ORIGINAL ARTICLE
A Study of Rate and Predictors of Fall Among Elderly Patients in a University Hospital Mahi Mahmoud al Tehewy, PhD, Ghada Essam Amin, Dr, and Nahla Wassem Nassar, MPH Introduction: Falls represent a serious problem facing hospital-admitted patients, and the severity of fall-related complications rises steadily after the age of 65 years. Objectives: The aims of this study were (a) to calculate the rate of falls among elderly patients in the internal medicine departments in Ain Shams University Hospital, (b) to identify different predictors and characteristics of falls, and (c) to assess clinical consequences and hospitalization outcomes of falls. Subjects and Methods: An observational longitudinal study has been conducted in Ain Shams University Hospital, where 411 elderly patients admitted to the internal medicine departments were included. Upon admission, the patients were assessed for their risk for falling using the Morse Fall Scale (MFS). Information about their medical condition and drugs administered was obtained. Functional assessment of the patients regarding their ability to perform different daily activities was also performed. The patients were followed up during their stay, and once a fall event occurred, complete details regarding the circumstances and consequences of that event were obtained. Results: The incidence rate of falls was found to be 16.9 per 1000 patient days. The fallers had a significantly high risk for falling according to the MFS (P = 0.02). The MFS was able to predict patients at risk for falling and identified correctly 82.6% of the fallers. The most common medical conditions associated with falls were diabetes (48.7%), hypertension (58.7%), and visual impairment (41.3%). Anemia (P = 0.05) and osteoporosis (P = 0.02) showed a statistically significant difference between the fallers and the nonfallers. Presence of a history of a fall and increased length of hospital stay were highly significant (P = 0.01) factors that predisposed to falls. Logistic regression analysis showed that anemia, osteoporosis, and history of a fall were independent predictors of falls. Most falls had no serious consequences, approximately 18% had contusions, 2% had subdural hematomas, and 4% had fractures and lacerations. Conclusions: Elderly patients with anemia, osteoporosis, and history of a fall are more prone to falls and should be considered in fall protective measures. Key Words: patient fall, elderly patient, patient safety (J Patient Saf 2014;11: 210–214)
A
n accidental fall has been defined as a sudden and nonintentional change of posture to the ground or a lower level, onto an object, floor, pavement, ground, or any other type of surface, and includes slipping, tripping (stumbling), falling on other people, loss of balance, and accidental stooping.1,2 Falls result from an interaction between predisposing and precipitating factors in people’s environment and in individual demographic and clinical characteristics. Hospitalization itself may be an important risk factor in falls, representing a deep change in elderly patients’ life habits. Morse3 classified falls into 3 types: From the Faculty of Medicine, Ain Shams University, Cairo, Egypt. Correspondence: Mahi Mahmoud al Tehewy, PhD, Faculty of Medicine, Ain Shams University, Abassia Square, Cairo, Egypt (e‐mail: [email protected]). The authors disclose no conflict of interest. Copyright © 2014 by Wolters Kluwer Health, Inc. All rights reserved.
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anticipated physiological fall, accidental fall, and unanticipated physiological fall. Studies showed that patient falls happened more frequently in geriatric wards followed by general medical and surgical wards.4,5 The rate of patient falls was approximately 2 per 1000 patient days in general hospitals and increased in geriatric departments up to approximately 10 per 1000 patient days.6–9 Consequences of falls include mild to severe injury, increased morbidity and mortality, loss of independence, fear, decreased activity, additional treatment expenses, and decreased quality of life.10 The severity of fall-related complications rises steadily after the age of 65 years including not only physical injury but also psychological and social consequences. In 2007, reducing the risk for patient harm resulting from falls became one of the international patient safety goals through assessing and periodically reassessing each patient’s risk for falling and taking action to decrease or eliminate any identified risks.11 Actually, up until now, fall as a patient safety issue is not highlighted in Ain Shams University Hospital, and little is known about the magnitude and determinants of this problem.
OBJECTIVES OF THE STUDY The objectives of this study were the following: 1. To calculate the rate of falls among elderly patients in the internal medicine departments of Ain Shams University Hospital 2. To identify different predictors and characteristics of falls 3. To assess clinical consequences and hospitalization outcomes of falls
SUBJECTS AND METHODS Study Design and Study Setting A longitudinal observational study was conducted in the internal medicine departments of Ain Shams University Hospital, a hospital with an 800-bed capacity. All medical departments, general and specialized, were included in the study.
Subjects Any patient, male or female, older than 60 years admitted to any of the internal medicine departments of Ain Shams University Hospital was included after agreeing to participate in the study. Exclusion criteria include psychiatric patients, patients who fell during physical therapy sessions, and patients admitted to the intensive care unit. Patients who fell during physical therapy sessions were excluded because such sessions encourage patients to be engaged in activities that may lead to postural instability. However, physical therapists already know that their patients are at higher risk for falling and thus apply special technical approaches to avoid body harm.
Sample Size and Sampling Technique The sample size was calculated to be 411 patients. Epi Info software version 6 was used, assuming that relative risk is 2, J Patient Saf • Volume 11, Number 4, December 2015
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J Patient Saf • Volume 11, Number 4, December 2015
Rate and Predictors of Fall Among Elderly Patients
prevalence among none exposed is 11.3%,9 confidence level is 95%, and power is 80%. A list of all admitted patients older than 60 years was obtained from the admission office each day, and a 25% systematic random sample of those patients was selected daily. On average, approximately 10 patients were included in the study each day.
• Fall incident sheet: only for patients who experienced a fall. It included details of the event such as date, time, and location of the fall. It also included data about the patient’s status just before the fall; relation of the fall to a specific activity, for example, toileting or turning the head suddenly; and clinical consequences of the fall.
Study Tools
Methods
• A data sheet was formulated to collect information extracted from patient file about personal data, habits, diagnosis, date of admission, date of discharge, duration of stay, associated medical conditions, the patient’s medications especially those affecting consciousness and balance, and condition of the patient upon his/her discharge. Data also included protective measures taken in the department to prevent the patients from falling obtained by direct observation or asking the patient’s health caregiver because these data were not documented routinely in the patient file. • Fall risk assessment using the Morse Fall Scale (MFS).12 The scale consists of the following 6 variables that are quick and easy to score: ○History of falling (no scores 0, yes scores 25) ○Secondary diagnosis (no scores 0, yes scores 15) ○Need for ambulatory aids (none/bed rest/nurse assist scores 0, crutches/cane/walker scores 15, furniture scores 30) ○Current intravenous therapy (no scores 0, yes scores 20) ○Gait or transferring (normal/bed rest/ wheelchair scores 0, weak scores 10, impaired scores 20) ○Mental status (oriented to own ability scores 0, forgets limitations scores 15)
Upon admission, the patients were assessed for their risk for falling using the MFS. Information about their personal and medical condition including drugs administered was obtained. Functional assessment of the patients regarding their ability to perform different daily activities and instrumental activities was also performed. The patients were then followed up during their stay in the hospital, and once a fall event occurred, complete details regarding the circumstances and consequences of that event were obtained. The study was conducted during the first 6 months of 2009.
The scores are then summed up, and the total identifies the risk for the patient to fall as follows: ○0 to 24 → No risk ○25 to 50 → Low risk ○51 or higher → High risk • Functional assessment for activities of daily living (ADLs). This was done using the progress in development of the index of ADLs done by Katz et al13 in 1970. It assesses the level of dependence of the patient according to the following activities: bathing, dressing, toileting, transferring, continence, and feeding. The patient is scored yes (1) or no (0) according to his/her independence in performing each of the previous functions. The scores are then summed up. The total points range from 0 to 6, with 0 being the lowest score (patient is totally dependent on others) and 6 being the highest score (patient is totally independent). • Functional assessment for instrumental ADLs (IADLs). This was done using the Lawton IADL scale, which was established in 1969.14 It assesses the level of the patient’s dependence according to skills that are considered more complex than the basic ADLs measured by the Katz Index of ADLs. These skills are ability to use the telephone, shopping, mode of transportation, responsibility for own medications, ability to handle finances, food preparation, housekeeping, and laundry. • Women are scored on all 8 areas of function, whereas for men, the areas of food preparation, housekeeping, and laundering are excluded. Patients are scored according to their highest level of functioning in that category. A summary score ranges from 0 (low function, dependent) to 8 (high function, independent) for women and 0 through 5 for men. © 2014 Wolters Kluwer Health, Inc. All rights reserved.
Data Management Data were collected, cleaned, coded, and introduced into a personal computer using the Statistical Package for the Social Sciences software version 17. Descriptive analysis was first done to determine the frequencies of different variables, then univariate analysis was done using the Pearson w2 test. Correlation coefficient was used to determine the correlation between MFS score and each of the following variables: patient’s age, ADL score, and IADL score. Multivariate analysis in the form of logistic regression analysis for fall-related factors was performed for variables that were statistically significant in the univariate analysis. Variables were entered together in the equation to identify independent predictors of falls. P value of less than 0.05 was considered significant in all performed analyses.
Ethical Considerations Administrative and ethical committee approval was obtained before conducting the study. Patient informed consent was obtained after explaining to him/her (or his/her caregiver) the importance and objectives of this study and what information would be needed to be given by the patient or obtained from reviewing his/ her medical file. The health caregivers of the patients who were proven to have a high risk for falling, according to the MFS, were informed to take necessary preventive measures. Confidentiality of personal data was also considered.
RESULTS The mean (±SD) age of the patients was 67.6 (6.7). The minimum age was 60 years, and the maximum was 90 years. Forty-six
FIGURE 1. Frequency and fall rate among elderly patients www.journalpatientsafety.com
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TABLE 1. Distribution of Sociodemographic Data and Patient Habits Among Faller and Nonfaller Groups of Patients No Fall (365) Item Age 60–69 70–79 ≥80 Sex Female Male Marital status Single Married Divorced Widowed Smoking Nonsmokers Smokers Alcohol Nonconsumers Consumers
Fall (46)
n
%
n
%
240 94 31
65.8 25.8 8.5
32 12 2
69.6 26.1 4.3
163 202
44.7 55.3
24 22
No Fall x2
P
0.97
0.62
0.93
0.34
3.23
0.36
52.2 47.8
3 264 6 92
0.8 72.3 1.6 25.2
1 29 2 14
2.2 63.1 4.3 30.4
272 93
74.5 25.5
37 9
80.4 19.6
357 8
97.8 2.2
45 1
97.8 2.2
0.77
—
0.38
—
(11.2%) of 411 patients experienced 50 falls. Forty-three of them fell once, 2 patients fell twice, and 1 patient fell 3 times (Fig. 1). The incidence rate of falls was calculated to be 16.9 per 1000 patient days. There was no significant difference between the fallers and the nonfallers regarding age distribution, sex, marital status, smoking, and alcohol consumption (Table 1). Table 2 shows fall preventive measures available for each patient. All measures, except bedside lockers, are absent for most of the patients, with no significant difference between the fallers and the nonfallers. More than 85% of the patients, fallers and nonfallers, were accompanied by an informal caregiver (relative), and most of them were not informed about fall risk of their patients. Comparison between the fallers and the nonfallers regarding medical condition showed that osteoporosis and anemia were significantly related to falls (P < 0.05). Other diseases showed no significant difference between the fallers and the nonfallers. In addition, number of drugs administered and administration of drugs that affect balance or consciousness showed no significant difference between the fallers and the nonfallers. On the other hand, there was a highly significant difference between the fallers and the nonfallers as regards history of a fall (P = 0.007) and hospital length of stay; increased duration of stay in hospital was significantly related to falls (P = 0.04). Table 3 shows distribution of the MFS among the studied group. The fallers had a significantly higher risk for falling than the nonfallers (P = 0.02). However, there was no significant difference between the fallers and the nonfallers as regards both ADL and IADL score (P 0.05). At the same time, there was a significant negative correlation between MFS score and both ADL score (r = −0.28, P < 0.01) and IADL score (r = −0.26, P < 0.01) (Fig. 2). There was also a positive correlation between age and MFS score (r = 0.16, P < 0.01). Logistic regression analysis for fall-related factors was performed for variables that were statistically significant in the univariate analysis (osteoporosis, anemia, history of a fall, and
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TABLE 2. Comparison of Available Fall Preventive Measures in the Faller and Nonfaller Groups of Patients
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n
Preventive Measure* Bedside locker Absent Present Side rails Absent Present Handrails Absent Present Medication evaluation Absent Present Fall risk assessment Absent Present Restraints Absent Present Informal caregiver (relative) Absent Present Informing patient/family about fall risk Absent Present
%
Fall n
P
x2
%
0.24 0.62 13 3.6 1 2.2 352 96.4 45 97.8 0.79 0.37 276 75.6 32 69.6 89 24.4 14 30.4 0.06 0.81 337 92.3 42 91.3 27 7.7 4 8.7 0.09 0.92 348 95.3 44 95.7 17 4.7 2 4.3 0.42 0.51 349 95.6 43 93.5 16 4.4 3 6.5 1.49 0.22 363 99.5 45 97.8 2 0.5 1 2.2 0.11 0.74 46 12.6 5 10.9 319 87.4 41 89.1 0.15 0.71 346 94.8 43 93.5 19 5.2 3 6.5
*Some fall preventive measures were available for all patients in all units such as locking the bed’s brakes, turning on a night-light, and having a clean and dry floor. Other measures were not available at all such as presence of a call bell in reach and placing a sign indicating that the patient was at a high risk for falling.
length of hospital stay). The analysis showed that the first 3 factors were the independent predictors of patient falls (Table 4).
DISCUSSION Patient falls represent a major problem that is not well recognized by the health care system in Egypt. Because falls are common and their consequences could be very devastating, especially at an older age,10,15 this study aimed to describe the different characteristics of elderly patients who fall in hospitals and the
TABLE 3. Comparison of MFS in the Faller and Nonfaller Groups of Patients No Fall (365) Item MFS score No risk (0–24) Low risk (25–50) High risk (≥51)
Fall (46)
Total
n
%
n
%
n
%
43 174 148
11.8 47.7 40.5
8 12 26
17.4 26.1 56.5
51 186 174
12.4 45.3 42.3
w2=7.71 and P=0.02.
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J Patient Saf • Volume 11, Number 4, December 2015
Rate and Predictors of Fall Among Elderly Patients
FIGURE 2. Scatter diagram showing the correlation between MFS score and ADL score.
circumstances surrounding these events to identify different contributing factors. Four hundred eleven patients were included in this study, with a total number of 2958 patient days. The incidence rate of falls was calculated to be 16.9 per 1000 patient days. This means that approximately 8 fall incidents occur each day after adjusting for bed capacity, occupancy rate, and mean proportion of admitted elderly patients in Ain Shams University Hospital. This incidence rate is very high when compared with other similar studies in which the rate of falls among elderly patients varied from 6 to 11.7 per 1000 patient days.5,8,9,16 The lack of a standardized implemented policy in the study hospital for assessment and management of falls was most probably behind this high incidence rate of falls. The results also showed that most fall preventive measures were not available for most of the patients. Only 25% of beds had side rails, and some fall preventive measures were not available at all such as presence of a call bell in reach and placing a sign indicating that the patient was at a high risk for falling. Other preventive measures were available for all patients in all units such as locking the bed’s brakes, turning on a night-light, and having a clean and dry floor. One important limitation of this study is that control for clustering within units was not addressed in data management. Most of the patients had a full-time companion, usually a family member, during their stay in the hospital. This may play a role in preventing patients’ falls, as described by Giles et al17 in 2006. Another study in Taiwan found that the presence of a family member with the patient 24 hours a day had no effect on preventing falls.18 We studied sociodemographic characteristics of both fallers and nonfallers and did not find a statistically significant difference between the 2 groups. This finding is in agreement with some studies9 and contradicts results of others who found men to be more prone to falls,10,19 whereas others found women to be more prone.2,20 In a study in New Zealand, alcohol was found to be strongly associated with unintentional falls and fall-related injuries.21 In our study, only 9 patients (2.2%) consumed alcohol, and 1 of them experienced a fall. This low percentage is most probably due to the religious beliefs of the patients. This made no significant difference among the fallers and the nonfallers because most were nonconsumers. Regarding associated medical condition, hypertension, diabetes, liver disease, and cardiovascular disease were the most common associated medical conditions in all studied patients. © 2014 Wolters Kluwer Health, Inc. All rights reserved.
However, presence of osteoporosis and anemia was found to be significantly higher in the fallers than the nonfallers. Anemia was found in 21.7% of the patients who fell and 14% of those who did not fall, and the difference was statistically significant (P < 0.05). A similar study also found that anemia was significantly associated with an increased risk for injurious falls, especially in the elderly population.22 This is because anemia in the elderly is associated with a number of health-related functional declines such as frailty, disability, and muscle weakness. As for osteoporosis, we found that 21.7% and 10% of the fallers and the nonfallers, respectively, had osteoporosis (P < 0.05). Osteoporosis leads to decreased bone strength, which is usually accompanied by acute or chronic pain.23 This makes the patient experience gait and balance deficits, thus rendering him/her more prone to falls. This was also concluded by Waters and his colleagues24 in their study in New Zealand. In this study, a history of a fall within the preceding 3 months of the study was found to be highly significant in relation to falls (P = 0.007). This finding is supported by other studies that also found that a history of a fall, in or out of the hospital, was a significant risk factor associated with falling.15,25 The patients who fell stayed significantly longer (>2 wk). One may think that the patients who fell stayed longer because of fall consequences; however, most falls (76%) had no serious consequences, approximately 18% had contusions, 2% had subdural hematomas, and 4% had fractures and lacerations. Other authors also stated that the majority of falls in their study resulted in no harm.26,27 What is more, the results of other studies confirmed that hospitalization itself is an important risk factor of falls5,9,10,28 and that fall risk increased in patients who stayed longer.
TABLE 4. Logistic Regression Analysis of Fall-Related Factors Showing Independent Predictors of Falls Variable Osteoporosis Anemia History of a fall
β
SE
OR
95% CI
P
0.79 0.78 0.03
0.41 0.38 0.01
2.21 2.19 1.03
0.99–4.92 1.03–4.67 1.01–1.06
0.05 0.04 0.004
CI, confidence interval; OR, odds ratio.
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In this study, most of the elderly patients were at risk for falls on admission according to the MFS, 45.3 at low risk and 42.3 at high risk for falls. The fallers showed a significantly high risk according to the MFS (P = 0.02). Fifteen percent of the highrisk patients experienced falls in comparison with 6.5% of the low-risk patients. However, what was astonishing is to find that 15.7% of the patients with no risk for falls experienced falls. Although the MFS was able to predict patients at risk for falls and identified correctly 82.6% of fallers, it gave us a high falsepositive rate, identifying more than 88% of nonfallers as having risk for falls (40% at high risk and 48% at low risk). This could be explained by having effective fall preventive measures for patients identified with risk for falls on admission; however, this was not the case in this study because there was no standardized protocol for fall prevention and protective measures were not available for most of the patients. O’Connell and Myers,29 in 2001, commented also that the MFS gave a high false-positive rate in their study, identifying more than 70% of nonfallers as having high risk for falls. Further analysis showed a positive correlation between level of fall risk by MFS and age. This confirms the fact that as age increases, the individual risk for falls increases and the patient becomes more susceptible to experience a fall. We also found a negative correlation between level of fall risk by the MFS and functional assessment by ADL and IADL scores. This means that a high MFS score associated with worse abilities to perform ADLs/IADLS. As patients become more dependent on others in performing different daily life activities, they become more prone to falls. Concerning fall circumstances in this study, most falls (94%) occurred on regular working days in the morning (8 AM to 2 PM). The commonest activity related to falls was toileting (60%). Most patients were alert and oriented. They were ambulating and fell either in their rooms (44%) or in the bathroom (42%). Very few falls (4%) occurred outside the department, while the patient was on his/her way to have a specific investigation or procedure done. These findings are in agreement with results of other studies that found that the majority of falls occurred in the patient’s room and the rest occurred while the patients were attempting to reach the bathroom.8,19,25,26
CONCLUSIONS The incidence rate of falls was found to be 16.9 per 1000 patient days in Ain Shams University Hospital. The MFS was able to predict patients at risk for falls and identified correctly 82.6% of fallers. Logistic regression identified independent predictors of falls to be anemia, osteoporosis, and history of a fall, which should be considered in formulating fall risk assessment and management policy in Ain Shams University Hospital. REFERENCES 1. Tinetti ME, Williams CS. Falls, injuries due to falls, and the risk of admission to a nursing home. N Engl J Med. 1997;337:1279–1284. 2. Tommasini C, Talamini R, Bidoli E, et al. Risk factors of falls in elderly population in acute care hospitals and nursing homes in north Italy: a retrospective study. J Nurs Care Qual. 2008;23:43–49. 3. Morse JM. Preventing Patient Falls: Creating a Fall Intervention Program. 2nd ed. New York, NY: Springer Publishing Co; 2009. 4. Salgado RI, Lord SR, Ehrlich F et al. Predictors of falling in elderly hospital patients. Arch Gerontol Geriatr. 2004;38:213–219. 5. Schwendimann R, Buhler H, De Geest S, et al. Characteristics of hospital inpatient fall across clinical departments. Gerontology. 2008;54:342–348. 6. Mahoney JE. Immobility and falls. Clin Geriatr Med. 1998;14:699–726.
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7. Halfon P, Eggli Y, Van Melle G, et al. Risk of falls for hospitalized patients: a predictive model based on routinely available data. J Clin Epidemiol. 2001;54:1258–1266. 8. Von Renteln-Kruse W, Krause T. Fall events in geriatric hospital in-patients. Results of prospective recording over a 3 year period. Z Gerontol Geriatr. 2004;37:9–14. 9. Corsinovi L, Bo M, Ricauda A, et al. Predictors of falls and hospitalization outcomes in elderly patients admitted to an acute geriatric unit. Arch Gerontol Geriatr. 2009:49:142–145. 10. Nakai A, Akeda M, Kawabata I. Incidence and risk factors for inpatient falls in an academic acute-care hospital. J Nippon Med Sch. 2006;73:265–270. 11. JCI (Joint Commission International). 2009. Available at: www. jointcommissioninternational.org/Quality-and-Safety-Risk-Areas/ Patient-Safety/. Accessed March 2012. 12. Morse J, Morse R, Tylko S. Development of a scale to identify the fall-prone patient. Can J Aging. 1989;8:366–367. 13. Katz S, Downs TD, Cash HR, et al. Progress in development of the index of ADL. Gerontologist. 1970;10:20–30. 14. Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969;9:179–186. 15. Krauss MJ, Nguyen SL, Dunagan WC, et al. Circumstances of patient falls and injuries in 9 hospitals in a midwestern healthcare system. Infect Control Hosp Epidemiol. 2007;28:544–550. 16. Tutuarima JA, van der Meulen JH, de Haan RJ, et al. Risk factors for falls of hospitalized stroke patients. Stroke. 1997;28:297–301. 17. Giles LC, Bolch D, Rouvray R, et al. Can volunteer companions prevent falls among inpatients? A feasibility study using a pre-post comparative design. BMC Geriatr. 2006;6:11. 18. Tzeng HM, Yin CY, Tsai SL, et al. Patient falls and open visiting hours: a case study in a Taiwanese medical center. J Nurs Care Qual. 2007;22:145–151. 19. Hitcho EB, Krauss MJ, Birge S, et al. Characteristics and circumstances of falls in a hospital setting: a prospective analysis. J Gen Intern Med. 2004;19:732–739. 20. Halil M, Ulger Z, Cankurtaran M, et al. Falls and the elderly: is there any difference in the developing world? A cross-sectional study from Turkey. Arch Gerontol Geriatr. 2006;43:351–359. 21. Kool B, Ameratunga S, Robinson E, et al. The contribution of alcohol to falls at home among working-aged adults. Alcohol. 2008;42:383–388. 22. Duh MS, Mody SH, Lefebvre P, et al. Anaemia and the risk of injurious falls in a community-dwelling elderly population. Drugs Aging. 2008;25:325–334. 23. Preisinger E. Physiotherapy and exercise in osteoporosis and its complications. Z Rheumatol. 2009;68:534–538. 24. Waters DL, Hale L, Grant AM, et al. Osteoporosis and gait and balance disturbances in older sarcopenic obese New Zealanders. Osteoporos Int. 2010;21:351–357. 25. Papaioannou A, Parkinson W, Cook R, et al. Prediction of falls using a risk assessment tool in the acute care setting. BMC Med. 2004;2:1. 26. Fischer ID, Krauss MJ, Dunagan WC, et al. Patterns and predictors of inpatient falls and fall-related injuries in a large academic hospital. Infect Control Hosp Epidemiol. 2005;26:822–827. 27. Healey F, Scobie S, Oliver D, et al. Falls in English and Welsh hospitals: a national observational study based on retrospective analysis of 12 months of patient safety incident reports. Qual Saf Health Care. 2008;17:424–430. 28. Vassallo M, Amersey RA, Sharma JC, et al. Falls on integrated medical wards. Gerontology. 2000;46:158–162. 29. O’Connell B, Myers H. A failed fall prevention study in an acute care setting: lessons from the swamp. Int J Nurs Pract. 2001;7:126–130.
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A Study of Rate and Predictors of Fall Among Elderly Patients in a University Hospital Mahi Mahmoud al Tehewy, PhD, Ghada Essam Amin, Dr, and Nahla Wassem Nassar, MPH Introduction: Falls represent a serious problem facing hospital-admitted patients, and the severity of fall-related complications rises steadily after the age of 65 years. Objectives: The aims of this study were (a) to calculate the rate of falls among elderly patients in the internal medicine departments in Ain Shams University Hospital, (b) to identify different predictors and characteristics of falls, and (c) to assess clinical consequences and hospitalization outcomes of falls. Subjects and Methods: An observational longitudinal study has been conducted in Ain Shams University Hospital, where 411 elderly patients admitted to the internal medicine departments were included. Upon admission, the patients were assessed for their risk for falling using the Morse Fall Scale (MFS). Information about their medical condition and drugs administered was obtained. Functional assessment of the patients regarding their ability to perform different daily activities was also performed. The patients were followed up during their stay, and once a fall event occurred, complete details regarding the circumstances and consequences of that event were obtained. Results: The incidence rate of falls was found to be 16.9 per 1000 patient days. The fallers had a significantly high risk for falling according to the MFS (P = 0.02). The MFS was able to predict patients at risk for falling and identified correctly 82.6% of the fallers. The most common medical conditions associated with falls were diabetes (48.7%), hypertension (58.7%), and visual impairment (41.3%). Anemia (P = 0.05) and osteoporosis (P = 0.02) showed a statistically significant difference between the fallers and the nonfallers. Presence of a history of a fall and increased length of hospital stay were highly significant (P = 0.01) factors that predisposed to falls. Logistic regression analysis showed that anemia, osteoporosis, and history of a fall were independent predictors of falls. Most falls had no serious consequences, approximately 18% had contusions, 2% had subdural hematomas, and 4% had fractures and lacerations. Conclusions: Elderly patients with anemia, osteoporosis, and history of a fall are more prone to falls and should be considered in fall protective measures. Key Words: patient fall, elderly patient, patient safety (J Patient Saf 2014;11: 210–214)
A
n accidental fall has been defined as a sudden and nonintentional change of posture to the ground or a lower level, onto an object, floor, pavement, ground, or any other type of surface, and includes slipping, tripping (stumbling), falling on other people, loss of balance, and accidental stooping.1,2 Falls result from an interaction between predisposing and precipitating factors in people’s environment and in individual demographic and clinical characteristics. Hospitalization itself may be an important risk factor in falls, representing a deep change in elderly patients’ life habits. Morse3 classified falls into 3 types: From the Faculty of Medicine, Ain Shams University, Cairo, Egypt. Correspondence: Mahi Mahmoud al Tehewy, PhD, Faculty of Medicine, Ain Shams University, Abassia Square, Cairo, Egypt (e‐mail: [email protected]). The authors disclose no conflict of interest. Copyright © 2014 by Wolters Kluwer Health, Inc. All rights reserved.
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anticipated physiological fall, accidental fall, and unanticipated physiological fall. Studies showed that patient falls happened more frequently in geriatric wards followed by general medical and surgical wards.4,5 The rate of patient falls was approximately 2 per 1000 patient days in general hospitals and increased in geriatric departments up to approximately 10 per 1000 patient days.6–9 Consequences of falls include mild to severe injury, increased morbidity and mortality, loss of independence, fear, decreased activity, additional treatment expenses, and decreased quality of life.10 The severity of fall-related complications rises steadily after the age of 65 years including not only physical injury but also psychological and social consequences. In 2007, reducing the risk for patient harm resulting from falls became one of the international patient safety goals through assessing and periodically reassessing each patient’s risk for falling and taking action to decrease or eliminate any identified risks.11 Actually, up until now, fall as a patient safety issue is not highlighted in Ain Shams University Hospital, and little is known about the magnitude and determinants of this problem.
OBJECTIVES OF THE STUDY The objectives of this study were the following: 1. To calculate the rate of falls among elderly patients in the internal medicine departments of Ain Shams University Hospital 2. To identify different predictors and characteristics of falls 3. To assess clinical consequences and hospitalization outcomes of falls
SUBJECTS AND METHODS Study Design and Study Setting A longitudinal observational study was conducted in the internal medicine departments of Ain Shams University Hospital, a hospital with an 800-bed capacity. All medical departments, general and specialized, were included in the study.
Subjects Any patient, male or female, older than 60 years admitted to any of the internal medicine departments of Ain Shams University Hospital was included after agreeing to participate in the study. Exclusion criteria include psychiatric patients, patients who fell during physical therapy sessions, and patients admitted to the intensive care unit. Patients who fell during physical therapy sessions were excluded because such sessions encourage patients to be engaged in activities that may lead to postural instability. However, physical therapists already know that their patients are at higher risk for falling and thus apply special technical approaches to avoid body harm.
Sample Size and Sampling Technique The sample size was calculated to be 411 patients. Epi Info software version 6 was used, assuming that relative risk is 2, J Patient Saf • Volume 11, Number 4, December 2015
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J Patient Saf • Volume 11, Number 4, December 2015
Rate and Predictors of Fall Among Elderly Patients
prevalence among none exposed is 11.3%,9 confidence level is 95%, and power is 80%. A list of all admitted patients older than 60 years was obtained from the admission office each day, and a 25% systematic random sample of those patients was selected daily. On average, approximately 10 patients were included in the study each day.
• Fall incident sheet: only for patients who experienced a fall. It included details of the event such as date, time, and location of the fall. It also included data about the patient’s status just before the fall; relation of the fall to a specific activity, for example, toileting or turning the head suddenly; and clinical consequences of the fall.
Study Tools
Methods
• A data sheet was formulated to collect information extracted from patient file about personal data, habits, diagnosis, date of admission, date of discharge, duration of stay, associated medical conditions, the patient’s medications especially those affecting consciousness and balance, and condition of the patient upon his/her discharge. Data also included protective measures taken in the department to prevent the patients from falling obtained by direct observation or asking the patient’s health caregiver because these data were not documented routinely in the patient file. • Fall risk assessment using the Morse Fall Scale (MFS).12 The scale consists of the following 6 variables that are quick and easy to score: ○History of falling (no scores 0, yes scores 25) ○Secondary diagnosis (no scores 0, yes scores 15) ○Need for ambulatory aids (none/bed rest/nurse assist scores 0, crutches/cane/walker scores 15, furniture scores 30) ○Current intravenous therapy (no scores 0, yes scores 20) ○Gait or transferring (normal/bed rest/ wheelchair scores 0, weak scores 10, impaired scores 20) ○Mental status (oriented to own ability scores 0, forgets limitations scores 15)
Upon admission, the patients were assessed for their risk for falling using the MFS. Information about their personal and medical condition including drugs administered was obtained. Functional assessment of the patients regarding their ability to perform different daily activities and instrumental activities was also performed. The patients were then followed up during their stay in the hospital, and once a fall event occurred, complete details regarding the circumstances and consequences of that event were obtained. The study was conducted during the first 6 months of 2009.
The scores are then summed up, and the total identifies the risk for the patient to fall as follows: ○0 to 24 → No risk ○25 to 50 → Low risk ○51 or higher → High risk • Functional assessment for activities of daily living (ADLs). This was done using the progress in development of the index of ADLs done by Katz et al13 in 1970. It assesses the level of dependence of the patient according to the following activities: bathing, dressing, toileting, transferring, continence, and feeding. The patient is scored yes (1) or no (0) according to his/her independence in performing each of the previous functions. The scores are then summed up. The total points range from 0 to 6, with 0 being the lowest score (patient is totally dependent on others) and 6 being the highest score (patient is totally independent). • Functional assessment for instrumental ADLs (IADLs). This was done using the Lawton IADL scale, which was established in 1969.14 It assesses the level of the patient’s dependence according to skills that are considered more complex than the basic ADLs measured by the Katz Index of ADLs. These skills are ability to use the telephone, shopping, mode of transportation, responsibility for own medications, ability to handle finances, food preparation, housekeeping, and laundry. • Women are scored on all 8 areas of function, whereas for men, the areas of food preparation, housekeeping, and laundering are excluded. Patients are scored according to their highest level of functioning in that category. A summary score ranges from 0 (low function, dependent) to 8 (high function, independent) for women and 0 through 5 for men. © 2014 Wolters Kluwer Health, Inc. All rights reserved.
Data Management Data were collected, cleaned, coded, and introduced into a personal computer using the Statistical Package for the Social Sciences software version 17. Descriptive analysis was first done to determine the frequencies of different variables, then univariate analysis was done using the Pearson w2 test. Correlation coefficient was used to determine the correlation between MFS score and each of the following variables: patient’s age, ADL score, and IADL score. Multivariate analysis in the form of logistic regression analysis for fall-related factors was performed for variables that were statistically significant in the univariate analysis. Variables were entered together in the equation to identify independent predictors of falls. P value of less than 0.05 was considered significant in all performed analyses.
Ethical Considerations Administrative and ethical committee approval was obtained before conducting the study. Patient informed consent was obtained after explaining to him/her (or his/her caregiver) the importance and objectives of this study and what information would be needed to be given by the patient or obtained from reviewing his/ her medical file. The health caregivers of the patients who were proven to have a high risk for falling, according to the MFS, were informed to take necessary preventive measures. Confidentiality of personal data was also considered.
RESULTS The mean (±SD) age of the patients was 67.6 (6.7). The minimum age was 60 years, and the maximum was 90 years. Forty-six
FIGURE 1. Frequency and fall rate among elderly patients www.journalpatientsafety.com
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TABLE 1. Distribution of Sociodemographic Data and Patient Habits Among Faller and Nonfaller Groups of Patients No Fall (365) Item Age 60–69 70–79 ≥80 Sex Female Male Marital status Single Married Divorced Widowed Smoking Nonsmokers Smokers Alcohol Nonconsumers Consumers
Fall (46)
n
%
n
%
240 94 31
65.8 25.8 8.5
32 12 2
69.6 26.1 4.3
163 202
44.7 55.3
24 22
No Fall x2
P
0.97
0.62
0.93
0.34
3.23
0.36
52.2 47.8
3 264 6 92
0.8 72.3 1.6 25.2
1 29 2 14
2.2 63.1 4.3 30.4
272 93
74.5 25.5
37 9
80.4 19.6
357 8
97.8 2.2
45 1
97.8 2.2
0.77
—
0.38
—
(11.2%) of 411 patients experienced 50 falls. Forty-three of them fell once, 2 patients fell twice, and 1 patient fell 3 times (Fig. 1). The incidence rate of falls was calculated to be 16.9 per 1000 patient days. There was no significant difference between the fallers and the nonfallers regarding age distribution, sex, marital status, smoking, and alcohol consumption (Table 1). Table 2 shows fall preventive measures available for each patient. All measures, except bedside lockers, are absent for most of the patients, with no significant difference between the fallers and the nonfallers. More than 85% of the patients, fallers and nonfallers, were accompanied by an informal caregiver (relative), and most of them were not informed about fall risk of their patients. Comparison between the fallers and the nonfallers regarding medical condition showed that osteoporosis and anemia were significantly related to falls (P < 0.05). Other diseases showed no significant difference between the fallers and the nonfallers. In addition, number of drugs administered and administration of drugs that affect balance or consciousness showed no significant difference between the fallers and the nonfallers. On the other hand, there was a highly significant difference between the fallers and the nonfallers as regards history of a fall (P = 0.007) and hospital length of stay; increased duration of stay in hospital was significantly related to falls (P = 0.04). Table 3 shows distribution of the MFS among the studied group. The fallers had a significantly higher risk for falling than the nonfallers (P = 0.02). However, there was no significant difference between the fallers and the nonfallers as regards both ADL and IADL score (P 0.05). At the same time, there was a significant negative correlation between MFS score and both ADL score (r = −0.28, P < 0.01) and IADL score (r = −0.26, P < 0.01) (Fig. 2). There was also a positive correlation between age and MFS score (r = 0.16, P < 0.01). Logistic regression analysis for fall-related factors was performed for variables that were statistically significant in the univariate analysis (osteoporosis, anemia, history of a fall, and
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TABLE 2. Comparison of Available Fall Preventive Measures in the Faller and Nonfaller Groups of Patients
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n
Preventive Measure* Bedside locker Absent Present Side rails Absent Present Handrails Absent Present Medication evaluation Absent Present Fall risk assessment Absent Present Restraints Absent Present Informal caregiver (relative) Absent Present Informing patient/family about fall risk Absent Present
%
Fall n
P
x2
%
0.24 0.62 13 3.6 1 2.2 352 96.4 45 97.8 0.79 0.37 276 75.6 32 69.6 89 24.4 14 30.4 0.06 0.81 337 92.3 42 91.3 27 7.7 4 8.7 0.09 0.92 348 95.3 44 95.7 17 4.7 2 4.3 0.42 0.51 349 95.6 43 93.5 16 4.4 3 6.5 1.49 0.22 363 99.5 45 97.8 2 0.5 1 2.2 0.11 0.74 46 12.6 5 10.9 319 87.4 41 89.1 0.15 0.71 346 94.8 43 93.5 19 5.2 3 6.5
*Some fall preventive measures were available for all patients in all units such as locking the bed’s brakes, turning on a night-light, and having a clean and dry floor. Other measures were not available at all such as presence of a call bell in reach and placing a sign indicating that the patient was at a high risk for falling.
length of hospital stay). The analysis showed that the first 3 factors were the independent predictors of patient falls (Table 4).
DISCUSSION Patient falls represent a major problem that is not well recognized by the health care system in Egypt. Because falls are common and their consequences could be very devastating, especially at an older age,10,15 this study aimed to describe the different characteristics of elderly patients who fall in hospitals and the
TABLE 3. Comparison of MFS in the Faller and Nonfaller Groups of Patients No Fall (365) Item MFS score No risk (0–24) Low risk (25–50) High risk (≥51)
Fall (46)
Total
n
%
n
%
n
%
43 174 148
11.8 47.7 40.5
8 12 26
17.4 26.1 56.5
51 186 174
12.4 45.3 42.3
w2=7.71 and P=0.02.
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J Patient Saf • Volume 11, Number 4, December 2015
Rate and Predictors of Fall Among Elderly Patients
FIGURE 2. Scatter diagram showing the correlation between MFS score and ADL score.
circumstances surrounding these events to identify different contributing factors. Four hundred eleven patients were included in this study, with a total number of 2958 patient days. The incidence rate of falls was calculated to be 16.9 per 1000 patient days. This means that approximately 8 fall incidents occur each day after adjusting for bed capacity, occupancy rate, and mean proportion of admitted elderly patients in Ain Shams University Hospital. This incidence rate is very high when compared with other similar studies in which the rate of falls among elderly patients varied from 6 to 11.7 per 1000 patient days.5,8,9,16 The lack of a standardized implemented policy in the study hospital for assessment and management of falls was most probably behind this high incidence rate of falls. The results also showed that most fall preventive measures were not available for most of the patients. Only 25% of beds had side rails, and some fall preventive measures were not available at all such as presence of a call bell in reach and placing a sign indicating that the patient was at a high risk for falling. Other preventive measures were available for all patients in all units such as locking the bed’s brakes, turning on a night-light, and having a clean and dry floor. One important limitation of this study is that control for clustering within units was not addressed in data management. Most of the patients had a full-time companion, usually a family member, during their stay in the hospital. This may play a role in preventing patients’ falls, as described by Giles et al17 in 2006. Another study in Taiwan found that the presence of a family member with the patient 24 hours a day had no effect on preventing falls.18 We studied sociodemographic characteristics of both fallers and nonfallers and did not find a statistically significant difference between the 2 groups. This finding is in agreement with some studies9 and contradicts results of others who found men to be more prone to falls,10,19 whereas others found women to be more prone.2,20 In a study in New Zealand, alcohol was found to be strongly associated with unintentional falls and fall-related injuries.21 In our study, only 9 patients (2.2%) consumed alcohol, and 1 of them experienced a fall. This low percentage is most probably due to the religious beliefs of the patients. This made no significant difference among the fallers and the nonfallers because most were nonconsumers. Regarding associated medical condition, hypertension, diabetes, liver disease, and cardiovascular disease were the most common associated medical conditions in all studied patients. © 2014 Wolters Kluwer Health, Inc. All rights reserved.
However, presence of osteoporosis and anemia was found to be significantly higher in the fallers than the nonfallers. Anemia was found in 21.7% of the patients who fell and 14% of those who did not fall, and the difference was statistically significant (P < 0.05). A similar study also found that anemia was significantly associated with an increased risk for injurious falls, especially in the elderly population.22 This is because anemia in the elderly is associated with a number of health-related functional declines such as frailty, disability, and muscle weakness. As for osteoporosis, we found that 21.7% and 10% of the fallers and the nonfallers, respectively, had osteoporosis (P < 0.05). Osteoporosis leads to decreased bone strength, which is usually accompanied by acute or chronic pain.23 This makes the patient experience gait and balance deficits, thus rendering him/her more prone to falls. This was also concluded by Waters and his colleagues24 in their study in New Zealand. In this study, a history of a fall within the preceding 3 months of the study was found to be highly significant in relation to falls (P = 0.007). This finding is supported by other studies that also found that a history of a fall, in or out of the hospital, was a significant risk factor associated with falling.15,25 The patients who fell stayed significantly longer (>2 wk). One may think that the patients who fell stayed longer because of fall consequences; however, most falls (76%) had no serious consequences, approximately 18% had contusions, 2% had subdural hematomas, and 4% had fractures and lacerations. Other authors also stated that the majority of falls in their study resulted in no harm.26,27 What is more, the results of other studies confirmed that hospitalization itself is an important risk factor of falls5,9,10,28 and that fall risk increased in patients who stayed longer.
TABLE 4. Logistic Regression Analysis of Fall-Related Factors Showing Independent Predictors of Falls Variable Osteoporosis Anemia History of a fall
β
SE
OR
95% CI
P
0.79 0.78 0.03
0.41 0.38 0.01
2.21 2.19 1.03
0.99–4.92 1.03–4.67 1.01–1.06
0.05 0.04 0.004
CI, confidence interval; OR, odds ratio.
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In this study, most of the elderly patients were at risk for falls on admission according to the MFS, 45.3 at low risk and 42.3 at high risk for falls. The fallers showed a significantly high risk according to the MFS (P = 0.02). Fifteen percent of the highrisk patients experienced falls in comparison with 6.5% of the low-risk patients. However, what was astonishing is to find that 15.7% of the patients with no risk for falls experienced falls. Although the MFS was able to predict patients at risk for falls and identified correctly 82.6% of fallers, it gave us a high falsepositive rate, identifying more than 88% of nonfallers as having risk for falls (40% at high risk and 48% at low risk). This could be explained by having effective fall preventive measures for patients identified with risk for falls on admission; however, this was not the case in this study because there was no standardized protocol for fall prevention and protective measures were not available for most of the patients. O’Connell and Myers,29 in 2001, commented also that the MFS gave a high false-positive rate in their study, identifying more than 70% of nonfallers as having high risk for falls. Further analysis showed a positive correlation between level of fall risk by MFS and age. This confirms the fact that as age increases, the individual risk for falls increases and the patient becomes more susceptible to experience a fall. We also found a negative correlation between level of fall risk by the MFS and functional assessment by ADL and IADL scores. This means that a high MFS score associated with worse abilities to perform ADLs/IADLS. As patients become more dependent on others in performing different daily life activities, they become more prone to falls. Concerning fall circumstances in this study, most falls (94%) occurred on regular working days in the morning (8 AM to 2 PM). The commonest activity related to falls was toileting (60%). Most patients were alert and oriented. They were ambulating and fell either in their rooms (44%) or in the bathroom (42%). Very few falls (4%) occurred outside the department, while the patient was on his/her way to have a specific investigation or procedure done. These findings are in agreement with results of other studies that found that the majority of falls occurred in the patient’s room and the rest occurred while the patients were attempting to reach the bathroom.8,19,25,26
CONCLUSIONS The incidence rate of falls was found to be 16.9 per 1000 patient days in Ain Shams University Hospital. The MFS was able to predict patients at risk for falls and identified correctly 82.6% of fallers. Logistic regression identified independent predictors of falls to be anemia, osteoporosis, and history of a fall, which should be considered in formulating fall risk assessment and management policy in Ain Shams University Hospital. REFERENCES 1. Tinetti ME, Williams CS. Falls, injuries due to falls, and the risk of admission to a nursing home. N Engl J Med. 1997;337:1279–1284. 2. Tommasini C, Talamini R, Bidoli E, et al. Risk factors of falls in elderly population in acute care hospitals and nursing homes in north Italy: a retrospective study. J Nurs Care Qual. 2008;23:43–49. 3. Morse JM. Preventing Patient Falls: Creating a Fall Intervention Program. 2nd ed. New York, NY: Springer Publishing Co; 2009. 4. Salgado RI, Lord SR, Ehrlich F et al. Predictors of falling in elderly hospital patients. Arch Gerontol Geriatr. 2004;38:213–219. 5. Schwendimann R, Buhler H, De Geest S, et al. Characteristics of hospital inpatient fall across clinical departments. Gerontology. 2008;54:342–348. 6. Mahoney JE. Immobility and falls. Clin Geriatr Med. 1998;14:699–726.
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