Factors Associated with Falls in Canadian Long Term Care Homes: a Retrospective Cohort Study

Background

Half of Canadians living in long-term care (LTC) homes will fall each year resulting in consequences to independence, quality of life, and health. The objective in this study was to analyze factors that contribute to, or are protective against, falls in Canadian LTC homes.

Methods

We analyzed of a retrospective cohort of a stratified random sample of Canadian LTC homes in Western Canada from 2011-2017. We accessed variables from the RAI-MDS 2.0 to assess the association of the dependent variable "fall within the last 31-180 days" with multiple independent factors, using generalized estimating equation models.

Results

A total of 28,878 LTC residents were analyzed. Factors found to increase the odds of falling were other fractures (OR 3.64 [95% confidence interval; CI 3.27, 4.05]), hip fractures (OR 3.58 [3.27, 3.93]), moderately impaired cognitive skills (OR 2.45 [2.28, 2.64]), partial support to balance standing (OR 2.44 [2.30, 2.57]), wandering (OR 2.31 [2.18, 2.44]).

Conclusion

A range of factors identified were associated with falls for people living in LTC homes. Individual physical ability represented the largest group of independent factors contributing to falls. Residents who experience any fracture or an acute change in behaviour, mobility, or activities of daily living (ADL) should be considered at increased risk of falls.

Fall risks and the related factors for the homebound older people with dementia: Evidence from East China

Purpose

Falls are a major public health problem, especially for older people. This research aimed to provide a direct illustration of fall risks among the homebound older people with dementia in China, and to identify the risk factors associated with it.

Methods

In 2020, a questionnaire-based field survey was used to assess 1,042 people aged over 60 years in Ningbo, Eastern China. The Morse Fall Risk Scale's result was employed as the dependent variable, while the basic health problems, living environment difficulties, social support problems, and behavioral awareness issues were utilized as the independent variables; subsequently, chi-squared tests and four multivariate ordinarily ordered logistic regression models were performed.

Results

Overall, nine hundred and thirty-one older people with dementia were included in this study (the effective rate was 89.34%), with the majority of them having severe dementia (27.9%). Furthermore, 16.2% had fallen in the past 3 months, and 16.8% were at a high risk of falling. The risk factors for the older people's cognitive function included 80-90 years old, vascular dementia, marital status, and history of falls (P < 0.05); the kinds of chronic diseases, the activities of daily living, living environment, caregiver burden, caregiver knowledge, the Cohen Mansfield Agitation Inventory results, and the Clinical Dementia Rating were the protective factors for the risk of falls in them (P < 0.05).

Conclusion

The risk of falling of the Chinese homebound older people with dementia was high. Their caregivers, such as relatives, need to pay attention to these risk factors and perform appropriate measures to prevent falls.

The association between fear of falling and occurrence of falls: a one-year cohort study

BACKGROUND

Both multiple fall experiences and fear of falling (FoF) would make people susceptible to another fall; however, the associations are unknown. This study investigates the association of FoF with fall occurrence among older adults according to their fall history.

METHODS

In this study, we adopted a longitudinal observational design. We visited 20 community centers to recruit 1,025 older adults (aged 65 years or older). At baseline, FoF was assessed using a single-item questionnaire. The number of falls in the past year was obtained via a self-questionnaire and participants were classified into three fall history groups (0: non-faller, 1: single faller, 2 or more: multiple faller). After a year of following-up, the number of falls during the year was considered as the main outcome. Poisson regression models clarified the influence of FoF on fall occurrence during the one-year follow-up, according to the participants' fall history.

RESULTS

The final sample comprised 530 individuals (follow-up rate: 530/801, 66.4%). Fall history, FoF, and interaction between multiple fallers and FoF were significant in the adjusted statistical model (rate ratio [95% confidence interval]: single faller = 2.81 [1.06, 6.30], multiple faller = 13.60 [8.00, 23.04], FoF = 3.70 [2.48, 5.67], multiple faller*FoF = 0.37 [0.20, 0.68]).

CONCLUSIONS

We found that FoF was associated with the occurrence of falls in community-dwelling older adults. However, its association was lower in multiple fallers.

Developing Self-Management Application of Fall Prevention Among Older Adults: A Content and Usability Evaluation

This paper presents a research and development project for studying aging and technology in fall prevention. Falls are an important global health problem in an aging global population. Up to 50% of serious falls may be fatal. Falls result from the cumulative effects of cognitive, musculoskeletal and sensory decline on postural control and substantially affect the activities of daily living, leading to a lower quality of life and physical injury. A near-fall, misstep and a prior fall are established risk factors for a more serious fall. The fear of falling may reduce physical activity and further predispose to falling. However, limitations in the reporting and documentation of fall events create “silent events”—events that are neither documented nor acted upon. An “Age-Techcare” Application (App) was designed using open innovation methods with local older adult populations and health care professionals through a mixed-methodology approach. The App comprised a digital diary for the self-reporting of fall events and an exercise video to strengthen balance as a fall-prevention intervention. The older adults recorded four fall events: a near-fall, the fear of falling, a fall, or no-fall. Prompts to watch the video and the number of times the video was watched were also recorded on the App. Reports retrieved from the App were analyzed after a 10-week pilot study among older adults accessing the App on their smartphones (n = 28) and through their smartTV (n = 23). All participants used the App to self-report fall events. Near-falls were the most frequently reported fall event among both smartphone and smartTV groups. The scale of silent falls (including a fear of falling and near falls) is greater than anticipated (according to prevailing literature) and significant, especially among the older cohort of participants who had previously experienced falls and are living alone. The exercise video was regularly accessed within a self-report–fall-prevention feedback loop. Watching a preventive exercise video clip as a preventive intervention is positively associated with self-reporting of all events. We have shown that the utility and effectiveness of an App in the self-management of fall events to raise self-awareness, document risk and prompt preventive action. As we address the health needs of an aging global population, Apps such as this will need to be further developed and interface with health and social care services. The facility for older adults to negotiate ideas and practices of risk and safety—the hallmark of the aging-in-place and healthy aging discourse—is important to them in their acceptance of dynamic and diverse technology.

Multidimensional risk score to stratify community-dwelling older adults by future fall risk using the Stopping Elderly Accidents, Deaths and Injuries (STEADI) framework

BACKGROUND

The Stopping Elderly Accidents, Deaths and Injuries (STEADI) screening algorithm aligns with current fall prevention guidelines and is easy to administer within clinical practice. However, the stratification into low, moderate and high risk categories limits the meaningful interpretation of the fall-related risk factors.

METHODS

Baseline measures from a modified STEADI were used to predict self-reported falls over 4 years in 3170 respondents who participated in the 2011-2015 National Health and Aging Trends Study. A point method was then applied to find coefficient-based integers and 4-year fall risk estimates from the predictive model. Sensitivity and specificity estimates from the point method and the combined moderate and high fall risk STEADI categories were compared.

RESULTS

There were 886 (27.95%) and 387 (12.21%) respondents who were classified as moderate and high risk, respectively, when applying the stratification method. Falls in the past year (OR: 2.16; 95% CI: 1.61 to 2.89), multiple falls (OR: 2.94; 95% CI: 1.89 to 4.55) and a fear of falling (OR: 1.77; 95% CI: 1.45 to 2.16) were among the significant predictors of 4-year falls in older adults. The point method revealed integers that ranged from 0 (risk: 27.21%) to 44 (risk: 99.71%) and a score of 10 points had comparable discriminatory capacity to the combined moderate and high STEADI categories.

CONCLUSION

Coefficient-based integers and their risk estimates can provide an alternative interpretation of a predictive model that may be useful in determining fall risk within a clinical setting, tracking changes longitudinally and defining the effectiveness of an intervention.

Incidence and circumstances of falls among middle-aged women: a cohort study

This was the first study assessing falls prospectively in middle-aged women. The 1-year incidence was 42% for any fall, which suggest falls are a major issue in middle-aged women. Middle-aged women, particularly those sustaining a fall, could be a target group for fall-prevention strategies.

PURPOSE

Incidence and circumstances of falls in middle-aged people are poorly understood. This cohort study aimed to elucidate the incidence and circumstances of falls over 1 year in middle-aged women.

METHODS

Falls were recorded monthly for 1 year by questionnaire in 2017-2019 in a population-based sample of women aged 41-62 years. The incidence of falls and injurious falls and related circumstances were descriptively analysed.

RESULTS

Of 273 women, 115 sustained 209 falls. The 1-year incidence was 42% for any fall, 17% for multiple (two or more) falls, and 24% for injurious falls. The incidence was greater in older age groups for any fall (33, 45, and 44% for people aged < 50, 50-55, and > 55 years, respectively), multiple falls (7, 14, and 22%) and injurious falls (15, 20, and 28%), although only the incidence of multiple falls was significantly increased across the three age groups (P = 0.01). Most falls occurred outdoors (71%) and were attributed to tripping and slipping (60%) CONCLUSIONS: Falls are a major issue in middle-aged women, a group that has been largely ignored in the prevention of falls. Middle-aged women, in particular those sustaining a fall, could be a target group for fall-prevention strategies. Future studies are needed to identify risk factors for falling in this population so as inform the development of strategies for preventing falls in middle-aged women.

Evaluation of the Predictive Accuracy of the interRAI Falls Clinical Assessment Protocol, Scott Fall Risk Screen, and a Supplementary Falls Risk Assessment Tool Used in Residential Long-Term Care: A Retrospective Cohort Study

Falls in residential long-term care (LTC) facilities continue to be a leading cause of injury for residents and cost for the health care system. Interdisciplinary clinical teams are responsible for assessing risk levels for their residents and developing appropriate care plans and interventions in response. This study compares the predictive accuracy of three separate fall risk assessment tools: the interRAI Falls Clinical Assessment Protocol (CAP), derived from the LTC Facility (LTCF) or Minimum Data Set (MDS) 2.0 assessments; the Scott Fall Risk Screen; and a modified Fall Risk Tool that was implemented as part of a provincial Fall Reduction Strategy in Nova Scotia. To conduct this retrospective cohort study, secondary data were collected from 1,553 LTC residents with interRAI assessments completed between March 1, 2015 and September 29, 2016, across Nova Scotia and New Brunswick. For each resident, data were collected regarding the three fall risk assessments, along with fall incident data for use in sensitivity, specificity, and logistic regression analyses. This study found that although all three tools had limitations with sensitivity or specificity thresholds, the interRAI Falls CAP delivered the highest accuracy with a c-statistic of 0.673, compared with the Scott Fall Risk Screen at 0.529 and the modified Fall Risk Tool at 0.609. When diseases that have been established to be a risk factor for falls were added to the model, the overall accuracy of the interRAI Falls CAP combined with those covariates increased to 0.749. These results suggest that the best practice guidelines for fall risk assessment be revisited, and that the interRAI Falls CAP could potentially be updated to include certain diseases and controls for optimal predictive ability.

Associations with rates of falls among home care clients in Ontario, Canada: a population-based, cross-sectional study

BACKGROUND

Accidental falls among older adults are a leading cause of injury-related hospitalizations. Reducing falls is an ongoing quality improvement priority for home care, given that many home care clients experience falls. In this study, we identify factors associated with the rate of falls among home care clients.

METHODS

We conducted a population-based, cross-sectional study using secondary data from the Hamilton, Niagara, Haldimand, and Brant health region of Ontario, Canada from January 1 - March 31, 2018. We captured person-level characteristics with falls from the Resident Assessment Instrument - Home Care (RAI-HC). Negative binomial regression was used to model the rate of falls.

RESULTS

Functional characteristics of home care clients had strong, statistically significant associations with the rate of falls. Declines in activities of daily living, assistive device use for locomotion indoors, polypharmacy, and health conditions, such as dizziness or lightheadedness, and parkinsonism, were associated with a higher rate of falls. Males who used assistive devices had a higher rate of falls compared to females; however, males with neurological and cardiovascular health conditions had a decrease in the rate of falls compared to females. Home care clients with parkinsonism who used a cane and took eight or more drugs had stronger associations with an increased rate of falls compared to those who do not have parkinsonism.

CONCLUSIONS

Functional characteristics, polypharmacy, and health conditions are associated with increased rates of falls among home care clients. Home care clients who are at a greater risk of falls may require environmental adjustments in their home to reduce or eliminate the possibility of falling.

Descriptive Evaluation and Accuracy of a Mobile App to Assess Fall Risk in Seniors: Retrospective Case-Control Study

BACKGROUND

Fall-risk assessment is complex. Based on current scientific evidence, a multifactorial approach, including the analysis of physical performance, gait parameters, and both extrinsic and intrinsic risk factors, is highly recommended. A smartphone-based app was designed to assess the individual risk of falling with a score that combines multiple fall-risk factors into one comprehensive metric using the previously listed determinants.

OBJECTIVE

This study provides a descriptive evaluation of the designed fall-risk score as well as an analysis of the app's discriminative ability based on real-world data.

METHODS

Anonymous data from 242 seniors was analyzed retrospectively. Data was collected between June 2018 and May 2019 using the fall-risk assessment app. First, we provided a descriptive statistical analysis of the underlying dataset. Subsequently, multiple learning models (Logistic Regression, Gaussian Naive Bayes, Gradient Boosting, Support Vector Classification, and Random Forest Regression) were trained on the dataset to obtain optimal decision boundaries. The receiver operating curve with its corresponding area under the curve (AUC) and sensitivity were the primary performance metrics utilized to assess the fall-risk score's ability to discriminate fallers from nonfallers. For the sake of completeness, specificity, precision, and overall accuracy were also provided for each model.

RESULTS

Out of 242 participants with a mean age of 84.6 years old (SD 6.7), 139 (57.4%) reported no previous falls (nonfaller), while 103 (42.5%) reported a previous fall (faller). The average fall risk was 29.5 points (SD 12.4). The performance metrics for the Logistic Regression Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gaussian Naive Bayes Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gradient Boosting Model were AUC=0.85, sensitivity=88%, specificity=62%, and accuracy=73%. The performance metrics for the Support Vector Classification Model were AUC=0.84, sensitivity=88%, specificity=67%, and accuracy=76%. The performance metrics for the Random Forest Model were AUC=0.84, sensitivity=88%, specificity=57%, and accuracy=70%.

CONCLUSIONS

Descriptive statistics for the dataset were provided as comparison and reference values. The fall-risk score exhibited a high discriminative ability to distinguish fallers from nonfallers, irrespective of the learning model evaluated. The models had an average AUC of 0.86, an average sensitivity of 93%, and an average specificity of 58%. Average overall accuracy was 73%. Thus, the fall-risk app has the potential to support caretakers in easily conducting a valid fall-risk assessment. The fall-risk score's prospective accuracy will be further validated in a prospective trial.

Fall Risk Classification in Community-Dwelling Older Adults Using a Smart Wrist-Worn Device and the Resident Assessment Instrument-Home Care: Prospective Observational Study

Background

 Little is known about whether off-the-shelf wearable sensor data can contribute to fall risk classification or complement clinical assessment tools such as the Resident Assessment Instrument-Home Care (RAI-HC).

Objective

 This study aimed to (1) investigate the similarities and differences in physical activity (PA), heart rate, and night sleep in a sample of community-dwelling older adults with varying fall histories using a smart wrist-worn device and (2) create and evaluate fall risk classification models based on (i) wearable data, (ii) the RAI-HC, and (iii) the combination of wearable and RAI-HC data.

Methods

 A prospective, observational study was conducted among 3 faller groups (G₀, G₁, G₂₊) based on the number of previous falls (0, 1, ≥2 falls) in a sample of older community-dwelling adults. Each participant was requested to wear a smart wristband for 7 consecutive days while carrying out day-to-day activities in their normal lives. The wearable and RAI-HC assessment data were analyzed and utilized to create fall risk classification models, with 3 supervised machine learning algorithms: logistic regression, decision tree, and random forest (RF).

Results

 Of 40 participants aged 65 to 93 years, 16 (40%) had no previous falls, whereas 8 (20%) and 16 (40%) had experienced 1 and multiple (≥2) falls, respectively. Level of PA as measured by average daily steps was significantly different between groups (P=.04). In the 3 faller group classification, RF achieved the best accuracy of 83.8% using both wearable and RAI-HC data, which is 13.5% higher than that of using the RAI-HC data only and 18.9% higher than that of using wearable data exclusively. In discriminating between {G₀+G₁} and G₂₊, RF achieved the best area under the receiver operating characteristic curve of 0.894 (overall accuracy of 89.2%) based on wearable and RAI-HC data. Discrimination between G₀ and {G₁+G₂₊} did not result in better classification performance than that between {G₀+G₁} and G₂₊.

Conclusions

 Both wearable data and the RAI-HC assessment can contribute to fall risk classification. All the classification models revealed that RAI-HC outperforms wearable data, and the best performance was achieved with the combination of 2 datasets. Future studies in fall risk assessment should consider using wearable technologies to supplement resident assessment instruments.

The Association Between Fall Frequency, Injury Risk, and Characteristics of Falls in Older Residents of Long-Term Care: Do Recurrent Fallers Fall More Safely?

Background

Although a fall is a necessary prerequisite to a fall-related injury, previous studies suggest that frequent fallers are at lower injury risk for a given fall. We tested the hypotheses that differences in protective responses or the circumstances of falls underlie differences in injury risk with fall frequency.

Methods

We analyzed video footage of 897 falls experienced by 220 long-term care residents (mean age 82 ± 9 years) to identify the cause of imbalance, activity leading to falling, direction of fall initiation, balance recovery and fall protective responses, and occurrence of impact to the head or hip. We further obtained injury information from the facilities' fall registration. We used generalized estimating equation models to examine the association between quartiles of fall frequency, injury risk, and fall characteristics.

Results

Residents with the highest fall frequency group (Q4; ≥5.6 falls/year) were less likely to sustain an injury per fall. They were less likely to fall during walking and more likely to fall during stand-to-sit transfers. Residents in the lowest fall frequency group (Q1; <1.15 falls/year) were more likely to fall during walking, and walking was associated with an increased risk for injury.

Conclusion

When compared to less frequent fallers, more frequent fallers had a lower risk for injury per fall. This appeared to be explained by differences in the circumstances of falls, and not by protective responses. Injury prevention strategies in long-term care should target both frequent and infrequent fallers, as the latter are more mobile and apt to sustain injury.

The Complex Association Between Fear of Falling and Mobility Limitation in Relation to Late-Life Falls: A SHARE-Based Analysis

OBJECTIVE

This study examines fear of falling (FOF) in relation to falls in light of mobility limitation.

METHOD

Data on community-dwelling older Europeans, aged 65+, were drawn from two consecutive waves of the Survey of Health, Ageing and Retirement in Europe (SHARE). The analysis regressed fall status in 2013 on reported FOF 2 to 3 years earlier, controlling for previous falls.

RESULTS

FOF predicted subsequent falls when mobility limitation was low to moderate. However, the effect of FOF on fall probability was reversed when mobility limitation was high.

DISCUSSION

The analysis underscores a complex association between FOF and mobility limitation in relation to late-life falls. People who are worried about falling tend to fall more. Those having high mobility limitation but lacking FOF are also more likely to fall. In cases of considerable mobility limitation, FOF may act as a protective buffer. The less worried in this group, however, may be subject to greater falling, and thus require greater attention.

Falls and fear of falling predict future falls and related injuries in ambulatory individuals with spinal cord injury: a longitudinal observational study

QUESTIONS

What is the 1-year incidence of falls and injurious falls in a representative cohort of community-dwelling ambulatory individuals with chronic spinal cord injury? What are the predictors of recurrent falls (more than two/year) and injurious falls in this population?

DESIGN

One-year longitudinal observational multi-centre study.

PARTICIPANTS

A representative sample of 68 (of 73 included) community-dwelling ambulatory individuals with traumatic SCI attending regular follow-up programs at rehabilitation centres.

OUTCOME MEASURES

Primary outcome measures were incidence and predictors of recurrent falls (more than two/year) and injurious falls reported every 2 weeks for 1year.

RESULTS

A total of 48% of participants reported recurrent falls. Of the 272 reported falls, 41% were injurious. Serious injuries were experienced by 4% of participants, all of whom were women. Multivariate logistic regression analysis showed that recurrent falls in the previous year (OR=111, 95% CI=8.6 to 1425), fear of falling (OR=6.1, 95% CI=1.43 to 26) and longer time taken to walk 10m (OR=1.3, 95% CI=1.0 to 1.7) were predictors of recurrent falls. Fear of falling (OR=4.3, 95% CI=1.3 to 14) and recurrent falls in the previous year (OR=4.2, 95% CI=1.2 to 14) were predictors of injurious falls.

CONCLUSION

Ambulatory individuals have a high risk of falling and of fall-related injuries. Fall history, fear of falling and walking speed could predict recurrent falls and injurious falls. Further studies with larger samples are needed to validate these findings. [Jørgensen V, Butler Forslund E, Opheim A, Franzén E, Wahman K, Hultling C, Seiger Å, Ståhle A, Stanghelle JK, Roaldsen KS (2017) Falls and fear of falling predict future falls and related injuries in ambulatory individuals with spinal cord injury: a longitudinal observational study. Journal of Physiotherapy 63: 108-113].