| Literature DB >> 34348933 |
Cindy George1, Suzaan Stoker2, Ikechi Okpechi3,4,5, Mark Woodward6,7, Andre Kengne2,8.
Abstract
Chronic kidney disease (CKD) is a global public health problem, seemingly affecting individuals from low-income and-middle-income countries (LMICs) disproportionately, especially in sub-Saharan Africa. Despite the growing evidence pointing to an increasing prevalence of CKD across Africa, there has not been an Africa-wide concerted effort to provide reliable estimates that could adequately inform health services planning and policy development to address the consequences of CKD. Therefore, we established the CKD in Africa (CKD-Africa) Collaboration. To date, the network has curated data from 39 studies conducted in 12 African countries, totalling 35 747 participants, of which most are from sub-Saharan Africa. We are, however, continuously seeking further collaborations with other groups who have suitable data to grow the network. Although many successful research consortia exist, few papers have been published (with none from Africa) detailing the challenges faced and lessons learnt in setting up and managing a research consortium. Drawing on our experience, we describe the steps taken and the key factors required to establish a functional collaborative consortium among researchers in Africa. In addition, we present the challenges we encountered in building our network, how we managed those challenges and the benefit of such a collaboration for Africa. Although the CKD-Africa Collaboration is focused primarily on CKD research, many of the lessons learnt can be applied more widely in public health research in LMICs. © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.Entities:
Keywords: epidemiology
Mesh:
Year: 2021 PMID: 34348933 PMCID: PMC8340290 DOI: 10.1136/bmjgh-2021-006454
Source DB: PubMed Journal: BMJ Glob Health ISSN: 2059-7908
Figure 1Distribution of African countries enrolled in the CKD-Africa Collaboration. The individual participant data (IPD) ranges from 300 participants to 12 247 participants per country. The nine shaded countries represent those for which IPD are currently available. The shading from light blue to dark blue represents the increasing number of IPD available per country, thus, the darkest shading represents the countries with the most available IPD. CKD, chronic kidney disease.
Characteristics of participating studies in CKD-Africa Collaboration
| Country | Study reference | Population | Cohort participants | Sample size (n) | Age range (years) | % Male | Creatinine measurement method | eGFR range | Proteinuria/ albuminuria |
| Burkina Faso† | Ali | GP | Geographical cohort | 2072 | 38–69 | 50.4 | Jaffe | 11.8 to >90 | Albuminuria |
| Burundi | Cailhol | HIV | HIV clinic cohort | 300 | 19–66 | 29.7 | Jaffe | 6.0 to >90 | Proteinuria |
| Cameroon | Choukem* | DM | Diabetes clinic cohort | 790 | 19–82 | 65.0 | Jaffe | 1.7 to >90 | NA |
| Cameroon | Feteh | DM | Diabetes clinic cohort | 645 | 20–86 | 53.0 | Jaffe | 4.0 to >90 | NA |
| Cameroon | Kaze* | GP | Geographical cohort | 433 | 21–90 | 49.0 | Modified Jaffe | 6.9 to >90 | Albuminuria |
| Cameroon | Kaze | GP | Geographical cohort | 500 | 19–83 | 53.4 | Modified Jaffe | 23.0 to >90 | Albuminuria |
| Cameroon | Kaze | GP | Geographical cohort | 439 | 19–90 | 42.1 | Modified Jaffe | 12.0 to >90 | Albuminuria |
| Cameroon | Kaze | HPT | HPT clinic cohort | 336 | 33–90 | 36.6 | Modified Jaffe | 5.0 to >90 | Albuminuria |
| Egypt | Gouda | Other | FDR of CKD cohort | 416 | 18–75 | 43.2 | Jaffe | 32.0 to >90 | Albuminuria |
| Ghana‡ | Adjei | GP | Geographical cohort | 2543 | 25–96 | 33.0 | Jaffe | 15.5 to >90 | Albuminuria |
| Ghana | Chadwick | HIV | HIV clinic cohort | 677 | 20–77 | 26.1 | Jaffe | 9.8 to >90 | Proteinuria |
| Ghana | Osafo | HPT | HPT clinic cohort | 754 | 19–90 | 21.3 | Jaffe | 1.4 to >90 | Proteinuria |
| Ghana† | Ali | GP | Geographical cohort | 2011 | 40–61 | 45.8 | Jaffe | 12.3 to >90 | Albuminuria |
| Kenya† | Ali | GP | Geographical cohort | 2000 | 35–67 | 46.0 | Jaffe | 13.9 to >90 | Albuminuria |
| Nigeria | Adedeji | HIV | HIV clinic cohort | 304 | 18–80 | 54.7 | Modified Jaffe | 5.9 to >90 | Albuminuria |
| Nigeria | Alasia | Other | Renal clinic cohort | 605 | 18–86 | 49.3 | Jaffe | 1.3 to >90 | Proteinuria |
| Nigeria | Ayodele* | GP | Cohort of students | 307 | 18–30 | 35.8 | Modified Jaffe | 39.9 to >90 | NA |
| Nigeria | Ayodele* | GP | Geographical cohort | 419 | 19–80 | 40.3 | Modified Jaffe | 37.1 to >90 | NA |
| Nigeria | Ayokunle | HIV | HIV clinic cohort | 335 | 20–75 | 43.9 | Jaffe | 4.7 to >90 | Albuminuria |
| Nigeria | Okoye | GP | Geographical cohort | 476 | 18–90 | 33.8 | Modified Jaffe | 22.4 to >90 | Proteinuria |
| Nigeria | Oluyombo | GP | Geographical cohort | 972 | 18–100 | 30.4 | Modified Jaffe | 23.9 to >90 | Albuminuria |
| Nigeria | Raji | Other | Renal clinic cohort | 309 | 18–73 | 45.3 | Modified Jaffe | 1.2 to >90 | NA |
| RoC | Ekat | HIV | HIV clinic cohort | 562 | 18–64 | 33.8 | Jaffe | 4.0 to >90 | NA |
| SA | Adeniyi | GP | Cohort of teachers | 455 | 22–71 | 29.7 | Modified Jaffe | 32.4 to >90 | Proteinuria |
| SA | Malan | GP | Cohort of teachers | 409 | 20–65 | 49.4 | Jaffe | 1.3 to >90 | Albuminuria |
| SA | Matsha | GP | Geographical cohort | 1620 | 18–91 | 24.8 | Jaffe | 8.2 to >90 | Albuminuria |
| SA | Peer | GP | Geographical cohort | 1086 | 22–81 | 35.9 | Jaffe | 9.0 to >90 | NA |
| SA | Rayner and Becker | HPT | HPT clinic cohort | 1107 | 18–94 | 48.8 | NA | NA | Albuminuria |
| SA | Schutte | GP | Geographical cohort | 750 | 20–70 | 46.1 | Jaffe | 47.8 to >90 | NA |
| SA | Schutte | GP | Geographical cohort | 1202 | 20–30 | 48.1 | Jaffe | 48.1 to >90 | Albuminuria |
| SA† | Ali | GP | Geographical cohort | 2312 | 40–81 | 42.2 | Jaffe | 3.1 to >90 | Albuminuria |
| SA† | Ali | GP | Geographical cohort | 1388 | 29–82 | 30.6 | Jaffe | 4.8 to >90 | Albuminuria |
| SA† | Ali | GP | Geographical cohort | 1918 | 39–61 | 50.6 | Jaffe | 9.4 to >90 | Albuminuria |
| Seychelles | Pruijm | GP | Geographical cohort | 1230 | 25–64 | 46.2 | Jaffe | 3.2 to >90 | Albuminuria |
| Seychelles | Heiniger | GP | Geographical cohort | 1240 | 26–64 | 42.8 | Jaffe | 4.5 to >90 | Albuminuria |
| Tanzania | Peck | GP | Geographical cohort | 1041 | 18–92 | 46.2 | Enzymatic | 25.1 to >90 | NA |
| Tanzania | Stanifer | GP | Geographical cohort | 468 | 18–88 | 25.6 | Enzymatic | 9.2 to >90 | Albuminuria |
| Uganda | Kalyesubula | GP | Geographical cohort | 955 | 18–87 | 33.0 | Enzymatic | 44.7 to >90 | Proteinuria |
| Uganda | Odongo | HIV | HIV clinic cohort | 361 | 18–66 | 36.3 | Jaffe | 4.6 to >90 | Proteinuria |
*Unpublished data
†Part of the multisite study, Africa Wits-INDEPTH partnership for Genomics studies.62
‡Part of the multisite study, Research on Obesity and Diabetes among African Migrants.40 GFR is estimated by the the CKD Epidemiology Collaboration equations,64 with the ethnicity correction factor omitted.
CKD, chronic kidney disease; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; FDR, first-degree relatives; GP, general population; HPT, hypertension; NA, not applicable; RoC, Republic of Congo; SA, South Africa.
Figure 2Average contribution of each subpopulation to the overall number of studies enrolled in the CKD-Africa Collaboration. CKD, chronic kidney disease.