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Mayo Clin Proc Innov Qual Outcomes ; 6(1): 77-85, 2022 Feb.
Article in English | MEDLINE | ID: covidwho-1560726


OBJECTIVE: To study associations between the Minnesota coronavirus disease 2019 (COVID-19) mitigation strategies on incidence rates of acute myocardial infarction (MI) or revascularization among residents of Southeast Minnesota. METHODS: Using the Rochester Epidemiology Project, all adult residents of a nine-county region of Southeast Minnesota who had an incident MI or revascularization between January 1, 2015, and December 31, 2020, were identified. Events were defined as primary in-patient diagnosis of MI or undergoing revascularization. We estimated age- and sex-standardized incidence rates and incidence rate ratios (IRRs) stratified by key factors, comparing 2020 to 2015-2019. We also calculated IRRs by periods corresponding to Minnesota's COVID-19 mitigation timeline: "Pre-lockdown" (January 1-March 11, 2020), "First lockdown" (March 12-May 31, 2020), "Between lockdowns" (June 1-November 20, 2020), and "Second lockdown" (November 21-December 31, 2020). RESULTS: The incidence rate in 2020 was 32% lower than in 2015-2019 (24 vs 36 events/100,000 person-months; IRR, 0.68; 95% CI, 0.62-0.74). Incidence rates were lower in 2020 versus 2015-2019 during the first lockdown (IRR, 0.54; 95% CI, 0.44-0.66), in between lockdowns (IRR, 0.70; 95% CI, 0.61-0.79), and during the second lockdown (IRR, 0.54; 95% CI, 0.41-0.72). April had the lowest IRR (IRR 0.48; 95% CI, 0.34-0.68), followed by August (IRR, 0.55; 95% CI, 0.40-0.76) and December (IRR, 0.56; 95% CI, 0.41-0.77). Similar declines were observed across sex and all age groups, and in both urban and rural residents. CONCLUSION: Mitigation measures for COVID-19 were associated with a reduction in hospitalizations for acute MI and revascularization in Southeast Minnesota. The reduction was most pronounced during the lockdown periods but persisted between lockdowns.

Mayo Clin Proc ; 96(10): 2528-2539, 2021 10.
Article in English | MEDLINE | ID: covidwho-1294052


OBJECTIVE: To identify risk factors associated with severe COVID-19 infection in a defined Midwestern US population overall and within different age groups. PATIENTS AND METHODS: We used the Rochester Epidemiology Project research infrastructure to identify persons residing in a defined 27-county Midwestern region who had positive results on polymerase chain reaction tests for COVID-19 between March 1, 2020, and September 30, 2020 (N=9928). Age, sex, race, ethnicity, body mass index, smoking status, and 44 chronic disease categories were considered as possible risk factors for severe infection. Severe infection was defined as hospitalization or death caused by COVID-19. Associations between risk factors and severe infection were estimated using Cox proportional hazard models overall and within 3 age groups (0 to 44, 45 to 64, and 65+ years). RESULTS: Overall, 474 (4.8%) persons developed severe COVID-19 infection. Older age, male sex, non-White race, Hispanic ethnicity, obesity, and a higher number of chronic conditions were associated with increased risk of severe infection. After adjustment, 36 chronic disease categories were significantly associated with severe infection. The risk of severe infection varied significantly across age groups. In particular, persons 0 to 44 years of age with cancer, chronic neurologic disorders, hematologic disorders, ischemic heart disease, and other endocrine disorders had a greater than 3-fold increased risk of severe infection compared with persons of the same age without those conditions. Associations were attenuated in older age groups. CONCLUSION: Older persons are more likely to experience severe infections; however, severe cases occur in younger persons as well. Our data provide insight regarding younger persons at especially high risk of severe COVID-19 infection.

COVID-19/epidemiology , Health Status Disparities , Severity of Illness Index , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , Child , Child, Preschool , Chronic Disease/epidemiology , Comorbidity , Humans , Infant , Male , Middle Aged , Midwestern United States , Risk Factors , Young Adult
Mayo Clin Proc ; 96(3): 699-707, 2021 03.
Article in English | MEDLINE | ID: covidwho-1002867


The success of vaccination programs is contingent upon irrefutable scientific safety data combined with high rates of public acceptance and population coverage. Vaccine hesitancy, characterized by lack of confidence in vaccination and/or complacency about vaccination that may lead to delay or refusal of vaccination despite the availability of services, threatens to undermine the success of coronavirus disease 2019 (COVID-19) vaccination programs. The rapid pace of vaccine development, misinformation in popular and social media, the polarized sociopolitical environment, and the inherent complexities of large-scale vaccination efforts may undermine vaccination confidence and increase complacency about COVID-19 vaccination. Although the experience of recent lethal surges of COVID-19 infections has underscored the value of COVID-19 vaccines, ensuring population uptake of COVID-19 vaccination will require application of multilevel, evidence-based strategies to influence behavior change and address vaccine hesitancy. Recent survey research evaluating public attitudes in the United States toward the COVID-19 vaccine reveals substantial vaccine hesitancy. Building upon efforts at the policy and community level to ensure population access to COVID-19 vaccination, a strong health care system response is critical to address vaccine hesitancy. Drawing on the evidence base in social, behavioral, communication, and implementation science, we review, summarize, and encourage use of interpersonal, individual-level, and organizational interventions within clinical organizations to address this critical gap and improve population adoption of COVID-19 vaccination.

COVID-19 Vaccines/pharmacology , COVID-19/prevention & control , Health Knowledge, Attitudes, Practice , Patient Acceptance of Health Care , SARS-CoV-2/immunology , Social Media , Vaccination/statistics & numerical data , COVID-19/epidemiology , Humans , Pandemics