1
|
Bielinski SJ, Manemann SM, Lopes GS, Jiang R, Weston SA, Reichard RR, Norman AD, Vachon CM, Takahashi PY, Singh M, Larson NB, Roger VL, St Sauver JL. The Importance of Estimating Excess Deaths Regionally During the COVID-19 Pandemic. Mayo Clin Proc 2024; 99:437-444. [PMID: 38432749 PMCID: PMC10914321 DOI: 10.1016/j.mayocp.2023.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 10/24/2023] [Accepted: 11/14/2023] [Indexed: 03/05/2024]
Abstract
National or statewide estimates of excess deaths have limited value to understanding the impact of the COVID-19 pandemic regionally. We assessed excess deaths in a 9-county geographically defined population that had low rates of COVID-19 and widescale availability of testing early in the pandemic, well-annotated clinical data, and coverage by 2 medical examiner's offices. We compared mortality rates (MRs) per 100,000 person-years in 2020 and 2021 with those in the 2019 reference period and MR ratios (MRRs). In 2020 and 2021, 177 and 219 deaths, respectively, were attributed to COVID-19 (MR = 52 and 66 per 100,000 person-years, respectively). COVID-19 MRs were highest in males, older persons, those living in rural areas, and those with 7 or more chronic conditions. Compared with 2019, we observed a 10% excess death rate in 2020 (MRR = 1.10 [95% CI, 1.04 to 1.15]), with excess deaths in females, older adults, and those with 7 or more chronic conditions. In contrast, we did not observe excess deaths overall in 2021 compared with 2019 (MRR = 1.04 [95% CI, 0.99 to 1.10]). However, those aged 18 to 39 years (MRR = 1.36 [95% CI, 1.03 to 1.80) and those with 0 or 1 chronic condition (MRR = 1.28 [95% CI, 1.05 to 1.56]) or 7 or more chronic conditions (MRR = 1.09 [95% CI, 1.03 to 1.15]) had increased mortality compared with 2019. This work highlights the value of leveraging regional populations that experienced a similar pandemic wave timeline, mitigation strategies, testing availability, and data quality.
Collapse
Affiliation(s)
- Suzette J Bielinski
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN.
| | - Sheila M Manemann
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Guilherme S Lopes
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Ruoxiang Jiang
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Susan A Weston
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - R Ross Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN
| | - Aaron D Norman
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Celine M Vachon
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN
| | - Mandeep Singh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Nicholas B Larson
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Véronique L Roger
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN; Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN; Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD
| | - Jennifer L St Sauver
- Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| |
Collapse
|
2
|
Takahashi PY, Thorsteinsdottir B, McCoy RG, Ramar P, Canning RE, Hanson GJ, Baumbach LJ, Chandra A, Philpot LM. Impact of Program Changes Including Telemedicine and Telephonic Care During the COVID-19 Pandemic in Preventing 30-Day Hospital Readmission for Patients in a Care Transitions Program. J Prim Care Community Health 2024; 15:21501319241226547. [PMID: 38270059 PMCID: PMC10812102 DOI: 10.1177/21501319241226547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 01/26/2024] Open
Abstract
INTRODUCTION/OBJECTIVES To describe health outcomes of older adults enrolled in the Mayo Clinic Care Transitions (MCCT) program before and during the COVID-19 pandemic compared to unenrolled patients. METHODS We conducted a retrospective cohort study of adults (age >60 years) in the MCCT program compared to a usual care control group from January 1, 2019, to September 20, 2022. The MCCT program involved a home, telephonic, or telemedicine visit by an advanced care provider. Outcomes were 30- and 180-day hospital readmissions, emergency department (ED) visit, and mortality. We performed a subgroup analysis after March 1, 2020 (during the pandemic). We analyzed data with Cox proportional hazards regression models and hazard ratios (HRs) with 95% CIs. RESULTS Of the 1,012 patients total, 354 were in the MCCT program and 658 were in the usual care group with a mean (SD) age of 81.1 (9.1) years overall. Thirty-day readmission was 16.9% (60 of 354) for MCCT patients and 14.7% (97 of 658) for usual care patients (HR, 1.24; 95% CI, 0.88-1.75). During the pandemic, the 30-day readmission rate was 15.1% (28 of 186) for MCCT patients and 14.9% (68 of 455) for usual care patients (HR, 1.20; 95% CI, 0.75-1.91). There was no difference between groups for 180-day hospitalization, 30- or 180-day ED visit, and 30- or 180-day mortality. CONCLUSIONS Numerous factors involving patients, providers, and health care delivery systems during the pandemic most likely contributed to these findings.
Collapse
Affiliation(s)
| | | | - Rozalina G. McCoy
- Mayo Clinic, Rochester, MN, USA
- University of Maryland School of Medicine, Baltimore, MD
- University of Maryland Institute for Health Computing, Bethesda, MD, USA
| | | | | | | | | | | | | |
Collapse
|
3
|
Manemann SM, Weston SA, Jiang R, Larson NB, Roger VL, Takahashi PY, Chamberlain AM, Singh M, St Sauver JL, Bielinski SJ. Health Care Utilization and Death in Patients With Heart Failure During the COVID-19 Pandemic. Mayo Clin Proc Innov Qual Outcomes 2023; 7:194-202. [PMID: 37229286 PMCID: PMC10099179 DOI: 10.1016/j.mayocpiqo.2023.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/31/2023] [Accepted: 04/05/2023] [Indexed: 05/27/2023] Open
Abstract
Objective To compare the 1-year health care utilization and mortality in persons living with heart failure (HF) before and during the coronavirus disease 2019 (COVID-19) pandemic. Patients and Methods Residents of a 9-county area in southeastern Minnesota aged 18 years or older with a HF diagnosis on January 1, 2019; January 1, 2020; and January 1, 2021, were identified and followed up for 1-year for vital status, emergency department (ED) visits, and hospitalizations. Results We identified 5631 patients with HF (mean age, 76 years; 53% men) on January 1, 2019, 5996 patients (mean age, 76 years; 52% men) on January 1, 2020, and 6162 patients (mean age, 75 years; 54% men) on January 1, 2021. After adjustment for comorbidities and risk factors, patients with HF in 2020 and patients with HF in 2021 experienced similar risks of mortality compared with those in 2019. After adjustment, patients with HF in 2020 and 2021 were less likely to experience all-cause hospitalizations (2020: rate ratio [RR], 0.88; 95% CI, 0.81-0.95; 2021: RR, 0.90; 95% CI, 0.83-0.97) compared with patients in 2019. Patients with HF in 2020 were also less likely to experience ED visits (RR, 0.85; 95% CI, 0.80-0.92). Conclusion In this large population-based study in southeastern Minnesota, we observed an approximately 10% decrease in hospitalizations among patients with HF in 2020 and 2021 and a 15% decrease in ED visits in 2020 compared with those in 2019. Despite the change in health care utilization, we found no difference in the 1-year mortality between patients with HF in 2020 and those in 2021 compared with those in 2019. It is unknown whether any longer-term consequences will be observed.
Collapse
Affiliation(s)
- Sheila M Manemann
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Susan A Weston
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Ruoxiang Jiang
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Nicholas B Larson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Véronique L Roger
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
- National Institutes of Health, Epidemiology and Community Health Branch, National Heart, Lung, and Blood Institute, Bethesda, MD
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, MN
| | - Alanna M Chamberlain
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | - Mandeep Singh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN
| | | | | |
Collapse
|
4
|
Vachon CM, Norman AD, Prasad K, Jensen D, Schaeferle GM, Vierling KL, Sherden M, Majerus MR, Bews KA, Heinzen EP, Hebl A, Yost KJ, Kennedy RB, Theel ES, Ghosh A, Fries M, Wi CI, Juhn YJ, Sampathkumar P, Morice WG, Rocca WA, Tande AJ, Cerhan JR, Limper AH, Ting HH, Farrugia G, Carter RE, Finney Rutten LJ, Jacobson RM, St. Sauver J. Rates of Asymptomatic COVID-19 Infection and Associated Factors in Olmsted County, Minnesota, in the Prevaccination Era. Mayo Clin Proc Innov Qual Outcomes 2022; 6:605-617. [PMID: 36277251 PMCID: PMC9578336 DOI: 10.1016/j.mayocpiqo.2022.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Objective To estimate rates and identify factors associated with asymptomatic COVID-19 in the population of Olmsted County during the prevaccination era. Patients and Methods We screened first responders (n=191) and Olmsted County employees (n=564) for antibodies to SARS-CoV-2 from November 1, 2020 to February 28, 2021 to estimate seroprevalence and asymptomatic infection. Second, we retrieved all polymerase chain reaction (PCR)-confirmed COVID-19 diagnoses in Olmsted County from March 2020 through January 2021, abstracted symptom information, estimated rates of asymptomatic infection and examined related factors. Results Twenty (10.5%; 95% CI, 6.9%-15.6%) first responders and 38 (6.7%; 95% CI, 5.0%-9.1%) county employees had positive antibodies; an additional 5 (2.6%) and 10 (1.8%) had prior positive PCR tests per self-report or medical record, but no antibodies detected. Of persons with symptom information, 4 of 20 (20%; 95% CI, 3.0%-37.0%) first responders and 10 of 39 (26%; 95% CI, 12.6%-40.0%) county employees were asymptomatic. Of 6020 positive PCR tests in Olmsted County with symptom information between March 1, 2020, and January 31, 2021, 6% (n=385; 95% CI, 5.8%-7.1%) were asymptomatic. Factors associated with asymptomatic disease included age (0-18 years [odds ratio {OR}, 2.3; 95% CI, 1.7-3.1] and >65 years [OR, 1.40; 95% CI, 1.0-2.0] compared with ages 19-44 years), body mass index (overweight [OR, 0.58; 95% CI, 0.44-0.77] or obese [OR, 0.48; 95% CI, 0.57-0.62] compared with normal or underweight) and tests after November 20, 2020 ([OR, 1.35; 95% CI, 1.13-1.71] compared with prior dates). Conclusion Asymptomatic rates in Olmsted County before COVID-19 vaccine rollout ranged from 6% to 25%, and younger age, normal weight, and later tests dates were associated with asymptomatic infection.
Collapse
Affiliation(s)
- Celine M. Vachon
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Aaron D. Norman
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Kavita Prasad
- Integrative Medicine, Zumbro Valley Health Center, Mayo Clinic, Rochester, MN
| | - Dan Jensen
- Department of Health, Housing and Human Services Administration, Olmsted County Public Health, Mayo Clinic, Rochester, MN
| | - Gavin M. Schaeferle
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | - Kristy L. Vierling
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | - Meaghan Sherden
- Department of Epidemiology, Surveillance and Preparedness Team, Olmsted County Public Health, Mayo Clinic, Rochester, MN
| | | | - Katherine A. Bews
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | - Ethan P. Heinzen
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN
| | - Amy Hebl
- Department of Human Resources, Olmsted County, Mayo Clinic, Rochester, MN
| | - Kathleen J. Yost
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Richard B. Kennedy
- Vaccine Research Group, Division of General Internal Medicine, Mayo Clinic, Rochester, MN
| | - Elitza S. Theel
- Department of Laboratory Medicine and Pathology, Division of Clinical Microbiology, Mayo Clinic, Rochester, MN
| | - Aditya Ghosh
- Department of Internal Medicine, Northeast Georgia Medical Center, Gainesville, GA
| | | | - Chung-Il Wi
- Department of Precision Population Science Lab, Mayo Clinic, Rochester, MN
| | - Young J. Juhn
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN
| | - Priya Sampathkumar
- Division of Infectious Diseases, Department of Medicine, Mayo Clinic, Rochester, MN
| | - William G. Morice
- Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, MN
| | - Walter A. Rocca
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
- Department of Neurology and Women’s Health Research Center, Mayo Clinic, Rochester, MN
| | - Aaron J. Tande
- Division of Public Health, Infectious Diseases, and Occupational Medicine, Mayo Clinic, Rochester, MN
| | - James R. Cerhan
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
| | - Andrew H. Limper
- Department of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
| | - Henry H. Ting
- Department of Cardiology, Emory University, Atlanta, GA
| | - Gianrico Farrugia
- Division of Gastroenterology & Hepatology, Department of Medicine, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN
| | - Rickey E. Carter
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL
| | | | - Robert M. Jacobson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN
- Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN
| | | |
Collapse
|