Saklayen MG. The global epidemic of the metabolic syndrome. Curr Hypertens Rep. 2018;20(2):12. https://doi.org/10.1007/s11906-018-0812-z.
O'Neill S, O'Driscoll L. Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obes Rev. 2015;16(1):1–12. https://doi.org/10.1111/obr.12229.
Alexander CM, Landsman PB, Teutsch SM. NCEP-defined metabolic syndrome, diabetes, and prevalence of coronary heart disease among NHANES III participants age 50 years and older. Diabetes. 2003;52(5):1210–4. https://doi.org/10.2337/diabetes.52.5.1210.
Stevenson M, Thompson J, de Sá TH, Ewing R, Mohan D, McClure R, et al. Land use, transport, and population health: estimating the health benefits of compact cities. Lancet. 2016;388(10062):2925–35. https://doi.org/10.1016/S0140-6736(16)30067-8.
GBD 2017 SDG Collaborators. Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related sustainable development goals for 195 countries and territories: a systematic analysis for the global burden of disease study 2017 [published correction appears in Lancet. 2019 Jun 22;393(10190):e44]. Lancet. 2018;392(10159):2091–2138. https://doi.org/10.1016/S0140-6736(18)32281-5.
Barnett DW, Barnett A, Nathan A, Van Cauwenberg J, Cerin E; Council on environment and physical activity (CEPA) – older adults working group. Built environmental correlates of older adults' total physical activity and walking: a systematic review and meta-analysis. Int J Behav Nutr Phys Act 2017;14(1):103. https://doi.org/10.1186/s12966-017-0558-z.
Cerin E, Nathan A, van Cauwenberg J, Barnett DW, Barnett A; Council on environment and physical activity (CEPA) – older adults working group. The neighbourhood physical environment and active travel in older adults: a systematic review and meta-analysis. Int J Behav Nutr Phys Act 2017;14(1):15. https://doi.org/10.1186/s12966-017-0471-5.
Van Cauwenberg J, Nathan A, Barnett A, Barnett DW, Cerin E. Council on environment and physical activity (CEPA)-older adults working group. Relationships between neighbourhood physical environmental attributes and older adults' leisure-time physical activity: a systematic review and meta-analysis. Sports Med. 2018;48(7):1635–60. https://doi.org/10.1007/s40279-018-0917-1.
McInerney M, Csizmadi I, Friedenreich CM, Uribe FA, Nettel-Aguirre A, McLaren L, et al. Associations between the neighbourhood food environment, neighbourhood socioeconomic status, and diet quality: an observational study. BMC Public Health. 2016;16:984. https://doi.org/10.1186/s12889-016-3631-7.
Gilham K, Gu Q, Dummer TJB, Spinelli JJ, Murphy RA. Diet quality and neighborhood environment in the Atlantic Partnership for Tomorrow's health project. Nutrients. 2020;12(10):3217. https://doi.org/10.3390/nu12103217.
Assah FK, Ekelund U, Brage S, Mbanya JC, Wareham NJ. Urbanization, physical activity, and metabolic health in sub-Saharan Africa. Diabetes Care. 2011;34(2):491–6.
Salas-Salvadó J, Guasch-Ferré M, Lee CH, Estruch R, Clish CB, Ros E. Protective effects of the Mediterranean diet on type 2 diabetes and metabolic syndrome. J Nutr. 2015;146(4):920S–7S. https://doi.org/10.3945/jn.115.218487.
Carroll SJ, Dale MJ, Taylor AW, Daniel M. Contributions of multiple built environment features to 10-year change in body mass index and waist circumference in a south Australian middle-aged cohort. Int J Environ Res Public Health. 2020;17:870. https://doi.org/10.3390/ijerph17030870.
Cerin E, Barnett A, Shaw JE, Martino E, Knibbs LD, Tham R, et al. Urban neighbourhood environments, cardiometabolic health and cognitive function: a national cross-sectional study of middle-aged and older adults in Australia. Toxics. 2022;10(1):23. https://doi.org/10.3390/toxics10010023.
Yim E, Lee K, Park I, Lee S. The prevalence of metabolic syndrome and health-related behavior changes: the Korea National Health Examination Survey. Healthcare. 2020;8:134. https://doi.org/10.3390/healthcare8020134.
Keita AD, Judd SE, Howard VJ, Carson AP, Ard JD, Fernandez JR. Associations of neighborhood area level deprivation with the metabolic syndrome and inflammation among middle- and older- age adults. BMC Public Health. 2014;14:1319. https://doi.org/10.1186/1471-2458-14-1319.
de Keijzer C, Basagaña X, Tonne C, Valentín A, Alonso J, Antó JM, et al. Long-term exposure to greenspace and metabolic syndrome: a Whitehall II study. Environ Pollut. 2019;255(Pt 2):113231. https://doi.org/10.1016/j.envpol.2019.113231.
Baldock K, Paquet C, Howard N, Coffee N, Hugo G, Taylor A, et al. Associations between resident perceptions of the local residential environment and metabolic syndrome. J Environ Public Health. 2012;2012:589409. https://doi.org/10.1155/2012/589409.
Yu Y, Paul K, Arah OA, Mayeda ER, Wu J, Lee E, et al. Air pollution, noise exposure, and metabolic syndrome - a cohort study in elderly Mexican-Americans in Sacramento area. Environ Int. 2020;134:105269. https://doi.org/10.1016/j.envint.2019.105269.
Zang S-T, Luan J, Li L, Wu QJ, Chang Q, Dai HX, et al. Air pollution and metabolic syndrome risk: evidence from nine observational studies. Environ Res. 2021;202:11546. https://doi.org/10.1016/j.envres.2021.111546.
Yang BY, Qian ZM, Li S, Fan S, Chen G, Syberg KM, et al. Long-term exposure to ambient air pollution (including PM1) and metabolic syndrome: the 33 communities Chinese health study (33CCHS). Environ Res. 2018;164:204–11. https://doi.org/10.1016/j.envres.2018.02.029.
Matthiessen C, Lucht S, Hennig F, Ohlwein S, Jakobs H, Jöckel KH, et al. Long-term exposure to airborne particulate matter and NO2 and prevalent and incident metabolic syndrome - results from the Heinz Nixdorf recall study. Environ Int. 2018;116:74–82. https://doi.org/10.1016/j.envint.2018.02.035.
Wallwork RS, Colicino E, Zhong J, Kloog I, Coull BA, Vokonas P, et al. Ambient fine particulate matter, outdoor temperature, and risk of metabolic syndrome. Am J Epidemiol. 2017;185(1):30–9. https://doi.org/10.1093/aje/kww157.
Cerin E. Building the evidence for an ecological model of cognitive health. Health Place. 2019;60:102206. https://doi.org/10.1016/j.healthplace.2019.102206.
Cerin E, Barnett A, Zhang CJP, Lai PC, Sit CHP, Lee RSY. How urban densification shapes walking behaviours in older community dwellers: a cross-sectional analysis of potential pathways of influence. Int J Health Geogr. 2020;19:14. https://doi.org/10.1186/s12942-020-00210-8.
Dunstan DW, Zimmet PZ, Welborn TA, Cameron AJ, Shaw J, de Courten M, et al. The Australian diabetes, obesity and lifestyle study (AusDiab)--methods and response rates. Diabetes Res Clin Pract 2002;57:119–129.
Tanamas SK, Magliano DJ, Lynch BM, Sethi P, Willenberg L, Polkinghorne KR, et al. AusDiab 2012: the Australian diabetes, obesity and lifestyle study. Melbourne: Baker Heart and Diabetes Institute; 2013.
Ho K, Jamsen KM, Bell JS, Korhonen MJ, Mc Namara KP, Magliano DJ, et al. Demographic, clinical and lifestyle factors associated with high-intensity statin therapy in Australia: the AusDiab study. Eur J Clin Pharmacol. 2018;74(11):1493–501. https://doi.org/10.1007/s00228-018-2518-1.
White IR, Carlin JB. Bias and efficiency of multiple imputation compared to complete-case analysis for missing covariate values. Stat Med. 2010;29(28):2920–31.
Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; National Heart, Lung, and Blood Institute; American Heart Association; world heart federation; international atherosclerosis society; and International Association for the Study of obesity. Circulation. 2009;120:1640–5.
Adams MA, Frank LD, Schipperijn J, Smith G, Chapman J, Christiansen LB, et al. International variation in neighborhood walkability, transit, and recreation environments using geographic information systems: the IPEN adult study. Int J Health Geogr. 2014;13:43. https://doi.org/10.1186/1476-072X-13-43.
Cerin E, Conway TL, Cain KL, Kerr J, De Bourdeaudhuij I, Owen N, et al. Sharing good NEWS across the world: developing comparable scores across 12 countries for the neighborhood environment walkability scale (NEWS). BMC Public Health. 2013;13:309. https://doi.org/10.1186/1471-2458-13-309.
Australia Bureau of Statistics. IRSAD, Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, (cat. no. 2033.0.55.001) Canberra: Australian Bureau of Statistics; 2011.
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. https://doi.org/10.1016/j.amepre.2004.04.011.
Gaio V, Roquette R, Dias CM, Nunes B. Ambient air pollution and lipid profile: systematic review and meta-analysis. Environ Pollut. 2019;254(Pt B):113036. https://doi.org/10.1016/j.envpol.2019.113036.
Knibbs LD, Hewson MG, Bechle MJ, Marshall JD, Barnett AG. A national satellite-based land-use regression model for air pollution exposure assessment in Australia. Environ Res. 2014;135:204–11. https://doi.org/10.1016/j.envres.2014.09.011.
Knibbs LD, Coorey CP, Bechle MJ, Cowie CT, Dirgawati M, Heyworth JS, et al. Independent validation of national satellite-based land-use regression models for nitrogen dioxide using passive samplers. Environ Sci Technol. 2016;50(22):12331–8. https://doi.org/10.1021/acs.est.6b03428.
Knibbs LD, van Donkelaar A, Martin RV, Bechle MJ, Brauer M, Cohen DD, et al. Satellite-based land-use regression for continental-scale long-term ambient PM2.5 exposure assessment in Australia. Environ Sci Technol. 2018;52(21):12445–55. https://doi.org/10.1021/acs.est.8b02328.
Cerin E, Leslie E, du Toit L, Owen N, Frank LD. Destinations that matter: associations with walking for transport. Health Place. 2007;13(3):713–24. https://doi.org/10.1016/j.healthplace.2006.11.002.
Lamb KE, Thornton LE, King TL, Ball K, White SR, Bentley R, et al. Methods for accounting for neighbourhood self-selection in physical activity and dietary behaviour research: a systematic review. Int J Behav Nutr Phys Act. 2020;17(1):45. https://doi.org/10.1186/s12966-020-00947-2.
Cerin E, Barnett A, Shaw JE, Martino E, Knibbs LD, Tham R, et al. From urban neighbourhood environments to cognitive health: a cross-sectional analysis of the role of physical activity and sedentary behaviours. BMC Public Health. 2021;21(1):2320. https://doi.org/10.1186/s12889-021-12375-3.
Collins LM, Lanza ST. Latent class and latent transition analysis with application in the social, behavioral, and health sciences. Hoboken: Wiley; 2010.
White A, Murphy TB. BayesLCA: An R package for Bayesian latent class analysis. J Stat Softw, 2014;61(13), 1–28. http://www.jstatsoft.org/v61/i13/. Accessed 2 June 2021.
Goodman LA. Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika. 1974;61(2):215–31.
Galindo Garre F, Vermunt JK. Avoiding boundary estimates in latent class analysis by Bayesian posterior mode estimation. Behaviometrika. 2006;33(1):43–59.
Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Series B. 2002;64(4):583–639.
Raftery AE, Newton MA, Satagopan JM, Krivitsky PN. Estimating the integrated likelihood via posterior simulation using the harmonic mean identity. In: Bernardo JM, Bayarri MJ, Berger JO, Dawid AP, Heckerman D, Smith AFM, West M, editors. Bayesian statistics. Oxford: Oxford University Press; 2007. p. 1–45.
Nylund KL, Asparouhov T, Muthen BO. Deciding on the number of classes in latent class analysis and growth mixture modelling: a Monte Carlo simulation study. Struct Equ Modeling. 2007;14(4):535–69.
Adams MA, Sallis JF, Conway TL, Frank LD, Saelens BE, Kerr J, et al. Neighborhood environment profiles for physical activity among older adults. Am J Health Behav. 2012;36(6):757–69. https://doi.org/10.5993/AJHB.36.6.4.
Boakye-Dankwa E, Nathan A, Barnett A, Busija L, Lee RSY, Pachana N, et al. Walking behaviour and patterns of perceived access to neighbourhood destinations in older adults from a low-density (Brisbane, Australia) and an ultra-dense city (Hong Kong, China). Cities. 2019;84:23–33. https://doi.org/10.1016/j.cities.2018.07.002.
R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/. Accessed 3 Apr 2021.
Textor J, van der Zander B, Gilthorpe MK, Liskiewicz M, Ellison GTH. Robust causal inference using directed acyclic graphs: the R package 'dagitty'. Int J Epidemiol. 2016;45(6):1887–94.
Wood S. Generalized additive models: an introduction with R. 2nd ed. Boca Raton: Chapman & Hall/CRC; 2017.
Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. New York, NY: Springer Verlag; 2002.
Michels KB, Rosner RA. Data trawling: to fish or not to fish. Lancet. 1996;348:1152–3.
Rothman KJ. No adjustments are needed for multiple comparisons. Epidemiology. 1990;1(1):43–6.
Riahi SM, Moamer S, Namdari M, Mokhayeri Y, Pourhoseingholi MA, Hashemi-Nazari SS. Patterns of clustering of the metabolic syndrome components and its association with coronary heart disease in the multi-ethnic study of atherosclerosis (MESA): a latent class analysis. Int J Cardiol. 2018;271:13–8. https://doi.org/10.1016/j.ijcard.2018.05.080.
Boyko EJ, Doheny RA, McNeely MJ, Kahn SE, Leonetti DL, Fujimoto WY. Latent class analysis of the metabolic syndrome. Diabetes Res Clin Pract. 2010;89(1):88–93. https://doi.org/10.1016/j.diabres.2010.02.013.
Liu X, Tao L, Cao K, Wang Z, Chen D, Guo J, et al. Association of high-density lipoprotein with development of metabolic syndrome components: a five-year follow-up in adults. BMC Public Health. 2015;15:412. https://doi.org/10.1186/s12889-015-1747-9.
Wang XR, Song GR, Li M, Sun HG, Fan YJ, Liu Y, et al. Longitudinal associations of high-density lipoprotein cholesterol or low-density lipoprotein cholesterol with metabolic syndrome in the Chinese population: a prospective cohort study. BMJ Open. 2018;8(5):e018659. https://doi.org/10.1136/bmjopen-2017-018659.
Fong KC, Hart JE, James P. A review of epidemiologic studies on greenness and health: updated literature through 2017. Curr Environ Health Reports. 2018;5(1):77–87.
Mlambo P, Kengne AP, De Villiers A, Lambert EV, Puoane T. Built environment, selected risk factors and major cardiovascular disease outcomes: a systematic review. PLoS One. 2016;11(11):e0166846. https://doi.org/10.1371/journal.pone.0166846.
Carroll SJ, Dale MJ, Niyonsenga T, Taylor AW, Daniel M. Associations between area socioeconomic status, individual mental health, physical activity, diet and change in cardiometabolic risk amongst a cohort of Australian adults: a longitudinal path analysis. PLoS One. 2020;15(5):e0233793. https://doi.org/10.1371/journal.pone.0233793.
Williams ED, Magliano DJ, Zimmet PZ, Kavanagh AM, Stevenson CE, Oldenburg BF, et al. Area-level socioeconomic status and incidence of abnormal glucose metabolism: the Australian diabetes, obesity and lifestyle (AusDiab) study. Diabetes Care. 2012;35(7):1455–61. https://doi.org/10.2337/dc11-1410.
Cerin E, Leslie E. How socio-economic status contributes to participation in leisure-time physical activity. Soc Sci Med. 2008;66(12):2596–609. https://doi.org/10.1016/j.socscimed.2008.02.012.
Zhu Y, Duan MJ, Riphagen IJ, Minovic I, Mierau JO, Carrero JJ, et al. Separate and combined effects of individual and neighbourhood socio-economic disadvantage on health-related lifestyle risk factors: a multilevel analysis. Int J Epidemiol. 2022;50(6):1959–69. https://doi.org/10.1093/ije/dyab079.
Grant TL, Edwards N, Sveistrup H, Andrew C, Egan M. Inequitable walking conditions among older people: examining the interrelationship of neighbourhood socio-economic status and urban form using a comparative case study. BMC Public Health. 2010;10:677. https://doi.org/10.1186/1471-2458-10-677.
Crouse DL, Ross NA, Goldberg MS. Double burden of deprivation and high concentrations of ambient air pollution at the neighbourhood scale in Montreal, Canada. Soc Sci Med. 2009;69(6):971–81.
Næss Ø, Piro FN, Nafstad P, Smith GD, Leyland AH. Air pollution, social deprivation, and mortality: a multilevel cohort study. Epidemiology. 2007;18(6):686–94.
Cerin E, Frank LD, Sallis JF, Saelens BE, Conway TL, Chapman JE, et al. From neighborhood design and food options to residents' weight status. Appetite. 2011;56(3):693–703. https://doi.org/10.1016/j.appet.2011.02.006.
McNeill LH, Kreuter MW, Subramanian SV. Social environment and physical activity: a review of concepts and evidence. Soc Sci Med. 2006;63(4):1011–22.
Cowie CT, Ding D, Rolfe MI, Mayne DJ, Jalaludin B, Bauman A, et al. Neighbourhood walkability, road density and socio-economic status in Sydney, Australia. Environ Health. 2016;15:58. https://doi.org/10.1186/s12940-016-0135-y.
Houstis N, Rosen ED, Lander ES. Reactive oxygen species have a causal role in multiple forms of insulin resistance. Nature. 2006;440(7086):944–8.
Perticone F, Ceravolo R, Candigliota M, Ventura G, Iacopino S, Sinopoli F, et al. Obesity and body fat distribution induce endothelial dysfunction by oxidative stress: protective effect of vitamin C. Diabetes. 2001;50(1):159–65.
Sun Q, Yue P, Deiuliis JA, Lumeng CN, Kampfrath T, Mikolaj MB, et al. Ambient air pollution exaggerates adipose inflammation and insulin resistance in a mouse model of diet-induced obesity. Circulation. 2009;119(4):538–46.
Mendez R, Zheng Z, Fan Z, Rajagopalan S, Sun Q, Zhang K. Exposure to fine airborne particulate matter induces macrophage infiltration, unfolded protein response, and lipid deposition in white adipose tissue. Am J Transl Res. 2013;5(2):224–34.
Zhang N, Wang L, Zhang M, Nazroo J. Air quality and obesity at older ages in China: the role of duration, severity and pollutants. PLoS One. 2019;14(12):e0226279. https://doi.org/10.1371/journal.pone.0226279.
Yang BY, Bloom MS, Markevych I, Qian ZM, Vaughn MG, Cummings-Vaughn LA, et al. Exposure to ambient air pollution and blood lipids in adults: the 33 communities Chinese health study. Environ Int. 2018;119:485–92. https://doi.org/10.1016/j.envint.2018.07.016.
Mao S, Chen G, Liu F, Li N, Wang C, Liu Y, et al. Long-term effects of ambient air pollutants to blood lipids and dyslipidemias in a Chinese rural population. Environ Pollut. 2020;256:113403. https://doi.org/10.1016/j.envpol.2019.113403.
Cai Y, Hansell AL, Blangiardo M, Burton PR, de Hoogh K, Doiron D, et al. Long-term exposure to road traffic noise, ambient air pollution, and cardiovascular risk factors in the HUNT and lifelines cohorts. Eur Heart J. 2017;38(29):2290–6.
Joseph RP, Vega-López S. Associations of perceived neighborhood environment and physical activity with metabolic syndrome among Mexican-Americans adults: a cross sectional examination. BMC Res Notes. 2020;13(1):306. https://doi.org/10.1186/s13104-020-05143-w.
Werneck AO, Christofaro DGD, Ritti-Dias RM, Cucato GG, Conceição RDO, Santos RD, et al. Self-initiated changes in physical activity and incidence of metabolic syndrome: a longitudinal follow-up study. Diabetes Res Clin Pract. 2020;165:108224. https://doi.org/10.1016/j.diabres.2020.108224.
Zając-Gawlak I, Pelclová J, Groffik D, Přidalová M, Nawrat-Szołtysik A, Kroemeke A, et al. Does physical activity lower the risk for metabolic syndrome: a longitudinal study of physically active older women. BMC Geriatr. 2021;21(1):11. https://doi.org/10.1186/s12877-020-01952-7.
An KY. Comparison between walking and moderate-to-vigorous physical activity: associations with metabolic syndrome components in Korean older adults. Epidemiol Health. 2020;42:e2020066. https://doi.org/10.4178/epih.e2020066.
Pruchno R, Wilson-Genderson M, Gupta AK. Neighborhood food environment and obesity in community-dwelling older adults: individual and neighborhood effects. Am J Public Health. 2014;104(5):924–9. https://doi.org/10.2105/AJPH.2013.301788.
Chen L, Caballero B, Mitchell DC. Reducing consumption of sugar-sweetened beverages is associated with reduced blood pressure: a prospective study among United States adults. Circulation. 2010;121:2398–406. https://doi.org/10.1161/CIRCULATIONAHA.109.911164.
Pereira MA, Kartashov AI, Ebbeling CB, Van Horn L, Slattery ML, Jacobs DR Jr, et al. Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis. Lancet. 2005;365(9453):36–42. https://doi.org/10.1016/S0140-6736(04)17663-0.
Bonaccorsi G, Manzi F, Del Riccio M, Setola N, Naldi E, Milani C, et al. Impact of the built environment and the neighborhood in promoting the physical activity and the healthy aging in older people: an umbrella review. Int J Environ Res Public Health. 2020;17(17):6127. https://doi.org/10.3390/ijerph17176127.
Sallis JF, Bowles HR, Bauman A, Ainsworth BE, Bull FC, Craig CL, et al. Neighborhood environments and physical activity among adults in 11 countries. Am J Prev Med. 2009;36(6):484–90. https://doi.org/10.1016/j.amepre.2009.01.031.
Zhang CJP, Barnett A, Johnston JM, Lai PC, Lee RSY, Sit CHP, et al. Objectively-measured neighbourhood attributes as correlates and moderators of quality of life in older adults with different living arrangements: the ALECS cross-sectional study. Int J Environ Res Public Health. 2019;16(5):876. https://doi.org/10.3390/ijerph16050876.
Zhong J, Cai XM, Bloss WJ. Coupling dynamics and chemistry in the air pollution modelling of street canyons: a review. Environ Pollut. 2016;214:690–704. https://doi.org/10.1016/j.envpol.2016.04.052.
Ishaque MM, Noland RB. Simulated pedestrian travel and exposure to vehicle emissions. Transp. 2008;1:27–46. https://doi.org/10.1016/j.trd.2007.10.005.
Araújo CAH, Giehl MWC, Danielewicz AL, Araujo PG, d'Orsi E, Boing AF. Built environment, contextual income, and obesity in older adults: evidence from a population-based study. Cad Saude Publica. 2018;34(5):e00060217. https://doi.org/10.1590/0102-311X00060217.
Hirsch JA, Moore KA, Barrientos-Gutierrez T, Brines SJ, Zagorski MA, Rodriguez DA, et al. Built environment change and change in BMI and waist circumference: multi-ethnic study of atherosclerosis. Obesity. 2014;22(11):2450–7. https://doi.org/10.1002/oby.20873.
Leonardi C, Simonsen NR, Yu Q, Park C, Scribner RA. Street connectivity and obesity risk: evidence from electronic health records. Am J Prev Med 2017;52(1S1):S40–7. https://doi.org/10.1016/j.amepre.2016.09.029.
Liu M, Huang Y, Jin Z, Ma Z, Liu X, Zhang B, et al. The nexus between urbanization and PM2.5 related mortality in China. Environ Pollut. 2017;227:15–23. https://doi.org/10.1016/j.envpol.2017.04.049.
Carozzi F, Roth S. Dirty density: air quality and density of American cities. Bonn: IZA Institute of Labor Economics IZA DP No. 13191; 2020.
James P, Hart JE, Laden F. Neighborhood walkability and particulate air pollution in a nationwide cohort of women. Environ Res. 2015;142:703–11. https://doi.org/10.1016/j.envres.2015.09.005.
Yang BY, Liu KK, Markevych I, Knibbs LD, Bloom MS, Dharmage SC, et al. Association between residential greenness and metabolic syndrome in Chinese adults. Environ Int. 2020;135:105388. https://doi.org/10.1016/j.envint.2019.105388.
Voss S, Schneider A, Huth C, Wolf K, Markevych I, Schwettmann L, et al. Long-term exposure to air pollution, road traffic noise, residential greenness, and prevalent and incident metabolic syndrome: results from the population-based KORA F4/FF4 cohort in Augsburg, Germany. Environ Int. 2021;147:106364. https://doi.org/10.1016/j.envint.2020.106364.
Sarkar C. Residential greenness and adiposity: findings from the UK biobank. Environ Int. 2017;106:1–10. https://doi.org/10.1016/j.envint.2017.05.016.
Luo YN, Huang WZ, Liu XX, Markevych I, Bloom MS, Zhao T, et al. Greenspace with overweight and obesity: a systematic review and meta-analysis of epidemiological studies up to 2020. Obes Rev. 2020;21(11):e13078. https://doi.org/10.1111/obr.13078.
Bauwelinck M, Zijlema WL, Bartoll X, Vandenheede H, Cirach M, Lefebvre W, et al. Residential urban greenspace and hypertension: a comparative study in two European cities. Environ Res. 2020;191:110032. https://doi.org/10.1016/j.envres.2020.110032.
Huang B, Xiao T, Grekousis G, Zhao H, He J, Dong G, et al. Greenness-air pollution-physical activity-hypertension association among middle-aged and older adults: evidence from urban and rural China. Environ Res. 2021;195:110836. https://doi.org/10.1016/j.envres.2021.110836.
Poulsen MN, Schwartz BS, Nordberg C, DeWalle J, Pollak J, Imperatore G, et al. Association of greenness with blood pressure among individuals with type 2 diabetes across rural to urban community types in Pennsylvania, USA. Int J Environ Res Public Health. 2021;18(2):614. https://doi.org/10.3390/ijerph18020614.
Li R, Chen G, Jiao A, Lu Y, Guo Y, Li S, et al. Residential green and blue spaces and type 2 diabetes mellitus: a population-based health study in China. Toxics. 2021;9:11. https://doi.org/10.3390/toxics9010011.
Twohig-Bennett C, Jones A. The health benefits of the great outdoors: a systematic review and meta-analysis of greenspace exposure and health outcomes. Environ Res. 2018;166:628–37. https://doi.org/10.1016/j.envres.2018.06.030.
Pandey KD, Wheeler D, Ostro B, Deichmann U, Hamilton K, Bolt K. Ambient particulate matter concentrations in residential areas of world cities: new estimates based on global model of ambient particulates (GMAPS). Washington, DC: Development Research Group and the Environment Department, World Bank; 2004.
Rodrigues PF, Alvim-Ferraz MCM, Martins FG, Saldiva P, Sá TH, Sousa SIV. Health economic assessment of a shift to active transport. Environ Pollut. 2020;258:113745. https://doi.org/10.1016/j.envpol.2019.113745.
Xia T, Nitschke M, Zhang Y, Shah P, Crabb S, Hansen A. Traffic-related air pollution and health co-benefits of alternative transport in Adelaide, South Australia. Environ Int. 2015;74:281–90. https://doi.org/10.1016/j.envint.2014.10.004.
UN-Habitat. Planning and design for sustainable urban mobility: global report on human settlements 2013. London: Routledge; 2013.
Coleman S. Built environment: increased urban footprint. In: Australia state of the environment. Australian Government Department of the Environment and Energy, Canberra. 2016. https://soe.environment.gov.au/theme/built-environment/topic/2016/increased-urban-footprint. https://doi.org/10.4226/94/58b65a5037ed8. Accessed 10 Sept 2021.