Abstract
Objective
This study aimed to determine whether specific factors of the built environment related to physical activity and diet are associated with inadequate and excessive gestational weight gain (GWG).
Study Design
This analysis is based on data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-To-Be, a prospective cohort of nulliparous women who were followed from the beginning of their pregnancies through delivery. At each study visit, home addresses were recorded and geocoded. Locations were linked to several built-environment characteristics such as the census tract National Walkability Score (the 2010 Walkability Index) and the number of gyms, parks, and grocery stores within a 3-km radius of residential address. The primary outcome of GWG (calculated as the difference between prepregnancy weight and weight at delivery) was categorized as inadequate, appropriate, or excessive based on weight gained per week of gestation. Multinomial regression (generalized logit) models evaluated the relationship between each factor in the built environment and excessive or inadequate GWG.
Results
Of the 8,182 women in the analytic sample, 5,819 (71.1%) had excessive GWG, 1,426 (17.4%) had appropriate GWG, and 937 (11.5%) had inadequate GWG. For the majority of variables examined, built environments more conducive to physical activity and healthful food availability were associated with a lower odds of excessive or inadequate GWG category. For example, a higher number of gyms or parks within 3 km of a participant’s residential address was associated with lower odds of having excessive (gyms: adjusted odds ratio [aOR]=0.93 [0.89–0.96], parks: 0.94 [0.90–0.98]) or inadequate GWG (gyms: 0.91 [0.86–0.96]; parks: 0.91 [0.86–0.97]). Similarly, a higher number of grocery stores was associated with lower odds of having excessive GWG (0.94 [0.91–0.97]).
Conclusion
Among a diverse population of nulliparous women, multiple aspects of the built environment are associated with excessive and inadequate GWG.
Keywords: neighborhood built environment, social determinants of health, gestational weight gain
Both excessive and inadequate gestational weight gain (GWG) have been associated with an increased risk of maternal (e.g., hypertensive disorders of pregnancy, cesarean delivery, and severe maternal morbidity) and perinatal (stillbirth, small for gestational age birth, and large for gestational age birth) complications.1-6 Moreover, there is evidence that GWG outside of the recommended range is associated with potentially long-lasting and adverse metabolic consequences for both a woman and her child.7-9
Multiple patient-level risk factors for inadequate and excessive GWG have been identified. These include maternal age, prepregnancy body mass index (BMI), and parity.10,11 In addition, many markers of socioeconomic status, including lower educational attainment, lower levels of family income, and race and ethnicity have been linked to GWG.10-12 Thus, it is apparent that GWG has a socially determined component, and correspondingly, is one pivot through which social determinants may operate to result in adverse pregnancy outcomes.
Nevertheless, the particular pathways by which social status translate into GWG outside of the recommended range have not been well elucidated. One potential, as well as potentially modifiable, exposure is the built environment, defined as “the human-made space in which people live, work, and recreate on a day-to-day basis.”13 Environments that are less conducive to leisure-time physical activity and healthy diet have been well documented in communities with fewer economic resources,14-16 and are plausibly related to weight changes in pregnancy. Accordingly, we hypothesized that specific factors of the built environment that are related to physical activity and diet would be associated with the risks of both inadequate and excessive GWG.
Materials and Methods
This analysis is based on data from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-To-Be (NuMoM2b), a prospective cohort of over 10,000 nulliparous women who were followed from the beginning of their pregnancies through delivery. Full details of the study protocol have been described previously.17 In brief, women were enrolled from eight clinical sites geographically spread throughout the United States17 between October 2010 and September 2013, and were eligible if they were nulliparous (defined as women without a prior pregnancy that progressed beyond 20 weeks of gestation) with a singleton pregnancy at less than 13 weeks of gestation. Women were excluded from enrollment in NuMoM2b if they were <13 years old, had three or more prior spontaneous abortions, had a suspected life-limiting fetal malformation or known fetal aneuploidy in the current pregnancy, used assisted reproduction with a donor oocyte, had a multifetal reduction, or planned to terminate the pregnancy. Women underwent three study visits (visit 1 between 6 and 13 weeks, visit 2 between 16 and 21 weeks, and visit 3 between 22 and 29 weeks) during which survey data and biologic specimens were collected, and physical examination was performed. After delivery, trained and certified chart abstractors reviewed the medical records of all participants and recorded additional information (e.g., weight at delivery) and birth outcomes. Each site’s local institutional review board approved the study and all women provided written informed consent prior to participation.
Height was measured at the first study visit using a stadiometer or measuring tape according to standard methodology, and weight was recorded at each study visit with a standardized scale and technique by study personnel. Each participant’s baseline BMI (kg/m2) was determined according to her measured height and weight, with BMI categories defined according to Institute of Medicine (IOM) criteria as underweight (<18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), or obese (≥30 kg/m2).
At each study visit, participants were asked to provide their current home address for use in research. Residential addresses were converted to standard formats and geocoded using ESRI ArcGIS.18 Addresses without an identifiable standardized point location were coded to street location or city centroid or were manually coded. Locations were then linked to several built-environment characteristics. For this analysis, the exposures in the built environment most relevant to the outcome of interest (GWG) were considered a priori to be those proximate to one’s residence that promoted access to physical activity and healthful food choices. Accordingly, geocodes were used to link to location-specific data that defined the census tract National Walkability Score (the 2010 Walkability Index)19 and, within a 3-km radius of residential address, the number of gyms, parks (recreation areas), grocery stores, and convenience stores. The National Walkability Index ranks block groups according to the degree to which their features (including land-use mix, employment types [e.g., retail, office, and industrial], commute modes, and street intersection density) promote walking trips. Scores are aggregated at the tract level and can range from 1 to 20, with a higher score indicating greater likelihood of walking trips. The numbers of gyms, parks, grocery stores, and convenience stores within a 3-km radius of each residential address were calculated from 2014 data from ESRI Business Data/ArcGIS Business Analyst (gyms, grocery stores, and convenience stores) and TeleAtlas (parks) using ArcGIS software.20,21 Gyms were identified using NAICS (North American Industry Classification System) code 713940, parks were identified using the Recreation Areas layer of ArcGIS (and included golf courses, amusement parks, beaches, and park and recreation areas), grocery stores were identified using NAICS code 445110, and convenience stores were identified using NAICS code 445120. The ratio of the number of grocery stores to the number of total markets (grocery stores+convenience stores) was calculated, as this was considered a measure that could provide insight into not just the number of stores, but the overall quality of the food-availability environment. A higher proportion in this ratio indicates not only food availability but an environment characterized by greater availability of more healthful options. If participants moved during the study, the values for the built-environment exposures of interest were averaged for each address available across the study visits.
For this analysis, participants were excluded if they did not provide at least one instance of residential data such that geocoding could be performed to the point or street location, experienced an early pregnancy loss or termination at <20 weeks’ gestational age, were missing covariate data, or did not have data available to permit calculation of GWG. The primary outcome of GWG was calculated as the difference between self-reported prepregnancy weight and weight at delivery abstracted from the medical record. This per-week GWG was then calculated based on the total weight gain and the length of pregnancy, and categorized based on BMI, as inadequate, appropriate, or excessive according to the 2009 IOM GWG goals.1 Of note, categorization of GWG was based on weight gained per week of gestation instead of total weight change across the total length of pregnancy, as utilizing the total GWG could misclassify the appropriateness of GWG during pregnancy for those with different lengths of gestation.
Characteristics of participants were compared according to whether they had inadequate, appropriate, or excessive GWG, as well as according to the factors of their built environment. Multinomial regression (generalized logit) models,22 an extension of logistic regression to situations where the dependent variable has more than two categories, formed the basis of our modeling approach. These models were developed for each environmental exposure to evaluate the relationship between that factor and excessive or inadequate GWG, considering appropriate GWG as the referent outcome category. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated from these models, with adjustment for potential confounders of maternal age (years), self-reported maternal race and ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, Asian, Other), insurance status (commercial, public, or none), and comorbidities (i.e., chronic hypertension and pregestational diabetes mellitus). Race and ethnicity were incorporated into this regression given its socially constructed nature and in an effort to account for other socially determined variables that may confound the association between the built environment and GWG. Similarly, insurance status was used as a proxy for income status, given that many participants chose not to specify their income level, and that insurance status provides insight into extremes of income, as well as health access. Education was not used as a covariate given its limitations in this regard in a population that is composed solely of individuals in the reproductive-age range (i.e., including teenagers and young adults). In this population, it may be as much a signifier of not yet having the time to complete a certain level of education versus not continuing education beyond a certain level. BMI was not used as a covariate given it is already incorporated into the categorization of the outcome. For these models, the counts of gyms, parks, grocery stores, and convenience stores were transformed using a log2 transformation due to their skewed distributions; thus, the ORs for these exposures are interpreted on a multiplicative scale (per doubling in exposure). Hierarchical modeling with clustering within census tract was not performed given that approximately 47% of census tracts had only one study participant; clustering at the higher site level was not performed due to lack of model convergence. All tests were two-tailed and p<0.05 was used to define statistical significance. Statistical modeling and data management was performed using SAS V9.4. This study was registered as NCT no.: 01322529.
Results
Of the 10,038 women in the nuMoM2b study, 1,055 were excluded from analysis due to inability to calculate GWG category, 727 due to low-quality or missing residential data, 69 due to missing covariate data, and 5 due to pregnancy loss or termination at <20 weeks (►Fig. 1). Thus, the final analytic sample included 8,182 women. Of these women, 5,819 (71.1%) had excessive GWG, 1,426 (17.4%) had appropriate GWG, and 937 (11.5%) had inadequate GWG. Characteristics of the population, stratified by GWG category are presented in ►Table 1. There were multiple differences among women based on the appropriateness of their GWG. Those with inadequate GWG were significantly more likely to be in the youngest age group and significantly less likely to identify as non-Hispanic White. Women with appropriate GWG were the most likely to have commercial insurance and the least likely to have chronic hypertension or pregestational diabetes. Women with excessive GWG were the most likely to have pregestational diabetes mellitus and to identify as non-Hispanic White.
Fig. 1.
Flow diagram of participation in analysis. nuMoM2b, the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-To-Be.
Table 1.
Participant characteristics stratified by gestational weight gain categories
| Baseline characteristics | Gestational weight gain | ||
|---|---|---|---|
| Excessive (n=5,819) | Appropriate (n=1,426) | Inadequate (n=937) | |
| Age (y) | |||
| Mean±SD | 27.05±5.50 | 27.26±5.74 | 25.84±5.76 |
| 13–21 | 1,129 (19.40) | 276 (19.35) | 266 (28.39) |
| 22–35 | 4,326 (74.34) | 1,035 (72.58) | 618 (65.96) |
| >35 | 364 (6.26) | 115 (8.06) | 53 (5.66) |
| Body mass index (kg/m2) | |||
| Mean±SD | 26.69±5.84 | 24.25±5.76 | 26.92±8.12 |
| <18.5 | 40 (0.69) | 73 (5.12) | 72 (7.68) |
| 18.5–24.9 | 2,706 (46.50) | 994 (69.71) | 478 (51.01) |
| 25–29.9 | 1,714 (29.46) | 166 (11.64) | 136 (14.51) |
| ≥30 | 1,359 (23.35) | 193 (13.53) | 251 (26.79) |
| Race/ethnicity | |||
| Non-Hispanic White | 3,684 (63.31) | 894 (62.69) | 457 (48.77) |
| Non-Hispanic Black | 705 (12.12) | 174 (12.20) | 201 (21.45) |
| Hispanic | 920 (15.81) | 219 (15.36) | 187 (19.96) |
| Asian | 200 (3.44) | 84 (5.89) | 39 (4.16) |
| Other | 310 (5.33) | 55 (3.86) | 53 (5.66) |
| Insurance | |||
| Commercial | 4,082 (70.15) | 1,061 (74.40) | 550 (58.70) |
| Public | 1,516 (26.05) | 321 (22.51) | 347 (37.03) |
| None | 221 (3.80) | 44 (3.09) | 40 (4.27) |
| Chronic hypertension | 141 (2.42) | 28 (1.96) | 30 (3.20) |
| Pregestational DM | 101 (1.74) | 9 (0.63) | 15 (1.60) |
Abbreviations: DM, diabetes mellitus; SD, standard deviation.
Notes: All data presented as mean±SD or n (%).
p<0.05 for all comparisons of gestational weight gain categories by all demographic characteristics except for chronic hypertension.
There also were many differences in the built environment according to maternal characteristics (►Table 2). For example, older women lived in neighborhoods with the highest walkability score, as well as with the greatest number of gyms, parks, and grocery stores. Conversely, women without insurance lived in neighborhoods with the lowest number of gyms, parks, and grocery stores. They also had the lowest proportion of food outlets that were grocery stores.
Table 2.
Neighborhood characteristics stratified by participant characteristics
| Neighborhood characteristics | |||||
|---|---|---|---|---|---|
| Participant characteristics | Walkability score | Number of gyms within 3 km |
Number of parks within 3 km |
Number of grocery stores within 3 km |
Grocery stores, fraction of all major food outlets |
| Age (y) | |||||
| 13–21 | 13.6 (11.5–15.2) | 7 (4–13.3) | 8 (4–13.7) | 14 (7–38) | 0.59 (0.47–0.75) |
| 22–35 | 13.8 (11.2–15.8) | 11 (5–28) | 9 (4–23) | 15 (6–72.5) | 0.60 (0.50–0.73) |
| >35 | 14.2 (11.8–16.2) | 20 (8–62) | 15.8 (6–34) | 42 (11–101.9) | 0.65 (0.54–0.76) |
| Body mass index (kg/m2) | |||||
| <18.5 | 13.7 (11–15.7) | 9 (4.3–17) | 8 (4–14) | 16 (7–48) | 0.59 (0.47–0.74) |
| 18.5–24.9 | 14 (11.5–15.9) | 12 (6–34) | 10 (5–25) | 17 (7–78) | 0.60 (0.50–0.73) |
| 25–29.9 | 13.7 (11.2–15.5) | 9.7 (5–21.8) | 8 (4–18) | 14 (6–70) | 0.60 (0.49–0.73) |
| ≥30 | 13.5 (11–15.3) | 8 (4–16.3) | 8 (3.8–15) | 13 (6–42.8) | 0.59 (0.48–0.73) |
| GWG | |||||
| Excessive | 13.8 (11.3–15.7) | 10 (5–24) | 9 (4–20) | 15 (6–68) | 0.60 (0.50–0.73) |
| Appropriate | 14 (11.6–16) | 12 (6–32) | 10 (5–24) | 18 (7–81) | 0.61 (0.50–0.75) |
| Inadequate | 13.7 (11.2–15.7) | 9.5 (5–20) | 9 (4–17) | 16 (7–71) | 0.62 (0.50–0.76) |
| Race/ethnicity | |||||
| Non-Hispanic White | 13.8 (10.7–15.7) | 9 (4.5–22) | 7.5 (3–18) | 11 (5–44) | 0.56 (0.47–0.67) |
| Non-Hispanic Black | 13.8 (12–15.3) | 9 (5–15) | 9 (4.8–16) | 22.4 (9–72.5) | 0.62 (0.51–0.79) |
| Hispanic | 13.8 (11.8–15.5) | 17 (7.5–38) | 15 (7.5–31) | 44 (13–270) | 0.74 (0.59–0.87) |
| Asian | 14.2 (12.2–16.2) | 18 (8–59) | 15 (8–32) | 41 (13–86.3) | 0.69 (0.56–0.79) |
| Other | 14 (12–15.9) | 9.5 (5–19) | 8.2 (4–17) | 17 (8–63.3) | 0.61 (0.50–0.74) |
| Insurance | |||||
| Commercial | 13.8 (11–15.8) | 11 (5–30) | 9 (4–24) | 14 (6–73) | 0.60 (0.50–0.72) |
| Public | 13.7 (11.7–15.2) | 9 (5–19) | 9.7 (5–17) | 20 (8.5–58.8) | 0.63 (0.50–0.80) |
| None | 13.8 (11–15.8) | 8 (5–15) | 7 (4–12.3) | 11.7 (7–29) | 0.56 (0.47–0.69) |
| Chronic hypertension | |||||
| Yes | 12.8 (10–15.3) | 9 (4–19) | 7.5 (3.7–14) | 15 (6–41) | 0.62 (0.52–0.75) |
| No | 13.8 (11.3–15.7) | 10 (5–25) | 9 (4–20.7) | 15 (6.5–71) | 0.60 (0.50–0.73) |
| Pregestational DM | |||||
| Yes | 13.3 (11.7–15.5) | 8 (5–16) | 7 (3–15) | 15 (6–42.8) | 0.58 (0.47–0.75) |
| No | 13.8 (11.3–15.7) | 10 (5–24.3) | 9 (4–20) | 15 (6.5–71) | 0.60 (0.50–0.73) |
Abbreviations: DM, diabetes mellitus; GWG, gestational weight gain.
Notes: All data presented as median (interquartile range).
p<0.05 for all comparisons of neighborhood characteristic categories by all demographic characteristics except for the association of “grocery stores, fraction of all major food outlets” by body mass index.
Women with appropriate GWG lived in neighborhoods with the highest median walkability score, and the highest number of gyms, parks, and grocery stores. In unadjusted analyses, a residential built environment that was more conducive to physical activity and healthful eating was associated with lower odds of either inadequate or excessive GWG (►Supplementary Table S1, available in the online version).
The results of the multinomial models are presented in ►Table 3. For most of the variables that were examined, those built environments more conducive to physical activity and healthful food availability were associated with GWG category. Specifically, living in a neighborhood with a higher walkability score was associated with a lower odds of having excessive or inadequate GWG. A higher number of gyms or parks within 3 km of a participant’s residential address also was associated with lower odds of having excessive or inadequate GWG. A higher number of grocery stores, or living in proximity to markets that were more likely to be grocery stores, also was associated with lower odds of having excessive GWG.
Table 3.
Associations of neighborhood characteristics with excessive or inadequate gestational weight gain
| Gestational weight gain | ||
|---|---|---|
| Excessivea | Inadequatea | |
| aOR (95% CI) | aOR (95% CI) | |
| Walkability score, per 3 units | 0.94 (0.89–0.98) | 0.92 (0.86–0.99) |
| Gyms count, per doubling in valueb | 0.93 (0.89–0.96) | 0.91 (0.86–0.96) |
| Parks count, per doubling in valueb | 0.94 (0.90–0.98) | 0.91 (0.86–0.97) |
| Grocery store count, per doubling in valueb | 0.94 (0.91–0.97) | 0.96 (0.92–1.00)c |
| Grocery stores as fraction of all major food outlets (per 10% change) | 0.96 (0.93–0.99) | 1.01 (0.96–1.05) |
Abbreviations: aOR, adjusted odds ratio; CI, confidence interval.
Note: Multinomial models are adjusted for maternal age, race and ethnicity, insurance status, chronic hypertension, and pregestational diabetes mellitus.
The referent category for this odds ratio is appropriate gestational weight gain.
This characteristic (x) is included in statistical models after a log2 transformation (log2 [x+1]).
The confidence interval shown includes 1.00 due to rounding. This symbol indicates that the nonrounded confidence interval includes unity.
Discussion
In this analysis of a diverse population of nulliparous women who were prospectively followed and had detailed exposure and outcome data available, we observed an association between multiple aspects of the built environment and GWG. Specifically, residential proximity to resources associated with a greater ability to engage in leisure-time physical activity (e.g., walkability, the number of gyms, and parks) and to a wider range of nutritional options (i.e., the number of grocery stores and the proportion of grocery stores among all food markets) was associated with lower odds of having excessive or inadequate GWG.
Both excessive GWG and inadequate GWG have shown strong and consistent associations with multiple adverse maternal, perinatal, and pediatric outcomes.2-11 Its relevance as a focus of investigation is further underscored because it is a potentially modifiable exposure. Multiple studies have evaluated whether behavioral interventions could be used to increase the chance that appropriate GWG could be achieved, and correspondingly that adverse outcomes could be lessened. Such interventions have included attempts to modify specific factors, such as diet or physical activity, while other studies have used multimodal approaches.23-26 However, no behavioral approach has proven consistently successful in materially improving GWG, let alone downstream adverse pregnancy outcomes.
One factor that has been less well studied in the context of GWG is the built environment. Composed of the physical spaces that surround individuals, this environment may be directly related both to an individual’s BMI at the beginning of pregnancy, the GWG that is incurred as pregnancy progresses, and the outcomes of pregnancy that are ultimately achieved. Indeed, in nonpregnant populations, the built environment has been shown repeatedly to be associated with an individual’s weight, as well as weight-associated health outcomes.27-29 Our study adds to this literature, and demonstrates the relationship between a specific social determinant of health and weight gain in pregnancy which itself is an antecedent to adverse pregnancy outcomes.
There are multiple strengths of this investigation. Because the cohort was prospectively enrolled with a priori standards and definitions for variable collection, the potential for bias due to recall or misascertainment is reduced. Similarly, because residential location was updated multiple times during the pregnancy, classification of the main exposure was enhanced. The population was geographically diverse and reflects the contemporary racial and ethnic demography of the United States, making the findings more generalizable to contemporary experience.
Nevertheless, as with any observational study, we cannot be certain of causality, whether there are confounding factors that were omitted, or the role of ascertainment bias. As one example, women who are inclined to be more active and more likely to have adequate GWG may choose to live in built environments more conducive to physical activity and healthful eating. Also, this analysis is founded on the belief that it is environment closest to one’s residence that is most likely to be associated with GWG, given that activities such as food shopping and exercise often take place in that setting. While such activities also may take place in other settings (e.g., near a place of employment), this dynamic would be expected to bias toward the null result due to misclassification. Similarly, we cannot be certain to what degree individuals performed physical activity within their home or had resources within the home to promote this activity, although the lack of this information also would be anticipated to bias toward the null. Also, there are many social determinants of health, and it is difficult to account for all of them, particularly because many are difficult to quantify or can be quantified in a variety of ways (such as the many ways economic status can be categorized or understood). Exposure data from commercially available databases were used, and it cannot be known if other approaches to quantify the built environment would yield similar results. Lastly, the applicability of the results among a population other than that analyzed (e.g., multiparas) cannot be known.
Conclusion
In conclusion this investigation has shown the association between the built environment, which is a modifiable exposure, and GWG. Person-level behavioral interventions have had relatively limited impact on GWG and its related outcomes, given the typically transient nature of these interventions, as well as their use long after adverse trajectories, have been established.23-26 Alterations to the physical environment, conversely, are interventions that may be long-lasting, promote wellbeing both before and during pregnancy, and do not require behavioral modification. Such inbuilt resources point to the potential role that public policy, and neighborhood-level investment may play in creating a milieu that enhances pregnancy-related health.
Supplementary Material
Key Points.
There are little data on the association between the built environment and pregnancy outcomes.
Multiple aspects of the built environment are associated with excessive and inadequate GWG.
These results suggest the role that neighborhood investment may play in improving pregnancy outcomes.
Funding
This work was supported by grants (cooperative agreements) from the National Heart, Lung, and Blood Institute and the Eunice Kennedy Shriver National Institute of Child Health and Human Development: U10-HL119991; U10-HL119989; U10-HL120034; U10-HL119990; U10-HL120006; U10-HL119992; U10-HL120019; U10-HL119993; and U10-HL120018. Support was also provided by the National Institutes of Health: Office of Disease Prevention; Office of Research on Women’s Health; Office of Behavioral and Social Sciences Research; and the National Center for Advancing Translational Sciences: UL-1-TR000124, UL-1-TR000153, UL-1-TR000439, and UL-1-TR001108. In addition, support was provided by respective Clinical and Translational Science Institutes to Indiana University (UL1TR001108) and University of California Irvine (UL1TR000153). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Heart, Lung, and Blood Institute, the National Institutes of Health, or the Department of Health and Human Services.
Footnotes
Conflict of Interest
None declared.
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