Skip to content

Advertisement

  • Research
  • Open Access
  • Open Peer Review

Environmental pollution and social factors as contributors to preterm birth in Fresno County

Environmental Health201817:70

https://doi.org/10.1186/s12940-018-0414-x

  • Received: 8 December 2017
  • Accepted: 21 August 2018
  • Published:
Open Peer Review reports

Abstract

Background

Environmental pollution exposure during pregnancy has been identified as a risk factor for preterm birth. Most studies have evaluated exposures individually and in limited study populations.

Methods

We examined the associations between several environmental exposures, both individually and cumulatively, and risk of preterm birth in Fresno County, California. We also evaluated early (< 34 weeks) and spontaneous preterm birth. We used the Communities Environmental Health Screening Tool and linked hospital discharge records by census tract from 2009 to 2012. The environmental factors included air pollution, drinking water contaminants, pesticides, hazardous waste, traffic exposure and others. Social factors, including area-level socioeconomic status (SES) and race/ethnicity were also evaluated as potential modifiers of the relationship between pollution and preterm birth.

Results

In our study of 53,843 births, risk of preterm birth was associated with higher exposure to cumulative pollution scores and drinking water contaminants. Risk of preterm birth was twice as likely for those exposed to high versus low levels of pollution. An exposure-response relationship was observed across the quintiles of the pollution burden score. The associations were stronger among early preterm births in areas of low SES.

Conclusions

In Fresno County, we found multiple pollution exposures associated with increased risk for preterm birth, with higher associations among the most disadvantaged. This supports other evidence finding environmental exposures are important risk factors for preterm birth, and furthermore the burden is higher in areas of low SES. This data supports efforts to reduce the environmental burden on pregnant women.

Keywords

  • Preterm birth
  • Environmental exposure
  • Social factors
  • Prematurity
  • Pollution

Background

Preterm birth (before 37 weeks gestation) is estimated to impact 10% of U.S. births annually with resultant potential for developmental and long-term adverse health consequences [14]. The estimated overall cost of preterm birth in the U.S. is approximately $26.2 billion per year [5]. Preterm birth is a complex phenotype with no single known mechanism or therapeutic strategy. Causes of preterm birth have remained largely unknown [5] and therefore, in most instances, not amenable to effective interventions or prevention.

Several studies have identified important environmental risk factors for preterm birth including prenatal exposure to air pollution [6, 7], contaminated water [812], pesticides [1315], traffic density (i.e. counts of motor vehicles within a given radius) [16], air toxins [17], and persistent organic pollutants [18]. In general, these studies have been relatively small and usually contaminants have been examined in isolation. Disparities in preterm birth have also been shown to exist by socioeconomic status (SES) wherein those with lower SES experience higher rates of preterm birth and other adverse pregnancy outcomes [19, 20].

Studies have also shown that exposures to pollutants differ by race/ethnicity and SES [21]. In previous work, we demonstrated that there are racial/ethnic disparities in exposure to air pollution during pregnancy [22]. Woodruff et al. found that Hispanic, African American and Asian/Pacific Islander mothers in the U.S. experienced higher mean levels of air pollution and were more than twice as likely to live in the most polluted counties compared with non-Hispanic white mothers after controlling for maternal risk factors, region and educational status [22]. Pregnant women who are exposed to multiple environmental chemicals and multiple psychosocial stressors such as neighborhood SES are at greater risk of adverse birth outcomes [23, 24]. The cumulative impacts and potential interactions between elevated exposures to chemical and psychosocial stressors have been referred to as a form of “double jeopardy” [25]. In other words, not only are such women at increased risk due to more cumulative risk factors, but the combination of risk factors is compounding the risk in a multiplicative rather than additive way. In a previous study, we found interactive effects of air pollution and SES that contribute to risk of preterm birth in the San Joaquin Valley of California [26].

Fresno County, in the San Joaquin Valley of California (CA), is an area of known environmental pollution burden [27] and a high prevalence of preterm birth (12.1% compared to 9.6% in CA in 2012). Additionally, Fresno County is characterized by diverse race/ethnicity and SES with a majority of the population being of non-white race and of lower SES, which may impact adverse health effects in conjunction with environmental exposure. Our study examines the association between multiple environmental, medical and social factors and preterm birth in Fresno County, CA from 2009 to 2012. Few studies have addressed how these factors may compound one another to contribute to preterm birth. The interaction of environmental, medical and social stressors may be critical in elucidating disparities in preterm birth. Furthermore, uncovering such compounding effects may focus policy and intervention efforts at reducing pollution burden in the most vulnerable communities.

Methods

Study population

Birth outcome and maternal demographic information were collected from a linked hospital discharge birth cohort database maintained by the CA Office of Statewide Health Planning and Development (OSHPD) that includes linked information from the State of CA vital records and hospital discharge records (comorbidities were identified from codes in the form of ICD-9-CM diagnoses). From this linked dataset, the study includes race/ethnicity, infant sex, maternal age at delivery, years of education, participation in the Women, Infants, and Children (WIC) food and nutrition service (a Federally-funded supplemental program), payer for delivery costs (i.e., heath insurance status), place of mother’s birth, body mass index (BMI) calculated from maternal height and pre-pregnancy weight, preexisting diabetes (ICD-9 code 250 and 648.0), gestational diabetes (648.8), preexisting hypertension (642.0, 642.1, 642.2, 642.7), gestational hypertension (642.3), preeclampsia/eclampsia (642.4, 642.4, 642.6), infection (646.5, 646.6, 647), anemia (648.2), mental illness (648.4), reported smoking, reported drug abuse, reported alcohol dependence, trimester when prenatal care began, parity, previous preterm birth, previous cesarean section, inter-pregnancy interval, premature rupture of membranes (658.1), mode of delivery (cesarean or vaginal), birth weight, birth date and gestational age at delivery (best obstetric estimate).

The sample was restricted to live-born singleton births with known birth date, birth weight between three standard deviations of mean by week of gestation [28] and gestational age between 20 and 44 weeks with complete information including census tract or zip code and births between 2009 and 2012 in Fresno County, CA.

Methods and protocols for the study were approved by the Committee for the Protection of Human Subjects within the Health and Human Services Agency of the State of California.

CalEnviroScreen

We used the California Communities Environmental Health Screening Tool (CalEnviroScreen 2.0, released in 2014) to estimate environmental exposures for each census tract in Fresno County [29]. The CalEnviroScreen was developed by CA’s Environmental Protection Agency’s (CalEPA) Office of Environmental Health Hazard Assessment to evaluate the cumulative existence of multiple pollutants and stressors in communities [30]. CalEnviroScreen is used to identify communities disproportionately burdened by cumulative impacts and identify disadvantaged communities for allocation of cap and trade funds generated under the Global Warming Solutions Act of 2006 [31]. CalEnviroScreen combines multiple sets of data on pollutants and stressors within a census tract into an overall index, which can be used to screen for places with the highest cumulative burdens (https://oehha.ca.gov/calenviroscreen).

CalEnviroScreen 2.0 consists of 19 environmental and population indicators in total, which are aggregated into a final, relative CalEnviroScreen Score (Table 1, Fig. 1). The CalEnviroScreen Score is made up of two key categories and four components of census tract-level indicators: Pollution Burden – Exposures score and Environmental Effects; and Population Characteristics – Sensitive Populations and Socioeconomic Factors (Fig. 1). Exposures score indicators include measures of pollutant sources, releases and environmental concentrations. Environmental Effects indicators are measures of threats to the environment and degraded ecosystems caused by pollution. In calculating the average Pollution Burden, the Environmental Effects indicators are weighted by half because CalEPA considers the Exposures score indicators to be more direct measures of exposures to pollution (e.g., air pollution monitoring). These indicators likely contribute more to a person’s total pollution burden than the impact of living near contaminated land or water, where the exposure is less immediate. Indicators of Sensitive Populations and Socioeconomic Factors include both biological traits (e.g., age and health conditions of tract residents) and factors related to tract-level SES (e.g., poverty and education) that can increase susceptibility to the adverse health impacts of pollutants. These together form the Population Characteristics score. The Pollution Burden and Population Characteristics scores are then multiplied together to arrive at a final relative CalEnviroScreen score ranging from 0 to 100. The indicators are ranked into percentiles, which allows them to be compared across the state. The indicator percentiles and component scores are also useful to evaluate and understand the key drivers of vulnerability in a community. The methodology and rationale for each specific indicator is described in detail in the CalEnviroScreen 2.0 report [31]. In addition, the individual drinking water contaminants are shown in Table 1. We used the Socioeconomic Factors score from the CalEnviroScreen, which includes the following variables derived from the US Census American Community Survey: educational attainment, linguistic isolation (households where no one over 14 years of age speaks English very well), poverty and unemployment.
Table 1

Description of pollution indicators in CalEnviroScreen 2.0

 

Indicators

Description

 

Pollution Burden

Average of percentiles from Exposure and Environmental Effects indicators, with a half weighting for the Environmental Effects indicators)

Pollution Burden

Exposures

Ozone

Amount of daily maximum 8-h Ozone concentration (ppm)

PM2.5

Annual mean particulate matter < 2.5 μm concentrations (μg/m3)

Diesel PM

Diesel PM emissions from on-road and non-road sources (kg/day)

Pesticides

Total pounds of selected active pesticide ingredients (filtered for hazard and volatility) used in production-agriculture per square mile in the census tract

Toxic Release

Toxicity-weighted concentrations of modeled chemical releases to air from facility emissions and off-site incineration

Traffic

Traffic density, in vehicle-kilometers per hour per road length, within 150 m of the census tract boundary

Individual Drinking Water Contaminants of Violation Measures

Drinking Water Score

Drinking water contaminant index for selected contaminants

Arsenic

Arsenic average (ppb)

Cadmium

Cadmium average (ppb)

DBCP

1,2-Dibromo-3-chloropropane average (ppb)

Lead

Lead average (ppb)

Nitrate

Nitrate (as NO3) average (ppm)

Perchlorate

Perchlorate average (ppb)

TCE

Trichloroethylene average (ppb)

TCP

1,2,3-trichloropropane average (ppb)

THM

Total trihalomethane average (ppb)

Uranium

Uranium average (PCI/L)

MCL Violations

The total number of Maximum Contaminant Level (MCL) violations for any chemical by system from 2008 to 2012 population weighted to the census tract

TCR Violations

Total coliform rule violations by system from 2008 to 2012 population weighted to the census tract

Environmental Effects

Groundwater Threats

Groundwater threats, sum of weighted GeoTracker leaking underground storage tank sites within buffered distances to populated blocks of census tracts

Hazardous Waste

Sum of weighted hazardous waste facilities and large quantity generators within buffered distances to populated blocks of census tracts

Impaired Water Bodies

Impaired water bodies, sum of number of pollutants across all impaired water bodies within buffered distances to populated blocks of census tracts

Solid Waste

Sum of weighted solid waste sites and facilities within buffered distances to populated blocks of census tracts

Cleanup Sites

Cleanup sites, sum of weighted EnviroStor cleanup sites within buffered distances to populated blocks of census tracts

Socioeconomic Factors

Poverty

Percent of population living below two times the federal poverty level

Unemployment

Percent of the population over the age of 16 that is unemployed and eligible for the labor force

Housing Burden

Percent housing burdened low income households

Linguistic Isolation

Percent limited English speaking households

Fig. 1
Fig. 1

Components of the CalEnviroScreen 2.0

We merged the OSHPD birth records with CalEnviroScreen 2.0 data by 2010 census tract. When birth records contained 2000 census tracts, we used the relationship files for 2000 to 2010 census tracts to create area-weighted values for the CalEnviroScreen variables [32]. If a census tract identifier for a birth record was missing or invalid, zip codes were used as surrogate and similar area-weighted adjustments were made using zip code to census tract relationship files (N = 1879; 3.5%).

Statistical analyses

Our primary outcome was preterm birth was defined as birth at less than 37 weeks gestation. We examined 24 exposure variables, which included the following scores and indicators from the CalEnviroScreen: Pollution Burden Score; Exposures score (component of Pollution Burden); Environmental Effects (component of Pollution Burden); 11 indicators (6 Exposures and 5 Environmental Effects); and 10 subcategories of the drinking water indicator (Fig. 1, Table 1). Each exposure variable was examined separately and classified dichotomously (split at the median) and by quintiles. We calculated Pearson correlation coefficients between the each of the indicators and scores from the CalEnviroScreen.

We examined several sets of covariates and their relationships to preterm birth and exposure indicators, which included socioeconomic variables (maternal education, payer for delivery), demographic characteristics (race/ethnicity, maternal age, maternal country of birth), obstetrical-related variables (diabetes, hypertension, smoking/alcohol/drug use during pregnancy, BMI, parity), and, among multiparous women, previous caesarean section, previous preterm birth, and inter-pregnancy interval from the previous live birth to the estimated conception of the index pregnancy. Inter-pregnancy interval was calculated from previous live birth (month and year) as reported in linked records and estimated as months to conception of the index pregnancy. Given that the day of previous live birth was not available, the middle of the month was used for calculation purposes [33]. We explored the association between the covariates and both the outcome (preterm birth) and exposure (above median levels of Pollution Burden).

We used logistic regression to evaluate the association between each indicator and preterm birth (< 37 weeks) and early preterm birth (< 34 weeks), comparing each of the higher 4 quintiles to the lowest to allow for non-monotonic relationships across the pollution distribution. We ran three sets of models: crude, adjusted with a priori variables and a stepwise selection. The covariates determined a priori included maternal education, age, race/ethnicity, and payer of delivery costs. The stepwise procedure included a forward and backward algorithm to estimate the association between environmental factors with preterm birth that allowed inclusion of covariates listed above that had p < 0.05 in crude risk calculations.

To explore the hypothesis that there is a double jeopardy when populations are vulnerable to both social and environmental stressors, we examined SES and race/ethnicity as potential modifiers in the relationship between environmental contaminants and preterm birth. We stratified analyses to examine the relationships between pollution and preterm birth by high and low SES of the census tract the woman lived in. The low SES group consisted of census tracts with below median levels of poverty, education, unemployment and linguistic isolation (Fig. 1, Table 1). We also stratified the analyses by broad race/ethnicity groups: White/non-Hispanic, non-White/non-Hispanic and Hispanic. These stratified analyses compared above versus below median levels of exposure in Fresno County and risk of preterm birth including early preterm birth.

In sensitivity analyses, we explored several alternative analytic decisions. We evaluated the pollutants continuously, both in individual models and a combined model with social factors. We chose more specific phenotypes of preterm birth including early preterm birth (< 34 weeks) and spontaneous preterm (i.e. premature labor or premature rupture of membranes) to restrict to preterm births that were not the consequence of a known cause or indication. We evaluated the raw scores of the exposure indicators (as opposed to the percentiles). Additionally, we mapped preterm birth prevalence across the county to visually observe the geographic variability.

Results

Population characteristics

After applying our exclusion criteria, our final study population included 53,843 births (Fig. 2). Our study population was highly diverse in both race/ethnicity and SES and pollution burden was higher in non-White and low SES areas (Table 2). We did not present cells with less than 16 women (for privacy purposes) nor calculate odds ratios with any cell less than 5. Our population in Fresno County was majority Hispanic (60%), followed by non-Hispanic white (19.7%), Asian (10.5%), and African American (5.8%). One quarter of mothers were born in Mexico (24.5%). More than 30% of the mothers had less than high school education and more than two-thirds of the mothers’ delivery costs were paid by Medi-Cal (California’s Medicaid). The prevalence of preterm birth (< 37 weeks), early preterm birth (< 34 weeks) and spontaneous preterm birth (< 37 weeks and premature rupture of membranes or preterm labor) were 8.5%, 2.1% and 7%, respectively.
Fig. 2
Fig. 2

Flow Chart of Our Study Population of Births in Fresno County, California

Table 2

Population characteristics in Fresno County, 2009–2012 (N = 53,843)

Population Characteristics

n (%)

Pollution Burden Quintile

1st

2nd

3rd

4th

5th

Race/ethnicity

 White non-Hispanic

10,620 (19.7)

139 (35.0)

583 (53.5)

2042 (38.3)

4113 (20.0)

3665 (14.0)

 Hispanic

32,302 (60.0)

212 (53.4)

357 (32.8)

2186 (41.0)

12,314 (59.7)

17,210 (65.5)

 Black

3095 (5.8)

*

17 (1.6)

226 (4.2)

1159 (5.6)

1689 (6.4)

 Asian

5675 (10.5)

*

100 (9.2)

512 (9.6)

2141 (10.4)

2899 (11.0)

 American Indian/Alaska native

546 (1.0)

19 (4.8)

*

70 (1.3)

201 (1.0)

245 (0.9)

 Hawaiian/Pacific Islander

70 (0.1)

*

*

*

40 (0.2)

19 (0.1)

 Other race

581 (1.1)

*

*

171 (3.2)

244 (1.2)

155 (0.6)

 Two or more races

954 (1.8)

*

*

122 (2.3)

406 (2.0)

395 (1.5)

Infant sex

 Male

27,354 (50.8)

208 (52.4)

569 (52.3)

2675 (50.1)

10,437 (50.6)

13,391 (51.0)

 Female

26,489 (49.2)

189 (47.6)

520 (47.8)

2663 (49.9)

10,180 (49.4)

12,886 (49.0)

Maternal age at delivery (years)

  < 18

2263 (4.2)

19 (4.8)

22 (2.0)

132 (2.5)

801 (3.9)

1288 (4.9)

 18–34

45,552 (84.6)

340 (84.6)

864 (79.3)

4420 (82.8)

17,506 (84.9)

22,331 (85.0)

  > 34

6028 (11.2)

38 (9.6)

203 (18.6)

786 (14.7)

2311 (11.2)

2658 (10.1)

Maternal education (years)

  < 12

16,607 (30.8)

85 (21.4)

132 (12.1)

990 (18.6)

5877 (28.5)

9522 (36.2)

 12

15,195 (28.2)

159 (40.1)

232 (21.3)

1275 (23.9)

6063 (29.4)

7456 (28.4)

  > 12

22,041 (40.9)

153 (38.5)

725 (66.6)

3073 (57.6)

8678 (42.1)

9299 (35.4)

WIC participanta

 Yes

39,404 (73.2)

287 (72.4)

436 (40.0)

2760 (51.7)

15,190 (73.7)

20,723 (79.0)

 No

14,439 (26.8)

150 (37.8)

655 (60.2)

2532 (47.4)

5305 (25.7)

5192 (9.8)

Payer for delivery costs

 Private insurance

13,949 (25.9)

150 (37.8)

655 (60.2)

2532 (47.4)

5305 (25.7)

5192 (9.8)

 Medi-Cal

39,040 (72.5)

222 (55.9)

399 (36.6)

2730 (51.1)

15,015 (72.8)

20,665 (78.6)

 Other government payer

651 (1.2)

*

22 (2.0)

20 (0.4)

36 (0.2)

43 (0.2)

 Self-pay

134 (0.3)

*

*

50 (0.9)

230 (1.1)

346 (1.3)

 Other payer

2 (0.0)

*

*

*

*

*

 No pay

67 (0.1)

*

*

*

31 (0.2)

30 (0.1)

Place of mother’s birth

 United States

35,911 (66.7)

317 (79.9)

825 (75.8)

3881 (72.7)

13,930 (67.6)

16,854 (64.1)

 Mexico

13,174 (24.5)

68 (17.1)

121 (11.1)

737 (13.8)

4865 (23.6)

7382 (28.1)

 Other

4758 (8.8)

12 (3.0)

143 (13.1)

720 (13.5)

1823 (8.8)

2041 (7.8)

Maternal conditionsb

 Diabetes, preexisting

565 (1.1)

*

*

33 (0.6)

220 (1.7)

302 (1.2)

 Diabetes, gestational

4875 (9.1)

42 (10.6)

86 (7.9)

429 (8.0)

1796 (8.7)

2517 (9.6)

 Hypertension, preexisting

915 (1.7)

*

*

103 (1.9)

351 (1.7)

436 (1.7)

  Without preeclampsia

649 (1.2)

*

*

75 (1.4)

254 (1.2)

298 (1.1)

  With preeclampsia

266 (0.5)

*

*

28 (0.5)

97 (0.5)

138 (0.5)

 Hypertension, gestational

3004 (5.6)

*

47 (4.3)

294 (5.5)

1083 (5.3)

1564 (6.0)

  Without preeclampsia

1224 (2.3)

*

21 (1.9)

140 (2.6)

424 (2.1)

629 (2.4)

  With preeclampsia

1780 (3.3)

*

26 (2.4)

154 (2.9)

659 (3.2)

936 (3.6)

 Infection

7402 (13.8)

45 (11.3)

102 (9.4)

562 (10.5)

2805 (13.6)

3789 (14.8)

 Anemia

4187 (7.8)

31 (7.8)

73 (6.7)

423 (7.9)

1887 (9.2)

2725 (10.4)

 Mental Illness

1231 (2.3)

*

35 (3.2)

155 (2.9)

680 (3.3)

954 (3.6)

 Reported Smoking

276 (0.5)

27 (6.8)

89 (8.2)

451 (8.5)

1664 (8.1)

1947 (7.4)

 Reported Drug Abuse

1835 (3.4)

*

*

94 (1.8)

423 (2.1)

697 (2.7)

 Reported Alcohol Dependence

5147 (9.6)

     

Trimester when prenatal care began

 1st

45,632 (84.8)

307 (77.3)

940 (86.3)

4659 (87.3)

17,575 (85.2)

22,032 (83.9)

 2nd

4846 (9.0)

66 (16.6)

93 (8.5)

341 (6.4)

1726 (8.4)

2619 (10.0)

 3rd

696 (1.3)

*

20 (1.8)

76 (1.4)

246 (1.2)

340 (1.3)

Multiparous sample

35,638

261

708

3354

13,591

17,651

 Previous Cesarean-section

9179 (25.8)

78 (29.9)

208 (29.4)

886 (26.4)

3520 (25.9)

4462 (25.3)

 Previous Preterm Birth

403 (1.1)

*

*

48 (1.4)

168 (1.2)

178 (1.0)

Interpregnancy Interval c

  < 6 months

2283 (6.4)

21 (8.1)

28 (4.0)

174 (5.2)

853 (6.3)

1207 (6.8)

 6–23 months

11,683 (32.8)

76 (29.1)

271 (38.3)

1142 (34.1)

4407 (32.4)

5748 (32.6)

 24–59 months

13,671 (38.4)

112 (42.9)

253 (35.7)

1233 (36.8)

5208 (38.3)

6843 (38.8)

  > 59 months

5371 (15.1)

34 (13.0)

95 (13.4)

527 (15.7)

2099 (15.4)

2610 (14.8)

*n < 16

aWIC Participation – Women, Infants and Children food and nutrition service

bDetermined by ICD-9 codes in maternal discharge records: preexisting diabetes (ICD-9 code 250 and 648.0), gestational diabetes (648.8), preexisting hypertension (642.0, 642.1, 642.2, 642.7), gestational hypertension (642.3), preeclampsia/eclampsia (642.4, 642.4, 642.6), infection (646.5, 646.6, 647), anemia (648.2), mental illness (648.4)

cNumber of months between the delivery date of the preceding live birth and the conception date of the index pregnancy

Correlations were moderate between diesel PM, ozone and traffic, ranging from 0.53 to 0.79 (Additional file 1: Appendix 1a). Nitrate and TCE were also moderately correlated (0.62; Additional file 1: Appendix 1b). Summary statistics of each of the indicators by preterm birth status is presented in Table 3. Although many are similar between the two groups, the Exposures score, PM2.5, Diesel PM, Toxic Release, Traffic, Drinking Water Score, Cadmium, Nitrate, Uranium, Solid Waste and Pollution Burden Score were all higher among preterm births.
Table 3

Descriptive statistics of environmental indicators by preterm birth status in Fresno County, 2009–2012 (N = 53,843)

Environmental exposure

Preterm Birth

Full Term Birth

< 37 weeks (N = 4560)

≥37 weeks (N = 49,283)

Exposures Score

   Mean (SD)

64.46 (9.83)

63.53 (10.34)

   Median (IQR)

65.23 (60.15–70.14)

64.71 (58.94–69.47)

 Ozone

   Mean (SD)

0.31 (0.09)

0.31 (0.09)

   Median (IQR)

0.32 (0.27–0.37)

0.32 (0.28–0.38)

 Pesticides

   Mean (SD)

452.78 (912.72)

477.23 (965.8)

   Median (IQR)

10.33 (0.00–505.73)

10.33 (0.00–524.69)

 PM2.5

   Mean (SD)

14.18 (1.13)

14.09 (1.26)

   Median (IQR)

14.28 (13.89–14.53)

14.25 (13.83–14.51)

 Diesel PM

   Mean (SD)

25.99 (17.66)

24.92 (17.54)

   Median (IQR)

22.96 (7.93–42.94)

20.79 (7.38–41.76)

 Toxic Release

   Mean (SD)

3111.81 (9510.54)

2874.46 (9198.78)

   Median (IQR)

469.15 (272.01–1109.65)

381.64 (236.93–1037.49)

 Traffic

   Mean (SD)

692.44 (471.82)

670.75 (467.61)

   Median (IQR)

621.44 (299.40–941.13)

605.60 (267.04–929.61)

 Drinking Water

   Mean (SD)

454.91 (114.09)

453.59 (117.42)

   Median (IQR)

406.83 (406.83–513.41)

406.83 (406.83–514.09)

  Arsenic

   Mean (SD)

1.38 (2.26)

1.40 (2.24)

   Median (IQR)

0.70 (0.70–0.84)

0.70 (0.70–0.86)

  Cadmium

   Mean (SD)

0.0007 (0.0076)

0.0006 (0.0068)

   Median (IQR)

0.00 (0.00–0.00)

0.00 (0.00–0.00)

  1,2-Dibromo-3-chloropropane (DBCP)

   Mean (SD)

0.03 (0.02)

0.03 (0.02)

   Median (IQR)

0.03 (0.03–0.03)

0.03 (0.03–0.03)

  Hexavalent chromium

   Mean (SD)

0.27 (0.63)

0.27 (0.62)

   Median (IQR)

0.00 (0.00–0.06)

0.00 (0.00–0.07)

  Lead

   Mean (SD)

0.13 (0.40)

0.14 (0.43)

   Median (IQR)

0.00 (0.00–0.02)

0.00 (0.00–0.02)

  Nitrate

   Mean (SD)

21.36 (7.36)

21.30 (7.68)

   Median (IQR)

25.30 (16.74–25.30)

25.30 (16.71–25.30)

  Perchlorate

   Mean (SD)

0.06 (0.33)

0.06 (0.32)

   Median (IQR)

0.00 (0.00–0.00)

0.00 (0.00–0.00)

  Trichloroethylene (TCE)

   Mean (SD)

0.10 (0.07)

0.09 (0.07)

   Median (IQR)

0.15 (0.00–0.15)

0.15 (0.00–0.15)

  Trihalomethane (THM)

   Mean (SD)

4.53 (9.80)

5.13 (11.22)

   Median (IQR)

2.66 (0.96–2.66)

2.66 (0.96–2.66)

  Uranium

   Mean (SD)

3.38 (1.75)

3.36 (1.88)

   Median (IQR)

3.12 (3.12–3.18)

3.12 (3.12–3.17)

  Maximum Contaminant Level (MCL) Violations

   Mean (SD)

0.85 (1.47)

0.85 (1.48)

   Median (IQR)

1.00 (0.00–1.00)

0.99 (0.00–1.00)

  Total coliform rule (TCR) Violations

   Mean (SD)

0.11 (0.31)

0.11 (0.32)

   Median (IQR)

0.00 (0.00–0.00)

0.00 (0.00–0.00)

Environmental Effects Score

   Mean (SD)

24.68 (19.50)

24.77 (19.33)

   Median (IQR)

20.15 (8.30–38.25)

20.45 (8.30–38.25)

 Cleanup Sites

   Mean (SD)

6.21 (13.05)

6.32 (13.00)

   Median (IQR)

1.00 (0.00–8.00)

1.15 (0.00–8.00)

 Groundwater Threats

   Mean (SD)

15.23 (18.67)

15.34 (18.78)

   Median (IQR)

9.56 (1.50–20.94)

9.56 (1.50–21.00)

 Hazardous Waste

   Mean (SD)

0.36 (1.07)

0.36 (1.10)

   Median (IQR)

0.05 (0.00–0.21)

0.05 (0.00–0.21)

 Imperial Water Bodies

   Mean (SD)

0.47 (1.35)

0.51 (1.36)

   Median (IQR)

0.00 (0.00–0.00)

0.00 (0.00–0.00)

 Solid Waste

   Mean (SD)

1.45 (2.76)

1.40 (2.76)

   Median (IQR)

0.00 (0.00–2.00)

0.00 (0.00–2.00)

Pollution Burden Score

   Mean (SD)

6.51 (1.04)

6.43 (1.07)

   Median (IQR)

6.33 (5.83–7.12)

6.24 (5.79–7.09)

Associations between the covariates and preterm birth included hypertension with pre-eclampsia, drug or alcohol abuse and previous preterm birth as maternal factors strongly associated with preterm birth (data not shown). Additionally, Hispanic, African-American and Asian mothers were more likely to have preterm birth compared to white mothers. Mothers with Medi-Cal payer status had higher risk of preterm birth. Additional risk factors for preterm birth included underweight BMI, diabetes, hypertension without pre-eclampsia, infection, anemia, mental illness, previous cesarean delivery, and short (< 6 months) or long (> 59 months) inter-pregnancy interval. Conversely, mothers that participated in WIC were less likely to deliver preterm.

Association between environmental pollutants and preterm birth

We found that the mothers in the highest quintile of Exposures score were two times as likely to have preterm birth (< 37 weeks), compared to the lowest quintile in the a priori variable adjustment regardless of different statistical adjustment settings (crude and stepwise adjustment, not shown). We also found the highest three quintiles of Pollution Burden score had statistically higher odds of preterm birth (Table 4).
Table 4

Crude and adjusted odds ratio of preterm birth across quintiles of CalEnviroScreen indicators and scores in Fresno County, 2009–2012 (N = 53,843)

Type of Preterm Birth

< 37 weeks (N = 4560)

≥37 weeks (N = 49,283)

  

Environmental exposure

N (%)

N (%)

cOR (95% CI)

aORa (95% CI)

Exposures Score

   0 – 19th percentile

17 (0.4)

380 (0.8)

Reference

Reference

   20 – 39th percentile

74 (1.6)

863 (1.8)

1.84 (1.09, 3.12)

1.73 (1.01, 2.97)

   40 – 59th percentile

151 (3.3)

1834 (3.7)

1.78 (1.08, 2.93)

1.85 (1.12, 3.06)

   60 – 79th percentile

673 (14.8)

8666 (17.6)

1.68 (1.04, 2.72)

1.64 (1.01, 2.65)

   80 – 100th percentile

3633 (79.7)

37,428 (76.0)

2.07 (1.28, 3.33)

2.00 (1.25, 3.23)

 Ozone

   0 – 19th percentile

Reference

Reference

   20 – 39th percentile

66 (0.1)

NC

NC

   40 – 59th percentile

178 (3.9)

2100 (4.3)

NC

NC

   60 – 79th percentile

631 (13.8)

6659 (13.5)

NC

NC

   80 – 100th percentile

3703 (81.2)

39,729 (80.6)

NC

NC

 Pesticides

   0 – 19th percentile

1768 (38.8)

19,587 (39.7)

Reference

Reference

   20 – 39th percentile

357 (7.8)

3486 (7.1)

1.12 (1.00, 1.26)

1.13 (1.01, 1.26)

   40 – 59th percentile

284 (6.2)

3082 (6.3)

1.02 (0.90, 1.16)

1.00 (0.88, 1.14)

  60 – 79th percentile

723 (15.9)

7565 (15.4)

1.05 (0.97, 1.15)

1.05 (0.96, 1.15)

   80 – 100th percentile

1428 (31.3)

15,563 (31.6)

1.02 (0.95, 1.09)

0.98 (0.92, 1.06)

 PM2.5

   0 – 19th percentile

20 (0.4)

315 (0.6)

Reference

Reference

   20 – 39th percentile

4.19 (0.56, 31.20)

4.02 (0.53, 30.23)

   40 – 59th percentile

38 (0.8)

650 (1.3)

0.93 (0.54, 1.59)

0.89 (0.51, 1.56)

   60 – 79th percentile

37 (0.8)

562 (1.1)

1.03 (0.60, 1.78)

1.07 (0.59, 1.94)

   80 – 100th percentile

4295 (94.2)

45,804 (92.9)

1.44 (0.93, 2.23)

1.36 (0.88, 2.11)

 Diesel Particulate Matter

   0 – 19th percentile

701 (15.4)

8391 (17.0)

Reference

Reference

   20 – 39th percentile

609 (13.4)

6616 (13.4)

1.09 (0.98, 1.22)

1.13 (1.02, 1.27)

   40 – 59th percentile

566 (12.4)

6582 (13.4)

1.03 (0.92, 1.15)

1.11 (0.99, 1.25)

   60 – 79th percentile

738 (16.2)

7690 (15.6)

1.14 (1.02, 1.26)

1.20 (1.08, 1.33)

   80 – 100th percentile

1946 (52.7)

20,004 (40.6)

1.15 (1.05, 1.25)

1.16 (1.06, 1.26)

 Toxic Release

   0 – 19th percentile

166 (3.6)

1915 (3.9)

Reference

Reference

   20 – 39th percentile

466 (10.2)

6073 (12.3)

0.89 (0.75, 1.07)

0.99 (0.82, 1.19)

   40 – 59th percentile

2256 (49.5)

25,163 (51.1)

1.03 (0.88, 1.21)

1.10 (0.94, 1.28)

   60 – 79th percentile

1049 (23.0)

10,059 (20.4)

1.18 (1.01, 1.39)

1.21 (1.03, 1.42)

   80 – 100th percentile

623 (13.7)

6073 (12.3)

1.17 (0.98, 1.38)

1.16 (0.97, 1.37)

 Traffic

   0 – 19th percentile

1668 (36.6)

18,996 (38.5)

Reference

Reference

   20 – 39th percentile

962 (21.1)

10,572 (21.5)

1.03 (0.95, 1.12)

1.04 (0.96, 1.12)

   40 – 59th percentile

982 (21.5)

10,003 (20.3)

1.11 (1.02, 1.20)

1.09 (1.01, 1.18)

   60 – 79th percentile

923 (20.2)

9423 (19.1)

1.11 (1.02, 1.20)

1.09 (1.00, 1.18)

   80 – 100th percentile

25 (0.6)

289 (0.6)

0.99 (0.66, 1.46)

0.99 (0.67, 1.47)

 Drinking Water

   0 – 19th percentile

29 (0.6)

527 (1.1)

Reference

Reference

   20 – 39th percentile

NC

NC

   40 – 59th percentile

382 (8.4)

4570 (9.3)

1.48 (1.01, 2.16)

1.50 (1.02, 2.19)

   60 – 79th percentile

2800 (61.4)

29,397 (59.7)

1.67 (1.16, 2.40)

1.67 (1.16, 2.41)

   80 – 100th percentile

1337 (29.3)

14,677 (29.8)

1.60 (1.11, 2.31)

1.67 (1.15, 2.41)

  Arsenic

   0 – 19th percentile

35 (0.8)

397 (0.8)

Reference

Reference

   20 – 39th percentile

190 (4.2)

2323 (4.7)

0.93 (0.65, 1.34)

0.91 (0.63, 1.31)

   40 – 59th percentile

3288 (72.1)

34,615 (70.2)

1.07 (0.77, 1.49)

1.04 (0.74, 1.45)

   60 – 79th percentile

455 (10.0)

5526 (11.2)

0.94 (0.67, 1.32)

0.93 (0.66, 1.31)

   80 – 100th percentile

580 (12.7)

6310 (12.8)

1.04 (0.74, 1.46)

0.98 (0.69, 1.38)

  Cadmium

   0 – 19th percentile

4336 (95.1)

46,850 (95.1)

Reference

Reference

   20 – 39th percentile

89 (0.2)

0.86 (0.41, 1.81)

0.86 (0.41, 1.82)

   40 – 59th percentile

NC

NC

   60 – 79th percentile

0.79 (0.11, 5.59)

0.69 (0.10, 4.90)

   80 – 100th percentile

216 (4.7)

2330 (4.7)

1.00 (0.87, 1.15)

1.00 (0.87, 1.14)

  1,2-Dibromo-3-chloropropane (DBCP)

   0 – 19th percentile

Reference

Reference

   20 – 39th percentile

NC

NC

   40 – 59th percentile

NC

NC

   60 – 79th percentile

216 (4.7)

2314 (4.7)

NC

NC

   80 – 100th percentile

4303 (94.4)

46,330 (94.0)

NC

NC

  Hexavalent chromium

   0 – 19th percentile

2731 (59.9)

28,808 (58.5)

Reference

Reference

   20 – 39th percentile

162 (3.6)

1799 (3.7)

0.95 (0.81, 1.12)

0.99 (0.85, 1.16)

   40 – 59th percentile

671 (14.7)

7035 (14.3)

1.01 (0.92, 1.09)

1.00 (0.92, 1.09)

   60 – 79th percentile

426 (9.3)

5077 (10.3)

0.89 (0.81, 0.99)

0.89 (0.81, 0.99)

   80 – 100th percentile

219 (4.8)

2151 (4.4)

1.07 (0.92, 1.22)

1.06 (0.92, 1.21)

  Lead

   0 – 19th percentile

2781 (61.0)

29,538 (59.9)

Reference

Reference

   20 – 39th percentile

51 (1.1)

544 (1.1)

1.00 (0.76, 1.31)

1.02 (0.77, 1.34)

   40 – 59th percentile

71 (1.6)

869 (1.8)

0.88 (0.69, 1.11)

0.85 (0.67, 1.07)

   60 – 79th percentile

772 (16.9)

8534 (17.3)

0.96 (0.89, 1.04)

0.98 (0.90, 1.06)

   80 – 100th percentile

873 (19.1)

9686 (19.7)

0.96 (0.89, 1.04)

1.00 (0.93, 1.08)

  Nitrate

   0 – 19th percentile

123 (2.7)

1232 (2.5)

Reference

Reference

   20 – 39th percentile

62 (1.4)

807 (1.6)

0.79 (0.58, 1.07)

0.77 (0.56, 1.06)

   40 – 59th percentile

56 (1.2)

969 (2.0)

0.60 (0.44, 0.83)

0.59 (0.42, 0.81)

   60 – 79th percentile

250 (5.5)

2570 (5.2)

0.98 (0.79, 1.21)

1.01 (0.81, 1.27)

   80 – 100th percentile

4057 (89.0)

43,593 (88.5)

0.94 (0.78, 1.12)

1.05 (0.88, 1.26)

  Perchlorate

   0 – 19th percentile

3928 (86.1)

42,060 (85.3)

Reference

Reference

   20 – 39th percentile

23 (0.5)

305 (0.6)

0.82 (0.54, 1.24)

0.88 (0.58, 1.33)

   40 – 59th percentile

90 (2.0)

1001 (2.0)

0.97 (0.78, 1.19)

0.99 (0.81, 1.23)

   60 – 79th percentile

164 (3.6)

1902 (3.9)

0.93 (0.80, 1.09)

0.97 (0.83, 1.14)

  80 – 100th percentile

355 (7.8)

4015 (8.2)

0.95 (0.85, 1.06)

0.97 (0.87, 1.08)

  Trichloroethylene (TCE)

   0 – 19th percentile

1244 (27.3)

13,123 (28.7)

Reference

Reference

   20 – 39th percentile

100 (0.2)

0.24 (0.06, 0.97)

0.27 (0.07, 1.07)

   40 – 59th percentile

24 (0.5)

388 (0.8)

0.72 (0.48, 1.08)

0.76 (0.51, 1.14)

   60 – 79th percentile

1144 (25.1)

12,506 (25.4)

1.04 (0.96, 1.12)

1.07 (0.98, 1.16)

   80 – 100th percentile

2134 (46.8)

22,054 (44.8)

1.09 (1.02, 1.17)

1.09 (1.01, 1.17)

  Trihalomethane (THM)

   0 – 19th percentile

4041 (88.6)

42,863 (87.0)

Reference

Reference

   20 – 39th percentile

182 (4.0)

2235 (4.5)

0.87 (0.75, 1.01)

0.86 (0.75, 1.00)

   40 – 59th percentile

270 (5.9)

3131 (6.4)

0.92 (0.81, 1.04)

1.01 (0.89, 1.15)

   60 – 79th percentile

112 (0.2)

1.12 (0.64, 1.98)

1.37 (0.78, 2.43)

   80 – 100th percentile

55 (1.2)

937 (1.9)

0.64 (0.49, 0.84)

0.62 (0.47, 0.81)

  Uranium

   0 – 19th percentile

29 (0.6)

527 (1.1)

Reference

Reference

   20 – 39th percentile

310 (6.8)

3787 (7.7)

1.45 (0.99, 2.12)

1.44 (0.98, 2.11)

   40 – 59th percentile

118 (2.6)

1614 (3.3)

1.31 (0.87, 1.96)

1.27 (0.85, 1.92)

   60 – 79th percentile

291 (6.4)

3061 (6.2)

1.66 (1.14, 2.44)

1.73 (1.18, 2.55)

   80 – 100th percentile

3668 (80.4)

38,646 (78.4)

1.66 (1.15, 2.40)

1.68 (1.17, 2.43)

  Maximum Contaminant Level (MCL)Violations

   0 – 19th percentile

1028 (22.5)

11,257 (22.8)

Reference

Reference

   20 – 39th percentile

65 (0.1)

0.18 (0.03, 1.29)

1.20 (0.03, 1.41)

   40 – 59th percentile

93 (0.2)

0.37 (0.12, 1.16)

0.40 (0.13, 1.25)

   60 – 79th percentile

43 (0.9)

634 (1.3)

0.76 (0.56, 1.03)

0.74 (0.54, 1.00)

   80 – 100th percentile

3473 (76.2)

37,122 (75.3)

1.02 (0.95, 1.10)

1.01 (0.94, 1.09)

  Total coliform rule (TCR) Violations

   0 – 19th percentile

3118 (68.4)

33,671 (68.3)

Reference

Reference

   20 – 39th percentile

113 (2.5)

1250 (2.5)

0.98 (0.81, 1.18)

1.00 (0.83, 1.21)

   40 – 59th percentile

95 (2.1)

953 (1.9)

1.07 (0.87, 1.31)

1.06 (0.86, 1.30)

   60 – 79th percentile

106 (2.3)

1267 (2.6)

0.91 (0.75, 1.11)

0.92 (0.76, 1.13)

   80 – 100th percentile

1128 (24.7)

12,142 (24.6)

1.00 (0.94, 1.07)

1.00 (0.93, 1.07)

Environmental Effects Score

   0 – 19th percentile

1725 (37.8)

18,282 (37.1)

Reference

Reference

   20 – 39th percentile

946 (20.8)

10,256 (20.8)

0.98 (0.90, 1.06)

0.97 (0.90, 1.06)

   40 – 59th percentile

556 (12.2)

6282 (12.8)

0.94 (0.86, 1.04)

0.92 (0.84, 1.01)

   60 – 79th percentile

837 (18.4)

9273 (18.8)

0.96 (0.88, 1.04)

0.93 (0.85, 1.01)

   80 – 100th percentile

496 (10.9)

5190 (10.5)

1.01 (0.92, 1.12)

0.94 (0.85, 1.05)

 Cleanup Sites

   0 – 19th percentile

2512 (55.1)

26,770 (54.3)

Reference

Reference

   20 – 39th percentile

537 (11.8)

5832 (11.8)

0.98 (0.90, 1.08)

1.01 (0.92, 1.11)

   40 – 59th percentile

561 (12.3)

6472 (13.1)

0.93 (0.85, 1.02)

0.90 (0.82, 0.99)

   60 – 79th percentile

486 (10.7)

5066 (10.3)

1.02 (0.93, 1.12)

1.01 (0.91, 1.11)

   80 – 100th percentile

464 (10.2)

5143 (10.4)

0.96 (0.87, 1.07)

0.94 (0.85, 1.03)

 Groundwater Threats

   0 – 19th percentile

1772 (38.9)

19,140 (38.8)

Reference

Reference

   20 – 39th percentile

683 (15.0)

8462 (15.1)

0.99 (0.91, 1.08)

1.00 (0.91, 1.09)

   40 – 59th percentile

976 (21.4)

10,124 (20.5)

1.04 (0.96, 1.12)

1.02 (0.95, 1.11)

   60 – 79th percentile

652 (14.3)

7472 (15.2)

0.95 (0.87, 1.04)

0.90 (0.82, 0.99)

   80 – 100th percentile

477 (10.5)

5085 (10.3)

1.01 (0.91, 1.12)

0.95 (0.86, 1.06)

 Hazardous Waste

   0 – 19th percentile

2274 (49.9)

24,740 (50.2)

Reference

Reference

   20 – 39th percentile

709 (15.6)

7424 (15.1)

1.04 (0.95, 1.13)

1.01 (0.93, 1.10)

   40 – 59th percentile

658 (14.4)

7179 (14.6)

1.00 (0.91, 1.09)

0.98 (0.90, 1.07)

   60 – 79th percentile

446 (9.8)

5020 (10.2)

0.97 (0.8, 1.07)

0.95 (0.86, 1.05)

   80 – 100th percentile

473 (10.4)

4920 (10.0)

1.04 (0.94, 1.15)

1.01 (0.92, 1.12)

 Imperial Water Bodies

   0 – 19th percentile

4031 (88.4)

42,996 (87.2)

Reference

Reference

   20 – 39th percentile

286 (6.3)

3410 (6.9)

0.90 (0.80, 1.02)

0.90 (0.80, 1.02)

   40 – 59th percentile

162 (3.6)

2011 (4.1)

0.87 (0.74, 1.02)

0.84 (0.72, 0.98)

   60 – 79th percentile

38 (0.8)

434 (0.9)

0.94 (0.68, 1.29)

0.88 (0.64, 1.21)

   80 – 100th percentile

43 (0.9)

432 (0.9)

1.06 (0.78, 1.43)

0.92 (0.68, 1.24)

 Solid Waste

   0 – 19th percentile

2858 (62.7)

31,070 (63.0)

Reference

Reference

   20 – 39th percentile

300 (6.6)

3482 (7.1)

0.94 (0.84, 1.06)

0.90 (0.79, 1.01)

   40 – 59th percentile

351 (7.7)

3939 (8.0)

0.97 (0.87, 1.09)

0.95 (0.85, 1.06)

   60 – 79th percentile

689 (15.1)

7190 (14.6)

1.04 (0.96, 1.13)

1.01 (0.93, 1.10)

   80 – 100th percentile

362 (7.9)

3602 (7.3)

1.08 (0.97, 1.21)

1.05 (0.94, 1.17)

 Pollution Burden Score

   0 – 19th percentile

17 (0.4)

380 (0.8)

Reference

Reference

   20 – 39th percentile

64 (1.4)

1025 (2.1)

1.37 (0.80, 2.34)

1.38 (0.79, 2.40)

   40 – 59th percentile

399 (8.8)

4929 (10.0)

1.75 (1.07, 2.84)

1.78 (1.09, 2.88)

   60 – 79th percentile

1780 (39.0)

18,838 (38.2)

2.02 (1.25, 3.25)

1.98 (1.23, 3.19)

   80 – 100th percentile

2288 (50.2)

23,989 (48.7)

2.03 (1.26, 3.28)

1.98 (1.23, 3.19)

cOR crude odds ratio, aOR adjusted odds ratio

aAdjusted for maternal race/ethnicity, age, education, payment for delivery

n < 16

NC not calculated (owing to lack of variability)

We found the highest quintile of drinking water contaminants was associated with higher odds of preterm birth (Table 4), especially spontaneous preterm birth (data not shown). Specifically, uranium concentrations in drinking water was associated with preterm birth and trichloroethylene (TCE) was associated with early preterm birth. Trihalomethanes (THM) concentrations were inversely associated with preterm birth.

The Exposures score, diesel PM and drinking water contaminants were more strongly associated with increased risk of early preterm birth in the low socioeconomic areas compared to the high socioeconomic areas (Table 5). Similar increases were also observed for early preterm birth among the low SES areas compared to high SES areas (Additional file 1: Appendix 3).
Table 5

Crude and adjusted* odds ratio of preterm birth comparing above versus below the median of environmental exposure stratified by census tract-level socioeconomic status (SES) in Fresno County, 2009–2012 (N = 53,843)

Environmental Exposure

Low SES

High SES

< 37 weeks

≥37 weeks

  

< 37 weeks

≥37 weeks

  

N (%)

N (%)

cOR (95% CI)

aOR* (95% CI)

N (%)

N (%)

cOR (95% CI)

aOR* (95% CI)

Sample

2455

24,998

  

2105

24,285

  

Exposures Score

    < 50th

924 (37.6)

10,360 (41.4)

Reference

Reference

1172 (55.7)

14,101 (58.1)

Reference

Reference

    ≥ 50th

1531 (62.4)

14,638 (58.6)

1.16 (1.07, 1.25)

1.16 (1.06, 1.25)

933 (44.3)

10,184 (41.9)

1.09 (1.00, 1.19)

1.07 (0.98, 1.17)

 Ozone

    < 50th

1481 (60.4)

15,063 (60.3)

Reference

Reference

777 (36.9)

8752 (36.0)

Reference

Reference

    ≥ 50th

974 (39.7)

9935 (39.7)

1.00 (0.92, 1.08)

1.00 (0.92, 1.08)

1295 (61.5)

14,916 (61.4)

0.98 (0.90, 1.07)

0.99 (0.90, 1.08)

 Pesticides

    < 50th

1019 (41.5)

9872 (39.5)

Reference

Reference

1244 (59.1)

14,691 (60.5)

Reference

Reference

    ≥ 50th

1436 (58.5)

15,126 (60.5)

0.93 (0.86, 1.00)

0.92 (0.85, 1.00)

861 (40.9)

9594 (39.5)

1.05 (0.97, 1.15)

1.05 (0.97, 1.15)

 PM2.5

    < 50th

592 (24.1)

6434 (25.7)

Reference

Reference

1425 (67.7)

17,259 (71.1)

Reference

Reference

    ≥ 50th

1736 (70.7)

17,286 (69.2)

1.08 (0.99, 1.19)

1.07 (0.98, 1.18)

650 (30.9)

6492 (26.7)

1.19 (1.09, 1.31)

1.15 (1.05, 1.26)

 Diesel PM

    < 50th

1096 (44.6)

12,166 (48.7)

Reference

Reference

1051 (49.9)

12,587 (51.8)

Reference

Reference

    ≥ 50th

1359 (55.4)

12,832 (51.3)

1.16 (1.07, 1.25)

1.16 (1.07, 1.25)

1054 (50.1)

11,698 (48.2)

1.07 (0.98, 1.17)

1.04 (0.96, 1.14)

 Toxic Release

    < 50th

735 (29.9)

8106 (32.4)

Reference

Reference

1315 (62.5)

16,391 (67.5)

Reference

Reference

    ≥ 50th

1720 (70.1)

16,892 (67.6)

1.11 (1.02, 1.21)

1.11 (1.02, 1.22)

790 (37.5)

7894 (32.5)

1.22 (1.12, 1.34)

1.18 (1.08, 1.29)

 Traffic

    < 50th

1240 (50.5)

13,288 (52.9)

Reference

Reference

972 (46.2)

11,579 (47.7)

Reference

Reference

    ≥ 50th

1215 (49.5)

11,770 (47.1)

1.09 (1.01, 1.18)

1.09 (1.01, 1.18)

1133 (53.8)

12,706 (52.3)

1.05 (0.97, 1.15)

1.03 (0.95, 1.13)

 Drinking Water

    < 50th

143 (5.8)

1863 (7.5)

Reference

Reference

402 (19.1)

4713 (19.4)

Reference

Reference

    ≥ 50th

2312 (94.2)

23,135 (92.6)

1.27 (1.08, 1.51)

1.29 (1.09, 1.52)

1703 (80.90

19,572 (80.6)

1.02 (0.91, 1.14)

1.00 (0.90, 1.12)

  Arsenic

    < 50th

234 (9.5)

2467 (9.9)

Reference

Reference

493 (23.4)

6230 (25.7)

Reference

Reference

    ≥ 50th

2221 (90.5)

22,531 (90.1)

1.04 (0.91, 1.19)

1.01 (0.88, 1.16)

1612 (76.6)

18,055 (74.4)

1.12 (1.01, 1.24)

1.09 (0.99, 1.21)

  Cadmium

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

2455 (100.0)

24,998 (100.0)

NC

NC

2105 (100.0)

24,285 (100.0)

NC

NC

  1,2-Dibromo-3-chloropropane (DBCP)

    < 50th

979 (39.9)

10,100 (40.4)

Reference

Reference

604 (28.7)

6924 (28.5)

Reference

Reference

    ≥ 50th

1476 (60.1)

14,898 (59.5)

1.02 (0.94, 1.11)

1.02 (0.94, 1.10)

1472 (69.9)

16,834 (69.3)

1.00 (0.91, 1.10)

1.00 (0.91, 1.10)

  Hexavalent Chromium

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

2147 (87.5)

21,281 (85.1)

NC

NC

2105 (100.0)

24,285 (100.0)

NC

NC

  Lead

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

2455 (100.0)

24,998 (100.0)

NC

NC

2105 (100.0)

24,285 (100.0)

NC

NC

  Nitrate

    < 50th

1019 (41.5)

10,330 (41.3)

Reference

Reference

1226 (58.2)

14,410 (59.3)

Reference

Reference

    ≥ 50th

1436 (58.5)

14,668 (58.7)

0.99 (0.92, 1.08)

0.99 (0.92, 1.07)

879 (41.8)

9875 (40.7)

1.04 (0.96, 1.14)

1.03 (0.94, 1.12)

  Perchlorate

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

2455 (100.0)

24,998 (100.0)

NC

NC

2105 (100.0)

24,285 (100.0)

NC

NC

  Trichloroethylene (TCE)

    < 50th

1036 (42.2)

11,114 (44.5)

Reference

Reference

1182 (56.2)

13,607 (56.0)

Reference

Reference

    ≥ 50th

1419 (57.8)

13,884 (55.5)

1.09 (1.00, 1.18)

1.07 (1.00, 1.17)

923 (43.9)

10,678 (44.0)

1.00 (0.91, 1.09)

0.99 (0.91, 1.08)

  Trihalomethane (THM)

    < 50th

1145 (46.6)

11,838 (47.4)

Reference

Reference

859 (40.8)

9593 (39.5)

Reference

Reference

    ≥ 50th

1310 (53.4)

13,160 (52.6)

1.03 (0.95, 1.11)

1.01 (0.93, 1.09)

1246 (59.2)

14,692 (60.5)

0.95 (0.87, 1.04)

0.96 (0.88, 1.04)

  Uranium

    < 50th

487 (19.8)

5535 (22.1)

Reference

Reference

294 (14.0)

3801 (15.7)

Reference

Reference

    ≥ 50th

1968 (80.2)

19,463 (77.9)

1.15 (1.04, 1.27)

1.14 (1.03, 1.25)

1679 (79.8)

18,9488 (78.0)

1.13 (1.00, 1.28)

1.12 (0.99, 1.27)

  Maximum Contaminant Level (MCL)Violations

    < 50th

1030 (42.0)

10,577 (42.3)

Reference

Reference

1207 (57.3)

13,998 (57.6)

Reference

Reference

    ≥ 50th

1425 (58.0)

14,4211 (57.7)

1.01 (0.94, 1.10)

1.00 (0.92, 1.09)

898 (42.7)

10,287 (42.4)

1.01 (0.93, 1.10)

1.00 (0.92, 1.09)

  Total coliform rule (TCR) Violations

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

2455 (100.0)

24,998 (100.0)

NC

NC

2105 (100.0)

24,285 (100.0)

NC

NC

Environmental Effects Score

    < 50th

1036 (42.2)

10,073 (40.3)

Reference

Reference

1254 (59.6)

14,381 (59.2)

Reference

Reference

    ≥ 50th

1419 (57.8)

14,925 (59.7)

0.93 (0.86, 1.01)

0.92 (0.85, 1.00)

851 (40.4)

9904 (40.8)

0.99 (0.90, 1.08)

0.98 (0.89, 1.07)

 Cleanup Sites

    < 50th

1168 (47.6)

11,805 (47.2)

Reference

Reference

1139 (54.1)

12,799 (52.7)

Reference

Reference

    ≥ 50th

1287 (52.4)

13,193 (52.8)

0.99 (0.91, 1.07)

0.97 (0.90, 1.05)

966 (45.9)

11,486 (47.3)

0.95 (0.87, 1.03)

0.95 (0.87, 1.04)

 Groundwater Threats

    < 50th

973 (39.6)

9545 (38.2)

Reference

Reference

1290 (61.3)

15,062 (62.0)

Reference

Reference

    ≥ 50th

1482 (60.4)

15,453 (61.8)

0.95 (0.87, 1.03)

0.93 (0.86, 1.01)

815 (38.7)

9223 (38.)

1.03 (0.94, 1.12)

1.02 (0.94, 1.12)

 Hazardous Waste

    < 50th

1023 (41.7)

10,195 (40.8)

Reference

Reference

1240 (58.9)

14,430 (59.4)

Reference

Reference

    ≥ 50th

1432 (58.3)

14,803 (59.2)

0.97 (0.89, 1.05)

0.98 (0.90, 1.06)

865 (41.1)

9855 (40.6)

1.02 (0.93, 1.11)

0.99 (0.91, 1.09)

 Impaired Water Bodies

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

2455 (100.0)

24,998 (100.0)

NC

NC

2105 (100.0)

24,285 (100.0)

NC

NC

 Solid Waste

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

2455 (100.0)

24,998 (100.0)

NC

NC

2105 (100.0)

24,285 (100.0)

NC

NC

Pollution Burden Score

    < 50th

827 (33.7)

8615 (35.5)

Reference

Reference

1380 (65.6)

16,068 (66.2)

Reference

Reference

    ≥ 50th

1628 (66.3)

16,383 (65.5)

1.03 (0.95, 1.12)

1.04 (0.96, 1.13)

725 (34.4)

8217 (33.8)

1.03 (0.94, 1.12)

1.03 (0.93, 1.11)

NC Not Calculated, cOR crude odds ratio, aOR adjusted odds ratio

*Adjusted for maternal race/ethnicity, age, education, payment for delivery

SES defined as “Socioeconomic Factors” score from the CalEnviroScreen, which includes the following variables derived from the US Census American Community Survey: educational attainment, linguistic isolation (households where no one over 14 years of age speaks English very well), poverty and unemployment

We also found the association between diesel PM and preterm birth was slightly higher among non-white and non-Hispanic women, particularly for early preterm birth after adjusting for age, education and payment for delivery costs (Table 6).
Table 6

Crude and adjusted odds ratio of preterm birth comparing above versus below the median of environmental exposure stratified by race/ethnicity in Fresno County, 2009–2012 (N = 53,843)

Environmental Exposure

White non-Hispanic

Non-White*, Non-Hispanic

Hispanic

<  37 weeks

≥37 weeks

cOR (95% CI)

aOR (95% CI)

<  37 weeks

≥37 weeks

cOR (95% CI)

aOR (95% CI)

<  37 weeks

≥37 weeks

cOR (95% CI)

aOR (95% CI)

N (%)

N (%)

N (%)

N (%)

N (%)

N (%)

Sample

773

9847

  

1081

9840

  

2706

29,596

  

Exposures Score

    < 50th

446 (57.7)

5952 (60.4)

Reference

Reference

363 (33.6)

3676 (37.4)

Reference

Reference

1287 (47.6)

14,833 (50.1)

Reference

Reference

    ≥ 50th

327 (42.3)

3895 (39.6)

1.11 (0.96, 1.28)

1.08 (0.93, 1.24)

718 (66.4)

6164 (62.6)

1.16 (1.02, 1.32)

1.08 (0.94, 1.22)

1419 (52.4)

14,763 (49.9)

1.10 (1.02, 1.18)

1.10 (1.02, 1.19)

 Ozone

    < 50th

298 (38.6)

3527 (35.8)

Reference

Reference

467 (43.2)

4402 (44.7)

Reference

Reference

1493 (55.2)

15,886 (53.7)

Reference

Reference

    ≥ 50th

466 (60.3)

6149 (62.5)

0.90 (0.78, 1.05)

0.90 (0.78, 1.04)

611 (56.5)

5377 (54.6)

1.06 (0.94, 1.20)

1.07 (0.95, 1.21)

1192 (44.1)

13,325 (45.0)

0.96 (0.89, 1.03)

0.97 (0.89, 1.04)

 Pesticides

    < 50th

456 (59.0)

5849 (59.4)

Reference

Reference

680 (62.9)

6035 (61.3)

Reference

Reference

1127 (41.7)

12,679 (42.8)

Reference

Reference

    ≥ 50th

317 (41.0)

3998 (40.6)

1.02 (0.88, 1.17)

1.03 (0.89, 1.19)

401 (37.1)

3805 (38.7)

0.94 (0.83, 1.07)

0.96 (0.84, 1.08)

1579 (58.4)

16,917 (57.2)

1.05 (0.97, 1.13)

1.03 (0.95, 1.11)

 PM2.5

    < 50th

526 (68.1)

7053 (71.6)

Reference

Reference

502 (46.4)

4926 (50.1)

Reference

Reference

989 (36.6)

11,714 (39.6)

Reference

Reference

    ≥ 50th

237 (30.7)

2658 (27.0)

1.18 (1.01, 1.38)

1.14 (0.97, 1.33)

558 (51.6)

4627 (47.0)

1.16 (1.03, 1.31)

1.10 (0.97, 1.24)

1591 (58.8)

16,493 (55.7)

1.13 (1.04, 1.22)

1.11 (1.02, 1.20)

 Diesel PM

    < 50th

391 (50.6)

5351 (54.3)

Reference

Reference

363 (33.6)

3891 (39.5)

Reference

Reference

1393 (51.5)

15,511 (52.4)

Reference

Reference

    ≥ 50th

382 (49.4)

4496 (45.7)

1.15 (1.00, 1.32)

1.10 (0.95, 1.27)

718 (66.4)

5949 (60.5)

1.26 (1.11, 1.43)

1.16 (1.02, 1.32)

1313 (48.5)

14,085 (47.6)

1.03 (0.96, 1.12)

1.03 (0.96, 1.12)

 Toxic Release

    < 50th

505 (65.3)

6961 (70.7)

Reference

Reference

381 (35.3)

4085 (41.5)

Reference

Reference

1164 (43.0)

13,451 (45.5)

Reference

Reference

    ≥ 50th

268 (34.7)

2886 (29.3)

1.26 (1.08, 1.46)

1.20 (1.03, 1.40)

700 (64.8)

5755 (58.5)

1.27 (1.12, 1.44)

1.17 (1.03, 1.33)

1542 (57.0)

16,145 (54.6)

1.09 (1.01, 1.18)

1.09 (1.01, 1.17)

 Traffic

    < 50th

402 (52.0)

5334 (54.2)

Reference

Reference

378 (35.0)

3960 (40.2)

Reference

Reference

1432 (52.9)

15,513 (52.4)

Reference

Reference

    ≥ 50th

371 (48.0)

4513 (45.8)

1.08 (0.94, 1.25)

1.04 (0.90, 1.20)

703 (65.0)

5880 (59.8)

1.23 (1.08, 1.39)

1.15 (1.01, 1.31)

1274 (47.1)

14,083 (47.60

0.98 (0.91, 1.06)

0.99 (0.91, 1.07)

 Drinking Water

    < 50th

158 (20.4)

1953 (19.8)

Reference

Reference

98 (9.1)

980 (10.0)

Reference

Reference

289 (10.7)

3643 (12.3)

Reference

Reference

    ≥ 50th

615 (79.6)

7894 (80.2)

0.97 (0.81, 1.15)

0.95 (0.80, 1.13)

983 (90.9)

8860 (90.0)

1.10 (0.89, 1.35)

1.00 (0.81, 1.24)

2417 (89.3)

25,953 (87.7)

1.16 (1.03, 1.31)

1.15 (1.02, 1.30)

  Arsenic

    < 50th

225 (29.1)

2843 (28.9)

Reference

Reference

130 (12.0)

1471 (15.0)

Reference

Reference

372 (13.8)

4383 (14.8)

Reference

Reference

    ≥ 50th

548 (70.9)

7004 (71.3)

0.99 (0.85, 1.16)

0.97 (0.83, 1.14)

951 (88.0)

8369 (85.1)

1.26 (1.05, 1.51)

1.13 (0.94, 1.36)

2334 (86.3)

25,213 (85.2)

1.08 (0.97, 1.21)

1.07 (0.96, 1.19)

  Cadmium

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

773 (100.0)

9847 (100.0)

NC

NC

1081 (100.0)

9840 (100.0)

NC

NC

2706 (100.0)

29,596 (100.0)

NC

NC

  1,2-Dibromo-3-chloropropane (DBCP)

    < 50th

249 (32.2)

3094 (31.4)

Reference

Reference

313 (39.0)

2891 (29.4)

Reference

Reference

1021 (37.7)

11,039 (37.3)

Reference

Reference

    ≥ 50th

518 (67.0)

6640 (67.4)

0.97 (0.84, 1.13)

0.97 (0.83, 1.13)

766 (70.9)

6909 (70.2)

1.02 (0.90, 1.17)

1.00 (0.87, 1.14)

1664 (61.5)

18,183 (61.4)

0.99 (0.92, 1.07)

1.00 (0.92, 1.08)

  Hexavalent Chromium

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

773 (100.0)

9847 (100.0)

NC

NC

1081 (100.0)

9840 (100.0)

NC

NC

2706 (100.0)

29,596 (100.0)

NC

NC

  Lead

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

773 (100.0)

9847 (100.0)

NC

NC

1081 (100.0)

9840 (100.0)

NC

NC

2706 (100.0)

29,596 (100.0)

NC

NC

  Nitrate

    < 50th

444 (57.4)

5792 (58.8)

Reference

Reference

430 (39.8)

4435 (45.1)

Reference

Reference

1371 (50.7)

14,513 (49.0)

Reference

Reference

    ≥ 50th

329 (42.6)

4055 (41.2)

1.05 (0.91, 1.22)

1.02 (0.89, 1.18)

651 (60.2)

5405 (54.9)

1.22 (1.08, 1.37)

1.12 (1.00, 1.27)

1335 (49.3)

15,083 (51.0)

0.94 (0.87, 1.02)

0.94 (0.87, 1.01)

  Perchlorate

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

773 (100.0)

9847 (100.0)

NC

NC

1081 (100.0)

9840 (100.0)

NC

NC

2706 (100.0)

29,596 (100.0)

NC

NC

  Trichloroethylene (TCE)

    < 50th

434 (556.1)

5472 (55.6)

Reference

Reference

387 (35.8)

3949 (40.1)

Reference

Reference

1397 (51.6)

15,300 (51.7)

Reference

Reference

    ≥ 50th

339 (43.9)

4375 (44.4)

0.98 (0.85, 1.13)

0.95 (0.82, 1.10)

694 (64.2)

5891 (59.9)

1.18 (1.04, 1.34)

1.07 (0.94, 1.22)

1309 (48.4)

14,296 (48.3)

1.00 (0.93, 1.08)

1.00 (0.93, 1.08)

  Trihalomethane (THM)

    < 50th

303 (39.2)

3904 (39.7)

Reference

Reference

389 (36.0)

3591 (36.5)

Reference

Reference

1312 (48.5)

13,936 (47.1)

Reference

Reference

    ≥ 50th

470 (60.8)

5943 (60.4)

1.02 (0.88, 1.18)

1.01 (0.87, 1.16)

692 (64.0)

6249 (63.5)

1.02 (0.90, 1.15)

0.98 (0.86, 1.11)

1394 (51.5)

15,660 (52.9)

0.95 (0.88, 1.02)

0.95 (0.88, 1.02)

  Uranium

    < 50th

121 (15.7)

1781 (18.1)

Reference

Reference

130 (12.0)

1304 (13.3)

Reference

Reference

530 (19.6)

6251 (21.1)

Reference

Reference

    ≥ 50th

584 (75.6)

7293 (74.1)

1.17 (0.96, 1.42)

1.17 (0.96, 1.42)

931 (86.1)

8222 (83.6)

1.12 (0.93, 1.35)

1.07 (0.89, 1.29)

2132 (78.8)

22,896 (77.4)

1.09 (0.99, 1.20)

1.10 (1.00, 1.21)

  Maximum Contaminant Level (MCL)Violations

    < 50th

451 (58.3)

5704 (57.9)

Reference

Reference

429 (39.7)

4234 (43.0)

Reference

Reference

1357 (50.2)

14,637 (49.5)

Reference

Reference

    ≥ 50th

322 (41.7)

4143 (42.1)

0.98 (0.85, 1.14)

0.95 (0.83, 1.11)

652 (60.3)

5606 (57.0)

1.13 (1.00, 1.28)

1.03 (0.91, 1.17)

1349 (49.9)

14,959 (50.5)

0.98 (0.90, 1.05)

0.97 (0.90, 1.04)

  Total coliform rule (TCR) Violations

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

773 (100.0)

9847 (100.0)

NC

NC

1081 (100.0)

9840 (100.0)

NC

NC

2706 (100.0)

29,596 (100.0)

NC

NC

Environmental Effects Score

    < 50th

444 (57.4)

5779 (58.7)

Reference

Reference

643 (59.5)

5668 (57.6)

Reference

Reference

1203 (44.5)

13,007 (44.0)

Reference

Reference

    ≥ 50th

329 (42.6)

4068 (41.3)

1.05 (0.91, 1.21)

1.04 (0.90, 1.20)

438 (40.5)

4172 (42.4)

0.93 (0.83, 1.05)

0.92 (0.81, 1.04)

1503 (55.5)

16,589 (56.1)

0.98 (0.91, 1.06)

0.97 (0.89, 1.04)

 Cleanup Sites

    < 50th

404 (52.3)

5228 (53.1)

Reference

Reference

584 (54.1)

5144 (52.3)

Reference

Reference

1318 (48.7)

14,232 (48.1)

Reference

Reference

    ≥ 50th

369 (47.7)

4619 (46.9)

1.03 (0.90, 1.19)

1.03 (0.90, 1.19)

496 (45.9)

4696 (47.7)

0.94 (0.83, 1.05)

0.93 (0.83, 1.05)

1388 (51.3)

15,364 (51.9)

0.98 (0.91, 1.05)

0.97 (0.90, 1.05)

 Groundwater Threats

    < 50th

440 (56.9)

5861 (59.5)

Reference

Reference

619 (57.3)

5456 (55.5)

Reference

Reference

1204 (44.5)

13,290 (44.9)

Reference

Reference

    ≥ 50th

333 (43.1)

3986 (40.5)

1.10 (0.96, 1.27)

1.09 (0.95, 1.26)

462 (42.7)

4384 (44.6)

0.94 (0.83, 1.06)

0.92 (0.81, 1.04)

1502 (55.5)

16,306 (55.1)

1.02 (0.94, 1.10)

1.00 (0.93, 1.08)

 Hazardous Waste

    < 50th

437 (56.5)

5951 (60.4)

Reference

Reference

592 (54.8)

5170 (52.5)

Reference

Reference

1234 (45.6)

13,504 (45.6)

Reference

Reference

    ≥ 50th

336 (43.5)

3896 (39.6)

1.16 (1.01, 1.34)

1.12 (0.97, 1.30)

489 (45.2)

4670 (47.5)

0.92 (0.82, 1.04)

0.90 (0.80, 1.02)

1472 (54.4)

16,092 (54.4)

1.00 (0.93, 1.08)

1.00 (0.92, 1.07)

 Impaired Water Bodies

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

773 (100.0)

9847 (100.0)

NC

NC

1081 (100.0)

9840 (100.0)

NC

NC

2706 (100.0)

29,596 (100.0)

NC

NC

 Solid Waste

    < 50th

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

0 (0.0)

0 (0.0)

Reference

Reference

    ≥ 50th

773 (100.0)

9847 (100.0)

NC

NC

1081 (100.0)

9840 (100.0)

NC

NC

2706 (100.0)

29,596 (100.0)

NC

NC

Pollution Burden Score

    < 50th

474 (61.3)

6263 (63.6)

Reference

Reference

515 (47.6)

4827 (49.1)

Reference

Reference

1218 (45.0)

13,593 (45.9)

Reference

Reference

    ≥ 50th

299 (38.7)

3584 (36.4)

1.09 (0.95, 1.26)

1.08 (0.93, 1.25)

566 (52.4)

5013 (51.0)

1.05 (0.93, 1.19)

1.00 (0.98, 1.12)

1488 (55.0)

16,003 (54.1)

1.03 (0.96, 1.12)

1.03 (0.95, 1.11)

NC Not Calculated, cOR crude odds ratio, aOR adjusted odds ratio

*Asian, African-American, Other

Adjusted for maternal race/ethnicity, age, education, payment for delivery

Sensitivity analyses

In logistic regression models of preterm birth (< 37 weeks gestation) examining one indicator at a time continuously, two pollutant measures were statistically associated with preterm birth: interquartile range increases in PM2.5 and Pollution Burden Score were associated with 6% increases in odds of preterm birth after adjustment for education, payer of delivery, maternal age and race/ethnicity. Diesel PM, traffic density and Trichloroethylene concentration (in drinking water) were associated with 26.3% increased odds of early preterm birth (26%, 10%, 16%, respectively, Additional file 1: Appendix 2). The associations were consistent between toxic releases and preterm across all race/ethnicity groups, but highest for white, non-Hispanic early preterm births (Additional file 1: Appendix 4). Pesticides were found to be inversely associated with early preterm birth (data not shown).

When all individual environmental indicators and social factors were included in the same model, PM2.5 and unemployment, maternal age > 34, Medi-Cal payer of delivery and African-American race were associated with preterm birth (data not shown). Results examining raw scores were comparable to those of the percentiles.

Discussion

Overall, the current study found small but consistent associations between pollution exposure and preterm birth in Fresno County. Although many of the individual pollutants were not associated with preterm birth, the cumulative scores were consistently associated with preterm birth, including the Exposures score, drinking water contaminants and Pollution Burden score. Novel exposures, such as the toxic releases from facilities, were identified as a potential contributor to preterm birth in Fresno County. There was an exposure-response of increased risk of preterm birth across quintiles of Pollution Burden scores. Furthermore, the relationship between pollution and preterm birth was stronger among areas with lower SES.

Some risk factors of preterm birth, such as hypertension, have large associations though only affect a small portion of the population. The associations found with pollution were smaller, but may affect a larger portion of births across the population. Pollution may be exacerbating diseases and health issues that lead to preterm birth (e.g., hypertension) [34], or operating directly through toxic exposures (through a variety of possible mechanisms) [35].

The results did not differ considerably when restricted to spontaneous preterm birth. In some cases, results were stronger among the more severe early preterm birth (less than 34 weeks). The drinking water contaminant, THM, was associated with a decrease in preterm birth; however, it can be inversely correlated with other contaminants because it is a disinfection by-product commonly found in metropolitan areas.

Our findings add to the literature on environmental risk factors and preterm birth. For example, in previous studies in CA, we found small but consistent effects of air pollution on risk of preterm birth using air pollution measurements at the geocoded residence [26, 36, 37]. Along with the current study, two additional studies found stronger associations between air pollutants and preterm birth for early preterm birth [26, 37]. Additionally, an interaction was also observed between air pollution and neighborhood SES using three U.S. Census indicators at the block group level (unemployment, poverty, income from public assistance) [26] in our previous study in the Central Valley of California. Compared to previous studies of air pollution with more precise exposure assessment, our current results are likely underestimated owing to non-differential exposure misclassification. The trade-off of the potential measurement errors is the ability to combine multiple exposures and examine cumulative pollution effects.

Consistent with previous work on environmental justice, we observed higher pollution burden among those who were non-White and of lower education and income. Additionally, we found stronger, though not statistically different, associations between some environmental indicators and preterm birth in low SES areas. This is consistent with the concept of ‘double jeopardy’ of environmental and socioeconomic stressors [24]. Further work in this area comparing the entire state of California may be more suitable to demonstrate this occurrence. Overall, there were not considerable differences in the association between pollution and preterm birth between racial/ethnic groups.

Notably, WIC participation, which was associated with high pollution burden and requires low SES, was protective against preterm birth. This is an example of a program that may be having a positive effect on reducing preterm birth in Fresno county. The addition of similar programs, which provide access to supplemental foods, healthcare referrals and nutritional education for pregnant women, may further reduce preterm birth in low-income areas.

Despite the large inclusion of the population, our study did have several limitations. One limitation is the imprecise exposure assessment both geographically and temporally. In some cases, the linkage between the birth records and the census tract were not available and this may have resulted in bias, given changes in census tracts are often a result of population growth. The exposure assessment was at the census tract level and the years were pooled for most data sources. Additionally, the CalEnviroScreen was designed as a screening level tool and does not include specific pollutants or chemical exposures that may be affecting this study population. We examined many indicators of pollution that included nested summary measures, which led to many comparisons. Although we did not adjust for multiple comparisons, we present these results as exploratory. Some women may have had two or possibly more births during this time period (2009–2012); however, we were unable to link them and control for these correlated events. Lastly, we assumed that mothers lived constantly throughout their pregnancy in the maternal residence recorded in the birth certificate without relocating from other regions and did not account for time activity patterns or time spent in other geographical areas.

The CalEnviroScreen is a unique tool devised to identify areas of high pollution burden and vulnerable populations and has the benefit of informing epidemiologic studies. Strengths of this study include our ability to include a large set of pollution indicators both individually and cumulatively across a broad geographic area. Additionally, we were able to include all singleton births in Fresno County with detailed demographic and medical information from medical discharge records. Further, our results find a stronger association with the Exposures score, which makes sense as this score consists of monitoring data that is likely to be more representative of actual exposures in the population.

Conclusion

Our study provides an initial investigation of the CalEnviroScreen as an epidemiologic tool to help elucidate a host of environmental and social factors that contribute to preterm birth. As a screening tool designed to discern communities that assume disproportionate environmental burdens in California, the CalEnviroScreen provides data for environmental justice research. Future studies could expand to the entire state of California and aim to include additional sources of data such as biomonitoring and genomics that could confirm exposure levels and identify pathways by which environmental pollutants contribute to preterm birth.

Abbreviations

BMI: 

Body mass index

CA: 

California

OSHPD: 

Office of Statewide Health Planning and Development

PM2.5

Particulate matter < 2.5 μm in aerodynamic diameter

SES: 

Socioeconomic status

Declarations

Funding

This research was supported by funding from Mark and Lynn Benioff – Preterm Birth Initiative (UCSF7027075) and the National Institutes of Health (NIEHS R00ES021470, NLM K01LM012381).

Availability of data and materials

Data for the CalEnviroScreen are publicly available online from the Office of Environmental Health Hazard Assessment of the State of California (https://oehha.ca.gov/calenviroscreen).

Data on the Birth Cohort File are not publicly available but were obtained from the Office of Statewide Health Planning and Development (https://www.oshpd.ca.gov).

Authors’ contributions

AP was the primary author of the manuscript. HH and RB analyzed the merged CalEnviroScreen and Birth Cohort File. LA provided additional data on water contaminants and insight into interpretation of the environmental data. MJ, LJ, MS, TW were major contributors in writing the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Methods and protocols for the study were approved by the Committee for the Protection of Human Subjects within the Health and Human Services Agency of the State of California.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, 550 16th Street, Mail Stop 0132, San Francisco, CA 94143, USA
(2)
Department of Pediatrics, University of California, San Francisco, USA
(3)
Department of Pediatrics, University of California, San Diego, USA
(4)
Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Sacramento, USA
(5)
Calit2/Qualcomm Institute, University of California, San Diego, USA
(6)
Department of Epidemiology and Biostatistics, University of California, San Francisco, USA

References

  1. McCormick MC, Litt JS, Smith VC, Zupancic JA. Prematurity: an overview and public health implications. Annu Rev Public Health. 2011;32:367–79.View ArticleGoogle Scholar
  2. Marlow N, Wolke D, Bracewell MA, Samara M. Neurologic and developmental disability at six years of age after extremely preterm birth. N Engl J Med. 2005;352(1):9–19.View ArticleGoogle Scholar
  3. Anderson P, Doyle LW, Group VICS. Neurobehavioral outcomes of school-age children born extremely low birth weight or very preterm in the 1990s. JAMA. 2003;289(24):3264–72.View ArticleGoogle Scholar
  4. Hamilton B, Martin J, Osterman M, et al.: Births: final data for 2014. National vital statistics reports. Hyattsville: National Center for Health Statistics 2015; 64(12).Google Scholar
  5. Behrman R, Butler A. Institute of Medicine (US). Committee on understanding premature birth and assuring healthy outcomes. Preterm birth: causes, consequences, and prevention. Washington, DC: National Academies Press; 2007.Google Scholar
  6. Sram RJ, Binkova B, Dejmek J, Bobak M. Ambient air pollution and pregnancy outcomes: a review of the literature. Environ Health Perspect. 2005;113(4):375–82.View ArticleGoogle Scholar
  7. Stieb DM, Chen L, Eshoul M, Judek S. Ambient air pollution, birth weight and preterm birth: a systematic review and meta-analysis. Environ Res. 2012;117:100–11.View ArticleGoogle Scholar
  8. Albouy-Llaty M, Limousi F, Carles C, Dupuis A, Rabouan S, Migeot V. Association between exposure to endocrine disruptors in drinking water and preterm birth, taking neighborhood deprivation into account: a historic cohort study. Int J Environ Res Public Health. 2016;13(8) https://doi.org/10.3390/ijerph13080796.
  9. Darrow LA, Stein CR, Steenland K. Serum perfluorooctanoic acid and perfluorooctane sulfonate concentrations in relation to birth outcomes in the mid-Ohio Valley, 2005-2010. Environ Health Perspect. 2013;121(10):1207–13.Google Scholar
  10. Kogevinas M, Bustamante M, Gracia-Lavedan E, Ballester F, Cordier S, Costet N, Espinosa A, Grazuleviciene R, Danileviciute A, Ibarluzea J, et al. Drinking water disinfection by-products, genetic polymorphisms, and birth outcomes in a European mother-child cohort study. Epidemiology. 2016;27(6):903–11.View ArticleGoogle Scholar
  11. Ruckart PZ, Bove FJ, Maslia M. Evaluation of contaminated drinking water and preterm birth, small for gestational age, and birth weight at marine Corps Base camp Lejeune, North Carolina: a cross-sectional study. Environ Health. 2014;13:99.View ArticleGoogle Scholar
  12. Laine JE, Bailey KA, Rubio-Andrade M, Olshan AF, Smeester L, Drobna Z, Herring AH, Styblo M, Garcia-Vargas GG, Fry RC. Maternal arsenic exposure, arsenic methylation efficiency, and birth outcomes in the biomarkers of exposure to ARsenic (BEAR) pregnancy cohort in Mexico. Environ Health Perspect. 2015;123(2):186–92.Google Scholar
  13. Mustafa M, Garg N, Banerjee BD, Sharma T, Tyagi V, Dar SA, Guleria K, Ahmad RS, Vaid N, Tripathi AK. Inflammatory-mediated pathway in association with organochlorine pesticides levels in the etiology of idiopathic preterm birth. Reprod Toxicol. 2015;57:111–20.View ArticleGoogle Scholar
  14. Kadhel P, Monfort C, Costet N, Rouget F, Thome JP, Multigner L, Cordier S. Chlordecone exposure, length of gestation, and risk of preterm birth. Am J Epidemiol. 2014;179(5):536–44.View ArticleGoogle Scholar
  15. Eskenazi B, Harley K, Bradman A, Weltzien E, Jewell NP, Barr DB, Furlong CE, Holland NT. Association of in utero organophosphate pesticide exposure and fetal growth and length of gestation in an agricultural population. Environ Health Perspect. 2004;112(10):1116–24.View ArticleGoogle Scholar
  16. Gehring U, Wijga AH, Fischer P, de Jongste JC, Kerkhof M, Koppelman GH, Smit HA, Brunekreef B. Traffic-related air pollution, preterm birth and term birth weight in the PIAMA birth cohort study. Environ Res. 2011;111(1):125–35.View ArticleGoogle Scholar
  17. Wilhelm M, Ghosh JK, Su J, Cockburn M, Jerrett M, Ritz B. Traffic-related air toxics and preterm birth: a population-based case-control study in Los Angeles County, California. Environ Health. 2011;10:89.View ArticleGoogle Scholar
  18. Vafeiadi M, Vrijheid M, Fthenou E, Chalkiadaki G, Rantakokko P, Kiviranta H, Kyrtopoulos SA, Chatzi L, Kogevinas M. Persistent organic pollutants exposure during pregnancy, maternal gestational weight gain, and birth outcomes in the mother-child cohort in Crete, Greece (RHEA study). Environ Int. 2014;64:116–23.View ArticleGoogle Scholar
  19. Larson K, Russ SA, Nelson BB, Olson LM, Halfon N. Cognitive ability at kindergarten entry and socioeconomic status. Pediatrics. 2015;135(2):E440–8.View ArticleGoogle Scholar
  20. America’s Children and the Environment - Health: Neurodevelopmental Disorders [https://www.epa.gov/ace/health-neurodevelopmental-disorders]. Accessed 23 Aug 2018.
  21. Evans GW, Kantrowitz E. Socioeconomic status and health: the potential role of environmental risk exposure. Annu Rev Public Health. 2002;23:303–31.View ArticleGoogle Scholar
  22. Woodruff TJ, Parker JD, Kyle AD, Schoendorf KC. Disparities in exposure to air pollution during pregnancy. Environ Health Perspect. 2003;111(7):942–6.View ArticleGoogle Scholar
  23. DeFur PL, Evans GW, Hubal EAC, Kyle AD, Morello-Frosch RA, Williams DR. Vulnerability as a function of individual and group resources in cumulative risk assessment. Environ Health Perspect. 2007;115(5):817–24.View ArticleGoogle Scholar
  24. Morello-Frosch R, Shenassa ED. The environmental "riskscape" and social inequality: implications for explaining maternal and child health disparities. Environ Health Perspect. 2006;114(8):1150–3.View ArticleGoogle Scholar
  25. Justice IoMCoE: Toward environmental justice: research, education, and health policy needs: National Academies Press (US); 1999.Google Scholar
  26. Padula AM, Mortimer KM, Tager IB, Hammond SK, Lurmann FW, Yang W, Stevenson DK, Shaw GM. Traffic-related air pollution and risk of preterm birth in the San Joaquin Valley of California. Ann Epidemiol. 2014;24(12):888–895e884.View ArticleGoogle Scholar
  27. Place Matters For Health in the San Joaquin Valley. Ensuring Opportunities for Good Health for All. Washington, DC: Joint Center for Political and Economic Studies; 2012.Google Scholar
  28. Talge NM, Mudd LM, Sikorskii A, Basso O. United States birth weight reference corrected for implausible gestational age estimates. Pediatrics. 2014;133(5):844–53.View ArticleGoogle Scholar
  29. California Communities Environmental Health Screening Tool, Version 2.0 (CalEnviroScreen 2.0): Guidance and Screening Tool. [https://oehha.ca.gov/media/CES20FinalReportUpdateOct2014.pdf]. Accessed 23 Aug 2018.
  30. CalEnviroScreen [http://oehha.ca.gov/calenviroscreen/report/calenviroscreen-version-20]. Accessed 23 Aug 2018.
  31. Alexeeff GV, Faust JB, August LM, Milanes C, Randles K, Zeise L, Denton J. A screening method for assessing cumulative impacts. Int J Environ Res Public Health. 2012;9(2):648–59.View ArticleGoogle Scholar
  32. Morello-Frosch R, Jesdale BM, Sadd JL, Pastor M. Ambient air pollution exposure and full-term birth weight in California. Environ Health. 2010;9:44.View ArticleGoogle Scholar
  33. Shachar BZ, Mayo JA, Lyell DJ, Baer RJ, Jeliffe-Pawlowski LL, Stevenson DK, Shaw GM. Interpregnancy interval after live birth or pregnancy termination and estimated risk of preterm birth: a retrospective cohort study. BJOG. 2016;123(12):2009–17.View ArticleGoogle Scholar
  34. Lavigne E, Yasseen AS 3rd, Stieb DM, Hystad P, van Donkelaar A, Martin RV, Brook JR, Crouse DL, Burnett RT, Chen H, et al. Ambient air pollution and adverse birth outcomes: differences by maternal comorbidities. Environ Res. 2016;148:457–66.View ArticleGoogle Scholar
  35. Ferguson KK, Chin HB. Environmental chemicals and preterm birth: biological mechanisms and the state of the science. Curr Epidemiol Rep. 2017;4(1):56–71.View ArticleGoogle Scholar
  36. Huynh M, Woodruff TJ, Parker JD, Schoendorf KC. Relationships between air pollution and preterm birth in California. Paediatr Perinat Epidemiol. 2006;20(6):454–61.View ArticleGoogle Scholar
  37. Padula AM, Noth EM, Hammond SK, Lurmann FW, Yang W, Tager IB, Shaw GM. Exposure to airborne polycyclic aromatic hydrocarbons during pregnancy and risk of preterm birth. Environ Res. 2014;135:221–6.View ArticleGoogle Scholar

Copyright

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate. Please note that comments may be removed without notice if they are flagged by another user or do not comply with our community guidelines.

Advertisement