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: Jenny.stewartwilliams@newcastle.edu.au

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 fall-

related 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 middle-

income countries (LMIC).[3,7] In 2010 the global burden of YLDs due to falls in adults aged 50-

69 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; fall-

related 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 fall-

related 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

Total Population***

N= 13,177 4,305 6,560 2,318 3,938 3,840 34,138

Residence % % % % % % % Urban 47.4 41.1 28.9 78.8 72.7 64.9 44.1 Rural 52.7 58.9 71.1 21.2 27.3 35.1 56.0

Sex Male 49.8 52.5 51.0 46.8 38.9 44.1 48.8 Female 50.3 47.6 49.0 53.2 61.1 56.0 51.2 Age Group

50-59 44.9 39.7 48.6 48.1 45.2 49.9 49.7 60-69 31.9 27.5 30.9 25.6 24.6 30.6 28.6 70-79 18.6 23.1 16.0 17.8 21.8 14.0 16.1 80+ 4.6 9.7 4.5 8.6 8.4 5.5 5.6

Marital Status Never married 1.1 1.3 0.7 7.0 2.7 14.3 1.4 Married/Cohabitating 85.0 59.3 76.9 73.0 58.3 55.9 80.1 Sep/Divorced/Widowed 13.8 39.4 22.3 20.0 39.0 29.8 18.5

Employment Not employed 22.9 93.8 99.7 42.3 97.0 46.7 32.2 Employed 77.1 6.2 0.3 57.8 3.0 53.3 67.8

Wealth Quintile Lowest 16.3 18.2 18.2 15.3 16.2 20.7 16.9 Second 18.1 19.1 19.5 24.7 19.6 19.9 18.8 Third 20.5 20.5 18.8 16.8 19.1 18.2 19.6 Fourth 23.4 20.7 19.6 16.6 20.5 19.8 21.9 Highest 21.8 21.6 23.9 26.6 24.6 21.3 22.9

Education Level No primary completed 24.6 22.6 20.6 46.3 1.3 32.8 22.2 Primary 27.3 23.8 30.4 29.0 5.6 29.7 25.3 Secondary/HS 42.2 45.8 38.5 14.9 74.7 30.0 44.5 University/College 5.8 7.8 10.5 9.8 18.4 7.5 8.0

*Individual country weights used. **Pooled country weights used ***Number of SAGE participants, aged 50+ years, who completed individual questionnaires.

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 China Ghana India Mexico Russian

Federation South Africa

Pooled Countries

Prevalence of all injury in previous 12 months N %

595 5.2%

256 5.8%

587 9.1%

118 4.3%

149 3.4%

52 1.4%

1,757 6.1%

Prevalence of fall-related injury among all injuries in previous 12 months N %

372 60.6%

118 44.4%

430 72.3%

101 67.2%

107 73.3%

31 66.9%

1159 65.7%

Sex Male Female

51.5% 68.2%

29.0% 59.2%

60.9% 80.1%

44.1% 88.8%

66.4% 76.9%

78.9% 53.5%

55.4% 73.4%

Age Group 50-59 60-69 70-79 80+

48.0% 58.6% 80.7% 93.9%

36.2% 42.2% 55.2% 59.8%

66.0% 76.7% 79.9% 87.1%

35.9% 97.1% 89.7% 87.4%

65.9% 78.5% 82.1% 84.5%

28.3% 73.3% 64.4% 94.5%

56.3% 66.9% 79.8% 89.7%

Prevalence of fall-related injury in population sample in previous 12 months N %

595 3.1%

256 2.6%

587 6.6%

118 2.8%

149 2.5%

52 0.9%

1,757 4.0%

Sex Male Female

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%

Age Group 50-59 60-69 70-79 80+

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%

Type of fall

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%

Place of fall

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% *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 Ghana India Mexico Russian Federation South Africa

1.00 0.67** (0.135) 1.42)** (0.248) 0.52** (0.166) 0.64 (0.189) 0.32** (0.143)

Age 50-59 years 60-69 years 70-79 years 80+ years

1.00 0.98 (0.158) 1.05 (0.175) 0.90 (0.244)

Sex Male Female

1.00 1.39*(0.238)

Cognition 0.90 (0.0835) BMI (WHO algorithm) Normal Underweight Pre-Obese/Obese

1.00 0.89 (0.123) 1.15 (0.212)

No. of SR chronic conditions None One Two Three or more

1.00 0.93 (0.194) 0.85 (0.256) 0.68 (0.271)

Depression (WHO algorithm) No Yes

1.0 2.03 (1.51-2.72)***

Arthritis (SR) No Yes

1.0 1.46** (0.277)

Stroke (SR) No Yes

1.0 1.39 (0.455)

Hypertension (SR) No Yes

1.0 1.26 (0.290)

Chronic lung disease (SR) No Yes

1.0 1.70* (0.541)

Cataracts (SR) No Yes

1.0 1.05 (0.171)

Most recent vision exam (SR) 0-3 years ago Never 4-+ years ago

1.00 0.90 (0.153) 1.22 (0.243)

Grip Strength (higher better) 0.98** (0.00941) Behavioral Country China Ghana India Mexico Russian Federation

1.00 0.58*** (0.0861) 1.31** (0.172) 0.69 (0.159) 0.57*** (0.115)

20

South Africa 0.21*** (0.0830) Intake of fruits and vegetables Insufficient Sufficient

1.0 0.68*** (0.0874)

Physical activity High Moderate Low

1.00 0.87 (0.0926) 0.83 (0.109)

Sleep duration No problems Severe/Extreme problems

1.00 2.53***(0.319)

Environmental Country China Ghana India Mexico Russian Federation South Africa

1.00 0.66*** (0.102) 1.65*** (0.206) 0.94 (0.236) 0.81 (0.178) 0.27*** (0.107)

Residence Urban Rural

1.00 1.26* (0.149)

Flooring Hard floor Earth floor

1.00 1.19 (0.170)

Water source Inside home Outside home

1.00 1.32** (0.173)

Safety Completely safe Very safe Moderately safe Slightly safe Not safe

1.00 0.86 (0.135) 0.75* (0.129) 1.04 (0.180) 1.14 (0.254)

Socioeconomic Country China Ghana India Mexico Russian Federation South Africa

1.00 1.10 (0.250) 2.12*** (0.302) 0.88 (0.251) 0.97 (0.221) 0.37 (0.231)

Wealth quintile Lowest (poorest) Second Third Fourth Highest

1.00 1.04 (0.191) 0.96 (0.180) 0.85 (0.152) 0.76 (0.157)

Marital status Never married Married Sep/Divorced/Widowed

1.00 1.80 (1.025) 1.95 (0.972)

Education No primary Completed primary Completed secondary Completed university/college

1.00 1.01 (0.159) 0.63*** (0.106) 0.85 (0.183)

Received healthcare if needed No

1.00

21

Yes 0.53* (0.184) Social network 0 (Low/poor) 1 2 3 4 (High/positive)

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

Model 1 Model 2 Model 3 Model 4 Estimate

(Standard Error) Estimate (Standard Error)

Estimate (Standard Error)

Estimate (Standard Error)

Country China 1.00 1.00 1.00 1.00 Ghana 0.71** (0.104) 0.57*** (0.0916) 0.45*** (0.0724) 0.70 (0.181) India 1.53*** (0.179) 1.08 (0.164) 0.87 (0.125) 1.11 (0.238) Mexico 0.67 (0.172) 0.54** (0.141) 0.54** (0.164) 0.57 (0.217) Russian Federation 0.75 (0.168) 0.60** (0.134) 0.60** (0.136) 0.78 (0.221) South Africa 0.30*** (0.129) 0.23*** (0.101) 0.23*** (0.0989) 0.28* (0.185)

Biological Sex Male Female

1.00 1.25* (0.160)

1.00 1.20 (0.147)

1.00 1.22 (0.152)

1.00 1.19 (0.184)

Depression (WHO algorithm) No Yes

1.00 2.33*** (0.295)

1.00 2.08*** (0.280)

1.00 2.05*** (0.281)

1.00 1.91*** (0.442)

Arthritis (SR) No Yes

1.00 1.39* (0.183)

1.00 1.35** 0.181)

1.00 1.37** (0.186)

1.00 1.32* (0.216)

Grip Strength (Higher better)

0.98*** (0.00650) 0.98*** (0.00626) 0.98*** (0.00629) 0.99** (0.00623)

Behavioral Intake of fruits and vegetables Insufficient Sufficient

1.00 0.72** (0.0948)

1.00 0.71***(0.0929)

1.00 0.68** (0.118)

Sleep duration No problems Severe/Extreme problems

1.00 1.77*** (0.263)

1.00 1.77*** (0.265) 1.00

1.69** (0.395) Environmental Water source Inside home Outside home

1.00 1.46*** (0.186)

1.00 1.48** (0.253)

Socioeconomic Education No primary Completed primary Completed secondary Completed university/college

1.00 1.00 (0.176) 0.68** (0.122) 1.03 (0.253)

Social network 0 (Low/poorest) 1 2 3 4 (High/positive)

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

References

1. Kannus P, Sievanen H, Palvanen M, Jarvinen T, Parkkari J (2005) Prevention of falls and consequent injuries in elderly people. Lancet 366: 1885-1893.

2. Kannus P, Palvanen M, Niemi S, Parkkari J (2007) Alarming rise in the number and incidence of fall-induced cervical spine injuries among older adults. Journal of Gerontology 62A: 180-183.

3. World Health Organization (2008) WHO global report on falls prevention in older age. Geneva: World Health Organization.

4. Murray CJL, Vos T, Lozano R, Naghavi M, Flaxman AD, et al. (2012) Disability-adjusted life years (DALYs) for 291 diseases and injuries in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet 380: 2198-2227.

5. Fang X, Shi J, Song X, Mitnitski A, Tang Z, et al. (2012) Frailty in relation to the risk of falls, fractures, and mortality in older Chinese adults: results from the Beijing Longitudinal Study of Aging. J Nutr Health Aging 16: 903-907.

6. Institute for Health Metrics and Evaluation (2013) http://www.healthmetricsandevaluation.org/gbd. Seattle: Bill and Melinda Gates Foundation.

7. De Ramirez SS, Hyder AA, Herbert HK, Stevens K (2012) Unintentional injuries: magnitude, prevention, and control. Ann Rev Public Health 33: 175-191.

8. Chandran A, Hyder AA, Peek-Asa C (2010) The global burden of unintentional injuries and an agenda for progress. Epidemiologic Reviews 32: 110-120.

9. Kalula SZ, Scott V, Dowd A, Brodrick K (2011) Falls and fall prevention programmes in developing countries: environmental scan for the adaptation of the Canadian falls prevention curriculum for developing countries. J Safety Res 42: 461-472.

10. Norton R, Kobusingye O (2013) Injuries. The New England Journal of Medicine 368: 1723-1730.

11. Yu PL, Qin ZH, Shi J, Zhang J, Xin MZ, et al. (2009) Prevalence and related factors of falls among the elderly in an urban community of Beijing. Biomed Environ Sci 22: 179-187.

12. Krishnaswamy B, Gnanasambandam U (2007) Falls in older people: national & regional review of India. WHO background paper to the global report on falls among older persons.

13. Kwan MM, Close JC, Wong AK, Lord SR (2011) Falls incidence, risk factors, and consequences in Chinese older people: a systematic review. J Am Geriatr Soc 59: 536-543.

25

14. Reyes-Ortiz CA, Al Snih S, Markides KS (2005) Falls among elderly persons in Latin America and the Caribbean and among elderly Mexican-Americans. Rev Panam Salud Publica 17: 362-369.

15. Cardona M, Joshi R, Ivers RQ, Iyengar S, Chow CK, et al. (2008) The burden of fatal and non-fatal injury in rural India. Inj Prev 14: 232-237.

16. Mukamal KJ, Mittleman MA, Longstreth WT, Jr., Newman AB, Fried LP, et al. (2004) Self-reported alcohol consumption and falls in older adults: cross-sectional and longitudinal analyses of the cardiovascular health study. J Am Geriatr Soc 52: 1174-1179.

17. Uthkarsh PS, Suryanarayana SP, Gautham MS, Shivraj NS, Murthy NS, et al. (2012) Profile of injury cases admitted to a tertiary level hospital in south India. Int J Inj Contr Saf Promot 19: 47-51.

18. Muir SW, Gopaul K, Montero Odasso MM (2012) The role of cognitive impairment in fall risk among older adults: a systematic review and meta-analysis. Age Ageing 41: 299-308.

19. Brassington GS, King AC, Bliwise DL (2000) Sleep problems as a risk factor for falls in a sample of community-dwelling adults aged 64-99 years. J Am Geriatr Soc 48: 1234-1240.

20. Chu LW, Chi I, Chiu AY (2005) Incidence and predictors of falls in the Chinese elderly. Ann Acad Med Singapore 34: 60-72.

21. Cunningham R, Carter K, Connor J, Fawcett J (2010) Does health status matter for the risk of injury? N Z Med J 123: 35-46.

22. Himes CL, Reynolds SL (2012) Effect of obesity on falls, injury, and disability. J Am Geriatr Soc 60: 124-129.

23. Bouchard DR, Pickett W, Janssen I (2010) Association between obesity and unintentional injury in older adults. Obes Facts 3: 363-369.

24. Li YH, Song GX, Yu Y, Zhou de D, Zhang HW (2013) Study on age and education level and their relationship with fall-related injuries in Shanghai, China. Biomed Environ Sci 26: 79-86.

25. Halil M, Ulger Z, Cankurtaran M, Shorbagi A, Yavuz BB, et al. (2006) Falls and the elderly: is there any difference in the developing world? A cross-sectional study from Turkey. Arch Gerontol Geriatr 43: 351-359.

26. Fabricio SC, Rodrigues RA, da Costa ML, Jr. (2004) Falls among older adults seen at a Sao Paulo State public hospital: causes and. Rev Saude Publica 38: 93-99.

26

27. Bilotta C, Bowling A, Nicolini P, Case A, Pina G, et al. (2011) Older People's Quality of Life (OPQOL) scores and adverse health outcomes at a one-year follow-up. A prospective cohort study on older outpatients living in the community in Italy. Health Qual Life Outcomes 9: 10.

28. Kang C (2011) Risks and characteristics of injuries in older adults in Korea. J Am Geriatr Soc 59: 1146-1148.

29. Mock CN, Abantanga F, Cummings P, Koepsell TD (1999) Incidence and outcome of injury in Ghana: a community-based survey. Bull World Health Organ 77: 955-964.

30. Kowal P, Chatterji S, Naidoo N, Biritwum R, Wu F (2012) Data resource profile: The World Health Organization Study on global AGEing and adult health (SAGE). Int J Epidemiol 41: 1639–1649.

31. He W, Muenchrath M, Kowal P (2012) Shades of Gray: A cross-country study of health and well-being of the older populations in SAGE countries, 2007–2010. Washington DC: U.S. Census Bureau 76 p.

32. Kessler RC, Birnbaum HG, Shahly V, Bromet E, Hwang I, et al. (2010) Age differences in the prevalence and co-morbidity of DSM-IV major depressive episodes: results from the WHO World Mental Health Survey Initiative. Depress Anxiety 27: 351-364.

33. Peltzer K, Phaswana-Mafuya N (2013) Depression and associated factors in older adults in South Africa. Glob Health Action 6: 1-9.

34. Peltzer K (2012) Sociodemographic and health correlates of sleep problems and duration in older adults in South Africa. S Afr J Psych 18: 150-156.

35. Lima MG, Bergamo Francisco PM, de Azevedo Barros MB (2012) Sleep duration pattern and chronic diseases in Brazilian adults (ISACAMP, 2008/09). Sleep Med 13: 139-144.

36. Hublin C, Partinen M, Koskenvuo M, Kaprio J (2007) Sleep and mortality: a population-based 22-year follow-up study. Sleep 30: 1245-1253.

37. Gu D, Sautter J, Pipkin R, Zeng Y (2010) Sociodemographic and health correlates of sleep quality and duration among very old Chinese. Sleep 33: 601-610.

38. Ferguson B, Murray CL, Tandon A, Gakidou E (2003) Estimating permanent income using asset and indicator variables. In: Murray CL, Evans DB, editors. Health systems performance assessment debates, methods and empiricism. Geneva: World Health Organization.

39. Howe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, et al. (2012) Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper. International Journal of Epidemiology doi:10.1093/ije/dys037: 16.

27

40. Berkman L, Syme SL (1979) Social networks, host resistance, and mortality: a nine-year follow-up study of Alameda County residents American Journal of Epidemiology 109: 186-204.

41. Wickham H (2009) ggplot2: elegant graphics for data analysis. New York: Springer.

42. R Core Team (2013) R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.

43. Kalula SZ, de Villiers L, Ross K, Ferreira M (2006) Management of older patients presenting after a fall--an accident and emergency department audit. S Afr Med J 96: 718-721.

44. Schiller JS, Kramarow EA, Dey AN (2007) Fall injury episodes among noninstitutionalized older adults: United States, 2001-2003. Adv Data: 1-16.

45. Launay C, De Decker L, Annweiler C, Kabeshova A, Fantino B, et al. (2013) Association of depressive symptoms with recurrent falls: a cross-sectional elderly population based study and a systematic review. J Nutr Health Aging 17: 152-157.

46. World Health Organization (2012) Women's Health Fact Sheet.

47. Dinsa GD, Goryakin Y, Fumagalli E, Suhrcke M (2012) Obesity and socioeconomic status in developing countries: a sytematic review. Obesity Reviews 13: 1067-1079.

48. Ancoli-Israel S, Ayalon L (2006) Disgnosis and treatment of sleep disorders in older adults. The American Journal of Geriatric Psychiatry 14: 95-103.

49. Latimer Hill E, Cumming RG, Lewis R, Carrington S, Le Couteur DG (2007) Sleep disturbances and falls in older people. J Gerontol A Biol Sci Med Sci 62: 62-66.

50. Korniloff K, Hakkinen A, Koponen HJ, Kautiainen H, Jarvenpaa S, et al. (2012) Relationships between depressive symptoms and self-reported unintentional injuries: the cross-sectional population-based FIN-D2D survey. BMC Public Health 12: 516.

51. Kvelde T, McVeigh C, Toson B, Greenaway M, Lord SR, et al. (2013) Depressive symptomatology as a risk factor for falls in older people: systematic review and meta-analysis. Journal of the American Geriatrics Society 61: 694-706.

52. Iaboni A, Flint AJ (2013) The complex interplay of depression and falls in older adults: a clinical review. The American Journal of Geriatric Psychiatry 21: 484-492.

28