Dataset Information

Cardiovascular Outcomes in Nova Scotia during the Early Phase of the COVID-19 Pandemic.

ABSTRACT:

Background

This study sought to determine the impact of the pandemic response to healthcare delivery on outcomes in patients with cardiovascular disease.

Methods

This is a population-based cohort study performed in the province of Nova Scotia (population 979,499), between Pre-COVID (March 1, 2017 - March 16, 2020) and in-COVID (March 17, 2020 - December 31, 2020) periods. Adult patients (≥18 years) with new onset or existing cardiovascular disease were included for comparison between periods. The main outcome measures included: cardiovascular emergency department visits or hospitalizations, mortality, and out-of-hospital cardiac arrest.

Results

In the first month of the in-COVID period, emergency department visits (n=51,750) for cardiac symptoms decreased by 20.8% (95% CI 14.0% - 27.0%, p<0.001). Cardiovascular hospitalizations (n=20 609) declined by 48.1% (95% CI 40.4% to 54.9%, p<0.001). In-hospital mortality rate increased in patients with cardiovascular admissions in secondary care institutions by 55.1% (95%CI 10.1%-118%, p=0.013). A decline of 20.4% - 44.0% occurred in cardiovascular surgical/interventional procedures. Out-of-hospital cardiac arrests (n=5528) increased from a monthly mean of 115±15 to 136±14, beginning in May, 2020. Mortality for ambulatory patients awaiting cardiac intervention (n=14,083) increased from 0.16% (n=12501) to 2.49% (n=361) in the in-COVID period (p<0.0001).

Conclusion

This study demonstrates increased cardiovascular morbidity and mortality during restrictions maintained during COVID-19, in an area with low burden of COVID-19 disease. As the healthcare system recovers or enters subsequent waves of COVID-19, these findings should inform communication to the public regarding cardiovascular symptoms and policy for delivery of cardiovascular care.

SUBMITTER: Greene A

PROVIDER: S-EPMC8704736 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportanceIn the absence of a national strategy in response to the COVID-19 pandemic, many public health decisions fell to local elected officials and agencies. Outcomes of such policies depend on a complex combination of local epidemic conditions and demographic features as well as the intensity and timing of such policies and are therefore unclear.ObjectiveTo use a decision analytical model of the COVID-19 epidemic to investigate potential outcomes if actual policies enacted in March 2020 (during the first wave of the epidemic) in the St Louis region of Missouri had been delayed.Design, setting, and participantsA previously developed, publicly available, open-source modeling platform (Local Epidemic Modeling for Management & Action, version 2.1) designed to enable localized COVID-19 epidemic projections was used. The compartmental epidemic model is programmed in R and Stan, uses bayesian inference, and accepts user-supplied demographic, epidemiologic, and policy inputs. Hospital census data for 1.3 million people from St Louis City and County from March 14, 2020, through July 15, 2020, were used to calibrate the model.ExposuresHypothetical delays in actual social distancing policies (which began on March 13, 2020) by 1, 2, or 4 weeks. Sensitivity analyses were conducted that explored plausible spontaneous behavior change in the absence of social distancing policies.Main outcomes and measuresHospitalizations and deaths.ResultsA model of 1.3 million residents of the greater St Louis, Missouri, area found an initial reproductive number (indicating transmissibility of an infectious agent) of 3.9 (95% credible interval [CrI], 3.1-4.5) in the St Louis region before March 15, 2020, which fell to 0.93 (95% CrI, 0.88-0.98) after social distancing policies were implemented between March 15 and March 21, 2020. By June 15, a 1-week delay in policies would have increased cumulative hospitalizations from an observed actual number of 2246 hospitalizations to 8005 hospitalizations (75% CrI: 3973-15 236 hospitalizations) and increased deaths from an observed actual number of 482 deaths to a projected 1304 deaths (75% CrI, 656-2428 deaths). By June 15, a 2-week delay would have yielded 3292 deaths (75% CrI, 2104-4905 deaths)-an additional 2810 deaths or a 583% increase beyond what was actually observed. Sensitivity analyses incorporating a range of spontaneous behavior changes did not avert severe epidemic projections.Conclusions and relevanceThe results of this decision analytical model study suggest that, in the St Louis region, timely social distancing policies were associated with improved population health outcomes, and small delays may likely have led to a COVID-19 epidemic similar to the most heavily affected areas in the US. These findings indicate that an open-source modeling platform designed to accept user-supplied local and regional data may provide projections tailored to, and more relevant for, local settings.