RENAL DATA FROM ASIA - AFRICA
|Year : 2016 | Volume
| Issue : 2 | Page : 362-370
|Identification of high-risk population and prevalence of kidney damage among asymptomatic central government employees in Delhi, India
Himanshu Sekhar Mahapatra1, Yadunanandan Prasad Gupta2, Neera Sharma3, Gurdeep Buxi4
1 Department of Nephrology, Post Graduate Institute of Medical Education and Research, Dr. Ram Manohar Lohia Hospital, New Delhi, India
2 Department of Financial Studies, Visiting Faculty, Delhi University, New Delhi, India
3 Department of Biochemistry, Post Graduate Institute of Medical Education and Research, Dr. Ram Manohar Lohia Hospital, New Delhi, India
4 Department of Pathology, Post Graduate Institute of Medical Education and Research, Dr. Ram Manohar Lohia Hospital, New Delhi, India
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|Date of Web Publication||11-Mar-2016|
| Abstract|| |
Chronic kidney disease (CKD) has attained epidemic proportions in India due to increased incidence of diabetes and hypertension (HTN). It was surmised that identification of only high-risk groups (HRGs) through a questionnaire would be sufficient to identify cases of kidney damage (KD). The study attempted to device a questionnaire to classify the subjects in to HRG and low-risk group (LRG) and assess the extent of early KD. The central government employees were classified into HRG and LRG based on "SCreening for Occult REnal Disease (SCORED)" and "EXTENDED" questionnaire formulated after addition of 10 more parameters apart from diabetes and HTN. Urine examination by dipstick, quantitative microalbumin, serum creatinine, and estimated glomerular filtration rate were assessed to determine KD. The data were analyzed for risk-group classification. Sensitivity was calculated based on the number of KD cases in the HRG. Of the 1104 employees screened, 58% and 42% were classified in HRG and LRG, respectively. There were 306 KD cases of whom, 65% were in the HRG. The sensitivity of the EXTENDED questionnaire to detect CKD was much higher (60%) compared to the SCORED questionnaire (25%). The prevalence of KD according to stage was: stage-1, 13.4%; stage-2, 9.9%; and late stages (3, 4, and 5), 4.5%. Microalbuminuria and dipstick-positive proteinuria showed statistically higher proportion in the HRG (25% and 4.1%) than in the LRG (19% and 1%, respectively) (P <0.05). Although the EXTENDED questionnaire was more sensitive in detecting KD, only screening the high-risk population will leave behind 35% of KD cases. There is, therefore, a need for mass screening at regular intervals.
|How to cite this article:|
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;27:362-70
|How to cite this URL:|
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 [serial online] 2016 [cited 2020 Oct 26];27:362-70. Available from: https://www.sjkdt.org/text.asp?2016/27/2/362/178564
| Introduction|| |
India holds the dubious distinction of having the world's largest diabetic population and about one-fifth of its population has hypertension. , The recently developed cross-sectional registry of chronic kidney disease (CKD) on 52,273 patients showed that about 85% of CKD patients were reporting to the nephrologists at stages-3 and above.  The underdetection of earlier stages of CKD, as well as determination of its risk factors, lead to a higher number of cases of end-stage renal disease. 
People with diabetes, hypertension (HTN), cardiovascular disease, lipid disorders, obesity, older adults (>40 years), smokers, patients with kidney stone disease, people with a family history of kidney disease, and high-risk ethnic groups have been defined as high-risk group (HRG) by most of the countries.  The importance of early detection of CKD was recognized as early as 2004 by Pol Nef in a study conducted in Poland.  Different preventive strategies of CKD have been propagated by various agencies. Kidney disease: improving global outcomes recommends screening patients with diabetes, HTN, or cardiovascular disease.  National Kidney Foundation's Kidney Early Evaluation Program (KEEP) used a questionnaire and obtained verbal responses on risk factors and diagnosed CKD by laboratory investigation.  Japan has proper mass CKD screening program but the USA negated this strategy because of its low prevalence. , In India also, screening of the HRG has been advocated.  However, there is no definite data on screening benefits among high-risk population compared to the mass screening.
It was therefore decided that the present study be conducted with an especially devised questionnaire to classify the asymptomatic central government employees into HRG and LRG for identifying CKD cases in the former group. Further, the study will try to identify the extent of early kidney damage (KD) in these groups.
| Subjects and Methods|| |
Sample population and data collection
A cross-sectional study was conducted from August to December 2011 with a convenience sample of 1104 asymptomatic employees from the six Central Government Offices. The sample consisted of all persons aged 18 years or above. Necessary Institutional Ethical Committee approval and administrative approval was sought to conduct the study (F No. 021/2011/Misc/IEC/PGIMER/RMLH/1999). The study subjects were administered a prestructured, standardized questionnaire covering demographic data, risk factors, detailed personal, family and medical history, lifestyle habits, and subsequently were subjected to laboratory investigations. Each subject was classified into HRG and LRG based on two sets of questionnaires, existing medical records and laboratory investigation reports. Further, they were classified into KD or no-KD group after obtaining the urine analysis report and the estimated glomerular filtration rate (e-GFR).
Description of activities
For classifying the subjects into HRG and LRG, the SCreening for Occult REnal Disease (SCORED) questionnaire using verbal response was used.  It consisted of nine parameters, namely age, gender, anemia, diabetes, HTN, history of heart attack, history of heart failure, peripheral vascular disease, and protein in the urine. In this model, the probability of CKD was worked out by assigning one point for each variable except age for which two, three, and four points were assigned for ages 50-59, 60-69, and 70 years and above, respectively. A total score of four or more was considered as HRG. Clinical examination and relevant laboratory investigations were performed to support the verbal responses. For confirmation of history of heart attack, history of heart failure and, peripheral vascular disease, existing medical records were examined. In addition to diabetes and HTN, 10 other parameters were included to enhance the HRG base and termed as the "EXTENDED questionnaire." Identification of these parameters was based on different questionnaires used in the KEEP study and European screening guidelines. ,, These were: use of indigenous medications, history of jaundice, systemic lupus erythematosus, prostatic disease, hematuria and/or nocturia, prolonged swelling, regular analgesic use, family history of CKD, urinary tract stone disease, and recurrent urinary tract infection. When the subjects' response was yes to any of these parameters, he/she was classified as HRG, otherwise the subject was placed under the LRG.
Thus, the final "EXTENDED" questionnaire was constructed by combining nine parameters of the "SCORED questionnaire" and 10 additional parameters. Since diabetes and hypertension were common to both, there were a total of 17 parameters. Of them, 10 parameters were measured through verbal responses, four through clinical examination and/or documentary evidences, and three through laboratory investigations. The questionnaire was validated on first two hundred subjects.
Definitions, inclusion, and exclusion criteria
Chronic kidney disease and its stages: The definition provided by the National Kidney Foundation Kidney Disease Outcomes Quality Initiative working group on CKD was followed. , For the KD stages 1 and 2, dipstick proteinuria and/or microalbuminuria (MAU) ≥30 mg and/or hematuria were included. Glomerular filtration rate equations: The Modification of Diet in Renal Diseases (MDRD) equation [186 × (Scr) −1.154 × (age) −0.203 × (0.742 if female)] mL/min/1.73 m 2 was used to calculate the e-GFR.  Hypertension: HTN was defined as per the Joint National Committee-7 guidelines.  Diabetes mellitus (DM): American Diabetes Association 2011 criteria was used for the diagnosis of DM. 
Obesity: Being overweight and having obesity was based on the body mass index and was defined as 23-25 kg/m 2 and 25 kg/m 2 or more, respectively. Truncal obesity was defined by waist-to-hip ratio of >0.9 for men and >0.8 for women. 
Anemia: Hemoglobin level less than 12.0 g/L for nonpregnant women (15 years of age and above) and 13.0 g/L for adult men (15 years of age and above) was considered as anemia.  Smoker: Persons smoking one or more packets of cigarettes per day for a period of two consecutive years.
Alcoholics: Persons drinking two or more measures (30 mL) of alcohol at least three times a week for a period of two consecutive years.
Analgesic abuse: Persons taking analgesic at least three days in a week for more than one year.
Menstruating and pregnant women on the day of investigation were excluded.
Anthropometric parameters and blood pressure were assessed using standardized techniques. Laboratory investigations such as spot morning urine and blood samples were collected and sent to the laboratory on the same day. Assessment for proteinuria, hematuria, and leukocyturia was done using dipsticks (Uriscan urine strip ® , YD Diagnostic ® , Korea), and the results were read on Uriscan strip ® . A reading of one plus (+), two plus (++), three plus (+++), and four plus (++++) blood corresponding to 10 red blood cells (RBCs), 50 RBC, 250 RBC, more than 250 RBC per high power field (HPF) and leukocytes corresponding to 25 white blood cells (WBCs), 75 WBC, 500 WBC, and more than 500 WBC per HPF was, respectively, considered positive for hematuria and leukocyturia. Quantitative urinary microalbumin was obtained using Hemocue ® , Sweden. Values above 30 mg/L were considered as MAU, while values <30 mg and >300 mg were considered as normal and macroalbuminuria, respectively. Serum creatinine (Scr) and fasting blood glucose were measured on fully automatic analyzer System Vitro 350 Chemistry analyzer (Ortho clinical diagnostic Johnson & Johnson ® Rochester, NY, USA). The glycosylated hemoglobin measurement was made by using boronate affinity chromatography method with the system of in2it (1) Biorad ® , UK. Hemoglobin was estimated by using electronic impedance principle for cell counting and sizing colorimetric method with the system Medonic CA 620 Merk ® , Sweden. The e-GFR was calculated by MDRD using the web site http://mdrd.com/.
Participants found to have diabetes, hypertension, proteinuria, or reduced e-GFR were advised regular follow-up with their primary health care physicians or were referred to the nephrology clinic if they had no such arrangements.
| Statistical Analysis|| |
The filled in questionnaires were edited for their completeness of responses. The data were tabulated on Microsoft Excel worksheets and analyzed using statistical software SPSS version 16.0. All quantitative data were presented as the mean ± standard deviation and all categorical data are presented as percentages. Equality of proportion between two groups was performed by using Z-test and equality of means between two groups was performed by using t-test. A P <0.05 was taken as statistically significant.
| Results|| |
The information obtained verbally was corroborated with clinical assessment and laboratory investigations in both the SCORED and EXTENDED questionnaire.
The results of the SCORED questionnaire were tested for their statistical significance of correlation as shown in [Table 1]. In this questionnaire, only two parameters viz., anemia and proteinuria showed poor correlation between verbal and investigation score. After investigations, we observed that a total of 16.3% of patients with diabetes, 26.6% with HTN, 63.6% with anemia, and 96.9% with urinary abnormalities were not aware of their current diseases. The prevalence of anemia, HTN, proteinuria, and diabetes were 44.3%, 27.2%, 23.5%, and 12.2%, respectively. Further, 20 subjects (1.8%) knew that they were currently suffering from kidney disease. The prevalence of other included parameters besides diabetes and HTN in the EXTENDED Questionnaire were: use of indigenous medications, 55 (5%); history of jaundice and/or systemic lupus erythematosus and/or prostatic disease, 136 (12.3%); hematuria and/or nocturia, 39 (3.5%); prolonged swelling, 58 (5.3%); regular analgesic use, 36 (3.3%); family history of CKD, 41 (3.7%); urinary tract stone disease, 71 (6.4%); and recurrent urinary tract infection, 36 (3.3%).
|Table 1: Correlation between verbal and investigation scores in SCORED questionnaire.|
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[Table 2] describes the output of the two questionnaires based on their capacity to pick up risk groups and subsequently, KD status. The SCORED questionnaire identified 10.3% HRG subjects which enhanced to 18.3% when investigation values were included. Similarly, EXTENDED questionnaire identified 46.7% in HRG, rising to 54.0% after incorporating investigation values. When the two questionnaires were combined, it yielded 51.5% and 58.0% of HRG subjects from verbal and investigation results, respectively. There were a total of 306 KD cases detected in the study group. After investigations, the number of KD cases detected by the EXTENDED questionnaire (60.4%) was much higher than the SCORED questionnaire (25.8%). When the two questionnaires were combined, detection of KD cases increased to 199 (65%).
|Table 2: Classification of respondents in to risk groups and detection of kidney disease subjects in the high-risk group.|
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Of the total KD subjects, 33.0% were suffering from diabetes and/or HTN and the remaining 67% had reported one or more risk factors other than diabetes or HTN. There were 26 (41.3%), 70 (30.6%), and 27 (37.5%) cases of KD among patients with diabetes alone, HTN alone, and both together, respectively.
The results of different parameters in the risk group are shown in [Table 3]. One in every seven was a smoker and almost 10% were alcoholic. Apart from the factors taken to categorize the two risk groups, HRG and LRG subgroups also differed significantly in terms of age, obesity, and truncal obesity (P <0.00, P <0.00 and P <0.01, respectively).
The prevalence of MAU in the total, HRG, and LRG samples was 22.7%, 25.3%, and 19.25%, respectively [Table 4]. Hematuria and leukocyturia separately did not show any significant difference between HRG and LRG, whereas MAU and dipstick proteinuria differed significantly between the two groups. When results were analyzed by combining all four measures, the two groups differed significantly. A total of 27.7% of the subjects were found to have KD. While there was a significant difference in the prevalence of KD stage-2 between the HRG and LRG, this difference was not statistically significant in stage1 or other stages [Table 5].
| Discussion|| |
This is the first study to identify KD subjects based on stratification of population into HRG and LRG. There were 58% and 42% subjects in HRG and LRG, respectively. The prevalence of KD among the total group and HRG were 27.7% and 31.1%, respectively.
We observed that many subjects were aware of their current illness from risk factors viz., diabetes, HTN, coronary artery disease, heart failure, and vascular disease. However, a large majority of subjects were unaware of anemia and urine abnormality. This is because it is not a practice in India to have a routine health check-up and hence, this low level of awareness. However, an abysmally low number of subjects (6.5%), currently suffering from KD, was aware of their disease.
The HRG subjects identified by SCORED questionnaire were low (10.3%), which increased to 18.3% when supported by clinical examination and laboratory investigations. SCORED method alone and EXTENDED method alone detected 25.8% and 60.4% KD cases, respectively. When both the methods were used in combination, 65% cases were detected. Although a sensitivity of 65% was obtained, it was much below the acceptable level of 90% or more, as evaluated in the original SCORED model and subsequently tested with patients having cardiovascular diseases. , The SCORED questionnaire proved futile in its classification to identify KD cases effectively. The reason for such a low sensitivity could be because the SCORED questionnaire gives higher weightage to older agegroups. Lack of health awareness and some unidentified risk factors could be other reasons. Therefore, the idea of classification into HRG and LRG did not achieve its objective.
The KEEP study on the prevalence of CKD among high-risk population concluded that there was one CKD patient from among three to six subjects examined.  However, population-based studies indicated that only one patient would be identified by screening 16-21 persons. , We found that screening of every three HRG subjects picked up one KD subject; and in the general population, of every four subjects, about one KD subject was detected. The reason why we got higher number in both the situations was perhaps due to one time estimation of quantitative MAU for defining KD, which is transient in nature.
For CKD screening, dipstick testing for proteinuria and/or albuminuria and e-GFR are sufficient parameters. Further testing with more sensitive methods for MAU could detect people with early stages, who cannot be identified by e-GFR alone. , We performed urine examination and quantitative assessment of MAU in all the subjects. The finding of prevalence of dipstick proteinuria of 2.7% and MAU of 22.7% supports the point. The only available Indian study reported a low prevalence of MAU (around 11%), which may be because of use of semiquantitative method for estimation.  The prevention of renal and vascular end-stage disease study observed that screening of only high-risk individuals identified 55% of those with MAU, and 87% of those who progressed to renal replacement therapy. Because 40-50% of individuals with MAU did not have risk factors for CKD, the authors advocated screening the general population rather than targeting screening to those at increased risk. 
In India, while three hospital-based studies conducted in early nineties focused on etiology of CKD, ,, the five population-based studies performed till date have reported on the prevalence of the disease. Regional and cohort heterogeneity, different methodologies, and definitions used to define CKD could be responsible for the varying results. While, Mani screened their cohort with questionnaire and urinary protein by sulfosalicylic acid method,  Agarwal et al used the Scr value >1.8 mg/dL as cut off value.  The two studies reported the prevalence of CKD of 0.68-1.39% and 0.8%, respectively. The third study utilized the criteria of GFR <60 mL/min and found the prevalence to be 4.2%.  The fourth study used urinary microalbumin and e-GFR and found the prevalence of MAU and reduced GFR to be 10% and 15%, respectively.  Finally, Singh et al in their recent study found an overall prevalence of CKD of around 17.2% and stage-wise prevalence of 7%, 4.3%, 4.3%, 0.8%, and 0.8%.  In comparison, among ethnic Indians in Singapore, the prevalence of albuminuria was 10.5% and e-GFR of <60 mL/min/1.73 m 2 was 4.1%.  NHANES (1999-2004) found the prevalence of CKD in the general population to be 13.3%. , A Chinese study found a prevalence of albuminuria of 94% with overall prevalence of CKD of 10·8%.  Among the HRG, the CKD prevalence was twice that of the general population as found by KEEP in the US and Japan. ,
The high prevalence in our study is because of inclusion of quantitative assessment of urine microalbumin and hematuria for defining KD. Past studies found that with repeat urine examination after three months, only 60% cases would retain positive abnormality.  With this criterion, our prevalence would reduce to about 16%. Other reasons could be nonvalidation of MDRD-GFR for the Indian population. , Again, taking Western norms of GFR cut-off value leads to inflation of KD prevalence rate. Barai and Gambhir commented that in the Indian population, the GFR cut-off value needs to reduce to 45 mL/min/1.73 m 2 for defining CKD.  We found that 33.8% of those who had diabetes or HTN had KD. This trend was similar to the KEEP study from Japan where the CKD prevalence was 35% among diabetic participants and 34.8% among hypertensive participants. 
If we compare the advantages and disadvantages of the two screening procedures, namely high risk and mass screening, we know that the latter will be cost prohibitive. Even then, it will not guarantee that all target population will participate. This may lead to selection bias. The former screening procedure will fail to detect more than one-third KD cases. Therefore, the benefit of early detection will be defeated.
In the present study, extensive efforts were put in to develop a questionnaire for the purpose of classifying the population in to HRG and LRG, to capture KD cases. There could be a "measurement bias" since no repeat examination of urine was performed. Perhaps, a perceptibly healthier group might have opted out from the study, thereby biasing the number of KD cases on a higher side. Longitudinal studies are required to estimate the true prevalence of MAU.
| Conclusions|| |
The EXTENDED questionnaire was more sensitive than the SCORED questionnaire in detecting KD. A high prevalence of MAU and KD in the present study is alarming. Despite worldwide recommendation of screening of HRG for KD, the present study brings out a case for mass screening. We suggest inclusion of urine test for albumin and blood test for creatinin for detection of KD in the screening tools of the diseases requiring mass screening such as diabetes, cardiovascular disease, and cancer. Further, there is a strong case for providing screening benefit through public universal health insurance scheme in developing countries.
| Acknowledgment|| |
We wish to thank the World Health Organization for giving us financial assistance to carry out the study. We also thank the hospital authorities for their administrative and laboratory support. We would like to place on record our sincere thanks to the central government organizations for their cooperation and support in the data collection. The research team deserves special thanks for their untiring efforts in data collection and analysis.
Conflict of interest: None declared.
| References|| |
Sicree R, Shaw J, Zimmet P. Diabetes and impaired glucose tolerance. In: Gan D, edr. Diabetes Atlas, International Diabetes Federation. 3rd ed. Belgium: International Diabetes Federation; 2006. p. 15-103.
Mohan V, Deepa M, Farooq S, Datta M, Deepa R. Prevalence, awareness and control of hypertension in Chennai - The Chennai Urban Rural Epidemiology Study (CURES-52). J Assoc Physicians India 2007;55:326-32.
Rajapurkar MM, John GT, Kirpalani AL, et al. What do we know about chronic kidney disease in India: first report of the Indian CKD registry. BMC Nephrol 2012;13:10.
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.
Smith JM, Mott SA, Hoy WE; International Federation of Kidney Foundations. Status of chronic kidney disease prevention programs: International Federation of Kidney Foundation Members 2005/2007. Kidney Int 2008;74: 1516-25.
Rutkowski B, Czekalski S, Sulowicz W, et al. Epidemiology of kidney diseases in Poland - pilot study (PolNef). Przegl Lek 2004;61:22-4.
Levey AS, Atkins R, Coresh J, 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;72:247-59.
Brown WW, Peters RM, Ohmit SE, et al. Early detection of kidney disease in community settings: the Kidney Early Evaluation Program (KEEP). Am J Kidney Dis 2003;42:22-35.
Yamagata K, Iseki K, Nitta K, et al. Chronic kidney disease perspectives in Japan and the importance of urinalysis screening. Clin Exp Nephrol 2008;12:1-8.
Powe NR, Boulware LE. Population-based screening for CKD. Am J Kidney Dis 2009;53 3 Suppl 3:S64-70.
Agarwal SK. Chronic kidney disease and its prevention in India. Kidney Int Suppl 2005; 98:S41-5.
Bang H, Vupputuri S, Shoham DA, et al. SCreening for Occult REnal Disease (SCORED): a simple prediction model for chronic kidney disease. Arch Intern Med 2007;167:374-81.
Brown WW, Collins A, Chen SC, et al. Identification of persons at high risk for kidney disease via targeted screening: the NKF Kidney Early Evaluation Program. Kidney Int Suppl 2003;83:S50-5.
Crowe E, Halpin D, Stevens P; Guideline Development Group. Early identification and management of chronic kidney disease: summary of NICE guidance. BMJ 2008;337: a1530.
Levey AS, Coresh J, Balk E, et al. National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med 2003;139:137-47.
Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999;130:461-70.
Chobanian AV, Bakris GL, Black HR, et al. The seventh report of the Joint National Committee on prevention, detection, evaluation, and treatment of high blood pressure: The JNC 7 report. JAMA 2003;289:2560-72.
American Diabetic Association, Standards of medical care in diabetes - 2011. Diabetes Care 2011;34:11-61.
Misra A, Chowbey P, Makkar BM, et al. Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management. J Assoc Physicians India 2009; 57:163-70.
Bang H, Mazumdar M, Newman G, et al. Screening for kidney disease in vascular patients: SCreening for Occult REnal Disease (SCORED) experience. Nephrol Dial Transplant 2009;24:2452-7.
Hallan SI, Dahl K, Oien CM, et al. Screening strategies for chronic kidney disease in the general population: follow-up of cross sectional health survey. BMJ 2006;333:1047.
Garg AX, Kiberd BA, Clark WF, Haynes RB, Clase CM. Albuminuria and renal insufficiency prevalence guides population screening: results from the NHANES III. Kidney Int 2002;61:2165-75.
de Jong PE, Halbesma N, Gansevoort RT. Screening for early chronic kidney disease - what method fits best? Nephrol Dial Transplant 2006;21:2358-61.
de Jong PE, Gansevoort RT. Screening techniques for detecting chronic kidney disease. Curr Opin Nephrol Hypertens 2005;14:567-72.
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;25:3011-7.
Atthobari J, Asselbergs FW, Boersma C, et al. Cost-effectiveness of screening for albuminuria with subsequent fosinopril treatment to prevent cardiovascular events: A pharmacoeconomic analysis linked to the prevention of renal and vascular end stage disease (PREVEND) study and the prevention of renal and vascular end-stage disease intervention trial (PREVEND IT). Clin Ther 2006;28:432-44.
Mani MK. Chronic renal failure in India. Nephrol Dial Transplant 1993;8:684-9.
Sakhuja V, Jha V, Ghosh AK, Ahmed S, Saha TK. Chronic renal failure in India. Nephrol Dial Transplant 1994;9:871-2.
Mittal S, Kher V, Gulati S, Agarwal LK, Arora P. Chronic renal failure in India. Ren Fail 1997;19:763-70.
Mani MK. Experience with a program for prevention of chronic renal failure in India. Kidney Int Suppl 2005;94:S75-8.
Agarwal SK, Dash SC, Irshad M, Raju S, Singh R, Pandey RM. Prevalence of chronic renal failure in adults in Delhi, India. Nephrol Dial Transplant 2005;20:1638-42.
Singh NP, Ingle GK, Saini VK, 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.
Singh AK, Farag YM, Mittal BV, 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.
Nang EE, Khoo CM, Tai ES, et al. Is there a clear threshold for fasting plasma glucose that differentiates between those with and without neuropathy and chronic kidney disease? the Singapore Prospective Study Program. Am J Epidemiol 2009;169:1454-62.
Coresh J, Byrd-Holt D, Astor BC, et al. Chronic kidney disease awareness, prevalence, and trends among U.S. adults, 1999 to 2000. J Am Soc Nephrol 2005;16:180-8.
Zhang L, Wang F, Wang L, et al. Prevalence of chronic kidney disease in China: a crosssectional survey. Lancet 2012;379:815-22.
National Kidney Foundation. Kidney Early Evaluation Program. Annual data report. Am J Kidney Dis 2009;53 Suppl 4:S1-135.
Takahashi S, Okada K, Yanai M. The Kidney Early Evaluation Program (KEEP) of Japan: results from the initial screening period. Kidney Int Suppl 2010;116:S17-23.
Srinivas S, Annigeri RA, Mani MK, Rao BS, Kowdle PC, Seshadri R. Estimation of glomerular filtration rate in South Asian healthy adult kidney donors. Nephrology (Carlton) 2008;13:440-6.
Barai S, Gambhir S. Need for a more realistic cut-off GFR value to define chronic renal failure. Kidney Int 2008;73:1325-6.
Himanshu Sekhar Mahapatra
Department of Nephrology, Post Graduate Institute of Medical Education and Research, Dr. Ram Manohar Lohia Hospital, New Delhi
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]
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