Open Access

CARRS Surveillance study: design and methods to assess burdens from multiple perspectives

  • Manisha Nair1,
  • Mohammed K Ali2,
  • Vamadevan S Ajay3,
  • Roopa Shivashankar4,
  • Viswanathan Mohan5,
  • Rajendra Pradeepa6,
  • Mohan Deepa6,
  • Hassan M Khan7,
  • Muhammad M Kadir7,
  • Zafar A Fatmi7,
  • K Srinath Reddy8,
  • Nikhil Tandon9,
  • KM Venkat Narayan10 and
  • Dorairaj Prabhakaran4Email author
BMC Public Health201212:701

DOI: 10.1186/1471-2458-12-701

Received: 27 June 2012

Accepted: 13 August 2012

Published: 28 August 2012

Abstract

Background

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.

Methods

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.

Results

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.

Discussion

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

Background

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 [1] accompanying socioeconomic and nutrition transitions [24]. 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, 57]. In 2010, globally, 4,000,000 deaths were due to diabetes, the highest in absolute numbers was (1,008,000) in India [8]. The largest fraction of deaths from CHD (37%) and stroke (30%) attributable to high blood glucose were in South-Asia [9]. Further, in people of South-Asian origin, onset of diabetes [1012], 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 [1623].

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 [24] and the creation of integrated, comprehensive, sustainable, on-going nationwide surveillance systems [25]. In South-Asia, current efforts are limited to local surveys with vast state-wise heterogeneity and variable data quality [2628]. Furthermore, projections of national income losses related to CMDs are based on models using inputs from limited local studies [29]; data on individual and household costs and social burdens are also scarce [30]. 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 [35] 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.

Methods

Study design

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) [36], Karachi (13 million) [37], and Delhi (16.3 million) [36], 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

Utilising risk factor prevalence estimates from previously published Indian and Pakistani studies and anticipating a response rate of 80% with a design effect factor of 1.5 (to account for cluster sampling), the sample size estimates were generated for males and females in three age strata in each urban setting. As shown in table 1, the highest required sample size (3983 rounded-off to 4000 participants) permits each site to reliably estimate one or more of the CMD risk factors for each of the gender and age strata leading to a total sample size of 12,000.
Table 1

Sample size estimation (per site)

Risk factors

Level of confidence

Margin of error

Baseline levels of indicators

Design effect

Expected response rate

No. of age/sex estimates

Sample size

Tobacco use

1.96

0.05

0.23

1.5

0.8

6

3062

Hypertension

1.96

0.05

0.36

1.5

0.8

6

3983

Diabetes

1.96

0.05

0.15

1.5

0.8

6

2204

Overweight (BMI ≥ 23)

1.96

0.05

0.65

1.5

0.8

6

3933

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.

Sampling method

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) [38] 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 [39]. 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 [35] 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

Inclusion criteria

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)][31].

Exclusion criteria

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

To provide consistency and reproducibility of the results across multiple sites, comprehensive and uniform data collection instruments were used to capture measurements (table 2). Household data were collected through interviewer administered paper questionnaires in English or the preferred local languages (Hindi, Tamil, and Urdu). Validated questions were derived from English questionnaires used in the WHO Multinational MONItoring of trends and determinants in CArdiovascular disease (MONICA) study [40], WHO STEPS studies [35], and from previous regional and national surveys. Using these, culturally-appropriate and methodologically-relevant closed-questions, an instrument for South-Asia was developed and pilot tested for face and construct validity prior to use in the study. Several sections of the baseline questionnaire (such as the QoL, CMD history, tobacco and alcohol consumption questions) were based on validated questionnaires that already exist in regional languages (Tamil, Hindi and Urdu). Questionnaires to elicit medical and treatment history, costs and QoL are being used to collect incident events during the ongoing cohort follow-up. Further, verbal autopsy is being performed using a reliable instrument to ascertain cause of death of participants who die during the course of follow-up and for whom either death certificate was unavailable or cause of death not certified. For the adapted instruments collecting a variety of CMD risks and diseases (e.g. tobacco, history of CMD, heart failure, and Chronic Obstructive Pulmonary Disease), the subjective history provided by participants was validated against laboratory and other diagnostic gold standards (e.g., salivary cotinine for tobacco consumption). These in-built steps to validate the self-reported data distinguishes the CARRS-Surveillance as a stronger model compared to the IDSP [31] and the INDEPTH network (International Network of field sites for continuous Demographic Evaluation of Populations and Their Health in developing countries (http://www.indepth-network.org) [41].
Table 2

Summary of the surveillance indicators, measures, methods and instruments

Indicators

Measures

Methods

Instruments

Demographic and Social Characteristics*

Age / Sex / Marital Status / Religion

Questionnaires

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

Household assets

Standard of Living Index (SLI)

Contact Details (and supplemental contacts)

 

Behavioral risk factors*

Tobacco use

Questionnaire / Cotinine in saliva (5 % of participants)

CUPS, CURES, Sentinel Surveillance Study

Alcohol use

Questionnaire

Dietary habits

Questionnaire/ validation by 24-hour dietary recall in a sub-sample

INTERHEART Study

Physical activity

Questionnaire

International Physical Activity Questionnaire (IPAQ)– short

Sleep

Sleep Heart Health Study (SHHS)

Physiological and biochemical risk factors**

Hypertension

Blood pressure measurement

Standardized method (American Heart Association) and validated instrument (certified by British Hypertensive Society and Association for the Advancement of Medical Instrumentation)

Dyslipidemia

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

Obesity

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

Diabetes

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

Questionnaire

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)

Questionnaire

European Quality of Life 5 Dimensions questionnaire (EQ-5D)

Morbidity**

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

Amputation/diabetes retinopathy

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

Questionnaire

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

Questionnaire

NHANES III and the present standards of the American Thoracic Society (ATS)

Family history*

Prevalence of CMDs and their risk factors in members of the family related to the participants

Questionnaire

Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial

Mortality***

All cause

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

  

*Only cross-sectional surveys; **both cross-sectional survey and cohort follow-up; ***only cohort follow-up.

Biological sample collection and storage

Biological sample collection involved drawing 15 ml of blood (in fasting state) from each participant, collecting urine (early morning void), and 1000 to 2000 μl of saliva (fasting) in Salivettes. While blood and urine were collected from all participants, saliva was collected from 5% of the study participants (i.e. 200 participants per site). The samples were transported from field sites in cold chain to the laboratories for analysis. Sample aliquots were also stored in cryo-vials at - 80 degrees Celsius for future studies. The methods of analysis and external quality control have been standardised for all biological samples across the study sites (table 3). There is one exception in that Apolipoprotein A and B analyses were not conducted in Karachi due to lack of required laboratory facilities for the test.
Table 3

Biological samples and their methods of analysis

Clinical parameter

Laboratory parameter

Methods used for analysis

Chennai

Delhi

Karachi

Diabetes

Fasting plasma glucose

Hexokinase/Kinetic

Hexokinase/Kinetic

Glucose Oxidase / End Point

Glycated haemoglobin (HbA1c)

High performance liquid chromatography (HPLC)

HPLC

HPLC

Dyslipidemia

Total cholesterol

Cholesterol Oxidase Peroxidase (CHOD-POD) end point

CHOD-POD end point

Enzymatic Colorimetric method (CHOD-PAP)

High density lipoprotein cholesterol

Direct

Direct

Direct

Low density lipoprotein cholesterol

Friedwald Formula

Friedwald Formula

Friedwald Formula

Very low density lipoprotein cholesterol

Calculation

Calculation

Calculation

Triglycerides

Enzymatic methods (GPO-PAP end point)

Enzymatic methods (GPO-PAP end point)

Enzymatic methods (GPO-PAP end point)

Apolipoprotein A, Apolipoprotein B

Immuno-turbidimetric

Immuno-turbidimetric

Will not be done

Kidney disease

Serum urea

Urease Glutamate Dehydrogenase (GLDH) / Kinetic

Urease GLDH/ Kinetic

Blood Urea Nitrogen (BUN): Enzymatic conductivity rate method

Serum creatinine

Jaffe Kinetic

Jaffe Kinetic

Modified Jaffe’s Method

Microalbuminuria

Immuno-turbidimetry

Immuno-turbidimetry

Rate nephelometry

Tobacco exposure

Salivary cotinine

Elisa kit

Elisa kit

Elisa kit

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 [42] and the third National Health and Nutrition Examination Survey (NHANES-III) [43].

Data management

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 control (QC) strategies were applied using a framework which comprehensively considers each phase of the study and applies inter-related themes to every level of the study and are described in table 4. Apart from standardisation of laboratory methods (table 3), QC involved laboratory procedure assessment at two levels. Level-1, internal quality control: Local laboratories attached with the study centre followed their own internal quality control standard operating procedures (SOPs) to ensure accuracy, precision, and reproducibility. Level-2; external quality control: Irrespective of the nature of existing laboratory accreditation and / or SOP’s, all study site laboratories were enrolled into an external quality assessment program for clinical chemistry, HbA1c (glycated haemoglobin), lipid and human urine. This was implemented with support from the Randox International Quality Assurance Scheme (RIQAS), UK. The frequency of external quality control sample was two per month for clinical chemistry, lipid and urine, and one per month for HbA1c.
Table 4

Quality assurance strategies

Levels of quality control

Phases

 

Design and planning

Pilot testing

Data collection

Data analysis

 

Critical review of protocols

Fluidity and feasibility of field operations assessed

Monitoring field activities

Audit and evaluate validity of findings prior to publication

Coordinating center

    
 

Common manual of operations for three study sites

   
    

Internal peer reviews prior to publication

 

Coordination of timelines & activities

   

Investigators

Reviewed the design and planning of the study

Results were audited after completion of the pilot

Monitoring

Validity checks

    

Results reviewed

 

Regular steering committee meetings

   

Field Personnel

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

   

Survey Questionnaires

Peer-reviewed

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

   

Measuring Equipment

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

   

Specimens

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

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

   

Communication

Reporting structures were established

Agility of transfers assessed

  
 

Data transfer planned

   

Documentation

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

   

Data Entry

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

Results

The first cross-sectional study was conducted between September 2010 and November 2011. A total of 14, 215 individuals in 8, 115 households were approached in the three study sites (5348 participants in Chennai, 4609 in Delhi, and 4258 in Karachi). From these, a total of 13,384 participants were recruited (4943 Chennai, 4425 Delhi, and 4016 Karachi). A total of 831 eligible participants in the three sites refused to participate in the study; overall response rate thus was 94.1% at the participant level (Chennai 92.4%, Delhi 95.7%, Karachi 94.3%). About eighty-one percent of the participants recruited into the study contributed biospecimens (Chennai 83.9%, Delhi 79.8% and Karachi 87.2%). Overall, 51.8% of the participants were females; a large proportion of the sample, 61.6% was below the age of 45 years, 27.5% in the 45–60 years age group and 10.9% > 60 years of age. In addition, the study provides an understanding of the existing political and social challenges in establishing a sustainable surveillance system for CMDs in the two countries and evidences of context specific successful measures that can be adopted to overcome most of these challenges (summarised in table 5).
Table 5

Challenges in the implementation of the study and methods used to overcome them

 

Challenges

Solutions

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.

Anthropometry

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).

Bio-impedance

  

Discussion

The CARRS-Surveillance model has been established in Chennai and New Delhi in India, and Karachi in Pakistan with successful completion of the first cross-sectional survey and initiation of the first year of cohort follow-up. The response rate for the first cross-sectional survey was more than 90% in all study sites with an overall rate of 94%. The combined cross-sectional and longitudinal study design lends it the advantages of:
  1. (i)

    serial cross-sectional survey based models such as the NHANES [43] and the Behaviour Risk Factor Surveillance Surveys (BRFSS) in the USA [44] and Jordan [45], and the national NCD risk factor examination surveys in Seychelles [46] and Cuba [47];

     
  2. (ii)

    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 [48] and other longitudinal studies which helped to estimate the psychosocial risk factors of CHD [49].

     

CMDs are among the top ten most costly diseases [50], 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 [53]. 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 [41]. 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 [41].

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 [55].

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 [56]. 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 [45]. The national examination survey of NCD risk factors in Seychelles has been collecting data for planning and evaluating interventions since 1989 [46], and the Cuban system since 1995–96 [47]. 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 [57]. 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 [4547, 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

Chennai:

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

Delhi:

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

Karachi:

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

Ethics approval

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.

Abbreviations

AAMI: 

American Association for Advancement of Medical Instrumentation

BMI: 

Body Mass Index

BP: 

Blood pressure

BRFSS: 

Behaviour Risk Factor Surveillance Surveys

CARRS: 

Centre for Cardiometabolic Risk Reduction in South-Asia

CDC: 

Centres for Disease Control and Prevention

CEB: 

Census enumeration blocks

CHD: 

Coronary heart disease

CKD: 

Chronic kidney disease

CMDs: 

Cardio-metabolic diseases

CVD: 

Cardiovascular diseases

DM: 

Diabetes mellitus

HbA1c: 

Glycated haemoglobin

HDSS: 

Health and Demographic Surveillance Systems

HINTS: 

Health Information National Trends Study

ICD-10: 

International Classification of Diseases 10 codes

IDSP: 

Integrated Disease Surveillance Project

LMICs: 

Low-and-middle-income countries

MONICA: 

Multinational MONItoring of trends and determinants in CArdiovascular disease

MySQL: 

My structured query language

NCDs: 

Non-communicable diseases

NHANES-III: 

National Health and Nutrition Examination Survey - third

NYC HANES: 

New York City Health and Nutrition Examination Survey

PHP: 

Hypertext preprocessor, scripting language for the web page/front end

PSUs: 

Primary sampling units

QC: 

Quality control

QoL: 

Quality of Life

RIQAS: 

Randox International Quality Assurance Scheme

SCORE: 

Systematic COronary Risk Evaluation

SOPs: 

Standard operating procedures

UK: 

United Kingdom

USA: 

United States of America

WHO: 

World Health Organisation

WHO STEP: 

World Health Organisation STEPwise approach to surveillance.

Declarations

Acknowledgements

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.

Authors’ Affiliations

(1)
Public Health Foundation of India (PHFI), New Delhi & CoE-CARRS, PHFI
(2)
Emory University
(3)
CoE-CARRS, PHFI
(4)
CoE-CARRS, Public Health Foundation of India & Centre for Chronic Disease Control (CCDC)
(5)
Madras Diabetes Research Foundation (MDRF)
(6)
MDRF
(7)
Aga Khan University
(8)
PHFI
(9)
All India Institute of Medical Sciences (AIIMS)
(10)
Emory University

References

  1. Kraushaar L, Kramer A: Are we losing the battle against cardiometabolic disease? The case for a paradigm shift in primary prevention. BMC Publ Health. 2009, 9 (1): 64-10.1186/1471-2458-9-64.View ArticleGoogle Scholar
  2. Economic Intelligence Unit: The silent epidemic: An economic study of diabetes in developed and developing countries. 2007, New York, London: Hong Kong: The EconomistGoogle Scholar
  3. Diabetes Atlas. http://www.eatlas.idf.org/media, 3,
  4. Gupta R, Joshi P, Mohan V, Reddy K, Yusuf S: Epidemiology and causation of coronary heart disease and stroke in India. Heart. 2008, 94 (1): 16-26. 10.1136/hrt.2007.132951.View ArticlePubMedGoogle Scholar
  5. World Health Organisation: Global status report on non-communicable diseases. 2011, Geneva: World Health OrganisationGoogle Scholar
  6. Mathers C, Lopez A, Murray C: The Burden of Disease and Mortality by Condition: Data, Methods, and Results for 2001. Global Burden of Disease and Risk Factors. Edited by: Lopez A, Mathers C, Ezzati M, Jamison D, Murray C. 2006, Oxford University Press, New YorkGoogle Scholar
  7. The human, social & economic impact of diabetes. http://www.idf.org/home/index.cfm?node=41,
  8. Roglic G, Unwin N: Mortality attributable to diabetes: Estimates for the year 2010. Diabetes Res Clin Pract. 2010, 87 (1): 15-19. 10.1016/j.diabres.2009.10.006.View ArticlePubMedGoogle Scholar
  9. Danaei G, Lawes CMM, Van der Hoorn S, Murray CJL, Ezzati M: Global and regional mortality from ischaemic heart disease and stroke attributable to higher-than-optimum blood glucose concentration: comparative risk assessment. Lancet. 2006, 368 (9548): 1651-1659. 10.1016/S0140-6736(06)69700-6.View ArticlePubMedGoogle Scholar
  10. Ramachandran A, Wan Ma RC, Snehalatha C: Diabetes in Asia. Lancet. 375 (9712): 408-418.
  11. Chan JCN, Malik V, Jia W, Kadowaki T, Yajnik CS, Yoon K-H, Hu FB: Diabetes in Asia. JAMA. 2009, 301 (20): 2129-2140. 10.1001/jama.2009.726.View ArticlePubMedGoogle Scholar
  12. Anjana RM, Pradeepa R, Deepa M, Datta M, Sudha V, Unnikrishnan R, Bhansali A, Joshi SR, Joshi PP, Yajnik CS, et al: Prevalence of diabetes and prediabetes (impaired fasting glucose and/or impaired glucose tolerance) in urban and rural India: Phase I results of the Indian Council of Medical Research-INdia DIABetes (ICMR-INDIAB) study. Diabetologia. 2011, 54: 3022-3027. 10.1007/s00125-011-2291-5.View ArticlePubMedGoogle Scholar
  13. Ramachandran A, Snehalatha C, Latha E, Satyavani K, Vijay V: Clustering of cardiovascular risk factors in urban Asian Indians. Diabetes Care. 1998, 21 (6): 967-971. 10.2337/diacare.21.6.967.View ArticlePubMedGoogle Scholar
  14. Pradeepa R, Mohan V: The changing scenario of the diabetes epidemic: implications for India. Indian J Med Res. 2002, 116: 121-132.PubMedGoogle Scholar
  15. Hughes L, Raval U, Raftery E: First myocardial infarctions in Asian and white men. BMJ. 1989, 298 (6684): 1345-1350. 10.1136/bmj.298.6684.1345.View ArticlePubMedPubMed CentralGoogle Scholar
  16. McKeigue P, Ferrie J, Pierpoint T, Marmot M: Association of early-onset coronary heart disease in South Asian men with glucose intolerance and hyperinsulinemia. Circulation. 1993, 87 (1): 152-161. 10.1161/01.CIR.87.1.152.View ArticlePubMedGoogle Scholar
  17. Ghaffar A, Reddy K, Singhi M: Burden of non-communicable diseases in South Asia. BMJ. 2004, 328 (7443): 807-810. 10.1136/bmj.328.7443.807.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Chambers J, Elliott P, Zabaneh D, LYZ W, Froguel P, Balding D, Scott J, Kooner J: Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat Genet. 2008, 40 (6): 716-718. 10.1038/ng.156.View ArticlePubMedGoogle Scholar
  19. Yajnik C: Early life origins of insulin resistance and type 2 diabetes in India and other Asian countries. J Nutr. 2004, 134 (1): 205-210.PubMedGoogle Scholar
  20. Joshi P, Islam S, Pais P, Reddy S, Dorairaj P, Kazmi K, Pandey M, Haque S, Mendis S, Rangarajan S, et al: Risk factors for early myocardial infarction in South Asians compared with individuals in other countries. JAMA. 2007, 297 (3): 286-294. 10.1001/jama.297.3.286.View ArticlePubMedGoogle Scholar
  21. Diaz V, Mainous A, Baker R, Carnemolla M, Majeed A: How does ethnicity affect the association between obesity and diabetes?. Diabet Med. 2007, 24 (11): 1199-1204. 10.1111/j.1464-5491.2007.02244.x.View ArticlePubMedGoogle Scholar
  22. McKeigue P, Marmot M, Court YS, Cottier D, Rahman S, Riemersma R: Diabetes, hyperinsulinaemia, and coronary risk factors in Bangladeshis in east London. Br Heart J. 1988, 60 (5): 390-396. 10.1136/hrt.60.5.390.View ArticlePubMedPubMed CentralGoogle Scholar
  23. McKeigue P, Shah B, Marmot M: Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians. Lancet. 1991, 337 (8738): 382-386. 10.1016/0140-6736(91)91164-P.View ArticlePubMedGoogle Scholar
  24. World Health Organisation: UN high-level meeting on NCDs (New York, 19–20 September 2011). Summary report of the discussions at the round tables. 2011, New York: United NationsGoogle Scholar
  25. Institute of Medicine: A nationwide framework for surveillance of cardiovascular and chronic lung diseases. 2011, Washington, DC: Institute of MedicineGoogle Scholar
  26. Eiseman E, Fossum D: The Challenges of Creating a Global Health Resource Tracking System. 2005, Santa Monica, CA: RAND CorporationGoogle Scholar
  27. Heathfield H, Pitty D, Hanka R: Evaluating information technology in health care: barriers and challenges. BMJ. 1998, 316 (7149): 1959-1961. 10.1136/bmj.316.7149.1959.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Gupta R: Recent Trends in Coronary Heart Disease Epidemiology in India. Indian Heart J. 2008, Suppl B: B4-B18.Google Scholar
  29. Abegunde D, Mathers C, Adam T, Ortegon M, Strong K: The burden and costs of chronic diseases in low-income and middle-income countries. Lancet. 2007, 370 (9603): 1929-1938. 10.1016/S0140-6736(07)61696-1.View ArticlePubMedGoogle Scholar
  30. Engelgau MM, El-Saharty S, Kudesia P, Rajan V, Rosenhouse S, Okamoto K: Capitalizing on the Demographic Transition: Tackling Noncommunicable Diseases in South Asia. 2011, Washington, D.C: The World Bank, 204-View ArticleGoogle Scholar
  31. Integrated Disease Surveillance Project (IDSP). http://idsp.nic.in/,
  32. Nishtar S, Bile KM, Ahmed A, Amjad S, Iqbal A: Integrated Population-Based Surveillance of Noncommunicable Diseases: The Pakistan Model. American Journal of Preventive Medicine. 2005, 29 (5, Supplement 1): 102-106. 10.1016/j.amepre.2005.07.034.View ArticlePubMedGoogle Scholar
  33. Morris A, Boyle D, MacAlpine R, Emslie-Smith A, Jung R, Newton R, MacDonald T: The diabetes audit and research in Tayside Scotland (darts) study: electronic record linkage to create a diabetes register. BMJ. 1997, 315 (7107): 524-528. 10.1136/bmj.315.7107.524.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Joshy G, Simmons D: Diabetes information systems: a rapidly emerging support for diabetes surveillance and care. Diabetes Technol Ther. 2006, 8 (5): 587-597. 10.1089/dia.2006.8.587.View ArticlePubMedGoogle Scholar
  35. STEPwise approach to surveillance (STEPS). http://www.who.int/chp/steps/manual/en/index.html,
  36. Census of India. 2011, http://censusindia.gov.in/,
  37. World Population Prospects: The. 2006, http://esa.un.org/unup, Revision and World Urbanization Prospects: The 2007 Revision,
  38. StataCorp: Statistical Software: Release 10.1 College Station. 2008, College Station, TX: StataCorp LPGoogle Scholar
  39. Rizzo L, Brick J, Park I: A minimally intrusive method for sampling persons in Random Digit Dial surveys. Public Opin Q. 2004, 68 (2): 267-274. 10.1093/poq/nfh014.View ArticleGoogle Scholar
  40. The WHO MONICA Project. http://www.ktl.fi/monica/index.html,
  41. Ng N, Minh HV, Juvekar S, Razzaque A, Bich TH, Kanungsukkasem U, Ashraf A, Ahmed SM, Soonthornthada K: Using the INDEPTH HDSS to build capacity for chronic non-communicable disease risk factor surveillance in low and middle-income countries. Global Health Action. 2009, 2 (NCD Supplement): 7-18.Google Scholar
  42. Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN, Jones DW, Kurtz T, Sheps SG, Roccella EJ: Recommendations for blood pressure measurement in humans and experimental animals: Part 1: blood pressure measurement in humans: a statement for professionals from the subcommittee of professional and public education of the American Heart Association Council on High Blood Pressure Research. Hypertension. 2005, 45: 142-161.View ArticlePubMedGoogle Scholar
  43. National Health and Nutrition Examination Survey. http://www.cdc.gov/nchs/nhanes.htm,
  44. Thacker SB, Stroup DF, Rothenberg RB: Public health surveillance for chronic conditions: a scientific basis for decisions. Stat Med. 1995, 14 (5–7): 629-641.View ArticlePubMedGoogle Scholar
  45. Al-Nsour M, Zindah M, Belbeisi A, Hadaddin R, Brown DW, Walke H: Prevalence of selected chronic, noncommunicable disease risk factors in jordan: results of the 2007 jordan behavioral risk factor surveillance survey. Prev Chronic Dis. 2012, 9: E25-PubMedGoogle Scholar
  46. Bovet P, Viswanathan B, Shamlaye C, Romain S, Gedeon J: Addressing non-communicable diseases in the Seychelles: towards a comprehensive plan of action. Glob Health Promot. 2010, 17 (2 suppl): 37-40. 10.1177/1757975910363930.View ArticlePubMedGoogle Scholar
  47. MS GR: New Survey Results Enhance Cuba’s NCD Surveillance: Mariano Bonet MD Director, National Hygiene, Epidemiology and Microbiology Institute. MEDICC Rev. 2011, 13: 11-13.Google Scholar
  48. Conroy RM, Pyörälä K, Fitzgerald AP, Sans S, Menotti A, De Backer G, De Bacquer D, Ducimetière P, Jousilahti P, Keil U, et al: Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003, 24 (11): 987-1003. 10.1016/S0195-668X(03)00114-3.View ArticlePubMedGoogle Scholar
  49. Hemingway H, Marmot M: Psychosocial factors in the aetiology and prognosis of coronary heart disease: systematic review of prospective cohort studies. BMJ. 1999, 318 (7196): 1460-1467. 10.1136/bmj.318.7196.1460.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Cohen JW, Krauss NA: Spending And Service Use Among People With The Fifteen Most Costly Medical Conditions, 1997. Health Aff. 2003, 22 (2): 129-138. 10.1377/hlthaff.22.2.129.View ArticleGoogle Scholar
  51. Malekzadeh F, Marshall T, Pourshams A, Gharravi M, Aslani A, Nateghi A, Rastegarpanah M, Khoshnia M, Semnani S, Salahi R: A pilot double-blind randomised placebo-controlled trial of the effects of fixed-dose combination therapy ("polypill") on cardiovascular risk factors. Int J Clin Pract. 2010, 64 (9): 1220-1227. 10.1111/j.1742-1241.2010.02412.x.View ArticlePubMedGoogle Scholar
  52. Mohan V: Effects of a polypill (Polycap) on risk factors in middle-aged individuals without cardiovascular disease (TIPS): a phase II, double-blind, randomised trial. Lancet. 2009, 373 (9672): 1341-1351.View ArticlePubMedGoogle Scholar
  53. Shah B, Mathur P: Surveillance of cardiovascular disease risk factors in India: The need & scope. Indian J Med Res. 2010, 132 (5): 634-642.PubMedPubMed CentralGoogle Scholar
  54. National Institute of Population Studies (NIPS) [Pakistan] aMII: Pakistan Demographic and Health Survey 2006–07. 2008, Islamabad, Pakistan: National Institute of Population Studies and Macro International IncGoogle Scholar
  55. Wilson PWF, Narayan KMV: New York City Health and Nutrition Examination Survey: A model for urban health surveillance. Diabetes Care. 2009, 32 (1): 204-205. 10.2337/dc08-1807.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Alwan A, MacLean DR, Riley LM, d’Espaignet ET, Mathers CD, Stevens GA, Bettcher D: Monitoring and surveillance of chronic non-communicable diseases: progress and capacity in high-burden countries. The Lancet. 2010, 376 (9755): 1861-1868. 10.1016/S0140-6736(10)61853-3.View ArticleGoogle Scholar
  57. Choi BCK, Corber SJ, McQueen DV, Bonita R, Zevallos JC, Douglas KA, Barceló A, Gonzalez M, Robles S, Stachenko S, et al: Enhancing regional capacity in chronic disease surveillance in the Americas. Rev Panam Salud Publica/Pan Am J Public Health. 2005, 17: 130-141.View ArticleGoogle Scholar
  58. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/12/701/prepub

Copyright

© Nair et al.; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.