Spatial clustering patterns and regional variations for food and physical activity environments across the United States

Wende, Marilyn E.; Stowe, Ellen W.; Eberth, Jan M.; McLain, Alexander C.; Liese, Angela D.; Breneman, Charity B.; Josey, Michele J.; Hughey, S. Morgan; Kaczynski, Andrew T.

doi:10.1080/09603123.2020.1713304

Cited by 18 publications

(25 citation statements)

References 80 publications

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“…Ultimately, the COEI combined ten variables deemed essential to assessing environmental supports (or lack thereof) for PA and HE according to a review of existing literature, ratings and feedback by expert reviewers, and the extensive knowledge and experience of our diverse study team. When aggregated into a composite score for each county, index values showed substantial variability across the US (ranging from 24 to 81 out of 100) and somewhat distinct patterns of geographic clustering [74]. For example, the vast majority of counties in the South region displayed COEI scores in the worst three deciles.…”

Section: Discussionmentioning

confidence: 99%

Development of a national childhood obesogenic environment index in the United States: differences by region and rurality

Kaczynski

Eberth

Stowe

et al. 2020

Int J Behav Nutr Phys Act

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Background: Diverse environmental factors are associated with physical activity (PA) and healthy eating (HE) among youth. However, no study has created a comprehensive obesogenic environment index for children that can be applied at a large geographic scale. The purpose of this study was to describe the development of a childhood obesogenic environment index (COEI) at the county level across the United States. Methods: A comprehensive search of review articles (n = 20) and input from experts (n = 12) were used to identify community-level variables associated with youth PA, HE, or overweight/obesity for potential inclusion in the index. Based on strength of associations in the literature, expert ratings, expertise of team members, and data source availability, 10 key variables were identifiedsix related to HE (# per 1000 residents for grocery/superstores, farmers markets, fast food restaurants, full-service restaurants, and convenience stores; as well as percentage of births at baby (breastfeeding)-friendly facilities) and four related to PA (percentage of population living close to exercise opportunities, percentage of population < 1 mile from a school, a composite walkability index, and number of violent crimes per 1000 residents). Data for each variable for all counties in the U.S. (n = 3142) were collected from publicly available sources. For each variable, all counties were ranked and assigned percentiles ranging from 0 to 100. Positive environmental variables (e.g., grocery stores, exercise opportunities) were reverse scored such that higher values for all variables indicated a more obesogenic environment. Finally, for each county, a total obesogenic environment index score was generated by calculating the average percentile for all 10 variables.

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Section: Discussionmentioning

confidence: 99%

Development of a national childhood obesogenic environment index in the United States: differences by region and rurality

Kaczynski

Eberth

Stowe

et al. 2020

Int J Behav Nutr Phys Act

Self Cite

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“…Most often, choropleth maps are used to present one or two data attributes, although dot maps, graduated symbol maps, and isarithmic maps are also commonplace [34]. Spatial regression techniques such as simultaneous autoregressive (SAR) models are often used to document health risks [35], and spatial clustering has been used for leprosy in Brazil [36], measle vaccination in sub-Saharan Africa [37], as well as food and physical activity in the United States [38]. Spatial analysis has been used to identify congenital heart disease in youth aged 4-18 in China as well [39].…”

Section: Relevant Methodsmentioning

confidence: 99%

Models for Heart Failure Admissions and Admission Rates, 2016 through 2018

et al. 2020

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Background: Approximately 6.5 to 6.9 million individuals in the United States have heart failure, and the disease costs approximately $43.6 billion in 2020. This research provides geographical incidence and cost models of this disease in the U.S. and explanatory models to account for hospitals’ number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables. Methods: The number of diagnoses is forecast using regression (constrained and unconstrained) and ensemble (random forests, extra trees regressor, gradient boosting, and bagging) techniques at the hospital unit of analysis. Descriptive maps of heart failure diagnostic-related groups (DRGs) depict areas of high incidence. State- and county-level spatial and non-spatial regression models of heart failure admission rates are performed. Expenditure forecasts are estimated. Results: The incidence of heart failure has increased over time with the highest intensities in the East and center of the country; however, several Northern states have seen large increases since 2016. The best predictive model for the number of diagnoses (hospital unit of analysis) was an extremely randomized tree ensemble (predictive R2 = 0.86). The important variables in this model included workload metrics and hospital type. State-level spatial lag models using first-order Queen criteria were best at estimating heart failure admission rates (R2 = 0.816). At the county level, OLS was preferred over any GIS model based on Moran’s I and resultant R2; however, none of the traditional models performed well (R2 = 0.169 for the OLS). Gradient-boosted tree models predicted 36% of the total sum of squares; the most important factors were facility workload, mean cash on hand of the hospitals in the county, and mean equity of those hospitals. Online interactive maps at the state and county levels are provided. Conclusions. Heart failure and associated expenditures are increasing. Costs of DRGs in the study increased $61 billion from 2016 through 2018. The increase in the more expensive DRG 291 outpaced others with an associated increase of $92 billion. With the increase in demand and steady-state supply of cardiologists, the costs are likely to balloon over the next decade. Models such as the ones presented here are needed to inform healthcare leaders.

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“…Most often, choropleth maps (maps that depict categorical and numerical data) are used to present one or two data attributes, although dot maps, graduated symbol maps, and isarithmic maps are also commonplace [26]. Spatial regression techniques such as simultaneous autoregressive (SAR) models are often used to document health risks [27], and spatial clustering has been used for leprosy in Brazil [28], measle vaccination in sub-Saharan Africa [29], as well as food and physical activity in the United States [30].…”

Section: Relevant Methodsmentioning

confidence: 99%

Geospatial and Explanatory Models for Heart Failure Admissions, 2016 through 2018

Kruse¹,

Beauvais²,

Brooks³

et al. 2020

Preprint

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Background. About 5.7 million individuals in the United States have heart failure, and the disease was estimated to cost about $42.9 billion in 2020. This research provides geographical incidence models of this disease in the U.S. and explanatory models to account for hospitals’ number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables. The research also provides updated financial and demand estimates based on inflationary pressures and disease rate increases. Understanding patterns is important to both policymakers and health administrators for cost control and planning. Methods. Maps of heart failure diagnosis-related groups (DRGs) from 2016 through 2018 depicted areas of high incidence as well as changes. Spatial regression identified no significant spatial correlations. Simple expenditure forecasts were calculated for 2016 through 2018. Linear, lasso, ridge, and Elastic Net models as well as ensembled tree regressors including were built on an 80% training set and evaluated on a 20% test set. Results: The incidence of heart failure has increased over time with highest intensities in the East and center of the country; however, several Northern states (e.g., Minnesota) have seen large increases in rates from 2016. The best traditional regression model explained 75% of the variability in the number of DRGs experienced by hospital using a small subset of variables including discharges, DRG type, percent Medicare reimbursement, hospital type, and medical school affiliation. The best ensembled tree models achieved R2 over .97 on the blinded test set and identified discharges, percent Medicare reimbursement, hospital acute days, affiliated physicians, staffed beds, employees, hospital type, emergency room visits, medical school affiliation, geographical location, and the number of surgeries as highly important predictors. Conclusions. Overall, the total cost of the three DRGs in the study has increased approximately $61 billion from 2016 through 2018 (average of two estimates). The increase in the more expensive DRG (DRG 291) has outpaced others with an associated increase of $92 billion in expenditures. With the increase in demand (linked to obesity and other factors) as well as the relatively steady-state supply of cardiologists over time, the costs are likely to balloon over the next decade.

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Spatial clustering patterns and regional variations for food and physical activity environments across the United States

Cited by 18 publications

References 80 publications

Development of a national childhood obesogenic environment index in the United States: differences by region and rurality

Development of a national childhood obesogenic environment index in the United States: differences by region and rurality

Models for Heart Failure Admissions and Admission Rates, 2016 through 2018

Geospatial and Explanatory Models for Heart Failure Admissions, 2016 through 2018

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