Socioeconomic status, neighborhood characteristics, and walking within the neighborhood among older Hong Kong Chinese.

Journal of Aging and Health http://jah.sagepub.com/

Socioeconomic Status, Neighborhood Characteristics, and Walking Within the Neighborhood Among Older Hong Kong Chinese Ester Cerin, Robin Mellecker, Duncan J. Macfarlane, Anthony Barnett, Man-chin Cheung, Cindy H. P. Sit and Wai-man Chan J Aging Health 2013 25: 1425 originally published online 18 November 2013 DOI: 10.1177/0898264313510034 The online version of this article can be found at: http://jah.sagepub.com/content/25/8/1425

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JAH25810.1177/0898264313510034Journal of Aging and HealthCerin et al.

Article

Socioeconomic Status, Neighborhood Characteristics, and Walking Within the Neighborhood Among Older Hong Kong Chinese

Journal of Aging and Health 25(8) 1425–1444 © The Author(s) 2013 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0898264313510034 jah.sagepub.com

Ester Cerin, PhD1,2, Robin Mellecker, PhD1, Duncan J. Macfarlane, PhD1, Anthony Barnett, PhD2, Man-chin Cheung, MPH3, Cindy H. P. Sit, PhD4, and Wai-man Chan, PhD3

Abstract Objective: We examined the associations of educational attainment and area socioeconomic status (SES) with total within-neighborhood walking patterns and percentage of walking undertaken for recreation purposes in Hong Kong elders. Environmental mediators of these associations were also examined. Method: Chinese-speaking elders (N = 484), cognitively unimpaired and able to walk unassisted, were recruited from 32 street blocks stratified by SES and walkability. Interviewer-administered surveys were conducted to collect data on walking and sociodemographics. Neighborhood environments were audited. Results: Educational attainment was positively related to walking 1The

University of Hong Kong, Hong Kong SAR, People’s Republic of China University, Burwood, Victoria, Australia 3Elderly Health Service, Hong Kong SAR, People’s Republic of China 4The Chinese University of Hong Kong, Hong Kong SAR, People’s Republic of China 2Deakin

Corresponding Author: Ester Cerin, PhD, Centre for Physical Activity and Nutrition Research, School of Exercise and Nutrition Sciences, Deakin University, 221 Burwood Highway, Burwood 3125, Victoria, Australia. Email: [email protected]


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outcomes, while area SES was only positively related to percentage of walking allocated to recreational purposes. While no mediators of area SES-walking associations were identified, several environmental attributes explained the associations of educational attainment with walking. Discussion: Educational attainment rather than area SES is a key determinant of walking in Hong Kong elders; these effects are mostly attributable to social and individual rather than environmental factors. Keywords built environment, elders, walking, socioeconomic status, mediators

China has a rapidly growing population of elders, projected to reach 500 million in 2060 (Li, Reuser, Kraus, & Alho, 2009). With the associated increases in health care and social welfare costs, such aging population trends have serious economic and social implications for China, and the rest of the world (Riley, 2004). To help mitigate these effects, consideration should be given to large-scale programs that could contribute to healthy aging in China, with particular focus on the socially disadvantaged segments who display higher mortality rates and more adverse health outcomes than their counterparts (Zimmer, Wen, & Kaneda, 2010). Regular engagement in physical activity can benefit health (Kruger, Ham, & Sanker, 2008). Walking is the most prevalent and preferred form of physical activity by elders (Cunningham & Michael, 2004) and, unlike many countries, provides the majority of activity-related energy expenditure in China/ Hong Kong (Bauman et al., 2009). Studies on Chinese (Cerin, Sit, Barnett, Cheung, & Chan, 2013) and Western elders (Kamphuis et al., 2009; Li, Fisher, & Brownson, 2005) have shown that individual-level SES indicators (e.g., household income and educational attainment) tend to be positively related to recreational walking, which may contribute to SES differentials in health outcomes. There is some evidence that neighborhood-level SES (e.g., neighborhood median household income) may contribute to recreational walking over and above individual-level SES indicators (e.g., Cerin & Leslie, 2008; Fisher, Li, Michael, & Cleveland, 2004). However, such evidence is limited to Western populations and is equivocal, suggesting that effects may be geographically specific. Walking does not occur only for recreational purposes, it can be also undertaken for transportation. This form of walking is highly prevalent in China, among adults (Lee et al., 2007) and elders (Cerin et al., 2012; Deng et al., 2008; Lee et al., 2007), especially in lower SES groups (Cerin et al., 2012; Lee et al.,


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2007). There is some evidence that, when compared with their low-SES counterparts, high-SES Chinese elders engage less in walking for transport (Cerin et al., 2012; Lee et al., 2007) and more in walking for recreation (Cerin, Sit, et al. 2013). Given these contrasting associations of SES with recreational and transport-related walking, SES differentials in overall walking remain unclear. An understanding of how individual- and neighborhood-level SES contributes to overall walking is important as the general physical activity literature suggests that health is affected by the total accumulated amount of walking. Yet, to complicate the issue, preliminary evidence suggests that the balance of recreational versus transportation walking may also impact on health. For example, studies indicate that recreational rather than transport-related physical activity is positively associated with self-reported mental (Cerin, Leslie, Sugiyama, & Owen, 2009) and physical well-being (Sugiyama, Leslie, Giles-Corti, & Owen, 2008). Although these findings were observed in Western adults, they may also apply to Chinese elders. Moreover, transport-related walking may be accompanied by increased exposure to environmental pollutants, which is associated with heightened health risks (de Nazelle & Rodriguez, 2009; Tonne et al., 2007). This is an especially relevant issue in Chinese urban environments where high levels of pollution are commonly reported (Health Effects Institute, 2004; World Bank, 2007). Although it may be argued that any type of outdoor walking in congested urban environments would increase exposure to pollutants, walking for transportation is more likely to occur along busy roads than is recreational walking. Thus, it is plausible to assume that the level of exposure to road-side pollution would be higher for those engaging in walking for transport. To help understand SES inequalities in health arising from differences in walking behaviors, it seems important to consider walking amounts and proportions allocated to different purposes. Thus, this study examined individuallevel (educational attainment) and area-level (neighborhood median household income) SES differentials in total weekly minutes of within-neighborhood walking and percentage of walking accrued for recreation versus transportation purposes among Hong Kong elders. We focused on within-neighborhood walking because neighborhood-level characteristics are assumed to impact on patterns of walking within rather than outside the neighborhood.

Environmental Correlates of SES Disparities in Walking Behavior To address SES inequalities in walking behaviors, it is important to identify potential mechanisms (mediators) that may be responsible for such discrepancies. Characteristics of the neighborhood environment have been linked to


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differences in walking among SES groups in Western populations (Ball et al., 2007; Cerin & Leslie, 2008; Cerin, Leslie, & Owen, 2009). For example, street connectivity, aesthetics, neighborhood safety, and design and access to facilities explained higher levels of walking in women with higher education (Ball et al., 2007). In a recent study, residents from low SES areas reported lower levels of neighborhood aesthetics, pedestrian/biking facilities, safety from traffic and crime, and access to recreational facilities, all of which are characteristics that support walking (Sallis et al., 2011). Perceived physical barriers to walking were found to explain the positive associations of area SES and individual income with walking for transport and recreation (Cerin & Leslie, 2008; Cerin, Leslie, & Owen, 2009), and environmental aesthetics those of area SES with walking for transport (Cerin, Leslie, & Owen, 2009). Yet, the above findings relate to young and middle-aged adults. To date, only one study, conducted in the Netherlands, examined environmental attributes associated with SES differences in walking among elders (Kamphuis et al., 2009). Thus, there is a general dearth of information on potential environmental mediators of SES-walking associations in elders, especially in non-Western populations. Consequently, one of the main aims of this study was to examine SES differentials in objectively measured attributes of the neighborhood environment and whether the latter were associated with walking outcomes after adjustment for SES indicators in Hong Kong Chinese elders. Attributes that are associated with SES indicators and explain walking behavior independently of SES represent potential mediators (mechanisms) of SES differentials in walking (Cerin & MacKinnon, 2009). At the same time, these analyses provide previously undocumented findings on objectively measured neighborhood environmental characteristics that may impact on total within-neighborhood walking and the proportion of walking allocated to recreational purposes among Hong Kong elders. Such knowledge is important in planning environmental and policy interventions aimed at promoting walking in general and reducing SES inequalities in health-enhancing patterns of walking. We hypothesized that elders with a higher level of educational attainment and living in high SES neighborhoods would accumulate a higher proportion of their total within-neighborhood walking by walking for recreational than transportation purposes as compared with their counterparts. Given that in Chinese elders the direction of the relationships between SES indicators and walking depends on walking purpose (Cerin et al., 2012; Lee et al., 2007), no specific hypotheses were formulated about SES differences in total withinneighborhood walking. We also expected that neighborhood attributes related to traffic, personal, and crime safety, and aesthetics, crowdedness, and pollution would be associated with SES indicators as well as (SES-adjusted)


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percentage of walking undertaken for recreational purposes. We therefore hypothesized that these attributes would cross-sectionally mediate the relationships of SES indicators with the proportional allocation of walking to recreational purposes.

Method Participants and Procedure This study was conducted in Hong Kong in 2007-2008, an ultra-dense metropolis (population density: 6,349 persons/km2). Ethical approval was obtained by the participating institutions (local university and Department of Health). A stratified two-stage sampling design was used to recruit 484 Chinese-speaking elders (42% men; 67% 65-74 years old; 33% 75+ years old) able to walk without assistance and with no diagnosis of cognitive impairment based on clinical assessments. The response rate was 78%. Approximately 13% of the sample had no formal education, 48% completed primary school, and 39% completed secondary or higher education. Participants were members of 4 out of 18 Hong Kong Elderly Health Centres (EHCs) representing low- and high-walkable catchment areas stratified by low- and high-SES (for details, see Cerin et al., 2012; Cerin et al., 2010). The EHCs were established by the Department of Health of the Hong Kong Special Administrative Region (Hong Kong SAR) to provide, on a membership basis, primary care services for elders aged 65+ residing in Hong Kong. While EHCs members are representative of the general Hong Kong population of elders in age, health status, and SES (Schooling et al., 2006), the participants recruited in this study represented a random sample of elders, able to walk unassisted and with no diagnosed cognitive impairment, living in communities selected to maximize the range of exposures (walkability and SES) and outcomes (walking). Thus, they do not represent a sample representative of the entire Hong Kong population of elders. The SES level of the EHC catchment areas was defined using census data on median monthly household income and percentage of owner-occupiers. Area transport-related walkability was established using Census and Centamap (www.centamap.com) data on household, intersection, and commercial/service densities (Cerin et al., 2010). The EHC catchment areas were first ranked by their SES and categorized into high- and low-SES based on median split. High- and low-SES areas were then separately ranked by their walkability. We selected EHC areas that were ranked among the highest and lowest on walkability and at the same time fell into the lowest or highest quartile of SES to represent the following strata: high walkable/low SES, high walkable/high


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SES, low walkable/low SES, low walkable/high SES. Eight street blocks with a minimum of 25 residing EHC members were randomly selected without replacement in each of 4 EHC catchment areas. On average, 15 eligible participants were recruited from each selected street block via an invitation letter followed up by a telephone call. After providing written informed consent, they took part in a face-to-face interviewer-administered survey lasting ~40 min. The survey included a sociodemographic questionnaire and the Neighborhood Walking Questionnaire–Chinese version for Seniors (NWQ-CS; Cerin, Barnett, et al., 2011). Grocery vouchers were offered as participation incentives. Environmental audits of the neighborhoods of residence of the study participants were conducted during daylight (10 a.m. to 6 p.m.).

Measures Individual-level measures. Sociodemographic measures obtained via interviewer-administered questionnaire were age, gender, and educational attainment. Educational attainment (illiterate; no formal education but can read and write; primary education; secondary or higher education) was used as the individual-level SES indicator and recoded into three categories (no formal education; primary education; and secondary or above education). Withinneighborhood walking was assessed using the NWQ-CS, a self-report measure validated in Chinese elders (Cerin, Barnett, et al., 2011). Participants were asked to report the frequency and duration (total minutes per week) of within-neighborhood walking for transport and recreation (separately) in a usual week. Neighborhood was defined as an area approximately 15-min walk from home. Total weekly minutes of within-neighborhood walking for recreation and transport were summed to provide total weekly minutes of within-neighborhood walking. The percentage of walking allocated to recreational purposes was computed by dividing the weekly minutes of withinneighborhood recreational walking by the total weekly minutes of within-neighborhood walking, and multiplying these by 100. Area-level measures. Area level SES was represented by a dichotomous variable (low = 0; high =1), which was derived from Census data on percentage owner-occupiers and median household income for the catchment areas of the four selected EHCs. On average, there were 62% owner-occupiers in high and 46% in low SES areas. The median monthly household income in low SES areas ranged from 12,200 to 13,000 Hong Kong dollars, while that in high SES areas ranged from 20,000 to 25,000 Hong Kong dollars. The Environment in Asia Scan Tool–Hong Kong version (EAST-HK; Cerin, Chan, Macfarlane, Lee, & Lai, 2011) was used to objectively assess attributes


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of the neighborhood environment. The EAST-HK has good test−retest reliability (Cerin, Chan, et al., 2011). For the purpose of this study we used items measuring neighborhood attributes that were plausibly related to SES (Sallis et al., 2011). These were safety (presence of stray animals [1 item], street lights [1 item], crime [4 items], pedestrian safety [3 items], traffic hazards [3 items]), infrastructure (good path conditions [2 items], physical barriers to walking [3 items], public facilities [2 items], indoor/covered places for walking [2 items]), aesthetics and orderliness (natural sights [1 items], presence of trees [1 item], parks [1 item], building attractiveness [2 items], perceptible pollution [2 items], litter [2 items], crowdedness [1 item]), and access to recreational facilities other than parks (4 items). High and low SES areas were, by study design, balanced in transport-related walkability (i.e., has similar average levels of walkability), including street connectivity, land use mix, and residential density. Hence, although such audit data were available, we did not include these neighborhood attributes in our analyses of SES differentials. Two trained auditors collected data on both sides of street segments (1,536 street segments) within 400 m road-network distance from the participants’ street blocks (32 street blocks). The selected distance was based on previous studies (Nagel, Carlson, Bosworth, & Michael, 2008), estimates of average distance walked by Hong Kong elders in 10 to 15 min of perceived time, and budgetary constraints. On average, 11 min were required to collect data from a street segment.

Data Processing and Analysis Preliminary data processing of audit data. Data collected using the EAST-HK were aggregated by street block representing neighborhoods. Aggregated scores of attributes gauged with a single item (e.g., presence of trees) represented the percentage of street segments within a neighborhood with a specific attribute. Scores of attributes gauged using multiple items represented the percentage of the highest obtainable score averaged across street segments within a neighborhood. For example, the highest obtainable score on perceptible pollution was 2. An average score of 1.7 across street segments within a neighborhood would correspond to 85% of the maximal score (i.e., a score of 85 out of 100) on pollution. All scores represented the level of a particular attribute and, thus, items gauging negative attributes (e.g., traffic or pollution) were not reverse-scored to reflect higher levels of walkability. Data analysis. Data were analyzed in several steps. First, associations of educational attainment and areas SES with weekly minutes of within-neighborhood walking and percentage of walking allocated to recreational purposes were


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estimated, after adjusting for age and gender. These outcome variables were positively skewed with an excess number of zeros, as evidenced by Vuong tests (Cheung, 2002). Hence, associations were estimated using zero-inflated negative binomial (ZINB) regression models with robust standard errors accounting for dependency in the data arising from the adopted sampling strategy. ZINB models simultaneously estimate associations of explanatory variables with the odds of scoring zero on the outcome (e.g., odds of nonparticipation in withinneighborhood walking) and with nonzero values on the outcome (e.g., weekly minutes of within neighborhood walking for those reporting some walking). One of the main aims of this article was to examine potential mediators of SES differentials in walking outcomes. This was done using the joint significance test (Cerin & MacKinnon, 2009), as procedures for the currently recommended product-of-coefficient test for ZINB models are not available. In the case of this study, the joint significance test consists of two steps: (a) estimating SES differences in environmental attributes, adjusting for potential sociodemographic confounders, (b) and estimating the significance of the effects of environmental attributes on the walking outcomes, while adjusting for confounders and SES indicators. Support for the hypothesized cross-sectional mediating effects is found if the regression coefficients of interest across the two sets of regression models are statistically significant at a probability level of .05. Generalized linear models (GLMs) accounting for clustering effects were used to evaluate the first step of the joint significance test, while ZINB models were used to evaluate the second step. Separate ZINB models were estimated for each environmental attribute. No adjustments for multiple statistical tests were used as these analyses were theory-driven rather than exploratory, that is, specific hypotheses were assessed (Perneger, 1998). All environmental attributes that were significant predictors in the single-attribute models were entered in multiple-attribute ZINB model to estimate their unique contribution to explaining walking outcomes and, for those that were associated with SES indicators, their unique contributions as cross-sectional mediators of SES-walking outcome associations. Final multi-attribute models including only environmental characteristics that significantly contributed to the explanation of the walking outcomes were estimated.

Results Descriptive Statistics and Amount of Within-Neighborhood Walking in Elders Walking for Different Purposes Overall, the prevalence of stray animals and signs of crime were low, while street lights, good path conditions, and pedestrian safety (e.g., presence of


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crossing aids and calming devices) were common features of the study neighborhoods (Table 1). Elders reported on average 498 (SD = 392) minutes of within-neighborhood walking in a usual week (Median = 420; IQR = 478). Only 23 out of 484 participants (4.75%) did not report any within-neighborhood walking. Among those reporting some walking, 23.2% engaged in walking for transport only, 3.3% engaged in walking for recreation only, while the remaining 73.5% reported walking for both purposes. The average percentage of walking undertaken for recreational purposes was 41.5% (SD = 35.0%; Median = 45.9%; IQR = 71.4%).

Associations of SES Indicators with Walking Outcomes Weekly minutes of within-neighborhood walking. Educational attainment was inversely related to the odds of not walking in the neighborhood (Table 2). Among walkers, those with secondary or higher education reported the largest amounts of walking, followed by respondents with no formal education. Participants with primary education reported significantly smaller amounts of walking than the other two educational groups. Area SES was unrelated to within-neighborhood walking (Table 2). Percentage of within-neighborhood walking allocated to recreational purposes. Educational attainment was inversely related to the odds of not reporting any recreational walking (Table 2). Among those who reported some recreational walking, participants with secondary or higher educational attainment and those living in high SES areas allocated a larger percentage of their total walking to this particular purpose. For example, when compared with those with no formal education, respondents with secondary or higher education allocated 70.7% (95% CI [45.0%, 101%]) more of their total walking to recreational purposes.

Analysis of Environmental Mediators and Correlates Associations of SES indicators with neighborhood attributes (Step 1 of mediation analyses). After adjustment for area SES, educational attainment was negatively related to the following neighborhood characteristics: presence of stray animals, physical barriers to walking, litter, and crowdedness (Table 1). For example, the prevalence of street segments with stray animals was 31% (95% CI [0%, 53%]) lower in the neighborhoods of residence of participants with primary education as compared with neighborhoods of those with no formal education. Area SES was negatively related to the presence of stray animals,


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Table 1. Objectively-Measured Neighborhood Attributes and Their Associations With Educational Attainment and Area SES. Educational attainment (ref: no formal education)

Neighborhood attribute Safety-related Stray animalsa Street lights

Primary

Secondary or above

High SES

M (SD)

b [95% CI]

b [95% CI]

b [95% CI]

2 (5)

0.69 [0.47, 1.00]* −1.76 [−3.95, 0.43] 0.88 [0.42, 1.85] −3.13 [−9.58, 3.31] 1.67 [−0.76, 4.11]

0.74 [0.51, 1.07] −1.34 [−3.19, 0.51] 0.50 [0.13, 1.88] −4.93 [−12.83, 2.96] 0.65 [−1.89, 3.18]

0.33 [0.25, 0.43]*** −3.41 [−11.01, 4.19] 0.15 [0.03, 0.88]* −1.82 [−13.50, 9.86] −1.51 [−7.11, 4.09]

0.95 [−1.59, 3.48] −4.75 [−8.60, −0.90]* 1.51 [−1.56, 4.59] −0.85 [−6.52, 4.82]

1.35 [−1.13, 3.83] −7.74 [−11.57, −3.91]*** −0.10 [−3.25, 3.05] −1.97 [−7.62, 3.68]

2.15 [−2.52, 6.82] −2.26 [−4.77, 0.24] −7.82 [−13.76, -1.88]** 0.69 [−14.70, 16.08]

0.95 [0.62, 1.46] 4.31 [−1.01, 9.64] 3.75 [−4.3, 12.23] 1.57 [−0.51, 3.65] −3.08 [−12.66, 6.49] 0.69 [0.49, 0.96]* 0.62 [0.42, 0.92]* 2.43 [−8.85, 13.71]

0.75 [0.43, 1.33] 4.54 [−2.46, 11.54] 2.12 [−8.43, 12.67] 1.87 [−0.20, 3.94] −7.48 [−18.11, 3.18] 0.68 [0.49, 0.95]* 0.54 [0.28, 1.00]* 2.26 [−7.45, 11.98]

1.46 [0.58, 3.71] −3.78 [−15.27, 7.70] −10.32 [−23.52, 7.12] 2.69 [1.34, 4.05]*** −0.97 [−26.50, 24.55] 0.80 [0.62, 0.99]* 1.40 [0.52, 3.72] 3.88 [−7.77, 15.53]

85 (12) 2 (4)

Crimea Pedestrian safety

55 (16)

Traffic hazards

Area SES (ref: low SES)

9 (10)

Infrastructure-related Good path conditions

90 (7)

Physical barriers to waking

17 (13)

Public facilities

16 (9)

Indoor/covered places for walking Aesthetics and orderliness Natural sightsa

25 (12)

Trees

35 (15)

Park

15 (10)

Building attractiveness

48 (7)

Perceptible pollution

36 (34)

Littera

11 (15)

Crowdednessa

12 (16)

Recreational facilities other than parks

43 (26)

21 (29)

Note. All generalized linear regression models (GLM) were adjusted for gender, age, SES indicators, and clustering effects at the neighborhood level. SES = socioeconomic status; ref = reference; b = regression coefficient; 95% CI = 95% confidence intervals. aFor these neighborhood attributes negative binomial and logarithmic link functions were used. Thus, values represent antilogarithms of the regression coefficients. These are interpreted as the proportional difference in neighborhood attributes between SES indicator categories with b > 1.00 indicating a positive difference and b < 1.00 a negative difference. All other models used Gaussian variance and identity link functions. *p < .05. **p < .01. ***p < .001.


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Cerin et al. Table 2. Associations of SES Indicators With Walking Outcomes.

Within-neighborhood walking

SES indicator

Percentage of within-neighborhood walking allocated to recreational purposes

Min/wk (negative binomial model)a

OR of being a nonwalker (logit model)b

% of walking allocated to recreational purposes (negative binomial model)a

OR of being a nonwalker for recreation (logit model)b

eb [e95% CI]

eb [e95% CI]

eb [e95% CI]

eb [e95% CI]

0.889 [0.765, 1.034] 1.707 [1.450, 2.009]***

0.293 [0.094, 0.481]*** 0.101 [0.040, 0.255]***

1.192 [1.057, 1.344]**

1.490 [0.571, 3.896]

Educational attainment (ref: no formal education) Primary education 0.844 0.153 [0.710, 1.003] [0.027, 0.884]* Secondary or 1.513 0.040 above education [1.260, 1.816]*** [0.015, 0.107]*** Area SES (ref: low SES) High SES 1.002 2.686 [0.856, 1.170] [0.731, 9.875]

Note. All models adjusted for age, gender, educational attainment, area SES, and clustering effects at the neighborhood level. SES = socioeconomic status; min/wk = minutes per week; OR = odds ratio; neg. = negative; eb = antilogarithm of regression coefficient; e95% CI = antilogarithms of the 95% confidence intervals of the regression coefficients; ref = reference category. aAntilogarithms of the regression coefficients representing the proportional difference in weekly minutes of walking between two categories of a specific SES indicator. bAntilogarithms of the regression coefficients representing the proportional difference in odds of being a nonwalker associated with 1 unit change in the predictor. *p < .05. **p < .01. ***p < .001.

public facilities (i.e., public toilets and benches), signs of crime, and litter. Also, high SES areas had significantly more attractive buildings (Table 1). SES-adjusted associations of neighborhood attributes with walking outcomes (Step 2 of mediation analyses). In the single-environmental predictor models, the presence of trees (b = .994; 95% CI [.989, .999]; p < .05) and stray animals (b = .986; 95% CI [.973, .999]; p < .05) were negatively, while street lights (b = 1.001; 95% CI [1.003, 1.009]; p < .01), signs of crime (b = 1.042; 95% CI [1.019, 1.065]; p < .001), indoor/covered places for walking (b = 1.003; 95% CI [1.000, 1.005]; p < .05), perceptible pollution (b = 1.003; 95% CI [1.000, 1.004]; p < .05), litter (b = 1.004; 95% CI [1.001, 1.007]; p < .01), crowdedness (b = 1.007; 95% CI [1.004, 1.010]; p < .001), and recreational facilities (b = 1.002; 95% CI [1.000, 1.005]; p < .05) were positively, related to amount of within-neighborhood walking, after adjusting for educational attainment and area SES. However, only litter, crowdedness, signs of crime, and the presence of stray animals were also related to SES indicators (Table 1) and, hence, identified as potential cross-sectional mediators of SES-walking relationships. Yet, after accounting for the effects of other significant environmental predictors


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(i.e., street lights), only presence of stray animals and signs of crime were retained as significant cross-sectional mediators (Table 3). These two characteristics, together with physical barriers to walking, were also identified as cross-sectional mediators of the relationships between SES indicators and the odds of being a nonwalker (Table 3). In contrast, good path conditions were a correlate of the odds of being a nonwalker (negative association). The above mediators explained only a small part of the associations of SES indicators with walking, as the regression coefficients for the SES indicators did not change substantially after accounting for the identified mediators (see Tables 2 and 3). While in single-environmental-predictor models signs of crime (b = .982; 95% CI [.972, .992]; p < .001), public facilities (b = .992; 95% CI [.986, .998]; p < .05), litter (b = 1.003; 95% CI [1.000, 1.004]; p < .01), and crowdedness (b = 1.003; 95% CI [1.000, 1.004]; p < .05) were identified as potential mediators of the associations of educational attainment with the percentage of walking allocated to recreational purposes, support for the mediating role of these characteristics was not found in multiple-predictor models (Table 3). Yet, natural sights and perceptible pollution emerged as significant correlates of this particular walking outcome (Table 3). Signs of crime were the only significant mediator of the associations between area SES and the odds of walking for recreational purposes.

Discussion Using data from a community sample of Hong Kong elders, this study examined SES differences in within-neighborhood walking patterns and identified potential environmental contributors to such differences. This information is important to understand whether previously observed SES disparities in health in Chinese urban elders (Zimmer et al., 2010) may, in part, arise from the amount and type of walking this population engages in and, if this is the case, the extent to which these discrepancies in walking may be due to environmental conditions.

Overall Within-Neighborhood Walking As noted previously (Cerin, Sit, et al., 2013; Cerin et al., 2012), the levels of walking in this sample of Chinese elders were substantially higher than those of elders living in Western countries (Mendes de Leon et al., 2009; Nagel et al., 2008). Study eligibility criteria (being able to walk unassisted) may have contributed to the findings. Yet, it is noteworthy that the levels of walking were even higher than those found in Western younger populations (Cerin, Leslie, Sugiyama, et al., 2009). Other studies on Chinese elders


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Cerin et al. Table 3. Associations of Neighborhood Attributes and SES Indicators With Walking Outcomes: Multiple-Environmental-Predictor Models.

Within-neighborhood walking

Min/wk (negative binomial model)a

Neighborhood attribute [SES indicator associated with attribute] Safety-related Stray animals [E; A] Street lights Crime [A] Infrastructure-related Good path conditions

eb [e95% CI]

% of walking allocated to recreational OR of being a nonwalker (logit purposes (neg. binomial model)a model)b

eb [e95% CI]

0.985 1.055 [0.970, 0.999]* [1.000, 1.067]* 1.005 NA [1.002, 1.008]*** 0.774 1.040 [1.017, 1.063]*** [0.682, 0.878]*** NA

Percentage of withinneighborhood walking allocated to recreational purposes

0.940 [0.894, 0.987]* 1.037 [1.010, 1.064]**

Physical barriers to walking [E] Aesthetics and orderliness Natural sights

NA

NA

NA

Perceptible pollution

NA

NA

OR of being a nonwalker for recreation (logit model)b

eb [e95% CI]

eb [e95% CI]

NA

NA

NA

NA

NA

1.165 [1.062, 1.279]***

NA

NA

NA

NA

1.004 [1.002, 1.004]*** 0.997 [0.996, 0.999]***

NA NA

Socio-economic status indicator Educational attainment (ref: no formal education) Primary education 0.865 0.298 0.849 0.325 [0.732, 1.022] [0.027, 3.261] [0.668, 1.079] [0.130, 0.813]** Secondary or higher 1.398 0.062 1.622 0.112 education [1.130, 1.730]*** [0.010, 0.384]*** [1.269, 2.073]*** [0.039, 0.322]*** Area SES (ref: low SES) High SES 1.043 1.148 1.163 1.849 [0.890, 1.223] [0.151, 8.740] [1.032, 1.311]** [0.982, 3.476] Note. All models adjusted for age, gender, educational attainment, area socioeconomic status (SES), significant environmental predictors (neighborhood attributes), and clustering effects at the neighborhood level. SES = socioeconomic status; min/wk = minutes per week; OR = odds ratio; eb = antilogarithm of regression coefficient; e95% CI = antilogarithms of the 95% confidence intervals of the regression coefficients; E = educational attainment; A = area SES; ref = reference category; NA = not applicable. aAntilogarithms of the regression coefficients representing the proportional difference in weekly minutes of walking associated with 1 unit change in the predictor. bAntilogarithms of the regression coefficients representing the proportional difference in odds of being a nonwalker associated with 1 unit change in the predictor. *p < .05. **p < .01. ***p < .001.


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reported similar findings (Deng et al., 2008). These exceptionally high amounts of walking may be due to environmental (e.g., high access to services and low levels of crime) and cultural factors (Cerin, Sit, et al., 2013). A large proportion of Chinese elders participate in regular morning exercise, which is traditionally considered to be a key contributor to good health and social ties (Belza et al., 2004). While area SES did not contribute to the explanation of the odds or amount of within-neighborhood walking, educational attainment did. More educated elders were more likely to engage in, and accrue greater amounts of, withinneighborhood walking, which is in line with previous findings in Western elders (e.g., Mendes de Leon et al., 2009). The associations of educational attainment with the odds of being a walker versus a nonwalker were in part explained by the presence of stray animals (typically, stray dogs) and physical barriers to walking in the neighborhood (e.g., hilly streets, vehicles parked on sidewalks, roadwork, and steep staircases). It appears that more educated Hong Kong elders may afford living in safer, less isolated (stray dogs are usually present in lower density, more isolated areas of Hong Kong), and more accessible areas, which mirrors previous research on Western adults (Cerin & Leslie, 2008; Cerin, Leslie, & Owen, 2009). It is unlikely that these effects were due to regular walkers self-selecting to live in more accessible communities free of stray dogs, as it is plausible to assume that these neighborhood attributes would somewhat concern all residents irrespective of their activity levels. While some types of physical barriers to walking may be difficult to change (e.g., hilly streets), the prevalence of unattended dogs could be reduced by local governmental efforts. In a previous report on the same sample, the presence of stray animals moderated the relationships between objectively measured outdoor recreational facilities and the odds of engaging in leisure-time physical activity, whereby facilities were associated with activity only among elders living in neighborhoods where no stray animals were observed (Cerin, Lee, et al., 2013). Similar detrimental effects of unattended dogs have been observed in Western samples (Griffin, Wilson, Wilcox, Buck, & Ainsworth, 2008), indicating that they constitute a significant environmental barrier to maintaining an active lifestyle in vulnerable segments of the population (e.g., elders), suggesting this area requires further policy and regulatory interventions at the local level. On the other hand, the presence of stray dogs may be an indicator of low levels of informal social control and social cohesion, which have been previously found to be related to walking in Western elders (Fisher et al., 2004; Mendes de Leon et al., 2009). Clearly, this is an issue that needs to be explored in future studies. Signs of crime, street lights, and good path conditions were positively associated with within-neighborhood walking. However, SES differences were


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found only for signs of crime. Street lights and well-maintained footpaths were highly prevalent in the study neighborhoods and relatively evenly distributed across areas varying in SES. In contrast, signs of crime were more prevalent in low SES areas. The positive association between crime and walking may seem counterintuitive; however, it has also been observed in other geographical locations, such as the United States and Japan (Inoue et al., 2011; Van Cauwenberg et al., 2011). It is possible that crime may measure aspects of SES that are not picked up by other individual- and area-level SES indicators based on income and education. Lower SES groups tend to report higher levels of walking for transportation that likely substantially contribute to their overall walking; this may be more common in areas where higher crime rates may discourage some public services (Clark et al., 2009) such as private mini-buses and taxis, thereby reducing motorized transportation options. Alternatively, crime may be more prevalent in destination-rich and crowded areas, which are known to promote walking for transportation (Van Cauwenberg et al., 2011). Another finding worth noting is that the identified potential environmental mediators of educational differentials in within-neighborhood walking explained only a small portion of the observed effects. It is possible that these effects were mostly due to individual and social factors. For example, previous studies have identified perceived benefits of physical activity, individual health, self-efficacy and social support for physical activity, and sense of community as important contributors to SES differentials in walking (Cerin & Leslie, 2008; Cerin, Leslie, & Owen, 2009). More educated, higher-income Hong Kong elders may be more aware of the benefits of an active lifestyle and, hence, regularly engage in recreational walking in addition to walking for transportation. This may not apply to low educated elders. Also, high educated Hong Kong elders can likely afford a domestic helper (quite common in Hong Kong) and the cost of public transportation, and, thus, have more time for leisure activities, such as walking for recreation. We found that participants who engaged in walking for transport and recreation accrued higher levels of walking than single-purpose walkers. Hong Kong destination-rich neighborhoods with a good pedestrian infrastructure may provide convenient opportunities for transport-related walking to all SES groups. In contrast, engagement in recreational walking may be more a function of personal preferences and circumstances rather than environmental conditions. A similar pattern of findings has been previously observed in Australian adults (Cerin & Leslie, 2008; Cerin, Leslie, & Owen, 2009). Thus, although high educated Hong Kong elders may accumulate smaller amounts of walking for transport than their low educated counterparts (Cerin et al., 2012), they appear to make up and even overcompensate for this difference by engaging in regular recreational walking.


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Percentage of Within-Neighborhood Walking Allocated to Recreational Purposes Educational attainment showed a strong positive association with the odds of engaging in walking for recreation and, together with area SES, a positive association with the percentage of walking allocated to recreational purposes in those who reported recreational walking. None of the measured neighborhood characteristics explained the former association, while the presence of litter and crowdedness explained the latter associations, however only when not accounting for the effects of perceptible pollution. Again, as noted earlier with respect to overall within-neighborhood walking, the lack of evidence for environmental mediators of SES differentials in proportional allocation to recreational walking suggest that the observed differences may be in the main due to social and individual factors. Pollution and natural sights were the only factors independently associated with proportional allocation of walking to recreational purposes. Environmental aesthetics has been found to be associated with recreational walking in elders (Cerin, Lee, et al., 2013; Van Cauwenberg et al., 2011). It has also been identified as a mediator of the relationship between individuallevel SES and walking for recreation (Kamphuis et al., 2009). However, no studies have looked at its predictive validity and mediating effects with respect to allocation of time to specific walking purposes, which, as mentioned earlier, may be a factor affecting health. This study indicates that the presence of natural sights might promote more engagement in recreational walking. The opposite is true for perceptible pollution. This is worrying as recreational rather than transport-related walking has been associated with physical and mental health (Sugiyama et al., 2008). In addition, access to outdoor recreational facilities was found to be positively related to elders’ leisure-time physical activity other than walking only in neighborhoods with low levels of perceptible pollution (Cerin, Lee, et al., 2013). Perceptible pollution appears to act as a universal, non-SES dependent deterrent to engaging in outdoor recreational activities in Hong Kong elders. Enhanced policies and regulatory interventions aimed at pollution reduction are therefore needed.

Limitations The cross-sectional design, the lack of a comprehensive assessment of participants’ health status (apart from cognitive impairment and mobility), and reliance on self-report measures of walking are study limitations. However, the percentage of walking undertaken for recreational purposes can only be gauged via self-reports. Given that being able to walk unassisted was one of


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the eligibility criteria for participation in the study, the examined sample likely represent a healthier segment of Hong Kong elders. However, as we aimed at capturing potential environmental mediators of SES differentials in walking behaviors, it was important to recruit participants who could walk. This study included a relatively small number of neighborhoods from four Hong Kong districts, which did not permit a more powerful analysis of arealevel effects on walking using continuous rather than dichotomous SES data. Future, larger scale studies need to address these limitations.

Conclusion We found that educational attainment was a stronger and more consistent predictor of within-neighborhood walking in Hong Kong elders than was area SES. These findings parallel those of another recent study conducted in Hong Kong, where educational attainment was found to be a significant correlate of self-reported health, while neighborhood-level income and inequality were not (Wong, Cowling, Lo, & Leung, 2009). Nevertheless, it should be noted that the overall level of self-reported walking was high and exceeded the current recommendations for health of 150 weekly minutes. This may be attributed to cultural factors as well as Hong Kong’s compact, high-density, and highly interconnected built environment, which facilitates social integration and enhances interarea accessibility. Only a few environmental characteristics explained SES differences in walking behavior, which indicated that individual and social factors may be more important in this respect within the context of Hong Kong. These findings may be generalizable to other densely populated urban locations with high average levels of accessibility to services and safety. This study has also highlighted the importance of several environmental factors for the promotion of walking in Hong Kong elders. Policy interventions aimed at improving streetlights, providing accessible and well-maintained footpaths, and reducing unattended dogs and pollution could help elders maintain an active lifestyle and address SES disparities in walking. Acknowledgment We thank the staff of the Elderly Health Centres for their assistance, which made it possible to successfully complete this project.

Authors’ Note The funding body had no input in the study design; the collection, analysis, and interpretation of data; the writing of the article; and the decision to submit it for publication. All authors are independent from the funding bodies.


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Declaration of Conflicting Interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Grant 04060671 by the Health and Health Service Research Fund (Food and Health Bureau, Hong Kong) for which we are grateful.

References Ball, K., Timperio, A., Salmon, J., Giles-Corti, B., Roberts, R., & Crawford, D. (2007). Personal, social and environmental determinants of educational inequalities in walking: A multilevel study. Journal of Epidemiology & Community Health, 61, 108-114. Bauman, A., Bull, F., Chey, T., Craig, C. L., Ainsworth, B. E., Sallis, J. F., . . . Pratt, M. (2009). The international prevalence study on physical activity: Results from 20 countries. International Journal of Behavioral Nutrition and Physical Activity, 6, Article 21. Belza, B., Walwick, J., Shiu-Thornton, S., Schwartz, S., Taylor, M., & LoGerfo, J. (2004). Older adult perspectives on physical activity and exercise: Voices from multiple cultures. Preventing Chronic Disease, 1, Article 9. Cerin, E., Barnett, A., Sit, C. H. P., Cheung, M. C., Lee, L. C. J., Ho, S. Y., & Chan, W. M. (2011). Measuring walking within and outside the neighborhood in Chinese elders: Reliability and validity. BMC Public Health, 11, Article 851. Cerin, E., Chan, K. W., Macfarlane, D. J., Lee, K. Y., & Lai, P. C. (2011). Objective assessment of walking environments in ultra-dense cities: Development and reliability of the environment in Asia Scan Tool–Hong Kong version (EAST-HK). Health & Place, 17, 937-945. Cerin, E., Lee, K. Y., Barnett, A., Sit, C. H. P., Cheung, M. C., & Chan, W. M. (2013). Objectively-measured neighborhood environments and leisure-time physical activity in Chinese urban elders. Preventive Medicine, 56, 86-89. Cerin, E., & Leslie, E. (2008). How socio-economic status contributes to participation in leisure-time physical activity. Social Science & Medicine, 66, 2596-2609. Cerin, E., Leslie, E., & Owen, N. (2009). Explaining socio-economic status differences in walking for transport: An ecological analysis of individual, social and environmental factors. Social Science & Medicine, 68, 1013-1020. Cerin, E., Leslie, E., Sugiyama, T., & Owen, N. (2009). Associations of multiple physical activity domains with mental well-being. Mental Health and Physical Activity, 2, 55-64. Cerin, E., & MacKinnon, D. P. (2009). A commentary on current practice in mediating variable analyses in behavioral nutrition and physical activity. Public Health Nutrition, 12, 1182-1188.


1443

Cerin et al.

Cerin, E., Sit, C. H. P., Barnett, A., Cheung, M. C., & Chan, W. M. (2013). Walking for recreation and perceptions of the neighborhood environment in older Chinese urban dwellers. Journal of Urban Health, 90, 56-66. Cerin, E., Sit, C. H. P., Barnett, A., Johnston, J. M., Cheung, M. C., & Chan, W. M. (2012). Aging in an ultra-dense metropolis: Perceived neighborhood characteristics and utilitarian walking in Hong Kong elders. Public Health Nutrition. Advance online publication. doi:10.1017/S136890012003862 Cerin, E., Sit, C. H. P., Cheung, M. C., Ho, S. Y., Lee, L. C., & Chan, W. M. (2010). Reliable and valid NEWS for Chinese seniors: Measuring perceived neighborhood attributes related to walking. International Journal of Behavioral Nutrition and Physical Activity,7, Article 84. Cheung, Y. B. (2002). Zero-inflated models of regression analysis of count data: A study of growth and development. Statistics in Medicine, 21, 1361-1469. Clark, C. R., Kawachi, I., Ryan, L., Ertel, K., Fay, M. E., & Berkman, L. F. (2009). Perceived neighborhood safety and incident mobility disability among elders: The hazards of poverty. BMC Public Health, 9, Article 162. Cunningham, G. O., & Michael, Y. L. (2004). Concepts guiding the study of the impact of the built environment on physical activity for older adults: A review of the literature. American Journal of Health Promotion, 18, 435-443. de Nazelle, A., & Rodriguez, D. A. (2009). Tradeoffs in incremental changes towards pedestrian-friendly environments: Physical activity and pollution exposure. Transportation Research Part D, 14, 255-263. Deng, H. B., Macfarlane, D. J., Thomas, G. N., Lao, X. Q., Jiang, C. Q., Cheng, K. K., & Lam, T. H. (2008). Reliability and validity of the IPAQ-Chinese: The Guangzhou Biobank Cohort study. Medicine & Science in Sports & Exercise, 40, 303-307. Fisher, K. J., Li, F., Michael, Y., & Cleveland, M. (2004). Neighborhood-level influences on physical activity among older adults: A multilevel analysis. Journal of Aging and Physical Activity, 11, 45-63. Griffin, S. F., Wilson, D. K., Wilcox, S., Buck, J., & Ainsworth, B. E. (2008). Physical activity influences in a disadvantaged African American community and the communities’ proposed solutions. Health Promotion Practice, 9, 180-190. Health Effects Institute. (2004). Effects of outdoor air pollution in developing countries of Asia. Boston, MA: Author. Inoue, S., Ohya, Y., Odagiri, Y., Takamira, T., Kamada, M., Okada, S., . . .Shimomitsu, T. (2011). Perceived neighborhood environment and walking for specific purposes among elderly Japanese. Journal of Epidemiology, 21, 481-490. Kamphuis, C. B. M., van Lenthe, F. J., Giskes, K., Huisman, M., Brug, J., & Mackenbach, J. P. (2009). Socioeconomic differences in lack of recreational walking among older adults: The role of neighborhood and individual factors. International Journal of Behavioral Nutrition and Physical Activity, 6, Article 1. Kruger, J., Ham, S. A., & Sanker, S. (2008).Physical inactivity during leisure time among older adults—Behavioral risk factor surveillance system, 2005. Journal of Physical Activity & Health, 16, 280-291.


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Lee, S. A., Xu, W. H., Li, H., Yang, G., Xiang, Y. B., & Shu, X. O. (2007). Physical activity patterns and their correlates among Chinese men in Shanghai. Medicine & Science in Sports & Exercise, 39, 1700-1707. Li, F., Fisher, K. J., & Brownson, R. C. (2005). A multilevel analysis of change in neighborhood walking activity in older adults. Journal of Aging and Physical Activity, 13, 145-159. Li, Q., Reuser, M., Kraus, C., & Alho, J. (2009). Aging of a giant: A stochastic population forecast for China, 2006-2060. Journal of Population Research, 26, 21-50. Mendes de Leon, C. F., Cagney, K. A., Bienias, J. L., Barnes, L. L., Skarupski, K. A., Scherr, P. A., & Evans, D. A. (2009). Neighborhood social cohesion and disorder in relation to walking in community-dwelling older adults: A multilevel analysis. Journal of Aging and Health, 21, 155-171. Nagel, C. L., Carlson, N. E., Bosworth, M., & Michael, Y. L. (2008). The relation between the neighborhood built environment and walking activity among older adults. American Journal of Epidemiology, 168, 461-468. Perneger, T. V. (1998). What’s wrong with Bonferroni adjustment. British Medical Journal, 316, 1236-1238. Riley, N. A. (2004). China’s population: New trends and challenges. Population Bulletin, 59, 1-36. Sallis, J. F., Slymen, D. J., Conway, T. L., Frank, L. D., Saelens, B. E., Cain, K., & Chapman, J. E. (2011). Income disparities in perceived built and social environment attributes. Health & Place, 17, 1274-1283. Schooling, C. M., Lam, T. H., Li, Z. B., Ho, S. Y., Chan, W. M., Ho, K. S., . . .Leung, G. M. (2006). Obesity, physical activity, and mortality in a prospective Chinese elderly cohort. Archives of Internal Medicine, 166, 1498-1504. Sugiyama, T., Leslie, E., Giles-Corti, B., & Owen, N. (2008). Associations of neighborhood greenness with physical and mental health: Do walking, social coherence and local social interaction explain the relationships? Journal of Epidemiology & Community Health, 62, e9. Tonne, C., Melly, S., Mittleman, M., Coull, B., Goldberg, R., & Schwartz, J. (2007). A case-control analysis of exposure to traffic and acute myocardial infarction. Environmental Health Perspectives, 115, 53-57. Van Cauwenberg, J., De Bourdeaudhuij, I., De Meester, F., Van Dyck, D., Salmon, J., Clarys, P., & Deforche, B. (2011). Relationship between the physical environment and physical activity in older adults: A systematic review. Health & Place, 17, 458-469. Wong, I. O. L., Cowling, B. J., Lo, S. V., & Leung, G. M. (2009). A multilevel analysis of the effects of neighborhood income inequality on individual self-rated health in Hong Kong. Social Science & Medicine, 68, 124-132. World Bank. (2007). Cost of pollution in China. Washington, DC: Author. Zimmer, Z., Wen, M., & Kaneda, T. (2010). A multi-level analysis of urban/rural and socioeconomic differences in functional health status transition among older Chinese. Social Science & Medicine, 71, 559-567.


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