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. Author manuscript; available in PMC: 2022 Jul 14.
Published in final edited form as: Environ Res. 2021 Feb 26;196:110937. doi: 10.1016/j.envres.2021.110937

Chronic exposure to air pollution and risk of mental health disorders complicating pregnancy

Jenna Kanner a, Anna Z Pollack b, Shamika Ranasinghe b,c, Danielle R Stevens a, Carrie Nobles a, Matthew CH Rohn a, Seth Sherman d, Pauline Mendola a,e,*
PMCID: PMC9280857  NIHMSID: NIHMS1818985  PMID: 33647295

Abstract

Background:

Air pollution is associated with mental health in the general population, but its influence on maternal mental health during pregnancy has not been assessed.

Objective:

We evaluated the relationship between unspecified mental disorders complicating pregnancy and depression with average air pollution exposure during 3-months preconception, first trimester and whole pregnancy.

Methods:

Ambient air pollution was derived from a modified Community Multiscale Air Quality model and mental health diagnoses were based on electronic intrapartum medical records. Logistic regression models assessed the odds of unspecified mental disorder complicating pregnancy (n = 11,577) and depression (n = 9793) associated with an interquartile range increase in particulate matter (PM) less than 2.5 μm (PM2.5), PM10, carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen oxide (NOx), sulfur dioxide (SO2), and ozone (O3). Pregnancies without mental health disorders were the reference group (n = 211,645). Models were adjusted for maternal characteristics and study site; analyses were repeated using cases with no additional mental health comorbidity.

Results:

Whole pregnancy exposure to PM10, PM2.5, NO2, and NOx was associated with a 29%–74% increased odds of unspecified mental disorders complicating pregnancy while CO was associated with 31% decreased odds. Results were similar for depression: whole pregnancy exposure to PM10, PM2.5, NO2, and NOx was associated with 11%–21% increased odds and CO and O3 were associated with 16%–20% decreased odds. SO2 results were inconsistent, with increased odds for unspecified mental disorders complicating pregnancy and decreased odds for depression. While most findings were similar or stronger among cases with no co-morbidity, PM2.5 and NOx were associated with reduced risk and SO2 with increased risk for depression only.

Discussion:

Whole pregnancy exposure to PM10, PM2.5, NO2, and NOx were associated with unspecified mental disorder complicating pregnancy and depression, but some results varied for depression only. These risks merit further investigation.

Keywords: Pregnancy, Air pollution, Depression, Psychiatric disorder, Environment

1. Introduction

Mental health problems during pregnancy adversely affect the mother’s wellbeing and are associated with severe outcomes, including postpartum depression and suicide (Lindahl et al., 2005), as well as adverse outcomes for the fetus, including pre-term birth (Mannisto et al., 2016), low birthweight (Grote et al., 2010), and future developmental and mental health problems (Junge et al., 2017). For large, population-based studies, mental health diagnoses in pregnancy are often based on medical record data, including reports of unspecified mental health disorders complicating pregnancy (International Classification of Diseases - 9 (ICD9) code 648.4) (Kelly et al., 1999, 2002; Mannisto et al., 2016). The code for “unspecified mental health disorders complicating pregnancy” is common and while it clearly indicates a relevant psychiatric concern during gestation, it can reflect a transient condition including psychosis (“Non-Organic Psychoses of Pregnancy,” 2017), or other conditions including maternal depression (Dietz et al., 2007).

Depression is one of the most common comorbidities during pregnancy, affecting around 10% of pregnant women worldwide (World Health Organization, 2020). Further, perinatal mental health disorders are more common in low-socioeconomic communities and communities of color, potentially contributing to intergenerational health disparities (Celaya et al., 2017). Risk factors for psychiatric disorders are diverse: adverse life experiences, social and institutional inequities, racism, family history, and underlying medical conditions all contribute (Kim et al., 2020; McGrath et al., 1990; Rusell et al., 2018). Given the high prevalence of mental health disorders and associated health effects, investigating potentially modifiable risk factors is important for both impacted women and their offspring (Ahmed et al., 2019).

Increasingly, air pollution has been recognized as potentially affecting mental health. Ambient air pollution is a well-studied environmental risk factor for health; chronic exposure is associated with cardiovascular (Fiordelisi et al., 2017), pulmonary (Kim et al., 2013), and metabolic dysfunction (Wei et al., 2016), leading to increased rates of premature mortality. More recently, air pollution has been linked with depression in older populations (Wang et al., 2020), hospitalizations due to depression (Gu et al., 2020; Wei et al., 2020), psychiatric emergency room visits (Buoli et al., 2018), and mental health disorders as summarized in a 2019 meta-analysis (Braithwaite et al., 2019). Particulate matter <2.5 μm (PM2.5) has been associated with cognitive impairments in mice, increased risk for depression onset among middle aged and older women (Kioumourtzoglou et al., 2017), and greater risk of major depressive disorders in the general population with long-term exposure (Kim et al., 2016). Further, particulate matter <10 μm (PM10), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) were all associated with emergency department visits for depressive episodes in a Korean cohort, with stronger risk associated with patients with pre-existing conditions (Cho et al., 2014).

The brain is susceptible to environmental factors due to high metabolic demands (Pun et al., 2017): air pollution crosses the blood-brain barrier, resulting in increased neuroinflammation and neurotoxicity, which can lead to pathological changes (Calderon-Garciduenas et al., 2015). In the context of pregnancy, the relationship between air pollution and diagnosed psychiatric disorders in pregnant women remains unclear. Lin et al. found a dose-dependent relationship between air pollution and emotional stress during pregnancy (Lin et al., 2017). Similarly, Niedzwiecki et al. reported an association between particulate matter and postpartum depression (Niedzwiecki et al., 2020). However, the relationship between air pollution and diagnosed psychiatric disorders in pregnant women has not been well-studied.

The aim of this study was to examine the association between preconception and pregnancy-related air pollution exposures and diagnosis of common psychiatric disorders in the Consortium on Safe Labor. Given the limits of prior research, we focused on two common conditions: unspecified mental health disorders during pregnancy (which ensures that the psychiatric problem was an active concern during pregnancy) and maternal depression (which has a high prevalence and relevant literature to support an association). We hypothesized that increased levels of air pollution would be associated with higher risk for these disorders in our sample of pregnant women.

2. Methods

2.1. Study participants

The Consortium on Safe Labor (CSL) was a retrospective nationwide cohort study of labor and delivery practices from 19 hospitals across the United States (2002–2008) (Zhang et al., 2010). Hospital delivery admission electronic medical records provided information on patient demographics, medical history, labor and delivery characteristics, immediate postpartum data and discharge summaries including International Classification of Diseases (ICD9) codes. From the full CSL cohort of singleton deliveries, 10 women were excluded due to exposure windows outside the modeled data parameters. We also excluded 2374 women who had only mental health disorders which were not the focus of the present study (e.g. bipolar, schizophrenia, etc.). Our analytic sample included 202,955 women with 221,794 singleton pregnancies who had available exposure data and either no mental health disorder recorded in their delivery medical record (reference) or at least one of the disorders under study. This study received ethical review and approval from the institutional review boards of all participating institutions; all records were anonymized.

2.2. Exposure data

Criteria air pollution exposure for the CSL participants was estimated in the Air Quality and Reproductive Health (AQRH) study. The modified Community Multiscale Air Quality models estimated exposure to particulate matter <10 μ (PM10), particulate matter < 2.5 μ (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), nitrous oxides (NOX), sulfur dioxide (SO2) and ozone (O3) from 2001 to 2009. The details of the exposure models are described elsewhere(Chen et al., 2014). Exposure for each pollutant was linked to each participant based on the average exposure in the hospital referral region for their delivery hospital. Hospital referral regions were in largely metropolitan areas with catchment areas between 415 and 312,644 km2. Concentrations are expressed as μg/m3 for PM10 and PM2.5 and in parts per billion for all other air pollutant species. Given the timing of diagnosis was not known, we estimated chronic exposure windows that represented the average exposure to air pollutants across a woman’s whole pregnancy, as well as during a 3-month preconception period, and during the first trimester to evaluate early pregnancy exposure (cut points as described in Supplemental Table 1).

2.3. Outcome data

Our primary endpoints were maternal diagnoses of unspecified mental disorder complicating pregnancy or maternal depression recorded in the hospital delivery admission medical record. We characterized these outcomes in two ways, first as any occurrence of the diagnosis of interest, including other psychiatric comorbidities (any) and second, when the only mental health condition reported was the diagnosis of interest (only):

Any unspecified mental disorder complicating pregnancy (N = 11,577). Pregnancies with diagnoses of unspecified mental disorder complicating pregnancy (ICD9 648.4), including those with another psychiatric disorder diagnosis (not mutually exclusive):

Depression, ICD9 296.2, 296.3, 311 (N = 3687).

Anxiety, ICD9 300 (N = 515).

Anxiety with depression, ICD9 300.4 (N = 847).

Schizophrenia, ICD9 295 (N = 132).

Bipolar disorder or bipolar disorder with depression and/or anxiety, ICD9 296.0, 296.2, 296.4–296.8 (N = 778).

Only unspecified mental disorders complicating pregnancy (N = 5617). Pregnancies with ICD9 code 648.4 in discharge summaries and no other mental health conditions noted.

Any depression (N = 9793) comprised pregnancies with a diagnosis of major depressive disorder (ICD9 296.2, 296.3) or depressive disorder not elsewhere classified (ICD9 311) in discharge summaries, or women with a history of depression recorded in electronic medical records. This category includes women who have another psychiatric disorder diagnosed (not mutually exclusive):

Anxiety with depression, ICD9 300.4 (N = 911).

Bipolar disorder with depression, ICD9 296.0, 296.2, 296.4–296.8 (N = 331).

Mental health disorder complicating pregnancy (ICD9 648.4) (N = 3687).

Pregnancies with only depression (N = 4862) included those with a diagnosis of major depressive disorder (ICD9 codes 296.2 and 296.3) or depressive disorder not elsewhere classified (ICD9 code 311) in discharge summaries, or women with a history of depression recorded in electronic medical records and no other mental health conditions noted.

2.4. Statistical analysis

Logistic regression models with generalized estimating equations (GEE) and robust variance estimators were used to estimate the odds of having a diagnosis of unspecified mental health disorder complicating pregnancy (with/without other mental health conditions) and depression (with/without other mental health conditions) associated with an interquartile range (IQR) increase in exposure to each criteria air pollutant compared to pregnancies with no psychiatric disorders recorded in their hospital delivery admission record (reference group) to give adjusted odds ratios (aOR) with 95% confidence intervals (CI). GEE and robust standard error estimates were calculated to account for multiple pregnancies among women. Most models were adjusted for hospital study site (except for depression only, which was adjusted for American College of Obstetrics and Gynecology region due to convergence issues), age (≤19, 20–24, 25–29, 30–34, ≥35), maternal race (Non-Hispanic White, Non-Hispanic Black, Hispanic, Asian/Pacific Islander, multiple/other, unknown/missing), pre-pregnancy body mass index (BMI, <18.5, 18.5–24.9, 25.0–29.9, ≥30.0 kg/m2), marital status (married, non-married), insurance (private, public, other), parity (0, 1, 2 or more), smoking status (yes/no) and alcohol consumption (yes/no), based upon a priori knowledge of confounding factors. We imputed data using chained equations with 5 datasets for BMI (2% missing) based on constituent air pollutants, hospital study site, age, maternal race, maternal height, pre-pregnancy weight, marital status, smoking status, alcohol consumption, insurance status, and parity. Sensitivity analyses were performed on women who experienced the psychiatric outcomes of interest with no other mental health conditions (only unspecified mental health disorder or depression). We also ran multipollutant models for whole pregnancy exposure to all pollutants except for NO2 given its high correlation with NOx (Supplemental Table 2). Furthermore, we ran all models with additional adjustment for season of conception, as well as hypertensive disorders of pregnancy and gestational diabetes.

Participant characteristics were compared by diagnostic status using PROC GENMOD to account for women with repeated pregnancies. All analyses were performed on SAS version 9.4 (Cary, NC, USA). A p-value of <0.05 was considered statistically significant.

3. Results

There were 11,577 pregnancies among 11,173 women with unspecified mental disorder complicating pregnancy and 9793 pregnancies among 9330 women with depression. Women in both diagnostic groups had significantly different demographics compared to women with no mental health disorders (Table 1). Women with unspecified mental disorder complicating pregnancy were more likely to be younger, white, overweight, non-married, have public insurance, be multiparous, smoke, and drink alcohol than women without any mental health diagnosis (Table 1). Women with depression were more likely to be white, obese, non-married, have public insurance, be multi-parous, smoke, and drink alcohol compared to women without any mental health disorders.

Table 1.

Demographic characteristics for pregnancies with and without mental health disorders complicating pregnancy in the Consortium on Safe Labor (2002–2008).

Pregnancies of Women Without Mental Health Diagnosis Pregnancies of Women with Any Unspecified Disorders During Pregnancy Pregnancies of Women with Only Unspecified Disorders During Pregnancy Pregnancies with Any Depressiona Pregnancies with Only Depression
Characteristics N % N % N % N % N %
Overall 211,645 11,577 5617 9793 4862
Age
 ≤19 12044 5.7 763 6.6 432 7.7 554 5.7 280 5.8
 20–24 59964 28.3 4081 35.3 2355 41.9 2703 27.6 1303 26.8
 25–29 58991 27.9 3002 25.9 1390 24.8 2791 28.5 1465 30.1
 30–34 55602 26.3 2487 21.5 929 16.5 2598 26.5 1297 26.7
 ≥35 25045 11.8 1244 10.8 511 9.1 1147 11.7 517 10.6
Race
 NH-White 102471 48.4 7432 64.2 3314 59.0 6480 66.1 3073 63.2
 NH-Black 47851 22.6 2648 22.9 1724 30.7 1540 15.7 839 17.3
 Hispanic 37991 17.9 984 8.5 360 6.4 1277 13.0 713 14.7
 API 9226 4.4 81 0.7 36 0.6 64 0.7 32 0.7
 Multi/Other 5188 2.5 143 1.2 86 1.5 119 1.2 66 1.4
 Unknown 8918 4.2 292 2.5 97 1.7 313 3.2 139 2.9
BMI
 ≤18.5 15395 7.3 924 8.0 508 9.0 604 6.1 263 5.4
 18.5–25 100418 47.5 4733 40.9 2380 42.4 3885 39.7 1935 39.8
 25–30 52790 24.9 2983 25.8 1426 25.4 2584 26.4 1311 26.9
 ≥30 43045 20.3 2937 25.4 1203 23.2 2720 27.8 1353 27.8
Marital Status
 Married 127643 60.3 4032 34.8 1266 22.5 4903 50.0 2572 52.9
 Non-Married 84002 39.7 7545 65.2 4351 77.5 4890 50.0 2290 47.1
Insurance Status
 Public 65130 30.8 6479 56.0 3933 70.0 3896 39.8 1902 39.1
 Private 120540 57.0 4550 39.3 1333 23.7 5536 56.5 2748 56.5
 Other 25975 12.3 548 4.7 351 6.3 361 3.7 212 4.4
Parity
 0 85784 40.5 3947 34.1 1803 32.1 3129 31.9 1395 28.7
 1 64761 30.6 3448 29.8 1639 29.2 3025 30.9 1521 31.3
 ≥2 61101 28.9 4182 36.1 2175 38.7 3639 37.2 1946 40.0
Smoking
 No 201108 95.0 7661 66.2 2953 52.6 7993 81.6 4111 84.6
 Yes 10538 5.0 3916 33.8 2664 47.4 1800 18.4 751 15.5
Alcohol
 No 208423 98.5 10818 93.4 5129 91.31 9403 96.0 4687 96.4
 Yes 3223 1.5 759 6.6 488 8.7 390 4.0 175 3.6
Gestational Diabetes
 No 180007 85.1 9526 82.3 4084 72.7 9165 93.6 4613 94.9
 Yes 7040 3.3 419 3.6 114 2.0 503 5.1 247 5.1
 Missing 24599 11.6 1632 14.1 1419 2.5 125 1.3 2 0
Hypertensive Disorders of Pregnancy
 No 196773 92.97 10738 92.8 5301 94.4 9029 92.2 4518 92.9
 Yes 14873 7/03 839 7.3 316 5.6 764 7.8 344 7.1
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Note: BMI, body mass index; NH, non-Hispanic; API, Asian-Pacific Islander.

b

Only Depression noted by ICD-9 and History of Depression in Medical Record Without Other Mental Health Comorbidities.

a

Any Depression noted by ICD-9 and/or History of Depression in Medical Record w/Mental Health Comorbidities.

Regarding the odds of any unspecified mental disorder complicating pregnancy (Table 2), a whole pregnancy IQR increase in several pollutants was associated with increased risk, from 29% for PM10 and 46% for NOx, to 73–74% for NO2 and SO2.Meanwhile, an IQR increase in CO across the whole pregnancy was associated with a decreased odds of any unspecified mental disorder complicating pregnancy (aOR: 0.69, 95% CI: 0.66, 0.72) and there was no association for O3 (aOR: 0.97, 95% CI 0.92, 1.03). The magnitude of these associations was smaller, but remained statistically significant, for the 3-month preconception and first trimester time periods. Analyses restricted to women with only unspecified disorders during pregnancy and no other mental health conditions yielded stronger associations, particularly for NO2 (aOR:4.40, 95% CI 3.68, 5.26) and SO2 (aOR: 4.63, 95% CI 3.90, 5.49) (Table 2).

Table 2.

Risk of unspecified mental disorder complicating pregnancy (noted by discharge ICD-9 code) for every IQR increase in average pollutant exposure for three months preconception, first trimester, and whole pregnancy, Air Quality and Reproductive Health study (2002–2008).

Mental Health Conditiona,b,c Pollutant 3 Months Preconception
 Trimester 1
 Whole Pregnancy
OR (95% CI) OR (95% CI) OR (95% CI)
Unspecified disorderd(any, N = 11,577) PM10 1.08 (1.04, 1.12) 1.09 (1.05, 1.12) 1.29 (1.24, 1.35)
PM2.5 1.12 (1.07, 1.18) 1.15 (1.10, 1.20) 1.73 (1.59, 1.88)
CO 0.85 (0.81, 0.89) 0.82 (0.78, 0.85) 0.69 (0.66, 0.72)
NO2 1.46 (1.36, 1.55) 1.47 (1.36, 1.58) 1.73 (1.56, 1.92)
NOx 1.30 (1.24, 1.37) 1.24 (1.17, 1.31) 1.46 (1.32, 1.62)
SO2 1.12 (1.06, 1.18) 1.25 (1.19, 1.33) 1.74 (1.57, 1.92)
O3 0.84 (0.81, 0.88) 0.92 (0.88, 0.96) 0.97 (0.92, 1.03)
Unspecified disordere(only, N = 5617) PM10 1.22 (1.16, 1.28) 1.14 (1.08, 1.19) 1.50 (1.40, 1.60)
PM2.5 1.37 (1.28, 1.46) 1.36 (1.28, 1.45) 2.57 (2.28, 2.90)
CO 0.83 (0.77, 0.90) 0.82 (0.76, 0.88) 0.70 (0.64, 0.76)
NO2 2.26 (2.04, 2.51) 2.50, 2.21, 2.81) 4.40 (3.68, 5.26)
NOx 1.67 (1.54, 1.81) 1.60 (1.46, 1.75) 2.53 (2.15, 2.98)
SO2 1.16 (1.08, 1.26) 1.52 (1.40, 1.66) 4.63 (3.90, 5.49)
O3 0.82 (0.77, 0.87) 0.90 (0.85, 0.95) 1.14 (1.04, 1.24)
Depressionf (any, N = 9793) PM10 1.03 (0.99, 1.07) 1.07 (1.03, 1.11) 1.14 (1.10, 1.19)
PM2.5 0.97 (0.92, 1.02) 1.03 (0.98, 1.08) 1.19 (1.08, 1.30)
CO 0.90 (0.87, 0.93) 0.89 (0.86, 0.92) 0.80 (0.76, 0.83)
NO2 1.05 (0.98, 1.12) 1.08 (1.01, 1.16) 1.21 (1.09, 1.32)
NOx 1.05 (0.99, 1.10) 1.03 (0.98, 1.09) 1.11 (1.00, 1.22)
SO2 1.03 (0.96, 1.09) 1.03 (0.96, 1.09) 0.90 (0.81, 1.00)
O3 0.90 (0.86, 0.94) 0.93 (0.89, 0.97) 0.84 (0.79, 0.89)
Depressiong (only, N = 4862) PM10 1.09 (1.05, 1.14) 1.12 (1.07, 1.17) 1.18 (1.13, 1.24)
PM2.5 0.96 (0.90, 1.03) 0.82 (0.76, 0.88) 0.51 (0.46, 0.57)
CO 0.82 (0.78, 0.86) 0.81 (0.77, 0.85) 0.69 (0.65, 0.74)
NO2 1.15 (1.06, 1.25) 1.25 (1.14, 1.36) 2.11 (1.88, 2.37)
NOx 0.79 (0.73, 0.85) 0.80 (0.75, 0.87) 0.55 (0.49, 0.62)
SO2 1.45 (1.34, 1.57) 1.45 (1.34, 1.57) 2.51 (2.22, 2.84)
O3 0.83 (0.78, 0.89) 0.84 (0.79, 0.89) 0.66 (0.61, 0.71)
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Abbreviations: OR, Odds Ratio; 95% CI, 95% Confidence Interval; CO, carbon monoxide; NO2, nitrogen dioxide; NOx, nitrogen oxides; PM10, particulate matter <10 pm; PM25, particulate matter <2.5 pm; SO2, sulfur dioxide; IQR, interquartile range.

a

Compared to women without any mental health disorders (N = 211,646).

b

Controlled for maternal age, rage, BMI, parity, insurance status, marital status, study site, and smoking and alcohol use.

c

BMI was imputed to account for missing data.

d

Noted by discharge ICD-9 code 648.4 with other mental health conditions (296.9, 300.0, 296.0, 296.2, 296.4, 296.8, 300.4, 295).

e

Noted by discharge ICD-9 code 648.4 without any other mental health condition.

f

Noted by discharge ICD-9 codes 296.2, 296.3, 311 with other mental health conditions (300.4, 309.28, 296.0–296.2 and 296.4–296.6).

g

Noted by discharge ICD-9 codes 296.2, 296.3, 311 without other mental health conditions.

With respect to any depression (Table 2), the associations were generally more modest with whole pregnancy average exposure to PM10, PM2.5, NO2, and NOx associated with 11–21% increased risk for each IQR increase. Measured across the whole pregnancy, an IQR increase in CO (aOR: 0.80, 95% CI: 0.76, 0.83), SO2 (aOR: 0.90, 95% CI: 0.81, 1.00), and O3 (aOR: 0.84, 95% CI: 0.79, 0.89) were associated with a decreased odds of any depression. Estimates were mostly closer to the null for the 3-month preconception and first trimester time periods. Restricting to women with depression only resulted in estimates in the same direction for PM10, CO, NO2, and O3 across whole pregnancy, but estimates in the opposite direction for PM2.5 (aOR: 0.51, 95% CI: 0.46, 0.57), NOx (aOR: 0.55, 95% CI: 0.49, 0.62), and SO2 (aOR: 2.51, 95% CI: 2.22, 2.84) (Table 2).

Multipollutant model results (Table 3) were less precise but generally similar with the exception of attenuated and reduced risk estimates for PM2.5. Results were consistent in models additionally adjusted for season of conception, or hypertensive disorders of pregnancy and gestational diabetes (data not shown).

Table 3.

Multipollutant models of constituent air pollutants during whole pregnancy.

Pollutant Any Unspecified Mental Disorders of Pregnancya
 Only Unspecified Mental Disorders of Pregnancyb
 Any Depressionc
 Only Depressiond
OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI)
PM10 1.32 (1.25, 1.41) 1.24 (1.13, 1.37) 1.38 (1.29, 1.46) 1.28 (1.20, 1.36)
PM2.5 0.99 (0.88, 1.13) 1.17 (0.97, 1.41) 0.81 (0.71, 0.93) 0.45 (0.39, 0.53)
CO 0.57 (0.54, 0.60) 0.61 (0.55, 0.68) 0.62 (0.59, 0.66) 0.64 (0.59, 0.69)
NOx 1.68 (1.40, 2.02) 2.52 (1.77, 3.59) 1.69 (1.45, 1.98) 0.73 (0.58, 0.88)
SO2 1.63 (1.44, 1.84) 4.03 (3.27, 4.95) 0.83 (0.73, 0.95) 2.52 (2.20, 2.89)
O3 1.04 (0.95, 1.13) 1.68 (1.42, 1.97) 0.84 (0.79, 0.90) 0.59 (0.53, 0.64)
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Abbreviations: OR, Odds Ratio; LCL, Lower Confidence Limit; UCL, Upper Confidence Limit.

a

Adjusted for six constituent pollutants: CO, carbon monoxide; NOx, nitrogen oxides; PM10, particulate matter <10 μm; PM2.5, particulate matter <2.5 μm; SO2, sulfur dioxide.

b

BMI was imputed to account for missing data.

c

Any Unspecified Mental Disorder of Pregnancy or Depression(noted by ICD-9 and/or History of Depression in Medical Record) including cases with other mental health comorbidities.

d

Only Unspecified Mental Disorder of Pregnancy or Depression (noted by ICD-9 and History of Depression in Medical Record) without other mental health comorbidities.

4. Discussion

In a diverse, nationwide cohort, we observed that increasing exposure to ambient air pollutants averaged over the whole pregnancy, particularly PM10 and NO2, was associated with 29–73% increased odds of unspecified mental disorder complicating pregnancy and 14–21% increased odds of depression. In contrast, CO was associated with a modest decrease in the odds of unspecified mental disorder complicating pregnancy (21%) and depression (20%). Exposure to SO2 increased odds of unspecified mental disorder complicating pregnancy and only depression but was not associated with any depression which included women with other psychiatric diagnoses. Results for cases with no other mental health co-morbidity (“only” cases) were similar or stronger for most pollutant-outcome relationships but, for depression only, we observed some changes in direction with reduced risks associated with PM2.5 and NOx while SO2 increased risk. We also found that our overall results were generally consistent, but not as strong, when evaluating exposure during a 3-month preconception period and across the first trimester of pregnancy. Our findings support our hypothesis that exposure to ambient air pollution, especially PM10, and NO2 during pregnancy, is associated with higher odds of these psychiatric disorders.

To our knowledge, this is the first study to investigate the association between air pollution and odds of psychiatric disorders during the perinatal period in a pregnant population. In line with our findings, another study investigated the relationship between air pollution and maternal stress during pregnancy, and found that acute increases in PM2.5, SO2, and NO2 were associated with higher scores on Global-Severity-Indices, indicating higher levels of emotional stress (Lin et al., 2017). Additionally, one study found mid-pregnancy PM2.5 exposure was associated with symptoms of depression and anhedonia postpartum (Sheffield et al., 2018). A study in Mexico City mirrors these results, finding associations between PM2.5 and postpartum depression and anhedonia (Niedzwiecki et al., 2020). While we were unable to investigate symptoms of depression, we also found increases in PM2.5 to increase odds of maternal depression recorded in the delivery admission medical record. Secondhand smoke contains many of the individual pollutants included in this study, such as PM2.5 (Semple et al., 2015). Second-hand smoke exposure during pregnancy has been shown to be associated with depressive symptoms in pregnant women (Suzuki et al., 2019). However, we note that our findings for PM2.5 were not consistent when restricted to the smaller group of women with depression only or in a multipollutant adjusted model, suggesting these relationships are likely to be complex and merit further study.

Acute exposure to air pollution has been studied in relation to depressive symptoms, suicide risk, and emergency department admittance. A Canadian study found that acute increases in constituent air pollutants, especially O3, increased risk of emergency department visits for depression(Szyszkowicz et al., 2016). Further, a study in China found that increases in PM2.5, PM10, and NO2 were associated with hospital admissions for depression (Gu et al., 2020). We were unable to investigate acute exposure but found chronic O3 exposure to be protective against depression. Our results for particulate matter and depression were similar to those observed in a 2014 Korean study (Cho et al., 2014). However, in contrast with our findings for SO2 and CO, this study also found that SO2, PM10, NO2, and CO were associated with greater risk of emergency department visits for depression, with stronger results for individuals with pre-existing cardiovascular disease, asthma, and diabetes mellitus (Cho et al., 2014). Our findings of PM2.5 associated with any depression and unspecified mental disorder complicating pregnancy were supported by a study in the US that found a 30 day moving-average of PM2.5 was associated with depressive symptoms in a population of older adults (Pun et al., 2017).

Many studies have investigated the associations between long-term exposure to air pollution and depression, mainly in populations of older adults. A study in Spain found that long-term exposure to air pollutants increased odds of self-reported history of depression and use of antidepressants (Vert et al., 2017). A Korean study also found evidence of an association of air pollution and depressive symptoms, with PM10, NO2, and O3 increasing emotional symptoms of depression (Lim et al., 2012). A Chinese study found exposure to PM2.5 increases depressive symptoms in older adults, possibly related to decreased social contact due to pollution (Wang et al., 2020). Further, in a population of elderly women in the US, increases in PM2.5 and O3 were associated with onset of depression for middle-aged and older women (Kioumourtzoglou et al., 2017). Even so, results are inconsistent between populations (Zijlema et al., 2016), but represent the possibility of an association between air pollution and psychiatric disorders.

The relationship between ambient air pollution and psychiatric disorders is biologically plausible. Small particulate matter, including PM10 and PM2.5, have a large surface area, leading to better lung penetration and diffusion into the respiratory tract and brain (Calderon-Garciduenas et al., 2015). Exposure to particulate matter increased inflammation in the brains of mice (Campbell et al., 2005). Chronic inflammation in the brain leads to the formation of reactive oxygen species and oxidative stress, can disrupt the blood-brain barrier and alter the response of the immune system which are risk factors for psychiatric disorders (Calderon-Garciduenas et al., 2015). Further, oxidative stress can lead to dopaminergic neurotoxicity which may lead to depression pathogenesis (Fan et al., 2020). Air pollution is also linked to other diseases, such as cardiovascular and pulmonary disease, that are predictors of depression and oxidative stress (Fan et al., 2020).

In our study, we saw similar patterns of risk among pollutants, with PM10 and NO2 associated with increased odds of unspecified disorders during pregnancy and depression. Although moderately correlated in our data (Spearman r = 0.31), these air pollutants may trend together because of shared emission sources (Cho et al., 2014). On the other hand, we saw increases in CO and O3 leading to decreased odds of unspecified disorders of pregnancy and depression. Ozone is a secondary pollutant generated from primary pollutants after photochemical reactions (Cho et al., 2014), which might explain why it is associated with lower risk in some time frames. In our data, ozone was negatively correlated with all other pollutants studied, most strongly with PM2.5 (Spearman r = −0.66), which is consistent with the suggestion that ozone levels are often inversely related to particulate matter (Jia et al., 2017). Further, while carbon monoxide is neurotoxic in high doses (Hsiu-Ling Chen, 2013), it may reduce oxidative stress and be therapeutic in low doses (Hanafy et al., 2013). In our study, increases in SO2 increased odds of unspecified mental disorders of pregnancy, and decreased odds of depression. Similarly, sulfur dioxide has a dose-dependent effect on brain damage, with low levels reducing neuronal apoptosis and alleviating neuronal damage in rats (Han et al., 2014).

Our study had several strengths. The Consortium on Safe Labor is a large, diverse, nationwide retrospective study in the US based on electronic medical records. We used detailed air quality models linked to hospital referral regions to establish proxies of exposure for each woman. We believe that our study is the first to investigate the relationship between air pollution and perinatal depression and unspecified mental disorder complicating pregnancy.

We recognize some limitations of our study, in particular, the potential for misclassification. Primarily, this study lacked geo-spatial data, such as maternal residential history, movement patterns and time spent indoors versus outdoors, which may bias the model of air pollution exposure, particularly if women with mental health disorders have differing patterns of local mobility. We also average exposure over the hospital referral region which does not allow for consideration of small area effects such as the influence of neighborhood deprivation. While adjusting for study site does control for variation in medical records or diagnostic preferences across sites, it will inevitably over-adjust for ambient conditions as well. The referral regions also vary considerably in size and averaging exposure over these distances, while supporting an estimate of local mobility, will also limit exposure variance. Further, we lack data on medication, timing of onset, control of mental health disorder, and inflammation during pregnancy. We determined psychiatric disorders from delivery admission medical records and do not have timing of diagnosis. Accordingly, some diagnoses likely preceded pregnancy, ruling out an interpretation of incident disease but we note that the discharge summary codes are likely to reflect current diagnoses that persist in pregnancy and for the unspecified mental disorder complicating pregnancy, we are confident that an active mental health problem occurred during the course of gestation as the ICD9 code is limited to pregnancy. In addition, we may have missed cases of depression that were undiagnosed during pregnancy (Ko et al., 2012). We were unable to investigate acute associations, as our data lacked timing of diagnosis. Given this limitation, we investigated exposures early, including a preconception window as well as a measure of chronic whole pregnancy exposures. Finally, we were limited in that our data did not have information on medication management or on postpartum mental health outcomes, which may have helped us to further elucidate the relationship between air pollution and psychiatric disorders during pregnancy.

There is growing evidence that air pollution is an environmental risk factor contributing to the etiology of psychiatric disorders. In our study, we found that exposure to some air pollutants increased the odds of unspecified psychiatric disorders during pregnancy and depression, while others decrease odds. Despite the limitations of our study noted above, we do not believe that women with mental health disorders are more likely to become pregnant when air pollution is high. This suggests that most of the potential misclassification is likely to be non-differential and may bias our findings towards the null. In this first investigation of the association between air pollution and psychiatric disorders during the perinatal period, we observe relations with ambient air pollutants that merit further attention. Having a pregnancy complicated by a psychiatric disorder can lead to poor outcomes for maternal mental health during postpartum, as well as for neurodevelopmental problems for the offspring (Junge et al., 2017; Lindahl et al., 2005). As such, it is important that future investigations build on and replicate this effort with longitudinal prospective data, with both indoor and outdoor air quality measures, to continue to elucidate the relationship between air quality and mental health across pregnancy.

Supplementary Material

Supplementary material

Acknowledgments

This research was supported by the Intramural Research Program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) under contracts for the Consortium on Safe Labor (Contract No. HHSN267200603425C) and the Air Quality and Reproductive Health Study (Contract No. HHSN275200800002I, Task Order No. HHSN27500008). The funding sources had no role in the study design, collection, analysis, interpretation of data, writing of the report, or the decision to submit for publication.

The Consortium on Safe Labor data is available through the NICHD Data and Specimen Hub (DASH) https://dash.nichd.nih.gov/and the Air Quality and Reproductive Health data is available with a data use agreement obtained by contacting the corresponding author.

Footnotes

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT

The authors have all contributed extensively to the manuscript. Jenna Kanner: Conceptualization, Methodology, Formal analysis, Writing – Original Draft, Writing – Review & Editing, Visualization. Anna Z. Pollack: Conceptualization, Methodology, Formal analysis, Writing – Review & Editing. Shamika Ranasinghe: Conceptualization, Methodology, Formal analysis, Writing – Review & Editing. Danielle R. Stevens: Methodology, Formal analysis, Writing – Review & Editing. Carrie Nobles: Methodology, Formal analysis, Writing – Review & Editing. Matthew C.H. Rohn: Methodology, Writing – Review & Editing. Seth Sherman: Methodology, Formal analysis, Investigation, Data Curation, Writing – Review & Editing. Pauline Mendola: Conceptualization, Methodology, Formal analysis, Investigation, Resources, Supervision, Writing – Review & Editing.

Prior presentation of data

None.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envres.2021.110937.

References

  1. Ahmed A, Bowen A, Feng CX, Muhajarine N, 2019. Trajectories of maternal depressive and anxiety symptoms from pregnancy to five years postpartum and their prenatal predictors. BMC Pregnancy Childbirth 19 (1), 26. 10.1186/s12884-019-2177-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Braithwaite I, Zhang S, Kirkbride JB, Osborn DPJ, Hayes JF, 2019. Air pollution (particulate matter) exposure and associations with depression, anxiety, bipolar, psychosis and suicide risk: a systematic review and meta-analysis. Environ. Health Perspect. 127 (12), 126002. 10.1289/EHP4595. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Buoli M, Grassi S, Caldiroli A, Carnevali GS, Mucci F, Iodice S, Bollati V, 2018. Is there a link between air pollution and mental disorders? Environ. Int. 118, 154–168. 10.1016/j.envint.2018.05.044. [DOI] [PubMed] [Google Scholar]
  4. Calderon-Garciduenas L, Calderon-Garciduenas A, Torres-Jardon R, Avila-Ramirez J, Kulesza RJ, Angiulli AD, 2015. Air pollution and your brain: what do you need to know right now. Prim. Health Care Res. Dev. 16 (4), 329–345. 10.1017/S146342361400036X. [DOI] [PubMed] [Google Scholar]
  5. Campbell A, Oldham M, Becaria A, Bondy SC, Meacher D, Sioutas C, Kleinman M, 2005. Particulate matter in polluted air may increase biomarkers of inflammation in mouse brain. Neurotoxicology 26 (1), 133–140. 10.1016/j.neuro.2004.08.003. [DOI] [PubMed] [Google Scholar]
  6. Celaya AK, Batra P, Jackson A, Robinson N, Dossett E, Reina A, 2017. Racial disparities in perinatal depression in an underserved los angeles county population [3OP]. Obsetrics & Gynecology 129 (5). [Google Scholar]
  7. Chen G, Li J, Ying Q, Sherman S, Perkins N, Sundaram R, Mendola P, 2014. Evaluation of observation-fused regional air quality model results for population air pollution exposure estimation. Sci. Total Environ. 485–486, 563–574. 10.1016/j.scitotenv.2014.03.107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cho J, Choi YJ, Suh M, Sohn J, Kim H, Cho SK, Shin DC, 2014. Air pollution as a risk factor for depressive episode in patients with cardiovascular disease, diabetes mellitus, or asthma. J. Affect. Disord. 157, 45–51. 10.1016/j.jad.2014.01.002. [DOI] [PubMed] [Google Scholar]
  9. Dietz PM, Williams SB, Callaghan WM, Bachman DJ, Whitlock EP, Hornbrook MC, 2007. Clinically identified maternal depression before, during, and after pregnancies ending in live births. Am. J. Psychiatr. 164 (10), 1515–1520. 10.1176/appi.ajp.2007.06111893. [DOI] [PubMed] [Google Scholar]
  10. Fan SJ, Heinrich J, Bloom MS, Zhao TY, Shi TX, Feng WR, Dong GH, 2020. Ambient air pollution and depression: a systematic review with meta-analysis up to 2019. Sci. Total Environ. 701, 134721. 10.1016/j.scitotenv.2019.134721. [DOI] [PubMed] [Google Scholar]
  11. Fiordelisi A, Piscitelli P, Trimarco B, Coscioni E, Iaccarino G, Sorriento D, 2017. The mechanisms of air pollution and particulate matter in cardiovascular diseases. Heart Fail. Rev. 22 (3), 337–347. 10.1007/s10741-017-9606-7. [DOI] [PubMed] [Google Scholar]
  12. Grote NK, Bridge JA, Gavin AR, Melville JL, Iyengar S, Katon WJ, 2010. A meta-analysis of depression during pregnancy and the risk of preterm birth, low birth weight, and intrauterine growth restriction. Arch. Gen. Psychiatr. 67 (10), 1012–1024. 10.1001/archgenpsychiatry.2010.111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Gu X, Guo T, Si Y, Wang J, Zhang W, Deng F, Wu S, 2020. Association between ambient air pollution and daily hospital admissions for depression in 75 Chinese cities. Am. J. Psychiatr. 10.1176/appi.ajp.2020.19070748appiajp202019070748. [DOI] [PubMed] [Google Scholar]
  14. Han Y, Yi W, Qin J, Zhao Y, Zhang J, Chang X, 2014. Dose-dependent effect of sulfur dioxide on brain damage induced by recurrent febrile seizures in rats. Neurosci. Lett. 563, 149–154. 10.1016/j.neulet.2013.12.042. [DOI] [PubMed] [Google Scholar]
  15. Hanafy KA, Oh J, Otterbein LE, 2013. Carbon monoxide and the brain: time to rethink the dogma. Curr. Pharmaceut. Des. 19 (15), 2771–2775. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hsiu-Ling Chen, P.-C. C, Cheng-Hsien Lu, Nai-Wen Hsu, Kun-Hsein Chou, Ching-Po Lin, Re-Wen Wu, Shau-Hsuan Li, Yu-Fan Cheng, Wei-Che Lin, 2013. Structural and cognitive deficits in chronic carbon monoxide intoxication: a voxel-based morphometry study. BMC Neurol. 13 (129). [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Jia M, Zhao T, Cheng X, Gong S, Zhang X, Tang L, Chen Y, 2017. Inverse relations of PM2.5 and O3 in air compound pollution between cold and hot seasons over an urban area of east China. Atmosphere 8 (12). 10.3390/atmos8030059. [DOI] [Google Scholar]
  18. Junge C, Garthus-Niegel S, Slinning K, Polte C, Simonsen TB, Eberhard-Gran M, 2017. The impact of perinatal depression on children’s social-emotional development: a longitudinal study. Matern. Child Health J. 21 (3), 607–615. 10.1007/s10995-016-2146-2. [DOI] [PubMed] [Google Scholar]
  19. Kelly RH, Danielsen BH, Golding JM, Anders TF, Gilbert WM, Zatzick DF, 1999. Adequacy of prenatal care among women with psychiatric diagnoses giving birth in California in 1994–1995. Psychiatr. Serv. 50 (12). [DOI] [PubMed] [Google Scholar]
  20. Kelly RH, Russo J, Holt VL, Danielsen BH, Zatzick DF, Walker E, Katon W, 2002. Psychiatric and substance use disorders as risk factors for low birth weight and preterm delivery. Obstet. Gynecol. 100 (2), 297–304. 10.1016/s0029-7844(02)02014-8. [DOI] [PubMed] [Google Scholar]
  21. Kim KH, Jahan SA, Kabir E, 2013. A review on human health perspective of air pollution with respect to allergies and asthma. Environ. Int. 59, 41–52. 10.1016/j.envint.2013.05.007. [DOI] [PubMed] [Google Scholar]
  22. Kim KN, Lim YH, Bae HJ, Kim M, Jung K, Hong YC, 2016. Long-term fine particulate matter exposure and major depressive disorder in a community-based urban cohort. Environ. Health Perspect. 124 (10), 1547–1553. 10.1289/EHP192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kim HG, Kuendig J, Prasad K, Sexter A, 2020. Exposure to racism and other adverse childhood experiences among perinatal women with moderate to severe mental illness. Community Ment. Health J. 56 (5), 867–874. 10.1007/s10597-020-00550-6. [DOI] [PubMed] [Google Scholar]
  24. Kioumourtzoglou MA, Power MC, Hart JE, Okereke OI, Coull BA, Laden F, Weisskopf MG, 2017. The association between air pollution and onset of depression among middle-aged and older women. Am. J. Epidemiol. 185 (9), 801–809. 10.1093/aje/kww163. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Ko JY, Farr SL, Dietz PM, Robbins CL, 2012. Depression and treatment among U. S. pregnant and nonpregnant women of reproductive age, 2005–2009. J Womens Health (Larchmt) 21 (8), 830–836. 10.1089/jwh.2011.3466. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lim YH, Kim H, Kim JH, Bae S, Park HY, Hong YC, 2012. Air pollution and symptoms of depression in elderly adults. Environ. Health Perspect. 120 (7), 1023–1028. 10.1289/ehp.1104100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lin Y, Zhou L, Xu J, Luo Z, Kan H, Zhang J, Zhang J, 2017. The impacts of air pollution on maternal stress during pregnancy. Sci. Rep. 7, 40956. 10.1038/srep40956. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Lindahl V, Pearson JL, Colpe L, 2005. Prevalence of suicidality during pregnancy and the postpartum. Arch Womens Ment Health 8 (2), 77–87. 10.1007/s00737-005-0080-1. [DOI] [PubMed] [Google Scholar]
  29. Mannisto T, Mendola P, Kiely M, O’Loughlin J, Werder E, Chen Z, Grantz KL, 2016. Maternal psychiatric disorders and risk of preterm birth. Ann. Epidemiol. 26 (1), 14–20. 10.1016/j.annepidem.2015.09.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. McGrath E, Keita GP, Strickland BR, Russo NF, 1990. Women and Depression: Risk Factors and Treatment Issues: Final Report of the American Psychological Association’s National Task Force on Women and Depression. American Psychological Association, Washington, DC, US. [Google Scholar]
  31. Niedzwiecki MM, Rosa MJ, Solano-Gonzalez M, Kloog I, Just AC, Martinez-Medina S, Wright RJ, 2020. Particulate air pollution exposure during pregnancy and postpartum depression symptoms in women in Mexico City. Environ. Int. 134, 105325. 10.1016/j.envint.2019.105325. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Non-Organic Psychoses of Pregnancy, 2017. In: Brockington I (Ed.), The Psychoses of Menstruation and Childbearing. Cambridge University Press, Cambridge, pp. 163–165. [Google Scholar]
  33. Pun VC, Manjourides J, Suh H, 2017. Association of ambient air pollution with depressive and anxiety symptoms in older adults: results from the NSHAP study. Environ. Health Perspect. 125 (3), 342–348. 10.1289/EHP494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Rusell DW, Clavel FD, Cutrona C, Abraham WT, Burzette RG, 2018. Neighborhood racial discrimination and the development of major depression. J. Abnorm. Psychol. 127 (2), 150–159. 10.1037/abn0000336. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Semple S, Apsley A, Azmina Ibrahim T, Turner SW, Cherrie JW, 2015. Fine particulate matter concentrations in smoking households: just how much secondhand smoke do you breathe in if you live with a smoker who smokes indoors? Tobac. Contr. 24 (e3), e205–211. 10.1136/tobaccocontrol-2014051635. [DOI] [PubMed] [Google Scholar]
  36. Sheffield PE, Speranza R, Chiu Y-HM, Hsu H-HL, Curtin PC, Renzetti S, Wright RJ, 2018. Association between particulate air pollution exposure during pregnancy and postpartum pscyhological functioning. PloS One. 10.1371/journal.10.1371/journal.pone.0195267. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Suzuki D, Wariki WMV, Suto M, Yamaji N, Takemoto Y, Rahman MM, Ota E, 2019. Association of secondhand smoke and depressive symptoms in nonsmoking pregnant women: a systematic review and meta-analysis. J. Affect. Disord. 245, 918–927. 10.1016/j.jad.2018.11.048. [DOI] [PubMed] [Google Scholar]
  38. Szyszkowicz M, Kousha T, Kingsbury M, Colman I, 2016. Air pollution and emergency department visits for depression: a multicity case-crossover study. Environ. Health Insights 10, 155–161. 10.4137/EHI.S40493. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Vert C, Sanchez-Benavides G, Martinez D, Gotsens X, Gramunt N, Cirach M, Gascon M, 2017. Effect of long-term exposure to air pollution on anxiety and depression in adults: a cross-sectional study. Int. J. Hyg Environ. Health 220 (6), 1074–1080. 10.1016/j.ijheh.2017.06.009. [DOI] [PubMed] [Google Scholar]
  40. Wang R, Yang B, Liu P, Zhang J, Liu Y, Yao Y, Lu Y, 2020. The longitudinal relationship between exposure to air pollution and depression in older adults. Int. J. Geriatr. Psychiatr. 35 (6), 610–616. 10.1002/gps.5277. [DOI] [PubMed] [Google Scholar]
  41. Wei Y, Zhang JJ, Li Z, Gow A, Chung KF, Hu M, Tang X, 2016. Chronic exposure to air pollution particles increases the risk of obesity and metabolic syndrome: findings from a natural experiment in Beijing. Faseb. J. 30 (6), 2115–2122. 10.1096/fj.201500142. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Wei F, Wu M, Qian S, Li D, Jin M, Wang J, Chen K, 2020. Association between short-term exposure to ambient air pollution and hospital visits for depression in China. Sci. Total Environ. 724, 138207. 10.1016/j.scitotenv.2020.138207. [DOI] [PubMed] [Google Scholar]
  43. World Health Organization, 2020. Maternal Mental Health. from World Health Organization. [Google Scholar]
  44. Zhang J, Troendle J, Reddy UM, Laughon SK, Branch DW, Burkman R, Consortium on Safe, L., 2010. Contemporary cesarean delivery practice in the United States. Am. J. Obstet. Gynecol. 203 (4) 10.1016/j.ajog.2010.06.058, 326 e321–326 e310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Zijlema WL, Wolf K, Emeny R, Ladwig KH, Peters A, Kongsgard H, Rosmalen JG, 2016. The association of air pollution and depressed mood in 70,928 individuals from four European cohorts. Int. J. Hyg Environ. Health 219 (2), 212–219. 10.1016/j.ijheh.2015.11.006. [DOI] [PubMed] [Google Scholar]

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