Abstract
Background:
Childhood obesity is a risk factor for poor cardiovascular, metabolic, and respiratory health. The studies examining influences of socio-ecologic factors on weight trajectories using longitudinal data are limited, often examine single measures (e.g., proximity to parks), and have not examined the specific trajectories of children with obesity.
Methods:
We examined influences on weight among 1518 children, 6–12 years of age, who had obesity using body mass index (BMI) criteria. BMI slope trajectories were categorized as decreasing, flat, or increasing, with a median of 2.1 years of follow-up. We examined socio-ecologic exposures, stratified by rural and urban settings, using census tracts to map indices, including food access, proximity to parks, normalized difference vegetation index, and area deprivation index (ADI). We used ordinal logistic regression to examine the associations between the socio-ecologic factors and BMI trajectories.
Results:
Among the 1518 children, 360 (24%) had a decreasing BMI trajectory with the remainder having flat (23%) or increasing (53%) trajectories. Children in rural areas were more likely to live in high disadvantage areas, 85%, compared with urban children, 46%. In the multivariable ordinal model, living in a lower ADI census tract had a 0.78 (95% CI 0.61–0.99) lower odds of being in an increasing BMI slope group, and no other socio-ecologic factor was associated.
Conclusions:
The area deprivation index captures a range of resources and social context compared with the built environment indicators, which had no association with BMI trajectory. Further work examining how to develop effective interventions in high deprivation areas is warranted.
Keywords: obesity, children, trajectories, poverty
Introduction
Environmental factors posited to influence weight status in children include access to healthy food, areas for play, and availability of other neighborhood resources. 1 –3 We used the socio-ecological framework to develop our approach to examining factors that could influence trajectories of weight for children with obesity in rural and urban settings. 4 There are critical differences in the socio-ecologic factors in rural versus urban environments and different ways in which these factors may interact in both environments. 5,6 Examining the association between contextual ecologic factors and longitudinal weight trajectories has been understudied, particularly for rural populations. Longitudinal data for weight have the potential to provide better insight into causality compared with cross-sectional assessments. A recent systematic review identified 24 cohorts of children, which examined food environment, physical environment, and social environments in association with longitudinal weight measures in children; however, the majority of these cohorts examined only one environmental factor (e.g., park proximity). 7 They found inconsistent evidence for both the food environment and the built environment, with a narrow majority (6 of 11 studies) supporting an association between access to parks or recreation space and decreased adiposity.
Measures of food access examine physical proximity of a household to healthy foods using grocery stores as a proxy, with the explanation being that easier access to healthy foods may improve healthy food intake. A recent meta-analysis showed no significant association between proximity to a grocery store and childhood obesity 8 ; however, these studies did not account for other environmental factors such as neighborhood deprivation. Proximity to parks has not consistently been found to be associated with decreased obesity; 9 however, increases in proximity to parks have been associated with a decrease in obesity, which may suggest capital investment. 10 The socio-economics of a neighborhood may be linked to its greenness, which is a significant confounder and highlights the need to study these environmental factors in concert. Neighborhood deprivation has been linked to a greater point prevalence of obesity 11,12 ; however, the relationship between deprivation and measures such as parks has been understudied.
In this study, we sought to examine whether socio-ecologic factors influenced weight trajectories in children with obesity, specifically examining food access, proximity to parks, greenspace, and area deprivation index (ADI), with rural and urban settings examined separately. Understanding the factors that may influence weight in this subset of children who already have obesity can provide better information on effective interventions in this population.
Methods
Sample Selection, Inclusion and Exclusion Criteria
We used the electronic medical record of a hospital system to extract data from January 1, 2015, through September 30, 2020, for children 6–12 years of age, with at least three outpatient weight values recorded since January 1, 2015, described previously. 13 We applied the Daymont algorithm implemented in the R package growthcleanr to identify and exclude implausible weight/height values (n = 5,507) extracted from the records (e.g., values carried forward from a prior visit) 14 (Fig. 1). Children with diagnoses of epilepsy, developmental disabilities, cerebral palsy, neoplasms, endocrine disorders, metabolic disorders, muscular dystrophy, end-stage renal disease, and organ transplant were excluded, given the potential to significantly influence growth (n = 917 excluded). We restricted this analysis to children who had a BMI ≥95th percentile for sex and age to examine the factors that influence weight trajectories in children with obesity, excluding 9,729 children with a BMI <95th percentile. This project was approved by the Oregon Health & Science University Institutional Review Board, STUDY00021599. Consent was waived for the extraction and analysis of data from medical records.
Figure 1.
Subject selection.
Geocoding
Addresses from medical record data were matched to census tracts using the US Census Bureau’s online geocoder. 15 Census tract groupings are represented by 11-digit codes and encompass a geographic region of approximately 1,200 to 8,000 people. The geocoder was unable to match post office (PO) boxes, mailing addresses in commercial zones, and some rural locations. Such addresses were replaced with residential addresses within the same census block.
Measures
Dependent variables
The dependent variable—weight trajectory—was defined as distance from the median BMI, using the slope over time for all measurement points. 16 We used linear mixed effect models to estimate slopes of distance from the median BMI for each child, with the intercept representing their baseline BMI at six years of age or older. Slopes were then categorized into decreasing (<−1), flat (−1 to 1), and increasing (>1); this categorization for trajectories using distance from the median BMI slope allows for better tracking of adiposity over time and categorization into trajectories that may be more meaningful clinically in terms of weight changes. 16
Independent variables
Food access, physical environment (distance to parks and normalized difference vegetation index (NDVI), and the ADI were examined as the primary independent variables. All data pulled were for the most recent year available at the time of analysis, ranging from 2019 to 2021. All socio-ecologic data were therefore accurate to the timeframe of the census tract data.
For food access, we used the US Department of Agriculture metric of low access at greater than 1 mile from a supermarket or grocery store for urban areas and 10 miles for rural. We coded this as either low access or not. 17
We used the CDC’s National Environmental Public Health Tracking Network 18 to measure NDVI, a robust measure of greenspace or vegetation, as viewed by satellite imaging, by census tract. NDVI values range from –1.0 to +1.0, where higher values represent higher density of green vegetation. The NDVI for this dataset is the highest (greenest) NDVI value for the year (e.g., summer versus winter), as determined by the US Geological Survey. Park proximity was defined as the percent of people living within ½ mile of a park, within a given census tract, using inclusion in the Protected Areas Databases of the US Geological Survey. We examined the relationship between NDVI and park access and found a nonlinear relationship. At NDVI scores between 0.2 and 0.4, the median park access at ½ mile was 98% (IQR 80, 100), and similar for NDVI scores in the 0.4–0.6 range, with median park access of 94% (IQR 81–100). In contrast, at high NDVI scores of >0.6, park access dropped to a median of 37% (IQR 21, 59). This pattern held even when only examining those children living in rural areas. When examining the mapping for NDVI in Oregon, the majority of the highest NDVI distribution falls across the heavily forested areas of the state. NDVI was categorized into low (<0.2), medium (0.2–0.4), high (0.4–0.6), and very high (>0.6). Park access was highly right skewed, and so it was dichotomized into census tracts with less than 90% of people having access to a park and those with ≥90% of people having access to a park.
We used the University of Wisconsin’s Neighborhood Atlas for state (decile) and national (percentage) data on ADI. 19 –21 The ADI uses 17 community-level metrics from census data to examine income, home values, home utilities, crowded living, percent living in poverty, employment, level of education, and single-parent households in a given census block. 20 A higher value represents higher measures of disadvantage. We dichotomized the state decile data into high and low deprivation.
Covariates
Home addresses were coded as rural or urban using rural–urban commuting area (RUCA) codes with RUCA codes 1–3 corresponding to urban/suburban and 4 and higher coded as rural. Poverty rates by census tracts were taken from the University of Wisconsin’s Neighborhood Atlas. Sex assigned at birth (male/female) and race and ethnicity data (Hispanic, non-Hispanic White) were abstracted from the record. Other racial and ethnic groups made up a very small minority and were dropped from further analysis. Sex has previously been associated differentially with environmental factors and weight trajectories, 7 and race/ethnicity used to determine if any systematic factor that varies by race could be influencing the association between environmental factors and weight trajectories.
Analysis Plan
We examined the associations between the independent variables described above with the premise based on prior literature that interactions between these variables may exert a significant effect on BMI slope. For example, a child may be more likely to engage in physical activity in a proximate park if their deprivation index is lower and not if it is higher, regardless of the park proximity.
BMI slope was examined using ordinal logistic regression models. In these models, we first examined the association at the individual level using baseline adiposity, age, race/ethnicity, and sex at birth. In subsequent models, we examine the associations between the independent socio-ecologic variables and the outcome. We included all the independent variables in the model and tested interaction terms between the independent variables. A p-value of <0.05 was used for determining statistical significance.
Results
Rural versus Urban/Suburban Comparison
Our final analytic sample consisted of 1518 children with an average age of 8.2 years (SD = 1.6) at first measurement (Table 1). Differences between the rural and urban groups include a higher poverty rate among rural children, median 15%, compared with urban, median 12%, p < 0.001, and a much higher proportion within the upper half of the state ADI at 85% in the rural group and only 46% in the urban, p < 0.001. There also was a different distribution of NDVI exposure between rural and urban groups, with more children living in the rural areas in the lowest NDVI category (10% vs. 5% for urban), p < 0.001.
Table 1.
Characteristics of Subjects Included in the Analysis, Stratified by Urban and Rural Census Tract
| All (n = 1518) | Rural (n = 419) | Urban (n = 1099) | p-value | |
|---|---|---|---|---|
| Baseline age in years, mean (SD) | 8.2 (1.6) | 8.0 (1.7) | 8.2 (1.6) | 0.10 |
| Child sex, female, n (%) | 848 (56) | 238 (57) | 610 (56) | 0.65 |
| Race/ethnicity, n (%) | 0.02 | |||
| White, non-Hispanic | 829 (55) | 249 (59) | 580 (53) | |
| Hispanic | 689 (45) | 170 (41) | 519 (47) | |
| Baseline percent of the 95th percentile BMI, median (IQR) | 114 (106–127) | 113 (105–126) | 114 (106–127) | 0.07 |
| BMI categories at baseline, n (%) | 0.13 | |||
| 100–<120% of 95th percentile BMI | 961 (63) | 276 (66) | 685 (62) | |
| ≥120–<140% of 95th percentile BMI | 404 (27) | 111 (27) | 293 (27) | |
| ≥140% of 95th percentile BMI | 153 (10) | 32 (8) | 121 (11) | |
| BMI slopes, n (%) | 0.002 | |||
| Decreasing | 360 (24) | 74 (18) | 286 (26) | |
| Flat | 348 (23) | 96 (23) | 252 (23) | |
| Increasing | 810 (53) | 249 (59) | 561 (51) | |
| Poverty rate, median (IQR) | 13 (8–19) | 15 (11–20) | 12 (8–19) | <0.001 |
| Low food access, n (%) | 511 (36) | 146 (41) | 365 (35) | 0.02 |
| NDVI, n (%) | <0.001 | |||
| 0.2 to ≤0.4 | 86 (6) | 35 (10) | 51 (5) | |
| >0.4 to ≤0.6 | 874 (62) | 197 (56) | 677 (64) | |
| >0.6 | 445 (32) | 120 (34) | 325 (31) | |
| Park access, ≥90% | 657 (47) | 150 (43) | 507 (48) | 0.07 |
| ADI (State), median (IQR) | 6 (4–8) | 8 (6–9) | 5 (3–7) | <0.001 |
| ADI (State), n (%) | <0.001 | |||
| Low disadvantage (1–5) | 622 (44) | 53 (15) | 569 (54) | |
| High disadvantage (6–10) | 782 (56) | 300 (85) | 482 (46) |
ADI, area deprivation index; BMI, body mass index; IQR, interquartile range; NDVI, normalized difference vegetation index.
At baseline, 961 (63%) children had a BMI between 100% and 120% of the 95th percentile, 404 (27%) had a BMI between 120% and 140% of the 95th percentile, and 153 (10%) had a BMI at 140% of the 95th percentile or higher, with no difference noted by rural versus urban (Table 1). In this sample, 360 (24%) had a decreasing slope for their BMI trajectory, 348 (23%) had a flat slope, and 810 (53%) had an increasing slope. There were fewer children with a decreasing slope in the rural group (18%) compared with the urban group (26%), p = 0.002.
Urban/Suburban Socio-Ecologic Influences
Among children living in urban/suburban areas (n = 1099), children with the highest degree of obesity at baseline (compared with children with lower degrees of obesity) had the lowest NDVI exposure (10%). Both the median poverty rate and the ADI were associated with the degree of obesity (Supplementary Table S1).
In examining the relationship between the different independent socio-ecologic risk factors, we found that in urban subjects, those with low deprivation had greater park access (53%) compared to those with high deprivation (42%), p < 0.001 (Table 2). Deprivation was associated, with NDVI with those in high deprivation areas having a shift toward lower greenspace at 7% in medium, 68% in medium-high, and 25% in high compared with 3% in medium, 61% in medium-high, and 36% in high for those with low deprivation, p < 0.001. No significant association between ADI and food access was observed, p = 0.18.
Table 2.
Association Between Area Deprivation Index (ADI) and Other Socio-Ecologic Factors, Stratified by Urban and Rural Settings
| ADI low | ADI high | p-value | |
|---|---|---|---|
| Urban | |||
| Poverty rate, median (IQR) | 10.9 (6.8) | 18.0 (8.4) | <0.001 |
| Low food access, n (%) | 207 (36) | 156 (32) | 0.18 |
| NDVI, n (%) | <0.001 | ||
| 0.2 to ≤0.4 | 17 (3) | 34 (7) | |
| >0.4 to ≤0.6 | 345 (61) | 329 (68) | |
| >0.6 | 206 (36) | 118 (25) | |
| Park access, ≥90% | 301 (53%) | 204 (42% | <0.001 |
| Rural | |||
| Poverty rate, median (IQR) | 11.8 (4.1) | 15.5 (6.0) | <0.001 |
| Low food access, n (%) | 21 (40) | 125 (42) | 0.77 |
| NDVI, n (%) | 0.003 | ||
| 0.2 to ≤0.4 | 4 (8) | 31 (10) | |
| >0.4 to ≤0.6 | 20 (38) | 176 (59) | |
| >0.6 | 29 (55) | 91 (31) | |
| Park access, ≥90% | 53 (26) | 298 (45) | 0.01 |
ADI, area deprivation index; IQR, interquartile range; NDVI, normalized difference vegetation index.
Examining the association of BMI slope with socio-ecologic risk factors, ADI was associated with BMI slope: 58% of those with a decreasing slope had low deprivation compared with 49% of those with increasing slopes living in a low deprivation census tract, p = 0.006 (Table 3). There was no significant association between BMI slope and park access (p = 0.12) or with low food access (p = 0.95). For NDVI, children with a decreasing BMI slope had predominantly medium-high NDVI (70%). Children living in areas with the lowest NDVI (0.2–0.4) had greater representation in the flat or increasing BMI slope (6%) compared with 3% of children with a decreasing BMI slope, p = 0.08.
Table 3.
Association of BMI Slope with Demographics and Environmental Factors, Stratified by Urban and Rural Address
| Urban subjects (n = 1099) | ||||
|---|---|---|---|---|
| Decreasing slopes (n = 286) | Flat slopes (n = 252) | Increasing slopes (n = 561) | p-value | |
| Baseline age in years, mean (SD) | 8.3 (1.6) | 8.6 (1.6) | 8.0 (1.5) | <0.001 |
| Child sex, female, n (%) | 138 (48) | 128 (51) | 223 (40) | 0.005 |
| Baseline percent of the 95th percentile BMI, median (IQR) | 113 (106, 128) | 111 (105, 122) | 117 (107, 129) | <0.001 |
| Race/ethnicity, n (%) | 0.17 | |||
| White, non-Hispanic | 162 (57) | 115 (46) | 280 (50) | |
| Hispanic | 124 (43) | 115 (46) | 280 (50) | |
| Poverty rate, median (IQR) | 12 (8, 19) | 12 (7, 18) | 12 (8, 19) | 0.60 |
| Low food access, n (%) | 93 (34) | 86 (35) | 186 (35) | 0.95 |
| NDVI, n (%) | 0.08 | |||
| 0.2 to ≤0.4 | 7 (3) | 14 (6) | 30 (6) | |
| >0.4 to ≤0.6 | 190 (70) | 143 (59) | 344 (64) | |
| >0.6 | 76 (28) | 85 (35) | 164 (31) | |
| Park access, ≥90% | 145 (53) | 107 (44) | 255 (47) | 0.12 |
| ADI (State), | 0.006 | |||
| Low deprivation (1–5) | 157 (58) | 147 (61) | 265 (49) | |
| High deprivation (6–10) | 116 (43) | 95 (39) | 271 (51) | |
| Rural subjects (n = 419) | ||||
|---|---|---|---|---|
| Decreasing slopes (n = 74) | Flat slopes (n = 96) | Increasing slopes (n = 249) | p-value | |
| Baseline age in years, mean (SD) | 8.3 (1.7) | 8.2 (1.7) | 7.9 (1.6) | 0.05 |
| Child sex, female, n (%) | 45 (61) | 55 (57) | 138 (55) | 0.71 |
| Baseline percent of the 95th percentile BMI, median (IQR) | 110 (103, 127) | 113 (104, 123) | 113 (106, 127) | 0.36 |
| Race/ethnicity, n (%) | 0.24 | |||
| White, non-Hispanic | 50 (68) | 58 (60) | 141 (57) | |
| Hispanic | 24 (32) | 38 (40) | 108 (43) | |
| Poverty rate, median (IQR) | 15 (11, 21) | 15 (11, 18) | 15 (10, 20) | 0.58 |
| Low food access, n (%) | 22 (34) | 33 (41) | 91 (44) | 0.41 |
| NDVI, n (%) | 0.17 | |||
| 0.2 to ≤0.4 | 5 (8) | 8 (10) | 22 (11) | |
| >0.4 to ≤0.6 | 29 (45) | 44 (55) | 124 (60) | |
| >0.6 | 30 (47) | 28 (35) | 62 (30) | |
| Park access, ≥90% | 25 (39) | 35 (44) | 90 (43) | 0.82 |
| ADI (State), n (%) | 0.84 | |||
| Low deprivation (1–5) | 11 (17) | 11 (14) | 31 (15) | |
| High deprivation (6–10) | 53 (83) | 69 (86) | 178 (85) | |
ADI, area deprivation index; BMI, body mass index; IQR, interquartile range; NDVI, normalized difference vegetation index.
In the multivariable ordinal logistic regression, higher baseline BMI, younger age, and male sex were associated with greater odds of having an increasing BMI slope (Model 1, Supplementary Table S2). In the full model with all socio-ecologic factors included, children living in a lower ADI census tract had a 0.78 (95% CI 0.61–0.99) lower odds of being in an increasing BMI slope group (Fig. 2 and Model 2, Supplementary Table S2). Food access was not associated with BMI slope, OR 1.0 (95% CI 0.78–1.26), and neither was park proximity, OR 1.11 (95% CI 0.84–1.47). Low NDVI, OR 1.42 (95% CI 0.78–2.59), and medium NDVI, OR 0.94 (95% CI 0.70–1.27), were not associated with BMI slope.
Figure 2.
Urban/suburban children, ordinal regression modeling. Odds ratios shown are odds of being in the increasing categories of BMI slope associated with a unit-increase in the independent variable listed.
Rural Socio-Ecologic Influences
For rural children (n = 419), baseline adiposity was not associated with food access, ADI, NDVI, or proximity to parks (Supplementary Table S1). For the association between the independent socio-ecologic risk factors, we found that, those with low deprivation had less park access (26%) compared to those with high deprivation (45%), p < 0.001 (Table 2). ADI in rural areas was also associated with NDVI, with a shift toward higher greenspace proximity with less deprivation. Of those with low deprivation, 8% had a medium NDVI, 38% had a medium-high, and 55% lived in high greenspace areas compared with 10% medium, 59% medium-high, and 31% in high greenspace for rural subjects living in high ADI areas, p = 0.003. No association between ADI and food access was observed, p = 0.77.
Younger children were more likely to have increasing BMI slopes compared with older children (Table 3). We found no difference by food access, NDVI, or ADI between the different BMI slope groups living in rural areas, nor did we find any difference by baseline BMI, race/ethnicity, or child sex. The multivariable analysis demonstrated that children in the high NDVI group had lower odds of being in an increasing slope category compared to those in the very high NDVI, OR 0.49 (95% CI: 0.28–0.86) (Supplementary Table S2).
Discussion
This study is one of the first to specifically examine the association between weight trajectories and socio-ecologic factors in children with obesity. The relevance of studying what influences weight trajectories among children with obesity rather than comparing children with obesity to children at a normal or healthy weight is important in understanding which interventions may be effective. We found that for urban children, only the ADI, and not food access, parks, or green space, was associated with BMI trajectory. Our findings suggest that mere proximity to a park or grocery store is not associated with an improved weight trajectory and that the social and economic context has a larger influence on weight trajectories. Understanding whether measures of area deprivation reflect a true underlying association between some factor in the built environment and weight trajectories or an alternative pathway of social disadvantage related to obesity has important policy implications.
Living in a rural area, compared with a suburban or urban area, is associated with an increased risk of childhood obesity 22 for postulated structural reasons. 23,24 In our analysis, rural children had a much higher average level of deprivation compared with their urban and suburban counterparts. We did not observe an association between the ADI and obesity trajectories. It may be that the restricted range (e.g., most are deprived) explains the finding of no association.
Food access was not associated with weight trajectories in this sample. Our results are similar to a recent meta-analysis that found no association between food access and obesity, using proximity to grocery stores. 8 However, this may be a lack of specificity, if a true association exists. For example, in contrast to our findings, recent work in Mexico found a relationship between change in BMI and availability of fruit and vegetable stores. 25 Examining exposure to convenience stores where high-caloric density foods are prevalent has been associated with greater adiposity in some studies, 26 but not others. 7 Similar to our findings, an association between store type and weight in rural settings has not been observed. 27
Although longitudinal studies examining park proximity show a significant association between greater proximity and BMI over time, 10,28 proximity to parks in this study was not associated with either baseline BMI or BMI trajectories. Results suggest that simple proximity may not be sufficient for engagement in physical activity and reaping the health benefits of parks. Studies on prescriptions for parks programs have highlighted some of the complexity involved related to family knowledge, barriers, and attitudes. 29
Proximity to greenspace using NDVI scores was not associated with weight trajectories for urban children and was associated for rural children. In Oregon, this could be due to high green space exposure overall, as evidenced by only 6% of this sample having a low NDVI (0.2–0.4). Although other studies have found an association between NDVI and early-life weight trajectories, the association does not remain significant once accounting for air pollution or population density. 30 Another recent study examining early childhood NDVI and adult adiposity found an inverse relationship. 31 However, other studies have shown a significant association between increased greenness and lower obesity in adults and children in different settings, including some longitudinal studies, 32 –35 suggesting this question requires further study.
The ADI has been examined previously in association with the point prevalence of obesity in both adults 36,37 and children, 38 –40 with studies finding consistent results of higher deprivation associated with greater obesity prevalence. Weight status over time has also been examined, and these studies have also documented a significant association between increase in weight and deprivation indices, with some variation noted by gender. 41 –44 A recent analysis of the Environmental Influences on Child Health Outcomes data (n = 20,677) showed that lower social vulnerability was associated with a lower risk of obesity over time. 45 They did not specifically examine children with obesity and how their trajectories changed. A recent study examined area deprivation in relation to diabetes and weight control in the context of the COVID-19 pandemic, finding that the ADI had a significant association with weight trajectory. 46 The ADI has been shown to be associated with obesity and diabetes in adults, 47 and in children, ADI has been shown to predict development of an unhealthy weight in survivors of leukemia. 39
Limitations
Examining whether these socio-ecologic risk factors are associated with intra-individual differences has important limitations. One limitation is that some home addresses changed over time, and we did not account for that in the analysis. The limited research done on this issue has not found that environmental exposures assessed by self-report versus medical record address vary substantially. 48 We examined the data at the census tract level, and some of these associations (e.g., NDVI) may be sensitive to very low spatial variability, which could have been lost in the spatial averaging process. The rural sample was smaller than the urban sample and the null findings in the rural sample could be due to a type 2 error.
Conclusion
We found that only area deprivation was associated with weight trajectories in urban children with obesity and not proximity to parks, green space, or food access. If this association is true and replicated in further studies, there are important policy implications to consider in addressing deprivation and the health consequences associated. We also need to better understand the mechanisms by which area deprivation leads to worsening weight trajectories.
Impact Statement
In children with obesity at baseline in this study, weight trajectories over time were influenced by the child’s area deprivation index and not their food environment, exposure to greenspace, or distance to parks. This study helps understand the role of social deprivation compared with other environmental factors driving weight trajectories and highlights the differences in rural and urban environments.
Authors’ Contributions
B.A.F. conceived of and designed the study, contributed to the writing, and assisted with the analysis. Z.B. contributed to the data collection, analysis, writing of the first draft, and revising multiple revisions of the article. C.M. contributed to the analysis, writing, and revision of multiple versions of the article. H.O.A. contributed to the writing and revision of the article.
Author Disclosure Statement
No competing financial interest exists and no other conflict of interests exists for any of the listed authors.
Funding Information
Research reported in this publication was supported by the National Institute of Diabetes And Digestive And Kidney Diseases of the National Institutes of Health under Award Number R03DK122011 to Dr. Foster. The project described was also supported by the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, through Grant Award Number UL1TR002369.
Supplementary Material
References
- 1. Yang S, Zhang X, Feng P, et al. Access to fruit and vegetable markets and childhood obesity: A systematic review. Obes Rev, 2021; 22 Suppl 1:e12980; doi: 10.1111/obr.12980 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Matsuzaki M, Sánchez BN, Acosta ME, et al. Food environment near schools and body weight-A systematic review of associations by race/ethnicity, gender, grade, and socio-economic factors. Obes Rev, 2020; 21(4):e12997; doi: 10.1111/obr.12997 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Malacarne D, Handakas E, Robinson O, et al. The built environment as determinant of childhood obesity: A systematic literature review. Obes Rev, 2022; 23 Suppl 1:e13385; doi: 10.1111/obr.13385 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Pereira M, Padez CMP, Nogueira H. Describing studies on childhood obesity determinants by Socio-Ecological Model level: A scoping review to identify gaps and provide guidance for future research. Int J Obes, 2019; 43(10):1883–1890; doi: 10.1038/s41366-019-0411-3 [DOI] [PubMed] [Google Scholar]
- 5. Sharifi M, Sequist TD, Rifas-Shiman SL, et al. The role of neighborhood characteristics and the built environment in understanding racial/ethnic disparities in childhood obesity. Prev Med, 2016; 91:103–109; doi: 10.1016/j.ypmed.2016.07.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Kraft AN, Thatcher EJ, Zenk SN. Neighborhood food environment and health outcomes in U.S. low-socioeconomic status, racial/ethnic minority, and rural populations: A systematic review. J Health Care Poor Underserved, 2020; 31(3):1078–1114; doi: 10.1353/hpu.2020.0083 [DOI] [PubMed] [Google Scholar]
- 7. Daniels KM, Schinasi LH, Auchincloss AH, et al. The built and social neighborhood environment and child obesity: A systematic review of longitudinal studies. Prev Med, 2021; 153:106790; doi: 10.1016/j.ypmed.2021.106790 [DOI] [PubMed] [Google Scholar]
- 8. Li Y, Luo M, Wu X, et al. Grocery store access and childhood obesity: A systematic review and meta-analysis. Obes Rev, 2021; 22 Suppl 1:e12945; doi: 10.1111/obr.12945 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Sánchez-Valdivia N, Pérez-Del-Pulgar C, de Bont J, et al. Residential proximity to urban play spaces and childhood overweight and obesity in Barcelona, Spain: A Population-Based Longitudinal Study. Int J Environ Res Public Health, 2022; 19(20); doi: 10.3390/ijerph192013676 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. White MJ, McClure E, Killeen J, et al. Changes in the recreational built environment and youth body mass index. Acad Pediatr, 2021; 21(1):76–83; doi: 10.1016/j.acap.2020.09.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Anderson LN, Fatima T, Shah B, et al. Income and neighbourhood deprivation in relation to obesity in urban dwelling children 0-12 years of age: A Cross-Sectional Study from 2013 to 2019. J Epidemiol Community Health, 2022; 76(3):274–280; doi: 10.1136/jech-2021-216455 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Clennin M, Brown A, Lian M, et al. Neighborhood socioeconomic deprivation associated with fat mass and weight status in youth. Int J Environ Res Public Health, 2020; 17(17); doi: 10.3390/ijerph17176421 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Foster BA, Latour E, Lim JY, Weinstein K. Weight trajectories and obesity remission among school-aged children. PLoS One, 2023; 18(9):e0290565; doi: 10.1371/journal.pone.0290565 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Daymont C, Ross ME, Russell Localio A, et al. Automated identification of implausible values in growth data from pediatric electronic health records. J Am Med Inform Assoc, 2017; 24(6):1080–1087; doi: 10.1093/jamia/ocx037 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Bureau UC. Geocoder. 2023. Available from: https://geocoding.geo.census.gov/geocoder/ [Last accessed: March 2, 2023. ].
- 16. Freedman DS, Davies AJG, Kompaniyets L, et al. A longitudinal comparison of alternatives to body mass index Z-scores for children with very high body mass indexes. J Pediatr, 2021; 235:156–162; doi: 10.1016/j.jpeds.2021.02.072 [DOI] [PubMed] [Google Scholar]
- 17. USDA. USDA Food Access Research Atlas. USDA. 2023. Available from: https://www.ers.usda.gov/data-products/food-access-research-atlas/go-to-the-atlas/ [Last accessed: Marc, h. ]. [Google Scholar]
- 18. CDC. National Environmental Public Health Tracking Network. CDC. 2023. Available from: https://ephtracking.cdc.gov/ [Last accessed: Marc, h. ]. [Google Scholar]
- 19. Singh GK. Area deprivation and widening inequalities in US mortality, 1969-1998. Am J Public Health, 2003; 93(7):1137–1143; doi: 10.2105/ajph.93.7.1137 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Kind AJH, Buckingham WR. Making neighborhood-disadvantage metrics accessible—the neighborhood atlas. N Engl J Med, 2018; 378(26):2456–2458; doi: 10.1056/NEJMp1802313 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Wisconsin Uo. Neighborhood Atlas. 2023. Available from: https://www.neighborhoodatlas.medicine.wisc.edu/ [Last accessed: March, 2023. ].
- 22. Johnson JA 3rd, Johnson AM. Urban-rural differences in childhood and adolescent obesity in the United States: A systematic review and meta-analysis. Child Obes, 2015; 11(3):233–241; doi: 10.1089/chi.2014.0085 [DOI] [PubMed] [Google Scholar]
- 23. Tarasenko YN, Chen C, Smalley KB, Warren J. Rural-Urban differences in perceptions of child overweight among children and adolescents, their guardians and health care professionals in the United States. J Rural Health, 2016; 32(1):63–71; doi: 10.1111/jrh.12135 [DOI] [PubMed] [Google Scholar]
- 24. Davis AM, Bennett KJ, Befort C, Nollen N. Obesity and related health behaviors among urban and rural children in the United States: Data from the National Health And Nutrition Examination Survey 2003–2004 and 2005–2006. J Pediatr Psychol, 2011; 36(6):669–676; doi: 10.1093/jpepsy/jsq117 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Ramírez-Toscano Y, Pérez-Ferrer C, Bilal U, et al. Longitudinal association between density of retail food stores and body mass index in Mexican school children and adolescents. Int J Obes (Lond), 2023; 47(5):365–374; doi: 10.1038/s41366-023-01273-w [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Ohri-Vachaspati P, Acciai F, Lloyd K, et al. Evidence that changes in community food environments lead to changes in children’s weight: Results from a Longitudinal Prospective Cohort Study. J Acad Nutr Diet, 2021; 121(3):419–434.e9; doi: 10.1016/j.jand.2020.10.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Gorski Findling MT, Wolfson JA, Rimm EB, Bleich SN. Differences in the neighborhood retail food environment and obesity among US children and adolescents by SNAP participation. Obesity, 2018; 26(6):1063–1071; doi: 10.1002/oby.22184 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28. Wolch J, Jerrett M, Reynolds K, et al. Childhood obesity and proximity to urban parks and recreational resources: A Longitudinal Cohort Study. Health Place, 2011; 17(1):207–214; doi: 10.1016/j.healthplace.2010.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Razani N, Morshed S, Kohn MA, et al. Effect of park prescriptions with and without group visits to parks on stress reduction in low-income parents: SHINE Randomized Trial. PLoS One, 2018; 13(2):e0192921; doi: 10.1371/journal.pone.0192921 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. de Bont J, Hughes R, Tilling K, et al. Early life exposure to air pollution, green spaces and built environment, and body mass index growth trajectories during the first 5 years of life: A Large Longitudinal Study. Environ Pollut, 2020; 266(Pt 3):115266; doi: 10.1016/j.envpol.2020.115266 [DOI] [PubMed] [Google Scholar]
- 31. Figaroa MNS, Gielen M, Casas L, et al. Early-life residential green spaces and traffic exposure in association with young adult body composition: A longitudinal birth cohort study of twins. Environ Health, 2023; 22(1):18; doi: 10.1186/s12940-023-00964-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32. Blas-Miranda NB, Lozada-Tequeanes AL, Miranda-Zuñiga JA, Jimenez MP. Green space exposure and obesity in the Mexican adult population. Int J Environ Res Public Health, 2022; 19(22); doi: 10.3390/ijerph192215072 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Jiang J, Xiang Z, Liu F, et al. Associations of residential greenness with obesity and BMI level among Chinese rural population: Findings from the Henan Rural Cohort Study. Environ Sci Pollut Res Int, 2022; 29(49):74294–74305; doi: 10.1007/s11356-022-20268-0 [DOI] [PubMed] [Google Scholar]
- 34. Petraviciene I, Grazuleviciene R, Andrusaityte S, et al. Impact of the social and natural environment on preschool-age children weight. Int J Environ Res Public Health, 2018; 15(3); doi: 10.3390/ijerph15030449 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35. Persson Å, Pyko A, Lind T, et al. Urban residential greenness and adiposity: A Cohort Study in Stockholm County. Environ Int, 2018; 121(Pt 1):832–841; doi: 10.1016/j.envint.2018.10.009 [DOI] [PubMed] [Google Scholar]
- 36. Nwana N, Taha MB, Javed Z, et al. Neighborhood deprivation and morbid obesity: Insights from the Houston Methodist Cardiovascular Disease Health System Learning Registry. Prev Med Rep, 2023; 31:102100; doi: 10.1016/j.pmedr.2022.102100 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37. Kimbro RT, Sharp G, Denney JT. Home and away: Area socioeconomic disadvantage and obesity risk. Health Place, 2017; 44:94–102; doi: 10.1016/j.healthplace.2017.02.001 [DOI] [PubMed] [Google Scholar]
- 38. Baker-Smith CM, Yang W, McDuffie MJ, et al. Association of area deprivation with primary hypertension diagnosis among youth medicaid recipients in delaware. JAMA Netw Open, 2023; 6(3):e233012; doi: 10.1001/jamanetworkopen.2023.3012 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39. Oluyomi A, Aldrich KD, Foster KL, et al. Neighborhood deprivation index is associated with weight status among long-term survivors of childhood acute lymphoblastic leukemia. J Cancer Surviv, 2021; 15(5):767–775; doi: 10.1007/s11764-020-00968-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Silverwood RJ, Williamson L, Grundy EM, De Stavola BL. Pathways between socioeconomic disadvantage and childhood growth in the Scottish Longitudinal Study, 1991-2001. PLoS One, 2016; 11(10):e0164853; doi: 10.1371/journal.pone.0164853 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41. Stewart R, Reilly JJ, Hughes A, et al. Trends in socioeconomic inequalities in underweight and obesity in 5-year-old children, 2011-2018: A population-based, repeated cross-sectional study. BMJ Open, 2021; 11(3):e042023; doi: 10.1136/bmjopen-2020-042023 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42. Staatz CB, Kelly Y, Lacey RE, Hardy R. Area-level and family-level socioeconomic position and body composition trajectories: Longitudinal analysis of the UK Millennium Cohort Study. Lancet Public Health, 2021; 6(8):e598–e607; doi: 10.1016/s2468-2667(21)00134-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Liu R, Shelton RC, Eldred-Skemp N, et al. Early exposure to cumulative social risk and trajectories of body mass index in childhood. Child Obes, 2019; 15(1):48–55; doi: 10.1089/chi.2018.0116 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44. Twaits A, Alwan NA. The association between area-based deprivation and change in body-mass index over time in primary school children: A population-based cohort study in Hampshire, UK. Int J Obes, 2020; 44(3):628–636; doi: 10.1038/s41366-019-0418-9 [DOI] [PubMed] [Google Scholar]
- 45. Aris IM, Perng W, Dabelea D, et al. Associations of neighborhood opportunity and social vulnerability with trajectories of childhood body mass index and obesity among US Children. JAMA Netw Open, 2022; 5(12):e2247957; doi: 10.1001/jamanetworkopen.2022.47957 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Vajravelu ME, Mani I, Malik S, et al. Race- and neighborhood-related disparities spanning the COVID-19 pandemic: Trajectories of combined glycemic control and BMI in youth with diabetes. Diabetes Care, 2023; 46(3):511–518; doi: 10.2337/dc22-1439 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Hu MD, Lawrence KG, Bodkin MR, et al. Neighborhood deprivation, obesity, and diabetes in residents of the US Gulf Coast. Am J Epidemiol, 2021; 190(2):295–304; doi: 10.1093/aje/kwaa206 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Hughes AE, Pruitt SL. The utility of EMR address histories for assessing neighborhood exposures. Ann Epidemiol, 2017; 27(1):20–26; doi: 10.1016/j.annepidem.2016.07.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.