Association between food, physical activity, and social assistance environments and the body mass index of schoolchildren from different socioeconomic strata

Association between food, physical activity, and social assistance environments and the body mass... Abstract The aim of this article was to evaluate associations between body mass index (BMI) and use of and distance from subjects homes of elements of the food and physical activity environments and use of social assistance environment, in schoolchildren from 7 to 14 years living in Florianópolis (South Brazil), stratified by monthly family income. A cross-sectional study was conducted with a probabilistic sample of 2152 schoolchildren. Univariate and multivariate linear regression analyses were conducted to test for associations between BMI and the use of and distance from supermarkets, bakeries and farmers’ markets; use of and distance from parks/playgrounds and football pitches; and use of health centers, Reference Centers for Social Assistance, instructional facilities, residents associations, religious groups and a Brazilian program for cash transfer. Overweight and obesity rates were 21.5 and 12.7%, respectively. Among schoolchildren from low-income families, living more than 11 min’ walk from parks/playgrounds was associated with higher BMI (β = 0.53; 95% CI = 0.33–0.73). In the high-income strata, a longer distance from home to football pitches was associated with lower BMI (β = −0.49; 95% CI = −0.69; −0.29). Neither food nor social assistance environments were associated with BMI of schoolchildren, even when analyzed by income strata. environment, places, young people Introduction The built environment in combination with the social, political and economic environments shapes the contexts in which people live.1 The built environment encompasses the food environment (restaurants, snack bars, etc.),2–6 the physical activity environment (beaches, parks/playgrounds, etc.),4,7,8 and the social assistance environment (centers for social assistance, instructional facilities, health centers, etc.).9–11 Environmental characteristics can influence eating habits,4 sedentary behavior8 and access to social and assistance programs.9–11 Living more than 800 m from grocery stores was related to higher body mass index (BMI) in adolescents from Connecticut (USA).4 Increased availability of facilities for physical activity and public assistance protected adolescents living in the USA from obesity, because these features improved walkability and opportunities for health promotion.9 Features related to the socioeconomic environment (i.e. educational level, rate of employment, residents’ incomes), and families’ socioeconomic characteristics have also been related to overweight/obesity.10–12 In one longitudinal study, children’s BMI was most influenced by their socioeconomic environment and the association was partially explained by their family’s educational level.12 Several Brazilian studies have evaluated associations between the food environment and food purchasing13–16 or overweight/obesity.17,18 Some have also focused on the quality of facilities available for physical activity19 and their associations with levels of physical activities.20,21 In Florianópolis, a coastal city situated in the South of Brazil, researchers have studied the spatial distribution of food vendors in areas with different socioeconomic features22 and the use of them and overweight/obesity in children.5,6 However, research seeking to evaluate the built environment in its multiple contexts and their influence on schoolchildren’s BMI is not available in developing settings.2–4,8–12 Although Florianópolis city has a very high Human Development Index (HDI: 0.847),23 it has a worryingly high Gini Index of 0.5474 (the closer to 1, the greater the social inequalities between residents),24 which could reflect differences in the built environments in wealthy and underprivileged areas. The aim of this article was to evaluate associations between BMI and use of and distance from subjects’ homes of elements of the food and physical activity environments and use of the social assistance environment, in schoolchildren aged 7–14 years living in Florianópolis (South Brazil), stratified by their families’ monthly incomes. Method Data came from a larger research analyzing the tendency and prevalence of obesity and associated factors among schoolchildren enrolled at public or private schools in Florianópolis (age 7–14 years). Several papers have been published from these data. In this investigation, a cross-sectional analysis with the whole probabilistic sample (2506 schoolchildren) was performed. The sample was selected by clusters, according to the number of students enrolled in each school. This procedure aimed to ensure that the sample was representative both of the regions in which the population lives and the variability of income in the population. The sampling methods used have been described in greater detail elsewhere.6,25 Weight and height were objectively measured by trained anthropometrists. The absolute intra-examiner and inter-examiner Technical Errors of Measurement (TEM) were used to select anthropometrists.26 Data were collected in accordance with the World Health Organization (WHO) technical standards.27 Overweight was defined as BMI for age and sex ≥+1 and <+2 z scores and obesity was defined as BMI for age and sex ≥+2 z scores.28 Schoolchildren and their families self-reported data about frequency of use and perceived distance from home to a list of facilities from three domains of the built environment that have been investigated in previous studies.29–33 We evaluated three elements from the food environment (bakeries, supermarkets and farmers’ markets) that had been significantly associated with overweight/obesity in studies of subsamples of the same original dataset;5,6 two facilities for physical activity (parks/playgrounds and football pitches); and four facilities for social assistance (health centers, Reference Centers for Social Assistance [RCSA], instructional facilities [Centers for Supplementary Education], and residents associations). The respondents also indicated their participation (yes/no) in three types of social activity: social projects (run by non-governmental organizations and/or the provisions of public policies), religious groups, and the Bolsa Família, a Brazilian cash transfer program. Data on use of facilities were categorized post hoc as follows: uses (grouping weekly and fortnightly) or does not use (combining never used, used rarely, and used monthly). Significant correlations between perceived and objective measures of proximity to parks have been reported for adults living in Cuernavaca, Mexico,34 and 64, 39 and 28% of a sample of adults from Ghent (Belgium), were able to correctly estimate the distances from home to bakeries, restaurants and supermarkets, respectively.35 We evaluated perceived distance from the family home to each item in the food and physical activity environments in terms of time in minutes taken to walk the distance. The answers were categorized post hoc as up to 10 or ≥11 min, on the assumption that places that take up to 10 min to reach on foot are close to adolescents and adults’ homes (~800 m)36,37 and can therefore be accessed actively. In the same questionnaire, monthly family incomes reported in Brazilian Reais (R$) were collected as a continuous variable, so the sample was stratified as higher-income families (HIF) (values >50th percentile) or lower-income families (LIF) (values ≤50th percentile). The value of the US Dollar varied from R$2.03 to R$2.37 during the data collection period. Parents’ educational level was also surveyed. Dietary data were obtained using another survey, the third version of the ‘Questionário Alimentar do Dia Anterior’ (Quada-3), which is a qualitative, illustrated questionnaire for evaluating children’s food consumption on the previous day. More detailed information on the QUADA is available elsewhere.25 Descriptive analyses were performed expressing categorical variables as absolute values and relative frequencies, and continuous variables as means with standard deviations. β Coefficients and their respective 95% confidence intervals, estimated by univariate linear regression analysis, were used to analyze factors associated with the outcome BMI. Exposure variables with P-values ≤0.25 for univariate associations were entered into the multivariate model with forward selection for strength of association.38 Categorical variables were tested for mutual collinearity using the chi-square test of independence. In cases of collinearity, the variable with the best fit to the model was chosen for the final regression. Age, sex and five variables related to consumption (yes/no) of non-healthy foods were included in the models as controls. Socioeconomic variables (type of school, and parents’ educational levels) were tested for relationships with BMI, using Pearson’s correlations. The results were weak (between 0.03 and 0.05) and these variables were not included in the multivariate models. The SVY command available in Stata, version 13.0, was used to account for complex sampling and sample weighting. This study was duly approved by the Human Research Ethics Committee at the Universidade Federal de Santa Catarina, under review process no. 120,341/2012. Results Valid BMI data were collected from 2484 schoolchildren, of whom valid family income data were available for 2152 (85.9% of the initial sample investigated). Table 1 lists characteristics for the whole sample and for the sample broken down by family income strata, showing that a majority of the schoolchildren were females (56.5%), aged 7–10 years (61.1%), and enrolled at public schools (65.3%). The age distribution and percentage of schoolchildren from public schools were as specified by the sample size calculation. Overall, prevalence of overweight was 21.5% (95% CI = 16.7–27.3%) and prevalence of obesity was 12.7% (95% CI = 11.0–14.5%). Table 1 Descriptive characteristics of the sample of 7–14-year-old schoolchildren, stratified by monthly family income, Florianópolis, Santa Catarina, Brazil, 2012/2013 Variables Categories Total LIFa (n = 1090) HIFa (n = 1062) P-value n % n % n % Sex (n = 2506) Female 1334 56.5 505 43.8 513 43.8 0.996 Male 1172 43.5 585 56.2 549 56.2 Age (years) (n = 2506) 7–10 1530 61.1 682 62.4 642 63.7 0.526 11–14 976 38.9 408 37.6 420 36.3 Type of school (n = 2506) Public 1637 65.3 1007 92.5 451 40.9 <0.001 Private 869 34.7 83 7.5 611 59.1 BMI for age and sex (n = 2484) Normal/low weightb 1658 65.8 705 65.4 704 65.9 0.902 Overweightc 511 21.5 219 22.0 224 20.8 Obesityd 315 12.7 151 12.6 129 13.3 Maternal education level (n = 2389) HS not graduated 851 33.3 606 55.3 174 14.9 0.003 HE not graduated 857 37.5 378 37.8 378 38.6 Graduated HE 681 29.2 66 6.9 497 46.5 Paternal education level (n = 2086) HS not graduated 806 35.6 529 58.2 213 19.5 0.016 HE not graduated 710 35.3 269 35.3 358 35.6 Graduated HE 570 29.1 64 6.5 398 44.9 Uses football pitches (n = 2341) Yes 661 25.4 326 26.4 256 23.5 0.344 No 1680 74.6 702 73.6 777 76.5 Uses parks/playgrounds (n = 2342) Yes 642 27.5 266 23.6 291 29.7 0.048 No 1700 72.5 751 76.4 749 70.3 Uses bakeries (n = 2279) Yes 2000 87.0 872 86.0 890 88.1 0.239 No 279 13.0 133 14.0 111 11.9 Uses supermarkets (n = 2397) Yes 2306 95.7 1008 94.3 1015 96.4 0.066 No 91 4.3 53 5.7 32 3.6 Uses farmers’ markets (n = 2318) Yes 2066 87.5 915 89.9 895 84.3 0.085 No 252 12.5 110 10.1 118 15.7 Distance to park/ playground (min) (n = 1830) 1–10 776 45.5 289 35.9 373 51.9 0.079 ≥11 1054 54.5 483 64.1 455 48.1 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to bakery (min) (n = 1946) 1–10 1035 69.1 549 64.2 602 73.3 0.171 ≥11 641 30.9 296 35.8 274 26.7 Distance to supermarket (min) (n = 2256) 1–10 894 40.6 377 37.3 411 43.9 0.322 ≥11 1362 59.4 614 62.7 584 56.1 Distance to farmers’ market (min) (n = 2006) 1–10 829 42.1 363 39.1 364 45.7 0.229 ≥11 1177 57.9 524 60.9 511 54.3 Uses health centers (n = 2359) Yes 939 37.9 555 50.4 287 27.0 0.089 No 1420 62.1 485 49.6 747 73.0 Uses RCSAe (n = 2261) Yes 52 1.6 44 3.3 4 0.2 0.012 No 2209 98.4 928 96.7 1019 99.8 Uses CSEf (n = 2270) Yes 197 7.7 138 12.2 45 4.7 0.029 No 2073 92.3 835 87.8 980 95.4 Uses social project (n = 2275) Yes 351 12.3 242 20.1 84 6.0 0.045 No 1924 87.7 748 79.9 935 94.0 Uses religious group (n = 2319) Yes 895 35.9 415 39.7 389 34.3 0.400 No 1424 64.1 592 60.3 648 65.7 Uses residents association (n = 2257) Yes 78 3.5 50 5.8 22 1.9 0.051 No 2179 96.5 917 94.2 1003 98.1 Receives Bolsa Família Program (n = 2394) Yes 217 8.5 178 15.6 20 2.4 0.013 No 2177 91.5 882 84.4 1025 97.6 Drinks soda (n = 2504) Yes 1473 61.1 714 68.0 759 56.0 0.103 No 1031 38.9 375 32.0 656 44.0 Eats sugary foods (n = 2504) Yes 1148 46.1 505 47.2 643 45.3 0.517 No 1356 53.9 584 52.8 772 54.7 Eats chips (n = 2504) Yes 437 17.4 219 20.2 218 15.4 0.113 No 2067 82.6 870 79.8 1197 84.6 Eats fried potatoes (n = 2504) Yes 465 19.9 234 23.9 231 16.9 0.293 No 2039 80.1 855 76.1 1184 83.1 Eats fast food (n = 2504) Yes 510 21.3 234 23.8 276 19.4 0.273 No 1994 78.7 855 76.2 1139 80.6 Variables Categories Total LIFa (n = 1090) HIFa (n = 1062) P-value n % n % n % Sex (n = 2506) Female 1334 56.5 505 43.8 513 43.8 0.996 Male 1172 43.5 585 56.2 549 56.2 Age (years) (n = 2506) 7–10 1530 61.1 682 62.4 642 63.7 0.526 11–14 976 38.9 408 37.6 420 36.3 Type of school (n = 2506) Public 1637 65.3 1007 92.5 451 40.9 <0.001 Private 869 34.7 83 7.5 611 59.1 BMI for age and sex (n = 2484) Normal/low weightb 1658 65.8 705 65.4 704 65.9 0.902 Overweightc 511 21.5 219 22.0 224 20.8 Obesityd 315 12.7 151 12.6 129 13.3 Maternal education level (n = 2389) HS not graduated 851 33.3 606 55.3 174 14.9 0.003 HE not graduated 857 37.5 378 37.8 378 38.6 Graduated HE 681 29.2 66 6.9 497 46.5 Paternal education level (n = 2086) HS not graduated 806 35.6 529 58.2 213 19.5 0.016 HE not graduated 710 35.3 269 35.3 358 35.6 Graduated HE 570 29.1 64 6.5 398 44.9 Uses football pitches (n = 2341) Yes 661 25.4 326 26.4 256 23.5 0.344 No 1680 74.6 702 73.6 777 76.5 Uses parks/playgrounds (n = 2342) Yes 642 27.5 266 23.6 291 29.7 0.048 No 1700 72.5 751 76.4 749 70.3 Uses bakeries (n = 2279) Yes 2000 87.0 872 86.0 890 88.1 0.239 No 279 13.0 133 14.0 111 11.9 Uses supermarkets (n = 2397) Yes 2306 95.7 1008 94.3 1015 96.4 0.066 No 91 4.3 53 5.7 32 3.6 Uses farmers’ markets (n = 2318) Yes 2066 87.5 915 89.9 895 84.3 0.085 No 252 12.5 110 10.1 118 15.7 Distance to park/ playground (min) (n = 1830) 1–10 776 45.5 289 35.9 373 51.9 0.079 ≥11 1054 54.5 483 64.1 455 48.1 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to bakery (min) (n = 1946) 1–10 1035 69.1 549 64.2 602 73.3 0.171 ≥11 641 30.9 296 35.8 274 26.7 Distance to supermarket (min) (n = 2256) 1–10 894 40.6 377 37.3 411 43.9 0.322 ≥11 1362 59.4 614 62.7 584 56.1 Distance to farmers’ market (min) (n = 2006) 1–10 829 42.1 363 39.1 364 45.7 0.229 ≥11 1177 57.9 524 60.9 511 54.3 Uses health centers (n = 2359) Yes 939 37.9 555 50.4 287 27.0 0.089 No 1420 62.1 485 49.6 747 73.0 Uses RCSAe (n = 2261) Yes 52 1.6 44 3.3 4 0.2 0.012 No 2209 98.4 928 96.7 1019 99.8 Uses CSEf (n = 2270) Yes 197 7.7 138 12.2 45 4.7 0.029 No 2073 92.3 835 87.8 980 95.4 Uses social project (n = 2275) Yes 351 12.3 242 20.1 84 6.0 0.045 No 1924 87.7 748 79.9 935 94.0 Uses religious group (n = 2319) Yes 895 35.9 415 39.7 389 34.3 0.400 No 1424 64.1 592 60.3 648 65.7 Uses residents association (n = 2257) Yes 78 3.5 50 5.8 22 1.9 0.051 No 2179 96.5 917 94.2 1003 98.1 Receives Bolsa Família Program (n = 2394) Yes 217 8.5 178 15.6 20 2.4 0.013 No 2177 91.5 882 84.4 1025 97.6 Drinks soda (n = 2504) Yes 1473 61.1 714 68.0 759 56.0 0.103 No 1031 38.9 375 32.0 656 44.0 Eats sugary foods (n = 2504) Yes 1148 46.1 505 47.2 643 45.3 0.517 No 1356 53.9 584 52.8 772 54.7 Eats chips (n = 2504) Yes 437 17.4 219 20.2 218 15.4 0.113 No 2067 82.6 870 79.8 1197 84.6 Eats fried potatoes (n = 2504) Yes 465 19.9 234 23.9 231 16.9 0.293 No 2039 80.1 855 76.1 1184 83.1 Eats fast food (n = 2504) Yes 510 21.3 234 23.8 276 19.4 0.273 No 1994 78.7 855 76.2 1139 80.6 aLIF, Lower-income families; HIF, higher-income families; the lower-income cutoff used was a monthly family income of ≤R$2000 (values ≤50th percentile); this was the equivalent of USD 985 at the start of data collection in September 2012, when 1 USD was worth R$2.03, and USD 843 at the end of data collection in August 2013, when 1 USD was worth R$2.37, and varied from 3.2 to 2.9 times the Brazilian minimum monthly wage over the same period; BMI, body mass index; 95% CI, 95% confidence interval; HS, high school; HE, higher education. bIn this Class 38 low-weight schoolchildren were included (1.2% of total evaluated). Normal/low-weight: BMI for age and sex <+1 z-score. coverweight: +1≤BMI for age and sex <+2 z-scores. dobesity: BMI for age and sex ≥+ 2 z-scores. eRCSA, Reference Center for Social Assistance. fCSE, Centers for Supplementary Education. Bold values indicate associations with P-values ≤ 0.05; Chisquare Pearson's test of independence. Table 1 Descriptive characteristics of the sample of 7–14-year-old schoolchildren, stratified by monthly family income, Florianópolis, Santa Catarina, Brazil, 2012/2013 Variables Categories Total LIFa (n = 1090) HIFa (n = 1062) P-value n % n % n % Sex (n = 2506) Female 1334 56.5 505 43.8 513 43.8 0.996 Male 1172 43.5 585 56.2 549 56.2 Age (years) (n = 2506) 7–10 1530 61.1 682 62.4 642 63.7 0.526 11–14 976 38.9 408 37.6 420 36.3 Type of school (n = 2506) Public 1637 65.3 1007 92.5 451 40.9 <0.001 Private 869 34.7 83 7.5 611 59.1 BMI for age and sex (n = 2484) Normal/low weightb 1658 65.8 705 65.4 704 65.9 0.902 Overweightc 511 21.5 219 22.0 224 20.8 Obesityd 315 12.7 151 12.6 129 13.3 Maternal education level (n = 2389) HS not graduated 851 33.3 606 55.3 174 14.9 0.003 HE not graduated 857 37.5 378 37.8 378 38.6 Graduated HE 681 29.2 66 6.9 497 46.5 Paternal education level (n = 2086) HS not graduated 806 35.6 529 58.2 213 19.5 0.016 HE not graduated 710 35.3 269 35.3 358 35.6 Graduated HE 570 29.1 64 6.5 398 44.9 Uses football pitches (n = 2341) Yes 661 25.4 326 26.4 256 23.5 0.344 No 1680 74.6 702 73.6 777 76.5 Uses parks/playgrounds (n = 2342) Yes 642 27.5 266 23.6 291 29.7 0.048 No 1700 72.5 751 76.4 749 70.3 Uses bakeries (n = 2279) Yes 2000 87.0 872 86.0 890 88.1 0.239 No 279 13.0 133 14.0 111 11.9 Uses supermarkets (n = 2397) Yes 2306 95.7 1008 94.3 1015 96.4 0.066 No 91 4.3 53 5.7 32 3.6 Uses farmers’ markets (n = 2318) Yes 2066 87.5 915 89.9 895 84.3 0.085 No 252 12.5 110 10.1 118 15.7 Distance to park/ playground (min) (n = 1830) 1–10 776 45.5 289 35.9 373 51.9 0.079 ≥11 1054 54.5 483 64.1 455 48.1 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to bakery (min) (n = 1946) 1–10 1035 69.1 549 64.2 602 73.3 0.171 ≥11 641 30.9 296 35.8 274 26.7 Distance to supermarket (min) (n = 2256) 1–10 894 40.6 377 37.3 411 43.9 0.322 ≥11 1362 59.4 614 62.7 584 56.1 Distance to farmers’ market (min) (n = 2006) 1–10 829 42.1 363 39.1 364 45.7 0.229 ≥11 1177 57.9 524 60.9 511 54.3 Uses health centers (n = 2359) Yes 939 37.9 555 50.4 287 27.0 0.089 No 1420 62.1 485 49.6 747 73.0 Uses RCSAe (n = 2261) Yes 52 1.6 44 3.3 4 0.2 0.012 No 2209 98.4 928 96.7 1019 99.8 Uses CSEf (n = 2270) Yes 197 7.7 138 12.2 45 4.7 0.029 No 2073 92.3 835 87.8 980 95.4 Uses social project (n = 2275) Yes 351 12.3 242 20.1 84 6.0 0.045 No 1924 87.7 748 79.9 935 94.0 Uses religious group (n = 2319) Yes 895 35.9 415 39.7 389 34.3 0.400 No 1424 64.1 592 60.3 648 65.7 Uses residents association (n = 2257) Yes 78 3.5 50 5.8 22 1.9 0.051 No 2179 96.5 917 94.2 1003 98.1 Receives Bolsa Família Program (n = 2394) Yes 217 8.5 178 15.6 20 2.4 0.013 No 2177 91.5 882 84.4 1025 97.6 Drinks soda (n = 2504) Yes 1473 61.1 714 68.0 759 56.0 0.103 No 1031 38.9 375 32.0 656 44.0 Eats sugary foods (n = 2504) Yes 1148 46.1 505 47.2 643 45.3 0.517 No 1356 53.9 584 52.8 772 54.7 Eats chips (n = 2504) Yes 437 17.4 219 20.2 218 15.4 0.113 No 2067 82.6 870 79.8 1197 84.6 Eats fried potatoes (n = 2504) Yes 465 19.9 234 23.9 231 16.9 0.293 No 2039 80.1 855 76.1 1184 83.1 Eats fast food (n = 2504) Yes 510 21.3 234 23.8 276 19.4 0.273 No 1994 78.7 855 76.2 1139 80.6 Variables Categories Total LIFa (n = 1090) HIFa (n = 1062) P-value n % n % n % Sex (n = 2506) Female 1334 56.5 505 43.8 513 43.8 0.996 Male 1172 43.5 585 56.2 549 56.2 Age (years) (n = 2506) 7–10 1530 61.1 682 62.4 642 63.7 0.526 11–14 976 38.9 408 37.6 420 36.3 Type of school (n = 2506) Public 1637 65.3 1007 92.5 451 40.9 <0.001 Private 869 34.7 83 7.5 611 59.1 BMI for age and sex (n = 2484) Normal/low weightb 1658 65.8 705 65.4 704 65.9 0.902 Overweightc 511 21.5 219 22.0 224 20.8 Obesityd 315 12.7 151 12.6 129 13.3 Maternal education level (n = 2389) HS not graduated 851 33.3 606 55.3 174 14.9 0.003 HE not graduated 857 37.5 378 37.8 378 38.6 Graduated HE 681 29.2 66 6.9 497 46.5 Paternal education level (n = 2086) HS not graduated 806 35.6 529 58.2 213 19.5 0.016 HE not graduated 710 35.3 269 35.3 358 35.6 Graduated HE 570 29.1 64 6.5 398 44.9 Uses football pitches (n = 2341) Yes 661 25.4 326 26.4 256 23.5 0.344 No 1680 74.6 702 73.6 777 76.5 Uses parks/playgrounds (n = 2342) Yes 642 27.5 266 23.6 291 29.7 0.048 No 1700 72.5 751 76.4 749 70.3 Uses bakeries (n = 2279) Yes 2000 87.0 872 86.0 890 88.1 0.239 No 279 13.0 133 14.0 111 11.9 Uses supermarkets (n = 2397) Yes 2306 95.7 1008 94.3 1015 96.4 0.066 No 91 4.3 53 5.7 32 3.6 Uses farmers’ markets (n = 2318) Yes 2066 87.5 915 89.9 895 84.3 0.085 No 252 12.5 110 10.1 118 15.7 Distance to park/ playground (min) (n = 1830) 1–10 776 45.5 289 35.9 373 51.9 0.079 ≥11 1054 54.5 483 64.1 455 48.1 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to bakery (min) (n = 1946) 1–10 1035 69.1 549 64.2 602 73.3 0.171 ≥11 641 30.9 296 35.8 274 26.7 Distance to supermarket (min) (n = 2256) 1–10 894 40.6 377 37.3 411 43.9 0.322 ≥11 1362 59.4 614 62.7 584 56.1 Distance to farmers’ market (min) (n = 2006) 1–10 829 42.1 363 39.1 364 45.7 0.229 ≥11 1177 57.9 524 60.9 511 54.3 Uses health centers (n = 2359) Yes 939 37.9 555 50.4 287 27.0 0.089 No 1420 62.1 485 49.6 747 73.0 Uses RCSAe (n = 2261) Yes 52 1.6 44 3.3 4 0.2 0.012 No 2209 98.4 928 96.7 1019 99.8 Uses CSEf (n = 2270) Yes 197 7.7 138 12.2 45 4.7 0.029 No 2073 92.3 835 87.8 980 95.4 Uses social project (n = 2275) Yes 351 12.3 242 20.1 84 6.0 0.045 No 1924 87.7 748 79.9 935 94.0 Uses religious group (n = 2319) Yes 895 35.9 415 39.7 389 34.3 0.400 No 1424 64.1 592 60.3 648 65.7 Uses residents association (n = 2257) Yes 78 3.5 50 5.8 22 1.9 0.051 No 2179 96.5 917 94.2 1003 98.1 Receives Bolsa Família Program (n = 2394) Yes 217 8.5 178 15.6 20 2.4 0.013 No 2177 91.5 882 84.4 1025 97.6 Drinks soda (n = 2504) Yes 1473 61.1 714 68.0 759 56.0 0.103 No 1031 38.9 375 32.0 656 44.0 Eats sugary foods (n = 2504) Yes 1148 46.1 505 47.2 643 45.3 0.517 No 1356 53.9 584 52.8 772 54.7 Eats chips (n = 2504) Yes 437 17.4 219 20.2 218 15.4 0.113 No 2067 82.6 870 79.8 1197 84.6 Eats fried potatoes (n = 2504) Yes 465 19.9 234 23.9 231 16.9 0.293 No 2039 80.1 855 76.1 1184 83.1 Eats fast food (n = 2504) Yes 510 21.3 234 23.8 276 19.4 0.273 No 1994 78.7 855 76.2 1139 80.6 aLIF, Lower-income families; HIF, higher-income families; the lower-income cutoff used was a monthly family income of ≤R$2000 (values ≤50th percentile); this was the equivalent of USD 985 at the start of data collection in September 2012, when 1 USD was worth R$2.03, and USD 843 at the end of data collection in August 2013, when 1 USD was worth R$2.37, and varied from 3.2 to 2.9 times the Brazilian minimum monthly wage over the same period; BMI, body mass index; 95% CI, 95% confidence interval; HS, high school; HE, higher education. bIn this Class 38 low-weight schoolchildren were included (1.2% of total evaluated). Normal/low-weight: BMI for age and sex <+1 z-score. coverweight: +1≤BMI for age and sex <+2 z-scores. dobesity: BMI for age and sex ≥+ 2 z-scores. eRCSA, Reference Center for Social Assistance. fCSE, Centers for Supplementary Education. Bold values indicate associations with P-values ≤ 0.05; Chisquare Pearson's test of independence. The median monthly income used to separate the sample into LIF and HIF strata was R$ 2000.00, which varied from 2.9 to 3.2 times the Brazilian minimal monthly wage, corresponded to 843.88–985.22 US Dollars, over the data collection period. Data on use of each of the built environment facilities showed that parks/playgrounds and football pitches were used by around a quarter of schoolchildren. Supermarkets were the most used facilities in the food environment (95.7%); around one-third of families were using health care centers and frequented religious groups, and 8.5% were beneficiaries of the ‘Bolsa Família’ program. Social assistance facilities were significantly most used by low-income families (P < 0.05). Data on perceived distance from physical activity facilities indicated that most of the schoolchildren live a long way from football pitches (57.6%) and parks (54.5%), which was more likely if they were from LIF (significant association for football pitches; P = 0.005). As for food environment, bakeries were closest to the children’s households (69.1% lived 10 min or less away) (Table 1). Table 2 lists the descriptive analysis for mean BMI among overweight/obese children using or do not using the facilities investigated, and the univariate and multivariate linear regression results for associations with BMI for the entire sample. Overweight/obese children who use parks/playgrounds had lower mean BMI than those who do not use parks/playgrounds (P < 0.001). Collinearity of variables was detected as follows: use of parks/playgrounds, use of football pitches, and distance from home to parks/playgrounds; use of football pitches and their distance from home; use of farmers’ markets with use of bakeries and supermarkets; distance from homes to bakeries, to supermarkets, and to farmers’ markets had mutual multicollinearity; use of health centers, RCSA, instructional facilities and residents associations had mutual collinearity. The environmental variables included in the multivariate model were not significantly associated with BMI. Table 2 Mean and standard deviation (SD) for body mass index among overweight/obese children, and univariate and multivariate analyses of association between use of and distance from home of facilities among 7–14-year-old schoolchildren from Florianópolis, SC, Brazil, 2012/2013 Environmental characteristic Body mass index (kg/m2) among overweight/obese children Univariate analyses Multivariate analyses n Mean; SD* β** 95% CI β*** 95% CI Uses football pitches Yes 333 22.37; 3.42 0.39 −0.30; 1.08 0.16 −0.73; 1.05 No 446 22.16; 3.32 0.00 Uses parks/playgrounds Yes 476 21.86; 3.24 −0.60 −1.88; 0.68 No 298 22.82; 3.46 0.00 Uses bakeries Yes 662 22.28; 3.42 0.13 −1.23; 1.50 No 86 22.07; 2.84 0.00 Uses supermarkets Yes 757 22.26; 3.38 0.07 −1.60; 1.73 No 34 21.86; 2.87 0.00 Uses farmers’ markets Yes 696 22.30; 3.38 0.40 −0.44; 1.23 −1.23 −4.84; 2.39 No 71 21.55; 2.93 0.00 Distance from football pitch (min) 1–10 193 22.28; 3.08 0.00 ≥ 11 238 22.57; 3.52 −0.51 −1.35; 0.32 Distance from park/playground (min) 1–10 256 21.89; 3.08 0.00 ≥ 11 346 22.17; 3.42 −0.08 −0.68; 0.52 Distance from bakery (min) 1–10 425 22.30; 3.27 0.00 ≥ 11 222 22.13; 3.61 −0.27 −0.82; 0.28 Distance from supermarket (min) 1–10 285 22.30; 3.34 0.00 ≥ 11 454 22.15; 3.50 0.16 −0.57; 0.89 Distance from farmers’ market (min) 1–10 280 22.50; 3.31 0.00 ≥ 11 392 22.12; 3.40 −0.17 −0.38; 0.05 −0.09 −0.42; 0.24 Uses health center Yes 306 22.50; 3.44 0.21 −0.54; 0.96 No 477 22.06; 3.28 0.00 Uses RCSA Yes 19 22.01; 3.05 0.21 −1.02; 1.45 No 738 22.24; 3.39 0.00 Uses CSE Yes 63 22.29; 3.43 −0.11 −1.19; 0.97 No 692 22.20; 3.36 0.00 Uses social projects Yes 111 22.59; 3.57 −0.02 −0.55; 0.50 No 644 22.15; 3.33 0.00 Uses religious groups Yes 305 22.43; 3.30 0.21 −1.12; 1.55 No 472 22.09; 3.40 0.00 Uses residents association Yes 21 22.57; 3.49 0.32 −1.05; 1.69 No 733 22.22; 3.38 0.00 Receives Bolsa Família Program Yes 57 22.08; 2.86 −0.76 −1.66; 0.14 −0.81 −1.81; 0.20 No 738 22.21; 3.39 0.00 Environmental characteristic Body mass index (kg/m2) among overweight/obese children Univariate analyses Multivariate analyses n Mean; SD* β** 95% CI β*** 95% CI Uses football pitches Yes 333 22.37; 3.42 0.39 −0.30; 1.08 0.16 −0.73; 1.05 No 446 22.16; 3.32 0.00 Uses parks/playgrounds Yes 476 21.86; 3.24 −0.60 −1.88; 0.68 No 298 22.82; 3.46 0.00 Uses bakeries Yes 662 22.28; 3.42 0.13 −1.23; 1.50 No 86 22.07; 2.84 0.00 Uses supermarkets Yes 757 22.26; 3.38 0.07 −1.60; 1.73 No 34 21.86; 2.87 0.00 Uses farmers’ markets Yes 696 22.30; 3.38 0.40 −0.44; 1.23 −1.23 −4.84; 2.39 No 71 21.55; 2.93 0.00 Distance from football pitch (min) 1–10 193 22.28; 3.08 0.00 ≥ 11 238 22.57; 3.52 −0.51 −1.35; 0.32 Distance from park/playground (min) 1–10 256 21.89; 3.08 0.00 ≥ 11 346 22.17; 3.42 −0.08 −0.68; 0.52 Distance from bakery (min) 1–10 425 22.30; 3.27 0.00 ≥ 11 222 22.13; 3.61 −0.27 −0.82; 0.28 Distance from supermarket (min) 1–10 285 22.30; 3.34 0.00 ≥ 11 454 22.15; 3.50 0.16 −0.57; 0.89 Distance from farmers’ market (min) 1–10 280 22.50; 3.31 0.00 ≥ 11 392 22.12; 3.40 −0.17 −0.38; 0.05 −0.09 −0.42; 0.24 Uses health center Yes 306 22.50; 3.44 0.21 −0.54; 0.96 No 477 22.06; 3.28 0.00 Uses RCSA Yes 19 22.01; 3.05 0.21 −1.02; 1.45 No 738 22.24; 3.39 0.00 Uses CSE Yes 63 22.29; 3.43 −0.11 −1.19; 0.97 No 692 22.20; 3.36 0.00 Uses social projects Yes 111 22.59; 3.57 −0.02 −0.55; 0.50 No 644 22.15; 3.33 0.00 Uses religious groups Yes 305 22.43; 3.30 0.21 −1.12; 1.55 No 472 22.09; 3.40 0.00 Uses residents association Yes 21 22.57; 3.49 0.32 −1.05; 1.69 No 733 22.22; 3.38 0.00 Receives Bolsa Família Program Yes 57 22.08; 2.86 −0.76 −1.66; 0.14 −0.81 −1.81; 0.20 No 738 22.21; 3.39 0.00 SD, standard deviation; RCSA, Reference Center for Social Assistance; CSE, Centers for Supplementary Education. *Bold values indicate significant differences between the mean BMIs of overweight/obese schoolchildren that use and do not use places/facilities (P = 0.0001). **Bold values indicate associations with P-values ≤0.25. ***Bold values indicate associations with P-values ≤0.05; Multivariate model adjusted by age, sex and food intake variables. Table 2 Mean and standard deviation (SD) for body mass index among overweight/obese children, and univariate and multivariate analyses of association between use of and distance from home of facilities among 7–14-year-old schoolchildren from Florianópolis, SC, Brazil, 2012/2013 Environmental characteristic Body mass index (kg/m2) among overweight/obese children Univariate analyses Multivariate analyses n Mean; SD* β** 95% CI β*** 95% CI Uses football pitches Yes 333 22.37; 3.42 0.39 −0.30; 1.08 0.16 −0.73; 1.05 No 446 22.16; 3.32 0.00 Uses parks/playgrounds Yes 476 21.86; 3.24 −0.60 −1.88; 0.68 No 298 22.82; 3.46 0.00 Uses bakeries Yes 662 22.28; 3.42 0.13 −1.23; 1.50 No 86 22.07; 2.84 0.00 Uses supermarkets Yes 757 22.26; 3.38 0.07 −1.60; 1.73 No 34 21.86; 2.87 0.00 Uses farmers’ markets Yes 696 22.30; 3.38 0.40 −0.44; 1.23 −1.23 −4.84; 2.39 No 71 21.55; 2.93 0.00 Distance from football pitch (min) 1–10 193 22.28; 3.08 0.00 ≥ 11 238 22.57; 3.52 −0.51 −1.35; 0.32 Distance from park/playground (min) 1–10 256 21.89; 3.08 0.00 ≥ 11 346 22.17; 3.42 −0.08 −0.68; 0.52 Distance from bakery (min) 1–10 425 22.30; 3.27 0.00 ≥ 11 222 22.13; 3.61 −0.27 −0.82; 0.28 Distance from supermarket (min) 1–10 285 22.30; 3.34 0.00 ≥ 11 454 22.15; 3.50 0.16 −0.57; 0.89 Distance from farmers’ market (min) 1–10 280 22.50; 3.31 0.00 ≥ 11 392 22.12; 3.40 −0.17 −0.38; 0.05 −0.09 −0.42; 0.24 Uses health center Yes 306 22.50; 3.44 0.21 −0.54; 0.96 No 477 22.06; 3.28 0.00 Uses RCSA Yes 19 22.01; 3.05 0.21 −1.02; 1.45 No 738 22.24; 3.39 0.00 Uses CSE Yes 63 22.29; 3.43 −0.11 −1.19; 0.97 No 692 22.20; 3.36 0.00 Uses social projects Yes 111 22.59; 3.57 −0.02 −0.55; 0.50 No 644 22.15; 3.33 0.00 Uses religious groups Yes 305 22.43; 3.30 0.21 −1.12; 1.55 No 472 22.09; 3.40 0.00 Uses residents association Yes 21 22.57; 3.49 0.32 −1.05; 1.69 No 733 22.22; 3.38 0.00 Receives Bolsa Família Program Yes 57 22.08; 2.86 −0.76 −1.66; 0.14 −0.81 −1.81; 0.20 No 738 22.21; 3.39 0.00 Environmental characteristic Body mass index (kg/m2) among overweight/obese children Univariate analyses Multivariate analyses n Mean; SD* β** 95% CI β*** 95% CI Uses football pitches Yes 333 22.37; 3.42 0.39 −0.30; 1.08 0.16 −0.73; 1.05 No 446 22.16; 3.32 0.00 Uses parks/playgrounds Yes 476 21.86; 3.24 −0.60 −1.88; 0.68 No 298 22.82; 3.46 0.00 Uses bakeries Yes 662 22.28; 3.42 0.13 −1.23; 1.50 No 86 22.07; 2.84 0.00 Uses supermarkets Yes 757 22.26; 3.38 0.07 −1.60; 1.73 No 34 21.86; 2.87 0.00 Uses farmers’ markets Yes 696 22.30; 3.38 0.40 −0.44; 1.23 −1.23 −4.84; 2.39 No 71 21.55; 2.93 0.00 Distance from football pitch (min) 1–10 193 22.28; 3.08 0.00 ≥ 11 238 22.57; 3.52 −0.51 −1.35; 0.32 Distance from park/playground (min) 1–10 256 21.89; 3.08 0.00 ≥ 11 346 22.17; 3.42 −0.08 −0.68; 0.52 Distance from bakery (min) 1–10 425 22.30; 3.27 0.00 ≥ 11 222 22.13; 3.61 −0.27 −0.82; 0.28 Distance from supermarket (min) 1–10 285 22.30; 3.34 0.00 ≥ 11 454 22.15; 3.50 0.16 −0.57; 0.89 Distance from farmers’ market (min) 1–10 280 22.50; 3.31 0.00 ≥ 11 392 22.12; 3.40 −0.17 −0.38; 0.05 −0.09 −0.42; 0.24 Uses health center Yes 306 22.50; 3.44 0.21 −0.54; 0.96 No 477 22.06; 3.28 0.00 Uses RCSA Yes 19 22.01; 3.05 0.21 −1.02; 1.45 No 738 22.24; 3.39 0.00 Uses CSE Yes 63 22.29; 3.43 −0.11 −1.19; 0.97 No 692 22.20; 3.36 0.00 Uses social projects Yes 111 22.59; 3.57 −0.02 −0.55; 0.50 No 644 22.15; 3.33 0.00 Uses religious groups Yes 305 22.43; 3.30 0.21 −1.12; 1.55 No 472 22.09; 3.40 0.00 Uses residents association Yes 21 22.57; 3.49 0.32 −1.05; 1.69 No 733 22.22; 3.38 0.00 Receives Bolsa Família Program Yes 57 22.08; 2.86 −0.76 −1.66; 0.14 −0.81 −1.81; 0.20 No 738 22.21; 3.39 0.00 SD, standard deviation; RCSA, Reference Center for Social Assistance; CSE, Centers for Supplementary Education. *Bold values indicate significant differences between the mean BMIs of overweight/obese schoolchildren that use and do not use places/facilities (P = 0.0001). **Bold values indicate associations with P-values ≤0.25. ***Bold values indicate associations with P-values ≤0.05; Multivariate model adjusted by age, sex and food intake variables. Table 3 summarizes the univariate and multivariate analyses for BMI against the environmental variables, stratified by income categories. These results show that schoolchildren from LIF who live a long way from parks/playgrounds had higher BMI values (β = 0.61; 95% CI = 0.24; 0.97). Notwithstanding, the association between use of parks/playgrounds and the outcome was not statistically significant in this income stratum. Among schoolchildren from HIF, a longer distance from homes to football pitches exhibited significant associations with lower BMI in the multivariate analysis (β = −0.49; 95% CI = −0.69; −0.29). Table 3 Univariate and multivariate analyses of association between use of and distance from home of facilities of the built environment and body mass index, by family income strata, among 7–14-year-old schoolchildren from Florianópolis, SC, Brazil, 2012/2013 Environmental variablec LIFa HIFa Univariate analyses Multivariate analysesb Univariate analyses Multivariate analysesb β 95% CI β 95% CI β 95% CI β 95% CI Uses football pitches Yesd 0.37 −0.01; 0.75 0.26 −0.09; 0.61 0.69 −0.95; 2.34 Uses parks/playgrounds Yes −0.47 −1.71; 0.77 −0.48 −1.87; 0.91 Uses bakeries Yes 0.29 −1.35; 1.93 −0.09 −1.50; 1.32 Uses supermarkets Yes 0.72 −0.98; 2.43 −0.67 −1.73; 0.38 −0.09 −2.11; 1.93 Uses farmers’ markets Yes 0.31 −1.28; 1.89 0.62 −0.23; 1.47 0.11 −1.33; 1.54 Distance to football pitch (min) ≥11d −0.38 −1.78; 1.01 −0.62 −1.04; −0.20 −0.49 −0.69; −0.29* Distance to park/playground (min) ≥11 0.54 0.34; 0.74 0.53 0.33; 0.73* −0.41 −0.96; 0.14 Distance to bakery (min) ≥11 −0.41 −1.34; 0.52 −0.25 −1.15; 0.66 Distance to supermarket (min) ≥11 0.28 −0.22; 0.78 0.13 −1.02; 1.28 0.18 −1.00; 1.36 Distance to farmers’ market (min) ≥11 −0.16 −1.07; 0.76 −0.22 −1.27; 0.83 Uses health center Yes −0.08 −0.86; 0.70 0.51 −0.45; 1.46 Uses RCSA Yes −0.09 −1.41; 1.23 0.00 −0.94; 0.95 Uses CSE Yes −0.12 −1.06; 0.81 −0.02 −2.71; 2.68 Uses social projects Yes −0.27 −0.93; 0.38 0.69 −0.86; 2.24 Uses religious groups Yes 0.20 −1.57; 1.97 0.26 −1.05; 1.57 Uses residents association Yes −0.25 −0.68; 0.18 0.26 −0.14; 0.66 1.98 −2.04; 5.99 Receives Bolsa Família Program Yes −0.58 −1.60; 0.45 −0.38 −3.31; 2.56 −2.38 −2.64; −2.11 −1.29 −2.89; 0.32 Environmental variablec LIFa HIFa Univariate analyses Multivariate analysesb Univariate analyses Multivariate analysesb β 95% CI β 95% CI β 95% CI β 95% CI Uses football pitches Yesd 0.37 −0.01; 0.75 0.26 −0.09; 0.61 0.69 −0.95; 2.34 Uses parks/playgrounds Yes −0.47 −1.71; 0.77 −0.48 −1.87; 0.91 Uses bakeries Yes 0.29 −1.35; 1.93 −0.09 −1.50; 1.32 Uses supermarkets Yes 0.72 −0.98; 2.43 −0.67 −1.73; 0.38 −0.09 −2.11; 1.93 Uses farmers’ markets Yes 0.31 −1.28; 1.89 0.62 −0.23; 1.47 0.11 −1.33; 1.54 Distance to football pitch (min) ≥11d −0.38 −1.78; 1.01 −0.62 −1.04; −0.20 −0.49 −0.69; −0.29* Distance to park/playground (min) ≥11 0.54 0.34; 0.74 0.53 0.33; 0.73* −0.41 −0.96; 0.14 Distance to bakery (min) ≥11 −0.41 −1.34; 0.52 −0.25 −1.15; 0.66 Distance to supermarket (min) ≥11 0.28 −0.22; 0.78 0.13 −1.02; 1.28 0.18 −1.00; 1.36 Distance to farmers’ market (min) ≥11 −0.16 −1.07; 0.76 −0.22 −1.27; 0.83 Uses health center Yes −0.08 −0.86; 0.70 0.51 −0.45; 1.46 Uses RCSA Yes −0.09 −1.41; 1.23 0.00 −0.94; 0.95 Uses CSE Yes −0.12 −1.06; 0.81 −0.02 −2.71; 2.68 Uses social projects Yes −0.27 −0.93; 0.38 0.69 −0.86; 2.24 Uses religious groups Yes 0.20 −1.57; 1.97 0.26 −1.05; 1.57 Uses residents association Yes −0.25 −0.68; 0.18 0.26 −0.14; 0.66 1.98 −2.04; 5.99 Receives Bolsa Família Program Yes −0.58 −1.60; 0.45 −0.38 −3.31; 2.56 −2.38 −2.64; −2.11 −1.29 −2.89; 0.32 aLIF, Lower-income families; HIF, higher-income families. The lower-income cutoff used was a monthly family income of R$2 000 (values ≤50th percentile); this was the equivalent of USD 985 at the start of data collection in September 2012, when 1 USD was worth R2.03, and USD 843 at the end of data collection in August 2013, when 1 USD was worth R$2.37, and varied from 3.2 to 2.94 times the Brazilian minimum monthly wage over the same period. bMultivariate model controlled by age, sex and food intake variables. cCollinearity among variables from each domain of the built environment was detected as follows: (i) physical activity environment: use of parks/playgrounds exhibited multicollinearity with use of football pitch and distance from home to parks/playgrounds; use of football pitches were collinear with distance from home to these same facilities; (ii) food environment: use of farmers’ market with use of bakery and use of supermarket; distance from homes to bakeries, to supermarkets and to farmers’ market have mutual multicollinearity; (iii) social assistance environment: use of health centers with use of Reference Center for Social Assistance, use of Centers for Supplementary Education, and use of residents association; use of Reference Center for Social Assistance with use of social projects, use of Centers for Supplementary Education and use of residents association. dCategories ‘no’ and ‘1–10 min away from home’ were the dummy variables. *P-value < 0.05. Table 3 Univariate and multivariate analyses of association between use of and distance from home of facilities of the built environment and body mass index, by family income strata, among 7–14-year-old schoolchildren from Florianópolis, SC, Brazil, 2012/2013 Environmental variablec LIFa HIFa Univariate analyses Multivariate analysesb Univariate analyses Multivariate analysesb β 95% CI β 95% CI β 95% CI β 95% CI Uses football pitches Yesd 0.37 −0.01; 0.75 0.26 −0.09; 0.61 0.69 −0.95; 2.34 Uses parks/playgrounds Yes −0.47 −1.71; 0.77 −0.48 −1.87; 0.91 Uses bakeries Yes 0.29 −1.35; 1.93 −0.09 −1.50; 1.32 Uses supermarkets Yes 0.72 −0.98; 2.43 −0.67 −1.73; 0.38 −0.09 −2.11; 1.93 Uses farmers’ markets Yes 0.31 −1.28; 1.89 0.62 −0.23; 1.47 0.11 −1.33; 1.54 Distance to football pitch (min) ≥11d −0.38 −1.78; 1.01 −0.62 −1.04; −0.20 −0.49 −0.69; −0.29* Distance to park/playground (min) ≥11 0.54 0.34; 0.74 0.53 0.33; 0.73* −0.41 −0.96; 0.14 Distance to bakery (min) ≥11 −0.41 −1.34; 0.52 −0.25 −1.15; 0.66 Distance to supermarket (min) ≥11 0.28 −0.22; 0.78 0.13 −1.02; 1.28 0.18 −1.00; 1.36 Distance to farmers’ market (min) ≥11 −0.16 −1.07; 0.76 −0.22 −1.27; 0.83 Uses health center Yes −0.08 −0.86; 0.70 0.51 −0.45; 1.46 Uses RCSA Yes −0.09 −1.41; 1.23 0.00 −0.94; 0.95 Uses CSE Yes −0.12 −1.06; 0.81 −0.02 −2.71; 2.68 Uses social projects Yes −0.27 −0.93; 0.38 0.69 −0.86; 2.24 Uses religious groups Yes 0.20 −1.57; 1.97 0.26 −1.05; 1.57 Uses residents association Yes −0.25 −0.68; 0.18 0.26 −0.14; 0.66 1.98 −2.04; 5.99 Receives Bolsa Família Program Yes −0.58 −1.60; 0.45 −0.38 −3.31; 2.56 −2.38 −2.64; −2.11 −1.29 −2.89; 0.32 Environmental variablec LIFa HIFa Univariate analyses Multivariate analysesb Univariate analyses Multivariate analysesb β 95% CI β 95% CI β 95% CI β 95% CI Uses football pitches Yesd 0.37 −0.01; 0.75 0.26 −0.09; 0.61 0.69 −0.95; 2.34 Uses parks/playgrounds Yes −0.47 −1.71; 0.77 −0.48 −1.87; 0.91 Uses bakeries Yes 0.29 −1.35; 1.93 −0.09 −1.50; 1.32 Uses supermarkets Yes 0.72 −0.98; 2.43 −0.67 −1.73; 0.38 −0.09 −2.11; 1.93 Uses farmers’ markets Yes 0.31 −1.28; 1.89 0.62 −0.23; 1.47 0.11 −1.33; 1.54 Distance to football pitch (min) ≥11d −0.38 −1.78; 1.01 −0.62 −1.04; −0.20 −0.49 −0.69; −0.29* Distance to park/playground (min) ≥11 0.54 0.34; 0.74 0.53 0.33; 0.73* −0.41 −0.96; 0.14 Distance to bakery (min) ≥11 −0.41 −1.34; 0.52 −0.25 −1.15; 0.66 Distance to supermarket (min) ≥11 0.28 −0.22; 0.78 0.13 −1.02; 1.28 0.18 −1.00; 1.36 Distance to farmers’ market (min) ≥11 −0.16 −1.07; 0.76 −0.22 −1.27; 0.83 Uses health center Yes −0.08 −0.86; 0.70 0.51 −0.45; 1.46 Uses RCSA Yes −0.09 −1.41; 1.23 0.00 −0.94; 0.95 Uses CSE Yes −0.12 −1.06; 0.81 −0.02 −2.71; 2.68 Uses social projects Yes −0.27 −0.93; 0.38 0.69 −0.86; 2.24 Uses religious groups Yes 0.20 −1.57; 1.97 0.26 −1.05; 1.57 Uses residents association Yes −0.25 −0.68; 0.18 0.26 −0.14; 0.66 1.98 −2.04; 5.99 Receives Bolsa Família Program Yes −0.58 −1.60; 0.45 −0.38 −3.31; 2.56 −2.38 −2.64; −2.11 −1.29 −2.89; 0.32 aLIF, Lower-income families; HIF, higher-income families. The lower-income cutoff used was a monthly family income of R$2 000 (values ≤50th percentile); this was the equivalent of USD 985 at the start of data collection in September 2012, when 1 USD was worth R2.03, and USD 843 at the end of data collection in August 2013, when 1 USD was worth R$2.37, and varied from 3.2 to 2.94 times the Brazilian minimum monthly wage over the same period. bMultivariate model controlled by age, sex and food intake variables. cCollinearity among variables from each domain of the built environment was detected as follows: (i) physical activity environment: use of parks/playgrounds exhibited multicollinearity with use of football pitch and distance from home to parks/playgrounds; use of football pitches were collinear with distance from home to these same facilities; (ii) food environment: use of farmers’ market with use of bakery and use of supermarket; distance from homes to bakeries, to supermarkets and to farmers’ market have mutual multicollinearity; (iii) social assistance environment: use of health centers with use of Reference Center for Social Assistance, use of Centers for Supplementary Education, and use of residents association; use of Reference Center for Social Assistance with use of social projects, use of Centers for Supplementary Education and use of residents association. dCategories ‘no’ and ‘1–10 min away from home’ were the dummy variables. *P-value < 0.05. Discussion Main findings of this study This study analyzed the use of and distance from subjects’ home to facilities from three different domains of the built environment and their associations with BMI, in schoolchildren aged 7–14 years living in Florianópolis (South Brazil), stratified by their families’ monthly incomes. The main findings are the association between longer distance from homes to parks/playgrounds and higher BMI in schoolchildren from LIF, and lower BMI values and longer distances to football pitches in schoolchildren from HIF. Additionally, the high prevalence rates of overweight and obesity indicate a public health concern. What is already known on this topic Previous studies10–12,39 found that low-income children are more susceptible to the effects of the built environment. In Massachusetts (US), a study conducted with 49 770 students found that proximity to open recreational spaces from home was significantly associated with lower BMI, and the direction of the association between the variables changed when adjusted for characteristics of the socioeconomic environment where students lived.40 This suggests that schoolchildren from low-income families spend more energy using the recreational spaces near to their homes than high-income children. In relation to the ideal distance from homes to parks, living more than 2.4 km from a new park in Alabama (US) did not predict changes in the BMI of schoolchildren under nineteen.41 With regard to the association among high-income schoolchildren between lower BMI and living further away from football pitches, Burgï et al.42 conducted a study in Zurich (Switzerland), observing that children living in neighborhoods with higher socioeconomic status did the majority of their moderate to intense physical activities in schools other than their own. This may suggest children from HIF living in Florianópolis are using football pitches to exercise, but they access these facilities far from home. With regard to the other domains of the built environment, only the socioeconomic environment was significantly associated with BMI in Kiel (Germany), where 485 children were evaluated at 5–7 years of age and at 9–11 years of age.43 Children living in economically deprived areas had an increase of 0.31 BMI units (kg/m2) compared to children living in wealthier areas, irrespective of features of the food, physical activity, and social environments. In an adjusted model, however, this longitudinal effect was partially explained by the educational level of the family.43 Taylor et al.44 did not detect associations between obesity and facilities for physical activity, food outlets or social assistance services in 911 children from 6 to 10 years of age living in the USA when educational level and area income were included as controls in the multivariate model. These results suggest that the availability of places for improving health may be influenced by a social inequality in distribution of services in areas where poorer people live, irrespective of whether a city is well-developed as a whole. In Brazil, a study conducted with 3425 adults only found a relationship between overweight/obesity and the availability of parks in the neighborhood, but did not find an association between this outcome and availability of hypermarkets, supermarkets and farmers’ markets, or the social environment.18 In the largest city in Brazil, São Paulo, findings among adults were similar, i.e. there was a significant correlation between the density of parks and BMI, but there were weak correlations between this outcome and density of supermarkets, farmers’ markets, fast food-restaurants, and other types of restaurants, or the social environment (crime rates).17 What this study adds Our results suggest that living up to 800 m from parks/playgrounds could be an ideal distance for schoolchildren to use these facilities at least once a week, resulting in a lower BMI among schoolchildren from LIF. Even though the variable ‘distance from’ parks/playgrounds was associated with BMI, the variable ‘use’ of the same type of facilities was not statistically significantly associated with the outcome in either of the income strata in our study. Similar results were found by Lavin-Fueyo et al.,45 when evaluating 1 777 children from Córdoba (Argentina). The authors used the same approach as in the present study—a self-report questionnaire on which parents reported the frequency of use and perceived distance from their households to parks, streets, empty lots and cul-de-sacs. They found that use of parks was not associated with increased levels of physical activity in children’s free time. One possible explanation for these results could be the way that the variable ‘use’ was categorized in our study (combining schoolchildren who reported going to places for physical activity on a weekly basis with those who went fortnightly), i.e. those who only go fortnightly may also be those who live further away (more than 10 min’ walk) and, as a consequence, those who have higher values for BMI. Along the same lines, schoolchildren who used parks/playgrounds weekly were possibly those who lived closer and, as a consequence, those who had lower values for the outcome variable. Our results also suggest that schoolchildren from HIF exercise more than once a week at football pitches even though these facilities are far from their homes. In the present study, there were no statistically significant associations between BMI and use of facilities from the food and the social assistance environments, even when stratified by income. In different subsamples of the same survey of schoolchildren, Motter et al.10 found a significant association between use of supermarkets and overweight/obesity among schoolchildren from private schools, as well as a significant association between the same outcome and use of bakeries by schoolchildren from public schools. Also, Corrêa et al.6 found an association between use of farmers’ markets and overweight/obesity. The main reason for these differences could be that the physical activity environment rather than the food or the social assistance environments is more strongly related to the body weight profiles of schoolchildren living in Florianópolis. In addition to these findings, it is important to point out that studies of the built environment should attempt to make greater use of geographical analysis techniques. Edwards et al.10 used a geographically weighted regression analysis to evaluate spatial correlations between obesity and environmental features, suggesting the need to employ other analytic statistical methods in addition to biostatistical methods when assessing environmental data. This was confirmed by Wall et al.,11 in a study conducted in the Minneapolis and Saint Paul metropolitan regions (USA), where they used three different analytic methods and observed that results diverged: they were significant in linear regression but not in other statistical analyses such as spatial latent class analysis and factor analysis. Future studies in Brazil could attempt to advance knowledge by using such analytical techniques. One strong point of this study is that the sample is representative of schools from all geographical regions of the target municipal district. Moreover, investigation of three domains of the built environment suggested that improvements to the physical activity environment could help prevent overweight/obesity. The weighting effect of each person in the sample was also taken into account, minimizing bias in the analysis of variables for which there were fewer responses. Limitations of this study The primary limitation of the study is its cross-sectional design, meaning that additional evidence is needed to confirm the findings. Additionally, our study could be affected by a cause–effect relationship between variables, since overweight or obese schoolchildren may have been using the facilities to treat obesity and this could mask a previously existing association with high BMI. Also, we did not assess variables related to physical activity levels because several such variables could not be properly fitted to the multivariate models. Likewise, it is possible that the self-report measures of use of and distance from schoolchildren’s homes of places/facilities may not correlate well with objective measures if younger children had answered the questionnaire, which reveals a need for future studies to investigate the feasibility of self-report measures in this sample. Conclusion Evaluation of the built environment in three different domains showed that the physical activity environment was the most strongly associated with BMI in schoolchildren enrolled at schools of Florianópolis (South of Brazil). Living a longer distance from parks/playgrounds was significantly associated with higher BMI in low-income schoolchildren, while living a longer distance from football pitches was associated with lower values of the same outcome in high-income schoolchildren. We suggest longitudinal studies should be conducted to confirm the temporal sequence of events. Conflict of interest The authors declare no conflict of interests. Authors’ contributions Wrote the article: CER and PFH; designed the study: CER, ENC and FAGV; collection of data: ENC; performed the analyses: CER; reviewed the article: JN, PFH and FAGV. All authors approved the article. Funding The authors are grateful to the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - National Council for Scientific and Technological Development) for financial support (Process nos. 014/2011-CNPq and 483955/2011-6). C.E.R. received a research grant from the Fundação de Pesquisa e Inovação do Estado de Santa Catarina, (FAPESC), a division of the Education Department of Santa Catarina State. References 1 Caiaffa WT , Ferreira FR , Ferreira AD et al. . Saude urbana: “a cidade é uma estranha senhora, que hoje sorri e amanhã te devora” . Ciên Saúde Col 2007 ; 13 ( 6 ): 1785 – 96 . Google Scholar CrossRef Search ADS 2 Papas MA , Alberg AJ , Ewing R et al. . The built environment and obesity . Epidemiol Rev 2007 ; 29 : 129 – 43 . Google Scholar CrossRef Search ADS PubMed 3 Feng J , Glass TA , Curriero FC et al. . The built environment and obesity: a systematic review of the epidemiologic evidence . Health Place 2010 ; 16 : 175 – 90 . Google Scholar CrossRef Search ADS PubMed 4 Carroll-Scott A , Gilstad-Haydena K , Rosenthal L et al. . Disentangling neighborhood contextual associations with child body mass index, diet, and physical activity: the role of built, socioeconomic, and social environments . Soc Sci Med 2013 ; 95 : 106 – 14 . Google Scholar CrossRef Search ADS PubMed 5 Motter AF , Vasconcelos FAG , Corrêa EM et al. . Pontos de venda de alimentos e associação com sobrepeso/obesidade em escolares de Florianópolis, Santa Catarina, Brasil . Cad Saúde Pública (Rio de Janeiro) 2015 ; 31 ( 3 ): 620 – 32 . Google Scholar CrossRef Search ADS 6 Corrêa EN , Rossi CE , das Neves J et al. . Utilization and environmental availability of food outlets and overweight/obesity among schoolchildren in a city in the south of Brazil . J Pub Health (Oxf) 2018 ; 40 : 106 – 13 . doi:10.1093/pubmed/fdx017 . Google Scholar CrossRef Search ADS 7 Lavin-Fueyo J , Berra S . Places children use for physical activity in peripheral neighborhoods of the city of Cordoba . Salud Colectiva (Buenos Aires) 2015 ; 11 ( 2 ): 223 – 34 . Google Scholar CrossRef Search ADS 8 Ding D , Sallis JF , Kerr J et al. . Neighborhood environment and physical activity among youth: a review . Am J Prev Med 2011 ; 41 ( 4 ): 442 – 55 . Google Scholar CrossRef Search ADS PubMed 9 Jones A . Residential instability and obesity over time: the role of the social and built environment . Health Place 2015 ; 32 : 74 – 82 . Google Scholar CrossRef Search ADS PubMed 10 Edwards KL , Clarke GP , Ransley JK et al. . The neighbourhood matters: studying exposures relevant to childhood obesity and the policy implications in Leeds, UK . J Epi Commun Health 2010 ; 64 : 194 – 201 . doi:10.1136/jech.2009.088906 . Google Scholar CrossRef Search ADS 11 Wall MM , Larson NI , Forsyth A et al. . Patterns of obesogenic neighborhood features and adolescent weight: a comparison of statistical approaches . Am J Prev Med 2012 ; 42 ( 5 ): e65 – 75 . Google Scholar CrossRef Search ADS PubMed 12 Gose M , Plachta-Danielzik S , Willié B et al. . Longitudinal influences of neighbourhood built and social environment on children’s weight status . Int J Environ Res Pub Health 2013 ; 10 : 5083 – 96 . Google Scholar CrossRef Search ADS 13 Cremm EC , Leite FHM , Abreu DSC et al. . Factors associated with overweight in children living in the neighbourhoods of an urban area of Brazil . Pub Health Nutr 2011 ; 15 ( 6 ): 1056 – 64 . Google Scholar CrossRef Search ADS 14 Leite FHM , Oliveira MA , Cremm EC et al. . Oferta de alimentos processados no entorno de escolas públicas em área urbana . J Pediatr 2012 ; 88 ( 4 ): 328 – 34 . Google Scholar CrossRef Search ADS 15 Vedovato GM , Trude ACB , Kharmats AY et al. . Degree of food processing of household acquisition patterns in a Brazilian urban area is related to food buying preferences and perceived food environment . Appetite 2015 ; 87 : 296 – 302 . Google Scholar CrossRef Search ADS PubMed 16 Duran AC Diez-Roux AV Latorre MRDO Jaime PC 2013 Neighborhood socioeconomic characteristics and differences in the availability of healthy food stores and restaurants in Sao Paulo, Brazil Health Place 23 39 47 Google Scholar CrossRef Search ADS PubMed 17 Jaime PC , Duran AC , Sarti FM et al. . Investigating environmental determinants of diet, physical activity, and overweight among adults in Sao Paulo, Brazil . J Urban Health 2011 ; 88 ( 3 ): 567 – 81 . Google Scholar CrossRef Search ADS PubMed 18 Velásquez-Meléndez G , Mendes LL , Padez CMP . Built environment and social environment: associations with overweight and obesity in a sample of Brazilian adults . Cad Saúde Pública 2013 ; 29 ( 10 ): 1988 – 96 . Google Scholar CrossRef Search ADS PubMed 19 Silva IJO , Alexandre MG , Ravagnani FCP et al. . Atividade física: espaços e condições ambientais para sua prática em uma capital brasileira . Rev Bras Cineantrop Mov 2014 ; 22 ( 3 ): 53 – 62 . Google Scholar CrossRef Search ADS 20 Fermino RC , Reis RS , Hallal PC et al. . Who are the users of urban parks? A study with adults from Curitiba, Brazil . J Phys Activ Health 2015 ; 15 : 58 – 67 . Google Scholar CrossRef Search ADS 21 Hino AAF , Rech CR , Gonçalves PB et al. . Projeto ESPAÇOS de Curitiba, Brasil: aplicabilidade de métodos mistos de pesquisa e informações georreferenciadas em estudos sobre atividade física e ambiente construído . Rev Pan Salud Publica 2012 ; 32 ( 3 ): 226 – 33 . Google Scholar CrossRef Search ADS 22 Corrêa EN , Padez CMP , de Abreu ÂH et al. . Geographic and socioeconomic distribution of food vendors: a case study of a municipality in the Southern Brazil . Cad Saúde Pública 2017 ; 33 ( 2 ): e00145015 . Google Scholar CrossRef Search ADS PubMed 23 Programa das Nações Unidas para o Desenvolvimento (PNUD)/Instituto de Pesquisa Econômica Aplicada (IPEA)/Fundação Joao Pinheiro . Índice de Desenvolvimento Humano Municipal Brasileiro. Brasília: PNUD, Ipea, FJP, 2013 . 96 p. http://www.ipea.gov.br/portal/index.php?option=com_content&id=24037m (25 November 2016, date last accessed). 24 Brazil, Ministério da Saude . Índice de Gini da Renda Domiciliar per Capita. http://tabnet.datasus.gov.br/cgi/idb2011/b09capc.htm (25 November 2016 , date last accessed). 25 Pinho MGM , Adami F , Benedet J et al. . Association between screen time and dietary patterns and overweight/obesity among adolescents . Braz J Nutr 2017 ; 30 : 377 – 89 . 26 Habicht J . Estandarización de métodos epidemiológicos cuantitativos sobre el terreno . Bol Oficina Sanit Panam 1974 ; 76 : 375 – 84 . Google Scholar PubMed 27 World Health Organization/WHO . Physical status: the use and interpretation of anthropometry. WHO Technical Report Series, 854. Geneva: World Health Organization; 1995 . 28 de Onis M , Adelheid M , Onyango W et al. . Development of a WHO growth reference for school-aged children and adolescents . Bull World Health Org 2007 ; 85 : 660 – 7 . Google Scholar CrossRef Search ADS PubMed 29 Jago R , Baranowski T , Baranowski JC et al. . Distance to food stores & adolescent male fruit and vegetable consumption: mediation effects . Int J Behav Nutr Phys Activ 2007 ; 4 : 35 . Google Scholar CrossRef Search ADS 30 Gebauer H , Laska MN . Convenience stores surrounding urban schools: an assessment of healthy food availability, advertising, and product placement . J Urban Health 2011 ; 88 ( 4 ): 616 – 22 . Google Scholar CrossRef Search ADS PubMed 31 Jilcott SB , Wade S , McGuirt JT et al. . The association between the food environment and weight status among eastern North Carolina youth . Pub Health Nutr 2011 ; 14 ( 9 ): 1610 – 7 . Google Scholar CrossRef Search ADS 32 Black JL , Day M . Availability of limited service food outlets surrounding schools in British Columbia . Can Pub Health Assoc 2012 ; 103 ( 4 ): 255 – 59 . 33 He M , Tucker P , Gilliland J et al. . The influence of local food environments on adolescents’ food purchasing behaviors . Int J Environ Res Pub Health 2012 ; 9 ( 4 ): 1458 – 71 . Google Scholar CrossRef Search ADS 34 Jáuregui A , Salvo D , Lamadrid-Figueroa H et al. . Perceived and objective measures of neighborhood environment for physical activity among Mexican adults, 2011 . Prev Chronic Dis 2016 ; 13 : 160009 . Google Scholar CrossRef Search ADS 35 Dewulf B , Neutens T , Van Dyck D et al. . Correspondence between objective and perceived walking times to urban destinations: influence of physical activity, neighbourhood walkability, and socio-demographics . Int J Health Geogr 2012 ; 11 : 43 . Google Scholar CrossRef Search ADS PubMed 36 Colabianchi N , Dowda M , Pfeiffer KA et al. . Towards an understanding of salient neighborhood boundaries: adolescent reports of an easy walking distance and convenient driving distance . Int J Behav Nutr Phys Activ 2007 ; 4 : 66 . Google Scholar CrossRef Search ADS 37 Austin SB , Melly SJ , Sanchez BN et al. . 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 : 1575 – 581 . Google Scholar CrossRef Search ADS PubMed 38 Hosmer DW , Lemeshow S . Applied Logistic Regression , 2nd edn . New York : John Wiley and Sons , 2000 . Google Scholar CrossRef Search ADS 39 Oreskovic NM , Kuhlthau KA , Romm D et al. . Built environment and weight disparities among children in high- and low-income towns . Acad Pediatr 2009 ; 9 : 315 – 21 . Google Scholar CrossRef Search ADS PubMed 40 Duncan DT , Sharifi M , Melly SJ et al. . Characteristics of walkable built environments and BMI z-scores in children: evidence from a large electronic health record database . Environ Health Perspect 2014 ; 122 : 1359 – 65 . Google Scholar PubMed 41 Goldsby TU , George BJ , Yeager VA et al. . Urban park development and pediatric obesity rates: a quasi-experiment using electronic health record data . Int J Environ Res Pub Health 2016 ; 13 : 411 . doi:10.3390/ijerph13040411 . Google Scholar CrossRef Search ADS 42 Burgï R , Tomatis L , Murer K et al. . Spatial physical activity patterns among primary school children living in neighbourhoods of varying socioeconomic status: a cross-sectional study using accelerometry and Global Positioning System . BMC Public Health 2016 ; 16 : 282 . Google Scholar CrossRef Search ADS PubMed 43 Wasserman JA , Suminski R , Xi J et al. . A multi-level analysis showing associations between school neighborhood and child body mass index . Int J Obes (Lond) 2014 ; 38 ( 7 ): 912 – 8 . Google Scholar CrossRef Search ADS PubMed 44 Taylor WC , Upchurch SL , Brosnan CA et al. . Features of the built environment related to physical activity friendliness and children’s obesity and other risk factors . Pub Health Nurs 2014 ; 31 ( 6 ): 545 – 55 . Google Scholar CrossRef Search ADS 45 Lavin-Fueyo J , Garcia LMT , Mamondi V et al. . Neighborhood and family perceived environments associated with children’s physical activity and body mass index . Prev Med 2016 ; 82 : 35 – 41 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Public Health Oxford University Press

Association between food, physical activity, and social assistance environments and the body mass index of schoolchildren from different socioeconomic strata

Loading next page...
 
/lp/ou_press/association-between-food-physical-activity-and-social-assistance-9w6t2B09HG
Publisher
Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
ISSN
1741-3842
eISSN
1741-3850
D.O.I.
10.1093/pubmed/fdy086
Publisher site
See Article on Publisher Site

Abstract

Abstract The aim of this article was to evaluate associations between body mass index (BMI) and use of and distance from subjects homes of elements of the food and physical activity environments and use of social assistance environment, in schoolchildren from 7 to 14 years living in Florianópolis (South Brazil), stratified by monthly family income. A cross-sectional study was conducted with a probabilistic sample of 2152 schoolchildren. Univariate and multivariate linear regression analyses were conducted to test for associations between BMI and the use of and distance from supermarkets, bakeries and farmers’ markets; use of and distance from parks/playgrounds and football pitches; and use of health centers, Reference Centers for Social Assistance, instructional facilities, residents associations, religious groups and a Brazilian program for cash transfer. Overweight and obesity rates were 21.5 and 12.7%, respectively. Among schoolchildren from low-income families, living more than 11 min’ walk from parks/playgrounds was associated with higher BMI (β = 0.53; 95% CI = 0.33–0.73). In the high-income strata, a longer distance from home to football pitches was associated with lower BMI (β = −0.49; 95% CI = −0.69; −0.29). Neither food nor social assistance environments were associated with BMI of schoolchildren, even when analyzed by income strata. environment, places, young people Introduction The built environment in combination with the social, political and economic environments shapes the contexts in which people live.1 The built environment encompasses the food environment (restaurants, snack bars, etc.),2–6 the physical activity environment (beaches, parks/playgrounds, etc.),4,7,8 and the social assistance environment (centers for social assistance, instructional facilities, health centers, etc.).9–11 Environmental characteristics can influence eating habits,4 sedentary behavior8 and access to social and assistance programs.9–11 Living more than 800 m from grocery stores was related to higher body mass index (BMI) in adolescents from Connecticut (USA).4 Increased availability of facilities for physical activity and public assistance protected adolescents living in the USA from obesity, because these features improved walkability and opportunities for health promotion.9 Features related to the socioeconomic environment (i.e. educational level, rate of employment, residents’ incomes), and families’ socioeconomic characteristics have also been related to overweight/obesity.10–12 In one longitudinal study, children’s BMI was most influenced by their socioeconomic environment and the association was partially explained by their family’s educational level.12 Several Brazilian studies have evaluated associations between the food environment and food purchasing13–16 or overweight/obesity.17,18 Some have also focused on the quality of facilities available for physical activity19 and their associations with levels of physical activities.20,21 In Florianópolis, a coastal city situated in the South of Brazil, researchers have studied the spatial distribution of food vendors in areas with different socioeconomic features22 and the use of them and overweight/obesity in children.5,6 However, research seeking to evaluate the built environment in its multiple contexts and their influence on schoolchildren’s BMI is not available in developing settings.2–4,8–12 Although Florianópolis city has a very high Human Development Index (HDI: 0.847),23 it has a worryingly high Gini Index of 0.5474 (the closer to 1, the greater the social inequalities between residents),24 which could reflect differences in the built environments in wealthy and underprivileged areas. The aim of this article was to evaluate associations between BMI and use of and distance from subjects’ homes of elements of the food and physical activity environments and use of the social assistance environment, in schoolchildren aged 7–14 years living in Florianópolis (South Brazil), stratified by their families’ monthly incomes. Method Data came from a larger research analyzing the tendency and prevalence of obesity and associated factors among schoolchildren enrolled at public or private schools in Florianópolis (age 7–14 years). Several papers have been published from these data. In this investigation, a cross-sectional analysis with the whole probabilistic sample (2506 schoolchildren) was performed. The sample was selected by clusters, according to the number of students enrolled in each school. This procedure aimed to ensure that the sample was representative both of the regions in which the population lives and the variability of income in the population. The sampling methods used have been described in greater detail elsewhere.6,25 Weight and height were objectively measured by trained anthropometrists. The absolute intra-examiner and inter-examiner Technical Errors of Measurement (TEM) were used to select anthropometrists.26 Data were collected in accordance with the World Health Organization (WHO) technical standards.27 Overweight was defined as BMI for age and sex ≥+1 and <+2 z scores and obesity was defined as BMI for age and sex ≥+2 z scores.28 Schoolchildren and their families self-reported data about frequency of use and perceived distance from home to a list of facilities from three domains of the built environment that have been investigated in previous studies.29–33 We evaluated three elements from the food environment (bakeries, supermarkets and farmers’ markets) that had been significantly associated with overweight/obesity in studies of subsamples of the same original dataset;5,6 two facilities for physical activity (parks/playgrounds and football pitches); and four facilities for social assistance (health centers, Reference Centers for Social Assistance [RCSA], instructional facilities [Centers for Supplementary Education], and residents associations). The respondents also indicated their participation (yes/no) in three types of social activity: social projects (run by non-governmental organizations and/or the provisions of public policies), religious groups, and the Bolsa Família, a Brazilian cash transfer program. Data on use of facilities were categorized post hoc as follows: uses (grouping weekly and fortnightly) or does not use (combining never used, used rarely, and used monthly). Significant correlations between perceived and objective measures of proximity to parks have been reported for adults living in Cuernavaca, Mexico,34 and 64, 39 and 28% of a sample of adults from Ghent (Belgium), were able to correctly estimate the distances from home to bakeries, restaurants and supermarkets, respectively.35 We evaluated perceived distance from the family home to each item in the food and physical activity environments in terms of time in minutes taken to walk the distance. The answers were categorized post hoc as up to 10 or ≥11 min, on the assumption that places that take up to 10 min to reach on foot are close to adolescents and adults’ homes (~800 m)36,37 and can therefore be accessed actively. In the same questionnaire, monthly family incomes reported in Brazilian Reais (R$) were collected as a continuous variable, so the sample was stratified as higher-income families (HIF) (values >50th percentile) or lower-income families (LIF) (values ≤50th percentile). The value of the US Dollar varied from R$2.03 to R$2.37 during the data collection period. Parents’ educational level was also surveyed. Dietary data were obtained using another survey, the third version of the ‘Questionário Alimentar do Dia Anterior’ (Quada-3), which is a qualitative, illustrated questionnaire for evaluating children’s food consumption on the previous day. More detailed information on the QUADA is available elsewhere.25 Descriptive analyses were performed expressing categorical variables as absolute values and relative frequencies, and continuous variables as means with standard deviations. β Coefficients and their respective 95% confidence intervals, estimated by univariate linear regression analysis, were used to analyze factors associated with the outcome BMI. Exposure variables with P-values ≤0.25 for univariate associations were entered into the multivariate model with forward selection for strength of association.38 Categorical variables were tested for mutual collinearity using the chi-square test of independence. In cases of collinearity, the variable with the best fit to the model was chosen for the final regression. Age, sex and five variables related to consumption (yes/no) of non-healthy foods were included in the models as controls. Socioeconomic variables (type of school, and parents’ educational levels) were tested for relationships with BMI, using Pearson’s correlations. The results were weak (between 0.03 and 0.05) and these variables were not included in the multivariate models. The SVY command available in Stata, version 13.0, was used to account for complex sampling and sample weighting. This study was duly approved by the Human Research Ethics Committee at the Universidade Federal de Santa Catarina, under review process no. 120,341/2012. Results Valid BMI data were collected from 2484 schoolchildren, of whom valid family income data were available for 2152 (85.9% of the initial sample investigated). Table 1 lists characteristics for the whole sample and for the sample broken down by family income strata, showing that a majority of the schoolchildren were females (56.5%), aged 7–10 years (61.1%), and enrolled at public schools (65.3%). The age distribution and percentage of schoolchildren from public schools were as specified by the sample size calculation. Overall, prevalence of overweight was 21.5% (95% CI = 16.7–27.3%) and prevalence of obesity was 12.7% (95% CI = 11.0–14.5%). Table 1 Descriptive characteristics of the sample of 7–14-year-old schoolchildren, stratified by monthly family income, Florianópolis, Santa Catarina, Brazil, 2012/2013 Variables Categories Total LIFa (n = 1090) HIFa (n = 1062) P-value n % n % n % Sex (n = 2506) Female 1334 56.5 505 43.8 513 43.8 0.996 Male 1172 43.5 585 56.2 549 56.2 Age (years) (n = 2506) 7–10 1530 61.1 682 62.4 642 63.7 0.526 11–14 976 38.9 408 37.6 420 36.3 Type of school (n = 2506) Public 1637 65.3 1007 92.5 451 40.9 <0.001 Private 869 34.7 83 7.5 611 59.1 BMI for age and sex (n = 2484) Normal/low weightb 1658 65.8 705 65.4 704 65.9 0.902 Overweightc 511 21.5 219 22.0 224 20.8 Obesityd 315 12.7 151 12.6 129 13.3 Maternal education level (n = 2389) HS not graduated 851 33.3 606 55.3 174 14.9 0.003 HE not graduated 857 37.5 378 37.8 378 38.6 Graduated HE 681 29.2 66 6.9 497 46.5 Paternal education level (n = 2086) HS not graduated 806 35.6 529 58.2 213 19.5 0.016 HE not graduated 710 35.3 269 35.3 358 35.6 Graduated HE 570 29.1 64 6.5 398 44.9 Uses football pitches (n = 2341) Yes 661 25.4 326 26.4 256 23.5 0.344 No 1680 74.6 702 73.6 777 76.5 Uses parks/playgrounds (n = 2342) Yes 642 27.5 266 23.6 291 29.7 0.048 No 1700 72.5 751 76.4 749 70.3 Uses bakeries (n = 2279) Yes 2000 87.0 872 86.0 890 88.1 0.239 No 279 13.0 133 14.0 111 11.9 Uses supermarkets (n = 2397) Yes 2306 95.7 1008 94.3 1015 96.4 0.066 No 91 4.3 53 5.7 32 3.6 Uses farmers’ markets (n = 2318) Yes 2066 87.5 915 89.9 895 84.3 0.085 No 252 12.5 110 10.1 118 15.7 Distance to park/ playground (min) (n = 1830) 1–10 776 45.5 289 35.9 373 51.9 0.079 ≥11 1054 54.5 483 64.1 455 48.1 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to bakery (min) (n = 1946) 1–10 1035 69.1 549 64.2 602 73.3 0.171 ≥11 641 30.9 296 35.8 274 26.7 Distance to supermarket (min) (n = 2256) 1–10 894 40.6 377 37.3 411 43.9 0.322 ≥11 1362 59.4 614 62.7 584 56.1 Distance to farmers’ market (min) (n = 2006) 1–10 829 42.1 363 39.1 364 45.7 0.229 ≥11 1177 57.9 524 60.9 511 54.3 Uses health centers (n = 2359) Yes 939 37.9 555 50.4 287 27.0 0.089 No 1420 62.1 485 49.6 747 73.0 Uses RCSAe (n = 2261) Yes 52 1.6 44 3.3 4 0.2 0.012 No 2209 98.4 928 96.7 1019 99.8 Uses CSEf (n = 2270) Yes 197 7.7 138 12.2 45 4.7 0.029 No 2073 92.3 835 87.8 980 95.4 Uses social project (n = 2275) Yes 351 12.3 242 20.1 84 6.0 0.045 No 1924 87.7 748 79.9 935 94.0 Uses religious group (n = 2319) Yes 895 35.9 415 39.7 389 34.3 0.400 No 1424 64.1 592 60.3 648 65.7 Uses residents association (n = 2257) Yes 78 3.5 50 5.8 22 1.9 0.051 No 2179 96.5 917 94.2 1003 98.1 Receives Bolsa Família Program (n = 2394) Yes 217 8.5 178 15.6 20 2.4 0.013 No 2177 91.5 882 84.4 1025 97.6 Drinks soda (n = 2504) Yes 1473 61.1 714 68.0 759 56.0 0.103 No 1031 38.9 375 32.0 656 44.0 Eats sugary foods (n = 2504) Yes 1148 46.1 505 47.2 643 45.3 0.517 No 1356 53.9 584 52.8 772 54.7 Eats chips (n = 2504) Yes 437 17.4 219 20.2 218 15.4 0.113 No 2067 82.6 870 79.8 1197 84.6 Eats fried potatoes (n = 2504) Yes 465 19.9 234 23.9 231 16.9 0.293 No 2039 80.1 855 76.1 1184 83.1 Eats fast food (n = 2504) Yes 510 21.3 234 23.8 276 19.4 0.273 No 1994 78.7 855 76.2 1139 80.6 Variables Categories Total LIFa (n = 1090) HIFa (n = 1062) P-value n % n % n % Sex (n = 2506) Female 1334 56.5 505 43.8 513 43.8 0.996 Male 1172 43.5 585 56.2 549 56.2 Age (years) (n = 2506) 7–10 1530 61.1 682 62.4 642 63.7 0.526 11–14 976 38.9 408 37.6 420 36.3 Type of school (n = 2506) Public 1637 65.3 1007 92.5 451 40.9 <0.001 Private 869 34.7 83 7.5 611 59.1 BMI for age and sex (n = 2484) Normal/low weightb 1658 65.8 705 65.4 704 65.9 0.902 Overweightc 511 21.5 219 22.0 224 20.8 Obesityd 315 12.7 151 12.6 129 13.3 Maternal education level (n = 2389) HS not graduated 851 33.3 606 55.3 174 14.9 0.003 HE not graduated 857 37.5 378 37.8 378 38.6 Graduated HE 681 29.2 66 6.9 497 46.5 Paternal education level (n = 2086) HS not graduated 806 35.6 529 58.2 213 19.5 0.016 HE not graduated 710 35.3 269 35.3 358 35.6 Graduated HE 570 29.1 64 6.5 398 44.9 Uses football pitches (n = 2341) Yes 661 25.4 326 26.4 256 23.5 0.344 No 1680 74.6 702 73.6 777 76.5 Uses parks/playgrounds (n = 2342) Yes 642 27.5 266 23.6 291 29.7 0.048 No 1700 72.5 751 76.4 749 70.3 Uses bakeries (n = 2279) Yes 2000 87.0 872 86.0 890 88.1 0.239 No 279 13.0 133 14.0 111 11.9 Uses supermarkets (n = 2397) Yes 2306 95.7 1008 94.3 1015 96.4 0.066 No 91 4.3 53 5.7 32 3.6 Uses farmers’ markets (n = 2318) Yes 2066 87.5 915 89.9 895 84.3 0.085 No 252 12.5 110 10.1 118 15.7 Distance to park/ playground (min) (n = 1830) 1–10 776 45.5 289 35.9 373 51.9 0.079 ≥11 1054 54.5 483 64.1 455 48.1 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to bakery (min) (n = 1946) 1–10 1035 69.1 549 64.2 602 73.3 0.171 ≥11 641 30.9 296 35.8 274 26.7 Distance to supermarket (min) (n = 2256) 1–10 894 40.6 377 37.3 411 43.9 0.322 ≥11 1362 59.4 614 62.7 584 56.1 Distance to farmers’ market (min) (n = 2006) 1–10 829 42.1 363 39.1 364 45.7 0.229 ≥11 1177 57.9 524 60.9 511 54.3 Uses health centers (n = 2359) Yes 939 37.9 555 50.4 287 27.0 0.089 No 1420 62.1 485 49.6 747 73.0 Uses RCSAe (n = 2261) Yes 52 1.6 44 3.3 4 0.2 0.012 No 2209 98.4 928 96.7 1019 99.8 Uses CSEf (n = 2270) Yes 197 7.7 138 12.2 45 4.7 0.029 No 2073 92.3 835 87.8 980 95.4 Uses social project (n = 2275) Yes 351 12.3 242 20.1 84 6.0 0.045 No 1924 87.7 748 79.9 935 94.0 Uses religious group (n = 2319) Yes 895 35.9 415 39.7 389 34.3 0.400 No 1424 64.1 592 60.3 648 65.7 Uses residents association (n = 2257) Yes 78 3.5 50 5.8 22 1.9 0.051 No 2179 96.5 917 94.2 1003 98.1 Receives Bolsa Família Program (n = 2394) Yes 217 8.5 178 15.6 20 2.4 0.013 No 2177 91.5 882 84.4 1025 97.6 Drinks soda (n = 2504) Yes 1473 61.1 714 68.0 759 56.0 0.103 No 1031 38.9 375 32.0 656 44.0 Eats sugary foods (n = 2504) Yes 1148 46.1 505 47.2 643 45.3 0.517 No 1356 53.9 584 52.8 772 54.7 Eats chips (n = 2504) Yes 437 17.4 219 20.2 218 15.4 0.113 No 2067 82.6 870 79.8 1197 84.6 Eats fried potatoes (n = 2504) Yes 465 19.9 234 23.9 231 16.9 0.293 No 2039 80.1 855 76.1 1184 83.1 Eats fast food (n = 2504) Yes 510 21.3 234 23.8 276 19.4 0.273 No 1994 78.7 855 76.2 1139 80.6 aLIF, Lower-income families; HIF, higher-income families; the lower-income cutoff used was a monthly family income of ≤R$2000 (values ≤50th percentile); this was the equivalent of USD 985 at the start of data collection in September 2012, when 1 USD was worth R$2.03, and USD 843 at the end of data collection in August 2013, when 1 USD was worth R$2.37, and varied from 3.2 to 2.9 times the Brazilian minimum monthly wage over the same period; BMI, body mass index; 95% CI, 95% confidence interval; HS, high school; HE, higher education. bIn this Class 38 low-weight schoolchildren were included (1.2% of total evaluated). Normal/low-weight: BMI for age and sex <+1 z-score. coverweight: +1≤BMI for age and sex <+2 z-scores. dobesity: BMI for age and sex ≥+ 2 z-scores. eRCSA, Reference Center for Social Assistance. fCSE, Centers for Supplementary Education. Bold values indicate associations with P-values ≤ 0.05; Chisquare Pearson's test of independence. Table 1 Descriptive characteristics of the sample of 7–14-year-old schoolchildren, stratified by monthly family income, Florianópolis, Santa Catarina, Brazil, 2012/2013 Variables Categories Total LIFa (n = 1090) HIFa (n = 1062) P-value n % n % n % Sex (n = 2506) Female 1334 56.5 505 43.8 513 43.8 0.996 Male 1172 43.5 585 56.2 549 56.2 Age (years) (n = 2506) 7–10 1530 61.1 682 62.4 642 63.7 0.526 11–14 976 38.9 408 37.6 420 36.3 Type of school (n = 2506) Public 1637 65.3 1007 92.5 451 40.9 <0.001 Private 869 34.7 83 7.5 611 59.1 BMI for age and sex (n = 2484) Normal/low weightb 1658 65.8 705 65.4 704 65.9 0.902 Overweightc 511 21.5 219 22.0 224 20.8 Obesityd 315 12.7 151 12.6 129 13.3 Maternal education level (n = 2389) HS not graduated 851 33.3 606 55.3 174 14.9 0.003 HE not graduated 857 37.5 378 37.8 378 38.6 Graduated HE 681 29.2 66 6.9 497 46.5 Paternal education level (n = 2086) HS not graduated 806 35.6 529 58.2 213 19.5 0.016 HE not graduated 710 35.3 269 35.3 358 35.6 Graduated HE 570 29.1 64 6.5 398 44.9 Uses football pitches (n = 2341) Yes 661 25.4 326 26.4 256 23.5 0.344 No 1680 74.6 702 73.6 777 76.5 Uses parks/playgrounds (n = 2342) Yes 642 27.5 266 23.6 291 29.7 0.048 No 1700 72.5 751 76.4 749 70.3 Uses bakeries (n = 2279) Yes 2000 87.0 872 86.0 890 88.1 0.239 No 279 13.0 133 14.0 111 11.9 Uses supermarkets (n = 2397) Yes 2306 95.7 1008 94.3 1015 96.4 0.066 No 91 4.3 53 5.7 32 3.6 Uses farmers’ markets (n = 2318) Yes 2066 87.5 915 89.9 895 84.3 0.085 No 252 12.5 110 10.1 118 15.7 Distance to park/ playground (min) (n = 1830) 1–10 776 45.5 289 35.9 373 51.9 0.079 ≥11 1054 54.5 483 64.1 455 48.1 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to bakery (min) (n = 1946) 1–10 1035 69.1 549 64.2 602 73.3 0.171 ≥11 641 30.9 296 35.8 274 26.7 Distance to supermarket (min) (n = 2256) 1–10 894 40.6 377 37.3 411 43.9 0.322 ≥11 1362 59.4 614 62.7 584 56.1 Distance to farmers’ market (min) (n = 2006) 1–10 829 42.1 363 39.1 364 45.7 0.229 ≥11 1177 57.9 524 60.9 511 54.3 Uses health centers (n = 2359) Yes 939 37.9 555 50.4 287 27.0 0.089 No 1420 62.1 485 49.6 747 73.0 Uses RCSAe (n = 2261) Yes 52 1.6 44 3.3 4 0.2 0.012 No 2209 98.4 928 96.7 1019 99.8 Uses CSEf (n = 2270) Yes 197 7.7 138 12.2 45 4.7 0.029 No 2073 92.3 835 87.8 980 95.4 Uses social project (n = 2275) Yes 351 12.3 242 20.1 84 6.0 0.045 No 1924 87.7 748 79.9 935 94.0 Uses religious group (n = 2319) Yes 895 35.9 415 39.7 389 34.3 0.400 No 1424 64.1 592 60.3 648 65.7 Uses residents association (n = 2257) Yes 78 3.5 50 5.8 22 1.9 0.051 No 2179 96.5 917 94.2 1003 98.1 Receives Bolsa Família Program (n = 2394) Yes 217 8.5 178 15.6 20 2.4 0.013 No 2177 91.5 882 84.4 1025 97.6 Drinks soda (n = 2504) Yes 1473 61.1 714 68.0 759 56.0 0.103 No 1031 38.9 375 32.0 656 44.0 Eats sugary foods (n = 2504) Yes 1148 46.1 505 47.2 643 45.3 0.517 No 1356 53.9 584 52.8 772 54.7 Eats chips (n = 2504) Yes 437 17.4 219 20.2 218 15.4 0.113 No 2067 82.6 870 79.8 1197 84.6 Eats fried potatoes (n = 2504) Yes 465 19.9 234 23.9 231 16.9 0.293 No 2039 80.1 855 76.1 1184 83.1 Eats fast food (n = 2504) Yes 510 21.3 234 23.8 276 19.4 0.273 No 1994 78.7 855 76.2 1139 80.6 Variables Categories Total LIFa (n = 1090) HIFa (n = 1062) P-value n % n % n % Sex (n = 2506) Female 1334 56.5 505 43.8 513 43.8 0.996 Male 1172 43.5 585 56.2 549 56.2 Age (years) (n = 2506) 7–10 1530 61.1 682 62.4 642 63.7 0.526 11–14 976 38.9 408 37.6 420 36.3 Type of school (n = 2506) Public 1637 65.3 1007 92.5 451 40.9 <0.001 Private 869 34.7 83 7.5 611 59.1 BMI for age and sex (n = 2484) Normal/low weightb 1658 65.8 705 65.4 704 65.9 0.902 Overweightc 511 21.5 219 22.0 224 20.8 Obesityd 315 12.7 151 12.6 129 13.3 Maternal education level (n = 2389) HS not graduated 851 33.3 606 55.3 174 14.9 0.003 HE not graduated 857 37.5 378 37.8 378 38.6 Graduated HE 681 29.2 66 6.9 497 46.5 Paternal education level (n = 2086) HS not graduated 806 35.6 529 58.2 213 19.5 0.016 HE not graduated 710 35.3 269 35.3 358 35.6 Graduated HE 570 29.1 64 6.5 398 44.9 Uses football pitches (n = 2341) Yes 661 25.4 326 26.4 256 23.5 0.344 No 1680 74.6 702 73.6 777 76.5 Uses parks/playgrounds (n = 2342) Yes 642 27.5 266 23.6 291 29.7 0.048 No 1700 72.5 751 76.4 749 70.3 Uses bakeries (n = 2279) Yes 2000 87.0 872 86.0 890 88.1 0.239 No 279 13.0 133 14.0 111 11.9 Uses supermarkets (n = 2397) Yes 2306 95.7 1008 94.3 1015 96.4 0.066 No 91 4.3 53 5.7 32 3.6 Uses farmers’ markets (n = 2318) Yes 2066 87.5 915 89.9 895 84.3 0.085 No 252 12.5 110 10.1 118 15.7 Distance to park/ playground (min) (n = 1830) 1–10 776 45.5 289 35.9 373 51.9 0.079 ≥11 1054 54.5 483 64.1 455 48.1 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to football pitch (min) (n = 1244) 1–10 538 42.4 234 36.7 240 45.6 ≥11 706 57.6 343 63.3 283 54.4 0.005 Distance to bakery (min) (n = 1946) 1–10 1035 69.1 549 64.2 602 73.3 0.171 ≥11 641 30.9 296 35.8 274 26.7 Distance to supermarket (min) (n = 2256) 1–10 894 40.6 377 37.3 411 43.9 0.322 ≥11 1362 59.4 614 62.7 584 56.1 Distance to farmers’ market (min) (n = 2006) 1–10 829 42.1 363 39.1 364 45.7 0.229 ≥11 1177 57.9 524 60.9 511 54.3 Uses health centers (n = 2359) Yes 939 37.9 555 50.4 287 27.0 0.089 No 1420 62.1 485 49.6 747 73.0 Uses RCSAe (n = 2261) Yes 52 1.6 44 3.3 4 0.2 0.012 No 2209 98.4 928 96.7 1019 99.8 Uses CSEf (n = 2270) Yes 197 7.7 138 12.2 45 4.7 0.029 No 2073 92.3 835 87.8 980 95.4 Uses social project (n = 2275) Yes 351 12.3 242 20.1 84 6.0 0.045 No 1924 87.7 748 79.9 935 94.0 Uses religious group (n = 2319) Yes 895 35.9 415 39.7 389 34.3 0.400 No 1424 64.1 592 60.3 648 65.7 Uses residents association (n = 2257) Yes 78 3.5 50 5.8 22 1.9 0.051 No 2179 96.5 917 94.2 1003 98.1 Receives Bolsa Família Program (n = 2394) Yes 217 8.5 178 15.6 20 2.4 0.013 No 2177 91.5 882 84.4 1025 97.6 Drinks soda (n = 2504) Yes 1473 61.1 714 68.0 759 56.0 0.103 No 1031 38.9 375 32.0 656 44.0 Eats sugary foods (n = 2504) Yes 1148 46.1 505 47.2 643 45.3 0.517 No 1356 53.9 584 52.8 772 54.7 Eats chips (n = 2504) Yes 437 17.4 219 20.2 218 15.4 0.113 No 2067 82.6 870 79.8 1197 84.6 Eats fried potatoes (n = 2504) Yes 465 19.9 234 23.9 231 16.9 0.293 No 2039 80.1 855 76.1 1184 83.1 Eats fast food (n = 2504) Yes 510 21.3 234 23.8 276 19.4 0.273 No 1994 78.7 855 76.2 1139 80.6 aLIF, Lower-income families; HIF, higher-income families; the lower-income cutoff used was a monthly family income of ≤R$2000 (values ≤50th percentile); this was the equivalent of USD 985 at the start of data collection in September 2012, when 1 USD was worth R$2.03, and USD 843 at the end of data collection in August 2013, when 1 USD was worth R$2.37, and varied from 3.2 to 2.9 times the Brazilian minimum monthly wage over the same period; BMI, body mass index; 95% CI, 95% confidence interval; HS, high school; HE, higher education. bIn this Class 38 low-weight schoolchildren were included (1.2% of total evaluated). Normal/low-weight: BMI for age and sex <+1 z-score. coverweight: +1≤BMI for age and sex <+2 z-scores. dobesity: BMI for age and sex ≥+ 2 z-scores. eRCSA, Reference Center for Social Assistance. fCSE, Centers for Supplementary Education. Bold values indicate associations with P-values ≤ 0.05; Chisquare Pearson's test of independence. The median monthly income used to separate the sample into LIF and HIF strata was R$ 2000.00, which varied from 2.9 to 3.2 times the Brazilian minimal monthly wage, corresponded to 843.88–985.22 US Dollars, over the data collection period. Data on use of each of the built environment facilities showed that parks/playgrounds and football pitches were used by around a quarter of schoolchildren. Supermarkets were the most used facilities in the food environment (95.7%); around one-third of families were using health care centers and frequented religious groups, and 8.5% were beneficiaries of the ‘Bolsa Família’ program. Social assistance facilities were significantly most used by low-income families (P < 0.05). Data on perceived distance from physical activity facilities indicated that most of the schoolchildren live a long way from football pitches (57.6%) and parks (54.5%), which was more likely if they were from LIF (significant association for football pitches; P = 0.005). As for food environment, bakeries were closest to the children’s households (69.1% lived 10 min or less away) (Table 1). Table 2 lists the descriptive analysis for mean BMI among overweight/obese children using or do not using the facilities investigated, and the univariate and multivariate linear regression results for associations with BMI for the entire sample. Overweight/obese children who use parks/playgrounds had lower mean BMI than those who do not use parks/playgrounds (P < 0.001). Collinearity of variables was detected as follows: use of parks/playgrounds, use of football pitches, and distance from home to parks/playgrounds; use of football pitches and their distance from home; use of farmers’ markets with use of bakeries and supermarkets; distance from homes to bakeries, to supermarkets, and to farmers’ markets had mutual multicollinearity; use of health centers, RCSA, instructional facilities and residents associations had mutual collinearity. The environmental variables included in the multivariate model were not significantly associated with BMI. Table 2 Mean and standard deviation (SD) for body mass index among overweight/obese children, and univariate and multivariate analyses of association between use of and distance from home of facilities among 7–14-year-old schoolchildren from Florianópolis, SC, Brazil, 2012/2013 Environmental characteristic Body mass index (kg/m2) among overweight/obese children Univariate analyses Multivariate analyses n Mean; SD* β** 95% CI β*** 95% CI Uses football pitches Yes 333 22.37; 3.42 0.39 −0.30; 1.08 0.16 −0.73; 1.05 No 446 22.16; 3.32 0.00 Uses parks/playgrounds Yes 476 21.86; 3.24 −0.60 −1.88; 0.68 No 298 22.82; 3.46 0.00 Uses bakeries Yes 662 22.28; 3.42 0.13 −1.23; 1.50 No 86 22.07; 2.84 0.00 Uses supermarkets Yes 757 22.26; 3.38 0.07 −1.60; 1.73 No 34 21.86; 2.87 0.00 Uses farmers’ markets Yes 696 22.30; 3.38 0.40 −0.44; 1.23 −1.23 −4.84; 2.39 No 71 21.55; 2.93 0.00 Distance from football pitch (min) 1–10 193 22.28; 3.08 0.00 ≥ 11 238 22.57; 3.52 −0.51 −1.35; 0.32 Distance from park/playground (min) 1–10 256 21.89; 3.08 0.00 ≥ 11 346 22.17; 3.42 −0.08 −0.68; 0.52 Distance from bakery (min) 1–10 425 22.30; 3.27 0.00 ≥ 11 222 22.13; 3.61 −0.27 −0.82; 0.28 Distance from supermarket (min) 1–10 285 22.30; 3.34 0.00 ≥ 11 454 22.15; 3.50 0.16 −0.57; 0.89 Distance from farmers’ market (min) 1–10 280 22.50; 3.31 0.00 ≥ 11 392 22.12; 3.40 −0.17 −0.38; 0.05 −0.09 −0.42; 0.24 Uses health center Yes 306 22.50; 3.44 0.21 −0.54; 0.96 No 477 22.06; 3.28 0.00 Uses RCSA Yes 19 22.01; 3.05 0.21 −1.02; 1.45 No 738 22.24; 3.39 0.00 Uses CSE Yes 63 22.29; 3.43 −0.11 −1.19; 0.97 No 692 22.20; 3.36 0.00 Uses social projects Yes 111 22.59; 3.57 −0.02 −0.55; 0.50 No 644 22.15; 3.33 0.00 Uses religious groups Yes 305 22.43; 3.30 0.21 −1.12; 1.55 No 472 22.09; 3.40 0.00 Uses residents association Yes 21 22.57; 3.49 0.32 −1.05; 1.69 No 733 22.22; 3.38 0.00 Receives Bolsa Família Program Yes 57 22.08; 2.86 −0.76 −1.66; 0.14 −0.81 −1.81; 0.20 No 738 22.21; 3.39 0.00 Environmental characteristic Body mass index (kg/m2) among overweight/obese children Univariate analyses Multivariate analyses n Mean; SD* β** 95% CI β*** 95% CI Uses football pitches Yes 333 22.37; 3.42 0.39 −0.30; 1.08 0.16 −0.73; 1.05 No 446 22.16; 3.32 0.00 Uses parks/playgrounds Yes 476 21.86; 3.24 −0.60 −1.88; 0.68 No 298 22.82; 3.46 0.00 Uses bakeries Yes 662 22.28; 3.42 0.13 −1.23; 1.50 No 86 22.07; 2.84 0.00 Uses supermarkets Yes 757 22.26; 3.38 0.07 −1.60; 1.73 No 34 21.86; 2.87 0.00 Uses farmers’ markets Yes 696 22.30; 3.38 0.40 −0.44; 1.23 −1.23 −4.84; 2.39 No 71 21.55; 2.93 0.00 Distance from football pitch (min) 1–10 193 22.28; 3.08 0.00 ≥ 11 238 22.57; 3.52 −0.51 −1.35; 0.32 Distance from park/playground (min) 1–10 256 21.89; 3.08 0.00 ≥ 11 346 22.17; 3.42 −0.08 −0.68; 0.52 Distance from bakery (min) 1–10 425 22.30; 3.27 0.00 ≥ 11 222 22.13; 3.61 −0.27 −0.82; 0.28 Distance from supermarket (min) 1–10 285 22.30; 3.34 0.00 ≥ 11 454 22.15; 3.50 0.16 −0.57; 0.89 Distance from farmers’ market (min) 1–10 280 22.50; 3.31 0.00 ≥ 11 392 22.12; 3.40 −0.17 −0.38; 0.05 −0.09 −0.42; 0.24 Uses health center Yes 306 22.50; 3.44 0.21 −0.54; 0.96 No 477 22.06; 3.28 0.00 Uses RCSA Yes 19 22.01; 3.05 0.21 −1.02; 1.45 No 738 22.24; 3.39 0.00 Uses CSE Yes 63 22.29; 3.43 −0.11 −1.19; 0.97 No 692 22.20; 3.36 0.00 Uses social projects Yes 111 22.59; 3.57 −0.02 −0.55; 0.50 No 644 22.15; 3.33 0.00 Uses religious groups Yes 305 22.43; 3.30 0.21 −1.12; 1.55 No 472 22.09; 3.40 0.00 Uses residents association Yes 21 22.57; 3.49 0.32 −1.05; 1.69 No 733 22.22; 3.38 0.00 Receives Bolsa Família Program Yes 57 22.08; 2.86 −0.76 −1.66; 0.14 −0.81 −1.81; 0.20 No 738 22.21; 3.39 0.00 SD, standard deviation; RCSA, Reference Center for Social Assistance; CSE, Centers for Supplementary Education. *Bold values indicate significant differences between the mean BMIs of overweight/obese schoolchildren that use and do not use places/facilities (P = 0.0001). **Bold values indicate associations with P-values ≤0.25. ***Bold values indicate associations with P-values ≤0.05; Multivariate model adjusted by age, sex and food intake variables. Table 2 Mean and standard deviation (SD) for body mass index among overweight/obese children, and univariate and multivariate analyses of association between use of and distance from home of facilities among 7–14-year-old schoolchildren from Florianópolis, SC, Brazil, 2012/2013 Environmental characteristic Body mass index (kg/m2) among overweight/obese children Univariate analyses Multivariate analyses n Mean; SD* β** 95% CI β*** 95% CI Uses football pitches Yes 333 22.37; 3.42 0.39 −0.30; 1.08 0.16 −0.73; 1.05 No 446 22.16; 3.32 0.00 Uses parks/playgrounds Yes 476 21.86; 3.24 −0.60 −1.88; 0.68 No 298 22.82; 3.46 0.00 Uses bakeries Yes 662 22.28; 3.42 0.13 −1.23; 1.50 No 86 22.07; 2.84 0.00 Uses supermarkets Yes 757 22.26; 3.38 0.07 −1.60; 1.73 No 34 21.86; 2.87 0.00 Uses farmers’ markets Yes 696 22.30; 3.38 0.40 −0.44; 1.23 −1.23 −4.84; 2.39 No 71 21.55; 2.93 0.00 Distance from football pitch (min) 1–10 193 22.28; 3.08 0.00 ≥ 11 238 22.57; 3.52 −0.51 −1.35; 0.32 Distance from park/playground (min) 1–10 256 21.89; 3.08 0.00 ≥ 11 346 22.17; 3.42 −0.08 −0.68; 0.52 Distance from bakery (min) 1–10 425 22.30; 3.27 0.00 ≥ 11 222 22.13; 3.61 −0.27 −0.82; 0.28 Distance from supermarket (min) 1–10 285 22.30; 3.34 0.00 ≥ 11 454 22.15; 3.50 0.16 −0.57; 0.89 Distance from farmers’ market (min) 1–10 280 22.50; 3.31 0.00 ≥ 11 392 22.12; 3.40 −0.17 −0.38; 0.05 −0.09 −0.42; 0.24 Uses health center Yes 306 22.50; 3.44 0.21 −0.54; 0.96 No 477 22.06; 3.28 0.00 Uses RCSA Yes 19 22.01; 3.05 0.21 −1.02; 1.45 No 738 22.24; 3.39 0.00 Uses CSE Yes 63 22.29; 3.43 −0.11 −1.19; 0.97 No 692 22.20; 3.36 0.00 Uses social projects Yes 111 22.59; 3.57 −0.02 −0.55; 0.50 No 644 22.15; 3.33 0.00 Uses religious groups Yes 305 22.43; 3.30 0.21 −1.12; 1.55 No 472 22.09; 3.40 0.00 Uses residents association Yes 21 22.57; 3.49 0.32 −1.05; 1.69 No 733 22.22; 3.38 0.00 Receives Bolsa Família Program Yes 57 22.08; 2.86 −0.76 −1.66; 0.14 −0.81 −1.81; 0.20 No 738 22.21; 3.39 0.00 Environmental characteristic Body mass index (kg/m2) among overweight/obese children Univariate analyses Multivariate analyses n Mean; SD* β** 95% CI β*** 95% CI Uses football pitches Yes 333 22.37; 3.42 0.39 −0.30; 1.08 0.16 −0.73; 1.05 No 446 22.16; 3.32 0.00 Uses parks/playgrounds Yes 476 21.86; 3.24 −0.60 −1.88; 0.68 No 298 22.82; 3.46 0.00 Uses bakeries Yes 662 22.28; 3.42 0.13 −1.23; 1.50 No 86 22.07; 2.84 0.00 Uses supermarkets Yes 757 22.26; 3.38 0.07 −1.60; 1.73 No 34 21.86; 2.87 0.00 Uses farmers’ markets Yes 696 22.30; 3.38 0.40 −0.44; 1.23 −1.23 −4.84; 2.39 No 71 21.55; 2.93 0.00 Distance from football pitch (min) 1–10 193 22.28; 3.08 0.00 ≥ 11 238 22.57; 3.52 −0.51 −1.35; 0.32 Distance from park/playground (min) 1–10 256 21.89; 3.08 0.00 ≥ 11 346 22.17; 3.42 −0.08 −0.68; 0.52 Distance from bakery (min) 1–10 425 22.30; 3.27 0.00 ≥ 11 222 22.13; 3.61 −0.27 −0.82; 0.28 Distance from supermarket (min) 1–10 285 22.30; 3.34 0.00 ≥ 11 454 22.15; 3.50 0.16 −0.57; 0.89 Distance from farmers’ market (min) 1–10 280 22.50; 3.31 0.00 ≥ 11 392 22.12; 3.40 −0.17 −0.38; 0.05 −0.09 −0.42; 0.24 Uses health center Yes 306 22.50; 3.44 0.21 −0.54; 0.96 No 477 22.06; 3.28 0.00 Uses RCSA Yes 19 22.01; 3.05 0.21 −1.02; 1.45 No 738 22.24; 3.39 0.00 Uses CSE Yes 63 22.29; 3.43 −0.11 −1.19; 0.97 No 692 22.20; 3.36 0.00 Uses social projects Yes 111 22.59; 3.57 −0.02 −0.55; 0.50 No 644 22.15; 3.33 0.00 Uses religious groups Yes 305 22.43; 3.30 0.21 −1.12; 1.55 No 472 22.09; 3.40 0.00 Uses residents association Yes 21 22.57; 3.49 0.32 −1.05; 1.69 No 733 22.22; 3.38 0.00 Receives Bolsa Família Program Yes 57 22.08; 2.86 −0.76 −1.66; 0.14 −0.81 −1.81; 0.20 No 738 22.21; 3.39 0.00 SD, standard deviation; RCSA, Reference Center for Social Assistance; CSE, Centers for Supplementary Education. *Bold values indicate significant differences between the mean BMIs of overweight/obese schoolchildren that use and do not use places/facilities (P = 0.0001). **Bold values indicate associations with P-values ≤0.25. ***Bold values indicate associations with P-values ≤0.05; Multivariate model adjusted by age, sex and food intake variables. Table 3 summarizes the univariate and multivariate analyses for BMI against the environmental variables, stratified by income categories. These results show that schoolchildren from LIF who live a long way from parks/playgrounds had higher BMI values (β = 0.61; 95% CI = 0.24; 0.97). Notwithstanding, the association between use of parks/playgrounds and the outcome was not statistically significant in this income stratum. Among schoolchildren from HIF, a longer distance from homes to football pitches exhibited significant associations with lower BMI in the multivariate analysis (β = −0.49; 95% CI = −0.69; −0.29). Table 3 Univariate and multivariate analyses of association between use of and distance from home of facilities of the built environment and body mass index, by family income strata, among 7–14-year-old schoolchildren from Florianópolis, SC, Brazil, 2012/2013 Environmental variablec LIFa HIFa Univariate analyses Multivariate analysesb Univariate analyses Multivariate analysesb β 95% CI β 95% CI β 95% CI β 95% CI Uses football pitches Yesd 0.37 −0.01; 0.75 0.26 −0.09; 0.61 0.69 −0.95; 2.34 Uses parks/playgrounds Yes −0.47 −1.71; 0.77 −0.48 −1.87; 0.91 Uses bakeries Yes 0.29 −1.35; 1.93 −0.09 −1.50; 1.32 Uses supermarkets Yes 0.72 −0.98; 2.43 −0.67 −1.73; 0.38 −0.09 −2.11; 1.93 Uses farmers’ markets Yes 0.31 −1.28; 1.89 0.62 −0.23; 1.47 0.11 −1.33; 1.54 Distance to football pitch (min) ≥11d −0.38 −1.78; 1.01 −0.62 −1.04; −0.20 −0.49 −0.69; −0.29* Distance to park/playground (min) ≥11 0.54 0.34; 0.74 0.53 0.33; 0.73* −0.41 −0.96; 0.14 Distance to bakery (min) ≥11 −0.41 −1.34; 0.52 −0.25 −1.15; 0.66 Distance to supermarket (min) ≥11 0.28 −0.22; 0.78 0.13 −1.02; 1.28 0.18 −1.00; 1.36 Distance to farmers’ market (min) ≥11 −0.16 −1.07; 0.76 −0.22 −1.27; 0.83 Uses health center Yes −0.08 −0.86; 0.70 0.51 −0.45; 1.46 Uses RCSA Yes −0.09 −1.41; 1.23 0.00 −0.94; 0.95 Uses CSE Yes −0.12 −1.06; 0.81 −0.02 −2.71; 2.68 Uses social projects Yes −0.27 −0.93; 0.38 0.69 −0.86; 2.24 Uses religious groups Yes 0.20 −1.57; 1.97 0.26 −1.05; 1.57 Uses residents association Yes −0.25 −0.68; 0.18 0.26 −0.14; 0.66 1.98 −2.04; 5.99 Receives Bolsa Família Program Yes −0.58 −1.60; 0.45 −0.38 −3.31; 2.56 −2.38 −2.64; −2.11 −1.29 −2.89; 0.32 Environmental variablec LIFa HIFa Univariate analyses Multivariate analysesb Univariate analyses Multivariate analysesb β 95% CI β 95% CI β 95% CI β 95% CI Uses football pitches Yesd 0.37 −0.01; 0.75 0.26 −0.09; 0.61 0.69 −0.95; 2.34 Uses parks/playgrounds Yes −0.47 −1.71; 0.77 −0.48 −1.87; 0.91 Uses bakeries Yes 0.29 −1.35; 1.93 −0.09 −1.50; 1.32 Uses supermarkets Yes 0.72 −0.98; 2.43 −0.67 −1.73; 0.38 −0.09 −2.11; 1.93 Uses farmers’ markets Yes 0.31 −1.28; 1.89 0.62 −0.23; 1.47 0.11 −1.33; 1.54 Distance to football pitch (min) ≥11d −0.38 −1.78; 1.01 −0.62 −1.04; −0.20 −0.49 −0.69; −0.29* Distance to park/playground (min) ≥11 0.54 0.34; 0.74 0.53 0.33; 0.73* −0.41 −0.96; 0.14 Distance to bakery (min) ≥11 −0.41 −1.34; 0.52 −0.25 −1.15; 0.66 Distance to supermarket (min) ≥11 0.28 −0.22; 0.78 0.13 −1.02; 1.28 0.18 −1.00; 1.36 Distance to farmers’ market (min) ≥11 −0.16 −1.07; 0.76 −0.22 −1.27; 0.83 Uses health center Yes −0.08 −0.86; 0.70 0.51 −0.45; 1.46 Uses RCSA Yes −0.09 −1.41; 1.23 0.00 −0.94; 0.95 Uses CSE Yes −0.12 −1.06; 0.81 −0.02 −2.71; 2.68 Uses social projects Yes −0.27 −0.93; 0.38 0.69 −0.86; 2.24 Uses religious groups Yes 0.20 −1.57; 1.97 0.26 −1.05; 1.57 Uses residents association Yes −0.25 −0.68; 0.18 0.26 −0.14; 0.66 1.98 −2.04; 5.99 Receives Bolsa Família Program Yes −0.58 −1.60; 0.45 −0.38 −3.31; 2.56 −2.38 −2.64; −2.11 −1.29 −2.89; 0.32 aLIF, Lower-income families; HIF, higher-income families. The lower-income cutoff used was a monthly family income of R$2 000 (values ≤50th percentile); this was the equivalent of USD 985 at the start of data collection in September 2012, when 1 USD was worth R2.03, and USD 843 at the end of data collection in August 2013, when 1 USD was worth R$2.37, and varied from 3.2 to 2.94 times the Brazilian minimum monthly wage over the same period. bMultivariate model controlled by age, sex and food intake variables. cCollinearity among variables from each domain of the built environment was detected as follows: (i) physical activity environment: use of parks/playgrounds exhibited multicollinearity with use of football pitch and distance from home to parks/playgrounds; use of football pitches were collinear with distance from home to these same facilities; (ii) food environment: use of farmers’ market with use of bakery and use of supermarket; distance from homes to bakeries, to supermarkets and to farmers’ market have mutual multicollinearity; (iii) social assistance environment: use of health centers with use of Reference Center for Social Assistance, use of Centers for Supplementary Education, and use of residents association; use of Reference Center for Social Assistance with use of social projects, use of Centers for Supplementary Education and use of residents association. dCategories ‘no’ and ‘1–10 min away from home’ were the dummy variables. *P-value < 0.05. Table 3 Univariate and multivariate analyses of association between use of and distance from home of facilities of the built environment and body mass index, by family income strata, among 7–14-year-old schoolchildren from Florianópolis, SC, Brazil, 2012/2013 Environmental variablec LIFa HIFa Univariate analyses Multivariate analysesb Univariate analyses Multivariate analysesb β 95% CI β 95% CI β 95% CI β 95% CI Uses football pitches Yesd 0.37 −0.01; 0.75 0.26 −0.09; 0.61 0.69 −0.95; 2.34 Uses parks/playgrounds Yes −0.47 −1.71; 0.77 −0.48 −1.87; 0.91 Uses bakeries Yes 0.29 −1.35; 1.93 −0.09 −1.50; 1.32 Uses supermarkets Yes 0.72 −0.98; 2.43 −0.67 −1.73; 0.38 −0.09 −2.11; 1.93 Uses farmers’ markets Yes 0.31 −1.28; 1.89 0.62 −0.23; 1.47 0.11 −1.33; 1.54 Distance to football pitch (min) ≥11d −0.38 −1.78; 1.01 −0.62 −1.04; −0.20 −0.49 −0.69; −0.29* Distance to park/playground (min) ≥11 0.54 0.34; 0.74 0.53 0.33; 0.73* −0.41 −0.96; 0.14 Distance to bakery (min) ≥11 −0.41 −1.34; 0.52 −0.25 −1.15; 0.66 Distance to supermarket (min) ≥11 0.28 −0.22; 0.78 0.13 −1.02; 1.28 0.18 −1.00; 1.36 Distance to farmers’ market (min) ≥11 −0.16 −1.07; 0.76 −0.22 −1.27; 0.83 Uses health center Yes −0.08 −0.86; 0.70 0.51 −0.45; 1.46 Uses RCSA Yes −0.09 −1.41; 1.23 0.00 −0.94; 0.95 Uses CSE Yes −0.12 −1.06; 0.81 −0.02 −2.71; 2.68 Uses social projects Yes −0.27 −0.93; 0.38 0.69 −0.86; 2.24 Uses religious groups Yes 0.20 −1.57; 1.97 0.26 −1.05; 1.57 Uses residents association Yes −0.25 −0.68; 0.18 0.26 −0.14; 0.66 1.98 −2.04; 5.99 Receives Bolsa Família Program Yes −0.58 −1.60; 0.45 −0.38 −3.31; 2.56 −2.38 −2.64; −2.11 −1.29 −2.89; 0.32 Environmental variablec LIFa HIFa Univariate analyses Multivariate analysesb Univariate analyses Multivariate analysesb β 95% CI β 95% CI β 95% CI β 95% CI Uses football pitches Yesd 0.37 −0.01; 0.75 0.26 −0.09; 0.61 0.69 −0.95; 2.34 Uses parks/playgrounds Yes −0.47 −1.71; 0.77 −0.48 −1.87; 0.91 Uses bakeries Yes 0.29 −1.35; 1.93 −0.09 −1.50; 1.32 Uses supermarkets Yes 0.72 −0.98; 2.43 −0.67 −1.73; 0.38 −0.09 −2.11; 1.93 Uses farmers’ markets Yes 0.31 −1.28; 1.89 0.62 −0.23; 1.47 0.11 −1.33; 1.54 Distance to football pitch (min) ≥11d −0.38 −1.78; 1.01 −0.62 −1.04; −0.20 −0.49 −0.69; −0.29* Distance to park/playground (min) ≥11 0.54 0.34; 0.74 0.53 0.33; 0.73* −0.41 −0.96; 0.14 Distance to bakery (min) ≥11 −0.41 −1.34; 0.52 −0.25 −1.15; 0.66 Distance to supermarket (min) ≥11 0.28 −0.22; 0.78 0.13 −1.02; 1.28 0.18 −1.00; 1.36 Distance to farmers’ market (min) ≥11 −0.16 −1.07; 0.76 −0.22 −1.27; 0.83 Uses health center Yes −0.08 −0.86; 0.70 0.51 −0.45; 1.46 Uses RCSA Yes −0.09 −1.41; 1.23 0.00 −0.94; 0.95 Uses CSE Yes −0.12 −1.06; 0.81 −0.02 −2.71; 2.68 Uses social projects Yes −0.27 −0.93; 0.38 0.69 −0.86; 2.24 Uses religious groups Yes 0.20 −1.57; 1.97 0.26 −1.05; 1.57 Uses residents association Yes −0.25 −0.68; 0.18 0.26 −0.14; 0.66 1.98 −2.04; 5.99 Receives Bolsa Família Program Yes −0.58 −1.60; 0.45 −0.38 −3.31; 2.56 −2.38 −2.64; −2.11 −1.29 −2.89; 0.32 aLIF, Lower-income families; HIF, higher-income families. The lower-income cutoff used was a monthly family income of R$2 000 (values ≤50th percentile); this was the equivalent of USD 985 at the start of data collection in September 2012, when 1 USD was worth R2.03, and USD 843 at the end of data collection in August 2013, when 1 USD was worth R$2.37, and varied from 3.2 to 2.94 times the Brazilian minimum monthly wage over the same period. bMultivariate model controlled by age, sex and food intake variables. cCollinearity among variables from each domain of the built environment was detected as follows: (i) physical activity environment: use of parks/playgrounds exhibited multicollinearity with use of football pitch and distance from home to parks/playgrounds; use of football pitches were collinear with distance from home to these same facilities; (ii) food environment: use of farmers’ market with use of bakery and use of supermarket; distance from homes to bakeries, to supermarkets and to farmers’ market have mutual multicollinearity; (iii) social assistance environment: use of health centers with use of Reference Center for Social Assistance, use of Centers for Supplementary Education, and use of residents association; use of Reference Center for Social Assistance with use of social projects, use of Centers for Supplementary Education and use of residents association. dCategories ‘no’ and ‘1–10 min away from home’ were the dummy variables. *P-value < 0.05. Discussion Main findings of this study This study analyzed the use of and distance from subjects’ home to facilities from three different domains of the built environment and their associations with BMI, in schoolchildren aged 7–14 years living in Florianópolis (South Brazil), stratified by their families’ monthly incomes. The main findings are the association between longer distance from homes to parks/playgrounds and higher BMI in schoolchildren from LIF, and lower BMI values and longer distances to football pitches in schoolchildren from HIF. Additionally, the high prevalence rates of overweight and obesity indicate a public health concern. What is already known on this topic Previous studies10–12,39 found that low-income children are more susceptible to the effects of the built environment. In Massachusetts (US), a study conducted with 49 770 students found that proximity to open recreational spaces from home was significantly associated with lower BMI, and the direction of the association between the variables changed when adjusted for characteristics of the socioeconomic environment where students lived.40 This suggests that schoolchildren from low-income families spend more energy using the recreational spaces near to their homes than high-income children. In relation to the ideal distance from homes to parks, living more than 2.4 km from a new park in Alabama (US) did not predict changes in the BMI of schoolchildren under nineteen.41 With regard to the association among high-income schoolchildren between lower BMI and living further away from football pitches, Burgï et al.42 conducted a study in Zurich (Switzerland), observing that children living in neighborhoods with higher socioeconomic status did the majority of their moderate to intense physical activities in schools other than their own. This may suggest children from HIF living in Florianópolis are using football pitches to exercise, but they access these facilities far from home. With regard to the other domains of the built environment, only the socioeconomic environment was significantly associated with BMI in Kiel (Germany), where 485 children were evaluated at 5–7 years of age and at 9–11 years of age.43 Children living in economically deprived areas had an increase of 0.31 BMI units (kg/m2) compared to children living in wealthier areas, irrespective of features of the food, physical activity, and social environments. In an adjusted model, however, this longitudinal effect was partially explained by the educational level of the family.43 Taylor et al.44 did not detect associations between obesity and facilities for physical activity, food outlets or social assistance services in 911 children from 6 to 10 years of age living in the USA when educational level and area income were included as controls in the multivariate model. These results suggest that the availability of places for improving health may be influenced by a social inequality in distribution of services in areas where poorer people live, irrespective of whether a city is well-developed as a whole. In Brazil, a study conducted with 3425 adults only found a relationship between overweight/obesity and the availability of parks in the neighborhood, but did not find an association between this outcome and availability of hypermarkets, supermarkets and farmers’ markets, or the social environment.18 In the largest city in Brazil, São Paulo, findings among adults were similar, i.e. there was a significant correlation between the density of parks and BMI, but there were weak correlations between this outcome and density of supermarkets, farmers’ markets, fast food-restaurants, and other types of restaurants, or the social environment (crime rates).17 What this study adds Our results suggest that living up to 800 m from parks/playgrounds could be an ideal distance for schoolchildren to use these facilities at least once a week, resulting in a lower BMI among schoolchildren from LIF. Even though the variable ‘distance from’ parks/playgrounds was associated with BMI, the variable ‘use’ of the same type of facilities was not statistically significantly associated with the outcome in either of the income strata in our study. Similar results were found by Lavin-Fueyo et al.,45 when evaluating 1 777 children from Córdoba (Argentina). The authors used the same approach as in the present study—a self-report questionnaire on which parents reported the frequency of use and perceived distance from their households to parks, streets, empty lots and cul-de-sacs. They found that use of parks was not associated with increased levels of physical activity in children’s free time. One possible explanation for these results could be the way that the variable ‘use’ was categorized in our study (combining schoolchildren who reported going to places for physical activity on a weekly basis with those who went fortnightly), i.e. those who only go fortnightly may also be those who live further away (more than 10 min’ walk) and, as a consequence, those who have higher values for BMI. Along the same lines, schoolchildren who used parks/playgrounds weekly were possibly those who lived closer and, as a consequence, those who had lower values for the outcome variable. Our results also suggest that schoolchildren from HIF exercise more than once a week at football pitches even though these facilities are far from their homes. In the present study, there were no statistically significant associations between BMI and use of facilities from the food and the social assistance environments, even when stratified by income. In different subsamples of the same survey of schoolchildren, Motter et al.10 found a significant association between use of supermarkets and overweight/obesity among schoolchildren from private schools, as well as a significant association between the same outcome and use of bakeries by schoolchildren from public schools. Also, Corrêa et al.6 found an association between use of farmers’ markets and overweight/obesity. The main reason for these differences could be that the physical activity environment rather than the food or the social assistance environments is more strongly related to the body weight profiles of schoolchildren living in Florianópolis. In addition to these findings, it is important to point out that studies of the built environment should attempt to make greater use of geographical analysis techniques. Edwards et al.10 used a geographically weighted regression analysis to evaluate spatial correlations between obesity and environmental features, suggesting the need to employ other analytic statistical methods in addition to biostatistical methods when assessing environmental data. This was confirmed by Wall et al.,11 in a study conducted in the Minneapolis and Saint Paul metropolitan regions (USA), where they used three different analytic methods and observed that results diverged: they were significant in linear regression but not in other statistical analyses such as spatial latent class analysis and factor analysis. Future studies in Brazil could attempt to advance knowledge by using such analytical techniques. One strong point of this study is that the sample is representative of schools from all geographical regions of the target municipal district. Moreover, investigation of three domains of the built environment suggested that improvements to the physical activity environment could help prevent overweight/obesity. The weighting effect of each person in the sample was also taken into account, minimizing bias in the analysis of variables for which there were fewer responses. Limitations of this study The primary limitation of the study is its cross-sectional design, meaning that additional evidence is needed to confirm the findings. Additionally, our study could be affected by a cause–effect relationship between variables, since overweight or obese schoolchildren may have been using the facilities to treat obesity and this could mask a previously existing association with high BMI. Also, we did not assess variables related to physical activity levels because several such variables could not be properly fitted to the multivariate models. Likewise, it is possible that the self-report measures of use of and distance from schoolchildren’s homes of places/facilities may not correlate well with objective measures if younger children had answered the questionnaire, which reveals a need for future studies to investigate the feasibility of self-report measures in this sample. Conclusion Evaluation of the built environment in three different domains showed that the physical activity environment was the most strongly associated with BMI in schoolchildren enrolled at schools of Florianópolis (South of Brazil). Living a longer distance from parks/playgrounds was significantly associated with higher BMI in low-income schoolchildren, while living a longer distance from football pitches was associated with lower values of the same outcome in high-income schoolchildren. We suggest longitudinal studies should be conducted to confirm the temporal sequence of events. Conflict of interest The authors declare no conflict of interests. Authors’ contributions Wrote the article: CER and PFH; designed the study: CER, ENC and FAGV; collection of data: ENC; performed the analyses: CER; reviewed the article: JN, PFH and FAGV. All authors approved the article. Funding The authors are grateful to the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq - National Council for Scientific and Technological Development) for financial support (Process nos. 014/2011-CNPq and 483955/2011-6). C.E.R. received a research grant from the Fundação de Pesquisa e Inovação do Estado de Santa Catarina, (FAPESC), a division of the Education Department of Santa Catarina State. References 1 Caiaffa WT , Ferreira FR , Ferreira AD et al. . Saude urbana: “a cidade é uma estranha senhora, que hoje sorri e amanhã te devora” . Ciên Saúde Col 2007 ; 13 ( 6 ): 1785 – 96 . Google Scholar CrossRef Search ADS 2 Papas MA , Alberg AJ , Ewing R et al. . The built environment and obesity . Epidemiol Rev 2007 ; 29 : 129 – 43 . Google Scholar CrossRef Search ADS PubMed 3 Feng J , Glass TA , Curriero FC et al. . The built environment and obesity: a systematic review of the epidemiologic evidence . Health Place 2010 ; 16 : 175 – 90 . Google Scholar CrossRef Search ADS PubMed 4 Carroll-Scott A , Gilstad-Haydena K , Rosenthal L et al. . Disentangling neighborhood contextual associations with child body mass index, diet, and physical activity: the role of built, socioeconomic, and social environments . Soc Sci Med 2013 ; 95 : 106 – 14 . Google Scholar CrossRef Search ADS PubMed 5 Motter AF , Vasconcelos FAG , Corrêa EM et al. . Pontos de venda de alimentos e associação com sobrepeso/obesidade em escolares de Florianópolis, Santa Catarina, Brasil . Cad Saúde Pública (Rio de Janeiro) 2015 ; 31 ( 3 ): 620 – 32 . Google Scholar CrossRef Search ADS 6 Corrêa EN , Rossi CE , das Neves J et al. . Utilization and environmental availability of food outlets and overweight/obesity among schoolchildren in a city in the south of Brazil . J Pub Health (Oxf) 2018 ; 40 : 106 – 13 . doi:10.1093/pubmed/fdx017 . Google Scholar CrossRef Search ADS 7 Lavin-Fueyo J , Berra S . Places children use for physical activity in peripheral neighborhoods of the city of Cordoba . Salud Colectiva (Buenos Aires) 2015 ; 11 ( 2 ): 223 – 34 . Google Scholar CrossRef Search ADS 8 Ding D , Sallis JF , Kerr J et al. . Neighborhood environment and physical activity among youth: a review . Am J Prev Med 2011 ; 41 ( 4 ): 442 – 55 . Google Scholar CrossRef Search ADS PubMed 9 Jones A . Residential instability and obesity over time: the role of the social and built environment . Health Place 2015 ; 32 : 74 – 82 . Google Scholar CrossRef Search ADS PubMed 10 Edwards KL , Clarke GP , Ransley JK et al. . The neighbourhood matters: studying exposures relevant to childhood obesity and the policy implications in Leeds, UK . J Epi Commun Health 2010 ; 64 : 194 – 201 . doi:10.1136/jech.2009.088906 . Google Scholar CrossRef Search ADS 11 Wall MM , Larson NI , Forsyth A et al. . Patterns of obesogenic neighborhood features and adolescent weight: a comparison of statistical approaches . Am J Prev Med 2012 ; 42 ( 5 ): e65 – 75 . Google Scholar CrossRef Search ADS PubMed 12 Gose M , Plachta-Danielzik S , Willié B et al. . Longitudinal influences of neighbourhood built and social environment on children’s weight status . Int J Environ Res Pub Health 2013 ; 10 : 5083 – 96 . Google Scholar CrossRef Search ADS 13 Cremm EC , Leite FHM , Abreu DSC et al. . Factors associated with overweight in children living in the neighbourhoods of an urban area of Brazil . Pub Health Nutr 2011 ; 15 ( 6 ): 1056 – 64 . Google Scholar CrossRef Search ADS 14 Leite FHM , Oliveira MA , Cremm EC et al. . Oferta de alimentos processados no entorno de escolas públicas em área urbana . J Pediatr 2012 ; 88 ( 4 ): 328 – 34 . Google Scholar CrossRef Search ADS 15 Vedovato GM , Trude ACB , Kharmats AY et al. . Degree of food processing of household acquisition patterns in a Brazilian urban area is related to food buying preferences and perceived food environment . Appetite 2015 ; 87 : 296 – 302 . Google Scholar CrossRef Search ADS PubMed 16 Duran AC Diez-Roux AV Latorre MRDO Jaime PC 2013 Neighborhood socioeconomic characteristics and differences in the availability of healthy food stores and restaurants in Sao Paulo, Brazil Health Place 23 39 47 Google Scholar CrossRef Search ADS PubMed 17 Jaime PC , Duran AC , Sarti FM et al. . Investigating environmental determinants of diet, physical activity, and overweight among adults in Sao Paulo, Brazil . J Urban Health 2011 ; 88 ( 3 ): 567 – 81 . Google Scholar CrossRef Search ADS PubMed 18 Velásquez-Meléndez G , Mendes LL , Padez CMP . Built environment and social environment: associations with overweight and obesity in a sample of Brazilian adults . Cad Saúde Pública 2013 ; 29 ( 10 ): 1988 – 96 . Google Scholar CrossRef Search ADS PubMed 19 Silva IJO , Alexandre MG , Ravagnani FCP et al. . Atividade física: espaços e condições ambientais para sua prática em uma capital brasileira . Rev Bras Cineantrop Mov 2014 ; 22 ( 3 ): 53 – 62 . Google Scholar CrossRef Search ADS 20 Fermino RC , Reis RS , Hallal PC et al. . Who are the users of urban parks? A study with adults from Curitiba, Brazil . J Phys Activ Health 2015 ; 15 : 58 – 67 . Google Scholar CrossRef Search ADS 21 Hino AAF , Rech CR , Gonçalves PB et al. . Projeto ESPAÇOS de Curitiba, Brasil: aplicabilidade de métodos mistos de pesquisa e informações georreferenciadas em estudos sobre atividade física e ambiente construído . Rev Pan Salud Publica 2012 ; 32 ( 3 ): 226 – 33 . Google Scholar CrossRef Search ADS 22 Corrêa EN , Padez CMP , de Abreu ÂH et al. . Geographic and socioeconomic distribution of food vendors: a case study of a municipality in the Southern Brazil . Cad Saúde Pública 2017 ; 33 ( 2 ): e00145015 . Google Scholar CrossRef Search ADS PubMed 23 Programa das Nações Unidas para o Desenvolvimento (PNUD)/Instituto de Pesquisa Econômica Aplicada (IPEA)/Fundação Joao Pinheiro . Índice de Desenvolvimento Humano Municipal Brasileiro. Brasília: PNUD, Ipea, FJP, 2013 . 96 p. http://www.ipea.gov.br/portal/index.php?option=com_content&id=24037m (25 November 2016, date last accessed). 24 Brazil, Ministério da Saude . Índice de Gini da Renda Domiciliar per Capita. http://tabnet.datasus.gov.br/cgi/idb2011/b09capc.htm (25 November 2016 , date last accessed). 25 Pinho MGM , Adami F , Benedet J et al. . Association between screen time and dietary patterns and overweight/obesity among adolescents . Braz J Nutr 2017 ; 30 : 377 – 89 . 26 Habicht J . Estandarización de métodos epidemiológicos cuantitativos sobre el terreno . Bol Oficina Sanit Panam 1974 ; 76 : 375 – 84 . Google Scholar PubMed 27 World Health Organization/WHO . Physical status: the use and interpretation of anthropometry. WHO Technical Report Series, 854. Geneva: World Health Organization; 1995 . 28 de Onis M , Adelheid M , Onyango W et al. . Development of a WHO growth reference for school-aged children and adolescents . Bull World Health Org 2007 ; 85 : 660 – 7 . Google Scholar CrossRef Search ADS PubMed 29 Jago R , Baranowski T , Baranowski JC et al. . Distance to food stores & adolescent male fruit and vegetable consumption: mediation effects . Int J Behav Nutr Phys Activ 2007 ; 4 : 35 . Google Scholar CrossRef Search ADS 30 Gebauer H , Laska MN . Convenience stores surrounding urban schools: an assessment of healthy food availability, advertising, and product placement . J Urban Health 2011 ; 88 ( 4 ): 616 – 22 . Google Scholar CrossRef Search ADS PubMed 31 Jilcott SB , Wade S , McGuirt JT et al. . The association between the food environment and weight status among eastern North Carolina youth . Pub Health Nutr 2011 ; 14 ( 9 ): 1610 – 7 . Google Scholar CrossRef Search ADS 32 Black JL , Day M . Availability of limited service food outlets surrounding schools in British Columbia . Can Pub Health Assoc 2012 ; 103 ( 4 ): 255 – 59 . 33 He M , Tucker P , Gilliland J et al. . The influence of local food environments on adolescents’ food purchasing behaviors . Int J Environ Res Pub Health 2012 ; 9 ( 4 ): 1458 – 71 . Google Scholar CrossRef Search ADS 34 Jáuregui A , Salvo D , Lamadrid-Figueroa H et al. . Perceived and objective measures of neighborhood environment for physical activity among Mexican adults, 2011 . Prev Chronic Dis 2016 ; 13 : 160009 . Google Scholar CrossRef Search ADS 35 Dewulf B , Neutens T , Van Dyck D et al. . Correspondence between objective and perceived walking times to urban destinations: influence of physical activity, neighbourhood walkability, and socio-demographics . Int J Health Geogr 2012 ; 11 : 43 . Google Scholar CrossRef Search ADS PubMed 36 Colabianchi N , Dowda M , Pfeiffer KA et al. . Towards an understanding of salient neighborhood boundaries: adolescent reports of an easy walking distance and convenient driving distance . Int J Behav Nutr Phys Activ 2007 ; 4 : 66 . Google Scholar CrossRef Search ADS 37 Austin SB , Melly SJ , Sanchez BN et al. . 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 : 1575 – 581 . Google Scholar CrossRef Search ADS PubMed 38 Hosmer DW , Lemeshow S . Applied Logistic Regression , 2nd edn . New York : John Wiley and Sons , 2000 . Google Scholar CrossRef Search ADS 39 Oreskovic NM , Kuhlthau KA , Romm D et al. . Built environment and weight disparities among children in high- and low-income towns . Acad Pediatr 2009 ; 9 : 315 – 21 . Google Scholar CrossRef Search ADS PubMed 40 Duncan DT , Sharifi M , Melly SJ et al. . Characteristics of walkable built environments and BMI z-scores in children: evidence from a large electronic health record database . Environ Health Perspect 2014 ; 122 : 1359 – 65 . Google Scholar PubMed 41 Goldsby TU , George BJ , Yeager VA et al. . Urban park development and pediatric obesity rates: a quasi-experiment using electronic health record data . Int J Environ Res Pub Health 2016 ; 13 : 411 . doi:10.3390/ijerph13040411 . Google Scholar CrossRef Search ADS 42 Burgï R , Tomatis L , Murer K et al. . Spatial physical activity patterns among primary school children living in neighbourhoods of varying socioeconomic status: a cross-sectional study using accelerometry and Global Positioning System . BMC Public Health 2016 ; 16 : 282 . Google Scholar CrossRef Search ADS PubMed 43 Wasserman JA , Suminski R , Xi J et al. . A multi-level analysis showing associations between school neighborhood and child body mass index . Int J Obes (Lond) 2014 ; 38 ( 7 ): 912 – 8 . Google Scholar CrossRef Search ADS PubMed 44 Taylor WC , Upchurch SL , Brosnan CA et al. . Features of the built environment related to physical activity friendliness and children’s obesity and other risk factors . Pub Health Nurs 2014 ; 31 ( 6 ): 545 – 55 . Google Scholar CrossRef Search ADS 45 Lavin-Fueyo J , Garcia LMT , Mamondi V et al. . Neighborhood and family perceived environments associated with children’s physical activity and body mass index . Prev Med 2016 ; 82 : 35 – 41 . Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

Journal of Public HealthOxford University Press

Published: May 29, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off