Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Burden of chronic kidney disease in the general population and high-risk groups in South Asia: A systematic review and meta-analysis

  • Nipun Shrestha ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

    Affiliation Community Health Development Nepal, Kathmandu, Nepal

  • Sanju Gautam,

    Roles Data curation, Writing – review & editing

    Affiliation Tokha Municipality, Kathmandu, Nepal

  • Shiva Raj Mishra,

    Roles Conceptualization, Methodology, Visualization, Writing – review & editing

    Affiliation Nepal Development Society, Bharatpur, Nepal

  • Salim S. Virani,

    Roles Writing – review & editing

    Affiliation Section of Cardiovascular Research, Baylor College of Medicine and Section of Cardiology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, United States of America

  • Raja Ram Dhungana

    Roles Data curation, Methodology, Writing – review & editing

    Affiliation Manmohan Memorial Institute of Health Sciences, Kathmandu, Nepal



Chronic kidney disease (CKD) is an emerging public health issue globally. The prevalence estimates on CKD in South Asia are however limited. This study aimed to examine the prevalence of CKD among the general and high-risk population in South Asia.


We conducted a systematic review and meta-analysis of population-level prevalence studies in South Asia (Afghanistan, Bangladesh, Bhutan, Maldives, Nepal, India, Pakistan, and Sri Lanka). Three databases namely PubMed, Scopus and Web of Science were systematically searched for published reports of kidney disease in South Asia up to 28 October 2020. A random-effect model for computing the pooled prevalence was used.


Of the 8749 identified studies, a total of 24 studies were included in the review. The pooled prevalence of CKD among the general population was 14% (95% CI 11–18%), and 15% (95% CI 11–20%) among adult males and 13% (95% CI 10–17%) in adult females. The prevalence of CKD was 27% (95% CI 20–35%) in adults with hypertension, 31% (95% CI 22–41%) in adults with diabetes and 14% (95% CI 10–19%) in adults who were overweight/obese. We found substantial heterogeneity across the included studies in the pooled estimates for CKD prevalence in both general and high-risk populations. The prevalence of CKD of unknown origin in the endemic population was 8% (95% CI 3–16%).


Our study reaffirms the previous reports that CKD represents a serious public health challenge in South Asia, with the disease prevalent among 1 in 7 adults in South Asian countries.


Chronic kidney disease (CKD) is an emerging public health issue globally. The Global Burden of Disease study estimated about 1.4 million deaths globally from CKD in 2019, a 20% increase from 2010, one of the largest rises among the top causes of death [1]. CKD disproportionately impacts low and middle-income countries where both prevalence and deaths due to CKD are significantly higher [2, 3]. The increasing burden of CKD across the globe has been attributed to the meteoric rise in the prevalence of its risk factors such as obesity, hypertension, diabetes, and other cardiovascular diseases (CVD) [4, 5]. Multiple Risk Factor Intervention Trial (MRFIT) showed that CKD patients with hypertension, compared to non-hypertensive, have nearly 22 times higher risk of end-stage renal disease (ESRD) [6]. Further, co-morbid hypertension and/or diabetes among CKD patients may exacerbate the prognosis and results in higher mortality and cardiovascular events [4]. But in some regions of South Asian countries such as Sri Lanka and India, other causes such as environmental toxins (heavy metals, pesticides) and underground water with high fluoride levels have also been implicated in the high burden of CKD also known as a CKD of unknown origin (CKDu) [7].

Limited access to renal replacement therapy (dialysis or kidney transplantation) in South Asia lead to premature deaths among people with CKD who go onto develop ESRD [8]. Even for those who receive the renal replacement therapy, the financial burden that it inflicts is often catastrophic and poses far-reaching implications for individuals and their families [8, 9]. Strengthening screening services and managing underlying metabolic risk factors should be the priority in the low economy countries of South-Asia. Thus, updated evidence of the prevalence of CKD in South Asia are urgently warranted. In this systematic review, we assessed the burden of CKD in the general population and high-risk groups in South Asian countries that included Afghanistan, Bangladesh, Bhutan, Maldives, Nepal, India, Pakistan, and Sri Lanka.


The review protocol has been published in PROSPERO: CRD42020220194 and has been conducted adhering to the Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines [10].

Search strategy and selection criteria

We searched PubMed, Scopus and Web of Science for published reports of kidney disease in South Asia up to 28 October 2020. We used Boolean logic with search terms including “kidney disease”, “renal function”, “renal insufficiency”, and “South Asia” (S1 Table). We also searched the reference list of included studies and systematic reviews on the similar topic identified in the database search.

Inclusion and exclusion criteria.

The studies retrieved through database research were screened for eligibility independently by two authors (RD, SG), and any disagreements were resolved through discussion with the third author (NS). For inclusion, studies had to fulfill the following criteria:

  1. Study population: Population or hospital-based, randomized controlled trial (RCT) or non-randomized studies including cross-sectional, cohort, and case-cohort studies that reported prevalence or provided data that allowed computation of the prevalence of CKD in adults (aged 18 and above), conducted in at least one of the South Asian Association for Regional Cooperation (SAARC) countries that included Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan in any age group or any gender.
  2. Exposure: chronic kidney disease should have been defined as
    1. the presence of kidney damage (proteinuria) and/or
    2. stimated Glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2, by the Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formulae or a creatinine clearance of less than 60 mL per min by the Cockcroft-Gault formula.

    These abnormalities should persist for three months for a diagnosis of CKD. However, for population-based studies, one measurement of kidney function is considered acceptable for the diagnosis of CKD. This definition is based on the NKF KDOQI (National Kidney Foundation Kidney disease outcomes quality initiative) or KDIGO (Kidney Disease: Improving Global Outcomes) guidelines [11, 12]. For CKDu, we relied on the definition reported by the included studies.
  3. Comparison: For prevalence estimate of CKD/CKDu in the general population, those studies which reported the number of CKD cases and sample population were included. Similarly, for prevalence estimate of CKD in high-risk population, those studies which reported the number of CKD cases and high-risk population (hypertension, diabetes and overweight/obesity) were included.
  4. Outcome: The included studies were required to report the prevalence of CKD/CKDu in the general population, CKD in high-risk population or provided data that allowed computation of the prevalence. In instances where there were multiple publications from the same dataset, we only included the most comprehensive article that reported the outcomes considered in this review.
    Exclusion criteria: Studies were excluded if they had no criteria for the diagnosis of CKD, did not include prevalence or were in a specialist restricted population (e.g. acute hospital patient cohort, nursing home). For the prevalence of CKD/CKDu in the general population, we excluded studies having a sample size less than 500. Similarly, for summarizing the prevalence of CKD in high-risk population, studies with a sample size of less than 300 were excluded. The smaller sample size studies were excluded to avoid selection bias from small studies.

Data extraction

Two authors (NS and SG) individually extracted data from the identified articles using a piloted data extraction table developed in excel for this review. This table included 1) author details: names and publication year, 2) Study characteristics: country, data collection period, setting, data source, sampling method, sample size, 3) Participant characteristics: mean age, gender, mean BMI, and 4) CKD characteristics: creatinine assessment method, eGFR equation, proteinuria assessment method, follow up, prevalence, number of participants tested and diagnosed with CKD overall and by subgroups of interest. The extracted data from both the authors were merged and later crosschecked by a third author (RD) to ensure consistency.

Assessment of the risk of bias in included studies

Two reviewers (RD & SG) independently assessed the risk of bias in included studies, with disagreements being resolved by discussion or by consulting a third reviewer (NS). A checklist with a ten-item rating developed by Hoy et al. [13] was used for risk of bias assessment. The checklist assesses the methodological quality of the included studies in the following domains: sampling, the sampling technique and size, outcome measurement, response rate, and statistical reporting. The reviewers assigned a score of 1 (yes) or 0 (no), for each item. The overall quality score (range: 0–10) was generated by adding the scores for the individual item. The studies with an overall score of higher than 8 were judged at high quality, a score of 6–8 were judged as moderate quality and a score of 5 or lower were judged low quality.

Statistical analysis

For each study, the unadjusted prevalence of CKD and standard errors were calculated (number of cases/sample size) based on the information on crude numerators and denominators provided in individual studies. The variances in the included studies was stabilised using the Freeman-Tukey Double Arcsine Transformation before estimating the pooled prevalence [14]. For the studies that reported eGFR from multiple equations, we used the Modification of Diet in Renal Disease (MDRD) then the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, and lastly the Cockcroft-Gault formula in the main analyses. We used the DerSimonian-Laird random-effects models to generate the pooled prevalence of CKD according to each diagnostic criterion. The random-effects model was chosen with the assumption that CKD prevalence estimates across the included studies would be variable. We also examined prevalence in high-risk populations (hypertension, diabetes mellitus and overweight/obese) and prevalence of CKDu using the random-effects model. The heterogeneity in the included studies was assessed using I2 statistics, with I2 values higher than 70% considered as evidence of substantial heterogeneity [15]. Subgroup analysis by country, gender, equation used for GFR estimation, and meta-regression by mean age, mean BMI and survey year was performed to identify the sources of heterogeneity across the studies. Publication bias was assessed by Egger’s test and funnel plot for pooled analyses with 10 or more studies [16]. All calculations were conducted using STATA (StataCorp LLC, USA).


Study selection

The search strategy yielded 8739 citations. An additional 10 studies were located from secondary searches. After removing duplicates, a total of 7439 studies were retrieved for the title and abstract screening, of which 47 studies were selected for full-text screening. Twenty-three studies were excluded for the following reasons: wrong study population, wrong outcomes, wrong study design, duplicate studies, the non-standard definition of CKD, sample size less than 500 and sample size less than 300 for studies conducted in high-risk groups. Finally, 24 studies [1740] were included in this systematic review and meta-analysis (Fig 1). Two studies reported prevalence of low GFR and proteinuria but did not report CKD prevalence [33, 35]. Out of 22 studies, 15 studies reported CKD prevalence in the general population [1720, 22, 23, 25, 27, 28, 32, 34, 3740], three studies reported CKDu prevalence [24, 28, 29, 31] in the general population and Two studies [28, 36] reported prevalence of both CKD and CKDu in the general population. Two studies were conducted in the diabetes population [26, 30] and one study [21] in the hypertensive population.

The characteristics of the included studies are summarised in Table 1. Most studies were from India (n = 13) [1719, 2730, 3436, 38, 39] and rest were from Bangladesh (n = 4) [20, 22, 23, 26], Nepal (n = 3) [32, 33, 40], Sri Lanka (n = 2) [24, 31], Pakistan (n = 1) [25]. One study reported data from Bangladesh, Sri Lanka and Pakistan [21]. However, both the studies from Sri Lanka reported only the prevalence of CKDu (12, 19). Altogether 16 studies were conducted in community settings [1719, 21, 2325, 28, 29, 3136, 40] and the remaining were hospital- or clinic-based studies. Out of 21 studies that reported CKD prevalence, 16 studies used single equation to estimate GFR: MDRD: 11 studies [20, 2628, 30, 32, 33, 36, 38, 40], CKD-EPI: 4 studies [17, 19, 21, 25], CG: one study [37]. Five studies reported GFR using two equations (MDRD and CG: three studies [18, 22, 23], MDRD and CKD-EPI: two studies [34, 39]. Most studies reported using Jaffe’s kinetic assay for creatinine assessment [1719, 22, 23, 29, 34, 35, 3740] (n = 12), while seven studies [20, 26, 27, 30, 32, 33] did not report assessment method for creatinine. Only four studies followed up participants at 3 months [22, 23, 26, 40]. Only seven studies reported calibration of creatinine measurement by isotope dilution mass spectrometry assays [17, 21, 25, 29, 31, 34, 37] which reduces variability in measured serum creatinine values [41]. Most commonly used method for measurement of proteinuria is urinary dipstick [1820, 22, 23, 27, 28, 30, 3235, 38, 39], however three studies did not report measurement method for proteinuria [26, 36, 37].

Table 1. Detailed characteristics of the included studies assessing the prevalence of CKD/CKDu in South Asia.

Assessment of risk of bias

Thirteen studies [20, 2224, 26, 27, 30, 3234, 3739] scored 2 or less out of 4 possible points in the external validity domain whereas twenty-two studies [1722, 2426, 2833, 3540] scored all 6 points and the remaining two studies [23, 34] scored 5 points in the internal validity domain. Overall, ten studies [1719, 21, 25, 28, 29, 31, 36, 40] were judged to be of high methodological quality and the remaining 14 studies [20, 2224, 26, 27, 30, 3235, 3739] were judged to be of moderate methodological quality (S2 Table).

Prevalence of CKD and CKDu in the general population

The overall prevalence of CKD was 14% (95% CI 11–18%, n  =  50,494, 16 studies, Fig 2) in the general population, 15% (95% CI 11–20%, n = 22, 973, 15 studies, S1 Fig) in adult males and 13% (95% CI 10–17%, n = 24,528, 15 studies, S2 Fig) in adult females. The prevalence of CKD in general population was 16% (95% CI 12–21%) in India, 14% (95% CI 12–17%) in Bangladesh, 12% (95% CI 11–14%) in Pakistan and 6% (95% CI 6–7%) in Nepal (Fig 2). One study [33] did not report estimated CKD prevalence therefore could not be pooled in the meta-analysis. It reported a 14.4% prevalence of low GFR (eGFR<60 mL/min/1.73 m2) and 7% prevalence of proteinuria in Nepal. Similarly, another study [35] from India which could not be pooled in a meta-analysis reported 13.3% prevalence of low GFR by CG equation and 4.2% prevalence of low GFR by MDRD equation. The same study [35] also reported a 2.2% prevalence of proteinuria in the Indian population. The prevalence of GFR < 60 mL/min/1.73 m2 was 6% (95% CI 4–9%, n = 64,143, 18 studies, S3 Fig). In subgroup analysis by GFR estimation equation, CKD prevalence was 13% (95% CI 10–17%) by MDRD, 15% (95% CI 10–21%) by CKD EPI and 18% (95% CI 15–21%) by CG (S4S6 Figs). Those studies that followed up participant at 3 months reported a slightly lower CKD prevalence of 12% (95% CI 5–20%) (S7 Fig). Similarly subgroup analysis also revealed that a higher prevalence of CKD in population of age group 40 years and older compared to age group below 40 years (18% vs. 6%, S8 Fig). The prevalence of CKDu in the endemic population was 8% (95% CI 3–16%, 5 studies) (S9 Fig).

Fig 2. Prevalence of CKD in general populations of adults in South Asia.

Black boxes represent the effect estimates (prevalence) and the horizontal bars are for the 95% confidence intervals (CIs). The diamond is for the pooled effect estimate and 95% CI and the dotted vertical line centered on the diamond has been added to assist visual interpretation.

We found substantial heterogeneity across the included studies in the pooled estimates for overall CKD prevalence, prevalence disaggregated by age groups, sex, across eGFR estimating equations and CKDu prevalence. In the meta-regression, the regression coefficient for the mean age, mean BMI and survey year was statistically non-significant (S3 Table). Publication bias was detected across studies reporting on the prevalence of CKD in the general population, with the asymmetrical funnel plot and significant Egger test (S10 Fig; p-value 0.002).

Prevalence of CKD in high-risk populations

The prevalence of CKD was 27% (95% CI 20–35%, 12 studies) in adults with hypertension, 31% (95% CI 22–41%, 13 studies) in adults with diabetes and 14% (95% CI 10–19%, 9 studies) in adults who were overweight/obese (Figs 35). We found substantial heterogeneity across the included studies in the pooled estimates for CKD prevalence in high risk populations. We found no evidence of publication bias across studies reporting on the prevalence of CKD in the adults with hypertension and the adults with diabetes, with the symmetrical funnel plot and non-significant Egger test (S11 and S12 Figs).

Fig 3. Prevalence of CKD in adults with hypertension in South Asia.

Fig 4. Prevalence of CKD in adults with diabetes in South Asia.

Fig 5. Prevalence of CKD in overweight/obese adults in South Asia.


Our study found that prevalence of CKD was nearly 14% in general population and among those who were overweight/obese. CKD prevalence was two times higher among adults with hypertension and diabetes compared to general population. These findings are comparable to CKD prevalence in African continent (15.8%, 95% CI 12.1–19.9%) [42] and global prevalence of CKD (13.4%, 95% CI 11.7–15.1%) [43]. Our pooled estimate in a high-risk population (adults with hypertension and adults with diabetes) is comparable to the prevalence of CKD in high risk population in the African continent [42]. There was high heterogeneity in the pooled estimates of CKD across the studies for the general population as well as in the high risk population. In subgroup analysis based on the three main equations used to estimate the GFR yielded different prevalence estimates. The prevalence estimates obtained from the Cockcroft formula was higher than the prevalence obtained using MDRD or CKD-EPI equations. While majority of included studies used Jaffe kinetic method, others lacked information on serum creatinine assessment. Therefore, we could not assess whether the variation in laboratory assessment of serum creatinine contributed to heterogeneity in pooled estimates. We noted variation in the burden of CKD by countries of the South Asia region: 16% in India to 6% in Nepal. Furthermore, the burden of CKD also varied widely within the countries itself, ranging from 6 to 32% in India, 13 to 17% in Bangladesh and 6 to 11% in Nepal. This might be because of diversity in geographic, socio-economic status, lifestyle and culture across South Asia [44]. The global burden of disease 2019 also suggests a wide variation in CKD prevalence in south Asian countries ranging from 4.5 to 13.5% [45] (S13 Fig). CKD was more prevalent in men (15%) than in women (13%). This variance could be explained by higher muscle mass in men than women in general as serum creatinine concentration in an individual depends on his/her muscle mass. These findings corroborate with the existing literature on gender differences in CKD prevalence [46, 47]. In subgroup analysis, prevalence of CKD was found three times higher in individuals aged 40 years and older compared to their younger counterparts. This might be because of gradual decline in renal function (GFR) at the rate of 0.75 to 1 ml/min/year after the age of 40 years [48]. Additionally, the higher prevalence of cardiometabolic risk factors like diabetes and hypertension in older age groups [49, 50] also explains the higher prevalence of CKD in in age group 40 years and older.

Metabolic risk factors, such as diabetes, hypertension, and obesity, have been described as strong risk factors of both ESRD and CVD events in large observational studies [6, 51]. Interestingly, countries, with a lower prevalence of hypertension reported a lower prevalence of CKD [49] and likewise modestly higher prevalence of CKD in India [52] and China [53] coincided with a higher prevalence of hypertension. Considering the rise in metabolic risk factors such as diabetes, hypertension, and obesity in the South Asian continent [49, 50, 5456], this might further increase the CKD burden in these countries in coming years. On the other hand, a significant proportion of deaths in CKD patients (around 40%) occur prematurely (before age 65) primarily due to cardiovascular complications [57, 58]. Therefore, these premature deaths can be prevented through strategies to slow the progression of CKD and optimal control of cardiometabolic risk factors.

On one hand, extrapolating the 14% prevalence of CKD in this study to South Asia’s 1.3 billion adult population (aged ≥20 years), we estimate that 143–234 million are at risk of some forms of kidney damage. This could mean nearly 22,880 to 37,440 would require treatment for ESRD every year [59]. However, the shortage of renal replacement services in South Asian countries is concerning. A study reported that there were only 900 Nephrologist and 5,500 dialysis centers in India which provide dialysis services to an estimated 55,000 ESRD patients [60]. The situation in other South Asian countries is even more disheartening such as in Nepal. There are only 36 Nephrologists and 42 dialysis centers that provide dialysis services to an estimated 2000 ESRD patients [61, 62]. Recent anecdotes also showed an increase in waiting time for patients with ESRD in dialysis centers and increasing demand for kidney transplantation for ESRD patients in South Asia [8]. Therefore, providing universal nephrology services remained an important priority in South Asia. Targeting risk factors for CKD, specifically blood pressure, blood glucose, and lipids, through a combination of active lifestyle intervention and pharmacotherapy can lead to better prognosis among those with kidney damage [63]. These interventions can be implemented at minimal cost and have been shown to decrease the burden of ESRD, risk of cardiovascular complications as well as morbidity and mortality from non communicable diseases (NCDs) [64].

In some regions in South Asia like the Indian states of Andhra Pradesh and Odisha [29] and the Anuradhapura district of Sri Lanka [31], chronic kidney disease in agricultural population without established risk factors (such as hypertension, diabetes) was identified and termed as CKDu. The studies that reported prevalence of CKDu identified in this review were from these endemic regions and therefore cannot be generalised to the entire population of the respective country. The pooled prevalence of CKDu in the endemic regions of South Asia was 8% (95% CI 3–16%).

The major strength of this study is an extensive search of literature in multiple electronic databases and reference list of identified studies. However, we might have missed some publications in the local language, including reports from national authorities/registries. The review was conducted strictly adhering to MOOSE guidelines and each was performed independently by two reviewers to minimise bias. The findings of this study, however, need to be interpreted with caution. There was substantial heterogeneity in the prevalence estimates across the studies, which could not be explained by subgroup analyses and meta-regression. The sources of these heterogeneities may stem from differences in characteristics of the study population and analytical variability in the measurement of serum creatinine [65, 66] and urinary albumin [67]. Moreover, most studies assessed urinary protein with dipstick instead of albumin which correlates poorly with albumin to creatinine ratio [68]. Most studies did not conduct the follow up at 3 months to demonstrate chronicity thus failed to detect the cases with transient changes in GFR and albuminuria [69]. We could not perform subgroup analysis for other covariates that may have contributed to heterogeneity across the included studies, such as age, residence.


This study highlighted CKD as an important health priority in South Asia. One in every seven participants in our study had CKD. CKD was nearly twice prevalent in adults with hypertension and diabetes compared to the general population. This shows an urgency for lifestyle intervention to target common NCD risk factors to reduce the progression of CKD and future CVD events.

Supporting information

S1 Table. Detailed search strategy for assessing the pooled prevalence of CKD/CKDu in South Asia.


S2 Table. Quality assessment of the included studies using the Hoy et al. risk of bias assessment tool.


S3 Table. Meta-regression analysis for the variation of the prevalence of CKD in the general population of adults in South Asia.


S1 Fig. Prevalence of CKD in adult males in South Asia.


S2 Fig. Prevalence of CKD in adult females in South Asia.


S3 Fig. Prevalence of GFR < 60 mL/min/1.73 m2 in the general population of adults in South Asia.


S4 Fig. Prevalence of CKD in the general population of adults in South Asia based on MDRD equation for GFR estimation.


S5 Fig. Prevalence of CKD in the general population of adults in South Asia based on CKD-EPI equation for GFR estimation.


S6 Fig. Prevalence of CKD in the general population of adults in South Asia based on Cockcroft-Gault equation for GFR estimation.


S7 Fig. Prevalence of CKD in general population of adults in South Asia (studies with 3 months follow up).


S8 Fig. Prevalence of CKD in age group <40 years and age group ≥ 40 years.


S9 Fig. Prevalence of CKDu in endemic populations of adults in South Asia.


S10 Fig. Egger’s test and funnel plot for publication bias for overall CKD prevalence.


S11 Fig. Egger’s test and funnel plot for publication bias for prevalence of CKD in adults with hypertension.


S12 Fig. Egger’s test and funnel plot for publication bias for prevalence of CKD in adults with diabetes.


S13 Fig. Forrest plot showing prevalence of chronic kidney disease in South Asian countries from global burden of disease 2019.



  1. 1. The Institute for Health Metrics and Evaluation. Global Burden of Disease (GBD 2019) 2019 [15 January 2021]. Available from: Available online:
  2. 2. Nugent RA, Fathima SF, Feigl AB, Chyung D. The burden of chronic kidney disease on developing nations: a 21st century challenge in global health. Nephron Clin Pract. 2011;118(3):c269–77. pmid:21212690.
  3. 3. Fraser SDS, Roderick PJ. Kidney disease in the Global Burden of Disease Study 2017. Nature Reviews Nephrology. 2019;15(4):193–4. pmid:30723305
  4. 4. Couser WG, Remuzzi G, Mendis S, Tonelli M. The contribution of chronic kidney disease to the global burden of major noncommunicable diseases. Kidney Int. 2011;80(12):1258–70. pmid:21993585.
  5. 5. Levey AS, Atkins R, Coresh J, Cohen EP, Collins AJ, Eckardt KU, et al. Chronic kidney disease as a global public health problem: approaches and initiatives—a position statement from Kidney Disease Improving Global Outcomes. Kidney Int. 2007;72(3):247–59. pmid:17568785.
  6. 6. Lewington S, Clarke R, Qizilbash N, Peto R, Collins R, Prospective Studies C. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet. 2002;360(9349):1903–13. pmid:12493255.
  7. 7. Singh NP, Gupta AK, Kaur G, Khanna T. Chronic Kidney Disease of Unknown Origin—What do we know? J Assoc Physicians India. 2020;68(2):76–9. pmid:32009367.
  8. 8. Mishra SR, Adhikari S, Sigdel MR, Nedkoff L, Briffa TG. Chronic kidney disease in south Asia. The Lancet Global Health. 2016;4(8):e523. pmid:27443779
  9. 9. Mushi L, Marschall P, Flessa S. The cost of dialysis in low and middle-income countries: a systematic review. BMC Health Serv Res. 2015;15:506. pmid:26563300; PubMed Central PMCID: PMC4642658.
  10. 10. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;283(15):2008–12. pmid:10789670.
  11. 11. National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39(2 Suppl 1):S1–266. pmid:11904577.
  12. 12. Stevens PE, Levin A, Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group M. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158(11):825–30. pmid:23732715.
  13. 13. Hoy D, Brooks P, Woolf A, Blyth F, March L, Bain C, et al. Assessing risk of bias in prevalence studies: modification of an existing tool and evidence of interrater agreement. J Clin Epidemiol. 2012;65(9):934–9. pmid:22742910.
  14. 14. Freeman M, Tukey J. Transformations Related to the Angular and the Square Root. The Annals of Mathematical Statistics. 1950;21(4)(4):607–11.
  15. 15. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60. pmid:12958120; PubMed Central PMCID: PMC192859.
  16. 16. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34. pmid:9310563; PubMed Central PMCID: PMC2127453.
  17. 17. Anand S, Shivashankar R, Ali MK, Kondal D, Binukumar B, Montez-Rath ME, et al. Prevalence of chronic kidney disease in two major Indian cities and projections for associated cardiovascular disease. Kidney Int. 2015;88(1):178–85. pmid:25786102; PubMed Central PMCID: PMC4490055.
  18. 18. Anupama YJ, Uma G. Prevalence of chronic kidney disease among adults in a rural community in South India: Results from the kidney disease screening (KIDS) project. Indian J Nephrol. 2014;24(4):214–21. pmid:25097333; PubMed Central PMCID: PMC4119333.
  19. 19. Farag YMK, Karai Subramanian K, Singh VA, Tatapudi RR, Singh AK. Occupational risk factors for chronic kidney disease in Andhra Pradesh: ’Uddanam Nephropathy’. Ren Fail. 2020;42(1):1032–41. pmid:33040645; PubMed Central PMCID: PMC7580562.
  20. 20. Fatema K, Abedin Z, Mansur A, Rahman F, Khatun T, Sumi N, et al. Screening for chronic kidney diseases among an adult population. Saudi J Kidney Dis Transpl. 2013;24(3):534–41. pmid:23640626.
  21. 21. Feng L, de Silva HA, Jehan I, Naheed A, Kasturiratne A, Himani G, et al. Regional variation in chronic kidney disease and associated factors in hypertensive individuals in rural South Asia: findings from control of blood pressure and risk attenuation-Bangladesh, Pakistan and Sri Lanka. Nephrol Dial Transplant. 2019;34(10):1723–30. pmid:29982770; PubMed Central PMCID: PMC6775474.
  22. 22. Hasan M, Kashem M, Rahman M, Qudduhush R, Rahman M, Sharmeen A, et al. Prevalence of Chronic Kidney Disease (CKD) and Identification of Associated Risk Factors among Rural Population by Mass Screening. Community Based Medical Journal. 2013;1(2):20–6.
  23. 23. Huda MN, Alam KS, Harun Ur R. Prevalence of chronic kidney disease and its association with risk factors in disadvantageous population. Int J Nephrol. 2012;2012:267329. pmid:22848823; PubMed Central PMCID: PMC3400350.
  24. 24. Jayatilake N, Mendis S, Maheepala P, Mehta FR, Team CKNRP. Chronic kidney disease of uncertain aetiology: prevalence and causative factors in a developing country. BMC Nephrol. 2013;14:180. pmid:23981540; PubMed Central PMCID: PMC3765913.
  25. 25. Jessani S, Bux R, Jafar TH. Prevalence, determinants, and management of chronic kidney disease in Karachi, Pakistan—a community based cross-sectional study. BMC Nephrol. 2014;15:90. pmid:24927636; PubMed Central PMCID: PMC4065316.
  26. 26. Khanam PA, Sayeed MA, Islam A, Begum T, Habib SH, Nahar N, et al. Hospital-based prevalence of chronic kidney disease among the newly registered patients with diabetes. Journal of Diabetology. 2016;7(3):2.
  27. 27. Mahapatra HS, Gupta YP, Sharma N, Buxi G. Identification of high-risk population and prevalence of kidney damage among asymptomatic central government employees in Delhi, India. Saudi J Kidney Dis Transpl. 2016;27(2):362–70. pmid:26997392.
  28. 28. Mohanty NK, Sahoo KC, Pati S, Sahu AK, Mohanty R. Prevalence of Chronic Kidney Disease in Cuttack District of Odisha, India. Int J Environ Res Public Health. 2020;17(2). pmid:31936746; PubMed Central PMCID: PMC7014305.
  29. 29. O’Callaghan-Gordo C, Shivashankar R, Anand S, Ghosh S, Glaser J, Gupta R, et al. Prevalence of and risk factors for chronic kidney disease of unknown aetiology in India: secondary data analysis of three population-based cross-sectional studies. BMJ Open. 2019;9(3):e023353. pmid:30850400; PubMed Central PMCID: PMC6429742.
  30. 30. Rajput R, Kumar P, Seshadri K, Agarwal P, Talwalkar P, Kotak B, et al. Prevalence of Chronic Kidney Disease (CKD) in Type 2 Diabetes Mellitus Patients: START-India Study. Journal of Diabetes & Metabolism. 2017;08.
  31. 31. Ruwanpathirana T, Senanayake S, Gunawardana N, Munasinghe A, Ginige S, Gamage D, et al. Prevalence and risk factors for impaired kidney function in the district of Anuradhapura, Sri Lanka: a cross-sectional population-representative survey in those at risk of chronic kidney disease of unknown aetiology. BMC Public Health. 2019;19(1):763. pmid:31200694; PubMed Central PMCID: PMC6570843.
  32. 32. Sharma SK, Dhakal S, Thapa L, Ghimire A, Tamrakar R, Chaudhary S, et al. Community-based screening for chronic kidney disease, hypertension and diabetes in Dharan. JNMA J Nepal Med Assoc. 2013;52(189):205–12. pmid:23591297.
  33. 33. Sharma SK, Zou H, Togtokh A, Ene-Iordache B, Carminati S, Remuzzi A, et al. Burden of CKD, proteinuria, and cardiovascular risk among Chinese, Mongolian, and Nepalese participants in the International Society of Nephrology screening programs. Am J Kidney Dis. 2010;56(5):915–27. pmid:20888105.
  34. 34. Singh AK, Farag YM, Mittal BV, Subramanian KK, Reddy SR, Acharya VN, et al. Epidemiology and risk factors of chronic kidney disease in India—results from the SEEK (Screening and Early Evaluation of Kidney Disease) study. BMC Nephrol. 2013;14:114. pmid:23714169; PubMed Central PMCID: PMC3848478.
  35. 35. Singh NP, Ingle GK, Saini VK, Jami A, Beniwal P, Lal M, et al. Prevalence of low glomerular filtration rate, proteinuria and associated risk factors in North India using Cockcroft-Gault and Modification of Diet in Renal Disease equation: an observational, cross-sectional study. BMC Nephrol. 2009;10:4. pmid:19220921; PubMed Central PMCID: PMC2663556.
  36. 36. Tatapudi RR, Rentala S, Gullipalli P, Komarraju AL, Singh AK, Tatapudi VS, et al. High Prevalence of CKD of Unknown Etiology in Uddanam, India. Kidney Int Reports. 2019;4(3):380–9. pmid:30899865
  37. 37. Trivedi H, Vanikar A, Patel H, Kanodia K, Kute V, Nigam L, et al. High prevalence of chronic kidney disease in a semi-urban population of Western India. Clin Kidney J. 2016;9(3):438–43. pmid:27274831; PubMed Central PMCID: PMC4886905.
  38. 38. Varma PP, Raman DK, Ramakrishnan TS, Singh P. Prevalence of Early Stages of Chronic Kidney Disease in Healthy Army Personnel. Medical Journal Armed Forces India. 2011;67(1):9–14. pmid:27365754
  39. 39. Varma PP, Raman DK, Ramakrishnan TS, Singh P, Varma A. Prevalence of early stages of chronic kidney disease in apparently healthy central government employees in India. Nephrol Dial Transplant. 2010;25(9):3011–7. pmid:20233739.
  40. 40. Nepal Health Research Council. Population based prevalence of selected non communicable disease in Nepal. Available online; http://nhrcgovnp/wp-content/uploads/2019/07/CKD-Report-pdf-resizepdf. 2019.
  41. 41. Lawson J, Switchenko JM, McKibbin T, Donald Harvey R. Impact of Isotope Dilution Mass Spectrometry (IDMS) Standardization on Carboplatin Dose and Adverse Events. Pharmacotherapy. 2016;36(6):617–22. pmid:27130286; PubMed Central PMCID: PMC5372694.
  42. 42. Kaze AD, Ilori T, Jaar BG, Echouffo-Tcheugui JB. Burden of chronic kidney disease on the African continent: a systematic review and meta-analysis. BMC Nephrol. 2018;19(1):125. pmid:29859046; PubMed Central PMCID: PMC5984759.
  43. 43. Hill NR, Fatoba ST, Oke JL, Hirst JA, O’Callaghan CA, Lasserson DS, et al. Global Prevalence of Chronic Kidney Disease—A Systematic Review and Meta-Analysis. PLoS One. 2016;11(7):e0158765. pmid:27383068; PubMed Central PMCID: PMC4934905.
  44. 44. Adhikari B, Mishra SR. Culture and epidemiology of diabetes in South Asia. J Glob Health. 2019;9(2):020301–. pmid:31448112.
  45. 45. Institute for Health Metrics and Evaluation. Global Burden of Disease Visualizations. 2019.
  46. 46. Carrero JJ. Gender differences in chronic kidney disease: underpinnings and therapeutic implications. Kidney Blood Press Res. 2010;33(5):383–92. pmid:20948227.
  47. 47. Eriksen BO, Ingebretsen OC. The progression of chronic kidney disease: a 10-year population-based study of the effects of gender and age. Kidney Int. 2006;69(2):375–82. pmid:16408129.
  48. 48. Muntner P. Longitudinal measurements of renal function. Semin Nephrol. 2009;29(6):650–7. pmid:20006797.
  49. 49. Mehata S, Shrestha N, Mehta R, Vaidya A, Rawal LB, Bhattarai N, et al. Prevalence, awareness, treatment and control of hypertension in Nepal: data from nationally representative population-based cross-sectional study. J Hypertens. 2018;36(8):1680–8. pmid:29621067.
  50. 50. Shrestha N, Mishra SR, Ghimire S, Gyawali B, Mehata S. Burden of Diabetes and Prediabetes in Nepal: A Systematic Review and Meta-Analysis. Diabetes Ther. 2020;11(9):1935–46. pmid:32712902; PubMed Central PMCID: PMC7434818.
  51. 51. Klag MJ, Whelton PK, Randall BL, Neaton JD, Brancati FL, Ford CE, et al. Blood pressure and end-stage renal disease in men. New England Journal of Medicine. 1996;334(1):13–8. pmid:7494564
  52. 52. Anchala R, Kannuri NK, Pant H, Khan H, Franco OH, Di Angelantonio E, et al. Hypertension in India: a systematic review and meta-analysis of prevalence, awareness, and control of hypertension. J Hypertens. 2014;32(6):1170. pmid:24621804
  53. 53. Wang Z, Chen Z, Zhang L, Wang X, Hao G, Zhang Z, et al. Status of hypertension in China: results from the China Hypertension Survey, 2012–2015. Circulation. 2018;137(22):2344–56. pmid:29449338
  54. 54. Hills AP, Arena R, Khunti K, Yajnik CS, Jayawardena R, Henry CJ, et al. Epidemiology and determinants of type 2 diabetes in south Asia. Lancet Diabetes Endocrinol. 2018;6(12):966–78. pmid:30287102.
  55. 55. Neupane D, McLachlan CS, Sharma R, Gyawali B, Khanal V, Mishra SR, et al. Prevalence of hypertension in member countries of South Asian Association for Regional Cooperation (SAARC): systematic review and meta-analysis. Medicine (Baltimore). 2014;93(13):e74. pmid:25233326; PubMed Central PMCID: PMC4616265.
  56. 56. Shrestha N, Mishra SR, Ghimire S, Gyawali B, Pradhan PMS, Schwarz D. Application of single-level and multi-level modeling approach to examine geographic and socioeconomic variation in underweight, overweight and obesity in Nepal: findings from NDHS 2016. Sci Rep. 2020;10(1):2406. pmid:32051421; PubMed Central PMCID: PMC7016110.
  57. 57. Fried LF, Shlipak MG, Crump C, Kronmal RA, Bleyer AJ, Gottdiener JS, et al. Renal insufficiency as a predictor of cardiovascular outcomes and mortality in elderly individuals. Journal of the American College of Cardiology. 2003;41(8):1364–72. pmid:12706933
  58. 58. Wen CP, Cheng TYD, Tsai MK, Chang YC, Chan HT, Tsai SP, et al. All-cause mortality attributable to chronic kidney disease: a prospective cohort study based on 462 293 adults in Taiwan. The Lancet. 2008;371(9631):2173–82. pmid:18586172
  59. 59. The World Bank. Population ages 15–64, total—South Asia. Available: 2019 [cited accessed 25 Jan 2021].
  60. 60. Navva PK, Venkata Sreepada S, Shivanand Nayak K. Present Status of Renal Replacement Therapy in Asian Countries. Blood Purif. 2015;40(4):280–7. pmid:26656132.
  61. 61. Jha V, Ur-Rashid H, Agarwal SK, Akhtar SF, Kafle RK, Sheriff RJKi. The state of nephrology in South Asia. Kidney Int. 2019;95(1):31–7. pmid:30612598
  62. 62. Mcgee J, Pandey B, Maskey A, Frazer T, Mackinney T. Free dialysis in Nepal: Logistical challenges explored. Hemodialysis International. 2018;22(3):283–9. pmid:29446212
  63. 63. De Boer IH, Rue TC, Cleary PA, Lachin JM, Molitch ME, Steffes MW, et al. Long-term renal outcomes of patients with type 1 diabetes mellitus and microalbuminuria: an analysis of the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications cohort. Arch Intern Med. 2011;171(5):412–20. pmid:21403038
  64. 64. Chen TK, Knicely DH, Grams ME. Chronic Kidney Disease Diagnosis and Management: A Review. JAMA. 2019;322(13):1294–304. pmid:31573641; PubMed Central PMCID: PMC7015670.
  65. 65. Coresh J, Astor BC, McQuillan G, Kusek J, Greene T, Van Lente F, et al. Calibration and random variation of the serum creatinine assay as critical elements of using equations to estimate glomerular filtration rate. Am J Kidney Dis. 2002;39(5):920–9. pmid:11979335.
  66. 66. Liu WS, Chung YT, Yang CY, Lin CC, Tsai KH, Yang WC, et al. Serum creatinine determined by Jaffe, enzymatic method, and isotope dilution-liquid chromatography-mass spectrometry in patients under hemodialysis. J Clin Lab Anal. 2012;26(3):206–14. pmid:22628238; PubMed Central PMCID: PMC6807401.
  67. 67. Bachmann LM, Nilsson G, Bruns DE, McQueen MJ, Lieske JC, Zakowski JJ, et al. State of the art for measurement of urine albumin: comparison of routine measurement procedures to isotope dilution tandem mass spectrometry. Clin Chem. 2014;60(3):471–80. pmid:24281781.
  68. 68. Guh JY. Proteinuria versus albuminuria in chronic kidney disease. Nephrology (Carlton). 2010;15 Suppl 2:53–6. pmid:20586950.
  69. 69. Bottomley MJ, Kalachik A, Mevada C, Brook MO, James T, Harden PN. Single estimated glomerular filtration rate and albuminuria measurement substantially overestimates prevalence of chronic kidney disease. Nephron Clin Pract. 2011;117(4):c348–52. pmid:20948233.