The association between state-level negative racial sentiment and maternal hypertension in the US from 2016 to 2021: An observational study using Twitter data

Laura B. Drew; Junaid Merchant; Marie E. Thoma; Heran Mane; Xiaohe Yue; Thu T. Nguyen

doi:10.1371/journal.pone.0346564

Abstract

Background

Racial disparities in maternal hypertension (i.e., prepregnancy and gestational) due to experiences of racism may contribute to ongoing racial disparities in US birth outcomes, including low birth weight and preterm birth. Despite evidence linking racism and adverse birth outcomes, no studies have examined the plausible association between area-level negative racial sentiment and disparities in maternal hypertension. To address this gap, we used 2016–2021 US birth certificate data to examine the associations between state-level Twitter-derived negative sentiments toward racial/ethnic minorities and maternal hypertension. We further examined if these associations increased during periods of heightened racial discrimination.

Methods

We used 2016–2021 US natality data with geographic identifiers for pregnancy data for singleton births (n = 22,618,566) and a random sample of 1% of publicly available tweets from 2016–2021 (n = 56,400,097) using Twitter’s Academic Application Programming Interface. We calculated annual state-level negative racial sentiment by averaging sentiment scores of all posts referencing a racial category. These scores, divided into quartiles, included five sentiment measures: one toward all racially minoritized groups and four race-specific (Black, Asian, Latinx, and White). We merged data for each year and used log-binomial regression to estimate prevalence rate ratios (PRRs) for prepregnancy and gestational hypertension, adjusting for individual maternal characteristics and state-level demographics. We additionally stratified analyses into time periods before and during the COVID-19 pandemic and Black Lives Matter movement (2016–2019 and 2020–2021, respectively).

Results

In our sample, 2.2% of individuals had prepregnancy hypertension and 7.7% had gestational hypertension. From 2016−2021, the prevalence of both types of maternal hypertension increased for individuals across all racial and ethnic groups. Individuals in states with the highest quartile of negative racial minority sentiment had a 36% higher (95% CI: 1%−83%) prevalence of prepregnancy hypertension and a 20% higher (95% CI: 0%−45%) prevalence of gestational hypertension compared to those in the lowest quartile. In 2020 and 2021, the prevalence of prepregnancy hypertension among individuals in racially minoritized groups was 51% greater (95% CI:12%−103%) in the 4^th quartile compared to the 1^st quartile and showed a higher magnitude of association compared to 2016−2019. In 2020−2021, among Black individuals, those in the highest quartile of anti-Black sentiment had a 31% higher (95% CI: 2%−69%) prevalence of prepregnancy hypertension, and the 19% increase in prepregnancy hypertension among Asian individuals in the highest quartile of anti-Asian sentiment was borderline significant (95% CI: 0%−44%). Patterns for gestational hypertension were not consistent across time and when examining race-specific sentiment.

Conclusion

Higher levels of state-level negative racial sentiment are associated with increased prevalence of prepregnancy and gestational hypertension among racially minoritized groups, and the association with prepregnancy hypertension appears to strengthen during periods of heightened racial tension and discrimination. These findings highlight the role of area-level racism as a contributor to maternal health disparities.

Citation: Drew LB, Merchant J, Thoma ME, Mane H, Yue X, Nguyen TT (2026) The association between state-level negative racial sentiment and maternal hypertension in the US from 2016 to 2021: An observational study using Twitter data. PLoS One 21(4): e0346564. https://doi.org/10.1371/journal.pone.0346564

Editor: Kwang-Sig Lee, Korea University - Seoul Campus: Korea University, KOREA, REPUBLIC OF

Received: July 11, 2025; Accepted: March 19, 2026; Published: April 29, 2026

Copyright: © 2026 Drew et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The Twitter datasets generated or analyzed during this study are not publicly available due to the discontinuation of the tools used to collect them. Twitter data were collected using Twitter’s application programming interface for academic research (now discontinued). More information on applying for access can be found from X.(71,72) Additionally, the manuscript uses restricted US natality files with geographic identifiers. The natality files were obtained after submitting a research proposal to and obtaining approval for data access from the National Center for Health Statistics. More information on applying for natality file data access can be found from the Centers for Disease Control and Prevention.(73).

Funding: Research in this publication was supported by the National Institute on Minority Health and Health Disparities (https://www.nimhd.nih.gov/; R01MD015716; TTN). The content is solely the responsibility of the authors and does not necessarily represent official views of the National Institutes of Health. The funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In the United States, Black women have higher rates of maternal morbidity and mortality as well as adverse birth outcomes, including low birth weight (LBW), preterm birth (PTB), and infant mortality compared to White women [1–8]. While most studies focus on risk and protective factors during pregnancy as the causes of these Black-White disparities, individual-level factors do not sufficiently explain why racial disparities in maternal health and birth outcomes persist [9]. Increasing evidence posits that racism is a root cause of racial health disparities in the U.S [9–11]. Hypertension preceding pregnancy (i.e., prepregnancy/chronic hypertension) and gestational hypertension are well-established risk factors for adverse birth outcomes, including low birth weight (LBW), very low birth weight, (VLBW), and preterm birth (PTB) [12,13]. Therefore, it is plausible that racial disparities in maternal hypertension—due to experiences of racism—may contribute to the persistent racial disparities in birth outcomes in the U.S.

Racism functions across many dimensions, including internalized, personally mediated (i.e., individual or interpersonal), and institutional/structural [14], and these types of racism can negatively impact one’s health. Internalized racism is when members of the stigmatized races accept the negative messages that they receive from others outside their race about their abilities and worth [14]. Personally mediated racism encompasses experiences of prejudice and discrimination enacted between individuals, both intentional and unintentional, and institutionalized/structural racism pertains to differential access to resources, such as education, safe housing, and medical facilities [14]. In addition to these three dimensions of racism, cultural racism can contribute to racial inequities in health [15]. Cultural racism is discriminatory cultural messaging that communicates and supports the belief that racial and ethnic groups have different statuses and rights, while systematically oppressing those who are non-White and reinforcing the belief in White cultural superiority [15]. Cultural racism is expressed through media, stereotyping, norms, and beliefs within society and its institutions [16], and it is strengthened through policies and practices within and across institutions that further sustain health inequities [17,18]. Collectively, these forms of racism maintain and underpin a hierarchical system of oppression and discrimination that contributes to racial disparities in the US [19]. From a life course perspective, addressing these overlapping forms of racism is critical; the cumulative impact of racial discrimination over time, including prior to pregnancy, can significantly affect maternal health, making preconception care and early intervention key strategies in reducing adverse birth outcomes [9].

Increasing evidence suggests racial bias and discrimination may contribute to the persistent disparities in LBW and PTB [10,11,20–22]. However, experiences with discrimination and racism are usually assessed at the individual level by self-reports, and these measures are subject to limitations including social desirability and self-censorship [23]. Additionally, they may not accurately account for the complex experience of racism [24]. Thus, more research is needed to investigate how experiences of other dimensions of racism can impact health and highlight areas that can be targeted for intervention. Measures of the social climate in particular locations via social media data, like Twitter (now known as X), have been shown as a proxy and/or complimentary measure for experiences of discrimination and racism in the U.S. at the state and county level [10,11,25–30]. Previous research assessing Twitter-derived negative racial sentiment found an increased incidence of LBW and PTB among Black and Latinx mothers who lived in states with high negative sentiment toward their respective races [11].

Our understanding of the vascular processes that mediate the relationship between social adversity experiences before and during pregnancy and adverse birth outcomes is still in development [31]. However, clinical studies have demonstrated that exposure to racial stressors can trigger physiological responses linked to hypertension, particularly among racial and ethnic minorities [32–34], and numerous lines of evidence indicate a relationship between racism and cardiovascular responses and hypertension [24,35–38]. Chronic stress during pregnancy can affect maternal cardiovascular, inflammatory, and neuroendocrine mechanisms, which can negatively impact infants’ birthweight and initiate preterm birth [31,39,40]. In addition, studies have shown socio-historical events such as police violence, the Black Lives Matter movement, and the COVID-19 pandemic, disproportionately increased stress and hypertension among racial minorities [41,42]. Similarly, adverse birth outcomes among non-white and immigrant mothers were observed following the 2016 US presidential election [43,44].

Despite the well-documented links between racism and adverse birth outcomes, no studies have examined the association between area-level negative racial sentiment and disparities in maternal hypertension (prepregnancy and gestational), which contributes to approximately 10% and 8% of the Black-White disparity in PTB and very preterm birth (VPTB), respectively [12]. To address this gap, this study utilizes 2016−2021 US birth certificate data to examine the associations between state-level Twitter-derived negative sentiments toward racial/ethnic minorities and maternal hypertension (i.e., prepregnancy and gestational hypertension). Our analyses further disaggregate this relationship by comparing the periods before (2016−2019) and during (2020−2021) the COVID-19 pandemic and Black Lives Matter demonstrations, which were periods of heightened racial discrimination toward Asian and Black communities [30,45–47].

Methods

Twitter data

A random sample of 1% of publicly available tweets from 2016–2021 was collected using Twitter’s Academic Application Programming Interface. Like Nguyen et al., [11] we restricted analyses to tweets that included a unique “tweet id,” were in English, originated from the United States, and used one or more race-related keywords, which were developed based on racial and ethnic terms from the US Census, prior studies that examined race-related online conversations [48], and a web-based database of racial slurs [49]. We removed duplicate tweets based on their “tweet_id,” but included retweets and quoted tweets as these account for nearly half of Twitter users’ posts [50]. The final analytic sample included 56,400,097 tweets referencing different racial categories from 3,699,646 users.

Sentiment analysis

A support vector machine (SVM) (i.e., a supervised machine learning model) assessed the sentiment of each tweet. A full description of our model has been previously described [10,11]. The training data included 6481 manually labeled tweets by our research group and labeled tweets from Sentiment140 (n = 498), Kaggle (n = 7086), and Sanders (n = 5113), which are publicly available training datasets for sentiment analysis. Five-fold cross-validation was conducted to assess model performance, which achieved a high level of accuracy for negative sentiment classification (91%) and a high F1 score (84%). We measured accuracy as the number of posts with the correct prediction over the total number of tweets in the testing dataset. The F1 score measure balances positive predicted value and recall sensitivity (i.e., precision). Therefore, a high F1 score suggests the model is robust in predicting posts labeled as 1 (i.e., negative). Finally, we used the trained SVM model to identify tweets based on overall emotional tone, which could include an unfavorable opinion, hostile language, or prejudice (which could be positive or negative) by classifying each tweet that mentioned race for negative sentiment (i.e., binary classification; 1 = negative, 0 = not negative). Our state-level negative racial sentiment measures were calculated by averaging the sentiment scores of all the posts referencing a racial category posted within each state (per year), such that negative racial sentiment reflects the proportion of posts referencing a group that are classified as negative. We used state-level negative racial sentiment quartiles in our statistical models examining the association between maternal hypertension and negative racial sentiment. The code for data collection and sentiment analysis has previously been published [11,51]. We used five negative racial sentiment measures – one global measure of negative sentiment toward all racially minoritized groups and four race-specific negative sentiment measures toward individual racial/ethnic groups (Black, Asian, Latinx, and White).

Individual-level pregnancy data

2016−2021 pregnancy data was obtained from US natality files (i.e., US Birth Certificate data files) with geographic identifiers. We selected this date range because all states began using the revised birth certificate in 2016, and we wanted to examine the relationship before and during the COVID-19 pandemic. We restricted analyses to singleton births due to the increased risk of hypertensive disorders among multifetal gestations [52,53]. The primary outcomes were prepregnancy and gestational hypertension. If neither prepregnancy hypertension nor gestational hypertension were checked, but eclampsia was checked, they were considered as having gestational hypertension. On the birth certificate data file, prepregnancy hypertension is defined as hypertension diagnosis prior to the onset of the current pregnancy, and does not include gestational/pregnancy-induced hypertension, while gestational hypertension is defined as hypertension diagnosis in the current pregnancy, including pregnancy-induced hypertension and preeclampsia, but the type of gestational hypertension is not specified [54]. Although the birth certificate data represents 100% of all registered live births in the US [55], hypertension is subject to underreporting [56,57].

Demographic characteristics and covariates

Based on the literature, we examined variables that could confound the relationship between area-level racial sentiment and maternal hypertension. Individual-level demographic characteristics and potential covariates included maternal age (years), race (non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Asian, non-Hispanic Native Hawaiian or other Pacific Islander, non-Hispanic American Indian Alaskan Native, non-Hispanic Multiple Races/Unknown/Other), education (less than high school, high school, some college, bachelor’s degree or higher), nativity (US-born or born outside of US), marital status (married, unmarried with paternity acknowledgment, or unmarried without paternity acknowledgment), infant sex (male or female), parity (first born or second born/higher), BMI (underweight, normal, overweight, obese), timing of prenatal care initiation (first trimester, second trimester, and third trimester or none), smoking during pregnancy (yes, no), and complete smoking history (never, before pregnancy only, during pregnancy only, or both before and during pregnancy). We additionally adjusted for the following state-level characteristics, which were retrieved from the American Community Survey [58]: 1) proportion of non-Hispanic Black and Latinx individuals, 2) population density (per square mile), and 3) economic disadvantage (standardized factor score), which summarizes the state-level percentage of a number of characteristics (unemployed, some college education, high school diploma, children in poverty, single parent household, and median household income) to account for compositional differences in demographic and economic characteristics at the state level. The economic disadvantage factor score has been previously validated and used elsewhere [11,59].

Statistical analyses

We merged the two data files (i.e., state-level negative racial sentiment data with birth data) by state and year and coded negative racial sentiment into quartiles to assess associations with birth outcomes. We calculated separate prevalence rate ratios (PRRs) for prepregnancy and gestational hypertension using log-binomial regression models. In our main models, the key independent variable was state-level negative racial sentiment, and we adjusted for individual maternal characteristics (age, race, nativity, and education) and state-level demographics (proportion of non-Hispanic Black and Latinx individuals, population density (per square mile), and economic disadvantage factor score). We conducted complete case analysis with <5% of the sample missing data on variables of interest and assessed statistical significance at p < 0.05. To examine how these associations might have varied over time, we stratified the analyses into time periods before and during the COVID-19 pandemic and Black Lives Matter movement (2016–2019 and 2020–2021, respectively). Our overall model investigated the relationship between state-level sentiment toward all racially minoritized groups and prepregnancy and gestational hypertension among the full sample, among pregnant people in racially minoritized groups, and White pregnant people separately. In our race-specific model, we individually explored the relationship between state-level negative sentiment toward specific racial groups (Black, Asian, Latinx, and White) and prepregnancy and gestational hypertension among pregnant people in each racial/ethnic group (Black, Asian, Latinx, and White).

To assess if additional factors influenced our findings, we conducted multiple sensitivity analyses. We stratified our main models by parity (primiparous and parous) and tested for an interaction with parity to assess if parity moderated the relationship between area-level racial sentiment and maternal hypertension. Because we could not differentiate the temporal relationship between other individual-level health risks and whether they operated as mediators or confounders in the association between racial sentiment and maternal hypertension, we further adjusted for these potential confounders separately: 1) BMI, 2) timing of prenatal care initiation, 3) smoking during pregnancy, and 4) complete smoking history. All statistical analyses were performed using Stata MP 18 [60]. We also conducted E-value analyses to assess the potential of unmeasured confounding [61]. The data collection and analysis methods for this study complied with the terms and conditions for the source of data.

Ethics statement

This study was exempt upon review by the University of Maryland College Park Institutional Review Board (1797788−1).

Results and discussion

In our sample (N = 22,618,566), 51.5% of births were to non-Hispanic White individuals, 23.6% were to Hispanic individuals, and 14.4% were to non-Hispanic Black individuals (Table 1). Most individuals were born in the US (77.5%), completed college or more (41.7%), had a second or higher-order birth (62.0%), and initiated prenatal care during the first trimester (77.6%). Overall, 2.2% of the sample had prepregnancy hypertension and 7.7% had gestational hypertension. From 2016–2021, the proportion of negative sentiment tweets toward all minority groups slightly increased and then decreased, overall resulting in a small change from 40.61% in 2016 to 39.93% in 2021 (Fig 1). Negative sentiment tweets toward Black individuals increased over time, but sharply decreased in May-July of 2020. The proportion of negative sentiment tweets referencing Asian individuals increased from 2016–2021 with peaks during March of 2020 and 2021. Prepregnancy and gestational hypertension increased among all pregnant people in every racial and ethnic group (White, Black, Latinx, and Asian) from 2016–2021 (Fig 2).

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Table 1. Demographic characteristics, covariates, and outcomes of mothers giving birth from 2016-2021 (overall and by quartiles).

https://doi.org/10.1371/journal.pone.0346564.t001

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Fig 1. Temporal trends in the proportion of tweets from 2016-2020 that were negative toward all minority groups and individual racial and ethnic groups (White, Black, Latinx, and Asian).

https://doi.org/10.1371/journal.pone.0346564.g001

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Fig 2. Temporal trends in prepregnancy and gestational hypertension from 2016-2021 among all pregnant mothers by racial and ethnic group (White, Black, Latinx, and Asian) based on US Birth Certificate Data.

https://doi.org/10.1371/journal.pone.0346564.g002

From 2016−2021 and after adjustment for potential confounders, individuals from minoritized groups in states with higher negative racial minority sentiment (quartiles 2−4) (i.e., tweets referencing racial and ethnic minoritized groups) were generally associated with higher risk of prepregnancy and gestational hypertension compared to those in quartile 1. In particular, those from states with the highest (4^th) quartile for negative racial minority sentiment had a 36% higher (95% CI: 1%−83%) prevalence of prepregnancy hypertension and a 20% higher (95% CI: 0%−45%) prevalence of gestational hypertension (Table 2). In 2020 and 2021, among racial minority groups, similar patterns held with all higher quartiles of negative racial minority sentiment associated with a significantly higher prevalence of prepregnancy hypertension among individuals in racially minoritized groups relative to quartile 1 and showed a higher magnitude of association compared with 2016−2019 data years. In particular, the prevalence of prepregnancy hypertension in 2020−2021 was 51% greater (95% CI: 12%−103%) among the 4th quartile compared with the 1st quartile, while the 4^th quartile of negative racial sentiment towards racial/ethnic minorities was only 1.23 times greater in 2016−2019 (95% CI: 0.88–1.73). From 2020−2021, there was an association between negative racial minority sentiment and maternal hypertension (prepregnancy and gestational) among White birthing people when comparing the highest quartile of negative racial sentiment to the 1st quartile (aPRR = 1.32, 95% CI:1.03–1.69 and aPRR = 1.18, 95% CI: 1.00–1.39, respectively) (Table 2).

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Table 2. Unadjusted and adjusted associations using prevalence rate ratios (PRRs) between state-level negative racial sentiment toward minoritized groups and hypertension type by pregnant individual’s race (all, racially minoritized groups, White).

https://doi.org/10.1371/journal.pone.0346564.t002

From 2016−2021, negative Black and Asian sentiments were associated with a significantly higher prevalence of prepregnancy hypertension among Black and Asian individuals in the highest quartile (q4) compared to the lowest quartile (Table 3). After stratifying into two time periods, this was not significant for Asian individuals from 2016−2019, but remained borderline significant in 2020−2021 with a higher magnitude of association (aPRR = 1.19, 95% CI: 0.99–1.44). Black individuals in the highest quartile of negative Black sentiment experienced a 31% (95% CI 2%−69%) higher prevalence in prepregnancy hypertension in 2020−2021. Other racial minority groups did not demonstrate consistent patterns or statistically significant associations with gestational hypertension, but gestational hypertension was higher among White individuals in the 4^th quartile of negative white sentiment in 2020−2021 (aPRR = 1.20, 95% CI: 1.02–1.20), compared to the lowest quartile. Sensitivity analyses did not result in significant changes to the models (Supplemental Tables 1 and 2 in S1 and S2 Tables), but did suggest the observed associations could be susceptible to modest, unmeasured confounding (S3 Table).

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Table 3. Adjusted associations using prevalence rate ratios (PRRs) between state-level negative sentiment toward specific racial groups (Asian, Black, Latinx, and White) and hypertension type by pregnant individual’s race (Black, Asian, Latinx, White).

https://doi.org/10.1371/journal.pone.0346564.t003

In this large, national study, we found that residence in a state with a higher level of negative racial sentiment on Twitter was associated with an increased prevalence of maternal hypertension, particularly prepregnancy hypertension among racially minoritized groups. This association was markedly stronger during the period of heightened racial tension in 2020–2021. Our findings provide evidence that negative racial sentiment contributes to maternal health disparities in prepregnancy and gestational hypertension among racially minoritized groups and may be heightened by distressing external events.

Notably, in states with the highest levels of negative racial minority sentiment, prepregnancy and gestational hypertension were higher among all populations during the pandemic – with higher magnitudes of association for prepregnancy hypertension. However, patterns for gestational hypertension were not consistent across time and when examining race-specific negative sentiment. This observation could be partially attributed to the differing time frames between the two conditions; gestational hypertension develops over a shorter period usually during the second half of pregnancy, while prepregnancy hypertension encompasses a longer time period and is more closely tied to long-term factors and experiences, which is aligned with a life course perspective.

Throughout the study period, negative racial sentiment on Twitter toward Black individuals persisted and remained higher than negative racial sentiment toward Latinx and Asian individuals, and the association between high negative Black sentiment and increased prevalence of prepregnancy hypertension among this group persisted over time. There was a brief dip in negative racial sentiment on Twitter toward Black individuals when the Black Lives Matter movement gained momentum in the summer of 2020. However, this time period was also marked by race-related stressors from counter-movements, such as All Lives Matter (ALM), White Lives Matter (WLM), and Blue Lives Matter (Blue LM), which were associated with higher levels of implicit racism against Black individuals and subsequent rates of obesity [47]. From 2020−2021, the prevalence of prepregnancy hypertension was higher (19%) among Asian individuals in states with the highest level of negative sentiment toward Asian individuals, compared to states with the lowest levels of these sentiments. Rising anti-Asian discrimination during the COVID-19 pandemic, including discriminatory rhetoric like “Chinese virus” and “Kung Flu” [30,62,63], has been associated with increased stress, hate crimes, and negative health outcomes for Asian populations [27,30,45,64,65]. The peaks of negative racial sentiment in tweets during March of 2020 and 2021 align with the beginning of the COVID-19 pandemic and the Atlanta Spa shootings, which were periods of increased anti-Asian discourse and discrimination [27,30,64]. Collectively, these findings suggest that rising racial hostility toward Black and Asian communities during these two years could have contributed to increased prepregnancy hypertension among these groups. The overall societal stress response to heightened racial animus during this period may also explain why the prevalence of prepregnancy hypertension was higher among White individuals in states with the highest level of negative sentiment toward minoritized groups, compared to states with the lowest levels.

Our findings are consistent with and build upon previous research that explored trends in negative sentiment toward minoritized groups on social media platforms and the association between negative racial sentiment and adverse health outcomes among racial/ethnic minorities. Findings by Nguyen and colleagues have shown negative racial sentiment toward minoritized groups is associated with adverse birth outcomes, including LBW, VLBW, and PTB [10,11,26]. Given prior research showing maternal hypertension, particularly prepregnancy hypertension, is a large contributor to Black-white disparities in PTB [12], our study may explain one potential pathway through which negative racial sentiment increases adverse birth outcomes among racially minoritized groups – maternal hypertension. A major strength of our study is utilizing Twitter data to measure area-level negative racial sentiment, which allows us to explore environmental contexts of and exposures to cultural racism. The relative anonymity that online spaces and social media platforms provide may also encourage individuals to express opinions they may not share in person [66]. Through this novel approach, we can examine changes in the association between negative racial sentiment and health disparities over time and place, including in the wake of race-related events, like the Black Lives Matter movement and anti-Asian discrimination during the COVID-19 pandemic [27,28,30,64].

While this method is innovative, it is not without limitations. To be comparable to our previous work [10,11,27,28], we utilized the same sentiment model, and the keyword list is not exhaustive of all keywords that could reference racially minoritized groups. Tweets referencing different racial groups were analyzed for sentiment or emotional tone, which is distinct from expressions of racial prejudice. However, prior work has demonstrated that this measure is associated with implicit and explicit racial bias [25], as well as elevated risk for preterm birth and low birthweight among racially minoritized mothers [11]. The birth certificate data file is nationally representative and encompasses 99% of all births [67], and while hypertension has been shown to be underreported on state birth certificates, it is measured consistently over time [68], but the specific type of gestational hypertension disorder (e.g., preeclampsia) and the time of prepregnancy hypertension diagnosis are not assessed. While prepregnancy hypertension could have developed before the sentiment measure from Twitter and study period (i.e., prior to 2016), we observed temporal trends in negative racial sentiment and that its associations with prepregnancy hypertension aligned with periods of heightened racial discrimination. We also controlled for individual and state-level factors; however, there may be unmeasured confounders, which could explain these findings. Our E-value assessment suggests our observed findings could be susceptible to modest, unmeasured confounding [61]. However, the E-value alone cannot infer or refute causality, rather careful consideration should be made with respect to study design, sample size, and biologic plausibility that may explain a statistically significant association between negative area-level racial sentiment and prepregnancy hypertension among racially minoritized groups. Additionally, only 1 in 4 US adults uses Twitter [50], and Twitter users’ sociodemographics differ from the general population; they are younger, more highly educated, wealthier, and more likely to be from minority backgrounds [69,70]. Therefore, our findings may not be generalizable to the general population. Collectively, these limitations could have influenced our observed findings.

Conclusions

Our study provides new insights into the impact of cultural racism on maternal health by integrating multiple years of nationally representative birth certificate data with Twitter data to examine the association between state-level negative racial sentiment and two types of maternal hypertension among multiple racial/ethnic groups. Our findings underscore the impact of societal-level racial sentiment on maternal health disparities and highlight the need for policies that address both overt racism and more subtle forms of racial sentiment, including online discourse. Further research is needed to examine how early-life and prepregnancy exposures to racism and discrimination, particularly during periods of heightened racial tension, affect maternal and child health inequities across the lifecourse. Findings from such research will provide additional insight into the long-term effects of racism and discrimination on maternal and child health inequities, and they may inform policies and interventions to reduce health inequities across racially minoritized groups. Public health and policy efforts to improve maternal health equity must extend beyond the clinic to address the pervasive and harmful effects of cultural racism. Policy efforts to combat online hate speech and promote racial justice may have direct and measurable benefits for the physical health of pregnant people and their infants [71–73].

Supporting information

S1 Table. Associations using prevalence rate ratios (PRRs) between state-level negative racial sentiment toward minoritized groups and hypertension type by pregnant individual’s race (all, racially minoritized groups, White) from 2016–2021, stratified by parity.

https://doi.org/10.1371/journal.pone.0346564.s001

(DOCX)

S2 Table. Sensitivity analyses for associations using prevalence rate ratios (PRRs) between state-level negative racial sentiment toward minoritized groups and hypertension type by pregnant individual’s race (all, racially minoritized groups, White) from 2016–2021, adjusted for final selection of covariates as well as BMI, prenatal care initiation trimester, smoking during pregnancy, and complete smoking history.

https://doi.org/10.1371/journal.pone.0346564.s002

(DOCX)

S3 Table. E-values for sensitivity analysis of unmeasured confounding in significant, adjusted associations using prevalence rate ratios (PRRs) between state-level negative racial sentiment toward minoritized groups and hypertension type by pregnant individual’s race.

https://doi.org/10.1371/journal.pone.0346564.s003

(DOCX)

References

1. Barreto A, Formanowski B, Peña MM, Salazar EG, Handley SC, Burris HH. Preterm Birth Risk and Maternal Nativity, Ethnicity, and Race. JAMA Network Open. 2024;7(3):e243194.
- View Article
- Google Scholar
2. Burgess APH, Dongarwar D, Spigel Z, Salihu HM, Moaddab A, Clark SL. Pregnancy-related mortality in the United States, 2003-2016: age, race, and place of death. Am J Obstet Gynecol. 2020;222(5):489.e1-489.e8.
- View Article
- Google Scholar
3. Callaghan WM. Overview of maternal mortality in the United States. Seminars in Perinatology. 2012;36(1):2–6.
- View Article
- Google Scholar
4. Howell EA. Reducing Disparities in Severe Maternal Morbidity and Mortality. Clin Obstet Gynecol. 2018;61(2):387–99. pmid:29346121
- View Article
- PubMed/NCBI
- Google Scholar
5. Liese KL, Mogos M, Abboud S, Decocker K, Koch AR, Geller SE. Racial and Ethnic Disparities in Severe Maternal Morbidity in the United States. J Racial Ethn Health Disparities. 2019;6(4):790–8. pmid:30877505
- View Article
- PubMed/NCBI
- Google Scholar
6. MacDorman MF, Thoma M, Declcerq E, Howell EA. Racial and ethnic disparities in maternal mortality in the United States using enhanced vital records, 2016‒2017. Am J Public Health. 2021;111(9):1673–81.
- View Article
- Google Scholar
7. Osterman M, Hamilton B, Martin J, Driscoll A, Valenzuela C. Births: Final Data for 2020 [Internet]. National Center for Health Statistics (U.S.); 2021 Feb [cited 2023 Apr 6]. Available from: https://stacks.cdc.gov/view/cdc/112078
- View Article
- Google Scholar
8. Petersen EE. Racial/Ethnic Disparities in Pregnancy-Related Deaths — United States, 2007–2016. MMWR Morb Mortal Wkly Rep [Internet]. 2019 [cited 2024 Dec 27];68. Available from: https://www.cdc.gov/mmwr/volumes/68/wr/mm6835a3.htm
- View Article
- Google Scholar
9. Lu MC, Kotelchuck M, Hogan V, Jones L, Wright K, Halfon N. Closing the Black-White gap in birth outcomes: a life-course approach. Ethn Dis. 2010;20(1 Suppl 2):S2-62–76. pmid:20629248
- View Article
- PubMed/NCBI
- Google Scholar
10. Nguyen TT, Adams N, Huang D, Glymour MM, Allen AM, Nguyen QC. The Association Between State-Level Racial Attitudes Assessed From Twitter Data and Adverse Birth Outcomes: Observational Study. JMIR Public Health Surveill. 2020;6(3):e17103. pmid:32298232
- View Article
- PubMed/NCBI
- Google Scholar
11. Nguyen TT, Merchant JS, Criss S, Makres K, Gowda KN, Mane H, et al. Examining Twitter-Derived Negative Racial Sentiment as Indicators of Cultural Racism: Observational Associations With Preterm Birth and Low Birth Weight Among a Multiracial Sample of Mothers, 2011-2021. J Med Internet Res. 2023;25:e44990. pmid:37115602
- View Article
- PubMed/NCBI
- Google Scholar
12. Thoma ME, Drew LB, Hirai AH, Kim TY, Fenelon A, Shenassa ED. Black-White Disparities in Preterm Birth: Geographic, Social, and Health Determinants. Am J Prev Med. 2019;57(5):675–86. pmid:31561920
- View Article
- PubMed/NCBI
- Google Scholar
13. Xiong W, Han L, Tang X, Wang Q, Chen W, Li R, et al. Association of maternal preconception blood pressure with preterm birth: a population-based cohort study. Hypertens Res. 2024;47(2):467–77. pmid:37907599
- View Article
- PubMed/NCBI
- Google Scholar
14. Jones CP. Levels of racism: a theoretic framework and a gardener’s tale. Am J Public Health. 2000;90(8):1212–5. pmid:10936998
- View Article
- PubMed/NCBI
- Google Scholar
15. Cogburn CD. Culture, race, and health: implications for racial inequities and population health. Milbank Q. 2019;97(3):736–61.
- View Article
- Google Scholar
16. Rodat S. Cultural racism: A conceptual framework. Revista de Stiinte Politice. 2017;54:129–40.
- View Article
- Google Scholar
17. Hicken MT, Miles L, Haile S, Esposito M. Linking History to Contemporary State-Sanctioned Slow Violence through Cultural and Structural Racism. Ann Am Acad Pol Soc Sci. 2021;694(1):48–58. pmid:34446942
- View Article
- PubMed/NCBI
- Google Scholar
18. Williams DR, Lawrence JA, Davis BA. Racism and Health: Evidence and Needed Research. Annu Rev Public Health. 2019;40(1):105–25.
- View Article
- Google Scholar
19. Michaels EK, Lam-Hine T, Nguyen TT, Gee GC, Allen AM. The Water Surrounding the Iceberg: Cultural Racism and Health Inequities. Milbank Quarterly. 2023;101(3):768–814.
- View Article
- Google Scholar
20. Alhusen JL, Bower KM, Epstein E, Sharps P. Racial Discrimination and Adverse Birth Outcomes: An Integrative Review. J Midwifery Womens Health. 2016;61(6):707–20. pmid:27737504
- View Article
- PubMed/NCBI
- Google Scholar
21. Black LL, Johnson R, VanHoose L. The relationship between perceived racism/discrimination and health among Black American women: a review of the literature from 2003 to 2013. J Racial Ethn Health Disparities. 2015;2(1):11–20.
- View Article
- Google Scholar
22. Giurgescu C, McFarlin BL, Lomax J, Craddock C, Albrecht A. Racial discrimination and the black-white gap in adverse birth outcomes: a review. J Midwifery Womens Health. 2011;56(4):362–70. pmid:21733107
- View Article
- PubMed/NCBI
- Google Scholar
23. Krumpal I. Determinants of social desirability bias in sensitive surveys: a literature review. Qual Quant. 2013;47(4):2025–47.
- View Article
- Google Scholar
24. Collins JW Jr, David RJ, Symons R, Handler A, Wall SN, Dwyer L. Low-income African-American mothers’ perception of exposure to racial discrimination and infant birth weight. Epidemiology. 2000;11(3):337–9. pmid:10784254
- View Article
- PubMed/NCBI
- Google Scholar
25. Nguyen TT, Huang D, Michaels EK, Glymour MM, Allen AM, Nguyen QC. Evaluating associations between area-level Twitter-expressed negative racial sentiment, hate crimes, and residents’ racial prejudice in the United States. SSM - Population Health. 2021;13:100750.
- View Article
- Google Scholar
26. Nguyen TT, Meng H-W, Sandeep S, McCullough M, Yu W, Lau Y, et al. Twitter-derived measures of sentiment towards minorities (2015-2016) and associations with low birth weight and preterm birth in the United States. Comput Human Behav. 2018;89:308–15. pmid:30923420
- View Article
- PubMed/NCBI
- Google Scholar
27. Nguyen TT, Criss S, Dwivedi P, Huang D, Keralis J, Hsu E, et al. Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19. Int J Environ Res Public Health. 2020;17(19):7032. pmid:32993005
- View Article
- PubMed/NCBI
- Google Scholar
28. Nguyen TT, Criss S, Michaels EK, Cross RI, Michaels JS, Dwivedi P. Progress and push-back: How the killings of Ahmaud Arbery, Breonna Taylor, and George Floyd impacted public discourse on race and racism on Twitter. SSM - Population Health. 2021;15:100922.
- View Article
- Google Scholar
29. Nguyen TT, Criss S, Kim M, De La Cruz MM, Thai N, Merchant JS, et al. Racism During Pregnancy and Birthing: Experiences from Asian and Pacific Islander, Black, Latina, and Middle Eastern Women. J Racial Ethn Health Disparities. 2023;10(6):3007–17. pmid:36449130
- View Article
- PubMed/NCBI
- Google Scholar
30. Wei H, Hswen Y, Merchant JS, Drew LB, Nguyen QC, Yue X, et al. From Tweets to Streets: Observational Study on the Association Between Twitter Sentiment and Anti-Asian Hate Crimes in New York City from 2019 to 2022. J Med Internet Res. 2024;26:e53050. pmid:39250221
- View Article
- PubMed/NCBI
- Google Scholar
31. Wadhwa PD, Culhane JF, Rauh V, Barve SS. Stress and preterm birth: neuroendocrine, immune/inflammatory, and vascular mechanisms. Matern Child Health J. 2001;5(2):119–25. pmid:11573837
- View Article
- PubMed/NCBI
- Google Scholar
32. Butler C, Tull ES, Chambers EC, Taylor J. Internalized racism, body fat distribution, and abnormal fasting glucose among African-Caribbean women in Dominica, West Indies. J Natl Med Assoc. 2002;94(3):143–8. pmid:11918383
- View Article
- PubMed/NCBI
- Google Scholar
33. Krieger N. Racial and gender discrimination: risk factors for high blood pressure? Soc Sci Med. 1990;30(12):1273–81. pmid:2367873
- View Article
- PubMed/NCBI
- Google Scholar
34. Krieger N, Sidney S. Racial discrimination and blood pressure: the CARDIA Study of young black and white adults. Am J Public Health. 1996;86(10):1370–8. pmid:8876504
- View Article
- PubMed/NCBI
- Google Scholar
35. Dolezsar CM, McGrath JJ, Herzig AJM, Miller SB. Perceived racial discrimination and hypertension: a comprehensive systematic review. Health Psychol. 2014;33(1):20–34. pmid:24417692
- View Article
- PubMed/NCBI
- Google Scholar
36. Panza GA, Puhl RM, Taylor BA, Zaleski AL, Livingston J, Pescatello LS. Links between discrimination and cardiovascular health among socially stigmatized groups: A systematic review. PLoS One. 2019;14(6):e0217623. pmid:31181102
- View Article
- PubMed/NCBI
- Google Scholar
37. Paradies Y, Ben J, Denson N, Elias A, Priest N, Pieterse A, et al. Racism as a Determinant of Health: A Systematic Review and Meta-Analysis. PLoS One. 2015;10(9):e0138511. pmid:26398658
- View Article
- PubMed/NCBI
- Google Scholar
38. Prokosch C, Fertig AR, Ojebuoboh AR, Trofholz AC, Baird M, Young M, et al. Exploring associations between social determinants of health and mental health outcomes in families from socioeconomically and racially and ethnically diverse households. Prev Med. 2022;161:107150. pmid:35809824
- View Article
- PubMed/NCBI
- Google Scholar
39. Matsas A, Panopoulou P, Antoniou N, Bargiota A, Gryparis A, Vrachnis N, et al. Chronic Stress in Pregnancy Is Associated with Low Birth Weight: A Meta-Analysis. J Clin Med. 2023;12(24):7686. pmid:38137756
- View Article
- PubMed/NCBI
- Google Scholar
40. Rich-Edwards J, Krieger N, Majzoub J, Zierler S, Lieberman E, Gillman M. Maternal experiences of racism and violence as predictors of preterm birth: rationale and study design. Paediatr Perinat Epidemiol. 2001;15 Suppl 2:124–35. pmid:11520405
- View Article
- PubMed/NCBI
- Google Scholar
41. Burch AE, Jacobs M. COVID-19, police violence, and educational disruption: the differential experience of anxiety for racial and ethnic households. J Racial Ethn Health Disparities. 2022;9(6):2533–50.
- View Article
- Google Scholar
42. Watson-Singleton NN, Mekawi Y, Wilkins KV, Jatta IF. Racism’s effect on depressive symptoms: Examining perseverative cognition and Black Lives Matter activism as moderators. J Couns Psychol. 2021;68(1):27–37. pmid:32271033
- View Article
- PubMed/NCBI
- Google Scholar
43. Gemmill A, Catalano R, Casey JA, Karasek D, Alcalá HE, Elser H, et al. Association of Preterm Births Among US Latina Women With the 2016 Presidential Election. JAMA Netw Open. 2019;2(7):e197084. pmid:31322687
- View Article
- PubMed/NCBI
- Google Scholar
44. Langer PD, Patler C, Hamilton ER. Adverse infant health outcomes increased after the 2016 U.S. presidential election among non-white U.S.-born and foreign-born mothers. Demography. 2024;61(4):1211–39.
- View Article
- Google Scholar
45. Dougan MM, Tzuang M, Nam B, Meyer OL, Tsoh JY, Park VMT. Discrimination Experiences among Asian American and Pacific Islander Adults during the COVID-19 Pandemic and Their Association with Mental Health Outcomes: Updated Findings from the COMPASS Study. Int J Environ Res Public Health. 2024;21(6):799. pmid:38929045
- View Article
- PubMed/NCBI
- Google Scholar
46. Lu P, Kong D, Shelley M. Discrimination and mental health during the Black Lives Matter movement and the COVID-19 pandemic. Innov Aging. 2021;5(Suppl 1):481.
- View Article
- Google Scholar
47. Park HJ, Francisco SC, Pang MR, Peng L, Chi G. Exposure to anti-Black Lives Matter movement and obesity of the Black population. Soc Sci Med. 2023;316:114265. pmid:34366168
- View Article
- PubMed/NCBI
- Google Scholar
48. Bartlett J, Krasodomski-Jones A. Counter-Speech: Examining Content that Challenges Extremism Online [Internet]. 2015. Available from: https://demos.co.uk/wp-content/uploads/2015/10/Counter-speech.pdf
- View Article
- Google Scholar
49. The Racial Slur Database [Internet]. [cited 2023 Apr 28]. Available from: http://www.rsdb.org/
- View Article
- Google Scholar
50. Dinesh S, Odabaş M. Pew Research Center. 2023 [cited 2025 Jan 3]. 8 facts about Americans and Twitter as it rebrands to X. Available from: https://www.pewresearch.org/short-reads/2023/07/26/8-facts-about-americans-and-twitter-as-it-rebrands-to-x/
- View Article
- Google Scholar
51. Nguyen TT, Merchant JS, Yue X, Mane H, Wei H, Huang D, et al. A Decade of Tweets: Visualizing Racial Sentiments Towards Minoritized Groups in the United States Between 2011 and 2021. Epidemiology. 2024;35(1):51.
- View Article
- Google Scholar
52. Day MC, Barton JR, O’Brien JM, Istwan NB, Sibai BM. The effect of fetal number on the development of hypertensive conditions of pregnancy. Obstet Gynecol. 2005;106(5 Pt 1):927–31. pmid:16260508
- View Article
- PubMed/NCBI
- Google Scholar
53. Francisco C, Wright D, Benkő Z, Syngelaki A, Nicolaides KH. Hidden high rate of pre-eclampsia in twin compared with singleton pregnancy. Ultrasound Obstet Gynecol. 2017;50(1):88–92. pmid:28317207
- View Article
- PubMed/NCBI
- Google Scholar
54. National Center for Health Statistics. Guide to Completing the Facility Worksheets for the Certificate of Live Birth and Report of Fetal Death [Internet]. 2022. Available from: https://www.cdc.gov/nchs/data/dvs/GuidetoCompleteFacilityWks.pdf
- View Article
- Google Scholar
55. Osterman MJK, Hamilton BE, Martin JA, Driscoll AK, Valenzuela CP. Births: Final Data for 2023. National Vital Statistics Reports [Internet]. 2025;74(1). Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr74/nvsr74-1.pdf
- View Article
- Google Scholar
56. Delker E, Baer RJ, Chambers CD, Bandoli G. Identification of Chronic Hypertension in Pregnancy in Three Administrative Data Sources Among Medicaid-Funded Births in California. Pharmacoepidemiol Drug Saf. 2024;33(12):e70059. pmid:39662981
- View Article
- PubMed/NCBI
- Google Scholar
57. Lydon-Rochelle MT, Holt VL, Cárdenas V, Nelson JC, Easterling TR, Gardella C, et al. The reporting of pre-existing maternal medical conditions and complications of pregnancy on birth certificates and in hospital discharge data. Am J Obstet Gynecol. 2005;193(1):125–34. pmid:16021070
- View Article
- PubMed/NCBI
- Google Scholar
58. US Census Bureau. Census.gov. 2024 [cited 2024 Dec 5]. American Community Survey (ACS). Available from: https://www.census.gov/programs-surveys/acs
- View Article
- Google Scholar
59. Nguyen QC, Li D, Meng H-W, Kath S, Nsoesie E, Li F, et al. Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity. JMIR Public Health Surveill. 2016;2(2):e158. pmid:27751984
- View Article
- PubMed/NCBI
- Google Scholar
60. StataCorp. Stata Statistical Software: Release 18 [Internet]. College Station, TX: StataCorp LLC; 2023 [cited 2024 Dec 18]. Available from: https://www.stata.com/company/
- View Article
- Google Scholar
61. VanderWeele TJ, Ding P. Sensitivity Analysis in Observational Research: Introducing the E-Value. Ann Intern Med. 2017;167(4):268–74. pmid:28693043
- View Article
- PubMed/NCBI
- Google Scholar
62. Hswen Y, Xu X, Hing A, Hawkins JB, Brownstein JS, Gee GC. Association of “#covid19” versus “#chinesevirus” with anti-Asian sentiments on Twitter: March 9–23, 2020. Am J Public Health. 2021;111(5):956–64.
- View Article
- Google Scholar
63. Huang JT, Krupenkin M, Rothschild D, Lee Cunningham J. The cost of anti-Asian racism during the COVID-19 pandemic. Nat Hum Behav. 2023;7(5):682–95. pmid:36658211
- View Article
- PubMed/NCBI
- Google Scholar
64. Criss S, Nguyen TT, Michaels EK, Gee GC, Kiang MV, Nguyen QC. Solidarity and strife after the Atlanta spa shootings: A mixed methods study characterizing Twitter discussions by qualitative analysis and machine learning. Front Public Health. 2023;11:952069.
- View Article
- Google Scholar
65. Zhou S, Banawa R, Oh H. Stop Asian hate: The mental health impact of racial discrimination among Asian Pacific Islander young and emerging adults during COVID-19. J Affect Disord. 2023;325:346–53.
- View Article
- Google Scholar
66. Suler J. The online disinhibition effect. Cyberpsychol Behav. 2004;7(3):321–6. pmid:15257832
- View Article
- PubMed/NCBI
- Google Scholar
67. Centers for Disease Control and Prevention. User Guide to the 2022 Natality Public Use File [Internet]. Available from: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/DVS/natality/UserGuide2022.pdf
- View Article
- Google Scholar
68. Thoma ME, De Silva DA, MacDorman MF. Examining interpregnancy intervals and maternal and perinatal health outcomes using U.S. vital records: Important considerations for analysis and interpretation. Paediatr Perinat Epidemiol. 2019;33(1):O60–72. pmid:30320453
- View Article
- PubMed/NCBI
- Google Scholar
69. Hughes SW, A. Pew Research Center. 2019 [cited 2024 June 17]. Sizing Up Twitter Users. Available from: https://www.pewresearch.org/internet/2019/04/24/sizing-up-twitter-users/
- View Article
- Google Scholar
70. Auxier B, Anderson M. Social Media Use in 2021 [Internet]. Pew Research Center; 2021. Available from: https://www.pewresearch.org/wp-content/uploads/sites/20/2021/04/PI_2021.04.07_Social-Media-Use_FINAL.pdf
- View Article
- Google Scholar
71. Dang S. Exclusive: Elon Musk’s X restructuring curtails disinformation research, spurs legal fears. Reuters [Internet]. 2023 Nov 6 [cited 2025 June 16]; Available from: https://www.reuters.com/technology/elon-musks-x-restructuring-curtails-disinformation-research-spurs-legal-fears-2023-11-06/
- View Article
- Google Scholar
72. X Developers. Starting February 9, we will no longer support free access to the Twitter API, both v2 and v1.1. A paid basic tier will be available instead. Twitter. 2023 [cited 2025 June 16]. Available from: https://x.com/XDevelopers/status/1621026986784337922
- View Article
- Google Scholar
73. Centers for Disease Control and Prevention. Restricted-Use Vital Statistics Data [Internet]. 2023 [cited 2025 June 16]. Available from: https://www.cdc.gov/nchs/nvss/nvss-restricted-data.htm
- View Article
- Google Scholar

[ref1] 1. Barreto A, Formanowski B, Peña MM, Salazar EG, Handley SC, Burris HH. Preterm Birth Risk and Maternal Nativity, Ethnicity, and Race. JAMA Network Open. 2024;7(3):e243194.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Burgess APH, Dongarwar D, Spigel Z, Salihu HM, Moaddab A, Clark SL. Pregnancy-related mortality in the United States, 2003-2016: age, race, and place of death. Am J Obstet Gynecol. 2020;222(5):489.e1-489.e8.
View Article
Google Scholar

[5] View Article

[6] Google Scholar

[ref3] 3. Callaghan WM. Overview of maternal mortality in the United States. Seminars in Perinatology. 2012;36(1):2–6.
View Article
Google Scholar

[8] View Article

[9] Google Scholar

[ref4] 4. Howell EA. Reducing Disparities in Severe Maternal Morbidity and Mortality. Clin Obstet Gynecol. 2018;61(2):387–99. pmid:29346121
View Article
PubMed/NCBI
Google Scholar

[11] View Article

[12] PubMed/NCBI

[13] Google Scholar

[ref5] 5. Liese KL, Mogos M, Abboud S, Decocker K, Koch AR, Geller SE. Racial and Ethnic Disparities in Severe Maternal Morbidity in the United States. J Racial Ethn Health Disparities. 2019;6(4):790–8. pmid:30877505
View Article
PubMed/NCBI
Google Scholar

[15] View Article

[16] PubMed/NCBI

[17] Google Scholar

[ref6] 6. MacDorman MF, Thoma M, Declcerq E, Howell EA. Racial and ethnic disparities in maternal mortality in the United States using enhanced vital records, 2016‒2017. Am J Public Health. 2021;111(9):1673–81.
View Article
Google Scholar

[19] View Article

[20] Google Scholar

[ref7] 7. Osterman M, Hamilton B, Martin J, Driscoll A, Valenzuela C. Births: Final Data for 2020 [Internet]. National Center for Health Statistics (U.S.); 2021 Feb [cited 2023 Apr 6]. Available from: https://stacks.cdc.gov/view/cdc/112078
View Article
Google Scholar

[22] View Article

[23] Google Scholar

[ref8] 8. Petersen EE. Racial/Ethnic Disparities in Pregnancy-Related Deaths — United States, 2007–2016. MMWR Morb Mortal Wkly Rep [Internet]. 2019 [cited 2024 Dec 27];68. Available from: https://www.cdc.gov/mmwr/volumes/68/wr/mm6835a3.htm
View Article
Google Scholar

[25] View Article

[26] Google Scholar

[ref9] 9. Lu MC, Kotelchuck M, Hogan V, Jones L, Wright K, Halfon N. Closing the Black-White gap in birth outcomes: a life-course approach. Ethn Dis. 2010;20(1 Suppl 2):S2-62–76. pmid:20629248
View Article
PubMed/NCBI
Google Scholar

[28] View Article

[29] PubMed/NCBI

[30] Google Scholar

[ref10] 10. Nguyen TT, Adams N, Huang D, Glymour MM, Allen AM, Nguyen QC. The Association Between State-Level Racial Attitudes Assessed From Twitter Data and Adverse Birth Outcomes: Observational Study. JMIR Public Health Surveill. 2020;6(3):e17103. pmid:32298232
View Article
PubMed/NCBI
Google Scholar

[32] View Article

[33] PubMed/NCBI

[34] Google Scholar

[ref11] 11. Nguyen TT, Merchant JS, Criss S, Makres K, Gowda KN, Mane H, et al. Examining Twitter-Derived Negative Racial Sentiment as Indicators of Cultural Racism: Observational Associations With Preterm Birth and Low Birth Weight Among a Multiracial Sample of Mothers, 2011-2021. J Med Internet Res. 2023;25:e44990. pmid:37115602
View Article
PubMed/NCBI
Google Scholar

[36] View Article

[37] PubMed/NCBI

[38] Google Scholar

[ref12] 12. Thoma ME, Drew LB, Hirai AH, Kim TY, Fenelon A, Shenassa ED. Black-White Disparities in Preterm Birth: Geographic, Social, and Health Determinants. Am J Prev Med. 2019;57(5):675–86. pmid:31561920
View Article
PubMed/NCBI
Google Scholar

[40] View Article

[41] PubMed/NCBI

[42] Google Scholar

[ref13] 13. Xiong W, Han L, Tang X, Wang Q, Chen W, Li R, et al. Association of maternal preconception blood pressure with preterm birth: a population-based cohort study. Hypertens Res. 2024;47(2):467–77. pmid:37907599
View Article
PubMed/NCBI
Google Scholar

[44] View Article

[45] PubMed/NCBI

[46] Google Scholar

[ref14] 14. Jones CP. Levels of racism: a theoretic framework and a gardener’s tale. Am J Public Health. 2000;90(8):1212–5. pmid:10936998
View Article
PubMed/NCBI
Google Scholar

[48] View Article

[49] PubMed/NCBI

[50] Google Scholar

[ref15] 15. Cogburn CD. Culture, race, and health: implications for racial inequities and population health. Milbank Q. 2019;97(3):736–61.
View Article
Google Scholar

[52] View Article

[53] Google Scholar

[ref16] 16. Rodat S. Cultural racism: A conceptual framework. Revista de Stiinte Politice. 2017;54:129–40.
View Article
Google Scholar

[55] View Article

[56] Google Scholar

[ref17] 17. Hicken MT, Miles L, Haile S, Esposito M. Linking History to Contemporary State-Sanctioned Slow Violence through Cultural and Structural Racism. Ann Am Acad Pol Soc Sci. 2021;694(1):48–58. pmid:34446942
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref18] 18. Williams DR, Lawrence JA, Davis BA. Racism and Health: Evidence and Needed Research. Annu Rev Public Health. 2019;40(1):105–25.
View Article
Google Scholar

[62] View Article

[63] Google Scholar

[ref19] 19. Michaels EK, Lam-Hine T, Nguyen TT, Gee GC, Allen AM. The Water Surrounding the Iceberg: Cultural Racism and Health Inequities. Milbank Quarterly. 2023;101(3):768–814.
View Article
Google Scholar

[65] View Article

[66] Google Scholar

[ref20] 20. Alhusen JL, Bower KM, Epstein E, Sharps P. Racial Discrimination and Adverse Birth Outcomes: An Integrative Review. J Midwifery Womens Health. 2016;61(6):707–20. pmid:27737504
View Article
PubMed/NCBI
Google Scholar

[68] View Article

[69] PubMed/NCBI

[70] Google Scholar

[ref21] 21. Black LL, Johnson R, VanHoose L. The relationship between perceived racism/discrimination and health among Black American women: a review of the literature from 2003 to 2013. J Racial Ethn Health Disparities. 2015;2(1):11–20.
View Article
Google Scholar

[72] View Article

[73] Google Scholar

[ref22] 22. Giurgescu C, McFarlin BL, Lomax J, Craddock C, Albrecht A. Racial discrimination and the black-white gap in adverse birth outcomes: a review. J Midwifery Womens Health. 2011;56(4):362–70. pmid:21733107
View Article
PubMed/NCBI
Google Scholar

[75] View Article

[76] PubMed/NCBI

[77] Google Scholar

[ref23] 23. Krumpal I. Determinants of social desirability bias in sensitive surveys: a literature review. Qual Quant. 2013;47(4):2025–47.
View Article
Google Scholar

[79] View Article

[80] Google Scholar

[ref24] 24. Collins JW Jr, David RJ, Symons R, Handler A, Wall SN, Dwyer L. Low-income African-American mothers’ perception of exposure to racial discrimination and infant birth weight. Epidemiology. 2000;11(3):337–9. pmid:10784254
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref25] 25. Nguyen TT, Huang D, Michaels EK, Glymour MM, Allen AM, Nguyen QC. Evaluating associations between area-level Twitter-expressed negative racial sentiment, hate crimes, and residents’ racial prejudice in the United States. SSM - Population Health. 2021;13:100750.
View Article
Google Scholar

[86] View Article

[87] Google Scholar

[ref26] 26. Nguyen TT, Meng H-W, Sandeep S, McCullough M, Yu W, Lau Y, et al. Twitter-derived measures of sentiment towards minorities (2015-2016) and associations with low birth weight and preterm birth in the United States. Comput Human Behav. 2018;89:308–15. pmid:30923420
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref27] 27. Nguyen TT, Criss S, Dwivedi P, Huang D, Keralis J, Hsu E, et al. Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19. Int J Environ Res Public Health. 2020;17(19):7032. pmid:32993005
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref28] 28. Nguyen TT, Criss S, Michaels EK, Cross RI, Michaels JS, Dwivedi P. Progress and push-back: How the killings of Ahmaud Arbery, Breonna Taylor, and George Floyd impacted public discourse on race and racism on Twitter. SSM - Population Health. 2021;15:100922.
View Article
Google Scholar

[97] View Article

[98] Google Scholar

[ref29] 29. Nguyen TT, Criss S, Kim M, De La Cruz MM, Thai N, Merchant JS, et al. Racism During Pregnancy and Birthing: Experiences from Asian and Pacific Islander, Black, Latina, and Middle Eastern Women. J Racial Ethn Health Disparities. 2023;10(6):3007–17. pmid:36449130
View Article
PubMed/NCBI
Google Scholar

[100] View Article

[101] PubMed/NCBI

[102] Google Scholar

[ref30] 30. Wei H, Hswen Y, Merchant JS, Drew LB, Nguyen QC, Yue X, et al. From Tweets to Streets: Observational Study on the Association Between Twitter Sentiment and Anti-Asian Hate Crimes in New York City from 2019 to 2022. J Med Internet Res. 2024;26:e53050. pmid:39250221
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref31] 31. Wadhwa PD, Culhane JF, Rauh V, Barve SS. Stress and preterm birth: neuroendocrine, immune/inflammatory, and vascular mechanisms. Matern Child Health J. 2001;5(2):119–25. pmid:11573837
View Article
PubMed/NCBI
Google Scholar

[108] View Article

[109] PubMed/NCBI

[110] Google Scholar

[ref32] 32. Butler C, Tull ES, Chambers EC, Taylor J. Internalized racism, body fat distribution, and abnormal fasting glucose among African-Caribbean women in Dominica, West Indies. J Natl Med Assoc. 2002;94(3):143–8. pmid:11918383
View Article
PubMed/NCBI
Google Scholar

[112] View Article

[113] PubMed/NCBI

[114] Google Scholar

[ref33] 33. Krieger N. Racial and gender discrimination: risk factors for high blood pressure? Soc Sci Med. 1990;30(12):1273–81. pmid:2367873
View Article
PubMed/NCBI
Google Scholar

[116] View Article

[117] PubMed/NCBI

[118] Google Scholar

[ref34] 34. Krieger N, Sidney S. Racial discrimination and blood pressure: the CARDIA Study of young black and white adults. Am J Public Health. 1996;86(10):1370–8. pmid:8876504
View Article
PubMed/NCBI
Google Scholar

[120] View Article

[121] PubMed/NCBI

[122] Google Scholar

[ref35] 35. Dolezsar CM, McGrath JJ, Herzig AJM, Miller SB. Perceived racial discrimination and hypertension: a comprehensive systematic review. Health Psychol. 2014;33(1):20–34. pmid:24417692
View Article
PubMed/NCBI
Google Scholar

[124] View Article

[125] PubMed/NCBI

[126] Google Scholar

[ref36] 36. Panza GA, Puhl RM, Taylor BA, Zaleski AL, Livingston J, Pescatello LS. Links between discrimination and cardiovascular health among socially stigmatized groups: A systematic review. PLoS One. 2019;14(6):e0217623. pmid:31181102
View Article
PubMed/NCBI
Google Scholar

[128] View Article

[129] PubMed/NCBI

[130] Google Scholar

[ref37] 37. Paradies Y, Ben J, Denson N, Elias A, Priest N, Pieterse A, et al. Racism as a Determinant of Health: A Systematic Review and Meta-Analysis. PLoS One. 2015;10(9):e0138511. pmid:26398658
View Article
PubMed/NCBI
Google Scholar

[132] View Article

[133] PubMed/NCBI

[134] Google Scholar

[ref38] 38. Prokosch C, Fertig AR, Ojebuoboh AR, Trofholz AC, Baird M, Young M, et al. Exploring associations between social determinants of health and mental health outcomes in families from socioeconomically and racially and ethnically diverse households. Prev Med. 2022;161:107150. pmid:35809824
View Article
PubMed/NCBI
Google Scholar

[136] View Article

[137] PubMed/NCBI

[138] Google Scholar

[ref39] 39. Matsas A, Panopoulou P, Antoniou N, Bargiota A, Gryparis A, Vrachnis N, et al. Chronic Stress in Pregnancy Is Associated with Low Birth Weight: A Meta-Analysis. J Clin Med. 2023;12(24):7686. pmid:38137756
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref40] 40. Rich-Edwards J, Krieger N, Majzoub J, Zierler S, Lieberman E, Gillman M. Maternal experiences of racism and violence as predictors of preterm birth: rationale and study design. Paediatr Perinat Epidemiol. 2001;15 Suppl 2:124–35. pmid:11520405
View Article
PubMed/NCBI
Google Scholar

[144] View Article

[145] PubMed/NCBI

[146] Google Scholar

[ref41] 41. Burch AE, Jacobs M. COVID-19, police violence, and educational disruption: the differential experience of anxiety for racial and ethnic households. J Racial Ethn Health Disparities. 2022;9(6):2533–50.
View Article
Google Scholar

[148] View Article

[149] Google Scholar

[ref42] 42. Watson-Singleton NN, Mekawi Y, Wilkins KV, Jatta IF. Racism’s effect on depressive symptoms: Examining perseverative cognition and Black Lives Matter activism as moderators. J Couns Psychol. 2021;68(1):27–37. pmid:32271033
View Article
PubMed/NCBI
Google Scholar

[151] View Article

[152] PubMed/NCBI

[153] Google Scholar

[ref43] 43. Gemmill A, Catalano R, Casey JA, Karasek D, Alcalá HE, Elser H, et al. Association of Preterm Births Among US Latina Women With the 2016 Presidential Election. JAMA Netw Open. 2019;2(7):e197084. pmid:31322687
View Article
PubMed/NCBI
Google Scholar

[155] View Article

[156] PubMed/NCBI

[157] Google Scholar

[ref44] 44. Langer PD, Patler C, Hamilton ER. Adverse infant health outcomes increased after the 2016 U.S. presidential election among non-white U.S.-born and foreign-born mothers. Demography. 2024;61(4):1211–39.
View Article
Google Scholar

[159] View Article

[160] Google Scholar

[ref45] 45. Dougan MM, Tzuang M, Nam B, Meyer OL, Tsoh JY, Park VMT. Discrimination Experiences among Asian American and Pacific Islander Adults during the COVID-19 Pandemic and Their Association with Mental Health Outcomes: Updated Findings from the COMPASS Study. Int J Environ Res Public Health. 2024;21(6):799. pmid:38929045
View Article
PubMed/NCBI
Google Scholar

[162] View Article

[163] PubMed/NCBI

[164] Google Scholar

[ref46] 46. Lu P, Kong D, Shelley M. Discrimination and mental health during the Black Lives Matter movement and the COVID-19 pandemic. Innov Aging. 2021;5(Suppl 1):481.
View Article
Google Scholar

[166] View Article

[167] Google Scholar

[ref47] 47. Park HJ, Francisco SC, Pang MR, Peng L, Chi G. Exposure to anti-Black Lives Matter movement and obesity of the Black population. Soc Sci Med. 2023;316:114265. pmid:34366168
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref48] 48. Bartlett J, Krasodomski-Jones A. Counter-Speech: Examining Content that Challenges Extremism Online [Internet]. 2015. Available from: https://demos.co.uk/wp-content/uploads/2015/10/Counter-speech.pdf
View Article
Google Scholar

[173] View Article

[174] Google Scholar

[ref49] 49. The Racial Slur Database [Internet]. [cited 2023 Apr 28]. Available from: http://www.rsdb.org/
View Article
Google Scholar

[176] View Article

[177] Google Scholar

[ref50] 50. Dinesh S, Odabaş M. Pew Research Center. 2023 [cited 2025 Jan 3]. 8 facts about Americans and Twitter as it rebrands to X. Available from: https://www.pewresearch.org/short-reads/2023/07/26/8-facts-about-americans-and-twitter-as-it-rebrands-to-x/
View Article
Google Scholar

[179] View Article

[180] Google Scholar

[ref51] 51. Nguyen TT, Merchant JS, Yue X, Mane H, Wei H, Huang D, et al. A Decade of Tweets: Visualizing Racial Sentiments Towards Minoritized Groups in the United States Between 2011 and 2021. Epidemiology. 2024;35(1):51.
View Article
Google Scholar

[182] View Article

[183] Google Scholar

[ref52] 52. Day MC, Barton JR, O’Brien JM, Istwan NB, Sibai BM. The effect of fetal number on the development of hypertensive conditions of pregnancy. Obstet Gynecol. 2005;106(5 Pt 1):927–31. pmid:16260508
View Article
PubMed/NCBI
Google Scholar

[185] View Article

[186] PubMed/NCBI

[187] Google Scholar

[ref53] 53. Francisco C, Wright D, Benkő Z, Syngelaki A, Nicolaides KH. Hidden high rate of pre-eclampsia in twin compared with singleton pregnancy. Ultrasound Obstet Gynecol. 2017;50(1):88–92. pmid:28317207
View Article
PubMed/NCBI
Google Scholar

[189] View Article

[190] PubMed/NCBI

[191] Google Scholar

[ref54] 54. National Center for Health Statistics. Guide to Completing the Facility Worksheets for the Certificate of Live Birth and Report of Fetal Death [Internet]. 2022. Available from: https://www.cdc.gov/nchs/data/dvs/GuidetoCompleteFacilityWks.pdf
View Article
Google Scholar

[193] View Article

[194] Google Scholar

[ref55] 55. Osterman MJK, Hamilton BE, Martin JA, Driscoll AK, Valenzuela CP. Births: Final Data for 2023. National Vital Statistics Reports [Internet]. 2025;74(1). Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr74/nvsr74-1.pdf
View Article
Google Scholar

[196] View Article

[197] Google Scholar

[ref56] 56. Delker E, Baer RJ, Chambers CD, Bandoli G. Identification of Chronic Hypertension in Pregnancy in Three Administrative Data Sources Among Medicaid-Funded Births in California. Pharmacoepidemiol Drug Saf. 2024;33(12):e70059. pmid:39662981
View Article
PubMed/NCBI
Google Scholar

[199] View Article

[200] PubMed/NCBI

[201] Google Scholar

[ref57] 57. Lydon-Rochelle MT, Holt VL, Cárdenas V, Nelson JC, Easterling TR, Gardella C, et al. The reporting of pre-existing maternal medical conditions and complications of pregnancy on birth certificates and in hospital discharge data. Am J Obstet Gynecol. 2005;193(1):125–34. pmid:16021070
View Article
PubMed/NCBI
Google Scholar

[203] View Article

[204] PubMed/NCBI

[205] Google Scholar

[ref58] 58. US Census Bureau. Census.gov. 2024 [cited 2024 Dec 5]. American Community Survey (ACS). Available from: https://www.census.gov/programs-surveys/acs
View Article
Google Scholar

[207] View Article

[208] Google Scholar

[ref59] 59. Nguyen QC, Li D, Meng H-W, Kath S, Nsoesie E, Li F, et al. Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity. JMIR Public Health Surveill. 2016;2(2):e158. pmid:27751984
View Article
PubMed/NCBI
Google Scholar

[210] View Article

[211] PubMed/NCBI

[212] Google Scholar

[ref60] 60. StataCorp. Stata Statistical Software: Release 18 [Internet]. College Station, TX: StataCorp LLC; 2023 [cited 2024 Dec 18]. Available from: https://www.stata.com/company/
View Article
Google Scholar

[214] View Article

[215] Google Scholar

[ref61] 61. VanderWeele TJ, Ding P. Sensitivity Analysis in Observational Research: Introducing the E-Value. Ann Intern Med. 2017;167(4):268–74. pmid:28693043
View Article
PubMed/NCBI
Google Scholar

[217] View Article

[218] PubMed/NCBI

[219] Google Scholar

[ref62] 62. Hswen Y, Xu X, Hing A, Hawkins JB, Brownstein JS, Gee GC. Association of “#covid19” versus “#chinesevirus” with anti-Asian sentiments on Twitter: March 9–23, 2020. Am J Public Health. 2021;111(5):956–64.
View Article
Google Scholar

[221] View Article

[222] Google Scholar

[ref63] 63. Huang JT, Krupenkin M, Rothschild D, Lee Cunningham J. The cost of anti-Asian racism during the COVID-19 pandemic. Nat Hum Behav. 2023;7(5):682–95. pmid:36658211
View Article
PubMed/NCBI
Google Scholar

[224] View Article

[225] PubMed/NCBI

[226] Google Scholar

[ref64] 64. Criss S, Nguyen TT, Michaels EK, Gee GC, Kiang MV, Nguyen QC. Solidarity and strife after the Atlanta spa shootings: A mixed methods study characterizing Twitter discussions by qualitative analysis and machine learning. Front Public Health. 2023;11:952069.
View Article
Google Scholar

[228] View Article

[229] Google Scholar

[ref65] 65. Zhou S, Banawa R, Oh H. Stop Asian hate: The mental health impact of racial discrimination among Asian Pacific Islander young and emerging adults during COVID-19. J Affect Disord. 2023;325:346–53.
View Article
Google Scholar

[231] View Article

[232] Google Scholar

[ref66] 66. Suler J. The online disinhibition effect. Cyberpsychol Behav. 2004;7(3):321–6. pmid:15257832
View Article
PubMed/NCBI
Google Scholar

[234] View Article

[235] PubMed/NCBI

[236] Google Scholar

[ref67] 67. Centers for Disease Control and Prevention. User Guide to the 2022 Natality Public Use File [Internet]. Available from: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/DVS/natality/UserGuide2022.pdf
View Article
Google Scholar

[238] View Article

[239] Google Scholar

[ref68] 68. Thoma ME, De Silva DA, MacDorman MF. Examining interpregnancy intervals and maternal and perinatal health outcomes using U.S. vital records: Important considerations for analysis and interpretation. Paediatr Perinat Epidemiol. 2019;33(1):O60–72. pmid:30320453
View Article
PubMed/NCBI
Google Scholar

[241] View Article

[242] PubMed/NCBI

[243] Google Scholar

[ref69] 69. Hughes SW, A. Pew Research Center. 2019 [cited 2024 June 17]. Sizing Up Twitter Users. Available from: https://www.pewresearch.org/internet/2019/04/24/sizing-up-twitter-users/
View Article
Google Scholar

[245] View Article

[246] Google Scholar

[ref70] 70. Auxier B, Anderson M. Social Media Use in 2021 [Internet]. Pew Research Center; 2021. Available from: https://www.pewresearch.org/wp-content/uploads/sites/20/2021/04/PI_2021.04.07_Social-Media-Use_FINAL.pdf
View Article
Google Scholar

[248] View Article

[249] Google Scholar

[ref71] 71. Dang S. Exclusive: Elon Musk’s X restructuring curtails disinformation research, spurs legal fears. Reuters [Internet]. 2023 Nov 6 [cited 2025 June 16]; Available from: https://www.reuters.com/technology/elon-musks-x-restructuring-curtails-disinformation-research-spurs-legal-fears-2023-11-06/
View Article
Google Scholar

[251] View Article

[252] Google Scholar

[ref72] 72. X Developers. Starting February 9, we will no longer support free access to the Twitter API, both v2 and v1.1. A paid basic tier will be available instead. Twitter. 2023 [cited 2025 June 16]. Available from: https://x.com/XDevelopers/status/1621026986784337922
View Article
Google Scholar

[254] View Article

[255] Google Scholar

[ref73] 73. Centers for Disease Control and Prevention. Restricted-Use Vital Statistics Data [Internet]. 2023 [cited 2025 June 16]. Available from: https://www.cdc.gov/nchs/nvss/nvss-restricted-data.htm
View Article
Google Scholar

[257] View Article

[258] Google Scholar

Figures

Abstract

Background

Methods

Results

Conclusion

Introduction

Methods

Twitter data

Sentiment analysis

Individual-level pregnancy data

Demographic characteristics and covariates

Statistical analyses

Ethics statement

Results and discussion

Conclusions

Supporting information

S1 Table. Associations using prevalence rate ratios (PRRs) between state-level negative racial sentiment toward minoritized groups and hypertension type by pregnant individual’s race (all, racially minoritized groups, White) from 2016–2021, stratified by parity.

S3 Table. E-values for sensitivity analysis of unmeasured confounding in significant, adjusted associations using prevalence rate ratios (PRRs) between state-level negative racial sentiment toward minoritized groups and hypertension type by pregnant individual’s race.

References