Phase V .Apa Seven

1

Phase 4- Outcomes

Falls in Long Care Term Settings

Nayaris Reyes

Florida National University

Nursing Research

Professor Dr. Barry E. Graham, DNP, MSN-Ed, RN

July 17, 2021

2

The issue of elderly falls has remained to be one of the major healthcare concerns that

affect the effective delivery of quality healthcare services in long-term care settings. Even though

there is the existence of such as intentional hourly rounding, the implementation process has

become a major issue thus leading to the increase in the reported case of elderly falls.

Communication other factors have been considered to be the contributory factors to the rise in the

reported cases of falls among patients. Other factors that have been considered to be the

predisposing factors in the high rate of falls are unsteady balance and gait, poor vision, weaker

muscles, dementia, and medications (Cameron et al., 2018). Additionally, patients with conditions

such as stroke, hypertension or high blood pressure, brain disorders, and poor management of

epilepsy increase the risk of falls among elderly patients.

Studies have been performed to help in the assessment on the effectiveness of the

multifactorial interventions in reducing the reported cases of fall rates in the long-term care

facilities for example the psycho-geriatric homecare that offers healthcare services to the frail

patients and those whose cognition have been affected as a result of their ages. These studies have

confirmed the existence of various interventions that can be adopted to help in addressing these

issues (Cameron et al., 2018). Some of them involve the general medical evaluation that focused

on falls, the use of specific risk assessment devices, assessment of the medication intake, assessing

the health history of the patients, assessment of their mobility, and the use of the protective or

assistive aids among others.

For this study, a random sampling was adopted to help in the investigation of falls amongst

elderly patients in long-term care institutions. This is aimed at establishing the gold standards to

help in the development of effective interventions that can be used to assist nurses and other

healthcare professionals in the reduction of the elderly falls within long-term care facilities. The

3

targeted population in the study were the elderly persons within the long-term care setting using

factors such as reach, timeliness, and adherence to the program targeted at reducing the fall rates

(Brewer et al., 2018).

According to the outcome of this study, the telephone fall risk assessment and the

management by the care managers are feasible and helping in the reduction of the medically

attended falls. However, the number of the medically treated falls in this study is lower compared

to the national data that reveal the annual incidence of 57.3 for 1000 falls that occur in the

emergency department among elderly patients. These differences can be associated with the

variations in the age distributions of the sample compared to the general population and the rate

whereby the rate of falls increases with age (Cameron et al., 2018).

The model that can be used to translate the evidence of fall prevention into practice can be

developed based on this outcome. For example, the fall clinics, the education outreach to the

healthcare providers, within a defined geographic area, and supportive information technology.

The success of these programs requires a care plan that considers patient input. It is also important

to advise on several risk factors, selecting the risk factor to be addressed, and the development of

the research evidence that considers the needs and preferences of the individuals (Radecki et al.,

2018).

The study also involved the assessment of the long-term care patients using the Assessment

Instrument-Home Care (RAI-HC) model. The validity of this fall algorithm was to be tested and

verified since it is required to incorporate assistive tools, age, and unsteady gait, pain, cognition,

and incontinence to different categories from low to high risk. Based on the outcome of the study,

RAI-HC failed to show the expected significant positive impacts on the outcome of the quality.

The comparison of the controlled group to the treatment groups reveals that the clients in the

4

control group are evolving more favourably as compared to the participants in the treatment group

even though such differences are not statistically significant. The clients in the treatment groups are

less likely to undergo admission into the facility or to be moved to the nursing home based on the

logistic multilevel regression. This difference is considered not to be statistically significant

(Cameron et al., 2018).

The number of falls was categorized as none (non-fallers), one or single faller, to or more

falls (repeat falls) during the 6 months. The fall rates were calculated as falls per individual per

month of the data collected. The rate of falls was computed for the individuals who returned four

diaries and made the completion of the questionnaire for specific time intervals and were presented

as unadjusted estimations of the rate ratios. The sample size of the participants was 800

Number of falls on equivalent questionnaires

0 380

1 70

2-4 50

≥5 100

Total 600

Table 1: The number of fall rates recorded for the participants during the study period.

Out of the total participants selected for the study (800), a total of 600 falls were recorded,

an indication there were high fall rates among the individuals for the duration of the study. This is

corresponding to the evidence from the literature studies that confirms the impacts of risk factors

such as high blood pressure among others on the fall rates of the elderly patients within the facility.

Concerning the number of falls based on the aspect of the Mini-mind Score Examination

(MMSE) and clinical dementia diagnostic, falls were common among the frail individuals in the

Long-Term Care facility (LTCF) in this study. There was 54.7 percent of the resident having fallen

not less than once before 6 months. These falls were linked to cognitive impairment, the male

5

gender, visual impairment, and the utilization of some medications. The inappropriate medication

and the use of the SSRI or the SNRI classes of medication were linked to the rise in the risk of

falls. The benzodiazepine was associated with the reduction in the risk of falls.

The study also reveals that visual impairment and cognition are associated with a high risk

of failure. This study confirms that visual impairment is increasing the risk of falls, nevertheless, it

does not reveal the intensity or even the type of the visual impairment since the study only

depended on the generalization of the visual impairment. The study also supports the role played

by dementia as a fall risk factor in LTCF. This is an indication that there is a need to have an

occasional screening process to help in the development of interventions aimed at reducing the risk

of falls among residents with dementia.

Factors Β value with 95 percent confidence interval

P value

Male -0.60 0.008

Dementia 0.80 0.006

Increase in frailty -1.16 0.38

Benzodiazepine -0.45 0.058

SSRI 0.42 0.084

Table 2: the relationship between the individual variables with falls (univariate analyses)

Based on the table above, it is clear that there were more overall rates of falls among the

residents diagnosed with dementia condition as compared to other categories at 59.62 percent with

a β value of 0.80 and p-value of 0.06. This is an indication that in a group of 10 residents, the

dementia patient will experience 8 falls as compared to 10 residents who are not diagnosed with

dementia. The results from the table also reveal that the increase in frailty was not significantly

associated with the rate of falls i.e. a p-value of 0.39. It indicates that it never significantly affected

the rate of falls as compared to other factors (Al Danaf et al., 2018).

6

Even though the issue of frailty is not statistically significant in this study, the trends with

this problem in the study were of much interest. The study finding reveals that there were moderate

and severe frail-related falls among the residents. This finding was also common among the

participants who were using benzodiazepine. Moderate and severe frailty are the most vulnerable

group of individuals experiencing falls due to their active movement and high rate of dependence

in most of their activities of daily living (ADLs) (Griffin et al., 2019). The severely frail and

terminally ill individuals in the residential homes spend most of their time in bed and are less likely

to fall as compared to the mild frailty who are presumably having a higher level of independence

with movement while in the facility.

Multivariate analyses were also performed using the linear regression model to study the

number of falls adjusted for medication count, dementia diagnosis, the potentially inappropriate

use of the medication, the visual impairment, benzodiazepine, SSRI, or the SNRI use, sex, and age

as variables. Based on the data presented in figure 1 below, these variables were associated with

the increase in the risk of falls (Tolentino et al., 2021). The outcomes are consistent with the

findings presented in the univariate analysis.

7

Figure 1: the individual variables concerning the risks of falls. The figure reveals linear regression

coefficients with a total of 95 percent confidence interval in a complete adjusted multivariate

regression model

8

References

Al Danaf, J., Chang, B. H., Shear, M., Johnson, K. M., Miller, S., Nester, L., . . . Amy, W. W.

(2018). Surfacing and addressing hospitalized patients’ needs: Proactive nurse rounding as

a tool. Journal of nursing management, 26(5), 540-547. https://doi.org/10.1111/jonm.12580

Brewer, B. B., Carley, K. M., Benham-Hutchins, M., Effken, J. A., & Reminga, J. (2018). Nursing

unit design, nursing staff communication networks, and patient falls: Are they related?.

HERD: Health Environments Research & Design Journal, 11(4), 82-94.

https://doi.org/10.1177/1937586718779223

Griffin, J., Lall, R., Bruce, J., Withers, E., Finnegan, S., Lamb, S. E., ... & Adjei, H. (2019).

Comparison of alternative falls data collection methods in the Prevention of Falls Injury

Trial (PreFIT). Journal of clinical epidemiology, 106, 32-40.

https://doi.org/10.1016/j.jclinepi.2018.09.006

Cameron, E. J., Bowles, S. K., Marshall, E. G., & Andrew, M. K. (2018). Falls and long-term care:

a report from the care by design observational cohort study. BMC Family Practice, 19.

https://doi.org/10.1186/s12875-018-0741-6

Panneman, M. J., Sterke, S. C., Eilering, M. J., Blatter, B. M., Polinder, S., & Van Beeck, E. F.

(2021). Costs and benefits of multifactorial fall prevention in nursing homes in the

Netherlands. Experimental gerontology, 143, 111173.

Radecki, B., Reynolds, S., & Kara, A. (2018). Inpatient fall prevention from the patient’s

perspective: a qualitative study. Applied Nursing Research, 43, 114-119.

https://doi.org/10.1016/j.apnr.2018.08.001

Tolentino, A., Amaral, S., Souza, L. S., Zeballos, D., & Brites, C. (2021). Frequency of falls and

associated risk factors in people living with HIV: a systematic review with meta-analysis

9

and meta-regression. JAIDS Journal of Acquired Immune Deficiency Syndromes, 86(5),

616-625. doi: 10.1097/QAI.0000000000002600