Geographic variation in the relationship between body mass index and the built environment

Non-traditional data sources in obesity research: a systematic review of their use in the study of obesogenic environments

BACKGROUND

The complex nature of obesity increasingly requires a comprehensive approach that includes the role of environmental factors. For understanding contextual determinants, the resources provided by technological advances could become a key factor in obesogenic environment research. This study aims to identify different sources of non-traditional data and their applications, considering the domains of obesogenic environments: physical, sociocultural, political and economic.

METHODS

We conducted a systematic search in PubMed, Scopus and LILACS databases by two independent groups of reviewers, from September to December 2021. We included those studies oriented to adult obesity research using non-traditional data sources, published in the last 5 years in English, Spanish or Portuguese. The overall reporting followed the PRISMA guidelines.

RESULTS

The initial search yielded 1583 articles, 94 articles were kept for full-text screening, and 53 studies met the eligibility criteria and were included. We extracted information about countries of origin, study design, observation units, obesity-related outcomes, environment variables, and non-traditional data sources used. Our results revealed that most of the studies originated from high-income countries (86.54%) and used geospatial data within a GIS (76.67%), social networks (16.67%), and digital devices (11.66%) as data sources. Geospatial data were the most utilised data source and mainly contributed to the study of the physical domains of obesogenic environments, followed by social networks providing data to the analysis of the sociocultural domain. A gap in the literature exploring the political domain of environments was also evident.

CONCLUSION

The disparities between countries are noticeable. Geospatial and social network data sources contributed to studying the physical and sociocultural environments, which could be a valuable complement to those traditionally used in obesity research. We propose the use of information available on the Internet, addressed by artificial intelligence-based tools, to increase the knowledge on political and economic dimensions of the obesogenic environment.

Geographical heterogeneity across England in associations between the neighbourhood built environment and body mass index

Effects of residential neighbourhood environments on health may vary across geographical space, with differences in local contexts influencing how much a given neighbourhood characteristic matters for the health of local residents. Linking UK Biobank data from 302,952 urban-dwelling adults in England, collected between 2006 and 2010, to publicly available Local Authority-level data, we examined (a) whether cross-sectional associations between body mass index (BMI) and two characteristics of the neighbourhood built environment (availability of formal physical activity facilities near home, and fast-food proximity) vary by Local Authority (LA), and (b) whether cross-level interactions with LA-level physical features (natural landcover) and socio-cultural attributes (local obesity norms) reveal evidence of effect modification by these features of the wider contexts in which neighbourhoods are located. We found variation across urban England in the relationship between availability of neighbourhood formal physical activity facilities and BMI, and some evidence suggesting this association was stronger among people living in areas with less natural landcover, especially in areas outside of London. We also found that the relationship between proximity of fast-food stores to people's homes and BMI varied geographically across England. Local descriptive obesity norms were not an important modifier of this association. This paper highlights the importance of considering potential geographical heterogeneity in relationships between the built environment and health, and the implications for generalisability of research findings. By seeking to better understand sources of geographical heterogeneity, we may be able to better adapt and target built environment interventions for population health improvement.

The cause and effect problem: Is there mutual obesity among Arab Israeli couples?

The influence of the health-related behavior of one spouse on that of the other is an important research question with public policy reprecussions. Yet, we are unaware of any previous study, which considered endogeneity problems between couples. Moreover, only a few studies considered ethnic origin differences among couples. Based on the 2016 wave of the Israeli longitudinal survey, we observe the cross-sectional correlation between the married couples' BMI, age, and accumulated wealth. The BMI (= [Formula: see text]) is a conventional measure of obesity, where BMI≥25 is considered overweight. Using a 3SLS methodology (in an effort to correct the endogeneity problem associated with BMI couples), the analysis tests the mutual obesity hypothesis among married couples. This hypothesis states that the BMI of the male influences that of a female and vice versa. Results indicate that on the one hand, a one-percent BMI increase among Arab Israeli males is associated with a projected 0.969 percent BMI increase among Arab Israeli females (p = 0.017); and in the case that an Arab Israeli male suffers from overweight, the projected probability of his Arab Israeli female counterpart to suffer from overweight as well rises (p = 0.050). On the other hand, one cannot reject the null hypothesis that projected BMI of the Arab Israeli male is unaffected by that of his Arab Israeli female counterpart (p = 0.907 and p = 0.853). As for the Jewish Israeli population, in the case that the 3SLS methodology is employed, so that the endogeneity problem among couples is considered, a one-percent BMI increase among Jewish Israeli females is associated with a projected 0.639 percent BMI increase among Jewish Israeli males (p = 0.091). Unlike Arab Israeli couples, no support is found to indicate the influence in the other direction, namely, the BMI of the male influences that of the female spouse. Research findings may thus be of relevance to public health and policy planners. Two limitations of this research lie in: 1) the self-reported BMI (which might be different from the measured BMI); and 2) missing confounders, such as regional dummies, which are not available in the dataset.

Frequency of Restaurant, Delivery and Takeaway Usage Is Not Related to BMI among Adults in Scotland

BACKGROUND

The frequency of visits to restaurants has been suggested to contribute to the pandemic of obesity. However, few studies have examined how individual use of these restaurants is related to Body Mass Index (BMI).

AIM

To investigate the association between the usage of different types of food outlets and BMI among adults in Scotland.

METHOD

The study was cross-sectional. Participants completed an online survey for seven consecutive days where all food purchased at food outlets was reported each day. We explored the relationship between BMI and usage of these food outlets.

RESULTS

The total number of participants that completed the survey was 681. The BMI of both males and females was not related to frequency of use of Full-Service Restaurants (FSRs), Fast-Food Restaurants (FFRs), delivery or takeaways, when assessed individually or combined (TFOs = total food outlets).

CONCLUSION

These cross-sectional data do not support the widespread belief that consumption of food out of the home at fast-food and full-service restaurants, combined with that derived from deliveries and takeaways, is a major driver of obesity in Scotland.

Impact of Changes in the Food, Built, and Socioeconomic Environment on BMI in US Counties, BRFSS 2003-2012

OBJECTIVE

Researchers have linked geographic disparities in obesity to community-level characteristics, yet many prior observational studies have ignored temporality and potential for bias.

METHODS

Repeated cross-sectional data were used from the Behavioral Risk Factor Surveillance System (BRFSS) (2003-2012) to examine the influence of county-level characteristics (active commuting, unemployment, percentage of limited-service restaurants and convenience stores) on BMI. Each exposure was calculated using mean values over the 5-year period prior to BMI measurement; values were standardized; and then variables were decomposed into (1) county means from 2003 to 2012 and (2) county-mean-centered values for each year. Cross-sectional (between-county) and longitudinal (within-county) associations were estimated using a random-effects within-between model, adjusting for individual characteristics, survey method, and year, with nested random intercepts for county-years within counties within states.

RESULTS

A negative between-county association for active commuting (β = -0.19; 95% CI: -0.23 to -0.16) and positive associations for unemployment (β = 0.17; 95% CI: 0.14 to 0.19) and limited-service restaurants (β = 0.13; 95% CI: 0.11 to 0.14) were observed. An SD increase in active commuting within counties was associated with a 0.51-kg/m² (95% CI: -0.72 to -0.31) decrease in BMI over time.

CONCLUSIONS

These results suggest that community-level characteristics play an important role in shaping geographic disparities in BMI between and within communities over time.

The Association of Pension Income with the Incidence of Type I Obesity among Retired Israelis

Previous studies have identified obesity and overweight as the fourth leading risk factor for global mortality. The objective of the current study is to investigate gender differences and the impact of wealth and income from pensions, sociodemographic variables, and self-assessment of health conditions on the projected probability to become obese in the postretirement age (67 years and older). We are unaware of previous studies, which explored the direct relationship between obesity, monetary income from pensions, wealth, and self-assessment of health conditions. To conduct this research, we make use of an extensive questionnaire concerning the economic and sociodemographic features and health and housing conditions of individuals administered within the framework of the 2015-2016 longitudinal survey conducted by the Israeli Central Bureau of Statistics (CBS). The survey is representative of the Israeli population and also includes information regarding the weight, height, gender, and age of each household member. Results of our study demonstrate that while for the female respondents older than 67, the projected probability of type I obesity (BMI ≥ 30) drops by 0.41% (p=0.0021) to 0.52% (p=0.0001) with an incremental 10,000 NIS (about $2,500) rise of gross annual income from a pension, for the male respondents above 67 years, the projected probability remains unchanged (p=0.4225). This outcome remains robust even when the 2015 BMI measurement of type I obesity (BMI ≥ 30) is controlled. This drop among women attenuates with a cutoff point increase from BMI ≥ 25 (overweight) to BMI ≥ 30 (type I obesity) to BMI ≥ 35 (type II obesity). Further results indicate that for both genders above 67 years and for men above 62 years, the projected BMI drop of one year decreases with income from a pension (p=0.013, p=0.039, and p=0.007, respectively), although the spread around the projection becomes wider. Compared with other martial status categories, for widowed females, the projected probability of obesity and self-reporting on improved health conditions drops by 6.58% (p=0.0419) to 11.28% (p=0.0048) and 6.55% (p=0.0190) to 7.47% (p=0.0036), respectively. For females older than 67, family status divorced drops the projected probability of obesity by 9.25% (p=0.0319). For males older than 67, results show a rise in projected obesity with car ownership by 6.10% (p=0.0897) to 6.41% (p=0.0469) and a drop in projected obesity with academic degree status by 9.93% (p=0.0106) to 10.14% (p=0.0118) and immigration status from American-European countries by 7.67% (p=0.0821) to 8.99% (p=0.0398) and Asian-African countries by 11.63% (p=0.0245) to 11.99% (p=0.02). Research findings stress the differences and similarities in male-female patterns of obesity after the retirement age of 67 years and may be of assistance to welfare and public health experts.

Identifying determinants of obesity in Athens, Greece through global and local statistical models

Individual socioeconomic status is linked to obesity risk, though, less is known about the influence of an area's socio-environmental conditions on obesity/overweight prevalence. This association was investigated using obesity/overweight data collected from 2445 individuals in Athens greater area, who were randomly enrolled in ATTICA study, during 2001 to 2002, and factors related to educational and economic level, population density, immigrants and green urban areas. Thematic mapping illustrated the socio-environmental status and highlighted the obesity/overweight prevalence across the municipalities of Athens. Global statistical models revealed that high socio-environmental status, educational and economic level were inversely associated with obesity/overweight (p < 0.001). Furthermore, local Geographically Weighted Regression revealed spatial non-stationarity in the relationship between green urban areas and obesity/overweight. The findings of the study lead to the better understanding of the factors affecting obesity/overweight and may support the development of policies that target to well-being, decrease of obesity/overweight prevalence and improvement of public health.

Spatial Technologies in Obesity Research: Current Applications and Future Promise

Geographic Information Systems (GIS), Global Positioning Systems (GPS), and remote sensing (RS) are revolutionizing obesity-related research. The primary applications of GIS have included visualizing obesity outcomes and risk factors, constructing obesogenic environmental indicators, and detecting geographical patterns of obesity prevalence and obesogenic environmental features. GPS was mainly used to delineate individual activity space and combined with other devices to measure obesogenic behaviors. RS has been understated for its role as important sources of data about natural and built environments. These spatial technologies, collectively called the 3S technologies, will be useful in measuring more facets of obesogenic environments and individual environmental exposure at finer levels and studying obesity etiology and interventions.

Area-Level Walkability and the Geographic Distribution of High Body Mass in Sydney, Australia: A Spatial Analysis Using the 45 and Up Study

Improving the walkability of built environments to promote healthy lifestyles and reduce high body mass is increasingly considered in regional development plans. Walkability indexes have the potential to inform, benchmark and monitor these plans if they are associated with variation in body mass outcomes at spatial scales used for health and urban planning. We assessed relationships between area-level walkability and prevalence and geographic variation in overweight and obesity using an Australian population-based cohort comprising 92,157 Sydney respondents to the 45 and Up Study baseline survey between January 2006 and April 2009. Individual-level data on overweight and obesity were aggregated to 2006 Australian postal areas and analysed as a function of area-level Sydney Walkability Index quartiles using conditional auto regression spatial models adjusted for demographic, social, economic, health and socioeconomic factors. Both overweight and obesity were highly clustered with higher-than-expected prevalence concentrated in the urban sprawl region of western Sydney, and lower-than-expected prevalence in central and eastern Sydney. In fully adjusted spatial models, prevalence of overweight and obesity was 6% and 11% lower in medium-high versus low, and 10% and 15% lower in high versus low walkability postcodes, respectively. Postal area walkability explained approximately 20% and 9% of the excess spatial variation in overweight and obesity that remained after accounting for other individual- and area-level factors. These findings provide support for the potential of area-level walkability indexes to inform, benchmark and monitor regional plans aimed at targeted approaches to reducing population-levels of high body mass through environmental interventions. Future research should consider potential confounding due to neighbourhood self-selection on area-level walkability relations.