Place-Based Factors Contributing to Sleep Health Disparities in the United States - The American Heart Association recently added “Get Healthy Sleep” to “Life’s Essential Eight” health behaviors. Key aspects of healthy sleep – and its converse, sleep health disparities (SHD), however, remain unknown. SHDs are persistent differences in one or more dimensions of sleep health (e.g., sleep deficiency) and circadian misalignment – each of which increases the risk of multiple cardiovascular and other diseases/disorders that disproportionately impact populations (i.e., health disparity populations [HDPs]). SHDs are likely partially driven by environmental and social factors, some of which are determined by geographic location. It is not known (i) how demographic factors impact the probability of SHD and (ii) how neighborhood-level/place-based factors (e.g. noise, longitudinal position within a time zone (as a measure of environmental circadian misalignment [ECM])) affect the probability of SHDs. This lack of information hampers planning of targeted interventions to improve sleep health and therefore other outcomes. Two NIH Institutes convened a 2020 workshop (led by co-Mentor Dr. Jackson) that identified a need to investigate the causes of SHDs and their health and social consequences. A related gap in the 2021 NIH Sleep Research Plan is what geographic factors contribute to SHDs. The proposed specific aims test hypotheses that address this need/gap: 1) estimates of sleep health (e.g., duration, quality, restorative sleep, and timing), and ECM are 1a) worse among HDPs and 1b) vary geographically in the United States, 2) both individual-level and place-based factors (e.g. noise) are associated with SHDs, and 3) Test whether place-based factors mediate sleep health disparities. These aims will be accomplished by linking data from a nationwide public health surveillance project to neighborhood-level factors using Geographic Information Science (GIS) and testing the association between individual-level and neighborhood-level factors and SHDs, and a mediation analysis between the same. The dataset includes validated surveys for sleep and circadian characteristics, detailed demographics, health-related quality of life, and precise respondent location. I am a skilled epidemiologist who requires additional training to conduct this project and become an independent investigator in the field of sleep medicine leading transdisciplinary research in sleep epidemiology with a focus on health disparities. The specific training includes 1) health disparities research, 2) GIS techniques and 3) clinical consequences of poor sleep health and circadian misalignment. I worked with the team of national experts assembled for this training to develop a comprehensive research and training program that builds on my current expertise and will provide the necessary skills for this project and my career. Upon completion, I will be uniquely qualified to study the causes and consequences of SHDs through prospective epidemiologic studies that include objective exposure assessments (leveraging GIS techniques) and robust incident outcome ascertainment.
