Next Article in Journal
Exploring Adverse Drug Reactions in Psychotropic Medications: A Retrospective Analysis of Portuguese Pharmacovigilance Data
Previous Article in Journal
Development and Validation of the Interprofessional Collaboration Practice Competency Scale (IPCPCS) for Clinical Nurses
Previous Article in Special Issue
An Importance-Performance Analysis of Korean Middle School Students’ Health Management Awareness during the Post-COVID-19 Era Depending on Sex
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Healthcare
Volume 12
Issue 7
10.3390/healthcare12070807
healthcare-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

Drivers of COVID-19 Outcomes in Long-Term Care Facilities Using Multi-Level Analysis: A Systematic Review

by
Mehri Karimi-Dehkordi
1,*,
Heather M. Hanson
2,
James Silvius
2 and
Adrian Wagg
3
1
Faculty of Medicine & Dentistry, Keyano College, University of Alberta, Edmonton, AB T6G 2R3, Canada
2
Seniors Health Strategic Clinical Network, Alberta Health Services, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
3
Seniors Health Strategic Clinical Network, Alberta Health Services, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada
*
Author to whom correspondence should be addressed.
Healthcare 2024, 12(7), 807; https://doi.org/10.3390/healthcare12070807
Submission received: 29 February 2024 / Revised: 30 March 2024 / Accepted: 5 April 2024 / Published: 8 April 2024
(This article belongs to the Special Issue The Impact of the COVID-19 Pandemic on Health: Time to Take a Longer-Term Perspective)
Browse Figures
Versions Notes

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.

1. Introduction

In unravelling the complexities of the impact of the COVID-19 pandemic on older adults in long-term care facilities (LTCFs), it becomes apparent that a comprehensive understanding of the substantial individual, organizational, and environmental factors is essential. The importance of this understanding is highlighted by the disproportionate impact and the huge toll of SARS-CoV-2 coronavirus (COVID-19) infection on older adults in LTCFs, especially those with underlying health conditions who experienced a range of predominantly adverse outcomes and death [1,2]. Thus, this study examined COVID-19 case(s)/outbreaks and COVID-related deaths among residents in LTCFs where the pandemic hit the hardest.
The COVID-19-related outcomes in LTCFs were highlighted in a systematic review, reporting an infection rate of 45% coupled with a 23% mortality rate [3]. Despite global efforts, by October 2021, the pandemic persisted and continued to spread; it appeared to differentially affect LTCFs worldwide, albeit with varying degrees of intensity. In the USA and across Europe, the proportion of COVID-19 cases per occupied LTCF bed ranged from 2.2% (in Finland) to 50% (in the USA) [4]. COVID-19-related mortality rates per total population were also widely variable, from 11% in the Czech Republic to 50% in Belgium [4]. Factors influencing COVID-19-related outcomes included variation in reporting information, the reliability, accuracy, and validity of the research, virus variants, healthcare infrastructure, population density, staffing levels and training, and geographic and cultural factors [5,6,7,8]. The phenomenon’s complexity was further evident as COVID-19 outcomes within a specific location fluctuated over time. For instance, the Canadian Institute for Health Information (CIHI) reported that in LTCFs, the count of COVID-19-related fatalities for March, April, May, and June 2020 were 13, 804, 3692, and 948, respectively [9].
Efforts have been made to dissect this intricate and complex phenomenon by either concentrating on a single domain or a particular topic within a domain separately or by examining all three domains/levels but failing to incorporate the relevant factors and synthesize them collectively comprehensively. In the existing body of literature, various drivers and predictors have been identified that can be classified as individual (e.g., age, sex), organizational (e.g., the ratio of staff to residents), and environmental factors (e.g., community prevalence). Reviews investigating determinants of COVID-19 outcomes in LTCFs have appeared in the scientific literature since 2020; to our knowledge, no published systematic review has yet collectively addressed these factors [10,11,12]. The synthesis of emerging research and a comprehensive appreciation of the individual and contextual factors which might influence COVID-19 outcomes is vital for effective management and mitigation of harm in future infectious outbreaks.
The aim of this systematic review was to identify the individual, organizational, and environmental factors which together might underly COVID-19-related outcomes in LTCF.
To achieve this goal, the following are research questions:
  • What individual factors or characteristics, such as Frailty Index and age, impacted COVID-19 outcomes within LTCFs?
  • What organizational characteristics and practices (e.g., staffing level, ownership status) within LTCFs affected the spread and mortality-related outcomes?
  • Did environmental factors, such as facility/room design and facility age, influence COVID-19 outbreak and death?
This approach prevents the skewing of the impact of one group of factors without considering their broader context and will suggest comprehensive areas that require further research.

2. Materials and Methods

The Preferred Reporting Items for Systematic reviews and Meta-Analyses statements (PRISMA-S) checklist guided the conduct and reporting of the current systematic review [13].
PROSPERO registration number: CRD42024519707.

2.1. Search Strategy and Data Sources

The systematic search was conducted with the collaboration of a librarian scientist using OVID MEDLINE, EMBASE, EBSCOhost CINAHL, and the Wiley Cochrane Database of Systematic Reviews. The search results included those published from inception to 23 May 2023. The details of search terms and strategies are shown in Table S1.

2.2. Eligibility Criteria

The PICO model was employed to design this review [14]; P (population): older adults residing in LTCFs or nursing homes regardless of sex and their health status, I (Intervention/exposure): LTCFs or nursing homes that reported case(s) with confirmed COVID-19-related outcomes, C (comparison): characteristics between LTCFs or residents with and without COVID-19-related outcomes, O (outcomes): the determinants that were associated with at least one confirmed COVID-19 case or death-related COVID-19 among residents in LTCFs.
To be included in the review, studies had to be original observational studies, published in peer-reviewed journals or as grey literature since 2020, and contain original statistical data on factors associated with COVID-19 outbreaks or COVID-19-related deaths among LTC residents. Studies were excluded if members of the study population were transferred to acute hospitals due to variations in care levels, staffing, and in the environment [15]. Inclusion of hospital transfers might introduce a potential sampling bias, for example, a study of hospitalized residents diagnosed with COVID-19 exhibited a significantly elevated mortality rate (ranging from 51.6% to 59.3%), contrasting with control samples (8.1% to 9.7%) within LTCFs [16]. Additional grounds for exclusion were met when the study’s participants resided in alternative types of facilities, such as retirement homes, where residents had limited medical and daily assistance needs, or when the studies were oriented towards assessing the effects of interventions on outcomes related to COVID-19. A search for publications in languages other than English confirmed that no substantial (>5%) body of literature was excluded from the review.

2.3. Data Extraction

After removing the duplicates and screening the titles and abstracts, the full text of all potentially eligible articles was retrieved and assessed against the inclusion criteria. The Joanna Briggs Institute (JBI) critical appraisal tools for cross-sectional studies (eight-item scale), case–control (ten-item scale), and cohort studies (eleven-item scale) were used to appraise the methodological quality of the included studies [17]. The studies were categorized into three levels based on quality assessment scores with scores ranging from zero to 8, 10, and 11 for each study type, respectively. Those in level one predominantly fulfilled the criteria and exhibited minimal bias risk, whereas level three studies failed to meet multiple criteria or demonstrated a significant risk of bias (Table S2). We also used a modified version of the JBI data extraction tool to include specific data about participants, context, methods, and outcomes (including individual, organizational, and environmental factors that may influence COVID-19 outbreaks and deaths). Prior to the study, the extraction tool was pilot tested on five studies by MKD and HMH using various methodological designs to make sure all related data were obtained. The tool was then refined and finalized through author consensus on conceptualizing and adding richness to the concepts of organizational, environmental, and individual factors using ecological models, focused on unravelling the complex interplay between factors which might influencing behaviour or care [18]. Each stage of the data collection was accompanied by team review and discussion until a consensus was reached.

2.4. Data Analysis

The multi-level analysis was employed to identify three levels of determinants by incorporating both deductive and inductive analysis [19]. The first author initially extracted tentative codes and themes. Then, the last author reviewed the results from each round and updated these. The analytical process was reviewed by the whole research team, and disagreements were resolved by discussion. We began by deductively coding the quantitative data against the three a priori codes: individual, organizational, and environmental factors. The data under each level were then categorized under two outcome groups: COVID-19 outbreaks and COVID-19-related deaths. Data under each category were then coded again, which led to emerging subcodes (the second level of the coding scheme) which revealed corresponding determinants for each level. Subcodes were then inductively clustered into two major categories for individual determinants and environmental factors and three major categories for organizational factors. The three a priori levels comprised seven themes and 98 subcodes. We synthesized data narratively and presented these under the three levels.

3. Results

Of the 2474 studies’ records identified via the systematic search strategy, 99 records met the inclusion criteria (Figure 1).
Data relating to the characteristics of the included studies are presented in Table 1.
Geographically, the studies originated from the USA (51), Canada (9), Spain (12), Italy (5), France (6), England (2), Australia (1), Ireland (1), Belgium (1), Netherlands (4), Andorra (1), Brazil (1), Germany (1), Korea (1), Switzerland (1), South Africa (1), and Wales (1). These studies followed an observational design and provided data on one or more of the strata of interest. Specifically, 45% of the articles focused on factors influencing both the occurrence of outbreaks and mortality; in comparison, 30% and 23% of the studies delved into the specifics of outbreaks and mortality outcomes, respectively.
A significant portion (40%) of the articles explored the impact of individual, organizational, and environmental factors concurrently.
The included articles examined these factors using 3 to 150 subcategorized variables, with a median of 23. Seventy-one percent of the articles were of moderate quality. Variability in findings was observed among studies classified as either low quality (6%) or high quality (22%). Variability in results was observed explicitly across articles that performed adjustment analyses or effect analyses (83%) and those that did not undertake them (16%).
The findings are organized into three primary sections, focusing on individual, organizational, and environmental factors. Within these sections, various subgroups are linked to both COVID-19 outbreak and COVID-related deaths. The presentation of data in Tables S3–S8 illustrates this structure, wherein each section is further divided into subsections emerged from an inductive analysis of the data.

3.1. Individual Factors

Individual factors were conceptualized as the attributes of LTCF residents that were related to COVID-19 infections and deaths.

3.1.1. Individual Factors Related to Outbreaks

Identified individual factors were linked to 13 areas and fell into two groups: (1) sociodemographic background (age, sex, and socioeconomic status) and (2) condition-specific factors (comorbid conditions, scores of health status instruments, smokers, co-existent medications, seroprevalence, body mass index (BMI), resident dependency level, frailty index, duration of stay in LTCFs, and hospitalization experience).
The individual factors that were most supported by the body of evidence with relation to COVID-19 outbreak include age [24,39,46,60,63,97,101,105,115], dependency level [22,46,54,58,63,72,101,105], frailty [24,58,115], sex (female) [45,56,91], and cognitive deterioration/dementia [105,107,115].
Table S3 details the individual factors associated with outbreaks in LTCFs.

3.1.2. Individual Factors Related to COVID-19-Related Deaths

Fifteen areas of individual factors associated with COVID-19-related death emerged and were categorized as follows: (1) sociodemographic background (age, sex, and social engagement level) and (2) condition-specific factors (cognitive/mental status, comorbidity, symptoms, lab test results, nutritional status, BMI, degree of dependence and level of needed care, frailty, co-existent medications, duration of stay in LTCFs, and hospitalization experience).
Several factors frequently linked to mortality include male sex [20,24,29,32,46,51,52,54,55,58,59,60,62,68,98,101,103], age [24,29,32,33,46,51,53,55,58,59,60,66,68,75,98,101,102,103,115], dependency and level of care needed, [2,20,32,44,46,54,56,63,101,103,110,113,116], cognitive deterioration [20,29,32,33,55,59,86,98,101,115], frailty [23,24,44,45,46,105,115,116], anticoagulation, antiplatelets, or antiplatelets and anticoagulants (inverse association) [20,29,32,46,52,68,98], comorbidity [24,29,52,55,58,98], cardiovascular disease [32,55,60,68,101], respiratory disease [45,59,60,101] hospitalization [29,58,98,113], chronic kidney disease [68,101,103,107], fever symptom [20,58,59,103], hypoxia/respiratory insufficiency symptoms [45,49,58,59,103], diabetes [101,103,107], hypertension [60,81,110], and immunocompromised status [20,29,98].
Table S4 details the individual factors associated with death in LTCFs.

3.2. Organizational Factors

Organizational factors were conceptualized as the internal attributes and organizational characteristics of an LTCF.

3.2.1. Organizational Factors Related to Infection Outbreaks

Eight areas were identified across three groups: (1) LTCFs quality indicators (quality rating star, quality performance), (2) staffing (staffing levels, infected staff, nursing staff assignment, and employment status, (3) ownership and membership affiliation (types of ownership, chain membership), (4) Medicare and Medicaid coverage, and (5) LTCF’s racial and ethnic composition.
The organizational factors that consistently emerged and were supported by a substantial body of evidence with relation to outbreak include staffing levels [26,28,34,48,56,59,70,72,76,79,80,82,84,91,92,94,97,99,106,113], star/subset domains ratings [59,63,69,71,74,76,79,82,83,88,89,94,99,106,111,113], LTCFs with a higher proportion of racial and ethnic minorities [67,70,73,77,81,83,84,89,94,95,99,101,105,106,109], type of ownership (for-profit facilities) [6,24,26,30,37,57,63,72,79,82,84,89,97,113], LTCFs with higher Medicaid-insured residents [71,72,73,91,94,95,97], presence of infected staff [36,42,47,54,56,89,113], quality performance [31,34,43,71,106], and chain membership status [21,30,70,97].
Table S5 contains details about the organizational factors associated with COVID-19 outbreaks in LTCFs.

3.2.2. Organizational Factors Related to COVID-Related Deaths

COVID-related deaths were associated with six organizational areas that we further categorized into five groups: (1) LTCFs quality indicators (star rating, quality performance), (2) staffing (staffing levels, infected staff), (3) ownership and chain affiliation (ownership types, chain membership status), (4) Medicaid and Medicare coverage, and (5) LTCF’s racial and ethnic composition.
The organizational factors that consistently emerged and were supported by a substantial body of evidence with relation to mortality include LTCF’s racial and ethnic composition [2,63,67,70,75,81,83,85,89,95,96,103,109,110], for-profit and/or private ownership [2,24,30,56,63,83,87,98,102,104,113], nursing staffing levels [2,48,56,75,80,81,92,94,98,110], star rating and subdomains (inverse association) [63,74,81,87,88,98,111,113], infected staff [27,56,66,89,113], chain membership [33,70,75,102,104], quality performance [29,40,62,75].
Table S6 depicts detailed information about the impact of organizational factors on COVID-19-related death.

3.3. Environmental Factors

Environmental factors were conceptualized as the social and physical environments in which residents reside.

3.3.1. Environmental Factors Related to COVID Outbreaks

Two groups of environmental factors covering 13 areas were found to affect COVID-19 outbreaks: (1) community factors (outbreaks in counties/communities, outbreak in the community where staff live, location of LTCF, community sociodemographic status (e.g., racial/ethnic composition of the community and socioeconomic status), and (2) physical characteristics (number of beds, crowding index, occupancy rate, new admissions, structural design of the rooms, using the Green House model, accessing ventilator-dependent units, mechanical recirculation of air, and type of care provided in the ward.
The environmental factors frequently emerged and supported by a substantial body of evidence linked to outbreaks include a higher number of beds, occupancy rate [21,25,26,30,31,34,47,54,56,60,61,63,65,67,72,73,82,83,84,91,94,97,106], presence of outbreaks in surrounding counties/communities [6,26,29,30,31,37,38,60,69,79,80,84,91,93,99,104], high-density communities [21,29,56,67,73,106] socioeconomic status of the community, [24,64,77,84,97,106], the structural design of the rooms [21,25,26,104,113], the racial/ethnic composition of the community [63,70,84,113], the location of the LTCF [21,24,43,56], older design and facility age [30,31,56,83].
Detailed information on these environmental factors is provided in Table S7.

3.3.2. Environmental Factors Related to COVID-Related Deaths

Ten environmental factors associated with COVID-19-related death were identified and further categorized into two groups: (1) community factors (outbreaks in counties/communities, high-density communities, public transportation use by workers, disability support program use, location of LTCF, community sociodemographic status (proportions of ethnic minorities and socially deprived communities), and (2) LTCFs physical characteristics (bed numbers, levels of crowding, structural design of the rooms, using the Green House model, accessing ventilator-dependent unit).
The environmental factors frequently associated with mortality include occupancy, levels of crowding [2,25,56,64,83,94,98,104], the larger size of the facility or the number of beds in the LTCF [2,32,33,62,63,65], the location of the LTCF [2,21,24,62,87], higher community or county incidences [60,77,80,104,113], and structural design of the rooms or number of beds in a room [21,25,66,113].
Table S8 contains detailed information about the impact of organizational factors on COVID-19-related death.

4. Discussion

The COVID-19 pandemic led to excess deaths of older adults in LTCFs, particularly early on, when there were few effective treatments and no vaccination programs [117]. This, coupled with staffing shortages, and the relative shortages of adequate personal protective equipment [118] exacerbated the already parlous state within LTCFs.
There is an abundance of information on the contributors to outbreaks and deaths. In looking across the included citations, the most influential factors begin to emerge. Figure 2 depicts factors with the greatest support.
Among the individual factors identified in contributing to outbreaks were a larger representation of older age, increased resident dependency, sex (female), and higher Frailty Index, as well as presence of comorbidities and cognitive decline/dementia.
Arranging individual factors based on the strongest research support reveals the following sequence of their influence on mortality: comorbidity, in particular older age, male sex, increased dependency, cognitive deterioration/dementia, and frailty levels, and co-morbidities.
The individual factors are unsurprising. The population residing in LTCFs represents the most vulnerable older adults, with complex comorbidities and a high prevalence of Alzheimer’s disease and related dementias. The greater likelihood of outbreaks in those exhibiting responsive behaviours is predictable, given that adherence to the strict infection control required is more difficult for these individuals and their dependency upon staff much greater. Cognitive impairment was the most extensively researched comorbidity and a significant predictor of COVID-related outcomes in LTCFs. This result ties well with a systematic review and meta-analysis [119,120]. Underlying mechanisms identified as being implicated in the increased risk were neuroinflammation, nonadherence to COVID-19 prevention measures, and the impact of coexisting comorbidities [119,120].
When looking at organizational factors, parallels emerged for outbreaks and mortality. For outbreaks, staffing levels (an inverse association), star and subset domain ratings for the facility, higher percentages of racial and ethnic minorities in LTCFs, ownership types (including private, for-profit, and chain membership), and presence of infected staff were corroborated across studies. The organizational factors with the highest level of research support for mortality were similar but with a slightly different rank order: LTCF’s racial and ethnic composition, private/for-profit ownership and chain membership, staffing levels, star and subset domain ratings, and the presence of infected staff. A lower quality of LTCF care has previously been linked to for-profit status ownership [121], and perhaps this finding illustrates the same paradigm.
Unsurprisingly, staffing levels, which also may be lower in private and for-profit homes, and which were further stressed during the pandemic, were identified as a significant risk in outbreak and death analyses. A higher risk of adverse COVID-19 outcomes was identified within LTCFs having larger proportions of racial/ethnic minority residents; it was noted that the disparities in nursing home outcomes attributed to race were not solely a result of race. Instead, these disparities were rooted in underlying inequalities inherent in healthcare and nonhealthcare sectors, ultimately leading to poorer health outcomes for racial/ethnic minority residents [89,95].
Not all studies attempted to control for socioeconomic factors underlying ethnic differences in susceptibility, either that of the residents, their care staff, or the socioeconomic status of the facility or of the community in which it resided, exposing the inter-relatedness of the three strata analyzed.
The variability regarding the influence of staffing levels can be attributed to three main factors. Firstly, discrepancies or challenges in methods and study quality may play a role, as different studies utilized diverse analysis techniques and might have overlooked adjusted analyses. This aligns with the findings of Harrington and colleagues [82], who found that low total nurse staffing hours were associated with outbreaks when the model was not adjusted for factors, including health deficiencies, bed size, ownership, and total nurse staffing hours. Adjusting for multi-level factors might have yielded different results. Studies also used various databases to gain information about the characteristics of the LTCFs in varying time periods before the pandemic; these might not reflect the characteristics of the LTCF staff attrition that occurred due to COVID-19 [122]. Furthermore, data related to the COVID-19 outcomes were often collected in different time frames, with little attention to longitudinal as the pandemic progressed. Methodological challenges may also arise in connection with interpretive analysis, leading to divergent results. For example, when interpreting staff–resident ratios, discrepancies may occur based on whether the numerator considers solely full-time staff or encompasses casual workers, as noted in a distinct systematic review [41]. Secondly, concerning differences in staffing policies, exemplified by variations in pre-COVID-19 staffing regulations governing casual and part-time employment or work across multiple LTCFs, could potentially contribute to the transmission of diseases [123]. Thirdly, contextual factors may contribute to the variation in the effects of staffing levels on outcomes; results could be influenced by whether the staff members come from communities experiencing active outbreaks, thereby affecting the potential for disease transmission within LTCFs [73]. The availability of personal protective equipment (PPE) and consistent infection control and prevention training throughout the pandemic could also shape outcomes [91].
Given the conflicting data regarding the effects of staffing. a more comprehensive investigation is warranted. This could involve retrospective longitudinal studies that span the entirety of the pandemic, utilizing repeated measures designs. Additionally, utilizing time-sensitive data related to COVID-19 outcomes and the specific attributes of the facilities under study would provide a more comprehensive understanding of the impact. To capture the whole picture of a phenomenon, performing an adjusted analysis by including the individual, organizational, and environmental covariates would be required.
The environmental factors that garnered the most substantial support regarding outbreaks and mortality alike were the number of beds/crowding index/occupancy rate, outbreaks in counties/communities, community sociodemographic status (racial/ethnic composition of the community/socioeconomic status), high-density communities and structural design of the rooms. Similarly, the strongest environmental factors contributing to COVID-19-related mortality included the number of beds/crowding index/occupancy rate, outbreaks in counties/communities, location of the LTCF, and structural design of the rooms.
A scoping and systematic review demonstrated that certain studies show a relationship between the for-profit status of care facilities and related attributes, like sufficient staffing, access to personal protective equipment (PPE), and testing provisions. These factors are then tied to increased adverse COVID-19 outcomes. The reviews also indicated that the influence of ownership is intricate and holds significance [124,125].
The higher occupancy metrics (including number of beds, crowding index, occupancy rate) and occurrences of outbreaks in counties and communities where LTCFs were located were predominant environmental factors, which is aligned with a previous systematic review wherein the congregate physical environment in LTCFs was described as a factor that exacerbates the outbreaks and mortality risk [10]. Larger LTCF size, location, and interaction with the community with high COVID-19 rates were described as the strongest and most consistent predictors of COVID-19 outcomes [10].
These findings illustrate the inter-relatedness of the classification of strata, homes in poorer neighbourhoods, drawing their staff from less privileged communities (all factors associated with outbreaks) may also have residents who similarly themselves have a high proportion of “at risk” factors. A lack of single rooms, making isolation in the face of infection more difficult, is perhaps a predictable finding, as is the existence of high-density living and older home design (perhaps more “institutional”). Findings such as those in the “Green House” model (smaller, more home-like communities) are perhaps less expected but again may illustrate resident-based risk, rather than an institutional factor.
Discrepancies within articles regarding both COVID-19 outbreaks and mortality may be partially attributed to outcome reporting bias. Notably, clarity is absent regarding whether the prevalence of COVID-19 encompasses asymptomatic residents as well or only those confirmed with tests. Similarly, for mortality rates it remains uncertain whether deaths among residents with positive COVID-19 status died because of, or with, COVID-19. This was evident in a study where 22.7% of COVID-19 cases resulted in death, of which only 24.8% were classified as COVID-19-related deaths [66].
Our study does have limitations. Observational studies are prone to biases, such as reverse causation and residual confounding [126]. The appraisal tool used for this study may not explicitly focus on issues like reverse causality or other forms of endogeneity in observational studies.
We did not include studies focusing mainly on residents transferred to the hospital to treat COVID-19 infection or those that implemented interventions that could be considered organizational. Included study designs were observational, describing strength of association rather than allowing inference of causality. Analyses also failed to consider the complexity of the interrelationship between factors; of the studies included here, only 40% took into account individual, organizational, and environmental (both internal and external to the facility) factors collectively, all including varying covariates/cofounders. Conversely, around 30% of the articles focused solely on one of these three strata, potentially introducing confounding effects due to unmeasured variables.
Many studies did not provide a fully adjusted analysis, which could have reduced the bias in the parameter estimates, so the results might have been overestimated. There was no consistent pattern of adjustment in analytical models across the papers, which may have affected the results. The sample size in seven studies was small and included only one to six LTCFs. More than half of the studies took place in the USA, with data from five studies obtained from one state. This may introduce a systematic bias, being based upon a common administrative structure. The remaining locations form an unrepresentative sample of international LTCF.
Furthermore, non-English papers were not included in this review; thus, a small percentage of the overall number of relevant articles have been excluded. We were also unable to take an intersectional lens in analyzing social determinants of the health of residents, their care providers, and the communities in which the LTCFs were situated. Some of the factors identified in this review are supported by very little evidence, requiring further studies. Furthermore, few LTCF care delivery models were covered, and the Green House model was the focus of only one study.
The results of this study offer comprehensive insights into the complexities of the phenomena and may support the development of modeling which incorporates the multi-level nature of factors influencing outcomes. The findings may also inform decision-makers about those that need to be taken into account to mitigate the impact of this and future pandemics in LTCFs. We have highlighted areas that have not been rigorously researched and those factors that can be considered covariates to control for in future analyses. We also encourage using concurrent data as the validity of the study’s results may be compromised if using non-contemporary data (e.g., two-year-old data regarding characteristics of the organizations).

5. Conclusions

This review has identified potentially modifiable individual, organizational, and environmental risk factors for COVID-19-related outbreaks and deaths in LTCFs for older adults. Action to address these factors is a matter of urgency. To address the risk factors identified in this systematic review, several important actions are recommended. Initially, focus should be devoted to enhancing staffing levels by employing recruitment of full-time staff and training programs to ensure that residents receive sufficient support and mitigating the risk of cross-contamination and transmission within the facility by proactively developing proper strategies. Furthermore, improving quality rankings and performance standards may enhance the overall quality of care. Addressing disparities and racial and ethnic barriers to effective healthcare services in LTCFs and communities requires strategic interventions to mitigate underlying healthcare inequalities. To address specific challenges for residents with dementia in LTCFs, specific plans need to be developed. Initiatives for community engagement and support can enhance resources and tackle social determinants of health. Lastly, prioritizing further research to gain a deeper understanding of these complex interactions will inform evidence-based interventions and policies for managing future pandemics.
In light of the factors identified, including age, sex, dependency level, dementia prevalence, quality performance metrics, staffing levels, racial compositions within LTCFs, ownership structure, bed count, occupancy rate, and community/county characteristics, we recommend a meta-analysis that includes more comparisons to estimate the effectiveness of these factors on COVID-19 outcomes in LTCFs.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/healthcare12070807/s1. File S1: PRISMA Checklist. Table S1: Search strategies. Table S2: The methodological quality assessment of the included studies. Table S3. Individual factors related to outbreaks. Table S4. Individual factors related to COVID-19-related deaths. Table S5. Organizational factors related to infection outbreaks. Table S6. Organizational factors related to COVID-related deaths. Table S7. Environmental factors related to COVID outbreaks. Table S8. Environmental factors related to COVID-related deaths. References [127,128] are cited in the supplementary materials.

Author Contributions

M.K.-D.: Conceptualization, Methodology, Investigation, Data Curation, Formal Analysis, Writing—Original Draft Preparation; H.M.H.: Supervision, Conceptualization, Methodology, Investigation, Formal Analysis, Validation; J.S.: Conceptualization, Validation, Reviewing, Writing and Editing; A.W.: Supervision. Conceptualization, Methodology, Formal Analysis, Data Curation, Validation, Reviewing, Writing and Editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. McMichael, T.M.; Currie, D.W.; Clark, S.; Pogosjans, S.; Kay, M.; Schwartz, N.G.; Lewis, J.; Baer, A.; Kawakami, V.; Lukoff, M.D.; et al. Epidemiology of COVID-19 in a long-term care facility in king county, Washington. N. Engl. J. Med. 2020, 382, 2005–2011. [Google Scholar] [CrossRef]
  2. Unruh, M.A.; Yun, H.; Zhang, Y.; Braun, R.T.; Jung, H.-Y. Nursing home characteristics associated with COVID-19 deaths in connecticut, New Jersey, and New York. J. Am. Med. Dir. Assoc. 2020, 21, 1001–1003. [Google Scholar] [CrossRef] [PubMed]
  3. Hashan, M.R.; Smoll, N.; King, C.; Ockenden-Muldoon, H.; Walker, J.; Wattiaux, A.; Graham, J.; Booy, R.; Khandaker, G. Epidemiology and clinical features of COVID-19 outbreaks in aged care facilities: A systematic review and meta-analysis. Eclinicalmedicine 2021, 33, 100771. [Google Scholar] [CrossRef]
  4. Aalto, U.L.; Pitkälä, K.H.; Andersen-Ranberg, K.; Bonin-Guillaume, S.; Cruz-Jentoft, A.J.; Eriksdotter, M.; Gordon, A.L.; Gosch, M.; Holmerova, I.; Kautiainen, H.; et al. COVID-19 pandemic and mortality in nursing homes across USA and Europe up to October 2021. Eur. Geriatr. Med. 2022, 13, 705–709. [Google Scholar] [CrossRef] [PubMed]
  5. Moolla, I.; Hiilamo, H. Health system characteristics and COVID-19 performance in high-income countries. BMC Health Serv. Res. 2023, 23, 244. [Google Scholar] [CrossRef]
  6. Bagchi, S.; Mak, J.; Li, Q.; Sheriff, E.; Mungai, E.; Anttila, A.; Soe, M.M.; Edwards, J.R.; Benin, A.L.; Pollock, D.A.; et al. Rates of COVID-19 among residents and staff members in nursing homes—United States, May 25–November 22, 2020. Morb. Mortal. Wkly. Rep. 2021, 70, 52. [Google Scholar] [CrossRef]
  7. Giri, S.; Chenn, L.M.; Romero-Ortuno, R. Nursing homes during the COVID-19 pandemic: A scoping review of challenges and responses. Eur. Geriatr. Med. 2021, 12, 1127–1136. [Google Scholar] [CrossRef]
  8. Lin, H.-M.; Liu, S.T.H.; Levin, M.A.; Williamson, J.; Bouvier, N.M.; Aberg, J.A.; Reich, D.; Egorova, N. Informative censoring-a cause of bias in estimating COVID-19 mortality using hospital data. Life 2023, 13, 210. [Google Scholar] [CrossRef] [PubMed]
  9. COVID-19′s Impact on Long-Term Care [Internet]. 2022. Available online: https://www.cihi.ca/en/COVID-19-resources/impact-of-COVID-19-on-canadas-health-care-systems/long-term-care (accessed on 1 December 2022).
  10. Konetzka, R.T.; White, E.M.; Pralea, A.; Grabowski, D.C.; Mor, V. A systematic review of long-term care facility characteristics associated with COVID-19 outcomes. J. Am. Geriatr. Soc. 2021, 69, 2766–2777. [Google Scholar] [CrossRef]
  11. Frazer, K.; Mitchell, L.; Stokes, D.; Lacey, E.; Crowley, E.; Kelleher, C.C. A rapid systematic review of measures to protect older people in long-term care facilities from COVID-19. BMJ Open 2021, 11, e047012. [Google Scholar] [CrossRef]
  12. Dykgraaf, S.H.; Matenge, S.; Desborough, J.; Sturgiss, E.; Dut, G.; Roberts, L.; McMillan, A.; Kidd, M. Protecting nursing homes and long-term care facilities from COVID-19: A rapid review of international evidence. J. Am. Med. Dir. Assoc. 2021, 22, 1969–1988. [Google Scholar] [CrossRef]
  13. Rethlefsen, M.L.; Kirtley, S.; Waffenschmidt, S.; Ayala, A.P.; Moher, D.; Page, M.J.; Koffel, J.B. Prisma-s: An extension to the prisma statement for reporting literature searches in systematic reviews. Syst. Rev. 2021, 10, 39. [Google Scholar] [CrossRef] [PubMed]
  14. Methley, A.M.; Campbell, S.; Chew-Graham, C.; McNally, R.; Cheraghi-Sohi, S. Pico, picos and spider: A comparison study of specificity and sensitivity in three search tools for qualitative systematic reviews. BMC Health Serv. Res. 2014, 14, 579. [Google Scholar] [CrossRef] [PubMed]
  15. Spilsbury, K.; Hewitt, C.; Stirk, L.; Bowman, C. The relationship between nurse staffing and quality of care in nursing homes: A systematic review. Int. J. Nurs. Stud. 2011, 48, 732–750. [Google Scholar] [CrossRef] [PubMed]
  16. Kohl, R.; Schwinger, A.; Jürchott, K.; Hering, C.; Gangnus, A.; Steinhagen-Thiessen, E.; Kuhlmey, A.; Gellert, P. Mortality among hospitalized nursing home residents with COVID-19. Dtsch. Arztebl. Int. 2022, 119, 293–294. [Google Scholar] [CrossRef] [PubMed]
  17. Joanna Briggs Institute. Critical Appraisal Tool 2020. Available online: https://jbi.global/critical-appraisal-tools (accessed on 25 May 2023).
  18. Taplin, S.H.; Price, R.A.; Edwards, H.M.; Foster, M.K.; Breslau, E.S.; Chollette, V.; Das, I.P.; Clauser, S.B.; Fennell, M.L.; Zapka, J. Introduction: Understanding and influencing multilevel factors across the cancer care continuum. J. Natl. Cancer Inst. Monogr. 2012, 2012, 2–10. [Google Scholar] [CrossRef] [PubMed]
  19. Lawrence, B.S. Levels of analysis and the qualitative study of quantitative data. In Multi-Level Issues in Organization Behavior and Processes; Francis, J., Yammarino, F.D., Eds.; Elsevier Science: Oxford, UK, 2004; pp. 231–250. [Google Scholar]
  20. Heras, E.; Garibaldi, P.; Boix, M.; Valero, O.; Castillo, J.; Curbelo, Y.; Gonzalez, E.; Mendoza, O.; Anglada, M.; Miralles, J.C.; et al. COVID-19 mortality risk factors in older people in a long-term care center. Eur. Geriatr. Med. 2021, 12, 601–607. [Google Scholar] [CrossRef] [PubMed]
  21. Ibrahim, J.E.; Li, Y.; McKee, G.; Eren, H.; Brown, C.; Aitken, G.; Pham, T. Characteristics of nursing homes associated with COVID-19 outbreaks and mortality among residents in Victoria, Australia. Australas. J. Ageing 2021, 40, 283–292. [Google Scholar] [CrossRef] [PubMed]
  22. Peckeu-Abboud, L.; Van Kleef, E.; Smekens, T.; Latour, K.; Dequeker, S.; Int Panis, L.; Laga, M. Factors influencing SARS-CoV-2 infection rate in Belgian nursing home residents during the first wave of COVID-19 pandemic. Epidemiol. Infect. 2022, 150, e72. [Google Scholar] [CrossRef]
  23. Frigotto, M.F.; Rodrigues, R.; Rabello, R.; Pietta-Dias, C. COVID-19 in older adult residents in nursing homes: Factors associated with mortality and impact on functional capacity. Sport Sci. Health 2023, 19, 527–535. [Google Scholar] [CrossRef]
  24. Akhtar-Danesh, N.; Baumann, A.; Crea-Arsenio, M.; Antonipillai, V. COVID-19 excess mortality among long-term care residents in Ontario, Canada. PLoS ONE 2022, 17, e0262807. [Google Scholar] [CrossRef] [PubMed]
  25. Brown, K.A.; Jones, A.; Daneman, N.; Chan, A.K.; Schwartz, K.L.; Garber, G.E.; Costa, A.P.; Stall, N.M. Association between nursing home crowding and COVID-19 infection and mortality in Ontario, Canada. JAMA Intern. Med. 2021, 181, 229–236. [Google Scholar] [CrossRef] [PubMed]
  26. Cox, M.B.; McGregor, M.J.; Poss, J.; Harrington, C. The association of facility ownership with COVID-19 outbreaks in long-term care homes in British Columbia, Canada: A retrospective cohort study. CMAJ Open 2023, 11, E267–E273. [Google Scholar] [CrossRef]
  27. Fisman, D.N.; Tuite, A.R.; Bogoch, I.; Lapointe-Shaw, L.; McCready, J. Risk factors associated with mortality among residents with coronavirus disease 2019 (COVID-19) in long-term care facilities in Ontario, Canada. JAMA Netw. Open 2020, 3, e2015957. [Google Scholar] [CrossRef] [PubMed]
  28. Kain, D.; Stall, N.; Brown, K.; McCreight, L.; Rea, E.; Kamal, M.; Brenner, J.; Verge, M.; Davies, R.; Johnstone, J. A longitudinal, clinical, and spatial epidemiologic analysis of a large COVID-19 long-term care home outbreak. J. Am. Med. Dir. Assoc. 2021, 22, 2003–2008.e2. [Google Scholar] [CrossRef]
  29. Lee, D.S.; Ma, S.; Chu, A.; Wang, C.X.; Wang, X.; Austin, P.C.; McAlister, F.A.; Kalmady, S.V.; Kapral, M.K.; Kaul, P.; et al. Predictors of mortality among long-term care residents with SARS-CoV-2 infection. J. Am. Geriatr. Soc. 2021, 69, 3377–3388. [Google Scholar] [CrossRef] [PubMed]
  30. Stall, N.M.; Jones, A.; Brown, K.A.; Rochon, P.A.; Costa, A.P. For-profit long-term care homes and the risk of COVID-19 outbreaks and resident deaths. Can. Med Assoc. J. 2020, 192, E1662–E1672. [Google Scholar] [CrossRef] [PubMed]
  31. Vijh, R.; Bharmal, A.; Ng, C.H.; Shirmaleki, M. Factors associated with transmission of COVID-19 in long-term care facility outbreaks. J. Hosp. Infect. 2022, 119, 118–125. [Google Scholar] [CrossRef] [PubMed]
  32. Zhang, X.S.; Charland, K.; Quach, C.; Nguyen, Q.D.; Zinszer, K. Institutional, therapeutic, and individual factors associated with 30-day mortality after COVID-19 diagnosis in Canadian long-term care facilities. J. Am. Geriatr. Soc. 2022, 70, 3210–3220. [Google Scholar] [CrossRef]
  33. Morciano, M.; Stokes, J.; Turner, A.J.; Kontopantelis, E.; Hall, I. Excess mortality for care home residents during the first 23 weeks of the COVID-19 pandemic in England: A national cohort study. BMC Med. 2021, 19, 71. [Google Scholar] [CrossRef]
  34. Shallcross, L.; Burke, D.; Abbott, O.; Donaldson, A.; Hallatt, G.; Hayward, A.; Hopkins, S.; Krutikov, M.; Sharp, K.; Wardman, L.; et al. Factors associated with SARS-CoV-2 infection and outbreaks in long-term care facilities in England: A national cross-sectional survey. Lancet Healthy Longev. 2021, 2, e129–e142. [Google Scholar] [CrossRef] [PubMed]
  35. Corvol, A.; Charras, K.; Prud’Homm, J.; Lemoine, F.; Ory, F.; Viel, J.F.; Somme, D. Structural and managerial risk factors for COVID-19 occurrence in French nursing homes. Int. J. Health Policy Manag. 2022, 11, 2630. [Google Scholar] [CrossRef] [PubMed]
  36. Piet, E.; Maillard, A.; Mallaval, F.O.; Dusseau, J.Y.; Galas-Haddad, M.; Ducki, S.; Creton, H.; Lallemant, M.; Forestier, E.; Gavazzi, G.; et al. Outbreaks of COVID-19 in nursing homes: A cross-sectional survey of 74 nursing homes in a French area. J. Clin. Med. 2021, 10, 4280. [Google Scholar] [CrossRef] [PubMed]
  37. Rabilloud, M.; Elsensohn, M.-H.; Riche, B.; Voirin, N.; Bénet, T.; Porcu, C.; Iwaz, J.; Étard, J.-F.; Vanhems, P.; Écochard, R. Stronger impact of COVID-19 in nursing homes of a french region during the second pandemic wave. J. Am. Med. Dir. Assoc. 2023, 24, 885–891.e3. [Google Scholar] [CrossRef] [PubMed]
  38. Rabilloud, M.; Riche, B.; Etard, J.F.; Elsensohn, M.-H.; Voirin, N.; Bénet, T.; Iwaz, J.; Ecochard, R.; Vanhems, P. COVID-19 outbreaks in nursing homes: A strong link with the coronavirus spread in the surrounding population, France, March to July 2020. PLoS ONE 2022, 17, e0261756. [Google Scholar] [CrossRef] [PubMed]
  39. Sacco, G.; Foucault, G.; Briere, O.; Annweiler, C. COVID-19 in seniors: Findings and lessons from mass screening in a nursing home. Maturitas 2020, 141, 46–52. [Google Scholar] [CrossRef]
  40. Tarteret, P.; Strazzulla, A.; Rouyer, M.; Gore, C.; Bardin, G.; Noel, C.; Benguerdi, Z.-E.; Berthaud, J.; Hommel, M.; Aufaure, S.; et al. Clinical features and medical care factors associated with mortality in French nursing homes during the COVID-19 outbreak. Int. J. Infect. Dis. 2021, 104, 125–131. [Google Scholar] [CrossRef] [PubMed]
  41. Preuß, B.; Fischer, L.; Schmidt, A.; Seibert, K.; Hoel, V.; Domhoff, D.; Heinze, F.; Brannath, W.; Wolf-Ostermann, K.; Rothgang, H. COVID-19 in German nursing homes: The impact of facilities’ structures on the morbidity and mortality of residents-an analysis of two cross-sectional surveys. Int. J. Environ. Res. Public Health 2023, 20, 610. [Google Scholar] [CrossRef] [PubMed]
  42. Kennelly, S.P.; Dyer, A.H.; Noonan, C.; Martin, R.; Kennelly, S.M.; Martin, A.; O’Neill, D.; Fallon, A. Asymptomatic carriage rates and case fatality of SARS-CoV-2 infection in residents and staff in irish nursing homes. Age Ageing 2021, 50, 49–54. [Google Scholar] [CrossRef]
  43. Cazzoletti, L.; Zanolin, M.E.; Tocco Tussardi, I.; Alemayohu, M.A.; Zanetel, E.; Visentin, D.; Fabbri, L.; Giordani, M.; Ruscitti, G.; Benetollo, P.P.; et al. Risk factors associated with nursing home COVID-19 outbreaks: A retrospective cohort study. Int. J. Environ. Res. Public Health 2021, 18, 8434. [Google Scholar] [CrossRef]
  44. Veronese, N.; Koyanagi, A.; Stangherlin, V.; Mantoan, P.; Chiavalin, M.; Tudor, F.; Pozzobon, G.; Tessarin, M.; Pilotto, A. Mortality attributable to COVID-19 in nursing home residents: A retrospective study. Aging Clin. Exp. Res. 2021, 33, 1745–1751. [Google Scholar] [CrossRef] [PubMed]
  45. Arienti, C.; Brambilla, L.; Campagnini, S.; Fanciullacci, C.; Giunco, F.; Mannini, A.; Patrini, M.; Tartarone, F.; Carrozza, M.C. Mortality and characteristics of older people dying with COVID-19 in lombardy nursing homes, Italy: An observational cohort study. J. Res. Med. Sci. 2021, 26, 40. [Google Scholar] [CrossRef] [PubMed]
  46. Cangiano, B.; Fatti, L.M.; Danesi, L.; Gazzano, G.; Croci, M.; Vitale, G.; Gilardini, L.; Bonadonna, S.; Chiodini, I.; Caparello, C.F.; et al. Mortality in an Italian nursing home during COVID-19 pandemic: Correlation with gender, age, adl, vitamin d supplementation, and limitations of the diagnostic tests. Aging 2020, 12, 24522. [Google Scholar] [CrossRef] [PubMed]
  47. Orlando, S.; Mazhari, T.; Abbondanzieri, A.; Cerone, G.; Ciccacci, F.; Liotta, G.; Mancinelli, S.; Marazzi, M.C.; Palombi, L. Characteristics of nursing homes and early preventive measures associated with risk of infection from COVID-19 in lazio region, italy: A retrospective case-control study. BMJ Open 2022, 12, e061784. [Google Scholar] [CrossRef]
  48. Lee, J.; Shin, J.H.; Lee, K.H.; Harrington, C.A.; Jung, S.O. Staffing levels and COVID-19 infections and deaths in Korean nursing homes. Policy Polit. Nurs. Pract. 2022, 23, 15–25. [Google Scholar] [CrossRef] [PubMed]
  49. Booij, J.A.; van de Haterd, J.C.; Huttjes, S.N.; van Deijck, R.H.; Koopmans, R.T. Short-and long-term mortality and mortality risk factors among nursing home patients after COVID-19 infection. J. Am. Med. Dir. Assoc. 2022, 23, 1274–1278. [Google Scholar] [CrossRef]
  50. Houben, F.; den Heijer, C.D.; Dukers-Muijrers, N.H.; Daamen, A.M.; Groeneveld, N.S.; Vijgen, G.C.; Martens, M.J.; Heijnen, R.W.; Hoebe, C.J. Facility- and ward-level factors associated with SARS-CoV-2 outbreaks among residents in long-term care facilities: A retrospective cohort study. Int. J. Infect. Dis. 2023, 130, 166–175. [Google Scholar] [CrossRef] [PubMed]
  51. Rutten, J.J.S.; van Loon, A.M.; van Kooten, J.; van Buul, L.W.; Joling, K.J.; Smalbrugge, M.; Hertogh, C.M.P.M. Clinical suspicion of COVID-19 in nursing home residents: Symptoms and mortality risk factors. J. Am. Med. Dir. Assoc. 2020, 21, 1791–1797.e1. [Google Scholar] [CrossRef] [PubMed]
  52. Visser, A.G.R.; Winkens, B.; Schols, J.M.G.A.; Janknegt, R.; Spaetgens, B. The impact of polypharmacy on 30-day covid-related mortality in nursing home residents: A multicenter retrospective cohort study. Eur. Geriatr. Med. 2022, 14, 51–57. [Google Scholar] [CrossRef]
  53. Arendse, T.; Cowper, B.; Cohen, C.; Masha, M.; Tempia, S.; Legodu, C.; Singh, S.; Ratau, T.; Geffen, L.; Heymans, A.; et al. SARS-CoV-2 cases reported from long-term residential facilities (care homes) in South Africa: A retrospective cohort study. BMC Public Health 2022, 22, 1035. [Google Scholar] [CrossRef]
  54. Agoües, A.B.; Gallego, M.S.; Resa, R.H.; Llorente, B.J.; Arabi, M.L.; Rodriguez, J.O.; Acebal, H.P.; Hernández, M.C.; Ayala, I.C.; Calero, P.P.; et al. Risk factors for COVID-19 morbidity and mortality in institutionalised elderly people. Int. J. Environ. Res. Public Health 2021, 18, 10221. [Google Scholar] [CrossRef] [PubMed]
  55. Aguilar-Palacio, I.; Maldonado, L.; Marcos-Campos, I.; Castel-Feced, S.; Malo, S.; Aibar, C.; Rabanaque, M.J. Understanding the COVID-19 pandemic in nursing homes (Aragon, Spain): Sociodemographic and clinical factors associated with hospitalization and mortality. Front. Public Health 2022, 10, 928174. [Google Scholar] [PubMed]
  56. Arnedo-Pena, A.; Romeu-Garcia, M.A.; Gascó-Laborda, J.C.; Meseguer-Ferrer, N.; Safont-Adsuara, L.; Prades-Vila, L.; Flores-Medina, M.; Rusen, V.; Tirado-Balaguer, M.D.; Sabater-Vidal, S.; et al. Incidence, mortality, and risk factors of COVID-19 in nursing homes. Epidemiologia 2022, 3, 179–190. [Google Scholar] [CrossRef] [PubMed]
  57. Escribà-Salvans, A.; Rierola-Fochs, S.; Farrés-Godayol, P.; Molas-Tuneu, M.; de Souza, D.L.B.; Skelton, D.A.; Goutan-Roura, E.; Minobes-Molina, E.; Jerez-Roig, J. Risk factors for developing symptomatic COVID-19 in older residents of nursing homes: A hypothesis-generating observational study. J. Frailty Sarcopenia Falls 2023, 8, 74–82. [Google Scholar] [CrossRef] [PubMed]
  58. Romero, M.M.; Céspedes, A.A.; Sahuquillo, M.T.T.; Zamora, E.B.C.; Ballesteros, C.G.; Alfaro, V.S.-F.; Bru, R.L.; Utiel, M.L.; Cifuentes, S.C.; Longobardo, L.M.P.; et al. COVID-19 outbreak in long-term care facilities from Spain. Many lessons to learn. PLoS ONE 2020, 15, e0241030. [Google Scholar]
  59. Meis-Pinheiro, U.; Lopez-Segui, F.; Walsh, S.; Ussi, A.; Santaeugenia, S.; Garcia-Navarro, J.A.; San-Jose, A.; Andreu, A.L.; Campins, M.; Almirante, B. Clinical characteristics of COVID-19 in older adults. A retrospective study in long-term nursing homes in Catalonia. PLoS ONE 2021, 16, e0255141. [Google Scholar] [CrossRef] [PubMed]
  60. Soldevila, L.; Prat, N.; Mas, M.À.; Massot, M.; Miralles, R.; Bonet-Simó, J.M.; Isnard, M.; Expósito-Izquierdo, M.; Garcia-Sanchez, I.; Rodoreda-Noguerola, S.; et al. The interplay between infection risk factors of SARS-CoV-2 and mortality: A cross-sectional study from a cohort of long-term care nursing home residents. BMC Geriatr. 2022, 22, 123. [Google Scholar] [CrossRef] [PubMed]
  61. San Román, J.; Candel, F.J.; del Mar Carretero, M.; Sanz, J.C.; Pérez-Abeledo, M.; Barreiro, P.; Viñuela-Prieto, J.M.; Ramos, B.; Canora, J.; Barba, R.; et al. Cross-sectional analysis of risk factors for outbreak of COVID-19 in nursing homes for older adults in the community of Madrid. Gerontology 2023, 69, 163–171. [Google Scholar] [CrossRef]
  62. Suñer, C.; Ouchi, D.; Mas, M.À.; Lopez Alarcon, R.; Massot Mesquida, M.; Prat, N.; Bonet-Simó, J.M.; Expósito Izquierdo, M.; Garcia Sánchez, I.; Rodoreda Noguerola, S.; et al. A retrospective cohort study of risk factors for mortality among nursing homes exposed to COVID-19 in Spain. Nat. Aging 2021, 1, 579–584. [Google Scholar] [CrossRef] [PubMed]
  63. Cooper, C. United States Nursing Homes and Health Equity during the COVID-19 Pandemic; Georgia Southern University: Statesboro, GA, USA, 2021. [Google Scholar]
  64. Torres, M.L.; Díaz, D.P.; Oliver-Parra, A.; Millet, J.-P.; Cosialls, D.; Guillaumes, M.; Rius, C.; Vásquez-Vera, H. Inequities in the incidence and mortality due to COVID-19 in nursing homes in Barcelona by characteristics of the nursing homes. PLoS ONE 2022, 17, e0269639. [Google Scholar] [CrossRef]
  65. Zunzunegui, M.V.; Beland, F.; Rico, M.; Lopez, F.J.G. Long-term care home size association with COVID-19 infection and mortality in Catalonia in March and April 2020. Epidemiologia 2022, 3, 369–390. [Google Scholar] [CrossRef] [PubMed]
  66. Scanferla, G.; Héquet, D.; Graf, N.; Münzer, T.; Kessler, S.; Kohler, P.; Nussbaumer, A.; Petignat, C.; Schlegel, M.; Flury, D. COVID-19 burden and influencing factors in swiss long-term-care facilities: A cross-sectional analysis of a multicentre observational cohort. Swiss Med. Wkly. 2023, 153, 40052. [Google Scholar] [CrossRef] [PubMed]
  67. Abrams, H.R.; Loomer, L.; Gandhi, A.; Grabowski, D.C. Characteristics of US nursing homes with COVID-19 cases. J. Am. Geriatr. Soc. 2020, 68, 1653–1656. [Google Scholar] [CrossRef] [PubMed]
  68. Adler, L.; Lipton, C.; Watson, C.C.; Lawrence, S.; Richie, A.; Spain, C.; Heltemes, K. Mortality reduction associated with coexistent antithrombotic use in nursing home residents with COVID-19. J. Am. Med. Dir. Assoc. 2022, 23, 440–441. [Google Scholar] [CrossRef] [PubMed]
  69. Bui, D.P.; See, I.; Hesse, E.M.; Varela, K.; Harvey, R.R.; August, E.M.; Winquist, A.; Mullins, S.; McBee, S.; Thomasson, E.; et al. Association between cms quality ratings and COVID-19 outbreaks in nursing homes—West Virginia, March 17–June 11, 2020. MMWR Morb. Mortal. Wkly. Rep. 2020, 69, 1300–1304. [Google Scholar] [CrossRef]
  70. Cai, S.; Yan, D.; Intrator, O. COVID-19 cases and death in nursing homes: The role of racial and ethnic composition of facilities and their communities. J. Am. Med. Dir. Assoc. 2021, 22, 1345–1351. [Google Scholar] [CrossRef] [PubMed]
  71. Chatterjee, P.; Kelly, S.; Qi, M.; Werner, R.M. Characteristics and quality of US nursing homes reporting cases of coronavirus disease 2019 (COVID-19). JAMA Netw. Open 2020, 3, e2016930. [Google Scholar] [CrossRef] [PubMed]
  72. Chen, A.T.; Ryskina, K.L.; Yun, H.; Jung, H.Y. Nursing home characteristics associated with resident COVID-19 morbidity in communities with high infection rates. JAMA Netw. Open 2021, 4, e211555. [Google Scholar] [CrossRef]
  73. Chen, M.K.; Chevalier, J.A.; Long, E.F. Nursing home staff networks and COVID-19. Proc. Natl. Acad. Sci. USA 2020, 118, e2015455118. [Google Scholar] [CrossRef]
  74. Cronin, C.J.; Evans, W.N. Nursing home quality, COVID-19 deaths, and excess mortality. J. Health Econ. 2022, 82, 102592. [Google Scholar] [CrossRef]
  75. Dean, A.; Venkataramani, A.; Kimmel, S. Mortality rates from COVID-19 are lower in unionized nursing homes. Health Aff. 2020, 39, 1993–2001. [Google Scholar] [CrossRef] [PubMed]
  76. Figueroa, J.F.; Wadhera, R.K.; Papanicolas, I.; Riley, K.; Zheng, J.; Orav, E.J.; Jha, A.K. Association of nursing home ratings on health inspections, quality of care, and nurse staffing with COVID-19 cases. JAMA 2020, 324, 1103–1105. [Google Scholar] [CrossRef] [PubMed]
  77. Engeda, J.C.; Karmarkar, E.N.; Mitsunaga, T.M.; Raymond, K.L.; Oh, P.; Epson, E. Resident racial and ethnic composition, neighborhood-level socioeconomic status, and COVID-19 infections in California snfs. J. Am. Geriatr. Soc. 2023, 71, 157–166. [Google Scholar] [CrossRef] [PubMed]
  78. Gilman, M.; Bassett, M.T. Trends in COVID-19 death rates by racial composition of nursing homes. J. Am. Geriatr. Soc. 2021, 69, 2442–2444. [Google Scholar] [CrossRef] [PubMed]
  79. Gopal, R.; Han, X.; Yaraghi, N. Compress the curve: A cross-sectional study of variations in COVID-19 infections across California nursing homes. BMJ Open 2021, 11, e042804. [Google Scholar] [CrossRef] [PubMed]
  80. Gorges, R.J.; Konetzka, R.T. Staffing levels and COVID-19 cases and outbreaks in U.S. Nursing homes. J. Am. Geriatr. Soc. 2020, 68, 2462–2466. [Google Scholar] [CrossRef] [PubMed]
  81. Gorges, R.J.; Konetzka, R.T. Factors associated with racial differences in deaths among nursing home residents with COVID-19 infection in the us. JAMA Netw. Open 2021, 4, e2037431. [Google Scholar] [CrossRef] [PubMed]
  82. Harrington, C.; Ross, L.; Chapman, S.; Halifax, E.; Spurlock, B.; Bakerjian, D. Nurse staffing and coronavirus infections in California nursing homes. Policy Politics Nurs. Pract. 2020, 21, 174–186. [Google Scholar] [CrossRef] [PubMed]
  83. He, M.; Fang, F.; Li, Y. Is there a link between nursing home reported quality and COVID-19 cases? Evidence from California skilled nursing facilities. J. Am. Med. Dir. Assoc. 2020, 21, 905–908. [Google Scholar] [CrossRef] [PubMed]
  84. Hege, A.; Lane, S.; Spaulding, T.; Sugg, M.; Iyer, L.S. County-level social determinants of health and COVID-19 in nursing homes, United States, June 1, 2020–January 31, 2021. Public Health Rep. 2022, 137, 137–148. [Google Scholar] [CrossRef]
  85. Hill, T.E.; Farrell, D.J. COVID-19 across the landscape of long-term care in alameda county: Heterogeneity and disparities. Gerontol. Geriatr. Med. 2022, 8, 23337214211073419. [Google Scholar] [CrossRef]
  86. Hua, C.L.; Cornell, P.Y.; Zimmerman, S.; Carder, P.; Thomas, K.S. Excess mortality among assisted living residents with dementia during the COVID-19 pandemic. J. Am. Med. Dir. Assoc. 2022, 23, 1743–1749.e6. [Google Scholar] [CrossRef] [PubMed]
  87. Iyanda, A.E.; Boakye, K.A. A 2-year pandemic period analysis of facility and county-level characteristics of nursing home coronavirus deaths in the United States, January 1, 2020-December 18, 2021. Geriatr. Nurs. 2022, 44, 237–244. [Google Scholar] [CrossRef]
  88. Khairat, S.; Zalla, L.C.; Adler-Milstein, J.; Kistler, C.E. US nursing home quality ratings associated with COVID-19 cases and deaths. J. Am. Med. Dir. Assoc. 2021, 22, 2021–2025.e1. [Google Scholar] [PubMed]
  89. Kim, S.J.; Hollender, M.; DeMott, A.; Oh, H.; Bhatia, I.; Eisenberg, Y.; Gelder, M.; Hughes, S. COVID-19 cases and deaths in skilled nursing facilities in cook county, Illinois. Public Health Rep. 2022, 137, 564–572. [Google Scholar] [CrossRef] [PubMed]
  90. Kumar, A.; Baldwin, J.A.; Roy, I.; Karmarkar, A.M.; Erler, K.S.; Rudolph, J.L.; Rivera-Hernandez, M. Shifting US patterns of COVID-19 mortality by race and ethnicity from June–December 2020. J. Am. Med. Dir. Assoc. 2021, 22, 966–970.e3. [Google Scholar] [CrossRef] [PubMed]
  91. Lane, S.J.; Sugg, M.; Spaulding, T.J.; Hege, A.; Iyer, L. Southeastern United States predictors of COVID-19 in nursing homes. J. Appl. Gerontol. 2022, 41, 1641–1650. [Google Scholar] [CrossRef]
  92. Levy-Storms, L.; Mueller-Williams, A. Certified nursing aides’ training hours and covid case and mortality rates across states in the U.S.: Implications for infection prevention and control and relationships with nursing home residents. Front. Public Health 2022, 10, 798779. [Google Scholar] [CrossRef]
  93. Longo, B.A.; Barrett, S.C.; Schmaltz, S.P.; Williams, S.C. A multistate comparison study of COVID-19 cases among accredited and nonaccredited nursing homes. Policy Polit. Nurs. Pract. 2022, 23, 26–31. [Google Scholar] [CrossRef] [PubMed]
  94. Li, Y.; Temkin-Greener, H.; Shan, G.; Cai, X. COVID-19 infections and deaths among connecticut nursing home residents: Facility correlates. J. Am. Geriatr. Soc. 2020, 68, 1899–1906. [Google Scholar] [CrossRef] [PubMed]
  95. Li, Y.; Temkin-Greener, H.; Cen, X.; Cai, X. Racial and ethnic disparities in COVID-19 infections and deaths across U.S. Nursing homes. J. Am. Geriatr. Soc. 2020, 68, 2454–2461. [Google Scholar] [CrossRef]
  96. Li, Y.; Cai, X.Y.; Mao, Y.J.; Cheng, Z.J.; Temkin-Greener, H. Trends in racial and ethnic disparities in coronavirus disease 2019 (COVID-19) outcomes among nursing home residents. Infect. Control. Hosp. Epidemiol. 2022, 43, 997–1003. [Google Scholar] [CrossRef] [PubMed]
  97. Lord, J.; Davlyatov, G.; Ghiasi, A.; Weech-Maldonado, R. Nursing homes located in socially deprived communities have been disproportionately affected by COVID-19. J. Health Care Financ. 2021, 48, 1–12. [Google Scholar]
  98. Lu, Y.; Jiao, Y.; Graham, D.J.; Wu, Y.; Wang, J.; Menis, M.; Chillarige, Y.; Wernecke, M.; Kelman, J.; Forshee, R.A.; et al. Risk factors for COVID-19 deaths among elderly nursing home medicare beneficiaries in the prevaccine period. J. Infect. Dis. 2022, 225, 567–577. [Google Scholar] [CrossRef] [PubMed]
  99. Mattingly, T.J.; Trinkoff, A.; Lydecker, A.D.; Kim, J.J.; Yoon, J.M.; Roghmann, M.C. Short-stay admissions associated with large COVID-19 outbreaks in Maryland nursing homes. Gerontol. Geriatr. Med. 2021, 7, 23337214211063103. [Google Scholar] [CrossRef]
  100. McGarry, B.E.; Gandhi, A.D.; Grabowski, D.C.; Barnett, M.L. Larger nursing home staff size linked to higher number of COVID-19 cases in 2020. Health Aff. 2021, 40, 1261–1269. [Google Scholar] [CrossRef] [PubMed]
  101. Mehta, H.B.; Li, S.; Goodwin, J.S. Risk factors associated with SARS-CoV-2 infections, hospitalization, and mortality among US nursing home residents. JAMA Netw. Open 2021, 4, e216315. [Google Scholar] [CrossRef] [PubMed]
  102. Olson, A.; Rajgopal, S.; Bai, G. Comparison of resident COVID-19 mortality between unionized and nonunionized private nursing homes. PLoS ONE 2022, 17, e0276301. [Google Scholar] [CrossRef] [PubMed]
  103. Panagiotou, O.A.; Kosar, C.M.; White, E.M.; Bantis, L.E.; Yang, X.; Santostefano, C.M.; Feifer, R.A.; Blackman, C.; Rudolph, J.L.; Gravenstein, S.; et al. Risk factors associated with all-cause 30-day mortality in nursing home residents with COVID-19. JAMA Intern. Med. 2021, 181, 439–448. [Google Scholar] [CrossRef] [PubMed]
  104. Shen, K. Relationship between nursing home COVID-19 outbreaks and staff neighborhood characteristics. PLoS ONE 2022, 17, e0267377. [Google Scholar] [CrossRef]
  105. Shi, S.M.; Travison, T.G.; Berry, S.D.; Bakaev, I.; Chen, H. Risk factors, presentation, and course of coronavirus disease 2019 in a large, academic long-term care facility. J. Am. Med. Dir. Assoc. 2020, 21, 1378–1383.e1. [Google Scholar] [CrossRef]
  106. Sugg, M.M.; Spaulding, T.J.; Lane, S.J.; Runkle, J.D.; Harden, S.R.; Hege, A.; Iyer, L.S. Mapping community-level determinants of COVID-19 transmission in nursing homes: A multi-scale approach. Sci. Total Environ. 2021, 752, 141946. [Google Scholar] [CrossRef] [PubMed]
  107. Tang, O.; Bigelow, B.F.; Sheikh, F.; Peters, M.; Zenilman, J.M.; Bennett, R.; Katz, M.J. Outcomes of nursing home COVID-19 patients by initial symptoms and comorbidity: Results of universal testing of 1970 residents. J. Am. Med. Dir. Assoc. 2020, 21, 1767–1773.e1. [Google Scholar] [CrossRef] [PubMed]
  108. Thorsness, R.; Raines, N.H.; White, E.M.; Santostefano, C.M.; Parikh, S.M.; Riester, M.R.; Feifer, R.A.; Mor, V.; Zullo, A.R. Association of kidney function with 30-day mortality following SARS-CoV-2 infection in nursing home residents: A retrospective cohort study. Am. J. Kidney Dis. 2022, 79, 305–307. [Google Scholar] [CrossRef]
  109. Travers, J.L.; Wu, B.; Agarwal, M.; Estrada, L.V.; Stone, P.W.; Dick, A.W.; Gracner, T. Assessment of coronavirus disease 2019 infection and mortality rates among nursing homes with different proportions of black residents. J. Am. Med. Dir. Assoc. 2021, 22, 893–898.e2. [Google Scholar] [CrossRef] [PubMed]
  110. Weech-Maldonado, R.; Orewa, G.; Lord, J.; Davlyatov, G.; Ghiasi, A. High-minority nursing homes disproportionately affected by COVID-19 deaths. Front. Public Health 2021, 9, 606364. [Google Scholar] [CrossRef] [PubMed]
  111. Williams, C.S.; Zheng, Q.; White, A.J.; Bengtsson, A.I.; Shulman, E.T.; Herzer, K.R.; Fleisher, L.A. The association of nursing home quality ratings and spread of COVID-19. J. Am. Geriatr. Soc. 2021, 69, 2070–2078. [Google Scholar] [CrossRef] [PubMed]
  112. Young, Y.; Shayya, A.; O’Grady, T.; Chen, Y.M. COVID-19 case and mortality rates lower in green houses compared to traditional nursing homes in New York state. Geriatr. Nurs. 2023, 50, 132–137. [Google Scholar] [CrossRef] [PubMed]
  113. Zhu, X.; Lee, H.; Yang, H.; Lee, C.; Sang, H.; Muller, J.; Ory, M. Nursing home design and COVID-19: Implications for guidelines and regulation. J. Am. Med. Dir. Assoc. 2022, 23, 272–279.e1. [Google Scholar] [CrossRef] [PubMed]
  114. Zimmerman, S.; Tandan, M.; Wretman, C.J.; Preisser, J.S.; Dumond-Stryker, C.; Howell, A.; Ryan, S. Nontraditional small house nursing homes have fewer COVID-19 cases and deaths. J. Am. Med. Dir. Assoc. 2021, 22, 489–493. [Google Scholar] [CrossRef]
  115. Emmerson, C.; Hollinghurst, J.; North, L.; Fry, R.; Akbari, A.; Humphreys, C.; Gravenor, M.B.; Lyons, R.A. The impact of dementia, frailty and care home characteristics on SARS-CoV-2 incidence in a national cohort of welsh care home residents during a period of high community prevalence. Age Ageing 2022, 51, afac250. [Google Scholar] [CrossRef]
  116. Beobide Telleria, I.; Ferro Uriguen, A.; Laso Lucas, E.; Sannino Menicucci, C.; Enriquez Barroso, M.; Lopez de Munain Arregui, A. Risk factors associated with COVID-19 infection and mortality in nursing homes. Aten. Primaria 2022, 54, 102463. [Google Scholar] [CrossRef]
  117. Ouslander, J.G.; Grabowski, D.C. COVID-19 in nursing homes: Calming the perfect storm. J. Am. Geriatr. Soc. 2020, 68, 2153–2162. [Google Scholar] [CrossRef] [PubMed]
  118. Gibson, D.M.; Greene, J. State actions and shortages of personal protective equipment and staff in US nursing homes. J. Am. Geriatr. Soc. 2020, 68, 2721–2726. [Google Scholar] [CrossRef]
  119. Naughton, S.X.; Raval, U.; Pasinetti, G.M. Potential novel role of COVID-19 in Alzheimer’s disease and preventative mitigation strategies. J. Alzheimer’s Dis. 2020, 76, 21–25. [Google Scholar] [CrossRef]
  120. Saragih, I.D.; Batubara, S.O.; Lin, C.J.; Saragih, I.S. Dementia as a mortality predictor among older adults with COVID-19: A systematic review and meta-analysis of observational study. Geriatr. Nurs. 2021, 42, 1230–1239. [Google Scholar] [CrossRef]
  121. Broms, R.; Dahlstrom, C.; Nistotskaya, M. Provider ownership and indicators of service quality: Evidence from swedish residential care homes. J. Public Adm. Res. Theory 2023, 34, 150–163. [Google Scholar] [CrossRef]
  122. Fosdick, B.K.; Bayham, J.; Dilliott, J.; Ebel, G.D.; Ehrhart, N. Model-based evaluation of policy impacts and the continued COVID-19 risk at long term care facilities. Infect. Dis. Model. 2022, 7, 463–472. [Google Scholar] [CrossRef]
  123. Patterson, P.B.; Weinberg, T.; McRae, S.; Pollack, C.; Dutton, D. Long-term care staffing policies pre-COVID-19 and pandemic responses: A case comparison of Ontario and British Columbia. Can. Public Policy-Anal. De Polit. 2023, 49, 94–113. [Google Scholar] [CrossRef]
  124. Bach-Mortensen, A.M.; Verboom, B.; Movsisyan, A.; Degli Esposti, M. A systematic review of the associations between care home ownership and COVID-19 outbreaks, infections and mortality. Nat. Aging 2021, 1, 948–961. [Google Scholar] [CrossRef]
  125. Kruse, F.M.; Mah, J.C.; Metsemakers, S.J.; Andrew, M.K.; Sinha, S.K.; Jeurissen, P.P. Relationship between the ownership status of nursing homes and their outcomes during the COVID-19 pandemic: A rapid literature review. J. Long-Term Care, 2021; 207–220. [Google Scholar] [CrossRef]
  126. Solmi, M.; Cobey, K.D.; Moher, D.; Ebrahimzadeh, S.; Dragioti, E.; Shin, J.I.; Radua, J.; Cortese, S.; Shea, B.; Veronese, N.; et al. Development of a reporting guideline for umbrella reviews on epidemiological associations using cross-sectional, case-control, and cohort studies: The preferred reporting items for umbrella reviews of cross-sectional, case-control, and cohort studies (priur-ccc). medRxiv 2022. medRxiv:28.22283572. [Google Scholar]
  127. Mitjà, O.; Reis, G.; Boulware, D.R.; Spivak, A.M.; Sarwar, A.; Johnston, C.; Webb, B.; Hill, M.D.; Smith, D.; Kremsner, P.; et al. Hydroxychloroquine for treatment of non-hospitalized adults with COVID-19: A meta-analysis of individual participant data of randomized trials. Clin. Transl. Sci. 2023, 16, 524–535. [Google Scholar] [CrossRef] [PubMed]
  128. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef] [PubMed]
Healthcare 12 00807 g001
Figure 1. PRISMA flow-chart for the systematic review process.
Figure 1. PRISMA flow-chart for the systematic review process.
Healthcare 12 00807 g001
Healthcare 12 00807 g002
Figure 2. Individual, organizational, and environmental factors affecting COVID-19 outcomes.
Figure 2. Individual, organizational, and environmental factors affecting COVID-19 outcomes.
Healthcare 12 00807 g002
Table 1. Description of studies included in the systematic review.
Table 1. Description of studies included in the systematic review.
First
Author		YearTime Frame Data CollectionCountry/
Region		Outcome TypeObjectivesLTCF #Study DesignFactors’ LevelAA/EA
Heras [20]202115 March 2020 to 5 June 2020AndorraMIdentification of the COVID-19 mortality risk factors in older people from a long-term care center-Cross-sectional Indiv+
Ibrahim [21]20217 July to 13 November 2020Australia/VictoriaO and MInvestigation of the LTCFs characteristics, including organizational and facility structures, location, and access to acute health care, associated with the COVID-19 outbreak766Cross-sectional Org, Envi-
Peckeu-Abboud [22]20228 April 2022, to 15 May 2020BelgiumOIdentification of the factors influencing SARS-CoV-2 infection rate in Belgian LTCFs residents during the first wave of the COVID-19 pandemic695Cross-sectionalIndiv, Org+
Frigotto [23]20235 November to 31 December 2020BrazilOInvestigation of whether the functional capacity prior to COVID-19 infection was different between survivor and non-survivor older adults2Cross-sectionalIndiv-
Akhtar -Danesh [24]2022January 2019 to December 2020Canada/OntarioO and MDetermination of the scale of pandemic-related deaths of long-term care residents in the province of Ontario, Canada, and to estimate excess mortality due to a positive COVID-19 test adjusted for demographics and regional variations626CohortIndiv, Org, Envi+
Brown [25]202129 March 2020 to 20 May 2020Canada/OntarioO and MDetermination of whether crowding was associated with COVID-19 cases and mortality in the first months of the COVID-19 epidemic618Cross-sectional Indiv, Org, Envi+
Cox [26]20231 March 2020, to 31 January 2021Canada/British ColumbiaO and MAssessment of whether facility ownership was associated with COVID-19 outbreaks among LTCFs293Cross-sectional Org, Envi+
Fisman [27]2020March 2020 to April 2020Canada/OntarioO and MIdentification of the trends and risk factors associated with COVID-19 death in LTCFs in Ontario, Canada627Cohort Org, Envi+
Kain [28]20211 March to 21 May 2020Canada/OntarioOUnderstanding of the way in which the virus spreads within these homes is critical to preventing further outbreaks.1Cross-sectional Indiv, Org, Envi-
Lee [29]20211 January to 31 August 2020Canada/OntarioMUnderstanding of the contributions to mortality among SARS-CoV-2-infected residents in LTC homes-Cross-sectional Indiv+
Stall [30]202029 March to 20 May 2020Canada/OntarioO and MExamination of the association between for-profit status and the risk of COVID-19 outbreaks and death during the peak of the epidemic in Ontario’s LTC homes623Cross-sectional Org, Envi+
Vijh [31]20211 March 2020, to 10 January 2021Canada/British ColumbiaOIdentification of the risk factors associated with outbreak severity to inform current outbreak management and future pandemic preparedness planning efforts48Cross-sectional Indiv, Org, Envi+
Zhang [32]202223 February to 11 July 2020Canada/MontrealMIdentification of the individual, therapeutic, and institutional factors associated with death in LTCFs17Cross-sectional Indiv, Org+
Morciano [33]20211 January 2017, to 7 August 2020EnglandMInvestigation of the excess mortality for care home residents during the COVID-19 pandemic in England, exploring associations with care home characteristics4428Cohort Indiv, Org, Envi+
Shallcross [34]202126 May to 19 June 2020EnglandOIdentification of the factors associated with SARS-CoV-2 infection and outbreaks among staff and residents in LTCFs5126Cross-sectional Indiv, Org, Envi+
Corvol [35]20221 March to 31 May 2020France/Brittany OIdentification of the structural and managerial factors associated with COVID-19 outbreaks in LTCFs231Cross-sectional Indiv, Org, Envi+
Piet [36]20211 March to 31 May 2020France/French AlpsO and MIdentification of the relationship between the occurrence of an outbreak of COVID-19 among residents and staff members225Cross-Sectional Indiv, Org, Envi+
Rabilloud [37]20231 March
to 31 July 2020, and 1 August to 31 December 2020		France/Auvergne-Rhône-AlpesO and MQuantification of the effects of characteristics of LTCFs and their surroundings on the spread of COVID-19 outbreaks and assessment of the changes in resident protection between the first 2 waves937Cross-sectional Org, Envi+
Rabilloud [38]20221 March to 31
July 2020		France/Auvergne-Rhône-Alpes OIdentification of the role SARS-CoV-2 virus spread in nearby population plays in introducing the disease in LTCFs943Cross-sectional Indiv-
Sacco [39]2020Prior to 17 March 2020France/Maine-et-LoireOComprehensive description of the symptoms and chronological aspects of the diffusion of the SARS-CoV-2 virus in an LTCF, among both residents and caregivers1Cross-sectional Indiv+
Tarteret [40]202118 March to 10 April 2020France/Ile-de-FranceMIdentification of the demographic, clinical, and medical care factors associated with mortality in LTCF residents3Cross-sectional Indiv, Org+
Preuß [41]202328 April to 12 May 2020 and 12 January to 7 February 2021GermanyO and MIdentification of the impact of Facilities’
Structures on the Morbidity and Mortality of Residents		824 and 385 during 1st and 2nd waves, respectively Cross-SectionalOrg, Envi+
Kennelly [42] 202129 February to 22 May 2020IrelandO and MExamination of characteristics of LTCFs across three Irish Community Health Organisations, proportions with COVID-19 outbreaks, staff and resident infection rates, symptom profile and resident case fatality45Cross-Sectional Indiv, Org, Envi-
Cazzoletti [43]2021March to May 2020Italy/TrentoOExamination of the association between certain measurable factors (structural, organizational, and practice-related) and the cumulative incidence of COVID-19 among LTCF residents in the Autonomous Province of Trento, Italy, during the peak of the COVID-19 outbreak57Cross-sectional Indiv, Org, Envi+
Veronese [44]20211 March to 31 December 2020.Italy/VeniceMExamination of whether COVID-19 was associated with a higher mortality rate in LTCF residents, considering frailty status assessed with the Multidimensional Prognostic Index31Cross-sectional Indiv+
Arienti [45] 20211 March to 7 May 2020Italy/LombardyO and MDescription of the epidemiological characteristics of LTCF residents infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and to compute the related case-fatality rate6Cross-sectional Indiv-
Cangiano [46]2020March to April 2020Italy Quantification of the impact of SARS-CoV-2 on mortality in LTCFs and to point out the factors related to its severity as well as the limitations of diagnostic tests used to manage the spread of this infective disease1Prospective Indiv-
Orlando [47]2022March to December 2020Italy/LazioOUnderstanding of which organizational–structural characteristics of LTCFs associated with the risk of a COVID-19 outbreak100Case–controlOrg, Envi+
Lee [48]202220 January to 20 October 2020KoreaO and MExamination of the organizational factors and characteristics of NH related to the COVD-19 outbreak and mortality during the COVID-19 pandemic’s peak3389Cross-sectional Org+
Booij [49]2022March to November 2020NetherlandsO and MInvestigation of short- and long-term mortality and risk factors in LTCFs patients with COVID-19 2Cross-sectional Indiv+
Houben [50]2023September 2020 to June 2021Netherlands/South LimburgOIdentification of the facility- and ward-level factors associated with SARS-CoV-2 outbreaks among LTCF residents60Cross-sectional Org, Envi+
Rutten [51]202018 March to 13 May 2020.NetherlandsO and MDescription of the symptomatology, mortality, and risk factors for mortality in a large group of Dutch LTCF residents with clinically suspected COVID-19 who were tested with a reverse transcription–polymerase chain reaction (RT–PCR) testNIProspective Indiv+
Visser [52]2023March 2020 to December 2021NetherlandMIdentification of the impact of polypharmacy on 30-day COVID-related mortality after adjustment for age, sex, CCI, BMI, and vaccination status15Cross-sectional Indiv+
Arendse [53]20225 March 2020 to 31 July 2022South AfricaMDescription of the temporal trends as well as the characteristics and risk factors for mortality45Cross-sectional Indiv+
Agoües [54]202115 March to 15 May 2020Spain/Sant Cugat del VallèsO and MIdentification of the factors related to morbidity and mortality of COVID-19 12Cross-sectional Indiv, Envi+
Aguilar-Palacio [55]20229 March 2020 to 14 March 2021Spain/AragónO and MDescription of the profile of institutionalized patients with a confirmed COVID-19 infection and the socioeconomic and morbidity factors associated with hospitalization and death1Cross-sectional Indiv+
Arnedo-Pena [56]2022March 2020 to January 2021Spain/CastellonO and MInvestigation of the incidence and mortality of COVID-19 in LTCFs, and their associated risk factors, before starting the vaccination against the disease27Cross-sectional Indiv, Org, Envi+
Escribà-Salvan [57]2022December 2019 to March 2021Spain/Central CataloniaOIdentification of the risk factors associated with developing COVID-19 infection with symptoms in institutionalized older people5LongitudinalIndiv, Org+
Romero [58]2020March 2020 to 5 April 2020Spain/AlbaceteO and MInvestigation of the mortality, costs, residents, and personnel characteristics in six long-term care facilities (LTCFs) during the outbreak of COVID-19 in Spain6Cross-sectional Indiv-
Meis-Pinheiro [59]20211 March to 31 May 2020Spain/CataloniaOIdentification of the clinical characteristics of COVID-19 in older adults in LTCFs80Cross-sectional Indiv+
Soldevila [60]20211 March to 30 June 2020Spain/Barcelona in CataloniaO and MIdentification of the interplay between infection risk factors of SARS-CoV-2 and prognosis168Cross-Sectional Indiv, Envi+
San Román [61]2023July to December 2020Spain/MadridOIdentification of the factors that facilitate COVID-19 outbreaks in LTCFs369Cross-sectional Indiv, Org+
Suñer [62]2021March to 1 June 2020,Spain/CataloniaMInvestigation of the determinants of mortality in LTC facilities during the COVID-19 outbreak167Cross-sectional Indiv, Org, Envi+
Telleria [63]2022March to December 2020Spain/Guipúzcoa O and MIdentification of the association of demographic, clinical, and pharmacological risk factors with the COVID-19 infection, and related death 4Case–control Indiv+
Torres [64]2022March to June 2020Spain/BarcelonaO and MIdentification of the inequalities in the cumulative incidences (CIs) and in the mortality rates (MRs) due to COVID-19 232Longitudinal Org, Envi+
Zunzunegui [65]2022March to April 2020Spain/CataloniaO and MUnderstanding how COVID-19 infection and mortality varied according to facility size 965Cross-SectionalOrg, Envi+
Scanferla [66]2023February 2020 to 31 May 2021SwissO and MIdentification of the influencing factors for a higher COVID-19 burden59Cross-sectionalIndiv, Org, Envi+
Abrams [67]2020By 11 May 2020USAOExamination of the characteristics of LTCFs with documented COVID-19 cases in 30 states reporting individual facilities affected9395Cross-SectionalIndiv, Org, Envi+
Adler [68]20221 March 2020, and 31 May 2020USAMEvaluation of the impact of the underlying use of antithrombotic on the 30-day mortality of individuals with COVID-19 and to assess the relationship between age and sex with 30-day all-cause mortality in patients with COVID-19NICross-sectional Indiv+
Bagchi [6]202125 May to 22 November 2020USAOIdentification of the rates of COVID-19 among residents and staff members in LTCFs15,342Cross-sectional Org, Envi-
Bui [69]202017 March to 11 June 2020USA/West VirginiaOIdentification of the relationship of the quality of LTCFs (using star ratings) and COVID-19 outbreak123Cross-SectionalOrg, Envi+
Cai [70] 20217 June 2020 to 23 August 2020USAO and MExamination of the racial and ethnic composition of LTCFs and communities with relation to the COVID-19 cases and death in LTCFs13,123Cross-sectionalIndiv, Org, Envi+
Chatterjee [71]202022 April to 29 April 2020USA/23 states and the District of ColumbiaODescription of the characteristics and quality of LTCFs with COVID-19 cases in states where public health departments have begun to publicly report their statuses8943Cross-sectionalOrg, Envi-
Chen [72]2021By 11 October 2020USAOIdentification of association between LTCF characteristics and resident COVID-19 morbidity in communities with high infection rates2017Cross-sectional Indiv, Org, Envi+
Chen [73]202013 April to 23 August 2020USAOInvestigation of LTCF staff networks and COVID-1913,165Cross-sectional Indiv, Org, Envi+
Cooper [63]2021June 2020 to January 2021USAO and MDetermination of whether LTCFs COVID-19 patient outcomes varied from one nursing facility to another based on location and resident characteristics, and the availability of COVID-19-specific medical equipment in the LTCFs impacts morbidity and mortality outcomes.14,405Cross-sectional Indiv, Org, Envi+
Cronin [74]2022May 2020 to September 2021USAO and MInvestigation of whether high-quality LTCFs measured by the CMS five-star ratings did a better job of preventing deaths from COVID-1914,905CohortOrg, Envi+
Dean [75]20201 March to 31 May 2020USA/New YorkO and MIdentification of the association between the presence of healthcare worker unions and additional facility-level factors and COVID-19 mortality rates355Cross-sectional Indiv, Org, Envi+
Figueroa [76]20201 January to 30 June 2020USA/8 statesOEvaluation of whether LTCFs, rated highly by the Centers for Medicare & Medicaid Services (CMS) across 3 unique domains—health inspections, quality measures, and nurse staffing—had lower COVID-19 cases than facilities with lower ratings4254Cross-sectional Org, Envi+
Engeda [77]202325 May to 16 August 2020USA/California OIdentification of the association between LTCFs COVID-19 infections and level of racial and ethnic compositions and facility- and neighborhood-level (census tract- and county-level) indicators of socioeconomic status 971Cross-sectionalIndiv, Envi+
Gilman [78]202125 May 2020, to 18 April 2021USAMIdentification of the trends in COVID-19 death rates by the racial composition of LTCFs through mid-April 202113,820Cross-sectional Indiv-
Gopal [79]20211 May 2020USA/CaliforniaOUnderstanding of why some LTCFs are more susceptible to larger COVID-19 outbreaks.713Cross-sectionalOrg, Envi+
Gorges [80]202025 June 2020USAO and MUnderstanding of whether baseline nurse staffing is associated with the presence of COVID-19 in LTCFs and whether staffing impacts outbreak severity13,167Cross-sectional Indiv, Org, Envi+
Gorges [81]20211 January 2020, to 13 September 2020USAMDescription of differences in the number of COVID-19 deaths by LTCFs racial composition and examine the factors associated with these differences13,312Cross-sectional Indiv, Org, Envi+
Harrington [82]2020March to 4 May 2020 USA/CaliforniaOExamination of the relationship of nurse staffing in California LTCFs and compare homes with and without COVID-19 residents1091Cross-sectional Org, Envi+
He [83]20202 June 2020USA/CaliforniaO and MDetermination of whether COVID-19 cases and deaths are related to the LTCFs reported quality1223Cross-sectional Indiv, Org, Envi+
Hege [84]20211 June 2020, to 31 January 2021USAOInvestigation of the relationship between US LTCF-associated COVID-19 infection rates and county-level and LTCFs attributes9990CohortIndiv, Org, Envi+
Hill [85]2022March 2020 to March
2021		USA/AlamedaMIdentification of the differential impacts across settings as well as racial and economic disparities among long-term care residents with relation to COVID-19-related death592Cross-sectionalIndiv-
Hua [86]202212 March to 31 December 2020 and 1 January 2019, to 11 March 2020USAMComparison of the weekly rate of excess all-cause mortality during the first several months of the COVID-19 pandemic among residents with ADRD in assisted living to those without ADRD and among residents with ADRD in memory care assisted living or in general assisted livingNICross-sectional Indiv+
Iyanda [87]20221 January 2020 to 18 December 2021USAMExamination of the determinants of COVID-19 deaths in LTCFs in the first 2-year pandemic period 13,350Cross-sectionalOrg, Envi+
Khairat [88]202125 May to 20 December 2020USAO and MIdentification of the relationship between LTCFs’ quality and the spread and severity of COVID-19 in LTCFs15,390Cross-sectional Org, Envi+
Kim [89]20221 January to 30 September 2021USA/IllinoisO and MExamination of the pathways through which community and facility factors may have affected COVID-19 cases and deaths177Cross-sectionalIndiv, Org, Envi+
Kumar [90]20211 June to 27 December 2020USAO and MExamination of the impact of a high proportion of minority residents in LTCFs on COVID-19-related mortality rates over a 30-week period.11,718Longitudinal Indiv, Org, Envi+
Lane [91]2022May to September 2020, September to December 2020, and December to February 2021USA-SouthernOIdentification of the LTCFs and county-level predictors of COVID-19 outbreaks in LTCFs in the southeastern region of the United States across three time periods.2951LongitudinalIndiv, Org, Envi+
Levy-Storms [92]20221 January 2020 to 2 July 2021USAO and MDetermination of the role of Certified Nursing Aides (CNAs) in the care of residents living in LTCFs-Cross-sectional Org-
Longo [93]2022June 2020 to January 2021USA/Illinois, Florida, and MassachusettsOTo compare COVID-19 cases among accredited and nonaccredited LTCFs Cross-sectional Envi+
Li [94]2020By 16 April 2020USAO and MDetermination of the associations of LTCFs registered nurse (RN) staffing, overall quality of care, and concentration of Medicaid or racial and ethnic minority residents with 2019 coronavirus disease (COVID-19) confirmed cases and deaths215Cross-sectional Indiv, Org, Envi+
Li [95]202025 May to 31 May 2020USAO and MDetermination of the racial/ethnic disparities in weekly counts of new COVID-19 cases and deaths among LTCFs residents or staff12,576Cross-sectional Indiv, Org, Envi+
Li [96]202213 April to 19 June 2020USAO and MEvaluation of the trends in racial and ethnic disparities in weekly cumulative rates of coronavirus disease 2019 (COVID-19) cases and deaths 211Longitudinal Indiv+
Lord [97]20211 January 2020 to 11 July 2021USAOExamination of the relationship between community resource scarcity, as conceptualized by the Social Deprivation Index (SD), and COVID-19 incidence rates in LTCFs.13,772Cross-sectional Indiv, Org, Envi+
Lu [98]20211 April 2020 to 22 December 2020USAMEvaluation of risk factors for COVID-19 deaths and hospitalizations among LTCFs Medicare beneficiaries in the prevaccine pandemic period.-Cross-sectional Indiv, Org, Envi+
Mattingly [99] 20211 January to 1 July 2020USA/MarylandOIdentification of the characteristics associated with large outbreaks216Cross-sectional Indiv, Org, Envi+
McGarry [100]2021By 30 September 2020USAO and MExamination of how the number of unique staff members might influence the likelihood of COVID-19 cases and deaths in LTCFs15,071Cross-sectional Indiv, Org, Envi +
Mehta [101]20211 April to 30 September 2020USAO and MIdentification of the risk factors for SARS-CoV-2 incidence, hospitalization, and mortality among LTCF residents in the US482, 323Cross-sectional Indiv+
Olson [102]202224 April
2022		USAO and MUnderstanding of the association between LTCFs unionization and resident COVID-19 mortality percentage14,380 Cross-sectional Indiv, Org, Envi+
Panagiotou [103]202116 March to 15 September 2020USAMIdentification of the risk factors for 30-day all-cause mortality among US LTCF residents with COVID-19.5256Cross-sectional Indiv+
Shen [104]20225 July–10 July 2020USA/18 statesMExamination of the relationship between LTCFs COVID-19 outbreaks and differences in the characteristics of the residential neighbourhoods6132Cross-sectionalIndiv, Org, Envi+
Shi [105]202016 March to 8 May 2020USA/Boston, MA O and MDescription of the clinical characteristics and risk factors associated with coronavirus disease 2019 (COVID-19) in long-stay LTCF residents1Cross-sectional Indiv+
Sugg [106]2021By 30 June 2020USAO and MDetermination of the association between LTCF-level metrics and county-level, place-based variables with COVID-19 confirmed cases in LTCFs across the United States13,709Cross-sectionalOrg, Envi+
Tang [107]20201 March to 12 June 2020USA/MarylandMIdentification of the association of symptom status and medical comorbidities on mortality and hospitalization risk associated with COVID-19 15Cross-sectional Indiv+
Thorsness [108]20221 March to 31 December 2020USAMIdentification of the association of kidney function with 30-day mortality following COVID-19 infection176Cross-sectional Indiv+
Travers [109]202120 January to 19 July 2020USAO and MExamination of the associations between the proportion of Black residents in LTCFs and COVID-19 infections and deaths, accounting for structural bias (operationalized as county-level factors) and stratifying by urbanicity/rurality11,587Cross-sectional Indiv, Org, Envi+
Unruh [2]202020 January–30 April
2020		USA/Connecticut, New Jersey, and New YorkMEvaluation of the characteristics of LTCFs with COVID-19 deaths compared to other LTCFs using data from Connecticut, New Jersey, and New York1162Cross-sectionalIndiv, Org, Envi+
Weech-Maldonado [110]20211 January to 25 October 2020USAMExamination of the relationship between LTCFs racial/ethnic mix and COVID-19 resident mortality12,914Cross-sectional Indiv, Org, Envi+
Williams [111]202110 January 2021USAO and MUnderstanding of the relationship of LTCFs quality ratings and measures of COVID-19 outbreak severity and persistence14,693Cross-sectionalIndiv, Org, Envi+
Young [112]202331 May 2020 to 27 March 2022USA/New YorkO and MEvaluation of the COVID-19 case and mortality rates in green houses compared to traditional LTCFs in New York state608Cross-sectional Org-
Zhu [113]20227 June to 20 December 2020USAO and MExamination of the associations of LTCFs design with COVID-19 cases, deaths, and transmissibility and provide relevant design recommendations7785Cross-sectional Indiv, Org, Envi+
Zimmerman [114]202120 January to 31 July 2020USAO and MComparison of the rates of COVID-19 infections, COVID-19 admissions/readmissions, and COVID-19 mortality, among Green House/small LTCFs with rates in other LTCFs43Cross-sectional Org-
Emmerson [115]20221 September to 31 December 2020WalesOIdentification of the impact of dementia, frailty, and care home
characteristics on SARS-CoV-2 incidence in a national cohort of Welsh care home residents during a period of high community prevalence		673Cross-sectional Indiv, Org, Envi-
Abbreviation: NI: Not identified, M: Mortality, O: Outbreak; ADRD: Alzheimer’s disease and related dementias, Envi: Environmental, Indiv: Individual; Org: Organizational; AA/EA: Adjusted analysis/Effect analysis, ‘+’ symbol indicates presence of Adjusted Analysis/Effect Analysis and ‘-’ symbol indicates absence.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Karimi-Dehkordi, M.; Hanson, H.M.; Silvius, J.; Wagg, A. Drivers of COVID-19 Outcomes in Long-Term Care Facilities Using Multi-Level Analysis: A Systematic Review. Healthcare 2024, 12, 807. https://doi.org/10.3390/healthcare12070807

AMA Style

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. 2024; 12(7):807. https://doi.org/10.3390/healthcare12070807

Chicago/Turabian Style

Karimi-Dehkordi, Mehri, Heather M. Hanson, James Silvius, and Adrian Wagg. 2024. "Drivers of COVID-19 Outcomes in Long-Term Care Facilities Using Multi-Level Analysis: A Systematic Review" Healthcare 12, no. 7: 807. https://doi.org/10.3390/healthcare12070807

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop