Background
Falling is a major threat to the elderly’s quality of life, often causing a decline in self-care ability and social activities. An estimated 646,000 elderly people around the world die from falls each year [
1]. Falls account for 40% of all injurious deaths [
2]. In Thailand and worldwide, falls are the second leading cause of injury death after road traffic accidents. Non-fatal falls resulted in minor to very severe injuries, with some of the fallers having disability and premature death [
2,
3]. The direct medical costs for falls total nearly $30 billion annually [
4].
A fall prevention program comprising screening for individual’s risk factors together with risk factor management is the most effective way to prevent accidental falls [
5‐
8]. If the program is managed properly, it can reduce the rate of falls by 24% [
8]. Therefore, a screening tool for fall risk is the first key and should be sensitive and specific in predicting fall risk as well as having the ability to identify the cause or risk factor(s) of fall. While a number of fall risk screening tools do exist currently, no information has clearly identified which tools are best [
9]. There are only recommendations mentioning that since there is no single tool showing sufficiently high predictive validity, multiple tools should be used in combination without specific detail on the suggested combined procedure [
10‐
12].
Currently, there is only one multi-tool fall risk screening algorithm based on sequential test, which was proposed by the U.S. Centers for Disease Control and Prevention (CDC) for use in its Stopping Elderly Accidents, Death & Injuries (STEADI) program [
4,
5,
13‐
15]. The first step identifies high fall-risk elderly population by using both a short self-assessment questionnaire “Stay Independent” brochure (SIB) comprising 12 questions and 3 key questions asked by clinicians about past fall history. Only those with the scores ≥ 4 on the Stay Independent brochure or “Yes” answer to any key question were considered at-risk of fall and would be further screened in the second step with a more sophisticated method such as physical fitness tests including Timed Up and Go (TUG) test, 30-S Chair Stand, and the 4-Stage Balance test. From these two steps, the elderly can be classified as having low, medium and high risk of fall. Those with high risk are further assessed for multiple risk factors for risk management. STEADI is an evidence-based intervention program that offers a coordinated approach to implementing the professionals’ clinical practice guidelines for fall prevention [
16]. Its screening algorithm had good psychometric properties including concurrent and predictive validity [
17‐
19], although improvement is needed [
20]. For example, the proposed screening guidelines for clinician’ 3 key questions combination with TUG or the application of a self-assessment with TUG are lacking predictive accuracy measurement [
19]. In addition, the generalizability and validity of the STEADI screening algorithm have never been examined outside the USA, especially in Asian context.
Thailand’s Ministry of Public Health (MOPH) has implemented the TUG as a fall risk screening tool for the elderly in community [
21]. Despite being one of the most evidence-supported and an initial screening tool for assessing fall risks, TUG is not recommended to be used as a single screening tool [
10,
12]. Therefore, we have developed multiple-tool screening algorithms for elderly fall-risk in Thailand. The algorithms account for local practicality, i.e., limited resources, and a disproportion between healthcare manpower and the rapidly increasing number of elderly in Thailand’ primary care setting where the fall risk screening is performed.
To examine the applicability of the US CDC’s STEADI screening algorithm in Thailand. This study aimed to determine the predictive validity (area under the receiver operating characteristic curve or AUC, sensitivity, specificity, positive predictive value or PPV, and negative predictive value or NPV) of the two-step sequential fall-risk screening algorithm of the STEADI program for Thai elderly in the community. In addition, to predictive validity of each component aforementioned, we also explored possible combinations of the components to maximize screening efficiency.
Results
Fall incidence
During the 12-month follow-up period, 148 out of 480 elderly reported the occurrence of at least one fall incidence, accounting for the cumulative incidence of 30.8 persons (95% CI 26.7, 34.9) per 100 persons per year. The corresponding number of incident falls was 320 during the total follow-up period of 174,354 person-days, resulting in a fall incidence density of 1.84 (95% CI 1.64, 2.05) falls per 1000 person-days. Among those who fell, 47 (31.8%) reported the occurrence of one fall incidence, and 101 (68.2%) recurrent falls. Out of 320 falls, 71 (22.2%) resulted in no injury, 232 (72.5%) mild and moderate injuries, such as contusion, abrasion, knee and leg pain, back pain, and foot injuries, and 17 (5.3%) experienced severe injuries such as hip fracture, arm fracture, leg fracture, and head injuries requiring treatment.
Baseline characteristic between fallers and non-fallers
The sample comprised of 480 community-dwelling older adults. The mean age was 73.3 ± 6.51 years (range 65–95 years) while 19.2% aged 80 years and older. Almost one third of participants were categorized as fallers (30.8%, 148 out of 480). Two thirds of the fallers were women (66.2%). The mean age of fallers was 74.34 ± 6.36 years (range 65–95 years) while the mean age of non-faller was 72.88 ± 6.54 years (range 65–94 years). Fallers and non-fallers significantly differed according to the composition of gender, marital status, education level, underlying disease including diabetes and dyslipidemia, smoking, and drinking behavior. They did not differ in terms of age and body mass index (Table
1). Their occupations, income, exercise, housing style (one-story non-elevated house, one-story elevated house, or two or more stories house), residential area (rural versus urban), and home fall hazard score were comparable (data not shown). Compared to non-fallers, fallers however had significantly higher baseline fall risk screening score (Thai-SIB 12 items) and lower physical fitness levels as assessed by the Time Up and Go test, and 30-s Chair Stand (Table
1).
Table 1
Baseline characteristics of the participants (n = 480)
Gendera |
Male | 50 | (33.8) | 178 | (53.6) | <0.001 |
Female | 98 | (66.2) | 154 | (46.4) | |
Age (year)a | | | | | 0.083 |
65–69 | 46 | (31.1) | 132 | (39.8) | |
70–74 | 31 | (21.0) | 79 | (23.8) | |
75–79 | 40 | (27.0) | 60 | (18.1) | |
≥ 80 | 31 | (21.0) | 61 | (18.4) | |
Mean (SD) | 74.34 | (6.36) | 72.88 | (6.54) | |
Marital statusa | | | | | 0.038 |
Single | 10 | (6.8) | 26 | (7.8) | |
Married | 80 | (54.0) | 215 | (64.8) | |
Widowed, separated | 58 | (39.2) | 91 | (27.4) | |
Educationa | | | | | 0.005 |
None | 15 | (10.1) | 17 | (5.1) | |
Primary school | 124 | (83.8) | 267 | (80.4) | |
Secondary school and above | 9 | (6.1) | 48 | (14.5) | |
Underlying diseasea |
Hypertension | 94 | (63.5) | 182 | (54.8) | 0.089 |
Diabetes | 50 | (33.8) | 67 | (20.2) | 0.002 |
Dyslipidemia | 44 | (29.7) | 70 | (21.1) | 0.048 |
Chronic renal failure | 7 | (4.7) | 7 | (2.1) | 0.142 |
Smokinga | | | | | 0.016 |
Never | 117 | (79.1) | 232 | (69.9) | |
Former | 23 | (15.5) | 54 | (16.3) | |
Current | 8 | (5.4) | 46 | (13.8) | |
Alcohol consumptiona | | | | | 0.019 |
Never | 110 | (74.3) | 208 | (62.6) | |
Former | 25 | (16.9) | 66 | (19.9) | |
Current | 13 | (8.8) | 58 | (17.5) | |
Body mass index (kg/m2)b | | | | | 0.509 |
Mean (SD) | 23.38 | (4.61) | 23.09 | (4.32) | |
Fall risk screening [mean (SD)]b |
Thai-SIB 12 items (14 points) | 5.93 | (3.06) | 1.72 | (0.95) | <0.001 |
Physical fitness tests [Mean (SD)]b |
Time Up and Go test (min.) | 13.43 | (5.45) | 11.49 | (4.25) | <0.001 |
30-s Chair Standa | | | | | 0.025 |
Less than 5 stand in 30 s | 13 | (8.8) | 10 | (3.0) | |
≥ 5 stand in 30 s | 135 | (91.2) | 322 | (97.0) | |
The 4-Stage Balance testa | | | | | 0.123 |
Did not complete all balance stage | 7 | (4.7) | 6 | (1.8) | |
Complete all balance stage | 141 | (95.3) | 326 | (98.2) | |
Home fall hazard assessment [Mean (SD)]b |
Thai Home-FAST (29 points) | 6.99 | (4.04) | 6.29 | (3.66) | 0.065 |
Predictive validity of the overall screening tools and algorithms
Results about predictive validity of the tools/procedures used in Steps 1 and 2 as well as the 6 sequential fall risk screening algorithms are shown in Table
2 and Additional file
2: Table S1. Between the two screening tools in the first step, the clinician’s 3 key questions had higher ability identify future fallers, as inferred from its higher sensitivity of 93.9% (95% CI 88.8, 97.2) (Table
2). Contrary to this, the Thai-SIB (12 items) had higher specificity, 88.0% (95% CI 84.0, 91.3).
Table 2
Predictive validity of the tools/procedures used in the Steps 1 and 2 and 6 sequential fall risk screening algorithms
STEP 1 |
Clinician’s 3 key questions | 0.845 | 1 | 93.9 | 75.0 | 62.6 | 96.5 | < 1 |
Thai-SIB 12 items | 0.828 | 4 | 77.7 | 88.0 | 74.2 | 89.8 | < 5 |
STEP 2 |
TUG | 0.584 | 10 | 75.0 | 41.9 | 36.5 | 79.0 | <1 |
30-s-Chair Stand | 0.526 | a | 8.8 | 96.4 | 52.0 | 70.3 | <1 |
4-Stage balance test | 0.515 | b | 4.7 | 98.2 | 53.8 | 69.8 | <2 |
Sequential screening |
Clinician’s 3 key questions followed by |
TUG | 0.774 | c | 71.6 | 83.1 | 65.4 | 86.8 | <2 |
30-s-Chair Stand | 0.539 | c | 8.8 | 99.1 | 81.3 | 70.9 | <2 |
4-Stage balance test | 0.521 | c | 4.7 | 99.4 | 77.8 | 70.1 | <3 |
Thai-SIB 12 items followed by |
TUG | 0.767 | c | 62.2 | 91.3 | 76.0 | 84.4 | <6 |
30-s-Chair Stand | 0.531 | c | 7.4 | 98.8 | 73.3 | 70.5 | <6 |
4-Stage balance test | 0.516 | c | 4.1 | 99.1 | 66.7 | 69.9 | <7 |
In the second step, among the individual physical tests, TUG had the highest ability to identify future fallers, with the sensitivity of 75.0% (95% CI 67.2, 81.7) (Table
2). The remaining two screening procedures including 30-s-chair stand and 4-stage balance test had very low ability to identify future fallers, with the sensitivity of only 8.8% (95% CI 4.8, 14.6) and 4.7% (95% CI 1.9, 9.5), respectively (Table
2). Compared to Step 1, all screening procedures in Step 2 had lower sensitivity.
Validity results of the 6 possible algorithms of the sequential Steps 1 and 2 screenings are shown on the lower portion of Table
2. Compared to the sole screening procedures in Step 1, all of these sequential screening algorithms had lower sensitivity, while their false positivity were slightly improved (lower).
The overall performance of the sequential screening algorithms were examined by dividing the participants into low, moderate, and high fall risk groups and proportional hazard modeling was conducted (Table
3). Result showed that the moderate and high-risk groups had significantly higher hazard ratios than the low-risk group with obvious dose-response patterns for almost all alternatives. These were particularly pronounced for the clinician’s 3 key questions & TUG and the Thai-SIB 12 items & TUG alternatives (Table
3). However, when categorizing risk based on the clinician’s 3 key questions and history of fall in the past one year, or simply basing on the number of positive responses of the clinician’s 3 key questions, results showed that their discriminative ability on future fall probability were even better, both in terms of the relative difference in fall probability and HR (Table
3).
Table 3
Relationship between the levels of risk from screening according to risk screening algorithm together with fall history in the past 1 year and chance of falling among elderly
Clinician’s 3 key questions (basing on the number of positive responses)b |
0 point | 258 | 9 (3.5) | 249 (96.5) | 1.00 | Reference | 1.00 | Reference | |
1 point | 57 | 13 (22.8) | 44 (77.2) | 7.29 | 3.12, 17.06 | 6.92 | 2.92, 16.40 | <0.001 |
≥2 points | 165 | 126 (76.4) | 39 (23.6) | 40.19 | 20.36, 79.31 | 40.35 | 20.28, 80.29 | <0.001 |
Clinician’s 3 key questions follow by history about the number and severity of previous fall |
Low risk | 258 | 9 (3.5) | 249 (96.5) | 1.00 | Reference | 1.00 | Reference | |
Moderate risk | 131 | 61 (46.6) | 70 (53.4) | 17.71 | 8.79, 35.68 | 18.32 | 9.01, 37.23 | <0.001 |
High risk | 91 | 78 (85.7) | 13 (14.3) | 52.48 | 26.18, 105.18 | 51.41 | 25.29, 104.50 | <0.001 |
Clinician’s 3 key questions & TUG |
Low risk | 318 | 42 (13.2) | 276 (86.8) | 1.00 | Reference | 1.00 | Reference | |
Moderate risk | 91 | 46 (50.6) | 45 (49.4) | 4.72 | 3.10, 7.18 | 4.75 | 3.08, 7.32 | <0.001 |
High risk | 71 | 60 (84.5) | 11 (15.5) | 11.82 | 7.92, 17.65 | 10.43 | 6.85, 15.90 | <0.001 |
Clinician’s 3 key questions & 30-s-Chair Stand |
Low risk | 464 | 135 (29.1) | 329 (70.9) | 1.00 | Reference | 1.00 | Reference | |
Moderate risk | 10 | 7 (70.0) | 3 (30.0) | 2.93 | 1.37, 6.27 | 3.02 | 1.36, 6.70 | 0.006 |
High risk | 6 | 6 (100.0) | 0 | 7.97 | 3.47, 18.30 | 4.49 | 1.86, 10.83 | <0.001 |
Clinician’s 3 key questions & 4-Stage balance test |
Low risk | 471 | 141 (29.9) | 330 (70.1) | 1.00 | Reference | 1.00 | Reference | |
Moderate risk | 6 | 4 (66.7) | 2 (33.3) | 2.40 | 0.89, 6.49 | 2.36 | 0.79, 7.08 | 0.124 |
High risk | 3 | 3 (100.0) | 0 | 5.68 | 1.80, 17.94 | 3.12 | 0.93, 10.46 | 0.066 |
Thai-SIB 12 items & TUG |
Low risk | 359 | 56 (15.6) | 303 (84.4) | 1.00 | Reference | 1.00 | Reference | |
Moderate risk | 69 | 40 (58.0) | 29 (42.0) | 5.12 | 3.41, 7.70 | 4.80 | 3.16, 7.29 | <0.001 |
High risk | 52 | 52 (100.0) | 0 | 16.03 | 10.76, 23.87 | 14.23 | 9.25, 21.88 | <0.001 |
Thai-SIB 12 items & 30-s-Chair Stand |
Low risk | 465 | 137 (29.5) | 328 (70.5) | 1.00 | Reference | 1.00 | Reference | |
Moderate risk | 9 | 5 (55.6) | 4 (44.4) | 2.20 | 0.90, 5.36 | 1.99 | 0.80, 4.95 | 0.137 |
High risk | 6 | 6 (100.0) | 0 | 7.83 | 3.41, 18.00 | 4.49 | 1.86, 10.83 | 0.001 |
Thai-SIB 12 items & 4-Stage balance test |
Low risk | 471 | 142 (30.2) | 329 (69.8) | 1.00 | Reference | 1.00 | Reference | |
Moderate risk | 6 | 3 (50.0) | 3 (50.0) | 1.79 | 0.57, 5.61 | 1.37 | 0.42, 4.45 | 0.598 |
High risk | 3 | 3 (100.0) | 0 | 5.63 | 1.78, 17.81 | 3.13 | 0.93, 10.51 | 0.065 |
Predictive validity of fall risk categorization
Performance of each step of the sequential screening and assessment was further examined in detail by determining its ability in predicting or identifying future fall risk (for person and event) (Table
4 and Additional file
2: Table S2). Results showed that those who were “not at risk” in Step 1 had a much lower future fall probability than those who were “at risk” irrespective of the assessment result from Step 2. For the Step 1 screening by the clinician’s 3 key questions, the average cumulative fall incidence in the former group ranged between 0 and 3.61 persons per 100 persons per year, while those for the latter group were 55.00 to 81.25 persons per 100 persons per year (Table
4). Proportions of multiple falls were also significantly higher among the “at-risk” (43.69%) than the “not at-risk (1.55%) groups. Within-group comparison between those with versus without gait, strength, or balance problems from the Step 2 assessment did not show any significant difference in the future fall probabilities. These patterns of group differences were also observed when Step 1 was screened by the Thai-SIB (12 items) (Additional file
2: Table S2). Since the numbers of those who were “at-risk” based on the 30-s-Chair Stand and 4-Stage balance test were too small, the following investigation then focused mainly on TUG test results.
Table 4
One-year fall incidences among study participants, stratified by Step 1 (the clinician’s 3 key questions) and Step 2 screening results
“Not at-risk” from Step 1 screening (n= 258) |
Timed-Up-and-Go | | | | | 1.000 | | | | | | | 0.670 |
Not at-risk | 116 | 4 | 3.45 | (0.95, 8.59) | | 112 | (96.55) | 3 | (2.59) | 1 | (0.86) | |
At-risk | 142 | 5 | 3.52 | (1.15, 8.03) | | 137 | (96.48) | 2 | (1.41) | 3 | (2.11) | |
30-s-Chair Stand | | | | | 1.000 | | | | | | | 1.000 |
Not at-risk | 249 | 9 | 3.61 | (1.67, 6.75) | | 240 | (96.39) | 5 | (2.01) | 4 | (1.61) | |
At-risk | 9 | 0 | 0.00 | (0.0, 33.63) | | 9 | (100) | 0 | (0) | 0 | (0) | |
4-Stage balance test | | | | | 1.000 | | | | | | | 1.000 |
Not at-risk | 254 | 9 | 3.54 | (1.63, 6.62) | | 245 | (96.46) | 5 | (1.97) | 4 | (1.57) | |
At-risk | 4 | 0 | 0.00 | (0.0, 60.24) | | 4 | (100.0) | 0 | (0) | 0 | (0) | |
Overall | 258 | 9 | 3.49 | (1.61, 6.52) | | 249 | (96.51) | 5 | (1.94) | 4 | (1.55) | |
“At-risk” from Step 1 screening (n= 222) |
Timed-Up-and-Go | | | | | 0.163 | | | | | | | 0.338 |
Not at-risk | 60 | 33 | 55.00 | (41.61, 67.88) | | 27 | (45.00) | 9 | (15.00) | 24 | (40.00) | |
At-risk | 162 | 106 | 65.43 | (57.57, 72.72) | | 56 | (34.57) | 33 | (20.37) | 73 | (45.06) | |
30-s-Chair Stand | | | | | 0.178 | | | | | | | 0.146 |
Not at-risk | 206 | 126 | 61.17 | (54.14, 67.86) | | 80 | (38.83) | 40 | (19.42) | 86 | (41.75) | |
At-risk | 16 | 13 | 81.25 | (54.35, 95.95) | | 3 | (18.75) | 2 | (12.50) | 11 | (68.75) | |
4-Stage balance test | | | | | 0.489 | | | | | | | 0.495 |
Not at-risk | 213 | 132 | 61.97 | (55.09, 68.52) | | 81 | (38.03) | 41 | (19.25) | 91 | (42.72) | |
At-risk | 9 | 7 | 77.78 | (39.99, 97.19) | | 2 | (22.22) | 1 | (11.11) | 6 | (66.67) | |
Overall | 222 | 139 | 62.61 | (55.89, 69.00) | <0.001‡ | 83 | (37.39) | 42 | (18.92) | 97 | (43.69) | <0.001‡ |
We further examined the performance of risk categorization basing the number and severity of fall(s) in the previous year among those who were “at risk” from Step 1 screening by the clinician’s 3 key questions (Table
5). Results showed that, compared to those without fall history in the previous year, those who had fallen at least once in the previous year had significantly higher future fall frequency, in terms of both cumulative incidence and the frequency of fall per person; while those who had fallen twice or more in the previous year had significantly higher frequency of fall per person than those with one fall in the previous year. However, among those with one fall in the previous year, future fall frequency did not significantly differ between those with versus without injury, neither in terms of cumulative incidence nor fall frequency per person. These trends were also observed when analyzing among those who were “at-risk” from Step 1 screening by the Thai-SIB 12 items, although less obvious (Additional file
2: Table S3).
Table 5
One-year fall incidences (persons per 100 persons per year) according to the number and severity of previous fall among those who were “at risk” from Step 1 screening by the clinician’s 3 key questions, stratified by the Timed-Up-and-Go test result in Step 2 assessment
Overall |
Previous fall history | | | | | <0.001 | | | | | | | <0.001 |
0 fall | 114 | 47 | 41.23 | (32.09, 50.83) | | 67 | (58.77) | 11 | (9.65) | 36 | (31.58) | |
1 fall, no injury | 17 | 14 | 82.35 | (56.57, 96.20) | a | 3 | (17.65) | 10 | (58.82) | 4 | (23.53) | a |
1 fall, injury | 53 | 42 | 79.25 | (65.89, 89.16) | a | 11 | (20.75) | 21 | (39.62) | 21 | (39.62) | a |
≥2 falls | 38 | 36 | 94.74 | (82.25, 99.36) | a | 2 | (5.26) | 0 | (0) | 36 | (94.74) | a,b,c |
Total | 222 | 139 | 62.61 | (55.89, 69.00) | | 83 | (37.39) | 42 | (18.92 | 97 | (43.69) | |
“NOT AT-RISK” from Step 2 assessment |
Previous fall history | | | | | <0.001 | | | | | | | <0.001 |
0 fall | 33 | 9 | 27.27 | (13.30, 45.52) | | 24 | (72.73) | 1 | (3.03) | 8 | (24.24) | |
1 fall, no injury | 7 | 6 | 85.71 | (42.13, 99.64) | a | 1 | (14.29) | 3 | (42.86) | 3 | (42.86) | a |
1 fall, injury | 12 | 10 | 83.33 | (51.59, 97.91) | a | 2 | (16.67) | 5 | (41.67) | 5 | (41.67) | a |
≥2 falls | 8 | 8 | 100.0 | (63.06, 100.0) | a | 0 | (0) | 0 | (0) | 8 | (100.0) | a,b,c |
Total | 60 | 33 | 55.00 | (41.61, 67.88) | | 27 | (45.00) | 9 | (15.00) | 24 | (40.00) | |
“AT-RISK” from Step 2 assessment |
Previous fall history | | | | | <0.001 | | | | | | | <0.001 |
0 fall | 81 | 38 | 46.91 | (35.73, 58.33) | | 43 | (53.09) | 10 | (12.35) | 28 | (34.57) | |
1 fall, no injury | 10 | 8 | 80.00 | 44.39, 97.48) | | 2 | (20.00) | 7 | (70.00) | 1 | (10.00) | a |
1 fall, injury | 41 | 32 | 78.05 | (62.39, 89.44) | a | 9 | (21.95) | 16 | (39.02) | 16 | (39.02) | a |
≥2 falls | 30 | 28 | 93.33 | (77.93, 99.18) | a | 2 | (6.67) | 0 | (0) | 28 | (93.33) | a,b,c |
Total | 162 | 106 | 65.43 | (57.57, 72.72) | 0.376‡ | 56 | (34.57) | 33 | (20.37 | 73 | (45.06) | 0.338‡ |
Discussion
This study showed that, in general, the fall risk sequential screening algorithms proposed by the US CDC in the STEADI program were well applicable in the Thai context. The results largely conformed with the official STEADI screening/assessment guideline, particularly about the suggested choices of screening/assessment tools/procedures used in Step 1 and 2 screening and the overall validity of the algorithms in predicting future fall risk. However, there were two discrepancies between our study result and the STEADI guideline concerning risk categorization after Steps 1 and 2 screening/assessment. Whether these discrepancies were reflective of fact or chance findings requires further investigation.
First conformity: choice of tool used in Step 1 screening. Our results demonstrated that the set of clinician’s 3 key questions is powerful and sufficient to identify future fallers who would benefit from fall preventive interventions. Its sensitivity is better than the Thai-SIB (12 items), which may be due to the higher cut-off of the latter tool. Its better sensitivity than the physical fitness tests (used in Step 2 screening) might relate to its more comprehensive consideration of broader intrinsic fall risk factors. These results also align with prior studies by Lusardi et al. [
12], Hesel et al. [
34], and Nithman and Vincenzo [
20]. When adverse risks of conducting TUG were predicted, either the clinician’s 3 key questions or Thai-SIB (12 items) may be used instead. Due to the high likelihood of serious health, social, and economic consequences of fall in older adults, high sensitivity of the clinician’s 3 key questions is therefore of clinical significance. Its brevity is also practical for utilization in primary care or busy clinical practice.
Second conformity: choice of physical fitness used in Step 2 screening. Our reported markedly high sensitivity of TUG compared to the 30-s-Chair Stand and 4-Stage balance test were also in agreement with the STEADI’s guideline in recommending the TUG as the first choice of physical fitness test, while the other two tests were optional. This was also supported by Lusardi et al.’s report of high post-test probability of the TUG over the Five Times Sit-to-Stand Test (which is comparable to 30-s-Chair Stand) and single-limb stance eyes open, which is a part of the 4-stage balance test in predicting fall risk [
12]. However, this was contrary to Nithman and Vincenzo who reported slightly higher sensitivities of 30-s-Chair Stand and 4-stage balance test compared to TUG [
20].
Third conformity: the overall validity of the algorithms in predicting future fall risk. Our reported high predictive validity of the sequential screening (composing the clinician’s 3 key questions or SIB in Step 1 screening and TUG in Step 2 assessment) with pronounced dose-response relationship between baseline fall risk level and future fall probability was also consistent with previous reports [
18‐
20]. However, our reported AUCs (0.774 and 0.767), sensitivities (71.6 and 62.2%), and specificities (83.1 and 91.3%) for these two algorithms were higher than those reported previously.
Concerning the two discrepancies, the first one was about the categorization of risk based on Step 1 screening and Step 2 physical fitness results. According to STEADI’s guideline, those who test positive from Step 1 can be categorized into low or moderate risk depending on the physical fitness test result in Step 2, that is, those without evidence of gait, strength, or balance problems will be categorized as “low risk” and otherwise as “moderate risk.” Our findings (Table
4) however showed that compared to those who were negative from Step 1 screening, the probability of future fall was significantly increased for those who were positive irrespective of the test result from the Step 2 assessment. In contrary, probabilities of future fall according to the physical fitness test results did not significantly differ when considering them in the same category of Step 1 screening results. This finding therefore suggested that those who were positive from Step 1 screening should be categorized at the least as “moderate of high risk,” as proposed by Lohman et al. in their investigation about predictive validity and adaptability of the STEADI algorithm to survey data of five annual rounds (2011–2015) of the National Health and Aging Trends Study (NHATS) [
19].
The second discrepancy was about the risk categorization based on the number and severity of previous falls. According to STEADI’s guideline, among the individuals who tested positive from Step 1 screening and had evidence of gait, strength, or balance problems in Step 2 assessment, those with no previous fall or had one non-injurious previous fall during the last year are categorized as “moderate risk,” while those with one injurious fall or two or more previous falls during the last year are categorized as “high risk.” In this study, we found that regardless of physical fitness test results, the probability of future fall among those with one previous fall differed significantly from those without previous fall, while these probabilities did not significantly differ for those with one non-injurious versus one injurious fall (Table
5). In addition, the probability of future falls of those with two or more previous fall differed significantly from those with one previous fall.
These two discrepant findings suggested deploying the clinician’s 3 key questions, together with details of the previous fall(s). Risk category may also be reclassified into “low risk” for those who answer “no” to any key question or having SIB score < 4 (with our reported probability or average 1-year incidence of future fall of 3.5%). For those who answer “yes” to any key question or having the SIB score of ≥4, they can be classified as “moderate risk” if no history of fall in the last year (with reported average 1-year incidence of fall of 25–50%). “High risk” classification can be made if individuals have history of one fall during the last year (with average reported 1-year incidence of fall of 70–80%) and “very high risk” classification if having two or more falls during the last year (with average reported 1-year incidence of fall of +90%).
Our study was however conducted only in one geographical location and the sample size was rather limited. In addition, these findings may be culturally specific since older adults in Thailand usually live with family caretakers [
6]. They therefore tend to limit their movement and rely on the help of caretakers whenever their physical fitness levels are reduced, resulting in lower than expected probability of future fall risk among those with gait, strength, or balance problems in the Step 2 assessment. These issues therefore need further investigation to acquire firmer evidence prior to inputting them for the consideration in the fall risk assessment guideline adaptation.
This is not to say that physical fitness tests are useless and have no role in the fall risk screening. They can still be utilized as parts of a multifactorial assessment to identify the root cause(s) of the individual’s fall risk or in detecting older individuals who require intervention to mitigate their gait, balance, or strength problems to promote better mobility and consequently improving quality of life.
Limitations of the study
This study was among the first to investigate the applicability of the US CDC’s STEADI screening algorithms outside the USA. Its prospective cohort design with monthly outcome tracking (falls) fostered valid causal inference. Its community-based nature also supported generalizability of the study findings. During our data collection process, the elderly who were too frail to complete the questionnaire and/or the 3 physical fitness tests were excluded from the study, thus our findings may not be generalized to all the elderly population, i.e., not for frail sub-group. The ceiling effect of the 30-s Chair Stand and 4-stage balance tests may also have occurred due to sample selection bias of the fit elderly; adding these tests to the screening algorithms could potentially decrease the sensitivity and specificity. During follows-up, fall preventive advice provided to those who had fallen might have modified the baseline fall risk for such individuals and introduced biased results to later fall events. Further studies are needed before firm generalizability of the study findings to other populations can be made.
Acknowledgements
The authors wish to thank all personnel of Si Salaloeng Sub-District Health Promoting Hospital, Khoksung Sub-District Health Promoting Hospital, Nong Prue Sub-District Health Promoting Hospital, Si Mum Sub-District Health Promoting Hospital, Yang Yai Sub-District Health Promoting Hospital, and all personnel of Phanao Sub-District Health Promoting Hospital as well as village health volunteers (VHVs) who gave assistance for data collection and survey of study subject older adults, which helped in successful completion of this study. We acknowledge all participants who enthusiastically participated in this study and provided good cooperation in the data collection. The authors would like to thank the Regional Health Promotion Center 9 Nakhon Ratchasima and Department of Health, Ministry of Public Health, Thailand.
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