The Effect of Neighborhood and Individual Characteristics on Pediatric Critical Illness

Abstract

The relationship between neighborhood/individual characteristics and pediatric intensive care unit (PICU) outcomes is largely unexplored. We hypothesized that individual- level racial/ethnic minority status and neighborhood-level low socioeconomic status and minority concentration would adversely affect children’s severity of illness on admission to the PICU. Retrospective analyses (1/1/2007–5/ 23/2011) of clinical, geographic, and demographic data were conducted at an academic, tertiary children’s hospital PICU. Clinical data included age, diagnosis, insurance, race/ethnicity, Pediatric Index of Mortality 2 score on presentation to the PICU (PIM2), and mortality. Residential addresses were geocoded and linked with 2010 US Census tract data using geographic information systems geocoding techniques. Repeated measures models to predict PIM2 and mortality were constructed using three successive models with theorized covariates including the patient’s race/ethnicity, the predominant neighborhood racial/ethnic group, interactions between patient race/ethnicity and neighborhood race/ethnicity, neighborhood socioeconomic status, and insurance type. Of the 5,390 children, 57.8 %were Latino and 70.1 %possessed government insurance. Latino children (β = 0.31; p < 0.01), especially Latino children living in a Latino ethnic enclave (β = 1.13; p < 0.05), had higher PIM2 scores compared with non-Latinos. Children with government insurance (β = 0.29; p < 0.01) had higher PIM2 scores compared to children with other payment types and median neighborhood income was inversely associated with PIM2 scores (β = −0.04 per $10,000/year of income; p < 0.05). Lower median neighborhood income, Latino ethnicity, Latino children living in a predominantly Latino neighborhood, and children possessing government insurance were associated with a higher severity of illness on PICU admission. The reasons why these factors affect critical illness severity require further exploration.

Hodgkin lymphoma incidence in ethnic enclaves in California

“…Our findings are consistent with prior research showing that residing in an ethnic enclave is related to risk of disease [16,19], including several cancers [13,20-26]. The direction of our observed associations was anticipated based on our previous findings regarding HL incidence by nativity [13,14], which is one component of our neighborhood enclave index.…”

“…[15,16,19,39-43]. Given established risk factors for HL, the observed association with ethnic enclave residence likely relates to relevant social community characteristics (e.g., education, family size [8], SES [4-6], household crowding [44]) and/or other environmental influences (e.g., smoking [45]).…”

“…For US Hispanics and APIs, living in an ethnic enclave has been shown to enhance social and economic engagement for recent immigrants, as well as to reinforce native customs [15,16]. As residence in ethnic enclaves relates to nativity and affects health behaviors [16,17] and illness [18,19], including numerous cancers [13, 20-26], it also may influence HL incidence.…”

Geographic Variation in Hospitalization for Lower Respiratory Tract Infections Across One County

“…Locally, our group found that measures of poverty and housing quality were associated with a return to the hospital for children with asthma. 20,31 Also, a recent study by Epstein et al 32 showed that lower neighborhood income is associated with higher severity of illness on admission to the pediatric intensive care unit across diagnoses. Such findings are not surprising given the extensive literature on the influence of socioeconomic deprivation on a child’s physical health.…”

Regional socioeconomic disparities in outcomes for workers with low back pain in the United States

“…There have been several other studies in the United States which found a link between neighborhood‐level SE disadvantages and morbidity and mortality independent of individual SE status 10, 11, 12. None of these studies, however, accounted for level of insurance coverage or cost‐sharing amount (eg, deductible amounts), which are important predictors of health care utilization and adverse health outcomes 13.…”