Measuring prevalence and risk factors for fall-related injury in older
adults in low- and middle-income countries: results from the WHO Study
on Global AGEing and Adult Health (SAGE)
SAGE Working Paper No. 6 July 2013.
Heather Hestekin1, Tristan O’Driscoll1, Jennifer Stewart Williams1, Paul
Kowal2, Karl Peltzer3, Somnath Chatterji2
1 Research Centre for Gender Health & Ageing, Faculty of Health, University of Newcastle, Australia. 2
World Health Organization Study on global AGEing and Adult Health, Geneva, Switzerland. 3 University
of Limpopo, Turfloop, South Africa.
Corresponding Author:
Jennifer Stewart Williams. Email: [email protected]
Acknowledgements:
This work was undertaken as part of University of Wisconsin/World Health Organization (WHO)
internships for 2013 PharmD candidates Heather Hestekin and Tristan O’Driscoll under the supervision
of Jennifer Stewart Williams and Paul Kowal. We would like to thank Professor Julie Byles, Director of
the Research Centre for Gender, Health & Ageing, University of Newcastle, Australia, for hosting the
internships and supporting this body of work. SAGE is supported by the Division of Behavioral and Social
Research of the US National Institute on Aging through Interagency Agreements (OGHA 04034785;
YA1323-08-CN-0020; Y1-AG-1005-01) with the WHO.
Key words: falls, fall-related injury, injuries, low- and middle-income countries (LMIC), older adults,
global health, SAGE, World Health Organization, WHO
Abstract
Background: In 2010 falls accounted for over 77% and 85% of years lived with disability (YLDs)
resulting from unintentional injuries excluding traffic accidents, in adults aged 50-69, and 70
and over. The global burden of YLDs due to falls in adults aged 50-69 was 66% in developing
countries compared with 34% in developed countries in 2010. This gap is expected to widen as
a consequence of rapid population ageing in developing countries, where over 70% of the
world’s older population currently lives, and as a result of effective falls prevention strategies
being implemented in higher-income countries. Developing countries urgently need sound
epidemiological data to develop and integrate falls prevention into their public health policy
frameworks.
Methods: The study uses household and individual data collected from the Study on global
AGEing and adult health (SAGE) in adults aged 50 years and older in six low- and middle-income
countries (LMIC). The objectives are to identify the annual prevalence of fall-related injury and
investigate and compare risk factors associated with fall-related injury. Multivariate logistic
regressions were separately conducted within biological, behavioral, environmental and
socioeconomic domains included in the falls prevention framework for older adults developed
by the World Health Organization. Statistically significant factors associated with fall-related
injury were tested across the domains using stepwise regression.
Results: Of the 34,138 survey participants, self-reported fall-related injury prevalence in the
previous 12 months was 4%, although this varied by country. The prevalence of fall-related
injury was higher among women. Risk factors significantly associated with an increased risk of
1
fall-related injury are depression (p<0.01), arthritis (p<0.1), grip strength (p<0.05), insufficient
intake of fruits and vegetables (p<0.05), severe or extreme sleep problems (p<0.05), water
source outside the home (p<0.05) and completed secondary education (p<0.05).
Conclusions: There is now, more than ever, a need to re-focus public health priorities for falls
prevention in older age in LMIC where populations are rapidly ageing. This study provides a
much needed platform for further investigation into fall-related injury in LMIC.
2
Background
Physical and mental change associated with advancing age and frailty increases the risk of fallrelated injury, resulting in substantial health and economic costs to individuals and
society.[1,2,3,4,5] In 2010 falls accounted for over 77% and 85% of years lived with disability
(YLDs) resulting from unintentional injuries other than traffic accidents in adults aged 50-69,
and 70 and over.http://viz.healthmetricsandevaluation.org/gbd-compare/ [6] With the rapid
ageing of the world’s populations, falls in older adults are a significant public health issue. Many
low cost interventions have been identified for falls prevention, yet health system development
and implementation is occurring mostly in high-income countries.
There is now, more than ever, a need to re-focus public health priorities in low-and middleincome countries (LMIC).[3,7] In 2010 the global burden of YLDs due to falls in adults aged 5069 was 66% in developing countries compared with 34% in developed
countries.http://www.healthmetricsandevaluation.org/gbd/visualizations/gbd-heatmap [6]
Furthermore this gap is expected to increase, firstly as a consequence of rapid population
ageing in developing countries, where over 70% of the world’s older population currently lives
[4], and secondly, due to the implementation of effective falls prevention strategies in
developed countries. Developing countries require data and resources to develop and integrate
falls prevention into their public health policy frameworks.[3,4,6,7,8,9,10] Epidemiological
research is urgently needed in order to identify the complexity of determinants and conditions
associated with fall-related injury in LMIC. This study uses consistent comparable national
3
health and ageing survey data, collected at the individual and household level from older adults
in six LMIC in different regions of the world, to measure prevalence and investigate risk factors
associated with fall-related injury.
Data on the incidence and prevalence of falls and fall-related injury in LMIC are of variable
quality. Methodological and sampling differences make it difficult to generalize across locations
and countries.[9,11] For example, a literature review of four studies conducted in India
reported annual fall rates for older adults between 14% and 51%.[12] Research in China, Hong
Kong, Macao, Singapore, and Taiwan[13] showed annual fall rates for adults aged 60+ years
between 14.7% and 34%, with rates of fall-related injury-ranging from 60% to 75% amongst
those reporting falls. A study of falls amongst community-dwelling older adults in Latin
America, the Caribbean and among older Mexican-Americans in the southwestern United
States, identified wide variation between countries.[14] A study in rural India found that 38.8%
of non-fatal injuries were due to falls, with one third of occurring in adults aged 60 and
over.[15]
Circumstances associated with falls, and fall-related injury in older adults, were identified
through a comprehensive review of the
literature.[5,11,12,13,14,16,17,18,19,20,21,22,23,24,25,26,27,28,29] Risk factors for fall-related
injury were classified using a conceptual framework developed by the World Health
Organization (WHO) as part of the WHO Risk Factor Model for Falls in Older Age [3].Under the
WHO framework, the determinants of falls in older adults were grouped under biological,
behavioral, environmental and socioeconomic domains. In this study data from Wave 1 of the
4
WHO Study on global AGEing and Adult Health (SAGE) [30] are analyzed within and across these
four domains.[3] The study population includes adults aged 50 years and older in the six SAGE
countries. The objectives are to: identify the annual prevalence of fall-related injury and
describe risk factors associated with fall-related injury.
Methods
SAGE is a longitudinal study with nationally representative samples of adults from China,
Ghana, India, Mexico, the Russian Federation and South Africa. The aim is to generate valid,
reliable and comparable information on a range of health and well-being outcomes of public
health importance in adult and older adult populations in LMIC. SAGE Wave 1 data were
collected via in-person structured interviews (2007-2010). Two types of questionnaires were
separately administered to gather information on individuals and their households. Additional
details about SAGE are provided elsewhere.[30]
Annual Fall-Related Injury: Outcome Variable
The outcome variable - fall-related injury - is defined using two key questions in the SAGE
individual questionnaire. Participants were asked “In the last 12 months, have you had any
other event where you suffered from bodily injury?” If the response was “yes” they were then
asked: “What was the cause of this injury?” “Fall” was one of several possible response
categories. Where respondents reported a fall as the cause of their injury in the previous 12
months they are identified here as having a fall-related injury.
5
Biological Covariates
Age is expressed categorically: 50-59 years (reference); 60-69 years; 70-79 years, and 80 plus
years. Male is the reference category for the binary variable “sex”. Chronic conditions are
identified as “no” (reference) vs. “yes” in answering the following questions: “have you ever
been diagnosed with - arthritis, hypertension, or chronic lung disease”; “have you ever been
told by a health professional that you have had a stroke” and “in the past five years, were you
diagnosed with a cataract in one or both of your eyes?” Most recent eye exam is based on the
number of years (self-reported) since the last eye examination by a medical professional. This is
categorized as 0 to 3 years (reference) vs. never. Depression and body mass index (BMI) were
derived using culturally appropriate WHO algorithms which are described elsewhere [31,32,33].
Depression is a symptom based measure and is categorized as “no” (reference) vs. “yes” and
BMI is categorized as “normal” (reference) vs. “underweight” vs. “pre-obese or obese”. The
number of chronic conditions is scored as the sum of self-reported responses to questions
asking respondents whether they had ever been diagnosed with any of the following - arthritis,
stroke, angina, diabetes mellitus, chronic lung disease, asthma, depression, or hypertension.
Scores for the number of chronic conditions are grouped into four categories –“none”
(reference) vs. “one” vs. “two” vs. “three or more”.
The results of physical tests conducted during the SAGE interviews, were used to derive proxy
measures of frailty. A cognition score was computed by summing scores on tests of verbal
recall, digit span (forward and backwards) and verbal fluency, a grip strength score was derived
from a test which required respondents to squeeze two pieces of metal together. Higher scores
6
indicated better performance. Gait strength was measured using a rapid walk test with
performance categorized as “completed walk” (reference) vs. “did not complete walk”.
Behavioral Covariates
Nutrition is measured by self-reported fruit and vegetable consumption: insufficient
(reference) <5 servings daily vs. sufficient ≥5 servings daily).[31] Sleep duration is a
dichotomous variable whereby respondents who reported having no problems with sleep
(reference) were compared with respondents who reported having had either severe or
extreme sleep problems in the previous thirty days.[34,35,36,37] The physical activity variable
high (reference) vs. moderate vs. low, was derived using the WHO Global Physical Activity
Questionnaire.[31] Responses to survey questions on use of alcohol and tobacco are grouped
here as “no” (reference) vs. “yes”.
Environmental Covariates
Environmental variables include place of residence “urban” (reference) vs. “rural”, type of
dwelling “hard material floor” (reference) vs. “earth floor”, and water source “in the home”
(reference) vs. “outside the home”. Environmental safety was measured by asking participants
how safe they felt walking alone after dark. Responses are based on a 5-point Likert scale
1=completely safe (reference) to 5=not safe at all.
Socioeconomic Covariates
A dichotomous hierarchical ordered probit model was used to develop an index of household
economic status based on owning selected assets.[38,39] The index was divided into wealth
quintiles within each country with the lowest quintile (reference) indicating the poorest
7
household economic status. Participants reported the highest level of education that they had
achieved. The education categories are: “no primary school” (reference) vs. “completed
primary school” vs. “completed secondary or high school” vs. “completed university/college or
a post-graduate degree”. Marital status was classified as: “never married” (reference) vs.
“married or cohabiting” vs. “divorced, widowed, or separated”. Access to healthcare is a binary
variable. Respondents are classified according to whether they reported receiving health care
the last time they needed health care (reference) vs. those who reported that they did receive
health care the last time they needed it. Social network is measured using a validated widely
used instrument with a zero score (reference) indicating low social interaction.[40]
Statistical Analyses
STATA Version 11 (StataCorp, 2009) was used for all statistical analyses. A multistage cluster
sampling strategy was utilized to create nationally representative cohorts. Sampling weights
available in the SAGE data set were applied.[31] Individual and household data sets were
merged using unique individual and household identifiers. All independent variables were
tested for correlation and multicollinearity. Variance inflation factors are reported.
Analyses were conducted on the pooled multi-country data. Univariate analyses were
conducted between each of the covariates and the dependent variable, fall-related injury.
Statistically significant (p<0.05) variables were included in multivariate logistic regressions in
which variables within domains were added in a stepwise process.
Logistic regressions tested association between biological, behavioral, environmental and
socioeconomic variables, as possible risk factors, and fall-related injury. Plots, developed using
8
the R statistical package,[41,42] show the distribution of data within countries. Statistical tests
of significance are reported at p<0.01, p<0.5 and p<0.1.
Results
Baseline Socio-Demographic Characteristics
Table 1 shows the socio-demographic characteristics of the study population by country. China
(N=13,177) and Mexico (N=2,318) had the largest and smallest samples. The distribution of the
population between urban and rural locations in the pooled dataset was 44.1% urban vs. 56.0%
rural. The sex distribution was similar across countries (48.8% female vs. 51.2% male), except in
the Russian Federation (38.9% males and 61.1% females). In China, Mexico and South Africa
over 50% of adults aged 50 and above self-reported that they had held an occupation or a job in
the past year. In the pooled weighted population approximately 45% of respondents were in
the two highest income quintiles. In most countries, the majority of participants completed
secondary or high school. The Russian Federation had the highest completion rates of
secondary or high school (74.7%) and post-secondary education (18.4%).
Annual Prevalence of Injury and Fall-Related Injury
Table 2 shows, for the previous twelve months, self-reported prevalence of: all injuries; fallrelated injury as a proportion of all injuries and fall-related injury as a proportion of the study
population. The data are shown by country, sex and age group. Data on type and place of falls
are included. Where prevalence is stratified by country, sex and age, the cells give the
proportion of respondents with fall-related injury within the country, age or sex sub-group. The
final column shows pooled country results by age and sex sub-groups.
9
Injury prevalence reported for the previous twelve months was highest in India (9.1%) and
lowest in south Africa (1.4%) and 6.1% across all countries. The prevalence of fall-related injury
among all injury was highest in the Russian Federation (73.3%) and the lowest in Ghana
(44.4%). Falls were a highly prevalent source of injury for women in Mexico, India and the
Russian Federation. In the pooled dataset the prevalence of falls as a major source of injury was
73.4% for women and 55.4% for men. Prevalence was higher in the older age groups.
The prevalence of fall-related injury in the study population was highest in India (6.6%) and
lowest in South Africa (0.9%). Fall-related injury was more prevalent among women than men
in all countries except South Africa. With the exception of the Russian Federation and South
Africa, the prevalence of fall-related injury in the study population in the previous twelve
months, was similar in the 70-79 and 80+ age groups.
Unintentional fall-related injuries were the most common type (91.4%), and intentional
(inflicted by another person) the least common type (2.6%). Fall-related injuries typically
occurred in the home.
Risk factors for Annual Fall-Related Injury
Table 3 gives the results of the logistic regression of factors associated with fall-related injury in
the biological, behavioral, environmental and socioeconomic domains. Within the biological
domain, symptom-based depression, arthritis and grip strength were statistically significant
(p<0.05). Nutrition and sleep duration were significant in the behavioral domain (p<0.05).
Water source was significant in the environmental domain (p<0.05). In the socioeconomic
domain, education (completed secondary or high school vs. non completion of primary
10
education) and social network (the highest or best vs. the lowest social network) were
statistically significant (p<0.05).
With the exception of sex (p<0.1) only factors statistically significant (p<0.05) within each of the
domains are included in the regressions in Table 4. Model 1 comprises the biological domain,
Model 2 the behavioral plus biological domains, Model 3 the behavioral plus biological plus
environmental domains and Model 4 (the final parsimonious model) comprises all four domains
- biological, behavioral, environmental and socioeconomic.
In Model 4 risk factors significantly associated with an increased risk of fall-related injury are
depression (p<0.01), arthritis (p<0.1), grip strength (p<0.05), insufficient intake of fruits and
vegetables (p<0.05), severe or extreme sleep problems (p<0.05), water source outside the
home (p<0.05) and completed secondary education (p<0.05).
Compared with China (the reference category) India had significantly higher odds of fall-related
injury in all domains, South Africa and Ghana had significantly lower odds of fall-related in the
biological, behavioral and environmental domains, the Russian Federation had significantly
lower odds of fall-related in the behavioral domain, and Mexico had significantly lower odds of
fall-related injury in the biological domain.
Distribution of Annual Fall-related Injury Conditioned by Country and Risk Factors
The plots in Figure 1 show the distribution of annual fall-related injury prevalence in each
country’s study population conditioned on binary variables that show statistically significant
association (p<0.05) with fall-related injury in Model 4. Each row of plots refers to a country.
11
The ordinate is the prevalence of fall-related injury in the study population in the previous
twelve months. (Note that the scale on the ordinate varies from country to country). For each
plot the four possible scores (insufficient nutrition; sufficient nutrition; water source inside the
home and water source outside the home) are shown with different color (nutrition) and
symbol (water access). In India, for example, approximately 12% of respondents who had
depression, yet no sleep problems, insufficient nutrition and water in the home, reported fallrelated injuries in the previous twelve months. Of the respondents in Mexico who were
depressed, had severe or extreme sleep problems, accessed water outside the home and had
sufficient nutrition, 50% reported fall related injuries. Care needs to be taken in the
interpretation of these results because some of the very high percentages result from low
numbers in the conditioned sub-group. This strong conditioning, relative to the sample size, has
also resulted in sub-groups with zero respondents, resulting in “missing” points in
corresponding plots.
Discussion
This study provides new evidence of prevalence and risk factors associated with self-reported
fall-related injury in LMIC. The highest annual prevalence of injuries and fall-related injury was
in India and the lowest in South Africa. When expressed as a proportion of all injuries, the
prevalence of annual fall-related injury was highest in the Russian Federation (73.3%) and
lowest in Ghana (44.4%). Although a standardized survey instrument was used to collect these
data, it is possible that these results were influenced by different cultural and social
interpretations of falls [3] and injuries from falls.[9,43] This study is the first to utilize national,
comparable population surveys to assess factors associated with fall-related injury across six
12
culturally disparate LMIC. To date, only a small number of studies in China and Latin America
have utilized nationally representative samples to evaluate fall rates and risk factors.[5,14,24]
Age is a common risk factor associated with increased risk of falls and fall-related injury. The
results of this study show fall prevalence was higher in older age groups.[3,13,44] However
when adjusted for other factors the multivariate regressions did not show statistically
significant associations between age and fall-related injury. This may have been mediated by
declining memory and higher fall-related mortality in the older age groups resulting in small
numbers and low statistical power in the sub-groups. A retrospective study in India found that
86% of all fatal falls occurred in the 60+ age group.[15]
Female sex is widely reported in the literature as being associated with increased risk of falls
and fall-related injury in LMIC.[11,15,24,45] This may be due in part to the fact that women are
more frail and live longer than men, which predisposes them to more fall-related injury[46] and
also because fall-related mortality is higher in men than in women.[7] The results of this study
showed statistically significant higher odds of fall-related injury among women compared with
men in the biological domain (Table 3) but sex was not significant in the multivariate models
(Table 4).
The pooled country analysis showed that having to access water outside the home was
associated with higher odds of fall-related injury. Environmental factors have been widely cited
in the literature as being associated with falls in developing countries [9,12] but this is to first
study of its kind to identify water source as a risk of fall-related injury.
13
Although obesity has been associated with falls in high income countries [22] obesity was not a
risk factor for fall-related injury in this analysis. One possible reason may be that overweight
and obesity is associated with higher socioeconomic status (SES) and less falls in LMIC.[47] The
association between insufficient intake of fruits and vegetables and fall-related injury in this
study is consistent with research showing that poor nutritional status is associated with falls in
older people in developing countries [9].
These findings show association between severe or extreme sleep problems and fall-related
injury. Sleep problems are common in older people and there is evidence that poor sleep
increases the risk of falls in older adults.[19,48,49]
Depressive symptomatology has been associated with increased risk of fall-related injury in
older adults in high-income countries [25,50,51,52] and there is a growing body of evidence
showing that depression is an independent risk factor for falls in developing countries.[9,13]
This is the first study of its kind to demonstrate statistically significant association between the
WHO measure of symptom-based depression [32] and fall-related injury in older adults in LMIC.
The covariates were identified from the literature as being determinants or risk factors for falls
and fall-related injury in older age groups.[3] Disability and quality of life were not tested as
determinants because they are also consequence of falls.[8] Analyses of fall-related injury as a
predictor of disability and quality of life in older adults in the SAGE countries are being
undertaken as part of a future body of work on fall-related injury in older adults in LMIC.
14
Strengths
Strengths include the use of uniform questionnaires individually administered by trained
interviewers to large representative samples of populations in LMIC from different geographic
regions of the world. The use of linked household and individual data allowed testing of a wider
range of factors.
Limitations
Recall bias and survivor bias are possible limitations. Survivor bias may help to explain the lack
of effect of age. Only 5.4% of the pooled study population was over 80 years. Cultural,
contextual structural factors may have differently impacted on the extent of under-reporting in
the countries. The numbers of respondents who reported fall-related injuries within countries
was relatively small (e.g. 31 in South Africa and 101 in Mexico) and a pooled analysis was
undertaken to address small samples sizes. However the pooling of country data to some
extent masked patterns within individual countries.
Older adults’ well-being and quality of life is both a cause and a consequence of falls.[13,22] In
this study the focus was on the determinants of annual fall-related injury. While falls result in
poorer quality of life and increased disability, proxy measures for quality of life (nutritional
status and quality of sleep) and frailty (cognition, grip strength and gait strength) are included
here as possible risk factors. The cross sectional nature of the data presents limitations in terms
of interpreting causal association. However SAGE is a longitudinal study and future waves will
provide information on temporal associations.
15
Conclusions
Given the limited data on fall-related injury prevalence and risk factors in LMIC, this study
provides a much needed platform for further epidemiological research. With the rapid ageing
of the populations in developing countries there is now, more than ever, a need for research to
provide evidence to inform public health policy about falls prevention in older populations in
LMIC.
Acknowledgements
The NIAs Division of Behavioral and Social Research, under the directorship of Dr Richard
Suzman, has been instrumental in providing continuous support to SAGE and has made the
entire endeavour possible. We thank the respondents in each country for their continued
contributions, and acknowledge the expertise and contributions of the country primary
investigators and their respective survey teams. Dr Brian Williams assisted in developing the
plots using R statistical software.
Ethics Statement
Informed consent was freely obtained from all participants and the SAGE study was approved
by the WHO Ethics Review Committee.
Contributors
JSW designed and directed the study with assistance from PK. HH and TO’D undertook the
literature review and wrote the first draft as part of their University of Wisconsin Pharmacy
Doctorate placement with the Research Centre for Gender Health & Ageing at the University of
16
Newcastle, Australia. Statistical analyses were undertaken by JSW, HH and TO’D. PK provided
input during the course of the study. JSW wrote the final draft with input from SC.
17
Table 1. Percentage distribution of baseline socio-demographic characteristics by country* and pooled
countries**, SAGE Wave 1, 50+ years
China
Ghana
India
Mexico
Russian
Federation
South
Africa
Pooled
countries
13,177
4,305
6,560
2,318
3,938
3,840
34,138
Total Population***
N=
%
%
%
%
%
%
Residence
Urban
47.4
41.1
28.9
78.8
72.7
64.9
Rural
52.7
58.9
71.1
21.2
27.3
35.1
Sex
Male
49.8
52.5
51.0
46.8
38.9
44.1
Female
50.3
47.6
49.0
53.2
61.1
56.0
Age Group
50-59
44.9
39.7
48.6
48.1
45.2
49.9
60-69
31.9
27.5
30.9
25.6
24.6
30.6
70-79
18.6
23.1
16.0
17.8
21.8
14.0
80+
4.6
9.7
4.5
8.6
8.4
5.5
Marital Status
Never married
1.1
1.3
0.7
7.0
2.7
14.3
Married/Cohabitating
85.0
59.3
76.9
73.0
58.3
55.9
Sep/Divorced/Widowed
13.8
39.4
22.3
20.0
39.0
29.8
Employment
Not employed
22.9
93.8
99.7
42.3
97.0
46.7
Employed
77.1
6.2
0.3
57.8
3.0
53.3
Wealth Quintile
Lowest
16.3
18.2
18.2
15.3
16.2
20.7
Second
18.1
19.1
19.5
24.7
19.6
19.9
Third
20.5
20.5
18.8
16.8
19.1
18.2
Fourth
23.4
20.7
19.6
16.6
20.5
19.8
Highest
21.8
21.6
23.9
26.6
24.6
21.3
Education Level
No primary completed
24.6
22.6
20.6
46.3
1.3
32.8
Primary
27.3
23.8
30.4
29.0
5.6
29.7
Secondary/HS
42.2
45.8
38.5
14.9
74.7
30.0
University/College
5.8
7.8
10.5
9.8
18.4
7.5
*Individual country weights used. **Pooled country weights used
***Number of SAGE participants, aged 50+ years, who completed individual questionnaires.
%
44.1
56.0
48.8
51.2
49.7
28.6
16.1
5.6
1.4
80.1
18.5
32.2
67.8
16.9
18.8
19.6
21.9
22.9
22.2
25.3
44.5
8.0
18
Table 2. Annual prevalence of all injuries and fall-related injury, among all injuries and the study
population, by sex and age group and type and place of fall, by country* and pooled countries**, SAGE
Wave 1, 50+ years
Russian
South
Pooled
China Ghana India Mexico
Federation Africa Countries
Prevalence of all injury in
previous 12 months
595
256
587
118
149
52
1,757
N
5.2%
5.8%
9.1%
4.3%
3.4%
1.4%
6.1%
%
Prevalence of fall-related injury
among all injuries in previous
12 months
372
118
430
101
107
31
1159
N
60.6% 44.4% 72.3% 67.2%
73.3%
66.9%
65.7%
%
Sex
Male
51.5% 29.0% 60.9% 44.1%
66.4%
78.9%
55.4%
Female
68.2% 59.2% 80.1% 88.8%
76.9%
53.5%
73.4%
Age Group
50-59
48.0% 36.2% 66.0% 35.9%
65.9%
28.3%
56.3%
60-69
58.6% 42.2% 76.7% 97.1%
78.5%
73.3%
66.9%
70-79
80.7% 55.2% 79.9% 89.7%
82.1%
64.4%
79.8%
80+
93.9% 59.8% 87.1% 87.4%
84.5%
94.5%
89.7%
Prevalence of fall-related injury
in population sample in previous
12 months
N
%
Sex
Male
Female
Age Group
50-59
60-69
70-79
80+
595
3.1%
256
2.6%
587
6.6%
118
2.8%
149
2.5%
52
0.9%
1,757
4.0%
2.4%
3.9%
1.6%
3.6%
4.4%
8.8%
1.9%
3.6%
2.0%
2.7%
1.3%
0.6%
2.9%
4.9%
2.2%
3.4%
4.7%
4.4%
2.0%
2.8%
3.0%
3.2%
6.2%
6.6%
7.4%
7.8%
1.4%
3.8%
4.5%
4.3%
2.4%
3.0%
1.7%
3.0%
0.2%
1.3%
0.8%
5.5%
3.1%
4.3%
5.4%
5.4%
Unintentional
92.9%
94.3%
89.8&
76.0%
92.0%
76.3%
91.4%
Intentional
1.0%
3.3%
3.6%
21.3%
1.6%
15.6%
2.6%
Self-inflicted
6.1%
0.0%
6.4%
2.5%
0.5%
1.2%
5.7%
Don’t know
0.0
2.4%
0.2%
0.2%
5.9%
6.9%
0.4%
Home
46.0%
41.3%
69.6
85.3%
46.0%
70.5%
56.4%
School
0.8%
2.2%
0.6
1.1%
1.5%
5.6%
0.8%
Work
31.7%
44.6%
14.7
13.6%
14.0%
12.0%
23.8%
Other
20.9%
11.1%
15.0
0.0
0.0
1.0%
16.8%
Don’t Know
0.7%
0.9%
0.1
0.0
38.6%
11.0%
2.2%
Type of fall
Place of fall
*Individual country weights used. ** Pooled country weights used. Fall-related injury in last 12 months derived
from Q4073 and Q4074 in SAGE Wave 1 individual questionnaires.
.
19
Table 3. Logistic regression of fall-related injury in previous 12 months#,
by WHO domains1, pooled countries##, SAGE Wave 1
Variable
Estimate (Standard Error)
Biological
Country
China
1.00
Ghana
0.67** (0.135)
India
1.42)** (0.248)
Mexico
0.52** (0.166)
Russian Federation
0.64 (0.189)
South Africa
0.32** (0.143)
Age
50-59 years
1.00
60-69 years
0.98 (0.158)
70-79 years
1.05 (0.175)
80+ years
0.90 (0.244)
Sex
Male
1.00
Female
1.39*(0.238)
0.90 (0.0835)
Cognition
BMI (WHO algorithm)
Normal
1.00
Underweight
0.89 (0.123)
Pre-Obese/Obese
1.15 (0.212)
No. of SR chronic conditions
None
1.00
One
0.93 (0.194)
Two
0.85 (0.256)
Three or more
0.68 (0.271)
Depression (WHO algorithm)
No
1.0
Yes
2.03 (1.51-2.72)***
Arthritis (SR)
No
1.0
Yes
1.46** (0.277)
Stroke (SR)
No
1.0
Yes
1.39 (0.455)
Hypertension (SR)
No
1.0
Yes
1.26 (0.290)
Chronic lung disease (SR)
No
1.0
Yes
1.70* (0.541)
Cataracts (SR)
No
1.0
Yes
1.05 (0.171)
Most recent vision exam (SR)
0-3 years ago
1.00
Never
0.90 (0.153)
4-+ years ago
1.22 (0.243)
Grip Strength (higher better)
0.98** (0.00941)
Behavioral
Country
China
1.00
Ghana
0.58*** (0.0861)
India
1.31** (0.172)
Mexico
0.69 (0.159)
Russian Federation
0.57*** (0.115)
20
South Africa
Intake of fruits and vegetables
Insufficient
Sufficient
Physical activity
High
Moderate
Low
Sleep duration
No problems
Severe/Extreme problems
Environmental
Country
China
Ghana
India
Mexico
Russian Federation
South Africa
Residence
Urban
Rural
Flooring
Hard floor
Earth floor
Water source
Inside home
Outside home
Safety
Completely safe
Very safe
Moderately safe
Slightly safe
Not safe
Socioeconomic
Country
China
Ghana
India
Mexico
Russian Federation
South Africa
Wealth quintile
Lowest (poorest)
Second
Third
Fourth
Highest
Marital status
Never married
Married
Sep/Divorced/Widowed
Education
No primary
Completed primary
Completed secondary
Completed university/college
Received healthcare if needed
No
0.21*** (0.0830)
1.0
0.68*** (0.0874)
1.00
0.87 (0.0926)
0.83 (0.109)
1.00
2.53***(0.319)
1.00
0.66*** (0.102)
1.65*** (0.206)
0.94 (0.236)
0.81 (0.178)
0.27*** (0.107)
1.00
1.26* (0.149)
1.00
1.19 (0.170)
1.00
1.32** (0.173)
1.00
0.86 (0.135)
0.75* (0.129)
1.04 (0.180)
1.14 (0.254)
1.00
1.10 (0.250)
2.12*** (0.302)
0.88 (0.251)
0.97 (0.221)
0.37 (0.231)
1.00
1.04 (0.191)
0.96 (0.180)
0.85 (0.152)
0.76 (0.157)
1.00
1.80 (1.025)
1.95 (0.972)
1.00
1.01 (0.159)
0.63*** (0.106)
0.85 (0.183)
1.00
21
Yes
Social network
0 (Low/poor)
1
2
3
4 (High/positive)
0.53* (0.184)
1.00
0.79 (0.247)
0.64 (0.290)
0.44* (0.213)
0.28** (0.153)
#
Fall-related injury in last 12 months derived from Q4073 and Q4074 in individual questionnaire.
Estimate is odds ratio (categorical variables) or coefficient (continuous variables)
##
Pooled country weights used. *** p<0.01, ** p<0.05, * p<0.1
SR=self-reported
1.World Health Organization. WHO Global Report on Falls Prevention in Older Age. Geneva: World Health
Organization; 2008.
22
Table 4. Stepwise logistic regression of factors associated with fall-related injury (previous 12 months)#
pooled countries##, SAGE Wave 1, 50+ years
Country
China
Ghana
India
Mexico
Russian Federation
South Africa
Biological
Sex
Male
Female
Depression (WHO
algorithm)
No
Yes
Arthritis (SR)
No
Yes
Grip Strength (Higher
better)
Behavioral
Intake of fruits and
vegetables
Insufficient
Sufficient
Sleep duration
No problems
Severe/Extreme
problems
Environmental
Water source
Inside home
Outside home
Socioeconomic
Education
No primary
Completed primary
Completed secondary
Completed
university/college
Social network
0 (Low/poorest)
1
2
3
4 (High/positive)
Model 1
Estimate
(Standard Error)
Model 2
Estimate
(Standard Error)
Model 3
Estimate
(Standard Error)
Model 4
Estimate
(Standard Error)
1.00
0.71** (0.104)
1.53*** (0.179)
0.67 (0.172)
0.75 (0.168)
0.30*** (0.129)
1.00
0.57*** (0.0916)
1.08 (0.164)
0.54** (0.141)
0.60** (0.134)
0.23*** (0.101)
1.00
0.45*** (0.0724)
0.87 (0.125)
0.54** (0.164)
0.60** (0.136)
0.23*** (0.0989)
1.00
0.70 (0.181)
1.11 (0.238)
0.57 (0.217)
0.78 (0.221)
0.28* (0.185)
1.00
1.25* (0.160)
1.00
1.20 (0.147)
1.00
1.22 (0.152)
1.00
1.19 (0.184)
1.00
2.33*** (0.295)
1.00
2.08*** (0.280)
1.00
2.05*** (0.281)
1.00
1.91*** (0.442)
1.00
1.39* (0.183)
0.98*** (0.00650)
1.00
1.35** 0.181)
0.98*** (0.00626)
1.00
1.37** (0.186)
0.98*** (0.00629)
1.00
1.32* (0.216)
0.99** (0.00623)
1.00
0.72** (0.0948)
1.00
0.71***(0.0929)
1.00
0.68** (0.118)
1.00
1.77*** (0.263)
1.00
1.77*** (0.265)
1.00
1.46*** (0.186)
1.00
1.69** (0.395)
1.00
1.48** (0.253)
1.00
1.00 (0.176)
0.68** (0.122)
1.03 (0.253)
1.0
1.46 (0.840)
1.33 (0.674)
0.97 (0.494)
0.68 (0.408)
#
Fall-related injury in last 12 months derived from Q4073 and Q4074 in individual questionnaire.
Estimate is odds ratio (categorical variables) or coefficient (continuous variables)
##
Pooled country weights used.
*** p<0.01, ** p<0.05, * p<0.1
Variance Inflation Factor for Model 4= 1.0142831
SR=self-reported
23
Figure 1 Percentage of fall-related injury (previous 12 months) conditioned on depression, sleep, country of
residence, nutrition and water access, SAGE Wave 1, 50+ years
Notes: Depression (symptom based) based on WHO algorithm. Water source: inside the home vs. outside the
home. Sleep: no problems vs. severe/extreme problems. Nutrition: Sufficient intake of fruit and vegetables vs.
insufficient intake of fruit and vegetables. Note there are different scales by countries in the ordinate.
24
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