Abstract
Background
With recent prioritization of equity in pediatric health outcomes, a shift to examine neighborhood-level health care disparities within pediatric populations has occurred, specifically in the context of critical illness.
Research Question
Does an association exist between individual indicators of neighborhood-level disadvantage and incidence of PICU admission?
Study Design and Methods
Pediatric patients younger than 18 years admitted to a PICU in a large urban tertiary pediatric hospital from January 1, 2016, through December 31, 2019, with a residential address in the city of Baltimore or Baltimore County on the day of admission were included in this ecological study. Demographic and clinical characteristics of children admitted to the PICU were summarized, with the primary outcome being PICU admission. Unadjusted negative binomial regression was used to examine the association between census tract-level PICU admissions and the previously described census tract-level indicators of neighborhood socioeconomic position. Regression models included an offset term for the population younger than 18 years for each census tract; results of models are reported as incidence rate ratios (IRRs) with corresponding 95% CIs.
Results
We identified 2,476 PICU admissions: 1,351 patients from the city of Baltimore (10.25 per 1,000 children) and 1,125 patients from Baltimore County (6.31 per 1,000 children). Most PICU admissions (n = 906 [68%]) for the city of Baltimore represented an area deprivation index (ADI) of > 60, whereas most Baltimore County PICU admissions (n = 919 [82.3%]) represented an ADI of < 60. At the neighborhood level, the percentage of families living below the poverty line was associated with greater incidence of PICU admission in the city of Baltimore (IRR, 1.09; 95% CI, 1.00-1.18) and Baltimore County (IRR, 1.19; 95% CI, 1.05-1.36). For every $10,000 increase in median household income, PICU admission rates dropped by 9% for the city of Baltimore (IRR, 0.91; 95% CI, 0.86-0.95) and Baltimore County (IRR, 0.91; 95% CI, 0.88-0.94). Neighborhoods with vacant housing units also were associated with a higher incidence of PICU admission in the city of Baltimore (IRR, 1.10; 95% CI, 1.01-1.21) and Baltimore County (IRR, 1.46; 95% CI, 1.21-1.77), as was a 10% increase in occupied homes without vehicles (city of Baltimore: IRR, 1.14; 95% CI, 1.07-1.21; Baltimore County: IRR, 1.23; 95% CI, 1.11-1.37).
Interpretation
Health outcomes of pediatric critical illness should be examined in the context of structural determinants of health, including neighborhood-level and environmental characteristics.
Key Words: disparities; health outcomes; neighborhood-level characteristics; pediatric critical care, neighborhood-level disparities
Graphical Abstract
FOR EDITORIAL COMMENT, SEE PAGE 1341
Take-home Points.
Research Question: Does an association exist between individual indicators of neighborhood-level disadvantage and incidence of PICU admission?
Results: In regression analyses examining the association of census tract-level PICU admissions and selected indicators of neighborhood socioeconomic position, housing quality, access to transportation, median income, education, and employment all are associated with incidence of PICU admission.
Interpretation: Neighborhood-level inequities in transportation infrastructure, housing quality, education, and financial disadvantage are a few key drivers related to disparities associated with increased incidence of PICU admission.
Health care disparities—unjust and preventable differences in the health status and health outcomes of minoritized and marginalized populations—have become evident in our most vulnerable pediatric populations.1, 2, 3, 4, 5, 6, 7 Such disparities, through systemic and structural racism,8 concentrated poverty, redlining,9, 10, 11 housing and environmental injustice,10 and racial and ethnic segregation,8,10,12 have been the foundation of neighborhood infrastructure and their subsequent inequities, from education to health. Medical literature has described the association between socioeconomic status and health outcomes,8,13,14 more recently highlighting the association between poverty and pediatric critical illness.2,12,15,16 Although race is a social construct and should not be considered a risk factor for poor health outcomes, it is important to understand the interconnectedness of race, ethnicity, and socioeconomic status with neighborhood-level health care inequities.2,3,15,16 With the prioritization of equity in pediatric health,17 the focus has shifted to include structural and neighborhood-level determinants as key drivers of inequity, specifically in the context of pediatric critical illness.18, 19, 20
Emerging evidence highlights the use of social deprivation and opportunity indexes21, 22, 23 to explore and describe associations between neighborhood-level determinants and pediatric health inequities.24, 25, 26, 27, 28 These composite indexes provide a broad multidimensional understanding of neighborhood-level socioeconomic position (SEP), whereas single neighborhood-level markers of SEP may be more specific, conclusive, and easier to interpret.28 Additionally, no individual indexes have been validated specifically to examine incidence of PICU admission.
Understanding the role of neighborhood-level markers of SEP on PICU admission warrants attention by the medical community and will inform future studies exploring unmet critical sociomedical needs at the neighborhood level, with a goal of improving pediatric health and health care value.29,30 Identifying specific markers of neighborhood-level SEP that are associated with increased incidence of PICU admission persists as a knowledge gap in the literature.
This study investigated associations between individual indicators of neighborhood-level SEP and incidence of PICU admission; PICU admission was the primary outcome. This study described PICU admissions (on the individual level) in the context of Area Deprivation Index (ADI) and the association between incidence of PICU admission and markers of neighborhood-level deprivation. We hypothesized that the incidence of PICU admission is disproportionately higher among disadvantaged neighborhoods with high rates of poverty and unemployment, low rates of education, lack of access to vehicles, and poor housing quality.
Study Design and Methods
Study Design
We conducted an ecological study to determine whether census track-level indicators of socioeconomic position are associated with PICU admission rates. We included all pediatric patients younger than 18 years who were admitted to a single urban tertiary care center PICU. The study time frame was from January 1, 2016, through December 31, 2019, and included only patients who resided within the city of Baltimore or Baltimore County on the day of PICU admission. We identified PICU admissions using the institutional Precision Medicine Analytic Platform, a data repository that pulls data from the electronic medical record and several research databases across the health system. PICU admissions included transfers from our center’s ED and inpatient acute care floor, as well as transfers from referring hospitals. PICU admission was identified as first (incident) admission to the Johns Hopkins PICU during the study period; repeat PICU admissions were not included. Exclusion criteria included patients older than 18 years, patients admitted to the pediatric cardiac ICU, and those admitted outside of the study time frame or residing outside of the city of Baltimore or Baltimore County at the time of PICU admission. This study was approved by the institutional review board (Identifier: 00245816) of the Johns Hopkins School of Medicine.
Setting
The Johns Hopkins Children’s Center is an urban tertiary pediatric care center located in the city of Baltimore and is the only level 1 pediatric trauma center in Maryland. Both the city of Baltimore and Baltimore County were selected for inclusion criteria, representing the major catchment areas for the Johns Hopkins PICU. The city of Baltimore is an independent city that functions governmentally at the place level and is considered a county equivalent. The city of Baltimore and Baltimore County are two separate entities covering different geographic areas in Maryland. The city of Baltimore, based on centrality and density, is considered an urban area, whereas Baltimore County is considered primarily suburban with few rural areas.
PICU Admissions
Patient admissions were identified using the Precision Medicine Analytic Platform, which mirrors clinical data from the electronic medical record and includes all patients in the PICU. Extracted data elements included patient identifiers (date of birth, patient name, medical record number), demographic characteristics (race, ethnicity, sex, primary language [English vs non-English]), admission data (admission and discharge dates from the PICU, length of stay, geocoded residential address), and clinical data (insurance type; International Classification of Diseases, 10th Revision, billing codes for encounter; Pediatric Logistic Organ Dysfunction score within 24 h of admission). The racial categories included Black, White, Asian, and Other. Patients identifying as Pacific Islander, Native American, and Hawaiian were categorized as Other because of the small numbers and potential for identification. The racial category of Other additionally represents all mixed race or multiracial patients; this large category potentially reflects the dynamic nature of an individual’s social ethnic identity. Ethnicity was characterized by Hispanic and non-Hispanic. Residential addresses at time of admission were recorded by hospital admission coordinators and encoded as 15-digit Federal Information Processing Series geographic codes representing census geographic data by Alteryx (Alteryx, Inc.); these were validated and corrected using the TomTom (TomTom NA, Inc.) geographic information systems database and the US Postal Service Coding Accuracy Support System (US Postal Service). The ADI,21 a multidimensional evaluation of neighborhood-level deprivation, was acquired from the Neighborhood Atlas and Wisconsin School of Medicine21 and was appended to each patient based on geocoded census block. Both state and national ADI were used in the descriptive analysis; the state scale measures 1 to 10 and the national scales measure 1 to 100, with higher numbers indicating greater degree of deprivation.
Census Tract-Level Data
The total number of PICU admissions during the study period was determined for each census tract based on the child’s residential address as recorded in the electronic medical record. The total population of children younger than 18 years within each census tract was collected from the US Census 2015 American Community Survey. In addition, we identified a priori neighborhood-level SEPs, including percent of families living below the poverty line, median household income, percent unemployment in those older than 16 years, median household education, percent of households without access to a car, percent of vacant homes, and percent of home structures built by decade. These markers of SEP were chosen because they represent the top five input domains of 15 commonly used social deprivation indexes used in the United States.28
Statistical Analysis
Demographic and clinical characteristics of children admitted to the PICU were summarized and compared between the city of Baltimore and Baltimore County using the Student t test or Mann-Whitney U test for continuous variables and the χ2 or Fisher exact tests for categorical variables. Unadjusted negative binomial regression was used to examine the association between census tract-level PICU admissions and the previously described census tract-level indicators of neighborhood SEP. Regression models included an offset term for the population younger than 18 years for each census tract; results of models are reported as incidence rate ratios (IRRs) with corresponding 95% CIs. Census tract-level PICU admission rates were plotted using ArcGIS version 10.4 software (ESRI). A P value of < .05 was considered statistically significant for all comparisons. All analyses were performed using STATA version 16.1 software (StataCorp).
Results
A total of 2,476 unique patient PICU admissions were identified during the study period, of which 1,351 (10.25 per 1,000 children < 18 years of age) were from the city of Baltimore and 1,125 (6.31 per 1,000 children) were from Baltimore County (Table 1). Census tract PICU incidence rates for the city of Baltimore and Baltimore County are presented in Figure 1.
Table 1.
Demographics of the Study Population for the City of Baltimore and Baltimore County
| Variable | Overall (n = 2,476) | City of Baltimore (n = 1,351) | Baltimore County (n = 1,125) | P Value |
|---|---|---|---|---|
| Age, y | ||||
| Median (IQR) | 4.3 (1.0-10) | 4.3 (1.2-10.7) | 4.3 (0.8-10.7) | .394 |
| No. (%) | .388 | |||
| < 5 | 1,322 (53.4) | 720 (53.3) | 602 (53.5) | |
| 5-9 | 477 (19.3) | 275 (20.4) | 202 (18.0) | |
| 10-14 | 337 (13.6) | 175 (13.0) | 162 (14.4) | |
| 15-18 | 340 (13.7) | 181 (13.4) | 159 (14.1) | |
| Sex | .993 | |||
| Male | 1,433 (57.9) | 782 (57.9) | 651 (57.9) | |
| Female | 1,043 (42.1) | 569 (42.1) | 474 (42.1) | |
| Race | < .001 | |||
| Black | 1,411 (57.0) | 981 (72.6) | 430 (38.2) | |
| White | 662 (26.7) | 189 (14.0) | 473 (42.0) | |
| Asian | 69 (2.8) | 14 (1.0) | 55 (4.9) | |
| Other | 326 (13.2) | 163 (12.1) | 163(14.5) | |
| Declined/Unknown/Missing | 8 (0.3) | 4 (0.3) | 4 (0.4) | |
| Ethnicity | .516 | |||
| Hispanic | 248 (10.0) | 127 (9.4) | 121 (10.8) | |
| Non-Hispanic | 2,205 (89.1) | 1,212 (89.7) | 993 (88.3) | |
| Declined/Unknown | 23 (0.9) | 12 (0.9) | 11 (1.0) | |
| Language | .482 | |||
| English | 2,256 (91.1) | 1,226 (90.8) | 1,030 (91.6) | |
| Other | 220 (8.9) | 125 (9.3) | 95 (8.5) | |
| PICU LOS, d | 1.5 (0.9-2.9) | 1.4 (0.8-2.6) | 1.6 (0.9-3.2) | < .001 |
| PELOD-2 score | 2 (2-3) | 2 (2-3) | 2 (2-3) | .054 |
| Mortality | 58 (2.3) | 23 (1.7) | 35 (3.1) | .021 |
Data are presented as No. (%) or median (interquartile range), unless otherwise indicated. IQR = interquartile range; LOS = length of stay; PELOD-2 = Pediatric Logistic Organ Dysfunction Score.
Figure 1.
Map showing PICU admissions per 1,000 children for the city of Baltimore and Baltimore County census tracts, 2016 through 2020.
Patients younger than 5 years made up 53.4% of PICU admissions (n = 1,322), consistent with PICU trends across the United States.31 Male children accounted for 57.9% (n = 1,433) of the admissions over the study period. Black patients were admitted with more frequency (n = 1,411 [57%]) than other racial groups, which is higher than the national average.31 Per census data, the racial composition of the city of Baltimore is 62% Black, 30% White, and 5% Hispanic,32 and that of Baltimore County is 30% Black, 60% White, and 6% Hispanic.32 A small percentage of patients (0.3% [n = 8]) declined to provide or were missing racial data. Ten percent (n = 248) of the study population identified as Hispanic. Most of the study population (91.1% [n = 2,256]) identified as primary English speakers.
Clinical Data
The median Pediatric Logistic Organ Dysfunction score for the overall population was 2 (interquartile range [IQR], 2-3), with no difference between the city of Baltimore and Baltimore County populations (P < .054). The median length of stay in the PICU was 1.5 days (IQR, 0.88-2.79 days), that for the city of Baltimore was 1.4 days (IQR, 0.83-2.63 days), and that for Baltimore County was 1.6 days (IQR, 0.92-3.17 days). Patient mortality within the overall study population was 58 deaths (2.3%), that for the city of Baltimore was 23 deaths (1.7%), and that for Baltimore County was 35 deaths (3.1%; P < .021).
Neighborhood Deprivation
On a national level (ADI scale, 1-100), the overall median ADI was 56 (IQR, 37-78). The PICU admissions from the city of Baltimore represented more disadvantaged individuals (median ADI, 75; IQR, 56-89]) compared with Baltimore County (median ADI, 39; IQR, 27-54) (Table 2). Most (n = 906 [68%]) PICU admissions from the city of Baltimore were from census tracts with an ADI of > 60, whereas most (n = 919 [82.3%]) of PICU admissions from Baltimore County were from less disadvantaged neighborhoods (ADI, < 60). On the state level (scale of 1-10), the overall study population showed a median ADI of 9 (IQR, 7-10). The PICU admissions from the city of Baltimore represented more disadvantaged neighborhoods (median ADI, 10; IQR, 9-10) compared with Baltimore County (median ADI, 7; IQR, 5-8) (Table 3).
Table 2.
Study Population National ADI
| National ADIa | Overall (n = 2,476) | City of Baltimore (n = 1,351) | Baltimore County (n = 1,125) | P Value |
|---|---|---|---|---|
| Median (IQR) | 56 (37-78) | 75 (56-89) | 39 (27-54) | < .001 |
| No. (%) | < .001 | |||
| 0-20 | 230 (9.4) | 50 (3.8) | 180 (16.1) | |
| 21-40 | 525 (21.4) | 109 (8.2) | 416 (37.3) | |
| 41-60 | 593 (24.2) | 270 (20.2) | 323 (28.9) | |
| 61-80 | 549 (22.4) | 383 (28.7) | 166 (14.9) | |
| 80+ | 554 (22.6) | 523 (39.2) | 31 (2.8) |
ADI = Area Deprivation Index; IQR = interquartile range.
National ADI values range from 1 to 10.
Table 3.
Study Population State ADI
| State ADIa | Overall (n = 2,476) | City of Baltimore (n = 1,351) | Baltimore County (n = 1,125) | P Value |
|---|---|---|---|---|
| Median (IQR) | 9 (7-10) | 10 (9-10) | 7 (5-8) | < .001 |
| No. (%) | < .001 | |||
| 1-2 | 112 (4.6) | 19 (1.4) | 93 (8.3) | |
| 3-4 | 174 (7.1) | 40 (3.0) | 134 (12.0) | |
| 5-6 | 289 (11.8) | 59 (4.4) | 230 (20.6) | |
| 7-8 | 571 (23.3) | 184 (13.8) | 387 (34.7) | |
| 9-10 | 1,305 (53.2) | 1,033 (77.4) | 272 (24.4) |
ADI = area deprivation index; IQR = interquartile range.
State ADI values range from 1 to 10.
Neighborhood-Level Indicators
Results of regression analyses examining the association of census tract-level PICU admissions and selected indicators of neighborhood SEP are presented in Table 4. Each 10% increase in the percentage of families living below the poverty line was associated with a 9% higher PICU admission rate in the City of Baltimore and a 19% higher PICU admission rate for Baltimore County. A $10,000 increase in median household income was associated with a 9% lower PICU admission rate in the city of Baltimore (IRR, 0.91; 95% CI, 0.86-0.95) and Baltimore County (IRR, 0.91; 95% CI, 0.88-0.94). Similarly, a 10% increase in occupied homes without vehicles was associated with higher PICU incidence in the city of Baltimore (IRR, 1.14; 95% CI, 1.07-1.21) and Baltimore County (IRR, 1.23; 95% CI, 1.11-1.37). A 10% increase in the percentage of vacant housing units in the city of Baltimore (IRR, 1.10; 95% CI, 1.01-1.21) and Baltimore County (IRR, 1.46; 95% CI, 1.21-1.77) was associated with higher PICU admission rates.
Table 4.
Negative Binomial Regression of Neighborhood-Level Sociodemographic Indicators With the Census Tract Rate of PICU Admissions for the City of Baltimore and Baltimore County
| Neighborhood-Level Sociodemographic Indicators | City of Baltimore PICU IRRa (95% CI) | Baltimore County PICU IRRa (95% CI) |
|---|---|---|
| Families below poverty level, % | 1.09 (1.00-1.18) | 1.19 (1.05-1.36) |
| Median household income, per $10,000 increase | 0.91 (0.86-0.95) | 0.91 (0.88-0.94) |
| Unemployed, 16 years of age or older, % | 1.06 (0.93-1.21) | 1.49 (1.25-1.79) |
| Without high school diploma, older than 18 years, % | 1.16 (1.04-1.30) | 1.37 (1.22-1.54) |
| Occupied housing without vehicle, % | 1.14 (1.07-1.21) | 1.23 (1.11-1.37) |
| Housing units that are vacant, % | 1.10 (1.01-1.21) | 1.46 (1.21-1.77) |
| Housing units built before 2010, % | 1.09 (0.69-1.74) | 2.03 (1.34-3.08) |
Bold represents significance. IRR = incidence rate ratio.
Reflects a 10% increase unless otherwise noted.
Discussion
Our study evaluated the association between PICU incidence and neighborhood-level indicators of SEP, including household income, education, vehicle access, and housing quality. We found that several indicators of neighborhood-level socioeconomic disadvantage in the city of Baltimore and Baltimore County were associated with higher PICU incidence. Within Baltimore County, all neighborhood-level indicators examined in the study (the percentage of families living below the poverty line, median household income, percentage of unemployed and without a high school diploma, percent of occupied houses without a vehicle, percent of vacant homes, and percent of homes built before 2010) were associated with increased PICU incidence. Within the city of Baltimore, median household income, percent without a high school diploma, percent of occupied houses without a vehicle, and percent of vacant homes were associated with increased PICU incidence. These differences may suggest variability in infrastructure and resource access among urban (the city of Baltimore) and suburban and rural (Baltimore County) populations.
These differences in rates of PICU admission fall along sociodemographic constructs, varying among privileged and disadvantaged neighborhoods, resulting in health disparities. This study continues to support foundational evidence that neighborhood-level deprivation and neighborhood-level poverty are associated increased PICU use.2 We add to the growing body of health disparities literature showing that several markers of neighborhood disadvantage, including housing quality, access to transportation, median income, education, and employment, are associated with incidence of PICU admission.
ICU admissions represent some of the most ill pediatric patients with some of the highest health care costs and resource use. Increased rates of pediatric mortality have been associated with socioeconomic disadvantage.33,34 Individual-level economic disadvantage within the PICU population has been associated with more critical care hospital days and increased hospital charges.3 It is estimated that by eliminating health disparities for minoritized populations, the US health system could save billions of dollars in direct medical care and trillions of dollars in indirect costs.29 Minimizing racial, ethnic, and socioeconomic health disparities, or a combination thereof within the pediatric critically ill population, may lead to less morbidity and mortality and may minimize health care expenditures.
Discerning patterns of neighborhood health care disparities in relationship to PICU admissions may lead to hospital system-level and community-level interventions to ameliorate the burden of pediatric critical illness.8 Targeting interventions first requires an understanding of the key drivers of health disparities. Multiple factors, including structural and intermediary determinants, affect how health systems influence health equity, as highlighted by the World Health Organization framework for understanding the impact of social determinants of health.35, 36 Variable sociodemographic factors operating on distinct levels (eg, individual, family, and neighborhood) affect the health status of individuals.37,38
This study additionally focused on the significance of place-based demographics in evaluation of disparities in PICU admissions and is generalizable to PICUs locally, regionally, and nationally. Geographic granularity on the census tract level allows for potential targeted interventions to improve pediatric health.39, 40, 41 The neighborhood-level indicators of socioeconomic position specific to health and health services were explored at the census tract level. However, more research is needed to establish a standard unit of place or neighborhood to explore pediatric critical care health disparities and community-level interventions.40
The interplay of individual-level and neighborhood-level disparities and PICU use is complex and likely multifactorial. Our findings indicate that inequities in transportation infrastructure (leading to decreased access to medical care), housing quality, education, and financial disadvantage are a few key drivers related to disparities associated with PICU incidence. These modifiable drivers of health care disparities are associated with unjust and unfavorable PICU admissions.
Pediatric patients in suburban and rural neighborhoods have farther distances to travel for pediatric subspecialty and outpatient care. Brown and McManus42 demonstrated that the distance to pediatric critical care services decreased with affluence, suggesting neighborhood-level variability in access to pediatric critical care resources. Although the underlying distributions of children based on ADI is not known for the city of Baltimore and Baltimore County, our study demonstrated increased PICU admissions from patients from more disadvantaged populations in the city of Baltimore (ADI, > 60) in areas closer to the Johns Hopkins Hospital. Patients from more affluent neighborhoods (ADI, < 60) in Baltimore County, with greater distance to Johns Hopkins, have more PICU admissions. This requires more information about the distribution of admissions and cause of patient admissions to draw conclusions about the juxtaposed characteristics of the Baltimore County and the city of Baltimore populations. This demonstrates that ADI may be used as a tool to minimize inequity and to support at-risk pediatric communities; however, more research is needed to elucidate its use as a predicative marker of PICU use.
The PICU incidence associated with neighborhoods without vehicles in the city of Baltimore and Baltimore County suggests the significant role of transportation infrastructure on pediatric health outcomes. Reliable transportation plays a role in health disparities because it contributes to minimization of social isolation and allows access to education and employment. Transportation has been linked to health disparities within the United States43 by influencing missed or delayed health care appointments, poorer health outcomes, and increased health expenditure. Parents have identified transportation as one of their greatest social needs affecting access to health care for their children.44 Public health initiatives focusing on transportation access, convenience, and cost of transit43 attempt to minimize disparities to improve health. The limited public transportation infrastructure of the city of Baltimore and Baltimore County not only limits potential access to health care, but also limits access to healthy food, education, and employment.
Although several studies have implicated the role of housing quality in health,45 this study further suggests the role of housing quality in pediatric critical illness. The role of the built environment regarding neighborhood safety, housing affordability, housing stability, and quality46, 47, 48 influences pediatric health. The current literature does not specifically identify types of housing assistance programs that improve health.48
The results of this study also suggest the importance of financial advantage on pediatric health outcomes. In both the city of Baltimore and Baltimore County, the incidence of PICU admission increases as neighborhood median household income decreases. Although median household income alone is not associated independently with PICU admission,2 it can be used to identify at-risk neighborhoods with the potential for specific population-level interventions. Safety net programs and other social policies can affect and address health care inequities within communities, at the population level, or both.49 Programs that minimize financial disadvantage—for example, the Earned Income Tax Credit, which is associated with improved birth outcomes,50 and the Child Tax Credit, which provided families with financial benefits to support food, utility, and basic expenses during the COVID-19 pandemic—can minimize pediatric health care inequities. This study provides evidence that supports maintenance and expansion of safety net programs to improve pediatric health and potentially to minimize pediatric critical illness.
Study Limitations
We acknowledge that this study examined a single center’s PICU admissions through an administrative lens. The primary limitation of this study is the presence of two large academic PICUs and a community PICU that serve both the city of Baltimore and County. Therefore, these data are not representative of all PICU admissions within the city of Baltimore and Baltimore County. The data suggest a large number of admissions in the eastern part of the city of Baltimore, potentially reflecting the catchment area of the Johns Hopkins PICU; patients in the western portion of the city may be frequenting different PICUs. Additionally, we did not include patients with addresses that were not able to be geocoded or those who were without housing.
Interpretation
Health outcomes long have been attributed to individual patient characteristics, but in the setting of structural and social determinants of health,6, 35 research should look to neighborhood-level and environmental characteristics that influence health and health outcomes of pediatric patients. This study attempted to swing the public health pendulum away from race and ethnicity as risk factors for disparate health and toward the examination of how infrastructure and neighborhood context play an instrumental role in health disparities within pediatric critical illness.51 As health care reform focuses on minimizing cost and optimizing value and equity, knowledge of the relationships between PICU resource use and socioeconomic disparities becomes relevant. Exploring disparities within the PICU based on neighborhood-level markers of deprivation can guide sociopolitical policy for the advancement of child health.
Funding/Support
This publication was made possible by the Johns Hopkins Institute for Clinical and Translational Research, which is funded in part by the National Center for Advancing Translational Sciences, a component of the National Institutes of Health and the National Institutes of Health Roadmap for Medical Research [Grant UL1 TR003098].
Financial/Nonfinancial Disclosures
None declared.
Acknowledgments
Author contributions: C. N. M., K. J. P., and J. P. B.. had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis, including and especially any adverse effects. C. N. M., A. C., K. J. P., J. P. B., and P. G. contributed substantially to the study design, data analysis and interpretation, and the writing of the manuscript.
Role of sponsors: The contents of this article are solely the responsibility of the authors and do not necessarily represent the official view of the Johns Hopkins Institute for Clinical and Translational Research, National Center for Advancing Translational Sciences, or the National Institutes of Health.
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