable costs. Most terminal phase costs were because of inpatient care for both early (100%) and late deaths (87%) (Figure 1; Appendix Table 3).

Mean (95% CI) attributable 10-day COVID-19 costs were lower for female than male patients for the acute-care phase, $193 ($182–$204) versus $289 ($274–$304), but higher for female than male patientss in the postacute care phase, $21 ($16–$26) versus $14 ($9–$19). Total mean attributable costs in both acute and postacute care phases were higher among older age groups, among those in the lower income quintiles, and in the 2 highest resource utilization bands (Appendix Tables 4–7).

Figure 2

Healthcare costs (2023 Canadian dollars) per 10-day intervals for the matched exposed and unexposed persons, Ontario, Canada. Costs of persons who died during the follow-up period were removed 60 days before death, consistent with the terminal phase length for late deaths.

Figure 2. Healthcare costs (2023 Canadian dollars) per 10-day intervals for the matched exposed and unexposed persons, Ontario, Canada. Costs of persons who died during the follow-up period were removed 60 days...

Healthcare costs were not sensitive to the preindex phase length. However, compared with the phase lengths in the main analysis, costs doubled for the acute and postacute phases when we considered shorter duration (30 days) for acute phase, with inpatient care remaining the main cost driver (Appendix Table 8). This change occurred because costs initially captured under the acute phase were now shifted to the postacute phase (Figure 2). Finally, mean cumulative costs adjusted for survival at 360 days were $2,553 ($2,348–$2,756); costs were lower for female patients, at $2,194 ($1,945–$2,446), versus male patients, at $2,921 ($2,602–$3,241) (Table 2). We summarized healthcare costs of persons who were hospitalized within 14 days of the index date with or without ICU admission (Appendix Table 9).

Discussion

We characterized the acute and long-term healthcare costs attributable to COVID-19 in Ontario, Canada, among persons who tested positive for SARS-CoV-2, compared with a group of persons who were unexposed to SARS-CoV-2. Exposed persons had higher costs than unexposed persons after the index date, and the largest difference occurred during the acute phase. Although the attributable costs were reduced in the postacute phase, mean costs remained greater among exposed persons compared with unexposed persons until the end of follow-up at 1-year post–index date.

The leading cost category in COVID-19–attributable cost across all phases was inpatient care, contributing 77% in the acute phase and 42% during the postacute phase. This result is consistent with descriptions of the natural history of SARS-CoV-2 acute infections and subsequent PCC. Acute infection often requires care to address acute symptoms, including potential respiratory distress, that would be best addressed in hospital, whereas PCC consists of several linked syndromic conditions are more likely to require complex care, rehabilitation, and outpatient visits (34).

We found the duration of acute illness to be 80 days. This duration is longer than durations reported in other studies (14) but lends empirical support to a growing global consensus that PCC is defined by symptoms that persist >12 weeks after infection onset (35–37).

The mean cumulative attributable cost for COVID-19 at 360 days was $2,553 per person testing positive for SARS-CoV-2, representing nearly half of the per capita healthcare spending in Ontario, which was $5,400 in 2020 (38). For the exposed cohort of 159,817 persons this translates to $408 million. Meanwhile, the estimated cost for PCC ($18 attributable cost in the postacute phase per person/10 days), amounts to about $75 million for the exposed cohort over 1 year.

Our findings are comparable with growing evidence on COVID-19-associated cost. Previous publications have estimated healthcare costs associated with COVID-19 during the first wave of the pandemic in 2 provinces of Canada, British Columbia and Ontario (39), stratified by the level of initial care required, including community, long-term care, hospital, and ICU settings. In Ontario, the net costs (in 2020) during the first 120 days of diagnosis were $28,329 for persons who were hospitalized and $96,308 for those admitted to the ICU. Those estimates were comparable to those reported in the United States (40,41) and countries in Europe (42). In our study, after stratifying by the level of initial care received (Appendix Table 9), the net costs over a 360-day period were $30,147 for persons who were hospitalized and $105,677 for persons who were admitted to ICU. The higher costs in our study, because of the longer time length, suggest the ongoing effects of PCC, particularly among those with initially severe disease. Consistent with our estimated cost for PCC ($18 attributable cost per person for every 10 days in the postacute phase), a previous study in Ontario reported the mean attributable healthcare costs of $487 (95% CI $394–$593) in a matched cohort of adults starting >56 days after a positive SARS-CoV-2 PCR test over 1 year of follow-up (15). Two United States studies examining costs up to 6 months by using data from private health insurance claims and Medicare beneficiaries showed that persons with COVID-19 had >1.4× higher direct medical costs after 31 days–6 months compared with matched controls (43), and, compared with historical controls, costs remained higher until the 5th month after diagnosis and were primarily driven by inpatient care (44), consistent with our findings. Further, we found that even after matching for comorbidities, frailty, and other factors, healthcare costs remained higher for persons living in lower-income neighborhoods. This finding aligns with a study conducted in Ontario, showing persons living in lower-income neighborhoods have a higher risk of death, a factor that likely contributes to increased end-of-life healthcare costs (45).

The first limitation of our study is that outcomes exclude costs not covered by Ontario’s public insurance system, such as medications for most persons <65 years of age, copayment costs covered by private insurance, or community-level services such as supportive housing. Second, although 96% of exposed persons were matched to unexposed persons, unmatched exposed persons were more likely to live in lower income neighborhoods, in urban areas, had higher ACG scores and were more likely to be frail compared with the matched exposed, making findings less generalizable to persons with these characteristics. Third, administrative data lack information to accurately define PCC, which is why we used costs incurred during the postacute phase as a proxy to estimate the economic burden attributable to PCC. However, as PCC diagnosis becomes more reliably captured within administrative datasets, future analyses could adopt a direct approach to estimate healthcare costs associated with PCC. Finally, our study reflects early pandemic experiences, when the likelihood of severe illness requiring hospitalization was higher, leading to increased healthcare costs. COVID-19 and associated healthcare costs may differ in contemporary times with the emergence of novel variants, vaccinations, treatments, and changes in clinical practice that were not assessed in this study.

Our study strengths included that because of Ontario’s public single-payer system, most of the community dwelling population was eligible for this study and the cohort was well matched. Of note, the absence of COVID-19–attributable costs at baseline suggested minimal residual confounding. Furthermore, matching to historic controls ensured that changes in healthcare delivery during the pandemic did not affect the unexposed group, which could bias attributable cost estimates for COVID-19. The use of several linked administrative databases enabled the characterization of COVID-19–attributable costs across a broad range of cost categories, providing a comprehensive view of COVID-19 healthcare costs. Finally, a phase-of-care approach enabled a comparison of acute versus postacute costs and the use of joinpoint regression enabled a data-driven approach to defining phase length.

Our findings offer insights into clinically relevant phase-specific costs, which can inform budget planning to ensure each healthcare sector is appropriately resourced. Inpatient care emerges as a major cost category, emphasizing the need for hospital resources. Of note, the costs associated with COVID-19 extend beyond the acute phase, indicating a growing effect of PCC on healthcare systems. This growth underscores the importance of allocating resources not only for immediate care but also for the long-term needs of persons with PCC, who will likely require ongoing access to healthcare services, including diagnostics, medications, and specialized treatments. Furthermore, our findings suggest that certain populations may experience disproportionate impacts, underscoring the need to address socioeconomic disparities through tailored healthcare policies. Policymakers can use those findings to prioritize investments and to assess the value of COVID-19 interventions to inform current policy decision-making and pandemic planning.

In conclusion, SARS-CoV-2 infection is associated with increased healthcare costs in the year following onset, with differential cost patterns in the acute and postacute phases, consistent with the evolving clinical understanding of PCC. Our findings have major implications for stakeholders responding to PCC at the health system level. Determining phase-specific healthcare costs for SARS-CoV-2 infection can inform future health sector budget and pandemic planning.

Dr. Sander is a senior scientist at the Toronto General Hospital Research Institute. Her interests include infectious disease economics, health economics, and simulation modeling.

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Acknowledgments

We thank IQVIA Solutions Canada Inc. for use of their Drug Information File and the Toronto Community Health Profiles Partnership for providing access to the Ontario Marginalization Index. We thank Poornima Goudar and Marian Hassan for helping with manuscript editing and Hong Lu for her coding contributions during analysis.

This document used data adapted from the Statistics Canada 2016 Census Area Profiles and the Statistics Canada Postal CodeOM Conversion File, which is based on data licensed from Canada Post Corporation ©, or data adapted from the Ontario Ministry of Health Postal Code Conversion File, which contains data copied under license from Canada Post Corporation © and Statistics Canada. Parts of this material are based on data and information compiled and provided by Statistics Canada, the Canadian Institute for Health Information (CIHI), Ontario Registrar General (ORG) information on deaths, Ontario Health (OH), and the Ontario Ministry of Health as well as Immigration, Refugees and Citizenship Canada (IRCC) September 2020–current. The analyses, conclusions, opinions, and statements expressed herein are solely those of the authors and do not reflect those of the funding or data sources, no endorsement is intended or should be inferred.

This study was approved by the university health network research ethics board. ICES is a prescribed entity under Ontario’s Personal Health Information Protection Act (PHIPA). Section 45 of the PHIPA authorizes the ICES to collect personal health information, without consent, for the purpose of analysis or compiling statistical information with respect to the management of, evaluation or monitoring of the allocation of resources to, or planning for all or part of the health system. Projects that use data collected by ICES under section 45 of PHIPA and use no other data are exempt from the research ethics board review. The use of the data in this project is authorized under Section 45 and approved by the ICES Privacy and Legal Office. Legal data-sharing agreements between ICES and data providers prohibit ICES from making the dataset publicly available, access may be granted to those who meet prespecified criteria for confidential access, available at https://www.ices.on.ca/DAS (email: das@ices.on.ca). The full dataset creation plan and underlying analytic code are available from the authors upon request.

This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health and the Ministry of Long-Term Care. This study received funding from the Canadian Institutes of Health Research (grant no. GA4-177757). This research was supported in part by a Tier 1 Canada Research Chair (CRC) in Economics of Infectious Diseases held by B.S. (grant no. CRC-2022-00362) and a Tier 2 CRC in Mathematical Modeling and Program Science held by S.M. (grant no. CRC-950-232643).

Author contributions: conceptualization J.K., S.M., J.N., S.H., and B.S.; design and methodology J.K., S.M., K.Q., and B.S.; analysis S.S.; original draft R.D., and Y.S.; review, editing, interpretation, and final approval all authors; study supervision J.K., S.M., K.Q., and B.S.; funding acquisition J.K., S.M., and B.S.

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References

  1. Canadian Institute for Health Information. Impact of COVID-19 on Canada’s health care systems. 2023 [cited 2024 Jun 17]. https://www.cihi.ca/en/covid-19-resources/impact-of-covid-19-on-canadas-health-care-systems
  2. Quinn  KL, Katz  GM, Bobos  P, Sander  B, McNaughton  CD, Cheung  AM, et al. Understanding the post COVID-19 condition (long COVID) in adults and the expected burden for Ontario. Science Briefs of the Ontario COVID-19 Science Advisory Table. 2022;3(65).
  3. Katz  GM, Bach  K, Bobos  P, Cheung  A, Décary  S, Goulding  S, et al. Understanding how post–COVID-19 condition affects adults and health care systems. JAMA Health Forum. 2023;4:e231933. DOIPubMedGoogle Scholar
  4. World Health Organization. Post COVID-19 condition, long COVID. 2022 [cited 2024 Jun 17]. https://www.who.int/europe/news-room/fact-sheets/item/post-covid-19-condition
  5. Blanchflower  DG, Bryson  A. Long COVID in the United States. PLoS One. 2023;18:e0292672. DOIPubMedGoogle Scholar
  6. Government of Canada. COVID-19 variants: wastewater monitoring dashboard [cited 2024 Dec 12]. https://health-infobase.canada.ca/wastewater
  7. McNaughton  CD, Austin  PC, Sivaswamy  A, Fang  J, Abdel-Qadir  H, Daneman  N, et al. Post-acute health care burden after SARS-CoV-2 infection: a retrospective cohort study. CMAJ. 2022;194:E1368–76. DOIPubMedGoogle Scholar
  8. Tartof  SY, Malden  DE, Liu  IA, Sy  LS, Lewin  BJ, Williams  JTB, et al. Health care utilization in the 6 months following SARS-CoV-2 infection. JAMA Netw Open. 2022;5:e2225657. DOIPubMedGoogle Scholar
  9. Whittaker  HR, Gulea  C, Koteci  A, Kallis  C, Morgan  AD, Iwundu  C, et al. GP consultation rates for sequelae after acute covid-19 in patients managed in the community or hospital in the UK: population based study. BMJ. 2021;375:e065834. DOIPubMedGoogle Scholar
  10. Patterson  B, Ruppenkamp  J, Richards  F, Debnath  R, ElKhoury  AC, DeMartino  JK, et al. Cost of long COVID following severe disease a US healthcare database analysis. Value Health. 2022;25:S375. DOIGoogle Scholar
  11. The COVID-19 Immunity Task Force. Seroprevalence in Canada [cited 2024 Jun 17]. https://www.covid19immunitytaskforce.ca/seroprevalence-in-canada
  12. Shrestha  SS, Kompaniyets  L, Grosse  SD, Harris  AM, Baggs  J, Sircar  K, et al. Estimation of coronavirus disease 2019 hospitalization costs from a large electronic administrative discharge database, March 2020–July 2021. Open Forum Infect Dis. 2021;8:ofab561. DOIPubMedGoogle Scholar
  13. Yang  J, Andersen  KM, Rai  KK, Tritton  T, Mugwagwa  T, Reimbaeva  M, et al. Healthcare resource utilisation and costs of hospitalisation and primary care among adults with COVID-19 in England: a population-based cohort study. BMJ Open. 2023;13:e075495. DOIPubMedGoogle Scholar
  14. Wolff Sagy  Y, Feldhamer  I, Brammli-Greenberg  S, Lavie  G. Estimating the economic burden of long-Covid: the additive cost of healthcare utilisation among COVID-19 recoverees in Israel. BMJ Glob Health. 2023;8:e012588. DOIPubMedGoogle Scholar
  15. McNaughton  CD, Austin  PC, Li  Z, Sivaswamy  A, Fang  J, Abdel-Qadir  H, et al. Higher post-acute health care costs following SARS-CoV-2 infection among adults in Ontario, Canada. J Multidiscip Healthc. 2024;17:5749–61. DOIPubMedGoogle Scholar
  16. Benchimol  EI, Smeeth  L, Guttmann  A, Harron  K, Moher  D, Petersen  I, et al.; RECORD Working Committee. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12:e1001885. DOIPubMedGoogle Scholar
  17. Government of Canada. COVID-19 vaccination: doses administered [cited 2024 Jun 17]. https://health-infobase.canada.ca/covid-19/vaccine-administration
  18. Dave  N, Sjöholm  D, Hedberg  P, Ternhag  A, Granath  F, Verberk  JDM, et al. Nosocomial SARS-CoV-2 infections and mortality during unique COVID-19 epidemic waves. JAMA Netw Open. 2023;6:e2341936. DOIPubMedGoogle Scholar
  19. Mitchell  R, Choi  KB, Pelude  L, Rudnick  W, Thampi  N, Taylor  G; CNISP COVID-19 Working Group. Patients in hospital with laboratory-confirmed COVID-19 in a network of Canadian acute care hospitals, Mar. 1 to Aug. 31, 2020: a descriptive analysis. CMAJ Open. 2021;9:E149–56. DOIPubMedGoogle Scholar
  20. Ontario Agency for Health Protection and Promotion; Provincial Infectious Diseases Advisory Committee on Infection Prevention and Control. Best practices for the prevention of acute respiratory infection transmission in all health care settings, 2024 [cited 2025 Mar 3]. https://www.publichealthontario.ca/-/media/Documents/A/24/acute-respiratory-infection-transmission.pdf
  21. Zeitouny  S, Cheung  DC, Bremner  KE, Pataky  RE, Pequeno  P, Matelski  J, et al. The impact of the early COVID-19 pandemic on healthcare system resource use and costs in two provinces in Canada: An interrupted time series analysis. PLoS One. 2023;18:e0290646. DOIPubMedGoogle Scholar
  22. Canadian Institute for Health Information. COVID-19 hospitalization and emergency department statistics, 2020–2021, August 2022 [cited 2025 Mar 3]. https://www.cihi.ca/en/covid-19-hospitalization-and-emergency-department-statistics
  23. Glazier  RH, Green  ME, Wu  FC, Frymire  E, Kopp  A, Kiran  T. Shifts in office and virtual primary care during the early COVID-19 pandemic in Ontario, Canada. CMAJ. 2021;193:E200–10. DOIPubMedGoogle Scholar
  24. Public Health Ontario. The story of COVID-19 testing in Ontario [cited 2024 Jun 25]. https://www.publichealthontario.ca/en/About/News/2020/Story-Covid-19-Testing-Ontario
  25. Austin  PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46:399–424. DOIPubMedGoogle Scholar
  26. Austin  PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10:150–61. DOIPubMedGoogle Scholar
  27. The Johns Hopkins Bloomberg School of Public Health. The Johns Hopkins ACG® System. Excerpt from version 13.0 technical reference guide, June 2022 [cited 2024 Jul 24]. https://www.healthpartners.com/content/dam/brand-identity/pdfs/care/acg-technical-guide.pdf
  28. Johns Hopkins Medicine. The ACG system is an essential tool in helping providers manage multiple chronic conditions [cited 2024 Jul 24]. https://www.hopkinsacg.org/the-acg-system-is-an-essential-tool-in-helping-providers-manage-multiple-chronic-conditions
  29. Matheson  FI, Moloney  G, van Ingen  T; Public Health Ontario. Ontario marginalization index: user guide, 2023 [cited 2025 Mar 3]. https://www.publichealthontario.ca/-/media/documents/o/2017/on-marg-userguide.pdf
  30. Tanuseputro  P, Wodchis  WP, Fowler  R, Walker  P, Bai  YQ, Bronskill  SE, et al. The health care cost of dying: a population-based retrospective cohort study of the last year of life in Ontario, Canada. PLoS One. 2015;10:e0121759. DOIPubMedGoogle Scholar
  31. Wodchis  WP, Bushmeneva  K, Nikitovic  M, Mckillop  I. Guidelines on person-level costing using administrative databases in Ontario, 2013 [cited 2024 Nov 20]. http://www.hsprn.ca/uploads/files/Guidelines_on_PersonLevel_Costing_May_2013.pdf
  32. Yabroff  KR, Lamont  EB, Mariotto  A, Warren  JL, Topor  M, Meekins  A, et al. Cost of care for elderly cancer patients in the United States. J Natl Cancer Inst. 2008;100:630–41. DOIPubMedGoogle Scholar
  33. Kim  HJ, Fay  MP, Feuer  EJ, Midthune  DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19:335–51. DOIPubMedGoogle Scholar
  34. Parotto  M, Gyöngyösi  M, Howe  K, Myatra  SN, Ranzani  O, Shankar-Hari  M, et al. Post-acute sequelae of COVID-19: understanding and addressing the burden of multisystem manifestations. Lancet Respir Med. 2023;11:739–54. DOIPubMedGoogle Scholar
  35. World Health Organization. A clinical case definition of post COVID-19 condition by a Delphi consensus, 6 October 2021 [cited 2024 Jun 14]. https://www.who.int/publications/i/item/WHO-2019-nCoV-Post_COVID-19_condition-Clinical_case_definition-2021.1
  36. Soriano  JB, Murthy  S, Marshall  JC, Relan  P, Diaz  JV; WHO Clinical Case Definition Working Group on Post-COVID-19 Condition. A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet Infect Dis. 2022;22:e102–7. DOIPubMedGoogle Scholar
  37. Centers for Disease Control and Prevention. Clinical overview of long COVID [cited 2024 Jun 24]. https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/post-covid-conditions.html
  38. Statistics Canada. Expenses of government classified by function, 2020 [cited 2025 Mar 3]. https://www150.statcan.gc.ca/n1/en/daily-quotidien/211126/dq211126a-eng.pdf?st=EL7ygEmS
  39. Tsui  TCO, Zeitouny  S, Bremner  KE, Cheung  DC, Mulder  C, Croxford  R, et al. Initial health care costs for COVID-19 in British Columbia and Ontario, Canada: an interprovincial population-based cohort study. CMAJ Open. 2022;10:E818–30. DOIPubMedGoogle Scholar
  40. Tsai  Y, Vogt  TM, Zhou  F. Patient characteristics and costs associated with covid-19-related medical care among medicare fee-for-service beneficiaries. Ann Intern Med. 2021;174:1101–9. DOIPubMedGoogle Scholar
  41. Kapinos  KA, Peters  RM Jr, Murphy  RE, Hohmann  SF, Podichetty  A, Greenberg  RS. Inpatient costs of treating patients with COVID-19. JAMA Netw Open. 2024;7:e2350145. DOIPubMedGoogle Scholar
  42. Czernichow  S, Bain  SC, Capehorn  M, Bøgelund  M, Madsen  ME, Yssing  C, et al. Costs of the COVID-19 pandemic associated with obesity in Europe: A health-care cost model. Clin Obes. 2021;11:e12442. DOIPubMedGoogle Scholar
  43. Pike  J, Kompaniyets  L, Lindley  MC, Saydah  S, Miller  G. Direct medical costs associated with post–COVID-19 conditions among privately insured children and adults. Prev Chronic Dis. 2023;20:E06. DOIPubMedGoogle Scholar
  44. DeMartino  JK, Swallow  E, Goldschmidt  D, Yang  K, Viola  M, Radtke  T, et al. Direct health care costs associated with COVID-19 in the United States. J Manag Care Spec Pharm. 2022;28:936–47. DOIPubMedGoogle Scholar
  45. Wang  L, Calzavara  A, Baral  S, Smylie  J, Chan  AK, Sander  B, et al. Differential patterns by area-level social determinants of health in coronavirus disease 2019 (COVID-19)-related mortality and non–COVID-19 mortality: a population-based study of 11.8 million people in Ontario, Canada. Clin Infect Dis. 2023;76:1110–20. DOIPubMedGoogle Scholar

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Suggested citation for this article: Sander B, Mishra S, Swayze S, Sahakyan Y, Duchen R, Quinn K, et al. Population-based matched cohort study of COVID-19 healthcare costs, Ontario, Canada. Emerg Infect Dis. 2025 Apr [date cited]. https://doi.org/10.3201/eid3104.241463

DOI: 10.3201/eid3104.241463

Table of Contents – Volume 31, Number 4—April 2025

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Beate Sander, Toronto Health Economics and Technology Assessment Collaborative, 10th Fl, 200 Elizabeth St, Toronto, ON M5G 2C4, Canada

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Page created: February 04, 2025
Page updated: March 10, 2025
Page reviewed: March 10, 2025
The conclusions, findings, and opinions expressed by authors contributing to this journal do not necessarily reflect the official position of the U.S. Department of Health and Human Services, the Public Health Service, the Centers for Disease Control and Prevention, or the authors' affiliated institutions. Use of trade names is for identification only and does not imply endorsement by any of the groups named above.
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