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. 2023 Feb 2;18(2):e0280817. doi: 10.1371/journal.pone.0280817

Anemia and its predictors among chronic kidney disease patients in Sub-Saharan African countries: A systematic review and meta-analysis

Mitku Mammo Taderegew 1,*, Alemayehu Wondie 1, Tamene Fetene Terefe 2, Tadesse Tsehay Tarekegn 2, Fisha Alebel GebreEyesus 2, Shegaw Tesfa Mengist 2, Baye Tsegaye Amlak 2, Mamo Solomon Emeria 2, Abebe Timerga 1, Betregiorgis Zegeye 3
Editor: Elizabeth S Mayne4
PMCID: PMC9894480  PMID: 36730249

Abstract

Introduction

Anemia is a serious complication of chronic kidney disease (CKD) with a significant adverse outcome on the burden and progression of the disease. Hence, the study intended to assess the pooled prevalence of anemia and its predictors among CKD patients in Sub-Saharan African nations.

Methods

To identify the relevant studies systematic searches were carried out in Medline, EMBASE, HINARI, Google Scholar, Science Direct, and Cochrane Library. From selected studies, data were taken out with a standardized data extraction format prepared in Microsoft Excel. Inverse variance (I2) tests were employed to evaluate the heterogeneity across the included studies. Due to substantial heterogeneity among the studies, a random-effects meta-analysis technique was employed to estimate the pooled prevalence of anemia. Subgroup analysis, sensitivity analysis, and meta-regression analysis were carried out to search the possible bases of heterogeneity. Funnel plot symmetry, Begg’s test, and Egger’s regression test were employed to assess the existence of publication bias. In addition, factors associated with anemia among CKD patients were examined. All statistical analyses were carried out with STATA™ Version 14 software.

Results

A total of 25 studies with 5042 study participants were considered in this study. The pooled prevalence of anemia among CKD patients was estimated to be 59.15% (95% CI, 50.02–68.27) with a substantial level of heterogeneity as evidenced by I2 statistics (I2 = 98.1%; p < 0.001). Stage of CKD (3–5) (pooled odds ratio (POR) = 5.33, 95% CI:4.20–6.76), presence of diabetes mellitus (POR = 1.75, 95% CI: 1.10–2.78), hemodialysis history (POR = 3.06, 95% CI: 1.63–5.73), and female sex (POR = 2.50, 95% CI: 1.76–3.55) were significantly related with anemia.

Conclusions

More than half of CKD patients were suffering from anemia. Stage of CKD, presence of DM, hemodialysis history, and being female sex were factors associated with anemia among CKD patients.

Introduction

Chronic kidney disease (CKD) is an emerging global public health issue that places a significant financial burden on both the patients’ families and the healthcare delivery system. CKD is a major basis of morbidity and mortality in both developed and developing nations, affecting more than 10% of the worldwide population in 2015 [1].

Evidences have shown that African descendants had a higher risk of developing CKD and advancement to end-stage renal disease (ESRD) [2, 3]. Due to rapid urbanization, adoption of Western lifestyles, increasing prevalence of obesity and physical inactivity, rapid population growth, and the growing prevalence of CKD risk factors with disproportionate impact in the developing nations, the rate of CKD is increasing promptly in the Sub-Saharan African nations with an estimated prevalence of 15.8% in 2018 [2–4].

Anemia is a communal and serious complication of CKD with a significant adverse outcome on the burden and progression of the disease. It is a common and avoidable risk factor for many adverse outcomes in CKD patients and it is related with a reduced quality of life, as well as increased morbidity and mortality [5, 6]. The presence of anemia among CKD patients is a significant indicator of cardiovascular events and has therefore been largely associated with a greater likelihood of hospitalization and prolonged hospital stay, poor quality of life, as well as increased morbidity and mortality [7]. According to the Kidney Disease Improving Global Outcomes (KDIGO) Anemia Work Group, anemia in CKD exists when the hemoglobin (Hb) value is <13 g/dL for men and <12 g/dL for women [8]. The documented causes of anemia in CKD patients are multifactorial and include deficiency of erythropoietin, resistance to erythropoietin, reduced red blood cell lifespan, iron deficiency, chronic inflammatory process, and uremic milieu [6, 9, 10].

Since anemia is a common finding among CKD patients and is related with high mortality, early screening and optimal intervention have been shown to reduce morbidity and mortality and improve the quality of life of the patients [6, 7, 11–13]. The burden of anemia among CKD patients varies greatly across many regions. For instance, it has been shown that 91.8% of CKD patients in central South Africa [14], 14.0% in Nigeria [15], and 39.5%-89.5% in Ethiopia [16, 17] suffered from anemia. Even though various primary studies in Sub-Saharan African nations revealed the prevalence of anemia among CKD patients, the majority of these studies was single-centered and had small sample sizes. The results of these studies also showed significant variation and inconsistency regarding the prevalence of anemia in the region. These uncertainties and inconsistency of findings across regions make it difficult for policymakers to make decisions based on such studies.

It is therefore imperative to estimate the overall magnitude of anemia and its associated factors among patients presenting with CKD. Hence, this study intended to estimate the overall prevalence of anemia and its associated factors among patients presenting with CKD in Sub-Saharan African nations. The results of the study would serve as baseline data for policymakers and other stakeholders to plan and apply proper interventions that stress routine screening, and appropriate management of anemia among patients presenting with CKD. The results will also be helpful for other investigators, as baseline data for further investigation. It is also hoped that the findings will help clinicians to appreciate the magnitude of anemia and implement appropriate interventions among CKD patients in the clinical setup.

Materials and methods

A systematic review and meta-analysis were carried out to estimate the pooled prevalence of anemia in CKD patients. The review was undertaken according to the guideline of the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) checklist (S1 Table) [18]. All literature accessible until January 2022 was considered in the study.

Searching strategy and study selection

To ascertain all eligible literature that reports the prevalence of anemia and its predictors among patients presenting with CKD in sub-Saharan African nations, a systematic exploration of the literature was conducted by three authors (MMT, AW, TFT) through Medline (PubMed), EMBASE, HINARI, Google Scholar, Science Direct, CINAHL, Popline, Cochrane Library African Journals Online (AJOL), and grey literature. Additionally, the reference lists of each retrieved article were also searched manually for search optimization. The search was carried out using the following key search terms and phrases: “anemia”, “anaemia”, “associated factors”, “risk factors”, “chronic renal impairment”, “chronic kidney injury”, “chronic renal insufficiency”, “chronic kidney disease”, “Sub Saharan Africa”, and “names of each the sub-Saharan Africa countries”. Boolean operators like “AND” and “OR” were used to combine search terms. The search of the article was limited to full texts, free articles, and published in peer-reviewed journals in the English language. Then, based on the eligibility criteria, two authors (TTT & FAG) independently reviewed all potential article titles, abstracts, and full-text quality. Finally, the screened articles were compiled together.

Inclusion and exclusion criteria

All studies in sub-Saharan African countries that reported the prevalence of anemia and its predictors among CKD patients were illegible for the study. However, the studies that did not report our outcome of interest (prevalence anemia and/ or its predictors) or if it is not possible to calculate from the available data; studies which were not fully accessible even after e-mailing the primary author twice; and studies having a low-quality score in accordance with the given quality assessment criteria were excluded from this study.

Data extraction and quality assessment

The selected articles were thoroughly and independently examined by the other two authors (STM & BTA), and the necessary data for the review were collected and summarized using a straightforward data extraction spreadsheet format prepared in Microsoft Office Excel software. If inconsistencies among data extractors were detected, a third author acted as the final arbiter. In instances of incomplete data, emails were sent to the corresponding author twice in an attempt to get further information, or calculations were conducted using the available information.

The data extraction tool comprises the name of the first author, publication year, country where the study was conducted, design of the study, sample size, the prevalence of anemia with 95% confidence interval (CI), dialysis status, and each specific factor (factors if two or more studies considered them as predictors of anemia among CKD patients). For every associated factor, in order to calculate the odds ratio, the data from the primary studies were extracted by three authors (MSE, AT, MMT) in the form of two by two tables.

The methodological quality of each incorporated study was evaluated by two authors (AW, TFT) independently, using the Newcastle-Ottawa scale (NOS) quality valuation tool that was adopted for the quality evaluation of cross-sectional studies [19]. The tool comprises three major parts; the first part is rated up to five stars, and measures the methodological quality of each study. The second part of the instrument assesses the comparability of each study and gives two points. The third part measures the quality of the original articles with regard to the appropriateness of the statistical methods employed to analyze the data and can be rated out of three stars. Any disagreement between the reviewers regarding each article was resolved by all authors, forwarding their suggestions and the final decision was reached by consensus. Finally, the studies were taken into the analysis if they scored ≥5 out of 10 points in three domains of ten modified NOS constituents for observational studies (S2 Table).

Additionally, the risk of bias in selected studies was evaluated using the Hoy et al. [20] risk of bias tool for prevalence studies. The tool has ten items, each of which was given a score of 1 (yes) or 0 (no). The scores from all the items were added up to generate an overall quality score, which ranged from 0 to 10. Two authors (TT, FAG) independently conducted the risk of bias assessment of the included articles. When the available data were not sufficient to help make a decision for a certain item, we contacted the corresponding authors for additional information and if uncertainty persisted we decided to grade that item as 0 (failure to satisfy a specific item) meaning a high-risk of bias. Then each study was ranked as being of low, moderate, or high methodological quality according to the number of items judged as “yes (low risk of bias)”. Studies were considered to be of high-, moderate- and low quality based on scores that were greater than 8, between 6 and 8, and equal to or lower than 5, respectively (S3 Table).

Statistical methods and analysis

For statistical analysis, the extracted data were imported into STATA version 14 software (StataCorp LP, College Station, TX, USA). The heterogeneity across the recorded studies was evaluated by using the heterogeneity I2 test and its p-values, with I2 values of below 25, 25–75, and above 75% demonstrating low, medium and, high heterogeneity, respectively [21]. The tests conducted for this meta-analysis show that there is significant high heterogeneity among the included studies (I2 = 98.1%, P-value 0.001).

As a result, a random-effects meta-analysis model was applied to estimate the pooled prevalence of anemia among CKD patients. Using a forest plot, the pooled prevalence with a 95% CI was produced and displayed.

Furthermore, subgroup analysis and meta-regression were employed to detect the probable cause of heterogeneity. In addition, sensitivity analysis was conducted to evaluate the relative effect of each study on the overall estimate by omitting each study one by one. Besides, the possibility of publication bias was also evaluated by using funnel plot symmetry, Egger’s regression test, and Begg’s test. Finally, the different factors associated with anemia among CKD patients were presented using pooled odds ratios (PORs) with a corresponding 95% CI.

Results

Selection of the studies

A total of 3644 records concerning the prevalence and predictors of anemia among CKD patients in sub-Saharan African regions were collected from the databases of PubMed, HINARI, EMBASE, Science Direct, Google Scholar, Cochrane Library, and grey literature. Of these studies, 1852 articles were removed due to repetition. After assessing the title and abstract, 1695 articles were removed from the remaining 1792 studies as they were found to be non-applicable for this systematic review and meta-analysis. The remaining 97 studies were then assessed for eligibility based on the pre-defined criteria, which resulted in the further exclusion of 72 studies. Finally, 25 studies that satisfied the eligibility criteria were considered in the analysis (Fig 1).

Fig 1. Flow chart showing the selection of studies for the systematic review and meta-analysis of prevalence and predictors of anemia among CKD patients in Sub-Saharan African countries.

Fig 1

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Baseline characteristics of included studies

A total of 25 original articles that revealed the prevalence and predictors of anemia among 5042 CKD patients in sub-Saharan African regions which were published between 2010 and 2021 were incorporated in this systematic review and meta-analysis. These studies were carried out in South Africa (5 studies) [14, 22–25], Nigeria (6 studies) [15, 26–30], Ethiopia (5 studies) [16, 17, 31–33], Tanzania (2 studies) [34, 35], Kenya (2 studies) [36, 37], Cameron (3 studies) [38–40], Sudan (1 study) [41], and Uganda (1 study) [42]. Of the studies incorporated in the final analysis, 22 (88.0%) of them were cross-sectional, 2 (8.0%) were case-control, and 1 (4.0%) was a prospective cohort study. The sample size of the incorporated studies ranged from 49 in South Africa [14] to 792 in Tanzania [34] (Table 1).

Table 1. Characteristics of included studies in the systematic review and meta-analysis of anemia among CKD patients, in Sub-Saharan African countries, 2022.

Authors Publication Year Country Sample Size Anemia Prevalence with 95% CI Quality (NOS)/ 10 pts Definition of anemia Definition of CKD Predictors
Adera et al. [31] 2019 Ethiopia 251 162 64.50 (58.58, 70.42) 7 WHO definition Not specified Residence, BMI, hemodialysis history
Nalado et al. [22] 2019 S. Africa 353 152 43.10 (37.93, 48.27) 7 WHO definition Not specified Stage of CKD, DM
Alemu et al. [32] 2021 Ethiopia 387 207 53.50 (48.53, 58.47) 8 WHO definition eGFR<60 mL/min/ 1.73 m2 for ≥ 3 months Sex, hemodialysis history, stage of CKD, DM, protein urea, Hypertension
Meremo AJ et al. [34] 2017 Tanzania 792 249 31.40 (28.17, 34.63) 7 WHO definition Not specified Sex, heart failure, stage of CKD
Akinola OL et al. [26] 2018 Nigeria 55 30 54.50 (41.34–67.66) 5 WHO definition Not specified Age, sex, DM, decline eGFR
Ljoma et al. [27] 2010 Nigeria 364 282 77.50 (73.21, 81.79) 6 Hb <12g/dL eGFR<60 mL/min/ 1.73 m2 for ≥ 3 months or albuminuria HIV, collagen vascular disease, chronic glomerulonephritis
Maina et al. [36] 2016 Kenya 212 142 67.00 (60.67,73.33) 6 WHO definition Not specified stage of CKD, DM, systemic lupus erythematous
Raji et al. [28] 2018 Nigeria 157 67 42.70 (34.96, 50.44) 8 K/DOQI. eGFR<60 mL/min/ 1.73 m2 for ≥ 3 months or albuminuria Sex, stage of CKD, hemodialysis history
Abate et al. [16] 2013 Ethiopia 57 51 89.50 (81.54, 97.46) 8 WHO definition eGFR<60 mL/min/ 1.73 m2 or albuminuria …….
George C et al. [23] 2018 South Africa 94 40 42.60 (32.60, 52.60) 7 K/DOQI eGFR<60 mL/min/ 1.73 m2 ……‥
Iyawe & Adejum [29] 2018 Nigeria 100 90 90.00 (84.12, 95.88) 5 WHO definition eGFR<60 mL/min/ 1.73 m2 for ≥ 3 months or marked kidney damage Stage of CKD
Emmanuel et al. [30] 2020 Nigeria 113 100 88.50 (82.62, 94.38) 7 WHO definition eGFR<60 mL/min/ 1.73 m2 for ≥ 3 months or marked kidney damage ……
Valerian et al. [37] 2019 Kenya 118 57 48.30 (39.28, 57.32) 9 Hb <10g/dL eGFR<60 mL/min/ 1.73 m2 for ≥ 3 months or marked kidney damage …….
Bashir et al. [41] 2016 Sudan 70 51 72.90 (62.49, 83.31) 8 WHO definition eGFR<60 mL/min/ 1.73 m2 or hemodialysis …….
Fiseha T et al. [17] 2019 Ethiopia 177 70 39.50 (32.30, 46.70) 8 WHO definition eGFR<60 mL/min/ 1.73 m2 or albuminuria …….
Nalado AM et al. [24] 2018 South Africa 258 91 35.30 (29.47, 41.13) 6 K/DOQI eGFR<60 mL/min/ 1.73 m2 ……‥
Francois et al. [38] 2015 Cameroon 95 75 78.90 (70.70, 87.10) 7 K/DOQI Not specified …….
Halle et al. [39] 2013 Cameroon 113 93 82.70 (75.73, 89.67) 6 Hb <11 g/dL Not specified …………‥
Haupt, & weyers [14] 2016 South Africa 49 45 91.80 (87.9, 95.7) 5 K/DOQI Not specified …….
Namuyimb et al. [42] 2018 Uganda 93 14 37.80 (27.95, 47.65) 7 Hb < 11.5 g/dL eGFR<60 mL/min/ 1.73 m2 or any eGFR with proteinuria ……
Nalado et al. [25] 2020 S.Africa 312 103 33.00 (27.78, 38.22) 8 WHO definition eGFR<60 mL/min/ 1.73 m2 ……
Iyawe IO et al. [15] 2018 Nigeria 100 14 14.00 (7.20, 20.80) 7 K/DOQI eGFR<60 mL/min/ 1.73 m2 for ≥ 3 months ……
Ruggajo et al. [35] 2019 Tanzania 233 161 69.00 (63.06, 74.94) 8 WHO definition Not specified …‥
Kaze et al. [40] 2020 Cameroon 105 91 86.70 (80.20, 93.20) 6 K/DOQI eGFR<60 mL/min/ 1.73 m2 …‥
Kidanewold et al. [33] 2021 Ethiopia 384 169 44.00 (39.00, 48.90) 8 WHO definition eGFR<60 mL/min/ 1.73 m2 Cardiovascular disease, DM, stage of CKD
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BMI: Body mass index; CI: Confidence interval; CKD: Chronic kidney disease; DM: Diabetes mellitus; eGFR: Estimated glomerular filtration rate; g/Dl: gram per deciliter; Hb: Hemoglobin; HIV: Human immune deficiency virus; K-DOQI: Kidney Disease Outcome Quality Initiative; NOS: Newcastle-Ottawa scale; WHO: World Health Organization

Meta-analysis

Prevalence of anemia among CKD patients

The pooled prevalence of anemia among CKD patients in Sub-Saharan countries was estimated to be 59.15% (95% CI, 50.02–68.27) with a substantial level of heterogeneity as evidenced by I2 statistic (I2 = 98.1%; p < 0.001), indicating a great variability in the prevalence of anemia among patients presented with CKD across the studies. Hence, a random effect model was employed to determine the pooled prevalence of anemia among CKD patients in sub-Saharan African regions (Fig 2).

Fig 2. Forest plot of the pooled prevalence of anemia among CKD patients in Sub-Saharan African countries, 2022.

Fig 2

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Subgroup analysis

To pinpoint the source of heterogeneity, subgroup analyses based on the study area, year of publication, sample size, dialysis status, and bias risk of the included studies were conducted. Based on the subgroup analysis, the pooled prevalence of anemia in the included studies ranged between 37.8% in Uganda and 83.30% in Cameroon, 54.76% among studies published after the years of 2015, and 81.63% up to the year 2015, and 65.51% among studies with moderate risk of bias and 51.00% among study with low-risk of bias.

Another subgroup analysis based on the sample size showed the highest prevalence (67.68%) of anemia was detected among the studies with a sample size of less than 150; whereas the lowest (50.04%) was observed among the studies with a sample size of 150 and above. Furthermore, the pooled prevalence of anemia in studies among dialysis and pre-dialysis patients was 71.01 and 55.42%, respectively (Table 2). In addition to subgroup analysis, a meta-regression analysis for the included studies was also carried out, by taking into consideration of factors such as publication year and sample size although none of these variables was found to be statistically significant.

Table 2. Subgroup analysis of the prevalence of anemia among CKD patients in Sub-Saharan African countries, 2022.
Sub-group Category Number of studies Sample size Prevalence (95% CI) Heterogeneity P-value I2 (%) Tau-squared
Country Ethiopia 5 1256 58.06 (43.45, 72.67) 119.09 0.000 96.6 267.40
S. Africa 5 1066 49.07 (30.34, 67.79) 174.24 0.000 97.7 443.66
Tanzania 2 1025 50.11 (13.27, 86.96) 118.8 0.000 99.2 700.93
Nigeria 6 889 61.31 (37.6, 84.97) 406.44 0.000 98.8 858.83
Kenya 2 330 57.94 (39.62, 76.25) 11.07 0.001 91.0 159.05
Sudan 1 70 72.90 (62.49, 83.31) 0.00 … … 0.000
Cameroon 3 313 83.30 (78.99, 87.61) 2.19 0.335 8.5 1.24
Uganda 1 93 37.80 (27.94, 47.65) 0.00 --- … 0.000
Publication years <2015 21 4413 54.76 (45.13, 64.39) 1015.95 0.000 98.0 492.61
≤2015 4 629 81.63 (76.43, 86.83) 7.32 0.062 59.0 16.27
Sample size ≥150 12 3880 50.04 (40.25, 59.83) 462.70 0.000 97.6 290.94
<150 13 1162 67.68 (53.28, 82.08) 534.02 0.000 97.8 682.86
Risk of bias Low risk 11 3040 51.00 (41.14, 60.87) 319.07 0.000 96.9 265.46
Moderate 14 2002 65.51 (52.28, 78.75) 711.00 0.000 98.2 624.19
Dialysis status Pre-dialysis 19 3690 55.42 (45.26, 65.59) 885.21 0.000 98.0 496.85
Dialysis 6 1352 71.01 (47.48, 94.55) 418.38 0.000 98.8 850.62
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Sensitivity analysis

A leave-one-out sensitivity analysis was also conducted to determine the cause of heterogeneity. The results of the random-effects model’s sensitivity analysis indicated that no single study had an impact on the overall prevalence of anemia among CKD patients (Fig 3).

Fig 3. Results of sensitivity test of 25 studies.

Fig 3

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Publication bias

The publication bias was evaluated with a funnel plot, Beggs’ and Eggers’ tests. The funnel plot (Fig 4) was symmetric and Egger’s regression test (P = 0.607), as well as Begg’s test (P = 0.191), provided no evidence of publication bias.

Fig 4. Test of publication bias of 25 studies using a funnel plot.

Fig 4

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Factors associated with anemia among CKD patients

In this systematic review and meta-analysis stage of CKD, the presence of diabetes mellitus (DM), female sex, and history of hemodialysis were recognized as factors significantly associated with the presence of anemia among CKD patients. For each identified factor except sex, sensitivity analysis was also carried out by excluding each study one by one, but the result showed that there was no strong evidence for the effect of a single study on the overall result.

Five studies [22, 28, 32, 33, 36] evaluated the association between the stage of CKD and the presence of anemia among CKD patients. The pooled odds ratio showed that CKD patients with stage 4–5 were five times more likely to have anemia (POR = 5.33, 95% CI:4.20–6.76) than those with stage 1–3 (Fig 5A). Similarly, the analysis of 5 studies [22, 26, 32, 33, 36] showed that the presence of DM among CKD patients was significantly associated with anemia than those without DM (POR = 1.75, 95% CI: 1.10, 2.78) (Fig 5B). Furthermore, the pooled result from the two studies [28, 32] considered in the meta-analysis have shown that being female was two times more likely to have anemia than being male (POR = 2.50, 95% CI: 1.76–3.55) (Fig 5C). Moreover, the association between hemodialysis history and anemia was evaluated according to the findings from three studies [28, 31, 32]. The pooled odds ratio from these studies indicated that CKD patients with a history of hemodialysis were three times more likely to have anemia than patients without a history of hemodialysis (POR = 3.06, 95% CI: 1.63–5.73) (Fig 5D).

Fig 5. Forest plot showing pooled odds ratio of the factors associated with anemia among CKD patients in Sub-Saharan African countries.

Fig 5

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A: stage of CKD; b: diabetes mellitus; c: sex of the study participants; d: history of hemodialysis.

Discussion

This systematic review and meta-analysis deliver evidence of an estimated pooled prevalence of anemia among CKD patients in Sub-Saharan African countries. Based on the review, the pooled prevalence of anemia was 59.15% (95% CI, 50.02–68.27). The finding is in line with the studies conducted in Turkey (55.9%) [43], Malaysia (60.3%) [44], Spain (58.5%) [45], Nepal (53.6%) [46] and China (51.5%) [47]. On the other hand, the prevalence of anemia among CKD patients is lower than the prevalence reported in Brazil (86.1%) [48], Malaysia (75.8%) [49] and Pakistan (80.5%) [50]. On the other hand, the observed magnitude of anemia in this finding was greater than the results in Korea (45.0%) [51], Nepal (47.8%) [52], Japan (32.3%) [53], and the USA (15.4%) [54].

These wide discrepancies in the burden of anemia among CKD patients might be explained by the differences in the socio-economic status, the definition of anemia, the study population, methodology, inclusion and exclusion criteria of the study, the stage of CKD, and the quality of healthcare services.

Sub-group analysis of this study indicated that the prevalence of anemia among dialysis patients 71.01% (95% CI: 47.48–94.55) was higher than their counterparts, 55.42% (95% CI: 45.26–65.59). It is not surprising to see anemia more commonly among dialysis patients than non-dialysis patients in this study, as the various studies revealed that erythropoietin deficiency and losing blood, either through blood tests or during dialysis were common among dialysis patients [28, 31, 32]. The prevalence of anemia also varies greatly across the regions with the highest prevalence observed in Cameroon 83.30% (95% CI: 78.99–87.61), followed by Sudan 72.90% (95% CI: 62.49–83.31) while the lowest prevalence was seen in Uganda 37.80% (95% CI: 27.94,-47.65). Since the study participants in Cameroon and Sudan were patients on dialysis, observing the high prevalence of anemia in these countries may support the acceptability of the above explanation. Moreover, these discrepancies in the prevalence of anemia among CKD patients across the regions may be due to the differences in methodology that the study employed and in the clinical features of the study participants, particularly the stage of CKD.

Predictors of anemia were also assessed in the present systematic review and meta-analysis. History of hemodialysis was identified as a predictor of anemia among CKD patients in which CKD patients with a history of hemodialysis were three times more likely to be suffered from anemia than those patients without a history of hemodialysis. This is most likely because hemodialysis-requiring patients were those with a long duration of the disease, advanced renal disease, and comorbidities, in which the presence and severity of anemia were prevalent [31, 32].

In this finding, the sex of the study participants was significantly associated with the presence of anemia in which being female is stated to be a non-modifiable risk factor for the occurrence of anemia among CKD patients, yielding a similar result to a study carried out in Turkey [43], Malaysia [44], Italy [55], United States [56], and Iran [57]. This may be simply elucidated by the biological susceptibility of the inevitable blood loss at the time of menstruation and pregnancy in pre-menopausal women as well as dietary inadequacy [28, 43].

The study also showed that the stage of CKD was significantly associated with the occurrence of anemia among CKD patients. It was found that the risk of anemia was gradually increased as the CKD advanced, which corresponds well with the results of earlier studies conducted in South Korea [51], Malaysia [44], Pakistan [50], USA [56], Greece [58], and Singapore [59]. Increased risk of anemia in advanced renal disease patients could be due to the known pathogenesis of CKD mainly impaired production of erythropoietin as kidney function worsens, urinary erythropoietin losses, metabolic disturbances, and reduced red blood cells life span because of uremic environment and the possible effect of circulating uremic-induced inhibition of erythropoiesis [9, 22, 36].

Finally, CKD patients with DM had an approximately two-fold increased risk of anemia than those without DM. Likewise; an increase in the risk of anemia among CKD patients with DM has been reported by various studies carried out in the United States [56], Greece [58], and Nepal [60]. This is mainly due to the impact of diabetes-related chronic hyperglycemia. Chronic hyperglycemia can cause a persistent hypoxic environment in the renal interstitium which results in impaired erythropoietin production. Additionally, erythrocyte precursor cells in the bone marrow may be exposed to prolonged direct glucose toxic effects, or mature red blood cells may be damaged by oxidative stress, both of which can influence erythrocyte production and red cell survival in patients with prolonged hyperglycemia. Chronic inflammatory activity, elevated levels of advanced glycation end products (AGEs), hyporesponsiveness to erythropoietin, the effects of oxidative stress, and anti-diabetic medicines are also other possible causes of anemia in DM patients [22, 26, 58].

Limitations of the study

There are a few potential significant limitations to this systematic review and meta-analysis. First of all, there is no uniformity of CKD definitions and anemia cut-off points in the included studies that may affect the estimation of the combined prevalence. Second, it might be challenging to apply the conclusions from a small number of sub-Saharan African nations included in this meta-analysis to all CKD patients in that region. Third, most of the studies incorporated in the review were cross-sectional in nature. As a consequence, a cause-effect relationship cannot be established. Hence further study with a strong study design and by considering other causes of anemia should be conducted. Additionally, there has been a substantial heterogeneity among the included studies and the cause/s for the heterogeneity thus remains elusive, which could make it difficult to interpret the findings.

Conclusion

Almost three out of five CKD patients in Sub- Saharan African countries were suffering from anemia, which revealed that anemia is highly prevailing in patients with CKD. Its prevalence varies across the region with the highest prevalence in Cameroon. It was also found that stage of CKD, presence of DM, being female sex, and history of hemodialysis were significantly associated with the presence of anemia among CKD patients. Consequently, a high index of suspicion for anemia should be implemented among CKD patients particularly in patients with advanced CKD stage, history of hemodialysis, comorbid condition of DM, and female sex to improve their outcomes.

Supporting information

S1 Table. The guideline of Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) checklist) (S1 Table).

(DOCX)

Click here for additional data file. (33.6KB, docx)
S2 Table. Methodological quality assessment of included studies using modified Newcastle—Ottawa Scale (NOS).

(DOCX)

Click here for additional data file. (36KB, docx)
S3 Table. The risk of bias assessment tool for the included studies.

(DOCX)

Click here for additional data file. (34.8KB, docx)

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. The guideline of Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) checklist) (S1 Table).

(DOCX)

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S2 Table. Methodological quality assessment of included studies using modified Newcastle—Ottawa Scale (NOS).

(DOCX)

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S3 Table. The risk of bias assessment tool for the included studies.

(DOCX)

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Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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