Introduction
When Hurricane Harvey struck Texas in 2017, over 30,000 people were displaced; it has been considered one of the costliest tropical cyclones in history.Reference Frame, Wehner, Noy and Rosier 1 These disasters affect the long-term health of survivors, including spikes in chronic and infectious diseases and mental health disorders.Reference Huang, Gao and Xu 2 Affected individuals often exhibit post-traumatic stress, depression, or anxiety after hurricanes, flooding, or earthquakes.Reference Keya, Leela, Habib, Rashid and Bakthavatchalam 3 Despite a growing body of research on the physical and mental health effects of natural disasters on human health, pregnant women remain underrepresented in these studies.
Health outcomes in the United States are strongly shaped by a person’s socioeconomic status, race/ethnicity, immigration status, access to health care, and gender identity.Reference McDowell 4 Women tend to have higher rates of health insurance and health care utilization; yet structural and systemic barriers mean they often receive less intensive treatment and face longer wait times.Reference Guzikevits, Gordon-Hecker and Rekhtman 5 Furthermore, foreign-born people encounter disproportionate health care access barriers compared to U.S.-born adults.Reference Chen, Sarkar, Hsu and Dizon 6
Events like Hurricane Harvey disproportionately impact vulnerable populations, including pregnant women. We have previously reported that maternal and neonatal morbidity increased after Hurricane Harvey, especially for gravidae of low socioeconomic status.Reference Mendez-Figueroa, Chauhan and Tolcher 7 We found that pregnant women affected by Hurricane Harvey had higher rates of blood transfusions, increased rates of newborn sepsis, and were more likely to suffer a neonatal death. In this current study, we aimed to identify the demographic characteristics of the pregnant individuals most severely impacted by Hurricane Harvey. This study focused on three key indicators: access to medical services (including hospitals with maternity care), financial hardship, and experiences of anxiety following the storm.
Methods
Study Design
This cross-sectional study was conducted using de-identified data from Baylor College of Medicine’s PeriBank database (IRB protocol H-26364).Reference Antony, Hemarajata and Chen 8 Following Hurricane Harvey, a supplemental survey was added to the PeriBank protocol to assess gravidae’s experiences during the storm (Supplemental Table 1). The dataset analyzed in this study includes gravidae who gave birth between October 28, 2017 and July 1, 2018.
Supplemental Survey and Outcomes
Participants were asked whether they had been affected by Hurricane Harvey, and those who answered “Yes” were classified as affected and included in the study, while a “No” answer was considered as unaffected. Affected gravidae were further asked about their access to medical and maternity services, financial hardship, and experiences of anxiety within four weeks of the storm. These outcomes were analyzed in relation to various demographic factors, including age, marital status, race/ethnicity, nativity, English proficiency, education level, household income, type of health insurance, and the hospital used for delivery.
Statistical Analyses
Data were abstracted from PeriBank and coded for analysis using Statistical Package for Social Sciences (SPSS). Descriptive statistics were reported using percentages, bivariate correlations, and Pearson chi-square tests. Multivariate logistic regression models were used to assess predictors of each outcome.
Results
Of the 4951 subjects pregnant during Hurricane Harvey, a total of 511 indicated that they were affected by the hurricane. The majority of the affected population was married (81.6%), with more US-born than foreign-born (55.5%). Almost half the subjects were Latinx (49.4%), with the majority less fluent in English (56.0%). More than half of the population had no college degree, relied more on public health insurance, had a household income of $35,000 and above, and utilized private hospitals (56.2%, 59.1%, 51.2%, and 65.5%, respectively).
The findings reveal that access to medical care or hospitals with maternity services was not equally distributed (Table 1). Foreign-born individuals, Latinxs, especially those with limited English proficiency, low-income gravidae, those on public insurance, and those who relied on county hospitals were significantly more likely to report having no access to either medical services or a hospital with maternity care during the disaster. Only 46.4% of those earning under $35,000 had access to medical services, versus 66.9% of those earning more. Insurance type and hospital choice also highlighted these disparities (Table 1). Multivariate logistic regression confirmed these patterns (Supplemental Table 2).
Table 1. Access to medical services and hospitals with maternity care
This study also focused on understanding which pregnant individuals in Houston experienced major financial hardship, defined as losses over $5000. Results showed that U.S.-born gravidae, those with higher incomes, private insurance, or those who used private hospitals were more likely to report financial difficulty than their foreign-born, lower-income, or publicly insured counterparts (Table 2). Multivariate models confirm that U.S.-born individuals and those with higher income were more likely to report losses (Supplemental Table 2).
Table 2. Demographics of those reporting financial difficulty and experiencing anxiety following Hurricane Harvey
Subjects were also asked if they experienced anxiety in the four weeks following Hurricane Harvey. The data reveal that higher socioeconomic status is associated with greater reports of anxiety. U.S.-born gravidae, those with college degrees, and those with household incomes above $35,000 were significantly more likely to report experiencing anxiety for at least four weeks after the storm. (Table 2B). Multivariate logistic regression confirmed these trends (Supplemental Table 2).
Discussion
This study reveals the disproportionate impact of Hurricane Harvey on pregnant individuals from marginalized communities in Houston. These individuals faced significant barriers to accessing both routine medical services and hospitals with maternity care during the storm, highlighting how social determinants of health compound vulnerability during disasters.
Surprisingly, however, U.S.-born, English-speaking Latinx individuals with higher incomes and education levels reported greater financial hardship and post-disaster anxiety. This unexpected finding suggests that disaster-related stress and economic strain may manifest differently across subgroups, possibly due to differences in expectations, social support networks, or access to recovery resources.
One of the most consistent predictors of poor outcomes was reliance on the county hospital, a publicly funded institution serving predominantly uninsured or publicly insured patients. This underscores the urgent need to bolster the resilience of public health infrastructure and ensure that safety-net institutions are equipped to serve vulnerable populations during crises.
Ultimately, these findings illuminate the structural inequities that shape both health care access and disaster resilience. Pregnant individuals from racially and economically marginalized backgrounds face compounded risks during climate-related emergencies. By leveraging a robust perinatal dataset and timely data collection, this study contributes critical insights into how disasters exacerbate existing health disparities.
Supplementary material
The supplementary material for this article can be http://doi.org/10.1017/dmp.2025.10234.
Acknowledgments
Collection of the data analyzed in this study was supported by the National Institutes of Health (R21ES029462) to K.M.A. This manuscript has been adapted from the master’s thesis of Cynthia D. Shope, submitted to the University of Houston Clear Lake. This work was funded, in part, by the Research Vision at Texas Children’s Hospital
Author’s contribution
CDS designed the study, analyzed the data, and wrote the manuscript; AEL and SMC advised on data analysis strategies; KMA designed the Harvey survey; MAS undertook the final draft of the manuscript. All authors have contributed to and approved the final manuscript.
Competing interests
None.
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