Social and Demographic Factors Influencing Maternal Breast Milk Intake in Preterm and Very Low Birthweight Infants

Social and Demographic Factors Influencing Maternal Breast Milk Intake in Preterm and Very Low Birthweight Infants | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Social and Demographic Factors Influencing Maternal Breast Milk Intake in Preterm and Very Low Birthweight Infants Thao Ho, Amornrat Sawangkum, Alexandra Hoeman, Willow Goff, Xiaoqi Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6486705/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Feb, 2026 Read the published version in Journal of Perinatology → Version 1 posted 9 You are reading this latest preprint version Abstract Objective: Identify demographic and social factors that influence the availability of maternal breast milk (MBM) to reduce barriers and improve outcomes for very low birthweight preterm infants. Study Design: This prospective cohort study analyzed demographic, socioeconomic, and clinical data from 300 maternal-infant dyads with infants born <1500 g. Data included residential distance from the hospital, comorbidities, and infant MBM intake measured as a percentage of total enteral intake. Results: Bivariate analysis revealed that maternal race, median income by zip code, marital status, and residential distance were significantly associated with MBM intake. In a multivariate regression model, only residential distance and marital status remained significant predictors, with greater distance from the hospital and marriage status associated with higher MBM intake. Conclusion: Residential distance from the hospital was not a significant barrier to breastfeeding in the study population. Health sciences/Health care/Paediatrics Health sciences/Medical research/Outcomes research Introduction The many benefits of maternal breast milk (MBM) consumption in infants are well-documented. MBM promotes the neurodevelopment of preterm infants ( 1 , 2 ), a population at increased risk of neurological deficits. MBM also enhances the immunological and microbiome development in infants, reducing the risk of gastrointestinal and respiratory tract infections throughout the lifespan ( 3 – 5 ). Numerous studies show that MBM provides greater benefits than donor breast milk, a common feeding option for preterm infants when MBM is not available ( 6 , 7 ). Mothers of preterm infants face unique challenges in initiating and sustaining breast milk supply. Infants requiring neonatal intensive care unit (NICU) admission are often unstable and cannot undergo breastfeeding and/or have diminished ability to coordinate suckling and swallowing at the breast ( 8 ). Additionally, the NICU environment can induce stress in both the infant and the mother, potentially inhibiting the initiation of breastfeeding and reducing MBM production ( 9 ). Given such challenges, it is critical to minimize other non-clinical barriers to breastfeeding in preterm and very low birthweight infants. Numerous non-clinical maternal factors influence the continuation of MBM feedings. Recent studies suggest that infants born to mothers of lower educational attainment are breastfed for a shorter duration ( 10 – 12 ) Additionally, mothers with a greater household income demonstrate increased rates of exclusive breastfeeding, possibly due to improved access to lactation consultants, supplies, and education regarding the benefits of MBM ( 13 , 14 ). Race and ethnicity may also play a role in MBM intake as some studies indicate that Hispanic and/or Black mothers have a higher risk of exclusive formula feeding than their White counterparts ( 15 – 17 ). However, other reports find that such disparities are diminished when controlling for maternal geographic residence ( 18 ). While not robustly examined in the current literature, maternal geographic location may be a stronger predictor of MBM feedings than race and ethnicity. Therefore, establishing geographic location or distance from the hospital as a potential barrier to breastfeeding may help hospitals implement solutions for at-risk mothers, such as transportation programs or mobile lactation support. We aimed to examine the influence of geographic distance from the hospital on MBM availability in preterm infants in addition to demographic and socioeconomic factors. This information will guide clinicians and hospitals to better support mothers in providing MBM for their preterm or very low birthweight infants. Methods Study design and patient population This was a prospective observational study of infants born at < 33 weeks of age or with birthweight < 1500 g admitted to the NICU at Tampa General Hospital between 2016 and 2022 to study nutrition and outcomes. Infants were included if the maternal residential address on file in the electronic medical record (EMR) was located within the state of Florida. Exclusion criteria were infants born to mothers with positive urinary drug screens or contraindications to breastfeeding, such as human immunodeficiency virus or human T-cell lymphotropic virus positivity or with active lesions of herpes simplex virus. Infants with classic galactosemia were also excluded from the study. Data collection MBM consumption was defined as the percentage by volume of MBM consumed enterally out of the total enteral intake. Feeding data was recorded weekly and the cumulative percentage of MBM consumption from birth until discharge was calculated as the primary dependent variable. Infant gestational age, maternal gravida, race, presence of maternal comorbidities (defined as presence of mental health diagnoses, hypertension, preeclampsia, or diabetes), and maternal marital status were also obtained. The demographic data and the maternal residential address including the 5-digit ZIP code (as defined by the United States Postal Service) were extracted from the EMR. Maternal residential distance was recorded as the distance in miles from the maternal residential address to the address of Tampa General Hospital. Distance from the hospital is used interchangeably with distance from the NICU. Additionally, yearly median household income for each ZIP code was determined from the United States Census Bureau database using the 2021 American Community Survey 5-Year Estimates ( 19 ). Statistical analysis Data descriptions were expressed in mean and standard deviations (SD) or median and interquartile range (IQR) for continuous variables and in percentages for categorical variables. The associations between categorical variables were evaluated by the Fisher exact test. Independent samples t -tests/ANOVA and Mann Whitney U/Kruskal-Wallis test were used to compare continuous variables for normally distributed and skewed data, respectively. Statistical interactions were tested based on research questions. Regression models were tested to examine the relationship between infant MBM intake and maternal residential distance, controlling for gestational age, gravida, birthweight, maternal age, marital status, race, mode of delivery, presence of infant comorbidities, presence of maternal comorbidities, and median income by residential ZIP code. The 95% confidence intervals were used to describe the precision of the estimates. The two-tailed statistical tests were considered significant at p < 0.05. All analyses were performed using IBM SPSS statistical software package (IBM Corp. released 2023, IBM SPSS Statistics, Version 29.0 Armonk, NY:IBM Corp). Results Patient characteristics Final analysis consisted of 300 infants who met the study criteria. Mean birth gestational age and birth weight were 29 ± 3 weeks and 1267 ± 427 g, respectively. The average residential distance from the hospital was 30 ± 31 miles. 55% of mothers had maternal comorbidities and 57% of infants had comorbidities during their NICU stay. The maternal race distribution was 50% White, 33% Black, and 17% other or unknown (Table 1 ). Table 1 Demographics and perinatal characteristics Characteristics N = 300 Gestational age (weeks, mean, SD) Minimum, maximum (weeks) 29 ± 3 23, 37 Birthweight (g, mean, SD) Minimum, maximum (g) 1273 ± 436 460, 2750 Maternal residential distance (miles, mean, SD) Minimum, maximum (miles) 30 ± 31 2.1, 178 Maternal age (years, mean, SD) 29 ± 6 Advanced maternal age ( \(\:\ge\:\) 35 years), n (%) 58 ( 19 ) Vaginal delivery, n (%) 224 (75) Cesarean delivery, n (%) 76 ( 25 ) White, n (%) 150 (50) Black, n (%) 99 (33) Other races, n (%) 51 ( 17 ) Maternal comorbidities, n (%) 164 (55) Infant comorbidities, n (%) 172 (57) Length of hospital stay (days, mean, SD) 61 ± 40 Reported maternal race Self-reported maternal race (Black, White, and others) was strongly associated with distance from the hospital ( \(\:\chi\:\) (2) = 42.37, p < 0.001) and median income by zip code ( \(\:\chi\:\) (2) = 32.17, p < 0.001). White mothers lived further from the hospital compared to Black mothers (median 28.9 [14.5, 49.4] vs. 11.6 [7.8, 23.6], p < 0.001) and other-race mothers (14.5 [10.9, 30.7], p = 0.003). MBM intake (%) was higher in infants of White mothers compared to infants of Black mothers (85% [34%, 99%] vs. 61% [17%, 94%], p = 0.029) (Fig. 1 ). Black mothers had greater numbers of pregnancies (3 [1, 4] vs. 2.4 [1, 3], p = 0.001) and lower maternal ages at current pregnancy (27 [22, 32] vs. 29 [25, 34], p = 0.016) compared to White mothers. There was a greater percentage of Black mothers who were not married compared to White mothers (71% vs 37%, p < 0.001). Maternal age Maternal age was analyzed in categories of advanced maternal age (defined as \(\:\ge\:\) 35 years) and non-advanced maternal age (defined as <35 years). Advanced maternal age was associated with a greater number of pregnancies (2 [1, 4] vs 3 [2, 5], p < 0.001), but not with infant MBM intake. Marital status About half (47%) of the mothers were married. Married mothers were older (30.7 \(\:\pm\:\) 5.4 vs. 27.0 \(\:\pm\:\) 5.7 years, p < 0.001), lived further from the hospital (23.1 [13, 42.5] vs. 14.2 [9.1, 35.5] miles, p = 0.001), had fewer pregnancies (2 [1, 3] vs. 3 [1, 4], p = 0.008), and had higher median income by zip code ( $ 64K [48K, 78K] vs. $ 45K [40K, 57K], p < 0.001) compared to single mothers. Infants born to married mothers had significantly higher MBM intake (92% [52%, 99%] vs. 50% [18%, 89%], p < 0.001) (Fig. 2 ). Median income by residential ZIP code Median income by residential ZIP code was correlated with MBM intake (r = 0.139, p = 0.016) and maternal age (r = 0.245, p = < 0.001) by Spearman’s correlation. Mothers from low-income households (defined as < $ 60,000 per year) had a greater number of pregnancies (3 [1, 4] vs 2 [1, 3], p = 0.011), younger ages at current pregnancy (27 [23, 31.5] vs. 31 [27, 34], p < 0.001), and their infants had lower MBM intake (65% [29%, 96%] vs. 86% [24%, 99%], p = 0.046) (Fig. 3 ). Residential distance from hospital The median distance was 21 (10.5, 39.2) miles. About 50% of the families were \(\:\le\:\) 20 miles, 75% of families were \(\:\le\:\) 30 miles, and 90% of families were \(\:\le\:\) 60 miles from the hospital. Distance from the hospital was positively associated with infant MBM intake (Kruskal-Wallis test, \(\:\chi\:\) (3) = 22.52, p < 0.001) (Fig. 4 ). Infant MBM intake About 23% infants received \(\:\le\:\) 25% MBM, 38% infants received \(\:\le\:\) 50% MBM, and 50% infants received \(\:\le\:\) 75% MBM during their NICU stay. Maternal age was significantly associated with MBM intake; infants who received \(\:\le\:\) 25% MBM or >75% MBM were born to older mothers (Kruskal-Wallis test, \(\:\chi\:\) 2 ( 3 ) = 19.75, p <0.001). Residential distance was greater (22.2 [10.9, 47.6] vs. 14.4 [10.3, 26.6] miles, p = 0.019) and birth weight was lower (1223 \(\:\pm\:\) 430 vs. 1371 \(\:\pm\:\) 463 g, p = 0.034) in infants who received >75% MBM compared to infants who received \(\:\le\:\) 25% MBM. For the logistic regression model, we defined “low MBM” group consisting of infants with MBM intake \(\:\le\:\) 75% and “high MBM” group consisting of infants with MBM intake >75%. Clinical and demographic factors (gestational age, mode of delivery, presence of infant comorbidities, presence of maternal comorbidities, gravida, maternal age, and maternal race) and socioeconomic factors (marital status, maternal residential distance, and median income by residential ZIP code stratified as less than or greater than $ 60,000 USD per year) were included in the logistic regression model. The logistic regression model demonstrated that further distance from the hospital and marital status were significant predictors of MBM intake > 75% in our preterm infants (B = 0.015, 95% CI [1.005–1.024], p= 0.002 and B = 1.293, 95% CI [2.001, 6.638], p < 0.001 respectively), when controlling for the above factors. The variance inflation factor for distance, income, race and marital status was 2.4 using McFadden’s R 2 . Discussion The current literature indicates that race, income, marital status, and maternal age are factors that may impact MBM production, breastfeeding initiation, and duration of breastfeeding ( 13 , 15 , 16 , 20 ). In this study, mothers living closer to the hospital more often identified as Black or unmarried. In bivariate comparisons, marriage status, greater maternal age, and higher income were protective factors, while maternal self-identification as Black was a risk factor for infant reduced MBM intake, reflective of reduced MBM production. Taking these demographic and socioeconomic factors into account, residential distance and marital status remained significant predictors of infant MBM intake. Breastfeeding and Distance to Hospital Currently, the relationship between residential distance to the hospital and neonatal outcomes, especially MBM intake, is largely undetermined( 21 ). A 2021 prospective cohort study found that shorter travel times ( 60 minutes) ( 22 ). This may suggest that mothers who live closer to the hospital spend more time with their infants and may have a greater ability to provide MBM. However, in our studied population, the opposite was observed: further residential distance was associated with higher infant MBM intake. This outcome reveals that the relationship between residential distance and breastfeeding outcome is affected by other social, economic and geographic factors. Therefore, hospitals should evaluate their own population characteristics before investing in transportation assistance. Breastfeeding and Maternal Age The relationship between maternal age and breastfeeding success is not straightforward, with older mothers providing \(\:\le\:\) 25% MBM or >75% MBM to their infants. The current literature regarding breastfeeding and maternal age has found that mothers over the age of 30 years are at an increased risk of early cessation of breastfeeding when compared to mothers aged 20 to 25 years ( 23 , 24 ). Interestingly, the Centers of Disease Control’s National Immunization Survey found that mothers aged 20–29 years are 6% less likely to ever breastfeed than mothers over the age of 30 years. However, younger mothers who initiated breastfeeding did so for 5.6 months longer than the older cohort ( 25 ). Therefore, our research supports the findings that advanced age is a risk factor for early cessation of breastfeeding and MBM intake, but not initiation of breastfeeding. This relationship could explain the weaker influence of maternal age on the outcomes of our study; although advanced maternal age is associated with early MBM cessation, children born to older mothers who self-identified as White and who reported higher incomes still had higher rates of MBM intake. Thus, factors such as income and race had a more powerful effect on MBM intake than maternal age. Breastfeeding and Marital Status According to our study, infants born to married mothers had a higher percentage of total MBM intake. This outcome is consistent with other studies, including a recent report which found that married mothers are two times more likely to breastfeed than their unmarried counterparts ( 26 ). This effect may be modulated by numerous variables; our data showed that married mothers were more likely to have a higher income, live further from the hospital, and self-identify as White, all of which are protective factors for MBM intake. However, when controlling for such social and demographic factors, logistic regression continued to yield a positive association between marital status and high MBM intake. This suggests that marital status independently influences MBM intake. Breastfeeding and Income Our study found that income is positively correlated with MBM intake, which is consistent with the current literature. Multiple studies report that mothers of low-income households are less likely to breastfeed their children than their higher-income counterparts ( 25 , 27 ). A recent study found that significantly fewer low-income mothers initiated breastfeeding and continued to breastfeed beyond six months ( 28 ). This relationship is likely due to the lack of paid maternity leave among low-income earners, as well as reduced access to lactation care due to cost barriers ( 13 ). As the current literature indicates, lack of financial accessibility may strongly influence breastfeeding behaviors. While our study results indicate that low-income mothers had greater number of pregnancies and at younger age at the current pregnancy, but income itself was not a predictor for MBM intake after controlling for other factors. Breastfeeding and Race Consistent with the current literature, our study found a significant relationship between race and MBM intake. Numerous studies report that those who self-identify as Black have lower rates of breastfeeding initiation and duration compared to those who identify as White. Patel et al. theorizes that this disparity arises as Black mothers are more likely to be low-income and sole supporters of households ( 17 ). However, another study found that lower rates of MBM intake in infants of Black mothers persist despite controlling for such social and economic variables. Instead, the researchers hypothesized that this group introduces solid food earlier than other racial groups, which could explain their lower rates MBM consumption ( 29 ). This explanation supports our finding that maternal race is a powerful predictor of MBM intake which may overcome other protective factors such as geographic proximity to healthcare. Strengths, Weaknesses, and Future Directions Strengths of this study include prospective longitudinal data from a diverse patient population, which allowed for analysis of various demographic characteristics. However, this study was limited by the inability to assess social support or educational status, which may play a significant role in milk production and the facilitation of breastfeeding in the NICU. Conclusions Identifying barriers to breastfeeding in the NICU is important to improve health outcomes for preterm infants. Our study demonstrated that preterm infants born to mothers who resided closer to the hospital took less MBM than those with mothers who resided farther from the hospital. Marital status was also a strong predictor of MBM consumption. Mothers whose infants had less MBM milk were more likely to self-identify as Black and have a lower income status, demographic variables identified as risk factors for breastfeeding. These suggest that hospitals may need to invest in supports other than transportational solutions to increase MBM supply to preterm infants. Additional research into the relationships between marital status, social networks, and breastfeeding may provide useful information for clinicians to support mothers in breastfeeding. Declarations Conflict Interests : The authors declare that there are no competing financial interests. Ethics : This study involved human participants and was reviewed by the University of South Florida Institutional Review Board, Pro00007168. The study was conducted in accordance with the Declaration of Helsinki. Availability of Data and Materials : De-identified data will be available upon request. Funding : NIH NHLBI K23HL150300 (Ho) and NIH NICHD R21HD112776 (Ho). Authorship contributions : AS designed the study, collected all clinical data and completed the first draft of manuscript. AH conducted literature review, prepared and edited the final manuscript. WG prepared and edited the final manuscript. XS performed biostatistical analyses. TTBH designed the study, supervised data collection, prepared and edited the final manuscript. Acknowledgements : We thank our research nurse, Marcia Kneusel, for assisting with enrollment and data collection. References Paduraru DI. The evidence for the benefits from breast milk in the neurodevelopment of premature babies–a literature review. Journal of Mind and Medical Sciences. 2018;5(2):151. Belfort MB, Inder TE. Human Milk and Preterm Infant Brain Development: A Narrative Review. Clin Ther. 2022;44(4):612–21. Dieterich CM, Felice JP, O’Sullivan E, Rasmussen KM. Breastfeeding and Health Outcomes for the Mother-Infant Dyad. Pediatric Clinics of North America. 2013;60(1):31–48. Cernadas JMC. Colostrum and breast milk in the neonatal period: The benefits keep adding up. Archivos argentinos de pediatría. 2018;116(4):234–5. Cartagena D, Penny F, McGrath JM, Reyna B, Parker LA, McInnis J. Differences in Neonatal Outcomes Among Premature Infants Exposed to Mother's Own Milk Versus Donor Human Milk. Adv Neonatal Care. 2022;22(6):539–49. Parker LA, Koernere R, Fordham K, Bubshait H, Eugene A, Gefre A, et al. Mother's Own Milk Versus Donor Human Milk: What’s the Difference? Critical Care Nursing Clinics of North America. 2024;36(1):119–33. Hope KLaMW-GaMTPaADF. Bacteria and Bioactivity in Holder Pasteurized and Shelf-Stable Human Milk Products. Current Developments in Nutrition. 2017;1(8):e001438. Lau C. Breastfeeding Challenges and the Preterm Mother-Infant Dyad: A Conceptual Model. Breastfeeding Medicine. 2017;13(1):8–17. Miles MS, Funk SG, Carlson J. Parental Stressor Scale: neonatal intensive care unit. Nurs Res. 1993;42(3):148–52. Lee HC, Gould JB. Factors influencing breast milk versus formula feeding at discharge for very low birth weight infants in California. J Pediatr. 2009;155(5):657–62 e1-2. Kair LR, Colaizy TT. Breastfeeding Continuation Among Late Preterm Infants: Barriers, Facilitators, and Any Association With NICU Admission? Hosp Pediatr. 2016;6(5):261–8. Riley B, Schoeny M, Rogers L, Asiodu IV, Bigger HR, Meier PP, et al. Barriers to Human Milk Feeding at Discharge of Very Low-Birthweight Infants: Evaluation of Neighborhood Structural Factors. Breastfeeding medicine: the official journal of the Academy of Breastfeeding Medicine. 2016;11(7):335–42. Heck KE, Braveman P, Cubbin C, Chávez GF, Kiely JL. Socioeconomic status and breastfeeding initiation among California mothers. Public Health Rep. 2006;121(1):51–9. Sankar MN, Weiner Ye, Chopra N, Kan P, Williams Z, Lee HC. Barriers to optimal breast milk provision in the neonatal intensive care unit. Barriers to Human Milk Feeding at Discharge of Very Low–Birthweight Infants: Evaluation of Neighborhood Structural Factors. Breastfeeding Medicine. 2016;11(7):335–42. Lee HC, Gould JB. Factors influencing breast milk versus formula feeding at discharge for very low birth weight infants in California. J Pediatr. 2009;155(5):657–62 .e1-2 . Patel AL, Johnson TJ, Meier PP. Racial and socioeconomic disparities in breast milk feedings in US neonatal intensive care units. Pediatr Res. 2021;89(2):344–52. Lee HJ, Elo IT, McCollum KF, Culhane JF. Racial/Ethnic Differences in Breastfeeding Initiation and Duration Among Low-income, Inner-city Mothers. Soc Sci Q. 2009;90(5):1251–71. Bureau USC. American Community Survey 5-Year Data. United States Census Bureau; 2024. Whitley MD, Ro A, Palma A. Work, race and breastfeeding outcomes for mothers in the United States. PLoS One. 2021;16(5):e0251125. Bourque SL, Williams VN, Scott J, Hwang SS. The Role of Distance from Home to Hospital on Parental Experience in the NICU: A Qualitative Study. Children (Basel). 2023;10(9). Bourque SL, Weikel BW, Palau MA, Greenfield JC, Hall A, Klawetter S, et al. The Association of Social Factors and Time Spent in the NICU for Mothers of Very Preterm Infants. Hosp Pediatr. 2021;11(9):988–96. Colombo L, Crippa BL, Consonni D, Bettinelli ME, Agosti V, Mangino G, et al. Breastfeeding Determinants in Healthy Term Newborns. Nutrients. 2018;10(1). Kitano N, Nomura K, Kido M, Murakami K, Ohkubo T, Ueno M, et al. Combined effects of maternal age and parity on successful initiation of exclusive breastfeeding. Prev Med Rep. 2016;3:121–6. CDC. Estimation of Breastfeeding Rates in the United States from the National Immunization Survey. 2023. Kihlstrom AC, Stiller T, Sultana N, Njau G, Schmidt M, Stepanov A, et al. The Impact of Marriage on Breastfeeding Duration: Examining the Disproportionate Effect of COVID-19 Pandemic on Low-Income Communities. Res Sq. 2024. Gynecologists ACoOa. Breastfeeding in underserved women: Increasing initiation and continuation of breastfeeding. Committee Opinion No. 570. Obstet Gynecol. 2013;122(2 pt 1):423–8. Foster SF, Vazquez C, Cubbin C, Nichols AR, Rickman RR, Widen EM. Breastfeeding, socioeconomic status, and long-term postpartum weight retention. Brand E, Kothari C, Stark MA. Factors related to breastfeeding discontinuation between hospital discharge and 2 weeks postpartum. J Perinat Educ. 2011;20(1):36–44. Additional Declarations There is NO conflict of interest to disclose. Cite Share Download PDF Status: Published Journal Publication published 03 Feb, 2026 Read the published version in Journal of Perinatology → Version 1 posted Editorial decision: revise 07 Jul, 2025 Review # 2 received at journal 05 Jul, 2025 Reviewer # 2 agreed at journal 14 Jun, 2025 Review # 1 received at journal 19 May, 2025 Reviewer # 1 agreed at journal 01 May, 2025 Reviewers invited by journal 30 Apr, 2025 Submission checks completed at journal 22 Apr, 2025 Editor assigned by journal 19 Apr, 2025 First submitted to journal 19 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6486705","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":450435950,"identity":"b4a0b617-4a24-4177-b960-beb1935ffba3","order_by":0,"name":"Thao Ho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnUlEQVRIiWNgGAWjYDACCQaGAx8YGPiBTAPidPBIMDAenMHAINlAihbmwzwkabGXbj5w2LatToKBvXmbBHG2yBxLOJzbdliCgedYGZFaJHIMgFoO1DFI5JiRoMUS5DD5N6RoYWxjlmCQ4CFWy420hIM95w5LsPGkFVsQpYV9RvLhDz/K6iT42Q9vvEGUFjhgI035KBgFo2AUjAK8AABQFiiQMZdztAAAAABJRU5ErkJggg==","orcid":"","institution":"University of South Florida","correspondingAuthor":true,"prefix":"","firstName":"Thao","middleName":"","lastName":"Ho","suffix":""},{"id":450435951,"identity":"9386b4ae-e40c-43a6-883a-cedd4cef7411","order_by":1,"name":"Amornrat Sawangkum","email":"","orcid":"","institution":"University of South Florida","correspondingAuthor":false,"prefix":"","firstName":"Amornrat","middleName":"","lastName":"Sawangkum","suffix":""},{"id":450435952,"identity":"b893ad41-f282-41b9-ae30-262a6552cd10","order_by":2,"name":"Alexandra Hoeman","email":"","orcid":"","institution":"University of South Florida","correspondingAuthor":false,"prefix":"","firstName":"Alexandra","middleName":"","lastName":"Hoeman","suffix":""},{"id":450435953,"identity":"ca111bd8-b702-4bfd-8cdf-31856fda9ed7","order_by":3,"name":"Willow Goff","email":"","orcid":"","institution":"University of South Florida","correspondingAuthor":false,"prefix":"","firstName":"Willow","middleName":"","lastName":"Goff","suffix":""},{"id":450435954,"identity":"28e5d190-5eb8-4bfb-af2c-ca49a57a9b5c","order_by":4,"name":"Xiaoqi Sun","email":"","orcid":"","institution":"Memorial Healthcare System","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqi","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-04-19 22:00:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6486705/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6486705/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41372-026-02577-1","type":"published","date":"2026-02-03T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101839209,"identity":"7af8317c-8fbb-4311-869d-c15c76ee3943","added_by":"auto","created_at":"2026-02-04 08:12:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":590436,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6486705/v1/192ff4eb-161c-48d1-b27a-514bff8e6994.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Social and Demographic Factors Influencing Maternal Breast Milk Intake in Preterm and Very Low Birthweight Infants","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe many benefits of maternal breast milk (MBM) consumption in infants are well-documented. MBM promotes the neurodevelopment of preterm infants (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), a population at increased risk of neurological deficits. MBM also enhances the immunological and microbiome development in infants, reducing the risk of gastrointestinal and respiratory tract infections throughout the lifespan (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Numerous studies show that MBM provides greater benefits than donor breast milk, a common feeding option for preterm infants when MBM is not available (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Mothers of preterm infants face unique challenges in initiating and sustaining breast milk supply. Infants requiring neonatal intensive care unit (NICU) admission are often unstable and cannot undergo breastfeeding and/or have diminished ability to coordinate suckling and swallowing at the breast (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Additionally, the NICU environment can induce stress in both the infant and the mother, potentially inhibiting the initiation of breastfeeding and reducing MBM production (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Given such challenges, it is critical to minimize other non-clinical barriers to breastfeeding in preterm and very low birthweight infants.\u003c/p\u003e \u003cp\u003eNumerous non-clinical maternal factors influence the continuation of MBM feedings. Recent studies suggest that infants born to mothers of lower educational attainment are breastfed for a shorter duration (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) Additionally, mothers with a greater household income demonstrate increased rates of exclusive breastfeeding, possibly due to improved access to lactation consultants, supplies, and education regarding the benefits of MBM (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Race and ethnicity may also play a role in MBM intake as some studies indicate that Hispanic and/or Black mothers have a higher risk of exclusive formula feeding than their White counterparts (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However, other reports find that such disparities are diminished when controlling for maternal geographic residence (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile not robustly examined in the current literature, maternal geographic location may be a stronger predictor of MBM feedings than race and ethnicity. Therefore, establishing geographic location or distance from the hospital as a potential barrier to breastfeeding may help hospitals implement solutions for at-risk mothers, such as transportation programs or mobile lactation support. We aimed to examine the influence of geographic distance from the hospital on MBM availability in preterm infants in addition to demographic and socioeconomic factors. This information will guide clinicians and hospitals to better support mothers in providing MBM for their preterm or very low birthweight infants.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patient population\u003c/h2\u003e \u003cp\u003eThis was a prospective observational study of infants born at \u0026lt;\u0026thinsp;33 weeks of age or with birthweight\u0026thinsp;\u0026lt;\u0026thinsp;1500 g admitted to the NICU at Tampa General Hospital between 2016 and 2022 to study nutrition and outcomes. Infants were included if the maternal residential address on file in the electronic medical record (EMR) was located within the state of Florida. Exclusion criteria were infants born to mothers with positive urinary drug screens or contraindications to breastfeeding, such as human immunodeficiency virus or human T-cell lymphotropic virus positivity or with active lesions of herpes simplex virus. Infants with classic galactosemia were also excluded from the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eMBM consumption was defined as the percentage by volume of MBM consumed enterally out of the total enteral intake. Feeding data was recorded weekly and the cumulative percentage of MBM consumption from birth until discharge was calculated as the primary dependent variable. Infant gestational age, maternal gravida, race, presence of maternal comorbidities (defined as presence of mental health diagnoses, hypertension, preeclampsia, or diabetes), and maternal marital status were also obtained. The demographic data and the maternal residential address including the 5-digit ZIP code (as defined by the United States Postal Service) were extracted from the EMR. Maternal residential distance was recorded as the distance in miles from the maternal residential address to the address of Tampa General Hospital. Distance from the hospital is used interchangeably with distance from the NICU. Additionally, yearly median household income for each ZIP code was determined from the United States Census Bureau database using the 2021 American Community Survey 5-Year Estimates (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData descriptions were expressed in mean and standard deviations (SD) or median and interquartile range (IQR) for continuous variables and in percentages for categorical variables. The associations between categorical variables were evaluated by the Fisher exact test. Independent samples \u003cem\u003et\u003c/em\u003e-tests/ANOVA and Mann Whitney U/Kruskal-Wallis test were used to compare continuous variables for normally distributed and skewed data, respectively. Statistical interactions were tested based on research questions. Regression models were tested to examine the relationship between infant MBM intake and maternal residential distance, controlling for gestational age, gravida, birthweight, maternal age, marital status, race, mode of delivery, presence of infant comorbidities, presence of maternal comorbidities, and median income by residential ZIP code. The 95% confidence intervals were used to describe the precision of the estimates. The two-tailed statistical tests were considered significant at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were performed using IBM SPSS statistical software package (IBM Corp. released 2023, IBM SPSS Statistics, Version 29.0 Armonk, NY:IBM Corp).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eFinal analysis consisted of 300 infants who met the study criteria. Mean birth gestational age and birth weight were 29\u0026thinsp;\u0026plusmn;\u0026thinsp;3 weeks and 1267\u0026thinsp;\u0026plusmn;\u0026thinsp;427 g, respectively. The average residential distance from the hospital was 30\u0026thinsp;\u0026plusmn;\u0026thinsp;31 miles. 55% of mothers had maternal comorbidities and 57% of infants had comorbidities during their NICU stay. The maternal race distribution was 50% White, 33% Black, and 17% other or unknown (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003e\u003cb\u003eDemographics and perinatal characteristics\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;300\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age (weeks, mean, SD)\u003c/p\u003e \u003cp\u003eMinimum, maximum (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003cp\u003e23, 37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirthweight (g, mean, SD)\u003c/p\u003e \u003cp\u003eMinimum, maximum (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1273\u0026thinsp;\u0026plusmn;\u0026thinsp;436\u003c/p\u003e \u003cp\u003e460, 2750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal residential distance (miles, mean, SD)\u003c/p\u003e \u003cp\u003eMinimum, maximum (miles)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026thinsp;\u0026plusmn;\u0026thinsp;31\u003c/p\u003e \u003cp\u003e2.1, 178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age (years, mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced maternal age (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e35 years), \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaginal delivery, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224 (75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCesarean delivery, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther races, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal comorbidities, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164 (55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfant comorbidities, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172 (57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of hospital stay (days, mean, SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u0026thinsp;\u0026plusmn;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReported maternal race\u003c/h2\u003e \u003cp\u003eSelf-reported maternal race (Black, White, and others) was strongly associated with distance from the hospital (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:\\)\u003c/span\u003e\u003c/span\u003e(2)\u0026thinsp;=\u0026thinsp;42.37, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and median income by zip code (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:\\)\u003c/span\u003e\u003c/span\u003e(2)\u0026thinsp;=\u0026thinsp;32.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). White mothers lived further from the hospital compared to Black mothers (median 28.9 [14.5, 49.4] vs. 11.6 [7.8, 23.6], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and other-race mothers (14.5 [10.9, 30.7], p\u0026thinsp;=\u0026thinsp;0.003). MBM intake (%) was higher in infants of White mothers compared to infants of Black mothers (85% [34%, 99%] vs. 61% [17%, 94%], p\u0026thinsp;=\u0026thinsp;0.029) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Black mothers had greater numbers of pregnancies (3 [1, 4] vs. 2.4 [1, 3], p\u0026thinsp;=\u0026thinsp;0.001) and lower maternal ages at current pregnancy (27 [22, 32] vs. 29 [25, 34], p\u0026thinsp;=\u0026thinsp;0.016) compared to White mothers. There was a greater percentage of Black mothers who were not married compared to White mothers (71% vs 37%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMaternal age\u003c/h3\u003e\n\u003cp\u003eMaternal age was analyzed in categories of advanced maternal age (defined as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\)\u003c/span\u003e\u003c/span\u003e35 years) and non-advanced maternal age (defined as \u0026lt;35 years). Advanced maternal age was associated with a greater number of pregnancies (2 [1, 4] vs 3 [2, 5], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not with infant MBM intake.\u003c/p\u003e\n\u003ch3\u003eMarital status\u003c/h3\u003e\n\u003cp\u003eAbout half (47%) of the mothers were married. Married mothers were older (30.7 \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\(\\:\\pm\\:\\) \u003c/span\u003e \u003c/span\u003e5.4 vs. 27.0 \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\(\\:\\pm\\:\\) \u003c/span\u003e \u003c/span\u003e 5.7 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lived further from the hospital (23.1 [13, 42.5] vs. 14.2 [9.1, 35.5] miles, p\u0026thinsp;=\u0026thinsp;0.001), had fewer pregnancies (2 [1, 3] vs. 3 [1, 4], p\u0026thinsp;=\u0026thinsp;0.008), and had higher median income by zip code (\u003cspan\u003e$\u003c/span\u003e64K [48K, 78K] vs. \u003cspan\u003e$\u003c/span\u003e45K [40K, 57K], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to single mothers. Infants born to married mothers had significantly higher MBM intake (92% [52%, 99%] vs. 50% [18%, 89%], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMedian income by residential ZIP code\u003c/h2\u003e \u003cp\u003eMedian income by residential ZIP code was correlated with MBM intake (r\u0026thinsp;=\u0026thinsp;0.139, p\u0026thinsp;=\u0026thinsp;0.016) and maternal age (r\u0026thinsp;=\u0026thinsp;0.245, p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001) by Spearman\u0026rsquo;s correlation. Mothers from low-income households (defined as \u0026lt;\u003cspan\u003e$\u003c/span\u003e60,000 per year) had a greater number of pregnancies (3 [1, 4] vs 2 [1, 3], p\u0026thinsp;=\u0026thinsp;0.011), younger ages at current pregnancy (27 [23, 31.5] vs. 31 [27, 34], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and their infants had lower MBM intake (65% [29%, 96%] vs. 86% [24%, 99%], p\u0026thinsp;=\u0026thinsp;0.046) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eResidential distance from hospital\u003c/h2\u003e \u003cp\u003eThe median distance was 21 (10.5, 39.2) miles. About 50% of the families were \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e 20 miles, 75% of families were \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e 30 miles, and 90% of families were \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e 60 miles from the hospital. Distance from the hospital was positively associated with infant MBM intake (Kruskal-Wallis test, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:\\)\u003c/span\u003e\u003c/span\u003e(3) = 22.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInfant MBM intake\u003c/h2\u003e \u003cp\u003eAbout 23% infants received \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e25% MBM, 38% infants received \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e50% MBM, and 50% infants received \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e75% MBM during their NICU stay. Maternal age was significantly associated with MBM intake; infants who received \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e25% MBM or \u0026gt;75% MBM were born to older mothers (Kruskal-Wallis test, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e2\u003c/sup\u003e(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) = 19.75, p \u0026lt;0.001). Residential distance was greater (22.2 [10.9, 47.6] vs. 14.4 [10.3, 26.6] miles, p\u0026thinsp;=\u0026thinsp;0.019) and birth weight was lower (1223 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 430 vs. 1371 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e 463 g, p\u0026thinsp;=\u0026thinsp;0.034) in infants who received \u0026gt;75% MBM compared to infants who received \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e25% MBM.\u003c/p\u003e \u003cp\u003eFor the logistic regression model, we defined \u0026ldquo;low MBM\u0026rdquo; group consisting of infants with MBM intake \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e \\(\\:\\le\\:\\) \u003c/span\u003e \u003c/span\u003e75% and \u0026ldquo;high MBM\u0026rdquo; group consisting of infants with MBM intake \u0026gt;75%. Clinical and demographic factors (gestational age, mode of delivery, presence of infant comorbidities, presence of maternal comorbidities, gravida, maternal age, and maternal race) and socioeconomic factors (marital status, maternal residential distance, and median income by residential ZIP code stratified as less than or greater than \u003cspan\u003e$\u003c/span\u003e60,000 USD per year) were included in the logistic regression model. The logistic regression model demonstrated that further distance from the hospital and marital status were significant predictors of MBM intake \u0026gt;\u0026thinsp;75% in our preterm infants (B\u0026thinsp;=\u0026thinsp;0.015, 95% CI [1.005\u0026ndash;1.024], p= 0.002 and B\u0026thinsp;=\u0026thinsp;1.293, 95% CI [2.001, 6.638], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 respectively), when controlling for the above factors. The variance inflation factor for distance, income, race and marital status was 2.4 using McFadden\u0026rsquo;s R\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current literature indicates that race, income, marital status, and maternal age are factors that may impact MBM production, breastfeeding initiation, and duration of breastfeeding (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In this study, mothers living closer to the hospital more often identified as Black or unmarried. In bivariate comparisons, marriage status, greater maternal age, and higher income were protective factors, while maternal self-identification as Black was a risk factor for infant reduced MBM intake, reflective of reduced MBM production. Taking these demographic and socioeconomic factors into account, residential distance and marital status remained significant predictors of infant MBM intake.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBreastfeeding and Distance to Hospital\u003c/h2\u003e \u003cp\u003eCurrently, the relationship between residential distance to the hospital and neonatal outcomes, especially MBM intake, is largely undetermined(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). A 2021 prospective cohort study found that shorter travel times (\u0026lt;\u0026thinsp;30 minutes) to the hospital is positively associated with time spent in the NICU when compared with longer travel times (\u0026gt;\u0026thinsp;60 minutes) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This may suggest that mothers who live closer to the hospital spend more time with their infants and may have a greater ability to provide MBM. However, in our studied population, the opposite was observed: further residential distance was associated with higher infant MBM intake. This outcome reveals that the relationship between residential distance and breastfeeding outcome is affected by other social, economic and geographic factors. Therefore, hospitals should evaluate their own population characteristics before investing in transportation assistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBreastfeeding and Maternal Age\u003c/h2\u003e \u003cp\u003eThe relationship between maternal age and breastfeeding success is not straightforward, with older mothers providing \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e25% MBM or \u0026gt;75% MBM to their infants. The current literature regarding breastfeeding and maternal age has found that mothers over the age of 30 years are at an increased risk of early cessation of breastfeeding when compared to mothers aged 20 to 25 years (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Interestingly, the Centers of Disease Control\u0026rsquo;s National Immunization Survey found that mothers aged 20\u0026ndash;29 years are 6% less likely to \u003cem\u003eever\u003c/em\u003e breastfeed than mothers over the age of 30 years. However, younger mothers who initiated breastfeeding did so for 5.6 months longer than the older cohort (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Therefore, our research supports the findings that advanced age is a risk factor for early cessation of breastfeeding and MBM intake, but not initiation of breastfeeding. This relationship could explain the weaker influence of maternal age on the outcomes of our study; although advanced maternal age is associated with early MBM cessation, children born to older mothers who self-identified as White and who reported higher incomes still had higher rates of MBM intake. Thus, factors such as income and race had a more powerful effect on MBM intake than maternal age.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eBreastfeeding and Marital Status\u003c/h2\u003e \u003cp\u003eAccording to our study, infants born to married mothers had a higher percentage of total MBM intake. This outcome is consistent with other studies, including a recent report which found that married mothers are two times more likely to breastfeed than their unmarried counterparts (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). This effect may be modulated by numerous variables; our data showed that married mothers were more likely to have a higher income, live further from the hospital, and self-identify as White, all of which are protective factors for MBM intake. However, when controlling for such social and demographic factors, logistic regression continued to yield a positive association between marital status and high MBM intake. This suggests that marital status independently influences MBM intake.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eBreastfeeding and Income\u003c/h2\u003e \u003cp\u003eOur study found that income is positively correlated with MBM intake, which is consistent with the current literature. Multiple studies report that mothers of low-income households are less likely to breastfeed their children than their higher-income counterparts (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). A recent study found that significantly fewer low-income mothers initiated breastfeeding and continued to breastfeed beyond six months (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). This relationship is likely due to the lack of paid maternity leave among low-income earners, as well as reduced access to lactation care due to cost barriers (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). As the current literature indicates, lack of financial accessibility may strongly influence breastfeeding behaviors. While our study results indicate that low-income mothers had greater number of pregnancies and at younger age at the current pregnancy, but income itself was not a predictor for MBM intake after controlling for other factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eBreastfeeding and Race\u003c/h2\u003e \u003cp\u003eConsistent with the current literature, our study found a significant relationship between race and MBM intake. Numerous studies report that those who self-identify as Black have lower rates of breastfeeding initiation and duration compared to those who identify as White. Patel et al. theorizes that this disparity arises as Black mothers are more likely to be low-income and sole supporters of households (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However, another study found that lower rates of MBM intake in infants of Black mothers persist despite controlling for such social and economic variables. Instead, the researchers hypothesized that this group introduces solid food earlier than other racial groups, which could explain their lower rates MBM consumption (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This explanation supports our finding that maternal race is a powerful predictor of MBM intake which may overcome other protective factors such as geographic proximity to healthcare.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStrengths, Weaknesses, and Future Directions\u003c/h2\u003e \u003cp\u003eStrengths of this study include prospective longitudinal data from a diverse patient population, which allowed for analysis of various demographic characteristics. However, this study was limited by the inability to assess social support or educational status, which may play a significant role in milk production and the facilitation of breastfeeding in the NICU.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIdentifying barriers to breastfeeding in the NICU is important to improve health outcomes for preterm infants. Our study demonstrated that preterm infants born to mothers who resided closer to the hospital took less MBM than those with mothers who resided farther from the hospital. Marital status was also a strong predictor of MBM consumption. Mothers whose infants had less MBM milk were more likely to self-identify as Black and have a lower income status, demographic variables identified as risk factors for breastfeeding. These suggest that hospitals may need to invest in supports other than transportational solutions to increase MBM supply to preterm infants. Additional research into the relationships between marital status, social networks, and breastfeeding may provide useful information for clinicians to support mothers in breastfeeding.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003e\u003cu\u003eConflict Interests\u003c/u\u003e\u003c/em\u003e\u003cstrong\u003e:\u003c/strong\u003e The authors declare that there are no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eEthics\u003c/u\u003e\u003c/em\u003e: This study involved human participants and was reviewed by the University of South Florida Institutional Review Board, Pro00007168. The study was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eAvailability of Data and Materials\u003c/u\u003e\u003c/em\u003e: De-identified data will be available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/em\u003e:\u0026nbsp;NIH NHLBI K23HL150300 (Ho) and NIH NICHD R21HD112776 (Ho).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eAuthorship contributions\u003c/u\u003e\u003c/em\u003e\u003cem\u003e:\u003c/em\u003e AS designed the study, collected all clinical data and completed the first draft of manuscript. AH conducted literature review, prepared and edited the final manuscript. WG prepared and edited the final manuscript. XS performed biostatistical analyses. TTBH designed the study, supervised data collection, prepared and edited the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cu\u003eAcknowledgements\u003c/u\u003e\u003c/em\u003e: We thank our research nurse, Marcia Kneusel, for assisting with enrollment and data collection.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePaduraru DI. The evidence for the benefits from breast milk in the neurodevelopment of premature babies\u0026ndash;a literature review. Journal of Mind and Medical Sciences. 2018;5(2):151.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelfort MB, Inder TE. Human Milk and Preterm Infant Brain Development: A Narrative Review. Clin Ther. 2022;44(4):612\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDieterich CM, Felice JP, O\u0026rsquo;Sullivan E, Rasmussen KM. Breastfeeding and Health Outcomes for the Mother-Infant Dyad. Pediatric Clinics of North America. 2013;60(1):31\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCernadas JMC. Colostrum and breast milk in the neonatal period: The benefits keep adding up. Archivos argentinos de pediatr\u0026iacute;a. 2018;116(4):234\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCartagena D, Penny F, McGrath JM, Reyna B, Parker LA, McInnis J. Differences in Neonatal Outcomes Among Premature Infants Exposed to Mother's Own Milk Versus Donor Human Milk. Adv Neonatal Care. 2022;22(6):539\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParker LA, Koernere R, Fordham K, Bubshait H, Eugene A, Gefre A, et al. Mother's Own Milk Versus Donor Human Milk: What\u0026rsquo;s the Difference? Critical Care Nursing Clinics of North America. 2024;36(1):119\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHope KLaMW-GaMTPaADF. Bacteria and Bioactivity in Holder Pasteurized and Shelf-Stable Human Milk Products. Current Developments in Nutrition. 2017;1(8):e001438.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLau C. Breastfeeding Challenges and the Preterm Mother-Infant Dyad: A Conceptual Model. Breastfeeding Medicine. 2017;13(1):8\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiles MS, Funk SG, Carlson J. Parental Stressor Scale: neonatal intensive care unit. Nurs Res. 1993;42(3):148\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee HC, Gould JB. Factors influencing breast milk versus formula feeding at discharge for very low birth weight infants in California. J Pediatr. 2009;155(5):657\u0026ndash;62 e1-2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKair LR, Colaizy TT. Breastfeeding Continuation Among Late Preterm Infants: Barriers, Facilitators, and Any Association With NICU Admission? Hosp Pediatr. 2016;6(5):261\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiley B, Schoeny M, Rogers L, Asiodu IV, Bigger HR, Meier PP, et al. Barriers to Human Milk Feeding at Discharge of Very Low-Birthweight Infants: Evaluation of Neighborhood Structural Factors. Breastfeeding medicine: the official journal of the Academy of Breastfeeding Medicine. 2016;11(7):335\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeck KE, Braveman P, Cubbin C, Ch\u0026aacute;vez GF, Kiely JL. Socioeconomic status and breastfeeding initiation among California mothers. Public Health Rep. 2006;121(1):51\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSankar MN, Weiner Ye, Chopra N, Kan P, Williams Z, Lee HC. Barriers to optimal breast milk provision in the neonatal intensive care unit.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarriers to Human Milk Feeding at Discharge of Very Low\u0026ndash;Birthweight Infants: Evaluation of Neighborhood Structural Factors. Breastfeeding Medicine. 2016;11(7):335\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee HC, Gould JB. Factors influencing breast milk versus formula feeding at discharge for very low birth weight infants in California. J Pediatr. 2009;155(5):657\u0026ndash;62\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.e1-2\u003c/span\u003e\u003cspan address=\"http://.e1-2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel AL, Johnson TJ, Meier PP. Racial and socioeconomic disparities in breast milk feedings in US neonatal intensive care units. Pediatr Res. 2021;89(2):344\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee HJ, Elo IT, McCollum KF, Culhane JF. Racial/Ethnic Differences in Breastfeeding Initiation and Duration Among Low-income, Inner-city Mothers. Soc Sci Q. 2009;90(5):1251\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBureau USC. American Community Survey 5-Year Data. United States Census Bureau; 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhitley MD, Ro A, Palma A. Work, race and breastfeeding outcomes for mothers in the United States. PLoS One. 2021;16(5):e0251125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBourque SL, Williams VN, Scott J, Hwang SS. The Role of Distance from Home to Hospital on Parental Experience in the NICU: A Qualitative Study. Children (Basel). 2023;10(9).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBourque SL, Weikel BW, Palau MA, Greenfield JC, Hall A, Klawetter S, et al. The Association of Social Factors and Time Spent in the NICU for Mothers of Very Preterm Infants. Hosp Pediatr. 2021;11(9):988\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColombo L, Crippa BL, Consonni D, Bettinelli ME, Agosti V, Mangino G, et al. Breastfeeding Determinants in Healthy Term Newborns. Nutrients. 2018;10(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitano N, Nomura K, Kido M, Murakami K, Ohkubo T, Ueno M, et al. Combined effects of maternal age and parity on successful initiation of exclusive breastfeeding. Prev Med Rep. 2016;3:121\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCDC. Estimation of Breastfeeding Rates in the United States from the National Immunization Survey. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKihlstrom AC, Stiller T, Sultana N, Njau G, Schmidt M, Stepanov A, et al. The Impact of Marriage on Breastfeeding Duration: Examining the Disproportionate Effect of COVID-19 Pandemic on Low-Income Communities. Res Sq. 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGynecologists ACoOa. Breastfeeding in underserved women: Increasing initiation and continuation of breastfeeding. Committee Opinion No. 570. Obstet Gynecol. 2013;122(2 pt 1):423\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFoster SF, Vazquez C, Cubbin C, Nichols AR, Rickman RR, Widen EM. Breastfeeding, socioeconomic status, and long-term postpartum weight retention.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrand E, Kothari C, Stark MA. Factors related to breastfeeding discontinuation between hospital discharge and 2 weeks postpartum. J Perinat Educ. 2011;20(1):36\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-perinatology","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"jp","sideBox":"Learn more about [Journal of Perinatology](http://www.nature.com/jp/)","snPcode":"41372","submissionUrl":"https://mts-jper.nature.com/cgi-bin/main.plex","title":"Journal of Perinatology","twitterHandle":"@jperinatology","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6486705/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6486705/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eIdentify demographic and social factors that influence the availability of maternal breast milk (MBM) to reduce barriers and improve outcomes for very low birthweight preterm infants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design:\u003c/strong\u003e This prospective cohort study analyzed demographic, socioeconomic, and clinical data from 300 maternal-infant dyads with infants born \u0026lt;1500 g. Data included residential distance from the hospital, comorbidities, and infant MBM intake measured as a percentage of total enteral intake.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eBivariate analysis revealed that maternal race, median income by zip code, marital status, and residential distance were significantly associated with MBM intake.\u003c/p\u003e\n\u003cp\u003eIn a multivariate regression model, only residential distance and marital status remained significant predictors, with greater distance from the hospital and marriage status associated with higher MBM intake.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eResidential distance from the hospital was not a significant barrier to breastfeeding in the study population.\u003c/p\u003e","manuscriptTitle":"Social and Demographic Factors Influencing Maternal Breast Milk Intake in Preterm and Very Low Birthweight Infants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-08 20:30:32","doi":"10.21203/rs.3.rs-6486705/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-07-07T08:51:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-07-06T01:44:03+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-06-14T18:14:06+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-05-20T01:49:09+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-05-01T23:22:54+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-04-30T23:50:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-22T12:42:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-19T21:59:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Perinatology","date":"2025-04-19T21:59:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-perinatology","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"jp","sideBox":"Learn more about [Journal of Perinatology](http://www.nature.com/jp/)","snPcode":"41372","submissionUrl":"https://mts-jper.nature.com/cgi-bin/main.plex","title":"Journal of Perinatology","twitterHandle":"@jperinatology","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"42dce366-5087-451e-bd00-67a02da0dcdc","owner":[],"postedDate":"May 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47932348,"name":"Health sciences/Health care/Paediatrics"},{"id":47932349,"name":"Health sciences/Medical research/Outcomes research"}],"tags":[],"updatedAt":"2026-02-04T08:11:53+00:00","versionOfRecord":{"articleIdentity":"rs-6486705","link":"https://doi.org/10.1038/s41372-026-02577-1","journal":{"identity":"journal-of-perinatology","isVorOnly":false,"title":"Journal of Perinatology"},"publishedOn":"2026-02-03 05:00:00","publishedOnDateReadable":"February 3rd, 2026"},"versionCreatedAt":"2025-05-08 20:30:32","video":"","vorDoi":"10.1038/s41372-026-02577-1","vorDoiUrl":"https://doi.org/10.1038/s41372-026-02577-1","workflowStages":[]},"version":"v1","identity":"rs-6486705","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6486705","identity":"rs-6486705","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}