Falls are an important health concern among older adults due to age-related changes in the body. Having a medical history of chronic health condition may pose even higher risk of falling. Only few studies have assessed a number of chronic health conditions as risk factor for falls over a large nationally representative sample of US older adults. In this study, Behavioral Risk Factor Surveillance System (BRFSS) 2014 participants aged 65 years and older (
Falls and associated health consequences are significant public health issues among older adults. Nearly 30% of older adult population experience a fall incident every year [
Many factors may contribute to falls. Literature identifies various biological, social, environmental, and behavioral risk factors for falls among different populations and age groups [
Also, using logistic regression to evaluate episodic data such as falls where many of the study participants do not experience a fall during the evaluation period does not capture the overdispersion due to excessive zeroes in the data. Thus, the results may be misleading. The zero-inflated regression modeling approach can address this issue. Zero-inflated regression models include both logistic and Poisson components, which allow for the identification of distinct predictors for the first-time (nonrecurrent) falling and recurrent falling, while accounting for excessive zeroes in the data at the same time. Previous studies repeatedly used the traditional method of logistic regression modeling, and, therefore, the risk of falling recurrently after the first event of falling could not be addressed.
The primary objective of this study was to quantify the current prevalence of falls, recurrent falls, and a number of CHCs in US older adult population. The secondary objective of this study was to examine those CHCs as risk factors for falls and recurrent falls in this population. This study also evaluated various demographic, socioeconomic, and behavioral risk factors as potential confounders for establishing the true relationship between CHCs and falls. Understanding the relationship between CHCs and falls is important to inform public health programs to increase the awareness regarding falls and CHCs as risk factors and to guide the development of interventions to decrease the risk of falls while addressing CHCs.
We performed a secondary data analysis using the Behavioral Risk Factor Surveillance System (BRFSS) survey 2014. BRFSS is a large cross-sectional US population based survey. It is conducted by the CDC every year in all the 50 states of US as well as the District of Columbia and three US territories [
A sample of 464,664 individuals, who participated in the BRFSS 2014, was restricted to the older adults aged 65 years and older (
The main outcome variable in this study was self-reported “falls” in the previous 12 months in response to the following question: “The next question asks about a recent fall. By a fall, we mean when a person unintentionally comes to rest on the ground or another lower level. In the past 12 months, how many times have you fallen?” [
We examined a number of CHCs as explanatory variables in this study in response to the survey question: “Have you ever been diagnosed by this chronic condition?” Participants were asked about ten chronic health conditions in BRFSS including the heart attack, angina, stroke, asthma, cancer, COPD, CKD, arthritis, depression, and diabetes [
Other than the CHCs as predictors, we included demographic (age, sex, race, and marital status), socioeconomic (education, employment, and income), and behavioral (smoking and drinking) covariates in our study. We identified these covariates as potential confounders as being associated with the risk of falls in previous literature. We considered two categories of age, “65–79” and “80+,” as the risk of falling increases substantially in people aged 80 and above [
We created two categories for employment. One for the people who are currently engaged in some kind of work or work-related activity named as “employed” and another category for people who are retired or unable to work named as “unemployed.” Unemployment may pose a higher risk of falling indirectly due to lack of access to the social and healthcare [
We used SAS version 9.4 for the descriptive and the inferential analyses. A
Characteristics of community-dwelling US older adults by fall, BRFSS 2014.
Characteristics | Overall sample | Fallers |
Nonfallers |
|
---|---|---|---|---|
|
||||
65–79 | 113747 | 32335 (27.2%) | 81412 (72.8%) | <0.0001 |
80+ | 36129 | 12215 (33.5%) | 23914 (66.5%) | |
|
||||
|
||||
White | 128911 | 38878 (29.5%) | 90033 (70.5%) | <0.0001 |
Black | 8870 | 2250 (23.3%) | 6620 (76.7%) | |
Hispanic | 6196 | 1687 (26.2%) | 4509 (73.8%) | |
Other | 5899 | 1735 (26.5%) | 4164 (73.5%) | |
|
||||
|
||||
Male | 56726 | 15842 (26.5%) | 40884 (73.5%) | <0.0001 |
Female | 93150 | 28708 (30.3%) | 64442 (69.7%) | |
|
||||
|
||||
Married/living-in | 73368 | 19806 (26.2%) | 53562 (73.8%) | <0.0001 |
Unmarried/single | 75900 | 24587 (31.7%) | 51313 (68.3%) | |
|
||||
|
||||
≤High school education | 61842 | 18136 (28.2%) | 43706 (71.8%) | 0.0771 |
>High school education | 87568 | 26284 (29.0%) | 61284 (71.0%) | |
|
||||
|
||||
Employed | 25181 | 6477 (23.9%) | 18704 (76.1%) | <0.0001 |
Unemployed | 124076 | 37895 (29.5%) | 86181 (70.5%) | |
|
||||
|
||||
<$50,000/Year | 80873 | 25528 (30.5%) | 55345 (69.5%) | <0.0001 |
≥$50,000/Year | 41008 | 10949 (25.3%) | 30059 (74.7%) | |
|
||||
|
||||
Yes | 5845 | 1835 (31.3%) | 4010 (68.7%) | 0.0248 |
No | 141342 | 41982 (28.6%) | 99360 (71.4%) | |
|
||||
|
||||
Yes | 12561 | 3748 (28.6%) | 8813 (71.4%) | 0.9896 |
No | 136200 | 40480 (28.6%) | 95720 (71.4%) | |
|
||||
|
||||
Yes | 16947 | 6383 (36.1%) | 10564 (63.9%) | <0.0001 |
No | 131974 | 37796 (27.6%) | 94178 (72.4%) | |
|
||||
|
||||
Yes | 17712 | 6662 (36.0%) | 11050 (64.0%) | <0.0001 |
No | 129887 | 37034 (27.4%) | 92853 (72.6%) | |
|
||||
|
||||
Yes | 11131 | 4790 (42.5%) | 6341 (57.5%) | <0.0001 |
No | 138208 | 39542 (27.5%) | 98666 (72.5%) | |
|
||||
|
||||
Yes | 17439 | 6501 (36.0%) | 10938 (64.0%) | <0.0001 |
No | 131948 | 37860 (27.6%) | 94088 (72.4%) | |
|
||||
|
||||
Yes | 26061 | 8591 (31.9%) | 17470 (68.1%) | <0.0001 |
No | 123428 | 35838 (27.9%) | 87590 (72.1%) | |
|
||||
|
||||
Yes | 18301 | 6781 (36.3%) | 11520 (63.7%) | <0.0001 |
No | 130525 | 37344 (27.4%) | 93181 (72.6%) | |
|
||||
|
||||
Yes | 8649 | 3552 (40.9%) | 5097 (59.1%) | <0.0001 |
No | 140558 | 40722 (27.7%) | 99836 (72.3%) | |
|
||||
|
||||
Yes | 80538 | 28533 (34.4%) | 52005 (65.6%) | <0.0001 |
No | 68519 | 15730 (21.7%) | 52789 (78.3%) | |
|
||||
|
||||
Yes | 23670 | 11174 (47.3%) | 12496 (52.7%) | <0.0001 |
No | 125631 | 33155 (25.2%) | 92476 (74.8%) | |
|
||||
|
||||
Yes | 31716 | 11279 (34.3%) | 20437 (65.7%) | <0.0001 |
No | 117956 | 33210 (26.9%) | 84746 (73.1%) |
Risk factors of falls among community-dwelling US older adults: logistic regression modeling, BRFSS 2014.
Explanatory variables (reference) | Crude odds |
Standard |
Adjusted odds |
Standard |
---|---|---|---|---|
|
||||
Heart attack | 1.48 (1.39–1.58) | 0.0332 | — | — |
Angina | 1.49 (1.40–1.59) | 0.0329 | 1.17 (1.09–1.27) | 0.0389 |
Stroke | 1.96 (1.81–2.11) | 0.0396 | 1.61 (1.46–1.76) | 0.0474 |
Asthma | 1.48 (1.38–1.58) | 0.0342 | 1.22 (1.13–1.32) | 0.0399 |
Cancer | 1.21 (1.14–1.28) | 0.0301 | 1.13 (1.06–1.20) | 0.0329 |
COPD |
1.51 (1.42–1.61) | 0.0323 | — | — |
CKD |
1.81 (1.63–1.99) | 0.0515 | 1.27 (1.14–1.42) | 0.0553 |
Arthritis | 1.89 (1.80–1.98) | 0.0237 | 1.61 (1.52–1.70) | 0.0277 |
Depression | 2.67 (2.52–2.83) | 0.0296 | 2.26 (2.11–2.42) | 0.0350 |
Diabetes | 1.42 (1.34–1.49) | 0.0268 | 1.32 (1.24–1.40) | 0.0320 |
|
||||
|
||||
Age group 80+ (65–79) | 1.35 (1.28–1.42) | 0.0263 | 1.27 (1.20–1.36) | 0.0330 |
Black (White) | 0.73 (0.66–0.80) | 0.0482 | 0.68 (0.60–0.75) | 0.0567 |
Hispanic (White) | 0.85 (0.75–0.96) | 0.0622 | 0.74 (0.65–0.85) | 0.0701 |
Other race (White) | 0.86 (0.71–1.04) | 0.0961 | 0.80 (0.66–0.97) | 0.0982 |
Female (male) | 1.21 (1.15–1.26) | 0.0234 | 1.06 (1.00–1.12) | 0.0284 |
Unmarried/single (married/living-in) | 1.31 (1.25–1.37) | 0.0231 | 1.13 (1.07–1.20) | 0.0293 |
Unemployed (employed) | 1.33 (1.25–1.42) | 0.0325 | 1.10 (1.02–1.18) | 0.0370 |
Income <$50,000/year (≥$50,000/year) | 1.30 (1.23–1.37) | 0.0268 | 1.07 (1.00–1.13) | 0.0305 |
Binge drinking |
1.14 (1.02–1.27) | 0.0571 | 1.24 (1.09–1.41) | 0.0654 |
CI = Confidence Interval.
Since 70.3% (
Risk factors of falls among community-dwelling US older adults: zero-inflated regression modeling, BRFSS 2014.
Explanatory variables (reference) | Zero-inflated Poisson | Zero-inflated negative binomial | ||
---|---|---|---|---|
Count part | RR (95% CI) | Standard |
RR (95% CI) | Standard |
|
||||
|
||||
Heart attack | 1.13 (1.10–1.16) | 0.0122 | 1.16 (1.11–1.21) | 0.0219 |
Angina | 1.08 (1.05–1.10) | 0.0118 | 1.17 (1.12–1.22) | 0.0209 |
Stroke | 1.32 (1.29–1.35) | 0.0115 | 1.50 (1.43–1.57) | 0.0249 |
Asthma | 1.15 (1.12–1.17) | 0.0113 | 1.22 (1.18–1.27) | 0.0201 |
COPD |
1.10 (1.08–1.13) | 0.0110 | 1.13 (1.08–1.17) | 0.0203 |
CKD |
1.18 (1.15–1.21) | 0.0132 | 1.24 (1.18–1.31) | 0.0276 |
Arthritis | 1.17 (1.15–1.19) | 0.0095 | 1.39 (1.34–1.44) | 0.0176 |
Depression | 1.68 (1.65–1.71) | 0.0089 | 2.12 (2.05–2.20) | 0.0186 |
Diabetes | 1.11 (1.09–1.13) | 0.0093 | 1.18 (1.14–1.22) | 0.0177 |
|
||||
|
||||
Age group 80+ (65–79) | 0.89 (0.87–0.90) | 0.0108 | 0.94 (0.91–0.98) | 0.0191 |
Black (White) | 0.74 (0.71–0.77) | 0.0223 | 0.70 (0.65–0.75) | 0.0355 |
Hispanic (White) | 0.89 (0.85–0.93) | 0.0231 | 0.85 (0.78–0.92) | 0.0401 |
Other race (White) | 1.10 (1.06–1.14) | 0.0190 | 1.09 (1.01–1.18) | 0.0394 |
Female (male) | 0.76 (0.74–0.77) | 0.0090 | 0.72 (0.70–0.75) | 0.0184 |
Unmarried/single (married/living-in) | 0.98 (0.96–0.99) | 0.0093 | 1.06 (1.03–1.10) | 0.0169 |
Unemployed (employed) | 1.11 (1.08–1.14) | 0.0120 | 1.14 (1.09–1.19) | 0.0216 |
Income <$50,000/year (≥$50,000/year) | 1.17 (1.14–1.19) | 0.0105 | 1.11 (1.08–1.14) | 0.0154 |
Smoking (no smoking) | 1.22 (1.19–1.25) | 0.0130 | 1.13 (1.07–1.19) | 0.0281 |
|
||||
Logit part | OR (95% CI) | Standard |
OR (95% CI) | Standard |
|
||||
|
||||
Heart attack | 0.92 (0.88–0.97) | 0.0253 | — | — |
Angina | 0.90 (0.86–0.94) | 0.0244 | — | — |
Stroke | 0.71 (0.68–0.75) | 0.0267 | 0.44 (0.30–0.64) | 0.1903 |
Asthma | 0.88 (0.85–0.93) | 0.0224 | — | — |
Cancer |
0.88 (0.84–0.92) | 0.0190 | 0.64 (0.53–0.79) | 0.1015 |
CKD |
0.84 (0.79–0.89) | 0.0298 | 0.56 (0.36–0.87) | 0.2216 |
Arthritis | 0.65 (0.63–0.68) | 0.0161 | 0.37 (0.31–0.44) | 0.0901 |
Depression | 0.55 (0.53–0.58) | 0.0189 | 0.22 (0.14–0.33) | 0.2171 |
Diabetes | 0.83 (0.79–0.85) | 0.0184 | 0.60 (0.50–0.72) | 0.0999 |
|
||||
|
||||
Age group 80+ (65–79) | 0.74 (0.71–0.77) | 0.0198 | 0.14 (0.07–0.26) | 0.3274 |
Black (White) | 1.21 (1.12–1.30) | 0.0370 | 1.56 (1.14–2.14) | 0.1618 |
Hispanic (White) | 1.19 (1.10–1.29) | 0.0401 | 1.58 (1.16–2.15) | 0.1579 |
Other race (White) | 1.13 (1.05–1.22) | 0.0377 | 1.73 (1.31–2.29) | 0.1431 |
Female (male) | 0.83 (0.80–0.86) | 0.0165 | 0.33 (0.27–0.40) | 0.1059 |
Unmarried/single (married/living-in) | 0.86 (0.83–0.89) | 0.0170 | 0.75 (0.64–0.88) | 0.0827 |
Unemployed (employed) | — | — | 1.34 (1.11–1.62) | 0.0950 |
Income <$50,000/year (≥$50,000/year) | 1.06 (1.03–1.10) | 0.0181 | — | — |
Smoking (no smoking) | 1.15 (1.03–1.10) | 0.0258 | 1.68 (1.38–2.06) | 0.1030 |
CI = Confidence Interval.
RR = relative risk; OR = odds ratio.
We further compared both model fits by AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), and standard errors of their estimates. Compared to ZIP model, most ZINB model parameters were found to be smaller in magnitude with larger standard errors. Both ZIP and ZINB accounted for excess zeroes, yet the Pearson statistic of ZIP model is indicated for the model misspecification, and in that case the observed standard errors may be biased. Therefore, we considered the ZINB model to be a better fit to explain CHCs as risk factors for falling for the first time and then recurrently among the community-dwelling US older population. Table
Comparison of model fit criteria of ZIP and ZINB models.
Criterion | ZIP |
ZINB |
---|---|---|
Scaled Pearson |
3.4216 | 2.4309 |
Full log likelihood | −144381.3621 | −118910.3002 |
AIC |
288836.7242 | 237890.6003 |
BIC |
289194.1387 | 238228.6952 |
This study includes 159,336 community-dwelling older adults who participated in the 2014 BRFSS. Of these, 29.7% (
While assessing the relationship between CHCs and falls with logistic regression analyses, we found angina, stroke, asthma, cancer, CKD, arthritis, depression, and diabetes as significant predictors for falls, while controlling for other sociodemographic and behavioral factors. A summary of the bivariate and multivariate logistic regression analyses is presented in Table
The logit part of the ZINB model addresses the significant predictors for membership between the zero group (nonfallers) and nonzero group (fallers). Older adults with a medical history of stroke, cancer, CKD, arthritis, depression, and diabetes had significantly lower odds of being in the zero group (nonfallers). Hence, they will have higher odds of experiencing a fall for the first time and moving into the nonzero group (fallers). Older adults with the depression had the lowest odds of being in the zero group (nonfallers), and thus they showed the highest risk of experiencing a fall event. Among sociodemographic factors, age, gender, race, marital status, and employment were significant predictors for membership between zero and nonzero group. Older adults who were 80 years or older, white, female, and those who were unmarried/single and unemployed had lower odds of being in the zero group and hence a higher risk of experiencing a fall. Also, being a smoker increases the odds of experiencing a fall event.
The count part of the ZINB model addresses the relative risk of experiencing recurrent falls among nonzero group (fallers) who already experienced a fall. We found a positive association between the number of fall episodes and the heart attack (RR: 1.16, 95% CI: 1.11–1.21), angina (RR: 1.17, 95% CI: 1.12–1.22), stroke (RR: 1.50, 95% CI: 1.43–1.57), asthma (RR: 1.22, 95% CI: 1.18–1.27), COPD (RR: 1.13 95% CI: 1.08–1.17), CKD (RR: 1.24, 95% CI: 1.18–1.31), arthritis (RR: 1.39, 95% CI: 1.34–1.44), depression (RR: 2.12, 95% CI: 2.05–2.20), and diabetes (RR: 1.18, 95% CI: 1.14–1.22) among fallers. In sociodemographic characteristics, being a male and white, being in the younger (65–79) age group, and being unmarried or single significantly increase the number of fall episodes after experiencing the first fall among fallers. Also, being unemployed, having lower income, and being a smoker significantly increase the risk of recurrent falls after the first event.
Summarizing the findings from this most appropriately fit ZINB model, we observed that having a medical history of stroke, CKD, arthritis, depression, and diabetes independently predict the risk of first-time falling as well as the risk of recurrent falling in older adult population while controlling for other factors. On the other hand, having a medical history of the heart attack, angina, asthma, and COPD did not predict the risk of first-time falling but did predict the risk of recurrent falling after experiencing the first fall in this population. Cancer was observed as the only health condition which predicted the risk of first-time falling but did not predict the risk of recurrent falling.
This study evaluates a number of CHCs as risk factor of falling for the first time and recurrently among a large and nationally representative sample of community-dwelling US older adults. We used the recently available BRFSS 2014 dataset for the analysis which includes a number of CHCs which has been identified as the most leading causes of mortality and morbidity in the US by CDC [
Methodologically, we used the zero-inflated regression modeling in addition to traditional logistic modeling, which is another major strength of this study. Most previous studies used logistic regression analysis to evaluate CHCs as risk factors for falls. However, fall data often consists of excess zeroes with a high proportion reporting no falls, and, due to this issue of overdispersion, results may be misleading. Literature reports that first-time fallers and recurrent fallers possess distinct characteristics and hence are two different populations [
Often studies on falls use different time frames and different age groups, so it is challenging to compare our findings with previous studies. Compared to a recent study in Canadian older adults, the prevalence of falls reported in our study (29.7%) is larger than the prevalence reported (20%) in this study [
An important finding in our study is the strong positive association between falls and depression. Both multivariate logistic regression and zero-inflated regression predict more than a twofold increased risk of falling among older adults with depression. Both parts of ZINB identify the depression as risk factor for fall, which means depression increases the risk of falling for the first time as well as falling recurrently among older adults. This supports previous findings on depression as a risk factor for falls and also adds to the evidence by adjusting for other CHCs and documenting the risk associated with the first fall as well as recurrent falls. Healthcare providers and researchers should pay particular attention to depressive symptoms while developing and implementing fall interventions, as depression shows the strongest relationship with the risk of fall among this population. Following depression, arthritis and stroke were found to be major risk factors for falls in our analysis. These chronic health conditions should particularly be targeted while developing fall assessments and interventions. Current fall prevention guidelines recommend a multifactorial fall assessment only when a person fails the test of postural balance [
Our study has some limitations as well. BRFSS includes land-line phones only, so there could be a selection bias while choosing the sample population. Also, BRFSS does not include older adults residing in the long-term care facilities/nursing homes; hence our results are not generalizable to the institutionalized older adult population. Recalling a fall event in the past 12 months may be challenging for some older adults, resulting in the underestimation of the prevalence of falls, so there could be a recall bias. Also, there is a possibility of information bias as participants might not report information accurately (e.g., diagnosis of the CHCs) which may lead to misclassification resulting in under- or overestimation of risk.
Some age-related health conditions, such as neurodegenerative diseases, poor vision, gait, and balance disorders that increase the risk for falling, were not investigated in BRFSS 2014. Some other factors which have been identified previously as important predictors for falls, such as previous falls, medications, and other environmental factors, were also not captured in BRFSS 2014 [
While falls remain a common health issue among older adults, this study concludes that there are two different “at-risk” populations. One group includes older adults who are at increased risk of experiencing a fall. This group includes people who are older (over 80 years of age), female, white, single/unmarried, employed, and diagnosed with the stroke, cancer, CKD, arthritis, depression, or diabetes. The other group includes older adults who are at increased risk of experiencing recurrent fall episodes after falling for the first time. This group includes people who are younger (65–79), male, white, and diagnosed with the heart attack, angina, stroke asthma, COPD, CKD, arthritis, depression, and diabetes. These groups may benefit from different risk stratification and interventions. Though demographic factors cannot be modified, CHCs can still be managed to reduce falls and related consequences. Fall prevention guidelines can be improved by recommending comprehensive fall assessment among older adults with CHCs who are significant predictors of fall.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors thank Dr. Adam P. Sima from the Department of Biostatistics and Dr. Steve Cohen from the Department of Epidemiology at VCU for their valuable guidance during the project.