Skip to main content
NCBI home page
UKPMC Funders Author Manuscripts logoLink to UKPMC Funders Author Manuscripts
. Author manuscript; available in PMC: 2025 Oct 4.
Published before final editing as: BJOG. 2025 Feb 19:10.1111/1471-0528.18082. doi: 10.1111/1471-0528.18082

Towards the Development of a Conceptual Framework of the Determinants of Pre-Eclampsia: A Hierarchical Systematic Review of Social Determinants

Mai-Lei Woo Kinshella 1,#, Kelly Pickerill 1,#, Sarina Prasad 1, Olivia Campbell 1, Jalila Devji 1, Lívia Vieira Lopes 1, Rosa Balleny 1, Terteel Elawad 2, Rachel Craik 2, Marie-Laure Volvert 2, Hiten D Mistry 2,3, Hannah Blencowe 4, Véronique Filippi 4, Peter von Dadelszen 1,2, Laura A Magee 1,2,#, Marianne Vidler 1,✉,#; the PRECISE Network
PMCID: PMC7618191  EMSID: EMS208247  PMID: 39973015

Abstract

Background

Existing reviews of pre-eclampsia determinants have focused on clinical and genetic risk factors.

Objective

To evaluate social determinants for pre-eclampsia prevention.

Search Strategy

Systematic searches were conducted from relevant electronic databases from inception of each database to 30th December 2024.

Selection Criteria

Reviews and large cohort studies (≥ 1000 participants), published between 2013 and 2024, reporting quantitative associations between social determinant exposures and pre-eclampsia outcomes.

Data Collection and Analysis

Titles and abstracts, then relevant full-texts were reviewed by two reviewers, independently. Strength of association was evaluated as ‘definite’ (odds ratios [OR] or relative risk [RR] ≥ 3.00 or < 0.33), ‘probable’ (OR or RR 1.50–2.99 or 0.33–0.67), ‘possible’ (OR or RR 1.10–1.49 or 0.68–0.89), or ‘unlikely’ (OR or RR 0.90–1.09). Quality of the evidence was high, moderate, low, or very-low, using GRADE.

Main Results

Twenty-seven publications found 24 associations of pre-eclampsia with socioeconomic status, social support/exclusion, healthcare access, and occupational and physical environmental factors. One association (polygamy) was definite (low-quality evidence). Probable associations included: work stress, lack of antenatal care and heat exposure in early pregnancy (high-quality evidence); prolonged occupational exposure to whole body vibrations or bending, distance to health facility, and UV-B radiation exposure (protective factor), all based on moderate-quality evidence; and neighbourhood deprivation, rotating work shifts, and Asian/Oceanian origins (protective factor), all based on low-quality evidence. There were 13 possible associations, which did not include education.

Conclusion

Our findings support recommendations to address climate change, strengthen occupational protection, and promote early antenatal attendance. Social determinants may be indicative of upstream factors (e.g., obesity) that increase likelihood of clinical risk factors for pre-eclampsia incidence and severity.

1. Introduction

Pre-eclampsia is a serious complication of pregnancy, characterised by new-onset hypertension at or after 20 weeks’ gestation with one or more of: proteinuria, maternal end-organ involvement, or uteroplacental dysfunction [1]. Pre-eclampsia is the second leading cause of maternal mortality worldwide, associated with over 46 000 maternal and 500 000 perinatal deaths annually [1]. This healthcare burden is disproportionately associated with resource-limited settings and economically-deprived communities, which highlights the need to consider the social environments where pregnant women develop the disease [2].

Social determinants of health are the non-medical factors that influence health outcomes [3] and include social or environmental factors that contribute to, or detract from, the ability to live a healthy life [4]. The Royal College of Obstetricians and Gynaecologists (2022), recently highlighted how socioeconomic status (SES), employment status, educational status, migration, ethnicity, health service factors, and physical environmental factors can contribute to maternal morbidity and mortality [4].

Despite the growing body of literature on the social determinants of maternal health [5–10], reviews of risk factors for pre-eclampsia have largely focused only on clinical and genetic risk factors [11–15]. This evidence review aims to compile current literature on the social determinants of pre-eclampsia, with any associations evaluated for their strength and the quality of the underlying evidence.

2. Methods

2.1. Search Strategy

Our review employed the methods of Hiatt et al., to develop a model of determinants using a systematic process [16], and is part of the larger aim of building a conceptual framework to describe a comprehensive multi-factorial model of the determinants of pre-eclampsia. A broad working model of known determinants were assembled by the ‘PREgnancy Care Integrating translational Science, Everywhere’ (PRECISE) Network [17], based on variables found to have significant associations with pre-eclampsia from pooled results within umbrella reviews of systematic reviews [14, 15]; however, social determinants of health were inadequately covered in these reviews. Informed by four prominent social determinant of health frameworks: Healthy People 2030 [18], the World Health Organisation [19], Public Health Agency of Canada [20], and the Dahlgren-Whitehead Rainbow model [21] (Table S2), we completed a search of social indicators, grouped into eight domains (SES, education, social support and exclusion, ethnicity and region of origin, occupation, healthcare access, and physical environment).

The search strategy was conducted by a team of researchers, graduate and undergraduate students and medical trainees (KP, OC, SP, MWK, JD, LVL, RD). Systematic searches were conducted on Medline, Embase, Health Technology Assessments, Database of Abstracts of Reviews of Effects, Cochrane Library, Google Scholar and reference lists. Searches were conducted for relevant articles in each domain, initially to 31st July 2023, with an update undertaken to 30th December 2024. Broad search terms are reported in Table 1. (File S1).

Table 1. Search terms.

Socioeconomic status, income, social gradient, poverty, deprivation AND Pre-eclampsia
Education, illiteracy, literate
Social capital, social support, community funds, women groups, social isolation, marital status, same-sex marriage, refugees, immigrant, migrant, housing instability
Ethnicity, region of origin, nativity
Occupation, occupational exposure, work exposure, work stress
Religion, Christian, Muslim, Islam, Hindu, Buddhism, agnostic, atheist
Health services accessibility, prenatal care, rural health services, health facility density, skilled birth attendant, homebirth, private healthcare, distance to health, rural, money for transport
Environmental pollution, air pollution, water pollution, noise, seasons, seasonality, temperature, humidity, rain, sunlight, climate change, physical environment
Open in a new tab

2.2. Eligibility Criteria

We included recent studies published between 2013 and 2024 that reported quantitative associations between social determinant exposures and pre-eclampsia. Our outcome of interest was pre-eclampsia incidence, as defined according to the individual publications reviewed. Studies that only reported pregnancy hypertension or hypertension outside of pregnancy were excluded. Following methods of Hiatt et al. [16], studies were selected according to a hierarchy of evidence that prioritised umbrella reviews (systematic reviews of reviews), followed by systematic reviews with meta-analyses and finally large observational cohort studies with a minimum sample size of 1000 pregnancies, as described by Bartsch et al. [11] to be more representative of the general population and to have sufficient statistical power to assess less prevalent, but potentially important, risk factors; we excluded smaller observational cohort studies, cross-sectional surveys, and case reports or series, which may be less representative of the population of interest, Additionally, qualitative reviews and editorials were excluded. Studies conducted in any country were eligible for inclusion.

2.3. Study Selection and Data Extraction

Titles and abstracts of search results were screened by the search team (KP, OC, SP, MWK, JD, LVL, RD) to determine potential eligibility. All potentially eligible studies independently underwent full-text review by two team members.

Data abstracted were general study characteristics, and strength of association between each social determinant and pre-eclampsia, expressed as relative risk (RR), odds ratios (OR), or hazard ratio (HR), unadjusted and adjusted if reported, or calculated from raw data provided.

2.4. Certainty of the Evidence

All indicators were assessed by the quality of the evidence and the strength of association. Evidence quality was rated independently by two reviewers, using Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) [22].

Following GRADE procedures, umbrella or systematic reviews were classed as high quality, while single observational studies were considered low quality that could be upgraded for large effect sizes or evidence of a dose response [23]. Evidence quality was downgraded for risk of bias, inconsistency (substantial heterogeneity I2 > 50%), indirectness (results not reported for pregnant populations), imprecision (wide confidence intervals) and publication bias (funnel plot asymmetry, Egger’s Test with p < 0.05). Directness and precision were supported by our eligibility criteria, as studies not conducted with pregnant populations and studies < 1000 were excluded. The strength of association was classified as definite (OR or RR ≥ 3.00 or < 0.33), probable (OR or RR 1.50–2.99 or 0.33–0.67), possible (OR or RR 1.10–1.49 or 0.68–0.89), or unlikely (OR or RR crosses one, or point estimate is in non-significant range, 0.90–1.09), adapted from Hiatt et al. [16]. Study countries were categorised as high, upper-middle, and low- or low-middle income countries according to World Bank classifications. Findings were discussed with six patient partners from the REACH BC Registry and the Preeclampsia Foundation.

3. Results

Searches across the eight domains yielded 25 281 records. After removal of duplicates and screening of titles and abstracts, and review of 183 full-texts (Figure 1), we included 27 articles. Included studies were primarily large individual cohort studies (n = 18) [24–41] or systematic reviews with meta-analyses (n = 8) [42–49], although there was one umbrella review [14] Cohort studies had an average of 831 364 participants (range: 2492–5 448 255) and were primarily from Europe (n = 7) and North America (n = 7), with representation from South America (n = 1), Asia (n = 1), the Middle East (n = 1) and sub-Saharan Africa (n = 1) (Table 2; Table S3; Figure S1). Meta-analyses contributed 25 comparisons with an average of 1 878 638 participants in each (range: 3490–30 310 610) and an average of 5 studies in each comparison (range: 2–22) (Table 2). Of the 137 studies included in the 25 comparisons by meta-analyses, most studies included in the systematic reviews were also from Europe (n = 35) and North America (n = 48), though there was more representation from Asia (n = 19), the Middle East (n = 12) and sub-Saharan Africa (n = 19) (Table S3; Figure S1). South America (n = 2) and Australasia (n = 1) were minimally represented. Pre-eclampsia was defined as high blood pressure (≥ 140/90 mmHg) after 20 weeks gestation with proteinuria, with proteinuria and other signs of organ damage, or with ICD 9/10 codes, all of which define pre-eclampsia by high blood pressure and proteinuria (File S2).

Figure 1. Search results.

Figure 1

Open in a new tab

Table 2. Social determinants examined for any association with risk of developing pre-eclampsia (any, unless otherwise stated).

Category and indicators Effect estimate
(95% CI)		 N
studies		 Countryab N participants I 2 Direction of
association		 Strength of
association		 Certainty of
evidence
Socioeconomic status
Higher household income (vs lower) OR 0.88 (0.86–0.89) 1 United States 5448255 NA Protective Possible Low
Higher neighbourhood deprivation RR1.52 (1.35–1.71) 1 United Kingdom 159125 NA Risk factor Probable Low
Absence of standard health insurance OR 1.98 (1.46–2.67) 1 Korea 461580 NA Risk factor Probable Low
Maternal education
Low maternal education OR 1.12 (0.59–1.65) 7c Cameroona, Mozambiquea,
Nigeriaa (2 studies), Sudana,
Tanzaniaa, Ugandaa 		4429 80% None Unlikely Moderate
Social support and exclusion
Polygamy RR 3.13 (2.51–3.90) 1 Israel 9872 NA Risk factor Definite Low
Immigrant status (vs native origin) OR 0.75 (0.69–0.80) 22c Australia, Canada, Chile, Greece, Israel, Norway (4 studies), Portugal (2 studies), Sweden (2 studies), Turkey† (6 studies), United States (3 studies) 30 310 610 93% Protective Possible Moderate
Refugee status OR 1.62 (1.55–1.69) 1 Ecuadorb 1165801 NA Risk factor Possible Low
Specifically for preterm pre-eclampsia (< 37 weeks) OR 1.18 (1.02–1.36) 1 Norway 1287270 NA Risk factor Possible Low
Specifically for very preterm pre-eclampsia (< 34 weeks) OR 1.41 (1.15–1.72) 1 Norway 1287270 NA Risk factor Possible Low
Unmarried marital status (vs married) OR 1.21 (1.11–1.32) 1 United States 205888 NA Risk factor Possible Low
Specifically for early-onset (< 34 weeks) pre-eclampsia HR 1.14 (1.10–1.19) 1 United States 456 668 NA Risk factor Possible Low
Specifically for late-onset (≥ 34 weeks) pre-eclampsia HR 1.14 (1.10–1.19) 1 United States 456 668 NA Risk factor Possible Low
Mental stress OR 1.49 (1.27–1.74) 12d Canada (2 studies), Finland, Germany, Iranb, Netherlands, Nigeria3, Pakistan3, Perub, United States (3 studies) 665893 68% Risk factor Possible Low
Unstable housing OR 1.0 (0.7–1.3) 1 United States 2898035 NA None Unlikely Very low
Ethnicity & region of origin
Asia and Oceania (vs. Swedish European) RR 0.60 (0.47–0.75) 1 Sweden 46618 NA Protective Probable Low
Africa (vs Swedish European) RR 1.05 (0.69–1.58) 1 Sweden 46618 NA None Unlikely Very low
Americas (vs Swedish European) RR 1.00 (0.65–1.53) 1 Sweden 46618 NA None Unlikely Very low
European Union countries (vs Swedish European) RR 0.97 (0.73–1.29) 1 Sweden 46618 NA None Unlikely Very low
Other European countries (vs Swedish European) RR 0.89 (0.69–1.14) 1 Sweden 46618 NA None Unlikely Very low
Non-Hispanic Black (vs. Non-Hispanic White) OR 1.17 (0.87–1.56) 1 United States 6096 NA None Unlikely Low
Hispanic (vs. Non-Hispanic White) OR 1.16 (0.84–1.59) 1 United States 6096 NA None Unlikely Very low
Occupation
Work stress OR 1.50 (1.15–1.97) 4d Canada (2 studies), Netherlands, United States 8742 0% Risk factor Probable High
Whole body vibrations (highest [≥0.5m/s2] vs. no exposure Orn/s2) OR 1.76 (1.41–2.20) 1 Sweden 646490 NA Risk factor Probable Moderate
Prolonged bending (≥ 1 h/day) OR 1.51 (1.09–2.08) 2c Canada, Norway 9970 12%, Risk factor Probable Moderate
Rotating shifts OR 1.75 (1.01–3.01) 2c Norway, Taiwan 29 588 75%, Risk factor Probable Low
Heavy lifting (≥ 11 kg/time) OR 1.35 (1.07–1.71) 5c Canada, Netherlands, Norway, United States (2 studies) 20716 0% Risk factor Possible Moderate
Passive job strain: within a low-demand job, low (vs high) decision-making authority RR 1.10 (1.06–1.14) 1 Sweden 1102230 NA Risk factor Possible Low
Active job strain: within high decision-making authority, high (vs low) demand job RR 0.90 (0.87–0.94) 1 Sweden 1102230 NA None Unlikely Low
High job strain: low decision-making authority and high demand job (vs high decision authority and low demand) RR 1.02 (0.98–1.06) 1 Sweden 1102230 NA None Unlikely Low
Long working hours (>40h/week) OR 1.27 (0.74–2.19) 5c Canada (2 studies), Netherlands, Taiwan, United States 34650 84% None Unlikely Low
Overnight shifts OR 1.05 (0.63–1.75) 3c Canada, Netherlands, Taiwan 33247 0% None Unlikely Moderate
Occupational noise exposure RR 1.07 (1.04–1.10) 4c Canada, Norway, Sweden, United States 1101240 41% None Unlikely Moderate
Prolonged standing (>4h/day) OR 0.95 (0.58–1.55) 6c Canada (2 studies), Netherlands, Norway, United States (2 studies) 26831 78% None Unlikely Low
Prolonged or walking (>4h/day) OR 0.70 (0.46–1.08) 3c Canada (2 studies), Netherlands 9777 41% None Unlikely Moderate
Heavy physical workload OR 1.30 (0.69–2.43) 2c Italy, United States 6085 3% None Unlikely Moderate
Healthcare access
No ANC visits (vs at least one visit) OR 2.71 (1.45–3.96) 6c Ethiopiaa, Nigeriaa, Sudana (3 studies), Ugandaa 3490 93% Risk factor Probable High
24 h MFM specialist coverage (vs no
24 h specialist coverage)		 OR 1.49 (1.01–2.19) 1 United States 205888 NA Risk factor Possible Low
Tertiary-level teaching hospital (vs community hospital) OR 0.78 (0.33–1.84) 1 United States 205888 NA None Unlikely Low
Distance to health facility (> 30miles/50km) OR 1.72 (1.23–2.45) 1 United States 2492 NA Risk factor Probable Moderate
Rural residence OR 0.91 (0.62–1.33) 1 United States 2492 NA Risk factor Unlikely Very low
Specifically for eclampsia OR 2.70 (1.80–4.07) 1 Canada 256220 NA Risk factor Probable Low
Physical environment
Ambient temperature
Heat exposure in early pregnancy (l–20weeks gestation) OR 1.54 (1.10–2.15) 4c Canada, Chinab, Israel, South Africab 4006445 99% Risk factor Probable High
Heat exposure in late pregnancy (l–20weeks gestation) OR 1.05 (0.67–1.64) 4c Canada, Chinab, Israel, South Africab 1964476 96%, None Unlikely Low
Cold exposure in early pregnancy (> 20 weeks gestation) OR 0.90 (0.84–0.97) 4c Canada, Chinab, Israel, South Africab 4006445 66%, None Unlikely Moderate
Cold exposure in late pregnancy (> 20 weeks gestation) OR 1.13 (0.84–1.53) 4c Canada, Chinab, Israel, South Africab 1964476 87%, None Unlikely Low
High (vs. lowest) exposure to UV-B radiation OR 0.57 (0.44–0.72) 1 Scotland 522896 NA Protective Probable Moderate
High (vs. lowest) exposure to sunlight OR 0.77 (0.62–0.95) 1 United States 205888 NA Protective Possible Low
Precipitation (rainy vs. dry season) OR 1.36 (1.11–1.65) 1 Rwandaa 19746 NA Risk factor Possible Very low
Noise pollution (65 vs. 50 dB) OR 1.09 (0.99–1.20) 1 Canada 269263 NA None Unlikely Low
Specifically for preterm pre-eclampsia (< 34 weeks) OR 1.71 (1.20–2.43) 1 Canada 269263 NA Risk factor Probable Moderate
Outdoor (ambient) air pollution
Air pollution (NO2) OR 1.10 (1.03–1.17) 4d Australia, Netherlands, Spain, United States 121126 0%, Risk factor Possible High
Nitrogen oxides (NOx) OR 1.03 (0.91–1.17) 3d Spain, Sweden, United States 50110 0%, None Unlikely Moderate
Ozone (O3) OR 1.10 (0.99–1.22) 3d Sweden, United States (2 studies) 115891 24%, None Unlikely Moderate
Carbon monoxide (CO) OR 1.03 (1.00–1.06) 3d Canada, United States (2 studies) 169 303 0%, None Unlikely High
PM10 OR 1.11 (1.03–1.21) 12d China (4 studies), Korea, Netherlands, Spain, Sweden, United States (4 studies) 1729 656 82%, Risk factor Possible Moderate
PM2.5 OR 1.12 (1.00–1.25) 10c China (4 studies), Spain, Sweden, United States (4 studies) 534430 69%, None Unlikely Low
Indoor (household) air pollution OR 1.10 (0.54–2.22) 3c Ghanaa, Indiaa (2 studies) 38 315 79%, None Unlikely Very low
Open in a new tab

Abbreviations: ANC, antenatal care; dB, decibels; h, hours; HELLP, haemolysis, elevated liver enzymes, low platelets syndrome; MFM, maternal fetal medicine; NO2, nitrogen dioxide; PM, particulate matter; UV-B, ultraviolet B.

a

A low or lower-middle income country.

b

An upper-middle income country according to the World Bank; All others are high-income countries.

c

N studies in a systematic review(s).

d

N studies in an umbrella review.

Overall, 66 indicators were evaluated across the eight domains, with the following associations observed with pre-eclampsia incidence: 24 indicators with definitive, probably, or possible associations (Table 2); 27 indicators that were unlikely to be associated; and 15 indicators for which there was insufficient evidence to make an assessment, according to our methodology (Files S1 and S2). GRADE assessments for all indicators are reported in File S3.

3.1. Socioeconomic Status

SES was evaluated by three large cohort studies from the United States, the UK and Sweden (6 068 960 participants total), all assessed as providing low-quality evidence (Table 2). The association with pre-eclampsia was: ‘probable’ for lower SES defined by the absence of standard health insurance [25] or higher levels of neighbourhood deprivation [26], and higher household income was ‘possible’ protective factor [24].

3.2. Maternal Education

A meta-analysis of seven studies (4429 participants) from sub-Saharan Africa found that low maternal education was not associated with higher odds of pre-eclampsia (OR 1.12, 95% CI: 0.59–1.65, I2 80%) [42], but the finding was downgraded to moderate-quality evidence due to high between-study heterogeneity in outcomes (i.e., I2 = 80%).

3.3. Social Support and Exclusion

The association between polygamy and the risk of pre-eclampsia was rated as ‘definite’ [29], but the quality was low, due to evidence availability from only one country in the Middle East.

Recent immigration was a ‘possible’ protective factor for pre-eclampsia, based on a meta-analysis of 24 studies (30 310 610 participants), but the evidence quality was moderate based on very high heterogeneity (I2 93%) [43]. Based on a large cohort study from Ecuador (1 154 891 participants), refugee status was a ‘probable’ risk factor, based on low-quality evidence [30]. Another cohort study from Norway reported that refugee status was specifically a ‘possible’ risk factor for preterm or very preterm pre-eclampsia [31].

Data informing the association between marital status and pre-eclampsia come from two large cohort studies (662 556 participants) undertaken in the United States. Being unmarried (vs. married) is a ‘possible’ risk factor for pre-eclampsia, based on low-quality evidence [27], as well as for early-onset (< 34 weeks) or late-onset (≥ 34 weeks) pre-eclampsia, again based on low-quality evidence [28].

Mental stress is another ‘possible’ risk factor for pre-eclampsia, based on an umbrella review that included 12 studies (665 893 participants), assessed with low-quality evidence due to high heterogeneity (I2 68%) and potential publication bias (Egger’s Test 0.02) [14].

Unstable housing was not associated with pre-eclampsia in a large cohort study from the United States (665 893 participants) providing very low-quality evidence [32].

3.4. Ethnicity and Region of Origin

Most ethnicities and region of origin were not significantly associated with pre-eclampsia risk.

Only Asia and Oceania region of origin was protective against pre-eclampsia incidence in a cohort study conducted in Sweden (RR 0.60, 95% CI: 0.47–0.75, 46 618 participants), while other global regions did not have significantly different rates from Swedish European, after adjusting for confounders [33].

An American cohort study did not find significant differences between Non-Hispanic Black (OR 1.17, 95% CI: 0.87–1.56, 6096 participants) or Hispanic (OR 1.16, 95% CI: 0.84–1.59, 6096 participants) compared with Non-Hispanic White pregnant people after adjusting for confounders [34]. A lack of published meta-analyses on ethnicity and region of origin contributed to low-/very low-quality of the evidence.

3.5. Occupation

There were numerous occupational risk factors for pre-eclampsia. ‘Probable’ associations included work stress (8742 participants, high-quality) from an umbrella review [14], prolonged bending for at least 1 h/day (9970 participants, moderate-quality) [45], and rotating shifts (29 588 participants, low-quality) [44] from two systematic reviews, and occupational exposure to whole body vibrations (moderate-quality) reported in a Swedish cohort study (646 490 participants) [35]. Heavy lifting of 11 kg or more at any one time (20 716 participants, moderate-quality) was evaluated as a ‘possible’ risk factor in a systematic review [45], and passive job strain, meaning within the context of low decision-making authority, particularly with low demand (e.g., no tight deadlines, high targets, or conflicting pressures) was evaluated as a ‘possible’ risk factor in a Swedish cohort study (1 102 230 participants, low-quality) [36]. Evaluated as ‘unlikely’ risk factors for pre-eclampsia were: high- (vs. low-) demand job within high decision-making authority or high-demand/low decision-making (vs low-demand/high decision-making) job [36], as well as long working hours (> 40 h/week), overnight shifts, occupational noise exposure, prolonged standing or walking (> 4 h/day), and heavy physical workload (all from meta-analyses [44–46], low/moderate quality).

3.6. Healthcare Access

Having no antenatal care (ANC) visits (vs at least one visit) was a ‘probable’ risk factor for pre-eclampsia in a meta-analysis of six African studies (3490 participants, I2 93%) (high-quality evidence, downgraded for high heterogeneity, but upgraded for large effect size) [42]. Having access to 24-h maternal-fetal medicine specialist coverage was a ‘possible’ risk factor, although rates did not significantly differ between hospital levels (tertiary-level teaching hospital vs. community hospital) in one study conducted in the United States [27]. A cohort study from the United States (2492 participants) found that over 30 miles (50 km) distance to health facility was probable risk factor, based on moderate-quality evidence, though rural residence was an unlikely risk factor [37]. While rural residence may not be associated with higher odds of pre-eclampsia, another cohort study from the United States found a probable association with eclampsia, though based on low-quality evidence [38].

3.7. Physical Environment

Heat exposure in early pregnancy (week 1–20 gestation) was ‘probable’ risk factor for pre-eclampsia in a meta-analysis of four studies from Canada, China, Israel and South Africa (4 006 445 participants, I2 99%) (high-quality evidence, heterogeneity upgraded for dose response: every 1°C temperature increase in early pregnancy OR 1.07, 95% CI: 1.02–1.12) [47]. Heat exposure in late pregnancy and cold exposure in early or late pregnancy were ‘unlikely’ risk factors based on moderate-low quality evidence [47].

Sunlight (solar radiation) was found to be a protective factor for pre-eclampsia, with associations that were ‘probable’ when exposure was UV-B radiation (moderate-quality, American cohort study with 205 888 participants), and ‘possible’ when there was direct exposure to sunlight (low-quality, cohort study from Scotland with 522 896 participants) [27, 39].

The rainy season was a ‘possible’ risk factor for pre-eclampsia (very low-quality), based in a cohort study from Rwanda (19 746 participants) [40].

Environmental noise pollution (neighbourhood exposure to 65.0 dB vs. 50 dB) was a risk factor only for very preterm pre-eclampsia (moderate-quality), but not pre-eclampsia overall (low-quality) in a Canadian cohort study (269 263 participants) [41].

Outdoor (ambient) air pollution consisting of nitrogen dioxide (NO2) (high-quality, umbrella review) [14] and particulate matter PM10 (moderate-quality, systematic review) during pregnancy [48] were ‘possible’ risk factors for pre-eclampsia. However, evaluated as ‘unlikely’ was a relationship between pre-eclampsia and other components of indoor (household) and outdoor air pollution, including: nitrogen oxides (NOx), ozone (O3), carbon monoxide (CO), and particulate matter PM2.5; all evaluated from systematic reviews with meta-analyses [14, 48, 49].

3.8. Results by Country Income Status

Table 3 reports a summary of associations by evidence quality, strength, and country income status. 71% (17 of 24) of indicators with significant associations in this review drew on studies conducted in HICs. Of the six other indicators, one risk factor (refugee status) was studied in an upper-middle income country, two risk factors (lack of ANC visits and rainy season) were studied in low-or low-middle income countries, and the risk factors of mental stress and heat exposure in early pregnancy, and immigrant status as a protective factor, were based on meta-analyses that included studies with multiple country income classifications. Of the 27 indicators with unlikely associations with pre-eclampsia incidence, 81% [22] were from studies conducted in HICs. Maternal education and indoor (household) air pollution were unlikely risk factors based on studies conducted in low- or low-middle income countries; heat exposure in late pregnancy, and cold exposure in early or late pregnancy, were unlikely risk factors based on meta-analyses that included studies from multiple country income classifications.

Table 3. Summary of social determinants of pre-eclampsia by evidence quality and strength.

Quality of the evidence
High Moderate Low Very Low
Strength of the
association		 Definite
Probable

  • Work stressc

  • Lack of antenatal care visitsa

  • Heat exposure in early pregnancyc

  • Occupational exposures to whole body vibrationsc

  • Occupational exposure to prolonged bendingc

  • Distance to health facilityc

  • UV-B exposurec

  • Polygamyc

  • Neighbourhood deprivationc

  • Asia and Oceania region of originc

  • Occupational exposure to rotating shiftsc
Possible

  • Air pollution (NO2)c

  • Immigrant statusd

  • Occupational exposure to heavy liftingc

  • PM10c

  • Higher household incomec

  • Absence of standard health Insurancec

  • Refugee statusb

  • Unmarried marital statusc

  • Mental stressd

  • Passive job strainc

  • 24h MFM specialist coveragec

  • Sunlight exposurec

  • Rainy seasona
Unlikely

  • Carbon monoxide (CO)c

  • Maternal education levela

  • Occupational exposure to overnight shiftsc

  • Occupational noise exposurec

  • Occupational exposure to prolonged walkingc

  • Heavy physical workloadc

  • Cold exposure in early pregnancyd

  • Nitrogen oxides (NOX)c

  • Ozone (O3)c

  • Non-Hispanic Blackc

  • Active job strainc

  • High job strainc

  • Occupational exposure to long working hoursc

  • Occupational exposure to prolonged standingc

  • Tertiary-level teaching hospitalc

  • Heat exposure in late pregnancyd

  • Cold exposure in late pregnancyd

  • Noise pollutionc

  • PM2.5c

  • Unstable housingc

  • Africa region of originc

  • Americas region of originc

  • European Union region of originc

  • Other European region of origin

  • Hispanicc

  • Rural residencec

  • Indoor (household) air pollutiona
Open in a new tab

Note: Protective factor.

Abbreviations: MFM, maternal-fetal medicine; PM, particulate matter.

a

Evidence from low or low-middle income countries.

b

Evidence from upper-middle income countries.

c

Evidence from high income countries.

d

Evidence from mixed country income categories.

4. Discussion

4.1. Summary of Findings

Our hierarchical systematic review found that access to care (lack of ANC and distance to health facility), occupational conditions (work stress, full body vibrations, and prolonged bending), and environmental conditions (exposure to elevated ambient temperatures in early pregnancy) have probable associations with pre-eclampsia, based on moderate-high quality evidence, largely from HICs. Exposure to UV-B radiation was the only protective factor found with probable association based on moderate quality evidence. Many associations were based on low quality evidence as assessed by GRADE, including SES (probable risk factors: neighbourhood deprivation and absence of standard medical insurance; possible protective factor: higher household income), social support (polygamy, unmarried marital status, mental stress, and refugee status risk factors) and region of origin (Asian and Oceanian origins as a protective factor), with the exception of immigrant status which was a possible protective factor based on moderate-quality evidence. Evidence did not support associations with maternal educational levels and we identified a lack of relevant evidence for religion, based on our methodology.

The certainty of evidence of included studies is challenged by many associations based largely on individual cohort studies, including for SES, polygamy, refugee status, unmarried marital status and unstable housing, ethnicity and region of origin, rural residence and distance to maternity health facility, hospital level, and exposure to sunlight, precipitation and noise. Evidence from individual cohort studies limits the generalisability of findings as the health impacts of social and socioeconomic context may differ by region. Moreover, of the included cohort studies, only one was from a low- and middle-income country, where the social determinants of health may both more variable and play a greater contributory part to the origins of pre-eclampsia. Associations with low maternal education, immigrant status, mental stress, work stress and occupational hazards, ambient temperature and air pollution were supported with evidence from reviews, though still largely from high-income countries, except for low maternal education and household air pollution. Certain determinants may have an adverse effect everywhere, such as the lack of access to adequate maternity care, poor working conditions and temperatures, while some determinants may be more dependent on context. For example, the effect of SES may vary between overall resources available in a high-income country compared to a low-income country, and experiences of polygamy may be culture-specific.

4.2. Comparison With Current Literature

We found that SES was associated with pre-eclampsia, but maternal education was not. Non-significant associations between pre-eclampsia and maternal education were reported both in sub-Saharan Africa [42] and Sweden [33], suggesting a similar relationship in high- and low- and middle-income countries. While studies have previously used maternal education as an indicator of SES, our findings suggest that this may be problematic [50, 51]. In fact, it has been demonstrated that economic indicators (e.g., wealth and family income) are most sensitive to health outcomes among women, particularly among those of reproductive age [52]. This may stem from societal gender inequities leading to women (compared with men) receiving lower income returns from a similar level of education, as well as different occupational opportunities [53]. Associations may also be driven by underlying medical risk factors, such as higher rates of overweight/obesity and early pregnancy blood pressure reported among pregnant people with lower educational backgrounds [51].

Similar to our findings, a previous systematic review about hypertensive disorders of pregnancy (HDPs) found that job strain and full body vibrations were risk factors for pre-eclampsia, although many factors were understudied and there was heterogeneity between studies in definitions and findings [54]. Of note, occupational risk factors in resource-limited settings are understudied in particular. Nevertheless, the aforementioned review included one study from Africa—a small case–control study from Nigeria—that found double the odds of pre-eclampsia among women with a stressful work environment during pregnancy (aOR 2.10; 95% CI 1.20–3.71) [55].

Although Black and African-American women are often reported to be disproportionately affected by pre-eclampsia [56–58], we did not find a significant association between pre-eclampsia and either African region of origin or non-Hispanic Black ethnicity, perhaps due to the protective effect of immigration. Immigrant populations tend to have lower rates of pre-eclampsia and other HDPs and the risk of pre-eclampsia increases by length of residence for migrant women [34, 43, 58–60]. Second, ethnicity and region of origin differences may be driven by existing medical conditions [61]. While non-Hispanic Black pregnant people initially appeared to have higher rates of pre-eclampsia in comparison to non-Hispanic White, the association was not significant after adding chronic hypertension, chronic diabetes, gestational diabetes, parity, smoking, and BMI to the statistical model [34]. These findings are consistent with a French study in which pre-pregnancy obesity mediated the heightened risk of severe pre-eclampsia among sub-Saharan African immigrants [62]. Disparities between groups may be related to how experiences of systemic racism intersect with SES, obesity, and access to care [26, 56, 57, 63–67].

While this review sought primarily to compile social risk factors for pre-eclampsia incidence, social inequities may also have a powerful impact on pre-eclampsia severity. Higher rates of preterm pre-eclampsia have been documented with refugee status [31], and higher rates of eclampsia and HELLP syndrome found with rural residency/greater distance to health facility [38, 68]. Poverty and deprivation have been associated with increased rates of eclampsia and higher blood pressure (systolic BP ≥ 160 mmHg) [69, 70] and significantly higher odds of blood transfusion ≥ 4 units and admission to intensive care among non-Hispanic Black (vs. non-Hispanic White) women diagnosed with HDP [71]. These differences may be related to access to care, which, as indicated by ANC visits, was one of the strongest social determinants we found. Across sub-Saharan Africa, lower healthcare worker densities was associated with lower likelihood of ANC, as well as reduced urine and blood pressure checks for pre-eclampsia [72]. Within a high-income country setting, differential prenatal care has been documented between African immigrant and native French populations, including blood pressure measurement and proteinuria testing procedures which may have delayed timely diagnosis of hypertension or pre-eclampsia among African immigrant groups [73]. Lack of access to standard health insurance has also been linked with more severe symptoms of pre-eclampsia [70, 74].

Physical environmental risk factors for pre-eclampsia around seasons, elevated temperatures and intense humidity, aligns with a growing body of literature on the potentially negative effects of climate change on pre-eclampsia incidence [75]. While the heterogeneity of global weather patterns complicates meta-analyses on seasonality, two reviews found an increased risk of pre-eclampsia associated with maternal exposure to heatwaves and high average temperature, potentially associated with reduced placental weight and volume [76, 77]. A review and a large time-to-event study in South Africa found that high temperatures, especially in early pregnancy (i.e., 2–5 weeks gestation), were associated with an increased risk of pre-eclampsia [77, 78]. The protective effect of sunlight exposure may be associated with synthesis of vitamin D, which is involved in the absorption of calcium, both associated with pre-eclampsia prevention [79–81].

4.3. Strengths and Limitations

To our knowledge, this is the first peer-reviewed evidence review on the social determinants of pre-eclampsia. A particular strength of our review is the systematic process of developing a model of determinants, study selection, and the use of GRADE and strength of association assessment to ascertain certainty of evidence. While our hierarchical approach to literature review systematically identified higher-quality evidence (from systematic reviews and large observational cohort studies), we accept that smaller observational studies were excluded, and this may have meant that some risk factors were not included in the final list of determinants of pre-eclampsia; as such, a broader range of determinants may have been identified a more traditional systematic review, that would have included lower-level evidence. Second, because large cohort studies were the primary type of report on which this evidence review was based, our review was limited by the availability and quality of evidence. As discussed, many reports originated from high-income countries. Some indicators, such social support and racism, may be hard to measure quantitatively, as seen by many reports having only qualitative analyses. Substantial heterogeneity in how indicators were conceptualised, such as ethnicity, SES, and seasonality, made meta-analyses difficult. A number of relevant findings were reported only as conference abstracts without further publication as manuscripts, such as maternal deprivation [69] and insurance status [70], suggesting potential publication biases within this field of study. Additionally, while systematic reviews along with other study designs included in our review were assessed using GRADE to ascertain quality of the evidence, meta-analyses typically pool raw effect estimates from individual studies without accounting for potential confounders, and this may bias outcomes. Lastly, the current review did not examine indirect associations between social determinants or between social and clinical determinants, which will be investigated in a follow-up review by the PRECISE Network.

5. Conclusion

Our hierarchical review of social determinants of pre-eclampsia supports recommendations to address climate change and strengthen occupational protection globally, as well as encourage early ANC attendance. All such determinants are potentially modifiable at the individual-level, with adequate knowledge and supportive labour policy for environmental exposures, and community awareness for early ANC attendance; clinicians should consider the modifiability of risk factors for pre-eclampsia, including inequities in SES and social support that may underly medical risk factors. Additionally, social determinants may be indicative of upstream factors (such as obesity) that increase the likelihood of clinical risk factors for pre-eclampsia, as well as its incidence and severity. While social determinants are critical to our understanding of pre-eclampsia, our review has highlighted heterogeneity in definitions, evidence gaps, and the low quality of existing evidence, particularly given that many results were derived from single large cohort studies, which presents serious limitations in terms of geographical contexts. Further high-quality research and meta-analyses on social determinants of pre-eclampsia are critically needed, especially studies in resource-limited settings.

Supplementary Material

Additional supporting information can be found online in the Supporting Information section.

figure 1
File S1
File S2
File S3

Acknowledgements

This manuscript is part of the PRECISE (PREgnancy Care Integrating translational Science, Everywhere) Network. The authors would like to express their gratitude to the PRECISE Team and Pathways to Preeclampsia patient partners for their support.

Funding

The PRECISE Network is funded by the UK Research and Innovation Grand Challenges Research Fund GROW Award scheme (MR/P027938/1). MWK is supported by the Vanier Canada Graduate Scholarship funded by the Government of Canada through the Canadian Institutes of Health Research (CIHR).

Footnotes

Author Contributions

M.V., L.A.M., P.v.D. and V.F. conceptualised the review. M.-L.W.K. and K.P. conducted the review with S.P., O.C., J.D., L.V.L. and R.B. M.-L.W.K. and K.P. wrote the first draft of the manuscript. M.V., L.A.M., P.v.D., V.F., H.B., H.D.M., M.-L.V., R.C. and T.E. reviewed all versions of the paper and contributed to the interpretation and the structure of the paper. All authors read and approved the final manuscript.

Ethics Statement

The review only utilised data from previous published studies.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that supports the findings of this study are available in the Supporting Information of this article.

References

  • 1.Magee LA, Nicolaides KH, von Dadelszen P. Preeclampsia. New England Journal of Medicine. 2022;386(19):1817–1832. doi: 10.1056/NEJMra2109523. [DOI] [PubMed] [Google Scholar]
  • 2.Wang W, Xie X, Yuan T, et al. Epidemiological Trends of Maternal Hypertensive Disorders of Pregnancy at the Global, Regional, and National Levels: A Population-Based Study. BMC Pregnancy and Childbirth. 2021;21(1):364. doi: 10.1186/s12884-021-03809-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.WHO. Social Determinants of Health. [cited 5 January 2025]. https://www.who.int/health-topics/social-determinants-of-health .
  • 4.Jones GL, Mitchell CA, Hirst JE, Anumba DOC, the Royal College of Obstetricians and Gynaecologists Understanding the Relationship Between Social Determinants of Health and Maternal Mortality. BJOG: An International Journal of Obstetrics & Gynaecology. 2022;129(7):1211–1228. doi: 10.1111/1471-0528.17044. [DOI] [PubMed] [Google Scholar]
  • 5.Wang E, Glazer KB, Howell EA, Janevic TM. Social Determinants of Pregnancy-Related Mortality and Morbidity in the United States: A Systematic Review. Obstetrics and Gynecology. 2020;135(4):896–915. doi: 10.1097/AOG.0000000000003762. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Amjad S, MacDonald I, Chambers T, et al. Social Determinants of Health and Adverse Maternal and Birth Outcomes in Adolescent Pregnancies: A Systematic Review and Meta-Analysis. Paediatric and Perinatal Epidemiology. 2019;33(1):88–99. doi: 10.1111/ppe.12529. [DOI] [PubMed] [Google Scholar]
  • 7.Weck RL, Paulose T, Flaws JA. Impact of Environmental Factors and Poverty on Pregnancy Outcomes. Clinical Obstetrics and Gynecology. 2008;51(2):349–359. doi: 10.1097/GRF.0b013e31816f276e. [DOI] [PubMed] [Google Scholar]
  • 8.Hamal M, Dieleman M, De Brouwere V, de Cock Buning T. Social Determinants of Maternal Health: A Scoping Review of Factors Influencing Maternal Mortality and Maternal Health Service Use in India. Public Health Reviews. 2020;41(1):13. doi: 10.1186/s40985-020-00125-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Hood CM, Gennuso KP, Swain GR, Catlin BB. County Health Rankings: Relationships Between Determinant Factors and Health Outcomes. American Journal of Preventive Medicine. 2016;50(2):129–135. doi: 10.1016/j.amepre.2015.08.024. [DOI] [PubMed] [Google Scholar]
  • 10.Mackenbach JP, Stirbu I, Roskam AJR, et al. Socioeconomic Inequalities in Health in 22 European Countries. New England Journal of Medicine. 2008;358(23):2468–2481. doi: 10.1056/NEJMsa0707519. [DOI] [PubMed] [Google Scholar]
  • 11.Bartsch E, Medcalf KE, Park AL, et al. Clinical Risk Factors for Pre-Eclampsia Determined in Early Pregnancy: Systematic Review and Meta-Analysis of Large Cohort Studies. British Medical Journal. 2016;353:i1753. doi: 10.1136/bmj.i1753. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Duckitt K, Harrington D. Risk Factors for Pre-Eclampsia at Antenatal Booking: Systematic Review of Controlled Studies. British Medical Journal. 2005;330(7491):565. doi: 10.1136/bmj.38380.674340.E0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Lee K, Brayboy L, Tripathi A. Pre-Eclampsia: A Scoping Review of Risk Factors and Suggestions for Future Research Direction. Regenerative Engineering and Translational Medicine. 2022;8(3):394–406. doi: 10.1007/s40883-021-00243-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Giannakou K, Evangelou E, Papatheodorou SI. Genetic and Non-Genetic Risk Factors for Pre-Eclampsia: Umbrella Review of Systematic Reviews and Meta-Analyses of Observational Studies. Ultrasound in Obstetrics & Gynecology. 2018;51(6):720–730. doi: 10.1002/uog.18959. [DOI] [PubMed] [Google Scholar]
  • 15.Townsend R, Khalil A, Premakumar Y, et al. Prediction of Pre-Eclampsia: Review of Reviews. Ultrasound in Obstetrics and Gynecology. 2019;54(1):16–27. doi: 10.1002/uog.20117. [DOI] [PubMed] [Google Scholar]
  • 16.Hiatt RA, Porco TC, Liu F, et al. A Multilevel Model of Post-menopausal Breast Cancer Incidence. Cancer Epidemiology, Biomarkers and Prevention. 2014;23(10):2078–2092. doi: 10.1158/1055-9965.EPI-14-0403. [DOI] [PubMed] [Google Scholar]
  • 17.von Dadelszen P, Poston L, et al. The PRECISE (PREgnancy Care Integrating Translational Science, Everywhere) Network’s First Protocol: Deep Phenotyping in Three Sub-Saharan African Countries. Reproductive Health. 2020;17(1):51. doi: 10.1186/s12978-020-0872-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.U.S. Department of Health and Human Services and Office of Disease Prevention and Health Promotion. Healthy People 2030. [cited 9 August 2023]. https://health.gov/healthypeople/priority-areas/social-determinants-health .
  • 19.World Health Organization. Social Determinants of Health. [cited 9 August 2023]. https://www.who.int/health-topics/social-determinants-of-health .
  • 20.Public Health Agency of Canada. Social Determinants of Health and Health Inequalities. 2001. [cited 9 August 2023]. https://www.canada.ca/en/public-health/services/health-promotion/population-health/what-determines-health.html .
  • 21.Dahlgran G, Whithead M. Policies and Strategies to Promote Equity in Health. Institute for Futures Studies; Stockholm: 1991. [Google Scholar]
  • 22.Guyatt GH, Oxman AD, Tugwell P, Knottnerus A. GRADE Guidelines: A New Series of Articles in the Journal of Clinical Epidemiology. Journal of Clinical Epidemiology. 2011;64(4):380–382. doi: 10.1016/j.jclinepi.2010.09.011. [DOI] [PubMed] [Google Scholar]
  • 23.Guyatt GH, Oxman AD, Sultan S, et al. GRADE Guidelines: 9. Rating Up the Quality of Evidence. Journal of Clinical Epidemiology. 2011;64(12):1311–1316. doi: 10.1016/j.jclinepi.2011.06.004. [DOI] [PubMed] [Google Scholar]
  • 24.Nicholls-Dempsey L, Badeghiesh A, Baghlaf H, Dahan MH. How Does High Socioeconomic Status Affect Maternal and Neonatal Pregnancy Outcomes? A Population-Based Study Among American Women. European Journal of Obstetrics, Gynecology, and Reproductive Biology X. 2023;20:100248. doi: 10.1016/j.eurox.2023.100248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Kim MK, Lee SM, Bae SH, et al. Socioeconomic Status Can Affect Pregnancy Outcomes and Complications, Even With a Universal Healthcare System. International Journal for Equity in Health. 2018;17(1):2. doi: 10.1186/s12939-017-0715-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Arechvo A, Wright A, Syngelaki A, et al. Incidence of Pre-Eclampsia: Effect of Deprivation. Ultrasound in Obstetrics & Gynecology. 2023;61(1):26–32. doi: 10.1002/uog.26084. [DOI] [PubMed] [Google Scholar]
  • 27.Lu D, Li M, Gan Y, Yu G, Zhang Q, Zhang J. Prenatal Exposure to Solar Radiation and Hypertensive Disorders of Pregnancy. BJOG: An International Journal of Obstetrics & Gynaecology. 2022;129(3):393–401. doi: 10.1111/1471-0528.16851. [DOI] [PubMed] [Google Scholar]
  • 28.Lisonkova S, Joseph KS. Incidence of Preeclampsia: Risk Factors and Outcomes Associated With Early-Versus Late-Onset Disease. American Journal of Obstetrics and Gynecology. 2013;209(6):544e1–544e12. doi: 10.1016/j.ajog.2013.08.019. [DOI] [PubMed] [Google Scholar]
  • 29.Berger-Polsky A, Daoud N, Sergienko R, Sheiner E, Shoham-Vardi I, Bilenko N. Polygamy and Birth Outcomes Among Bedouin Women of the Negev: The Contribution of Social Determinants and Pregnancy Complications. Health Care for Women International. 2020;41(1):54–74. doi: 10.1080/07399332.2019.1639708. [DOI] [PubMed] [Google Scholar]
  • 30.Weigel MM, Armijos RX. Obstetrical Complications in Venezuelan Refugee and Migrant Women: Analysis of Ecuadorian National Hospital Discharge Data, 2018–2021. Journal of Immigrant and Minority Health. 2024;26(5):830–840. doi: 10.1007/s10903-024-01600-x. [DOI] [PubMed] [Google Scholar]
  • 31.Nilsen RM, Vik ES, Rasmussen SA, et al. Preeclampsia by Maternal Reasons for Immigration: A Population-Based Study. BMC Pregnancy and Childbirth. 2018;18(1):423. doi: 10.1186/s12884-018-2034-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Pantell MS, Baer RJ, Torres JM, et al. Associations Between Unstable Housing, Obstetric Outcomes, and Perinatal Health Care Utilization. American Journal of Obstetrics & Gynecology MFM. 2019;1(4):100053. doi: 10.1016/j.ajogmf.2019.100053. [DOI] [PubMed] [Google Scholar]
  • 33.Mattsson K, Juárez S, Malmqvist E. Influence of Socio-Economic Factors and Region of Birth on the Risk of Preeclampsia in Sweden. International Journal of Environmental Research and Public Health. 2022;19(7):4080. doi: 10.3390/ijerph19074080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Boakye E, Kwapong YA, Obisesan O, et al. Nativity-Related Disparities in Preeclampsia and Cardiovascular Disease Risk Among a Racially Diverse Cohort of US Women. JAMA Network Open. 2021;4(12):e2139564. doi: 10.1001/jamanetworkopen.2021.39564. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Skröder H, Pettersson H, Albin M, et al. Occupational Exposure to Whole-Body Vibrations and Pregnancy Complications: A Nationwide Cohort Study in Sweden. Occupational and Environmental Medicine. 2020;77(10):691–698. doi: 10.1136/oemed-2020-106519. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Lissåker C, Hemmingsson T, Kjellberg K, Lindfors P, Selander J. Occupational Stress and Pregnancy-Related Hypertension and Diabetes: Results From a Nationwide Prospective Cohort. Scandinavian Journal of Work, Environment & Health. 2022;48(3):239–247. doi: 10.5271/sjweh.4004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Bujimalla PV, Kenne KA, Steffen HA, et al. Effects of Rurality and Distance to Care on Perinatal Outcomes Over a 1-Year Period During the COVID-19 Pandemic. Journal of Rural Health. 2024;40(3):520–530. doi: 10.1111/jrh.12820. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Lisonkova S, Haslam MD, Dahlgren L, Chen I, Synnes AR, Lim KI. Maternal Morbidity and Perinatal Outcomes Among Women in Rural Versus Urban Areas. CMAJ. 2016;188(17–18):E456–E465. doi: 10.1503/cmaj.151382. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Hastie CE, Mackay DF, Clemens TL, et al. Antenatal Exposure to UV-B Radiation and Preeclampsia: A Retrospective Cohort Study. Journal of the American Heart Association. 2021;10(13):e020246. doi: 10.1161/JAHA.120.020246. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Mutabazi L, Bazzett-Matabele L, Small M, Ntasumbumuyange D, Rulisa S, Magriples U. Seasonal Variation in Preeclampsia and Eclampsia in Kigali. Rwanda Medical Journal. 2020;77(1):1–5. [Google Scholar]
  • 41.Auger N, Duplaix M, Bilodeau-Bertrand M, Lo E, Smargiassi A. Environmental Noise Pollution and Risk of Preeclampsia. Environmental Pollution. 2018;239:599–606. doi: 10.1016/j.envpol.2018.04.060. [DOI] [PubMed] [Google Scholar]
  • 42.Meazaw MW, Chojenta C, Muluneh MD, Loxton D. Systematic and Meta-Analysis of Factors Associated With Preeclampsia and Eclampsia in Sub-Saharan Africa. PLoS One. 2020;15(8):e0237600. doi: 10.1371/journal.pone.0237600. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Behboudi-Gandevani S, Bidhendi-Yarandi R, Panahi MH, et al. Adverse Pregnancy Outcomes and International Immigration Status: A Systematic Review and Meta-Analysis. Annals of Global Health. 2022;88(1):44. doi: 10.5334/aogh.3591. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Cai C, Vandermeer B, Khurana R, et al. The Impact of Occupational Shift Work and Working Hours During Pregnancy on Health Outcomes: A Systematic Review and Meta-Analysis. American Journal of Obstetrics and Gynecology. 2019;221(6):563–576. doi: 10.1016/j.ajog.2019.06.051. [DOI] [PubMed] [Google Scholar]
  • 45.Cai C, Vandermeer B, Khurana R, et al. The Impact of Occupational Activities During Pregnancy on Pregnancy Outcomes: A Systematic Review and Metaanalysis. American Journal of Obstetrics and Gynecology. 2020;222(3):224–238. doi: 10.1016/j.ajog.2019.08.059. [DOI] [PubMed] [Google Scholar]
  • 46.Wang Z, Qian R, Xiang W, et al. Association Between Noise Exposure During Pregnancy and Pregnancy Complications: A Meta-Analysis. Frontiers in Psychology. 2022;13:1026996. doi: 10.3389/fpsyg.2022.1026996. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Mao Y, Gao Q, Zhang Y, et al. Associations Between Extreme Temperature Exposure and Hypertensive Disorders in Pregnancy: A Systematic Review and Meta-Analysis. Hypertension in Pregnancy. 2023;42(1):2288586. doi: 10.1080/10641955.2023.2288586. [DOI] [PubMed] [Google Scholar]
  • 48.Zhu J, Chen J, Wang K, et al. Exposure to Ambient Black Carbon and Particulate Matter During Pregnancy in Associations With Risk of Pre-Eclampsia: A Meta-Analysis Based on Population-Based Studies. Environmental Pollution. 2024;343:123230. doi: 10.1016/j.envpol.2023.123230. [DOI] [PubMed] [Google Scholar]
  • 49.Adekoya A, Tyagi SK, Duru CN, Satia I, Paudyal V, Kurmi OP. Effects of Household Air Pollution (HAP) on Cardiovascular Diseases in Low- and Middle-Income Countries (LMICs): A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health. 2022;19(15):9298. doi: 10.3390/ijerph19159298. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Heshmati A, Mishra G, Koupil I. Childhood and Adulthood Socio-Economic Position and Hypertensive Disorders in Pregnancy: The Uppsala Birth Cohort Multigenerational Study. Journal of Epidemiology and Community Health. 2013;67(11):939–946. doi: 10.1136/jech-2012-202149. [DOI] [PubMed] [Google Scholar]
  • 51.Silva L, Coolman M, Steegers E, et al. Maternal Educational Level and Risk of Gestational Hypertension: The Generation R Study. Journal of Human Hypertension. 2008;22(7):483–492. doi: 10.1038/jhh.2008.22. [DOI] [PubMed] [Google Scholar]
  • 52.Duncan GJ, Daly MC, McDonough P, Williams DR. Optimal Indicators of Socioeconomic Status for Health Research. American Journal of Public Health. 2002;92(7):1151–1157. doi: 10.2105/ajph.92.7.1151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.McDonough P, Williams DR, House JS, Duncan GJ. Gender and the Socioeconomic Gradient in Mortality. Journal of Health and Social Behavior. 1999;40(1):17–31. [PubMed] [Google Scholar]
  • 54.Spadarella E, Leso V, Fontana L, Giordano A, Iavicoli I. Occupational Risk Factors and Hypertensive Disorders in Pregnancy: A Systematic Review. International Journal of Environmental Research and Public Health. 2021;18(16):8277. doi: 10.3390/ijerph18168277. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Anorlu RI, Iwuala NC, Odum CU. Risk Factors for Pre-Eclampsia in Lagos, Nigeria. Australian and New Zealand Journal of Obstetrics and Gynaecology. 2005;45(4):278–282. doi: 10.1111/j.1479-828X.2005.00411.x. [DOI] [PubMed] [Google Scholar]
  • 56.Fasanya HO, Hsiao CJ, Armstrong-Sylvester KR, Beal SG. A Critical Review on the Use of Race in Understanding Racial Disparities in Preeclampsia. Journal of Applied Laboratory Medicine. 2021;6(1):247–256. doi: 10.1093/jalm/jfaa149. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Johnson JD, Louis JM. Does Race or Ethnicity Play a Role in the Origin, Pathophysiology, and Outcomes of Preeclampsia? An Expert Review of the Literature. American Journal of Obstetrics and Gynecology. 2022;226(2S):S876–S885. doi: 10.1016/j.ajog.2020.07.038. [DOI] [PubMed] [Google Scholar]
  • 58.Burger RJ, Delagrange H, van Valkengoed IGM, et al. Hypertensive Disorders of Pregnancy and Cardiovascular Disease Risk Across Races and Ethnicities: A Review. Frontiers in Cardiovascular Medicine. 2022;9:933822. doi: 10.3389/fcvm.2022.933822. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Naimy Z, Grytten J, Monkerud L, Eskild A. The Prevalence of Pre-Eclampsia in Migrant Relative to Native Norwegian Women: A Population-Based Study. BJOG: An International Journal of Obstetrics and Gynaecology. 2015;122(6):859–865. doi: 10.1111/1471-0528.12978. [DOI] [PubMed] [Google Scholar]
  • 60.Mogos MF, Salinas-Miranda AA, Salemi JL, Medina IM, Salihu HM. Pregnancy-Related Hypertensive Disorders and Immigrant Status: A Systematic Review and Meta-Analysis of Epidemiological Studies. Journal of Immigrant and Minority Health. 2017;19(6):1488–1497. doi: 10.1007/s10903-016-0410-6. [DOI] [PubMed] [Google Scholar]
  • 61.Elawad T, Scott G, Bone JN, et al. Risk Factors for Pre-Eclampsia in Clinical Practice Guidelines: Comparison With the Evidence. BJOG: An International Journal of Obstetrics & Gynaecology. 2024;131(1):46–62. doi: 10.1111/1471-0528.17320. [DOI] [PubMed] [Google Scholar]
  • 62.Siddiqui A, Deneux-Tharaux C, Luton D, et al. Maternal Obesity and Severe Pre-Eclampsia Among Immigrant Women: A Mediation Analysis. Scientific Reports. 2020;10(1):5215. doi: 10.1038/s41598-020-62032-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.White K, Lawrence JA, Tchangalova N, Huang SJ, Cummings JL. Socially-Assigned Race and Health: A Scoping Review With Global Implications for Population Health Equity. International Journal for Equity in Health. 2020;19(1):25. doi: 10.1186/s12939-020-1137-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Crear-Perry J, Correa-de-Araujo R, Lewis Johnson T, Mc-Lemore MR, Neilson E, Wallace M. Social and Structural Determinants of Health Inequities in Maternal Health. Journal of Women’s Health. 2021;30(2):230–235. doi: 10.1089/jwh.2020.8882. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Bryant AS, Worjoloh A, Caughey AB, Washington AE. Racial/Ethnic Disparities in Obstetric Outcomes and Care: Prevalence and Determinants. American Journal of Obstetrics and Gynecology. 2010;202(4):335–343. doi: 10.1016/j.ajog.2009.10.864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Gadson A, Akpovi E, Mehta PK. Exploring the Social Determinants of Racial/Ethnic Disparities in Prenatal Care Utilization and Maternal Outcome. Seminars in Perinatology. 2017;41(5):308–317. doi: 10.1053/j.semperi.2017.04.008. [DOI] [PubMed] [Google Scholar]
  • 67.Johnson JD, Green CA, Vladutiu C, Manuck TA. 521 the Legacy of Racial Inequality and Risk of Modern-Day Placentally Mediated Adverse Pregnancy Outcomes. American Journal of Obstetrics and Gynecology. 2021;224(2):S329–S330. [Google Scholar]
  • 68.Engjom HM, Morken NH, Høydahl E, Norheim OF, Klungsøyr K. Risk of Eclampsia or HELLP-Syndrome by Institution Availability and Place of Delivery - A Population-Based Cohort Study. Pregnancy Hypertension. 2018;14:1–8. doi: 10.1016/j.preghy.2018.05.005. [DOI] [PubMed] [Google Scholar]
  • 69.Azria E, Estellat C, Alfaiate T, et al. 152: Impact of Maternal Social Deprivation on Maternal and Perinatal Severe Adverse Outcomes: The PreCARE Cohort Study. American Journal of Obstetrics and Gynecology. 2016;214(1):S99 [Google Scholar]
  • 70.Greiner KJ, Speranza RJ, Rincon M, Burwick RM. 640: Impact of Insurance Status on Outcomes in Women With Hypertensive Disorders in Pregnancy. American Journal of Obstetrics and Gynecology. 2018;218:S381–S382. [Google Scholar]
  • 71.Palatnik A, McGee P, Bailit JL, et al. The Association of Race and Ethnicity With Severe Maternal Morbidity Among Individuals Diagnosed With Hypertensive Disorders of Pregnancy. American Journal of Perinatology. 2023;40(5):453–460. doi: 10.1055/a-1886-5404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Rosser JI, Aluri KZ, Kempinsky A, Richardson S, Bendavid E. The Effect of Healthcare Worker Density on Maternal Health Service Utilization in Sub-Saharan Africa. American Journal of Tropical Medicine and Hygiene. 2022;106(3):939–944. doi: 10.4269/ajtmh.21-0727. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Sauvegrain P, Azria E, Chiesa-Dubruille C, Deneux-Tharaux C. Exploring the Hypothesis of Differential Care for African Immigrant and Native Women in France With Hypertensive Disorders During Pregnancy: A Qualitative Study. BJOG: An International Journal of Obstetrics and Gynaecology. 2017;124(12):1858–1865. doi: 10.1111/1471-0528.14658. [DOI] [PubMed] [Google Scholar]
  • 74.Haelterman E, Qvist R, Barlow P, Alexander S. Social Deprivation and Poor Access to Care as Risk Factors for Severe Pre-Eclampsia. European Journal of Obstetrics & Gynecology and Reproductive Biology. 2003;111(1):25–32. doi: 10.1016/s0301-2115(03)00161-1. [DOI] [PubMed] [Google Scholar]
  • 75.Poursafa P, Keikha M, Kelishadi R. Systematic Review on Adverse Birth Outcomes of Climate Change. Journal of Research in Medical Sciences: The Official Journal of Isfahan University of Medical Sciences. 2015;20(4):397–402. [PMC free article] [PubMed] [Google Scholar]
  • 76.Dalugoda Y, Kuppa J, Phung H, Rutherford S, Phung D. Effect of Elevated Ambient Temperature on Maternal, Foetal, and Neonatal Outcomes: A Scoping Review. International Journal of Environmental Research and Public Health. 2022;19(3):1771. doi: 10.3390/ijerph19031771. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Beltran AJ, Wu J, Laurent O. Associations of Meteorology With Adverse Pregnancy Outcomes: A Systematic Review of Preeclampsia, Preterm Birth and Birth Weight. International Journal of Environmental Research and Public Health. 2014;11(1):91–172. doi: 10.3390/ijerph110100091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Part C, le Roux J, Chersich M, et al. Ambient Temperature During Pregnancy and Risk of Maternal Hypertensive Disorders: A Time-To-Event Study in Johannesburg, South Africa. Environmental Research. 2022;212:113596. doi: 10.1016/j.envres.2022.113596. [DOI] [PubMed] [Google Scholar]
  • 79.Kinshella MLW, Omar S, Scherbinsky K, et al. Effects of Maternal Nutritional Supplements and Dietary Interventions on Placental Complications: An Umbrella Review, Meta-Analysis and Evidence Map. Nutrients. 2021;13(2):472. doi: 10.3390/nu13020472. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Kinshella MLW, Omar S, Scherbinsky K, et al. Maternal Nutritional Risk Factors for Pre-Eclampsia Incidence: Findings From a Narrative Scoping Review. Reproductive Health. 2022;19(1):1–13. doi: 10.1186/s12978-022-01485-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Woo Kinshella ML, Sarr C, Sandhu A, et al. Calcium for Pre-Eclampsia Prevention: A Systematic Review and Network Meta-Analysis to Guide Personalised Antenatal Care. BJOG: An International Journal of Obstetrics & Gynaecology. 2022;129(11):1833–1843. doi: 10.1111/1471-0528.17222. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

figure 1
EMS208247-supplement-figure_1.docx (115.6KB, docx)
File S1
EMS208247-supplement-File_S1.xlsx (25.5KB, xlsx)
File S2
EMS208247-supplement-File_S2.xlsx (24.1KB, xlsx)
File S3
EMS208247-supplement-File_S3.xlsx (25KB, xlsx)

Data Availability Statement

The data that supports the findings of this study are available in the Supporting Information of this article.

RESOURCES