Modeling spatiotemporal associations of obesity prevalence with biking, housing cost and green spaces in Chicago, IL, USA, 2015–2017

Hohl, Alexander; Lotfata, Aynaz

doi:10.1016/j.jth.2022.101412

Cited by 10 publications

(10 citation statements)

References 47 publications

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“…When households spend >50% of their income on housing, access to healthy foods once again becomes a burden. 45,46 Economic status and high housing costs likewise affect housing quality and conditions, which in turn, play a role in sleep patterns-an obesity-related behaviour. 47 Beyond food and housing insecurity, lack of health insurance represents an additional risk factor associated with adult obesity.…”

Section: Discussionmentioning

confidence: 99%

“…Populations living in areas of high rent burden are more likely to have obesity, which has been demonstrated by modelling investigations in Chicago, Illinois. When households spend >50% of their income on housing, access to healthy foods once again becomes a burden 45,46 . Economic status and high housing costs likewise affect housing quality and conditions, which in turn, play a role in sleep patterns—an obesity‐related behaviour 47 .…”

Section: Discussionmentioning

confidence: 99%

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County‐level socio‐environmental factors and obesity prevalence in the United States

Salerno,

Qian,

Dong

et al. 2024

Diabetes Obesity Metabolism

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AimsTo investigate high‐risk sociodemographic and environmental determinants of health (SEDH) potentially associated with adult obesity in counties in the United States using machine‐learning techniques.Materials and MethodsWe performed a cross‐sectional analysis of county‐level adult obesity prevalence (body mass index ≥30 kg/m2) in the United States using data from the Diabetes Surveillance System 2017. We harvested 49 county‐level SEDH factors that were used in a classification and regression trees (CART) model to identify county‐level clusters. The CART model was validated using a ‘hold‐out’ set of counties and variable importance was evaluated using Random Forest.ResultsOverall, we analysed 2752 counties in the United States, identifying a national median (interquartile range) obesity prevalence of 34.1% (30.2%, 37.7%). The CART method identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, seven key SEDH variables were identified by CART to guide the categorization of clusters, including Physically Inactive (%), Diabetes (%), Severe Housing Problems (%), Food Insecurity (%), Uninsured (%), Population over 65 years (%) and Non‐Hispanic Black (%).ConclusionThere is significant county‐level geographical variation in obesity prevalence in the United States, which can in part be explained by complex SEDH factors. The use of machine‐learning techniques to analyse these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo‐specific strategic interventions and optimize resource allocation to help battle the obesity pandemic.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

County‐level socio‐environmental factors and obesity prevalence in the United States

Salerno,

Qian,

Dong

et al. 2024

Diabetes Obesity Metabolism

View full text Add to dashboard Cite

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“…When households spend >50% of their income on housing, access to healthy foods once again becomes a burden. 31,32 Beyond food and housing insecurity, lack of health insurance represents an additional risk factor associated with adult obesity. Analyses have revealed disparities in obesity prevalence in disadvantaged neighborhoods and conversely, people with obesity constitute one population that are more often uninsured.…”

Section: Discussionmentioning

confidence: 99%

County-level Socio-Environmental Factors and Obesity Prevalence in the United States

Salerno,

Qian,

Dong

et al. 2023

Preprint

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AimsThere are substantial geographical variations in obesity prevalence. Sociodemographic and environmental determinants of health (SEDH), understood as upstream determinants of obesogenic behaviors, may be contributing to this disparity. Thus, we investigated high-risk SEDH potentially associated with adult obesity in American counties using machine learning (ML) techniques.Materials and methodsWe performed a cross-sectional analysis of county-level adult obesity prevalence (≥30 kg/m2) in the U.S. using data from the Diabetes Surveillance System 2017. We harvested 49 county-level SEDH factors that were used by a Classification and Regression Trees (CART) model to identify county-level clusters. CART was validated using a “hold-out” set of counties and variable importance was evaluated using Random Forest.ResultsOverall, we analyzed 2,752 counties in the U.S identifying a national median obesity prevalence of 34.1% (IQR, 30.2, 37.7). CART identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, 7 key SEDH variables were identified by CART to guide the categorization of clusters, includingPhysically Inactive(%),Diabetes, Severe Housing Problems(%),Food Insecurity(%),Uninsured(%),Population over 65 years(%), andNon-Hispanic Black(%).ConclusionThere is significant county-level geographical variation in obesity prevalence in the United States which can in part be explained by complex SEDH factors. The use of ML techniques to analyze these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo-specific strategic interventions and optimize resource allocation to help battle the obesity pandemic.Article HighlightsWhy did we undertake this study?To improve the understanding of the association between complex sociodemographic and environmental determinants of health (SEDH) and obesity prevalence in the U.S.What is the specific question(s) we wanted to answer?What are the SEDH associated with obesity prevalence?What did we find?Seven key SEDH variables were identified by CART to guide the categorization of clusters, includingPhysically Inactive(%),Diabetes, Severe Housing Problems(%),Food Insecurity(%),Uninsured(%),Population over 65 years(%), andNon-Hispanic Black(%).What are the implications of our findings?Our study shows the importance of SEDH for the regional variation of obesity prevalence and aids in the development of geo-specific strategies to reduce disparities.

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“…The maximum window size for spatiotemporal analysis was set at 50% for the study area and time using SatScan software (M Kulldorff and Information Management Services Inc, Cambridge, MA, USA). A circular shape was chosen to scan and detect all significant spatio-temporal clusters [ 86 ]. A Poisson model was used for COVID-19 mortality calculations [ 85 ].…”

Section: Methodsmentioning

confidence: 99%

“…While exploratory regression is similar to stepwise regression, rather than only looking for models with high adjusted R 2 values, exploratory regression looks for models that meet all of the requirements and assumptions of the OLS model. The variance inflation factor (VIF) indicates multicollinearity, and values between 5 and 10 are recommended, with 10 as the maximum VIF level [ 86 , 90 ]. The max VIF was set at 7.5, as ESRI recommended [ 91 , 92 ], and the model was executed eight times to select the final significant variables.…”

Section: Methodsmentioning

confidence: 99%

The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis

et al. 2023

Self Cite

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There are different area-based factors affecting the COVID-19 mortality rate in urban areas. This research aims to examine COVID-19 mortality rates and their geographical association with various socioeconomic and ecological determinants in 350 of Tehran’s neighborhoods as a big city. All deaths related to COVID-19 are included from December 2019 to July 2021. Spatial techniques, such as Kulldorff's SatScan, geographically weighted regression (GWR), and multi-scale GWR (MGWR), were used to investigate the spatially varying correlations between COVID-19 mortality rates and predictors, including air pollutant factors, socioeconomic status, built environment factors, and public transportation infrastructure. The city's downtown and northern areas were found to be significantly clustered in terms of spatial and temporal high-risk areas for COVID-19 mortality. The MGWR regression model outperformed the OLS and GWR regression models with an adjusted R2 of 0.67. Furthermore, the mortality rate was found to be associated with air quality (e.g., NO2, PM10, and O3); as air pollution increased, so did mortality. Additionally, the aging and illiteracy rates of urban neighborhoods were positively associated with COVID-19 mortality rates. Our approach in this study could be implemented to study potential associations of area-based factors with other emerging infectious diseases worldwide.

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Modeling spatiotemporal associations of obesity prevalence with biking, housing cost and green spaces in Chicago, IL, USA, 2015–2017

Cited by 10 publications

References 47 publications

County‐level socio‐environmental factors and obesity prevalence in the United States

County‐level socio‐environmental factors and obesity prevalence in the United States

County-level Socio-Environmental Factors and Obesity Prevalence in the United States

The COVID-19 Mortality Rate Is Associated with Illiteracy, Age, and Air Pollution in Urban Neighborhoods: A Spatiotemporal Cross-Sectional Analysis

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