Introduction

Human immunodeficiency virus (HIV) infection and antiretroviral therapy (ART) can present with neurological complications. HIV persists in the central nervous system (CNS) because brain macrophages, microglial cells, and astrocytes serve as reservoirs1,2,3. As of 2022, there were 39.0 million HIV-positive individuals worldwide, with 76% of them residing in sub-Saharan Africa (SSA)4. According to the Ethiopia Public Health Institute’s 2023 report, the country recorded 603,537 people living with HIV5.

HIV/ADIS infected individuals are more vulnerable to sleep disturbances6. Poor sleep quality is a symptom that includes dysfunction related to the sleep and sleep stage, excessive somnolence, trouble falling and staying asleep, and difficulty starting and maintaining sleep7. According to various studies, between 50 and 70 million individuals in the United States experience sleep disturbances8. Sleep is one of the most important around the clock cycles in the human body system, which plays an important role in physiological and psychological functions9.

Sleep disturbance is frequently reported among individuals with chronic diseases, such as HIV infection10. The exact cause of sleep disturbance is not well known, but it may relate to HIV itself, antiretroviral drug side effects, and other HIV-related disorders11,12. Previous studies indicate that people living with HIV experience insomnia and other sleep difficulties at varying rates, ranging from 40 to 100%13. A meta-analysis reported a global pooled prevalence of sleep disturbances among adults living with HIV at 58%13. In Africa, adults living with HIV show significant rates of sleep disturbances, ranging from 39.4 to 94%14. Sleep disturbance rates vary significantly across different regions in Ethiopia. For instance, at Hawassa University Comprehensive Specialized Hospital, it was reported as 92.1%15; at Zewditu Memorial Hospital, the rate was 55.6%16; at Mettu Karl Referral Hospital, it was 57.1%17; and at Finote Selam General Hospital, the reported rate was 55.1%18.

Sleep disturbance is prevalent among individuals living with HIV/AIDS and significantly impacts treatment adherence, quality of life7,15,16,19,20,21. These issues may be overlooked due to sleep disturbances being seen as a typical consequence of the disease and its treatment or considered less significant compared to other HIV-related complications10,16. Additionally, cytokines such as TNF-alpha and interleukin-1 (IL-1), along with medications like Efavirenz used in treatment, are associated with sleep disturbances among HIV/AIDS patients22,23,24.

The impact of HIV/AIDS in SSA countries includes increased mortality, morbidity, orphan hood, poor sleep quality, depression, anxiety, reduced productivity, as well as stigma and discrimination25,26. Individual study on poor sleep quality among HIV/AIDS patients in SSA have reported different levels of sleep disturbances, indicating the necessity for a systematic review and meta-analysis. This research aimed to determine the prevalence of poor sleep quality and identify associated factors among HIV/AIDS patients in the region.

Review questions

  1. 1.

    What is the estimated pooled prevalence of poor sleep quality among patients with HIV/ADIS in sub-Saharan African countries?

  2. 2.

    What factors are associated with poor sleep quality among HIV/ADIS patients in sub-Saharan African countries?

Methods

This protocol for this review has been officially registered in PROSPERO under protocol number CD42024517229, which can be access ay https://www.crd.york.ac.uk/PROSPERO# my PROSEPERO. This systematic review and meta-analysis strictly followed the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Checklists27.

The eligibility criteria

Inclusion criteria

Condition Poor sleep quality.

Context Both published and unpublished observational studies that reported the prevalence and/or associated factors of poor sleep quality in SSA countries were the context.

Population People living with HIV/ADIS were the population for this review.

Study design All observational studies that reported the prevalence and/or associated factors of poor sleep quality among people living with HIV/ADIS were included.

Language Studies written in the English language were included.

Publication year Studies published between 2014 and 2023.

Exclusion criteria

This study excluded case reports, case series, letters to the editor, studies without full-text availability, or studies that did not disclose the prevalence of poor sleep quality among patients with HIV/AIDS (Supplementary material 1).

Measurement of the outcome variable

The outcome variable in this study was poor sleep quality, defined using specific assessment tools. The PSQI consists of 19 questions across seven components: sleep quality, sleep onset latency, sleep duration, sleep efficiency, sleep disturbances, use of sleeping medications, and daytime dysfunction. Each item is scored from 0 to 3, contributing to the global PSQI score, which ranges from 0 to 21. A global PSQI score above 5 indicates poor sleep quality28. Additionally, the Epworth Sleepiness Scale (ESS) was used, where individuals scoring > 7 out of 24 were classified as having abnormal sleepiness or sleep apnea29.

Search strategies

We systematically searched across various databases, including PubMed, African Journals Online, Scopus, Cochrane Library, HINARI, and Science Direct. Additionally, we conducted searches using Google and Google Scholar search engines, to identify primary studies relevant to our research objectives. Our search strategy was based on the CoCoPo principle (conditions, context, and population), ensuring that we defined all key components before commencing the review process30. Specifically, our aim was to identify studies that reported on the prevalence of poor sleep quality among individuals living with HIV/AIDS in sub-Saharan African (SSA) countries. Each database received a customized search strategy, utilizing Medical Subject Headings (MeSH) terms.

Study selection

All studies retrieved from various electronic databases were transferred to EndNote version 8. Following the removal of duplicate articles, the titles of all remaining articles were screened. Subsequently, both the abstracts and full texts were meticulously and independently reviewed by two authors (BA and LW). Any discrepancies between the reviewers were resolved through further discussion involving additional reviewers (MM and DE) when necessary.

Data extraction and management

Two authors (LL and MH) summarized the findings regarding the prevalence and associated factors of poor sleep quality among HIV/AIDS patients using a data extraction format developed with guidance from the Joanna Briggs Institute (JBI) data extraction tool for prevalence studies31. The extracted data were compared by two authors (EF and AD). Any discrepancies were resolved through consensus after discussion. The details of each study, including the first author’s name, publication year, study design, sample size, criteria for poor sleep quality, prevalence of poor sleep quality, and estimates of associated factors (such as sex, viral load, CD4 count) along with their standard errors, were extracted.

Risk of bias and quality assessment

We assessed the quality of the included studies using a validated modified version of a quality assessment tool specifically designed for prevalence studies32. Two authors, TD and AD, independently assessed the quality of the studies. Any disagreements in the quality appraisal between these two authors were resolved through consultation with a third reviewer, NW. The quality assessment tool consists of nine items that assess the risk of bias, with a maximum score of “9” and a minimum score of “0”. The risk of bias was classified as low (0–3), moderate (4–6), or high (7–9)33,34.

Data synthesis and analysis

The data extracted in Microsoft Excel were transferred to STATA version 17.0 software for further analysis35. The pooled estimate of the prevalence of poor sleep quality and its associated factors was determined using the random-effects model with Der Simonian Laird weights. Statistical heterogeneity was assessed using the Cochrane Q test and I2 statistics36. High heterogeneity among the included studies was observed in the random-effects model analysis. To address this, sensitivity, subgroup, and meta-regression analyses were conducted. Sensitivity analysis indicated that one study significantly influenced the overall estimates (Fig. 4).

Publication bias

Publication bias, specifically the small study effect, was assessed through the use of funnel plots and statistically through Egger’s test37. To identify factors associated with poor sleep quality among individuals living with HIV/AIDS, odds ratios (ORs) and their corresponding 95% confidence intervals were calculated (Fig. 3).

Results

We conducted an electronic database search that yielded 5724 article records. After identifying and removing 689 duplicate entries, we reviewed the titles and abstracts of the remaining articles. Subsequently, 5709 articles were deemed unsuitable and excluded from the study. One article was excluded from the full-text review because of differences in the study population. During the full-text review, one article was excluded because of differences in the population under study. In addition, one article lacked full text, and another study lacked a validated assessment tool, resulting in their exclusion38,39. Ultimately, we included 15 full-text articles, comprising a total sample size of 5176, for further analysis (see Fig. 1). Of these 15 studies, two were from Cameroon32,40, eight were from Ethiopia14,22,31,41,42,43,44,45, and four were from Nigeria11,24,42,46,47 (Table 1). According to the Epworth Sleepiness Scale, poor sleep quality is defined by individual scores exceeding seven of 24 points32,40. According to the Pittsburgh Sleep Quality Index (PSQI), poor sleep quality is identified when an individual score above five out of 21 possible points14,31,41 (Fig. 1).

Figure 1
figure 1

Prisma flow diagram showing the multiple steps of relevant study selection for the systematic review and meta-analysis of poor sleep quality among HIV/ADIS patients living in sub-Saharan African countries, 2024.

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Table 1 Characteristics of the included studies and the prevalence of poor sleep quality in individual studies, 2024.
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Prevalence of poor sleep quality among people living with HIV/AIDS in sub-Saharan Africa

The pooled prevalence of poor sleep quality among people living with HIV/AIDS in sub-Saharan Africa was 49.32% (95% CI 41.97–56.8%). The studies showed significant heterogeneity (I2 = 96.8%, p < 0.001). The prevalence ranged from 21.7% in Nigeria47, the lowest reported rate, to 73.7% in Ethiopia, the highest reported rate42 (Fig. 2).

Figure 2
figure 2

Forest plot for poor sleep quality among HIV patients in sub-Saharan African countries.

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Publication bias

Publication bias was assessed using both a Funnel Plot and Egger's regression test. The Funnel Plot analysis showed a symmetrical distribution, and Egger’s test produced non-significant results (estimated bias coefficient = 5.6 with a standard error of 8.5, p = 0.957), indicating that publication bias was not present (Fig. 3). Sensitivity analysis was conducted to detect influential studies on the pooled prevalence of poor sleep quality. As shown in Fig. 4, no study was found to significantly influence the pooled estimate. Omitting studies using leave-one-out meta-analysis did not affect the overall prevalence of poor sleep quality (Fig. 5).

Figure 3
figure 3

Funnel plot for assessing publication bias of the prevalence of poor sleep quality in sub-Saharan African (n = 15), 20,224.

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Figure 4
figure 4

Sensitivity analysis between studies included in the meta-analysis.

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Figure 5
figure 5

Subgroup analysis of poor sleep quality based on assessment tools among HIV/AIDS patients living in sub-Saharan Africa in 2024 (n = 15).

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We also performed and reported estimates from a subgroup analysis, considering other possible sources of variation, including assessment tools, study country, and sample size.

Sub group analysis of poor sleep quality based on assessment tools

The subgroup analysis of the prevalence of poor sleep quality, based on the assessment tool used, indicated variations between tools, with significant heterogeneity within studies (see Fig. 4). The results showed that the pooled prevalence of poor sleep quality was 50.33% (95% CI 39.34% 61.32%, I2 = 72.0%, p = 0.059) in studies with a moderate risk of bias.

Subgroup analysis of poor sleep quality based on study country

The subgroup analysis of the prevalence of poor sleep quality, based on study country, indicated variations in the prevalence across regions, with significant heterogeneity within studies (see Figs. 6, 7, and 8). The pooled prevalence of poor sleep quality in Cameroon among patients living with HIV/AIDS was 49.36% (95% CI 38.19–60.53%, I2 = 88.2%, p = 0.004). This was higher than the pooled prevalence in Nigeria among patients living with HIV/AIDS, which was reported at 41.61% (95% CI 25.09–54.12%, I2 = 95.7%, p < 0.001) (see Fig. 4).

Figure 6
figure 6

Sub group analysis of poor sleep quality based on study country among HIV/AIDS patients living in sub-Saharan Africa in 2024 (n = 15).

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Figure 7
figure 7

Sub group analysis of poor sleep quality based on study country among HIV/AIDS patients living in sub-Saharan Africa in 2024 (n = 15).

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Figure 8
figure 8

Subgroup analysis of poor sleep quality based on the country among HIV/AIDS patients living in sub-Saharan Africa in 2024 (n = 15).

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Subgroup analysis was also performed on studies conducted in Ethiopia and Nigeria by omitting those from Cameroon to identify the influential studies on the pooled prevalence of poor sleep quality. However, omitting studies using leave-one-out meta-analysis did not influence the overall prevalence of poor sleep quality (Fig. 7). For instance, the estimated prevalence of poor sleep quality in Ethiopia was higher than in Nigeria, reported at 54% (95% CI 46–63%, I2 = 96.22, p value < 0.001).

Finally, we conducted another subgroup analysis focusing on country-specific studies. However, even after omitting certain studies, there was no influence on the overall prevalence of poor sleep quality (Fig. 8). Consequently, we observed a lower pooled prevalence of poor sleep quality in Nigeria, reported at 39% (95% CI 28–50%; I2 = 96.9%, p value < 0.001).

Sub group analysis based on sample size

To address heterogeneity, we conducted a subgroup analysis of the prevalence of poor sleep quality based on sample size, categorizing studies with mean sample sizes of ≥ 300 and < 300 (Fig. 9). The analysis revealed a higher prevalence of poor sleep quality among studies with a sample size of ≥ 300, reported at 52% (95% CI 44–59%; I2 = 96.45; p value < 0.001).

Figure 9
figure 9

Subgroup analysis of poor sleep quality based on the sample size among HIV/AIDS patients living in sub-Saharan Africa in 2024 (n = 15).

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Sub group analysis of poor sleep quality based on study year

We conducted another subgroup analysis to address heterogeneity, and as depicted in Fig. 10, no individual study was found to significantly influence the overall pooled prevalence of sleep quality. In this analysis, a higher prevalence of poor sleep quality was observed in studies conducted from 2010 to 2023, reported at 48% (95% CI 39–57%; I2 = 97.51%; p value < 0.001).

Figure 10
figure 10

Subgroup analysis of poor sleep quality based on the study year among HIV/AIDS patients living in sub-Saharan Africa in 2024 (n = 15).

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Factors associated with pooled quality among people living with HIV/AIDS

To explore factors associated with the pooled prevalence of poor sleep quality among people living with HIV/AIDS in sub-Saharan African (SSA) countries, we conducted separate random-effects pooled estimate analyses on extracted factors. The results indicated that variables such as depression and CD4 count < 200 were statistically significant predictors of poor sleep quality. According to the pooled meta-analysis (Fig. 11), individuals with HIV/AIDS in SSA who experienced depression were 2.78 times more likely to have poor sleep quality compared to those without depression (OR 2.78, 95% CI 1.21–6.40). Similarly, in Fig. 12, the analysis showed that HIV/AIDS patients in SSA with a CD4 count less than 200 had a 3.15 times higher risk of poor sleep quality than those with a CD4 count greater than 200 (OR 3.15, 95% CI 2.40–4.05).

Figure 11
figure 11

Factors depression with the prevalence of poor sleep quality in sub-Saharan African countries.

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Figure 12
figure 12

Forest plot showing the association between poor sleep quality and CD4+ T cells (< 200 cells/mm3) in HIV/ADIS patients in SSA.

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Discussion

Poor sleep quality is a major health concern among people with HIV in sub-Saharan Africa. Many studies across the region show varied findings. Hence, this systematic review and meta-analysis estimated the pooled prevalence and associate factors poor sleep quality among people living with HIV/ADIS in sub-Saharan Africa.

According to the study, the pooled prevalence of poor sleep quality among people living with HIV/AIDS in sub-Saharan African countries was 49.32% (95% CI 41.97–56.8%). This finding is consistent with the 49.54% reported in studies conducted in Iran12.

However, it was lower than a result reported by Jie Wu et al. at prevalence 58%13, and studies in the USA48, and in Mexico49, which reported 75% and 58.6%, respectively. These differences might be attributed to variations in assessment tool cut-off points, sociodemographic factors, viral load, CD4 count, the presence of opportunistic infections, and levels of depression and anxiety. Overall, these factors contribute to the variation in poor sleep quality among HIV patients15,29,50.

A significant heterogeneity was observed between the included primary studies. To address this, we conducted a subgroup analysis considering factors such as the country, assessment tool, study year and sample size. Despite this effort, heterogeneity persisted.

Our analysis indicated that studies using the Pittsburgh Sleep Quality Index (PSQI) reported a higher prevalence of poor sleep quality compared to those using the Epworth Sleepiness Scale and the Insomnia Severity Index (ISI). This study is in line with a study conducted by Márcio Flávio Moura de Araujo et al.51. This discrepancy might be due to the use of different questionnaires or scales to measure sleep quality, each with its own criteria and cut-off points. These variations can lead to differences in what is classified as “poor sleep quality” across studies52,53,54,55.

Result from sub group analysis based on the country indicated that Ethiopia had the highest prevalence of poor sleep quality, estimated at 54.29%. In contrast, Nigeria exhibited the lowest prevalence among the countries studied. The prevalence of poor sleep quality in Ethiopia is consistent with earlier research, such as Kathryn et al., who reported a prevalence of 56%56. However, it was lower than result from Taiwan, which reported, 60%57, and higher than those in China, 15%58, Bangladesh, 38.8%59, and Vietnam, 41.2%60. Differences in HIV/AIDS care across countries, influenced by healthcare infrastructure, could explain varying levels of sleep quality. Better access to antiretroviral therapy, specialized clinics, and mental health support improves sleep outcomes. Conversely, limited access to treatment and support services in certain nations may exacerbate stress, anxiety, and depression among patients, worsening sleep quality. Socioeconomic factors like income and housing stability also contribute to sleep disturbances. Cultural attitudes toward HIV/AIDS, including stigma, often deter patients from seeking care and adhering to treatment, leading to increased psychological distress and poorer sleep quality48,61,62.

The subgroup analysis based on sample size revealed that studies with ≥ 300 participants consistently showed a higher prevalence of poor sleep quality compared to those with < 300 participants. This suggests that larger sample sizes may increase the likelihood of detecting poor sleep quality. Larger samples provide greater statistical power for more precise prevalence estimates and may capture a broader range of individuals affected by poor sleep quality, enhancing the study's comprehensiveness. The subgroup analysis based on study year found no individual study significantly influencing the overall pooled prevalence of sleep quality. However, it revealed a higher prevalence of poor sleep quality from 2010 to 2023 compared to the period from 2013 to 2018. This trend might be attributed to several factors such as changes in healthcare practices, evolving treatment protocols, or shifts in the population demographics over time.

According to this review, poor sleep quality showed a significant correlation with CD4 count and depression. Specifically, participants with depression were more likely to experience poor sleep quality compared to HIV/AIDS patients without depression, which aligns with findings from a study conducted in Iran19,63,64,65. People living with HIV/AIDS and comorbid depression are particularly vulnerable to poor sleep quality. Depression often disrupts sleep cycles, leading to difficulties in falling asleep, staying asleep, or achieving restorative sleep. This may result in hypersomnia, insomnia, or other sleep-related issues. Additionally, psychological distress such as rumination, worry, and negative thoughts associated with depression can significantly interfere with sleep onset and maintenance64,66,67,68. In addition, the complex relationship between depression and HIV/AIDS exacerbates insomnia. The regulation of sleep, emotional processing, alterations in the hypothalamic–pituitary–adrenal axis, psychopathological symptoms, and disturbances in the sleep–wake cycle are all interconnected in this complex situation. Additionally, both circumstances are linked to a decline in69,70.

According to the study, people living with HIV/AIDS who had a CD4 count of less than 200 cells/mm3 were more likely to have poor sleep quality than those who had a count of more than 200 cells/mm3. This study is supported by a study conducted in Mexico49 and the USA71. This could be because a CD4 count of less than 200 cells/mm3 indicates severe immune suppression, which increases a person's susceptibility to opportunistic infections and comorbidities, some of which have a direct or indirect impact on sleep quality72.

Limitations of the study

Despite conducting sensitivity, subgroup, and meta-regression analyses to mitigate the impact of heterogeneity, the level of heterogeneity among the included studies remained high. This could be attributed to variations in the study area, methodology, study period, assessment tools for diagnosing poor sleep quality, and other unexplained factors. Therefore, it is crucial for clinicians and policymakers to consider these factors when interpreting the results. Another limitation of our study was the restriction of literature searches to specific languages, predominantly English, which might have led to the exclusion of relevant studies published in other languages. Additionally, limited access to databases or journals could have resulted in incomplete retrieval of important studies, particularly those published in local or regional journals.

Conclusions

This review revealed that HIV/AIDS patients in SSA had a significantly greater prevalence of poor sleep quality than did the general population, highlighting the urgent need for mental health services and treatments to improve participant health. This astounding frequency highlights how urgently programs to enhance sleep quality are needed. The results from the HIV/AIDS study revealed relationships between participant income, depression, and poor sleep quality.