Research in Gerontological Nursing

Empirical Research 

Falls and Social Isolation of Older Adults in the National Health and Aging Trends Study

Janet S. Pohl, PhD, RN; Barbara B. Cochrane, PhD, RN, FAAN; Karen G. Schepp, PhD, RN, FAAN; Nancy F. Woods, PhD, RN, FAAN

Abstract

A longitudinal secondary analysis of 2 years of data from the National Health and Aging Trends Study was undertaken to determine the extent to which social isolation predicts falls in older adults. Social isolation during Year 1 (baseline) was operationalized as a multiple-indicator measure based on Social Network Index participation domains. Falling during the previous year was self-reported using Year 2 data. Logistic regression models revealed social isolation significantly predicted falls (odds ratio [OR] = 1.11; 95% confidence interval [CI] [1.05, 1.17]). The relationship remained significant after adjusting for age, gender, and education (OR = 1.08; 95% CI [1.02, 1.14]). The relationship weakened after adjusting for self-reported general health, depression risk, and worry about falling (OR = 1.02; 95% CI [0.96, 1.08]). Adjusting for Short Physical Performance Battery (SPPB), assistive mobility device, and activities of daily living further weakened the relationship (OR = 0.99; 95% CI [0.94, 1.04]). SPPB demonstrated the strongest correlation with social isolation (r = −0.42; p < 0.01). Fall prevention intervention studies specifically targeting social isolation may incorporate physical performance as a shorter-term and cost-effective proxy outcome for falls.

[Res Gerontol Nurs. 2018; 11(2):61–70.]

Abstract

A longitudinal secondary analysis of 2 years of data from the National Health and Aging Trends Study was undertaken to determine the extent to which social isolation predicts falls in older adults. Social isolation during Year 1 (baseline) was operationalized as a multiple-indicator measure based on Social Network Index participation domains. Falling during the previous year was self-reported using Year 2 data. Logistic regression models revealed social isolation significantly predicted falls (odds ratio [OR] = 1.11; 95% confidence interval [CI] [1.05, 1.17]). The relationship remained significant after adjusting for age, gender, and education (OR = 1.08; 95% CI [1.02, 1.14]). The relationship weakened after adjusting for self-reported general health, depression risk, and worry about falling (OR = 1.02; 95% CI [0.96, 1.08]). Adjusting for Short Physical Performance Battery (SPPB), assistive mobility device, and activities of daily living further weakened the relationship (OR = 0.99; 95% CI [0.94, 1.04]). SPPB demonstrated the strongest correlation with social isolation (r = −0.42; p < 0.01). Fall prevention intervention studies specifically targeting social isolation may incorporate physical performance as a shorter-term and cost-effective proxy outcome for falls.

[Res Gerontol Nurs. 2018; 11(2):61–70.]

Social isolation and falls in older community-dwelling adults can have life-altering outcomes. Although evidence that supports an association between social isolation and falls is described below, the nature of the relationship is not well documented. Understanding the association between these two geriatric conditions could be important for the development of fall prevention interventions.

There is a wide range of health outcomes associated with social isolation, including cognitive impairment, depression, changes in general health, cardiovascular disease, cancer, and death (Courtin & Knapp, 2015; Uchino, 2006). Coyle and Dugan (2012) found that social isolation was associated with 43% greater likelihood of an individual having fair or poor health. In addition, social isolation has been found to impact health-related behaviors, including smoking and low physical activity (Pantell et al., 2013; Shankar, McMunn, Banks, & Steptoe, 2011).

Falls in older adults can be devastating in human and financial terms. The risk of falling is closely associated with gait, balance, strength, the use of an assistive mobility device, and deficits in activities of daily living (ADL; Stevens & Phelan, 2013). Falls are the leading cause of injury-related death among older adults (Haegerich et al., 2014) and account for 6% of health care costs for this population (Lach, 2010). An economic analysis estimated the direct cost of all fall injuries in 2000 at $1.9 billion; in 2013, costs increased to $34 billion (Centers for Disease Control and Prevention, 2015). Falls are expected to increase with population trends in aging and costs are expected to reach $67.7 billion by 2020 (National Council on Aging, 2015). Social isolation and falls impact the health of community-dwelling older adults, and the relationship between these two geriatric conditions has conceptual and empirical support.

The Convoy Model of social relations and health provides a conceptual framework for the longitudinal relationship between falls and social isolation of older adults (Antonucci, Birditt, & Akiyama, 2009). The Convoy Model is a unifying framework within which social connections are key elements recognized as having cumulative and reciprocal effects on mental and physical health (Antonucci, Ajrouch, & Birditt, 2014). However, use of this model in research still needs conceptual clarity, as there has been a lack of such clarity regarding social isolation. An abundance of social isolation measures has limited understanding of the relationship between falls and social isolation in older adults and makes study comparisons difficult. The ongoing advancement of research on this subject requires a definition and measure of social isolation that reflects domains of participation (e.g., network, integration), rather than perceptions (e.g., loneliness). Identifying domains of participation offers an opportunity to identify social isolation targets for intervention and more objectively measure outcomes in future intervention research.

Living alone has been used most often in the literature as a social isolation measure to test the association between social isolation and falls (Elliott, Painter, & Hudson, 2009; Flabeau et al., 2013; Kharicha et al., 2007). However, living-alone status excludes relevant social participation domains for older adults who are more likely to live alone but continue to have connections to their social network (Lubben, 1988). The consistent use of a comprehensive and domain-inclusive measure of participation may offer insights to the social isolation experienced by older adults and facilitate comparison of results across studies. Only two published studies that investigated social isolation and falls measured other domains in addition to living-alone status (Cwikel, 1992; Faulkner, Cauley, Zmuda, Griffin, & Nevitt, 2003). Both studies addressed the relationship between social isolation and falls but did not include a temporal element or a nationally representative sample of Medicare recipients.

A clearer understanding of the association between social isolation and falls will help inform the development of fall prevention interventions and broaden the implementation of relevant best practices. The aim of the current study was to examine the extent to which social isolation at one point predicts a fall during the following year in a nationally representative sample of older adults.

Method

Research Design

The current study was a longitudinal secondary analysis, using data from 2011 (Year 1, baseline) and 2012 (Year 2) of the National Health and Aging Trends Study (NHATS). The NHATS was designed to support the study of disability trends and dynamics in a national sample of non-nursing home Medicare beneficiaries age 65 and older living in the contiguous United States. It is an ongoing study being conducted by the Johns Hopkins University Bloomberg School of Public Health, with funding from the National Institute on Aging.

Sample

Random subsamples from the Centers for Medicare & Medicaid Services Medicare enrollment database provided the sampling frame for the NHATS (Montaquila, Freedman, Edwards, & Kasper, 2012). To ensure a nationally representative sample of Medicare beneficiaries age 65 and older, the NHATS study design included baseline stratification of 5-year age groups (i.e., 65 to 89, ≥90). Two subgroups often underrepresented in research were oversampled—individuals who were Black and individuals age 90 and older. Selection into the sample was designed to generate equal probability samples, including targeted sample sizes by age group and race/ethnicity. NHATS protocols were approved by the Johns Hopkins University review board, and all study participants provided written informed consent. Face-to-face annual interviews were conducted by trained personnel with the same participants each year and provided the main source of data for the NHATS. Participants who resided in the community and completed the first (N = 7,609) and second (N = 6,056) year participant interviews were included in the current study. No exclusion criteria were used.

Starting in Year 2, some of the first-year community-dwelling participants transitioned into nursing homes and completed the participant interview in their new setting. Others did not complete their Year 2 interview, were lost to follow-up, or had died, such that Year 2 N = 6,056 (including 64 [1.06%] nursing home residents). Falls are a risk factor for nursing home placement (Lach, 2010), so participants who were residing in a nursing home in Year 2 and had participated in the interview in Year 1 were not removed from the analytic sample for the current analysis.

The NHATS is a publicly available data set accessed by registering online (access http://www.nhats.org) and downloading the data files. Additional demographic data that were viewed as sensitive, such as age, were available through a simple application process. The University of Washington Human Subjects Division established that the current study did not meet the definition of research concerning human participants because the data were de-identified.

Measures

A data subset was created for the current study with NHATS variables that are known to be associated with or predictive of falls (Berry, Lee, Cai, & Dore, 2013; Stevens & Phelan, 2013; Tinetti, Gordon, Sogolow, Lapin, & Bradley, 2006) and social isolation (Coyle & Dugan, 2012; DiNapoli, Wu, & Scogin, 2014; Iliffe et al., 2007; McCrae et al., 2005; Pantell et al., 2013; Shankar et al., 2011; Shub et al., 2011). The variables in the data subset included living-alone status, sleep medicine used, current smoker or non-smoker, hearing device used, visual correction used, ADL, instrumental activities of daily living (IADL), worry about falling, assistive mobility device, taking walks, vigorous activity, Short Physical Performance Battery (SPPB), overall health, heart disease, arthritis, osteoporosis, diabetes, stroke, Alzheimer's disease or other dementia, and depression risk.

Outcome Variable. Several questions regarding falling were asked, prefaced by the definition of falling as “any fall, slip, or trip in which you lose your balance and land on the floor or ground or at a lower level” (Gadkaree, Sun, Huang, Varadhan, & Agrawal, 2015, p. 3). The outcome variable for the current study was assessed by asking participants if they had experienced a fall in the past 12 months. Participants were also asked if they worried about falling in the past 1 month, with a possible response of yes or no (Gell, Wallace, LaCroix, Mroz, & Patel, 2015).

Independent Variable. For the current study, social isolation was defined as a social circumstance in which an older adult had a deficiency of network contacts and integrating relationships with contacts (Nicholson, 2010; Pohl, Cochrane, Schepp, & Woods, 2017). Social isolation was operationalized as a comprehensive and domain-inclusive measure based on Berkman and Syme's (1979) Social Network Index (SNI) using comparable items (i.e., indicators) available in the NHATS data set. Berkman and Syme (1979) suggested the SNI is a strong predictor of health and mortality outcomes. The four SNI conceptual domains include marriage, family and friend contact, church, and club participation, and have been operationalized within many research studies to create a constructed measure of social network and integration using data items (indicators) that varied from study to study. The continued use of the SNI domains over time support its validity for identifying individuals who are socially isolated (Nicholson, 2010; Pantell et al., 2013; Shankar et al., 2011).

Indicators of the SNI domains of social isolation in the current study used NHATS data items on marriage/partner status; talked with family; talked with friends; visited in family's, friends', or own homes; attended religious services; and participated in club/community activities. In the current study, the domain items were recoded to ensure a higher score indicated greater isolation. One point was recorded for each negative (isolated) response to an item, and the total score (range = 0 to 6) was based on the sum of the negative responses. Adapted from Nicholson (2010), the Figure characterizes the relationships among social isolation, SNI network contacts and integrating relationship domains, and the indicators (items) in the NHATS interview. A complete description of this measure development was published previously (Pohl et al., 2017).

Conceptualization of the social isolation measure, the Social Network Index network contact, and integrating relationship domains, and the indicators in the National Health and Aging Trends Study interview. Adapted from Nicholson (2010). Reprinted with permission from Pohl, J.S., Cochrane, B.B., Schepp, K.G., & Woods, N.F. (2017). Measuring social isolation in the National Health and Aging Trends Study. Research in Gerontological Nursing, 10, 277–287. Copyright © SLACK Incorporated

Figure.

Conceptualization of the social isolation measure, the Social Network Index network contact, and integrating relationship domains, and the indicators in the National Health and Aging Trends Study interview. Adapted from Nicholson (2010). Reprinted with permission from Pohl, J.S., Cochrane, B.B., Schepp, K.G., & Woods, N.F. (2017). Measuring social isolation in the National Health and Aging Trends Study. Research in Gerontological Nursing, 10, 277–287. Copyright © SLACK Incorporated

Demographic Variables. Age, sex, self-identified race/ethnicity, and education, as reported in the baseline interview, were included. Education was included as an indicator of socioeconomic status (Winkleby, Jatulis, Frank, & Fortmann, 1992). Race/ethnicity was categorized into White non-Hispanic, Black non-Hispanic, or Hispanic. Additional non-Hispanic groups including American Indian, Asian, and Native Hawaiian/Pacific Islander were reported as other. Nine education levels were collapsed into three: no schooling to 12th grade schooling but no diploma, high school graduate to high school graduate with some college, and associate to graduate degree.

Behavioral and Sensory Variables. Living-alone status was measured as a dichotomous variable. Living alone is the most frequently reported measure of social isolation related to falls (Elliott et al., 2009; Flabeau et al., 2013; Kharicha et al., 2007). The use of sleep medicines, current cigarette smoking, and sensory impairment (i.e., hearing device used in the past 1 month, glasses or contacts needed to see things at a distance) were also measured as dichotomous variables.

Health Variables. Participants rated their general health as excellent, very good, good, fair, or poor. Participants were asked if they had been told by their health care professional that they had a broken bone, broken hip, heart attack, heart disease, arthritis, osteoporosis, diabetes, stroke, cancer, or dementia, with possible answers of yes or no. Depression risk was identified with the Patient Health Questionnaire-2 (PHQ-2), a brief screening instrument (Löwe et al., 2010). The items began with the question, “Over the last month, how often have you…” and included: (a) had little interest or pleasure in doing things, and (b) felt down, depressed, or hopeless. The ordinal response options were rated from not at all to nearly every day, and the score was the sum of both items, with a higher score indicating the need for further depression assessment (Löwe et al., 2010). The reliability and validity of this measure has been established in the general population (Kroenke, Spitzer, & Williams, 2003).

Physical Function Variables. The SPPB was used to measure physical performance (Kasper, Freedman, & Niefeld, 2012; Sun, Huang, Varadhan, & Agrawal, 2016). This assessment consists of balance stands, gait speed, and chair stands. Scores for each measure ranged from 0 to 4, with 0 indicating an inability to complete the test and 4 indicating highest performance. The total SPPB score was the sum of the three scores. The internal consistency reliability of the SPPB has been previously reported with a 0.76 Cronbach's alpha in older adults (Guralnik et al., 1994). The use of an assistive mobility device was assessed by asking if in the past 1 month, a device (e.g., cane, scooter, walker, wheel-chair) was used. Physical activity was assessed with two questions that asked participants if they ever go for walks or spend time doing a vigorous activity that increases their breathing and heart rate. Response options for these measures were yes/no. ADL scores were assessed with questions regarding help needed for dressing, eating, bathing, and toileting. IADL scores were assessed with questions regarding help needed to do laundry, shop, cook hot meals, handle bills and banking, keep track of medications, and drive. Higher scores indicated more help was needed. The validity of the ADL and IADL measures and a high degree of internal consistency reliability have been documented in previous research with older adults (Katz, 1983).

Data Analysis

Analytic sample weights were developed in the NHATS to account for the unequal probabilities of selection into the NHATS sample. Specifically, the weights accounted for the differential selection into the sample of underrep-resented groups and were adjusted for nonresponse. Variance estimates were calculated using a Taylor series linearization. Data management and statistical analysis were performed using SPSS Statistics version 23.

To examine the baseline sample, demographic, household, behavioral, and health characteristics of study participants in Year 1 were compared by fall status using chi-square and independent t test analysis. Logistic regression models were used to test the relationship between social isolation scores and falls. Model 1 consisted of the social isolation score in Year 1 as the independent variable and whether the individual reported a fall in Year 2 as the dependent variable. Predictive equations of Model 1 results were calculated and revealed the odds of falling associated with a one-unit change in the social isolation score. Model 2 consisted of demographic covariates including age, gender, and education added simultaneously to the independent variable. The performance of all study variables was examined to further interpret Model 1 results. Pearson's r correlations of all study variables at Year 1 determined the adjustment covariates for Models 3 and 4. Variables that correlated significantly with falls and social isolation (p < 0.01) and had a Pearson correlation coefficient ≥ 0.10 with the social isolation measure were considered for model testing. This coefficient strength was chosen because in social science research, a correlation coefficient of r = 0.10 to 0.29 is considered to demonstrate low to moderate strength (de Vaus, 2002; Koenig, 2012). Further psychosocial variables that are known clinically to have a strong relationship will seldom exceed a correlation of r = 0.25 to 0.3 (Koenig, 2012). In Model 3, general health, depression risk, and worry about falls were added simultaneously to Model 2. In Model 4, the SPPB, assistive mobility device, and ADL were added simultaneously to Model 3.

Results

Baseline Sample Characteristics

Participants' mean age at Year 1 was 78.4 (SD = 8.19) years, and at Year 2 was 79.2 (SD = 8.04) years. Table 1 presents participant characteristics by falls in Year 1. The mean age at Year 1 was higher for those who fell than for those who did not; participants in older age groups were more likely to report falling.

Distribution of Demographic, Household, Behavioral and Health Characteristics of Participants According to Fall Status at the National Health and Aging Trends Study Year 1 (N = 7,609)Distribution of Demographic, Household, Behavioral and Health Characteristics of Participants According to Fall Status at the National Health and Aging Trends Study Year 1 (N = 7,609)

Table 1:

Distribution of Demographic, Household, Behavioral and Health Characteristics of Participants According to Fall Status at the National Health and Aging Trends Study Year 1 (N = 7,609)

Reporting the significant differences, females were more likely to fall than males, and participants who were non-Hispanic White and Hispanic were more likely to fall than not to fall. Social isolation mean score was higher for those who fell (2.78, SD = 1.31) than those who did not (2.63, SD = 1.24). Those who fell were more likely to live alone and report in the past 1 month use of sleep medicine, visual correction, and an assistive mobility device, as well as needing help with ADL and IADL and worry about falling. Those who fell were less likely to report taking walks and participating in vigorous activity. The SPPB mean score was lower for those who fell (6.57, SD = 4.02) than those who did not (8.14, SD = 3.6). Participants who reported poor health were more than twice as likely to report falling in the past 1 year, and a higher percentage of participants who fell reported a comorbid condition compared to those who had not fallen. Finally, the mean depression risk score was higher for those who fell (1.35, SD = 1.60) than those who had not fallen (0.84, SD = 1.29).

Correlations

Pearson's r correlation between falls and social isolation in Year 1 was weak (r = 0.05, p < 0.01). The strongest correlation was a negative, moderate relationship between the SPPB and social isolation (r = −0.42, p < 0.01). Additional significant correlations between other health covariates and social isolation were: general health, r = 0.28; depression risk, r = 0.23; worry about falling, r = 0.14; assistive mobility device use, r = 0.28; and ADL, r = 0.29.

Logistic Regression Models

To examine the extent to which social isolation in Year 1 predicted a fall in Year 2, a series of logistic regression models were analyzed (Table 2). The current study's primary variables in Model 1, social isolation, significantly predicted falling (odds ratio [OR] = 1.1; 95% confidence interval [1.05, 1.17]). The weighted probability of a reported fall at Year 2 increased with each increase in social isolation scores at Year 1. For example, participants with a social isolation score of 0 had a 19.7% predicted probability of falling compared to those with a social isolation score of 6, who had a 30.5% predicted probability of falling. In Model 2, after adjusting for age, gender, and education, the social isolation measure remained significantly associated with falls. In Model 3, this relationship was weakened by the addition of self-reported covariates (i.e., general health, depression risk, worrying about falls). General health was predictive of falls in Model 3, but this relationship was not significant in Model 4. Depression risk and worry about falls were significantly predictive of falling in Models 3 and 4. When adjusted for physical performance covariates (i.e., SPPB, assistive mobility device, ADL) the relationship between social isolation and falls was weakened. In Model 4, the SPPB was predictive of falling, as was the assistive mobility device use variable, which accounted for much of the variance in that model.

Logistic Regression Models: Falls Year 2 By Social Isolation Measure Year 1 and Covariates Year 1

Table 2:

Logistic Regression Models: Falls Year 2 By Social Isolation Measure Year 1 and Covariates Year 1

Discussion

The current study was the first to investigate a multiple indicator measure of social isolation and falls with a nationally representative sample of Medicare beneficiaries in the United States. The large, nationally representative NHATS sample is a strength. The current study was the first to use a comprehensive and domain-inclusive social isolation measure that specifically expands on the SNI dimensions of Berkman and Syme (1979) to examine the relationship between social isolation and falls. Cross-sectional studies have constructed various multiple indicator measures of social isolation as predictors of falls, but specific domains of the SNI have not been examined. Cwikel (1992) found that those who reported the highest levels of social interaction reported the fewest falls in a nationally representative sample of older adults in Israel. Using the Lubben Social Network Scale, Faulkner et al. (2003) reported that social isolation was not significantly predictive of falls. However, the family data item indicators from the scale were significant predictors of falls in a sample of non-Black women from four major U.S. cities.

The current study provides evidence to support use of a comprehensive, domain-inclusive social isolation measure in future research on falls. Although living alone has predicted falls among older adults (Elliott et al., 2009; Flabeau et al., 2013; Kharicha et al., 2007), it is not a comprehensive measure of social isolation (Lubben, 1988). Operationalizing social isolation with multiple indicators of participation may be more useful than a single item on living-alone status for future research on fall prevention and other gerontological health outcomes, because it more thoroughly accounts for the experience of social isolation. Consistently measuring social isolation as domains of participation, regardless of living-alone status, will allow for comparison of results across studies.

The current study was the first to report a temporal element in the relationship between falling and social isolation in older adults. Social isolation in Year 1 predicted a fall in Year 2, and the relationship remained significant after adjusting for age, gender, and education. In addition, a higher social isolation score predicted a higher probability of falling, and examination of covariates helped explain and interpret these results. The relationship between social isolation and falls was weakened after adjusting for self-reported health, depression risk, and worrying about falls in Model 3. However, depression risk and worrying about falls continued to predict falls after controlling for physical performance and function variables in Model 4. Worry about falling has been associated with future falls (McKee et al., 2002) and having fewer social contacts (Howland et al., 1998).

Physical performance partially explained the relationship between social isolation and falls in the current study, and two of three physical function covariates are important to consider. First, the performance of the SPPB was predictive of falling and demonstrated a significant relationship with social isolation. The SPPB has been identified in previous research as an indicator of lower extremity strength that is associated with falls among older adults (Veronese et al., 2014). The activity involved in visiting family and friends, going to church, and participating in a club requires a level of physical function that helps maintain physical health, such that community engagement may be an important target for fall prevention strategies. Second, the performance of the mobility device use covariate suggests that physical function is important to the relationship between social isolation and falls. However, in previous NHATS research, no association between falls and assistive mobility device use was found (Gell et al., 2015). The third physical function covariate, ADL, was not predictive of falls in the current study, but ADL have been associated with falls as well as physical frailty among older adults in other research (Mamikonian-Zarpas & Laganá, 2015). Although no causal relationship has been established, all covariates except ADL helped explain the relationship between social isolation and falls. Understanding these relationships may be important for development of fall prevention strategies.

Implications

Social isolation as a predictor of falls is partially explained by the relationship between social isolation and physical performance, and these findings have implications for future research in that intervention studies targeting both variables could have an impact on falls. Further, intervention studies that specifically target social isolation could incorporate physical performance as a shorter-term and cost-effective proxy outcome for falls. Knowledge regarding the relationship between social isolation domains and falls may inform decisions made by nurses concerning planning and implementation of care for older adults.

Conclusion

In the nationally representative sample, social isolation predicted a fall 1 year later, and each increase in social isolation score increased the probability of falling. Covariates were identified that offer an increased understanding of the association between social isolation and falls in a geriatric population and provide information for the development of fall intervention studies as well as for the implementation of relevant best care practices.

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Distribution of Demographic, Household, Behavioral and Health Characteristics of Participants According to Fall Status at the National Health and Aging Trends Study Year 1 (N = 7,609)

CharacteristicFell in Past Year (Weighted %)Did Not Fall in Past Year (Weighted %)p Value
Age (years) (M = 78.4 [SD = 8.19])M = 78.56 (SD = 8.11)M = 77.15 (SD = 7.71)<0.001a
  65 to 69259 (25.9)1,026 (29.1)<0.001b
  70 to 74286 (23.0)1,163 (26.4)
  75 to 79305 (19.5)1,065 (19.2)
  80 to 84305 (15.1)1,001 (13.9)
  85 to 89233 (11.2)572 (7.7)
  ≥90162 (5.3)388 (3.6)
Female997 (63.5)2,946 (54.4)<0.001b
Race/ethnicity
  White, non-Hispanic1,115 (83.4)3,473 (80.7)0.016b
  Black, non-Hispanic295 (7.3)1,206 (8.6)
  Hispanic96 (7.0)306 (6.7)
  Other31 (2.3)167 (4.0)
Education0.055b
  No school to 12th grade but no diploma412 (21.0)1,338 (20.7)
  High school graduate to some college743 (50.9)2,452 (48.8)
  Associate degree to graduate degree382 (28.7)1,359 (30.5)
Social isolation (M = 2.71 [SD = 1.27])M = 2.78 (SD = 1.31)M = 2.63 (SD = 1.24)<0.001a
Lives alone523 (32.6)1,665 (28.4)0.009b
Sleep medicine used554 (38.2)1,319 (26.5)<0.001b
Smokes now117 (16.7)415 (16.1)<0.001b
Hearing device used in past month219 (12.1)631 (11.0)0.525b
Visual correction used in past month1,012 (65.8)3,105 (59.3)<0.001b
ADL help needed367 (19.9)564 (7.9)<0.001b
IADL help needed958 (61.0)2,716 (50.8)<0.001b
Worry about falling717 (44.8)1,100 (19.0)<0.001b
Assistive mobility devices used (e.g., cane, scooter, walker, and/or wheelchair)657 (35.3)1,190 (16.9)<0.001b
Ever go for a walk790 (53.8)3,235 (65.6)<0.001b
Vigorous activityc461 (33.9)1,931 (42.1)<0.001b
SPPB scored (M = 7.51 [SD = 3.91])M = 6.57 (SD = 4.02)M = 8.14 (SD = 3.63)<0.001a
Overall health self-report<0.001b
  Excellent138 (10.9)740 (17.0)
  Very good372 (27.4)1,516 (31.7)
  Good466 (29.1)1,713 (31.4)
  Fair395 (22.8)966 (15.6)
  Poor178 (9.9)275 (4.3)
Heart disease483 (28.8)1,231 (21.9)<0.001b
Arthritis1,011 (63.8)2,649 (48.8)<0.001b
Osteoporosis406 (27.8)940 (18.4)<0.001b
Diabetes444 (27.9)1,212 (21.6)<0.001b
Stroke230 (12.7)496 (8.0)0.002b
Alzheimer's or other dementia127 (6.2)213 (2.8)<0.001b
Depression risk (PHQ-2)e (M = 0.04 [SD = 1.45])M = 1.35 (SD = 1.60)M = 0.84 (SD = 1.29)<0.001a

Logistic Regression Models: Falls Year 2 By Social Isolation Measure Year 1 and Covariates Year 1

OR (95% CI)
Model 1a (N = 5,298)Model 2b (N = 5,250)Model 3c (N = 5,239)Model 4d (N = 4,608)
Intercept0.25 [0.21, 0.29]0.11 [0.08, 0.17]0.06 [0.04, 0.10]0.13 [0.07, 0.23]
SI measure1.11 [1.05, 1.17]1.08 [1.02, 1.14]1.02 [0.96, 1.08]0.99 [0.94, 1.04]
Age1.13 [1.06, 1.20]1.11 [1.04, 1.18]1.04 [0.98, 1.12]
Gender1.36 [1.14, 1.62]1.25 [1.04, 1.50]1.20 [0.99, 1.46]
Education1.00 [0.97, 1.04]1.03 [0.99, 1.08]1.02 [0.98, 1.07]
General health report
scoree1.15 [1.07, 1.23]1.07 [0.99, 1.17]
Depression risk (PHQ-2)f1.12 [1.06, 1.92]1.11 [1.04, 1.19]
Worry about fallsg1.65 [1.39, 1.97]1.48 [1.20, 1.82]
SPPBh0.97 [0.94, 1.00]
Assistive mobility devicei1.35 [1.02, 1.76]
ADLj1.06 [0.94, 1.19]
Authors

Dr. Pohl is PhD Graduate, School of Nursing, Dr. Cochrane is de Tornyay Endowed Professor for Healthy Aging, Family and Child Nursing, Dr. Schepp is Professor and Chair, Psychosocial & Community Health, and Dr. Woods is Professor Dean Emerita, Biobehavioral Nursing and Health Systems, University of Washington, Seattle, Washington.

The authors have disclosed no potential conflicts of interest, financial or otherwise. This work was supported by the Myrene C. McAninch Doctoral Scholarship, de Tornyay Center for Healthy Aging, University of Washington School of Nursing.

Address correspondence to Janet S. Pohl, PhD, RN, PhD Graduate, School of Nursing, University of Washington, Box 357262, Seattle, WA 98195; e-mail: janpohl@uw.edu.

Received: September 12, 2017
Accepted: December 04, 2017
Posted Online: March 02, 2018

10.3928/19404921-20180216-02

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