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1.
Ogden  CL, Carroll  MD, Kit  BK, Flegal  KM.  Prevalence of childhood and adult obesity in the United States, 2011-2012.  JAMA. 2014;311(8):806-814.PubMedGoogle ScholarCrossref
2.
Fryar  CD, Carroll  MD, Ogden  CL; National Center for Health Statistics, Centers for Disease Control and Prevention. NCHS health e-stat: prevalence of overweight, obesity, and extreme obesity among adults: United States, trends 1960-1962 through 2009-2010. http://www.cdc.gov/nchs/data/hestat/obesity_adult_09_10/obesity_adult_09_10.htm#table2. Accessed May 14, 2015.
3.
Geiss  LS, Wang  J, Cheng  YJ,  et al.  Prevalence and incidence trends for diagnosed diabetes among adults aged 20 to 79 years, United States, 1980-2012.  JAMA. 2014;312(12):1218-1226.PubMedGoogle ScholarCrossref
4.
Nichols  GA, Schroeder  EB, Karter  AJ,  et al; SUPREME-DM Study Group.  Trends in diabetes incidence among 7 million insured adults, 2006-2011: the SUPREME-DM project.  Am J Epidemiol. 2015;181(1):32-39.PubMedGoogle ScholarCrossref
5.
Lipscombe  LL, Hux  JE.  Trends in diabetes prevalence, incidence, and mortality in Ontario, Canada 1995-2005: a population-based study.  Lancet. 2007;369(9563):750-756.PubMedGoogle ScholarCrossref
6.
Danaei  G, Finucane  MM, Lu  Y,  et al; Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Blood Glucose).  National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants.  Lancet. 2011;378(9785):31-40.PubMedGoogle ScholarCrossref
7.
Sallis  JF, Cerin  E, Conway  TL,  et al.  Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study.  Lancet. doi:10.1016/S0140-6736(15).PubMedGoogle Scholar
8.
Saelens  BE, Handy  SL.  Built environment correlates of walking: a review.  Med Sci Sports Exerc. 2008;40(7)(suppl):S550-S566.PubMedGoogle ScholarCrossref
9.
Frank  LD, Andresen  MA, Schmid  TL.  Obesity relationships with community design, physical activity, and time spent in cars.  Am J Prev Med. 2004;27(2):87-96.PubMedGoogle ScholarCrossref
10.
Glazier  RH, Creatore  MI, Weyman  JT,  et al.  Density, destinations or both? a comparison of measures of walkability in relation to transportation behaviors, obesity and diabetes in Toronto, Canada.  PLoS One. 2014;9(1):e85295.PubMedGoogle ScholarCrossref
11.
Booth  GL, Creatore  MI, Moineddin  R,  et al.  Unwalkable neighborhoods, poverty, and the risk of diabetes among recent immigrants to Canada compared with long-term residents.  Diabetes Care. 2013;36(2):302-308.PubMedGoogle ScholarCrossref
12.
Christine  PJ, Auchincloss  AH, Bertoni  AG,  et al.  Longitudinal associations between neighborhood physical and social environments and incident type 2 diabetes mellitus: the Multi-Ethnic Study of Atherosclerosis (MESA).  JAMA Intern Med. 2015;175(8):1311-1320.PubMedGoogle ScholarCrossref
13.
Müller-Riemenschneider  F, Pereira  G, Villanueva  K,  et al.  Neighborhood walkability and cardiometabolic risk factors in Australian adults: an observational study.  BMC Public Health. 2013;13:755.PubMedGoogle ScholarCrossref
14.
Walks  A, Maaranen  R. Neighbourhood gentrification and upgrading in Montreal, Toronto and Vancouver. http://www.urbancentre.utoronto.ca/pdfs/researchbulletins/CUCS_RB_43-Walks-Gentrification2008.pdf. Accessed December 11, 2015.
15.
Huynh  M, Maroko  AR.  Gentrification and preterm birth in New York City, 2008–2010.  J Urban Health. 2014;91(1):211-220. PubMedGoogle ScholarCrossref
16.
Statistics Canada. Other reference periods, Canadian Community Health Survey, annual component (CCHS). http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getInstanceList&Id=164081. Accessed July 16, 2015.
17.
Barba  C, Cavalli-Sforza  T, Cutter  J,  et al; WHO Expert Consultation.  Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies.  Lancet. 2004;363(9403):157-163.PubMedGoogle ScholarCrossref
18.
Wen  CP, David Cheng  TY, Tsai  SP,  et al.  Are Asians at greater mortality risks for being overweight than Caucasians? redefining obesity for Asians.  Public Health Nutr. 2009;12(4):497-506.PubMedGoogle ScholarCrossref
19.
Chiu  M, Austin  PC, Manuel  DG, Shah  BR, Tu  JV.  Deriving ethnic-specific BMI cutoff points for assessing diabetes risk.  Diabetes Care. 2011;34(8):1741-1748.PubMedGoogle ScholarCrossref
20.
Hux  JE, Ivis  F, Flintoft  V, Bica  A.  Diabetes in Ontario: determination of prevalence and incidence using a validated administrative data algorithm.  Diabetes Care. 2002;25(3):512-516.PubMedGoogle ScholarCrossref
21.
Data Management Group. Transportation Tomorrow Survey 2011: version 1.0 data guide. http://www.dmg.utoronto.ca/pdf/tts/2011/dataguide2011.pdf. Accessed March 31, 2015.
22.
Data Management Group. Transportation Tomorrow Survey 2006: version 1.0 data expansion and validation. http://dmg.utoronto.ca/pdf/tts/2006/validation2006.pdf. Accessed December 21, 2015.
23.
Data Management Group. Transportation Tomorrow Survey 2011: version 1.0 data expansion and validation. http://dmg.utoronto.ca/pdf/tts/2011/validation2011.pdf. Accessed December 21 2015.
24.
Auchincloss  AH, Diez Roux  AV, Brown  DG, Erdmann  CA, Bertoni  AG.  Neighborhood resources for physical activity and healthy foods and their association with insulin resistance.  Epidemiology. 2008;19(1):146-157.PubMedGoogle ScholarCrossref
25.
Berry  TR, Spence  JC, Blanchard  C, Cutumisu  N, Edwards  J, Nykiforuk  C.  Changes in BMI over 6 years: the role of demographic and neighborhood characteristics.  Int J Obes (Lond). 2010;34(8):1275-1283.PubMedGoogle ScholarCrossref
26.
Lysy  Z, Booth  GL, Shah  BR, Austin  PC, Luo  J, Lipscombe  LL.  The impact of income on the incidence of diabetes: a population-based study.  Diabetes Res Clin Pract. 2013;99(3):372-379.PubMedGoogle ScholarCrossref
27.
Ludwig  J, Sanbonmatsu  L, Gennetian  L,  et al.  Neighborhoods, obesity, and diabetes: a randomized social experiment.  N Engl J Med. 2011;365(16):1509-1519.PubMedGoogle ScholarCrossref
28.
Hu  FB, Li  TY, Colditz  GA, Willett  WC, Manson  JE.  Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women.  JAMA. 2003;289(14):1785-1791.PubMedGoogle ScholarCrossref
29.
Norberg  M, Lindvall  K, Stenlund  H, Lindahl  B.  The obesity epidemic slows among the middle-aged population in Sweden while the socioeconomic gap widens.  Glob Health Action. 2010;3. PubMedGoogle Scholar
30.
Eriksson  M, Holmgren  L, Janlert  U,  et al.  Large improvements in major cardiovascular risk factors in the population of northern Sweden: the MONICA study 1986-2009.  J Intern Med. 2011;269(2):219-231.PubMedGoogle ScholarCrossref
31.
García-Alvarez  A, Serra-Majem  L, Ribas-Barba  L,  et al.  Obesity and overweight trends in Catalonia, Spain (1992-2003): gender and socio-economic determinants.  Public Health Nutr. 2007;10(11A):1368-1378.PubMedGoogle ScholarCrossref
32.
Faeh  D, Bopp  M.  Excess weight in the canton of Zurich, 1992-2009: harbinger of a trend reversal in Switzerland?  Swiss Med Wkly. 2010;140:w13090.PubMedGoogle Scholar
33.
Health and Social Care Information Centre. Health survey for England, 2011: trend tables. http://www.hscic.gov.uk/catalogue/PUB09302. Accessed July 15, 2015.
34.
US Department of Health and Human Services.  The Surgeon General’s Call to Action to Prevent and Decrease Overweight and Obesity. Rockville, MD: US Dept of Health & Human Services, Public Health Service, Office of the Surgeon General; 2001.
35.
Public Health Agency of Canada. 2005 Integrated pan-Canadian healthy living strategy. http://www.phac-aspc.gc.ca/hp-ps/hl-mvs/ipchls-spimmvs/index-eng.php. Accessed July 16, 2015.
36.
Knowler  WC, Barrett-Connor  E, Fowler  SE,  et al; Diabetes Prevention Program Research Group.  Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.  N Engl J Med. 2002;346(6):393-403.PubMedGoogle ScholarCrossref
37.
Polsky  JY, Moineddin  R, Dunn  JR, Glazier  RH, Booth  GL.  Absolute and relative densities of fast-food versus other restaurants in relation to weight status: does restaurant mix matter?  Prev Med. 2016;82(82):28-34. PubMedGoogle ScholarCrossref
38.
Shields  M, Connor Gorber  S, Tremblay  MS.  Estimates of obesity based on self-report versus direct measures.  Health Rep. 2008;19(2):61-76.PubMedGoogle Scholar
39.
Creatore  MI, Booth  GL, Manuel  DG, Moineddin  R, Glazier  RH.  Diabetes screening among immigrants: a population-based urban cohort study.  Diabetes Care. 2012;35(4):754-761.PubMedGoogle ScholarCrossref
40.
Rosella  LC, Lebenbaum  M, Fitzpatrick  T, Zuk  A, Booth  GL.  The prevalence of undiagnosed diabetes in Canada (2007-2011): diabetes screening according to fasting plasma glucose and HbA1c screening criteria.  Diabetes Care. 2016;33:395-403.PubMedGoogle Scholar
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    Original Investigation
    May 24/31, 2016

    Association of Neighborhood Walkability With Change in Overweight, Obesity, and Diabetes

    Author Affiliations
    • 1Li Ka Shing Knowledge Institute of St Michael’s Hospital, Toronto, Ontario, Canada
    • 2The Institute of Clinical Evaluative Sciences, Toronto, Ontario, Canada
    • 3Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
    • 4Institute of Health Policy, Management and Evaluation, University of Toronto
    • 5Centre for Research on Inner City Health, St Michael’s Hospital, Toronto
    • 6Department of Family and Community Medicine, University of Toronto
    • 7Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
    • 8Department of Medicine, University of Toronto
    JAMA. 2016;315(20):2211-2220. doi:10.1001/jama.2016.5898
    Abstract

    Importance  Rates of obesity and diabetes have increased substantially in recent decades; however, the potential role of the built environment in mitigating these trends is unclear.

    Objective  To examine whether walkable urban neighborhoods are associated with a slower increase in overweight, obesity, and diabetes than less walkable ones.

    Design, Setting, and Participants  Time-series analysis (2001-2012) using annual provincial health care (N ≈ 3 million per year) and biennial Canadian Community Health Survey (N ≈ 5500 per cycle) data for adults (30-64 years) living in Southern Ontario cities.

    Exposures  Neighborhood walkability derived from a validated index, with standardized scores ranging from 0 to 100, with higher scores indicating more walkability. Neighborhoods were ranked and classified into quintiles from lowest (quintile 1) to highest (quintile 5) walkability.

    Main Outcomes and Measures  Annual prevalence of overweight, obesity, and diabetes incidence, adjusted for age, sex, area income, and ethnicity.

    Results  Among the 8777 neighborhoods included in this study, the median walkability index was 16.8, ranging from 10.1 in quintile 1 to 35.2 in quintile 5. Resident characteristics were generally similar across neighborhoods; however, poverty rates were higher in high- vs low-walkability areas. In 2001, the adjusted prevalence of overweight and obesity was lower in quintile 5 vs quintile 1 (43.3% vs 53.5%; P < .001). Between 2001 and 2012, the prevalence increased in less walkable neighborhoods (absolute change, 5.4% [95% CI, 2.1%-8.8%] in quintile 1, 6.7% [95% CI, 2.3%-11.1%] in quintile 2, and 9.2% [95% CI, 6.2%-12.1%] in quintile 3). The prevalence of overweight and obesity did not significantly change in areas of higher walkability (2.8% [95% CI, −1.4% to 7.0%] in quintile 4 and 2.1% [95% CI, −1.4% to 5.5%] in quintile 5). In 2001, the adjusted diabetes incidence was lower in quintile 5 than other quintiles and declined by 2012 from 7.7 to 6.2 per 1000 persons in quintile 5 (absolute change, −1.5 [95% CI, −2.6 to −0.4]) and 8.7 to 7.6 in quintile 4 (absolute change, −1.1 [95% CI, −2.2 to −0.05]). In contrast, diabetes incidence did not change significantly in less walkable areas (change, −0.65 in quintile 1 [95% CI, −1.65 to 0.39], −0.5 in quintile 2 [95% CI, −1.5 to 0.5], and −0.9 in quintile 3 [95% CI, −1.9 to 0.02]). Rates of walking or cycling and public transit use were significantly higher and that of car use lower in quintile 5 vs quintile 1 at each time point, although daily walking and cycling frequencies increased only modestly from 2001 to 2011 in highly walkable areas. Leisure-time physical activity, diet, and smoking patterns did not vary by walkability (P > .05 for quintile 1 vs quintile 5 for each outcome) and were relatively stable over time.

    Conclusions and Relevance  In Ontario, Canada, higher neighborhood walkability was associated with decreased prevalence of overweight and obesity and decreased incidence of diabetes between 2001 and 2012. However, the ecologic nature of these findings and the lack of evidence that more walkable urban neighborhood design was associated with increased physical activity suggest that further research is necessary to assess whether the observed associations are causal.

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