Journal of Aging Research
Volume 2018 (2018), Article ID 8546085, 10 pages
https://doi.org/10.1155/2018/8546085

Research Article

Correlates of Falls among Community-Dwelling Elderly in Thailand

Titaporn Worapanwisit,¹ Somkid Prabpai,¹ and Ed Rosenberg²

¹Health Promotion and Health Education, Faculty of Education, Kasetsart University, Bangkok 10903, Thailand
²Department of Sociology, Appalachian State University, Boone, NC 28608, USA

Correspondence should be addressed to Ed Rosenberg; rosenberge@appstate.edu

Received 24 January 2018; Accepted 7 May 2018; Published 24 May 2018

Academic Editor: Barbara Shukitt-Hale

Copyright © 2018 Titaporn Worapanwisit et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Nearly every nation is experiencing rapid population aging. One area of major concern is health; a major health risk for older adults is falling, and there are multiple negative consequences of falling. This is a global concern yet is underresearched in many nations. This study examines demographic, health, and environmental correlates of falling among community-dwelling Thai elderly. Data were collected from a sample of 406 adults aged 60–69. Significant () fall correlates were urban residence, older age, greater BMI, impaired and uncorrected vision, chronic health conditions, medication use and medication side effects, poor muscle tone, and hazardous indoor and outdoor home environments. Results lead to recommendations for interventions to reduce fall risk that are both evidence-based and culturally acceptable.

1. Introduction

In the twenty-first century, most nations are experiencing rapid growth of their older populations, both in number and as a percent of the population. This growth is largely attributable to decreasing mortality due to advances in medicine and health promotion, and to fertility declines. The portion of the world’s population aged 60+, 12% in 2015, is projected to rise to 22%, to 2.1 billion, by 2050.

The older adult growth rate is not uniform across nations: less-developed nations will experience more rapid growth. While those aged 60+ in developed countries grew by 29% from 2000 to 2015, less-developed countries can expect a 60–71% increase by 2030. By 2050 nearly 80% of the world’s older population will live in these less-developed countries. Many Asian nations are in this latter category. By 2030, 60% of Asians will be 60+ and Asia will be home to more than half of the world’s oldest-old persons (aged 80+) [1].

In this context, Thailand is typical of Asian nations. The World Health Organization [2] estimates that those aged 60+ will comprise 29.6% of the Thai population in 2050.

Population aging has serious implications for individuals, families, communities, and nations. The faster the aging, the more quickly and severely the challenges arrive, and the more difficult it is for governments to react effectively. Large-scale proactive initiatives, if and where they exist, seem the exception that proves the underpreparedness rule.

Adapting health care for an aging population is one such major challenge. As people live longer, all else equal, they will live longer with illness and other impairments. Extending healthy life expectancy is more difficult than extending life expectancy, and the “rectangularization of the survival curve” [3], where we live a long, healthy life ending in a brief, precipitous terminal drop, remains the Holy Grail of life extension.

One typology of causes of death is composed of noncommunicable diseases, communicable disease, and injuries. The World Health Organization [4] believes the distribution of noncommunicable disease deaths will remain relatively constant, while communicable disease deaths will decrease. In contrast, deaths due to injuries—the focus of this research—will grow through 2030. Injury-related deaths are a large burden on society, financially and otherwise, but many of these deaths are preventable [5]. Unintentional-injury deaths, the five leading causes of which are falls, motor vehicle traffic crashes, suffocation, poisoning, and fire [6], focus the current study further.

A fall is “an unexpected event in which the participant comes to rest on the ground, floor, or lower level” [7]. Similarly, Bekibele and Gureje [8] described a fall as an unintentional loss of balance, causing one to make unexpected or unprepared contact with the ground or floor. Weir and Culmer [9] noted that a fall results from a complex combination of individual factors operating alone or in association with precipitating environmental factors.

Falls are by far the most common cause of injury and unintentional-injury death among older persons in the United States [10], accounting for more than one-half (55%) of the total; in contrast, the second most common cause, motor vehicle accidents, accounts for only 14% [11]. Both the NCHS and the Centers for Disease Control and Prevention [12] report that the rank of falls as a cause of unintentional-injury death rises with age, from tenth among those aged 25–34 to first for those aged 60+.

Falls also have greater consequences for older adults compared to younger adults. Elderly falls may cause serious, perhaps fatal injuries such as fractures, joint dislocation, and/or head trauma. But even if falls have no lasting impact on older adults’ physical health, they can have significant effects on psychosocial health, including greater fear of falling that can lead to social isolation [13]. It is difficult to determine the definitive risk factors for falling [14]. Nonetheless, fall risk factors can be trichotomized as follows: (1) intrinsic risk factors related to normal age changes, gender, race, physical problems, medical conditions, cognitive impairment, and degree of physical inactivity, (2) extrinsic risk factors, such as outdated vision correction, taking multiple prescribed medications, improper footwear, and nonuse of assistive devices, and (3) environmental risk factors, such as loose carpet, lack of grab bars in the bathroom, poor lighting, unsafe stairs, and uneven or slippery surfaces. Over one-third of elderly fall at least once annually due to various intrinsic and extrinsic risk factors [15]; 30–50% of falls among community-dwelling elderly are due to environmental factors [16].

Each year, more than one-third of the American elderly fall, with outdoor falls comprising nearly half the total—about 45%——of all elderly falls [17]. Outdoor falls comprise nearly half the total—about 48%—of all elderly falls [18]. Since Americans spend only 7% of their time outdoors [19], this is greatly disproportionate. The same is true in Thailand, where the Health Insurance System Research Office (HISRO) [20] reported that Thai women and men aged 60–69 are more likely to fall in public settings than in the home.

Most outdoor falls are precipitated by environmental factors, such as uneven surfaces and tripping or slipping on objects and usually occur on sidewalks, curbs, and streets [21]. This finding has cross-cultural support from studies in Canada [22], England [23], Norway [24], Finland [25, 26], Israel [27], and Japan [28]. Several researchers [29–31] reported that prescription medications can contribute to fall risk. Jung et al. [32] found that mobility impairments (transferring, gait imbalance, and lack of equilibrium) increase fall risk. Guzman et al. [33] related nonuse of assistive devices to increased odds of falling. In a Thai study, Kuhirunyaratn et al. [34] reported that elderly who use psychotropic medications are almost twice as likely to fall as those who do not (see also [35, 36]). Gait disturbance, visual impairment, and depression also doubled fall risk, and inappropriate indoor environment (cluttered rooms, slippery floors, nonadapted bathrooms, and dim lighting) nearly tripled the risk of falling.

Still, measures to reduce fall risk can be taken, especially at the individual and community levels. Fall-reduction improvements can be made inside and outside the home [37], often inexpensively.

Since falls incidence and prevalence rise with age, since nearly every nation is experiencing rapid population aging, and since the health, economic, and quality of life impacts of falls are significant on both micro- and macrolevels, there is a need for evidence-based research on effective fall prevention strategies and programs that reduce fall risks. Fortunately, many such studies exist, looking at the efficacy of both single- and multi-factor interventions to reduce risk factors and fall incidence. Exercise is the most common intervention used to reduce fall risk factors in community-dwelling older adults. Balance, gait, and strength training have been shown to be effective methods to reduce and prevent falls among community-dwelling older adults [38]. For example, a study of older adults [39] found that fall evaluation, balance training, home hazard management, fall prevention education, exercise, and home visitations resulted in significant fall reduction. Other multifaceted interventions, such as exercise plus dietary supplements [40] and exercise plus fall prevention education [41], yielded reduced fall rates among older adults.

Overall, falls are the leading cause of unintentional-injury deaths and can result from multiple causes, either singly or in combination. The risk of such death rises with age. Even nonfatal falls have more serious consequences for older adults than younger age groups. Major findings include (1) compared to time spent outside the home, older adults are disproportionately likely to fall there, often due to lack of familiarity and uneven surfaces; (2) prescription medications, mobility impairments, and nonuse of assistive devices are associated with greater likelihood of falling; (3) lower perceived fall risk is related to higher likelihood of falling; and (4) many evidence-based fall-reduction interventions exist. Some of these findings have been demonstrated to have cross-cultural application.

It is utopian to believe fall-related injuries can be eradicated, but one can realistically hope to reduce fall incidence and prevalence, even as nations’ population age and, as they do, the risk of falls increase. The aim of this research, by reporting demographic, health, and behavioral correlates of falls for community-dwelling Thai elderly, was to add to the knowledge base of universal versus culture-specific fall-reduction knowledge, attitudes, and practices. For older people, there seem to be cross-cultural commonalities in risk of falling. But there is likely variation in preventive or intervention activities; these can depend on aspects like a society’s view of aging and older people and the cultural acceptability of a proposed intervention (thus the importance of properly framing the intervention).

Two types of relationships are of interest: those that support prior research and those that vary from prior research. The latter case may merely reflect methodological variance from prior studies but also could indicate the impact of cultural variation on the risk of falls. In both cases, the findings could help identify fall prevention strategies that could reduce falls among community-dwelling older adults, promote safe, independent, healthy aging, and be culturally appropriate.

2. Methods

2.1. Sampling, Sample, and Data Collection

Thailand, a country in Southeast Asia, comprises 77 provinces and is divided into four regions: North, Central, Northeast, and South. For this study, one province—Surat Thani—was randomly chosen from the Southern region due to the residence of the lead author (“researcher”). The province has 19 districts containing a total of 131 subdistricts. Ten districts were randomly selected, and from each, a convenience sample of one subdistrict was chosen. Convenience criteria were ease of access by the researcher (transportation and relative proximity), researcher safety, and ease of access to older adults. Each subdistrict has a hospital. The researcher met with each hospital’s director; with the director’s consent, the researcher then met with the hospital’s Health Promotion Director to obtain a list of older adults. In each subdistrict, a list of all older adults was extracted from a database made available by public health officers. Each resulting list contained 80–200 older adults; the total for all ten subdistricts was 2203.

A random sample of 50 was drawn from each list. The 500 randomly selected Thai older adults then had to meet the following inclusion criteria: aged 60–69, voluntary participation, neither a current psychiatric diagnosis nor a history of such diagnoses, and sufficiently literate to complete a questionnaire with minimal assistance. Some were excluded due to being older than 69, and some chose not to participate. Nine older adults with a current psychiatric diagnosis or history of psychiatric diagnoses were excluded from the sample. No one was excluded due to illiteracy.

After exclusions, a sample contained 406 older adults. Demographic, health, and falls history data were collected via surveys administered at participants’ homes or other convenient locations (e.g., senior clubs, temples) between 15 June and 31 October 2017. Demographic variables were age, gender, marital status, residential location, education, occupational status, residence type, number of family members in household, living arrangement, income, main source of income, and health service provider. Health variables were body mass index, vision, hearing, chronic condition diagnosis, medication use, and alcohol use. Fall history variables were falls since turning 60, falls in the past six months, falls in the past three months, general cause of falls (e.g., medication side effects, hazardous home environment), specific cause of falls (e.g., footwear, drowsiness, poor lighting), general (indoors and outdoors), and specific (e.g., kitchen, sidewalk, temple) locations of falls, fall-related health problems, and treatment (if any) after falling.

2.2. Data Analysis

Descriptive statistics (mean, median, standard deviation, frequency, and percentage distributions) were used to describe the demographic, health, and falls history data of the sample. Data were analyzed using SPSS version 17. Due to most variables being measured at the nominal level, including the dependent variable (falls measured as yes/no or never/ever), chi-square was calculated, and was chosen to indicate statistical significance. In some cross tabulations, a cell had a frequency of zero; these instances related never/ever fall to muscle weakness, medical condition/lack of assistive devices, hazardous home environment, footwear/clothing, and indoor/outdoor area. In these cases, the Fisher Exact Test was calculated; was chosen to indicate statistical significance [42].

2.3. Research Ethics

This study was approved by the ethics committee at Boromarajonani College of Nursing, Surat Thani, Thailand. It was also approved by the Ethics Committee of the Provincial Public Health Office in Thailand for the research involving human subjects. The purpose of the research was explained to participants, as were voluntary participation, identity protection (confidentiality), and researcher contact information. Informed consent was obtained from all participants. Survey data were entered into a password-protected database on the first author’s secured computer.

3. Results

The demographic characteristics of the sample are presented in Table 1. There were 406 community-dwelling older adults aged 60–69 (M = 64.60, SD = 3.20), with slightly more (52.2%) in the older category. There were slightly more females (56.0%) than males. About two-thirds (63.85%) were living in towns, the rest in cities. Nearly half (46.8%) were married, with another 31% self-identifying as part of a couple; 12.1% were single, with smaller percentages separated or divorced. Over half (53.9%) had primary school education or less. Just over one-quarter (27.9%) considered themselves working (merchant or employee), with the rest reporting themselves as unemployed, pensioners, or gardeners. Four of five (80.5%) lived in their own homes, with another 12.3% living with relatives.

Table 1: Demographic characteristics of the sample.

Variable
Number ()
Percent

Age

    60–64
194
47.8

    65–69
212
52.2

Mean: 64.6 years

Standard deviation: 3.2 years

Gender

    Male
175
43.1

    Female
231
56.9

Residential location

    City
147
36.2

    Town
259
63.8

Marital status

    Single
49
12.1

    Couple
126
31.0

    Married
190
46.8

    Divorced
14
3.4

    Separated
27
6.7

Education

    Uneducated
24
5.9

    Primary school
195
48.0

    Some secondary school
71
17.5

    High school graduate
43
10.6

    Vocational education
22
5.4

    Bachelor’s degree
6
1.5

    Master’s degree
45
11.1

Occupational status

    Unemployed
143
35.2

    Pensioner
36
8.9

    Merchant
62
15.3

    Employee
51
12.6

    Gardener
114
28.1

Residence

    Own house
327
80.5

    Relative’s house
50
12.3

    Rental house
29
7.1

Number of family members in household

    Range: 1–9

    Mean: 3.51

    Standard deviation: 1.53

Living arrangement

    Alone
9
1.2

    Spouse
268
35.1

    Children
265
34.9

    Nephew/niece
168
22.0

    Cousin
26
3.4

    Others
26
3.4

Income (Thai baht per month, 1000 baht = US$ 30–31)

    Minimum: 500

    Maximum: 112,000

    Mean: 10,953

    Standard deviation: 12,520

Main source of income

    Children
181
25.4

    Spouse
68
9.5

    Cousin
17
2.4

    Pension
38
5.3

    Government support
219
30.7

    Working
190
26.6

Health service provider

    Government hospital
405
99.8

    Private hospital
1
0.2

The number of family members in the household where the participant lived ranged from 1 to 9 persons, with two-thirds of participants living in households with 2–5 family members. Seventy percent lived with spouses and/or children; another 22% were living with nephews or nieces. Hardly any older Thais (1.2%) lived alone. Participants’ mean income was 10,953 Thai baht per month (about US$ 335). A bit less than one-third (30.7%) came from government support, followed by employment (26.6%) and children (25.4%). Thai elderly in the sample had, essentially, no savings. All but one of the 406 participants received health care from the government hospital.

Data on participants’ health status are presented in Table 2. Body mass index (BMI) showed well over half (63.3%) to be overweight or obese. About three-fifths (61.3%) had normal vision. Of those with vision problems, more than four-fifths were diagnosed with presbyopia or blurry vision. Nine of ten participants had normal hearing; of those who did not, less than one in five used any type of hearing aid. The majority (60.1%) of participants had a diagnosed medical condition, with the primary ones being hypertension, hyperlipidemia, diabetes mellitus, benign prostate hypertrophy, or heart disease. Two-thirds (66.0%) used medications to help with their medical conditions, with the most common being antihypertensive drugs, antihyperlipidemia drugs, and antihyperglycemic drugs. Nine of ten participants (92.1%) reported not drinking alcohol.

Table 2: Health status of the sample.

Variable
Number ()
Percentage

Body mass index

    Underweight (<17.50 kg/m²)
3
0.7

    Normal weight (17.50–22.99 kg/m²)
146
36.0

    Overweight (23.00–27.99 kg/m²)
192
47.3

    Obese (≥28 kg/m²)
65
16.0

    Minimum: 15.56 kg/m²

    Maximum: 43.75 kg/m²

    Mean: 24.60 kg/m²

    Standard deviation: 4.15 kg/m²

Vision

    Normal
249
61.3

    Abnormal
157
38.7

        Presbyopia
98
51.9

        Blurry vision
64
33.9

        Cataract
12
6.3

        Glaucoma
3
1.6

        Pterygium
12
6.3

Hearing

    Normal
367
90.4

    Abnormal
39
9.6

        Hearing aids
6
17.1

        Deaf
29
82.9

Diagnosis of chronic condition

    No
162
39.9

    Yes
244
60.1

        Diabetes
76
19.2

        Heart
18
4.6

        Hypertension
167
42.3

        Hyperlipidemia
99
25.1

        Benign prostate hypertrophy
35
8.9

Medication use

    No
138
34.0

    Yes
268
66.0

        Antianalgesic drugs
51
11.0

        Bone and joint medicine
15
3.21

        Antidiuretic drugs
6
1.3

        Antihyperglycemic drugs
77
16.6

        Cardiac medications
16
3.4

        Antihypertensive drugs
168
36.2

        Antihyperlipidemia drugs
96
20.7

        Antidepressive
1
0.2

        Sleeping pill
14
3.0

        Others
20
4.3

Alcohol drinking

    No
374
92.1

    Yes
32
7.9

Participants’ fall history is described in Table 3. About three-quarters (73.9%) of the sample reported no falls since age 60, with the rest acknowledging one or more falls. Of the 26.1% reporting falls, most (21.1%) reported falling one or two times since turning 60. Only about one in ten had fallen in the six months preceding the study; even fewer had fallen in the preceding three months.

Table 3: Falls history.

Event
Number ()
Percentage

Falls since 60 years old

    None
300
73.9

    Some
106
26.1

        1 time
57
14.0

        2 times
29
7.1

        3 times
11
2.7

        4 times
5
1.2

        5 times
2
0.5

        6 times
1
0.2

        10 times
1
0.2

Falls in previous 6 months

    None
360
88.7

    Some
46
11.3

        1 time
33
8.1

        2 times
9
2.2

        3 times
3
0.7

        4 times
1
0.2

Falls in previous 3 months

    None
376
92.6

    Some
30
7.4

        1 time
22
5.4

        2 times
7
1.7

        3 times
1
0.2

Attributed general causes of falls

    Muscle weakness

        No
339
83.5

        Yes
67
16.5

    Medication side effect

        No
334
82.3

        Yes
72
17.7

    Medical condition/lack of assistive devices

        No
357
87.9

        Yes
49
12.1

    Hazardous home environment

        No
309
76.1

        Yes
97
23.9

Specific causes of falls

    Slippery shoes
51
10.8

    Contact with other person/object
47
10.0

    Slippery floor
40
8.5

    Fatigue
39
8.3

    Headache
25
5.3

    Blurry vision
24
5.1

    Muscle pain
24
5.1

    Muscle weakness
22
4.7

    Presbyopia
20
4.2

    Dizziness
17
3.6

    Slope/uneven ground
15
3.2

    Cramp
13
2.8

    Numb leg
13
2.8

    Hard heeled shoes
13
2.8

    Flushing toilet
12
2.5

    Slippery bathroom
11
2.3

    Foot abnormality
10
2.1

    Long dress or sarong
10
2.1

    Stumbling
9
1.9

    Cesspool
9
1.9

    Drowsiness
8
1.7

    In a hurry to void
8
1.7

    Foot numbness
7
1.5

    General numbness
6
1.3

    Poor lighting
5
1.1

    Stairs
5
1.1

    Muscle tension
3
0.6

    High-heeled shoes
3
0.6

    Skewness
2
0.4

Falls location

    Outdoor
69
65.1

    Indoor
25
23.6

    Both
12
11.3

Indoor falls location

    Kitchen
22
30.6

    Bedroom
14
19.4

    Living room
11
15.3

    Terraces
11
15.3

    Bathroom
7
9.7

    Stairs
6
8.3

    Roof
1
1.4

Outdoor falls location

    House surroundings
52
69.3

    Footpath/sidewalk
12
16.0

    Street/road
4
5.3

    Hospital/clinic
2
2.7

    Sky walk/overpass
2
2.7

    Mall/supermarket
1
1.3

    Theatre
1
1.3

    Temple
1
1.3

Falls-related health problems

    None
3
2.8

    Some
103
97.2

        Physical consequences

            Swelling/bruising
63
50.0

            Pain
30
23.8

            Abrasion
15
11.9

            Fracture/dislocation
10
7.9

            Laceration
6
4.8

            Head injury
2
1.6

    Treatment

        Buying drugs at pharmacy
29
27.4

        Hospital
28
26.4

        No treatment
26
24.5

        Resting at home for 1-2 days
14
13.2

        Resting for “a while”
6
5.7

        Clinic
3
2.8

Although the great majority of the sample did not identify specific causes for their falls, some of the attributed causes, such as a hazardous home environment, were more common than others. Physical weakness, medication side effects, and lack of assistive devices were also reported. Regarding specific causes, improper footwear and “contact falls” (losing one’s balance due to being jostled, bumping into furniture, etc.) were the most commonly mentioned.

More participants fell outdoors (65.1%) than indoors; about one in ten (11.3%) reported falls in both locations. As one might expect, the majority of outdoor falls (69.3%) occurred in the area surrounding the house. Indoor falls were most common in frequently used areas: kitchen, bedroom, and living room.

For Thai elderly, a fall comes with physical consequences; nearly all (97.2%) reported fall-related health problems. Of those reporting physical consequences, fortunately, most cited relatively minor problems: swelling/bruising, pain, and abrasions comprised 85.7% of reported consequences. More serious outcomes, such as lacerations, fractures, dislocations, or head injuries, were far less common, accounting for 14.3% of reported problems. Treatment behavior was quite varied: about one-quarter went to the hospital, another quarter obtained medications from a pharmacy, and another quarter used no treatment whatsoever.

Table 4 reports bivariate analyses of likelihood of falling and several independent variables. For community-dwelling Thais aged 60–69, falls are significantly more likely to occur among (1) city residents versus residents of towns; (2) older participants; (3) those with the least or the most education; (4) overweight and obese (measured by BMI) elderly compared to normal weight and underweight elderly; (5) those with visual impairment compared to those with normal vision; (6) those with a chronic health condition; (7) those using medications versus those who do not; (8) those reporting muscle weakness compared to those claiming no muscle weakness; (9) those reporting side effects from medications; (10) those with physical impairments who lack assistive devices; (11) those reporting a hazardous home environment; and (12) those using footwear or clothing that can contribute to falling.

Table 4: Likelihood of falling by selected factors.

Variable
Falling, number (%)
value

Never
Ever

Residential location

<0.001

    City
88 (59.9)
59 (40.1)

    Town
212 (81.9)
47 (18.1)

Gender

0.19

    Male
135 (77.1)
40 (22.9)

    Female
165 (71.4)
66 (28.6)

Age

0.029

    60–64
153 (78.9)
41 (21.1)

    65–69
147 (69.3)
65 (30.7)

Marital status

0.9

    Single
34 (69.4)
15 (30.6)

    Couple
97 (77.0)
29 (23.0)

    Married
138 (72.6)
52 (27.4)

    Divorced
10 (71.4)
4 (28.6)

    Separated
21 (77.8)
6 (22.2)

Education

0.009

    Uneducated
19 (79.2)
5 (20.8)

    Primary school
137 (70.3)
58 (29.7)

    Secondary school
60 (84.5)
11 (15.5)

    High school graduate
38 (88.4)
5 (11.6)

    Vocational education
12 (54.5)
10 (16.7)

    Bachelor’s degree
5 (83.3)
1 (16.7)

    Master’s degree
29 (64.4)
16 (35.6)

Residence

0.45

    Own house
241 (73.7)
86 (26.3)

    Relative’s house
35 (70.0)
15 (30.0)

    Rental house
24 (82.8)
5 (17.2)

Number of family members in household

0.13

    1 person
9 (52.9)
8 (47.1)

    2–4 persons
228 (75.0)
76 (25.0)

    5+ persons
63 (74.1)
22 (25.9)

Living arrangement

0.08

    Spouse
203 (75.7)
65 (24.3)

    Children
199 (74.8)
67 (25.2)

    Nephew/niece
118 (70.2)
50 (29.8)

    Cousin
16 (61.5)
10 (38.5)

    Others
20 (76.9)
6 (23.1)

    Alone
5 (55.6)
4 (44.4)

Occupation

0.59

    Unemployed
99 (69.2)
44 (30.8)

    Pensioner
27 (75.0)
9 (25.0)

    Merchant
48 (77.4)
14 (22.6)

    Employee
41 (80.4)
10 (19.6)

    Gardener
85 (74.6)
29 (25.4)

Income

0.5

    Below poverty line (<2,647 baht/month)
33 (70.2)
14 (29.8)

    Above poverty line (2,647+ baht/month)
267 (74.4)
92 (25.6)

BMI

0.046

    Underweight (<17.50 kg/m²)
3 (100)
0

    Normal weight (17.50–22.99 kg/m²)
115 (78.8)
31 (21.2)

    Overweight (23.00–27.99 kg/m²)
142 (74.0)
50 (26.0)

    Obese (≥28 kg/m²)
40 (61.5)
25 (38.5)

Vision

0.001

    Normal
198 (79.5)
51 (20.5)

    Abnormal
102 (65.0)
55 (35.0)

        Presbyopia
63 (64.3)
35 (35.7)

        Blurry vision
41 (64.1)
23 (35.9)

        Cataract
7 (58.3)
5 (41.7)

        Glaucoma
3 (100.0)
0

        Pterygium
7 (58.3)
5 (41.7)

Hearing

0.065

    Normal
276 (75.2)
91 (24.8)

    Abnormal
24 (61.5)
15 (38.5)

        Hearing aids
5 (83.3)
1 (16.7)

        Deaf
17 (58.6)
12 (41.4)

Diagnosis of chronic condition

<0.001

    No
144 (88.9)
18 (11.1)

    Yes
156 (63.9)
88 (36.1)

        Diabetes
53 (69.7)
23 (30.3)

        Heart
12 (66.7)
6 (33.3)

        Hypertension
105 (62.9)
62 (37.1)

        Hyperlipidemia
64 (64.6)
35 (35.4)

        Benign prostate     hypertrophy
21 (60.0)
14 (40.0)

Medication use

<0.001

    No
125 (90.6)
13 (9.4)

    Yes
175 (65.3)
93 (34.7)

        Antianalgesic drugs
29 (56.9)
22 (43.1)

        Bone and joint medicine
5 (33.3)
10 (66.7)

        Antidiuretic drugs
1 (16.7)
5 (83.3)

        Antihyperglycemic drugs
53 (68.8)
24 (31.2)

        Cardiac medications
13 (81.3)
3 (18.8)

        Antihypertensive drugs
108 (64.3)
60 (35.7)

        Antihyperlipidemia drugs
62 (64.3)
34 (35.4)

        Antidepressive
1 (100.0)
0

        Sleeping pill
11 (78.6)
3 (21.4)

        Others
12 (60.0)
8 (40.0)

Alcohol drinking

0.051

    No
281 (75.1)
93 (24.9)

    Yes
19 (59.4)
13 (40.6)

Muscle weakness

<0.001

    No
300 (88.5)
39 (11.5)

    Yes
0
67 (100.0)

        Weak

22 (33.8)

        Fatigue

39 (60.0)

        Cramp

13 (20.0)

        Numb leg

13 (20.0)

        Numb foot

7 (10.8)

        Muscle tension

3 (4.6)

Medication side effect

<0.001

    No
319 (95.5)
15 (4.5)

    Yes
42 (58.3)
30 (41.7)

        Headaches

25 (34.2)

        Blurry vision

24 (32.9)

        Drowsiness

8 (11.0)

        Diuretic

8 (11.0)

        Muscle aches

24 (32.9)

        Numbness

6 (8.2)

        Dizziness

17 (23.3)

Medical condition/lack of assistive devices

<0.001

    No
300 (84.0)
57 (16.0)

    Yes
0
49 (100.0)

        Myopia

18 (36.7)

        Presbyopia

20 (40.8)

        Skewness

2 (4.1)

        Abnormal foot

10 (20.4)

Home hazard environment

<0.001

    No
300 (97.1)
9 (2.9)

    Yes
0
97 (100.0)

        Slippery floor

40 (41.2)

        Slope/uneven ground

15 (15.5)

        Stumbling

9 (9.3)

        Contact with other     person/object

47 (48.5)

        Poor lighting

5 (5.2)

        Stairs

5 (5.2)

        Flushing toilet

12 (12.4)

        Cesspool

9 (9.3)

        Slippery bathroom

11 (11.3)

Footwear/clothing

<.0001

    No
300 (89.6)
35 (10.4)

    Yes
0
71 (100.0)

        Slippery shoes

51 (71.8)

        High-heeled shoes

3 (4.2)

        Hard-heeled shoes

13 (18.3)

        Long dress/sarong

10 (14.1)

Indoor area

<0.001

    No
300 (85.2)
52 (14.8)

    Yes
0
54 (100.0)

        Bedroom

14 (25.5)

        Kitchen

22 (40.0)

        Living room

11 (20.0)

        Terraces

11 (20.0)

        Stairs

6 (10.9)

        Bathroom

7 (12.7)

        Roof

1 (1.8)

Outdoor area

<0.001

    No
300 (86.7)
46 (13.3)

    Yes
0
60 (100.0)

        Home surrounding

52 (82.5)

        Foot path

12 (19.0)

        Department store

1 (1.6)

        Sky walk/overpass

2 (3.2)

        Street/road

4 (6.3)

        Theatre

1 (1.6)

        Temple

1 (1.6)

        Hospital/clinic

2 (3.3)

4. Discussion

The sample of 406 community-dwelling Thai adults aged 60–69 had many traits that imply representativeness of the Thai population and, in fact, make this sample similar to those of other studies. There were more females than males, more in rural than urban areas, most were living in their own residences, and nearly all received health care from government facilities.

On the other hand, three-quarters of the Thai sample were married or cohabiting, educational attainment was low (over half had not attended secondary school), nearly all (about 19 of 20) lived with immediate or extended family, and over one-quarter reported their main source of income as their children (31% reported “government support”).

In addition, nearly two-thirds (63.3%) were overweight or obese based on BMI; a solid majority (61.3%) reported normal vision, and 90.4% reported normal hearing. Six in ten had a chronic condition diagnosis, and two-thirds were taking one or more medications. As might be expected in a heavily Buddhist nation, over nine in ten (92.1%) reported not drinking alcohol.

Regarding falls history, only one-quarter (26.1%) of the sample reported one or more falls since turning 60 (compared to the one-third of American elderly who fall in a given year); however, the maximum age in the sample was 69, and it can be expected that falls incidence and prevalence will rise as the sample lives into its 70s, 80s, and 90s. Despite the disproportionate likelihood of falling outside (versus inside the home) based on percent of time spent outside, the most commonly attributed cause of falls was indoors: a “hazardous home environment.”

Bivariate analyses found several factors were associated with higher fall risk: age, urban residence, low or high educational attainment, being overweight or obese, impaired vision, chronic health conditions, medication use and side effects, muscle weakness, nonuse of assistive devices, a hazardous home environment, and certain types of footwear/clothing.

One counterintuitive finding was the high percent (35.6%) of the sample with master’s degrees () reporting ever falling (Table 4). In general, the percent reporting a fall declined as education increased. This could result from increasing educational attainment leading to less physical and physically risky jobs, better health education and health promotion opportunities, and better overall health. The literature on the education-falls relationship reaches no consensus: studies from Australia and Finland found the logically expected inverse relationship [26, 43], while other studies found a positive ([44], in America) or insignificant ([45], in Turkey) relationship between educational attainment and falls among older adults. Additional cross cultural and multivariate studies are needed to understand the relationship, if any, between educational attainment and falls among older adults.

In some ways, Thai culture can affect the risk and rate of elderly falls. Thai culture, historically and even today, is collectivistic and family oriented. In a more individualistic and independent culture, such as in the United States, the government often provides economic, health care and other supports for older adults based on the assumption that the family will not or cannot do this. Within any given nation, this shift from a more communal to a more individualistic society tends to occur with modernization [46, 47]. However, the effects of modernization on the lived experience of older adults can be and are mediated by culture.

For example, in Thailand, China, and other Asian nations including highly modernized Japan, there is a strong cultural norm of filial piety that (adult) children will honor their aging parents by caring for them, even to the point of doing as much as they can for them. Regarding the focus of this research, this operationalization of filial piety can include activities the aging parents might still be quite capable of doing themselves, leading to less physical activity, less muscle tone, and thus increased fall risk.

The data suggest recommendations to reduce fall risk for older Thais. First, the interior home environment could be modified, often at little or no cost, to reduce the risk of falling. Examples include better lighting, nonskid flooring, and grab bars/handrails in the bathrooms and on stairs. Second, the external environment could be modified, given the disproportionate number of falls that occur outside and the 70% that occur on the “house surroundings.” Smoothing/repairing uneven surfaces, and adding handrails on outside stairs or replacing stairs with ramps are examples of such modifications. Older adults could be strongly encouraged to maintain a healthy weight, to seek treatment for vision problems, to learn about and practice chronic disease self-management, and maintain regular physical activity. When it comes to clothing and footwear, safety should be a greater concern than vanity.

As is well known, the physical and psychosocial consequences of elderly falls are significant and negative, a drain on the time, energy, and resources of the older adult and his/her family. But these consequences, and thus the recommendations above, pertain to more macrolevels as well—the community and the nation. The World Health Organization, on a global level, and national organizations like AARP in the United States have developed guides to age-friendly homes, communities, and societies. Many of their assessments and recommendations are relevant and applicable cross culturally. New regulations for construction of home and business environments, subsidization of age-friendly renovations, and macrolevel initiatives to promote physical activity, fight obesity, and manage chronic health conditions can be implemented on community and national levels and adapted for cultural acceptability as necessary.

5. Conclusions

Globally and nationally, populations are aging. Rising life expectancy is due mainly to advances in medical science and technology (especially in reducing infant and child mortality). The rapid increase in the percent of older adults is due also to decades-long declines in fertility. The world and nations are aging, quickly and permanently, and this must be planned for.

Falling has significant and negative consequences for older adults and their families, communities, and societies. These consequences affect families, communities, and societies along several dimensions, including economic, psychosocial, and health care. Age is strongly related to falling; as populations age, it is more important than ever to take evidence-based steps to change knowledge, attitudes, and behaviors to reduce fall risk at home, in the community, and in society.

Data in this study came from a nonrepresentative sample of older Thai adults in one province of southern Thailand. Thus, generalizability is limited. Replication in the three other regions of Thailand would help assess both validity and reliability; more representative samples would also be advantageous.

Some results of this study of community-dwelling older Thais support prior research in other nations and thus add to the knowledge base of falls, fall risk, and fall-reduction strategies that have cross-cultural applicability. At the same time, some results vary from prior research and show the necessity of cultural awareness. It is only through such awareness that some questions—for example, why are the least- and most-educated Thai elderly more likely to fall?—will find answers, and only through such awareness can evidence-based interventions be developed to adapt to ensure cultural acceptability.

Data Availability

Data availability inquiries should be addressed to the first author.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

All authors have contributed significantly to the research concept, literature review, and objectives. They all are in agreement with the content of the manuscript. All authors approved the manuscript and this submission.

Acknowledgments

This research was supported by the Institute for Health Workforce Development, Ministry of Public Health, Thailand; the Boromarajonani College of Nursing, Surat Thani, Thailand; and the Department of Physical Education, Kasetsart University, Thailand.

References

United Nations, Department of Economic and Social Affairs, and Population Division, World Population Ageing 2015, Department of Economic and Social Affairs, and Population Division (UN), New York, NY, USA, 2015.

World Health Organization, What are the Main Risk Factors for Falls Amongst Older People and What are the Most Effective Interventions to Prevent These Falls? World Health Organization, Geneva, Switzerland, 2004.

J. R. Wilmoth and S. Horiuchi, “Rectangularization Revisited: Variability of Age at Death within Human Populations,” Demography, vol. 36, no. 4, pp. 475–495, 1999.

World Health Organization, The Global Burden of Disease: 2004 Update, World Health Organization, Geneva, Switzerland, 2004.

G. Bergen, L. H. Chen, M. Warner, and L. A. Fingerhut, Injury in the United States: 2007, National Center for Health Statistics, Hyattsville, MD, USA, 2008.

Department of Health and Human Services, Office of Disease Prevention and Health Promotion. Healthy People 2020, 2013, http://www.healthypeople.gov/2020.

L. D. Gillespie, M. Robertson, W. Gillespie et al., “Interventions for preventing falls in older people living in the community,” Cochrane Database of Systematic Reviews, vol. 2, 2009.

C. O. Bekibele and O. Gureje, “Fall incidence in a population of elderly persons in Nigeria,” Gerontology, vol. 56, no. 3, pp. 278–283, 2010.

E. Weir and L. Culmer, “Fall prevention in the elderly population,” CMAJ, vol. 171, no. 7, p. 724, 2004.

N. R. Hooyman and H. A. Kiyak, Social Gerontology: A Multidisciplinary Perspective, Allyn and Bacon, Boston, MA, USA, 9th edition, 2011.

National Center for Health Statistics, Mortality Multiple Cause Files, 2000–2013, 2013, http://www.cdc.gov/nchs/data_access/vitalstatsonline.htm.

Centers for Disease Control and Prevention, Ten Leading Causes of Injury Deaths by Age Group Highlighting Unintentional Injury Death, Centers for Disease Control and Prevention, Atlanta, GA, USA, 2014.

M. Rogerson and C. Emes, “Fostering resilience within an adult day support program,” Activities, Adaptation and Aging, vol. 32, no. 1, pp. 1–18, 2008.

R. A. Newton, “Maximizing independence: reducing/preventing falls,” Geriatric Care Management Journal, vol. 12, no. 2, pp. 16–19, 2002.

K. Frick, J. Kung, J. Parrish, and M. Narrett, “Evaluating the cost-effectiveness of fall prevention programs that reduce fall-related hip fractures in older adults,” Journal of the American Geriatrics Society, vol. 58, no. 1, pp. 136–141, 2010.

G. Feder, C. Cryer, S. Donovan, and Y. Carter, “Guidelines for the prevention of falls in older people,” BMJ, vol. 321, no. 7267, pp. 1007–1011, 2000.

M. E. Tinetti, “Clinical practice. Preventing falls in elderly persons,” New England Journal of Medicine, vol. 348, no. 1, pp. 42–49, 2003.

T. Chippendale and V. Raveis, “Knowledge, behavioral practices, and experiences of outdoor fallers: Implications for prevention programs,” Archives of Gerontology and Geriatrics, vol. 72, pp. 19–24, 2017.

N. E. Klepeis, W. C. Nelson, W. R. Ott et al., “The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants,” Journal of Exposure Analysis and Environmental Epidemiology, vol. 11, no. 3, pp. 231–252, 2001.

Health Insurance System Research Office (HISRO), Fall in Thai Elderly, HISRO, Mueang Nonthaburi, Thailand, 2010.

W. Li, T. H. Keegan, B. Sternfeld, S. Sidney, C. P. Quesenberry, and J. L. Kelsey, “Outdoor falls among middle-aged and older adults: a neglected public health problem,” American Journal of Public Health, vol. 96, no. 7, pp. 1192–1200, 2006.

L. E. Weinberg and L. A. Strain, “Community-dwelling older adults’ attributions about falls,” Archives of Physical Medicine and Rehabilitation, vol. 76, no. 10, pp. 955–960, 1995.

P. A. Bath and K. Morgan, “Differential risk factor profiles for indoor and outdoor falls in older people living at home in Nottingham, UK,” European Journal of Epidemiology, vol. 15, no. 1, pp. 65–73, 1999.

A. Bergland, G. B. Jarnlo, and K. Laake, “Predictors of falls in the elderly by location,” Aging Clinical and Experimental Research, vol. 15, no. 1, pp. 43–50, 2003.

O. P. Ryynanen, S. L. Kivela, and R. Honkanen, “Times, places, and mechanisms of falls among the elderly,” Zeitschrift Fur Gerontologie, vol. 24, pp. 154–161, 1991.

O. P. Ryynänen, S. L. Kivelä, and R. Honkanen, “Incidence of falling injuries leading to medical treatment in the elderly,” Public Health, vol. 105, no. 5, pp. 373–386, 1991.

J. Cwikel, “Falls among elderly people living at home: medical and social factors in a national sample,” Israel Journal of Medical Sciences, vol. 28, pp. 446–453, 1992.

S. Yasumura, H. Haga, and N. Niino, “Circumstances of injurious falls leading to medical care among elderly people living in a rural community,” Archives of Gerontology and Geriatrics, vol. 23, no. 2, pp. 95–109, 1996.

B. Resnick and P. Junlapeeya, “Falls in a community dwelling older adults: Findings and implications for practice,” Applied Nursing Research, vol. 17, no. 2, pp. 81–91, 2004.

M. Callisaya, J. E. Sharman, J. Close, S. R. Lord, and V. K. Srikanth, “Greater daily defined dose of antihypertensive medication increases the risk of falls in older people-a population-based study,” Journal of Gerontological Society, vol. 62, pp. 1527–1533, 2014.

H. Huang, M. Gau, C. Lin, and G. Kernohan, “Assessing risk of falling in older adults,” Public Health Nursing, vol. 20, no. 5, pp. 399–411, 2003.

Y. Jung, D. Shin, K. S. Chung, and S. E. Lee, “Health status and fall-related factors among older Korean women,” Journal of Gerontological Nursing, vol. 33, no. 10, pp. 12–20, 2007.

A. B. Guzman, J. M. Garcia, J. P. Garcia et al., “A multinomial regression model of risk for falls (RFF) factors among Filipino elderly in a community setting,” Educational Gerontology, vol. 39, no. 9, pp. 669–683, 2013.

P. Kuhirunyaratn, P. Prasomrak, and B. Jindawong, “Factor related to falls among community dwelling elderly,” Southeast Asian Journal of Tropical Medicine and Public Health, vol. 44, no. 5, pp. 906–915, 2013.

F. Baranzini, N. Poloni, and M. Diurni, “Polypharmacy and psychotropic drugs as risk factors for falls in long-term care setting for elderly patients in Lombardy,” Recenti Progressi in Medicina, vol. 100, pp. 9–16, 2009.

K. D. Hill and R. Wee, “Psychotropic drug-induced falls in older people: a review of interventions aimed at reducing the problem,” Drugs and Aging, vol. 29, no. 1, pp. 15–30, 2012.

J. Knodel, P. Vipan, and C. Napaporn, The Changing Well-being of Thai Elderly: An Update from the 2011 Survey of Older Persons in Thailand. Chiang Mai, Help Age International, London, UK, 2013.

Panel on Prevention of Falls in Older Persons, American geriatrics society, and British Geriatrics Society, “Summary of the updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons,” Journal of the American Geriatrics Society, vol. 59, no. 1, pp. 148–157, 2011.

P.-C. Sze, W.-H. Cheung, P.-S. Lam, H.-S. D. Lo, K.-S. Leung, and T. Chan, “The efficacy of a multidisciplinary falls prevention clinic with an extended step-down community program,” Archives of Physical Medicine of Rehabilitation, vol. 89, no. 7, pp. 1329–1334, 2008.

J. Swanenburg, E. D. de Bruin, M. Stauffacher, T. Mulder, and D Uebelhart, “Effects of exercise and nutrition on postural balance and risk of falling in elderly people with decreased bone mineral density: randomized controlled trial pilot study,” Clinical Rehabilitation, vol. 21, no. 6, pp. 523–534, 2007.

H.-C. Huang, C.-U. Liu, Y.-U. Huang, and W. G. Kernohan, “Community-based interventions to recede falls among older adults in Taiwan-long time follow-up randomized controlled study,” Journal of Clinical Nursing, vol. 19, no. 7-8, pp. 959–968, 2010.

J. Fleiss, Statistical Methods for Rates and Proportions, John Wiley and Sons, New York, NY, USA, 2nd edition, 1981.

J. Dolinis, J. E. Harrison, and G. R. Andrews, “Factors associated with falling in older Adelaide residents,” Australian and New Zealand Journal of Public Health, vol. 21, no. 5, pp. 462–468, 1997.

J. T. Hanlon, L. R. Landerman, and G. G. Fillenbaum, “Falls in African American and white community-dwelling elderly residents,” Journals of Gerontology Series A: Biological Sciences and Medical Sciences, vol. 577, no. 7, pp. M473–M478, 2002.

T. C. Ozturk, R. Ak, U. A. Ebru, O. Onur, S. Eroglu, and S. Murat, “Factors associated with multiple falls among elderly patients admitted to emergency department,” International Journal of Gerontology, vol. 11, no. 2, pp. 85–89, 2017.

D. Cowgill, “Aging and modernization: a revision of the theory,” in Late Life Communities and Environmental Policy, J. F. Gubrium, Ed., Charles C. Thomas, Springfield, IL, USA, 1974.

M. Pipher, Another Country, Riverhead, New York, NY, USA, 1999.