Neighborhoods: place-based opportunities for advancing sleep health

Neighborhoods matter for overall health [1], and particularly sleep health as growing research indicates [2–5]. The link between the neighborhood environment and health evolved based on research that found individual-level factors did not fully explain associations with health outcomes. Thus, a socio-ecological model emerged as a framework for studying health, and health disparities more specifically. This model emphasizes the interconnected and complex nature between individual, household, neighborhood, and societal-level factors [6]. Furthermore, this model is important for considering how individuals are nested within broader environments that can shape sleep health.

Structural racism/discrimination has led to segregated and unequal neighborhood environments [4, 7], which may determine an individual’s exposure to social, built/physical, and ambient features. Historical redlining, a discriminatory practice used to identify areas suitable for home loans or investment, is an example of structural racism that is closely tied to neighborhood deprivation. The areas deemed unworthy were “redlined” and densely populated by Black and Brown communities. There are long-lasting effects of this historical discriminatory practice, which has contributed to a vicious cycle by which residential segregation promotes the differential access to opportunities, resources, and services that in turn reinforce residential segregation by race, socioeconomic position, and immigration status [8]. This has led to present-day environments consisting of health-harming (e.g. air pollution, inopportune light exposure, and noise) and/or health-promoting factors (e.g. safety and social cohesion).

As illustrated by Savin et al., data support that the neighborhood's social and physical environment is associated with various dimensions of sleep health. However, this literature is limited by self-reported assessments of the neighborhood and sleep. Additionally, as highlighted above, Hispanic/Latinx and Black individuals disproportionately reside in disadvantaged neighborhoods, thus these populations may be most vulnerable to the effects of the neighborhood environment on sleep [9]. Therefore, among Hispanic/Latino adults from the San Diego site of the HCHS/SOL and its Community and Surrounding Areas (SOL CASAS) and Sueño ancillary studies, Savin et al. examined objectively defined neighborhood features including socioeconomic deprivation, social disorder, air pollution, and traffic in relation to sleep health measured as a composite variable and separately with the individual dimensions [10]. The authors also explored neighborhood assets such as walkability and greenness in relation to indicators of sleep health. Addresses were geocoded to ascertain the neighborhood characteristics within a buffer that translated to around a 10-minute walking distance around the home. To my surprise, the findings from this research were mainly null. However, there were some notable findings. Savin et al. reported that lower walkability was associated with greater wake after sleep onset, and this association may operate through sleep apnea severity [10]. Additionally, after excluding shift workers, air pollution and traffic density were associated with more wakefulness after sleep onset [10]. The authors concluded that enhancing walkability and/or reducing air pollution may promote better sleep health.

Despite the largely null associations, which were generally inconsistent with the literature, there are several key takeaways from this research. I commend the authors for extending the literature by thoroughly investigating multiple neighborhood domains in relation to a composite measure of sleep health using both objective and subjective data. The main analyses considered a composite measure of sleep health but found associations with specific dimensions of sleep health. This is important to note, given associations can be masked within composite measures, thus underscoring the importance of identifying subgroups and specific dimensions. A broad limitation of the neighborhood and sleep literature is the different measurements of the neighborhood environment, which limits comparisons between studies. The authors included objective measures of crime and social disorder, which were not associated with sleep in this sample. However, while objective measurements of the neighborhood environment enhance precision, they may not accurately capture perceptions, which are relevant to health outcomes. For example, objectively measured neighborhood-level crime may not relate to perceptions of safety. Thus, exploring both objective measures and perceptions may provide important insight into the neighborhood-sleep relation.

Racial and ethnic disparities in sleep have been well documented [11], thus to advance sleep disparities research, conducting within-group studies where multiple determinants can be examined is advantageous. Furthermore, Hispanic/Latinx populations are underrepresented in sleep research, and are a heterogenous group. Thus, it was a strength of Savin et al. to conduct a within-group analysis of mostly adults of Mexican heritage. While beyond the scope of the paper, future research should examine sociocultural factors relevant to the Mexican population, that may buffer some of the adverse effects of the neighborhood environment on sleep.

Some of the limitations highlighted by Savin et al., create an opportunity for the advancement of future research on this topic. For example, the household is an important contextual factor, that is unfortunately understudied in sleep. The household may modify or exacerbate neighborhood effects on sleep. For example, soundproof windows could block neighborhood noise, thus measuring neighborhood noise without a more proximate measure of household-level noise may yield an imprecise result and lead to measurement bias. Therefore, future studies should consider the household as a potential modifier of the neighborhood-sleep relation and/or a unit of measurement that is more proximal. While Savin et al., did not include length of residence in the neighborhood in the study, this is an important confounder that should be considered in future studies. In addition to length of residence in a neighborhood, time within the residence should be considered. Many individuals work or play outside of their neighborhood, thus, to accurately capture environmental exposures, 24-hour assessments should be ascertained as opposed to solely relying on place of residence without context of time spent within the environment. Additionally, Savin et al., conducted the analysis among a small sample size of 342 individuals, which may explain some of the null findings. Thus, it is critical that larger studies be conducted to ensure adequate power to identify key associations of neighborhood and social disparities with sleep health.

In conclusion, neighborhood characteristics are key determinants of sleep health, and as indicated by Savin et al., some characteristics may be more relevant than others. Neighborhood interventions are limited in sleep, but emerging evidence suggests neighborhood investment is beneficial to sleep health [12], thus potentially countering the long-lasting effects of historical redlining. Additional important next steps in this research include studying the mechanisms connecting neighborhoods and sleep and translating findings into interventions. The results by Savin et al. have clinical and policy implications—from using addresses in the clinic to identify individuals most at risk to promoting policies around mixed-income neighborhoods or enhancing public transportation to limit traffic and vehicle-related air pollution. To combat sleep deserts [13], it will be critical to partner with other fields/professionals such as urban planners to ensure environments are intentionally built for healthy sleep. Lastly, employing a socio-ecological approach may be an effective strategy to improve population sleep health, and eliminate sleep health inequities.

Funding

This work was funded in part by the National Institutes of Health, National Heart, Lung, and Blood Institute R01HL157954.

Disclosure Statement

Financial disclosure: The author has nothing to disclose. Nonfinancial disclosure: The author has nothing to disclose.