Neighborhood commuting environment and obesity in the United States: an urban-rural stratified multilevel analysis

The longitudinal association between neighbourhood quality and cardiovascular risk factors among youth receiving obesity treatment

BACKGROUND

Neighbourhood factors are associated with cardiovascular health in adults, but these relationships are under-explored in youth.

OBJECTIVES

To characterize the associations between neighbourhood factors and child and adolescent health among youth with obesity.

METHODS

Data were drawn from patient health records at a pediatric weight management clinic (n = 2838) and the Child Opportunity Index (COI). Exposures were area-level neighbourhood factors (commute duration, walkability, greenspace and industrial pollutants). Outcomes included BMI relative to the 95th percentile (BMIp95) and blood pressure (continuous variables). Longitudinal models examined associations between COI indicators and outcomes.

RESULTS

Shorter commute duration (β = -4.31, 95% CI: -5.92, -2.71) and greater walkability (β = -4.40, 95% CI: -5.98, -2.82) were negatively associated with BMIp95. Increased greenspace availability was positively associated with BMIp95 (β = 1.93, 95% CI: 0.19, 3.67). None of the COI indicators were associated with cardiovascular outcomes in the full sample. Analyses stratified by sex and race/ethnicity showed similar patterns for BMIp95. For commute duration, there was a negative association with blood pressure for female, non-Hispanic White and other race/ethnicity youth.

CONCLUSIONS

Neighbourhood factors should be considered as contextual factors when treating youth with obesity. Additional research is needed to understand the relationship between neighbourhood factors and cardiovascular outcomes.

Associations Between Neighborhood Opportunity and Indicators of Physical Fitness for New York City Public School Youth

Background: Fewer than 1/4th of US children and adolescents meet physical activity (PA) guidelines, leading to health disparities that track into adulthood. Neighborhood opportunity may serve as a critical modifiable factor to improve fitness attainment and reduce these disparities. We drew data from the Child Opportunity Index to examine associations between neighborhood indicators of opportunity for PA and multiple fitness indicators among New York City public school youth. Methods: Multilevel generalized linear mixed models were used to estimate the overall and sex-stratified associations between neighborhood indicators (green space, healthy food, walkability, commute time) and indicators for physical fitness [curl-ups, push-ups, Progressive Aerobic Cardiovascular Endurance Run (PACER), sit-and-reach] using the New York City FITNESSGRAM data set. Results: The analytic sample [n = 299,839; median (interquartile range) age = 16 (12-17)] was 50.1% female, 37.5% Hispanic, 26.2% non-Hispanic Black, and most (69.5%) qualified for free/reduced price school meals. Neighborhood indicators were positively associated with higher values of indicators for physical fitness. The strongest associations were observed between walkability and both BMI and PACER, and commute time with BMI, push-ups, and PACER. For example, walkability had the greatest magnitude of effects for BMI and muscular strength and endurance (BMI: β: -0.75, 95% confidence interval, CI: -1.01 to -0.49; PACER: β: 1.98, 95% CI: 1.59 to 2.37), and particularly for girls compared with boys (BMI, girls: β: -0.91, 95% CI: -1.22 to -0.66); BMI, boys: β: -0.56, 95% CI: -0.86 to -0.25); PACER, girls: β: 2.11, 95% CI: 1.68 to 2.54; push-ups, boys: β: 1.71, 95% CI: 1.31 to 2.12). Conclusion: Neighborhood indicators were associated with multiple measures of youth fitness. Continued research on neighborhood opportunity and youth fitness may better inform place-based public health interventions to reduce disparities.

Assessing the association between food environment and dietary inflammation by community type: a cross-sectional REGARDS study

BACKGROUND

Communities in the United States (US) exist on a continuum of urbanicity, which may inform how individuals interact with their food environment, and thus modify the relationship between food access and dietary behaviors.

OBJECTIVE

This cross-sectional study aims to examine the modifying effect of community type in the association between the relative availability of food outlets and dietary inflammation across the US.

METHODS

Using baseline data from the REasons for Geographic and Racial Differences in Stroke study (2003-2007), we calculated participants' dietary inflammation score (DIS). Higher DIS indicates greater pro-inflammatory exposure. We defined our exposures as the relative availability of supermarkets and fast-food restaurants (percentage of food outlet type out of all food stores or restaurants, respectively) using street-network buffers around the population-weighted centroid of each participant's census tract. We used 1-, 2-, 6-, and 10-mile (~ 2-, 3-, 10-, and 16 km) buffer sizes for higher density urban, lower density urban, suburban/small town, and rural community types, respectively. Using generalized estimating equations, we estimated the association between relative food outlet availability and DIS, controlling for individual and neighborhood socio-demographics and total food outlets. The percentage of supermarkets and fast-food restaurants were modeled together.

RESULTS

Participants (n = 20,322) were distributed across all community types: higher density urban (16.7%), lower density urban (39.8%), suburban/small town (19.3%), and rural (24.2%). Across all community types, mean DIS was - 0.004 (SD = 2.5; min = - 14.2, max = 9.9). DIS was associated with relative availability of fast-food restaurants, but not supermarkets. Association between fast-food restaurants and DIS varied by community type (P for interaction = 0.02). Increases in the relative availability of fast-food restaurants were associated with higher DIS in suburban/small towns and lower density urban areas (p-values < 0.01); no significant associations were present in higher density urban or rural areas.

CONCLUSIONS

The relative availability of fast-food restaurants was associated with higher DIS among participants residing in suburban/small town and lower density urban community types, suggesting that these communities might benefit most from interventions and policies that either promote restaurant diversity or expand healthier food options.

Spatial analysis of overweight prevalence in China: exploring the association with air pollution

BACKGROUND

Overweight is a known risk factor for various chronic diseases and poses a significant threat to middle-aged and elderly adults. Previous studies have reported a strong association between overweight and air pollution. However, the spatial relationship between the two remains unclear due to the confounding effects of spatial heterogeneity.

METHODS

We gathered height and weight data from the 2015 China Health and Retirement Long-term Survey (CHARLS), comprising 16,171 middle-aged and elderly individuals. We also collected regional air pollution data. We then analyzed the spatial pattern of overweight prevalence using Moran's I and Getis-Ord Gi* statistics. To quantify the explanatory power of distinct air pollutants for spatial differences in overweight prevalence across Southern and Northern China, as well as across different age groups, we utilized Geodetector's q-statistic.

RESULTS

The average prevalence of overweight among middle-aged and elderly individuals in each city was 67.27% and 57.39%, respectively. In general, the q-statistic in southern China was higher than that in northern China. In the north, the prevalence was significantly higher at 54.86% compared to the prevalence of 38.75% in the south. SO2 exhibited a relatively higher q-statistic in middle-aged individuals in both the north and south, while for the elderly in the south, NO2 was the most crucial factor (q = 0.24, p < 0.01). Moreover, fine particulate matter (PM2.5 and PM10) also demonstrated an important effect on overweight. Furthermore, we found that the pairwise interaction between various risk factors improved the explanatory power of the prevalence of overweight, with different effects for different age groups and regions. In northern China, the strongest interaction was found between NO2 and SO2 (q = 0.55) for middle-aged individuals and PM2.5 and SO2 (q = 0.27) for the elderly. Conversely, in southern China, middle-aged individuals demonstrated the strongest interaction between SO2 and PM10 (q = 0.60), while the elderly showed the highest interaction between NO2 and O3 (q = 0.42).

CONCLUSION

Significant spatial heterogeneity was observed in the effects of air pollution on overweight. Specifically, air pollution in southern China was found to have a greater impact on overweight than that in northern China. And, the impact of air pollution on middle-aged individuals was more pronounced than on the elderly, with distinct pollutants demonstrating significant variation in their impact. Moreover, we found that SO2 had a greater impact on overweight prevalence among middle-aged individuals, while NO2 had a greater impact on the elderly. Additionally, we identified significant statistically interactions between O3 and other pollutants.

Longitudinal Associations Between Neighborhood Child Opportunity and Physical Fitness for New York City Public School Youth

Neighborhood environments can support fitness-promoting behavior, yet little is known about their influence on youth physical fitness outcomes over time. We examined longitudinal associations between neighborhood opportunity and youth physical fitness among New York City (NYC) public school youth. The Child Opportunity Index (COI), a composite index of 29 indicators measuring neighborhood opportunity at the census-tract level, along with scores on 4 selected COI indicators were linked to NYC FITNESSGRAM youth data at baseline. Fitness outcomes (measured annually, 2011-2018) included body mass index, curl-ups, push-ups, and Progressive Aerobic Cardiovascular Endurance Run (PACER) laps. Unstratified and age-stratified, adjusted, 3-level generalized linear mixed models, nested by census tract and time, estimated the association between COI and fitness outcomes. The analytical sample (n = 204,939) lived in very low (41%) or low (30%) opportunity neighborhoods. Unstratified models indicated that overall COI is modestly associated with improved youth physical fitness outcomes. The strongest opportunity-fitness associations were observed for PACER. Stratified models show differences in associations across younger vs. older youth. We find that neighborhood factors are associated with youth fitness outcomes over time, with the strength of the associations dependent on age. Future implications include better informed place-based interventions tailored to specific life stages to promote youth health.

Youth-onset type 2 diabetes mellitus: an urgent challenge

The incidence and prevalence of youth-onset type 2 diabetes mellitus (T2DM) and its complications are increasing worldwide. Youth-onset T2DM has been reported in all racial and ethnic groups, but Indigenous peoples and people of colour are disproportionately affected. People with youth-onset T2DM often have a more aggressive clinical course than those with adult-onset T2DM or those with type 1 diabetes mellitus. Moreover, the available treatment options for children and adolescents with T2DM are more limited than for adult patients. Intermediate complications of youth-onset T2DM, such as increased albuminuria, often develop in late childhood or early adulthood, and end-stage complications, including kidney failure, develop in mid-life. The increasing frequency, earlier onset and greater severity of childhood obesity in the past 50 years together with increasingly sedentary lifestyles and an increasing frequency of intrauterine exposure to diabetes are important drivers of the epidemic of youth-onset T2DM. The particularly high risk of the disease in historically disadvantaged populations suggests an important contribution of social and environmental factors, including limited access to high-quality health care, healthy food choices and opportunities for physical activity as well as exposure to stressors including systemic racism and environmental pollutants. Understanding the mechanisms that underlie the development and aggressive clinical course of youth-onset T2DM is key to identifying successful prevention and management strategies.

Mediating Effect of Work-Family Conflict on the Relationship Between Long Commuting Time and Workers' Anxiety and Insomnia

Background

Our study aimed to investigate the mediating role of work-family conflict (WFC) on the relationship between long commutes and workers' anxiety and insomnia.

Methods

Our study measured the two dimensions of WFC, time-related, and strain-related, which were considered multiple mediators. The mediating effect of WFC on anxiety and insomnia was investigated by decomposing the total effect into a direct effect (long commuting time → anxiety or insomnia) and an indirect effect (long commuting time → WFC → anxiety or insomnia). The combined indirect effect (joint indirect effect) of strain-related WFC and time-related WFC was estimated. The effects were presented as odds ratios and 95% confidence intervals (CIs).

Results

The direct effect of 120 min or longer of commuting time was 1.39 (95% CI: 1.17-1.65) times increase in the odds of anxiety and 1.64 (95% CI: 1.41-1.90) times increase in the odds of insomnia than those whose commuting time was less than 60 min. In the case of indirect effects, those whose commuting time was 120 min or longer had 1.13 times higher odds of anxiety (95% CI: 1.07-1.18) and 1.12 times higher odds of insomnia (95% CI: 1.07-1.17) via WFC. The joint indirect effects accounted for 26.4% and 18.5% of the total effect on anxiety and insomnia, respectively. The longer the commuting time, the stronger both direct and indirect effects.

Conclusions

Our findings highlight the mediating effect of WFC on the relationship between long commuting times and workers' anxiety and insomnia.

City-Level Travel Time and Individual Dietary Consumption in Latin American Cities: Results from the SALURBAL Study

There is limited empirical evidence on how travel time affects dietary patterns, and even less in Latin American cities (LACs). Using data from 181 LACs, we investigated whether longer travel times at the city level are associated with lower consumption of vegetables and higher consumption of sugar-sweetened beverages and if this association differs by city size. Travel time was measured as the average city-level travel time during peak hours and city-level travel delay time was measured as the average increase in travel time due to congestion on the street network during peak hours. Vegetables and sugar-sweetened beverages consumption were classified according to the frequency of consumption in days/week (5-7: "frequent", 2-4: "medium", and ≤1: "rare"). We estimate multilevel ordinal logistic regression modeling for pooled samples and stratified by city size. Higher travel time (Odds Ratio (OR) = 0.65; 95% Confidence Interval (CI) 0.49-0.87) and delay time (OR = 0.57; CI 0.34-0.97) were associated with lower odds of frequent vegetable consumption. For a rare SSB consumption, we observed an inverse association with the delay time (OR = 0.65; CI 0.44-0.97). Analysis stratified by city size show that these associations were significant only in larger cities. Our results suggest that travel time and travel delay can be potential urban determinants of food consumption.

Drivers to Obesity-A Study of the Association between Time Spent Commuting Daily and Obesity in the Nepean Blue Mountains Area

Obesity has become a public health challenge in every country on this planet, with a substantial contribution to global mortality and morbidity. Studies of the built environment have shown some promise in understanding the drivers of this obesity pandemic. This paper contributes to this knowledge, by focusing on one aspect of the urban environment and asking whether there is an association between commuting and obesity in residents of the Nepean Blue Mountains area on the fringes of Sydney. This is a cross-sectional study with obesity being the dependent variable, and commuting the independent variable, where 45 min or less was defined as local and distant commute was more than 45 min. In the sample of 158 respondents, the risk of obesity was twice as likely in the distant commuters than in the local commuters (OR 2.04, 95% CI 1.051 to 3.962, p = 0.034). Investigation of possible mediators of this association was limited by sample size; however, mode of transport was found to be a significant mediator. The results support the design of cities to provide health supporting environments for all residents, including equitable access to employment at a reasonable distance and effective public transport.

Research on the Difference Between Recreational Walking and Transport Walking Among the Elderly in Mega Cities With Different Density Zones: The Case of Guangzhou City

Walking is the easiest method of physical activity for older people, and current research has demonstrated that the built environment is differently associated with recreational and transport walking. This study modelled the environmental characteristics of three different building density zones in Guangzhou city at low, medium, and high densities, and examined the differences in walking among older people in the three zones. The International Physical Activity Questionnaire (IPAQ) was used to investigate the recreational and transport walking time of older people aged 65 years and above for the past week, for a total of three density zones (N = 597) and was analysed as a dependent variable. Geographic Information Systems (GIS) was used to identify 300, 500, 800, and 1,000 m buffers and to assess differences between recreational and transport walking in terms of the built environment [e.g., land-use mix, street connectivity, Normalised Difference Vegetation Index (NDVI) data]. The data were processed and validated using the SPSS software to calculate Pearson's correlation models and stepwise regression models between recreation and transit walking and the built environment. The results found that land use mix and NDVI were positively correlated with transport walking in low-density areas and that transport walking was negatively correlated with roadway mediated centrality (BtE) and Point-of-Interest (PoI) density. Moreover, recreational walking in medium density areas was negatively correlated with self-rated health, road intersection density, and PoI density while positively correlated with educational attainment, population density, land use mix, street connectivity, PoIs density, and NDVI. Transport walking was negatively correlated with land-use mix, number of road crossings while positively correlated with commercial PoI density. Street connectivity, road intersection density, DNVI, and recreational walking in high-density areas showed negative correlations. Moreover, the built environment of older people in Guangzhou differed between recreational and transport walking at different densities. The richness of PoIs has different effects on different types of walking.

Unhealthy Neighbourhood “Syndrome”: A Useful Label for Analysing and Providing Advice on Urban Design Decision-Making?

Even before the COVID-19 pandemic, there was growing interest in designing healthier neighbourhoods. Adopting this perspective brings attention to how conditions in neighbourhoods (directly and indirectly) affect their inhabitants’ physical health and mental wellbeing. However, considerably less attention has been paid to how to alleviate such conditions through integrated interventions designed to operate specifically at the neighbourhood scale. To address this gap, this paper introduces the term “unhealthy neighbourhood syndrome” (UNS). The conceptual clarity and practical utility offered by using this term are critically examined. The paper contains a rigorous review and critical analysis of academic and grey literature on what are held to be the relationships between key features of the built environment and people’s health and wellbeing. It also examines literature offering advice on how urban designers should make neighbourhoods healthier. It illustrates the complexity of the range of issues involved and the complicated web of top down, bottom up and middling out actors that need to be involved in making decisions about them. Despite having inherent weaknesses, the term “unhealthy neighbourhood syndrome” is judged to be useful. It illustrates how seemingly separate issues operate in urban design, promoted for tackling specific symptoms of ill health, need to be addressed jointly through an integrated programme of parallel work streams operating at the neighbourhood scale. The paper is innovative in identifying the wide cluster of symptoms used to describe unhealthy neighbourhoods in the literature as being a “syndrome”. Its significance lies in its injunction that this syndrome needs to be tackled through integrated streams of remedial action drawing on experience and expertise that lie beyond those offered by the traditional membership of urban design teams.

The association between neighborhood quality, youth physical fitness, and modifiable cardiovascular disease risk factors

PURPOSE

Striking disparities persist in cardiovascular disease risk factors among minority youth. We examined the association between multiple indicators of neighborhood quality and minority youth fitness.

METHODS

The primary exposure was the Child Opportunity Index (COI), a measure comprised of indicators that facilitate healthy child development. Outcome data were drawn from the 2018-2019 Fit2Play Study (Miami-Dade County, FL). Hotspot analysis evaluated COI spatial clustering. Generalized linear mixed models examined cross-sectional COI-fitness associations.

RESULTS

The sample included 725 youth (53% Black, 43% Hispanic; 5-17 years). Significant neighborhood quality spatial clusters were identified (Gi*_z-score = -4.85 to 5.36). Adjusting for sociodemographics, walkability was associated with lower percentiles in body mass index (BMI) and diastolic blood pressure percentiles (DBP) (β = -5.25, 95% CI: -8.88, -1.62 and β = -3.95, 95% CI: -7.02, -0.89, respectively) for all, lower skinfold thickness (β = -4.83, 95% CI: -9.97, 0.31 and higher sit-ups (β = 1.67, 95% CI: -0.17, 3.50) among girls, and lower systolic blood pressure percentiles (SBP) (β = -4.75, 95% CI: -8.99, -0.52) among boys. Greenspace was associated with higher BMI (β = 6.17, 95% CI: 2.47, 9.87), SBP (β = 3.47, 95% CI: -0.05, 6.99), and DBP (β = 4.11, 95% CI: 1.08, 7.13).

CONCLUSIONS

COI indicators were positively associated with youth fitness. Disparities in youth cardiovascular disease risk may be modifiable through community interventions and built environment initiatives targeting select neighborhood factors.

Twenty years of pediatric diabetes surveillance: what do we know and why it matters

SEARCH for Diabetes in Youth (SEARCH) was initiated in 2000 as a multicenter study to address major gaps in the understanding of childhood diabetes in the United States. An active registry of youth diagnosed with diabetes at age <20 years since 2002 assessed prevalence, annual incidence, and trends by age, race/ethnicity, sex, and diabetes type. An observational cohort nested within the population-based registry was established to assess the natural history and risk factors for acute and chronic diabetes-related complications, as well as the quality of care and quality of life of children and adolescents with diabetes from diagnosis into young adulthood. SEARCH findings have contributed to a better understanding of the complex and heterogeneous nature of youth-onset diabetes. Continued surveillance of the burden and risk of type 1 and type 2 diabetes is important to track and monitor incidence and prevalence within the population. SEARCH reported evidence of early diabetes complications highlighting that continuing the long-term follow-up of youth with diabetes is necessary to further our understanding of its natural history and to develop the most appropriate approaches to primary, secondary, and tertiary prevention of diabetes and its complications. This review summarizes two decades of research and suggests avenues for further work.

Commuting time to work and behaviour-related health: a fixed-effect analysis

Objectives

Long commuting times are linked to poor health outcomes, but the evidence is mainly cross-sectional. We examined longitudinal within-individual associations between commuting time and behaviour-related health.

Methods

Data were from the Swedish Longitudinal Occupational Survey of Health study. We selected workers who responded to a minimum of two surveys conducted every other year between 2008 and 2018. We included all study waves with self-reported commuting time (ie, the exposure, 1–5, 6–10, 11–15 or ≥15 hours/week), body mass index (based on weight and height), physical (in)activity, smoking, alcohol use and sleep problems (ie, the outcomes) (N_individuals=20 376, N_observations=46 169). We used conditional logistic regression for fixed effects analyses that controls for time-varying confounders by design. Analyses were stratified by working hours: normal (30–40 hours/week) or longer than normal (>40 hours/week) and adjusted for time dependent covariates: age, marital status, occupational position, presence of children, chronic disease, depressive symptoms, job strain and shift work.

Results

Those working >40 hours/week had higher odds of physical inactivity (OR 1.25, 95% CI 1.03 to 1.51) and sleep problems (OR 1.16, 95% CI 1.00 to 1.35) when they were commuting >5 hours/week than when they were commuting 1–5 hours/week. Among women working normal hours, longer commuting time associated with lower odds of problem drinking.

Conclusion

Our findings suggest that lengthy commuting time increases the risk of physical inactivity and sleep problems if individuals have longer than normal weekly working hours. Effects of work arrangements that decrease commuting time should be examined in relation to health behaviours.

Neighborhood Commute to Work Times and Self-Reported Caregiver Health Behaviors and Food Access

OBJECTIVE

Time spent commuting is associated with obesity. The objective of this study was to assess the relationship between neighborhood-level commute to work (CTW) times and self-reported health behaviors and food access.

METHODS

We conducted a cross-sectional analysis of caregivers with infants as part of the Greenlight Study, a multisite obesity trial in Chapel Hill, New York City, Nashville, and Miami. ZIP code-based commuting estimates were determined using the US Census American Community Survey. Self-reported health behaviors and food access data were collected by directed interview. Logistic and linear regression models were used to determine associations between neighborhood CTW times and health behaviors and food access.

RESULTS

The average neighborhood CTW time for all ZIP codes was 29 minutes (n = 846). Caregivers in longer CTW time neighborhoods were more likely to endorse fewer food choices (adjusted odds ratio [AOR], 1.39; 95% confidence interval [CI], 1.15-1.69; P = .001) and difficulty accessing markets with fresh produce (AOR, 1.51; 95% CI, 1.02-2.25; P = .04). Neighborhood CTW time >30 minutes was associated with less caregiver physical activity (AOR, 0.58; 95% CI, 0.34-0.98; P = .044). Neighborhood CTW time was inversely related to infant television time (adjusted mean, 399 minutes/day for ≤30 minutes and 256 minutes/day for >30 minutes; P = .025). New York families in longer CTW neighborhoods were more likely to report difficulty accessing markets with fresh produce (AOR, 1.80; 95% CI, 1.03-3.14; P = .039).

CONCLUSIONS

Neighborhood CTW time is associated with several self-reported health behaviors and perceived food access among caregivers with children. Neighborhood CTW times may represent city-specific features, including transportation infrastructure, which may impact the health of families.

Actual neighborhood-level crime predicts body mass index z-score changes in a multi-racial/ethnic sample of children

Longitudinal studies are warranted to clarify the influence crime has on health outcomes in children especially children representing multiple racial/ethnic backgrounds. To address this need, the current study examined whether neighborhood-level crime predicted changes in body mass index z (BMIz) scores in 373 White (W), 627 African American (AA), 1020 Hispanic (H), and 88 Asian (A), five to ten year-old boys and girls living in urban neighborhoods. Heights and weights were assessed at baseline (2012) and three-years later and used to calculate BMIz scores. Characteristics of zip codes where students lived during the three-year period were obtained at baseline from various sources. The Crime Risk Index (CRI) for each zip code was calculated using actual crime statistics. Multiple linear regression analyses were conducted to examine associations between baseline CRI and follow-up BMIz scores while controlling for other variables including BMIz at baseline. The CRI and BMIz scores differed significantly by race/ethnicity with the highest values for both noted in H. Regression analyses indicated that the CRI accounted for a significant percentage of the variance in follow-up BMIz scores in the overall sample. When race/ethnicity was considered, the CRI predicted follow-up BMIz scores only in W children. The CRI was not significantly associated with BMIz scores in the other races/ethnicities. The impact actual, neighborhood-level crime has on BMI in children is complex. Based on the existing evidence, considering actual crime as a primary target in obesity prevention would be premature especially in racial/ethnicity minority children living in urban areas.

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Crime and body mass index differ significantly by race/ethnicity in children.

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The association between crime and body mass index varies by race/ethnicity.

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Crime predicts changes in body mass index only in White children.

Differences in Obesity Prevalence by Demographic Characteristics and Urbanization Level Among Adults in the United States, 2013-2016

IMPORTANCE

Differences in obesity by sex, age group, race and Hispanic origin among US adults have been reported, but differences by urbanization level have been less studied.

OBJECTIVES

To provide estimates of obesity by demographic characteristics and urbanization level and to examine trends in obesity prevalence by urbanization level.

DESIGN, SETTING, AND PARTICIPANTS

Serial cross-sectional analysis of measured height and weight among adults aged 20 years or older in the 2001-2016 National Health and Nutrition Examination Survey, a nationally representative survey of the civilian, noninstitutionalized US population.

EXPOSURES

Sex, age group, race and Hispanic origin, education level, smoking status, and urbanization level as assessed by metropolitan statistical areas (MSAs; large: ≥1 million population).

MAIN OUTCOMES AND MEASURES

Prevalence of obesity (body mass index [BMI] ≥30) and severe obesity (BMI ≥40) by subgroups in 2013-2016 and trends by urbanization level between 2001-2004 and 2013-2016.

RESULTS

Complete data on weight, height, and urbanization level were available for 10 792 adults (mean age, 48 years; 51% female [weighted]). During 2013-2016, 38.9% (95% CI, 37.0% to 40.7%) of US adults had obesity and 7.6% (95% CI, 6.8% to 8.6%) had severe obesity. Men living in medium or small MSAs had a higher age-adjusted prevalence of obesity compared with men living in large MSAs (42.4% vs 31.8%, respectively; adjusted difference, 9.8 percentage points [95% CI, 5.1 to 14.5 percentage points]); however, the age-adjusted prevalence among men living in non-MSAs was not significantly different compared with men living in large MSAs (38.9% vs 31.8%, respectively; adjusted difference, 4.8 percentage points [95% CI, -2.9 to 12.6 percentage points]). The age-adjusted prevalence of obesity was higher among women living in medium or small MSAs compared with women living in large MSAs (42.5% vs 38.1%, respectively; adjusted difference, 4.3 percentage points [95% CI, 0.2 to 8.5 percentage points]) and among women living in non-MSAs compared with women living in large MSAs (47.2% vs 38.1%, respectively; adjusted difference, 4.7 percentage points [95% CI, 0.2 to 9.3 percentage points]). Similar patterns were seen for severe obesity except that the difference between men living in large MSAs compared with non-MSAs was significant. The age-adjusted prevalence of obesity and severe obesity also varied significantly by age group, race and Hispanic origin, and education level, and these patterns of variation were often different by sex. Between 2001-2004 and 2013-2016, the age-adjusted prevalence of obesity and severe obesity significantly increased among all adults at all urbanization levels.

CONCLUSIONS AND RELEVANCE

In this nationally representative survey of adults in the United States, the age-adjusted prevalence of obesity and severe obesity in 2013-2016 varied by level of urbanization, with significantly greater prevalence of obesity and severe obesity among adults living in nonmetropolitan statistical areas compared with adults living in large metropolitan statistical areas.

Resident Perceptions of Neighborhood Conditions, Food Access, Transportation Usage, and Obesity in a Rapidly Changing Central City

There is a lack of research on obesity that uses primary data and fine-grained information on neighborhoods. I use primary data for 367 participants in Detroit to examine neighborhood predictors of obesity. These data were supplemented with public data. I considered multilevel and spatial modeling, but the data lent itself best to ordinary least squares (OLS) regressions. I find that socioeconomic factors, the built environment, transportation usage, and perceptions of neighborhoods are important predictors of obesity. Importantly, litter is associated with higher levels of obesity. Planners can take measures to reduce litter and collaborate with other policy-makers to encourage less driving, though drawing direct lines of causality is complicated.

Neighborhood characteristics, food deserts, rurality, and type 2 diabetes in youth: Findings from a case-control study

Little is known about the influence of neighborhood characteristics on risk of type 2 diabetes (T2D) among youth. We used data from the SEARCH for Diabetes in Youth Case-Control Study to evaluate the association of neighborhood characteristics, including food desert status of the census tract, with T2D in youth. We found a larger proportion of T2D cases in tracts with lower population density, larger minority population, and lower levels of education, household income, housing value, and proportion of the population in a managerial position. However, most associations of T2D with neighborhood socioeconomic characteristics were attributable to differences in individual characteristics. Notably, in multivariate logistic regression models, T2D was associated with living in the least densely populated study areas, and this finding requires further exploration.

Characterizing the neighborhood obesogenic environment in the Multiethnic Cohort: a multi-level infrastructure for cancer health disparities research

PURPOSE

We characterized the neighborhood obesogenic environment in the Multiethnic Cohort (MEC) by examining the associations of obesity with attributes of the social and built environment, establishing a multi-level infrastructure for future cancer research.

METHODS

For 102,906 African American, Japanese American, Latino, and white MEC participants residing predominately in Los Angeles County, baseline residential addresses (1993-1996) were linked to census and geospatial data, capturing neighborhood socioeconomic status (nSES), population density, commuting, food outlets, amenities, walkability, and traffic density. We examined neighborhood attributes and obesity (body mass index ≥ 30 kg/m2) associations using multinomial logistic regression, adjusting for individual-level (e.g., demographics, physical activity, and diet) and neighborhood-level factors.

RESULTS

NSES was associated with obesity among African Americans, Latinos, and whites (p-trend ≤ 0.02), with twofold higher odds (adjusted odds ratios, 95% confidence intervals) for living in the lowest versus highest quintile among African American women (2.07, 1.62-2.65), white men (2.11, 1.29-3.44), and white women (2.50, 1.73-3.61). Lower density of businesses among African American and white women and lower traffic density among white men were also associated with obesity (p-trends ≤ 0.02).

CONCLUSIONS

Our study highlights differential impacts of neighborhood factors across racial/ethnic groups and establishes the foundation for multi-level studies of the neighborhood context and obesity-related cancers.

Walkable neighborhoods and obesity: Evaluating effects with a propensity score approach

Background

Research investigating the connection between neighborhood walkability and obesity often overlooks the issue of nonrandom residential selection.

Methods

We use propensity score methods to adjust for the nonrandom selection into residential neighborhoods in this cross-sectional, observational study. The sample includes 103,912 women residing in Salt Lake County, Utah age 20 or older. We measured percentage living in neighborhoods with more walkability, area level measures of neighborhood characteristics, and obesity (body mass index (BMI) > 30).

Results

Our findings confirm previous work that observes an association between living in more walkable neighborhoods and lower obesity. After adjusting for nonrandom selection, the odds of being obese when living in a less walkable neighborhood increase. Specifically, the odds ratio for being obese without the propensity score correction is 1.12. After adjusting for nonrandom selection, the odds ratio for being obese is 1.19, an increase of six percent.

Conclusion

Results demonstrate that residential selection bias inherent in cross-sectional analysis slightly attenuates the true association between neighborhood walkability and obesity. Results lend support to the growing body of research suggesting that more walkable neighborhoods have residents with a lower prevalence of obesity. Absent propensity score controls, the causal relationship between environment and obesity would be underestimated by 6%. Our analysis suggests there is an association between neighborhood walkability and obesity.

Rural and Urban Differences in Air Quality, 2008-2012, and Community Drinking Water Quality, 2010-2015 - United States

Problem/Condition

The places in which persons live, work, and play can contribute to the development of adverse health outcomes. Understanding the differences in risk factors in various environments can help to explain differences in the occurrence of these outcomes and can be used to develop public health programs, interventions, and policies. Efforts to characterize urban and rural differences have largely focused on social and demographic characteristics. A paucity of national standardized environmental data has hindered efforts to characterize differences in the physical aspects of urban and rural areas, such as air and water quality.

Reporting Period

2008–2012 for air quality and 2010–2015 for water quality.

Description of System

Since 2002, CDC’s National Environmental Public Health Tracking Program has collaborated with federal, state, and local partners to gather standardized environmental data by creating national data standards, collecting available data, and disseminating data to be used in developing public health actions. The National Environmental Public Health Tracking Network (i.e., the tracking network) collects data provided by national, state, and local partners and includes 21 health outcomes, exposures, and environmental hazards. To assess environmental factors that affect health, CDC analyzed three air-quality measures from the tracking network for all counties in the contiguous United States during 2008–2012 and one water-quality measure for 26 states during 2010–2015. The three air-quality measures include 1) total number of days with fine particulate matter (PM_2.5) levels greater than the U.S. Environmental Protection Agency’s (EPA’s) National Ambient Air Quality Standards (NAAQS) for 24-hour average PM_2.5 (PM_2.5 days); 2) mean annual average ambient concentrations of PM_2.5 in micrograms per cubic meter (mean PM_2.5); and 3) total number of days with maximum 8-hour average ozone concentrations greater than the NAAQS (ozone days). The water-quality measure compared the annual mean concentration for a community water system (CWS) to the maximum contaminant level (MCL) defined by EPA for 10 contaminants: arsenic, atrazine, di(2-ethylhexyl) phthalate (DEHP), haloacetic acids (HAA5), nitrate, perchloroethene (PCE), radium, trichloroethene (TCE), total trihalomethanes (TTHM), and uranium. Findings are presented by urban-rural classification scheme: four metropolitan (large central metropolitan, large fringe metropolitan, medium metropolitan, and small metropolitan) and two nonmetropolitan (micropolitan and noncore) categories. Regression modeling was used to determine whether differences in the measures by urban-rural categories were statistically significant.

Results

Patterns for all three air-quality measures suggest that air quality improves as areas become more rural (or less urban). The mean total number of ozone days decreased from 47.54 days in large central metropolitan counties to 3.81 days in noncore counties, whereas the mean total number of PM_2.5 days decreased from 11.21 in large central metropolitan counties to 0.95 in noncore counties. The mean average annual PM_2.5 concentration decreased from 11.15 μg/m³ in large central metropolitan counties to 8.87 μg/m³ in noncore counties. Patterns for the water-quality measure suggest that water quality improves as areas become more urban (or less rural). Overall, 7% of CWSs reported at least one annual mean concentration greater than the MCL for all 10 contaminants combined. The percentage increased from 5.4% in large central metropolitan counties to 10% in noncore counties, a difference that was significant, adjusting for U.S. region, CWS size, water source, and potential spatial correlation. Similar results were found for two disinfection by-products, HAA5 and TTHM. Arsenic was the only other contaminant with a significant result. Medium metropolitan counties had 3.1% of CWSs reporting at least one annual mean greater than the MCL, compared with 2.4% in large central counties.

Interpretation

Noncore (rural) counties experienced fewer unhealthy air-quality days than large central metropolitan counties, likely because of fewer air pollution sources in the noncore counties. All categories of counties had a mean annual average PM_2.5 concentration lower than the EPA standard. Among all CWSs analyzed, the number reporting one or more annual mean contaminant concentrations greater the MCL was small. The water-quality measure suggests that water quality worsens as counties become more rural, in regards to all contaminants combined and for the two disinfection by-products individually. Although significant differences were found for the water-quality measure, the odds ratios were very small, making it difficult to determine whether these differences have a meaningful effect on public health. These differences might be a result of variations in water treatment practices in rural versus urban counties.

Public Health Action

Understanding the differences between rural and urban areas in air and water quality can help public health departments to identify, monitor, and prioritize potential environmental public health concerns and opportunities for action. These findings suggest a continued need to develop more geographically targeted, evidence-based interventions to prevent morbidity and mortality associated with poor air and water quality.

Geography, Race/Ethnicity, and Physical Activity Among Men in the United States

Engaging in regular physical activity reduces one’s risk of chronic disease, stroke, cardiovascular disease, and some forms of cancer. These preventive benefits associated with physical activity are of particular importance for men, who have shorter life expectancy and experience higher rates of chronic diseases as compared to women. Studies at the community and national levels have found that social and environmental factors are important determinants of men’s physical activity, but little is known about how regional influences affect physical activity behaviors among men. The objective of this study is to examine the association between geographic region and physical activity among men in the United States, and to determine if there are racial/ethnic differences in physical activity within these geographic regions. Cross-sectional data from men who participated the 2000 to 2010 National Health Interview Survey (N = 327,556) was used. The primary outcome in this study was whether or not men had engaged in sufficient physical activity to receive health benefits, defined as meeting the 2008 Physical Activity Guidelines for Americans. Race/ethnicity and geographic region were the primary independent variables. Within every region, Hispanic and Asian men had lower odds of engaging in sufficient physical activity compared to white men. Within the Northeast, South, and West, black men had lower odds of engaging in sufficient physical activity compared to white men. The key findings indicate that the odds of engaging in sufficient physical activity among men differ significantly between geographic regions and within regions by race/ethnicity.

Travel distance to prenatal care and high blood pressure during pregnancy

OBJECTIVE

To assess whether poor geographic accessibility to prenatal care, as indicated by long distance trips to prenatal care, produced high blood pressure (HPB) during pregnancy.

METHODS

Using the 2007 Los Angeles Mommy and Baby Study for women without hypertension prior to pregnancy (n = 3405), we compared self-reported HBP by travel distance to prenatal care controlling for age, race/ethnicity, marital status, education, household income, weight status, and physical activity.

RESULTS

Results of the multilevel logistic regression shows traveling more than 50 mi to prenatal care is associated with an increased odds for having HPB during pregnancy (odds ratio [OR] = 2.867, 95% confidence interval [CI] = 1.079,7.613), as compared with a travel distance shorter than 5 mi. Traveling 5-14 mi (OR = 0.917, 95% CI = 0.715-1.176), 15-29 mi (OR = 0.955, 95% CI = 0.634-1.438), or 30-50 mi (OR = 1.101, 95% CI = 0.485-2.499) were not significantly associated with more risk of HBP during pregnancy.

CONCLUSION

To our knowledge, no previous studies have examined the association between poor geographic accessibility to care and the possible harms of travel burdens for pregnant women. Future research that replicates these findings can assist in developing recommendations for pregnant women and health-care accessibility.

Built Environments and Active Living in Rural and Remote Areas: a Review of the Literature

Rural children and adults are more likely to have obesity than their urban counterparts even after adjustment for individual-level behaviors, suggesting that rural environments may promote obesity. The rural built environment may be an important area of research that can help us understand rural-urban disparities in obesity. The purpose of this review is to summarize the rural built environment and active living literature, and to address key issues, gaps, and observations in the field. A literature review was conducted in spring 2015 to identify research published from 2000 to 2015. Our review suggests that limited active living built environments in rural communities and unique rural barriers to physical activity may contribute to a higher prevalence of obesity compared to urban populations. More empirical research is needed to build the evidence-base for the association between rural built environments, active living, and obesity. School- and community-based policies that expand active living opportunities in rural areas should also be closely examined.

Geography, Race/Ethnicity, and Obesity Among Men in the United States

The prevalence of obesity in the United States has increased significantly and is a particular concern for minority men. Studies focused at the community and national levels have reported that geography can play a substantial role in contributing to obesity, but little is known about how regional influences contribute to obesity among men. The objective of this study is to examine the association between geographic region and obesity among men in the United States and to determine if there are racial/ethnic differences in obesity within these geographic regions. Data from men, aged 18 years and older, from the National Health Interview Survey were combined for the years 2000 to 2010. Obesity was defined as body mass index (BMI) ≥30 kg/m(2) Logistic regression models were specified to calculate the odds ratio (OR) and 95% confidence interval (CI) for the association between geographic region and obesity and for race and obesity within geographic regions. Compared to men living in the Northeast, men living in the Midwest had significantly greater odds of being obese (OR = 1.09, 95% CI [1.02, 1.17]), and men living in the West had lower odds of being obese (OR = 0.82, 95% CI [0.76, 0.89]). Racial/ethnic differences were also observed within geographic region. Black men have greater odds of obesity than White men in the South, West, and Midwest. In the South and West, Hispanic men also have greater odds of obesity than White men. In all regions, Asian men have lower odds of obesity than White men.