{"paper_id":"3baedd27-4c27-4ea2-9e62-a3a3ea2a02c7","body_text":"Navigating Geographical Disparities: Access to Obstetric Hospitals in Maternity Care Deserts and across the United States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Navigating Geographical Disparities: Access to Obstetric Hospitals in Maternity Care Deserts and across the United States Jazmin Fontenot, Christina Brigance, Ripley Lucas, Ashley Stoneburner This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3900489/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 May, 2024 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted 10 You are reading this latest preprint version Abstract BACKGROUND Access to maternity care in the U.S. remains inequitable, impacting over two million women in maternity care “deserts.\" These areas, exacerbated by hospital closures and workforce shortages, heighten risks of pregnancy-related complications, particularly in rural regions. This study investigates travel distances and time to obstetric hospitals, emphasizing disparities faced by those in maternity care deserts and rural areas, while also exploring variances across races and ethnicities. METHODS The research adopted a retrospective secondary data analysis, utilizing the American Hospital Association and Centers for Medicaid and Medicare Provider of Services Files to classify obstetric hospitals. The study population included census tract estimates of birthing individuals sourced from the U.S. Census Bureau's 2017–2021 American Community Survey. Using ArcGIS Pro Network Analyst, drive time and distance calculations to the nearest obstetric hospital were conducted. Furthermore, Hot Spot Analysis was employed to identify areas displaying significant spatial clusters of high and low travel distances. RESULTS The mean travel distance and time to the nearest obstetric facility was 8.3 miles and 14.1 minutes. The mean travel distance for maternity care deserts and rural counties was 28.1 and 17.3 miles, respectively. While birthing people living in rural maternity care deserts had the highest average travel distance overall (33.4 miles), those living in urban maternity care deserts also experienced inequities in travel distance (25.0 miles). States with hotspots indicating significantly higher travel distances included: Montana, North Dakota, South Dakota, and Nebraska. Census tracts where the predominant race is American Indian/Alaska Native (AIAN) had the highest travel distance and time compared to those of all other predominant races/ethnicities. CONCLUSIONS Our study revealed significant disparities in obstetric hospital access, especially affecting birthing individuals in maternity care deserts, rural counties, and communities predominantly composed of AIAN individuals, resulting in extended travel distances and times. To rectify these inequities, sustained investment in the obstetric workforce and implementation of innovative programs are imperative, specifically targeting improved access in maternity care deserts as a priority area within healthcare policy and practice. maternity care deserts maternal health disparities in maternity care access spatial analysis Figures Figure 1 Figure 2 Figure 3 BACKGROUND Despite a decrease in the global maternal mortality rate, the rate in the United States has increased for decades [ 1 ]. Nearly 80% of pregnancy-related deaths in the U.S. are preventable [ 2 ], indicating ample opportunity for improvement. Access to healthcare is critical during the perinatal period; however, hospitals that offer maternity care services are not equitably distributed across the country. Further, access to maternity care depends on several other factors, including the availability of obstetric providers, risk-appropriate care, and health insurance [ 3 – 5 ]. More than 2 million women of childbearing age live in maternity care “deserts* [ 6 ], defined as counties without birthing facilities or maternity care providers [ 7 ]. Areas of inadequate access to maternity care are created through systems and policies that deplete community resources. Hospital and maternity unit closures [ 8 , 9 ], obstetric workforce shortages [ 8 , 9 ], inadequate Medicaid reimbursement rates [ 9 , 10 ], and systemic racism and classism [ 11 ], have contributed to the increase in more counties with low or no access to care. One-third of all U.S. counties are maternity care deserts, and 60% are in rural areas [ 7 ]. Maternity care deserts are associated with an increased risk of pregnancy-related death up to one year postpartum [ 12 ]. Birthing people living in rural areas have a 9% greater risk of severe maternal morbidity and mortality from pregnancy and childbirth and are more likely to report difficulties in accessing quality care compared to urban residents [ 13 – 16 ]. Across the nation, the closure of obstetric hospitals has played a role in the rise of maternity care deserts, resulting in increased distance to care [ 5 , 7 ]. These extended distances not only discourage the use of preventive care but also impact the overall health and quality of care for individuals going through childbirth [ 17 , 18 ]. In addition to the financial strain and heightened stress and anxiety [ 19 ], the risk of adverse maternal outcomes and neonatal intensive care unit admission [ 20 ] also increases with longer travel distances and time to care. Unlike established standards for reaching a hospital promptly in medical emergencies like stroke [ 21 ], there is currently no standardized guideline for the travel time to reach a hospital during an obstetric emergency [ 22 , 23 ]. Study Purpose This research aims to assess access to obstetric hospitals across the U.S., focusing on inequities for birthing people in maternity care deserts and rural areas. A secondary purpose is to highlight the differing distance and time to obstetric hospitals by the predominant race/ ethnicity in each census tract. Using spatial analysis techniques, our study maps the typical travel distance and time needed to reach the nearest obstetric hospital in the U.S. METHODS Research Design We conducted a retrospective analysis of secondary data to estimate the travel distance and time to obstetric hospitals across the U.S. The main outcomes were geographic distance, in miles, and drive time, in minutes, from residential census tracts to the nearest hospital that provided obstetric care. Analyses were cross-sectional and estimated travel distances for all birthing people in the U.S. and used census tract fertility data and 2021 hospital availability. We examined differences in driving distance and driving time by maternity care desert designation, rurality, and predominant race/ethnicity. Data Sources Births. This analysis used five-year fertility estimates from the U.S. Census Bureau’s 2017–2021 American Community Survey (ACS) [ 24 ]. The ACS estimates the number of people who reported giving birth in the past year for all U.S. census tracts. Demographic variables from the ACS were used to analyze distance by the predominant race/ethnicity in each census tract. Census Tracts. The spatial analysis files included point centroid locations for U.S. census tracts linked with ACS birth estimates. Point centroid locations were spatially weighted to account for population density within each census tract using data from the IPUMS National Historical Geographic Information System (NHGIS) [ 25 ]. U.S. Census Topologically Integrated Geographic Encoding and Referencing (TIGER/Line) shapefiles were used for data visualization at the county and level [ 26 ]. Hospitals. Hospital location data were obtained from the American Hospital Association (AHA) 2021 Survey [ 27 ]. The AHA Annual Survey provides data for more than 6,200 hospitals and healthcare systems and includes addresses for geocoding. The use of AHA data is consistent with federal government agencies as the most comprehensive hospital data source for health research [ 28 ]. The AHA survey uses self-reported data to classify hospitals with obstetric care services. To check for missing obstetric hospitals, we performed a secondary validation of AHA hospital locations using the Centers for Medicare and Medicaid (CMS) Provider of Service (POS) files for 2021 and followed enhanced methods of identification described by prior research [ 29 , 30 ]. Only hospitals that listed providing obstetric services according to CMS POS data OB_SRV_CD > = 1 were included. Obstetric hospitals were selected if they met the following criteria (see Fig. 1 ). Designations for Maternity Care Access. Maternity care access designations were created by the Perinatal Data Center at March of Dimes [ 7 ]. U.S. counties are classified using data from the Human Resources Service Administration’s (HRSA) Area Health Resource Files (2021–2022) for obstetric providers (OB/GYN, nurse midwives) and obstetric hospitals; counts of family physicians who deliver babies from the American Board of Family Medicine (2017–2020); birth estimates from the National Center for Health Statistics (NCHS) 2021 natality data; and birth center data from the 2021 American Association of Birth Centers (AABC) [ 31 – 34 ]. For this analysis, we combined the low and moderate maternity care access categories into one “limited” access designation. Levels of maternity care access are defined as follows: Maternity care desert: A county with no hospitals providing obstetric care, no birth centers, and no obstetric providers, which include certified nurse midwives, certified midwives, and family physicians who reported delivering babies. ​ Limited access: A county with fewer than two hospitals or birth centers offering obstetric care and fewer than 60 obstetric providers per 10,000 births. Full access: A county with two or more hospitals or birth centers offering obstetric services or more than 60 obstetric providers per 10,000 births. Rural Areas. Rural-Urban Continuum codes, developed by the U.S. Department of Agriculture, Economic Research Service, classify counties into nine categories by population size based on census-defined urbanized areas and by adjacency to metropolitan areas [ 35 ]. The categories can be further classified as metropolitan/nonmetropolitan or urban/rural. Metropolitan categories were defined as a county with a metropolitan area of 1 million people (about the population of Delaware) or more, 250,000 to 1 million, or fewer than 250,000 (one, two, or three on the Rural-Urban Continuum). All the other categories (four or more on the Rural-Urban Continuum) are considered nonmetropolitan. Urban areas include all metropolitan areas and/or areas adjacent to a metropolitan area with a population greater than 2,500 (one through four and six on the Rural-Urban Continuum). Rural includes areas with a population of 2,500 or more not adjacent to a metropolitan area and areas with fewer than 2,500 people (five and seven or higher on the Rural-Urban Continuum). Analyses ArcGIS Pro, version 3.0 [ 36 ], was used to geocode validated hospital locations. Linked ACS birth data with population-weighted census tract point locations were used as the residential incidents (starting point locations for GIS calculations). The ESRI Network Analyst Extension Closest Facility Solver [ 37 ] calculated the driving time and mileage distance from each birth to the nearest obstetric hospital location. ArcGISOnline network data was used for routing services, a regularly maintained database of comprehensive street data that includes historical, live, and predictive road networks. To increase the generalizability of our results and because labor can occur at any time of the day, we did not account for fluctuations in traffic conditions. All the statistical analyses were performed using SAS software, version 9.4 [ 38 ]. Imported GIS census tract calculations were aggregated at the county-level for comparison with county-level maternity care desert designations, rurality, metropolitan status, and predominant race/ethnicity. We tested significant differences in travel time by county-level factors using a one-way ANOVA test for factors with three or more levels (maternity care desert designation) and t-tests for all others. Travel time cutoffs of 30 and 60 minutes were used to describe the percentage of birthing people who live far from obstetric care. To define areas in the U.S. with statistically significant high and low travel distances, we conducted hotspot analyses for the continental U.S. at the county level. Counties with 10 or fewer births were suppressed. RESULTS We identified 3,991,060 birthing people among 85,396 census tracts across the U.S., D.C., and Puerto Rico and 2,630 hospitals that provide obstetric care. Obstetric hospitals were geocoded with a match score of 99.0%; nine hospitals did not have a match and were manually reviewed using Google Maps to obtain point location data. Four hospitals were excluded after manual review yielded no location data. Drive-time routes were calculated for 99.6% of the total estimated births; eight census tracts did not have calculable road network routes, accounting for less than 0.4% of all estimated births. Distance overall. Figure 2 displays the quintile distribution of mean travel distance, in miles, by county across the U.S. The mean distance and time to the nearest obstetric hospital were 8.3 miles and 14.1 minutes, respectively (Table 1 ). Nearly all of the U.S. population lived within 1 hour of the nearest obstetric care hospital (99.7%), and 93.6% lived within 30 minutes. Table 1 Mean Miles and Minutes to Nearest Obstetric Hospital by Census Tract Births N Mean distance miles (SD) Mean time minutes (SD) US Census tracts 3,991,060 8.3 (9.0) 14.1 (21.8) MCD Designation Maternity care desert 145,146 28.1 (21.2) 36.5 (102.3) Limited access 248,347 14.2 (10.8) 20.1 (11.8) Full access 3,597,567 7.1 (6.6) 12.7 (8.5) Rurality Urban 3,785,735 7.8 (7.4) 13.5 (8.6) Rural 205,325 17.3 (21.9) 24.6 (88.1) Metropolitan Residence Nonmetro 536,349 16.0 (16.8) 22.3 (55.6) Metro 3,454,711 7.1 (6.3) 12.8 (7.5) Predominant race/ethnicity White 2,763,765 9.2 (8.8) 15.0 (11.0) Hispanic 713,750 6.1 (6.7) 11.3 (7.5) Black 406,972 6.3 (6.5) 11.9 (7.4) Asian 92,886 5.2 (18.3) 12.5 (107.7) AIAN 9,772 27.7 (41.3) 45.9 (203.3) Multiracial 1,967 12.8 (10.8) 23.4 (18.5) NH/PI 1,360 17.2 (10.5) 26.4 (14.2) Travel Time to Closest Obstetric Care (%) <=15 minutes 2,692,070 - 67.5% <=30 minutes 3,736,131 - 93.6% <=45 minutes 3,933,929 - 98.6% <=60 minutes 3,977,229 - 99.7% Distance by maternity care designation. The mean time and distance to the closest obstetric hospital increased as access to care decreased. The mean distance and time to care by maternity care access designation was 7.1 miles or 12.7 minutes for full access areas; 14.2 miles or 20.1 minutes for limited access areas; and 28.1 miles or 36.5 minutes in maternity care deserts. The mean travel time among birthing people in maternity care deserts was 3.9 times greater than in full-access areas. Distance by rurality. The travel distance and time to care for birthing people living in rural and urban areas was 17.3 miles or 24.5 minutes and 7.8 miles or 13.5 minutes, respectively. Differences in travel distance and time were also observed when comparing metropolitan (7.1 miles or 12.8 minutes) to nonmetropolitan (16.0 miles or 22.3 minutes) areas. When examining rural and urban distance differences by maternity care access designation, we found that distances were similar for low-access and full-access areas but were greater regardless of rural designation for maternity care deserts (Table 2 ). Those living in rural maternity care deserts traveled 1.9 times farther than the average birthing person in a rural area and 4.0 times farther than the average birthing person overall. In urban maternity care deserts, the average travel distance was 3.2 times farther than the average person living in an urban area and 3.0 times farther than the average birthing person. These discrepancies highlight that even in urban areas, those living in maternity care deserts travel farther than those living in rural areas. Distance by predominant race. Census tracts classified as predominantly American Indian and Alaska Native (AIAN) had the highest travel distance and time to obstetric hospitals. On average, those living in predominantly AIAN census tracts travel 27.8 miles or 45.9 minutes to their nearest obstetric hospital, 3.3 times farther than the average travel time in the U.S. Driving distance and time differences are exacerbated for AIAN living in maternity care deserts, where the closest obstetric hospital is 59.0 miles or 161.1 minutes away, on average. This disparity is 2.0 times greater than that of all other races and ethnicities in maternity care deserts. Predominant AIAN census tracts are 2.2 times more likely to be in maternity care deserts than census tracts with predominantly White residents. Table 2 Distance and Time by Geographic Characteristics and Maternity Care Designation Full access Limited access Maternity care desert Miles Time Miles Time Miles Time Overall 7.1 12.7 14.2 20.1 28.1 36.5 Rurality Urban 6.9 (6.1)* 12.6 (7.5)* 14.2 (10.6)* 20.1 (11.5)* 25.0 (11.2)* 31.4 (11.9)* Rural 11.4 (13.6)* 17.1 (20.1)* 14.6 (13.7)* 19.8 (15.1)* 33.4 (31.3)* 45.4 (169.4)* Predominate race/ethnicity White 7.8 (6.9)* 13.5 (8.3)* 14.9 (10.5)* 20.9 (11.6)* 28.0 (13.4)* 34.8 (23.8)* Hispanic 5.6 (5.5)* 10.9 (6.6)* 10.7 (11.5)* 15.7 (11.9)* 24.2 (17.2)* 29.3 (16.1)* Black 5.3 (4.4)* 10.9 (5.5)* 10.7 (10.8)* 16.3 (11.7)* 27.6 (10.7)* 33.2 (11.7)* Asian 4.8 (3.4)* 10.2 (4.9)* -- -- -- -- AIAN 24.7 (27.7)* 35.8 (58.0)* 26.2 (19.5)* 31.4 (20.7)* 59.0 (105.3)* 161.1 (657.3)* * p-value < 0.001 Travel distance by state and hot spot analysis. The travel times and distances by state are shown in more detail Additional File 1. The states with the highest overall travel distances were Alaska, West Virginia, Montana, Mississippi, and South Dakota. The states with the highest travel times included Hawaii and North Dakota. The states with the lowest overall travel distances were D.C., New Jersey, Connecticut, New York, Rhode Island and California, all with mean travel distances lower than six miles. Hot spot analysis revealed areas with statistically significant high and low travel distances in the U.S. (Fig. 2 ). From this analysis, we found that regions of Montana, South Dakota, North Dakota and Nebraska all had the highest concentrations of maternity care deserts, and these were also areas with statistically significant hot spots for high travel distances to obstetric care. States with large populations living in maternity care deserts also had statistically significant spatial clustering, indicating long travel distances; these included Texas, Mississippi, Oklahoma, and Missouri. Areas for low travel times across the U.S. were concentrated in the northeast and included states with predominantly metropolitan areas such as D.C., Rhode Island, and New Jersey. Additional clusters of low travel times were found for the East North Central region of the U.S., Northern California, Minnesota, and North Carolina (Fig. 3 ). DISCUSSION Consistent with the findings of prior literature, we found that most of the U.S. population lived within one hour of their nearest obstetric hospital [ 15 , 39 – 44 ]. Our analysis revealed that nearly 94% of the birthing population in the U.S. lived within 30 minutes of an obstetric hospital; however, this percentage decreased to 86% among the birthing population who lived in maternity care deserts. Although the estimated mean travel distance and time to reach the nearest hospital with obstetric services were relatively low (8.3 miles and 14.1 minutes), this study is the first to characterize the geographic accessibility of maternity care deserts. Birthing people living in maternity care deserts traveled nearly four times farther to reach their closest obstetric hospital than those living in full-access counties (28.1 miles vs. 7.1 miles). In some states, this difference exceeded 40 miles. It is well documented that healthcare access is limited in rural areas; however, our analysis further highlights access barriers for people living in maternity care deserts in urban areas (40% of all classified maternity care deserts). In contrast to the mean travel distance for those living in urban counties (7.8 miles) and rural counties (17.3 miles), the mean travel distance in an urban maternity care desert was 25.0 miles, a difference of 3.2 and 1.5 times farther, respectively. These findings highlight that living in a maternity care desert, whether urban or rural, significantly impacts travel distance to the nearest obstetric hospital. Given the relationship between poor maternal health outcomes and living in a rural area [ 12 – 14 ], further research is necessary to assess health outcomes among birthing people living in maternity care deserts. Analysis of mean travel distance and time to the closest obstetric hospital does not account for additional barriers that birthing people in areas of no or low access may face to reach risk-appropriate care. Higher level care is typically available in highvolume hospitals with greater resources, including NICUs and specialized staff better equipped to handle rare maternal and infant complications. Studies have shown that maternal and infant outcomes are better in hospitals with high birth volumes than those with low birth volumes. For example, infant survival is greater in high-volume hospitals for both high- and low-risk infants [ 45 ]. Additionally, the risk of severe maternal morbidity is greater among obstetric patients who deliver at lower-volume hospitals in rural areas [ 41 , 46 ]. High-volume hospitals are often located in metropolitan areas where most infants are delivered. In contrast, high-volume hospitals account for only 10% of all obstetric hospitals in rural areas where less than 20% of infants born had a high-volume hospital within 30 miles [ 41 ]. Future research should quantify barriers faced by birthing people living in maternity care deserts when seeking more comprehensive care, either by choice or necessity. Our findings were consistent with others, which found disparities in travel distance by race/ethnicity [ 47 – 49 ]. For birthing people living in predominantly AIAN census tracts that are located within maternity care deserts, the mean distance to reach obstetric care was 59.0 miles, 2.1 times farther than the distance traveled by those living in predominantly White census tracts in maternity care deserts. We found that, regardless of maternity care access designation, those living in predominantly AIAN census tracts travel the farthest to reach obstetric care compared to birthing people living in all other census tracts. Travel distance is exacerbated for birthing people living in rural areas and on American Indian reservations, where access is limited, and bypassing the nearest hospital to give birth is more common and necessary for risk-appropriate care [ 47 ]. States identified in our hot spot analysis for statistically high travel distances to care were overwhelmingly concentrated in areas with high AIAN populations compared to other U.S. states [Additional File 1]. AIANs are two times more likely to die from pregnancy complications than White mothers and Indigenous people living in rural areas have the highest rates of severe maternal morbidity and mortality [ 48 , 49 ]. These findings highlight the need to address inequities and implement policies that support maternity care for AIAN communities with barriers in distance and time to care. Strengths and Limitations There are several limitations of this study worth noting. We analyzed driving time and distance to the nearest obstetric hospital in the U.S.; however, the average birthing person may bypass their closest obstetric hospital to receive more comprehensive or better-quality care. In some circumstances, insurance coverage may not extend past a birthing person’s state of residence, and several of the closest points of care in our analysis included obstetric hospitals in states that crossed residential borders. We did not specify day start or stop times to account for fluctuations in traffic conditions or weather seasonality where driving conditions could impact travel time. GIS analyses of drive times were based on car transport calculations and are not generalizable for bus or public transit travel. Due to these limitations, in addition to using census tract weighted point locations rather than patient addresses, the results likely underestimate the actual travel distance and time to reach obstetric care. Despite these limitations, our results are derived from extensive and validated datasets and are generalizable to hospital deliveries, accounting for 98% of all U.S. births in 2022 [ 33 ]. In addition, response rates of the AHA hospital data vary across states and health systems; however, validation using CMS data allowed for accurate identification of hospitals with obstetric care available across the nation. Our GIS analysis used population-weighted centroid locations to account for where the majority of birthing people reside in each census tract. Census tract centroids allowed for greater granularity in calculations of travel distance and time. ArcGIS Pro Network Analyst Extension allowed us to obtain the shortest driving distance and times to care using live data for streets, railroads, and ferries. The use of transport network analysis enabled us to model real-time world phenomena in road travel and is the recommended method to estimate geographic accessibility instead of using straight-line Euclidian distance [ 40 , 50 – 52 ]. Implications A lack of access to maternity care is a complicated issue that requires innovative and diverse solutions. Although the mean distance and time to care is low in much of the U.S., additional barriers persist. Maternity care deserts deserve closer study to determine how we can continue and improve services in these areas. Continuing investment in healthcare infrastructure is critical—this includes creating a sustainable maternity care workforce and providing communities without sufficient access to a maternity care hospital with the additional resources needed to reach care. This research supports expanding programs and policies to address inadequate access to maternity care deserts, including those in urban areas. The White House Blueprint for Addressing the Maternal Health Crisis outlines several goals that target improvements in access for rural communities and investments in the maternal health workforce [ 53 ]. One program expanded under these goals is the HRSA-funded Rural Maternity and Obstetrics Management Strategies (RMOMS) program [ 54 ]. Rural communities across 11 states are working to identify innovative solutions that increase access to obstetric care and that can be applied to other communities nationwide. Policymakers and hospital administrators should consider the impact of closures on the distance traveled for birthing individuals in both urban and rural maternity care deserts. Understanding the implications of closures on travel burden is essential for crafting effective policies and interventions to mitigate these challenges. Telehealth, which includes virtual visits, remote patient monitoring, mobile healthcare, and real-time telemedicine interactions between patients and providers, has proven effective in mitigating obstetric provider shortages, particularly in rural areas with limited access to specialty care [ 55 ]. Supporting and incorporating innovative telehealth initiatives ensures equitable access to obstetric care, regardless of geographical location. Despite having a limited impact on obstetric unit closures [ 56 ], policies such as Medicaid extension and expansion have shown positive effects on birth and maternal health outcomes for individuals in poverty [ 57 ]. Policymakers should consider expanding Medicaid coverage in all states to mitigate the travel burden for individuals with low income, ensuring access to a broader range of potential hospitals offering obstetric care services regardless of socioeconomic status. Moreover, Medicaid expansion allows for greater continuity in insurance coverage [ 58 ] and improved overall health even before pregnancy [ 59 ], thereby reducing the potential for complications during pregnancy. The obstetric workforce must increase not only in number but also in geographic distribution and racial/ethnic diversity to meet the needs of the U.S. birthing population. One way to do this is to support expanding midwifery services, which can improve outcomes, increase culturally appropriate care and lower costs of obstetric care [ 60 , 61 ]. Widespread acceptance of practices that eliminate cumbersome licensing requirements, increase reimbursement rates for midwifery care, and address hospital resistance to employing midwives could bolster the obstetric workforce [ 53 ]. Providing incentives and continued investment in training programs for clinicians in rural and underserved areas is imperative to sustain the obstetric workforce. Finally, HRSA’s development of Maternity Care Target Areas (MCTA) informs the optimal placement of obstetricians and certified nurse midwives in the National Health Service Corps [ 62 ]. MCTA’s present unique funding opportunities and internal research has shown a high degree of overlap between designated MCTA’s and maternity care deserts. Future research should explore the impact of living in areas with unmet need for maternity care, focusing on adverse health outcomes for birthing people and infants. While access to healthcare should be a human right [ 63 ], this study shows that where a person lives greatly impacts the ability to access maternity care. Not only are maternity care deserts lacking the obstetric care facilities and providers needed to care for birthing people, living in these areas has a fourfold impact on the time and distance to reach maternity care. This study adds to extensive research that demonstrates inequities in access to maternity care across the U.S., which are created and perpetuated through the failure of our policies and systems. To enact change, we must address the underlying systemic issues that persist. Conclusion Our findings revealed disparities in access to obstetric hospitals, for birthing individuals residing in maternity care deserts, rural areas, and predominantly AIAN census tracts. These findings highlight the importance of finding solutions to support populations that reside farther away from maternity care to reduce the risk of adverse birthing outcomes associated with extended travel during childbirth. To mitigate these disparities, sustained investment in the obstetric workforce is crucial, along with implementing innovative practices and programs to expand access, especially in maternity care deserts. Addressing systemic inequities demands a multifaceted, multi-sectoral approach that prioritizes healthcare access as a fundamental right and actively dismantles disparities in obstetric care nationwide. Abbreviations AIAN American Indian/Alaska Native AHA American Hospital Association ESRI Environmental Systems Research Institute GIS Geographic Information Systems HRSA Health Resources and Services Administration MCTA Maternity Care Target Area NH/PI Native Hawaiian/Pacific Islander U.S. United States Declarations Ethics Approval and Consent to Participate: Exempt approval was granted by Solutions IRB, LLC (Approved November 20, 2022. IRB Protocol ID: 2022/07/20). Exempt status based on secondary data used during research. The ethics committee of Solutions IRB, LLC waived the need for informed consent due to retrospective analysis of secondary data. Availability of Data and Materials: The data supporting this study's findings are available from the American Hospital Association (AHA), but restrictions apply to the availability of these data, which were used under license for the current research and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of AHA. Competing Interests: All author(s) declare no potential conflicts of interest concerning this article's research, authorship, and/or publication. Funding: This research was funded by a grant from the Elevance Health Foundation. Statements in this report are those of the authors and do not necessarily reflect the views of the Elevance Health Foundation or its affiliates unless explicitly noted. Consent for Publication: Not applicable. Authors Contributions: Jazmin Fonten ot: Conceptualization, Data Curation, Analysis, Writing, Editing. Christina Brigance: Conceptualization, Writing, Editing. Ripley Lucas: Conceptualization, Data Curation, Writing, Editing. Ashley Stoneburner: Conceptualization, Data Curation, Writing, Editing, Supervision. Jazmin Fontenot, MPH is a Data Analyst at March of Dimes’ Perinatal Data Center. Christina Brigance, MPH is a Data Analyst at March of Dimes’ Perinatal Data Center. Ripley Lucas, MPH is a Data Analyst at March of Dimes’ Perinatal Data Center. Ashley Stoneburner, MPH is the Director of Applied Research and Analytics at March of Dimes’ Perinatal Data Center. References Douthard RA, Martin IK, Chapple-Mcgruder T, Langer A, Chang S. U.S. Maternal Mortality Within a Global Context: Historical Trends, Current State, and Future Directions. J Womens Health. 2021;30:168. https://doi.org/10.1089/JWH.2020.8863 . Centers for Disease Control and Prevention (CDC). Four in 5 pregnancy-related deaths in the U.S. are preventable. https://www.cdc.gov/media/releases/2022/p0919-pregnancy-related-deaths.html (accessed December 14, 2023). Backes EP, Scrimshaw S, National Academies of Sciences E, National Academies of Sciences E., National Academies of Sciences E, National Academies of Sciences E. Birth settings in America: outcomes, quality, access, and choice. n.d. Eliason EL, Daw JR, Allen HL. Association of Medicaid vs Marketplace Eligibility on Maternal Coverage and Access with Prenatal and Postpartum Care. JAMA Netw Open 2021. https://doi.org/10.1001/jamanetworkopen.2021.37383 . Brigance C, Lucas R, Jones E, Davis A, Oinuma M, Mishkin K et al. Nowhere to Go: Maternity Care Deserts Across the U.S. (Report No. 3). 2022. Kozhimannil KB. Declining access to US maternity care is a systemic injustice. BMJ 2023;382. https://doi.org/10.1136/BMJ.P 2038. Fontenot J, Lucas R, Stoneburner A, Brigance C, Hubbard K, Jones E et al. Where You Live Matters: Maternity Care Deserts and the Crisis of Access and Equity in [All States]. 2023. Kramer KJ, Elena Rhoads-Baeza M, Sadek S, Chao CR, Bell C, Recanati M-A. Trends and Evolution in Women’s Health Workforce in the First Quarter of the 21 st Century. World J Gynecol Womens Health 2022;5. https://doi.org/10.33552/WJGWH.2022.05.000622 . Hung P, Kozhimannil KB, Casey MM, Moscovice IS. Why Are Obstetric Units in Rural Hospitals Closing Their Doors? Health Serv Res. 2016;51:1546. https://doi.org/10.1111/1475-6773.12441 . Carroll C, Planey A, Kozhimannil KB. Reimagining and reinvesting in rural hospital markets. Health Serv Res. 2022;57:1001–5. https://doi.org/10.1111/1475-6773.14047 . McGregor AJ, Hung P, Garman D, Amutah-Onukagha N, Cooper JA. Obstetrical unit closures and racial and ethnic differences in severe maternal morbidity in the state of New Jersey. Am J Obstet Gynecol MFM 2021;3. https://doi.org/10.1016/J.AJOGMF.2021.100480 . Wallace M, Dyer L, Felker-Kantor E, Benno J, Vilda D, Harville E, et al. Maternity Care Deserts and Pregnancy-Associated Mortality in Louisiana. Women’s Health Issues. 2021;31:122–9. https://doi.org/10.1016/J.WHI.2020.09.004 . Kozhimannil KB, Interrante JD, Henning-Smith C, Admon LK. Rural-Urban Differences In Severe Maternal Morbidity And Mortality In The US, 2007-15. Health Aff (Millwood). 2019;38:2077–85. https://doi.org/10.1377/HLTHAFF.2019.00805 . Hostetter M, Klein S. Restoring Access to Maternity Care in Rural America. Commonwealth Fund. 2021. https://www.commonwealthfund.org/publications/2021/sep/restoring-access-maternity-care-rural-america (accessed January 24, 2024). Parker K, Horowitz JM, Brown A, Fry R, Cohn D, Igielnk R. Similarities and differences between urban, suburban and rural communities in America. 2018. Lam O, Broderick B, Toor S. How far Americans live from the closest hospital differs by community type. 2018. Care Institute of Medicine (US). Committee to Study Outreach for Prenatal. Barriers to the Use of Prenatal Care. In: Brown SS, editor. Prenatal Care: Reaching Mothers, Reaching Infants. National Academies Press (US); 1988. DiPietro Mager NA, Zollinger TW, Turman JE, Zhang J, Dixon BE. Routine Healthcare Utilization Among Reproductive-Age Women Residing in a Rural Maternity Care Desert. J Community Health. 2021;46:108–16. https://doi.org/10.1007/S10900-020-00852-6/METRICS . Kozhimannil KB, Leonard SA, Handley SC, Passarella M, Main EK, Lorch SA, et al. Obstetric Volume and Severe Maternal Morbidity Among Low-Risk and Higher-Risk Patients Giving Birth at Rural and Urban US Hospitals. JAMA Health Forum. 2023;4:e232110–0. https://doi.org/10.1001/JAMAHEALTHFORUM.2023.2110 . Minion SC, Krans EE, Brooks MM, Mendez DD, Haggerty CL. Association of Driving Distance to Maternity Hospitals and Maternal and Perinatal Outcomes. Obstet Gynecol. 2022;140:812–9. https://doi.org/10.1097/AOG.0000000000004960 . American Heart Association. Target: Stroke - When Seconds Count n.d. https://www.heart.org/en/professional/quality-improvement/target-stroke/learn-more-about-target-stroke (accessed January 24, 2024). Nageotte MP, Vander Wal B. Achievement of the 30-minute standard in obstetrics can it be done? Am J Obstet Gynecol. 2012;206:104–7. https://doi.org/10.1016/j.ajog.2011.09.008 . Boehm FH. Decision to incision: Time to reconsider. Am J Obstet Gynecol. 2012;206:97–8. https://doi.org/10.1016/j.ajog.2011.09.009 . United States Census Bureau. S1301: Fertility. American Community Survey, 2017–2021. Manson S, Schroeder J, Van Riper D, Knowles K, Kugler T, Roberts F et al. Centers of Population GIS File 2017–2021. Minneapolis, MN: 2023. United States Census Bureau. TIGER/Line Shapefiles. 2021. https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html . American Hospital Association. Hospital Data, 2021. American Hospital Association. About the AHA. https://www.aha.org/about (accessed January 24, 2024). Interrante JD, Carroll C, Handley SC, Kozhimannil K. An Enhanced Method for Identifying Hospital-Based Obstetric Unit Status. University of Minnesota Rural Health Research Center; 2022. Centers for Medicare and Medicaid. Provider of Service Files- Hospital & Non-Hospital Facilities 2021. U.S. Health Resources and Services Administration (HRSA). Area Health Resources Files; 2022. Peterson LE, Fang B, Phillips RL, Avant R, Puffer JC. The American Board of Family Medicine’s Data Collection Method for Tracking Their Specialty. J Am Board Family Med. 2019;32:89–95. https://doi.org/10.3122/JABFM.2019.01.180138 . National Center for Health Statistics. Final Natality Data, 2021. American Association of. Birth Centers, 2021. Economic Research Service US Department of Agriculture. Urban Influence Codes 2021. Environmental Systems Research Institute (ESRI). ArcGIS Pro 2022. ESRI. What is the ArcGIS Network Analyst extension? https:// pro.arcgis.com/en/pro-app/latest/help/analysis/networks/what-is-network-analyst-.htm (accessed January 24, 2024). SAS. Version 9.4 Cary. NC: SAS Institute Inc; 2020. Brantley MD, Davis NL, Goodman DA, Callaghan WM, Barfield WD. Perinatal regionalization: a geospatial view of perinatal critical care, United States, 2010–2013. Am J Obstet Gynecol. 2017;216. https://doi.org/10.1016/j.ajog.2016.10.011 . :185.e1-185.e10. Weiss AJ, Roemer M, Pickens GT. Methods for Calculating Patient Travel Distance to Hospital in HCUP Data. HCUP Methods Series Report US Agency for Healthcare Research and Quality; 2021. Handley SC, Passarella M, Herrick HM, Interrante JD, Lorch SA, Kozhimannil KB, et al. JAMA Netw Open. 2021;4:2125373. https://doi.org/10.1001/jamanetworkopen.2021.25373 . Birth Volume and Geographic Distribution of US Hospitals With Obstetric Services From 2010 to 2018 Key Points + Supplemental content. Shabo V, Friedman H. Distances to Hospital-Based and Skilled Nursing Care Make Paid Leave Critical for Rural Communities. New America. 2022. Hung P, Casey MM, Kozhimannil KB, Karaca-Mandic P, Moscovice IS. Rural-urban differences in access to hospital obstetric and neonatal care: how far is the closest one? J Perinatol. 2018;38:645–52. https://doi.org/10.1038/S41372-018-0063-5 . Guo J, Hernandez I, Dickson S, Tang S, Essien UR, Mair C, et al. Income disparities in driving distance to health care infrastructure in the United States: a geographic information systems analysis. BMC Res Notes. 2022;15. https://doi.org/10.1186/S13104-022-06117-W . Walther F, Kuester D, Bieber A, Malzahn J, Rüdiger M, Schmitt J. Are birth outcomes in low risk birth cohorts related to hospital birth volumes? A systematic review. BMC Pregnancy Childbirth. 2021;21:1–16. https://doi.org/10.1186/S12884-021-03988-Y/FIGURES/4 . Kozhimannil KB, Leonard SA, Handley SC, Passarella M, Main EK, Lorch SA, et al. Obstetric Volume and Severe Maternal Morbidity Among Low-Risk and Higher-Risk Patients Giving Birth at Rural and Urban US Hospitals. JAMA Health Forum. 2023;4:e232110–0. https://doi.org/10.1001/JAMAHEALTHFORUM.2023.2110 . Thorsen ML, Harris S, Palacios JF, McGarvey RG, Thorsen A. American Indians travel great distances for obstetrical care: Examining rural and racial disparities. Soc Sci Med 2023;325. https://doi.org/10.1016/J.SOCSCIMED.2023.115897 . Centers for Disease Control and Prevention (CDC). Disparities and Resilience among American Indian and Alaska Native People who are Pregnant or Postpartum. https://www.cdc.gov/hearher/aian/disparities.html (accessed January 25, 2024). Kozhimannil KB, Interrante JD, Tofte AN, Admon LK, Kozhimannil KB. Severe Maternal Morbidity and Mortality Among Indigenous Women in the United States. Obstet Gynecol. 2020;135:294. https://doi.org/10.1097/AOG.0000000000003647 . Haynes R, Jones AP, Sauerzapf V, Zhao H. Validation of travel times to hospital estimated by GIS. Int J Health Geogr. 2006;5:40. https://doi.org/10.1186/1476-072X-5-40 . Delamater PL, Messina JP, Shortridge AM, Grady SC. Measuring geographic access to health care: raster and network-based methods. Int J Health Geogr. 2012;11:1–18. https://doi.org/10.1186/1476-072X-11-15/FIGURES/13 . Phibbs CS, Luft HS. Correlation of travel time on roads versus straight line distance. Med Care Res Rev. 1995;52:532–42. https://doi.org/10.1177/107755879505200406 . The White House. White House Blueprint for Addressing the Maternal Health Crisis 2022. Health Resources and Services Administration (HRSA). Rural Maternity and Obstetrics Management Strategies (RMOMS) Program | HRSA 2022. https://www.hrsa.gov/rural-health/grants/rural-community/rmoms (accessed January 25, 2024). Tenorio B, Whittington JR. Increasing Access: Telehealth and Rural Obstetric Care. Obstet Gynecol Clin North Am. 2023;50:579–88. https://doi.org/10.1016/J.OGC.2023.03.014 . Carroll C, Interrante JD, Daw JR, Kozhimannil KB. Association Between Medicaid Expansion And Closure Of Hospital-Based Obstetric Services. Health Aff (Millwood). 2022;41:531–9. https://doi.org/10.1377/HLTHAFF.2021.01478 . Searing A, Corcoran A, Alker J. accessed January 25,. Medicaid Expansion’s Effects on Families: More coverage, improved maternal health, better preventive care. Center For Children and Families 2021. https://ccf.georgetown.edu/2021/02/19/medicaid-expansions-effects-on-families-more-coverage-improved-maternal-health-better-preventive-care (2024). Corallo B, Frederiksen B. accessed January 25,. How Does the ACA Expansion Affect Medicaid Coverage Before and During Pregnancy? Kaiser Family Foundation 2022. https://www.kff.org/medicaid/issue-brief/how-does-the-aca-expansion-affect-medicaid-coverage-before-and-during-pregnancy (2024). Myerson R, Crawford S, Wherry LR. Medicaid Expansion Increased Preconception Health Counseling, Folic Acid Intake, And Postpartum Contraception. Health Aff. 2020;39:1883–90. https://doi.org/10.1377/HLTHAFF.2020.00106 . Sonenberg A, Mason DJ. Maternity Care Deserts in the US. JAMA Health Forum. 2023;4:e225541–1. https://doi.org/10.1001/JAMAHEALTHFORUM.2022.5541 . Stoll K, Titoria R, Turner M, Jones A, Butska L. Perinatal outcomes of midwife-led care, stratified by medical risk: a retrospective cohort study from British Columbia (2008–2018). Can Med Assoc J. 2023;195:E292–9. https://doi.org/10.1503/CMAJ.220453 . Health Resources and Services Administration (HRSA). What Is Shortage Designation? 2023. https://bhw.hrsa.gov/workforce-shortage-areas/shortage-designation#mcta (accessed January 25, 2024). World Health Organization (WHO). Human rights. https://www.who.int/news-room/fact-sheets/detail/human-rights-and-health (accessed January 25, 2024). Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Cite Share Download PDF Status: Published Journal Publication published 08 May, 2024 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted Editorial decision: Revision requested 20 Mar, 2024 Reviews received at journal 19 Mar, 2024 Reviews received at journal 11 Feb, 2024 Reviewers agreed at journal 06 Feb, 2024 Reviewers agreed at journal 06 Feb, 2024 Reviewers invited by journal 06 Feb, 2024 Editor assigned by journal 06 Feb, 2024 Editor invited by journal 06 Feb, 2024 Submission checks completed at journal 06 Feb, 2024 First submitted to journal 26 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-3900489\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":271498339,\"identity\":\"25a2620c-07f3-4124-bcf2-0bc78d09f3fd\",\"order_by\":0,\"name\":\"Jazmin Fontenot\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBAC9vkPYKwGEHkASCc24NXCOAOsjoGBh+cARAvPAaK1SCTAtCTgdxjjDOZjjz9U3JGzl3yd+Lmi5o4cD3ty64YPfxjkzPsXYNcyvy3d4MCZZ8Y80rmbJc8cAzJ4HrbdnNnGYCxz4wF2LTN4zCQOth1O7JHO3SDZ2HA4cb9EYttt3gaGxBkSB/Bo+QfUInl280+Qlh6Qlj9/cGsRBGsBq+TdJgnXwsAG1MLfgFWLtARbmsSZY4eNec7kbrNsADFAfultkzCWkMAeYnwSzMckKmoOy7G3n918swHI4GFPf3bjxx8bOQl+7A7DCYBWQGKKJECqLaNgFIyCUTBcAQDF4mlwBbNGLwAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"March of Dimes\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Jazmin\",\"middleName\":\"\",\"lastName\":\"Fontenot\",\"suffix\":\"\"},{\"id\":271498340,\"identity\":\"9504be1f-5ff8-4317-b5de-62fa82dee8dc\",\"order_by\":1,\"name\":\"Christina Brigance\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"March of 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16:22:41\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3900489/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3900489/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12884-024-06535-7\",\"type\":\"published\",\"date\":\"2024-05-08T04:01:33+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":50926508,\"identity\":\"9696d64d-8fcb-4073-aec1-f2c30cc2aa09\",\"added_by\":\"auto\",\"created_at\":\"2024-02-09 17:11:30\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":53655,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eFlow Diagram for Obstetric Hospital Validation\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3900489/v1/a64df3d1f60687465fecb5c6.png\"},{\"id\":50924244,\"identity\":\"2dbdab0e-b5e7-4f95-afae-54d0e9e87011\",\"added_by\":\"auto\",\"created_at\":\"2024-02-09 17:03:30\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":417085,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eDistribution of Travel Distance to Nearest Obstetric Hospital in the U.S. by County\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3900489/v1/e12963ab7c46a3962a7bef3a.png\"},{\"id\":50924245,\"identity\":\"2e307539-d8f3-4537-9df4-5adddbffef3d\",\"added_by\":\"auto\",\"created_at\":\"2024-02-09 17:03:30\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":403336,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cem\\u003eHot Spot Analysis for Low and High Travel Distance to Nearest Obstetric Hospital\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3900489/v1/ff8d048f4b769d2410510093.png\"},{\"id\":56140435,\"identity\":\"9e892536-6c69-4bc4-bf13-8cb321c728cc\",\"added_by\":\"auto\",\"created_at\":\"2024-05-09 04:25:00\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1311729,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3900489/v1/681d0753-908a-426c-bd50-4a91051a7765.pdf\"},{\"id\":50924242,\"identity\":\"1f594abb-09db-49a9-801b-1970e9047086\",\"added_by\":\"auto\",\"created_at\":\"2024-02-09 17:03:30\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":34945,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Additionalfile1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3900489/v1/ccbde0de3f039f3b43790088.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Navigating Geographical Disparities: Access to Obstetric Hospitals in Maternity Care Deserts and across the United States \",\"fulltext\":[{\"header\":\"BACKGROUND\",\"content\":\"\\u003cp\\u003eDespite a decrease in the global maternal mortality rate, the rate in the United States has increased for decades [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Nearly 80% of pregnancy-related deaths in the U.S. are preventable [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e], indicating ample opportunity for improvement. Access to healthcare is critical during the perinatal period; however, hospitals that offer maternity care services are not equitably distributed across the country. Further, access to maternity care depends on several other factors, including the availability of obstetric providers, risk-appropriate care, and health insurance [\\u003cspan additionalcitationids=\\\"CR4\\\" citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eMore than 2\\u0026nbsp;million women of childbearing age live in maternity care \\u0026ldquo;deserts* [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e], defined as counties without birthing facilities or maternity care providers [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Areas of inadequate access to maternity care are created through systems and policies that deplete community resources. Hospital and maternity unit closures [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e], obstetric workforce shortages [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e], inadequate Medicaid reimbursement rates [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e], and systemic racism and classism [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e], have contributed to the increase in more counties with low or no access to care. One-third of all U.S. counties are maternity care deserts, and 60% are in rural areas [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Maternity care deserts are associated with an increased risk of pregnancy-related death up to one year postpartum [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Birthing people living in rural areas have a 9% greater risk of severe maternal morbidity and mortality from pregnancy and childbirth and are more likely to report difficulties in accessing quality care compared to urban residents [\\u003cspan additionalcitationids=\\\"CR14 CR15\\\" citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eAcross the nation, the closure of obstetric hospitals has played a role in the rise of maternity care deserts, resulting in increased distance to care [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. These extended distances not only discourage the use of preventive care but also impact the overall health and quality of care for individuals going through childbirth [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. In addition to the financial strain and heightened stress and anxiety [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e], the risk of adverse maternal outcomes and neonatal intensive care unit admission [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e] also increases with longer travel distances and time to care. Unlike established standards for reaching a hospital promptly in medical emergencies like stroke [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e], there is currently no standardized guideline for the travel time to reach a hospital during an obstetric emergency [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cdiv id=\\\"Sec2\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy Purpose\\u003c/h2\\u003e \\u003cp\\u003eThis research aims to assess access to obstetric hospitals across the U.S., focusing on inequities for birthing people in maternity care deserts and rural areas. A secondary purpose is to highlight the differing distance and time to obstetric hospitals by the predominant race/ ethnicity in each census tract. Using spatial analysis techniques, our study maps the typical travel distance and time needed to reach the nearest obstetric hospital in the U.S.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"METHODS\",\"content\":\"\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eResearch Design\\u003c/h2\\u003e \\u003cp\\u003eWe conducted a retrospective analysis of secondary data to estimate the travel distance and time to obstetric hospitals across the U.S. The main outcomes were geographic distance, in miles, and drive time, in minutes, from residential census tracts to the nearest hospital that provided obstetric care. Analyses were cross-sectional and estimated travel distances for all birthing people in the U.S. and used census tract fertility data and 2021 hospital availability. We examined differences in driving distance and driving time by maternity care desert designation, rurality, and predominant race/ethnicity.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData Sources\\u003c/h2\\u003e \\u003cp\\u003e \\u003cb\\u003eBirths.\\u003c/b\\u003e This analysis used five-year fertility estimates from the U.S. Census Bureau\\u0026rsquo;s 2017\\u0026ndash;2021 American Community Survey (ACS) [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. The ACS estimates the number of people who reported giving birth in the past year for all U.S. census tracts. Demographic variables from the ACS were used to analyze distance by the predominant race/ethnicity in each census tract.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eCensus Tracts.\\u003c/b\\u003e The spatial analysis files included point centroid locations for U.S. census tracts linked with ACS birth estimates. Point centroid locations were spatially weighted to account for population density within each census tract using data from the IPUMS National Historical Geographic Information System (NHGIS) [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. U.S. Census Topologically Integrated Geographic Encoding and Referencing (TIGER/Line) shapefiles were used for data visualization at the county and level [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eHospitals.\\u003c/b\\u003e Hospital location data were obtained from the American Hospital Association (AHA) 2021 Survey [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. The AHA Annual Survey provides data for more than 6,200 hospitals and healthcare systems and includes addresses for geocoding. The use of AHA data is consistent with federal government agencies as the most comprehensive hospital data source for health research [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. The AHA survey uses self-reported data to classify hospitals with obstetric care services. To check for missing obstetric hospitals, we performed a secondary validation of AHA hospital locations using the Centers for Medicare and Medicaid (CMS) Provider of Service (POS) files for 2021 and followed enhanced methods of identification described by prior research [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Only hospitals that listed providing obstetric services according to CMS POS data OB_SRV_CD\\u0026thinsp;\\u0026gt;\\u0026thinsp;=\\u0026thinsp;1 were included. Obstetric hospitals were selected if they met the following criteria (see Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003cdiv class=\\\"Heading\\\"\\u003e\\u003c/div\\u003e \\u003cp\\u003e \\u003cb\\u003eDesignations for Maternity Care Access.\\u003c/b\\u003e Maternity care access designations were created by the Perinatal Data Center at March of Dimes [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. U.S. counties are classified using data from the Human Resources Service Administration\\u0026rsquo;s (HRSA) Area Health Resource Files (2021\\u0026ndash;2022) for obstetric providers (OB/GYN, nurse midwives) and obstetric hospitals; counts of family physicians who deliver babies from the American Board of Family Medicine (2017\\u0026ndash;2020); birth estimates from the National Center for Health Statistics (NCHS) 2021 natality data; and birth center data from the 2021 American Association of Birth Centers (AABC) [\\u003cspan additionalcitationids=\\\"CR32 CR33\\\" citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. For this analysis, we combined the low and moderate maternity care access categories into one \\u0026ldquo;limited\\u0026rdquo; access designation. Levels of maternity care access are defined as follows:\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eMaternity care desert: A county with no hospitals providing obstetric care, no birth centers, and no obstetric providers, which include certified nurse midwives, certified midwives, and family physicians who reported delivering babies. ​\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eLimited access: A county with fewer than two hospitals or birth centers offering obstetric care and fewer than 60 obstetric providers per 10,000 births.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eFull access: A county with two or more hospitals or birth centers offering obstetric services or more than 60 obstetric providers per 10,000 births.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eRural Areas.\\u003c/b\\u003e Rural-Urban Continuum codes, developed by the U.S. Department of Agriculture, Economic Research Service, classify counties into nine categories by population size based on census-defined urbanized areas and by adjacency to metropolitan areas [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. The categories can be further classified as metropolitan/nonmetropolitan or urban/rural. Metropolitan categories were defined as a county with a metropolitan area of 1\\u0026nbsp;million people (about the population of Delaware) or more, 250,000 to 1\\u0026nbsp;million, or fewer than 250,000 (one, two, or three on the Rural-Urban Continuum). All the other categories (four or more on the Rural-Urban Continuum) are considered nonmetropolitan. Urban areas include all metropolitan areas and/or areas adjacent to a metropolitan area with a population greater than 2,500 (one through four and six on the Rural-Urban Continuum). Rural includes areas with a population of 2,500 or more not adjacent to a metropolitan area and areas with fewer than 2,500 people (five and seven or higher on the Rural-Urban Continuum).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAnalyses\\u003c/h2\\u003e \\u003cp\\u003eArcGIS Pro, version 3.0 [\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e], was used to geocode validated hospital locations. Linked ACS birth data with population-weighted census tract point locations were used as the residential incidents (starting point locations for GIS calculations). The ESRI Network Analyst Extension Closest Facility Solver [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e] calculated the driving time and mileage distance from each birth to the nearest obstetric hospital location. ArcGISOnline network data was used for routing services, a regularly maintained database of comprehensive street data that includes historical, live, and predictive road networks. To increase the generalizability of our results and because labor can occur at any time of the day, we did not account for fluctuations in traffic conditions.\\u003c/p\\u003e \\u003cp\\u003eAll the statistical analyses were performed using SAS software, version 9.4 [\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Imported GIS census tract calculations were aggregated at the county-level for comparison with county-level maternity care desert designations, rurality, metropolitan status, and predominant race/ethnicity. We tested significant differences in travel time by county-level factors using a one-way ANOVA test for factors with three or more levels (maternity care desert designation) and t-tests for all others. Travel time cutoffs of 30 and 60 minutes were used to describe the percentage of birthing people who live far from obstetric care. To define areas in the U.S. with statistically significant high and low travel distances, we conducted hotspot analyses for the continental U.S. at the county level. Counties with 10 or fewer births were suppressed.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cp\\u003eWe identified 3,991,060 birthing people among 85,396 census tracts across the U.S., D.C., and Puerto Rico and 2,630 hospitals that provide obstetric care. Obstetric hospitals were geocoded with a match score of 99.0%; nine hospitals did not have a match and were manually reviewed using Google Maps to obtain point location data. Four hospitals were excluded after manual review yielded no location data. Drive-time routes were calculated for 99.6% of the total estimated births; eight census tracts did not have calculable road network routes, accounting for less than 0.4% of all estimated births.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eDistance overall.\\u003c/b\\u003e Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e displays the quintile distribution of mean travel distance, in miles, by county across the U.S. The mean distance and time to the nearest obstetric hospital were 8.3 miles and 14.1 minutes, respectively (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Nearly all of the U.S. population lived within 1 hour of the nearest obstetric care hospital (99.7%), and 93.6% lived within 30 minutes.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMean Miles and Minutes to Nearest Obstetric Hospital by Census Tract\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBirths\\u003c/p\\u003e \\u003cp\\u003eN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMean distance miles (SD)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMean time\\u003c/p\\u003e \\u003cp\\u003eminutes (SD)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eUS Census tracts\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3,991,060\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.3 (9.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.1 (21.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMCD Designation\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMaternity care desert\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e145,146\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28.1 (21.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e36.5 (102.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLimited access\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e248,347\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14.2 (10.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20.1 (11.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFull access\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3,597,567\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.1 (6.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12.7 (8.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRurality\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUrban\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3,785,735\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.8 (7.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13.5 (8.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRural\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e205,325\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17.3 (21.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e24.6 (88.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMetropolitan Residence\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNonmetro\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e536,349\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16.0 (16.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e22.3 (55.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMetro\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3,454,711\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.1 (6.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12.8 (7.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePredominant race/ethnicity\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWhite\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2,763,765\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.2 (8.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15.0 (11.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHispanic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e713,750\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.1 (6.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11.3 (7.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBlack\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e406,972\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.3 (6.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11.9 (7.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAsian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e92,886\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.2 (18.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12.5 (107.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAIAN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9,772\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.7 (41.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e45.9 (203.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMultiracial\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1,967\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12.8 (10.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23.4 (18.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNH/PI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1,360\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17.2 (10.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e26.4 (14.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c4\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTravel Time to Closest Obstetric Care (%)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;=15 minutes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2,692,070\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e67.5%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;=30 minutes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3,736,131\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e93.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;=45 minutes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3,933,929\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e98.6%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026lt;=60 minutes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3,977,229\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e99.7%\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eDistance by maternity care designation.\\u003c/b\\u003e The mean time and distance to the closest obstetric hospital increased as access to care decreased. The mean distance and time to care by maternity care access designation was 7.1 miles or 12.7 minutes for full access areas; 14.2 miles or 20.1 minutes for limited access areas; and 28.1 miles or 36.5 minutes in maternity care deserts. The mean travel time among birthing people in maternity care deserts was 3.9 times greater than in full-access areas.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eDistance by rurality.\\u003c/b\\u003e The travel distance and time to care for birthing people living in rural and urban areas was 17.3 miles or 24.5 minutes and 7.8 miles or 13.5 minutes, respectively. Differences in travel distance and time were also observed when comparing metropolitan (7.1 miles or 12.8 minutes) to nonmetropolitan (16.0 miles or 22.3 minutes) areas. When examining rural and urban distance differences by maternity care access designation, we found that distances were similar for low-access and full-access areas but were greater regardless of rural designation for maternity care deserts (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Those living in rural maternity care deserts traveled 1.9 times farther than the average birthing person in a rural area and 4.0 times farther than the average birthing person overall. In urban maternity care deserts, the average travel distance was 3.2 times farther than the average person living in an urban area and 3.0 times farther than the average birthing person. These discrepancies highlight that even in urban areas, those living in maternity care deserts travel farther than those living in rural areas.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eDistance by predominant race.\\u003c/b\\u003e Census tracts classified as predominantly American Indian and Alaska Native (AIAN) had the highest travel distance and time to obstetric hospitals. On average, those living in predominantly AIAN census tracts travel 27.8 miles or 45.9 minutes to their nearest obstetric hospital, 3.3 times farther than the average travel time in the U.S. Driving distance and time differences are exacerbated for AIAN living in maternity care deserts, where the closest obstetric hospital is 59.0 miles or 161.1 minutes away, on average. This disparity is 2.0 times greater than that of all other races and ethnicities in maternity care deserts. Predominant AIAN census tracts are 2.2 times more likely to be in maternity care deserts than census tracts with predominantly White residents.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDistance and Time by Geographic Characteristics and Maternity Care Designation\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eFull access\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eLimited access\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eMaternity care desert\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMiles\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTime\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMiles\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eTime\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eMiles\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eTime\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eOverall\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e20.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e28.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e36.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRurality\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUrban\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.9 (6.1)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12.6 (7.5)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.2 (10.6)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e20.1 (11.5)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e25.0 (11.2)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e31.4 (11.9)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRural\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e11.4 (13.6)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e17.1 (20.1)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.6 (13.7)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e19.8 (15.1)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e33.4 (31.3)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e45.4 (169.4)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c3\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePredominate race/ethnicity\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWhite\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.8 (6.9)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.5 (8.3)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.9 (10.5)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e20.9 (11.6)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e28.0 (13.4)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e34.8 (23.8)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHispanic\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.6 (5.5)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.9 (6.6)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10.7 (11.5)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e15.7 (11.9)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e24.2 (17.2)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e29.3 (16.1)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBlack\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.3 (4.4)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.9 (5.5)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e10.7 (10.8)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e16.3 (11.7)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e27.6 (10.7)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e33.2 (11.7)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAsian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.8 (3.4)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.2 (4.9)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e--\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e--\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e--\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e--\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAIAN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24.7 (27.7)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e35.8 (58.0)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e26.2 (19.5)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e31.4 (20.7)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e59.0 (105.3)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e161.1 (657.3)*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"7\\\"\\u003e* p-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eTravel distance by state and hot spot analysis.\\u003c/b\\u003e The travel times and distances by state are shown in more detail Additional File 1. The states with the highest overall travel distances were Alaska, West Virginia, Montana, Mississippi, and South Dakota. The states with the highest travel times included Hawaii and North Dakota. The states with the lowest overall travel distances were D.C., New Jersey, Connecticut, New York, Rhode Island and California, all with mean travel distances lower than six miles.\\u003c/p\\u003e \\u003cp\\u003eHot spot analysis revealed areas with statistically significant high and low travel distances in the U.S. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). From this analysis, we found that regions of Montana, South Dakota, North Dakota and Nebraska all had the highest concentrations of maternity care deserts, and these were also areas with statistically significant hot spots for high travel distances to obstetric care. States with large populations living in maternity care deserts also had statistically significant spatial clustering, indicating long travel distances; these included Texas, Mississippi, Oklahoma, and Missouri. Areas for low travel times across the U.S. were concentrated in the northeast and included states with predominantly metropolitan areas such as D.C., Rhode Island, and New Jersey. Additional clusters of low travel times were found for the East North Central region of the U.S., Northern California, Minnesota, and North Carolina (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eConsistent with the findings of prior literature, we found that most of the U.S. population lived within one hour of their nearest obstetric hospital [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR40 CR41 CR42 CR43\\\" citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. Our analysis revealed that nearly 94% of the birthing population in the U.S. lived within 30 minutes of an obstetric hospital; however, this percentage decreased to 86% among the birthing population who lived in maternity care deserts. Although the estimated mean travel distance and time to reach the nearest hospital with obstetric services were relatively low (8.3 miles and 14.1 minutes), this study is the first to characterize the geographic accessibility of maternity care deserts.\\u003c/p\\u003e \\u003cp\\u003eBirthing people living in maternity care deserts traveled nearly four times farther to reach their closest obstetric hospital than those living in full-access counties (28.1 miles vs. 7.1 miles). In some states, this difference exceeded 40 miles. It is well documented that healthcare access is limited in rural areas; however, our analysis further highlights access barriers for people living in maternity care deserts in urban areas (40% of all classified maternity care deserts). In contrast to the mean travel distance for those living in urban counties (7.8 miles) and rural counties (17.3 miles), the mean travel distance in an urban maternity care desert was 25.0 miles, a difference of 3.2 and 1.5 times farther, respectively. These findings highlight that living in a maternity care desert, whether urban or rural, significantly impacts travel distance to the nearest obstetric hospital. Given the relationship between poor maternal health outcomes and living in a rural area [\\u003cspan additionalcitationids=\\\"CR13\\\" citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e], further research is necessary to assess health outcomes among birthing people living in maternity care deserts.\\u003c/p\\u003e \\u003cp\\u003eAnalysis of mean travel distance and time to the closest obstetric hospital does not account for additional barriers that birthing people in areas of no or low access may face to reach risk-appropriate care. Higher level care is typically available in highvolume hospitals with greater resources, including NICUs and specialized staff better equipped to handle rare maternal and infant complications. Studies have shown that maternal and infant outcomes are better in hospitals with high birth volumes than those with low birth volumes. For example, infant survival is greater in high-volume hospitals for both high- and low-risk infants [\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. Additionally, the risk of severe maternal morbidity is greater among obstetric patients who deliver at lower-volume hospitals in rural areas [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]. High-volume hospitals are often located in metropolitan areas where most infants are delivered. In contrast, high-volume hospitals account for only 10% of all obstetric hospitals in rural areas where less than 20% of infants born had a high-volume hospital within 30 miles [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. Future research should quantify barriers faced by birthing people living in maternity care deserts when seeking more comprehensive care, either by choice or necessity.\\u003c/p\\u003e \\u003cp\\u003eOur findings were consistent with others, which found disparities in travel distance by race/ethnicity [\\u003cspan additionalcitationids=\\\"CR48\\\" citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. For birthing people living in predominantly AIAN census tracts that are located within maternity care deserts, the mean distance to reach obstetric care was 59.0 miles, 2.1 times farther than the distance traveled by those living in predominantly White census tracts in maternity care deserts. We found that, regardless of maternity care access designation, those living in predominantly AIAN census tracts travel the farthest to reach obstetric care compared to birthing people living in all other census tracts. Travel distance is exacerbated for birthing people living in rural areas and on American Indian reservations, where access is limited, and bypassing the nearest hospital to give birth is more common and necessary for risk-appropriate care [\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]. States identified in our hot spot analysis for statistically high travel distances to care were overwhelmingly concentrated in areas with high AIAN populations compared to other U.S. states [Additional File 1]. AIANs are two times more likely to die from pregnancy complications than White mothers and Indigenous people living in rural areas have the highest rates of severe maternal morbidity and mortality [\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. These findings highlight the need to address inequities and implement policies that support maternity care for AIAN communities with barriers in distance and time to care.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStrengths and Limitations\\u003c/h2\\u003e \\u003cp\\u003eThere are several limitations of this study worth noting. We analyzed driving time and distance to the nearest obstetric hospital in the U.S.; however, the average birthing person may bypass their closest obstetric hospital to receive more comprehensive or better-quality care. In some circumstances, insurance coverage may not extend past a birthing person\\u0026rsquo;s state of residence, and several of the closest points of care in our analysis included obstetric hospitals in states that crossed residential borders. We did not specify day start or stop times to account for fluctuations in traffic conditions or weather seasonality where driving conditions could impact travel time. GIS analyses of drive times were based on car transport calculations and are not generalizable for bus or public transit travel. Due to these limitations, in addition to using census tract weighted point locations rather than patient addresses, the results likely underestimate the actual travel distance and time to reach obstetric care.\\u003c/p\\u003e \\u003cp\\u003eDespite these limitations, our results are derived from extensive and validated datasets and are generalizable to hospital deliveries, accounting for 98% of all U.S. births in 2022 [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. In addition, response rates of the AHA hospital data vary across states and health systems; however, validation using CMS data allowed for accurate identification of hospitals with obstetric care available across the nation. Our GIS analysis used population-weighted centroid locations to account for where the majority of birthing people reside in each census tract. Census tract centroids allowed for greater granularity in calculations of travel distance and time. ArcGIS Pro Network Analyst Extension allowed us to obtain the shortest driving distance and times to care using live data for streets, railroads, and ferries. The use of transport network analysis enabled us to model real-time world phenomena in road travel and is the recommended method to estimate geographic accessibility instead of using straight-line Euclidian distance [\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR51\\\" citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e].\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eImplications\\u003c/h2\\u003e \\u003cp\\u003eA lack of access to maternity care is a complicated issue that requires innovative and diverse solutions. Although the mean distance and time to care is low in much of the U.S., additional barriers persist. Maternity care deserts deserve closer study to determine how we can continue and improve services in these areas. Continuing investment in healthcare infrastructure is critical\\u0026mdash;this includes creating a sustainable maternity care workforce and providing communities without sufficient access to a maternity care hospital with the additional resources needed to reach care. This research supports expanding programs and policies to address inadequate access to maternity care deserts, including those in urban areas. The White House Blueprint for Addressing the Maternal Health Crisis outlines several goals that target improvements in access for rural communities and investments in the maternal health workforce [\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]. One program expanded under these goals is the HRSA-funded Rural Maternity and Obstetrics Management Strategies (RMOMS) program [\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e]. Rural communities across 11 states are working to identify innovative solutions that increase access to obstetric care and that can be applied to other communities nationwide.\\u003c/p\\u003e \\u003cp\\u003ePolicymakers and hospital administrators should consider the impact of closures on the distance traveled for birthing individuals in both urban and rural maternity care deserts. Understanding the implications of closures on travel burden is essential for crafting effective policies and interventions to mitigate these challenges. Telehealth, which includes virtual visits, remote patient monitoring, mobile healthcare, and real-time telemedicine interactions between patients and providers, has proven effective in mitigating obstetric provider shortages, particularly in rural areas with limited access to specialty care [\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e]. Supporting and incorporating innovative telehealth initiatives ensures equitable access to obstetric care, regardless of geographical location. Despite having a limited impact on obstetric unit closures [\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e], policies such as Medicaid extension and expansion have shown positive effects on birth and maternal health outcomes for individuals in poverty [\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e]. Policymakers should consider expanding Medicaid coverage in all states to mitigate the travel burden for individuals with low income, ensuring access to a broader range of potential hospitals offering obstetric care services regardless of socioeconomic status. Moreover, Medicaid expansion allows for greater continuity in insurance coverage [\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e] and improved overall health even before pregnancy [\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e], thereby reducing the potential for complications during pregnancy.\\u003c/p\\u003e \\u003cp\\u003eThe obstetric workforce must increase not only in number but also in geographic distribution and racial/ethnic diversity to meet the needs of the U.S. birthing population. One way to do this is to support expanding midwifery services, which can improve outcomes, increase culturally appropriate care and lower costs of obstetric care [\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e]. Widespread acceptance of practices that eliminate cumbersome licensing requirements, increase reimbursement rates for midwifery care, and address hospital resistance to employing midwives could bolster the obstetric workforce [\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]. Providing incentives and continued investment in training programs for clinicians in rural and underserved areas is imperative to sustain the obstetric workforce. Finally, HRSA\\u0026rsquo;s development of Maternity Care Target Areas (MCTA) informs the optimal placement of obstetricians and certified nurse midwives in the National Health Service Corps [\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e]. MCTA\\u0026rsquo;s present unique funding opportunities and internal research has shown a high degree of overlap between designated MCTA\\u0026rsquo;s and maternity care deserts. Future research should explore the impact of living in areas with unmet need for maternity care, focusing on adverse health outcomes for birthing people and infants.\\u003c/p\\u003e \\u003cp\\u003eWhile access to healthcare should be a human right [\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e], this study shows that where a person lives greatly impacts the ability to access maternity care. Not only are maternity care deserts lacking the obstetric care facilities and providers needed to care for birthing people, living in these areas has a fourfold impact on the time and distance to reach maternity care. This study adds to extensive research that demonstrates inequities in access to maternity care across the U.S., which are created and perpetuated through the failure of our policies and systems. To enact change, we must address the underlying systemic issues that persist.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eOur findings revealed disparities in access to obstetric hospitals, for birthing individuals residing in maternity care deserts, rural areas, and predominantly AIAN census tracts. These findings highlight the importance of finding solutions to support populations that reside farther away from maternity care to reduce the risk of adverse birthing outcomes associated with extended travel during childbirth. To mitigate these disparities, sustained investment in the obstetric workforce is crucial, along with implementing innovative practices and programs to expand access, especially in maternity care deserts. Addressing systemic inequities demands a multifaceted, multi-sectoral approach that prioritizes healthcare access as a fundamental right and actively dismantles disparities in obstetric care nationwide.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eAIAN\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eAmerican Indian/Alaska Native\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eAHA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eAmerican Hospital Association\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eESRI\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eEnvironmental Systems Research Institute\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eGIS\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eGeographic Information Systems\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eHRSA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eHealth Resources and Services Administration\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eMCTA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eMaternity Care Target Area\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eNH/PI\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eNative Hawaiian/Pacific Islander\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eU.S.\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eUnited States\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics Approval and Consent to Participate:\\u0026nbsp;\\u003c/strong\\u003eExempt approval was granted by Solutions IRB, LLC (Approved November 20, 2022. IRB Protocol ID: 2022/07/20). Exempt status based on secondary data used during research. The ethics committee of Solutions IRB, LLC waived the need for informed consent due to retrospective analysis of secondary data.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of Data and Materials:\\u003c/strong\\u003e The data supporting this study\\u0026apos;s findings are available from the American Hospital Association (AHA), but restrictions apply to the availability of these data, which were used under license for the current research and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of AHA.\\u003cstrong\\u003e\\u0026nbsp; \\u0026nbsp; \\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests:\\u003c/strong\\u003e All author(s) declare no potential conflicts of interest concerning this article\\u0026apos;s research, authorship, and/or publication.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u003c/strong\\u003e This research was funded by a grant from the Elevance Health Foundation. Statements in this report are those of the authors and do not necessarily reflect the views of the Elevance Health Foundation or its affiliates unless explicitly noted.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for Publication:\\u0026nbsp;\\u003c/strong\\u003eNot applicable. \\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors Contributions: Jazmin Fonten\\u003c/strong\\u003e\\u003cstrong\\u003eot:\\u0026nbsp;\\u003c/strong\\u003eConceptualization, Data Curation, Analysis, Writing, Editing. \\u003cstrong\\u003eChristina Brigance:\\u003c/strong\\u003e Conceptualization, Writing, Editing.\\u003cstrong\\u003e\\u0026nbsp;Ripley Lucas:\\u0026nbsp;\\u003c/strong\\u003eConceptualization, Data Curation, Writing, Editing. \\u003cstrong\\u003eAshley Stoneburner:\\u0026nbsp;\\u003c/strong\\u003eConceptualization, Data Curation, Writing, Editing, Supervision.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eJazmin Fontenot, MPH\\u003c/strong\\u003e is a Data Analyst at March of Dimes\\u0026rsquo; Perinatal Data Center.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eChristina Brigance, MPH\\u003c/strong\\u003e is a Data Analyst at March of Dimes\\u0026rsquo; Perinatal Data Center.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRipley Lucas, MPH\\u003c/strong\\u003e is a Data Analyst at March of Dimes\\u0026rsquo; Perinatal Data Center.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAshley Stoneburner, MPH\\u003c/strong\\u003e is the Director of Applied Research and Analytics at March of Dimes\\u0026rsquo; Perinatal Data Center.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eDouthard RA, Martin IK, Chapple-Mcgruder T, Langer A, Chang S. U.S. Maternal Mortality Within a Global Context: Historical Trends, Current State, and Future Directions. J Womens Health. 2021;30:168. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1089/JWH.2020.8863\\u003c/span\\u003e\\u003cspan address=\\\"10.1089/JWH.2020.8863\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCenters for Disease Control and Prevention (CDC). 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Human rights. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.who.int/news-room/fact-sheets/detail/human-rights-and-health\\u003c/span\\u003e\\u003cspan address=\\\"https://www.who.int/news-room/fact-sheets/detail/human-rights-and-health\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e (accessed January 25, 2024).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-pregnancy-and-childbirth\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"prch\",\"sideBox\":\"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/prch/default.aspx\",\"title\":\"BMC Pregnancy and Childbirth\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"maternity care deserts, maternal health, disparities in maternity care access, spatial analysis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3900489/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3900489/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBACKGROUND\\u003c/h2\\u003e \\u003cp\\u003eAccess to maternity care in the U.S. remains inequitable, impacting over two million women in maternity care \\u0026ldquo;deserts.\\\" These areas, exacerbated by hospital closures and workforce shortages, heighten risks of pregnancy-related complications, particularly in rural regions. This study investigates travel distances and time to obstetric hospitals, emphasizing disparities faced by those in maternity care deserts and rural areas, while also exploring variances across races and ethnicities.\\u003c/p\\u003e\\u003ch2\\u003eMETHODS\\u003c/h2\\u003e \\u003cp\\u003eThe research adopted a retrospective secondary data analysis, utilizing the American Hospital Association and Centers for Medicaid and Medicare Provider of Services Files to classify obstetric hospitals. The study population included census tract estimates of birthing individuals sourced from the U.S. Census Bureau's 2017\\u0026ndash;2021 American Community Survey. Using ArcGIS Pro Network Analyst, drive time and distance calculations to the nearest obstetric hospital were conducted. Furthermore, Hot Spot Analysis was employed to identify areas displaying significant spatial clusters of high and low travel distances.\\u003c/p\\u003e\\u003ch2\\u003eRESULTS\\u003c/h2\\u003e \\u003cp\\u003eThe mean travel distance and time to the nearest obstetric facility was 8.3 miles and 14.1 minutes. The mean travel distance for maternity care deserts and rural counties was 28.1 and 17.3 miles, respectively. While birthing people living in rural maternity care deserts had the highest average travel distance overall (33.4 miles), those living in urban maternity care deserts also experienced inequities in travel distance (25.0 miles). States with hotspots indicating significantly higher travel distances included: Montana, North Dakota, South Dakota, and Nebraska. Census tracts where the predominant race is American Indian/Alaska Native (AIAN) had the highest travel distance and time compared to those of all other predominant races/ethnicities.\\u003c/p\\u003e\\u003ch2\\u003eCONCLUSIONS\\u003c/h2\\u003e \\u003cp\\u003eOur study revealed significant disparities in obstetric hospital access, especially affecting birthing individuals in maternity care deserts, rural counties, and communities predominantly composed of AIAN individuals, resulting in extended travel distances and times. To rectify these inequities, sustained investment in the obstetric workforce and implementation of innovative programs are imperative, specifically targeting improved access in maternity care deserts as a priority area within healthcare policy and practice.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Navigating Geographical Disparities: Access to Obstetric Hospitals in Maternity Care Deserts and across the United States \",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-02-09 17:03:25\",\"doi\":\"10.21203/rs.3.rs-3900489/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-03-20T11:36:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-03-20T02:04:28+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-02-12T02:43:31+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"4b7f2117-1f13-4371-a8ef-22237f6eff13\",\"date\":\"2024-02-06T15:15:47+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"e59ff28c-c2be-437a-b169-e9c2924c50e7\",\"date\":\"2024-02-06T13:51:24+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-02-06T13:15:01+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-02-06T13:08:50+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2024-02-06T10:10:55+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-02-06T10:08:24+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Pregnancy and Childbirth\",\"date\":\"2024-01-26T16:13:17+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-pregnancy-and-childbirth\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"prch\",\"sideBox\":\"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/prch/default.aspx\",\"title\":\"BMC Pregnancy and Childbirth\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"a68cc5a2-7c18-40e8-ab82-bd6ae634eee5\",\"owner\":[],\"postedDate\":\"February 9th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-05-09T04:01:34+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-3900489\",\"link\":\"https://doi.org/10.1186/s12884-024-06535-7\",\"journal\":{\"identity\":\"bmc-pregnancy-and-childbirth\",\"isVorOnly\":false,\"title\":\"BMC Pregnancy and Childbirth\"},\"publishedOn\":\"2024-05-08 04:01:33\",\"publishedOnDateReadable\":\"May 8th, 2024\"},\"versionCreatedAt\":\"2024-02-09 17:03:25\",\"video\":\"\",\"vorDoi\":\"10.1186/s12884-024-06535-7\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12884-024-06535-7\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3900489\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3900489\",\"identity\":\"rs-3900489\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}