Factors associated with poor balance ability in older ...

Archives of Gerontology and Geriatrics 77 (2018) 108–114

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Factors associated with poor balance ability in older adults of nine highaltitude communities

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Diego Urrunaga-Pastora, Enrique Moncada-Mapellia, Fernando M. Runzer-Colmenaresb,c,d, , Zaira Bailon-Valdeza, Rafael Samper-Ternente, Leocadio Rodriguez-Mañasf, Jose F. Parodib,c a

Universidad de San Martín de Porres, Sociedad Científica de Estudiantes de Medicina, Lima, Peru Universidad de San Martín de Porres, Facultad de Medicina Humana, Centro de Investigación del Envejecimiento (CIEN), Lima, Peru c Bamboo Seniors Health Services, Lima, Peru d Universidad Cientifica del Sur, Facultad de Ciencias de la Salud, Lima, Peru e Internal Medicine/Geriatrics – Sealy Center on Aging, University of Texas Medical Branch, Galveston, USA f Department of Geriatrics, Hospital Universitario de Getafe, Madrid, Spain b

A R T I C LE I N FO

A B S T R A C T

Keywords: Postural balance Falls Altitude Elderly Latin America Peru

Introduction: Poor balance ability in older adults result in multiple complications. Poor balance ability has not been studied among older adults living at high altitudes. In this study, we analysed factors associated with poor balance ability by using the Functional Reach (FR) among older adults living in nine high-altitude communities. Material and methods: Analytical cross-sectional study, carried out in inhabitants aged 60 or over from nine highaltitude Andean communities of Peru during 2013–2016. FR was divided according to the cut-off point of 8 inches (20.32 cm) and two groups were generated: poor balance ability (FR less or equal than 20.32 cm) and good balance ability (greater than 20.32 cm). Additionally, we collected socio-demographic, medical, functional and cognitive assessment information. Poisson regression models were constructed to identify factors associated with poor balance ability. Prevalence ratio (PR) with 95% confidence intervals (95CI%) are presented. Results: A total of 365 older adults were studied. The average age was 73.0 ± 6.9 years (range: 60–91 years), and 180 (49.3%) participants had poor balance ability. In the adjusted Poisson regression analysis, the factors associated with poor balance ability were: alcohol consumption (PR = 1.35; 95%CI: 1.05–1.73), exhaustion (PR = 2.22; 95%CI: 1.49–3.31), gait speed (PR = 0.67; 95%CI: 0.50–0.90), having had at least one fall in the last year (PR = 2.03; 95%CI: 1.19–3.46), having at least one comorbidity (PR = 1.60; 95%CI: 1.10–2.35) and having two or more comorbidities (PR = 1.61; 95%CI: 1.07–2.42) compared to none. Conclusions: Approximately a half of the older adults from these high-altitude communities had poor balance ability. Interventions need to be designed to target these balance issues and prevent adverse events from concurring to these individuals.

1. Introduction Aging in high-altitude populations is a poorly explored phenomenon. There is little knowledge on whether geriatric syndromes present in the same way among older adults living in such conditions. It is known that lung function decreases with aging, altering oxygen absorption and ventilatory responses from partial carbon dioxide pressure and partial oxygen pressure; however, there is no evidence suggesting that older adults are more prone to altitude sicknesses compared to other age groups (Lipsitz, 2002; Rodway, Hoffman, & Sanders, 2004). Dizziness and vertigo are common among older adults and part of the ⁎

Lake Louiśs diagnostic criteria of altitude sickness (Roach, Bartsch, Hackett, & Oelz, 1993). These criteria however, are not exclusive to older adults, and no connection has been identified between them and increased risk of adverse events such as falls or balance impairment (Carod-Artal, 2014). Poor balance ability is also common in older adults and predict multiple complications such as falls, fractures, brain injury, disability and death (Ambrose, Paul, & Hausdorff, 2013; Okubo et al., 2015). The four-square step test, performing dual tasks and functional reach (FR) have all been used to assess balance among older populations (Ambrose et al., 2013; Duncan, Studenski, Chandler, & Prescott, 1992; Duncan,

Corresponding author at: Geriatrics Department Office, Bamboo Seniors Health Services Building, Caminos del Inca Ave. 556, Surco 33, Lima, Peru. E-mail addresses: [email protected] (D. Urrunaga-Pastor), [email protected] (E. Moncada-Mapelli), [email protected] (F.M. Runzer-Colmenares), [email protected] (Z. Bailon-Valdez), [email protected] (R. Samper-Ternent), [email protected] (L. Rodriguez-Mañas), [email protected] (J.F. Parodi). https://doi.org/10.1016/j.archger.2018.04.013 Received 6 November 2017; Received in revised form 30 April 2018; Accepted 30 April 2018 Available online 01 May 2018 0167-4943/ © 2018 Elsevier B.V. All rights reserved.


D. Urrunaga-Pastor et al.

(falls, polypharmacy, comorbidities, tobacco, alcohol and coca leaf consumption), functional status (Barthel Index, Edmonton test, exhaustion), physical performance (gait speed), anthropometric measurements (height and weight) and cognitive status (Pfeiffer Questionnaire).

Weiner, Chandler, & Studenski, 1990; Gabbard & Cordova, 2013; Gobbo, Bergamin, Sieverdes, Ermolao, & Zaccaria, 2014; Whitney, Poole, & Cass, 1998). Unfortunately, only a few of these measures have demonstrated reliability, validity and sensitivity to change. Unlike other measures, the FR test uses a continuous scoring system, is simple to use and is easily performed in the clinical setting (Duncan et al., 1992). Despite these benefits, few authors have used it to evaluate poor balance ability in older populations living in high-altitude (Otsuka et al., 2005; Sakamoto et al., 2016). These authors have shown that among older adults climbing to high-altitude cities, chemoreceptors of the carotid body detect a decrease in PaO2, and trigger balance-related symptoms such as dizziness or vertigo affecting the FR (Otsuka et al., 2005; Sakamoto et al., 2016). Some authors have therefore hypothesized there may be a pathophysiological relationship between high-altitude and balance problems among older adults (Carod-Artal, 2014). Based on the reported impact poor balance ability have on mortality and disability in older adults (Kwan, Close, Wong, & Lord, 2011; Rubenstein, 2006) and the gap in knowledge identified among older adults living at high altitudes, we will explore the factors associated with poor balance ability among a group of older adults living in such conditions. The aim of this study was to evaluate the factors associated with poor balance ability by using the FR among older adults from nine high-altitude communities.

2.5. Measures 2.5.1. Outcome: poor balance ability We used the FR test to assess the presence of poor balance ability in the participants. This test was performed on a flat surface and with a wall or a supporting point in which the participant, standing and using the arm closest to the wall stretched, leaned forward without moving the feet of the flat surface and without losing the balance; registering the maximum reach (in cm) of his fist from the wall. The FR was divided according to the cut-off point of 8 inches (20.32 cm) and two groups were generated: poor balance ability (FR less or equal than 20.32 cm) and good balance ability (greater than 20.32 cm) (Bradley, 2011; Murphy, Olson, Protas, & Overby, 2003; Scott, Votova, Scanlan, & Close, 2007; Wolf, 1999). 2.5.2. Other variables 2.5.2.1. Sociodemographic characteristics. The sociodemographic characteristics were included and evaluated by self-report: age (less than or equal to 70 years, 71 to 80 years, over 80 years), gender (male, female), educational level (no education/incomplete elemental school, complete elementary school, complete high school), marital status (single, married, widowed/divorced), work (yes or no), current occupation (agriculture, trading, others) and altitude (masl). The sociodemographic information was corroborated with the participant's national identity document (ID card).

2. Material and methods 2.1. Design and population Analytical cross-sectional study, carried out in inhabitants aged 60 or over from nine high-altitude (≥1500 m above sea level) Andean communities of Peru: La Jalca, Leimebamba (Amazonas), Llupa, San Pedro de Chaná, Atipayan (Áncash), Pampamarca (Huánuco), Ayahuanco (Ayacucho), Paucarcolla (Puno) and Vilca (Huancavelica) between 2013 and 2016.

2.5.2.2. Medical background. The following variables were evaluated by self-report: falls in the last year (none, at least 1), polypharmacy (5 drugs or more, of frequent use, under medical prescription) (Viktil, Blix, Moger, & Reikvam, 2007), tobacco consumption (yes or no), alcohol consumption (yes or no), coca leaf consumption (yes or no), high blood pressure (HBP) (yes or no), diabetes mellitus type 2 (DM2) (yes or no), chronic obstructive pulmonary disease (COPD) (yes or no) and low back pain (yes or no). Likewise, a variable of comorbidities (obesity defined according to body mass index (BMI) + HBP + COPD + DM2 + low back pain + urinary incontinence) was constructed. The medical background information was confirmed by the caregiver/family member at the time of data collection. According to the body mass index (BMI), we categorized the population as: malnutrition (< 18.5 kg/m2), normal (18.5-24.99 kg/m2), overweight (25.0-29.99 kg/m2) and obesity (> 30.0 kg/m2) (Flegal, Carroll, Ogden, & Johnson, 2002).

2.2. Description of the study area The National Statistics Institute of Peru (Instituto Nacional de Estadística e Informática −INEI) classifies communities with 100 houses not in a capital district, having more than 100 individuals, located in a dispersed way without forming blocks as rural communities (Sociales, 2008). Therefore, most of individuals included in this study are considered members of rural communities in Peru. The communities were located in the Peruvian highlands as follows: a) La Jalca: urban settlement located at 2800 masl (meters above sea level); B) Leimebamba: rural village located at 2158 masl; C) Llupa: rural village located at 3511 masl; D) San Pedro de Chaná: rural village located at 3413 masl; E) Atipayán: rural village located at 3364 masl; F) Pampamarca: urban village located at 3445 masl; G) Ayahuanco: rural village located at 3414 masl; H) Paucarcolla: urban village located at 3847 masl; I) Vilca: rural village located at 3275 masl.

2.5.2.3. Functional assessment. We included gait speed independently from the Short Physical Performance Battery (SPPB); this was considered as a continuous variable and divided by 4, because the speed recorded was during the 4 m to obtain gait speed per meter (m/s) (Guralnik et al., 1994). We used the Barthel Index, a questionnaire about 10 basic activities of daily living (ADL) with a total score between 0 and 100. People not reaching the highest score (100) were classified as dependent (< 100) (Collin, Wade, Davies, & Horne, 1988). Additionally, we use 2 items from the Edmonton test (Rolfson, Majumdar, Tsuyuki, Tahir, & Rockwood, 2006): 1) Social support: When you need help, do you have someone who meets your needs? (Always, sometimes/never); 2) Urinary Incontinence: Do you have trouble holding urine when you do not feel like urinating? (yes or no). In the present study, we evaluated exhaustion, which was defined by 3 items that the patient must respond according to the way he felt during the last 2 weeks: a) Did you feel full of energy? (yes or no); B)

2.3. Sample type, sample size and analysis unit A non-probabilistic, census-type sampling was performed, registering all older adults in the highland communities previously described. We included all or most (approximately 95%) of the geriatric population of each community (urban/rural). The analysis unit was older person from high-altitude Andean communities (urban/rural). The final sample included 368 adults and they all signed informed consent. 2.4. Evaluation Participants were visited in their homes for the interview. Data was collected on sociodemographic characteristics, medical background 109



Table 1 Sociodemographic characteristics of the study sample and bivariate analysis (N = 365). Variables

Gender Female Male Age ≤70 years 71–80 years > 80 years Marital status Single Married Widowed/divorced Educational level No education/Incomplete elemental school Complete elemental school Complete high school Currently works Yes Current occupation Agriculture Trading Others Altitude (masl)

n

%

Mean ± SD1

Balance ability

P value

Good n = 185 (50.7%)

Poor n = 180 (49.3%)

131 (54.1) 49 (39.8) 73.0 ± 7.1 72 (49.0) 79 (49.7) 27 (48.2)

0.010 242 123

66.3 33.7

147 159 56

40.6 43.9 15.5

111 (45.9) 74 (60.2) 73.0 ± 6.7 75 (51.0) 80 (50.3) 29 (51.8)

35 214 116

9.6 58.6 31.8

22 (62.9) 97 (45.3) 66 (56.9)

13 (37.1) 117 (54.7) 50 (43.1)

302 61 2

82.7 16.7 0.6

149 (49.3) 35 (57.4) 1 (50.0)

153 (50.7) 26 (42.6) 1 (50.0)

175

47.9

93 (53.1)

82 (46.9)

241 37 37

76.5 11.8 11.8

128 (53.1) 27 (73.0) 20 (54.1) 3414 (2800–3445)

113 (46.9) 10 (27.0) 17 (45.9) 3445 (3364–3511)

73.0 ± 6.9

0.993 0.980

0.042

0.631

0.367 0.076

3414 (3275–3511)

< 0.001

Data expressed as mean ± standard deviation, median (interquartile range) or number (percentage). 1 SD: Standard deviation.

Did you feel that you could not go on? (yes or no); C) Did you feel that all you did was with effort? (yes or no). A score greater than or equal to 2 was considered positive for exhaustion (Fried et al., 2001; Nascimento, Batistoni, & Neri, 2016).

drugs used as a continuous variable in the adjusted model, because they presented statistical collinearity with the comorbidities variable.

2.5.2.4. Cognitive assessment. We used the Pfeiffer Questionnaire, a 10item questionnaire for evaluation of cognitive impairment. The strata were generated as follows: no intellectual impairment (0–2 errors), mild intellectual impairment (3–4 errors), moderate intellectual impairment (5–7 errors) and severe intellectual impairment (8–10). Those with 8 or more errors were excluded (Pfeiffer, 1975).

The research project was approved by the Institutional Review Board of the Peruvian Naval Medical Centre, located in Lima, Peru. In addition, informed consent was obtained from participants. The anonymity of the participants and confidentiality of the data were ensured.

2.7. Ethical issues

3. Results 3.1. Sociodemographic characteristics of the study sample and bivariate analysis

2.6. Statistical analysis We used STATA v14.0 for our analysis. Descriptive results were presented using measures of central tendency, dispersion measures, absolute frequencies, and relative frequencies. The characteristics of the participants with normal and poor balance ability were compared using the Chi square test, the Fisher exact test, the student T test or the Wilcoxon rank sum test as appropriate. We decided to use Poisson regression due to the cross-sectional design. Two models (1 crude and 1 adjusted model) were constructed using robust variance with the objective of evaluating factors associated with poor balance ability in the participants. The reported measure was the prevalence ratio (PR) with their respective 95% confidence intervals (95% CI). The adjusted model included the following variables: gender, alcohol consumption, tobacco consumption, coca leaf consumption, gait speed per meter (m/s), falls in the last year, exhaustion, comorbidities and altitude (masl). We included the comorbidities variable in the multivariate analysis to avoid losing statistical power by individually entering each of the evaluated comorbidities. These variables were included in the adjusted model because they had statistically significant association with poor balance ability in the crude Poisson regression analysis. Additionally, we evaluated the possible collinearity between the exposure variables entered in the adjusted model and the main variable. Thus, we did not include polypharmacy or the number of

A total of 368 adults 60 years or older were eligible to participate in the study, two participants were excluded because of severe intellectual impairment, and one participant was excluded because of visual and auditory impairment that prevented safe performance of the tests. Data from 365 elderly adults from nine high-altitude communities were analysed. A total of 180 (49.3%) participants had poor balance ability; the mean in cm of FR in participants with balance impairment was 14.1 ± 3.3, while in those participants without balance impairment was 25.4 ± 4.8. The average age was 73.0 ± 6.9 years (range: 60–91 years), 242 (66.3%) were female, 116 (31.8%) were widowed or divorced, 302 (82.7%) had no education or had not finished elementary school and 241 (76.5%) worked in agriculture. Only statistically significant differences were found in gender, marital status and altitude (masl) among functional reach groups (Table 1). 3.2. Medical background, functional and cognitive tests in the study sample and bivariate analysis Of the 365 adults evaluated, 237 (65.1%) had at least 1 fall in the last year, 47 (12.9%) consumed coca leaf, 161 (55.9%) had at least one comorbidity, 111 (35.2%) were classified as overweight and 61 (19.4%) were obese according to BMI, 109 (84.9%) had disability (Barthel 110



Table 2 Medical background, functional assessment and cognitive evaluation in the study sample and bivariate analysis (N = 365). Variables

Medical background Falls in the last year None At least 1 Tobacco consumption Yes Alcohol consumption Yes Coca leaf consumption Yes Number of drugs used Polypharmacy Yes Comorbidities None 1 2 or more HBP2 COPD3 DM24 Low back pain Urinary incontinence BMI5 Malnutrition Normal Overweight Obesity Functional assessment Gait speed per meter (m/s) Barthel Index Independent Dependent Social support Always Sometimes/never Exhaustion Positive Cognitive Assessment Pfeiffer Questionnaire No intellectual impairment Mild intellectual impairment Moderate intellectual impairment

N

%

Mean ± SD1

Balance ability

P value

Good n = 185 (50.7%)

Poor n = 180 (49.3%)

< 0.001 127 237

34.9 65.1

90 (70.9) 94 (39.7)

37 (29.1) 143 (60.3)

47

12.9

12 (25.5)

35 (74.5)

111

30.4

22 (19.8)

89 (80.2)

47

12.9

32 (68.1) 1 (0–1)

15 (31.9) 1 (0–3)

< 0.001 < 0.001 0.011 1 (0–2) 13

3.6

1 (7.7)

12 (92.3)

127 100 61 35 14 30 63 103

44.1 34.7 21.2 9.6 3.8 8.8 18.2 32.3

102 (80.3) 48 (48.0) 20 (32.8) 14 (40.0) 7 (50.0) 15 (50.0) 29 (46.0) 35 (34.0)

25 (19.7) 52 (52.0) 41 (67.2) 21 (60.0) 7 (50.0) 15 (50.0) 34 (54.0) 68 (66.0)

3 140 111 61

1.0 44.4 35.2 19.4

0 (0.0) 106 (75.7) 59 (53.2) 18 (29.5)

3 (100.0) 34 (24.3) 52 (46.8) 43 (70.5)

2.0 (1.9–2.1)

1.2 (1.0–1.9)

0.001 0.002 < 0.001

1.9 (1.1–2.0) 55 309

15.1 84.9

24 (43.6) 161 (52.1)

31 (56.4) 148 (47.9)

156 206

43.1 56.9

86 (55.1) 96 (46.6)

70 (44.9) 110 (53.4)

131

43.4

44 (33.6)

87 (66.4)

258 95 12

70.7 26.0 3.3

129 (50.0) 52 (54.7) 4 (33.3)

129 (50.0) 43 (45.3) 8 (66.7)

0.184 0.958 0.920 0.411 < 0.001 < 0.001

< 0.001 0.247

0.108

< 0.001

0.347

Data expressed as mean ± standard deviation, median (interquartile range) or number (percentage). 1 SD: Standard Deviation. 2 HBP: High blood pressure. 3 COPD: Chronic obstructive pulmonary disease. 4 DM2: Diabetes mellitus type 2. 5 BMI: Body mass index.

having at least one comorbidity (PR = 1.60; 95% CI: 1.10–2.35) and having two or more comorbidities (PR = 1.61; 95% CI: 1.07–2.42) compared to none. The only protective factor found was gait speed (PR = 0.67; 95% CI: 0.50-0.90) (Table 4).

Index) and 107 (29.3%) had cognitive intellectual impairment (mildmoderate) (Table 2). 3.3. Functional reach mean (in cm) according to altitude (masl) We found that 178 (48.8%) of the participants resided at an altitude between 3001 and 3500 masl, while 18 (4.9%) lived at an altitude of 2501–3000 masl. Likewise, the FR means (in cm) did not follow a determined pattern according to altitude (masl) (Table 3).

4. Discussion This study presents data from 365 older adults from nine high-altitude communities, 49.3% of which had poor balance ability. The factors associated to the presence of poor balance ability were: alcohol consumption, having suffered at least one fall in the last year, exhaustion and at having at least one comorbidity. Although the design of the study precludes to establish causal relationships, it is sensible to say that alcohol consumption, exhaustion and the presence of comorbidities are potential causes while increase number falls is a consequence. Better walking speed was associated with a lower probability of poor

3.4. Factors associated with poor balance ability In the adjusted Poisson regression analysis, the associated factors with poor balance ability were: alcohol consumption (PR = 1.35; 95% CI: 1.05–1.73), having had at least one fall in the last year (PR = 2.03; 95% CI: 1.19–3.46), exhaustion (PR = 2.22; 95% CI: 1.49–3.31), 111



and gait are distinct domains and are affected in a different form and intensity by aging (Park, Mancini, Carlson-Kuhta, Nutt, & Horak, 2016). Additionally, studies in Andean populations with instruments including gait speed assessment, detected better performance in older adults in the highlands compared to population living at sea level (EstelaAyamamani, Espinoza-Figueroa, Columbus-Morales, & RunzerColmenares, 2013). Bit according to our data, indeed in this population with a high-performance gait speed remains associated with poor balance ability. Additionally, we report a significant association between the presence of at least one comorbidity and poor balance ability was found, a finding not previously reported in high-altitude populations. Herrera et al. (Herrera et al., 2011) indicates that there are predictors of cardiovascular risk, including impaired gait speed, immobility syndrome, sedentary lifestyle and cognitive impairment. Changes inherent to aging, coupled with the presence of cardiovascular, neurological and vestibular comorbidities, would favour the deterioration of posture and balance, altering the FR (Lawson & Bamiou, 2005; Welmer, Kåreholt, Angleman, Rydwik, & Fratiglioni, 2012), similar to what was noted in our findings. We decided to create the comorbidities variable to assess its impact as deficit accumulation as previously described by other authors (Pinto, Fontaine, & Neri, 2016). No significant results in relation to gender and age were found. In relation to these variables, there is controversial evidence about their role in poor balance ability, but most of the evidence suggests that female gender and advanced age are linked to a higher prevalence of poor balance ability, except that, in the few publications related to FR, no such association was found, supporting our results (Hageman, Leibowitz, & Blanke, 1995; Wallmann, 2001). However, these studies did not assess comorbidities or frailty, so the association with age and gender could differ according to the presence of these covariates. We did not found studies that evaluated the association between exhaustion and poor balance ability in high-altitude populations. It has been described that exhaustion and balance are components included in the assessment of frailty, whereby they have a close relationship. Additionally, muscle strength, cognition and nutrition (often operationalized as nutritional status or weight change) are components also involved in this association, so the etiology would be multifactorial and not only due to a muscular problem (Ferrucci et al., 2004). We found an association between altitude (masl) and poor balance ability in the crude model of Poisson regression analysis, however, it lost statistical significance in the adjusted model. The distribution of FR means values (in cm) did not follow an evident pattern according to the altitude (masl), which could explain the lack of statistically significant association. In addition, the altitude (masl) could be affected by functional status and medical background covariates, also evaluated in the adjusted model of Poisson regression. This study has some limitations. First, the sampling performed was not probabilistic, but included a very percentage of the population,

Table 3 Functional reach mean (in cm) according to altitude (N = 365). Altitude (masl)

n (%)

Mean ± SD1

2158–2500 2501–3000 3001–3500 3501–3847

55 (15.1) 18 (4.9) 178 (48.8) 114 (31.2)

22.4 18.4 22.0 15.5

1

± ± ± ±

3.1 4.7 7.3 5.9

SD: Standard Deviation.

balance ability. Previous studies have used the FR test as a tool to evaluate poor balance ability among older adults, reporting different prevalences. A study carried out in five cities in Ladakh (3250–4647 masl) found that the average measure of FR in the participants was 18.1 cm, slightly lower than the average measure that we found in our study population (19.8 cm) (Otsuka et al., 2005). Another study conducted in 112 older adults from several cities in India (2800–4200 masl), found that the average measure of FR was 17.8 cm, 2 cm lower than the average measure found in our study (Sakamoto et al., 2016). A study in Brazil found 36.4% of patients at risk of falls, which is lower than our results; however, this study was performed in 147 older adults and the cutpoint used was 15 cm (Lopes, Costa, Santos, Castro, & Bastone, 2009). Similarly, in a study performed in Callao, Peru (0 masl) in 2010 with 643 older adults, 57.4% of the population had suffered at least one fall during a 2-year follow-up, and the average measure of FR in this population was 15.8 cm, much smaller than the average measure that we found in older adults with poor balance ability (Díaz-Villegas et al., 2016). On the other hand, a study in Malaysia found that the population at high risk of falls had an average of 26.7 cm with the FR test, taking a higher cut-off point than the one we used (Singh, Pillai, Tan, Tai, & Shahar, 2015). We did not found studies that evaluated the association between alcohol consumption and poor balance ability in high-altitude populations. A cohort study in the United States found that low alcohol consumption (≥1 drinks per week to < 7 drinks per week) reduced the risk of falls in older adults (Cawthon et al., 2006). Similarly, another study determined that the risk of falls was greater in those people consuming 14 or more drinks per week, at four years of follow-up (Mukamal et al., 2004); thus reinforcing what was found in the present study. However, in the United States, another research was conducted where there was no statistically significant connection between alcohol consumption and falls (Wong, Heuberger, Logomarsino, & Hewlings, 2016). In this study, we found an association between gait speed and poor balance ability, a finding previously not described in high-altitude populations. Preliminary evidence suggests that gait speed is an independent predictor of adverse events such as falls, fractures, institutionalization and death in the elderly (Pinedo, Saavedra, & Jimeno, 2010). In relation to this, there is evidence that, neurologically, balance Table 4 Poisson regression to determine factors associated with poor balance ability. Variables

Crude Model: PR (95%CI)

P value

Adjusted Model: PR (95%CI)

P value

Male gender Alcohol consumption Tobacco consumption Coca leaf consumption Gait speed per meter (m/s) Falls in the last year Exhaustion Comorbidities 0 1 2 or more Altitude (masl)1

0.74 2.24 1.63 0.62 0.45 2.07 3.79

(0.58–0.94) (1.85–2.70) (1.33–2.01) (0.40–0.95) (0.39–0.54) (1.55–2.77) (2.67–5.36)

0.015 < 0.001 < 0.001 0.027 < 0.001 < 0.001 < 0.001

0.94 1.35 1.18 0.63 0.67 2.03 2.22

0.630 0.018 0.191 0.072 0.009 0.009 < 0.001

Reference 2.64 (1.77–3.94) 3.41 (2.30–5.06) 1.50 (1.16–1.93)

< 0.001 < 0.001 0.002

Reference 1.60 (1.10–2.35) 1.61 (1.07–2.42) 0.77 (0.59–1.00)

1

Altitude for each 1000 masl. 112

(0.73–1.21) (1.05–1.73) (0.92–1.53) (0.37–1.04) (0.50–0.90) (1.19–3.46) (1.49–3.31)

0.015 0.023 0.052



making it representative of the population living in these 9 high-altitude communities. The study design was cross-sectional, which does not allow us to determine causality between poor balance ability and associated factors. Nevertheless, we identified useful markers that can help us to design future longitudinal studies. Additionally, some variables evaluated in this study were collected through a self-report and may be affected by recall bias. However, 70% of the sample were mentally preserved and our main variable was a performed-based measure, having more reliability against self-report collected variables (Reuben, Siu, & Kimpau, 1992). The accuracy of self-report to collect information of complex diseases would be affected due to low educational level of the study population (Okura, Urban, Mahoney, Jacobsen, & Rodeheffer, 2004); for this reason, we decided to evaluate the most common comorbidities and corroborated the interview information in the presence of a relative of the respondent. We could not measure the quantity of alcohol ingested by study participants because of their low educational level. Finally, some of the evaluated variables have missing values, however, the percentage does not exceed 20%, being possible its analysis (Dong & Peng, 2013).

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