# Urban environment in pregnancy and postpartum depression: An individual participant data meta-analysis of 12 European birth cohorts

Tim Cadman, Katrine Strandberg-Larsen, Lucinda Calas, Malina

Christiansen, Iryna Culpin, Payam Dadvand, Montserrat de Castro, Maria

Foraster, Serena Fossati, Mònica Guxens, et al.

► To cite this version:

Tim Cadman, Katrine Strandberg-Larsen, Lucinda Calas, Malina Christiansen, Iryna Culpin, et al. Urban environment in pregnancy and postpartum depression: An individual participant data meta-analysis of 12 European birth cohorts. Environment International, 2024, 185, pp.108453. 10.1016/j.envint.2024.108453. hal-04660249

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Urban environment in pregnancy and postpartum depression: An individual participant data meta-analysis of 12 European birth cohorts

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Tim Cadman<sup>a, l, s, *</sup>, Katrine Strandberg-Larsen<sup>a</sup>, Lucinda Calas<sup>b</sup>, Malina Christiansen<sup>a</sup>, Iryna Culpin<sup>c, d</sup>, Payam Dadvand<sup>e, f, g</sup>, Montserrat de Castro<sup>e, f, g</sup>, Maria Foraster<sup>e, f, g</sup>, Serena Fossati<sup>e, f, g</sup>, Mònica Guxens<sup>e, f, g, i</sup>, Jennifer R. Harris<sup>h</sup>, Manon Hillegers<sup>i</sup>, Vincent Jaddoe<sup>j</sup>, Yunsung Lee<sup>h</sup>, Johanna Lepeule<sup>k</sup>, Hanan el Marroun<sup>i, j</sup>, Milena Maule<sup>m</sup>, Rosie McEachen<sup>n</sup>, Chiara Moccia<sup>m</sup>, Johanna Nader<sup>o</sup>, Mark Nieuwenhuijsen<sup>e, f, g</sup>, Anne-Marie Nybo Andersen<sup>a</sup>, Rebecca Pearson<sup>c, d, p</sup>, Morris Swertz<sup>q, r</sup>, Marina Vafeiadi<sup>r</sup>, Martine Vrijheid<sup>e, f, g</sup>, John Wright<sup>n</sup>, Deborah A Lawlor<sup>c, d</sup>, Marie Pedersen<sup>a</sup>

ARTICLE INFO

Handling Editor: Olga Kalantzi

Keywords:

Postpartum depression

Ambient air pollution

Natural spaces

Traffic noise

Urban

Built environment

A B S T R A C T

**Background:** Urban environmental exposures associate with adult depression, but it is unclear whether they are associated to postpartum depression (PPD).

**Objectives:** We investigated associations between urban environment exposures during pregnancy and PPD.

**Methods:** We included women with singleton deliveries to liveborn children from 12 European birth cohorts (N with minimum one exposure = 30,772, analysis N range 17,686–30,716 depending on exposure; representing 26–46 % of the 66,825 eligible women). We estimated maternal exposure during pregnancy to ambient air pollution with nitrogen dioxide (NO₂) and particulate matter (PM₂.₅ and PM₁₀), road traffic noise (Lₐ₇₉), natural spaces (Normalised Difference Vegetation Index; NDVI, proximity to major green or blue spaces) and built environment (population density, facility richness and walkability). Maternal PPD was assessed 3–18 months after birth using self-completed questionnaires. We used adjusted logistic regression models to estimate cohort-specific associations between each exposure and PPD and combined results via *meta*-analysis using DataSHIELD.

<footnote><sup>a</sup> Section for Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark</footnote>

<footnote><sup>b</sup> Inserm, UMR1153 Center for Research in Epidemiology and Statistics (CRESS), Early Life Research on Later Health Team (EARoH), Paris, France</footnote>

<footnote><sup>c</sup> MRC Integrative Epidemiology Unit at the University of Bristol, United Kingdom</footnote>

<footnote><sup>d</sup> Population Health Science, Bristol Medical School, University of Bristol, United Kingdom</footnote>

<footnote><sup>e</sup> Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain</footnote>

<footnote><sup>f</sup> Pompeu Fabra University, Barcelona, Spain</footnote>

<footnote><sup>g</sup> Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Av. Monforte de Lemos, 3-5. Pabellón 11, 28029 Madrid, Spain</footnote>

<footnote><sup>h</sup> Center for Fertility and Health, Norwegian Institute of Public Health, Olso, Norway</footnote>

<footnote><sup>i</sup> Department of Child and Adolescent Psychiatry, University Medical Center, Erasmus MC, Rotterdam, the Netherlands</footnote>

<footnote><sup>j</sup> Department of Pediatrics, Erasmus MC, University Medical Center Rotterdam, the Netherlands</footnote>

<footnote><sup>k</sup> Université Grenoble Alpes INSERM CNRS Institute for Advanced Biosciences Team of Environmental Epidemiology Applied to Development and Respiratory Health, F-38700 La Tronche, France</footnote>

<footnote><sup>l</sup> Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, the Netherlands</footnote>

<footnote><sup>m</sup> Cancer Epidemiology Unit, Department of Medical Sciences, University of Turin and CPO-Piemonte, Turin, Italy</footnote>

<footnote><sup>n</sup> Bradford Institute for Health Research, Bradford BD9 6RJ, United Kingdom</footnote>

<footnote><sup>o</sup> Department of Genetics and Bioinformatics, Division of Health Data and Digitalisation, Norwegian Institute of Public Health, Oslo, Norway</footnote>

<footnote><sup>p</sup> Manchester Metropolitan University, All Saints Building, All Saints, Manchester, United Kingdom</footnote>

<footnote><sup>q</sup> Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands</footnote>

<footnote><sup>r</sup> Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands</footnote>

<footnote><sup>s</sup> Department of Social Medicine, School of Medicine, University of Crete, Greece</footnote>

<footnote>* Corresponding author at: Øster Farimagsgade 5A, Section of Epidemiology, Department of Public Health, University of Copenhagen, 1014 Copenhagen K, Denmark.</footnote>

<footnote>E-mail address: tica@sund.ku.dk (T. Cadman).</footnote>

<footnote>https://doi.org/10.1016/j.envint.2024.108453</footnote>

<footnote>Received 17 October 2023; Received in revised form 17 January 2024; Accepted 18 January 2024</footnote>

<footnote>Available online 13 February 2024</footnote>

<footnote>0160-4120/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</footnote>
**Results:** Of the 30,772 women included, 3,078 (10 %) reported having PPD. Exposure to PM₁₀ was associated with slightly increased odds of PPD (adjusted odd ratios (OR) of 1.08 [95 % Confidence Intervals (CI): 0.99, 1.17] per inter quartile range increment of PM₁₀) whilst associations for exposure to NO₂ and PM₂.₅ were close to null. Exposure to high levels of road traffic noise (≥65 dB vs. < 65 dB) was associated with an OR of 1.12 [CI: 0.95, 1.32]. Associations between green spaces and PPD were close to null; whilst proximity to major blue spaces was associated with increased risk of PPD (OR 1.12, 95 %CI: 1.00, 1.26). All associations between built environment and PPD were close to null. Multiple exposure models showed similar results.

**Discussion:** The study findings suggest that exposure to PM₁₀, road traffic noise and blue spaces in pregnancy may increase PPD risk, however future studies should explore this causally.

# 1. Introduction

Maternal postpartum depression (PPD) is defined within the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) as depressed mood, anxiety and anhedonia within four weeks of giving birth (American Psychiatric Association, 2013). PPD is a major public health challenge estimated to affect 12–25 % of women in high-income countries (Norhayati et al., 2015; Shorey et al., 2018). Risk factors include low socioeconomic position, previous history of depression, intimate partner violence, substance misuse and limited social support (Norhayati et al., 2015; Yang et al., 2022). There is debate over the nosology of PPD, with International Classification of Diseases (ICD)-10 codes not including a specific category for PPD (World Health Organization, 1992), and the DSM-V classifying PPD as a subset of perinatal depression (American Psychiatric Association, 2013). However there is evidence that PPD is characterised by more anxious features than other adult depression, and a higher risk of PPD in subsequent pregnancies (Cooper and Murray, 1995). Furthermore, the postpartum period is important because depression during this time may interfere with the mother's ability to care for the baby and PPD is a risk factor for child mental health problems (Stein et al., 2014).

Currently more than 50 % of the world population lives in cities, a figure projected to rise to 70 % by 2050 (Un, 2015). Notwithstanding the multiple potential benefits of living in urban areas, there is growing evidence that exposure to urban environmental stressors such as ambient air pollution, road traffic noise and lack of access to natural spaces may be detrimental to mental health (Callaghan et al., 2020: Gong et al., 2016; Kotzeva and Brandmüller, 2016; Tzivian et al., 2015). These types of environmental exposures could impact mental health through biological and social mechanisms. For example, exposure to ambient air pollutants has been linked with neurotoxic effects and disturbance of hypothalamus–pituitary–adrenal (HPA) functioning (Amitai et al., 1998; Gruzieva et al., 2017; Thomson, 2019), road traffic noise exposure may cause stress and disrupt sleep (Smith et al., 2022), and lack of natural spaces may reduce opportunities to relax, socialize and exercise (Pirrera et al., 2010; Hartig et al., 2014).

There is a growing epidemiological evidence that urban environmental exposures (such as air pollution) (Fan et al., 2020) may increase the risk of adult depression, however few studies have focused on PPD. Whilst the risk factors are similar (American Psychiatric Association, 2013), the unique context of pregnancy and the transition to motherhood may make women in the perinatal period more susceptible to such exposures. In the few epidemiological studies that have been conducted, pregnancy exposure to higher levels of ambient air pollution with particulate matter (PM) with a diameter smaller than 10 µm (PM₁₀) (Bastain et al., 2021; Duan et al., 2022; Niedzwiecki et al., 2020) and nitrogen dioxide (NO₂) (Bastain et al., 2021; Duan et al., 2022; Shih et al., 2021) have been associated with higher risk of PPD, however evidence for associations between PM₂.₅ exposure and PPD are less consistent (Bastain et al., 2021; Duan et al., 2022; Niedzwiecki et al., 2020; Pourhoseini et al., 2022; Sheffield et al., 2018; Shih et al., 2021; Zhang et al., 2021). Studies have also found that exposure to a range of air pollutants including those mentioned above is associated with increased risk of depression during pregnancy (Lamichhane et al., 2021; Kanner et al.,

2021).

Evidence for associations between other urban environmental exposures and PPD is very limited. To our knowledge only two studies have examined associations between exposure to residential road traffic noise and perinatal depression. They suggested that noise exposure was associated with increased risk of antenatal depression (Jigeer et al., 2022), and that a high level of night time noise (>70 dB) was associated with an increased risk of hospitalisation for PPD (He et al., 2019). A recent study investigating the association between surrounding green space and PPD found a small protective effect for some, but not all measures of greenness (Sun et al., 2023). Two studies investigating natural space exposure and antenatal depression reported mixed findings (McEachan et al., 2016; Nichani et al., 2017), and we are unaware of any studies investigating pregnancy exposure to natural spaces and PPD. There is emerging evidence that living in a more walkable neighbourhood may be protective against depression and loneliness (Joshi et al., 2017; Domènech-Abella et al., 2020) including for mothers in the postnatal period (Zhang et al., 2021). Given that social isolation is a risk factor for PPD (Norhayati et al., 2015), we also hypothesise that living in a neighbourhood with greater availability of facilities (such as sport and cultural facilities) could be a protective factor for PPD, however as far as we are aware this has not yet been examined.

In summary, evidence that urban environmental exposures constitute risk factors for PPD is limited and inconsistent. The aim of the present study was to estimate associations between several urban environmental exposures during pregnancy and PPD using harmonised data from multiple European birth cohorts. We hypothesised that (i) residential exposure to ambient air pollution (NO₂, PM₂.₅ and PM₁₀) and road traffic noise (Lₐden) would be associated with higher risk of PPD, and (ii) that residential exposure to natural spaces (Normalised Difference Vegetation Index, NDVI and proximity to major green and blue spaces), increased facility richness and walkability would be associated with lower risk of PPD. As there is very limited previous research, we did not specify a direction of association between population density and PPD.

# 2. Materials and methods

## 2.1. Study population

Our study is based on the EU Child Cohort Network (EUCN) (Jaddoe et al., 2020; Pinot de Moira et al., 2021). We obtained data from twelve (out of 22 in the network) European birth cohort studies from eight countries (Table 1), with available data on at least one of the selected environmental exposures and PPD. Together, these twelve cohorts included data on 78,916 participants. We restricted the study population to mothers with (i) singleton pregnancies resulting in a liveborn infant, (ii) no previous history of depression and (iii) complete data on at least one of the selected environmental exposures, PPD, and confounders (selected a priori and defined as detailed below). Mothers with a previous history of depression were excluded to identify a sample of women for whom depression began during or after pregnancy (thus corresponding as closely as possible to the DSM-V criteria of depression with perinatal onset). The analysis sample ranged from N = 17,686 (26 % of eligible) in the PM₁₀ analyses to N = 30,716 (46 % of eligible women) in
the analyses of green space (Fig. 1).

All participant women gave written informed consent and ethical approval was provided by cohort-specific ethics boards. A summary of the cohort characteristics is provided in Table 1. The analysis plan provided to all cohorts can be viewed at https://osf.io/7cv2d/.

## 2.2. Exposure assessment

Maternal residential address(es) during pregnancy were used to assign environmental exposures, for further detail see (Robinson et al., 2018; Binter et al., 2022; Maitre et al., 2018; Torres Toda et al., 2022). For all exposures we estimated the average level across pregnancy. The mean concentrations of NO₂, PM₂.₅, and PM₁₀ were estimated using land-use regression (LUR) models developed within the European Study of Cohorts for Air Pollution Effects (ESCAPE) project (Eeftens et al., 2012) or the Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) project (de Hoogh et al., 2018). We used the available air pollution data from the nearest routine air quality monitoring data and ratio-methods to back-extrapolate the estimated time-weighted mean air pollution levels to the exact time period of the individual pregnancies as air sampling campaigns were conducted years after the pregnancies of the cohort participants (Pedersen et al., 2013). NO₂ and PM₂.₅ were available for all cohorts and PM₁₀ exposure was available for nine cohorts (ABCD, BiB, EDEN-Nancy, EDEN-Poitiers, GEN-R, INMA Sabadell, MoBa, NINFEA & Rhea).

The mean road traffic noise levels over days, evenings and nights (Lₐ₇₅) were estimated using existing European road traffic noise maps, which were generated under EC Directive 2002/49/EC (Assessment and Management of Environmental Noise) in the framework of the European Noise Directive (END). Noise exposure for the Rhea cohort was calculated using a newly developed noise map generated in the EXPOSOMICS project. Exposure to road traffic noise was coded as a binary variable (exposed defined as >= 65 dB), as not all cohorts had available continuous data. Data was available for all cohorts except INMA Gipuzkoa.

Three indicators of exposure to natural spaces were selected for the present study. Average exposure to green vegetation (e.g. trees, parks shrubland) was captured using NDVI within a 300 m buffer from the centroid of the residential address derived from the Landsat 4–5 Thematic Mapper I, Landsat 7 Enhanced Thematic Mapper Plus (ETM +), and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) with 30 m × 30 m resolution (Nieuwenhuisen et al., 2014). NDVI quantifies vegetation by measuring the difference between near-infrared and red-light reflection based on satellite imagery. Values range from -1 to 1: negative values represent water, low values (0 to 0.1) indicate areas of barren rock, sand or snow; moderate values (0.2 to 0.5) indicate sparse vegetation such as shrubs and grasslands, whilst high values (0.6 to 1) indicate dense vegetation (Weier). Since our focus was on vegetation, negative values were removed leaving a range of 0 to 1. Two additional binary variables were derived indicating residential proximity to green and blue spaces, as indicators of access to these spaces.

These were defined as living within a straight-line distance of 300 m of a major green space (parks or countryside) or blue space (sea, lakes, fish ponds, rivers, canals) with an area > 5,000 m² (yes or no). These were calculated using the European Environmental Protection Agency Urban Atlas for all cohorts except INMA Gipuzkoa (European Nature Information System data used) and MoBa (local data used) (Urban Atlas). Data was available for all cohorts.

We included three features of the built environment: (i) facility richness (300 m buffer), (ii) walkability (300 m buffer) and (iii) population density (100 m buffer). Facility richness was calculated as the number of different facility types (e.g. restaurants, shops, schools, medical centres) within a 300 m buffer divided by the maximum (ten) potential types (range 0 – 1; possible facility types given in Table S1). Population density was expressed as the number of inhabitants per km² at the home address and was calculated using the Global Human Settlement Layer (Data from: GHS-POP R, 1975) for all cohorts except MoBa for which local data were used (Robinson et al., 2018). Finally, a walkability index was developed consisting of an average of four other indices: (i) Land use Shannon's Evenness Index (an indicator of the evenness of land use (range 0–1) with higher values indicating a more even distribution of land) (Shannon, 1948), (ii) Facility richness, (iii) Population density and (iv) Connectivity index (number of street intersections within 300 m buffer) with a range of 0–1 (1 = most walkable) (Binter et al., 2022; Frank et al., 2006). Information on built environment was available for all cohorts.

## 2.3. Outcome definition

We categorized maternal PPD as a binary variable (yes/no) based on maternal self-report, with eleven out of twelve cohorts using validated questionnaires. Six cohorts used versions of the Edinburgh Postnatal Depression Scale (ALSPAC, EDEN-Nancy, EDEN-Poitiers, GEN-R, MoBa, Rhea) (Cox et al., 1987), three cohorts used the General Health Questionnaire (BiB, INMA Gipuzkoa, INMA Sabadell) (Goldberg et al., 1997), one cohort used the Center for Epidemiologic Studies Depression Scale (ABCD), and one cohort used data from two questionnaires (Symptom Distress Checklist and the General Health Questionnaire; DNBC). Validated cut-offs were used to define the risk of clinical depression. The cohort not applying a validated questionnaire collected data on women-reported doctor diagnoses (NINFEA). Harmonised data on PPD were available at one time point in each cohort, with the time of measurement varying from 2 months (GEN-R) to 18 months after birth (INMA Gipuzkoa, INMA Sabadell & NINFEA). Full details of the measures, cut-offs and measurement periods are described in Table S2 and accompanying text in the Supplemental Materials).

## 2.4. Confounder definition

We used Directed Acyclic Graphs (DAGs) to depict confounders and determine whether there was any evidence of colliders that we should not adjust for (Figure S1). In our main analysis we included maternal

<table><caption>**Table 1**

Characteristics of the prospective birth cohorts.</caption><thead><tr><th>Cohort name</th><th>Country</th><th>City/area</th><th>Subsample<sup>a</sup></th><th>N included</th><th>Birth year(s)</th></tr></thead><tbody><tr><td>ABCD</td><td>Netherlands</td><td>Amsterdam</td><td>Prospective</td><td>4,490</td><td>2003—2004</td></tr><tr><td>ALSPAC</td><td>The United Kingdom</td><td>Greater Bristol</td><td>Greater Bristol</td><td>7,141</td><td>1991—1992</td></tr><tr><td>BiB</td><td>The United Kingdom</td><td>Bradford</td><td>–</td><td>1,024</td><td>2007—2011</td></tr><tr><td>DNBC</td><td>Denmark</td><td>Nationwide</td><td>Greater Copenhagen</td><td>4,750</td><td>1996—2002</td></tr><tr><td>EDEN Nancy</td><td>France</td><td>Nancy</td><td>–</td><td>749</td><td>2003—2005</td></tr><tr><td>EDEN Poitiers</td><td>France</td><td>Poitiers</td><td>–</td><td>677</td><td>2003—2005</td></tr><tr><td>Gen-R</td><td>The Netherlands</td><td>Rotterdam</td><td>–</td><td>3046</td><td>2002—2006</td></tr><tr><td>INMA Gipuzkoa</td><td>Spain</td><td>Gipuzkoa</td><td>–</td><td>4,64</td><td>1997—2008</td></tr><tr><td>INMA Sabadell</td><td>Spain</td><td>Sabadell</td><td>–</td><td>4,82</td><td>1997—2008</td></tr><tr><td>MoBa</td><td>Norway</td><td>Nationwide</td><td>Greater Oslo</td><td>6,475</td><td>1999—2008</td></tr><tr><td>NINFEA</td><td>Italy</td><td>Nationwide</td><td>Florence, Rome & Turin</td><td>1,181</td><td>2005—2016</td></tr><tr><td>Rhea</td><td>Greece</td><td>Crete</td><td>Heraklion</td><td>293</td><td>2007—2008</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Flow chart of included participants<sup>a</sup>Refers to participants within each cohort living within areas for which environmental exposures were modelled (Table 1)<sup>b</sup>Data on birth outcome unavailable for MoBa;<sup>c</sup>Data on multiple birth unavailable for GEN-R;<sup>d</sup>Defined as women self-reporting either a history or depression or other psychiatric problems prior to pregnancy.</figcaption></figure>

education in pregnancy (low, medium, high), disposable family income in quintiles (Pizzi et al., 2020), area-specific deprivation for the residential address in pregnancy (low, medium, high; detail of the indices provided in Table S3), parity prior to the index pregnancy (nulliparous vs other), maternal age (years), year and season of birth. Maternal ethnicity (defined as Western vs other) fit our definition of a confounder, but was only available with sufficient variability in four out of the twelve cohorts (ABCD, ALSPAC, BiB, Gen-R, maximum n = 15,607, representing 58 % of the 30,772 participants) and thus was only

included in a sensitivity analysis.

## 2.5. Statistical analysis

We performed federated analysis using DataSHIELD version 6.10 (Gaye et al., 2014) and R version 3.52 (code available at: https://github.com/lifecycle-project/env-pnd). DataSHIELD is a platform for federated data analysis which prevents disclosure of personal identifiable data by preventing researchers from viewing individual participant data and
involves no data transfer. Instead, analysis commands are sent to local servers on which harmonised individual participant data are stored and processed. Only non-disclosive summary statistics are returned.

Our analysis proceeded as follows. First, we described the distribution of the exposures, outcomes and covariates along with (Pearson) correlations between exposures. We then estimated associations between each exposure and PPD (adjusting for confounders) separately for each cohort using logistic regression and combined results via random-effects meta-analysis. We chose random-effects meta-analysis because we hypothesised that due to differences in demographics and urban design of cities in the study centres, the true effect size may vary between study (Borenstein et al., 2010; Robinson et al., 2018). We also hypothesised that some exposures may confound the association between other environmental exposures and PPD (as depicted in the DAG via maternal residential address as an unmeasured common cause); therefore we additionally fit models adjusting air pollutants for road traffic noise, natural spaces and built environment, and road traffic noise for air pollution, natural spaces and built environment (Figure S1). We described heterogeneity between cohorts both qualitatively and using Cochran's Q statistic. Continuous exposure variables were scaled by the interquartile range calculated across all studies whilst binary and categorical exposures were left untransformed.

We conducted the following sensitivity analyses. First, we repeated analyses additionally adjusting for ethnicity in the four cohorts with available data. We also compared findings where PPD was assessed using the EPDS (a specific PPD questionnaire) vs other depression measures.

We handled missing data through complete case analysis, as neither multiple imputation nor inverse probability weighting functionalities were available in DataShield at the time of analysis. To explore missingness of data, we compared the characteristics of participants included in the analysis sample (minimum one environmental exposure, PPD and core confounders, N = 30,772) with those excluded. Some differences were observed: those excluded had on average lower income, lived in more deprived areas and had higher rates of PPD (Table S4).

# 3. Results

## 3.1. Study participant characteristics

Approximately a third of participants were from Scandinavian countries and a quarter from the United Kingdom. Participants were mostly highly educated, nulliparous, and cohabiting women of Western European origin (Table 2). A total of 3,078 (10 %) PPD cases were identified, with the percentage of women meeting threshold for PPD ranging from 3 % in NINFEA to 19 % in BiB (Table S5). Women with PPD had on average lower education, lower income and were from more deprived areas compared to those without PPD. There were considerable differences between cohorts in demographics, for example 85 % of women from BiB lived in highly deprived neighbourhoods compared to 1 % in INMA Gipuzkoa, and the proportion of nulliparous women varied from 41 % in BIB to 73 % in NINFEA. The proportion with complete data who contributed to at least one exposure-PPD analyses varied from 25 % (Rhea) to 78 % (INMA Sabadell), with full details for each study provide in Table S6.

The median pregnancy concentrations of NO₂ exposure ranged from 11.6 (Rhea) to 46.6 µg/m³ (Copenhagen area of DNBC); PM₂.₅ levels ranged from 9.3 (BiB) to 23.3 µg/m³ (NINFEA), and PM₁₀ levels ranged from 13.6 (Oslo area of MoBa) to 40.1 µg/m³ (NINFEA). The proportion of women exposed to high levels of road traffic noise (>= 65 dB) was greatest in NINFEA (36 %) and lowest in ALSPAC (2 %). NDVI levels ranged from 0.16 (Rhea) to 0.53 (Eden Poitiers). In all cohorts, most women lived within 300 m of a major green space, but only two cohorts had a high percentage of women living close to blue spaces (GEN-R & INMA Gipuzkoa). ABCD had the highest levels of facility richness and walkability whilst EDEN Poitiers had lowest levels. Population density

<table><caption>**Table 2**

Study population characteristics for all and by postpartum depression (PPD).</caption><thead><tr><th rowspan="2">Variable</th><th colspan="3">Median (IQR) for continuous variables / N (%) for<br/>categorical variables</th></tr><tr><th>All (N =<br/>30,772)</th><th>No PPD (N =<br/>27,694)</th><th>PPD (N =<br/>3,078)</th></tr></thead><tbody><tr><td>Birth season</td><td></td><td></td><td></td></tr><tr><td>January - March</td><td>8708 (28.3)</td><td>7843 (28.3)</td><td>863 (28.4)</td></tr><tr><td>April - June</td><td>6875 (22.3)</td><td>6170 (22.3)</td><td>694 (22.8)</td></tr><tr><td>July - September</td><td>7851 (25.5)</td><td>7056 (25.5)</td><td>783 (25.8)</td></tr><tr><td>October - December</td><td>7338 (23.8)</td><td>6625 (23.9)</td><td>701 (23)</td></tr><tr><td>Parity</td><td></td><td></td><td></td></tr><tr><td>Nulliparous</td><td>13,306 (43.2)</td><td>11,855 (42.8)</td><td>1451 (47.1)</td></tr><tr><td>Not nulliparous</td><td>17,466 (56.8)</td><td>15,839 (57.2)</td><td>1627 (52.9)</td></tr><tr><td>Maternal age at birth<br/>(years)</td><td></td><td></td><td></td></tr><tr><td>15-20</td><td>498 (1.63)</td><td>421 (1.5)</td><td>34 (1.5)</td></tr><tr><td>21-30</td><td>14,149 (46.4)</td><td>12,873 (46.5)</td><td>974 (43.3)</td></tr><tr><td>31-40</td><td>15,419 (50.6)</td><td>14,031 (50.7)</td><td>1203 (53.5)</td></tr><tr><td>41-50</td><td>413 (1.4)</td><td>369 (1.3)</td><td>38 (1.7)</td></tr><tr><td>Maternal education at<br/>birth</td><td></td><td></td><td></td></tr><tr><td>High</td><td>17,275 (56.1)</td><td>15,671 (56.6)</td><td>1116 (44.1)</td></tr><tr><td>Medium</td><td>10,086 (32.8)</td><td>9174 (33.1)</td><td>854 (33.8)</td></tr><tr><td>Low</td><td>3411 (11.1)</td><td>2849 (10.3)</td><td>560 (22.1)</td></tr><tr><td>Household disposable<br/>income</td><td></td><td></td><td></td></tr><tr><td>First quintile</td><td>4503 (14.6)</td><td>3855 (13.9)</td><td>648 (21)</td></tr><tr><td>Second quintile</td><td>5695 (18.5)</td><td>5038 (18.2)</td><td>657 (21.4)</td></tr><tr><td>Third quintile</td><td>7935 (25.8)</td><td>7202 (26)</td><td>733 (23.8)</td></tr><tr><td>Fourth quintile</td><td>6861 (22.3)</td><td>6300 (22.8)</td><td>561 (18.2)</td></tr><tr><td>Fifth quintile</td><td>5778 (18.8)</td><td>5299 (19.1)</td><td>479 (15.6)</td></tr><tr><td>Maternal ethnicity</td><td></td><td></td><td></td></tr><tr><td>Western origin</td><td>14,598 (82.0)</td><td>12,469 (82.8)</td><td>1034 (62.8)</td></tr><tr><td>Non-western origin</td><td>3204 (18)</td><td>2584 (17.2)</td><td>613 (37.2)</td></tr><tr><td>NO₂ (µg/m³)</td><td>31.4 (27.4,<br/>35.7)</td><td>31.2 (27.2,<br/>35.5)</td><td>32.8 (28.9,<br/>37.0)</td></tr><tr><td>PM₂.₅ (µg/m³)</td><td>15.3 (14.1,<br/>16.6)</td><td>15.3 (14.0,<br/>16.6)</td><td>15.5 (14.4,<br/>16.7)</td></tr><tr><td>PM₁₀ (µg/m³)</td><td>24.5 (22.3,<br/>27.2)</td><td>24.4 (22.2,<br/>27.2)</td><td>25 (23.3, 27.3)</td></tr><tr><td>NDVI (range 0-1; 300 m<br/>buffer)</td><td>0.4 (0.34,<br/>0.47)</td><td>0.4 (0.34, 0.47)</td><td>0.38 (0.32,<br/>0.46)</td></tr><tr><td>L₃ₐ₇ₐ₈</td><td></td><td></td><td></td></tr><tr><td>&lt;55 dB</td><td>21,085 (88.0)</td><td>19,017 (88.0)</td><td>2042 (88.1)</td></tr><tr><td>&gt;=65 dB</td><td>2,871 (12.0)</td><td>2593 (12.0)</td><td>276 (11.9)</td></tr><tr><td>Major green space &gt;<br/>5,000 m² within 300 m</td><td></td><td></td><td></td></tr><tr><td>No</td><td>8,517 (28.3)</td><td>7603 (28.1)</td><td>913 (30.6)</td></tr><tr><td>Yes</td><td>21,597 (71.7)</td><td>19,490 (71.9)</td><td>2076 (69.4)</td></tr><tr><td>Major blue space &gt; 5,000<br/>m² within 300 m</td><td></td><td></td><td></td></tr><tr><td>No</td><td>21,920 (72.8)</td><td>19,875 (73.4)</td><td>2014 (67.4)</td></tr><tr><td>Yes</td><td>8,194 (27.2)</td><td>7218 (26.6)</td><td>974 (32.6)</td></tr><tr><td>Facility richness (0-1;<br/>300 m buffer)</td><td>0.09<br/>(0.04,0.15)</td><td>0.09 (0.04,0.15)</td><td>0.1 (0.05,0.17)</td></tr><tr><td>Walkability (0-1; 300 m<br/>buffer)</td><td>0.33<br/>(0.28,0.38)</td><td>0.33 (0.28,0.38)</td><td>0.34 (0.29,0.4)</td></tr><tr><td>Population density<br/>(persons km² in 100 m)</td><td>5900<br/>(4020,9190)</td><td>5860<br/>(3990,9150)</td><td>6270<br/>(4280,9700)</td></tr><tr><td>Area deprivation</td><td></td><td></td><td></td></tr><tr><td>Low</td><td>10,493 (34.1)</td><td>9531 (34.4)</td><td>960 (33.3)</td></tr><tr><td>Medium</td><td>8344 (27.1)</td><td>7592 (27.4)</td><td>727 (25.2)</td></tr><tr><td>High</td><td>11,935 (38.8)</td><td>10,571 (38.2)</td><td>1196 (41.5)</td></tr></tbody></table>

ranged from 1,660 inhabitants/km² (EDEN Poitiers) to 12,000 inhabitants/km² (INMA Sabadell).

Figure S2 shows correlations between exposures for each cohort. Correlations between air pollutants were moderate to high (r = 0.25 to 0.96); the correlation between NO₂ and L₃ₐ₇ₐ₈ ranged from low to moderate (r = 0.01 to 0.26), and air pollutants were negatively correlated with NDVI (r = -0.01 to -0.70). Built environment exposures were highly correlated, especially walkability and facility richness (r = 0.38 to 0.82).
## 3.2. Associations between urban environment exposures and PPD in single exposure models

Meta-analysed results are shown in Fig. 2 with study-specific results in Figures S3–S6. Exposure to PM₁₀ was associated with increased risk of PPD, although estimates were compatible with no association. Associations between exposure to NO₂, PM₂.₅ and PPD were close to null. Heterogeneity between cohorts was low (NO₂ Q = 13, p = 0.30; PM₂.₅ Q = 13, p = 0.31, PM₁₀ Q = 5.3, p = 0.72), and ORs > 1 observed in 6 out of 12 studies for NO₂, 7 out of 12 studies for PM₂.₅ and 6 out of 9 studies for PM₁₀ (Figure S3).

Exposure to road traffic noise > = 65 dB (vs < 65 dB) was associated with an increased risk of PPD (OR = 1.12, CI 0.95, 1.32) though results were also compatible with no association. Heterogeneity between cohorts was low (Q = 14.1, p = 0.17), with 5 out of 11 cohorts having an OR > 1 (Figure S4).

Associations between NDVI and the presence of major green spaces near the home residence were close to null (Fig. 2), and heterogeneity was low (NDVI Q = 4.2, p = 0.99; access to green space Q = 5.5, p = 0.92). Exposure to blue spaces near the home was associated with an increased risk of PPD (OR = 1.12, CI 1.00, 1.26), with 9 out of 12 cohorts having an OR > 1 (Figure S5).

In terms of the built environment, exposure to greater facility richness and walkability were associated with increased risk of PPD; however results were also consistent with no association (Figure S6). Heterogeneity was low (Facility richness Q = 9.6, p = 0.57; walkability Q = 11.5, p = 0.41), with 7 out of 12 cohorts showing OR > 1 for facility richness and 7 out of 12 cohorts for walkability. Associations between population density were null, with low statistical heterogeneity (Q = 16.8, p = 0.08).

## 3.3. Associations between urban environment exposures and PPD in multiple exposure models

Additionally adjusting air pollutant models for population density and NDVI (potential confounders) moved estimates slightly away from

null (Fig. 2). Additionally adjusting the traffic noise model for population density and NDVI made minimal difference to estimates. Additionally adjusting air pollutant models for traffic noise and vice versa also did not notably change results.

## 3.4. Sensitivity analyses

We repeated analyses in the subset of 4 cohorts (ABCD, ALSPAC, BiB, GEN-R) with up to 15,607 participants with available data on ethnicity, and found that additionally adjusting for ethnicity changed estimates slightly with the direction varying between exposure (Table S7). We also stratified analysed depending on whether PPD was measured by EPDS or other measures. We found that the strength of association for air pollutants was slightly higher when PPD was measured using the EPDS vs. other measures, whilst for other exposures there was not a consistent pattern of differences. For all exposures 95 % CIs for estimates overlapped comparing estimates where PPD was assessed via the EPDS vs other measures (Table S8).

## 4. Discussion

In this study of individual level data on up to 30,772 women from twelve European birth cohorts and eight countries, we found suggestive evidence for associations between some of the selected environmental exposures and PPD, but not for all of them. As hypothesised, we found some indication that increased exposure to PM₁₀ and high levels of road traffic noise were associated with increased risk of PPD. However, associations for the pooled study population were null for NO₂ and PM₂.₅ and contrary to hypotheses we found that having a residence near to a major blue space was associated an increased risk. For many of the examined associations we observed considerable heterogeneity between cohorts, and except for proximity to a major blue space all pooled effect estimates were compatible with no association.

<table><thead><tr><th>Exposure</th><th>Adjustment</th><th>K</th><th>No PPD</th><th>PPD</th><th>I2</th><th>OR [95% CI]</th></tr></thead><tbody><tr><td colspan="7"><strong>Air pollutants</strong></td></tr><tr><td rowspan="3">NO2</td><td>Single exposure</td><td>12</td><td>27556</td><td>3048</td><td>0.05</td><td>0.98 [0.93, 1.03]</td></tr><tr><td>Pop. density &amp; NDVI</td><td>11</td><td>26792</td><td>2990</td><td>0</td><td>0.97 [0.92, 1.03]</td></tr><tr><td>Pop. density, NDVI &amp; Lden</td><td>11</td><td>21433</td><td>2314</td><td>0</td><td>0.98 [0.91, 1.04]</td></tr><tr><td rowspan="3">PM2.5</td><td>Single exposure</td><td>12</td><td>27573</td><td>3049</td><td>11.87</td><td>1.04 [0.97, 1.11]</td></tr><tr><td>Pop. density &amp; NDVI</td><td>11</td><td>26809</td><td>2991</td><td>8.5</td><td>1.03 [0.96, 1.11]</td></tr><tr><td>Pop. density, NDVI &amp; Lden</td><td>11</td><td>21448</td><td>2315</td><td>13.8</td><td>1.05 [0.96, 1.14]</td></tr><tr><td rowspan="3">PM10</td><td>Single exposure</td><td>9</td><td>15916</td><td>1770</td><td>0</td><td>1.08 [0.99, 1.17]</td></tr><tr><td>Pop. density &amp; NDVI</td><td>9</td><td>15776</td><td>1756</td><td>0</td><td>1.06 [0.97, 1.16]</td></tr><tr><td>Pop. density, NDVI &amp; Lden</td><td>9</td><td>12903</td><td>1275</td><td>0</td><td>1.06 [0.97, 1.17]</td></tr><tr><td colspan="7"><strong>Road traffic noise</strong></td></tr><tr><td rowspan="3">Lden >= 65 dB</td><td>Single exposure</td><td>11</td><td>21610</td><td>2346</td><td>12.95</td><td>1.12 [0.95, 1.32]</td></tr><tr><td>Pop. density &amp; NDVI</td><td>11</td><td>21549</td><td>2340</td><td>11.09</td><td>1.12 [0.95, 1.32]</td></tr><tr><td>Pop. density, NDVI &amp; PM2.5</td><td>11</td><td>21448</td><td>2315</td><td>13.64</td><td>1.12 [0.94, 1.33]</td></tr><tr><td colspan="7"><strong>Natural spaces</strong></td></tr><tr><td>NDVI</td><td>Single exposure</td><td>12</td><td>27645</td><td>3071</td><td>0</td><td>0.99 [0.93, 1.05]</td></tr><tr><td>Major green space</td><td>Single exposure</td><td>12</td><td>27093</td><td>3021</td><td>2.22</td><td>0.98 [0.89, 1.07]</td></tr><tr><td>Major blue space</td><td>Single exposure</td><td>12</td><td>27093</td><td>3021</td><td>0</td><td>1.12 [1.00, 1.26]</td></tr><tr><td colspan="7"><strong>Built environment</strong></td></tr><tr><td>Facility richness</td><td>Single exposure</td><td>12</td><td>26782</td><td>3003</td><td>11.37</td><td>1.04 [0.98, 1.11]</td></tr><tr><td>Walkability score</td><td>Single exposure</td><td>12</td><td>26230</td><td>2950</td><td>17.04</td><td>1.05 [0.98, 1.12]</td></tr><tr><td>Population density</td><td>Single exposure</td><td>11</td><td>26958</td><td>3022</td><td>0.05</td><td>0.99 [0.95, 1.04]</td></tr></tbody></table>

**Fig. 2.** Associations between urban environmental exposures and postpartum depression in single and multiple exposure models for the full study population using *meta*-analysis with random-effects on cohort. Note: Models adjusted for maternal education, disposable family income, area deprivation, parity, maternal age at birth, birth month and birth year. K = Number of cohorts. "Major green space" = green space > 5000 m² within 300 m or residential address; "Major blue space" = blue space > 5000 m² within 300 m of residential address. For binary variables (Lden, major green space, major blue space) estimates represent odds ratio of exposure category vs reference category, for all other (continuous) variables estimates represent odds ratio by IQR change in exposure.
## 4.1. Strengths and limitations

One of the major strengths of our study is its large geographical coverage including different regions of Europe, enabling us to cover different urban and cultural settings in our analysis. We included harmonised individual level data on multiple urban environmental exposures, PPD and a range of potential confounders. We were able to include multiple urban environmental exposures and account for potential confounding of one exposure by another. Since we had selected relevant exposures a priori for planned analyses and interpreted results based on point estimates and confidence intervals, we did not believe that adjustment for multiple testing was required. We had self-reported data on PPD which captures milder cases than if PPD was based on hospital contact and avoids selection bias into health services.

There were however limitations. First, due to technical limitations with the DataSHIELD infrastructure multiple imputation was not available at the time of analysis. We were therefore limited to complete case analysis, with the percentage of complete cases varying from 25 % (Rhea) to 78 % (EDEN Nancy). Complete case analysis is susceptible to bias where the outcome is related to probability of being a complete case (Hughes et al., 2019), and we observed that on average women included in the analysis sample had slightly lower rates of PPD compared to those excluded (10 % vs. 14 %). Technical limitations with DataSHIELD also meant that normality of variables had to be assessed via inspecting histograms rather than formal tests (e.g. Shaprio-Wilk test). In addition, whilst having participants from different study areas increases the exposure contrast, it also increases the risk of exposure misclassification as the methods and data available for each cohort were not identical. Our natural space indicators provide information about quantity of green and blue spaces within proximity to residential address, however they do not provide information about the access or quality of spaces (e. g. facilities, safety) which may affect their use. All exposures were estimated based on maternal residential address(es) during pregnancy and we did not consider maternal exposure in other microenvironments or other time periods. All our estimates for exposure were based on the location of the home and do not account for the location of the bedroom or apartments within buildings. This could have contributed to non- differential exposure misclassification, diluting the result towards the null. A further limitation was that the same questionnaires to assess PPD were not used in all cohorts and thus the reported rates of PPD are not directly comparable across cohorts. Self-reported assessment of mental health could also be influenced by recall bias and individual characteristics. It is less objective than clinical assessment and we also cannot rule out that self-reported assessment of PPD could have been influenced by unmeasured social and cultural differences that differentiate between and within the study areas such as intimate partner, household and/or residential neighbourhood level of support and crime. Furthermore, there was significant heterogeneity in the time points at which PPD was assessed in each cohort. This may have affected the prevalence and severity of PPD captured within each study. Due to the harmonisation process within the EUCCN, we were limited to a binary variable indicating PPD. Future research could use a continuous measure of PPD which may confer more power to detect associations. In order to focus on women for whom depression begun during or after pregnancy, we excluded women who reported a previous history of depression. Whilst this made our sample more homogeneous (and potentially more in line with the DSM-V criteria of depression with perinatal onset), we acknowledge the risk of introducing collider bias. In addition, as with all observational studies, there is a likelihood that associations are subject to unmeasured and residual confounding. Finally, we focused on long-term exposure to urban environmental exposures and it was not within the scope of this study to evaluate effects or effect modification by short-term exposure.

## 4.2. Interpretation of findings

Our finding that exposure to PM₁₀ was associated with a small increased risk of PPD was consistent with previous studies. For example, a recent study in Shanghai, Hangzhou and Shaoxing, China (n = 10,209) reported that a 10 µg/m³ increase in PM₁₀ exposure in pregnancy was associated with an approximately 50 % increase in self-reported PPD risk (Duan et al., 2022). However, unlike previous studies we found the association between NO₂ and PPD to be close to null. By contrast, a study including 180 mothers in Los Angeles, USA reported that a standard deviation change in pregnancy NO₂ exposure was associated with a two- fold risk of self-reported PPD (Bastain et al., 2021), whilst a nation-wide study in Taiwan (n = 21,248) also reported a positive association be- tween NO₂ exposure in the first trimester and self-reported PPD (Shih et al., 2021).

Our finding that residential exposure to high levels of road-traffic noise was associated with increased PPD risk was also consistent with our hypothesis and the previous research. Only one previous study (Montreal, Canada) has explored the relationship between traffic noise and hospitalisation for PPD (n = 140, 456) (He et al., 2019). Whilst they reported a close to null association between Lden and PPD, they found an increased risk of PPD with greater exposure to night-time noise. Two recent *meta*-analyses have also both reported a 4 % increased risk of adult depression per 10 dB increase in Lden (Dzhambov and Lercher, 2019; Hegewald et al. 2020). We lacked information on low noise levels in some cohorts and thus were unable to study effects below 55 dB, although we are aware that noise effects could start at lower levels (World Health Organization, 2018). Further research should replicate our findings using noise as a continuous variable including more fine- grained information on bedroom location relative to the street.

We found little evidence for an association between green space and PPD. A recent study conducted in California also found no association between NDVI and PPD, but did find an association between higher total green space exposure and lower PPD. Two previous studies which explored associations between NDVI and depression risk during preg- nancy showed mixed findings. Whilst a previous study using the BiB cohort (n = 7,547) reported a 20 % reduced risk of depression in pregnancy for mothers living in the greenest quintile, a study in New Zealand (n = 6,772) reported close to null associations between the proportion of green space within a census unit and pregnancy depression (Nichani et al., 2017). Contrary to hypotheses, we found that exposure to blue spaces was associated with increased risk of depression. This was consistent with a previous study of 958 adults in Barcelona which similarly reported that adults living within 300 m of a major blue space had increased risk of self-reported depression. As we are not aware of a plausible mechanism by which blue space exposure could increase risk of PPD, it is likely that these findings are due either to residual con- founding, or to a major blue space exposure being associated with other aspects of the built environment which themselves are associated with increased risk of PPD, such as ambient air pollution emissions from ships or unmeasured social features that cluster in areas near major blue spaces in our study areas.

In terms of the built environment, we found little evidence that either population density, facility richness or walkability were associated with PPD, with *meta*-analysed estimates close to one. Whilst no previous studies have been conducted using these exposures and PPD, two studies with older adults reported that increased walkability was associated with a lower risk of depression (Joshi et al., 2017, Domènech-Abella et al., 2020). However, again, the context of PPD is different to (older) adult depression and thus the risk factors also may differ.

## 5. Conclusions

Overall, we found some evidence that greater exposure to PM10, road traffic noise and blue spaces were associated with a higher risk of PPD. Most other associations evaluated were close to null. Many aspects
of the urban environment are highly correlated, so future research could extend on our findings by modelling how exposure to the whole urban exposome is associated with PPD. (Vrijheid, Sep, 2014) Causal inference methods should also be used to estimate the causal effects of these exposures.

## Funding

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 874853 (ATHLETE project). Tim Cadman was supported by a Marie Curie-Sklodowska Individual Fellowship (URBINEX, 120616).

IS Global acknowledges support from the Spanish Ministry of Science and Innovation through the "Centro de Excelencia Severo Ochoa 2019-2023" Program (CEX2018-000806-S), and support from the Generalitat de Catalunya through the CERCA Program to ISGlobal.

HEM was supported by the Stichting Volksbond Rotterdam, the Netherlands Organization for Scientific Research (NWO) Aspasia grant (No. 015.016.056), the European Union's Horizon 2020 Research and Innovation Program (LifeCycle, Grant Agreement 733 206 & Happy-Mums 101057390), and the Marie Sklodowska-Curie Actions Innovative Training Network (MSCA-ITN) programme (Serotonin and BEYOND, Grant agreement No: 953327). The funding agencies had no role in the design and conduct of the study, collection, management, analyses and interpretation of the data; and preparation, review or approval of the manuscript and the decision to submit it for publication.

DAL works in a unit that is supported by the University of Bristol and UK Medical Research Council (MC_UU_00011/6).

Details of funding for each cohort is provided in Supplementary Materials S3.

## CRediT authorship contribution statement

**Tim Cadman:** Writing – original draft, Visualization, Software, Project administration, Methodology, Funding acquisition, Formal analysis. **Katrine Strandberg-Larsen:** Writing – review & editing, Methodology, Conceptualization. **Lucinda Calas:** Writing – review & editing. **Malina Christiansen:** Writing – review & editing. **Iryna Culpin:** Writing – review & editing. **Payam Dadvand:** Writing – review & editing, Methodology. **Montserrat de Castro:** Writing – review & editing, Methodology. **Maria Foraster:** Writing – review & editing. **Serena Fossati:** Writing – review & editing, Methodology. **Mónica Guxens:** Writing – review & editing, Methodology. **Jennifer R. Harris:** Writing – review & editing. **Manon Hillegers:** Writing – review & editing. **Vincent Jaddoe:** Writing – review & editing. **Yunsung Lee:** Writing – review & editing. **Johanna Lepeule:** Writing – review & editing. **Hanan el Marroun:** Writing – review & editing. **Milena Maule:** Writing – review & editing. **Rosie McEachen:** Writing – review & editing, Methodology. **Chiara Moccia:** Writing – review & editing. **Johanna Nader:** Writing – review & editing. **Mark Nieuwenhuijsen:** Writing – review & editing, Methodology. **Anne-Marie Nybo Andersen:** Writing – review & editing. **Rebecca Pearson:** Writing – review & editing. **Morris Swertz:** Writing – review & editing. **Marina Vafeiadi:** Writing – review & editing. **Martine Vrijheid:** Writing – review & editing. **John Wright:** Writing – review & editing, Methodology. **Deborah A Lawlor:** Writing – review & editing, Methodology, Funding acquisition, Conceptualization. **Marie Pedersen:** Writing – review & editing, Methodology.

## 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.

## Data availability

The data that has been used is confidential.

## Acknowledgements

For each cohort are provided in Supplementary Materials S2.

## Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envint.2024.108453.

## References

*   American Psychiatric Association, 2013. Diagnostic and Statistical Manual of Mental Disorders: DSM-5, vol 10. American psychiatric association, Washington, DC.

*   Amitai, Y., Zlotogorski, Z., Golan-Katzav, V., Wexler, A., Gross, D., 1998. Neuropsychological Impairment From Acute Low-Level Exposure to Carbon Monoxide. Arch. Neurol. 55 (6), 845-848.

*   Bastain, T.M., Chavez, T., Habre, R., et al., Nov 27 2021; Prenatal ambient air pollution and maternal depression at 12 months postpartum in the MADRES pregnancy cohort. Environ Health. 20 https://doi.org/10.1186/s12940-021-00807-x.

*   Binter, A.C., Bernard, J.Y., Mon-Williams, M., et al., Jan 2022. Urban environment and cognitive and motor function in children from four European birth cohorts. Environ Int. 158, 106933 https://doi.org/10.1016/j.envint.2021.106933.

*   Borenstein, M., Hedges, L.V., Higgins, J.P.T., Rothstein, H.R., 2010. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res. Synth. Methods 1 (2), 97-111. https://doi.org/10.1002/jrsm.12.

*   Callaghan, A., McCombe, G., Harrold, A., et al. The impact of green spaces on mental health in urban settings: a scoping review. J. Mental Health. 2020.1:1-15. doi: 10.1080/09638237.2020.1755027.

*   Cooper, P.J., Murray, L. (1995). Course and recurrence of postnatal depression. Evidence for the specificity of the diagnostic concept. Br. J. Psychiatry 1995;166(2):191-5.

*   Cox, J.L., Holden, J.M., Sagovsky, R., 1987. Detection of postnatal depression. Development of the 10-item Edinburgh postnatal depression scale. Br J Psychiatry. Jun 1987;150:782-6. doi:10.1192/bjp.150.6.782.

*   Data from: GHS-POP R2015A - GHS population grid, derived from GW4, multitemporal (1975, 1990, 2000, 2015) - OBSOLETE RELEASE. (2015). 2015.

*   de Hoogh, K., Chen, J., Gulliver, J., et al., 2018. Spatial PM2.5, NO2, O3 and BC models for Western Europe – Evaluation of spatiotemporal stability. Environ. Int. 120, 81-92. https://doi.org/10.1016/j.envint.2018.07.036.

*   Domènech-Abella, J., Mundó, J., Leonardí, M., et al., 2020. Loneliness and depression among older European adults: The role of perceived neighborhood built environment. Health & Place. 2020/03/01/ 2020;62:102280. doi: https://doi.org/10.1016/j.healthplace.2019.102280.

*   Duan, C.C., Li, C., Xu, J.J., et al., Jun 2022. Association between prenatal exposure to ambient air pollutants and postpartum depressive symptoms: a multi-city cohort study. Environ Res. 209, 112786 https://doi.org/10.1016/j.envres.2022.112786.

*   Dzhambov, A.M., Lercher, P., 2019. Road traffic noise exposure and depression/anxiety: an updated systematic review and meta-analysis. Int. J. Environ. Res. Public Health. 16 (21), 4134.

*   Eftens, M., Beelen, R., de Hoogh, K., et al., 2012/10/16 2012., Development of land use regression models for PM2.5, PM2.5 absorbance, PM10 and PMcoarse in 20 european study areas; results of the ESCAPE project. Environ. Sci. Tech. 46 (20), 11195-11205. https://doi.org/10.1021/es301948k.

*   Fan, S.-J., Heinrich, J., Bloom, M.S., et al., 2020. Ambient air pollution and depression: a systematic review with meta-analysis up to 2019. Sci. Total Environ. 701, 134721 https://doi.org/10.1016/j.scitotenv.2019.134721.

*   Frank, L.D., Sallis, J.F., Conway, T.L., Chapman, J.E., Saelens, B.E., Bachman, W., 2006. Many pathways from land use to health: associations between neighborhood walkability and active transportation, body mass index, and air quality. J. Am. Plann. Assoc. 72 (1), 75-87. https://doi.org/10.1080/01944360608976725.

*   Gaye, A., Marcon, Y., Isaeva, J., et al., 2014. DataSHIELD: taking the analysis to the data, not the data to the analysis. Int. J. Epidemiol. 43 (6), 1929-1944. https://doi.org/10.1093/ije/dyu188.

*   Goldberg, D.P., Gater, R., Sartorius, N., et al., Jan 1997. The validity of two versions of the GHQ in the WHO study of mental illness in general health care. Psychol Med. 27 (1), 191-197. https://doi.org/10.1017/s0033291796004242.

*   Gong, Y., Palmer, S., Gallacher, J., Marsden, T., Fone, D., 2016. A systematic review of the relationship between objective measurements of the urban environment and psychological distress. Environ. Int. 96, 48-57.

*   Gruzieva, O., Merid, S.K., Gref, A., et al., 2017. Exposure to traffic-related air pollution and serum inflammatory cytokines in children. Environ Health Perspect. 177(12):067007.

*   Hartig, T., Mitchell, R., Vries, S.d., Frumkin, H., 2014. Nature and health. Annu. Rev. Public Health 35 (1), 207-228. https://doi.org/10.1146/annurev-publhealth-032013-182443.

*   He, S., Smargiassi, A., Low, N., Bilodeau-Bertrand, M., Ayoub, A., Auger, N., 2019. Residential noise exposure and the longitudinal risk of hospitalization for pregnancy after pregnancy: postpartum and beyond. Environ Res. 170, 26-32. https://doi.org/10.1016/j.envres.2018.12.001.

*   Hegewald, J., Schubert, M., Freiberg, A., et al., 2020. Traffic noise and mental health: a systematic review and meta-analysis. Int. J. Environ. Res. Public Health. 17 (17), 6175.
Hughes, R.A., Heron, J., Sterne, J.A.C., Tilling, K., 2019. Accounting for missing data in statistical analyses: multiple imputation is not always the answer. Int. J. Epidemiol. 48 (4), 1294–1304. https://doi.org/10.1093/ije/dyz032.

Jaddoe, V.W.V., Felix, J.F., Andersen, A.-M.-N., et al., 2020/07/01 2020. The LifeCycle Project-EU Child Cohort Network: a federated analysis infrastructure and harmonized data of more than 250,000 children and parents. Eur. J. Epidemiol. 35 (7), 709–724. https://doi.org/10.1007/s10654-020-00662-z.

Jigeer, G., Tao, W., Zhu, Q., et al., Aug 17 2022: Association of residential noise exposure with maternal anxiety and depression in late pregnancy. Environ Int. 168, 107473 https://doi.org/10.1016/j.envint.2022.107473.

Joshi, S., Mooney, S.J., Rundle, A.G., Quinn, J.W., Beard, J.R., Cerdá, M., 2017. Pathways from neighborhood poverty to depression among older adults. Health Place 43, 138–143. https://doi.org/10.1016/j.healthplace.2016.12.003.

Kanner, J., Pollack, A.Z., Ranasinghe, S., et al., May 2021. Chronic exposure to air pollution and risk of mental health disorders complicating pregnancy. Environ Res. 196, 110937 https://doi.org/10.1016/j.envres.2021.110937.

Kotzeva, M.M., Brandmüller, T., 2016. Urban Europe: statistics on cities, towns and suburbs. Publications office of the European Union.

Lamichhane, D.K., Jung, D.Y., Shin, Y.J., et al., Aug 2021. Association of ambient air pollution with depressive and anxiety symptoms in pregnant women: a prospective cohort study. Int. J. Hyg. Environ. Health. 237, 113823 https://doi.org/10.1016/j.ijheh.2021.113823.

Maitre, L., de Bont, J., Casas, M., et al., 2018. Human Early Life Exposome (HELIX) study: a European population-based exposome cohort. BMJ Open 8 (9), e021311.

McEachan, R.R., Prady, S.L., Smith, G., et al., Mar 2016. The association between green space and depressive symptoms in pregnant women: moderating roles of socioeconomic status and physical activity. J. Epidemiol. Community Health 70 (3), 253–259. https://doi.org/10.1136/jech-2015-205954.

Nichani, V., Dirks, K., Burns, B., Bird, A., Grant, C., 2017. Green space and depression during pregnancy: results from the growing up in New Zealand study. Int. J. Environ. Res. Public Health. 14 (9), 1083. https://doi.org/10.3390/ijerph14091083.

Niedzwiecki, M.M., Rosa, M.J., Solano-González, M., et al., 2020. Particulate air pollution exposure during pregnancy and postpartum depression symptoms in women in Mexico City. Environ. Int. 134, 105325.

Nieuwenhuijsen, M.J., Kruize, H., Gidlow, C., et al., 2014. Positive health effects of the natural outdoor environment in typical populations in different regions in Europe (PHENOTYPE): a study programme protocol. BMJ Open. Apr 16 2014;4(4):e004951. doi:10.1136/bmjopen-2014-004951.

Norhayati, M.N., Hazlina, N.H., Asrenee, A.R., Emilin, W.M., 2015. Magnitude and risk factors for postpartum symptoms: a literature review. J Affect Disord. 175, 34–52. https://doi.org/10.1016/j.jad.2014.12.041.

Organization, W.H., 2018. Environmental noise guidelines for the European region. World Health Organization, Regional Office for Europe.

Pedersen, M., Giorgis-Allemand, L., Bernard, C., et al., Nov 2013. Ambient air pollution and low birthweight: a European cohort study (ESCAPE). Lancet Respir Med. 1 (9), 695–704. https://doi.org/10.1016/s2213-2600(13)70192-9.

Pinot de Moira, A., Haakma, S., Strandberg-Larsen, K., et al., 2021/05/01 2021,. The EU Child Cohort Network's core data: establishing a set of findable, accessible, interoperable and re-usable (FAIR) variables. Eur. J. Epidemiol. 36 (5), 565–580. https://doi.org/10.1007/s10654-021-00733-9.

Pirrera, S., De Valck, E., Cluydts, R., 2010. Nocturnal road traffic noise: a review on its assessment and consequences on sleep and health. Environ. Int. 36 (5), 492–498. https://doi.org/10.1016/j.envint.2010.03.007.

Pizzi, C., Richiardi, M., Charles, M.-A., et al., 2020. Measuring child socio-economic position in birth cohort research: the development of a novel standardized household income indicator. Int. J. Environ. Res. Public Health. 17 (5), 1700. https://doi.org/10.3390/ijerph17051700.

Pourhoseini, S.A., Akbary, A., Mahmoudi, H., Akbari, M., Heydari, S.T., 2022. Association between prenatal period exposure to ambient air pollutants and

development of postpartum depression: a systematic review and meta-analysis. Int. J. Environ. Health Res. 2022:1–11. doi:10.1080/09603123.2022.2153808.

Robinson, O., Tamayo, I., Castro, Md, et al., 2018. The urban exposome during pregnancy and its socioeconomic determinants. Environ. Health Perspect. 2018;126 (7):077005. doi: 10.1289/EHP2862.

Robinson, O., Tamayo, I., de Castro, M., et al., Jul 2018. The urban exposome during pregnancy and its socioeconomic determinants. Environ Health Perspect. 126 (7), 077005.

Shannon, C.E., 1948. A mathematical theory of communication. Bell Syst. Tech. J. 27 (3), 379–423.

Sheffield, P., Speranza, R., Chiu, Y.-H.-M., et al., 2018. Association between particulate air pollution exposure during pregnancy and postpartum maternal psychological functioning. PLoS One 13 (4), e0195267.

Shih, P., Wu, C.-D., Chiang, T.-L., et al., 2021. The association between postpartum depression and air pollution during pregnancy and postpartum period: a national population study in Taiwan. Environ. Res. Lett. 16 (8), 084021 https://doi.org/10.1088/1748-9326/ac1224.

Shorey, S., Chee, C.Y.I., Ng, E.D., Chan, Y.H., Tam, W.W.S., Chong, Y.S., 2018. Prevalence and incidence of postpartum depression among healthy mothers: a systematic review and meta-analysis. J. Psychiatr. Res. 104, 235–248. https://doi.org/10.1016/j.jpsychires.2018.08.001.

Smith, M.G., Cordoza, M., Basner, M., 2022. Environmental noise and effects on sleep: an update to the WHO systematic review and meta-analysis. Environ. Health Perspect. 130 (7), 076001 https://doi.org/10.1289/EHP10197.

Stein, A., Pearson, R.M., Goodman, S.H., et al., 2014. Effects of perinatal mental disorders on the fetus and child. Lancet 384 (9956), 1800–1819. https://doi.org/10.1016/S0140-6736(14)61277-0.

Sun, Y., Molitor, J., Benmarhnia, T., et al., 2023. Association between urban green space and postpartum depression, and the role of physical activity: a retrospective cohort study in Southern California. Lancet Regional Health-Am. 21.

Thomson, E.M., 2019. Air pollution, stress, and allostatic load: linking systemic and central nervous system impacts. J Alzheimers Dis. 69 (3), 597–614. https://doi.org/10.3233/jad-190015.

Torres Toda, M., Avraam, D., James Cadman, T., et al., Dec 2022. Exposure to natural environments during pregnancy and birth outcomes in 11 European birth cohorts. Environ Int. 170, 107648 https://doi.org/10.1016/j.envint.2022.107648.

Tzivian, L., Winkler, A., Dlugaj, M., et al., 2015. Effect of long-term outdoor air pollution and noise on cognitive and psychological functions in adults. Int. J. Hyg. Environ. Health. 218 (1), 1–11. https://doi.org/10.1016/j.ijheh.2014.08.002.

UN D. World urbanization prospects: The 2014 revision. United Nations Department of Economics and Social Affairs, Population Division: New York, NY, USA. 2015:41.

Urban Atlas. European Environment Agency. http://www.eea.europa.eu/data-and-maps/data/urban-atlas.

Vrijheid, M., Sep 2014. The exposome: a new paradigm to study the impact of environment on health. Thorax 69 (9), 876–878. https://doi.org/10.1136/thoraxjnl-2013-204949.

Weier J. HD. Measuring vegetation (NDVI & EVI). http://earthobservatory.nasa.gov/Features/MeasuringVegetation/measuring_vegetation_2.php.

World Health Organization, 1992. The ICD-10 classification of mental and behavioural disorders: clinical descriptions and diagnostic guidelines. World Health Organization.

Yang, K., Wu, J., Chen, X., 2022. Risk factors of perinatal depression in women: a systematic review and meta-analysis. BMC Psychiatry 22. https://doi.org/10.1186/s12888-021-03684-3.

Zhang, Y., Tayarani, M., Wang, S., et al., 2021. Identifying urban built environment factors in pregnancy care and maternal mental health outcomes. BMC Pregnancy Childbirth 21. https://doi.org/10.1186/s12884-021-04056-1.