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Adamou H, François D, Lebel A, Paquette MC. Life-course socioeconomic status and obesity: a scoping review protocol. BMJ Open 2024; 14:e077750. [PMID: 38367976 PMCID: PMC10875501 DOI: 10.1136/bmjopen-2023-077750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 01/04/2024] [Indexed: 02/19/2024] Open
Abstract
OBJECTIVE We aim to explore the literature that studies the links between life-course socioeconomic status and weight status and characterize the life-course approach used. INTRODUCTION Obesogenic environments are increasing rapidly in deprived environments, and cross-sectional studies have shown limitations in explaining the links between these environments and obesity. The life-course approach has been proposed recently to better understand the links between socioeconomic status and weight status. INCLUSION CRITERIA Studies that identify life-course socioeconomic status and longitudinal built environment indicators and associate them with body weight indicators between January 2000 and January 2023. METHODS Studies in French or English were searched in Medline (PubMed), Web of Science and GeoBase (Embase) according to the strategies formulated for each database. The selected studies were exported to Covidence for evaluation according to the inclusion/exclusion criteria. RESULTS The main results retained are the association between longitudinal socioeconomic indicators and weight measures; longitudinal built environment indicators and the measures of weight.
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Affiliation(s)
- Habila Adamou
- Graduate School of Land Management and Urban Planning, Laval University, Quebec, Québec, Canada
- Institut universitaire de cardiologie et de pneumologie de Quebec, Evaluation Platform on Obesity Prevention, Laval University, Quebec, Québec, Canada
| | - Dener François
- Graduate School of Land Management and Urban Planning, Laval University, Quebec, Québec, Canada
| | - Alexandre Lebel
- Graduate School of Land Management and Urban Planning, Laval University, Quebec, Québec, Canada
- Institut universitaire de cardiologie et de pneumologie de Quebec, Evaluation Platform on Obesity Prevention, Laval University, Quebec, Québec, Canada
| | - Marie-Claude Paquette
- Institut national de santé publique du Québec, Quebec, Québec, Canada
- Department of Nutrition, Université de Montréal, Montreal, Quebec, Canada
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Verde L, Barrea L, Bowman-Busato J, Yumuk VD, Colao A, Muscogiuri G. Obesogenic environments as major determinants of a disease: It is time to re-shape our cities. Diabetes Metab Res Rev 2024; 40:e3748. [PMID: 38287716 DOI: 10.1002/dmrr.3748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Obesity rates are increasing in almost all high- and low-income countries, and population-based approaches are necessary to reverse this trend. The current global efforts are focused on identifying the root causes of obesity and developing effective methods for early diagnosis, screening, treatment, and long-term management, both at an individual and health system level. However, there is a relative lack of effective options for early diagnosis, treatment, and long-term management, which means that population-based strategies are also needed. These strategies involve conceptual shifts towards community- and environment-focused approaches. This review aimed to provide evidence on how environmental factors contribute to the risk of obesity and how reshaping cities can help slow down obesity prevalence rates and improve long-term management.
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Affiliation(s)
- Ludovica Verde
- Department of Public Health, University of Naples Federico II, Naples, Italy
- Centro Italiano per la cura e il Benessere del Paziente con Obesità (C.I.B.O), Dipartimento di Medicina Clinica e Chirurgia, Unità di Endocrinologia, Diabetologia e Andrologia, Università degli Studi di Napoli Federico II, Naples, Italy
| | - Luigi Barrea
- Centro Italiano per la cura e il Benessere del Paziente con Obesità (C.I.B.O), Dipartimento di Medicina Clinica e Chirurgia, Unità di Endocrinologia, Diabetologia e Andrologia, Università degli Studi di Napoli Federico II, Naples, Italy
- Dipartimento di Scienze Umanistiche, U-niversità Telematica Pegaso, Napoli, Italy
| | | | - Volkan Demirhan Yumuk
- Division of Endocrinology, Metabolism and Diabetes, Istanbul University-Cerrahpaşa, Cerrahpaşa Medical Faculty, Istanbul, Turkey
| | - Annamaria Colao
- Centro Italiano per la cura e il Benessere del Paziente con Obesità (C.I.B.O), Dipartimento di Medicina Clinica e Chirurgia, Unità di Endocrinologia, Diabetologia e Andrologia, Università degli Studi di Napoli Federico II, Naples, Italy
- Dipartimento di Medicina Clinica e Chirurgia, Unità di Endocrinologia, Diabetologia e Andrologia, Università degli Studi di Napoli Federico II, Naples, Italy
- Cattedra Unesco "Educazione alla salute e allo sviluppo sostenibile", University Federico II, Naples, Italy
| | - Giovanna Muscogiuri
- Centro Italiano per la cura e il Benessere del Paziente con Obesità (C.I.B.O), Dipartimento di Medicina Clinica e Chirurgia, Unità di Endocrinologia, Diabetologia e Andrologia, Università degli Studi di Napoli Federico II, Naples, Italy
- Dipartimento di Medicina Clinica e Chirurgia, Unità di Endocrinologia, Diabetologia e Andrologia, Università degli Studi di Napoli Federico II, Naples, Italy
- Cattedra Unesco "Educazione alla salute e allo sviluppo sostenibile", University Federico II, Naples, Italy
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Spoelder M, Schoofs MCA, Raaphorst K, Lakerveld J, Wagtendonk A, Hartman YAW, van der Krabben E, Hopman MTE, Thijssen DHJ. A positive neighborhood walkability is associated with a higher magnitude of leisure walking in adults upon COVID-19 restrictions: a longitudinal cohort study. Int J Behav Nutr Phys Act 2023; 20:116. [PMID: 37752497 PMCID: PMC10521432 DOI: 10.1186/s12966-023-01512-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/07/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND Previous cross-sectional and longitudinal observational studies revealed positive relationships between contextual built environment components and walking behavior. Due to severe restrictions during COVID-19 pandemic lockdowns, physical activity was primarily performed within the immediate living area. Using this unique opportunity, we evaluated whether built environment components were associated with the magnitude of change in walking activity in adults during COVID-19 restrictions. METHODS Data on self-reported demographic characteristics and walking behaviour were extracted from the prospective longitudinal Lifelines Cohort Study in the Netherlands of participants ≥ 18 years. For our analyses, we made use of the data acquired between 2014-2017 (n = 100,285). A fifth of the participants completed the questionnaires during COVID-19 restrictive policies in July 2021 (n = 20,806). Seven spatial components were calculated for a 500m and 1650m Euclidean buffer per postal code area in GIS: population density, retail and service destination density, land use mix, street connectivity, green space density, sidewalk density, and public transport stops. Additionally, the walkability index (WI) of these seven components was calculated. Using multivariable linear regression analyses, we analyzed the association between the WI (and separate components) and the change in leisure walking minutes/week. Included demographic variables were age, gender, BMI, education, net income, occupation status, household composition and the season in which the questionnaire was filled in. RESULTS The average leisure walking time strongly increased by 127 min/week upon COVID-19 restrictions. All seven spatial components of the WI were significantly associated with an increase in leisure walking time; a 10% higher score in the individual spatial component was associated with 5 to 8 more minutes of leisure walking/week. Green space density at the 500m Euclidean buffer and side-walk density at the 1650m Euclidean buffer were associated with the highest increase in leisure walking time/week. Subgroup analysis revealed that the built environment showed its strongest impact on leisure walking time in participants not engaging in leisure walking before the COVID-19 pandemic, compared to participants who already engaged in leisure walking before the COVID-19 pandemic. CONCLUSIONS These results provide strong evidence that the built environment, corrected for individual-level characteristics, directly links to changes observed in leisure walking time during COVID-19 restrictions. Since this relation was strongest in those who did not engage in leisure walking before the COVID-19 pandemic, our results encourage new perspectives in health promotion and urban planning.
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Affiliation(s)
- Marcia Spoelder
- Department of Physiology, Radboud Institute for Health Sciences, Radboud University Medical Center, Philips Van Leydenlaan 15, Nijmegen, 6525 EX, The Netherlands.
- Present affiliation: Department of Primary and Community Care, Radboud Institute for Health Sciences, Radboud University Medical Center, Geert Grooteplein Noord 21, Nijmegen, 6525 EZ, The Netherlands.
| | - Merle C A Schoofs
- Department of Physiology, Radboud Institute for Health Sciences, Radboud University Medical Center, Philips Van Leydenlaan 15, Nijmegen, 6525 EX, The Netherlands
| | - Kevin Raaphorst
- Department of Geography, Planning and Environment, Institute for Management Research, Radboud University, Nijmegen, The Netherlands
| | - Jeroen Lakerveld
- Amsterdam UMC, Department of Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, Boelelaan 1089a, Amsterdam, 1081HV, The Netherlands
- Amsterdam Public Health, Health Behaviours and Chronic Diseases, Amsterdam, The Netherlands
- Upstream Team, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alfred Wagtendonk
- Amsterdam UMC, Department of Epidemiology and Data Science, Vrije Universiteit Amsterdam, Amsterdam Public Health Research Institute, Boelelaan 1089a, Amsterdam, 1081HV, The Netherlands
- Amsterdam Public Health, Health Behaviours and Chronic Diseases, Amsterdam, The Netherlands
- Upstream Team, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Yvonne A W Hartman
- Department of Physiology, Radboud Institute for Health Sciences, Radboud University Medical Center, Philips Van Leydenlaan 15, Nijmegen, 6525 EX, The Netherlands
| | - Erwin van der Krabben
- Department of Geography, Planning and Environment, Institute for Management Research, Radboud University, Nijmegen, The Netherlands
| | - Maria T E Hopman
- Department of Physiology, Radboud Institute for Health Sciences, Radboud University Medical Center, Philips Van Leydenlaan 15, Nijmegen, 6525 EX, The Netherlands
| | - Dick H J Thijssen
- Department of Physiology, Radboud Institute for Health Sciences, Radboud University Medical Center, Philips Van Leydenlaan 15, Nijmegen, 6525 EX, The Netherlands
- Research Institute for Sports and Exercise Sciences, Liverpool John Moores University, Liverpool, UK
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Wood SM, Alston L, Beks H, Mc Namara K, Coffee NT, Clark RA, Wong Shee A, Versace VL. Quality appraisal of spatial epidemiology and health geography research: A scoping review of systematic reviews. Health Place 2023; 83:103108. [PMID: 37651961 DOI: 10.1016/j.healthplace.2023.103108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/19/2023] [Accepted: 08/22/2023] [Indexed: 09/02/2023]
Abstract
A scoping review of peer-reviewed literature was conducted to understand how systematic reviews assess the methodological quality of spatial epidemiology and health geography research. Fifty-nine eligible reviews were identified and included. Variations in the use of quality appraisal tools were found. Reviews applied existing quality appraisal tools with no adaptations (n = 32; 54%), existing quality appraisal tools with adaptations (n = 9; 15%), adapted tools or methods from other reviews (n = 13; 22%), and developed new quality appraisal tools for the review (n = 5; 8%). Future research should focus on developing and validating a quality appraisal tool that evaluates the spatial methodology within studies.
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Affiliation(s)
- Sarah M Wood
- Deakin Rural Health, School of Medicine, Faculty of Health, Deakin University, Warrnambool Campus, Vic, Australia.
| | - Laura Alston
- Deakin Rural Health, School of Medicine, Faculty of Health, Deakin University, Warrnambool Campus, Vic, Australia; Research Unit, Colac Area Health, Colac, Vic, Australia
| | - Hannah Beks
- Deakin Rural Health, School of Medicine, Faculty of Health, Deakin University, Warrnambool Campus, Vic, Australia
| | - Kevin Mc Namara
- Deakin Rural Health, School of Medicine, Faculty of Health, Deakin University, Warrnambool Campus, Vic, Australia; Grampians Health, Ballarat, Vic, Australia
| | - Neil T Coffee
- Deakin Rural Health, School of Medicine, Faculty of Health, Deakin University, Warrnambool Campus, Vic, Australia; Australian Centre for Housing Research, The University of Adelaide, Adelaide, SA, Australia
| | - Robyn A Clark
- Caring Futures Institute, Flinders University, SA, Australia; Southern Adelaide Health Care Services, SA, Australia
| | - Anna Wong Shee
- Deakin Rural Health, School of Medicine, Faculty of Health, Deakin University, Warrnambool Campus, Vic, Australia; Grampians Health, Ballarat, Vic, Australia
| | - Vincent L Versace
- Deakin Rural Health, School of Medicine, Faculty of Health, Deakin University, Warrnambool Campus, Vic, Australia; Grampians Health, Ballarat, Vic, Australia
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Kinsey EW, Widen EM, Quinn JW, Huynh M, Van Wye G, Lovasi GS, Neckerman KM, Caniglia EC, Rundle AG. Neighborhood Food Environment and Birth Weight Outcomes in New York City. JAMA Netw Open 2023; 6:e2317952. [PMID: 37306998 PMCID: PMC10261997 DOI: 10.1001/jamanetworkopen.2023.17952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 04/21/2023] [Indexed: 06/13/2023] Open
Abstract
Importance Infants born with unhealthy birth weight are at greater risk for long-term health complications, but little is known about how neighborhood characteristics (eg, walkability, food environment) may affect birth weight outcomes. Objective To assess whether neighborhood-level characteristics (poverty rate, food environment, and walkability) are associated with risk of unhealthy birth weight outcomes and to evaluate whether gestational weight gain mediated these associations. Design, Setting, and Participants The population-based cross-sectional study included births in the 2015 vital statistics records from the New York City Department of Health and Mental Hygiene. Only singleton births and observations with complete birth weight and covariate data were included. Analyses were performed from November 2021 to March 2022. Exposures Residential neighborhood-level characteristics, including poverty, food environment (healthy and unhealthy food retail establishments), and walkability (measured by both walkable destinations and a neighborhood walkability index combining walkability measures like street intersection and transit stop density). Neighborhood-level variables categorized into quartiles. Main Outcomes and Measures The main outcomes were birth certificate birth weight measures including small for gestational age (SGA), large for gestational age (LGA), and sex-specific birth weight for gestational age z-score. Generalized linear mixed-effects models and hierarchical linear models estimated risk ratios for associations between density of neighborhood-level characteristics within a 1-km buffer of residential census block centroid and birth weight outcomes. Results The study included 106 194 births in New York City. The mean (SD) age of pregnant individuals in the sample was 29.9 (6.1) years. Prevalence of SGA and LGA were 12.9% and 8.4%, respectively. Residence in the highest density quartile of healthy food retail establishments compared with the lowest quartile was associated with lower adjusted risk of SGA (with adjustment for individual covariates including gestational weight gain z-score: risk ratio [RR], 0.89; 95% CI 0.83-0.97). Higher neighborhood density of unhealthy food retail establishments was associated with higher adjusted risk of delivering an infant classified as SGA (fourth vs first quartile: RR, 1.12; 95% CI, 1.01-1.24). The RR for the association between density of unhealthy food retail establishments and risk of LGA was higher after adjustment for all covariates in each quartile compared with quartile 1 (second: RR, 1.12 [95% CI, 1.04-1.20]; third: RR, 1.18 [95% CI, 1.08-1.29]; fourth: RR, 1.16; [95% CI, 1.04-1.29]). There were no associations between neighborhood walkability and birth weight outcomes (SGA for fourth vs first quartile: RR, 1.01 [95% CI, 0.94-1.08]; LGA for fourth vs first quartile: RR, 1.06 [95% CI, 0.98-1.14]). Conclusions and Relevance In this population-based cross-sectional study, healthfulness of neighborhood food environments was associated with risk of SGA and LGA. The findings support use of urban design and planning guidelines to improve food environments to support healthy pregnancies and birth weight.
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Affiliation(s)
- Eliza W. Kinsey
- Department of Family Medicine & Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Elizabeth M. Widen
- Department of Nutritional Sciences and Population Research Center, University of Texas at Austin
| | - James W. Quinn
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
| | - Mary Huynh
- Bureau of Vital Statistics, New York City Department of Health and Mental Hygiene, New York
| | - Gretchen Van Wye
- Bureau of Vital Statistics, New York City Department of Health and Mental Hygiene, New York
| | - Gina S. Lovasi
- Epidemiology and Biostatistics, Urban Health Collaborative, Dornsife School of Public Health, Drexel University, Philadelphia, Pennsylvania
| | | | - Ellen C. Caniglia
- Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Andrew G. Rundle
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York
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Tewahade S, Berrigan D, Slotman B, Stinchcomb DG, Sayer RD, Catenacci VA, Ostendorf DM. Impact of the built, social, and food environment on long-term weight loss within a behavioral weight loss intervention. Obes Sci Pract 2023; 9:261-273. [PMID: 37287525 PMCID: PMC10242259 DOI: 10.1002/osp4.645] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 10/04/2022] [Accepted: 10/10/2022] [Indexed: 11/10/2022] Open
Abstract
Background Behavioral weight loss interventions can lead to an average weight loss of 5%-10% of initial body weight, however there is wide individual variability in treatment response. Although built, social, and community food environments can have potential direct and indirect influences on body weight (through their influence on physical activity and energy intake), these environmental factors are rarely considered as predictors of variation in weight loss. Objective Evaluate the association between built, social, and community food environments and changes in weight, moderate-to-vigorous physical activity (MVPA), and dietary intake among adults who completed an 18-month behavioral weight loss intervention. Methods Participants included 93 adults (mean ± SD; 41.5 ± 8.3 years, 34.4 ± 4.2 kg/m2, 82% female, 75% white). Environmental variables included urbanicity, walkability, crime, Neighborhood Deprivation Index (includes 13 social economic status factors), and density of convenience stores, grocery stores, and limited-service restaurants at the tract level. Linear regressions examined associations between environment and changes in body weight, waist circumference (WC), MVPA (SenseWear device), and dietary intake (3-day diet records) from baseline to 18 months. Results Grocery store density was inversely associated with change in weight (β = -0.95; p = 0.02; R 2 = 0.062) and WC (β = -1.23; p < 0.01; R 2 = 0.109). Participants living in tracts with lower walkability demonstrated lower baseline MVPA and greater increases in MVPA versus participants with higher walkability (interaction p = 0.03). Participants living in tracts with the most deprivation demonstrated greater increases in average daily steps (β = 2048.27; p = 0.02; R 2 = 0.039) versus participants with the least deprivation. Limited-service restaurant density was associated with change in % protein intake (β = 0.39; p = 0.046; R 2 = 0.051). Conclusion Environmental factors accounted for some of the variability (<11%) in response to a behavioral weight loss intervention. Grocery store density was positively associated with weight loss at 18 months. Additional studies and/or pooled analyses, encompassing greater environmental variation, are required to further evaluate whether environment contributes to weight loss variability.
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Affiliation(s)
- Selam Tewahade
- Department of EpidemiologyColorado School of Public HealthUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - David Berrigan
- Division of Cancer Control and Population SciencesNational Cancer InstituteBethesdaMarylandUSA
| | | | | | - R. Drew Sayer
- Department of Nutrition SciencesUniversity of Alabama at BirminghamBirminghamAlabamaUSA
| | - Victoria A. Catenacci
- Division of Endocrinology, Metabolism, and DiabetesDepartment of MedicineUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
- Anschutz Health and Wellness CenterDepartment of MedicineUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
| | - Danielle M. Ostendorf
- Division of Endocrinology, Metabolism, and DiabetesDepartment of MedicineUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
- Anschutz Health and Wellness CenterDepartment of MedicineUniversity of Colorado Anschutz Medical CampusAuroraColoradoUSA
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Saucy A, Gehring U, Olmos S, Delpierre C, de Bont J, Gruzieva O, de Hoogh K, Huss A, Ljungman P, Melén E, Persson Å, Pieterson I, Tewis M, Yu Z, Vermeulen R, Vlaanderen J, Tonne C. Effect of residential relocation on environmental exposures in European cohorts: An exposome-wide approach. ENVIRONMENT INTERNATIONAL 2023; 173:107849. [PMID: 36889121 DOI: 10.1016/j.envint.2023.107849] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/26/2023] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Residential relocation is increasingly used as a natural experiment in epidemiological studies to assess the health impact of changes in environmental exposures. Since the likelihood of relocation can be influenced by individual characteristics that also influence health, studies may be biased if the predictors of relocation are not appropriately accounted for. Using data from Swedish and Dutch adults (SDPP, AMIGO), and birth cohorts (BAMSE, PIAMA), we investigated factors associated with relocation and changes in multiple environmental exposures across life stages. We used logistic regression to identify baseline predictors of moving, including sociodemographic and household characteristics, health behaviors and health. We identified exposure clusters reflecting three domains of the urban exposome (air pollution, grey surface, and socioeconomic deprivation) and conducted multinomial logistic regression to identify predictors of exposome trajectories among movers. On average, 7 % of the participants relocated each year. Before relocating, movers were consistently exposed to higher levels of air pollution than non-movers. Predictors of moving differed between the adult and birth cohorts, highlighting the importance of life stages. In the adult cohorts, moving was associated with younger age, smoking, and lower education and was independent of cardio-respiratory health indicators (hypertension, BMI, asthma, COPD). Contrary to adult cohorts, higher parental education and household socioeconomic position were associated with a higher probability of relocation in birth cohorts, alongside being the first child and living in a multi-unit dwelling. Among movers in all cohorts, those with a higher socioeconomic position at baseline were more likely to move towards healthier levels of the urban exposome. We provide new insights into predictors of relocation and subsequent changes in multiple aspects of the urban exposome in four cohorts covering different life stages in Sweden and the Netherlands. These results inform strategies to limit bias due to residential self-selection in epidemiological studies using relocation as a natural experiment.
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Affiliation(s)
- Apolline Saucy
- Barcelona Institute of Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Ulrike Gehring
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Sergio Olmos
- Barcelona Institute of Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Cyrille Delpierre
- Centre for Epidemiology and Research in POPulation Health (CERPOP) UMR1295, Inserm, Université Toulouse III Paul Sabatier, Toulouse, France
| | - Jeroen de Bont
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Olena Gruzieva
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Kees de Hoogh
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland; University of Basel, Basel, Switzerland
| | - Anke Huss
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Petter Ljungman
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; Danderyd Hospital, Department of Cardiology, Stockholm, Sweden
| | - Erik Melén
- Department of Clinical Sciences and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - Åsa Persson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Inka Pieterson
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Marjan Tewis
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Zhebin Yu
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Roel Vermeulen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Jelle Vlaanderen
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Cathryn Tonne
- Barcelona Institute of Global Health (ISGlobal), Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; CIBER Epidemiología y Salud Pública, Madrid, Spain.
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Kowalski K, Auerbach J, Martenies SE, Starling AP, Moore B, Dabelea D, Magzamen S. Neighborhood Walkability, Historical Redlining, and Childhood Obesity in Denver, Colorado. J Urban Health 2023; 100:103-117. [PMID: 36622547 PMCID: PMC9918655 DOI: 10.1007/s11524-022-00703-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/01/2022] [Indexed: 01/10/2023]
Abstract
Childhood obesity is a precursor to future health complications. In adults, neighborhood walkability is inversely associated with obesity prevalence. Recently, it has been shown that current urban walkability has been influenced by historical discriminatory neighborhood disinvestment. However, the relationship between this systemic racism and obesity has not been extensively studied. The objective of this study was to evaluate the association of neighborhood walkability and redlining, a historical practice of denying home loans to communities of color, with childhood obesity. We evaluated neighborhood walkability and walkable destinations for 250 participants of the Healthy Start cohort, based in the Denver metropolitan region. Eligible participants attended an examination between ages 4 and 8. Walkable destinations and redlining geolocations were determined based on residential addresses, and a weighting system for destination types was developed. Sidewalks and trails in Denver were included in the network analyst tool in ArcMap to calculate the precise walkable environment for each child. We implemented linear regression models to estimate associations between neighborhood characteristics and child body mass index (BMI) z-scores and fat mass percent. There was a significant association between child BMI and redlining (β: 1.36, 95% CI: 0.106, 2.620). We did not find an association between walkability measures and childhood obesity outcomes. We propose that cities such as Denver pursue built environment policies, such as inclusionary zoning and direct investments in neighborhoods that have been historically neglected, to reduce the childhood health impacts of segregated poverty, and suggest further studies on the influences that redlining and urban built environment factors have on childhood obesity.
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Affiliation(s)
- Katharina Kowalski
- Department of Environmental and Radiological Health Sciences, Colorado State University, CO, Fort Collins, USA
| | - Jeremy Auerbach
- Department of Environmental and Radiological Health Sciences, Colorado State University, CO, Fort Collins, USA
| | - Sheena E Martenies
- Department of Environmental and Radiological Health Sciences, Colorado State University, CO, Fort Collins, USA
- Department of Community Health and Kinesiology, University of Illinois Urbana-Champaign, IL, Champaign, USA
| | - Anne P Starling
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Brianna Moore
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Dana Dabelea
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Sheryl Magzamen
- Department of Environmental and Radiological Health Sciences, Colorado State University, CO, Fort Collins, USA.
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9
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Lam TM, Wagtendonk AJ, den Braver NR, Karssenberg D, Vaartjes I, Timmermans EJ, Beulens JWJ, Lakerveld J. Development of a neighborhood obesogenic built environment characteristics index for the Netherlands. Obesity (Silver Spring) 2023; 31:214-224. [PMID: 36541154 PMCID: PMC10108038 DOI: 10.1002/oby.23610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/26/2022] [Accepted: 08/04/2022] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Environmental factors that drive obesity are often studied individually, whereas obesogenic environments are likely to consist of multiple factors from food and physical activity (PA) environments. This study aimed to compose and describe a comprehensive, theory-based, expert-informed index to quantify obesogenicity for all neighborhoods in the Netherlands. METHODS The Obesogenic Built Environment CharacterisTics (OBCT) index consists of 17 components. The index was calculated as an average of componential scores across both food and PA environments and was scaled from 0 to 100. The index was visualized and summarized with sensitivity analysis for weighting methods. RESULTS The OBCT index for all 12,821 neighborhoods was right-skewed, with a median of 44.6 (IQR = 10.1). Obesogenicity was lower in more urbanized neighborhoods except for the extremely urbanized neighborhoods (>2500 addresses/km2 ), where obesogenicity was highest. The overall OBCT index score was moderately correlated with the food environment (Spearman ρ = 0.55, p <0.05) and with the PA environment (ρ = 0.38, p <0.05). Hierarchical weighting increased index correlations with the PA environment but decreased correlations with the food environment. CONCLUSIONS The novel OBCT index and its comprehensive environmental scores are potentially useful tools to quantify obesogenicity of neighborhoods.
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Affiliation(s)
- Thao Minh Lam
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health, Health Behaviours and Chronic Diseases, Amsterdam, the Netherlands
- Upstream Team, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Alfred J Wagtendonk
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health, Health Behaviours and Chronic Diseases, Amsterdam, the Netherlands
- Upstream Team, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Nicolette R den Braver
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health, Health Behaviours and Chronic Diseases, Amsterdam, the Netherlands
- Upstream Team, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Derek Karssenberg
- Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands
| | - Ilonca Vaartjes
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Erik J Timmermans
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Joline W J Beulens
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health, Health Behaviours and Chronic Diseases, Amsterdam, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Jeroen Lakerveld
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health, Health Behaviours and Chronic Diseases, Amsterdam, the Netherlands
- Upstream Team, Amsterdam University Medical Centers, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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10
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Le VT, Rhew IC, Kosterman R, Lovasi GS, Frank LD. Associations of Cumulative and Point-in-Time Neighborhood Poverty and Walkability with Body Mass from Age 30 to 39. J Urban Health 2022; 99:1080-1090. [PMID: 36222973 PMCID: PMC9727000 DOI: 10.1007/s11524-022-00688-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/13/2022] [Indexed: 12/31/2022]
Abstract
Few studies examining the effects of neighborhood exposures have accounted for longitudinal residential history. This study examined associations of body mass index (BMI, kg/m2) with neighborhood-level walkability and poverty, both assessed concurrently and cumulatively in the years leading up to BMI assessment. Participants (N = 808) were from a cohort study of individuals originally recruited from public schools in Seattle, Washington, in fifth grade in 1985. Height and weight for BMI were obtained at four assessments at ages: 30 (in 2005), 33, 35, and 39. Participants also completed residential timelines listing each address where they lived from ages 28 to 39, creating a continuous record of addresses and moves. Neighborhood-level walkability and poverty were based on census block groups of each address. Generalized estimating equation models estimated associations of standardized neighborhood variables, both at point-in-time concurrently with assessment of BMI and cumulatively up to the time of BMI assessment. Mean BMI across observations was 28.8 (SD = 7.1). After adjusting for covariates, cumulative walkability was associated with lower BMI (b = - 0.28; 95% CI: - 0.55, - 0.02), and cumulative neighborhood poverty was associated with higher BMI (b = 0.35; 95% CI: 0.09, 0.60). When examining point-in-time concurrent walkability and poverty with BMI, adjusted associations were close to the null and non-significant. This study provides evidence for a significant role of cumulative exposure to neighborhood built and socioeconomic environments predicting BMI. It underscores the relative strength and importance of cumulative assessments to capture neighborhood exposure not captured through point-in-time assessments.
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Affiliation(s)
- Vi T Le
- Social Development Research Group, School of Social Work, University of Washington, 9725 3rd Ave NE, Suite 401, Seattle, WA, 98115, USA.
| | - Isaac C Rhew
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
- Department of Psychiatry and Behavioral Sciences, Center for the Study of Health and Risk Behaviors, University of Washington, Seattle, WA, USA
| | - Rick Kosterman
- Social Development Research Group, School of Social Work, University of Washington, 9725 3rd Ave NE, Suite 401, Seattle, WA, 98115, USA
| | - Gina S Lovasi
- Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, PA, USA
| | - Lawrence D Frank
- Department of Urban Studies and Planning, University of California San Diego, San Diego, CA, USA
- Urban Design 4 Health, Seattle, WA, USA
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11
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Albanese NNY, Lin I, Friedberg JP, Lipsitz SR, Rundle A, Quinn JW, Neckerman KM, Nicholson A, Allegrante JP, Wylie-Rosett J, Natarajan S. Association of the built environment and neighborhood resources with obesity-related health behaviors in older veterans with hypertension. Health Psychol 2022; 41:701-709. [PMID: 35389690 PMCID: PMC10110294 DOI: 10.1037/hea0001161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE To evaluate the association of the built environment and neighborhood resources with exercise, diet, and body mass index (BMI). METHOD Person-level data were collected from 533 veterans with uncontrolled hypertension. Neighborhood measures were: (a) census-tract level walkability; and (b) healthy food proximity (HFP). Robust or logistic regression (adjusting for age, race, education, comorbidity, and clustered by provider) was used to evaluate associations between neighborhood and exercise duration (hours/week), exercise adherence (% adherent), saturated fat index (0-10), Healthy Eating Index (HEI; 0-100), HEI adherence (≥ 74 score), stage of change (SOC) for exercise and diet (% in action/maintenance), BMI (kg/m²), and obesity (BMI ≥ 30 kg/m²). RESULTS The adjusted difference in HEI score (standard error [SE]) between the highest and lowest walkability tertiles was 3.67 (1.35), p = .006; the corresponding comparison for the saturated fat index was 1.03 (.50), p = .041 and BMI was -1.12 (.45), p = .013. The adjusted odds ratio (OR; 95% confidence intervals [CI]) between the highest and lowest walkability tertiles for HEI adherence was 2.16 [1.22, 3.82], p = .009 and for action/maintenance for exercise SOC was 1.78 [1.15, 2.76], p = .011. The adjusted difference (SE) between the highest and lowest HFP tertiles for exercise duration was .65 (.31), p = .03. The adjusted OR [95% CI] between the highest and lowest HFP tertiles for exercise adherence was 1.74 [1.08, 2.79], p = .023 and for action/maintenance for exercise SOC was 1.75 [1.10, 2.79], p = .034. CONCLUSIONS Geographical location is associated with exercise and diet. Environment-tailored health recommendations could promote healthier lifestyles and decrease obesity-related cardiovascular disease. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Affiliation(s)
| | - Iris Lin
- Research and Development Service
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12
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Neighbourhood walkability and dietary attributes: effect modification by area-level socio-economic status. Public Health Nutr 2022; 25:2593-2600. [PMID: 35583044 PMCID: PMC9991640 DOI: 10.1017/s1368980022001197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Higher neighbourhood walkability would be expected to contribute to better health, but the relevant evidence is inconsistent. This may be because residents' dietary attributes, which vary with socio-economic status (SES) and influence their health, can be related to walkability. We examined associations of walkability with dietary attributes and potential effect modification by area-level SES. DESIGN The exposure variable of this cross-sectional study was neighbourhood walkability, calculated using residential density, intersection density and destination density within 1-km street-network buffer around each participant's residence. The outcome variables were dietary patterns (Western, prudent and mixed) and total dietary energy intake, derived from a FFQ. Main and interaction effects with area-level SES were estimated using two-level linear regression models. SETTING Participants were from all states and territories in Australia. PARTICIPANTS The analytical sample included 3590 participants (54 % women, age range 34 to 86). RESULTS Walkability was not associated with dietary attributes in the whole sample. However, we found interaction effects of walkability and area-level SES on Western diet scores (P < 0·001) and total energy intake (P = 0·012). In low SES areas, higher walkability was associated with higher Western dietary patterns (P = 0·062) and higher total energy intake (P = 0·066). In high SES areas, higher walkability was associated with lower Western diet scores (P = 0·021) and lower total energy intake (P = 0·058). CONCLUSIONS Higher walkability may not be necessarily conducive to better health in socio-economically disadvantaged areas. Public health initiatives to enhance neighbourhood walkability need to consider food environments and socio-economic contexts.
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13
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Lam TM, Wang Z, Vaartjes I, Karssenberg D, Ettema D, Helbich M, Timmermans EJ, Frank LD, den Braver NR, Wagtendonk AJ, Beulens JWJ, Lakerveld J. Development of an objectively measured walkability index for the Netherlands. Int J Behav Nutr Phys Act 2022; 19:50. [PMID: 35501815 PMCID: PMC9063284 DOI: 10.1186/s12966-022-01270-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 03/10/2022] [Indexed: 12/03/2022] Open
Abstract
Background Walkability indices have been developed and linked to behavioural and health outcomes elsewhere in the world, but not comprehensively for Europe. We aimed to 1) develop a theory-based and evidence-informed Dutch walkability index, 2) examine its cross-sectional associations with total and purpose-specific walking behaviours of adults across socioeconomic (SES) and urbanisation strata, 3) explore which walkability components drive these associations. Methods Components of the index included: population density, retail and service density, land use mix, street connectivity, green space, sidewalk density and public transport density. Each of the seven components was calculated for three Euclidean buffers: 150 m, 500 m and 1000 m around every 6-digit postal code location and for every administrative neighbourhood in GIS. Componential z-scores were averaged, and final indices normalized between 0 and 100. Data on self-reported demographic characteristics and walking behaviours of 16,055 adult respondents (aged 18–65) were extracted from the Dutch National Travel Survey 2017. Using Tobit regression modelling adjusted for individual- and household-level confounders, we assessed the associations between walkability and minutes walking in total, for non-discretionary and discretionary purposes. By assessing the attenuation in associations between partial indices and walking outcomes, we identified which of the seven components drive these associations. We also tested for effect modification by urbanization degree, SES, age and sex. Results In fully adjusted models, a 10% increase in walkability was associated with a maximum increase of 8.5 min of total walking per day (95%CI: 7.1–9.9). This association was consistent across buffer sizes and purposes of walking. Public transport density was driving the index’s association with walking outcomes. Stratified results showed that associations with minutes of non-discretionary walking were stronger in rural compared to very urban areas, in neighbourhoods with low SES compared to high SES, and in middle-aged (36–49 years) compared to young (18–35 years old) and older adults (50–65 years old). Conclusions The walkability index was cross-sectionally associated with Dutch adult’s walking behaviours, indicating its validity for further use in research. Supplementary Information The online version contains supplementary material available at 10.1186/s12966-022-01270-8.
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Affiliation(s)
- Thao Minh Lam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Boelelaan 1089a, 1081HV, Amsterdam, Netherlands. .,Upstream Team, Vrije Universiteit, Amsterdam, Netherlands.
| | - Zhiyong Wang
- Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, Princetonlaan 8a, 3584, Utrecht, CB, Netherlands
| | - Ilonca Vaartjes
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.,Global Geo Health Data Center, University Medical Center Utrecht & Utrecht University, Utrecht, Netherlands
| | - Derek Karssenberg
- Global Geo Health Data Center, University Medical Center Utrecht & Utrecht University, Utrecht, Netherlands.,Department of Physical Geography, Utrecht University, Princetonlaan 8a, 3584, Utrecht, CB, Netherlands
| | - Dick Ettema
- Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, Princetonlaan 8a, 3584, Utrecht, CB, Netherlands
| | - Marco Helbich
- Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, Princetonlaan 8a, 3584, Utrecht, CB, Netherlands
| | - Erik J Timmermans
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Lawrence D Frank
- Department of Urban Studies and Planning, UC San Diego, La Jolla, San Diego, USA.,Urban Design 4 Health, Inc, Rochester, NY, USA
| | - Nicolette R den Braver
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Boelelaan 1089a, 1081HV, Amsterdam, Netherlands.,Upstream Team, Vrije Universiteit, Amsterdam, Netherlands
| | - Alfred J Wagtendonk
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Boelelaan 1089a, 1081HV, Amsterdam, Netherlands.,Upstream Team, Vrije Universiteit, Amsterdam, Netherlands
| | - Joline W J Beulens
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Boelelaan 1089a, 1081HV, Amsterdam, Netherlands.,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.,Upstream Team, Vrije Universiteit, Amsterdam, Netherlands
| | - Jeroen Lakerveld
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health research institute, Boelelaan 1089a, 1081HV, Amsterdam, Netherlands.,Upstream Team, Vrije Universiteit, Amsterdam, Netherlands
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14
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Joint associations between neighborhood walkability, greenness, and particulate air pollution on cardiovascular mortality among adults with a history of stroke or acute myocardial infarction. Environ Epidemiol 2022; 6:e200. [PMID: 35434462 PMCID: PMC9005250 DOI: 10.1097/ee9.0000000000000200] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 01/29/2022] [Indexed: 11/26/2022] Open
Abstract
Fine particulate matter (PM2.5) is a known risk factor for cardiovascular disease (CVD). Neighborhood walkability and greenness may also be associated with CVD, but there is limited evidence on their joint or interacting effects with PM2.5.
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15
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Lang IM, Antonakos CL, Judd SE, Colabianchi N. A longitudinal examination of objective neighborhood walkability, body mass index, and waist circumference: the REasons for Geographic And Racial Differences in Stroke study. Int J Behav Nutr Phys Act 2022; 19:17. [PMID: 35151322 PMCID: PMC8841052 DOI: 10.1186/s12966-022-01247-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 01/10/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Studies have shown neighborhood walkability is associated with obesity. To advance this research, study designs involving longer follow-up, broader geographic regions, appropriate neighborhood characterization, assessment of exposure length and severity, and consideration of stayers and movers are needed. Using a cohort spanning the conterminous United States, this study examines the longitudinal relationship between a network buffer-derived, duration-weighted neighborhood walkability measure and two adiposity-related outcomes.
Methods
This study included 12,846 Black/African American and White adults in the REasons for Geographic And Racial Differences in Stroke study. Body mass index (BMI) and waist circumference (WC) were assessed at baseline and up to 13.3 years later (M (SD) = 9.4 (1.0) years). BMI and WC were dichotomized. Walk Score® was duration-weighted based on time at each address and categorized as Very Car-Dependent, Car-Dependent, Somewhat Walkable, Very Walkable, and Walker’s Paradise. Unadjusted and adjusted logistic regression models tested each neighborhood walkability-adiposity association. Adjusted models controlled for demographics, health factors, neighborhood socioeconomic status, follow-up time, and either baseline BMI or baseline WC. Adjusted models also tested for interactions. Post-estimation Wald tests examined whether categorical variables had coefficients jointly equal to zero. Orthogonal polynomial contrasts tested for a linear trend in the neighborhood walkability-adiposity relationships.
Results
The odds of being overweight/obese at follow-up were lower for residents with duration-weighted Walk Score® values in the Walker’s Paradise range and residents with values in the Very Walkable range compared to residents with values in the Very Car-Dependent range. Residents with duration-weighted Walk Score® values classified as Very Walkable had significantly lower odds of having a moderate-to-high risk WC at follow-up relative to those in the Very Car-Dependent range. For both outcomes, the effects were small but meaningful. The negative linear trend was significant for BMI but not WC.
Conclusion
People with cumulative neighborhood walkability scores in the Walker’s Paradise range were less likely to be overweight/obese independent of other factors, while people with scores in the Very Walkable range were less likely to be overweight/obese and less likely to have a moderate-to-high risk WC. Addressing neighborhood walkability is one approach to combating obesity.
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16
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Kinsey EW, Widen E, Quinn JW, Huynh M, Van Wye G, Lovasi GS, Neckerman K, Rundle A. Neighborhood walkability and poverty predict excessive gestational weight gain: A cross-sectional study in New York City. Obesity (Silver Spring) 2022; 30:503-514. [PMID: 35068077 PMCID: PMC8830702 DOI: 10.1002/oby.23339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 10/14/2021] [Accepted: 10/23/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE This study evaluated associations between neighborhood-level characteristics and gestational weight gain (GWG) in a population-level study of 2015 New York City births. METHODS Generalized linear mixed-effects models were used to estimate odds ratios (ORs) for associations between neighborhood-level characteristics (poverty, food environment, walkability) within 1 km of a residential Census block centroid and excessive or inadequate GWG compared with recommended GWG. All models were adjusted for individual-level sociodemographic characteristics. RESULTS Among the sample of 106,285 births, 41.8% had excessive GWG, and 26.3% had inadequate GWG. Residence in the highest versus lowest quartile of neighborhood poverty was associated with greater odds of excessive GWG (OR: 1.17, 95% CI: 1.08-1.26). Residence in neighborhoods in the quartile of highest walkability compared with the quartile of lowest walkability was associated with lower odds of excessive GWG (OR: 0.87, 95% CI: 0.81-0.93). Adjustment for prepregnancy BMI attenuated the associations for neighborhood poverty, but not for walkability. Neighborhood variables were not associated with inadequate GWG. CONCLUSIONS These analyses indicate that greater neighborhood walkability is associated with lower odds of excessive GWG, potentially from differences in pedestrian activity during pregnancy. This research provides further evidence for using urban design to support healthy weight status during pregnancy.
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Affiliation(s)
- Eliza W. Kinsey
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Elizabeth Widen
- Department of Nutritional Sciences and Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - James W. Quinn
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Mary Huynh
- Bureau of Vital Statistics, New York City Department of Health and Mental Hygiene, New York, NY, USA
| | - Gretchen Van Wye
- Bureau of Vital Statistics, New York City Department of Health and Mental Hygiene, New York, NY, USA
| | - Gina S. Lovasi
- Epidemiology and Biostatistics, Urban Health Collaborative, Dornsife School of Public Health, Drexel University, Philadelphia, PA, USA
| | - Kathryn Neckerman
- Columbia Population Research Center, Columbia University, New York, NY, USA
| | - Andrew Rundle
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
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17
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Florindo AA, Turrell G, Garcia LMT, Dos Anjos Souza Barbosa JP, Cruz MS, Failla MA, de Aguiar BS, Barrozo LV, Goldbaum M. Mix of destinations and sedentary behavior among Brazilian adults: a cross-sectional study. BMC Public Health 2021; 21:347. [PMID: 33579233 PMCID: PMC7881484 DOI: 10.1186/s12889-020-10123-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Accepted: 12/23/2020] [Indexed: 11/10/2022] Open
Abstract
Background Sedentary behavior is influenced by contextual, social, and individual factors, including the built environment. However, associations between the built environment and sitting time have not been extensively investigated in countries with economies in transition such as Brazil. The objective of this study is to examine the relationship between sitting-time and access to a mix of destinations for adults from Sao Paulo city, Brazil. Methods This study uses data from the Health Survey of Sao Paulo. Sedentary behavior was assessed by a questionnaire using two questions: total sitting time in minutes on a usual weekday; and on a usual weekend day. The mix of destinations was measured by summing the number of facilities (comprising bus stops, train/subway stations, parks, squares, public recreation centres, bike paths, primary health care units, supermarkets, food stores, bakeries, and coffee-shops) within 500 m of each participant’s residence. Minutes of sitting time in a typical weekday and weekend day were the outcomes and the mix of destinations score in 500 m buffers was the exposure variable. Associations between the mix of destinations and sitting time were examined using multilevel linear regression: these models accounted for clustering within census tracts and households and adjusted for environmental, sociodemographic, and health-related factors. Results After adjustment for covariates, the mix of destinations was inversely associated with minutes of sitting time on a weekday (β=− 8.8, p=0.001) and weekend day (β=− 6.1, p=0.022). People who lived in areas with a greater mix of destinations had shorter average sitting times. Conclusion Greater mix of destinations within 500 m of peoples’ residences was inversely associated with sitting time on a typical weekday and weekend day. In Latin American cities like Sao Paulo built environments more favorable for walking may contribute to reducing sedentary behavior and prevent associated chronic disease.
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Affiliation(s)
- Alex Antonio Florindo
- School of Arts, Sciences and Humanities, University of Sao Paulo, Rua Arlindo Bettio, Sao Paulo, SP, 1000, Brazil. .,Graduate Program in Nutrition in Public Health, School of Public Health, University of Sao Paulo, Sao Paulo, Brazil. .,Physical Activity Epidemiology Group, University of Sao Paulo, Sao Paulo, Brazil.
| | - Gavin Turrell
- Centre for Research and Action in Public Health, Health Research Institute, University of Canberra, Canberra, Australia
| | - Leandro Martin Totaro Garcia
- Physical Activity Epidemiology Group, University of Sao Paulo, Sao Paulo, Brazil.,Centre for Public Health, Queen's University Belfast, Belfast, UK
| | | | - Michele Santos Cruz
- Graduate Program in Nutrition in Public Health, School of Public Health, University of Sao Paulo, Sao Paulo, Brazil.,Physical Activity Epidemiology Group, University of Sao Paulo, Sao Paulo, Brazil
| | - Marcelo Antunes Failla
- Department of Epidemiology and Information, Municipal Government of Sao Paulo, Sao Paulo, Brazil
| | - Breno Souza de Aguiar
- Department of Epidemiology and Information, Municipal Government of Sao Paulo, Sao Paulo, Brazil
| | - Ligia Vizeu Barrozo
- Department of Geography, School of Philosophy, Literature and Human Sciences, University of Sao Paulo, Sao Paulo, Brazil
| | - Moises Goldbaum
- Department of Preventive Medicine, School of Medicine, University of Sao Paulo, Sao Paulo, Brazil
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18
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Nichani V, Koohsari MJ, Oka K, Nakaya T, Shibata A, Ishii K, Yasunaga A, Turley L, McCormack GR. Associations between the traditional and novel neighbourhood built environment metrics and weight status among Canadian men and women. CANADIAN JOURNAL OF PUBLIC HEALTH = REVUE CANADIENNE DE SANTE PUBLIQUE 2021; 112:166-174. [PMID: 32696142 PMCID: PMC7851194 DOI: 10.17269/s41997-020-00365-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Accepted: 06/08/2020] [Indexed: 01/09/2023]
Abstract
OBJECTIVES Neighbourhood characteristics can impact the health of residents. This study investigated associations between objectively derived neighbourhood characteristics, including novel space syntax metrics, and self-reported body mass index (BMI) among Canadian men and women. METHODS Our study included survey data collected from a random cross-section of adults residing in Calgary, Alberta (n = 1718). The survey, conducted in 2007/2008, captured participants' socio-demographic characteristics, health, and weight status (BMI). Participants' household postal codes were geocoded and 1600-m line-based network buffers estimated. Using a geographical information system, we estimated neighbourhood characteristics within each buffer including business destination density, street intersection density, sidewalk length, and population density. Using space syntax, we estimated street integration and walkability (street integration plus population density) within each buffer. Using adjusted regression models, we estimated associations between neighbourhood characteristics and BMI (continuous) and BMI categories (healthy weight vs. overweight including obese). Gender-stratified analysis was also performed. RESULTS Business destination density was negatively associated with BMI and the odds of being overweight. Among men, street intersection density and sidewalk length were negatively associated with BMI and street intersection density, business destination density, street integration, and space syntax walkability were negatively associated with odds of being overweight. Among women, business destination density was negatively associated with BMI. CONCLUSION Urban planning policies that impact neighbourhood design have the potential to influence weight among adults living in urban Canadian settings. Some characteristics may have a differential association with weight among men and women and should be considered in urban planning and in neighbourhood-focussed public health interventions.
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Affiliation(s)
- Vikram Nichani
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, TRW 3rd Floor, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4Z6, Canada.
| | - Mohammad Javad Koohsari
- Faculty of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama, 359-1192, Japan
- Behavioural Epidemiology Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - Koichiro Oka
- Faculty of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama, 359-1192, Japan
| | - Tomoki Nakaya
- Graduate School of Environmental Studies, Tohoku University, Sendai, Miyagi, 980-8577, Japan
| | - Ai Shibata
- Faculty of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama, 359-1192, Japan
- Faculty of Health and Sport Sciences, University of Tsukuba, Tsukuba, Ibaraki, 305-8577, Japan
| | - Kaori Ishii
- Faculty of Sport Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama, 359-1192, Japan
| | - Akitomo Yasunaga
- Faculty of Liberal Arts and Sciences, Bunka Gakuen University, Shibuya City, Tokyo, 151-8523, Japan
| | - Liam Turley
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, TRW 3rd Floor, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4Z6, Canada
| | - Gavin R McCormack
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, TRW 3rd Floor, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4Z6, Canada
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19
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Kartschmit N, Sutcliffe R, Sheldon MP, Moebus S, Greiser KH, Hartwig S, Thürkow D, Stentzel U, van den Berg N, Wolf K, Maier W, Peters A, Ahmed S, Köhnke C, Mikolajczyk R, Wienke A, Kluttig A, Rudge G. Walkability and its association with walking/cycling and body mass index among adults in different regions of Germany: a cross-sectional analysis of pooled data from five German cohorts. BMJ Open 2020; 10:e033941. [PMID: 32350013 PMCID: PMC7213856 DOI: 10.1136/bmjopen-2019-033941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
OBJECTIVES To examine three walkability measures (points of interest (POI), transit stations and impedance (restrictions to walking) within 640 m of participant's addresses) in different regions in Germany and assess the relationships between walkability, walking/cycling and body mass index (BMI) using generalised additive models. SETTING Five different regions and cities of Germany using data from five cohort studies. PARTICIPANTS For analysing walking/cycling behaviour, there were 6269 participants of a pooled sample from three cohorts with a mean age of 59.2 years (SD: 14.3) and of them 48.9% were male. For analysing BMI, there were 9441 participants of a pooled sample of five cohorts with a mean age of 62.3 years (SD: 12.8) and of them 48.5% were male. OUTCOMES (1) Self-reported walking/cycling (dichotomised into more than 30 min and 30 min and less per day; (2) BMI calculated with anthropological measures from weight and height. RESULTS Higher impedance was associated with lower prevalence of walking/cycling more than 30 min/day (prevalence ratio (PR): 0.95; 95% CI 0.93 to 0.97), while higher number of POI and transit stations were associated with higher prevalence (PR 1.03; 95% CI 1.02 to 1.05 for both measures). Higher impedance was associated with higher BMI (ß: 0.15; 95% CI 0.04 to 0.25) and a higher number of POI with lower BMI (ß: -0.14; 95% CI -0.24 to 0.04). No association was found between transit stations and BMI (ß: 0.005, 95% CI -0.11 to 0.12). Stratified by cohort we observed heterogeneous associations between BMI and transit stations and impedance. CONCLUSION We found evidence for associations of walking/cycling with walkability measures. Associations for BMI differed across cohorts.
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Affiliation(s)
- Nadja Kartschmit
- Institute of Med. Epidemiology, Biometrics and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Robynne Sutcliffe
- Centre for Urban Epidemiology, University Clinics Essen, Essen, Germany
| | | | - Susanne Moebus
- Centre for Urban Epidemiology, University Clinics Essen, Essen, Germany
| | - Karin Halina Greiser
- Institute of Med. Epidemiology, Biometrics and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
- German Cancer Research Centre, Heidelberg, Baden-Württemberg, Germany
| | - Saskia Hartwig
- Institute of Med. Epidemiology, Biometrics and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Detlef Thürkow
- Institute of Geosciences and Geography, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Ulrike Stentzel
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Neeltje van den Berg
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Kathrin Wolf
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
| | - Werner Maier
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Helmholtz Zentrum München, German Research Center for Environmental Health, Institute of Health Economics and Health Care Management, Neuherberg, Germany
| | - Annette Peters
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany
| | - Salman Ahmed
- Centre for Urban Epidemiology, University Clinics Essen, Essen, Germany
| | - Corinna Köhnke
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Nordrhein-Westfalen, Germany
| | - Rafael Mikolajczyk
- Institute of Med. Epidemiology, Biometrics and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Andreas Wienke
- Institute of Med. Epidemiology, Biometrics and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Alexander Kluttig
- Institute of Med. Epidemiology, Biometrics and Informatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Gavin Rudge
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
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