A new method to visualize obesity prevalence in Seattle‐King County at the census block level

Drewnowski, Adam; Buszkiewicz, James; Aggarwal, Anju; Cook, Andrea J.; Moudon, Anne Vernez

doi:10.1002/osp4.144

Cited by 7 publications

(10 citation statements)

References 14 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They may capture net worth and SES more consistently and with a clearer gradient than either self-reported educational attainment or household incomes (Lovasi et al, 2012, Rundle et al, 2013, Duncan et al, 2005, Fiechtner et al, 2013, Duncan et al, 2012, Rundle et al, 2011, Wall et al, 2012, Dunton et al, 2009, Auchincloss et al, 2009). In past studies, key characteristics of “obesogenic” neighborhoods were associated with lower property values (Drewnowski et al, 2014, Drewnowski et al, 2018, Drewnowski et al, 2016b).…”

Section: Discussionmentioning

confidence: 99%

“…Each SOS participant completed a general-select (G-SEL) version of the validated (Neuhouser et al, 1999, Patterson et al, 1999, Kristal et al, 2000) FFQ. This FFQ has been widely used in health studies in the past (Drewnowski et al, 2016a, Drewnowski et al, 2018, Neuhouser et al, 1999, Patterson et al, 1999, Kristal et al, 2000, Lippman et al, 2005, Masset et al, 2009). The G-SEL is a semi-quantitative FFQ, collecting information on both frequency (with response categories ranging from “never or less than once per month” to “2+ times per day”) and portion size (small, medium, and large) for 125 food items.…”

Section: Methodsmentioning

confidence: 99%

“…incomes and education. In past studies, residential property values have shown significant associations with overall diet quality measures and obesity rates (Drewnowski et al, 2015, Drewnowski et al, 2016a, Drewnowski et al, 2018). Third, tax parcel property values, obtained from the County tax assessor and aggregated to the census block level, will serve as the objective measure of both individual and area SES and will provide a useful tool to visualize geospatial disparities in soda and salad consumption (Berrigan et al, 2015, Moudon et al, 2011, Coffee et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Soda, salad, and socioeconomic status: Findings from the Seattle Obesity Study (SOS)

Drewnowski

Buszkiewicz

Aggarwal

2019

SSM - Population Health

View full text Add to dashboard Cite

BackgroundDocumenting geographic disparities in dietary behaviors can help inform public health interventions at the local level.ObjectiveTo study and visualize socioeconomic gradient in soda and salad consumption using a geo-localized measure of socioeconomic status in contrast to more traditional measures.MethodsGeo-localized dietary intake data came from the Seattle Obesity Study I, a population-based sample of King County adults (n=1099). Socio-demographic data and soda and salad consumption frequencies (times/week) were obtained by 20-min telephone survey. Food frequency questionnaire (FFQ) data were used to construct Healthy Eating Index (HEI) scores. Individual residential property values obtained from the King County tax assessor. Multivariable linear regressions examined socioeconomic gradient in the frequency of soda and salad consumption by residential property values, the primary independent variable, in comparison to annual household incomes and educational attainment, with adjustment for age, gender, and race/ethnicity. Geographic disparities in soda and salad consumption by property value metric were illustrated at the census block level using modeled predicted marginal means.ResultsAmong all three socioeconomic indicators (income, education and residential property values), residential property values captured strongest gradient in soda and salad consumption. Higher quintiles of residential property values were associated with lower soda and higher salad consumption. Respondents living in the highest quintile of property values −1.04 fewer sodas per week (95% CI= −1.87, −0.21) and 0.89 more salads per week (95% CI= 0.36, 1.42), adjusting for sociodemographic covariates. Residential property values illustrated geographic disparities in soda and salad consumption at the census-block level.ConclusionGeo-localized disparities in food consumption patterns by neighborhood can inform current discourse on the socioeconomic determinants of health, while providing a useful tool for targeted interventions at the local level.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Soda, salad, and socioeconomic status: Findings from the Seattle Obesity Study (SOS)

Drewnowski

Buszkiewicz

Aggarwal

2019

SSM - Population Health

View full text Add to dashboard Cite

show abstract

“…The RLF overcomes other problems related to aggregated spatial data obtained from census and other governmental statistics that are available only for predefined administrative areas, which can yield results tainted with the modifıable areal unit problem (Vernez Moudon et al ., ). Based on local property sales data, the RLF has a further benefit as a composite measure of wealth potentially reducing measurement error, co‐linearity, and over‐adjustment in analysis and conferring ease of explanation (Drewnowski et al ., ; Feng et al ., ). Property values have been found to be a stronger predictor of health over traditional measures of SES (Rehm et al ., ; Vernez Moudon et al ., ).…”

Section: Discussionmentioning

confidence: 99%

Concurrent assessment of urban environment and cardiometabolic risk over 10 years in a middle‐aged population‐based cohort

Daniel

Carroll

Niyonsenga

et al. 2018

Geographical Research

View full text Add to dashboard Cite

Inference regarding the impact of urban areas on health is limited by cross-sectional studies assessing few dimensions and ignoring area-level socio-economic status. This study simultaneously assessed several dimensions of the built environment against incident cardiometabolic risk (CMR) arising over 10 years. It tested the hypothesis that, accounting for local area relative wealth, features of the built environment would not predict incident CMR. Initially, disease-free adults in a biomedical cohort in Adelaide, Australia, provided address and clinical data over three waves of follow-up. CMR was defined as the count of five clinical CMR factors. Built environment measures were derived for urban form, and natural, and food environments. Local area wealth was expressed using the relative location factor index. Poisson growth models accounting for within-suburb clustering, age, sex, and education were used to estimate associations between built environment measures and increasing CMR. Fitted linear trajectories had statistically significant mean values of intercepts and slopes. CMR trajectories were associated with age, male sex, and low education. In models including measures of the food, natural, and urban form environments, per standard deviation increase, only POS count predicted incident CMR, which was more strongly predicted by relative location factor. Not accounting for local area socio-economic status may overestimate the strength of relationships between health and the built environment. Inequity in accessible POS is robustly related to incident CMR.

show abstract

“…In Australian adults, greater increases in weight [5,30] and waist girth have been observed for people in areas with greatest area-level socioeconomic disadvantage [5]. Recent studies have also included residential property values in the features of physical built environment to derive indices of both individual-level and area-level SES [31][32][33][34]. Studies showed that lower property values were linked to higher BMI and higher risk of obesity [8,9,34] as well as to higher cardiometabolic risks [31,35].…”

Section: Introductionmentioning

confidence: 99%

Are changes in depressive symptoms, general health and residential area socio-economic status associated with trajectories of waist circumference and body mass index?

et al. 2020

View full text Add to dashboard Cite

This study sought to assess whether changes in depressive symptoms, general health, and area-level socioeconomic status (SES) were associated to changes over time in waist circumference and body mass index (BMI). Methods A total of 2871 adults (18 years or older), living in Adelaide (South Australia), were observed across three waves of data collection spanning ten years, with clinical measures of waist circumference, height and weight. Participants completed the Centre for Epidemiologic Studies Depression (CES-D) and Short Form 36 health questionnaires (SF-36 general health domain). An area-level SES measure, relative location factor, was derived from hedonic regression models using residential property features but blind to location. Growth curve models with latent variables were fitted to data. Results Waist circumference, BMI and depressive symptoms increased over time. General health and relative location factor decreased. Worsening general health and depressive symptoms predicted worsening waist circumference and BMI trajectories in covariate-adjusted models. Diminishing relative location factor was negatively associated with waist circumference and BMI trajectories in unadjusted models only.

show abstract

A new method to visualize obesity prevalence in Seattle‐King County at the census block level

Cited by 7 publications

References 14 publications

Soda, salad, and socioeconomic status: Findings from the Seattle Obesity Study (SOS)

Soda, salad, and socioeconomic status: Findings from the Seattle Obesity Study (SOS)

Concurrent assessment of urban environment and cardiometabolic risk over 10 years in a middle‐aged population‐based cohort

Are changes in depressive symptoms, general health and residential area socio-economic status associated with trajectories of waist circumference and body mass index?

Contact Info

Product

Resources

About