Association Between the Social Vulnerability Index and Adverse Pregnancy Outcomes

Higher social vulnerability was associated with greater Black–White disparities.

Abstract

OBJECTIVE:

To assess the association between the Social Vulnerability Index (SVI) and racial disparities in pregnancy outcomes across U.S. counties and to quantify these racial disparities.

METHODS:

This was a cross-sectional study using restricted Centers for Disease Control and Prevention data sets, including natality data sets, fetal death data sets, and all-cause mortality data sets from 2016 to 2021. We limited analyses to Black or White individuals aged 15–44 years from 3,114 U.S. counties. Participants were categorized into quartiles based on county-level SVI. The primary outcome was maternal mortality rate while pregnant or within 42 days of the end of pregnancy, and secondary outcomes were pregnancy-related mortality while pregnant or within 365 days of the end of pregnancy, stillbirth, and preterm birth. Mixed-effect generalized linear models with negative binomial distribution were used to quantify disparities, using difference-in-difference analysis to measure the difference in outcomes between Black and White individuals across different levels of social vulnerability (first quartile as referent).

RESULTS:

A total of 20,189,328 individuals were included, distributed across SVI quartiles as follows: first quartile 2,558,131, second quartile 4,945,774, third quartile 6,827,503, and fourth quartile 5,857,920. Black individuals experienced significantly higher rates of maternal mortality, pregnancy-related mortality, stillbirth, and preterm birth compared with White individuals regardless of SVI quartiles. Difference-in-difference analyses demonstrated that disparities in maternal mortality rate were significantly larger in the second, third, and fourth quartiles compared with the first quartile (difference-in-difference 14.22 [95% CI, 2.11–26.33], 12.53 [95% CI, 1.26–23.81], and 18.82 [95% CI, 6.67–30.98], respectively). A worsening disparity in pregnancy-related mortality was observed in the fourth quartile, whereas disparities in stillbirth and preterm birth did not show significant differences across SVI quartiles.

CONCLUSION:

Racial disparities in maternal mortality intensified in counties with higher social vulnerability. These findings underscore the need for targeted interventions to address social determinants of health.

Racial disparities in pregnancy outcomes remain a critical public health issue in the United States.¹ Non-Hispanic Black women experience maternal mortality rates 3.5 times higher than that of their White counterparts,² with stillbirth rates more than double (10.3 vs 4.7/1,000 live births).³ Preterm birth also disproportionately affects Black individuals (14% vs 9% among White individuals in 2020).⁴ Although factors such as socioeconomic status, stress, and structural racism contribute to these disparities,^5–8 a significant portion remain unexplained, highlighting the need for broader analyses.⁹

The Social Vulnerability Index (SVI), developed by the Centers for Disease Control and Prevention (CDC), measures community resilience to external stresses such as natural disasters or health crises.¹⁰ It incorporates factors such as socioeconomic status, minority status, language barriers, and housing conditions, identifying populations more vulnerable to experience adverse outcomes. Communities with higher SVI scores often experience compounded health challenges, including increased vulnerability to heat-related illnesses,¹¹ severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seropositivity,¹² and reduced youth physical fitness.¹³ However, the link between SVI and severe pregnancy outcomes such as maternal mortality, stillbirth, and preterm births remains understudied.

This study aimed to address this gap by examining the association between community-level social vulnerability, as measured by the SVI, and severe pregnancy outcomes such as maternal mortality, stillbirth, and preterm birth. Furthermore, we sought to explore whether higher SVI scores amplify Black–White disparities in these outcomes.

METHODS

This cross-sectional study analyzed deidentified county-level data on individuals who delivered in the United States, including Alaska and Hawaii, from 2016 to 2021. Live-birth data were obtained from the CDC natality data sets¹⁴; stillbirth data were obtained from the fetal death data sets¹⁵; and maternal death data were obtained from all-cause mortality data sets.¹⁶ These restricted data sets included all outcomes within the United States from all U.S. residents and were accessed under an approved research proposal with the CDC. The study population included individuals aged 15–44 years at the time of delivery or maternal death. Those aged 45 years or older were excluded because of an increased likelihood of false-positive maternal death reports in this age group.¹⁷ To focus on racial disparities, analyses were limited to Black and White populations. The CDC data sets use self-reported race information, which is categorized with predefined racial and ethnic classifications. Race and ethnicity in SVI derive from the American Community Survey. Individuals and counties with missing SVI data or gestational age at delivery were excluded. This study was approved by the Eastern Virginia Medical School IRB. This study followed the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) reporting guideline.

The primary exposure was the SVI, derived from publicly available data sets.¹⁸ The SVI is a composite measure that reflects vulnerability across four themes: socioeconomic status, household characteristics, racial and ethnic minority status, and housing type and transportation. The methodology behind the SVI development has been detailed in previous publications.¹⁰ The county-level SVI information was linked to other data sets.

In brief, the 2016 and 2018 versions of the SVI were derived by summing percentile rankings at both the census tract and county levels for 15 indicators grouped into four themes: socioeconomic status (including below poverty level, unemployment, income, and lack of a high school diploma), household characteristics (including individuals aged 65 years or older, individuals aged 17 years or younger, individuals with disabilities, and single-parent households), minority status and language (minority status and individuals aged 5 years or older who speak English “less than well”), and housing type and transportation (including multiunit structures, mobile homes, overcrowding, lack of vehicle access, and group quarters).

The 2020 version of the SVI expanded to 16 social factors and refined several measures. In particular, it replaced “below poverty level” with “below 150% of the poverty level,” added “housing cost burden,” introduced “no health insurance,” and changed “minority status and language” to two separate components: “racial and ethnic minority status” and an updated measure of “English language proficiency.” Despite these refinements, the 2020 index remained organized under the same four overarching themes (socioeconomic status, household characteristics, minority status, and housing type and transportation).

The 2020 SVI was applied to births occurring in 2020–2021, the 2018 SVI to births in 2018–2019, and the 2016 SVI to births in 2016–2017. The SVI ranges from 0 to 1, with higher values indicating greater vulnerability. For the analysis, the SVI and theme-specific indices were categorized into quartiles: first quartile (lowest vulnerability), second quartile, third quartile, and fourth quartile (highest vulnerability).

The primary outcome of this analysis was maternal mortality rate, defined as maternal deaths per 100,000 live births from live-birth data. Maternal death is defined by the World Health Organization as, “the death of a woman while pregnant or within 42 days of termination of pregnancy, irrespective of the duration and the site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management, but not from accidental or incidental causes.”¹⁹ Therefore, those with the underlying cause of death assigned to the International Statistical Classification of Diseases, Tenth Revision codes A34, O00–O95, and O98–O99 were used.¹⁶

Secondary outcomes included pregnancy-related mortality rate, stillbirth, and preterm birth before 37 weeks of gestation. Pregnancy-related mortality was defined as the number of maternal deaths regardless of gestational age per 100,000 live births up to 1 year postpartum (International Statistical Classification of Diseases, Tenth Revision codes O96 and O97 in addition to maternal mortality rate) resulting from any cause related to or aggravated by the pregnancy or its management.²⁰ Stillbirth was defined as fetal death at or after 20 weeks of gestation. Preterm birth was defined as delivery before 37 weeks of gestation. For any missing or unreported outcomes, the event was treated as not having occurred.

Demographic characteristics and outcomes were compared across SVI quartiles with χ² tests for categorical variables and one-way analysis of variance for continuous variables. We conducted analyses on annual county-level aggregates. We plotted the rate of the primary outcome according to the county to visualize geographic variation. To account for the random effects of state and county-level clustering, we used a mixed-effect generalized linear model using robust SEs.²¹ Given that the outcomes were count variables, we specified a negative binomial distribution with a log link to address overdispersion. We used the total number of live births as the denominator for each outcome and incorporated the log of the total births of each county as an offset in the model. This approach standardizes the analysis across counties with differing birth counts, allowing more accurate comparisons of outcome rates.

Interaction terms were created to assess the modifying effect of race on the relationship between SVI and outcomes. Marginal standardization was applied to estimate incidence rates of outcomes according to race and SVI categories. Difference-in-difference analysis was used to compare incidence rate differences, with the least vulnerable counties serving as the reference group.²²

To account for confounding, we included the county-specific proportion of age (35 years or older), obesity (body mass index [BMI, calculated as weight in kilograms divided by height in meters squared] 30 or higher), chronic hypertension, pregestational diabetes, education (less than a high school degree), and WIC (Special Supplemental Nutrition Program for Women, Infants, and Children) participation as covariates.^23,24 In addition, the year of pregnancy was included to address the increased risk of adverse outcomes linked to the coronavirus disease 2019 (COVID-19) pandemic.²⁵

We conducted several sensitivity analyses using the same covariates as in the main models. First, given that the 2016 and 2018 versions included 15 indicators and the 2020 version included 16 indicators, we stratified the analysis by years (2016–2019 vs 2020–2021). Second, we also explored associations between the four SVI themes and the outcomes. All hypothesis tests were two sided, with statistical significance set at a type I error rate of 0.05. Statistical analyses were performed with Stata 18.5.

RESULTS

The cohort diagram is illustrated in Figure 1. Of the 20,189,328 individuals included from 3,114 counties, the distribution across SVI quartiles was as follows: 2,558,131 individuals in the first quartile, 4,945,774 in the second quartile, 6,827,503 in the third quartile, and 5,857,920 in the fourth quartile. Demographic characteristics and outcomes stratified by SVI quartiles are presented in Table 1. Individuals in the fourth quartile (highest social vulnerability) were more likely to be Black and younger; to have obesity, less than a high school education, chronic hypertension, and pregestational diabetes; and to have participated in WIC. Furthermore, individuals in the fourth quartile exhibited significantly higher rates of adverse outcomes, including maternal mortality, pregnancy-related mortality, stillbirth, and preterm birth.

Fig. 1. Cohort diagram. SVI, Social Vulnerability Index; GA, gestational age.

Kawakita. Social Vulnerability Index and Adverse Pregnancy Outcomes. Obstet Gynecol 2025.

Table 1.

Demographics and Outcomes According to Social Vulnerability Index Quartile*

Appendix 1, available online at http://links.lww.com/AOG/E52, illustrates the distribution of maternal mortality rate per 100,000 births across the United States, along with SVI categories. The maternal mortality rate map reveals that although most counties exhibit relatively low mortality rates (0–10/100,000), pronounced clusters of elevated maternal mortality rate (40 or more/100,000) extend along the East Coast, in the southwestern and southeastern regions, and in select areas of Alaska and Hawaii. The SVI map shows that counties with the highest social vulnerability (fourth quartile, dark blue) are located predominantly in the South, Southwest, and Alaska, whereas counties with the lowest vulnerability (first quartile, light blue) are more common in the Midwest and Northeast (Appendix 2, available online at http://links.lww.com/AOG/E52). A visual comparison of these maps suggests a geographic overlap between areas with high maternal mortality rate and those with greater social vulnerability, particularly in the South and West. The distributions of secondary outcomes are shown in Appendices 3–5, available online at http://links.lww.com/AOG/E52.

Appendix 6, available online at http://links.lww.com/AOG/E52, and Figures 2 and 3 and display the association between SVI and adverse pregnancy outcomes, stratified by race. Black individuals consistently experienced higher incidences of maternal mortality rate, pregnancy-related mortality, stillbirth, and preterm birth across all SVI quartiles. Difference-in-difference analyses demonstrated that disparities in maternal mortality rate were significantly larger in the second, third, and fourth quartiles compared with the first quartile (adjusted difference in difference 14.22 [95% CI, 2.11–26.33], P=.021 for interaction; 12.53 [95% CI, 1.26–23.81], P=.029 for interaction; and 18.82 [95% CI, 6.67–30.98], P=.002 for interaction, respectively). A worsening disparity in pregnancy-related mortality was observed in the fourth quartile, whereas disparities in stillbirth and preterm birth did not show significant differences across SVI quartiles. Unadjusted analyses were consistent with the adjusted analyses except for the significant difference in difference for preterm birth in the fourth quartile.

Fig. 2. Association between Social Vulnerability Index (SVI) quartile and adverse pregnancy outcomes, including the maternal mortality rate (MMR) per 100,000 live births (A) and pregnancy-related mortality (PRM) per 100,000 live births (B), stratified by race. Outcomes are represented for Black (black circles) and White (blue circles) individuals. A and B. Adjusted (solid lines) and unadjusted (dashed lines) estimates for each SVI quartile. C and D. Results from the difference-in-difference (DID) analysis for each outcome, quantifying changes in racial disparities across SVI quartiles (with the first quartile as the reference). The DID estimates are represented by green and orange circles, with solid green lines indicating adjusted estimates and dashed orange lines indicating unadjusted estimates. Statistically significant findings are marked with plus signs.

Kawakita. Social Vulnerability Index and Adverse Pregnancy Outcomes. Obstet Gynecol 2025.

Fig. 3. Association between Social Vulnerability Index (SVI) quartile and adverse pregnancy outcomes, including stillbirths, per 1,000 live births (A) and preterm births per 100 live births (B), stratified by race. Outcomes are represented for Black (black circles) and White (blue circles) individuals. A and B. Adjusted (solid lines) and unadjusted (dashed lines) estimates for each SVI quartile. C and D. Results from the difference-in-difference (DID) analysis for each outcome, quantifying changes in racial disparities across SVI quartiles (with the first quartile as the reference). The DID estimates are represented by green and orange circles, with solid green lines indicating adjusted estimates and dashed orange lines indicating unadjusted estimates. Statistically significant findings are marked with plus signs. Adjusted estimates account for year of pregnancy, maternal age (35 years or older), obesity, chronic hypertension, pregestational diabetes, education level (less than high school), and participation in WIC (Special Supplemental Nutrition Program for Women, Infants, and Children).

Kawakita. Social Vulnerability Index and Adverse Pregnancy Outcomes. Obstet Gynecol 2025.

Appendix 7, available online at http://links.lww.com/AOG/E52, illustrates the association between SVI and adverse pregnancy outcomes, stratified by race and across two time periods: 2016–2019 and 2020–2021. Results from 2020 to 2021 were largely consistent with the primary analysis, showing significant disparities. In contrast, results from 2016 to 2019 were generally not statistically significant, with the exception of a significant difference in difference for maternal mortality rate in the fourth quartile. Appendix 8, available online at http://links.lww.com/AOG/E52, explores the association between the four SVI themes and adverse pregnancy outcomes. Within the socioeconomic theme, significantly worsening disparities were observed in maternal mortality rate and pregnancy-related mortality across the second, third, and fourth quartiles, as well as in preterm birth in the second and third quartiles.

DISCUSSION

In this cross-sectional analysis of restricted CDC data sets, Black individuals experienced significantly higher rates of maternal mortality, pregnancy-related mortality, stillbirth, and preterm birth compared with White individuals, regardless of SVI quartiles. Counties with higher SVI were associated with greater Black–White disparities in these outcomes, and these findings were robust across sensitivity analyses. An analysis of individual SVI themes showed that socioeconomic vulnerability was significantly associated with worsening disparities in mortality and preterm birth, whereas the other SVI themes were not associated with such disparities.

The relationship between racial disparities and social determinants of health has become an increasingly important focus of research. Previous studies such as those using the Maternal Vulnerability Index, which was developed to quantify county and census tract–level indicators of vulnerability to adverse pregnancy outcomes, demonstrated that Black individuals were more likely to reside in more vulnerable areas compared with White individuals.²⁶ Furthermore, individuals living in counties with high Maternal Vulnerability Index scores were more likely to experience extreme preterm birth before 28 weeks of gestation compared with those in counties with low Maternal Vulnerability Index scores.²⁷ Our study is unique because we quantified widening disparities according to the social vulnerability of counties.

Our study using the SVI aligns with prior research and highlights how socially vulnerable counties are associated with exacerbating racial disparities in maternal health. We also found that the exacerbating disparities in more vulnerable counties were pronounced in 2020–2021, suggesting that the COVID-19 pandemic further intensified disparities. These findings underscore the role of structural racism in shaping social vulnerability; systemic inequities in housing, health care, and economic opportunities disproportionately affect marginalized communities. Policy makers and researchers can leverage the SVI to identify these populations at high risk and to design interventions aimed at dismantling the structural racism embedded in social and health care systems that drives these disparities.²⁸

Black individuals in highly vulnerable counties face disproportionate risks for maternal mortality rate, pregnancy-related mortality, stillbirth, and preterm birth. Health care professionals may consider incorporating tools such as the SVI to identify patients at high risk and to tailor interventions accordingly. This could include increased surveillance, early interventions, and enhanced prenatal care for individuals in vulnerable areas. Given that the socioeconomic theme was strongly associated with adverse outcomes, interventions addressing poverty, unemployment, and educational barriers should be prioritized. Screening for social risk factors during prenatal care and connecting patients with support services such as financial assistance, educational resources, and community programs could mitigate some of the adverse effects in high-SVI counties.

This study uses area-level social vulnerability rather than individual-level measures of vulnerability. Although the overall level of social vulnerability at the individual level generally corresponds to the area-level social vulnerability,²⁹ it is crucial to assess the association between social vulnerability at the individual level and worsening disparity in pregnancy outcomes. Future research should explore the relationship between individual-level social vulnerability and adverse pregnancy outcomes, potentially with tools such as the Accountable Health Communities Health-Related Social Needs Screening Tool.³⁰ In addition, future studies should assess the effect of specific community-level interventions and determine whether SVI-based screening in clinical settings can improve maternal outcomes and reduce racial disparities.

This study has several strengths. First, the use of the difference-in-difference analysis allowed us to quantify racial disparities in maternal outcomes across different levels of social vulnerability, providing a more granular assessment of these disparities. Second, the SVI, a publicly accessible tool, makes the findings replicable and applicable to public health efforts. Third, our results were validated through robust sensitivity analyses, confirming the consistency and reliability of our findings across different model specifications. Last, because we used comprehensive U.S. birth certificate data, which includes all pregnant individuals nationwide, our results are both generalizable and reflective of the broader population.

Our study is not without limitations. First, given the cross-sectional study design, we can assess only the association, not the causal relationships. Second, as we discussed, the SVI, area-level social vulnerability, may not fully capture individual-level factors such as household income, education, employment, and household structure. Third, birth certificate data, although comprehensive, may have errors and lack the granularity of census tract–level data. Fourth, although the difference-in-difference method is a robust tool for quantifying disparities, it cannot fully account for unmeasured confounders that may vary across counties and affect maternal outcomes. Finally, because of the lack of linkage across individual-level data sets, we could not account for random effects attributable to individuals who experienced several pregnancies during the study period.

In conclusion, Black individuals face greater risks of maternal mortality and adverse pregnancy outcomes. These disparities are intensified in counties with higher social vulnerability. Social determinants such as socioeconomic status drive these disparities. The SVI provides a valuable tool for identifying vulnerable populations and guiding targeted interventions. Future studies could use more granular community-level data sets to isolate specific components of social vulnerability such as access to perinatal care, food insecurity, and housing instability and to develop targeted interventions.

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