Prevalence of Anemia in Ethiopia Systematic Review and Meta-analysis

• Periodical Listing
• SAGE Open Med
• five.9; 2021
• PMC8274127

SAGE Open Med. 2021; 9: 20503121211031126.

Magnitude and factors associated with anemia among diabetic patients in Ethiopia: A systematic review and meta-analysis

Daniel Atlaw

¹Section of Beefcake, Schoolhouse of Medicine, Madda Walabu Academy Goba Referral Hospital, Bale-Goba, Federal democratic republic of ethiopia

Zerihun Tariku

ⁱⁱSection of Public Wellness, College of Medicine and Health Sciences, Dire Dawa University, Dire Dawa, Federal democratic republic of ethiopia

Received 2021 Mar 8; Accepted 2021 Jun 21.

Supplementary Materials

sj-doc-1-smo-10.1177_20503121211031126 – Supplemental fabric for Magnitude and factors associated with anemia amidst diabetic patients in Ethiopia: A systematic review and meta-analysis

GUID: F6D3771F-ECF3-4EAC-AAC9-9BC7772BB6E3

Supplemental material, sj-doc-one-smo-10.1177_20503121211031126 for Magnitude and factors associated with anemia among diabetic patients in Ethiopia: A systematic review and meta-analysis by Daniel Atlaw and Zerihun Tariku in SAGE Open Medicine

sj-docx-ii-smo-10.1177_20503121211031126 – Supplemental textile for Magnitude and factors associated with anemia among diabetic patients in Ethiopia: A systematic review and meta-analysis

GUID: 8232D203-0CDF-4A5D-8BDE-2345C2D19E42

Supplemental textile, sj-docx-ii-smo-ten.1177_20503121211031126 for Magnitude and factors associated with anemia amid diabetic patients in Federal democratic republic of ethiopia: A systematic review and meta-analysis past Daniel Atlaw and Zerihun Tariku in SAGE Open Medicine

sj-xlsx-3-smo-10.1177_20503121211031126 – Supplemental material for Magnitude and factors associated with anemia among diabetic patients in Ethiopia: A systematic review and meta-analysis

GUID: 0CA656A0-E297-408A-B453-0FA65BC81E70

Supplemental material, sj-xlsx-three-smo-10.1177_20503121211031126 for Magnitude and factors associated with anemia among diabetic patients in Ethiopia: A systematic review and meta-analysis by Daniel Atlaw and Zerihun Tariku in SAGE Open up Medicine

sj-docx-4-smo-10.1177_20503121211031126 – Supplemental fabric for Magnitude and factors associated with anemia amidst diabetic patients in Federal democratic republic of ethiopia: A systematic review and meta-assay

GUID: B492424D-0DD0-43A5-84E1-519938B5BF92

Supplemental material, sj-docx-4-smo-10.1177_20503121211031126 for Magnitude and factors associated with anemia amongst diabetic patients in Ethiopia: A systematic review and meta-analysis by Daniel Atlaw and Zerihun Tariku in SAGE Open up Medicine

Abstract

Groundwork:

In Ethiopia, diabetes is estimated to affect well-nigh half a million people. Most 35% of individuals with diabetes are complicated past microvascular diseases similar retinopathy, nephropathy, cardiovascular, and anemia. Even though there are some studies conducted on prevalence and associated factors of anemia in diabetic patients, their findings were variable. Therefore, this meta-analysis is aimed to determine the pooled prevalence and factors associated with anemia amongst diabetic patients.

Methods:

PubMed, CINAHL, POPLINE, ScienceDirect, African Journals Online, and Google Scholar were systematically searched to identify related studies. The heterogeneity of studies was assessed using Cochran's Q test and I ² tests. A random-effects model was used to estimate the pooled prevalence of anemia among diabetic patients in Ethiopia. Publication bias was evaluated by employing Egger's tests.

Results:

Afterward reviewing 503 manufactures, 6 articles fulfilled inclusion criteria and remained for the final meta-analysis. The pooled prevalence of anemia among diabetic patients was 24.81% (95% conviction interval: nineteen.38–30.25). Age greater than 60 years old (pooled odds ratio, 95% confidence interval: iii.73 (two.23–vi.77)), glomerular filtration rate less than lx mL/min/1.73 gⁱⁱ (pooled odds ratio, 95% confidence interval: 12.65 (eight.71–18.37)), and existence diabetic for more ten years (pooled odds ratio, 95% confidence interval: 10.21 (7.00–15.04)) were found to be determinants of anemia among diabetic patients in Ethiopia.

Conclusion:

Overall, 1 in four diabetic patients develops anemia in Ethiopia. Age, glomerular filtration rate, and duration of beingness diabetic are factors significantly associated with the occurrence of anemia in diabetic patients.

Keywords: Anemia, diabetes, Federal democratic republic of ethiopia, meta-analysis, systematic review

Background

Diabetes mellitus is increasing rapidly worldwide and reached 463 million people in 2019. ⁱ Available data were indicating a rapid increment in the prevalence of diabetes in Africa, which is estimated to increment by twofold in 2030 every bit overweight, fast-food consumption, and urbanization increment. ^two In Ethiopia, diabetes is estimated to affect half a million people. ³ About 35% of individuals with diabetes are complicated by microvascular complications similar retinopathy, nephropathy, cardiovascular, and anemia.^4,5

Anemia is one of the complications of patients with diabetes.^6–viii It occurs more than frequently in many chronic diseases but is not recognized. ^nine Diabetes is i of the common causes of anemia.^ten,11 Some studies identified anemia as two times more likely among diabetic than non-diabetic patients.^12,13 Besides, hematological indices were shown to touch on blood glucose levels.^14,15

Anemia is besides becoming an indicator of nephropathy in diabetic patients.^16–18 It is too identified every bit a determinant factor for prognosis and microvascular complications in diabetic patients. ⁸ The degree of anemia roughly estimates the phase of renal failure, and the presence of anemia increases the hazard of developing terminate-stage renal failure in blazon 2 diabetic patients.^eighteen,xix Prevalence of anemia was as well shown to increase in diabetic patients even without renal impairment. ²⁰

The occurrence of anemia in diabetic patients was afflicted past factors like age, glomerular filtration rate, serum creatinine, early glycemic command, albuminuria, and nutritional condition.^7,12,21,22 Anemia occurs five times more likely among diabetics with a glomerular filtration rate less than <threescore mL/min/1.73 m². ²³ Anemia among diabetic was shown to impact men more than women. ¹⁹

The pathophysiologic mechanism of anemia amidst diabetic patient was stated to vary, every bit causes of anemia are multifactorial. Reduction in erythropoietin production was among the usually mentioned causes of anemia in a diabetic patient at whatever glomerular filtration rate. ²⁴ A subtract in erythropoietin production was associated with microvascular complications.^24,25 In addition, renal impairment by itself will decrease erythropoietin production which will end upwards in anemia. ²¹ The presence of diabetes will predispose to systemic inflammation that affects interstitial tissue of the kidney that in turn will cause anemia. ²⁶ Inflammation is as well stated to impact atomic number 26 metabolism either by induction of autoimmune disorders^27,28 or by the release of multiple inflammatory cytokines and free radicals that increase hepcidin. ²⁹ Hepcidin increases ferroprotein degradation and results in iron deficiency anemia. ^thirty Furthermore, the degradation of ferroprotein results in a blockade of the duodenal iron transfer. ³¹

Early diagnosis and direction of anemia in diabetes were shown to accept improvement of complications, ³² as it happens early in the progression of diabetic nephropathy and other complications. ³³ In Federal democratic republic of ethiopia, some studies have been conducted on the magnitude and associated factors of anemia among patients with diabetes, but their findings were inconsistent ranging from 17.ix% ³⁴ to 34.eight%. ³⁵ Therefore, this meta-analysis is aimed to determine the pooled prevalence and factors associated with anemia among diabetic patients.

Methods

Study pattern and reporting

A review and meta-analysis were conducted to make up one's mind the pooled prevalence of anemia amidst diabetic patients. This meta-analysis was conducted co-ordinate to the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (Supplementary File 1).

Eligibility criteria

Studies that were conducted in Ethiopia to decide the prevalence and associated factors of anemia amid diabetic patients and that satisfied the following conditions were recruited for the concluding assay.

• Study area: Whatsoever studies conducted in Ethiopia were involved.

• Population: All studies that had reported prevalence and associated factors of anemia among adult diabetic patients were involved.

• Written report designs: Observational studies reporting the prevalence and associated factors of anemia amongst diabetic patients were eligible for this systematic review and meta-analysis.

• Publication status: Published manufactures were considered.

• Year of publication: All publications reported upwards to January 31, 2021, were considered.

• Exclusion criteria: Studies that reported complication of diabetes but practice not have separate outcomes for anemia were excluded.

The outcome of this systematic review and meta-analysis

Prevalence of anemia in adult (historic period ⩾eighteen years) diabetic patients: Number of diabetic patients reported hemoglobin <12 mg/dL for females and <13 mg/dL for males ³⁶ per full number of diabetic patients × 100.

Search strategy

A systematic search of the literature was conducted by the authors to identify all relevant chief studies. All articles on the prevalence of anemia amidst diabetic patients in Ethiopia were identified through a literature search. The databases used to search for studies were PubMed, ScienceDirect, Google Scholar, CINAHL, POPLINE, Cochrane Library, and African Journals Online (AJOL), and greyness literature was searched on Google until 31 January 2021. The central search terms and Medical Subject Headings [MeSH]—"prevalence" OR "magnitude" AND "anemia" AND "diabetic patient" AND "Ethiopia [MeSH]"—were used separately or in combination with the Boolean operator's terms "AND" and "OR" (Supplementary File 2). Moreover, the reference lists of the retrieved studies were likewise scanned to access additional articles and screened against our eligibility criteria.

Report selection

In this review, all the searched articles were exported into the EndNote version X8 software, and subsequently, the duplicate manufactures were removed. Screening of retrieved article titles, abstracts, and the total text was conducted independently by 2 review authors (D.A. and Z.T.) based on the eligibility criteria. Afterward, full-text articles were retrieved and appraised to approve eligibility. Finally, the screened articles were compiled together past the two investigators.

Risk of bias assessment

The qualities of the included studies were assessed, and the risks for biases were judged using the Joanna Briggs Institute (JBI) quality assessment tool for the prevalence studies. Two reviewers (D.A. and Z.T.) assess the quality of included studies independently, and a discrepancy between the two reviewers resolved with word. The evaluation tool comprises nine parameters: (1) appropriate sampling frame, (2) right sampling technique, (3) acceptable sample size, (4) study subject and location explanation, (five) appropriate data investigation, (6) use of valid methods for the identified conditions, (7) valid measurement for all participants, (viii) using appropriate statistical analysis, and (9) adequate response charge per unit. ³⁷ Failure to satisfy each parameter was scored as 1 if not 0. When the information provided was not satisfactory to aid in deciding on a specific item, nosotros agreed to grade that item as 1 (failure). The risks for biases were classified equally either low (total score: 0–two), moderate (full score: iii or four), or high (full score: 5–ix) (Supplementary Figure 3).

Data extraction

The selected articles were thoroughly reviewed, and the required information for the systematic review was extracted and summarized using an extraction table in Microsoft Office Excel software. The data extraction was conducted past the 2 authors (D.A. and Z.T.) based on prespecified headings that are agreed upon past discussion. The data extraction tool consists of the name of the author(s), year of publication, region, study design, study setting, subtype of diabetes, sample size, prevalence, odds ratio with 95% confidence interval (CI), risk of bias, and results of associated factors in diabetic patients.

Statistical methods and analysis

The extracted data were imported into STATA version 14 software for statistical assay. The heterogeneity among all included studies was assessed by I ² statistics and Cochran's Q test. In this meta-analysis, the tests indicate the presence of significant heterogeneity among included studies (I ² = 87.7%, p < 0.001). Thus, a random-effect model was used to analyze the data. Pooled prevalence forth their corresponding 95% CI was presented using a forest plot. Subgroup analyses for the prevalence of anemia among diabetic patients was performed. Meta-regression analysis was used to evaluate the association between the prevalence of anemia amidst diabetic patients and publication yr and sample size in the selected studies. To determine the associated factors, data extracted were manually entered into Review Managing director version 5.three software, and fixed-effect model pooled odds ratios (PORs) with 95% CI were used to declare the association. In this meta-assay, the presence of publication bias was evaluated using funnel plots and Egger's tests at a significance level of less than 0.05.

Results

Description of included studies

Almost 503 studies were retrieved from initial electronic searches using international databases and Google search. The databases included PubMed (n = 18), ScienceDirect (n = 236), Google Scholar (n = 23), and AJOL (northward = 224), and the remaining (north = two) studies were identified through manual search. Of these, 333 duplicates were removed, the remaining 170 articles were screened by championship, and 161 manufactures were excluded after reading their titles. Ix total-text articles remained and were further assessed for their eligibility. Finally, based on the predefined inclusion and exclusion criteria, a total of six articles were included in the meta-analysis, and data were extracted for the last assay (Figure 1).

Flow diagram of systematic review and meta-analysis on the prevalence of anemia among diabetic patients in Ethiopia, 2021.

Characteristics of the included studies

A total number of 1978 diabetic patients participated in the study. All included studies are cantankerous-exclusive and hospital-based. The latest study was published in 2020, ³⁸ and the earliest study was published in 2013. ³⁹ Depending on sample size, 2 studies have a sample size greater than or equal to 384,^39,forty and 4 studies have a sample size less than 384.^34,35,38,41 Four studies were conducted in the Amhara region,^38–41 i report in the Harari region, ³⁵ and one study in the Tigray region ³⁴ Table 1). The common associated factors reported by included studies were glomerular filtration charge per unit, sex, duration of diabetes, and historic period of diabetic patients (Table 2).

Table 1.

Characteristics of included study for meta-analysis of pooled prevalence of anemia among diabetic patients in Ethiopia.

Author's proper name	Year of publication	Setting	Region	Study design	Blazon of diabetes	Study population	Sample size	Prevalence of anemia	SE	Hazard of bias
Allay A.	2013	Hospital-based	Amhara	Cross-sectional	Mixed type i and type ii	Adults	384	xix	2.01	Moderate
Engidaw MT.	2020	Infirmary-based	Amhara	Cross-sectional	Mixed type i and type 2	Adults	297	29.eight	2.64	Low
Tadergew One thousand.	2020	Hospital-based	Amhara	Cross-sectional	Type 2	Adults	249	20.1	2.54	Moderate
Fiseha A.	2019	Hospital-based	Amhara	Cross-exclusive	Mixed type 1 and type 2	Adults	412	27.half dozen	ii.22	Low
Bekele A.	2019	Hospital-based	Harari	Cross-sectional	Type 2	Adults	374	34.viii	ii.48	Low
Hailu NA.	2020	Infirmary-based	Tigray	Cross-exclusive	Mixed type i and type 2	Adults	262	17.nine	2.38	Moderate

Table 2.

Characteristics of included study for meta-analysis of factors associated with anemia among diabetic patients in Ethiopia.

Authors	Year of publication	Study design	Setting	Sample size	Gamble of bias	Result	OR (95% CI)
Engidaw MT.	2020	Cross-sectional	Hospital-based	297	Low	Sex of diabetic patient (male person)	0.52 (0.30–0.89)
Abate A.	2013	Cantankerous-sectional	Hospital-based	384	Moderate	Glomerular filtration rate (<threescore)	14.38 (eighteen.23–89.48)
						Age of diabetic patient (>60 years)	10.65 (4.32–26.23)
						Duration of being diabetic (>10 years)	sixteen.86 (22.12–145.91)
Tadergew Yard.	2020	Cross-sectional	Hospital-based	249	Moderate	Glomerular filtration rate (<60)	6.58 (ii.42–17.93)
						Age of diabetic patient (>60 years)	3.06 (i.32–7.11)
						Duration of being diabetic (>10 years)	2.75 (ane.17–6.48)
Fiseha T.	2019	Cross-sectional	Hospital-based	412	Low	Age of diabetic patient (>60 years)	three.89 (ii.23–vi.77)
						Duration of being diabetic (>10 years)	8.69 (iv.57–16.52)
						Sex of diabetic patient (male)	2.25 (1.44–3.51)
						Glomerular filtration rate (<60)	vi.32 (3.41–xi.73)
Hailu Northward.	2020	Cross-exclusive	Hospital-based	262	Moderate	Sex of diabetic patient (female)	iii.43 (ane.58–seven.46)
						Age of diabetic patient (>lx years)	4.01 (one.53–x.51)
Bekele A.	2019	Cross-sectional	Hospital-based	374	Depression	Sexual practice of diabetic patient (male person)	1.75 (1.14–2.69)
Melaku T.	2020	Cross-sectional	Infirmary-based	297	Depression	Sex of diabetic patient (male)	0.52 (0.xxx–0.89)
Mitiku Yard.	2020	Cross-sectional	Infirmary-based	249	Moderate	Glomerular filtration charge per unit (<threescore)	6.58 (ii.42–17.93)
						Historic period of diabetic patient (>60 years)	three.06 (1.32–7.11)
						Duration of being diabetic (>ten years)	2.75 (1.17–6.48)

The publication biases

The presence of publication bias was evaluated using funnel plots and Egger'south tests at a significance level of less than 0.05. The findings revealed that publication bias was not significant for the studies on the prevalence of anemia in diabetic patients (p = 0.38; Figure ii).

Forest plot showing the publication bias of study on anemia among diabetic patients in Ethiopia.

Prevalence of anemia in diabetic patients in Federal democratic republic of ethiopia

The pooled prevalence of anemia in diabetic patients in Ethiopia using the random-furnishings model was estimated to be 24.81% (95% CI: 19.38–thirty.25) with a significant level of heterogeneity (I ^two = 87.9 %, p < 0.001; Figure iii). Subgroup analysis was conducted past the chance of bias, sample size, type of diabetes, year of publication, and region of studies. The prevalence of anemia in diabetes was identified to be eighteen.96% (95% CI: 16.38–21.53) amidst moderate-risk bias of studies and xxx.65% (95% CI: 26.38–34.93) amid low-run a risk bias of studies. The heterogeneity was shown to decrease to 58.5% after subgroup assay (Effigy 4). The prevalence of anemia was 25.40% (95% CI: 22.97–27.88) among studies having a sample size of less than 384, and 22.87% (95% CI: xix.95–25.79) among studies with a sample size of greater than or equal to 384. Studies published before 2020 were shown to have a college prevalence of anemia 26.04 % (95% CI: 23.53–28.54). Subgroup analysis conducted on the prevalence of anemia was college amid studies conducted in type 2 diabetic patients (27.63%) when compared with studies conducted on both type 1 and blazon 2 diabetic patients (23.02%) (Table 3).

Forest plot showing a pooled prevalence of anemia among diabetic patients in Ethiopia.

Forest plot showing subgroup assay of anemia among diabetic patients past risk of bias in Ethiopia.

Table 3.

Subgroup analysis by sample size, publication year, regions, and subtypes of diabetes on the prevalence of anemia among diabetic patients.

	Prevalence of anemia	95% confidence interval	Heterogeneity (I two%)	p value
Subgroup assay by sample size
1. Less than 384	25.42	22.97–27.88	90.5	p < 0.001
2. 384 and in a higher place	22.87	19.95–25.79	87.9	p = 0.004
Subgroup analysis by year of publication
1. Before 2020	26.04	23.53–28.54	92.one	p < 0.001
2. Later on and in 2020	22.21	19.37–25.06	83.7	p = 0.002
Subgroup assay by regions of Federal democratic republic of ethiopia
ane. Amhara region	23.63	22.36–25.89	83.eight	p = 0.001
2. Other (Harari and Tigray) regions	26.00	22.34–29.67	95.9	p < 0.001
Subgroup analysis by types of diabetes
1. Type two	27.63	24.fifteen–31.10	94.2	p < 0.001
ii. Mixed type 2 and type ane	23.02	22.79–25.26	84.6	p < 0.001

Factors associated with anemia in diabetic patients

The determinant factors included in this analysis were age, sex, elapsing of being diabetes, and glomerular filtration rate. A separate analysis was conducted for each variable.

Historic period and anemia in diabetic patients

Iv studies^34,39,40,41 examined the association betwixt the age of diabetic patients and the occurrence of anemia. The POR indicated that diabetic patients aged greater than 60 years old were iv times more likely to have anemia (POR, 95% CI: 3.73 (2.23–6.77)). The studies showed moderate heterogeneity (I ² = 74.0%, p < 0.001; Effigy v).

Wood plot showing the association between anemia and historic period amid diabetic patients in Federal democratic republic of ethiopia.

Glomerular filtration rate and anemia in diabetes

The clan of glomerular filtration rate and anemia was examined based on the findings from three studies.^39–41 The POR indicated that diabetic patients with glomerular filtration charge per unit less than lx mL/min/1.73 m² were 13 times more than likely to develop anemia than glomerular filtration charge per unit greater than threescore mL/min/1.73 m² (POR, 95% CI: 12.65 (8.71–xviii.37)). The studies showed significant heterogeneity (I ² = 93.0%, p < 0.001). Hence, a random-furnishings model was considered for the terminal analysis (Figure 6).

Forest plot showing the association between anemia and sex among diabetic patients in Federal democratic republic of ethiopia.

Elapsing of existence diabetic and anemia

This meta-analysis was employed on three studies,^39–41 and POR revealed that exposure to high claret glucose level for more than ten years was 10 times more likely to develop anemia than those who were exposed less than ten years for diabetes (POR, 95% CI: x.21 (7.00–15.04)). The studies showed depression heterogeneity (I ² = 14.0%, p = 0.21; Figure seven).

Wood plot showing the association between anemia and GFR among diabetic patients in Ethiopia.

Sex of diabetic patient and anemia

Four studies^34,35,38,40 included in the meta-analysis have revealed that there was no difference among male and female person diabetic patients on the occurrence of anemia (POR, 95% CI: 1.two (0.94–1.52)) with a significant level of heterogeneity (I ⁱⁱ = 89%, p < 0.001; Figure 8).

Woods plot showing the association between anemia and elapsing of diabetes amidst diabetic patients in Ethiopia.

Sensitivity assay

To detect the source of heterogeneity, a go out-one-out sensitivity analysis was employed. The upshot of sensitivity analysis using the random-effects model revealed that at that place was no single report that influenced the overall prevalence of anemia among diabetic patients (Supplementary File 4).

Meta-regression

In a meta-regression analysis, the publication year and sample size were not significant sources of heterogeneity for the prevalence of anemia in diabetic patients. In this study, no significant human relationship was identified between the prevalence of anemia and the publication year (b = 0.08, SE = 0.04, and p = 0.15), and sample size (b = 1.87, SE =1.07, and p = 0.xviii).

Word

This meta-analysis is conducted to determine the pooled prevalence and associated factors of anemia in diabetic patients. The pooled prevalence of anemia amidst diabetic patients was 24.81% (95% CI: nineteen.38–30.25) in Ethiopia. The pooled prevalence of anemia amongst diabetics in Ethiopia is almost similar to a survey conducted in Chinese diabetic patients (22%). ¹² Similarly, the written report was supported past some other study conducted in Bangladesh in 2018 (21%). This may be due to near similar poor glycemic control levels among countries: 34% in Ethiopia, ⁴² 32% in People's republic of bangladesh, ⁴³ and 32.v% in Communist china. ⁴⁴ The current finding is significantly lower than the prevalence reported in Pakistan (63%). ⁴⁵ This can exist explained by the difference in poor glycemic command levels among countries: 34% in Ethiopia ⁴² and 46.7% in Pakistan. ⁴⁵

Among determinant factors investigated in this review and meta-analysis, the historic period of diabetic patients, duration of diabetes, and glomerular filtration rate have shown a significant clan. Diabetic patients aged greater than 60 years former were iv times more likely to have anemia than those with age less than 60 years former. This study is supported by different individual studies conducted worldwide.^xviii,19 This may be due to higher cherry claret prison cell turnover increases with advanced historic period, and compensatory mechanisms become inadequate which leads to the evolution of anemia. The situation is exacerbated in a patient with diabetes mellitus. ⁴⁶ Furthermore, erythropoietin secretion will be depleted as age increases. ⁴⁷

Diabetic patients with glomerular filtration rates less than 60 mL/min/1.73 m² were thirteen times more likely to develop anemia than with glomerular filtration rates greater than sixty mL/min/1.73 m². This upshot is supported by a pooled result conducted in 2020 that showed patients with Phase v chronic kidney diseases were thirteen times more likely to develop anemia when compared with Stage 1 chronic kidney affliction. ⁴⁸ This tin be explained past the depletion of erythropoietin which stimulates the erythropoiesis process as a result of kidney damage by renal fibrosis. ⁴⁹ Furthermore, erythropoietin proportionally decreases with a decrease in the glomerular filtration charge per unit. ⁵⁰

The duration of being diabetic for more than 10 years was ten times more likely to develop anemia than those who were exposed less than ten years to diabetes. This may be explained by microvascular complications occurring later on a long time of exposure to hyperglycemia.^51,52 In addition, every bit the duration of exposure for hyperglycemia increases, the glomerular filtration rate will decrease which may subtract the level of erythropoietin. ⁵³

There are some limitations to this review that may inform time to come research. Outset, we pooled only six studies due to the absence of original published studies. Second, our pooled finding represents only published studies because many Ethiopian universities and enquiry institutes practice not have repositories that are hands available online. 3rd, fifty-fifty if the subgroup assay has been conducted based on the type of diabetes, the pooled result of blazon one and blazon 2 diabetes is not comparable, which might bear on the pooled prevalence of anemia among diabetic patients.

Decision

Generally, 1 in 4 diabetic patients develops anemia in Ethiopia. Age, glomerular filtration rate, and elapsing of being diabetic are factors significantly associated with the occurrence of anemia in diabetic patients. Therefore, early screening and direction of anemia are important to subtract mortality and morbidity related to microvascular complications of diabetic patients.

We recommend the Minister of Wellness to incorporate anemia screening as one of the component of diabetic patient care in Ethiopia. The health facilities should provide anemia screening priority to diabetic patients as they are i of the commonly affected groups by anemia.

Information technology is recommended that researchers carry primary studies separately for type 1 and type 2 diabetes in Federal democratic republic of ethiopia as the 2 populations may non be equally afflicted past anemia.

Supplemental Material

sj-medico-1-smo-10.1177_20503121211031126 – Supplemental material for Magnitude and factors associated with anemia among diabetic patients in Ethiopia: A systematic review and meta-analysis:

Supplemental cloth, sj-md-1-smo-10.1177_20503121211031126 for Magnitude and factors associated with anemia amid diabetic patients in Federal democratic republic of ethiopia: A systematic review and meta-assay by Daniel Atlaw and Zerihun Tariku in SAGE Open up Medicine

sj-docx-ii-smo-x.1177_20503121211031126 – Supplemental cloth for Magnitude and factors associated with anemia amidst diabetic patients in Ethiopia: A systematic review and meta-analysis:

Supplemental material, sj-docx-2-smo-x.1177_20503121211031126 for Magnitude and factors associated with anemia among diabetic patients in Federal democratic republic of ethiopia: A systematic review and meta-analysis by Daniel Atlaw and Zerihun Tariku in SAGE Open up Medicine

sj-xlsx-iii-smo-x.1177_20503121211031126 – Supplemental material for Magnitude and factors associated with anemia amid diabetic patients in Ethiopia: A systematic review and meta-analysis:

Supplemental material, sj-xlsx-iii-smo-10.1177_20503121211031126 for Magnitude and factors associated with anemia amongst diabetic patients in Ethiopia: A systematic review and meta-assay by Daniel Atlaw and Zerihun Tariku in SAGE Open Medicine

sj-docx-4-smo-10.1177_20503121211031126 – Supplemental material for Magnitude and factors associated with anemia among diabetic patients in Ethiopia: A systematic review and meta-analysis:

Supplemental textile, sj-docx-iv-smo-10.1177_20503121211031126 for Magnitude and factors associated with anemia amidst diabetic patients in Ethiopia: A systematic review and meta-analysis by Daniel Atlaw and Zerihun Tariku in SAGE Open Medicine

Acknowledgments

We sincerely thank all the authors of original articles who accept responded timely to our queries through emails. Nosotros are also grateful to Madda Walabu University Goba Referral Hospital, for providing evidence-based grooming that was helpful for this meta-analysis.

Footnotes

Contributed past

Author contributions: D.A. conceptualized study protocol, data extraction, and assay, and wrote the original draft of the manuscript. D.A. and Z.T. conducted written report blueprint, literature review, statistical analysis of the review, critical appraisement, information extraction, and critically revised the manuscript. Both the authors read and approved the last version before submission.

Availability of data and materials: The office of the data analyzed during this report is included in this manuscript. Other data will be available from the corresponding author upon reasonable request.

Declaration of alien interests: The author(s) alleged no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial back up for the research, authorship, and/or publication of this article.

ORCID iD: Daniel Atlaw https://orcid.org/0000-0002-2968-4958

Supplemental material: Supplemental material for this commodity is available online.

References

one. IDF Diabetes Atlas Commission. International Diabetes Federation 2020, www.diabetesatlas.org

2. Mellitus D, Women P. Gestational diabetes mellitus in HIV-infected and -uninfected pregnant women in Cameroon. Diabetes Care 2013; 36: 141–143. [PMC free article] [PubMed] [Google Scholar]

three. Tesfaye B, Alebel A, Gebrie A, et al.. Prevalence of diabetes mellitus and its association with hypertension in Ethiopia: a systematic review and meta-analysis. Diabetes Res Clin Pract 2019; 2019: 107838. [PubMed] [Google Scholar]

4. Hosseini MS, Rostami Z. Anemia and microvascular complications in patients with Type 2 diabetes mellitus. Nephrourol Mon 2014; half-dozen(four): e19976. [PMC free article] [PubMed] [Google Scholar]

5. Ezenwaka CE, Jones-lecointe A, Nwagbara E, et al.. Anaemia and kidney dysfunction in Caribbean Type 2 diabetic patients. BMC Diabetol 2008; 6(25): 2–7. [PMC complimentary article] [PubMed] [Google Scholar]

6. Li Vecchi M, Fuiano G, Francesco Thou, et al.. Prevalence and severity of anaemia in patients with Type 2 diabetic nephropathy and dissimilar. Nephron Clin Pract 2007; 105(2): c62–c67. [PubMed] [Google Scholar]

7. Rani PK, Raman R, Rachepalli SR, et al.. Anemia and diabetic retinopathy in type ii diabetes. J Assoc Physicians India 2010; 58: 91–94. [PubMed] [Google Scholar]

eight. Grossman C, Dovrish Z, Koren-Morag N, et al.. Diabetes mellitus with normal renal office is associated with anaemia. Diabetes Metab Res Rev 2014; 30(4): 291–296. [PubMed] [Google Scholar]

9. Wright JA, Oddy MJ, Richards T. Presence and characterisation of anaemia in diabetic foot Ulceration. Anemia 2014; 2014: 104214. [PMC costless article] [PubMed] [Google Scholar]

x. Al-Khoury S, Afzali B, Shah N, et al.. Anaemia in diabetic patients with chronic kidney disease—prevalence and predictors. Diabetologia 2006; 49(6): 1183–1189. [PubMed] [Google Scholar]

11. Of P, In A, With P, et al.. Prevalence of anaemia in patients with diabetes mellitus. Ir J Med Sci 2006; 175(2): 25–27. [PubMed] [Google Scholar]

12. He BB, Xu K, Wei L, et al.. Relationship betwixt anemia and chronic complications in Chinese Patients with Type two diabetes mellitus. Arch Iran Med 2015; 18(5): 277–283. [PubMed] [Google Scholar]

13. Thambiah SC, Samsudin IN, George Due east, et al.. Anaemia in Type 2 diabetes mellitus (T2DM) patients in Hospital Putrajaya. Malaysian J Med Heal Sci 2015; 11(1): 49–61. [Google Scholar]

xiv. Biadgo B, Melku Chiliad, Abebe SM, et al.. Hematological indices and their correlation with fasting blood glucose level and anthropometric measurements in type ii diabetes mellitus patients in Gondar, Northwest Ethiopia. Diabetes Metab Syndr Obes 2016; 9: 91–99. [PMC gratuitous article] [PubMed] [Google Scholar]

fifteen. Adane T, Getaneh Z, Asrie F. Red blood jail cell parameters and their correlation with renal function tests amid diabetes mellitus patients: a comparative cross-sectional study. Diabetes Metab Syndr Obes 2020; 13: 3937–3946. [PMC costless commodity] [PubMed] [Google Scholar]

xvi. Stevens PE, O'Donoghue DJ, Lameire NR. Anaemia in patients with diabetes: unrecognised, undetected and untreated. Curr Med Res Opin 2003; xix(5): 395–401. [PubMed] [Google Scholar]

17. Ahmed AT, Go AS, Warton EM, et al.. Ethnic differences in anemia amongst patients with diabetes mellitus: the diabetes study of Northern California (Distance). Am J Hematol 2010; 85(one): 57–61. [PMC costless article] [PubMed] [Google Scholar]

eighteen. Idris I, Tohid H, Muhammad NA, et al.. Anaemia among primary care patients with type ii diabetes mellitus (T2DM) and chronic kidney disease (CKD): a multicentred cantankerous-sectional report. BMJ 2018; 8: 1–9. [PMC free article] [PubMed] [Google Scholar]

xix. El-Achkar TM, Ohmit SE, McCullough PA, et al.. Higher prevalence of anemia with diabetes mellitus in moderate kidney insufficiency: the kidney early evaluation program. Kidney Int 2005; 67(iv): 1483–1488. [PubMed] [Google Scholar]

20. Roberto Due north, Lucio J, Chavez E, et al.. Iron deficiency in chronic kidney illness patients with diabetes mellitus. Diabetes Metab Syndr Clin Res Rev 2018; 12: 933–937. [PubMed] [Google Scholar]

21. Loutradis C, Skodra A, Georgianos P, et al.. Diabetes mellitus increases the prevalence of anemia in patients with chronic kidney disease: a nested case-control study. World J Nephrol 2016; 5(iv): 358–366. [PMC gratuitous commodity] [PubMed] [Google Scholar]

22. Thomas C, Cooper ME, Rossing K, et al.. Anaemia in diabetes: is there a rationale to TREAT? Diabetologia 2006; 49(6): 1151–1157. [PubMed] [Google Scholar]

23. Awofisoye OI, Adeleye JO, Olaniyi JA, et al.. Prevalence and correlates of anemia in type 2 diabetes mellitus: a study of a Nigerian outpatient diabetic population. Sahel Med J 2019; 22: 55–63. [Google Scholar]

24. Khoshdel A, Carney S, Gillies A, et al.. Potential roles of erythropoietin in the management of anaemia and other complications diabetes. Diabetes Obes Metab 2008; 10(ane): 1–nine. [PubMed] [Google Scholar]

25. Thomas MC, Tsalamandris C, MacIsaac R, et al.. Functional erythropoietin deficiency in patients with Type two diabetes and anaemia. Diabet Med 2006; 23(5): 502–509. [PubMed] [Google Scholar]

26. Thomas MC, MacIsaac RJ, Tsalamandris C, et al.. Unrecognized anemia in patients with diabetes. Diabetes Care 2003; 26: 1164–1169. [PubMed] [Google Scholar]

27. Greenburg AG, Island R. Pathophysiology of anemia. Gerson Greenbg 1996; 101: vii–xi. [PubMed] [Google Scholar]

28. Al-Salman M. Anemia in patients with diabetes mellitus: prevalence and progression general medicine: open access. Gen Med 2015; iii(1): 9–12. [Google Scholar]

29. Barbieri J, Fontela PC, Winkelmann ER, et al.. Anemia in patients with Blazon ii diabetes mellitus. Hindawi 2015; 2015: 354737. [PMC free commodity] [PubMed] [Google Scholar]

30. De R, Allende C, Entresotos LD, et al.. Anaemia of chronic diseases: pathophysiology, diagnosis and treatment ଝ. Med Clínica 2021; 156(5): 235–242. [PubMed] [Google Scholar]

31. Madu AJ, Ughasoro Dr.. Anaemia of chronic disease: an in-depth review. Med Princ Pract 2017; 26(one): 1–9. [PMC free article] [PubMed] [Google Scholar]

32. Thomas Southward, Rampersad M. Anaemia in diabetes. Acta Diabetol 2004; 41: xiii–17. [PubMed] [Google Scholar]

33. Stevens E. Anaemia, diabetes and chronic kidney disease: where are nosotros now. J Ren Intendance 2012; 38(Suppl. 1): 67–77. [PubMed] [Google Scholar]

34. Hailu AN, Tolessa T, Gufue ZH, et al.. The magnitude of anemia and associated factors amongst adult diabetic patients in Third Teaching Hospital, Northern Ethiopia, 2019, cross-sectional written report. PLoS ONE 2020; 15(eleven): e0240678. [PMC free article] [PubMed] [Google Scholar]

35. Bekele A, Teji Roba K, Egata G, et al.. Anemia and associated factors amidst type-2 diabetes mellitus patients attending public hospitals in Harari Region, Eastern Federal democratic republic of ethiopia. PLoS ONE 2019; 14(12): e0225725. [PMC free article] [PubMed] [Google Scholar]

38. Engidaw MT, Feyisa MS. Prevalence of anemia and its associated factors amidst developed diabetes mellitus patients at Debre. Diabetes Metab Syndr Obes 2020; 13: 5017–5023. [PMC free article] [PubMed] [Google Scholar]

39. Allay A, Birhan Westward, Alemu A. Clan of anemia and renal function test among diabetes mellitus patients attending Fenote Selam Hospital, West Gojam, Northwest Ethiopia: a cross sectional study. BMC Hematol 2013; xiii(6): 6. [PMC gratis commodity] [PubMed] [Google Scholar]

forty. Fiseha T, Adamu A, Tesfaye M, et al.. Prevalence of anemia in diabetic adult outpatients in Northeast Ethiopia. PLoS I 2019; 14(9): e0222111. [PMC free article] [PubMed] [Google Scholar]

41. Taderegew MM, Gebremariam T, Tareke AA, et al.. Anemia and its associated factors among type 2 diabetes mellitus patients attention Debre Berhan Referral Infirmary, Due north-E Ethiopia: a cross-sectional study. J Blood Med 2020; 11: 47–58. [PMC free article] [PubMed] [Google Scholar]

42. Gebreyohannes EA, Netere AK, Belachew SA. Glycemic control amidst diabetic patients in Ethiopia: a systematic review and meta-analysis. PLoS ONE 2019; 14(8): e0221790–1022114. [PMC free article] [PubMed] [Google Scholar]

43. AlDallal SM, Jena N. Prevalence of anemia in Type ii diabetic patients. J Hematol 2018; 7(two): 57–61. [PMC free article] [PubMed] [Google Scholar]

44. Chen R, Ji L, Chen L, et al.. Glycemic control rate of T2DM outpatients in People's republic of china: a multi-center survey. Med Sci Monit 2015; 21: 1440–1446. [PMC complimentary commodity] [PubMed] [Google Scholar]

45. Sharif A, Younus Southward, Baig Chiliad, et al.. Prevalence and risk of anemia in Type-2 diabetic patients. Health 2014; 06(12): 1415–1419. [Google Scholar]

46. Ershler WB, Sheng South, McKelvey J, et al.. Serum erythropoietin and aging: a longitudinal analysis. J Am Geriatr Soc 2005; 53(8): 1360–1365. [PubMed] [Google Scholar]

47. Ble A, Fink JC, Woodman RC, et al.. Renal function, erythropoietin, and anemia of older persons. Arch Intern Med 2005; 165(nineteen): 2222–2227. [PubMed] [Google Scholar]

48. Shiferaw WS, Akalu TY, Aynalem YA. Risk factors for anemia in patients with chronic renal failure: a systematic review and meta-analysis. Ethiop J Health Sci 2020; 30(5): 829–842. [PMC free article] [PubMed] [Google Scholar]

49. Winkler AS, Marsden J, Chaudhuri KR, et al.. Erythropoietin depletion and anaemia in diabetes mellitus. Diabet Med 1999; 16(10): 813–819. [PubMed] [Google Scholar]

fifty. Angelousi A, Larger E. Anaemia, a mutual but often unrecognized risk in diabetic patients: a review. Diabetes Metab 2015; 41(1): 18–27. [PubMed] [Google Scholar]

51. Ramanathan Amnath S. Correlation of duration, hypertension and glycemic control with microvascular complications of diabetes mellitus at a tertiary care infirmary. Integr Mol Med 2017; 4(1): 1–4. [Google Scholar]

52. Zoungas S, Woodward M, Li Q, et al.. Impact of age, age at diagnosis and elapsing of diabetes on the risk of macrovascular and microvascular complications and death in type two diabetes. Diabetologia 2014; 57(12): 2465–2474. [PubMed] [Google Scholar]

53. Vistisen D, Andersen GS, Hulman A, et al.. Progressive reject in estimated glomerular filtration rate in patients with diabetes subsequently moderate loss in kidney part—even without albuminuria. Diabetes Intendance 2019; 42(10): 1886–1894. [PubMed] [Google Scholar]

---

Articles from SAGE Open Medicine are provided here courtesy of SAGE Publications

---

bridgeforthupeor1956.blogspot.com

Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8274127/

Share this post