
==== Front
BMC Health Serv Res
BMC Health Serv Res
BMC Health Services Research
1472-6963
BioMed Central London

39223588
11479
10.1186/s12913-024-11479-x
Research
Falls in focus: an analysis of the rate of falls in 25 Australian residential aged care facilities from 2019 to 2021, pre- and during COVID-19 lockdowns
Meulenbroeks Isabelle isabelle.meulenbroeks@mq.edu.au

Wabe Nasir
Raban Magdalena Z.
Seaman Karla
Westbrook Johanna
https://ror.org/01sf06y89 grid.1004.5 0000 0001 2158 5405 Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Road North Ryde, Sydney, 2122 Australia
2 9 2024
2 9 2024
2024
24 101524 2 2024
22 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Introduction

During 2020–2021 Australia maintained comparatively low rates of COVID-19 in the community and residential aged care facilities (RAC) due to stringent public health measures such as lockdowns. However, the public health measures implemented may have had unintended impacts on critical RAC resident health outcomes, such as falls, due to routine care disruptions and aged care resident isolation. We utilised a longitudinal data to assess the association between COVID-19 lockdowns and the rate of falls in RAC settings.

Methods

A longitudinal cohort study was conduct using routinely collected data from 25 RAC facilities from one non-profit aged care provider in Sydney, Australia. The study included 2,996 long term residents between March 2019 and March 2021. The outcome measures were all falls, injurious falls, and falls assessed as requiring hospitalisation. Generalised estimating equations (GEE) were applied to determine the association between COVID-19 lockdown periods and fall-related outcomes while adjusting for confounders and seasonality.

Results

During the study period 11,658 falls were recorded. Residents frequently experienced at least one fall during the study period (median: 1, interquartile range: 0–4). During Lockdown 1 (March-June 2020) the rate of all falls increased 32% (IRR 1.32, 95% CI 1.19–1.46, p < 0.01) and the rate of injurious falls increased by 28% (IRR 1.28, 95% CI 1.12–1.46, p < 0.01) compared to pre-pandemic rates. The rate of falls assessed as requiring hospitalisation remained unchanged during Lockdown 1 (IRR 1.07, 95% CI 0.86–1.32, p = 0.519). During Lockdown 2 (Dec 2020-Jan 2021) the rate of all falls, injurious falls, and falls assessed as requiring hospitalisation did not change significantly compared to pre-pandemic rates.

Conclusion

These findings suggest that the consequences of stringent COVID-19 restrictions, as seen in Lockdown 1, produced changes in residents’ care which contributed to more falls and associated harm. The subsequent lockdown, which were less restrictive and occurred after staff had gained experience, was associated with no significant increase in falls rate. The nature and extent of lockdowns implemented for infection control in RAC need to balance multiple potential adverse effects. Factors which facilitated resilience during this period require exploration in future research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-024-11479-x.

Keywords

COVID-19
Residential aged care
Falls
Routinely collected data
Lockdown
National Health and Medical Research Council,AustraliaAPP2013953 APP1143941 APP1170898 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcIntroduction

The COVID-19 pandemic triggered a crisis in residential aged care facilities (RAC) (i.e., nursing homes and long-term facilities) internationally as RAC residents were more susceptible to the negative outcomes associated with COVID-19 and aged care services were often poorly positioned to cope with an increase in care needs [1–3]. Globally, aged care services have long faced systemic issues such as chronic under resourcing, limited governance, poor design, and infection control standards [4, 5]. The scale of the disaster in RAC is demonstrated in global COVID-19 mortality statistics. In 2021, it was estimated that COVID-19 related deaths in RAC accounted for 30% of the COVID-19 death toll in 38 Organisation for Economic Co-operation and Development nations even though RAC populations frequently make up less than 1% of the overall population in these countries [3].

The Australian experience of COVID-19 in RAC between 2020 and 2021 differed from the international experience due to comparatively low rates of COVID-19. Between 2020 and late 2021 Australia, a country with a population of over 25 million people including 180,000 living in RAC [6], reported fewer than 400,000 COVID-19 cases approximately 3,700 of these occurred in RAC facilities [7]. By comparison, in December 2021 regions of the United Kingdom (UK) estimated that 5% of the population had an active COVID-19 infection in a single week [8]. Australia was able to maintain low rates of COVID-19 in 2020–2021 largely due to strict public health measures which included: heavily restricted international and inter-state travel; work from home orders; mask mandates; high levels of testing and isolation requirements for COVID-19 close contracts and COVID-19 cases [9–11]. In Australian RAC facilities, key public health restrictive measures included facility lockdowns (i.e., limiting community access and visitors), routine COVID-19 testing of staff and residents, isolation requirement for COVID-19 cases and close contracts, and the use of personal protective equipment (PPE) for residents, staff, and visitors [9, 10].

While public health measures implemented in Australian RAC from 2020 to 2021 protected residents and the health system from COVID-19, they may have inadvertently harmed RAC residents by disrupting care routines and isolating residents from their social networks. During lockdowns, Australian RAC facilities paused allied health and lifestyle and leisure services, such as group exercise, music, and mental stimulation activities and community outings, sometimes for months at a time [12, 13]. Services which continued during Australian RAC lockdowns, such as support for activities of daily living (i.e., feeding and showering), were reportedly carried out with poorer quality (e.g., delayed care and limited flexibility, and choice for residents) due to staffing limitations [14, 15]. Residents also spent more time alone as family and friends were not permitted to enter facilities, communal activities were restricted, and other forms of social connection were limited [12, 16, 17]. While these measures were effective in reducing COVID-19 outbreaks, these public health measures are also believed to have negatively impacted the quality and quantity of care, and physical, and psychological health of residents in RAC facilities [16].

The rate of falls is a critical measure of care quality and resident harm in RAC settings that may have been impacted by the pandemic. Research conducted in community settings indicates that falls increased in older adults during waves of COVID-19 cases due to an increase in fall risk factors including loneliness and physical inactivity [18]. However, limited research has been conducted on falls in RAC settings. The association between public health restrictions and falls in RAC settings is critical to explore to understand the potential risks and benefits of implementing facility lockdowns in future infectious disease outbreaks. To fill this knowledge gap, we aimed to investigate rates of fall and their outcomes during COVID-19 lockdowns in Australian RAC facilities from 2020 to 2021 compared to pre-pandemic using routinely collected data.

Methods

Setting and design

We conducted this study using a longitudinal retrospective cohort analysis before and after COVID-19 lockdown periods in Sydney, Australia (March 2019 to March 2021) using electronic health record data of 25 RAC facilities. All facilities belonged to the same not-for-profit aged care provider and were in Sydney.

This study is reported according to the REporting of studies Conducting using Observational Routinely-collected health Data (RECORD) guidelines [19]. The study forms part of a broader research project which investigates the application of predictive analytics and decision support to prevent falls in aged care [20–22]. Ethics approval, including a waiver of participant consent for the use of routinely collected data, was received from Macquarie University Human Research Ethics Committee (Project ID: 6144).

Participants

Study inclusion criteria were limited to aged care residents who resided in the participating RAC facilities for > 30 days between March 2019 and March 2021. Respite residents were excluded from the analysis.

Data source

De-identified resident electronic health records were obtained from the aged care provider. Data sets included in this study were resident demographic data (e.g., gender, health conditions at admission, age, date of entry and exist into aged care) and incident report datasets containing information related to falls (e.g., time and date of fall, injurious/non-injurious fall, and need to attend hospital following the fall). Health conditions in this data set were recorded as free text, and we applied a health macro, developed by our research team, to categorise the health conditions [23]. Information on the dates of COVID-19 restrictions was sourced from national and state government websites [9, 10].

Outcome measures

The outcomes were all falls, falls resulting in injury, and falls requiring hospitalisation. All falls was defined as the occurrence of any fall, as entered in the resident’s incident report, regardless of whether they resulted in injury. Injurious falls were falls resulting in any injury as assessed and entered in the incident report by the responding RAC staff member. Injurious falls include minor injury (e.g., skin tears), and major injury, (e.g., hip fracture). Falls assessed as requiring hospitalisation were falls assessed as requiring hospitalisation by RAC staff. Falls requiring hospitalisation is a check box in the fall’s incident form at the RAC provider and not the actual number of people who attend hospital. All outcomes were expressed as counts in the dataset.

COVID-19 lockdown periods

A monthly categorical variable was generated to describe pre-COVID-19, COVID-19 lockdown periods, and periods of eased restriction during COVID-19 in Sydney, Australia (Fig. 1). The dates of COVID-19 lockdowns and periods of eased restrictions were derived from New South Wales (NSW) Health public health orders [9, 10].

Fig. 1 COVID-19 lockdowns in Sydney Australia

In the community in Sydney, Australia, during Lockdown 1 businesses such as gyms and restaurants closed, public events and gatherings were banned, businesses transitioned to work from home, and social distancing was implemented in all settings. During this time in RAC, staff wore PPE, group activities (e.g., group meals and social and exercise groups) were limited, residents spent more time in their rooms, and residents had restricted or no access to external visitors [12, 16]. By the end of Lockdown 1 in NSW Australia, there had been 61 COVID-19 cases in RAC settings [24].

Lockdown 2 was triggered by an outbreak of COVID-19 in the community in the Northern Beaches Local Government Area of greater Sydney. While none of the facilities included in this study were in the Northern Beaches, COVID-19 restrictions were tightened in all health facilities, including RAC facilities, across NSW. NSW public health orders during this time stated that RAC facilities must exclude all visits from family and friends, conduct routine COVID-19 testing on staff and residents, and restrict staff to work in only one health facility. RAC facilities however were encouraged to increase allied health staff ratios to facilitate social, mental, and physical stimulation [25]. No COVID-19 cases were recorded in NSW RAC facilities during Lockdown 2 [9, 10].

In between COVID-19 lockdown periods in 2020 and 2021 COVID-19 restrictions eased in the community and RAC facilities. However, some restrictions remained in place. For example, RAC facilities often continued to limit the number of external visitors, and while group activities resumed, they often were modified to enforce infection control policies such as social distancing and mask wearing [26]. Additionally, staff needed to comply with increased use of PPE, compared to pre-pandemic, and all staff, visitors, and residents needed to isolate for two weeks if they tested positive to COVID-19 or were in close contact with someone who had tested positive.

Statistical analysis

We used descriptive statistics such as medians, interquartile ranges (IQR), and counts to report cohort characteristics. We assessed the distribution of time-invariant covariates during COVID-19 lockdowns and pre-COVID-19 using chi-squared analysis for categorical variables and Kruskal-Wallis tests for continuous variables as continuous variables, such as age, are not normally distributed in this cohort. Multicollinearity between variables was assessed with a 0.80 correlation coefficient and at a 95% confidence level (P < 0.05). Unadjusted fall outcome descriptive statistics are presented either over the total study period or monthly, as falls incidence is too rare an event to present over smaller time periods (i.e., days or weeks). For analysis, all fall count data were converted to monthly rates per 1,000 residents to account for monthly fluctuations in cohort size.

We applied Generalised Estimating Equations (GEE) to model the association between COVID-19 lockdowns and each outcome measure (all falls, injurious falls, falls requiring hospitalisation) while adjusting for confounders. Several time-invariant and time-variant variables were considered in the analysis. The time-invariant variables included age, gender, medical conditions, and length of stay in residential aged care. Four binary COVID-19 indicator variables were created and entered as time-variant variables in the analysis. The GEE model was clustered by resident. We adjusted for seasonality using Fourier terms [27]. We applied robust standard errors to accommodate for the panel nature of the data and negative binomial log link function to handle the over-dispersed data. We used an unstructured working correlation matrix to allow for unconstrained correlations between measures. The GEE calculated incident rate ratio (IRR) and 95% confidence intervals (CI). Statistical significance was assessed at P < 0.05. We applied established model selection metrics, quasilikelihood under the independence model criterion (QIC), to select the best working correlation structure and subset of explanatory variables [28]. All analyses were conducted using Stata Version 18 (StataCorp LP, College Station, TX).

Results

The study sample included 2,996 residents over the two-year period. Table 1 presents cohort characteristics. Approximately three quarters of participants were female, and half had a history of falls and dementia. During the study period residents frequently experienced one fall (median: 1, IQR: 0–4). No statistical differences were observed in time-invariant demographics when comparing the cohort admitted during the pandemic compared to the cohort before the pandemic (Appendix 1).

Table 1 Cohort demographics (n = 2,996)*

Age, median (IQR)	86 (80–90)	
Female, n (%)	2030 (67.7%)	
Admitted prior to 2019, n (%)	1,969 (65.7%)	
Length of stay (days), median (IQR)	533 (244–761)	
Respite history with the provider prior to the study period, n (%)	666 (18.9%)	
Falls during study period, median (IQR)		
All falls	1 (0–4)	
Injurious falls	0 (0–2)	
Falls requiring hospitalisation	0 (0–1)	
Health status [ref = No disease], n (%)		
Falls history	1,623 (54.2%)	
Chronic pain	1,579 (52.7%)	
Dementia	1,578 (52.7%)	
Depression, mood & affective disorders	1,414 (47.2%)	
Cognitive impairment	1,291 (43.1%)	
Hypertension	1,128 (37.7%)	
Anxiety & stress-related disorders	1,040 (34.7%)	
Osteoporosis	848 (28.3%)	
Cerebrovascular accident	755 (25.2%)	
Visual impairment	577 (19.3%)	
Chronic respiratory condition	525 (17.5%)	
Delirium	363 (12.1%)	
Parkinson’s disease	177 (5.9% )	

Incidence of falls before and during the pandemic

Over two years the cohort experienced a total of 11,658 falls. The number of falls each month ranged from 401 (December 2020) to 589 (March 2020). The proportion of residents who experienced a fall each month during the study period ranged from 13.5% (April 2019) to 17.8% (May 2020) (Table 2).

Over the study period, 35.0% (n = 4,076) of all falls resulted in injury (injurious fall). The total number of injurious falls per month ranged from 135 (August 2018) to 206 (March 2020). The proportion of residents who experienced an injurious fall each month ranged from 5.9% (June 2019) to 8.5% (May 2020).

During the two-year period, 11.3% of all falls (n = 1,313) were assessed as requiring hospitalisation. The number of falls requiring hospitalisation per month ranged from 31 (June 2019) to 81 (March 2021). The proportion of residents who experienced falls requiring hospitalisation per month ranged from 1.7% (April 2019) to 3.9% (March 2021).

Table 2 Number of fall related incidents per month and number of residents who experienced the incident

COVID-19 period	Month	Residents (n)	All falls (n)	Falls resulting injury (n)	Fall assessed as requiring hospitalisation (n)	
Incidents	Residents	Incidents	Residents	Incidents	Residents	
Pre-COVID-19	2019m3	2,031	427	287	177	139	46	43	
2019m4	2,035	421	275	144	121	35	35	
2019m5	2,025	464	302	158	132	58	53	
2019m6	2,004	421	278	140	119	35	31	
2019m7	2,013	456	306	150	133	54	48	
2019m8	2,012	460	311	135	124	34	33	
2019m9	1,989	418	281	147	121	43	39	
2019m10	2,005	442	301	178	152	55	52	
2019m11	2,011	503	325	178	149	59	55	
2019m12	2,018	527	360	191	171	73	69	
2020m1	2,030	524	331	185	153	75	62	
2020m2	2,035	478	311	170	148	70	68	
Lockdown 1	2020m3	2,045	589	362	206	158	63	57	
2020m4	2,045	558	356	187	155	41	39	
2020m5	2,013	565	360	198	172	50	46	
2020m6	1,986	457	319	157	136	42	38	
Reduced restriction period 1	2020m7	1,949	547	326	177	145	52	49	
2020m8	1,958	456	299	139	126	39	39	
2020m9	1,941	411	278	144	127	52	51	
2020m10	1,913	479	321	161	132	49	46	
2020m11	1,898	429	291	142	121	49	43	
Lockdown 2	2020m12	1,893	401	280	144	129	58	56	
2021m1	1,876	402	281	160	139	50	46	
Reduced restriction period 2	2021m2	1,882	387	270	141	125	50	47	
2021m3	1,850	436	283	169	150	81	73	

The rate of fall-related outcomes during COVID-19

During Lockdown 1 there was a 32% increase in the rate of falls compared to pre-pandemic (IRR 1.32, 95% CI 1.19–1.46, p < 0.01) (Table 3). The increase in all falls was also reflected in the rate of injurious falls which increased by 28% compared to pre-pandemic (IRR 1.28, 95% CI 1.12–1.46, p < 0.01). Despite this, the rate of falls assessed as requiring hospitalisation did not significantly change by the first COVID-19 lockdown (IRR 1.07, 95% CI 0.86–1.32, p = 0.519).

The rate of all falls remained high following the end of Lockdown 1 compared to pre-pandemic (IRR 1.15, 95% CI 1.04–1.28, p < 0.01). The rate of injurious falls also remained slightly elevated. However, this was not statistically significant (IRR 1.12 0.98–1.27, p = 0.09). The rate of falls assessed as requiring hospitalisation remained unchanged following the end of the first lockdown (IRR 1.05, 95% CI 0.85–1.29, p = 0.68).

In Lockdown 2 the fall rate returned to pre-pandemic levels (IRR: 0.95, 95% CI 0.83–1.09, p = 0.44), including for falls resulting in injury (IRR: 0.95 95% CI 0.81–1.13, p = 0.58). This trend continued into the period following Lockdown 2. Following the end of the Lockdown 2, the rate of falls assessed as requiring hospitalisation increased by 32% compared to pre-pandemic (IRR 1.32 95% CI 1.02–1.70, p = 0.03).

Table 3 GEE results for all falls, injurious falls, and falls assessed as requiring hospitalisation for COVID-19 indicator variables (n = 2,996).A

	IRR	95% CI	P	
Lower	Upper	
All falls [ref: pre-pandemic]	
Lockdown 1	1.32	1.19	1.46	0.000*	
Reduced restriction period 1	1.15	1.04	1.28	0.006*	
Lockdown 2	0.95	0.83	1.09	0.435	
Reduced restriction period 2	1.10	0.97	1.25	0.131	
Injurious falls [ref: pre-pandemic]	
Lockdown 1	1.28	1.12	1.46	0.000*	
Reduced restriction period 1	1.12	0.98	1.27	0.086	
Lockdown 2	0.95	0.81	1.13	0.574	
Reduced restriction period 2	1.08	0.91	1.27	0.389	
Falls assessed as requiring hospitalisation [ref: pre-pandemic]	
Lockdown 1	1.07	0.86	1.32	0.547	
Reduced restriction period 1	1.05	0.85	1.29	0.676	
Lockdown 2	0.81	0.61	1.08	0.146	
Reduced restriction period 2	1.32	1.02	1.70	0.033*	
amodels are adjusted for gender, age at first admission, Parkinson’s disease, dementia, depression, anxiety, visual impairment, falls history, length of stay, and seasonality. The full GEE model results can be viewed in Appendix 2

Discussion

Lockdown 1 was associated with 32% increase in the rate of all falls and a 28% increase in injurious falls compared to the 12 months prior to COVID-19. Despite the significant increase in falls, there was not increase in the rate of residents assessed as requiring hospitalisation during Lockdown 1 The rate of all falls, injurious falls, and falls assessed as requiring hospitalisation remained unchanged compared to pre-pandemic rates in Lockdown 2. However, interestingly, after Lockdown 2 there was a 32% increase in falls assessed as requiring hospitalisation without an increase in the rate of all falls.

International community-based research has attributed the increase in falls during waves of COVID-19 to physical deconditioning caused by either lower limb weakness post COVID-19 infection or physical inactivity due to lockdowns or self-imposed isolation [18, 29, 30]. Physical deconditioning may have also played a role in the increase in all falls observed in our RAC cohort. A Canadian study of almost 200,000 older adults in RAC found that residents were 20% more likely to experience functional decline during the first lockdown of the pandemic compared to pre-pandemic [31]. However, in our RAC cohort, the factors causing physical deconditioning are likely to vary from the community-setting and some of the experiences of residents in RAC in other countries. First, the incidence of COVID-19 infection was low in our cohort and therefore side effects from COVID-19 infection are not likely to have significantly impacted the overall rate of falls. Second, RAC residents may have limited ability to self-impose an increase or decrease in physical activity as they often require assistance to facilitate or participate in activity. Instead, in RAC settings, residents may have physically deconditioned as they received less opportunity to participate in physical activity as routine exercise classes and falls prevention activities performed by allied health professionals were paused and because nursing staff had less time to assist with mobility [12, 13]. An analysis of over 8,000 RAC facilities in the UK found that staff shortages during the pandemic were associated with greater loss of mobility among residents compared to those who in facilities which maintained pre-pandemic staffing levels [32]. Finally, in RAC settings, compared to the community, there are likely to be a number of factors, beyond physical deconditioning, that may have contributed to an increase in falls during Lockdown 1. Factors such as disrupted routines and separation from friends and family during the pandemic increased RAC resident agitation and delirium, particularly for residents with dementia, and may have heightened the risk of falls [33]. Additionally, prescribing patterns of fall-risk increasing drugs such as psychotropics and opioids prescribing increased during lockdowns in healthcare settings, if the prescribing patterns also changed in RAC facilities this may have further increased the risk of falls [34, 35].

In our analysis, the rate of falls assessed as requiring hospitalisation did not increase during Lockdown 1 despite an overall increase in all falls and injurious falls during the same period. Interestingly, after Lockdown 2 the rate of falls assessed as requiring hospitalisation increased 32% compared to pre-pandemic despite no increase in the rate of all falls. Using routinely collected data, other Australia researchers have noted a reduction in hospital transfers from Australian RAC facilities during COVID-19 outbreaks. The researchers suggested that RAC facility imposed hospital-in-the-home policies, established during the initial lockdowns, for COVID-19 positive cases may have influenced staff members assessment to refer the resident to hospital for all other conditions [36]. International evidence also suggests that staff hesitation to recommend transfer to hospital may also reflect resident preferences. A study from the United States reported that almost half residents updated their advanced care plan during the pandemic to avoid hospitalisation [37]. Irrespective of the factors driving lower rates of falls assessed as requiring hospitalisation, the reduction is potentially concerning as residents may not have received necessary medical care post-fall. Further qualitative and quantitative research, such as analysis of linked hospital data and staff and resident interviews, is required to understand how COVID-19 lockdowns impacted resident transfer to hospital and health outcomes. This research is important to understand how to manage and monitor hospital transfers in future facility lockdowns and infectious disease outbreaks.

In our analysis, COVID-19 lockdowns were not associated with consistent changes in the rate of all falls in RAC. Falls increased during Lockdown 1 but not in Lockdown 2, a finding that was also observed among older people in the community [38]. As other authors have suggested, the variability in the relationship between falls and COVID-19 restrictions may be attributed to shifts in policies, behaviours, and perceptions of COVID-19. In our cohort, Lockdown 2 may not have impacted falls because the lockdown was only implemented for a short period of time and staff and residents were acclimatised to infection control practices. Additionally, public health orders in Lockdown 2 placed an emphasis on continuing care as normal in RAC facilities, including the rostering of additional lifestyle and allied health staff [25]. The inconsistency in the relationship between lockdowns and the rate of falls suggests that not all lockdowns have a negative impact on resident health outcomes. In the future more measured approaches such as short lockdown periods, visitor screening, and support to continue routine activities with heightened infection control standards may balance the impact of the disease outbreaks and the negative impacts of service disruption and isolation.

The strengths of our study include its longitudinal design, use of person level data, sample size, and division of COVID-19 into significant periods rather than a pre-post cross-sectional study. However, some weaknesses exist. Our study was not able to gather linked hospital data, therefore in this analysis falls are only assessed as requiring hospitalisation and the exact rate at which residents attended hospital is unknown. Additionally, person level data such as past medical history is only entered at admission into RAC and is not updated throughout the stay. Therefore, past medical history entered in the analysis is time-invariant and likely underestimates the true number of people with any of the medical conditions included in our analyses. The analysis also relies on incident reports entered by aged care staff and therefore it may misrepresent the true number of falls occurring or be influenced by trends in reporting practices. Lastly, we were not able to collect and analyse data on contextual factors, such as staffing levels, that may have affected the fall rates. Australian RAC facilities had limited mandatory data collection standards during the study period. As a result, we can only speculate, drawing on available qualitative studies, about other factors could have influenced the rate of falls in our cohort. RAC data collection is improving under new mandates and more detailed modelling could be possible in the future.

Conclusion

Our analysis revealed distinct patterns in falls within RAC settings during COVID-19 lockdowns. During Lockdown 1 all falls increased 32% and injurious falls increased 28% compared to pre-pandemic, however, falls assessed as requiring hospitalisation did not change. During Lockdown 2 fall rates were not significantly different from pre-pandemic rates. The patterns in this analysis indicate the need for further research into, and careful consideration of specific areas of success and concern. Namely, how the extent and nature of lockdowns can be implemented with minimal impact on other resident outcomes in future infectious disease outbreaks.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

None.

Author contributions

IM designed the study, conducted the analysis, and drafted the manuscript. NW, JW, MR, and KS oversaw study design, analysis, and provided feedback and helped to develop the final manuscript. JW established funding and the relationship with the aged care provider to facilitate data sharing.

Funding

This work was funded by an Australian National Health and Medical Research Council (NHMRC) Postgraduate Scholarship (APP2013953) awarded to IM and was further supported by and NHMRC Partnership Grant (APP1170898) awarded to JW. MZR is supported by a NHMRC Early Career Fellowship (APP1143941).

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to patient privacy but are available from the corresponding author on reasonable request.

Declarations

Ethics approval

Ethics approval was granted by Macquarie University Medicine and Health Sciences Human Research Ethics Subcommittee (ID: 6144). The Macquarie University Medicine and Health Sciences Human Research Ethics Subcommittee approved a waiver of participants consent for the use of routinely collected healthcare data in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Abbreviations

CI Confidence interval

GEE Generalised Estimating Equations

IQR Internal quartile range

IRR Incident rate ratio

NSW New South Wales

PPE Personal protective equipment

QIC Quasilikelihood under the independence model criterion

RAC Residential aged care

RECORD REporting of studies Conducting using Observational Routinely-collected health Data

UK United Kingdom

US United States

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Booth A Reed AB Ponzo S Yassaee A Aral M Plans D Labrique A Mohan D Population risk factors for severe disease and mortality in COVID-19: a global systematic review and meta-analysis PLoS ONE 2021 16 3 e0247461 10.1371/journal.pone.0247461 33661992
Booth A, Reed AB, Ponzo S, Yassaee A, Aral M, Plans D, Labrique A, Mohan D. Population risk factors for severe disease and mortality in COVID-19: a global systematic review and meta-analysis. PLoS ONE. 2021;16(3):e0247461.33661992 10.1371/journal.pone.0247461
2. Dessie ZG Zewotir T Mortality-related risk factors of COVID-19: a systematic review and meta-analysis of 42 studies and 423,117 patients BMC Infect Dis 2021 21 1 855 10.1186/s12879-021-06536-3 34418980
Dessie ZG, Zewotir T. Mortality-related risk factors of COVID-19: a systematic review and meta-analysis of 42 studies and 423,117 patients. BMC Infect Dis. 2021;21(1):855.34418980 10.1186/s12879-021-06536-3
3. Rocard E, Sillitti P, Llena-Nozal A. COVID-19 in long-term care. OECD Health Working Papers 2021, No. 131.
4. Organisation for Economic Co-operation and Development Health at a glance 2021 2021 Switzerland Geneva
Organisation for Economic Co-operation and Development. Health at a glance 2021. Switzerland: Geneva; 2021.
5. Gilbert GL COVID-19 in a Sydney nursing home: a case study and lessons learnt Med J Aust 2020 213 9 393 e396391 10.5694/mja2.50817 33037646
Gilbert GL. COVID-19 in a Sydney nursing home: a case study and lessons learnt. Med J Aust. 2020;213(9):393–e396391.33037646 10.5694/mja2.50817
6. Australian Institute of Health Welfare Older australians 2023 Canberra, Australia AIHW
Australian Institute of Health Welfare. Older australians. Canberra, Australia: AIHW; 2023.
7. Department of Health: COVID-19 at a glance. 2021. [https://www.health.gov.au/sites/default/files/documents/2022/01/coronavirus-covid-19-at-a-glance-31-december-2021.pdf]
8. Welsh Government: Coronavirus (COVID-19) infection survey (positivity estimates): 25 to 31 December 2021. 2022. [https://www.gov.wales/coronavirus-covid-19-infection-survey-positivity-estimates-25-31-december-2021-html]
9. New South Wales Health. : 2020 media releases from NSW Health. 2020. [https://www.health.nsw.gov.au/news/Pages/2020-nsw-health.aspx]
10. New South Wales Health. : 2021 media releases from NSW Health. 2021. [https://www.health.nsw.gov.au/news/Pages/2021-nsw-health.aspx]
11. Braithwaite J Tran Y Ellis LA Westbrook J The 40 health systems, COVID-19 (40HS, C-19) study Int J Qual Health Care 2021 33 1 mzaa113 10.1093/intqhc/mzaa113 32991713
Braithwaite J, Tran Y, Ellis LA, Westbrook J. The 40 health systems, COVID-19 (40HS, C-19) study. Int J Qual Health Care. 2021;33(1):mzaa113.32991713 10.1093/intqhc/mzaa113
12. Meulenbroeks I, Seaman K, Raban MZ, Wabe N, Westbrook J. Just visiting: a qualitative study of Australian allied health professionals’ experiences working in residential aged care facilities during the COVID-19 pandemic. Australas J Ageing 2023.
13. Meulenbroeks I Raban MZ Seaman K Wabe N Westbrook J Allied health service delivery in residential aged care and the impact of the coronavirus (COVID-19) pandemic: a survey of the allied health workforce Aust Health Rev 2023 47 6 729 34 10.1071/AH23093 37758280
Meulenbroeks I, Raban MZ, Seaman K, Wabe N, Westbrook J. Allied health service delivery in residential aged care and the impact of the coronavirus (COVID-19) pandemic: a survey of the allied health workforce. Aust Health Rev. 2023;47(6):729–34.37758280 10.1071/AH23093
14. Thomas S, Bolsewicz K, Latta R, Hewitt J, Byles J, Durrheim D. The Impact of Public Health Restrictions in residential aged care on residents, families, and Staff during COVID-19: getting the Balance Right. J Aging Soc Policy:1–20.
15. Murray CM Milanese S Guerin M Bilton R Baldock KL Parfitt G Exploring what matters to residents of Australian aged care facilities with the Happy Life Index: comparison of qualitative responses between pre- and mid-COVID-19 pandemic time points Qual Life Res 2023 32 8 2247 57 10.1007/s11136-023-03387-0 36928652
Murray CM, Milanese S, Guerin M, Bilton R, Baldock KL, Parfitt G. Exploring what matters to residents of Australian aged care facilities with the Happy Life Index: comparison of qualitative responses between pre- and mid-COVID-19 pandemic time points. Qual Life Res. 2023;32(8):2247–57.36928652 10.1007/s11136-023-03387-0
16. Royal Commission into Aged Care Quality and Safety. Aged care and COVID-19: a speacil report. 2020.
17. Human Rights Watch. Submission by Human Rights Watch to the Royal Commission into Aged Care Quality and Safety on the impact of the coronavirus on the aged care sector. 2020. [https://www.hrw.org/sites/default/files/media_2020/08/200731 HRW Submission to Aged%20Care%20Royal%20Commission%20on%20Impact%20of%20Covid-19_2.pdf].
18. Hoffman GJ Malani PN Solway E Kirch M Singer DC Kullgren JT Changes in activity levels, physical functioning, and fall risk during the COVID-19 pandemic J Am Geriatr Soc 2022 70 1 49 59 10.1111/jgs.17477 34536288
Hoffman GJ, Malani PN, Solway E, Kirch M, Singer DC, Kullgren JT. Changes in activity levels, physical functioning, and fall risk during the COVID-19 pandemic. J Am Geriatr Soc. 2022;70(1):49–59.34536288 10.1111/jgs.17477
19. Benchimol EI Smeeth L Guttmann A Harron K Moher D Peteresen I Sørensen HT von Elm E Langan SM The REporting of studies conducted using Observational routinely-collected health data (RECORD) statement PLoS Med 2015 12 10 e1001885 1001885 10.1371/journal.pmed.1001885 26440803
Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Peteresen I, Sørensen HT, von Elm E, Langan SM. The REporting of studies conducted using Observational routinely-collected health data (RECORD) statement. PLoS Med. 2015;12(10):e1001885–1001885.26440803 10.1371/journal.pmed.1001885
20. Wabe N Siette J Seaman KL Nguyen AD Raban MZ Close JCT Lord SR Westbrook JI The use and predictive performance of the Peninsula Health Falls Risk Assessment Tool (PH-FRAT) in 25 residential aged care facilities: a retrospective cohort study using routinely collected data BMC Geriatr 2022 22 1 271 10.1186/s12877-022-02973-0 35365078
Wabe N, Siette J, Seaman KL, Nguyen AD, Raban MZ, Close JCT, Lord SR, Westbrook JI. The use and predictive performance of the Peninsula Health Falls Risk Assessment Tool (PH-FRAT) in 25 residential aged care facilities: a retrospective cohort study using routinely collected data. BMC Geriatr. 2022;22(1):271.35365078 10.1186/s12877-022-02973-0
21. Wabe N, Seaman KL, Nguyen AD, Siette J, Raban MZ, Hibbert P, Close JCT, Lord SR, Westbrook JI. Epidemiology of falls in 25 Australian residential aged care facilities: a retrospective longitudinal cohort study using routinely collected data. J Qual Health Care 2022, 34(3).
22. Huang G Wabe N Raban MZ Seaman KL Silva SM Westbrook JI The relationship between fall incidents and place of birth in residential aged care facilities: a retrospective longitudinal cohort study BMC Geriatr 2023 23 1 257 10.1186/s12877-023-03954-7 37118675
Huang G, Wabe N, Raban MZ, Seaman KL, Silva SM, Westbrook JI. The relationship between fall incidents and place of birth in residential aged care facilities: a retrospective longitudinal cohort study. BMC Geriatr. 2023;23(1):257.37118675 10.1186/s12877-023-03954-7
23. Lind KE Raban MZ Brett L Jorgensen ML Georgiou A Westbrook JI Measuring the prevalence of 60 health conditions in older australians in residential aged care with electronic health records: a retrospective dynamic cohort study Popul Health Metrics 2020 18 1 25 25 10.1186/s12963-020-00234-z
Lind KE, Raban MZ, Brett L, Jorgensen ML, Georgiou A, Westbrook JI. Measuring the prevalence of 60 health conditions in older australians in residential aged care with electronic health records: a retrospective dynamic cohort study. Popul Health Metrics. 2020;18(1):25–25.10.1186/s12963-020-00234-z
24. Department of Health: COVID-19 statistics. 2020. [https://www.health.gov.au/sites/default/files/documents/2020/08/coronavirus-covid-19-at-a-glance-17-august-2020.pdf].
25. Department of Health and Aged Care. Update advice for NSW aged care providers. 2020. [https://www.health.gov.au/news/announcements/update-advice-for-nsw-aged-care-providers-0]
26. Aged Care Quality and Safety Commission Aged Care Quality and Safety Commission Residential care visitor access survey report 2021 Canberra, Australia Australian Government
Aged Care Quality and Safety Commission. Aged Care Quality and Safety Commission Residential care visitor access survey report. Canberra, Australia: Australian Government; 2021.
27. Stolwijk AM Straatman H Zielhuis GA Studying seasonality by using sine and cosine functions in regression analysis J Epidemiol Community Health 1999 53 4 235 10.1136/jech.53.4.235 10396550
Stolwijk AM, Straatman H, Zielhuis GA. Studying seasonality by using sine and cosine functions in regression analysis. J Epidemiol Community Health. 1999;53(4):235.10396550 10.1136/jech.53.4.235
28. Cui J QIC program and model selection in GEE analyses Stata J 2007 7 2 209 20 10.1177/1536867X0700700205
Cui J. QIC program and model selection in GEE analyses. Stata J. 2007;7(2):209–20.10.1177/1536867X0700700205
29. Anezaki S, Sakka M, Yamamoto-Mitani N. Association between prevention from going out and incidence of falls among community-dwelling older adults during COVID-19 pandemic. Int J Environ Res Public Health 2023, 20(3).
30. Nguyen HT Nguyen CC Le Hoang T Falls among older adults during the COVID-19 pandemic: a multicenter cross-sectional study in Vietnam Clin Interv Aging 2022 17 1393 404 10.2147/CIA.S382649 36172531
Nguyen HT, Nguyen CC, Le Hoang T. Falls among older adults during the COVID-19 pandemic: a multicenter cross-sectional study in Vietnam. Clin Interv Aging. 2022;17:1393–404.36172531 10.2147/CIA.S382649
31. Egbujie BA, Turcotte LA, Heckman GA, Morris JN, Hirdes JP. Functional decline in long-term care homes in the first wave of the COVID-19 pandemic: a population-based longitudinal study in five Canadian provinces. J Am Med Dir Assoc 2023.
32. Chen M Goodwin JS Bailey JE Bowblis JR Li S Xu H Longitudinal associations of staff shortages and staff levels with health outcomes in nursing homes J Am Med Dir Assoc 2023 24 11 1755 e17601757 10.1016/j.jamda.2023.04.017 37263319
Chen M, Goodwin JS, Bailey JE, Bowblis JR, Li S, Xu H. Longitudinal associations of staff shortages and staff levels with health outcomes in nursing homes. J Am Med Dir Assoc. 2023;24(11):1755–e17601757.37263319 10.1016/j.jamda.2023.04.017
33. Battams S Martini A The impact of the COVID-19 pandemic on people with cognitive impairment residing in aged care facilities: an integrative review Inquiry 2023 60 00469580231160898 37278270
Battams S, Martini A. The impact of the COVID-19 pandemic on people with cognitive impairment residing in aged care facilities: an integrative review. Inquiry. 2023;60:00469580231160898.37278270
34. Ferro Uriguen A Laso Lucas E Sannino Menicucci C Iturrioz Arrechea I Alaba Trueba J Echevarría Orella E Gil Goikouria J Beobide Telleria I: psychotropic drug prescription in nursing homes during the COVID-19 pandemic Drugs Aging 2022 39 6 467 75 10.1007/s40266-022-00948-9 35726042
Ferro Uriguen A, Laso Lucas E, Sannino Menicucci C, Iturrioz Arrechea I, Alaba Trueba J, Echevarría Orella E, Gil Goikouria J. Beobide Telleria I: psychotropic drug prescription in nursing homes during the COVID-19 pandemic. Drugs Aging. 2022;39(6):467–75.35726042 10.1007/s40266-022-00948-9
35. Dai Z Raban MZ Sezgin G McGuire P Datta S Wabe N Pearce C Woodman R Georgiou A Opioid prescribing among aged care residents during the first year of the COVID-19 pandemic: an analysis using general practice health records in Australia BMC Geriatr 2023 23 1 111 10.1186/s12877-023-03821-5 36829128
Dai Z, Raban MZ, Sezgin G, McGuire P, Datta S, Wabe N, Pearce C, Woodman R, Georgiou A. Opioid prescribing among aged care residents during the first year of the COVID-19 pandemic: an analysis using general practice health records in Australia. BMC Geriatr. 2023;23(1):111.36829128 10.1186/s12877-023-03821-5
36. Nair SP Quigley AL Moa A Chughtai AA Macintyre CR Monitoring the burden of COVID-19 and impact of hospital transfer policies on Australian aged-care residents in residential aged-care facilities in 2020 BMC Geriatr 2023 23 1 507 10.1186/s12877-023-04154-z 37608356
Nair SP, Quigley AL, Moa A, Chughtai AA, Macintyre CR. Monitoring the burden of COVID-19 and impact of hospital transfer policies on Australian aged-care residents in residential aged-care facilities in 2020. BMC Geriatr. 2023;23(1):507.37608356 10.1186/s12877-023-04154-z
37. Ye P Fry L Champion JD Changes in advance care planning for nursing home residents during the COVID-19 pandemic J Am Med Dir Assoc 2021 22 1 209 14 10.1016/j.jamda.2020.11.011 33290730
Ye P, Fry L, Champion JD. Changes in advance care planning for nursing home residents during the COVID-19 pandemic. J Am Med Dir Assoc. 2021;22(1):209–14.33290730 10.1016/j.jamda.2020.11.011
38. Bowling CB Wei RX Qian L Shimbo D Schwartz JE Muntner P Cannavale KL Harrison TN Reynolds K Changes in fall rates from before to during the COVID-19 pandemic: findings from the prospective AMBROSIA study J Gerontol Biol Sci Med Sci 2023 78 4 624 9 10.1093/gerona/glac131
Bowling CB, Wei RX, Qian L, Shimbo D, Schwartz JE, Muntner P, Cannavale KL, Harrison TN, Reynolds K. Changes in fall rates from before to during the COVID-19 pandemic: findings from the prospective AMBROSIA study. J Gerontol Biol Sci Med Sci. 2023;78(4):624–9.10.1093/gerona/glac131
