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Kidney damage and associated risk factors in the rural Eastern Cape, South Africa: A cross-sectional study
Kidney damage and risk factors in rural South Africa
https://orcid.org/0000-0002-8811-4154
Rosales Gonzalez Ernesto Conceptualization Data curation Formal analysis Investigation Methodology Writing – original draft 1 *
Yogeswaran Parimalanie Conceptualization Formal analysis Methodology 1
Chandia Jimmy Methodology Supervision Writing – review & editing 1
Pulido Estrada Guillermo Alfredo Conceptualization Data curation Formal analysis Methodology 2
Adeniyi Oladele Vincent Supervision Writing – review & editing 3
1 Department of Family Medicine and Rural Health, Faculty of Medicine and Health Sciences, Mthatha, South Africa
2 Department of Public Health, Faculty of Medicine and Health Sciences, Mthatha, South Africa
3 Department of Family Medicine and Rural Health, Faculty of Medicine and Health Sciences, East London, South Africa
Bello Ibrahim Sebutu Editor
Osun State University, NIGERIA
Competing Interests: The authors have declared that no competing interests exist.

* E-mail: ernestorosalesgonzalez@gmail.com.
9 9 2024
2024
19 9 e029241619 9 2023
19 6 2024
© 2024 Rosales Gonzalez et al
2024
Rosales Gonzalez et al
https://creativecommons.org/licenses/by/4.0/ 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.

Background

The colliding epidemic of infectious and non-communicable diseases in South Africa could potentially increase the prevalence of kidney disease in the country. This study determines the prevalence of kidney damage and known risk factors in a rural community of the Eastern Cape province, South Africa.

Methods

This observational cross-sectional study was conducted in the outpatient department of the Mbekweni Community Health Centre in the Eastern Cape between May and July 2022. Relevant data on demography, medical history, anthropometry and blood pressure were obtained. The glomerular filtration rate was estimated using the Chronic Kidney Disease Epidemiology Collaboration Creatinine (CKD-EPICreatinine) equation and the re-expressed four-variable Modification of Diet in Renal Disease (MDRD) equation, without any adjustment for black ethnicity. Prevalence of kidney damage was defined as the proportion of individuals with low eGFR (<60mL/min per 1.73m2). The presence of proteins in the spot urine samples was determined with the use of test strips. We used the logistic regression model analysis to identify the independent risk factors for significant kidney damage.

Results

The mean (±standard deviation) age of the 389 participants was 52.3 (± 17.5) years, with 69.9% female. The prevalence of significant kidney damage was 17.2% (n = 67), as estimated by the CKD-EPICreatinine, with a slight difference by the MDRD equation (n = 69; 17.7%), while the prevalence of proteinuria was 7.2%. Older age was identified as a significant risk factor for CKD, with an odds ratio (OR) = 1.08 (95% confidence interval [CI]: 1.06–1.1, p < 0.001). Hypertension was strongly associated with proteinuria (OR = 4.17, 95% CI 1.67–10.4, p<0.001).

Conclusions

This study found a high prevalence of kidney damage (17.2%) and proteinuria (7.97%) in this rural community, largely attributed to advanced age and hypertension, respectively. Early detection of proteinuria and decreased renal function at community health centres should trigger a referral to a higher level of care for further management of patients.

Discovery Foundation Awards Award Reference: 035996 Parimalane YogeswaranThe research project was supported by the Discovery Foundation awards The Award to PY (Reference: 035996). https://www.discovery.co.za/corporate/discovery-foundation-awards The Discovery Foundation had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Data AvailabilityThe restrictions on sharing the participant's data are ethical and imposed by the Walter Sisulu University EC Research and Ethics Committee (HREC). If you have any inquiries about our ethical research guidelines, please feel free to contact Professor E. Ndevia, head of the Walter Sisulu University EC Research and Ethics Committee (HREC), at endevia@wsu.ac.za. The ethics committee also makes data from the study available upon request. Please contact Professor E. Ndevia. His contact information is endevia@wsu.ac.za.
Data Availability

The restrictions on sharing the participant's data are ethical and imposed by the Walter Sisulu University EC Research and Ethics Committee (HREC). If you have any inquiries about our ethical research guidelines, please feel free to contact Professor E. Ndevia, head of the Walter Sisulu University EC Research and Ethics Committee (HREC), at endevia@wsu.ac.za. The ethics committee also makes data from the study available upon request. Please contact Professor E. Ndevia. His contact information is endevia@wsu.ac.za.
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pmcIntroduction

Chronic kidney disease (CKD) is a growing problem in developing countries [1, 2]. In most of sub-Saharan Africa, most patients with CKD die because of a lack of adequate treatment and renal replacement therapy (RRT) [3]. RRT is very expensive, making it unaffordable to people in low- and middle-income countries. Evidence suggests an increase in the demand for RRT in South Africa, from 70 per million of the population in 1994 to 190 per million of the population in 2017, which is a more than two-fold increase [4, 5]. The true burden of CKD in South Africa is unknown, owing to a lack of nationally representative studies in the country. Evidence suggests an increasing burden of non-communicable diseases in South Africa (hypertension, obesity and Type-2 diabetes mellitus) which are known risk factors for end-stage kidney disease (ESKD), particularly in black ethnic groups [6–8].

CKD often goes unnoticed because there are no specific symptoms, leading to delays in diagnosis or diagnosis at an advanced stage. Investigations for CKD are very simple and freely available in South Africa. The diagnosis of CKD is made in the presence of a persistently low glomerular filtration rate (GFR), which is estimated from the serum creatinine concentration or proteinuria over a period of at least three months. Evidence shows unequivocally that the Chronic Kidney Disease Epidemiology Collaboration Creatinine (CKD-EPICreatinine) equation is more accurate than the previous Modification of Diet for Renal Disease equation (MDRD) [2, 9]. The diagnostic challenges of CKD cannot be ignored, especially in rural communities where timeous access to relevant tests remains a concern. It has been estimated that 10% of the world’s population has some degree of CKD, with trends suggesting an alarming increase in the incidence globally [10]. Current data estimates that about 5 million South Africans over the age of 20 years have CKD, and in black South Africans, the figure is almost certainly higher [3, 10]. In sub-Saharan Africa, the overall prevalence in the general population is 15,8% for CKD Stages 1–3 and 4.6% for Stages 3–5 in the general population [11].

By December 2012, there were 8 559 patients receiving chronic RRT in SA—6 952 on dialysis and 1 607 with a functioning kidney transplant. More than half of these patients were from the private sector, which serves less than 20% of the population. Facilities in the public sector, which not only serve more than 80% of the population but where the burden of Chronic Renal Failure (CRF) is about three times that in the private sector, are strictly limited. This means that only 15–20% of those who require RRT obtain such treatment because of limited resources. The approximate annual cost of dialysis is R200 000 per patient, and that of transplantation is R300 000 in the first year, and the cost rises to R160 000—R180 000 in subsequent years [10].

CKD complications represent a considerable burden on global healthcare resources, and only a few countries have sufficiently robust economies to meet the challenge posed by this disease [1]. As such, an intensified screening programme aimed at prioritising at-risk individuals at the population level might assist in early diagnosis and management to prevent further progression to end-stage kidney failure and mortality [4]. Without a nationally representative study sample, it is difficult to gain a full understanding of the true burden of CKD in the country. Worse still, there is a paucity of published literature on CKD among people living in rural communities, especially in the Eastern Cape province. This has serious implications for crafting an effective national health promotion and disease prevention policy for the country. In order to contribute much-needed data on the burden of CKD among rural residents, which is currently unavailable in the country, this study determined the prevalence of kidney damage and examined the associated risk factors among individuals accessing care at the rural Mbekweni Community Health Centre in the Eastern Cape province, South Africa.

Methods

Ethical considerations

This study received ethical approval from the Research Ethics Committee of the Faculty of Medicine and Health Sciences, Walter Sisulu University (Reference Number: 025/2017). Permission for the study’s implementation was granted by the Eastern Cape Department of Health, OR Tambo District Department of Health and the facility manager. Each participant gave written informed consent, indicating voluntary participation in the study. The study was implemented in accordance with the Helsinki Declaration and Good Clinical Practice Guidelines.

Study design, setting and population

This observational cross-sectional study was conducted in the outpatient department of the Mbekweni Community Health Centre (CHC) in the King Sabata Dalindyebo (KSD) sub-district of OR Tambo district in the Eastern Cape province between May and July 2022. The Eastern Cape province of South Africa, specifically its north-eastern area, known as the wild coast, encompassing the district of Mbashe, is the most deprived area in the country, with a disproportionate burden of unemployment, poverty and disease [12, 13]. The area falls below national and regional standards for clean water, employment and access to healthcare services [12, 13]. The Eastern Cape covers 13.8% of the total area of the country and is home to 12.7% of the population, which utilises 10.2% of the domestic electrification and 6.5% of the domestic piped water [13, 14]. The incidence of infectious and chronic diseases, as well as malnutrition, is higher than the national average, and the coverage of immunisation and healthcare service delivery is the lowest [14].

The Mbekweni CHC serves the deeply rural residents of the Mbekweni location, providing health care services for patients with multi-morbidity, comprising an estimated population of 24 284 inhabitants predominantly of the black ethnic group [15]. The adult population (20 years and above) of Mbekweni is estimated to be 13 716 [15]. The sample size of 389 was estimated by using the free Software Epidat 3.1. Xunta de Galicia, Spain (Pan-American Health Organization/World Health Organization), using a confidence level of 95%, an expected proportion of 10%, and a maximum error of 3%.

In order to ensure inclusivity and minimise selection bias, participants were recruited through a simple random sampling technique; a hundred numbers were given to the adults attending the outpatient department of Mbekweni CHC, and 50 numbers were chosen randomly using the free Software Epidat 3.1. Xunta de Galicia Spain (Pan-American Health Organization/World Health Organization) run on Dr ERG’s personal computer.

Participants were considered eligible if they were ≥ 20 years old at the time of the study, attending the outpatient department of Mbekweni CHC, and willing to participate in the research study. However, participants were excluded if they had acute medical (vomiting, diarrhoea, burns) or psychiatric emergencies that required urgent care at the health facility, pregnancy, or any other limitations judged to interfere with study participation or their ability to follow study procedures (cognitive impairment, depression or psychotic disorders). In addition, first-degree relatives of participants were excluded.

To avoid bias, a 40 per cent was calculated over the estimated sample, a total of 545 participants were approached at the outpatient department during the study period. Of the total (N = 545), 112 declined participations (refused consent). A total of 44 participants were excluded for different reasons; 10 had a positive pregnancy test, who were promptly referred to the antenatal clinic, 10 with urinary tract infection were treated, eight patients with Dementia, and six with moderate to severe depression, five first degree relatives of the participants, and another five with acute diarrhoea.

Study procedure

The lead author (ERG) and a research nurse trained in the study process implemented the protocol. The research nurse administered the interviewer-assisted questionnaire by reviewing the medical records and conducting the direct interviews, while ERG drew 5mL of venous blood sample from each consenting participant for additional investigation. Participants also submitted spot urine samples. Each questionnaire was issued a unique identifier code to link the participants’ data to the laboratory investigation while maintaining privacy and confidentiality of medical information. The questionnaire comprised demographic information (age and sex), medical history (family history of hypertension and/or diabetes, current hypertension, current diabetes mellitus, current CKD and HIV) and laboratory results (urine dipstick for proteinuria, blood glucose, total cholesterol, low-density lipoprotein, high-density lipoprotein triglycerides, and serum creatinine). The blood pressure, weight and height of each participant was measured by the research nurse in accordance with standard protocols. All the participants with abnormal findings (proteinuria in the urine dipstick, decreased glomerular filtration as well as dyslipidaemia were followed up for further management at the health facility by ERG.

Measures

Current kidney function was assessed by estimating the serum creatinine and confirming the presence of protein in urine samples. For the detection of proteinuria, spot urine samples were collected from the participants in specimen jars of 40mL. To avoid interpretation bias, the urine test strips were processed in a Urine Analysis Machine Model SLSSUA and interpreted as Protein Negative, Protein trace (15 mg/dL), Protein 1+ (30 mg/dL), Protein 2+ (100 mg/dL) or Protein 3+ (300 mg/dL).

To determine the serum creatinine, 5 mL of venous blood samples were drawn by ERG (a medical doctor) from each participant and sent on ice daily to the Nelson Mandela National Health Laboratory Services for processing, in accordance with standard protocols. The estimated glomerular filtration rate (eGFR) was estimated by using the Chronic Kidney Disease Epidemiology Collaboration Creatinine (CKD-EPIcreatinine) and the re-expressed four-variable Modified Diet in Renal Disease (MDRD) equations without any adjustment for black ethnicity. There is currently insufficient information in the literature about AmaXhosa people of South Africa and therefore, it is unclear which method is better at detecting kidney damage in all the diverse populations in the country [3, 16].

Participants were classified according to the Kidney Disease Outcomes Quality Initiatives CKD classification: GFR categories (ml/min/1.73m2); G1 –Normal or High ≥ 90; G2 –Mildly Decreased (60–90); G3a –Mildly to Moderately Decreased (45–59); G3b –Moderately to Severely Decreased (30–44); G4 –Severely Decreased (15–29); G5 –Kidney Failure (≤15) [17].

CKD was defined as an eGFR < 60ml/min/1.73 by CKD-Epicreatinine or the presence of kidney structural abnormalities for more than three months [18]. Hypertension (HTN) was defined as a persistent elevation of office blood pressure (BP) 140/90mmHg, in two or three readings several hours or days apart or a personal history of HTN and/or treatment with anti-hypertensive drugs [19]. Diabetes mellitus (DM) was defined as a persistent elevation of random blood sugar ≥ 11.1 mmol/l on two or more consecutive clinic visits or a history of diabetes and/or treatment with hypoglycaemic drugs [20].

Statistical analysis

Data were analysed with the IBM SPSS Statistics for Windows, Version 27.0 (IBM Corp., Armonk, New York, USA). Descriptive statistics were used to summarise the baseline characteristics and were presented as counts and frequencies for categorical variables, means (± standard deviations) for normally distributed continuous variables, and medians (interquartile range) for non-normally distributed variables. The chi-squared test was used in the bivariate analysis to identify the association between baseline characteristics and CKD. Significant associations between the baseline characteristics and the main outcome (chronic kidney disease) were assessed by applying a multivariate logistic regression model analysis (both unadjusted and adjusted odds ratios) with a 95% confidence interval (95% CI). A p-value of less than 0.05 was considered statistically significant.

Results

All 389 participants were black Africans, 69,9% of whom were females. The mean age of the participants was 52.3 (±17.5) years. The mean eGFR CK-EPIcreatinine was 87,42 (±26.93) ml/min/1.73m2. The prevalence of CKD (GFR categories G3–G5) was 17.22% (n = 67) as determined by CK-EPIcreatinine, and 17.73% (n = 69) as determined by the MDRD equation. Ten participants reported a prior diagnosis of CKD at the time of the study (Table 1).

10.1371/journal.pone.0292416.t001 Table 1 Demographic and clinical characteristics of the participants.

Variables		
Age (Years) (n = 389)	52.36 ± 17.5	
Sex (n = 389)	
 Female	272 (69.9)	
 Male	117 (30.1)	
Kidney function assessment (n = 389)	
 Serum Creatinine(μmol/l)	73 [26]	
 Serum Creatinine(mg/dl)	0.80 [0.30]	
 Proteins (mg/dl)	2 [7]	
Estimated Glomerular Filtration rate (ml/min/1.73m2) (n = 389)	
 eGFR CKD-Epicreatinine	87.42 ± 26.93	
 eGFR CK MDRD	89.08 ± 38.05	
Chronic Kidney Disease (CKD) (n = 389)	
 eGFR CKD-Epicreatinine	67 (17.22)	
 CKD (eGFR by MDRD)	69 (17.73)	
Participants with previous history of CKD	10	
n (%), Mean ± Standard deviation, Median [Interquartile range IQR] CKD-EPIcreatinine = Chronic Kidney Disease Epidemiology Collaboration Creatinine, MDRD = Modified Diet in Renal Disease, CKD = chronic kidney disease, Source: Participants’ questionnaire

Associations of CKD with potential risk factors

There is a significant relationship between CKD and increasing age; prevalence ranged from 3% at 30–39 years to a maximum of 32.8% at 60–69 years, followed by 28.4% at 70–79 years to 16.4% at 80 years and over (p-value = 0.000) (Fig 1).

10.1371/journal.pone.0292416.g001 Fig 1 Association between CKD-EPIcreatinine and age categories.

Red box Percentage (%) of participants with CKD by the CKDEPI-creatinine values. Blue box Percentage (%) of patients without CKD by the CKDEPI-creatinine values.

Table 2 shows the distribution of the participants with CKD grades from 3 to 5 by age group and sex.

10.1371/journal.pone.0292416.t002 Table 2 Distribution of the CKD grades 3–5 (KDIGO classification) by age group and sex.

Age Groups	Grade 3	Grade 4	Grade 5	CKD (Total grades 3–5)	Total	
F	M	F	M	F	M	F	M	
20–29	0	0	0	0	0	0	0 (0.0)	0 (0.0)	0 (0.0)	
30–39	1	1	0	0	0	0	1 (2.0)	1 (5.6)	2 (3.0)	
40–49	1	1	0	0	2	0	3 (6.1)	1 (5.6)	4 (6.0)	
50–59	5	1	0	2	1	0	6 (12.2)	3 (16.7)	9 (13.4)	
60–69	15	3	4	0	0	0	19 (38.8)	3 (16.7)	22 (32.8)	
70–79	5	3	5	3	2	1	12 (24.5)	7 (38.9)	19 (28.4)	
= > than 80	6	3	1	0	1	0	8 (16.3)	3 (16.7)	11 (16.4)	
Total	33	12	10	5	6	1	49 (73.1)	18 (26.9)	67 (17.2)	
n (%), Source: Participants’ questionnaire

Table 3 shows the distribution of the associated risk factors and patients with a previous CKD history by sex and CKD Grades 3 to 5.

10.1371/journal.pone.0292416.t003 Table 3 Distribution of the associated risk factors and previous CKD history by sex and CKD grades 3 to 5 (KDIGO classification).

Risk Factors	Grade 3	Grade 4	Grade 5	CKD stages Grades 3–5	CKD (n = 67)	
F	M	F	M	F	M	Total By Sex	Total	
F	M	
HTN only	18
(69.2)	5
(71.4)	6
(23.1)	1
(14.3)	2
(7.7)	1
(14.3)	26
(53.1)	7
(38.1)	33
(49.3)	
DM only	0
(0.0)	1
(14.3)	0
(0.0)	0
(0.0)	0
(0.0)	0
(0.0)	0
(0.0)	1
(5.6)	1
(1.5)	
HIV only	2
(7.7)	2
(28.6)	1
(3.8)	0
(0.0)	1
(3.8)	0
(0.0)	4
(8.2)	2
(11.1)	6
(9.0)	
HTN + DM	8
(30.8)	1
(14.3)	3
(11.5)	2
(28.6)	1
(3.8)	0
(0.0)	12
(24.5)	3
(16.3)	15
(22.4)	
HTN + HIV	1
(3.8)	0
(0.0)	0
(0.0)	0
(0.0)	0
(0.0)	0
(0.0)	1
(2.0)	0
(0.0)	1
(1.5)	
HTN + DM + HIV	1
(3.8)	0
(0.0)	0
(0.0)	0
(0.0)	0
(0.0)	0
(0.0)	1
(2.0)	0
(0.0)	1
(1.5)	
Previous History of CKD	
HTN only	0
(0.0)	1
(14.3)	0
(0.0)	1
(14.3)	0
(0.0)	0
(0.0)	0
(0.0)	2
(11.1)	2
(3.0)	
HTN + DM	3
(11.5)	1
(14.3)	0
(0.0)	0
(0.0)	1
(3.8)	0
(0.0)	4
(8.2)	1
(5.6)	5
(7.5)	
HTN + HIV	0
(0.0)	1
(14.3)	0
(0.0)	0
(0.0)	0
(0.0)	0
(0.0)	0
(0.0)	1
(5.6)	1
(1.5)	
HTN + DM + HIV	0
(0.0)	0
(0.0)	0
(0.0)	1
(14.3)	1
(3.8)	0
(0.0)	1
(2.0)	1
(5.6)	2
(3.0)	
HTN = Hypertension, HIV = Human Immunodeficiency Virus, DM = Diabetes Mellitus, Source: Participants’ questionnaire

In the bivariate analysis, the following potential risk factors were significantly associated with CKD: age (p<0.001), HTN (p<0.001), DM (p <0.001), HIV (p<0.001) and level of education (p<0.001). There were no sex differences in CKD prevalence (p = 0.529) (Table 4).

10.1371/journal.pone.0292416.t004 Table 4 Bivariate analysis showing risk factors between participants with and without CKD.

Variables	CKD	No CKD	Unadjusted Odds Ratios 95%(CI)	p-value	
Age	67.82±12.94	49.12±15.99	1.08 (1.06–1.1)	<0.001	
Gender				0.529	
 Female (Ref)	49(73.13)	223(69.25)	1	
 Male	18 (26.87)	99 (30.75)	0.83 (0.46–1.49)	
Hypertension	60 (89.55)	144(44.72)	10.6 (4.7–23.89)	<0.001	
Diabetes mellitus	24 (35.82)	56 (17.35)	2.65 (1.49–4.72)	<0.001	
HIV	11 (16.42)	181 (56.21)	6.54 (3.3–12.94)	<0.001	
FHx of Hypertension	16(23.88)	54(16.77)	0.64 (0.34–1.21)	0.17	
FHx of Diabetes mellitus	10(14.93)	39(12.11)	0.79 (0.37–1.66)	0.529	
Alcohol consumption	5 (7.46)	27 (8.71)	0.88 (0.33–2.38)	0.800	
Current Smoking	2 (2.98)	13 (4.03)	0.73 (0.16–3.32)	0.674	
Level of Education				0.000	
 Illiterate (ref)	14 (20.9)	20 (6.21)	1		
 Primary	29 (43.28)	101 (31.37)	0.41 (0.19–0.91)	0.029	
 Secondary	20 (29.85)	197 (61.18)	0.15 (0.06–0.33)	0.000	
 Tertiary	4 (5.97)	4 (1.24)	1.43 (0.31–6.70)	0.651	
BMI	24.29 ± 5.27	23.76 ± 5.81	0.98 (0.94–1.03)	0.489	
Urine Protein(≥30mg/dl)	22 (32.8)	9 (2.8)	1.02 (1.01–1.03)	<0.001	
Cholesterol	4.46 ± 0.91	4.42 ± 1.12	0.96 (0.73–1.28)	0.802	
Triglycerides	1.83 ± 1.01	1.59 ± 0.8	0.73 (0.44–1.23)	0.236	
HIV = Human immunodeficiency virus, BMI = Body mass index, CKD = Chronic kidney disease, FHx = Family History

There was no association between the anti-hypertensive treatment and the CKD. At the moment of the study, four patients classified as grade 5 were on treatment with enalapril. It was stopped, and the patients were changed to amlodipine (Table 5).

10.1371/journal.pone.0292416.t005 Table 5 Relationship between the Antihypertensive treatment and CKD grades (KDIGO classification).

Antihypertensive treatment	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Total Grades 1–2	Total Grades 3–5	Unadjusted Odds Ratio. CI (95%)	p-value	
Amlodipine	21	53	19	9	2	74	30	0.73(0.42–1.26)	0.254	
Enalapril	24	49	27	10	4	73	41	1.45 (0.83–2.53)	0.189	
Diuretics	45	86	38	12	6	131	56	1.68 (0.61–4.6)	0.314	
Atenolol	6	5	8	3	1	11	12	2.48(0.97–6.35)	0.058	
Source: Participants’ questionnaire

There was no association between diabetes treatment and CKD. At the time of the study, two patients classified as grade 5 were on treatment with Metformin, which was changed to Insulin (Table 6).

10.1371/journal.pone.0292416.t006 Table 6 Relationship between the Diabetes Mellitus treatment and CKD grades (KDIGO classification).

Diabetes Mellitus Treatment	Grade 1	Grade 2	Grade 3	Grade 4	Grade 5	Total Grades 1–2	Total Grades 3–5	Unadjusted Odds Ratio. CI (95%)	p-value	
Metformin	18	32	14	4	2	50	20	3.37 (0.67–17.01)	0.196	
Sulfonylureas	9	11	9	3	0	20	12	0.57 (0.23–1.43)	0.232	
Insulin	6	7	2	2	1	13	5	1.21 (0.41–3.54)	0.793	
Source: Participants’ questionnaire

In the multiple logistic regression analysis, age as a continuous variable was significantly associated with the development of CKD in the study sample. Older age was identified as a significant risk factor for CKD, with an odds ratio (OR) = 1.08 (95% confidence interval [CI]: 1.06–1.1, p < 0.001) (Table 7).

10.1371/journal.pone.0292416.t007 Table 7 Multiple logistic regression analysis of risk factors associated with CKD by CKD-Epicreatinine.

Variables	Adjusted Odds Ratios (95% CI)	p-value	
Age	1.08 (1.06–1.1)	<0.001	
Hypertension	0.48 (0.16–1.4)	0.179	
Diabetes mellitus	0.64 (0.33–1.23)	0.182	
HIV	0.78 (0.3–1.98)	0.595	
Level of Education			
 Illiterate (ref)	1		
 Primary	1.3 (0.54–3.13)	0.555	
 Secondary	1.28 (0.48–3.39)	0.617	
HIV = Human immunodeficiency virus.

Thirty-one participants (7.97%) had significant levels of protein in the urine (≥ 30 mg/dl); their mean age was 56.68 (±15.45) years. In the bivariate analysis, HTN was significantly associated with proteinuria: HTN (p = 0.001) (OR = 4.17, 95% CI 1.67–10.4) (Table 8).

10.1371/journal.pone.0292416.t008 Table 8 Bivariate analysis showing association between potential risk factors and proteinuria.

Variables	Urine Proteins < 30 mg/dl
(n = 358)	Urine Proteins ≥30 mg/dl
(n = 31)	Unadjusted Odds Ratios. CI (95%)	p-value	
Age	51.97±17.13	56.68 ± 15.45	1.02 (0.99–1.04)	0.141	
Sex				0.894	
Female (ref)	250 (69.83)	22 (70.97)	1.06 (0.47–2.37)	
Male	108 (30.17)	9 (29.03)	
Hypertension	179 (50.0)	25 (80.65)	4.17 (1.67–10.4)	0.001	
Diabetes mellitus	22 (70.97)	9 (29.03)	1.65 (0.73–3.75)	0.243	
HIV	182 (50.84)	10 (32.26))	0.46 (0.21–1.01)	0.52	
BMI	23.8 ± 5.66	24.21 ± 6.38	1.01 (0.95–1.08)	0.707	
Cholesterol	4.4 ± 1.07	4.74 ± 1	1.32 (0.91–1.92)	0.148	
Triglycerides	1.67 ± 0.9	1.56 ± 0.55	1.18 (0.55–2.53)	0.674	
HIV = Human immunodeficiency virus; BMI = Body mass index. Source: Participants’ questionnaire. Data expressed as number (percentage) and mean ± standard deviation. Urine strip results from urine analysis machine model SLSSUA.

The study did not find any association between the treatment (as a risk factor) and the presence of proteinuria (Table 9).

10.1371/journal.pone.0292416.t009 Table 9 Relationship between the treatment and proteinuria.

Antihypertensive treatment	Urine Proteins	p-value	Unadjusted Odds Ratio. CI (95%)	
Equal or more than 30 mg/dl	Less than 30 mg/dl	
Amlodipine	7	97	0.866	0.92(0.34–2.48)	
Enalapril	12	102	0.398	0.7 (0.3–1.61)	
Atenolol	4	19	0.415	0.56(0.15–2.11)	
Diuretics	22	165	0.482	0.62 (0.17–2.34)	
Diabetes treatment	
Metformin	7	63	0.301	0.3(0.03–2.95)	
Sulfonylureas	4	28	0.913	1.09 (0.23–5.16)	
Insulin	2	16	0.622	0.55(0.05–0.598)	

Discussion

Despite the increasing prevalence of hypertension, diabetes mellitus and other non-communicable diseases, all known risk factors for the development of CKD in South Africa, the true burden of CKD in the country is unknown. The study reports on the prevalence of CKD and examines some of its known risk factors among the residents of a rural community accessing Mbekweni Community Health Centre in the King Sabata Dalindyebo sub-district municipality of the Eastern Cape province. Findings from this study provide much-needed CKD data on the rural population of South Africa that may inform an effective kidney health prevention programme in the country.

The study found a prevalence of CKD of 17.2% in the sample, with no significant differences shown between the CKD-Epicreatinine and MDRD results. This prevalence (17.2%) is higher than the 6.1% reported by Adeniyi et al. among a cohort of teachers in an urban area of Cape Town in the Western Cape province in 2017 [9]. It should also be noted that in the Adeniyi et al. (2017) study, there was a considerable difference between the CKD results shown by the two equations; 6.1% according to MDRD and 1.8% according to CKD-EPIcreatinine [9]. Similarly, the current study found a much higher prevalence of CKD than the 3.4% found by Peer et al in Cape Town in 2020 [21] and the 6.7% described by Fabian et al. in the rural area of Mpumalanga between 2017 and 2018 [22]. It should be noted that the CKD prevalence from Fabian et al. [22] was reported mainly for persistent albuminuria, which differs methodologically from the present study. The prevalence of 17.2% from the present study is lower than the 29.2% reported by Navise et al. in a community-based study from the North-West Province [23]. It is necessary to highlight that in Navise et al. [23], the crude prevalence is based on an eGFR < 90 ml/min/1.73m2. The prevalence for the eGFR < 60 ml/min/1.73m2 is 3% lower than in our study. The CKD definition differs from the one used in our study. However, the prevalence of CKD in the current study is similar to the 17.3% reported by Matsha et al in Cape Town in 2013 [24]. The wide disparity in the prevalence of CKD can be explained by the peculiarities of each study population. While the participants in the current study were older (mean age 52.36 years) and had a high prevalence of hypertension (52.4%), diabetes mellitus (20.6%) and other cardiovascular risk factors, similar to the participants in Matsha et al in Cape Town [24], relatively younger (mean age 44.1–46.3 years) and more active cohorts, with a lower prevalence of cardiovascular disease, were included in Booysen et al [16], Adeniyi et al [9] Peer et al [21] and Navise et al. [23], the median age in the Fabian et al. study was 35 years [22].

Several studies have compared different glomerular filtration rate equations measured by nuclear medicine methods [16, 21, 25]. The authors suggest that more studies are needed to validate these equations in the diverse local populations of South Africa. Booysen et al. [16] acknowledged that the CKD-Epicreatinine equation may not be as accurate for eGFR assessments among black Africans as it is among the white population. However, this equation represents the most appropriate equation for detecting pre-clinical cardiac and vascular end-organ damage beyond the conventional risk factors found in black Africans [13]. Moreover, Holnes et al. [26] reported that both the MDRD and CKD-EPI equations have shown satisfactory accuracy in the South African mixed-ancestry adult population.

In the current study, female participants were predominant (69.9%), which is not surprising, given that this cohort was drawn from the local community health centre. This is in keeping with other studies in South Africa, where the percentages of female participants were 70.3% [3], 64.1% [21], 58% [22], 62,8% [23] and 75.2% [24]. There are few studies in rural South Africa to compare. However, a study by Kaze et al. in Cameroon found a slightly higher male predominance at 53.4% [27]. Although Matsha et al. [24] reported a significant relationship between female sex and CKD, the current study found no significant association with sex. This is in agreement with Peer et al. [21], Fabian et al. [22], and Kaze et al. [27], whose studies demonstrated no significant association between sex and CKD. The impact of sex on CKD remains controversial; although some studies document a higher risk of developing CKD among women [23, 24, 28, 29], others report higher odds among men [30–33].

Previous studies have reported many risk factors for the development of CKD in different population groups in South Africa, especially in the urban parts of Cape Town [9, 21, 24]. However, such risk factors have not been sufficiently investigated in the rural communities of South Africa, especially in the Eastern Cape province. The current study showed a significant association between CKD and the ageing population. The mean age of the participants with CKD was 67.82 years, with those above 60 years most affected. This finding corroborates the findings of Peer et al. [21] and Kaze et al [27]. However, Adeniyi et al. [9] reported the presence of CKD in younger age groups (mean age 47.3 years) among teachers in Cape Town.

The current study found a proteinuria prevalence of 7.97%, which was significantly associated with the presence of hypertension. A similar prevalence of proteinuria (7.2%) and association with hypertension was reported by Kaze et al. [27]. However, a lower prevalence of proteinuria (4.5%) in a younger cohort of teachers was reported by Adeniyi et al [9]. It should be pointed out that the urine—protein creatinine ratio was not assessed in the current study, as it was in Adeniyi et al. study [9]. Although other studies report higher rates of proteinuria among women than men [3, 28, 33], the current study found no association with sex. In other studies, proteinuria was not evaluated or was related to groups of participants with specific co-morbidities such as HIV ART naïve, HIV on ART, hypertension, or only diabetes mellitus [30, 34–39].

Study limitations

First, this is a facility-based study with respondents recruited from the outpatient clinic thus, limits the ability to generalise the findings to the general population. In addition, the study population consists exclusively of black South Africans from the rural KSD sub-district, limiting the applicability of the findings to the diverse ethnicities of the rural South African population.

In addition, the cohort’s predominance of females reflects a common trend in health service utilisation in South Africa, as elucidated in many population studies in the country [3, 21, 24]. Notwithstanding, there was no difference by sex in the CKD prevalence in this study. Future studies on CKD prevalence should target the broader rural population of the country. Furthermore, creatinine was measured using an assay based on the Jaffe reaction, which is more susceptible to interferences than the enzymatic method.

Finally, the use of the CKD-EPIcreatinine equation for the detection of CKD needs further validation in the local context, even though both the MDRD and CKD-EPI equations have shown a satisfactory performance in the South African mixed-ancestry adult population. The cross-sectional nature of the study design did not allow for chronicity and might have overestimated chronic kidney disease prevalence in the setting. A previous study showed an overestimation of CKD in an older population and an underestimation in a younger population [40]. Although individuals with probable acute kidney injuries (vomiting, diarrhoea and burns) were excluded from the study, a repeat measurement of creatinine and proteinuria at three months would have established the diagnosis of chronic kidney disease.

Conclusions

This study found a high prevalence of CKD (17.2%) and proteinuria (7.97%) in this rural community, largely attributed to advanced age and hypertension, respectively. This finding supports the ideal hospital framework of the National Department of Health in South Africa, which focuses on screening for cardiovascular and other non-communicable diseases at every facility. Early detection of proteinuria and decreased renal function at community health centres should trigger a referral to a higher level of care for further management of patients.

The authors thank the doctors and nurses of the Mbekweni Community Health Centre for their support during the study.

10.1371/journal.pone.0292416.r001
Decision Letter 0
McGrowder Donovan Anthony Academic Editor
© 2024 Donovan Anthony McGrowder
2024
Donovan Anthony McGrowder
https://creativecommons.org/licenses/by/4.0/ 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.
Submission Version0
30 Oct 2023

PONE-D-23-27552Kidney Damage and Associated Risk Factors in the Rural Eastern Cape, South Africa: A Cross-Sectional StudyPLOS ONE

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Additional Editor Comments:

Your manuscript “Kidney Damage and Associated Risk Factors in the Rural Eastern Cape, South Africa: A Cross-Sectional Study” has been assessed by our reviewers. They have raised a number of points which we believe would improve the manuscript and may allow a revised version to be published in PLOS ONE. Their reports, together with any other comments, are below.

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Reviewers' comments:

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Reviewer #1: Partly

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: Partly

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: No

Reviewer #5: Yes

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5. Review Comments to the Author

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Reviewer #1: The concept of this study is interesting. However, this study examined only a limited number cases in a certain region. I think it would be better to submit this paper to a journal of relevant regions.

1. It is necessary to show in more detail the clinical and social background of the regions.

2. It is necessary to make comparisons with other regions and clarify the characteristics of the target region.

3. Information about treatment for HT, DM and CKD is needed.

Reviewer #2: The authors investigated the prevalence of CKD in the Rural Eastern Cape, South Africa. Although the findings were partially meaningful, there are several serious problems. Especially, it is not surprising that both older age and the presence of proteinuria are strongly associated with the presence of CKD. Although they aimed to clarify the prevalence of CKD in this area, they included only outpatients of the hospitals. In contrast, the study by Adeniyi AB et al. [Ref. 9] included young school teachers, and the prevalence of CKD was lower as compared to this study. From this study design, they cannot conclude that early detection of proteinuria and decreased renal function could lead to prompt initiation of preventative measures and management to delay the progression to end-stage kidney failure and mortality.

Abstract

P.2, L.15: The authors described that significant kidney damage was defined as low eGFR (<60mL/min per 1.73m2) and/or the presence of proteinuria. However, the chief outcome of this study seems the prevalence of CKD defined by KDIGO Guideline.

P.2, L.21: Although the authors described that older age and the presence of proteinuria were associated with the significant kidney damage, both ORs were less than 1.

Introduction

P.3, L.30-32: Abbreviations of RRT should be spelled out at first usage and the abbreviated form used thereafter. Please check all abbreviations including CKD, eGFR, CKD-EPIcreatinine or MDRD.

Methods

P.8, L.133: Ref.14 is guideline for the management of glomerular diseases. CKD should be defined by KDIGO guideline [Levey AS, et al. Kidney Int 2005；67：2089‒100].

Results

P.9, L.156 and Table 1: The authors described that ten participants reported prior diagnosis of CKD at the time of the study (Table 1). However, there was no description in Table 1. There is no need to present the number and proportion of both female and male participants. Not-normally distributed variables, especially serum creatinine and urine protein, should be presented as medians and ranges or interquartile ranges (IQR).

P.11, Table 2: The number of HIV patients without CKD is odd. Was the prevalence of CKD higher among HIV patients? Urine protein of CKD and non-CKD were odd.

P.12, Table 3: ORs of high age and urine protein should be more than 1. P-value of diabetic mellitus is odd. Although the authors described the abbreviations of HTN, DM, eGFR and CKD-EPI below the Table 3, they did not use in the Table.

P.13, Table 4: The data of both patients with urine protein >30 and those with urine protein<30 should be presented. Although the OR of HIV is 0.46 (0.21-1.01), p-value was less than 0,05. OR of triglyceride is odd.

P.14, Table 5: The objective variable of this multivariate analysis is unclear. It is not much meaning to find that hypertension and higher serum creatinine level were associated with higher prevalence of proteinuria.

Reviewer #3: This cross-sectional study describes the prevalence of CKD in patients attending an outpatient clinic of a rural community in the eastern cape of South Africa. The article is well written and easy to understand. There are some limitations to the study including the homogeneity of the study population, the fact that this study was conducted in patients attending the out-patient department of a community clinic but these have been mentioned by the authors.

There are a few clarifications which should be addressed.

1. Please describe the outpatient department in more detail. Does it provide wellness and preventive services or it caters to patients who are being followed up for several illnesses? Although the results suggest the latter, this should be clearly stated in the methodology.

2. References are not always well written, some references do not have year of publications, and different styles have been used. Please refer to Plos one referencing style.

3. Some of the references do not seem relevant to the text, for example ref 19 refers to prevalence of CKD in Cameroonians. References should be sought from South African patients while discussing the demographics.

Reference 27 refers to precision nephrology, it may be better to quote the article quoted in that write-up: Minutolo R et al... Sex differences in the progression of CKD.......

In Conclusion, the study adds to the body of knowledge on prevalence of CKD to a subset of South Africans. Minor corrections noted above should be made.

Reviewer #4: This observational study investigates the prevalence of kidney damage within the rural community of the Eastern Cape province in South Africa. Notably, the research delves into the association of kidney damage with various risk factors linked to Chronic Kidney Disease (CKD). This study serves as a crucial endeavor in understanding the prevalence of CKD within the context of rural South Africa.

However, upon careful review, certain aspects require further elucidation and refinement.

1. Firstly, it is imperative to delineate the relationship between kidney function and specific factors, such as diabetes, hypertension, and proteinuria, differentiating the data based on gender. Analyzing these associations separately for males and females could provide a more comprehensive understanding of the impact of these factors on kidney health.

2. Moreover, the report highlights that 67 patients were classified between G3-G5 stages of CKD. However, the distribution of these patients in terms of gender remains ambiguous. It is essential to clarify the proportion or percentage of male and female patients within these stages for a more nuanced interpretation of the data.

3. Additionally, exploring the relationship between the grades of CKD and associated risk factors might offer deeper insights into the progression of the disease and its underlying causes. By examining how different risk factors correspond with the severity of CKD, a clearer understanding of the disease's trajectory can be obtained.

4. In reference to Table 2, while the data for Female CKD (49) and No-CKD (223) is presented, the information pertaining to males seems to be absent. Clarification is required regarding whether Table 2 is exclusively related to female subjects or if the data for male subjects was inadvertently omitted.

5. Lastly, in the paragraph discussing the 'Associations of CKD with potential risk factors,' an apparent inconsistency is noted regarding the relationship between age and the incidence of CKD. Contrary to the initial suggestion that the incidence of CKD declines with age, Figure 1 clearly illustrates an increasing ratio of CKD to Non-CKD cases with advancing age. This inconsistency should be rectified to ensure accurate interpretation and reporting of the data.

Reviewer #5: This is an interesting study as very few data exist about CKD in rural areas of South Africa.

The authors should be congratulated for their efforts to investigate CKD epidemiology in a region with shortages of health facilities.

However, I do have some comments.

1. The authors should give more information about the region. How much remoted is this? What are the main demographic data of the whole population and study group (employment, length of life, poverty, educational status, access to health facilities and so on)

2. There are no data about smoking and alcohol.

3. There is a possibility of selection bias as the participants were outpatients of the Health Centre. The authors should comment on this.

4. The authors should give a chart about the initial number of persons asked to participate. How many people refused or were excluded?

5. I miss information about the questionnaire given to patients and how it was filled in (assisted? , could all patients read?)

6. In the discussion, the authors could add comments/comparisons with other South African rural areas data ( Navise et al. https://doi.org/10.1186/s12882-023-03068-7 June Fabian et al https://doi.org/10.12688/wellcomeopenres.18016.2)

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Ngozi Virginia Aikpokpo

Reviewer #4: No

Reviewer #5: No

**********

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Attachment Submitted filename: Review PONE-D-23-27552.docx

10.1371/journal.pone.0292416.r002
Author response to Decision Letter 0
Submission Version1
8 Feb 2024

Dear Editor,

Thanks for the insightful comments of the reviewers. Indeed, the manuscript has improved remarkably with the suggestions and queries from the reviewers. Please find below the responses to the reviewers’ comments.

Regards

Ernesto Rosales Gonzalez

Reviewer #1:

1. It is necessary to show in more detail the clinical and social background of the regions.

Response: Thanks for the comment. The clinical and social background has been included from lines 54 to 61 and 88 to 95.

2. It is necessary to make comparisons with other regions and clarify the characteristics of the target region.

Response: Thanks for the comment. We have added more information on the study setting.

3. Information about treatment for HT, DM and CKD is needed.

Response: Thanks for the comment. We have made corrections

Reviewer #2:

1. The authors investigated the prevalence of CKD in the Rural Eastern Cape, South Africa. Although the findings were partially meaningful, there are several serious problems. Especially, it is not surprising that both older age and the presence of proteinuria are strongly associated with the presence of CKD. Although they aimed to clarify the prevalence of CKD in this area, they included only outpatients of the hospitals.

Response: Thanks for the comment. It should be clarified that the study comprised of stable patients attending the primary health care outpatient setting. This is the first point of contact with patients in the rural South African settings. More studies are needed to elucidate on the in-patient in rural hospital settings in South Africa.

2. In contrast, the study by Adeniyi AB et al. [Ref. 9] included young school teachers, and the prevalence of CKD was lower as compared to this study. From this study design, they cannot conclude that early detection of proteinuria and decreased renal function could lead to prompt initiation of preventative measures and management to delay the progression to end-stage kidney failure and mortality.

Response: Thanks for the insight. We have rephrase our conclusion on this.

Abstract

P.2, L.15: The authors described that significant kidney damage was defined as low eGFR (<60mL/min per 1.73m2) and/or the presence of proteinuria. However, the chief outcome of this study seems to be the prevalence of CKD defined by KDIGO Guideline.

Response: Thanks for the comment. We have made corrections

P.2, L.21: Although the authors described that older age and the presence of proteinuria were associated with the significant kidney damage, both ORs were less than 1.

Response: Thanks for the comment. We have made corrections.

Introduction

P.3, L.30-32: Abbreviations of RRT should be spelled out at first usage and the abbreviated form used thereafter. Please check all abbreviations including CKD, eGFR, CKD-EPIcreatinine or MDRD.

Response: Thanks for the comment. We have made corrections as suggested

Methods

P.8, L.133: Ref.14 is guideline for the management of glomerular diseases. CKD should be defined by KDIGO guideline [Levey AS, et al. Kidney Int 2005；67：2089‒100].

Response: Thanks for the comment. We have made corrections as suggested

Results

P.9, L.156 and Table 1: The authors described that ten participants reported prior diagnosis of CKD at the time of the study (Table 1). However, there was no description in Table 1. There is no need to present the number and proportion of both female and male participants. Not-normally distributed variables, especially serum creatinine and urine protein, should be presented as medians and ranges or interquartile ranges (IQR).

Response: Thanks for the comment. We have made corrections as suggested

P.11, Table 2: The number of HIV patients without CKD is odd. Was the prevalence of CKD higher among HIV patients? Urine protein of CKD and non-CKD were odd.

Response: Thanks for the comment. We have made corrections as suggested

P.12, Table 3: ORs of high age and urine protein should be more than 1. P-value of diabetic mellitus is odd. Although the authors described the abbreviations of HTN, DM, eGFR and CKD-EPI below the Table 3, they did not use in the Table.

Response: Thanks for the comment. We have made corrections as suggested

P.13, Table 4: The data of both patients with urine protein >30 and those with urine protein<30 should be presented. Although the OR of HIV is 0.46 (0.21-1.01), p-value was less than 0,05. OR of triglyceride is odd.

Response: Thanks for the comment. We have made corrections as suggested

P.14, Table 5: The objective variable of this multivariate analysis is unclear. It is not much meaning to find that hypertension and higher serum creatinine level were associated with higher prevalence of proteinuria.

Response: Thanks for the comment. We have made corrections as suggested

Reviewer #3:

This cross-sectional study describes the prevalence of CKD in patients attending an outpatient clinic of a rural community in the eastern cape of South Africa. The article is well written and easy to understand. There are some limitations to the study including the homogeneity of the study population, the fact that this study was conducted in patients attending the out-patient department of a community clinic but these have been mentioned by the authors.

There are a few clarifications which should be addressed.

1. Please describe the outpatient department in more detail. Does it provide wellness and preventive services or it caters to patients who are being followed up for several illnesses? Although the results suggest the latter, this should be clearly stated in the methodology.

Response: Thanks for the comment. We have made corrections

2. References are not always well written, some references do not have year of publications, and different styles have been used. Please refer to Plos one referencing style.

Response: Thanks for the comment. We have made corrections as suggested

3. Some of the references do not seem relevant to the text, for example ref 19 refers to prevalence of CKD in Cameroonians. References should be sought from South African patients while discussing the demographics.

Reference 27 refers to precision nephrology, it may be better to quote the article quoted in that write-up: Minutolo R et al... Sex differences in the progression of CKD.......

In Conclusion, the study adds to the body of knowledge on prevalence of CKD to a subset of South Africans. Minor corrections noted above should be made.

Response: Thanks for the comment. We have made corrections as suggested.

Reviewer #4:

This observational study investigates the prevalence of kidney damage within the rural community of the Eastern Cape province in South Africa. Notably, the research delves into the association of kidney damage with various risk factors linked to Chronic Kidney Disease (CKD). This study serves as a crucial endeavor in understanding the prevalence of CKD within the context of rural South Africa.

However, upon careful review, certain aspects require further elucidation and refinement.

1. Firstly, it is imperative to delineate the relationship between kidney function and specific factors, such as diabetes, hypertension, and proteinuria, differentiating the data based on gender. Analyzing these associations separately for males and females could provide a more comprehensive understanding of the impact of these factors on kidney health.

Response: Thanks for the comment. We have made corrections as suggested

2. Moreover, the report highlights that 67 patients were classified between G3-G5 stages of CKD. However, the distribution of these patients in terms of gender remains ambiguous. It is essential to clarify the proportion or percentage of male and female patients within these stages for a more nuanced interpretation of the data.

Response: Thanks for the comment. We have made corrections as suggested

3. Additionally, exploring the relationship between the grades of CKD and associated risk factors might offer deeper insights into the progression of the disease and its underlying causes. By examining how different risk factors correspond with the severity of CKD, a clearer understanding of the disease's trajectory can be obtained.

Response: Thanks for the comment. We have made corrections as suggested

4. In reference to Table 2, while the data for Female CKD (49) and No-CKD (223) is presented, the information pertaining to males seems to be absent. Clarification is required regarding whether Table 2 is exclusively related to female subjects or if the data for male subjects was inadvertently omitted.

Response: Thanks for the comment. We have made corrections as suggested

5. Lastly, in the paragraph discussing the 'Associations of CKD with potential risk factors,' an apparent inconsistency is noted regarding the relationship between age and the incidence of CKD. Contrary to the initial suggestion that the incidence of CKD declines with age, Figure 1 clearly illustrates an increasing ratio of CKD to Non-CKD cases with advancing age. This inconsistency should be rectified to ensure accurate interpretation and reporting of the data.

Response: Thanks for the comment. We have made corrections as suggested

Reviewer #5:

This is an interesting study as very few data exist about CKD in rural areas of South Africa. The authors should be congratulated for their efforts to investigate CKD epidemiology in a region with shortages of health facilities.

However, I do have some comments.

1. The authors should give more information about the region. How much remoted is this? What are the main demographic data of the whole population and study group (employment, length of life, poverty, educational status, access to health facilities and so on.

Response: Thanks for the comment. We have made corrections as suggested.

2. There are no data about smoking and alcohol.

Response: Thanks for the comment. We have made corrections as suggested

3. There is a possibility of selection bias as the participants were outpatients of the Health Centre. The authors should comment on this.

Response: Thanks for the comment. This primary health care study is the first in the Eastern Cape province, however, future studies should focus on CKD at the community level.

4. The authors should give a chart about the initial number of persons asked to participate. How many people refused or were excluded?

Response: Thanks for the comment. We have made corrections as suggested

5. I miss information about the questionnaire given to patients and how it was filled in (assisted? , could all patients read?)

Response: Thanks for the comment. The research nurse provided assistance to the participants when required (line 107)

6. In the discussion, the authors could add comments/comparisons with other South African rural areas data ( Navise et al. https://doi.org/10.1186/s12882-023-03068-7 June Fabian et al https://doi.org/10.12688/wellcomeopenres.18016.2).

Response: Thanks for the comment. We have made corrections as suggested

This observational study investigates the prevalence of kidney damage within the rural community of the Eastern Cape province in South Africa. Notably, the research delves into the association of kidney damage with various risk factors linked to Chronic Kidney Disease (CKD). This study serves as a crucial endeavor in understanding the prevalence of CKD within the context of rural South Africa. However, upon careful review, certain aspects require further elucidation and refinement.

1. Firstly, it is imperative to delineate the relationship between kidney function and specific factors, such as diabetes, hypertension, and proteinuria, differentiating the data based on gender. Analyzing these associations separately for males and females could provide a more comprehensive understanding of the impact of these factors on kidney health.

Response: Thanks for the comment. We have made corrections as suggested

2. Moreover, the report highlights that 67 patients were classified between G3-G5 stages of CKD. However, the distribution of these patients in terms of gender remains ambiguous. It is essential to clarify the proportion or percentage of male and female patients within these stages for a more nuanced interpretation of the data.

Response: Thanks for the comment. We have made corrections as suggested

3. Additionally, exploring the relationship between the grades of CKD and associated risk factors might offer deeper insights into the progression of the disease and its underlying causes. By examining how different risk factors correspond with the severity of CKD, a clearer understanding of the disease's trajectory can be obtained.

Response: Thanks for the comment. We have made corrections as suggested

4. In reference to Table 2, while the data for Female CKD (49) and No-CKD (223) is presented, the information pertaining to males seems to be absent. Clarification is required regarding whether Table 2 is exclusively related to female subjects or if the data for male subjects was inadvertently omitted.

Response: Thanks for the comment. We have made corrections as suggested

5. Lastly, in the paragraph discussing the 'Associations of CKD with potential risk factors,' an apparent inconsistency is noted regarding the relationship between age and the incidence of CKD. Contrary to the initial suggestion that the incidence of CKD declines with age, Figure 1 clearly illustrates an increasing ratio of CKD to Non-CKD cases with advancing age. This inconsistency should be rectified to ensure accurate interpretation and reporting of the data.

Response: Thanks for the comment. We have made corrections as suggested

Attachment Submitted filename: REBUTTAL Gonzalez.docx

10.1371/journal.pone.0292416.r003
Decision Letter 1
Bello Ibrahim Sebutu Academic Editor
© 2024 Ibrahim Sebutu Bello
2024
Ibrahim Sebutu Bello
https://creativecommons.org/licenses/by/4.0/ 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.
Submission Version1
27 May 2024

PONE-D-23-27552R1Kidney Damage and Associated Risk Factors in the Rural Eastern Cape, South Africa: A Cross-Sectional StudyPLOS ONE

Dear Dr. Rosales Gonzalez,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

Kind regards,

Ibrahim Sebutu Bello, MBBS, MPH, MD, FMCGP

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

I want to commend the authors for their work on this important topic and for the diligence with which they have incorporated the reviewers' corrections and recommendations. Please kindly see below a few pending issues that need to be sorted out before we can proceed to the final acceptance of the manuscript.

General Comment:

The candidate uploaded the corrected manuscript to the manuscript with Track changes but not to the unmarked manuscript with no Track changes. Table 6 needs to be included in the corrected manuscript, and there was no corresponding correction in the abstract section. Hence, the corrected Manuscript in the unmarked should be uploaded, the table numbering corrected, and the abstract should reflect the changes made in the new tables 8.

Abstract: Page 2, Line 22-23 - Based on the changes the authors have effected in the body of the manuscript, the following statement should be corrected: “Risk factors for significant kidney damage were older age (OR=1.08, 95% CI 1.06-1.1, p<0.001)

Specifically, I request that you review the abstract to ensure it accurately reflects the changes made in the manuscript. This includes the correction of the statement regarding the risk factors for significant kidney damage.

Page 15, line 289: In line with reviewers' comments, the authors have changed the data points for Age on Table 8 (Multiple regression). Please make the same changes in the text and in the abstract. Similarly, the authors have removed urine protein from the table. Please make the same change in the abstract.

Table 8: Please clarify if the variable ‘Age’ as used in the logistic regression models was a continuous or categorical variable.

Limitations: Page 20, line 392—Being a facility-based study with respondents recruited from the outpatient clinic limits the ability to generalise the findings to the general population. Please kindly indicate this fact as part of the study limitations.

References: Please kindly ensure that all the references are in alignment with the journal requirements

Once these issues are addressed, we anticipate being able to accept the manuscript for publication. We kindly request that you resubmit as soon as possible. Your prompt action will greatly assist in expediting the publication process. Thank you.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

Reviewer #5: All comments have been addressed

**********

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Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: No

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #3: I Don't Know

Reviewer #4: Yes

Reviewer #5: Yes

**********

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Reviewer #1: Yes

Reviewer #3: No

Reviewer #4: Yes

Reviewer #5: Yes

**********

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Reviewer #1: Yes

Reviewer #3: Yes

Reviewer #4: (No Response)

Reviewer #5: Yes

**********

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Reviewer #1: (No Response)

Reviewer #3: The authors have addressed all my comments. The limitations of the study are clearly stated and the need for further studies involving a wider range of participants have been noted.

Reviewer #4: (No Response)

Reviewer #5: I have no more comments.

............................................................... ..............................................

**********

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Reviewer #1: No

Reviewer #3: Yes: Ngozi Virginia Aikpokpo

Reviewer #4: No

Reviewer #5: No

**********

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10.1371/journal.pone.0292416.r004
Author response to Decision Letter 1
Submission Version2
11 Jun 2024

Reviewer’s comments

Comment 1: The candidate uploaded the corrected manuscript with Track changes but not to the unmarked manuscript with no Track changes. Table 6 needs to be included in the corrected manuscript, and there was no corresponding correction in the abstract section.

Response: We have provided the marked and clean copies.

Comment 2: Hence, the corrected Manuscript in the unmarked should be uploaded, the table numbering corrected, and the abstract should reflect the changes made in the new tables 8.

Response: Thanks for the suggestions. We also made changes in the abstract.

Comment 3: Abstract: Page 2, Line 22-23 - Based on the changes the authors have effected in the body of the manuscript, the following statement should be corrected: “Risk factors for significant kidney damage were older age (OR=1.08, 95% CI 1.06-1.1, p<0.001)

Specifically, I request that you review the abstract to ensure it accurately reflects the changes made in the manuscript. This includes the correction of the statement regarding the risk factors for significant kidney damage.

Response: We have made changes as suggested.

Comment 4: Page 15, line 289: In line with reviewers' comments, the authors have changed the data points for Age on Table 8 (Multiple regression). Please make the same changes in the text and in the abstract.

Response: We also made changes as suggested.

Comment 5: Similarly, the authors have removed urine protein from the table. Please make the same change in the abstract.

Table 8: Please clarify if the variable ‘Age’ as used in the logistic regression models was a continuous or categorical variable.

Response: Age was used as a continuous variable.

Comment 6: Limitations: Page 20, line 392—Being a facility-based study with respondents recruited from the outpatient clinic limits the ability to generalise the findings to the general population. Please kindly indicate this fact as part of the study limitations.

Response: We have made changes as suggested.

Comment 7: References: Please kindly ensure that all the references are in alignment with the journal requirements

Response: We have made changes as suggested.

Attachment Submitted filename: Response letter Editor Reviewers comments.docx

10.1371/journal.pone.0292416.r005
Decision Letter 2
Bello Ibrahim Sebutu Academic Editor
© 2024 Ibrahim Sebutu Bello
2024
Ibrahim Sebutu Bello
https://creativecommons.org/licenses/by/4.0/ 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.
Submission Version2
20 Jun 2024

Kidney Damage and Associated Risk Factors in the Rural Eastern Cape, South Africa: A Cross-Sectional Study

PONE-D-23-27552R2

Dear Dr. Rosales Gonzalez,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Ibrahim Sebutu Bello, MBBS, MPH, MD, FMCGP

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

All issues raised have been addressed

Reviewers' comments:

10.1371/journal.pone.0292416.r006
Acceptance letter
Bello Ibrahim Sebutu Academic Editor
© 2024 Ibrahim Sebutu Bello
2024
Ibrahim Sebutu Bello
https://creativecommons.org/licenses/by/4.0/ 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.
26 Jul 2024

PONE-D-23-27552R2

PLOS ONE

Dear Dr. Rosales Gonzalez,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Ibrahim Sebutu Bello

Academic Editor

PLOS ONE
==== Refs
References

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4 Davids M, Marais N, Jacobs J. South African Renal Registry Annual Report 2012 [Internet]. South African Renal Society; 2014. http://www.sa-renalsociety.org.
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20 SEMDSA 2017 Guidelines for the Management of Type 2 diabetes mellitus. SEMDSA Type 2 Diabetes Guidelines Expert Committee. JEMDSA 2017; 22(1) (Supplement 1): S1-S196. http://www.jemdsa.co.za/index.php/JEMDSA/article/download/647/937.
21 Peer N , George J , Lombard C , Steyn K , Levitt N , Kengne AP . Prevalence, concordance and associations of chronic kidney disease by five estimators in South Africa. BMC Nephrology. 2020 Aug 27;21 (1 ). Available from: https://pubmed.ncbi.nlm.nih.gov/32854641. 32854641
22 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 Research [Internet]. 2022 Nov 3; 7 :236. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9674890/. doi: 10.12688/wellcomeopenres.18016.2 36457874
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24 Matsha TE , Yako YY , Rensburg MA , Hassan MS , Kengne AP , Erasmus RT . Chronic kidney diseases in mixed ancestry south African populations: prevalence, determinants, and concordance between kidney function estimators. BMC Nephrology. 2013 Apr 2;14 (1 ).Available from: http://www.biomedcentral.com/1471-2369/14/75. doi: 10.1186/1471-2369-14-75 23547953
25 Moodley N , Hariparshad S , Peer F , Gounden V . Evaluation of the CKD-EPI creatinine-based glomerular filtration rate estimating equation in Black African and Indian adults in KwaZulu-Natal, South Africa. Clinical Biochemistry. 2018 Sep; 59 :43–9. Available from: 10.1016/j.clinbiochem.2018.06.014. 29940141
26 Holness JL , Bezuidenhout K , Davids MR , Warwick JM . Validation of equations to estimate glomerular filtration rate in South Africans of mixed ancestry. South African Medical Journal. 2020 Feb 26;110 (3 ):229. Available from: https://pubmed.ncbi.nlm.nih.gov/32657701/ 32657701
27 Kaze FF , Halle MP , Mopa HT , Ashuntantang G , Fouda H , Ngogang J , et al . Prevalence and risk factors of chronic kidney disease in urban adult Cameroonians according to three common estimators of the glomerular filtration rate: a cross-sectional study. BMC Nephrology. 2015 Jul 7;16 (1 ). Available from: https://pubmed.ncbi.nlm.nih.gov/26149764. 26149764
28 Rodriguez-Poncelas A , Garre-Olmo J , Franch-Nadal J , Diez-Espino J , Mundet-Tuduri X , Barrot-De la Puente J , et al . Prevalence of chronic kidney disease in patients with type 2 diabetes in Spain: PERCEDIME2 study. BMC Nephrology [Internet]. 2013 Feb 22;14 (1 ). Available from: https://bmcnephrol.biomedcentral.com/articles/10.1186/1471-2369-14-46. 23433046
29 Ephraim Richard KD , Arthur E , Owiredu WKBA , Adoba P , Agbodzakey H , Eghan B . Chronic kidney disease stages among diabetes patients in the Cape Coast Metropolis. Saudi Journal of Kidney Diseases and Transplantation. 2016;27 (6 ):1231. Available from: 10.4103/1319-2442.194658. 27900971
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