Dataset Information

The Impact of the COVID-19 Pandemic on Cardiac Procedure Wait List Mortality in Ontario, Canada.

ABSTRACT:

Background

The novel SARS-CoV-2 (COVID-19) pandemic has dramatically altered the delivery of healthcare services, resulting in significant referral pattern changes, delayed presentations, and procedural delays. Our objective was to determine the effect of the COVID-19 pandemic on all-cause mortality in patients awaiting commonly performed cardiac procedures.

Methods

Clinical and administrative data sets were linked to identify all adults referred for: (1) percutaneous coronary intervention; (2) coronary artery bypass grafting; (3) valve surgery; and (4) transcatheter aortic valve implantation, from January 2014 to September 2020 in Ontario, Canada. Piece-wise regression models were used to determine the effect of the COVID-19 pandemic on referrals and procedural volume. Multivariable Cox proportional hazards models were used to determine the effect of the pandemic on waitlist mortality for the 4 procedures.

Results

We included 584,341 patients who were first-time referrals for 1 of the 4 procedures, of whom 37,718 (6.4%) were referred during the pandemic. The pandemic period was associated with a significant decline in the number of referrals and procedures completed compared with the prepandemic period. Referral during the pandemic period was a significant predictor for increased all-cause mortality for the percutaneous coronary intervention (hazard ratio, 1.83; 95% confidence interval, 1.47-2.27) and coronary artery bypass grafting (hazard ratio, 1.96; 95% confidence interval, 1.28-3.01), but not for surgical valve or transcatheter aortic valve implantation referrals. Procedural wait times were shorter during the pandemic period compared with the prepandemic period.

Conclusions

There was a significant decrease in referrals and procedures completed for cardiac procedures during the pandemic period. Referral during the pandemic was associated with increased all-cause mortality while awaiting coronary revascularization.

SUBMITTER: Tam DY

PROVIDER: S-EPMC8481095 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundMathematical and statistical models are used to project the future time course of infectious disease epidemics and the expected future burden on health care systems and economies. Influenza is a particularly important disease in this context because it causes annual epidemics and occasional pandemics. In order to forecast health care utilization during epidemics-and the effects of hospitalizations and deaths on the contact network and, in turn, on transmission dynamics-modellers must make assumptions about the lengths of time between infection, visiting a physician, being admitted to hospital, leaving hospital, and death. More reliable forecasts could be be made if the distributions of times between these types of events ("delay distributions") were known.MethodsWe estimated delay distributions in the province of Ontario, Canada, between 2006 and 2010. To do so, we used encrypted health insurance numbers to link 1.34 billion health care billing records to 4.27 million hospital inpatient stays. Because the four year period we studied included three typical influenza seasons and the 2009 influenza pandemic, we were able to compare the delay distributions in non-pandemic and pandemic settings. We also estimated conditional probabilities such as the probability of hospitalization within the year if diagnosed with influenza.ResultsIn non-pandemic [pandemic] years, delay distribution medians (inter-quartile ranges) were: Service to Admission 6.3 days (0.1-17.6 days) [2.4 days (-0.3-13.6 days)], Admission to Discharge 3 days (1.4-5.9 days) [2.6 days (1.2-5.1 days)], Admission to Death 5.3 days (2.1-11 days) [6 days (2.6-13.1 days)]. (Service date is defined as the date, within the year, of the first health care billing that included a diagnostic code for influenza-like-illness.) Among individuals diagnosed with either pneumonia or influenza in a given year, 19% [16%] were hospitalized within the year and 3% [2%] died in hospital. Among all individuals who were hospitalized, 10% [12%] were diagnosed with pneumonia or influenza during the year and 5% [5%] died in hospital.ConclusionOur empirical delay distributions and conditional probabilities should help facilitate more accurate forecasts in the future, including improved predictions of hospital bed demands during influenza outbreaks, and the expected effects of hospitalizations on epidemic dynamics.

Project description:ImportanceNursing home residents have been disproportionately affected by coronavirus disease 2019 (COVID-19). Prevention recommendations emphasize frequent testing of health care personnel and residents, but additional strategies are needed.ObjectiveTo develop a reproducible index of nursing home crowding and determine whether crowding was associated with COVID-19 cases and mortality in the first months of the COVID-19 epidemic.Design, setting, and participantsThis population-based retrospective cohort study included more than 78 000 residents across more than 600 nursing homes in Ontario, Canada, and was conducted from March 29 to May 20, 2020.ExposuresThe nursing home crowding index equaled the mean number of residents per bedroom and bathroom.Main outcomes and measuresThe cumulative incidence of COVID-19 cases confirmed by a validated nucleic acid amplification assay and mortality per 100 residents; the introduction of COVID-19 into a home (≥1 resident case) was a negative tracer.ResultsOf 623 homes in Ontario, we obtained complete information on 618 homes (99%) housing 78 607 residents (women, 54 160 [68.9%]; age ≥85 years, 42 919 [54.6%]). A total of 5218 residents (6.6%) developed COVID-19 infection, and 1452 (1.8%) died of COVID-19 infection as of May 20, 2020. COVID-19 infection was distributed unevenly across nursing homes; 4496 infections (86%) occurred in 63 homes (10%). The crowding index ranged across homes from 1.3 (mainly single-occupancy rooms) to 4.0 (exclusively quadruple occupancy rooms); 308 homes (50%) had a high crowding index (≥2). Incidence in high crowding index homes was 9.7% vs 4.5% in low crowding index homes (P < .001), while COVID-19 mortality was 2.7% vs 1.3%, respectively (P < .001). The likelihood of COVID-19 introduction did not differ (high = 31.3% vs low = 30.2%; P = .79). After adjustment for regional, nursing home, and resident covariates, the crowding index remained associated with an increased incidence of infection (relative risk [RR] = 1.73, 95% CI, 1.10-2.72) and mortality (RR, 1.69; 95% CI, 0.99-2.87). A propensity score analysis yielded similar conclusions for infection (RR, 2.09; 95% CI, 1.30-3.38) and mortality (RR, 1.83; 95% CI, 1.09-3.08). Simulations suggested that converting all 4-bed rooms to 2-bed rooms would have averted 998 COVID-19 cases (19.1%) and 263 deaths (18.1%).Conclusions and relevanceIn this cohort of Canadian nursing homes, crowding was common and crowded homes were more likely to experience larger and deadlier COVID-19 outbreaks.