(2024) 24:565
Mitchell et al. BMC Pediatrics
https://doi.org/10.1186/s12887-024-05031-3

BMC Pediatrics

RESEARCH

Open AccessA multi‑state analysis on the effectof deprivation and race on PICU admission
and mortality in children receiving Medicaid
in United States (2007–2014)
Hannah K. Mitchell1*, Joshua Radack2, Molly Passarella2, Scott A. Lorch2,4 and Nadir Yehya3,4

Abstract
Introduction In the United States (US), racial and socioeconomic disparities have been implicated in pediatric intensive care unit (PICU) admissions and outcomes, with higher rates of critical illness in more deprived areas. The degree
to which this persists despite insurance coverage is unknown. We investigated whether disparities exist in PICU
admission and mortality according to socioeconomic position and race in children receiving Medicaid.
Methods Using Medicaid data from 2007–2014 from 23 US states, we tested the association between area level
deprivation and race on PICU admission (among hospitalized children) and mortality (among PICU admissions). Racewas categorized as Black, White, other and missing. Patient-level ZIP Code was used to generate a multicomponent
variable describing area-level social vulnerability index (SVI). Race and SVI were simultaneously tested for associations
with PICU admission and mortality.
Results The cohort contained 8,914,347 children (23·0% Black). There was no clear trend in odds of PICU admission
by SVI; however, children residing in the most vulnerable quartile had increased PICU mortality (aOR 1·12 (95%CI
1·04–1·20; p = 0·0021). Black children had higher odds of PICU admission (aOR 1·04; 95% CI 1·03–1·05; p < 0·0001)
and higher mortality (aOR 1·09; 95% CI 1·02–1·16; p = 0·0109) relative to White children. Substantial state-level variation
was apparent, with the odds of mortality in Black children varying from 0·62 to 1·8.
Conclusion In a Medicaid cohort from 2007–2014, children with greater socioeconomic vulnerability had increased
odds of PICU mortality. Black children were at increased risk of PICU admission and mortality, with substantial statelevel variation. Our work highlights the persistence of sociodemographic disparities in outcomes even among insured
children.
Keywords Pediatric critical care, Health equity, Health care disparities, Deprivation, Racism, Medicaid, Pediatrics*Correspondence:
Hannah K. Mitchellhannahkmitchell@gmail.com
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To
view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Mitchell et al. BMC Pediatrics(2024) 24:565

Introduction
Background

There is substantial evidence from the United States(US) of disparities in incidence and outcome of childhood critical illness by race, ethnicity and socioeconomic
position [1, 2]. Disparities in mortality according to race
(as a social construct and a proxy for individual or systemic racism) have been reported in critically ill children
with oncologic diagnoses and sepsis [1, 3–5]. However,
the association of race with mortality has not been consistently reported, and inclusion of severity of illness orrelated socioeconomic variables, such as insurance status
and access to healthcare, can affect the reported relationship [6, 7]. Admission to the pediatric intensive care unit
(PICU) is often used either as evidence of illness severity,and occasionally as inclusion criteria for a study. However, this creates a selection bias if PICU admission itself
is impacted by race. Evidence regarding disparities in
PICU admission according to race and/or ethnicity show
mixed results [8–10]. Overall, accuracy of recorded ofrace, ethnicity and socioeconomic position, unintentional
selection biases, and inconsistent control for confounders
contribute to conflicting results across studies [11, 12].
One important plausible confounder of the association between race and outcomes (PICU admission or
mortality) is the social vulnerability of the area where achild resides, which is intrinsically linked to racism andsocioeconomics in the US [13]. The most common reasons for PICU admission are respiratory illness [14, 15],congenital illness [14], and injury or poisoning [14, 15].
Prior studies from the US have shown higher rates of
PICU admission in children residing in more socioeconomically deprived areas [16–19], and certain common
PICU diagnoses, specifically severe asthma and traumatic
injuries, are also associated with socioeconomic deprivation [20, 21].
Prior research has yielded conflicting evidence on
the role of neighborhood-level socioeconomic position and race and/or ethnicity on rates of PICU admissions and outcomes, highlighting a significant gap in our
understanding of these dynamics [1, 2]. This ambiguity underscores the need for targeted investigation into
how specific racial disparities influence these healthcare
metrics.
Objectives

We analyzed a multistate Medicaid database, aiming
to isolate the effects of social vulnerability and race on
PICU admission and mortality. We reasoned that usinga Medicaid database would restrict our analysis to children with a baseline level of access to public insurance,thus minimizing access due to insurance as a potential

Page 2 of 11

confounder or mediator of any reported associations.
Recognizing Medicaid’s state-level administration, we
also consider the potential for state-specific variations
to elucidate the complex interplay between state policy,
cultural milieu, sociodemographic factors and outcomes.
We acknowledge the significant changes in healthcare
access and policy, particularly post-ACA, and the shifting
perceptions regarding race as a social construct following
recent societal events. Although this study looks at data
which is 10 years old, this paper can still provide a usefulbenchmark for future research using more recent data.
We hypothesised that racial disparities exist in PICU
admission rates and mortality, influenced by area-levelsocioeconomic vulnerability.

Methods
Design and settingThis was a population-based, retrospective cohort study,
using Medicaid Analytic eXtract (MAX) data maintainedby the Centers for Medicare and Medicaid Services
from the years 2007 to 2014. MAX includes person-level
claims on Medicaid demographics, eligibility, and utilization on individuals with qualifying socioeconomic
status. Medicaid, one of the largest healthcare payers in
the United States, covers approximately 37% of children.
We chose to use MAX rather than more recent (2015and later) Transformed Medicaid Statistical Information
Systems (T-MSISs) because MAX provided extensive
quality-control, data validation, and harmonization at
the federal level, permitting a cleaner multistate analysis;by comparison, T-MSIS data quality and validation varies by state. We included states based on data availability
and quality for the years studied. Quality was assessed by
how complete the data were longitudinally over time, andwhether managed care claims were available in the dataset, versus just including information on enrolment ofchildren in a managed care plan [22, 23]. Variables wereassessed using individual variable quality reports [24].
Data sources

We used Medicaid data from 23 US states (Arizona,
Arkansas, Connecticut, Georgia, Hawaii, Indiana, Iowa,
Louisiana, Michigan, Minnesota, Mississippi, Missouri,
New York, Ohio, Oklahoma, Oregon, South Dakota, Tennessee, Utah, Vermont, Virginia, Washington and Wyoming) from the years 2007 to 2014.
Participants

The study population included patients under 21 years ofage (who had not yet reached their 21st birthday) in the
MAX database from 2007 to 2014. Neonates managed
in the NICU were excluded from the study by excluding
patients with the neonatal CPT codes (appendix 1).

Mitchell et al. BMC Pediatrics(2024) 24:565

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PICU population was defined by using CPT codes andrevenue codes (appendix 1). Revenue codes have beenshown in the adult population to be a sensitive and specific way of identifying an ICU population [25].

Sample size

Variables

Statistical methods

The co-primary independent variables under investigation were area-level social vulnerability index (SVI) andrace. We selected SVI because it encompasses a comprehensive set of indicators across multiple domains of socioeconomic status, which allowed us to see what aspects
of deprivation are more closely associated with PICU
admission and outcome. Socioeconomic position was
defined using patient-level ZIP Code to extract socioeconomic variables from the CDC/ATSDR SVI, allowing us
to generate a multicomponent variable describing arealevel SVI [26]. The SVI uses 16 variables from the US
census data to determine the social vulnerability of every
census tract. Variables are grouped into the following
four themes. 1) socioeconomic (below poverty, unemployed, income, no high school diploma, 2) householdcomposition/disability (aged 65 and over, aged 17 andyounger, civilian with a disability, single-parent household), 3) minority status/language (minority, speaks
English “Less than Well”), 4) housing type/transportation (multi-unit structures, mobile homes, crowding, novehicle, group quarters). We report composite SVI as themain area-level socioeconomic exposure, but also separately analyzed each of the four themes above. A higher
SVI score indicates higher vulnerability (quartile 1 least
vulnerable and quartile 4 most vulnerable).
Race was categorized as Black, White, Other, or Missing. These were examined separately in univariable
analyses and then included together in models. Due tosubstantial state-level inconsistencies regarding theavailability and accuracy of ethnicity coding (Hispanic/
Latino versus not Hispanic/Latino), we could not reliably
capture Hispanic/Latino ethnicity. Hispanic patients areincluded in the “other” category.
The primary outcome was any PICU admission during the 8 years of the analysis. The secondary outcome
was mortality (patient disposition codes 20,40,41 and 42
were flagged as death) in an analysis restricted to PICU
patients.
Other included variables based on relevant individual variables (age categories, sex, presence of complex
chronic condition (as defined by Feudtner [27]) reason
for being on Medicaid (poverty, expansion waver, CASH
or other). Federal Information Processing System (FIPS)
codes (numbers which uniquely identify counties andcounty equivalents in the United States average population 100,000) were included as a random effect in theoverall and state-specific models.

A pragmatic approach was taken, and all eligible
records were used. No formal sample size calculation
was done.
Descriptive statistics were used to characterize thecohort. For composite SVI and race, and separately foreach of the four component SVI themes, univariate andmultivariable logistic regression were used to test for
association with PICU admission and PICU mortality.
SVI and race were modelled together in the main analyses. FIPS (i.e., local geographic area) was modelled as arandom effect. We performed separate regressions forthe entire cohort and individually by state to investigate state-level variability. In the analysis by state somemodels failed to converge with FIPS as a random effectdue to small numbers in some local geographic areas;in those states, effect estimates are presented without
accounting for FIPS.
A pre-planned interaction tests were done to assess
whether there was any evidence of effect modificationof race by SVI.
The amount of missing data on race and ethnicity
varied substantially by state, with South Dakota andConnecticut recording no missing data on race or ethnicity and Iowa recording 50·5% patients with missing
ethnicity. Most states had < 10% missing data (supplemental Table 1 and supplemental Fig. 1)· There was no
missing data for patient age and sex, and 1.7% of thecohort were missing data on ZIP Code. The percentage of records missing area level socioeconomic variables was 9·8% which again varied substantially by state(supplemental Table 1). Because of conceptual limitations introduced by imputing missing race or SVI withincomplete information (e.g., without surnames), we
restricted our analyses to analyses where race or SVI
were not missing. Analyses were done using SAS version 9.4 (2016) and R Core Team (2022).
Ethics approval and consent to participate

The Institutional Review Board of The Children’s
Hospital of Philadelphia reviewed this project anddeclared that it met the criteria for non-human subjects research, compliant with the terms outlined in adata use agreement with the Centers for Medicare andMedicaid Services for use of the data. Because patientswere not contacted or identified in the dataset, consent
to participate was waived by the Institutional Review
Board of The Children’s Hospital of Philadelphia.

Mitchell et al. BMC Pediatrics(2024) 24:565

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Results
Participants

The final cohort included 8,914,347 patients of twenty
years of age or less, of whom 425,330 were admitted to
PICU between 2007 and 2014 (Table 1). Data from 23
states were included. New York contributed the largest number of patients (n = 1,675,492) and Vermont theleast (n = 24,359).
Description of the cohort

Most children in the cohort were less than 1 year of age(57·3% n = 5,106,440), with the median age of the PICU
cohort (5 years [IQR 1–15 years]) being older than the
non-PICU cohort (median 0 years [IQR 0–13 years]).
The prevalence of chronic complex condition was significantly higher in the PICU cohort (69·1%) compared
to children not admitted to the PICU (15·9%). Mortality
in the PICU cohort was 2·2% compared to 0·1% in theremainder of the cohort (Table 1). In the entire cohort,
23.0% had race recorded as Black and 42·8% as White,
11·9% of the cohort were missing data on race and/or
ethnicity, with substantial variation between states (supplemental Fig. 1).

When restricted to PICU admissions, non-survivors
were significantly more likely than survivors to be lessthan 1 year of age (30·5% vs 22·1% respectively) and tohave a chronic complex condition (92·4% vs 68·6%)(Table 2). Among PICU survivors, 26·8% were of Black
race, while 43·4% were White. Among the children who
died, 27·2% were of Black race and 41·8% were White.
There was a clear association between SVI and race:
across all states, a higher proportion of Black children
resided in the most vulnerable SVI quartile compared to
White (Fig. 1).
Outcomes according to composite SVI

When investigating the association between PICU admission and composite SVI, in univariable analysis there
were reduced odds of PICU admission for children living in more deprived quartiles, this was not significant in
multivariable analysis (Table 3). Children residing in themost vulnerable quartile had increased PICU mortality(aOR1·12, 95%CI 1·04–1·20; p = 0·002) (Table 4). In theanalysis looking at the association of the most vulnerable
composite SVI quartile and PICU admission and mortality across states, there was some variability, although not

Table 1 Baseline characteristics of entire cohort by PICU admission status (n=9,439,919)
Not admitted to PICU

Admitted to PICU

8,489,017

425,330nAge (years)(%)

nP-value(%)<1 year5,011,529(59.04%)

94,931(22.32%)

1-5

804,781(9.48%)

122,935(28.9%)

6-12

543,735(6.41%)

64,780(15.23%)

13-16

532,461(6.27%)

53,054(12.47%)<0.0001

17-20

1,596,511(18.81%)

89,630(21.07%)

Presence of chronic complex condition

1,350,678(15.91%)

293,791(69.07%)<0.0001

Died in hospital

10,680(0.13%)

9,189(2.16%)<0.0001

Foster child

137,136(1.62%)

14,012(3.29%)<0.0001

Male sex

3,735,086(44%)

225,299(52.97%)<0.0001

Year

Black

1,938,628(22.84%)

114,044(26.81%)

White

3,628,472(42.74%)

184,277(43.33%)All other race

1,919,397(22.6%)

72,418(17.0%)

Missing

1,002,520(11.81%)

54,591(12.83%)

2007

1,033,412(12.17%)43,244(10.17%)2008

1,030,862(12.14%)45,099(10.6%)

2009

1,094,879(12.9%)

53,508(12.58%)2010

1,023,096(12.05%)52,890(12.44%)2011

1,107,725(13.05%)57,768(13.58%)2012

1,107,683(13.05%)59,039(13.88%)2013

1,084,014(12.77%)57,539(13.53%)

2014

1,007,346(11.87%)56,243(13.22%)<0.0001

Mitchell et al. BMC Pediatrics(2024) 24:565

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Table 2 Baseline characteristics of PICU cohort by mortality (n = 425,330)
Did not die

Died416,141

9,189

n
Age (years)(%)

nP-value(%)< 1 year

92,131(22.14%)2,800(30.47%)

1–5

120,572(28.97%)2,363(25.72%)

6–12

63,638(15.29%)1,142(12.43%)

13–16

51,976(12.49%)1,078(11.73%)< 0.0001

17–20

87,824(21.1%)

1,806(19.65%)Presence of chronic complex condition

285,304(68.56%)

8,487(92.36%)< 0.0001

Foster child

13,750(3.3%)

262(2.85%)

0.0161< 0.0001

Male sexBlack

220,056(52.88%)5,243(57.06%)

111,548(26.81%)

2,496(27.16%)

White

180,441(43.36%)

3,836(41.75%)All other race

70,894(17.0%)

1,524(16.6%)

Missing

53,258(12.8%)

1,333(14.51%)

as pronounced as for race (supplemental Fig. 2) for PICU
admission.
Outcomes according to individual SVI themes

In the analysis exploring the association between individual SVI themes, PICU admission, and risk of mortality(supplementary Table 2), four key themes were examined: socioeconomic (theme 1), household composition/
disability (theme 2), minority status/language (theme 3),
and housing type/transportation (theme 4). Divergent
results between themes were found for both outcome
measures.
For Theme 1 (socioeconomic)multivariable analysis,
patients showed increasing odds of PICU admission withhigher degrees of vulnerability. When testing the association between social vulnerability and mortality amongchildren admitted to PICU there were non-significant
trends towards increased mortality in the more vulnerable quartile compared to the least for socioeconomic
vulnerability.
In Theme 2 (household composition/disability), analyses indicated a trend towards lower odds of PICU admission in the more vulnerable quartile. The most vulnerable
quartile exhibited a 5% decrease in the odds of admission
compared to the least vulnerable quartile (aOR 0·95; 95%
confidence interval [CI] 0·94–0·97, p < 0·0001). There wasa non-significant trend towards increased mortality inmore vulnerable quartiles.
In the analysis for Theme 3 (minority status/language),the most deprived quartile showed a 2% reduction in theodds of PICU admission compared to the least deprived
quartile (aOR 0·98; 95% CI 0·96–1·00, p = 0·0179).
No significant trend was observed in the analysis for

Theme 4 (housing type/transportation). Significantly
increased mortality was seen in children residing in themost vulnerable quartile for housing type/transportation compared to the least (aOR 1·09; 95%CI 1·00–1·19
p = 0·0421).
Outcomes according to race

In the fully adjusted multivariable models for all states(with SVI included in the model), Black race was associated with higher odds of PICU admission (aOR 1·04;
95% CI 1·03–1·05; p < 0·0001) (Fig. 2, Table 3). Blackrace was associated with 1·08 increased odds of mortality once admitted to PICU, compared to White (95% CI
1·01–1·15; p = 0·0176) (Fig. 3, Table 4). When looking at
racial disparities in admission and mortality by state, significant variance was seen (Figs. 2 and 3). The state withthe highest odds of mortality for Black children, relative
to White children, had nearly double the mortality odds
(aOR 1·75; 95% CI 1·04–2·97 p = 0·04). There was no evidence of interaction between race and deprivation for theoutcome of PICU mortality (p = 0.898). For the outcomeof PICU admission the test is significant for the inclusionof an interaction (p < 0.0001. Results are presented stratified in the supplemental material (supplemental Table 3).

Discussion
In our large nationally representative study using datafrom 2007–2014, we found disparities by race and by SVI
for PICU admissions and mortality rates in children covered by Medicaid. Black children had higher odds of both
PICU admission and increased risk of mortality once
admitted to PICU, even when conditioned on Medicaid
and controlling for SVI. Relative odds of PICU admission

Mitchell et al. BMC Pediatrics(2024) 24:565

Fig. 1 Proportion of Black and White Children in the Most Deprived Social Vulnerability Quartile by State

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Mitchell et al. BMC Pediatrics(2024) 24:565

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Table 3 Univariable and multivariable association of composite social vulnerability index and race with PICU admission (quartile 1
indicates least vulnerable with quartile 4 being the most vulnerable)
Univariable
Quartile
Composite SVI

Race

OR

Multivariable*
95% CI

P-valueOR

95% CI

P-value

1

ref

2

0.981

0.971

0.991

0.0002

ref
1.01

1.00

1.02

0.2616

3

0.975

0.964

0.985

<0.0001

0.99

0.98

1.00

0.1604

4

0.982

0.971

0.993

0.0011

0.99

0.98

1.00

0.1222

White

ref

Black

1.11

1.1

1.12

<0.0001

1.04

1.03

1.05

<0.0001

All other race

0.91

0.9

0.92

<0.0001

0.97

0.96

0.98

<0.0001

Missing

1.18

1.17

1.2

<0.0001

1.02

1.01

1.03

0.0035*adjusted for other variables in the table, reason for being on Medicaid (CASH, expansion waver, poverty, other), sex, age, and presence of chronic complex condition

Table 4 Univariable and multivariable association of social vulnerability index with PICU mortality (quartile 1 indicates least vulnerablewith quartile 4 being the most vulnerable)
Univariable
OR
Composite SVI

Race

Multivariable*
95% CI

P-valueOR

95% CI

P-value

1

ref

2

1.01

0.94

1.08

0.8092

1.04

ref
0.97

1.11

0.3126

3

1.01

0.95

1.08

0.7072

1.03

0.97

1.11

0.3381

4

1.09

1.02

1.17

0.0122

1.12

1.04

1.20

0.0021

White

ref

ref

Black

1.05

0.99

1.11

0.1135

1.08

1.01

1.15

0.0176All other race

1.03

0.96

1.1

0.436

1.02

0.95

1.1

0.6056

Missing

1.17

1.09

1.25

<0.0001

0.97

0.90

1.05

0.4418*adjusted for other variables in the table, reason for being on Medicaid (CASH, expansion waver, poverty, other), sex, age, and presence of chronic complex condition

and mortality in Black children varied according to thestate in which they were admitted, with some states
showing over 50% increased odds of mortality in Blackchildren. Race was significantly associated with neighborhood SVI, with Black children more likely to reside in
more vulnerable ZIP Codes. There was increased mortality observed in PICU in children living in the most vulnerable areas after accounting for race, with higher odds
of mortality in children residing in the most vulnerable
quartile of housing type/transportation (Theme 4) whenanalyzing SVI components.
We found limited evidence of an association between
PICU admissions and worsening SVI in our multistate
analysis, with slightly increased odds of PICU admissionin children in the most vulnerable quartile for socioeconomics (Theme 1) and lower odds of PICU admission
in the most vulnerable quartile for household composition/disability (Theme 2) and minority status/language(Theme 3). One prior study investigating the area surrounding a single major children’s hospital found that

census tracts with the highest rates of childhood poverty
had the highest rates of PICU admissions and most PICU
bed-day rates [16]. Additionally, worse socioeconomic
status has been associated with higher rates of asthma
and injury [14, 15], which are two of the most common
reasons for admission to the PICU [20, 21]. Deeper analyses into whether specific medical diagnoses or ability toaccess care despite insurance (e.g., household composition, vehicle ownership) drive associations with PICU
admission (either higher or lower odds) are warranted.
Specifically, an assessment as to whether household composition/disability and minority status/language represent barriers to recognition of illness severity, and thus
lower odds of PICU admission, are indicated.
Our study confirms strong overall and regional trends
in racial disparities in outcomes of hospitalized children
across the US, with worse outcomes for Black children[3, 4]. The persistence of racial disparities in this cohort,
despite restricting to a Medicaid cohort and adjusting
for area-level SVI, suggests that racism, either systemic,

Mitchell et al. BMC Pediatrics(2024) 24:565

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Fig. 2 Multivariable odds of PICU admission in Black children with White children as reference by state (n = number of hospital admissions for state)(entire cohort n = 9,439,919)

institutional, or personal, is a strong residual mechanism
behind the disparities found. Importantly, the interrelatedness of race, socioeconomic deprivation, healthcare access, and related factors makes it challenging todetermine which variables are driving worse outcomes inBlack children. The state-level disparities suggest possible
effects of state-level variation in Medicaid implementation or in patient case-mix, suggesting that higher PICU
admissions and higher mortality for Black children may
be modifiable by state-level policies. Our study timeperiod ends at the time of the 2014 Affordable Care Act(ACA) Medicaid expansion, and future studies assessing longitudinal rates of PICU admission and mortality
in Black children in states with and without Medicaid
expansion are necessary. Medicaid expansion has been
associated with some improved insurance coverage inpediatric oncology in expansion states [28]. Overall, theinteraction between states’ economic metrics, racial
demographics and Medicaid policy remains understudied in pediatrics.

It is notable that while some signal towards worse
outcome is apparent in children from more socially vulnerable areas, this is not particularly pronounced. There
are several plausible explanations for this. Patient-levelsocioeconomic position and community SVI, while correlated, are not precise analogues [29]. Area-level SVI
may not be a true marker of individual socioeconomic
position, risking ecological bias. This dataset is also
conditioned on patients who are on Medicaid and so isselecting a more homogenous group, and residual socioeconomic differences may not be as apparent within
this dataset. Many children’s hospitals are located inhigh poverty areas with high needs [30], and it is possible that this might mitigate some of the pejorative
effects of socioeconomic disparities due to improved
access to care, thereby explaining the limited evidence
for disparities found by area-level SVI. Finally, modelling SVI and race together risks over-adjusting, andthus potentially biasing the causal association between
SVI (and race) and outcomes.

Mitchell et al. BMC Pediatrics(2024) 24:565

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Fig. 3 Multivariable odds of PICU mortality in Black children with White children as reference by state (PICU cohort n = 476,886)

Our study had some important limitations. As routine
data there may be coding errors and misclassification ofrace/ethnicity, socioeconomic position or outcome. We
identified our pediatric intensive care cohort using CPT
billing codes and it is possible that these may not accurately capture the PICU population; however, we can see
clear differences in this cohort, such as higher mortality
and greater number of complex chronic condition, making it plausible that this cohort reflects a typical PICU
cohort. We also were not able to identify patients withmultiple readmissions, risking inappropriate precision of
our effect estimates. As an administrative dataset, there
was a substantial amount of missing data, and inconsistency with coding Hispanic/Latino patients meant wecould not reliably capture patients with Hispanic/Latino
ethnicity. Not analyzing Hispanic/Latino children separately limits our understanding of the unique healthcare
challenges and disparities faced by this demographic
group. Hispanic/Latino children represent a significant
and growing portion of the pediatric population in the
United States, and prior research has shown that theyoften face unique barriers to healthcare access, such as
language barriers, and potential immigration-relatedfears, which can all contribute to disparities in health outcomes [31]. By grouping Hispanic/Latino children withother races/ethnicities, we may not capture the specificfactors influencing their PICU admissions and outcomes,
potentially overlooking critical areas for intervention andpolicy improvement.
Studies have shown that patients who are missing dataon race/ethnicity differ systematically from those whodo, such as being more likely to be low income [32]. It is
unclear what resources are dedicated to collecting andcleaning this data, with some data being gathered by selfreport and other data being assigned with varying accuracy [33]. To account for this missing data, we included
patients missing data as a category in our analysis andpresent results for this group. We are also only able tocapture PICU mortality, and as such our study potentially
underestimates mortality, although is likely a reasonable estimate of hospital mortality. Pre-hospital mechanisms contributing to childhood mortality, such as from

Mitchell et al. BMC Pediatrics(2024) 24:565

accidents or injuries, could not be assessed, including if
subjects died in an emergency department and were nothospitalized.
The limitations of our study are partly offset by some
important strengths. We use a large dataset including
over 4 million patients over 7 years. The dataset allowed
us to look at ZIP Code-level socioeconomic indicators using a multi-themed metric. Although this data
is 10 years old it is the most complete set of multi-state
Medicaid data available, making it potentially more generalizable. This study provides a landscape of the state
disparities in PICU prior to expansion of Medicaid, andwill provide a baseline for future studies to assess for
improvements. Additionally, the dataset includes diagnostic codes allowing us to identify and adjust for children with chronic complex conditions, which is an
important confounder. [28] A range of states are represented geographically making our study more representative than many single-center and regional databases.
Medicaid covered approximately 26.8% of children in
the US in 2008, increasing to 37.7% in 2014, providing
robust demographic information on individuals with low
socioeconomic status at the national level, allowing us
to account for the geographic heterogeneity in the USA
with respect to racial disparities and outcome [34].

Conclusion
Children covered by Medicaid of Black race were atincreased risk of PICU admission and mortality, with
substantial state-level variation, emphasizing geographic variability as a major driver of systemic racism
in Pediatrics. Race was associated with neighborhoodsocioeconomic vulnerability, and children in areas
with higher levels of vulnerability had higher mortality. Further work is needed to develop targeted policy
and healthcare interventions to reduce racial and socioeconomic disparities. Future research should focus on
evaluating the effectiveness of any interventions andexploring strategies to ensure equitable healthcare
access. A decade of Medicaid access in some of thesestates will provide a natural experiment for assessing
whether disparities were impacted. Accurate documentation of a child’s race/ethnicity in administrative
records is essential to understand if healthcare disparities occur. The Transformed Medicaid Statistical Information System (T-MSIS) the current largest national
database of children’s Medicaid race/ethnicity datacollection quality is low of for the majority of states.
T-MSIS could improve its data collection on racial disparities by standardizing race and ethnicity categories
and formatting, and supporting complete and accurate
data collection across states to understand the racial

Page 10 of 11

disparities experienced by Black children. Our study
findings underscore the complex interplay between
race, social vulnerability and outcomes, emphasizing
the need for comprehensive assessment and tailoredinterventions to address disparities in PICU utilizationand outcomes.
Abbreviations
ATSDR	Agency for Toxic Substances and Disease Registry
CDC	Centers for Disease Control and Prevention
CPT	Current Procedural Terminology
FIPS	Federal Information Processing SystemMAX	Medicaid Analytic eXtract
PICU	Pediatric intensive care unit
SVI	Social vulnerability index
US	United States

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12887-​024-​05031-3.
Supplementary Material 1.
Authors’ contributions
HM and NY conceptualized and designed the study, drafted the initial manuscript, and reviewed and revised the manuscript. SL helped devise the statistical analysis plan and reviewed and revised the manuscript for important intellectual content. JR and MP coordinated data management and carried out theinitial analyses, drafted methods, and critically reviewed the manuscript forimportant intellectual content. All authors approved the final manuscript as
submitted and agree to be accountable for all aspects of the work.
Funding
Nadir Yehya is supported by National Institutes of Health (NIH) grant
number K23-HL136688.
Hannah Mitchell is supported by a National Institute for Health and Care
Research Academic Clinical Fellowship.
Availability of data and materials
The datasets generated and/or analyzed during the current study are from theMedicaid Analytic eXtract (MAX) repository. These data are publicly accessible
but require an application for access through the Research Data AssistanceCenter (ResDAC). To apply for access or find more information, please visit the
ResDAC website at https://​resdac.​org and navigate to the ’Get Data’ section.

Declarations
Ethics approval and consent to participate
The Institutional Review Board of The Children’s Hospital of Philadelphia
reviewed this project and declared that it met the criteria for non-humansubjects research, compliant with the terms outlined in a data use agreement with the Centers for Medicare and Medicaid Services for use of the data.
Because patients were not contacted or identified in the dataset, consent toparticipate was waived by the Institutional Review Board of The Children’s
Hospital of Philadelphia.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Author details
1
Institute of Child Health, University College London, London, UK. 2 Divisionof Neonatology, Department of Pediatrics, Children’s Hospital of Philadelphia,
Philadelphia, USA. 3 Department of Anesthesiology and Critical Care Medicine,Mitchell et al. BMC Pediatrics(2024) 24:565

Children’s Hospital of Philadelphia, Philadelphia, USA. 4 Leonard Davis Instituteof Health Economics, University of Pennsylvania, Philadelphia, PA, USA.
Received: 7 May 2024 Accepted: 23 August 2024

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