- Email: [email protected]
Fall risk: the clinical relevance of falls and how to integrate fall risk with fracture risk
Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
Contents lists available at ScienceDirect
Best Practice & Research Clinical Rheumatology journal homepage: www.elsevierhealth.com/berh
8
Fall risk: the clinical relevance of falls and how to integrate fall risk with fracture risk G. Peeters, MSc, Postdoc researcher, Epidemiologist a,1, Natasja M. van Schoor, PhD, Postdoc researcher, Epidemiologist a, 2, Paul Lips, PhD, MD, Professor of endocrinology b, * a
Department of Epidemiology and Biostatistics, EMGO Institute for health and care research, VU University Medical Center, van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands b Department of Internal Medicine, Section Endocrinology, VU University Medical Center, Postbus 7057, 1007 MB Amsterdam, The Netherlands
Keywords: accidental falls fracture risk profiles osteoporosis
In old age, 5–10% percent of all falls result in a fracture, and up to 90% of all fractures result from a fall. This article describes the link between fall risk and fracture risk in community-dwelling older persons. Which factors attribute to both the fall risk and the fracture risk? Which falls result in a fracture? Which tools are available to predict falls and fractures? Directions for the use of prediction tools in clinical practice are given. Challenges for future research include further validation of existing prediction tools and evaluation of the cost-effectiveness of treatment after screening. Ó 2009 Elsevier Ltd. All rights reserved.
Falling is a major health problem in old age. It is considered as one of the geriatric giants. Since the late1980s, the number of publications on the subject of falling has risen steeply. These studies cover different settings: community-dwelling older persons, hospitalised older persons and home for the elderly/nursing home residents. The risk factors, risk profiles and preventive interventions for falling differ across these populations. The current article focusses on the epidemiology, risk factors and prediction models for falls and fractures in community-dwelling older persons.
* Corresponding author. Tel.: þ31 204440614; Fax: þ31 204440502. E-mail addresses: [email protected] (G. Peeters), [email protected] (N.M. van Schoor), [email protected] (P. Lips). 1 Tel.: þ31 204449336; Fax: þ31 204446775. 2 Tel.: þ31 204448439; Fax: þ31 204446775. 1521-6942/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.berh.2009.09.004
798
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
Epidemiology of falls and fractures in old age Annually, about 30% of the community-dwelling persons of 65 years and older fall once and 15% fall twice or more times [1,2]. The consequences of falling can be severe: 68% reports a physical injury, 6% suffers a major injury and 29–92% reports fear of falling, depending on the definition used [3–5]. Subsequently, a fall can lead to decreased physical functioning, loss of independence, nursing home admittance and even death [1,4,6,7]. About 5–10% of all falls result in a fracture, whereas 90% of all fractures are attributable to falls [8,9]. Moreover, the risk of hip and proximal humerus fractures seems to be 40% and 79% higher in women with higher fall rates, respectively [10]. The economic consequences of falls and fractures vary greatly between countries, because of differences in health-care costs and treatment policies. With respect to falls, little information on the costs is available. The average direct medical costs of a fall have been estimated at about Euro 3400 for any fall in the Netherlands and at about Canadian Dollar $ 6088 for falls leading to hospitalisation in Canada [11,12]. The average medical costs of a fracture vary strongly between fracture types, with a hip fracture being the most expensive. In the Netherlands, the incremental costs for a hip fracture were estimated at US$ 9540 during the first year and US$ 1017 during the second year as compared with matched controls [13]. In the same study, vertebral fractures were associated with a yearly recurrent incremental cost of over US$ 1000. However, almost half of the cost difference was already present before the occurrence of the fracture. The total costs of osteoporotic fractures in men and women combined in Europe was estimated at Euro 36 248 million, of which Euro 24 353 million were for hip fractures, Euro 719 million for vertebral fractures and Euro 11 177 million for other osteoporotic fractures [14]. Definitions A fall is defined as ‘‘an unintentional change in position resulting in coming to rest on the ground or other lower level.’’[15] In the literature, a distinction is made between once-fallers and recurrent fallers. Falling refers to any fall and includes occasional falls. Occasional falls may be caused mainly by extrinsic factors (i.e., environmental factors that act upon the person), whereas recurrent falls are usually caused by intrinsic factors (i.e., physical, cognitive and behavioural factors within the person, e.g., mobility limitations) accompanied by an environmental hazard. Recurrent falling has been defined as two or more falls within 6 months [16]. Fractures are often classified according to the site of fracture or according to the circumstances, for example, osteoporotic fractures or fall-related fractures. Osteoporotic fractures are associated with bone fragility and may occur at any site, but typically at the proximal femur, distal radius, proximal humerus and vertebrae [17]. Fractures of head, fingers and toes, as well as fractures caused by traffic accidents or malignancies, are usually considered non-osteoporotic. Aetiology When discussing predictors, a distinction must be made between risk factors and risk indicators. Risk factors are factors that have a causal relationship with the outcome measure. For instance, walking impairment is a risk factor for falling, because impaired ability to walk directly increases the fall risk and if the walking ability is improved, the fall risk decreases. Risk indicators are factors that are associated with the outcome measure, but without causality. Having grey hair, for instance, may be associated with falling because of the link with ageing, but dyeing the hair brown would not alter the person’s fall risk. Note that both risk factors and risk indicators may add to the prediction of a certain outcome, but only risk factors offer possibilities for preventive interventions. Since most fractures result from falls, the aetiology of falls and fractures is partly overlapping (Fig. 1). The causality of falls is complex: many risk factors have been identified (Table 1). However, the attributable risk of each of these risk factors is limited. The most important predictor for falling is having a history of falling. In community-dwelling persons of 65 years and older, the fall risk is more than twice as high in persons with a history of falls [18]. In case of a fracture, the force impact on the bone exceeds the strength of the bone. Risk factors for fractures can be split into factors affecting bone strength and factors affecting the impact of forces on the bone [19,20]. Major risk factors affecting bone strength are age (>70 years), weight loss and low body
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
799
Factors leading to a fall Mobility impairments Perception impairments Depression/cognitive impairments
Fall risk
Chronic diseases/medication use Environment Factors modifying a fall Protective responses Anthropometry Impact of fall
Fracture risk
Local shock absorbers Circumstances (surface, height, obstacles) Factors decreasing bone strength Decreased bone mass Impaired bone structure
Bone strength
Impaired bone quality Fig. 1. Conceptual model of factors influencing the risk of falling and fractures.
weight, physical inactivity, hypogonadism, use of glucocorticoids, anticonvulsants, primary hyperparathyroidism, hyperthyroidism, diabetes mellitus type I, anorexia nervosa and gastrectomy [21]. Furthermore, there is a series of additional risk factors including female gender (postmenopausal status), current smoking status, low vitamin D status due to low sunlight exposure, family history of osteoporotic fracture, surgical/early menopause and low calcium intake [21]. Risk factors affecting the impact on bone include all fall-risk factors as well as conditions of the fall such as the height and acceleration and local factors such as subcutaneous fat around the hip acting as a shock absorber. Table 1 shows which risk factors are relevant for both falls and fractures. The number of overlapping risk factors is small. This is likely to be due to the multicausality of falls; the risk that each of these factors contributes to the risk of fractures is, therefore, small. Prediction of falls and fractures Prediction of (recurrent) falling In the past 2 decades, several prediction tools for falling and recurrent falling have been developed. Table 2 provides an overview of these prediction tools. Six tools were developed using statistical techniques, whereas four were developed based on expertise. Six tools predict the risk of falling, three predict the risk of recurrent falling and one predicts both the risk of falling and recurrent falling. The complexity of the tools varies from two questions and a simple test[22], to 26 questions[23] or a series of physical tests [24]. Each of these models has strengths and limitations. The overall predictive validity can be expressed by the area under the receiver-operating curve (AUC). Optimal predictive validity (AUC ¼ 1) suggests that in randomly chosen pairs of fallers and non-fallers, 100% of the persons are correctly classified as a faller and non-faller, respectively. If the AUC is 0.50, the tool does not add any information to chance (e.g., flip a coin to classify persons into fallers and non-fallers). The AUC scores of the risk profiles described here vary between 0.71 and 0.79 in the samples in which they have been developed.
800
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
Table 1 Risk factors for (any) falls and (any) fractures in community-dwelling older persons.
Age (for 10 years increase in age) Age (>80 years) Age of menopause (youngest versus oldest age) Body weight (for 10 kg decrease in weight) Chronic diseases (1 diseases) Diabetes Mellitus (type I) Diabetes Mellitus (type II) Rheumatoid Arthritis Stroke Current smoking Depressive symptoms Dizziness Fall history Family history of hip-fracture Female gender Fracture history Fear of falling High levels of physical activity Low levels of physical activity Low bone mineral density (per SD decrease) Low calcium intake Limitations in daily activities Mobility problems Muscle weakness Orthostatic hypotension Reduced sun exposure Urinary incontinence Use of psychotropic medication Use of corticosteroids Vision impairments
Falls
Fractures
1.1a 1.5–1.8a
1.27 (1.22–1.33)b 1.26 (1.08–1.47)b 2.35 (1.75–3.14)b
1.3a 2.91–3.79c 1.67 (1.37–2.08)b 1.44 (1.19–1.73)b 1.9–5.0a 1.37 (1.21–1.55)b 1.4–4.8a 1.2–3.1a 2.0–5.0a 1.2–1.8a 1.8–3.0a 0.7–2.0a 2.9a
1.94 (1.44–2.60)b 1.62 (1.48–1.77)b 1.35–21.5c
1.18–7.1c 1.5a 1.51 (1.28–1.78)b
1.5–1.8a 1.3–4.1a 1.1–2.0a 0.8–1.5a 1.38–5.31c 1.5–2.2a 1.6–3.1a 1.78 (1.37–2.32)b 1.3–1.8a
a
Intervals of risk ratios reported in the literature. The ranges were based on a convenience literature search. Pooled risk ratios and (95% confidence intervals) for fractures were adapted from Espallargues et al (2001) [21]. c Intervals of risk ratios reported in the literature were adapted from the same review [21]. Note that definitions and methods used differed across studies. b
Since the predictive validity is always higher in the sample in which the tool was developed than in other similar samples (optimism), validation of a prediction rule in a different sample is important to give insight into the generalisability (external validation). Moreover, the characteristics of a population influence the prevalence of the items and outcome measure, as a result of which the predictive validity varies among different populations. Most tools based on statistical techniques were developed in unselected samples of patients 65–70 years and older. However, the characteristics of these samples differ from the persons who consult the Emergency Department or general practitioner after a fall. Only one tool was developed in a population with a fall history[25], and three were validated among persons who consulted the Emergency Department and/or general practitioner after a fall [22,23,26]. The predictive validity of these three tools was moderate to good with AUCs ranging from 0.65 to 0.73 [22,23,26]. Prediction of fractures Case finding for osteoporosis varies from population screening with dual-energy X-ray absorptiometry (DXA) to step-wise case finding using clinical risk factors first, followed by DXA if indicated. Bone mineral density (BMD) as measured with DXA is used for the diagnosis for osteoporosis and is also the most important predictor for fractures with risk ratios per standard deviation change in BMD for hip fractures varying from 3.68 (CI 2.61–5.19) at the age of 50 years to 1.70 at the age of 90 years (CI 1.50–1.93) and for other osteoporotic fractures of 1.19 (CI 1.05–1.34) at the age of 50 years and 1.56 (CI 1.40–1.75) at the age of 90 years [19]. However, the predictive value of this measure is limited: more
Table 2 Overview of prediction models for (recurrent) falling in community-dwelling older persons. name of instrument (Acronym)
method of development
outcome
development populationa
content of instrument
Tinetti (1988)
Risk model for falls
logistic regression
fall
6 items
Stalenhoef (2000)
Patient record-based risk model
logistic regression
recurrent falls
genetic algorithm neural network
fall
unselected, 75þ n ¼ 336 unselected 70þ n ¼ 1660 unselected 65þ n ¼ 435 unselected 70þ n ¼ 557 positive fall history 70þ n ¼ 311
Bath (2000)
6 items available in GP records 16 questions
Covinsky (2001)
History and mobility exam index
logistic regression
fall
Stalenhoef (2002)
Risk model for recurrent falls
logistic regression
recurrent falls
Lord (2003)
Physiological profile approach (PPA)
expertise
fall, multiple falls
series of physical tests
Nandy (2004)
Falls risk assessment tool (FRAT)
systematic review and expert panel
fall
5 questions
Pluijm (2006)
LASA fall risk profile
logistic regression
recurrent falls
Russell (2008)
Falls risk for older people in the community (FROP-Com)
expertise
fall
26 questions
Russell (2009)
Falls risk for older people in the community (FROP-Com)
expertise
fall
2 questions, 1 test
unselected 65þ n ¼ 1365
2 questions, 4 tests 3 questions, 3 tests
7 questions, 2 tests
diagnostic values internal validity
external validation populationa
diagnostic values external validity
(un)selected groups varying in age and sample size unselected 65þ n ¼ 510 consulting ED/GP after a fall 65þ n ¼ 408 consulting ED after a fall 65þ n ¼ 344 consulting ED after a fall 65þ n ¼ 344
AUC multiple falls ¼ 0.75 intrarater reliability > 0.50
AUC ¼ 0.73 Se ¼ 64 Sp ¼ 71 PPV ¼ 42 NPV ¼ 86 Se ¼ 31 Sp ¼ 92 PPV ¼ 57 NPV ¼ 79 AUC ¼ 0.71 Se ¼ 59 Sp ¼ 73 PPV ¼ 38 NPV ¼ 86 AUC ¼ 0.79 Se ¼ 59 Sp ¼ 87 PPV ¼ 52 NPV ¼ 90
AUC ¼ 0.71 Se ¼ 59 Sp ¼ 71 PPV ¼ 39 NPV ¼ 85
YI ¼ 0.387 Se ¼ 59 Sp ¼ 80 PPV ¼ 43 NPV ¼ 88 AUC ¼ 0.65 Se ¼ 57 Sp ¼ 74 PPV ¼ 34 NPV ¼ 86 AUC ¼ 0.68, YI ¼ 0.30, Se ¼ 66 Sp ¼ 64 intrarater reliability ¼ 0.93 interrater reliability ¼ 0.81 AUC ¼ 0.73, YI ¼ 0.34 Se ¼ 67 Sp ¼ 67 PPV ¼ 65 NPV ¼ 69 intrarater reliability ¼ 0.87 interrater reliability ¼ 0.89
801
AUC ¼ area under the ROC-curve, YI ¼ Youden index, Se ¼ sensitivity, Sp ¼ specificity, PPV ¼ positive predictive value, NPV ¼ negative predictive value, ED ¼ Emergency Department, GP ¼ general practitioner. a Reported are the selection criteria, age range, and sample size (n).
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
Author
802
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
than half of the fractures occur in persons with T-scores higher than –2.5, that is, in persons with osteopenea or normal BMD [19]. Moreover, BMD measurement is expensive and not always available. To overcome the limitations of BMD measurement to predict fractures, the World Health Organization (WHO) has developed the Fracture Risk Assessment Tool (FRAX), using individual participant data of almost 60 000 men and women from 12 population-based cohort studies [19]. The FRAX is available on the Internet (http://www.shef.ac.uk/FRAX/index.htm) and consists of the predictors age, sex, weight and height (from which BMI is computed), prior fragility fracture, parental history of hip fracture, current tobacco smoking, ever long-term use of oral glucocorticoids, rheumatoid arthritis, other causes of secondary osteoporosis and daily consumption of more than 2 units of alcohol. The predictive validity of the model can be further improved by including a BMD measurement, but this is optional. Weighted scores are assigned to each item and the 10-year probability of major osteoporotic fractures and hip fractures is computed. The FRAX tool has been calibrated for different countries by adjusting the weighted scores per item. The advantages of the FRAX tool are that the absolute 10-year fracture risk is easier to understand for patients as compared with T-scores or Z-scores of the DXA scan. Furthermore, the tool is easily accessible on the Internet. Several studies were performed on the costeffectiveness of osteoporosis treatment in women in the United Kingdom using the FRAX tool and intervention thresholds were established [27,28].
Which falls result in fractures? A few studies have studied the circumstances of fall-related fractures and identified risk factors for falls resulting in fractures. One study compared 82 fallers with fractures to 82 fallers with soft-tissue injuries and concluded that fear of falling, decreased muscle strength, high social participation and poor distance vision increased the risk of a fall-related fracture [29]. A second study among 980 older persons (over 70 years of age) showed that fall-related fractures occurred more often during slips, trips and falls due to extrinsic factors such as being pulled or due to collisions as compared with falls due to intrinsic factors (i.e., falls with no clear extrinsic contribution) [30]. Although the first study revealed some intrinsic fall-risk factors that are associated with fractures, the second study suggested that external factors that increase the impact of a fall on the bone might further increase the fracture risk. However, since these external factors are situation-specific (e.g., height of fall and slippery surfaces), they are not suitable for use in prospective risk profiles. To our knowledge, no risk profiles have been developed that predict the fracture risk following a fall. Use of prediction models in clinical practice Even if the capacity is maximally expanded, treating every older person is not feasible or costeffective. Therefore, it is important to predict which persons are most in need of preventive measures. Since fractures have a large impact on both the individual’s quality of life and health-care costs, it is even more important to predict fall-related fractures. Since 90% of all fractures result from falls, it is recommended to assess the fracture risk in persons with a high fall risk, for example, those who experienced recurrent falls, and to assess the fall risk in persons who experienced a fracture. Which tool should be used depends on the setting and population in which the tool will be used, and the diagnostic properties that are required in that situation. In an ideal situation, both the sensitivity and specificity of a measurement tool are high. However, there is a trade-off between sensitivity and specificity and one has to compromise on a tool with the optimal balance between sensitivity and specificity for the purpose of the screening. For example, if a tool is used by a general practitioner to select older persons who may benefit from an effortless and cheap preventive intervention, the tool needs to be easy and fast to administer with a high sensitivity. Since the costs and efforts of the intervention for the patients and the caregivers are low, the unnecessary treatment of persons with a low risk due to the low specificity is acceptable. However, when a tool is used to select older persons who may benefit from a more intensive and costly intervention, one may chose a tool with a higher specificity and negative predictive value to minimise the misclassification of persons with a low risk, and thus increase the cost-effectiveness.
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
803
For the prediction of fractures, the predictive features of the FRAX tool are promising. FRAX has been validated in 11 independent cohorts that did not participate in the model synthesis. The AUCs in the validation cohorts were, in general, comparable to the original cohorts. In all cohorts, except for one small cohort, the addition of BMD to clinical risk factors alone improved the performance of the model. These improvements were greater for hip-fracture prediction (AUC for hip fractures ¼ 0.67 without BMD and 0.78 with BMD in original cohort; mean AUC ¼ 0.66 without BMD and 0.74 with BMD in validation cohorts) than for the prediction of other osteoporotic fractures (AUC for other osteoporotic fractures ¼ 0.62 without BMD and 0.63 with BMD in original cohort; mean AUC ¼ 0.60 without BMD and 0.62 with BMD in validation cohorts) [19]. Challenges for future research An important limitation of the existing fall-risk profiles is lack of external validation, mainly of the statistics-based profiles. Future research should focus on the external validation and optimising of existing fall-risk profiles, rather than developing new profiles. The external validation should be done in the population in which the tool will be used. Moreover, it would be interesting to compare the accuracy of these profiles with clinical judgement. For the prediction of fractures, the FRAX tool seems promising. Its predictive validity may be improved by adding risk factors for falls. Its validity has been described for some but not all countries. Cost-effective treatment thresholds were identified for women in the UK, but should also be established for other countries and for men. The impact of FRAX screening on the cost-effectiveness of preventive fracture treatment is not yet clear, and should be tested in randomised controlled trials. Summary In brief, in old age, 5–10% of all falls result in a fracture, and up to 90% of all fractures result from a fall. In community-dwelling older persons, risk factors for both falling and fractures are age, female gender and chronic diseases. Interestingly, the number of overlapping risk factors is small. Although many fall-prediction tools have been developed, there is no consensus on which fall prediction tool should be used in clinical practice, and most tools have not been validated. For the prediction of fractures, the FRAX tool has very promising features. It would be interesting to examine whether fracture prediction can be improved by adding risk factors for falls to the FRAX tool. Challenges for future research include further validation of existing prediction tools and evaluation of the costeffectiveness of a treatment after screening.
Practice points It is recommended to assess the fracture risk in persons with a high fall risk or who experienced recurrent falls and to assess the fall risk in persons who experienced a fracture. Which prediction tool should be used to predict the fall risk depends on the setting and population and the diagnostic properties that are required in that situation. For the prediction of fractures, the FRAX tool is recommended.
Research agenda Fall-prediction tools need to be validated in the population where it will be used in clinical practice. The FRAX has been validated for some countries, but needs to be validated in others. The cost-effectiveness of treatment after screening needs to be evaluated.
804
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
References [1] Tinetti ME, Speechley M, Ginter SF. Risk factors for falls among elderly persons living in the community. N Engl J Med 1988 Dec 29;319(26):1701–7. [2] Tromp AM, Smit JH, Deeg DJ, et al. Predictors for falls and fractures in the Longitudinal Aging Study Amsterdam. J Bone Miner Res 1998 Dec;13(12):1932–9. [3] Jorstad EC, Hauer K, Becker C, Lamb SE. Measuring the psychological outcomes of falling: a systematic review. J Am Geriatr Soc 2005 Mar;53(3):501–10. [4] Stel VS, Smit JH, Pluijm SM, Lips P. Consequences of falling in older men and women and risk factors for health service use and functional decline. Age Ageing 2004 Jan;33(1):58–65. [5] Tinetti ME, Williams CS. The effect of falls and fall injuries on functioning in community-dwelling older persons. J Gerontol A Biol Sci Med Sci 1998 Mar;53(2):M112–9. [6] Dunn JE, Rudberg MA, Furner SE, Cassel CK. Mortality, disability, and falls in older persons: the role of underlying disease and disability. Am J Public Health 1992 Mar;82(3):395–400. [7] Zijlstra GA, van Haastregt JC, van Eijk JT, et al. Prevalence and correlates of fear of falling, and associated avoidance of activity in the general population of community-living older people. Age Ageing 2007 May;36(3):304–9. *[8] Parkkari J, Kannus P, Palvanen M, et al. Majority of hip fractures occur as a result of a fall and impact on the greater trochanter of the femur: a prospective controlled hip fracture study with 206 consecutive patients. Calcif Tissue Int 1999 Sep;65(3):183–7. *[9] Youm T, Koval KJ, Kummer FJ, Zuckerman JD. Do all hip fractures result from a fall? Am J Orthop 1999 Mar;28(3):190–4. *[10] Schwartz AV, Nevitt MC, Brown Jr BW, Kelsey JL. Increased falling as a risk factor for fracture among older women: the study of osteoporotic fractures. Am J Epidemiol 2005 Jan 15;161(2):180–5. [11] Consument en Veiligheid. Letsel informatie systeem, 1998–2001. [12] Osei W, Rasali D, Hawkey J, McCrae S, Johnson S. Cost Implications of Falls Injury Hospitalizations in Older Residents in Saskatchewan, Canada. Annals of Epidemiology 2007;17(9). 739–739. [13] De Laet CE, van Hout BA, Burger H, et al. Incremental cost of medical care after hip fracture and first vertebral fracture: the Rotterdam study. Osteoporos Int 1999;10(1):66–72. *[14] Kanis JA, Johnell O. Requirements for DXA for the management of osteoporosis in Europe. Osteoporos Int 2005 Mar;16(3): 229–38. [15] The prevention of falls in later life. A report of the Kellogg International Work Group on the prevention of falls by the elderly. Dan Med Bull 1987 Apr;34(Suppl. 4):1–24. [16] Stel VS, Pluijm SM, Deeg DJ, et al. A classification tree for predicting recurrent falling in community-dwelling older persons. J Am Geriatr Soc 2003 Oct;51(10):1356–64. [17] Seeley DG, Browner WS, Nevitt MC, et al. Which fractures are associated with low appendicular bone mass in elderly women? The Study of Osteoporotic Fractures Research Group. Ann Intern Med 1991 Dec 1;115(11):837–42. [18] Tromp AM, Pluijm SM, Smit JH, et al. Fall-risk screening test: a prospective study on predictors for falls in communitydwelling elderly. J Clin Epidemiol 2001 Aug;54(8):837–44. *[19] Kanis JA. Assessment of osteoporosis at the primary health care level: report of a WHO Scientific Group. UK: University of Sheffield Medical School; 2007. World Health Organization Collaborating Centre for Metabolic Bone Diseases. [20] Hayes CW, Myers ER. Biomechanics of fractures. In: Riggs BL, Melton LJ, editors. Osteoporosis: etiology, diagnosis, and management. 2nd edn. Philadelphia: Lippincott-Raven Publishers; 1995. p. 93–114. *[21] Espallargues M, Sampietro-Colom L, Estrada MD, et al. Identifying bone-mass-related risk factors for fracture to guide bone densitometry measurements: a systematic review of the literature. Osteoporos Int 2001;12(10):811–22. [22] Russell MA, Hill KD, Day LM, et al. Development of the Falls Risk for Older People in the Community (FROP-Com) screening tool. Age Ageing 2009 Jan;38(1):40–6. [23] Russell MA, Hill KD, Blackberry I, et al. The reliability and predictive accuracy of the falls risk for older people in the community assessment (FROP-Com) tool. Age Ageing 2008 Nov;37(6):634–9. [24] Lord SR, Menz HB, Tiedemann A. A physiological profile approach to falls risk assessment and prevention. Phys Ther 2003 Mar;83(3):237–52. [25] Stalenhoef PA, Diederiks JP, Knottnerus JA, et al. The construction of a patient record-based risk model for recurrent falls among elderly people living in the community. Fam Pract 2000 Dec;17(6):490–6. [26] Peeters GM. Prevention of falling in older persons with a high risk of recurrent falling. EMGO Institute for Health and Care Research, VU University Medical Center; 2009. [27] Borgstrom F, Strom O, Coelho J, et al. The cost-effectiveness of risedronate in the UK for the management of osteoporosis using the FRAX(R). Osteoporos Int 2009 Jun 30. PMID 19565175. [28] Borgstrom F, Strom O, Coelho J, et al. The cost-effectiveness of strontium ranelate in the UK for the management of osteoporosis. Osteoporos Int 2009 Jun 10. PMID 19513577. *[29] Luukinen H, Koski K, Laippala P, Kivela SL. Factors predicting fractures during falling impacts among home-dwelling older adults. J Am Geriatr Soc 1997 Nov;45(11):1302–9. *[30] Luukinen H, Herala M, Koski K, et al. Fracture risk associated with a fall according to type of fall among the elderly. Osteoporos Int 2000;11(7):631–4.
Contents lists available at ScienceDirect
Best Practice & Research Clinical Rheumatology journal homepage: www.elsevierhealth.com/berh
8
Fall risk: the clinical relevance of falls and how to integrate fall risk with fracture risk G. Peeters, MSc, Postdoc researcher, Epidemiologist a,1, Natasja M. van Schoor, PhD, Postdoc researcher, Epidemiologist a, 2, Paul Lips, PhD, MD, Professor of endocrinology b, * a
Department of Epidemiology and Biostatistics, EMGO Institute for health and care research, VU University Medical Center, van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands b Department of Internal Medicine, Section Endocrinology, VU University Medical Center, Postbus 7057, 1007 MB Amsterdam, The Netherlands
Keywords: accidental falls fracture risk profiles osteoporosis
In old age, 5–10% percent of all falls result in a fracture, and up to 90% of all fractures result from a fall. This article describes the link between fall risk and fracture risk in community-dwelling older persons. Which factors attribute to both the fall risk and the fracture risk? Which falls result in a fracture? Which tools are available to predict falls and fractures? Directions for the use of prediction tools in clinical practice are given. Challenges for future research include further validation of existing prediction tools and evaluation of the cost-effectiveness of treatment after screening. Ó 2009 Elsevier Ltd. All rights reserved.
Falling is a major health problem in old age. It is considered as one of the geriatric giants. Since the late1980s, the number of publications on the subject of falling has risen steeply. These studies cover different settings: community-dwelling older persons, hospitalised older persons and home for the elderly/nursing home residents. The risk factors, risk profiles and preventive interventions for falling differ across these populations. The current article focusses on the epidemiology, risk factors and prediction models for falls and fractures in community-dwelling older persons.
* Corresponding author. Tel.: þ31 204440614; Fax: þ31 204440502. E-mail addresses: [email protected] (G. Peeters), [email protected] (N.M. van Schoor), [email protected] (P. Lips). 1 Tel.: þ31 204449336; Fax: þ31 204446775. 2 Tel.: þ31 204448439; Fax: þ31 204446775. 1521-6942/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.berh.2009.09.004
798
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
Epidemiology of falls and fractures in old age Annually, about 30% of the community-dwelling persons of 65 years and older fall once and 15% fall twice or more times [1,2]. The consequences of falling can be severe: 68% reports a physical injury, 6% suffers a major injury and 29–92% reports fear of falling, depending on the definition used [3–5]. Subsequently, a fall can lead to decreased physical functioning, loss of independence, nursing home admittance and even death [1,4,6,7]. About 5–10% of all falls result in a fracture, whereas 90% of all fractures are attributable to falls [8,9]. Moreover, the risk of hip and proximal humerus fractures seems to be 40% and 79% higher in women with higher fall rates, respectively [10]. The economic consequences of falls and fractures vary greatly between countries, because of differences in health-care costs and treatment policies. With respect to falls, little information on the costs is available. The average direct medical costs of a fall have been estimated at about Euro 3400 for any fall in the Netherlands and at about Canadian Dollar $ 6088 for falls leading to hospitalisation in Canada [11,12]. The average medical costs of a fracture vary strongly between fracture types, with a hip fracture being the most expensive. In the Netherlands, the incremental costs for a hip fracture were estimated at US$ 9540 during the first year and US$ 1017 during the second year as compared with matched controls [13]. In the same study, vertebral fractures were associated with a yearly recurrent incremental cost of over US$ 1000. However, almost half of the cost difference was already present before the occurrence of the fracture. The total costs of osteoporotic fractures in men and women combined in Europe was estimated at Euro 36 248 million, of which Euro 24 353 million were for hip fractures, Euro 719 million for vertebral fractures and Euro 11 177 million for other osteoporotic fractures [14]. Definitions A fall is defined as ‘‘an unintentional change in position resulting in coming to rest on the ground or other lower level.’’[15] In the literature, a distinction is made between once-fallers and recurrent fallers. Falling refers to any fall and includes occasional falls. Occasional falls may be caused mainly by extrinsic factors (i.e., environmental factors that act upon the person), whereas recurrent falls are usually caused by intrinsic factors (i.e., physical, cognitive and behavioural factors within the person, e.g., mobility limitations) accompanied by an environmental hazard. Recurrent falling has been defined as two or more falls within 6 months [16]. Fractures are often classified according to the site of fracture or according to the circumstances, for example, osteoporotic fractures or fall-related fractures. Osteoporotic fractures are associated with bone fragility and may occur at any site, but typically at the proximal femur, distal radius, proximal humerus and vertebrae [17]. Fractures of head, fingers and toes, as well as fractures caused by traffic accidents or malignancies, are usually considered non-osteoporotic. Aetiology When discussing predictors, a distinction must be made between risk factors and risk indicators. Risk factors are factors that have a causal relationship with the outcome measure. For instance, walking impairment is a risk factor for falling, because impaired ability to walk directly increases the fall risk and if the walking ability is improved, the fall risk decreases. Risk indicators are factors that are associated with the outcome measure, but without causality. Having grey hair, for instance, may be associated with falling because of the link with ageing, but dyeing the hair brown would not alter the person’s fall risk. Note that both risk factors and risk indicators may add to the prediction of a certain outcome, but only risk factors offer possibilities for preventive interventions. Since most fractures result from falls, the aetiology of falls and fractures is partly overlapping (Fig. 1). The causality of falls is complex: many risk factors have been identified (Table 1). However, the attributable risk of each of these risk factors is limited. The most important predictor for falling is having a history of falling. In community-dwelling persons of 65 years and older, the fall risk is more than twice as high in persons with a history of falls [18]. In case of a fracture, the force impact on the bone exceeds the strength of the bone. Risk factors for fractures can be split into factors affecting bone strength and factors affecting the impact of forces on the bone [19,20]. Major risk factors affecting bone strength are age (>70 years), weight loss and low body
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
799
Factors leading to a fall Mobility impairments Perception impairments Depression/cognitive impairments
Fall risk
Chronic diseases/medication use Environment Factors modifying a fall Protective responses Anthropometry Impact of fall
Fracture risk
Local shock absorbers Circumstances (surface, height, obstacles) Factors decreasing bone strength Decreased bone mass Impaired bone structure
Bone strength
Impaired bone quality Fig. 1. Conceptual model of factors influencing the risk of falling and fractures.
weight, physical inactivity, hypogonadism, use of glucocorticoids, anticonvulsants, primary hyperparathyroidism, hyperthyroidism, diabetes mellitus type I, anorexia nervosa and gastrectomy [21]. Furthermore, there is a series of additional risk factors including female gender (postmenopausal status), current smoking status, low vitamin D status due to low sunlight exposure, family history of osteoporotic fracture, surgical/early menopause and low calcium intake [21]. Risk factors affecting the impact on bone include all fall-risk factors as well as conditions of the fall such as the height and acceleration and local factors such as subcutaneous fat around the hip acting as a shock absorber. Table 1 shows which risk factors are relevant for both falls and fractures. The number of overlapping risk factors is small. This is likely to be due to the multicausality of falls; the risk that each of these factors contributes to the risk of fractures is, therefore, small. Prediction of falls and fractures Prediction of (recurrent) falling In the past 2 decades, several prediction tools for falling and recurrent falling have been developed. Table 2 provides an overview of these prediction tools. Six tools were developed using statistical techniques, whereas four were developed based on expertise. Six tools predict the risk of falling, three predict the risk of recurrent falling and one predicts both the risk of falling and recurrent falling. The complexity of the tools varies from two questions and a simple test[22], to 26 questions[23] or a series of physical tests [24]. Each of these models has strengths and limitations. The overall predictive validity can be expressed by the area under the receiver-operating curve (AUC). Optimal predictive validity (AUC ¼ 1) suggests that in randomly chosen pairs of fallers and non-fallers, 100% of the persons are correctly classified as a faller and non-faller, respectively. If the AUC is 0.50, the tool does not add any information to chance (e.g., flip a coin to classify persons into fallers and non-fallers). The AUC scores of the risk profiles described here vary between 0.71 and 0.79 in the samples in which they have been developed.
800
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
Table 1 Risk factors for (any) falls and (any) fractures in community-dwelling older persons.
Age (for 10 years increase in age) Age (>80 years) Age of menopause (youngest versus oldest age) Body weight (for 10 kg decrease in weight) Chronic diseases (1 diseases) Diabetes Mellitus (type I) Diabetes Mellitus (type II) Rheumatoid Arthritis Stroke Current smoking Depressive symptoms Dizziness Fall history Family history of hip-fracture Female gender Fracture history Fear of falling High levels of physical activity Low levels of physical activity Low bone mineral density (per SD decrease) Low calcium intake Limitations in daily activities Mobility problems Muscle weakness Orthostatic hypotension Reduced sun exposure Urinary incontinence Use of psychotropic medication Use of corticosteroids Vision impairments
Falls
Fractures
1.1a 1.5–1.8a
1.27 (1.22–1.33)b 1.26 (1.08–1.47)b 2.35 (1.75–3.14)b
1.3a 2.91–3.79c 1.67 (1.37–2.08)b 1.44 (1.19–1.73)b 1.9–5.0a 1.37 (1.21–1.55)b 1.4–4.8a 1.2–3.1a 2.0–5.0a 1.2–1.8a 1.8–3.0a 0.7–2.0a 2.9a
1.94 (1.44–2.60)b 1.62 (1.48–1.77)b 1.35–21.5c
1.18–7.1c 1.5a 1.51 (1.28–1.78)b
1.5–1.8a 1.3–4.1a 1.1–2.0a 0.8–1.5a 1.38–5.31c 1.5–2.2a 1.6–3.1a 1.78 (1.37–2.32)b 1.3–1.8a
a
Intervals of risk ratios reported in the literature. The ranges were based on a convenience literature search. Pooled risk ratios and (95% confidence intervals) for fractures were adapted from Espallargues et al (2001) [21]. c Intervals of risk ratios reported in the literature were adapted from the same review [21]. Note that definitions and methods used differed across studies. b
Since the predictive validity is always higher in the sample in which the tool was developed than in other similar samples (optimism), validation of a prediction rule in a different sample is important to give insight into the generalisability (external validation). Moreover, the characteristics of a population influence the prevalence of the items and outcome measure, as a result of which the predictive validity varies among different populations. Most tools based on statistical techniques were developed in unselected samples of patients 65–70 years and older. However, the characteristics of these samples differ from the persons who consult the Emergency Department or general practitioner after a fall. Only one tool was developed in a population with a fall history[25], and three were validated among persons who consulted the Emergency Department and/or general practitioner after a fall [22,23,26]. The predictive validity of these three tools was moderate to good with AUCs ranging from 0.65 to 0.73 [22,23,26]. Prediction of fractures Case finding for osteoporosis varies from population screening with dual-energy X-ray absorptiometry (DXA) to step-wise case finding using clinical risk factors first, followed by DXA if indicated. Bone mineral density (BMD) as measured with DXA is used for the diagnosis for osteoporosis and is also the most important predictor for fractures with risk ratios per standard deviation change in BMD for hip fractures varying from 3.68 (CI 2.61–5.19) at the age of 50 years to 1.70 at the age of 90 years (CI 1.50–1.93) and for other osteoporotic fractures of 1.19 (CI 1.05–1.34) at the age of 50 years and 1.56 (CI 1.40–1.75) at the age of 90 years [19]. However, the predictive value of this measure is limited: more
Table 2 Overview of prediction models for (recurrent) falling in community-dwelling older persons. name of instrument (Acronym)
method of development
outcome
development populationa
content of instrument
Tinetti (1988)
Risk model for falls
logistic regression
fall
6 items
Stalenhoef (2000)
Patient record-based risk model
logistic regression
recurrent falls
genetic algorithm neural network
fall
unselected, 75þ n ¼ 336 unselected 70þ n ¼ 1660 unselected 65þ n ¼ 435 unselected 70þ n ¼ 557 positive fall history 70þ n ¼ 311
Bath (2000)
6 items available in GP records 16 questions
Covinsky (2001)
History and mobility exam index
logistic regression
fall
Stalenhoef (2002)
Risk model for recurrent falls
logistic regression
recurrent falls
Lord (2003)
Physiological profile approach (PPA)
expertise
fall, multiple falls
series of physical tests
Nandy (2004)
Falls risk assessment tool (FRAT)
systematic review and expert panel
fall
5 questions
Pluijm (2006)
LASA fall risk profile
logistic regression
recurrent falls
Russell (2008)
Falls risk for older people in the community (FROP-Com)
expertise
fall
26 questions
Russell (2009)
Falls risk for older people in the community (FROP-Com)
expertise
fall
2 questions, 1 test
unselected 65þ n ¼ 1365
2 questions, 4 tests 3 questions, 3 tests
7 questions, 2 tests
diagnostic values internal validity
external validation populationa
diagnostic values external validity
(un)selected groups varying in age and sample size unselected 65þ n ¼ 510 consulting ED/GP after a fall 65þ n ¼ 408 consulting ED after a fall 65þ n ¼ 344 consulting ED after a fall 65þ n ¼ 344
AUC multiple falls ¼ 0.75 intrarater reliability > 0.50
AUC ¼ 0.73 Se ¼ 64 Sp ¼ 71 PPV ¼ 42 NPV ¼ 86 Se ¼ 31 Sp ¼ 92 PPV ¼ 57 NPV ¼ 79 AUC ¼ 0.71 Se ¼ 59 Sp ¼ 73 PPV ¼ 38 NPV ¼ 86 AUC ¼ 0.79 Se ¼ 59 Sp ¼ 87 PPV ¼ 52 NPV ¼ 90
AUC ¼ 0.71 Se ¼ 59 Sp ¼ 71 PPV ¼ 39 NPV ¼ 85
YI ¼ 0.387 Se ¼ 59 Sp ¼ 80 PPV ¼ 43 NPV ¼ 88 AUC ¼ 0.65 Se ¼ 57 Sp ¼ 74 PPV ¼ 34 NPV ¼ 86 AUC ¼ 0.68, YI ¼ 0.30, Se ¼ 66 Sp ¼ 64 intrarater reliability ¼ 0.93 interrater reliability ¼ 0.81 AUC ¼ 0.73, YI ¼ 0.34 Se ¼ 67 Sp ¼ 67 PPV ¼ 65 NPV ¼ 69 intrarater reliability ¼ 0.87 interrater reliability ¼ 0.89
801
AUC ¼ area under the ROC-curve, YI ¼ Youden index, Se ¼ sensitivity, Sp ¼ specificity, PPV ¼ positive predictive value, NPV ¼ negative predictive value, ED ¼ Emergency Department, GP ¼ general practitioner. a Reported are the selection criteria, age range, and sample size (n).
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
Author
802
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
than half of the fractures occur in persons with T-scores higher than –2.5, that is, in persons with osteopenea or normal BMD [19]. Moreover, BMD measurement is expensive and not always available. To overcome the limitations of BMD measurement to predict fractures, the World Health Organization (WHO) has developed the Fracture Risk Assessment Tool (FRAX), using individual participant data of almost 60 000 men and women from 12 population-based cohort studies [19]. The FRAX is available on the Internet (http://www.shef.ac.uk/FRAX/index.htm) and consists of the predictors age, sex, weight and height (from which BMI is computed), prior fragility fracture, parental history of hip fracture, current tobacco smoking, ever long-term use of oral glucocorticoids, rheumatoid arthritis, other causes of secondary osteoporosis and daily consumption of more than 2 units of alcohol. The predictive validity of the model can be further improved by including a BMD measurement, but this is optional. Weighted scores are assigned to each item and the 10-year probability of major osteoporotic fractures and hip fractures is computed. The FRAX tool has been calibrated for different countries by adjusting the weighted scores per item. The advantages of the FRAX tool are that the absolute 10-year fracture risk is easier to understand for patients as compared with T-scores or Z-scores of the DXA scan. Furthermore, the tool is easily accessible on the Internet. Several studies were performed on the costeffectiveness of osteoporosis treatment in women in the United Kingdom using the FRAX tool and intervention thresholds were established [27,28].
Which falls result in fractures? A few studies have studied the circumstances of fall-related fractures and identified risk factors for falls resulting in fractures. One study compared 82 fallers with fractures to 82 fallers with soft-tissue injuries and concluded that fear of falling, decreased muscle strength, high social participation and poor distance vision increased the risk of a fall-related fracture [29]. A second study among 980 older persons (over 70 years of age) showed that fall-related fractures occurred more often during slips, trips and falls due to extrinsic factors such as being pulled or due to collisions as compared with falls due to intrinsic factors (i.e., falls with no clear extrinsic contribution) [30]. Although the first study revealed some intrinsic fall-risk factors that are associated with fractures, the second study suggested that external factors that increase the impact of a fall on the bone might further increase the fracture risk. However, since these external factors are situation-specific (e.g., height of fall and slippery surfaces), they are not suitable for use in prospective risk profiles. To our knowledge, no risk profiles have been developed that predict the fracture risk following a fall. Use of prediction models in clinical practice Even if the capacity is maximally expanded, treating every older person is not feasible or costeffective. Therefore, it is important to predict which persons are most in need of preventive measures. Since fractures have a large impact on both the individual’s quality of life and health-care costs, it is even more important to predict fall-related fractures. Since 90% of all fractures result from falls, it is recommended to assess the fracture risk in persons with a high fall risk, for example, those who experienced recurrent falls, and to assess the fall risk in persons who experienced a fracture. Which tool should be used depends on the setting and population in which the tool will be used, and the diagnostic properties that are required in that situation. In an ideal situation, both the sensitivity and specificity of a measurement tool are high. However, there is a trade-off between sensitivity and specificity and one has to compromise on a tool with the optimal balance between sensitivity and specificity for the purpose of the screening. For example, if a tool is used by a general practitioner to select older persons who may benefit from an effortless and cheap preventive intervention, the tool needs to be easy and fast to administer with a high sensitivity. Since the costs and efforts of the intervention for the patients and the caregivers are low, the unnecessary treatment of persons with a low risk due to the low specificity is acceptable. However, when a tool is used to select older persons who may benefit from a more intensive and costly intervention, one may chose a tool with a higher specificity and negative predictive value to minimise the misclassification of persons with a low risk, and thus increase the cost-effectiveness.
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
803
For the prediction of fractures, the predictive features of the FRAX tool are promising. FRAX has been validated in 11 independent cohorts that did not participate in the model synthesis. The AUCs in the validation cohorts were, in general, comparable to the original cohorts. In all cohorts, except for one small cohort, the addition of BMD to clinical risk factors alone improved the performance of the model. These improvements were greater for hip-fracture prediction (AUC for hip fractures ¼ 0.67 without BMD and 0.78 with BMD in original cohort; mean AUC ¼ 0.66 without BMD and 0.74 with BMD in validation cohorts) than for the prediction of other osteoporotic fractures (AUC for other osteoporotic fractures ¼ 0.62 without BMD and 0.63 with BMD in original cohort; mean AUC ¼ 0.60 without BMD and 0.62 with BMD in validation cohorts) [19]. Challenges for future research An important limitation of the existing fall-risk profiles is lack of external validation, mainly of the statistics-based profiles. Future research should focus on the external validation and optimising of existing fall-risk profiles, rather than developing new profiles. The external validation should be done in the population in which the tool will be used. Moreover, it would be interesting to compare the accuracy of these profiles with clinical judgement. For the prediction of fractures, the FRAX tool seems promising. Its predictive validity may be improved by adding risk factors for falls. Its validity has been described for some but not all countries. Cost-effective treatment thresholds were identified for women in the UK, but should also be established for other countries and for men. The impact of FRAX screening on the cost-effectiveness of preventive fracture treatment is not yet clear, and should be tested in randomised controlled trials. Summary In brief, in old age, 5–10% of all falls result in a fracture, and up to 90% of all fractures result from a fall. In community-dwelling older persons, risk factors for both falling and fractures are age, female gender and chronic diseases. Interestingly, the number of overlapping risk factors is small. Although many fall-prediction tools have been developed, there is no consensus on which fall prediction tool should be used in clinical practice, and most tools have not been validated. For the prediction of fractures, the FRAX tool has very promising features. It would be interesting to examine whether fracture prediction can be improved by adding risk factors for falls to the FRAX tool. Challenges for future research include further validation of existing prediction tools and evaluation of the costeffectiveness of a treatment after screening.
Practice points It is recommended to assess the fracture risk in persons with a high fall risk or who experienced recurrent falls and to assess the fall risk in persons who experienced a fracture. Which prediction tool should be used to predict the fall risk depends on the setting and population and the diagnostic properties that are required in that situation. For the prediction of fractures, the FRAX tool is recommended.
Research agenda Fall-prediction tools need to be validated in the population where it will be used in clinical practice. The FRAX has been validated for some countries, but needs to be validated in others. The cost-effectiveness of treatment after screening needs to be evaluated.
804
G. Peeters et al. / Best Practice & Research Clinical Rheumatology 23 (2009) 797–804
References [1] Tinetti ME, Speechley M, Ginter SF. Risk factors for falls among elderly persons living in the community. N Engl J Med 1988 Dec 29;319(26):1701–7. [2] Tromp AM, Smit JH, Deeg DJ, et al. Predictors for falls and fractures in the Longitudinal Aging Study Amsterdam. J Bone Miner Res 1998 Dec;13(12):1932–9. [3] Jorstad EC, Hauer K, Becker C, Lamb SE. Measuring the psychological outcomes of falling: a systematic review. J Am Geriatr Soc 2005 Mar;53(3):501–10. [4] Stel VS, Smit JH, Pluijm SM, Lips P. Consequences of falling in older men and women and risk factors for health service use and functional decline. Age Ageing 2004 Jan;33(1):58–65. [5] Tinetti ME, Williams CS. The effect of falls and fall injuries on functioning in community-dwelling older persons. J Gerontol A Biol Sci Med Sci 1998 Mar;53(2):M112–9. [6] Dunn JE, Rudberg MA, Furner SE, Cassel CK. Mortality, disability, and falls in older persons: the role of underlying disease and disability. Am J Public Health 1992 Mar;82(3):395–400. [7] Zijlstra GA, van Haastregt JC, van Eijk JT, et al. Prevalence and correlates of fear of falling, and associated avoidance of activity in the general population of community-living older people. Age Ageing 2007 May;36(3):304–9. *[8] Parkkari J, Kannus P, Palvanen M, et al. Majority of hip fractures occur as a result of a fall and impact on the greater trochanter of the femur: a prospective controlled hip fracture study with 206 consecutive patients. Calcif Tissue Int 1999 Sep;65(3):183–7. *[9] Youm T, Koval KJ, Kummer FJ, Zuckerman JD. Do all hip fractures result from a fall? Am J Orthop 1999 Mar;28(3):190–4. *[10] Schwartz AV, Nevitt MC, Brown Jr BW, Kelsey JL. Increased falling as a risk factor for fracture among older women: the study of osteoporotic fractures. Am J Epidemiol 2005 Jan 15;161(2):180–5. [11] Consument en Veiligheid. Letsel informatie systeem, 1998–2001. [12] Osei W, Rasali D, Hawkey J, McCrae S, Johnson S. Cost Implications of Falls Injury Hospitalizations in Older Residents in Saskatchewan, Canada. Annals of Epidemiology 2007;17(9). 739–739. [13] De Laet CE, van Hout BA, Burger H, et al. Incremental cost of medical care after hip fracture and first vertebral fracture: the Rotterdam study. Osteoporos Int 1999;10(1):66–72. *[14] Kanis JA, Johnell O. Requirements for DXA for the management of osteoporosis in Europe. Osteoporos Int 2005 Mar;16(3): 229–38. [15] The prevention of falls in later life. A report of the Kellogg International Work Group on the prevention of falls by the elderly. Dan Med Bull 1987 Apr;34(Suppl. 4):1–24. [16] Stel VS, Pluijm SM, Deeg DJ, et al. A classification tree for predicting recurrent falling in community-dwelling older persons. J Am Geriatr Soc 2003 Oct;51(10):1356–64. [17] Seeley DG, Browner WS, Nevitt MC, et al. Which fractures are associated with low appendicular bone mass in elderly women? The Study of Osteoporotic Fractures Research Group. Ann Intern Med 1991 Dec 1;115(11):837–42. [18] Tromp AM, Pluijm SM, Smit JH, et al. Fall-risk screening test: a prospective study on predictors for falls in communitydwelling elderly. J Clin Epidemiol 2001 Aug;54(8):837–44. *[19] Kanis JA. Assessment of osteoporosis at the primary health care level: report of a WHO Scientific Group. UK: University of Sheffield Medical School; 2007. World Health Organization Collaborating Centre for Metabolic Bone Diseases. [20] Hayes CW, Myers ER. Biomechanics of fractures. In: Riggs BL, Melton LJ, editors. Osteoporosis: etiology, diagnosis, and management. 2nd edn. Philadelphia: Lippincott-Raven Publishers; 1995. p. 93–114. *[21] Espallargues M, Sampietro-Colom L, Estrada MD, et al. Identifying bone-mass-related risk factors for fracture to guide bone densitometry measurements: a systematic review of the literature. Osteoporos Int 2001;12(10):811–22. [22] Russell MA, Hill KD, Day LM, et al. Development of the Falls Risk for Older People in the Community (FROP-Com) screening tool. Age Ageing 2009 Jan;38(1):40–6. [23] Russell MA, Hill KD, Blackberry I, et al. The reliability and predictive accuracy of the falls risk for older people in the community assessment (FROP-Com) tool. Age Ageing 2008 Nov;37(6):634–9. [24] Lord SR, Menz HB, Tiedemann A. A physiological profile approach to falls risk assessment and prevention. Phys Ther 2003 Mar;83(3):237–52. [25] Stalenhoef PA, Diederiks JP, Knottnerus JA, et al. The construction of a patient record-based risk model for recurrent falls among elderly people living in the community. Fam Pract 2000 Dec;17(6):490–6. [26] Peeters GM. Prevention of falling in older persons with a high risk of recurrent falling. EMGO Institute for Health and Care Research, VU University Medical Center; 2009. [27] Borgstrom F, Strom O, Coelho J, et al. The cost-effectiveness of risedronate in the UK for the management of osteoporosis using the FRAX(R). Osteoporos Int 2009 Jun 30. PMID 19565175. [28] Borgstrom F, Strom O, Coelho J, et al. The cost-effectiveness of strontium ranelate in the UK for the management of osteoporosis. Osteoporos Int 2009 Jun 10. PMID 19513577. *[29] Luukinen H, Koski K, Laippala P, Kivela SL. Factors predicting fractures during falling impacts among home-dwelling older adults. J Am Geriatr Soc 1997 Nov;45(11):1302–9. *[30] Luukinen H, Herala M, Koski K, et al. Fracture risk associated with a fall according to type of fall among the elderly. Osteoporos Int 2000;11(7):631–4.