Economic and social determinants of health disparities in India: A systematic review of sleep literature

Among multiple determinants affecting sleep health, there is people socioeconomic status (SES), a multidimensional concept of an individual's social, economic and ecological position associated to public health inequalities at different levels. No systematic review on the rela tion between SES and sleep health has been previously conducted in India. Following Prisma protocol, seven articles were selected. Findings revealed that all studies were cross-sectional. The combined number of participants is N=12,746 participants, composed of 81.15% of adults (n=10,343), 10.56% of children (n=1346) and 8.29% of adolescents (n=1057). The smallest sample was N=268 and the larger was N=7017. The socioeconomic determin ants the most reported by authors were perceived SES/composite indices, education, income and employment/occupation. The most reported s leep disturbances were obstructive sleep apnea (OSA), insomnia, restless legs syndrome (RLS) and sleep quality. Higher SES (specifically high education and high income) was associated on one hand in adults, with insomnia and a lower risk for OSA; and on the other hand, in adolescents, with poor quality of sleep and shorter sleep duration. Unemployment was significantly associated with insomnia and risk for pediatric OSA (specifically maternal employment). These findings are coherent with the conceptual socioeconomic model of sleep health published by Etindele Sosso et al. and one previous ecological model of sleep published by Grandner et al., both explaining the relationship between SES and sleep disparities. More studies on the subject and more longitudinal research are necessary to support public health programs related to sleep health disparities in India.

India; determinant; socioeconomic status; sleep; health disparities; systematic review; public health . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 3 1- INTRODUCTION Health disparities are associated to socioeconomic gradient that can be measured through indicators like education, income, mar ital status or type of employment [1-7]. These indicators were previously employed in social epidemiology and biomedical research such as those relate d to cardiovascular system [8], breathing system [9] or sleep mechanisms [1, 2]. They helped established how environments can affect the pathway of an individual’s health status and it was documented extensively that, this relationship was behind a lot of public health issues [ 4, 6, 10]. Among others important public health issues potentially linked to the social and physical environment, there is sleep health, which is decre asing considerably worldwide since the last decade [10-14]. Sleep is a multifactorial mechanism very sensitive to external inputs with a complex construction at the corner of physiology, sociology, psychology and public health [4, 6, 15]. The concept of sleep health, which is relatively new, promote a multidimensional sleep research’s approach considering a wide range of clinical parameters such as sleep duration, sleep continuity, sleep efficiency or total sleep time [1, 2, 11]; an d also no clinical parameters such as sleep quality or sleep insufficiency [12, 16, 17]. Sleep health inequalities represents a public health outc ome similar to public health issues previously reported for cardiovascular, mental health and metabolic diseases [18] and among factors influencing v ariations of these inequalities; socioeconomic status (SES) is one of the most important but strangely also one of the less documented in developi ng countries [6, 10]. SES is an invisible multidimensional concept of an individual's social, economic and ecological position associated to public h ealth inequalities at different levels; generated by subjective norms and social ladder defined or adopted by the individual’s community [4, 6, 10, 1 8-20]. Thus, sleep . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 4 health disparity is a complex assessment of a socio-ideological an d theoretical construct measured in a variety of ways usually considering several determinants such as employment, income, education, occupation and social position [3, 15, 18, 21]. Trends in terms of sleep health disparities seems to be similar everywhere regardless the country [6, 15]. An extensive screening of empirical literature revealed that India was one of the biggest countries with a lack of literature about sleep health relationship with socioeconomic determinants of health disparities. This screening also revealed that, no systematic review on the relation between SES and sleep health has been previously conducted in India. Its pertinent to understand if public health inequalities in terms of sleep observed elsewhere, are the same in this important country with documented variety of national’s health burdens and economic disparities among their multiple ethnocultural populations [22, 23]. The goals of this systematic review is to 1) document socioeconomic determinants of sleep h ealth inequalities in India and 2) recommend future actions and research directions based on evidence. 2- METHODS 2.1- Literature search Relevant citations for this review were identified by searching the databases PubMed/Medline and Google scholar between January 2000 and July 2022. A combination of search terms “socioeconomic”, “socio-economic”, ‘’social position’’, ‘’social class’’, ‘’socioeconomic p osition’’, “determinant*”, “health disparities”, “sleep”, ‘’sleep disorders’’, ‘’sleep disturbances’’, ‘’sleep complains’’, “sleep outcome”, “sleep health” and “India*” was used. All included articles were identified on the basis of relevance to the association between SES determinants and sleep outcomes following the PRISMA guidelines (Fig. 1). . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint Figure 1. Prisma flowchart of study selection process. 5 . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 6 2.2- Inclusion and exclusion criteria Empirical studies were defined as peer-reviewed scientific articles of any design (cross-sectional, retrospective or longitudinal) that assess the relation SES and sleep, including a human sample of any sex, race/ethnicity, gender or age from the general population of India. The stu dy had to include an

such as education, income, assets, occupation, employment status, perceived SES or a qualitative measure of SES inclu ding self-reported items by participants. Aggregate measures of SES (neighbourhood SES or area deprivation indices) were included if participant’s d a t a w e r e n o t available or reported by authors. For studies with children or adolescents’ participants, perceived family SES measures such as parental education, parental profession or household income were used. Articles were not included excluded when they met one or many of the followi ng criteria: 1) They were reviews or meta-analyses, case series, editorial, case reports, and/or did not present original research, 2) they wer e not written in English or French, 3) the full text was not available, 4) samples included participants with conditions potentially influence the relat ion SES and sleep base (for example sleeping pills, chronic sleep disturbances, diseases with sleep symptoms, etc.…), 5) they did not provide statisti cal significance in cases where the relation between SES indicators and sleep parameters were evaluated. 2.3- Quality assessment The National Institute of Health’s Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was used to rate the quality of included studies [7]. It assesses 14 quality criteria, asking equal numbers of questions about study objectives, population, exposures, outcomes, follow-up rates, and statistical analysis. Overall quality ratings were calculated by taking the proportion of positive ratings over the sum of applicable criteria. Studies with <50% positive rating were judged as poor quality, 65% as good quality and the rest as fair quality. Complete evaluations criteria of all articles are available in Table 2. . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 7 3- RESULTS Table 1 Characteristics of included studies investigating determinants of sleep health disparities in India. Study Study design Population % Women Age (mean ± SD or range) Sample size SES measures Sleep measures Main effects Interactions/ Mediations Odds ratio, p- value Quality rating Rangarajan 2007 Cross- sectional Adults from the general population in Bangalore, India 44.8 38.1 ± 14.2 1266 Education (below high school vs high school and above) Monthly per-capita income (2 cut-offs: US$2/day and US$1/day) NIH/IRLSSG criteria for diagnosis of RLS (questionnaire) RLS was associated with education less than high school level in the group with per- capita income less than US$2/day Education less than high school was associated with the occurrence of RLS: Adjusted OR= 2.76 [1.17–6.55] Good Reddy 2009 Cross- sectional Adults 30-65 y from the general population in South Delhi, India 44.7 N/A 360 Kuppuswami socioeconomic status score OSA (AHI ≥ 5 in PSG) Prevalence of OSA was not significantly different across the socio- economic strata 95 % confidence interval of adjusted OR contain one Good Bapat 2017 Cross- sectional 5 th-9th grade adolescents from two public and four private schools in Pune, India 43.3 13.8 ± 1.3 268 4-item Family Affluence Scale (divided in 3 categories) Self-reported sleep time Children with a higher SES slept shorter than children with a lower SES This relation was significantly mediated by screen time (low SES children reported more screen time and thus less sleep time) and academic work (high SES children reported more academic SES related to sleep time (r= −0.32, p = 0.001) *Academic time was a stronger mediator than both screen time [point estimate = 8.44, 95 % CI (5.14 to 12.96)], and physical Good . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 8 work and thus less sleep time) activity [point estimate = −6.02, 95 % CI (−9.80 to−3.29)] in the relation between SES and sleep time. * Screen time was a stronger mediator than physical activity in the relation between SES and sleep time: [point estimate = 2.42, 95 % CI (.82 to 5.73)]. Goyal 2018 Cross- sectional Children 5- 10y from 3 schools in Bhopal, India 37.9 N/A 1346 Maternal education (illiterate vs literate) Maternal employment status (yes vs no) OSA risk (score >0.33 in the Sleep-Related Breathing Disorder scale of the Pediatric Sleep Questionnaire) Maternal employment was associated with OSA risk Working mother was a significant risk factor for OSA: Adjusted OR= 1.8 [1.2–2.7] Good Jaisoorya 2018 Cross- sectional Adults 18- 60y attending 71 primary health centers in the State of Kerala, India 65.5 41.1± 11.0 7017 Education ( ≤ 10y vs >10y) Income (below vs above poverty line) Employment status (unemployed vs employed) Insomnia (ISI score 0, <15, ≥ 15) Lower education was associated with both subclinical and clinical insomnia. Unemployment was associated only with subclinical insomnia. Clinical insomnia decreases in educated >10y than ≤ 10y: Adjusted OR= 0.64 [0.42– 0.98]. Subclinical insomnia decreases in educated >10y than ≤ 10y: Adjusted OR= Good . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 9 0.79 [0.64– 0.98] and employed than unemployed: Adjusted OR= 0.74 [0.62– 0.87] Khan 2018 Cross- sectional Adults ≥ 20y from the general population of Dehradun district, India 48.8 N/A 1700 Education (none, high school, intermediate, graduate and above) Employment (not working, service, agriculture, self- employed) Socio-economic class (upper, middle, lower) Insomnia (ISI score >7) Higher education and unemployment increased the odds for having clinical insomnia Clinical insomnia decreases in lower educated (None/high school) than graduated: Adjusted OR= 0.10 [0.04– 0.20]/ Adjusted OR= 0.38 [0.20– 0.72]; and in workers (Services/Self- employed) than unemployed: Adjusted OR= 0.50 [0.30– 0.83]/ Adjusted OR= 0.45 [0.22– 0.92] Good Sarveswaran 2019 Cross- sectional Adolescents 10-19y from the general population of two villages in rural Puducherry, India 44.2 14.1± 2.4 789 Income (Modified BG Prasad’s scale; lower and lower middle, middle, upper and upper middle) Sleep quality (PSQI global score ≥ 5) Higher education and higher income was found to be significant determinant for poor quality of sleep Education≥ 11 y was Significantly associated with poor quality of sleep: aPR=3.43 [1.66–12.35] Good . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 10 Upper and upper middle income were Significantly associated with poor quality of sleep: aPR=5.48 [1.61–49.40] SES = socio-economic status; SD = Standard deviation; PSQI = Pittsburgh Sleep Quality Index; NIH = National Institutes of Healt h; IRLSSG = International Restless Legs Syndrome Study Group; RLS = restless legs syndrome; OSA = obstructive sleep apnea; PSG = polysomnography; AHI = apnea-hypopnea index; ISI = Insomnia Severity Index; OR = odds ratio; aPR= adjusted prevalence ratio; CI = Confidence Interval Table 2 Quality assessment of included studies according to NHLBI’s Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies Study Q1 Q2 Q3 Q4 Q5 Q6 Q7 Q8 Q9 Q10 Q11 Q12 Q13 Q14 Quality rating Rangarajan 2007 Y Y Y Y Y N N N Y N Y NA NA Y Good Reddy 2009 Y Y Y Y N N N Y Y N Y Y NA Y Good Bapat 2017 Y N NR N N N N Y Y N N NA NA Y Poor Goyal 2018 Y Y Y Y Y N N N Y N N NA NA N Fair Jaisoorya 2018 Y Y Y Y Y N N N Y N N NA NA Y Fair Khan 2018 Y N NR Y Y N N Y Y N N NA NA N Poor Sarveswaran 2019 Y Y Y Y Y N N Y Y N N NA NA Y Good Y = Yes; N = No; CD = cannot determine; NA = not applicable; NR = not reported. Q1. Was the research question or objective in this paper clearly stated? Q2. Was the study population clearly specified and defined? Q3. Was the participation rate of eligible persons at least 50%? Q4. Were all the subjects selected or recruited from the same or similar populations (including the same time period)? Were inc lusion and exclusion criteria for being in the study prespecified and applied uniformly to all participants? Q5. Was a sample size justification, power description, or variance and effect estimates provided? Q6. For the analyses in this paper, were the exposure(s) of interest measured prior to the outcome(s) being measured? Q7. Was the timeframe sufficient so that one could reasonably expect to see an association between exposure and outcome if it existed? . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 11 Q8. For exposures that can vary in amount or level, did the study examine different levels of the exposure as related to the ou tcome (e.g., categories of exposure, or exposure measured as continuous variable)? Q9. Were the exposure measures (independent variables) clearly defined, valid, reliable, and implemented consistently across al l study participants? Q10. Was the exposure(s) assessed more than once over time? Q11. Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? Q12. Were the outcome assessors blinded to the exposure status of participants? Q13. Was loss to follow-up after baseline 20% or less? Q14. Were key potential confounding variables measured and adjusted statistically for their impact on the relationship between exposure(s) and outcome(s)? 3.1- Characteristics of studies selected Seven articles [24-30] were included in the final sample. All these articles were cross-sectional studies (Table 1) and evaluat ed as of good quality (Table 2). The combined number of participants is N= 12,746 participants, composed of 81.15% of adults (n = 10,343), 10.56% of children (n = 1346) and 8.29% of adolescents (n= 1057). The smallest sample was N= 268, and the largest was N= 7017. The socioeconomic indica tors used were perceived SES/composite indices in three studies [24-26], education in five studies [26-29], income in three studies [27, 29, 3 0] and employment/occupation in three studies [26, 28, 29]. The measurement instruments and the sleeps disturbances reported were : self-reported sleep time [25], apnea-hypopnea index (AHI) [24] and sleep- related breathing disorder scale (SRBD) [28] for obstructive sleep apnea (OSA), the Insomnia Severity Index (ISI) for insomnia [26, 29], sleep quality using the Pittsburgh Sleep Quality Index (PSQI) [30] and sleep disturbance via National Institutes of Health/Internatio nal Restless Legs Syndrome Study Group (NIH/IRLSSG) for diagnosis of restless legs syndrome (RLS) [27]. 3.2- Determinants of sleep health disparities in India . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 12 Disparities in sleep apnea In the study by Goyal et al. [28], a significant higher risk for pediatric OSA was observed in association with maternal employ ment (adjusted odds ratio 1.8; 95% CI: [1.2-2.7]) in school children aged 5-10y. Prevalence of OSA was not significantly different across the updat ed Kuppuswami socioeconomic status score in the study by Reddy et al. [24] among adults aged 30-65y (p > 0.05). Disparities in insomnia Two studies reported that employment status and education are associated with insomnia (clinical and subclinical) in adults [26 , 29]. In the study by Jaisoorya et al. [29], Subclinical insomnia was considerably lower in employed (adjusted odds ratio 0.74; 95% CI: [0.62-0.87]) and higher educated /g3408 10y (adjusted odds ratio 0.79; 95% CI: [0.64-0.98]). Also, Clinical insomnia was considerably higher in unemployed [26, 29]. But for education, prevalence of clinical insomnia decreased with higher educated /g3408 10y in the study by Jaisoorya et al. [29] (adjusted odds ratio 0.64; 95% CI: [0.42- 0.98]); while it decreased in the lower educated : none (adjusted odds ratio 0.10; 95% CI: [0.04-0.20]) and high school (adjusted odds ratio 0.38; 95% CI: [0.20-0.72]) in the study by Khan et al. [26]. Disparities in restless legs syndrome One study [27] indicated positive association between education and RLS. Occurrence of RLS was significantly associated with ed ucation less than high school in the group with higher income cut-off ($2/day) (adjusted odds ratio 2.76; 95% CI: [1.17-6.55]). Disparities in sleep quality . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 13 One study [30] showed that education and income was associated with poor quality of sleep among the adolescents. Adolescents wi th a high educational level /g3410 11/g1877 /g4666adjusted prevalence ratio 3.43; 95% CI: /g46701.66 /g3398 12.35/g4671/g4667 or a high socio-economic class (adjusted prevalence ratio 5.48; 95% CI: [1.61-49.40]) were more likely to suffer from poor sleep quality. Disparities in sleep duration Bapat et al. [25] observed a positive association between socioeconomic status and sleep time mediated by academic work mainly and screen time (point estimate = 8.44, 95% CI [5.14 -12.96]), but not by physical activity among adolescents. In the sense that children from a higher SES sleep less as a result of school demands, than children from a lower SES that reporting more screen time which is negatively related to ti me spent sleeping (p=0.001). 4- DISCUSSION A. Summary of findings The main findings of the qualitative analyses were as follows: (1) higher SES (specifically education and income) was significa ntly associated with insomnia and a lower risk of OSA in adults populations, (2) higher SES (specifically education and income) was associated with poor sleep quality and shorter sleep duration in adolescents populations, (3) Unemployment was significantly associated with insomnia, and (4) maternal employment was significantly associated with risk for pediatric OSA. . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 14 B. Relation with current knowledge The findings of this systematic review are coherent with previous literature including a recent socioeconomic model of sleep health [2, 10, 18] and different socioecological model of sleep [19, 31], stating the strong association existing between the individual socioeconomic status and sleep health. Biological needs for sleep are met by engaging in behaviors that are largely influenced by the environment, social norms and demands, and societal influences and pressures [19]. Understanding the etiology of socioeconomic disparities in sleep could assist public health authorities in preventing the morbidity of socially disadvantaged individuals, in western countries as well in developing countries [32]. Findings of this research supports theories stating that low SES induced sleep disturbances or in other terms, an individual SES is mediating his sleep health following a social gradient measured through markers like education and income [4, 6, 10, 19, 32]. A narrative synthesis of three decades of empirical literature demonstrated that, unhealthy behaviors, increased stress levels and limited access to healthcare in low SES individuals may explain this SES-sleep health gradient [10], because low SES people often reported more sleep disturbances than high SES people. Similarly, it was established that environmental stressors related to climate changes like noise, heat stress and respirable dust are related to an increase of sleep disturbance [33]. In addition, it was showed recently that these disparities are present in several rich countries where social inequities are reduced. For example, a recent systematic review found that in canadian populations, sleep health disparities among children and adolescent are strongly correlated to parental socioeconomic indicators [18]. Findings revealed also that poor parental income, poor family SES and poor parental education are associated with higher sleep disturbances among children and adolescents; same thing with lower education which acts as a predictor of increased sleep disturbances for adults [18]. The same trends were observed with adults and old populations, with low SES associated with high sleep disturbances and low income which was significantly associated with short sleep duration [10, 18]. These results clearly highlight the importance of considering multiple psychosocial and environment risk factors for implementing occupational health and ergonomics interventional programs to prevent sleep disturbances for the entire population, . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 15 including adolescents and the country’s workforce [33], if governments and employers wish to prevent major expenditures related to inevitable consequences due to an unhealthy sleep [4, 12, 34]. However, the cross-sectional design of most studies related to this relationship and the high heterogeneity in employed measures of SES, reduce the larger promotion of better sleep hygiene and a global standardization of evidence-based policies to improve sleep health of populations across the world [4]. Further research in India is warranted due to important implications for health issues and policy changes. C. Recommendations for future research SES has an unrecognized influence on behavioral risk factors as well as public health strategies related to sleep health disparities. In several countries with a wide range of public health policies and economic challenges, sleep appears to be the main visible consequence of stress induced by difficult living conditions regardless population [13, 35-38]. Obviously, at a more macro level, country’s economic policy influences population’s SES as well as the funding of public health programs. The national and regional public health programs can target directly sleep health, while the same sleep health is affected by stress generated by the individual SES. Thus, SES, economic policy, public health and sleep are linked together. The socioeconomic model of sleep health (Figure 2, Figure 3) developed in previous research [2, 4, 18, 31] may explains all these interconnexions and can be a good start for a more national thinking about the management of Indian’s sleep health. The comparison of sleep health determinants can be made with other diseases determinants (cardiovascular diseases, mental disorders, etc…) to assess the magnitude of their influence, knowing that influence of SES on sleep can be measured objectively and quantitatively [1, 2, 11]. . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint Figure 2. Socioeconomic Model of Sleep Health (adapted with permission from Etindele Sosso FA et al. Eur. J. Investig. Health Psychol. Edu 16 duc 2022) . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 17 Figure 3. Socioeconomic Model of Sleep (adapted with permission from Etindele Sosso FA et al. Sleep Health 2021) . CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) The copyright holder for this preprint this version posted March 14, 2023. ; https://doi.org/10.1101/2023.03.13.23287175doi: medRxiv preprint 18 Authors’ Statement of Conflict of Interest and Adherence to Ethical Standards The author reports no conflict of interest.

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