Social Determinants of Health and Language Outcomes in Preterm Infants with Public and Private Insurance

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Study Design Single center study of 375 neonates born ≤ 28 weeks. Perinatal characteristics were collected, and the Bayley III was administered at 18–24 months. Primary outcome was language scores of < 85. Bivariate and multivariable analyses were used to compare groups. Results Mothers with public insurance had higher rates of psychosocial risk factors. In regression analysis, People of Color (aOR 2.4, 1.47–4.04), non-English speaking household (aOR 4.05, 1.47–11.15) and public insurance (aOR 2.03, 1.18–3.49) significantly increased the odds of having a language composite score of < 85, whereas breast milk (aOR 0.47, 0.28–0.79) was protective. Conclusions Preterm children with public insurance combined with specific SDOH are at increased risk of language delay. Providers have an opportunity to reshape health-care protocols and policies to address social determinants that impact outcomes. Health sciences/Health care/Paediatrics Health sciences/Risk factors Introduction In the United States, 1 in every 10 infants born is preterm,( 1 ) and social determinants of health (SDOH) are known to be linked to preterm birth.( 2 ) Prematurity is associated with a spectrum of unfavorable neurodevelopmental sequelae, but when coupled with SDOH and maternal psychosocial factors, adverse outcomes can be exacerbated for both mother and baby.( 3 – 8 ) Preterm birth itself can be a traumatic event for mothers, exposing them to increased risk of depression, anxiety and acute stress disorder.( 9 ) This stressful event can lead to negative effects on caregiving and have a significant impact on cognitive and language development.( 9 , 10 ) The Centers for Disease Control and Prevention (CDC) defines health disparities as preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health that are experienced by socially disadvantaged populations, and defines SDOH as conditions that exist in the places where people live, learn, work, and play.( 11 , 12 ) Socioeconomic factors such as poverty, public insurance, race, and maternal education, and psychosocial factors including maternal mental health disorders, substance abuse, and domestic abuse are associated with adverse childhood outcomes.( 13 – 15 ) However, there are limited data on the combined effects of neonatal morbidities, type of health insurance, and specific maternal SDOH on preterm language outcomes.( 16 – 18 ) The primary objective was to evaluate associations of maternal SDOH on the language outcomes of premature infants with public compared to private insurance. The secondary objective was to evaluate the effects of SDOH on the cognitive and motor outcomes of premature infants with public compared to private insurance. It was hypothesized that SDOH, including public health insurance and psycho-social risk factors, increase the risk of delayed BSID-III language scores in preterm infants at 18–24 months corrected age. Methods This single facility retrospective study was conducted using maternal and infant data previously collected in the Women & Infants Hospital Neonatal Follow-up Clinic electronic database and included infants born between 1/1/2012 and 12/28/2018. Inclusion criteria included all preterm survivors born at ≤ 28 weeks gestation who had the Bayley Scales of Infant and Toddler Development III (BSID-III) administered at age 18 to 24 months. Exclusion criteria included infants born with chromosomal or congenital anomalies (n = 17 (3%)). The study was approved by the Women and Infants Hospital Institutional Review Board with consent waived because of the retrospective study design. This study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines. Subjects were analyzed to determine effects of social determinants of health by public and private insurance. Maternal demographic characteristics as reported by the mother included maternal age, race, ethnicity, education, and primary language. Social workers collected data regarding psychosocial risks including maternal current or history of mental health disorders (i.e. depression, anxiety or psychosis), and current or history of domestic abuse, substance abuse, or child protective services (CPS) involvement. Neonatal characteristics include birthweight, gestational age, days on oxygen, days in hospital, gender, singleton birth, neonatal complications (necrotizing enterocolitis, sepsis – culture positive, intraventricular hemorrhage (IVH III/IV), bronchopulmonary dysplasia (BPD), discharge feeding (mother’s breast milk, formula, or both), and discharge oxygen therapy requirement. The BSID-III ( 19 ) was administered at the 18–24 month corrected age neonatal follow-up clinic visit by examiners blinded to maternal and neonatal history. Cognitive, language, and motor composite scores were obtained with a mean and standard deviation (SD) of 100 ± 15.( 19 , 20 ) The standard cut-points for composite scores of < 85 for 1 standard deviation and < 70 for 2 standard deviations below the mean were analyzed. The primary outcome was language scores, specifically a language composite score of < 85. Subscores were obtained for BSID-III language and motor domains. Although there is no standardized Spanish version of the BSID-III, translators were used as needed during test administration, based on parental request. Statistical Analyses The available sample sizes of 212 (public insurance) and 163 (private insurance) provided 80% power at an alpha of p = 0.05 to detect minimum differences of 10 to 14 percentage points in the proportion with BSID-III scores < 85 between groups when assuming the percentage in the private insurance group was between 10% and 30%. Bivariate analyses of study groups were conducted using the chi-square test for categorical variables and t-test/Wilcoxon tests (depending on distribution) for continuous variables. Multivariable logistic regression analysis was used to determine independent social, psychosocial, and medical predictors of language scores. Covariates entered in the regressions were those clinical and social factors statistically significant in bivariate analyses and known to be associated with language outcomes. People of Color (POC) groups including Black, Asian/Pacific Islander, American Indian, Alaskan Native, Hispanic or Latino, and Other were combined into one variable (POC) for the regression. Generalized estimating equations were used to adjust the infant data for the presence of multiple births. The final model included the following predictors: gestational age, IVH 3–4, male, POC, non-English speaking household, public insurance, and breast milk at discharge. Results Study Population Characteristics A total of 375 neonates who met the inclusion criteria were evaluated at 18-24 months. Table 1 shows that mothers with public insurance were noted to have significantly younger maternal age, higher rates of POC groups, lower level of education, non-English as primary language, and psycho-social risk factors (mental health disorders, domestic abuse, substance abuse, and CPS involvement) compared to mothers with private insurance. Infant characteristics that were associated with public insurance included singleton birth, necrotizing enterocolitis, and formula feeding at NICU discharge (Table 2). Outcomes Table 3 shows the BSID-III scores at the 18-24 month corrected age follow-up visit relative to type of insurance. Composite language, receptive communication, expressive communication, and composite cognitive scores were significantly lower (p < 0.0001) in the public insurance group. The language composite scores were approximately 11 points lower in the public insurance group, and compared to the private insurance group, 29% more children scored <85 in the public insurance group. Receptive communication subscores of <7 in the public insurance group were almost double the rate in the private insurance group. The cognitive composite score was 6.6 points lower in the public insurance group, and 14% more children scored <85 than in the private insurance group. Infants with public insurance scored significantly lower on all BSID-III scales except for the motor composite score <85 and the fine and gross motor scores <7. The order of BSID-III administration was cognitive, language, and motor. Although 100% of children completed the cognitive test, 95% and 96% completed the language composite, and 98% and 96% the motor composite for public and private insurance respectively. The primary reasons for not completing all components of the BSID-III were child fatigue or behavior. Table 4 shows the logistic regression analysis to predict BSID-III language composite scores of <85. IVH increased the odds by 234% (aOR 3.34), male sex increased the odds by 123% (aOR 2.23), POC race increased the odds by 144% (aOR 2.44), non-English primary language increased the odds by 305% (aOR 4.05), and public insurance increased the odds by 103% (aOR 2.03) of having a language composite score of <85. Provision of breast milk was the only significant protective factor, and it decreased the odds by 53% (aOR 0.47) of having lower language composite scores <85. Discussion Findings from this study support our hypothesis that SDOH, specifically public insurance (as a marker of poverty), minority race, and non-English primary language, independently increase the risk of low BSID-III language scores in preterm infants at 18–24 months corrected age. Infants with public insurance had a significantly lower mean BSID-III language composite score, and were more likely to have a score more than one SD below the mean compared to those with private insurance. Mean receptive and expressive communication subscales were also significantly lower than those of children with private insurance. According to Rhode Island Department of Health, from 2012 to 2018 the percentage of individuals in the state who had public insurance increased from 31–42.2%.( 21 ) Data from Rhode Island Kids Count 2023 Factbook indicates 45% of infants born in RI were on public insurance.( 22 ) Among the infants participating in this preterm study, 57% had public insurance. With the increasing percentage of families with public insurance, more infants are at risk of developmental delays. Green et al.( 23 ) previously reported that public insurance was associated with decreased BSID-III total language composite score and receptive language subscale test scores between 8 to 20 months. Other studies have shown that race/ethnicity and primary language have effects on language outcomes with POC children scoring lower on BSID-III language compared to White race children.( 7 , 24 ) Family health literacy can also amplify the differences in health outcomes by affecting healthcare use, relationship with provider, and health behavior, especially if there is a language barrier or cultural dissimilarities.( 25 ) In addition, decreased access to resources has been linked to cognitive impairment and adverse effects on learning.( 15 ) Comfort et al.( 13 ) and Jang et al.( 17 ) describe the effects that type of health insurance can have on the accessibility and quality of services offered. Studies have also shown that public insurance is a risk factor associated with poverty, and with less optimal perinatal and post-discharge outcomes.( 14 , 16 , 17 ) As expected, public insurance was also associated with lower cognitive scores in bivariate analysis and independently in multivariable analysis, demonstrating that type of health insurance is a valid marker for SDOH. Language development in infants starts through daily interactions and experiences with caregivers, even prior to speaking their first words.( 26 – 28 ) The exposure to early sensory experiences and language postnatally has a considerable influence on the developing brain, since the first few years of life represent a critical time for neural circuitry expansion related to language development, hearing, vision, and socialization, especially for preterm neonates.( 29 – 31 ) Maternal report of adverse mental health was more prevalent in the public insurance group, but did not achieve significance in our regression modeling when controlled for other factors. There continues to be growing prevalence rates of adverse maternal mental health disorders world-wide with reported antenatal depression and/or anxiety ranging from 8–30%.( 32 , 33 ) In the maternal cohort of this study, rates were significantly higher for those with public versus private insurance (57% and 34%) respectively. Multiple adverse outcomes have been linked to maternal depression in the perinatal period including social isolation, marital discord, restricted fetal growth, increased stress reactivity in infants, and delays in infant motor or intellectual development.( 32 ) Maternal mental health can impact the relationship between mother and infant, affecting interactions and provision of stimuli needed for language development.( 24 , 31 ) Although multiple additional social risk factors were associated in bivariate analyses with public insurance, they did not achieve independent effects on BSID-III language scores in adjusted analyses. This may be attributed to our specific patient population, inadequate sample size, and collinearity since many of our mothers had multiple co-morbidities. Premature infants are also at higher risk of language impairments associated with prolonged hospitalization in a NICU in conjunction with increased rates of neonatal medical morbidities. Studies have also shown an association between low birth weight, low gestational age, and medical conditions associated with prematurity and linguistic development.( 7 , 8 , 23 , 34 ) Although our findings did not show a relationship between public insurance and birth weight or length of stay, it did confirm a significant association between public insurance and necrotizing enterocolitis. Immune mediated responses caused by infection, such as necrotizing enterocolitis, have been thought to impact the central nervous system, and a history of sepsis has also been associated with reduced BSID-III language scores.( 23 ) Multivariable logistic regression analysis did show a protective effect of exclusive or partial breast milk feeding at discharge. Breast milk is well known to have protective effects, which include decreased risk of necrotizing enterocolitis, and multiple studies have reported the positive relationship between breast milk and development.( 35 – 38 ) Belfort et al.( 38 ) found that higher maternal milk intake during the NICU hospitalization and after discharge was associated with better academic achievement and IQ, with stronger beneficial associations of maternal milk feeding with neurodevelopment for neonates with lower gestational ages. Secondary outcomes evaluated in our study included assessing the relationship between BSID-III cognitive and motor scores and socioeconomic factors. Infants with public insurance were more likely to have lower mean cognitive and motor composite scores compared to infants with private insurance, consistent with other studies of very preterm infants. ( 23 ) Gross and fine motor subscores were also lower for the public Insurance group, although the fine and gross motor subscore cutpoints of < 7 indicating a more significant delay did not differ between the groups. Predictive value of Bayley motor scores for later development remains uncertain.( 23 , 39 , 40 ) Strengths of this study include data collection of social determinants of health in the NICU, and neurodevelopmental assessment at 2 years of age. Limitations include that this is a single tertiary care center retrospective analysis, which may limit the generalizability of the study’s findings and there is no standardized Spanish version of the BSID-III. Furthermore, the maternal data regarding some of the psychosocial variables could be affected by maternal reporting. A small subset of children did not complete all components of Bayley testing either due to lack of cooperation or fatigue. Conclusion Results of the study support the independent associations of social determinants of health, including type of health insurance, race, and primary language in the household, with preterm language outcomes at 18–24 months of age. In addition, infants with public insurance are at significantly increased risk of exposure to multiple SDOH. These findings emphasize the importance of monitoring both medical risk factors and social determinants of health in order to tailor management and facilitate access to needed support services. It is proposed that these efforts would contribute to management changes that optimize post-discharge outcomes of extremely preterm neonates. Declarations Contributions: All authors are responsible for the reported research and all authors have participated in the study concept and design, data interpretation, drafting and revising the manuscript and they have all approved the manuscript as submitted. 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Tables Table 1: Maternal demographics in relation to type of insurance Public Insurance Private Insurance P-value N 194 132 Maternal Age, y, M ± SD 27.2 ± 6.2 31.4 ± 4.7 <.0001 Race and Ethnicity White Non-Hispanic Black Non-Hispanic Hispanic or Latino Asian/Pacific Islander American Indian/Alaskan Native Other/Unknown 81 (42) 41 (21) 48 (25) 3 (2) 1 (1) 20 (10) 101 (77) 18 (14) 2 (2) 6 (5) 1 (1) 4 (3) <.0001 Maternal Education Less than High School High School/GED College Incomplete College N = 193 37 (19) 81 (42) 46 (24) 29 (15) N = 132 4 (3) 16 (12) 41 (31) 71 (54) <.0001 Primary Language Non-English, N (%) 35/194 (18) 6/132 (5) 0.0003 Mental Health Disorders (current/history) N (%) 110 (57) 44/131 (34) <.0001 Domestic Abuse N (%) 34/192 (18) 0/125 (0) <.0001 Substance Abuse N (%) 44/193 (23) 5/129 (4) <.0001 Child Protective Services Involvement N (%) 55 (28) 3 (2) <.0001 Social Risks* 0, N (%) 1 2 3 4 64 (33) 70 (36) 22 (11) 23 (12) 15 (8) 87 (66) 39 (30) 5 (4) 1 (1) 0 (0) <.0001 Data shown as M ± SD or N (%) * Social risk factors included mental health disorders, domestic abuse, substance abuse, and CPS involvement Table 2: Infant’s demographics in relation to insurance Public Insurance Private Insurance P-value N 212 163 Birthweight, grams, 859 ± 223 880 ± 217 0.42 Gestational Age, weeks 25.9 ± 1.5 26.0 ± 1.5 0.51 Male 108 (51) 88 (54) 0.42 Singleton 170/211 (81) 87/160 (54) <.0001 Bronchopulmonary dysplasia 123/205 (60) 97/160 (61) 0.99 Necrotizing Enterocolitis 21/209 (10) 1/161 (1) 0.0051 Sepsis 49/211 (23) 24 (15) 0.0632 Intraventricular Hemorrhage (III/IV) 11/210 (5) 11/162 (7) 0.61 Discharge Feeding, N (%) Breast Milk Formula Both Breast Milk and Formula N = 208 9 (4) 151 (73) 48 (23) N = 155 25 (16) 69 (45) 61 (39) <.0001 Days on Oxygen Therapy 61 ± 42 59 ± 40 0.64 Discharged with Oxygen Therapy 53/209 (25) 42/162 (26) 0.84 Days in Hospital 106 ± 35 102 ± 30 0.25 Data shown as M ± SD or N (%) Table 3: BSID-III scores of infants in relation to type of insurance at 18-24 months corrected age BSID-III Scores Insurance Public Private P-value N 212 163 Language composite score 80.2 ± 16 201(95%) 91.2 ± 20 157(96%) <.0001 Language composite score < 85 118 (59) n = 201 47 (30) n = 157 0.0001 Receptive communication subscore 6.4 ± 3 n = 207 8.6 ± 4 n = 160 <.0001 Expressive communication subscore 6.9 ± 3 n = 199 8.5 ± 4 n = 155 <.0001 Receptive communication subscore < 7 101/206 (49) 41/160 (26) <.0001 Expressive communication subscore <7 89/198 (45) 46/155 (30) 0.0088 Cognitive composite score 83.8 ± 13 212(100%) 90.4 ± 163(100%) <.0001 Cognitive composite score <85 84 (40) 42 (26) 0.02 Motor composite score 88.2 ± 16 n = 207(98%) 93.4 ± 15 n = 157(96%) 0.0027 Motor composite score < 85 61/207 (29) 33/157 (21) 0.0797 Fine motor subscore 8.9 ± 3 n = 207 10.0 ± 3 n = 161 0.0007 Gross motor subscore 7.3 ± 2 n = 203 7.9 ± 2 n = 155 0.0361 Fine motor score <7 39/207 (19) 19/161 (12) 0.0564 Gross motor score <7 54/203 (27) 29/155 (19) 0.1132 Data shown as M ± SD or N (%) Table 4: Logistic regressions analysis to predict BSID-III language composite score of <85 Predictors aOR (95% CI) Gestational age 0.86 (0.73 – 1.02) Intraventricular Hemorrhage 3.34 (1.14 – 9.79) Male 2.23 (1.33 – 3.73) People of Color 2.44 (1.47 – 4.04) Non-English-Speaking Household 4.05 (1.47 – 11.15) Public Insurance 2.03 (1.18 – 3.49) Breast Milk at Discharge 0.47 (0.28 – 0.79) Adjusted for multiple births via generalized estimating equations Additional Declarations There is NO conflict of interest to disclose. Cite Share Download PDF Status: Published Journal Publication published 31 Jul, 2024 Read the published version in Journal of Perinatology → Version 1 posted Editorial decision: revise 25 Mar, 2024 Review # 1 received at journal 10 Feb, 2024 Reviewer # 1 agreed at journal 27 Jan, 2024 Reviewers invited by journal 25 Jan, 2024 Submission checks completed at journal 22 Jan, 2024 First submitted to journal 20 Jan, 2024 Editor assigned by journal 20 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Vohr","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACe4kENgaGisP2BvePPyBOi+EMkJYzh3kMbiQ2EKfF4AZQC2MbKVoMew4fe1w5D6rl455awlrseY6lG57ddpvH/v7BBsYZz44TY8sZM8lGoBaD+4wNzDwHjhHWYnC8B6hlDlDLzUaStDT8B/rlIEhLDREOa2ZLk2w49hzs/YMzDhwgrMWemfmYZEMNMCpvpD988OFAHWEtKABoxWEStQABqbaMglEwCkbBSAAARENI39tBeIAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-1200-7413","institution":"Alpert Medical School of Brown University, Women and Infants Hospital","correspondingAuthor":true,"prefix":"","firstName":"Betty","middleName":"","lastName":"Vohr","suffix":""},{"id":269341256,"identity":"6a105a4d-0738-421a-b604-cde6ccd35191","order_by":1,"name":"Arya Batta","email":"","orcid":"","institution":"Alpert Medical School of Brown University, Women and Infants Hospital","correspondingAuthor":false,"prefix":"","firstName":"Arya","middleName":"","lastName":"Batta","suffix":""},{"id":269341257,"identity":"ab6e77d1-28f6-4ffd-af61-ac3dd0fb995e","order_by":2,"name":"Elisabeth McGowan","email":"","orcid":"","institution":"Women \u0026 Infants Hospital/Brown University","correspondingAuthor":false,"prefix":"","firstName":"Elisabeth","middleName":"","lastName":"McGowan","suffix":""},{"id":269341258,"identity":"35d612d2-8951-4711-94eb-8d70edc51f8c","order_by":3,"name":"Richard Tucker","email":"","orcid":"","institution":"Women and Infants Hospital of Rhode Island","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Tucker","suffix":""}],"badges":[],"createdAt":"2024-01-20 21:20:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3882610/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3882610/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41372-024-02082-3","type":"published","date":"2024-07-31T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61554589,"identity":"ea096b49-b9a4-424b-a8b3-f0d5e79a383e","added_by":"auto","created_at":"2024-08-01 07:14:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":423441,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3882610/v1/54e1eb5f-cb10-48be-8607-0b133686bb50.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Social Determinants of Health and Language Outcomes in Preterm Infants with Public and Private Insurance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the United States, 1 in every 10 infants born is preterm,(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) and social determinants of health (SDOH) are known to be linked to preterm birth.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Prematurity is associated with a spectrum of unfavorable neurodevelopmental sequelae, but when coupled with SDOH and maternal psychosocial factors, adverse outcomes can be exacerbated for both mother and baby.(\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) Preterm birth itself can be a traumatic event for mothers, exposing them to increased risk of depression, anxiety and acute stress disorder.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) This stressful event can lead to negative effects on caregiving and have a significant impact on cognitive and language development.(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) The Centers for Disease Control and Prevention (CDC) defines health disparities as preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health that are experienced by socially disadvantaged populations, and defines SDOH as conditions that exist in the places where people live, learn, work, and play.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) Socioeconomic factors such as poverty, public insurance, race, and maternal education, and psychosocial factors including maternal mental health disorders, substance abuse, and domestic abuse are associated with adverse childhood outcomes.(\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) However, there are limited data on the combined effects of neonatal morbidities, type of health insurance, and specific maternal SDOH on preterm language outcomes.(\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe primary objective was to evaluate associations of maternal SDOH on the language outcomes of premature infants with public compared to private insurance. The secondary objective was to evaluate the effects of SDOH on the cognitive and motor outcomes of premature infants with public compared to private insurance. It was hypothesized that SDOH, including public health insurance and psycho-social risk factors, increase the risk of delayed BSID-III language scores in preterm infants at 18\u0026ndash;24 months corrected age.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis single facility retrospective study was conducted using maternal and infant data previously collected in the Women \u0026amp; Infants Hospital Neonatal Follow-up Clinic electronic database and included infants born between 1/1/2012 and 12/28/2018. Inclusion criteria included all preterm survivors born at \u0026le;\u0026thinsp;28 weeks gestation who had the Bayley Scales of Infant and Toddler Development III (BSID-III) administered at age 18 to 24 months. Exclusion criteria included infants born with chromosomal or congenital anomalies (n\u0026thinsp;=\u0026thinsp;17 (3%)). The study was approved by the Women and Infants Hospital Institutional Review Board with consent waived because of the retrospective study design. This study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.\u003c/p\u003e \u003cp\u003eSubjects were analyzed to determine effects of social determinants of health by public and private insurance. Maternal demographic characteristics as reported by the mother included maternal age, race, ethnicity, education, and primary language. Social workers collected data regarding psychosocial risks including maternal current or history of mental health disorders (i.e. depression, anxiety or psychosis), and current or history of domestic abuse, substance abuse, or child protective services (CPS) involvement. Neonatal characteristics include birthweight, gestational age, days on oxygen, days in hospital, gender, singleton birth, neonatal complications (necrotizing enterocolitis, sepsis \u0026ndash; culture positive, intraventricular hemorrhage (IVH III/IV), bronchopulmonary dysplasia (BPD), discharge feeding (mother\u0026rsquo;s breast milk, formula, or both), and discharge oxygen therapy requirement.\u003c/p\u003e \u003cp\u003eThe BSID-III (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) was administered at the 18\u0026ndash;24 month corrected age neonatal follow-up clinic visit by examiners blinded to maternal and neonatal history. Cognitive, language, and motor composite scores were obtained with a mean and standard deviation (SD) of 100\u0026thinsp;\u0026plusmn;\u0026thinsp;15.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) The standard cut-points for composite scores of \u0026lt;\u0026thinsp;85 for 1 standard deviation and \u0026lt;\u0026thinsp;70 for 2 standard deviations below the mean were analyzed. The primary outcome was language scores, specifically a language composite score of \u0026lt;\u0026thinsp;85. Subscores were obtained for BSID-III language and motor domains. Although there is no standardized Spanish version of the BSID-III, translators were used as needed during test administration, based on parental request.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eThe available sample sizes of 212 (public insurance) and 163 (private insurance) provided 80% power at an alpha of p\u0026thinsp;=\u0026thinsp;0.05 to detect minimum differences of 10 to 14 percentage points in the proportion with BSID-III scores\u0026thinsp;\u0026lt;\u0026thinsp;85 between groups when assuming the percentage in the private insurance group was between 10% and 30%. Bivariate analyses of study groups were conducted using the chi-square test for categorical variables and t-test/Wilcoxon tests (depending on distribution) for continuous variables. Multivariable logistic regression analysis was used to determine independent social, psychosocial, and medical predictors of language scores. Covariates entered in the regressions were those clinical and social factors statistically significant in bivariate analyses and known to be associated with language outcomes. People of Color (POC) groups including Black, Asian/Pacific Islander, American Indian, Alaskan Native, Hispanic or Latino, and Other were combined into one variable (POC) for the regression. Generalized estimating equations were used to adjust the infant data for the presence of multiple births. The final model included the following predictors: gestational age, IVH 3\u0026ndash;4, male, POC, non-English speaking household, public insurance, and breast milk at discharge.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cu\u003eStudy Population Characteristics\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eA total of 375 neonates who met the inclusion criteria were evaluated at 18-24 months. Table 1 shows that mothers with public insurance were noted to have significantly younger maternal age, higher rates of POC groups, lower level of education, non-English as primary language, and psycho-social risk factors (mental health disorders, domestic abuse, substance abuse, and CPS involvement) compared to mothers with private insurance. Infant characteristics that were associated with public insurance included singleton birth, necrotizing enterocolitis, and formula feeding at NICU discharge (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eOutcomes\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 shows the BSID-III scores\u0026nbsp;at the 18-24 month corrected age follow-up visit\u0026nbsp;relative to type of insurance. Composite language, receptive communication, expressive communication, and composite cognitive scores were significantly lower (p \u0026lt; 0.0001) in the public insurance group. The language composite scores were approximately 11 points lower in the public insurance group, and compared to the private insurance group, 29% more children scored \u0026lt;85 in the public insurance group. Receptive communication subscores of \u0026lt;7 in the public insurance group were almost double the rate in the private insurance group. The cognitive composite score was 6.6 points lower in the public insurance group, and 14% more children scored \u0026lt;85 than in the private insurance group. Infants with public insurance scored significantly lower on all BSID-III scales except for the motor composite score \u0026lt;85 and the fine and gross motor scores \u0026lt;7.\u0026nbsp;The order of BSID-III administration was cognitive, language, and motor. Although 100% of children completed the cognitive test, 95% and 96% completed the language composite, and 98% and 96% the motor composite for public and private insurance respectively. The primary reasons for not completing all components of the BSID-III were child fatigue or behavior.\u003c/p\u003e\n\u003cp\u003eTable 4 shows the logistic regression analysis to predict BSID-III language composite scores of \u0026lt;85. IVH increased the odds by 234% (aOR 3.34), male sex increased the odds by 123% (aOR 2.23), POC race increased the odds by 144% (aOR 2.44), non-English primary language increased the odds by 305% (aOR 4.05), and public insurance increased the odds by 103% (aOR 2.03) of having a language composite score of \u0026lt;85. Provision of breast milk was the only significant protective factor, and it decreased the odds by 53% (aOR 0.47) of having lower language composite scores \u0026lt;85. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFindings from this study support our hypothesis that SDOH, specifically public insurance (as a marker of poverty), minority race, and non-English primary language, independently increase the risk of low BSID-III language scores in preterm infants at 18\u0026ndash;24 months corrected age. Infants with public insurance had a significantly lower mean BSID-III language composite score, and were more likely to have a score more than one SD below the mean compared to those with private insurance. Mean receptive and expressive communication subscales were also significantly lower than those of children with private insurance.\u003c/p\u003e \u003cp\u003eAccording to Rhode Island Department of Health, from 2012 to 2018 the percentage of individuals in the state who had public insurance increased from 31\u0026ndash;42.2%.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) Data from Rhode Island Kids Count 2023 Factbook indicates 45% of infants born in RI were on public insurance.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Among the infants participating in this preterm study, 57% had public insurance. With the increasing percentage of families with public insurance, more infants are at risk of developmental delays. Green et al.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) previously reported that public insurance was associated with decreased BSID-III total language composite score and receptive language subscale test scores between 8 to 20 months. Other studies have shown that race/ethnicity and primary language have effects on language outcomes with POC children scoring lower on BSID-III language compared to White race children.(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) Family health literacy can also amplify the differences in health outcomes by affecting healthcare use, relationship with provider, and health behavior, especially if there is a language barrier or cultural dissimilarities.(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) In addition, decreased access to resources has been linked to cognitive impairment and adverse effects on learning.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) Comfort et al.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and Jang et al.(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) describe the effects that type of health insurance can have on the accessibility and quality of services offered. Studies have also shown that public insurance is a risk factor associated with poverty, and with less optimal perinatal and post-discharge outcomes.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) As expected, public insurance was also associated with lower cognitive scores in bivariate analysis and independently in multivariable analysis, demonstrating that type of health insurance is a valid marker for SDOH.\u003c/p\u003e \u003cp\u003eLanguage development in infants starts through daily interactions and experiences with caregivers, even prior to speaking their first words.(\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) The exposure to early sensory experiences and language postnatally has a considerable influence on the developing brain, since the first few years of life represent a critical time for neural circuitry expansion related to language development, hearing, vision, and socialization, especially for preterm neonates.(\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) Maternal report of adverse mental health was more prevalent in the public insurance group, but did not achieve significance in our regression modeling when controlled for other factors. There continues to be growing prevalence rates of adverse maternal mental health disorders world-wide with reported antenatal depression and/or anxiety ranging from 8\u0026ndash;30%.(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) In the maternal cohort of this study, rates were significantly higher for those with public versus private insurance (57% and 34%) respectively. Multiple adverse outcomes have been linked to maternal depression in the perinatal period including social isolation, marital discord, restricted fetal growth, increased stress reactivity in infants, and delays in infant motor or intellectual development.(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) Maternal mental health can impact the relationship between mother and infant, affecting interactions and provision of stimuli needed for language development.(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) Although multiple additional social risk factors were associated in bivariate analyses with public insurance, they did not achieve independent effects on BSID-III language scores in adjusted analyses. This may be attributed to our specific patient population, inadequate sample size, and collinearity since many of our mothers had multiple co-morbidities.\u003c/p\u003e \u003cp\u003ePremature infants are also at higher risk of language impairments associated with prolonged hospitalization in a NICU in conjunction with increased rates of neonatal medical morbidities. Studies have also shown an association between low birth weight, low gestational age, and medical conditions associated with prematurity and linguistic development.(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) Although our findings did not show a relationship between public insurance and birth weight or length of stay, it did confirm a significant association between public insurance and necrotizing enterocolitis. Immune mediated responses caused by infection, such as necrotizing enterocolitis, have been thought to impact the central nervous system, and a history of sepsis has also been associated with reduced BSID-III language scores.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) Multivariable logistic regression analysis did show a protective effect of exclusive or partial breast milk feeding at discharge. Breast milk is well known to have protective effects, which include decreased risk of necrotizing enterocolitis, and multiple studies have reported the positive relationship between breast milk and development.(\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) Belfort et al.(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) found that higher maternal milk intake during the NICU hospitalization and after discharge was associated with better academic achievement and IQ, with stronger beneficial associations of maternal milk feeding with neurodevelopment for neonates with lower gestational ages.\u003c/p\u003e \u003cp\u003eSecondary outcomes evaluated in our study included assessing the relationship between BSID-III cognitive and motor scores and socioeconomic factors. Infants with public insurance were more likely to have lower mean cognitive and motor composite scores compared to infants with private insurance, consistent with other studies of very preterm infants. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) Gross and fine motor subscores were also lower for the public Insurance group, although the fine and gross motor subscore cutpoints of \u0026lt;\u0026thinsp;7 indicating a more significant delay did not differ between the groups. Predictive value of Bayley motor scores for later development remains uncertain.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eStrengths of this study include data collection of social determinants of health in the NICU, and neurodevelopmental assessment at 2 years of age. Limitations include that this is a single tertiary care center retrospective analysis, which may limit the generalizability of the study\u0026rsquo;s findings and there is no standardized Spanish version of the BSID-III. Furthermore, the maternal data regarding some of the psychosocial variables could be affected by maternal reporting. A small subset of children did not complete all components of Bayley testing either due to lack of cooperation or fatigue.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eResults of the study support the independent associations of social determinants of health, including type of health insurance, race, and primary language in the household, with preterm language outcomes at 18\u0026ndash;24 months of age. In addition, infants with public insurance are at significantly increased risk of exposure to multiple SDOH. These findings emphasize the importance of monitoring both medical risk factors and social determinants of health in order to tailor management and facilitate access to needed support services. It is proposed that these efforts would contribute to management changes that optimize post-discharge outcomes of extremely preterm neonates.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors are responsible for the reported research and all authors have participated in the study concept and design, data interpretation, drafting and revising the manuscript and they have all approved the manuscript as submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosures:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest relevant to this article to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Source:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePrevention CfDCa. 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Effect of health insurance on the use and provision of maternal health services and maternal and neonatal health outcomes: a systematic review. J Health Popul Nutr. 2013;31(4 Suppl 2):81\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUninsurance IoMUCotCo. Health-Related Outcomes for Children, Pregnant Women, and Newborns. Health Insurance is a Family Matter: National Academies Press; 2002.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarrero-Castillero A, Morton SU, Nelson CA, 3rd, Smith VC. Psychosocial Stress and Adversity: Effects from the Perinatal Period to Adulthood. Neoreviews. 2019;20(12):e686-e96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEinarsdottir K, Haggar FA, Langridge AT, Gunnell AS, Leonard H, Stanley FJ. Neonatal outcomes after preterm birth by mothers' health insurance status at birth: a retrospective cohort study. BMC Health Serv Res. 2013;13:40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJang W, Flatley C, Greer RM, Kumar S. Comparison between public and private sectors of care and disparities in adverse neonatal outcomes following emergency intrapartum cesarean at term - A retrospective cohort study. PLoS One. 2017;12(11):e0187040.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKermode M, Fisher J, Jolley D. Health insurance status and mood during pregnancy and following birth: a longitudinal study of multiparous women. Aust N Z J Psychiatry. 2000;34(4):664\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBayley N. Bayley Scales of Infant and Toddler Development - Third Edition San Antonio, TX: Harcourt Assessment 2006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDel Rosario C, Slevin M, Molloy EJ, Quigley J, Nixon E. How to use the Bayley Scales of Infant and Toddler Development. Arch Dis Child Educ Pract Ed. 2021;106(2):108\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmerman A. Rhode Island Current State Assessment The Rhode Island Executive Office of Health and Human Services; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRhode Island Kids Count Factbook Policy \u0026amp; Advocacy for Rhode Island\u0026rsquo;s Children [updated 2023; cited 2023 May 25]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rikidscount.org/Portals/0/2023%20Factbook%20Files/2023_Factbook.pdf?ver=2023-05-\u003c/span\u003e\u003cspan address=\"https://www.rikidscount.org/Portals/0/2023%20Factbook%20Files/2023_Factbook.pdf?ver=2023-05-\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e10-100640-057.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreene MM, Patra K, Silvestri JM, Nelson MN. Re-evaluating preterm infants with the Bayley-III: patterns and predictors of change. Res Dev Disabil. 2013;34(7):2107\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwab JF, Lew-Williams C. Language learning, socioeconomic status, and child-directed speech. Wiley Interdiscip Rev Cogn Sci. 2016;7(4):264\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYee LM, Silver R, Haas DM, Parry S, Mercer BM, Wing DA, et al. Association of Health Literacy Among Nulliparous Individuals and Maternal and Neonatal Outcomes. JAMA Netw Open. 2021;4(9):e2122576.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMongan D, Lynch J, Hanna D, Shannon C, Hamilton S, Potter C, et al. Prevalence of self-reported mental disorders in pregnancy and associations with adverse neonatal outcomes: a population-based cross-sectional study. BMC Pregnancy Childbirth. 2019;19(1):412.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurray L, Cooper P. Effects of postnatal depression on infant development. Arch Dis Child. 1997;77(2):99\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLam-Cassettari C, Kohlhoff J. Effect of maternal depression on infant-directed speech to prelinguistic infants: Implications for language development. PLoS One. 2020;15(7):e0236787.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoward K, Roberts G, Lim J, Lee KJ, Barre N, Treyvaud K, et al. Biological and environmental factors as predictors of language skills in very preterm children at 5 years of age. J Dev Behav Pediatr. 2011;32(3):239\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanchez K, Spittle AJ, Cheong JL, Thompson DK, Doyle LW, Anderson PJ, et al. Language in 2-year-old children born preterm and term: a cohort study. Arch Dis Child. 2019;104(7):647\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVandormael C, Schoenhals L, Huppi PS, Filippa M, Borradori Tolsa C. Language in Preterm Born Children: Atypical Development and Effects of Early Interventions on Neuroplasticity. Neural Plast. 2019;2019:6873270.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeth S, Lewis AJ, Galbally M. Perinatal maternal depression and cortisol function in pregnancy and the postpartum period: a systematic literature review. BMC Pregnancy Childbirth. 2016;16(1):124.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatyanarayana VA, Lukose A, Srinivasan K. Maternal mental health in pregnancy and child behavior. Indian J Psychiatry. 2011;53(4):351\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZambrana IM, Vollrath ME, Jacobsson B, Sengpiel V, Ystrom E. Preterm birth and risk for language delays before school entry: A sibling-control study. Dev Psychopathol. 2021;33(1):47\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalfisch A, Sermer C, Cressman A, Koren G. Breast milk and cognitive development\u0026ndash;the role of confounders: a systematic review. BMJ Open. 2013;3(8):e003259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNwanne OY, Rogers ML, McGowan EC, Tucker R, Smego R, Vivier PM, et al. High-Risk Neighborhoods and Neurodevelopmental Outcomes in Infants Born Preterm. J Pediatr. 2022;245:65\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLechner BE, Vohr BR. Neurodevelopmental Outcomes of Preterm Infants Fed Human Milk: A Systematic Review. Clin Perinatol. 2017;44(1):69\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelfort MB, Knight E, Chandarana S, Ikem E, Gould JF, Collins CT, et al. Associations of Maternal Milk Feeding With Neurodevelopmental Outcomes at 7 Years of Age in Former Preterm Infants. JAMA Netw Open. 2022;5(7):e2221608.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreene MM, Patra K, Nelson MN, Silvestri JM. Evaluating preterm infants with the Bayley-III: patterns and correlates of development. Res Dev Disabil. 2012;33(6):1948\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontgomery C, Setanen S, Kaul YF, Farooqi A, Brostrom L, Aden U, et al. Predictive value of Bayley-III Motor Index for later motor difficulties in children born extremely preterm. Acta Paediatr. 2023;112(4):742\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u0026nbsp;\u003c/strong\u003eMaternal demographics in relation to type of insurance\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.26923076923077%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\"\u003e\n \u003cp\u003ePublic Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\"\u003e\n \u003cp\u003ePrivate Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.26923076923077%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.26923076923077%\"\u003e\n \u003cp\u003eMaternal Age, y, M \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\"\u003e\n \u003cp\u003e27.2 \u0026plusmn; 6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\"\u003e\n \u003cp\u003e31.4 \u0026plusmn; 4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.26923076923077%\"\u003e\n \u003cp\u003eRace and Ethnicity\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;White Non-Hispanic\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black Non-Hispanic\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Hispanic or Latino\u003cins cite=\"mailto:Betty%20Vohr\" datetime=\"2024-01-12T14:45\"\u003e\u0026nbsp;\u003c/ins\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Asian/Pacific Islander\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;American Indian/Alaskan Native\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Other/Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e81 (42)\u003c/p\u003e\n \u003cp\u003e41 (21)\u003c/p\u003e\n \u003cp\u003e48 (25)\u003c/p\u003e\n \u003cp\u003e3 (2)\u003c/p\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003cp\u003e20 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e101 (77)\u003c/p\u003e\n \u003cp\u003e18 (14)\u003c/p\u003e\n \u003cp\u003e2 (2)\u003c/p\u003e\n \u003cp\u003e6 (5)\u003c/p\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003cp\u003e4 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.26923076923077%\"\u003e\n \u003cp\u003eMaternal Education\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Less than High School\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;High School/GED\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;College Incomplete\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;College\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\"\u003e\n \u003cp\u003eN = 193\u003c/p\u003e\n \u003cp\u003e37 (19)\u003c/p\u003e\n \u003cp\u003e81 (42)\u003c/p\u003e\n \u003cp\u003e46 (24)\u003c/p\u003e\n \u003cp\u003e29 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\"\u003e\n \u003cp\u003eN = 132\u003c/p\u003e\n \u003cp\u003e4 (3)\u003c/p\u003e\n \u003cp\u003e16 (12)\u003c/p\u003e\n \u003cp\u003e41 (31)\u003c/p\u003e\n \u003cp\u003e71 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.26923076923077%\"\u003e\n \u003cp\u003ePrimary Language\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Non-English, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35/194 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6/132 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.26923076923077%\"\u003e\n \u003cp\u003eMental Health Disorders (current/history)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e110 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e44/131 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.26923076923077%\"\u003e\n \u003cp\u003eDomestic Abuse\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e34/192 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0/125 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.26923076923077%\"\u003e\n \u003cp\u003eSubstance Abuse\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e44/193 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5/129 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.26923076923077%\"\u003e\n \u003cp\u003eChild Protective Services Involvement\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e55 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.26923076923077%\"\u003e\n \u003cp\u003eSocial Risks*\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0, N (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;3\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e64 (33)\u003c/p\u003e\n \u003cp\u003e70 (36)\u003c/p\u003e\n \u003cp\u003e22 (11)\u003c/p\u003e\n \u003cp\u003e23 (12)\u003c/p\u003e\n \u003cp\u003e15 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.192307692307693%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e87 (66)\u003c/p\u003e\n \u003cp\u003e39 (30)\u003c/p\u003e\n \u003cp\u003e5 (4)\u003c/p\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.384615384615385%\"\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\u003eData shown as M \u0026plusmn; SD or N (%)\u003c/p\u003e\n\u003cp\u003e* Social risk factors included mental health disorders, domestic abuse, substance abuse, and CPS involvement\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e Infant\u0026rsquo;s demographics in relation to insurance \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003ePublic Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\"\u003e\n \u003cp\u003ePrivate Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e212\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003eBirthweight, grams,\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e859 \u0026plusmn; 223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\"\u003e\n \u003cp\u003e880 \u0026plusmn; 217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003eGestational Age, weeks\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e25.9 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\"\u003e\n \u003cp\u003e26.0 \u0026plusmn; 1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e108 (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\"\u003e\n \u003cp\u003e88 (54)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003eSingleton\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e170/211 (81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\"\u003e\n \u003cp\u003e87/160 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003eBronchopulmonary dysplasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e123/205 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\"\u003e\n \u003cp\u003e97/160 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003eNecrotizing Enterocolitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e21/209 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\"\u003e\n \u003cp\u003e1/161 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\"\u003e\n \u003cp\u003e0.0051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e49/211 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\"\u003e\n \u003cp\u003e24 (15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\"\u003e\n \u003cp\u003e0.0632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003eIntraventricular Hemorrhage (III/IV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e11/210 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\"\u003e\n \u003cp\u003e11/162 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eDischarge Feeding, N (%)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Breast Milk\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Formula\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Both Breast Milk and Formula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\" valign=\"top\"\u003e\n \u003cp\u003eN = 208\u003c/p\u003e\n \u003cp\u003e9 (4)\u003c/p\u003e\n \u003cp\u003e151 (73)\u003c/p\u003e\n \u003cp\u003e48 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eN = 155\u003c/p\u003e\n \u003cp\u003e25 (16)\u003c/p\u003e\n \u003cp\u003e69 (45)\u003c/p\u003e\n \u003cp\u003e61 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003eDays on Oxygen Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e61 \u0026plusmn; 42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\"\u003e\n \u003cp\u003e59 \u0026plusmn; 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\" valign=\"top\"\u003e\n \u003cp\u003eDischarged with Oxygen Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\" valign=\"top\"\u003e\n \u003cp\u003e53/209 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e42/162 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\" valign=\"top\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"46.15384615384615%\"\u003e\n \u003cp\u003eDays in Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03205128205128%\"\u003e\n \u003cp\u003e106 \u0026plusmn; 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.673076923076923%\"\u003e\n \u003cp\u003e102 \u0026plusmn; 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.14102564102564%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData shown as\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eM \u0026plusmn; SD or N (%)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e BSID-III scores of infants in relation to type of insurance at 18-24 months corrected age\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003eBSID-III Scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"40.449438202247194%\" colspan=\"2\"\u003e\n \u003cp\u003eInsurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003ePublic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003ePrivate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003eLanguage composite score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e80.2 \u0026plusmn; 16 201(95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e91.2 \u0026plusmn; 20\u0026nbsp;157(96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003eLanguage composite score \u0026lt; 85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e118 (59)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;n = 201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e47 (30)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en = 157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Receptive communication subscore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e6.4 \u0026plusmn; 3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en = 207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e8.6 \u0026plusmn; 4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en = 160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Expressive communication subscore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e6.9 \u0026plusmn; 3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en = 199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e8.5 \u0026plusmn; 4\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en = 155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Receptive communication subscore \u0026lt; 7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e101/206 (49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e41/160 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Expressive communication subscore \u0026lt;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e89/198 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e46/155 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e0.0088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003eCognitive composite score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e83.8 \u0026plusmn; 13 212(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e90.4 \u0026plusmn;\u0026nbsp;163(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e\u0026lt;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003eCognitive composite score \u0026lt;85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e84 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e42 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003eMotor composite score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e88.2 \u0026plusmn; 16\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en = 207(98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e93.4 \u0026plusmn; 15\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en = 157(96%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e0.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003eMotor composite score \u0026lt; 85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e61/207 (29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e33/157 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e0.0797\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Fine motor subscore\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e8.9 \u0026plusmn; 3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en = 207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e10.0 \u0026plusmn; 3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en = 161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e0.0007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Gross motor subscore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e7.3 \u0026plusmn; 2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en = 203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e7.9 \u0026plusmn; 2\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en = 155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e0.0361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Fine motor score \u0026lt;7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e39/207 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e19/161 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e0.0564\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"48.31460674157304%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Gross motor score \u0026lt;7\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e54/203 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.224719101123597%\"\u003e\n \u003cp\u003e29/155 (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.235955056179776%\"\u003e\n \u003cp\u003e0.1132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData shown as\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eM \u0026plusmn; SD or N (%)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Logistic regressions analysis to predict BSID-III language composite score of \u0026lt;85\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.884086444007856%\" valign=\"top\"\u003e\n \u003cp\u003ePredictors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.115913555992144%\" valign=\"top\"\u003e\n \u003cp\u003eaOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.884086444007856%\" valign=\"top\"\u003e\n \u003cp\u003eGestational age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.115913555992144%\" valign=\"top\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003cp\u003e(0.73 \u0026ndash; 1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.884086444007856%\" valign=\"top\"\u003e\n \u003cp\u003eIntraventricular Hemorrhage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.115913555992144%\" valign=\"top\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003cp\u003e(1.14 \u0026ndash; 9.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.884086444007856%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.115913555992144%\" valign=\"top\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003cp\u003e(1.33 \u0026ndash; 3.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.884086444007856%\" valign=\"top\"\u003e\n \u003cp\u003ePeople of Color\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.115913555992144%\" valign=\"top\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003cp\u003e(1.47 \u0026ndash; 4.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.884086444007856%\" valign=\"top\"\u003e\n \u003cp\u003eNon-English-Speaking Household\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.115913555992144%\" valign=\"top\"\u003e\n \u003cp\u003e4.05\u003c/p\u003e\n \u003cp\u003e(1.47 \u0026ndash; 11.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.884086444007856%\" valign=\"top\"\u003e\n \u003cp\u003ePublic Insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.115913555992144%\" valign=\"top\"\u003e\n \u003cp\u003e2.03\u003c/p\u003e\n \u003cp\u003e(1.18 \u0026ndash; 3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.884086444007856%\" valign=\"top\"\u003e\n \u003cp\u003eBreast Milk at Discharge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.115913555992144%\" valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003cp\u003e(0.28 \u0026ndash; 0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Adjusted for multiple births via generalized estimating equations\u003c/p\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-3882610/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3882610/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate associations of maternal social determinants of health (SDOH) with language outcomes of preterm infants with public and private insurance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle center study of 375 neonates born ≤ 28 weeks. Perinatal characteristics were collected, and the Bayley III was administered at 18–24 months. Primary outcome was language scores of \u0026lt; 85. Bivariate and multivariable analyses were used to compare groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMothers with public insurance had higher rates of psychosocial risk factors. In regression analysis, People of Color (aOR 2.4, 1.47–4.04), non-English speaking household (aOR 4.05, 1.47–11.15) and public insurance (aOR 2.03, 1.18–3.49) significantly increased the odds of having a language composite score of \u0026lt; 85, whereas breast milk (aOR 0.47, 0.28–0.79) was protective.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreterm children with public insurance combined with specific SDOH are at increased risk of language delay. Providers have an opportunity to reshape health-care protocols and policies to address social determinants that impact outcomes.\u003c/p\u003e","manuscriptTitle":"Social Determinants of Health and Language Outcomes in Preterm Infants with Public and Private Insurance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-30 09:56:58","doi":"10.21203/rs.3.rs-3882610/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-03-25T11:53:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-02-10T18:20:58+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-01-27T13:30:29+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-01-26T00:50:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-22T17:27:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Perinatology","date":"2024-01-20T21:18:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-20T21:18:10+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":"10c4050a-f6f0-4a6e-b94d-3165b715b1cb","owner":[],"postedDate":"January 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28380962,"name":"Health sciences/Health care/Paediatrics"},{"id":28380963,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-08-01T07:14:33+00:00","versionOfRecord":{"articleIdentity":"rs-3882610","link":"https://doi.org/10.1038/s41372-024-02082-3","journal":{"identity":"journal-of-perinatology","isVorOnly":false,"title":"Journal of Perinatology"},"publishedOn":"2024-07-31 04:00:00","publishedOnDateReadable":"July 31st, 2024"},"versionCreatedAt":"2024-01-30 09:56:58","video":"","vorDoi":"10.1038/s41372-024-02082-3","vorDoiUrl":"https://doi.org/10.1038/s41372-024-02082-3","workflowStages":[]},"version":"v1","identity":"rs-3882610","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3882610","identity":"rs-3882610","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}