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Prevalence and factors associated with adult diabetes mellitus in the African Great Lakes Region: a systematic review and meta-analysis

Prevalence and factors associated with adult diabetes mellitus in the African Great Lakes Region: a systematic review and meta-analysis

David Dyikpanu Tibasima1,2, Yves Wasnyo3, Guy Sadeu Wafeu4, Brice Mbock Ekwalla5, Philomème Missi Mbassi6, Joëlle Laure Sobngwi Tambekou7, Eugène Sobngwi7,8,9,&

 

1Department of Public Health, School of Health Sciences, Catholic University of Central Africa, Yaoundé, Cameroon, 2Department of Epidemiology, Community Health, Nyankunde Higher Institute of Medical Techniques, Bunia, Democratic Republic of the Congo, 3Department of Public Health, Faculty of Medicine and Biomedical Sciences, University of Yaoundé I, Yaoundé, Cameroon, 4Building Medical Research in Africa, Yaoundé, Cameroon, 5Department of the General Secretariat, Ministry of Scientific Research and Innovation Cameroon, Yaoundé, Cameroon, 6Departement of Nursing Sciences, School of Health Sciences, Catholic University of Central Africa, Yaoundé, Cameroon, 7Research, Health and Development (RSD Institute), Yaoundé, Cameroon, 8Departement of Internal Medicine and Specialties, Faculty of Medicine and Biomedical Sciences, University of Yaoundé I, Yaoundé, Cameroon, 9Directorate of Health Care Organization and Health Technology, Ministry of Public Health, Yaoundé, Cameroon

 

 

&Corresponding author
Eugène Sobngwi, Research, Health and Development (RSD Institute), Yaoundé, Cameroon

 

 

Abstract

Diabetes mellitus, with a global prevalence of 10.5% and a mortality increase of 77.6% in ten years, represents a global health emergency that requires in-depth analysis to guide public health policies, particularly in the African Great Lakes Region, which is characterised by armed conflicts and a particular socioeconomic profile. A systematic review of articles published until December 2023 was conducted using PubMed/MEDLINE, Google Scholar, Embase, and ScienceDirect. The included studies were assessed for quality using the JBI checklist and analyzed using a random-effects model due to data heterogeneity. Heterogeneity was explored through subgroup analyses with formal difference testing (Q statistic), meta-regression, and sensitivity analyses. Of the 59 studies selected, 57 contributed to the prevalence analysis (101,412 participants; I2=99.2%), revealing an overall diabetes prevalence of 8.1% (95% CI: 6.3-10.1%). Prevalence was 4.5% (95% CI: 3.0-6.3%; 19 studies) in men and 5.9% (95% CI: 3.6-8.6%; 18 studies) in women, with no statistically significant difference (p=0.373). Factors significantly associated with diabetes included age >40 years (OR=3.89; 95% CI: 2.55-5.93), overweight/obesity (OR=2.90; 95% CI: 1.70-4.93), and hypertension (OR=4.32; 95% CI: 2.45-7.62). The prevalence of diabetes in the African Great Lakes Region varies according to local context and is associated with older age, overweight/obesity, and hypertension. These findings, limited by substantial heterogeneity between studies, call for prevention strategies targeting high-risk groups, tailored to the specific characteristics of each country.

 

 

Introduction    Down

Diabetes mellitus is one of the most concerning non-communicable diseases worldwide, with increasing prevalence and mortality over the last two decades [1]. According to the International Diabetes Federation (IDF), approximately 10.5% of the world's adult population (aged 20-79 years) is affected by this disease, and the prevalence is expected to reach 11.3% by 2030 and 12.2% by 2045 [2]. In 2021, 6.7 million deaths of adults aged 20 to 79 were attributed to diabetes and its complications [2], compared to 1.5 million in 2012 [3], an increase of approximately 77.6% in a decade. Once considered a disease of developed countries, diabetes is now ubiquitous and affects people regardless of their economic status [3,4]. The prevalence of diabetes mellitus in Africa is estimated to be 5.3% [2]. Although this figure seems low compared to other parts of the world, it could increase by 134% by 2045 [2]. The prevalence of diabetes in Africa varies by country and region [2]. Indeed, the results of systematic reviews show that it varies from 2.6% to 20.0 in North Africa and from 1% to 12% in sub-Saharan Africa [5,6].

The African Great Lakes Region is located near the equator in East Africa and includes seven lakes and the surrounding countries, namely, the DRC, Rwanda, Burundi, Kenya, Uganda, and Tanzania [7,8]. In addition to the great social, economic, and ethnic differences that exist between the populations of this region, recurrent armed conflicts, refugee movements, and lifestyle changes due to rapid urbanization and modernization significantly influence the health profiles of its inhabitants [9-11].

Every year, the International Diabetes Federation publishes estimates of the prevalence of diabetes mellitus in different countries and regions worldwide. For countries without internal data, this prevalence is extrapolated from data from a similar country, leading to potential sources of error due to the differences that exist between countries in the same region [2,4]. In addition, the estimates of the latest edition of the IDF report only consider 10 countries in sub-Saharan Africa, including three in the African Great Lakes Region. Although many studies have been conducted in these countries, data compiled at the regional level are not available. Considering the geographical proximity between these countries and the policy and decision-making framework (including health decisions) that exists between them, an estimate of the prevalence of diabetes in this region is crucial for informing decisions. In addition, the estimation of the factors associated with diabetes in this region will make it possible to improve strategies to combat this condition. Therefore, we conducted this systematic review and meta-analysis to determine the prevalence and factors associated with diabetes in the African Great Lakes Region.

 

 

Methods Up    Down

Protocol and registration: in accordance with PRISMA-P's recommendations for the presentation of systematic review and meta-analysis protocols, the protocol of this systematic review was registered in the PROSPERO database (registration number: CRD42024496404).

Search strategy: we searched the PubMed/MEDLINE, Google Scholar, Embase, and ScienceDirect databases, covering all publications up to December 2023, with no restrictions on the publication date. The research methodology included the use of keywords and their synonyms, selected according to the Condition, Context, and Population (CoCoPop) framework [12], to ensure comprehensive coverage of relevant topics. This selection of terms was specifically adapted to the language and particularities of each database consulted, thus optimising the efficiency of the search. The keywords were combined using the Boolean operators "AND" and "OR", structuring the search to capture both the specificity and breadth of the relevant studies. In addition to the electronic search, we carefully reviewed the reference lists of the selected articles to identify other relevant publications that may have escaped our initial search strategy. The results of the search are summarised in the PRISMA flowchart presented in Figure 1, which illustrates the different steps of the selection process and the number of articles ultimately selected for analysis.

Study eligibility criteria: this review included full articles of observational studies published in scientific journals until December 2023, in either English or French. These studies had to be conducted in one or more of the countries of the African Great Lakes region (DRC, Rwanda, Burundi, Kenya, Uganda, and Tanzania), on an adult population aged ≥18 years (or contain sufficient information to extract data from this population), and report data on diabetes mellitus. We excluded non-observational studies (such as clinical trials or diagnostic test evaluations, abstracts, and article reviews [13-16].

Selection of articles: the selection of articles included in this review was performed in several stages. After the literature search, all the items found were imported into Rayyan software [17], or duplicates were identified and excluded from the review. Two team members selected the articles based on the titles and abstracts, according to the eligibility criteria described above. The discrepancies between these two authors were dealt with by a third author, independently of the results of the first two. Full articles from the selected studies were uploaded for review. For articles that were not available online, we contacted the authors via email to obtain a copy of the article. For this stage, full articles were reviewed by two independent authors for inclusion, and conflicts were addressed by a third independent author. For the meta-analysis of associated factors, only articles with data on factors associated with diabetes were included.

Data extraction: data were extracted from the articles included in the selection stage. This included information such as the name of the first author, year of publication, country where the study was conducted, definition of diabetes considered in the study, study population, prevalence of pre-diabetes and diabetes, and factors associated with diabetes. This sheet was implemented in a Google Form that was used to enter the extracted data. This data extraction was performed by a single author, and a discussion with the other members of the team was held in case of doubt about the data to be extracted for one of the articles.

Risk of bias assessment: the quality of the included studies was assessed using the JBI assessment tool for cross-sectional, case-control, and observational cohort. We designed a tool that combined the questions extracted from each of these tools. These questions assessed factors such as sampling quality, sample size, procedure, reliable and reproducible measurements, and adequate statistical analyses. For each question, four possible answers were given: (i) "yes" was assigned when the article fully met the criterion evaluated; (ii) "no" indicated a failure of the study to integrate the criterion in question; (iii) "not clear" was used when there was ambiguity or lack of precision in the way the criterion was addressed in the study; (iv) and "not applicable" referred to situations where the specified criterion did not apply to the context of the study in question (Table 1, Table 1.1). The results obtained for each study were without further analysis.

Data analysis: the data were analysed using R software version 4.2.3 (2023-03-15 ucrt) and RStudio version 2023.6.1.524 (Integrated Development Environment for R. Posit Software, PBC, Boston, MA). We used meta packages to perform the meta-analyses [18]. The level of heterogeneity between the included studies was estimated using the inverse variance and Cochrane Q statistic. The I2 allowed us to classify the articles into three categories of heterogeneity: low for values below 50%, moderate for values between 50 and 80%, and high for values above 80% [19,20].

Statistical indicators (prevalence and odds ratio) were compiled using a random-effects model and are represented by a forest diagram. This approach was chosen because of the wide variability observed in the characteristics of the studies and the statistical tools for assessing heterogeneity described above. The causes of heterogeneity were explored mainly by examining the characteristics of the studies and subgroup analyses according to the variables that could affect the prevalence of diabetes, such as the location of the study, age, and country where the study was conducted. To assess the robustness of the overall prevalence estimate, a sensitivity analysis was conducted using leave-one-out analysis and Baujat plots to identify influential studies, followed by a sensitivity analysis excluding studies with fewer than 200 participants. To assess publication bias, we used a funnel chart, as published by Egger et al. [21]. The trim-and-fill method was used to extrapolate potentially missing studies to the funnel plot with estimator adjustment as needed [19]. Figure 1 shows the number of papers obtained at each stage.

For each subgroup analysis, a formal between-group comparison was performed using the subgroup difference test (Q statistic, mixed-effects model) to determine whether the variable considered contributed significantly to the observed heterogeneity. Studies were also classified according to the diagnostic criterion used to define diabetes (fasting glucose, random glucose, HbA1c, oral glucose tolerance test, or self-report) and according to methodological quality as assessed by the JBI score (high quality: score ≥7/10; low quality: score <7/10), in order to assess their contribution to the observed heterogeneity. A univariate meta-regression was also conducted for each of the following moderators: diagnostic criterion, JBI quality score, study setting, country, year of publication, and sample size (log-transformed), using the metafor package in R. p-values were reported exactly, except when below 0.001 (reported as p <0.001), and all prevalences were reported to one decimal place.

 

 

Results Up    Down

Selection of studies: a total of 4,039 articles were selected from the available databases. After eliminating 1,397 duplicates, the titles and abstracts of 2,642 articles were evaluated, of which 2,520 were excluded. The full texts of 137 articles were searched, of which 131 were found and evaluated for their eligibility. Finally, 59 articles were included in the systematic review and meta-analysis, of which 57 were on prevalence, and 2 had only data on associated factors (Figure 1).

Study characteristics: the characteristics of the studies are described in Table 2 and Table 2.1. Of the 59 studies selected, 20 were conducted in Tanzania [22-41], 18 in Kenya [42-58], 7 in the DRC [59-65], 12 in Uganda [66-77], 1 in Rwanda [78], and 1 simultaneously in Uganda and Tanzania [79]. These studies covered the period from 2010 to 2023. The full set of study characteristics used for the subgroup analyses (study setting, location, country, diagnostic criterion, JBI quality score) is detailed in Table 2 and Table 2.1, together with the references corresponding to each study.

Risks of study bias: of the 10 criteria assessed for risk of bias, seven were met by more than 75% of the included articles, while the other three were met by 50 - 75% of the articles. Table 1 and Table 1.1 describes the details of the responses to each criterion.

Summary of results on the prevalence of diabetes: the 57 included studies represented a total population of 101,412 adults aged ≥18 years, with 8194 individuals with diabetes, for an overall prevalence of 8.1% (95% CI: 6.3-10.1%) (Figure 2). However, there was a high degree of heterogeneity between studies (I2 = 99.2%, statistic Q = 6,946.34, df = 56, p < 0.001), as described in Figure 2.

The prevalence of diabetes differed according to the sex of participants, with a pooled prevalence of 4.5% (95% CI: 3.0-6.3%; k=19 studies, I2=96.4%) in males and 5.9% (95% CI: 3.6-8.6%; k=18, I2=95.0%) in females; this difference was not statistically significant (Q=0.79, df=1, p=0.373).

Prevalence also varied according to study setting, with a pooled estimate of 7.2% (95% CI: 4.3-10.7%; k=19, I2=98.8%) for community-based studies, 9.6% (95% CI: 4.8-15.8%; k=6, I2=92.8%) for hospital-based studies, and 3.8% (95% CI: 1.7-6.7%; k=3, I2=91.6%) for enterprise/workplace-based studies. The test for subgroup differences showed a borderline non-significant trend (Q=4.93, df=2, p=0.085). Similarly, prevalence differed between studies conducted in rural areas, with a pooled estimate of 3.8% (95% CI: 2.3-5.7%; k=9, I2=91.7%), and those conducted in urban areas, at 4.7% (95% CI: 1.9-8.6%; k=6, I2=95.5%), although this difference was not statistically significant (Q=0.22, df=1, p=0.640).

Regarding geographical distribution, the prevalence by country was 6.5% (95% CI: 3.7-10.1%; k=6, I2=99.0%) in Uganda, 7.0% (95% CI: 4.7-9.6%; k=5, I2=99.2%) in the Democratic Republic of Congo and 7.5% (95% CI: 4.4-11.4%; k=7, I2=97.9%) in Kenya. In addition, the pooled prevalence of diabetes in Tanzania was 8.7% (95% CI: 5.7-12.4%; k=9, I2=95.1%). In contrast to the other subgroup comparisons, the test for subgroup differences showed a statistically significant difference in pooled prevalence between countries (Q=16.50, df=4, p=0.0024), suggesting that country-level factors contribute meaningfully to the heterogeneity observed in this meta-analysis. From the 17 studies that reported the prevalence of prediabetes, the overall pooled prevalence was 10.8% (95% CI: 6.9-15.5%).

Diabetes prevalence also varied according to the diagnostic criterion used across studies (Q=27.00, df=6, p<0.001), with the lowest prevalence observed in studies based solely on self-reported diagnosis (2.6%; 95% CI: 1.4-4.1%; k=6 studies) compared with other diagnostic criteria (fasting glucose, random glucose, HbA1c, or oral glucose tolerance test), for which pooled prevalence estimates ranged from 8.4% to 10.0%. Conversely, no statistically significant difference was observed between studies of high methodological quality (JBI score ≥7/10; 8.0%; 95% CI: 5.8-10.4%; k=42) and those of lower quality (JBI score <7/10; 8.4%; 95% CI: 5.2-12.2%; k=15) (Q=0.04, df=1, p=0.839), suggesting that the overall methodological quality of the studies did not substantially bias the pooled prevalence estimate.

Synthesis of findings on factors associated with diabetes: the factors associated with diabetes reported in the different included studies were gender, age over 40 years, marital status, obesity, family history of diabetes, high blood pressure, smoking, alcohol consumption, and location (Table 2, Table 2.1). Factors significantly associated with diabetes were age > 40 years (OR: 3.89; 95% CI: 2.55 - 5.93), overweight/obesity (OR: 2.90; 95% CI: 1.70 - 4.93), and hypertension (OR: 4.32; 95% CI: 2.45 - 7.62). Male sex (OR=1.13; 95% CI: 0.84-1.52), urban residence (OR=1.38; 95% CI: 0.55-3.48), smoking, alcohol consumption, marital status, and family history of diabetes were not significantly associated with diabetes in our analyses. As these analyses are based on a limited number of studies per factor, these findings should be interpreted as exploratory.

Publication bias: the tunnel diagram was skewed (Figure 3), and the Egger regression test confirmed the publication bias of the articles included in the prevalence meta-analysis (p = 0.0023). Although the correction to the trim-and-fill model did not report any potentially missing studies, the standard error of the number of missing studies was 4.06.

Sensitivity analysis: the leave-one-out analysis showed that no single study substantially altered the pooled estimate: the largest shift occurred when Jutta Jorgensen et al. [37] was excluded, changing the pooled prevalence from 8.1% to 7.6% (a difference of 0.45 percentage points), followed by Nizeyimana et al. [78] (0.39 points) and Ayah et al. [44] (0.32 points). The Baujat plot (Figure 4) identified eleven studies as substantial contributors to overall heterogeneity, most notably Jutta Jorgensen et al. [37] and Nizeyimana et al. [78]; however, their exclusion individually did not meaningfully change the pooled prevalence, suggesting that the high heterogeneity observed (I2=99.2%) reflects genuine variability across the study population rather than being driven by one or a few extreme studies. When the four studies with sample sizes below 200 participants were excluded (Epafra et al. 2019, n=50; Asiimwe et al. 2020, n=139; Jordan Amanyire et al. 2019, n=156; and Nyombi et al. 2016, n=180) [32,72-74], the pooled prevalence remained stable at 7.9% (95% CI: 6.1-10.0%; k=53), a change of only 0.16 percentage points from the main estimate, with I2 remaining virtually unchanged (99.2%).

Univariable meta-regression (Annex 1) identified country (QM=11.05, df=4, p=0.026) and study sample size (QM=9.18, df=1, p=0.003) as significant contributors to the heterogeneity observed in the pooled prevalence estimate, together explaining 11.4% and 13.0% of the between-study variance, respectively; prevalence estimates were higher in Rwanda compared with Kenya (coefficient=0.402, 95% CI: 0.146-0.657, p=0.002) and decreased with increasing sample size. Diagnostic criterion, study setting, JBI quality score, and publication year were not significant moderators of prevalence estimates (all p>0.05).

 

 

Discussion Up    Down

The African Great Lakes Region, which has experienced multiple armed conflicts and considerable socio-economic and ethnic disparities, offers a particular context for the study of chronic diseases such as diabetes mellitus. This geographical area, which is not often considered in diabetes epidemiological data, requires special attention because of its unique conditions that directly influence the health of its inhabitants. This review compiled data on the prevalence and factors associated with diabetes mellitus in this region, including articles published between 2010 and 2023.

The combined prevalence of diabetes in this region was 8.1% (95% CI: 6.27 - 10.09%). This value is lower than the prevalence found in Iran (10.8%) and Afghanistan (12.3%) [80,81]. However, in the sub-Saharan Africa region, the result of this study appears to be superior to that of the research conducted by Asamoah et al. in Ghana, according to which the prevalence of diabetes mellitus was 6.46% [82].

However, this high variability observed between the primary studies in our study and those conducted elsewhere reflects the fact that the prevalence of diabetes is closely dependent on the location of the study. In addition, methodological variations, such as the criteria for defining diabetes and the non-inclusion of small sample sizes in these reviews, could explain the observed differences in prevalence. However, this prevalence is still high, as it represents approximately one in ten people who are affected overall. This demonstrates the need to strengthen diabetes prevention and management in these countries, most of which are low- and middle-income countries. The implementation of a regionally coordinated strategy may be particularly beneficial. Considering the socio-cultural similarities and challenges common to the countries of the sub-region, such an approach would make it possible to pool resources, share best practices, and optimize the impact of interventions. These recommendations should nevertheless be adapted and validated at the level of each country, given the substantial heterogeneity observed between national contexts, rather than applied uniformly at the regional level. This regional cooperation could also facilitate the establishment of training programs for health professionals, improvement of infrastructure, and development of awareness-raising campaigns adapted to local contexts.

The prevalence of diabetes was numerically higher in women than in men (5.9% vs. 4.5%), although this difference did not reach the threshold of statistical significance (p=0.373) in our subgroup analysis. This lack of a significant difference should be interpreted with caution given the aggregated and observational nature of the data; it does not rule out the existence of underlying sex-related biological mechanisms (particularly the protective effect of oestrogen before menopause) already documented in the literature [83-85], but suggests that individual-level data would be needed to explore these differences more robustly. Diabetes control strategies remain necessary for both sexes.

Although a numerically higher prevalence was observed in studies conducted in urban areas compared with rural areas (4.7% vs. 3.8%), this difference was not statistically significant (p=0.640) in our subgroup analysis, in contrast to what has been reported in other regional contexts by Flood et al. [86]. This lack of significance may be explained by the limited number of studies reporting this stratification (n=15) and by the substantial residual heterogeneity within each subgroup (I2>90%). Hypotheses relating to sedentary lifestyles and diets high in processed foods [6] remain plausible, along with chronic stress in urban environments [87,88], but these are not confirmed by our aggregated data and would warrant testing using individual-level data. In addition, a statistically significant difference in prevalence was observed between countries in the region (p=0.002), with a higher estimate in Rwanda compared with Kenya in meta-regression; however, as this result is based on a very limited number of studies per country (only one study for Rwanda), it should be interpreted as exploratory. The socio-economic particularities, health systems, and public policies specific to each country should nevertheless be taken into account in the development of national diabetes control strategies.

Risk factors associated with the development of diabetes in the Great Lakes Region were analyzed, highlighting key elements such as age, obesity, and hypertension. Indeed, this study revealed a significant association between these factors and the prevalence of diabetes. Comparing our findings with those of other regional and international studies revealed a similar trend, suggesting that the risk factors for diabetes are broadly constant despite cultural and socioeconomic variations. Focusing on prevention in individuals aged > 40 years, as well as interventions to reduce obesity rates and control hypertension, could potentially decrease the burden of diabetes in this region. This could take the form of specific awareness campaigns, early detection programs, and lifestyle modification initiatives tailored to the needs of this population. These associations, although consistent with the international literature, are based on a limited number of studies per factor and should be interpreted as exploratory.

This study has several strengths, notably the use of strict eligibility criteria, the systematic assessment of the methodological quality of included studies using the JBI tool, the formal exploration of sources of heterogeneity through subgroup analyses with formal statistical tests and a meta-regression, as well as sensitivity analyses confirming the robustness of the main prevalence estimate. However, it also has several limitations. First, we only included studies involving a population aged 18 years and older, which excluded studies that considered a population aged 15 years and older. Second, despite our efforts to categorize studies according to the diagnostic criterion used, the included articles did not consistently report information allowing type 1 diabetes to be distinguished from type 2 diabetes among participants; although we excluded studies explicitly including type 1 diabetic patients, it is possible that a limited number of unidentified type 1 diabetes cases were included in the prevalence studies, which constitutes a limitation inherent to the data available in the existing literature. Third, data extraction was performed by a single author, which may increase the risk of error, although uncertainties were discussed collegially with the team. Finally, the substantial residual heterogeneity between studies (I2=99.2%), only partially explained by country and sample size in our meta-regression analyses, together with methodological and diagnostic-criterion variability between primary studies, warrants caution in interpreting the pooled prevalence and subgroup estimates, particularly for subgroups based on a small number of studies (for example, Rwanda, represented by a single study).

 

 

Conclusion Up    Down

This systematic review and meta-analysis estimated the pooled prevalence of diabetes mellitus among adults in the African Great Lakes Region at 8.1% (95% CI: 6.3-10.1%), with substantial heterogeneity between studies partially explained by country and sample size. Age over 40 years, overweight/obesity, and hypertension were significantly associated with diabetes in this region. In contrast, differences in prevalence according to sex, study setting, and area of residence (urban/rural) did not reach the threshold of statistical significance in our analyses. These findings underscore the need for diabetes prevention and management strategies targeting high-risk groups (people over 40 years of age, overweight or obese, or hypertensive), tailored to the specific characteristics of each country in the region rather than applied uniformly, given the heterogeneity observed between national contexts.

What is known about this topic

  • Diabetes mellitus is a non-communicable disease whose global and African prevalence is continually increasing;
  • Diabetes prevalence varies markedly across regions of Africa, with aggregated estimates available for North Africa and sub-Saharan Africa;
  • Data specific to the African Great Lakes Region, marked by conflict, have until now remained fragmented and unsynthesized.

What this study adds

  • This study provides the first pooled regional estimate of diabetes prevalence in the countries of the African Great Lakes Region (8.1%);
  • It identifies older age, overweight/obesity, and hypertension as factors significantly associated with diabetes in this region;
  • It demonstrates, through subgroup analyses and meta-regression, that country and sample size contribute significantly to the observed heterogeneity.

 

 

Competing interests Up    Down

The authors declare no competing interests.

 

 

Authors' contributions Up    Down

Study conception and design: David Dyikpanu Tibasima and Eugène Sobngwi; data collection: David Dyikpanu Tibasima, Guy Sadeu Wafeu, and Brice Mbock Ekwalla; data analysis and interpretation: David Dyikpanu Tibasima, Guy Sadeu Wafeu, and Philomème Missi Mbassi; manuscript drafting: David Dyikpanu Tibasima, Yves Wasnyo and Guy Sadeu Wafeu; manuscript revision: Philomème Missi Mbassi, Joëlle Laure Sobngwi Tambekou, Brice Mbock Ekwalla, Yves Wasnyo, and Eugène Sobngwi; guarantor of the study: Eugène Sobngwi. All the authors read and approved the final version of this manuscript.

 

 

Acknowledgments Up    Down

We thank Dr. Nancy Houser for her financial contribution to this research. We also thank Mr Tito Hecheked and Pascal Napoléon Amani Kagadju for the selection of articles.

 

 

Tables and figures Up    Down

Table 1: description of the risks of bias in the studies selected for the systematic review and meta-analysis on the prevalence and factors associated with diabetes in the African Great Lakes Region, 2010 to 2023 (n=59)

Table 1.1: description of the risks of bias in the studies selected for the systematic review and meta-analysis on the prevalence and factors associated with diabetes in the African Great Lakes Region, 2010 to 2023 (n=59)

Table 2: description of the characteristics of the studies included in the systematic review and meta-analysis on prevalence and factors associated with diabetes in the African Great Lakes Region, 2010 to 2023 (n=59)

Table 2.1: description of the characteristics of the studies included in the systematic review and meta-analysis on prevalence and factors associated with diabetes in the African Great Lakes Region, 2010 to 2023 (n=59)

Figure 1: PRISMA flowchart of the studies included in this systematic review and meta-analysis

Figure 2: forest plot of the overall prevalence of diabetes mellitus in the adult population of the Great Lakes Africa, 2010 - 2023

Figure 3: funnel plot of prevalence studies of diabetes mellitus in the African Great Lakes Region, 2010 to 2023 (n=59)

Figure 4: Baujat plot identifying the studies contributing most to heterogeneity

 

 

Annex Up    Down

Annex 1: supplementary material (PDF - 288KB)

 

 

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Table 1: description of the characteristics of the studies included in the systematic review and meta-analysis on prevalence and factors associated with diabetes in the African Great Lakes Region, 2010 to 2023 (n=59)
Table 1.1: description of the characteristics of the studies included in the systematic review and meta-analysis on prevalence and factors associated with diabetes in the African Great Lakes Region, 2010 to 2023 (n=59)
Table 2: description of the risks of bias in the studies selected for the systematic review and meta-analysis on the prevalence and factors associated with diabetes in the African Great Lakes Region, 2010 to 2023 (n=59)
Table 2.1: description of the risks of bias in the studies selected for the systematic review and meta-analysis on the prevalence and factors associated with diabetes in the African Great Lakes Region, 2010 to 2023 (n=59)
Figure 1: PRISMA flowchart of the studies included in this systematic review and meta-analysis
Figure 2: forest plot of the overall prevalence of diabetes mellitus in the adult population of the Great Lakes Africa, 2010 - 2023
Figure 3: funnel plot of prevalence studies of diabetes mellitus in the African Great Lakes Region, 2010 to 2023 (n=59)
Figure 4: Baujat plot identifying the studies contributing most to heterogeneity

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