Background Little is known about the neighbourhood characteristics of workplaces, the extent to which they are independently and synergistically correlated with residential environments, and their impact on health.
Methods This study investigated cross-sectional relationships between home and workplace neighbourhood environments with body mass index (BMI) in 1503 working participants of the Multi-Ethnic Study of Atherosclerosis with mean age 59.6 (SD=7.4). Neighbourhood features were socioeconomic status (SES), social environment (aesthetic quality, safety and social cohesion) and physical environment (walking environment, recreational facilities and food stores) derived from census data, locational data on businesses and survey data. Paired t tests and correlations compared environments overall and by distance between locations. Cross-classified multilevel models estimated associations with BMI.
Results Home neighbourhoods had more favourable social environments while workplaces had more favourable SES and physical environments. Workplace and home measures were correlated (0.39–0.70), and differences between home and workplaces were larger as distance increased. Associations between BMI and neighbourhood SES and recreational facilities were stronger for home environment (p≤0.05) but did not significantly differ for healthy food, safety or social cohesion. Healthy food availability at home and work appeared to act synergistically (interaction p=0.01).
Conclusions Consideration of workplace environment may enhance our understanding of how place affects BMI.
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The high prevalence of obesity and overweight in the USA is well established.1 ,2 A growing body of work has examined environmental factors that may affect obesity. Persons who live in more walkable environments have been found to have lower body mass index (BMI).3 ,4 Availability of healthy food outlets and supermarkets have been inversely associated with BMI and obesity in some,5–8 but not all, studies.9 ,10 Access to fast food and all types of restaurants have been found to be associated with higher levels of BMI in some studies,5 ,11 but null and even inverse associations have also been reported.6 ,12 ,13 In general, neighbourhoods that are more unsafe or have lower levels of aesthetic quality have been linked to higher levels of BMI and obesity,14 ,15 whereas findings for other features of neighbourhood social environments, such as social cohesion and psychosocial hazards, have been more mixed.16 ,17
A major challenge in studying the impact of environmental factors on health is defining the relevant environment or ‘neighbourhood’. Most research has operationalised neighbourhood exposures using characteristics of the residential environment because home address is commonly the only address available and it is assumed that people spend much of their time around their residence. However, US adults spend an average of 7.6 h/day at work18 and individuals may choose to perform some activities potentially related to obesity (such as food purchasing or engaging in physical activity) around their work; this environment could influence health outcomes. Despite the importance of non-residential environments,19–22 a review found that fewer than 5% of studies use any non-residential location information.20
Little is known about the neighbourhood characteristics of workplaces and the extent to which features of the home and work environments are correlated. One recent study found that non-home environments had greater numbers of restaurants, parks and traffic volume than home environments, but this study did not directly compare the correlation between measures around the home and non-home.23 Understanding the degree to which home and workplace measures are correlated, and the extent to which this correlation differs for different kinds of environmental features would shed light on whether home environments can be used to reasonably proxy work environments. It may also help understand discordant findings reported in studies using only the home environment.
Using unique data on the neighbourhood features of home and work locations for a population sample participating in the Multi-Ethnic Study of Atherosclerosis (MESA), we examined the extent to which neighbourhood environments surrounding an individual's home and workplace were correlated in terms of their socioeconomic status (SES) and social and physical environments. We also evaluated how residential and work environments may separately and jointly relate to BMI.
MESA is a longitudinal study of cardiovascular disease among adults aged 45–84 years at six field sites (Forsyth County, North Carolina; New York City, New York; Baltimore, Maryland; St Paul, Minnesota; Chicago, Illinois and Los Angeles, California) in the USA. Persons with a history of clinically overt cardiovascular disease were excluded. The study recruited 6814 participants at baseline. Baseline assessment was conducted from 2000 to 2002, with three follow-up exams occurring at approximately 1.5-year to 2-year intervals and follow-up phone calls occurring every 18 months.24 The study was approved by the institutional review boards at each site and all participants gave written informed consent.
Addresses for workplaces were collected as part of the MESA Air Questionnaire from 2005 to 2007 as part of Exams 3 and 4 and home address information was also updated.25 All addresses were geocoded using the TeleAtlas EZ-Locate web-based geocoding software.26 ,27 Of the 6814 MESA participants at baseline, 6179 agreed to participate in both the Neighbourhood and Air ancillary studies. The following exclusions were then made: 317 did not complete a visit during 2005–2007; 3036 reported not currently working at least part-time; 1044 where the workplace address was unavailable; and 279 where the home or workplace address was not geocoded to the street level. This yielded a final sample size of 1503 with at least one neighbourhood measure available for both the home and the workplace. Participants included in the analysis were slightly younger (60 vs 63 years), female (50% vs 42%) and had higher household income ($66 000 vs $56 000/year) and education (14.3 vs 13.8 years) than working MESA participants excluded due to missing street address data (all p<0.0001).
Neighbourhood environment exposures (home and workplace)
Three datasets were used to assess neighbourhood features: sociodemographic census data, Geographic Information System (GIS) data, and survey-based measures of neighbourhood physical and social environments.
Neighbourhood socioeconomic characteristics
Measures of neighbourhood (census tract) were obtained from the American Community Survey 2005–2009 estimates.28 To derive a measure of SES, we conducted principal factor analysis with orthogonal rotation of 16 census variables, which reflected aspects of education, occupation, household income and wealth, poverty, employment and housing. Variables were standardised, and those which represented a less favourable SES environment were reverse coded such that higher values indicate increasing socioeconomic advantage. Four factors representing 74% of the variance were retained. Weighted scales were created by multiplying the standardised variables by the factor weights. These analyses used the first factor called ‘SES’ (49% of the variance). Variables with high loading on this factor included education, occupation, housing value and income.
Densities of recreational facilities and a healthy food environment were derived from GIS data using Dun and Bradstreet data as compiled by Walls and Associates in the National Establishment Time Series database29 for 2005–2007. Addresses were geocoded using the TeleAtlas EZ-Locate web-based geocoding software.26 For the recreational facilities, 114 Standard Industrial Classification (SIC) codes were selected to represent the recreational and physical activity establishments such as indoor conditioning, dance, bowling, golf, team and racquet sports, and water activities derived from lists used in previous studies.30 ,31 Healthy food stores were defined as fruit and vegetable markets (SIC #5431) and supermarkets (grocery stores (SIC #5411) with at least $2 million in annual sales or at least 25 employees or name being on standardized supermarket chain name lists as described elsewhere32). Weighted-kernel densities per square mile were created for 1-mile (1609m) buffers around both the home and workplace addresses using the kernel density command in ArcGIS V.9.3.33 ,34
Questionnaires on neighbourhood characteristics were administered to MESA participants during 2003–2005. Similar questionnaires were administered to a random sample of residents of selected census tracts in three of the MESA study sites (Baltimore; Forsyth County; and New York) between January and August 2004. This sample was identified through random digit dialling and one adult aged 18 or older within the household was randomly selected to complete the survey.35 To increase the sample size and reliability of the scale estimates, responses from this sample were pooled with neighbourhood survey responses from the MESA respondents.
In the survey, participants were asked to rate the area within approximately 1 mile around their home. On the basis of a conceptual model36 and prior work,37 five neighbourhood dimensions were assessed: aesthetic quality (three items), safety (two items), social cohesion (four items), walking environment (four items) and healthy food availability (two items) as described elsewhere.35 Responses for each item ranged from 1 (strongly agree) to 5 (strongly disagree). Questions were reverse coded when needed such that a higher score indicates a more favourable environment. Scales had acceptable internal consistency (Cronbach α 0.64–0.82).35 Scales based on a 1-mile buffer around the home and workplace addresses were created by taking the crude mean of the responses for all respondents living within a 1-mile buffer, excluding themselves. Only respondents who answered all questions within the domain were included. Analyses were restricted to only those who had five or more respondents living within a 1-mile buffer to increase reliability.
We created summary measures of each person's exposure to the neighbourhood characteristics by calculating weighted averages of the home and work measures, with the weights being the proportion of the hours during the week spent at each location. The number of hours per week spent at the work address was obtained via questionnaire.
BMI was calculated as weight in kilograms divided by height in metres squared. Information on sociodemographic factors were obtained via questionnaire. These included age, race/ethnicity, gender, education and household income. Race/ethnicity was classified as Hispanic, non-Hispanic white, non-Hispanic Chinese and non-Hispanic black. Participants selected their education from eight categories and continuous years of education was assigned as the interval midpoint of the selected category. Participants selected their total combined family income from 13 categories and continuous income in US dollars was assigned as the interval midpoint of the selected category. The distance between the home and workplace locations was calculated as Euclidean distance in miles.
Paired t tests and Spearman rank correlations were used to compare neighbourhood features at each participant’s work and home location. To determine whether the home and work neighbourhood environment measures varied as a linear function of the distance between them, the distance in miles between the home and the workplace was divided into quartiles and analysis of variance (ANOVA) was used to test for trend.
Estimates of the associations between home and workplace neighbourhood environments with BMI in separate models were obtained using two-level mixed regression models with a neighbourhood-level random intercept (model 1). Differences between the β coefficients for the home and workplace environments were tested using methods described elsewhere.38
Estimates of the associations between the home and workplace neighbourhood environments with BMI simultaneously in the same model were obtained using a two-level cross-classified regression model (model 2).39 Product terms were added to test for interactions between the home and workplace environments (model 3). We also contrasted results obtained with home and work measures separately to those obtained using the weighted average (model 4). Each neighbourhood exposure was analysed separately.
The 1503 participants included in the analyses had a mean age of 59 years, were 49% men and predominantly non-Hispanic white (42%) followed by non-Hispanic black (27%), Hispanic (20%) and non-Hispanic Chinese (11%). Over 78% of the sample was working full-time (mean number of hours spent at the workplace: 40.4 h during the winter and 38.3 h during the summer; table 1). The mean distance between the home and workplace addresses was 6.0 miles (SD=7.0), but this varied from a mean of 3.6 miles in New York to 7.0 miles in California and North Carolina. Only 4.7% had a home and workplace within the same census tract, but one-mile buffers around the home and the workplace overlapped in 29.8% of participants (range: 12% in California to 42% in New York).
Persons travelling farther distances to work were more likely than those travelling shorter distances to be men (60% and 46% for highest vs lowest distance quartile), non-Hispanic white (38% and 50%) and have higher household income ($71 905 vs $62 363) and education (14.8 years vs 14.2 years). Among those working farther from home, over 86% were working full-time compared with 77% of those travelling the least distance.
Home and workplace neighbourhoods differed in neighbourhood features (table 2). Home environments had greater population density and more favourable aesthetic quality, safety and social cohesion compared with work environments (all p≤0.005). However, workplace environments had more favourable SES, greater density of recreational facilities and healthy food stores, as well as a more favourable walking environment and healthy food availability compared with home environments (all p≤0.0003). Despite these differences, the home and work environments for participants were substantially correlated for each type of neighbourhood exposure (ranging from 0.39 to 0.70).
For most of the neighbourhood features, the differences between the home and work environments became greater as the distance increased (table 3). Neighbourhood SES was similar for locations close together, but the workplace had higher SES at greater distances (p = 0.01). At greater distances, the densities of recreational facilities and healthy food stores were lower around the home than around the work (p < 0.0001), yet population density became more similar (p < 0.0001). In general, persons who travelled further for work both lived and worked in areas with lower population densities than those who travelled less for work.
Aesthetic quality, safety and social cohesion were similar for home and work environments located close to each other but differences increased as the distance between locations increased (p<0.0001 for all), with home environments showing a more favourable aesthetic quality, safety and cohesion than workplace environments as the distance between them increased. There was little difference for walking environments between home and work locations and no clear pattern by distance (p=0.79). A survey reported that healthy food availability was similar when the home and workplace environments were closer but became less similar at greater distances; workplace environments had more favourable healthy food availability (p=0.02).
In general, point estimates of associations revealed that higher neighbourhood SES, recreational facilities, safety, social cohesion, walkability and healthy food availability were associated with lower BMI for both the home and workplace environments, although associations were not statistically significant for the workplace neighbourhood SES or for home environment safety and social cohesion (table 4, model 1). In the case of neighbourhood SES, the home environment had a much stronger association with lower BMI than the workplace environment (p for difference in coefficients=0.047). The magnitude of the coefficients for density of healthy food stores, safety, social cohesion and healthy food availability were higher for the workplace environment, although the differences were not statistically significant. The density of recreational facilities had a stronger association for the home environment (p for difference in coefficients=0.052). Associations of the walking environment with BMI were of similar magnitude for both environments. Associations became weaker but patterns were approximately similar when both environments were included in the same model (model 2).
Interactions between the home and work environments were not statistically significant at the p<0.05 level except for the survey reporting healthy food availability. The negative interaction term suggested a synergistic effect of greater healthy food availability at both locations on lower BMI (model 3). Weighted estimates for neighbourhood SES, recreational facilities and walking environment suggested a synergy between the home and the workplace, although uncertainty was higher (p<0.1). In general, the magnitude of estimates using weighted averages of the home and workplace environments fell between the home and workplace estimates; the exception was walking environment for which the weighted measure had a stronger association than the separate measures for home and work (model 4). This would suggest a synergistic effect between the home and the workplace, consistent with the negative interaction term (model 3), although the latter was not statistically significant.
We found that the neighbourhood environments around the home and workplace were correlated, but differed in SES and social and physical characteristics. Neighbourhoods around the home tended to have a more favourable aesthetic quality, safety and social cohesion, whereas those around the workplace had a more favourable SES and availability of foods and recreational resources. The differences between the home and work locations increased as the distance travelled to work increased.
Few studies have directly examined the correlation between residential and workplace environments. One recent study found greater numbers of restaurants, parks and traffic around non-home environments, while home environments had greater numbers of supermarkets, fitness facilities and street density, but the analyses were based on a small sample in one US city.23 In our multicity sample, analogous measures for work and home environments tended to be correlated, suggesting that the use of the home environment as a proxy for place-based exposures may not be entirely inadequate. However, the correlations were moderate for SES and social environment features, like safety and social cohesion, and higher for population density and density of food stores and recreational resources; suggesting that the impact of proxing environmental exposures through the home environment may differ depending on the construct being examined. In addition, the study found that the home and work environments differed as a function of the distance between the locations. Home and work neighbourhood correlations may differ from sample to sample depending on home-work geographic patterning, which is most likely strongly influenced by region.40
Prior analyses of the full MESA sample showed that greater access to healthy foods and physical activity resources was associated with lower BMI.16 Contrary to expectations, a more favourable social environment was linked to a higher BMI in males, but the social environment was not associated with BMI in women.16 Moreover, MESA participants living in environments with greater access to healthy foods experienced a lower incidence of obesity over time.8 Consistent with these findings, our analysis of a subsample of working MESA participants shows that better physical activity and food environments were associated with lower BMI. Associations were generally of similar direction and magnitude for the home and work environments, with the exception that recreational density around the home was more predictive of BMI than recreational densities around the workplace. Findings from a study examining physical activity also found stronger associations between built environment measures at the workplace compared with the home; however, the joint effects or a time-weighted measure were not explored.41 Interestingly, in our study, a weighted measure of the walking environment (which reflected time spent at home and at work) was more strongly related to BMI than separate measures for home and work. In principle, this aligns with other work that found impacts of walking to work and walking around work as important contributors to lower BMI.42 ,43 Our study was able to explicitly examine synergistic effects and found that protective associations between food environment and BMI were more evident when both the work and home environments had favourable food access. Others have reported similarly strong findings between the workplace food environment and BMI. Stronger positive associations between BMI and fast food restaurants were found for the work (or entire activity space) environment than the home environment,13 ,19 but these studies did not examine the synergistic effect. Taken as a whole, our results suggest that the place effects may be underestimated when the workplace environment is not considered.
In contrast to prior MESA work which was based on only three of the study sites,16 we found no statistically significant association of residential social environment features including safety, social cohesion or aesthetic quality with BMI. Workplace safety and social cohesion were inversely associated with BMI, although differences between the home and work coefficients were not statistically significant. The limited sample size precluded investigation of the sex interactions with social environments documented in prior work.16 Future work is needed to examine the impact of residential and workplace social environments on BMI by sex.
The finding that workplace features were related to BMI in the expected direction is notable because they are less likely to be confounded by individual-level SES, which is often closely associated with residential environments as a result of residential segregation by SES and race. Neighbourhood SES, which can proxy a range of social and physical environment features, was only associated with BMI for residential measures which align with what others have found with self-reported health.44 That said, workplace population-based census characteristics can be difficult to interpret when they are non-residential neighbourhoods, such as commercial business parks, semiurban malls and corporate parks, for example.
Previous studies that incorporated both residential and non-residential environments were generally limited to samples taken from one geographic area.13 ,19 ,23 ,41 ,44 A strength of our work is that we are able to use information from multiple study sites across the USA. We also had information on the amount of time a person spent at the workplace location, which allowed us to evaluate a weighted average for the amount of time spent at each location. Whereas most prior work incorporating both residential and non-residential measures has focused on either SES44 or the physical environment,13 ,19 ,41 we were able to include a range of measures constructed using various measurement techniques including measures of social environments.
A limitation of this study is that the sample is of an older working population over 50 years of age. The associations of the workplace environment could differ in younger populations. The differences between work and residential environments may vary depending on the patterns of commuting to work and may be highly sample-dependent. Another limitation is that the survey-based scales asked respondents only about the area around their home. When linking these measures to the workplace locations, this leads to missing data around workplaces that do not lie within residential areas. Workplace addresses for which we were unable to calculate the survey-based measures were in areas with greater density of recreational facilities and food stores and lower population density.
Although neighbourhood environments have been identified as possible important factors in understanding obesity and other health outcomes, research has been focused mainly on the residential environment. This study suggests that even when work and home residence have correlated features, differences remain, and for some environmental domains, the neighbourhood surrounding the workplace may be as influential as or possibly more influential than the area surrounding the home. More work needs to be performed in this area to characterise similarities and differences between home and work environments and to understand how work and residential environments may interact to affect health and changes in health across time.
What is already known on this subject
Neighbourhood characteristics such as higher socioeconomic status (SES), healthy food availability, more favourable walking environment and safety have been associated with lower body mass index (BMI) and obesity.
The majority of studies until now have only utilised information about the residential or home neighbourhood environment and not taken environments around the workplace into account.
What this study adds
Home neighbourhoods had more favourable social environments while workplaces had more favourable socioeconomic and physical environments but were correlated.
Associations between body mass index and neighbourhood socioeconomic status and recreational facilities were stronger for home environment but did not significantly differ for healthy food, safety or social cohesion.
Healthy food availability at home and work appeared to act synergistically.
We thank the other investigators, staff and participants of the MESA study for their contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org. The authors thank Shannon Brines for his contribution in creating the GIS-based measurements.
Contributors The coauthors have contributed to the work as follows: KM performed statistical analysis and drafted all sections of the paper. AVDR aided in methodological and conceptual design, interpretation of findings and critically reviewed the drafts. AA aided in conceptual design, interpretation of results and edited the drafts. KRE aided in conceptual design and edited the drafts. JK aided in the design of the study and data collection and edited the drafts. MM aided in conceptual design and edited the drafts. KW aided with data collection and processing and edited drafts.
Funding This research was supported by contracts N01-HC-95159 through N01-HC-95169 from the National Heart, Lung, and Blood Institute and by grants UL1-RR-024156 and UL1-RR-025005 from the NCRR, R01 HL071759 from the National Heart, Lung, and Blood Institute at the National Institutes of Health, and US EPA—Science to Achieve Results (STAR) Program Grant # RD831697. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Competing interests None.
Ethics approval Institutional Review boards at each study site.
Provenance and peer review Not commissioned; externally peer reviewed.
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