Background

The prevalence of type 2 diabetes mellitus has been increasing worldwide, with low and middle-income countries bearing the brunt of this growth in terms of morbidity, mortality, and economic costs [1, 2]. As such, Africa has been experiencing the greatest increase of all the World Health Organization (WHO) regions. The epidemiological transition due to the adoption of the Western lifestyle and urbanization, among other things, has played a major role in the progression of diabetes [3]. The growing burden of diabetes has been a barrier to the wellness of families and the effectiveness of the health system.

One of the main goals of diabetes mellitus management is to achieve glycaemic control to delay or prevent the onset of diabetes complications. Worldwide, only approximately 50% of patients achieve glycaemic control [4] and in sub-Saharan Africa (SSA), glycaemic control rates are generally poor. In sub-Saharan Africa, the proper management of diabetes faces numerous challenges including inadequate resources, coexisting traditional health priorities, ill-preparedness for chronic disease management and low health insurance coverage [5].

Glycaemic control represents an emergency to alleviate the burden of the disease in sub-Saharan Africa [6]. The design and implementation of effective glycaemic control strategies require accurate knowledge of the factors underlying glycaemic control to enable the identification of effective interventions. The factors associated with poor glycaemic control are numerous and vary in importance depending on the population [7, 8]. Empirical evidence suggests that higher socioeconomic status, greater dietary knowledge, and higher self-efficacy and empowerment improve glycaemic control [9]. Factors driving poor glycaemic control include patients, diabetes disease, treatment, health system, and physician-related factors [8, 10]. However, there is a paucity of literature on factors that influence glycaemic control in the sub-Saharan region. Therefore, this systematic review aims to determine the prevalence and factors associated with glycaemic control among type 2 diabetes patients in sub-Saharan Africa. The review will comprise all articles on glycaemic control among patients with type 2 diabetes from January 2012 to May 2022 to have enough studies to have a broad view of the phenomenon.

Methods

The protocol of this systematic review and meta-analysis was registered on PROSPERO with reference CRD 41021237941. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used to report the entire process of this systematic review [11].

Eligibility criteria

Eligible studies were those that reported glycaemic control in persons with type 2 diabetes mellitus in sub-Saharan Africa. Only peer-reviewed articles were eligible to ensure the inclusion of valid research and avoid falsified data. Data from January 2012 to May 2022 without language restrictions were included. As we planned to estimate the prevalence and identify factors associated with glycaemic control, the following types of study designs were considered: randomized controlled trials, quasi-experimental trials, cohort studies, case–control studies, and cross-sectional studies. Only studies that reported a multivariate analysis were included in the systematic review.

Information sources

The search was conducted in five databases: African Index Medicus, Africa-Wide Information, Global Health, PubMed, and Web of Science. In addition, the reference lists of the selected articles were reviewed for any other eligible articles. The last search date was 02 May 2022.

Search strategy

The search term domains were “Type 2 diabetes mellitus”, “glycaemic control”, and “sub-Saharan Africa”. Additional file 1: Table S1 presents the search strategy at the level of the five databases.

Selection process

Two investigators (JPF and JMF) independently reviewed the studies using the eligibility criteria and selected studies for inclusion in the review. The first investigator (JPF) reviewed all articles, and the second investigator (JMF) randomly assessed 10% of the selected articles. Differences in selection were assessed by consensus. The software used for the selection and recording of decisions was EndNote 20.

Data extraction

Data from eligible studies were captured using a Microsoft Excel file. The first investigator (JPF) performed data extraction on all the articles and the second investigator (JMF) randomly assessed 10% of the extracted information. Any differences of opinion between individual judgments were resolved through consensus. For the metaanalysis of the proportions of glycaemic control, we contacted eight authors for missing information and clarification. One provided us with its study dataset, and another showed us how to access the information needed to calculate the glycaemic control.

Study variables

Main outcomes

Reported glycaemic control: pooled prevalence of samples reported to have glycaemic control.

Exposure: reported independent risk factors for glycaemic control.

Data items

The following information was extracted from the studies: the last name of the first author, study type, publication year, country, study population, total sample size, glycaemic level assessment method, glycaemic control definition, number or proportion of persons with good glycaemic control, factors associated with glycaemic control, and measure of association for glycaemic control.

Study risk of bias assessment

The assessment of the methodological quality of the selected studies was performed by two reviewers (JPF and JMF) using the Joanna Briggs Institute Critical Appraisal Tools [12]; any differences of opinion between the two reviewers were resolved by consensus. The Joanna Briggs Institute Critical Appraisal Tools were used to classify each selected study as good, moderate, or poor regarding the risk of bias. The percentage of "Yes" votes was equal to or less than 50%, 51–80%, and more than 80% for poor, moderate, or good quality respectively [13]. The assessment of an item was marked "Yes" if the description met the criteria set for the assessment, otherwise, the assessment was "No". If the description was insufficient, the assessment was "Unknown".

Synthesis methods

To estimate the overall prevalence of glycaemic control, we carried out a meta-analysis with the random-effects model of the proportions of good glycaemic control. The statistical software used was Stata 17.0 [14]. The data needed for analysis are summarized in Additional file 3: Table S3. Only 51 studies were eligible for the estimation of the prevalence of glycaemic control as randomized control trials, quasi-experimental and case–control studies were excluded. We observed high heterogeneity of the studies as shown by the I2 [15], and therefore reported the pooled prevalence estimate and the glycaemic control patterns in sub-Saharan Africa. Forest plots were used to visually display the results of individual studies and the syntheses. We explored heterogeneity by performing subgroups analysis of the prevalence by region of sub-Saharan Africa (Eastern, Western, Central, Southern), study type (case–control, cohort, cross-sectional, quasi-experimental, randomized control trial), and method (glycosylated haemoglobin, glycaemia) used to assess the control.

To assess the factors driving glycaemic control, due to the heterogeneity of the studies, we performed a narrative synthesis of reported factors. Each reported factor is presented with the studies in which it was assessed, and the measure of association —and its 95% confidence interval—with glycaemic control found in each study is reported. The factors were categorized into six groups: sociodemographic, lifestyle, clinical, treatment modalities, adherence, and interventions.

Results

Study selection

We retrieved 6656 publications from the information sources. A total of 105 publications were removed due to duplication. Of the remaining 6551 articles, 6425 were excluded after the titles and abstracts were reviewed, and 126 were retained for full-text evaluation based on the inclusion criteria. Of the 126, eighty-three articles were excluded after full-text review and 43 articles were retained. An additional search of the reference lists of selected articles yielded 2623 publications. We selected 53 publications for full-text evaluation according to the selection criteria. Of these, 22 were excluded because they did not meet inclusion criteria, and 31 articles were retained. The total number of included studies was 74 articles by both search strategies. The selection process is summarized in the PRISMA flow diagram (Fig. 1). The characteristics of the excluded studies and the reason(s) for exclusion are summarized in Additional file 2: Table S2.

Fig. 1
figure 1

Selection of studies

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General characteristics of the included studies

A total of 74 studies reporting on 21,133 patients with type 2 diabetes were included in the review. The studies were conducted in 16 sub-Saharan African countries, with Ethiopia (n = 26, 35.1%) being the most represented, followed by South Africa (n = 11, 14.9%) and Nigeria (n = 10, 13.5%). The majority of the studies (n = 51, 68.9%) were conducted in the last five years (2017-2022). Of the 74 studies selected, 55 (74.3%) were cross-sectional studies, ten (13.5%) were randomized controlled studies, four (5.4%) were quasi-experimental studies, three (4.1%) were case–control studies, and two (2.7%) were cohort studies. The general characteristics of the included studies are presented in Table 1.

Table 1 General characteristics of included studies
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Assessment of risk of bias

Of the 74 studies selected for the review, only 14 (18.9%) were assessed as being of good quality, 54 (73.0%) were of moderate quality, and six (8.1%) were of poor quality. Additional file 4: Tables S4, Additional file 5: Table S5, Additional file 6: Table S6, Additional file 7: Table S7, Additional file 8: Table S8, Additional file 9 detail the assessment of study methodological quality. Of the 55 cross-sectional studies, only four (7.3%) were able to formally identify confounding factors, while ten (1 8.2%) reported the method used to address confounding factors. In the four quasi-experimental studies, one study (25.0%) did not measure the outcomes consistently or in a reproducible way. In two of the ten randomized controlled trials (20.0%), participants and treatment providers were not blinded to treatment allocation nor were the staff members assessing outcomes blinded to treatment allocation. Moreover, for one of these two studies, the treatment groups were not similar at baseline. For two of the three case–control studies, confounding factors were not identified, and for one study, cases and controls were mismatched.

Assessment of glycaemic control

Glycaemic control was assessed by glycosylated haemoglobin in 43(58.1%) studies, fasting blood glucose in 25 (33.8%) studies and a combination of both methods in 6(8.1%) studies. The cut-off points for good glycaemic control varied across studies and were: HbA1c < 7%, HbA1c ≤ 7%, HbA1c < 8%, HbA1c < 53 mmol/mol, FBG: 70–130 mg/dL, FBG < 126 mg/dL, FBG ≤ 126 mg/dL, FBG: 70–130 mg/dL, FBG < 154 mg/dL, FBS ≤ 130 mg/dl, FPG: 100–130 mg/dL, FBG: 4–7 mmol/L, FBG ≤ 130 mg/dL or 7. 2 mmol/L.

Prevalence of glycaemic control

The estimated pooled prevalence of good glycaemic control in sub-Saharan Africa was 30.3% (95% CI: 27.6–32.9). The analysis showed considerable heterogeneity (I2: 93.9%, p < 0.001), and glycaemic control prevalence ranged from 10 to 60% (Fig. 2). The subgroup analysis by region showed that most of the studies in the Central (n = 5, 83.3%) and the Southern (n = 5, 62.5%) regions had a prevalence of glycaemic control of < 30% while most of the studies in the Eastern region had a prevalence of glycaemic control > 30% (Fig. 3).

Fig.2
figure 2

Prevalence of glycaemic control in sub-Sharan Africa

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Fig.3
figure 3

Prevalence of glycaemic control by sub-Saharan Africa regions

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Factors associated with glycaemic control

The reported sociodemographic, lifestyle, clinical, adherence, treatment factors, and reported glycaemic control optimization interventions factors are summarized in Tables 2, 3, 4, 5, 6, 7.

Table 2 Sociodemographic factors and glycaemic control in sub-Saharan Africa
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Table 3 Lifestyle factors and glycaemic control in sub-Saharan Africa
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Table 4 Clinical factors and glycaemic control in sub-Saharan Africa
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Table 5 Adherence to treatment plans and glycaemic control in sub-Saharan Africa
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Table 6 Treatment modalities and glycaemic control in sub-Saharan Africa
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Table 7 Glycaemic optimization interventions and glycaemic control in sub-Saharan Africa
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Sociodemographic characteristics

Table 2 presents the sociodemographic factors with respect to their relationship with glycaemic control. Five studies assessed the relationship between increasing age and glycaemic control [27, 31, 34, 58, 61], two found that it was negatively associated with glycosylated haemoglobin [31, 61], and one found that it was associated with good glycaemic control [57]. Older age was associated with poor glycaemic control in twelve studies [22, 29, 32, 36, 39, 65, 68, 69, 73, 77, 83, 86]. Eight studies assessed the relationship between the female gender and glycaemic control [18, 29, 34, 51, 61, 64, 65, 73], two studies found that the female gender was significantly associated with poor glycaemic control [18, 34], and one study linked it to good glycaemic control [28]. Male gender with respect to glycaemic control was assessed by eleven studies [27, 31, 39, 44, 58, 66, 75, 77, 83, 85, 87]; two studies associated it with good glycaemic control [58, 75], while two studies linked it to poor glycaemic control [27, 87]. Fifteen studies assessed the relationship between educational level and glycaemic control; in one study, primary, secondary, or tertiary education levels were associated with good glycaemic control [28]. A lack of formal education and a low level of education were associated with poor glycaemic control in three studies [39, 48, 87]. In respectively two studies, low monthly income [18, 87], absence of health insurance [47, 58], and being a farmer [25, 48] were associated with poor glycaemic control. In respectively one study, living in urban areas [49], and a high frequency of seeking traditional medicine practitioners [30] were associated with poor glycaemic control. Residing less than 100 kms from a health facility [25], residing in Guinea compared to residing in Cameroon [32], self-reporting a positive perception of family support [68], and the frequency of participating in religious activities [31] were associated with good glycaemic control in respectively one study.

Lifestyle factors

The lifestyle factors assessed were dietary adherence, the practice of exercise, smoking, and alcohol consumption (Table 3). Good dietary adherence was associated with good glycaemic control in five studies [29, 36, 40, 61, 86] while low adherence to dietary recommendations was associated with poor glycaemic control in two studies [35, 67]. Exercise was associated with good glycaemic control in two studies [29, 36]. The inadequate practice of exercise was associated with poor glycaemic control in two studies [39, 53]. In respectively one study, smoking [39], and alcohol consumption [29] were associated with poor glycaemic control.

Clinical factors

The clinical factors—history of diabetes and comorbidities—with respect to glycaemic control are summarized in Table 4. A family history of diabetes was significantly associated with poor glycaemic control in one study [80]. A long duration of diabetes was associated with poor glycaemic control in seven studies [18, 26, 27, 32, 58, 61]. As a corollary, treatment of > 10 years was associated with poor glycaemic in one study [38]. In one study, patients who always had fluctuating/unstable blood glucose levels or had blood glucose levels not improved from diagnosis were prone to poor glycaemic control [60].

Four studies found that the presence of comorbidities was associated with poor glycaemic control [53, 75, 77, 87]. The presence of hypertension led to poor glycaemic control in one study [16]. Dyslipidaemia was associated with poor glycaemic control in three studies [18, 53, 82]. Concerning body mass index (BMI), all categories, such as being underweight [60], having a normal BMI [46], or being overweight/obese [18, 34, 87] have been significantly associated with poor glycaemic control. Central obesity was associated with poor glycaemic control in four studies [16, 30, 52, 56]. In respectively one study, the presence of anaemia [17], non-alcoholic fatty liver disease [19], vitamin B12 deficiency [20], metabolic syndrome [28], cognitive impairment [32], congestive cardiac failure [46], HIV infection [46], thyroid autoimmunity [74], and hypogonadism [45] had a significant association with poor glycaemic control. The presence of peripheral neuropathy [83] or a high-level tooth mobility index [59] was associated with poor glycaemic control. Overall health-related quality of life was inversely associated with FBG [42]. The global disability burden was significantly associated with poor glycaemic control [70]. A unit reduction in the estimated glomerular filtration rate (eGFR) was also associated with HbA1c ≥ 7% [16].

Adherence to treatment plans

Adherence modalities, as represented by adherence to scheduled appointments or medication adherence, are presented in Table 5. Regular attendance at scheduled appointments was associated with good glycaemic control in two studies [49, 85]. Good medication adherence was associated with good glycaemic control in two studies [40, 77], while two other studies showed no association [75, 78]. Low medication adherence had a significant association with poor glycaemic control in three studies [33, 48, 86]. Medium medication adherence was associated with poor glycaemic control in one study [48].

Treatment modalities

The findings on the treatment modalities with respect to glycaemic control are summarized in Table 6. The pill burden was associated with poor glycaemic control in one study. Combination therapy with oral hypoglycaemic agents (OHA) was associated with poor glycaemic control in two studies [48, 53] while it was linked to good glycaemic control in one study [75]. Insulin plus OHA was associated with poor glycaemic control in three studies [44, 48, 69], while it was linked to good glycaemic control in one study [75]. The use of insulin alone was associated with poor glycaemic control in two studies [53, 78]. The presence of drug-related problems was associated with poor glycaemic control as shown in one study [86]. Rwegerera et al. found that being on diet and OHA was associated with suboptimal glycaemic control [73]. A South African study found that the use of statin and antihypertensives was associated with higher glycaemic levels [50]. Non-surgical periodontal management was associated with good glycaemic control after three months in one study [82]. Diabetes information from non-health workers was significantly associated with poor glycaemic control [18], while having a high diabetes health literacy [77] was significantly associated with good glycaemic control. In one study, the absence of clarity in pharmacists’ advice was associated with poor glycaemic control [44].

Reported glycaemic control optimization interventions

The interventions retrieved from the included studies are presented along with their effect on glycaemic control in Table 7. Only one study [43] out of five reported an educational program associated with good glycaemic control. None of the self-management programs was associated with glycaemic control as found in three studies [40, 61, 62]. All exercise programs were associated with improved glycaemic control as found in four studies [37, 38, 52, 76, 84]. Adding a second OHA was associated with poor glycaemic control in one study [49]. The effectiveness of a community-based multilevel peer support intervention was associated with a significant reduction in glycosylated haemoglobin in the intervention group in one study [24].

Discussion

We sought to determine the prevalence and factors associated with glycaemic control in sub-Saharan Africa (SSA) in the past 10 years (2012–2022). Our review shows that poor glycaemic control is common in SSA with only 10–60% of patients having optimal glycaemic control. In addition, glycaemic control was associated with sociodemographic factors (younger and older age, gender, lower income, absence of health insurance, low level of education, place of residence, family support, coping strategies), lifestyle (dietary adherence, practice of exercise, smoking, alcohol consumption), clinical factors (family history of diabetes, longer duration of diabetes, presence of comorbidities/complications), adherence (adhering to follow-up appointments and medication), treatment modalities (pill burden, treatment regimen, use of statins or anti-hypertensives, drug-related problems, diabetes information from non-health workers, high diabetes health literacy, absence of clarity in pharmacists’ advice, failure to set glycaemic goals), and reported glycaemic control optimization interventions.

The assessment of glycaemic control was variable in the studies included in our review; in only 43 (58.1%) studies, glycosylated haemoglobin was used. This renders it difficult to estimate the real extent of glycaemic control, compare the results and, even in daily clinical practice, manage patients [90]. Nevertheless, our estimated prevalence of good glycaemic control in SSA is similar to the prevalence found by a meta-analysis in Ethiopia in which only one-third of patients were adequately controlled [90] and in a study in Central, East and West Africa with approximately 29% of good glycaemic control [91]. The prevalence of poor glycaemic control in sub-Saharan Africa is far lower than that found in eight European countries by The Panorama study (62.6%) [10], and in the United States of America by Fang and colleagues (55.3%) in 2015–2018 [92]. The poor glycaemic control in sub-Saharan Africa is the result of poor quality of diabetes care due, in turn, to a weak disease management framework and fragmented health systems [93]. Changes are required in the organization of healthcare systems in sub-Saharan African countries for better management of non-communicable diseases in general, with effective implementation of diabetes care into primary care [4, 93].

Several studies have reported significant associations between sociodemographic factors and glycaemic control. Advancing in age was negatively associated with poor glycaemic control, indicating the vulnerability of young patients as found by several studies [93]. Young patients are confronted with many barriers to effective self-management. Older age was associated with poor glycaemic control in our review, corroborating the findings of several studies and explained by insulin resistance and the presence of comorbidities [8, 90, 95]. Although both genders were linked to poor and good glycaemic control in our review, it is recognized that women are traditionally prone to poor glycaemic control [96]. Women with type 2 diabetes in sub-Saharan Africa have a greater risk of death due to poor access to care [97]. Thus, young, and older patients along with women represented vulnerable categories, in terms of propensity to poor glycaemic control and issues of accessing care. Caution must be taken when managing diabetes in sub-Saharan Africa to ease access to care and provide adequate responses to the needs of these categories.

Poor socioeconomic conditions (low income, poor education) have been associated with poor glycaemic control due to poor access to adequate care and poor health-seeking behaviors [18, 48, 87, 98, 99]. Increasing universal health coverage could address these problems and lead to better outcomes [100]. Factors such as food insecurity and depression have been identified as mediators in the relationship between poor living conditions and glycaemic control [98]. Family support and adequate coping strategies such as participation in religious activities were beneficial for glycaemic control and could act through these mediating factors. Management interventions to optimize glycaemic control for patients with type 2 diabetes with poor socioeconomic conditions should consider these interconnected factors.

A long distance from home to the healthcare facility has been associated with poor glycaemic control while having less distance was found to be beneficial in many studies, as the latter favors access, adherence and monitoring of care [101, 102]. However, for the nearness of health facilities to have a meaningful impact, these facilities must have adequate equipment, and trained personnel for diabetes care [32].

As expected, adherence to dietary recommendations and physical exercise have been associated with good glycaemic control [103, 104]. In our review smoking was associated with poor glycaemic control. The literature shows that smoking has a confusing relationship with poor glycaemic control [104]. Indeed, if smoking was related to poor glycaemic control due to reduced effectiveness of insulin, quitting smoking has also been linked to poorer glycaemic control [106, 107]. Nevertheless, smoking cessation is one goal of diabetes care. One study in our review linked patients who ever drunk alcohol regularly to poor glycaemic control [29], and the author did not provide details on the quantity used and the term. The literature showed that drinking moderately in the short or medium-term did not affect glycaemic control [108]. Current guidelines support moderate alcohol consumption as excessive chronic alcohol consumption or acute intoxication that adversely has detrimental effects on all organs and affects mortality and morbidity [109, 110]. In sub-Saharan Africa, careful recommendations on alcohol use need to be developed for patients with type 2 diabetes as alcohol might represent a concurrent source of expenses of the few resources available. The real nature of alcoholic beverages found in sub-Saharan Africa is not accurately known.

Several studies confirmed our findings concerning clinical factors with respect to glycaemic control. With a longer duration of diabetes, there is a deterioration of the function of the pancreas due to failure in beta cells, and the emergence of disease-related complications, which in turn can have effects on glycaemic control [8, 111]. The presence of comorbidities/complications poses a problem with respect to pill burden, adherence to treatment and cost, or as an intricate mechanism linked to beta-cell impairment or aggravation of insulin resistance [8, 94, 112,113,114,115,116,117]. In sub-Saharan Africa, there is a high proportion of undiagnosed diabetes mellitus, and the diagnosis is often delayed. At diagnosis, many patients will present with complications or comorbidities, thus complicating the management and the attainment of glycaemic targets [118]. Strategies to improve the diagnosis of diabetes mellitus must be considered by policy makers in sub-Saharan Africa for a reduction in diabetes-related complications and mortality [118].

Good medication adherence was associated with good glycaemic control. This finding is in line with that found in several studies [111, 119, 120], particularly that good adherence improves glycaemic control, leads to fewer emergency department visits, decreases hospitalizations, and lowers medical costs [121]. In sub-Saharan Africa, medication adherence is confronted by the issues of access and affordability of drugs [122]. The organization of a regular and reliable system for the supply of medicines at affordable prices, even in remote areas is essential to improve diabetes care.

Oral hypoglycaemic agents (OHAs), either insulin only or combined with the former, were associated with poor glycaemic control in our review. In the included studies, the matters surrounding medication use, such as adherence, reason for prescribing one agent or a combination, were not reported. The use of statins and some antihypertensive agents (thiazide diuretics and non-selective beta-blockers) are linked to comorbidities, with hypertension being the most frequent [123] and having been linked in several studies with high levels of glycosylated haemoglobin [124,125,126]. Since many patients have comorbidities, present late and may need these adjunct treatments, these findings have implications for the management of patients with type 2 diabetes in sub-Saharan Africa. They call for the judicious use of these agents and adherence of healthcare professionals to evidence-based diabetes care in SSA.

Concerning the reported interventions, all the exercise programs, and one educational program for self-management were associated with good glycaemic control. Nevertheless, the full integration of exercise into routine healthcare in Africa is challenged by poor knowledge and attitudes of patients and healthcare providers [127]. In the same way, self-management of diabetes is poor in Africa as it faces numerous barriers [128, 129]. Peer-support interventions have been increasingly recognized worldwide, but one may note that the transferability of interventions across different cultures might be difficult [24]. Research is needed to identify effective interventions to optimize glycaemic control in the context of sub-Saharan Africa.

To the best of our knowledge, this systematic review is the first to provide a prevalence estimate of glycaemic control and an overview of factors associated with glycaemic control in patients with type 2 diabetes in SSA. The review only considered studies in which multivariate analysis was performed in the data analysis and therefore excluded factors without an uncertain link to glycaemic control. The findings of this systematic review are also as good as the quality of the studies included, more so that most (70.2%) were of moderate quality. Most of the studies were observational and one cannot ascertain causality between factors identified and glycaemic control. Glycaemic control was assessed using different methods across the studies, with only 58.1% of the included studies using the recommended glycosylated haemoglobin. There were also different thresholds for glycaemic control through studies even if the same glycaemic control assessment was used. These variations in assessment standards have the potential for errors in estimates and misclassifications.

Beyond these limitations, this systematic review is, to our knowledge, the first to provide a broad view of the extent and multifactorial drivers of glycaemic control among patients with type 2 diabetes in SSA. The review highlights the need for changes in the organization of the healthcare systems in sub-Saharan Africa while ensuring effective funding. Health providers must be trained, and health facilities equipped for adequate diabetes care. The screening of diabetes mellitus must be improved as well as access to care for vulnerable patients. While this review highlights the need for multipronged interventions to improve glycaemic control and diabetes care in this region, further studies are needed to assess their feasibility, effectiveness, affordability and acceptability.

Conclusion

Suboptimal glycaemic control is pervasive among patients with type-2 diabetes in sub-Saharan Africa and poses a significant public health challenge. While urgent interventions are required to optimize glycaemic control in this region, these should consider sociodemographic, lifestyle, clinical, and treatment-related factors.