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Browsing Research Data by Keyword "Africa South of the Sahara"

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    Data Set : Prevalence, characterization and response to chronic kidney disease in an urban and rural setting in South Africa
    (2016-11-18) Naicker, Saraladevi; Fabian, June; Jaya A George; Harriet R Etheredge; Manuel van Deventer; Robert Kalyesubula; Alisha N Wade; Laurie A Tomlinson; Stephen Tollman
    Globally, chronic kidney disease (CKD) is an emerging public health challenge but accurate data on its true prevalence are scarce, particularly in poorly resourced regions such as sub-Saharan Africa (SSA). Limited funding for population-based studies, poor laboratory infrastructure and the absence of a validated estimating equation for kidney function in Africans are contributing factors. Consequently, most available studies used to estimate population prevalence are hospital-based, with small samples of participants who are at high risk for kidney disease. While serum creatinine is most commonly used to estimate glomerular filtration, there is considerable potential bias in the measurement of creatinine that might lead to inaccurate estimates of kidney disease at individual and population level. To address this, the Laboratory Working Group of the National Kidney Disease Education Program published recommendations in 2006 to standardize the laboratory measurement of creatinine. The primary objective of this review was to appraise implementation of these recommendations in studies conducted in SSA after 2006. Secondary objectives were to assess bias relating to choice of estimating equations for assessing glomerular function in Africans and to evaluate use of recommended diagnostic criteria for CKD. This study was registered with Prospero (CRD42017068151), and using PubMed, African Journals Online and Web of Science, 5845 abstracts were reviewed and 252 full-text articles included for narrative analysis. Overall, two-thirds of studies did not report laboratory methods for creatinine measurement and just over 80% did not report whether their creatinine measurement was isotope dilution mass spectroscopy (IDMS) traceable. For those reporting a method, Jaffe was the most common (93%). The four-variable Modification of Diet in Renal Disease (4-v MDRD) equation was most frequently used (42%), followed by the CKD Epidemiology Collaboration (CKD-EPI) equation for creatinine (26%). For the 4-v MDRD equation and CKD-EPI equations, respectively, one-third to one half of studies clarified use of the coefficient for African-American (AA) ethnicity. When reporting CKD prevalence, <15% of studies fulfilled Kidney Disease: Improving Global Outcomes criteria and even fewer used a population-based sample. Six studies compared performance of estimating equations to measured glomerular filtration rate (GFR) demonstrating that coefficients for AA ethnicity used in the 4-v MDRD and the CKD-EPI equations overestimated GFR in Africans. To improve on reporting in future studies, we propose an 'easy to use' checklist that will standardize reporting of kidney function and improve the quality of studies in the region. This research contributes some understanding of the factors requiring attention to ensure accurate assessment of the burden of kidney disease in SSA. Many of these factors are difficult to address and extend beyond individual researchers to health systems and governmental policy, but understanding the burden of kidney disease is a critical first step to informing an integrated public health response that would provide appropriate screening, prevention and management of kidney disease in countries from SSA. This is particularly relevant as CKD is a common pathway in both infectious and non-communicable diseases, and multimorbidity is now commonplace, and even more so when those living with severe kidney disease have limited or no access to renal replacement therapy.
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    Dataset from Ark Consortium - Understanding kidney disease in rural central Uganda - Findings from a qualitative study.
    (2016-11-09) Saraladevi, Naicker; June, Fabian; Working group ARK Consortium,; Seeley, Janet; Kabunga, Elizabeth; Laurie, Tomlinson; Liam, Smeeth; Moffat, Nyirenda; Robert, Newton; Robert, Kalyesubula; Dominic, Bukenya; Joseph Ssembatya
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    Dataset from: Chronic kidney disease (CKD) and associated risk in rural South Africa: a population-based cohort study
    (2022-07-13) Fabian, June; Gondwe, Mwawi; Mayindi, Nokthula; Khoza, Bongekile; Gaylard, Petra; Wade, Alisha N.; Gómez‑Olivé, F. Xavier; Tomlinson, Laurie A.; Ramsay, Michele; Tollman, Stephen Meir; Winkler, Cheryl; George, Jaya Anna; Naicker, Saraladevi; Study data were collected and managed using opensource REDCap electronic data capture tools hosted at the University of the Witwatersrand
    Study Methods This longitudinal cohort study was conducted from November 2017 to September 2018 in the Medical Research Council (MRC)/Wits Rural Public Health and Health Transitions Research Unit (otherwise referred to as "Agincourt") in Bushbuckridge, a rural subdistrict of the Mpumalanga province in north-eastern South Africa. Agincourt is a Health and Socio-Demographic Surveillance System (HDSS) site that includes approximately 115,000 people. For this study, a minimum sample size of 1800 was required to provide at least 80% power to determine CKD prevalence of at least 5%, provided the true prevalence was equal to or more than 6.5%. Proportional allocation of Black African adults aged 20 to 79 years ensured a representative sample based on the most recent annual population census. Sample size was increased proportionately to 2759 individuals to accommodate a 25% non-participation rate. Dataset is 2022 cases Variables are: 1. age 2. Gender 3. Years of Education (refers to completed years of schooling) 4. Height (cm) (one decimal place) 5. weight (kg) (one decimal place) 6. BMI (body mass index) 7. POC random cholesterol (mmol/L) (2 decimal places) 8. POC random glucose (mmol/L) (1 decimal place) 9. HIV status is: Based on (i) prior HIV testing history OR (ii) HIV PCR testing for ARK 10. Using the urine pregnancy test, is this participant pregnant? ( 11. ERY (erythrocytes, blood) 12. Hb (haemoglobin, blood) LEU (leucocytes) 13. NIT (nitrites) 14. PRO (protein) 15. hepatitis B surface antigen 16. Serum creatinine (umol/L) 17. Systolic BP(1) 18. Diastolic BP (1) 19. Systolic BP(2) 20. Diastolic BP (2) 21. Systolic BP(3) 22. Diastolic BP (3) 23. Serum creatinine (umol/L) 24. Urine microalbumin (mg/L) 25. Urine creatinine (mmol/L) 26. Urine microalbumin (mg/L) 27. Urine creatinine (mmol/L) 28. APOL1 haplotype