Hypertension and Diabetes as Drivers of Severe Maternal Morbidity Across Age Race Region and Income in the United States

Hypertension and Diabetes as Drivers of Severe Maternal Morbidity Across Age Race Region and Income in 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 Hypertension and Diabetes as Drivers of Severe Maternal Morbidity Across Age Race Region and Income in the United States Saanie Sulley, Julia Liu, Alicia Aroche, Deidre McDaniel, Deborah Frazier This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6666416/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Dec, 2025 Read the published version in Discover Public Health → Version 1 posted 9 You are reading this latest preprint version Abstract Background Severe Maternal Morbidity (SMM), defined as unexpected outcomes of labor and delivery with significant health consequences, continues to rise in the United States and disproportionately affects marginalized communities. Chronic conditions such as hypertension and diabetes are known risk factors, but the interplay of these comorbidities with social determinants of health remains poorly characterized. Methods We analyzed 2019–2020 delivery hospitalizations from the National Inpatient Sample (NIS), a nationally representative dataset. SMM was defined using CDC and AIM criteria (excluding transfusion-related events). We conducted multivariate logistic regression to estimate adjusted odds ratios (aORs) for SMM, stratified by diabetes and hypertension status, while controlling demographic, clinical, and socioeconomic variables. Results Among 7 million delivery hospitalizations, 0.8% involved SMM. SMM prevalence was 1.4% among individuals with diabetes and 3.2% with hypertension. Younger individuals (ages 20–24) had higher odds of SMM in the hypertension subgroup (aOR 1.49; 95% CI: 1.23–1.82), contrary to general trends. Racial and income disparities persisted across subgroups. Black and Hispanic patients had lower adjusted SMM odds in diabetic and hypertensive subgroups, respectively. Increased healthcare utilization (longer stays, higher diagnosis/procedure counts) was consistently associated with SMM. Conclusions This study highlights how age, race, income, and regional factors interact with hypertension and diabetes to shape SMM risk. Findings underscore the urgency of integrating chronic disease management into maternal care and tailoring interventions to the unique needs of socioeconomically and demographically vulnerable populations. Community-based care models and enhanced preconception screening are essential to reducing disparities. Severe maternal morbidity hypertension diabetes health disparities maternal health social determinants comorbidity pregnancy outcomes community care NIS dataset Introduction Severe Maternal Morbidity (SMM) has emerged as a pervasive, albeit, underrecognized crisis in the landscape of maternal health. Defined by life-threatening complications during labor and delivery, SMM not only endangers the lives of pregnant individuals, but often results in everlasting physical, emotional, and systemic harm. 1 – 3 The rising prevalence of SMM in the United States exposes critical fractures in the nation’s healthcare infrastructure and reflects deeper social inequities that persist across race, geography, and income. While chronic conditions such as hypertension and diabetes are well-established risk factors, their impact acts as an amplifier of inequality that continuously disproportionately impacts marginalized communities. Hypertension, including gestational forms, affects up to 10% of pregnancies, and is linked to severe complications such as preeclampsia, placental abruption, and preterm birth. 4 – 10 Similar with diabetes, whether preexisting or gestational, can increase the likelihood of cesarean deliveries, fetal macrosomia, and congenital anomalies. 4 , 6 , 11 , 12 These conditions, when exacerbated by socioeconomic stressors, such as limited access to high quality care, underinsurance, and environmental risk, it consequently deepens vulnerabilities. 13 – 17 Research has consistently shown that individuals from lower-income households, or those residing in rural areas face greater barriers to care and experience worse maternal outcomes. 18 , 19 Geographic disparities further widen this gap where in rural areas, there is limited availability of obstetric services and transportation challenges that can delay critical and timely care, heightening the risk of severe complications. 20 , 21 This study examines the relationship of hypertension, diabetes, and SMM through the lens of demographic, geographic, and socioeconomic variation. By leveraging a nationally representative dataset, this analysis examines how these chronic conditions interact with structural and social determinants to shape maternal outcomes. Methods Data Source This study used the 2019–2020 National Inpatient Sample (NIS) from the Healthcare Cost and Utilization Project (HCUP), the largest publicly available all-payer inpatient database in the US. 22 The NIS provides a nationally representative sample, enabling robust analysis of hospitalizations across diverse demographic and geographic populations. Measures The SMM metric used in this analysis was derived from the Centers for Disease Control and Prevention (CDC) and the Alliance for Innovation on Maternal Health (AIM) standardized definitions, with the exclusion of transfusion-related indicators. 23 SMM indicators were individually extracted using corresponding ICD-10 codes, then systematically combined into a composite variable representing overall SMM status. Independent variables incorporated into the analysis included grouped age categories, length of inpatient stay (LOS), number of diagnoses (NDX), race and ethnicity, household income, primer payer, urban-rural classification, and the U.S. census region. Records with missing data were excluded. Statistical Analysis Data were analyzed in accordance with HCUP guidelines. 24 Statistical procedures were conducted using IBM SPSS Statistics version 23.0. 25 Subgroup differences were assessed via chi-square tests, focusing on delivery hospitalizations among individuals with hypertension and diabetes. Logic regression was used to estimate adjusted odds ratios (aORs) and 95% confidence intervals to assess the associations between demographic, socioeconomic, and clinical characteristics and the likelihood of experiencing SMM. Odds ratio was chosen to model the binary outcome of SMM. Given the rarity of SMM in this study population, the odds ratio closely approximates risk ratios. All p-values were two-tailed, with significance defined as p < 0.05. Additionally, the weights provided in NIS data were used to account for the clustering and unequal probability in the complex data structure. 26 , 27 The application of the weights also helps achieve nationally representative estimations with statistical accuracy. 26 Results This analysis included 7,037,395 delivery hospitalizations in the US from 2019–2020. Of these, 99.2% (n = 6,983,421) had no SMM, while 0.8% (n = 53,975) experienced SMM as shown in Table 1 . The cohort was stratified into three categories: all delivery hospitalizations, individuals with diabetes without chronic complications (n = 411,845), and those with hypertension without chronic complications (n = 46,140). Cases that involved complicated diabetes or hypertension were excluded based on HCUP NIS coding. Demographically, 60.4% of the total cohort were aged 25–34, with 52.1% identifying as White and 20.9% as Hispanic. Most patients resided in central metropolitan counties with populations ≥ 1 million (32.3%). Medicaid (41.3%) and private insurance (51.9%) were the primary payers. The South Atlantic (19.2%) and Pacific (15.6%) regions accounted for the largest geographic representation. Among the diabetes group, 1.4% (n = 5,505) had SMM; in the hypertension group, 3.2% (n = 1,430) had SMM. SMM was associated with a notably longer average length of stay (LOS: 6.0 days; SD 7.5) compared to those without SMM (LOS: 2.57 days; SD 2.1). Table 1 Demographic, Clinical, and Socioeconomic Characteristics by Comorbidity and SMM Status (NIS 2019–2020) Characteristic Total: No SMM Total: SMM Diabetes: No SMM Diabetes: SMM Hypertension: No SMM Hypertension: SMM N (Unweighted) 6,983,421 53,975 398,655 5,505 43,700 1,430 Length of Stay (Mean, SD) 2.57 (2.1) 6.0 (7.5) 2.6 (2.2) 7.26 (9.3) 2.6 (2.5) 8.4 (10.5) Number of Procedures (NPR) 7.3 (3.6) 14.8 (6.1) 2.6 (1.5) 3.9 (2.9) 2.6 (1.8) 4.0 (3.5) Number of Diagnoses (NDX) 2.6 (1.5) 3.9 (3.0) 7.3 (3.6) 16.9 (5.9) 7.3 (3.6) 18.3 (5.9) Age 20–24 1,302,845 (19.5%) 8,275 (0.6%) 38,345 (9.5%) 500 (0.1%) 4,425 (9.8%) 170 (0.4%) Age 25–34 4,248,020 (60.4%) 28,295 (0.4%) 244,045 (61.2%) 3,310 (0.6%) 26,690 (61.1%) 690 (1.5%) Age 35–44 1,432,556 (20.3%) 14,380 (0.2%) 116,265 (29.3%) 1,695 (0.4%) 12,585 (28.8%) 570 (1.3%) White 4,234,400 (60.2%) 19,995 (0.3%) 176,235 (44.1%) 1,950 (0.5%) 19,560 (43.5%) 400 (0.9%) Black or African American 1,658,400 (23.6%) 18,380 (0.4%) 144,165 (35.3%) 2,140 (0.5%) 18,675 (40.9%) 655 (1.5%) Other Race 1,150,595 (16.3%) 15,600 (0.2%) 78,255 (20.6%) 1,415 (0.4%) 5,465 (12.0%) 200 (0.4%) Income: 0–25th Percentile 1,927,711 (27.6%) 16,860 (0.2%) 111,095 (27.2%) 1,655 (0.4%) 15,705 (34.3%) 575 (1.3%) Income: 26–50th Percentile 1,915,120 (27.4%) 15,900 (0.2%) 113,345 (27.5%) 1,720 (0.4%) 13,625 (31.2%) 420 (1.1%) Income: 51–75th Percentile 1,725,315 (24.7%) 13,820 (0.2%) 106,775 (26.2%) 1,455 (0.3%) 9,350 (21.5%) 275 (0.9%) Income: 76–100th Percentile 1,415,275 (20.3%) 11,395 (0.2%) 87,530 (21.4%) 1,175 (0.3%) 7,420 (16.2%) 160 (0.5%) Medicaid (Payer) 2,758,730 (39.5%) 25,395 (0.4%) 170,330 (41.4%) 2,720 (0.7%) 20,475 (44.4%) 660 (1.4%) Private Insurance 3,423,765 (48.9%) 22,145 (0.3%) 213,965 (52.0%) 2,650 (0.6%) 21,990 (47.7%) 685 (1.5%) Medicare 43,540 (0.6%) 690 (0.1%) 1,360 (0.3%) 130 (0.3%) 370 (0.8%) 45 (0.1%) Central Urban 2,269,090 (32.3%) 20,580 (0.3%) 135,950 (33.0%) 2,185 (0.5%) 15,330 (33.3%) 580 (1.3%) Fringe Urban 2,332,800 (33.4%) 18,865 (0.3%) 135,765 (32.9%) 1,920 (0.5%) 15,875 (35.3%) 535 (1.2%) Micropolitan 1,030,560 (14.8%) 6,455 (0.1%) 55,210 (13.3%) 630 (0.3%) 5,245 (11.5%) 85 (0.2%) Non-Metro 1,351,305 (19.5%) 8,075 (0.1%) 71,730 (17.4%) 770 (0.2%) 7,250 (16.0%) 130 (0.2%) Comorbidity Status and Healthcare Utilization Among delivery hospitalizations, individuals with SMM experienced significantly greater healthcare utilization. The average number of procedures (NPR) was 14.8 (SD 6.1), and diagnosis codes (NDX) averaged 3.9 (SD 3.0) in the SMM group, substantially higher than those without SMM. Within the diabetes subgroup, individuals with SMM had a higher mean NPR (3.9 vs. 2.6) and markedly elevated NDX (16.9 vs. 7.3). A similar pattern was observed among individuals with hypertension. Length of stay (LOS) was significantly associated with SMM in the general population (AOR 1.29; 95% CI: 1.29–1.30, p < 0.001). Among diabetics with SMM, the number of procedures was significantly increased (AOR 1.21; 95% CI: 1.20–1.23, p < 0.001), with a comparable trend observed among hypertensives (AOR 1.14; 95% CI: 1.11–1.17, p < 0.001) as shown in Table 2 . Table 2 Adjusted Odds Ratios (aOR) for Severe Maternal Morbidity (SMM) by Population Group Covariate General Population aOR (95% CI) Diabetes aOR (95% CI) Hypertension aOR (95% CI) Age 20–24 vs. 35–44 0.78 (0.75–0.80) *** 0.97 (0.89–1.05) 1.49 (1.23–1.82) *** Age 25–34 vs. 35–44 0.98 (0.96–1.01) 0.93 (0.87–0.98) * 1.02 (0.89–1.17) White (ref: other) 0.63 (0.60–0.66) *** 0.96 (0.84–1.10) 0.92 (0.79–1.07) Black (ref: other) 1.10 (1.05–1.16) *** 0.80 (0.70–0.91) ** 1.06 (0.90–1.25) Hispanic (ref: other) 0.95 (0.91–1.00) 0.76 (0.66–0.87) *** 0.70 (0.51–0.97) * Asian/PI (ref: other) 0.88 (0.81–0.96) ** 0.89 (0.76–1.04) 0.65 (0.47–0.89) ** Income: 0–25th Percentile 1.07 (1.04–1.11) *** 1.04 (0.94–1.14) 1.13 (1.00–1.28) * Income: 26–50th Percentile 1.03 (1.00–1.07) 1.27 (1.16–1.39) *** 1.08 (0.96–1.22) Income: 51–75th Percentile 1.01 (0.98–1.05) 1.12 (1.02–1.23) * 1.06 (0.93–1.20) Income: 76–100th (Ref) Ref Ref Ref East South-Central Region 1.29 (1.23–1.36) *** 1.13 (0.92–1.38) 1.61 (1.22–2.14) ** New England Region 0.85 (0.78–0.92) *** 0.59 (0.49–0.72) *** 0.87 (0.63–1.21) South Atlantic Region 1.11 (1.07–1.16) *** 1.05 (0.93–1.18) 1.23 (1.01–1.51) * West North Central 1.04 (0.99–1.09) 0.98 (0.86–1.12) 1.08 (0.86–1.35) LOS (per day increase) 1.29 (1.29–1.30) *** 1.08 (1.07–1.09) *** 1.10 (1.08–1.12) *** NPR (per unit increase) 1.18 (1.17–1.18) *** 1.21 (1.20–1.23) *** 1.14 (1.11–1.17) *** *Significance levels: *p < 0.05, **p < 0.01, ***p < 0.001 Age Distribution Younger maternal age was associated with varying risk across subgroups. Compared to the reference group (35–44 years), individuals aged 20–24 had reduced odds of SMM in the general population (AOR 0.78; 95% CI: 0.75–0.80, p < 0.001), but increased odds in the hypertension subgroup (AOR 1.49; 95% CI: 1.23–1.82, p < 0.001). Among diabetics aged 25–34, a modest reduction in SMM was observed (AOR 0.93; 95% CI: 0.87–0.98, p = 0.017). Racial Disparities In the general population, White individuals had significantly lower odds of SMM compared to other racial/ethnic groups (AOR 0.63; 95% CI: 0.60–0.66, p < 0.001). Among diabetics, Black individuals showed reduced SMM odds (AOR 0.80; 95% CI: 0.70–0.91, p = 0.001), contrary to their higher crude prevalence. In the hypertension group, Hispanic individuals had significantly reduced SMM odds (AOR 0.70; 95% CI: 0.51–0.97, p = 0.034). These findings suggest that crude racial disparities in SMM may partially reflect confounding by comorbidity and other factors, warranting further stratified or mediation analysis. Income-Level Differences Low income was associated with higher SMM risk. In the general population, individuals in the lowest income quartile had increased odds of SMM (AOR 1.07; 95% CI: 1.04–1.11, p < 0.001). Among individuals with diabetes, those in the 26th–50th percentile income group had significantly elevated odds (AOR 1.27; 95% CI: 1.16–1.39, p < 0.001), indicating that income-related disparities persist even within clinical risk groups. Urban-Rural Disparities In regional analyses, individuals in the East South-Central U.S. exhibited higher odds of SMM compared to other regions (AOR 1.29; 95% CI: 1.23–1.36, p < 0.001). Among individuals with diabetes, those in the New England region had significantly reduced odds (AOR 0.59; 95% CI: 0.49–0.72, p < 0.001). In the hypertension subgroup, the East South-Central region was again associated with increased risk (AOR 1.61; 95% CI: 1.22–2.14, p = 0.001), underscoring regional disparities in maternal health outcomes. Urban areas demonstrated a higher prevalence of SMM, which may reflect the concentrated referrals of high-risk cases to tertiary care centers or increased regionalized of complex deliveries. Insurance Coverage Medicaid and private insurance were the predominant payers across all groups. In the hypertension subgroup, both payers had similar SMM rates (~ 1.5%), while Medicare patients had slightly higher SMM prevalence (though numerically small). These patterns reflect the need to further examine how payer type may interact with comorbidity status and access to obstetric care. Discussion This study provides a comprehensive evaluation of SMM across diverse populations, stratified by key comorbidities – hypertension and diabetes without chronic complications, and examined through demographic, geographic, and socioeconomic lenses. The findings reinforce known disparities, but add complexity by uncovering how age, race, income, and comorbidities interact to shape SMM risk. This underscores the need for public health responses to consider all the factors together. One of the most notable findings was the elevated adjusted odds of SMM among individuals aged 20–24 years within the hypertension group. Despite this age group generally having a lower obstetric risk in the broader population, this unexpected vulnerability may indicate an early-onset or undiagnosed hypertension because of limited engagement in preventative or preconception care. Additionally, younger individuals often face heightened social vulnerability such as unstable or inconsistent insurance coverage, fragmented healthcare access, and limited experience navigating complex medical systems. 1 These barriers consequently result in delay diagnosis, interfere with care continuity, and chronic conditions during pregnancy that can increase the likelihood of severe maternal complications. While crude prevalence rates of SMM were higher among Black individuals, our adjusted models revealed lower odds of SMM among Black individuals with diabetes, and lower odds among Hispanic individuals with hypertension. This highlights the complexity of interpreting racial disparities in health outcomes and reinforces those structural inequities—not race itself—underlie these differences. 28 , 29 It is plausible that in certain subpopulations, enhanced clinical monitoring may lead to earlier detection and intervention, resulting in lower observed adjusted SMM. The analysis reaffirmed the association between lower income and increased SMM risk, particularly among those with diabetes in the mid-income quartile, potentially reflecting the coverage gap where individuals earn too much to qualify for Medicaid but cannot afford high-quality private insurance. The persistence of SMM across all payer types, especially among Medicaid recipients, suggests gaps in quality, coordination, and continuity of care. Individuals covered by Medicare, though a small group, had slightly elevated SMM prevalence, pointing to potential disability-related vulnerability. Insurance coverage alone does not guarantee good health outcomes. Persistent social and structural barriers must also be addressed to ensure access to comprehensive care. Limitations This study is not without limitations. The HCUP NIS presents limitations that needs to be considered in the interpretation of the findings. Primarily, the restricted two-year time frame inhibits the ability to detect long-term trends or ascertain the effects that may be specific to the particular period. Due to the nationally representative design of NIS, it does not eliminate the potential regional differences in healthcare practices, policies, and accessibility. These variations can still limit the applicability of the findings across different geographical areas or healthcare settings, ultimately limiting generalizability. Additionally, there is also the risk of misclassification or coding errors that can affect the precise identification of diagnoses, procedures, or other relevant variables. Lastly, while logistic regression was appropriate for binary outcomes and rare events, the use of odds ratio may limit the interpretability in subgroups where SMM is more common. In such, odds ratio can overstate the strength of the association compared to risk ratios. Future analyses may benefit from modified Poisson regression to enhance the clarity and applicability of findings. Conclusion This study emphasizes on the need for community-based interventions tailored to reduce SMM among individuals with diabetes and hypertension. By embracing localized, person-centered approaches during the prenatal, perinatal, and postpartum periods, it may help mitigate the disparities observed, particularly among younger individuals with hypertension and those facing socioeconomic disadvantaged. 30 – 32 Future research should prioritize on the effective implementation of community-based strategies, aiming for comprehensive and sustainable healthcare solutions for diverse pregnant populations. Given the disproportionate risk among younger individuals with hypertension and among socioeconomically disadvantaged groups, the findings advocate for community-based care models. However, there is the limitation of individuals who deliver locally may systematically differ from those who were referred to tertiary centers. Future analyses should stratify outcomes by planned vs. final delivery location to better understand care dynamics. There is also a need to integrate chronic disease screening and management into early reproductive and preconception care, particularly for younger individuals. Additionally, prenatal care models should be developed to explicitly address comorbid risk. This can include learning sessions or monitoring for chronic diseases in group prenatal care or deploying risk stratification tools to indicate elevated SMM risk early in the pregnancy. Furthermore, investment in community-based care is imperative where designing interventions aligns with the population’s unique social needs. Such as expanding the roles of midwives and doulas can help mitigate institutional mistrust, ultimately improving care engagement. Declarations This study was supported by the National Healthy Start Association and Alliance for Innovation on Maternal Health Community Care Initiative (AIM CCI), Health Resources Services Administration (U7BMC33635). The content is solely the authors' responsibility and does not necessarily reflect the views of the National Healthy Start Association, Alliance for Innovation on Maternal Health Community Care Initiative or the Health Resources Services Administration. Ethical Considerations None. Clinical trial number Not applicable Consent for publication All authors reviewed the manuscript and provided their consent for publication Availability of data and materials This study used the 2019-2020 National Inpatient Sample (NIS) from the Healthcare Cost and Utilization Project (HCUP). The data can be obtained from HCUP system after completion of required training at https://cdors.ahrq.gov/ Competing interests The authors declare that they have no competing interests. Acknowledgement We would like to thank the National Healthy Start Association for their support. Author Contributions S.S.: Conceptualization/design, Methodology, Investigation, Data curation, Formal analysis, Validation, Visualization, Writing – original draft, Writing – review and editing. JL.: Review & editing AA: Review & editing DM: Methodology, review & editing. DF: Conceptualization/design, review & editing, Supervision. Funding This study was supported by the National Healthy Start Association and Alliance for Innovation on Maternal Health Community Care Initiative (AIM CCI), Health Resources Services Administration (U7BMC33635). References Fink DA, Kilday D, Cao Z, et al. Trends in Maternal Mortality and Severe Maternal Morbidity During Delivery-Related Hospitalizations in the United States, 2008 to 2021. JAMA Netw Open 2023;6(6):e2317641. doi: 10.1001/jamanetworkopen.2023.17641 [published Online First: 20230601] Callaghan WM, Creanga AA, Kuklina EV. Severe among delivery and postpartum hospitalizations in the United States. 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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-6666416","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470532862,"identity":"a159368a-b16e-4f35-aa87-f3cc33cd4681","order_by":0,"name":"Saanie Sulley","email":"data:image/png;base64,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","orcid":"","institution":"National Healthy Start Association","correspondingAuthor":true,"prefix":"","firstName":"Saanie","middleName":"","lastName":"Sulley","suffix":""},{"id":470532863,"identity":"0790fe22-702a-4ea9-9cc6-499da7a65132","order_by":1,"name":"Julia Liu","email":"","orcid":"","institution":"National Healthy Start Association","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Liu","suffix":""},{"id":470532864,"identity":"c715a31d-9760-4f54-83eb-e75312cfcc00","order_by":2,"name":"Alicia Aroche","email":"","orcid":"","institution":"National Healthy Start Association","correspondingAuthor":false,"prefix":"","firstName":"Alicia","middleName":"","lastName":"Aroche","suffix":""},{"id":470532865,"identity":"b2d1494e-73e6-4a59-9cfc-2279e97c9f2f","order_by":3,"name":"Deidre McDaniel","email":"","orcid":"","institution":"National Healthy Start Association","correspondingAuthor":false,"prefix":"","firstName":"Deidre","middleName":"","lastName":"McDaniel","suffix":""},{"id":470532866,"identity":"4919e805-8b73-481e-95ec-76e4979ea067","order_by":4,"name":"Deborah Frazier","email":"","orcid":"","institution":"National Healthy Start Association","correspondingAuthor":false,"prefix":"","firstName":"Deborah","middleName":"","lastName":"Frazier","suffix":""}],"badges":[],"createdAt":"2025-05-14 17:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6666416/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6666416/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12982-025-01099-z","type":"published","date":"2025-12-08T15:59:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":98244772,"identity":"4d26c62f-236c-4e84-a694-63b340a3daeb","added_by":"auto","created_at":"2025-12-15 16:14:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1102630,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6666416/v1/dcb86726-6bea-4fea-b583-a822eb32e006.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hypertension and Diabetes as Drivers of Severe Maternal Morbidity Across Age Race Region and Income in the United States","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSevere Maternal Morbidity (SMM) has emerged as a pervasive, albeit, underrecognized crisis in the landscape of maternal health. Defined by life-threatening complications during labor and delivery, SMM not only endangers the lives of pregnant individuals, but often results in everlasting physical, emotional, and systemic harm.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e \u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The rising prevalence of SMM in the United States exposes critical fractures in the nation\u0026rsquo;s healthcare infrastructure and reflects deeper social inequities that persist across race, geography, and income. While chronic conditions such as hypertension and diabetes are well-established risk factors, their impact acts as an amplifier of inequality that continuously disproportionately impacts marginalized communities.\u003c/p\u003e \u003cp\u003eHypertension, including gestational forms, affects up to 10% of pregnancies, and is linked to severe complications such as preeclampsia, placental abruption, and preterm birth. \u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Similar with diabetes, whether preexisting or gestational, can increase the likelihood of cesarean deliveries, fetal macrosomia, and congenital anomalies. \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e These conditions, when exacerbated by socioeconomic stressors, such as limited access to high quality care, underinsurance, and environmental risk, it consequently deepens vulnerabilities. \u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Research has consistently shown that individuals from lower-income households, or those residing in rural areas face greater barriers to care and experience worse maternal outcomes. \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Geographic disparities further widen this gap where in rural areas, there is limited availability of obstetric services and transportation challenges that can delay critical and timely care, heightening the risk of severe complications.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThis study examines the relationship of hypertension, diabetes, and SMM through the lens of demographic, geographic, and socioeconomic variation. By leveraging a nationally representative dataset, this analysis examines how these chronic conditions interact with structural and social determinants to shape maternal outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source\u003c/h2\u003e \u003cp\u003eThis study used the 2019\u0026ndash;2020 National Inpatient Sample (NIS) from the Healthcare Cost and Utilization Project (HCUP), the largest publicly available all-payer inpatient database in the US.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e The NIS provides a nationally representative sample, enabling robust analysis of hospitalizations across diverse demographic and geographic populations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eThe SMM metric used in this analysis was derived from the Centers for Disease Control and Prevention (CDC) and the Alliance for Innovation on Maternal Health (AIM) standardized definitions, with the exclusion of transfusion-related indicators.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e SMM indicators were individually extracted using corresponding ICD-10 codes, then systematically combined into a composite variable representing overall SMM status. Independent variables incorporated into the analysis included grouped age categories, length of inpatient stay (LOS), number of diagnoses (NDX), race and ethnicity, household income, primer payer, urban-rural classification, and the U.S. census region. Records with missing data were excluded.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed in accordance with HCUP guidelines.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Statistical procedures were conducted using IBM SPSS Statistics version 23.0.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Subgroup differences were assessed via chi-square tests, focusing on delivery hospitalizations among individuals with hypertension and diabetes. Logic regression was used to estimate adjusted odds ratios (aORs) and 95% confidence intervals to assess the associations between demographic, socioeconomic, and clinical characteristics and the likelihood of experiencing SMM. Odds ratio was chosen to model the binary outcome of SMM. Given the rarity of SMM in this study population, the odds ratio closely approximates risk ratios. All p-values were two-tailed, with significance defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Additionally, the weights provided in NIS data were used to account for the clustering and unequal probability in the complex data structure. \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e The application of the weights also helps achieve nationally representative estimations with statistical accuracy. \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis analysis included 7,037,395 delivery hospitalizations in the US from 2019\u0026ndash;2020. Of these, 99.2% (n\u0026thinsp;=\u0026thinsp;6,983,421) had no SMM, while 0.8% (n\u0026thinsp;=\u0026thinsp;53,975) experienced SMM as shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The cohort was stratified into three categories: all delivery hospitalizations, individuals with diabetes without chronic complications (n\u0026thinsp;=\u0026thinsp;411,845), and those with hypertension without chronic complications (n\u0026thinsp;=\u0026thinsp;46,140). Cases that involved complicated diabetes or hypertension were excluded based on HCUP NIS coding.\u003c/p\u003e \u003cp\u003eDemographically, 60.4% of the total cohort were aged 25\u0026ndash;34, with 52.1% identifying as White and 20.9% as Hispanic. Most patients resided in central metropolitan counties with populations\u0026thinsp;\u0026ge;\u0026thinsp;1\u0026nbsp;million (32.3%). Medicaid (41.3%) and private insurance (51.9%) were the primary payers. The South Atlantic (19.2%) and Pacific (15.6%) regions accounted for the largest geographic representation. Among the diabetes group, 1.4% (n\u0026thinsp;=\u0026thinsp;5,505) had SMM; in the hypertension group, 3.2% (n\u0026thinsp;=\u0026thinsp;1,430) had SMM. SMM was associated with a notably longer average length of stay (LOS: 6.0 days; SD 7.5) compared to those without SMM (LOS: 2.57 days; SD 2.1).\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\u003eDemographic, Clinical, and Socioeconomic Characteristics by Comorbidity and SMM Status (NIS 2019\u0026ndash;2020)\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=\"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 \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 \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal: No SMM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal: SMM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiabetes: No SMM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDiabetes: SMM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHypertension: No SMM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHypertension: SMM\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\u003eN (Unweighted)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,983,421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53,975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e398,655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5,505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of Stay (Mean, SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.57 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.0 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.26 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6 (2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.4 (10.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Procedures (NPR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.0 (3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Diagnoses (NDX)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.6 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.3 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.9 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.3 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.3 (5.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge 20\u0026ndash;24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,302,845 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,275 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38,345 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e500 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,425 (9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e170 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge 25\u0026ndash;34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,248,020 (60.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28,295 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244,045 (61.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,310 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26,690 (61.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e690 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge 35\u0026ndash;44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,432,556 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14,380 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116,265 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,695 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12,585 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e570 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,234,400 (60.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19,995 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176,235 (44.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,950 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19,560 (43.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e400 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlack or African American\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,658,400 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,380 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144,165 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,140 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18,675 (40.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e655 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther Race\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,150,595 (16.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,600 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78,255 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,415 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,465 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e200 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome: 0\u0026ndash;25th Percentile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,927,711 (27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,860 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111,095 (27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,655 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,705 (34.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e575 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome: 26\u0026ndash;50th Percentile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,915,120 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,900 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113,345 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,720 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13,625 (31.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e420 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome: 51\u0026ndash;75th Percentile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,725,315 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,820 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106,775 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,455 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9,350 (21.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e275 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome: 76\u0026ndash;100th Percentile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,415,275 (20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11,395 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87,530 (21.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,175 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,420 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e160 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedicaid (Payer)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,758,730 (39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,395 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170,330 (41.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,720 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20,475 (44.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e660 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrivate Insurance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,423,765 (48.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,145 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e213,965 (52.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,650 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21,990 (47.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e685 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedicare\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43,540 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e690 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,360 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e370 (0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCentral Urban\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,269,090 (32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,580 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135,950 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,185 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,330 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e580 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFringe Urban\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,332,800 (33.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18,865 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135,765 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,920 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,875 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e535 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMicropolitan\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,030,560 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6,455 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55,210 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e630 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5,245 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-Metro\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,351,305 (19.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8,075 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71,730 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e770 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7,250 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e130 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eComorbidity Status and Healthcare Utilization\u003c/h3\u003e\n\u003cp\u003eAmong delivery hospitalizations, individuals with SMM experienced significantly greater healthcare utilization. The average number of procedures (NPR) was 14.8 (SD 6.1), and diagnosis codes (NDX) averaged 3.9 (SD 3.0) in the SMM group, substantially higher than those without SMM. Within the diabetes subgroup, individuals with SMM had a higher mean NPR (3.9 vs. 2.6) and markedly elevated NDX (16.9 vs. 7.3). A similar pattern was observed among individuals with hypertension. Length of stay (LOS) was significantly associated with SMM in the general population (AOR 1.29; 95% CI: 1.29\u0026ndash;1.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among diabetics with SMM, the number of procedures was significantly increased (AOR 1.21; 95% CI: 1.20\u0026ndash;1.23, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a comparable trend observed among hypertensives (AOR 1.14; 95% CI: 1.11\u0026ndash;1.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eAdjusted Odds Ratios (aOR) for Severe Maternal Morbidity (SMM) by Population Group\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 \u003cp\u003eCovariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral Population aOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiabetes aOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHypertension aOR (95% CI)\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\u003eAge 20\u0026ndash;24 vs. 35\u0026ndash;44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78 (0.75\u0026ndash;0.80) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.89\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49 (1.23\u0026ndash;1.82) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge 25\u0026ndash;34 vs. 35\u0026ndash;44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.96\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.87\u0026ndash;0.98) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.89\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite (ref: other)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63 (0.60\u0026ndash;0.66) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.84\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92 (0.79\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlack (ref: other)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.10 (1.05\u0026ndash;1.16) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80 (0.70\u0026ndash;0.91) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.90\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHispanic (ref: other)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.91\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.66\u0026ndash;0.87) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.51\u0026ndash;0.97) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAsian/PI (ref: other)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88 (0.81\u0026ndash;0.96) **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89 (0.76\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65 (0.47\u0026ndash;0.89) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome: 0\u0026ndash;25th Percentile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.04\u0026ndash;1.11) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04 (0.94\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (1.00\u0026ndash;1.28) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome: 26\u0026ndash;50th Percentile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (1.00\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27 (1.16\u0026ndash;1.39) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.96\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome: 51\u0026ndash;75th Percentile\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.98\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12 (1.02\u0026ndash;1.23) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.93\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome: 76\u0026ndash;100th (Ref)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEast South-Central Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29 (1.23\u0026ndash;1.36) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.92\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61 (1.22\u0026ndash;2.14) **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNew England Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.78\u0026ndash;0.92) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59 (0.49\u0026ndash;0.72) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87 (0.63\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSouth Atlantic Region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.07\u0026ndash;1.16) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.93\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23 (1.01\u0026ndash;1.51) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWest North Central\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.99\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.86\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.86\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLOS (per day increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29 (1.29\u0026ndash;1.30) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08 (1.07\u0026ndash;1.09) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.10 (1.08\u0026ndash;1.12) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNPR (per unit increase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18 (1.17\u0026ndash;1.18) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21 (1.20\u0026ndash;1.23) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14 (1.11\u0026ndash;1.17) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Significance levels: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAge Distribution\u003c/h2\u003e \u003cp\u003eYounger maternal age was associated with varying risk across subgroups. Compared to the reference group (35\u0026ndash;44 years), individuals aged 20\u0026ndash;24 had reduced odds of SMM in the general population (AOR 0.78; 95% CI: 0.75\u0026ndash;0.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but increased odds in the hypertension subgroup (AOR 1.49; 95% CI: 1.23\u0026ndash;1.82, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among diabetics aged 25\u0026ndash;34, a modest reduction in SMM was observed (AOR 0.93; 95% CI: 0.87\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.017).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRacial Disparities\u003c/h3\u003e\n\u003cp\u003eIn the general population, White individuals had significantly lower odds of SMM compared to other racial/ethnic groups (AOR 0.63; 95% CI: 0.60\u0026ndash;0.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among diabetics, Black individuals showed reduced SMM odds (AOR 0.80; 95% CI: 0.70\u0026ndash;0.91, p\u0026thinsp;=\u0026thinsp;0.001), contrary to their higher crude prevalence. In the hypertension group, Hispanic individuals had significantly reduced SMM odds (AOR 0.70; 95% CI: 0.51\u0026ndash;0.97, p\u0026thinsp;=\u0026thinsp;0.034).\u003c/p\u003e \u003cp\u003eThese findings suggest that crude racial disparities in SMM may partially reflect confounding by comorbidity and other factors, warranting further stratified or mediation analysis.\u003c/p\u003e\n\u003ch3\u003eIncome-Level Differences\u003c/h3\u003e\n\u003cp\u003eLow income was associated with higher SMM risk. In the general population, individuals in the lowest income quartile had increased odds of SMM (AOR 1.07; 95% CI: 1.04\u0026ndash;1.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among individuals with diabetes, those in the 26th\u0026ndash;50th percentile income group had significantly elevated odds (AOR 1.27; 95% CI: 1.16\u0026ndash;1.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that income-related disparities persist even within clinical risk groups.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUrban-Rural Disparities\u003c/h2\u003e \u003cp\u003eIn regional analyses, individuals in the East South-Central U.S. exhibited higher odds of SMM compared to other regions (AOR 1.29; 95% CI: 1.23\u0026ndash;1.36, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among individuals with diabetes, those in the New England region had significantly reduced odds (AOR 0.59; 95% CI: 0.49\u0026ndash;0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the hypertension subgroup, the East South-Central region was again associated with increased risk (AOR 1.61; 95% CI: 1.22\u0026ndash;2.14, p\u0026thinsp;=\u0026thinsp;0.001), underscoring regional disparities in maternal health outcomes. Urban areas demonstrated a higher prevalence of SMM, which may reflect the concentrated referrals of high-risk cases to tertiary care centers or increased regionalized of complex deliveries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInsurance Coverage\u003c/h2\u003e \u003cp\u003eMedicaid and private insurance were the predominant payers across all groups. In the hypertension subgroup, both payers had similar SMM rates (~\u0026thinsp;1.5%), while Medicare patients had slightly higher SMM prevalence (though numerically small). These patterns reflect the need to further examine how payer type may interact with comorbidity status and access to obstetric care.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a comprehensive evaluation of SMM across diverse populations, stratified by key comorbidities \u0026ndash; hypertension and diabetes without chronic complications, and examined through demographic, geographic, and socioeconomic lenses. The findings reinforce known disparities, but add complexity by uncovering how age, race, income, and comorbidities interact to shape SMM risk. This underscores the need for public health responses to consider all the factors together.\u003c/p\u003e \u003cp\u003eOne of the most notable findings was the elevated adjusted odds of SMM among individuals aged 20\u0026ndash;24 years within the hypertension group. Despite this age group generally having a lower obstetric risk in the broader population, this unexpected vulnerability may indicate an early-onset or undiagnosed hypertension because of limited engagement in preventative or preconception care. Additionally, younger individuals often face heightened social vulnerability such as unstable or inconsistent insurance coverage, fragmented healthcare access, and limited experience navigating complex medical systems.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e These barriers consequently result in delay diagnosis, interfere with care continuity, and chronic conditions during pregnancy that can increase the likelihood of severe maternal complications.\u003c/p\u003e \u003cp\u003eWhile crude prevalence rates of SMM were higher among Black individuals, our adjusted models revealed lower odds of SMM among Black individuals with diabetes, and lower odds among Hispanic individuals with hypertension. This highlights the complexity of interpreting racial disparities in health outcomes and reinforces those structural inequities\u0026mdash;not race itself\u0026mdash;underlie these differences. \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e It is plausible that in certain subpopulations, enhanced clinical monitoring may lead to earlier detection and intervention, resulting in lower observed adjusted SMM. The analysis reaffirmed the association between lower income and increased SMM risk, particularly among those with diabetes in the mid-income quartile, potentially reflecting the coverage gap where individuals earn too much to qualify for Medicaid but cannot afford high-quality private insurance. The persistence of SMM across all payer types, especially among Medicaid recipients, suggests gaps in quality, coordination, and continuity of care. Individuals covered by Medicare, though a small group, had slightly elevated SMM prevalence, pointing to potential disability-related vulnerability. Insurance coverage alone does not guarantee good health outcomes. Persistent social and structural barriers must also be addressed to ensure access to comprehensive care.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study is not without limitations. The HCUP NIS presents limitations that needs to be considered in the interpretation of the findings. Primarily, the restricted two-year time frame inhibits the ability to detect long-term trends or ascertain the effects that may be specific to the particular period. Due to the nationally representative design of NIS, it does not eliminate the potential regional differences in healthcare practices, policies, and accessibility. These variations can still limit the applicability of the findings across different geographical areas or healthcare settings, ultimately limiting generalizability. Additionally, there is also the risk of misclassification or coding errors that can affect the precise identification of diagnoses, procedures, or other relevant variables. Lastly, while logistic regression was appropriate for binary outcomes and rare events, the use of odds ratio may limit the interpretability in subgroups where SMM is more common. In such, odds ratio can overstate the strength of the association compared to risk ratios. Future analyses may benefit from modified Poisson regression to enhance the clarity and applicability of findings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study emphasizes on the need for community-based interventions tailored to reduce SMM among individuals with diabetes and hypertension. By embracing localized, person-centered approaches during the prenatal, perinatal, and postpartum periods, it may help mitigate the disparities observed, particularly among younger individuals with hypertension and those facing socioeconomic disadvantaged.\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Future research should prioritize on the effective implementation of community-based strategies, aiming for comprehensive and sustainable healthcare solutions for diverse pregnant populations. Given the disproportionate risk among younger individuals with hypertension and among socioeconomically disadvantaged groups, the findings advocate for community-based care models. However, there is the limitation of individuals who deliver locally may systematically differ from those who were referred to tertiary centers. Future analyses should stratify outcomes by planned vs. final delivery location to better understand care dynamics. There is also a need to integrate chronic disease screening and management into early reproductive and preconception care, particularly for younger individuals. Additionally, prenatal care models should be developed to explicitly address comorbid risk. This can include learning sessions or monitoring for chronic diseases in group prenatal care or deploying risk stratification tools to indicate elevated SMM risk early in the pregnancy. Furthermore, investment in community-based care is imperative where designing interventions aligns with the population\u0026rsquo;s unique social needs. Such as expanding the roles of midwives and doulas can help mitigate institutional mistrust, ultimately improving care engagement.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis study was supported by the National Healthy Start Association and Alliance for Innovation on Maternal Health Community Care Initiative (AIM CCI), Health Resources Services Administration (U7BMC33635). The content is solely the authors\u0026apos; responsibility and does not necessarily reflect the views of the National Healthy Start Association, Alliance for Innovation on Maternal Health Community Care Initiative or the Health Resources Services Administration. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNot applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors reviewed the manuscript and provided their consent for publication\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used the 2019-2020 National Inpatient Sample (NIS) from the Healthcare Cost and Utilization Project (HCUP). The data can be obtained from HCUP system after completion of required training at https://cdors.ahrq.gov/\u003c/p\u003e\n\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the National Healthy Start Association for their support.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.S.: Conceptualization/design, Methodology, Investigation, Data curation, Formal analysis, Validation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review and editing.\u003c/p\u003e\n\u003cp\u003eJL.: Review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eAA: Review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eDM: Methodology, review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eDF: Conceptualization/design, review \u0026amp; editing, Supervision.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Healthy Start Association and Alliance for Innovation on Maternal Health Community Care Initiative (AIM CCI), Health Resources Services Administration (U7BMC33635).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFink DA, Kilday D, Cao Z, et al. 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Obstetric Volume and Severe Maternal Morbidity Among Low-Risk and Higher-Risk Patients Giving Birth at Rural and US Hospitals. \u003cem\u003eJAMA Health Forum\u003c/em\u003e 2023;4(6):e232110-e10. doi: 10.1001/jamahealthforum.2023.2110\u003c/li\u003e\n\u003cli\u003eKozhimannil KB, Interrante JD, Henning-Smith C, et al. Rural- Differences In Severe Maternal Morbidity And Mortality In The US, 2007\u0026ndash;15. \u003cem\u003eHealth Affairs\u003c/em\u003e 2019;38(12):2077-85. doi: 10.1377/hlthaff.2019.00805\u003c/li\u003e\n\u003cli\u003eHealthcare Cost and Utilization Project (HCUP) Statistical Briefs2023.\u003c/li\u003e\n\u003cli\u003eHow Does CDC Identify Severe Maternal Morbidity? \u003cem\u003eMMWR Recomm Rep\u003c/em\u003e 2023;41(RR-13):1-76.\u003c/li\u003e\n\u003cli\u003eAgency for Healthcare Research and Quality. \u003cstrong\u003eHCUP Databases: Overview of the HCUP Databases\u003c/strong\u003e. Healthcare Cost and Utilization Project (HCUP). Published 2024. 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Accessed March 31, 2025. https://www.aimcci.org/who-we-are/\u003c/li\u003e\n\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":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Severe maternal morbidity, hypertension, diabetes, health disparities, maternal health, social determinants, comorbidity, pregnancy outcomes, community care, NIS dataset","lastPublishedDoi":"10.21203/rs.3.rs-6666416/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6666416/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSevere Maternal Morbidity (SMM), defined as unexpected outcomes of labor and delivery with significant health consequences, continues to rise in the United States and disproportionately affects marginalized communities. Chronic conditions such as hypertension and diabetes are known risk factors, but the interplay of these comorbidities with social determinants of health remains poorly characterized.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe analyzed 2019\u0026ndash;2020 delivery hospitalizations from the National Inpatient Sample (NIS), a nationally representative dataset. SMM was defined using CDC and AIM criteria (excluding transfusion-related events). We conducted multivariate logistic regression to estimate adjusted odds ratios (aORs) for SMM, stratified by diabetes and hypertension status, while controlling demographic, clinical, and socioeconomic variables.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAmong 7\u0026nbsp;million delivery hospitalizations, 0.8% involved SMM. SMM prevalence was 1.4% among individuals with diabetes and 3.2% with hypertension. Younger individuals (ages 20\u0026ndash;24) had higher odds of SMM in the hypertension subgroup (aOR 1.49; 95% CI: 1.23\u0026ndash;1.82), contrary to general trends. Racial and income disparities persisted across subgroups. Black and Hispanic patients had lower adjusted SMM odds in diabetic and hypertensive subgroups, respectively. Increased healthcare utilization (longer stays, higher diagnosis/procedure counts) was consistently associated with SMM.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study highlights how age, race, income, and regional factors interact with hypertension and diabetes to shape SMM risk. Findings underscore the urgency of integrating chronic disease management into maternal care and tailoring interventions to the unique needs of socioeconomically and demographically vulnerable populations. Community-based care models and enhanced preconception screening are essential to reducing disparities.\u003c/p\u003e","manuscriptTitle":"Hypertension and Diabetes as Drivers of Severe Maternal Morbidity Across Age Race Region and Income in the United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 12:15:19","doi":"10.21203/rs.3.rs-6666416/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-22T09:47:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-22T02:00:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225432526016220769113603636736216766318","date":"2025-07-10T10:40:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-28T19:06:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249128484920452696284803485630241463344","date":"2025-06-12T13:35:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-12T13:18:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-05T09:26:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-28T22:49:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2025-05-28T22:48:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c01bd74a-7d47-4564-a296-4ad0d2cc021a","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:09:01+00:00","versionOfRecord":{"articleIdentity":"rs-6666416","link":"https://doi.org/10.1186/s12982-025-01099-z","journal":{"identity":"discover-public-health","isVorOnly":false,"title":"Discover Public Health"},"publishedOn":"2025-12-08 15:59:16","publishedOnDateReadable":"December 8th, 2025"},"versionCreatedAt":"2025-06-16 12:15:19","video":"","vorDoi":"10.1186/s12982-025-01099-z","vorDoiUrl":"https://doi.org/10.1186/s12982-025-01099-z","workflowStages":[]},"version":"v1","identity":"rs-6666416","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6666416","identity":"rs-6666416","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}