Neighborhood Disadvantage and Sleep Health in Children with Sickle Cell Disease
Authors: Rani Seema , DiVirgilio Nicole , Hildenbrand Aimee K. , Canter Kimberly , Miller Robin E , Chidekel Aaron , Strang Abigail , Dyer Kaitlin , Lewis Amanda M. , Patterson Freda 
Published: December 2024

Background: Optimal sleep health in children is complex and multifactorial and is determined by an interplay of individual and family factors, as well as environment and broader socio-cultural aspects (for e.g. neighborhood factors like green space, lighting, noise pollution).

Living in disadvantaged neighborhoods can adversely affect sleep including sleep quality and has been associated with sleep health disparities.

Area deprivation index (ADI) created by HRSA (Health resources and services administration) allows for rankings of neighborhoods by socioeconomic disadvantage in a region of interest. It incorporates domains of income, education, employment and housing quality at state and national levels and has been used to inform health delivery and policy in disadvantaged neighborhoods. State ADI deciles are ranked from 1 to10 for each individual state and are constructed by ranking the ADI from low to high within each state. Group 1 is lowest ADI being least disadvantaged and 10 is the highest ADI (most disadvantaged). Similarly, national percentile rankings are at the block group level 1 to 100 ranging from low to high for the nation, with group 1 as lowest ADI block (lowest disadvantaged) to group 100 as higher ADI (most disadvantaged).

Aim: To evaluate the role of ADI in sleep health in children with SCD compared to healthy controls.

Methods: Cross-sectional study was conducted between November 2022 and June 2024. Youth between the ages of 8-17 years with a diagnosis of SCD (any genotype) in their baseline state of health were eligible to participate. Age-, race-, and sex-matched children without chronic illness and in their baseline state of health without conditions causing chronic pain or inflammation were enrolled as controls

Sleep health was measured utilizing Patient-Reported Outcomes Measurement Information System (PROMIS) Sleep Disturbance (SD) Short Form 8A, PROMIS Sleep-Related Impairment (SRI) Short Form 8A, Child Sleep Hygiene Scale (CSHS) and Adolescent Sleep Hygiene Scale (ASHS) questionnaires.

Results: We obtained ADI indices for a total of 71 children in our cohort, Mean age= 12.9 years, SD (2.8), 60% female and 98% were Black/African American. We had 49 children with SCD and 22 age, race and sex matched controls. There was no significant difference in age, sex and race between the SCD and control group.

Children with SCD had significantly increased neighborhood disadvantage compared to controls. ADI for state deciles for the entire cohort was 5.66 (3.18) and was significantly elevated in children with SCD compared to controls; 6.53(2.98) vs 3.73(2.78), p value 0.000. ADI for national rank for the entire cohort was 48.6(24.91) and was significantly elevated in SCD population (Mean =54.53, SD=23.51) compared to controls (M=35.59, SD=23.39), p= 0.002.

There was no significant correlation between measures of sleep health (including sleep disturbance, sleep related impairment, and sleep hygiene scales) and ADI (either state or national ranks) for the entire cohort. Although there was no correlation between the ADI and composite sleep hygiene scales, ASHS environmental sleep subscale factors were found to be significantly and negatively correlated with ADI in adolescents with SCD (Pearson correlation -466, p 0.016).

Conclusions: Children with SCD were at a higher neighborhood disadvantage compared to children in the control group, despite both populations being of similar ages and race. ASHS environmental sleep subscale was negatively correlated with ADI in children with SCD.

Future Directions: Comprehensive evaluation of sleep health in children should include assessment of neighborhood factors, in addition to family and individual factors. Larger studies using longitudinal designs and multiple methods of sleep assessment are needed to determine specific socio-economic factor/s contributions. These are needed to inform sleep and health outcomes in this population.

Acknowledgements:

Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number P20GM109021

Research reported in this publication is supported by the National Institute of General Medical Sciences of the NIH under grant number P20GM144270. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health