Abstract Background and Objectives: Falls are a major source of morbidity and mortality among older adults

Abstract Background and Objectives: Falls are a major source of morbidity and mortality among older adults; however, little is known regarding fall occurrence during a nursing home (NH) to community transition. This study sought to examine whether the presence of supports and services impacts the relationship between fall-related risk factors and fall occurrence post NH discharge. Research Design and Methods: Participants in the Minnesota Return to Community Initiative who were assisted in achiev- ing a community discharge (N = 1459) comprised the study sample. The main outcome was fall occurrence within 30 days of discharge. Factor analyses were used to estimate latent models from variables of interest. A structural equation model (SEM) was estimated to determine the relationship between the emerging latent variables and falls. Results: Fifteen percent of participants fell within 30 days of NH discharge. Factor analysis of fall-related risk factors pro- duced three latent variables: fall concerns/history; activities of daily living impairments; and use of high-risk medications. A supports/services latent variable also emerged that included caregiver support frequency, medication management assist- ance, durable medical equipment use, discharge location, and receipt of home health or skilled nursing services. In the SEM model, high-risk medications use and fall concerns/history had direct positive effects on falling. Receiving supports/services did not affect falling directly; however, it reduced the effect of high-risk medication use on falling (p < .05). Discussion and Implications: Within the context of a state-implemented transition program, findings highlight the import- ance of supports/services in mitigating against medication-related risk of falling post NH discharge. Keywords: Home and community-based services, Caregivers, Structural equation modeling, Nursing home transition, High-risk medica- tions, Medication management Background It is estimated that a quarter to a third of adults aged 65 years and older fall annually (American Geriatrics Society/British Geriatrics Society, 2011; Centers for Disease Control and Prevention [CDC], 2017; Marrero, Fortinsky, Kuchel, & Robison, 2017). Falls are the leading cause of injury-related mortality and a well-studied source of sig- nificant morbidity and diminished quality of life among The Gerontologist cite as: Gerontologist, 2018, Vol. 58, No. 6, 1075–1084 doi:10.1093/geront/gnx133 Advance Access publication September 4, 2017 D ow nloaded from https://academ ic.oup.com /gerontologist/article/58/6/1075/4103220 by guest on 02 A ugust 2022 mailto:mnoureldin@manchester.edu?subject= older adults (American Geriatrics Society/British Geriatric Society, 2011; CDC, 2017; Lim, Hoffmann, & Brasel, 2007). Falls are also a major contributor of trauma-related hospitalizations for older adults, ranging from fractures to brain injury (Moncada, 2011), with costs of fall-related treatments totaling more than $31 billion annually (CDC, 2017). Risk factors associated with falls have been exten- sively studied and falls have been described as multifactor- ial events resulting from both patient-specific (intrinsic) as well as environmental (extrinsic) factors (Bueno-Cavanillas, Padilla-Ruiz, Jimenez-Moleon, Peinado-Alonso, & Galvez- Vargas, 2000; Ganz, Bao, Shekelle, & Rubenstein, 2007; Marrero et al., 2017; Moncada, 2011). In addition to being a major cause for hospitalization, falling among older adults is also a predictor for both nursing home (NH) admission and readmissions (American Geriatrics Society/ British Geriatric Society, 2011; Howell, Silberberg, Quinn, & Lucas, 2007; Lim et al., 2007). Although incidence of falls among older adults and risk factors leading to these events have been examined in multiple settings (Bueno- Cavanillas et al., 2000; Ganz et al., 2007; Lim et al., 2007; Phelan, Mahoney, Voit, & Stevens, 2015), few studies have explored falls as an outcome during an older adult’s tran- sition from a NH to the community (Howell et al., 2007; Marrero et al., 2017). NH Transitions Transition from a NH to the community presents unique challenges. NHs provide care to a range of individuals based on their needs; short-stay residents (less than 100 days) are typically admitted following an acute-care hospitalization, whereas long-stay residents receive care for prolonged dis- ease or disability. A recent analysis indicated that a large proportion (40%) of previously community dwelling indi- viduals discharged to a NH following acute hospitaliza- tion did not return to the community, or they returned but were eventually readmitted to a NH (Hakkarainen, Arbabi, Willis, Davidson, & Flum, 2016). Studies examining tran- sition-related outcomes have focused on NH readmission or hospitalizations (Howell et al., 2007; Robison, Porter, Shugrue, Kleppinger, & Lambert, 2015; Wysocki et al., 2014). Wysocki and colleagues (2014) reported that dually eligible older adults who transitioned from the NH into the community had an increased risk of hospitalizations compared to NH residents. On the other hand, Bogaisky and Dezieck (2015) reported that NH residents had 41% higher risk of 30-day rehospitalization compared to adults discharged to the community. State-Implemented Transition Programs and Falls Over the last several decades, federal and state policymak- ers have advanced initiatives to assist individuals with long- term care needs to transition from long-term care settings to the community and to remain in the community after a transition (Bardo, Applebaum, Kunkel, & Carpio, 2014; Fries & James, 2012; Reinhard, 2010). These initiatives have mainly focused on Medicaid paying or dual Medicare/ Medicaid paying residents through the Money Follows the Person (MFP) programs. Some studies have explored readmission outcomes associated with NH to community transition within the context of these state-implemented transition programs (Howell et al., 2007; Marrero et al., 2017; Robison et al., 2015). Howell and colleagues (2007) examined New Jersey’s nursing home transition program participants and found that falls within 8 to 10 weeks of a NH to community transition were a significant predictor of long-stay NH readmissions. Another study evaluating the Connecticut Money Follows the Person (MFP) program examined fall incidence at two time points post NH dis- charge (6 and 12 months) and reported that 25% of par- ticipants fell in the first 6 months following a NH transition and 25% fell between 6 and 12 months (Marrero et al., 2017). Predictors of falling at 12 months included previous falls, depressive symptoms, unmet medical care needs, and older adult physical/verbal mistreatment. Services and Supports A major component of state-implemented transition pro- grams is the provision of home and community-based ser- vices (HCBS), including both health-related and personal care services to ease transitions and assist individuals in maintaining independence in the community (Centers for Medicare and Medicaid Services [CMS], 2016; Reinhard, 2010). Although some studies have examined transition outcomes in the context of these state-implemented transi- tion programs, these studies have not examined specifically the impact of home and community service accessibility on transition-related outcomes, including falls. In addi- tion, these transition studies have not fully explored the impact of caregiver availability and support on fall occur- rence among older adults. Hoffman and colleagues (2017) reported that receiving high levels of informal caregiving (≥14 hours a week) was associated with reduced fall risk among community dwelling older adults. Older adults who had physical limitations and cognitive impairments and who were receiving high levels of informal care experienced the greatest reduction in the risk of falling (Hoffman et al., 2017). The purpose of our study was to examine whether the presence of supports and services impacts the relation- ship between factors typically associated with falls and the occurrence of falls within 30-days post-discharge from the NH. This time-frame is a critical period when older adults are re-adjusting to their community setting and can be at an increased risk for falls (Davenport et al., 2009). This study examines the relationship within the context of state-imple- mented transition program aimed at assisting private-pay NH residents. As previously mentioned, studies examin- ing NH to community transitions have mainly focused on The Gerontologist, 2018, Vol. 58, No. 61076 D ow nloaded from https://academ ic.oup.com /gerontologist/article/58/6/1075/4103220 by guest on 02 A ugust 2022 the Money Follows the Person initiatives that are targeted toward Medicaid populations and there is a lack of knowl- edge about programs tailored to other populations (Bardo et al., 2014; Howell et al., 2007; Marrero et al., 2017). Study Context The Minnesota Return to Community Initiative (RTCI) is a state-implemented transition program that assists private- pay NH residents to transition into the community. It pro- vides a context for us to explore fall occurrence during a transition and to investigate the role of HCBS and various supports in fall prevention. Administered by the Minnesota Department of Human Services, RTCI targets transition- related assistance to NH residents who have a preference for discharge, fit a discharge “target” profile (Arling, Kane, Cooke, & Lewis, 2010), and have been in the NH for at least 60 days (Minnesota Board on Aging, 2017). RTCI has a staff of Community Living Specialists (CLS) that assists in care planning and offers information about community services and other resources to older adults and their fami- lies both during the NH stay and after discharge. However, they do not provide specific interventions or services related to falls. Conceptual Framework Previous literature on fall-related risk factors and fall pre- vention helped guide this study’s conceptual framework. As formerly mentioned, falls can result from both patient- specific factors as well as environmental factors. Patient- specific factors include age, gender, having a history of falls, having certain musculoskeletal or neurologic conditions, depression, being cognitively impaired, and experiencing problems with balance (Bueno-Cavanillas et al., 2000; Moncada, 2011). Environmental factors include presence of home safety issues, use of certain high-risk medications or multiple medications, and having impaired abilities to perform activities of daily living (Bueno-Cavanillas et al., 2000; Ganz et al., 2007; Moncada, 2011). Interventions recommended in fall prevention guidelines are focused on screening for older adults at high risk for falls and modify- ing some of their risk factors (American Geriatrics Society/ British Geriatric Society, 2011; Moncada, 2011; Phelan et al., 2015). Current guidelines recommend multifac- eted interventions for fall prevention, including providing patient education, assessing and modifying medication regimens, ensuring a safe home environment, and enroll- ing older adults in physical therapy and exercise programs among other strategies (American Geriatrics Society/British Geriatric Society, 2011). However, there has been less focus on how other types of strategies, such as caregiver assis- tance, use of durable medical equipment, or use of HCBS- based services, can modify fall risk, especially following a transition from the NH to the community setting. In our conceptual framework, we hypothesize that fall- related risk factors, including previous history of falls in the NH, concerns related to balance, falling, or the home environment, activities of daily living (ADL) deficits, and use of potentially inappropriate medications will contrib- ute to falls among NH residents transitioning into the com- munity. We also hypothesize that HCBS as well as various informal supportive strategies will have a moderating effect and ameliorate the impact of fall-related risk factors on fall occurrence. Modeling the effects of supports and services on falls is complex. We expect individuals with greater fall risk, e.g., ADL impairment, history of falls, or high-risk medication use will receive more supports and services. Consequently, a simple bivariate model might result in the counterintuitive finding that greater supports and services contribute to falls. We employed a structural equation model (SEM) to test our conceptual framework because this approach can be more effective at addressing direct, indirect, and moderating effects of both fall-related risk factors and supports and services. A figure of the concep- tual framework is included in the Supplementary Figure 1. Design and Methods Study Sample The analytic sample included NH residents who were tran- sitioned from the NH to the community by the Minnesota RTCI between April 2014 and October 2016 (N = 1,459). Data came from the comprehensive Community Planning Tool (CPT), completed by CLS prior to discharge for all NH residents who participated in RTCI. The CPT is a compre- hensive assessment and includes demographic information, medical diagnoses, health, functional, and cognitive status of the residents, medication use and medication management, discharge location, as well as caregiver availability and fre- quency of assistance. The CLS personnel use various sources to collect information for the CPT, including Minimum Data Set (MDS) assessments, NH charts, and NH resident and fam- ily caregivers. CLS staff conduct follow-up interviews with older adults (in person or by phone) at 3, 10, and 30 days post-discharge. The follow-up assessments provide informa- tion regarding fall occurrence and health care utilization. Variables Study variables were derived from the CPT and follow-up assessments. The outcome variable of interest was occurrence of falls within 30 days of discharge, dichotomously coded (yes/ no). Independent variables included age, gender, presence of at least one musculoskeletal condition (arthritis, hip fracture, osteoporosis, etc.), or presence of at least one neurological condition (dementia, stroke, seizures, etc.). Medical diagnoses were collected from MDS assessments and were based on NH records. Additional variables included presence of moderate- to-severe cognitive impairment (moderate-to-severe score The Gerontologist, 2018, Vol. 58, No. 6 1077 D ow nloaded from https://academ ic.oup.com /gerontologist/article/58/6/1075/4103220 by guest on 02 A ugust 2022 ≤12, cognitively intact 13–15), based on the Brief Interview for Mental Status (BIMS; Saliba et al., 2012) and presence of moderate-to-severe depression based on the Patient Health Questionnaire-9 (PHQ-9 score; moderate-to-severe score ≥10, mild to no depression score 0–9; Kroenke, Spitzer, & Williams, 2001). Prior history of NH falls (yes/no) as well as resident concerns at discharge regarding falling in the com- munity (yes/no) and concerns about balance/vertigo affect- ing daily activities (yes/no) were also included in the analysis. Home environmental safety issues were defined as older adult concern about getting around within at least one of seven areas in the home, including the basement, bathroom, bed- room, kitchen, laundry room, stairs, and entrances/exits (yes/no). Functional variables included three items assessing whether assistance is sometimes needed with ADLs, specific- ally toileting, walking, and bed mobility (yes/no). Toileting was defined as getting to and on the toilet, adjusting clothes, and cleaning after toilet use. Walking referred to the ability to walk short distances around the house. Bed mobility was defined as sitting up in bed or moving around in bed. Use of medications considered inappropriate or high risk in the eld- erly was obtained from medication lists provided to the CLS staff by the NH at discharge. Medications were categorized as psychotropics, analgesics, and anticholinergics (American Geriatric Society, 2015). Psychotropics encompassed use of antidepressants, hypnotics or sedatives, and anti-psychotics; analgesics included use of opioid medications; and varying types of medications with known anticholinergic effects that can lead to dizziness comprised the third category. Variables related to various supports and services included assistance with medication management (independent, somewhat dependent, or dependent); older adult use of durable medical equipment (yes/no); receipt of at least one of the following HCBS: skilled nursing, home health, or personal care assis- tants; discharge location (alone vs with someone else), and caregiver frequency of support (once weekly or less vs daily or several times a week). Data Analysis Descriptive statistics provide an overview of RTCI partici- pant characteristics and 30-day post-discharge outcomes. In the SEMs, we tested the relationships between: (a) fall- related risk factors and receipt of supports and services, (b) fall-related risk factors and the occurrence of falls, and (c) moderating effect of supports and services on the relation- ship between fall-related risk factors and the occurrence of falls. The SEM approach allows us to model latent con- structs from observed measures and to examine complex relationships between observed variables and latent con- structs simultaneously (Lei & Wu, 2007; Weston & Gore, 2006). Data management, descriptive statistics, and prelim- inary regression analyses were conducted using SAS version 9.3 (SAS Institute, Cary, NC), whereas factor analysis and SEM were conducted using Mplus version 7.4 (Muthen & Muthen, Los Angeles, CA). In developing the SEM models, we first conducted bivariate logistic regression analyses to examine associa- tions between variables identified in the study’s conceptual framework with the outcome of falls, to provide preliminary assessment of the relationships, and to assist in SEM model specification. Next, exploratory factor analyses (EFAs) and confirmatory factor analyses (CFAs) were used sequentially to estimate individual latent variable models (measurement models). Initially, we hypothesized the presence of two latent constructs, supports and services, and fall-related risk fac- tors. Results of factor analyses indicated that fall-related risk factors were better represented by three constructs instead of one: fall concerns and fall history, ADLs impairment, and use of high-risk medications. Finally, two SEM models were esti- mated, one without interactions and one with interactions, including the support and services and fall risk constructs as well as the outcome of falls. We tested all direct and indirect effects and interaction terms between each fall-related risk construct and the supports and services construct; however, for parsimony, the final model included only the significant interaction term. Model co-variates for the full SEM model included age (>85 years vs ≤85 years), gender, diagnosis with at least one musculoskeletal condition, diagnosis with at least one neurological condition, depression, and cogni- tive status. Results from the model without interactions are included in the Supplementary Figure 2.

SEM model fit is typically assessed based on several indi- ces including the comparative fit index (CFI), the root mean- square error of approximation (RMSEA), and the maximum likelihood χ2 test (Lei & Wu, 2007; Weston & Gore, 2006). The χ2 test is a measure of how well the models fit the observed data with a nonsignificant χ2 indicating good fit; however, it is extremely sensitive to large sample sizes (Weston & Gore, 2006). The CFI is an incremental fit index that measures improvement in fit with values more than 0.9 or 0.95 indi- cating improved fit. The RMSEA is an index that corrects for model complexity with values less than 0.06 indicating good fit between the hypothesized model and sample data (Lei & Wu, 2007; Weston & Gore, 2006). Model fit indices were used to assess the individual latent variable models and SEM model with no interaction terms. Due to the dichoto- mous nature of some variables in the SEM model, Mplus uses maximum likelihood to estimate a model with interaction terms. This estimation technique does not provide traditional fit statistics. We compared our SEM models using the receiver operating curve (ROC) to assess which model was the most predictive of falls (closer to 1.0 indicates more predictive accuracy). The research was approved by the Institutional Review Board at Purdue University.


Descriptive and Bivariate Associations Fifteen percent of RTCI participants (N = 1,459) who transitioned from the NH to the community fell within 30 days of discharge. An overview of RTCI participant

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characteristics is presented in Table 1. The mean age of par- ticipants was 79.6 years, 59.5% were female and 57.4% were married. The majority (92.8%) had been admitted to the NH from an acute-care hospital and 16.6% had expe- rienced a fall in the NH prior to discharge. Based on the BIMS, 11.7% of participants had moderate-to-severe cog- nitive impairment. In terms of assistance with ADLs, 69.8%

needed some assistance with walking, 12% needed some assistance with toileting, and 9.7% needed some assist- ance with moving within bed. A majority (54.1%) of par- ticipants were using psychotropic medications and almost 40% were using analgesics prior to NH discharge. In terms of medication management, 54.7% indicated some level of assistance needed (somewhat dependent or dependent).

Table 1. Participant Demographics and Key Variables

Total % (N) Fall % (N) No fall % (N)

Variables N = 1,459 N = 219 N = 1,240

Age (mean ± standard deviation) 79.6 ± 9.8 78 ± 10.1 79.9 ± 9.7 Female 59.5% (868) 47.7% (106) 61.6% (762)c

Married 57.4% (835) 50.2% (111) 58.7% (724)c

Prior nursing home stay in previous 2 years 61.5% (894) 60.6% (134) 61.6% (760) Admission from acute hospital 92.8% (1,354) 92.3% (205) 92.9% (1,149) Mean length of stay 76.7 ± 92.9 78.2 ± 109.9 76.4 ± 89.5 Medical conditions Depression (moderate to severe)a 8.2% (119) 11.7% (26) 7.5% (93)c

Diabetes 32.6% (476) 35.6% (79) 32.1% (397) Heart disease 47.4% (692) 46.4% (103) 47.6% (589) Musculoskeletal conditions (arthritis, osteoporosis) 49.5% (722) 43.7% (97) 50.5% (625)c

Neurological conditions (dementia, stroke) 33.9% (494) 51.4% (114) 30.7% (380)c

Cognitive impairmentb (moderate to severe) 11.7% (170) 16.8% (37) 10.8% (133)c

Functional variables (sometimes need assistance) ADL-toileting 12.0% (175) 15.8% (35) 11.3% (140)c

ADL-walking 69.8% (1,018) 68.9% (153) 69.9% (865) ADL-bed movement 9.7% (141) 11.3% (25) 9.4% (116) Previous fall in nursing home 16.6% (242) 28.8% (64) 14.4% (178)c

Fear of falling in the community 50.4% (735) 56.8% (126) 49.2% (609)c

Concern with vertigo/balance 41.8% (610) 55.4% (123) 39.4% (487)c

Concern with home safety 35.6% (520) 39.6% (88) 34.9% (432) Use of durable medical equipment 31.7% (462) 31.5% (70) 31.7% (392) High-risk medication use Psychotropics 54.1% (789) 68.0% (151) 51.6% (638)c

Analgesics 39.5% (577) 37.4% (83) 39.9% (494) Anticholinergics 10.8% (158) 10.4% (23) 10.9% (135) Medication management Independent 45.2% (660) 36.9% (82) 46.7% (578) Somewhat dependent 36.9% (539) 41.0% (91) 36.2% (448) Dependent 17.8% (260) 22.1% (49) 17.1% (211)c

Caregiver support Once weekly or less 24.1% (352) 19.4% (43) 25.0% (309)c

Weekly or more frequently 75.9% (1107) 80.6% (179) 75.0% (928) Post-discharge living arrangement Alone 30.2% (441) 18.9% (42) 32.3% (399) With family 49.8% (727) 59.9% (133) 48.0% (594)c

Assisted living 19.9% (291) 21.2% (47) 19.7% (244) Use of HCBS-based services Skilled nursing 48.3% (704) 46.8% (104) 48.4% (599) Home health aides 50% (729) 53.6% (119) 49.3% (610) Personal care assistants 1.9% (27) 2.7% (6) 1.7% (21)

Note: ADL = activities of daily living; HCBS = home and community-based services. aBased on PHQ-9(score ≥ 10 = moderate to severe). bBased on BIMS (score of ≤ 12 = moderate to severe). cBivariate association (fall/did not fall) significant at alpha = .1.

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Three quarters of participants (75.9%) had caregiver sup- port daily or multiple times a week, and most were living with a spouse, other relative or significant other (69.8%). At discharge, 48.3% accepted skilled nursing services and 50% accepted home health aide services.

Bivariate analyses indicated statistically significant associations (alpha < .10) between falls and several of the variables considered in our conceptual framework, includ- ing health and functional variables as well as medication- related variables and caregiver support (Table 1). EFA and CFA for the Fall-Related Risk Factors EFA with the fall-related risk variables, suggested three latent constructs: fall concerns/fall history, ADLs impairment, and use of high-risk medications. CFA was used to fit the three latent variables for the SEM models. The first item of each latent variable was set to 1 to allow for model estimation. For the fall concerns/fall history latent variable, four indica- tors loaded significantly, including concern with home safety (1.00), previous NH fall (1.03), fear of falling in the commu- nity (2.17), and concerns with vertigo/balance (3.10). For the ADLs impairment latent variable, three indicators loaded sig- nificantly including needing some assistance within bed move- ment (1.00), with toileting (0.40), and walking (−0.31). For the use of high-risk medications latent variable, three indica- tor variables loaded significantly, including use of analgesics (1.00), anticholinergics (0.90), and psychotropics (1.13). Each of the three latent variable models had good fit to the data individually. We combined the three latent vari- ables into one model to examine how well they fit the data collectively. Figure 1 shows standardized parameter esti- mates and correlations between latent variables in the com- bined model. The combined model also had good model fit with a χ2(32) = 78.44, p < .01; CFI = .94, RMSEA = .03 (90% confidence interval [CI] = 0.02, 0.04). EFA and CFA for the Support and Services Construct In the EFA, we found that five indicators of supports and services loaded significantly onto a single-latent construct. We conducted CFA for these variables and the latent vari- able supports and services (Figure 2). The first item (receiv- ing at least one HCBS) was set to 1. The loadings were 0.96 for use of durable medical equipment, 2.46 for caregiver support frequency, 2.61 for medication management assis- tance, and 3.04 for discharge location. The latent variable was influenced mainly by discharge location, medication management assistance, and caregiver support. This latent variable had good model fit with a χ2(5) = 10.36, p = .07; CFI = .99; RMSEA = .03 (90% CI = 0.00, 0.05). SEM Model We estimated two SEM models: one model without inter- action terms and the second model with the addition of interaction terms. The initial model indicated that con- structs of ADL impairment and use of high-risk medica- tion were positively related to supports and services, while supports and services had no significant effect on falls at 30 days (Supplementary Figure 2 and Supplementary Table 1). Next, we tested the same conceptual model with interaction terms between supports and services and each of the fall-related risk constructs. Nonsignificant interac- tion terms were then dropped and a more parsimonious model was tested. This final model was similar to the ini- tial model except for the inclusion of an interaction term .93*** .38 *** .31*** .65*** .41 *** .23 .39 .16 .52*** .30*** .46*** -.29 *** .94*** High risk medication use Analgesics Anticholinergics Psychotropics Bed mobility Toileting Walking Fall concerns/fall history Home safety concern Previous nursing home fall Fear of falls Vertigo/balance concern Activities of daily living impairments Figure 1. Confirmatory factor analysis for fall-related risk factor latent variables. Standardized coefficients are presented. Correlations between latent variables are also presented. χ2(32) = 78.44, p < .01; CFI = .94, RMSEA = .03 (90% confidence interval = 0.02, 0.04). ***p < .001. CFI = comparative fit index; RMSEA = root mean-square error of approximation. The Gerontologist, 2018, Vol. 58, No. 61080 D ow nloaded from https://academ ic.oup.com /gerontologist/article/58/6/1075/4103220 by guest on 02 A ugust 2022 between the latent variables of supports and services with use of high-risk medications (Figure 3). To evaluate the two SEM models (with and without the interaction term), we compared C-statistics from the ROC for each model. Based on the ROC, the model with the interaction term predicted falling within 30 days of post-discharge more accurately than the model with no interactions, with a C-statistic of 0.757 versus 0.719, respectively. Results from the final model indicated a significant posi- tive effect of the falls concern/falls history latent variable on falls, a significant positive effect of use of high-risk med- ications on falls, and a significant positive effect of ADL impairments on receiving supports and services (Figure 3). In addition, the interaction between supports and services and use of high-risk medications was negatively associated with falling (p = .03). Given a specific level of high-risk medication use, as receipt of supports and services increase, the risk of falling decreases. In addition, being female was negatively associated with falling while having at least one neurological condition had a significant positive effect on falling. Unstandardized and standardized model coeffi- cients for direct effects are presented in Table 2. Discussion This is one of few studies examining fall outcomes within 30 days of older adults transitioning from the NH to the community within the context of a state-implemented tran- sition program. Previous studies have typically examined fall incidence during NH stays or during/after hospitali- zations. Among this study’s participants, 15% fell within 30 days of NH discharge, which is lower than reported fall rates for both NH and community dwelling older adults (American Geriatrics Society/British Geriatric Society, 2011; CMS, 2015). Marrero and colleagues (2017)reported that among older adults transitioning from the NH to the com- munity, 25% experienced a fall within the first 6 months of discharge. This study’s participants were specifically tar- geted for assistance through the RTCI based on health and functional characteristics (Arling et al., 2010), which may partially explain the lower fall rate. However, RTCI offered information about community resources and was not an intervention specifically aimed at fall prevention. Factor analysis and structural equation modeling pro- vided a unique and innovative approach to examining risk factors related to falling post NH discharge. Fall-related risk has been typically examined as a unidimensional construct with a fall score derived through conventional regression analysis, and fall screening and prevention guidelines typi- cally list risk factors for assessment without discriminating among types of risk (American Geriatrics Society/British Geriatric Society, 2011; Moncada, 2011; Phelan et al., 2015). .22** -.24 .02 .20*** Fall concerns/fall history Activities of daily living impairments High risk medication use Supports and services Falls within 30 days Age Female Depression Cognitive impairment Musculoskeletal disease Neurological condition .93*** -.17** .26 .04 -.04 .15*** -.12 *** -.05 Figure 3. Falls SEM. The three fall risk factor latent variables are correlated (not shown for simplicity). Standardized coefficients presented. Because of the interaction term, Mplus did not provide fit statistics. Significance bolded. **p < .05, ***p < .001. SEM = structural equation model. .76 ***.25*** Supports and services Use of HCBS (home health, skilled nursing, etc.) Durable medical equipment use Caregiver support frequency Medication management assistance Discharge location .24 *** .62 *** .65 *** Figure 2. Confirmatory factor analysis for supports and services latent variable. Standardized coefficients presented. χ2(5) = 10.36, p = .07; CFI = .99; RMSEA = .03 (90% confidence interval = 0.00, 0.05), ***p < .001. CFI = comparative fit index; HCBS = home and community- based services; RMSEA = root mean-square error of approximation. The Gerontologist, 2018, Vol. 58, No. 6 1081 D ow nloaded from https://academ ic.oup.com /gerontologist/article/58/6/1075/4103220 by guest on 02 A ugust 2022 Our study moves the discussion forward by examining how various fall risk factors are related to each other as well as to the receipt of supports and services. We found three clinically meaningful fall-related risk constructs represented by the latent variables: fall concerns and fall history; ADL impair- ments; and use of high-risk medications. The fall concerns/ fall history latent variable is comprised of older adults’ con- cerns with balance, fear of falling, and concern with home safety along with previous NH fall history. The ADL impair- ments latent variable indicates that three ADL impairments are related, some difficulty with toileting, with walking, and with bed mobility. Likewise, the high-risk medication latent variable highlights three medication classes related to falling with psychotropic medications having a higher loading than the other two. The three latent variables were significantly correlated and had good model fit. Results of the SEM model indicated that fall concerns/fall history and use of high-risk medications had a significant positive direct effect on falls, whereas ADL impairments were not signifi- cantly related. These findings provide a unique view when examining fall risk from a clinical perspective and further strengthen empirical evidence for fall predictors in this older adult population undergoing a care transition. For example, older adults’ concerns about issues related to falling, such as fear of falling or concerns with balance, can be vital consid- erations when assessing fall risk. Results also highlight the importance of high-risk medications, as a main contributor to falls in the community after NH discharge. This finding emphasizes the need for continued reviews of medications lists by health care professionals and adjustment of medica- tion regimens to minimize use of unnecessary and potentially inappropriate medications in older adults. Another key contribution of our findings is the role of supports and services in ameliorating the effects of fall- related risk factors. The latent variable for supports and services included several items that had not been exten- sively examined in the fall risk literature. Frequency of caregiver support, assistance with medication manage- ment, use of durable medical equipment, post-discharge living arrangement, and receipt of home health or skilled nursing services were all correlated within the latent vari- able. Durable medical equipment use has been previously considered as a risk factor for fall (Moncada, 2011) rather than a potential support, and variables such as assistance with managing medications had not been examined. This finding brings forward a new perspective on the interrela- tionship between different types of supports and services and provides insight into the potential benefit of both fam- ily and other informal supports in combination with HCBS in transitioning from the NH. We found that receipt of supports and services had no significant direct effect on fall occurrence. This result highlights the complexity of relationships between the fall- related risk factors and support and services. In our study, older adults who had some ADL impairments were more likely to receive supports and services. Although this may have led to a lack of significant relationship between ADL impairments and falls, this finding is encouraging since it indicates those who need assistance seem to be receiving it among RTCI participants. More importantly, individu- als who used high-risk medications and also received sup- port tended to benefit from that support with a reduced likelihood of falling. It is not clear if one component of the supports and services latent variable is influencing this Table 2. Structural Equation Model of Falls Within 30 Days Post-Discharge Model with interaction Variables Unst. C SE Std. C Direct effects: falls Fall concern/fall history 0.939** 0.376 0.203 Activities of daily living impairment −0.588 1.704 −0.236 High-risk medication use 2.461*** 0.739 0.218 Supports and services 1.203 2.949 0.258 Neurological condition 0.628*** 0.170 0.147 Musculoskeletal condition −0.161 0.156 −0.040 Depression (moderate to severe) 0.155 0.255 0.021 Cognitive impairment (moderate to severe) 0.249 0.211 0.043 Female −0.477*** 0.155 −0.116 Age ≥ 85 years −0.189 0.171 −0.045 Support and services × high-risk medication use (interaction term) −4.525** 2.252 −0.174 Direct effects: supports and services Fall concern/fall history — — — Activities of daily living impairment 0.500*** 0.065 0.933 High-risk medication use — — — Note: Unstd. C = unstandardized coefficient; SE = standard error; Std. C = standardized coefficient. **p < .05, ***p < .01. The Gerontologist, 2018, Vol. 58, No. 61082 D ow nloaded from https://academ ic.oup.com /gerontologist/article/58/6/1075/4103220 by guest on 02 A ugust 2022 relationship or if it is a combination of the assistance pro- vided, including assistance with medication management. Additional research is needed to further examine these sup- portive strategies and evaluate how they might vary across the older adult population and their potential impact on health outcomes. Limitations There are several limitations to this study that should be noted. First, the study sample was primarily short-stay private-pay NH residents who had met specific targeting criteria for discharge. As such, these results may not be generalizable to Medicaid residents or private paying older adults transitioning into the community but who do not fit the RTCI targeting profile. Based on RTCI’s design, some information was only collected prior to discharge, such as type and number of high-risk medications taken. Since medication lists tend to be dynamic in nature, participants’ medication lists may have changed within the first 30 days. Additionally, not all fall-related risk factors were examined due to the nature and type of data collected. For example, gait and balance were not objectively assessed, and partici- pants were only asked if they had concerns with balance or vertigo. Methodologically, this study has unique strengths including the use of advance modeling (latent variables and structural equation modeling) that go beyond regression or hazards modeling commonly seen when studying fall risk. Given the complexity and multifactorial nature of fall occurrence and the dynamic relationships between various factors, higher levels of modeling provide a broader pic- ture of the factors associated with falls, both positively and negatively. Moreover, other studies have focused on NH to community transition among Medicaid populations, and there is limited information on other populations, such as the private-pay population, which comprises approxi- mately a third of NH users (CDC, 2016). Implications Within the context of a state-implemented transition pro- gram and using structural equation modeling, results indi- cate that fall risk factors can be viewed as latent constructs relating to older adults’ fall concerns and fall history, ADL deficits, and use of high-risk medications. Supports and ser- vices are essential when assessing fall risk. Although they were not related directly to the occurrence of falls, they moderated the relationship between using high-risk medi- cations and falls. Individuals with greater fall risk due to high-risk medications were less likely to fall if they had sup- ports and services. This result points to the importance of both informal supports and receipt of HCBS in influencing older adult NH to community transition outcomes. Results emphasize the importance of conducting fall assessment and medication reviews in older adults who are transitioning from an institutionalized to a community set- ting similar to current guidelines for fall prevention in the community (American Geriatrics Society/British Geriatric Society, 2011; Casey et al., 2016). Furthermore, it is also essential for health care providers to recognize the role older adults’ concerns and attitudes, such as concerns with balance or with falling, can play in fall risk and address these concerns in a patient-centered manner. From a policy perspective, findings can help inform other state-imple- mented transition programs aimed at achieving successful NH to community transitions. Supplementary Material Supplementary data is available at The Gerontologist online. 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