Walkable home neighbourhood food environment and children's overweight and obesity: proximity, density or price?

Le, Ha ; Engler-Stringer, Rachel ; Muhajarine, Nazeem 等

With the increasing prevalence of overweight and obesity in Canada over the past few decades (1) and the minimal success of downstream interventions (educational, behavioural and pharmacological) that target the individual, (2) many have now turned to understanding the role of environments (neighbourhoods, schools, communities) to find solutions within them to stem the growing problem of overweight and obesity. (3-5) This paper focuses on one such environment--food environment closest to home--to understand its relationship with overweight and obesity in children, and to propose solutions for mitigation.

Broadly conceptualized, the food environment includes any opportunity to obtain food, such as accessibility to and availability of food stores, as well as marketing and advertising of food and food products. (6) Glanz and colleagues (6) have proposed a model of the food environment consisting of four interlinked components: the community nutrition environment (food sources available in a community at large), the consumer nutrition environment (typically food available within stores or establishments serving food), the organizational nutrition environment (food available in organizational settings such as schools, hospitals, workplaces) and the information environment (all information related to food typically through marketing or mass media channels).

Of these, researchers have argued that community and consumer nutrition environments are likely to have the broadest effects. (6,7) According to Holsten, (7) the research gaps that are most in need of filling include collecting primary data and conducting direct measures of the consumer and community nutrition environments. Additionally, all types of food outlets (grocery, convenience, restaurant) should be examined together to paint a more complete picture of the community and consumer nutrition environments in a particular locale.

The purpose of this study, then, is to identify factors within a walkable home neighbourhood food environment associated with overweight and obesity in young adolescents in Saskatoon, SK. Specifically, we studied three characteristics of the community and consumer food environments as they relate to child overweight and obesity, namely proximity to food outlets, density of available food outlets within a specified geographic area and costs of food or services available within food retailers or restaurants. We hypothesize that children who had convenient access (proximity) to more sources of healthy food, as compared with unhealthy food, at lower costs are less likely to be overweight or obese, and that these effects will be independent of selected dietary and socio-demographic factors related to children.

METHODS

The data used in this paper are from the Smart Cities, Healthy Kids: Food Environment study begun in 2011 in Saskatoon, SK. The design and methods of the Smart Cities, Healthy Kids study have been described in detail previously. (8) Briefly, this cross-sectional, multi-method study used data collected at multiple levels (children, neighbourhoods, food retail stores and services) and focused on 10-14-year-old children and their food environments. There were 1,469 students recruited from 43 of the 79 elementary schools in Saskatoon who agreed to a written request to participate. This sample of children accounted for 11.5% of the 10-14-year age group of the Saskatoon population according to the 2011 census. Since elementary schools are equally represented in all residential neighbourhoods in Saskatoon, the study sample was a good representation (socio-economically, geographically) of the population of children aged 10-14 years in Saskatoon.

The outcome measure, overweight or obese status vs. underweight and normal weight, was derived by measuring standing height without shoes to the nearest 0.1 cm and weight to the nearest 0.1 kg on a calibrated digital scale. The inputs for calculating the body mass index (BMI) were measured height and weight, and the instrument used was the age- and sex-specific BMI calculator from the World Health Organization (WHO) AnthroPlus version 3.1. Using the 2007 WHO reference standards, (9) we classified children as normal weight ([+ or -] 1 SD of the age-sex specific mean), overweight or obese (>1 SD) or obese (>2 SD).

Children's data were obtained from the Youth and Adolescent Food Frequency Questionnaire (YAQ), (10) anthropometric measurements and demographic data. YAQ was initially developed in the US (10) and has been adapted for Canadian use. (11) Detailed procedures for conducting dietary assessment in our study are given elsewhere. (8) For the purpose of this paper, we included as covariables derived nutrition-related factors such as food groups and macro- and micro-nutrient status, since earlier studies have found them to be related to BMI or weight status in children. (11,12) Since demographic characteristics and socio-economic status may influence children's weight status, we considered the following covariables as well: age in years, sex, Aboriginal status and self-reported family economic situation. (13)

A comprehensive database inventory of all restaurants, grocery stores, convenience stores and specialty food stores located within the city limits of Saskatoon was built, initially using the City of Saskatoon business licences database. This list was cross-checked with information from the phone book. The list of food outlets was later confirmed and completed in February 2011, when research assistants visited each neighbourhood in Saskatoon to conduct a census of the food environments. For the research reported here, we focused on all manner of grocery stores, convenience stores and fast-food restaurants. Grocery stores included both large supermarkets and small ones, as well as ethnic groceries, as long as they contained a full range of food items. (14) The convenience store category included gasoline stations and pharmacies where food items are sold. (14) The fast-food restaurants included all types of fast-food restaurant--burger and chicken, pita and sandwich, pizza and ethnic fast-food restaurants, as well as chain coffee shops, which are similar to fast-food restaurants in offering high-calorie foods and beverages (e.g., donuts, pastries) at lower price points and with minimal table service. (15)

The Nutrition Environment Measures Survey for Stores (NEMS-S) (14) and the Nutrition Environment Measures Survey for Restaurants (NEMS-R) (15) are structured observational tools that were used to characterize the nutrition environments of Saskatoon restaurants and retail food stores. The NEMS-S instrument measures the availability and pricing differences between healthier and less healthy options and the quality of fruit and vegetables (based on the percentage of acceptable ratings, and the total amount of varieties available). The scoring procedures for NEMS-S (14) involve positive scores for the availability of healthy food options in a store and the acceptability of fruit and vegetable quality, and negative scores for higher prices for healthy food options: the higher the score the better the consumer food environment. On the basis of the survey results, a total score (ranging from -9, least healthy, to 54, most healthy) was calculated by summing the scores for each NEMS-S item assessed.

The NEMS-R instrument measures the healthfulness of foods and beverages available on restaurants' menus, the main menu and children's, with a focus on availability of healthy entrees, side dishes and beverages; facilitators or supports for healthy eating; barriers to healthy eating; and relative pricing for healthy and less healthy choices. The scoring procedures for NEMS-R (15) involve positive scores for the availability of healthy options in the restaurant, nutrition information and facilitators encouraging healthful eating, and negative scores for barriers to healthy eating as well as extra costs for healthy food. On the basis of the survey results, a total quality score for restaurant food environments (ranging from -27, least healthy, to 63, most healthy) was calculated by summing the scores for each NEMS-R item assessed.

Children's walkable neighbourhood food environment was defined using a buffer zone area of a defined geographic distance from a child's residence. We considered distances of 500 m and 800 m from a child's home along the street network to be within walking distance, labeled "walkable neighbourhood from home". Most urban planners assume a half mile (805 m) to be walking distance. (16) Previous research has also used the half mile measure of proximity. (17,18)

Using ArcGIS 10.2 (Environmental Systems Research Institute Inc, Redlands, CA, 2010), the locations of food outlets were geocoded, along with the children's home addresses. Using these geocoded data we created the following walkable neighbourhood food environment indicators: 1) proximity to a food outlet (closest distance, via street network, from a student's home to each type of food outlet); 2) density of food outlets within the 500 and 800 m network buffer zones (counts of each type of food outlet); and 3) price of the overall quality of food consumable within restaurants and retail stores. Overall quality of food was calculated by summing two totals: the total of NEMS-S scores of each type of food stores (grocery, convenience) and the total of NEMS-R scores of fast-food restaurants within each defined neighbourhood.

Data analysis

After data entry was completed we omitted data for 103 children from further analysis. These non-retained respondents comprised 2 children whose data were incomplete, 59 who resided outside the Saskatoon city boundaries, 3 whose BMIs were either greater than 3 SD from the age- and sex-specific mean or were less than -3 SD, (19) and 39 who reported average energy intakes of less than 500 kcal or greater than 5000 kcal/day. (11,19) A further 145 students did not provide accurate address information, therefore the final sample remaining for logistic regression consisted of 1,221 students.

We used multivariable regression models to estimate associations between respondents' weight status and the variables of the walkable neighbourhood food environment. Variables were entered into multivariable logistic models in blocks. Block 1 consisted of socio-demographic variables such as sex, age, Aboriginal status, self-reported family economic situation; Block 2 dietary intake variables such as food group consumption, macro-and micro-nutrient intakes; and Block 3 the food environment indicators as described above, proximity to food outlets, availability of food outlets and relative price.

Odds ratios (OR) and 95% confidence intervals (CI) were calculated. The final model included only variables that were significant at a p value less than 0.05. SPSS version 18 was used for data analysis.

RESULTS

Of our sample of children aged 10-14 years, 55% were girls and 45% were boys; 15% of children self-identified as Aboriginal; 10.5% of the children reported their family economic situation as well-off, 68% as average and 5% as poor (17% had a missing value). In this sample 24.3% (95% CI 21.2-25.7) were classified as overweight, and 12.0% (95% CI 12.4-16.0) were obese (Table 1).

Table 2 shows the mean, median, minimum and maximum closest distance (all measures in metres) to a grocery or a convenience store or a fast-food restaurant using road network distance. Overall, the mean closest network distance from children's residence to a grocery store was 1381 m, to a convenience store 803 m and to a fast-food restaurant 1236 m.

Table 3 presents the number and percentage of children who had 0, 1, or 2 or more of the food outlets and fast food-restaurants within walking distance from home. As shown, a large percentage of children (89% within 500 m or 76% within 800 m road network buffers) did not have access to a grocery store within walking distance from their homes; in contrast, 58% and 32% of children could access at least one convenience store or fast-food restaurant respectively within an 800 m walk of their home.

Table 4 presents the final multivariable logistic model showing significant covariables that were associated with overweight or obesity in the participants of this study. We found that a healthier consumer nutrition environment--i.e., healthy food options, at lower prices, in grocery stores or restaurants--was significantly associated with lower odds of overweight or obesity. Children who had access to higher quality and more affordable healthy food options in grocery and convenience stores in their home neighbourhoods had a significantly lower risk of being overweight or obese (OR = 0.87, 95% CI 0.77-0.99). Similarly, children whose walkable neighbourhoods offered more affordable prices and fewer barriers for healthy food options in fast-food restaurants had a lower risk of being overweight or obese (OR = 0.97, 95% CI 0.95-0.99). We did not find, however, statistically significant associations between distance to grocery stores or restaurants, or the density (number of retail or food services outlets within a given geographic area) of food outlets, and overweight and obesity in this study.

We found several other significant factors independently associated with overweight or obesity. The frequency of meat and meat-alternatives consumption increased the odds of being overweight or obese--the more the consumption the higher the odds of being overweight or obese. Children who reported the highest or moderate levels of meat consumption (3rd or 2nd tertiles), compared with those who consumed at the lowest level, had significantly increased odds of overweight or obesity: more than 2 times (OR = 2.14, 95% CI 1.33-3.45) or 77% greater odds (OR = 1.77, 95% CI 1.21-2.56) respectively. In contrast, high intake of monounsaturated fat or low intake of sodium was associated with lower odds of overweight or obesity (OR for monounsaturated fat 0.51, 95% CI 0.34-0.78, and OR for sodium 0.56, 95% CI 0.36-0.87). Significant associations were also found indicating increased odds of overweight and obesity for males, children 11, 12, 13 or 14 years of age compared with 10 years, and for children of Aboriginal status.

DISCUSSION

Prior to this not many studies have described the walkable community nutrition environment centred on children's place of residence (proximity to and density of food outlets and fast-food restaurants) and the consumer nutrition environment (pricing, quality of food items within the stores or restaurants) together. The results here suggest that young children in Saskatoon have greater access to potentially unhealthy food sources, compared with healthy food, within walking distance of 500-800 m from their home. The nearest grocery store was, on average, 1381 m from home, whereas the distance to a convenience store was 803 m and to a fast-food restaurant was 1236 m. A large percentage of children did not have access to a grocery store within walking distance from home (89% of children did not have access within 500 m of their home, and 76% children had no access within 800 m). It is normally assumed that grocery stores offer a fuller range of options, including healthy foods at an affordable cost, (20) and that convenience stores and fast-food restaurants sell mostly unhealthy food items. (21) The relevance of designating food stores in this manner for children has been questioned, however; furthermore, as reported here, proximity to food outlets or how many outlets are available within easy access may not be the primary factor of concern in terms of increased risk of overweight or obesity in children. (22,23)

This study reports that another type of accessibility--specifically the cost of food within the stores and meals within restaurants and their quality--around children's homes has a significant association with weight status, independent of factors such as the type of food children consume (i.e., meat and meat alternatives, fat or salt content in food) or key demographic factors such as age, sex, Aboriginal status or economic situation of the family. Children who had access to affordable healthy food options within walking distance from home (800 m) had a lower likelihood of being overweight or obese. These results agree with the findings from a review by Powell and Chaloupka, who found significant effects of food prices on weight outcomes. (24) Similarly, in a longitudinal study, Sturm and Datar showed that changes in children's weight were positively related to the price of fruits and vegetables. (25)

In another study, in which Sturm and Datar followed children from kindergarten up to fifth grade, they confirmed their previous finding, that children's BMI was sensitive to changes in fruit and vegetable prices. (26) These results suggest that lower prices for healthy food options such as fruits and vegetables within a walkable distance from home may help to mitigate development of overweight or obesity in children.

Similar to An and Sturm, (22) we found no evidence, however, to support the hypotheses that improved access, i.e., proximity or distance to supermarkets, or decreased exposure to fast-food restaurants or convenience stores within walking distance, is associated with lower odds of overweight or obesity. This may be due to several reasons. First, our study applied 500 and 800 m definitions of street network buffers around children's homes to operationally define a walkable neighbourhood food environment from home. However, these definitions assume that children, or indeed their parents, do in fact shop for food items closer to where they live. These assumptions need to be tested and measures incorporated in future studies to show that participants actually shop at food outlets closer to their home. Some studies have reported that most people do not shop for food primarily at stores near where they reside. (23) Drewnowski et al.'s study, on adults, reported that only 14% of the respondents in Seattle and 11.4% in Paris shopped for food either at the closest supermarket or in their own residential neighbourhood. They argued that shoppers seem to be willing to travel longer distances from home to arrive at the supermarket of their choice or that they use supermarkets on their daily activity routes rather than specifically near their home. (23)

Second, although it was normally assumed that grocery stores offer healthy foods at an affordable cost (20) and that convenience stores and fast-food restaurants sell mostly unhealthy food items, (21) Powell and Chaloupka argued that much of the revenue in supermarkets comes from the wider selection of soft drinks, sweets, salty snacks or frozen dinners, which are available at lower prices and in larger packaged sizes. (24) According to these arguments, one interpretation and implication of our data is that categorizing food outlets by general types, e.g., grocery stores, convenience stores, fast-food restaurants, is at best a crude and shorthand way of classifying a complex phenomenon and, at worst, would tend to produce uninformative or even misleading results. Future research on food environments should either break with or significantly improve on precedence when using broad classification systems to identify healthy and unhealthy food environments. As our study has shown, continuing to assume that healthy food at affordable prices is available on the basis simply of distance to or availability of broadly classified food outlets (grocery stores, convenience stores) or fast-food restaurants is no longer helpful in this field of study.

Strengths and limitations

Our study has a number of strengths. We conducted a census of food retail and food service establishments in one city, at one point in time, using direct observation and standardized tools. This allowed us to comprehensively document and describe, by direct observation, the neighbourhood food environment: location of food outlets, types, food quality and price. Second, we defined neighbourhood food environments in relation to participating children's homes and used two different distances (500 and 800 m) to define a walkable environment. We used children's actual home address for geocoding, which allows for the correct classification of the presence or absence of certain environmental features. (27,28) Third, our characterization of the local neighbourhood food environment was theoretically driven (i.e., Glanz et al.), and we specifically operationalized Glanz et al.'s food environment dimensions of community and consumer food environments. (6) This enabled us to focus on not only measures that are often used in other studies, such as availability of food establishments and closest distance to establishments, (21,29) but also the price and quality of food within these establishments. Fourth, we also directly measured height and weight, which allows for accurate classification of children's weight status. Socio-economic and demographic data and detailed dietary intake of children allow adjustment for potential confounders in the analyses.

The current study also has limitations. Its cross-sectional nature does not allow for the detection of any cause-and-effect relationship in the association observed. However, Hanibuchi et al. argued that even with longitudinal data the causal association between food outlets or dietary practices and BMI can be problematic because of residential selection and store location preferences. (30) Papas et al. argued that many "desirable" characteristics of neighbourhoods tend to cluster, therefore it is important to check that any putative influence of the food environment on obesity is not confounded by co-occurring built environment characteristics. (29) The measure of children's family socio-economic status from self-reported data likely has limitations (misclassification). Finally, generalizability issues need to be taken into account before applying the results of this study to other cities with similar characteristics of the food environment.

CONCLUSIONS

Guided by a theoretical understanding of the food environment specifically, community and consumer food environments (Glanz et al.) --this study aimed to provide answers to the questions, Do children have greater (or lesser) access to healthy versus unhealthy food sources from their homes, and What characteristics of the neighbourhood food environment (proximity, density or costs of food and quality) are associated with overweight and obesity in children. A majority of children 10-14 years of age in Saskatoon do not have easy access to healthy food retail establishments. Most important, lower prices for healthy food options in grocery and convenience stores and fast-food restaurants are associated with decreased odds of overweight or obesity. Interventions to reduce food prices for healthy options in food outlets and restaurants in neighbourhoods may have favourable effects on children's weight outcomes.

REFERENCES

(1.) Roberts KC, Shields M, De Groh M, Aziz A, Gilbert JA. Overweight and obesity in children and adolescents: Results from the 2009 to 2011 Canadian Health Measures Survey. Health Rep 2012; 23(3):37-41. PMID: 23061263. Catalogue no. 82-003-XPE.

(2.) Summerbell CD, Waters E, Edmunds LD, Kelly S, Brown T, Campbell KJ. Interventions for preventing obesity in children. Cochrane Database Syst Rev 2005; 3:1-70. PMID: 16034868. doi: 10.1002/14651858.CD001871.pub2.

(3.) Morland K, Wing S, Roux AD. The contextual effect of the local food environment on residents' diets: The atherosclerosis risk in communities study. Am J Public Health 2002; 92(11):1761-68. PMID: 12406805. doi: 10.2105/ AJPH.92.11.1761.

(4.) Laraia BA, Siega-Riz AM, Kaufman JS, Jones SJ. Proximity of supermarkets is positively associated with diet quality index for pregnancy. Prev Med 2004; 39(5):869-75. PMID: 15475018. doi: 10.1016/j.ypmed.2004.03.018.

(5.) Wang MC, Gonzalez AA, Ritchie LD, Winkleby MA. The neighborhood food environment: Sources of historical data on retail food stores. Int J Behav Nutr PhysAct 2006; 3(1):15. PMID: 16846518. doi: 10.1186/1479-5868-3-15.

(6.) Glanz K, Sallis JF, Saelens BE, Frank LD. Healthy nutrition environments: Concepts and measures. Am J Health Promot 2005; 19(5):330-33. PMID: 15895534. doi: 10.4278/0890-1171-19.5.330.

(7.) Holsten JE. Obesity and the community food environment: A systematic review. Public Health Nutr 2009; 12(3):397-405. PMID: 18477414. doi: 10.1017/ S1368980008002267.

(8.) Engler-Stringer R, Muhajarine N, Le H, Del Canto S, Ridalls T. Characterizing the Food Environment in Saskatoon for Families with Children: Research Methods and Descriptive Results. Saskatchewan Population Health and Evaluation Research Unit, Saskatoon 2014. Available at: http://www.spheru. ca/publications/files/FINAL%20Food%20Environment%20Technical% 20Report.pdf (Accessed April 17, 2016).

(9.) WHO AnthroPlus for personal computers Manual: Software for assessing growth of the world's children and adolescents. Geneva: World Health Organization, 2009. Available at: http://www.who.int/growthref/tools/en/.

(10.) Rockett HR, Berkey CS, Colditz GA. Comparison of a short food frequency questionnaire with the Youth/Adolescent Questionnaire in the Growing Up Today Study. Int J Pediatr Obes 2007; 2(1):31-39. PMID: 17763008. doi: 10.1080/17477160601095417.

(11.) Veugelers PJ, Fitzgerald AL. Prevalence of and risk factors for childhood overweight and obesity. Can Med Assoc J 2005; 173(6):607-13. PMID: 16157724. doi: 10.1503/cmaj.050445.

(12.) Matthews VL, Wien M, Sabate J. The risk of child and adolescent overweight is relatedto types of food consumed. NutrJ 2011; 10(1):71-74. PMID: 21702912. doi: 10.1186/1475-2891-10-71.

(13.) Shrewsbury V, Wardle J. Socioeconomic status and adiposity in childhood: A systematic review of cross-sectional studies 1990-2005. Obesity 2008; 16(2):275-84. PMID: 18239633. doi: 10.1038/oby.2007.35.

(14.) Glanz K, Sallis JF, Saelens BE, Frank LD. Nutrition Environment Measures Survey in stores (NEMS-S): Development and evaluation. Am J Prev Med 2007; 32(4):282-89. PMID: 17383559. doi: 10.1016/j.amepre.2006.12.019.

(15.) Saelens BE, Glanz K, Sallis JF, Frank LD. Nutrition environment measures study in restaurants (NEMS-R): Development and evaluation. Am J Prev Med 2007; 32(4):273-81. PMID: 17383558. doi: 10.1016/j.amepre.2006.12.022.

(16.) Agrawal AW, Schlossberg M, Irvin K. How far, by which route and why? A spatial analysis of pedestrian preference. J Urban Des 2008; 13(1):81-98. doi: 10.1080/13574800701804074.

(17.) Austin SB, Melly SJ, Sanchez BN, Patel A, Buka S, Gortmaker SL. Clustering of fast-food restaurants around schools: A novel application of spatial statistics to the study of food environments. Am J Public Health 2005; 95(9):1575-81. PMID: 16118369. doi: 10.2105/AJPH.2004.056341.

(18.) Zenk SN, Powell LM. U.S. secondary schools and food outlets. Health Place 2008; 14(2):336-46. PMID: 17881277. doi: 10.1016/j.healthplace.2007.08.003.

(19.) Rockett HR, Berkey CS, Field AE, Colditz GA. Cross-sectional measurement of nutrient intake among adolescents in 1996. Prev Med 2001; 33(1):27-37. PMID: 11482993. doi: 10.1006/pmed.2001.0850.

(20.) Larson NI, Story MT, Nelson MC. Neighborhood environments: Disparities in access to healthy foods in the U.S. Am J Prev Med 2009; 36(1):74-81.e10. PMID: 18977112. doi: 10.1016/j.amepre.2008.09.025.

(21.) Fleischhacker SE, Evenson KR, Rodriguez DA, Ammerman AS. A systematic review of fast food access studies. Obes Rev 2011; 12(5):e460-71. PMID: 20149118. doi: 10.1111/j.1467-789X.2010.00715.x.

(22.) An R, Sturm R. School and residential neighborhood food environment and diet among California youth. Am J Prev Med 2012; 42(2):129-35. PMID: 22261208. doi: 10.1016/j.amepre.2011.10.012.

(23.) Drewnowski A, Moudon AV, Jiao J, Aggarwal A, Charreire H, Chaix B. Food environment and socioeconomic status influence obesity rates in Seattle and in Paris. IntJ Obes 2014; 38(2):306-14. PMID: 23736365. doi: 10.1038/ijo. 2013.97.

(24.) Powell LM, Chaloupka FJ. Food prices and obesity: Evidence and policy implications for taxes and subsidies. Milbank Q 2009; 87(1):229-57. PMID: 19298422. doi: 10.1111/j.1468-0009.2009.00554.x.

(25.) Sturm R, Datar A. Body mass index in elementary school children, metropolitan area food prices and food outlet density. Public Health 2005; 119(12):1059-68. PMID: 16140349. doi: 10.1016/j.puhe.2005.05.007.

(26.) Sturm R, Datar A. Food prices and weight gain during elementary school: 5-year update. PublicHealth 2008; 122(11):1140-43. PMID: 18539306. doi: 10. 1016/j.puhe.2008.04.001.

(27.) Bow CJD, Waters NM, Faris PD, Seidel JE, Galbraith PD, Knudtson ML, Ghali WA. Accuracy of city postal code coordinates as a proxy for location of residence. Int J Health Geogr 2004; 3(1):5. PMID: 15028120. doi: 10.1186/ 1476-072X-3-5.

(28.) Healy MA, Gilliland JA. Quantifying the magnitude of environmental exposure misclassification when using imprecise address proxies in public health research. Spat Spatiotemporal Epidemiol 2012; 3(1):55-67. PMID: 22469491. doi: 10.1016/j.sste.2012.02.006.

(29.) Papas MA, Alberg AJ, Ewing R, Helzlsouer KJ, Gary TL, Klassen AC. The built environment and obesity. Epidemiol Rev 2007; 29(1):129-43. PMID: 17533172. doi: 10.1093/epirev/mxm009.

(30.) Hanibuchi T, Kondo K, Nakaya T, Nakade M, Ojima T, Hirai H, Kawachi I. Neighborhood food environment and body mass index among Japanese older adults: Results from the Aichi Gerontological Evaluation Study (AGES). IntJHealth Geogr 2011; 10:43. PMID: 21777439. doi: 10.1186/ 1476-072X-10-43.

Ha Le, MSc, [1] Rachel Engler-Stringer, PhD, [1,2] Nazeem Muhajarine, PhD [1,2]

Author Affiliations

[1.] Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, SK

[2.] Saskatchewan Population Health and Evaluation Research Unit, Saskatoon, SK

Correspondence: Nazeem Muhajarine, PhD, Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, SK S7N 5E5, Tel: 306-966 7940, E-mail: nazeem.muhajarine@usask.ca

Source of Funding: This research was funded by the Canadian Institutes of Health Research (Grant #106643).

Conflict of Interest: None to declare. Table 1. Characteristics of the study participants * Variable Frequency (n) Percentage (%) Sex (n = 1408) Female 776 55.1 Male 632 44.9 Age (n = 1408) 10 265 18.9 11 399 28.3 12 363 25.8 13 279 19.8 14 102 7.2 Aboriginal status (n = 1408) Yes 208 14.8 No 1184 84.1 Missing 16 1.1 Self-rated family economic situation (n = 1408) Well-off 148 10.5 Average 958 68.0 Poor 66 4.7 Missing 236 16.8 Body mass index (n = 1331) Normal 678 51.0 Overweight 323 24.3 Obese 160 12.0 Underweight 170 12.7 * 1469 children agreed to participate; 59 cases were removed because they resided outside of Saskatoon, 2 cases were removed because of incomplete information. For calculation of BMI, an additional 43 cases were removed because of extreme or improbable values: 3 with BMI < -3 SD and BMI > 3 SD, and 39 with total calories consumed of <500/d and >5000/d. Table 2. Closest distance (all in metres) from children's residence to a food outlet or a fast food restaurant Mean Median distance distance (SD) Grocery store Network distance 1381 (717) 1274 Convenience store Network distance 803 (483) 691 Fast-food restaurant Network distance 1236 (760) 1078 Minimum Maximum distance distance Grocery store 22 4014 Convenience store 5 3556 Fast-food restaurant 15 3804 Table 3. Density of food outlets or fast-food restaurants within walking distance from home (metres) Network buffer distance Counts 500 m, 800 m, Food outlet n (%) n (%) Grocery stores 0 1102 (89.2) 939 (76.0) 1 110 (8.9) 204 (16.5) 2 24 (1.9) 93 (7.5) Convenience stores 0 872 (70.6) 517 (41.8) 1 228 (18.4) 345 (27.9) 2 or more 136 (11.0) 374 (30.3) Fast-food restaurants 0 1037 (83.9) 846 (68.4) 1 89 (7.2) 129 (10.4) 2 or more 110 (8.9) 261 (21.1) Retail food outlets 839 (67.9) 489 (39.6) (convenience 0 and 1 212 (17.2) 306 (24.8) grocery stores) 2 or more 185 (15.0) 441 (35.7) Table 4. Neighbourhood food environment factors (within an 800 m network buffer zone from home), nutrient intake and socio-demographic factors associated with overweight/obesity in children aged 10-14 years in Saskatoon Associated factor Odds ratio (95% p value confidence interval) Higher quality and lower price (score) 0.87 (0.77-0.99) 0.032 for healthier food options in grocery and convenience stores Higher quality and lower price (score) 0.97 (0.95-0.99) 0.014 for healthier food options in fast-food restaurants Meat and meat alternatives (number of servings daily) Low (bottom third) 1.00 Moderate (middle third) 1.77 (1.21-2.56) 0.003 High (top third) 2.14 (1.33-3.45) 0.002 Monounsaturated fats intake High (top half) 0.51 (0.34-0.78) Low (bottom half) 1.00 0.002 Sodium intake Low (equal to or less than 2000 mg/d) 0.56 (0.36-0.87) High (more than 2000 mg/d) 1.00 0.010 Sex Female 1.00 Male 2.20 (1.66-2.93) <0.001 Age in years 10 1.00 11 2.17 (1.34-3.50) 0.002 12 2.10 (1.30-3.41) 0.003 13 4.30 (2.63-7.03) <0.001 14 4.13 (2.22-7.68) <0.001 Aboriginal status Non-Aboriginal 1.00 Aboriginal 2.92 (1.92-4.40) <0.001 Family economic situation Well-off 1.00 Average 1.27 (0.81-1.99) 0.294 Poor 1.99 (0.97-4.10) 0.061