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

The neighourhood obesogenic built environment characteristics (OBCT) index: Practice versus theory

BACKGROUND

Obesity is a key risk factor for major chronic diseases such as type 2 diabetes and cardiovascular diseases. To extensively characterise the obesogenic built environment, we recently developed a novel Obesogenic Built environment CharacterisTics (OBCT) index, consisting of 17 components that capture both food and physical activity (PA) environments.

OBJECTIVES

We aimed to assess the association between the OBCT index and body mass index (BMI) in a nationwide health monitor. Furthermore, we explored possible ways to improve the index using unsupervised and supervised methods.

METHODS

The OBCT index was constructed for 12,821 Dutch administrative neighbourhoods and linked to residential addresses of eligible adult participants in the 2016 Public Health Monitor. We split the data randomly into a training (two-thirds; n = 255,187) and a testing subset (one-third; n = 127,428). In the training set, we used non-parametric restricted cubic regression spline to assess index's association with BMI, adjusted for individual demographic characteristics. Effect modification by age, sex, socioeconomic status (SES) and urbanicity was examined. As improvement, we (1) adjusted the food environment for address density, (2) added housing price to the index and (3) adopted three weighting strategies, two methods were supervised by BMI (variable selection and random forest) in the training set. We compared these methods in the testing set by examining their model fit with BMI as outcome.

RESULTS

The OBCT index had a significant non-linear association with BMI in a fully-adjusted model (p<0.05), which was modified by age, sex, SES and urbanicity. However, variance in BMI explained by the index was low (<0.05%). Supervised methods increased this explained variance more than non-supervised methods, though overall improvements were limited as highest explained variance remained <0.5%.

DISCUSSION

The index, despite its potential to highlight disparity in obesogenic environments, had limited association with BMI. Complex improvements are not necessarily beneficial, and the components should be re-operationalised.

Community and Organizational Readiness to Adopt a Physical Activity Intervention in Micropolitan Settings

BACKGROUND

Assessing community and organizational readiness is key to successfully implementing programs. The purpose of this study was to assess the baseline readiness of micropolitan communities to adopt an evidence-based physical activity (PA) intervention by exploring three dimensions: (1) attitudes and current efforts toward prevention, (2) community and organizational climate that facilitates (or impedes) change, and (3) capacity to implement change.

METHOD

Data were collected from community leaders in 14 communities through an online survey in June 2021 (n = 149). Data were analyzed in aggregate using descriptive statistics for multiple-choice responses and content analysis for open ended responses. One-way repeated analyses of variance were used to compare mean score differences.

RESULTS

In reference to their attitudes prior to the pandemic, respondents said that addressing PA was "somewhat a priority" in their professional positions (M = 2.01, SD = 0.94), their organizations (M = 2.08, SD = 0.91), and their communities (M = 2.28, SD = 0.88). Current PA efforts included statewide initiatives, community sponsored events/clubs, and youth sports leagues. The community climate included both PA facilitators (mainly outdoor PA resources) and barriers (cost, lack of social services, and an unsupportive PA environment). Individual-level capacity (M = 2.94; SD = 1.21) to adopt a PA program was regarded lower than the community's capacity (M = 3.95; SD = 0.82), and perceptions of capacity at the community level improved even more if technical assistance (M = 3.96; SD = 0.84) or financial support (M = 4.12; SD = 0.80) were provided.

CONCLUSION

Readiness varied by dimension, suggesting the need for tailored implementation supports including technical assistance and financial support.

Experiences of Food Pantry Stakeholders and Emergency Food Providers in Rural Minnesota Communities

OBJECTIVE

This study aimed to describe the experiences influencing food pantry stakeholders' and emergency food providers' ability to meet their shoppers' needs.

DESIGN

We conducted 5 focus groups.

SETTING

Food pantries in Minnesota in late 2019 and early 2020.

PARTICIPANTS

The sample included 37 participants with various roles in the emergency food system.

PHENOMENON OF INTEREST

Barriers and challenges facing emergency food providers/stakeholders and practices and resources providers employ.

ANALYSIS

We identified major themes using a thematic analysis approach.

RESULTS

Participants reported multiple barriers to accessing food pantries, that shopper demographics were changing, and shoppers needed nonfood support, such as personal hygiene items and mental health services. Food pantries required appropriate and sustainable food supplies, additional financial, labor, technical support, and physical infrastructure improvements. Participants described the benefits of their relationship with the University of Minnesota Cooperative Extension, explained how pantries offered healthier foods, highlighted innovative service delivery models, and stressed that their organization connected to many facets of their community.

CONCLUSION AND IMPLICATIONS

Food pantries serving rural areas reported meeting community needs by distributing food to their shoppers in an inclusive and health-promoting way but require additional support. These data support nutrition practitioners working to understand the local, place-based context and needs of emergency food providers while building wider and deeper connections between nutrition professionals and the emergency food system.

Head-Toes-Knees-Shoulders task and EF in two samples of adolescents in Brazil and United States

Executive function (EF) is a foundational cognitive construct, which is linked to better cognitive and physical health throughout development. The present study examines the construct validity of an EF task, the Head-Toes-Knees-Shoulders task (HTKS) that was initially developed for young children, in a sample of adolescents. We investigate the initial validity and range in scores between 54 adolescents from Brazil (mean age 12.58) and 56 US adolescents (mean age 12.48) from different socioeconomic contexts. Results indicated that the HTKS showed sufficient variability in both samples, especially for a measure of HTKS efficiency (completion time divided by the total score). The US sample performed better on all cognitive measures. For the Brazilian sample, regression models controlling for age and sex showed a significant relationship between the digit span working memory task, the HTKS total score, and the HTKS efficiency score. The Heart and Flowers cognitive flexibility measure was also included as an independent variable only for the Brazil sample, showing a significant relationship with both HTKS scores. For the US sample, results showed that only the HTKS efficiency score was significantly related to the digit span working memory task. This study highlights the importance of cognitive efficiency measures to achieve greater validity, as they can assess a broader range of performance with different populations. The HTKS showed good ecological validity with two adolescent samples, as it differentiated between populations with high and low socioeconomic status from different cultural contexts.

Urban-rural disparities in childhood obesogenic environments in the United States: Application of differing rural definitions

BACKGROUND

Research is needed that identifies environmental resource disparities and applies multiple rural definitions. Therefore, this study aims to examine urban-rural differences in food and physical activity (PA) environment resource availability by applying several commonly used rural definitions. We also examine differences in resource availability within urban-rural categories that are typically aggregated.

METHODS

Six food environment variables (access to grocery/superstores, farmers' markets, fast food, full-service restaurants, convenience stores, and breastfeeding-friendly facilities) and 4 PA environment variables (access to exercise opportunities and schools, walkability, and violent crimes) were included in the childhood obesogenic environment index (COEI). Total COEI, PA environment, and food environment index scores were generated by calculating the average percentile for related variables. US Department of Agriculture Urban Influence Codes, Office of Management and Budget codes, Rural-Urban Continuum Codes, Census Bureau Population Estimates for percent rural, and Rural Urban Commuting Area Codes were used. One-way ANOVA was used to detect urban-rural differences.

RESULTS

The greatest urban-rural disparities in COEI (F=310.2, P<.0001) and PA environment (F=562.5, P<.0001) were seen using RUCC codes. For food environments, the greatest urban-rural disparities were seen using Census Bureau percent rural categories (food: F=24.9, P<.0001). Comparing remote rural categories, differences were seen for food environments (F=3.1, P=.0270) and PA environments (F=10.2, P<.0001). Comparing metro-adjacent rural categories, differences were seen for PA environment (F=4.7, P=.0090).

CONCLUSION

Findings inform future research on urban and rural environments by outlining major differences between urban-rural classifications in identifying disparities in access to health-promoting resources.

Measuring Obesogenicity and Assessing Its Impact on Child Obesity: A Cross-Sectional Ecological Study for England Neighbourhoods

Both major influences on changing obesity levels (diet and physical activity) may be mediated by the environment, with environments that promote higher weight being denoted obesogenic. However, while many conceptual descriptions and definitions of obesogenic environments are available, relatively few attempts have been made to quantify obesogenic environments (obesogenicity). The current study is an ecological study (using area units as observations) which has as its main objective to propose a methodology for obtaining a numeric index of obesogenic neighbourhoods, and assess this methodology in an application to a major national dataset. One challenge in such a task is that obesogenicity is a latent aspect, proxied by observed environment features, such as poor access to healthy food and recreation, as well as socio-demographic neighbourhood characteristics. Another is that obesogenicity is potentially spatially clustered, and this feature should be included in the methodology. Two alternative forms of measurement model (i.e., models representing a latent quantity using observed indicators) are considered in developing the obesogenic environment index, and under both approaches we find that both food and activity indicators are pertinent to measuring obesogenic environments (though with varying relevance), and that obesogenic environments are spatially clustered. We then consider the role of the obesogenic environment index in explaining obesity and overweight rates for children at ages 10-11 in English neighbourhoods, along with area deprivation, population ethnicity, crime levels, and a measure of urban-rural status. We find the index of obesogenic environments to have a significant effect in elevating rates of child obesity and overweight. As a major conclusion, we establish that obesogenic environments can be measured using appropriate methods, and that they play a part in explaining variations in child weight indicators; in short, area context is relevant.

Area-Level Determinants in Colorectal Cancer Spatial Clustering Studies: A Systematic Review

The increasing pattern of colorectal cancer (CRC) in specific geographic region, compounded by interaction of multifactorial determinants, showed the tendency to cluster. The review aimed to identify and synthesize available evidence on clustering patterns of CRC incidence, specifically related to the associated determinants. Articles were systematically searched from four databases, Scopus, Web of Science, PubMed, and EBSCOHost. The approach for identification of the final articles follows PRISMA guidelines. Selected full-text articles were published between 2016 and 2021 of English language and spatial studies focusing on CRC cluster identification. Articles of systematic reviews, conference proceedings, book chapters, and reports were excluded. Of the final 12 articles, data on the spatial statistics used and associated factors were extracted. Identified factors linked with CRC cluster were further classified into ecology (health care accessibility, urbanicity, dirty streets, tree coverage), biology (age, sex, ethnicity, overweight and obesity, daily consumption of milk and fruit), and social determinants (median income level, smoking status, health cost, employment status, housing violations, and domestic violence). Future spatial studies that incorporate physical environment related to CRC cluster and the potential interaction between the ecology, biology and social determinants are warranted to provide more insights to the complex mechanism of CRC cluster pattern.

A New Decade of Healthy People: Considerations for Comparing Youth Physical Activity Across 2 Surveillance Systems

BACKGROUND

Healthy People 2030 includes objectives to increase meeting the aerobic physical activity guideline for ages 6-13 years (of ages 6-17 y, monitored by National Survey of Children's Health [NSCH]) and grades 9 to 12 (mostly aged 14-18+ y, monitored by Youth Risk Behavior Survey [YRBS]). This study compares methodologies, prevalence, and patterns of meeting the guideline, particularly for overlapping ages 14-17 years.

METHODS

Nationally representative surveys, 2016-2017 NSCH (adult proxy report, 6-17 y) and 2015 and 2017 YRBS (self-report, grades 9-12), assess meeting the guideline of ≥60 minutes of daily moderate to vigorous physical activity. Prevalence and odds ratios were estimated by age group and demographics.

RESULTS

For youth aged 14-17 years, 17.4% (95% confidence interval [CI], 16.1-18.7; NSCH) and 27.0% (95% CI, 25.6-28.5; YRBS) met the guideline. 25.9% (95% CI, 24.8-27.2) aged 6-13 years (NSCH) and 26.6% (95% CI, 25.3-28.0) in grades 9 to 12 (YRBS) met the guideline. Across surveys, fewer females (P < .001) and Asian youth (P < .001 except among NSCH 14-17 y) met the guideline.

CONCLUSIONS

Neither methodology nor estimates for meeting the aerobic guideline are similar across surveys, so age continuity between juxtaposed estimates should not be assumed by magnitude nor age for separate Healthy People 2030 youth physical activity objectives.

Environmental Factors Associated with Physical Activity in Rural U.S. Counties

Background: Rural U.S. adults’ prevalence of meeting physical activity (PA) guidelines is lower than urban adults, yet rural-urban differences in environmental influences of adults’ PA are largely unknown. The study’s objective was to identify rural-urban variations in environmental factors associated with the prevalence of adults meeting PA guidelines. Methods: County-level data for non-frontier counties (n = 2697) were used. A five-category rurality variable was created using the percentage of a county’s population living in a rural area. Factor scores from Factor Analyses (FA) were used in subsequent Multiple Linear Regression (MLR) analyses stratified by rurality to identify associations between environmental factor scores and the prevalence of males and females meeting PA guidelines. Results: FA revealed a 13-variable, four-factor structure of natural, social, recreation, and transportation environments. MLR revealed that natural, social, and recreation environments were associated with PA for males and females, with variation by sex for social environment. The natural environment was associated with PA in all but urban counties; the recreation environment was associated with PA in the urban counties and the two most rural counties. Conclusions: Variations across the rural-urban continuum in environmental factors associated with adults’ PA, highlight the uniqueness of rural PA and the need to further study what succeeds in creating active rural places.

Incongruency of youth food and physical activity environments in the United States: Variations by region, rurality, and income

Local environments are increasingly the focus of health behavior research and practice to reduce gaps between fruit/vegetable intake, physical activity (PA), and related guidelines. This study examined the congruency between youth food and PA environments and differences by region, rurality, and income across the United States. Food and PA environment data were obtained for all U.S. counties (N = 3142) using publicly available, secondary sources. Relationships between the food and PA environment tertiles was represented using five categories: 1) congruent-low (county falls in both the low food and PA tertiles), 2) congruent-high (county falls in both the high food and PA tertiles), 3) incongruent-food high/PA low (county falls in high food and low PA tertiles), 4) incongruent-food low/PA high (county falls in low food and high PA tertiles), and 5) intermediate food or PA (county falls in the intermediate tertile for food and/or PA). Results showed disparities in food and PA environment congruency according to region, rurality, and income (p < .0001 for each). Nearly 25% of counties had incongruent food and PA environments, with food high/PA low counties mostly in rural and low-income areas, and food low/PA high counties mostly in metropolitan and high-income areas. Approximately 8.7% of counties were considered congruent-high and were mostly located in the Northeast, metropolitan, and high-income areas. Congruent-low counties made up 10.0% of counties and were mostly in the South, rural, and low-income areas. National and regional disparities in environmental obesity determinants were identified that can inform targeted public health interventions.

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

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.

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

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 identified – six 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.

Results

The average COEI percentile ranged from 24.5–81.0 (M = 50.02,s.d. = 9.01) across US counties and was depicted spatially on a choropleth map. Obesogenic counties were more prevalent (F = 130.43,p < .0001) in the South region of the U.S. (M = 53.0,s.d. = 8.3) compared to the Northeast (M = 43.2,s.d. = 6.9), Midwest (M = 48.1,s.d. = 8.5), and West (M = 48.4,s.d. = 9.8). When examined by rurality, there were also significant differences (F = 175.86,p < .0001) between metropolitan (M = 46.5,s.d. = 8.4), micropolitan (M = 50.3,s.d. = 8.1), and rural counties (M = 52.9,s.d. = 8.8) across the U.S.

Conclusion

The COEI can be applied to benchmark obesogenic environments and identify geographic disparities and intervention targets. Future research can examine associations with obesity and other health outcomes.