CARRS Surveillance study: design and methods to assess burdens from multiple perspectives
© Nair et al.; licensee BioMed Central Ltd. 2012
Received: 27 June 2012
Accepted: 13 August 2012
Published: 28 August 2012
Cardio-metabolic diseases (CMDs) are a growing public health problem, but data on incidence, trends, and costs in developing countries is scarce. Comprehensive and standardised surveillance for non-communicable diseases was recommended at the United Nations High-level meeting in 2011.
Aims: To develop a model surveillance system for CMDs and risk factors that could be adopted for continued assessment of burdens from multiple perspectives in South-Asian countries.
Design: Hybrid model with two cross-sectional serial surveys three years apart to monitor trend, with a three-year prospective follow-up of the first cohort.
Sites: Three urban settings (Chennai and New Delhi in India; Karachi in Pakistan), 4000 participants in each site stratified by gender and age.
Sampling methodology: Multi-stage cluster random sampling; followed by within-household participant selection through a combination of Health Information National Trends Study (HINTS) and Kish methods.
Culturally-appropriate and methodologically-relevant data collection instruments were developed to gather information on CMDs and their risk factors; quality of life, health-care utilisation and costs, along with objective measures of anthropometric, clinical and biochemical parameters. The cohort follow-up is designed as a pilot study to understand the feasibility of estimating incidence of risk factors, disease events, morbidity, and mortality.
The overall participant response rate in the first cross-sectional survey was 94.1% (Chennai 92.4%, n = 4943; Delhi 95.7%, n = 4425; Karachi 94.3%, n = 4016). 51.8% of the participants were females, 61.6% < 45years, 27.5% 45–60years and 10.9% >60 years.
This surveillance model will generate data on prevalence and trends; help study the complex life-course patterns of CMDs, and provide a platform for developing and testing interventions and tools for prevention and control of CMDs in South-Asia. It will also help understanding the challenges and opportunities in establishing a surveillance system across countries.
Keywords“Cardio-metabolic diseases” Surveillance Risk-factors South-Asia
Cardio-metabolic diseases (CMDs) broadly comprise of diabetes mellitus, cardiovascular diseases (CVD), chronic kidney disease (CKD) and their common interconnected risk factors such as obesity, insulin resistance, glucose intolerance, dyslipidaemia, and hypertension. They are a growing public health problem worldwide  accompanying socioeconomic and nutrition transitions [2–4]. Coronary heart disease (CHD), cerebrovascular disease, and diabetes together account for 30% of global mortality and 80% of these deaths occur in low-and-middle-income countries (LMICs) [2, 5–7]. In 2010, globally, 4,000,000 deaths were due to diabetes, the highest in absolute numbers was (1,008,000) in India . The largest fraction of deaths from CHD (37%) and stroke (30%) attributable to high blood glucose were in South-Asia . Further, in people of South-Asian origin, onset of diabetes [10–12], other cardio-metabolic risk factors [13, 14], and late-stage disease events [15, 16] occur at lower body mass indices and younger ages than other ethnic groups [16–23].
Key recommendations of the 2011 United Nations high-level meeting on non-communicable diseases (NCDs) and the US Institute of Medicine are initiation and strengthening of surveillance for NCDs  and the creation of integrated, comprehensive, sustainable, on-going nationwide surveillance systems . In South-Asia, current efforts are limited to local surveys with vast state-wise heterogeneity and variable data quality [26–28]. Furthermore, projections of national income losses related to CMDs are based on models using inputs from limited local studies ; data on individual and household costs and social burdens are also scarce . Current efforts by the Governments of India and Pakistan in setting up nationwide surveillance of NCDs are limited to self-reported surveys [31, 32]. A robust surveillance system would need to be representative of the population of interest, utilise standardised methods that are not solely reliant on self-reporting, be amenable to scaling up, would be sustainably financed by the country/region itself, and also become a platform for further research opportunities and policy guidance (much like the role of the Centres for Disease Control and Prevention [CDC] in the United States) [33, 34].
We present the design and methods of a model surveillance system for CMDs, the CARRS (Centre for cArdiometabolic Risk Reduction in South-Asia)-Surveillance Study, which could be adopted for continuing assessments of burdens in South-Asian countries. The CARRS-Surveillance study builds on the WHO STEPS (World Health Organisation stepwise Approach to Surveillance) model  to capture prevalence of risk factors, CMDs, and their socioeconomic impact in serial representative surveys to understand trends, but goes a step further to convert the cross-sectional survey into a large, urban, sub-continent wide prospective cohort at lower-costs, to understand the incidence of risk factors, diseases, complications, and mortality. Thus, apart from estimating burdens, it can be used to develop South-Asian assessment and clinical management systems to tailor care and preventive approaches.
This is a hybrid cohort-modelled cross-sectional multi-centre surveillance study to be conducted over a period of four years. Two cross-sectional surveys conducted three years apart on standalone representative samples of each of the three city-wide populations, using objective measures will permit estimation of the prevalence and trends of CMDs and their risk factors. Those enrolled in the first cross-sectional survey will be followed as a cohort in a three-year study to estimate (i) the incidence of new risk factors (such as obesity, hypertension, diabetes,); (ii) incidence of later-stage target organ diseases such as peripheral vascular disease, stroke, myocardial infarction, congestive heart failure, chronic stable angina, CKD, retinopathy, neuropathy, and amputation; (iii) assessment of health service utilisation and costs including hospitalisation and outpatient use and (iv) morbidity and mortality associated with CMDs.
The first cross-sectional survey has been completed with ongoing first year of cohort-follow-up. The survey was comprehensive, undertaking assessments of quality of Life (QoL), and socioeconomic burdens on individuals and families with regards to these diseases. Participants underwent anthropometric measurements, blood pressure (BP) assessment, and provided biochemical specimens. The cohort follow-up was limited to patient reports with recording of BP and anthropometry. CMDs and their complications were diagnosed using standard definitions and coded using the International Classification of Diseases 10 (ICD-10) codes.
The study sites are metropolitan urban settings with large, growing (due to continued births and migration from various parts of the country), and heterogeneous populations. Estimates suggest that population size in Chennai (4.68 million) , Karachi (13 million) , and Delhi (16.3 million) , and the diversity in their composition make these cities current and future archetypes of rapid socio-economic, demographic, epidemiologic, and nutrition/lifestyle transitions in the South-Asian region.
Sample size estimation
Sample size estimation (per site)
Level of confidence
Margin of error
Baseline levels of indicators
Expected response rate
No. of age/sex estimates
Overweight (BMI ≥ 23)
With regards to the cohort follow-up, separate consent has been taken from participants to be followed up for three years or longer. An overall 15 - 25% loss-to-follow-up by the 3-year data collection time-period is anticipated due to the high probability of migration among the young population for job opportunities, marriage (in case of females), etc. Retention efforts (in the form of maintaining updated contact information; collecting contact details of friends and relatives; periodic reminder calls; courtesy calls/visits) have been put in place to keep track of participants and minimise loss-to-follow-up. Although the study at present is not powered to estimate incidence of CMDs and their risk factors, it has the potential to determine such incidence rates if the follow-up period is increased and the study is scaled up by adding follow up of subsequent cross-sectional samples.
Households were selected in each of the three cities using a multi-stage cluster random sampling technique. Each city has its own distinctive municipal sub-divisions, encompassing municipal corporations, wards and Census Enumeration Blocks (CEB), which were used sequentially as sampling frames to randomly select households. While wards were the primary sampling units (PSUs) for Chennai and Delhi, CEBs or clusters were the PSUs for Karachi. STATA version 10.1 (Statacorp, TX)  and data from the most recent census were used to randomly select the wards, CEBs, and households (defined below.). To give each household an equal chance of being selected for the study and to identify households constructed after the last census survey, manual listing and mapping of all households in each CEB was done before randomly selecting them.
Two participants, one male and one female, aged 20 years or older, were selected from each household based on inclusion and exclusion criteria given below. Two methods were used for within household sampling – (i) for households with one to two adults (≥20 years), the sampling strategy described in the 2002 Health Information National Trends Study (HINTS) in the USA was used . According to HINTS, one or both individuals (one male and one female) were selected and enrolled into the study based on eligibility criteria and informed consent; (ii) for households with more than two eligible adults, the “Kish method” used in the WHO’s STEPS surveys  was applied. Recruitment of participants, and data and specimen collection were conducted through three visits to each participant’s place of residence, respectively (Visit-0, Visit-1, and Visit-2).
Inclusion and exclusion criteria for CARRS – Surveillance Study
Any individual aged ≥20 years and permanently residing in the selected household.
For the purpose of this study, a permanent resident was defined as a person living in the selected household, was related to the household head and ate at least 3 meals in a week with the family.
Households were defined as “a group of people who live together, usually pool their income and eat at least one meal together a day when they are at home. This does not include people who have migrated permanently or are considered visitors” [(Integrated Disease Surveillance Project (IDSP)].
Pregnant women were not included in the study since their biochemical parameters would vary from the normal physiology due to pregnancy, further their patterns of diet and physical activity would also be different from usual.
Bed-ridden individuals were excluded because of the difficulty in taking anthropometric measurements in these individuals. However, reasons for being confined to bed were collected from such individuals to estimate prevalence of CMDs among this excluded group (since CMDs can be the cause for being bed-ridden).
Surveillance indicators and study instruments
Summary of the surveillance indicators, measures, methods and instruments
Demographic and Social Characteristics*
Age / Sex / Marital Status / Religion
Chennai Urban Population Study (CUPS), Chennai Urban Rural Epidemiological Study (CURES), Establishment of Sentinel Surveillance System for CVD in Indian Industrial Populations (Sentinel Surveillance Study)
Education / Income / Occupation
Standard of Living Index (SLI)
Contact Details (and supplemental contacts)
Behavioral risk factors*
Questionnaire / Cotinine in saliva (5 % of participants)
CUPS, CURES, Sentinel Surveillance Study
Questionnaire/ validation by 24-hour dietary recall in a sub-sample
International Physical Activity Questionnaire (IPAQ)– short
Sleep Heart Health Study (SHHS)
Physiological and biochemical risk factors**
Blood pressure measurement
Standardized method (American Heart Association) and validated instrument (certified by British Hypertensive Society and Association for the Advancement of Medical Instrumentation)
Laboratory estimation of serum total cholesterol, low density lipoprotein cholesterol, very low density lipoprotein cholesterol, high density lipoprotein cholesterol, triglycerides, Apolipoprotein A and B (not done in Karachi)
Standardized across all three study sites
Anthropometry (height / weight / body circumferences / skinfold thickness / body composition/bio-impedance)
Standard procedures based on National Health And Nutrition Examination Survey-III with instruments used in epidemiological studies on South Asian population
Laboratory estimation of fasting plasma glucose, glycated haemoglobin (HbA1c)
Standardized across all three study sites
Female Reproductive history*
Menarche/ gestational history (pregnancy induced hypertension, gestational diabetes), menopause (surgical / physiological / whether on hormone replacement therapy) / contraception
CUPS, CURES, India Health Study (IHS)
Quality of Life*
Mobility, self care, usual activities, pain/discomfort, anxiety/depression (related to cardiometabolic diseases; CMDs and their risk factors)
European Quality of Life 5 Dimensions questionnaire (EQ-5D)
Stroke / Myocardial infarction / Congestive heart failure / Chronic stable angina
Questionnaires including medication history;
Rose Angina, CURES, IHS, Sentinel Surveillance, Community Heart Failure questionnaire
Medical records of documented events or procedures, serum urea and creatinine and albumin for CKD
Chronic kidney disease (CKD)/ Dialysis / Renal transplantation
Procedures, Revascularization, Hospitalization
Initiative for Cardiovascular Health Research in the developing countries (IC-Health) macroeconomic study
Treatment history, health services, quality of care and health care costs**
Awareness and risk factor control
IC-Health macroeconomic study
Access to health care services
Utilization of services
Health insurance / coverage
Costs of treating CMDs and their risk factors
Chronic Obstructive Pulmonary Disease (COPD), Asthma*
Prevalence of COPD & asthma in the population
NHANES III and the present standards of the American Thoracic Society (ATS)
Prevalence of CMDs and their risk factors in members of the family related to the participants
Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial
Follow-up surveys; Death Certificates; Verbal Autopsy
Modified version of Registrar General of India – Center for Global Health Research (RGI-CGHR) Prospective Study on Million Deaths (Form 10C)
Cardiovascular disease specific; Diabetes-specific
Biological sample collection and storage
Biological samples and their methods of analysis
Methods used for analysis
Fasting plasma glucose
Glucose Oxidase / End Point
Glycated haemoglobin (HbA1c)
High performance liquid chromatography (HPLC)
Cholesterol Oxidase Peroxidase (CHOD-POD) end point
CHOD-POD end point
Enzymatic Colorimetric method (CHOD-PAP)
High density lipoprotein cholesterol
Low density lipoprotein cholesterol
Very low density lipoprotein cholesterol
Enzymatic methods (GPO-PAP end point)
Enzymatic methods (GPO-PAP end point)
Enzymatic methods (GPO-PAP end point)
Apolipoprotein A, Apolipoprotein B
Will not be done
Urease Glutamate Dehydrogenase (GLDH) / Kinetic
Urease GLDH/ Kinetic
Blood Urea Nitrogen (BUN): Enzymatic conductivity rate method
Modified Jaffe’s Method
Clinical and anthropometric assessments
Two clinical (BP and pulse rate) and eight anthropometric measurements of participants were taken during the visits: Clinical measurements - BP and Pulse rate. Anthropometric measurements - Mid-arm circumference, Waist circumference, Hip circumference, Triceps skin-fold, Sub-scapular skin-fold, Supra-patellar skin-fold, Height (Standing) and Body composition analysis by Bio-impedance.
The equipment and methods used for BP and anthropometric measurements were standardised and certified, and have been used in other epidemiological studies in the South-Asian population. BP was measured using electronic sphygmomanometer; Omron HEM-7080 and HEM-7080IT-E; Omron Corporation, Tokyo, Japan (certified by the British Hypertensive Society and the American association for Advancement of Medical Instrumentation [AAMI] protocols). Skinfold Calipers (Holtain Ltd., UK) and non-stretch measuring tape (Gulick II, Country Technology, Gays Mills, WI) were used to measure skin-fold thickness and body circumferences, respectively. Height was measured using a portable Stadiometer (SECA Model 213, SecaGmbh Co, Hamburg, Germany). Apart from these, body-composition analysers (instrument which measures body fat by sending out weak electric currents to measure impedance/electrical resistance by different tissues of the body); Tanita BC-418 in Delhi and Chennai, and BC-545 in Karachi were used to measure compartmental body fat distribution. To ensure standardisation, both instruments were tested in 50 male and 50 female participants to compare the parameters measured; i.e. weight, body mass index, basal metabolic rate, body fat and visceral fat. The results showed that all measured parameters were highly co-related for both males and females (r > 0.95, p < 0.05) between the two instruments, except body fat in males (r = 0.67, p = 0.67). Methods for BP measurement and anthropometric measures were based on the recommendation of the American Heart Association’s Council on High Blood Pressure Research  and the third National Health and Nutrition Examination Survey (NHANES-III) .
An online system was developed in an ‘open source’ platform PHP (Hypertext PreProcessor, scripting language for the web page/front end) and MySQL (My Structured Query Language) for data entry and database management at each site. This online database has been programmed to have automated in-built checks for logic which are ‘clinically reasonable’ (such as ranges, absolute and relative values, context and structure). It provides an efficient means of data entry, storage, and quality control. Data are available at the coordinating site for immediate feedback and timely corrections. The data have been stored in pass-word protected files and questionnaires in locked cabinets in all study sites, and only the study personnel have access to these. All information related to participant identification was de-linked from the data files before analysis to maintain anonymity.
Quality control strategies
Quality assurance strategies
Levels of quality control
Design and planning
● Critical review of protocols
● Fluidity and feasibility of field operations assessed
● Monitoring field activities
● Audit and evaluate validity of findings prior to publication
● Common manual of operations for three study sites
● Internal peer reviews prior to publication
● Coordination of timelines & activities
● Reviewed the design and planning of the study
● Results were audited after completion of the pilot
● Validity checks
● Results reviewed
● Regular steering committee meetings
● Extensive training over a period of 7–10 days – theory and practical, field visits and shadowing by the study managers
● Evaluated all field and documenting techniques
● Random checks, re-training
● Easy-to-carry operations guide provided
● Established clarity and face validity in small field sample
● Regular checks done to assess completeness
● Compromised or inadequately completed questionnaires identify and discard
● Translated into local languages
● Internal consistency estimates and reliability exercises through review of literature on survey instruments and their published data
● Centrally procured
● Evaluated calibration techniques, acceptability of use in field
● Regular calibration of equipment; faulty equipment replaced as and when required
● Central training
● Calibration guidelines and checks developed
● Kits and equipment procured centrally
● Evaluated adherence to protocols, labeling, processing, storage and handling
● Interim analysis conducted to detect outliers
● Random checks done
● Samples stored for future investigation
● External temperature gauge labels to monitor sample temperature
● Compromised samples identify and discard
● Specific protocols for each biochemical assay was developed
● Extensive training (labeling, handling, storage)
● Laboratory selected and reference laboratory identified based on National Accreditation Board for Testing and Calibration Laboratories, Department of Science and Technology, Government of India (NABL) or College of American Pathologists, Northfield, IL, USA(CAP) certification
● Evaluated procedural fluidity
● Internal quality checks and calibration
Assessment of intra- and inter-laboratory coefficients of variation
●Regular external validation – lyophilized samples from reference laboratory
● Evaluated intra- and inter-laboratory variability
● Analysis conducted to detect outliers
● Internal and external quality assessment protocols and schedule of regularity developed
● Reporting structures were established
● Agility of transfers assessed
● Data transfer planned
● Checklists and logbooks were maintained
● Recording legibility assessed
● Audit logbooks for response rates and field activity indicators maintained
Training in appropriate and legible documentation
Data Storage & Confidentiality
● Data back-up and protection policies have been established
● Accessibility, simplicity and flexibility of software assessed
● Locked and password-protected data storage
● Datasets de-identified
● Access to personal identifiers limited
● Active back-up
● Training of all staff
● Protocols, consistent data cleaning methods and verification systems were established
● Variability assessments conducted
● Interim analyses to identify duplicate entries
● Reporting on outliers
● Validity checks
● Decision log to document issues
● Database errors tracked
Challenges in the implementation of the study and methods used to overcome them
Mapping and listing of households
Reference data Delhi and Chennai: 2001 census. Karachi: 1998 census.
Complete listing of all the households in all randomly selected CEBs was done by field workers and structural maps of the areas were developed manually.
Lot of changes in structure and population had taken place by 2010
Training of trainers (ToT) and site managers for uniform implementation of the study
Challenges with regards to organising the ToT in either India or Pakistan due to visa issues for trainers and participants.
The ToT was organised in Kathmandu, Nepal with assistance from the Nepal Public Health Foundation.
Participant recruitment and interviews
Poor response from upper socioeconomic status localities and gated communities
Resident Welfare Association, Societies and Unions of the localities were approached for cooperation.
Recruiting and interviewing male participants - who could not be contacted on working days
Interviews were scheduled on weekends, early mornings and late evenings, and more field workers were recruited to conduct these weekend surveys.
Frequent electricity breakdowns in Karachi in the evenings.
Emergency lights were arranged for interviewing the participants in the evenings.
The socio-political climate in Karachi posed challenges to the safety of interviewers and in completion of surveys.
Field work was scheduled accordingly to target safe areas as per the socio-political situation of the city on a day-to-day basis.
Blood sample collection
Fear of providing blood samples among the participants of lower socio economic status
Team leader and supervisor contacted and counselled the participants.
Not coming fasting to the blood collection camps – some participants consumed tea or juices early in the morning.
The blood samples for these participants were not collected in the camp on that day, but were collected on another day from their homes ensuring that the participant was in fasting state. The samples were transported to the laboratory in appropriate cold chain.
Blood samples could not be collected during the month of Ramadan (Islamic fasting month) in Karachi.
During the month of Ramadan, non-Muslim participants (mainly from the Christian communities) were recruited.
Difficulty in conducting blood collection camps during extreme (cold and hot) weather conditions.
As far as possible camps were avoided on extreme cold and hot days in Delhi.
The instrument purchased for the other two sites Tanita BC-418 was not available at Karachi and also could not be shipped in to the country.
A different model of Tanita was used in Karachi, BC-554, but the two models were compared by measuring the correlation of their parameters in 100 participants (described in the text).
serial cross-sectional survey based models such as the NHANES  and the Behaviour Risk Factor Surveillance Surveys (BRFSS) in the USA  and Jordan , and the national NCD risk factor examination surveys in Seychelles  and Cuba ;
longitudinal prospective models such as the SCORE (Systematic COronary Risk Evaluation) project which helped to develop a risk scoring system for management of CVD in Europe  and other longitudinal studies which helped to estimate the psychosocial risk factors of CHD .
CMDs are among the top ten most costly diseases , but have the advantage of being largely predictable through identification of distal and intermediate risk factors, and also substantially preventable through changes in lifestyle and/or use of preventive pharmacology [1, 51, 52]. The surveillance model if scaled up has the potential to estimate secular trends and incidence rates of mortality and morbidity due to CMDs, their complications and risk factors, thereby providing means of prioritizing and measuring the impact of public health interventions.
In a recent review of prevailing methods of NCD surveillance, particularly of CVD in India, the authors reiterate the need for harmonising all existing efforts, at least in measurement tools and quality assurance methods, to establish an integrated, comprehensive, and standardised surveillance system . CARRS-Surveillance provides an understanding of the challenges in establishing such a surveillance system for CMDs, and elucidates the means to address them. However, we suggest that such an effort should not be limited to individual countries, but should be consolidated for South-Asia as a whole because the entire region is experiencing an epidemiological transition leading to increased incidence of CMDs and their risk factors. Also, there are shared demographic, socio-economic, behavioural, and physiological determinants among South-Asians. One such multi-site collaborating surveillance network is INDEPTH which regularly collates cross-sectional survey data from 34 sites in 17 LMICs . However, these are based on the self-selected samples of Health and Demographic Surveillance Systems (HDSS) in each site and are only representative of a district, therefore, the findings cannot be generalised to the region or the country .
Strengths and limitations
Apart from robust study methods and quality control mechanisms, the sample population recruited in our study conforms to the current age profile of the population in the two countries; about 65% of the population in India and 75% of population in Pakistan are below the age of 35 years [36, 54]. This demonstrates the success of the sampling strategy employed and has implications for the generalisability of findings. However, a limitation of the CARRS model is that the study setting is urban and does not include the larger rural population where the burden of CMD is also growing. An urban model, however, would anticipate the growth of urban areas the world over, and also provide insights into operational aspects of surveillance systems, and empirical evidence of successful implementation at lower costs. The New York City Health and Nutrition Examination Survey (NYC HANES) provides an aspirational model suggesting that surveillance in such metropolitan cities with diverse populations might provide a reasonable reflection of diverse and growing cities to each individual nation’s disease burdens .
Several LMICs have some structure to estimate the burden of NCDs, but a recent study by the WHO in 23 high burden countries (which includes India) showed that the existing systems are deficient in standardised data collection tools and often lack accuracy and quality . The CARRS-Surveillance model addresses these technical standardisation and quality challenges in setting up national and regional CMD surveillance systems in South-Asia, but the task of scaling up will require political commitment, funds, and human resources. Although challenging, this is achievable and has been accomplished by a few LMICs. For example, the Ministry of Health in Jordan, in partnership with WHO and CDC, established the Jordan BRFSS in 2002 which conducts cross-sectional surveys every two to three years . The national examination survey of NCD risk factors in Seychelles has been collecting data for planning and evaluating interventions since 1989 , and the Cuban system since 1995–96 . Eleven Latin American countries (Argentina, Brazil, Chile, Colombia, Dominican Republic, Guatemala, Mexico, Panama, Peru, Uruguay and Venezuela) have new or emerging systems for serial national NCD and risk factor surveys in various stages of development . These countries have demonstrated the utility of continuous surveillance in identifying high risk communities, planning interventions, and evaluating the effects of existing policies, thereby creating an evidence-base for steering national policies on NCD prevention and health promotion [45–47, 57].
COE-CARRS Surveillance Investigators’ Group
Steering Committee: Dorairaj Prabhakaran, K. M. Venkat Narayan, K Srinath Reddy, Nikhil Tandon, V. Mohan, Muhammed M. Kadir, Mohammed K. Ali, Vamadevan S Ajay
Operations: Dorairaj Prabhakaran, Nikhil Tandon, K. M. Venkat Narayan, Mohammed K Ali, Muhammed M. Kadir, S. Roopa, Hassan M. Khan, R. Pradeepa, M. Deepa, Vamadevan S Ajay, Dimple Kondal, Ruby Gupta, Pragya Sharma
Coordinating Centre (Delhi): Dorairaj Prabhakaran, Nikhil Tandon, S. Roopa, Vamadevan S Ajay, Manisha Nair, Nivedita Devasenapathy, Divya Pillai
Development of questionnaires and manual of operations: Dorairaj Prabhakaran, Nikhil Tandon, K. M. Venkat Nararayan, Mohammed K. Ali, Manisha Nair, Nivedita Devasenapathy, R. Pradeepa , Ed Gregg, Anwar Merchant, Romaina Iqbal
Data management and statistical team: Dimple Kondal, Shivam Pandey, Praggya, Naveen
Laboratory: Lakshmy Ramakrishnan, Ruby Gupta, Savita
Information Technology: Ramanathan K, Ansel J D’Cruz, Gnanashekaran K.
Online data entry software: Mahesh Dorairaj
Data collection team
Field supervisor: Rahul T
Field interviewers: Alagarsamy, Anthony JV, Arul Dass.A, Arul Pitchai.S, Ashok Kumar, Balaji V, Dhanasekar L, KalaiVani D, Kumar M, Nandhakumar, Prathiban K, Sampath, Saravana Kumar P, SaravananR, Senthil RajaR, ShenbagaValliE, SivamanikandanK, SureshT, Uma Sankari G
Laboratory assistants: Geetha Priya L, Gowri, Irin Jayakumari A, Padmapriya, Ramakrishnan R, Revathy, Satish Raj S, Sudha M, Suresh, Vijay Baskar S
Data entry operators: Narayanan, Nirmala
Field supervisor: Liladhar Dorlikar
Field interviewers: Parag Jyoti Das, Kulwant Kaur, Sweta Kumari, Meena Thakur, Garima Rautela, Avijeet Malik, Anita Yadav, Makhan, Rishi Garg, Arun
Laboratory assistants: Priyanka Nautiyal, Sunil Dogra, Geetha
Data entry operators: Naveen Kaushik, Avnish
Field supervisor: Mehboob John Samuel
Field interviewers & laboratory assistants: Yousuf Sadiq, Shukrat Khan, Shahirah Ziarat Khan, Nadia Khan, Noureen Khan, Naseem Sehar, Asif Shabaz, Fakhrah Perveen, Karan Inayat, Tajir Hussain, Tariq Hussain, Nasreen Khan
Data entry operator: Sayed Arif Hussain Kazmi
CARRS-surveillance study has been approved by the Institutional Review Boards (IRBs) of Public Health Foundation of India, New Delhi, All India Institute of Medical Sciences, New Delhi, Madras Diabetes Research Foundation, Chennai, India, Aga Khan University, Karachi, Pakistan, and Emory University, Atlanta, USA. In addition the study has received regulatory approval from the National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), USA and the Health Ministry Screening Committee of India, New Delhi.
American Association for Advancement of Medical Instrumentation
Body Mass Index
Behaviour Risk Factor Surveillance Surveys
Centre for Cardiometabolic Risk Reduction in South-Asia
Centres for Disease Control and Prevention
Census enumeration blocks
Coronary heart disease
Chronic kidney disease
Health and Demographic Surveillance Systems
Health Information National Trends Study
International Classification of Diseases 10 codes
Integrated Disease Surveillance Project
Multinational MONItoring of trends and determinants in CArdiovascular disease
My structured query language
National Health and Nutrition Examination Survey - third
- NYC HANES:
New York City Health and Nutrition Examination Survey
Hypertext preprocessor, scripting language for the web page/front end
Primary sampling units
Quality of Life
Randox International Quality Assurance Scheme
Systematic COronary Risk Evaluation
Standard operating procedures
United States of America
World Health Organisation
- WHO STEP:
World Health Organisation STEPwise approach to surveillance.
This study is coordinated by CoE-CARRS (Center of Excellence - Center for CArdio-metabolic Risk Reduction in South Asia ) based at Public Health Foundation of India (PHFI), New Delhi, India in collaboration with Centre for Chronic Disease Control (CCDC), New Delhi, Emory University, Atlanta, U.S.A, All India Institute of Medical Sciences (AIIMS), New Delhi, Madras Diabetes Research Foundation (MDRF), Chennai, India and Aga Khan University, Karachi, Pakistan. We hereby, acknowledge the contributions of the field and research staff of the “CARRS Surveillance Investigators’ Group” (a list of all members is included above).
This project is funded in whole or in part by the National Heart, Lung, and Blood Institute, National Institutes of Health (NIH), Department of Health and Human Services, under Contract No. HHSN268200900026C, and the United Health Group, Minneapolis, Mn, USA.
Several members of the research team at PHFI, Emory University, and CCDC were/are supported by the Fogarty International Clinical Research Scholars – Fellows programme (FICRS-F) through Grant Number 5R24TW007988 from NIH, Fogarty International Center (FIC) through Vanderbilt University, Emory’s Global Health Institute, and D43 NCDs in India Training Program through Award Number D43HD05249 from the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD) and FIC. However, the contents of this paper are solely the responsibility of the writing group and do not necessarily represent the official views of FIC, Vanderbilt University, Emory University, PHFI, NICHD, or the NIH.
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