Our results and subsequent interpretations are bolstered by the adherence to systematic review principles in meta-analysis [19
]. This includes the reliance on separate data sources (as opposed to independent studies), systematic identification and retrieval of studies, a cross-referencing approach conducted to the point of saturation, and a reliance on studies and data sources that were representative of the elderly population in the U.S. or a region thereof. However, there were several limitations to this meta-analysis that are important to note. Few studies distinguished between short-term NH stays for rehabilitative purposes and long-term admissions [22
]. Due to available resources, only the principal author screened available abstracts and extracted data for the meta-analysis. Guidelines to establish quality in meta-analyses emphasize that inter-rater agreement of multiple reviewers enhances the validity of the data extracted and synthesized in systematic reviews [19
]. Multiple studies and their data sources either did not report complete data for the purposes of the meta-analysis or utilized analytical approaches that were unable to be integrated into the pooled findings reported in this study (e.g., multinomial logistic regression). Similarly, as it is an issue in all meta-analyses of observational studies, extracted predictors of NH admission were derived from studies that included models with varying numbers of adjusting covariates; variations in adjustment across predictive models pose challenges to standardization when pooling estimates in meta-analyses. Related to this issue, variation in the outcome rates (i.e., percentages of participants who entered NHs; see Tables and ) may have led to the observed statistical heterogeneity. Concerns have also been raised in the use of odds ratios as measures of association (the most common statistic used to report pooled effects in meta-analyses), particularly in instances where there is existence of heterogeneity [45
Several of the results showed significant heterogeneity, suggesting a lack of sensitivity in the pooled empirical associations [19
]. The published data available did not offer the opportunity to test the clinical sources of heterogeneity (i.e., subgroup or meta-regression analyses) beyond visual inspection of effect sizes across data sources and predictors. This is a potential weakness of meta-analyses, particularly when attempting to synthesize across observational studies (as opposed to randomized controlled trials), as various methods related to sampling, measurement, and research design could mask associations between predictors and clinical outcomes of interest.
Another important limitation to note is that the meta-analysis was limited to individual predictors of NH admission. Interactions between sociodemographic characteristics, indicators of functional impairment, and similar predictors is likely to occur given the wide range of variables that potentially influence NH entry. The reliance on published data limited this meta-analysis to pool similar variables across studies; future meta-analysis of predictors of NH admission or other healthcare transitions could utilized pooled individual data methods to conduct a meta-analysis of key interaction terms [20
In order to maximize the relevance of these findings, we relied on the pooled logistic regression estimates to determine whether an older patient is at risk for NH admission or not at some point in the future (i.e., 1 year or more). Older adults with 3 or more ADL dependencies had approximately 3.25 times the odds to enter a nursing over a 2–6 year interval. Similarly, elderly patients with 4 or more errors on a short screening tool (Short Portable Mental Status Questionnaire) [40
] had more than twice the odds to enter a NH 3 years in the future. In some instances, older adults or their caregivers have to deal with the consequences of a cataclysmic event (e.g., an injurious fall) [46
] that sets into motion a cascade of crises where instant and unavoidable long-term care decisions are made on an ad-hoc basis. However, awareness of the important thresholds reported here may inform older adults and their caregiving families in the years prior to a potential admission event. Earlier intervention in the long-term care decision-making process may also prompt the mobilization of community-based resources or clinical services to forestall a NH admission [47
The findings in this meta-analysis suggest that once certain functional or cognitive thresholds are reached, future risk of NH admission increases substantially (net a host of other factors). As suggested in prior research, intervention in the earlier stages of a chronic disease trajectory that offers respite and support to older adults or their caregiving families may help to potentially delay NH entry [47
]. However, experimental research (such as randomized controlled trials) would better inform whether intervention in earlier stages of chronic disease trajectories for older adults may help to delay clinical outcomes such as NH admission.
The results may offer useful prognostic information for clinicians, families, and older patients. For example, the significant predictors from the current meta-analyses could be converted into a practical screening tool of NH admission risk. A series of single dichotomous codes (1 = yes; 0 = no) could be applied for each indicator and then further weighted according to the effect size reported here. Summing these numbers and standardizing the sum would create a "risk score" on a 0–10 metric that offers guidance as to whether a geriatric patient is at risk for NH entry in the future. An almost identical algorithm has been utilized to predict NH admission in a smaller, clinic-based sample of persons with Alzheimer's disease where presence of a particular risk factor was calculated (i.e., the "value;" 1 = present; 0 = absent) and multiplied with the coefficient value of that risk factor as a weight (derived from Cox regression models) [16
]. The products of the values and weights were then summed, and this value was successfully applied as a prognostic tool to predict NH care and death among a second, validation sample of individuals with Alzheimer's disease. Relying on published data for this meta-analysis did not allow us to test the predictive accuracy of this proposed prognostic index because individual level data were not available to model. The next step in testing such a tool is to apply it to individual patient data [20
] across nationally representative samples (e.g., the publicly available data sources described in Table ) and determine its specificity and sensitivity to NH admission over various follow-up intervals, thus offering empirical evidence for its efficacy as a screening tool for risk of NH entry. It is important to note that similar tools used to predict clinical outcomes such as hospital admission have explained areas under receiver operant curves of .70 or below [49
]. This suggests that creating risk prognostic tools are helpful for population- or group-based planning strategies but less advantageous for individual clinical decision-making.
Approaching the prediction of future NH admission in terms of a threshold model may provide improved accounting of risk. Studies that have incorporated cut-points on functional or cognitive variables have generally accounted for greater variance in NH entry (Cox-Snell R2
of .40–.42) [21
] and the results of this meta-analysis confirm such an approach when identifying the presence or absence of risk factors for inclusion in predictive algorithms. However, a considerable amount of unexplained variance in the prediction of NH admission still remains in many studies. Predictions across several years are always risky because too many intervening events may occur. Nonetheless, adapting more complex conceptual models that differentiate between long-term predictors and immediate crisis events may illuminate the process leading to immediate or future NH entry, and may inform clinicians of the need to direct patients to early community-based service use (when distal predictors such as those presented here are identified) or develop immediate care plans for those at impending risk of NH admission.
Future descriptive and clinical research could approach NH placement as less of an endpoint and instead as an important transition where timing of NH admission, type and characteristics of the institution, and preplacement factors all operate to influence outcomes after admission. Relatively little research has explored these issues or has considered data at these variable levels of analysis when examining the impact of the NH transition on key health outcomes [4
]. By building research in this area, future clinical efforts could not only strive to delay placement, but also improve patient and family outcomes if such an event must occur.