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. 2023 Mar 8;53(14):6792–6805. doi: 10.1017/S0033291723000314

Impact of sedentary behavior and emotional support on prenatal psychological distress and birth outcomes during the COVID-19 pandemic

Alison E Hipwell 1,*,✉, Irene Tung 2,*, Phillip Sherlock 3, Xiaodan Tang 3, Kim McKee 4, Monica McGrath 5, Akram Alshawabkeh 6, Tracy Bastain 7, Carrie V Breton 7, Whitney Cowell 8, Dana Dabelea 9, Cristiane S Duarte 10, Anne L Dunlop 11, Assiamira Ferrera 12, Julie B Herbstman 13, Christine W Hockett 14, Margaret R Karagas 15, Kate Keenan 16, Robert T Krafty 17, Catherine Monk 18, Sara S Nozadi 19, Thomas G O'Connor 20, Emily Oken 21, Sarah S Osmundson 22, Susan Schantz 23, Rosalind Wright 24, Sarah S Comstock 25, program collaborators for Environmental influences on Child Health Outcomes
PMCID: PMC10485176  NIHMSID: NIHMS1909386  PMID: 36883203

Abstract

Background

Studies have reported mixed findings regarding the impact of the coronavirus disease 2019 (COVID-19) pandemic on pregnant women and birth outcomes. This study used a quasi-experimental design to account for potential confounding by sociodemographic characteristics.

Methods

Data were drawn from 16 prenatal cohorts participating in the Environmental influences on Child Health Outcomes (ECHO) program. Women exposed to the pandemic (delivered between 12 March 2020 and 30 May 2021) (n = 501) were propensity-score matched on maternal age, race and ethnicity, and child assigned sex at birth with 501 women who delivered before 11 March 2020. Participants reported on perceived stress, depressive symptoms, sedentary behavior, and emotional support during pregnancy. Infant gestational age (GA) at birth and birthweight were gathered from medical record abstraction or maternal report.

Results

After adjusting for propensity matching and covariates (maternal education, public assistance, employment status, prepregnancy body mass index), results showed a small effect of pandemic exposure on shorter GA at birth, but no effect on birthweight adjusted for GA. Women who were pregnant during the pandemic reported higher levels of prenatal stress and depressive symptoms, but neither mediated the association between pandemic exposure and GA. Sedentary behavior and emotional support were each associated with prenatal stress and depressive symptoms in opposite directions, but no moderation effects were revealed.

Conclusions

There was no strong evidence for an association between pandemic exposure and adverse birth outcomes. Furthermore, results highlight the importance of reducing maternal sedentary behavior and encouraging emotional support for optimizing maternal health regardless of pandemic conditions.

Keywords: Birth outcomes, depression, pandemic, pregnancy, stress

Introduction

The coronavirus disease 2019 (COVID-19) pandemic has dramatically impacted families globally, exacerbating existing stressors and racial and socioeconomic inequities across a wide range of psychological and health domains (Purtle, 2020; Tai, Shah, Doubeni, Sia, & Wieland, 2021). Common pandemic stressors include health and economic concerns, social isolation, and restrictions on movement (Ammar et al., 2020; Hall, Laddu, Phillips, Lavie, & Arena, 2021). Epidemiological studies have reported an increased prevalence of pandemic-related psychiatric morbidity and psychological distress in the general population (Lei et al., 2020; Smith et al., 2020) with effects projected to continue beyond the current pandemic (Cullen, Gulati, & Kelly, 2020). Studies have also shown increases in the prevalence of psychological distress among women who were pregnant during the COVID-19 pandemic (Berthelot et al., 2020; King, Feddoes, Kirshenbaum, Humphreys, & Gotlib, 2020; Lebel, MacKinnon, Bagshawe, Tomfohr-Madsen, & Giesbrecht, 2020). These trends are particularly concerning given the large body of literature linking prenatal stress and distress with adverse intrauterine development and birth outcomes, such as preterm birth (PTB, < 37 weeks gestation) and low infant birthweight (LBW, <2500 g) (Harville, Xiong, & Buekens, 2010; Lima et al., 2018; Stein et al., 2014). Although evidence suggests that exposure to stress during pregnancy leads to negative birth outcomes, in part via heightened maternal psychological distress (e.g. depressive symptoms) (Glover, 2015), there has been limited opportunity to examine the impact of the pandemic as a stressor on prenatal mental health as most studies have been descriptive in nature.

Globally, there have been inconsistent findings about the effect of the pandemic on rates of PTB and low birthweight (Ashish et al., 2020; Been et al., 2020; Hedermann et al., 2021; Kirchengast & Hartmann, 2021; Matheson et al., 2021; Pasternak et al., 2021; Philip et al., 2020). Similarly, in the United States, some studies have reported overall reductions in PTB (Berghella, Boelig, Roman, Burd, & Anderson, 2020; Harvey et al., 2021) or reductions specific to women of White race or from more advantaged neighborhoods (Lemon, Edwards, & Simhan, 2021) relative to rates before the COVID-19 pandemic onset. Other studies have reported no differences (Greene, Kilpatrick, Wong, Ozimek, & Naqvi, 2020; Handley et al., 2021; Wood et al., 2021) or increased rates of very preterm birth specifically among Hispanic or Latinx women (Main et al., 2021). Results are also equivocal with regard to birthweight, with variable evidence for greater infant birthweight (Kirchengast & Hartmann, 2021; Yang et al., 2021), reduced rates of very low birthweight (Philip et al., 2020), or no change (Chmielewska et al., 2021; Matheson et al., 2021) relative to pandemic exposure.

While partly attributable to geographic differences in the timing and extent of pandemic mitigation measures, these mixed results may also reflect differences in the quality and rigor of study designs. Most birth outcome studies have drawn on electronic records to compare rates of PTB and infant birthweight categories before or during the pandemic. However, record-based studies have limited data on important covariates (e.g. maternal race/ethnicity, socioeconomic status) that are associated with experiences of stress and birth outcomes. Because the pandemic disproportionally affected people of color and individuals in low resourced environments (Maroko, Nash, & Pavilonis, 2020), these studies cannot clarify whether the pandemic itself is a driving factor of health outcomes. Methods such as propensity-score matching, a quasi-experimental approach, enable the risks for birth outcomes conferred by the pandemic to be examined separately from those related to sociodemographic factors.

Individual differences in daily behavior and social interactions could also modify the impact of the pandemic on prenatal distress and subsequent birth outcomes. For some individuals, social isolation, loss of daily routines, and enforced working from home led to increased time in sedentary behaviors (Stockwell et al., 2021). Time spent in sedentary behavior is a known risk factor for poor health outcomes, independent of physical activity levels (Clark et al., 2009; Pate, O'Neill, & Lobelo, 2008), and is also associated bi-directionally with mental health problems and perceived stress (Chekroud et al., 2018). Although pregnant women typically spend more than 50% of their waking hours in sedentary behaviors (Fazzi, Saunders, Linton, Norman, & Reynolds, 2017), evidence for birth outcome risks is unclear (Both, Overvest, Wildhagen, Golding, & Wildschut, 2010; Reid, McNeill, Alderdice, Tully, & Holmes, 2014; Ruifrok et al., 2014). Sedentary behavior may exacerbate the impact of the pandemic on prenatal psychological distress and subsequent birth outcomes.

Studies examining pandemic-related health in pregnant women have focused largely on negative impacts. However, to inform strengths-based preventative care, studies need to identify protective factors that can be readily implemented to improve prenatal health and support positive birth outcomes. One factor broadly linked to stress resilience is perceived social support (Panagioti, Gooding, Taylor, & Tarrier, 2014; Sim, Bowes, & Gardner, 2019), particularly emotional support that has been shown to influence stress physiology during pregnancy and may buffer the effects of stress on health outcomes (Coburn, Gonzales, Luecken, & Crnic, 2016; Nierop, Wirtz, Bratsikas, Zimmermann, & Ehlert, 2008; Tung et al., 2021). Although some evidence suggests that higher levels of prenatal support during the pandemic are associated with less psychological distress (Lebel et al., 2020), no studies to our knowledge have directly investigated emotional support as a psychosocial buffer of pandemic effects.

Studies have reported mixed findings with respect to the impact of the COVID-19 pandemic on pregnant women and birth outcomes; differences that may be explained in part by sampling, geographical differences and social determinants of health. In the current study, we used propensity-score matching to examine the effects of the pandemic on psychological distress (i.e. perceived stress, depressive symptoms) during pregnancy and on birth outcomes (see conceptual model in Fig. 1). We hypothesized that after controlling for potential sociodemographic confounds, pandemic exposure would be associated with shorter infant gestational age and lower birthweight for gestational age. We also hypothesized that perceived stress and depressive symptoms during pregnancy would mediate the association between pandemic exposure and adverse birth outcomes. Finally, we expected that sedentary behavior would exacerbate, and emotional support would buffer, the negative effects of the pandemic on both prenatal distress and birth outcomes.

Fig. 1.

Fig. 1.

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Conceptual model.

Methods

Participants

The Environmental influences on Child Health Outcomes (ECHO) Program is an NIH-funded nationwide consortium of multiple cohort studies across the United States designed to investigate the effects of early life exposures on child health and development (Paneth & Monk, 2018; Romano, Buckley, Elliott, Johnson, Paneth, & program collaborators for Environmental influences on Child Health Outcomes, 2022). The ECHO program combines existing prenatal and pediatric data collected via cohort-specific protocols with a standardized ECHO-wide protocol that was established in 2019 (Gillman & Blaisdell, 2018; Knapp et al., in press; LeWinn, Caretta, Davis, Anderson, Oken & program collaborators for Environmental influences on Child Health Outcomes, 2022) (https://echochildren.org/echo-program-protocol/). The ECHO study was approved by the local and/or central ECHO Institutional Review Board, and written informed consent was obtained for participation in specific cohorts and the ECHO-wide data collection protocol.

The current study focused on individuals enrolled in prenatal ECHO cohorts who had a singleton gestation pregnancy and who delivered during or before the COVID-19 pandemic. Between 12 March 2020 and 30 May 2021, 501 pandemic-exposed pregnant women delivered a live infant and had complete sociodemographic data on maternal age, race and ethnicity, and child sex assigned at birth. Given that the decision or ability to participate in research before and during a pandemic may vary for different individuals as a function of sociodemographic characteristics, and evidence that child sex differences can emerge under conditions of stress (Walsh et al., 2019), participants were propensity-score-matched in a 1:1 (pandemic: pre-pandemic) design on the above sociodemographic characteristics with 501 women who delivered before the pandemic onset between 1 January 2009 and 10 March 2020.

Measures

Birth outcomes

Gestational age at birth (GA) in completed weeks and infant birthweight (in grams) were obtained from maternal medical record abstraction (15% GA; 5% birthweight), childbirth/neonatal medical record abstraction (28% GA; 38% birthweight), childbirth information (18% GA; 32% birthweight) or other maternal report (39% GA; 25% birthweight). Sex-specific birthweight adjusted for gestational age z scores (BWGA-z scores) were assigned based on prior work (Aris, Kleinman, Belfort, Kaimal, & Oken, 2019).

Sociodemographic variables

Sociodemographic variables were obtained from maternal medical record abstraction, childbirth/neonatal medical record abstraction, or via maternal report during pregnancy, depending on the ECHO cohort. Maternal age at delivery was calculated from maternal and child dates of birth. Maternal self-reported race was categorized as American Indian or Alaska Native, Asian, Black, White, multiple race, and other race. Self-reported ethnicity was categorized as Hispanic or non-Hispanic. Child sex assigned at birth was coded as female or male.

Psychological distress

Severity of prenatal stress was assessed via self-report using the Perceived Stress Scale [PSS, (Cohen, Kamarck, & Mermelstein, 1983). Three versions of the PSS (consisting of 4, 10, or 14 items) were administered across the ECHO cohorts; each item was rated on a 5-point Likert scale. Raw scores were normed to a common, standardized T score metric (Mean = 50, s.d. = 10) (McDonald, 1999). Maternal depressive symptoms during pregnancy were measured by self-report on at least one of the following: (1) the Patient Reported Outcomes Measurement Information System (PROMIS) Depression 8a (Cella et al., 2010; Pilkonis et al., 2011); (2) the Edinburgh Postnatal Depression Scale (Cox, Holden, & Sagovsky, 1987); (3) the Adult Self-Report Achenbach System Depression Problems Syndrome Scale (Rescorla & Achenbach, 2004); (4) the Brief Symptom Inventory (Derogatis & Melisaratos, 1983); (5) the Center for Epidemiological Studies Depression Scale (Radloff & Locke, 1986); (6) the Patient Health Questionnaire-9 (Kroenke, Spitzer, & Williams, 2001); (7) the Beck Depression Inventory (Beck & Steer, 1984); and (8) the Kessler 6 Mental Health Scale (Kessler et al., 2003). Depression measures were harmonized to the PROMIS T score metric using validated crosswalk tables (Blackwell et al., 2021; Cella et al.,; Choi et al., 2022; Kaat, Newcomb, Ryan, & Mustanski, 2017). After harmonization, depression scores were expected to have a mean of 50 and standard deviation (s.d.) of 10 on the PROMIS T score (normed for the general population).

Sedentary behavior

Sedentary behavior was measured via self-report on the five-item inactivity/sedentary behavior subscale of the Pregnancy Physical Activity Questionnaire (PPAQ), (Chasan-Taber et al., 2004). This PPAQ subscale is a validated and widely used measure for pregnant women (Chasan-Taber et al., 2015; Nascimento, Surita, Godoy, Kasawara, & Morais, 2015).

Emotional support

The self-report PROMIS-Emotional Support 4a measure (Cella et al., 2010) assesses the availability of confidante relationships and feeling cared for and valued as a person. PROMIS-Emotional Support 4a applies item response theory to generate T scores with scores greater than 50 indicating levels of emotional support higher than in the general population.

Covariates

Highest level of maternal education reported during pregnancy was reduced to three categories: less than high school, high school completion, or some college and above. Participants reported receipt of any (yes/no) prenatal public assistance (e.g. State Children's Health Insurance Program, Supplemental Nutrition Assistance Program, Head Start, housing assistance, Medicaid, Supplemental Security Income, Temporary Assistance for Needy Families). Prenatal employment status was coded as working or not working for wages. Pre-pregnancy body mass index (BMI) was self-reported or calculated from measured pre-pregnancy weight and height.

Analytic approach

The study employed a quasi-experimental longitudinal design with women who delivered during or before pandemic onset. Propensity-score matching was used to maximize comparability of the two groups and account for systematic differences in sociodemographic characteristics (i.e. maternal age, maternal race and ethnicity, child sex) based on a 1:1 (pandemic: pre-pandemic) design using the nearest neighbor matching method. A propensity score in the form of probability of belonging to the pandemic or pre-pandemic group conditional on the matching sociodemographic variables was estimated for each case. The pair of cases in the two groups was matched if they had very similar propensity scores (Austin, 2011b). Remaining cases with discrepant propensity scores were removed from the data. Consistent with prior propensity score modeling studies (Austin, 2011a; Rosenbaum & Rubin, 1983), we estimated ‘treatment’ effects (in this case, pandemic exposure) by directly comparing outcomes across matched groups.

We first estimated two multiple regression models with pandemic exposure as the independent variable and infant GA and birthweight for GA z score (BWGA-z score) as the dependent variables (DV). Models included the following covariates: maternal education level, receipt of public assistance, employment status and pre-pregnancy BMI. We then estimated four structural equation models (SEMs) comprising mediator/DVs as follows: (1) perceived stress/GA; (2) perceived stress/BWGA-z score; (3) depressive symptoms/GA; and (4) depressive symptoms/BWGA-z score using semTools in the R software package (Jorgensen, Pornprasertmanit, Schoemann, & Rosseel, 2022). Models were conducted in a stepwise fashion to test for the incremental prediction of the predictor and moderator variables. Step 1 tested direct and indirect effects between pandemic exposure, psychological distress (perceived stress or depressive symptoms), and birth outcomes (GA or BWGA-z score). In Step 2, the main (direct) effects of sedentary behavior (Step 2a) or emotional support (Step 2b) were added to examine the incremental association effect of these factors on prenatal psychological distress and birth outcomes, above and beyond the effect of the pandemic. Finally, in Step 3, the interactions between sedentary behavior × pandemic (Step 3a) or emotional support × pandemic (Step 3b) were added to examine moderation of the direct effects of pandemic exposure on prenatal psychological distress and birth outcomes. In addition, we used the Index of Moderated Mediation (Hayes, 2015) to test whether sedentary behavior and emotional support moderated the indirect associations of pandemic exposure on birth outcomes via prenatal psychological distress. The SEM included the following covariates' direct effects on the mediators: education level, receipt of public assistance and employment status, and the same covariates with the addition of pre-pregnancy BMI on birth outcomes. Rates of covariate missingness varied between 4.2% and 31.4% (mean = 13.5%, see Table 1). To minimize parameter biases associated with listwise deletion, missing data on covariates, mediator and moderator variables were imputed using the mice package in R (Van Buuren & Groothuis-Oudshoorn, 2011). Multiple imputation (MI) can result in unbiased results with up to 90% missingness with a properly specified MI model that includes all variables related to missingness when data are missing at random (Madley-Dowd, Hughes, Tilling, & Heron, 2019). This resulted in 10 imputed datasets associated with each of the two mediators. The two sets of ten datasets were used to estimate their respective models in lavaan (Rosseel, 2012) in R and we reported pooled results.

Table 1.

Participant characteristics

Pre-pandemic Pandemic Overall
Variable Category (N = 501) (N = 501) (N = 1002) p value
Maternal age (years)*
Mean (s.d.) 30.9 (5.04) 30.5 (5.01) 30.7 (5.03) 0.161
Median [min, max] 31.0 [19.0–44.0] 31.0 [19.0–42.0] 31.0 [19.0–44.0]
Missing 0 0 0
Race* N (%)
White 388 (77.4%) 371 (74.1%) 759 (75.7%) 0.333
Black 26 (5.19%) 32 (6.39%) 58 (5.79%)
Asian 15 (2.99%) 9 (1.80%) 24 (2.40%)
American Indian or Alaska Native 12 (2.40%) 17 (3.39%) 29 (2.89%)
Multiple Race 36 (7.19%) 50 (9.98%) 86 (8.58%)
Other Race 24 (4.79%) 22 (4.39%) 46 (4.59%)
Missing 0 0 0
Ethnicity* N (%)
Hispanic 121 (24.2%) 141 (28.1%) 262 (26.1%)
Non-Hispanic 380 (75.8%) 360 (71.9%) 740 (73.9%) 0.172
Missing 0 0 0
Child sex* N (%)
Female 238 (47.5%) 242 (48.3%) 480 (47.9%) 0.85
Male 263 (52.5%) 259 (51.7%) 522 (52.1%)
Missing 0 0 0
Educational level N (%)
Less than high school 20 (4.13%) 16 (3.36%) 36 (3.75%) 0.378
High school 64 (13.2%) 77 (16.2%) 141 (14.7%)
Some college or above 400 (82.6%) 383 (80.5%) 783 (81.6%)
Missing 17 (3.4%) 25 (5.0%) 42 (4.2%)
Prenatal receipt of public assistance N (%)
No 233 (69.9%) 284 (59.7%) 517 (63.7%) < 0.001
Yes 102 (30.4%) 192 (40.3%) 294 (36.3%)
Missing 163 (32.7%) 22 (4.4%) 185 (18.6%)
Income level N (%)
<$ 30 000 60 (24.8%) 91 (21.7%) 151 (22.8%) 0.366
$ 30 000–$ 49 999 32 (13.2%) 57 (13.6%) 89 (13.4%)
$ 50 000–$ 74 999 36 (14.9%) 68 (16.2%) 104 (15.7%)
$ 75 000–$ 99 999 35 (14.5%) 84 (20.0%) 119 (18.0%)
$ 100 000 or more 79 (32.6%) 120 (28.6%) 199 (30.1%)
Missing 259 (51.7%) 81 (16.2%) 340 (33.9%)
Prenatal employment for wages, biological mother N(%)
No (work without pay; homemaker; unemployed) 37 (16.2%) 122 (26.6%) 159 (23.1%) 0.003
Yes (employed part-time/full-time; self-employed; active duty; on leave and expect to return to work) 191 (83.8%) 337 (73.4%) 528 (76.9%)
Missing 273 (54.5%) 42 (8.4%) 315 (31.4%)
Parity N (%)
0 176 (45.2%) 34 (13.8%) 210 (33.0%) < 0.001
1 121 (31.1%) 134 (54.3%) 255 (40.1%)
2 61 (15.7%) 37 (15.0%) 98 (15.4%)
3 19 (4.88%) 21 (8.50%) 40 (6.29%)
⩾ 12 (3.08%) 21 (8.50%) 33 (5.19%)
Missing 112 (22.4%) 254 (50.7%) 366 (36.5%)
Gestational age at birth (weeks)
Mean (s.d.) 39.0 (1.88) 38.7 (1.75) 38.8 (1.83) 0.002
Median [min, max] 39.0 [23.0–42.0] 39.0 [23.0–43.0] 39.0 [23.0–43.0]
Missing <5 8 (1.6%) 8 (0.8%)
Gestational age category N (%)
Extremely and very preterm (22–33 weeks) <10 <10 13 (1.31%) <0.001
Late preterm (34–36 weeks) 24 (4.79%) 26 (5.27%) 50 (5.03%)
Early term (37–38 weeks) 100 (20.0%) 153 (31.0%) 253 (25.5%)
Full term (39–40 weeks) 299 (59.7%) 267 (54.2%) 566 (56.9%)
Late term (> 41 weeks) 71 (14.2%) 41 (8.32%) 112 (11.3%)
Missing <5 <10 8 (0.8%)
Birthweight (grams)
Mean (s.d.) 3370 (537) 3400 (508) 3380 (525) 0.507
Median [min, max] 3390 [600–4930] 3430 [539–4620] 3400 [539–4930]
Missing 20 (4.0%) 164 (32.7%) 184 (18.4%)
Birthweight category N (%)
Low birthweight (<2500 g) 24 (4.99%) 12 (3.56%) 36 (4.40%) 0.172
Normal birthweight (⩾2500 g and <4000 g) 402 (83.6%) 297 (88.1%) 699 (85.5%)
Macrosomia (⩾4000 g and <5000 g) 50 (10.4%) 27 (8.1%) 83 (10.2%)
Missing <25 <170 184 (18.4%)
Birthweight for GA z score (BWGA-z score)
Mean (s.d.) 0.0174 (1.06) 0.234 (0.960) 0.107 (1.02) 0.002
Median [min, max] 0.00858 [−3.04 to 3.08] 0.256 [−2.37 to 2.54] 0.131 [−3.04 to 3.08]
Missing 20 (4.0%) 164 (32.7%) 184 (18.4%)
Pre-pregnancy BMI
Mean (s.d.) 26.6 (6.61) 28.1 (7.26) 27.2 (6.94) 0.002
Median [min, max] 24.8 [16.8–60.4] 26.9 [13.9–67.7] 25.6 [13.9–67.7]
Missing 16 (3.2%) 112 (22.4%) 128 (12.8%)
Perceived stress harmonized T score
Mean (s.d.) 48.3 (9.54) 47.1 (10.1) 47.7 (9.83) 0.064
Median [min, max] 48.5 [22.4–72.5] 46.6 [22.4–78.2] 47.3 [22.4–78.2]
Missing 31 (6.2%) 15 (3.0%) 46 (4.6%)
Depressive symptoms harmonized T score
Mean (s.d.) 46.0 (8.61) 48.0 (8.50) 46.9 (8.61) 0.003
Median [min, max] 45.9 [33.0–81.8] 47.8 [33.0–71.6] 45.9 [33.0–81.8]
Missing 136 (27.1%) 194 (38.7%) 330 (32.9%)
PPAQ Sedentary subscale
Mean (s.d.) 15.6 (9.46) 14.6 (7.29) 15.3 (8.75) 0.11
Median [min, max] 13.9 [0–46.7] 14.5 [0–39.8] 14.2 [0–46.7]
Missing 29 (5.8%) 232 (46.3%) 261 (26.0%)
PROMIS emotional support 4a T score
Mean (s.d.) 57.6 (6.95) 57.5 (6.92) 57.5 (6.92) 0.93
Median [min, max] 62.0 [36.9–62.0] 62.0 [30.4–62.0] 62.0 [30.4–62.0]
Missing 431 (86.0%) 147 (29.3%) 578 (57.7%)
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BMI, body mass index; BWGA, birthweight for gestational age; GA, gestational age; max, maximum; min, minimum; PPAQ, Pregnancy Physical Activity Questionnaire; PROMIS, Patient Reported Outcomes Measurement Information System; s.d., standard deviation.

Note. Cell sizes smaller than 5 are suppressed for privacy in accordance with ECHO's publication and data use policy. Variables with* were covariates used in propensity score matching. Complete data of these variables were available

Groups were compared using t tests for continuous variables. For categorical variables, p values for χ2 tests were computed across categories excluding the missing category between the pre-pandemic and pandemic groups

Results

Descriptive Statistics

Sample characteristics are shown in Table 1. In the overall sample (N = 1002, drawn from 16 ECHO cohorts, see online Supplementary Table S1), participants were on average 30.7 years old (s.d. = 5.03). Most women self-identified as White (75.7%) with 5.8% as Black, 2.4% as Asian, 2.9% as American Indian or Alaska Native, 8.6% more than one race and 4.6% another race, and most participants reported non-Hispanic ethnicity (73.9%). Infants (47.9% female) had an average GA of 38.8 weeks (s.d. = 1.83); 6.3% were born preterm (<37 weeks) and mean birthweight was 3380 g (s.d. = 525). GA was unrelated to BWGA-z score (r = −0.01, ns) indicating their independence for later model estimation. Mean harmonized perceived stress and depression T scores for the overall sample were 47.7 (s.d. = 9.83) and 46.9 (s.d. = 8.61) respectively, close to the population norm. Approximately half of the participants in the exposed group (n = 261, 52%) became pregnant after the start of the pandemic, whereas 97 participants (19.4%) were in the third trimester.

By design, the pandemic and pre-pandemic groups did not differ on maternal age, race, ethnicity and child sex. There was also no group difference on education or income level. However, relative to women in the pre-pandemic group, pandemic-exposed women were more likely to receive public assistance, less likely to be employed and had higher pre-pregnancy BMI (ps < 0.01). These variables were covaried in the predictive and mediation models to account for these group differences.

Effects of pandemic exposure on birth outcomes

Results of the multiple regression models after controlling for covariates showed a small effect of prenatal pandemic exposure on shorter GA at birth [β = −0.56 weeks, 95% CI (−0.89 to −0.24)]. In contrast, pandemic exposure was unrelated to adjusted birthweight [β = 0.01, 95% CI (−0.17 to 0.20)]. Maternal education, receipt of public assistance, and employment status were not significantly associated with birth outcomes.

Mediation models

Pandemic, perceived stress and GA at birth

As shown in Table 2, after adjusting for covariates in Step 1, women who were pregnant during the pandemic reported higher levels of stress compared to those who were pregnant pre-pandemic [B = 2.53, standard error (s.e.) = 0.99, 95% CI (0.59–4.47)]. Furthermore, after adjusting for covariates and perceived stress during pregnancy, pandemic exposure had a small direct effect on GA at birth (B = −0.55, s.e. = 0.18): between 0.20 and 0.90 weeks shorter than pre-pandemic births. However, prenatal stress did not mediate the association between pandemic exposure and infant GA. Higher levels of sedentary behavior were associated with higher levels of perceived stress beyond the significant effects of pandemic status and public assistance (Table 2 GA; Step 2a), but no main effect of sedentary behavior on GA at birth was observed. In Step 2b, higher levels of emotional support were associated with less perceived stress but did not directly predict GA at birth. Neither sedentary behavior nor emotional support moderated the direct and indirect effects of the pandemic on perceived stress and infant GA at birth (Steps 3a and 3b, results not shown).

Table 2.

Structural equation models examining perceived stress as mediating the effect of pandemic on birth outcomes

Gestational age at birth
Perceived stress B s.e. 95% CI [min, max] B s.e. 95% CI [min, max]
Step 1
Pandemic exposure 2.53 0.99 0.590–4.470 −0.55 0.18 −0.903 to −0.197
Perceived stress NA NA NA −0.004 0.01 −0.024 to 0.016
Education −1.13 1.06 −3.208 to 0.948 −0.11 0.20 −0.502 to 0.282
Public assistance 1.55 1.05 −0.051 to 3.608 0.05 0.19 −0.322 to 0.422
Employment −1.52 1.12 −3.715 to 0.675 −0.18 0.21 −0.592 to 0.232
BMI NA NA NA −0.03 0.01 −0.050 to −0.010
Step 2a
Pandemic exposure 4.28 1.09 2.144–6.416 −0.61 0.22 −1.041 to 0.179
Perceived Stress NA NA NA −0.004 0.01 −0.024 to 0.016
Education −0.57 1.18 −2.883 to 1.743 0.03 0.24 −0.440 to 0.500
Public assistance 2.52 1.18 0.207–4.833 0.14 0.24 −0.330 to 0.610
Employment −1.92 1.34 −4.546 to 0.706 −0.39 0.27 −0.919 to 0.139
BMI NA NA NA −0.03 0.01 −0.050 to −0.010
Sedentary behavior 0.19 0.05 0.092–0.288 −0.002 0.01 −0.022 to 0.018
Step 2b
Pandemic exposure 0.94 1.43 −1.863 to 3.743 −0.01 0.29 −0.578 to 0.558
Perceived stress NA NA NA −0.01 0.01 −0.030 to 0.010
Education 0.16 1.11 −2.016 to 2.336 −0.22 0.22 −0.651 to 0.211
Public assistance 1.36 1.02 −0.639 to 3.359 −0.04 0.21 −0.452 to 0.372
Employment −0.31 1.19 −2.642 to 2.022 −0.24 0.24 −0.710 to 0.230
BMI NA NA NA −0.03 0.01 −0.050 to −0.010
Emotional support −0.62 0.07 −0.757 to −0.483 0.01 0.02 −0.029 to 0.049
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B, unstandardized beta; BMI, body mass index; CI, confidence interval; max, maximum; min, minimum; NA, not applicable; s.e., standard error.

Note: Significant effects are bolded for emphasis.

Pandemic, perceived stress and BWGA

In adjusted models, pandemic exposure showed no direct effect on offspring BWGA-z score (Table 2 BWGA; Step 1). Additionally, there was no main effect of sedentary behavior on adjusted birthweight after accounting for sociodemographic and health covariates, including the significant effects of pre-pregnancy BMI (Table 2 BWGA; Step 2a). Similarly, emotional support did not directly predict BWGA-z score (Step 2b). Neither sedentary behavior nor emotional support moderated direct or indirect effects of the pandemic (Steps 3a and 3b, results not shown).

Pandemic, depressive symptoms and GA at birth

Models examining depressive symptoms as mediating the effect of the pandemic on birth outcomes are shown in Table 3. In Step 1, pandemic-exposed women reported higher levels of prenatal depressive symptoms [B = 3.12, s.e. = 1.07, 95% CI (1.02–5.22)] after adjusting for covariates. In addition, infants delivered during the pandemic had somewhat shorter GA at birth compared to infants delivered pre-pandemic [B = −0.71, s.e. = 0.25, 95% CI (−1.20 to −0.22)]. However, prenatal depressive symptoms did not predict variability in GA, nor did they mediate the association between pandemic exposure and GA at birth. More sedentary behavior was associated with higher levels of prenatal depressive symptoms over and above the significant effects of pandemic status (Table 3 GA; Step 2a). However, no main effect of sedentary behavior on infant GA was observed beyond the significant effect of pandemic status and adjustment for covariates. In Step 2b, emotional support was uniquely associated with lower levels of prenatal depressive symptoms but was unrelated to GA at birth. After adjusting for emotional support, pandemic exposure remained significantly associated with higher prenatal depressive symptoms, although it no longer predicted shorter GA at birth. Neither emotional support nor sedentary behavior moderated the direct and indirect effects of the pandemic on prenatal depressive symptoms and infant GA (Steps 3a and 3b, results not shown).

Table 3.

Structural equation models examining depressive symptoms as mediating the effect of the pandemic on birth outcomes

Depressive symptoms Gestational age at birth
B s.e. 95% CI [min, max] B s.e. 95% CI [min, max]
Step 1
Pandemic exposure 3.12 1.07 1.023–5.217 −0.71 0.25 −1.200to −0.220
Depressive symptoms NA NA NA −0.01 0.01 −0.030 to 0.010
Education −0.91 1 −2.870 to 1.050 −0.02 0.23 −0.471 to 0.431
Public assistance 2.08 1 0.120–4.040 0.06 0.23 −0.391 to 0.511
Employment 0.47 1.1 −1.686 to 2.626 −0.48 0.25 −0.970 to 0.010
BMI NA NA NA −0.02 0.01 −0.040 to −0.001
Step 2a
Pandemic exposure 3.63 1.03 1.611–5.649 −0.76 0.27 −1.289 to −0.231
Depressive symptoms NA NA NA −0.01 0.01 −0.030 to 0.010
Education −1.29 1.06 −3.367 to 0.788 0.01 0.27 −0.519 to 0.539
Public assistance 2.64 0.99 0.700–4.580 0.10 0.26 −0.410 to 0.610
Employment −0.79 1.19 −3.122 to 1.542 −0.53 0.30 −1.118 to 0.058
BMI NA NA NA −0.02 0.02 −0.059 to 0.019
Sedentary behavior 0.22 0.07 0.083–0.357 0.001 0.02 −0.038 to 0.040
Step 2b
Pandemic exposure 3.23 1.51 0.270–6.190 0.17 0.36 −0.536 to 0.876
Depressive symptoms NA NA NA −0.02 0.01 −0.043 to 0.011
Education −0.16 1.01 −2.140 to 1.820 −0.18 0.24 −0.650 to 0.290
Public assistance 1.29 0.97 −0.611 to 3.191 0.09 0.23 −0.361 to 0.541
Employment 1.69 1.14 −0.544 to 3.924 −0.37 0.27 −0.899 to 0.159
BMI NA NA NA −0.03 0.02 −0.069 to 0.010
Emotional support −0.43 0.07 −0.567 to −0.293 0.003 0.02 −0.036 to 0.042
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B, unstandardized beta; BMI, body mass index; CI, confidence interval; max, maximum; min, minimum; NA, not applicable; s.e., standard error.

Note: Significant effects are bolded for emphasis.

Pandemic, depressive symptoms and BWGA

In adjusted models, neither pandemic exposure, nor prenatal depressive symptoms, predicted offspring BWGA-z score (Table 3 BWGA; Step 1). In Step 2a, sedentary behavior was associated with higher depressive symptoms beyond the significant effects of pandemic status, whereas in Step 2b, emotional support was associated with fewer depressive symptoms. BWGA-z score was unrelated to sedentary behavior or emotional support in adjusted models. Sedentary behavior and emotional support did not moderate any direct or indirect effects of the pandemic (Steps 3a and 3b).

Discussion

There is an urgent need for rigorously designed studies to examine the impact of the pandemic on women's prenatal health and subsequent birth outcomes, as well as studies that can identify modifiable daily life factors that could exacerbate or attenuate pandemic effects. The ECHO study provides a valuable opportunity to fill these gaps via common data elements collected before and during the pandemic from cohorts located across the United States. The current study used propensity-score matching to increase causal inferences made about the effect of the pandemic on birth outcomes and determine whether heightened psychological distress associated with the pandemic explained these effects.

The results showed that women pregnant during the pandemic reported higher levels of stress and depressive symptoms compared with a propensity-score matched group of women who delivered prior to the pandemic. This increase may reflect the disruptions to daily life and health, social, and financial concerns experienced by many during the pandemic (Fitzpatrick, Drawve, & Harris, 2020; Tai et al., 2021), and is consistent with prior descriptive studies showing increased prevalence of psychiatric disorders and psychological distress. However, by leveraging a quasi-experimental design, the current study could increase the sociodemographic comparability of the pandemic and pre-pandemic groups to provide a more rigorous test of exposure on prenatal distress. This approach, combined with inclusion of additional covariates, allowed us to delineate the effects of the pandemic from the effects of various sociodemographic confounders.

Contrary to our hypothesis, the study did not reveal a substantial negative effect of pandemic exposure on birth outcomes. Although the results showed a shorter GA in the pandemic relative to the pre-pandemic group, this effect translated to an overall mean difference of about half a week, which may be important for preterm births, but may have little clinical significance for early term and term births. This result is commensurate with several other U.S.-based studies that have shown no, or only a small association, between pandemic exposure and categorical definitions of preterm birth (Greene et al., 2020; Handley et al., 2021; Wood et al., 2021). In addition, the current study revealed no main effects of the pandemic on GA-adjusted infant birthweight, similar to some prior descriptive studies focused on (unadjusted) birthweight (Chmielewska et al., 2021) but at odds with others conducted outside the United States (Yang et al., 2021). Taken together, our results suggest that pandemic mitigation measures (e.g. focus on hygiene, physical distancing, reduced physical demands of work and travel) while not reducing psychological distress, may have been generally effective in protecting some women' (Goldenberg, Culhane, Iams, & Romero, 2008).

Despite the elevated rates of psychological distress among women pregnant during the pandemic, neither perceived stress nor depressive symptoms predicted birth outcomes beyond the effect of the pandemic. Thus, our hypothesis that psychological distress would mediate the association between prenatal pandemic exposure and negative birth outcomes was not supported. However, this study may only partially capture the range of stress and depression, or birth outcomes experienced by pregnant women in the United States. Specifically, most participants in the analytic sample identified as White (75.7%), non-Hispanic (73.9%), and college educated (81.6%), whereas other racial and ethnic groups and individuals with fewer resources were under-represented. Thus, our observation of minimal effects of the pandemic on birth outcomes may be most relevant to highly educated White women in the United States; an important consideration given that systemic racism and structural processes underlying economic disparities significantly contribute to known inequities in prenatal stress and birth outcomes (Alhusen, Bower, Epstein, & Sharps, 2016; Braveman et al., 2015; Mendez, Hogan, & Culhane, 2013). Given the elevated rates of preterm birth among Black, American Indian, and Hispanic/Latinx infants (March of Dimes Foundation, 2022), there is a clear need for additional studies that focus specifically on the impact of the pandemic on prenatal distress and birth outcomes for these groups. Future work should also consider the contributions of psychological resources, given evidence that resilience, optimism and life satisfaction are associated with offspring birth outcomes (Bhatia, Chao, Higgins, Patel, & Crespi, 2015; Maxson, Edwards, Valentiner, & Miranda, 2016) and may explain variability beyond prenatal distress (Ramiro-Cortijo et al., 2021). Thus, it is possible that the current findings masked subgroups differentially characterized by personal resources.

An important strength of the current study was examination of potential pandemic-related effect modifiers (sedentary behavior and emotional support) with relevance for health policy and practice. The results showed a consistent pattern of main effects, whereby sedentary behavior was associated with higher levels of perceived stress and depressive symptoms, and emotional support was robustly associated with lower levels of each. However, none of the hypothesized moderating effects were observed. Although sedentary behavior did not exacerbate the negative effects of the pandemic on distress or birth outcomes in the current analysis, the additive risk to psychological distress highlights a universal need for targeted interventions that reduce sedentary behavior to improve psychological health during pregnancy (DiPietro et al., 2019; Kołomańska, Zarawski, & Mazur-Bialy, 2019), regardless of pandemic conditions. Furthermore, despite a lack of association with birth outcomes in the current study, sedentary behavior likely confers risk for maternal cardiovascular diseases such as hypertension, diabetes, and metabolic syndrome (Narici et al., 2021) that could impact the health of future pregnancies (Xie, Madkour, & Harville, 2015).

Emotional support was robustly associated with lower levels of prenatal stress and depressive symptoms and, in most cases, the negative effect of the pandemic on psychological distress became negligible once emotional support was accounted for. These results support the utility of emotional support as a critical target for healthcare efforts in terms of both screening and intervention (Dunkel Schetter, 2011; Marques, Bjørke-Monsen, Teixeira, & Silverman, 2015). Emotional support can take many forms such as having a confidante, friends and family in the community, connections with health workers (Hans, Edwards, & Zhang, 2018; Orr, 2004), and/or perinatal support groups (Chae, Chae, Kandula, & Winter, 2017; Chan & Chen, 2019). Further research is needed to understand how pregnant women best access/receive emotional support, and the types that are most impactful on psychological well-being during pregnancy.

Limitations

The findings should be considered in the context of several limitations. First, given some constraints on the availability of data, propensity-score matching of the pandemic and pre-pandemic groups was limited to four sociodemographic variables. Although the groups were comparable on educational level and income level, and all women had a singleton pregnancy, descriptive data indicated that some important differences remained on variables including receipt of public assistance, paid employment, and pre-pregnancy BMI. In addition, limited data on parity prevented inclusion of this variable in analyses. Given associations with birth outcomes, including PTB (Koullali et al., 2020), this is an important covariate for future studies. Unmeasured cohort or period effects (e.g. political climate, population health, mental health awareness) could have affected outcomes. Second, data were gathered from a 15-month period during the pandemic (11 March 2020 to 30 May 2021) during which infection rates and mitigation measures varied. While this extended interval fully captured the entire pregnancy for more than half the women unlike some prior studies, there was likely a range in the type, duration, and severity of stress experienced by women (e.g. disruptions to prenatal health care, risk for infection, social isolation, job loss) as well as differences in local and state-level mitigation policies at varying times across the pregnancy that were not modeled. Future studies are needed to examine more fine-grained pandemic experiences in relation to birth outcomes, and to capture the full range of pregnancy experiences and birth outcomes in diverse groups of women. Finally, sample bias may have been introduced by the focus on GA and birthweight among live births included in the ECHO study given some evidence suggesting a higher incidence of stillbirths during the pandemic (Khalil et al., 2020).

Conclusion

Using a quasi-experimental design, our results showed that exposure to the COVID-19 pandemic during pregnancy was associated with heightened psychological distress during pregnancy and marginally shorter GA at birth. In addition, we observed a general, but not a pandemic-specific, effect of sedentary behavior and emotional support on prenatal stress and depressive symptoms, highlighting the importance of these factors for maternal health regardless of pandemic exposure.

Acknowledgments

The authors wish to thank our ECHO colleagues; the medical, nursing and program staff, and the children and families participating in the ECHO cohorts. We also acknowledge the contribution of the following ECHO program collaborators:

ECHO Components – Coordinating Center: Duke Clinical Research Institute, Durham, North Carolina: Smith PB, Newby KL.

Data Analysis Center: Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland: Jacobson LP: Research Triangle Institute, Durham, North Carolina: Parker CB.

Person-Reported Outcomes Core: Northwestern University, Evanston, Illinois: Gershon R, Cella D.

Pediatric Cohorts: Northeastern University, Boston, Massachusetts: Alshawabkeh AN, University of Southern California, Los Angeles: Breton CV, Bastain T, Farzan S, Habre R, Memorial Hospital of Rhode Island, Pawtucket: Deoni S, D'Sa VA, University of Colorado Denver, Denver, CO: Dabelea D, New York State Psychiatric Institute at Columbia University, New York: Duarte CS, Canino GJ, Monk CE, Posner JE, Emory University, Atlanta, Georgia: Dunlop AL, Brennan PA, Corwin EJ, Avera McKennan Hospital and University Health Center, Sioux Falls, South Dakota: Elliott AJ, Kaiser Permanente, Oakland, California: Ferrara A, Croen LA, University of Wisconsin- Madison: Gern J, Columbia University Medical Center, New York, NY: Herbstman J, University of Pittsburgh, Pennsylvania: Hipwell AE, Keenan KE, Dartmouth College, Hanover, New Hampshire: Karagas MR, University of Rochester, New York: O'Connor TG, Buss C, Miller RK, Simhan H, Wadhwa PD, Michigan State University, East Lansing, Michigan: Paneth N, Kerver JM, Ruden DM, University of Illinois, Urbana: Schantz SL, and Icahn School of Medicine at Mount Sinai, New York, New York: Wright RJ, Wright RO.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Supplementary material

For supplementary material accompanying this paper visit https://doi.org/10.1017/S0033291723000314.

S0033291723000314sup001.docx (41.4KB, docx)

click here to view supplementary material

Financial support

Research reported in this publication was supported by the Environmental influences on Child Health Outcomes (ECHO) program, Office of the Director, National Institutes of Health, under Award Numbers U2COD023375 (Coordinating Center), U24OD023382 (Data Analysis Center), U24OD023319 (PRO Core) and UH3OD023251, UH3OD023287, UH3OD023313, UH3OD023248, UH3OD023328, UH3OD023318, UH3OD023279, UH3OD023289, UH3OD023282, UH3OD023290, UH3OD023244, UH3OD023275, UH3OD023349, UH3OD023285, UH3OD023272 and UH3OD023337 (Pediatric Cohorts).

Conflict of interest

None.

Ethical standards

The study protocol was approved by the local [or single ECHO] institutional review board. Written informed consent or parent's/guardian's permission was obtained for ECHO-wide Cohort Data Collection Protocol participation and for participation in specific cohorts.

References

  1. Alhusen, J. L., Bower, K. M., Epstein, E., & Sharps, P. (2016). Racial discrimination and adverse birth outcomes: An integrative review. Journal of Midwifery & Women's Health, 61(6), 707–720. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ammar, A., Brach, M., Trabelsi, K., Chtourou, H., Boukhris, O., Masmoudi, L., … Ahmed, M. (2020). Effects of COVID-19 home confinement on eating behaviour and physical activity: Results of the ECLB-COVID19 international online survey. Nutrients, 12(6), 1583. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Aris, I. M., Kleinman, K. P., Belfort, M. B., Kaimal, A., & Oken, E. (2019). A 2017 US reference for singleton birth weight percentiles using obstetric estimates of gestation. Pediatrics, 144(1), e20190076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Ashish, K., Gurung, R., Kinney, M. V., Sunny, A. K., Moinuddin, M., Basnet, O., … Shrestha, M. P. (2020). Effect of the COVID-19 pandemic response on intrapartum care, stillbirth, and neonatal mortality outcomes in Nepal: A prospective observational study. The Lancet Global Health, 8(10), e1273–e1281. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Austin, P. C. (2011a). Comparing paired vs non-paired statistical methods of analyses when making inferences about absolute risk reductions in propensity-score matched samples. Statistics in medicine, 30(11), 1292–1301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Austin, P. C. (2011b). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Beck, A. T., & Steer, R. A. (1984). Internal consistencies of the original and revised Beck Depression Inventory. Journal of Clinical Psychology, 40(6), 1365–1367. [DOI] [PubMed] [Google Scholar]
  8. Been, J. V., Ochoa, L. B., Bertens, L. C., Schoenmakers, S., Steegers, E. A., & Reiss, I. K. (2020). Impact of COVID-19 mitigation measures on the incidence of preterm birth: A national quasi-experimental study. The Lancet Public Health, 5(11), e604–e611. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Berghella, V., Boelig, R., Roman, A., Burd, J., & Anderson, K. (2020). Decreased incidence of preterm birth during coronavirus disease 2019 pandemic. American Journal of Obstetric and Gynecology MFM, 2(4), 100258. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Berthelot, N., Lemieux, R., Garon-Bissonnette, J., Drouin-Maziade, C., Martel, É, & Maziade, M. (2020). Uptrend in distress and psychiatric symptomatology in pregnant women during the coronavirus disease 2019 pandemic. Acta Obstetricia et Gynecologica Scandinavica, 99(7), 848–855. [DOI] [PubMed] [Google Scholar]
  11. Bhatia, N., Chao, S. M., Higgins, C., Patel, S., & Crespi, C. M. (2015). Association of mothers’ perception of neighborhood quality and maternal resilience with risk of preterm birth. International Journal of Environmental Research and Public Health, 12(8), 9427–9443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Blackwell, C. K., Tang, X., Elliott, A. J., Thomes, T., Louwagie, H., Gershon, R., … Cella, D. (2021). Developing a common metric for depression across adulthood: Linking PROMIS depression with the Edinburgh postnatal depression scale. Psychological Assessment, 33(7), 610–618. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Both, M. I., Overvest, M. A., Wildhagen, M. F., Golding, J., & Wildschut, H. I. (2010). The association of daily physical activity and birth outcome: A population-based cohort study. European Journal of Epidemiology, 25(6), 421–429. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Braveman, P. A., Heck, K., Egerter, S., Marchi, K. S., Dominguez, T. P., Cubbin, C., … Curtis, M. (2015). The role of socioeconomic factors in black–white disparities in preterm birth. American Journal of Public Health, 105(4), 694–702. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., … Choi, S. (2010). The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179–1194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Cella, D., Schalet, B. D., Kallen, D., Lai, J.-S., Cook, K., Rutsohn, J., … Choi, S. PROsetta Stone Analysis Report: PROMIS Depression and Kessler Psychological Distress Scale (K6). 2. Retrieved from https://static1.squarespace.com/static/60c7c36a1afd4a3ab90af0a6/t/60d8a1ae14fd3e66d3232e84/1624809903375/PROMIS + Depression + and + K6 + Full + Report.pdf. Accessed July 11, 2022.
  17. Chae, S. Y., Chae, M. H., Kandula, S., & Winter, R. O. (2017). Promoting improved social support and quality of life with the CenteringPregnancy® group model of prenatal care. Archives of Women's Mental Health, 20(1), 209–220. [DOI] [PubMed] [Google Scholar]
  18. Chan, K. L., & Chen, M. (2019). Effects of social media and mobile health apps on pregnancy care: Meta-analysis. JMIR mHealth and uHealth, 7(1), e11836. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Chasan-Taber, L., Schmidt, M. D., Roberts, D. E., Hosmer, D., Markenson, G., & Freedson, P. S. (2004). Development and validation of a pregnancy physical activity questionnaire. Medicine and Science in Sports and Exercise, 36(10), 1750–1760. [DOI] [PubMed] [Google Scholar]
  20. Chasan-Taber, L., Silveira, M., Pekow, P., Braun, B., Manson, J. E., Solomon, C. G., & Markenson, G. (2015). Physical activity, sedentary behavior and risk of hypertensive disorders of pregnancy in Hispanic women. Hypertension in Pregnancy, 34(1), 1–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Chekroud, S. R., Gueorguieva, R., Zheutlin, A. B., Paulus, M., Krumholz, H. M., Krystal, J. H., & Chekroud, A. M. (2018). Association between physical exercise and mental health in 1⋅ 2 million individuals in the USA between 2011 and 2015: A cross-sectional study. The Lancet Psychiatry, 5(9), 739–746. [DOI] [PubMed] [Google Scholar]
  22. Chmielewska, B., Barratt, I., Townsend, R., Kalafat, E., van der Meulen, J., Gurol-Urganci, I., … Thangaratinam, S. (2021). Effects of the COVID-19 pandemic on maternal and perinatal outcomes: A systematic review and meta-analysis. The Lancet Global Health, 9(6), e759–e772. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Choi, S., Podrabsky, T., McKinney, N., Schalet, B. D., Cook, K., & Cella, D. (2022). PROsetta Stone Analysis Report: PROMIS Depression and SF-36 Mental Health. Retrieved from https://static1.squarespace.com/static/60c7c36a1afd4a3ab90af0a6/t/60d8ab32aac1033985c2c480/1624812339054/PROMIS + Depression + and + SF-36 + Mental + Health + Full + Report.pdf.
  24. Clark, B. K., Sugiyama, T., Healy, G. N., Salmon, J., Dunstan, D. W., & Owen, N. (2009). Validity and reliability of measures of television viewing time and other non-occupational sedentary behaviour of adults: A review. Obesity Reviews, 10(1), 7–16. [DOI] [PubMed] [Google Scholar]
  25. Coburn, S. S., Gonzales, N., Luecken, L., & Crnic, K. (2016). Multiple domains of stress predict postpartum depressive symptoms in low-income Mexican American women: The moderating effect of social support. Archives of Women's Mental Health, 19(6), 1009–1018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24(4), 385–396. [PubMed] [Google Scholar]
  27. Cox, J., Holden, J., & Sagovsky, R. (1987). Detection of postnatal depression: Development of the 10-item Edinburgh postnatal depression scale. British Journal of Psychiatry, 150, 782–786. doi: 10.1192/bjp.150.6.782 [DOI] [PubMed] [Google Scholar]
  28. Cullen, W., Gulati, G., & Kelly, B. D. (2020). Mental health in the COVID-19 pandemic. QJM: An International Journal of Medicine, 113(5), 311–312. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Derogatis, L. R., & Melisaratos, N. (1983). The brief symptom inventory: An introductory report. Psychological Medicine, 13(3), 595–605. [PubMed] [Google Scholar]
  30. DiPietro, L., Evenson, K. R., Bloodgood, B., Sprow, K., Troiano, R. P., Piercy, K. L., … Powell, K. E. (2019). Benefits of physical activity during pregnancy and postpartum: An umbrella review. Medicine and Science in Sports and Exercise, 51(6), 1292. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Dunkel Schetter, C. (2011). Psychological science on pregnancy: Stress processes, biopsychosocial models, and emerging research issues. Annual Review of Psychology, 62, 531–558. [DOI] [PubMed] [Google Scholar]
  32. Fazzi, C., Saunders, D. H., Linton, K., Norman, J. E., & Reynolds, R. M. (2017). Sedentary behaviours during pregnancy: A systematic review. International Journal of Behavioral Nutrition and Physical Activity, 14(1), 1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Fitzpatrick, K. M., Drawve, G., & Harris, C. (2020). Facing new fears during the COVID-19 pandemic: The state of America's mental health. Journal of Anxiety Disorders, 75, 102291. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Gillman, M. W., & Blaisdell, C. J. (2018). Environmental influences on child health outcomes, a research program of the NIH. Current Opinion in Pediatrics, 30(2), 260. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Glover, V. (2015). Prenatal stress and its effects on the fetus and the child: Possible underlying biological mechanisms. In Antonelli M (Ed.), Perinatal Programming of Neurodevelopment. Advances in Neurobiology (Vol. 10, pp. 269–283). New York, NY: Springer. doi: 10.1007/978-1-4939-1372-5_13 [DOI] [PubMed] [Google Scholar]
  36. Goldenberg, R. L., Culhane, J. F., Iams, J. D., & Romero, R. (2008). Epidemiology and causes of preterm birth. The Lancet, 371(9606), 75–84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Greene, N. H., Kilpatrick, S. J., Wong, M. S., Ozimek, J. A., & Naqvi, M. (2020). Impact of labor and delivery unit policy modifications on maternal and neonatal outcomes during the coronavirus disease 2019 pandemic. American Journal of Obstetrics & Gynecology MFM, 2(4), 100234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Hall, G., Laddu, D. R., Phillips, S. A., Lavie, C. J., & Arena, R. (2021). A tale of two pandemics: How will COVID-19 and global trends in physical inactivity and sedentary behavior affect one another? Progress in Cardiovascular Diseases, 64, 108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Handley, S. C., Mullin, A. M., Elovitz, M. A., Gerson, K. D., Montoya-Williams, D., Lorch, S. A., & Burris, H. H. (2021). Changes in preterm birth phenotypes and stillbirth at 2 Philadelphia hospitals during the SARS-CoV-2 pandemic, March–June 2020. JAMA, 325(1), 87–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Hans, S. L., Edwards, R. C., & Zhang, Y. (2018). Randomized controlled trial of doula-home-visiting services: Impact on maternal and infant health. Maternal and Child Health Journal, 22(1), 105–113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Harvey, E. M., McNeer, E., McDonald, M. F., Shapiro-Mendoza, C. K., Dupont, W. D., Barfield, W., & Patrick, S. W. (2021). Association of preterm birth rate with COVID-19 statewide stay-at-home orders in Tennessee. JAMA Pediatrics, 175(6), 635–637. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Harville, E., Xiong, X., & Buekens, P. (2010). Disasters and perinatal health: A systematic review. Obstetrical and Gynecological Survey, 65(11), 713. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Hayes, A. F. (2015). An index and test of linear moderated mediation. Multivariate Behavioral Research, 50(1), 1–22. [DOI] [PubMed] [Google Scholar]
  44. Hedermann, G., Hedley, P. L., Bækvad-Hansen, M., Hjalgrim, H., Rostgaard, K., Poorisrisak, P., … Christiansen, M. (2021). Danish premature birth rates during the COVID-19 lockdown. Archives of Disease in Childhood-Fetal and Neonatal Edition, 106(1), 93–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Jorgensen, T., Pornprasertmanit, S., Schoemann, A., & Rosseel, Y. (2022). semTools: Useful tools for structural equation modeling. Retrieved from https://cran.r-project.org/web/packages/semTools/semTools.pdf.
  46. Kaat, A. J., Newcomb, M. E., Ryan, D. T., & Mustanski, B. (2017). Expanding a common metric for depression reporting: Linking two scales to PROMIS® depression. Quality of Life Research, 26(5), 1119–1128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Kessler, R. C., Barker, P. R., Colpe, L. J., Epstein, J. F., Gfroerer, J. C., Hiripi, E., … Walters, E. E. (2003). Screening for serious mental illness in the general population. Archives of General Psychiatry, 60(2), 184–189. [DOI] [PubMed] [Google Scholar]
  48. Khalil, A., Von Dadelszen, P., Draycott, T., Ugwumadu, A., O'Brien, P., & Magee, L. (2020). Change in the incidence of stillbirth and preterm delivery during the COVID-19 pandemic. JAMA, 324(7), 705–706. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. King, L. S., Feddoes, D. E., Kirshenbaum, J. S., Humphreys, K. L., & Gotlib, I. H. (2020). Pregnancy during the pandemic: The impact of COVID-19-related stress on risk for prenatal depression. Psychological Medicine, 53(1), 1–11. doi: 10.1017/S003329172100132X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Kirchengast, S., & Hartmann, B. (2021). Pregnancy outcome during the first COVID 19 lockdown in Vienna, Austria. International Journal of Environmental Research and Public Health, 18(7), 3782. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Knapp, E., Kress, A., Parker, C. B., Page, G., McArthur, K., Gachigi, K., … Jacobson, L. (in press). The Environmental influences on Child Health Outcomes (ECHO)-wide Cohort. American Journal of Epidemiology. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Kołomańska, D., Zarawski, M., & Mazur-Bialy, A. (2019). Physical activity and depressive disorders in pregnant women – a systematic review. Medicina, 55(5), 212. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Koullali, B., Van Zijl, M. D., Kazemier, B. M., Oudijk, M. A., Mol, B. W., Pajkrt, E., & Ravelli, A. C. (2020). The association between parity and spontaneous preterm birth: A population based study. BMC Pregnancy and Childbirth, 20(1), 1–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Lebel, C., MacKinnon, A., Bagshawe, M., Tomfohr-Madsen, L., & Giesbrecht, G. (2020). Elevated depression and anxiety symptoms among pregnant individuals during the COVID-19 pandemic. Journal of Affective Disorders, 277, 5–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Lei, L., Huang, X., Zhang, S., Yang, J., Yang, L., & Xu, M. (2020). Comparison of prevalence and associated factors of anxiety and depression among people affected by versus people unaffected by quarantine during the COVID-19 epidemic in Southwestern China. Medical Science Monitor: International Medical Journal of Experimental and Clinical Research, 26, e924609–e924601. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Lemon, L., Edwards, R. P., & Simhan, H. N. (2021). What is driving the decreased incidence of preterm birth during the coronavirus disease 2019 pandemic? American Journal of Obstetrics & Gynecology MFM, 3(3), 100330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. LeWinn, K. Z., Caretta, E., Davis, A., Anderson, A. L., Oken, E., & program collaborators for Environmental influences on Child Health Outcomes. (2022). SPR Perspectives: Environmental influences on Child Health Outcomes (ECHO) program: Overcoming challenges to generate engaged, multidisciplinary science. Pediatric Research, 92(5), 1262–1269. doi: 10.1038/s41390-021-01598-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Lima, S. A. M., El Dib, R. P., Rodrigues, M. R. K., Ferraz, G. A. R., Molina, A. C., Neto, C. A. P., … Rudge, M. V. C. (2018). Is the risk of low birth weight or preterm labor greater when maternal stress is experienced during pregnancy? A systematic review and meta-analysis of cohort studies. PloS One, 13(7), e0200594. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Madley-Dowd, P., Hughes, R., Tilling, K., & Heron, J. (2019). The proportion of missing data should not be used to guide decisions on multiple imputation. Journal of Clinical Epidemiology, 110, 63–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Main, E. K., Chang, S.-C., Carpenter, A. M., Wise, P. H., Stevenson, D. K., Shaw, G. M., & Gould, J. B. (2021). Singleton preterm birth rates for racial and ethnic groups during the coronavirus disease 2019 pandemic in California. American Journal of Obstetrics and Gynecology, 224(2), 239–241. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. March of Dimes Foundation. (2022). A profile of prematurity in the United States. Retrieved from https://www.marchofdimes.org/peristats/tools/prematurityprofile.aspx?reg=99.
  63. Maroko, A. R., Nash, D., & Pavilonis, B. T. (2020). COVID-19 and inequity: A comparative spatial analysis of New York City and Chicago hot spots. Journal of Urban Health, 97(4), 461–470. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Marques, A. H., Bjørke-Monsen, A.-L., Teixeira, A. L., & Silverman, M. N. (2015). Maternal stress, nutrition and physical activity: Impact on immune function, CNS development and psychopathology. Brain Research, 1617, 28–46. [DOI] [PubMed] [Google Scholar]
  65. Matheson, A., McGannon, C. J., Malhotra, A., Palmer, K. R., Stewart, A. E., Wallace, E. M., … Rolnik, D. L. (2021). Prematurity rates during the coronavirus disease 2019 (COVID-19) pandemic lockdown in Melbourne, Australia. Obstetrics and Gynecology, 137(3), 405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Maxson, P. J., Edwards, S. E., Valentiner, E. M., & Miranda, M. L. (2016). A multidimensional approach to characterizing psychosocial health during pregnancy. Maternal and Child Health Journal, 20(6), 1103–1113. [DOI] [PubMed] [Google Scholar]
  67. McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Lawrence Erlbaum Associates Publishers. [Google Scholar]
  68. Mendez, D. D., Hogan, V. K., & Culhane, J. F. (2013). Stress during pregnancy: The role of institutional racism. Stress and Health, 29(4), 266–274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Narici, M., Vito, G. D., Franchi, M., Paoli, A., Moro, T., Marcolin, G., … Biolo, G. (2021). Impact of sedentarism due to the COVID-19 home confinement on neuromuscular, cardiovascular and metabolic health: Physiological and pathophysiological implications and recommendations for physical and nutritional countermeasures. European Journal of Sport Science, 21(4), 614–635. [DOI] [PubMed] [Google Scholar]
  70. Nascimento, S. L., Surita, F. G., Godoy, A. C., Kasawara, K. T., & Morais, S. S. (2015). Physical activity patterns and factors related to exercise during pregnancy: A cross sectional study. PloS One, 10(6), e0128953. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Nierop, A., Wirtz, P. H., Bratsikas, A., Zimmermann, R., & Ehlert, U. (2008). Stress-buffering effects of psychosocial resources on physiological and psychological stress response in pregnant women. Biological Psychology, 78(3), 261–268. [DOI] [PubMed] [Google Scholar]
  72. Orr, S. T. (2004). Social support and pregnancy outcome: A review of the literature. Clinical Obstetrics and Gynecology, 47(4), 842–855. [DOI] [PubMed] [Google Scholar]
  73. Panagioti, M., Gooding, P. A., Taylor, P., & Tarrier, N. (2014). Perceived social support buffers the impact of PTSD symptoms on suicidal behavior: Implications into suicide resilience research. Comprehensive Psychiatry, 55(1), 104–112. [DOI] [PubMed] [Google Scholar]
  74. Paneth, N., & Monk, C. (2018). The importance of cohort research starting early in life to understanding child health. Current Opinion in Pediatrics, 30(2), 292. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Pasternak, B., Neovius, M., Söderling, J., Ahlberg, M., Norman, M., Ludvigsson, J. F., & Stephansson, O. (2021). Preterm birth and stillbirth during the COVID-19 pandemic in Sweden: A nationwide cohort study. Annals of Internal Medicine, 174(6), 873–875. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Pate, R. R., O'Neill, J. R., & Lobelo, F. (2008). The evolving definition of” sedentary”. Exercise and Sport Sciences Reviews, 36(4), 173–178. [DOI] [PubMed] [Google Scholar]
  77. Philip, R. K., Purtill, H., Reidy, E., Daly, M., Imcha, M., McGrath, D., … Dunne, C. P. (2020). Unprecedented reduction in births of very low birthweight (VLBW) and extremely low birthweight (ELBW) infants during the COVID-19 lockdown in Ireland: A ‘natural experiment’ allowing analysis of data from the prior two decades. BMJ Global Health, 5(9), e003075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Pilkonis, P. A., Choi, S. W., Reise, S. P., Stover, A. M., Riley, W. T., Cella, D., & Group, P. C. (2011). Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): Depression, anxiety, and anger. Assessment, 18(3), 263–283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Purtle, J. (2020). COVID-19 and mental health equity in the United States. Social Psychiatry and Psychiatric Epidemiology, 55(8), 969–971. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Radloff, L. S. & Locke, B. (1986). The community mental health assessment survey and the CES-D scale. In Weissman M., Meyer J., & Ross C. (Eds.), Community Surveys of Psychiatric Disorders (pp. 177–189). New Brunswick, NJ: Rutgers University Press. [Google Scholar]
  81. Ramiro-Cortijo, D., De la Calle, M., Gila-Díaz, A., Moreno-Jiménez, B., Martin-Cabrejas, M. A., Arribas, S. M., & Garrosa, E. (2021). Maternal resources, pregnancy concerns, and biological factors associated to birth weight and psychological health. Journal of Clinical Medicine, 10(4), 695. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Reid, E., McNeill, J. A., Alderdice, F. A., Tully, M. A., & Holmes, V. A. (2014). Physical activity, sedentary behaviour and fetal macrosomia in uncomplicated pregnancies: A prospective cohort study. Midwifery, 30(12), 1202–1209. [DOI] [PubMed] [Google Scholar]
  83. Rescorla, L. A., & Achenbach, T. (2004). The achenbach system of empirically based assessment (ASEBA) or ages 18 to 90+ years. In Maruish M. (Ed.), The use of psychological testing for treatment planning and outcomes assessment (Vol. 3, pp. 115–152). Mahwah, NJ: Lawrence Erlbaum Associates. [Google Scholar]
  84. Romano, M. E., Buckley, J. P., Elliott, A. J., Johnson, C. C., Paneth, N., & program collaborators for Environmental influences on Child Health Outcomes. (2022). SPR Perspectives: Scientific opportunities in the environmental influences on child health outcomes program. Pediatric Research, 92(5), 1255–1261. doi: 10.1038/s41390-021-01577-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. [Google Scholar]
  86. Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36. [Google Scholar]
  87. Ruifrok, A. E., Althuizen, E., Oostdam, N., Van Mechelen, W., Mol, B. W., De Groot, C. J., … Van Poppel, M. N. (2014). The relationship of objectively measured physical activity and sedentary behaviour with gestational weight gain and birth weight. Journal of Pregnancy, 2014, 567379 [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Sim, A., Bowes, L., & Gardner, F. (2019). The promotive effects of social support for parental resilience in a refugee context: A cross-sectional study with Syrian mothers in Lebanon. Prevention Science, 20(5), 674–683. [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. Smith, L., Jacob, L., Yakkundi, A., McDermott, D., Armstrong, N. C., Barnett, Y., … Tully, M. A. (2020). Correlates of symptoms of anxiety and depression and mental wellbeing associated with COVID-19: A cross-sectional study of UK-based respondents. Psychiatry Research, 291, 113138. [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. Stein, A., Pearson, R. M., Goodman, S. H., Rapa, E., Rahman, A., McCallum, M., … Pariante, C. M. (2014). Effects of perinatal mental disorders on the fetus and child. The Lancet, 384(9956), 1800–1819. [DOI] [PubMed] [Google Scholar]
  91. Stockwell, S., Trott, M., Tully, M., Shin, J., Barnett, Y., Butler, L., … Smith, L. (2021). Changes in physical activity and sedentary behaviours from before to during the COVID-19 pandemic lockdown: A systematic review. BMJ Open Sport & Exercise Medicine, 7(1), e000960. [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Tai, D. B. G., Shah, A., Doubeni, C. A., Sia, I. G., & Wieland, M. L. (2021). The disproportionate impact of COVID-19 on racial and ethnic minorities in the United States. Clinical Infectious Diseases, 72(4), 703–706. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Tung, I., Krafty, R. T., Delcourt, M. L., Melhem, N. M., Jennings, J. R., Keenan, K., & Hipwell, A. E. (2021). Cardiac vagal control in response to acute stress during pregnancy: Associations with life stress and emotional support. Psychophysiology, 58(6), e13808. [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate imputation by chained equations in R. Journal of Statistical Software, 45, 1–67. [Google Scholar]
  95. Walsh, K., McCormack, C. A., Webster, R., Pinto, A., Lee, S., Feng, T., … Champagne, F. A. (2019). Maternal prenatal stress phenotypes associate with fetal neurodevelopment and birth outcomes. Proceedings of the National Academy of Sciences, 116(48), 23996–24005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  96. Wood, R., Sinnott, C., Goldfarb, I., Clapp, M., McElrath, T., & Little, S. (2021). Preterm birth during the coronavirus disease 2019 (COVID-19) pandemic in a large hospital system in the United States. Obstetrics and Gynecology, 137(3), 403. [DOI] [PMC free article] [PubMed] [Google Scholar]
  97. Xie, Y., Madkour, A. S., & Harville, E. W. (2015). Preconception nutrition, physical activity, and birth outcomes in adolescent girls. Journal of Pediatric and Adolescent Gynecology, 28(6), 471–476. [DOI] [PMC free article] [PubMed] [Google Scholar]
  98. Yang, J., D'souza, R., Kharrat, A., Fell, D. B., Snelgrove, J. W., Murphy, K. E., & Shah, P. S. (2021). COVID-19 pandemic and population-level pregnancy and neonatal outcomes: A living systematic review and meta-analysis. Acta Obstetricia et Gynecologica Scandinavica, 100(10), 1756–1770. [DOI] [PMC free article] [PubMed] [Google Scholar]

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