Impact of social and built environment factors on body size among breast cancer survivors: the Pathways Study

Abstract

Background

As social and built environment factors have been shown to be associated with physical activity, dietary patterns, and obesity in the general population, they likely also influence these health behaviors among cancer survivors, and thereby impact survivorship outcomes.

Methods

Enhancing the rich, individual-level survey and medical record data from 4,505 breast cancer survivors in the Pathways Study, a prospective cohort drawn from Kaiser Permanente Northern California, we geocoded baseline residential addresses and appended social and built environment data. With multinomial logistic models, we examined associations between neighborhood characteristics and body mass index and whether neighborhood factors explained racial/ethnic/nativity disparities in overweight/obesity.

Results

Low neighborhood socioeconomic status, high minority composition, high traffic density, high prevalence of commuting by car, and a higher number of fast food restaurants were independently associated with higher odds of overweight or obesity. The higher odds of overweight among African Americans, US-born Asian Americans/Pacific Islanders and foreign-born Hispanics and the higher odds of obesity among African Americans and US-born Hispanics, compared to non-Hispanic Whites, remained significant though somewhat attenuated when accounting for social and built environment features.

Conclusions

Addressing aspects of neighborhood environments may help breast cancer survivors maintain a healthy body weight.

Impact

Further research in this area, such as incorporating data on individuals’ perceptions and use of their neighborhood environments, is needed to ultimately inform multilevel interventions that would ameliorate such disparities and improve outcomes for breast cancer survivors, regardless of their social status (e.g., race/ethnicity, socioeconomic status, nativity).

Introduction

As social and built environment factors have been shown to be associated with physical activity, dietary patterns, and obesity in the general population (1–4), these neighborhood factors likely also influence these health behaviors among cancer survivors, impacting survivorship outcomes including quality of life, patient-reported outcomes, disease recurrence, and mortality. Neighborhoods can influence health outcomes through environmental exposures, material deprivation (e.g., inadequate housing), psychosocial mechanisms (e.g., stress and social support), health behaviors (e.g., physical activity, smoking, diet), and access to resources (5–9). The built environment, i.e., the man-made attributes of a neighborhood, provides the context for individuals to engage in healthful behaviors. For example, street connectivity, traffic density, parks, businesses, or the food environment may influence opportunities or create barriers for physical activity or healthful food choices. In addition, neighborhood socioeconomic status (nSES) as well as demographic and social environment characteristics of the neighborhood, have been associated with opportunities for education, employment, social support, collective efficacy, stress and coping, health behaviors, prognostic factors, and ultimately health outcomes (5, 6, 8).

The recognition of the importance of neighborhood context is illustrated in several conceptual frameworks that emphasize the relevance of factors at multiple levels impacting outcomes across the cancer continuum (10, 11). Yet, few studies of outcomes across the cancer continuum have considered the influence of social and built neighborhood environments, and to date, only seven published studies have examined and found significant associations between neighborhood characteristics and cancer survivorship outcomes including self-rated health and behavioral factors (reviewed in (12)).

We recently found an association of nSES with breast cancer survival after accounting for individual education and other prognostic factors (13, 14), suggesting an independent effect of nSES, or other neighborhood factors related to nSES, on survival. The nSES associations with overall mortality were stronger in some racial/ethnic groups (i.e., African-Americans, Hispanics, and Asian Americans/Pacific Islanders (AAPI)), than in others (i.e., non-Hispanic (NH) Whites), and associations between nSES and breast-cancer specific mortality was seen only for AAPI women (14, 15). In addition to nSES, prior studies have also shown neighborhood ethnic composition, ethnic enclave, or racial/ethnic residential segregation to be independently associated with breast cancer mortality (16–22). Together, these findings point to the relevance of neighborhood factors in breast cancer survival, and the importance of distinguishing effects among racial/ethnic groups and in combination with patient-level factors.

With this motivation, we incorporated small-area level neighborhood social and built environment data from the California Neighborhoods Data System (23) into the Pathways Study, a prospective cohort study of 4,505 women with incident breast cancer in the Kaiser Permanente Northern California (KPNC) integrated health care system. Here we describe associations between baseline neighborhood social and built environment factors and body size (overweight/obesity), as being overweight and obese may lead to worse breast cancer survival (24–27). We capitalize on the diversity in Pathways to focus on racial/ethnic differences in these associations.

METHODS

Study Sample and Data Collection

The Pathways Study is designed to examine the effects of lifestyle, use of complementary and alternative therapies, and molecular and biologic factors on cancer outcomes, while considering factors known to influence prognosis. From 2006 through 2013, women with invasive breast cancer were identified from computerized pathology reports and recruited into the study on average within 2 months of diagnosis. In addition to baseline and follow-up questionnaire data, the study also collected biological specimens at baseline, and updates vital status and clinical data from KPNC electronic data sources, including the KPNC Cancer Registry. Ninety-seven percent of the participants were residents of the San Francisco Bay Area (75%) and Sacramento (22%) metropolitan regions. Detailed information on the study design and the cohort has been previously published (28).

The baseline data collection included interviewer- and self-administered questionnaires, with information on demographics, reproductive and family histories, lifestyle, and other factors. All women who participated in this study provided informed consent upon enrollment. The study protocol was approved by the Institutional Review Boards of all of the participating institutions.

Geocoding

Residential address at baseline was geocoded to latitude and longitude coordinates and then assigned a 2010 Census block group. Addresses were standardized to conform to U.S. Postal Service specifications using ZP4 software (ZP4. Monterey, CA: Semaphore Corp., 2011). ZP4 is software certified by the U.S. Postal Service that uses official USPS databases to correct, standardize, confirm and validate addresses, which can greatly improve geocoding success. Batch geocoding was performed using ArcGIS with both current address point and street geocoding reference files (ArcGIS. Redlands, CA: Environmental Systems Research Institute, Inc., 2011). Manual review was performed to geocode addresses that did not batch geocode, resulting in 97% of all Pathways addresses being assigned latitude and longitude coordinates. The 151 addresses that could not be geocoded were post office box addresses. The total geocoded sample size was 4,354. Sixty-seven percent of block groups had one Pathways participant, 23% had two, and 10% had three or more.

Neighborhood Measures of the Social and Built Environment

As Pathways patients were recruited from 2006 to 2013, we used neighborhood data anchored around the 2010 Census (see Table 1). At the block group level, we included measures of nSES, population density, racial/ethnic composition, street connectivity, and urban/rural status. NSES was measured with a composite measure using American Community Survey (ACS) data based on seven indicator variables at the census block group level (29, 30). Population density (the number of people per square meter), percent of racial/ethnic population and urban/rural status were derived from 2010 Census data. Street connectivity was measured using Gamma, the ratio of actual number of street segments to maximum possible number of intersections, with a higher ratio indicating more street connectivity (i.e., more walkable neighborhoods), and was derived using NavTeq’s NavStreets dataset (31, 32). The level of urbanization was developed from census-defined variables for urbanized areas, urban clusters, population and population density, and has five categories to capture the range of neighborhoods in the urban/rural spectrum: metropolitan urban (highest quartile of population density within a census-defined urbanized area with a population of one million or more), metropolitan suburban (the rest of the population within an urbanized area with a population of one million or more), city (census-designated places with more than 50,000 people outside of a metropolitan area with a population of one million or more), town (places with less than 50,000 people, outside of an urbanized area, and not the lowest quartile of population density), and rural (places with less than 50,000 people, outside of an urbanized area, and in the lowest quartile of population density).

Table 1.

Description of neighborhood social and built environment measures.

Contextual Data	Data Source	Description of measure
Socioeconomic status	2007–2011 American Community Survey (ACS)(29)	Block group-level composite measure for income, education, poverty, employment, occupation, housing and rent values (53)
Racial/ethnic composition	US Census 2010 short form data (54)	Block group-level measures of % of each racial/ethnic group
Immigration/acculturation characteristics	2007–2011 American Community Survey (ACS)	Block group-level measures of residential composition on % foreign-born; Tract-level measure of ethnic enclave (Hispanic, Asian)
Population density	US Census 2010 short form data	Block group-level measures of population size per square mile
Urbanization (Rural/Urban)	US Census 2010 short form data	Block group-level composite measure based on census defined urbanized area, population size and population density
Businesses	Dunn & Bradstreet annual business listings (1990–2008), via Walls & Associates (35)	Residential buffer (1600m) measures of total businesses, total number of recreational facilities, retail food environment index(38) and restaurant environment index
Commuting by car	2007–2011 American Community Survey (ACS)	Tract- level measures of proportion of population who drive to work (car, motorcycle, taxicab, and other)
Street connectivity	NAVTEQ (32)	Block group-level measure of walkability, using the gamma index (31)
Parks	NAVTEQ (32)	Residential buffer (1600m) measure of total of parks
Farmers Markets	California Department of Food and Agriculture (36)	Tract-level counts of farmers’ markets
Traffic density	California Department of Transportation (33)	Residential buffer (500m) measure of volume of traffic (34)

The percent of the population that was foreign-born was not available at the census block group level from ACS data, therefore we used the census tract measure. Similarly, for stable measures of commuting, including percent of residents commuting to work by car (including taxicab, motorcycle and other), we used tract-level ACS data.

We created a series of racial/ethnic composition variables based on the block group population being above or below statewide median for each of the three non-White racial/ethnic groups (African American, Hispanic, and AAPI). We combined this variable into mutually-exclusive categories as follows: above median for all 3 groups (predominantly minority neighborhoods), above AAPI median only, above AAPI and African American medians, and all other combinations.

Several neighborhood features were developed based on residential buffers. Data on traffic counts from the California Department of Transportation (33) were used to obtain traffic density within a 500-meter buffer of each participant’s residence (34). Neighborhood amenities were based on business listings from Walls & Associates’ National Establishment Time-Series Database (35), farmers’ markets listings from the California Department of Food and Agriculture (36), and parks from NavTeq’s NavStreets database. Using ArcGIS software, neighborhood amenities within a 1,600-meter pedestrian network distance (37) from a participant’s residence at diagnosis were averaged over a 4-year window of 2005–2008 (the latest available business data for this study). The average number of recreational facilities included places where recreational activities could take place (e.g., fitness centers, sports clubs). The Restaurant Environment Index (REI) is the ratio of the average number of fast food restaurants to other restaurants, and the Retail Food Environment Index (RFEI) (38) is the ratio of the average number of convenience stores, liquor stores, and fast food restaurants to supermarkets and farmers’ markets.

Quintiles/quartiles for neighborhood measures were based on either distributions in California (nSES, population density, racial/ethnic composition, percent foreign born) or among study participants (street connectivity, commuting by car, traffic density, businesses, recreational facilities). RFEI was categorized into neighborhoods with no unhealthy food outlets (ratio=0), fewer unhealthy vs. healthy outlets (ratio <1), equal or more unhealthy vs. healthy outlets (ratio ≥1) and neighborhoods without any retail food outlets. REI was categorized so that 0 indicates a neighborhood with no fast food restaurants; for neighborhoods with fast food restaurants, we used the median value of the ratio of fast food to other restaurants to split the sample into those living in neighborhoods with relatively fewer fast food to other restaurants, and those living in neighborhoods with relatively more fast foods to other restaurants, where the latter includes those who have a numerator value >0 and a denominator=0.

Individual-level characteristics

In the baseline questionnaire, women were asked to report their race/ethnicity, nativity, education level, and annual household income. For these analyses, we combined the race/ethnicity and nativity variable into a single variable resulting in eight racial/ethnic/nativity groups: NH White, African American, AAPI/foreign-born, AAPI/US-born, Hispanic/foreign-born, Hispanic/US-born, and Other. The numbers of foreign-born NH White and African Americans were too small to examine separately (8.6% and 5.2%, respectively). We also combined education (1=≤high school, 2=some college, 3=college graduate, 4=post graduate) and income (1=<$25,000, 2=$25,000–49,000, 3=$50,000–89,000 4=≥ $90,000) into an individual-level summary SES variable with possible values ranging from 2 to 8. Lowest scores (2 and 3) combined the lowest education and income group. The highest score (8) was obtained in women in both the highest income and highest education categories. We also included a measure of self-reported physical activity at baseline, categorized as quartiles of metabolic hours per week of moderate/vigorous leisure time activities.

BMI is the primary outcome of interest, calculated from self-reported height and weight at baseline as weight (kilograms) divided by squared height (meters): underweight/normal (BMI <25 kg/m²), overweight (25–29.9 kg/m²), and obese (≥30 kg/m²). For AAPIs, we used the WHO Asian-specific cut-points where underweight/normal, overweight and obese are defined as <23, 23.0–27.4, and ≥27.5, respectively (39, 40).

Analysis

Our analytic sample included 4,312 women, after excluding participants with addresses that could not be geocoded and 42 participants with unknown BMI. For all other variables with missing responses, we created a missing category to preserve our sample size. For ordinal variables with missing responses, such as the neighborhood attributes, we did not include the missing category when testing for trends. We used multinomial logistic regression to calculate adjusted odds ratios (OR) and 95% confidence intervals (CI) of the social and built environment features for overweight and obese compared to normal weight and underweight women. Covariates and neighborhood characteristics that were significant at p<0.05 in minimally-adjusted (age at diagnosis, race/ethnicity/nativity and individual SES) models were included in the multivariable models. Tests for linear trend were used to evaluate associations between body size and increasing ordinal categories of neighborhood characteristics (41). P-values <0.05 were considered statistically significant, and all tests of significance were two-sided.

To examine whether observed racial/ethnic/nativity disparities in overweight or obesity was explained when accounting for social and built environment features of the residential neighborhood, we used a series of multinomial logistic regression models: (1) minimally-adjusted models including race/ethnicity/nativity, age, marital status, and physical activity; (2) model 1 + individual-level SES; (3) model 2 + nSES; (4) model 3 + social and built environment characteristics that were associated with BMI in minimally-adjusted models. Analyses were conducted in SAS Version 9.4 using PROC GLIMMIX (SAS Institute Inc., Cary, NC). We used this method for modeling to account for clustering within block groups. We also checked for multicollinearity with a weighted regression model with (as explained at http://support.sas.com/kb/32/471.html) but did not find evidence of it.

RESULTS

The majority of Pathways Study participants were over 50 years of age at diagnosis (78%), were of non-Hispanic White race/ethnicity (64%), had at least some college education (84%), had a household income of at least $50,000 (59%), and were married or living as married (61%) (Table 2). One third of participants worked full-time (34%) and almost another third were retired (31%).

TABLE 2.

Individual Characteristics for Breast Cancer Survivors with Geocoded Addresses (N=4,354), Pathways Study, Kaiser Permanente Northern California, 2006–2013.

Individual Characteristics	N	%
Body Mass Index1	45	1.0
Underweight
Normal weight	1404	32.2
Overweight	1352	31.1
Obese	1511	34.7
Unknown	42	1.0
Age at Breast Cancer Diagnosis (years)	960	22.0
< 50
50–59	1271	29.2
60–69	1252	28.8
>70	871	20.0
Race/ethnicity and nativity
White, non-Hispanic	2786	64.0
African American	348	8.0
Asian American Pacific Islander (AAPI), foreign-born	423	9.7
AAPI, US-born	141	3.2
Hispanic, foreign-born	228	5.2
Hispanic, US-born	314	7.2
Other	114	2.6
Educational level	688	15.8
High school or less
Some college	1510	34.7
College graduate	1204	27.7
Post graduate	942	21.6
Unknown	10	0.2
Household income	404	9.3
<25K
$25–49K	802	18.4
$50–89K	1227	28.2
≥$90K	1351	31.0
Unknown	570	13.1
Combined education +income (individual level SES)2
1: Lowest SES	462	10.6
2	582	13.4
3	789	18.1
4	750	17.2
5	730	16.8
6: Highest SES	469	10.8
Unknown	572	13.1
Employment status	1452	33.3
Full time
Part time	517	11.9
Unemployed	188	4.3
Retired	1347	30.9
Disability	408	9.4
Other	238	5.5
Unknown	204	4.7
Marital status	2653	60.9
Married or live as married
Widow	489	11.2
Separated/divorced	841	19.3
Single	353	8.1
Unknown	18	0.4

¹BMI for Asians were defined using Asian-specific cut-points from World Health Organization (WHO).

²Combined education and income variable created by adding education value 1–4 and income value 1–4. Baseline education 1= ≤High School, 2=some college, 3=college graduate, 4= post graduate. Baseline Income: 1= < $25,000, 2=$25,000–49,000, 3=$50,000–89,000 4= ≥ $90,000. Possible values 2 through 8. Lowest scores (2 and 3) combined for the lowest education and income group. Reference group score 8, women in both the highest income and highest education categories.

Study participants resided in 2,933 unique block groups. The majority of study participants resided in neighborhoods that were in the highest two statewide quintiles of SES (63%) (Table 3). Thirty-three percent of the women resided in neighborhoods where the percent of AAPI and African American residents was higher than the state median, and another 22% resided in neighborhoods with a percent of AAPI higher than the state median. Most participants resided in neighborhoods with lower proportions of foreign-born residents (72%). Just over half of participants resided in block groups in the lowest two categories of population density (57%). Forty-two percent lived in neighborhoods where the number of unhealthy food outlets outnumbered healthy ones and the majority resided within 1600m walking network distance of one or more parks (74%).

TABLE 3.

Neighborhood Characteristics for Breast Cancer Survivors with Geocoded Addresses (N=4,354), Pathways Study, Kaiser Permanente Northern California, 2006–2013.

Neighborhood Characteristics	N	%
Neighborhood socioeconomic status (nSES), statewide quintiles, (Yang Index1, block group)	209	4.8
Quintile 1: Lowest nSES
Quintile 2	522	12.0
Quintile 3	898	20.6
Quintile 4	1278	29.4
Quintile 5: Highest nSES	1447	33.2
Neighborhood racial/ethnic composition2 (block group)	530	12.2
Above state medians for all 3 groups (predominantly minority)
Other combinations	682	15.7
Above AAPI and African American state medians	1465	33.6
Above AAPI state median	970	22.3
Below state medians for all 3 groups	707	16.2
Percent of population foreign-born, statewide quintiles (census tract)	532	12.2
Quintile 1: Highest % foreign-born
Quintile 2	693	15.9
Quintile 3	1040	23.9
Quintile 4	1066	24.5
Quintile 5: Lowest % foreign-born	1023	23.5
Population density, statewide quartiles (persons/square km, block group)	1139	26.2
Quartile 1: Lowest population density
Quartile 2	1356	31.1
Quartile 3	1112	25.5
Quartile 4: Highest population density	747	17.2
Traffic density3, study-specific quintiles (500m buffer)	871	20,0
Quintile 1: Highest traffic density
Quintile 2	871	20.0
Quintile 3	871	20.0
Quintile 4	871	20.0
Quintile 5: Lowest traffic density	870	20.0
Percent of population commuting by car, study-specific quintiles (census tract)	873	20.1
Quintile 1: Highest % commuting by car
Quintile 2	868	19.9
Quintile 3	873	20.1
Quintile 4	868	19.9
Quintile 5: Lowest % commuting by car	872	20.0
Number of total businesses within 1600m walking network distance, study-specific quintiles	871	20.0
Quintile 1: Lowest # of total businesses
Quintile 2	867	19.9
Quintile 3	874	20.1
Quintile 4	871	20.0
Quintile 5: Highest # of total businesses	871	20.0
Retail Food Environment Index4 within 1600m walking network distance	330	7.6
0
<1	1698	39.0
1+	1836	42.2
No businesses of interest	490	11.3
Restaurant Environment Index5 within 1600m walking network distance
None	1197	27.5
>0 but less than median among those with a value (0.15)	1383	31.8
>0 and above median	1338	30.7
No businesses of interest	436	10.0
Number of recreational facilities6 within 1600m walking network distance, sample specific quintiles	576	13.2
Quintile 1: Lowest (none)
Quintile 2: (0.25–0.5)	974	22.4
Quintile 3: (0.75–1.25)	1066	24.5
Quintile 4: (1.5–2.5)	889	20.4
Quintile 5: Highest (2.75+)	849	19.5
Number of parks within 1600m walking network distance	1150	26.4
None
1 park	1149	26.4
2 parks	906	20.8
3 or more	1149	26.4
Street connectivity—Gamma7, study-specific quintiles (block group)	871	20.0
Quintile 1: Lowest street connectivity
Quintile 2	873	20.1
Quintile 3	864	19.8
Quintile 4	872	20.0
Quintile 5: Highest street connectivity	874	20.1
Urbanicity8 (block group)	258	5.9
Small town/Rural
City	1192	27.4
Suburban	2449	56.2
Metropolitan urban	455	10.5
AAPI Enclave Index,9 statewide quintiles (census tract)	1290	29.6
Quintile 1: Highest enclave
Quintile 2	1043	24.0
Quintile 3	899	20.6
Quintile 4	722	16.6
Quintile 5: Lowest enclave	400	9.2
Hispanic Enclave Index,10 statewide quintiles (census tract)	215	4.9
Quintile 1: Highest enclave
Quintile 2	582	13.4
Quintile 3	1091	25.1
Quintile 4	1231	28.3
Quintile 5: Lowest enclave	1235	28.4

¹Yang SES Index is a composite measure of seven indicator variables for Census block groups (Liu education index, proportion blue collar job, proportion older than age 16 in the workforce without a job, median household income, percent below 200% of federal poverty line, median rent, median house value).

²Neighborhood Racial/Ethnic composition is based on the block group population being above or below state median for each non-White racial/ethnic group.

³Traffic density is based on traffic counts within a 500m buffer in units of vehicle miles traveled per square mile.

⁴Retail Food Environment Index is a ratio of unhealthy food outlets (fast food restaurants, liquor stores and convenient stores) to healthy food outlets (grocery stores and farmers’ markets). 0 indicates that the neighborhood has no unhealthy food outlets, a ratio of <1 indicates that there are fewer unhealthy food outlets compared to healthy food outlets, where as a ratio greater than 1 indicates that there are more unhealthy food outlets compared to healthy ones.

⁵Restaurant Environment Index is a ratio of the average number of fast food restaurants to other restaurants. 0 indicates that the neighborhood has no fast food restaurants; for neighborhoods with fast food restaurants, we used the median value of the ratio of fast food to other restaurants to split the sample into those living in neighborhoods with relatively fewer fast food to other restaurants, and those living in neighborhoods with relatively more fast foods to other restaurants, where the latter includes those who have a numerator value >0 and a denominator=0.

⁶Recreational facilities included places where recreational activities could take place (e.g., fitness centers, sports clubs, yoga centers, dance schools).

⁷Gamma is the ratio of actual number of street segments to maximum possible number of intersections, with a higher ratio indicating more street connectivity/walkability.

⁸Urbanicity is based on a combination of census-defined metropolitan areas and population density, with five categories: metropolitan urban (highest quartile of population density within a census-defined urbanized area with a population of one million or more), metropolitan suburban (the rest of the population within an urbanized area with a population of one million or more), city (census-designated places with more than 50,000 people outside of a metropolitan area with a population of one million or more), town (places with less than 50,000 people, outside of an urbanized area, and not the lowest quartile of population density), and rural (places with less than 50,000 people, outside of an urbanized area, and in the lowest quartile of population density).

⁹AAPI Enclave Index is a composite measure of four indicator variables for census tracts (% recent immigrants, % API language-speaking households that were linguistically isolated, % API language speakers with limited English proficiency, and % API).

¹⁰Hispanic Enclave Index is a composite measure of seven indicator variables for census tracts (% foreign-born, % recent immigrants, % households that were linguistically isolated, % of Spanish language-speaking households that were linguistically isolated, % all language speakers with limited English proficiency, % of Spanish language-speakers with limited English proficiency, and % Hispanic).

The neighborhood characteristics for the study participants stratified by race/ethnicity/nativity are shown in Supplemental Table 1. The distribution of neighborhood characteristics varied considerably by race/ethnicity and nativity among Hispanics and AAPIs. For example, nearly half of US-born AAPIs lived in the highest nSES quintile, compared to fewer than 20% among African Americans and among foreign-born Hispanics on the other extreme. Generally, individuals were more likely to live in neighborhoods with similar racial/ethnic composition as their own race/ethnicity. More than one-third of African Americans and foreign-born Hispanics lived in the highest quartile of population density, compared to 12% among Whites and 8% among those of other races/ethnicities.

Neighborhood factors associated with overweight

Several social and built environment attributes were associated with overweight compared to normal/underweight, when modeled on their own with adjustment for race/ethnicity/nativity, individual-level SES, and age at diagnosis. Lower neighborhood SES was associated with overweight (Q1/lowest nSES compared to Q5: OR=1.31, 95% CI=0.85–2.01; p-trend=0.017) (Table 4, Model 1). Certain neighborhood racial/ethnic compositions were associated with higher odds of overweight: those with higher than statewide median percentages of AAPIs, African Americans and Hispanics (OR=1.54, 95% CI=1.14–2.08) and those with higher than statewide median percentages of AAPIs and African Americans (OR=1.31, 95% CI=1.04–1.65) compared to neighborhoods with percentages of AAPIs, African Americans and Hispanics that were lower than the statewide median. In addition, the following neighborhood characteristics were associated with overweight: higher traffic density (Q1/highest % traffic density versus Q5: OR=1.26, 95% CI=0.99–1.60, p-trend=0.04); higher proportion of workers commuting by car (Q1/highest % commuting versus Q5: OR=1.35, 95% CI=1.06–1.71; p-trend=0.01); higher ratio of unhealthy to healthy food outlets compared to having only healthy food outlets (RFEI ≥1 OR=1.35, 95% CI=1.01–1.82, p-trend=0.02); and more fast food restaurants compared to only non-fast food restaurants (REI > median OR=1.42, 95% CI=1.16–1.74, p-trend=<0.01).

Table 4.

Odds Ratios for Associations between Neighborhood Characteristics and Odds of Overweight or Obesity among Breast Cancer Survivors (N=4,312), Pathways Study, Kaiser Permanente Northern California, 2006–2013.¹

	Overweight						Obesity
Neighborhood Characteristics	Model 12 OR	LCI	UCI	Model 23 OR	LCI	UCI	Model 12 OR	LCI	UCI	Model 23 OR	LCI	UCI
Neighborhood socioeconomic status, statewide quintiles, (Yang Index,4 block group)
Quintile 1: Lowest nSES	1.31	0.85	2.01	0.92	0.57	1.47	2.32	1.55	3.47	1.35	0.86	2.12
Quintile 2	1.73	1.31	2.29	1.38	1.00	1.90	2.38	1.79	3.14	1.64	1.18	2.27
Quintile 3	0.90	0.72	1.12	0.73	0.57	0.94	1.43	1.15	1.77	1.07	0.83	1.39
Quintile 4	1.17	0.97	1.41	1.06	0.86	1.30	1.52	1.25	1.85	1.28	1.03	1.59
Quintile 5: Highest nSES (ref)	1.00			1.00			1.00			1.00
p-trend	0.017			0.898			<.0001			0.046
Neighborhood racial/ethnic composition,5 (block groups)
Above state medians for all 3 groups ((predominantly minority)	1.54	1.14	2.08	1.50	1.03	2.19	2.03	1.50	2.75	1.37	0.93	2.00
Other combinations	1.45	1.10	1.91	1.42	1.04	1.94	2.13	1.62	2.79	1.66	1.21	2.28
Above AAPI and African American state medians	1.31	1.04	1.65	1.41	1.07	1.86	1.64	1.30	2.08	1.51	1.13	2.01
Above AAPI state median	1.11	0.88	1.41	1.15	0.86	1.53	1.17	0.91	1.50	1.21	0.89	1.64
Below state medians for all 3 groups (ref)	1.00			1.00			1.00			1.00
Percent of population foreign born, statewide quintiles (census tract)
Quintile 1: Highest % foreign-born	1.08	0.81	1.44	0.84	0.60	1.19	1.53	1.15	2.04	1.18	0.83	1.68
Quintile 2	1.09	0.85	1.41	0.83	0.61	1.12	1.47	1.14	1.89	1.07	0.79	1.45
Quintile 3	0.93	0.75	1.16	0.74	0.58	0.96	1.23	0.98	1.53	0.95	0.73	1.24
Quintile 4	0.96	0.78	1.19	0.82	0.65	1.05	0.99	0.80	1.24	0.85	0.66	1.09
Quintile 5: Lowest % of foreign-born (ref)	1.00			1.00			1.00			1.00
p-trend	0.471			0.276			<.0001			0.184
Population density, statewide quartiles (persons/square km, block group)
Quartile 1: Lowest population density	0.92	0.72	1.17	0.84	0.59	1.19	0.82	0.64	1.05	0.88	0.62	1.26
Quartile 2	1.02	0.81	1.29	0.90	0.66	1.23	0.95	0.75	1.20	0.85	0.62	1.16
Quartile 3	1.16	0.91	1.48	1.03	0.77	1.37	1.14	0.90	1.45	0.94	0.70	1.26
Quartile: 4 Highest population density (ref)	1.00			1.00			1.00			1.00
p-trend	0.181			0.181			0.016			0.408
Traffic density6, study-specific quintiles (500m buffer)
Quintile 1: Highest traffic density	1.26	0.99	1.60	1.36	1.00	1.85	1.25	0.98	1.59	1.19	0.87	1.62
Quintile 2	1.38	1.09	1.76	1.45	1.07	1.97	1.52	1.20	1.94	1.40	1.03	1.91
Quintile 3	1.11	0.88	1.41	1.12	0.84	1.49	1.11	0.88	1.41	0.96	0.72	1.29
Quintile 4	1.25	0.99	1.58	1.28	0.99	1.67	1.34	1.05	1.70	1.20	0.92	1.56
Quintile 5: Lowest traffic density (ref)	1.00			1.00			1.00			1.00
p-trend	0.037			0.053			0.037			0.189
Percent commuting by car, study-specific quintiles (census tract)
Quintile 1: Highest % commuting by car	1.35	1.06	1.71	1.14	0.85	1.54	1.93	1.51	2.47	1.46	1.07	1.99
Quintile 2	1.18	0.94	1.50	1.06	0.80	1.40	1.42	1.11	1.82	1.18	0.88	1.58
Quintile 3	1.16	0.92	1.47	1.05	0.80	1.38	1.48	1.16	1.89	1.30	0.97	1.74
Quintile 4	0.98	0.77	1.23	0.90	0.69	1.17	1.29	1.01	1.65	1.20	0.90	1.58
Quintile 5: Lowest % commuting by car (ref)	1.00			1.00			1.00			1.00
p-trend	0.005			0.149			<.0001			0.050
Number of total businesses within 1600m walking network distance, study-specific quintiles
Quintile 1: Lowest # of businesses	1.03	0.81	1.29	1.21	0.73	2.00	1.01	0.79	1.29	1.22	0.73	2.03
Quintile 2	1.01	0.79	1.28	0.96	0.64	1.45	1.44	1.13	1.83	1.24	0.82	1.89
Quintile 3	1.10	0.87	1.40	0.87	0.61	1.24	1.44	1.13	1.83	1.06	0.74	1.52
Quintile 4	1.19	0.94	1.50	0.97	0.72	1.31	1.33	1.04	1.70	1.06	0.78	1.45
Quintile 5: Highest # of businesses (ref)	1.00			1.00			1.00			1.00
p-trend	0.688			0.690			0.689			0.353
Retail Food Environment Index within 1600m walking network distance7
1+	1.35	1.01	1.82	1.09	0.76	1.56	1.36	1.01	1.83	1.33	0.92	1.90
<1	1.16	0.86	1.55	1.17	0.83	1.64	1.20	0.89	1.62	1.28	0.91	1.81
0 (ref.)	1.00			1.00			1.00			1.00
No businesses of interest	1.14	0.81	1.60	1.11	0.77	1.61	0.87	0.61	1.24	1.03	0.70	1.51
p-trend	0.016			0.573			0.029			0.306
Restaurant Environment Index within 1600m walking network distance8
None	1.00			1.00			1.00			1.00
>0 but less than median among those with a value (0.15)	0.99	0.82	1.21	0.89	0.67	1.18	0.85	0.70	1.04	0.79	0.60	1.04
>0 and above median	1.42	1.16	1.74	1.26	0.98	1.61	1.29	1.06	1.58	1.06	0.83	1.36
No businesses of interest	1.09	0.83	1.42	1.15	0.83	1.61	0.75	0.57	0.99	0.97	0.68	1.38
p-trend	0.001			0.028			0.011			0.282
Number of Recreational facilities within 1600m walking network distance, study-specific quintiles9
Quintile 1: Lowest (none)	1.14	0.88	1.48	1.09	0.71	1.68	1.22	0.93	1.60	1.21	0.78	1.88
Quintile 2 (0.25–0.5)	1.03	0.82	1.30	1.02	0.70	1.47	1.31	1.04	1.66	1.12	0.77	1.64
Quintile 3	1.20	0.95	1.50	1.20	0.86	1.66	1.46	1.16	1.84	1.22	0.87	1.72
Quintile 4	1.42	1.12	1.79	1.44	1.08	1.93	1.53	1.20	1.96	1.33	0.99	1.80
Quintile 5: Highest (2.75 or more) (ref)	1.00			1.00			1.00			1.00
p-trend	0.820			0.588			0.284			0.801
Number of parks within 1600m walking network distance
None	0.99	0.81	1.21				0.97	0.79	1.20
1 park	1.09	0.88	1.34				1.14	0.92	1.40
2 parks	1.05	0.84	1.32				1.22	0.98	1.52
3 or more parks (ref)	1.00						1.00
p-trend	0.992						0.704
Street Connectivity--Gamma10, study-specific quintiles (block group)
Quintile 1: Lowest street connectivity	0.97	0.77	1.23				0.87	0.68	1.11
Quintile 2	1.10	0.86	1.39				1.17	0.92	1.48
Quintile 3	1.13	0.89	1.44				1.26	0.99	1.60
Quintile 4	1.15	0.90	1.46				1.23	0.97	1.56
Quintile 5: Highest street connectivity (ref)	1.00						1.00
p-trend	0.644						0.224
Urbanicity (block group)11
Small town/Rural	0.99	0.66	1.47				1.03	0.70	1.52
City	1.19	0.90	1.57				1.15	0.87	1.52
Suburban	1.09	0.84	1.41				1.05	0.81	1.37
Metropolitan urban (ref)	1.00						1.00

¹N=42 women with missing BMI excluded. Bolded estimates are statistically significant at p<.05, OR=odds ratio, UCI=upper 95% confidence interval, LCI=lower 95% confidence interval.

²Model 1 and 2 adjusted for age at diagnosis, race/ethnicity/nativity, individual SES.

³Model 2 additionally adjusted for neighborhood SES, Neighborhood Racial/Ethnic Composition, Percent foreign born, Population Density, Traffic Density, Percent commuting to work by car, Number of total businesses within 1600m walking network distance, Retail Food Environment Index within 1600m walking network distance, Restaurant Environment Index within 1600m walking network distance, Number of Recreational facilities within 1600m walking network distance. Neighborhood variables from Model 1 that were not associated with BMI (p<0.05) were not included in Model 2.

⁴Yang SES Index is a composite measure of seven indicator variables for Census block groups (Liu education index, proportion blue collar job, proportion older than age 16 in the workforce without a job, median household income, percent below 200% of federal poverty line, median rent, median house value).

⁵Neighborhood Racial/Ethnic composition is based on the block group population being above or below state median for each non-White racial/ethnic group.

⁶Traffic density is based on traffic counts within a 500m buffer in units of vehicle miles traveled per square mile.

⁷Gamma is the ratio of actual number of street segments to maximum possible number of intersections, with a higher ratio indicating more street connectivity/walkability.

⁸Retail Food Environment Index is a ratio of unhealthy food outlets (fast food restaurants, liquor stores and convenient stores) to healthy food outlets (grocery stores and farmers’ markets). 0 indicates that the neighborhood has no unhealthy food outlets, a ratio of <1 indicates that there are fewer unhealthy food outlets compared to healthy food outlets, where as a ratio greater than 1 indicates that there are more unhealthy food outlets compared to healthy ones.

⁹Restaurant Environment Index is a ratio of the average number of fast food restaurants to other restaurants. 0 indicates that the neighborhood has no fast food restaurants; for neighborhoods with fast food restaurants, we used the median value of the ratio of fast food to other restaurants to split the sample into those living in neighborhoods with relatively fewer fast food to other restaurants, and those living in neighborhoods with relatively more fast foods to other restaurants, where the latter includes those who have a numerator value >0 and a denominator=0.

¹⁰Recreational facilities included places where recreational activities could take place (e.g., fitness centers, sports clubs, yoga centers, dance schools).

¹¹Urbanicity is based on a combination of census-defined metropolitan areas and population density, with five categories: metropolitan urban (highest quartile of population density within a census-defined urbanized area with a population of one million or more), metropolitan suburban (the rest of the population within an urbanized area with a population of one million or more), city (census-designated places with more than 50,000 people outside of a metropolitan area with a population of one million or more), town (places with less than 50,000 people, outside of an urbanized area, and not the lowest quartile of population density), and rural (places with less than 50,000 people, outside of an urbanized area, and in the lowest quartile of population density).

In multivariable models adjusting for all neighborhood factors associated with overweight (Table 4, Model 2), neighborhood racial/ethnic composition, specifically neighborhoods with high minority representation (predominantly minority OR=1.50, 95% CI=1.03–2.19; > median for AAPI and African American OR=1.41, 95% CI=1.07–1.86), higher traffic density (Q1/highest % traffic density versus Q5: OR=1.36, 95% CI=1.00–1.85, p-trend=0.04), and higher number of fast food restaurants (REI > median OR=1.26, 95% CI=0.98–1.61, p-trend=0.03) remained associated with higher odds of overweight.

Neighborhood factors associated with obesity

When considering social and built environment attributes individually, with adjustment for race/ethnicity/nativity, individual-level SES, and age at diagnosis, several neighborhood attributes were associated with obesity compared to normal/underweight (Table 4, Model 1): lower nSES (Q1/lowest nSES compared to Q5: OR=2.32, 95% CI=1.55–3.47; p-trend<0.01); higher proportion of foreign-born residents (Q1/highest % foreign-born versus Q5: OR=1.53, 95% CI=1.15–2.04; p-trend<0.01); higher traffic density (Q1/highest % traffic density versus Q5: OR=1.25, 95% CI=0.98–1.59, p-trend=0.04); higher commuting to work by car (Q1/highest % of commuting by car OR=1.93, 95% CI=1.51–2.47, p-trend<0.01); higher ratio of unhealthy to healthy food outlets (Ratio >1 versus none: OR=1.36, 95% CI=1.01–1. 38; p-trend=0.03); and more fast food restaurants compared to only non-fast food restaurants (REI > median OR=1.29, 95% CI=1.06–1.58, p-trend=0.01). Residing in neighborhoods with more businesses and with more recreational facilities was associated with obesity although no significant trends were observed. Residing in a lower versus higher population density neighborhood was associated with lower odds of being obese (Q1/lowest population density compared to Q5: OR=0.82, 95% CI=0.64–1.05; p-trend=0.02). In addition, neighborhood racial/ethnic composition was also associated with obesity—those with higher percentage of AAPI, African American and Hispanic than the statewide median (OR=2.03, 95% CI=1.50–2.75) and those with a higher percentage of AAPI and African American (OR=1.64, 95% CI=1.30–2.08) compared to those with lower percentages of AAPI, African American and Hispanic than the statewide median.

In a model including all of the neighborhood variables, residing in a neighborhood with lower SES (Q1/lowest nSES OR=1.35,95% CI=0.86–2.12, p-trend=0.05), high percent of AAPIs and African-Americans (OR=1.51, 95% CI=1.13–2.01), and higher proportion of workers commuting by car (Q1/highest % commuting compared to Q5: OR=1.46, 95% CI=1.07–1.99, p=trend=0.05) remained associated with higher odds of obesity (Table 4, Model 2).

Racial/ethnic disparities in body mass index

Using sequential models (Table 5), we show the persistence of racial/ethnic/nativity disparities in overweight and obesity after accounting for individual-level SES (Model 2), nSES (Model 3) and other social and built environment attributes (Model 4). In minimally-adjusted models, we observed racial/ethnic disparities in overweight with African Americans (OR=1.79, 95% CI=1.26–2.55), AAPIs (foreign-born OR=1.31, 95% CI=1.02–1.68; US-born OR=1.67, 95% CI=1.11–2.52), and foreign born Hispanics (OR=1.84, 95% CI=1.29–2.64) having increased odds of overweight compared to NH Whites. Adjusting for individual SES and nSES slightly attenuated these associations; further adjusting for neighborhood features fully attenuated the higher odds observed among foreign-born (OR=1.25, 95% CI=0.96–1.63), but not US-born AAPIs, African Americans, and foreign-born Hispanics relative to NH Whites.

Table 5.

Associations Between Race/Ethnicity/Nativity with Odds of Being Overweight or Obese (vs. Normal/Underweight) among Breast Cancer Survivors (N=4,312), Pathways Study, Kaiser Permanente Northern California.

	Model 1: adjusted for age at dx, marital status, and physical activity.			Model 2: Model 1+ individual SES.			Model 3: Model 2 + neighborhood SES.			Model 4: Model 3 + other neighborhood attributes.
Race/ethnicity and nativity	OR	LCI	UCI	OR	LCI	UCI	OR	LCI	UCI	OR	LCI	UCI
Overweight
White, non-Hispanic	1.00			1.00			1.00			1.00
African American	1.79	1.26	2.55	1.72	1.21	2.45	1.66	1.16	2.37	1.61	1.11	2.31
Asian American Pacific Islander (AAPI), foreign-born	1.31	1.02	1.68	1.30	1.02	1.68	1.31	1.02	1.69	1.25	0.96	1.63
AAPI, US-born	1.67	1.11	2.52	1.73	1.15	2.63	1.76	1.16	2.66	1.78	1.16	2.72
Hispanic, foreign-born	1.84	1.29	2.64	1.70	1.18	2.44	1.64	1.14	2.36	1.55	1.06	2.25
Hispanic, US-born	1.35	0.98	1.85	1.27	0.92	1.74	1.26	0.92	1.74	1.23	0.89	1.70
Other	1.05	0.64	1.75	0.98	0.59	1.63	0.92	0.56	1.54	0.88	0.53	1.47
Obesity
White, non-Hispanic	1.00			1.00			1.00			1.00
African American	3.50	2.55	4.80	3.20	2.33	4.40	2.88	2.08	3.97	2.70	1.93	3.77
AAPI, foreign-born	0.71	0.54	0.94	0.71	0.54	0.94	0.72	0.54	0.95	0.61	0.46	0.83
AAPI, US-born	1.03	0.65	1.62	1.11	0.70	1.76	1.16	0.73	1.85	1.11	0.69	1.79
Hispanic, foreign-born	1.50	1.04	2.16	1.26	0.87	1.83	1.17	0.80	1.70	1.04	0.70	1.53
Hispanic, US-born	1.80	1.34	2.43	1.63	1.21	2.20	1.57	1.16	2.13	1.51	1.11	2.05
Other	1.82	1.15	2.90	1.59	1.00	2.54	1.46	0.91	2.33	1.45	0.90	2.34

¹N=42 women with missing BMI excluded. Bolded estimates are statistically significant at p<.05, OR=odds ratio, UCI=upper 95% confidence interval, LCI=lower 95% confidence interval.

For obesity, we also observed racial/ethnic disparities in minimally-adjusted models with African Americans (OR=3.50, 95% CI=2.55–4.80) and Hispanics (foreign-born OR=1.50, 95% CI=1.04–2.16; US-born OR=1.80, 95% CI=1.34–2.43) having higher odds of obesity compared to NH Whites; foreign-born AAPIs had lower odds of obesity (OR=0.71, 95% CI=0.54–0.94). Additionally adjusting for individual level SES fully attenuated the increased odds of obesity among foreign-born Hispanics relative to NH Whites. Addition of nSES slightly attenuated the associations for African Americans and US-born Hispanics. Further adjustment for neighborhood factors slightly attenuated associations in African Americans and US-born Hispanics, but strengthened associations in foreign-born AAPIs (OR=0.61, 95% CI=0.46–0.83).

Discussion

Among a diverse cohort of breast cancer survivors within an integrated healthcare system in Northern California, we found that select neighborhood social and built environment factors, including low nSES, high minority composition, high traffic density, high prevalence of commuting by car, and a higher number of fast food restaurants were independently associated with higher odds of being overweight or obese. These neighborhood features also somewhat attenuated the higher odds of overweight among African Americans, US born AAPIs and foreign born Hispanics and the higher odds of obesity among African Americans and US-born Hispanics, relative to NH Whites. However, racial/ethnic/nativity disparities in overweight and obesity persisted, suggesting that additional research is warranted to understand other potential mediating factors. In addition, this is the first study, to our knowledge, that has examined whether social and built environment variables may explain these disparities, and one of a few studies to focus on the role of these environmental factors among breast cancer survivors. As maintaining a healthy body weight is a key modifiable factor for optimizing breast cancer survivorship outcomes, our study suggests that addressing aspects of survivors’ neighborhood environments may help to lower their risks of recurrence, low quality of life, and poor survival.

Higher BMI is associated with increased disease morbidity and mortality in general (42), and with higher mortality among breast cancer survivors (25, 43–49). As a result, breast cancer survivors are encouraged to achieve and/or maintain a healthy weight after diagnosis (50). Consistent with the broader literature on neighborhoods and obesity, we found that lower nSES, higher minority racial/ethnic composition, higher traffic density, higher commuting by car, and more fast food restaurants were associated with being overweight or obese (3, 51). In the breast cancer literature, only two studies, both from our group, have looked at neighborhood factors and body size among breast cancer survivors, finding similar results of lower nSES (measured similarly as in the current study) associated with lower odds of having larger body size (13, 24).

Racial/ethnic disparities in obesity have also been previously reported, though only descriptively, in studies of breast cancer survivors, with findings showing African Americans and Hispanics are more likely, and Asian Americans less likely, to be overweight or obese compared with NH Whites (44, 45). However, this is the first study to provide a more nuanced look at these disparities by considering nativity in Hispanics and AAPIs concurrently with race/ethnicity. For example, after adjusting for individual-level covariates including SES, we found that all groups, excluding other races/ethnicities, are at higher odds of being overweight compared with NH Whites. For obesity, these disparities differ, with African Americans and US-born Hispanics at higher odds of obesity and foreign-born AAPIs at lower odds compared to NH Whites. The opposite direction of associations observed among foreign-born AAPIs was unexpected. While it may be partly a function of the more conservative cut-points used to define the overweight and obese categories among AAPIs, these associations should be further explored in future studies. These findings also suggest that neighborhoods may be differently experienced by racial/ethnic and nativity groups. Further research in this area, such as incorporating data on individuals’ perceptions and their use of their neighborhood environments, is needed to ultimately inform multilevel interventions that would ameliorate such disparities and improve outcomes for breast cancer survivors, regardless of their social status (e.g., race/ethnicity, SES, nativity).

We demonstrated that the racial/ethnic disparities in overweight were slightly attenuated with the addition of nSES into the model, but still persisted, and only the foreign-born AAPI association was fully attenuated after accounting for the other social and built environment attributes. Similarly, the addition of nSES only slightly attenuated the observed disparities in obesity, and the addition of the other social and built environment attributes into the model resulted in a stronger association for foreign-born AAPIs.

Despite the strengths of this diverse breast cancer survivorship cohort including rich, multilevel data, our study had several limitations. The data for these analyses are cross-sectional and based on self-reported measures of height and weight to calculate BMI and physical activity; yet these data provided a unique opportunity to explore these associations among breast cancer survivors. Our findings regarding associations of neighborhood factors with body size and their influence on racial/ethnic/nativity differences may not be generalizable to other patient populations as breast cancer patients from the KPNC integrated healthcare system live in more middle SES, suburban and higher minority neighborhoods relative to other breast cancer patients in the same catchment area (52). Our study uses secondary geospatial data to describe neighborhood environments, and thus does not capture how residents perceive and use their environments. However, secondary geospatial data for capturing social and built environment characteristics are commonly used, capture objective assessments of neighborhoods, and show robust associations with health behaviors and health outcomes (3, 6). Finally, even with the large overall sample size, the relatively small number of minorities precluded our ability to assess neighborhood associations in specific racial/ethnic groups.

Selected self-reported neighborhood characteristics are being collected in the Pathways cohort 72-month interview, and will be assessed in future work as the cohort matures. With these data, we will be able to study the impact of neighborhood social and built environment characteristics on health-related quality of life and other breast cancer outcomes, as well as potential interaction with molecular factors. With these integrated sources of neighborhood data, we will be able to assess how cancer survivors’ neighborhoods enable healthy behaviors and shape breast cancer outcomes, and which neighborhood features influence breast cancer survivorship.