Mild cognitive impairment is associated with falls among older adults findings from the irish longitudinal study on ageing (TILDA)

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Mild cognitive impairment is associated with falls among older adults:
findings from the Irish Longitudinal Study on Ageing (TILDA)
Stefanos Tyrovolas, Ai Koyanagi, Elvira Lara, Ziggi Santini, Josep Maria Haro
PII:
DOI:
Reference:

S0531-5565(15)30107-8
doi: 10.1016/j.exger.2015.12.008
EXG 9756

To appear in:

Experimental Gerontology

Received date:
Revised date:
Accepted date:

17 August 2015
15 December 2015
16 December 2015

Please cite this article as: Tyrovolas, Stefanos, Koyanagi, Ai, Lara, Elvira, Santini, Ziggi,
Haro, Josep Maria, Mild cognitive impairment is associated with falls among older adults:
findings from the Irish Longitudinal Study on Ageing (TILDA), Experimental Gerontology
(2015), doi: 10.1016/j.exger.2015.12.008

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Mild cognitive impairment is associated with falls among
older adults: findings from the Irish Longitudinal Study on

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Ageing (TILDA)

Stefanos Tyrovolas1,2, Ai Koyanagi1,2, Elvira Lara1,2, Ziggi Santini1, Josep Maria

Parc Sanitari Sant Joan de Déu, Universitat de Barcelona. Fundació Sant Joan de

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Haro1,2

Déu, Dr Antoni Pujades, 42, SantBoi de Llobregat, Barcelona, 08830, Spain
Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud

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Mental, CIBERSAM, Monforte de Lemos 3-5. Pabellón 11, 28029, Madrid, Spain

Address for Correspondence
Dr. Stefanos Tyrovolas
Parc Sanitari Sant Joan de Déu, Fundació Sant Joan de Déu, CIBERSAM,
Dr. Antoni Pujadas, 42, 08830 – Sant Boi de Llobregat, Barcelona, Spain.
Email:


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Abstract
Introduction: The role of mild cognitive impairment (MCI) on falls among older

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adults remains under-investigated. The aim of this study was to evaluate the

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association between MCI and number of falls or occurrence of non-accidental falls

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among older adults. Methods: Data from the first wave of the Irish longitudinal Study
on Ageing (TILDA) was analysed. The analytical sample consisted of 5364

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individuals aged ≥50 years. MCI was defined as: Montreal Cognitive Assessment
(MoCA) score<26; presence of subjective cognitive complaints; Mini-Mental State

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Examination (MMSE) score≥14; and no limitations in activities of daily living
(ADL). Multivariable poisson and logistic regression analyses were conducted to

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assess the association between MCI and number of falls or presence of non-accidental

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falls in the past 12 months. Results: The prevalence of MCI was 10.1%. In the fullyadjusted model, MCI was associated with a higher rate of falls (PR=1.41

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95%CI=1.05-1.89) and odds for non-accidental falls in the past 12 months (OR=1.67
95%CI=1.07-2.61). Muscle strength and performance indicators, and medical health

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conditions were influential factors in the association between MCI and falls but did
not fully explain the association. Conclusion: MCI is related with higher rates of falls
and the occurrence of non-accidental falls among older adults. Future studies are
warranted to clarify the underlying mechanism linking MCI and falls, and to establish
interventions targeting MCI to reduce the risk of falls.

Keywords: Mild cognitive impairment; Falls; Gait speed; Muscle strength


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Introduction
The European commission has recognized population aging as one of the most

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challenging policy issues of this century in Europe (European Commission 2006).

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Advanced age is accompanied by various co-morbidities that affect health status and

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quality of life including falls (Janssen et al., 2002; Landi et al., 2013; Newman et al.,
2006). Falls are a major health care problem for the elders. Almost 30% of the older

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population have been reported to experience a fall accident at least once per year
(Muir et al., 2012). Moreover, falls are associated with a higher risk of loss of

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independence, autonomy, and confidence. Falls are one of the major contributors to
the increased need for specialized care and hospitalization among older adults, while

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it is also associated with higher rates of morbidity, mortality, and institutionalization

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(Tinetti et al., 1995; Tinetti and Williams, 1997). Additionally, the cost of falls for the
public health services is high. For example, in the UK, the cost of fall-related

2003).

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hospitalizations among older adults is almost £1 billion per year (Scuffham et al.,

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Various factors such as vision and hearing problems, abnormal blood pressure,
mobility limitation, neuropsychiatric disorders, sarcopenia, and frailty have been
associated with falls (Tinetti et al., 1986; Robbins et al., 1989; Shumway-Cook et al.,
1997; Vellas et al., 1997; Mühlberg and Sieber, 2004). Among neuropsychiatric
disorders, decline in cognitive function has been related with greater risk of falls in
the older population. Recent studies have reported an increased frequency of falls with
lower Mini-Mental State Examination (MMSE) scores (i.e., loss of global cognitive
ability) (Gleason et al., 2009). Impairments in attention (Amboni et al., 2013),
processing speed (Chen et al., 2012), and executive functions (Banich, 2009) have
been proposed as a set of interrelated factors in the pathway between cognitive


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impairment and falls. Based on these previous findings, some researchers have
proposed that fall and injury prevention strategies may benefit from focusing on the

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early prevention of cognitive decline (Montero-Odasso et al., 2009). In particular, in

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recent years, mild cognitive impairment (MCI), which is considered an intermediate

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state between normal aging and dementia, is gaining further attention from the
viewpoint of prevention of dementia or cognitive decline. However, despite the
potentially important role that cognitive function plays in the occurrence of falls, the

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association between MCI and falls among older adults still remain under-investigated

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(Delbaere et al., 2012).

Given the rapid aging occurring in Europe, the scarcity of studies on MCI and

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falls, and a complete lack of studies on this topic from Ireland, the aim of the present

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work was to evaluate the associations between MCI and frequency of falls or
occurrence of non-accidental falls in a large, nationally-representative sample of non-

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Methods

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institutionalized older Irish individuals.

Study design and sample
Data from the first wave of the Irish Longitudinal Study on Ageing (TILDA)
was analyzed. The full description of the survey and the sampling procedures can be
found elsewhere (Cronin et al., 2013). Briefly, TILDA was an Irish nationallyrepresentative, cross-sectional study on the economic, health, and social status of the
non-institutionalized population, and was conducted between 2009 and 2010 by
Trinity College in Dublin (Cronin et al., 2013). The sample included a total of 8504
people [individuals aged ≥50 years (n=8175) and their spouses or partners younger
than 50 years (n=329)]. Of these individuals, 5895 completed a health assessment.


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Information was obtained by face-to-face interviews conducted by trained
professionals using Computer Assisted Personal Interviewing (CAPI). The response

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rate was 62% (Whelan and Savva, 2013).

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The Trinity College Dublin approved the design and procedures of the study.

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Informed consent was obtained from all participants. Individuals were not eligible for
inclusion if they reported a doctor’s diagnosis of dementia. Furthermore, individuals
who were not able to consent personally because of severe cognitive impairment (at

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interviewer’s discretion) were also excluded.

Number of falls and the presence of non-accidental falls
The number of falls in the past 12 months was assessed by the question “How many

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times have you fallen in the last year?” Information on the presence of non-accidental

falls in the past 12 months was assessed by the question “Were any of these falls non-

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accidental, i.e., with no apparent or obvious reason?” among those who had fallen in

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the past 12 months. The answer options were “Yes” or “No”.

Mild cognitive impairment
The case definition of MCI was based on the core criteria outlined by the National
Institute on Aging-Alzheimer´s Association (Albert et al., 2011):
a) Concern about a change in cognition: Subjective cognitive complaints were
assessed by the question “How would you rate your day-to-day memory at present
time?” with answer options: excellent, very good, good, fair, and poor. Those who
replied fair or poor were considered to have subjective cognitive complaints.
b) Objective evidence of impairment in one or more cognitive domains, typically
including memory: Cognitive function was assessed with the Montreal Cognitive


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Assessment (MoCA) (score range: 0–30). This tool has been demonstrated to be
sensitive to mild cognitive deficits when applied in cognitively intact older adults

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(Kenny et al., 2013), and includes measures of executive function, language, memory,

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defined as a MoCA score<26 (Freitas et al., 2013).

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attention, orientation, calculation, and visuospatial ability. Cognitive impairment was

c) Preservation of independence in functional abilities: The participants were
presented with a list of six basic standard ADLs on dressing, walking, bathing, eating,

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getting in or out of bed, and using the toilet (Katz et al., 1963), and were asked if they

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have difficulty with these activities. They were also asked to exclude any difficulties
that are expected to last for less than three months. Those who claimed to have

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difficulty with any of the six abovementioned ADLs were excluded from the analysis.

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d) Not demented: Individuals who obtained a score <14 on the MMSE were excluded

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from the analytical sample (Shigemori et al., 2010).

Sociodemographic and lifestyle characteristics

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Sociodemographic and lifestyle characteristics included age (50-59, 60-69, 70-79, ≥80
years), gender, education (primary, secondary, tertiary), wealth, living arrangement
(alone or not), residence [urban (Dublin city or county/another town or city) or rural],
physical activity, and problem drinking. Wealth (financial strain) was assessed by the
statement “shortage of money stops me from doing the things I want to do” with
answer options never, rarely, sometimes, and often. Physical activity was measured
using the short form of the International Physical Activity Questionnaire, which
converts levels of physical activity of various domains into predicted kilocalories
expended per week (Craig et al., 2003). Problem drinking was assessed by the CAGE


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screening test with scores of ≥2 being used as a cut-off for problem drinking

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(Mayfield et al., 1974).

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Muscle strength and performance

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Handgrip strength and gait speed were considered indicators of muscle strength and
performance respectively (Tyrovolas et al., 2015). Grip strength was assessed using a
dynamometer. Two readings from the dominant hand were taken, and the mean

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strength was calculated. Gait speed was measured using the GAITRite portable

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electronic walkway system (CIR Systems, Inc., Havertown, PA). Participants walked
at their usual pace along a 4.88-m (16-foot) walkway with an extra 2.5 m at each end

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to allow for acceleration and deceleration. Gait speed was then calculated as meters

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per second and then transformed to centimeters per second.

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Obesity and medical health conditions
Weight and height were measured using standard procedures. Body mass index (BMI)

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was calculated as weight in kilograms divided by height in meters squared. Obesity
was defined as BMI≥30kg/m2. The presence of medical conditions was assessed by
asking the respondents about whether they were ever told by a doctor that they have
angina, arthritis (including osteoarthritis and rheumatism), congestive heart failure,
diabetes or high blood sugar, heart attack (including myocardial infarction and
coronary thrombosis), stroke (cerebral vascular disease), or Parkinson's disease. Heart
disease referred to having at least one of: angina, congestive heart failure, and heart
attack. Depression was measured with the 20-item Center for Epidemiologic Studies
Depression (CES-D) (Radloff et al., 1977) based on symptoms experienced in the past
week, and was defined as a CES-D score of ≥ 16 (Beekman et al., 1997).


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Statistical analysis
A descriptive analysis was conducted to characterize the study sample by the presence

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of MCI. The differences in sample characteristics by the presence of MCI were tested

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by chi-squared tests and student’s t-tests for categorical and continuous variables

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respectively. Poisson and logistic regression analyses were done with number of falls
and presence of non-accidental falls in the past 12 months as the outcome respectively.
MCI was the main covariate of interest. Since it is possible that the inclusion of

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different blocks of control variables in the model affects the association between MCI

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and falls in different ways, we conducted hierarchical analyses where three different
models were constructed for each outcome: Model 1 - adjusted for sociodemographic

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and lifestyle characteristics; Model 2 - adjusted for covariates in model 1 and grip

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strength and gait speed; Model 3 - adjusted for covariates in model 2 and obesity and
medical health conditions. All variables were included in the models as categorical

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variables with the exception of grip strength and gait speed (continous variables). The
selection of the covariates was based on past literature (Muir et al., 2012; Tinetti et al.,

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1995; Tinetti et al., 1986; Robbins et al., 1989). In order to assess the influence of
multicolinearity, we calculated the variance inflation factor (VIF) value for each
independent variable. The highest VIF was 2.44, which is much lower than the
commonly used-cut off of 10 (O'Brien RM, 2007), indicating that multicolinearity
was unlikely to be a problem in our analyses. The analyses were done with Stata
version 13.1 (Stata Corp LP, College Station, Texas). In order to generate nationallyrepresentative estimates, in all analyses, the sample weighting and the complex study
design were taken into account with Taylor linearization methods. Prevalence ratios
(PR) and odds ratios (OR) and 95% confidence intervals (95%CI) are reported. The
level of statistical significance was set at P<0.05.


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Results
The analytical sample consisted of 5364 individuals aged ≥50 years with no

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limitations in ADL and a MMSE score of ≥14. The prevalence of MCI was 10.1%.

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Overall, 18.9% had fallen at least once and 4.6% had non-accidental falls in the past

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12 months. The characteristics of the study participants by MCI status are summarized
in Table 1. The following characteristics were significantly associated with MCI:

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older age, lower levels of education, higher levels of financial strain, living alone,
rural residence, low physical activity, weaker hand grip strength, slower gait speed,

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obesity, and presence of medical conditions (arthritis, stroke, heart disease, and
depression). The association of MCI and other covariates with number of falls in the

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past 12 months is shown in Table 2. In the model adjusted for sociodemographic and

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lifestyle characteristics (Model 1), MCI was associated with a higher rate of falls 1.51
(95%CI 1.15-1.97). After the addition of hand grip strength and gait speed, the PR

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(95%CI) became 1.47 (95%CI 1.11-1.95). This association remained significant even
after further adjustment for obesity and other medical health conditions [PR 1.41

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(95%CI 1.05-1.89)]. In the fully-adjusted model (Model 3), gender, grip strength, and
arthritis were also significantly associated with falls.
The association of MCI and other covariates with non-accidental falls in the

past 12 months is shown in Table 3. MCI was associated with 1.93 (95%CI 1.282.93) times higher odds for non-accidental falls in the model adjusted for
sociodemographic and lifestyle factors (Model 1). Further adjustment for grip strength
and gait speed (Model 2), or obesity and medical health conditions in addition to grip
strength and gait speed (Model 3) attenuated the ORs [Model 2: OR 1.68 (95%CI
1.09-2.60); Model 3: OR 1.67 (95%CI 1.07-2.61)] when compared to Model 1, but
results were still statistically significant in both Model 2 and 3. In the fully-adjusted


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model (Model 3), apart from MCI, the factors significantly associated with non-

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accidental falls were financial strain, grip strength, gait speed, and arthritis.

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Discussion

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The present work revealed a strong association between MCI and number of falls or
presence of non-accidental falls in the past 12 months in the older Irish population.

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Muscle strength and performance, and health conditions were influential factors in the
association between MCI and falls but did not fully explain the association. While

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there is a growing literature on cognitive decline and falls, studies specifically on the
topic of MCI and falls are scarce. To date, a small number of studies (Liu-Ambrose et

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al., 2008; Borges et al., 2015; Dealbaere et al., 2012; Uemura et al., 2014) have

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assessed this association. However, with the exception of one study (Uemura et al.,
2014), the rest had very small sample size and were conducted in limited geographical

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areas. To the best of our knowledge, this is the first study that examined this
association using a large nationally-representative dataset of the older European

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(Irish) population.

Our findings on the association between MCI and falls are in line with

previous studies. For example, Liu-Ambrose et al. (2008), in a sample of 158 older
Canadians, reported that females with MCI had higher physiological risk of falling
and increased postural sway compared to females without MCI. Additionally,
Dealbaere et al. (2012) analyzed a sample of 419 non-demented communitydwelling adults in Sydney, and reported that MCI was associated with a 1.72 (95%CI
1.03-2.89) greater risk for falls. Also, Borges et al. (2015), in a sample of 104
community-dwelling elders in Brazil, showed that the prevalence of falls in MCI was
higher than in cognitively healthy older adults. Finally Uemura et al., (2014) analyzed


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a sample of 4474 community-dwelling older Japanese adults and concluded that MCI
has an effect on fear of falling.

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Our analysis revealed a consistent association between MCI and the number of

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falls or the presence of non-accidental falls in the past 12 months. Furthermore, the

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inclusion of different blocks of covariates in the model attenuated the association but
even after inclusion of all potentially influential variables, the association between
MCI and falls remained significant. The exact mechanism linking MCI with falls is

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unclear but attention deficit, impaired psychomotor processing, problem-solving, and

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spatial awareness, which are characteristics associated with MCI, have been reported
to be related with balance control and consequently with falls (Alexander and

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Hausdorff, 2008). Differences in brain structures between those with and without MCI

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have also been reported. For example, individuals with MCI may have reduced
integrity of the posterior regions of the brain, and their medial temporal lobe, insula,

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and thalamus may constitute of reduced gray matter (Medina et al., 2006). Since these
regions of the brain are known to be associated with attention and balance control

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(Zimmerman et al., 2006), individuals with MCI may be more likely to fall due to
impairment in attention and equilibrium.
Apart from MCI, factors such as handgrip strength, gait speed and arthritis,

were also associated with falls. Muscle strength and performance have been reported
to be significant predictors of falls (Mühlberg and Sieber, 2004). Furthermore, an
interrelated pathway among gait speed, balance control, cognitive decline and falls in
older adults has been proposed. In addition, falls commonly occur in patients with
arthritis (Kaz Kaz et al., 2004), and the role of arthritis on the epidemiology of falls
has previously been reported. Since falls are associated with high healthcare
expenditures (Ambrose et al., 2013), the prevention of MCI and other co-morbidities


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may be important to promote healthy aging and minimize disability and their resulting

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distress and costs.

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Strengths and limitations

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The present study has several strengths. It is one of the few studies that evaluated the
association between MCI and falls (especially with large sample size), while it is also
the first study from Ireland on this topic. In terms of limitations, since some data were

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obtained using self-reported measures, reporting-bias may be present. Also, because

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the survey was not designed to generate clinical diagnoses for dementia, some
individuals with dementia may have been included in our analytical sample. However,

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we made use of information on dementia diagnosis obtained from the participant or

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family members, and excluded those with MMSE score<14, which is a cut-off for
dementia that has been used previously (Shigemori et al., 2010). Furthermore, there

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are currently no standard definitions for the identification of MCI and a variety of
definitions have been used in previous studies. In our study, objective cognitive

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impairment (one of the MCI criteria) was based on a MoCA score<26 which has been
reported to have high sensitivity and specificity for detecting MCI (Nasreddine et al.,
2005ref). However, as with most population-based studies, a thorough clinical
evaluation to detect MCI was lacking. Next we did not have information on spatial
and temporal aspects of gait (e.g., step length and time). Conducting analyses with
more detailed information could have lead to a better understanding of the association
between MCI and falls. Finally, due to the cross-sectional design, we were unable to
establish causal relationships.

Conclusions


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The present work evaluated the role of MCI on falls among older Irish adults. In view
of rapid population aging accompanied with an increase in the prevalence of cognitive

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impairment, it is of major interest nowadays to study the negative health outcomes of

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cognitive impairment. In our study, the potential importance of early detection and

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management of MCI to prevent falls among the elderly has been highlighted. Also the
prevention or treatment of arthritis and muscle function decline, which are becoming
increasingly prevalent among the elders, may also constitute effective means for

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reducing falls and to promote healthy aging among the older population.

Acknowledgments

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The authors are particularly grateful to the men and women who participated in the

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Conflicts of interest

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TILDA survey.

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The authors report no relationships that could be construed as a conflict of interest.

Source of funding

Stefano Tyrovolas received a scholarship from the Foundation for Education and
European Culture (IPEP) to undertake his post-doctoral research, of which this work
is a part. Ai Koyanagi’s work was supported by the Miguel Servet contract financed
by the CP13/00150 project, integrated into the National R+D+I and funded by the
ISCIII - General Branch Evaluation and Promotion of Health Research - and the
European Regional Development Fund (ERDF-FEDER). Elvira Lara’s work is
supported by the FPU predoctoral grant (FPU13/03573) from the Spanish Ministry of
Education, Culture and Sports. Ziggi Santini’s work has received funding from the


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People Programme (Marie Curie Actions) of the European Union's Seventh

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Framework Programme FP7/2007 – 2013 under REA grant agreement n° 316795.


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Table 1 Sample characteristics by presence of mild cognitive impairment

Characteristics

Categories

Mild cognitive
impairment
No
Yes

Overall

50-59
44.7
46.4
29.7
<0.0001
60-69
32.8
32.5
35.5
70-79
17.1
16.0
26.6
≥80
5.4
5.1
8.1
Gender

Female
51.8
52.0
50.2
0.4883
Male
48.2
48.0
49.8
Education
Primary
31.4
28.9
53.3
<0.001
Secondary
46.7
47.6
38.6
Tertiary
21.9
23.4
8.0
Financial strain
Never
22.4
22.2
23.5
0.0017
Rarely

23.2
23.7
18.6
Sometimes 37.4
37.7
34.4
Often
17.1
16.4
23.4
Living alone
No
80.9
81.6
74.5
0.0004
Yes
19.1
18.4
25.5
Residence
Rural
48.0
46.6
60.5
<0.0001
Urban
52.0
53.4
39.5

Physical activity
Low
28.4
27.7
35.5
0.0015
Medium
35.2
35.4
33.1
High
36.4
36.9
31.5
Problem drinking
No
87.1
87.0
87.5
0.8132
Yes
12.9
13.0
12.5
Grip strength (kg)
Mean (SD) 26.7 (10.1) 26.9 (10.1) 25.2 (9.6) 0.0013
Gait speed (cm/s)
Mean (SD) 11.9 (2.5)
12.0 (2.4)
10.9 (2.5) <0.0001

Obesity (BMI ≥30kg/m2)
No
66.4
66.9
61.3
0.0185
Yes
33.6
33.1
38.7
Arthritis
No
75.0
76.0
65.5
<0.0001
Yes
25.0
24.0
34.5
Parkinson's disease
No
99.7
99.7
99.3
0.2018
Yes
0.3
0.3
0.7

Stroke
No
98.7
98.8
97.2
0.0038
Yes
1.3
1.2
2.8
Diabetes
No
93.3
93.6
91.3
0.0657
Yes
6.7
6.4
8.7
Heart disease
No
92.4
92.9
87.5
<0.0001
Yes
7.6
7.1
12.5

Depression
No
92.2
93.4
81.1
<0.0001
Yes
7.8
6.6
18.9
Abbreviation: BMI Body Mass Index
Data are percentage (%) unless otherwise stated.
* P-values were calculated by Chi-square tests and Student's t-tests for categorical and continuous
variables respectively.

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Age (years)

P-value*


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Table 2 Correlates of number of falls in the past 12 months assessed by multivariable Poisson regression
Characteristics

Categories

Model 1
PR
95%CI

Model 2
PR
95%CI

Model 3
PR
95%CI

Mild cognitive impairment
Age (years)

Yes vs. No
50-59
60-69
70-79
≥80
Female
Male
Primary
Secondary
Tertiary
Never
Rarely
Sometimes
Often
Yes vs. No
Rural
Urban
Low
Medium
High
Yes vs. No

1.51**
1.00
1.07
1.28

1.47
1.00
1.08
1.00
1.06
1.04
1.00
1.27
1.26
1.34*
1.28*
1.00
1.23*
1.00
1.18
0.95
1.11

1.47**
1.00
1.00
1.08
1.13
1.00
1.37*
1.00
1.10
1.12
1.00
1.26

1.24
1.31
1.24*
1.00
1.20
1.00
1.20
0.99
1.10
0.98*
0.97

1.41*
1.00
0.97
1.05
1.07
1.00
1.37*
1.00
1.11
1.09
1.00
1.27
1.23
1.26
1.23
1.00
1.20
1.00

1.21
1.01
1.09
0.98*
0.97
0.99
1.24*
2.45
1.39
1.10
1.02
1.24

Living alone
Residence

[0.96,1.68]
[0.97,1.62]
[1.01,1.78]
[1.04,1.57]
[1.02,1.48]

[0.95,1.48]
[0.76,1.19]
[0.87,1.42]

[1.11,1.95]

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[0.82,1.23]
[0.81,1.43]
[0.72,1.78]

IP

SC
R

[0.85,1.32]
[0.82,1.31]

CE
P

AC

Problem drinking
Grip strength (kg)
Gait speed (cm/s)
Obesity (BMI ≥30kg/m2)
Arthritis
Parkinson's disease
Stroke
Diabetes
Heart disease
Depression

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Physical activity

[0.91,1.29]

NU

Financial strain

[0.87,1.31]
[1.00,1.64]
[0.98,2.20]

MA

Education

D

Gender

[1.15,1.97]

Yes vs. No
Yes vs. No
Yes vs. No
Yes vs. No
Yes vs. No
Yes vs. No
Yes vs. No

Abbreviations: PR Prevalence Ratio; CI Confidence Interval; BMI Body Mass Index
The models are mutually adjusted for all covariates in the respective columns.
Grip strength and gait speed were included in the models as continuous variables.
* p<0.05, ** p<0.01, *** p<0.001

[1.05,1.79]
[0.88,1.38]
[0.87,1.43]
[0.95,1.67]
[0.95,1.60]
[0.98,1.75]
[1.00,1.53]
[1.00,1.46]
[0.96,1.51]
[0.79,1.24]
[0.86,1.42]
[0.97,1.00]
[0.92,1.02]

[1.05,1.89]
[0.79,1.19]
[0.80,1.38]
[0.68,1.69]
[1.04,1.81]
[0.88,1.39]
[0.85,1.41]
[0.95,1.69]
[0.94,1.61]
[0.93,1.70]
[0.98,1.53]

[0.99,1.45]
[0.97,1.52]
[0.80,1.27]
[0.85,1.39]
[0.97,1.00]
[0.92,1.03]
[0.81,1.19]
[1.02,1.53]
[0.55,10.97]
[0.72,2.71]
[0.84,1.46]
[0.70,1.48]
[0.86,1.77]


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Table 3 Correlates of non-accidental falls in the past 12 months assessed by multivariable logistic regression
Model 1
Model 2
Model 3
Characteristics

Categories

OR

95%CI

OR

95%CI

OR

95%CI

Mild cognitive impairment
Age (years)

Yes vs. No
50-59
60-69
70-79
≥80
Female
Male
Primary
Secondary
Tertiary
Never
Rarely
Sometimes
Often
Yes vs. No
Rural
Urban
Low
Medium

High
Yes vs. No

1.93**
1.00
1.30
1.78*
2.35*
1.00
0.62**
1.00
1.25
0.89
1.00
1.49
1.46
1.88*
1.30
1.00
1.33
1.00
1.17
0.83
1.09

[1.28,2.93]

1.68*
1.00
1.13

1.12
0.97
1.00
1.22
1.00
1.41
1.08
1.00
1.44
1.39
1.81*
1.23
1.00
1.30
1.00
1.22
0.90
1.09
0.95***
0.90**

[1.09,2.60]

1.67*
1.00
1.04
1.05
0.86
1.00
1.22

1.00
1.41
1.01
1.00
1.48
1.39
1.83*
1.24
1.00
1.30
1.00
1.19
0.90
1.11
0.95***
0.90**
0.90
1.58**
3.41
1.45
1.09
0.75
0.82

[1.07,2.61]

Physical activity

[0.95,1.88]
[0.81,1.69]

[0.56,1.23]
[0.69,1.72]

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[0.77,1.66]
[0.67,1.85]
[0.43,2.23]
[0.78,1.91]

SC
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NU

[0.95,2.35]
[0.96,2.22]
[1.16,3.04]
[0.89,1.91]

Yes vs. No
Yes vs. No
Yes vs. No
Yes vs. No
Yes vs. No
Yes vs. No
Yes vs. No

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Problem drinking
Grip strength (kg)
Gait speed (cm/s)
Obesity (BMI ≥30kg/m2)
Arthritis
Parkinson's disease
Stroke
Diabetes
Heart disease
Depression

[0.83,1.91]
[0.58,1.39]

MA

Living alone
Residence

D

Financial strain

TE

Education

[0.45,0.86]

CE
P

Gender

[0.89,1.89]
[1.12,2.82]
[1.18,4.71]

Abbreviations: OR Odds Ratio; CI Confidence Interval; BMI Body Mass Index
The models are mutually adjusted for all covariates in the respective columns.
Grip strength and gait speed were included in the models as continuous variables.
* p<0.05, ** p<0.01, *** p<0.001

[0.91,2.17]
[0.69,1.71]
[0.91,2.28]
[0.91,2.12]
[1.12,2.94]
[0.83,1.80]
[0.92,1.85]
[0.84,1.77]
[0.61,1.34]
[0.68,1.74]
[0.92,0.97]
[0.84,0.96]

[0.70,1.54]
[0.63,1.75]

[0.36,2.04]
[0.76,1.95]
[0.91,2.19]
[0.64,1.60]
[0.93,2.37]
[0.89,2.16]
[1.11,3.02]
[0.84,1.84]
[0.92,1.85]
[0.81,1.73]
[0.60,1.34]
[0.70,1.77]
[0.93,0.98]
[0.84,0.97]
[0.63,1.29]
[1.15,2.16]
[0.73,15.99]
[0.56,3.74]
[0.63,1.87]
[0.39,1.44]
[0.49,1.37]


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Highlights

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The prevalence of mild cognitive impairment was 10% in the Irish older population
Mild cognitive impairment is related with higher rates of falls among older adults.
Mild cognitive impairment is related with the occurrence of non-accidental falls.

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


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