| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
RESEARCH AND PRACTICE |
Nancy Ross and Daniel Crouse are with the Department of Geography, McGill University, Montreal, Quebec. Stephane Tremblay, Saeeda Khan, Mark Tremblay, and Jean-Marie Berthelot, are with Statistics Canada, Ottawa, Ontario.
Correspondence: Requests for reprints should be sent to Nancy A. Ross, PhD, Department of Geography, McGill University, 805 Sherbrooke St West, Montreal, PQ, H3A 2K6, Canada (e-mail: nancy.ross{at}mcgill.ca).
 |
ABSTRACT |
|---|
 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
Objectives. We investigated the influence of neighborhood and metropolitan area characteristics on body mass index (BMI) in urban Canada in 2001.
Methods. We conducted a multilevel analysis with data collected from a cross-sectional survey of men and women nested in neighborhoods and metropolitan areas in urban Canada during 2001.
Results. After we controlled for individual sociodemographic characteristics and behaviors, the average BMIs of residents of neighborhoods in which a large proportion of individuals had less than a high school education were higher than those BMIs of residents in neighborhoods with small proportions of such individuals (P< .01). Living in a neighborhood with a high proportion of recent immigrants was associated with lower BMI for men (P<.01), but not for women. Neighborhood dwelling density was not associated with BMI for either gender. Metropolitan sprawl was associated with higher BMI for men (P=.02), but the effect was not significant for women (P= .09).
Conclusions. BMI is strongly patterned by an individual’s social position in urban Canada. A neighborhood’s social condition has an incremental influence on the average BMI of its residents. However, BMI is not influenced by dwelling density. Metropolitan sprawl is associated with higher BMI for Canadian men, which supports recent evidence of this same association among American men. Individuals and their environments collectively influence BMI in urban Canada.
|
INTRODUCTION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
25 kg/m2) in Canada increased from 48% to 57% among men and from 30% to 35% among women during the 15 years between 1981 and 1996.7 The increase was indeed a national phenomenon: the rates increased in each province.8 The speed of the rise in obesity rates suggests that the root of the obesity pandemic in developed countries is an environment that supports obesity,9–11 rather than a shift in the genetic composition of the population. Individual BMI is associated with multiple factors, including genotype, metabolism, energy intake, and level of physical activity. Socioeconomic, cultural, and environmental factors influence health-related behaviors, which in turn influence weight.2 It is these influences—the interplay between adult BMI, social position, behavior, and environment—that are the principal focus of this paper. We take the approach that BMI is a function of individual characteristics (e.g., age, income level, immigrant status, exercise patterns, diet) along with neighborhood (e.g., neighborhood educational level, density of dwellings) and metropolitan area contexts (e.g., sprawl).
Sobal and Stunkard12 reviewed 144 studies published between 1933 and 1988 that examined the relation between socioeconomic status (SES) and obesity. The vast majority of these studies found an inverse association between social position and obesity for women, but the findings for men were inconsistent. Studies that followed the 1989 review by Sobal and Stunkard have generally supported their findings,13–16 but recent American research suggests the disparity in obesity across SES has decreased in the past 30 years.17
Many variables act as mediators in the association of social position and obesity, including smoking18 and psychological stress.19,20 However, individual factors alone (e.g., social position, health behaviors) cannot explain variations in BMI.21 Studies that consider the relation between BMI and the environment tend to focus on 2 broad aspects: sociodemographic characteristics of neighborhoods and overall urban form (density, land-use mix, and street connectivity).
Although a large body of literature exists regarding neighborhood health effects,22–26 researchers have only recently attempted to examine the relation between neighborhood socioeconomic conditions, urban form, and body weight. Ellaway et al.27 interviewed 691 individuals from 4 socially contrasting neighborhoods in Glasgow, Scotland. They found twice the number of obese individuals in the most economically deprived area of the city compared with individuals from the most affluent area. In a Dutch study,28 after adjusting for the educational level, age, and gender of neighborhood residents, investigators found that risk of becoming overweight increased with level of neighborhood social deprivation. The authors of the Dutch study suggest that differences in neighborhood resources, such as the availability and price of healthy foods and the presence and quality of sports facilities and parks, may be related to both dietary intake and physical activity levels.
Modern suburban neighborhoods, which are characterized by work, school, and commercial land uses that are not easily accessible on foot or bicycle, likely constrain the amount of time people spend walking or cycling for utilitarian purposes. As a result, levels of physical activity for people who live in sprawling neighborhoods tend to be lower than for those who live in higher density, more compact neighborhoods.29–34 Frank et al.35 demonstrated that mean BMI for White men decreased significantly across neighborhoods as land-use mix, density, and street connectivity increased.
|
METHODS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
7 ft or < 3 ft), who resided in a census metropolitan area (CMA), were included. CMAs are the 27 largest Canadian urban areas.
Outcome Variable
BMI is the standard unit of measure for weight and obesity in populations and is calculated by dividing a person’s weight in kilograms by their height in meters squared. BMI was calculated for CCHS respondents according to self-reported height and weight. Self-reporting of height and weight is a limitation of this study, because individuals may over report their height or underreport their weight.37
Explanatory Variables
We assumed that BMI is influenced by factors at 3 levels: individual, neighborhood, and metropolitan area. We hypothesized that, at the individual level, demographic characteristics (age, marital status, immigrant status), social position (income, educational attainment), health-related behaviors (smoking, physical activity, and diet), and stress are important predictors of BMI (Table 1
).
|
5 years), the proportion of individuals who have low educational attainment, and the neighborhood median household income. A measure of dwelling density (dwellings per km2) was a proxy for the "walkability" of a neighborhood (a physical attribute). We also considered the characteristics of the larger metropolitan areas and tested our hypothesis that living in a sprawling metropolitan area has an incremental effect on BMI. Investigators have used a variety of methods and data sources (including population, land use and transportation data, and remotely sensed images)38–42 to define, model, and measure sprawl in American cities. Lopez43 developed a method based on population density and compactness to examine the association between urban sprawl and being overweight among American adults.
The method presented by Lopez43 was attempted here, although it proved to be ineffective for Canadian metropolitan areas that had several low-density CTAs. Our index (similar to the one presented in Razin and Rosentraub42) was composed of 3 equally weighted dimensions of sprawl: proportion of CMA dwellings that are single or detached units, dwelling density, and percentage of CMA population living in the urban core (an urban area around which a CMA is delineated and contains a minimum of 100,000 residents). CMAs were sorted from least to most sprawling for each measure and then assigned a value of 1 to 27. The 3 ranked scores were summed together to produce a cumulative sprawl rank; the lower values reflected less sprawl (Table 2). The validity of the sprawl index is suggested by the low sprawl score for Montreal, an island city in which architectural styles favor multidwelling units. The western Canadian prairie has sprawling cities (e.g., Edmonton, Regina, Saskatoon) because of abundant, relatively inexpensive land that surrounds these areas. Affluence was measured by median CMA household income (Canadian dollars). A dummy variable was added to indicate CMAs in the province of Quebec, because exploratory analyses showed that the BMI profile of these CMAs was systematically lower than the other CMAs.
|
An intraclass (neighborhood and metropolitan levels) correlation coefficient was used to judge the effect of explanatory variables included in the model.44 The coefficient is the ratio between the neighborhood-based variations or the metropolitan area–based variation and the total variation. A decline in the intraclass correlation coefficient indicates that differences between metropolitan areas or neighborhoods have been reduced by the inclusion of explanatory variables.
|
RESULTS |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
). Dummy variables for missing information about income and physical activity were created to retain cases (not shown in the results). Men were distributed among 2615 CTAs and women were distributed among 2660 CTAs. Separate multilevel models were created for men and women because factors associated with BMI tend to differ by gender. Mean BMI for men was 26.1 and for women it was 24.7; the standard deviation of BMI was higher for women (5.1 vs 4.2). Mean BMIs for men and women tended to be lowest in Vancouver and Victoria, British Columbia; Toronto, Ontario; and 4 CMAs in Quebec (Sherbrooke, Chicoutimi, Quebec, and Montreal).
Body Mass Index in Men
The amount of variation in BMI among men that was attributable to neighborhoods and metropolitan areas was 4.39% and 1.35%, respectively (Table 3, model A). These values tended to decline but remain significant across the models as covariates were added, which indicates that most, but not all, of the variation in BMI between neighborhoods and metropolitan areas can be attributed to the composition of the population living in those areas. The intercept in the null model (Table 3, model A) provides the estimated average BMI for men aged 20–64 years in urban Canada (26.265), which is classified as overweight.
|
, model B). Recent immigration had a large effect on BMI (–1.787, P < .01) and the effect declined for individuals who had been in Canada longer than 5 years (–0.847, P < .01). BMI was significantly higher for older men than for younger men and for married and previously married men than for unmarried men. The relation of BMI in men with income was characterized by a gradient of lower BMI across decreasing income categories. A poor diet and physical inactivity were associated with higher BMI values. Men who reported high stress also had significantly higher BMIs than those who reported little or no daily stress (0.287, P < .01). Smokers (–0.545, P < .01) had significantly lower BMIs than nonsmokers. The –2 log likelihood measure indicates the steady improvement in models A through D with additional explanatory variables at the neighborhood and metropolitan levels.
Two neighborhood characteristics were statistically significant, and after including individual-level covariates, approximately 3.5% of neighborhood-level variation remained (Table 3, model C). Men who resided in neighborhoods with a high proportion of recent immigrants had lower BMI scores (–0.022, P= .02) than men who lived in other neighborhoods. Men in neighborhoods with a high proportion of individuals of low educational attainment had incrementally higher BMI scores (0.021, P< .01). Dwelling density and median household income of the neighborhood were not associated with BMI in men.
Living in a sprawling metropolitan area was associated with higher BMI scores in men, even after neighborhood and individual factors were accounted for (0.010, P= .02) (Table 3, model D). CMA median household income was not associated with higher BMI scores for men, nor was there an incremental effect of Quebec residence for men.
Body Mass Index in Women
The amount of variation in BMI among women attributable to neighborhoods and metropolitan areas was 4.44% and 1.42%, respectively (Table 4, model A). These values tended to decline but remain significant across the models as covariates were added. The intercept in the null model (Table 4, model A) provides the estimated average BMI for women aged 20–64 years in urban Canada (24.946), which is classified as normal weight.
|
, model B). We found an association between advanced age and increased BMI for both men and women, although the magnitude of this association was larger for women than for men (0.079, P< .01 vs 0.043, P< .01) (Tables 3 and 4, model B). Being a recent immigrant had a large effect on BMI among women (–1.764, P< .01), but the effect declined for immigrants who had been in Canada longer than 5 years (–0.803, P< .01). BMI was significantly higher (1.080, P< .01) for women who did not complete high school when compared with women with at least some college education. The magnitude of the educational effect was far greater for women than for men. The BMI of married women was not significantly different from single women, although the BMI for previously married women was significantly lower (–0.180, P= .02) than for single women. The relation between BMI in women and income was roughly characterized by a reverse gradient; higher BMI is seen in lower levels of income adequacy. When we looked at behavioral influences, physical inactivity was associated with higher BMI values in women, and the magnitude of that association was much higher for women than for men (1.153, P< .01 vs 0.526, P< .01). Some patterns, however, were similar for men and women. Women who reported high stress also had significantly higher BMIs than women who reported low or no daily stress (0.318, P< .01). Also like men, female smokers (–0.491, P< .01) had significantly lower BMIs than nonsmokers. The –2 log likelihood measure indicates the steady improvement in models A through D with additional explanatory variables at the neighborhood and metropolitan levels.
The only variable that contributed explanatory significance to the 2.91% variation in the neighborhood-level model for women was low educational attainment (Table 3, model C). Women who reside in neighborhoods where there is a high proportion of individuals who have low educational attainment had incrementally higher BMI scores (0.042, P< .01) than women who live in neighborhoods populated with more highly educated individuals. Dwelling density and median household income of the neighborhood were not associated with BMI for women or for men.
Metropolitan affluence was not associated with BMI for Canadian women and the association with sprawl was in the expected direction but showed marginal significance (Table 4, model D). There remained, however, a large association between BMI in women and residing in a metropolitan area in Quebec (–0.972, P= .01), even after accounting for neighborhood and individual factors. The variation at the CMA level was statistically significant but small at 0.62%.
|
DISCUSSION |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
The magnitude of the association between low educational attainment and BMI for women suggests that strategies to keep girls in high school could have dramatic effects on the distribution of BMI for women. The hypothetical BMI increase for women who do not graduate from high school relative to women who complete college studies was nearly a full BMI unit. The relation between income and BMI was shown to be different for men and women. It has been argued that in the United Kingdom obesity is a marker for social position,47 and obesity is associated with being lower down the social ladder; however, this is not the case for Canadian men. This gender contingency in the BMI social gradient is likely rooted in complex social factors including societal roles and norms as well as access to resources that support healthy body weights, such as time for activity.
We found small incremental effects of neighborhood- and metropolitan-level environments on the BMI of men and women in urban Canada. These effects related primarily to 2 neighborhood characteristics (low education levels and the presence of immigrants [men only]) and 2 metropolitan area characteristics (sprawl [BMI in men] and residence in a Quebec CMA [BMI in women]). The fact that low education levels were associated with incrementally higher BMI values for both men and women may be related to norms and practices around diet and exercise in those neighborhoods, but might also be related to issues of neighborhood safety, availability and quality of recreational opportunities, or access to healthy food. One might surmise from the neighborhood-level findings that recent immigrants bring with them customs and norms regarding diet or physical activity that become part of local practice and influence behaviors beyond the immigrant community. This is a contextual healthy-immigrant effect that would be worthy of more study on a local scale.
Our study provides some support for the findings of recent American research40,43,48 (although we used a more extensive set of control variables) that suggests that sprawling cities and their characteristic low-density suburbs—and concomitant dependence upon the automobile for transportation—produce heavier and less healthy populations. Most of the research in this area has focused on American urban environments, and other studies49 have shown that Canadian urban environments have historically been more protective of population health than their American counterparts. The fact that the association with sprawl also appears to hold in Canada (at least for men) suggests that health and urban sustainability issues cross international boundaries. Although the average man in urban Canada already has a BMI score in the overweight range (approximately 26), an inactive, married man under high stress who lives in a sprawling metropolis has a hypothetical BMI over 27.
Quebec is a Canadian province with a predominantly French-speaking population. Women who live in Quebec CMAs have significantly lower BMIs than do women who live in other CMAs, which suggests that there is a true environmental effect that may represent unmeasured cultural norms or genetic predisposition factors related to body mass. We also tested for the presence of other regional effects (data not shown) but found none. This suggests that differences in average BMI in women between metropolitan areas outside of Quebec are largely attributable to differences in population composition.
A constellation of individual, neighborhood, and metropolitan area factors is associated with BMI in urban Canada. Although the overwhelming amount of variation in BMI occurred at the individual level for both men and women, we found small incremental effects of neighborhood and metropolitan area environments. These environments probably set the stage for many of the individual characteristics and behaviors, so that the neighborhood and metropolitan area effects revealed here are likely underestimated. Rose50 has argued that small changes that influence the distribution of risk factors across populations have the best potential to improve the health of entire populations. Our results suggest that Canadian urban environments play a small but significant role in shaping the distribution of BMI. They also provide support for altering the contexts in which health improvement behavior occurs and for informing urban sustainability and design policy with human health research.
|
Acknowledgments |
|---|
Human Participant Protection
No institutional review board approval was required for this study.
|
Footnotes |
|---|
Contributors
N. Ross originated the idea for the study and led all aspects of the work including data analysis and writing. S. Tremblay, S. Khan, and D. Crouse performed analyses and assisted with writing. M. Tremblay and J-M. Berthelot provided conceptual and methodological expertise and assisted with writing.
Accepted for publication October 30, 2005.
|
References |
|---|
|
TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
|---|
2. Huot I, Paradis G, Ledoux M. Factors associated with overweight and obesity in Quebec adults. Int J Obes Relat Metab Disord.2004;28:766–774.[CrossRef][Web of Science][Medline]
3. Global strategy on diet, physical activity and health. Geneva, Switzerland: World Health Organization; 2004.
4. Torrance GM, Hooper MD, Reeder BA. Trends in overweight and obesity among adults in Canada (1970–1992): evidence from national surveys using measured height and weight. Int J Obes Relat Metab Disord.2002;26:797–804.[CrossRef][Web of Science][Medline]
5. Obesity: preventing and managing the global epidemic. Geneva, Switzerland: World Health Organization; 1998. WHO Technical Report Series, No. 894.
6. Macdonald SM, Reeder BA, Chen Y, Despres JP. Obesity in Canada: a descriptive analysis. Canadian Heart Health Surveys Research Group. Can Med Assoc J.1997;157(Suppl 1):S3–S9.
7. Tremblay MS, Katzmarzyk PT, Willms JD. Temporal trends in overweight and obesity in Canada, 1981–1996. Int J Obes Relat Metab Disord.2002;26:538–543.[CrossRef][Web of Science][Medline]
8. Katzmarzyk PT. The Canadian obesity epidemic 1985–1998. Can Med Assoc J.2002;1666:1039–1040.
9. Egger G, Swinburn B. An ecological approach to the obesity pandemic. BMJ.1997;315:477–480.
10. Hill JO, Peters JC. Environmental contributions to the obesity epidemic. Science.1998;280:1371–1374.
11. Poston WSC, Foreyt JP. Obesity is an environmental issue. Atherosclerosis.1999;146:201–209.[CrossRef][Web of Science][Medline]
12. Sobal J, Stunkard AJ. Socioeconomic status and obesity: a review of the literature. Psychol Bull. 1989; 105(2): 260–275.[CrossRef][Web of Science][Medline]
13. Ball K, Mishra GD, Crawford D. Which aspects of socioeconomic status are related to obesity among men and women? Int J Obes Relat Metab Disord.2002;26:559–565.[CrossRef][Web of Science][Medline]
14. Wamala SP, Wolk A, Orth-Gomer K. Determinants of obesity in relation to socioeconomic status among middle-aged Swedish women. Prev Med.1997; 26:734–744.[CrossRef][Web of Science][Medline]
15. Jeffery RW, French SA. Socioeconomic status and weight control practices among 20 to 45 year old women. Am J Public Health.1996;86:1005–1010.
16. Enzi G. Socioeconomic consequences of obesity: the effect of obesity on the individual. PharmacoEconomics.1994;5(Suppl 1):54–57.[Web of Science][Medline]
17. Zhang Q, Wang Y. Trends in the association between obesity and socioeconomic status in US adults: 1971–2000. Obes Res.2004;12:1622–1632.[Web of Science][Medline]
18. Sobal J. Obesity and socioeconomic status: a framework for examining relationships between physical and social variables. Med Anthropol.1991;13:231–247.[Medline]
19. Epel EE, McEwen B, Seeman T, et al. Stress and body shape: stress-induced cortisol secretion is consistently greater among women with central fat. Psychosom Med.2000;62:623–632.
20. Epel EE, Moyer AE, Martin CD, et al. Stress-induced cortisol, mood, and fat distribution in men. Obes Res.1999;7:9–15.[Web of Science][Medline]
21. Ball K, Mishra GD, Crawford D. Social factors and obesity: an investigation of the role of health behaviors. Int J Obes Relat Metab Disord.2003;27:394–403.[CrossRef][Web of Science][Medline]
22. Macintyre S, Maciver S, Sooman A. Area, class and health: should we be focusing on places or people? J Social Policy.1993;22:213–234.[Web of Science]
23. Diez Roux AV, Nieto FJ, Montaner C, et al. Neighborhood environments and coronary heart disease: a multilevel analysis. Am J Epidemiol.1997;156:48–53.
24. Macintyre S, Ellaway A, Cummins S. Place effects on health: how can we conceptualize, operationalize and measure them? Soc Sci Med.2002;55:125–139.[CrossRef][Web of Science][Medline]
25. Kawachi I, Berkman LF, eds. Neighborhoods and Health. New York, NY: Oxford University Press; 2003.
26. Ross NA, Tremblay S, Graham K. Neighbourhood influences on health in Montreal, Canada. Soc Sci Med.2004;59:1485–1494.[CrossRef][Web of Science][Medline]
27. Ellaway A, Anderson A, Macintyre S. Does area of residence affect body size and shape? Int J Obes.1997;21:304–308.[CrossRef][Web of Science][Medline]
28. van Lenthe FJ, Mackenbach JP. Neighbourhood deprivation and overweight: the GLOBE study. Int J Obes Relat Metab Disord.2002;26:234–240.[CrossRef][Web of Science][Medline]
29. Ewing R, Schmid T, Killingsworth R, Zlot A, Raudenbush S. Relationship between urban sprawl and physical activity, obesity and morbidity. Am J Health Promotion.2003;18:47–57.[Web of Science][Medline]
30. Frank LD, Engelke PO, Schmid TL. Health and Community Design: The Impact of the Built Environment on Physical Activity. Washington, DC: Island Press; 2003.
31. Frank LD, Schmid TL, Sallis JF, et al. Linking objectively measured physical activity with objectively measured urban form: findings from SMARTRAQ. Am J Prev Med.2005;28(252):117–125.[CrossRef][Web of Science][Medline]
32. Frumkin H. Urban sprawl and public health. Public Health Rep.2002;117:201–217.
33. Jackson RL. The impact of the built environment on health: an emerging field. Am J Public Health.2003; 93:1382–1384.
34. Saelens BE, Sallis JF, Black JB, Chen D. Neighborhood-based differences in physical activity: an environment scale evaluation. Am J Public Health.2003;93:1552–1558.
35. Frank LD, Andresen MA, Schmid TL. Obesity relationships with community design, physical activity, and time spent in cars. Am J Prev Med.2004;27:87–96.[CrossRef][Web of Science][Medline]
36. Beland Y. Canadian community health survey—methodological overview. Health Reports.2002;13:9–14.[Medline]
37. Rowland ML. Self-reported weight and height. Am J Clin Nutr.1990;52:1125–1133.
38. Vandegrift D, Yoked T. Obesity rates, income and suburban sprawl: an analysis of US states. Health and Place.2004;10:221–229.
39. Sutton PC. A scale-adjusted measure of "urban sprawl" using nighttime satellite imagery. Remote Sensing Environ.2003;86:353–369.[CrossRef]
40. Ewing R, Pendall R, Chen D. Measuring sprawl and its impact: the character and consequences of metropolitan expansion. Available at: www.smart-growthamerica.org/sprawlindex/sprawlindex.html. Accessed November 1, 2004.
41. Fulton W, Pendall R, Nguyen M, Harrison A. Who Sprawls Most? How Growth Patterns Differ Across the US. Washington, DC: Brookings Institute; 2001.
42. Razin E, Rosentraub M. Are fragmentation and sprawl interlinked? North American evidence. Urban Affairs Rev.2000;35:821–836.
43. Lopez R. Urban sprawl and risk for being overweight or obese. Am J Public Health. 2004;94:1574–1579.
44. Snijders TAB, Bosker RJ. Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. London: Sage; 1999.
45. Benfante R. Studies of cardiovascular disease and cause-specific mortality trends in Japanese-American men living in Hawaii and risk factor comparisons with other Japanese populations in the Pacific region: a review. Hum Biol.1992;64:791–805.[Web of Science][Medline]
46. Tremblay MS, Pérez CE, Ardern CI, Bryan SN, Katzmarzyk PT. Obesity, overweight and ethnicity: evidence from the Canadian community health survey. Health Reports.2005;16(4):23–34.[Medline]
47. Shaw M, Tunstall H, Davey Smith G. Seeing social position: visualizing class in life and death. Int J Epidemiol.2003;32:332–335.
48. Sturm R, Cohen DA. Suburban sprawl and physical and mental health. Am J Public Health.2004;118:488–496.
49. Ross NA, Wolfson MC, Dunn JR, Berthelot J-M, Kaplan G, Lynch J. Relation between income inequality and mortality in Canada and in the United States: cross sectional assessment using census data and vital statistics. BMJ.2000;320:898–902.
50. Rose G. Sick individuals and sick populations. Int J Epidemiol.1985;14(1):32–38.
This article has been cited by other articles:
|
|
 |
|
  M. S. Mujahid, A. V. D. Roux, M. Shen, D. Gowda, B. Sanchez, S. Shea, D. R. Jacobs Jr., and S. A. Jackson Relation between Neighborhood Environments and Obesity in the Multi-Ethnic Study of Atherosclerosis Am. J. Epidemiol., June 1, 2008; 167(11): 1349 - 1357. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
