Chronic kidney disease and associated factors among adult population in Southwest Ethiopia.

In Ethiopia, data on the burden and determinants of chronic kidney disease (CKD) in the general population is limited. This community-based study was conducted to assess the burden and associated factors of CKD among adults in Southwest Ethiopia. The study was conducted from August 23, 2018-October 16, 2018. Study participants were selected using a random sampling method. A structured questionnaire was used to collect relevant data. Blood pressure and anthropometric indices were measured following standard procedures. About 5 ml of urine sample was collected and the dipstick test was performed immediately. A blood sample of 3-5ml was collected for serum creatinine and blood glucose level determination. The three commonest estimators of glomerular filtration rate and the National Kidney Foundation Kidney Disease Outcomes Quality Initiative were used to define and stage CKD. Data were analyzed using SPSS 21. Multivariable logistic regression was employed and p-value <0.05 was used to indicate statistically significant results. A total of 326 participants with a mean age of 39.9(SD±11.2) years were enrolled in the study. The proportions of female participants (59.8%) were relatively higher than male participants (40.2%). The mean eGFR using CKD-EPI, CG and MDRD was 124.34 (SD±23.8) mL/min/1.73m2, 110.67(SD±33.0) mL/min/1.73m2 and 131.29 (SD±32.5) mL/min/1.73m2 respectively. The prevalence of CKD was 7.4% using CKD-EPI & MDRD and 8% using CG. Similar finding using CKD-EPI & MDRD may indicate that either CKD-EPI or MDRD can be used to estimate GFR in this study area. In the age and sex-adjusted logistic regression model, hypertension was significantly associated with CKD using CKD-EPI & MDRD and age ≥40 years old was significantly associated with CKD using CG. Behavioral characteristics and other traditional risk factors were not significantly associated with CKD in the current study. The prevalence of CKD was high in the study area. Only hypertension and age ≥40 years old were significantly associated with CKD. More of the increased prevalence of CKD in the current study remained unexplained and deserves further study.

Introduction

Chronic kidney disease (CKD) is among Non-communicable diseases (NCD) which cause significant morbidity and mortality globally [1]. A recent systematic review and meta-analysis estimated that the global pooled prevalence of CKD ranges from 11–13% and 13.9% prevalence in sub-Saharan Africa [2]. The epidemiology of CKD in sub-Saharan Africa is different from other regions that it mainly affects the economically productive young age group. The risk of CKD like HIV infection, hypertension, diabetes, and other infectious diseases are increasing in sub-Saharan Africa [2-5].

Although Ethiopia is among the countries that strive to achieve sustainable development goals 2016–2030, the progress towards the reduction of NCD associated deaths is limited. More than one third of, (39%) annual deaths are associated with NCD [6]. The burden of CKD is increasing and mainly associated with poor community awareness, insufficient data, and poor healthcare infrastructure [7]. In Ethiopia, studies that assess the prevalence and associated factors of CKD are very limited. The available studies are not representative because they are taken from high-risk groups, like, diabetes and HIV patients [7, 8]. A high prevalence of CKD, 18.2% and 23.8% using the Modification of Diet in Renal Disease (MDRD) and Cockcroft-Gault (C-G) equations respectively was reported among diabetic patients [7]. Owing to limited community based published reports available so far on CKD in Ethiopia, this study was conducted to assess the prevalence and associated factors of CKD among adults in Southwest Ethiopia.

Materials and methods

Study area, study design, and participants

A community-based cross-sectional study was conducted in the Sheka zone, Southern Nation, Nationality and People Region (SNNPR), Southwest Ethiopia. It is bordered on the South by Bench Maji, on the West by the Gambella region, on the North by the Oromia region and on the East by the Kaffa zone. The administrative center of Sheka is Masha which is located approximately 610 Km Southwest of Addis Ababa. Based on the 2007 census conducted by the central statistical agency, the zone had a total population of 199,314 of whom 101,059 were men and 98,255 were women. About 34, 2227 (17.17%) were urban inhabitants.

The study was conducted from August 23, 2018 to October 16, 2018. The minimum sample size (N) was determined by using single population proportion formula [N = (Z α/2) 2 P (1-P) /d2], where Zα/2 = the value under standard normal table at 95% level of confidence which is 1.96, prevalence P, 15.2% prevalence of CKD among urban adults [9], margin of error d, which was set at 4%. Including a 10% non-response rate, the final sample size was 384 adults. Permanent residents aged ≥18 years in the households of Masha and Tepi town were included in the study. However, pregnant women and participants with acute illness with fever during data collection were excluded. Mentally disabled and those unable to give response were also excluded from the study.

Sampling procedure

A total of two urban areas of Sheka zone was selected randomly i.e. Tepi and Masha from a total of three urban areas (Tepi, Mash and Andracha). Tepi town had three kebeles, while Masha town had two kebeles during the study period. A total of 3 kebeles were randomly included in the study from a total of 5 kebeles in the selected urban areas. Then, the lists of households were taken from the administration of each kebele. All households in the selected urban kebeles with adults ≥18 years old were enumerated with the help of health extension workers to generate the sampling frame. After the generation of the sample framing, the sample size was allocated proportionally to the total household of each kebele. Finally, eligible participants were selected using a random sampling method. In the case of more than one adult population in a single household, one was selected using the lottery method. In case where eligible respondents were not available at the time of data collection, a revisit for three times was made. Respondents who were not available after three visits were considered as non-respondents.

Data collection and laboratory methods

A structured questionnaire adapted from various literatures [9-11] was used to collect data on socio-demographic, economic, behavioral and co-morbidity related characteristics of participants.

Height, weight, blood pressure and waist circumference of all participants were measured by trained data collectors following standardized procedures. Blood pressure readings were performed using a digital blood pressure apparatus after the participant had remained in a sited position for three to five minutes on his or her right arm resting on the chair. Weight, height and waist circumference were measured in light clothing without shoe. Height was taken to the nearest 0.1 cm with a portable stadiometer (Seca 274, Germany) and weight was measured to the nearest 0.1k.g on mechanical Seca (Seca761, Germany). Waist circumference was measured using tape measures on the midway between the top of the hip bone and the bottom of the ribs. Participants were instructed to put off their heavy clothes and breathe out normally before waist circumference measurements. Waist circumference measurements were taken to the nearest 0.1cm. Blood pressure and all anthropometric measurements were made twice, and their averages were used in all analysis.

About 5 ml random urine sample was collected using a clean, leak-proof urine cup for urine chemical analysis. Chemical analysis of urine specimens was performed immediately after sample collection using urine dipsticks test (Multistix® Henry Schein, Inc. https://www.henryschein.com/medical-multistix.aspx). Semi-quantitative chemical analysis of CKD markers (protein) was performed.

Blood sample of 3–5 ml for serum creatinine and blood glucose level determination was collected using a syringe with needle and transferred to a gel containing serum separator test tube. Blood specimens were transported to the nearest general hospital (Tepi general hospital) for serum separation. A serum was separated after the sample clotted and centrifuged at 1000 – 2000g for 10 minutes by trained laboratory technologist. Serum sample was immediately separated from the whole blood and transferred to nunc tube. The serum was kept frozen at -20°C until processed and then transported on ice-cooled containers to Jimma teaching and referral hospital, Ethiopia for analysis.

The creatinine and blood glucose levels were measured using the ABX pentra400 chemistry analyzer and reported in mg/dL or μmol/L (HORIBA, ABX, Japan). The ABX Pentra 400 is a multiparametric analyzer in which it has good reliability and practicability for routine and specialized clinical chemistry analyses, evaluated according to the National Committee for Clinical Laboratory Standards [12].

Variables’ definition

The three common equations were used to estimate glomerular filtration rate (eGFR), and the National Kidney Foundation Kidney Disease Outcomes Quality Initiative (NKF/KDOQI) guidelines to define and stage CKD [13]. We defined a participant as having CKD if he or she had GFR below 60 ml/min/1.73 m2 and/or had proteinuria (positive dipstick at least +1). Glomerular filtration rate classification was based on the following: stage 1: GFR > 90 with proteinuria; stage 2: GFR 60–89 with proteinuria; stage 3a: GFR 45–59; stage 3b: GFR 30–44; stage 4: GFR 15–29 and stage 5 end-stage renal disease (ESRD): GFR < 15 (Fig 1). Hypertension was defined as a systolic (SBP) ≥140 mmHg and/or a diastolic blood pressure of (DBP) ≥90 mmHg or self-reported on anti-hypertension drugs. Diabetes was defined as random blood sager >200mg/dl and/or self-reported on anti-hyperglycemic medication. A separate cut of points was used to level central obesity for men and women: women were leveled as central obese if their waist circumference was ≥88 cm and men were leveled as central obese if their waist circumference ≥102 cm.

Fig 1

Definitions and stages of chronic kidney disease [13].

The nutritional status of participants was assessed using body mass index (BMI) and leveled as follows: underweight: <18.5; normal weight: 18.5–24.9; overweight: 25–29.9 and obese: >30.

Traditional causes of CKD

Refer to the well-known causes of CKD such as hypertension, diabetes, obesity, and HIV.

Heart diseases

In this study, it means a self-reported history of heart diseases such as coronary disease (vessel surgery), or heart failure, or heart attack or stroke.

Moderate activities/exercise

Brisk walking or carrying light loads, gardening, household /domestic chores, roofing, painting for 30 minutes per day for five days a week.

Vigorous exercise/activity

Carrying or lifting heavy loads, digging or construction work, running, fast cycling, aerobics, fast swimming, playing football & volleyball for 30 minutes per day for five days a week.

Data analysis and interpretation

Data were entered into Epidata version 3.1 and exported to SPSS 21.0 for further analysis. Continuous data were presented as mean (± standard deviation) and categorical data as proportions. We used chi-square and fishers exact test for comparison of proportions and independent t-test for comparing mean differences. Logistic regression models were used to investigate the predictors of CKD. Variables with p-value of <0.2 in the bivariate analysis were included in the multivariable analysis. A p-values <0.05 were used to indicate statistically significant results.

Data quality control

Data collectors were trained and supervised during the data collection process. The questionnaire was first prepared in the English language and translated to the local language (Amharic). About 10% of the questionnaire was pretested among urban Kebeles of Sheka zone which were not included in this study before the actual study period. Completeness and consistency of the collected data were checked during data collection by the supervisors. Anthropometric equipment’s were calibrated frequently for every five measurements to control fallacy. Both intra-observer and interobserver variations were checked using SPSS version 21.0 during training of anthropometric measurement and the training was continued until an acceptable level of variation reached. Laboratory instruments were calibrated to guarantee reliability test result and laboratory tests were done according to manufacturer’s instructions, with their respective controls.

Ethics approval and consent to participate

Ethical approval was obtained from the ethics committee of Mizan Tepi University research directorate (Ref No: MTU/27/514/44/10). Permission of Sheka Zone, Southwest Ethiopia Health office and the head of the woreda were guaranteed. After explaining the objectives of the study, written consent was obtained from each study participant. For the participants who were not able to read and write, a fingerprint was used as a signature. The referral was considered for study participants with CKD and other medical risk factors.

Results

Socio-demographic characteristics

A total of 326 participants which gives a 95.9% response rate were included in this study. Most participants were within the age category of 18–39 (241; 73.9%) years. The proportions of female participants (59.8%) were relatively higher than male participants (40.2%). Significant differences in age, marital status and occupation were observed between male and females (p<0.05) as indicated in Table 1.

Table 1

Socio-economic and demographic characteristics of study participants in Sheka zone, Southwest Ethiopia, 2018.

Characteristics	Overall	Men	Women	p-value
Age (Mean = 33.90 ± 11.2 years)				0.011
18–39 years old	241(73.9)	87(66.4)	154(79.0)
≥ 40 years old	85(26.1)	44(33.6)	41(21.0)
Education status				0.163
Not attended formal education	53(16.3)	17(13.0)	36(18.5)
Primary (1–8)	116(35.6)	42(32.1)	74(37.9)
Secondary (9–12)	90(27.6)	44(33.6)	46(23.6)
Above (12+)	67(20.6)	28(21.4)	39(20.0)
Marital status				0.032
Married	267(81.9)	100(76.3)	167(85.6)
Others*	59(18.1)	31(23.7)	28(14.4)
Religion				0.477
Christian	242(74.2)	100(41.3)	31(36.9)
Muslim	84(25.8)	142(58.7)	53(63.1)
Occupation				<0.001
Employed**	53(16.3)	22(16.8)	31(15.9)
Farming	56(17.2)	37(28.2)	19(9.7)
Merchant	49(15.0)	36(27.5)	13(6.7)
Housewife	104(31.9)	3(2.3)	101(51.8)
Students	42(12.9)	18(13.7)	24(12.3)
Others***	22(6.7)	15(11.5)	7(3.6)
Monthly income ((Median 2000(IQR = 2200 EB))				0 .110
<2000EB	113(34.7)	38(36.9)	75(46.9)
≥2000EB	150(46.0)	65(63.1)	85(53.1)
Main source of water for drinking				0.771
Pip water	171(52.5)	70(53.4)	101(51.8)
Wall/spring	155(47.5)	61(46.6)	94(48.2)

EB = Ethiopian birr*Single, divorced or widowed ** Private and government employed ***house maid, retired, unemployed

Behavioral and Lifestyle characteristics

Majority of participants didn’t engage in vigorous activity/exercise (83.1%) or moderate activity/exercise (72.4%). A very small proportion (3.1%) of participants ever used herbal medicines. About 43 (13.2%) of the participants reported that they used nonsteroidal anti-inflammatory drugs. Nearly one-third of the participants (34.0%) reported that they drink <2 liters of water per day. A significant difference in alcohol and cigarette consumption, moderate exercise/activity, vigorous exercise/activity, and consumption of canned food between males and female were observed as described in Table 2.

Table 2

Behavioral related characteristics of study participants in Sheka zone, Southwest Ethiopia, 2018.

Characteristics	Overall	Men	Women	P-value
Ever drink alcohol				<0.001
Yes	46(14.1)	34(26.0)	12(6.2)
No	280(85.9)	97(74.0)	183(93.8)
Ever smoke cigarette
Yes	17(5.2)	17(13.0)	0(0.0)
No	309(94.8)	114(87.0)	195(100.0)
Smoking year				0.098
<10 years	13(76.5)	49(37.4)	91(46.7)
≥10 years	4(23.5)	82(62.6)	104(53.3)
Herbal medicine intake				0.326 #
Yes	10(3.1)	2(1.5%)	8(4.1)
No	316(96.9)	129(98.5)	187(95.9)
Vigorous exercise/activity				0.037
Yes	55(16.9)	29(22.1)	26(13.3)
No	271(83.1)	102(77.9)	169(86.7)
Moderate exercise/activity				0.013
Yes	90(27.6)	46(35.1)	44(22.6)
No	236(72.4)	85(64.9)	151(77.4)
Added salt to food				0.931
Never	18(5.5)	8(6.1)	10(5.1)
Occasionally	55(16.9)	22(16.8)	33(16.9)
With each meal	253(77.6)	101(77.1)	152(77.9)
Eat canned/processed food				<0.001
Never	193(59.2)	63(48.1)	130(66.7)
Yes (occasionally and often)	133(40.8)	68(51.9)	65(33.3)
Daily water intake
<2 letters per day	111(34.0)	46(35.1)	65(33.3)	0.739
≥ 2 letters per day	215(66.0)	85(64.9)	130(66.7)
Use of over NSAIDs drugs regularly				0.002
Yes	43(13.2)	8(6.1)	35(17.9)
No	283(86.8)	123(93.9)	160(82.1)

# Fishers exact test was used

Clinical characteristics/Co-morbidities

One fifth (21.1%; n = 71) of the participants were hypertensive: defined as being on antihypertensive drug or having a systolic blood pressure of ≥140mmHg and /or diastolic blood pressure of ≥ 90mmHg. Similarly, one-fifth of the participants (20.2%; n = 66) reported history of anemia. However, small proportion of the participants (3.4%; n = 11) were diabetic defined as being on drugs for diabetes and/or having blood glucose level >200mg/dl. Overall, there is a significant difference in the proportion of self-reported history of anemia and kidney stone between men and females (p<0.05) as described in Table 3.

Table 3

Clinical/co-morbidity related characteristics of study participants in Sheka zone, Southwest Ethiopia, 2018.

Characteristics	Overall	Men	Women	P-value
Hypertension				0.215
Yes	71(21.8)	24(18.3)	47(24.1)
No	255(78.2)	107(81.7)	148(75.9)
Diabetes				0.761#
Yes	11(3.4)	5(3.8)	6(3.1)
No	315(96.6)	126(96.2)	189(96.9)
Anemia				<0.001
Yes	66(20.2)	6(4.6)	60(30.8)
No	260(79.8)	125(95.4)	135(69.2)
Heart Disease *				0.374 #
Yes	12(3.7)	3(2.3)	9(4.6)
No	314(96.3)	128(97.7)	186(95.4)
Kidney stone				0.049
Yes	34(10.4)	19(14.5)	15(7.7)
No	292(89.6)	112(85.5)	180(92.3)

* Heart Disease includes coronary disease (vessel surgery), heart failure, or heart attack or stork, # Fishers exact test was used

Anthropometric and laboratory measurements

Table 4 describes the mean differences of the anthropometric measurements and laboratory tests between men and women which were assessed using an independent t-test. A significant mean difference in height, BMI, serum creatinine, and eGFR using CKD-EPI and CG were observed between men and women (p<0.05). As expected, serum creatinine was higher in men than women (p < 0.001) but they had a significantly lower mean estimated creatinine clearance. We also found significantly high level of obesity among women as compared to men (p<0.001).

Table 4

Anthropometric and laboratory measurement results of study participants in Sheka zone, Southwest Ethiopia, 2018.

Characteristics	Overall	Men	Women	P-value
Mean weight (Kg.)	60.96±9.63	61.49± 9.13	60.61±9.96	0.414
Mean height(meter)	1.60± 0.08	1.66±0.08	1.57±0.06	<0.001
Mean SBP, mmHg (SD)	121.01±15.1	121.14±12.5	120.92±16.6	0.896
Mean DBP, mmHg (SD)	80.11± 10.7	79.30±9.2	80.66±11.6	0.262
Mean BMI, kg/m2 (SD)	23.84±4.10	22.52±3.7	24.73±4.1	<0.001
Mean serum creatinine, mg/dl (SD)	0.74±0.22	0.99±.31	0.69±.14	<0.001
Mean creatinine clearance, ml/min (SD)
MDRD	131.29±32.5	114.79±37.6	135.03±30.5	0.075
CG	110.67±33.0	90.20±32.6	115.32±32.7	0.033
CKD-EPI	124.34±23.8	107.10±33.3	128.26±19.5	0.010
Obesity				<0.001
Yes	25(7.7)	2(1.5)	23(11.8)
No	301(92.3)	129(98.5)	172(88.2)

Staging of kidney function and prevalence of chronic kidney disease

Using CKD-EPI estimator, about 23 (7.05%) of the participants had stage 1 and stage 2 CKD. Whereas, only 1 (0.3%) of them had chronic renal failure (stage 3a-5). The overall prevalence of CKD defined as proteinuria of ≥+1 and/or GFR<60 mL/min/1.73m2 was 7.4% ((95% confidence interval (CI) [4.6–10.4]).

Using CG- estimator, about 21 (6.4%) of the participants had stage 1 and stage 2 CKD. Whereas, only 5 (1.5%) of them had chronic renal failure (stage 3a-5). The overall prevalence of CKD using CG-estimator and defined as proteinuria of ≥+1 and/or GFR <60 was 8.0% ((95% confidence interval (CI) [5.2–11.0]). whereas, using MDRD- estimator, about 23 (7.0%) of the participants had stage 1 and 2 CKD. Only 0.3% of them had chronic renal failure (stage 3a-5). The overall prevalence of CKD Using MDRD-estimator and defined as proteinuria of ≥+1and/or GFR<60 mL/min/1.73m2 was 7.4% ((95% confidence interval (CI) [4.6–10.1]) as showed in Table 5.

Table 5

Stages of kidney functions using the three eGFR estimators in Sheka zone, Southwest Ethiopia, 2018.

Stages	GFR estimation	CKD-Epi n (%)	CG n (%)	MDRD n (%)
1	≥ 90, with proteinuria (≥+1)	19(5.8)	17(5.2)	20(6.1)
2	60–89, with proteinuria (≥+1)	4(1.2)	4(1.2)	3(0.9)
3a	45–59.9, with or without proteinuria (≥+1)	1(0.3)	4(1.2)	1(0.3)
3b	30–44.9, with or without proteinuria (≥+1)	0(0)	1(0.3)	0(0)
4	15–29.9, with or without proteinuria (≥+1)	0(0)	0(0)	0(0)
5	< 15, with or without proteinuria (≥+1)	0(0)	0(0)	0(0)
	Overall CKD	24(7.4)	26(8.0)	24(7.4)

Bivariate and multivariable analysis of associated factors

As the outcome variable is dichotomous, we applied logistic regression model. Before applying the model, the assumptions of logistic regression were checked. The linear relationship between continuous independent variables (age and income) and their logit transformation were checked. We found that there was no linear relationship between age and income and their log transformations. As a result, we included age and income as categorical variables in the model. We also investigated the multicollinearity between independent variables using the variance inflation factor. A significant multicollinearity was not identified. We used three separate logistic regression models for three common GFR estimators. However, the models explained only up 41% (Nagelkerke R2) of the variance in the CKD. The Hosmer & Lemeshow test of the three models was not significant (p>0.05) which suggests good model fitness.

Using all the three GFR estimators, age, hypertension, heart disease, central obesity and use of over the counter NSAIDs drugs regularly were significantly associated with CKD in the Bivariate analysis (p<0.05). In the full adjusted model, only hypertension was significantly associated with CKD using CKD-EPI & MDRD- estimators described in Tables 6 and 7 respectively. On the other hand, age was significantly associated with CKD using CG- estimator in the full adjusted model as describe in Table 8. Using CKD-EPI & MDRD- estimators, participants who had hypertension were nearly 2.61 times more likely to have CKD than those who didn’t have hypertension (p<0.001). Using CG- estimator, participants whose age ≥40 were 3.187 times more likely to exhibit CKD than whose age was 18–39 years (p = 0.005). However, central obesity and age showed borderline significant using CKD-EPI & MDRD estimators described in Tables 6 and 7 respectively.

Table 6

Factors associated with CKD in age and sex adjusted logistic regressions using CKD-EPI estimator in Sheka Zone, Southwest Ethiopia, 2018.

Variables	Chronic Kidney Disease		COR with 95% CI	P value	AOR with 95% CI
Socio-demographic characteristics	Yes (n, %)	No (n, %)
Sex
Male	6(25.0)	125(41.4)	1		1
Female	18(75.0)	177(58.6)	2.12(0.82–5.49)	0.122	1.031(0.295–3.598)
Age
18–39	13(54.2)	228(75.5)	1
≥40	11(45.8)	74(24.5)	2.607(1.120–6.067)	0.026	2.347(0.902–6.109)
Education status
Not attended formal education	1(4.2)	52(17.2)	0.642(0.277–1.492)	0.303
Attended formal education	23(95.8)	250(82.8)	1
Marital status
Married	21(87.5)	246(81.5)	1
Others	3(12.5)	56(18.5)	0.628(0.181–2.177)	0.463
Religion
Christian	17(70.8)	225(74.5)	1
Muslim	7(29.2)	77(25.5)	1.203(0.481–3.012)	0.693
Occupation
Employed*	4(7.5)	49(92.5)
Farming	2(3.6)	54(96.4)	0.454(0.080–2.587)	0.374
Merchant	4(8.2)	45(91.8)	1.089(0.257–4.613)
Housewife	11(10.6)	93(89.4)	1.449(0.438–4.789)
Students	2(4.8)	40(95.2)	0.613(0.107–3.518)
Others**	1(4.5)	21(95.5)	0.583(0.061–5.535)
Ethnicity
Sheka (natives)	9(37.5)	77(25.5)	1.753(0.737–4.168)	0.204
Others***	15(62.5)	225(74.5)	1
Monthly income
<2000EB	9(45.0)	104(42.8)	1
≥2000EB	11(55.0)	139(57.2)	0.914(0.366–2.287)	0.848
Main source of water for drinking
Wall/spring	11(45.8)	144(47.7)	0.928(0.403–2.138)	0.861
Pip water	13(54.2)	158(52.3)	1
Lifestyle/behavioral characteristics
Ever drink alcohol
Yes	1(4.2)	45(14.9)	0.248(0.033–1.885)	0.178	0.269(0.033–2.176)
No	23(95.8)	257(85.1)	1
Ever smoke cigarette
Yes	1(4.2)	16 5.3%	0.777(0.099–6.125)	0.811
No	23(95.8)	286(94.7)	1
Smoking year
<10 years	0(0.0)	13(81.3)	≅0	.999
≥10 years	1(100.0)	3(18.8)	1
Herbal supplement/ medicine
Yes	2(8.3)	8(2.6)	3.341(0.669–16.696)	0.142	0.997(0.150–6.621
No	22(91.7)	294(97.4)	1
Use of over the counter NSAIDs drugs regularly
Yes	7(29.2)	36(11.9)	3.042(1.181–7.840)	0.021	2.219(0.760–6.483
No	17(70.8)	266(88.1)	1
Vigorous exercise /activity
Yes	2(8.3)	53(17.5)	0.427(0.097–1.872	0.259
No	22(91.7)	249(82.5)	1
Moderate exercise/activity
Yes	9(37.5)	81(26.8)	1.637(0.689–3.887)	0.264
No	15(62.5)	221(73.2)	1
Eat canned/processed foods
Never	13(54.2)	180(59.6)	1
Yes (occasionally and often)	11(45.8)	122(40.4)	1.248(0.542–2.878)	0.603
Added salt to food
Never	1(4.2)	17(5.6)	0.871(0.109–6.970)		0.878(0.096–8.007)
Occasionally	7(29.2)	48(15.9)	2.160(0.843–5.534)	0.109	1.593(0.546–4.647
With each meal	16(66.7)	237(78.5)	1
Daily water intake
<2 letters per day	11(45.8)	100(33.1)	1.709(0.739–3.951)	0.210
≥ 2 letters per day	13(54.2)	202(66.9)	1
Co-morbidities/clinical characteristics
Hypertension
Yes	12(50.0)	59(19.5)	4.119(1.762–9.629)	0.001	2.614(1.016–6.727
No	12(50.0)	243(80.5)	1
Diabetes mellitus
Yes	1(4.2)	10(3.3)	1.270(0.156–10.357)	0.824
No	23(95.8)	292(96.7)	1
Anemia
Yes	6(25.0)	60(19.9)	1.344(0.512–3.533	0.548
No	18(75.0)	242(80.1)	1
Heart Disease*
Yes	3(12.5)	9(3.0)	4.651(1.170–18.479)	0.029	2.539(0.456–14.142
No	21(87.5)	293(97.0)	1
Kidney stone
Yes	4(16.7)	30(9.9)	1.813(0.581–5.657)	0.305
No	20(83.3)	272(90.1)	1
Central obesity
Yes	13(54.2)	69(22.8)	3.991(1.711–9.306)	0.001	2.352(0.770–7.188
No	11(45.8)	233(77.2)	1
Nutritional status based on BMI
Under weight	0(0.0)	26(8.8)	≅0
Normal weight	15(62.5)	175(58.9)	0.986(0.212–4.589)
Overweight	7(29.2)	73(24.6)	1.103(0.214–5.684)	0.907
Obese	2(8.3)	23(7.7)	1

Table 7

Factors associated with CKD in age and sex adjusted logistic regressions using MDRD estimator in Sheka Zone, Southwest Ethiopia, 2018.

Variables	Chronic Kidney Disease		COR with 95% CI	P value	AOR with 95% CI
Socio-demographic characteristics	Yes (n, %)	No (n, %)
Sex
Male	6(25.0)	125(41.4)	1		1
Female	18(75.0)	177(58.6)	2.119(0.818–5.489)	0.122	1.031(0.295–3.598)
Age
18–39	13(54.2)	228(75.5)	1
≥40	11(45.8)	74(24.5)	2.607(1.120–6.067)	0.026	2.347(0.902–6.109)
Education status
Not attended formal education	1(4.2)	52(17.2)	0.209(0.028–1.582)	0.303
Attended formal education	23(95.8)	250(82.8)	1
Marital status
Married	21(87.5)	246(81.5)	1
Others	3(12.5)	56(18.5)	0.628(0.181–2.177)	0.463
Religion
Christian	17(70.8)	225(74.5)	1
Muslim	7(29.2)	77(25.5)	1.203(0.481–3.012)	0.693
Occupation
Employed*	4(16.7)	49(16.2)
Farming	2(8.3)	54(17.9)	0.454(0.080–2.587)	0.374
Merchant	4(16.7)	45(14.9)	1.089(0.257–4.613)
Housewife	11(45.8)	93(30.8)	1.449(0.438–4.789)
Students	2(8.3)	40(13.2)	0.613(0.107–3.518)
Others**	1(4.2)	21(7.0)	0.583(0.061–5.535)
Ethnicity
Sheka(natives)	9(37.5)	77(25.5)	1.753(0.737–4.168)	0.204
Others***	15(62.5)	225(74.5)	1		1
Monthly income
<2000EB	9(45.0)	104(42.8)	1		1
≥2000EB	11(55.0)	139(57.2)	0.914(0.366–2.287)	0.848
Main source of water for drinking
Wall/spring	11(45.8)	144(47.7)	0.928(0.403–2.138)	0.861
Pip water	13(54.2)	158(52.3)	1		1
Lifestyle/behavioral characteristics
Ever drink alcohol
Yes	1(4.2)	45(14.9)	0.248(0.033–1.885)	0.178	0.269(0.033–2.176)
No	23(95.8)	257(85.1)	1		1
Ever smoke cigarette
Yes	1(4.2)	16(5.3)	0.777(0.099–6.125)	0.811
No	23(95.8)	286(94.7)	1
Smoking year
<10 years	0(0.0)	13(81.3)	≅0	0.999
≥10 years	1(100.0)	3(18.8)	1
Herbal supplements medicine
Yes	2(8.3)	8(2.6)	3.341(0.669–16.696)	0.142	0.997(0.150–6.621)
No	22(91.7)	294(97.4)	1		1
Use of over the counter NSAIDs drugs regularly
Yes	7(29.2)	36(11.9)	3.042(1.181–7.840)	0.021	2.219(0.760–6.483)
No	17(70.8)	266(88.1)	1		1
Vigorous exercise /activity
Yes	2(8.3)	53(17.5)	0.427(0.097–1.872)	0.259
No	22(91.7)	249(82.5)	1
Moderate exercise/activity
Yes	9(37.5)	81(26.8)	1.637(0.689–3.887)	0.264
No	15(62.5)	221(73.2)	1
Eat canned/processed foods
Never	13(54.2)	180(59.6)	1
Yes (occasionally and often)	11(45.8)	122(40.4)	1.248(0.542–2.878)	0.603
Added salt to food
Never	1(4.2)	17(5.6)	0.871(0.109–6.970)		0.878(0.096–8.007)
Occasionally	7(29.2)	48(15.9)	2.160(0.843–5.534)	0.109	1.593(0.546–4.647)
With each meal	16(66.7)	237(78.5)	1		1
Daily water intake
<2 letters per day	11(45.8)	100(33.1)	1.709(0.739–3.951)	0.210
≥ 2 letters per day	13(54.2)	202(66.9)	1
Co-morbidities/clinical characteristics
Hypertension
Yes	12(50.0)	59(19.5)	4.119(1.762–9.629)	0.001	2.614(1.016–6.727)
No	12(50.0)	243(80.5)	1		1
Diabetes
Yes	1(4.2)	10(3.3)	1.270(0.156–10.357)	0.824
No	23(95.8)	292(96.7)	1
Anemia
Yes	6(25.0)	60(19.9)	1.344(0.512–3.533)	0.548
No	18(75.0)	242(80.1)	1
Heart Disease*
Yes	3(12.5)	9(3.0)	4.651(1.170–18.479)	0.029	2.539(0.456–14.142)
No	21(87.5)	293(97.0)	1		1
Kidney stone
Yes	4(16.7)	30(9.9)	1.813(0.581–5.657)	0.305
No	20(83.3)	272(90.1)	1
Central Obesity
Yes(obese)	13(54.2)	69(22.8)	3.991(1.711–9.306)	0.001	2.352(0.770–7.188)
No (not obese)	11(45.8)	233(77.2)	1		1
Nutritional status based on BMI
Under weight	0(0.0)	26(8.8)	≅0
Normal weight	15(62.5)	175(58.9)	0.986(0.212–4.589)
Overweight	7(29.2)	73(24.6)	1.103(0.214–5.684)	0.907
Obese	2(8.3)	23(7.7)	1

Table 8

Factors associated with CKD in age and sex adjusted logistic regressions using CG estimator in Sheka Zone, Southwest Ethiopia, 2018.

Variables	Chronic Kidney Disease		Unadjusted	P-value	Adjusted
Socio-demographic characteristics	Yes (n, %)	No n, %)
Sex
Male	6(23.1)	125(41.7)	1		1
Female	20(76.9)	175(58.3)	2.381(0.929–6.100)	0.071	1.340(0.404–4.442)
Age
18–39	13(50.0)	228(76.0)	1
≥40	13(50.0)	72(24.0)	3.167(1.404–7.141)	0.005	3.187(1.281–7.926)
Education status
Not attended formal education	2(7.7)	51(17.0)	0.407(0.093–1.776)	0.232
Attended formal education	24(92.3)	249(83.0)	1		1
Marital status
Married	23(88.5)	244(81.3)	1		1
Others	3(11.5)	56(18.7)	0.568(0.165–1.959)	0.371
Religion
Christian	18(69.2)	224(74.7)	1		1
Muslim	8(30.8)	76(25.3)	1.310(0.547–3.135)	0.544
Occupation
Employed*	4(15.4)	49(16.3)	1
Farming	3(11.5)	53(17.7)	0.693(0.148–3.256)
Merchant	4(15.4)	45(15.0)	1.089(0.257–4.613)	0.438
Housewife	12(46.2)	92(30.7)	1.598(0.489–5.218)
Students	2(7.7)	40(13.3)	0.613(0.107–3.518)
Others**	1(3.8)	21(7.0)	0.583(0.061–5.535)
Ethnicity
Sheka (natives)	10(38.5)	76(25.3)	1.842(0.802–4.232)	0.150
Others***	16(61.5)	224(74.7)	1		1
Monthly income
<2000EB	11(50.0)	102(42.3)	1		1
≥2000EB	11(50.0)	139(57.7)	0.734(0.306–1.758)	0.488
Main source of water for drinking
Wall/spring	13(50.0)	142(47.3)	1.113(0.499–2.480)	0.794
Pip water	13(50.0)	158(52.7)	1		1
Lifestyle/behavioral characteristics
Ever drink alcohol
Yes	1(3.8)	45(15.0)	0.227(0.030–1.715)	0.151	0.253(0.032–2.025)
No	25(96.2)	255(85.0)	1
Ever smoke cigarette
Yes	1(3.8)	16(5.3)	0.710(0.090–5.578)	0.745
No	25(96.2)	284(94.7)	1
Smoking year
<10 years	0(0.0)	13(81.3)	≅0	0.999
≥10 years	1(100.0)	3(18.8)	1
Herbal supplements /medicine
Yes	2(7.7)	8(2.7)	3.042(0.611–15.132)	0.174	1.106(0.167–7.342)
No	24(92.3)	292(97.3)	1
Use of over the counter NSAIDs drugs regularly
Yes	7(26.9)	36(12.0)	2.702(1.062–6.875)	0.037	1.937(0.673–5.576)
No	19(73.1)	264(88.0)	1
Vigorous exercise /activity
Yes	2(7.7)	53(17.7)	0.388(0.089–1.694)	0.208
No	24(92.3)	247(82.3)	1
Moderate exercise/activity
Yes	10(38.5)	80(26.7)	1.719(0.749–3.944)	0.201
No	16(61.5)	220(73.3)	1
Eat canned/processed foods
Never	15(57.7)	178(59.3)	1
Yes (occasionally and often)	11(42.3)	122(40.7)	1.070(0.475–2.409)	0.870
Added salt to food
Never	1(3.8)	17(5.7)	0.768(0.097–6.104)		0.716(0.078–6.543)
Occasionally	7(26.9)	48(16.0)	1.904(0.754–4.809)	0.173	1.471(0.511–4.233)
With each meal	18(69.2)	235(78.3)	1
Daily water intake
<2 letters per day	11(42.3)	100(33.3)	1.467(0.650–3.311)	0.357
≥ 2 letters per day	15(57.7)	200(66.7)	1
Co-morbidities/clinical
Hypertension
Yes	12(46.2)	59(19.7)	3.501(1.539–7.965)	0.003	2.105(0.836–5.299)
No	14(53.8)	241(80.3)	1
Diabetes
Yes	1(3.8)	10(3.3)	1.160(0.143–9.433)	0.890
No	25(96.2)	290(96.7)	1
Anemia
Yes	6(23.1)	60(20.0)	1.200(0.462–3.119)	0.708
No	20(76.9)	240(80.0)	1
Heart Disease*
Yes	3(11.5)	9(3.0)	4.217(1.068–16.661)	0.040	2.555(0.462–14.133)
No	23(88.5)	291(97.0)	1
Kidney stone
Yes	4(15.4)	30(10.0)	1.636(0.529–5.066)	0.393
No	22(84.6)	270(90.0)	1
Central Obesity
Yes	14(53.8)	68(22.7)	3.980(1.758–9.011)	0.001	2.147(0.748–6.162)
No	12(46.2)	232(77.3)	1
Nutritional status based on BMI
Under weight	1(3.8)	25(8.5)	0.460(0.039–5.418)	0.537
Normal weight	15(57.7)	175(59.3)	0.986(0.212–4.589)
Overweight	8(30.8)	72(24.4)	1.278(0.253–6.451)
Obese	2(7.7)	23(7.8)	1

Discussion

In the current study, we assessed the prevalence and associated risk factors of chronic kidney diseases among the urban adult population in Sheka zone using the three commonest estimators of kidney functions. Over 7% of urban adults had CKD regardless of whether CKD-EPI, CG or MDRD GFR estimators were used. This finding is comparable with the prevalence of CKD reported in other sub-Saharan African counties; 10.4% in Nigeria [14], 12.4% in Kinshasa Congo [15], 10% in Cameroon [10], and 7% in Tanzania with 15.2% in the urban population [16]. The prevalence of CKD was slightly higher using the CG GFR estimator. It has been documented that CG overestimates the true prevalence of CKD [10, 16]. But, a similar prevalence of CKD was estimated using CKD-EPI and MDRD estimators. This may indicate that either CKD-EPI or MDRD can be used to estimate GFR in this study area. Other study findings, for instance, in Southern Africa and Ghana also showed that there is a high agreement between these two GFR estimators [17, 18].

Using NKF/KDOQI staging, we found that most of the participants were in stage I & II. The finding is in line with several study findings in developing countries [9-11]. People within the early stage of CKD may not aware of their status. In fact, several studies showed that the majority of CKD patients don’t aware their kidney problems [15, 19, 20]. Identification of people in the early stage of CKD is important. This is because the cardiovascular risks associated with stage I & II is nearly equal to that of stage III [21].

After adjusting socio-demographic, lifestyle and co-morbidities related variables, only hypertension and advanced age were found to be significantly associated with CKD using CKD-EPI/MDRD & CG estimators respectively. However, more of the increased prevalence of CKD in this study area seems to be unexplained. In line with our study finding, a recent study finding in Tanzania and Uganda showed that the majority of the increase in the risk of CKD was not explained [9, 11]. Unexplained risk factors of CKD in this study area may suggest that other non-traditional risk factors might contribute to the high burden of CKD. Some study findings in another part of the world showed that nontraditional risk factors such as heavy metals and agro-chemicals may contribute to CKD in tropical and semi-tropical areas [22, 23]. Some other studies suggested that infectious diseases such as HIV/AIDS, Schistosomiasis and Leishmaniasis are also associated with CKD [24-26]. However, as a limitation of this study, we didn’t control the confounding effect of these risk factors of CKD. Large-scale epidemiological studies are needed to better understand the specific risk factors of CKD and, to examine the potential but unmeasured above risk factors.

As documented in the broad literature [7, 10, 11, 19], advanced age is a well-known risk factor of CKD. As age increases, there may be salt sensitization, hardening of arteries and scarring of the tiny blood vessels in the kidney. This may, in turn, enhance protein excretion and decrease GFR [27-29]. However, as many of the adult participants in the current study didn’t have a birth certificate, they may underestimate their age. Underestimation of age could lead to overestimation of GFR using any of the methods used to calculate GFR. This may in turn underestimate the true prevalence of CKD in this study area since the level of creatinine is affected by socio-demographic factors such as age [30].

In the current study, among the traditional risk factors of CKD, hypertension was found to be significantly associated with CKD. This is in line with several community-based study findings [10, 11, 14, 20]. Hypertension may elevate intraglomerular pressure which results in glomerulosclerosis and eventual protein trafficking [31, 32]. However, as a limitation of this study (chicken egg dilemma), it is hard to distinguish whether hypertension causes CKD or CKD causes hypertension. An increased risk of CKD among hypertensive participants may warrant urban adults to adhere to treatment and control their blood pressures. Studies have shown that interventions that decrease blood pressure levels in patients with proteinuria delay progression to CKD [33, 34].

Co-morbidity related factors such as diabetes, anemia, kidney stone; heart disease, central obesity and nutritional status measured by BMI were not significantly associated with CKD. However, central obesity showed a borderline significant. It has been documented that waist circumference or waist to hip ratio predicts cardiovascular risks than BMI [35]. The low prevalence of the above-mentioned co-morbidities in our participants makes any comment of their association difficult to ascertain.

In the current study, behavioral factors such as alcohol intake and cigarette smoking were not significantly associated with CKD. This is in line with several study findings [14, 36, 37]. Alcohol intake and cigarette smoking are often considered anti-social habits in Ethiopia so people may give a negative response. This may, in turn, nullify the true association between alcohol intake, cigarette smoking and, CKD.

Overall, in this study area, CKD is significantly associated with age ≥40 years old and hypertension. Early screening of high-risk populations (elderly and hypertensive peoples in the case of this study area) may help to diagnose people at the early stage of CKD. This is very important as Ethiopian health care institutions may not be able to support the economic burden of providing dialysis and renal replacement therapy to all patients. Dialysis and renal replacement therapy are not free in Ethiopia. This may result catastrophic out of the pocket expenditure. Thus, early screening of high-risk populations to CKD is important to minimize complications and out of pocket expenditures.

This study was the first community-based assessment of CKD in Southwest Ethiopia using the three common GFR estimators. The finding of the study will provide information about the urban prevalence of CKD in Sheka zone. However, this study has several limitations. The cross-sectional nature of this study restricted us to infer causality. The common GFR measures used in this study were not validated in Ethiopia. The absence of reliable and validated measures of kidney function tests has made difficult the estimation of CKD in Africa [16, 18]. This study exclusively focused on the urban population. Thus, the estimated CKD may not reflect the prevalence and risk factors of rural communities. Because of security and financial restrictions, we didn’t make a three months control of positive findings as suggested by the KDIGO guideline. Some risk factors identified elsewhere such as heavy metals, agrochemicals, Schistosomiasis, Leishmaniasis and HIV [22-26] were not investigated in this study.

Conclusions

The prevalence of CKD was high in the study area. Only hypertension and age ≥ years old were significantly associated with CKD. More of the increased prevalence of CKD in the current study remained unexplained and deserves further study. Policymakers and programmers need to design strategies and encourage high-risk populations to be screened as early as possible. It is better if clinicians should also consider kidney function marker tests for hypertensive and patients older than 40 years old.

Questionnaire in English and local language (Amharic).

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22 Jun 2020

PONE-D-20-08526

Chronic Kidney Disease and Associated factors among adult population in Southwest Ethiopia

PLOS ONE

Dear Dr. Abateneh,

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.

We recommend to include the potential impact of the kebeles in the results of the survey.

We requiere to supress these first lines and start the paper directly with the CKD.

Authors are required to re-define what advanced age means or modify the age ranges. > 40 years cannot be considered advanced age

The increased prevalence of CKD in the current study remains unexplained and scarcely discussed. Authors are requiered to recommend the use of CKD-EPI, MDRD or CG to estimate CKD prevalence in order to make recommendations.

The paper is suitable for publication but needs to focus in the novelty and originality of the studied population. We do not identify  any conflicts of interest.

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PLOS ONE

Additional Editor Comments:

There are similar papers as Sumaili et al in 2009 about prevalence of Chronic Kidney Disease in the Democratic Republic of Congo. Although there is no significant new clinical data, the study is based in a survey and there are some socio demographic data that are of interest. The survey is based in an administrative Ethiopian distribution of kebeles, making it original. However these points are not discussed along the paper. It would be of interest to compare the urban and rural areas.

Moreover, the introduction is initially focused in the global burden of noncommunicable diseases (NCD) but this not the main point along the paper.

Hypertension and advanced age were significantly associated with the excess risk of CKD, however the advanced age criteria is > 40 years. Authors must re-define what advanced age means or modify the age ranges.

The increased prevalence of CKD in the current study remains unexplained and scarcely discussed. Authors must recommend the use of CKD-EPI, MDRD or CG to estimate CKD prevalence in order to make recommendations.

The paper is suitable for publication but needs to focus in the novelty and originality of the studied population. There are no conflicts of interest.

I consider that after this corrections in the introduction (eliminate the initial lines related to NCD, and start with CKD prevalence and the collected data related with general and CKD populations

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27 Jul 2020

Ref No: PONE-D-20-08526

Title: Chronic Kidney Disease and Associated Factors among Adult Population in Southwest Ethiopia

Response to reviewers

1. We recommend to include the potential impact of the kebeles in the results of the survey.

Response: Dear reviewer, all the kebeles included in this study were urban kebeles and assumed to be homogenous. Therefore, the prevalence of CKD is not expected to vary among the included kebeles.

2. We require to suppress these first lines and start the paper directly with the CKD.

Response: We have deleted the first paragraph and started the paper directly with the CKD (Introduction section, page 2, line 38-44).

3. Authors are required to re-define what advanced age means or modify the age ranges. > 40 years cannot be considered advanced age

Response: Dear reviewer thank you very much for this comment. We couldn’t find a standard definition for advanced age for sub-Saharan countries. For this reason, we have removed the term “advanced age” and simply we categorized age as ≥40 years old and <40 years old. The age category was decided for comparison (Compare with other studies) (Abstract section, page 2, line 30; Discussion section, page 25, line 316; Conclusions section, page 26, line 337and 341).

4. The increased prevalence of CKD in the current study remains unexplained and scarcely discussed. Authors are required to recommend the use of CKD-EPI, MDRD or CG to estimate CKD prevalence in order to make recommendations.

Response: Even though the aim of this study is not to recommend the use of CKD-EPI, MDRD or CG, we have incorporated recommendations on the use of CKD-EPI, MDRD, or CG to estimate CKD prevalence in this study area (Abstract section, page 2, line 27-28).

5. The paper is suitable for publication but needs to focus in the novelty and originality of the studied population.

Response: Dear reviewer, thank you for this comment. At this stage, we can’t change the study population. It is difficult to say that the study population is unique. This study was conducted among the urban population in the Sheka zone. Therefore, the population may share similar characteristics with other urban populations in Ethiopia. However, this study is the first community-based study in the study area and in Ethiopia at large. In fact, this study has certain limitations. We have included those limitations in the manuscript.

Response to Editor

1. There are similar papers as Sumaili et al in 2009 about prevalence of Chronic Kidney Disease in the Democratic Republic of Congo. Although there is no significant new clinical data, the study is based in a survey and there are some socio-demographic data that are of interest. The survey is based in an administrative Ethiopian distribution of kebeles, making it original. However, these points are not discussed along the paper.

Response: Dear editor, all the kebeles included in this study were urban kebeles and assumed to be homogenous. Therefore, the prevalence of CKD is not expected to vary among the included kebeles.

2. It would be of interest to compare the urban and rural areas.

Response: Dear editor, this study was exclusively studied in urban areas. Therefore, it is difficult to compare urban and rural areas.

3. Moreover, the introduction is initially focused in the global burden of noncommunicable diseases (NCD) but this not the main point along the paper.

Response: We have deleted the first paragraph and started the paper directly with the CKD (page 2, line 38-44).

4. Hypertension and advanced age were significantly associated with the excess risk of CKD, however the advanced age criteria is > 40 years. Authors must re-define what advanced age means or modify the age ranges.

Response: Dear editor, thank you very much for this comment. We couldn’t find a standard definition for advanced age for sub-Saharan countries. For this reason, we removed the term “advanced age” and simply we categorized age as ≥ 40 years old and <40 years old. The age category was decided for comparison (Compare with other studies) (Abstract section, page 2, line 30; Discussion section, page 25, line 316; Conclusions section, page 26, line 337 and 341).

5. The increased prevalence of CKD in the current study remains unexplained and scarcely discussed. Authors must recommend the use of CKD-EPI, MDRD or CG to estimate CKD prevalence in order to make recommendations.

Response: Even though the aim of this study is not to recommend the use of CKD-EPI, MDRD, or CG, we have incorporated recommendation on use of CKD-EPI, MDRD or CG to estimate CKD prevalence in this study area (page 2, line 27-28)

6. The paper is suitable for publication but needs to focus in the novelty and originality of the studied population.

Response: Dear editor, thank you for this comment. At this stage, we can’t change the study population. It is difficult to say that the study population is unique. This study was conducted among the urban population in the Sheka zone. Therefore, the population may share similar characteristics with other urban populations in Ethiopia. However, this study is the first community-based study in the study area and in Ethiopia at large. In fact, this study has certain limitations. We have included those limitations in the manuscript.

7. I consider that after this correction in the introduction (eliminate the initial lines related to NCD, and start with CKD prevalence and the collected data related with general and CKD populations

Response: Corrected based on your comment.

8. If you developed a questionnaire as part of this study and it is not under a copyright more restrictive than CC-BY, please include a copy, in both the original language and English, as Supporting Information.

Response: We have uploaded a questionnaire both in English and local language (Amharic) as supporting information.

Submitted filename: Response to Reviewers.docx

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30 Jun 2021

PONE-D-20-08526R1

Chronic Kidney Disease and Associated Factors among Adult Population in Southwest Ethiopia

PLOS ONE

Dear Dr. Derseh,

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Additional Editor Comments (if provided):

Authors respond to all the comments of both reviewers and editor. All significant points are corrected in the final version and the paper is suitable for being puiblished. Although the novelty of the resultas are scarce, the study is well designed and the methodology have some interest for the region.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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Reviewer #1: Partly

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: 1. The work is original and will contribute to body of knowledge

2. The author should pay attention and correction the minor edits in the manuscript

3. Figures should carry units

4. Presentation of tables should not be merged together like tables 6-8. Each should be presented separately, highlighting what is significant and relevant to the study.

5. Discussions should focus more on the findings based on the objectives of the study

6. The conclusion should be derived from the outcomes or findings of the study and should not be inferred or extrapolated.

Reviewer #2: 1. On page 13, row 229, please clarify the sentence “As the outcome variable is dictums, we applied logistic regression model”.

2. Please indicate in the methods how was central obesity defined. The methods seem to define obesity only and waist circumference, which is not the same as central obesity. The latter is habitually defined as waist to height ratio.

3. There are a couple places in the results where the statement “developed chronic renal failure” is being used. Since this is a cross-sectional study, time-dependent terminology should be avoided. The authors may wish to use something on the line of “had chronic renal failure” instead.

4. The lack of associations with other risk factors than HT might be due to the low prevalence of these other factors, i.e. CVD, diabetes, in the studied population. The authors may wish to comment on this aspect.

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Reviewer #1: Yes: Timothy Olusegun Olanrewaju

Reviewer #2: No

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Submitted filename: PONE-D-20-08526_R1_reviewer (1).pdf

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Submitted filename: The Editor PlosOne.docx

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17 Aug 2021

Ref No: PONE-D-20-08526R1

Title: Chronic Kidney Disease and Associated Factors among Adult Population in Southwest Ethiopia

Response to reviewers

Reviewer #1:

1. The work is original and will contribute to body of knowledge

Response: Dear reviewer, thanks for your remark.

2. The author should pay attention and correction the minor edits in the manuscript

Response: Dear Reviewer, thanks for your comment and we have corrected the minor edits within the manuscript accordingly.

3. Figures should carry units

Response: We have added the units with figures accordingly (Abstract section, page 2, line 26-27, Result section, page 13, line 217, 229 ).

4. Presentation of tables should not be merged together like tables 6-8. Each should be presented separately, highlighting what is significant and relevant to the study.

Response: We have cited the tables separately (Result section, page 14, line 251).

5. Discussions should focus more on the findings based on the objectives of the study

Response: Dear reviewer, we appreciate your comment. In the discussion section, we tried to be focused based on the two specific objectives of the study. i. the prevalence of CKD based on the GFR estimators and ii. associated factors of CKD. The discussion is based on the finding of the two objectives.

6. The conclusion should be derived from the outcomes or findings of the study and should not be inferred or extrapolated.

Response: Dear reviewer, we appreciate your comment and we have corrected the conclusion section mainly on the conclusion on the prevalence of CKD and avoid extrapolation during the conclusion (Conclusions, page 26, line 339).

Reviewer #2:

1. On page 13, row 229, please clarify the sentence “As the outcome variable is dictums, we applied logistic regression model”.

Response: Thanks for your comment. The sentence “As the outcome variable is dictums, we applied logistic regression model” is to means “As the outcome variable is dichotomous, we applied logistic regression model.” We re-wrote the sentence. It’s to indicate the outcome variable CKD has one of two possible values and fulfills one of the logistic regression assumptions (Result section, page 14, line 232).

2. Please indicate in the methods how was central obesity defined. The methods seem to define obesity only and waist circumference, which is not the same as central obesity. The latter is habitually defined as waist to height ratio.

Response: Thanks for your comment and keen observation. The waist circumference is used to assess central obesity and it’s indicated in previous studies [1, 2]. The waist circumference in the current study is used to define central obesity with the cut-point for male ≥102 cm and female ≥88cm. We have corrected the statement accordingly in the definition of variables section of the manuscript (Methods section, Variables’ definition, page 6, line 132-133).

1. Ahmad N, Adam SI, Nawi AM, Hassan MR, Ghazi HF. Abdominal Obesity Indicators: Waist Circumference or Waist-to-hip Ratio in Malaysian Adults Population. Int J Prev Med. 2016 Jun 8;7:82. doi: 10.4103/2008-7802.183654. PMID: 27330688; PMCID: PMC4910307.

2. Lean MEJ, Han TS & Morrison CE (1995): Waist circumference as a measure for indicating the need for weight management. Br. Med. J. 311, 158–161.

3. There are a couple of places in the results where the statement “developed chronic renal failure” is being used. Since this is a cross-sectional study, time-dependent terminology should be avoided. The authors may wish to use something on the line of “had chronic renal failure” instead.

Response: We appreciate your comment and have used “had chronic renal failure” rather than “developed chronic renal failure” accordingly (Result section, page 12, 13, line 18,222, 225).

4. The lack of associations with other risk factors than HT might be due to the low prevalence of these other factors, i.e. CVD, diabetes, in the studied population. The authors may wish to comment on this aspect.

Response: Dear Reviewer, we appreciate your comment. We strongly believe that the lack of association between CKD and factors such as Diabetes and CVD in the current study might be the low prevalence or the low number of study participants with diabetes and CVD that result from no association during logistic regression analysis. Chronic diseases such as diabetes and CVD are known risk factors indicated in different studies.

Submitted filename: Response to Reviewers.docx

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9 Feb 2022

14 Feb 2022

RESPONSE TO EDITOR AND REVIEWER COMMENTS

Dear Editor and Reviewers,

The authors sincerely appreciate the time and resources invested in giving such a thorough review. Thank you for the previous comments, suggestions and corrections on the manuscript. We have made efforts to address them and revise the manuscript in line with the comments. The changes have been highlighted in red and tracked in the manuscript. We look forward to a favorable and kind re-consideration.

Editor comment

The article is much improved after addressing the reviewers comments. However, I think that it needs further improvement and incorporation of relevant literature. In this connection, I suggest to cite the following very relevant articles either in Introduction or Discussion section of the manuscript:

https://link.springer.com/article/10.1007/s11845-018-1813-2

https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-019-6796-z

https://link.springer.com/article/10.1007/s11255-018-1834-9

https://link.springer.com/article/10.1186/s40545-019-0169-y

https://www.hindawi.com/journals/bmri/2016/9710965/

Response: Dear Editor, thank you for your insights and the suggested articles to be incorporated within the manuscript. The articles are very interesting. We have added a reference that supports a statement in the discussion section (Ref#30, Page 25, Line 29). However, the other articles are studies on hemodialysis patients (focuses on depression among hemodialysis patients, hypertension control among hemodialysis patients, management of patient care in hemodialysis, and on factors affecting to achieve dry weight in post-dialysis) and we couldn’t cite as our study mainly focuses on the prevalence of CKD through a community-based cross-sectional study. But we will use it for other studies in the future.

Submitted filename: Response to Reviewers.docx

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15 Feb 2022

Chronic Kidney Disease and Associated Factors among Adult Population in Southwest Ethiopia

PONE-D-20-08526R3

Dear Dr. Derseh,

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Amjad Khan, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Thank you for considering and addressing my comments.

Reviewers' comments:

24 Feb 2022

PONE-D-20-08526R3

Chronic Kidney Disease and Associated Factors among Adult Population in Southwest Ethiopia

Dear Dr. Abateneh:

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on behalf of

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PLOS ONE