https://orcid.org/0000-0002-8587-7172
ABSTRACT
Falls remain a major public health issue, particularly for frail older adults, such as those who receive home-delivered meals (HDMs). Social workers who assess the needs of HDM clients and routinely monitor their care are uniquely positioned to address fall prevention; however, the degree to which HDM social workers currently manage fall risk is unknown. To close this knowledge gap, we conducted a retrospective chart review and evaluated HDM social workers’ current practices relative to identifying clients at risk for falling and the client characteristics associated with social workers’ perceptions of fall risk. A total of 230 client charts were included in our analysis. Thirty-eight percent of HDM clients were determined to be at risk of falling. Advanced age, activity limitations, and specific health conditions (e.g., diabetes mellitus) were associated with social workers’ fall risk concerns. However, over 80% of our sample presented with well-established fall risk factors (e.g., mobility impairment) suggesting that HDM social workers might be under-identifying fall-risks. Though HDM social workers are well positioned to play a critical role in fall risk management, systematic efforts are needed to optimize social workers’ capacity for effectively identifying HDM clients at risk for falling.
Introduction
Despite the development of effective, evidence-based fall prevention programs and strategies (Gillespie et al., Citation2012; Hopewell et al., Citation2018; Sherrington et al., Citation2019), falls continue to plague the older adult community. Community-dwelling older adults at particularly elevated risk for falling are those with chronic diseases and disabilities who are unable to safely leave their homes and are reliant on home-based services to address their basic needs (Lloyd & Wellman, Citation2015; Thomas et al., Citation2018). It is estimated that 41% of those who receive home-delivered meals (HDMs) have experienced falls – a substantially higher rate than the 25% of older adults nationwide who are estimated to fall each year (Bergen et al., Citation2016; Choi et al., Citation2019). Additionally, 44% of HDM clients have mobility impairments, 45% have six or more chronic health conditions, and 57% have multiple (two or more) activity of daily living impairments – all of which are well-established fall risk factors (Lloyd & Wellman, Citation2015; Zhao et al., Citation2018). Any combination of these fall risk factors can place HDM clients at an increased risk for experiencing a fall and subsequent injuries, leading to hospitalization, institutionalization, and even death.
Though evidence has indicated the alarmingly high prevalence of fall risk factors among HDM clients, research exploring fall risk management in the HDM population is sparse (Choi et al., Citation2019; Thomas et al., Citation2018). Recently, however, fall prevention advocates have acknowledged the unique role that HDM social workers may play in fall risk management due to the routine contact social workers maintain with HDM clients (Juckett & Robinson, Citation2019). To receive HDMs, such as those funded through the Older Americans Act, older adults (60 years and over) must undergo a comprehensive in-take evaluation conducted in the home, often by a social worker, to determine each client’s extent of service need (Anderson et al., Citation2018). During the evaluation, HDM social workers gather information such as the recipient’s sociodemographic characteristics, health history, physical impairments, need for HDMs as well as the need for other health services (Jenei, Citation2010). This HDM evaluation period is a prime opportunity for social workers to evaluate fall risk and identify clients who may be in need of general fall risk education or preventive follow-up care (Brewster et al., Citation2019; Yamashita et al., Citation2011).
As a first step toward managing fall risk in the HDM setting, social workers must have the capacity to effectively recognize clients who have an increased likelihood of falling. As mandated by the Older Americans Act, HDM organizations and personnel (e.g., social workers) must evaluate fall risk with all older adults who receive HDM services. Often, fall risk is evaluated based on the personnel’s judgment of each client’s fall risk factors and their perceptions of each client’s likelihood for experiencing a fall. Although there are well-established and highly feasible fall-risk screening tools available (e.g., Timed Up and Go, 4-stage balance test), the Older Americans Act does not mandate that specific fall risk screening tools be used. Therefore, we expect that there is wide variation in how and who HDM social workers identify as being at risk of falling and in need of fall prevention services. As such, the purpose of this study was to (a) identify the HDM clients at risk of falling as perceived by HDM social workers, (b) examine HDM client characteristics associated with social workers’ perception of fall risk and (c) examine the accuracy of HDM social workers’ assessments by comparing them to independent chart reviewers’ assessment of fall risk. By addressing these objectives, we plan to establish the extent to which HDM social workers appropriately identify clients at risk of falling and underscore opportunities to optimize fall risk management among HDM social workers and their organizations.
Materials and methods
Study setting
To address our study objectives, we partnered with one HDM organization located in the Midwestern United States with over 100 years of experience providing health and human services to the local older adult community. This regional nonprofit organization serves more than 30,000 older adults annually through various programs, with the HDM program being the largest. Similar to HDM providers nationwide, our partner organization’s HDM program provided daily or weekly meals to older adults with difficulties accessing and preparing nutritious food independently. Older adults enrolled in the HDM program participated in initial in-take evaluations that were conducted by the organization’s social workers who obtained and documented client characteristics such as clients’ sociodemographic information (e.g., age, gender, race), fall risk status (yes/no), health history (e.g., number and type of health conditions), social history (e.g., living situation), and mobility status (e.g., impaired given the need for assistive device(s) in the home).
Data source
To assess HDM social workers’ identification of fall risk among clients, we conducted a retrospective chart review informed by seminal retrospective chart review guidelines (Gearing et al., Citation2006; Prusaczyk et al., Citation2018). Chart data reflected HDM in-take evaluations completed by social workers between January 1 – December 31, 2018 which and were all conducted in the client’s home environment. Our decision to conduct a chart review that spanned a 12-month period was guided by retrospective chart audit recommendations set forth by the Agency for Healthcare Quality and Research (AHRQ, Citation2013). Client charts were eligible for inclusion in our retrospective review if they met all the following criteria: (a) represented an adult age 60 years and over, (b) represented a homebound older adult receiving HDMs funded by the Older Americans Act (OAA), Title III, and (c) contained intake evaluations conducted between January 1, 2018 through December 31, 2018. Prior to data collection, all research activities were approved based on responsible research standards set forth at The Ohio State University’s Institutional Review Board (#2019E0340).
Variables
Fall risk
Social workers identified clients at risk for falling through use of a dichotomous “fall risk” variable (yes = social worker perceives client is at risk for falling; no = social worker does not perceive client to be at risk for falling). Determination of fall risk was made based solely on social workers’ interpretation of clients’ fall risk characteristics, such as observed in-home mobility status, self-reported fall history, and/or self-reported assistance needed with activities of daily living. Established fall risk screening tools, such as the Timed Up and Go, were not administered.
Client health conditions
Given the association between chronic diseases and falls, we extracted all documented chronic health conditions of HDM clients. Examples of conditions included cardiopulmonary disease (e.g., hypertension), musculoskeletal impairments (e.g., arthritis), diabetes, obesity, cancer, neurological impairments (e.g., dementia), mental health disorders (e.g., depression), and visual deficits. The presence of a mobility impairment (yes/no) and the use of an assistive mobility device (e.g., cane, walker, wheelchair) were variables also collected during HDM evaluations and were extracted during our chart review process as these are well-established fall risk factors.
Client activity limitations and sociodemographics
We collected scores depicting a clients’ limitations in their activities of daily living (ADL) and instrumental activities of daily living (IADL) as well as their risk for malnutrition. Adapted from the Barthel ADL Scale (Wade & Collin, Citation1988), a 6-item instrument (e.g., assistance with bathing) was used to represent ADL limitations that were rated on a scale from 0 (independent) to 5 (activity does not occur). An eight-item instrument using a rating scale of 0 (independent) to 4 (activity does not occur), adapted from the Lawton IADL Scale (1969), was used to determine IADL independence (e.g., independence with meal preparation). Higher scores on the ADL and IADL instruments indicated that a client required a greater level of assistance with daily activities and routines. Risk for malnutrition was assessed through the Determine Your Own Nutritional Risk checklist, otherwise known as the Nutrition Risk Assessment (NRA). Scores on the NRA ranged from 0 to 21 with scores ≥6 indicating high nutritional risk. All three of these instruments, the ADL scale, IADL scale, and NRA, are required for use by the Ohio Department of Aging, which provides administrative oversight to our partner organization (ODA, Citation2020). Four sociodemographic characteristics were also extracted and consisted of age, gender, living situation (e.g., alone, with spouse, with relative, or with non-relative), and race/ethnicity.
Comparing perceptions of fall risk
After extracting all variables of interest from HDM client charts, our chart review team, with expertise in fall prevention and implementation research, began to examine clients’ documented health characteristics that were consistent with common fall risk factors. In line with the dichotomous nature of the HDM social workers’ assessment of fall risk, the chart reviewers rated each case as a fall risk (yes) or not a fall risk (no). Clients were considered by the chart reviewers to be a fall risk if the “health conditions” section of the chart indicated that the client (a) had a mobility impairment and/or (b) used an ambulatory device in the home. Reported mobility impairments or use of an ambulatory device are two major factors that increase an older adult’s susceptibility for falling, and therefore were used as the standard for identifying fall risk which is consistent with several studies and practice guidelines (Ambrose et al., Citation2013; Phelan et al., Citation2015). These two major factors were also the most relevant health characteristics by which chart reviewers could ascertain fall risk given that chart reviewers were unable to observe each client’s mobility status in person.
Analysis
Descriptive statistics were used to assess the proportion of clients at risk for falling as perceived by social workers, characteristics of HDM clients, and prevalence of key fall risk factors. To examine characteristics associated with clients identified as a fall risk, we conducted multiple logistic regressions in a two-step process informed by Zhao et al.’s (Citation2019) evaluation of falls among homebound older adults. First, we ran a series of regression models to preliminarily assess client characteristics that were predictive of “fall risk” as determined by social work personnel. Characteristics in Model 1 included four sociodemographic characteristics; Model 2 included 11 health conditions, and Model 3 included six characteristics relative to clients’ functional status (e.g., ADL score, device use). The second step of our analysis was to select variables for our combined logistic regression model. In each preliminary model, client characteristics with p values ≤0.2 were entered into our combined model with the final model consisting of 10 variables. Given the exploratory nature of this study, this two-step process was appropriate for our analyses to understand current practice patterns rather than test hypotheses. To examine discrepancies in assessment of fall risk, we used chi-square analyses comparing the proportion of clients who were identified as being at risk of falling by HDM social workers to the proportion of clients who were found to be at risk of falling by HDM chart reviewers.
Results
A total of 242 new clients were enrolled in OAA-funded HDM services between Jan – Dec 2018. We excluded 10 clients from our chart review who were not 60 years of age at the time of initial evaluation. Two client charts were excluded due to incomplete demographic information, resulting in a total of 230 HDM client charts included in our retrospective chart review. Clients were primarily female, white, and lived alone. On average, clients took 7.15 (SD = 4.5) daily medications and had 2.7 (SD = 1.2) total health conditions, with cardiovascular disease (n = 141) and mobility impairments (n = 120) being the most prevalent. Complete sociodemographics and client characteristics can be found in .
Table 1. Characteristics of home-delivered meal clients
Client characteristics associated with fall risk
, , represent the sociodemographic, health conditions, and functional status models that we ran in order to identify variables to be included in our final 10-variable model. Results from the final model indicated that clients who were of older age (OR = 1.04, p = .03) were more likely to be identified by social workers as a fall risk compared to younger clients. Clients who had a diagnosis of diabetes (OR = 2.15, p = .03) were approximately twice as likely to be perceived as a fall risk compared to clients without a diabetes diagnosis, and clients with higher ADL scores (i.e. greater daily activity limitations) were more likely to be considered a fall risk by social work personnel (OR = 1.90, p < .01; ).
Table 2 Model 1—HDM client sociodemographic characteristics
Table 3 Model 2—HDM client health conditions
Table 4 Model 3—HDM client functional status indicators
Table 5 Final model of characteristics associated with fall risk as perceived by social workers
Comparing assessments of fall risk
Thirty-eight percent (n = 88) of HDM clients were found to be at risk of falling, based on documentation in HDM client charts. Our chart reviewers, however, noted that 82% of clients (n = 189) presented with key fall risk factors as evidenced by documentation of mobility impairments and/or ambulatory device use. The difference between these proportions (38% vs 82%) was statistically significant (x2 = 7.46, p < .01), indicating a clear discrepancy between social work personnel’s perceptions of fall risk and HDM chart reviewers’ assessment of fall risk.
Discussion
This study examined the implementation of fall risk management practices within one organization providing HDMs to vulnerable older adults. Prior literature has suggested that social workers, such as those working within HDM organizations, are in a prime position to address fall risk with HDM clients (Juckett & Robinson, Citation2019), however, this study was the first of its kind to (a) assess social workers’ ability to identify fall risk among clientele, (b) examine the client characteristics associated with perceived fall risk, and (c) evaluate the extent to which social workers can accurately identify clients at risk of falling. Our retrospective chart review found that social workers identified 38% of HDM clients as being at risk for falling, yet 82% of clients presented with one or more key fall risk factors. Social workers tended to perceive that clients were at risk for falling if they were older, had greater daily activity limitations, or had diabetes, which is consistent with findings from prior studies (Ambrose et al., Citation2013; Gravesande & Richardson, Citation2017; Mamikonian-Zarpas & Laganá, Citation2015). Although these characteristics are common fall risk factors, we expected to note other characteristics associated with social workers’ perception fall risk, such as having mobility impairments, using ambulatory devices, or taking multiple daily medications (Ambrose et al., Citation2013; Haddad et al., Citation2018; Henwood et al., Citation2020).
In our sample, social workers identified 38% of HDM clients were at risk for falling. We believe this is an underestimation of fall risk. Notably, other studies have documented that up to 41% of HDM clients have actually experienced a fall (Choi et al., Citation2019), thus it is likely that there are more older adults who are at risk for falling than have been identified by the social workers in our sample. Identifying this risk provides critical information that can guide social workers’ decision-making process when selecting potential options for follow-up care. For example, an HDM client who is perceived to be at risk of falling may benefit from a home safety evaluation performed by an occupational therapist, a comprehensive fall risk evaluation from a physician, or transportation to a community-based fall prevention program, all of which an HDM social worker can help coordinate. Services that have been coordinated by HDM social workers have been well received by HDM clients (Marie Joosten, Citation2015), which further validates the important role of social workers in fall risk management.
Though HDM social workers are well positioned to mitigate fall risk among vulnerable older adults, findings from this study indicate that additional efforts are needed to build social workers’ capacity for effectively identify clients at risk of falling. Fall risk screens and assessments have often been administered by physicians, nurses, occupational therapists, and physical therapists (Phelan et al., Citation2015; Stevens & Phelan, Citation2013); however, recent evidence points to promising strategies and approaches for integrating other disciplines in the proactive management of fall risk. For instance, the Massachusetts Department of Health has leveraged community health workers to serve as community liaisons who implement fall risk screens and assist with coordinating follow-up medical care for community-dwelling older adults at high risk for falling (Coe et al., Citation2017). Building from this model’s success and the findings from our present study, we propose the following three strategies to optimize social workers’ preparedness to implement fall risk management practices with HDM clients: (a) evaluate social workers’ perceptions of fall risk and capacity for implementing fall risk screens, (b) create a fall prevention learning collaborative, and (c) modify electronic health record systems in HDM organizations
Social workers’ perceptions of fall risk
The proportion of HDM clients in our sample who possessed one or more key fall risk factors greatly exceeded the proportion of clients who were identified as being a fall risk by HDM social workers. Such incongruity points to the need for further understanding of social workers’ decision-making process and the factors they consider when evaluating fall risk. Qualitative interviews with HDM social workers as well as HDM administrators may help illuminate this decision-making process and may also point to opportunities for systematically implementing fall risk screens (e.g., Timed Up and Go) that are compatible with the clinical skills of HDM social workers and the expectations of HDM organizations (Juckett et al., Citation2020).
Creating a fall prevention learning collaborative
HDM social workers in our partner organization were given the autonomy to decide if clients were at risk of falling based solely on their own clinical judgment. However, given the number of older adults whose level of fall risk was underestimated by social workers, learning collaboratives that are developed to provide fall prevention training and allow for peer-to-peer knowledge sharing may benefit HDM social workers and other organization personnel. Fall prevention training programs have been effective when designed for community-based health professionals (St John. et al., Citation2015), and HDM social workers would be a prime target audience for training programs oriented to the community setting. Training programs have been effective for producing practice change when accompanied by follow-up coaching/consultation sessions, as opposed to single training sessions alone, and could be valuable in building social workers’ fall risk management capacity (Beidas et al., Citation2012; Edmunds et al., Citation2013).
In addition to providing training and coaching, the Massachusetts Department of Health’s learning collaborative also included fall prevention “champions” and toolkits/resources for community health workers to maximize their skills in fall prevention (Coe et al., Citation2017). When tailored to the community setting, learning collaboratives can help professionals connect with local experts, colleagues at their own organization, and those in other professional disciplines who can support learning and collaboration (Bunger et al., Citation2016; Hanson et al., Citation2018). Moreover, collaborations with fall prevention leaders and advocates have the potential to influence policy-level change that may facilitate social workers’ capacity for managing fall risk effectively. HDM intake evaluations are designed to obtain client information required by the OAA, Title III. Though social workers have a unique opportunity to evaluate fall risk in HDM clients’ own homes, OAA policymakers have yet to mandate that evidence-based tools be used to assess levels of fall risk in the HDM setting. This insufficient guidance on fall prevention from the OAA serves as a missed opportunity to (a) identify HDM clients most susceptible to falls and (b) connect at-risk clients to other federally funded fall prevention programs (e.g., Matter of Balance). Linking at-risk clients to these programs has the potential to streamline the delivery of fall prevention care, reduce the rates of falls among older adults, and decrease healthcare costs associated with falls and fall-related injuries (Sherrington et al., Citation2019; Stevens & Lee, Citation2018).
Modify record systems
Our chart reviews indicated that fall risk documentation consisted of a singular field in which social workers indicated “yes” the client was perceived to be at risk for falling or “no” the client was not at risk. If HDM personnel were to implement standardized fall risk screening tools or assist with coordinating fall prevention care, modifications to the HDM organization’s electronic health record will be imperative. The lack of fields to capture fall risk screening/assessment information has been a consistent barrier in the identification and management of fall risk (Casey, Citation2017; Coe et al., Citation2017), but electronic health record systems can be effective for enhancing care coordination and fall risk documentation (Casey, Citation2017; Stoeckle et al., Citation2019).
Limitations
While our study does make unique contributions to the fall prevention and HDM bodies of literature, it is not without limitations. Inherent with retrospective chart reviews, completeness of HDM client chart data was reliant on the documentation practices of HDM social workers (Prusaczyk et al., Citation2018). Therefore, our understanding of client characteristics and fall risk factors was dependent on social workers’ documentation habits and routines as well as the health information self-reported by clients. Additionally, these charts were drawn from an organization that provides HDMs to older adults funded through the Older Americans Act – a source that funds HDMs for low-income older adults across the U.S.; however, we recognize that our partner organization may not be representative of all HDM organizations nationwide, especially those situated outside the Midwest. There are also limitations in our use of a mobility device as a criterion for fall risk. Few studies have examined populations using a mobility device and whether the device was professionally assessed and accompanied by education from a trained clinician. Studies indicate the dangers of incorrect use of a mobility device with an increased risk for those who had not been professionally educated (Renfro & Fehrer, Citation2011; Sheehan & Millicheap, Citation2008). On the contrary, in a small study of Turkish older adults, 80% of the study sample who selected a walking stick without professional advice demonstrated improved balance after recognizing their own needs and independently selecting a mobility device (Dogru et al., Citation2016). Given the inconsistencies and general gaps in knowledge in the literature, more research to investigate the associations between presence/absence of mobility device training and fall risk is needed, and our findings relative to the use of mobility devices should be interpreted with caution.
Conclusions
With the rapid growth of the older adult population, the risk of falls and fall-related injuries is expected to rise as well. Innovative solutions are urgently needed to identify older adults at risk for falling, especially those older adults with extensive health needs and limited resources. The present study explored the concept of addressing fall risk by leveraging HDM social workers and organizations whose services reach millions of older adults across the country. Our study found that though social workers are well positioned to assess fall risk of HDM clients during initial in-take evaluations, there is an urgent need to build social workers’ capacity for accurately evaluating fall risk among vulnerable, older adults who receive HDMs. To build this capacity, we propose that HDM organizations and their personnel implement specific strategies (e.g., deploying training programs, creating learning collaboratives) to optimize the implementation of fall risk management practices by social workers who serve the older adult community.
This study was approved by the Institutional Review Board at The Ohio State University (#2019E0340).
Conflicts of interest
The authors have no conflicts of interest to disclose
Acknowledgments
The authors would like to extend their gratitude to LifeCare Alliance in Columbus, Ohio for their support and involvement throughout all phases of this study. We also would like to thank Drs. Shannon Jarrott, Holly Dabelko-Schoeny, and Jessica Krok-Schoen for their guidance during study development.
Additional information
Funding
References
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