Sage Journals: Discover world-class research

Abstract

This study aimed to identify the risk factors for falls among older individuals living at home in a community and develop a nomogram to predict falls. This study included 74 492 people aged 65 years or older who participated in the 2021 Community Health Survey conducted in Korea. The data analysis methods used included the Rao-Scott χ² test, a complex sample t-test, and complex binary logistic regression using SPSS 26.0. Using logistic regression analysis, a fall-risk prediction nomogram was created based on regression coefficients, and the reliability of the nomogram was calculated using a receiver operating characteristic (ROC) curve and values of the area under the curve (AUC). The fall incidence rate among older adults was 16.4%. Factors affecting the subject’s fall experience included being more than 85 years old (OR = 1.40); living alone (OR = 1.13); receiving basic welfare (OR = 1.18); subjective health status (OR = 1.72); number of days spent walking (OR = 0.98); obesity (OR = 1.08); severe depression (OR = 2.84); sleep duration time (OR = 1.11); experiencing cognitive decline (OR = 1.34); and diabetes (OR = 1.12). In the nomogram, the depression score exhibited the greatest discriminatory power, followed by subjective health status, gender, experience of cognitive decline, age, basic livelihood security, adequacy of sleep, living alone, diabetes, and number of days of walking. The AUC value was 0.66. An intervention plan that comprehensively considers physical, psychological, and social factors is required to prevent falls in older adults. The nomogram developed in this study will help local health institutions assess all these risk factors for falling and create and implement systematic education and intervention programs to prevent falls and fall-related injuries among older individuals.

Keywords

aged accidental falls risk factors nomograms secondary analysis

What do we already know about this topic?

Approximately 80% of the patients hospitalized for injuries caused by falls or slips in 2020 were older. Falls have recently been identified as a risk factor for patient safety.

How does your research contribute to the field?

The incidence of falls among older adults was 16.8%. The fall prediction nomogram developed in this study revealed that depression had the greatest influence on the occurrence of falls among older adults, followed by subjective health status, gender, experience of cognitive impairment, age, amount of stress, welfare status, adequacy of sleep, and living alone.

What are your research’s implications toward theory, practice, or policy?

The nomogram developed based on the risk factors identified in this study will help local health institutions develop and apply systematic education and intervention programs to adequately assess risk factors and prevent falls and fall-related injuries among the older population.

Introduction

The older population in Korea has been growing rapidly, from approximately 7% of the total population in 2000 to 15.5% of the population in 2019, and the country is expected to become a super-aging society, with more than 20% aging by 2025.¹ Due to aging, issues related to older adults—such as the burden of medical expenses, geriatric diseases, depression, and increased suicide rates—have emerged as social concerns. The increase in chronic geriatric disease, which imposes a significant burden on families, society, and the nation, has become a crucial public health issue.² In an aging society, health-related issues among older adults’ manifest in various forms, primarily due to the deterioration of physical functions, such as balance and muscle strength, as well as attention deficits resulting from aging. Falls represent the most prevalent health concern among individuals aged >65 years old and constitute one-third of health problems in this demographic.³ A fall refers to an accident in which a person collapses or falls on the floor owing to a sudden, unintentional change in posture.³ According to a survey by the Korea Centers for Disease Control and Prevention,⁴ approximately 80% of the patients hospitalized for injuries caused by falls or slips in 2020 were older. Among patients with falls who visited the emergency room in 2022, those aged 70 years or older accounted for the highest rate, at 29.3%; 36.0% were hospitalized, and the death rate was 2.2%.⁵ Falls are a major factor in lowering the quality of life for older people; they lead to physical damage, reduced activity, and social isolation due to fear of repeated falls.⁶

Given the fact of the rapidly increasing older population, social and economic losses, including medical treatment costs for falls that commonly occur among older people, are expected to continue rising. In Korea, the total medical expenses incurred owing to injuries, including falls, are estimated to exceed 5 trillion won by 2023,⁷ indicating that significant social and economic costs are attributed to falls. Falls among older people not only threaten healthy life in old age but also become a factor that continuously increases the country’s economic losses. Therefore, it is necessary to recognize falls as a social problem rather than an unfortunate accident for individuals, and implement national measures. Screening high-risk groups for falls and appropriately managing fall risk factors can improve the health of older people and reduce medical costs for society as a whole.

Within medical institutions, falls have recently been identified as a risk factor for patient safety. Each institution actively assesses the risk of falls and implements interventions for prevention.⁸ The importance of fall prevention has been emphasized at the national level by incorporating fall risk-assessment and prevention activities as “patient safety assurance activities” in the Ministry of Health and Welfare’s evaluation of medical institution certification. Older individuals hospitalized in medical institutions or nursing facilities receive interventions from medical staff and caregivers to prevent falls and provide prompt responses and management after a fall. However, this level of care is not extended to older people in local communities, where the rate of fall accidents is the highest.⁹

Several factors can cause falls among older people. Deterioration of physical function due to aging, taking various medications for complex chronic diseases, and experiencing dizziness can significantly contribute to falls, particularly in relation to conditions that affect body control, such as stroke, orthostatic hypotension, and dementia. In addition, the risk of falling increases as cognitive ability declines.¹⁰ Therefore, it is necessary to analyze fall-related risk factors from various perspectives and identify the influence of specific predictors among this high-risk population.

Numerous studies have suggested fall-related risk factors, such as older age, being female, living alone, low education level and economic status,¹¹ depression,¹² cognitive decline, dementia,¹³ high blood pressure, and diabetes.¹⁴ The majority of these studies were conducted in specific regions by conveniently selecting subjects from these areas, leading to limitations in identifying the risk factors for falls among all older people who live in the community. Accordingly, this study aimed to develop a nomogram to select risk factors for falls among older people and to predict falls using data from the 2021 Community Health Survey, a representative sample survey conducted in Korea. Using the nomogram developed in this study, local health institutions can systematically predict and manage the risk of falls among older people at home. They can establish the groundwork for a fall-prevention strategy based on scientific evidence by using it as a specifically visualized educational resource for older people.

Methods

Research Design

This study utilized a secondary data analysis to identify the risk factors for falls and estimate the risk for older people aged 65 years or older using data from the 2021 Community Health Survey in Korea.

Research Participants

The Community Health Survey, which has been conducted by the Korea Disease Control and Prevention Agency since 2008, aims to identify the health status of residents in Korea. The report’s data is fundamental for developing health policies and influencing practice decisions to improve population health.¹⁵ The Community Health Survey targets adults aged 19 years and older, stratifies them by town type and housing type, and uses a proportional sampling probability based on the number of households of each housing type. After the initial extraction, the number of households in tong, ban, and ri, chosen as sampling points, is determined, and a second extraction is conducted using systematic sampling. Of the 229 242 participants in the 2021 Community Health Survey, 74 492 aged 65 years or older were selected as the final research participants.

Measures

General Characteristics of the Participants

The general variables included age, gender, living arrangements, type of residential area, housing type, level of financial security, and employment status. Age was categorized as “65-74 years old,” “75-84 years old,” and “85 years old or older,” and participants were categorized as “recipients” or “non-recipients” of welfare.

Fall Experience

The experience of falling was encapsulated in a single question: “Have you fallen in the past year?” The survey included slips, trips, and falls, and responses were limited to “yes” or “no.” The number of falls was categorized as 1, 2, or 3 or more falls per year. Fall treatment experience was assessed with a single question: “Have you ever received hospital treatment for a fall?” Possible responses were “yes” and “no.”

Health-Related Characteristics

Health-related variables included subjective health status, number of days of moderate physical activity, number of days of walking, number of days of strength training, number of days of eating breakfast, body mass index (BMI), sleep time, depression score, experience of cognitive decline, and presence or absence of high blood pressure. The presence or absence of a diagnosis of diabetes was also considered. Subjective health status was classified as “good” for very good or good health, “average” for average health, and “poor” for poor or very bad health. Regarding the number of strength-training days, participants were divided into 2 groups: those who engaged in strength training for more than 3 days a week and those who did not. BMI was calculated based on height and weight. According to the standards of the Korean Obesity Society, underweight is defined as a BMI of less than 18.5 kg/m², while normal weight falls within the range of 18.5 to 22.9 kg/m². Overweight and obese individuals were classified as having a BMI of 23.0 kg/m² or higher. Sleep time was categorized as “yes” if the duration was 6 to 8 h and “no” for other durations, based on the optimal sleep time (6-8 h) recommended by the Korean Sleep Society.¹⁶ Depression was assessed using the Patient Health Questionnaire (PHQ-9), which comprises 9 questions rated from 0 to 3, totaling 27 points. Scores of 0 to 4 indicate no depression, 5 to 9 suggest mild depression, 10 to 19 indicate moderate depression, and 20 to 27 indicate severe depression.

Data Analysis

The data analysis in this study utilized SPSS/Windows software (version 26.0; IBM Corp, Armonk, NY, USA). The stratification variables employed in the sample design of the 2021 Community Health Survey and the survey districts extracted by stratum were designated as cluster variables. After applying integrated weights to create a complex sample design analysis file, a complex sample data analysis was performed. The participants’ general and health-related characteristics were analyzed using unweighted counts, weighted percentages, weighted averages, and average errors. Additionally, the participants’ demographic characteristics, physical attributes, and health-related lifestyles were examined. Differences in fall experiences based on habits were analyzed using the Rao-Scott χ² test and complex sample t-test. To determine the risk ratio of falls experienced among seniors aged 65 years or older, a complex sample binary logistic regression was conducted by including variables that exhibited a significant impact in univariate analysis. Based on the results of logistic regression analysis, a fall-risk prediction nomogram was created according to regression coefficients. Receiver operating characteristic (ROC) curves and areas under the curve (AUC) were calculated to assess the reliability of the nomogram.

Ethical Considerations

This study was conducted as a secondary data analysis (IRB no. JBNU 2023-08-003) with an exemption from review by the Bioethics Committee of the researcher’s institution. The community health survey was conducted after receiving approval from the Korea Disease Control and Prevention Agency’s Medical Research Ethics Review Committee, and consent was obtained from all participants. Article 17 of the Statistics Act allows for only non-identifying data, which cannot be used to identify an individual. This study obtained approval for the use of raw data from the Community Health Survey website (http://chs.cdc.go.kr/chs/index.do) and assigned the data a unique identification number from the Korea Disease Control and Prevention Agency.

Results

General Characteristics of the Participants

Regarding the general characteristics of the subjects in this study, the average age was 73.91 years, with the highest proportion being 65 to 74 years old (58.6%). Concerning gender, 55.1% were female, 22.3% lived alone, and 72.9% lived in the same region. Regarding housing type, 57.5% of the participants were residents, which exceeded the percentage of apartment residents. Additionally, 8.1% were recipients of basic welfare and 31.5% were economically active. Regarding health-related characteristics, 29.6% reported poor subjective health status, and the average number of days spent walking was 4.20 days. Among the participants, 13.7% engaged in strength training for more than 3 days a week, while the majority (91.5%) reported eating breakfast 5 to 7 times a week. The mean sleep time was 6.48 h. Of the participants, 68.0% slept for 6 to 8 h a day. The average BMI was 23.5 kg/m², with obesity being the most common status (55.7%). Of the participants, 80.0% did not have depression, and the average depression score was 2.64 points (out of 27). Additionally, 37.8% of the participants experienced cognitive decline, 53.3% had high blood pressure, and 23.5% had diabetes mellitus. The percentage of people who had experienced falls was 16.8%, and the average number of falls per year was 1.48. Instances of 3 or more falls accounted for 15.2%, and 49.0% of individuals received hospital treatment for falls (Table 1).

Table 1.

Demographic Characteristics of the Participants (N = 74 492).

Variable	Categories	n ^a	%^b	M ± SE
General characteristics
Age (years)	65-74	40 333	58.6	73.91 ± 0.04
	75-84	27 401	33.8
	≥85	6758	7.7
Gender	Male	31 471	44.9
Gender	Female	43 021	55.1
Living together	No	20 058	22.3
Living together	Yes	54 434	77.7
Residence	City	30 855	72.9
Residence	Town/Village	43 637	27.1
Residence type	House	56 213	57.5
Residence type	Apartment	18 279	42.5
Receiving basic welfare	Yes	5381	8.1
Receiving basic welfare	No	69 099	91.9
Economic activity	Yes	30 505	31.5
Economic activity	No	43 986	68.5
Health related characteristic
Subjective health status	Good	19 616	29.1
	Moderate	29 914	41.3
	Bad	24 960	29.6
Number of walking days				4.20 ± 0.01
Strength training (>3 days/week)	Yes	7756	13.7
Strength training (>3 days/week)	No	66 736	86.3
Breakfast days per week	No breakfast	2492	4.4
	1-2	782	1.3
	3-4	1907	2.8
	5-7	69 311	91.5
BMI (kg/m²)	<18.5	3604	4.3	23.51 ± 0.03
	>22.9	29 430	40.0
	18.5-22.9	38 208	55.7
Sleep duration time (6-8 h)	Yes	50 915	68.0	6.48 ± 0.01
Sleep duration time (6-8 h)	No	23 577	32.0	6.48 ± 0.01
PHQ score (0-27)	Severe (20-27)	324	0.5	2.64 ± 0.02
	Moderate (10-19)	780	1.1
	Mild (5-9)	12 726	18.4
	Not (0-4)	60 642	80.0
Experience of cognitive decline	Yes	27 811	37.8
Experience of cognitive decline	No	46 661	62.2
Hypertension	Yes	40 922	53.3
Hypertension	No	33 552	46.7
Diabetes mellitus	Yes	17 232	23.5
Diabetes mellitus	No	57 249	76.5
Falls related characteristics
Fall experience	Yes	12 634	16.8
Fall experience	No	61 854	83.2
Number of falls (times/annual)	1	8232	66.8	1.48 ± 0.02
	2	2207	18.0
	>3	2195	15.2
Fall treatment experience	Yes	6223	49.0
	No	6410	51.0

Note. BMI = body mass index; M = mean; PHQ = Patient Health Questionnaire; SE = standard error.

Unweighted count.

Weighted %.

Differences in Fall Experiences Based on the Participants’ Characteristics

The participants’ general characteristics, as factors influencing fall experience, were age (P < .001), gender (P < .001), living alone or with 1 or more others (P < .001), housing type (P < .001), and welfare status (P < .001). As shown in Table 1, a statistically significant difference in falls was observed based on economic activity (P < .001). Falls, according to health-related characteristics, were associated with subjective health status (P < .001), number of days spent walking (P < .001), number of days dedicated to strength training (P < .001), frequency of eating breakfast (P < .001), BMI (P < .001), sleep duration (P < .001), depression (P < .001), cognitive decline (P < .001), hypertension (P < .001), and diabetes mellitus (P < .001). A significant difference was observed between the groups (Supplemental Table 1).

Factors Influencing the Participants’ Experience of Falling

Factors affecting the subjects’ fall experience indicated that the probability of falling was 1.21 times higher in the group aged 75 to 84 years compared to the group aged 65 to 74 years (95% CI = 1.14-1.29), and 1.40 times higher in the group aged 85 or older (95% CI = 1.27-1.56). Regarding gender, women had 1.49 times higher odds than men (95% CI = 1.40-1.58). Those living alone had a 1.13 times higher risk compared to those not living alone (95% CI = 1.07-1.20), and those receiving basic livelihood assistance were 1.18 times more likely than those not receiving basic welfare (95% CI = 1.08-1.30).

Additionally, compared to the group that perceived their subjective health status as good, the group with average health status had a 1.16 times higher likelihood of a fall (95% CI = 1.07-1.25), and the group with poor subjective health status was 1.72 times more likely (95% CI = 1.59-1.87). As the number of days of walking practice increased by 1 day, the probability of falling became .99 times less likely (95% CI = 0.98-0.99). The group that had breakfast 5 to 7 times a week was 0.86 times less likely to have a fall than the group that did not have breakfast. The obesity group’s BMI was 1.08 times higher than that of the normal group (95% CI = 1.02-1.15), and in depression, the mildly depressed group was more likely to experience fall than the non-depressed group. The non-depressed group had a 1.78 times higher risk of a fall (95% CI = 1.57-2.02), the group with moderate depression had a 2.22 times higher risk (95% CI = 1.89-2.85), and the group with severe depression had a 2.84 times higher risk (95% CI = 2.11-3.82). Compared to the group with adequate sleep, the group without sleep had a 1.11 times higher risk of a fall (95% CI = 1.04-1.17). The group that experienced cognitive decline was 1.34 times more likely than the group that did not experience it (95% CI = 1.27-1.42), and the rate in the group diagnosed with diabetes mellitus was 1.12 times higher than that of the group that was not diagnosed with diabetes mellitus (95% CI = 1.05-1.19) (Table 2).

Table 2.

Factors Influencing Fall Experiences (N = 74 492).

Variable	Categories	B	SE	P	OR	95% CI
Variable	Categories	B	SE	P	OR	Lower	Upper
General characteristics
Age (years) (Ref. 65-74)	≥85	0.34	0.05	<.001	1.40	1.27	1.56
Age (years) (Ref. 65-74)	75-84	0.19	0.03	<.001	1.21	1.14	1.29
Gender (Ref. Male)	Female	0.40	0.03	<.001	1.49	1.40	1.58
Living together (Ref. No)		0.12	0.03	<.001	1.13	1.07	1.20
Residence type (Ref. Apartment)	House	0.02	0.03	.456	1.02	0.96	1.08
Receiving basic welfare (Ref. No)	Yes	0.17	0.05	<.001	1.18	1.08	1.30
Economic activity (Ref. No)	Yes	0.04	0.03	.247	1.04	0.97	1.10
Health-related characteristic
Subjective health status (Ref. Good)	Bad	0.54	0.04	<.001	1.72	1.59	1.87
Subjective health status (Ref. Good)	Moderate	0.15	0.04	<.001	1.16	1.07	1.25
Number of walking days (days)		–0.01	0.01	.014	0.99	0.98	0.99
Strength training (>3 days/week) (Ref. <3days)	≥3	0.02	0.05	.731	1.02	0.92	1.12
Breakfast days per week (week/days) (Ref. <1)	5-7	–0.15	0.07	.031	0.86	0.75	0.99
	3-4	0.04	0.10	.655	1.05	0.86	1.27
	1-2	0.09	0.13	.493	1.09	0.85	1.41
BMI (kg/m²) (Ref. 18.5-22.9)	<18.5	0.10	0.06	.113	1.10	0.98	1.24
BMI (kg/m²) (Ref. 18.5-22.9)	>22.9	0.08	0.03	.006	1.08	1.02	1.15
PHQ (0-27) (Ref. 0-4)	Severe (20-27)	1.04	0.15	<.001	2.84	2.11	3.82
	Moderate (10-19)	0.84	0.10	<.001	2.32	1.89	2.85
	Mild (5-9)	0.58	0.06	<.001	1.78	1.57	2.02
Sleep duration time (6-8 h) (Ref. No)	Yes	0.10	0.03	.001	1.11	1.04	1.17
Experience of cognitive decline (Ref. No)	Yes	0.29	0.03	<.001	1.34	1.27	1.42
Hypertension (Ref. No)	Yes	0.01	0.03	.843	1.01	0.95	1.06
Diabetes mellitus (Ref. No)	Yes	0.11	0.03	<.001	1.12	1.05	1.19

Note. BMI = body mass index; CI = critical ratio; OR = odds ratio; PHQ = Patient Health Questionnaire; Ref. = reference; SE = standard error.

Fall-Risk Nomogram

The nomogram converts the influence of individual factors into scores using variables that have a significant impact on the likelihood of falling. It calculates a total score, which is the sum of these scores, and provides the probability of experiencing a fall as a measure for the total score. Among the variables that affect the experience of falling, the depression score has the largest distribution, from 0 to 100 points, and can be seen as having the greatest discriminatory power, followed by subjective health status (0-60 points) and gender (0-38 points). Experience of cognitive impairment ranged from 0 to 32 points, age from 0 to 26 points, welfare status from 0 to 17 points, adequacy of sleep from 0 to 15 points, living alone or with 1 or more others and presence or absence of diabetes mellitus from 0 to 11 points, and number of days of walking practice from 0 to 8 points (Figure 1A). To assess the consistency of the predicted values derived from the fall experience variables used in the nomogram with respect to actual fall occurrences, an ROC curve was constructed, and the AUC area, representing the area under the ROC curve, was computed. The AUC was 0.66 (Figure 1B).

Figure 1.

(A) Nomogram for fall experiences; (B) Accuracy of the fall experiences nomogram using the ROC curve.

Discussion

This study aimed to develop a nomogram to screen for risk factors for falls and predict falls in community-dwelling older people aged 65 years or older using data from Korea’s 2021 Community Health Survey. The results of this study indicated that 16.8% of the older individuals residing in the community had experienced a fall within the prior year. In a previous study, the 1-year fall experience rate among older people living at home in Korea was established as 16.3% to 51.5%,^9,10,17 which varied depending on the location or subject. However, a national study in the United States established that the prevalence was 16.4% among individuals aged 65 years or older.¹⁸ This result is similar to the 16.5%¹⁹ reported in a study of individuals more than 65 years of age in Taiwan.

In this study, the average annual number of falls experienced per year was 1.48. Of the participants, 15.2% had experienced 3 or more falls, and 49.0% sought treatment due to falls. In another study by Lee²⁰ based on data from the 2020 Survey on the Status of the Elderly, the average number of falls per year was reported to be 1.56, which is similar to the results of this study. Kim²¹ reported that the rate of physical damage after a fall in older individuals was 75.7%; however, only 56.8% of them received treatment. Approximately 30% to 50% of physical injuries, such as abrasions, were minor, but 30% of the cases required surgery, such as fractures or subdural hematomas. As individuals age, the incidence of falls increases not only because of decreased physical function resulting from physiological changes or chronic diseases but also because of the serious damage and prognosis associated with falls.⁶ Therefore, effective measures for fall prevention are required. Older people require consistent practice of suitable strength training and walking exercises, as well as ongoing education programs, to maintain a safe living environment.

In this study, risk factors affecting fall experiences among older people included age, gender, living alone, welfare status, subjective health status, number of walking days, BMI, adequacy of sleep, depression, degree of cognitive impairment, and diabetes. Previous studies have reported various factors related to falls, such as age; gender; type of residence; frequency of assistive-device usage; obesity level; presence or absence of physical diseases, dementia, and depression; and amount of drinking, smoking, and exercise.^10
-14 Even though our findings relied on the data collected from Korea’s 2021 Community Health Survey, we found risk factors similar to those reported by the previous studies.

Most studies have reported that the incidence of falls increases with age.^10,12 In this study, the risk of falls was 1.22 times higher in the group aged 75 to 84 years than in the group aged 65 to 74, and 1.42 times higher in the group aged 85 years or older. In a longitudinal study conducted in China, the incidence of falls increased by 1.15 times in individuals aged 80 years or older compared to those aged 64 years or younger.²² Additionally, Lee²⁰ reported that the fall rate increases with age, which aligns with the findings of this study. With age, the weakening of physical and mental functions is an unavoidable physiological phenomenon commonly experienced by many older adults. A decline in physical function increases the risk of unstable gait and falls.²² Therefore, an appropriate exercise program is necessary to periodically assess lower-extremity muscle strength and balance ability to prevent muscle-strength decline in older people.

Another interesting result of this study indicated that women had a 1.47 times higher risk of falling than men. In studies by Lee²⁰ and Kim,²³ there was no difference in the incidence of falls according to gender. However, a study by the Korea Disease Control and Prevention Agency⁷ reported a 1.35 times higher incidence in women than in men. Additionally, a study on Europeans by Almada et al²⁴ demonstrated that the incidence of falls was twice as high in elderly women as in older men. Although figures vary across studies, the incidence of falls is higher among women. In general, older women have a greater fear of falling than older men. The greater the fear of falling, the slower the movement, which increases the risk of falling.²⁴ Additionally, women’s physical function, muscle strength, and homeostasis are weakened compared to men’s due to hormonal changes associated with menopause, which increases the risk of falls.²⁵ Therefore, gender-specific fall-prevention strategies are needed, as well as research that considers the characteristics of men and women.

In this study, we established that the rate of falls among basic livelihood recipients was 1.17 times higher than that among non-recipients. According to Kim et al,¹¹ the incidence of falls is 1.774 times higher among low-income older people than among high-income older people. For older people, a weak economic status increases the likelihood of being exposed to the risk of low health and chronic stress, but a lack of resources to cope with these issues²⁶ can be interpreted as a greater risk of health damage.

Additionally, this study found that the risk of falling among older people living alone was 1.14 times higher than among those living with family. However, Wu and Ouyang²² found that the risk of falling was higher when living with a housemate than when living alone. This is because when there is a housemate, less consideration is given to appropriate furniture and lighting to ensure safety. Contrarily, studies by the Ministry of Public Administration and Security⁹ and Lee²⁰ established that the risk of falling was higher in older individuals living alone than in those living with others, which matches the result of this study.

Depression due to loneliness and isolation has been reported to increase the incidence of falls in older people living alone, and social support from family or friends lowers the incidence of falls in older people living alone.²⁰ Depression causes a decrease in neurotransmitters such as serotonin and norepinephrine, leading to decreased motor skills, difficulty walking, and cognitive decline.¹² In Korea, the average prevalence of depression among older people in 2020 was 13.5%.²⁷ Decreased movement, drug use, and issues with dynamic balance in older individuals with depression contribute to falls.¹² Moreover, this study found that the risk of falling increased 1.06 times as the depression score increased by 1 point. Lee²⁰ reported that the incidence of falls among depressed older people was 1.14 times higher than that among non-depressed older people, supporting the relationship between depression and falls.

This current study established that the risk of falling was 1.34 times higher in individuals who experienced a decline in cognitive function than in those who did not experience such a decline. This is because, as Zhang et al²⁸ established, older people with cognitive decline differ from those with normal cognitive function. This supports the study results, which reported that falls were more than twice as frequent in older individuals with cognitive decline. When cognitive function is reduced, spatial cognitive function and concentration are diminished, leading to an unstable gait and a decreased sense of balance, thereby increasing the risk of falls.¹³ In Korea, 63.3% of falls among older people occur at home and 72.4% occur during daily activities.⁹ Therefore, to prevent falls among older adults, especially those living alone, we provide support for housing modifications and fall-prevention products. We also offer assistance and social support in daily life through home nursing visits or integrated community care services, and address stress and depression in old age. Government-led measures are necessary to reduce the risk of cognitive decline.

Quantitative and qualitative sleep disorders increase with age, and sleep quality is related to stress and depression.¹⁶ In this study, based on the optimal sleep time of 6 to 8 h suggested by the Korean Society of Sleep Medicine,¹⁶ the risk of falling in the group that did not have adequate sleep was 1.09 times higher than that in the group that did sleep well and enough. Kim and Kim²⁹ also indicated that sleep disorders increased the risk of falling, which is similar to the findings of this study. As age increases, deep sleep becomes more difficult to maintain. Sleep disorders cause fatigue, confusion, decreased concentration, and impaired cognitive function, resulting in an increase of accidents such as falls among the older population.¹⁶ Therefore, a sleep promotion program tailored to older people that can improve sleep quality through appropriate physical activity or stress management is required.

Subsequently, as the number of days of walking practice increased by 1 day, the probability of falling decreased by .99 times. Walking is an aerobic exercise that older people can easily engage in. It reduces physical stress on the joints; improves cardiopulmonary function, joint flexibility, balance, and walking ability; and strengthens the lower-extremity muscles, thereby reducing the risk of falls.³⁰ Additionally, physical activities such as walking are known to be effective in improving sleep quality and suppressing or alleviating depression and cognitive decline.³⁰ Therefore, physical activity programs should be implemented that older adults can enjoy, engage in, and continue to practice.

The risk of falling in the obese group was 1.09 times higher than that in the normal BMI group, which supports the study by Neri et al³¹ that reported that the incidence of falls in obese older women was 2.13 times higher than that in normal-weight older individuals. If BMI is high, increased weight weakens the lower-extremity muscles, affects balance and stability, and increases the likelihood of falls.³¹ Therefore, effective physical activity intervention programs such as walking, dancing, and tai chi exercises, which older people can safely and consistently engage in at senior welfare centers, will be beneficial in helping them maintain appropriate weight and physical function in old age.

Similar to previous studies, this study found that the risk of falling was 1.14 times higher in the group with an average subjective health status than in the group that perceived their subjective health status as good. Additionally, the risk of falling was 1.65 times higher in the group with poor subjective health status. Subjective health status is based on age, and subjective health perception is widely used as a reliable proxy variable to predict actual health status.³² The research results of Lee²⁰ also support the findings of this study, reporting that the risk of falling was 1.25 times higher when individuals perceived their health status as poor than when they perceived it as good.

Additionally, in this study, the fall rate in the group diagnosed with diabetes was 1.12 times higher than that in the group that was not diagnosed. Chronic diseases and medications are closely related to falls,¹⁴ and a longitudinal study by Pijpers et al³³ reported that older people with diabetes had a 67% higher rate of falling than those without diabetes. The prevalence of diabetes among older people is thought to increase the risk of falls because persistent diabetes can lead to reduced vision, dizziness due to hypoglycemia, and balance disorders.³³

As discussed above, falls have the characteristic of being predictable and preventable.⁹ The World Falls Group (WFG) has proposed 12 guidelines for preventing falls in older adults, which include fall risk assessment, cardiovascular risk factors related to falls, and the association between cognition and falls.³⁴ Eckstrom et al³⁵ compared the guidelines of the WFG and the American Geriatrics Society (AGS) and recommended that annual fall screening tests be conducted for seniors aged 65 and older in the U.S. community. If identified as a high-risk group, multifactorial risk assessments and tailored interventions are provided. Therefore, predicting the risk of falls in advance and providing appropriate preventive services can be a highly effective way to enhance individuals’ quality of life and alleviate the social burden. Predicting the risk of falls in advance and providing appropriate preventive services can be highly effective in improving not only the individual’s quality of life but also in reducing the social burden. Medical institutions use tools such as the Morse Fall Scale (MFS) and St. Thomas’s Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) to predict the risk of falls among hospitalized patients.⁸ However, these tools are designed to be applied in a clinical environment and are therefore inappropriate for assessing the risk of falls among older people living at home in the community.

Accordingly, fall risk factors were identified using secondary data from a sample survey representing Korea, and a nomogram was developed. Among the variables that affect the experience of falling, the depression score exhibits the largest distribution, from 0 to 100 points, and can be seen as having the greatest discriminatory power, followed by subjective health status (0-32 points) and gender (0-21 points). Experience with cognitive impairment (0-17 points), age (0-16 points), basic livelihood security (0-8 points), adequacy of sleep (0-7 points), living alone (0-6 points), presence of diabetes (0-6 points), and the distribution of walking practice days (0-4 points) also contributed to the risk of falling. The accuracy of the values estimated using these variables was an AUC of 0.66. According to previous research,³⁶ displaying an AUC value above 0.6 is significant. The nomogram in this study can be considered reliable for predicting the risk of falling. Therefore, the nomogram constructed in this study makes it possible to visualize and intuitively validate the fall-risk score. This will enable a more accurate prediction of fall risk for older individuals living at home in the community and facilitate intensive management of high-risk groups. However, the screening tool for older persons’ prescriptions in older adults with a high risk of falling (STOPPFall)³⁷ is utilized worldwide to identify medications that elevate the risk of falls and to recommend precautions when these medications are prescribed to older adults. However, this study has a limitation in that medication use data was not available in the secondary data, which prevented its inclusion in the nomogram. Similarly, because this was a cross-sectional study that used data from a community health survey, fall risk factors were examined using only the variables included in the original dataset. Therefore, we believe that the accuracy of the nomogram can be further enhanced if future research targets older individuals in homes and incorporates additional variables such as individual physical function and health indicators. Nevertheless, using the nomogram developed in this study, local health institutions can predict and manage fall risk more systematically and establish fall-prevention strategies based on scientific evidence that can reduce an individual’s risk of falling. We hope that this will make a valuable contribution to the field.

Conclusion

In this study, the incidence of falls among older adults was 16.8%. The fall prediction nomogram developed in this study revealed that depression had the greatest influence on the occurrence of falls among older adults, followed by subjective health status, gender, experience of cognitive impairment, age, amount of stress, welfare status, adequacy of sleep, and living alone. The presence of diabetes and the number of days of walking practice also played an important role. To prevent falls among older people, it is necessary to develop an intervention program that comprehensively considers not only physical but also psychological and social factors. Therefore, the nomogram developed based on the risk factors identified in this study will help local health institutions develop and apply systematic education and intervention programs to adequately assess risk factors and prevent falls and fall-related injuries among the older population. We also suggest future research to confirm the relationship between falls and medication use and between falls and living environment among older people, which were not identified in this study.

Supplemental Material

sj-docx-1-inq-10.1177_00469580241273173 – Supplemental material for Nomogram for Predicting the Risk Factors for Falls in Older People: A Secondary Data Analysis Based on the 2021 Community Health Survey

Supplemental material, sj-docx-1-inq-10.1177_00469580241273173 for Nomogram for Predicting the Risk Factors for Falls in Older People: A Secondary Data Analysis Based on the 2021 Community Health Survey by Sook Kyoung Park, Hyuk Joon Kim, Young-Me Lee and Hye Young Kim in INQUIRY: The Journal of Health Care Organization, Provision, and Financing

Footnotes

Acknowledgements

None

Authorship Statement

Conceptualization, SKP, HJK, YML, and HYK; methodology, SKP, HJK, YML, and HYK; formal analysis, SKP & HJK; investigation, and data curation, SKP & HJK; writing-original draft, SKP, HJK, YML, and HYK; writing—review and editing, SKP, HJK, YML, and HYK; visualization, HJK. All authors read and approved the final manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This paper was supported by international research funds for humanities and social science of Jeonbuk National University in 2023.

Institutional Review Board Statement

This study was performed after receiving approval from the Institutional Review Board (IRB) of the Jeonbuk National University to which the researcher belongs (JBNU 2023-08-003).

ORCID iDs

Sook Kyoung Park

Hyuk Joon Kim

Hye Young Kim

Supplemental Material

Supplemental material for this article is available online.

References

Korean Statistical Information Service. Elderly Population Ratio. Korean Statistical Information Service. Accessed July 7, 2023. https://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1YL20631&vw_cd=MT_GTITLE01&list_id=101&seqNo=⟨mode=ko&language=kor&obj_var_id=&itm_id=&conn_path=MT_GTITLE01

World Health Organisation. WHO Health Organization Global Report on Fall Prevention in Older Age. World Health Organization. 2021. Accessed July 7, 2023. https://www.who.int/en/news-room/fact-sheets/detail/falls

Clemson

Stark

Pighills

, et al. Environmental interventions to prevent falls among community-dwelling older adults. Cochrane Database Syst Rev. 2023;3(3):1-19. doi:10.1002/14651858.CD013258.pub2

Korea Disease Control and Prevention Agency. Injury Statistics. Korea Disease Control and Prevention Agency. Published April 19, 2022. Updated April 20, 2022. Accessed October 10, 2023. https://kdca.go.kr/contents.es?mid=a20205021300

Korea Disease Control and Prevention Agency. Injury Type and Cause Statistics. Korea Disease Control and Prevention Agency; 2023:33-35.

Hajek

Bock

König

HH.

Psychological correlates of fear of falling: Findings from the German Aging Survey. Geriatr Gerontol Int. 2018;18(3):396-406. doi:10.1111/ggi.13190

Korea Disease Control and Prevention Agency. National Injury Factbook 12th. Korea Disease Control and Prevention Agency; 2023:36-48.

Sohng

Park

Chaung

Park

HJ.

Validation of the Short Form Bobath Memorial Hospital Fall Risk Assessment Scale at a specialized geriatric hospital in Korea. J Korean Public Health Nurs. 2014;28(3):495-508. doi:10.5932/JKPHN.2014.28.3.495

Ministry of the Interior and Safety. Increased Falls Occur Due to Aging and Changes in the Social Environment. Ministry of the Interior and Safety; 2023:1-30.

10.

Park

Shin

YS.

Predictors of accidental falls in the community-dwelling elderly by age. J Korean Acad Community Health Nurs. 2019;30(2):141-149. doi:10.12799/jkachn.2019.30.2.141

11.

Kim

Choi

Xiong

Epidemiology of fall and its socioeconomic risk factors in community-dwelling Korean elderly. PLoS One. 2020;15(6):1-14. doi:10.1371/journal.pone.0234787

12.

Serrano-Checa

Hita-Contreras

Jiménez-García

, et al. Sleep quality, anxiety, and depression are associated with fall risk factors in older women. Int J Environ Res Public Health. 2020;17(11):4043-4053. doi:10.3390/ijerph17114043

13.

Wong

Pang

Factors associated with falls in psychogeriatric inpatients and comparison of two fall risk assessment tools. East Asian Arch Psychiatry. 2019;29(1):10-14.

14.

Kim

Jang

, et al. Urinary incontinences are related with fall and fragility fractures in elderly population: Nationwide Cohort Study. J Bone Metab. 2018;25(4):267-274. doi:10.11005/jbm.2018.25.4.267

15.

Korea Disease Control and Prevention Agency. Guide for Using Raw Data from the 2021 Community Health Survey. Korea Disease Control and Prevention Agency; 2021:1-232.

16.

Korean Society of Sleep Medicine. Appropriate Sleep Time. Korean Society of Sleep Medicine. Accessed October 10, 2023. https://www.sleepmed.or.kr/content/info/sleeptime.html

17.

Kim

Yang

Park

KS.

Fall experience and risk factors for falls among the community-dwelling elderly. J Muscle Joint Health. 2013;20(2):91-101. doi:10.5953/JMJH.2013.20.2.91

18.

Verma

Willetts

Corns

, et al. Falls and fall-related injuries among community-dwelling adults in the United States. PLoS One. 2016;11(3):1-14. doi:10.1371/journal.pone.0150939

19.

Tsai

Yang

Lin

Wang

YW.

Prevalence and risk factors of falls among community-dwelling older people: results from three consecutive waves of the national health interview survey in Taiwan. BMC Geriatr. 2020;20(1):529-611. doi:10.1186/s12877-020-01922-z

20.

Lee

CK.

Factors influencing fall in the community-dwelling elderly: data from the 2020 National Survey of Older People: a secondary analysis study. J Korean Gerontol Nurs. 2023;25(3):320-331. doi:10.17079/jkgn.2023.00094

21.

Kim

SM.

The comparison of risk factors for falls in the community-dwelling elderly. J Korean Proprioceptive Neuromuscul Facil Assoc. 2016;14(2):93-103. doi:10.21598/JKPNFA.2016.14.2.93

22.

Ouyang

Fall prevalence, time trend and its related risk factors among elderly people in China. Arch Gerontol Geriatr. 2017;73:294-299. doi:10.1016/j.archger.2017.08.009

23.

Kim

Factors associated with falls in the elderly: Based on 2014 the Korean elderly survey. J Korea Contents Assoc. 2017;17(6):479-489. doi:10.5392/JKCA.2017.17.06.479

24.

Almada

Brochado

Portela

Midão

Costa

Prevalence of falls and associated factors among community-dwelling older adults: a cross-sectional study. J Frailty Aging. 2021;10(1):10-16. doi:10.14283/jfa.2020.44

25.

Zhao

Liang

Huang

, et al. Identification of risk factors for falls in postmenopausal women: a systematic review and meta-analysis. Osteoporos Int. 2020;31(10):1895-1904. doi:10.1007/s00198-020-05508-8

26.

Grzywacz

Almeida

Neupert

Ettner

SL.

Socioeconomic status and health: a micro-level analysis o exposure and vulnerability to daily stressors. J Health Soc Behav. 2004;45(1):1-16. doi:10.1177/002214650404500101

27.

Korean Institute for Health and Social Affairs. National Survey of Older Koreans. Ministry of Health and Welfare; 2020:259-644.

28.

Zhang

Low

Schwenk

, et al. Review of gait, cognition, and fall risks with implications for fall prevention in older adults with dementia. Dement Geriatr Cogn Disord. 2019;48(1-2):17-29. doi:10.1159/000504340

29.

Kim

JI.

Relationship among the health state, daily living activities (ADL, IADL), sleep state, and depression among old people at elderly care facilities. J Korea Acad Coop Soc. 2015;16(4):2609-2619. doi:10.5762/KAIS.2015.16.4.2609

30.

Rodio

Fattorini

Downhill walking to improve lower limb strength in healthy young adults. Eur J Sport Sci. 2014;14(8):806-812. doi:10.1080/17461391.2014.908958

31.

Neri

SGR

Harvey

Tiedemann

Gadelha

Lima

. Obesity and falls in older women: mediating effects of muscle quality, foot loads and postural control. Gait Posture. 2020;77:138-143. doi:10.1016/j.gaitpost.2020.01.025

32.

Benyamini

Why does self-rated health predict mortality? An update on current knowledge and a research agenda for psychologists. Psychol Heal. 2011;26(11):1407-1413. doi:10.1080/08870446.2011.621703

33.

Pijpers

Ferreira

de Jongh

, et al. Older individuals with diabetes have an increased risk of recurrent falls: analysis of potential mediating factors: the longitudinal ageing study Amsterdam. Age Ageing. 2012;41(3):358-365. doi:10.1093/ageing/afr145

34.

Montero-Odasso

van der Velde

Martin

FC.

World guidelines for falls prevention and management for older adults: a global initiative. Age Ageing. 2023;51(9):afac205. doi:10.1093/ageing/afac205

35.

Eckstrom

Vincenzo

Casey

, et al. American Geriatrics Society response to the World Falls Guidelines. J Am Geriatr Soc. 2024;72(6):1669-1686. doi:10.1111/jgs.18734

36.

Simundic

AM.

Diagnostic accuracy—Part 1: Basic concepts sensitivity and specificity, ROC analysis, STARD statement. Point Care. 2012;11(1):6-8. doi:10.1097/POC.0b013e318246a5d6

37.

Seppala

Petrovic

Ryg

, et al. STOPPFall (Screening tool of older persons prescriptions in older adults with high fall risk): a Delphi study by the EuGMS task and finish group on Fall-risk-increasing drugs. Age Ageing. 2021;50(4):1189-1199. doi:10.1093/ageing/afaa249

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB