SCCM Resource Library-Pediatric Health Disparities: The Role of Built Environment on Pediatric Critical Illness

Video Transcription

Good morning, everyone. My name is Carly Myers, and I'm currently a third-year pediatric critical care fellow at Johns Hopkins in Baltimore, Maryland.  Today I present to you our project on pediatric health disparities, the role of built environment on pediatric critical illness.  Socioeconomic position at the individual level has been shown to influence health outcomes in children.  However, few studies have investigated the role of SEP in the critically ill pediatric population.  Emerging evidence highlights the importance of neighborhood-level SEP in the health of children, but the influence of neighborhood-level disparities on pediatric critical illness  is yet to be described. Focusing on neighborhood as a domain that affects health outcomes, we examined the association  between indicators of neighborhood-level SEP and rates of pediatric critical illness.  For our background, we used the WHO framework for understanding the impact of social determinants  of health, remembering that social determinants are conditions in the environment where people  are born, live, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes.  Note the interplay here between the structural determinants and the intermediate determinants.  The health system interacts with one's socioeconomic position and one's material circumstance, all to impact equity in health and one's well-being.  In terms of neighborhood and pediatric critical illness, there are two articles that come  to light, Epstein et al. and the Effect of Neighborhood and Individual-Level Characteristics  on Pediatric Critical Illness and Andrus et al. Neighborhood Poverty in Pediatric Intensive Care.  Note that the field of critical care medicine, both adult and pediatric, is still trying  to identify neighborhood-level and individual-level risk factors of healthcare disparities and methodology for evaluating such disparities.  One metric used for neighborhood-level disadvantage is the Area of Deprivation Index, also referred to as ADI.  Note in the Baltimore-Washington area, the ADI here, blue is least disadvantaged and red is most disadvantaged.  Zooming into Baltimore City, you can see the focus and concentration of deep red, which is the most disadvantaged, on the outskirts of the city, and the confluence of light blue  or light yellow down the center of the city. This figure will parallel a lot of our data moving forward.  For our project, our hypothesis is that there is a strong association between the built environment and pediatric health, such that neighborhood characteristics influence PICU  presentations. This is an ecologic retrospective analysis of admissions to the Johns Hopkins Children's Center PICU.  Our inclusion criteria were pediatric patients less than 18 years of age, those admitted to the PICU from August 2016 to July of 2020, and those with addresses within Baltimore  City or Baltimore County at the time of admission. We excluded those patients that required admission to the Pediatric Cardiac Intensive Care Unit.  This study took place at the Johns Hopkins Children's Center PICU, which is a tertiary  pediatric care center, and it's the only level one pediatric trauma center in Maryland, serving both Baltimore City and Baltimore County.  The primary objective of our study was to describe the sociodemographic profiles of PICU admissions in both Baltimore City and Baltimore County, with a secondary objective  being to determine the associations between various census tract level SEP indicators and rates of PICU admissions. Our census tract SEP indicators were decided a priori.  For our data analysis, PICU admissions were identified and aggregated in total number to the census tract level based on census tract data available from our EMR.  Census tract level characteristics for Baltimore City and Baltimore County were collected from the 2015 American Community Survey.  In terms of merging, the number of cases at the census tract level were merged with census tract characteristic data.  A Mann-Whitney PICU test was used for analysis of continuous variables, and a Fisher-Exak test was used for categorical variables.  Negative binomial regression was used to evaluate the association of census tract level characteristics with rate of PICU admissions.  The outcome was the total number of PICU admissions per census tract, and an offset term for the total census tract population less than 18 years of age, resulting in a standardized  PICU incident ratio, or IRR. PICU admissions during the study period were identified in 190 census tracts within Baltimore  City, which is 98% of all census tracts, and 204 census tracts within Baltimore County, which equally represents 98%.  PICU admissions from Baltimore City were 1,351, which represents 10.25 per 1,000 children,  and those from Baltimore County represented an N of 1,121, which is 6.31 per 1,000 children. The demographics of our study reveal that our patients from Baltimore City and Baltimore  County have the same age and gender makeup. The difference lies in the racial makeup. In Baltimore City, per census tract data, 62% of the population is black and 30% is  white, yet here we see 72% being black in Baltimore City and 14% of our patients being white from Baltimore City.  In Baltimore County census tract data, 60% of the population is white and 30% of the population is black, where here our patient population is 42% white and 38% black.  I point out the racial differences not to focus on race as a risk factor, but to force  us to examine the socioeconomic and political context in which our patients live, understanding  that structural racism or a system in which public policies, institutional practice, cultural  representations, and other norms work in various ways, often reinforcing ways to perpetuate racial group inequity, in this case, health.  In terms of ADI at the national level, both in Baltimore City and Baltimore County, a large portion of our patient population comes from the most disadvantaged patient populations.  Note that the national scale is zero to 100, so greater than 50 is greater than the average in terms of deprivation.  This figure here represents our PICU admissions per 1,000 children in quartiles, with yellow being the least and red being the most.  Note the high concentration in the east of Baltimore City, which has to do with location of the Johns Hopkins PICU catchment area.  In terms of our results, the bolded results are statistically significant, but the Baltimore City incident rate ratio had a 95% confidence interval with a P-value less than 0.05.  Note that both in Baltimore City and Baltimore County, median household income was statistically  significant, so for every $10,000 increase of median household income, the PICU incident rate decreased by 9%.  Note the importance of education, of transportation, and of housing quality in terms of association with increased PICU incidents.  The limitations of this study include the fact that there are three total pediatric  ICUs within Baltimore City and counting serving the same patient population, thus our representation  of the patients in Baltimore City and Baltimore County are split with these other two institutions.  There is also a heavy reliance on administrative data and administrative data entry in terms of looking at EMR data, specifically as we look at race and ethnicity of our patients.  And lastly, ecological fallacy, which is an error of attributing the characteristics of a whole population to an individual.  This data gives us insight to an additional vital sign describing the patient, but not the entire picture.  In conclusion, PICU admission can be a barometer for pediatric health equity as it represents healthcare utilization by vulnerable and sick pediatric patients.  Pursuing an understanding of geodemographics and place-based disparities, especially as  it pertains to housing quality and conditions, transportation, and poverty can improve pediatric health.  Public health and health system-based approaches are really key to reducing disparities. Thank you for listening to my presentation.  I believe we have a few moments for some questions.

Video Summary

The project presented is about pediatric health disparities and the role of the built environment in pediatric critical illness. The study explores the influence of socioeconomic position (SEP) at both the individual and neighborhood levels on pediatric health outcomes. The analysis focuses on the association between neighborhood-level SEP indicators and rates of pediatric critical illness, using data from the Johns Hopkins Children's Center PICU in Baltimore. The study finds significant associations between various census tract-level SEP indicators and PICU admissions. The research highlights the importance of understanding the impact of social determinants of health on pediatric health equity and suggests that public health and health system-based approaches can help reduce disparities.

Asset Subtitle

Research, Administration, Pediatrics, 2022

Asset Caption

INTRODUCTION: Socioeconomic position (SEP) at the individual level has been shown to influence health outcomes in children; however, few studies have investigated the role of SEP in the critically ill pediatric population. Emerging evidence highlights the importance of neighborhood-level SEP in the health of children, but the influence of neighborhood-level disparities on pediatric critical illness has yet to be described. Focusing on neighborhood as a domain effecting health outcomes, we examined the association between indicators of neighborhood-level SEP and rates of pediatric critical illness. METHODS: We conducted an ecologic study of patients ages 0-18 years who were admitted to the Johns Hopkins pediatric intensive care unit (PICU) between January 1, 2016, and December 31, 2019, and resided within Baltimore City or County. Unadjusted negative binomial regression models were used to examine the association between census tract (CT)-level PICU admission rates (per 1000 population < 18 years of age) and CT-level indicators of SEP, reported as incidence rate ratios (IRR) with corresponding 95% confidence intervals (CI). RESULTS: PICU admissions included 1351 from Baltimore City (10.25 per 1000 children) and 1125 from Baltimore County (6.31 per 1000 children). At the CT level, PICU admission rates were associated with a higher percentage of families living below poverty in Baltimore City (IRR=1.09; 95%CI: 1.00, 1.18) and County (IRR=1.19; 95%CI: 1.05, 1.36). Similarly, a 10% increase in occupied homes without vehicles was associated with higher PICU admission rates in Baltimore City (IRR=1.14; 95%CI: 1.07, 1.21) and County (IRR=1.23; 95%CI: 1.11, 1.37), as well as for vacant housing units (IRR=1.10; 95%CI: 1.01, 1.21 and IRR: 1.46; 95%CI: 1.21, 1.77). Increases in median household income by $10,000 were associated with lower PICU admission rates for Baltimore City (IRR=0.91; 95%CI: 0.86, 0.95) and County (IRR=0.91; 95%CI: 0.88, 0.94). CONCLUSION: Pursuing an understanding of geo-demographics and place-based disparities, specifically housing quality and conditions, transportation, and poverty, in relation to pediatric critical illness offers a sub-framework for disparities research. Understanding the interplay between place and health allows for tailored community-level interventions to improve pediatric health.

Meta Tag

Content Type Presentation

Knowledge Area Research

Knowledge Area Administration

Knowledge Area Pediatrics

Knowledge Level Advanced

Membership Level Select

Tag Outcomes Research

Tag Patient and Family Support

Tag Pediatrics

Year 2022

Keywords

pediatric health disparities

built environment

socioeconomic position

neighborhood levels

pediatric health outcomes

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