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Original Contribution |

Change in Disability After Hospitalization or Restricted Activity in Older Persons FREE

Thomas M. Gill, MD; Heather G. Allore, PhD; Evelyne A. Gahbauer, MD, MPH; Terrence E. Murphy, PhD
[+] Author Affiliations

Author Affiliations: Yale University School of Medicine, Department of Internal Medicine, New Haven, Connecticut.


JAMA. 2010;304(17):1919-1928. doi:10.1001/jama.2010.1568.
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Context Disability among older persons is a complex and highly dynamic process, with high rates of recovery and frequent transitions between states of disability. The role of intervening illnesses and injuries (ie, events) on these transitions is uncertain.

Objectives To evaluate the relationship between intervening events and transitions among states of no disability, mild disability, severe disability, and death and to determine the association of physical frailty with these transitions.

Design, Setting, and Participants Prospective cohort study conducted in greater New Haven, Connecticut, from March 1998 to December 2008 of 754 community-living persons aged 70 years or older who were nondisabled at baseline in 4 essential activities of daily living: bathing, dressing, walking, and transferring. Telephone interviews were completed monthly for more than 10 years to assess disability and ascertain exposure to intervening events, which included illnesses and injuries leading to either hospitalization or restricted activity. Physical frailty (defined as gait speed >10 seconds on the rapid gait test) was assessed every 18 months through 108 months.

Main Outcome Measure Transitions between no disability, mild disability, and severe disability and 3 transitions from each of these states to death, evaluated each month.

Results Hospitalization was strongly associated with 8 of the 9 possible transitions, with increased multivariable hazard ratios (HRs) as high as 168 (95% confidence interval [CI], 118-239) for the transition from no disability to severe disability and decreased HRs as low as 0.41 (95% CI, 0.30-0.54) for the transition from mild disability to no disability. Restricted activity also increased the likelihood of transitioning from no disability to both mild and severe disability (HR, 2.59; 95% CI, 2.23-3.02; and HR, 8.03; 95% CI, 5.28-12.21), respectively, and from mild disability to severe disability (HR, 1.45; 95% CI, 1.14-1.84), but was not associated with recovery from mild or severe disability. For all 9 transitions, the presence of physical frailty accentuated the associations of the intervening events. For example, the absolute risk of transitioning from no disability to mild disability within 1 month after hospitalization for frail individuals was 34.9% (95% CI, 34.5%-35.3%) vs 4.9% (95% CI, 4.7%-5.1%) for nonfrail individuals. Among the possible reasons for hospitalization, fall-related injury conferred the highest likelihood of developing new or worsening disability.

Conclusions Among older persons, particularly those who were physically frail, intervening illnesses and injuries greatly increased the likelihood of developing new or worsening disability. Only the most potent events, ie, those leading to hospitalization, reduced the likelihood of recovery from disability.

Figures in this Article

Among older persons, disability in essential activities of daily living, such as bathing, dressing, walking, and transferring, is common and associated with increased mortality, institutionalization, and greater use of formal and informal home services.1 Active life expectancy, a metric that is often used by policy makers to forecast the functional health of older persons, is based on disability.2,3

According to several conceptual models,4,5 disability is thought to arise when a vulnerable host is exposed to a new or worsening insult or intervening event. Supporting this theoretical framework, we have shown that intervening events, including illnesses and injuries leading to either hospitalization or restricted activity, are strongly associated with the development of disability in activities of daily living.6 These results, however, were based solely on the initial onset of disability. We have subsequently demonstrated that disability is a complex and highly dynamic process, with high rates of recovery and frequent transitions between states of disability.7,8 The role of intervening events on these transitions is uncertain.

The objectives of the current study were to evaluate the association of intervening events with transitions between states of no disability, mild disability, severe disability, and death and to determine the association of physical frailty, the vulnerability factor with the strongest epidemiologic link to disability,9,10 with these transitions. To accomplish our objectives, we used data from a longitudinal study that includes monthly assessments of intervening events and activities of daily living for more than 10 years in a cohort of community-living older persons.

Study Population

Participants were members of the Precipitating Events Project, an ongoing longitudinal study of 754 community-living persons aged 70 years or older who were initially nondisabled (ie, required no personal assistance) in 4 activities of daily living—bathing, dressing, walking inside the house, and transferring from a chair.11 Exclusion criteria included significant cognitive impairment with no available proxy,12 inability to speak English, diagnosis of a terminal illness with a life expectancy less than 12 months, and a plan to move out of the New Haven area during the next 12 months.

The assembly of the cohort, which took place between March 1998 and October 1999, is summarized in the eFigure and has been described in detail elsewhere.7,11 In brief, potential participants were identified from a computerized list of 3157 age-eligible members of a large health plan in greater New Haven, Connecticut. Eligibility was determined during a screening telephone interview and was confirmed during an in-home assessment. Persons who were physically frail, as denoted by a timed score of greater than 10 seconds on the rapid gait test (ie, walk back and forth over a 10-ft [3-m] course as quickly as possible), were oversampled to ensure a sufficient number of participants at increased risk for disability.9 In the absence of a gold standard, operationalizing physical frailty as slow gait speed is justified by its high face validity,13 clinical feasibility,14 and strong epidemiologic link to functional decline and disability.9,10,15 Only 4.6% of the 2753 health plan members who were alive and could be contacted refused to complete the screening telephone interview, and 75.2% of the eligible members agreed to participate in the project. Persons who refused to participate did not differ significantly from those who were enrolled in terms of age or sex. The study protocol was approved by the Yale human investigation committee, and all participants provided verbal informed consent.

Data Collection

Comprehensive home-based assessments were completed at baseline and subsequently at 18-month intervals for 108 months, while telephone interviews were completed monthly through December 2008 (median, 111 months). For participants with significant cognitive impairment, the monthly telephone interviews were completed with a designated proxy. The accuracy of these proxy reports was high, with κ = 1.0 for disability, 1.0 for hospitalization, and 0.63 for restricted activity.6,12 Deaths were ascertained by review of the local obituaries or from an informant during a subsequent telephone interview. Four hundred five participants (53.7%) died after a median follow-up of 68 months, while 35 (4.6%) dropped out of the study after a median follow-up of 24 months. Data were otherwise available for 99.2% of the 66 425 monthly telephone interviews, with no difference between the decedents and nondecedents.

Physical Frailty and Covariates

During each of the comprehensive assessments, data were collected on physical frailty as previously described, and several covariates, including demographic characteristics; cognitive status as assessed by the Mini-Mental State Examination (MMSE)16; depressive symptoms as assessed by the Center for Epidemiologic Studies Depression (CES-D) Scale17; and 9 self-reported, physician-diagnosed chronic conditions: hypertension, myocardial infarction, congestive heart failure, stroke, diabetes mellitus, arthritis, hip fracture, chronic lung disease, and cancer. Data on these covariates were 100% complete at baseline and greater than 95% complete during the subsequent comprehensive assessments. Participants were considered to be cognitively impaired if they scored less than 24 on the MMSE16 and to have significant depressive symptoms if they scored 20 or higher on the CES-D.18 Participants were asked by a trained nurse researcher to identify their race/ethnicity. These data were collected primarily for descriptive purposes and to fulfill federal regulations regarding the inclusion of minority participants in studies funded by the US National Institutes of Health.

Intervening Events

The intervening events included illnesses and injuries leading to either hospitalization or restricted activity. During the monthly telephone interviews, participants were asked whether they had stayed at least overnight in a hospital since the last interview, ie, during the past month. The accuracy of these reports, based on an independent review of hospital records among a subgroup of 94 participants, was high, with κ = 0.94.6 Participants who were hospitalized were also asked to provide the primary reason for their admission. These reasons were subsequently grouped into distinct diagnostic categories using a revised version of the protocol described by Ferrucci et al.19 Agreement relative to an independent review of hospital records among a subgroup of 172 admissions was 82%.6

To ascertain less potent intervening events, participants were asked 2 questions related to restricted activity using a standardized protocol with high reliability, ie, κ = 0.9011: (1) “Since we last talked on (date of last interview), have you stayed in bed for at least half a day due to an illness, injury, or other problem?” and (2) “Since we last talked on (date of last interview), have you cut down on your usual activities due to an illness, injury, or other problem?” Participants who answered yes to one or both of these questions were considered to have restricted activity during a specific month.11 These participants were subsequently asked to identify the reasons for their restricted activity using a standardized protocol that included 24 prespecified problems and an open-ended response. We have previously demonstrated that older persons usually attribute their restricted activity to several concurrent health-related problems.11

For participants who had died, a final interview was completed with a proxy informant who was asked about any hospitalizations since the prior interview. Given the nature of the questions, asking about restricted activity in the last month of life was not feasible. For each month during the follow-up period, exposure to the intervening events was categorized as hospitalization; restricted activity without hospitalization, referred to hereafter as simply restricted activity; or neither hospitalization or restricted activity.

Disability

During the monthly telephone interviews, participants were assessed for disability using standard questions that were identical to those used during the screening telephone interview.12 For each of the 4 essential activities of daily living, we asked, “At the present time, do you need help from another person to (complete the task)?” Disability was operationalized as the need for personal assistance, and the severity of disability was denoted by the number of disabled activities of daily living (from 0 to 4) in a specific month. Disability in 1 or 2 activities of daily living was considered mild, while disability in 3 or 4 activities of daily living was considered severe.7,8 The reliability of our disability protocol was substantial (κ = 0.75) for reassessments completed within 48 hours and excellent (κ = 1.0) for reassessments performed the same day.12 Complete details regarding these procedures, including formal tests of reliability and accuracy, are provided elsewhere.7,12 To address the small amount of missing data on disability (ie, less than 1% of observations), we used multiple imputation with 50 random draws per missing observation according to procedures described in an earlier report.20

Functional Transitions

Based on prior research,8 4 states were defined: no disability, mild disability, severe disability, and death. Transitions were possible between each of the nondecedent states and from each nondecedent state to death.

Statistical Analysis

For each of the 9 possible transitions, we calculated the cumulative incidence rate per 1000 person-months spent in the state immediately preceding the transition. We also calculated the median time (months) spent in a specific state before the next transition or end of follow-up for no disability, mild disability, and severe disability, respectively. To quantify exposure to the 2 types of intervening events—hospitalization and restricted activity—we calculated the mean rate for each as the number of person-months exposed divided by all person-months observed. Confidence intervals (CIs) for the transition rates and exposure rates were calculated by bootstrapping, using sampling with replacement. One thousand samples were created, and the 2.5th and 97.5th percentiles were used to form the CIs. Bootstrap-derived CIs provide nonparametric estimates of dispersion. For each of these analyses, the results were stratified according to physical frailty.

To evaluate the multivariable relationships between the independent variables and 9 functional transitions, we used a competing-risk Cox model for recurrent events.21 In this model, persons are simultaneously at risk for several “competing” outcomes. For example, participants with severe disability could transition to no disability, mild disability, and death. The model calculates the respective associations based on the amount of time participants spend in a specific state prior to transitioning to another state. The competing-risk Cox model accounts for the correlation among observations within individuals through the use of robust sandwich variance estimators for standard errors of the coefficients22; is fairly robust to the distribution of time to event; and can be used for nonproportional hazards, which may occur with time-dependent variables.23 The independent variables included physical frailty, which was time-varying, and the 2 types of intervening events. For the intervening events, the calculated hazard ratio (HR) refers to the risk of making a specific transition between month t and month t + 1 based on exposure to hospitalization or restricted activity, respectively, relative to participants who had no hospitalization or restricted activity, during this 1-month interval. We have previously shown that intervening events occurring prior to this 1-month interval are not independently associated with the development of disability.6 Similarly, the HR for physical frailty refers to the risk of making a specific transition between month t and month t + 1 among participants who were physically frail relative to those who were not physically frail.

The multivariable model, which evaluated associations between the independent variables and each of the functional transitions, included 3 fixed covariates—sex, race/ethnicity, and years of education—and 5 time-varying covariates—age 85 years or older, living alone, number of chronic conditions, cognitive impairment, and depressive symptoms. These covariates represent important sociodemographic factors and pertinent clinical factors that have been linked to disability in prior studies.6 The time-varying covariates and physical frailty were updated using data from the comprehensive assessment that immediately preceded entry into the state. P values were adjusted for multiple comparisons assuming a false discovery rate of 5%.24 Potential interactions between physical frailty and the intervening events were tested and statistically significant interactions were retained in the final model. Because restricted activity could not be ascertained among decedents, transitions to death were not evaluated for this exposure.

To enhance the interpretability of our findings, we calculated the absolute risk per month of each functional transition for hospitalization, restricted activity, and no intervening event, respectively, in the presence and absence of physical frailty.25 Values for the absolute risk represent the probability of developing a specific outcome per unit of time in the setting of the competing outcomes. The coefficients used to calculate the absolute risks were obtained from a set of pooled logistic models (one for each transition) that approximate a simple exponential model over the entire follow-up period. Each logistic model included the 3 independent variables, 8 covariates, and transition-specific interaction terms that were statistically significant in the competing-risk Cox model. The 8 covariates were set as male sex, age less than 85 years, white race, greater than high school education, living alone, 2 or fewer chronic conditions, no cognitive impairment, and no depressive symptoms. In a separate set of analyses, the absolute risks were further stratified by sex and age, with the remaining covariates set as described herein.

To be conservative, power calculations were based on the lowest frequency of observed transitions out of each state. Covariates were assumed to contribute an R2 of 0.20. Using Cox regression and assuming 20% exposure for either physical frailty or hospitalization, we had 80% power with a type I error of 5% to detect HRs of 2.05, 2.35, and 1.85 for transitions from no disability, mild disability, and severe disability, respectively. For restricted activity, we had 80% or greater power to detect an HR of 1.5 or higher for all transitions except from severe disability to no disability, which had 46% power. All statistical tests were 2-tailed with P < .05 denoting statistical significance, and all analyses were performed using SAS version 9.2 (SAS Institute, Cary, North Carolina).

The baseline characteristics of the study participants are shown in Table 1. Participants who were physically frail at baseline were older; were more likely to be female, live alone, be cognitively impaired, and have depressive symptoms; were less likely to be non-Hispanic white; and had less education and more chronic conditions than participants who were not physically frail. The median duration of follow-up was 81 months (interquartile range [IQR], 42-115 months) for participants who were physically frail at baseline and 117 months (IQR, 77.5-123) for those who were not physically frail.

Table Graphic Jump LocationTable 1. Baseline Characteristics of Study Participants According to Physical Frailty

Of the 754 participants, 117 (15.5%) remained nondisabled and alive through the end of follow-up and therefore made no transitions during a median follow-up of 121 months (IQR, 117-124). Of these, 27 (23.1%) were physically frail at baseline. Figure 1 provides the rates of transitions between states of disability and death among the 637 participants who had at least 1 transition. Overall, the rates were highest for transitions from mild disability to no disability and from severe disability to mild disability. As compared with participants who were not physically frail, those who were physically frail had substantially higher rates of developing new or worsening disability and lower rates of recovering from mild and severe disability. Although differences in rates among participants with vs without physical frailty were relatively small for those who died after having no disability or mild disability, the rate of death among those with severe disability was modestly lower among participants who were physically frail than those who were not physically frail. Participants who were physically frail spent a median of 3 months (IQR, 1-12), 2 months (IQR, 1-3), and 1 month (IQR, 1-3), respectively, in a state of no disability, mild disability, and severe disability before the next transition or end of follow-up. The corresponding values for participants who were not physically frail were 13 months (IQR, 3-49), 1 month (IQR, 1-1), and 1 month (IQR, 1-1), respectively.

Place holder to copy figure label and caption
Figure 1. Rates of Functional Transitions per 1000 Person-Months According to Physical Frailty
Graphic Jump Location

Point estimates are accompanied by 95% bootstrap confidence intervals (error bars). Physical frailty was updated every 18 months during the comprehensive assessments. Participants who did not make any transitions were included in the denominators for the calculation of rates. The y-axis ranges shown in blue indicate 0 to 80 transitions per 1000 person-months.

Among the 117 participants without a functional transition, 68 (58.1%) had at least 1 hospital admission and 107 (91.5%) had at least 1 month of restricted activity; they had a median of 2 hospital admissions (IQR, 1-3) and 7 months (IQR, 3-13) of restricted activity. Among the 637 participants who had at least 1 functional transition, 578 (90.7%) had at least 1 hospital admission and 601 (94.3%) had at least 1 month of restricted activity during a median follow-up of 102 months (IQR, 52-119); they had a median of 3 hospital admissions (IQR, 2-6) and 10 months (IQR, 5-18) of restricted activity. As shown in Figure 2, the exposure rates to both hospitalization and restricted activity were considerably greater among participants who were physically frail than those who were not physically frail.

Place holder to copy figure label and caption
Figure 2. Exposure Rates to Intervening Events per 1000 Months According to Physical Frailty
Graphic Jump Location

Point estimates are accompanied by 95% bootstrap confidence intervals (error bars). Physical frailty was updated every 18 months during the comprehensive assessments. As described in the “Methods” section, the 2 intervening events are mutually exclusive.

The multivariable associations between the 3 independent variables—physical frailty, hospitalization, and restricted activity—and each of the functional transitions are shown in Table 2. Participants who were frail were more likely to transition to new or worsening disability and less likely to recover from mild and severe disability, but frailty was associated with the transition to death only for those with no disability. Hospitalization was associated with disability for 8 of the 9 transitions (the ninth being severe disability to no disability), with HRs as high as 168 for the transition from no disability to severe disability and as low as 0.41 for the transition from mild disability to no disability. Restricted activity increased the likelihood of transitioning from no disability to both mild and severe disability, respectively, and from mild disability to severe disability, but was not associated with recovery from mild or severe disability.

Table Graphic Jump LocationTable 2. Associations of Physical Frailty, Hospitalization, and Restricted Activity With Functional Transitionsa

Table 3 provides the absolute risk per month of having a functional transition, based on exposure to intervening events and stratified by physical frailty. Regardless of intervening event, the absolute risk of transitions to new or worsening disability or death was consistently higher in participants with frailty, while transitions representing functional recovery were consistently more likely in those who were not frail. As shown in the eTable, similar results were observed for those 85 years and older vs those younger than 85 years, although the differences in absolute risk were generally smaller than the differences in absolute risk observed for frailty. The absolute risk of death was consistently higher in men than women, while the functional outcomes related to both hospitalization and restricted activity were worse in women than men, with few exceptions (eg, severe disability to mild disability). These differences were generally small. Regardless of sex or age, the absolute risks for new or worsening disability or death were greatest for those who were frail and hospitalized, with probabilities as high as 41.1% for the transition from no disability to mild disability among women aged 85 years or older. In contrast, for those without an intervening event or physical frailty, the absolute risk of transitioning to new or worsening disability or dying was very low, while the probability of functional recovery ranged widely—from 19.0% for the transition from severe disability to no disability among women older than 85 years to 44.1% for the transition from mild disability to no disability among men younger than 85 years.

Table Graphic Jump LocationTable 3. Absolute Risk of Functional Transitions Per Month, Based on Intervening Events and Stratified by Physical Frailtya

The primary reasons that individuals were hospitalized are provided in Table 4. Although cardiac causes (coronary heart disease, congestive heart failure, arrhythmia, etc) and infection were the most common diagnostic categories (as seen in the total columns), fall-related injury conferred the highest likelihood of developing new or worsening disability. For example, of the 97 hospital admissions for a fall-related injury among nondisabled participants, 32 (33.0%) and 43 (44.3%) resulted in transitions to mild and severe disability, respectively. Table 5 provides comparable information for episodes of restricted activity. For each set of transitions, fatigue was the most common reason for restricted activity. Of all the reasons, however, a fall or injury conferred the highest likelihood of transitioning from no disability to mild and severe disability, respectively, and the second highest likelihood (after problem with memory or difficulty thinking) of transitioning from mild disability to severe disability. For example, of the 665 episodes of restricted activity attributed to a fall or injury among nondisabled participants, 100 (15.0%) and 25 (3.8%) resulted in transitions to mild and severe disability, respectively.

Table Graphic Jump LocationTable 4. Reasons for Hospitalization According to Functional Transition
Table Graphic Jump LocationTable 5. Reasons for Restricted Activity According to Functional Transition

In this prospective cohort study of community-living older persons, we found that illnesses and injuries leading to hospitalization were associated with worsening functional ability for nearly all transitions between states of no disability, mild disability, severe disability, and death over the course of more than 10 years. Furthermore, illnesses and injuries leading to restricted activity but not hospitalization increased the likelihood of transitioning from no disability to both mild and severe disability and from mild disability to severe disability, but were not associated with recovery from mild or severe disability. Finally, these associations of hospitalization and restricted activity with functional transitions were accentuated by the presence of physical frailty. These results provide strong evidence that intervening events have an important role in precipitating and, subsequently, perpetuating the disabling process.

Unlike conditions that invariably progress, such as Alzheimer disease, disability is a recurrent disorder, characterized by high rates of recovery.7 The dynamic nature of disability has only recently been elucidated,8 and relatively little is known about the factors that are associated with clinically relevant transitions in functional status. We have previously shown that illnesses and injuries leading to either hospitalization or restricted activity are strongly associated with the initial onset of disability.6 The current study extends this earlier work by demonstrating that exposure to these intervening illnesses and injuries is also associated with the subsequent course of disability.

Hospitalization was associated with a particularly pronounced risk, with HRs for developing new and worsening disability much greater than those for physical frailty, which is the single strongest risk factor for disability and functional decline.9,10 For the transition from no disability to severe disability, the high HR associated with hospitalization is likely attributable not only to the potent disabling effects of serious illness, and hospitalization itself,26 but also to the low incidence of severe disability in the comparison group of nondisabled participants without an acute hospital admission or restricted activity. Our results support the hypothesis that illnesses and injuries leading to hospitalization act not only to precipitate and worsen disability, but also to hasten death and to impede recovery from disability, thereby prolonging the disabling process. Illnesses and injuries leading to hospitalization were not associated with the transition from severe disability to no disability; this was likely due to the short duration of severe disability among participants who regained independence (median, 1 month; IQR, 1-1) and, for participants with severe disability, the strong association of hospitalization with the 2 competing outcomes of mild disability and death.

Illnesses and injuries leading to restricted activity were also associated with developing new and worsening disability. These associations were not as strong as those for acute hospital admissions, although they are important because restricted activity was much more common than hospitalization. We were unable to calculate population attributable fractions, but in our earlier report,6 which focused only on the initial onset of disability, the population attributable fractions were considerably greater for hospitalization than restricted activity for 3 distinct disability outcomes. In the current study, we found that episodes of restricted activity were not associated with transitions from mild disability to no disability or from severe disability to mild disability, suggesting that intervening events less potent than those leading to hospitalization do not impede recovery from disability.

Physical frailty was independently associated with each of the 6 transitions between no disability, mild disability, and severe disability and accentuated the associations of the intervening events in absolute terms for all 9 of the possible transitions. For both hospitalization and restricted activity, the influence of physical frailty was most striking for transitions from no disability to mild disability, with increases in absolute risk within 1 month of hospitalization as high as 30%. Because physical frailty was assessed every 18 months but could have changed over shorter periods of time, it is possible that its effects were underestimated. Nonetheless, in the setting of an intervening event, the change in absolute risks associated with physical frailty on transitions to new or worsening disability was considerably greater than that of sex and age.

We determined absolute risks for subgroups defined on the basis of physical frailty, sex, and age, which greatly enhances the clinical relevance of our findings. For example, in the setting of an acute illness or injury leading to hospitalization, the absolute risk of transitioning from no disability to severe disability within 1 month ranged from only 3.3% in men younger than 85 years without physical frailty to 24.4% in physically frail women aged 85 years or older. Although the multivariate nature (ie, 9 different outcomes) of our analytic strategy did not permit us to evaluate the risks associated with specific reasons for hospitalization or restricted activity, we found that falls and fall-related injuries almost invariably conferred the highest likelihood for developing new or worsening disability.

The results of the current study, coupled with those of our earlier report,6 provide strong evidence that disability among older persons is driven largely by illnesses and injuries leading to hospitalization or restricted activity. Both types of intervening events greatly increased the likelihood of developing new or worsening disability, while only the most potent events, ie, those leading to hospitalization, reduced the likelihood of recovery from disability. Given the central role of intervening illnesses and injuries on the disabling process, more aggressive efforts are warranted to prevent their occurrence2730; to manage them more effectively and reduce subsequent complications, especially in the hospital setting3134; and, after an event, to enhance restorative interventions in the subacute, home care, and outpatient settings.3537

Although causality cannot be established by an observational study, the frequency of our assessments increases the likelihood that the intervening events at least preceded the functional transitions. However, our data do not allow us to determine how often the intervening events resulted immediately in new or worsening disability, as may occur with a sudden acute process such as a stroke or hip fracture. The validity of our results is strengthened by the nearly complete ascertainment of intervening events and disability, by the high reliability and accuracy of these assessments, by the low rate of attrition, and by adjustment for several relevant covariates at 18-month intervals with few missing data.

Our study has at least 2 additional limitations. First, information was not available on the duration of the intervening events. It is possible that the likelihood of recovery may be reduced by long episodes of restricted activity but not by short episodes. Second, because our study participants were members of a single health plan in a small urban area and were oversampled for slow gait speed, our results may not be generalizable to older persons in other settings. However, the demographic characteristics of our cohort did reflect those of older persons in New Haven County, Connecticut, which are similar to the characteristics of the US population as a whole, with the exception of race or ethnic group.38 The generalizability of our results is enhanced by our high participation rate, which was greater than 75%.

Despite the reductions observed in the prevalence of disability over the past 2 decades,39 the absolute number of disabled older Americans could increase substantially in the coming years with the aging of the baby boom generation.40 To obviate this increase, more aggressive efforts will be needed to prevent and manage intervening illnesses and injuries, given their apparent role in precipitating and perpetuating the disabling process.

Corresponding Author: Thomas M. Gill, MD, Yale University School of Medicine, Adler Geriatric Center, 20 York St, New Haven, CT 06504 (thomas.gill@yale.edu).

Author Contributions: Dr Gill had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Gill, Allore.

Acquisition of data: Gill, Gahbauer.

Analysis and interpretation of data: Gill, Allore, Murphy.

Drafting of the manuscript: Gill, Allore, Gahbauer, Murphy.

Critical revision of the manuscript for important intellectual content: Gill, Allore, Murphy.

Statistical analysis: Allore, Gahbauer, Murphy.

Obtained funding: Gill.

Administrative, technical, or material support: Gill.

Study supervision: Gill.

Financial Disclosures: None reported.

Funding/Support: The work for this report was funded by grants from the National Institute on Aging (R37AG17560, R01AG022993). The study was conducted at the Yale Claude D. Pepper Older Americans Independence Center (P30AG21342). Dr Gill is the recipient of a Midcareer Investigator Award in Patient-Oriented Research (K24AG021507) from the National Institute on Aging.

Role of the Sponsor: The funding agencies had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.

Additional Contributions: Denise Shepard, BSN, MBA; Andrea Benjamin, BSN; Paula Clark, RN; Martha Oravetz, RN; Shirley Hannan, RN; Barbara Foster; Alice Van Wie, BSW; Patricia Fugal, BS; Amy Shelton, MPH; and Alice Kossack, Yale University School of Medicine, assisted with data collection. Wanda Carr and Geraldine Hawthorne, BS, Yale University School of Medicine, assisted with data entry and management. Peter Charpentier, MPH, Yale University School of Medicine, designed and developed the study database and participant tracking system. Linda Leo-Summers, MPH, Yale University School of Medicine, assisted with figures. Joanne McGloin, MDiv, MBA, Yale University School of Medicine, provided leadership and advice as the project director. None of these members of the research staff received compensation for the contributions beyond salary support. Haiqun Lin, MD, PhD, Yale University School of Medicine, advised on the statistical methods, for which she received no compensation.

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PubMed   |  Link to Article
Gill TM, McGloin JM, Gahbauer EA, Shepard DM, Bianco LM. Two recruitment strategies for a clinical trial of physically frail community-living older persons.  J Am Geriatr Soc. 2001;49(8):1039-1045
PubMed   |  Link to Article
Abellan van Kan G, Rolland Y, Andrieu S,  et al.  Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force.  J Nutr Health Aging. 2009;13(10):881-889
PubMed   |  Link to Article
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician.  J Psychiatr Res. 1975;12(3):189-198
PubMed   |  Link to Article
Kohout FJ, Berkman LF, Evans DA, Cornoni-Huntley J. Two shorter forms of the CES-D (Center for Epidemiological Studies Depression) depression symptoms index.  J Aging Health. 1993;5(2):179-193
PubMed   |  Link to Article
Barry LC, Allore HG, Guo Z, Bruce ML, Gill TM. Higher burden of depression among older women: the effect of onset, persistence, and mortality over time.  Arch Gen Psychiatry. 2008;65(2):172-178
PubMed   |  Link to Article
Ferrucci L, Guralnik JM, Pahor M, Corti MC, Havlik RJ. Hospital diagnoses, Medicare charges, and nursing home admissions in the year when older persons become severely disabled.  JAMA. 1997;277(9):728-734
PubMed   |  Link to Article
Gill TM, Guo Z, Allore HG. Subtypes of disability in older persons over the course of nearly 8 years.  J Am Geriatr Soc. 2008;56(3):436-443
PubMed   |  Link to Article
Lin H, Guo Z, Peduzzi PN, Gill TM, Allore HG. A semiparametric transition model with latent traits for longitudinal multistate data.  Biometrics. 2008;64(4):1032-1042
PubMed   |  Link to Article
Lin DY, Wei LJ. The robust inference for the proportional hazards model.  J Am Stat Soc. 1989;84:1074-1078
Link to Article
Allison PD, edSurvival Analysis Using the SAS System: A Practical Guide. Cary, NC: SAS Institute Inc; 1995
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.  J R Stat Soc Ser B. 1995;57:289-300
Benichou J, Gail MH. Estimates of absolute cause-specific risk in cohort studies.  Biometrics. 1990;46(3):813-826
PubMed   |  Link to Article
Creditor MC. Hazards of hospitalization of the elderly.  Ann Intern Med. 1993;118(3):219-223
PubMed   |  Link to Article
Tinetti ME, Baker DI, King M,  et al.  Effect of dissemination of evidence in reducing injuries from falls.  N Engl J Med. 2008;359(3):252-261
PubMed   |  Link to Article
Straus SE, Majumdar SR, McAlister FA. New evidence for stroke prevention: clinical applications.  JAMA. 2002;288(11):1396-1398
PubMed   |  Link to Article
Beswick AD, Rees K, Dieppe P,  et al.  Complex interventions to improve physical function and maintain independent living in elderly people: a systematic review and meta-analysis.  Lancet. 2008;371(9614):725-735
PubMed   |  Link to Article
Advisory Committee on Immunization Practices.  Recommended adult immunization schedule: United States, 2010.  Ann Intern Med. 2010;152(1):36-39
PubMed   |  Link to Article
Rich MW. Heart failure in the 21st century: a cardiogeriatric syndrome.  J Gerontol A Biol Sci Med Sci. 2001;56(2):M88-M96
PubMed   |  Link to Article
Landefeld CS, Palmer RM, Kresevic DM, Fortinsky RH, Kowal J. A randomized trial of care in a hospital medical unit especially designed to improve the functional outcomes of acutely ill older patients.  N Engl J Med. 1995;332(20):1338-1344
PubMed   |  Link to Article
Cohen HJ, Feussner JR, Weinberger M,  et al.  A controlled trial of inpatient and outpatient geriatric evaluation and management.  N Engl J Med. 2002;346(12):905-912
PubMed   |  Link to Article
Inouye SK. Delirium in older persons.  N Engl J Med. 2006;354(11):1157-1165
PubMed   |  Link to Article
Hoenig H, Nusbaum N, Brummel-Smith K. Geriatric rehabilitation: state of the art.  J Am Geriatr Soc. 1997;45(11):1371-1381
PubMed
Tinetti ME, Baker D, Gallo WT, Nanda A, Charpentier P, O’Leary J. Evaluation of restorative care vs usual care for older adults receiving an acute episode of home care.  JAMA. 2002;287(16):2098-2105
PubMed   |  Link to Article
Binder EF, Brown M, Sinacore DR, Steger-May  K, Yarasheski KE, Schechtman KB. Effects of extended outpatient rehabilitation after hip fracture: a randomized controlled trial.  JAMA. 2004;292(7):837-846
PubMed   |  Link to Article
 American FactFinder. US Census Bureau. http://factfinder.census.gov. Accessed May 29, 2003
Manton KG, Gu X, Lamb VL. Change in chronic disability from 1982 to 2004/2005 as measured by long-term changes in function and health in the US elderly population.  Proc Natl Acad Sci U S A. 2006;103(48):18374-18379
PubMed   |  Link to Article
Olshansky SJ, Goldman DP, Zheng Y, Rowe JW. Aging in America in the twenty-first century: demographic forecasts from the MacArthur Foundation Research Network on an Aging Society.  Milbank Q. 2009;87(4):842-862
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1. Rates of Functional Transitions per 1000 Person-Months According to Physical Frailty
Graphic Jump Location

Point estimates are accompanied by 95% bootstrap confidence intervals (error bars). Physical frailty was updated every 18 months during the comprehensive assessments. Participants who did not make any transitions were included in the denominators for the calculation of rates. The y-axis ranges shown in blue indicate 0 to 80 transitions per 1000 person-months.

Place holder to copy figure label and caption
Figure 2. Exposure Rates to Intervening Events per 1000 Months According to Physical Frailty
Graphic Jump Location

Point estimates are accompanied by 95% bootstrap confidence intervals (error bars). Physical frailty was updated every 18 months during the comprehensive assessments. As described in the “Methods” section, the 2 intervening events are mutually exclusive.

Tables

Table Graphic Jump LocationTable 1. Baseline Characteristics of Study Participants According to Physical Frailty
Table Graphic Jump LocationTable 2. Associations of Physical Frailty, Hospitalization, and Restricted Activity With Functional Transitionsa
Table Graphic Jump LocationTable 3. Absolute Risk of Functional Transitions Per Month, Based on Intervening Events and Stratified by Physical Frailtya
Table Graphic Jump LocationTable 4. Reasons for Hospitalization According to Functional Transition
Table Graphic Jump LocationTable 5. Reasons for Restricted Activity According to Functional Transition

References

Fried LP, Guralnik JM. Disability in older adults: evidence regarding significance, etiology, and risk.  J Am Geriatr Soc. 1997;45(1):92-100
PubMed
Katz S, Branch LG, Branson MH, Papsidero JA, Beck JC, Greer DS. Active life expectancy.  N Engl J Med. 1983;309(20):1218-1224
PubMed   |  Link to Article
Manton KG, Stallard E, Liu K. Forecasts of active life expectancy: policy and fiscal implications.  J Gerontol. 1993;48(Spec No):11-26
PubMed   |  Link to Article
Campbell AJ, Buchner DM. Unstable disability and the fluctuations of frailty.  Age Ageing. 1997;26(4):315-318
PubMed   |  Link to Article
Rockwood K, Fox RA, Stolee P, Robertson D, Beattie BL. Frailty in elderly people: an evolving concept.  CMAJ. 1994;150(4):489-495
PubMed
Gill TM, Allore HG, Holford TR, Guo Z. Hospitalization, restricted activity, and the development of disability among older persons.  JAMA. 2004;292(17):2115-2124
PubMed   |  Link to Article
Hardy SE, Gill TM. Recovery from disability among community-dwelling older persons.  JAMA. 2004;291(13):1596-1602
PubMed   |  Link to Article
Hardy SE, Dubin JA, Holford TR, Gill TM. Transitions between states of disability and independence among older persons.  Am J Epidemiol. 2005;161(6):575-584
PubMed   |  Link to Article
Gill TM, Williams CS, Tinetti ME. Assessing risk for the onset of functional dependence among older adults: the role of physical performance.  J Am Geriatr Soc. 1995;43(6):603-609
PubMed
Guralnik JM, Ferrucci L, Pieper CF,  et al.  Lower extremity function and subsequent disability: consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery.  J Gerontol A Biol Sci Med Sci. 2000;55(4):M221-M231
PubMed   |  Link to Article
Gill TM, Desai MM, Gahbauer EA, Holford TR, Williams CS. Restricted activity among community-living older persons: incidence, precipitants, and health care utilization.  Ann Intern Med. 2001;135(5):313-321
PubMed   |  Link to Article
Gill TM, Hardy SE, Williams CS. Underestimation of disability in community-living older persons.  J Am Geriatr Soc. 2002;50(9):1492-1497
PubMed   |  Link to Article
Goodwin JS. Ambling towards Nirvana.  Lancet. 2002;359(9314):1358
PubMed   |  Link to Article
Gill TM, McGloin JM, Gahbauer EA, Shepard DM, Bianco LM. Two recruitment strategies for a clinical trial of physically frail community-living older persons.  J Am Geriatr Soc. 2001;49(8):1039-1045
PubMed   |  Link to Article
Abellan van Kan G, Rolland Y, Andrieu S,  et al.  Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force.  J Nutr Health Aging. 2009;13(10):881-889
PubMed   |  Link to Article
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician.  J Psychiatr Res. 1975;12(3):189-198
PubMed   |  Link to Article
Kohout FJ, Berkman LF, Evans DA, Cornoni-Huntley J. Two shorter forms of the CES-D (Center for Epidemiological Studies Depression) depression symptoms index.  J Aging Health. 1993;5(2):179-193
PubMed   |  Link to Article
Barry LC, Allore HG, Guo Z, Bruce ML, Gill TM. Higher burden of depression among older women: the effect of onset, persistence, and mortality over time.  Arch Gen Psychiatry. 2008;65(2):172-178
PubMed   |  Link to Article
Ferrucci L, Guralnik JM, Pahor M, Corti MC, Havlik RJ. Hospital diagnoses, Medicare charges, and nursing home admissions in the year when older persons become severely disabled.  JAMA. 1997;277(9):728-734
PubMed   |  Link to Article
Gill TM, Guo Z, Allore HG. Subtypes of disability in older persons over the course of nearly 8 years.  J Am Geriatr Soc. 2008;56(3):436-443
PubMed   |  Link to Article
Lin H, Guo Z, Peduzzi PN, Gill TM, Allore HG. A semiparametric transition model with latent traits for longitudinal multistate data.  Biometrics. 2008;64(4):1032-1042
PubMed   |  Link to Article
Lin DY, Wei LJ. The robust inference for the proportional hazards model.  J Am Stat Soc. 1989;84:1074-1078
Link to Article
Allison PD, edSurvival Analysis Using the SAS System: A Practical Guide. Cary, NC: SAS Institute Inc; 1995
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.  J R Stat Soc Ser B. 1995;57:289-300
Benichou J, Gail MH. Estimates of absolute cause-specific risk in cohort studies.  Biometrics. 1990;46(3):813-826
PubMed   |  Link to Article
Creditor MC. Hazards of hospitalization of the elderly.  Ann Intern Med. 1993;118(3):219-223
PubMed   |  Link to Article
Tinetti ME, Baker DI, King M,  et al.  Effect of dissemination of evidence in reducing injuries from falls.  N Engl J Med. 2008;359(3):252-261
PubMed   |  Link to Article
Straus SE, Majumdar SR, McAlister FA. New evidence for stroke prevention: clinical applications.  JAMA. 2002;288(11):1396-1398
PubMed   |  Link to Article
Beswick AD, Rees K, Dieppe P,  et al.  Complex interventions to improve physical function and maintain independent living in elderly people: a systematic review and meta-analysis.  Lancet. 2008;371(9614):725-735
PubMed   |  Link to Article
Advisory Committee on Immunization Practices.  Recommended adult immunization schedule: United States, 2010.  Ann Intern Med. 2010;152(1):36-39
PubMed   |  Link to Article
Rich MW. Heart failure in the 21st century: a cardiogeriatric syndrome.  J Gerontol A Biol Sci Med Sci. 2001;56(2):M88-M96
PubMed   |  Link to Article
Landefeld CS, Palmer RM, Kresevic DM, Fortinsky RH, Kowal J. A randomized trial of care in a hospital medical unit especially designed to improve the functional outcomes of acutely ill older patients.  N Engl J Med. 1995;332(20):1338-1344
PubMed   |  Link to Article
Cohen HJ, Feussner JR, Weinberger M,  et al.  A controlled trial of inpatient and outpatient geriatric evaluation and management.  N Engl J Med. 2002;346(12):905-912
PubMed   |  Link to Article
Inouye SK. Delirium in older persons.  N Engl J Med. 2006;354(11):1157-1165
PubMed   |  Link to Article
Hoenig H, Nusbaum N, Brummel-Smith K. Geriatric rehabilitation: state of the art.  J Am Geriatr Soc. 1997;45(11):1371-1381
PubMed
Tinetti ME, Baker D, Gallo WT, Nanda A, Charpentier P, O’Leary J. Evaluation of restorative care vs usual care for older adults receiving an acute episode of home care.  JAMA. 2002;287(16):2098-2105
PubMed   |  Link to Article
Binder EF, Brown M, Sinacore DR, Steger-May  K, Yarasheski KE, Schechtman KB. Effects of extended outpatient rehabilitation after hip fracture: a randomized controlled trial.  JAMA. 2004;292(7):837-846
PubMed   |  Link to Article
 American FactFinder. US Census Bureau. http://factfinder.census.gov. Accessed May 29, 2003
Manton KG, Gu X, Lamb VL. Change in chronic disability from 1982 to 2004/2005 as measured by long-term changes in function and health in the US elderly population.  Proc Natl Acad Sci U S A. 2006;103(48):18374-18379
PubMed   |  Link to Article
Olshansky SJ, Goldman DP, Zheng Y, Rowe JW. Aging in America in the twenty-first century: demographic forecasts from the MacArthur Foundation Research Network on an Aging Society.  Milbank Q. 2009;87(4):842-862
PubMed   |  Link to Article
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