Previous research suggests increasing numbers of and variation in NICU admissions. We explored whether these trends were reflected in California by examining NICU admissions and birth data in aggregate and among patient and hospital subpopulations more susceptible to variations in care.
In this retrospective cohort study, we evaluated NICU utilization between 2008 and 2018 for all live births at hospitals that provide data to the California Perinatal Quality Care Collaborative. We compared hospital- and admission-level data across birth weight (BW), gestational age (GA), and illness acuity categories. Trends were analyzed by using linear regression models.
We identified 472 402 inborn NICU admissions and 3 960 441 live births across 144 hospitals. Yearly trends in NICU admissions remained stable among all births and higher acuity births (mean admission rates 11.9% and 4.1%, respectively). However, analysis of the higher acuity births revealed significant increases in NICU admission rates for neonates with higher BW and GA (BW ≥ 2500g: 1.8% in 2008, 2.1% in 2018; GA ≥ 37 weeks: 1.5% in 2010, 1.8% in 2018). Kaiser hospitals had a decreasing trend of NICU admissions compared to non-Kaiser hospitals (Kaiser: 13.9% in 2008, 10.1% in 2018; non-Kaiser: 11.3% in 2008, 12.3% in 2018).
Overall NICU admission rates in California were stable from 2008–2018. However, trends similar to national patterns emerged when stratified by infant GA, BW, and illness acuity as well as Kaiser or non-Kaiser hospitals, with increasing admission rates for infants born at higher BW and GA and within non-Kaiser hospitals.
Advances in the field of neonatology have transformed birth outcomes, enabling newborns with complex medical conditions to receive life-saving care in the NICU. However, research has revealed changes in NICU utilization patterns over the years. A national study reported increases in the rate of NICU admissions from 2007 to 2012.1 Compared to years past, neonates of higher gestational age (GA), greater birth weight (BW), and lower illness acuity are being admitted more frequently to NICUs.2 Although we know that NICU care can improve outcomes for high-risk infants, the question remains whether this changing population of NICU infants reflects appropriate stewardship of resources.
In addition to trends of increased NICU admissions, several studies have reported variations in clinical management across practice settings: the Dartmouth Atlas reported considerable differences between states in NICU admission rates for moderately low and normal BW newborns.2,3 In California, the percentage of inborn NICU admissions for neonates born at ≥34 weeks GA varied 34-fold between hospitals.4 Such large variations in care and practice patterns raise concerns because infants that may have less to gain from the NICU’s highly specialized care are still exposed to its risks, including iatrogenic complications, parental emotional distress, and poorer breastfeeding patterns.5–8 On the other hand, some populations may benefit from NICU admission but are not receiving appropriate services because of lack of available resources.9–12 It is important to investigate the outcomes of greater BW and GA newborns that may be most at risk for practice variations and understand how admissions patterns have evolved to produce this different composition of NICU patients.
Although previous studies have predominantly studied these patterns and variations in care in aggregate, there remains a need to examine the temporal trends in NICU admissions on a population-based level and identify hospital or patient subpopulations that may be contributing to these changing practice patterns.3,13–16 Braun et al previously described trends in NICU utilization for newborns within the Kaiser Permanente Southern California health care system, revealing a decrease from 2010 to 2018 in NICU admissions without an increase in readmissions or mortality.17 As a large California integrated health care delivery system, Kaiser Permanente’s population-based financial payment structure positions it as both health care provider and payer. Accordingly, Kaiser hospitals may be uniquely incentivized to safely reduce medical care that does not provide value for patients and families.18 Comparing patterns of care between Kaiser and non-Kaiser hospitals would offer insights into how NICU admission practices may be impacted by aligned financial incentives compared to management by separate health insurers or payers.
The aim of this study was to evaluate NICU admissions in California over a 10-year study period (2008–2018), in aggregate and across GA, BW, and illness acuity categories, as well as between Kaiser and non-Kaiser hospitals. We hypothesized that NICU admission trends in California would reflect the increasing rates nationwide and that infants of higher BW and GA would be increasingly admitted over the study period, suggesting a pattern of greater care utilization in these patients over time.
Methods
This retrospective cohort study used linked data of hospitals participating in the California Perinatal Quality Care Collaborative (CPQCC), which includes overall birth and NICU admission data, as well as more detailed outcomes of neonates with higher acuity medical needs. This latter population comprises approximately one-third of all NICU admissions. In California, all hospitals with an accredited NICU are required to report data on live births and NICU admissions to California Children’s Services (CCS) annually. CPQCC collects additional variables on overall NICU admissions and outcomes that it shares with CCS for all CCS-accredited NICUs. NICUs that are not CCS-accredited can additionally submit their data to CPQCC to gain insight on their NICU operations and statistics. Although the number of participating hospitals varies year to year, CPQCC collected data from 144 partner hospitals in California during the study period, 131 of which are CCS-approved. We estimate our analysis collectively represents >90% of NICU admissions in California.19
We examined inborn NICU admissions only, meaning NICU admissions of in-hospital births, as to minimize differences in management patterns that may be affected by transfers of care. We also limited our study population to live births at hospitals that directly participated in the CPQQC and CCS NICU database, which includes 3 960 441 of the 5 293 310 total live births in California in the 2008 to 2018 study period. Live births that resulted in delivery room death or death within the first 12 hours were excluded. We also excluded CPQCC and CCS hospitals without onsite births.
The inborn NICU admission rates and CPQCC-eligible inborn NICU admission rates were defined as their fraction among all live inborn births. CPQCC eligibility was used as a measure for higher illness acuity because by definition infants had to meet specific criteria for inclusion. Although all infants with a BW of 1500 g or less or a GA of <32 weeks are automatically considered as high acuity, infants with a BW >1500 g are also eligible for CPQCC data collection and classified as high acuity if at least 1 of the following criteria is met: intubated- or nonintubated-assisted ventilation for 4 hours or more, early bacterial sepsis, major surgery requiring anesthesia, acute transport in to the NICU, suspected encephalopathy or suspected perinatal asphyxia, active therapeutic hypothermia, or death. The percentage of infants meeting the CPQCC definition of high illness acuity was then calculated.
Proportions of inborn NICU admission rates were similarly calculated for the high-acuity subpopulation as stratified by infant characteristics (GA <37 weeks vs ≥37 weeks, BW <2500 g vs ≥2500 g) or hospital type (defined as Kaiser vs. non-Kaiser). Information on infant BW was available over the entire study period, whereas infant GA was only available from 2010 to 2018.
Patient and hospital characteristics were summarized using descriptive statistics. CCS levels of care are assigned by CCS according to publicly available standardized definitions and American Academy of Pediatrics (AAP) levels of care are self-reported by each NICU on the basis of the level of services they provide in comparison with the AAP’s outlined tiers.20,21 We performed trend analyses with Cochran-Armitage trend tests and used linear regression models across the available study period to examine the significance of statewide trends in live births and inborn admission rates. P values < 0.05 were considered statistically significant. All analysis was conducted by using SAS statistical software version 9.4 (SAS Institute, Cary, NC).
The need for informed consent was waived by the institutional review board at our institution because the data set contained no individual patient identifiers.
Results
During the study period of 2008 to 2018, there were 3 960 441 live births in California that were included in the hospitals covered by CPQCC. Among these live births, 472 402 births were designated as inborn NICU admissions, representing a NICU admission rate of 11.9%. Furthermore, 162 091 inborn NICU admissions were classified as high acuity (CPQCC-eligible), representing 4.1% of all live births. These births and NICU admissions occurred at 144 unique hospitals in California. In 2018, data were obtained from 131 of these hospitals, representing a range of CCS levels of care, NICU sizes, and hospital type designations; 16% of the hospitals were designated as Kaiser Northern California or Kaiser Southern California. The payer mix among non-Kaiser hospitals was ∼56.6% California Medical Assistance Program (Medi-Cal), 36.4% private insurance, 2.7% self-pay, 2.6% other government assistance, and 1.7% other funding sources. Table 1 further describes the sampling from the overall California birth cohort and hospital characteristics.
Study Population Sample During 2008 to 2018 Study Period, Birth and Hospital Characteristics
Sample Characteristics . | No. of Live Births . |
---|---|
California live birth cohort | 5 293 310 |
Live births at all GAs at CPQCC/CCS birth hospitals with NICUs | 3 960 441 |
Live births at GA <34 wka | 90 078 |
Inborn NICU admissions at all GA | 472 402 |
Inborn NICU admission at GA <34 wka | 87 818 |
Inborn NICU admission at any GA that is CPQCC-qualifying (high illness acuity or severity) | 111 137 |
Hospital Characteristics | No. of Hospitals (%) |
Total no. of hospitals in California live birth cohort | 349 |
Cumulative no. of unique CPQCC/CCS hospitals with onsite births | 144 unique OSHPD IDs across 2008–2018 study period |
CCS level of careb | |
Regional | 18 (14) |
Community | 84 (64) |
Intermediate | 15 (11) |
Non-CCS | 14 (11) |
AAP level of careb | |
1 | 2 (2) |
2 | 22 (17) |
3 | 85 (65) |
4 (designated for 2012–2018) | 19 (14) |
None of the above | 3 (2) |
Number of NICU bedsb | |
0 | 10 (8) |
1–10 | 15 (11) |
11–25 | 69 (53) |
26–50 | 28 (21) |
51–75 | 6 (5) |
75+ | 2 (2) |
Undefined no. | 1 (1) |
Perinatal region (all captured)b | |
North Coast East Bay | 10 (8) |
Northeastern | 9 (7) |
San Joaquin-Central Valley-Sierra Nevada | 9 (7) |
Midcoastal | 12 (9) |
Southern Inland Counties | 12 (9) |
Central-North LA-Coastal Valley | 29 (22) |
LA-San Gabriel-Inland Orange | 10 (8) |
South Coastal LA-Orange | 8 (6) |
San Diego and Imperial | 11 (8) |
Kaiser North | 7 (5) |
Kaiser South | 14 (11) |
Sample Characteristics . | No. of Live Births . |
---|---|
California live birth cohort | 5 293 310 |
Live births at all GAs at CPQCC/CCS birth hospitals with NICUs | 3 960 441 |
Live births at GA <34 wka | 90 078 |
Inborn NICU admissions at all GA | 472 402 |
Inborn NICU admission at GA <34 wka | 87 818 |
Inborn NICU admission at any GA that is CPQCC-qualifying (high illness acuity or severity) | 111 137 |
Hospital Characteristics | No. of Hospitals (%) |
Total no. of hospitals in California live birth cohort | 349 |
Cumulative no. of unique CPQCC/CCS hospitals with onsite births | 144 unique OSHPD IDs across 2008–2018 study period |
CCS level of careb | |
Regional | 18 (14) |
Community | 84 (64) |
Intermediate | 15 (11) |
Non-CCS | 14 (11) |
AAP level of careb | |
1 | 2 (2) |
2 | 22 (17) |
3 | 85 (65) |
4 (designated for 2012–2018) | 19 (14) |
None of the above | 3 (2) |
Number of NICU bedsb | |
0 | 10 (8) |
1–10 | 15 (11) |
11–25 | 69 (53) |
26–50 | 28 (21) |
51–75 | 6 (5) |
75+ | 2 (2) |
Undefined no. | 1 (1) |
Perinatal region (all captured)b | |
North Coast East Bay | 10 (8) |
Northeastern | 9 (7) |
San Joaquin-Central Valley-Sierra Nevada | 9 (7) |
Midcoastal | 12 (9) |
Southern Inland Counties | 12 (9) |
Central-North LA-Coastal Valley | 29 (22) |
LA-San Gabriel-Inland Orange | 10 (8) |
South Coastal LA-Orange | 8 (6) |
San Diego and Imperial | 11 (8) |
Kaiser North | 7 (5) |
Kaiser South | 14 (11) |
no., number.
Data not available until 2010.
Data representative of 131 total hospitals in 2018.
The rate of live births and NICU admissions did not demonstrate significant variability across the study period (Fig 1, p for trend = .31). The admission rate remained stable, with an average rate of admission of 11.9% (SD 0.3%).
Trends in total inborn NICU admissions and CPQCC high-acuity admissions among total live births, 2008–2018.
Trends in total inborn NICU admissions and CPQCC high-acuity admissions among total live births, 2008–2018.
Over the study period, the rate of high-acuity admissions (defined as CPQCC-qualifying) remained stable at an average of 4.1% of all live births (SD 0.2%) and did not reveal a significant trend (p for trend = .10). This reflects a constant proportion of newborns among all live births that met CPQCC high-acuity criteria and warranted NICU admission.
Further stratifying the CPQCC-qualifying cohort by infant characteristics reinforced a constant admission rate among newborns born at <2500 g BW or <37 weeks GA, with no significant trend over the study period (p for trend = .62 and p for trend = .09, respectively). However, subanalyses of infants born at greater GA and BW revealed increasing admission patterns over the study period. Among high-acuity newborns with BW ≥2500 g, the NICU admission rate increased significantly from 1.8% of live births in 2008 to 2.1% of live births in 2018 (Fig 2a, p for trend = .01). Similarly, among high -acuity newborns born at ≥37 weeks GA, the NICU admission rate increased significantly from 1.5% of live births in 2010 to 1.8% of live births in 2018 (Fig 2b, p for trend = .004).
Trends in composition of CPQCC high-acuity admissions by newborn risk factors. A, Proportion of admissions by birth weight, 2008–2018. B, Proportion of admissions by gestational age, 2010–2018.
Trends in composition of CPQCC high-acuity admissions by newborn risk factors. A, Proportion of admissions by birth weight, 2008–2018. B, Proportion of admissions by gestational age, 2010–2018.
For all live births that resulted in inborn NICU admissions, hospital type impacted the trends in admission patterns over time (Fig 3a). The proportion of inborn admissions decreased among Kaiser hospitals, with an inborn admission rate of 13.9% in 2008 compared to 10.1% in 2018 (p for trend < .001). For non-Kaiser hospitals, these rates increased over the study period from 11.3% in 2008 to 12.3% in 2018 (p for trend = .003).
Trends in composition of NICU admissions by hospital type (Kaiser and non-Kaiser hospitals). A, Proportion of total inborn NICU admissions, 2008-2018. B, Proportion of CPQCC high-acuity admissions, 2008–2018.
Trends in composition of NICU admissions by hospital type (Kaiser and non-Kaiser hospitals). A, Proportion of total inborn NICU admissions, 2008-2018. B, Proportion of CPQCC high-acuity admissions, 2008–2018.
These trends persisted for the subpopulation of high-acuity newborns born at Kaiser hospitals; the CPQCC-qualifying inborn admission rate increased from 3.1% in 2008 to 4.4% in 2018 (Fig 3b, p for trend < .001). When evaluating high-acuity newborns born at non-Kaiser hospitals, however, the overall admission rate did not change (Fig 3a, p for trend = 0.34). This trend among non-Kaiser hospitals is consistent with the broader admission trends among all newborns (Fig 1).
When stratified on the basis of AAP level of care designations, we did not identify significant trends in NICU admissions based on increasing level of care. Trend analysis on the basis of CCS level of care identified no significant changes for community NICUs, which make up the majority of the study cohort. For the remaining NICUs, there was an increase in admissions for regional NICUs (p for trend = .005) but no significant change for intermediate NICUs.
Discussion
This retrospective, population-based cohort analysis leverages a statewide network of California’s NICUs to examine temporal trends in NICU admission patterns. The findings reveal overall stable admission patterns statewide, with a mean admission rate of 11.9% of live births for 2008 to 2018. The admission rate for infants classified as higher acuity was similarly stable at 4.1% across this study period. These temporal trends offer a different perspective on NICU admission trends compared to 2 studies that also examined how admission patterns have developed over time: (1) Harrison and Goodman’s retrospective analysis of birth certificate data representing 72.9% of all births nationwide, which found increased overall admission rates from 6.4% in 2007 to 7.8% in 2012 and increasing admission rates for all BW categories,1 and (2) Braun et al’s cohort study from Kaiser Permanente Southern California hospitals, which contrasted national trends in reporting that admission rates to the NICU decreased from a mean of 14.5% in 2010 to 10.9%. in 2018.17
A strength of our study is the systematic data collection in California NICUs through CPQCC.22 The overall NICU admission rates identified in our study were similar to rates found in Braun et al of 10.9% to 14.5%, as well as other nontemporal studies.2,4,23 The lower rates found by Harrison and Goodman can perhaps be explained by their usage of birth certificate data, which limits NICU admissions to infants admitted to AAP-designated level III or IV nurseries and can contain potential miscoding, as discussed in Braun et al.
Our analyses also revealed different trends in NICU admission rates when we stratified the cohort by GA, BW, and illness acuity categories, as well as between Kaiser and non-Kaiser hospitals. For high-acuity newborns born at higher BW and GA, the admission rate increased significantly. These findings build on previous studies that have reported variations in admission patterns for newborns with fewer medical indications for admission.13,16,24 Although these studies focused on newborns that may be considered as lower acuity, a previous study from CPQCC using the same shared population database for the 2015 calendar year identified a 34-fold variation in percentage of high illness acuity infants born at GA ≥34 weeks admitted to the NICU.4 Our study reinforces the theory that increasing admission rates are driven by admission practices for infants born at higher GA and BW and illustrates how these variations have emerged over time.
Although our cumulative analyses of admission rates statewide suggested a constant NICU admission rate, our investigation into the role of hospital type on admission rates unveiled a different trend: from 2008 to 2018, the rate of inborn admissions increased for non-Kaiser hospitals but decreased among Kaiser hospitals. These findings concur with Braun et al’s study highlighting decreasing admission rates among the Kaiser Permanente Southern California health system and shed light on the larger context of admission practices at different hospital types within California. Although a changing case mix of insurance coverage may be 1 reason for the admission patterns in Kaiser hospitals, Kaiser’s integrated managed care model may reflect uniquely aligned incentives to decrease NICU admissions, as opposed to other health systems wherein provider and payer are separate. This positions the Kaiser system as one that may be particularly motivated to reduce costs. Braun et al postulate with their risk adjustment model that the changes in NICU admissions are more likely associated with changes in decision-making around NICU care. These include systemwide quality improvement initiatives and protocols implemented during the study period that aimed to reduce unnecessary admissions for higher GA and BW subgroups, including a revised policy that raised the threshold for routine NICU admissions of well-appearing preterm infants NICU admissions to <35 weeks GA and <2000 g BW. In addition, risk assessment tools such as The Kaiser Neonatal Sepsis Calculator and the Eat Sleep Console function-based assessment tool for neonatal opioid withdrawal syndrome helped to facilitate clinical decision-making around an infants’ individual risk of and need for more intensive care services.25–28 Lastly, it is possible that variable adoption of policies aimed to reduce NICU admissions, such as provision of phototherapy or dextrose gel in well newborn nurseries, may contribute to different NICU utilization practices.29
Although admission rates did increase over the study period for non-Kaiser hospitals, our results suggest relatively slower rates of increase in admission patterns compared to national trends.1 Compared to a national study that identified an overall increase in NICU admission rates of 37% from 2008 to 2018,30 our California-focused study showed that the NICU admission rate increased by 1.7% when comparing 2008 to 2018, although the overall trend in the study period was not significant. State-wide collaboratives such as CPQCC aim to standardize care practices across its network with a combination of education, quality improvement, and research initiatives. Our study demonstrates the importance of examining admission patterns at a more granular geographic, hospital-based, or patient- and family-based level to understand sources of change or variability in newborn clinical management. Furthermore, the substantial difference in proportion of overall admissions compared to high-acuity admissions is an important area for further investigation. A previous study from CPQCC has shown that less than 15% of admissions ≥34 weeks were high acuity,4 reinforcing the need to enhance current epidemiologic and state quality databases to better capture the care practices for more mature and lower-risk newborns.31 Racial and ethnic disparities in quality measures, outcomes, and family experiences exist both across and within NICUs.32–37 Further work is needed to examine whether inconsistent resource allocation may be perpetuating these inequities. One area for further exploration would be to incorporate analyses that account for patient demographics and the social determinants of health to investigate disparities in NICU practice patterns. Our findings raise the question whether changing admission patterns may disproportionately impact medically underserved and historically minoritized populations, and how this may lead to increased health care burden and suboptimal health outcomes. More work is needed to ensure evidence-based, equitable medical decision-making that facilitates better quality of care and reduced mortality and morbidity. Upholding the “Choosing Wisely” approach of minimizing unnecessary medical intervention and escalation cannot only reduce costs and health care waste,38 but also potentially benefit the health and wellbeing of infants and their whole families, preserving the mother-infant dyad in the first days and weeks of life and promoting well-researched outcomes such as breastfeeding and bonding that facilitate beneficial long-term outcomes.5–8,39
We recognize several limitations of this study. First, our database lacks individual patient-level data that may be found in medical record data. However, use of this data source allows us to examine population-level trends at a larger scale, thus improving its generalizability beyond single-institution or single-health care system studies. Additionally, our interpretation of overall trends and differences by hospital type may be impacted by changes in patient cohorts over time; because of the limited information available at an individual patient level, we were not able to examine the case mix of each subpopulation. Interpretation of these trends should be considered in the context of overall trends in preterm birth rates, which have increased over time largely because of a rise in late preterm births.40 Lastly, although our study period predated the onset of the coronavirus disease 2019 pandemic, the pandemic may have additionally altered patterns of NICU admissions and overall newborn hospitalizations. Early studies of the aftermath of the pandemic onset have identified mixed findings on the role of the pandemic on preterm birth rates, and its impact on hospitalization pattern remains an important area of future study.41,42
Conclusions
We found that the NICU admission rate remained constant in California from 2008 to 2018, differing from previously published studies that revealed increasing NICU admission rates nationwide in relation to the live birth rate. However, we found that this general population-level analysis may mask variabilities in admission practices among newborns classified as higher acuity: among this subpopulation, admission patterns remained stable for lower BW and GA infants but in fact increased for infants born at higher BW and GA. Admission rates increased at non-Kaiser hospitals but decreased at Kaiser hospitals, suggesting that the overall constant admission rates may be attributable to a combination of these opposing practice patterns. Further investigation is needed to evaluate what systemwide initiatives and developments may have contributed to these shifting trends, and how these practice patterns translate to patient outcomes. Additionally, it remains important to examine how variations in NICU utilization may be reflective of and contribute to health disparities and what quality improvement efforts and protocols can ensure appropriate and equitable care.
COMPANION PAPER: A companion to this article can be found online at www.hosppeds.org/cgi/doi/10.1542/hpeds.2023-007473.
Ms Pang conceptualized and designed the study, selected data for inclusion in analyses, analyzed the data, interpreted the results, drafted the initial manuscript, and critically reviewed and revised the manuscript; Dr Liu selected data for inclusion in analyses, analyzed the data, helped to interpret the results, and critically reviewed and revised the manuscript; Dr Lu selected data for inclusion in analyses and critically reviewed and revised the manuscript; Drs Joshi and Gould helped to interpret the results and critically reviewed and revised the manuscript; Dr Lee conceptualized and designed the study, selected data for inclusion in analyses, interpreted the results, drafted the initial manuscript, and critically reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
FUNDING: Ms Pang is supported by the Stanford Medical Scholars Research Fellowship. Dr Joshi is supported by the Stanford Maternal and Child Health Research Institute, National Institute for Child Health and Human Development (1F32HD106763-01A1), and the Gerber Foundation. The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of sponsors. Sponsors were not involved in the study design, data collection, data analysis, interpretation of data, writing of the report, or the decision to submit the article for publication.
CONFLICT OF INTEREST DISCLOSURES: The authors have no conflicts of interest relevant to this article to disclose.
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