Journal of Safety Research 45 (2013) 7–13 Contents lists available at SciVerse ScienceDirect Journal of Safety Research journal homepage: www.elsevier.com/locate/jsr Health and lifestyle risk factors for falls in a large population-based sample of older people in Australia Rebecca J Mitchell a,⁎, Wendy L Watson b, Andrew Milat c, Amy Z.Q Chung d, Stephen Lord e a Falls and Injury Prevention Group, Neuroscience Research Australia, University of New South Wales, Australia NSW Injury Risk Management Research Centre, University of New South Wales, Australia Evidence and Evaluation Branch, NSW Ministry of Health, Australia d School of Aviation, University of New South Wales, Australia e Falls and Balance Research Group, Neuroscience Research Australia, University of New South Wales, Australia b c a r t i c l e i n f o Article history: Received 27 February 2012 Received in revised form 19 September 2012 Accepted 29 November 2012 Available online 14 December 2012 Keywords: accidental falls aged lifestyle risk factors health policy a b s t r a c t Introduction: Fall-related injuries among older people is a significant public health issue Method: To identify medical, general health and lifestyle factors associated with falls and multiple falls in older persons, a representative sample of people aged 65+ years living in the community in New South Wales (NSW) Australia were surveyed regarding their falls experience, lifestyle and general health Results: One-quarter of respondents indicated they had fallen in the past 12 months People who fell were more likely to be aged 85 + years, have cataracts, musculoskeletal system and connective tissue disorders, major diseases of the circulatory, respiratory and nervous systems, use four or more medications, use a mobility aid and be overweight than non-fallers Individuals aged 85+ years and those who experienced circulatory diseases, used four or more medications and used mobility aids were more likely to experience multiple falls Discussion: This representative population-based survey reinforces the multi-factorial nature of falls and the complex interaction of risk factors that increase the likelihood of individuals having a fall or multiple falls Agencies focused on community-based fall prevention strategies should adopt a systematic, integrated approach to reduce the burden of fall injury at the population-level and should have mechanisms in place at the population-level to monitor the success of fall reduction strategies © 2013 National Safety Council and Elsevier Ltd All rights reserved Introduction Fall-related injury morbidity and mortality among older people is a significant public health issue worldwide (Aschkenasy & Rothenhaus, 2006; Bradley & Pointer, 2009; Peden McGee, & Sharma, 2002) It is estimated that approximately one-third of individuals aged 65 years and older living in the community fall each year with many older individuals falling more than once (Campbell, Borrie, & Spears, 1989; Tinetti, Speechley, & Ginter, 1988) Fall-related injuries among older people represent a significant cost in health care (Schuffham, Chaplin, & Legood, 2003; Stevens, Corso, Finkelstein, & Miller, 2006), with the annual direct cost to the health system in 2006-07 estimated at $558.5 million in New South Wales (NSW), the most populous state in Australia, with an estimated 460,000 people aged 65 years and older (Watson, Clapperton, & Mitchell, 2011) The causes of falls are multi-factorial, involving both intrinsic and extrinsic factors (Graafmans et al., 1996; Morris et al., 2004) In particular, fall injury risk has been associated with a number of physical and ⁎ Corresponding author at: Neuroscience Research Australia, University of New South Wales, Sydney NSW 2052, Australia Tel.: +61 9385 7555; fax: +61 9385 6040 E-mail address: r.mitchell@unsw.edu.au (R.J Mitchell) lifestyle-related factors, such as the presence of chronic health conditions, like stroke, being female, having mobility problems, using multiple medications, alcohol consumption, and a low body mass index (O'Loughlin, Robitaille, Boivin, & Suissa, 1993; Tinetti et al., 1988; Tinetti, Doucette, & Claus, 1995) In addition to causing physical injuries, falls can have a detrimental effect on an individuals’ confidence Following a fall, older people can develop a fear of falling, often decreasing their levels of activity in an attempt to prevent further falls (King & Tinetti, 1995) With the ageing of the population and the growing costs to the health system of fall-related injuries, government policies have sought to reduce the fall injury morbidity burden by encouraging participation in activities that promote healthy ageing that are likely to prevent, or reduce, the number of fall injuries (Campbell & Robertson, 2010; Scott, Wagar, Sum, Metcalfe, & Wagar, 2010) These strategies have included encouraging involvement in physical activities, particularly strength and balance exercises, medication reviews and cataract surgery (Gillespie et al., 2009) In Australia, there have been few attempts at the population-level to monitor the success of fall reduction strategies by conducting representative population-based surveys to collect information across a range of factors pertinent to falls In order to create a baseline for monitoring government strategies to reduce fall injury in NSW, information on lifestyle and health differences between fallers and non-fallers, and between 0022-4375/$ – see front matter © 2013 National Safety Council and Elsevier Ltd All rights reserved http://dx.doi.org/10.1016/j.jsr.2012.11.005 R.J Mitchell et al / Journal of Safety Research 45 (2013) 7–13 of 5,681 older people (aged 65+ years) living in the community, with a private telephone, were surveyed across NSW regarding their falls experience, knowledge and perception of falls, participation and awareness of physical activity and health status The full details concerning the development of the survey and the methods used are described in full elsewhere (Centre for Health Advancement and Centre for Epidemiology and Research, 2010) and summarised below and in Fig A two-stage sampling process was used, with the sample stratified by each of the former eight NSW Area Health Services (AHS) Within each AHS, households were randomly selected using a computergenerated list of telephone numbers A single respondent was then randomly selected from each household for a computer-assisted single fallers and repeat fallers, need to be described The aim of this paper is to use such baseline data to compare health and lifestyle factors between fallers and non-fallers and between single and multiple fallers in persons aged 65 years and over in a population-based sample Method 2.1 Sampling design The NSW Falls Prevention Baseline Survey was undertaken in 2009 by the NSW Ministry of Health (Centre for Health Advancement and Centre for Epidemiology and Research, 2010) A representative sample Geocoding of the Australia on-disc electronic white pages telephone book ↓ Percent of telephone numbers with each telephone prefix by AHS calculated All prefixes were expanded with suffixes ranging from 0000 to 9999 Geocoded telephone numbers assigned Statitical Local Areas and Area Health Services (AHS) ↓ ↓ Matching of created telephone numbers to electronic telephone book All telephone numbers that matched the electronic telephone book flagged and assigned an AHS ↓ Unlisted telephone numbers assigned to an AHS containing the greatest proportion of telephone numbers with that prefix ↓ Telephone numbers filtered to eliminate contiguous unused blocks of greater than 10 numbers ↓ Remaining numbers checked against business telephone numbers in the electronic phone book and eliminated ↓ Telephone numbers randomly sorted by each AHS and a sample identified for interview and each household sent a letter describing the aims and methods of the survey weeks prior to any telephone contract A 1800 telephone number provided for any questions ↓ Households contacted via random digit dialing Seven call -backs made to contact each household ↓ n=109,151 telephone calls made Of these: n=10,912 unable to contact; n=42,104 number not connected; n=6,610 business or institution telephone; n=6,067 fax number; n=172 household not in NSW or holiday house; n=236 away for duration of survey; n=549 unable to answer (eg confused, deaf); n=1,351 non-translated language; n=3,669 refusal; n=31,800 no one 65+ years in household ↓ Where more than individual per household was aged 65+ years, random selection of respondent was conducted ↓ n=5,681 completed telephone calls, with at least n=675 from each AHS Of these, n=361 completed by proxies and n=5,320 by respondents Fig Sampling flow chart R.J Mitchell et al / Journal of Safety Research 45 (2013) 7–13 telephone interview (CATI) Proxy respondents were chosen for 361 participants who were unable to answer on their own behalf due to various conditions, such as hearing impairments, poor health, dementia or cognitive impairment (Centre for Health Advancement and Centre for Epidemiology and Research, 2010) Interviews were conducted between March and July 2009 Households selected for a telephone interview that had postal addresses in the electronic phone book were sent a letter describing the aims and methods of the survey two weeks prior to the initial attempts at telephone contact Interviews were conducted by trained Health Survey Program CATI interviewers and by interviewers from McNair Ingenuity Research Ltd Up to seven call backs were made to establish initial contact with a household and up to five call backs were made to contact a selected respondent Almost all respondents (96.0%) were interviewed in English Other languages included Arabic, Chinese, Greek, Italian, and Vietnamese (Centre for Health Advancement and Centre for Epidemiology and Research, 2010) The survey response rate was 60.8% (number of completed interviews divided by the completed interviews and refusals) (Centre for Health Advancement and Centre for Epidemiology and Research, 2010) living in each household, the number of residential telephone connections for the household, and the varying sampling fraction in each AHS Results Around one-quarter (25.6%) of older individuals in 2009 indicated that they had fallen in the preceding 12 months and of these, 38.1% reported falling more than once Of all those who fell, 65.9% of falls resulted in an injury Of the injurious falls, 19.9% required a visit to hospital and 10.7% required hospital admission 3.1 Gender and age There were no significant differences in the odds of falling for males and females Individuals aged 85 years and over had almost twice the likelihood of falling than those aged in the comparison group of 65-69 years (OR = 1.93; 95%CI 1.52-2.44, p b 0.0001) For multiple fallers, individuals aged 70-74 years were around 10% less likely (OR = 0.89; 95%CI 0.63-1.26, p = 0.03) and individuals aged 85 years and over were one and a half times more likely (OR = 1.66; 95%CI 1.10-2.50, p = 0.01) to fall than single fallers 2.2 The survey instrument 3.2 Physical co-morbidities During each interview, information obtained from all respondents included demographic information on age, gender, and health status, such as physician-diagnosed co-morbidities, physical activity participation, prescription medication use, alcohol consumption, and smoking status Information on fall experience, knowledge and perception of falls was also obtained Respondents were asked if in the last 12 months they had suffered a fall (i.e accidentally lost their balance, tripped or slipped and found themselves on the floor or ground), how many times they had fallen, how many of the falls resulted in an injury, how many of the falls resulted in going to hospital, and how many of the falls resulted in being admitted to hospital Questions were field tested prior to use Identification of risk alcohol drinking was defined as individuals who drank more than two standard drinks on any day (the Australian standard drink contains 10 g of alcohol; equivalent to 12.5 mL of pure alcohol) (National Health and Medical Research Council, 2009) Identification of individuals who were overweight or obese was conducted using body mass index (BMI) scores of 25.0 to 29.9 and 30.0 and higher, respectively Ethics approval was obtained from the NSW Health's Population and Health Services Research Ethics Committee (2008/12/114; HREC/08/CIPHS/55) 2.3 Data analysis Analysis was performed using SAS version 9.1 (SAS Institute, 2003) The SURVEYFREQ procedure was used to calculate Rao-Scott designadjusted chi-square tests The SURVEYLOGISTIC procedure was used to calculate odds ratios and ninety-five percent confidence intervals (95% CI) The method of purposeful selection was used in order to select the variables for the multiple variable logistic regression model (Bursac, Gauss, Williams, & Hosmer, 2008) Initially, a univariate logistic regression model was developed for fallers and non-fallers and for single and multiple fallers for each variable to determine if it should be included in the full model, based on its statistical significance at the 0.05 level The full multivariate logistic regression model was then developed, and each included variable was considered for retention in the model based on its significance, adjusted for age group and gender Where possible, physical conditions were combined using International Classification of Diseases-based categories into diseases of the nervous system, circulatory system, respiratory system, and musculoskeletal system and connective tissue Data were stratified by AHS and a sampling weight was applied to adjust for differences in the probabilities of selection among respondents These differences were due to the varying number of people Compared to those who did not report falling in the last 12 months, those who reported falling in this period had significantly higher odds of being diagnosed with diabetes, arthritis, osteoporosis, heart disease/ angina, poor circulation in the legs/peripheral vascular disease, reduced sensation in the legs or feet, stroke, Parkinson's disease, cataracts, emphysema or lung disease, dementia/cognitive impairment/Alzheimer's disease, and neck and back problems Those who fell multiple times had significantly higher odds of being diagnosed with heart disease/ angina, poor circulation in legs/peripheral vascular disease, reduced sensation in legs or feet, stroke, and cataracts than those who fell once (Table 1) 3.3 Prescription medications Fallers were two and a half times more likely to be taking four or more prescription medications compared to non-fallers (p b 0.0001) Fallers were regularly consuming a higher proportion of all prescribed medications, including twice the proportion of tranquillisers and anti-depressants, than non-fallers Multiple fallers were significantly more likely to be taking multiple prescription medications than single fallers and consumed twice the proportion of sleeping tablets and anti-depressants compared to single fallers (Fig 2) 3.4 Health status Non-fallers and single fallers were significantly more likely to rate their health as excellent, very good or good compared to fallers and multiple fallers, respectively (OR= 0.49; 95%CI 0.42-0.57, p b 0.0001 and OR= 0.55; 95%CI 0.43-0.72, p b 0.0001) Non-fallers and single fallers were also significantly less likely to have to rush unexpectedly to the toilet to urinate compared to fallers and multiple fallers, respectively (OR = 1.65; 95%CI 1.44-1.89, p b 0.0001 and OR= 1.27; 95%CI 1.00-1.63, p = 0.05) 3.5 Mobility, physical activity and lifestyle Individuals who fell were over two and a half times more likely to regularly use mobility aids than non-fallers (pb 0.0001) and multiple fallers were twice as likely to regularly use mobility aids than single fallers (pb 0.0001) Non-fallers and single fallers were significantly more likely to agree that being physically active for 30 minutes or more each day reduced fall risk compared to fallers (p= 0.006) and 10 R.J Mitchell et al / Journal of Safety Research 45 (2013) 7–13 Table Comparison of demographic, co-morbidity, medication and health status characteristics of fallers and non-fallers and single fallers and multiple fallers, NSW Falls Prevention Baseline Survey, 2009 Non-fallers % Fallers % Odds Ratio Single fallers % Multiple fallers % Odds Ratio Gender Male Female 46.1 53.9 44.1 55.9 1.01 0.95–1.25 0.24 42.8 57.2 45.8 54.2 0.89 0.69–1.13 0.32 Age group 65-69 70-74 75-79 80-84 85+ 31.5 24.6 20.2 14.4 9.3 25.0 23.2 21.6 16.1 14.2 1.12 1.35 1.41 1.93 0.98–1.43 1.10–1.64 1.14–174 1.52–2.44 0.06 0.95 0.51 b0.0001 26.1 25.2 20.7 16.4 11.6 23.5 20.3 22.7 16.2 17.3 0.89 1.22 1.10 1.66 0.63–1.26 0.86–1.74 0.76–1.60 1.10–2.50 0.03 0.6 0.7 0.01 Physical co-morbidities Diabetes Arthritis (all types) Osteoporosis Heart disease/angina Poor circulation in legs/peripheral vascular disease Reduced sensation in legs or feet High blood pressure Stroke Cancer Parkinson's disease Cataracts Emphysema or lung disease Dementia/Alzheimer's disease/cognitive impairment Neck and back problems 16.2 47.4 16.7 23.5 17.4 13.0 51.0 7.5 16.3 0.7 35.5 9.2 0.3 1.1 20.1 58.7 21.0 29.7 28.1 21.4 53.5 10.6 18.6 2.4 45.4 13.6 0.8 1.7 1.30 1.57 1.33 1.38 1.85 1.82 1.11 1.46 1.18 3.41 1.51 1.55 2.73 1.77 1.10–1.55 1.37–1.80 1.12–1.57 1.18–1.60 1.58–2.16 1.53–2.17 0.97–1.27 1.16–1.85 0.98–1.40 1.98–5.88 1.32–1.74 1.26–1.91 1.01–6.85 1.01–3.10 0.0027 b0.0001 0.0009 b0.0001 b0.0001 b0.0001 0.14 0.0013 0.075 b0.0001 b0.0001 b0.0001 0.032 0.045 20.2 57.2 19.9 26.6 23.0 18.6 52.0 7.2 18.0 1.9 42.9 12.4 0.9 2.0 19.9 60.9 22.1 33.7 35.3 24.9 56.3 15.8 19.3 3.2 49.0 15.1 0.9 1.2 0.98 1.17 1.14 1.40 1.82 1.45 1.19 2.43 1.09 1.7 1.28 1.26 0.64 0.82 0.73–1.32 0.91–1.50 0.86–1.52 1.08–1.82 1.40–2.38 1.08–1.93 0.93–1.51 1.64–3.58 0.80–1.49 0.78–3.82 1.01–1.63 0.89–1.78 0.16–2.67 0.30–2.26 0.90 0.21 0.37 0.01 b0.0001 0.01 0.16 b0.0001 0.58 0.17 0.05 0.2 0.54 0.7 Number of prescription medications None One Two Three Four or more 13.4 14.8 16.6 15.0 39.8 6.8 11.3 14.3 13.8 53.1 1.50 1.69 1.81 2.62 1.10–2.03 1.26–2.27 1.35–2.44 2.03–3.39 0.28 0.72 0.24 b0.0001 8.0 14.1 16.0 13.3 48.1 5.0 6.6 11.9 14.9 60.6 0.76 1.20 1.81 2.04 0.40–1.44 0.66–2.18 1.00–3.29 1.21–3.45 0.004 0.69 0.02 b0.0001 Health status Excellent, very good or good rated health status Incontinence – urgency some/most time 81.3 40.0 68.3 51.3 0.49 1.65 0.42–0.57 1.44–1.89 b0.0001 b0.0001 73.7 49.3 60.9 54.7 0.55 1.27 0.43–0.72 1.00–1.63 b0.0001 0.05 multiple fallers (p= 0.009), respectively Fallers were more likely to have participated in some form of balance training in the last week than non-fallers (p=0.02) The proportion of individuals who smoked, were risk alcohol drinkers, and who had their eyesight tested in the last 12 months were similar for all Fallers and multiple fallers were significantly more likely to be overweight or obese than non-fallers (p=0.01) and single fallers (p= 0.04), respectively (Table 2) Compared to non-fallers, fallers were significantly more likely to have made some changes to their lifestyle in the last 12 months to prevent falls (pb 0.0001), with fallers reporting a higher proportion now 95% CI P-value 95% CI p-value using a walking aid, having a balance, walking and/or muscle strength assessment conducted, and beginning balance exercises Just over one-third of multiple fallers reported limiting their activities in the last 12 months to prevent falls and around one-third of multiple fallers reported commencing using a walking aid (Fig 3) 3.6 Co-morbidities and medications The logistic regression analyses revealed the following variables were significant and independent risk factors for falls: taking four or Fig Type of medications prescribed and taken regularly by non-fallers and fallers and single and multiple fallers (% yes) R.J Mitchell et al / Journal of Safety Research 45 (2013) 7–13 11 Table Comparison of physical activity, mobility, and lifestyle characteristics of fallers and non-fallers and single fallers and multiple fallers, NSW Falls Prevention Baseline Survey, 2009 Physical activity, mobility and lifestyle Regularly uses mobility aids (eg cane, walking stick, walker or frame, mobility scooter) Agrees being active for 30 minutes a day reduces the risk of a fall Did balance training each day in last week Did flexibility activities each day in last week (i.e tai chi; yoga; lawn bowls or other types of bowls; dancing) Did strength or resistance training, such as lifting weights or push ups each day in last week Current smoker Risk alcohol drinking Overweight (BMI 25.0–29.9) or obese (BMI 30.0 and over) Had eyesight tested in last 12 months Lifestyle changes made to prevent falls in last 12 months Non-fallers % Fallers % Odds Ratio 95% CI P-value Single fallers % Multiple fallers % Odds Ratio 95% CI p-value 13.5 29.2 2.67 2.26–3.13 b0.0001 22.8 38.0 2.07 1.59–2.70 b0.0001 68.3 1.2 1.1 64.3 2.2 1.1 0.78 1.84 1.05 0.65–0.93 1.10–3.07 0.54–2.04 0.006 0.02 0.89 68.5 1.8 1.5 59.2 2.6 0.6 0.66 1.39 0.39 0.48–0.90 0.62–3.10 0.10–1.55 0.009 0.42 0.18 7.4 2.1 0.8 0.64–1.03 0.08 4.6 3.6 1.27 0.83–1.92 0.27 7.2 13.2 51.8 65.3 78.7 7.0 11.0 55.3 68.5 88.0 0.98 0.82 1.20 1.15 1.99 0.75–1.28 0.66–1.02 1.04–1.38 1.00–1.33 1.63–2.42 0.89 0.07 0.01 0.06 b0.0001 6.7 11.1 53.1 69.1 86.7 7.5 11.1 59.4 67.0 89.7 1.13 0.98 1.32 0.91 1.31 0.70–1.82 0.66–1.45 1.02–1.70 0.71–1.18 1.00–1.92 0.61 0.91 0.04 0.49 0.16 more prescription medications, untreated cataracts and the presence of circulatory, respiratory, nervous system and musculoskeletal system and connective tissue diseases The model for multiple falls (compared to a single fall) included taking four or more prescription medications, untreated cataracts and the presence of circulatory diseases (Table 3) Discussion The growth of the ageing population has resulted in an increase in the incidence of fall-related injury requiring hospitalisation in a number of countries (Bradley & Pointer, 2009; Scott et al., 2010) Fall-related injury morbidity and recurrent falls can leave older individuals with ongoing health concerns and impaired mobility which can adversely impact on their quality of life (Pinheiro, Ciconelli, Martini, & Ferraz, 2010) To ensure that older individuals have the best opportunity to remain in their own home with no detrimental impact on their quality of life, there is a need to reduce the incidence of fall injuries in older age In order to achieve this, fall-related risk factors need to be addressed and community-wide, as well as individually focused, preventive strategies need to be implemented and their impact evaluated (Australian Commission on Safety and Quality in Health Care, 2009) In the current study, one-quarter of respondents fell once and over a third (38.1%) of these fell more than once in the last 12 months Two-thirds of the reported falls resulted in injury and of those injured, one in five went to hospital for treatment and one in ten was admitted to hospital Similar proportions of falls have been found in other studies in the United States, where 32% of individuals aged 75 years and over reported falling at least once in the last year (Tinetti et al., 1988) and in Montreal where 29% of individuals aged 65 years and over reporting falling in the last 12 months; 18% of all respondents reported falling once and 12% fell twice or more (O'Loughlin et al., 1993) Similar to other studies (O'Loughlin et al., 1993; Morris et al., 2004), females reported experiencing more falls than males However, like O'Loughlin et al (1993), the current study identified no significant differences in the proportion of males and females that reported multiple falls This is unlike Morris et al (2004) and Graafmans et al (1996) who found higher proportions of females experiencing two or more falls In the current study, individuals aged 85 years and over had twice the odds of falling compared to those aged 65-69 years and were almost one and a half times more likely to have multiple falls As individuals age, the number of fall-related risk factors increase, contributing to the increased rate of fall-related injuries (O'Loughlin et al., 1993) In general, fallers were more likely to have co-morbidities than non-fallers, with fallers three and a half times more likely to have Parkinson's disease than non-fallers Other studies have also found Parkinson's disease to be associated with fall risk (Cesari et al., 2002) Individuals with Parkinson's disease often experience postural instability, difficulty rising from low chairs, and a shuffling gait which are all likely to affect mobility and balance (Aita, 1982) Compared to non-fallers, fallers were over 2.5 times more likely to experience some form of dementia, cognitive impairment or Alzheimer's disease Other studies have also found a similar risk of falls for individuals with dementia (Tinetti et al., 1988; van Doorn et al., 2003; Anstey, von Sanden, & Luszcz, 2006) Multiple fallers were almost 2.5 times more likely to have had a stroke than single fallers Likewise Graafmans et al (1996), in a prospective study of falls, found recurrent fallers almost 3.5 times more likely to have had a stroke than non-fallers and Fig Type of lifestyle changes made in the last 12 months to prevent falls by non-fallers and faller and single and multiple fallers (% yes) 12 R.J Mitchell et al / Journal of Safety Research 45 (2013) 7–13 Table Major medical and medication-related risk factors for falls, NSW Falls Prevention Baseline Survey 2009 Adjusted 95%CI Odds Ratio p-value Fallers and non-fallers Diabetes Musculoskeletal system and connective tissue1 Circulatory system2 Cataracts Respiratory system3 Nervous system4 Four or more medications 1.16 1.42 1.39 1.24 1.32 2.75 1.24 0.96–1.39 0.12 1.22–1.87 b0.0001 1.20–1.62 b0.0001 1.06–1.44 0.006 1.06–1.64 0.01 1.68–4.51 b0.0001 1.06–1.46 0.007 Multiple fallers and single fallers Circulatory2 Cataracts Four or more medications 1.44 1.13 1.43 1.10–1.87 0.87–2.14 1.10–1.86 0.008 0.36 0.008 Includes arthritis, osteoporosis and neck and back problems Includes heart disease/angina, poor circulation in legs, peripheral vascular disease, reduced sensation in legs or feet and stroke Includes emphysema or lung disease Includes dementia, Alzheimer's disease, cognitive impairment, Parkinson's disease single fallers combined At a population-level, tailored interventions of a multi-factorial nature may be of benefit for health conditions, such as nervous system diseases (Shaw & Kenny, 1988), especially with the growth of dementia suffers aged 65 years and over in Australia set to double from 212,000 in 2011 to 452,600 by 2031 (Australian Institute of Health and Welfare, 2007) Individuals using four or more medications had an increased risk of falling compared to non-fallers and of having multiple falls compared to a single fall Polypharmacy increases the risk of falling, with the use of four or more medications an indicator of falls risk (Tinetti, 2003) Fallers and multiple fallers were more likely to be taking psychoactive medications, such as anti-depressants, compared to non-fallers and single fallers, respectively Where possible, mechanisms to decrease medication load among older individuals need to be considered and medication reviews promoted (Tinetti, 2003) How this strategy is implemented at a population-level becomes less clear as voluntary annual health assessments for those aged 75 years and over in Australia are only accessed by 191 in every 1,000 individuals (Australian Institute of Health and Welfare, 2007) While the older people surveyed recognised that 30 minutes of exercise a day was a preventive factor for falling, their ability to put this knowledge into practice was not evident When asked about the physical activities that they had conducted in the last week, only 2% of individuals who fell reported participating in any balance, flexibility or strength enhancing activities This suggests a need to increase opportunities for strength and balance enhancing activities in this age group, possibly through greater advertising of activities for older individuals and their health benefits Self-reported smoking or drinking behaviours were similar for fallers and non-fallers alike Tinetti et al (1988) also found a similar likelihood of smoking and drinking behaviours reported by fallers and non-fallers and hypothesised that heavy smokers and high risk drinkers were more likely to have early deaths and not reach older age On the other hand, Peel et al (Peel, Bartlett, & McClure, 2007) found a protective effect for some lifestyle-related behaviours, such as not smoking, moderate alcohol consumption and being active, and the risk of fall-related hip fracture However, hip fracture is one of the more serious injuries following a fall and this study included both serious and non-serious injuries Limitations of the current study include the self-reported nature of the data and the possibility of recall bias associated with respondents reporting events over a 12 month period, compared to other methods, such as weekly or monthly diaries or telephone calls (Ganz, Higashi, & Rubenstein, 2005) It is also well known that cognitive deficits associated with age can undermine the validity of self reported survey measures (New England States Consortium, 2001) However, to minimise its impact survey items were pretested, interviewers were trained and proxy respondents were used, all measures consistent with international survey best practice (New England States Consortium, 2001) There were no physiological measurements or mobility examinations conducted to verify the conditions reported, nor were all vision problems identified, such as glaucoma and macular degeneration, despite vision problems being an important risk factor for falls (Tinetti et al., 1988) This study demonstrates the ability to achieve population-level baseline data to monitor the future impact of fall injury reduction strategies in NSW Initial investment in fall injury prevention in NSW has seen the development of a statewide policy for fall injury prevention (NSW Health Department, 2011), the establishment of a statewide and AHS fall coordinator positions and promotion of balance and strength training programs (NSW Health Department, 2011) The baseline results reinforce the multi-factorial nature of falls causalities and the complex interaction of risk factors that increase the likelihood of individuals having a fall or multiple falls In addition, the consistency in the findings of this study with previous research may suggest that the aetiology of falls in community-dwelling older people in developed countries is relatively stable Findings also strengthen the case for developing different strategies to address the needs of sub-populations as it is clear that risk factors vary between single and multiple fallers The complexity of the issue has meant that, while research has identified a number of efficacious interventions that can significantly decrease the incidence of falls, costeffective fall prevention programs at the population-level has been more difficult to demonstrate The implementation and evaluation of fall injury prevention strategies at the population-level requires an integrated approach across all settings and health care providers, as well as more effective partnerships with older adults and their caregivers As Ganz et al points out, “It takes a village of stakeholders working together to prevent falls and reduce fall risk, tasks that no one stakeholder can accomplish alone” (Ganz, Alkema, & Wu, 2008) Acknowledgements R Mitchell was supported by an ARC-linkage post-doctoral fellowship (LP0990057) and the NSW Ministry of Health W Watson was supported by the NSW Ministry of Health The 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