Falls result from an interaction between an individual’s underlying vulnerabilities and their exposure to environmental conditions.People experiencing homelessness have a high prevalence of factors known to be associated with falls in the general population, including chronic diseases, functional impairment, alcohol and opioid use problems.Homeless older adults have high prevalence of other factors that could be associated with falls, such as substance use and heightened exposure to physical violence.People who are homeless live in a variety of environments, including homeless shelters and unsheltered spaces that expose them to environmental hazards and violence. In each of these settings, homeless individuals have limited control over their environment, especially when living in unsheltered environments. We examined the prevalence of and risk factors for falls in a longitudinal cohort of adults aged 50 and older who were homeless at study entry. We hypothesized that homeless adults would have a high prevalence of falls and high exposure to environmental hazards. We hypothesized that factors known to be associated with falls in the general population would be associated with falls in our cohort. We further hypothesized that several factors that are plausibly related, but have not been studied , would be associated. We conducted a 3-year prospective cohort study of 350 homeless adults aged 50 and older, the Health Outcomes in People Experiencing Homelessness in Older Middle agE study.We interviewed participants at baseline and every 6 months for three years; at each interview, trained research staff administered a structured interview and conducted clinical assessments. The institutional review board of the University of California, San Francisco approved this study. The datasets we analyzed during the current study are available from the corresponding author on request. Between July 2013 and June 2014,vertical grow systems we recruited 350 adults age 50 or older who were homeless at study entry.
We recruited from all local shelters open to older adults , all free and low-cost meal programs that served at least three meals a week , one recycling center, and areas where adults slept unsheltered in Oakland, California . To create a sample that best represented the target population, including the high number of people living unsheltered in Oakland, we randomly selected potential participants using sampling frames that included encampment sites, recycling centers, shelters, and meal programs.We describe our Methods in more detail elsewhere .Eligibility criteria included: homeless according to the Homeless Emergency Assistance and Rapid Transition to Housing Act definition that includes any person living unsheltered, staying in an emergency shelter, or facing eviction in the next 14 days, age 50 years or older, English-speaking, and able to provide informed consent as determined by a teach-back mechanism.20Participants received $25 for the screening and enrollment interview, $5 for monthly check-ins, and $15 for follow-up interviews. Our primary outcome was self-reported falls in the prior six months, assessed at each study interview. We defined falling as “a sudden, unintentional change in position from an upright posture coming to rest on the floor or ground.” For descriptive purposes, among participants who reported a fall, we asked how many times the participant fell and whether they sought medical treatment for their fall. We identified demographic risk factors as time-constant and other risk factors, health status, and health-related behaviors as time-varying . We assessed age, gender, and race/ethnicity.In our analyses, we dichotomized race as Black versus non-Black. Participants reported their highest educational attainment. We classified participants as having graduated from high school or earned a General Educational Development certificate versus no high school diploma/GED. Using modified questions from the National Health and Nutrition Examination Survey , we asked participants whether a healthcare provider told them they had: myocardial infarction, congestive heart failure, stroke, arthritis, diabetes, or chronic lung disease ; we included these as separate variables.If a participant reported a medical condition at any time point, we considered them to have that condition in subsequent visits. We assessed visual impairment using the Snellen test, and defined visual impairment as corrected visual acuity <20/100.We defined hearing impairment as self-reported difficulty hearing.
To evaluate cognitive impairment, we used the Modified Mini-Mental State Examination . Those who scored below the 7th percentile or were unable to complete the assessment were defined as cognitively impaired. We asked participants about their ability to complete activities of daily living . We defined an ADL impairment as reporting difficulty with bathing, transferring, toileting, dressing or eating.We assessed lower extremity function with the Short Physical Performance Battery test and classified those who scored ≤10 as having reduced physical performance.We assessed urinary incontinence in the past six months by asking participants whether they had “leaked urine, even a small amount.” To assess exposure to environmental hazards at each visit, we used a residential follow-back calendar in which we asked participants to report each place they had stayed and number of nights in each setting during the prior six months.We considered being unsheltered as indicative of the highest environmental exposure. We defined an unsheltered environment as sleeping outdoors or any place not meant for human habitation .In preliminary analyses, we evaluated nights unsheltered as a 3-level variable and as a 6-level variable . Neither alternative exhibited a dose-response effect. Therefore, we used a dichotomous measure of any nights unsheltered in our analysis. Statistical Analyses To identify risk factors for falls, we chose independent variables based on our hypotheses. We assessed bivariate associations between a priori independent variables and recent falls using generalized estimating equations . We built our multivariable model by including variables with bivariate Type 1 p-values <0.20. If a categorical variable had more than two levels, we included all levels in our multivariable model if any Type 1 p-value was p<0.20. We reduced the model using backwards elimination retaining variables with p-values <0.05 in our final multivariable model. We conducted our analysis in SAS 9.4 using complete case analysis and robust confidence intervals . In a sensitivity analysis, we assessed whether we had underestimated the probability of falls due to incomplete follow-up or mortality. We examined the prevalence of falls amongthose: 1) with complete follow-up, 2) who had died during follow-up, or 3) who had not died but had missed any study visits over the 36-month study period. We used GEE to examine whether those who had died or missed visits were more likely to have experienced a fall in the past 6 months than those with complete follow-up. We included participants with a minimum of two visits.
We used weighted linear regression with a second order polynomial and zero intercept term to plot a trend line.At baseline,curing marijuana over one-third reported one or more falls in the past six months. Of the 118 participants who reported falling at baseline, 28.0% reported 4 or more falls, 35.6% two to three falls, and 36.4% one fall. One-third of participants who fell required medical treatment due to a fall. During the 36-month study, 28 participants died. Of those who survived, 183 completed all six follow-up interviews; 72 completed 4-5, 32 completed 2-3, and 21 completed one follow-up interview. We found a higher mean number of falls at baseline among those who died during follow-up 0.50) and those who had not died but had missed visits than among those who completed follow-up . Of the 350 participants, 218 reported one or more falls in at least one study visit; 107 reported falls in at least half of the visits; and 34 reported falls at all visits. Factors Associated with Falls Those who reported falls at baseline had a higher prevalence of known risk factors for falls than those who had not fallen . People with falls were significantly more likely to have less than a high school education, a history of stroke, difficulty with ADLs, mobility impairment, use of assistive device, increased urinary incontinence, and depressive symptoms. We found that those who fell were more likely to have moderate-to-high risk opioid and marijuana use, fewer social confidants, have spent at least one night unsheltered, or experienced physical assault. In models adjusted for key covariates, individual risk factors associated with significantly higher odds of falls included older age 1.00-1.06), being a woman , having non-Black race , having a history of stroke , reporting an ADL impairment , urinary incontinence , and use of an assistive device . Moderate-to-severe marijuana use and moderate-tosevere opioid use were associated with increased odds of falling. Experiencing physical assault and spending any night unsheltered in the last six months were as well. In this longitudinal study of adults 50 and older who were homeless at study enrollment, we found a high prevalence of falls. Despite a median age of 58 years, study participants reported a prevalence of falls higher than older adults with a mean age of 78 in the general population.3 Many participants fell repeatedly throughout the three-year study period; over a third of the cohort reported falls in at least half of their study visits. We found an association between falls and several factors known to increase fall risk within the general population, including older age, gender, functional impairment, urinary incontinence, use of an assistive device, and stroke. Our findings indicate that the increased risk for falls in homeless older adults results, in part, from a high prevalence of geriatric conditions and substance use known to increase fall risk.2 Some of these risk factors may be modifiable via physical and occupational therapy, although it is more difficult to intervene while someone is experiencing homelessness. As the average age of the homeless population continues to increase, the population will have increasing prevalence of geriatric risk factors.We identified novel risk factors: using marijuana, experiencing physical assault, and spending time unsheltered that contributed to the high fall prevalence in our population. Both opioid use and marijuana use were associated with increased odds of falling. Opioid use is associated with increased fall risk among older adults in the general population.However,despite research on marijuana use and injuries in community-dwelling older adults, little is known about how marijuana use impacts falls.Marijuana—like opioids—may increase falls by affecting the sensorium, inducing dizziness, confusion, and drowsiness.We found a high prevalence of marijuana use among study participants. People born in the study’s age cohort have had high prevalence of marijuana use their whole lives, including in older adulthood.As marijuana use among older adults increases, due to changes in legal status and cohort effects, there may be increased falls associated with its use. Experiencing physical assault is common among older adults who are homeless.Physical assault can increase fall risk directly , or indirectly, by causing injuries that enhance underlying individual vulnerabilities associated with falls.Future research should evaluate the role of marijuana use and physical assault in falls among housed older adults to determine whether these risk factors are unique to older adults experiencing homelessness. People who are unsheltered have increased exposure to unsafe environments, with minimal control. They may stay in isolated locations with uneven surfaces and physical barriers, such as abandoned buildings, under bridges, or along highways. Unsheltered environments lack lighting or protection against environmental hazards. Avoiding falls requires intact executive function and physical agility to be able to process external stimuli and modify movements to remain upright.For older adults with vulnerabilities—such as those common among homeless older adults—small external triggers may precipitate falls. Housed older adults are able to modify their behaviors to avoid high-risk environmental exposures that predispose them to falls. For example, they can decrease how often they walk outside on uneven surfaces or minimize their public transit use. In contrast, adults living in unsheltered settings have less ability to avoid high-risk environmental exposures.Our finding that non-Black race was associated with increased falls is consistent with research in housed adults.Homelessness is caused by an interaction between structural factors and individual risk factors. Because Black Americans face structural racism, Black Americans with less individual vulnerability are at risk for homelessness. While we adjusted for these conditions, there may be unmeasured confounders that we were unable to account for.Our study has several limitations. We rely on six-month recall of falls. Other studies of falls in older adults use time frames that range from monthly to biennial.Participants without complete study follow-up had a higher prevalence of falls, indicating that our model may have underestimated the odds of experiencing falls.