Exploring Housing Determinants of Obesity Prevalence Using Multiscale Geographically Weighted Regression in Chicago, Illinois

Lotfata, Aynaz; Tomal, Mateusz

doi:10.1080/00330124.2022.2111692

Cited by 9 publications

(8 citation statements)

References 39 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

show abstract

“…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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Section: Methodsmentioning

confidence: 99%

“…GLPR is not capable of capturing spatial dependence in data, and it ignores the spatial correlation in the estimation of relationships [ 42 , 44 ]. Equally important, it is more unlikely that one single coefficient per explanatory variable can reflect the true spatial relationship between the dependent variable and the explanatory variable since spatial data vary in space [ 44 , 45 ]. This study explored the effectiveness of Geographically Weighted Regression (GWR) [ 46 ] that detects spatial heterogeneity in the dataset, relaxing the assumption of spatial stationarity associated with global models.…”

Section: Methodsmentioning

confidence: 99%

Exploring Determinants of HIV/AIDS Self-Testing Uptake in South Africa Using Generalised Linear Poisson and Geographically Weighted Poisson Regression

et al. 2023

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Increased HIV/AIDS testing is of paramount importance in controlling the HIV/AIDS pandemic and subsequently saving lives. Despite progress in HIV/AIDS testing programmes, most people are still reluctant to test and thus are still unaware of their status. Understanding the factors associated with uptake levels of HIV/AIDS self-testing requires knowledge of people’s perceptions and attitudes, thus informing evidence-based decision making. Using the South African National HIV Prevalence, HIV Incidence, Behaviour and Communication Survey of 2017 (SABSSM V), this study assessed the efficacy of Generalised Linear Poisson Regression (GLPR) and Geographically Weighted Poisson Regression (GWPR) in modelling the spatial dependence and non-stationary relationships of HIV/AIDS self-testing uptake and covariates. The models were calibrated at the district level across South Africa. Results showed a slightly better performance of GWPR (pseudo R2 = 0.91 and AICc = 390) compared to GLPR (pseudo R2 = 0.88 and AICc = 2552). Estimates of local intercepts derived from GWPR exhibited differences in HIV/AIDS self-testing uptake. Overall, the output of this study displays interesting findings on the levels of spatial heterogeneity of factors associated with HIV/AIDS self-testing uptake across South Africa, which calls for district-specific policies to increase awareness of the need for HIV/AIDS self-testing.

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Exploring Housing Determinants of Obesity Prevalence Using Multiscale Geographically Weighted Regression in Chicago, Illinois

Cited by 9 publications

References 39 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

Exploring Determinants of HIV/AIDS Self-Testing Uptake in South Africa Using Generalised Linear Poisson and Geographically Weighted Poisson Regression

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