“Sleep disparity” in the population: poor sleep quality is strongly associated with poverty and ethnicity | BMC Public Health | Full Text

The public health ramifications of sleep are far-reaching and under-recognized as stated in a recent report by the Institute of Medicine [44]. Many have reported the ill effects that poor sleep, defined by suboptimal sleep duration or the presence of sleep disorders, confers upon health (including mortality) [1–4, 45], well-being, and society [44]. The collective economic impact of impaired/restricted sleep is enormous and has been conservatively estimated at $107 billion [44]. It is, therefore, critical to advance our understanding of the determinants of sleep attainment given the impact.

This study included and adjusted for several covariates that plausibly influence sleep quality that have not been addressed in prior studies [6, 8, 11–13], with interesting results. A significant “sleep disparity” exists in the population sample, with African-American and Latino groups overall having poorer sleep quality than the White, non-poor, referent group. Amongst these minority subjects, the impoverished subgroups reported the highest odds for poor sleep. The inclusion of other socio-economic and health covariates, however, significantly modified these findings. Of particular interest, health covariates markedly attenuated the relationship between poor sleep and race/ethnicity for impoverished individuals: impoverished African-American subjects demonstrated a diminution of the odds ratio for poor sleep upon including socio-economic covariates such as employment and education (model 2), but the odds ratio became non-significant only when health covariates were included (model 3). This offers insights into potential targets for intervention by suggesting that minorities are particularly vulnerable to the effects of poor health on sleep quality.

These results are consistent with literature linking SES [6, 9, 12, 33, 46–49] and race/ethnicity disparities to sleep attainment [7, 9, 50]. However, our analysis advances on these studies by reporting for the first time the interactive effects of race and SES upon sleep quality in a large population sample. There is a growing appreciation that race and SES may operate in an interactive manner on health outcomes [51]. Thus, instead of the issue being an issue of race or class, it may be race and class [52]. This may be true for sleep attainment and the associated health outcomes.

Our observation that impoverished White subjects demonstrated the highest odds for poor sleep in contrast to other race/ethnic groups who did not have increased likelihood of poor sleep after adjusting for the same covariates is intriguing when examined in the context of existing literature on health disparities- typically poor minority groups experience the most health disadvantage [53]. We believe this adds new perspective to the minority poverty hypothesis that refers to the unique disadvantage experienced by impoverished African-Americans [51]. The underlying mechanism of poorer reported sleep quality in this group is unclear and may represent biological, psychosocial, cultural and environmental processes. It is possible, for example, that “expectations” of sleep quality led to a greater sense of impaired sleep quality in Whites. This warrants further investigating to assist in future public health campaigns and sleep-health policy.

African-Americans above the poverty line had significantly increased odds for poor sleep compared to the White referent group and African-Americans below the poverty line in multivariable adjusted analysis. This raises further questions: 1) What are the explanations for sleep quality disparity amongst not poor African-Americans and Whites? This may be related to societal structure and/or due to a differential SES/sleep relationship in African-Americans and Whites [53] and; 2) Why do African-Americans above the poverty line have increased likelihood of poor sleep than African-Americans below the poverty line? Sleep disadvantage in the latter group, as noted earlier, may be explained by education, employment and health factors. In African-Americans above the poverty line, though, their higher SES has been hypothesized to foster positive social, psychological, and economic skills that shield against the effect of adversity [54] which in theory would protect sleep attainment. The persistence of poor sleep in the fully adjusted Model 3 analysis in non-poor African-Americans may instead be related to other factors such as higher sleep/health expectations, career demands, and social roles [5]. As noted earlier, though, this same finding did not hold true for Whites: Impoverished Whites demonstrated worse sleep quality than non-impoverished Whites even in adjusted analysis. This raises the intriguing question of whether poverty has differential effects on symptom perception in different race/ethnic groups even after adjusting for SES and health factors. Several explanations in a variety of socio-ecological domains may be posited for this sleep-race-SES gradient: differences in health behavior (for example, self-efficacy, perception, attitudes and value expectancy), psychosocial circumstances, and environment (social and physical) can disparately affect sleep. Future research relying on qualitative methods and grounded in health behavior theory could add to our understanding of these factors. Of note, this finding of a statistically significant elevated OR for impaired sleep in not poor African-Americans even in multivariate modeling that was observed in the weighted model was not as prominent in the unweighted model: for the unweighted model, there remained an elevated OR (1.25), but the lower limit of the confidence interval was 0.97, thus it represented a trend towards statistical significance in the unweighted model. Thus, the findings were similar in the weighted and unweighted models.

The literature linking sleep and health continues to grow. Our study is consistent with this as we observed that poor health was associated with an almost 4-fold increased likelihood of poor sleep in the final model and represented one of the most significant factors (Model 3). It is important to note that the relationship between health and sleep quality is likely bidirectional and/or parallel: sleep can influence health and vice-versa.

Assessing sleep quality over sleep quantity may have several advantages. We believe that assessing sleep quality captures multiple domains of sleep including quantity, consolidation, daytime functioning, and sleep satisfaction. Sleep quality, compared with sleep quantity, has superior relation to measures of health, well-being, and sleepiness [55]. Sleep duration studies typically define a referent duration as an optimal. This can overlook sleep sufficiency, disruption and of course known biological differences in sleep requirements [56]. Finally, there is a reported discrepancy between subjective and objective sleep duration [15]. Theoretically, inquiry about sleep quality may encompass these concerns.

Limitations

Respondents were from Philadelphia metropolitan area thereby limiting generalizability to similar locales. Nonetheless, the respondents were from diverse race/ethnic backgrounds. Telephone surveys are inherently limited by coverage (low socioeconomic status, disabled, and institutionalized subjects may not have access to a telephone) and response rate. A response rate of 28% is consistent with reported attrition discussed in the literature [57] and does not necessarily translate to non-response bias [58]. However, this is still a low response rate by absolute standards, and may reflect a number of biases in the sample, systematically excluding those who cannot be on the phone for longer periods of time and/or are not willing or able to participate in research and reflecting biases caused by social desirability and/or demand characteristics. Importantly, this study’s sample characteristics are generally similar to Census data for the same geographical area. In addition, we have presented both weighted (correcting for differences relative to Census data) and unweighted data; the results are fairly similar for both methods.

The cross-sectional design limits inference of causality in the effect of SES on sleep quality. SES measurement is challenging and not completely captured by any single or combination of factors [59]. Nonetheless, all measured SES dimensions (income status, low education level, and unemployment) were significantly associated with poor sleep. Furthermore, we recognize that different socioeconomic factors may affect health at different times in the lifespan [59].