Can the International Classification of Functioning, Disability and Health (ICF) be used to understand risk factors for falls in older Australian women?


Purpose: To evaluate the relevance and accuracy of determining and predicting risk factors for falls in older women using the International Classification of Functioning, Disability and Health (ICF). Methods: We tested the accuracy of the ICF against risk of falls amongst 568 community dwelling participants of the Australian Longitudinal Survey on Women’s Health (ALSWH). We linked health-related variables to the ICF using ten linking rules. The logistic regression analysis evaluated the relationship between the variables and the outcome of falls. Self-report surveys measured daily functioning, health service use, medications, housing and social support. Results: Variables aligned with the ICF components of body function, health conditions, environment, activity and participation (ADL/IADL), and general health were significantly associated with falls. Discussion and conclusion: Mapping ALSWH health-related data to ICF components identified significant risk factors for falls are related to health conditions, functional limitations and home hazards. Biopsycho-social approaches guided by the ICF framework are crucial for fall prevention.


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Mehraban, A. , Mackenzie, L. , Byles, J. , Gibson, R. and Curryer, C. (2013) Can the International Classification of Functioning, Disability and Health (ICF) be used to understand risk factors for falls in older Australian women?. Health, 5, 39-48. doi: 10.4236/health.2013.512A006.


Fall is a major source of morbidity and mortality in older people. Preventing falls is a key health priority. Fall is the second leading cause of accidental or unintentional injury deaths worldwide [1]. Injuries from falls contribute to increased disability and mobility limitations for older people, and therefore, as the proportion of the aged population increases, the research of the risk factors for falls is becoming increasingly important. The cost of hospitalizations due to fall related injury for people aged 65 and over is projected to increase to US $240 billion by the year 2040 [2]. Due to the complexity of risk factors for falls, it is crucial to refine conceptual and methodological frameworks for understanding and predicting falls in the population to aid the formulation of more effective fall interventions. One such framework which may be applied to understanding and predicting risks of falls is the International Classification of Functioning, Disability and Health (ICF) [3].

The ICF is a classification system that documents the complex interactions of a person with his physical, social and psychological environments, and how this interaction affects his health status [3-5]. Together with the ICD10 [6], the World Health Organisation’s diagnostic epidemiological, clinical and health management classification system, the ICF can describe health for an individual or a population and identify relationships between factors that are known to contribute to falls, or assist in detecting those most at risk [7]. The level of physical functioning experienced by an individual is an important factor in falls. The ICF’s emphasis on personal functioning—on how an individual engages in activities and participates in society in the context of environmental and personal factors [3], and the dynamic interaction between health states, the person and his/her environment [3], makes it particularly suitable for extending understandings of falls and falls risk. As a classification framework for fall prediction and intervention, the ICF can conceivably provide an inter-professional scientific basis for understanding and studying fall behaviour and risk, and ensure that important concepts are identified and measured.  

The ICF allows a comprehensive study of fall risk that includes medical, social and psychological risk factors. As fall risk is multidimensional, the ICF is appropriate to systematically investigate falls in the clinical practice [8]. Further, the ICF has already been embraced across a number of falls-related disciplines, such as occupational therapy and rehabilitation [7,9], but has not been specifically related to fall risk. The interactions between the ICF components are reciprocal. Therefore, the ICF accommodates the dynamic interactions underpinning fall risk, so that any change in one of the ICF components can influence other interactions within the framework. In a study of the ICF and clinical assessment measures in relation to falls following stroke [7], the ICF was found to match the multidimensional nature of fall risk. However, this relationship was not empirically tested. Furthermore, little evidence exists on how to reliably map the ICF to fall risk, or whether an analysis of fall risk using the ICF components is able to confirm the theoretical assumptions underpinning the ICF [10].

The Australian Longitudinal Study on Women’s Health (ALSWH) is well positioned to explore the prevalence of falls and serious injury among a large sample of older women with varying degrees of health, wellbeing, mobility and functional capacities or limitations and disability within the Australian community, and to observe and study the antecedents and outcomes of falls over the course of the study [11]. An exploratory cross-sectional study of the baseline older cohort (n = 12,000) of the ALSWH demonstrated that serious falls were significantly associated with the physical component score of the SF36 (Short-form-36 Health Survey) [12], taking drugs for nerves, having had a serious life event other than a fall in the previous year, and feeling dejected [11]. These results suggested that psychological, social, and occupational factors consistent with the ICF were important to understand serious falls in older women. However, previous studies have argued that the ICF is limited by its failure to meaningfully address the influence of personal factors such as socioeconomic status and gender which are critical to understanding the lives of individuals [13], and by inherent difficulties such as those posed by a lack of differentiation between conceptual boundaries, for example, the boundaries between the concepts of Participation and Activity [4,14]. Further, it is argued that it is necessary to clarify these boundaries for the ICF to be scientifically useful for the empirical research [4]. We sought to map risk factors for falls (measured in the ALSWH) to the ICF, and to determine whether this conceptual model predicted falls statistically significantly.


2.1. Data Collection

Participants in this study were drawn from the Australian Longitudinal Study on Women’s Health (ALSWH), a population-based study of changes in health and wellbeing of three different age-cohorts of women living in Australia. The sample was randomly selected from the Medicare Australia database, which is the universal provider of health insurance in Australia [15], and surveys have been conducted at 3-yearly intervals since 1996. The study was approved by the University of Newcastle Human Research Ethics Committee. Further details on the ALSWH are available from the website, For this study, 650 women were randomly selected from ALSWH participants who were born in 1921-1926, and who had completed Survey 3 in 2002.

Women in this sub-sample were invited to complete a postal survey collecting additional falls information including the Modified Falls Efficacy Scale [16], Fear of Falling scale [17,18], the Home Falls and Accidents Screening Tool (HOME FAST-SR) for measuring home hazards [19], and use of walking aids, and activities of daily living (Lambeth Disability Scale) [20]. This survey supplemented the data collected within the main ALSWH surveys known to be significantly related to falls. The outcome of falls was assessed in the sub-study and again in Survey 4 (2005) of the main study from the question: In the last 12 months have you had a fall to the ground (Yes, No). This self-report item has been shown to have reasonable sensitivity and specificity when compared with falls calendars [21].

2.2. Mapping to the ICF

Items included in the four main surveys and sub-study were mapped to the corresponding ICF components using ICF definitions (but not the levels of classification) [3] and the linking rules developed by Cieza [22,23]. These linking rules have been used in many studies [24-27] and were the only available tool to use in assigning the ALSWH survey items to the ICF components. Initial mapping was done by two of the authors who were occupational therapists (AM, LM) according to their interpretations of the meaning of the ICF and the linking rules. Demographic information, use of health services, lifestyle factors and living arrangements were mapped to personal factors, despite these being unclassified within the ICF [3,28].

2.3. Longitudinal Analysis

Univariate analyses (chi-square and t-tests) were applied to assess associations between the variables aligned to the ICF and self-reported falls at Survey 4. Explanatory variables were selected as those items measured on the sub-study or on the most recent survey prior to the sub-study. Most variables were measured at Survey 3 but some variables were measured at Survey 2 and education and country of birth were measured at Survey 1 (See Table 1). Correlation matrices [29] were constructed to identify highly correlated explanatory variables. Where variables were highly correlated, the variable most strongly associated with falls at the univariate level was retained for inclusion in the multivariate modelling.

Logistic regression was used to construct sub-models of factors associated with falls at Survey 4 for each ICF component. Variables with P ≤ 0.3 at the univariate level were included in multivariate analyses for each submodel, with backward stepwise removal of variables [30]. A final composite model was constructed by systematically combining each sub-model into a forward stepwise regression model-building process in order of the strongest Akaike Information Criteria (AIC) [31] for each submodel. The composite model examined statistically adjusted effects of the sub-models on the log odds of falls. A total of eight steps were conducted to create the final composite model from the sub-models. Statistical interactions between the strongest variables from each submodel were also explored. All these analyses were performed by Stata v8.2 [32] (StataCorp, 2004) and SAS v9.1 [33]. (SAS Institute, 2007).


3.1. Results of the Mapping Process

Table 1 shows a summary of the item categories contained within the four surveys of ALSWH, and their relationship to the ICF components after the mapping process was completed. Some scale items appear in multiple components of the ICF, due to the nature of each scale item.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] World Health Organisation (2010) Falls. Fact Sheet 344.
[2] World Health Organisation (2007) Global report on falls prevention in older age. World Health Organization, Geneva.
[3] World Health Organization (2001) International Classification of Functioning, Disability and Health: ICF. World Health Organization, Geneva.
[4] Jette, A., Haley, S. and Kooyoomjianet, J. (2003) Are the ICF activity and participation dimensions distinct? Journal of Rehabilitation Medicine, 35, 145-149.
[5] Simeonsson, R., Lollar, D., Hollowell, J. and Adams, M. (2000) Revision of the international classification of impairments, disabilities, and handicaps: Developmental issues. Journal of Clinical Epidemiology, 53, 113-124.
[6] World Health Organisation (2007) International classification of diseases: ICD-10. World Health Organisation, Geneva.
[7] Bennineto, M., Portneym, L. and Sullivan, P. (2009) Using the International Classification of Functioning, Disability and Health as a framework to examine the association between falls and clinical assessment tools in people with stroke. Physical Therapy, 89, 816-825.
[8] Cieza, A., Hilfiker, R., Chatterji, S., Kostanjsek, N., üstün, B and Stucki, G. (2009) The International Classification of Functioning, Disability and Health could be used to measure functioning. Journal of Clinical Epidemiology, 62, 899-911.
[9] Darzins, P., Fone, S and Darzins, S. (2006) The International Classification of Functioning, Disability and Health can help to structure and evaluate therapy. Australian Occupational Therapy Journal, 53,127-131.
[10] Mackenzie, L., Hassani Mehraban, A., Byles, J. and Gibson, R. (2010) Can the International Classification of Functioning, Disability and Health (ICF) be used as a framework to explain falls in older Australian women?
[11] Mackenzie, L., Byles, J and Mishra, G. (2004) An occupational focus on falls with serious injury among older women in Australia. Australian Occupational Therapy Journal, 51, 144-154.
[12] Ware, J and Sherbourne, C. (1992) The MOS 36-item short form health survey (SF-36): Conceptual framework and item selection, Part 1. Medical Care, 30, 473-481.
[13] Conti-Becker, A. (2009) Between the ideal and the real: Reconsidering the International Classification of Functioning, Disability and Health. Disability and Rehabilitation, 31, 2125-2129.
[14] Grimby, G and Smedby, B. (2001) ICF approved as the successor of ICIDH (editorial). Journal of Rehabilitation Medicine, 33, 193-194.
[15] Lee, C., Dobson, A. and Brown, W. (2005) Cohort profile: The australian longitudinal study on women’s health. International Journal of Epidemiology, 34, 987-991.
[16] Hill, K., Schwarz, J., Kalogeropoulos, A. and Gibson, S. (1996) Fear of falling revisited. Archives of Physical Medicine and Rehabilitation, 77, 1025-1029.
[17] Howland, J., Peterson, E., Levin, W., Fried, L., Pordon, D. and Bak, S. (1993) Fear of falling among the community-dwelling elderly. Journal of Aging and Health, 5, 229-243.
[18] Howland, J., Lachman, M., Peterson, E., Cote, J., Kasten, L. and Jette, A. (1998) Covariates of fear of falling and associated activity curtailment. The Gerontologist, 38, 549-555.
[19] Hassani Mehraban, A., Mackenzie, L. and Byles, J. (2011) A self-report home environment screening tool identified older women at risk of falls. Journal of Clinical Epidemiology, 64, 191-199.
[20] Charlton, J., Patrick, D. and Peach, H. (1983) Use of multivariate measures of disability in health surveys. Journal of Epidemiology & Community Health, 37, 296304.
[21] Mackenzie, L., Byles, J. and D’Este, C. (2006) Validation of self-reported fall events in intervention studies. Clinical Rehabilitation, 20, 331-339.
[22] Cieza, A., Brockow, T. and Ewert, T. (2002) Linking health-status measurements to the International Classification of Functioning, Disability and Health. Journal of Rehabilitation Medicine, 34, 205-210.
[23] Cieza, A., Geyh, S. and Chatterji, S. (2005) ICF linking rules: An update based on lessons learned. Journal of Rehabilitation Medicine, 37, 212-218.
[24] Sigl, T., Cieza, A. and Brockow, T. (2006) Content comparison of low back pain-specific measures based on the International Classification of Functioning, Disability and Health (ICF). The Clinical Journal of Pain, 22, 147-153.
[25] Stamm, T., Cieza, A. and Stucki, G. (2006) Exploration of the link between conceptual occupational therapy models and the International Classification of Functioning, Disability and Health. Australian Occupational Therapy Journal, 53, 9-17.
[26] Stucki, A., Stucki, G., Cieza, A., Schuurmans, M., Kostanjsek, N. and Ruof, J. (2007) Content comparison of health-related quality of life instruments for COPD. Respiratory Medicine, 101, 1113-1122.
[27] Weigl, M., Cieza, A. and Harder, M. (2003) Linking osteoarthritis-specific health status measures to the International Classification of Functioning, Disability and Health (ICF). Osteoarthritis and Cartilage, 11, 519-523.
[28] Australian Institute Health & Welfare (AIHW) (2003) ICF Australian user guide, V.1.0.
[29] Drasgow, F. (2004) Polychoric and polyserial correlations. In: Kotz, L. and Johnson, N., Eds., Encyclopedia of Statistical Sciences, Wiley, New York, 68-74.
[30] Hosmer, D and Lemshow, S. (2000) Applied logistic regression. 2nd Edition, Wiley-Interscience, John Wiley & Sons Inc., Hoboken.
[31] Akaike, H. (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716-723.
[32] Stata Statistical Software (2004) (Version 8.2) Stata Corporation, College Station.
[33] SAS (2007) (Version 9.1) [software]. SAS Institute, Carey.
[34] Abdelhafiz, A. and Austin, C. (2003) Visual factors should be assessed in older people presenting with falls or hip fracture. Age and Ageing, 32, 26-30.
[35] Campbell, A., Robertson, M. and La Grow, S. (2005) Randomised controlled trial of prevention of falls in people aged >75 with severe visual impairment: The VIP trial. British Medical Journal, 331, 817-824.
[36] Lord, S., Ward, J. and Williams, P. (1993) An epidemiological study of falls in older community-dwelling women: The Randwick falls and fractures study. Australian and New Zealand Journal of Public Health, 17, 240-245.
[37] Harwood, R. (2001) Visual problems and falls. Age and Ageing, 30, 13-18.
[38] Harwood, R., Foss, A., Osborn, F., Gregson, R., Zaman, A. and Masud, T. (2005) Falls and health status in elderly women following first eye cataract surgery: A randomised controlled trial. British Journal of Ophthalmology, 89, 53-59.
[39] Foss, A., Harwood, R., Osborn, F., Gregson, R., Zaman, A. and Masud, T. (2006) Falls and health status in elderly women following second eye cataract surgery: A randomised controlled trial. Age and Ageing, 35, 66-71.
[40] Dharmarajan, T., Auula, S. and Norkus, E. (2006) Anaemia increases risk for fall in hospitalized older adults: An evaluation on falls in 362 hospitalized, ambulatory, long term care, and community patients. Journal of American Medical Directors Association, 7, 287-293.
[41] Penninx, B., Pluijm, S. and Lips, P. (2005) Late-life anaemia is associated with increased risk of recurrent falls. Journal of the American Geriatrics Society, 53, 2106-2111.
[42] Hartikainen, S., Lonnroos, E. and Louhivuori, K. (2007) Medication as a risk factor for fall: Critical systematic review. Journals of Gerontology A Biological Sciences Medical Sciences, 62, 1172-1181.
[43] Schwartz, A., Hillier, T. and Sellmeyer, D. (2002) Older women with diabetes have a higher risk of falls. A prospective study. Diabetes Care, 25, 1749-1754.
[44] Herndon, J., Helmick, C. and Sattin, R. (1997) Chronic medical conditions and risk of fall injury events at home in older adults. Journal of the American Geriatrics Society, 45, 739-743.
[45] van Daele, P., Stolk, R. and Burger, H. (1995) Bone density in non-insulin-dependent diabetes mellitus: The rotterdam study. Annals of Internal Medicine, 122, 409-414.
[46] Clemson, L., Mackenzie, L., Ballinger, C., Close, J. and Cumming, R. (2008) Environmental interventions to prevent falls in community dwelling older people: A metaanalysis of randomized trials. Journal of Aging and Health, 20, 954-71.
[47] Gillespie, L., Robertson, M. and Gillespie, W. (2012) Interventions for preventing falls in older people living in the community. Cochrane Database of Systematic Reviews, Article ID: CD007146.
[48] Judge, J., Schechman, K. and Cress, E. (1996) The relationship between physical performance measures and independence in instrumental activities of daily living. The FICSIT group. Frailty and injury: co-operative studies of intervention trials. Journal of the American Geriatrics Society, 44, 1332-1341.
[49] Wojszel, Z. and Bien, B. (2004) Falls amongst older people living in the community. Annales Academiae Medicae Bialostocensis, 49, 280-284.
[50] Bruyère, S. and Van Looy, S. (2005) The International Classification of Functioning, Disability and Health: Contemporary literature overview. Rehabilitation Psychology, 50, 113-121.
[51] Nordenfelt, L. (2003) Action theory, disability, and ICF. Disability and Rehabilitation, 25, 1075-1079.
[52] Chan, B., Marshall, L., Winters, K., Faulkner, K., Schwartz, A. and Orwoll, E. (2007) Incident fall risk and physical activity and physical performance among older men. American Journal of Epidemiology, 165, 696-703.
[53] Cerniauskaite, M., Quintas, R. and Boldt, C. (2011) Systematic literature review on ICF from 2001 to 2009: Its use, implementation and operationalisation. Disability and Rehabilitation, 33, 281-309.
[54] National Collaborating Centre for Nursing and Supportive Care (2004) Clinical guideline 21, falls: The assessment and prevention of falls in older people. National Institute for Clinical Excellence, London.
[55] Geyh, S., Peter, C. and Müller, R. (2011) The personal factors of the International Classification of Functioning, Disability and Health in the literature—A systematic review and content analysis. Disability and Rehabilitation, 33, 1089-1102.

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