Abstract
Objective:
To assess factors influencing neonatal respiratory distress syndrome (RDS) risk, incorporating maternal demographics, behaviors, medical conditions, pregnancy-related factors, and PM2.5 speciation pollutant exposures.
Methods:
Using Florida de-identified birth records, logistic regression analyses were conducted to assess associations between maternal exposure to PM2.5 speciation metals during pregnancy and the risk of RDS, adjusting for various covariates.
Results:
Our findings highlight the multifaceted nature of RDS risk, reaffirming known risk factors such as preterm birth, low birth weight, and maternal health conditions. Complex interactions among pollutants and maternal health factors were observed, emphasizing the importance of considering synergistic effects in risk assessment. Additionally, race and ethnicity were identified as significant factors, with nuances observed within Hispanic subgroups.
Conclusion:
Targeted interventions aimed at reducing exposure to harmful pollutants, particularly among high-risk populations, may help mitigate RDS burden.
Keywords: respiratory distress syndrome (RDS), PM2.5 speciation chemicals, air pollutants, metals interactions, interaction
Neonatal respiratory distress syndrome (RDS) is a significant cause of neonatal mortality, impacting 1% of all newborns, and is the leading cause of death in premature infants.1 Known risk factors of RDS include cesarean delivery, preterm birth, low birth weight, and male infant’s sex.2–4 Maternal characteristics including age and having diabetes mellitus or gestational diabetes are associated with RDS in their infants.5,6 In addition, other pregnancy complications are associated with RDS risk factors. For example, maternal hypertension, preeclampsia, multiple birth, preterm delivery, and premature rupture of membranes are associated with an increased incidence of cesarean delivery,7,8 hence a higher risk of RDS. Preeclampsia is linked to low birth weight and preterm delivery. Previous studies show that Black and Hispanic preterm infants in Connecticut9 and Black, Asian, and Hispanic term and preterm infants in North Carolina2 are less likely to be born with RDS compared to White infants. However, a recent study of Florida mothers and infants indicated that race modifies the effect of ethnicity on RDS such that Hispanic Black and Hispanic Asian mothers were more likely to birth infants with RDS compared to non-Hispanic White mothers.10 These relationships highlight the interconnectivity of pregnancy outcomes and the complexity of risk assessment.
Adverse pregnancy outcomes have been associated with several modifiable risk factors including poor maternal nutrition, maternal smoking and alcohol consumption, and maternal exposure to environmental toxins.11,12 Prenatal exposure to metals has recently gained attention in research as they are also associated with adverse birth outcomes. Exposure of pregnant women to lead, cadmium, or arsenic is associated with low birth weight, and exposure to lead or cadmium is associated with preterm birth.13,14 Additionally, recent research has indicated that prenatal exposure to air pollution is associated with impaired lung function and development.15 Understanding poor birth outcomes and pregnancy complications is crucial as they not only present immediate health concerns for mothers and newborns but also have long-term implications as seen by an increased risk of chronic conditions in adulthood, such as cardiovascular disease, hypertension, obesity, and diabetes.16
Although fossil fuel emissions have typically been a focal source of air pollution, biomass burning, and wildfire activity have also been recognized as contributing to over 25% of particulate matter with a diameter less than 2.5 μm (PM2.5) polluting the air.17 Particulate matter in the air has several components that differentially contribute to toxicity, and key components include metals, metalloids, and nonmetal elements, such as lead, arsenic, and sulfur, respectively.18 Previous studies of adverse birth outcomes have focused on either single metal exposures or aggregated PM2.5 pollutant measurements that do not take composition into account. It is important to recognize that exposure to multiple pollutants concurrently provides a more accurate representation of real-world exposure. To address this issue, there has been a shift toward using mixture analysis to better understand the health impacts of exposure to multiple pollutants,19 because pollutants may exert a joint and potential interactive effect on health outcomes.
Furthermore, the association between adverse pregnancy outcomes and later cardiovascular disease risk introduces another layer of complexity. Understanding the link between adverse pregnancy outcomes like preeclampsia, hypertension, and gestational diabetes and the subsequent risk of respiratory distress syndrome in infants contributes to a comprehensive understanding of the continuum from pregnancy to cardiovascular health. The objective of this work is to study association between maternal exposure to PM2.5 speciation metals during pregnancy and the increased odd of having an infant born with RDS, after adjusting for maternal sociodemographic risk factors, maternal behavioral factors, and other maternal medical conditions and pregnancy-related factors
METHODS
Study Population
This study utilized the de-identified birth records data spanning from 2011 to 2020, sourced from the Florida Bureau of Vital Statistics. Study data include all live births occurring in Hillsborough and Broward counties, Florida, and the dataset contains birth-related details pertaining to both mothers and newborns. The study was approved by the Institutional Review Board (IRB) of Florida International University and the Florida Department of Health IRB and classified as exempt from IRB. Participants (or their parent or legal guardian for those under 16) provided informed consent to partake in the study. Individuals with missing or unknown data in any of the variables of interest were excluded, resulting in a final sample of 358,385 subjects.
Measures
Respiratory distress syndrome (RDS) is recorded as yes or no. Given the limited representation of American Indian or Alaskan Native (AIAN) and other Pacific Islander (NPI) groups in the data, the classification of race was refined to include White, Black, Asian or Pacific Islander (API), and Others (encompassing various races, AIAN, and multiple races). Maternal demographic covariates included maternal age (<18, 18–35, >35 years), education level (≤high school, >high school), and ethnicity (non-Hispanic, Hispanic). Maternal behavioral factor covariates included tobacco use during pregnancy, alcohol use during pregnancy, prenatal care, and Women, Infants, and Children (WIC) program status, all recorded as yes/no. Pregnancy-related covariates assessed were gestational weeks (<34, 34–37, >37 weeks), birth weight (<2500, ≥2500 g), birth route (vaginal, cesarean), plurality (singleton, twin, multiple), and infant sex (female, male). Additionally, maternal medical information used was prepregnancy body mass index (BMI; <30, ≥30), history of diabetes (yes/no), gestational diabetes (yes/no), history of preterm birth (yes/no), and premature rupture of membranes (PRoM; yes/no).
PM2.5 Speciation Chemicals Data
PM2.5 is a complex mixture of small particles and liquid droplets. The components of this mixture include metals, metalloids, and nonmetal elements. PM2.5 chemical speciation data for Broward and Hillsborough counties were obtained from the environmental protection agency (EPA) and used to approximate county-level concentrations of PM speciation elements that include the following, which contribute substantially to PM2.5 total mass and have been suspected to have potential adverse health consequences: aluminum, arsenic, cadmium, calcium, chlorine, lead, mercury, nickel, silicon, sodium, sulfur, titanium, vanadium, and zinc.20 The full list of PM2.5 components included in the data can be found in the study by Ibrahimou et al.20 Estimates of maternal exposure during pregnancy were assigned based on the 24-hour pollutant readings obtained from the monitoring stations located in the county matching each mother’s zip code and residence. Reliance on estimations of exposure based on maternal residence is due to the absence of individual-level exposure data coupled with the difficulty of assessing exposure at the population level. Exposure estimates as daily reading averages for the entire pregnancy duration were calculated for this study using the gestational age and the delivery dates included in the birth records data. All exposure estimates were used as continuous variables.
Statistical Analysis
The objective of this work is to study association between maternal exposure to PM2.5 speciation metals during pregnancy and the increased odd of having an infant born with RDS, after adjusting for other risk factors. The maternal and pregnancy factors and their interactions used in our adjusted models were selected based on previous work by Ibrahimou et al.10 To avoid unreliable analyses, PM2.5 speciation elements were excluded from analysis if more than 50% of measurements were below the limit of detection. Pollutants were used as continuous variables, and the odds ratio and 95% confidence intervals (CIs) are estimated per interquartile range (IQR) change. Chi-squared tests and logistic regression were used to determine the effects of speciation chemicals on risk of RDS. Unadjusted models were used to assess crude odds for each pollutant. Base models were built for each pollutant while adjusting for covariates and interaction terms. The AIC values from these models were used to perform stepwise selection of main pollutant effects, pollutant-health interaction effects, and pollutant-pollutant interaction effects. Statistical significance at P = 0.05 level was used for inclusion into the final logistic model. R statistical package (version 4.1.2; R Foundation for Statistical Computing, Vienna, Austria) was used for the analysis.
RESULTS
A total of 358,385 records, including 3231 (0.09%) cases of RDS were included in the study. Descriptive statistics of the study population can be found in Table 1. Summary statistics of PM2.5 chemical measurements, including mean and median representing mother’s daily exposure during the whole pregnancy period, are presented in Table 2. Of the 33 PM2.5 chemicals measured, 15 had less than 50% of measurements below the detection limit including aluminum, bromine, calcium, chlorine, chromium, copper, iron, magnesium, nickel, potassium, silicon, sodium, sulfur, titanium, and zinc. The most abundant elements meeting the inclusion criteria included sulfur, sodium, chlorine, silicon, and aluminum. The least abundant elements were nickel, bromine, zinc, and chromium.
TABLE 1.
Descriptive Statistics of Birth Records in Broward and Hillsborough Counties, Florida, 2011–2020
| Variable | Respiratory Distress Syndrome |
P | ||
|---|---|---|---|---|
| No, n (%) | Yes, n (%) | |||
| Total | n = 355,154 | n = 3231 | ||
| Race | White | 196,665 (55.37) | 924 (28.60) | *** |
| Black | 112,753 (31.75) | 1750 (54.16) | ||
| API | 16,748 (4.72) | 122 (3.78) | ||
| Other | 28,988 (8.16) | 435 (13.46) | ||
| Ethnicity | Non-Hispanic | 219,919 (61.92) | 1972 (61.03) | 0.309 |
| Hispanic | 135,235 (38.08) | 1259 (38.97) | ||
| Maternal age | <18 y | 4381 (1.23) | 48 (1.49) | ** |
| 18–35 y | 295,042 (83.07) | 2608 (80.72) | ||
| >35 y | 55,731 (15.69) | 575 (17.80) | ||
| Education | ≤High school | 208,307 (58.65) | 1433 (44.35) | *** |
| >High school | 146,847 (41.35) | 1798 (55.65) | ||
| Insurance | None | 35,005 (9.86) | 665 (20.58) | *** |
| Medicaid | 157,416 (44.32) | 1497 (46.33) | ||
| Private | 156,900 (44.18) | 895 (27.70) | ||
| Other | 5833 (1.64) | 174 (5.39) | ||
| WIC status | No | 190,455 (53.63) | 1185 (36.68) | *** |
| Yes | 164,699 (46.37) | 2046 (63.32) | ||
| BMI | <30 | 258,512 (72.79) | 2212 (68.46) | *** |
| ≥30 | 96,642 (27.21) | 1019 (31.54) | ||
| Alcohol use | No | 353,240 (99.46) | 3208 (99.29) | 0.225 |
| Yes | 1914 (0.54) | 23 (0.71) | ||
| Tobacco use | No | 347,719 (97.91) | 3157 (97.71) | 0.474 |
| Yes | 7435 (2.09) | 74 (2.29) | ||
| Prenatal care | No | 6227 (1.75) | 146 (4.52) | *** |
| Yes | 348,927 (98.25) | 3085 (95.48) | ||
| Method of delivery | Vaginal | 216,274 (60.90) | 1332 (41.23) | *** |
| Cesarean | 138,880 (39.10) | 1899 (58.77) | ||
| Diabetes | No | 352,874 (99.36) | 3139 (97.15) | *** |
| Yes | 2280 (0.64) | 92 (2.85) | ||
| Gestational age | >37 wk | 310,263 (87.36) | 1669 (51.66) | *** |
| 34–37 wk | 32,168 (9.06) | 606 (18.76) | ||
| <34 wk | 12,723 (3.58) | 956 (29.59) | ||
| Gestational diabetes | No | 337,633 (95.07) | 2933 (90.78) | *** |
| Yes | 17,521 (4.93) | 298 (9.22) | ||
| Plurality | Singleton | 343,747 (96.79) | 2811 (87.00) | *** |
| Twin | 11,118 (3.13) | 395 (12.23) | ||
| Multiple | 289 (0.08) | 25 (0.77) | ||
| Preeclampsia | No | 338,314 (95.26) | 2852 (88.27) | *** |
| Yes | 16,840 (4.74) | 379 (11.73) | ||
| Previous preterm | No | 350,481 (98.68) | 3045 (94.24) | *** |
| Yes | 4673 (1.32) | 186 (5.76) | ||
| PRoM | No | 340,275 (95.81) | 2947 (91.21) | *** |
| Yes | 14,879 (4.19) | 284 (8.79) | ||
| Birth weight | ≥2500 g | 324,315 (91.32) | 1714 (53.05) | *** |
| <2500 g | 30,839 (8.68) | 1517 (46.95) | ||
| Sex | Female | 173,995 (48.99) | 1446 (44.75) | *** |
| Male | 181,159 (51.01) | 1785 (55.25) | ||
p-value < 0.001
p-value < 0.01
API, Asian or Pacific Islander; PRoM, premature rupture of membranes.
TABLE 2.
Mean (Standard Error), First Quartile, Third Quartile, IQR, and Percentage Undetectable for Measurements of PM2.5 Pollutant Speciation Measured in Broward and Hillsborough Counties, Florida, 2011–2020
| Pollutant | Mean (SE), μg/m3 | First Quartile (μg/m3) | Third Quartile (μg/m3) | IQR (μg/m3) | Percent Undetectable |
|---|---|---|---|---|---|
| Aluminum | 0.114 (5.50E – 03) | 0.016 | 0.092 | 0.076 | 30.00 |
| Antimony | 0.016 (4.74E – 04) | 0.006 | 0.021 | 0.015 | 59.55 |
| Arsenic | 0.002 (9.31E – 05) | 0.001 | 0.002 | 0.001 | 80.17 |
| Barium | 0.022 (1.19E – 03) | 0.005 | 0.030 | 0.025 | 60.79 |
| Bromine | 0.003 (8.32E – 05) | 0.002 | 0.004 | 0.003 | 25.40 |
| Cadmium | 0.007 (1.99E – 04) | 0.003 | 0.010 | 0.007 | 63.46 |
| Calcium | 0.060 (1.15E – 03) | 0.030 | 0.070 | 0.040 | 0.97 |
| Cerium | 0.026 (9.08E – 04) | 0.008 | 0.039 | 0.031 | 70.78 |
| Cesium | 0.014 (4.73E – 04) | 0.004 | 0.021 | 0.017 | 59.00 |
| Chlorine | 0.195 (6.69E – 03) | 0.019 | 0.243 | 0.224 | 3.87 |
| Chromium | 0.005 (3.34E – 04) | 0.001 | 0.004 | 0.003 | 43.95 |
| Cobalt | 0.001 (2.64E – 05) | 0.001 | 0.002 | 0.001 | 68.48 |
| Copper | 0.009 (3.83E – 04) | 0.002 | 0.010 | 0.008 | 22.96 |
| Indium | 0.009 (2.42E – 04) | 0.004 | 0.013 | 0.009 | 62.45 |
| Iron | 0.069 (2.26E – 03) | 0.023 | 0.060 | 0.037 | 0.51 |
| Lead | 0.004 (1.24E – 04) | 0.002 | 0.006 | 0.004 | 53.80 |
| Magnesium | 0.044 (1.39E – 03) | 0.014 | 0.056 | 0.042 | 26.09 |
| Manganese | 0.003 (7.68E – 05) | 0.001 | 0.003 | 0.002 | 51.54 |
| Nickel | 0.002 (8.42E – 05) | 0.001 | 0.002 | 0.001 | 48.55 |
| Phosphorus | 0.004 (3.86E – 04) | 0.001 | 0.004 | 0.003 | 88.68 |
| Potassium | 0.085 (6.09E – 03) | 0.035 | 0.082 | 0.047 | 0.18 |
| Rubidium | 0.002 (5.56E – 05) | 0.001 | 0.003 | 0.002 | 70.09 |
| Selenium | 0.002 (4.60E – 05) | 0.001 | 0.002 | 0.001 | 69.03 |
| Silicon | 0.182 (8.28E – 03) | 0.027 | 0.133 | 0.106 | 4.14 |
| Silver | 0.007 (1.88E – 04) | 0.003 | 0.009 | 0.006 | 66.27 |
| Sodium | 0.245 (5.15E – 03) | 0.083 | 0.345 | 0.262 | 6.53 |
| Strontium | 0.003 (3.73E – 04) | 0.001 | 0.003 | 0.002 | 57.89 |
| Sulfur | 0.477 (5.80E – 03) | 0.303 | 0.579 | 0.276 | 0.23 |
| Tin | 0.012 (3.48E – 04) | 0.005 | 0.018 | 0.013 | 62.54 |
| Titanium | 0.008 (3.39E – 04) | 0.002 | 0.007 | 0.005 | 27.47 |
| Vanadium | 0.002 (5.13E – 05) | 0.001 | 0.003 | 0.002 | 51.45 |
| Zinc | 0.004 (9.73E – 05) | 0.002 | 0.005 | 0.003 | 13.39 |
| Zirconium | 0.009 (3.34E – 04) | 0.003 | 0.013 | 0.010 | 68.06 |
Crude odds of birthing an infant with RDS in association to exposure to PM speciation chemicals per IQR increase are presented in Table 3. Exposure to calcium (OR = 2.07, 95% CI: 1.93–2.21), chlorine (OR= 1.41, 95% CI: 1.31–1.51), chromium (OR = 1.19, 95% CI: 1.15–1.23), copper (OR = 1.50, 95% CI: 1.42–1.58), iron (OR= 1.40, 95% CI: 1.33–1.48), nickel (OR = 1.51, 95% CI: 1.46–1.56), sodium (OR = 1.64, 95% CI: 1.47–1.83), or sulfur (OR = 1.50, 95% CI: 1.37–1.64) significantly increased the risk of RDS (all P < 0.001), whereas exposure to bromine (OR = 0.878, 95% CI: 0.814–0.948, P < 0.001), silicon (OR = 0.947, 95% CI: 0.899–0.998), P < 0.05), or zinc (OR = 0.693, 95% CI: 0.621–0.774, P < 0.001) significantly decreased the risk.
TABLE 3.
Crude Odds Ratios Per IQR Change of PM2.5 Pollutants
| Metal | OR | 95% CI | P |
|---|---|---|---|
| Aluminum | 1.01 | (0.95–1.07) | 0.67 |
| Bromine | 0.88 | (0.81–0.95) | *** |
| Calcium | 2.07 | (1.93–2.21) | *** |
| Chlorine | 1.41 | (1.31–1.51) | *** |
| Chromium | 1.19 | (1.15–1.23) | *** |
| Copper | 1.50 | (1.42–1.58) | *** |
| Iron | 1.40 | (1.33–1.48) | *** |
| Magnesium | 1.01 | (0.93–1.10) | 0.83 |
| Nickel | 1.51 | (1.46–1.56) | *** |
| Potassium | 1.05 | (0.99–1.10) | 0.12 |
| Silicon | 0.95 | (0.90–1.00) | * |
| Sodium | 1.64 | (1.47–1.83) | *** |
| Sulfur | 1.50 | (1.37–1.64) | *** |
| Titanium | 1.06 | (0.99–1.14) | 0.09 |
| Zinc | 0.69 | (0.62–0.77) | *** |
p-value < 0.001
p-value < 0.05
Results from three adjusted models can be found in Table 4. The “base” model covariates for all three models were taken from Ibrahimou et al,10 which includes preeclampsia, maternal race, maternal ethnicity, tobacco use during pregnancy, premature rupture of membranes, gestational age, insurance, maternal age, maternal education, prepregnancy BMI, prenatal care, alcohol use during pregnancy, WIC status, method of delivery, plurality, birth weight, birth sex, diabetes, gestational diabetes, previous preterm, race-ethnicity interaction, race-preeclampsia interaction, preeclampsia-PRoM interaction, and preeclampsia–gestational age interaction. Model 1 assesses the risk of RDS adjusting for the base plus the main effects of pollutants with less than 50% of measurements missing. Model 2 adjusts for the base, pollutant main effects, and pollutant–health factor interactions, and model 3 adjusts for the base, pollutant main effects, pollutant–health factor interactions, and pollutant-pollutant interactions. In model 1, maternal exposure to calcium, sulfur, aluminum, chlorine, copper, and nickel significantly increased risk of RDS in infants, whereas exposure to chromium, potassium, silicon, bromine, and sodium significantly reduced the risk of RDS.
TABLE 4.
Adjusted Odds Ratios of Respiratory Distress Occurrence From Multivariable Models
| Variables | Model 1 |
Model 2 |
Model 3 |
||||||
|---|---|---|---|---|---|---|---|---|---|
| aOR | 95% CI | Sig | aOR | 95% CI | Sig | aOR | 95% CI | Sig | |
| Pollutant main effects | |||||||||
| Nickel | 1.16 | (1.07–1.25) | *** | 1.15 | (1.06–1.25) | *** | 0.23 | (0.14–0.39) | *** |
| Calcium | 4.16 | (3.53–4.91) | *** | 4.60 | (3.89–5.45) | *** | 14.88 | (7.18–30.83) | *** |
| Copper | 1.16 | (1.07–1.26) | *** | 1.16 | (1.07–1.26) | *** | 2.42 | (1.45–4.05) | *** |
| Chromium | 0.89 | (0.84–0.94) | *** | 0.90 | (0.85–0.95) | *** | 0.56 | (0.39–0.81) | ** |
| Sulfur | 2.53 | (2.09–3.07) | *** | 2.54 | (2.10–3.08) | *** | 2.84 | (0.87–9.23) | |
| Chlorine | 1.66 | (1.15–2.39) | ** | 1.63 | (1.13–2.35) | ** | 0.01 | (0.00–0.08) | *** |
| Sodium | 0.12 | (0.07–0.20) | *** | 0.13 | (0.08–0.24) | *** | 31.81 | (0.81–1242.15) | |
| Silicon | 0.34 | (0.30–0.40) | *** | 0.35 | (0.30–0.41) | *** | 5.07 | (1.96–13.12) | *** |
| Bromine | 0.33 | (0.28–0.39) | *** | 0.30 | (0.26–0.36) | *** | 0.09 | (0.03–0.24) | *** |
| Potassium | 0.78 | (0.73–0.84) | *** | 0.78 | (0.73–0.84) | *** | 1.53 | (1.11–2.11) | ** |
| Aluminum | 2.05 | (1.73–2.42) | *** | 2.02 | (1.71–2.39) | *** | 0.02 | (0.01–0.07) | *** |
| Health factor–pollutant interactions | |||||||||
| Preeclampsia–Calcium | 0.45 | (0.35–0.58) | *** | 0.47 | (0.37–0.61) | *** | |||
| PRoM–Calcium | 0.59 | (0.42–0.84) | ** | 0.70 | (0.49–0.99) | * | |||
| PRoM–Sodium | 0.27 | (0.15–0.49) | *** | 0.22 | (0.12–0.41) | *** | |||
| PRoM–Bromine | 1.92 | (1.36–2.72) | *** | 1.94 | (1.38–2.73) | *** | |||
| Preeclampsia–Bromine | 1.64 | (1.27–2.12) | *** | 1.68 | (1.29–2.17) | *** | |||
| Pollutant-pollutant interactions | |||||||||
| Calcium–Bromine | 0.28 | (0.17–0.44) | *** | ||||||
| Calcium–Copper | 0.57 | (0.43–0.76) | *** | ||||||
| Calcium–Potassium | 0.71 | (0.60–0.84) | *** | ||||||
| Chlorine–Bromine | 4.99 | (3.26–7.65) | *** | ||||||
| Chromium–Sodium | 1.59 | (1.17–2.16) | ** | ||||||
| Nickel–Calcium | 3.38 | (2.50–4.57) | *** | ||||||
| Nickel–Chromium | 0.89 | (0.83–0.94) | *** | ||||||
| Sodium–Silicon | 1.72 | (1.35–2.21) | *** | ||||||
| Sulfur–Aluminum | 18.13 | (7.72–42.53) | *** | ||||||
| Sulfur–Bromine | 2.84 | (1.70–4.77) | *** | ||||||
| Sulfur–Chlorine | 9.98 | (2.21–45.08) | ** | ||||||
| Sulfur–Silicon | 0.14 | (0.08–0.25) | *** | ||||||
| Sulfur–Sodium | 0.02 | (0.00–0.17) | *** | ||||||
| Health factor main effects | |||||||||
| Preeclampsia | |||||||||
| No | |||||||||
| Yes | 2.14 | (1.68–2.73) | *** | 4.62 | (2.79–7.65) | *** | 3.96 | (2.39–6.56) | *** |
| Race | |||||||||
| White | |||||||||
| Black | 1.33 | (1.19–1.48) | *** | 1.30 | (1.16–1.45) | *** | 1.26 | (1.13–1.41) | *** |
| API | 0.93 | (0.75–1.16) | 0.92 | (0.74–1.14) | 0.91 | (0.73–1.14) | |||
| Other | 0.73 | (0.48–1.11) | 0.71 | (0.47–1.08) | 0.70 | (0.46–1.06) | |||
| Ethnicity | |||||||||
| Non-Hispanic | |||||||||
| Hispanic | 0.38 | (0.33–0.44) | *** | 0.38 | (0.33–0.45) | *** | 0.38 | (0.33–0.44) | *** |
| Tobacco use | |||||||||
| No | |||||||||
| Yes | 0.97 | (0.76–1.24) | 0.95 | (0.74–1.22) | 0.94 | (0.74–1.21) | |||
| PRoM | |||||||||
| No | |||||||||
| Yes | 1.20 | (1.04–1.38) | * | 5.73 | (2.75–11.95) | *** | 5.14 | (2.49–10.63) | *** |
| Gestational age | |||||||||
| >37 wk | |||||||||
| 34–37 wk | 1.79 | (1.59–2.01) | *** | 1.78 | (1.59–2.00) | *** | 1.81 | (1.61–2.03) | *** |
| <34 wk | 3.98 | (3.52–4.50) | *** | 3.97 | (3.51–4.49) | *** | 4.07 | (3.58–4.62) | *** |
| Insurance | |||||||||
| None | |||||||||
| Medicaid | 0.36 | (0.32–0.40) | *** | 0.36 | (0.32–0.40) | *** | 0.36 | (0.32–0.40) | *** |
| Private ins | 0.37 | (0.33–0.42) | *** | 0.37 | (0.33–0.42) | *** | 0.37 | (0.33–0.42) | *** |
| Other | 1.00 | (0.84–1.21) | 1.01 | (0.84–1.21) | 0.96 | (0.80–1.15) | |||
| Mother’s age | |||||||||
| <18 y | |||||||||
| 18–35 y | 1.01 | (0.75–1.36) | 1.00 | (0.74–1.36) | 1.00 | (0.74–1.35) | |||
| >35 y | 0.96 | (0.70–1.31) | 0.96 | (0.70–1.31) | 0.95 | (0.70–1.30) | |||
| Education | |||||||||
| ≤High school | |||||||||
| >High school | 1.24 | (1.14–1.34) | *** | 1.24 | (1.14–1.34) | *** | 1.23 | (1.14–1.34) | *** |
| BMI | |||||||||
| <30 | |||||||||
| ≥30 | 0.93 | (0.86–1.01) | 0.93 | (0.86–1.00) | 0.92 | (0.85–1.00) | * | ||
| Prenatal care | |||||||||
| No | |||||||||
| Yes | 0.67 | (0.56–0.81) | *** | 0.68 | (0.57–0.82) | *** | 0.67 | (0.56–0.80) | *** |
| Alcohol use | |||||||||
| No | |||||||||
| Yes | 1.73 | (1.12–2.67) | * | 1.75 | (1.13–2.70) | * | 1.60 | (1.03–2.48) | * |
| WIC status | |||||||||
| No | |||||||||
| Yes | 1.65 | (1.51–1.81) | *** | 1.67 | (1.52–1.83) | *** | 1.68 | (1.53–1.84) | *** |
| Method of delivery | |||||||||
| Vaginal | |||||||||
| Cesarean | 1.54 | (1.43–1.67) | *** | 1.53 | (1.42–1.65) | *** | 1.53 | (1.42–1.65) | *** |
| Plurality | |||||||||
| Singleton | |||||||||
| Twin | 1.13 | (0.999–1.27) | 1.13 | (1.001–1.28) | * | 1.13 | (0.998–1.27) | ||
| Multiple | 1.27 | (0.82–1.97) | 1.27 | (0.82–1.97) | 1.30 | (0.83–2.01) | |||
| Birth weight | |||||||||
| ≥2500 g | |||||||||
| <2500 g | 3.74 | (3.37–4.15) | *** | 3.73 | (3.36–4.14) | *** | 3.79 | (3.42–4.21) | *** |
| Birth sex | |||||||||
| Female | |||||||||
| Male | 1.23 | (1.15–1.33) | *** | 1.23 | (1.15–1.32) | *** | 1.23 | (1.15–1.32) | *** |
| Diabetes | |||||||||
| No | |||||||||
| Yes | 2.56 | (2.04–3.22) | *** | 2.57 | (2.04–3.23) | *** | 2.50 | (1.99–3.14) | *** |
| Previous preterm | |||||||||
| No | |||||||||
| Yes | 1.91 | (1.62–2.25) | *** | 1.89 | (1.60–2.23) | *** | 1.87 | (1.58–2.20) | *** |
| Gestational diabetes | |||||||||
| No | |||||||||
| Yes | 1.73 | (1.52–1.96) | *** | 1.71 | (1.50–1.94) | *** | 1.66 | (1.46–1.89) | *** |
| Health factor interactions | |||||||||
| Race–Ethnicity | |||||||||
| Black–Hispanic | 3.15 | (2.62–3.78) | *** | 3.10 | (2.58–3.72) | *** | 3.16 | (2.63–3.80) | *** |
| API–Hispanic | 9.14 | (5.08–16.45) | *** | 8.83 | (4.90–15.93) | *** | 9.22 | (5.12–16.59) | *** |
| Other–Hispanic | 5.22 | (3.34–8.16) | *** | 5.12 | (3.27–8.01) | *** | 5.21 | (3.33–8.15) | *** |
| Race–Preeclampsia | |||||||||
| Black–Preeclampsia | 0.51 | (0.40–0.66) | *** | 0.60 | (0.47–0.78) | *** | 0.62 | (0.48–0.80) | *** |
| API–Preeclampsia | 0.58 | (0.30–1.12) | 0.64 | (0.33–1.24) | 0.67 | (0.35–1.29) | |||
| Other–Preeclampsia | 0.59 | (0.39–0.88) | * | 0.69 | (0.46–1.04) | 0.72 | (0.48–1.08) | ||
| Preeclampsia–PRoM | |||||||||
| Preeclampsia–PRoM | 0.30 | (0.16–0.58) | *** | 0.28 | (0.15–0.54) | *** | 0.28 | (0.14–0.53) | *** |
| Preeclampsia–Gestational age | |||||||||
| Preeclampsia: 34–37 wk | 1.12 | (0.84–1.49) | 1.11 | (0.84–1.49) | 1.10 | (0.82–1.46) | |||
| Preeclampsia: <34 wk | 0.94 | (0.72–1.24) | 0.93 | (0.71–1.23) | 0.94 | (0.71–1.23) | |||
P-value < 0.001.
P-value < 0.01.
P-value < 0.05.
The main effects for pollutants in model 2 were similar to model 1. Maternal exposure to bromine interacted with preeclampsia (aOR = 1.64, 95% CI: 1.27–2.12, P < 0.001) and PRoM (aOR = 1.92, 95% CI: 1.36–2.72, P < 0.001) and resulted in significantly increased risk of RDS. Conversely, significantly reduced risk of RDS was seen in interaction with maternal exposure to calcium and either preeclampsia (aOR = 0.45, 95% CI: 0.35–0.58, P < 0.001) or PRoM (aOR = 0.59, 95% CI: 0.42–0.84, P < 0.01) as well as an interaction with maternal exposure to sodium and PRoM (aOR = 0.27, 95% CI: 0.15–0.49, P < 0.001). In model 3, the following pollutant-pollutant interaction terms significantly increased the risk of RDS: chlorine-bromine (aOR = 4.99, 95% CI: 3.26–7.65, P < 0.001), chromium-sodium (aOR = 1.59, 95% CI: 1.17–2.16, P<0.01), nickel-calcium (aOR = 3.38, 95% CI: 2.50–4.57, P < 0.001), sodium-silicon (aOR = 1.72, 95% CI: 1.35–2.21, P < 0.001), sulfur-aluminum (aOR = 18.13, 95% CI: 7.72–42.53, P < 0.001), sulfur-bromine (aOR = 2.84, 95% CI: 1.70–4.77, P < 0.001), and sulfur-chlorine (aOR = 9.98, 95% CI: 2.21–45.08, P < 0.01). Several pollutant-pollutant interaction terms were associated with significant decreased risk of RDS including calcium-bromine (aOR = 0.28, 95% CI: 0.17–0.44, P < 0.001), calcium-copper (aOR = 0.57, 95% CI: 0.43–0.76, P < 0.001), calcium-potassium (aOR = 0.71, 95% CI: 0.60–0.84, P < 0.001), nickel-chromium (aOR = 0.89, 95% CI: 0.83–0.94, P < 0.001), sulfur-silicon (aOR = 0.14, 95% CI: 0.08–0.25, P < 0.001), and sulfur-sodium (aOR = 0.02, 95% CI: 0.002–0.17, P < 0.001). Pollutant–health factor interactions in model 3 had results similar to model 2, but the main effects for pollutants changed from what was seen in the first two models. The differences in main effects and interaction terms change the overall adjusted odds (Table 5), which is calculated by multiplying the odds of the interaction term with the odds of the main effects making up the interaction term.
TABLE 5.
Overall Adjusted Odds Ratios for Interactions in Multivariable Models
| Variables | Model 1 | Model 2 | Model 3 | ||
|---|---|---|---|---|---|
| Health factor–health factor interactions | Race–Ethnicity | Black–Hispanic | 1.584 | 1.542 | 1.514 |
| API–Hispanic | 3.224 | 3.104 | 3.182 | ||
| Other–Hispanic | 1.445 | 1.400 | 1.381 | ||
| Race–Preeclampsia | Black–Preeclampsia | 1.451 | 3.610 | 3.110 | |
| API–Preeclampsia | 1.152 | 2.719 | 2.413 | ||
| Other–Preeclampsia | 0.918 | 2.277 | 1.990 | ||
| Preeclampsia–PRoM | 0.779 | 7.430 | 5.672 | ||
| Preeclampsia–Gestational age | Preeclampsia: 34–37 wk | 4.274 | 9.174 | 7.866 | |
| Preeclampsia: <34 wk | 8.011 | 17.092 | 15.101 | ||
| Health factor–pollutant interactions | Preeclampsia–Calcium | 9.504 | 27.842 | ||
| PRoM–Calcium | 15.671 | 53.301 | |||
| PRoM–Sodium | 0.207 | 36.212 | |||
| PRoM–Bromine | 3.319 | 0.870 | |||
| Preeclampsia–Bromine | 2.281 | 0.579 | |||
| Pollutant-pollutant interactions | Calcium–Bromine | 0.357 | |||
| Calcium–Copper | 20.583 | ||||
| Calcium–Potassium | 16.078 | ||||
| Chlorine–Bromine | 0.003 | ||||
| Chromium–Sodium | 28.363 | ||||
| Nickel–Calcium | 11.771 | ||||
| Nickel–Chromium | 0.116 | ||||
| Sodium–Silicon | 278.409 | ||||
| Sulfur–Aluminum | 0.966 | ||||
| Sulfur–Bromine | 0.705 | ||||
| Sulfur–Chlorine | 0.204 | ||||
| Sulfur–Silicon | 2.000 | ||||
| Sulfur–Sodium | 1.766 | ||||
API, Asian or Pacific Islander; PRoM, premature rupture of membranes.
DISCUSSION
Neonatal respiratory distress syndrome (RDS) poses a significant challenge in neonatal care, particularly affecting premature infants. Our study aimed to assess the role of maternal exposure to PM2.5 speciation pollutants, on risk of neonatal RDS. We explored several factors contributing to RDS risk, incorporating various domains such as maternal demographics, behaviors, medical conditions, and pregnancy-related factors. Through comprehensive analyses, we sought to identify potential associations and interactions among these factors to inform targeted interventions and policies.
Our findings indicate several risk factors associated with RDS. Consistent with existing literature, preterm birth, low birth weight, cesarean delivery, and male infant sex were identified as known risk factors.2–4 Additionally, maternal characteristics such as age, diabetes mellitus, and gestational diabetes demonstrated associations with RDS, underscoring the importance of maternal health in neonatal outcomes.
The influence of race and ethnicity on RDS risk warrants attention. Although previous studies indicated lower RDS rates among Black, Asian, and Hispanic infants compared to White infants,2,9 recent evidence suggests a more complex interplay between race and ethnicity.10 Our study supports recent findings and highlights nuances within Hispanic subgroups, with Hispanic Black and Hispanic Asian mothers showing increased likelihood of birthing infants with RDS compared to non-Hispanic White mothers. This underscores the complexity of racial and ethnic disparities in neonatal health outcomes, necessitating tailored interventions to address specific community needs. For example, implementing culturally sensitive prenatal education programs that are accessible and relevant to diverse racial and ethnic communities can help improve awareness and understanding of prenatal care practices. Additionally, initiatives such as community health worker programs, language interpretation services, and efforts to improve access to healthcare services for underserved communities can address barriers to care and ensure equitable access to prenatal screenings and interventions. By collaborating with community organizations and government agencies to implement targeted interventions aimed at addressing these underlying factors, we can work toward improving overall maternal and neonatal health outcomes for marginalized communities.
A meta-analysis of studies of maternal exposure to PM2.5 air pollution for whole pregnancy indicates that maternal exposure to PM2.5 is associated with significantly decreased infant birth weight and increased risk of low birth weight, being small for gestational age, and being born prematurely.21,22 Much of the existing literature tends to focus on PM2.5 as a whole rather than on specific PM2.5 speciation elements. However, studies of maternal exposure to PM2.5 aluminum and elemental carbon revealed an increased risk of preeclampsia and placental abruption,20,23 and a study of maternal exposure to PM2.5 aluminum or PM2.5 sodium showed an increased risk of low birth weight and very low birth weight.24 Our study explored the role of individual PM2.5 speciation pollutants and their associations with neonatal RDS. We found that maternal exposure to certain elements such as calcium, sulfur, and aluminum was associated with an increased risk of RDS, whereas exposure to bromine, silicon, and zinc exhibited protective effects. These findings underscore the importance of considering environmental factors in maternal and neonatal health, particularly in regions with high levels of particulate matter pollution with specific elemental compositions such as regions prone to wildfires.
Furthermore, our study explored interactions among pollutants and between pollutants and maternal health factors, revealing complex interplays that influence RDS risk. For instance, interactions between bromine and maternal conditions such as preeclampsia and premature rupture of membranes were associated with heightened RDS risk, highlighting the synergistic effects of environmental and maternal factors. Conversely, antagonistic effects of environmental and maternal factors were seen with interactions between calcium and preeclampsia or PRoM as well as sodium and PRoM, which were associated with decreased risk of RDS. Similarly, several pollutant-pollutant interactions were found to significantly increase (chlorine-bromine, chromium-sodium, etc.) or decrease (calcium-bromine, calcium-copper, etc) risk of RDS. Although our study highlights the presence of numerous interactive effects, the use of logistic regression with stepwise forward selection presents a limitation as it potentially oversimplifies the relationship among pollutants and maternal health factors. Future studies using methodologies capable of assessing potential nonlinear relationships as well as pollutant mixture analysis would help improve the understanding of how the elemental pollutants of PM2.5 speciation interact with each other to affect the risk of RDS.
It is important to note that we are limited in the conclusions that can be drawn because the PM2.5 speciation measurements only provide information about the total concentration of a particular element and do not provide any information about the chemical form(s) the elements take in the air samples.25 More advanced measurement methodologies, such as infrared spectroscopy, would need to be performed on air samples to identify the chemical form(s) present in the air. As an example, chlorine may be present in the air from sea spray in the relatively innocuous form of sodium chloride (NaCl) that we also know as table salt, but it can also be in the form of the corrosive air pollutant hydrochloric acid (HCl) from industrial processes and chemical manufacturing. Other limitations of our study include its retrospective design and reliance on birth records data. Future research should explore longitudinal associations between prenatal exposures and neonatal outcomes, incorporating additional environmental and genetic factors to further understand the underlying mechanisms contributing to RDS risk.
Our study offers significant insights for both clinical practice and public health initiatives. By shedding light on the factors influencing RDS risk, such as environmental exposures, our findings emphasize the necessity of thorough risk evaluation during prenatal care. Implementing targeted interventions, like improving air quality in high-risk areas, can be effective in reducing the burden of RDS. Our findings advocate for the integration of air quality alerts into public health initiatives, offering timely warnings about pollutant levels and their potential impact on neonatal health, thereby enabling proactive measures to safeguard vulnerable populations.
CONCLUSION
In conclusion, our study examines the various factors contributing to neonatal respiratory distress syndrome (RDS), focusing on maternal, environmental, and sociodemographic influences on neonatal health outcomes. Through thorough analyses of maternal demographics, behaviors, medical conditions, pregnancy-related factors, and specific PM2.5 speciation pollutants, we have identified key determinants of RDS risk.
Our findings reaffirm established risk factors such as preterm birth, low birth weight, and certain maternal health conditions, while also uncovering nuances within racial and ethnic groups. Specifically, our study highlights variations in RDS risk among Hispanic subgroups, underscoring the complexity of racial and ethnic disparities in neonatal health outcomes. Moreover, our exploration of the associations between maternal exposure to PM2.5 speciation pollutants and RDS risk unveils significant findings. Although certain elements like calcium, sulfur, and aluminum are associated with increased RDS risk, others such as bromine, silicon, and zinc demonstrate protective effects. These insights underscore the critical importance of considering environmental factors in maternal and neonatal health, particularly in regions characterized by high levels of particulate matter pollution with specific elemental compositions. Furthermore, our investigation into interactions among pollutants and between pollutants and maternal health factors reveals complex relationships that shape RDS risk and highlight the need for advanced modeling methodologies. Furthermore, these findings emphasize the need for a holistic approach to risk assessment and intervention, considering the complex interactions among maternal health, sociodemographic, and environmental factors.
Considering these findings, our study offers valuable implications for clinical practice and public health initiatives. Implementing targeted interventions aimed at reducing exposure to harmful pollutants, integrating air quality alerts into public health initiatives, and tailoring interventions to address disparities in neonatal health outcomes are essential steps in mitigating the burden of RDS and improving neonatal health outcomes. Our study contributes to a deeper understanding of the multifactorial nature of RDS risk, providing a foundation for informed decision-making and policy development aimed at enhancing neonatal health outcomes.
CME Learning Objectives.
After completing this enduring educational activity, the learner will be better able to:
Assess factors influencing Neonatal Respiratory Distress Syndrome (RDS) risk,
Identify which combination of metals, medical and sociodemographic factors is the most significant exposure associated with respiratory distress syndrome (RDS)
Estimate the total effects of metal on RDS as the exposure changes in the index with interaction changes.
ACKNOWLEDGMENTS
The authors would like to thank the Florida Department of Health for providing us with the vital statistics data. They also acknowledge that any published findings and conclusions of this research are those of the authors and do not necessarily represent the official position of the Florida Department of Health.
NO AI was utilized at any stage during research development and design, data collection, and manuscript preparation.
Funding Sources:
This work is supported by the NIH/National Heart Lung and Blood Institute (grant no. K01HL146944).
Dr Boubakari Ibrahimou is the recipient of the NIH K01 grant and is the main author. He oversees the whole project from protocol/project development, data analysis, and overall production of the manuscript. Ms Shelbie Burchfield: graduate assistant, assisted with the data management, data analysis, and manuscript writing/editing Ms Ning Sun: graduate assistant, oversaw the data management and manuscript editing.
Footnotes
Ibrahimou, Burchfield, and Sun have no relationships/conditions/circumstances that present potential conflict of interest.
The JOEM editorial board and planners have no financial interest related to this research.
Ethics Approval: This study is exempt from the IRB with FIU IRB-20-0033.
Availability of Data and Materials:
Air pollution data are publicly available on the EPA website, and the health data are available upon request after approval from the Florida Department of Health.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Air pollution data are publicly available on the EPA website, and the health data are available upon request after approval from the Florida Department of Health.