BACKGROUND AND OBJECTIVES:

Exposures to environmental chemicals are ubiquitous in the US. Little is known about how neighborhood factors contribute to exposures.

METHODS:

Growing Up Healthy is a prospective cohort study of environmental exposures and growth and development among Hispanic and African American children (n = 506) in New York City. We sought to determine associations between neighborhood-level factors (eg, housing type, school, time spent indoors versus outdoors) and urinary biomarkers of chemical exposures suspected to be associated with these characteristics (cotinine, 2,5-dichlorophenol, and phthalate metabolites) adjusted by age, sex, race, and caregiver education and language.

RESULTS:

Urinary cotinine concentrations revealed a prevalent exposure to secondhand smoke; children living in public housing had higher concentrations than those in private housing. In homes with 1 smoker versus none, we found significant differences in urinary cotinine concentrations by housing, although not in homes with 2 or more smokers. Children in charter or public schools had higher urinary cotinine concentrations than those in private schools. School type was associated with exposures to both low- and high-molecular-weight phthalates, and concentrations of both exposure biomarkers were higher for children attending public versus private school. 2,5-Dichlorophenol concentrations declined from 2004 to 2007 (P = .038) and were higher among charter school children.

CONCLUSIONS:

Housing and school type are associated with chemical exposures in this minority, inner city population. Understanding the role of neighborhood on environmental exposures can lead to targeted community-level interventions, with the goal of reducing environmental chemical exposures disproportionately seen in urban minority communities.

Through biomonitoring conducted by the Centers for Disease Control and Prevention (CDC) for the National Report on Human Exposure to Environmental Chemicals, it is demonstrated that environmental chemical exposures are widespread in the US population.1 Many of these exposures may be higher in children than adults, with children ages 6 to 11 years often having higher biomarker concentrations than their adult counterparts, whereas adolescents have intermediate concentrations.1 Certain biomarker concentrations are higher in racial and ethnic minority groups compared with non-Hispanic white individuals.1 In addition to sociodemographic factors, the contribution of individual behaviors, such as smoking frequency or personal care product use, and their respective contributions to exposure have also been examined.2,5 Thus, what is known about exposure sources has focused largely on individual factors. For these reasons, much of the clinical counseling on exposure reduction has targeted behavioral changes at the individual level.

In emerging research, authors have identified how exposures might be magnified by neighborhood-level factors, including housing or school type.6,8 This is rooted in the concept of environmental justice, which arose from the knowledge that persons living in high poverty communities can have higher exposures.9,11 This is true whether we are considering established chemicals of concern, such as nicotine, or emerging chemicals of concern, including those classified as potential endocrine disruptors (EDs). EDs are chemicals that may influence hormone action and thus have the potential to disrupt the endocrine system.12 Researchers have demonstrated a potential role of EDs such as phthalates in a wide variety of health conditions from reproductive effects to alteration of growth and neurodevelopment.12 

We sought to determine associations between neighborhood-level factors and chemicals that are both potential respiratory irritants and suspected to be associated with neighborhood characteristics in inner city, low income, and minority girls and boys using urinary concentrations of cotinine, phthalate metabolites, and 2,5-dichlorophenol. Cotinine is the primary metabolite of nicotine.1 Phthalates are a class of chemicals used to make plastics more flexible and durable. Phthalates can be found in products such as vinyl flooring, adhesives, detergents, lubricating oils, automotive plastics, toys, plastic clothes (eg, raincoats), medical tubing, and personal care products (eg, soaps, shampoos, hair sprays, and nail polishes; see Supporting Information, Supplemental Table 6).1 2,5-Dichlorophenol is a metabolite of 1,4-dichlorobenzene (paradichlorobenzene), which can be found in mothballs and room and toilet deodorizers.1 Environmental exposures to these chemicals or their precursors by neighborhood factors in our sample compared with the US general population in NHANES may in part contribute to disparities in adverse childhood health conditions.13 

Growing Up Healthy in East Harlem is a community-based, prospective cohort study in which researchers examine environmental factors and their influence on the growth and development of boys and girls ages 6 to 8 years. Children (n = 506) were recruited from 2005 to 2007 from East Harlem and the greater New York City area, and recruited subjects were predominantly girls because of a parallel study of puberty in girls. Informed consent was obtained from a parent or guardian along with child assent, and the study was approved by the institutional review boards of Mount Sinai and the CDC. Eligibility included age (6–8 years), having no underlying endocrine medical conditions, and African American or Hispanic race and/or ethnicity.

Urinary concentrations of cotinine, phthalate metabolites, and 2,5-dichlorhophenol were measured at the CDC and analyzed, correcting for creatinine.14,15 Cotinine levels, however, are only available for girls because of the parallel study of puberty in girls, which allowed for these additional urinary analyses. We examined molar sums of the phthalate metabolites as low- and high-molecular-weight compounds on the basis of their usual sources of exposure16 to reduce multiple comparisons and stabilize the variance. We fit multivariable models predicting geometric means (GMs) of biomarker concentrations according to neighborhood variables. We considered additional covariates as potential confounders that should be controlled for in the analysis. We included child age, sex, and race in the models. Additional covariates were retained by using backward elimination if they altered estimates for the neighborhood variables by >10%. We also verified that exclusion of a covariate did not degrade the precision of neighborhood estimates. Final models that included >1 neighborhood variable were further examined with each variable singly. In addition, because charter schools may have unique population characteristics that distinguish them from either private or public school populations, final models with school type as a predictor were further tested for the influence of charter schools (N = 23 children). We tested interactions by including in the models the cross products for biologically plausible effect modification; for example, we included housing by smoking and school type by year of urine collection.

Our cohort of inner city minority children ages 6 to 8 years were mainly from East Harlem in New York City (70%). They were predominantly Hispanic (61%), English-speaking (59%), from nonsmoking households (65%), with caregivers who had less than a high school education (61%), living in privately owned housing (61%), and attending public school (89%) (Table 1). Urine specimens and interview data were collected mostly during the school year (Table 1). Boys and girls had similar characteristics, except boys were recruited mainly between 2005 and 2006, whereas some girls were recruited between 2004 and 2007.

TABLE 1

Participant Characteristics (N = 506)

CharacteristicsVariableCategoryAll%Girls%Boys%χ2P
nnn
Individual characteristics Language of interview English 300 0.59 248 0.61 52 0.51 .07 
Spanish or Spanish and English 206 0.41 157 0.39 49 0.49 
Child’s race and/or ethnicity African American 135 0.27 111 0.27 24 0.24 .69 
Hispanic 307 0.61 242 0.6 65 0.64 
Hispanic African American 64 0.13 52 0.13 12 0.12 
Age at baseline questionnaire (in y) 6–6.99 189 0.37 151 0.37 38 0.38 .92 
7–7.99 159 0.31 126 0.31 33 0.33 
8–8.99 158 0.31 128 0.32 30 0.3 
Caregiver’s education ≤ HS diploma 303 0.61 235 0.59 68 0.69 .06 
≥ Some college 192 0.39 162 0.41 30 0.31 
Neighborhood characteristics: housing Public or private housing Privately owned housing 294 0.61 238 0.62 56 0.58 .53 
Public housing 187 0.39 147 0.38 40 0.42 
No. HH smokers 0 HH smokers 328 0.65 258 0.64 70 0.69 .11 
1 HH smokers 124 0.25 98 0.24 26 0.26 
2+ HH smokers 54 0.11 49 0.12 0.05 
Neighborhood characteristics: school Type of school child attends Charter school 23 0.05 22 0.05 0.01 .07 
Private school 33 0.07 29 0.07 0.04 
Public school 450 0.89 354 0.87 96 0.95 
Y of urine sample collection 2004 20 0.04 20 0.05 <.001 
2005 153 0.3 117 0.29 36 0.36 
2006 230 0.45 169 0.42 61 0.6 
2007 103 0.2 99 0.24 0.04 
In school yesterday In school yesterday 277 0.55 222 0.55 55 0.54 .89 
Not in school yesterday 226 0.45 180 0.45 46 0.46 
Indoor time ≥ 3 h/wk in outside play < 3 h outside in play per wk 258 0.51 213 0.53 45 0.45 .14 
≥ 3 h outside in play per wk 248 0.49 192 0.47 56 0.55 
≥8.5 (median) of sedentary activity per d < 8.5 h of sedentary activity per wk 242 0.48 190 0.47 52 0.51 .41 
≥ 8.5 h of sedentary activity per wk 264 0.52 215 0.53 49 0.49 
D of week yesterday Weekday yesterday 423 0.84 341 0.84 82 0.81 .47 
Weekend yesterday 83 0.16 64 0.16 19 0.19 
Season of urine collection Fall and winter: September to February 250 0.49 203 0.5 47 0.47 .96 
Spring: March to June 185 0.37 145 0.36 40 0.4 
Summer: July to August 71 0.14 57 0.14 14 0.14 
Total   — — 405 — 101 — — 
CharacteristicsVariableCategoryAll%Girls%Boys%χ2P
nnn
Individual characteristics Language of interview English 300 0.59 248 0.61 52 0.51 .07 
Spanish or Spanish and English 206 0.41 157 0.39 49 0.49 
Child’s race and/or ethnicity African American 135 0.27 111 0.27 24 0.24 .69 
Hispanic 307 0.61 242 0.6 65 0.64 
Hispanic African American 64 0.13 52 0.13 12 0.12 
Age at baseline questionnaire (in y) 6–6.99 189 0.37 151 0.37 38 0.38 .92 
7–7.99 159 0.31 126 0.31 33 0.33 
8–8.99 158 0.31 128 0.32 30 0.3 
Caregiver’s education ≤ HS diploma 303 0.61 235 0.59 68 0.69 .06 
≥ Some college 192 0.39 162 0.41 30 0.31 
Neighborhood characteristics: housing Public or private housing Privately owned housing 294 0.61 238 0.62 56 0.58 .53 
Public housing 187 0.39 147 0.38 40 0.42 
No. HH smokers 0 HH smokers 328 0.65 258 0.64 70 0.69 .11 
1 HH smokers 124 0.25 98 0.24 26 0.26 
2+ HH smokers 54 0.11 49 0.12 0.05 
Neighborhood characteristics: school Type of school child attends Charter school 23 0.05 22 0.05 0.01 .07 
Private school 33 0.07 29 0.07 0.04 
Public school 450 0.89 354 0.87 96 0.95 
Y of urine sample collection 2004 20 0.04 20 0.05 <.001 
2005 153 0.3 117 0.29 36 0.36 
2006 230 0.45 169 0.42 61 0.6 
2007 103 0.2 99 0.24 0.04 
In school yesterday In school yesterday 277 0.55 222 0.55 55 0.54 .89 
Not in school yesterday 226 0.45 180 0.45 46 0.46 
Indoor time ≥ 3 h/wk in outside play < 3 h outside in play per wk 258 0.51 213 0.53 45 0.45 .14 
≥ 3 h outside in play per wk 248 0.49 192 0.47 56 0.55 
≥8.5 (median) of sedentary activity per d < 8.5 h of sedentary activity per wk 242 0.48 190 0.47 52 0.51 .41 
≥ 8.5 h of sedentary activity per wk 264 0.52 215 0.53 49 0.49 
D of week yesterday Weekday yesterday 423 0.84 341 0.84 82 0.81 .47 
Weekend yesterday 83 0.16 64 0.16 19 0.19 
Season of urine collection Fall and winter: September to February 250 0.49 203 0.5 47 0.47 .96 
Spring: March to June 185 0.37 145 0.36 40 0.4 
Summer: July to August 71 0.14 57 0.14 14 0.14 
Total   — — 405 — 101 — — 

HH, household; HS, high school; —, not applicable.

We observed significant differences between the biomarker GMs (P ≤ .1) and school type, public versus private housing ownership, and more outside activity time. Neighborhood variables that showed no associations with biomarker levels were not further examined (owning versus renting a home, number in household, number of rooms in home, number of plastic household items, use of an afterschool program, urine collection site, neighborhood safety perception).

The urinary cotinine concentrations in girls revealed prevalent exposures to secondhand smoke (median 1.3 µg/g creatinine compared with <0.5 µg/g creatinine in the NHANES (Tables 2 and 3).1 Children who lived in public housing had higher urinary cotinine concentrations than those whose homes were in privately owned buildings. When reported household smoking was taken into account, this difference was significant among homes with 1 smoker (GM = 4.2 vs 1.9 µg/g respectively); there were no cotinine differences in children who lived in public or private homes with 2 or more smokers. Children in charter and public schools had higher cotinine concentrations than those in private schools, even after adjustment for demographics.

TABLE 2

Median Concentrations of Urinary Metabolites in Growing Up Healthy Children, 2004 to 2007 (µg/g Creatinine)

NHANESAllGirlsBoys
Child Median (2005–2006)nMedianIQRnMedianIQRnMedianIQR
Cotinine (girls) <0.5a 386 1.3 0.6–4.7 386 1.3 0.6–4.7 — — — 
HMWPsb ∼150 506 281 158–500 405 270 149–479 101 316 185–583 
Mono-benzyl phthalateb 35.6 506 26 13–54 405 24 13–48 101 35 21–71 
Mono-(3-carboxypropyl) phthalate 5.06 506 5.1 3.4–8.9 405 5.1 3.3–9.4 101 5.2 3.6–8.1 
Sum di-(2-ethylhexyl) phthalate ∼120 506 212 113–420 405 206 110–410 101 242 131–471 
Mono-(2-ethyl-5-carboxypentyl) phthalate 54.2 506 110 62–215 405 110 61–208 101 117 69–240 
Mono-2-ethylhexyl phthalate 3.26 506 6.2 3.2–12.9 405 3.1–12.6 101 6.8 3.7–13.9 
Mono-(2-ethyl-5-hydroxyhexyl) phthalate 37 506 68 35–133 405 66 33–128 101 74 40–156 
Mono-(2-ethyl-5-oxohexyl) phthalate 24.4 506 43 22–84 405 41 21–82 101 51 26–98 
LMWPs ∼70 506 212 136–405 405 216 135–409 101 198 141–368 
Mono-ethyl phthalate 75.9 506 116 67–261 405 117 67–287 101 98 67–184 
Mono-isobutyl phthalate 9.46 506 22 14–36 405 22 13–36 101 23 15–36 
Mono-n-butyl phthalate 33.9 506 63 39–97 405 61 38–95 101 73 46–113 
2,5-Dichlorophenolb 8c 505 68 31–189 405 64 30–160 100 79 33–259 
NHANESAllGirlsBoys
Child Median (2005–2006)nMedianIQRnMedianIQRnMedianIQR
Cotinine (girls) <0.5a 386 1.3 0.6–4.7 386 1.3 0.6–4.7 — — — 
HMWPsb ∼150 506 281 158–500 405 270 149–479 101 316 185–583 
Mono-benzyl phthalateb 35.6 506 26 13–54 405 24 13–48 101 35 21–71 
Mono-(3-carboxypropyl) phthalate 5.06 506 5.1 3.4–8.9 405 5.1 3.3–9.4 101 5.2 3.6–8.1 
Sum di-(2-ethylhexyl) phthalate ∼120 506 212 113–420 405 206 110–410 101 242 131–471 
Mono-(2-ethyl-5-carboxypentyl) phthalate 54.2 506 110 62–215 405 110 61–208 101 117 69–240 
Mono-2-ethylhexyl phthalate 3.26 506 6.2 3.2–12.9 405 3.1–12.6 101 6.8 3.7–13.9 
Mono-(2-ethyl-5-hydroxyhexyl) phthalate 37 506 68 35–133 405 66 33–128 101 74 40–156 
Mono-(2-ethyl-5-oxohexyl) phthalate 24.4 506 43 22–84 405 41 21–82 101 51 26–98 
LMWPs ∼70 506 212 136–405 405 216 135–409 101 198 141–368 
Mono-ethyl phthalate 75.9 506 116 67–261 405 117 67–287 101 98 67–184 
Mono-isobutyl phthalate 9.46 506 22 14–36 405 22 13–36 101 23 15–36 
Mono-n-butyl phthalate 33.9 506 63 39–97 405 61 38–95 101 73 46–113 
2,5-Dichlorophenolb 8c 505 68 31–189 405 64 30–160 100 79 33–259 

See Supplemental Table 6 phthalates. IQR, interquartile range; —, not applicable.

a

Urine test as 5*serum cot: 0.050; 75th percentile, 0.22; 90th percentile, 1.22.

b

Wilcoxon rank test for boys and girls differ, P < .05.

c

75th percentile: 24.7 µg/g creatinine.

TABLE 3

Relationships Between Urinary Cotinine Concentrations (µg/g Creatinine) and Neighborhood Characteristics

Cotinine (n = 386 Girls)nUnadjusted GMs (µg/g Creatinine)PnAdjusted GMs (µg/g Creatinine)P
Private housing       
 0 smoker 213 1.0 (0.8, 1.2) .006* 208 0.9 (0.7, 1.2) <.01* 
 1 smoker 59 2.5 (1.8, 3.4) — 59 1.9 (1.3, 2.9) — 
 2+ smokers 22 11.7 (6.7, 20.2) — 22 7.0 (3.9, 12.4) — 
Public housing       
 0 smoker 100 0.9 (0.7, 1.2) — 97 0.7 (0.5, 0.9) — 
 1 smoker 56 6.9 (4.8, 9.9) — 56 4.2 (2.6, 6.6) — 
 2+ smokers 31 10.4 (6.8, 16.0) — 29 6.1 (3.7, 10.1) — 
School       
 Charter 23 3.21 (1.70, 6.06) .01* 21 2.9(1.7, 5.1) .06 
 Private 33 0.93 (0.54, 1.61) — 29 1.7(1.1, 2.7) — 
 Public 450 1.86 (1.59, 2.18) — 421 3.0(2.5, 3.5) — 
Cotinine (n = 386 Girls)nUnadjusted GMs (µg/g Creatinine)PnAdjusted GMs (µg/g Creatinine)P
Private housing       
 0 smoker 213 1.0 (0.8, 1.2) .006* 208 0.9 (0.7, 1.2) <.01* 
 1 smoker 59 2.5 (1.8, 3.4) — 59 1.9 (1.3, 2.9) — 
 2+ smokers 22 11.7 (6.7, 20.2) — 22 7.0 (3.9, 12.4) — 
Public housing       
 0 smoker 100 0.9 (0.7, 1.2) — 97 0.7 (0.5, 0.9) — 
 1 smoker 56 6.9 (4.8, 9.9) — 56 4.2 (2.6, 6.6) — 
 2+ smokers 31 10.4 (6.8, 16.0) — 29 6.1 (3.7, 10.1) — 
School       
 Charter 23 3.21 (1.70, 6.06) .01* 21 2.9(1.7, 5.1) .06 
 Private 33 0.93 (0.54, 1.61) — 29 1.7(1.1, 2.7) — 
 Public 450 1.86 (1.59, 2.18) — 421 3.0(2.5, 3.5) — 

Adjusted for child age, race, sex, caregiver education, and Spanish language of caregiver. —, not applicable.

*

P < .05.

Urinary phthalate metabolites (Supplemental Table 6), both high-molecular-weight phthalates (HMWPs) and low-molecular-weight phthalates (LMWPs), in this cohort were higher than among 2005 to 2006 NHANES children (medians shown in Table 2), which is consistent with the disproportionate burden of environmental exposures often seen in high poverty, predominantly minority populations.1 Boys and girls also had similar concentrations of urinary biomarkers. Phthalate metabolites differ by demographic factors including across race-specific subgroups. For example, LMWP concentrations in children in NHANES from 2005 to 2006 were lower than in adults, but monoethyl phthalate, the major LMWP, was higher among African American and Hispanic children.1 

HMWPs and LMWPs were both higher in children who attended public school compared with private school or charter school; however, the difference was only significant for HMWPs in public school children when compared with private school children (Table 4). Increasing time spent in outdoor activity was associated with decreasing concentrations of HMWPs and increasing concentrations of LMWPs, but neither association remained significant in adjusted models (P > .1). In addition, LMWP concentrations were ∼30% higher among samples collected in summer. Models in which researchers used only 1 of these factors to predict LMWPs or HMWPs were similar to those presented in Table 3. Removing charter schools from these models did not change the findings.

TABLE 4

Relationships Between Urinary Phthalate Metabolite Concentrations (µg/g Creatinine) and Neighborhood Characteristics (N = 506)

Neighborhood CharacteristicsnUnadjusted GMsPnAdjusted GMsP
HMWPs School  µg/g creatinine   µg/g creatinine  
 Charter 23 269 (186 387) .009* 23 301 (208 437) .01* 
 Private 33 191 (141 259)  33 201 (148 273) — 
 Public 450 311 (286 338)  447 321 (291 355) — 
Hours outside in play per week       
 <3 h 258 324 (290 362) .04* 255 287 (236 349) .11 
 ≥3 h 248 275 (246 308)  248 252 (210 303)  
LMWPs School       
 Charter 23 192 (136 272) .06 22 184 (126 268) .10 
 Private 33 190 (142 254)  33 190 (139 258) — 
 Public 450 254 (235 275)  437 245 (215 279) — 
Hours outside in play per week       
 <3 h 258 231 (208 256) .09 251 194 (157 241) .22 
 ≥3 h 248 263 (236 292)  241 215 (178 260) — 
Season sample collected       
 Fall and winter: September to February 250 236 (212 262) .008* 245 189 (156 229) .04* 
 Spring: March to June 185 233 (206 263)  179 182 (148 223) — 
 Summer: July to August 71 329 (271 401)  68 249 (190 325) — 
Neighborhood CharacteristicsnUnadjusted GMsPnAdjusted GMsP
HMWPs School  µg/g creatinine   µg/g creatinine  
 Charter 23 269 (186 387) .009* 23 301 (208 437) .01* 
 Private 33 191 (141 259)  33 201 (148 273) — 
 Public 450 311 (286 338)  447 321 (291 355) — 
Hours outside in play per week       
 <3 h 258 324 (290 362) .04* 255 287 (236 349) .11 
 ≥3 h 248 275 (246 308)  248 252 (210 303)  
LMWPs School       
 Charter 23 192 (136 272) .06 22 184 (126 268) .10 
 Private 33 190 (142 254)  33 190 (139 258) — 
 Public 450 254 (235 275)  437 245 (215 279) — 
Hours outside in play per week       
 <3 h 258 231 (208 256) .09 251 194 (157 241) .22 
 ≥3 h 248 263 (236 292)  241 215 (178 260) — 
Season sample collected       
 Fall and winter: September to February 250 236 (212 262) .008* 245 189 (156 229) .04* 
 Spring: March to June 185 233 (206 263)  179 182 (148 223) — 
 Summer: July to August 71 329 (271 401)  68 249 (190 325) — 

Adjusted for child age, race, and sex. HMWPs were further adjusted for having been a weekday. LMWPs were adjusted for caregiver education and Spanish language. —, not applicable.

*

P < .05.

Urinary 2,5-dichlorophenol concentrations were elevated in the children in our study compared with the US sample. Median concentrations (overall median 68 µg/g creatinine) were twice as high as NHANES 75th percentile (28 µg/g creatinine in 2005–2006) (Table 2). 2,5-Dichlorophenol concentrations declined over the years the samples were collected (P = .038 for adjusted model; see Table 5). 2,5-Dichlorophenol was higher among children in charter schools and in homes with no smokers. An apparent relationship of 2,5-dichlorophenol with seasonality was no longer significant after adjustment for covariates.

TABLE 5

Relationships Between Urinary 2,5-Dichlorophenol Concentrations (µg/g Creatinine) and Neighborhood Characteristics N = 506

Neighborhood CharacteristicsnUnadjusted GMs (µg/g Creatinine)PnAdjusted GMs (µg/g Creatinine)P
School       
 Charter 23 211 (115–390) .02* 22 197 (99–394) .07 
 Private 33 200 (148–270) — 33 76 (43–134) — 
 Public 450 87 (76–100) — 440 99 (74–131) — 
Household smokers       
 0 smoker 328 100 (85–118) .10 320 146 (101–210) .03* 
 1 smoker 124 72 (55–94) — 124 99 (65–153) — 
 2+ smokers 54 81 (54–121) — 51 102 (61–170) — 
Year sample collected   —    
 2004 20 177 (92–342) .06 20 185 (89–387) .19 
 2005 153 95 (75–121) — 149 108 (74–156) — 
 2006 230 93 (77–113)  226 100 (69–144) — 
 2007 103 69 (52–92) — 100 84 (53–134) — 
Season sample collected       
 Fall and winter: September to February 250 106 (88–127) .04* 246 129 (89–186) .25 
 Spring: March to June 185 73 (59–91) — 181 100 (67–149) — 
 Summer: July to August 71 90 (64–128) — 68 114 (69–189) — 
Neighborhood CharacteristicsnUnadjusted GMs (µg/g Creatinine)PnAdjusted GMs (µg/g Creatinine)P
School       
 Charter 23 211 (115–390) .02* 22 197 (99–394) .07 
 Private 33 200 (148–270) — 33 76 (43–134) — 
 Public 450 87 (76–100) — 440 99 (74–131) — 
Household smokers       
 0 smoker 328 100 (85–118) .10 320 146 (101–210) .03* 
 1 smoker 124 72 (55–94) — 124 99 (65–153) — 
 2+ smokers 54 81 (54–121) — 51 102 (61–170) — 
Year sample collected   —    
 2004 20 177 (92–342) .06 20 185 (89–387) .19 
 2005 153 95 (75–121) — 149 108 (74–156) — 
 2006 230 93 (77–113)  226 100 (69–144) — 
 2007 103 69 (52–92) — 100 84 (53–134) — 
Season sample collected       
 Fall and winter: September to February 250 106 (88–127) .04* 246 129 (89–186) .25 
 Spring: March to June 185 73 (59–91) — 181 100 (67–149) — 
 Summer: July to August 71 90 (64–128) — 68 114 (69–189) — 

Adjusted for child age, race, sex, caregiver education, year of urine collection, and whether yesterday was a weekday. —, not applicable.

*

P < .05.

Interactions between housing and smoking as well as school type and year of urine collection for all urinary biomarkers were not significant (data not shown).

We report associations between neighborhood characteristics, specifically, type of housing where a child lives, type of school a child attends, and amount of time spent outdoors, with exposures to chemicals or their precursors known to be high in our study cohort, including cotinine, LMWPs and HMWPs, and 2,5-dichlorophenol. A strength of this analysis is the availability of both descriptive neighborhood-level data along with individual level biomarker data, which allows for a detailed assessment of neighborhood factors unique to the urban built environment and its potential role in every day chemical exposures.

Urinary cotinine concentrations revealed a prevalent exposure to secondhand smoke; girls living in public housing had higher concentrations than those in privately owned housing. Secondhand smoke exposures in multiunit housing have been documented despite implementation of smoke-free housing policies.17,18 In homes with 1 smoker versus none, we found significant differences in urinary cotinine concentrations by housing type, although these differences by housing type were not seen in homes with 2 or more smokers. Girls in charter or public schools had higher urinary cotinine concentrations than those in private schools. Both housing and school type are associated with chemical exposures in this minority, inner city population. These findings are of particular concern given the higher prevalence of asthma in children living in public housing, providing further support for the need for environmental interventions partnered with individualized medical treatment to reduce asthma exacerbations.19 

Although in an emerging body of literature researchers have demonstrated an association between housing factors and phthalate exposures, such as indoor dust concentrations of phthalates, and presence of polyvinylchloride products, or vinyl flooring,20,21 we did not see differences between broader neighborhood characteristics, including public or private housing type and urinary concentrations of phthalate biomarkers. With these findings, we suggest that for this particular population, housing type is not a good surrogate for indoor characteristics that have been associated with phthalate exposures in previous studies. An important limitation, however, is that the population characteristics of our study may not capture the full variability in public versus private housing that one typically sees across the economic spectrum given that our population is almost entirely low income. A second limitation is the lack of environmental exposure data for both homes and schools to further assess their contribution to children’s exposure levels.

We originally hypothesized that time outdoors would reduce environmental chemical exposures, reflecting exposure sources in the indoor setting to a variety of chemicals. However, in adjusted models, no significant relationships were seen with time outdoors.

School factors were associated with a number of exposures that we assessed, including attendance at public schools versus charter or private schools. Schools differ on a number of factors, from age of building to renovation status to years in operation to class composition, all of which may potentially play a role in why school type influenced exposure levels.22 It is further possible that school type (public versus private versus charter) is a surrogate marker for socioeconomic status and hence may in part explain socioeconomic status differences on a population level rather than neighborhood level, although the possibility of an association because of chance cannot be excluded.22 

Notably, urinary cotinine1 and 2,5-dichlorophenol23 concentrations declined in general over the course of the study years, and these trends were also noted in NHANES from 2003 to 2010. A 2004 ban on use of paradichlorobenzene, the precursor of 2,5-dichlorophenol, in school buildings24 and the ban on smoking in indoor public spaces and certain outdoor areas in New York City25 may in part account for these trends, demonstrating the importance of public policy in reducing exposures.

Understanding existing health disparities commonly seen in low income, predominantly minority communities requires an enhanced understanding of neighborhood-level contributors to health.7,9 This is especially true for environmental exposures, which are disproportionately higher in these same communities and are increasingly linked with a wide range of health outcomes, the most common of which includes respiratory conditions, such as asthma.26,27 Enhanced understanding of neighborhood-level factors and their association with concentrations of exposure biomarkers can lead to targeted community-level interventions, with the goal of reducing the cumulative burden of environmental chemicals exposure disproportionately seen in urban minority communities.

     
  • CDC

    Centers for Disease Control and Prevention

  •  
  • ED

    endocrine disruptor

  •  
  • GM

    geometric mean

  •  
  • HMWP

    high-molecular-weight phthalate

  •  
  • LMWP

    low-molecular-weight phthalate

Dr Galvez conceptualized the study and drafted the initial manuscript; Ms McGovern conducted the initial analyses and assisted in drafting, reviewing, and revising the manuscript; Dr Teitelbaum, as coinvestigator, supervised the overall study design and analyses and reviewed and revised the manuscript; Dr Windham, as coinvestigator, contributed to study design and supervision of the data collection, acted as liaison with the Centers for Disease Control and Prevention laboratory for cotinine measures, and reviewed and revised the manuscript; Dr Wolff, as principal investigator, supervised the overall study design and analyses, and reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the California Department of Public Health.

FUNDING: Supported by grants from the National Institutes of Environmental Health Sciences (ES009584, ES012771, ES019454, ES012645, ES12801, ES019435, and P30ES023515), National Institutes of Health (UL1TR001433), Environmental Protection Agency (R827039 and RD831711), National Cancer Institute (CA93447), and National Center for Research Resources (MO1-RR-00071). Funded by the National Institutes of Health (NIH).

We thank Antonia M. Calafat, Connie S. Sosnoff, John T. Bernert, Charles Dodson, Catherine Knuff, Shravani Vundavalli, Jessica Montana, Sofia Bengoa, Rochelle Osborne, and Barbara Brenner for their valuable support of this project.

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Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

Supplementary data