Older adults are at high risk of nursing home placement. In developed countries, 1.5% to 8% of older adults 65 years or older are living in nursing homes.1 Most older adults prefer to stay in their familiar home environment rather than transfer to a nursing home because the transition to nursing home can cause loss of autonomy and independence, poor quality of life, and negative psychological impacts.2,3 In addition, the cost of nursing home placement is substantial and imposes a huge burden on patients and their family as well as heath care systems, stretching limited health care resources.4 With a growing number of older adults worldwide, there have been increasing implications to avoid or delay nursing home placement and extend quality life in old age.
Determining modifiable risk factors of nursing home placement is of interest to older adults, their families, and all related parties, including clinicians, researchers, and policy makers. High-risk individuals can be identified and targeted for interventions to prevent or delay admission to the nursing home. Moreover, those at high risk can be educated to prepare themselves to make decisions in advance and be guided in their future financial planning, leading to a better overall quality of life in old age. Among various risk factors associated with nursing home placement reported in the literature, functional disability, such as difficulty in performing basic activities of daily living, has been extensively studied and almost uniformly shown to be a strong predictor.5 Once disability is fully developed, it may be difficult to modify. An old but recently reconceptualized entity, frailty, has been gaining the attention of recent research. Frailty was once used synonymously with disability as these 2 often coexist in older adults, with overlapping features. However, frailty is now considered as a distinct state with a biological basis and may be a predisability state.6
Frailty has been described as a medical syndrome characterized by decreased physiological reserve in multiple systems and vulnerability to poor resolution of homeostasis after internal or external stressors because of accumulated age-related health deficits.6–8 The most frequently used frailty definition is the phenotype criteria proposed by Fried et al,6 using the Cardiovascular Health Study (CHS) data. They defined frailty categories on the basis of 5 physical components: weight loss, exhaustion, weakness, slow walking speed, and low physical activity.6 An individual is classified as robust, prefrail, and frail when they meet 0, 1 to 2, and 3 to 5 components.6 Frailty is associated with multiple adverse health outcomes, including disability, falls, hospitalization, dementia, poor quality of life, and mortality.6–14 Prevalence of frailty among community-dwelling older adults is approximately 10%,15 whereas frailty is highly prevalent in nursing homes, with as many as 50% of nursing home patients being frail.16 Given the dynamic nature of frailty, which can progress or improve dramatically,17 and its potential to be a precursor of disability,6 frailty may be a promising target to reduce nursing home placement risks in older adults. Although frailty can easily be expected to be a cause of nursing home placement, only a limited amount of evidence on frailty as a predictor of nursing home placement exists in the literature. Several prospective studies have examined associations between baseline frailty and a subsequent risk of nursing home admission, with inconsistent results.18–24
One previous systematic review article included studies published in 1990 to 2010, examining associations between frailty and nursing home placement in general elderly populations.25 The review article identified only 2 studies,23,24 both of which showed frailty was a significant predictor of nursing home placement, but did not perform a meta-analysis to show pooled evidence.25 Since this review, an increasing number of relevant studies have been published,18–21 as this research field has been making remarkable advances. Thus, the current study aimed (1) to systematically review the literature for prospective studies investigating frailty as a risk factor for nursing home placement among community-dwelling older adults and (2) to conduct a meta-analysis to combine the risk measures to provide pooled evidence.
A systematic search of the literature was conducted by 1 geriatric clinician researcher (GK) with clinical experience in a nursing home setting in July 2015 according to a protocol developed within the scope of the Meta-analysis of Observational Studies in Epidemiology (MOOSE) statements.26 Any cohort studies examining associations between frailty and a subsequent nursing home placement risk among community-dwelling older adults, published from 2000 through July 2015, were searched for without language restriction using 6 electronic databases (Scopus, Embase, CINAHL Plus, MEDLINE, PsycINFO, and the Cochrane Library). The search terms used were “Institutionalization (Medical Subject Heading [MeSH]),” “Patient admission (MeSH),” “Facility admission (MeSH),” “institutionalization*,” “institutionalisation*,” “nursing home placement*,” “nursing home admission*,” “Frailty syndrome (MeSH),” and “frailty.” Bibliographies of the relevant articles were also hand-searched. An expert in this field (Dr Yu Taniguchi) was consulted for any additional articles.
Inclusion criteria were (1) original articles; (2) involving noninstitutionalized older adults 65 years or older living in the community; (3) longitudinally examining a risk of nursing home placement according to baseline frailty status defined by validated criteria or its modified versions; and (4) providing odds ratio (OR) as a risk measure or data sufficient enough to calculate OR. Authors of potentially eligible studies were contacted for sharing data. Exclusion criteria were (1) defining frailty using surrogate measures, such as morbidity or walking speed; (2) using a nongeneral population, such as patients with heart failure or dementia; or (3) using a continuous index to grade frailty status without categorizing frailty status. If the same cohort was used by multiple eligible studies, the study with the largest number of participants was included. All eligible studies were assessed for methodological quality using the Newcastle-Ottawa scale for cohort studies.27 This scale was chosen because it was developed by international experts to assess the quality of nonrandomized studies for meta-analysis.27 It consists of 9 items to assess selection, compatibility, and outcome domains of cohort studies.27 In this review, a study was considered to have adequate methodological quality to be included in the meta-analysis if 5 or more out of 9 criteria were met. Data extracted were first author, study cohort name, publication year, location (country), sample size, proportion of female individuals, age (mean, range, or age criterion for inclusion), frailty criteria, effect measure, and follow-up period.
A flow diagram of the systematic literature search and selection process, along with the number of studies at each stage, is shown in Figure 1. Of these 885 studies identified, 5 were included for this review. All 5 studies were considered to have adequate methodological quality (mean number of criteria met = 5.8, range = 5-7) (Table 1).
Table 1. -
Summary of Included Studies on Frailty and Nursing Home Placement Among Community-Dwelling Older Adults
|Coelho et al21
|Drubbel et al19
|Bilotta et al18
|Forti et al20 CSBA
|Bandeen-Roche et al23 WHAS
Abbreviations: aHR, adjusted hazard ratio; CSBA, Conselice Study of Brain Aging; FI, frailty index; mCHS, Modified Cardiovascular Health Study frailty index (Fried's phenotype); SOF, Study of Osteoporotic Fractures frailty index; NOS, Newcastle-Ottawa scale for cohort studies; u/cOR, unadjusted/calculated odds ratio; WHAS, Women's Health and Aging Studies.
The numbers of participants who were admitted to nursing homes and who were not according to frailty categories (frail, prefrail, and robust) were collected from the included studies and used to calculate pooled OR using the Mantel-Haenszel method. The heterogeneity among the included studies was assessed using Cochran's Q statistic and the magnitude of the heterogeneity was assessed using the I2 statistic. I2 values of 25%, 50%, and 75% were considered as low, moderate, and high heterogeneity, respectively.28 A fixed-effects model was used if the heterogeneity was low to moderate and a random-effects model was used if the heterogeneity was high. The numbers of participants who were admitted to nursing homes and who were not according to baseline frailty categories (frail, prefrail, and robust) from the 5 included studies were used for the meta-analysis. Pooled OR was calculated using fixed-effects models on the basis of low to moderate heterogeneity across the studies (5 studies: P = .11, I2 = 46% for frailty; 3 studies: P = .60, I2 = 0% for prefrailty). Publication bias was assessed using Begg-Mazumdar's and Egger's tests and visually inspecting funnel plots. All analyses were performed using Review Manager 5 (version 5.2, The Cochrane Collaboration, Copenhagen, Denmark) and StatsDirect (version 2.8, StatsDirect, Cheshire, UK).
Table 1 presents characteristics of the 5 included studies encompassing 3528 community-dwelling older adults.18–21,23 Four studies were from Europe18–21 (Italy, Portugal, and Netherlands) and 1 from the United States.23 All studies included relatively small numbers of participants, ranging from 9521 to 1679.19 One study included only women.23 Not all studies presented mean age, but the participants were mostly 70 years or older. Various criteria were used to define frailty, including the CHS criteria,21,23 the Study of Osteoporotic Fractures (SOF) frailty index,18,20 the Conselice Study of Brain Aging (CSBA) index,20 and the Frailty Index (FI).19 Bilotta et al18 used the SOF frailty index, which consists of 3 components, and defined frail, prefrail, and robust as having 2 or more, 1, and 0 components, respectively. One study using the FI19 divided the cohort into tertiles; therefore, the highest, middle, and lowest tertiles were treated as frail, prefrail, and robust groups, respectively, in the current review. Two studies20,21 dichotomized the cohort into frail and nonfrail and 3 studies18,19,23 divided the cohort into frail, prefrail, and robust. Among the 5 included studies, 3 studies19,20,23 showed that frailty was associated with significantly higher risks of nursing home admission whereas the other 2 studies18,21 did not. Three studies examined the risks of nursing home placement according to prefrail and all showed nonsignificant results.18,19,23 Follow-up periods ranged from 10 months21 up to 4 years,20 with the mean of 2.2 years.
Frailty as a Predictor of Nursing Home Placement
Frailty and prefrailty were significantly associated with a higher risk of nursing home placement compared with nonfrailty (5 studies: pooled OR = 5.58, 95% confidence interval [CI] = 2.94-10.60, P < .00001; 3 studies: pooled OR = 3.26, 95% CI = 1.21-8.78, P = .02, respectively) (Figure 2)
Publication Bias Assessment
Figures 3A and 3B are funnel plots for studies examining a nursing home placement risk on the basis of frailty and prefrailty, respectively. No apparent asymmetry suggestive of publication bias was observed. Begg-Mazumdar's and Egger's tests showed no evidence of publication bias for studies of frailty (both P = .82). These tests could not assess the 3 studies for prefrailty because of the small number of studies.
Both frailty and prefrailty have been demonstrated to be a significant predictor of nursing home placement on the basis of the pooled data of 5 studies included in this systematic review and meta-analysis.
Among the included 5 studies, 3 defined frailty on the basis of physical components using the CHS criteria21,23 or the SOF frailty index.18 The physical components used included unintentional weight loss, self-reported exhaustion/low energy, weakness (hand grip), slow walking speed, low physical activity, and inability to rise from a chair.6,29 Meanwhile, 2 studies employed multidimensional frailty tools, the FI19 and the CSBA index,20 which encompass cognitive, psychological, and social components in addition to physical components to conceptualize frailty. Although the CHS criteria, focusing on the physical dimension, have been most commonly used in the literature, the multidimensional approaches to define frailty have recently been more debated.30–32 The multidimensional criteria were compared with the CHS criteria and shown to have better discriminating abilities than the CHS criteria in some studies.33,34 In the current review, a subgroup analysis according to frailty criteria showed no significant nursing home placement risk difference (P for group difference = .79) between 2 studies19,20 using multidimensional frailty criteria (pooled OR = 5.99, 95% CI = 2.60-13.81, P < .0001) and 3 studies18,21,23 using physical frailty criteria (pooled OR = 5.00, 95% CI = 1.84-13.59, P = .03).
Frail and prefrail older adults are approximately 5 times and 3 times more likely to be institutionalized, respectively, compared with those who are nonfrail. The mechanisms of how frailty predisposes older adults to being institutionalized are not clear, but may be multifactorial because the reasons for nursing home placement are often complex and are subject to social and cultural circumstances. One possible explanation is disability. Frail older adults are at a high risk of developing disability,35 which can be the direct cause of nursing home placement. One of the included studies adjusted for an array of potential confounders including age, ethnicity, education, smoking, congestive heart failure, cognitive and depressive status test scores, number of diseases, ankle-arm blood pressure, and diuretic use without history of hypertension or congestive heart failure, but not disability;23 therefore, it is not clear whether the nursing home placement risk predicted by frailty was independent of disability. Another possibility is cognitive impairment including dementia, which can lead to disability and dependence.36 Frailty has been shown to be associated with worse cognitive functions and to predict incident mild cognitive impairment and dementia,14,37 and adding cognitive impairment to frailty defined by physical components improved predictive validity for adverse outcomes.38 Furthermore, cognition has recently been considered as a part of frailty,37 and a new entity, cognitive frailty, has been proposed by an international consensus group as a heterogeneous clinical manifestation characterized by coexistence of physical frailty and cognitive impairment without concurrent dementia.32 Future research focusing on cause-specific nursing home placement may contribute to a further understanding of the associations between frailty and nursing home placement.
Mounting evidence shows that frailty generally deteriorates with age15 but also has the potential to be modified by appropriate interventions.7,17 Physical exercise may be a promising strategy to prevent or treat frailty.39 In particular, aerobic and resistance exercise can be expected to address most of the core physical frailty components, such as weakness, slow gait speed, low physical activity, or sarcopenia.39 Previous interventional trials have shown that physical exercise improved muscle strength, gait speed, and physical performance.40–44 Furthermore, a 12-month multifactorial interdisciplinary intervention, including tailored exercise programs, was conducted in frail community-dwelling older adults 70 years or older in Australia and decreased the prevalence of frailty compared with the usual care group.45 It is of note that the adherence to the exercise programs was high and adverse events were not reported in most of the trials.39 These findings suggest that, potentially, physical exercise can prevent or reverse frailty and may lead to decreased risks of nursing home placement in older adults.
The findings must be interpreted with caution. Unadjusted ORs had to be used for the present meta-analysis because of limited data on adjusted effect measures available; therefore, the findings may have been affected by important confounding factors, such as age, gender, education, or socioeconomic status. Future research should address the independent association between frailty and nursing home placement by controlling these factors.
Another potential limitation is that only 1 investigator conducted this study. It would have been theoretically better if 2 independent investigators had been involved in some processes of the systematic review. It is also of note that various types of frailty definitions were used by the included studies, and therefore different aspects of frailty may have been captured. Inconsistent use of frailty criteria by the included studies as well as other factors, including age, gender, or socioeconomic status, may have contributed to moderate heterogeneity across the studies.
One of the major strengths of this review is that this is, to the best of my knowledge, the first meta-analysis on frailty as a predictor of nursing home placement. In addition to the meta-analysis, distinctive features of the current study include extensive search strategy and assessment of heterogeneity and publication bias across the included studies. Furthermore, additional data were obtained by contacting a corresponding author and were incorporated into the meta-analysis.
In summary, although it should be noted that the numbers of participants who were admitted to nursing homes were fairly small in all of the included studies, this systematic review and meta-analysis suggests that frailty may be a significant predictor of nursing home placement among community-dwelling older adults.
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