1035281

81Therapeutic Advances in Chronic DiseaseMHR Sarker, M Moriyama
research-article20212021

TAJ0010.1177/204062232110352

Chronic Kidney Disease

Special Collection

Therapeutic Advances in Chronic DiseaseOriginal ResearchCommunity-based screening to determine
the prevalence, health and nutritional
status of patients with CKD in ruraland peri-urban Bangladesh

Ther Adv Chronic Dis
2021, Vol. 12: 1–13
https://doi.org/10.1177/20406223211035281
DOI: 10.1177/
https://doi.org/10.1177/20406223211035281
20406223211035281© The Author(s), 2021.
Article reuse guidelines:
sagepub.com/journalspermissions

Mohammad Habibur Rahman Sarker , Michiko Moriyama, Harun Ur Rashid,
Mohammod Jobayer Chisti, Md Moshiur Rahman, Sumon Kumar Das, Aftab Uddin,
Samir Kumar Saha, Shams El Arifeen, Tahmeed Ahmed and ASG Faruque

Abstract
Background: Studies have labelled chronic kidney disease (CKD) among the adult populationin urban Bangladesh. To address knowledge gaps on CKD, we aimed to generate data on
prevalence, health and nutrition of CKD individuals living in rural and peri-urban Bangladesh.
Methods: Participants were recruited from the Mirzapur Demographic Surveillance System
by age-stratified random sampling. We screened participants by measuring serum creatinine
and urine albumin to creatinine ratio, and collected socio-demographic, lifestyles and healthinformation (phase I). After 3 months (phase II), we repeated the urine and blood tests as per
the Kidney Disease Outcomes Quality Initiative guidelines. The glomerular filtration rate wascalculated using the CKD Epidemiology Collaboration equation.
Results: Among 928 participants, 872 completed the study. In phase I, probable CKD cases
were 281 (32.2%); in phase II, confirmed cases were 192 (22.0%) (stage 1, 4.0%; stage 2, 11.8%;
stage 3, 5.5%; stage 4, 0.6%; stage 5, 0.1%). In multivariable analysis, associated factors
for prevalent CKD included aged ⩾60 years [adjusted odds ratio (aOR) 5.02; 95% confidence
interval (CI) 1.85–13.65], hypertension (aOR 3.08; 95% CI 2.07–4.59), diabetes (aOR 2.52; 95% CI
1.60–3.96), presence of red blood cell in urine (aOR 3.20; 95% CI 1.71–5.98) and anemia (aOR
2.50; 95% CI 1.63–3.84).
Conclusions: This is the first ever research on CKD prevalence in rural and peri-urban
Bangladesh and recorded about 22%, which is higher than urban settings. Monitoring systems
are needed to evaluate the overall burden and to mitigate risk factors with an emphasis on therural and peri-urban population.
Keywords: Bangladesh, chronic kidney disease, health and nutritional status, prevalence,rural and peri-urban
Received: 11 February 2021; revised manuscript accepted: 5 July 2021.

Introduction
Globally, the prevalence of chronic kidney disease(CKD) is increasing and is documented as a rising public health threat for the future.1 CKD is
responsible for at least 2.4 million deaths eachyear2 and the overall mortality rate has increased
by 41.5% between 1990 and 2017.3 Diabetes,
hypertension and obesity are the world’s leadingrisk factors for CKD.4 Proper management andmitigation of the risks associated with CKD are of
critical importance as they have been reported toprolong the progression to end-stage renal disease(ESRD) and other related health consequences.5
Approximately 3 million patients worldwide are
currently receiving renal replacement therapy,
which is only half of the total case load, and this

Correspondence to:Mohammad Habibur
Rahman Sarker
Graduate School of
Biomedical and Health
Sciences, Hiroshima
University, 1-2-3, Kasumi,
Minami-ku, Hiroshima,734-8553, Japan
International Centre
for Diarrhoeal Disease
Research, Bangladesh(ICDDR,B), Dhaka,Bangladeshd185558@hiroshima-u.
ac.jp; habibur.rahman@
icddrb.org
Michiko Moriyama
Md Moshiur Rahman
Graduate School of
Biomedical and Health
Sciences, Hiroshima
University, Hiroshima,
Japan
Harun Ur Rashid
Kidney Foundation
Hospital and ResearchInstitute, Dhaka,Bangladesh
Mohammod Jobayer Chisti
Aftab Uddin
Shams El Arifeen
Tahmeed Ahmed
ASG FaruqueInternational Centre
for Diarrhoeal Disease
Research, Bangladesh(ICDDR,B), Dhaka,Bangladesh
Sumon Kumar Das
Menzies – School of
Health Research, Charles
Darwin University, Darwin,
Australia
Samir Kumar Saha
Dhaka Shishu Hospital,
Dhaka, BangladeshChild Health Research
Foundation, Dhaka,Bangladesh

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1

Therapeutic Advances in Chronic Disease 12
figure is projected to rise to between 5 and 10 million by 2030.6 The treatment costs of CKD and
its complications differ and therefore are notaffordable in many parts of the world.7,8 In low
and middle-income countries (LMICs), mostpatients with kidney failure have limited access tolife-saving dialysis and kidney transplantation.9
Early detection of CKD and reduction of risk factors, therefore, would be the most pragmatic
strategy to prevent premature mortality and morbidity in those countries.
The prevalence of CKD is increasing among thegeneral and disadvantaged population in
LMICs,10 and community-based screening services to assess the prevalence and identify the
related factors can be a crucial intervention forearly case detection. A few studies on CKD havebeen conducted in Bangladesh, and most of themwere hospital based among urban and slum populations,11–13 which measured the prevalence of
CKD as ranging at 16–18%. Although 65% of the
Bangladeshi population reside in rural areas andthey are in greater need due to lack of diagnostic
facilities, only one study has been conducted inrural Bangladesh. This study, however, contains
several methodological flaws in which the study
population and the study procedure were notclearly mentioned.14 Taking this previous study
as the point of departure, we designed and carriedout the population-based screening and early
assessment of CKD project to document theprevalence and determine factors associated withCKD in rural and peri-urban Bangladesh.
Methods
Study site
This cross-sectional epidemiological research
was performed in the Mirzapur sub-district ofTangail, located approximately 60 km northwestof Dhaka, the capital city of Bangladesh fromJanuary to June 2020. It has a mixed (rural andperi-urban) population of 407,781 (men 50%,
women 50%); men are mostly engaged in agriculture, whereas women are mostly busy withhousehold chores.15 Mirzapur has 13 unions (thesmallest administrative unit with an average population size of 25,000) and 219 villages. The
Mirzapur Demographic Surveillance System(DSS), established in 2007 consists of approximately 300,000 individuals from 10 unions of2

Mirzapur who are visited every 4 months for
updating demographic information on births,
deaths, and profile of the population.16
Study population
The study participants were stratified into three
age groups (18–30 years, 31–45 years, and 46 years
and older) and were selected randomly with theassistance of the DSS database of Mirzapur fieldsite. Adult individuals of either sex, aged 18 years
and older who are residents of the DSS area, havebeen living steadily in the locality for at least
5 years and have given written informed consent
to participate in the study were considered forenrolment in the study. However, individuals
hospitalised at the time of enrolment and having
any known serious illness with questionable prognosis that is, malignancy, mental illness, congenital disease and physical disability (if they haveevidence for getting treatments) were excluded
from the study. Among eight unions in Mirzapurthat comprise the DSS area, three unions(Mirzapur, Bhatgram and Gorai) nearest to oursentinel health facility, Kumudini Hospital, wereselected purposively due to budgetary constraints
and time limitations (Supplemental Figure 1).
Sample size calculation
The study participants were stratified into different age groups and selected randomly with theassistance of the DSS database. Sample size estimation was calculated based on an estimation of
the prevalence of CKD in urban Dhaka,Bangladesh; in which the proportion of CKD
among adults was 6.4%, 9.4%, 29.3% amongages 18–30 years, 31–45 years and 46 years andabove, respectively.13 The sample size was calculated using the following formula: n = (z)2 p(1−p)/d2, where n = sample size; z = level of confidence according to the standard normal distribution (level of confidence was chosen at 95%,z = 1.96); d = tolerated margin of error (d = 0.04);
and p = estimated proportion of the populationaccording to the prevalence of CKD valid among
different age groups. The cumulative sample size
was 844 (18–30 years 143, 31–45 years 204,
46 years and above 497). The final sample size
was 928, considering a 10% attrition rate becauseof out-migration, absence, non-response, andrefusal for the invasive blood specimen collection
procedure.

journals.sagepub.com/home/tajMHR Sarker, M Moriyama et al.
Enrolment of study participantsCommunity healthcare workers (CHWs), trained
in interview techniques and anthropometric andblood pressure (BP) measurement, made home
visits and performed interviews and physicalexaminations after getting written informed consent. Interviews were performed by administeringa field-tested structured questionnaire and information was obtained on age, gender, marital status, occupation, educational background, income/month, smoking status, participants’s current
medical history, past medical history, sleeping
hours, and medical history of family (up to thethird generation). Physical examinations were
performed to measure BP, pulse, height, weight,
waist circumference, hip circumference. The participants were then advised to visit Kumudini
Hospital laboratory for their study-related
investigations.
Sample collection of study participantsFirst assessment during initial visit: at Kumudini
Hospital laboratory, blood samples were collected
to measure serum creatinine, serum albumin,
hemoglobin (Hb), fasting blood glucose (FBG),
total cholesterol (TC), high density lipoprotein(HDL) cholesterol, and triglyceride (TG); andurine specimens were collected to measure thealbumin to creatinine ratio (ACR) and routine
microscopic examination. Serum creatinine wasmeasured using the Jaffe colorimetric method ofSiemens Dimension EXL 200/Siemens Dimension
RXL Max. ACR was calculated using the urine
albumin concentration/urine creatinine concentration equation. Urinary albumin was assessedusing a nephelometric method, and urinary creatinine was measured using a Flex reagent cartridge
principle based on Jaffe reaction. Tests were carried out using Siemens Dimension EXL 200/
Siemens Dimension RXL Max.
Second assessment (3 months after first assessment): after the first assessment, we evaluated theparticipants’ CKD status following the Chronic
Kidney Disease Epidemiology Collaboration(CKD-EPI) equation and estimated their glomerular filtration rate (GFR). Those participants who
had an estimated glomerular filtration rate (eGFR)
below 60 ml/min/1.73 m2 and/or ACR ⩾30 mg/g
were considered for the second assessment.
CHWs made phone calls and advised them to visit
the hospital laboratory after 3 months of their firstjournals.sagepub.com/home/taj

assessment. Blood samples were collected tomeasure serum creatinine, and urine specimens
were also collected to measure ACR. After completing investigations, the CKD status of the participants was confirmed following the National
Kidney Foundation Kidney Disease Outcomes
Quality Initiative (NKF/KDOQI) guidelines.17
Definitions
Chronic kidney disease. The CKD-EPI equation
was used to estimate the GFR, and the NKF/
KDOQI guidelines were used to define and stage
the CKD. A participant was diagnosed as a caseof CKD if (s)he had eGFR below 60 ml/
min/1.73 m2 or had albuminuria (ACR ⩾30 mg/g)
for more than 3 months.17
Stages of CKD. Stage 1 (eGFR ⩾90 mL/
min/1.73 m2 and ACR ⩾30 mg/g); stage 2 (eGFR
60–89 mL/min/1.73 m2 and ACR ⩾30 mg/g);
stage 3 (eGFR 30–59 mL/min/1.73 m2 regardlessof ACR); stage 4 (eGFR 15–29 mL/min/1.73 m2
regardless of ACR); and stage 5 (eGFR < 15 mL/
min/1.73 m2 regardless of ACR).17,18
Hypertension. Participants who currently requireantihypertensive therapy to control their BP or
those with a systolic blood pressure (SBP) of⩾140 mmHg and/or diastolic blood pressure(DBP) of ⩾90 mmHg at screening were considered to have hypertension.19
Diabetes. Participants with diabetes had a historyof diabetes or fasting blood glucose >7 mmol/L[according to the World Health Organization(WHO)].
Obesity, overweight and underweight. [(Weight inkilograms)/(square of the height in meters)] wasused to calculate the body mass index (BMI).
BMI ⩾ 30 was defined as obese, those between 25
and 29.9 were considered as overweight, andthose <18.5 were considered underweight(according to WHO).
Abdominal obesity. Abdominal obesity was a
waist circumference of ⩾94 cm in men and⩾80 cm in women (according to WHO).
Hypercholesterolemia and hypertriglyceridemia
and low HDL-cholesterol. Serum total cholesterol>200 mg/dL, serum triglyceride >150 mg/dL

3

Therapeutic Advances in Chronic Disease 12
and serum HDL-cholesterol <40 mg/dL atscreening was considered as hypercholesterolemia, hypertriglyceridemia and low HDL-cholesterol, respectively.20
Anemia. Anemia was a blood hemoglobin level<13 g/dL and <12 g/dL for men and women,
respectively (according to WHO).
Undernutrition. Mid upper arm circumference(MUAC) <25 cm for men and <24 cm for womenwas considered as undernutrition in developing
countries.21
Income (<$100/month). Income was a monthly
earnings from households <8400 Bangladeshi
Taka ($1 = 84 Bangladeshi Taka).
Illiterate. Participants without formal education
were regarded as illiterate.
Present tobacco smoker. Participants who currently smoked any form of bidi or cigarette wereconsidered present tobacco smokers.
Present smokeless tobacco user. Participants
who currently consumed any form of smokelesstobacco product were considered smokelesstobacco users.22
Data analysis
Statistical analysis was performed using the SPSSv. 20.0 (IBM Co., Armonk, NY, USA). In thedescriptive analysis, continuous variables wereexpressed as mean ± standard deviation (SD) andcategorical variables were expressed as count(percentages). The outcome variable was theprevalence of CKD and exposure variables
included age [18–30 years (reference), 31–
45 years, 46–59 years and ⩾60 years], sex [male(reference), female], education [literate (reference), illiterate], occupation [housewife, farmer,
other than housewife and farmer (reference)],marital status [married, widowed, other thanmarried and widowed (reference)], monthly
income [⩾USD 100, t-test or Mann–Whitney U
test was performed for continuous variables after
checking the data distribution. For categoricalvariables, we used the Pearson chi-square test to
see the associations between outcome and4

exposure variables. Variables with p-values lessthan 0.05 were simultaneously included into themultivariate logistic model and adjusted odds
ratios (aORs) and 95% confidence intervals (CIs)were estimated. Multivariate logistic regression
analysis was also performed when non-CKD was
the reference group (non-CKD = 0, CKD = 1) tomeasure the aORs. aORs were calculated toassess the association between the outcomeand exposure variables. Odds ratios (ORs) also
indicated the strength of association. In addition
to ORs, their 95% CIs were also estimated. A
probability of less than 0.05 was consideredsignificant.
Ethical statement
The Research Review Committee and the Ethical
Review Committee of the International Centre
for Diarrhoeal Disease Research, Bangladesh(ICDDR,B) approved this study (registration no.
19081).
At the time of enrolment, written consents weretaken from the study participants. They wereassured about the non-disclosure of informationcollected from them. However, without revealing
the name or identification, they were also
informed about the use of data for evaluation anduse of findings to enhance patient care activities,
as well as publication.
Results
Demographic characteristics
A total of 928 patients were enrolled from the
DSS. Out of them 872 (94.0%) completed thestudy procedure and were included in this study(Figure 1).
Overall, the mean ± SD of age was 48.2 ± 16.4, themean ± SD of BMI was 23.5 ± 4.1 and themean ± SD of waist circumference was 82.3 ± 11.5.
The majority of the population were women(56.2%), married (79.0%), illiterate (36.2%) andhousewives (49.0%). The minority of them werecurrent smokers (19.6%) and smokeless tobaccousers (28.9%). The remaining demographic, clinical and laboratory findings are also summarized inTable 1.

journals.sagepub.com/home/tajMHR Sarker, M Moriyama et al.
Total population (3 Unions),
Mirzapur DSS;
123,050

Total adult population (Age 18
years and above);85,056 (69%)

Total enrolled participants;
n= 928
Excluded from study; 37
Refused first time lab investigation.
Completed interview, physicalexamination and laboratory
investigation;

CKD assessment at baseline
Probable CKD;
n=281

Non-CKD;

Excluded from study; 19
Refused second time lab investigation (17)Died (2)Completed laboratory
investigation; n= 262

CKD assessment after 3 months ofbaseline assessment baseline
CKD; n= 192 (22%)

Non-CKD; n=70

Total non-CKD;
n=680 (78%)

Figure 1. Study population of the community-based screening for chronic kidney diseases in rural and periurban Bangladesh.
DSS, demographic surveillance system; CKD, chronic kidney disease.

CKD prevalence
The overall prevalence of CKD was 22.0% (192cases of 872 screened participants); of them theprevalence of CKD was 4.0% in stage 1, 11.8% instage 2, 5.5% in stage 3, 0·6% in stage 4, and
0·1% in stage 5 (Table 2).
However, the age-specific prevalence of CKD
was 4.9% (stage 1: 4.2%, stage 2: 0.7%), 14.7%(stage 1: 6.9%, stage 2: 6.4%, stage 3: 1.4%),
20.1% (stage 1: 2.8%, stage 2: 14.4%, stage 3:2.9%) and 46.0% (stage 1: 2.6%, stage 2:
journals.sagepub.com/home/taj

21.7%, stage 3: 18.6%, stage 4: 2.6%, stage 5:0.5%) among the age groups 18–30 years,
31–45 years, 46–59 years and ⩾60 years, respectively (Figure 2).
Factors associated with CKD
In the univariate analysis, aged 31–45 years (OR0.53; 95% CI 0.35–0.81), aged ⩾60 years (OR4.78; 95% CI 3.36–0.80), illiterate (OR 1.59;
95% CI 1.15–2.20), housewife (OR 1.49; 95%
CI 1.08–2.06), widowed (OR 3.07; 95% CI
56319 (36.6)
193 (22.1)

46–59

60 and older

78 (8.9)

Farmer242 (27.8)
171 (19.6)
252 (28.9)355 (40.7)

Sleeping duration (<7 h/24 h)

Present tobacco smoker

Present smokeless tobacco user

Hypertension

19 (9.9)

23.5 ± 4.1
91 (10.4)

BMI (mean ± SD)

Underweight (BMI <18.5)

24.0 ± 4.1

147 (16.9)

61 (31.8)

7.1 ± 3.1

133 (69.3)

71 (37.0)

Diabetes

6.2 ± 2.2

78 (40.6)

136 (15.6)

Income (<$100/month)

Fasting blood glucose (mmol/L) (mean ± SD)

36 (18.8)

104 (11.9)

Widowed29 (15.1)

44 (22.9)

689 (79.0)

143 (74.5)

14 (7.3)

109 (56.8)

86 (44.8)

119 (62.0)

89 (46.4)

64 (33.3)

32 (16.7)

7 (3.6)58.9 ± 16.0

CKD n = 192(%)

MarriedMarital status

427 (49.0)

316 (36.2)

Housewife

Occupation

Illiterate

Literacy

Female490 (56.2)

217 (24.9)

31–45

Gender143 (16.4)

48.2 ± 16.4

Age in years (mean ± SD)

18–30

All n = 872(%)

Variable72 (10.6)

23.3 ± 4.0

86 (12.6)

6.0 ± 1.7

222 (32.6)

181 (26.6)

142 (20.9)

164 (24.1)

100 (14.7)

60 (8.8)546 (80.3)

64 (9.4)

318 (46.8)

230 (33.8)

371 (54.6)

104 (15.3)

255 (37.5)

185 (27.2)

136 (20.0)

45.2 ± 15.2

Non-CKDn = 680 (%)(Continued)

1.11 (0.55–2.22)––
0.93 (0.54–1.58)

2.52 (1.60–3.96)*––
3.22 (2.20–4.70)*

3.08 (2.07–4.59)*

0.86 (0.55–1.32)–

1.20 (0.80–1.81)–

2.86 (0.85–9.57)

1.82 (0.61–5.45)

0.56 (0.26–1.17)

1.03 (0.65–1.61)

1.08 (0.69–1.68)–

5.02 (1.85–13.65)*

1.63 (0.63–4.20)

1.49 (0.58–3.81)

4.65 (3.29–6.57)*

1.62 (1.15–2.27)*

0.67 (0.43–1.04)

2.15 (1.53–3.01)*

1.34 (0.88–2.04)

3.07 (2.00–4.71)*

0.72 (0.49–1.04)

0.76 (0.41–1.38)

1.49 (1.08–2.06)*

1.59 (1.15–2.20)*

1.36 (0.98–1.88)

4.78 (3.36–6.80)*

0.83 (0.59–1.17)

0.53 (0.35–0.81)*

–

–

–
–

Adjusted OR (95%CI), p-value

Unadjusted OR(95% CI), p-value

Table 1. Demographic characteristics and risk factors data between CKD and non-CKD participants (t-test and multivariate logistic regression was performed).

Therapeutic Advances in Chronic Disease 12

journals.sagepub.com/home/tajjournals.sagepub.com/home/taj

34 (17.7)
12.8 ± 1.5
70 (36.5)

168 (19.3)
13.2 ± 1.5
197 (22.6)
63 (7.2)

Undernutrition (MUAC, men <25 cm, women <24 cm)

Hemoglobin (g/dL) (mean ± SD)

Anemia (hemoglobin, men <13 g/dL, women <12 g/dL)

Presence of RBCs in urine

49 (25.5)
47.1 ± 12.9
61 (31.8)
163.1 ± 113.7
83 (43.2)

187 (21.4)
46.4 ± 12.1
287 (32.9)
140.8 ± 99.8
286 (32.8)
40 (4.6)
31 (3.6)
232 (26.6)
339 (38.9)

Hypercholesterolemia (total cholesterol >200 mg/dL)

Serum HDL-cholesterol (mg/dL) (mean ± SD)

Low HDL-cholesterol (HDL-cholesterol <40 mg/dL)

Serum triglyceride (mg/dL) (mean ± SD)

Hypertriglyceridemia (triglyceride >150 mg/dL)

History of heart disease

History of stroke

Family history of diabetes

Family history of hypertension

259 (38.1)

178 (26.2)

20 (2.9)22 (3.2)

203 (29.9)

134.5 ± 94.6

226 (33.2)

46.3 ± 11.9

138 (20.3)

169.5 ± 36.8

34 (5.0)

3.9 ± 0.3

34 (5.0)127 (18.7)

13.3 ± 1.5

134 (19.7)

27.22 ± 3.24

246 (36.2)

81.6 ± 11.3

222 (32.6)

Non-CKDn = 680 (%)

1.16 (0.84–1.61)

1.10 (0.77–1.58)

2.00 (0.94–4.26)

3.09 (1.62–5.90)*

–

–

–

1.66 (0.76–3.66)

1.23 (0.82–1.85)––
1.79 (1.29–2.49)*–––
0.93 (0.66–1.32)–––
1.34 (0.92–1.96)

1.11 (0.55–2.22)––
2.46 (1.40–4.31)*

3.20 (1.71–5.98)*

3.38 (2.00–5.71)*

2.50 (1.63–3.84)*––
2.50 (1.76–3.55)*–––
0.88 (0.58–1.33)

0.78 (0.45–1.34)––
1.73 (1.25–2.39)*

1.67 (0.98–2.83)

Adjusted OR (95%CI), p-value

1.50 (1.08–2.09)*

Unadjusted OR(95% CI), p-value

Dependent variable: Presence of CKD when non-CKD was considered as reference group (non-CKD = 0, CKD = 1).
Reference categories for independent variable were: (18–30 years, male sex, literate, other than housewife and farmer, other than married and widowed, income.
*p < 0.05.
BMI, body mass index; CKD, chronic kidney disease; HDL, high-density lipoprotein; MUAC, mid-upper arm circumference; OR, odds ratio; RBC, red blood cell; SD, standard deviation.

80 (41.7)

54 (28.1)

11 (5.7)

18 (9.4)

179.7 ± 37.2

171.8 ± 37.2

Serum cholesterol (mg/dL) (mean ± SD)

22 (11.5)

56 (6.4)

3.7 ± 0.3

Low serum albumin (albumin <3.5 g/dL)

3.8 ± 0.3

27.24 ± 3.37

27.23 ± 3.27

MUAC (cm) (mean ± SD)

Serum albumin (g/dL) (mean ± SD)

95 (49.5)

341 (39.1)

Abdominal obesity (waist circumference, men ⩾94 cm,
women ⩾80 cm)

29 (15.1)

85.0 ± 11.7

81 (42.2)

303 (34.7)
82.3 ± 11.5

CKD n = 192(%)

All n = 872(%)

Waist circumference (cm) (mean ± SD)

Overweight/Obese (BMI ⩾25)

Variable

Table 1. (Continued)

MHR Sarker, M Moriyama et al.

7Therapeutic Advances in Chronic Disease 12
Table 2. Overall distribution of study participants based on repeated estimated glomerular filtration rate and albumin to creatinine
ratio in rural and peri-urban Bangladesh.
Chronic
kidney disease(CKD) stages

Estimatedglomerular
filtration rate (ml/min/1.73 m2)Albumin to creatinine ratio (ACR)

Total
n (%)

*CKD
prevalence,
n (%)

A1
(<30 mg/g)

A2(30–300 mg/g)

A3(>300 mg/g)

G1

⩾90

287

32

3

322 (36.9)

35 (4.0)

G2

60–89

393

100

3

496 (56.9)

103 (11.8)

G3a

45–59

20

10

2

32 (3.7)

32 (3.7)

G3b

30–44

8

8

0

16 (1.8)

16 (1.8)

G4

15–29

1

1

3

5 (0.6)5 (0.6)

G5

<15

0

1

0

1 (0.1)1 (0.1)709 (81.3)

Total, n (%)

152 (17.4)

11 (1.3)

872

192 (22.0)

Low risk (If no other marker of kidney disease. no CKD)
Moderately increased risk
High risk
Very high risk

Chronic kidney diseases prevalence (%)*CKD: estimated glomerular filtration rate below 60 ml/min/1.73 m2 and/or ACR ⩾30 mg/g.

25
21.7
18.6

20
14.4

15
10
6.9
5

4.2

6.4
2.8 2.6
0.7

00Stage 1

Stage 2

1.4

2.9

2.6
0

Stage 30

0

Stage 4

0

0

0

0.5

Stage 5Chronic kidney diseases Stages
18-30 years

31-45 years

46- 59 years≥60 years

Figure 2. Prevalence of chronic kidney diseases by ages and stages (stages 1–5) in rural and peri-urban Bangladesh.

2.00–4.71), sleeping duration less than 7 h/24 h(OR 2.15; 95% CI 1.53–3.01), present smokeless tobacco user (OR 1.62; 95% CI 1.15–2.27),
hypertension (OR 4.65; 95% CI 3.29–6.57),
diabetes (OR 3.22; 95% CI 2.20–4.70), overweight or obesity (OR 1.50; 95% CI 1.08–2.09),
8

abdominal obesity (OR 1.73; 95% CI 1.25–
2.39) anemia (OR 2.50; 95% CI 1.76–3.55),
presence of RBCs in the urine (OR 3.38; 95%
CI 2.00–5.71), low serum albumin (OR 2.46;
95% CI 1.40–4.31), hypertriglyceridemia (OR1.79; 95% CI 1.29–2.49), and history of heart
journals.sagepub.com/home/tajMHR Sarker, M Moriyama et al.
disease (OR 3.09; 95% CI 1.62–5.90) were associated with CKD. However, there was no significant association between CKD and aged
46–59 years, female gender, farmer, married,
income (<$100/month), present tobaccosmoker, underweight, undernutrition, hypercholesterolemia, low HDL-cholesterol, history ofstroke; and history of diabetes and hypertension
in the family (Table 1).
The multivariate logistic regression analysis
revealed that aged ⩾60 years (aOR 5.02; 95% CI
1.85–13.65), hypertension (aOR 3.08; 95% CI
2.07–4.59), diabetes (aOR 2.52; 95% CI 1.60–
3.96), anemia (aOR 2.50; 95% CI 1.63–3.84)
and presence of RBCs in the urine (aOR 3.20;
95% CI 1.71–5.98) were associated with CKD(Table 1).
In the physical measurement and laboratory
findings, age (years) [CKD (mean ± SD) versus
non-CKD (mean ± SD); 58.9 ± 16.0 versus
45.2 ± 15.2], BMI (kg/m2) (24.0 ± 4.1 versus
23.3 ± 4.0), waist circumference (cm) (85.0 ±11.7 versus 81.6 ± 11.3), FBG (mmol/L)(7.1 ± 3.1 versus 6.0 ± 1.7), serum cholesterol(mg/dL) (179.7 ± 37.2 versus 169.5 ± 36.8), ser­um triglyceride (mg/dL) (163.1 ± 113.7 versus
134.5 ± 94.6) were significantly higher; conversely, hemoglobin (g/dL) (12.8 ± 1.5 versus
13.3 ± 1.5) and serum albumin (g/dL) (3.7 ± 0.3
versus 3.9 ± 0.3) were significantly lower among
CKD participants compared to non-CKD participants (Table 1).
Discussion
The main objective of this study was to determine
the prevalence and factors associated with CKD
among adults in rural and peri-urban Bangladesh.
The overall CKD prevalence documented in ourstudy (22%) is higher than the pooled prevalence(17.3%) in Bangladesh.23 However, the prevalenceof CKD is rising in Bangladesh24 and the exact figure varies from study to study based on the defining
criteria and study settings. These studies are mostly
from urban Dhaka, Bangladesh; and the majorityof these studies have not repeated serum creatinine
and urine ACR measurements over 3 months todefine CKD,11,13 as suggested by the KDIGO
guidelines.17 This is critical because we identified
281 participants initially with probable CKD

journals.sagepub.com/home/taj(Supplemental Table 1) and, on retesting after
3 months, almost 27% proved to be non-CKD
individuals. This could be due to false-positive
findings or the resolution of acute kidney injury.25
The overall CKD prevalence identified in our study
is higher than the prevalence of the global (9%),3
developed (United States 11%,26 Japan 13%27) anddeveloping (Pakistan 21.2%,28 India 10.2%28)
countries. Our study also ascertained a higher prevalence of CKD in stages 1–2 (15.82% versus global
5.0%, India 11.3%) and stage 3 (5.50% versus
global 3.90%,3 India 4.3%28). The increasing
trend of non-communicable diseases (e.g. diabetes,
hypertension, stroke), unhealthy lifestyle and dietary behaviors, lack of physical exercise may potentially be the key reasons behind our findings. In
addition, a higher prevalence of CKD among theage group ⩾60 years (46.4% versus United States
39.4%,29 India 40.6)30 may be the explanation of
the higher prevalence of CKD in our current study.
Other causes may include heterogeneity in themethodological approach, inclusion criteria andsize of the study sample that were able to capture
the diversity as much as possible. Although theprevalence of CKD was remarkably high in ourstudy, the participants had a low awareness about
their kidney status. Only 6.8% of participants wereconscious of being a CKD patient prior to thestudy, which is lower than that of the neighboring
country India (7.9%).31 This also reflects the lackof provision of healthcare and diagnostic facilities
available to this population.
Our study reported higher odds among the diabetes
and hypertensive individuals, which is consistent
with several previous studies;31,32 and might partially contribute to the higher prevalence of CKD in
our sample population. The prevalence of diabetes
and hypertension has dramatically increased in
Bangladesh over the past two decades33,34 and particular attention should be paid to manage thesefactors in order to prevent or slow down CKD.
In the course of CKD, anemia can occur earlyand may worsen in advanced stages.35 We foundthat anemia is substantially high among CKD
patients, which is expected and has also beenreported in other studies.36–38 The presence of
RBCs in the urine is also documented as a strong
factor associated with CKD in our research,which may be attributed to kidney pathology that9

Therapeutic Advances in Chronic Disease 12
blocks the urinary tract and triggers decreased
kidney function.39
Although CKD can develop at any time, we foundthat increasing age particularly aged ⩾60 years
and older is a risk and lower age (18–45 years) is
protective against developing CKD. After the ageof 40 years, kidney filtration starts to decline;40
and in addition to the natural aging of the kidneys,
in the elderly, certain diseases including diabetes,
high BP, and heart disease that affect the kidneys
to become more prevalent to CKD. For the elderly
population, eGFR up to 45 ml/min/1.73 m2 is
acceptable without proteinuria or primary kidneydisease diagnosis,41 and eGFR less than 45 ml/
min/1.73 m2 with or without proteinuria are associated with a high risk of mortality.42 Therefore, itwill be prudent to screen the elderly population togather further evidence. Moreover, we observedthat lower serum albumin levels are independently
correlated with decreased renal function among
our participants, which has also been reported inother studies, particularly in the elderly population,43 Further study is required to understand thebiological mechanisms that relate low serum albumin to decreased renal function.
An interesting finding from our research was thatthe widows were more likely to develop CKD.
We do not have a ready explanation for this finding; however, evidence from Bangladesh suggeststhat 12% of widows live alone. They are more at
risk of poverty because of the absence of a maleearner. In addition, they suffer from social isolation, and higher mortality and morbidity including stress that shortens the duration of their
sleeping hours at night, thus increasing their susceptibility to CKD. A high prevalence of CKD isobserved among the illiterate and housewives. In
Bangladesh, rural and peri-urban residents havea lower rate of literacy, particularly among housewives and the elderly population.44 This group islargely ignorant of their unhealthy lifestyle anddietary patterns which may contribute to thedevelopment of risk factors for CKD such as dyslipidemia, overweight or obesity. Dyslipidemia isassociated with a decrease in GFR; thus the lipid
profile depends on the degree of renal function
and the degree of proteinuria.45 Our study demonstrated hypertriglyceridemia is a risk factor for
CKD, but hypercholesterolemia and low HDLcholesterol showed no relevant relation. CKD
has been found to be more prevalent in the general overweight or obese and abdominal obese
10

population. Overweight and obesity are reported
to increase kidney disease, particularly by causing endocrine dysfunction.46 Another important
lifestyle factor associated with the development
of CKD documented in our study is sleeping lessthan 7 h per day which can be explained by theregulation of kidney function by the sleep–wakecycle47 and the relationship between melatoninsecretion and sleeping hours.48 Further studies
are needed to establish the mechanism to explain
our observations.
Although several studies documented a strong
relationship between smoking and CKD,49,50 wehave not recognized any significant association
with CKD. Instead, we found that smokelesstobacco use is a distinct associated factor of CKD,
even though we have not found sufficient literature to reinforce this issue. In Taiwan, a study
showed a greater prevalence of CKD among betel
nut users than the prevalence of CKD among
non-betel nut users.51 Tobacco compounds hinder the formation of vascular endothelial cells andinduce endothelial dysfunction and might beresponsible for the development of CKD.
Surprisingly, the use of smokeless tobacco is culturally accepted in Bangladesh, and there are
more smokeless tobacco consumers than smokers(27% versus 23%).22 There is a need for publichealth awareness along with the implementationof a national tobacco control act to curb smokeless tobacco.
Limitations and strengths
We used the CKD-EPI equation that was createdon the basis of the population of the UnitedStates, most of which were whites and blacks withdiverse characteristics. So, this equation would beless valid in the context of citizens in Bangladesh.
In addition, we were unable to retest 19 of the281 participants who screened positive in phase I.
Two of these 19 participants had died, and others
refused laboratory investigation. They may havesuffered from kidney disease; however, during
screening they denied any history of kidney disease. Moreover, the cross-sectional design, purposive inclusion of three unions, observation only
based on Mirzapur sub-district and assessment ofundernutrition status using only the MUAC
measurement are the main limitations of ourstudy. The main strengths of this analysis include
the unbiased age-stratified sampling method toenrol patients, use of an accredited laboratory,
journals.sagepub.com/home/tajMHR Sarker, M Moriyama et al.
use of standardised equipment and trained
workforce.
Implications of all the available evidence
The evidence presented in our study will help tostrengthen public health strategies to reduce theprevalence of CKD and slowing down the progression of the disease, particularly in the ruraland per-urban settings of the developing countries. Strategies include the development of a
CKD screening programme based on a nationallyestablished equation to confirm diagnosis, awareness through health education, proper management of CKD risk factors such as diabetes,
hypertension, the presence of RBCs in the urine
and special care for the elderly population.
Conclusion
Our research shows that patients who reside inrural and peri-urban settings have a high prevalence of CKD, in which diabetes, hypertension,
anemia, old age, the presence of RBCs in theurine, hypertriglyceridemia, obesity or overweight, widowed and smokeless tobacco use havea significant role. Therefore, the scale-up of preventive and therapeutic steps to manage thesemajor risk factors may help to reduce CKDrelated ramifications.
Acknowledgements
The authors are thankful to the study participantsand CHWs (Ayesha Akter, Sompa Bakali, Shikha
Akter, Shanta Islam, Tanni Sikder, Hazera
Begum, Hiramon Akter, Afrin Sultana) for theirgenerous support. They are grateful to Yuko Itofor her enormous support, Yasmin Jahan for her
assistance in CHW recruitment and Ashir Ahmed(Kyushu University, Japan) for providing the
Portable Health Clinic device. They would like toexpress their sincere gratitude to Soroar Hossain
Khan for his data analysis assistance and all thelaboratory staff for their invaluable support inlaboratory investigations.
Author contributions
MHRS, MM and ASGF designed the study;
MHRS, MM and ASGF performed the main statistical analysis; HUR, SKS, SKD, MMR, SEA,
AU, TA and MJC provided assistance for themain statistical analysis and data interpretation;

journals.sagepub.com/home/taj

MM, MMR, MJC, SKS, AU, SEA, TA, ASGF
and MHRS provided material support during thestudy; and all authors participated in drafting andreviewing the manuscript, and approved the final
version of the manuscript to be published.
Conflict of interest statement
The authors declare that there is no conflict ofinterest.
Funding
The authors disclosed receipt of the following
financial support for the research, authorship,
and/or publication of this article: This research
project was funded by the Grants-in-Aid for
Scientific Research Program (KAKENHI),
Japan, grant number Kiban-B # 18H03113.
Supplemental material
Supplemental material for this article is available
online.
ORCID iD
Mohammad Habibur Rahman Sarker
orcid.org/0000-0001-9614-0806

https://

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