Obesity and Places Where People Living in Sao Paulo City, Brazil

doi:10.21203/rs.3.rs-17052/v1

Background The built environment characteristics and urban form can influence health outcomes like obesity in people living in high-income countries. However, there are few studies in megacities from middle-high income countries like Brazil in which the built environment has been modified and obesity has been growing slightly. Therefore, the objectives of this study were: 1) to describe the body mass index (BMI) and obesity in different health administrative areas in Sao Paulo; 2) to investigate the association between BMI and obesity with the places where people lived according to social and demographics variables, health variables, built environment, and family per capita income.

Methods This was a cross-sectional study that used the Sao Paulo Health Survey dataset (2015) with 3,145 individuals (18 years or older). The weight and height were self-reported and was calculated the BMI. Residential locations were geocoded, types and the mix of destinations were calculated in 500m buffers. We used multilevel models to examine the association between BMI and obesity with the places where the people lived.

Results The Midwest region showed the highest mean of the mix of destinations than other areas and the lowest prevalence of overweight and obesity. The BMI was higher for people that lived in North, Southeast, South, and East than Midwest after adjusted. Individuals that lived in North (OR=1.69 CI95% 1.18-2.43) and Southeast (OR=1.66 CI 95%1.17-2.37) had increased the likelihood for obesity compared with Midwest after adjusted by social and demographic variables, physical activity level, mix of destinations, and family per capita income.

Conclusion This study found that individuals that lived in the North, Southeast, South, and East had higher BMI than people who lived in Midwest, and people that lived in the North and Southeast had increased the likelihood of obesity compared with the Midwest area. The place where people living can influence BMI and obesity in megacities like Sao Paulo, Brazil. Key words: Body Mass Index, Obesity, Built Environment, Multilevel analysis.

Health Policy

Body Mass Index

Obesity

Built Environment

Multilevel analysis

Obesity is characterized as abnormal or excessive fat accumulation that may decrease health. It is increasing in every region and was estimated to affect 650 million (13%) of people in the world [1]. This is a public health concern because obesity is an important risk factor and determinant for three of the four leading causes of non-communicable diseases (NCDs) worldwide, including cardiovascular diseases, type 2 diabetes, and certain cancers [2]. Nowadays, the major challenge faced relates to the fact that these arguments are multi-factorials problems related mainly to behavioral variables, such as level of physical activity and diet [3], and these variables are associated with health policies and the environmental variables [4].

Several studies have shown associations between built environment and obesity. Adults living in American metropolitan areas had higher BMI scores and were more likely to be obese than those living in brief areas [5]. However, another studies have shown that the association between built environment with obesity is not consensual [6–8].

Regarding the built environment at the neighborhood scale, Frank et al. [9, 10] found similar associations for adults living in metropolitan versus brief areas walkable. Several studies since 2004 have confirmed the inverse association between the land-use mix with obesity [11–14]. Some authors consider that the presence of walkable destinations are important for healthy weight [15, 16]. This assertion is reinforced by other studies that found proximity to parks [17–19] and transit-stations [12, 20] to be associated with lower prevalence of obesity. Other built environment components may include of food access and types of food outlets. The proximity of grocery stores and supermarkets are associated with lower prevalence of obesity [21–23], while the presence of fast-food restaurants and convenience stores is associated with higher prevalence of obesity [24].

Nowadays, other important environment variable is the mix of destinations, defined as the sum of destinations diversity [25–27]. Recently, the mix of destinations in 500 m was associated with walking trips in adults living in Sao Paulo [28].

The most consistent evidence available for an association between built environment components and variations in obesity prevalence across different neighborhoods comes from high-income countries [12, 29]. However, it is not clear yet whether these variations and associations are also true in megacities from middle-income countries like Sao Paulo, Brazil [30–32]. Therefore, the objectives of this study were: 1) to describe the BMI and obesity in different health areas in Sao Paulo city, Brazil; 2) to investigate the association between BMI and obesity with the places where people lived according to social and demographics variables, health variables, built environment, and family per capita income.

Sao Paulo Health Survey

This was the cross-sectional study that used the Sao Paulo Health Survey (ISA) dataset (2015). The final face-to-face interviews were conducted with 4,043 study individuals (12 years or older). More details can be obtained in Alves et al. [33].

ISA: Physical Activity and Environment

This study is part of the longitudinal project to verify the relationship between built environment, physical activity, and nutritional status in adults that living in Sao Paulo city, Brazil.

Outcome variable

Weight and height variables were obtained by self-reported according to two questions: “What is your weight?”, whose response was given in kilograms and grams; and: “What is your height?”, with response options in meters and centimeters. There was calculated the body mass index (BMI) from the formula of weight in kilograms divided by the square of height in meters (Kg/m²), in order to classify their nutritional status according to WHO standards [34]. Adults were considered as overweight (≥ 25.0 Kg/m²) and obese (≥ 30.0 Kg/m²) [34]. The self-report data was validated in a previous study with the same population and the study showed acceptable results [35].

Places of the City Where People Lived

We used the classification of the health administration areas of the municipality government of Sao Paulo city [36]. These areas are divided into five regions until 2015: East, South, Southeast, North, and Midwest [37].

Built Environment

The mix of destinations was characterized by the presence of the following items: a) bus stop; b) train and subway stations; c) parks; d) squares; e) public recreation centers; f) bike paths; g) primary health care units; h) supermarkets; i) bakeries; j) restaurants (food stores), and k) coffee-shops. These variables were calculated using a geographic information system (QGIS 2.14). We delineated radial buffers of 500 meters according to residential address. More details about the mix of destination score can be obtained in Florindo et al. [28].

Family Per Capita Income

The family per capita income using the total residence income divided by total the number of persons living in the same household was measured through the question “What was the average overall net household income last month?” and analyzed in quartiles: 1^st quartile (lowest), second, third and fourth (highest).

Physical Activity

The physical activity was evaluated by International Physical Activity Questionnaire long version. The score was calculated in minutes per week by sum of minutes in each domain: occupational, leisure, transportation and household [38-40]. We used the cutoff point of 150 minutes per week (0-149 minutes/week, ≥150 minutes/week).

Social and Demographic Variables

Age groups (18–29, 30–39, 40–49, 50–59, and 60 years or older), sex (male, female), level of education (incomplete elementary school, complete elementary to incomplete high school, complete high school, undergraduate incomplete to complete) and, length of living in the same residence (up to one year, between one and five years, >five years).

Statistical Analysis

Chi-square test for overweight and obesity was calculated according to social and demographics variables, and health characteristics. Complex sample design according to the census tract (primary unit of the sample) in five health areas in Sao Paulo (strata), and the sample weight was used.

Two outcomes were used, 1) BMI in Kg/m²; and 2) Obesity (BMI ≥ 30.0 Kg/m²; yes or no). The place where people lived were the main independent variable and was based in five health administrative areas of the city: Midwest, North, Southeast, South, and East. Normality test was performed (Kolmogorov-Smirnov), since the data had no normal distribution, the mean was compared by the Kruskal-Wallis test and multiple post-hoc comparisons were performed using the Bonferroni test.

The modeling was undertaken took into account clustering by census tract and household in four stages: 1) Firstly, we conducted the analysis of BMI and obesity with places where people lived without adjust; 2) Secondly, we examined the analysis with adjustment for sex, age groups, education, length of living in the same residence and total physical activity; 3) Thirdly, we used all variables of the model 2 with mix of destinations 500 m buffers’ size and 4) Finally, we used all variables of the model 3 with family per capita income. We used the xtmixed command for linear models and the results were presented as beta coefficients (β) with 95% confidence intervals, and the xtmelogit command for logistic models and the results were presented as odds ratios (OR) with 95% confidence intervals.

All analyses were conducted in Stata software (Stata version SE 12.1, StataCorp).

Ethical Committee

The Ethics Committee of the School of Arts, Sciences, and Humanities at the University of Sao Paulo approved this study (process number 55846116.6.0000.5390).

The distribution of the adult population with BMI complete data regarding to five places was showed in Figure 1. Southeast area had a higher number of individuals (n=704) and the Midwest (n=502) was the smallest. The prevalence of overweight and obesity was high in adults who lived in the North (61.0% and 23.3%, respectively); in the other hand, Midwest area had a lower prevalence of both overweight (49.8%) and obesity (18.0%). There was no statistically significant difference between the five health administrative areas of both overweight (p=0.07) and obesity (p=0.19) (Table 1).

Insert Figure 1

Insert Table 1

The summary of a mix of destinations variables according to the five areas in Sao Paulo city was showed in Table 2. The Midwest region showed higher mean that other areas in 500 m buffers’ size and the difference between areas was significant (p<0.05). Other differences were shown with family per capita income and total physical activity variables (Table 2).

Insert Table 2

Table 3 presents the results of the multilevel linear regression explaining the influence of BMI values in the health area administration where living according to social, demographics, environmental and health variables. After adjustment for all these variables, compared to the Midwest area, the North area had a greater increase in the BMI and all results were statistically significant to the other areas.

Insert Table 3

The people that lived in North area had increased the likelihood compared with people that lived in Midwest, independently of social and demographic variables, physical activity level, mix of destinations, and family per capita income (Table 4). For people lived in Southeast, the likelihood for obesity lost the significance after the adjustment by social and demographic variables, and for physical activity level, however, after the adjustment by mix of destinations, and family per capita income, the likelihood for obesity returned.

Insert Table 4

The people that lived in South had more likelihood compared with people that lived in Midwest only after the adjustment for social and demographic variables, for physical activity level, and for a mix of destinations, however, the significance was lost after the adjustment by family per capita income (Table 4).

The main findings of the present study were: 1) The Midwest region showed the highest mean of mix of destinations than other areas and the lowest prevalence of overweight and obesity in Sao Paulo; 2) The BMI was high for people living in North, Southeast, South, and East than comparison with people from Midwest after adjusted for all variables; 3) Individuals that lived in North and Southeast had increased likelihood for overweight and obesity compared with Midwest.

Despite of the other study verified environmental determinants of diet, physical activity, and overweight among adults in Sao Paulo [41], this is the first study in a representative sample that investigated differences between BMI and obesity according to places where people lived.

In the Midwest area, we found the largest difference mean in the score of a mix of destinations compared to other areas, and lower prevalence for both overweight and obesity. This place presents an average income superior to other areas of the municipality, with a greater supply of public equipment, and jobs. A very diversified area with a great emphasis on green areas, with more than fifteen municipal parks, squares and shopping centers, restaurants, besides the main subway lines and bus stops [42], and this seems to explain our results. Differently, the North area in Sao Paulo, despite having extensive green areas and native forest and increased population density, in this area, 31.3% of the population total deaths in men and 34.2% in women are due to cardiovascular diseases. Overweight and obesity are risk factors that have contributed to the high of deaths due to this cause [43].

The male had a higher prevalence for overweight, and female had a higher prevalence for obesity. This pattern is detected in high-income countries over time [44, 45]. The observed differences across the cities suggest that environmental factors, and social contexts [46–49], can establish an important role regarding sex differences in overweight and obesity. It is important to note that the length of residence was associated with overweight and obesity and corroborated with other studies [50–52] showing that this variable is important for analyzing outcomes as overweight and obesity in upper-middle-income countries.

The socioeconomic status like the family per capita income is important to be considered and explains possible differences between places where people lived and the relationship with overweight and obesity. Despite family per capita income did not show association with overweight and obesity when analyzed by the chi-square test, in the hierarchical model, people that lived in North and Southeast areas had increased in the likelihood for overweight and obesity compared with people that lived in Midwest area. In addition, people that lived in South and East areas had lost significance for obesity after adjustment by family per capita income. The East area has a high population density, region with the lowest per capita income in the municipality, with the worst infrastructure, with the highest incidence of poverty and the lowest Human Development Index [42]. The South is a region that is observed economic distress, beyond social exclusion, with the presence of places of poverty and housing centers along brooks and watershed areas [53]. This shows that per capita household income is very important in these areas when we analyzed the nutritional status of the population.

In the city of Sao Paulo, those who earn more than one minimum wage per person per month in the household are 14% more likely to have obesity. And, these results may be associated specifically with other factors as food environment and food security [31, 46, 54]. These results were similar with other studies [55–58] that showed that disadvantage areas are associated with health problems in adults and support the need to focus on improving local environments to reduce socioeconomic and other inequalities.

The mix of destinations modified some results about the association between the places where people lived with obesity. A systematic review and meta-analysis of longitudinal studies about built environment and cardio-metabolic health showed no evidence was found, but this results were shown in high-income countries [59]. Other systematic review of the observational studies not found associations between walkability and obesity in longitudinal studies conducted with adults in high-income countries [60].

About the mix of destinations, Florindo et al. [28] showed that people that lived within 500 m with highest destinations had a greater likelihood of walking for transportation. The presence of destinations near of residences where people living are important for people walking, however, is important to investigate the different types to destinations, because the proximity of supermarkets is associated with lower rates of overweight and obesity [21–23], but the presence of fast-food restaurants and convenience stores is associated with higher rates of overweight and obesity [24]. In addition, should be considered that the rich access to healthy and affordable food and better local stores with no limited variety, higher quality and poorer prices, no compromising food security and potentially decrease inequities [61, 62].

Some limitations should be considered in interpreting the findings of the study. The caution should be exercised when extrapolating these results due to neighborhood self-selection bias, as people who are not obese and who live a healthier lifestyle that prevents obesity may choose to live in neighborhoods with better conditions [63, 64]. To obtain stronger causal inferences, further longitudinal and quasi-experimental studies must be conducted in addition to natural experimental studies [65]. The values for height and weight used to calculate the body mass index were self-reported by the individuals during the household interview. Although the self-reported data have been validated in a previous study with Sao Paulo Health Survey [35], they may be subject to inaccuracies. Moreover, the environment variable analyzed in our study does not fully represent the different aspects of the environment, since only the mix of destinations represents little to characterize the environment, and it is necessary to have other variables as walkability and the indicators that are part of the index as land-use mix, residential density, street connectivity. Family per capita income as a classification of socioeconomic status also has limitations due to the number of missing values, to avoid this it would be important to use other socioeconomic indicators. And, there was no evaluation of the eating pattern of the individuals.

Despite some limitations, this study showed that people lived in the Midwest had the lowest prevalence of overweight and obesity than in other regions. In addition, we found that individuals that lived in the North, Southeast, South, and East had higher BMI than people who lived in Midwest, and people that lived in the North and Southeast had increased the likelihood of obesity compared with the Midwest independent of social and demographic variables, physical activity, environmental variables, and socioeconomic status. Family per capita income modified the results for people living in East and South. The place where people living can influence health outcomes like BMI and obesity in megacities like Sao Paulo and these results may foster relevant information for decision-makers and researchers to discuss interventions to contribute to preventing this health problem in these places.

IPAQ

International Physical Activity Questionnaire; CI–Confidence interval; OR–Odds Ratio; NCDs–Non-communicable diseases; BMI–Body Mass Index; SE–Standard error.

Ethics approval and consent to participate

The Ethics Committee of the School of Arts, Sciences, and Humanities at the University of Sao Paulo approved the study (process number 55846116.6.0000.5390). The consent for participation in the study was written and obtained where individuals were children (under 16 years old) from their parents or guardian.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

João Paulo dos Anjos Souza Barbosa holds a postdoctoral scholarship from the Postdoctoral National Program (PNPD/CAPES – Brazil). Alex Antonio Florindo received an international scholarship from Sao Paulo Research Foundation (grant 2014/12681-1) to develop this study and is receiving a research fellowship from the Brazilian National Council for Scientific and Technological Development (CNPq) (grant 306635/2016-0). Ligia Vizeu Barrozo is supported by the Brazilian National Council for Scientific and Technological Development (CNPq) (grant 301550/2017-4). ISA study was supported by Sao Paulo Research Foundation (grant 2012/22113-9) and The Sao Paulo Municipal Health Department (no grant number). ISA – Physical Activity and Environment Study is supported by Sao Paulo Research Foundation (grant 2017/17049-3). All these fundings contributed since the design of the study and collection to analysis, and interpretation of data and in writing.

Authors’ contributions

JPASB and AAF had the idea of this study. JPASB and AAF contributed to data statistical analysis. JPASB, BSA, MAF, LVB, RMF and AAF contributed to interpretation and drafted the manuscript. JPASB, BSA, MAF and LVB contributed to georeferencing of built environment variables. All authors contributed to drafting, critically revising and approved the final manuscript.

Acknowledgements

Acknowledgments to ISA Study Group (Marilisa Berti de Azevedo Barros, PhD, University of Campinas; Maria Cecília Goi Porto Alves, PhD, Health of Institute, Sao Paulo; Regina Mara Fisberg, PhD, University of Sao Paulo; and Chester Luiz Galvão Cesar, PhD, University of Sao Paulo). Acknowledgments to The University of Melbourne for the reception of the international visit of Alex Antonio Florindo to develop this project in the Melbourne School of Population and Global Health. Acknowledgments to Professor Billie Giles-Corti for his support of the work in Melbourne School of Population and Global Health.

Fact sheets: Obesity and overweight. [http://www.who.int/news-room/fact-sheets/detail/obesity-andoverweight. ]
Swinburn BA, Kraak VI, Allender S, Atkins VJ, Baker PI, Bogard JR, Brinsden H, Calvillo A, De Schutter O, Devarajan R et al: The Global Syndemic of Obesity, Undernutrition, and Climate Change: <em>The Lancet</em> Commission report. The Lancet 2019, 393(10173):791-846.
Bouchard C: Physical activity and obesity: Manole; 2003.
Sallis JF, Cervero RB, Ascher W, Henderson KA, Kraft MK, Kerr J: An ecological approach to creating active living communities. Annual Review of Public Health 2006, 27(1):297-322.
Ewing R, Schmid T, Killingsworth R, Zlot A, Raudenbush S: Relationship between Urban Sprawl and Physical Activity, Obesity, and Morbidity. American Journal of Health Promotion 2003, 18(1):47-57.
Plantinga AJ, Bernell S: The association between urban sprawl and obesity: Is it a two-way street? Journal of Regional Science 2007, 47(5):857-879.
Feng J, Glass TA, Curriero FC, Stewart WF, Schwartz BS: The built environment and obesity: A systematic review of the epidemiologic evidence. Health & Place 2010, 16(2):175-190.
Gregson J: Poverty, Sprawl, and Restaurant Types Influence Body Mass Index of Residents in California Counties. Public Health Reports 2011, 126(1_suppl):141-149.
Frank LD, Sallis JF, Conway TL, Chapman JE, Saelens BE, Bachman W: Many pathways from land use to health - Associations between neighborhood walkability and active transportation, body mass index, and air quality. Journal of the American Planning Association 2006, 72(1):75-87.
Frank LD, Kerr J, Sallis JF, Miles R, Chapman J: A hierarchy of sociodemographic and environmental correlates of walking and obesity. Prev Med 2008, 47(2):172-178.
Mobley LR, Root ED, Finkelstein EA, Khavjou O, Farris RP, Will JC: Environment, Obesity, and Cardiovascular Disease Risk in Low-Income Women. American Journal of Preventive Medicine 2006, 30(4):327-332.e321.
Rundle A, Roux AVD, Freeman LM, Miller D, Neckerman KM, Weiss CC: The Urban Built Environment and Obesity in New York City: A Multilevel Analysis. American Journal of Health Promotion 2007, 21(4_suppl):326-334.
Rutt CD, Coleman KJ: Examining the relationships among built environment, physical activity, and body mass index in El Paso, TX. Preventive Medicine 2005, 40(6):831-841.
Li F, Harmer PA, Cardinal BJ, Bosworth M, Acock A, Johnson-Shelton D, Moore JM: Built Environment, Adiposity, and Physical Activity in Adults Aged 50–75. American Journal of Preventive Medicine 2008, 35(1):38-46.
Brown BB, Yamada I, Smith KR, Zick CD, Kowaleski-Jones L, Fan JX: Mixed land use and walkability: Variations in land use measures and relationships with BMI, overweight, and obesity. Health & Place 2009, 15(4):1130-1141.
Yamada I, Brown BB, Smith KR, Zick CD, Kowaleski-Jones L, Fan JX: Mixed Land Use and Obesity: An Empirical Comparison of Alternative Land Use Measures and Geographic Scales. Professional Geographer 2012, 64(2):157-177.
Berry TR, Spence JC, Blanchard CM, Cutumisu N, Edwards J, Selfridge G: A longitudinal and cross-sectional examination of the relationship between reasons for choosing a neighbourhood, physical activity and body mass index. International Journal of Behavioral Nutrition and Physical Activity 2010, 7(1):57.
Rundle A, Quinn J, Lovasi G, Bader MDM, Yousefzadeh P, Weiss C, Neckerman K: Associations between Body Mass Index and Park Proximity, Size, Cleanliness, and Recreational Facilities. American Journal of Health Promotion 2013, 27(4):262-269.
West ST, Shores KA, Mudd LM: Association of available parkland, physical activity, and overweight in America's largest cities. Journal of Public Health Management and Practice 2012, 18(5):423-430.
Werner CM: Before and After a New Light Rail Stop: Resident Attitudes, Travel Behavior, and Obesity AU - Brown, Barbara B. Journal of the American Planning Association 2008, 75(1):5-12.
Chen S, Florax RJ, Snyder S, Miller CC: Obesity and access to chain grocers. Economic geography 2010, 86(4):431-452.
Drewnowski A, Aggarwal A, Hurvitz PM, Monsivais P, Moudon AV: Obesity and Supermarket Access: Proximity or Price? American Journal of Public Health 2012, 102(8):e74-e80.
Inagami S, Cohen DA, Finch BK, Asch SM: You Are Where You Shop: Grocery Store Locations, Weight, and Neighborhoods. American Journal of Preventive Medicine 2006, 31(1):10-17.
Pereira MA, Kartashov AI, Ebbeling CB, Van Horn L, Slattery ML, Jacobs DR, Ludwig DS: Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis. The Lancet 2005, 365(9453):36-42.
Gunn LD, King TL, Mavoa S, Lamb KE, Giles-Corti B, Kavanagh A: Identifying destination distances that support walking trips in local neighborhoods. Journal of Transport & Health 2017, 5:133-141.
McCormack GR, Giles-Corti B, Bulsara M: The relationship between destination proximity, destination mix and physical activity behaviors. Preventive Medicine 2008, 46(1):33-40.
King TL, Bentley RJ, Thornton LE, Kavanagh AM: Does the presence and mix of destinations influence walking and physical activity? International journal of behavioral nutrition and physical activity 2015, 12(1):115.
Florindo AA, Barbosa JPdAS, Barrozo LV, Andrade DR, de Aguiar BS, Failla MA, Gunn L, Mavoa S, Turrell G, Goldbaum M: Walking for transportation and built environment in Sao Paulo city, Brazil. Journal of Transport & Health 2019, 15:100611.
Maharana A, Nsoesie EO: Use of Deep Learning to Examine the Association of the Built Environment With Prevalence of Neighborhood Adult ObesityDeep Learning to Examine the Built Environment and Neighborhood Adult Obesity PrevalenceDeep Learning to Examine the Built Environment and Neighborhood Adult Obesity Prevalence. JAMA Network Open 2018, 1(4):e181535-e181535.
Vigilância de fatores de Risco e Proteção para Doenças Crônicas por Inquérito Telefônico [http://bvsms.saude.gov.br/bvs/publicacoes/vigitel_brasil_2017_vigilancia_fatores_riscos.pdf]
Pereira JL, Vieira DADS, Alves MCGP, César CLG, Goldbaum M, Fisberg RM: Excess body weight in the city of São Paulo: panorama from 2003 to 2015, associated factors and projection for the next years. BMC public health 2018, 18(1):1332-1332.
Pereira JL, Vieira DAdS, Alves MCGP, César CLG, Goldbaum M, Fisberg RM: Excess body weight in the city of São Paulo: panorama from 2003 to 2015, associated factors and projection for the next years. BMC Public Health 2018, 18(1):1332.
Alves MCGP, Escuder MML, Goldbaum M, Barros MBdA, Fisberg RM, Cesar CLG: Sampling plan in health surveys, city of São Paulo, Brazil, 2015. Revista de Saúde Pública 2018, 52.
World Health Organization: Preventing and Managing the Global Epidemic. Report of a WHO Consultation on Obesity, Geneva: World Health Organization. Obesity 1997:p. 107-158.
Carvalho AMd, Piovezan LG, Selem SSAdC, Fisberg RM, Marchioni DML: Validação e calibração de medidas de peso e altura autorreferidas por indivíduos da cidade de São Paulo. Revista Brasileira de Epidemiologia 2014, 17(3):735-746.
Portaria Intersecretarial Secretaria Municipal da Saúde - SMS nº 1 de 2 de Setembro de 2005 [http://legislacao.prefeitura.sp.gov.br/leis/portaria-intersecretarial-secretaria-municipal-da-saude-1-de-2-de-setembro-de-2005/consolidado]
Decreto Nº 57.857 DE 5 de Setembro de 2017 [http://legislacao.prefeitura.sp.gov.br/leis/decreto-57857-de-05-de-setembro-de-2017]
Florindo AA, Guimarães VV, Cesar CLG, de Azevedo Barros MB, Alves MCGP, Goldbaum M: Epidemiology of leisure, transportation, occupational, and household physical activity: prevalence and associated factors. Journal of Physical Activity and Health 2009, 6(5):625-632.
Hallal PC, Victora CG, Wells JCK, Lima RC: Physical inactivity: prevalence and associated variables in Brazilian adults. Medicine & Science in Sports & Exercise 2003, 35(11):1894-1900.
Ferrari TK, Cesar CLG, Alves MCGP, Barros MBdA, Goldbaum M, Fisberg RM: Estilo de vida saudável em São Paulo, Brasil. Cadernos de Saúde Pública 2017, 33.
Jaime PC, Duran AC, Sarti FM, Lock K: Investigating environmental determinants of diet, physical activity, and overweight among adults in Sao Paulo, Brazil. Journal of urban health : bulletin of the New York Academy of Medicine 2011, 88(3):567-581.
Plano de Desenvolvimento Urbano Integrado - Região Metropolitana de São Paulo [https://www.pdui.sp.gov.br/rmsp/]
Boletim CCD DANT [https://www.prefeitura.sp.gov.br/cidade/secretarias/upload/chamadas/dant_norte_final1_1424352957.pdf]
Vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico [http://portalarquivos2.saude.gov.br/images/pdf/2019/julho/25/vigitel-brasil-2018.pdf]
Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, Mullany EC, Biryukov S, Abbafati C, Abera SF et al: Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet 2014, 384(9945):766-781.
Malik VS, Willett WC, Hu FB: Global obesity: trends, risk factors and policy implications. Nature Reviews Endocrinology 2012, 9:13.
Florindo AA, Barrozo LV, Cabral-Miranda W, Rodrigues EQ, Turrell G, Goldbaum M, Cesar CLG, Giles-Corti B: Public Open Spaces and Leisure-Time Walking in Brazilian Adults. International Journal of Environmental Research and Public Health 2017, 14(6):553.
Papas MA, Alberg AJ, Ewing R, Helzlsouer KJ, Gary TL, Klassen AC: The Built Environment and Obesity. Epidemiologic Reviews 2007, 29(1):129-143.
Fraser B: Latin America's urbanisation is boosting obesity. The Lancet 2005, 365(9476):1995-1996.
Obiang-Obounou BW: The Length of Residence is Associated with Cardiovascular Disease Risk Factors among Foreign-English Teachers in Korea. Behavioral Sciences 2018, 8(1):2.
Commodore‐Mensah Y, Ukonu N, Obisesan O, Aboagye Jonathan K, Agyemang C, Reilly Carolyn M, Dunbar Sandra B, Okosun Ike S: Length of Residence in the United States is Associated With a Higher Prevalence of Cardiometabolic Risk Factors in Immigrants: A Contemporary Analysis of the National Health Interview Survey. Journal of the American Heart Association, 5(11):e004059.
da Costa LP, Dias SF, Martins MdRO: Association between length of residence and overweight among adult immigrants in Portugal: A nationwide cross-sectional study. BMC Public Health 2017, 17(1):316.
Boletim CCD DANT [https://www.prefeitura.sp.gov.br/cidade/secretarias/upload/chamadas/boletim_dantsul_final_link-2_1422448981.pdf]
Bhattacharya J, Currie J, Haider S: Poverty, food insecurity, and nutritional outcomes in children and adults. Journal of Health Economics 2004, 23(4):839-862.
King T, Kavanagh AM, Jolley D, Turrell G, Crawford D: Weight and place: a multilevel cross-sectional survey of area-level social disadvantage and overweight/obesity in Australia. International Journal Of Obesity 2005, 30:281.
Ellaway A, Anderson A, Macintyre S: Does area of residence affect body size and shape? International journal of obesity 1997, 21(4):304.
Sundquist J, Malmström M, Johansson S-E: Cardiovascular risk factors and the neighbourhood environment: a multilevel analysis. International journal of epidemiology 1999, 28(5):841-845.
Diez-Roux AV, Link BG, Northridge ME: A multilevel analysis of income inequality and cardiovascular disease risk factors. Social science & medicine 2000, 50(5):673-687.
Chandrabose M, Rachele JN, Gunn L, Kavanagh A, Owen N, Turrell G, Giles-Corti B, Sugiyama T: Built environment and cardio-metabolic health: systematic review and meta-analysis of longitudinal studies. Obesity Reviews 2019, 20(1):41-54.
Paulo dos Anjos Souza Barbosa J, Henrique Guerra P, de Oliveira Santos C, de Oliveira Barbosa Nunes AP, Turrell G, Antonio Florindo A: Walkability, Overweight, and Obesity in Adults: A Systematic Review of Observational Studies. International Journal of Environmental Research and Public Health 2019, 16(17):3135.
Coveney J, O’Dwyer LA: Effects of mobility and location on food access. Health & Place 2009, 15(1):45-55.
Winkler E, Turrell G, Patterson C: Does living in a disadvantaged area mean fewer opportunities to purchase fresh fruit and vegetables in the area? Findings from the Brisbane food study. Health & Place 2006, 12(3):306-319.
McCormack GR, Shiell A: In search of causality: a systematic review of the relationship between the built environment and physical activity among adults. International Journal of Behavioral Nutrition and Physical Activity 2011, 8(1):125.
Giles-Corti B, Bull F, Knuiman M, McCormack G, Van Niel K, Timperio A, Christian H, Foster S, Divitini M, Middleton N et al: The influence of urban design on neighbourhood walking following residential relocation: Longitudinal results from the RESIDE study. Social Science & Medicine 2013, 77:20-30.
Garfinkel-Castro A, Kim K, Hamidi S, Ewing R: Obesity and the built environment at different urban scales: examining the literature*. Nutrition Reviews 2017, 75(suppl_1):51-61.

Table 1. Descriptive analysis of body mass index, overweight and obesity according to health administrative area where people lived, Sao Paulo Health Survey, 2015.

Exposure variables	Overweight			Obesity			BMI
Exposure variables	BMI <25.0 Kg/m²	BMI ≥25.0 Kg/m²	p	BMI <30 Kg/m²	BMI ≥30 Kg/m²	p	Mean (SE)	p
Overall (n=3145)	43.6	56.4	-	79.2	20.8	-	26.3 (0.1)	-

Health administrative areas (n=3077)			0.07			0.19		0.01
Midwest	50.2	49.8		82.0	18.0		25.8 (0.3)
North	39.0	61.0		76.7	23.3		26.7 (0.3)
Southeast	42.9	57.1		77.5	22.5		26.4 (0.2)
South	43.0	57.0		79.1	20.9		26.3 (0.3)
East	45.0	55.0		82.0	18.0		26.1 (0.2)

BMI – Body Mass Index; SE – Standard error.

Table 2. Descriptive variables for mix of destinations, family per capita income, and physical activity according to health administration area where people lived, Sao Paulo Health Survey, 2015.

Exposure variables	Overall (n=3145) % (95% CI); Mean (SE)	Health administrative areas in Sao Paulo city, Brazil
Exposure variables	Overall (n=3145) % (95% CI); Mean (SE)	Midwest (I) (n=502) % (95% CI); Mean (SE)	North (II) (n=608) % (95% CI); Mean (SE)	Southeast (III) (n=704) % (95% CI); Mean (SE)	South (IV) (n=645) % (95% CI); Mean (SE)	East (V) (n=618) % (95% CI); Mean (SE)	p

Mix of destinations 500 m buffers’ size (n = 3077)							<0.001
Lowest	38.9 (32.6; 45.7)	16.2 (6.7; 34.2)	33.8 (21.3; 49.0)	35.4 (23.2; 49.9)	52.9 (38.3; 67.0)	50.9 (36.1; 65.5)
Middle	22.9 (18.0; 28.7)	18.2 (6.8; 40.3)	19.8 (10.9; 33.2)	29.4 (19.4; 41.8)	19.3 (11.9; 29.8)	24.7 (14.3; 39.1)
Highest	38.2 (31.1; 45,9)	65.7 (44.9; 81.7)	46.4 (28.5; 65.4)	35.1 (22.0; 50.9)	27.8 (15.1; 45.5)	24.5 (13.3; 40.7)
Mean (SE) Mix of destinations 500 m buffers (score)	3.2 (0.1)	4.5 (0.4) °+#&	3.3 (0.3) *#&	3.3 (0.3) *#&	2.5 (0.2) *°+	2.5 (0.2) *°+	<0.001
Family per capita income (n = 2497)							<0.001
Lowest	23.1 (20.1; 26.3)	11.4 (5.5; 22.0)	26.2 (19.7; 34.0)	16.6 (12.1; 22.3)	30.9 (24.8; 37.8)	30.4 (24.1; 37.4)
Second	23.8 (21.4; 26.4)	20.0 (13.9; 28.0)	23.3 (19.1; 28.0)	20.8 (16.1; 26.5)	28.2 (23.1; 33.8)	27.3 (22.4; 32.8)
Third	25.0 (22.8; 27.4)	24.9 (19.1; 31.6)	23.4 (18.6; 29.0)	25.6 (21.6; 30.1)	21.9 (17.0; 27.7)	28.5 (23.7; 33.8)
Highest	28.1 (24.3; 32.3)	43.7 (32.7; 55.4)	27.1 (18.6; 37.7)	37.1 (28.8; 46.2)	19.1 (12.5; 27.9)	13.8 (10.0; 18.8)
Mean (SE) Brazilian Currency	1332.0 (70.8)	2089.3 (258.2) °+#&	1163.1 (104.1) *&	1592.9 (182.8) *#&	1015.6 (95.0) *+	874.2 (47.6) *°+	<0.001
Total physical activity (n = 3050)
≥150 min/week	80.9 (78.8; 82.9)	77.3 (70.5; 83.0)	81.6 (76.0; 86.1)	82.7 (78.4; 86.3)	77.9 (72.8; 82.4)	83.5 (79.9; 86.5)	0.04
0-149 min/week	19.1 (17.1; 21.2)	22.7 (17.0;29.5)	18.4 (13.9; 24.0)	17.3 (13.7; 21.6)	22.1 (17.6; 27.2)	16.5 (13.5; 20.1)
Mean (SE) minutes per week	1150.3 (40.1)	1052.7 (98.6) #&	1177.6 (94.9)	1016.4 (84.3) #&	1196.2 (78.0) *+	1325.3 (91.3) *+	<0.001

Mean difference of mix of destinations 500 m buffers (score): *Significant difference between I with II, III, IV and V; ° Significant difference between II with I, IV and V; + Significant difference between III with I, IV and V; # Significant difference between IV with I, II and III; & Significant difference between V with I, II and III.

Mean difference of family per capita income (Brazilian Currency): *Significant difference between I with II, III, IV and V; ° Significant difference between II with I and V; + Significant difference between III with I, IV and V; # Significant difference between IV with I and III; & Significant difference between V with I, II and III.

Mean difference of total physical activity (minutes per week): *Significant difference between I with IV and V; ° No significant difference; + Significant difference between III with IV and V; # Significant difference between IV with I and III; & Significant difference between V with I and III.

Table 3. Multilevel linear regression results explaining the influence of BMI values in the health administration area where living according to social, demographics, environmental and health variables, Sao Paulo Health Survey, 2015.

Exposure variable	BMI (Kg/m²)
	β (95% CI) Model 1*	β (95% CI) Model 2**	β (95% CI) Model 3***	β (95% CI) Model 4****
Health administration area where living
Midwest	Ref.	Ref.	Ref.	Ref
North	0.94 (0.33-1.55)^#	0.97 (0.36-1.58)^#	1.02 (0.41-1.63)^#	1.35 (0.67-2.02)^#
Southeast	0.62 (0.03-1.22)^#	0.64 (0.51-1.23)^#	0.73 (0.13-1.32)^#	1.06 (0.41-1.72)^#
South	0.63 (0.22-1.23)^#	0.78 (0.17-1.39)^#	0.89 (0.27-1.51)^#	1.07 (0.36-1.78)^#
East	0.38 (-0.23-0.99)	0.47 (-0.15-1.08)	0.57 (-0.05-1.20)	0.69 (0.01-1.37)^#

*Model 1 without adjust; **Model 2 adjusted by sex, age groups, education, length of residence and total physical activity; *** Model 3 variables of the model 2 with mix of destinations 500 m buffers’ size; **** Model 4 variables of the model 3 with family per capita income; # p<0.05.

Table 4. Association between health administration area where people lived and obesity according to social, demographics, environmental and health variables, Sao Paulo Health Survey, 2015.

Exposure variable	Obesity BMI (≥30 Kg/m²; yes or no)	Obesity (BMI ≥30 Kg/m²; yes or no)	Obesity (BMI ≥30 Kg/m²; yes or no)	Obesity (BMI ≥30 Kg/m²; yes or no)
Exposure variable	Model 1* OR (95% CI)	Model 2** OR (95% CI)	Model 3*** OR (95% CI)	Model 4**** OR (95% CI)
Health administration area where living
Midwest	1	1	1	1
North	1.46 (1.06-2.00)^#	1.45 (1.04-2.02)^#	1.50 (1.08-2.10)^#	1.69 (1.18-2.43)^#
Southeast	1.37 (1.00-1.87)^#	1.38 (1.00-1.89)	1.46 (1.05-2.02)^#	1.66 (1.17-2.37)^#
South	1.36 (0.99-1.86)	1.34 (0.96-1.87)	1.46 (1.04-2.05)^#	1.45 (0.99-2.13)
East	1.10 (0.79-1.53)	1.10 (0.78-1.55)	1.19 (0.84-1.68)	1.16 (0.80-1.70)

*Model 1 without adjust; **Model 2 adjusted by sex, age groups, education, length of residence and total physical activity; *** Model 3 variables of the model 2 with mix of destinations 500 m buffers’ size; **** Model 4 variables of the model 3 with family per capita income; # p<0.05.

Obesity and Places Where People Living in Sao Paulo City, Brazil

Status:

Version 1

Abstract

Figures

Background

Methods

Results

Discussion

Conclusions

Abbreviations

Declarations

References

Tables

Status:

Version 1