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Falling is a risk factor for functional dependence in adults 75 years and older, but has not been systematically evaluated for younger and healthier older adults. This younger group of older adults may benefit from earlier identification of their risk. We hypothesizedthat falling would bea marker for future difficulty with activities of daily (ADL)that would vary by fall frequency and associated injury.
Nationally representative cohort of 2,020 community-living, functionally independent older adults 65-69 years of age at baseline followed between 1998-2008.
Experiencing one fall with injury in the prior 2 years (Odds = 1.78, 95% CI 1.29-2.48), at least 2 falls without injury in the prior 2 years (Odds = 2.36, 95% CI 1.80-3.09), or at least 2 falls with at least one injury in the prior 2 years (Odds = 3.75, 95% CI 2.55-5.53) were independently associated with higher rates of ADL difficulty after adjustment for socio-demographic, behavioral, and clinical covariates.
HRS data are self-reported
Falling is an important marker for future ADL difficulty in youngerfunctionally independent older adults. Individuals who fall frequently or report injury are at highest risk.
Disability in older adults is an important public health concern. Recent studies document increasing disability among older Americans following three decades of decline (1,2). A recent analysis of the National Health and Nutrition Examination Surveys (NHANES) concluded that the rate of new disability increased among individuals aged 60 to 69 years between 1998-2004. Minorities and overweight/obese individuals experienced higher rates of disability; the overall trend was independent of other measured socio-demographic factors, chronic conditions, and health behaviors (3). This finding suggests that younger groups of older adults may spend an increasing proportion of their lives with disability.
Falling is a common event in older adults, and a knownrisk factor for future disability in individuals 75 years and older (4-6).Falls can result in injury, higher rates of skilled nursing home placement, expensive medical costs, and loss of patient confidence leading to voluntary restriction of activity (7-9). The relationship between falls and disability has not been systematically explored in younger groups of healthier older adults. Many of these individuals are newly retired and eligible for Medicare, and may have the most to gain from earlier identification for future disability.
We assessed the relationship between falls and difficulty with activities of daily living (ADL) in a nationally representative cohort of functionally independent, community living older adults 65-69 years of age at baseline followed for 10 years. Wehypothesized that falling would be an important marker for future ADL difficulty that varies byfall frequency and associatedinjury.
We analyzed data from the Health and Retirement Study (HRS), a nationally representative, biennial longitudinal survey of adults aged 51 years and older in the United States, designed to evaluate the socioeconomic and health dynamics of older adults. The HRS is sponsored by the National Institute on Aging and administered by the Institute for Social Research at the University of Michigan. The study uses a national probability sample of U.S. households with systematic oversampling of black persons, Hispanic persons, and residents of Florida. Interviews are conducted both in person and by telephone, and last for at least one hour with approximately 40% of the interview devoted to health topics. Proxy respondents are permitted when the study participant is unable to participate due to physical or cognitive impairments. Records for survey respondents that die during the follow up period are matched to the National Death Index (NDI). In accordance with the policy of our institution for this publicly available data source, this project was not submitted to the Institutional Review Board of our institution.
For this longitudinal analysis, we selected an initial sample of 2,120community-living, functionally independent individuals (no difficulty withADL or instrumental activities of daily living, or IADL, as defined below) without a proxy respondent, aged 65-69 years of age at baseline from the 1998 HRS interview wave. Interview waves were defined as 2-year periods (1998, 2000, 2002, 2004, 2006, and 2008). The subgroup represented 77% of all 65-69 year olds and approximately 10% of the entire 1998 HRS cohort. The analysis sample consisted of 1,998 individuals (8,486 measurement occasions) who completed at least two consecutive interview waves without missing data on ADL status in either interview, and without missing fall status data during the second interview.Within this analysis sample, 5.7% of the observations were missing covariate values, exclusive of falls or ADL difficulty status, yielding 8,001 observations across 1,985 individuals.
Interviewers asked respondents the following questions about falls during each interview wave: 1) “Since the last interview period, did you fall?” 2) “Since the last interview period, did you experience a fall that required medical attention (fall with injury)?” 3) “Since the last interview period, how many falls did you experience?” The question pertaining to fall with injury is similar to questions used in other large national surveys(7). The severity of the fall related injury was not specified.
We created categories of fall frequency and self-reported injury similar toTinetti and Williams’ (6). The mutually-exclusive fall status categories for each interview wave were no falls in the prior 2 years; one fall without injury in the prior 2 years; one fall with injury in the prior 2 years; at least 2 falls without injury in the prior 2 years, and at least 2 falls with at least one injury in the prior 2 years.
We defined difficultyas a limitation in performance of any one of 6 activities of daily living (ADL). We specified a dichotomous outcome as “no difficulty” or “any difficulty” during each survey wave. The questions were framed as “because of a health problem, do you have any difficulty with [activity].” The six ADLs were bathing, eating, dressing, walking across a room, getting into or out of bed, and toileting. We included a continuous composite measure of ADL difficulty (0-6) at the previous interview to account for accrued or improved difficulty over the study follow up period.
We selected these covariates based on their potential association with either fall status or ADL difficulty and classified them according to a disability model specified by Verbrugge and Jette (10). This model includes: socio-demographic factors; active pathology (chronic conditions); behavioral risk factors; impairment (dysfunction in one or more organ systems), and functional limitations. In addition to the previous ADL difficulty measure noted above, we also included a continuous composite measure of instrumental activities of daily living (IADL) difficulty (0-5) at the previous interview to account for accrued IADL difficultyover the study follow up period. The five IADLs were using a telephone, taking medication, handling money, shopping, or preparing meals. The socio-demographic category included age, gender, race (white, black, Hispanic, other), education (coded as <12 years, 12 years, >12 years), marital status, and household wealth quartiles (coded as <$45k, $45,001-134,000, $134,001-319,000, >$319,00). The health behavioral risk factors included body mass index (BMI), current alcohol use (none, 1-2 drinks, >2 drinks per day) and smoking status at the 1998 cohort inception. The impairment domain included urinary incontinence, poor hearing, poor vision, self-reported memory impairment, self-reported health status, the CES-D8 depressive symptom score (zero, 1-3, ≥4 symptoms), and chronic pain (mild, moderate, or severe on most days). The chronic condition categories were a self-reported physician-diagnosed history of cancer, hypertension, heart disease, stroke, diabetes, lung disease, and arthritis. Functional impairment included measures of mobility (zero, 1-2, or ≥3 limitations in walking several blocks, walking one block, walking across the room, climbing one flight of stairs, and climbing several flights of stairs) and strength (zero, 1, 2, or ≥3 limitations in sitting for two hours, getting up from a chair, stooping/kneeling/crouching, and pushing/pulling a large object). For all covariate values with the exception of gender, race, and education, we used responses reported during the previous interview in the model.
We evaluated the relationship between fall status and ADL difficulty over successive 2-year intervals during the 10-year period. We used multivariate logistic regression with clustering at the individual level to account for repeated measures. We used the model coefficients toestimate the average predicted adjusted risk of ADL difficulty for each of the five fall status groups (Stata 12 command predict).
We tested the impact of missing databy using multiple imputation with switching regression (Stata 12 command, ice) for missing covariate values. The model remained robust to the imputation. We report the results for the non-imputed model. We adjusted for the complex survey design using HRS probability weights (Stata 12 command, svy).All statistical analyses were performed using Stata 12 (College Station, Texas).
The 1998 baseline characteristics for the analysis sample are shown in Table 1. The mean age of the respondents was 67 (SD +/-1.28) years; 56% were female and 79% were White. 75% of the sample had 1-3 chronic conditions, and only 5% had ≥4 conditions. The most common conditions were arthritis (52.8%), hypertension (45.4%), and heart disease (17.6%). Approximately 20% of individuals described themselves as having fair or poor health status at baseline. The mean body mass index was 27 (SD +/-4.6) kg/m2.
Figure 1 shows the progression of the 1998 analysis cohort through the 10-year follow up period. Over the 10-year period, 233 (11%) individuals in the cohort died, and 1,819 (85.6%) eligible individuals completed all follow up interviews. The prevalence of ADL difficulty during each 2-year follow up ranged from a minimum of 10% to a maximum of 21% with a trend that increased over time. The prevalence of falling (data not shown) during each 2-year follow-up period ranged from a minimum of 19% to a maximum of 35% also with a trend that increased over time.
We constructed logistic regression models according to the major domains described in Verbrugge and Jette’s established disability model (Table 2). In the base model, we included a measure of prior ADL and IADL difficulty reported during the previous interview to account for any change and fall status during the previous 2 years. After adjusting for a combination of socio-demographic, clinical, and functional covariates, one fall with injury OR = 1.78 (1.29-2.48), ≥2 falls without injury OR = 2.36 (1.80-3.09) and ≥2 falls with at least one injury OR = 3.75 (2.55-5.53) over the 2-year period were independently associated with ADL difficulty within 2 years. Experiencing one fall without injury in the prior 2 years was not.
We used the model coefficients to predict 2-year risk for ADL difficulty by fall status (Table 2). The results suggest an adjusted risk of 7.3% in individuals who reported no falls during the observation period. This risk progressively increased with more frequent and severe falls, peaking to more than 39% for frequent falls with injury. Frequent falls with or without injury were strong predictors for ADL difficulty within 2 years for this functionally independent group of young older adults.
We found that fall status in the prior 2 years was an important independent predictor of subsequent ADL difficulty in a nationally representative, community-living,functionally independent cohort of adults 65-69 years at baseline followed for 10 years.A key finding in this study was that individuals who fell multiple times without presenting to health care attention (the group with ≥2 falls without injury) in the prior 2 years experienced a 17% higher risk of newADL difficulty at 2 yearscompared to individuals with no falls.
Previous studies have shown the association between falls and ADL difficulty but did not include or were not sufficiently powered to test the association for individuals younger than 75 years of age (6). These younger individuals are newly eligible for Medicare services, are generally healthier, and may benefit from earlier targeted efforts to reduce future ADL difficulty. These efforts areimportant because ADL difficulty, even if transient, represents an important social cost for older adults, their families, and society.
There are several strengths to our observational analysis. First, our cohort was a nationally representative group of older adults who were close in age andfunctionally independent at baseline. Second, we assessed a comprehensive set of measures over time to improve measurement reliability. This includedchange in ADL or IADL difficultyover time and a detailed set of demographic and clinical characteristics (including chronic disease status, body mass index, other geriatric conditions, and functional status). The fall status categories were similar to Tinetti and Williams (6), allowing us to categorize levels of fall risk based on frequency and severity. Third, the HRS data captures individuals who may never present to medical attention for fall related issues and still be at high risk for subsequent ADL difficulty. This complements information from national surveillance databases (NEISS-AIP and HCUP) identifying nonfatal falls presenting to medical attention.
There are several limitations of this study. First, the observational data obtained from the HRS are self-reported and recall bias of falls is possible. A systematic review by Ganz and colleagues suggests more accurate recall in non-fallers and those falling with injury (11). Recall bias would be more likelyto lead to under-estimation of ADL difficulty risk for less severe falls. Second, although we were able to capture ADL responses at the end of each wave, the data are not granular enough to establish temporal precedence between falls and ADL difficulty. Third, we included limited medication data in our model. We included diabetes medication use as a proxy for diabetes severity, but did not include psychoactive medication use (an established risk factor) given uncertainty about the consistency of the data with regards to our research question. Fourth, although we included a comprehensive set of observed covariates, individuals who fall more frequently or severely are likely to have a distinct physiologic phenotype that predisposes them to an accelerated path of functional decline. We cannot infer causality between falls and subsequent ADL difficulty with this observational data. We also did not have access to physical performance data or clinical diagnostic information to include in the model.
Many interviewees reported falling frequently and not seeking specific medical attention (≥2 falls without injury) in nearly 1 out of 10 cases. These individuals are at high risk for subsequent ADL difficulty. Unfortunately, several studies say medical providers do an insufficient job in performing comprehensive post-fall assessments or teaching patients how to prevent future falls (12,13). An initialfalls screen is easily obtained and can be as simple as inquiring if an individual has fallen before(14). For individuals who report falling, assessment of hemodynamics, balance, visual-spatial ability, medications, cognition, home environment, and ADL-IADL performance ability are key next steps (15). Motivating insurers, such as Medicare, to support comprehensive fall prevention and treatment programsmay be an important policy intervention since these programs arealso likely to address multiple predisposing risk factors for ADL difficulty (16).
Future studies can evaluatefunctional ADL trajectories in younger groups of older adult fallers over time to get a better sense of how transient or persistent the associated difficulties may be.
Role of the Funding Source
An abstract of the manuscript has been presented at the Robert Wood Johnson Foundation Clinical Scholars National Meeting (November 2011). The HRS is sponsored by the National Institute on Aging (U01 AG009740), and administered by the Institute for Social Research at the University of Michigan.
We would like to thank Anna Xu (University of Michigan) for assistance with data management and Nick Moloci, BS (University of Michigan) for editing assistance.
Sponsor’s Role: The funding sources were not involved in the design, conduct, or reporting of this study.
Conflict of Interest