https://orcid.org/0000-0002-3603-3926
Figures
Abstract
Background
The global burden of chronic kidney disease (CKD) is rising, disproportionately impacting on low- and middle-income countries. In African populations, use of serum creatinine to estimate glomerular filtration rate (GFR) significantly underestimates CKD prevalence, contributing to its under-recognition as a health problem. Serum cystatin C provides more accurate estimates of Iohexol measured GFR than creatinine. Early diagnosis and treatment of CKD is essential to reduce premature morbidity and mortality. However, little is known about risk factors for CKD development and progression in Africa owing to limited longitudinal data. This study aims to determine risk factors for progressive kidney function decline among adults living in rural, northern Malawi.
Methods
This protocol describes a prospective study being conducted in a general population Health and Demographic Surveillance Site in rural Karonga, Malawi (2024–2025). Eligible participants are adults aged 18 years and over who participated in two population-based surveys of long-term health conditions (2013–2016 and 2021–2025), with availability of baseline measures. New household-level data is being prospectively collected on CKD risk factors, alongside blood and urine samples. Cystatin C and creatinine will be tested on individual-level paired, stored serum samples collected at three longitudinal time points. Urine will undergo dipstick urinalysis, microscopy and testing for albumin and creatinine to quantify proteinuria. The primary outcome will be sustained 25% reduction in estimated GFR (eGFR) from baseline and change in eGFR category, determined using serum cystatin C. Multivariable logistic regression will be used to determine effect size estimates of key risk factors for kidney function decline.
Discussion
This study will provide important data on risk factors for eGFR decline and CKD progression amongst Malawian adults. The findings will inform future research into important context-specific risk factors, and could directly inform health policies in Malawi for targeting CKD screening, prevention and treatment strategies to high-risk patient groups.
Citation: Snead CM, Mkandawire C, Kambiya P, Munthali-Mkandawire S, Kalobekamo F, Dube A, et al. (2026) Risk factors for decline in estimated glomerular filtration rate amongst Malawian adults living in rural Karonga: Protocol for a prospective cohort study using cystatin C- and creatinine-based eGFR. PLoS One 21(7): e0329042. https://doi.org/10.1371/journal.pone.0329042
Editor: Diego Moriconi, University of Pisa, ITALY
Received: July 11, 2025; Accepted: June 8, 2026; Published: July 27, 2026
Copyright: © 2026 Snead et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data from the MEIRU NCD survey and the African Research on Kidney Disease (ARK) Consortium study was used to create the tables in Supplementary files S4 and S5. Data from both these studies is publicly available. The full data from the MEIRU NCD survey can be accessed via the following public repository; https://datacompass.lshtm.ac.uk/id/eprint/3834/; with some creatinine data available in an additional public repository; https://datacompass.lshtm.ac.uk/id/eprint/1150/ Data from the ARK study is available publicly at: https://github.com/ARKconsortium/iohexol_mGFR_eGFR. This is a standardised set of relevant data from all three study sites involved in that study: Malawi, South Africa and Uganda. The dataset includes deidentified individual participant data related to estimated and measured GFR, with accompanying analytic code used for development of a new creatinine-based GFR-estimating equation. The specific merged dataset used to create supplementary tables S4 and S5 used anonymised participant-level data from both the MEIRU NCD survey and the Malawi arm of the ARK study (all individuals had taken part in the MEIRU NCD survey; a subset had additional taken part in the ARK study). This data will be publicly available by the time the currently described study is completed and published, but is not yet accessible in a public repository as work is ongoing to finalise open datasets. However while this work is ongoing the dataset can be accessed on request through application to the MEIRU data access committee (mia.crampin@glasgow.ac.uk; paul.kambiya@meiru.mw). This and the deidentified data from the currently ongoing Impso study, described in this protocol paper, will be made publicly available the study is completed and published.
Funding: CMS, 223502/Z/21/Z, Wellcome Trust https://wellcome.org/ ACC, 098610/Z/12/Z, 098610/B/12/A, 217073/Z/9/Z, Wellcome Trust https://wellcome.org/ JF, Project number:8111, GSK Africa Non-Communicable Disease Open Lab https://www.gsk.com/en-gb/responsibility/africa-open-lab/ Segun Fatumo, 220740/Z/20/Z, Wellcome Trust Segun Fatumo, UKRI/MRC MR/Z504853/ The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The global burden of chronic kidney disease (CKD) and associated mortality is rising [1], with CKD predicted to be the fifth leading cause of life years lost by 2040 [2]. At the 78th World Health Assembly in May 2025, the World Health Organization (WHO) adopted a long-awaited resolution recognising CKD as a global health priority and committing to promotion of kidney health through prevention and control of kidney disease [3]. The burden is disproportionately impacting on low- and middle-income countries (LMICs), where over 77% of people living with CKD reside, the majority undiagnosed [4]. Set against this, stark disparities in access to appropriate diagnostic tests, prevention and treatments remain [5]. Only 7% of individuals receiving kidney replacement therapy (KRT) live in LMICs [6] and there are huge workforce shortages for delivery of kidney care. Compared to high-income countries, nephrologist densities in low-income counties and lower-middle income countries are more than 80 times and 10 times lower, respectively [7]. Notably, Malawi has only two adult nephrologists for a population of over 21 million people.
In most of Africa, the prevalence of CKD is thought to be between 13 and 15% [8–10]. However, accurate estimates of burden are hampered by limited access to essential diagnostic blood and urine tests [11,12], heterogeneity of methods for measuring CKD prevalence across studies [10], and uncertainty about the most appropriate methods for estimating kidney function in African settings [13]. Importantly, the most readily available creatinine-based equations for estimating glomerular filtration rate (eGFRcreat) have been shown to perform poorly in African populations, significantly underestimating the overall burden of CKD when compared to gold-standard measured GFR (mGFR) determined by Iohexol plasma clearance [13–15]. This poor performance is most marked at lower GFR levels and is relative to the performance of creatinine-based estimating equations developed and modelled among predominantly North American or European populations [13,14]. Their use among continental African populations with little validation has further contributed to under-recognition of CKD as a public health problem in many African countries [13]. In Malawi, population prevalence of eGFR less than 60 ml/min/1.73m2 determined using the internationally recommended creatinine-based CKD-EPI (2021) equation [16] is only 0.49% (95% CI 0.31–0.73) and using the CKD-EPI 2009 equation [17] without adjustment for African-American ethnicity, 1.25% (95% CI 0.95–1.61), compared to 11.91% (95% CI 10.13–13.69) when using imputed GFR modelled on Iohexol mGFR [13]. Estimating GFR using serum cystatin C (eGFRcysC) more closely mirrors mGFR in African populations [13], but this assay is significantly more expensive than serum creatinine and is not currently routinely clinically available in African settings. The reasons why serum creatinine performs less well than cystatin C when compared to measured GFR in continental African populations are not yet fully understood but may could potentially relate to differences in lean muscle mass (body mass index, body surface area, height and weights are lower when compared to European ancestry and African American cohorts) [13,15,18–20]. Possible explanations include nutritional factors (such as low red meat consumption, under-five childhood malnutrition and stunting) [21–23], chronic wasting conditions (such as HIV, tuberculosis and endemic infections) and undiagnosed liver disease, all of which can affect muscle mass and therefore creatinine production [24], as well as genetic determinants of serum creatinine [25].
Given the limited available resources in many LMICs for treating advanced kidney disease, early detection, prevention and treatment, targeted towards those at highest risk, is essential. Early initiation of disease-modifying treatments to slow CKD progression, reduce risk of kidney failure and mitigate CKD-related complications will reduce premature morbidity and mortality, including the significantly heightened risk of CKD-attributable cardiovascular morbidity and mortality [5,26–31].
CKD fits the WHO criteria for screening that has public health benefit, given its asymptomatic nature and the existence of available treatments, however evidence for cost-effectiveness of mass population screening is lacking [32–35]. Instead, screening in high-risk groups, including individuals with diabetes and hypertension, is recommended, provided treatment can be provided [36–39]. However, in LMICs, including those in Africa, the rising prevalence of NCDs intersects with ongoing high burdens of infectious diseases, poverty, malnutrition, maternal health challenges, environmental and climate factors. As a result, the potential causes of CKD are diverse, and the groups of individuals at highest risk for CKD progression may therefore differ from those in high income settings [40–44]. Indeed, several cross-sectional studies conducted across East and Southern Africa have suggested that only one-fifth to two-thirds of CKD appears to be associated with traditional risk factors [19,45–47]. As such, it remains unclear in many African settings which individuals should be prioritised for CKD screening and prevention programs, above and beyond those with hypertension, diabetes and proteinuria [32].
Improved knowledge on the individuals at highest risk for rapid eGFR decline, CKD development, progression and future kidney failure would help target resources to those at the highest risk of adverse CKD outcomes, and would also contribute to future development of context-specific clinical risk prediction tools, (not yet validated in African settings [48,49]) to aid identification of high-risk patients, risk-stratification and prognostication. Understanding this requires longitudinal studies of kidney function trajectories, however very few studies of this nature have been conducted in African settings. Those that exist have generally been conducted in hospital settings [50,51], amongst specific patient groups [52,53,54–58], and/or over relatively short follow-up periods. [50,52,51,58] Furthermore, all have assessed eGFR trajectories using creatinine-based eGFR measures (eGFRcreat), and not using cystatin C (eGFRcysC), now known to be a much more accurate predictor of kidney function in African populations.
Aim and objectives
The aim of this prospective cohort study is to determine key risk factors for progressive decline in eGFR amongst adults participating in a general population cohort in rural northern Malawi. The primary objective is to determine predictors for progressive decline in eGFRcysC. A secondary objective is to determine risk factors for decline in eGFRcreat and development of CKD.
Methods
Study design and setting
The “Impso” (“kidney” in Chichewa, a local language) study is a prospective cohort study set within Karonga Health and Demographic Surveillance System (HDSS), an open, population-based cohort established in 2002 and covering an area of 135km2 of rural subsistence farming and fishing communities in northern Malawi [59] managed by the Malawi Epidemiology and Intervention Research Unit (MEIRU) [Fig 1].
The HDSS includes a population of over 53,000 (data to early 2025) and is served by four health facilities (rural hospitals and health centres) [59]. The nearest secondary referral hospital is located 80 km to the north in Karonga boma. The HDSS undergoes continual population surveillance of births and deaths, with annual census of inward- and outward migration and collection of detailed sociodemographic data. All deaths undergo verbal autopsy (VA) with physician-assigned cause of death following a medical interview and case review using the standardised WHO VA tool [59]. Use of unique identifiers within the HDSS creates capacity for linkage across longitudinal studies.
The Karonga HDSS, alongside a more recently established peri-urban HDSS in the outskirts of Lilongwe, is the site of two comprehensive population-based surveys of long-term health conditions (LTCs): a non-communicable disease (NCD) survey of cardiometabolic conditions conducted 2013−16 [60,61]; and the currently ongoing Healthy Lives Malawi (HLM) LTC survey; 2021−25 [62], which aims to re-survey prevalence and assess progression of cardiometabolic conditions, in addition to newly determining prevalence of additional LTCs and more detailed risk factor data. Both studies contribute to a biobank.
The currently described study capitalises on these two larger studies [Fig 1] and additionally leverages data from a large multinational study conducted in 2018−19 by the African Research on Kidney Disease (ARK) Consortium to investigate methods for measuring kidney function in African populations [13], which included 510 adults residing in the Karonga HDSS. Available data from all three linked studies includes household data (socioeconomic status; geolocators), interview data (demographics; lifestyle factors; clinical history, including prior diagnosis, screening and medications for chronic conditions), examination and physical measures (anthropometry; blood pressure; hand grip strength; peripheral arterial measures) and biological samples (serum, plasma and whole blood samples stored at −80 Celsius and other biological material). Baseline cystatin C and creatinine have already been tested on stored serum samples collected between 2013 and 2019 [13,19] for 2792 and 4637 adults respectively.
Sample size calculation
The target sample size is 1000 participants. A sample of n = 1000 participants with eGFRcysc available at baseline will give ≥80% power to detect odds ratio (OR) of between 1.66 and 4.38 for the effect of risk factors on eGFR decline. This is assuming that; (1) the risk factors of interest increase probability and rate of eGFR decline; (2) risk factor prevalences range between 5% and 30% [19,60]; and (3) eGFR decline outcome measures occur in 5–15% of individuals unexposed to the risk factors of interest.
Participants
This study is prospectively recruiting adults living within the Karonga HDSS, who meet the following eligibility criteria [Fig 2]:
- Alive, self-defined as resident within the Karonga HDSS, and provided consent to ongoing participation in the HDSS and sampling for further studies (in the event of internal migration within the HDSS, resident status is maintained).
- Participated in both the NCD (2013−16) and HLM-LTC (2021−25) population surveys.
- Aged ≥ 18 years at the time of participation in the NCD (2013−16) survey.
- Availability of baseline cystatin C tested on a stored historical serum sample (2013–2019).
- Baseline eGFRcysC < 90 ml/min/1.73m2.
Exclusion criteria include:
- Unable to provide informed written consent, or assent with proxy informed written consent from a nominated guardian.
- Acute presentation of physical or mental illness or rapidly declining poor health at the time of attempted recruitment, which might be exacerbated by participation in the study.
- Currently a hospital inpatient at time of follow-up (at time of screening for participation in Impso study)
- Pregnant at the time of baseline sample collection for cystatin C testing, and/or when approached for recruitment into the current study, with pregnancy to continue beyond the end of the study recruitment period.
Recruitment and consent
Individuals potentially meeting the inclusion criteria are identified by screening the MEIRU database. For this purpose, the MEIRU-authorised study team members were granted access to linked datasets (secured in encrypted servers, only accessible by authorised personnel through provided credentials) on 08/08/2023 for the purposes of screening, study visits and results dissemination. All participants in the datasets have given consent for use of their data in future linked studies. Final datasets are stripped of personal identifying data. No study team members have access to identifying after data collection and results dissemination are complete. All study team members undergo training in Good Clinical Practice in research.
Potentially eligible participants are approached at their household to invite participation in the study, a minimum of three months after their participation in the HLM LTC survey. Up to three attempts are made to visit each potentially eligible participant, including at different times of day, to facilitate recruitment of adults of working age who may be less likely to be at home and thereby minimise selection bias. After checking for exclusion criteria, individuals who accept receive information about the study in the form of a participant information sheet, provided in their preferred language (Chitumbuka, Chichewa or English). The information sheet is read aloud to the potential participant by a member of the study team and the individual is given the opportunity to ask questions.
Individuals who wish to take part proceed to provide informed, written consent. Individuals who are unable to read or write provide a thumbprint instead of a signature. In this event, an impartial witness is present throughout the information-giving and consent process, and also signs the informed consent form. For individuals who lack capacity to provide informed consent, but are able to provide assent, proxy consent is sought from the nominated guardian. Informed written consent is obtained for all planned study activities, including for long-term storage of study data and biological samples.
Household-level data collection
Data collection takes place over the course of two to four household visits [Fig 3].
Interviewer-led questionnaires.
The first household visit involves delivery of several questionnaires, covering; (1) context-specific risk factors for CKD, including detailed questions on medical history, symptoms and medication use, guided by a priori hypotheses on exposures of importance in this setting [19,41], and supported by review of the participant’s health passport if available; (2) health-related quality of life (HrQOL), using the RAND 36-Item Short Form Survey Instrument (SF-36), and the EQ-5D-5L, both translated into Chitumbuka and Chichewa, with the SF-36 adapted where appropriate for relevance to the Malawi setting [63], and use of the EQ-5D-5L translations (already validated in Chichewa) [64] approved by EuroQol; and (3) screening questions to determine optimal timing for early morning biological sample collection. Sample collection is delayed in the event of urinary tract infection symptoms, current or recent (within past 24 hours) menstruation, and hospital admission >24 hours duration within the preceding 90 days [Fig 3].
HIV testing and counselling (HTC) using point-of-care DetermineTM rapid test kits is offered to participants who have never been tested, are unsure about their HIV status, or whose most recent test with a non-reactive result was conducted over six months previously (ascertained from both self-report and health passport documentation). Study staff performing HTC have undergone formal training and certification though Malawi Ministry of Health HTC training providers. Any participants with a reactive result on the DetermineTM rapid HIV test are referred to the HIV clinic at Chilumba Rural Hospital for repeat counselling and full testing using the three-test diagnostic algorithm, following Ministry of Health guidelines. [65]
All questionnaire data is captured electronically on Android tablets using the Open Data Kit (ODK) platform. [66,67] Before completing the first household visit, the study staff ensure that clear instructions have been given to participants in preparation for mid-stream urine (MSU) sample collection using a standardised information sheet provided in Chitumbuka, Chichewa or English [S1 File].
Biological sample collection.
Collection of biological samples (MSU and single venepuncture serum sample) takes place at a second, early morning household visit, the day after the interview date, or else on the first alternative convenient date for the participant. Immediately prior to sample collection, study staff repeat screening questions for menstruation and UTI symptoms, and check that the MSU collection instructions have been followed. MSU samples are collected in sterile containers, contained with sealed sterile packaging until the time of collection, and are inspected by the study team prior to dipstick urinalysis testing. Samples collected incorrectly, that are clearly contaminated, leaking, or that are inadequate (< 20 ml) in volume are discarded without testing and arrangements are made for re-collection.
Dipstick urinalysis is being conducted on acceptable MSU samples at the household using Siemens Multistix® 10 SG urine dipsticks following a strict standard operating procedure, including use of a sterile Pasteur pipette to apply urine to dipstick test pads, and use of an electronic timer to ensure dipstick results for each reagent are read at the times specified by manufacturer. Urine dipstick results are captured electronically at the household using ODK and are photographed at two minutes for quality control purposes. Any abnormalities on dipstick urinalysis triggers a third household visit in ≥ three weeks’ time for collection and testing of a repeat (second) MSU sample. In the event of microscopic haematuria newly present on a second sample, a third and final MSU collection visit takes place [Fig 3]. Venepuncture serum samples are collected in 10 ml plain serum tubes using vacutainer needles.
Samples are placed upright in cool boxes maintained at 2–8 ˚C immediately after collection and urinalysis, and are transported back to the MEIRU laboratory within four hours of sample collection. Within this four-hour window, urine samples spend a maximum of two hours at ambient temperature prior to the cold storage.
Laboratory methods
The condition of all biological specimens is inspected on arrival at the laboratory; samples that are leaking, contaminated or inadequate are rejected and arrangements are made for recollection.
Urine microscopy involves both wet mount examination and Gram stain of urine sediment. After mixing by inversion, 10 ml from each mid-stream urine sample is centrifuged at 720 g for five minutes immediately after reaching the laboratory. After separating from the supernatant, urine sediment is emulsified and the sediment examined by light microscopy at both low power field (LPF, x10 objective) and high power field (HPF, x40 objective), the latter for identification and quantification of cells (erythrocytes, leucocytes, epithelial cells), casts, crystals and Schistosoma ova. Cell counts are reported per HPF, and Schistosoma ova per 10 ml of urine.
Prior to Gram stain, urine sediment is mounted on the slide and gently heat fixed for five minutes. Gram stain is then performed by sequentially flooding slides, first with 0.5% crystal violet (left for 60 seconds), rinsed off with water, then with Gram’s iodine (left for 60 seconds), washed off with acetone decolouriser before rinsing with water, and finally with safranin (left for 30 seconds), again rinsed off with water. After blotting dry, slides are examined using x100 objective lens with emersion oil. Participants with organisms identified on Gram stain of the baseline urine sample receive a follow-up visit for repeat sample collection, with reiteration of MSU collection instructions. Urine culture is not being performed owing to budget constraints of the study.
The remaining uncentrifuged portion of the urine sample for each participant is mixed by inversion and used to extract three 1 ml aliquots using 2 ml cryotubes, which are stored at −20°C temporarily, then transferred to the −80°C freezer within 12 hours of sample collection for short-term storage until the time of urine chemistry analysis, which will take place after participant recruitment is complete.
Blood samples collected in plain tubes are centrifuged within two hours of reaching the laboratory by spinning for ten minutes at 1620g in a swing bucket centrifuge. The separated serum is used to make five aliquots of 0.5 ml using 2 ml cryotubes, stored at −20°C temporarily, then transferred to −80°C freezer within 12 hours of sample collection for long-term storage.
Prior to chemistry testing, stored serum and urine samples will be removed from the −80°C freezer and allowed to thaw for 30 minutes to 1 hour. After completing thawing, samples will be vortexed and put in sample cups ready for testing. All chemistry testing will use Beckman Coulter AU480 chemistry analysers.
Serum samples from this study, and individual level paired serum samples from the HLM-LTC survey, will be tested for serum cystatin C (immunoturbidimetric method, standardised using ERM- DA471/IFCC reference material) and creatinine (kinetic compensated Jaffe method, method A, standardised to an isotope-dilution mass spectroscopy [IDMS] assay). For cystatin C testing, serum will be mixed with Gentian Cystatin C reagent containing cystatin C immunoparticles. Cystatin C from the serum and anti-cystatin C from the immunoparticles form aggregates. The complex particles created absorb light, and by turbidimetry the absorption is related to cystatin C concentration via interpolation on an established standard calibration curve. The AU480 platforms automatically calculate the results. The machine will be calibrated using Gentian Cystatin C Calibrator which is standardised against the international calibrator standard ERM-DA471/IFCC. A six-point standard curve will be established using standards one to six with assigned values given on the analytical value sheet provided with the calibrator. In the kinetic compensated Jaffe method, creatinine will form a coloured compound with picric acid in alkaline medium. The rate of change in absorbance at 520/800nm is proportional to the creatinine concentration in the sample. Interference from protein will be mathematically corrected by subtracting 18 μmol/L from each test result. The Beckman Coulter AU480 analyser will automatically compute the creatinine concentration of each sample. Serum calibrator creatinine value for method A is traceable to the Isotope Dilution Mass Spectroscopy (IDMS) method via National Institute of Standards and Technology (NIST) Standard Reference Material (SRM) 967.
Urine will be tested for albumin (turbidimetric method) and creatinine (kinetic compensated Jaffe method). In the turbidimetric method for urine albumin testing, anti-human serum albumin antibodies combine with albumin from the sample to form immune complexes that scatter light in proportion to their size, shape and concentration. The absorbance of these aggregates is proportional to the albumin concentration in the sample. Change in absorbance is measured at 380nm with subtraction of a reference wavelength at 800nm. The urine calibrator creatinine value is traceable to the IDMS method. Urine albumin calibrator values are traceable to the International Federation of Clinical Chemistry Certified Reference Material CRM470.
Urine samples will be discarded after validation of results; but serum samples will be stored long-term to allow additional testing in future studies.
Laboratory quality control processes.
Prior to microscopic examination of urine (for both Gram staining and wet mount examination of urine sediment) the performance of the objective lenses is verified using standardised positive and negative control slides. Quality control of the microscopy procedures is maintained by independent verification of findings by two qualified technologists. During serum cystatin C and creatinine testing, low and high concentration controls will be tested each day before any samples are measured to validate the calibration curve. The controls have an assigned value range that will be met before measuring samples; these are given in the Analytical Value sheet included with the Gentian Cystatin C and creatinine control kits. During urine albumin and creatinine testing, low and high concentration controls of Biorad Liquichek assayed quality control material will be tested a minimum of once a day on each day of testing.
Pilot phase
The data collection tools and field activities strategy were piloted both internally and externally to understand and address in a timely manner any issues associated with the workflow, logistics and time required, for the information giving, consent, medical interview, and biological sample collection processes. During this pilot phase, the questionnaire and field procedures were reviewed and revised based on feedback raised. This included improvements to some questionnaire questions, answer options and translations to improve understandability and cultural acceptability, in addition to revision of working procedures for more efficient collection of interview data and biological samples.
Timelines
Recruitment for the pilot phase took place between 14 February and 1 March 2024. Recruitment for the main study commenced on 5 March 2024 and is ongoing, anticipated to complete by 30 September 2025. Data collection is anticipated to complete by 30 November 2025, and results are anticipated by March 2026.
Analysis plan
eGFR equations.
By the end of the study, each participant will have up to three stored serum samples tested for cystatin C and creatinine, allowing calculation of eGFRcysC and eGFRcreat at different points in time: (i) baseline (blood collected between 2013 and 2019); (ii) HLM LTC survey (blood collected between 2021 and 2025); and (iii) Impso study (blood collected between 2024 and 2025). HLM LTC and Impso study samples are collected a minimum of 90 days apart.
All eGFRcysC values will be calculated using the CKD-EPI 2012 equation [68]. eGFRcreat will be calculated using the CKD-EPI (2009) equation [17] without adjustment for African American ethnicity, as this has been demonstrated to have slightly higher accuracy and lower relative bias than the newer CKD-EPI (2021) equation when compared to Iohexol mGFR in African populations, including in adults from rural Karonga [13]. However sensitivity analyses for will also be conducted using the CKD-EPI (2021) creatinine equation (which has no ethnicity coefficient), the CKD-EPI (2021) combined creatinine-cystatin C equation and the European Kidney Function Consortium (EFKC) equations for both creatinine and cystatin C [16,69–71]. Details of all eGFR equations are provided in full in S2 File.
Outcome measures.
The study outcome measures are guided by the Kidney Disease Improving Global Outcomes (KDIGO) as well as the National Kidney Foundation and Food and Drug Administration guidance on surrogate end points for CKD progression [72,73]. In each of the below definitions, for the purposes of this study, the term “sustained” means that at an individual participant level, the outcome is present on testing both the sample collected in the 2021−25 HLM LTC survey, and on the sample collected in the currently described 2024−25 Impso study, when compared to testing of the individual-level paired baseline sample (2013−19). “Non-sustained” means that the outcome has been reached for the first time at the point of the most recent (Impso study) sample collection.
The primary outcome measure will be a sustained 25% reduction in eGFRcysC from baseline and change in eGFRcysC category.
The secondary outcome measures will be:
- Non-sustained 25% reduction in eGFRcysC from baseline and change in eGFRcysC category
- (a) Sustained and (b) non-sustained 25% reduction in eGFRcreat from baseline and change in eGFRcreat category
- (a) Sustained and (b) non-sustained decline in (a) eGFRcysC and (b) eGFRcreat by:
- 57% (equates to doubling of serum creatinine) [73]
- 40%
- 30%
- Estimated annual decline* in (a) eGFRcysC and (b) eGFRcreat of:
- -5 ml/min/1.73m2/year
- -3 ml/min/1.73m2/year
*(Data from other settings suggests that age-related GFR decline in healthy adults without hypertension is no more than −1.07 mL/min/1.73m2/year) [74–76]
- 5. Incident CKD, defined using eGFR criteria as:
- (a) Sustained and (b) non-sustained new eGFR < 60ml/min/1.73m2 (not present at baseline) using both eGFRcysC and eGFRcreat
- 6. Kidney failure, defined as:
- (a) Sustained and (b) non-sustained eGFRcysC < 15ml/min/1.73m2; or
- (a) Sustained and (b) non-sustained eGFRcreat <15ml/min/1.73m2; or
- Commencement of maintenance KRT (≥4 weeks duration)**
**(Commencement of maintenance KRT forms part of the KDIGO definition of kidney failure, however we do not expect to find any individuals meeting this outcome criterion in our cohort).
Statistical analysis
Logistic regression (univariable and multivariable models) will be used to determine effect size estimates (odds ratios) of key covariates (risk factors) for kidney function decline (primary and secondary outcomes) to interrogate how much of kidney disease progression is explained by potential novel risk factors of interest, such as self-reported history of severe malaria or sepsis, HIV infection, previous TB or schistosomiasis infection, use of non-steroidal anti-inflammatory medications, use of traditional medicines, use of antibiotics, pregnancy-related complications and occupational exposures. Adjustment will be made for traditional risk factors (hypertension, diabetes, smoking and cardiovascular disease), established confounders (age, sex, body mass index, socioeconomic status) and for baseline eGFR. Potential interaction between sex and the effect of risk factors on outcome measures will be examined, as there is evidence from other settings that sex may modulate the relationship with renal function decline, in particular cardiometabolic risk factors [77]. Heterogeneity by age in the associations with outcome measures will also be investigated. In addition linear mixed effects models will be used to determine eGFR trajectories over time and estimate differences in eGFR slopes according to baseline risk factors [78].
Ethics, governance and regulations
Ethical approval and informed consent.
This study was approved by the National Health Sciences Research Committee (NHSRC) in Malawi (protocol #23/04/4049) and by the Liverpool School of Tropical Medicine, UK (protocol #23−019). All participants provided written, informed consent to participate.
Feedback of individual participant test results and clinical referral.
Individual urine dipstick results are communicated to study participants immediately after testing and are recorded in health passports. Participants with abnormal baseline urine results requiring follow-up urine collection (see ‘Biological Sample Collection’), receive results of the laboratory urine microscopy at the follow-up visit.
Individual baseline eGFR results are communicated to participants at the point of recruitment, accompanied by provision of general CKD lifestyle advice using a standardised information sheet. Repeat kidney function results (eGFR, uACR) obtained by testing of serum and urine samples collected in this study, and on paired LTC samples, as well as remaining normal urine test results, will be communicated once recruitment and testing is complete. S3 File outlines the indications for clinical referral and assessment. Clinical assessment will take place at an NCD clinic at Chilumba Rural Hospital, which was established by MEIRU to support the wider HLM LTC survey. Any onward referrals required to secondary or tertiary facilities will take place via existing referral pathways.
Study governance and quality control.
The study field staff all underwent Good Clinical Practice training in addition to formal training in all study activities as outlined in the Standard Operating Procedures prior to study initiation. This included formal training in study information, informed consent, survey administration, electronic data capture, biological sample collection and transportation. Intermittent spot checks are carried out of field data collection activities, and regular team meetings are held to facilitate discussion of study progress, challenges in the field, and for performance-based feedback based on review of incoming study data. Photographs of a random 10% sample of urine dipsticks will be reviewed by the investigator team. In the laboratory, calibrators and controls will be used to ensure reliability and consistency of assay results; in addition to use of standard operating procedures for sample processing (see ‘Laboratory Methods, Quality control’). Spot checks are carried out to ensure the temperature of the cool boxes used for sample transportation are maintained within the required range.
Patient and public involvement
Prior to initiation of this study, approval for the study was granted by the Karonga District Health Office, and meetings were held with community leaders through the Area Development Committee. HDSS residents had been engaged at an individual- and community-levels on all aspects of the wider MEIRU programme of census activities, HLM LTC survey and its nested studies, including this study, through a series of open community meetings to raise awareness and provide opportunities for questions and discussion. Additional meetings with group village heads and community representatives of each area of the HDSS due to be specifically visited by this study took place one to two weeks before initiation of the study in that particular area, to discuss finer details of this study, enabling wider dissemination of information by community representatives and providing a more focused opportunity for questions and concerns to be raised and addressed.
On completion of the study, community meetings will be held to feedback the results of the study and wider HLM LTC survey, including with the more recently formed community advisory group. This will include opportunity for questions, discussion, and priorities to be raised with regards to future research directions.
Discussion
The methodology of the Impso study, an ongoing prospective population cohort study aiming to determine risk factors for progressively impaired kidney function amongst adults living in rural northern Malawi, is described.
This study has been designed as a prospective cohort study nested within two large, rural population-based surveys of long-term health conditions, in order to leverage existing longitudinal follow-up data, including stored paired, individual-level serum samples. The overall study’s sample size is limited by the available resources (time and funds). This has necessitated convenience sampling of individuals within the HDSS with pre-existing baseline eGFR results available, calculated from serum cystatin C and creatinine already tested on serum samples collected in previous studies. In order to ensure the internal and external validity of the study findings, the characteristics of individuals who participate in the current study will be compared to those of the general population of the HDSS. We have already compared the characteristics of individuals with and without availability of existing cystatin C test results within the wider HDSS adult population [S4 Table].
We are targeting recruitment to individuals with a baseline eGFRcysC less than 90 ml/min/1.73m2 in order to prioritise inclusion of individuals with existing evidence of at least mildly impaired kidney function. It is known from other settings that the risks of adverse clinical outcomes including incident kidney failure and mortality are highest in individuals with lower baseline eGFR, specifically those with a baseline eGFR less than 60 ml/min/1.73m2, proteinuria, and history of prior rapid eGFR decline, amongst other clinical risk factors [79–82]. In some settings, studies of older adults and people living with HIV, have also suggested an increased risk of CKD development among individuals with baseline eGFR between 60 and 90 ml/min/1.73m2 compared to eGFR ≥ 90 ml/min/1.73m2 [83,84]. It is not yet known if the clinical cut-offs used to define normal range for eGFR (≥ 60 ml/min/1.73m2) and CKD classification are appropriate in African settings [13,85]. Targeting inclusion to individuals with eGFRcysC < 90 ml/min/1.73m2 will allow us to interrogate in more detail the differences in clinical outcomes between individuals with baseline eGFR either side of established classification values. Inclusion of individuals with eGFR < 90 ml/min/1.73m2 is also of more clinical relevance as most laboratories do not routinely report actual eGFR values that are ≥ 90 ml/min/1.73m2 [86,87].
It is important to note that in some study populations, higher baseline eGFR attributed to hyperfiltration can be associated with rapid eGFR decline [88–91]. We may therefore fail to capture some of these high-risk individuals in our study. However, this phenomenon is mostly observed in specific conditions such as diabetes and metabolic syndrome, and not necessarily in other patient groups [92,93]. We have already compared the baseline characteristics of individuals with baseline eGFRcysC < 90 ml/min/1.73m2 and (≥ 90 ml/min/1.73m2) [S5 Table]. To build understanding of longer-term health of those not recruited, we plan to investigate differences in clinical characteristics, eGFR measures and all-cause mortality (to be reported in a separate survival analysis) between individuals recruited and not recruited to the study. We will be adjusting for baseline eGFR in our analysis. Furthermore, although our recruitment criteria are based on baseline eGFRcysC criteria, we also plan to look at a range of outcome measures using eGFRcreat, meaning that we will have outcome data for individuals with higher starting eGFR within the eGFRcreat analysis, and will also be able to interrogate outcome measures according to baseline eGFRdiff) [94,95].
Overall the inclusion criteria for this study are broad, including measures to facilitate inclusion of adults with reduced mental capacity to provide consent, for example due to mental health conditions, learning disability and dementia; often neglected groups in clinical research [96]. Exclusion of individuals pregnant at the time of either historical (baseline) blood sampling or at the time of the current study is due to the known temporary physiological changes in eGFR that occur during pregnancy. Inclusion of these individuals would limit conclusions about overall longitudinal trends in eGFR using on the available blood samples collected at these time points. For individuals hospitalised for over 24 hours, biological sample collection will be delayed until three months post-discharge due to the known high prevalence of acute kidney injury among hospital inpatients, including in low-resource settings – often undiagnosed [97].
Strengths of this study include the relatively long duration of follow-up (anticipated to be over nine years on average), and richness of paired, individual-level participant risk factor data from linked studies including sociodemographic, anthropometric, clinical and biochemical data. We anticipate a high (>85%) participation rate in line with that seen in other studies conducted in the Karonga HDSS [61]. A particular strength of this study is that it is the first study use of both cystatin C- and creatinine-based eGFR to examine predictors for longitudinal decline in kidney function in an African setting. This is important because creatinine-based estimates significantly underestimate the burden of CKD in African settings, including in Malawi, when compared to Iohexol plasma clearance mGFR; in contrast, cystatin C based eGFR more closely mirrors mGFR [13]. Use of both measures will therefore enable more accurate determination of longitudinal trends in eGFR decline, and association with different risk factors, in this Malawian setting, and to compare findings using the two methods for estimating GFR.
This study also has limitations. Being conducted in rural Karonga, findings may not be generalisable to other regions of Malawi, or to other African settings, in particular urban areas, although over 80% of Malawi’s population live in rural areas [98]. In addition, a maximum of three longitudinal eGFR measures per participant will be available, limiting the ability to precisely determine annual rate of eGFR decline. However, we will be able to assess a range internationally-recognised outcome measures for sustained eGFR decline and CKD progression, as well as new development of CKD, due to the fact that the two most recent measures for each participant will be collected more than three months apart. This is in contrast to many cross-sectional studies of CKD prevalence conducted in sub-Saharan Africa, which have commonly had to rely on a single eGFR measures, limiting ability to distinguish between acute and chronic kidney disease [8–10].
This study will be limited by survivorship bias, as individuals who are older and those who have more advanced kidney disease at baseline, including those who have already progressed to untreated kidney failure, are more likely to have died during the years preceding this study. The CKD risk factors we can interrogate will be limited by availability of measurements and information that can be collected without limitation by significant information bias. Pregnancy status will be based on self-report rather than testing so it is possible that some individuals in early pregnancy may be recruited into the study. Furthermore, as this is set in an open population cohort, some potentially eligible individuals may have left the area. The fact that rich baseline data are available for all individuals will allow interrogation of differences between individuals who do, and do not, participate or meet eligibility for the study.
The sample size for this study is relatively small, albeit adequate to detect reasonable effect size estimates (OR 1.66–4.38) for the association of different risk factors with progressive decline in kidney function. There is limited power to detect more modest effects, as well as the effects of less common risk factors. Our outcome measures for CKD progression are guided by those recommended by KDIGO however it is important to note that these were not derived from African populations.
Finally, the use of the Jaffe colorimetric kinetic method for quantification of serum creatinine, with its inferior accuracy to the recommended enzymatic method [39,99,100] is a limitation. The decision to use this method is based on the fact that the individual paired serum samples for study participants collected in the earlier linked studies have already been tested using the Jaffe method – as occurs in over 90% of studies from Africa due to resource limitations [10]. In order to determine eGFR change over time, the same quantification method must therefore be used to test the newly collected serum samples in this study [101]. In mitigation, the primary outcome measure of this study will use eGFRcysC rather than eGFRcreat. Furthermore, serum samples will be centrifuged within a maximum of eight hours from the time of collection; well within the recommended 24-hour window after which delay in sample separation can add significant positive bias when using the Jaffe method [102,103]. The consent obtained for long-term storage of all serum samples will enable testing and analyses using the enzymatic method for creatinine quantification to be conducted as part of future work.
In conclusion, the Impso study will be one of very few longitudinal studies to date to study risk factors for progressive decline in kidney function in a low-resource, rural, population-based African setting, and the first in this setting to use the more sensitive kidney function biomarker, cystatin C, to examine longitudinal eGFR trajectories. This study is likely to contribute important data about individuals at the highest risk of rapid eGFR decline and CKD progression in rural Malawi, markers for multiple adverse health outcomes [79,82]. This will inform future research into specific risk factor exposures of importance, and could directly inform future health policies in Malawi for targeting CKD screening, prevention and treatment strategies to the highest risk patient groups.
Supporting information
S1 File. Participant mid-stream urine sample collection instructions (English version).
https://doi.org/10.1371/journal.pone.0329042.s001
(PDF)
S2 File. eGFR equations for use in planned analyses.
https://doi.org/10.1371/journal.pone.0329042.s002
(.PDF)
S3 File. Indications for referral for clinical assessment.
https://doi.org/10.1371/journal.pone.0329042.s003
(PDF)
S4 Table. Comparison of sociodemographic and clinical characteristics of adults aged ≥ 18 years living within the Karonga HDSS, with and without availability of cystatin C tested on stored serum samples.
https://doi.org/10.1371/journal.pone.0329042.s004
(PDF)
S5 Table. Baseline sociodemographic and clinical characteristics of adults with cystatin C tested on stored serum samples, according to baseline eGFRcysC.
https://doi.org/10.1371/journal.pone.0329042.s005
(PDF)
S8 File. Inclusivity in global research questionnaire.
https://doi.org/10.1371/journal.pone.0329042.s008
(PDF)
Acknowledgments
CMS is also affiliated with Malawi Epidemiology and Intervention Research Unit (MEIRU) and with the Malawi Liverpool Wellcome Research Programme, Blantyre, Malawi.
We thank participants of all the named studies in addition to the wider MEIRU research, management and support teams for their contribution. We thank Professor Segun Fatumo (Queen Mary, University of London) for his support in provision of serum creatinine and cystatin C assays. We acknowledge the wider ARK consortium for their work pre-dating this study and ongoing collaboration. CMS also thanks the Liverpool Clinical PhD Programme for Health Priorities in the Global South (Professor Neil French, Elly Wallis, and steering group) for their support and the Liverpool School of Tropical Medicine for sponsorship of this study.
References
- 1. GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709–33. pmid:32061315
- 2. Foreman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories. Lancet. 2018;392(10159):2052–90. pmid:30340847
- 3.
World Health Organization. Resolution EB156(20): Reducing the burden of noncommunicable diseases through promotion of kidney health and strengthening prevention and control of kidney disease. Geneva: World Health Organization; 2025.
- 4. Mills KT, Xu Y, Zhang W, Bundy JD, Chen C-S, Kelly TN, et al. A systematic analysis of worldwide population-based data on the global burden of chronic kidney disease in 2010. Kidney Int. 2015;88(5):950–7. pmid:26221752
- 5. Okpechi IG, Bello AK, Ameh OI, Swanepoel CR. Integration of care in management of CKD in resource-limited settings. Semin Nephrol. 2017;37(3):260–72. pmid:28532555
- 6. Bello AK, Levin A, Lunney M, Osman MA, Ye F, Ashuntantang GE, et al. Status of care for end stage kidney disease in countries and regions worldwide: international cross sectional survey. BMJ. 2019;367:l5873. pmid:31672760
- 7. Bello AK, Okpechi IG, Levin A, Ye F, Damster S, Arruebo S, et al. An update on the global disparities in kidney disease burden and care across world countries and regions. Lancet Glob Health. 2024;12(3):e382–95. pmid:38365413
- 8. Kaze AD, Ilori T, Jaar BG, Echouffo-Tcheugui JB. Burden of chronic kidney disease on the African continent: a systematic review and meta-analysis. BMC Nephrol. 2018;19(1):125. pmid:29859046
- 9. Stanifer JW, Jing B, Tolan S, Helmke N, Mukerjee R, Naicker S, et al. The epidemiology of chronic kidney disease in sub-Saharan Africa: a systematic review and meta-analysis. Lancet Glob Health. 2014;2(3):e174-81. pmid:25102850
- 10. Fabian J, George JA, Etheredge HR, van Deventer M, Kalyesubula R, Wade AN, et al. Methods and reporting of kidney function: a systematic review of studies from sub-Saharan Africa. Clin Kidney J. 2019;12(6):778–87. pmid:31807291
- 11. Htay H, Alrukhaimi M, Ashuntantang GE, Bello AK, Bellorin-Font E, Benghanem Gharbi M, et al. Global access of patients with kidney disease to health technologies and medications: findings from the Global Kidney Health Atlas project. Kidney Int Suppl (2011). 2018;8(2):64–73. pmid:30675440
- 12. Chikowe I, Mwapasa V, Kengne AP. Analysis of rural health centres preparedness for the management of diabetic patients in Malawi. BMC Res Notes. 2018;11(1):267. pmid:29720279
- 13. Fabian J, Kalyesubula R, Mkandawire J, Hansen CH, Nitsch D, Musenge E, et al. Measurement of kidney function in Malawi, South Africa, and Uganda: a multicentre cohort study. Lancet Glob Health. 2022;10(8):e1159–69. pmid:35839814
- 14. Bukabau JB, Yayo E, Gnionsahé A, Monnet D, Pottel H, Cavalier E, et al. Performance of creatinine- or cystatin C-based equations to estimate glomerular filtration rate in sub-Saharan African populations. Kidney Int. 2019;95(5):1181–9. pmid:30910379
- 15. Wyatt CM, Schwartz GJ, Owino Ong’or W, Abuya J, Abraham AG, Mboku C, et al. Estimating kidney function in HIV-infected adults in Kenya: comparison to a direct measure of glomerular filtration rate by iohexol clearance. PLoS One. 2013;8(8):e69601. pmid:23950899
- 16. Inker LA, Eneanya ND, Coresh J, Tighiouart H, Wang D, Sang Y, et al. New creatinine- and cystatin c-based equations to estimate gfr without race. N Engl J Med. 2021;385(19):1737–49.
- 17. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12. pmid:19414839
- 18. Eastwood JB, Kerry SM, Plange-Rhule J, Micah FB, Antwi S, Boa FG, et al. Assessment of GFR by four methods in adults in Ashanti, Ghana: the need for an eGFR equation for lean African populations. Nephrol Dial Transplant. 2010;25(7):2178–87. pmid:20100724
- 19. Nakanga WP, Prynn JE, Banda L, Kalyesubula R, Tomlinson LA, Nyirenda M, et al. Prevalence of impaired renal function among rural and urban populations: findings of a cross-sectional study in Malawi. Wellcome Open Res. 2019;4:92. pmid:31656860
- 20. Kalyesubula R, Hau JP, Asiki G, Ssebunya B, Kusemererwa S, Seeley J, et al. Impaired renal function in a rural Ugandan population cohort. Wellcome Open Res. 2019;3:149. pmid:31223661
- 21. Kolliesuah NP, Olum S, Ongeng D. Status of household dietary diversity and associated factors among rural and urban households of Northern Uganda. BMC Nutr. 2023;9(1):83. pmid:37430346
- 22. Tesema GA, Yeshaw Y, Worku MG, Tessema ZT, Teshale AB. Pooled prevalence and associated factors of chronic undernutrition among under-five children in East Africa: a multilevel analysis. PLoS One. 2021;16(3):e0248637. pmid:33765094
- 23. Kimani-Murage EW, Kahn K, Pettifor JM, Tollman SM, Dunger DB, Gómez-Olivé XF, et al. The prevalence of stunting, overweight and obesity, and metabolic disease risk in rural South African children. BMC Public Health. 2010;10:158. pmid:20338024
- 24. Spearman CW, Sonderup MW. Health disparities in liver disease in sub-Saharan Africa. Liver Int. 2015;35(9):2063–71. pmid:26053588
- 25. Fatumo S, Chikowore T, Kalyesubula R, Nsubuga RN, Asiki G, Nashiru O, et al. Discovery and fine-mapping of kidney function loci in first genome-wide association study in Africans. Hum Mol Genet. 2021;30(16):1559–68. pmid:33783510
- 26. Li X, Lindholm B. Cardiovascular risk prediction in chronic kidney disease. Am J Nephrol. 2022;53(10):730–9. pmid:36481730
- 27. Tonelli M, Nkunu V, Varghese C, Abu-Alfa AK, Alrukhaimi MN, Fox L, et al. Framework for establishing integrated kidney care programs in low- and middle-income countries. Kidney Int Suppl (2011). 2020;10(1):e19–23. pmid:32149006
- 28. Tonelli M, Wiebe N, Culleton B, House A, Rabbat C, Fok M, et al. Chronic kidney disease and mortality risk: a systematic review. J Am Soc Nephrol. 2006;17(7):2034–47. pmid:16738019
- 29. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu C. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351(13):1296–305. pmid:15385656
- 30. Jha V, Garcia-Garcia G, Iseki K, Li Z, Naicker S, Plattner B, et al. Chronic kidney disease: global dimension and perspectives. Lancet. 2013;382(9888):260–72. pmid:23727169
- 31. Chronic Kidney Disease Prognosis Consortium, Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010;375(9731):2073–81. pmid:20483451
- 32. Perico N, Remuzzi G. Prevention programs for chronic kidney disease in low-income countries. Intern Emerg Med. 2016;11(3):385–9. pmid:26983956
- 33. Bottomley MJ, Kalachik A, Mevada C, Brook MO, James T, Harden PN. Single estimated glomerular filtration rate and albuminuria measurement substantially overestimates prevalence of chronic kidney disease. Nephron Clin Pract. 2011;117(4):c348-52. pmid:20948233
- 34. Levin A, Okpechi IG, Caskey FJ, Yang C-W, Tonelli M, Jha V. Perspectives on early detection of chronic kidney disease: the facts, the questions, and a proposed framework for 2023 and beyond. Kidney Int. 2023;103(6):1004–8. pmid:37125982
- 35. Cusick MM, Tisdale RL, Chertow GM, Owens DK, Goldhaber-Fiebert JD. Population-wide screening for chronic kidney disease: a cost-effectiveness analysis. Ann Intern Med. 2023;176(6):788–97. pmid:37216661
- 36.
International Society of Nephrology. CKD Early Identification and Intervention Toolkit. https://www.theisn.org/initiatives/toolkits/ckd-early-screening-intervention/]
- 37. Komenda P, Ferguson TW, Macdonald K, Rigatto C, Koolage C, Sood MM, et al. Cost-effectiveness of primary screening for CKD: a systematic review. Am J Kidney Dis. 2014;63(5):789–97. pmid:24529536
- 38. Shlipak MG, Tummalapalli SL, Boulware LE, Grams ME, Ix JH, Jha V, et al. The case for early identification and intervention of chronic kidney disease: conclusions from a kidney disease: improving global outcomes (KDIGO) controversies conference. Kidney Int. 2021;99(1):34–47. pmid:33127436
- 39. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int. 2024;105(4S):S117–314. pmid:38490803
- 40. Luyckx VA, Al-Aly Z, Bello AK, Bellorin-Font E, Carlini RG, Fabian J, et al. Sustainable development goals relevant to kidney health: an update on progress. Nat Rev Nephrol. 2021;17(1):15–32. pmid:33188362
- 41. Stanifer JW, Muiru A, Jafar TH, Patel UD. Chronic kidney disease in low- and middle-income countries. Nephrol Dial Transplant. 2016;31(6):868–74. pmid:27217391
- 42. Slama S, Luyckx VA, Hemmingsen B. Kidney health within the broader non-communicable disease agenda. Nat Rev Nephrol. 2024;20(7):424–5. pmid:38760475
- 43. Kalyesubula R, Sekitoleko I, Tomlin K, Hansen CH, Ssebunya B, Makanga R, et al. Association of impaired kidney function with mortality in rural Uganda: results of a general population cohort study. BMJ Open. 2022;12(4):e051267. pmid:35473721
- 44. Perico N, Bravo RF, De Leon FR, Remuzzi G. Screening for chronic kidney disease in emerging countries: feasibility and hurdles. Nephrol Dial Transplant. 2009;24(5):1355–8. pmid:19218536
- 45. Fabian J, Gondwe M, Mayindi N, Chipungu S, Khoza B, Gaylard P, et al. Chronic kidney disease (CKD) and associated risk in rural South Africa: a population-based cohort study. Wellcome Open Res. 2022;7:236. pmid:36457874
- 46. Muiru AN, Charlebois ED, Balzer LB, Kwarisiima D, Elly A, Black D, et al. The epidemiology of chronic kidney disease (CKD) in rural East Africa: a population-based study. PLoS One. 2020;15(3):e0229649. pmid:32130245
- 47. Stanifer JW, Maro V, Egger J, Karia F, Thielman N, Turner EL, et al. The epidemiology of chronic kidney disease in Northern Tanzania: a population-based survey. PLoS One. 2015;10(4):e0124506. pmid:25886472
- 48. Tangri N, Grams ME, Levey AS, Coresh J, Appel LJ, Astor BC, et al. Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta-analysis. JAMA. 2016;315(2):164–74. pmid:26757465
- 49. Matsushita K, Ballew SH, Astor BC, Jong PE de, Gansevoort RT, Hemmelgarn BR, et al. Cohort profile: the chronic kidney disease prognosis consortium. Int J Epidemiol. 2013;42(6):1660–8. pmid:23243116
- 50. Meremo A, Paget G, Duarte R, Bintabara D, Naicker S. Progression of chronic kidney disease among black patients attending a tertiary hospital in Johannesburg, South Africa. PLoS One. 2023;18(2):e0276356. pmid:36780543
- 51. Patrice HM, Pascal KA, François KF, Hilaire D, Solange DM, Gloria AE, et al. Markers and risk factors for chronic kidney disease in sub-Saharan Africans: baseline levels and 12-month trajectories in newly referred patients in Cameroon. BMC Nephrol. 2020;21(1):101. pmid:32188410
- 52. Ofori E, Gyan KF, Gyabaah S, Nguah SB, Sarfo FS. Predictors of rapid progression of estimated glomerular filtration rate among persons living with diabetes and/or hypertension in Ghana: findings from a multicentre study. J Clin Hypertens (Greenwich). 2022;24(10):1358–69. pmid:36067082
- 53. Blazer A, Dey ID, Nwaukoni J, Reynolds M, Ankrah F, Algasas H, et al. Apolipoprotein L1 risk genotypes in Ghanaian patients with systemic lupus erythematosus: a prospective cohort study. Lupus Sci Med. 2021;8(1):e000460. pmid:33461980
- 54. Kaboré NF, Poda A, Zoungrana J, Da O, Ciaffi L, Semdé A, et al. Chronic kidney disease and HIV in the era of antiretroviral treatment: findings from a 10-year cohort study in a west African setting. BMC Nephrol. 2019;20(1):155. pmid:31064340
- 55. Chadwick DR, Barker F, Smith C, Perditer O, Hardy Y, Owusu D, et al. Prevalence and predictors of long-term progression of chronic kidney disease in people with HIV in Ghana from 2003-2018. BMC Nephrol. 2024;25(1):241. pmid:39075393
- 56. Roberts NLS, Fadhil S, Willkens M, Ruselu G, Desderius B, Kanenda S, et al. HIV and CKD in the tenofovir era: a prospective parallel-group cohort study from Tanzania. Kidney Med. 2024;7(1):100937. pmid:39790232
- 57. Wu E-L, Christian B, Rivera AS, Fabian E, Macha I, Aris E, et al. Renal outcomes in adults with HBV, HIV and HBV/HIV coinfection after 3 years of antiviral therapy in urban Tanzania. J Antimicrob Chemother. 2024;79(1):36–45. pmid:37935111
- 58. Chikwapulo B, Ngwira B, Sagno JB, Evans R. Renal outcomes in patients initiated on tenofovir disoproxil fumarate-based antiretroviral therapy at a community health centre in Malawi. Int J STD AIDS. 2018;29(7):650–7. pmid:29334883
- 59. Crampin AC, Dube A, Mboma S, Price A, Chihana M, Jahn A, et al. Profile: the karonga health and demographic surveillance system. Int J Epidemiol. 2012;41(3):676–85. pmid:22729235
- 60. Crampin AC, Kayuni N, Amberbir A, Musicha C, Koole O, Tafatatha T, et al. Hypertension and diabetes in Africa: design and implementation of a large population-based study of burden and risk factors in rural and urban Malawi. Emerg Themes Epidemiol. 2016;13:3. pmid:26839575
- 61. Price AJ, Crampin AC, Amberbir A, Kayuni-Chihana N, Musicha C, Tafatatha T, et al. Prevalence of obesity, hypertension, and diabetes, and cascade of care in sub-Saharan Africa: a cross-sectional, population-based study in rural and urban Malawi. Lancet Diabetes Endocrinol. 2018;6(3):208–22. pmid:29371076
- 62.
Malawi epidemiology and intervention research unit. Healthy lives Malawi. https://www.meiru.info/healthy-lives-malawi/]
- 63. Masina T, Chimera B, Kamponda M, Dreyer G. Health related quality of life in patients with end stage kidney disease treated with haemodialysis in Malawi: a cross sectional study. BMC Nephrol. 2016;17(1):61. pmid:27389582
- 64. Chokotho L, Mkandawire N, Conway D, Wu H-H, Shearer DD, Hallan G, et al. Validation and reliability of the Chichewa translation of the EQ-5D quality of life questionnaire in adults with orthopaedic injuries in Malawi. Malawi Med J. 2017;29(2):84–8. pmid:28955412
- 65.
Malawi Ministry of Health. HIV, Syphilis and Hepatitis B Integrated Rapid Testing and Counselling Guidelines and Standard Operating Procedures (1st edition). 2023.
- 66. Hartung C, Lerer A, Anokwa Y, Tseng C, Brunette W, Borriello G. Open data kit: tools to build information services for developing regions. Proceedings of the 4th ACM/IEEE international conference on information and communication technologies and development; 2010.
- 67.
Open data kit software.
- 68. Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367(1):20–9. pmid:22762315
- 69. Pottel H, Hoste L, Dubourg L, Ebert N, Schaeffner E, Eriksen BO, et al. An estimated glomerular filtration rate equation for the full age spectrum. Nephrol Dial Transplant. 2016;31(5):798–806. pmid:26932693
- 70. Pottel H, Björk J, Courbebaisse M, Couzi L, Ebert N, Eriksen BO, et al. Development and validation of a modified full age spectrum creatinine-based equation to estimate glomerular filtration rate: a cross-sectional analysis of pooled data. Ann Intern Med. 2021;174(2):183–91. pmid:33166224
- 71. Pottel H, Björk J, Rule AD, Ebert N, Eriksen BO, Dubourg L, et al. Cystatin C-based equation to estimate GFR without the inclusion of race and sex. N Engl J Med. 2023;388(4):333–43. pmid:36720134
- 72. Levin A, Agarwal R, Herrington WG, Heerspink HL, Mann JFE, Shahinfar S, et al. International consensus definitions of clinical trial outcomes for kidney failure: 2020. Kidney Int. 2020;98(4):849–59. pmid:32998816
- 73. Badve SV, Palmer SC, Hawley CM, Pascoe EM, Strippoli GFM, Johnson DW. Glomerular filtration rate decline as a surrogate end point in kidney disease progression trials. Nephrol Dial Transplant. 2016;31(9):1425–36. pmid:26163881
- 74. Guppy M, Thomas ET, Glasziou P, Clark J, Jones M, O’Hara DV, et al. Rate of decline in kidney function with age: a systematic review. BMJ Open. 2024;14(11):e089783. pmid:39609029
- 75. Correction: sex differences in age-related loss of kidney function. J Am Soc Nephrol. 2024;35(8):1137. pmid:38652688
- 76. Melsom T, Norvik JV, Enoksen IT, Stefansson V, Mathisen UD, Fuskevåg OM, et al. Sex differences in age-related loss of kidney function. J Am Soc Nephrol. 2022;33(10):1891–902. pmid:35977806
- 77. Sullivan MK, Lees JS, Rosales BM, Cutting R, Wyld ML, Woodward M, et al. Sex and the relationship between cardiometabolic risk factors and estimated GFR decline: a population-based cohort study. Am J Kidney Dis. 2024;84(6):731-741.e1. pmid:39053834
- 78. Shou H, Hsu JY, Xie D, Yang W, Roy J, Anderson AH, et al. Analytic considerations for repeated measures of eGFR in cohort studies of CKD. Clin J Am Soc Nephrol. 2017;12(8):1357–65. pmid:28751576
- 79. Writing group for the CKD Prognosis Consortium, Grams ME, Coresh J, Matsushita K, Ballew SH, Sang Y, et al. Estimated glomerular filtration rate, albuminuria, and adverse outcomes: an individual-participant data meta-analysis. JAMA. 2023;330(13):1266–77.
- 80. Major RW, Shepherd D, Medcalf JF, Xu G, Gray LJ, Brunskill NJ. Correction: the kidney failure risk equation for prediction of end stage renal disease in UK primary care: an external validation and clinical impact projection cohort study. PLoS Med. 2020;17(7):e1003313. pmid:32706776
- 81. Van Pottelbergh G, Bartholomeeusen S, Buntinx F, Degryse J. The evolution of renal function and the incidence of end-stage renal disease in patients aged ≥ 50 years. Nephrol Dial Transplant. 2012;27(6):2297–303. pmid:22102615
- 82. Kovesdy CP, Coresh J, Ballew SH, Woodward M, Levin A, Naimark DM, et al. Past decline versus current eGFR and subsequent ESRD Risk. J Am Soc Nephrol. 2016;27(8):2447–55.
- 83. Ibrahim F, Hamzah L, Jones R, Nitsch D, Sabin C, Post FA, et al. Baseline kidney function as predictor of mortality and kidney disease progression in HIV-positive patients. Am J Kidney Dis. 2012;60(4):539–47. pmid:22521282
- 84. Drak D, Mcmanus H, Vickers T, Heron JE, Vaccher S, Zablotska I, et al. Renal impairment in a large-scale HIV preexposure prophylaxis implementation cohort. AIDS. 2021;35(14):2319–26. pmid:34310371
- 85. Nasuuna EM, Tomlinson LA, Kalyesubula R, Dziva Chikwari C, Castelnuovo B, Manabe YC, et al. Comparison of the prevalence and associated factors of chronic kidney disease diagnosed by serum creatinine or cystatin C among young people living with HIV in Uganda. BMC Nephrol. 2024;25(1):422. pmid:39587464
- 86. Mathew TH, Johnson DW, Jones GRD, Australasian Creatinine Consensus Working Group. Chronic kidney disease and automatic reporting of estimated glomerular filtration rate: revised recommendations. Med J Aust. 2007;187(8):459–63. pmid:17937643
- 87.
National Institute for Health and Care Excellence. NICE guideline NG203. Chronic kidney disease: assessment and management; 2021.
- 88. The PLOSONE Staff. Correction: longitudinal study of the decline in renal function in healthy subjects. PLoS One. 2015;10(8):e0135992.
- 89. Fujisawa T, Suzuki S, Arita T, Yagi N, Otsuka T, Kano H, et al. Decline in eGFR over time and incidence of cardiovascular events: Shinken database analysis. J Cardiol. 2021;77(6):626–33. pmid:33386217
- 90. Bjornstad P, Cherney DZ, Snell-Bergeon JK, Pyle L, Rewers M, Johnson RJ, et al. Rapid GFR decline is associated with renal hyperfiltration and impaired GFR in adults with Type 1 diabetes. Nephrol Dial Transplant. 2015;30(10):1706–11. pmid:26050268
- 91. Bystad EW, Stefansson VTN, Eriksen BO, Melsom T. The association between metabolic syndrome, hyperfiltration, and long-term GFR decline in the general population. Kidney Int Rep. 2023;8(9):1831–40. pmid:37705899
- 92. Rifkin DE, Shlipak MG, Katz R, Fried LF, Siscovick D, Chonchol M, et al. Rapid kidney function decline and mortality risk in older adults. Arch Intern Med. 2008;168(20):2212–8. pmid:19001197
- 93. Lin Y-C, Lai T-S, Lin S-L, Chen Y-M, Chu T-S, Tu Y-K. The impact of baseline glomerular filtration rate on subsequent changes of glomerular filtration rate in patients with chronic kidney disease. Sci Rep. 2021;11(1):7894. pmid:33846427
- 94. Carrero JJ, Fu EL, Sang Y, Ballew S, Evans M, Elinder CG, et al. Discordances between creatinine- and cystatin C-based estimated GFR and adverse clinical outcomes in routine clinical practice. Am J Kidney Dis. 2023;82(5):534–42.
- 95. Lees JS, Fabian J, Shlipak MG. Cystatin C should be routinely available for estimating kidney function. Curr Opin Nephrol Hypertens. 2024;33(3):337–43. pmid:38411195
- 96. Shepherd V, Wood F, Griffith R, Sheehan M, Hood K. Protection by exclusion? The (lack of) inclusion of adults who lack capacity to consent to research in clinical trials in the UK. Trials. 2019;20(1):474. pmid:31382999
- 97. Evans RDR, Sharma SK, Claure-Del Granado R, Cullis B, Burdmann EA, Franca F, et al. Identification and outcomes of acute kidney disease in patients presenting in Bolivia, Brazil, South Africa, and Nepal. PLoS Med. 2024;21(11):e1004495. pmid:39541400
- 98.
The National Statistical Office, Malawi and the DHS Program, ICF. Malawi demographic and health survey 2024: key indicators report. Zomba, Malawi, and Rockville, Maryland, USA: National Statistical Office and ICF; 2024.
- 99. Currin S, Gondwe M, Mayindi N, Chipungu S, Khoza B, Khambule L, et al. Evaluating chronic kidney disease in rural South Africa: comparing estimated glomerular filtration rate using point-of-care creatinine to iohexol measured GFR. Clin Chem Lab Med. 2021;59(8):1409–20. pmid:33711217
- 100. Boutten A, Bargnoux A-S, Carlier M-C, Delanaye P, Rozet E, Delatour V, et al. Enzymatic but not compensated Jaffe methods reach the desirable specifications of NKDEP at normal levels of creatinine. Results of the French multicentric evaluation. Clin Chim Acta. 2013;419:132–5. pmid:23415696
- 101. Syme NR, Stevens K, Stirling C, McMillan DC, Talwar D. Clinical and analytical impact of moving from jaffe to enzymatic serum creatinine methodology. J Appl Lab Med. 2020;5(4):631–42. pmid:32447368
- 102. Ford L, Berg J. Delay in separating blood samples affects creatinine measurement using the Roche kinetic Jaffe method. Ann Clin Biochem. 2008;45(Pt 1):83–7. pmid:18275679
- 103. Chikomba CE, Padoa CJ, Tanyanyiwa D. Evaluation of the impact of delayed centrifugation on the diagnostic performance of serum creatinine as a baseline measure of renal function before antiretroviral treatment. South Afr J HIV Med. 2020;21(1):1056. pmid:32832109