Development and testing of a multicomponent obesogenic built environment measure for youth using kernel density estimations

Convenience stores: an obesogenic promoter in a metropolitan area of northern Mexico?

Introduction

The prevalence of obesity in the Mexican school-age (5-11 years old) population increased from 8.9 to 18.1% between 1999 and 2022. Although overweight and obesity (OW + Ob) is a complex and multifactorial phenomenon, alongside its increasing trend, changes in eating patterns as a result of obesogenic environments that promote higher energy intake have been documented. The objective of the present study was to detect possible associations between schools and their proximity to and density of convenience stores in Monterrey, Mexico from 2015 to 2018.

Materials and methods

Anthropometric data were obtained from a subset of measurements of the National Registry of Weight and Height (RNPT) performed in the Monterrey Mexico metropolitan area in 2015 and 2018, and obesity prevalence was computed and classified into quintiles at the school level. Convenience store data were obtained from the National Directory of Economic Units (DNUE). The analyses consisted of densities within 400-800 m buffers, distance to the nearest stores, and cartographic visualization of the store's kernel density versus OW + Ob hotspots for both periods.

Results

A total of 175,804 children in 2015 and 175,964 in 2018 belonging to 1,552 elementary schools were included in the study; during this period, OW + Ob prevalence increased from 38.7 to 39.3%, and a directly proportional relationship was found between the quintiles with the higher OW + Ob prevalence and the number of stores for both radii. Hotspots of OW + Ob ranged from 63 to 91 between 2015 and 2018, and it was visually confirmed that such spots were associated with areas with a higher density of convenience stores regardless of socioeconomic conditions.

Conclusion

Although some relationships between the store's proximity/density and OW + Ob could be identified, more research is needed to gather evidence about this. However, due to the trends and the magnitude of the problem, guidelines aimed at limiting or reducing the availability of junk food and sweetened beverages on the school's periphery must be implemented to control the obesogenic environment.

Built and social indices for hazards in Children's environments

Leveraging the capabilities of the Historical Spatial Data Infrastructure (HSDI) and composite indices we explore the importance of children's built and social environments on health. We apply contemporary GIS methods to a set of 2000 historical school records contextualized within an existing HSDI to establish seven variables measuring the relative quality of each child's built and social environments. We then combined these variables to create a composite index that assesses acute (short-term) health risks generated by their environments. Our results show that higher acute index values significantly correlated with higher presence of disease in the home. Further, higher income significantly correlated with lower acute index values, indicating that the relative quality of children's environments in our study area were constrained by familial wealth. This work demonstrates the importance of analyzing multiple activity spaces when assessing built and social environments, as well as the importance of spatial microdata.

Comparative Review of Environmental Audit Tools for Public Open Spaces from the Perspective of Children's Activity

Public open spaces are important venues for children's participation in outdoor activities and social life. This study performs a comparative and qualitative review of the tools that can be used to audit the environments of children-focused public open spaces. The analysis reviews 25 studies involving 11 tools for comparison. The results reveal that (1) the tools were developed in different fields; (2) the tools use two data resources, field investigation and geographic databases; (3) the tool dimensions are diverse, as are the number of items covered, and are generally related to four categories: surrounding environment and accessibility, activity and perceived safety, children's sports and play opportunities, and aesthetic and comfort of the environment; (4) the reliability of most tools has been verified, with some validity still to be confirmed; (5) there are differences in tool users, settings, and aims. Among the tools, the CPAT and the EAPRS are the most comprehensive. Comparative analysis of the tools provides a reference for studies on children-focused public open spaces and for the development and improvement of corresponding tools in the future.

Validating a geospatial healthfulness index with self-reported chronic disease and health outcomes

Leveraging community engagement from past research may yield frameworks on which to build new inquiries. We previously integrated community voice into the development of a healthfulness index to increase awareness of social determinants of health in the built environment and inform deployment of public health interventions in the Flint (Michigan, USA) Center for Health Equity Solutions. Here we combine the healthfulness index with self-reported chronic disease and health outcomes (n = 12,279) from a community-based healthcare entity, the Genesee Health Plan. The healthfulness index purports to predict how health-promoting a neighborhood is based on many spatially varying characteristics; by linking our health plan data to this index, we validate the effectiveness of the healthfulness index. After geocoding all enrollees and joining their healthfulness scores, we conducted a series of logistic regressions to compare the relationship between self-reported outcomes and healthfulness. Matching the two intervention projects of our center (revolving around healthy eating & physical activity in project 1 and mental health sustainment & substance use prevention in project 2), our analyses also focused on classes of outcomes related to a) cardiovascular disease and b) mental health. In only select cases, higher (better) healthfulness scores from each project were independently associated with better cardiovascular and mental health outcomes, controlling for age, race, and sex. Generally, however, healthfulness did not add predictive strength to the association between health and sociodemographic covariates. Even so, the use of composite healthfulness indices to describe the health-promoting or degrading qualities of a neighborhood could be valuable in identifying differences in health outcomes. Future researchers could further explore healthcare claims datasets to increase understanding of the links between healthfulness and health outcomes. This and future work will be valuable in advocacy toward additional healthfulness indices to aid other communities in enriching understanding between the built environment and health.

Changes in neighborhood environments and the increasing socioeconomic gap in child obesity risks: Evidence from Singapore

Most empirical research studying the link between neighborhood environments and child obesity risks are conducted in contexts such as the U.S., with pronounced patterns of residential segregation, making it difficult to extrapolate how far built environment characteristics contribute to socioeconomic disparities in obesity risks in less segregated contexts. Using a large national dataset of almost 625,000 students' height and weight data collected at ages 7, 11 and 14, between 2004 and 2015, this paper explores whether differences in eight neighborhood characteristics measuring access to different type of food outlets, parks and other active spaces, and public transport infrastructure might be responsible for socioeconomic differences in child obesity risks in Singapore, a city-state with relatively low levels of residential segregation. Through descriptive analyses we find that socioeconomic disparities in child BMIz in Singapore widened from 2004 onwards. However, while longitudinal regression models with individual and time fixed effects suggest that family socioeconomic status modified the relationship between environmental exposures and BMIz, there does not seem to be a clear, unequivocal relationship between built environment changes and the observed widening of the socioeconomic obesity gap.

Role of Built Environments on Physical Activity and Health Promotion: A Review and Policy Insights

As urbanization and motorization continue worldwide, various health issues have emerged as a burden between individuals, families and governments at all levels. Under the prevalence of chronic disease, this review synthesizes research on the impact of the various built environments on the multiple health outcomes from a methodological and mechanistic perspective. Besides, it attempts to provide useful planning and policy implications to promote physical activity and health benefits. The finds show that: (1) Current literature has used a variety of dataset, methods, and models to examine the built environment-health benefit connections from the perspective of physical activity; (2) The prevalence of chronic diseases is inextricably linked to the built environment, and policy interventions related to physical activity and physical and mental wellbeing of urban residents should be emphasized; (3) The impact of the built environment on health is manifested in the way various elements of the physical environment guide the lifestyle of residents, thereby influencing physical activity and travel; (4) Given the changes that have occurred in the built environment during the current urban expansion, the link between urban planning and the public health sector should be strengthened in the future, and the relevant authorities should actively pursue policies that promote urban public health in order to improve the health of residents. Finally, it proposes potential policy insights for urban planning and development toward a healthier city and society.

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

This study examined spatial patterns of obesogenic environments for US counties. We mapped the geographic dispersion of food and physical activity (PA) environments, assessed spatial clustering, and identified food and PA environment differences across U.S. regions and rurality categories. Substantial low food score clusters were located in the South and high score clusters in the Midwest and West. Low PA score clusters were located in the South and high score clusters in the Northeast and Midwest (p < .0001). For region, the South had significantly lower food and PA environment scores. For rurality, rural counties had significantly higher food environment scores and metropolitan counties had significantly higher PA environment scores (p < .0001). This study highlights geographic clustering and disparities in food and PA access nationwide. State and region-wide environmental inequalities may be targeted using structural interventions and policy initiatives to improve food and PA access.

Does the built environment have independent obesogenic power? Urban form and trajectories of weight gain

OBJECTIVE

To determine whether selected features of the built environment can predict weight gain in a large longitudinal cohort of adults.

METHODS

Weight trajectories over a 5-year period were obtained from electronic health records for 115,260 insured patients aged 18-64 years in the Kaiser Permanente Washington health care system. Home addresses were geocoded using ArcGIS. Built environment variables were population, residential unit, and road intersection densities captured using Euclidean-based SmartMaps at 800-m buffers. Counts of area supermarkets and fast food restaurants were obtained using network-based SmartMaps at 1600, and 5000-m buffers. Property values were a measure of socioeconomic status. Linear mixed effects models tested whether built environment variables at baseline were associated with long-term weight gain, adjusting for sex, age, race/ethnicity, Medicaid insurance, body weight, and residential property values.

RESULTS

Built environment variables at baseline were associated with differences in baseline obesity prevalence and body mass index but had limited impact on weight trajectories. Mean weight gain for the full cohort was 0.06 kg at 1 year (95% CI: 0.03, 0.10); 0.64 kg at 3 years (95% CI: 0.59, 0.68), and 0.95 kg at 5 years (95% CI: 0.90, 1.00). In adjusted regression models, the top tertile of density metrics and frequency counts were associated with lower weight gain at 5-years follow-up compared to the bottom tertiles, though the mean differences in weight change for each follow-up year (1, 3, and 5) did not exceed 0.5 kg.

CONCLUSIONS

Built environment variables that were associated with higher obesity prevalence at baseline had limited independent obesogenic power with respect to weight gain over time. Residential unit density had the strongest negative association with weight gain. Future work on the influence of built environment variables on health should also examine social context, including residential segregation and residential mobility.

Improving spatial data in health geographics: a practical approach for testing data to measure children's physical activity and food environments using Google Street View

Background

Geographic information systems (GIS) are often used to examine the association between both physical activity and nutrition environments, and children’s health. It is often assumed that geospatial datasets are accurate and complete. Furthermore, GIS datasets regularly lack metadata on the temporal specificity. Data is usually provided ‘as is’, and therefore may be unsuitable for retrospective or longitudinal studies of health outcomes. In this paper we outline a practical approach to both fill gaps in geospatial datasets, and to test their temporal validity. This approach is applied to both district council and open-source datasets in the Taranaki region of Aotearoa New Zealand.

Methods

We used the ‘streetview’ python script to download historic Google Street View (GSV) images taken between 2012 and 2016 across specific locations in the Taranaki region. Images were reviewed and relevant features were incorporated into GIS datasets.

Results

A total of 5166 coordinates with environmental features missing from council datasets were identified. The temporal validity of 402 (49%) environmental features was able to be confirmed from council dataset considered to be ‘complete’. A total of 664 (55%) food outlets were identified and temporally validated.

Conclusions

Our research indicates that geospatial datasets are not always complete or temporally valid. We have outlined an approach to test the sensitivity and specificity of GIS datasets using GSV images. A substantial number of features were identified, highlighting the limitations of many GIS datasets.

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.

Frequency of Neighborhood Park Use Is Associated With Physical Activity Among Adults in Four US Cities

BACKGROUND

Neighborhood parks are recognized as important spaces for facilitating physical activity (PA); however, it remains unclear how the frequency of park use is associated with PA. The purpose of this study was to examine associations between minutes of moderate to vigorous PA and multiple park use indicators: (1) use of a neighborhood park, (2) unique number of neighborhood parks used, and (3) frequency of neighborhood park use.

METHODS

Adults were surveyed from 4 US cities (Brooklyn, NY; Greenville County, SC; Raleigh, NC; and Seattle, WA). Using a map-based survey platform, participants indicated all neighborhood parks they used and the frequency of use in the past 30 days. Participants self-reported their weekly moderate to vigorous PA. Quantile regression was used to examine associations between PA and park use indicators.

RESULTS

Of all respondents (N = 360), 60% indicated visiting a neighborhood park in the past 30 days, with an average of about 13 total neighborhood park visits (SD = 17.5). Significant, positive associations were found between moderate to vigorous PA and both unique neighborhood park visits and total number of neighborhood parks visits.

CONCLUSIONS

Frequency of park visitation is associated with PA among US adults. Ensuring equitable and safe access to neighborhood parks has the potential for population-level PA health benefits.

ParkIndex: Validation and application of a pragmatic measure of park access and use

Composite metrics integrating park availability, features, and quality for a given address or neighborhood are lacking. The purposes of this study were to describe the validation, application, and demonstration of ParkIndex in four diverse communities. This study occurred in Fall 2018 in 128 census block groups within Seattle(WA), Brooklyn(NY), Raleigh(NC), and Greenville County(SC). All parks within a half-mile buffer were audited to calculate a composite park quality score, and select households provided data about use of proximal parks via an online, map-based survey. For each household, the number of parks, total park acreage, and average park quality score within one half-mile were calculated using GIS. Logistic regression was used to identify a parsimonious model predicting park use. ParkIndex values (representing the probability of park use) were mapped for all study areas and after scenarios involving the addition and renovation/improvement of parks. Out of 360 participants, 23.3% reported visiting a park within the past 30 days. The number of parks (OR = 1.36, 95% CI = 1.15-1.62), total park acreage (OR = 1.13, 95% CI = 1.07-1.19), and average park quality score (OR = 1.04, 95% CI = 1.01-1.06) within one half-mile were all associated with park use. Composite ParkIndex values across the study areas ranged from 0 to 100. Hypothetical additions of or renovations to study area parks resulted in ParkIndex increases of 22.7% and 19.2%, respectively. ParkIndex has substantial value for park and urban planners, citizens, and researchers as a common metric to facilitate awareness, decision-making, and intervention planning related to park access, environmental justice, and community health.

Where we eat is who we are: a survey of food-related travel patterns to Singapore's hawker centers, food courts and coffee shops

Background

The development of empirically-grounded policies to change the obesogenic nature of urban environment has been impeded by limited, inconclusive evidence of the link between food environments, dietary behaviors, and health-related outcomes, in part due to inconsistent methods of classifying and analyzing food environments. This study explores how individual and built environment characteristics may be associated with how far and long people travel to food venues,that can serve as a starting point for further policy-oriented research to develop a more nuanced, context-specific delineations of ‘food environments’ in an urban Asian context.

Methods

Five hundred twenty nine diners in eight different neighborhoods in Singapore were surveyed about how far and long they travelled to their meal venues, and by what mode. We then examined how respondents’ food-related travel differed by socioeconomic characteristics, as well as objectively-measured built environment characteristics at travel origin and destination, using linear regression models.

Results

Low-income individuals expended more time traveling to meal destinations than high-income individuals, largely because they utilized slower modes like walking rather than driving. Those travelling from areas with high food outlet density travelled shorter distances and times than those from food-sparse areas, while those seeking meals away from their home and work anchor points had lower thresholds for travel. Respondents also travelled longer distances to food-dense locations, compared to food-sparse locations.

Conclusion

Those seeking to improve food environments of poor individuals should consider studying an intervention radius pegged to typical walking distances, or ways to improve their transport options as a starting point. Policy-focused research on food environments should also be sensitive to locational characteristics, such as food outlet densities and land use.

Healthy and unhealthy food environments are linked with neighbourhood socio-economic disadvantage: an innovative geospatial approach to understanding food access inequities

Objective:

This study examined the separate relationships between socio-economic disadvantage and the density of multiple types of food outlets, and relationships between socio-economic disadvantage and composite food environment indices.

Design:

Cross-sectional data were analysed using geospatial kernel density techniques. Food outlet data included convenience stores, discount stores, fast-food and fast casual restaurants, and grocery stores. Controlling for urbanicity and race/ethnicity, multivariate linear regression was used to examine the relationships between socio-economic disadvantage and density of food outlets.

Setting:

This study occurred in a large Southeastern US county containing 255 census block groups with a total population of 474 266, of which 77·1 % was Non-Hispanic White, the median household income was $48 886 and 15·0 % of residents lived below 125 % of the federal poverty line.

Participants:

The unit of analysis was block groups; all data about neighbourhood socio-economic disadvantage and food outlets were publicly available.

Results:

As block group socio-economic disadvantage increased, so too did access to all types of food outlets. The total food environment index, calculated as the ratio of unhealthy food outlets to all food outlets, decreased as block group disadvantage increased.

Conclusions:

Those who reside in more disadvantaged block groups have greater access to both healthy and unhealthy food outlets. The density of unhealthy establishments was greater in more disadvantaged areas; however, because of having greater access to grocery stores, disadvantaged populations have less obesogenic total food environments. Structural changes are needed to reduce access to unhealthy food outlets to ensure environmental injustice and reduce obesity risk.

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.

A social marketing perspective of young adults' concepts of eating for health: is it a question of morality?

BACKGROUND

Poor dietary choices are a risk factor for non-communicable diseases. Young adults have low levels of engagement towards their health and may not see the importance in the adoption of healthy eating behaviours at this stage in their lives. Here we utilise social marketing principles, digital ethnography and online conversations to gain insights into young adults' attitudes and sentiments towards healthy eating.

METHODS

Young Australian adults who use social media at least twice a day were recruited by a commercial field house. Using a mixture of methods, combining online polls, forums and conversations, participants (n = 195, 18-24 years old) engaged in facilitated discussions over an extended 4 week period about health and eating-related topics. Data were analysed using thematic analysis constant comparison approach. A post-hoc conceptual framework related to religion was theorised and used as a metaphor to describe the results.

RESULTS

Findings demonstrate that different segments of young adults with varying attitudes and interest towards healthy eating exist. We developed a conceptual framework based on consumer segmentation which adopted religious metaphors as a typology of 'consumers'. Some young adults practice and believe in the message of healthy eating (saints), whilst some oppose these messages and are not motivated to make any change (sinners), another segment are both aware of and interested in the issues but do not put healthy eating behaviours as a current priority (person in the pew).

CONCLUSIONS

Consumer segmentation and social marketing techniques assist health professionals to understand their target audience and tailor specific messages to different segments. Segmentation provides insights on which groups may be most easily influenced to adopt the desired behaviours. The typology presented may be a useful tool for health professionals and social marketers to design strategies to engage young adults in healthy eating, particularly those in the pew who are contemplating a change but lacking the motivation. The utilisation of marketing segmentation in health promotion has the potential to enhance health messaging by tailoring messages to specific segments based on their needs, beliefs and intentions and therefore drive the efficient use of resources towards those most likely to change.

Associations between Walkability and Youth Obesity: Differences by Urbanicity

Background: Attributes of the built environment, such as neighborhood walkability, have been linked to increased physical activity and reduced obesity risk. This relationship, however, has primarily been documented in adults; less is known about neighborhood walkability and youth obesity, as limited prior research has produced mixed findings. The purpose of this study was to examine the association between neighborhood walkability and youth obesity, including differences by urbanicity. Methods: Data were collected in 2013 from youth aged 7-14 years (n = 13,469) in a Southeastern county school district. Height and weight were objectively measured and utilized to calculate body mass index (BMI) z-scores. Youth demographic characteristics and addresses were obtained, and a Walk Score^® was gathered for each youth's home address. Multilevel linear regression analysis, accounting for nesting within census block groups, was conducted to examine the association between Walk Score and BMI z-score and to test for the moderating effect of urbanicity. Separate multilevel analyses examined Walk Score and BMI z-score among urban, urban-rural mixed, and rural youth subsamples. Results: Overall, as Walk Score increased, youth BMI z-score decreased. Walk Score was positively associated with BMI z-score among urban youth and negatively associated with BMI z-score among rural youth; no relationship was observed between Walk Score and youth in urban-rural mixed areas. Conclusions: Neighborhood walkability may impact youth differently across geographic areas. Further study is warranted about how youth utilize a walkable environment and mechanisms through which walkability influences youth physical activity and obesity risk.