Almost 14 million, or more than one in four community-dwelling (i.e., noninstitutionalized) older adults (aged 65 or older) in the United States fall annually (Moreland et al., 2020). Among those who fall, around five million report injuries (Moreland et al., 2020) and about 980,000 are hospitalized due to a fall injury each year (Centers for Disease Control and Prevention, 2020). Falls are the leading cause of unintentional injury death among older adults and resulted in more than 36,000 deaths in 2020 (Centers for Disease Control and Prevention, 2020).

Multiple factors are related to increased fall risk among older adults including demographics (e.g., sex, age, race/ethnicity), health conditions (e.g., depression, arthritis), and functional limitations (e.g., ability to climb stairs, cognitive ability) (Ambrose et al., 2013; Bergen et al., 2019). The prevalence of certain risk factors [e.g., depression (Girgus et al., 2017), dementia (van der Flier & Scheltens, 2005), stroke (Appelros et al., 2009), binge drinking (Blazer & Wu, 2009), diabetes (Andes et al., 2019), and difficulty performing activities of daily living (ADL) (Liang J. et al., 2008)] differ by age and sex in the older adult population. The strength of association of falls with some risk factors [e.g., ADLs (O & El Fakiri, 2015), alcohol consumption (Chang & Do, 2015; O & El Fakiri, 2015), depression (Gale et al., 2016; Gale et al., 2018), and balance (Gale et al., 2016, 2018)] differs by sex. The number of co-morbidities increase with age (Vogeli et al., 2007), and some diseases cluster together more often than others (Bhattacharjee et al., 2017; Sinnige J. et al., 2013). However, knowledge about how these risk factors together influence fall risk, especially across age groups or by sex (Vu T., Finch C.F., & Day L., 2011) is limited.

Research aimed at identifying fewer risk factors or at examining how they cluster into subpopulations of varying fall risk is also lacking (Brodie et al., 2015; Helgadottir et al., 2015; Ward et al., 2019; Yamashita et al., 2012). Approaches varied among the few studies conducted, the risk factors considered, the methods used to measure them (e.g., self-report, clinical assessment), and the statistical methods used for analyses (e.g., factor analysis, cluster analysis). As a result, risk factor clusters or identified subpopulations were diverse and therefore not generalizable. Our study aims to add to this research by employing a statistical method that has already been used (Ward et al., 2019) (i.e., exploratory factor analysis) on a nationally representative dataset that collects information on both falls and fall risk factors through self-report. Exploratory factor analysis helps identify the few unmeasured or latent constructs/themes underlying multiple measured risk factors (Kim & Mueller, 1978). These latent constructs/themes, referred to as factors, explain most of the shared variance between the measured variables (Kim & Mueller, 1978). A previous study that used factor analysis did not examine the effect of the identified factors on falls by age group and sex (Ward et al., 2019).

The first objective of this study was to identify whether a smaller set of underlying constructs/themes (factors) represent 13 known fall risk factors. The second objective was to examine how the effects of these underlying factors differed by age and sex subgroup.

Through exploratory factor analysis, we determined that eight of the 13 older adult fall risk factors had two common underlying factors. Specifically, a major portion of the co-variance in eight of the 13 could be explained by physical health limitations and mental health limitations.

A previous study that performed factor analysis on fall risk factors (Ward et al., 2019) showed three underlying factors: neuromuscular attributes, memory-related cognition, and non–memory-related cognition. Neuromuscular attributes (Ward et al., 2019), which was the underlying factor for leg strength, leg velocity, trunk-extensor-muscle endurance, pain, and mobility self-efficacy scores could be a construct similar to our physical health limitations factor, which underlies difficulty walking/climbing stairs, difficulty dressing/bathing, difficulty performing errands alone, general health status, and number of bad physical health days. More research is needed to investigate how well the physical health limitations factor measures the neuromuscular attributes described in Ward et al. (2019). The mental health limitations factor in our study, however, includes variables measuring both mental health (depression, stress, and emotional problems) and cognition (difficulty concentrating, remembering, or making decisions), unlike Ward et al.’s (2019) memory-related cognition, and non–memory-related cognition factors. Our mental health limitation factor had a Cronbach’s alpha of 0.58 and consisted of only three items. Although, Cronbach’s alpha underestimates the internal consistency when there are fewer than 8 items (Henson, 2001; O’Rourke & Hatcher, 2013), our mental health limitations factor could also be measuring more than one construct. Future research may benefit from using more comprehensive data on both mental health and cognition.

Physical and mental health limitations in this sample were correlated. Physical and cognitive functioning are known to be associated with each other in the older adult population (Clouston et al., 2013). Several studies showed that the interaction of impaired physical and cognitive functioning increased fall risk (Callisaya et al., 2016; Martin et al., 2013). Depression is strongly associated with falls and has similar risk factors as falls including cognitive impairment, poor self-rated health, impaired ADL, slow walking speed, poor balance, weakness, and low energy and activity levels (Biderman et al., 2002; Caligiuri & Ellwanger, 2000; Kvelde et al., 2010). The correlation between the factors supports the need for using mental health indicators (Hoffman et al., 2017) with the more commonly used physical health indicators for screening falls.

Both physical and mental health limitations were strongly associated with falls among both sexes and across all age groups. However, subgroups of men aged 65–74 and 85+ had unique patterns. As physical health limitations increased, men aged 65–74 were more likely to report falls than women and older men. This contrasts with the findings in the overall older adult population where women were more likely to report falls than men; older age groups more likely to report falls than those aged 65–74 (Moreland et al., 2020). One probable explanation to this is mathematical. In the absence of physical health limitations, fewer men aged 65–74 reported falls compared with other sub groups. Therefore, with each unit increase in physical health limitations, the relative rise in the percentage of men aged 65–74 who reported falls (prevalence ratio) was higher than in other subgroups. However, this can only be a partial explanation because at the highest physical health limitation score, more men aged 65–74 reported falls than women in any of the three subgroups. Men aged 85+ had a unique pattern in that, unlike women of the same age group, a consistently high percentage reported falls across all physical and mental health limitation scores. At the highest physical health limitations score, more men aged 85+ reported falls than women aged 75–84 and 85+. At the highest mental health limitation score, more men aged 85+ reported falls than all other subgroups.

Men with reported physical and mental health limitations may be different from women with similar limitations. This is an important finding because men and women’s fall risk and their attitudes towards fall prevention could be more different than we previously understood. Studies show that while a lower proportion of men report nonfatal falls than women (Moreland et al., 2020), a higher proportion of men have fall-related fatalities (Burns & Kakara, 2018). Although the reasons for this difference are unknown, men’s view of fall prevention is dissimilar to women. They are less receptive to fall prevention messages and are less likely to participate in fall prevention programs (Liddle et al., 2017; Sandlund et al., 2017). Fewer men than women seek medical care and/or discuss falls with a healthcare provider (Stevens et al., 2012).

While adhering to gender norms could be a barrier to fall prevention, it could also influence behavior and provide more opportunities for a fall. Men are more likely to report falling while performing hazardous activities or tasks with high levels of difficulty compared with women (Pereira et al., 2013). Older men, in general, report more activity than older women (Keadle et al., 2016; Sun F., Norman I.J., & While A., 2013). Older adults in the 65–74-year age group are the most physically active, however, activity decreases with age (Keadle et al., 2016; Sun F. et al., 2013). Additionally, twice as many adults aged 65–74 years were in the labor force in 2016, compared with those in the 75–84 year age group (Roberts, Ogunwole, Blakeslee, & Rabe, 2018) and more men aged 65–74 years are in the labor force (30%) than women in the same age group (22%) (Roberts et al., 2018). Potentially, men, especially those in the 65–74 age group, are more likely to perform activities that expose them to a higher fall risk even when they have physical health limitations. Data on daily activity, activity at the time of a fall, and on fall risk factors including physical health limitations are needed to fully examine the relationship.

Although being mobile and active may have provided more opportunities for a fall among men aged 65–74 years with physical health limitations, it should be stressed that physical activity in the form of structured exercises that focus on improving strength and balance reduce fall risk (Bauman et al., 2016). Moderate-intensity physical activity was shown to prevent the onset and progression of limitations in basic ADLs among community-dwelling older adults (Tak et al., 2013). Studies have shown that physically active older adults had a reduced risk of developing dementia (Bauman et al., 2016) and that a combination of strength, balance, and endurance training improved basic ADL ability in patients with all types of dementia (Blankevoort et al., 2010). Additionally, physical activity is known to improve mental well-being (Bauman et al., 2016).

This study has limitations. First, the names of the factors, physical and mental health limitations, were chosen based on what we thought best represented the variables and might not actually assess an older adult’s “true” limitations. Second, the functional limitation questions in BRFSS (e.g., difficulty walking/climbing stairs, difficulty dressing/bathing) ask only if the respondent has the limitation and do not assess severity (Centers for Disease Control and Prevention, 2016, 2018b). Therefore, determining if the severity of physical health limitations differed between age-sex subgroups was not possible. Older adults with very severe ADL limitations may fall less often than those with moderate limitations (Henry-Sanchez et al., 2012). Third, BRFSS does not collect data on some important fall risk factors such as osteoporosis, fall risk medications, fear of falling, pain, walking aids, and other ADLs and IADLs not included in the study. Hence, we may be missing factors that contribute to differences by age and sex. Fourth, BRFSS is self-reported data and is subject to recall bias. A thorough clinical assessment of physical and mental health factors (including cognitive health) may provide rich physiologic data that could explain the underlying mechanisms leading to falls. Fifth, the median response rates across the nation in 2016 and 2018 were 47% and 50%, respectively, and could have caused a non-response bias. However, BRFSS mitigates non-response with their weighting procedures (Centers for Disease Control and Prevention, 2016, 2018a).

The effects of the individual risk factors considered in our study were previously reported (Bergen et al., 2019). However, we arrived at three conclusions by identifying the common themes underlying these risk factors. First, screening for and addressing mental health limitations, as part of fall prevention, may be as important as screening and addressing physical health limitations. The Updated American Geriatrics Society/British Geriatrics Society (AGS/BGS) recommendations for falls assessment, for example, includes questions about gait and balance but does not assess for depression (Panel on Prevention of Falls in Older Persons, 2011). Second, men with physical and mental health limitations could be different from women with similar limitations. We may need to understand men’s perspectives, make fall prevention more appealing for them, and motivate them to take action (Liddle et al., 2017; Sandlund et al., 2017). Two possibly beneficial strategies are motivational interviewing techniques to understand men’s attitudes towards falls prevention (Kiyoshi-Teo et al., 2020) and conducting fall prevention exercise programs in “male-friendly” places (Liddle et al., 2017). However, more research about falls risk among men with physical and mental health limitations and effective strategies to increase male participation in fall prevention is needed.

Finally, the composite relationship between physical health limitations, mental health limitations, age, and sex in association with older adult falls reinforces the benefits of a multifactorial approach to prevention. Multiple risk factors exist for older adult falls and the risk factor profiles may vary between older adults. Multifactorial falls interventions are known to be effective in reducing falls (Lee & Yu, 2020). In a multifactorial intervention, a healthcare provider (e.g., physician, nurse) screens and assesses an older adult for fall risk and based on the individual’s unique risk factor profile administers or refers the older adult to other providers (e.g., physical therapist, occupational therapist, pharmacist) for interventions (e.g., physical therapy, home-modification, medication management). While assessing for risk factors, healthcare providers could enquire older men (and women) about daily activities that may increase their fall risk (e.g., cleaning roof gutters, shoveling snow) and counsel them. Centers for Disease Control and Prevention’s STEADI initiative provides multiple resources, for healthcare providers to screen, assess, and intervene, which could be accessed here—https://www.cdc.gov/steadi/index.html.