Prevalence and determinants of fall risk among elderly inpatients at a tertiary care public teaching hospital
Background. Falls in elderly people are a major concern in terms of disability, institutionalization, mortality and
socioeconomic burden and are considered as one of the “Geriatric Giants”.
Aim. To evaluate the risk of fall and risk factors associated with falls among elderly inpatients at a tertiary care
public teaching hospital.
Materials and methods. This questionnaire based study utilized the “John Hopkins Fall Risk Assessment
Tool” to assess the risk of falls among elderly inpatients. The data obtained from 235 participants reporting
in the medicine department is presented. Based upon JHFRAT parameters, a well-determined score was obtained;
and, the patients were categorized into “low” fall risk, “moderate” fall risk and “high” fall risk. The data
was analyzed by performing Multinomial logistic regression.
Results. The patients in the study had an average age of 68.2 ± 0.4 years; and, had a high frequency of “moderate”
fall risk and “high” fall risk (65.1 & 20%). Only 10.2% of patients have experienced a fall event within 6
months before hospitalization. It has been reported that the mobility functions, cognitive functions, high fall risk
drugs and advanced age are major contributing factors. A positive correlation between high fall risk drugs and
average fall risk score was observed.
Conclusions. In India, falls among the elderly is an emerging public health problem and a hurdle to active
ageing. Due to paucity of epidemiological research on falls in Indian population, further studies are required in
order to minimize the negative impacts of falling on the patient’s physical, psychological, and social functional
Falls in elderly people are a major concern in terms of disability, institutionalization, mortality and socioeconomic burden and are considered as one of the “Geriatric Giants”. Falls and unstable balance rank high among serious clinical problems faced by elderly. Unintentional injuries are the fifth leading cause of death in elderly people after cardiovascular disease, cancer, stroke and pulmonary disorders and falls constitute two-thirds of these deaths 1.
The incidence of falls is reported to vary among countries. For instance, the incidence of falls among elderly in China lies between 6-31, 20% in Japan, 21.6% in Barbados and up to 34% in Chile 2. In India, the prevalence of falls among elderly population has been reported to be 14-53% 3 4. These reports vary in terms of sample size, geographical region, fall history criteria and methods.
Falls lead to 20-30% of mild to severe injuries and are underlying cause of 10-15% of all emergency department visits 5. The major clinical conditions for fall-related hospital admissions are hip fractures, traumatic brain injuries, and upper limb injuries. Subsequently to falls, 20% die within a year due to hip fracture 6. In addition, falls may also result in a post-fall syndrome that includes dependence, loss of autonomy, confusion, immobilization and depression, which will lead to a further restriction in daily activities.
A combination of risk factors is responsible for the complex phenomenon of falls in the elderly people. Rawsky has reported that cognitive impairment/psychological status, acute/chronic illness and mobility, sensory deficits, fall history, and elimination concern were identified most often intrinsic risk factors in a variety of setting (e.g., inpatient hospital, community, psychiatry facility, rehabilitation center, and long-term care facility) 7. Rubenstein and Josephson as well as the Amer Ger Soc 8 9 have underlined that muscle weakness, history of falls, gait deficits, balance deficits, use of assistive devices, visual deficits, arthritis, impaired activities of daily living, depression, cognitive impairments, and advanced age are major intrinsic fall risk factors.
Falls can be caused by almost any drug that acts on the brain or on the circulation 10 11. It has been reported that stopping cardiovascular medication reduces syncope and falls by 50%, and also reduces the prevalence of fall associated syndromes like paroxysmal hypotension (orthostatic hypotension, vaso-vagal syndrome and vaso-depressor-carotid sinus hypersensitivity) and bradycardia, tachycardia or periods of asystole 12 13.
Fall risk assessment is an initial step for the prevention of falls among elderly. This helps to identify persons at highest risk upon whom to target specific interventions. Fall risk assessment, however, is not standardized within or across settings. The instruments like Morse Fall Scale, STRATIFY, Resident Assessment Instrument (RAI), Fall Risk Assessment Tool, Hendrich Fall Risk Model, High Risk for Falls Assessment Form and John Hopkins Fall Risk Assessment Tool are used to identify who is likely to fall on the basis of intrinsic or medical characteristics of the patient (e.g., psychological status, mobility dysfunction, fall history, elimination frequency/dependence, acute/chronic illnesses, and sensory deficits).
There is a scarcity of epidemiological research on falls in Indian population. In India, only a limited number of large-scale community based studies to evaluate the fall risk among elderly patients have been carried out. This rapid population aging has emphasized the relevance of investigating the fall risk among elderly patients.
This study has evaluated the risk of fall and its risk factors among elderly inpatients at a tertiary care public teaching hospital.
MATERIALS AND METHODS
STUDY DESIGN, SAMPLE AND SETTING
This prospective cross sectional study was conducted at selected inpatient departments (IPDs) of a tertiary care public teaching hospital. The patients aged 60 years or more were then rated using JHFRAT; fall events before and during hospitalization were monitored. In this study, the data from 235 patients were analyzed.
DATA COLLECTION PROCEDURE
Data collection was started after receiving approval from the “research committee” and “ethics committee” of Government Medical College and Hospital, (GMCH) Chandigarh and National Institute of Pharmaceutical Education and Research, (NIPER) Mohali. The verbal and written explanation of the study was performed and the written informed document from patients or patient’s legal guardian(s) prior to enrolling subjects in the study was obtained.
The definition of fall in this study was “any unplanned descent to the floor with or without injury” (National Database of Nursing Quality Indicators 2012).
The patients aged 60 year or more, either sex, patients with one or more concurrent diseases, patients with one or multiple drugs and patients willing to participate were included. The patients with incomplete documentation and completely immobilized patients and/or unable to answer the questions were excluded from the study.
All the relevant information was collected from the patient record file and a patient interview was conducted for each patient in the study. Patient related information included demographic characteristics (age, gender, body weight, and height), co-morbidities, diagnosis and drugs prescribed. All this information was collected in a standard case record form. Patient’s rights were respected and the personal health information provided by the patient was confidential and in no circumstances it was disclosed to anyone.
JOHN HOPKINS FALL RISK ASSESSMENT TOOL (JHFRAT)
The JHFRAT consists of seven variables including age (1-3 points), fall history (0 or 5 points), elimination of bowel and urine (2 or 4 points), medications and sedative procedure (3, 5 or 7 points), patient care equipments (1-3 points), mobility (2, 4 or 6 points) and cognition (1-7 points). Based upon the inputs on these parameters, a well determined score was obtained for all patients. On the basis of these scores, the patients were categorized into ‘low’ fall risk (< 6 points), ‘moderate’ fall risk (6-13 points) and ‘high’ fall risk (> 13 points). The fall risk score calculation was not processed further when any of the following condition(s) was met: history of more than one fall within 6 months before admission; patient has experienced a fall during this hospitalization; patient is deemed high fall-risk per protocol (e.g., seizure precautions); complete paralysis or completely immobilized.
The patients who met the “not processed further” criteria were excluded from the study. Permission to use John Hopkins Fall Risk Assessment tool, a pre-validated tool, for evaluating risk of fall among elderly patients was obtained from the developer 14. The frequencies of fall risk factors were calculated based on their individual scores for each parameter using (JHFRAT) John Hopkins Fall Risk Assessment Tool. The individual items/parameters i.e. elimination of bowel and urine, mobility and cognition were evaluated by the help of predetermined scores of their sub-parameters.
The elimination, bowel and urine parameter was assessed by using three sub-parameters: incontinence; urgency or frequency; urgency/frequency and incontinence.
The mobility parameter was assessed by using three sub-parameters: requires assistance or supervision for mobility, transfer, or ambulation; unsteady gait; visual or auditory impairment affecting mobility.
The cognition parameter was assessed by using three sub-parameters: altered awareness of the immediate physical environment; impulsive; lack of understanding of one’s physical and cognitive limitation.
All the data obtained during the study was organized into a spread sheet. The descriptors of central tendency and variations were computed using Microsoft excel® and Free version of SPSS. The results were presented in percentages and average supported with SEM.
Multinomial logistic regression was performed in order to study the influence of age, gender, and other socio-demographic details on the prevalence of fall risk among geriatric patients. Strength of multinomial logistic regression relationship was evaluated by using pseudo R square. In this case, the Cox and Snell R Square and the Nagelkerke R square value was used, which provides an indication of the amount of variation in the dependent variable. The overall classification accuracy in the predictive table was used to evaluate the utility of logistic model or to characterize the model as useful. The likelihood ratio test was used to evaluate the overall relationship between an independent variable and dependent variable. P < 0.05 was taken to indicate statistical significance.
In this cross-sectional study, 246 elderly patients were evaluated by means of personal interview and validated scales/questionnaire to predict fall risk in inpatient (IPD) setting at a public teaching hospital. Eleven patients were excluded from this study (4 with incomplete information and 7 unwilling to participate in the study). Therefore, the results of this study are based on data obtained from 235 patients.
Out of 235 patients, 114 were male and 121 were female (48.5 and 51.5%). The average age of the patients was 68.2 ± 0.4 years, with range of 60-92 years. The median age was found to be 68 years and mode was 70 years. The average age of male patients was 68.8 ± 0.6 years and the average age of female patients was 67.7 ± 0.5 years.
Further, the patients were categorized into three age groups like “young-old” (60-69 yrs), “middle-old” (70-79 yrs) and very-old” (≥ 80 yrs) according to internationally accepted criteria (15). The most common diagnoses of the patients enrolled were diabetes mellitus, hypertension, CKD, chronic liver disease, acute pancreatitis, coronary artery disease, COPD, parkinson’s disease and cholelithiasis.
FALL RISK EVALUATION
A total of 235 patients were evaluated for fall risk by using JHFRA-tool.
- ‘Low’ Fall Risk Category: 35 patients were found to fall under ‘low’ fall risk category (14.9%). Out of these, 15 were females and 20 were males; 29 belonged to 60-69 year age group, 4 to 70-79 year age group and 2 to over 80 year age group. The average age of these patients was found to be 65.0 ± 0.8 years;
- ‘Moderate’ Fall Risk category: 153 patients (81 female and 72 male) were found to fall under ‘moderate’ fall risk category (65.1%). Out of these, 86 belonged to 60-69 year age group, 58 to 70-79 year age group and 9 were over 80 years of age. The average age of these patients was found to be 68.0 ± 0.5 years;
- ‘High’ Fall Risk Category: 47 patients (25 female and 22 male) were found to fall under ‘high’ fall risk category (20%). Out of these, 15 were in the age group 60-69 years, 23 in 70-79 years and 9 were over 80 years of age. The average age of these patients was found to be 71.4 ± 1.0 years.
The analysis of the fall history showed that 24 patients had experienced a fall event before admission to the hospital. The average age of these patients was found to be 70.5 ± 1.5 years. Of these 24, 15 were females and 9 were male. And, 11 belonged to 60-69 years age group, 10 to 70-79 years and 3 were over 80 years of age. The assessment of fall risk based on socio-demographic details is presented in (Tab. I).
Table II represents the likelihood ratio test which was used to evaluate the overall relationship between an independent variable and dependent variable while performing multinomial logistic regression analysis. The amount of variation in the dependent variable is described as pseudo R square. Nagelkerke R square value was found to be 0.270, respectively.
This indicates that 27% of the variability in the dependent variable is explained by using this model. The overall predictive accuracy for the present model is 66.0%, suggesting that the model was useful.
Most commonly drugs prescribed to the patients included antidiabetics, antihypertensives, diuretics, antimicrobials, proton pump inhibitors, analgesics, laxatives and antipsychotic agents. 59.1% of the patients were prescribed with 6-10 medications and 9.7% were prescribed with more than 10 medications. These categories of patients were having 76.5% of population with 2 or more comorbidities.
The most common classes of drugs responsible for higher fall risk among elderly patients were antihypertensives, diuretics followed by laxatives, antipsychotics, hypnotics, anticonvulsants, antiparkinsons, opioid analgesics, sedatives, and antianginals. Figure 1 shows frequency of involvement of ‘high’ fall risk drugs.
Figure 2 represents the correlation between the number of high fall risk drugs prescribed plotted on X-axis and JHFRAT score plotted on Y-axis, which showed that with an increase in HFR-drugs there is an increment in average JHFRAT score. They were found to be positively correlated (r = 0.95) with each other.
FALL RISK FACTORS
Figure 3 represents the various fall risk factors identified which includes mobility functions, age, high fall risk drugs, cognitive functions, elimination (bowel and urine) concerns and patient care equipments. The frequencies of fall risk factors were calculated based on their individual scores for each parameter using (JHFRAT) John Hopkins Fall Risk Assessment Tool. The individual items/parameters like elimination of bowel and urine, mobility and cognition were evaluated by the help of predetermined scores of their sub-parameters.
The results of this study are based upon data obtained from a set of 235 patients comprising a nearly balanced gender criterion (48.5% males and 51.5% females). 130 patients belonged to the elderly age group 60-69 years (55.3%), 85 belonged to age bracket of 70-79 years (36.2%) and 20 belonged to oldest old (80 or more years) category. The average number of diagnoses was found to be 2.26 ± 0.07. 70.6% of the patients were found to have two or more diagnoses.
The results of Kim et al. 14 using JHFRA-tool in South Korean elderly showed that among 356 patients, 71 patients experienced fall events and 285 have not experienced any fall event (20 vs 80%). This study confirms that 24 (10.2%) patients have experienced a fall event before hospitalization and 209 (89.8%) patients have not experienced any fall event, which reflects that the percentage of fall events in this study is quite smaller in comparison to Kim et al. Between the fall risk categories, there was no statistically significant difference for gender (p = 0.50).
The average number of medications was found to be 6.9 ± 0.1. The current study confirms that the patients who are receiving more than 5 drugs are at higher fall risk than those who are receiving less than 5 medications. High fall risk drugs are one of the major contributors of falls among elderly population. The average number of high fall risk drugs was found to be 1.11 ± 0.07. The most common classes of high fall risk drugs prescribed to the patients enrolled were antihypertensives, diuretics followed by laxatives, antipsychotics, hypnotics, anticonvulsants, antiparkinsons, opioid analgesics, sedatives and antianginals which was in consonance with the earlier reports 11-14. Linear relationship between the number of HFRDs and the average JHFRAT score demonstrates that HFRDs are a major contributor of fall risk among elderly.
The analysis of relationship between different variables with fall risk categories showed a statistically significant relationship for age (p = 0.00), food habits (p = 0.005) and treatment funding (p = 0.019). The association between educational status (p = 0.278) & occupation (p = 0.948) with the falls, however, was found to be statistically insignificant. This finding matches with the report of Philip et al. 16. Further, there was insignificant association for alcoholic status, smoking status, gender, marital status and residential (urban/rural) status.
There are number of risk factors for falls in elderly such as muscle weakness, history of falls, gait deficit, balance deficit, use of assistive device, visual deficit, arthritis, impaired activities of daily living, depression, cognitive impairment, Parkinson’s disease and advanced age (patients aged 70 years or more). Based on the evaluated scores of JHFRAT, the current results confirm that the mobility functions 17 18, advanced age (70 years or more) 21, use of high fall risk drugs 10 11 22 and cognitive functions 18-20 as major fall risk factors.
This cross sectional study was conducted at selected IPDs of a tertiary care public teaching hospital; and, the conclusions are significant drawn based on data collected from the records of 235 inpatients.
This study evaluated the fall risk and its risk factors among elderly patients using “John Hopkins fall risk assessment tool”. The ‘moderate’ risk group was the largest contributor (65.1%) in comparison to ‘low’ risk and ‘high’ risk groups. Mobility functions followed by cognitive functions, high fall risk medications, advanced age, patient care equipments, elimination (bowel and urine) and fall history were fall risk factors observed. High fall risk drugs, like antihypertensives, followed by diuretics, laxatives, hypnotics, antipsychotics, opioid analgesics and anticonvulsant were most frequently used. A association was observed between use of HFRDs and average JHFRAT scores. This study has also provided hard real-time evidence about the distribution of fall risk among different sex, age groups, and other demographic parameters.
Figures and tables
|Socio-demographic details||N||At low fall risk (0-5 points)||At moderate fall risk (6-13points)||At high fall risk (> 13 points)|
|Classification of patients||235||35 (14.9%)||153 (65.1%)||47 (20%)|
|Marital status Married Widow/widower||188 (80%)47 (20%)||31 (13.2%)4 (1.7%)||127 (54%)26 (11.1%)||30 (12.8%)17 (7.2%)|
|Residential status Rural Urban||139 (59.1%)96 (40.9%)||21 (8.9%)14 (6%)||85 (36.2%)68 (28.9%)||33 (14%)14 (6.0%)|
|Food habits Veg Non veg||176 (74.9%)59 (24.1%)||21 (8.9%)14 (6%||112 (47.7%)41 (17.4%)||43 (18.3%)4 (1.7%)|
|Smoking Yes No||58 (24.7%)177 (75.3%)||14 (6.0%)21 (8.9%)||36 (15.3%)117 (49.8%)||8 (3.4%)39 (16.6%)|
|Alcoholic Yes No||81 (34.5%)154 (65.5%)||16 (6.8%)19 (8.1%)||50 (21.3%)103 (43.8%)||15 (6.4%)32 (13.6%)|
|Occupation government Private Unemployed||24 (10.2%)21 (8.9%)190 (80.9%)||2 (0.9%)5 (2.1%)28 (11.9%)||18 (7.7%)13 (5.5%)122 (51.9%)||4 (1.7%)3 (1.3%)40 (17.0%)|
|Education middle High College Uneducated||61 (26%)45 (19.1%)8 (3.4%)121 (51.5%)||11 (4.7%)4 (1.7%)2 (0.9%)18 (7.7%)||38 (16.2%)33 (14.0%)5 (2.1%)77 (32.8%)||12 (5.1%)8 (3.4%)1 (0.4%)26 (11.1%)|
|Treatment funding reimbursement None||26 (11.1%)209 (88.9%)||035 (14.9%)||20 (8.5%)133 (56.6%)||6 (2.6%)41 (17.4%)|
|Effect||Likelihood ratio tests|
|Food habits (veg/non-veg)||10.777||2||0.005|
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