Comparison of hospitalization events among residents of assisted living and nursing homes during COVID-19: Do settings respond differently during public health crises?

Colleen J Maxwell; Eric McArthur; David B Hogan; Hana Dampf; Jeffrey Poss; Joseph E Amuah; Susan E Bronskill; Erik Youngson; Zoe Hsu; Matthias Hoben

doi:10.1371/journal.pone.0306569

. 2024 Jul 12;19(7):e0306569. doi: 10.1371/journal.pone.0306569

Comparison of hospitalization events among residents of assisted living and nursing homes during COVID-19: Do settings respond differently during public health crises?

Colleen J Maxwell ^1,^2,^*, Eric McArthur ³, David B Hogan ⁴, Hana Dampf ⁵, Jeffrey Poss ⁶, Joseph E Amuah ⁷, Susan E Bronskill ^2,⁸, Erik Youngson ^9,¹⁰, Zoe Hsu ^9,¹⁰, Matthias Hoben ^5,¹¹

Editor: Peng Wu¹²

¹School of Pharmacy, University of Waterloo, Waterloo, Ontario, Canada

²ICES, Toronto, Ontario, Canada

³London Health Sciences Centre, London, Ontario, Canada

⁴Division of Geriatric Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

⁵Faculty of Nursing, College of Health Sciences, University of Alberta, Edmonton, Alberta, Canada

⁶School of Public Health Sciences, University of Waterloo, Waterloo, Ontario, Canada

⁷School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada

⁸Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada

⁹Provincial Research Data Services, Alberta Health Services, Alberta, Canada

¹⁰Data and Research Services, Alberta SPOR SUPPORT Unit, Alberta, Canada

¹¹School of Health Policy and Management, Faculty of Health, York University, Toronto, Ontario, Canada

¹²The University of Hong Kong School of Public Health, HONG KONG

Competing Interests: The authors have declared that no competing interests exist.

^✉

* E-mail: colleen.maxwell@uwaterloo.ca

Roles

Colleen J Maxwell: Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Visualization, Writing – original draft

Eric McArthur: Conceptualization, Formal analysis, Investigation, Methodology, Visualization, Writing – review & editing

David B Hogan: Conceptualization, Writing – review & editing

Hana Dampf: Conceptualization, Formal analysis, Investigation, Methodology, Visualization, Writing – review & editing

Jeffrey Poss: Conceptualization, Methodology, Writing – review & editing

Joseph E Amuah: Conceptualization, Writing – review & editing

Susan E Bronskill: Conceptualization, Writing – review & editing

Erik Youngson: Data curation, Resources, Software, Writing – review & editing

Zoe Hsu: Data curation, Resources, Software, Writing – review & editing

Matthias Hoben: Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – review & editing

Peng Wu: Editor

PMCID: PMC11244779 PMID: 38995897

Abstract

Background

COVID-19 and resulting health system and policy decisions led to significant changes in healthcare use by nursing homes (NH) residents. It is unclear whether healthcare outcomes were similarly affected among older adults in assisted living (AL). This study compared hospitalization events in AL and NHs during COVID-19 pandemic waves 1 through 4, relative to historical periods.

Methods

This was a population-based, repeated cross-sectional study using linked clinical and health administrative databases (January 2018 to December 2021) for residents of all publicly subsidized AL and NH settings in Alberta, Canada. Setting-specific monthly cohorts were derived for pandemic (starting March 1, 2020) and comparable historical (2018/2019 combined) periods. Monthly rates (per 100 person-days) of all-cause hospitalization, hospitalization with delayed discharge, and hospitalization with death were plotted and rate ratios (RR) estimated for period (pandemic wave vs historical comparison), setting (AL vs NH) and period-setting interactions, using Poisson regression with generalized estimating equations, adjusting for resident and home characteristics.

Results

On March 1, 2020, there were 9,485 AL and 14,319 NH residents, comparable in age (mean 81 years), sex (>60% female) and dementia prevalence (58–62%). All-cause hospitalization rates declined in both settings during waves 1 (AL: adjusted RR 0.60, 95%CI 0.51–0.71; NH: 0.74, 0.64–0.85) and 4 (AL: 0.76, 0.66–0.88; NH: 0.65, 0.56–0.75) but unlike NHs, AL rates were not significantly lower during wave 2 (and increased 27% vs NH, January 2021). Hospitalization with delayed discharge increased in NHs only (during and immediately after wave 1). Both settings showed a significant increase in hospitalization with death in wave 2, this increase was larger and persisted longer for AL.

Conclusions

Pandemic-related changes in hospitalization events differed for AL and NH residents and by wave, suggesting unique system and setting factors driving healthcare use and outcomes in these settings in response to this external stress.

Introduction

Assisted living (AL) represents an increasingly common residential setting for older adults with health and support needs [1, 2]. Primarily aligned with a social model of care, AL homes typically provide a secure environment, personal support services and limited health care, emphasizing a home-like setting [2]. Relative to nursing homes (NHs), AL settings have lower levels of professional staffing (no or limited on-site registered nurses, physicians, nurse practitioners), fewer health and medical services and a greater emphasis on family involvement in resident care [3–6]. These features contrast with the clinical complexity of the AL population, with residents exhibiting a high prevalence of dementia, multimorbidity, frailty and complex polypharmacy [1, 2, 6, 7]. Research on the ability of AL to provide quality care to residents with changing health needs remains sparse though studies have raised concerns about insufficient staffing levels and mix [8–10], medication use and oversight [11], and high rates of care transitions, including hospital and NH admissions [2, 8, 12, 13]. As such, AL settings may be particularly sensitive to external stresses because of their limited reserve.

Hospitalization events are common and an important indicator of the quality and availability of care in AL [8, 13]. Associated risk factors include both AL resident (e.g., health instability, polypharmacy) and home characteristics (e.g., smaller settings, lower staffing levels and the absence of on-site professional staff) [8–10]. Prior to the COVID-19 pandemic, studies within the U.S. [12, 14] and Canada [8, 13, 15] showed a significantly higher rate of hospitalization (and prolonged stays with delayed discharge) for AL residents relative to the NH population. The relative absence of professional staff, medical services and integrated interprofessional care, arguably resulting in less timely and/or appropriate care (e.g., failure to address upstream risks for falls, delirium, and infections; delayed detection or treatment of acute health changes), is felt to contribute to the higher hospitalizations observed for AL [6, 16, 17]. These hospitalization events often lead to lengthy hospital stays and worsening functional and cognitive health status for residents [18] (prompting a NH admission) [2] and increased distress for family caregivers. Yet, it is important to acknowledge that many hospitalizations are necessary and that reduced access to needed hospital care (e.g., during healthcare crises) also constitutes a relevant system quality indicator for at-risk older populations [19]. Given these considerations, it is likely that the healthcare disruptions associated with the COVID-19 pandemic (e.g., staffing shortages, reduced availability of social/medical services and family to support care) were especially challenging for AL homes [3–5, 20] potentially leading to heightened risks of adverse health outcomes for residents.

Studies of older adults in community [21, 22] and nursing home [23–28] settings showed that hospitalization and emergency department rates declined dramatically during the first pandemic wave and typically remained below historical rates during the second wave [22, 27, 28]. Other than a descriptive study of hospitalizations from residential care and nursing homes in England early in the pandemic, that showed few differences between the settings [24], research has yet to rigorously examine whether the COVID-19 pandemic and different pandemic waves (that varied in case numbers, infection virulence, vaccination rates, public health restrictions, and availability of staff and services) [28] differentially affected hospitalizations among AL compared with NH residents. This is a particularly salient research question given historical patterns of hospitalizations evident for these two settings caring for vulnerable older residents.

Our objectives were to compare rates of hospitalization events (all-cause hospitalization, hospitalization with delayed discharge, hospitalization with death) among residents of AL and NH settings in Alberta, before and following the onset of the COVID-19 pandemic, and to explore variation in rates by pandemic waves 1–4 for these two settings. We hypothesized that hospitalization rates would be higher for AL than NH residents (pre- and post-pandemic onset) and there would be significant decreases in rates for all-cause and delayed hospitalizations (but increases in hospitalization with death) that would be more pronounced for NH than AL residents and vary by pandemic wave.

Methods

Study design, setting and data

We conducted a population-based, repeated cross-sectional study of hospitalizations among all residents of publicly subsidized assisted living (AL) and nursing home (NH) settings in Alberta Canada between January 1, 2018 and December 31, 2021. Alberta Health Services (AHS) coordinate and provide continuing care services through a single point of access, including licensed publicly subsidized AL (designated supportive living in Alberta) [29] and NH (or residential long-term care) care in the province. Starting March 20, 2020, both settings were under a Provincial Chief Medical Officer of Health order to restrict visitor access, allowing a single essential visitor only [30].

The following provincial clinical and health administrative databases were used (extracted from the AHS Enterprise Data Warehouse and provided by AHS as individual level de-identified data and linked using unique resident identifiers): Alberta Continuing Care Information System (ACCIS), capturing data on residents and homes, including the Resident Assessment Instrument–Home Care (RAI-HC) and Minimum Data Set 2.0 (RAI-MDS 2.0) used in AL and NH, respectively; Discharge Abstract Database (DAD) for inpatient hospitalizations; Provincial Laboratory for COVID-19 testing data (month/year only); Immunization & Adverse Reactions to Immunization for aggregate data on vaccine administration; and Vital Statistics database for death dates (S1 Table). These de-identified data were accessed for this research on the 29^th of April, 2022 (with continued access for analyses until December 31, 2023). The RAI-HC and RAI-MDS 2.0 are validated assessments [31, 32] routinely completed on admission, after a significant health change and annually, and for NH only—on a quarterly basis. These assessments capture residents’ physical, cognitive and psychosocial status, chronic conditions, and service use.

Study population

All AL and NH residents with one or more RAI assessments completed during the study period were identified and all assessments for this period (plus those completed during a 1-year look back period) were captured. We identified residents alive and living in AL or NH on the first day of each month (monthly index date). Included were 26 historical (pre-pandemic) monthly periods (January 1, 2018 to February 29, 2020) and 22 pandemic monthly periods (March 1, 2020 to December 31, 2021). On each monthly index date, we excluded residents with a hospital stay overlapping this index date. Relevant admission, discharge, and death dates to define monthly cohorts were obtained from the ACCIS, DAD, and Vital Statistics databases.

Measures

Exposures

Our main exposures were period (COVID-19 pandemic month relative to comparable historical [2018–2019 combined] month) and setting (AL vs NH). Specific focus was on months that represented peaks of the first 4 pandemic waves in Alberta (April 2020, December 2020, April 2021, September 2021) [33], a month with low count of COVID-19 cases that was prior to the start of vaccinations in AL and NH (August 2020), and a month with an observed peak in hospitalizations for AL residents (January 2021).

Outcomes

For each monthly period of interest, we utilized the DAD to capture all-cause inpatient hospitalizations (primary outcome), hospitalizations with a delayed discharge (termed alternate level of care [ALC] in Canada), and hospitalizations with a discharge disposition of death (secondary outcomes). An ALC designation occurs when a patient occupying a hospital bed no longer requires acute-care resources or services and is awaiting ongoing supportive care in the community, AL or NH setting [34]. Monthly outcome rates were derived with person-days as the denominator (censoring for residents’ date of transfer/discharge from the setting, death or monthly end date).

For each setting, we used the DAD to examine the top 10 ICD-10-CA [35] diagnoses/chapters for the most responsible diagnosis recorded for hospitalizations during April 2020 and January 2021 (relative to comparable historical [2018–2019] months). We identified setting-specific monthly COVID-19 related hospitalizations during the pandemic period using ICD-10-CA codes (any diagnosis field) of U07.1, U07.2, or U07.3 [36].

Resident & home characteristics

The most recent RAI assessment before each monthly index date was used to capture residents’ age, sex, scores on validated health outcomes derived from RAI items (the Activities of Daily Living Self-Performance Hierarchy Scale [ADL-H] [37], Cognitive Performance Scale [CPS] [38], Depression Rating Scale [DRS] [39], Changes in Health, End-Stage Disease, Signs and Symptoms Scale [CHESS; assessing health instability] [40]), frailty status, behaviors, and chronic health conditions. Frailty was defined using a validated frailty index (FI), derived as the proportion of accumulated to potential health deficits (72 items on the RAI), and categorized as robust (FI<0.2), pre-frail (FI 0.2–0.3) and frail (FI>0.3) [41].

For each AL and NH, four home characteristics (health zone, urban/rural location, bed size, and for-profit vs non-profit ownership) were obtained from the home’s postal code, ACCIS database, and AHS continuing care registries. In Alberta, AHS is organized into five health zones to facilitate healthcare decisions and delivery.

Analyses

Descriptive analyses compared resident characteristics between AL and NH annually (on March 1^st) across study years. Standardized differences [42] of greater than 0.10 were used to illustrate meaningful differences between AL and NH populations at each time point and within each care setting over time (comparing study end date to March 1, 2019). The monthly hospitalization event rates (per 100 person-days) were plotted for historical (2018–2019 combined) and 2020–2021 pandemic periods, by setting.

For each month of interest, separate Poisson regression models with an offset term for log-transformed follow-up time and generalized estimating equations (to account for repeated measures within individuals across months) were used to estimate rate ratios (RR) (and 95% confidence intervals [CI]) for period (pandemic vs historical), setting (AL vs NH) and period-setting interactions. Models were adjusted for age, sex, CPS score, ADL-H score, CHESS, number of chronic conditions, and home health zone and ownership status (derived at the resident level to account for regional health systems and owner/operator policies).

In sensitivity analyses we (i) restricted all-cause hospitalizations to urgent (unplanned) hospitalizations only, and (ii) estimated rate ratios for all-cause hospitalizations using a generalized linear mixed model with a Poisson distribution and random effects for individuals nested within homes to account for clustering by individual and home.

We employed p <0.05 as the level of statistical significance (two-tailed) and all analyses were conducted with SAS version 9.4 (SAS Institute Inc.).

This study received ethics clearance from the University of Alberta Health Research Ethics Board (Pro00116520), University of Calgary Conjoint Health Research Ethics Board (pSite-22-0001), York University Office of Research Ethics-Human Participants Review Sub-Committee (e2022-239) and operation approval from AHS. This study was granted a waiver of informed consent as the study team received only de-identified data and had no contact with the study population. The de-identified data were accessed for this research on the 29^th of April, 2022.

The study is reported per RECORD guidelines (S1 Text).

Results

During the study period, there were 439,218 AL and 673,486 NH monthly records included after exclusions for deaths or hospitalizations at the time of monthly index dates (1–2% of records), representing 20,997 unique AL and 35,487 NH residents.

On March 1, 2020, there were 9,485 AL and 14,319 NH residents, representing 251 AL and 181 NH sites. The two settings were comparable in terms of resident age (mean 81 years), sex (63–67% female), number of chronic conditions (34% with 5+ conditions) and the presence of select conditions including dementia (58–62%) (Table 1). Relative to NH residents, those in AL showed significantly lower levels of ADL and cognitive impairment, depressive symptoms, health instability, frailty, and responsive behaviours. There were few differences in AL resident characteristics over time except for a significant decrease in the proportion of residents with meaningful depressive symptoms. Several NH resident characteristics also remained stable over time, however there were declines during the study period in the prevalence of frailty, multiple chronic conditions (5+) and specific conditions (dementia, arthritis) among residents in this setting.

Table 1. Yearly characteristics of Alberta Assisted Living (AL) and Nursing Home (NH) residents,^a comparing select time periods.

Characteristic	March 2018		March 2019		March 2020		March 2021		Dec 2021
	AL (n = 8431)	NH (n = 13695)	AL (n = 9069)	NH (n = 14191)	AL (n = 9485)	NH (n = 14319)	AL (n = 9051)	NH (n = 13396)	AL (n = 9323)	NH (n = 14155)
Age, mean (SD), y	80.4(12.8)^b	81.9 (12.5)	80.7 (12.5)	81.7 (12.6)	80.7 (12.4)	81.4 (12.9)	80.5 (12.5)	81.1 (12.9)	80.5 (12.5)	81.1 (12.8)
Age group, y
<65	1060 (12.6)	1395 (10.2)	1054 (11.6)	1478 (10.4)	1079 (11.4)	1535 (10.7)	1100 (12.2)	1497 (11.2)	1101 (11.8)	1535 (10.8)
65–74	1077 (12.8)	1674 (12.2)	1161 (12.8)	1873 (13.2)	1298 (13.7)	1975 (13.8)	1261 (13.9)	1957 (14.6)	1312 (14.1)	2106 (14.9)
75–84	2312 (27.4)	3645 (26.6)	2603 (28.7)	3753 (26.5)	2731 (28.8)	3761 (26.3)	2562 (28.3)	3537 (26.4)	2695 (28.9)	3751 (26.5)
85+	3982 (47.2)	6981 (51.0)	4251 (46.9)	7087 (49.9)	4377 (46.2)	7048 (49.2)	4128 (45.6)	6405 (47.8)	4215 (45.2)	6763 (47.8)
Female	5664 (67.2)	8767 (64.0)	6020 (66.4)	8958 (63.1)	6330 (66.7)	8995 (62.8)	6075 (67.1)	8408 (62.8)	6208 (66.6)	8867 (62.6)
ADL-H 3+
Extensive assist/Dependent	2219 (26.3)^b	11355 (82.9)	2329 (25.7)^b	11824 (83.3)	2430 (25.6)^b	12034 (84.0)	2387 (26.4)^b	11325 (84.5)	2465 (26.4)^b	11924 (84.2)
CPS 3+
Moderate-severe impairment	2488 (29.5)^b	8554 (62.5)	2647 (29.2)^b	8740 (61.6)	2670 (28.2)^b	8717 (60.9)	2509 (27.7)^b	8154 (60.9)	2664 (28.6)^b	8580 (60.6)
DRS 3+
Depressive symptoms	1532 (18.2)^b	4073 (29.7)	1686 (18.6)^b	3997 (28.2)	1634 (17.2)^b	3907 (27.3)	1431 (15.8)^b	3523 (26.3)	1338 (14.4)^b^,^c	3675 (26.0)
CHESS Scale 2+
Higher health instability	986 (11.7)^b	2955 (21.6)	1074 (11.8)^b	2968 (20.9)	1060 (11.2)^b	2975 (20.8)	930 (10.3)^b	2809 (21.0)	1019 (10.9)^b	2968 (21.0)
Frailty (FI value)
Not frail (<0.2)	2977 (35.3)^b	1404 (10.3)	3175 (35.0)^b	1598 (11.3)	3375 (35.6)^b	1725 (12.1)	3133 (34.6)^b	1721 (12.9)	3271 (35.1)^b	1960 (13.9)
Prefrail (0.2–0.3)	3065 (36.4)^b	3985 (29.1)	3446 (38.0)^b	4234 (29.8)	3604 (38.0)^b	4470 (31.2)	3425 (37.8)^b	4320 (32.3)	3509 (37.6)	4732 (33.4)
Frail (>0.3)	2389 (28.3)^b	8305 (60.6)	2448 (27.0)^b	8359 (58.9)	2506 (26.4)^b	8124 (56.7)	2493 (27.5)^b	7355 (54.9)	2543 (27.3)^b	7456 (52.7)^c
# Responsive behaviours^d
2+	787 (9.3)^b	3194 (23.3)	820 (9.0)^b	3300 (23.3)	810 (8.5)^b	3281 (22.9)	741 (8.2)^b	3153 (23.5)	739 (7.9)^b	3284 (23.2)
Chronic Conditions
mean (SD)	3.77 (1.89)	3.87 (1.88)	3.83 (1.91)	3.84 (1.88)	3.85 (1.89)	3.82 (1.89)	3.85 (1.89)	3.71 (1.89)	3.84 (1.9)^b	3.56 (1.86)^c
#
0–2	2206 (26.2)	3309 (24.2)	2278 (25.1)	3491 (24.6)	2323 (24.5)	3613 (25.2)	2225 (24.6)	3703 (27.6)	2304 (24.7)^b	4263 (30.1)^c
3,4	3490 (41.4)	5698 (41.6)	3720 (41.0)	5893 (41.5)	3901 (41.1)	5868 (41.0)	3744 (41.4)	5452 (40.7)	3828 (41.1)	5812 (41.1)
5+	2735 (32.4)	4688 (34.2)	3071 (33.9)	4807 (33.9)	3261 (34.4)	4838 (33.8)	3082 (34.1)	4241 (31.7)	3191 (34.2)^b	4080 (28.8)^c
Selected Health Conditions
Dementia	4781 (56.7)^b	8757 (63.9)	5232 (57.7)^b	8937 (63.0)	5500 (58.0)	8830 (61.7)	5277 (58.3)	8012 (59.8)	5439 (58.3)	8225 (58.1)^c
Arthritis	3899 (46.3)	5815 (42.5)	4205 (46.4)	6048 (42.6)	4387 (46.3)	6015 (42.0)	4241 (46.9)^b	5320 (39.7)	4311 (46.2)^b	5247 (37.1)^c
Osteoporosis	2174 (25.8)	4008 (29.3)	2300 (25.4)	3982 (28.1)	2384 (25.1)	3991 (27.9)	2215 (24.5)	3511 (26.2)	2332 (25.0)	3457 (24.4)
Diabetes	1937 (23.0)	3586 (26.2)	2119 (23.4)	3780 (26.6)	2211 (23.3)	3826 (26.7)	2110 (23.3)	3575 (26.7)	2177 (23.4)	3720 (26.3)
COPD	1721 (20.4)	2807 (20.5)	1883 (20.8)	2824 (19.9)	1951 (20.6)	2734 (19.1)	1801 (19.9)	2489 (18.6)	1828 (19.6)	2357 (16.7)
Congestive Heart Failure	980 (11.6)	2028 (14.8)	1055 (11.6)	2032 (14.3)	1080 (11.4)	1941 (13.6)	1029 (11.4)	1693 (12.6)	1034 (11.1)	1670 (11.8)
Cancer	535 (6.4)^b	1487 (10.9)	609 (6.7)^b	1582 (11.2)	620 (6.5)^b	1612 (11.3)	571 (6.3)^b	1474 (11.0)	615 (6.6)^b	1506 (10.6)

Open in a new tab

Abbreviations: ADL-H, activities of daily living self-performance hierarchy scale; CHESS, Changes in Health, End-Stage Disease, Signs & Symptoms; COPD, chronic obstructive pulmonary disease; CPS, Cognitive Performance Scale; DRS, Depression Rating Scale; FI, frailty index; SD, standard deviation.

a Unless indicated otherwise, data are expressed as Column No. (%) of residents with percentages rounded.

b Standardized difference of >0.10 considered clinically meaningful difference (comparing AL to NH)

c Standardized difference of >0.10 considered clinically meaningful difference (comparing Dec 2021 to Mar 2019)

d Sum of 4 behaviours assessed on the RAI-HC (verbal abuse, physical abuse, socially inappropriate/disruptive care, resisting care)

AL residents were more likely than NH residents to reside in the South zone, smaller homes and those owned/operated by voluntary non-profit and private for-profit organizations (rather than by AHS or contracted non-profit operators) (Table 2). The urban/rural distribution of residents was comparable for both settings. COVID-19 vaccination of both AL and NH residents started January 1, 2021 and by May 28, 2021, 85% had received their second dose. By study end about 70–80% had received a third dose.

Table 2. Yearly home characteristics of Alberta Assisted Living (AL) and Nursing Home (NH) residents,^a comparing select time periods.

Home Characteristic	March 2018		March 2019		March 2020		March 2021		Dec 2021
	AL (n = 8431)	NH (n = 13695)	AL (n = 9069)	NH (n = 14191)	AL (n = 9485)	NH (n = 14319)	AL (n = 9051)	NH (n = 13396)	AL (n = 9323)	NH (n = 14155)
Geographic Health Zone
Calgary	2460 (29.2)^b	5005 (36.6)	2607 (28.8)^b	5363 (37.8)	2882 (30.4)^b	5480 (38.3)	2717 (30.0)^b	5181 (38.7)	2777 (29.8)^b	5701 (40.3)
Central	1216 (14.4)	2088 (15.3)	1351 (14.9)	2123 (15.0)	1418 (15.0)	2117 (14.8)	1494 (16.5)	2072 (15.5)	1555 (16.7)	2069 (14.6)
Edmonton	2843 (33.7)	4663 (34.1)	2830 (31.2)	4774 (33.6)	2929 (30.9)	4842 (33.8)	2661 (29.4)	4277 (31.9)	2811 (30.2)	4606 (32.5)
North	630 (7.5)	1160 (8.5)	754 (8.3)	1124 (7.9)	760 (8.0)	1107 (7.7)	742 (8.2)	1086 (8.1)	800 (8.6)	1024 (7.2)
South	1282 (15.2)^b	779 (5.7)	1527 (16.8)^b	807 (5.7)	1496 (15.8)^b	773 (5.4)	1437 (15.9)^b	780 (5.8)	1380 (14.8)^b	755 (5.3)
Location
Rural	848 (10.1)^b	1844 (13.5)	981 (10.8)	1769 (12.5)	953 (10.1)	1732 (12.1)	1003 (11.1)	1747 (13.0)	1012 (10.9)	1642 (11.6)
Urban	7583 (89.9)^b	11851 (86.5)	8088 (89.2)	12422 (87.5)	8532 (90.0)	12587 (87.9)	8048 (88.9)	11649 (87.0)	8311 (89.2)	12513 (88.4)
Bed Size, median (25^th, 75^th)	86 (45,107)	129 (73,220)	85 (45,107)	132 (74,210)	84 (48,107)	132 (74,210)	84 (46,107)	127 (69,208)	84 (48,107)	132 (74,208)
Bed Size (median cut-point)
≤102	5830 (69.2)^b	5361 (39.2)	6258 (69.0)^b	5542 (39.1)	6559 (69.2)^b	5627 (39.3)	6318 (69.8)^b	5453 (40.7)	6509 (69.8)^b	5506 (38.9)
>102	2592 (30.7)^b	8334 (60.9)	2805 (30.9)^b	8649 (61.0)	2926 (30.9)^b	8692 (60.7)	2733 (30.2)^b	7943 (59.3)	2814 (30.2)^b	8649 (61.1)
Ownership Status
AB Health Services (gov’t)	393 (4.7)^b	4323 (31.6)	434 (4.8)^b	4276 (30.1)	435 (4.6)^b	4277 (29.9)	480 (5.3)^b	4074 (30.4)	499 (5.4)^b	4126 (29.2)
Non-Profit / Voluntary	3651 (43.3)^b	4516 (33.0)	3846 (42.4)^b	4580 (32.3)	3973 (41.9)^b	4624 (32.3)	3748 (41.4)^b	4344 (32.4)	3770 (40.4)^b	4596 (32.5)
For-Profit / Private	4387 (52.0)^b	4856 (35.5)	4789 (52.8)^b	5335 (37.6)	5077 (53.5)^b	5418 (37.8)	4823 (53.3)^b	4978 (37.2)	5054 (54.2)^b	5433 (38.4)

Open in a new tab

a Unless indicated otherwise, data are expressed as Column No. (%) of residents with percentages rounded.

b Standardized difference of >0.10 considered clinically meaningful difference (comparing AL to NH)

There was a statistically significant decrease in the rate of all-cause hospitalization during the peak of pandemic wave 1 (April 2020) for residents of both AL (adjRR 0.60, 95% CI 0.51–0.71) and NHs (adjRR 0.74, 95% CI 0.64–0.85) relative to historical comparisons (Table 3, Fig 1 and S1 Fig). Over subsequent months up to and including pandemic wave 3, the hospitalization rate among NH residents increased slightly but remained significantly lower relative to historical comparisons (adjRR 0.82 to 0.85). For AL residents, the hospitalization rate increased after the initial wave 1 decline and showed no statistically significant difference relative to historical periods during the peak of wave 2 (December 2020) and the subsequent month (January 2021). During January 2021, the hospitalization rate among AL residents increased by 27% relative to that among NH residents (period*setting interaction p = 0.013). Both settings showed significantly lower hospitalization rates during pandemic waves 3 (April 2021) and 4 (September 2021), with a more pronounced decrease during wave 4 (AL: adjRR 0.76, 95% CI 0.66–0.88; NH: adjRR 0.65, 95% CI 0.56–0.75).

Table 3. Adjusted rate ratios^a for all-cause hospitalization, hospitalization with delayed discharge, and hospitalization with death, comparing COVID-19 pandemic vs historical (2018/19) monthly periods, among Alberta Assisted Living (AL) and Nursing Home (NH) residents.

	Rate Ratio (95% CI) for Hospital Outcome Associated with Select COVID-19 Pandemic Time Periods
	April 2020	August 2020	December 2020	January 2021	April 2021	September 2021
Hospital Outcome & Setting	[Peak Wave 1]		[Peak Wave 2]		[Peak Wave 3]	[Peak Wave 4]
All-Cause Hospitalization
Period (pandemic vs 2018/19)
AL	0.60 (0.51–0.71)	0.87 (0.76–1.00)	0.92 (0.80–1.04)	1.05 (0.92–1.18)^c	0.84 (0.73–0.97)	0.76 (0.66–0.88)
NH	0.74 (0.64–0.85)	0.82 (0.71–0.95)	0.82 (0.72–0.95)	0.82 (0.72–0.95) ^c	0.85 (0.74–0.98)	0.65 (0.56–0.75)
Urgent Hospitalization
Period (pandemic vs 2018/19)
AL	0.62 (0.52–0.73)	0.89 (0.77–1.02)	0.93 (0.81–1.06)	1.05 (0.93–1.19)^c	0.83 (0.72–0.97)	0.76 (0.65–0.88)
NH	0.71 (0.61–0.83)	0.83 (0.72–0.96)	0.82 (0.71–0.95)	0.83 (0.72–0.95) ^c	0.86 (0.74–0.99)	0.67 (0.57–0.78)
Hospitalization with Delayed Discharge ^b
Period (pandemic vs 2018/19)
AL	0.98 (0.71–1.34)^d	1.14 (0.87–1.51)	0.98 (0.73–1.33)	1.16 (0.88–1.52)	0.81 (0.57–1.15)	0.71 (0.50–1.01)
NH	1.94 (1.31–2.85) ^d	1.84 (1.19–2.86)	1.39 (0.89–2.15)	1.15 (0.73–1.80)	0.82 (0.49–1.38)	0.63 (0.35–1.15)
Hospitalization with Death
Period (pandemic vs 2018/19)
AL	0.80 (0.55–1.17)	1.27 (0.88–1.82)	1.89 (1.40–2.54)	1.47 (1.07–2.01)	0.50 (0.31–0.79)	1.20 (0.77–1.87)
NH	1.05 (0.75–1.46)	0.81 (0.57–1.14)	1.53 (1.14–2.05)	0.99 (0.72–1.37)	0.71 (0.48–1.05)	0.90 (0.63–1.29)

Open in a new tab

a For each time period, separate Poisson regression generalized estimating equations (GEE) models were used to estimate rate ratios for period (COVID-19 pandemic vs historical [2018/19]), setting (AL vs NH) and period-setting interactions; Models adjusted for age, sex, ADL, CPS, CHESS, #chronic conditions, health zone & ownership status. See S2 Table for all model estimates, including setting.

b Note: An adjusted model for October 2020 was also computed given the rise in rate of hospitalization with a delayed discharge evident in Fig 2 and S2 Fig: for AL the adjusted RR = 1.55 (1.17–2.04), for NH the adjusted RR = 2.57 (1.69–3.91), with a statistically significant interaction (period*setting), p = 0.048

c Test of statistical significance for interaction of period*setting, p = 0.013

d Test of statistical significance for interaction of period*setting, p = 0.008

Among AL residents, there were no statistically significant associations between the selected pandemic periods and rate of hospitalization with a delayed discharge (Table 3, Fig 2 and S2 Fig), whereas among NH residents, there was a statistically significant increase during both the peak of wave 1 (adjRR 1.94, 95% CI 1.31–2.85, with a statistically significant period*setting interaction, p = 0.008) and August 2020 (adjRR 1.84, 95% CI 1.19–2.86). Of note, both settings showed a statistically significant increase in rate of hospitalization with a delayed discharge during October 2020 (not an a priori exposure period of interest). This increase was more pronounced for NH (adjRR 2.57 (1.69–3.91) than AL (adjRR 1.55 (1.17–2.04) residents (period*setting interaction, p = 0.048).

Among AL residents, there was a statistically significant increase in rate of hospitalization with death during the peak of wave 2 and in January 2021, followed by a significant decrease in this outcome during the peak of wave 3 (Table 3, S3 Fig). For NH residents, the only statistically significant association was for an increase in rate of hospitalization with death during wave 2 relative to historical period.

Both sensitivity analyses produced comparable findings to those presented in Table 3.

Rates of all-cause hospitalization and hospitalization with a delayed discharge were consistently higher among AL than NH residents throughout the study period (adjRR of 1.4–1.6 and 4.5–6.5, respectively; Figs 1 and 2, S2 Table). AL residents consistently showed higher median delayed discharge bed days (e.g., during January 2021, median 30 [IQR 6–52] bed days for AL vs median 9.5 [IQR 4–28] for NH residents, S3 Table).

Among the top 10 ICD-10-CA codes for the most responsible diagnosis (for hospitalizations during April 1, 2020 and January 1, 2021 vs historical counterparts) (S3 Table), dementia/mental health/delirium and injuries/fractures were more common and ranked higher among AL vs NH residents (especially during the pandemic months), whereas sepsis and renal failure/UTIs were more common and ranked higher during pandemic months among NH residents. Respiratory diseases were among the top two diagnoses for both settings but accounted for smaller proportion of diagnoses during pandemic periods (replaced by COVID-19 as the top condition in January 2021).

The monthly rates of COVID-19 hospitalizations were highest during wave 2, decreased following the implementation of vaccinations, and increased again during wave 4 (S4 Fig). During waves 2 and 4 the rates were higher among AL than NH residents. The proportion of all hospitalizations that were for COVID-19 was comparable for AL and NHs during December 2020 (27.0% vs 26.1%) and slightly higher for AL in January 2021 (19.5% vs 14.9%) and September 2021 (7.7% vs 5.6%). Monthly rates of positive COVID-19 tests were similar in AL and NHs (except for wave 1) and aligned with peak surges observed for COVID-19 hospitalizations (S5 Fig).

Discussion

In this population-based study of residents of AL and NHs in Alberta Canada, both settings showed pronounced decreases in all-cause hospitalizations during the first (40% and 26%, respectively) and fourth (24% and 35%, respectively) COVID-19 pandemic peaks but they varied significantly in this outcome during wave 2, and in the secondary hospital outcomes examined during selected pandemic periods. Whereas hospitalization rates among NH residents remained significantly below historical levels throughout, they increased for AL residents (by 27% relative to NHs) and were not significantly different from pre-pandemic rates during this period. For hospitalization with a delayed discharge, rates increased significantly during wave 1 (and into August 2020) for NH residents only, and though both settings showed a significant increase in rates of hospitalization with death during wave 2, this increase was larger and persisted longer (beyond peak of wave 2) for AL residents. The two settings also showed differences in the distribution of most responsible hospital diagnoses during pandemic periods and in COVID-19 associated hospitalizations (higher in AL than NH for waves 2 and 4), the latter despite generally comparable rates of positive COVID-19 tests.

The substantial decline in hospitalizations noted for both settings during the peak of wave 1 (consistent for all-cause and emergent admissions) aligns with other studies of chronically ill older adults in community [21] and long-term care settings [23–25, 28]. Few studies of congregate care have examined changes in healthcare use for AL residents [24] or for periods beyond 2020 (following the introduction of COVID-19 vaccinations). Expanding on earlier work, our findings reveal that with one exception (wave 2), pandemic periods examined up to the end of 2021 were associated with significantly lower hospitalizations from both settings. Complex and inter-related health system, home and resident/family factors may explain these lower rates, including the protection of acute care beds for the influx of expected COVID-19 cases early on in the pandemic [21], a reluctance by hospitals to accept AL or NH residents and/or by care homes or family to transfer residents to hospital because of the perceived risk of infection [25], changes in resident conditions typically associated with hospitalization [23, 24, 27], and a failure to recognize emergent health conditions requiring acute care given staffing shortages and family absence [27]. From a resident quality of care perspective, these findings may be viewed positively (e.g., there were fewer unnecessary hospital transfers from care home settings) [18, 43, 44] or negatively (e.g., there were more residents with serious unmet clinical care needs at heightened risk for death or functional and cognitive decline) [19, 24, 45]. Both possibilities warrant further investigation.

The evident rise in hospital admissions for AL residents in wave 2 (back to historical levels), may reflect an increased likelihood for AL homes to transfer residents with COVID-19 and other conditions to hospital and/or greater illness severity in AL vs NH residents (perhaps facilitated by less effective isolation measures or greater interactions among AL residents) during this period [20]. In both settings, positive COVID-19 cases among residents were especially high during wave 2, dropped dramatically following widespread resident vaccinations, and increased again during wave 4. It may also be that AL residents experienced more adverse health outcomes (e.g., inappropriate medication use [46], falls/injuries [47], worse mental health and/or disease progression) and potentially avoidable hospitalizations during this surge because of reductions in already low levels of professional staffing and services [8–10, 12–14] and absence of family caregivers [3]. Our data regarding setting-specific changes in the distribution of most responsible diagnoses for hospitalizations during waves 1 and 2 and consistently elevated rates of hospitalization (including admissions with delayed discharge and higher bed stays) for AL relative to NHs throughout pre- and post-pandemic periods partially support this explanation.

Though there is no evidence of changes in AL or NH admission and discharge criteria during the pandemic [48], there may have been differences in the enactment of these criteria across care settings and/or adoption of strategies to prevent avoidable hospitalizations (e.g., increased communication between NH sites and emergency department physicians or involvement of community paramedics, [49]) that contributed to the setting-specific hospitalization findings noted above.

The increased rate ratios for hospital admission with a delayed discharge shown for NH residents early in the pandemic, and for both settings during Oct 2020, may be related to the reduced availability of NH and AL beds as homes addressed concerns of overcrowding and the need for isolation spaces during periods of peak COVID-19 cases [21, 50]. These findings may also reflect hesitation among physicians, staff and/or families to have residents discharged back to AL or NHs during peak surges because of a limited availability of staff to care for residents who likely had increased health needs.

Both settings experienced a significant increase in hospitalization with death during the peak of wave 2 relative to historical periods (coinciding with peak COVID-19 cases and hospitalizations), but the magnitude of this increase was larger and persisted into early 2021 for AL residents. As suggested by our data on most responsible diagnoses, AL residents were more likely to be admitted with health conditions associated with increased hospital bed days and mortality (e.g., dementia, mental health issues, delirium) [51]. They may also have presented with more severe health conditions related to less timely detection of emerging health issues and limited ability to augment care in the AL home [8, 13]. These hospital deaths are disconcerting given residents’ and families’ preferred place of death (at home) and that many residents likely died alone because of public health restrictions.

Study strengths & limitations

Strengths of this study include its use of population-based clinical and health administrative data and rigorous methodology (including adjustment for relevant confounding factors), comparison of hospitalization trends for residents of AL (a less researched setting) and NHs, and examination of outcomes beyond initial pandemic waves and for periods following widespread COVID-19 vaccinations among residents. Limitations include the lack of data on setting-specific differences in staffing, service availability, advance directives (or residents’ goals of care), policy changes and resident health outcomes during the pandemic waves examined. Though our models adjusted for key covariates, there may have been temporary disruptions in completing RAI assessments early in the pandemic. We cannot comment on the appropriateness of the hospitalization trends observed and future investigations of associated outcomes for residents and families are warranted. We did not have access to other data of potential relevance to hospitalization events observed across pandemic waves, including the variants of SARS-CoV-2 present throughout the study period. As our study was restricted to publicly subsidized AL in Alberta, caution is needed in generalizing our findings to other AL settings in Canada and elsewhere.

Conclusions

Hospitalizations represent an important quality of care indicator for AL and NH populations, reflecting the importance of potentially preventable hospital transfers as well as equitable access to appropriate and timely medical care. This study showed that the associations between various pandemic waves and hospitalization events varied in important ways for AL compared with NH residents, further highlighting the unique challenges faced by different congregate care settings during COVID-19 [4, 5] and their varying responses to this external stress. Our findings support the presence of setting-specific drivers of residents’ healthcare use and outcomes both prior to and during COVID-19 that require careful consideration by continuing care operators, health care providers and policy decision makers. These setting-specific differences should also inform future interventions [44, 52, 53] aimed at strengthening the integration, communication and support between congregate care and acute care settings. Considerations of the clinical complexity of AL residents and hospitalization trends observed (both historically and during COVID-19) strongly support previous calls for a blended social-medical model of care in AL homes [1, 6, 16]. Strategies to be investigated include more routine access to primary care physicians and/or nurse practitioners (both on-site and via telemedicine) [54], enhanced levels of well-trained professional staff, clearly documented advance directives, and the development of policies and procedures specific to the appropriate detection, treatment, and management of the chronic physical and mental health conditions common in AL residents.

Supporting information

S1 Table. Description of Alberta provincial clinical and health administrative databases.

(DOCX)

pone.0306569.s001.docx^{(35.9KB, docx)}

S2 Table. Adjusted rate ratios for all-cause hospitalization, hospitalization with delayed discharge, and hospitalization with death, comparing COVID-19 pandemic vs historical (2018/19) monthly periods, among Alberta Assisted Living (AL) and Nursing Home (NH) residents.

(DOCX)

pone.0306569.s002.docx^{(39.2KB, docx)}

S3 Table. A) Distribution of top 10 ICD-10-CA diagnosis (grouped) for most responsible diagnosis associated with hospitalizations during April 2020 and January 2021 (relative to comparable historical 2018/19 months), by setting.

B) Distribution of top 10 ICD-10-CA diagnosis / chapters for most responsible diagnosis associated with hospitalizations during April 2020 and January 2021 (relative to comparable historical 2018/19 months), by setting.

(DOCX)

pone.0306569.s003.docx^{(53.7KB, docx)}

S1 Fig. Monthly all-cause hospitalization rate (per 100 person-days) across study period, January 1, 2018 to December 31, 2021, among Assisted Living (AL) and Nursing Home (NH) residents.

(DOCX)

pone.0306569.s004.docx^{(50.1KB, docx)}

S2 Fig. Monthly hospitalization with delayed discharge rate (per 100 person-days) across study period, January 1, 2018 to December 31, 2021, among Assisted Living (AL) and Nursing Home (NH) residents.

(DOCX)

pone.0306569.s005.docx^{(51.4KB, docx)}

S3 Fig. Monthly hospitalization with death rate (per 100 person-days) across study period, January 1, 2018 to December 31, 2021, among Assisted Living (AL) and Nursing Home (NH) residents.

(DOCX)

pone.0306569.s006.docx^{(43.7KB, docx)}

S4 Fig. Monthly COVID-19 hospitalization rate (per 100 person-days) across pandemic period, March 1, 2020 to December 31, 2021, among Assisted Living (AL) and Nursing Home (NH) residents.

(DOCX)

pone.0306569.s007.docx^{(43KB, docx)}

S5 Fig. Monthly rate (per 100 residents) of positive COVID-19 tests, across pandemic period, March 1, 2020 to December 31, 2021, among Assisted Living (AL) and Nursing Home (NH) residents.

(DOCX)

pone.0306569.s008.docx^{(524.9KB, docx)}

S1 Text. RECORD statement.

(PDF)

pone.0306569.s009.pdf^{(69.7KB, pdf)}

Acknowledgments

We would like to thank the Alberta SPOR SUPPORT Unit for their assistance in accessing the data used in this study.

Data Availability

The dataset from this study is held securely in coded form within Alberta Health Services. While ethical restrictions and legal data sharing agreements between the investigators, the Alberta SPOR SUPPORT Unit and data providers (Alberta Health Services and Alberta Health) prohibit making the dataset publicly available, access may be granted to those who meet pre-specified criteria for access, available at: https://absporu.ca/. email: absporu@albertainnovates.ca.

Funding Statement

This study was funded by a Canadian Institutes of Health Research (CIHR) Operating Grant (reference # 179467), co-held by MH & CJM. The funders had no role in the study design, data collection and analyses, decision to publish, or preparation of the manuscript.

References

1.Zimmerman S, Sloane PD, Wretman CJ, Cao K, Silbersack J, Carder P, et al. Recommendations for medical and mental health care in assisted living based on an expert delphi consensus panel: A consensus statement. JAMA Netw Open. 2022;5(9):e2233872. doi: 10.1001/jamanetworkopen.2022.33872 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Maxwell CJ, Soo A, Hogan DB, Wodchis WP, Gilbart E, Amuah J, et al. Predictors of nursing home placement from assisted living settings in Canada. Can J Aging. 2013;32(4):333–348. doi: 10.1017/S0714980813000469 [DOI] [PubMed] [Google Scholar]
3.Hoben M, Li W, Dampf H, Hogan DB, Corbett K, Chamberlain SA, et al. Caregiver involvement and concerns with care of residents of assisted living before and during the COVID-19 pandemic. Gerontology. 2023;69(7):839–851. doi: 10.1159/000530622 [DOI] [PubMed] [Google Scholar]
4.Schwartz LB, Lieblich C, Laxton CE, Kaes L, Barnett DP, Port C, et al. COVID-19 in Assisted Living: Protecting a critical long-term care resource. J Am Med Dir Assoc. 2023;24(2):134–139. doi: 10.1016/j.jamda.2022.12.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Zimmerman S, Sloane PD, Katz PR, Kunze M, O’Neil K, Resnick B.The need to include assisted living in responding to the COVID-19 pandemic. J Am Med Dir Assoc. 2020;21(5):572–575. doi: 10.1016/j.jamda.2020.03.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Katz PR, Howd S, Rust C. Reimagining medical care in Assisted living. J Am Med Dir Assoc. 2022;23(2):201–203. doi: 10.1016/j.jamda.2021.12.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Zimmerman S, Sloane PD, Reed D. Dementia prevalence and care in assisted living. Health Aff (Millwood). 2014;33(4):658–666. doi: 10.1377/hlthaff.2013.1255 [DOI] [PubMed] [Google Scholar]
8.Hogan DB, Amuah JE, Strain LA, Wodchis WP, Soo A, Eliasziw M, et al. High rates of hospital admission among older residents in assisted living facilities: opportunities for intervention and impact on acute care. Open Med. 2014;8(1):e33–45. [PMC free article] [PubMed] [Google Scholar]
9.Thomas KS, Cornell PY, Zhang W, Smith L, Hua C, Kaskie B, et al. The relationship between states’ staffing regulations and hospitalizations of assisted living residents. Health Aff (Millwood). 2021;40(9):1377–1385. doi: 10.1377/hlthaff.2021.00598 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Stearns SC, Park J, Zimmerman S, Gruber-Baldini AL, Konrad TR, Sloane PD. Determinants and effects of nurse staffing intensity and skill mix in residential care/assisted living settings. Gerontologist. 2007;47(5):662–671. doi: 10.1093/geront/47.5.662 [DOI] [PubMed] [Google Scholar]
11.Lei L, Samus QM, Thomas KS, Maust DT. Medication costs and use of older Americans in assisted living settings: a nationally representative cross-sectional study. J Gen Intern Med. 2023;38(2):294–301. doi: 10.1007/s11606-022-07434-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Sloane PD, Zimmerman S, Gruber-Baldini AL, Hebel JR, Magaziner J, Konrad TR. Health and functional outcomes and health care utilization of persons with dementia in residential care and assisted living facilities: comparison with nursing homes. Gerontologist. 2005;45 Spec No 1(1):124–132. doi: 10.1093/geront/45.suppl_1.124 [DOI] [PubMed] [Google Scholar]
13.Maxwell CJ, Amuah JE, Hogan DB, Cepoiu-Martin M, Gruneir A, Patten SB, et al. Elevated hospitalization risk of assisted living residents with dementia in Alberta, Canada. J Am Med Dir Assoc. 2015;16(7):568–577. doi: 10.1016/j.jamda.2015.01.079 [DOI] [PubMed] [Google Scholar]
14.Thomas KS, Zhang W, Cornell PY, Smith L, Kaskie B, Carder PC. State variability in the prevalence and healthcare utilization of assisted living residents with dementia. J Am Geriatr Soc. 2020;68(7):1504–1511. doi: 10.1111/jgs.16410 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Manis DR, Poss JW, Jones A, Rochon PA, Bronskill SE, Campitelli MA, et al. Rates of health services use among residents of retirement homes in Ontario: a population-based cohort study. CMAJ. 2022;194(21):E730–E738. doi: 10.1503/cmaj.211883 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Kronhaus A, Zimmerman S, Fuller S, Reed D. Implementing integrated care into assisted living-the road (finally) taken. J Am Med Dir Assoc. 2018;19(10):914–915. doi: 10.1016/j.jamda.2018.05.003 [DOI] [PubMed] [Google Scholar]
17.Zimmerman S, Greene A, Sloane PD, Mitchell M, Giuliani C, Nyrop K, et al. Preventing falls in assisted living: Results of a quality improvement pilot study. Geriatr Nurs. 2017;38(3):185–191. doi: 10.1016/j.gerinurse.2016.09.003 [DOI] [PubMed] [Google Scholar]
18.Gill TM, Allore HG, Gahbauer EA, Murphy TE. Change in disability after hospitalization or restricted activity in older persons. JAMA. 2010;304(17):1919–1928. doi: 10.1001/jama.2010.1568 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Mukamel DB, Saliba D, Ladd H, Konetzka RT. Associations between daily nurse staffing levels and daily hospitalizations and ED visits in nursing homes. J Am Med Dir Assoc. 2022;23(11):1793–1799.e3 doi: 10.1016/j.jamda.2022.06.030 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Zimmerman S, Sloane PD, Hickey JS, Wretman CJ, Gizlice SP, Thomas KS, et al. Dementia and COVID-19 infection control in assisted living in seven states. J Am Geriatr Soc. 2022;70(9):2653–2658. doi: 10.1111/jgs.17923 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Bronskill SE, Maclagan LC, Maxwell CJ, Iaboni A, Jaakkimainen RL, Marras C, et al. Trends in health service use for Canadian adults with dementia and parkinson disease during the first wave of the COVID-19 pandemic. JAMA Health Forum. 2022;3(1):e214599. doi: 10.1001/jamahealthforum.2021.4599 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Maclagan LC, Wang X, Emdin A, Jones A, Jaakkimainen RL, Schull MJ, et al. Visits to the emergency department by community-dwelling people with dementia during the first 2 waves of the COVID-19 pandemic in Ontario: a repeated cross-sectional analysis. CMAJ Open. 2022;10(3):E610–E621. doi: 10.9778/cmajo.20210301 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Betini R, Milicic S, Lawand C. The impact of the COVID-19 pandemic in long-term care in Canada. Healthc Q. 2021;24(3):13–15. [DOI] [PubMed] [Google Scholar]
24.Grimm F, Hodgson K, Brine R, Deeny SR. Hospital admissions from care homes in England during the COVID-19 pandemic: a retrospective, cross-sectional analysis using linked administrative data. Int J Popul Data Sci. 2021;5(4):1663. doi: 10.23889/ijpds.v5i4.1663 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Sundaram M, Nasreen S, Calzavara A, He S, Chung H, Bronskill SE, et al. ; Canadian Immunization Research Network (CIRN) investigators. Background rates of all-cause mortality, hospitalizations, and emergency department visits among nursing home residents in Ontario, Canada to inform COVID-19 vaccine safety assessments. Vaccine. 2021;39(37):5265–5270. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Tan LF, Soh RYH, Seetharaman SK. Impact of the COVID-19 pandemic on hospital admissions of nursing home residents. Australas J Ageing. 2022;41(2):330–334. doi: 10.1111/ajag.13032 [DOI] [PubMed] [Google Scholar]
27.Jones A, Mowbray FI, Falk L, Stall NM, Brown KA, Malikov K, et al. Variations in long-term care home resident hospitalizations before and during the COVID-19 pandemic in Ontario. PLoS One. 2022;17(11):e0264240. doi: 10.1371/journal.pone.0264240 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Canadian Institute for Health Information (CIHI). COVID-19’s impact on long-term care. Available from https://www.cihi.ca/en/covid-19-resources/impact-of-covid-19-on-canadas-health-care-systems/long-term-care.
29.Alberta Health Services. Designated Supportive Living, Continuing Care. Available from: https://www.albertahealthservices.ca/cc/Page15490.aspx.
30.Alberta Health—Chief Medical Office of Health (CMOH) order 03–2020: 2020. COVID-19 response. Available from: https://open.alberta.ca/publications/cmoh-order-03-2020-2020-covid-19-response. [Google Scholar]
31.Hogeveen SE, Chen J, Hirdes JP. Evaluation of data quality of interRAI assessments in home and community care. BMC Med Inform Decis Mak. 2017;17(1):150. doi: 10.1186/s12911-017-0547-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Poss JW, Jutan NM, Hirdes JP, Fries BE, Morris JN, Teare GF, et al. A review of evidence on the reliability and validity of Minimum Data Set data. Healthc Manage Forum. 2008;21(1):33–39. doi: 10.1016/S0840-4704(10)60127-5 [DOI] [PubMed] [Google Scholar]
33.COVID-19 Alberta statistics. Available from: https://www.alberta.ca/stats/covid-19-alberta-statistics.htm#total-cases.
34.Canadian Institute for Health Information (CIHI). Definitions and guidelines to support ALC designation in acute inpatient care. Available from: https://www.cihi.ca/sites/default/files/document/acuteinpatientalc-definitionsandguidelines_en.pdf.
35.Canadian Institute for Health Information. Canadian Coding Standards for Version 2022 ICD-10-CA and CCI. Ottawa, ON: CIHI; 2022. Available from: https://secure.cihi.ca/free_products/canadian-coding-standards-2022-en.pdf. [Google Scholar]
36.Canadian Institute for Health Information (CIHI). Mapping COVID-19 Codes: ICD-10-CA to WHO’s Base ICD-10. Available from: https://www.cihi.ca/sites/default/files/document/mapping-covid-19-codes-icd-10-ca-to-base-icd-10-job-aid-en.pdf.
37.Morris JN, Fries BE, Morris SA. Scaling ADLs with the MDS. J Gerontol A Biol Sci Med Sci. 1999;54(11):M546–M553. [DOI] [PubMed] [Google Scholar]
38.Hartmaier SL, Sloane PD, Guess HA, Koch GG, Mitchell CM, Phillips CD. Validation of the Minimum Data Set Cognitive Performance Scale: Agreement with the Mini-Mental State Examination. J Gerontol A Biol Sci Med Sci. 1995;50A(2):M128–M133. doi: 10.1093/gerona/50a.2.m128 [DOI] [PubMed] [Google Scholar]
39.Burrows AB, Morris JN, Simon SE, Hirdes JP, Phillips C. Development of a minimum data set-based depression rating scale for use in nursing homes. Age Ageing. 2000;29(2):165–172. doi: 10.1093/ageing/29.2.165 [DOI] [PubMed] [Google Scholar]
40.Hirdes J, Frijters DH, Teare GF. The MDS-CHESS Scale: A new measure to predict mortality in institutionalized older people. J Am Geriatr Soc. 2003;51(1):96–100. doi: 10.1034/j.1601-5215.2002.51017.x [DOI] [PubMed] [Google Scholar]
41.Campitelli MA, Bronskill SE, Hogan DB, Diong C, Amuah JE, Gill S, et al. The prevalence and health consequences of frailty in a population-based older home care cohort: A comparison of different measures. BMC Geriatr. 2016;16:133. doi: 10.1186/s12877-016-0309-z [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28(25):3083–3107. doi: 10.1002/sim.3697 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.McGarry BE, Mao Y, Nelson DL, Temkin-Greener H. Hospital proximity and emergency department use among assisted living residents. J Am Med Dir Assoc. 2023;24(9):1349–1355.e5. doi: 10.1016/j.jamda.2023.05.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Ouslander JG, Naharci I, Engstrom G, Shutes J, Wolf DG, Alpert G, et al. Root cause analyses of transfers of skilled nursing facility patients to acute hospitals: lessons learned for reducing unnecessary hospitalizations. J Am Med Dir Assoc. 2016;17(3):256–262. doi: 10.1016/j.jamda.2015.11.018 [DOI] [PubMed] [Google Scholar]
45.Janke AT, Jain S, Hwang U, Rosenberg M, Biese K, Schneider S, et al. Emergency department visits for emergent conditions among older adults during the COVID-19 pandemic. J Am Geriatr Soc. 2021;69(7);1713–1721. doi: 10.1111/jgs.17227 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Maxwell CJ, Dampf H, Alkabbani W, Cotton CA, Gamble JM, Hogan DB, et al. Psychotropic, anticonvulsant and opioid use in assisted living residents before and during the COVID-19 pandemic. J Am Med Dir Assoc. 2024;25(1):121–129. doi: 10.1016/j.jamda.2023.09.009 [DOI] [PubMed] [Google Scholar]
47.Hua CL, Cornell PY, White EM, Thomas KS. Injury-related emergency department use among assisted living residents with Alzheimer’s disease and related dementias. J Am Geriatr Soc. 2023;71(2):538–545. doi: 10.1111/jgs.18207 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Auditor General of Alberta. COVID-19 in Continuing Care Facilities, 2023. Available from: https://www.oag.ab.ca/wp-content/uploads/2023/04/oag-covid19-cont-care-facilities-feb2023.pdf [Google Scholar]
49.Wyer L, Guterman Y, Ewa V, Lang E, Faris P, Holroyd-Leduc J. The impact of the COVID-19 pandemic on transfers between long-term care and emergency departments across Alberta. BMC Emerg Med. 2024;24(1):9. doi: 10.1186/s12873-023-00926-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Brown KA, Jones A, Daneman N, Chan AK, Schwartz KL, Garber GE, et al. Association between nursing home crowding and COVID-19 infection and mortality in Ontario, Canada. JAMA Intern Med. 2021;181(2):229–236. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Kennedy M, Helfand BKI, Gou RY, Gartaganis SL, Webb M, Moccia JM, et al. Delirium in older patients with COVID-19 presenting to the emergency department. JAMA Netw Open 2020;3(11):e2029540. doi: 10.1001/jamanetworkopen.2020.29540 [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Kane RL, Huckfeldt P, Tappen R, Engstrom G, Rojido C, Newman D, et al. Effects of an intervention to reduce hospitalizations from nursing homes: A randomized implementation trial of the INTERACT Program. JAMA Intern Med. 2017;177(9):1257–1264. doi: 10.1001/jamainternmed.2017.2657 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Wong BM, Rotteau L, Feldman S, Lamb M, Liang K, Moser A, et al. A novel collaborative care program to augment nursing home care during and after the COVID-19 pandemic. J Am Med Dir Assoc. 2022;23(2):304–307. doi: 10.1016/j.jamda.2021.11.018 [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Hua Y, Temkin-Greener H, Cai S. Primary care telemedicine use among assisted living residents with dementia during COVID-19: Race and dual enrollment status. J Am Med Dir Assoc. 2023;24(8):1157–1158.e3. doi: 10.1016/j.jamda.2023.05.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0306569.r001

Decision Letter 0

Mario Ulises Pérez-Zepeda

7 May 2024

PONE-D-24-03317Comparison of hospitalization events among residents of assisted living and nursing homes during COVID-19: do settings respond differently during public health crises?PLOS ONE

Dear Dr. Maxwell,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Please submit your revised manuscript by Jun 21 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Mario Ulises Pérez-Zepeda, M.D., Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you have indicated that there are restrictions to data sharing for this study. For studies involving human research participant data or other sensitive data, we encourage authors to share de-identified or anonymized data. However, when data cannot be publicly shared for ethical reasons, we allow authors to make their data sets available upon request. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

Before we proceed with your manuscript, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., a Research Ethics Committee or Institutional Review Board, etc.). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of recommended repositories, please see https://journals.plos.org/plosone/s/recommended-repositories. You also have the option of uploading the data as Supporting Information files, but we would recommend depositing data directly to a data repository if possible.

Please update your Data Availability statement in the submission form accordingly.

4. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Well written.

The practical problems of AL have been brought out in the first paragraph itself.

The residents of the NH were frailer, had more comorbidities, cognitively worse and were dependent on ADLs, as expected. Did this approach statistical significance?

Among AL residents, there was a statistically significant increase in rate of hospitalization with death during the peak of wave 2 and in January 2021, followed by a significant decrease in this outcome during the peak of wave 3. For NH residents, the only statistically significant association was for an increase in rate of hospitalization with death during wave 2 relative to historical period. This is true all over the world. Wave 2 was the worst and by wave 3 and 4 – vaccinations had been given, and others would have been exposed to the virus. But what about variants?

During waves 2 and 4 the rates were higher among AL than NH residents- this could be attributed to the fact that AL patients mingled, while NH patients were bedbound and exposed to fewer people – both inmates and carers at the facilities.

Did death and burden of comorbidities correlate? What about morbidity?

Did the admission and discharge criteria vary between waves, and between NH and AL?

Reviewer #2: The article is well constructed and gives an interesting perspective of how different settings of assisted living and nursing homes respond during health crises. Some data should be clarified and be less dispersive to ensure that readers undestand exactly the researches point of view. I did appreciate the strategies to be investigated in the conclusions and I think it worths finding out more about.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2024 Jul 12;19(7):e0306569. doi: 10.1371/journal.pone.0306569.r002

Author response to Decision Letter 0

14 May 2024

Response to Reviewers: Note: Comments from Reviewers are presented verbatim using italics. The location of revised text refers to lines in the revised manuscript with track changes.

Reviewer #1:

1. Well written. The practical problems of AL have been brought out in the first paragraph itself.

Response: Thank you for noting these strengths of our manuscript.

2. The residents of the NH were frailer, had more comorbidities, cognitively worse and were dependent on ADLs, as expected. Did this approach statistical significance?

Response: In our comparison of the characteristics of residents in AL vs NH, we reported which differences were larger than a standardized difference of 0.10 as a measure of a meaningful difference (please see footnotes used in Tables 1 and 2). We used standardized differences rather than p values given the large sample sizes involved - which would result in small differences being statistically significant but not necessarily clinically relevant. In our original manuscript, we noted the rationale for this in the Analysis section (lines 172-174) with a supporting reference (reference #42).

3. Among AL residents, there was a statistically significant increase in rate of hospitalization with death during the peak of wave 2 and in January 2021, followed by a significant decrease in this outcome during the peak of wave 3. For NH residents, the only statistically significant association was for an increase in rate of hospitalization with death during wave 2 relative to historical period. This is true all over the world. Wave 2 was the worst and by wave 3 and 4 – vaccinations had been given, and others would have been exposed to the virus. But what about variants?

Response: We do acknowledge that COVID-19 case rates were most pronounced (in Alberta and globally) during the period of the pandemic prior to widespread vaccinations. This is also evident for residents in our study as illustrated in our S5 Fig. Regarding our findings for rate of hospitalization with death – a key point we wished to highlight with these statements were the differences in this outcome observed between AL and NH beyond the peak of wave 2 – i.e., during January 2021 and during the peak of wave 3 (as also emphasized in our Discussion – see lines 305-307 and lines 356-359). Related to this was our finding of considerably higher COVID-19 hospitalization rates among residents of AL vs NH during the peak of wave 2 and in the subsequent month of January 2021 (as shown in S4 Fig.). As noted throughout our manuscript, there were several notable differences in study outcomes between those in AL vs NH – including during wave 2 of the COVID-19 pandemic.

We are not able to comment on the relevance of differences in specific variants of SARS-CoV-2 present throughout the study period as we did not have access to these data (we have added this statement to the limitation section of our Discussion, see lines 377-379). We do not believe that the key differences we observed in outcomes between AL and NH settings would be due to differences in the variants present, as the dominant variants would be expected to be comparable across settings at specific points in time.

4. During waves 2 and 4 the rates were higher among AL than NH residents- this could be attributed to the fact that AL patients mingled, while NH patients were bedbound and exposed to fewer people – both inmates and carers at the facilities.

Response: Thank you for this comment regarding potential contributing factors to the observed differences between AL and NH settings in the monthly rates of COVID-19 hospitalizations. As we noted in our Results (lines 290-295) and Discussion (lines 309-310) sections, these differences were observed during waves 2 and 4 even though monthly rates of positive COVID-19 tests were relatively similar in ALs and NHs during these waves (as illustrated in our S5 Fig.). We do comment on possible reasons for these differences in our Discussion (including an increased likelihood for AL homes to transfer residents with COVID-19 and/or greater illness severity in AL vs NH residents) (lines 330-331). As suggested, we have added a brief comment about the potential role of less effective isolation of active cases and greater interactions among AL residents (lines 331-332); however, while more physically impaired, it would be inaccurate to state that most NH residents were bedbound. We also believe it would be misleading to assume that NH residents were exposed to fewer people than AL residents - given that similar visitor restrictions were implemented in both settings during the pandemic and the expected higher numbers of staff available in NH vs AL settings.

5. Did death and burden of comorbidities correlate? What about morbidity?

Response: We believe this comment relates to the likely associations between level of resident comorbidity and the outcome of hospitalization with death. For this outcome (as with all our outcomes), our models were adjusted for several covariates, including number of chronic conditions as noted in our Analysis section (see lines 181-183) and in the footnotes provided for Table 3.

6. Did the admission and discharge criteria vary between waves, and between NH and AL?

Response: Thank you for this interesting question. As would be expected, there always have been differences in the admission and discharge criteria between licensed publicly subsidized AL and NH settings in Alberta as these criteria define initial and ongoing eligibility for setting-specific care as per Alberta Health Services (as per our reference #29). There is no evidence, however, that admission and discharge criteria changed in either of the two settings with the emergence of the COVID-19 pandemic (or by pandemic wave) (Auditor General of Alberta. COVID-19 in Continuing Care Facilities, 2023. https://www.oag.ab.ca/wp-content/uploads/2023/04/oag-covid19-cont-care-facilities-feb2023.pdf ). However, we can’t be sure whether there were temporary differences in how existing admission and/or discharge criteria across AL and/or NH sites were enacted by the homes during the pandemic waves under study. It is also possible that strategies were adopted during the pandemic (e.g., increased interactions between NH sites and ED physicians or paramedic services [Wyer et al., 2024]) that differentially affected hospitalization events during the four pandemic waves. We have added a brief comment regarding the above to our Discussion along with the references noted above (lines 343-348; new references #48 & #49).

Reviewer #2:

1. The article is well constructed and gives an interesting perspective of how different settings of assisted living and nursing homes respond during health crises.

Response: Thank you for these positive comments regarding the strengths of our manuscript.

2. Some data should be clarified and be less dispersive to ensure that readers undestand exactly the researches point of view.

Response: Thank you. We are unclear what specific data should be clarified and/or rearranged (which we think is what was meant by the suggestion to be less dispersive). We believe that our revisions made in response to comments provided by Reviewer #1 help to address this comment.

3. I did appreciate the strategies to be investigated in the conclusions and I think it worths finding out more about.

Response: Thank you for this positive comment.

Attachment

Submitted filename: Response to Reviewers_COVID Hosp AL NH_PLOS ONE 14May2024.docx

pone.0306569.s010.docx^{(1.2MB, docx)}