Area-Level Geospatial Analysis to Examine Smoking During Pregnancy with Social Risk Factors in North Carolina, United States

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Ventura, Qian Huang, Adeola Ololade Ayo, Kate E. Beatty This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7895692/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Smoking during pregnancy remains a public health concern in the United States, with disproportionately higher rates observed in rural populations. These differences are influenced by factors at system and community levels. This study examines the association between smoking during pregnancy, social risk factors, and geographic context in North Carolina, a state in the Southeast United States. Methods An ecological cross-sectional study was conducted at the county-level using publicly available secondary data from 2018–2022, incorporating spatial statistical methods to examine geographic variation and area-level drivers of smoking during pregnancy. Key independent variables included rural-urban status (2023 Rural-Urban Continuum Codes), managed care regions (North Carolina Department of Health and Human Services designations), and county-level indicators of socioeconomic instability (e.g., median household income), healthcare access (e.g., insurance coverage, OB-GYN physician rate), and neighborhood/built environment (e.g., broadband access, food availability). Descriptive analyses, Spearman correlations, and ordinary least squares (OLS) regression were conducted. Spatial dependence was evaluated using Global Moran’s I and Lagrange Multiplier tests, with a spatial lag regression model applied to adjust for spatial autocorrelation. Results The average county-level rate of smoking during pregnancy was 10.3%, ranging from 1.6% to 21.6%. Significant spatial clustering was observed (Global Moran’s I = 0.55, p < 0.01), with high-high clusters in western counties (Regions 1 and 2) and low-low clusters in central counties (Region 4). Rural counties had significantly higher smoking rates (11.98%) than urban counties (8.19%; p < 0.0001) and significantly greater socio-economic disadvantage. In the OLS model (R² = 0.58), rurality, lower median household income, fewer OB-GYNs, and regional location were significantly associated with higher smoking rates. Spatial lag modeling improved model fit (R² = 0.71) and confirmed spatial dependence (ρ = 0.53, p < 0.001). Rurality, OB-GYN rates, and regional location remained significant predictors. Conclusions Smoking during pregnancy demonstrates distinct geographic clustering in North Carolina, in the Southeast United States. Rates are influenced by rurality, healthcare access, and regional context. Spatial models are crucial for informing place-based prevention efforts. Leveraging tobacco taxes, Master Settlement Agreement funds, and investments in rural economic development promise to reduce tobacco-related maternal and child health disparities. Healthcare Disparities Health Services Accessibility Maternal Health Medicaid Pregnancy Public Health Rural Community Spatial Analysis Tobacco Use United States Figures Figure 1 Figure 2 Background The prevalence of smoking during pregnancy in the United States (U.S.) was 11.8% in 2015–2020 ( 1 ). Smoking during pregnancy is associated with low birth weight, infants who are small for gestational age, and preterm birth ( 2 , 3 ). Adverse health outcomes may persist throughout the infants’ lifespan, contributing to chronic diseases (e.g., obesity, diabetes, heart disease), cognitive and learning challenges, sleep problems, and respiratory infections and asthma ( 4 ). In the U.S., women who live in rural areas are significantly more likely than their urban counterparts to smoke during pregnancy and to have nicotine dependence, which reduces the likelihood of successful smoking cessation attempts ( 5 , 6 ). Disparities in smoking during pregnancy between rural and urban populations are associated with factors at multiple levels of influence according to the socioecological model, including system- and community-level factors ( 7 ). Tobacco-related policies, such as tobacco pricing and taxes, smoke-free indoor air laws, and state tobacco control spending, influence the prevalence of smoking and have a minor effect on successful cessation efforts among pregnant women who smoke ( 8 ). Disparities are further reinforced by the tobacco industry, which targets advertising campaigns to marginalized populations, including people who are Indigenous, who have low socioeconomic status, and who live in rural areas ( 9 ). The historical marginalization of Indigenous communities further perpetuates intergenerational cycles of tobacco smoking and nicotine dependence ( 7 ). Additionally, economic reliance on tobacco crops as a crucial source of income is particularly influential in the Southern Appalachian region, which is predominantly rural and has the highest rates of smoking during pregnancy across the country ( 10 , 11 ). Disadvantaged socioeconomic positioning is linked with a higher prevalence of smoking during pregnancy, including low levels of educational attainment, unemployment or underemployment, low levels of household income, or a vulnerable neighborhood ecological index ( 12 ). Smoking during pregnancy among women with lower socioeconomic conditions may pose challenges to having sufficient amounts of healthy foods ( 7 , 9 ). Healthcare access is similarly pivotal, whereby, among a sample of pregnant women in Southern Appalachia, having access to private health insurance or receiving sufficient prenatal care resulted in significantly higher likelihoods of successful cessation attempts ( 11 ). Among rural communities, the digital divide, which is characterized by a lack of access to broadband internet, is particularly pronounced and has implications for less access to perinatal health care, including essential medical services and programs to support smoking cessation ( 13 , 14 ). Given the multi-level influences, including geographic differences in rurality, on disparities in the prevalence of smoking during pregnancy ( 7 , 15 ), robust modeling approaches are indicated to assess area-level risk factors ( 16 ). Geographic context underlies community-level determinants of health, which influence outcomes, and nonstationarity among these factors is evident when comparing geographic areas ( 17 ). Spatial investigation at sub-national levels is particularly indicated to highlight localized inequities in maternal health and to tailor community-driven solutions to improve outcomes and reduce disparities ( 18 ). North Carolina, situated within the Southeast U.S., ranks 34th of 50 states for preterm birth, whereby 17% of all preterm births are attributed to smoking during pregnancy ( 19 ). The state is geographically diverse, with a 21% of the population residing in rural-designated areas ( 20 ). Geographically, there are three distinct regions within the state: Coastal, Piedmont, and Mountain. The Mountain Region in the west is situated within Southern Appalachia ( 20 ). This study aims to examine the effect of geography, including rurality and managed care regions as designated by the North Carolina Department of Health and Human Services (DHHS), on smoking during pregnancy throughout the state. This study also investigates the role of area-level socioeconomic instability (e.g., median household income), healthcare access (e.g., obstetrician-gynecologist (OB-GYN) physician rates), and the neighborhood and built environment (e.g., broadband internet access) on county-level rates of smoking during pregnancy throughout North Carolina. Through applying a spatial approach, we investigate the unique interplay between geography, area-level social risk factors, and smoking during pregnancy in a geographically diverse state in the Southeast U.S. Findings have the potential to inform system- and community-level policies for reducing and preventing smoking during pregnancy and associated adverse health outcomes that are perpetuated through generations. Methods Data Sources An ecological cross-sectional study was conducted at the county-level, encompassing all 100 counties in North Carolina. Data were drawn from publicly available, deidentified secondary sources provided by state, federal, and philanthropic organizations, including: ( 1 ) the North Carolina State Center for Health Statistics, ( 2 ) the U.S. Census Bureau, ( 3 ) the Health Resources and Services Administration (HRSA) Area Health Resources Files, ( 4 ) the U.S. Department of Agriculture (USDA) Economic Research Service (ERS), ( 5 ) County Health Rankings. The county was selected as the unit of analysis because it represents the smallest policy-relevant unit and has practical importance for ongoing community health assessment and improvement initiatives in the U.S. ( 21 ). Variables The outcome variable was the county-level rate of smoking during pregnancy from 2018 to 2022, defined as the percentage of infants born to mothers who reported smoking during any trimester. The rural-urban classification was derived from the USDA ERS 2023 Rural-Urban Continuum Codes (RUCC), where RUCC codes 1–3 were categorized as urban and codes 4–9 as rural ( 22 ). The region classification was constructed based on the six managed care regions, defined by North Carolina DHHS, which operationalizes the provision of the state-sponsored health insurance plans (Fig. 1 ) ( 23 ). Covariates included county-level measures of socioeconomic instability, healthcare access, and the neighborhood and built environment characteristics. To measure income, we used the natural log of median household income, which is a widely used and acceptable measure of community-level economic well-being ( 24 ). Educational attainment was included as the percentage of the population 25 years of age and older with no high school diploma or equivalent. Measures of healthcare access were the rate of individuals with no health insurance, the rate of OB-GYN physicians per 100,000, and the percentage of the population initiating prenatal care in the first trimester, which is considered optimal prenatal care ( 25 ). Measures for the neighborhood and built environment included no household vehicle access, lack of access to healthy foods, and no broadband internet access, which were derived from Fixed Broadband Deployment Data (U.S. Census Bureau). All variables are continuous, except for the two geographic variables (rurality and managed care region), which are categorical. All data were obtained, quality assured, and curated in the North Carolina Maternal and Child Health Dashboard , which was co-developed with community partners throughout Western North Carolina ( 26 ). All variables and data sources used in this study are presented in Table 1 . Table 1 Description of Study Variables and Secondary Data Sources Outcome variable Description Year(s) Data Source Smoking during pregnancy Percent of mothers who smoked during any trimester of pregnancy 2018–2022 NC State Center for Health Statistics Geography Rurality RUCC codes 1–3 classified as urban and ( 4 – 9 ) were classified as rural 2023 USDA ERS Managed Care Regions Regions identified by NC DHHS for public health service provision 2011 NC DHHS Socioeconomic instability Median household income The natural log of median household income in U.S. dollars 2018–2022 U.S. Census Bureau No high school diploma Percent of individuals (18 years of age and older) with no high school diploma or equivalent 2018–2022 U.S. Census Bureau Healthcare access No Health insurance Estimated percent of adults without health insurance 2018–2022 U.S. Census Bureau OB/GYN Rate Rate per 100,000 population 2018–2022 HRSA Area Health Resources Files First trimester prenatal care Percent of individuals who initiated prenatal care during the first trimester 2018–2022 NC State Center for Health Statistics Neighborhood and Built Environment No household vehicle access Estimated percent of occupied households without access to at least 1 vehicle per household 2018–2022 U.S. Census Bureau Lack of healthy food access Measure of access to healthy foods via distance to/from grocery stores, supermarkets, or other locations to obtain healthy food 2018–2022 County Health Rankings No broadband internet access Estimated percent of the population with no access to broadband internet (standard broadband access is 25 Mbps download, 3 Mbps upload) 2018–2022 U.S. Census Bureau Abbreviations: U.S.-United States; USDA ERS-United States Department of Agriculture Economic Research Service; DHHS-Department of Health and Human Services; HRSA-Human Resources and Service Administration; OB/GYN-Obstetrician-Gynecologist. Statistical Analyses To assess geographic differences in smoking during pregnancy and area-level social risk factors across the state, we applied a two-tailed independent t-test. We conducted a Spearman’s Correlation to examine the univariate relationships between each social risk factor, rural-urban, and managed care region variables with smoking during pregnancy, respectively (Appendix 1). To further estimate the impact of social factors on smoking during pregnancy, we first employed an ordinary least squares (OLS) regression model as a baseline. Heteroskedasticity was assessed using the Breusch–Pagan and Koenker–Bassett tests, while spatial dependence in OLS residuals was evaluated using Global Moran’s I and Lagrange Multiplier (LM) tests to determine the need for spatial regression. Based on these diagnostics, a spatial lag model was applied to account for the influence of smoking during pregnancy rates in neighboring counties and to improve the precision of coefficient estimates. The univariate analyses were conducted with SAS Enterprise Guide 8.3 (SAS Institute; Cary, NC, USA). All regression analyses were conducted in ArcGIS Pro 3.0.3 and GeoDa 1.22.0.10. A first-order rook contiguity spatial weights matrix, based on shared borders and identified using Federal Information Processing Standards (FIPS) codes, was applied and row-standardized. Statistical significance was set at p < 0.05 for all analyses. This study was designated exempt from review by the Institutional Review Board at XXXX XXXX. Data are from publicly available aggregated data with no personal identifiable information. Results Spatial Distribution of Smoking During Pregnancy Rate in North Carolina The average county-level rate of smoking during pregnancy in North Carolina from 2018 to 2022 was 10.3%, with a wide range across the state. The lowest rate was observed in Alamance County (1.6%), located in central North Carolina (Region 4), while the highest rate was recorded in Graham County (21.6%) in the west (Region 1). Overall, counties in western North Carolina exhibited higher rates of smoking during pregnancy, whereas central counties reported the lowest rates (Fig. 2 a). Spatial autocorrelation analysis using Global Moran’s I indicated a statistically significant clustering of smoking during pregnancy across North Carolina (Moran’s I = 0.55, z = 7.50, p < 0.01), suggesting that neighboring counties tend to have similar rates. Further cluster analysis using Local Moran’s I (Fig. 2 b) identified several spatial clusters and outliers. High–high clusters—counties with high smoking rates surrounded by similarly high rates—were primarily located in the western mountain region, especially within Region 1 and parts of Region 2. Low–low clusters, where counties with low smoking rates are surrounded by others with similarly low rates, were concentrated in central North Carolina, particularly in Region 4 and portions of Region 5. One low–high outlier—a county with a low smoking rate surrounded by high-rate counties—was identified in Watauga County, which accounted for 7.9% of such spatial outliers. Urban and Rural Differences in Smoking During Pregnancy Across North Carolina, rural-urban differences in rates of smoking during pregnancy and area-level social risk factors were evident. The rate of smoking during pregnancy was 11.98% in rural counties as compared to 8.19% in urban counties, which exceeds the statewide average (10.27%) (p < .0001). Median household income (p < .0001) and no high school diploma (p < .0001) were significantly different between rural and urban counties, respectively. Rural counties had significantly higher rates of individuals with no health insurance (12.22%) as compared to urban counties (10.02%) (p < .0001) and significantly lower rates of OB-GYN physicians (5.61% in rural counties vs. 9.07% in urban counties; p = 0.03). However, the rate of individuals initiating prenatal care in the first trimester did not differ significantly between rural and urban counties, whereas the statewide average was 75.14%. Measures of the neighborhood and built environment were similarly significantly different between rural and urban areas whereby rural counties were characterized by higher rates of households with no vehicle access (6.53% vs. 4.80%; p = .000), lower rates of access to healthy foods (4.86% vs. 6.84%; p = .03), and higher rates of households with no access to broadband internet (18.67% vs. 5.20%; p < .0001). Rural-urban differences for all variables are presented in Table 2. Table 2: Rural-Urban Differences in Smoking During Pregnancy and Social Risk Factors in North Carolina, United States (2018-2022) Rural (N=55) Urban (N=45) Statewide (N=100) P Smoking during pregnancy *** 11.98 8.19 10.27 <.0001 Median household income *** 4.71 4.81 4.76 <.0001 No high school diploma *** 9.70 7.42 8.67 <.0001 No health insurance *** 12.22 10.02 11.23 <.0001 OB/GYN Rate * 5.61 9.07 7.17 0.03 First trimester prenatal care 75.07 75.22 75.14 0.91 No household vehicle access *** 6.53 4.80 5.75 0.000 Lack of healthy food access * 4.86 6.84 5.75 0.03 No broadband internet access *** 18.67 5.20 12.6 <.0001 * p < 0.05; ** p < 0.01; *** p < 0.0001 Underlying Driving Factors of Smoking During Pregnancy To explore the factors contributing to the geographic variation in smoking during pregnancy, an OLS regression model was fitted using county-level data (Adjusted R² = 0.58, F = 14.60, p < 0.001, AICc = 502.27). In the OLS model, median household income (β = − 20.50, p = 0.002), OB/GYN rate (β = − 0.13, p = 0.007), rurality (β = − 1.86, p = 0.022), and managed care regions (β = − 1.08, p < 0.001) were statistically significant. These findings suggest that rural counties, located in certain managed care regions, have fewer OB-GYN physicians per capita, lower median household income, and are more likely to exhibit higher rates of smoking during pregnancy. Other explanatory variables were not statistically significant in the OLS model (Table 3 ). Table 3 Results from Ordinary Least Squares Regression Model Examining Smoking During Pregnancy in North Carolina, Southeast United States (2018–2022) Variable Coefficient Std. Error t-statistic Probability [Constant] 110.54 32.45 3.41 .001 Rurality * -1.86 .80 -2.33 .02 Managed Care Regions *** -1.08 .19 -5.87 .000 Median household income *** -20.50 6.56 -3.12 .002 No high school diploma − .02 .17 − .12 .91 No Health insurance .01 .14 .05 .96 OB/GYN rate * − .13 .05 -2.75 .01 First trimester prenatal care .05 .05 1.07 .29 No household vehicle access − .04 .16 − .22 .83 Lack of healthy food access − .04 .07 − .56 .57 No broadband internet access − .01 .02 − .47 .64 * p < 0.05; ** p < 0.01; *** p < 0.0001 Diagnostic tests revealed evidence of spatial dependence not accounted for by the OLS model. Residuals demonstrated significant positive spatial autocorrelation (Moran’s I = 0.23, p < 0.001), suggesting spatial clustering of unobserved factors. Heteroskedasticity was not evident, as both the Breusch–Pagan (χ² = 5.52, p = 0.85) and Koenker–Bassett (χ² = 5.82, p = 0.83) tests were not statistically significant, suggesting constant variance across residuals. To account for spatial dependence, LM tests supported the use of a spatial lag model (LM = 16.03, Robust LM = 4.12, p < 0.001), with a non-significant spatial error term (Robust LM error = 0.03, p = 0.86). The spatial lag model showed improved model fit (R² = 0.71, log-likelihood = − 230.73, AICc = 485.46). The spatially lagged dependent variable (W_smoking during pregnancy) was statistically significant and positively associated with smoking rates (ρ = 0.53, p < 0.001), indicating strong spatial dependence. In the spatial lag model, rurality (β = − 1.57, p = 0.018), managed care region (β = − 0.63, p < 0.001), and OB-GYN rate (β = − 0.13, p < 0.001) remained statistically significant, reinforcing their relevance in explaining spatial variation in smoking during pregnancy. Other covariates, including income, education, and healthcare access indicators, were not statistically significant in the spatial model. The Likelihood Ratio Test (χ² = 18.81, p < 0.001) confirmed that the spatial lag model provided a significantly better fit than the OLS model (Table 4 ). Table 4 Results from Spatial Lag Model Examining Smoking During Pregnancy in North Carolina, Southeast United States (2018–2022) Variable Coefficient Std. Error z-value Probability Smoking during pregnancy *** .526 .092 5.711 .000 [Constant] 48.904 28.018 1.745 .081 Rurality * -1.57 .663 -2.367 .018 Managed Care Regions *** − .629 .172 -3.664 .000 Median household income -8.347 5.657 -1.475 .140 No high school diploma 0.190 .138 1.384 .167 No Health insurance − .104 .120 − .871 .384 OB/GYN rate *** − .134 .038 -3.559 0.000 First trimester prenatal care − .007 .039 − .188 .851 No household vehicle access .049 .136 .364 .716 Lack of healthy food access − .047 .058 − .812 .417 No broadband internet access − .016 .020 − .834 .405 * p < 0.05; ** p < 0.01; *** p < 0.0001 Discussion This study examined the effect of area-level geographic and social risk factors on smoking during pregnancy throughout North Carolina, a geographically diverse state in the Southeast U.S. County-level rates of smoking during pregnancy were significantly greater in rural as compared to urban counties (11.98% in rural vs. 8.19% in urban; p < .0001). Statistically significant clustering in rates of smoking during pregnancy was evident across the state (Moran’s I = 0.55, z = 7.50, p < 0.01), with high-high clusters in the Mountain region, which contained one low-high outlier in Watauga County. Rurality (β = − 1.57, p = 0.018) and managed care region (β = − 0.63, p < 0.001) were significant in explaining the spatial variations in smoking during pregnancy. Similarly, OB-GYN rate (β = − 0.13, p < 0.001) was a significant predictor of smoking during pregnancy when accounting for spatial dependence. These findings underscore the importance of healthcare access in smoking during pregnancy. Rural disparities in access to hospital-based obstetrics care and licensed OB-GYN physicians have significantly worse outcomes pertaining to health behaviors, such as smoking during pregnancy, as well as outcomes including preterm birth and maternal mortality ( 27 ). In North Carolina, rural counties have significantly lower rates of OB-GYN physicians compared to urban counties (5.61% vs. 9.07%; p = 0.03). The closure of labor and delivery units is particularly pronounced in Western North Carolina, which has the highest rates of smoking during pregnancy ( 28 , 29 ). Families in Western North Carolina may drive up to 185 minutes to a planned birthing facility due to labor and delivery unit closures and rural mountain road conditions ( 28 ). The closure of labor and delivery units disproportionately impacts families with fewer resources, including those covered by Medicaid, the state-sponsored health insurance ( 29 ). Lower area-level socioeconomic instability, often operationalized through indicators like median household income, is consistently associated with higher rates of smoking during pregnancy ( 30 ). While rural counties fared worse in nearly all measures of social risk, including socioeconomic instability (e.g., median household income and no high school diploma), geography had a greater impact on smoking during pregnancy than social risk factors alone. Specifically, median household income was not significant when accounting for spatial dependence, although it was strongly and significantly predictive in the OLS model (β = − 20.50, p = 0.002). These findings highlight the unique interplay between geography and social risk factors. Notably, access to educational opportunities and economic earning potential are closely linked to geography. Region 4 is a low-low cluster of smoking during pregnancy - whereby counties with low smoking rates are surrounded by others with similarly low rates - and contains Alamance County, which has the lowest rate of smoking during pregnancy in the state (1.6%). This managed care region encompasses Alamance, Caswell, Chatham, Durham, Franklin, Granville, Johnston, Nash, Orange, Person, Vance, Warren, Wake, and Wilson Counties, which, in total, include twenty institutions of higher education (8 community colleges, 3 University of North Carolina-system schools, and 9 independent institutions) ( 31 ). These findings highlight the deterministic triad that encompasses geographic location, economic, and cultural factors in rural counties, which presents unique challenges for perpetuating rural health disparities ( 32 ). Furthermore, the relationship between socioeconomic instability and smoking during pregnancy is driven by interconnected mechanisms: heightened stress due to financial strain and limited economic opportunities ( 33 , 34 ), increased tobacco retail density and targeted marketing in disadvantaged neighborhoods ( 35 ), and community norms whereby tobacco use is more accepted and cessation support is less accessible and available ( 36 , 37 ). Region 1 and Region 2 include high-high clusters of smoking during pregnancy. These regions are within Southern Appalachia, which is characterized by particularly high rates of smoking during pregnancy ( 38 , 39 ). Region 1 also contains the Qualla Boundary, which is an Indigenous territory governed by the Eastern Band of Cherokee Indians ( 40 ). In rural communities, with a cultural propensity towards tobacco use and a tight-knit social fabric, social cohesion can pose a challenge to cessation and can reinforce tobacco-use behaviors ( 41 ). Additional inequities in smoking during pregnancy may be reinforced among Indigenous communities that are targeted by tobacco companies with pro-tobacco messaging ( 9 ), and where the cultural milieu may include tobacco as a healing, ritual, or spiritual practice ( 7 ). There may be intergenerational effects on smoking during pregnancy. Individuals growing up in low-income environments with historic economic reliance on tobacco crop, such as in Appalachia, are more likely to initiate smoking earlier, experience greater nicotine dependence, and remain embedded in social networks whereby tobacco use is normative, perpetuating cycles of disadvantage ( 34 , 42 – 44 ). Given the persistent rural and socioeconomic disparities in smoking during pregnancy identified in this study, policy responses should consider reallocating portions of North Carolina’s tobacco-related revenues toward maternal tobacco cessation initiatives. Effective and strategic use of funds from tobacco taxes and the Master Settlement Agreement (MSA) presents an important opportunity to advance tobacco control in the state. Under the MSA, tobacco companies agreed to pay states billions of dollars annually in perpetuity to offset the healthcare costs of tobacco-related diseases and to support prevention efforts ( 45 ). North Carolina receives nearly $ 140M each year from the MSA and about $ 245M from tobacco tax revenues ( 46 , 47 ). While a portion of the MSA funds was originally earmarked for youth tobacco control and economic transition in historically tobacco-dependent communities through mechanisms such as the Golden LEAF Foundation ( 48 ), over time, a significant share has been diverted to cover general budgetary expenses, severely limiting sustained investment in comprehensive tobacco prevention and cessation programming ( 47 ). The significant differences in smoking during pregnancy between managed care regions in North Carolina further highlight the role of state-sponsored health insurance policy in perpetuating health disparities. For example, differences in the availability of insurance plans between regions and in the accessibility of healthcare services can maintain inequalities ( 49 ). Medicaid, the largest payer for pregnancy and delivery in the U.S., is a state-sponsored and state-administered health insurance program ( 50 ). Expanding state-sponsored health insurance coverage beyond pregnancy and delivery throughout the reproductive life course to include family planning and contraceptive care, cervical cancer screening, and mental health services has the potential to improve outcomes broadly ( 50 ). Managed care regions may be leveraged to address disparities, such as increasing spending for cessation programs in regions with higher rates of smoking and increasing the number of qualified health professionals in regions with high rates of smoking during pregnancy. However, in the U.S., Medicaid spending will be reduced by $ 1 trillion in the next decade and nearly 17 million people will lose their health insurance coverage ( 51 ). Findings from this study underscore the need for concerted policy- and community-level interventions aimed at addressing smoking during pregnancy, specifically recognizing the non-homogeneity among rural communities. With the pending collapse of state-sponsored health insurance in the U.S., disparities in smoking during pregnancy are likely to worsen. Strategic planning to leverage available resources for developing and delivering evidence-based smoking cessation programs is imperative. Clinic-based cessation support tailored to local needs can improve quit attempts and birth outcomes ( 52 ). Investments to support rural economic development promise to improve infrastructure to expand access to services including broadband and telehealth and remote education and employment pathways. Women-owned businesses and readily available, high quality childcare programs can reduce the financial stressors that contribute to tobacco use. Community-based participatory research can yield results that emphasize local nuance and lead to tailored interventions that address the complexities of culture, economic disparities and geographic context in rural-designated areas ( 32 ). The study is not without limitations. The county-level unit of analysis, while practically relevant for public health practice in North Carolina, may omit nuance found with more granular units such as census tract and zip code areas. The outcome measure for smoking during pregnancy did not consider mothers who may have successfully quit smoking during pregnancy. As such this measure did not allow for a dose-response assessment. However, prior research suggests there is no safe level of smoking during pregnancy at any given trimester ( 2 ). Given the deterministic triad of economic, cultural, and geographic factors on smoking during pregnancy, qualitative research is warranted to deeply investigate these relationships and to identify appropriate prevention strategies across policy- and community-levels of influence. Conclusions Findings reveal substantial geographic disparities in smoking during pregnancy across North Carolina, with higher rates in rural counties and distinct spatial clustering in the western mountain region. While socioeconomic instability contributes to these patterns, geography, healthcare access, and cultural context play critical and interconnected roles. Spatially informed and multilevel strategies are needed to address these disparities. Redirecting tobacco taxes and Master Settlement Agreement funds towards maternal tobacco cessation programs, improving rural healthcare infrastructure (e.g., OB-GYN services, telehealth access), and strengthening economic development (e.g., childcare support, broadband expansion) could yield substantial public health gains. Importantly, integrating strengths-based approaches, geographic outliers like Watauga County, surrounded by high-smoking areas yet exhibiting low rates, should be evaluated as “bright spots” to identify successful local strategies. Overall, integrating spatial analysis into public health planning can help advance equitable, evidence-based solutions to reduce smoking during pregnancy and promote maternal-child health across diverse geographic communities. Abbreviations United States (U.S.) Department of Health and Human Services (DHHS) obstetrician-gynecologist (OB-GYN) Economic Research Service (ERS) Federal Information Processing Standards (FIPS) Rural-Urban Continuum Codes (RUCC) Ordinary Least Squares (OLS) Lagrange Multiplier (LM) Declarations Ethics approval and consent to participate: This study was designated exempt from review by the Institutional Review Board at East Tennessee State University. Data are from publicly available aggregated data with no personal identifiable information. Consent for publication: Not Applicable Funding: This work was supported by the Dogwood Health Trust (grant #2747). Author Contribution Liane Ventura: Conceptualization, Methodology, Formal Analysis, Writing – Original Draft Qian Huang: Data Curation, Methodology, Formal Analysis, Writing – Original Draft Adeola Ayo: Writing – Original Draft Kate Beatty: Funding Acquisition, Supervision, Methodology, Writing – Review & Editing Acknowledgement The authors would like to thank Mathew Beer for assistance with Data Curation and our many community partners in Western North Carolina who contributed to the North Carolina Maternal and Child Health Dashboard. Data Availability The datasets analyzed during the current study are available from the corresponding author on reasonable request. References Masud N, Hamilton W, Tarasenko Y. Prevalence of Cigarette Smoking, E-cigarette Use, and Dual Use Among Urban and Rural Women During the Peripartum Period, PRAMS 2015–2020. Public Health Rep. 2024;139(6):708–16. Liu B, Xu G, Sun Y, Qiu X, Ryckman KK, Yu Y et al. 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Denslow S, Wingert JR, Hanchate AD, Rote A, Westreich D, Sexton L, et al. Rural-urban outcome differences associated with COVID-19 hospitalizations in North Carolina. PLoS ONE. 2022;17(8):e0271755. Hung A, Bush C, Greiner M, Campbell H, Hammill B, Maciejewski ML, et al. Risk Factors and Outcomes of Opioid Users with and Without Concurrent Benzodiazepine Use in the North Carolina Medicaid Population. J managed care specialty Pharm. 2020;26(2):169. Oka M, Williams F, Whiteside M. Comparing proxy and formal measures of county-level racial isolation in race-stratified models: A case study in Tennessee, 2005–2014. SSM Popul Health 2022 June 14;19:101146. Bloch JR, Dawley K, Suplee PD. Application of the Kessner and Kotelchuck Prenatal Care Adequacy Indices in a Preterm Birth Population. Public Health Nurs. 2009;26(5):449–59. Ventura LM, Dailey R, Strahm AM, Rhoads S, Martin MY, Persad-Clem R. Upstream approaches for improving health equity through community engagement in maternal and child health research. Ann Behav Med. 2024;58(Supplement1):S136–8. Lemas DJ, Layton C, Ballard H, Xu K, Smulian JC, Gurka M, et al. Perinatal Health Outcomes Across Rural and Nonrural Counties Within a Single Health System Catchment. Women’s Health Rep. 2023;4(1):169–81. Woodward R, Mazure ES, Belden CM, Denslow S, Fromewick J, Dixon S, et al. Association of prenatal stress with distance to delivery for pregnant women in Western North Carolina. Midwifery. 2023;118:103573. Sullivan MH, Denslow S, Lorenz K, Dixon S, Kelly E, Foley KA. Exploration of the Effects of Rural Obstetric Unit Closures on Birth Outcomes in North Carolina. J Rural Health. 2021;37(2):373–84. DWSRF Disadvantaged Community Definitions. a Reference for States [Internet]. Available from: https://www.epa.gov/system/files/documents/2022-10/DWSRF%20DAC%20Definitions%20Report_October%202022%20Updates_FINAL_508.pdf College of North Carolina [Internet]. [cited 2025 July 27]. Map of North Carolina Colleges. Available from: https://www.cfnc.org/media/1jhefszg/nc-college-map-letter.pdf Thomas TL, DiClemente R, Snell S. Overcoming the triad of rural health disparities: How local culture, lack of economic opportunity, and geographic location instigate health disparities. Health Educ J. 2014;73(3):285–94. Kendzor DE, Businelle MS, Costello TJ, Castro Y, Reitzel LR, Cofta-Woerpel LM, et al. Financial Strain and Smoking Cessation Among Racially/Ethnically Diverse Smokers. Am J Public Health. 2010;100(4):702–6. Cardarelli K, Westneat S, Dunfee M, May B, Schoenberg N, Browning S. Persistent disparities in smoking among rural Appalachians: evidence from the Mountain Air Project. BMC Public Health. 2021;21:270. Galiatsatos P, Brigham E, Krasnoff R, Rice J, Van Wyck L, Sherry M, et al. Association between neighborhood socioeconomic status, tobacco store density and smoking status in pregnant women in an urban area. Prev Med. 2020 July;136:106107. van Wijk EC, Landais LL, Harting J. Understanding the multitude of barriers that prevent smokers in lower socioeconomic groups from accessing smoking cessation support: A literature review. Prev Med 2019 June 1;123:143–51. Hiscock R, Bauld L, Amos A, Fidler JA, Munafò M. Socioeconomic status and smoking: a review. Ann N Y Acad Sci. 2012;1248:107–23. Bailey. Factors predicting pregnancy smoking in southern Appalachia. AM J HEALTH BEHAV. 2006;30(4):413–21. Bailey BA, Cole LKJ. Rurality and Birth Outcomes: Findings From Southern Appalachia and the Potential Role of Pregnancy Smoking. J Rural Health. 2009;25(2):141–9. Miller C, Linzey D, Hallerman E. Morphological and Genetic Assessments of Coyote Diet in Qualla Boundary, North Carolina, Show Interaction with Humans. Animals. 2025;15(5):741. Shoff C, Yang TC. Understanding Maternal Smoking during Pregnancy. Does Residential Context Matter? Soc Sci Med. 2013;78:50–60. Horn K, Schoenberg N, Rose S, Romm K, Berg CJ. Tobacco use among Appalachian adolescents: An urgent need for virtual scale out of effective interventions. Tob Prev Cessat. 2022;8:39. HealthDispairitiesRelatedtoSmokinginAppalachiaApr2019.pdf [Internet]. [cited 2025 July 24]. Available from: https://www.arc.gov/wp-content/uploads/2020/06/HealthDispairitiesRelatedtoSmokinginAppalachiaApr2019.pdf cutting-tobaccos-rural-roots.pdf [Internet]. [cited 2025 July 28]. Available from: https://healthforward.org/wp-content/uploads/2015/07/cutting-tobaccos-rural-roots.pdf The Master Settlement Agreement and Attorneys General [Internet]. National Association of Attorneys General. [cited 2025 July 24]. Available from: https://www.naag.org/our-work/naag-center-for-tobacco-and-public-health/the-master-settlement-agreement/ Hoban JB, Rose. Up in smoke: Federal cuts threaten to derail NC’s progress on tobacco prevention [Internet]. North Carolina Health News. 2025 [cited 2025 July 24]. Available from: https://www.northcarolinahealthnews.org/2025/05/21/tobacco-prevention-cuts/ WideEye. Tobacco Settlement Funds Should Be Used to Fight Smoking in North Carolina [Internet]. Roosevelt Institute. 2015 [cited 2025 July 24]. Available from: https://rooseveltinstitute.org/blog/tobacco-settlement-funds-should-be-used-to-fight-smoking-in-north-carolina/ Golden LEAF. Foundation [Internet]. [cited 2025 July 24]. The Golden Leaf Foundation. Available from: https://goldenleaf.org/ Lucas SR. Health insurance in the United States: A case of effectively maintained inequality? SSM - Popul Health. 2024;28:101687. Jarlenski M. Invited Commentary: Medicaid Policy and Pregnancy Outcomes—Toward a Reproductive Autonomy Framework. Am J Epidemiol. 2021;190(8):1499–501. Evans EJ. The Cost of a Beautiful Bill: Medicaid Retrenchment and the Moral Crisis for Social Work. Health & Social Work; 2025. Sept 15;hlaf041. Bailey BA. Effectiveness of a Pregnancy Smoking Intervention: The Tennessee Intervention for Pregnant Smokers Program. Health Educ Behav. 2015;42(6):824–31. Additional Declarations No competing interests reported. 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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-7895692","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551388747,"identity":"a8bb6f49-8562-41fe-bc2b-2f31ce07fa35","order_by":0,"name":"Liane M. 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1","display":"","copyAsset":false,"role":"figure","size":3492653,"visible":true,"origin":"","legend":"\u003cp\u003eNorth Carolina Counties by Managed Care Region and by Urban/Rural Status.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7895692/v1/d0ee570103c91e39d97522dc.jpeg"},{"id":97095899,"identity":"1e77b359-d481-4b0b-959f-b73a80c9af27","added_by":"auto","created_at":"2025-11-30 23:26:59","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1253284,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Smoking During Pregnancy Rates in North Carolina and (b) Results from Local Moran’s I.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7895692/v1/902864964afb3a6daec5f15a.jpeg"},{"id":97145222,"identity":"ce0acac3-d624-47c2-8018-c06ac72eab9a","added_by":"auto","created_at":"2025-12-01 10:13:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5565312,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7895692/v1/bb54d024-82e2-46e1-a846-f7ef01e276a5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Area-Level Geospatial Analysis to Examine Smoking During Pregnancy with Social Risk Factors in North Carolina, United States","fulltext":[{"header":"Background","content":"\u003cp\u003eThe prevalence of smoking during pregnancy in the United States (U.S.) was 11.8% in 2015\u0026ndash;2020 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Smoking during pregnancy is associated with low birth weight, infants who are small for gestational age, and preterm birth (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Adverse health outcomes may persist throughout the infants\u0026rsquo; lifespan, contributing to chronic diseases (e.g., obesity, diabetes, heart disease), cognitive and learning challenges, sleep problems, and respiratory infections and asthma (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In the U.S., women who live in rural areas are significantly more likely than their urban counterparts to smoke during pregnancy and to have nicotine dependence, which reduces the likelihood of successful smoking cessation attempts (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDisparities in smoking during pregnancy between rural and urban populations are associated with factors at multiple levels of influence according to the socioecological model, including system- and community-level factors (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Tobacco-related policies, such as tobacco pricing and taxes, smoke-free indoor air laws, and state tobacco control spending, influence the prevalence of smoking and have a minor effect on successful cessation efforts among pregnant women who smoke (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Disparities are further reinforced by the tobacco industry, which targets advertising campaigns to marginalized populations, including people who are Indigenous, who have low socioeconomic status, and who live in rural areas (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The historical marginalization of Indigenous communities further perpetuates intergenerational cycles of tobacco smoking and nicotine dependence (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Additionally, economic reliance on tobacco crops as a crucial source of income is particularly influential in the Southern Appalachian region, which is predominantly rural and has the highest rates of smoking during pregnancy across the country (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDisadvantaged socioeconomic positioning is linked with a higher prevalence of smoking during pregnancy, including low levels of educational attainment, unemployment or underemployment, low levels of household income, or a vulnerable neighborhood ecological index (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Smoking during pregnancy among women with lower socioeconomic conditions may pose challenges to having sufficient amounts of healthy foods (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Healthcare access is similarly pivotal, whereby, among a sample of pregnant women in Southern Appalachia, having access to private health insurance or receiving sufficient prenatal care resulted in significantly higher likelihoods of successful cessation attempts (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Among rural communities, the digital divide, which is characterized by a lack of access to broadband internet, is particularly pronounced and has implications for less access to perinatal health care, including essential medical services and programs to support smoking cessation (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven the multi-level influences, including geographic differences in rurality, on disparities in the prevalence of smoking during pregnancy (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), robust modeling approaches are indicated to assess area-level risk factors (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Geographic context underlies community-level determinants of health, which influence outcomes, and nonstationarity among these factors is evident when comparing geographic areas (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Spatial investigation at sub-national levels is particularly indicated to highlight localized inequities in maternal health and to tailor community-driven solutions to improve outcomes and reduce disparities (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNorth Carolina, situated within the Southeast U.S., ranks 34th of 50 states for preterm birth, whereby 17% of all preterm births are attributed to smoking during pregnancy (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The state is geographically diverse, with a 21% of the population residing in rural-designated areas (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Geographically, there are three distinct regions within the state: Coastal, Piedmont, and Mountain. The Mountain Region in the west is situated within Southern Appalachia (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study aims to examine the effect of geography, including rurality and managed care regions as designated by the North Carolina Department of Health and Human Services (DHHS), on smoking during pregnancy throughout the state. This study also investigates the role of area-level socioeconomic instability (e.g., median household income), healthcare access (e.g., obstetrician-gynecologist (OB-GYN) physician rates), and the neighborhood and built environment (e.g., broadband internet access) on county-level rates of smoking during pregnancy throughout North Carolina. Through applying a spatial approach, we investigate the unique interplay between geography, area-level social risk factors, and smoking during pregnancy in a geographically diverse state in the Southeast U.S. Findings have the potential to inform system- and community-level policies for reducing and preventing smoking during pregnancy and associated adverse health outcomes that are perpetuated through generations.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Sources\u003c/h2\u003e\u003cp\u003eAn ecological cross-sectional study was conducted at the county-level, encompassing all 100 counties in North Carolina. Data were drawn from publicly available, deidentified secondary sources provided by state, federal, and philanthropic organizations, including: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the North Carolina State Center for Health Statistics, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the U.S. Census Bureau, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the Health Resources and Services Administration (HRSA) Area Health Resources Files, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) the U.S. Department of Agriculture (USDA) Economic Research Service (ERS), (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) County Health Rankings. The county was selected as the unit of analysis because it represents the smallest policy-relevant unit and has practical importance for ongoing community health assessment and improvement initiatives in the U.S. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cp\u003eThe outcome variable was the county-level rate of smoking during pregnancy from 2018 to 2022, defined as the percentage of infants born to mothers who reported smoking during any trimester. The rural-urban classification was derived from the USDA ERS 2023 Rural-Urban Continuum Codes (RUCC), where RUCC codes 1\u0026ndash;3 were categorized as urban and codes 4\u0026ndash;9 as rural (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The region classification was constructed based on the six managed care regions, defined by North Carolina DHHS, which operationalizes the provision of the state-sponsored health insurance plans (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Covariates included county-level measures of socioeconomic instability, healthcare access, and the neighborhood and built environment characteristics. To measure income, we used the natural log of median household income, which is a widely used and acceptable measure of community-level economic well-being (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Educational attainment was included as the percentage of the population 25 years of age and older with no high school diploma or equivalent. Measures of healthcare access were the rate of individuals with no health insurance, the rate of OB-GYN physicians per 100,000, and the percentage of the population initiating prenatal care in the first trimester, which is considered optimal prenatal care (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Measures for the neighborhood and built environment included no household vehicle access, lack of access to healthy foods, and no broadband internet access, which were derived from Fixed Broadband Deployment Data (U.S. Census Bureau). All variables are continuous, except for the two geographic variables (rurality and managed care region), which are categorical. All data were obtained, quality assured, and curated in the \u003cem\u003eNorth Carolina Maternal and Child Health Dashboard\u003c/em\u003e, which was co-developed with community partners throughout Western North Carolina (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). All variables and data sources used in this study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eDescription of Study Variables and Secondary Data Sources\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\u003eOutcome variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYear(s)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData Source\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking during pregnancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercent of mothers who smoked during any trimester of pregnancy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u0026ndash;2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNC State Center for Health Statistics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGeography\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\u003eRurality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRUCC codes 1\u0026ndash;3 classified as urban and (\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) were classified as rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUSDA ERS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManaged Care Regions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegions identified by NC DHHS for public health service provision\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNC DHHS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocioeconomic instability\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\u003eMedian household income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe natural log of median household income in U.S. dollars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u0026ndash;2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU.S. Census Bureau\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo high school diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercent of individuals (18 years of age and older) with no high school diploma or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u0026ndash;2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU.S. Census Bureau\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealthcare access\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\u003eNo Health insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimated percent of adults without health insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u0026ndash;2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU.S. Census Bureau\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB/GYN Rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRate per 100,000 population\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u0026ndash;2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHRSA Area Health Resources Files\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst trimester prenatal care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercent of individuals who initiated prenatal care during the first trimester\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u0026ndash;2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNC State Center for Health Statistics\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeighborhood and Built Environment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo household vehicle access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimated percent of occupied households without access to at least 1 vehicle per household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u0026ndash;2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU.S. Census Bureau\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of healthy food access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeasure of access to healthy foods via distance to/from grocery stores, supermarkets, or other locations to obtain healthy food\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u0026ndash;2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCounty Health Rankings\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo broadband internet access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimated percent of the population with no access to broadband internet (standard broadband access is 25 Mbps download, 3 Mbps upload)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2018\u0026ndash;2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eU.S. Census Bureau\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: U.S.-United States; USDA ERS-United States Department of Agriculture Economic Research Service; DHHS-Department of Health and Human Services; HRSA-Human Resources and Service Administration; OB/GYN-Obstetrician-Gynecologist.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eStatistical Analyses\u003c/h3\u003e\n\u003cp\u003eTo assess geographic differences in smoking during pregnancy and area-level social risk factors across the state, we applied a two-tailed independent t-test. We conducted a Spearman\u0026rsquo;s Correlation to examine the univariate relationships between each social risk factor, rural-urban, and managed care region variables with smoking during pregnancy, respectively (Appendix 1).\u003c/p\u003e\u003cp\u003eTo further estimate the impact of social factors on smoking during pregnancy, we first employed an ordinary least squares (OLS) regression model as a baseline. Heteroskedasticity was assessed using the Breusch\u0026ndash;Pagan and Koenker\u0026ndash;Bassett tests, while spatial dependence in OLS residuals was evaluated using Global Moran\u0026rsquo;s I and Lagrange Multiplier (LM) tests to determine the need for spatial regression. Based on these diagnostics, a spatial lag model was applied to account for the influence of smoking during pregnancy rates in neighboring counties and to improve the precision of coefficient estimates.\u003c/p\u003e\u003cp\u003eThe univariate analyses were conducted with SAS Enterprise Guide 8.3 (SAS Institute; Cary, NC, USA). All regression analyses were conducted in ArcGIS Pro 3.0.3 and GeoDa 1.22.0.10. A first-order rook contiguity spatial weights matrix, based on shared borders and identified using Federal Information Processing Standards (FIPS) codes, was applied and row-standardized. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses.\u003c/p\u003e\u003cp\u003eThis study was designated exempt from review by the Institutional Review Board at XXXX XXXX. Data are from publicly available aggregated data with no personal identifiable information.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eSpatial Distribution of Smoking During Pregnancy Rate in North Carolina\u003c/h2\u003e\u003cp\u003eThe average county-level rate of smoking during pregnancy in North Carolina from 2018 to 2022 was 10.3%, with a wide range across the state. The lowest rate was observed in Alamance County (1.6%), located in central North Carolina (Region 4), while the highest rate was recorded in Graham County (21.6%) in the west (Region 1). Overall, counties in western North Carolina exhibited higher rates of smoking during pregnancy, whereas central counties reported the lowest rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSpatial autocorrelation analysis using Global Moran’s I indicated a statistically significant clustering of smoking during pregnancy across North Carolina (Moran’s I = 0.55, z = 7.50, p \u0026lt; 0.01), suggesting that neighboring counties tend to have similar rates. Further cluster analysis using Local Moran’s I (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) identified several spatial clusters and outliers. High–high clusters—counties with high smoking rates surrounded by similarly high rates—were primarily located in the western mountain region, especially within Region 1 and parts of Region 2. Low–low clusters, where counties with low smoking rates are surrounded by others with similarly low rates, were concentrated in central North Carolina, particularly in Region 4 and portions of Region 5. One low–high outlier—a county with a low smoking rate surrounded by high-rate counties—was identified in Watauga County, which accounted for 7.9% of such spatial outliers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eUrban and Rural Differences in Smoking During Pregnancy\u003c/h2\u003e\u003cp\u003eAcross North Carolina, rural-urban differences in rates of smoking during pregnancy and area-level social risk factors were evident. The rate of smoking during pregnancy was 11.98% in rural counties as compared to 8.19% in urban counties, which exceeds the statewide average (10.27%) (p \u0026lt; .0001). Median household income (p \u0026lt; .0001) and no high school diploma (p \u0026lt; .0001) were significantly different between rural and urban counties, respectively. Rural counties had significantly higher rates of individuals with no health insurance (12.22%) as compared to urban counties (10.02%) (p \u0026lt; .0001) and significantly lower rates of OB-GYN physicians (5.61% in rural counties vs. 9.07% in urban counties; p = 0.03). However, the rate of individuals initiating prenatal care in the first trimester did not differ significantly between rural and urban counties, whereas the statewide average was 75.14%. Measures of the neighborhood and built environment were similarly significantly different between rural and urban areas whereby rural counties were characterized by higher rates of households with no vehicle access (6.53% vs. 4.80%; p = .000), lower rates of access to healthy foods (4.86% vs. 6.84%; p = .03), and higher rates of households with no access to broadband internet (18.67% vs. 5.20%; p \u0026lt; .0001). Rural-urban differences for all variables are presented in Table\u0026nbsp;2.\u003c/p\u003e\u003c/div\u003e\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"534\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 528px;\"\u003e\n \u003cp\u003eTable 2: Rural-Urban Differences in Smoking During Pregnancy and Social Risk Factors in North Carolina, United States (2018-2022)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eRural\u0026nbsp;\u003cbr\u003e\u0026nbsp;(N=55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eUrban\u0026nbsp;\u003cbr\u003e\u0026nbsp;(N=45)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eStatewide\u0026nbsp;\u003cbr\u003e\u0026nbsp;(N=100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eSmoking during pregnancy ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e11.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e8.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e10.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eMedian household income ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e4.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e4.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e4.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eNo high school diploma ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e9.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e7.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eNo health insurance ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e12.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e10.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e11.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eOB/GYN Rate *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e5.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e9.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eFirst trimester prenatal care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e75.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e75.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e75.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eNo household vehicle access ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e4.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e5.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eLack of healthy food access *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e4.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e6.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e5.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 240px;\"\u003e\n \u003cp\u003eNo broadband internet access ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e18.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 78px;\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* p \u0026lt; 0.05; ** p \u0026lt; 0.01; *** p \u0026lt; 0.0001\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eUnderlying Driving Factors of Smoking During Pregnancy\u003c/h3\u003e\n\u003cp\u003eTo explore the factors contributing to the geographic variation in smoking during pregnancy, an OLS regression model was fitted using county-level data (Adjusted R² = 0.58, F = 14.60, p \u0026lt; 0.001, AICc = 502.27). In the OLS model, median household income (β = − 20.50, p = 0.002), OB/GYN rate (β = − 0.13, p = 0.007), rurality (β = − 1.86, p = 0.022), and managed care regions (β = − 1.08, p \u0026lt; 0.001) were statistically significant. These findings suggest that rural counties, located in certain managed care regions, have fewer OB-GYN physicians per capita, lower median household income, and are more likely to exhibit higher rates of smoking during pregnancy. Other explanatory variables were not statistically significant in the OLS model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults from Ordinary Least Squares Regression Model Examining Smoking During Pregnancy in North Carolina, Southeast United States (2018–2022)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et-statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProbability\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[Constant]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRurality *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManaged Care Regions ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian household income ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-20.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo high school diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e− .12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Health insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB/GYN rate *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst trimester prenatal care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo household vehicle access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e− .22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of healthy food access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e− .56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo broadband internet access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e− .47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e* p \u0026lt; 0.05; ** p \u0026lt; 0.01; *** p \u0026lt; 0.0001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eDiagnostic tests revealed evidence of spatial dependence not accounted for by the OLS model. Residuals demonstrated significant positive spatial autocorrelation (Moran’s I = 0.23, p \u0026lt; 0.001), suggesting spatial clustering of unobserved factors. Heteroskedasticity was not evident, as both the Breusch–Pagan (χ² = 5.52, p = 0.85) and Koenker–Bassett (χ² = 5.82, p = 0.83) tests were not statistically significant, suggesting constant variance across residuals.\u003c/p\u003e\u003cp\u003eTo account for spatial dependence, LM tests supported the use of a spatial lag model (LM = 16.03, Robust LM = 4.12, p \u0026lt; 0.001), with a non-significant spatial error term (Robust LM error = 0.03, p = 0.86). The spatial lag model showed improved model fit (R² = 0.71, log-likelihood = − 230.73, AICc = 485.46). The spatially lagged dependent variable (W_smoking during pregnancy) was statistically significant and positively associated with smoking rates (ρ = 0.53, p \u0026lt; 0.001), indicating strong spatial dependence.\u003c/p\u003e\u003cp\u003eIn the spatial lag model, rurality (β = − 1.57, p = 0.018), managed care region (β = − 0.63, p \u0026lt; 0.001), and OB-GYN rate (β = − 0.13, p \u0026lt; 0.001) remained statistically significant, reinforcing their relevance in explaining spatial variation in smoking during pregnancy. Other covariates, including income, education, and healthcare access indicators, were not statistically significant in the spatial model. The Likelihood Ratio Test (χ² = 18.81, p \u0026lt; 0.001) confirmed that the spatial lag model provided a significantly better fit than the OLS model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eResults from Spatial Lag Model Examining Smoking During Pregnancy in North Carolina, Southeast United States (2018–2022)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProbability\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking during pregnancy ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e[Constant]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRurality *\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManaged Care Regions ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian household income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo high school diploma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Health insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e− .871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.384\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOB/GYN rate ***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.559\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst trimester prenatal care\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e− .188\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.851\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo household vehicle access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.716\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of healthy food access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e− .812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.417\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo broadband internet access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e− .016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e− .834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.405\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e* p \u0026lt; 0.05; ** p \u0026lt; 0.01; *** p \u0026lt; 0.0001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\n\n"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the effect of area-level geographic and social risk factors on smoking during pregnancy throughout North Carolina, a geographically diverse state in the Southeast U.S. County-level rates of smoking during pregnancy were significantly greater in rural as compared to urban counties (11.98% in rural vs. 8.19% in urban; p \u0026lt; .0001). Statistically significant clustering in rates of smoking during pregnancy was evident across the state (Moran’s I = 0.55, z = 7.50, p \u0026lt; 0.01), with high-high clusters in the Mountain region, which contained one low-high outlier in Watauga County. Rurality (β = − 1.57, p = 0.018) and managed care region (β = − 0.63, p \u0026lt; 0.001) were significant in explaining the spatial variations in smoking during pregnancy. Similarly, OB-GYN rate (β = − 0.13, p \u0026lt; 0.001) was a significant predictor of smoking during pregnancy when accounting for spatial dependence.\u003c/p\u003e\u003cp\u003eThese findings underscore the importance of healthcare access in smoking during pregnancy. Rural disparities in access to hospital-based obstetrics care and licensed OB-GYN physicians have significantly worse outcomes pertaining to health behaviors, such as smoking during pregnancy, as well as outcomes including preterm birth and maternal mortality (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). In North Carolina, rural counties have significantly lower rates of OB-GYN physicians compared to urban counties (5.61% vs. 9.07%; p = 0.03). The closure of labor and delivery units is particularly pronounced in Western North Carolina, which has the highest rates of smoking during pregnancy (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Families in Western North Carolina may drive up to 185 minutes to a planned birthing facility due to labor and delivery unit closures and rural mountain road conditions (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The closure of labor and delivery units disproportionately impacts families with fewer resources, including those covered by Medicaid, the state-sponsored health insurance (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLower area-level socioeconomic instability, often operationalized through indicators like median household income, is consistently associated with higher rates of smoking during pregnancy (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). While rural counties fared worse in nearly all measures of social risk, including socioeconomic instability (e.g., median household income and no high school diploma), geography had a greater impact on smoking during pregnancy than social risk factors alone. Specifically, median household income was not significant when accounting for spatial dependence, although it was strongly and significantly predictive in the OLS model (β = − 20.50, p = 0.002). These findings highlight the unique interplay between geography and social risk factors. Notably, access to educational opportunities and economic earning potential are closely linked to geography.\u003c/p\u003e\u003cp\u003eRegion 4 is a low-low cluster of smoking during pregnancy - whereby counties with low smoking rates are surrounded by others with similarly low rates - and contains Alamance County, which has the lowest rate of smoking during pregnancy in the state (1.6%). This managed care region encompasses Alamance, Caswell, Chatham, Durham, Franklin, Granville, Johnston, Nash, Orange, Person, Vance, Warren, Wake, and Wilson Counties, which, in total, include twenty institutions of higher education (8 community colleges, 3 University of North Carolina-system schools, and 9 independent institutions) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). These findings highlight the deterministic triad that encompasses geographic location, economic, and cultural factors in rural counties, which presents unique challenges for perpetuating rural health disparities (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, the relationship between socioeconomic instability and smoking during pregnancy is driven by interconnected mechanisms: heightened stress due to financial strain and limited economic opportunities (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), increased tobacco retail density and targeted marketing in disadvantaged neighborhoods (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), and community norms whereby tobacco use is more accepted and cessation support is less accessible and available (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Region 1 and Region 2 include high-high clusters of smoking during pregnancy. These regions are within Southern Appalachia, which is characterized by particularly high rates of smoking during pregnancy (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Region 1 also contains the Qualla Boundary, which is an Indigenous territory governed by the Eastern Band of Cherokee Indians (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). In rural communities, with a cultural propensity towards tobacco use and a tight-knit social fabric, social cohesion can pose a challenge to cessation and can reinforce tobacco-use behaviors (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Additional inequities in smoking during pregnancy may be reinforced among Indigenous communities that are targeted by tobacco companies with pro-tobacco messaging (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), and where the cultural milieu may include tobacco as a healing, ritual, or spiritual practice (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). There may be intergenerational effects on smoking during pregnancy. Individuals growing up in low-income environments with historic economic reliance on tobacco crop, such as in Appalachia, are more likely to initiate smoking earlier, experience greater nicotine dependence, and remain embedded in social networks whereby tobacco use is normative, perpetuating cycles of disadvantage (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e–\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGiven the persistent rural and socioeconomic disparities in smoking during pregnancy identified in this study, policy responses should consider reallocating portions of North Carolina’s tobacco-related revenues toward maternal tobacco cessation initiatives. Effective and strategic use of funds from tobacco taxes and the Master Settlement Agreement (MSA) presents an important opportunity to advance tobacco control in the state. Under the MSA, tobacco companies agreed to pay states billions of dollars annually in perpetuity to offset the healthcare costs of tobacco-related diseases and to support prevention efforts (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). North Carolina receives nearly \u003cspan\u003e$\u003c/span\u003e140M each year from the MSA and about \u003cspan\u003e$\u003c/span\u003e245M from tobacco tax revenues (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). While a portion of the MSA funds was originally earmarked for youth tobacco control and economic transition in historically tobacco-dependent communities through mechanisms such as the Golden LEAF Foundation (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), over time, a significant share has been diverted to cover general budgetary expenses, severely limiting sustained investment in comprehensive tobacco prevention and cessation programming (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe significant differences in smoking during pregnancy between managed care regions in North Carolina further highlight the role of state-sponsored health insurance policy in perpetuating health disparities. For example, differences in the availability of insurance plans between regions and in the accessibility of healthcare services can maintain inequalities (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Medicaid, the largest payer for pregnancy and delivery in the U.S., is a state-sponsored and state-administered health insurance program (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Expanding state-sponsored health insurance coverage beyond pregnancy and delivery throughout the reproductive life course to include family planning and contraceptive care, cervical cancer screening, and mental health services has the potential to improve outcomes broadly (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Managed care regions may be leveraged to address disparities, such as increasing spending for cessation programs in regions with higher rates of smoking and increasing the number of qualified health professionals in regions with high rates of smoking during pregnancy. However, in the U.S., Medicaid spending will be reduced by \u003cspan\u003e$\u003c/span\u003e1 trillion in the next decade and nearly 17\u0026nbsp;million people will lose their health insurance coverage (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFindings from this study underscore the need for concerted policy- and community-level interventions aimed at addressing smoking during pregnancy, specifically recognizing the non-homogeneity among rural communities. With the pending collapse of state-sponsored health insurance in the U.S., disparities in smoking during pregnancy are likely to worsen. Strategic planning to leverage available resources for developing and delivering evidence-based smoking cessation programs is imperative. Clinic-based cessation support tailored to local needs can improve quit attempts and birth outcomes (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Investments to support rural economic development promise to improve infrastructure to expand access to services including broadband and telehealth and remote education and employment pathways. Women-owned businesses and readily available, high quality childcare programs can reduce the financial stressors that contribute to tobacco use. Community-based participatory research can yield results that emphasize local nuance and lead to tailored interventions that address the complexities of culture, economic disparities and geographic context in rural-designated areas (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study is not without limitations. The county-level unit of analysis, while practically relevant for public health practice in North Carolina, may omit nuance found with more granular units such as census tract and zip code areas. The outcome measure for smoking during pregnancy did not consider mothers who may have successfully quit smoking during pregnancy. As such this measure did not allow for a dose-response assessment. However, prior research suggests there is no safe level of smoking during pregnancy at any given trimester (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Given the deterministic triad of economic, cultural, and geographic factors on smoking during pregnancy, qualitative research is warranted to deeply investigate these relationships and to identify appropriate prevention strategies across policy- and community-levels of influence.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eFindings reveal substantial geographic disparities in smoking during pregnancy across North Carolina, with higher rates in rural counties and distinct spatial clustering in the western mountain region. While socioeconomic instability contributes to these patterns, geography, healthcare access, and cultural context play critical and interconnected roles. Spatially informed and multilevel strategies are needed to address these disparities. Redirecting tobacco taxes and Master Settlement Agreement funds towards maternal tobacco cessation programs, improving rural healthcare infrastructure (e.g., OB-GYN services, telehealth access), and strengthening economic development (e.g., childcare support, broadband expansion) could yield substantial public health gains. Importantly, integrating strengths-based approaches, geographic outliers like Watauga County, surrounded by high-smoking areas yet exhibiting low rates, should be evaluated as \u0026ldquo;bright spots\u0026rdquo; to identify successful local strategies. Overall, integrating spatial analysis into public health planning can help advance equitable, evidence-based solutions to reduce smoking during pregnancy and promote maternal-child health across diverse geographic communities.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eUnited States (U.S.)\u003c/p\u003e\u003cp\u003eDepartment of Health and Human Services (DHHS)\u003c/p\u003e\u003cp\u003eobstetrician-gynecologist (OB-GYN)\u003c/p\u003e\u003cp\u003eEconomic Research Service (ERS)\u003c/p\u003e\u003cp\u003eFederal Information Processing Standards (FIPS)\u003c/p\u003e\u003cp\u003eRural-Urban Continuum Codes (RUCC)\u003c/p\u003e\u003cp\u003eOrdinary Least Squares (OLS)\u003c/p\u003e\u003cp\u003eLagrange Multiplier (LM)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003cp\u003eThis study was designated exempt from review by the Institutional Review Board at East Tennessee State University. Data are from publicly available aggregated data with no personal identifiable information.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003cp\u003eNot Applicable\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis work was supported by the Dogwood Health Trust (grant #2747).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLiane Ventura: Conceptualization, Methodology, Formal Analysis, Writing \u0026ndash; Original Draft Qian Huang: Data Curation, Methodology, Formal Analysis, Writing \u0026ndash; Original Draft Adeola Ayo: Writing \u0026ndash; Original Draft Kate Beatty: Funding Acquisition, Supervision, Methodology, Writing \u0026ndash; Review \u0026amp; Editing\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to thank Mathew Beer for assistance with Data Curation and our many community partners in Western North Carolina who contributed to the North Carolina Maternal and Child Health Dashboard.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMasud N, Hamilton W, Tarasenko Y. 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SSM - Popul Health. 2024;28:101687.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJarlenski M. Invited Commentary: Medicaid Policy and Pregnancy Outcomes\u0026mdash;Toward a Reproductive Autonomy Framework. Am J Epidemiol. 2021;190(8):1499\u0026ndash;501.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEvans EJ. The Cost of a Beautiful Bill: Medicaid Retrenchment and the Moral Crisis for Social Work. Health \u0026amp; Social Work; 2025. Sept 15;hlaf041.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBailey BA. Effectiveness of a Pregnancy Smoking Intervention: The Tennessee Intervention for Pregnant Smokers Program. Health Educ Behav. 2015;42(6):824\u0026ndash;31.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","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":"Healthcare Disparities, Health Services Accessibility, Maternal Health, Medicaid, Pregnancy, Public Health, Rural Community, Spatial Analysis, Tobacco Use, United States","lastPublishedDoi":"10.21203/rs.3.rs-7895692/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7895692/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eSmoking during pregnancy remains a public health concern in the United States, with disproportionately higher rates observed in rural populations. These differences are influenced by factors at system and community levels. This study examines the association between smoking during pregnancy, social risk factors, and geographic context in North Carolina, a state in the Southeast United States.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAn ecological cross-sectional study was conducted at the county-level using publicly available secondary data from 2018\u0026ndash;2022, incorporating spatial statistical methods to examine geographic variation and area-level drivers of smoking during pregnancy. Key independent variables included rural-urban status (2023 Rural-Urban Continuum Codes), managed care regions (North Carolina Department of Health and Human Services designations), and county-level indicators of socioeconomic instability (e.g., median household income), healthcare access (e.g., insurance coverage, OB-GYN physician rate), and neighborhood/built environment (e.g., broadband access, food availability). Descriptive analyses, Spearman correlations, and ordinary least squares (OLS) regression were conducted. Spatial dependence was evaluated using Global Moran\u0026rsquo;s I and Lagrange Multiplier tests, with a spatial lag regression model applied to adjust for spatial autocorrelation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe average county-level rate of smoking during pregnancy was 10.3%, ranging from 1.6% to 21.6%. Significant spatial clustering was observed (Global Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with high-high clusters in western counties (Regions 1 and 2) and low-low clusters in central counties (Region 4). Rural counties had significantly higher smoking rates (11.98%) than urban counties (8.19%; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and significantly greater socio-economic disadvantage. In the OLS model (R\u0026sup2; = 0.58), rurality, lower median household income, fewer OB-GYNs, and regional location were significantly associated with higher smoking rates. Spatial lag modeling improved model fit (R\u0026sup2; = 0.71) and confirmed spatial dependence (ρ\u0026thinsp;=\u0026thinsp;0.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Rurality, OB-GYN rates, and regional location remained significant predictors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eSmoking during pregnancy demonstrates distinct geographic clustering in North Carolina, in the Southeast United States. Rates are influenced by rurality, healthcare access, and regional context. Spatial models are crucial for informing place-based prevention efforts. Leveraging tobacco taxes, Master Settlement Agreement funds, and investments in rural economic development promise to reduce tobacco-related maternal and child health disparities.\u003c/p\u003e","manuscriptTitle":"Area-Level Geospatial Analysis to Examine Smoking During Pregnancy with Social Risk Factors in North Carolina, United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-30 23:26:54","doi":"10.21203/rs.3.rs-7895692/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"253654734354897587335119190701305761095","date":"2025-12-01T12:02:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174724139138366190975508739208163236162","date":"2025-11-24T17:16:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77895587673388689631480973401261890633","date":"2025-11-24T16:23:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-19T16:13:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-24T09:04:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-23T00:57:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-23T00:55:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-10-18T23:02:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"afb8b0bd-a92d-4790-afaf-edf1a4b3b27d","owner":[],"postedDate":"November 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-30T23:26:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-30 23:26:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7895692","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7895692","identity":"rs-7895692","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}