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Grady SC, Pavan A, Qiong Z, Rachael P, Ligmann-Zielinska A. COVID-19 in nursing homes: Geographic diffusion and regional risk factors from January 1 to July 26, 2020 of the pandemic. PLoS One 2024; 19:e0308339. [PMID: 39146332 PMCID: PMC11326555 DOI: 10.1371/journal.pone.0308339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 07/22/2024] [Indexed: 08/17/2024] Open
Abstract
BACKGROUND COVID-19 deaths in nursing homes accounted for 30.2% of all COVID-19 deaths in the United States during the early weeks (1-January to 26-July, 2020) of the pandemic. This study presents the geographic diffusion of COVID-19 cases and deaths in nursing homes during this time period, while also providing explanation of regional risk factors. METHODS AND FINDINGS Nursing home COVID-19 data on confirmed cases (n = 173,452) and deaths (n = 46,173) were obtained from the Centers for Medicare and Medicaid Services. Weekly COVID-19 case counts were spatially smoothed to identify nursing homes in areas of high COVID-19 infection. Bivariate spatial autocorrelation was used to visualize High vs. Low-case counts and related deaths. Zero-inflated negative binomial models were estimated within Health and Human Service (HHS) Regions at three-week intervals to evaluate facility and area-level risk factors. The first reported nursing home resident to die of COVID-19 was in the state of Washington on 28-February, 2020. By 24-May, 2020 there were simultaneous epicenters in the Northeast (HHS Regions 1 and 2) and Midwest (HHS Region 5) with diffusion into the South (HHS Regions 4 and 6) from 15-June to 5-July, 2020. The case-fatality rate was highest from 25-May to 14-June, 2020 (30.9 deaths per 1000 residents); thereafter declining to 24.1 (15-June to 5-July, 2020) and 19.4 (6-July to 26-July, 2020) (overall case-fatality rate 1-January to 26-July = 26.6). Statistically significant risk factors for COVID-19 deaths were admission of patients with COVID-19 into nursing homes, staff confirmed infections and nursing shortages. COVID-19 deaths were likely to occur in nursing homes in high minority and non-English speaking neighborhoods and neighborhoods with a high proportion of households with disabilities. CONCLUSIONS Enhanced communication between HHS regional administrators about "lessons learned" could provide receiving state health departments with timely information to inform clinical practice to prevent premature death in nursing homes in future pandemics.
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Affiliation(s)
- Sue C Grady
- Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, Michigan, United States of America
| | - Amanda Pavan
- Newborn Screening Section, Minnesota Department of Health, Minneapolis, Minnesota, United States of America
| | - Zhang Qiong
- Division of Public Health, Henry Ford Health System, Detroit, Michigan, United States of America
| | - Portelli Rachael
- Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, Champaign, Illinois, United States of America
| | - Arika Ligmann-Zielinska
- Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, Michigan, United States of America
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Karimi-Dehkordi M, Hanson HM, Silvius J, Wagg A. Drivers of COVID-19 Outcomes in Long-Term Care Facilities Using Multi-Level Analysis: A Systematic Review. Healthcare (Basel) 2024; 12:807. [PMID: 38610229 PMCID: PMC11011537 DOI: 10.3390/healthcare12070807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/30/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024] Open
Abstract
This study aimed to identify the individual, organizational, and environmental factors which contributed to COVID-19-related outcomes in long-term care facilities (LTCFs). A systematic review was conducted to summarize and synthesize empirical studies using a multi-level analysis approach to address the identified influential factors. Five databases were searched on 23 May 2023. To be included in the review, studies had to be published in peer-reviewed journals or as grey literature containing relevant statistical data. The Joanna Briggs Institute critical appraisal tool was employed to assess the methodological quality of each article included in this study. Of 2137 citations identified after exclusions, 99 records met the inclusion criteria. The predominant individual, organizational, and environmental factors that were most frequently found associated with the COVID-19 outbreak comprised older age, higher dependency level; lower staffing levels and lower star and subset domain ratings for the facility; and occupancy metrics and co-occurrences of outbreaks in counties and communities where the LTCFs were located, respectively. The primary individual, organizational, and environmental factors frequently linked to COVID-19-related deaths comprised age, and male sex; higher percentages of racial and ethnic minorities in LTCFs, as well as ownership types (including private, for-profit, and chain membership); and higher occupancy metrics and LTCF's size and bed capacity, respectively. Unfolding the risk factors collectively may mitigate the risk of outbreaks and pandemic-related mortality in LTCFs during future endemic and pandemics through developing and improving interventions that address those significant factors.
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Affiliation(s)
- Mehri Karimi-Dehkordi
- Faculty of Medicine & Dentistry, Keyano College, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Heather M. Hanson
- Seniors Health Strategic Clinical Network, Alberta Health Services, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada; (H.M.H.); (J.S.)
| | - James Silvius
- Seniors Health Strategic Clinical Network, Alberta Health Services, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada; (H.M.H.); (J.S.)
| | - Adrian Wagg
- Seniors Health Strategic Clinical Network, Alberta Health Services, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada;
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Fung V, Levison JH, Wilson A, Cheng D, Chau C, Krane D, Trieu HD, Irwin K, Cella E, Bird B, Shellenberger K, Silverman P, Batson J, Fathi A, Gamse S, Wolfe J, Holland S, Donelan K, Samuels R, Becker JE, Freedberg KA, Reichman JL, Keller T, Tsai AC, Hsu J, Skotko BG, Bartels S. COVID-19-Related Outcomes Among Group Home Residents with Serious Mental Illness in Massachusetts in the First Year of the Pandemic. ADMINISTRATION AND POLICY IN MENTAL HEALTH AND MENTAL HEALTH SERVICES RESEARCH 2024; 51:60-68. [PMID: 37938475 PMCID: PMC10872570 DOI: 10.1007/s10488-023-01311-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/06/2023] [Indexed: 11/09/2023]
Abstract
This study examined COVID-19 infection and hospitalizations among people with serious mental illness who resided in residential care group homes in Massachusetts during the first year of the COVID-19 pandemic. The authors analyzed data on 2261 group home residents and COVID-19 data from the Massachusetts Department of Public Health. Outcomes included positive COVID-19 tests and COVID-19 hospitalizations March 1, 2020-June 30, 2020 (wave 1) and July 1, 2020-March 31, 2021 (wave 2). Associations between hazard of outcomes and resident and group home characteristics were estimated using multi-level Cox frailty models including home- and city-level frailties. Between March 2020 and March 2021, 182 (8%) residents tested positive for COVID-19, and 51 (2%) had a COVID-19 hospitalization. Compared with the Massachusetts population, group home residents had age-adjusted rate ratios of 3.0 (4.86 vs. 1.60 per 100) for COVID infection and 13.5 (1.99 vs. 0.15 per 100) for COVID hospitalizations during wave 1; during wave 2, the rate ratios were 0.5 (4.55 vs. 8.48 per 100) and 1.7 (0.69 vs. 0.40 per 100). In Cox models, residents in homes with more beds, higher staff-to-resident ratios, recent infections among staff and other residents, and in cities with high community transmission risk had greater hazard of COVID-19 infection. Policies and interventions that target group home-specific risks are needed to mitigate adverse communicable disease outcomes in this population.Clinical Trial Registration Number This study provides baseline (i.e., pre-randomization) data from a clinical trial study NCT04726371.
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Affiliation(s)
- Vicki Fung
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge Street, Suite 1600, Boston, MA, 02114, USA.
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Julie H Levison
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge Street, Suite 1600, Boston, MA, 02114, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Anna Wilson
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge Street, Suite 1600, Boston, MA, 02114, USA
| | - David Cheng
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Biostatistics Center, Massachusetts General Hospital, Boston, MA, USA
| | - Cindy Chau
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge Street, Suite 1600, Boston, MA, 02114, USA
| | - David Krane
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge Street, Suite 1600, Boston, MA, 02114, USA
| | - Hao D Trieu
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge Street, Suite 1600, Boston, MA, 02114, USA
| | - Kelly Irwin
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge Street, Suite 1600, Boston, MA, 02114, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | | | | | | | | | | | | | | | | | | | - Karen Donelan
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge Street, Suite 1600, Boston, MA, 02114, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ronita Samuels
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge Street, Suite 1600, Boston, MA, 02114, USA
| | - Jessica E Becker
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Kenneth A Freedberg
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge Street, Suite 1600, Boston, MA, 02114, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Alexander C Tsai
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge Street, Suite 1600, Boston, MA, 02114, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - John Hsu
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge Street, Suite 1600, Boston, MA, 02114, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Brian G Skotko
- Down Syndrome Program, Division of Medical Genetics and Metabolism, Department of Pediatrics, Massachusetts General Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Stephen Bartels
- Mongan Institute, Massachusetts General Hospital, Harvard Medical School, 100 Cambridge Street, Suite 1600, Boston, MA, 02114, USA
- Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Jeong W, Kang H, Patel A, Singh M, Prachand N, Stewart WP. Financial, Social, and Health Impacts from the COVID-19 Pandemic: Findings from the Healthy Chicago Survey. Int J Behav Med 2023:10.1007/s12529-023-10220-y. [PMID: 37700149 DOI: 10.1007/s12529-023-10220-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/05/2023] [Indexed: 09/14/2023]
Abstract
BACKGROUND This study examines self-reported impacts of the COVID-19 pandemic among Chicago residents and disparities of these impacts across demographic characteristics. Six logistic regression models were developed to identify demographic and socioeconomic factors associated with each COVID-19 impact. METHOD The study used de-identified and weighted data from the 2020 Healthy Chicago Survey (HCS). HCS is an annual survey using an address-based random sampling method administered by the Chicago Department of Public Health (CDPH) across the city of Chicago on adults aged 18 or older living between July 17 and November 11, 2020, and received 4517 responses. RESULTS Chicago residents with lower socioeconomic status had a higher likelihood of food and housing insecurity. Non-Hispanic (NH) Black residents were more likely to report being unable to obtain food (OR: 2.996; 95% CI: 2.268-2.324); being unable to pay for rent, mortgage, or bills (OR: 2.352; 95% CI: 2.325-2.380); and grief from a loss of someone to COVID-19 (OR: 2.037; 95% CI: 2.013-2.061) compared to NH White residents. NH White residents were more likely than other racial/ethnic groups to report loss of social connections, worsened mental health, and canceling or postponing medical care. CONCLUSIONS The analysis showed higher odds of social contact loss and worsened mental health from COVID-19 in NH White and higher education populations. By implementing strategies to address specific challenges faced by different racial groups, Chicago may effectively mitigate pandemic's adverse effects. These strategies can promote a more inclusive approach to distributing COVID investments for programs and policies.
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Affiliation(s)
- Wonjin Jeong
- Department of Recreation, Sport and Tourism, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
| | - Hyojung Kang
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | - Ajanta Patel
- Chronic Disease Prevention and Health Promotion, Chicago Department of Public Health, Chicago, IL, USA
| | - Meha Singh
- Healthy Chicago Survey, Chicago Department of Public Health, Chicago, IL, USA
| | - Nikhil Prachand
- Epidemiology, Chicago Department of Public Health, Chicago, IL, USA
| | - William P Stewart
- Department of Recreation, Sport and Tourism, University of Illinois at Urbana-Champaign, Champaign, IL, USA
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