Next Article in Journal
Ventilatory Management of Patients with Acute Respiratory Distress Syndrome Due to SARS-CoV-2
Next Article in Special Issue
Mothers with Dysregulated Eating and Their Offspring’s Emotional/Behavioral Functioning during the COVID-19 Pandemic: A Descriptive Study
Previous Article in Journal
Frailty in Older Patients with Transthyretin Cardiac Amyloidosis
Previous Article in Special Issue
Health-Related Quality of Life after Pediatric Traumatic Brain Injury: A Quantitative Comparison between Children’s and Parents’ Perspectives of the QOLIBRI-KID/ADO Questionnaire
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

Prevalence of Sleep Disturbances in Latin American Populations and Its Association with Their Socioeconomic Status—A Systematic Review and a Meta-Analysis

1
Department of Global Health and Ecoepidemiology, Redavi Institute, Montréal, QC H4J 1C5, Canada
2
Pneumonology Department, Centro Hospitalar de Trás-os-Montes e Alto Douro, 5000-508 Vila Real, Portugal
3
Laboratory of Epidemiology, Faculty of Medicine and Pharmacy of Casablanca, University Hassan II, Casablanca 20250, Morocco
4
Faculté de Médecine de L’université Paris-Saclay, 75015 Paris, France
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2023, 12(24), 7508; https://doi.org/10.3390/jcm12247508
Submission received: 18 September 2023 / Revised: 20 November 2023 / Accepted: 25 November 2023 / Published: 5 December 2023
(This article belongs to the Special Issue Personalized Medicine in Children and Adolescent Psychology)

Abstract

:
Background: The worldwide increase in the prevalence and incidence of sleep disturbances represents a major public health issue. Among multiple determinants affecting sleep health, an individual’s socioeconomic status (SES) is the most ignored and underestimated throughout the literature. No systematic review on the relation between SES and sleep health has been previously conducted in Latin America. Methods: PRISMA guidelines were used. Results: Twenty articles were included in the final sample (all cross-sectional studies), and twelve among them were rated as fair or poor quality. Among these studies, 80.0% (n = 16) were performed in Brazil, 10.0% (n = 2) were performed in Peru, 5.0% (n = 1) were performed in Chile, and 5.0% (n = 1) were multicentric (11 countries). The combined total number of participants was N = 128.455, comprising 3.7% (n = 4693) children, 16.0% (n = 20,586) adolescents, and 80.3% (n = 103,176) adults. The results show the following: (1) The sleep outcomes analyzed were sleep duration, sleep quality/sleep disturbance, insomnia, excessive daytime sleepiness (EDS), obstructive sleep apnea (OSA)/sleep-disordered breathing (SDB) symptoms, and bruxism. (2) The most used determinants were income, education level, employment status/occupation, wealth/assets, and composite indices. (3) Higher SES was associated with shorter sleep duration. (4) Lower SES was associated with a decrease in sleep quality, less frequent snoring, more prevalent EDS, and sleep bruxism. (5) Lower education was associated with insomnia. (6) Higher education was associated with more sleep bruxism. (7) The pooled prevalence using a meta-analysis of the random effects model was 24.73% (95%CI, 19.98–30.19), with high heterogeneity (I2 = 100%). (8) The prevalence of sleep disturbances decreased with high education (OR, 0.83; 95%CI, [0.69–0.99]; I2 = 79%), while it increased with low income (OR, 1.26; 95%CI, [1.12–1.42]; I2 = 59%), unemployment (OR, 2.84; 95%CI, [2.14–3.76]; I2 = 0%), and being a housewife (OR, 1.72; 95%CI, [1.19–2.48]; I2 = 55%). Discussion: This meta-analysis shows that lower SES (education, income, and work) was associated with sleep disturbances in Latin America. Therefore, sleep disturbance management should be addressed with a multidimensional approach, and a significant investment in targeted public health programs to reduce sleep disparities and support research should be made by the government before the situation becomes uncontrollable.

1. Introduction

Promoting a healthy sleep is an ongoing project which never ends. Sleep management, with our modern lifestyle, is a complex public health outcome requiring multidimensional interventions at the economic level, the populational level, and the societal level [1,2]. Mental health is also highly dependent on sleep, which has a recognized impact on several brains functions [3] and global health status, as well as stress [4]. In addition, sleep has a significant influence on mental health due to its relationship with people’s socioeconomic status (SES) and its connection with multiple biological systems involved in neurological disorders, such as anxiety [1,5,6,7,8,9,10]. In others words, sleep disturbances are mental disorders resulting from complex socioecological and economic interactions between the brain, the society in which we live, global health, and SES [7,11,12]. Thus, sleep health inequalities represent a mental health outcome similar to the public health issues previously reported for cardiovascular, mental health, and metabolic diseases [11]. The empirical literature in Western countries seems to validate the hypothesis that low-SES individuals reported more sleep disturbances than high-SES people [13,14,15,16,17,18]. Similar evidence exists in Asia [19,20] and Oceania [21,22]. Thus, sleep health disparity could be a public health issue in other areas, like Africa and Latin America. Few studies have documented sleep health disparities in South and Central America due to a wide range of determinants, such as employment, income, education, occupation, and social position [11,12,23]. An exhaustive evaluation of the public health literature related to Latin America revealed that no systematic review on the relation between SES and sleep health has been previously conducted in Latin America. It is important to analyze if trends related to the influence of SES determinants of mental health on sleep health observed in Western countries are following the same patterns as in Latin America. The goals of this systematic review are to (1) document the global prevalence of sleep disturbances in Latin American populations and (2) document the influence of SES on different types of sleep disturbances.

2. Methods

2.1. Literature Search

Relevant peer-reviewed studies included in this systematic review were identified by searching the databases Web of Science, Scopus, PubMed/Medline, and Google scholar between January 1990 and July 2022. A combination of the terms “socioeconomic”, “socio-economic”, “social position”, “social class”, “socioeconomic position”, “determinant*”, “indicators*”, “markers*”, “health inequalities”, “sleep”, “sleep disorders”, “sleep disturbances”, ‘’sleep complains”, “sleep outcome”, “sleep health”, “south america*”, “central america*”, and “latin america” was used. All included articles were identified following the PRISMA guidelines detailed in Figure 1 [24].

2.2. Inclusion and Exclusion Criteria

Empirical studies were defined as those of any design (cross-sectional, retrospective, or longitudinal) assessing humans of any gender, race/ethnicity, and age from the general population of any country from Latin America. The article should include an objective or a subjective measure of SES, such as income (monthly personal income, monthly family income, per capita income, and annual household income), educational level, wealth, profession/occupation, employment status, and perceived SES or self-reported SES. Proxy measures of SES, such as neighborhood and social class, were also included. Every sleep component, like sleep duration and sleep quality; and every sleep disturbance, like insomnia, excessive daytime sleepiness (EDS), obstructive sleep apnea (OSA), sleep-disordered breathing (SDB) symptoms, and bruxism, were considered dependent variables. For articles with samples composed of children and adolescents, perceived family SES measures, such as parental education, parental occupation, or annual household income, were used instead. Studies were excluded based on the following criteria: (1) they were interventional trials, every type of reviews (narrative, overview, systematic, umbrella, and meta-analyses), case series, case reports, conference series, or any writing without original research (editorial, commentary, letter to editors); (2) they were articles that did not provide statistical significance in cases where either SES or sleep was evaluated as a covariate or mediator; (3) the full text was not accessible; (4) the authors/researchers recruited participants with medical conditions at the beginning of study (for example, people with medications, including sleep pills; people with cancers; people with neurodegenerative diseases, etc.); and (5) articles that were not written in English, French, Portuguese, Arabic, or Spanish (the languages of the authors).

2.3. Selection of Evidence and Data Extraction

Two reviewers (FAES and SZ) independently reviewed the titles and abstracts of the studies identified by the search strategy and determined eligibility for inclusion, while disagreements were resolved by consensus with a third external reviewer. For studies that passed the initial screening, the entire text was collected, and the screening process was repeated by the same co-authors to conclude with the final articles included, validated by the third reviewer. Then, four reviewers (FTS, RQR, MC, and FAES) extracted from each report the following study characteristics: population, % women, age, sample size, SES measures, relevant statistics, interaction or mediation, sleep measures, conclusions/main effects, statistical methods, and results’ significance.

2.4. Quality Rating of Studies

The National Institute of Health’s (NIH) Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was used to rate the quality of the included studies [25]. This quality rating tool was used to analyze fourteen quality criteria, asking an equal number of questions about study objectives, population, exposures, outcomes, follow-up rates, and statistical analysis. SES was considered the exposure variable, and sleep measures the outcome variable, respectively. Overall quality ratings were calculated by taking the proportion of positive ratings on the sum of applicable criteria. Studies with a <50% positive rating were judged as poor quality, ≥65% as good quality, and the rest as fair quality [25].

2.5. Study Outcomes

The primary outcomes were (1) to determine the prevalence of sleep disturbances (sleep duration, sleep quality, insomnia, EDS, OSA/SDB symptoms, and bruxism) in Latin American populations and (2) to document the influence of SES (income, education, employment/occupation, wealth/assets, and composite indices) on different type of sleep disturbances. These were explored through an analysis of education, income, employment status, and perceived SES, if they were reported by at least two independent studies. The secondary outcomes were to (1) analyze the relationship of our findings with current public health literature and (2) promote a multidimensional approach for sleep management.

2.6. Data Analysis

A meta-analysis was performed using the meta package on R with RStudio interface (Version 4.1.3, R Core Team, 2022) to analyze the collected data.
In each study, the prevalence of sleeping problems in Latin America was obtained. Studies that did not report the prevalence were excluded. The random effects model was used with the logit transformation for obtaining the pooled results, because it produces more conservative results than fixed-effects models, regardless of the heterogenicity scores [26]. The pooled prevalence estimates of sleeping problems in Latin America were presented as a percentage with 95% confidence intervals (CIs), using a forest plot. Also, a subgroup analysis was conducted for different sleep issues, cities, study quality, and age group to assess the contribution of each study to overall heterogeneity in the prevalence of sleep disturbance.
To measure the relationship between SES and sleeping problems, we extracted from the selected publications the adjusted odds ratio (aOR) with 95% confidence intervals (CIs). Their standard errors were calculated from the respective CIs. The value from each study and the corresponding standard error were transformed into their natural logarithms to stabilize the variances and to normalize the distribution. The pooled OR (and 95%CI) was estimated using a DerSimonian–Laird random effect model. In situations in which a study reported effect estimates for independent subgroups, the subgroups were treated as individual studies in the meta-analysis.
The test of the overall effect was assessed by using Z-statistics at p < 0.05. The heterogeneity among the included studies was assessed using Cochran’s Q test and I2 statistics. The thresholds 25%, 50%, and 75% were used to indicate low, moderate, and high heterogeneity, respectively [27,28]. A funnel plot based on Egger’s regression test was used to evaluate the publication bias [29]. In all analyses, a p-value less than 0.05 was considered statistically significant.

3. Results

3.1. Characteristics of Included Articles

Twenty articles were included [30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49] in the final sample, all of which are cross-sectional studies, and they are listed in Table 1. Among these studies, 80.0% (n = 16) were performed in Brazil [30,31,32,34,36,37,40,41,42,43,44,45,46,47,48,49], 10.0% (n = 2) were performed in Peru [38,39], 5.0% (n = 1) were performed in Chile [33], and 5.0% (n = 1) were multicentric (11 countries) [35]. The combined total number of participants was N = 128,455, comprising 3.7% (n = 4693) children, 16.0% (n = 20,586) adolescents [37,38,40,41,44], and 80.3% (n = 103,176) adults [30,31,32,33,34,35,36,38,39,45,46,47,48,49]. The smallest sample was n = 851 [42] and the largest was n = 60,202 [48]. The socioeconomic indicators used were income (monthly personal income, monthly family income, per capita income, and annual household income) [30,31,32,33,34,36,37,40,42,43,44,45,46], wealth/assets [36,38,39,48,49], number of residents in the household [45], employment status/occupation [30,31,33,34,36,37,38,45,46,49], accessed healthcare system [36], and composite indices [41]. The sleep variables used were excessive daytime sleepiness [31,39,48], sleep duration [36,37,38,40,43,44,45,49], sleep quality/sleep disturbance [32,33,35,37,43,46,48], insomnia [30,35], obstructive sleep apnea (OSA)/sleep-disordered breathing (SDB) symptoms [34,39], and bruxism [41,42,47]. A majority of the articles were of poor quality (55%), and a detailed qualitative evaluation is available in Table 2.

3.2. Descriptive Synthesis of Articles

Quantitative analyses are presented in the results section. Presented below is a descriptive analysis, which provides a deeper examination of the overall findings that were not considered in the quantitative analysis. Table 1 presents details of the individual studies included in the descriptive analysis.

3.2.1. Sleep Duration

Seven cross-sectional studies examined the relation between socioeconomic status (SES) and sleep duration [36,37,38,40,43,45,49]. One of these was conducted with children (from 3 to 48 months old) [43], two studied adolescents (12 to 19 years old) [37,40], three involved adults [36,45,49], and one encompassed both adolescents and adults [38].
Overall, a higher SES was associated with a shorter sleep duration. Specifically, the highest level of education [36,37,38,40,45], being employed [36,37,38,45], higher income [37,38,40,45], and living with more residents [45] were associated with a shorter sleep duration in samples of adults and adolescents. In addition, long sleep was more prevalent among housewives [36], adolescents with black maternal skin color [40], and adolescents whose mothers had lower schooling [40].
In contrast, the poorest wealth index and being unemployed or not studying were associated with lower sleep percentage in another study with adults [49]. One study did not find consistent associations between sleep duration and maternal education or family income in children [43]. The overall quality of the selected studies was good for three studies [37,40,49] and fair for four studies [36,38,43,45].

3.2.2. Sleep Quality/Sleep Disturbance

Eight cross-sectional studies assessed the relationship between sleep quality or disturbance and SES [32,33,35,37,43,44,46,48]. One of these studies was conducted with infants (from 3 to 48 months) [43], two with students (14 to 19 years old) [37,44], and the others with adults [32,46,48]. One study focused only on adult women [35]. The overall quality of the studies selected was good for three studies [35,37,44], fair for three studies [32,33,46], and poor for one study [48].
Globally, a lower SES was associated with diminished sleep quality. Specifically, low income [32,37] and unemployment [33,46] were associated with impaired sleep quality. Two studies indicated a higher prevalence of sleep disturbance in women [33,46]. More educated adults had significantly less sleep disturbances [32,35,48]. In contrast, one study found that higher maternal education was associated with lower quality of sleep in students [44], but this association was not found in infants in another study [43]. Additionally, psychiatric comorbidities [32,33] and alcohol and drug consumption [33,35] were also associated with sleep disturbance.

3.2.3. Insomnia

Concerning insomnia, two cross-sectional studies assessed its relationship with SES [30,35]. One study evaluated only women [35], and one study assessed adults in general [30]. The overall quality of the two studies was good for one study [35] and fair for the other [30].
In both of the aforementioned studies, insomnia was independently associated with individuals with less education [30,35]. Moreover, alcohol and drug consumption was also associated with insomnia, according to another study [35].

3.2.4. Excessive Daytime Sleepiness

Three cross-sectional studies approached the association between excessive daytime sleepiness and SES [31,39,48]. All studies assessed adults. The overall quality of the studies selected was good for one study [31], fair for another study [39] and poor for the third study [48].
Largely, excessive daytime sleepiness was associated with a lower SES in one the studies [39]. Additionally, it was also significantly more prevalent in individuals with a lower family income [31] and less education [48].

3.2.5. Obstructive Sleep Apnea (OSA)/Sleep-Disordered Breathing (SDB) Symptoms

Concerning OSA/SDB, two cross-sectional studies assessed their relationship with SES [34,39]. Both studies evaluated adults in the general population. The overall quality was good in one study [34], and it was fair in the other study [39].
In one study, lower SES was associated with less frequent snoring. However, no significant association was found between SES and observed apneas [34]. The other study did not find any association between SES and OSA [39].

3.2.6. Bruxism

Three cross-sectional studies assessed the relation between SES and bruxism [41,42,47]. One of the studies was conducted with infants (ages 6–12 years) [42], one with adolescents (aged 13–15 years) [41], and the last one with adults [47]. The overall quality was good in two studies [41,42] and fair in one study [47].
Among these three studies, two studies reported that sleep bruxism was independently associated with higher SES, including higher education [41,47]. However, the other study, which was conducted by Mota-Veloso et al. [42], found that SES had a significant indirect effect on bruxism via sucking habits (finger sucking and biting nails or other objects) and that a lower SES was associated with more sleep bruxism [42].

3.3. Prevalence of Sleep Disturbances in Latin America

Based on our selection criteria, eighteen studies [30,31,32,33,34,35,36,37,38,39,41,42,43,44,45,46,47,48] were eligible. Whenever results in the same article were reported for different sleep disturbance types separately, they were entered into the analysis as separate studies. Therefore, a total of 18 papers including 28 studies were included in the final meta-analysis, which is shown in Figure 2A.
The overall pooled prevalence for sleep disturbance in Latin America was 24.73% (95% CI, 19.98–30.19; I2 = 100%) (Figure 2A). To decide whether to include all of the articles examining sleep disturbances or not, a publication bias chart was created. The results showed that publication bias was not significant (p = 0.059) (Figure 3).
We divided eighteen papers into seven categories according to different types of sleep disturbances. The subgroup analysis was manipulated based on seven categories (Figure 4). The highest prevalence was for insomnia, with 39.52% (95% CI, 31.79–47.82; p < 0.01), and the lowest prevalence was for EDS, with 15.52% (95% CI, 11.85–20.07; p < 0.01). The overall pooled prevalence for sleep disturbance was 33.30% (95% CI, 22.64–45.99; p = 0) across eight articles [32,33,35,37,43,44,46,48]; for OSA/SDB, it was 31.32% (95% CI, 28.77–34.00; p = 0.11) across two articles [34,39]; and for bruxism, it was 16.60% (95% CI, 8.83–29.04; p < 0.01) across three articles [41,42,47]. The overall pooled prevalence for short sleep duration was 16.65% (95% CI, 8.15–31.02; p = 0) across four articles [36,37,38,45], and for long sleep duration, it was 21.67% (95% CI, 15.95–28.75; p < 0.01) across three articles [36,38,45].

3.4. Sleep Length in Latin America

A total of two articles [40,49] reported the sleep length (Figure 2B). Where results were reported for men and women separately, they were entered into the analysis as separate studies. In the pooled analysis, sleep length was higher in men with sleep disturbances than women without any significant differences, with a standardized mean of 0.40 h (95% CI, 0.34–0.47; p = 0.11; I2 = 100%).

3.5. Subgroup Analysis

Because of a high level of heterogeneity across the included studies, a subgroup analysis was performed by region (cities), age group, and quality of study in relation to the principal outcome variable. The analysis revealed that the prevalence of sleep disturbances among infants, i.e., 56.52 (95% CI 54.11–58.89), was greater than that of adolescents, i.e., 20.33 (95% CI 12.68–30.95), and that of adults, i.e., 24.24 (95% CI 18.93–30.48). Also, the pooled prevalence of sleep disturbances was higher in Brazil, 25.00 (95% CI 19.54–31.40), than in Peru, 15.91 (95% CI 6.17–35.27) (Table 3).

3.6. Risk Factors

A meta-analysis was possible for sleep disturbance prevalence with three SES factors (education, income, and employment status).

3.6.1. Education and Sleep Disturbances

Eight studies [30,32,35,36,38,44,47,48] evaluated the association between education and the prevalence of sleep disturbances, as shown in Figure 5A. No association was found between low education and prevalent sleep disturbances, with a pooled OR of 1.42 (95%CI, [0.87–2.29]; p < 0.01). However, the meta-analysis showed that the highly educated population had a lower prevalence of sleep disturbances (OR, 0.83; 95%CI, [0.69–0.99]; p < 0.01), with a high heterogeneity between studies (I2 = 79%).
Separating the education analyses according to the quality of the studies did not reveal a significant subgroup effect for sleep disturbance prevalence (p = 0.70; Figure 6A). Similarly, when the education analyses were separated according to city, no significant subgroup effect for the prevalence of sleep disturbances was observed (p = 0.70; Figure 6B).

3.6.2. Income and Sleep Disturbances

Six studies [31,32,34,37,38,45] considered income as a risk factor of sleep disturbances in Latin America. The meta-analysis showed a significant relationship between low income and the prevalence of sleep problems (OR, 1.26; 95%CI, [1.12–1.42]), with moderate heterogeneity between studies (I2 = 59%). However, it did not show a significant association between mid-income (OR, 1.07; 95%CI, [0.78–1.46]; p < 0.01) or high income (OR, 0.76; 95%CI, [0.34–1.69]; p < 0.01) and prevalence of sleep problems (Figure 5B).

3.6.3. Work and Sleep Disturbances

Data on the relationship between work and sleep disturbances are shown in Figure 5C. The meta-analysis showed a significant relationship between unemployment and the prevalence of sleep disturbances (OR, 2.84; 95%CI, [2.14–3.76]), with no heterogeneity between studies (I2 = 0%). Similarly, in the pooled analysis, being a housewife was associated with a high prevalence of sleep disturbances (OR, 1.72; 95%CI, [1.19–2.48]), with moderate heterogeneity between studies (I2 = 55%).
Figure 6. Subgroup analyses to explore sources of heterogeneity in risk factors of sleep disturbances. (A) Forest plot demonstrating that higher education was associated with sleep disturbance prevalence by quality of the study (good vs. fair/poor). (B) Forest plot demonstrating that higher education was associated with sleep disturbance prevalence by city (Brazil vs. multicentric).
Figure 6. Subgroup analyses to explore sources of heterogeneity in risk factors of sleep disturbances. (A) Forest plot demonstrating that higher education was associated with sleep disturbance prevalence by quality of the study (good vs. fair/poor). (B) Forest plot demonstrating that higher education was associated with sleep disturbance prevalence by city (Brazil vs. multicentric).
Jcm 12 07508 g006

4. Discussion

4.1. Detailed Summary of Findings

The sleep outcomes analyzed were sleep duration, sleep quality/sleep disturbance, insomnia, excessive daytime sleepiness (EDS), obstructive sleep apnea (OSA)/sleep-disordered breathing (SDB) symptoms, and bruxism. The most used determinants were income, education level, employment status/occupation, wealth/assets, and composite indices.
A higher SES was associated with lower sleep duration, and a lower SES was associated with a decrease in sleep quality, less frequent snoring, more prevalent EDS, and sleep bruxism. Lower education was associated with insomnia, and higher education was associated with more sleep bruxism. For the 20 articles included, 12 were rated as fair or poor in study quality. Therefore, a meta-analysis was performed to estimate the prevalence of sleep disturbances in Latin America and the main SES risk factors that could be associated with it. The pooled prevalence, using a meta-analysis of the random effects model, was 24.73% (95%CI, 19.98–30.19), with high heterogeneity (I2 = 100%). The meta-analysis showed that the prevalence of sleep disturbances decreased with high education (OR, 0.83; 95%CI, [0.69–0.99]; I2 = 79%), while it increased with low income (OR, 1.26; 95%CI, [1.12–1.42]; I2 = 59%), unemployment (OR, 2.84; 95%CI, [2.14–3.76]; I2 = 0%), and being a housewife (OR, 1.72; 95%CI, [1.19–2.48]; I2 = 55%).

4.2. Relationship with Public Health Literature

Epidemiologic data continue to increase the literature about the influence of sleep on the general population’s health status. Sleep plays a vital role in several body functions, as well as health disparities. The scientific community is still investigating external and environmental factors affecting sleep mechanisms, but there is still a lot that is unknown. Based on the current findings, it can be hypothesized that sleep disturbances are associated with socioeconomic status, as suggested by many other studies [17,50,51,52,53,54,55,56,57]. The fact is that the gradient of health disparity existing for some diseases, like cardiovascular illness, seems the same for sleep. Regardless of the region of the world where the investigation is conduced, sleep disparities are observed because our findings on Latin America support the previous results [17,50,51,52,53,54,55,56,57]. Our findings are consistent with those of previous studies from places other than Latin America, and they are additional arguments in favor of the establishment of a more efficient worldwide program framing sleep health management.

4.3. The Necessity of a Multidimensional Sleep Management

Sleep disturbance management should be addressed by a multidimensional approach. Recent epidemiological studies performed outside Latin America in different public health contexts reported significant associations of sleep with stress [4], work conditions [8], environment [58], and employment [17,59,60], and they also revealed latent interactions existing between government policy and public health strategies [22,54,61,62,63] (Figure 7). Obviously, a government’s economic policy influences the funding of public health programs. Similarly, SES directly influences health status regardless of the disease, as assessed through individuals’ living conditions and their resulting behavioral risk factors and stress. Knowing that sleep disparities can be measured objectively and quantitatively [6,7,49], our suggestion for governments is to invest as soon as possible in preventive management programs for sleep disturbances before they become uncontrollable. It was already documented how expensive absenteeism and presenteeism due to sleep disturbances are for the economy [64,65], but diverse governments did not move forward yet with strong regulations to reduce these important losses [66,67].
In this meta-analysis, there is an inequal distribution of research because 80% of studies came from Brazil (Table 1). Even if Brazil is representative of Latin American populations, its public health’s context regarding sleep management is not necessarily identical to that of its neighbors. More research should be performed in other Latin American countries to obtain an accurate overview of sleep disparities in this continent. Our suggestion for scientists is to not forget that cross-sectionals studies are often used to understand determinants of health and establish preliminary evidence [68]; however, they are useless when it is necessary to consider the correlation between theoretical determinants and health outcomes. This first meta-analysis on sleep determinants in Latin America highlighted the high quality of cross-sectional studies published, as well as the lack of systematic review and longitudinal studies, similar to what has been found recently with the African population [69]. To support public health strategies, randomized controlled trials and longitudinal studies are required with a broader objective related to the SES–sleep health gradient, including the role of unhealthy behaviors, chronic diseases, and psychological factors [68].

5. Conclusions

This meta-analysis reveals that the prevalence of sleep disturbances accounts for almost a quarter of the health issues in Latin America, and they have been associated with lower SES, especially in terms of education, income, and work. Despite the growing body of literature around the importance of sleep, it seems non-significant enough for decision makers who still do not pay attention to this public health matter. Governmental programs should consider scientific evidence and could be funded to allow fast and permanent results in the near future, before reaching an uncontrollable point.

Author Contributions

F.A.E.S., Conceptualization, Methodology, Validation, Investigation, Data Extraction, Writing—Original and Revised Draft, and Review and Editing; F.T.S., Data Extraction and Writing—Original Draft; R.Q.R., Data Extraction and Writing—Original Draft. M.M.C., Data Extraction and Writing—Original Draft; S.Z., Methodology, Data Analysis, Meta-Analysis, and Writing—Review and Editing; G.C.Z., Writing—Original Draft and Editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare that they have nothing to disclose.

References

  1. Hale, L.; Troxel, W.; Buysse, D.J. Sleep Health: An Opportunity for Public Health to Address Health Equity. Annu. Rev. Public Health 2020, 41, 81–99. [Google Scholar] [CrossRef]
  2. Grandner, M.A. Sleep, Health, and Society. Sleep Med. Clin. 2020, 15, 319–340. [Google Scholar] [CrossRef]
  3. Carroll, J.E.; Irwin, M.R.; Merkin, S.S.; Seeman, T.E. Sleep and Multisystem Biological Risk: A Population-Based Study. PLoS ONE 2015, 10, e0118467. [Google Scholar] [CrossRef]
  4. Lo Martire, V.; Caruso, D.; Palagini, L.; Zoccoli, G.; Bastianini, S. Stress & Sleep: A Relationship Lasting a Lifetime. Neurosci. Biobehav. Rev. 2020, 117, 65–77. [Google Scholar] [CrossRef] [PubMed]
  5. Vermeiren, A.P.; Willeboordse, M.; Oosterhoff, M.; Bartelink, N.; Muris, P.; Bosma, H. Socioeconomic Multi-Domain Health Inequalities in Dutch Primary School Children. Eur. J. Public Health 2018, 28, 610–616. [Google Scholar] [CrossRef] [PubMed]
  6. Etindele Sosso, F.A. Measuring Sleep Health Disparities with Polysomnography: A Systematic Review of Preliminary Findings. Clocks Sleep 2022, 4, 80–87. [Google Scholar] [CrossRef] [PubMed]
  7. Etindele Sosso, F.A.; Holmes, S.D.; Weinstein, A.A. Influence of Socioeconomic Status on Objective Sleep Measurement: A Systematic Review and Meta-Analysis of Actigraphy Studies. Sleep Health 2021, 7, 417–428. [Google Scholar] [CrossRef]
  8. Etindele-Sosso, F.A. Insomnia, Excessive Daytime Sleepiness, Anxiety, Depression and Socioeconomic Status among Customer Service Employees in Canada. Sleep Sci. 2020, 13, 54–64. [Google Scholar] [CrossRef] [PubMed]
  9. Berwick, D.M. The Moral Determinants of Health. JAMA 2020, 324, 225–226. [Google Scholar] [CrossRef] [PubMed]
  10. Rodriguez, J.M.; Karlamangla, A.S.; Gruenewald, T.L.; Miller-Martinez, D.; Merkin, S.S.; Seeman, T.E. Social Stratification and Allostatic Load: Shapes of Health Differences in the MIDUS Study in the United States. J. Biosoc. Sci. 2019, 51, 627–644. [Google Scholar] [CrossRef]
  11. Etindele Sosso, F.A.; Kreidlmayer, M.; Pearson, D.; Bendaoud, I. Towards A Socioeconomic Model of Sleep Health among the Canadian Population: A Systematic Review of the Relationship between Age, Income, Employment, Education, Social Class, Socioeconomic Status and Sleep Disparities. Eur. J. Investig. Health Psychol. Educ. 2022, 12, 1143–1167. [Google Scholar] [CrossRef] [PubMed]
  12. Grandner, M.A. Chapter 5—Social-Ecological Model of Sleep Health. In Sleep and Health; Grandner, M.A., Ed.; Academic Press: Cambridge, MA, USA, 2019; pp. 45–53. ISBN 978-0-12-815373-4. [Google Scholar]
  13. van de Straat, V.; Cheval, B.; Schmidt, R.E.; Sieber, S.; Courvoisier, D.; Kliegel, M.; Burton-Jeangros, C.; Cullati, S.; Bracke, P. Early Predictors of Impaired Sleep: A Study on Life Course Socioeconomic Conditions and Sleeping Problems in Older Adults. Aging Ment. Health 2020, 24, 322–332. [Google Scholar] [CrossRef]
  14. Papadopoulos, D.; Sosso, F.E.; Khoury, T.; Surani, S.R. Sleep Disturbances Are Mediators Between Socioeconomic Status and Health: A Scoping Review. Int. J. Ment. Health Addict. 2022, 20, 480–504. [Google Scholar] [CrossRef]
  15. Mayne, S.L.; Mitchell, J.A.; Virudachalam, S.; Williamson, A.A. 0397 Neighborhood Physical and Social Environments and Sleep Among Children and Adolescents: A Systematic Review. Sleep 2020, 43, A152. [Google Scholar] [CrossRef]
  16. Petrovic, D.; Haba-Rubio, J.; Carmeli, C.; Vollenweider, P.; Heinzer, R.; Stringhini, S. Social Inequalities in Sleep-Disordered Breathing: Evidence from the CoLaus|HypnoLaus Study. J. Sleep Res. 2019, 28, e12799. [Google Scholar] [CrossRef] [PubMed]
  17. Mai, Q.D.; Hill, T.D.; Vila-Henninger, L.; Grandner, M.A. Employment Insecurity and Sleep Disturbance: Evidence from 31 European Countries. J. Sleep Res. 2019, 28, e12763. [Google Scholar] [CrossRef]
  18. Papadopoulos, D.; Etindele Sosso, F.A. Socioeconomic Status and Sleep Health: A Narrative Synthesis of Three Decades of Empirical Research. J. Clin. Sleep Med. JCSM Off. Publ. Am. Acad. Sleep Med. 2023, 19, 605–620. [Google Scholar] [CrossRef]
  19. Wu, W.; Jiang, Y.; Wang, N.; Zhu, M.; Liu, X.; Jiang, F.; Zhao, G.; Zhao, Q. Sleep Quality of Shanghai Residents: Population-Based Cross-Sectional Study. Qual. Life Res. Int. J. Qual. Life Asp. Treat. Care Rehabil. 2020, 29, 1055–1064. [Google Scholar] [CrossRef]
  20. Visvalingam, N.; Sathish, T.; Soljak, M.; Chua, A.-P.; Dunleavy, G.; Divakar, U.; Nazeha, N.; Bajpai, R.; Soh, C.K.; Woon, K.K.; et al. Prevalence of and Factors Associated with Poor Sleep Quality and Short Sleep in a Working Population in Singapore. Sleep Health 2020, 6, 277–287. [Google Scholar] [CrossRef]
  21. Metse, A.P.; Bowman, J.A. Prevalence of Self-Reported Suboptimal Sleep in Australia and Receipt of Sleep Care: Results from the 2017 National Social Survey. Sleep Health J. Natl. Sleep Found. 2020, 6, 100–109. [Google Scholar] [CrossRef]
  22. Muller, D.; Paine, S.-J.; Wu, L.J.; Signal, T.L. How Long Do Preschoolers in Aotearoa/New Zealand Sleep? Associations with Ethnicity and Socioeconomic Position. Sleep Health 2019, 5, 452–458. [Google Scholar] [CrossRef] [PubMed]
  23. Ribeiro, A.I.; Amaro, J.; Lisi, C.; Fraga, S. Neighborhood Socioeconomic Deprivation and Allostatic Load: A Scoping Review. Int. J. Environ. Res. Public Health 2018, 15, 1092. [Google Scholar] [CrossRef]
  24. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar] [CrossRef] [PubMed]
  25. National Institute of Health. Study Quality Assessment Tool; National Institutes of Health: Bethesda, MD, USA, 2018.
  26. Barendregt, J.J.; Doi, S.A.; Lee, Y.Y.; Norman, R.E.; Vos, T. Meta-Analysis of Prevalence. J. Epidemiol. Community Health 2013, 67, 974–978. [Google Scholar] [CrossRef] [PubMed]
  27. Borenstein, M.; Higgins, J.P.T.; Hedges, L.V.; Rothstein, H.R. Basics of Meta-Analysis: I2 Is Not an Absolute Measure of Heterogeneity. Res. Synth. Methods 2017, 8, 5–18. [Google Scholar] [CrossRef] [PubMed]
  28. Higgins, J.P.T.; Thompson, S.G.; Spiegelhalter, D.J. A Re-Evaluation of Random-Effects Meta-Analysis. J. R. Stat. Soc. Ser. A Stat. Soc. 2009, 172, 137–159. [Google Scholar] [CrossRef] [PubMed]
  29. Egger, M.; Davey Smith, G.; Schneider, M.; Minder, C. Bias in Meta-Analysis Detected by a Simple, Graphical Test. BMJ 1997, 315, 629–634. [Google Scholar] [CrossRef]
  30. Rocha, F.L.; Guerra, H.L.; Lima-Costa, M.F.F. Prevalence of Insomnia and Associated Socio-Demographic Factors in a Brazilian Community: The Bambuí Study. Sleep Med. 2002, 3, 121–126. [Google Scholar] [CrossRef]
  31. Hara, C.; Lopes Rocha, F.; Lima-Costa, M.F.F. Prevalence of Excessive Daytime Sleepiness and Associated Factors in a Brazilian Community: The Bambuí Study. Sleep Med. 2004, 5, 31–36. [Google Scholar] [CrossRef]
  32. Blay, S.L.; Andreoli, S.B.; Gastal, F.L. Prevalence of Self-Reported Sleep Disturbance among Older Adults and the Association of Disturbed Sleep with Service Demand and Medical Conditions. Int. Psychogeriatr. 2008, 20, 582–595. [Google Scholar] [CrossRef]
  33. Fritsch Montero, R.; Lahsen Martínez, P.; Romeo Gómez, R.; Araya Baltra, R.; Rojas Castillo, G. Sleep Disorders in the Adult Population of Santiago of Chile and Its Association with Common Psychiatric Disorders. Actas Esp. Psiquiatr. 2010, 38, 358–364. [Google Scholar]
  34. Tufik, S.; Santos-Silva, R.; Taddei, J.A.; Bittencourt, L.R.A. Obstructive Sleep Apnea Syndrome in the Sao Paulo Epidemiologic Sleep Study. Sleep Med. 2010, 11, 441–446. [Google Scholar] [CrossRef]
  35. Blümel, J.E.; Cano, A.; Mezones-Holguín, E.; Barón, G.; Bencosme, A.; Benítez, Z.; Bravo, L.M.; Calle, A.; Flores, D.; Espinoza, M.T.; et al. A Multinational Study of Sleep Disorders during Female Mid-Life. Maturitas 2012, 72, 359–366. [Google Scholar] [CrossRef] [PubMed]
  36. Lima, M.G.; Bergamo Francisco, P.M.S.; de Azevedo Barros, M.B. Sleep Duration Pattern and Chronic Diseases in Brazilian Adults (ISACAMP, 2008/09). Sleep Med. 2012, 13, 139–144. [Google Scholar] [CrossRef]
  37. Hoefelmann, L.P.; Lopes, A.D.S.; da Silva, K.S.; Moritz, P.; Nahas, M.V. Sociodemographic Factors Associated with Sleep Quality and Sleep Duration in Adolescents from Santa Catarina, Brazil: What Changed between 2001 and 2011? Sleep Med. 2013, 14, 1017–1023. [Google Scholar] [CrossRef]
  38. Carrillo-Larco, R.M.; Bernabé-Ortiz, A.; Miranda, J.J.; Rey de Castro, J. Peruvians’ Sleep Duration: Analysis of a Population-Based Survey on Adolescents and Adults. PeerJ 2014, 2, e345. [Google Scholar] [CrossRef]
  39. Schwartz, N.G.; Rattner, A.; Schwartz, A.R.; Mokhlesi, B.; Gilman, R.H.; Bernabe-Ortiz, A.; Miranda, J.J.; Checkley, W. Sleep Disordered Breathing in Four Resource-Limited Settings in Peru: Prevalence, Risk Factors, and Association with Chronic Diseases. Sleep 2015, 38, 1451–1459. [Google Scholar] [CrossRef]
  40. Schäfer, A.A.; Domingues, M.R.; Dahly, D.L.; Meller, F.O.; Gonçalves, H.; Wehrmeister, F.C.; Assunção, M.C.F. Correlates of Self-Reported Weekday Sleep Duration in Adolescents: The 18-Year Follow-up of the 1993 Pelotas (Brazil) Birth Cohort Study. Sleep Med. 2016, 23, 81–88. [Google Scholar] [CrossRef]
  41. Fulgencio, L.B.; Corrêa-Faria, P.; Lage, C.F.; Paiva, S.M.; Pordeus, I.A.; Serra-Negra, J.M. Diagnosis of Sleep Bruxism Can Assist in the Detection of Cases of Verbal School Bullying and Measure the Life Satisfaction of Adolescents. Int. J. Paediatr. Dent. 2017, 27, 293–301. [Google Scholar] [CrossRef] [PubMed]
  42. Mota-Veloso, I.; Celeste, R.K.; Fonseca, C.P.; Soares, M.E.C.; Marques, L.S.; Ramos-Jorge, M.L.; Ramos-Jorge, J. Effects of Attention Deficit Hyperactivity Disorder Signs and Socio-Economic Status on Sleep Bruxism and Tooth Wear among Schoolchildren: Structural Equation Modelling Approach. Int. J. Paediatr. Dent. 2017, 27, 523–531. [Google Scholar] [CrossRef]
  43. Netsi, E.; Santos, I.S.; Stein, A.; Barros, F.C.; Barros, A.J.D.; Matijasevich, A. A Different Rhythm of Life: Sleep Patterns in the First 4 Years of Life and Associated Sociodemographic Characteristics in a Large Brazilian Birth Cohort. Sleep Med. 2017, 37, 77–87. [Google Scholar] [CrossRef] [PubMed]
  44. de Lima, T.R.; Silva, D.A.S. Association of Sleep Quality with Sociodemographic Factors and Lifestyle in Adolescents from Southern Brazil. World J. Pediatr. WJP 2018, 14, 383–391. [Google Scholar] [CrossRef] [PubMed]
  45. Lima, M.G.; Barros, M.B.D.A.; Ceolim, M.F.; Zancanella, E.; Cardoso, T.A.M.D.O. Sleep Duration, Health Status, and Subjective Well-Being: A Population-Based Study. Rev. Saude Publica 2018, 52, 82. [Google Scholar] [CrossRef] [PubMed]
  46. Barros, M.B.D.A.; Lima, M.G.; Ceolim, M.F.; Zancanella, E.; Cardoso, T.A.M.D.O. Quality of Sleep, Health and Well-Being in a Population-Based Study. Rev. Saúde Pública 2019, 53, 82. [Google Scholar] [CrossRef] [PubMed]
  47. Pontes, L.D.S.; Prietsch, S.O.M. Sleep bruxism: Population based study in people with 18 years or more in the city of Rio Grande, Brazil. Rev. Bras. Epidemiol. Braz. J. Epidemiol. 2019, 22, e190038. [Google Scholar] [CrossRef]
  48. Wendt, A.; Costa, C.S.; Machado, A.K.F.; Costa, F.S.; Neves, R.G.; Flores, T.R.; Santos, I.; Wehrmeister, F.C. Sleep Disturbances and Daytime Fatigue: Data from the Brazilian National Health Survey, 2013. Cad. Saude Publica 2019, 35, 00086918. [Google Scholar] [CrossRef]
  49. Wendt, A.; da Silva, I.C.M.; Gonçalves, H.; Assunção, M.C.F.; Menezes, A.M.B.; Wehrmeister, F.C. Sleep Parameters Measured by Accelerometry: Descriptive Analyses from the 22-Year Follow-up of the Pelotas 1993 Birth Cohort. Sleep Med. 2020, 67, 83–90. [Google Scholar] [CrossRef] [PubMed]
  50. Holstein, B.E.; Ammitzbøll, J.; Damsgaard, M.T.; Pant, S.W.; Pedersen, T.P.; Skovgaard, A.M. Difficulties Falling Asleep among Adolescents: Social Inequality and Time Trends 1991–2018. J. Sleep Res. 2020, 29, e12941. [Google Scholar] [CrossRef]
  51. Williamson, A.A.; Mindell, J.A.; Hiscock, H.; Quach, J. Sleep Problem Trajectories and Cumulative Socio-Ecological Risks: Birth to School-Age. J. Pediatr. 2019, 215, 229–237. [Google Scholar] [CrossRef]
  52. Sarveswaran, G.; Arikrishnan, K.; Krishnamoorthy, Y.; Sakthivel, M.; Majella, M.G.; Lakshminarayanan, S. Prevalence and Determinants of Poor Quality of Sleep among Adolescents in Rural Puducherry, South India. Int. J. Adolesc. Med. Health 2019, 33, 20180101. [Google Scholar] [CrossRef]
  53. Ren, Y.; Liu, Y.; Meng, T.; Liu, W.; Qiao, Y.; Gu, Y.; Li, Y.; Liu, Y.; Yu, Y.; Cheng, Y. Social-Biological Influences on Sleep Duration among Adult Residents of Northeastern China. Health Qual. Life Outcomes 2019, 17, 47. [Google Scholar] [CrossRef] [PubMed]
  54. Peltzer, K.; Pengpid, S. Prevalence, Social and Health Correlates of Insomnia among Persons 15 Years and Older in Indonesia. Psychol. Health Med. 2019, 24, 757–768. [Google Scholar] [CrossRef] [PubMed]
  55. Magee, C.; Gopaldasani, V.; Bakand, S.; Coman, R. The Physical Work Environment and Sleep: A Latent Class Analysis. J. Occup. Environ. Med. 2019, 61, 1011–1018. [Google Scholar] [CrossRef] [PubMed]
  56. Doane, L.D.; Breitenstein, R.S.; Beekman, C.; Clifford, S.; Smith, T.J.; Lemery-Chalfant, K. Early Life Socioeconomic Disparities in Children’s Sleep: The Mediating Role of the Current Home Environment. J. Youth Adolesc. 2019, 48, 56–70. [Google Scholar] [CrossRef] [PubMed]
  57. Chen, J.-H. Experience of Poverty and Problem Sleep in Later Life. Res. Aging 2019, 41, 697–722. [Google Scholar] [CrossRef] [PubMed]
  58. Johnson, D.A.; Billings, M.E.; Hale, L. Environmental Determinants of Insufficient Sleep and Sleep Disorders: Implications for Population Health. Curr. Epidemiol. Rep. 2018, 5, 61–69. [Google Scholar] [CrossRef] [PubMed]
  59. Maeda, M.; Filomeno, R.; Kawata, Y.; Sato, T.; Maruyama, K.; Wada, H.; Ikeda, A.; Iso, H.; Tanigawa, T. Association between Unemployment and Insomnia-Related Symptoms Based on the Comprehensive Survey of Living Conditions: A Large Cross-Sectional Japanese Population Survey. Ind. Health 2019, 57, 701–710. [Google Scholar] [CrossRef]
  60. Lee, E.-S.; Park, S. Patterns of Change in Employment Status and Their Association with Self-Rated Health, Perceived Daily Stress, and Sleep among Young Adults in South Korea. Int. J. Environ. Res. Public Health 2019, 16, 4491. [Google Scholar] [CrossRef]
  61. Sheehan, C.M.; Frochen, S.E.; Walsemann, K.M.; Ailshire, J.A. Are U.S. Adults Reporting Less Sleep?: Findings from Sleep Duration Trends in the National Health Interview Survey, 2004–2017. Sleep 2019, 42, zsy221. [Google Scholar] [CrossRef]
  62. Poulain, T.; Vogel, M.; Sobek, C.; Hilbert, A.; Körner, A.; Kiess, W. Associations Between Socio-Economic Status and Child Health: Findings of a Large German Cohort Study. Int. J. Environ. Res. Public Health 2019, 16, 677. [Google Scholar] [CrossRef]
  63. Chami, H.A.; Bechnak, A.; Isma’eel, H.; Talih, F.; Nasreddine, L.; Nasrallah, M.; Tamim, H. Sleepless in Beirut: Sleep Difficulties in an Urban Environment With Chronic Psychosocial Stress. J. Clin. Sleep Med. JCSM Off. Publ. Am. Acad. Sleep Med. 2019, 15, 603–614. [Google Scholar] [CrossRef]
  64. Sadeghniiat-Haghighi, K.; Najafi, A.; Eftekhari, S.; Tarkhan, S. Insomnia and Its Association with Absenteeism: A Cross-Sectional Study among Iranian Nursing Team. Sleep Sci. 2021, 14, 305–310. [Google Scholar] [CrossRef]
  65. Hwang, J.; Cho, S.-S.; Kim, J.I. Association between Insomnia and Absenteeism or Presenteeism among Korean Employees. Ann. Occup. Environ. Med. 2022, 34, e41. [Google Scholar] [CrossRef] [PubMed]
  66. Huyett, P.; Bhattacharyya, N. Incremental Health Care Utilization and Expenditures for Sleep Disorders in the United States. J. Clin. Sleep Med. JCSM Off. Publ. Am. Acad. Sleep Med. 2021, 17, 1981–1986. [Google Scholar] [CrossRef] [PubMed]
  67. Wickwire, E.M. Value-Based Sleep and Breathing: Health Economic Aspects of Obstructive Sleep Apnea. Fac. Rev. 2021, 10, 40. [Google Scholar] [CrossRef] [PubMed]
  68. Wang, X.; Cheng, Z. Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. Chest 2020, 158, S65–S71. [Google Scholar] [CrossRef]
  69. Sosso, F.A.E.; Matos, E.; Papadopoulos, D. Social Disparities in Sleep Health of African Populations: A Systematic Review and Meta-Analysis of Observational Studies. Sleep Health 2023, S2352-7218(23)00198-5. [Google Scholar] [CrossRef]
Figure 1. PRISMA flowchart of study selection process.
Figure 1. PRISMA flowchart of study selection process.
Jcm 12 07508 g001
Figure 2. Forest plot showing the primary outcomes in 31 cross sectional studies from 20 published reports in Latin America: (A) prevalence of sleep disturbances and (B) sleep length (hours).
Figure 2. Forest plot showing the primary outcomes in 31 cross sectional studies from 20 published reports in Latin America: (A) prevalence of sleep disturbances and (B) sleep length (hours).
Jcm 12 07508 g002
Figure 3. Funnel plot for meta-analysis of the prevalence of sleep disturbances in Latin America. Egger’s test: p = 0.0598.
Figure 3. Funnel plot for meta-analysis of the prevalence of sleep disturbances in Latin America. Egger’s test: p = 0.0598.
Jcm 12 07508 g003
Figure 4. Subgroup analysis on the prevalence of sleep disturbances in Latin America by type of sleep disturbance. The black dot point is the estimate, and the horizontal line is the 95% CI for prevalence plotted for each study. The black diamond at the bottom of each type of sleep disturbance is the estimated average prevalence. CI: confidence interval.
Figure 4. Subgroup analysis on the prevalence of sleep disturbances in Latin America by type of sleep disturbance. The black dot point is the estimate, and the horizontal line is the 95% CI for prevalence plotted for each study. The black diamond at the bottom of each type of sleep disturbance is the estimated average prevalence. CI: confidence interval.
Jcm 12 07508 g004
Figure 5. (A) Forest plot for education and sleep disturbance prevalence (compared to the reference group). (B) Forest plot for income and sleep disturbance prevalence (compared to the reference group). (C) Forest plot for employment status and sleep disturbance prevalence (compared to the reference group). Box sizes reflect the weights of studies included in the meta-analysis, horizontal lines are the 95% CIs, and the summary ORs are represented by the diamond. OR, odds ratio; CI, confidence interval.
Figure 5. (A) Forest plot for education and sleep disturbance prevalence (compared to the reference group). (B) Forest plot for income and sleep disturbance prevalence (compared to the reference group). (C) Forest plot for employment status and sleep disturbance prevalence (compared to the reference group). Box sizes reflect the weights of studies included in the meta-analysis, horizontal lines are the 95% CIs, and the summary ORs are represented by the diamond. OR, odds ratio; CI, confidence interval.
Jcm 12 07508 g005
Figure 7. The Socioeconomic and Environmental Model of Health (SEEMOH).
Figure 7. The Socioeconomic and Environmental Model of Health (SEEMOH).
Jcm 12 07508 g007
Table 1. Characteristics of included studies investigating determinants of sleep health in Latin America.
Table 1. Characteristics of included studies investigating determinants of sleep health in Latin America.
StudyStudy DesignPopulation% WomenAge (Years) Sample SizeSES MeasuresSleep MeasuresStatistically Significant FindingsMain EffectsInteractions/Mediations
Rocha 2002 [30]Cross-sectionalAdults from Bambui, Brazil55.539 (N/R)1066Years of education (0, 1–3, 4–7, 8+)
Current employment situation (working, not working, retired)
Monthly family income (<2.0, 2.0–3.9, ≥4 Brazilian minimum wage)
Insomnia symptoms (difficulty initiating sleep, frequency of disrupted sleep, and frequency of early morning awakening) during the last 30 days, at least three times a week or more, with any level of distressInsomnia was higher in females with age 60+ (OR, 1.8; CI, 1.1–1.3) and with 1–3 or no years of schooling (OR = 1.8, CI = 1.1–3.0; OR = 2.6, CI = 1.3–5.1) and males with 1–3 years of schooling (OR, 2.2; CI, 1.1–4.1)Insomnia was independently associated with less education
Hara 2004 [31]Cross-sectionalAdults from Bambui, Brazil55.539 (N/R)1066Years of education (0, 1–3, 4–7, 8+)
Monthly personal income (none, <1, 1.0–1.9, ≥2.0 Brazilian minimum wages) Monthly family income
(<2.0, ≥2.0 Brazilian minimum wages)
Current employment situation (student, working, unemployed, retired)
Excessive daytime sleepiness three or more times per week with consequent impairment
of daily activities
EDS was associated with insomnia (OR, 2.24; CI, 1.6–3.15) and lower family income (OR, 1.47; CI, 1.02–2.12)Lower family income was associated with EDS
Blay 2008 [32]Cross-sectionalAdults ≥ 60 y from Rio Grande do Sul, Brazil66.0N/R6961Income (< vs. ≥USD 200/month)
Years of education (0–3 vs. ≥4)
Sleep disturbance (single question, yes vs. no) in the last 30 daysLower income (OR, 1.26; CI, 1.11–1.43) and lower education (OR, 1.8; CI, 1.24–1.76) were related to disturbed sleepLow income and low education were independent risk factors for self-reported sleep disturbance.
Fritsch Montero 2010 [33]Cross-sectionalAdults 18–64 y from Gran Santiago, Chile52.3N/R3867Schooling (upper, middle, basic, none)
Employment status (10 levels)
Per capita income quartiles (high, middle high, middle low, low), decrease of income.
Revised Clinical Interview Schedule sleep score > 1Being an occasional worker (OR, 1.75; CI, 1.11–2.77), unemployed and looking (OR, 2.8; CI, 1.32–5.93) and not looking (OR, 4.63; CI, 1.7–12.51) for a job, having adequate (OR, 2.05; CI, 1.21–3.47) and poor living conditions (OR, 2.04; CI, 1.24–3.47), being a housewife (OR, 1.46; CI, 1.09–1.95) or female (OR, 1.43; CI, 1.14–1.80) were considered to be risk factors for sleep disturbanceUnemployed and occasional workers patients, housewives, patients with common mental disorders had higher odds of having sleep disorders
Tufik 2010 [34]Cross-sectionalAdults 20–80 y from the general population in Sao Paulo, Brazil55N/R1042Annual household income (high, mid, low according to the Brazilian Economic Classification Criteria),
Employment status (working vs. not working)
OSA ICSD-2 criteria (AHI from PSG, items 2 and 5 from Berlin Questionnaire, ESS > 10 and/or item 8 from PSQI, Chalder Fatigue Scale > 4)Increasing age (OR, 3.9; 6.6; 10.8; 34.5) and gender (OR, 4.1; CI, 2.9–5.8) were independent and strong associated factors for the presence of OSA.SES was not associated with OSALow income was a protective factor for males (OR, 0.4; CI, 0.1–0.9), but not significant in females (p = 0.057)
Blümel 2012 [35]Cross-sectional40–59 y women recruited from 20 healthcare centers in 11 Latin American countries10049.8 (5.4)6079Education (≤ vs. >12 years), Insomnia (AIS score > 5). Sleep quality (PSQI global score > 5).Education > 12 years was associated with less insomnia (OR, 0.84; CI, 0.74–0.9) and less poor sleep quality (OR, 0.83; CI, 0.73–0.94).Higher educational level was an independent risk factor related to less insomnia and better sleep quality
Lima 2012 [36]Cross-sectionalAdults from general population in Campinas, Brazil52.341.8
(95% CI: 40.7–42.9)
2637Education (0–3, 4–7, 8–11, ≥12 y), per capita monthly household income (1 minimum salary or less, 1–3 times the minimal salary, 3 or more times the minimum salary), work status (working, not working, housewife), number of household appliances (≤10, >10).Sleep duration (≤6, 7–8, ≥9 h)Long sleep (≥9 h) was less prevalent among those with 4–11 or ≥12 years of schooling (OR = 0.38, CI = 0.25–0.60; OR = 0.26, CI = 0.13–0.50) and more prevalent among those not working or housewives (OR = 2.70, CI = 1.96–3.73; OR = 2.13, CI = 1.43–3.18)Long sleep was more prevalent among those with a lower level of education, those who did not work and housewives.
Hoefelmann 2013 [37]Cross-sectionalAdolescents 15–19 years from high-schools in Santa Catarina, Brazil57.215–19 years5932 (2001)
and
5932 (2011)
Work status (yes vs. no), monthly family income (in terciles), school grading and school shiftInsufficient sleep duration (<8 h), poor sleep quality (sometimes/hardly ever/never vs. always/nearly always)Working (OR = 1.66, CI = 1.41–1.96; OR = 1.38, CI = 1.21–1.57 in 2001 and 2011, respectively), school grade (OR third year in 2001 = 1.42; CI, 1.02–1.99), income (OR second tercile in 2011 = 1.19; CI, 1.03–1.38), and school shift (OR night in 2011 = 1.26; CI, 1.07–1.49) were related to poor sleep quality.
Working (OR = 2.41, CI = 1.94–2.99; and OR = 1.64, CI = 1.42–1.89 in 2001 and 2011, respectively), monthly family income (OR third tercile in 2011 = 1.68; CI, 1.24–2.28) and higher school grading (OR third year in 2011 = 1.48; CI, 1.15–1.91) were associated with insufficient sleep duration.
Working and higher family income was associated with both short sleep and poor sleep quality. Higher school grading was associated with both short sleep and poor sleep quality. Night school shift was associated with poor sleep quality.
Carrillo-
Larco 2014 [38]
Cross-sectionalAdolescents and adults ≥ 12 y from the general population of Peru49.435.8 ± 17.712,424Education (none/primary school, high school, higher), asset index (in tertiles), job status (yes vs. no)Self-reported sleep duration (short-sleep < 6, regular-sleep 6–8, long-sleep > 8 h)Higher probability of being a short-sleeper was found in those currently employed (OR, 1.5; CI, 1.09–2.06) and lower probability in those with high school (OR, 0.53; CI, 0.32–0.86) or none/primary education (OR, 0,63; CI, 0.57–0.7). Higher probability of being a long-sleeper was found in those with high school (OR, 1.42; CI, 1.34–1.51) or none/primary education (OR, 2.17; CI, 1.72–2.73) and lesser probability in those with middle (OR, 0.77; CI, 0.63–0.94) or highest (OR, 0.54; CI, 0.48–0.60) assets index and currently working (OR, 0.59; CI, 0.46–0.75).Participants with lower education were more likely to have long sleep duration and less likely to have short sleep duration. Those with higher asset index were less likely to report long sleep. Employed individuals had a higher probability of being short sleepers and lower probability of being long sleepers than unemployed.
Schwartz 2015 [39]Cross-sectionalAdults > 35 y from the general population of 4 Peruvian settings49.554.1 (18.8)2682Wealth index—based on current occupation, household income, assets, and household facilities (in tertiles)SDB symptoms: habitual snoring (self-reported snoring at least 3 nights per week); observed apnea (pauses in breathing or choking during sleep reported by a spouse or bed partner); excessive daytime sleepiness (modified ESS score > 6)More excessive daytime sleepiness was associated with medium SES (OR, 1.41; CI, 1.10–1.80; p = 0.006).
Less habitual snoring was associated with medium (OR, 0.79; CI, 0.64–0.97; p = 0.027) and low (OR, 0.7; CI, 0.55–0.90; p = 0.005) SES.
Lower SES was associated with less habitual snoring but more excessive daytime sleepiness. No significant association was found between SES and observed apnea.
Schäfer 2016 [40]Cross-sectionalAdolescents 18 y members of a population-based birth cohort in Pelotas, Brazil50.918 (N/R)4016Family income at birth and at 18 y (in quintiles)
Maternal schooling in completed years at birth (0, 1–4, 5–8, 9–11, ≥12 y)
Maternal skin color (white, black, other)
Currently enrolled in school (yes, no)
Adolescent schooling in completed years (≤4, 5–8, ≥9 y)
Self-reported sleep duration (h/day)Maternal schooling was associated with sleep duration in both genders, with an inverse linear trend in girls (p < 0.001).
Girls whose mothers had no schooling showed an increase of 1.40 (ß) hours per day (95%CI, 0.77–2.04) compared to those whose mothers had ≥12 y of schooling.
Girls whose mothers were black had 0.37 h more sleep per day (95%CI, 0.17–0.58) than those whose mothers were white.
Girls in the lowest fifth of family income at birth had a higher sleep duration (ß = 0.58; 95%CI, 0.30–0.87) compared to girls in the highest fifth.
Adolescents who were currently studying showed lower sleep duration (ß = −0.34; 95%CI, −0.51 to −10.17 for males; ß = −0.75, 95%CI, −0.92 to −0.59 for females).
Boys with lower schooling showed higher sleep duration (ß = 0.45; 95%CI, 0.09–0.81 for males). Girls with intermediate schooling (5–8 years) showed higher sleep duration (ß = 0.46; 95%CI, 0.27–0.65).
Boys and girls in the lowest fifth family income at 18 years had higher sleep duration (ß = 0.58; 95%CI, 0.33–0.84) and (ß = 0.69; 95%CI, 0.41–0.96), respectively
Lower maternal and adolescent schooling and lower family income was associated with higher sleep duration.
Black maternal skin color was associated with higher sleep duration in girls.
Fulgencio 2017 [41]Cross-sectionalAdolescents 13–15 y from 14 public and private schools in Itabira, Brazil56.113–15 years1344SES—composite variable (goods owned by the family and educational level of its head), categorized as higher vs. lowerParent-reported possible sleep bruxism (single question, yes vs. no)Higher SES was associated with higher prevalence of possible sleep bruxism (PR, 1.51; 95%CI, 1.23–1.86)Greater prevalence of possible sleep bruxism was observed among adolescents with a higher SES
Mota-Veloso 2017 [42]Cross-sectionalChildren 6–12 y from seven public and two private schools in Diamantina, Brazil54.86–12
years
851SES—composite variable of 3 indicators: equivalized household income (10 levels), mother’s and father’s schooling (9 levels)Sleep bruxism (reports of parents/caregivers and oral clinical evaluation)SES had a significant indirect effect on bruxism via sucking habits (SC = −0.08; p = 0.01).
SES had a significant direct effect (SC = −0.16; p = 0.01) and the total effect on tooth wear was also significant (SC = −0.17; p = 0.00).
Lower SES was associated with more sleep bruxismThe effect was mediated by sucking behavior (finger sucking, biting nails or other objects)
Netsi 2017 [43]Cross-sectionalInfants from Pelotas, BrazilN/RN/R3842Maternal education (0–4 years, 5–8 years, ≥9 years)
Family income (in quintiles)
Parent-reported sleep duration, awakenings, and sleep disturbances (nightmares/night terrors, restless sleep, difficulty going to sleep, wakes up at night, and wakes up early)There were no consistent associations between sleep duration or sleep disturbances and sociodemographic characteristicsMaternal education and family income were not associated with infant sleep duration or disturbances
De Lima 2018 [44]Cross-sectionalStudents with 14–19 y, from high schools in São José, Brazil54.216.1 ± 1.11110Maternal education (<8 vs. ≥8 years)
Family income (up to two minimum wages; two to ten times the minimum wage; more than ten times the minimum wage)
Sleep quality (perception of sleep quality, single question, categorized in almost never/seldom/sometimes vs. with relative frequency/almost always)The prevalence of low quality of sleep was higher in adolescents whose mothers had up to 8 years of study (OR, 1.44; CI, 1.13–1.84).
Those who had sedentary behavior of risk based on screen time (OR, 0.54; CI, 0.42–0.70).
Students whose mothers had a high level of education were more likely to have a low quality of sleep. Students with sedentary risk behavior were less likely to report poor sleep quality.
Lima 2018 [45]Cross-sectionalAdults ≥ 20 y from the general population of Campinas, Brazil52.743.7 (CI 42.3–45.2)1969Work status (yes vs. no)
Per capita family income (<1; 1–2; 3 or more minimum wages)
Schooling (0–4; 5–8; 9–11; 12 or more years of schooling) Number of residents in the household (1;2;3 or more)
Sleep duration (6 h or less; 7–8 h; 9 h or more)Associated with short sleep: highest level of schooling (OR, 1.73; CI, 1.08–2.75).
The probability
of long sleep was lower in individuals who work (OR, 0.39; CI, 0.28–0.55), with higher income (OR, 0.49; CI, 0.29–0.85)
Those with higher schooling were more likely to have short sleep. The chance of long sleep was lower in those who have more years of schooling, have higher income, and worked.Adjusting for chronic diseases and health disturbances attenuated the effects of education on short sleep
Barros 2019 [46]Cross-sectionalAdults ≥ 20 y from the general population in Campinas, Brazil52.743.7 (CI 42.3–45.3)1998Education (0–3 y, 4–8 y, 9–11 y, ≥12 y)
Per capita family income (<1 minimum wage, 1–3, >3)
Employment (working vs. not working)
Sleep quality (single question; excellent/
very good/good vs. regular/poor/very poor)
Poor sleep quality was more frequent in women (OR, 1.36; CI, 1.14–1.63), older individuals (OR, 1.5; CI, 1.20–1.87), unemployed (OR, 1.26; CI, 1.03–1.54), and in those with the highest number of children (OR, 1.33; CI, 1.02–1.74). Adjusting for amount of health disturbances, self-rated health, common mental disorders, and life satisfaction attenuated the effect
Pontes 2019 [47]Cross-sectionalAdults ≥ 18 y from the general population in Rio Grande, Brazil56.645.9 ± 17.21280Education (0 to 11 years vs. 12 years or more)Sleep bruxism (ICSD criteria)In the bivariate analysis, the schooling (OR, 1.66; CI, 1.14–2.42) and stress (OR, 1.66; CI, 1.14–2.42) variables were associated with sleep bruxism. In the adjusted analysis, age (OR, 1.63; CI, 1.07–2.46), schooling (OR, 1.92; CI, 1.35–2.72), and stress (OR, 1.76; CI, 1.11–2.81) were associated with sleep bruxism.Higher education and psychological stress were associated with higher prevalence of sleep bruxism
Wendt 2019 [48]Cross-sectionalAdults from the general population in Brazil52.9N/R60,202Education (none, incomplete primary level, complete primary level, secondary level, higher education)
Wealth index (assets index score, in quintiles)
Sleep disturbance frequency and daytime fatigue in last two weeks (none; up to seven days; more than seven days; almost every day)Sleep disturbances and daytime fatigue had lower prevalence in highly educated individuals with, respectively, (OR, 0.73; CI, 0.64–0.83) and (OR, 0.79; CI, 0.69–0.92)Highly educated individuals had lower prevalence of sleep disturbance than those with no formal education
Wendt 2020 [49]Cross-sectional22-year-old adults from a population-based birth cohort in Pelotas, Brazil53.222 (N/R)2462Wealth index (asset index, in quintiles), occupation (none, only study, only work, both)Sleep duration and efficiency (7-day accelerometry)Women not working or studying presented higher Sleep Time Window (OR, 7.5; CI, 7.3–7.6) and lower Sleep Percent (OR, 82; CI, 81.1–83.7).
Those in the poorest quintile of wealth index
presented lower SP (OR, 82.4; CI, 81.9–83.7)
Women in the poorest quintile of wealth index presented with lower sleep efficiency
AHI = apnea-hypopnea index; AIS = Athens Insomnia Scale; CI = confidence interval; EDS = excessive daytime sleepiness; ESS = Epworth Sleepiness Scale; ICSD = International Classification of Sleep Disorders; OR = odds ratio; OSA = obstructive sleep apnea; PSG = polysomnography; PSQI = Pittsburgh Sleep Quality Index; SDB = sleep-disordered breathing; SES = socioeconomic status. Age is presented as mean ± standard deviation or median (interquartile range or range).
Table 2. Quality rating of the included studies, using the NIH quality assessment tool.
Table 2. Quality rating of the included studies, using the NIH quality assessment tool.
StudyQ1Q2Q3Q4Q5Q6Q7Q8Q9Q10Q11Q12Q13Q14Quality Rating
Rocha 2002 [30]YYYYYNNYNNYNANAYFair
Hara 2004 [31]YYYYYNNYNNYNANAYFair
Blay 2008 [32]YYYYYNNYYNNNANAYFair
Fritsch Montero 2010 [33]YYNYNNNYYNYNANAYFair
Tufik 2010 [34]YYYYYNNYYNYNANAYGood
Blümel 2012 [35]YYYYYNNYYNYNANAYGood
Lima 2012 [36]YYYYYNNYNNNNANAYFair
Hoefelmann 2013 [37]YYYYYNNYNYYNANRYGood
Carrillo-Larco 2014 [38]YYYYNNNYYNYNANAYFair
Schwartz 2015 [39]YYYYNNNYYNYNANAYFair
Schäfer 2016 [40]YYYYNNNYYYNNYYGood
Fulgencio 2017 [41]YYYYYNNYYNYNANAYGood
Mota-Veloso 2017 [42]YYYYYNNYYNYNANAYGood
Netsi 2017 [43]YYYYNNNYNYNNYYFair
De Lima 2018 [44]YYYYYNNYYNYNANAYGood
Lima 2018 [45]YYYYYNNYYNNNANAYFair
Barros 2019 [46]YYYYYNNYYNNNANAYFair
Pontes 2019 [47]YYYYYNNNYNYNANAYFair
Wendt 2019 [48]YYYYYNNNNNNNANAYPoor
Wendt 2020 [49]YYYYYNNYYNYNANYGood
N = no; NA = Not applicable; NR = Not Reported; Y = Yes.
Table 3. Subgroup analyses of the prevalence of sleep disturbances in Latin America.
Table 3. Subgroup analyses of the prevalence of sleep disturbances in Latin America.
SubgroupNumber of StudiesPooled Prevalence (95 CI%)I2 (%)
Total2824.73 [19.62–30.66]100
Cities
Brazil2125.00 [19.54–31.40]100
Chile1--
Peru415.91 [6.17–35.27]100
Multicentric244.89 [42.36–47.45]88
Age group
Adult1524.24 [18.93–30.48]100
Adolescents1020.33 [12.68–30.95]100
Children1--
Infant256.52 [54.11–58.89]78
Study’s quality
Good1227.97 [20.59–36.76]100
Fair1424.08 [16.61–33.55]100
Poor213.38 [10.79–16.49]100
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Etindele Sosso, F.A.; Torres Silva, F.; Queiroz Rodrigues, R.; Carvalho, M.M.; Zoukal, S.; Zarate, G.C. Prevalence of Sleep Disturbances in Latin American Populations and Its Association with Their Socioeconomic Status—A Systematic Review and a Meta-Analysis. J. Clin. Med. 2023, 12, 7508. https://doi.org/10.3390/jcm12247508

AMA Style

Etindele Sosso FA, Torres Silva F, Queiroz Rodrigues R, Carvalho MM, Zoukal S, Zarate GC. Prevalence of Sleep Disturbances in Latin American Populations and Its Association with Their Socioeconomic Status—A Systematic Review and a Meta-Analysis. Journal of Clinical Medicine. 2023; 12(24):7508. https://doi.org/10.3390/jcm12247508

Chicago/Turabian Style

Etindele Sosso, F. A., Filipa Torres Silva, Rita Queiroz Rodrigues, Margarida M. Carvalho, Sofia Zoukal, and Gabriel Cordova Zarate. 2023. "Prevalence of Sleep Disturbances in Latin American Populations and Its Association with Their Socioeconomic Status—A Systematic Review and a Meta-Analysis" Journal of Clinical Medicine 12, no. 24: 7508. https://doi.org/10.3390/jcm12247508

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop