Association of Asian American, Native Hawaiian, and Pacific Islander Very Low Birth Weight infants’ Outcomes with Neonatal Intensive Care Unit Cultural Familiarity

Association of Asian American, Native Hawaiian, and Pacific Islander Very Low Birth Weight infants’ Outcomes with Neonatal Intensive Care Unit Cultural Familiarity | 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 Association of Asian American, Native Hawaiian, and Pacific Islander Very Low Birth Weight infants’ Outcomes with Neonatal Intensive Care Unit Cultural Familiarity Igbagbosanmi Oredein, Xin Cui, Elliot Main, Salma Shariff-Marco, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7405103/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: To assess whether cultural familiarity, measured by Asian American, Native Hawaiian, and Pacific Islander (AANHPI) hospital patient volume, is associated with care and outcomes among very low birth weight infants. Study Design: We analyzed 43,067 infants, including 6,534 (15.2%) AANHPI infants, from 142 California neonatal intensive care units (NICUs) in the California Perinatal Quality Care Collaborative database (2011–2019). Hospitals were grouped into tertiles by AANHPI VLBW admissions. Outcomes were assessed using multivariable Poisson regression adjusted for infant, maternal, and hospital factors. Result: Compared to low-tertile NICUs, AANHPI infants in high- and middle-tertile NICUs had greater human milk use at discharge (79.2% and 77.0% vs. 69.6%, p<.001), and those in high-tertile NICUs had higher growth velocity (13.3 vs. 12.8 g/kg/day, p<.001), although attenuated after adjustment. Mortality and major morbidities showed no association with AANHPI patient volume. Conclusion: Higher AANHPI patient volume was linked to feeding outcomes but not broader neonatal outcomes. Health sciences/Health care/Paediatrics Health sciences/Medical research/Epidemiology Health sciences/Health care/Health services Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Disparities in neonatal intensive care unit (NICU) outcomes among racial and ethnic minority groups remain a significant public health concern. 1 – 4 The Asian American, Native Hawaiian, and Pacific Islander (AANHPI) community is the fastest growing racial/ethnic group in the U.S., comprising 6% of the national population and 18% of California’s. 5 – 7 We define AANHPI using standard descriptions in the literature. 8 Asian Americans are defined as individuals with origins in any of the original peoples of Central or East Asia, Southeast Asia, or South Asia, including, for example, Chinese, Asian Indian, Filipino, Vietnamese, Korean, and Japanese. 8 Native Hawaiians are defined as individuals with origins in the original peoples of Hawaii. 8 Pacific Islanders are defined as individuals with origins in the peoples of Guam, Samoa, or other Pacific Islands, such as Tonga, Fiji, and the Marshall Islands. 8 Studies use differing terms to describe AANHPIs and heretofore we will use the descriptor in these studies. AANHPI populations are culturally, socioeconomically, and ethnically highly diverse and may experience unique challenges in neonatal care. 9 Despite generally favorable neonatal outcomes reported among AANHPI populations, recent studies highlight disparities in access, care, and outcomes both across AANHPI ethnic groups and compared to other racial and ethnic groups. 10 – 16 For example, variations exist in human milk feeding rates and in NICU outcomes for very low birth weight (VLBW; ≤1500g) infants across AANHPI ethnic groups in California. 12 – 16 Additionally, differences in healthcare access — such as longer appointment wait times and access influenced by neighborhood ethnic enclaves — have been documented. 10 – 11 A growing area of interest in addressing these inequities is cultural familiarity in healthcare —defined as the degree to which health systems and providers reflect and understand the cultural norms, beliefs, and practices of the populations they serve. 17 – 22 Cultural familiarity may foster trust, improve communication, and enhance care quality. Hospitals serving higher volumes of AANHPI patients may, by necessity or design, offer more culturally responsive care, making patient volume a potential proxy for cultural familiarity. However, the relationship between hospital-level AANHPI volume and neonatal outcomes remains underexplored. Using data from the California Perinatal Quality Care Collaborative (CPQCC), this study explores the association between NICU-level AANHPI patient volume, as a proxy for cultural familiarity, and care quality for AANHPI VLBW infants. Specifically, we aim to: (1) describe the distribution of AANHPI infants across hospital tertiles by AANHPI patient volume, and (2) test for differences in care processes and outcomes across these tertiles. We hypothesize that hospitals with higher AANHPI patient volumes may deliver more equitable care and achieve better outcomes for AANHPI infants. Findings from this study may inform targeted efforts to improve culturally responsive care and reduce differences in neonatal outcomes. METHODS Study Design and Population We conducted a retrospective cohort study of clinical data from the linked California Perinatal Quality Care Collaborative (CPQCC) and Vital Statistics (VS) data registries, spanning 2011–2019 across 142 NICUs. 24 CPQCC captures data from > 90% of all NICUs in California, covering more than 95% of all VLBW births in the state with < 2% missing data variables. 1 , 23 This study was approved by the IRBs from Stanford University and the State of California. We included infants born between 22–29-week gestation or with birth weight between 401 − 1500 grams. Infants who died in the delivery room or within 12 hours of life (n = 5,575) were excluded. AANHPI infants were identified via maternal self-reported race and ethnicity on birth certificates, and included Chinese, Japanese, Filipino, Korean, Vietnamese, Asian Indian, Cambodian, Thai, Laotian, Hmong, other Asian, Native Hawaiian, Guamanian, Samoan, and other Pacific Islander ethnic groups. 23 Race and ethnicity were missing for 2.5% of birth records. Measures Hospitals were ranked at top 33.3%, middle 33.3% and bottom 33.3% by their cumulative AANHPI VLBW infant volume and were categorized into high (n = 47), middle (n = 47), and low (n = 48) tertile groups. Hospital factors such as each NICU’s American Academy of Pediatrics (AAP) level (II-IV) and Vermont Oxford Network (VON) NICU safety net status were obtained from the California Department of Health Care Access and Information (HCAI) Hospital Utilization data. 25 – 27 Safety net NICUs, defined based on greater than two thirds share of Medi-Cal (California's Medicaid program) patients, predominantly serve low-income, uninsured and underserved populations with limited care access. 25 Additional variables including inborn NICU admission rate, annual reported registered nurse staffing hours and annual total number of patient-days, expected principal source of payment for delivery, and annual CPQCC admissions were extracted from linked clinical and organizational data from CPQCC with race and ethnicity from VS and financial data from HCAI. Clinical data were extracted from CPQCC using VON definitions. For the current analysis, measures were expressed as binary variables at the patient level and as proportions at the unit level. Clinical data were extracted from CPQCC using VON definitions. For this analysis, measures were recorded as binary variables at the patient level and as proportions at the unit level. Care and outcome measures included survival without major morbidity , defined as survival to hospital discharge or being alive in-hospital at 1 year of age, and absence of the following: (1) chronic lung disease (CLD), defined by the National Institute of Health (NIH) as supplemental oxygen requirement at 36 weeks postmenstrual age (PMA) among infants born < 32 weeks gestation 28 ; (2) severe intraventricular hemorrhage (IVH; grade 3 or 4 periventricular hemorrhage on or before day 28) 29 – 30 ; (3) healthcare-associated infection (HAI), defined as a late bacterial, coagulase-negative Staphylococcus, or fungal infection occurring after day 3 31 ; (4) necrotizing enterocolitis (NEC), diagnosed surgically, postmortem, or clinically (bilious gastric aspirate or emesis, abdominal distension, or blood in stool) and radiographically (pneumatosis intestinalis, hepatobiliary gas, or pneumoperitoneum) 31 ; (5) severe retinopathy of prematurity (ROP; stages 3–5) or ROP surgery 32 ; and (6) cystic periventricular leukomalacia (PVL). We also used the Baby-MONITOR composite 23 , 33 to assess nine process and outcome measures that span birth hospitalization in the NICU. Five process measures included (1) any breastmilk at discharge (defined as receiving any human milk in the 24 hours prior to discharge home, either initially or after being transported out and readmitted to the reporting NICU) 33 ; (2) hypothermia on admission; (3) timely eye exam for ROP (ROP screening at the age recommended by the AAP 34 ; (4) HAI; and (5) use of antenatal steroids. Four outcome measures included (1) in-hospital survival; (2) CLD; (3) pneumothorax; and (4) and growth velocity. 23 , 33 Particular attention was given to these two feeding related measures - any breastmilk at discharge and growth velocity, as previous work has demonstrated sensitivity in these feeding related measures being indicative of racial and ethnic differences in care in the NICU. 23 Statistical analyses Hospital-level characteristics were described using sample size (n) and percentage (%) (for categorical variables) as well as median and interquartile range (IQR) (for continuous variables) for each tertile group. Number of average daily NICU admissions was calculated as annual total admissions divided by 365 days. Registered nursing hours per patient day were calculated as annual total reported registered nurse staffing hours divided by annual total number of patient-days. Inborn NICU admission rate was calculated as annual total inborn NICU admissions divided by annual total live births. The percentage of Medi-Cal patients was calculated as number of births using Medi-Cal as the expected principal source of payment for delivery divided by total number of births. Patient-level analyses were performed on the patient admission level. The distribution of care, process and outcome measures were presented using percentage (%) and mean (standard deviation (SD)) for each tertile group among all and AANHPI VLBW patients, respectively. Multivariable generalized estimating equations (GEE) Poisson regression models with robust error variance were then fitted to compare the process and outcome measures across tertile groups comparing high and middle tertile groups with low tertile group. In order to investigate factors that might contribute to the variability in the distribution of care and outcome measures across the tertile groups, we conducted sequential modeling as follows: 1) Model 1: Unadjusted; 2) Model 2: Adjusted for infant factors (sex, birth weight, gestational age, size for gestational age, location of birth (inborn vs. outborn), delivery mode (vaginal vs. cesarean section), multiple birth, and 5-minute Apgar score); 3) Model 3: Model 2 plus maternal factors (age, education, birth place, tobacco use during pregnancy, payer source for delivery, and parity); and 4) Model 4: Model 3 plus hospital-level factors (AAP level, hospital ownership, VLBW volume, and safety net hospital). Random effect at hospital level was included in all models to consider the clustering structure for patients within the same hospitals. We estimated risk ratio (for binary variables) and risk difference (for continuous variables) with 95% confidence intervals (CIs). We conducted trend tests to examine the distributions of the measures across tertile groups. In sensitivity analyses, we repeated the same analyses in different population groups: 1) All patients; 2) All patients excluding AANHPI; 3) American Asian (AA); 4) Native Hawaiian and Pacific Islander (NHPI); and 5) Non-Hispanic White. We also evaluated the incremental influence of maternal factors on effect estimates and re-ran models between Model 2 and Model 3. P- values were adjusted for multiple comparisons using the Benjamini-Hochberg method (FDR = 0.05). All statistical analyses were performed with SAS (Ver. 9.4; SAS Institute, Cary, North Carolina). RESULTS Our study cohort included 43,067 VLBW infants from 48,642 NICU admissions, of whom 6,534 (14.8%) were AANHPI. The median percentage of AANHPI infants was 24.3% (IQR 18.3-34.5) in high tertile hospitals, 11.5% (IQR 9.8-14) in middle, and 4.8% (IQR 2.6-6.4) in low (Table 1). Table 2 shows process and outcome comparisons across the hospital tertiles for all infants and AANHPI VLBW infants specifically. For AANHPI infants, no consistent differences were observed by tertile in major outcomes such as infant death during hospitalization up to age 1 year, development of CLD, pneumothorax, severe IVH, HAI, NEC and ROP. However, in univariate analyses, when comparing high vs. low AANHPI tertile groups, statistically significant differences in outcomes were noted for these measures – no cystic PVL, any breastmilk at discharge, no hypothermia, antenatal steroid use, growth velocity, change in weight z-score from birth to discharge and no growth failure. When adjusting for infant level covariates (Model 2), infant process and outcome measures by AANHPI tertile showed little clinical significance for AANHPI VLBW infants except for two feeding related measures – use of any breastmilk at discharge and growth velocity (Table 2). For AANHPI infants, receiving care in a high and middle tertile AANHPI NICU was associated with in increased rates of human milk use at discharge (79.2% (high) vs. 77.0% (middle) vs. 69.6% (low), P-value < .001; risk ratio [95% CI] 1.10 [1.01, 1.18] (middle vs. low) and 1.14 [1.06, 1.22] (high vs. low)) (Table 2). However, after additionally adjusting for maternal and hospital level covariates (Models 3 and 4), associations were no longer significant for comparisons of the high and middle vs. the low tertile group (Figure 1a-d, 2a-d). After adjusting for infant and maternal covariates (Models 2 and 3), receiving care in a high tertile AANHPI NICU was associated with higher growth velocity (13.3 g/kg/d (high) vs. 12.8 (middle) vs. 12.8 (low), P-value < .001; risk difference [95% CI] 0.44 [0.11, 0.77] (high vs. low)) among AANHPI VLBW patients (Table 2). However, after additional adjustment for hospital level covariates (Model 4), associations were no longer significant for comparisons with the low tertile group (Figure 3a-d, 4a-d). There was no significant difference in growth velocity in any of the models for high or middle compared with low tertile group among the AANHPI VLBW infants (Figure 3a-d, 4a-d). Trends were identified for any breast milk at discharge and growth velocity from the same models where the likelihood of these two measures were increased as AANHPI VLBW volume increased from one tertile to another. Effect estimates remained significant after adjusting for multiple testing. Results by race/ethnicity are shown in the supplementary materials, with similar results seen across patient populations (see Appendix). Further sensitivity analysis suggested that maternal education and insurance were primary drivers of changes in the association between hospital tertile and breastmilk use at discharge for the comparison of both the middle and high tertiles vs. the low tertile group (risk ratio [95% CI] 1.07 (0.99, 1.15) (middle vs low) and 1.03 [0.96, 1.11] (high vs low)). Other maternal level covariates, including maternal age, birth place, smoking status during pregnancy, and parity, had minimal impact. DISCUSSION The main findings from our study are that a higher hospital-level AANHPI patient volume—a proxy for cultural familiarity – was associated with better feeding-related outcomes, including higher rates of human milk use at discharge and improved growth velocity, decreased rates of cystic PVL, hypothermia and antenatal steroid use in AANHPI VLBW infants. However, these associations were attenuated after adjusting for maternal and hospital-level factors, suggesting potential confounding or mediation by these factors. Notably, maternal education and insurance status accounted for much of the observed difference in human milk use, aligning with prior studies. 15 , 35 Other clinical measures of survival without morbidity in the NICU including infant death during the birth hospitalization, development of CLD, pneumothorax, severe IVH, HAI, NEC and ROP, showed no significant differences across tertiles for AANHPI infants. Cultural Familiarity and Feeding Practices Previous studies have reported high rates of human milk feeding at discharge among infants from the AANHPI population. 23 , 36 – 37 Lee et al. reported highest rates of human milk feeding among Asian/Pacific Islander infants compared to White, Native American and African American infants. 36 Parker et al. found that non-Hispanic Asian mothers were least likely to stop providing human milk to their infants during their NICU hospitalization compared to other racial and ethnic groups. 37 Our study adds to this literature in that it highlights how cultural familiarity may be contributing to these findings in the preterm population. Specifically, for AANHPI infants, the rates of human milk use at discharge were higher in high (79.2%) and middle (77.0%) tertile NICUs, compared to 69.6% in low tertile NICUs. These differences suggest that the racial and ethnic composition of the hospital patient population may translate into clinically relevant differences in feeding practices potentially based on better cultural familiarity, including in language and culturally relevant resources as well as clinical staff who are familiar with cultural norms, practices and preferences. Prior research highlights disparities in breastfeeding receipt among AANHPI population groups such as NHPI mothers compared to Asian Americans, often linked to systemic barriers and limited culturally tailored support. 13 , 38 A meta-analysis of nine studies highlighted differences in human milk provision among certain AANHPI groups, reporting that less than half of NHPI women initiated breastfeeding or breastfed exclusively. 11 These suboptimal rates are below the recommended national and international goals and guidelines and have been suggested to contribute to the higher incidence of obesity among these groups. 11 A qualitative study identified system-level barriers to breastfeeding among Native Hawaiian mothers, including limited provider knowledge and inadequate access to comprehensive culturally appropriate lactation services and support. 38 Another qualitative study that examined infant feeding beliefs and experiences of Marshallese women (one Pacific Islander ethnic group) living in the US reported social factors like acculturation to US customs as a barrier to human milk feeding. 14 Evidence suggests that neonatal health care providers can support lactation in the NICU and ameliorate differences in the provision of human milk. 39 Culturally competent care delivery, including promoting and supporting human milk feeding in the NICU, may be more effectively implemented in settings with higher cultural familiarity. Previous studies have highlighted that human milk feeding is associated with improved health outcomes for VLBW infants, including lower rates of NEC, better neurodevelopmental outcomes, and lower long-term health care costs. 4 , 14 Therefore, the finding that AANHPI infants in high tertile hospitals had increased rates of human milk feeding at discharge could have significant policy implications for efforts addressing feeding differences in the NICU like the current CPQCC led quality improvement collaborative, MOMMS (Motivating & Optimizing Maternal Milk in Safety Net NICUs), which focuses on improving human milk provision across the state of CA. Cultural Familiarity and Growth Velocity Our study found that AANHPI infants in high-tertile NICUs had growth rates that were 0.05 g/kg/day higher than those in low-tertile NICUs. While this difference may seem modest, it is clinically meaningful as improvement collaboratives targeting growth velocity often struggle to achieve gains of this magnitude. 40 – 42 Growth velocity is an important indicator of neonatal health, as it is associated with long term neurodevelopmental outcomes and overall survival in VLBW infants. 4 , 13 , 43 – 44 Previous studies have reported higher rates of low birth weight, small for gestational age at birth and discharge as well as postnatal growth failure among VLBW infants from Asian American populations, compared to White, Hispanic and African American populations. 4 , 44 – 45 The higher growth velocity among AANHPI infants in high tertile NICUs found in our study could be related to more effective care nutrition strategies, which may be influenced by the hospital’s cultural familiarity with the specific needs of infants/families from AANHPI populations. Our study highlights the potential benefits of cultural familiarity on extrauterine growth and long-term clinical outcomes. However, it is important to acknowledge that the association between cultural familiarity and growth outcomes is complex, and neonatal growth velocity is influenced by multiple factors, including infant comorbidities, maternal morbidities, environmental factors and social determinants of health. 4 , 40 , 46 While cultural familiarity may play a role, it is likely that other systemic factors such as hospital infrastructure, access to resources, and the presence of standardized feeding guidelines and specialized growth management teams also contribute to these outcomes. 41 – 42 Clinical and Policy Implications Our study provides valuable insights into the potential role of cultural familiarity in shaping neonatal care and outcomes for AANHPI infants. While the differences observed in feeding and growth outcomes suggest that higher volumes of AANHPI admissions may improve certain aspects of care, the overall significance of these findings should be seen as hypothesis generating. The lack of major differences in other process and outcome measures suggests that while cultural familiarity, as measured by racial/ethnic-specific patient volume, may have its greatest impact in areas of care that require coordinated “team effort,” such as supporting breastfeeding, where shared cultural understanding may facilitate stronger engagement with families. In contrast, some medical neonatal outcomes, such as NEC and CLD, may be less influenced by cultural awareness and more dependent on standardized clinical protocols. However, given these observed associations in the AANHPI population, future studies should examine whether similar effects of cultural familiarity on patient outcomes are present among other underrepresented groups, such as Black and Hispanic populations to broaden understanding of cultural factors in patient care and outcomes and influence policy changes. In addition, research should explore how structural factors such as staff diversity, language support services, and family-centered care models interact with cultural familiarity to impact infant health outcomes. Studies specifically designed to delineate these pathways should investigate whether potential mediators attenuate these associations, thereby clarifying the mechanisms through which cultural familiarity impacts care and outcomes. Finally, longitudinal studies are needed to determine whether the observed differences in feeding practices and growth velocity translate into lasting health and developmental benefits for AANHPI infants. This study has to be viewed in light of its design. First, our use of hospital patient volume as a proxy measure for cultural familiarity is indirect and may not fully capture its complexity. Whether this proxy measure is a valid measure of cultural familiarity needs confirmational research through surveys and interviews. Furthermore, the retrospective nature of the study limits causal inference and does not account for all potential confounders. Additionally, the AANHPI population is highly heterogeneous, and some associations may be unidentified with data aggregation. A key strength, however, is the use of population-based data from the diverse network of California NICUs, which enhances the generalizability of our findings. CONCLUSIONS This study suggests that hospital-level cultural familiarity may influence care and outcomes for AANHPI VLBW infants. Higher AANHPI patient volumes were initially associated with greater human milk use at discharge and higher growth velocity, though the human milk association was attenuated after risk adjustment, indicating maternal and hospital-level factors may play a substantial role. Future research should use disaggregated AANHPI data to identify specific practices and interventions that drive improved outcomes, and prospective studies should examine the roles of provider cultural congruence, community engagement, and targeted care strategies to better address differences in neonatal care for AANHPI infants. References Ravi D, Iacob A, Profit J. Unequal care: Racial/ethnic disparities in neonatal intensive care delivery. Seminars in Perinatology . 2021;45(4):151411. doi: 10.1016/j.semperi.2021.151411 Barfield WD. Public Health Implications of Very Preterm Birth. 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Factors Influencing Breast Milk versus Formula Feeding at Discharge for Very Low Birth Weight Infants in California. The Journal of Pediatrics . 2009;155(5):657–662.e2. doi: https://doi.org/10.1016/j.jpeds.2009.04.064 Parker MG, Gupta M, Melvin P, et al. Racial and Ethnic Disparities in the Use of Mother’s Milk Feeding for Very Low Birth Weight Infants in Massachusetts. The Journal of Pediatrics . 2019;204:134–141.e1. doi: https://doi.org/10.1016/j.jpeds.2018.08.036 Oneha M, Dodgson J. Community influences on breastfeeding described by Native Hawaiian mothers. Pimatisiwin . 2009;7(1):75–97. Parker MG, Stellwagen LM, Noble L, Kim JH, Poindexter BB, Puopolo KM. Promoting Human Milk and Breastfeeding for the Very Low Birth Weight Infant. Pediatrics . 2021;148(5):e2021054272. doi: https://doi.org/10.1542/peds.2021-054272 Sung TH, Lin CS, Jeng MJ, Tsao PC, Chen WY, Lee YS. Weight growth velocity and growth outcomes in very-low-birth-weight infants developing major morbidities. Pediatrics & Neonatology . 2023;65(2):177–182. doi: https://doi.org/10.1016/j.pedneo.2022.05.022 Alhamad M, Ben Ayad A, Fugate K, et al. Improving Growth Velocity in Very Low Birth Weight Infants: a Quality Improvement Project. Pediatrics . 2019;144(2_MeetingAbstract):650–650. doi: https://doi.org/10.1542/peds.144.2ma7.650 Bagga N, Kiran Kumar Reddy, Mohamed A, Nalinikant Panigrahy, Dinesh Kumar Chirla. Quality improvement initiative to decrease extrauterine growth restriction in preterm neonates. Nutrition in clinical practice . 2021;36(6):1296–1303. doi: https://doi.org/10.1002/ncp.10735 Ehrenkranz RA. Growth in the Neonatal Intensive Care Unit Influences Neurodevelopmental and Growth Outcomes of Extremely Low Birth Weight Infants. PEDIATRICS . 2006;117(4):1253–1261. doi: https://doi.org/10.1542/peds.2005-1368 Lee HC, Ramachandran P, Madan A. Morbidity Risk at Birth for Asian Indian Small for Gestational Age Infants. American Journal of Public Health . 2010;100(5):820–822. doi: https://doi.org/10.2105/ajph.2009.165001 Kurtyka K, Gaur S, Mehrotra N, Chandwani S, Janevic T, Demissie K. Adverse Outcomes Among Asian Indian Singleton Births in New Jersey, 2008–2011. Journal of Immigrant and Minority Health . 2014;17(4):1138–1145. doi: https://doi.org/10.1007/s10903-014-0075-y Jerome M, Chandler-Laney P, Affuso O, Li P, Salas AA. Racial Differences in Growth Rates and Body Composition of Infants Born Preterm. Journal of perinatology: official journal of the California Perinatal Association . 2022;42(3):385–388. doi: https://doi.org/10.1038/s41372-021-01305- Tables Table 1. AANHPI VLBW patient distribution across hospital tertiles Tertile Number of NICUs AANHPI % Patient level NICU level Mean (SD) Median (Q1-Q3) High 47 25.3 27.9 (11.9) 24.3 (18.3-34.5) Middle 47 11.8 11.8 (2.4) 11.5 (9.8-14.0) Low 48 5.4 4.5 (2.5) 4.8 (2.6-6.4) Total 142 14.8 14.7 (12.1) 11.4 (6.4-18.3) Table 2. Care and outcomes by AANHPI volume tertile All VLBW patients AANHPI VLBW patients AANHPI tertile P-value Middle vs. Low P-value High vs. Low AANHPI tertile P-value Middle vs. Low P-value High vs. Low High Middle Low High Middle Low Survival without major morbidity (%) 66.0 63.0 62.5 0.385 <.001 1 67.8 64.7 65.2 0.829 0.169 No infant death during hospitalization up to age 1 year (%) 92.8 91.7 91.4 0.312 <.001 1 93.5 91.6 92.4 0.523 0.282 No chronic lung disease (%) 77.7 76.3 76.4 0.900 0.016 78.0 77.0 77.6 0.774 0.826 No severe peri-intraventricular hemorrhage (%) 93.5 91.7 92.5 0.017 1 0.002 1 94.9 93.2 94.2 0.395 0.487 No nosocomial infection (%) 92.1 91.1 90.1 0.002 1 <.001 1 93.1 91.8 91.7 0.980 0.204 No NEC (%) 96.5 95.8 96.1 0.132 0.042 96.9 95.9 96.7 0.339 0.833 No severe retinopathy of prematurity or surgery for retinopathy of prematurity (%) 93.4 93.1 93.2 0.661 0.597 92.1 92.2 91.7 0.694 0.747 No cystic periventricular leukomalacia (%) 98.2 97.5 97.2 0.090 <.001 1 98.5 97.9 97.4 0.417 0.033 Baby-Monitor Process Measures Any breastmilk at discharge (%) 72.4 70.2 60.1 <.001 1 <.001 1 79.2 77.0 69.6 <.001 1 <.001 1 No hypothermia (%) 90.8 95.1 93.9 <.001 1 <.001 1 90.7 95.4 94.0 0.158 0.007 1 Antenatal steroids (%) 92.6 92.3 87.5 <.001 1 <.001 1 92.5 92.1 88.0 0.006 1 0.001 1 No HAI (%) 92.7 91.9 90.7 0.001 1 <.001 1 93.7 92.2 91.9 0.818 0.105 Timely eye exam (%) 97.5 97.6 95.7 <.001 1 <.001 1 97.8 98.2 96.5 0.040 0.139 Baby-Monitor Outcome Measures In hospital survival (%) 93.5 92.6 93.1 0.103 0.163 94.5 92.4 93.1 0.551 0.157 No chronic lung disease (%) 78.9 79.0 77.9 0.033 0.068 79.0 79.5 78.2 0.504 0.670 No pneumothorax (%) 96.2 96.1 97.0 <.001 1 <.001 1 97.0 96.5 97.4 0.262 0.585 Growth velocity (mean (SD)) 13.2 (2.4) 12.9 (2.4) 12.8 (2.6) 0.013 1 <.001 1 13.3 (2.3) 12.8 (2.2) 12.8 (2.3) 0.759 <.001 1 Postnatal growth Z-score at birth (mean (SD)) -0.41 (1.07) -0.33 (1.11) -0.35 (1.09) 0.145 <.001 1 -0.51 (1.04) -0.43 (1.07) -0.46 (1.13) 0.573 0.284 Z-score at discharge (mean (SD)) -1.18 (1.06) -1.24 (1.08) -1.23 (1.06) 0.520 <.001 1 -1.26 (1.05) -1.30 (1.07) -1.32 (1.11) 0.618 0.195 Change in weight z-score from birth to discharge (mean (SD)) 0.78 (0.72) 0.92 (0.77) 0.87 (0.77) <.001 1 <.001 1 0.76 (0.71) 0.87 (0.73) 0.89 (0.78) 0.573 <.001 1 No growth failure (%) 55.0 46.7 49.1 <.001 1 <.001 1 56.2 49.3 48.8 0.856 0.001 1 Inclusion criteria: VLBW (GA: 22-29 weeks or 401-1500g). Exclusion criteria: Infants with delivery room death and death by 12 hours of age. Analysis was performed at the admission level. Numbers were calculated based on non-missing data for each clinical outcome Weight z-score at birth and discharge was calculated based on Fenton Growth Chart (2013). Change in weight z-score from birth to discharge: Defined as z-score at birth - z-score at discharge. Postnatal growth failure: Defined as change in weight z-score from birth to discharge > 0.8. 1 P-value was significant after adjusting for multiple testing, assuming false discovery rate = 0.05. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files AANHPIManuscriptSupplementaryMaterial1.docx Table S1 AANHPIManuscriptSupplementaryMaterial2.docx Table S2 Cite Share Download PDF Status: Posted Version 1 posted 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-7405103","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503791403,"identity":"f89acf4b-2d31-43db-88a2-72c7ed4e5bb3","order_by":0,"name":"Igbagbosanmi Oredein","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACNgYeBgbGBgjnAEMFkGRmbiBFyxmQFhgXJ0DSwsDYBibxa+HjX3vw49cdNvb8s7sTD3ycVxvN3w7U8qNiG26HSbxLlpY9k5Y4487ZDQdnbjueO+MwYwNjz5nbeLScMZCWbDucwHAjd8Nh3m3HchuAWpgZ2/BqMf4N1GIvD9Yy51jufIJa+HvMJD+2HWbcANbSUAMkCdrCl2bNCPTLRqCWgzOOHcjdCNRyEJ9f5PvPHr75ExhicjdyN3/4UFOXO+/84YMPflTg1sIgkcDAzIPgHgaTB3CrBwL+AwyMPxDcOryKR8EoGAWjYGQCAB+aZNPeEGr3AAAAAElFTkSuQmCC","orcid":"","institution":"Stanford University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Igbagbosanmi","middleName":"","lastName":"Oredein","suffix":""},{"id":503791404,"identity":"211d3124-ec68-4e02-9634-9ed2331bd834","order_by":1,"name":"Xin Cui","email":"","orcid":"https://orcid.org/0000-0002-4277-7972","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Cui","suffix":""},{"id":503791405,"identity":"3f072272-20dc-4332-830a-9e50a22fdb18","order_by":2,"name":"Elliot Main","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Elliot","middleName":"","lastName":"Main","suffix":""},{"id":503791406,"identity":"da978ea7-791d-424c-a6d3-33da0d4d5f3b","order_by":3,"name":"Salma Shariff-Marco","email":"","orcid":"","institution":"Stanford University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Salma","middleName":"","lastName":"Shariff-Marco","suffix":""},{"id":503791407,"identity":"3130c314-5bd2-49c2-ace0-51bcec0a0847","order_by":4,"name":"Scarlett Gomez","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Scarlett","middleName":"","lastName":"Gomez","suffix":""},{"id":503791408,"identity":"79388710-6371-43d1-a729-a151b5642689","order_by":5,"name":"Jochen Profit","email":"","orcid":"https://orcid.org/0000-0002-3782-9248","institution":"Stanford University","correspondingAuthor":false,"prefix":"","firstName":"Jochen","middleName":"","lastName":"Profit","suffix":""}],"badges":[],"createdAt":"2025-08-19 06:25:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7405103/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7405103/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90315448,"identity":"4f197b4d-a59a-4469-a3e5-c4b18da8a695","added_by":"auto","created_at":"2025-09-01 10:12:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151763,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCrude and risk adjusted risk ratio with 95% Confidence Interval for any breast milk at discharge comparing high with low tertiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1a. Model 1: Crude RR; 1b. Model 2: Adjusted for infant factors; 1c. Mode 3: Adjusted for infant and maternal factors; 1d. Model 4: Adjusted for infant, maternal and hospital factors.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7405103/v1/b1fb624c6a07bc774136dc1a.png"},{"id":90315446,"identity":"d07dd6ad-1e99-434b-9e46-2d884d194ff8","added_by":"auto","created_at":"2025-09-01 10:12:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156229,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCrude and risk adjusted risk ratio with 95% Confidence Interval for any breast milk at discharge comparing middle with low tertiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2a. Model 1: Crude RR; 2b. Model 2: Adjusted for infant factors; 2c. Mode 3: Adjusted for infant and maternal factors; 2d. Model 4: Adjusted for infant, maternal and hospital factors.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7405103/v1/5076e1f3183890d6f0795077.png"},{"id":90315447,"identity":"36de3b7c-370d-4380-a456-88020c2fc138","added_by":"auto","created_at":"2025-09-01 10:12:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":138175,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCrude and risk adjusted risk difference with 95% Confidence Interval for growth velocity comparing high with low tertiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3a. Model 1: Crude RD; 3b. Model 2: Adjusted for infant factors; 3c. Mode 3: Adjusted for infant and maternal factors; 3d. Model 4: Adjusted for infant, maternal and hospital factors.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7405103/v1/5add80b95344a97a8fd4d97f.png"},{"id":90315468,"identity":"45a6f255-577f-4406-986b-885ed8641404","added_by":"auto","created_at":"2025-09-01 10:12:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":151698,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCrude and risk adjusted risk difference with 95% Confidence Interval for growth velocity comparing middle with low tertiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4a. Model 1: Crude RD; 4b. Model 2: Adjusted for infant factors; 4c. Mode 3: Adjusted for infant and maternal factors; 4d. Model 4: Adjusted for infant, maternal and hospital factors.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7405103/v1/5a9ede3e81314dec14e7d2ce.png"},{"id":91442121,"identity":"10281eb1-ba1c-4e03-b239-a9a352d6fe6c","added_by":"auto","created_at":"2025-09-16 14:17:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1745791,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7405103/v1/152d96e8-7822-4f4c-a6ed-bcd978c80d2c.pdf"},{"id":90317738,"identity":"f9a4609a-ffdf-4b44-a66f-7c3f35ba0f2f","added_by":"auto","created_at":"2025-09-01 10:28:40","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":23334,"visible":true,"origin":"","legend":"Table S1","description":"","filename":"AANHPIManuscriptSupplementaryMaterial1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7405103/v1/3c70ea6b22ef7f63559192c4.docx"},{"id":90315470,"identity":"bf68d1b3-54f7-4445-8752-479209a08b4d","added_by":"auto","created_at":"2025-09-01 10:12:41","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":20865,"visible":true,"origin":"","legend":"Table S2","description":"","filename":"AANHPIManuscriptSupplementaryMaterial2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7405103/v1/024cfb2905c900f42fbcd29f.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Association of Asian American, Native Hawaiian, and Pacific Islander Very Low Birth Weight infants’ Outcomes with Neonatal Intensive Care Unit Cultural Familiarity","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDisparities in neonatal intensive care unit (NICU) outcomes among racial and ethnic minority groups remain a significant public health concern.\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The Asian American, Native Hawaiian, and Pacific Islander (AANHPI) community is the fastest growing racial/ethnic group in the U.S., comprising 6% of the national population and 18% of California\u0026rsquo;s.\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e We define AANHPI using standard descriptions in the literature.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Asian Americans are defined as individuals with origins in any of the original peoples of Central or East Asia, Southeast Asia, or South Asia, including, for example, Chinese, Asian Indian, Filipino, Vietnamese, Korean, and Japanese.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Native Hawaiians are defined as individuals with origins in the original peoples of Hawaii.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Pacific Islanders are defined as individuals with origins in the peoples of Guam, Samoa, or other Pacific Islands, such as Tonga, Fiji, and the Marshall Islands.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Studies use differing terms to describe AANHPIs and heretofore we will use the descriptor in these studies.\u003c/p\u003e\u003cp\u003eAANHPI populations are culturally, socioeconomically, and ethnically highly diverse and may experience unique challenges in neonatal care.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Despite generally favorable neonatal outcomes reported among AANHPI populations, recent studies highlight disparities in access, care, and outcomes both across AANHPI ethnic groups and compared to other racial and ethnic groups.\u003csup\u003e\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e For example, variations exist in human milk feeding rates and in NICU outcomes for very low birth weight (VLBW; \u0026le;1500g) infants across AANHPI ethnic groups in California.\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Additionally, differences in healthcare access \u0026mdash; such as longer appointment wait times and access influenced by neighborhood ethnic enclaves \u0026mdash; have been documented.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eA growing area of interest in addressing these inequities is cultural familiarity in healthcare \u0026mdash;defined as the degree to which health systems and providers reflect and understand the cultural norms, beliefs, and practices of the populations they serve.\u003csup\u003e\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Cultural familiarity may foster trust, improve communication, and enhance care quality. Hospitals serving higher volumes of AANHPI patients may, by necessity or design, offer more culturally responsive care, making patient volume a potential proxy for cultural familiarity. However, the relationship between hospital-level AANHPI volume and neonatal outcomes remains underexplored.\u003c/p\u003e\u003cp\u003eUsing data from the California Perinatal Quality Care Collaborative (CPQCC), this study explores the association between NICU-level AANHPI patient volume, as a proxy for cultural familiarity, and care quality for AANHPI VLBW infants. Specifically, we aim to: (1) describe the distribution of AANHPI infants across hospital tertiles by AANHPI patient volume, and (2) test for differences in care processes and outcomes across these tertiles. We hypothesize that hospitals with higher AANHPI patient volumes may deliver more equitable care and achieve better outcomes for AANHPI infants. Findings from this study may inform targeted efforts to improve culturally responsive care and reduce differences in neonatal outcomes.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eStudy Design and Population\u003c/p\u003e\u003cp\u003eWe conducted a retrospective cohort study of clinical data from the linked California Perinatal Quality Care Collaborative (CPQCC) and Vital Statistics (VS) data registries, spanning 2011\u0026ndash;2019 across 142 NICUs.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e CPQCC captures data from \u0026gt;\u0026thinsp;90% of all NICUs in California, covering more than 95% of all VLBW births in the state with \u0026lt;\u0026thinsp;2% missing data variables.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e This study was approved by the IRBs from Stanford University and the State of California.\u003c/p\u003e\u003cp\u003eWe included infants born between 22\u0026ndash;29-week gestation or with birth weight between 401 \u0026minus;\u0026thinsp;1500 grams. Infants who died in the delivery room or within 12 hours of life (n\u0026thinsp;=\u0026thinsp;5,575) were excluded. AANHPI infants were identified via maternal self-reported race and ethnicity on birth certificates, and included Chinese, Japanese, Filipino, Korean, Vietnamese, Asian Indian, Cambodian, Thai, Laotian, Hmong, other Asian, Native Hawaiian, Guamanian, Samoan, and other Pacific Islander ethnic groups.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Race and ethnicity were missing for 2.5% of birth records.\u003c/p\u003e\u003cp\u003eMeasures\u003c/p\u003e\u003cp\u003eHospitals were ranked at top 33.3%, middle 33.3% and bottom 33.3% by their cumulative AANHPI VLBW infant volume and were categorized into high (n\u0026thinsp;=\u0026thinsp;47), middle (n\u0026thinsp;=\u0026thinsp;47), and low (n\u0026thinsp;=\u0026thinsp;48) tertile groups. Hospital factors such as each NICU\u0026rsquo;s American Academy of Pediatrics (AAP) level (II-IV) and Vermont Oxford Network (VON) NICU safety net status were obtained from the California Department of Health Care Access and Information (HCAI) Hospital Utilization data.\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Safety net NICUs, defined based on greater than two thirds share of Medi-Cal (California's Medicaid program) patients, predominantly serve low-income, uninsured and underserved populations with limited care access.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Additional variables including inborn NICU admission rate, annual reported registered nurse staffing hours and annual total number of patient-days, expected principal source of payment for delivery, and annual CPQCC admissions were extracted from linked clinical and organizational data from CPQCC with race and ethnicity from VS and financial data from HCAI.\u003c/p\u003e\u003cp\u003eClinical data were extracted from CPQCC using VON definitions. For the current analysis, measures were expressed as binary variables at the patient level and as proportions at the unit level. Clinical data were extracted from CPQCC using VON definitions. For this analysis, measures were recorded as binary variables at the patient level and as proportions at the unit level. Care and outcome measures included \u003cem\u003esurvival without major morbidity\u003c/em\u003e, defined as survival to hospital discharge or being alive in-hospital at 1 year of age, and absence of the following: (1) chronic lung disease (CLD), defined by the National Institute of Health (NIH) as supplemental oxygen requirement at 36 weeks postmenstrual age (PMA) among infants born\u0026thinsp;\u0026lt;\u0026thinsp;32 weeks gestation\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e; (2) severe intraventricular hemorrhage (IVH; grade 3 or 4 periventricular hemorrhage on or before day 28)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e; (3) healthcare-associated infection (HAI), defined as a late bacterial, coagulase-negative Staphylococcus, or fungal infection occurring after day 3\u003csup\u003e31\u003c/sup\u003e; (4) necrotizing enterocolitis (NEC), diagnosed surgically, postmortem, or clinically (bilious gastric aspirate or emesis, abdominal distension, or blood in stool) and radiographically (pneumatosis intestinalis, hepatobiliary gas, or pneumoperitoneum)\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e; (5) severe retinopathy of prematurity (ROP; stages 3\u0026ndash;5) or ROP surgery\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e; and (6) cystic periventricular leukomalacia (PVL).\u003c/p\u003e\u003cp\u003eWe also used the Baby-MONITOR composite\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e to assess nine process and outcome measures that span birth hospitalization in the NICU. Five process measures included (1) any breastmilk at discharge (defined as receiving any human milk in the 24 hours prior to discharge home, either initially or after being transported out and readmitted to the reporting NICU)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e; (2) hypothermia on admission; (3) timely eye exam for ROP (ROP screening at the age recommended by the AAP\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e; (4) HAI; and (5) use of antenatal steroids. Four outcome measures included (1) in-hospital survival; (2) CLD; (3) pneumothorax; and (4) and growth velocity.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Particular attention was given to these two feeding related measures - any breastmilk at discharge and growth velocity, as previous work has demonstrated sensitivity in these feeding related measures being indicative of racial and ethnic differences in care in the NICU.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eStatistical analyses\u003c/p\u003e\u003cp\u003eHospital-level characteristics were described using sample size (n) and percentage (%) (for categorical variables) as well as median and interquartile range (IQR) (for continuous variables) for each tertile group. Number of average daily NICU admissions was calculated as annual total admissions divided by 365 days. Registered nursing hours per patient day were calculated as annual total reported registered nurse staffing hours divided by annual total number of patient-days. Inborn NICU admission rate was calculated as annual total inborn NICU admissions divided by annual total live births. The percentage of Medi-Cal patients was calculated as number of births using Medi-Cal as the expected principal source of payment for delivery divided by total number of births.\u003c/p\u003e\u003cp\u003ePatient-level analyses were performed on the patient admission level. The distribution of care, process and outcome measures were presented using percentage (%) and mean (standard deviation (SD)) for each tertile group among all and AANHPI VLBW patients, respectively. Multivariable generalized estimating equations (GEE) Poisson regression models with robust error variance were then fitted to compare the process and outcome measures across tertile groups comparing high and middle tertile groups with low tertile group. In order to investigate factors that might contribute to the variability in the distribution of care and outcome measures across the tertile groups, we conducted sequential modeling as follows: 1) Model 1: Unadjusted; 2) Model 2: Adjusted for infant factors (sex, birth weight, gestational age, size for gestational age, location of birth (inborn vs. outborn), delivery mode (vaginal vs. cesarean section), multiple birth, and 5-minute Apgar score); 3) Model 3: Model 2 plus maternal factors (age, education, birth place, tobacco use during pregnancy, payer source for delivery, and parity); and 4) Model 4: Model 3 plus hospital-level factors (AAP level, hospital ownership, VLBW volume, and safety net hospital). Random effect at hospital level was included in all models to consider the clustering structure for patients within the same hospitals. We estimated risk ratio (for binary variables) and risk difference (for continuous variables) with 95% confidence intervals (CIs).\u003c/p\u003e\u003cp\u003eWe conducted trend tests to examine the distributions of the measures across tertile groups.\u003c/p\u003e\u003cp\u003eIn sensitivity analyses, we repeated the same analyses in different population groups: 1) All patients; 2) All patients excluding AANHPI; 3) American Asian (AA); 4) Native Hawaiian and Pacific Islander (NHPI); and 5) Non-Hispanic White. We also evaluated the incremental influence of maternal factors on effect estimates and re-ran models between Model 2 and Model 3. \u003cem\u003eP-\u003c/em\u003evalues were adjusted for multiple comparisons using the Benjamini-Hochberg method (FDR\u0026thinsp;=\u0026thinsp;0.05). All statistical analyses were performed with SAS (Ver. 9.4; SAS Institute, Cary, North Carolina).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOur study cohort included 43,067 VLBW infants from 48,642 NICU admissions, of whom 6,534 (14.8%) were AANHPI. The median percentage of AANHPI infants was 24.3% (IQR 18.3-34.5) in high tertile hospitals, 11.5% (IQR 9.8-14) in middle, and 4.8% (IQR 2.6-6.4) in low (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eTable 2\u003c/u\u003e shows process and outcome comparisons across the hospital tertiles for all infants and AANHPI VLBW infants specifically. For AANHPI infants, no consistent differences were observed by tertile in major outcomes such as infant death during hospitalization up to age 1 year, development of CLD, pneumothorax, severe IVH, HAI, NEC and ROP. However, in univariate analyses, when comparing high vs. low AANHPI tertile groups, statistically significant differences in outcomes were noted for these measures – no cystic PVL, any breastmilk at discharge, no hypothermia, antenatal steroid use, growth velocity, change in weight z-score from birth to discharge and no growth failure. When adjusting for infant level covariates (Model 2), infant process and outcome measures by AANHPI tertile showed little clinical significance for AANHPI VLBW infants except for two feeding related measures – use of any breastmilk at discharge and growth velocity (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor AANHPI infants, receiving care in a high and middle tertile AANHPI NICU was associated with in increased rates of human milk use at discharge (79.2% (high) vs. 77.0% (middle) vs. 69.6% (low), P-value \u0026lt; .001; risk ratio [95% CI] 1.10 [1.01, 1.18] (middle vs. low) and 1.14 [1.06, 1.22] (high vs. low)) (Table 2). However, after additionally adjusting for maternal and hospital level covariates (Models 3 and 4), associations were no longer significant for comparisons of the high and middle vs. the low tertile group (Figure 1a-d, 2a-d).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter adjusting for infant and maternal covariates (Models 2 and 3), receiving care in a high tertile AANHPI NICU was associated with higher growth velocity (13.3 g/kg/d (high) vs. 12.8 (middle) vs. 12.8 (low), P-value \u0026lt; .001; risk difference [95% CI] 0.44 [0.11, 0.77] (high vs. low)) among AANHPI VLBW patients (Table 2). However, after additional adjustment for hospital level covariates (Model 4), associations were no longer significant for comparisons with the low tertile group (Figure 3a-d, 4a-d). There was no significant difference in growth velocity in any of the models for high or middle compared with low tertile group among the AANHPI VLBW infants (Figure 3a-d, 4a-d). Trends were identified for any breast milk at discharge and growth velocity from the same models where the likelihood of these two measures were increased as AANHPI VLBW volume increased from one tertile to another. Effect estimates remained significant after adjusting for multiple testing. Results by race/ethnicity are shown in the supplementary materials, with similar results seen across patient populations (see Appendix).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther sensitivity analysis suggested that maternal education and insurance were primary drivers of changes in the association between hospital tertile and breastmilk use at discharge for the comparison of both the middle and high tertiles vs. the low tertile group (risk ratio [95% CI] 1.07 (0.99, 1.15) (middle vs low) and 1.03 [0.96, 1.11] (high vs low)). Other maternal level covariates, including maternal age, birth place, smoking status during pregnancy, and parity, had minimal impact.\u0026nbsp;\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe main findings from our study are that a higher hospital-level AANHPI patient volume\u0026mdash;a proxy for cultural familiarity \u0026ndash; was associated with better feeding-related outcomes, including higher rates of human milk use at discharge and improved growth velocity, decreased rates of cystic PVL, hypothermia and antenatal steroid use in AANHPI VLBW infants.\u003c/p\u003e\u003cp\u003eHowever, these associations were attenuated after adjusting for maternal and hospital-level factors, suggesting potential confounding or mediation by these factors. Notably, maternal education and insurance status accounted for much of the observed difference in human milk use, aligning with prior studies. \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Other clinical measures of survival without morbidity in the NICU including infant death during the birth hospitalization, development of CLD, pneumothorax, severe IVH, HAI, NEC and ROP, showed no significant differences across tertiles for AANHPI infants.\u003c/p\u003e\u003cp\u003eCultural Familiarity and Feeding Practices\u003c/p\u003e\u003cp\u003ePrevious studies have reported high rates of human milk feeding at discharge among infants from the AANHPI population.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Lee et al. reported highest rates of human milk feeding among Asian/Pacific Islander infants compared to White, Native American and African American infants.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Parker et al. found that non-Hispanic Asian mothers were least likely to stop providing human milk to their infants during their NICU hospitalization compared to other racial and ethnic groups.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Our study adds to this literature in that it highlights how cultural familiarity may be contributing to these findings in the preterm population. Specifically, for AANHPI infants, the rates of human milk use at discharge were higher in high (79.2%) and middle (77.0%) tertile NICUs, compared to 69.6% in low tertile NICUs. These differences suggest that the racial and ethnic composition of the hospital patient population may translate into clinically relevant differences in feeding practices potentially based on better cultural familiarity, including in language and culturally relevant resources as well as clinical staff who are familiar with cultural norms, practices and preferences.\u003c/p\u003e\u003cp\u003ePrior research highlights disparities in breastfeeding receipt among AANHPI population groups such as NHPI mothers compared to Asian Americans, often linked to systemic barriers and limited culturally tailored support.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e A meta-analysis of nine studies highlighted differences in human milk provision among certain AANHPI groups, reporting that less than half of NHPI women initiated breastfeeding or breastfed exclusively.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e These suboptimal rates are below the recommended national and international goals and guidelines and have been suggested to contribute to the higher incidence of obesity among these groups.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e A qualitative study identified system-level barriers to breastfeeding among Native Hawaiian mothers, including limited provider knowledge and inadequate access to comprehensive culturally appropriate lactation services and support.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Another qualitative study that examined infant feeding beliefs and experiences of Marshallese women (one Pacific Islander ethnic group) living in the US reported social factors like acculturation to US customs as a barrier to human milk feeding.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eEvidence suggests that neonatal health care providers can support lactation in the NICU and ameliorate differences in the provision of human milk.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e Culturally competent care delivery, including promoting and supporting human milk feeding in the NICU, may be more effectively implemented in settings with higher cultural familiarity. Previous studies have highlighted that human milk feeding is associated with improved health outcomes for VLBW infants, including lower rates of NEC, better neurodevelopmental outcomes, and lower long-term health care costs.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Therefore, the finding that AANHPI infants in high tertile hospitals had increased rates of human milk feeding at discharge could have significant policy implications for efforts addressing feeding differences in the NICU like the current CPQCC led quality improvement collaborative, MOMMS (Motivating \u0026amp; Optimizing Maternal Milk in Safety Net NICUs), which focuses on improving human milk provision across the state of CA.\u003c/p\u003e\u003cp\u003eCultural Familiarity and Growth Velocity\u003c/p\u003e\u003cp\u003eOur study found that AANHPI infants in high-tertile NICUs had growth rates that were 0.05 g/kg/day higher than those in low-tertile NICUs. While this difference may seem modest, it is clinically meaningful as improvement collaboratives targeting growth velocity often struggle to achieve gains of this magnitude.\u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Growth velocity is an important indicator of neonatal health, as it is associated with long term neurodevelopmental outcomes and overall survival in VLBW infants.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e Previous studies have reported higher rates of low birth weight, small for gestational age at birth and discharge as well as postnatal growth failure among VLBW infants from Asian American populations, compared to White, Hispanic and African American populations.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e The higher growth velocity among AANHPI infants in high tertile NICUs found in our study could be related to more effective care nutrition strategies, which may be influenced by the hospital\u0026rsquo;s cultural familiarity with the specific needs of infants/families from AANHPI populations. Our study highlights the potential benefits of cultural familiarity on extrauterine growth and long-term clinical outcomes. However, it is important to acknowledge that the association between cultural familiarity and growth outcomes is complex, and neonatal growth velocity is influenced by multiple factors, including infant comorbidities, maternal morbidities, environmental factors and social determinants of health.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e While cultural familiarity may play a role, it is likely that other systemic factors such as hospital infrastructure, access to resources, and the presence of standardized feeding guidelines and specialized growth management teams also contribute to these outcomes.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eClinical and Policy Implications\u003c/p\u003e\u003cp\u003e Our study provides valuable insights into the potential role of cultural familiarity in shaping neonatal care and outcomes for AANHPI infants. While the differences observed in feeding and growth outcomes suggest that higher volumes of AANHPI admissions may improve certain aspects of care, the overall significance of these findings should be seen as hypothesis generating. The lack of major differences in other process and outcome measures suggests that while cultural familiarity, as measured by racial/ethnic-specific patient volume, may have its greatest impact in areas of care that require coordinated \u0026ldquo;team effort,\u0026rdquo; such as supporting breastfeeding, where shared cultural understanding may facilitate stronger engagement with families. In contrast, some medical neonatal outcomes, such as NEC and CLD, may be less influenced by cultural awareness and more dependent on standardized clinical protocols.\u003c/p\u003e\u003cp\u003eHowever, given these observed associations in the AANHPI population, future studies should examine whether similar effects of cultural familiarity on patient outcomes are present among other underrepresented groups, such as Black and Hispanic populations to broaden understanding of cultural factors in patient care and outcomes and influence policy changes. In addition, research should explore how structural factors such as staff diversity, language support services, and family-centered care models interact with cultural familiarity to impact infant health outcomes. Studies specifically designed to delineate these pathways should investigate whether potential mediators attenuate these associations, thereby clarifying the mechanisms through which cultural familiarity impacts care and outcomes. Finally, longitudinal studies are needed to determine whether the observed differences in feeding practices and growth velocity translate into lasting health and developmental benefits for AANHPI infants.\u003c/p\u003e\u003cp\u003eThis study has to be viewed in light of its design. First, our use of hospital patient volume as a proxy measure for cultural familiarity is indirect and may not fully capture its complexity. Whether this proxy measure is a valid measure of cultural familiarity needs confirmational research through surveys and interviews. Furthermore, the retrospective nature of the study limits causal inference and does not account for all potential confounders. Additionally, the AANHPI population is highly heterogeneous, and some associations may be unidentified with data aggregation. A key strength, however, is the use of population-based data from the diverse network of California NICUs, which enhances the generalizability of our findings.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study suggests that hospital-level cultural familiarity may influence care and outcomes for AANHPI VLBW infants. Higher AANHPI patient volumes were initially associated with greater human milk use at discharge and higher growth velocity, though the human milk association was attenuated after risk adjustment, indicating maternal and hospital-level factors may play a substantial role. Future research should use disaggregated AANHPI data to identify specific practices and interventions that drive improved outcomes, and prospective studies should examine the roles of provider cultural congruence, community engagement, and targeted care strategies to better address differences in neonatal care for AANHPI infants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRavi D, Iacob A, Profit J. Unequal care: Racial/ethnic disparities in neonatal intensive care delivery. \u003cem\u003eSeminars in Perinatology\u003c/em\u003e. 2021;45(4):151411. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.semperi.2021.151411\u003c/span\u003e\u003cspan address=\"10.1016/j.semperi.2021.151411\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarfield WD. 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Weight growth velocity and growth outcomes in very-low-birth-weight infants developing major morbidities. \u003cem\u003ePediatrics \u0026amp; Neonatology\u003c/em\u003e. 2023;65(2):177\u0026ndash;182. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.pedneo.2022.05.022\u003c/span\u003e\u003cspan address=\"10.1016/j.pedneo.2022.05.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlhamad M, Ben Ayad A, Fugate K, et al. Improving Growth Velocity in Very Low Birth Weight Infants: a Quality Improvement Project. \u003cem\u003ePediatrics\u003c/em\u003e. 2019;144(2_MeetingAbstract):650\u0026ndash;650. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1542/peds.144.2ma7.650\u003c/span\u003e\u003cspan address=\"10.1542/peds.144.2ma7.650\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBagga N, Kiran Kumar Reddy, Mohamed A, Nalinikant Panigrahy, Dinesh Kumar Chirla. Quality improvement initiative to decrease extrauterine growth restriction in preterm neonates. \u003cem\u003eNutrition in clinical practice\u003c/em\u003e. 2021;36(6):1296\u0026ndash;1303. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ncp.10735\u003c/span\u003e\u003cspan address=\"10.1002/ncp.10735\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEhrenkranz RA. Growth in the Neonatal Intensive Care Unit Influences Neurodevelopmental and Growth Outcomes of Extremely Low Birth Weight Infants. \u003cem\u003ePEDIATRICS\u003c/em\u003e. 2006;117(4):1253\u0026ndash;1261. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1542/peds.2005-1368\u003c/span\u003e\u003cspan address=\"10.1542/peds.2005-1368\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee HC, Ramachandran P, Madan A. Morbidity Risk at Birth for Asian Indian Small for Gestational Age Infants. \u003cem\u003eAmerican Journal of Public Health\u003c/em\u003e. 2010;100(5):820\u0026ndash;822. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2105/ajph.2009.165001\u003c/span\u003e\u003cspan address=\"10.2105/ajph.2009.165001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKurtyka K, Gaur S, Mehrotra N, Chandwani S, Janevic T, Demissie K. Adverse Outcomes Among Asian Indian Singleton Births in New Jersey, 2008\u0026ndash;2011. \u003cem\u003eJournal of Immigrant and Minority Health\u003c/em\u003e. 2014;17(4):1138\u0026ndash;1145. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10903-014-0075-y\u003c/span\u003e\u003cspan address=\"10.1007/s10903-014-0075-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJerome M, Chandler-Laney P, Affuso O, Li P, Salas AA. Racial Differences in Growth Rates and Body Composition of Infants Born Preterm. \u003cem\u003eJournal of perinatology: official journal of the California Perinatal Association\u003c/em\u003e. 2022;42(3):385\u0026ndash;388. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41372-021-01305-\u003c/span\u003e\u003cspan address=\"10.1038/s41372-021-01305-\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"414\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"width: 414px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1. AANHPI VLBW patient distribution across hospital tertiles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 54px;\"\u003e\n \u003cp\u003eTertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNumber of NICUs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 294px;\"\u003e\n \u003cp\u003eAANHPI %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 84px;\"\u003e\n \u003cp\u003ePatient level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 210px;\"\u003e\n \u003cp\u003eNICU level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eMedian (Q1-Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e27.9 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e24.3 (18.3-34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e11.8 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e11.5 (9.8-14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.5 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e4.8 (2.6-6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e14.7 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003e11.4 (6.4-18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"880\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" valign=\"bottom\" style=\"width: 880px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2. Care and outcomes by AANHPI volume tertile\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"bottom\" style=\"width: 331px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 271px;\"\u003e\n \u003cp\u003eAll VLBW patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 277px;\"\u003e\n \u003cp\u003eAANHPI VLBW patients\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAANHPI tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eP-value Middle vs. Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eP-value High vs. Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 155px;\"\u003e\n \u003cp\u003eAANHPI tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003eP-value Middle vs. Low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 60px;\"\u003e\n \u003cp\u003eP-value High vs. Low\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003eLow\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eLow\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 331px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvival without major morbidity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e66.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e63.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e62.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001 \u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e67.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e64.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e65.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 331px;\"\u003e\n \u003cp\u003eNo infant death during hospitalization up to age 1 year (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e92.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e91.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e91.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e93.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e91.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e92.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 331px;\"\u003e\n \u003cp\u003eNo chronic lung disease (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e77.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e76.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e76.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e78.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e77.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e77.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 331px;\"\u003e\n \u003cp\u003eNo severe peri-intraventricular hemorrhage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e93.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e91.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e92.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017 \u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002 \u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e94.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e93.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e94.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 331px;\"\u003e\n \u003cp\u003eNo nosocomial infection (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e92.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e91.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e90.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002 \u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e93.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e91.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e91.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 331px;\"\u003e\n \u003cp\u003eNo NEC (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e96.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e95.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e96.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.042\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e96.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e95.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e96.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 331px;\"\u003e\n \u003cp\u003eNo severe retinopathy of prematurity or surgery for retinopathy of prematurity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e93.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e93.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e93.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e92.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e92.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e91.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 331px;\"\u003e\n \u003cp\u003eNo cystic periventricular leukomalacia (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e98.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e97.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e97.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e98.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e97.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e97.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 331px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaby-Monitor Process Measures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003eAny breastmilk at discharge (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e72.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e70.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e60.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e79.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e77.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e69.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003eNo hypothermia (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e90.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e95.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e93.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e90.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e95.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e94.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007 \u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003eAntenatal steroids (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e92.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e92.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e87.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e92.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e92.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e88.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006 \u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001 \u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003eNo HAI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e92.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e91.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e90.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001 \u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e93.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e92.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e91.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003eTimely eye exam (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e97.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e97.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e95.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e97.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e98.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e96.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaby-Monitor Outcome Measures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003eIn hospital survival (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e93.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e92.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e93.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e92.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e93.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003eNo chronic lung disease (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e78.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e79.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e77.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e79.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e79.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e78.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003eNo pneumothorax (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e96.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e96.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e97.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e97.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e96.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e97.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003eGrowth velocity (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e13.2 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e12.9 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e12.8 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013 \u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e13.3 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e12.8 (2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e12.8 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostnatal growth\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003eZ-score at birth (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.41 (1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.33 (1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-0.35 (1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.51 (1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.43 (1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e-0.46 (1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003eZ-score at discharge (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-1.18 (1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-1.24 (1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e-1.23 (1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-1.26 (1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-1.30 (1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e-1.32 (1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003eChange in weight z-score from birth to discharge (mean (SD))\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.78 (0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.92 (0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.87 (0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.76 (0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.87 (0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.89 (0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 331px;\"\u003e\n \u003cp\u003eNo growth failure (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e55.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e46.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e49.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;.001\u003csup\u003e\u0026nbsp;1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e56.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e49.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e48.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001 \u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 880px;\"\u003e\n \u003cp\u003eInclusion criteria: VLBW (GA: 22-29 weeks or 401-1500g).\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 880px;\"\u003e\n \u003cp\u003eExclusion criteria: Infants with delivery room death and death by 12 hours of age.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 880px;\"\u003e\n \u003cp\u003eAnalysis was performed at the admission level.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 880px;\"\u003e\n \u003cp\u003eNumbers were calculated based on non-missing data for each clinical outcome\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 880px;\"\u003e\n \u003cp\u003eWeight z-score at birth and discharge was calculated based on Fenton Growth Chart (2013).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 880px;\"\u003e\n \u003cp\u003eChange in weight z-score from birth to discharge: Defined as z-score at birth - z-score at discharge.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 880px;\"\u003e\n \u003cp\u003ePostnatal growth failure: Defined as change in weight z-score from birth to discharge \u0026gt; 0.8.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\" style=\"width: 880px;\"\u003e\n \u003cp\u003e\u003csup\u003e1\u003c/sup\u003e P-value was significant after adjusting for multiple testing, assuming false discovery rate = 0.05.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7405103/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7405103/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To assess whether cultural familiarity, measured by Asian American, Native Hawaiian, and Pacific Islander (AANHPI) hospital patient volume, is associated with care and outcomes among very low birth weight infants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design:\u003c/strong\u003e We analyzed 43,067 infants, including 6,534 (15.2%) AANHPI infants, from 142 California neonatal intensive care units (NICUs) in the California Perinatal Quality Care Collaborative database (2011–2019). Hospitals were grouped into tertiles by AANHPI VLBW admissions. Outcomes were assessed using multivariable Poisson regression adjusted for infant, maternal, and hospital factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003e Compared to low-tertile NICUs, AANHPI infants in high- and middle-tertile NICUs had greater human milk use at discharge (79.2% and 77.0% vs. 69.6%, p\u0026lt;.001), and those in high-tertile NICUs had higher growth velocity (13.3 vs. 12.8 g/kg/day, p\u0026lt;.001), although attenuated after adjustment. Mortality and major morbidities showed no association with AANHPI patient volume.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Higher AANHPI patient volume was linked to feeding outcomes but not broader neonatal outcomes.\u003c/p\u003e","manuscriptTitle":"Association of Asian American, Native Hawaiian, and Pacific Islander Very Low Birth Weight infants’ Outcomes with Neonatal Intensive Care Unit Cultural Familiarity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:12:35","doi":"10.21203/rs.3.rs-7405103/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32614c20-81da-4b4e-a5ea-975682ec318a","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53524535,"name":"Health sciences/Health care/Paediatrics"},{"id":53524536,"name":"Health sciences/Medical research/Epidemiology"},{"id":53524537,"name":"Health sciences/Health care/Health services"}],"tags":[],"updatedAt":"2025-09-16T14:09:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 10:12:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7405103","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7405103","identity":"rs-7405103","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}