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Open AcceResearch articleItalian network for obesity and cardiovascular disease surveillance: A pilot projectChiara Donfrancesco*1, Cinzia Lo Noce1, Ovidio Brignoli2, Gabriele Riccardi3, Paola Ciccarelli1, Francesco Dima1, Luigi Palmieri1 and Simona Giampaoli1Address: 1Centro Nazionale di Epidemiologia, Sorveglianza e Promozione della Salute, Istituto Superiore di Sanità, Rome, Italy, 2Società Italiana di Medicina Generale, Rome, Italy and 3Università Federico II, Naples, Italy
Email: Chiara Donfrancesco* - [email protected]; Cinzia Lo Noce - [email protected]; Ovidio Brignoli - [email protected]; Gabriele Riccardi - [email protected]; Paola Ciccarelli - [email protected]; Francesco Dima - [email protected]; Luigi Palmieri - [email protected]; Simona Giampaoli - [email protected]
* Corresponding author
AbstractBackground: Also in Mediterranean countries, which are considered a low risk population forcardiovascular disease (CVD), the increase in body mass index (BMI) has become a public healthpriority. To evaluate the feasibility of a CVD and obesity surveillance network, forty GeneralPractitioners (GPs) were engaged to perform a screening to assess obesity, cardiovascular risk,lifestyle habits and medication use.
Methods: A total of 1,046 women and 1,044 men aged 35–74 years were randomly selected fromGPs' lists stratifying by age decade and gender. Anthropometric and blood pressure measurementswere performed by GPs using standardized methodologies. BMI was computed and categorized innormal weight (BMI 18.5–24.9 kg/m2), overweight (BMI 25.0–29.9 kg/m2) and obese (BMI ≥ 30 kg/m2). Food frequency (per day: fruits and vegetables; per week: meat, cheese, fish, pulses, chocolate,fried food, sweet, wholemeal food, rotisserie food and sugar drink) and physical activity (at workand during leisure time) were investigated through a questionnaire. CVD risk was assessed usingthe Italian CUORE Project risk function.
Results: The percentage of missing values was very low. Prevalence of overweight was 34% inwomen and 50% in men; prevalence of obesity was 23% in both men and women. Level of physicalactivity was mostly low or very low. BMI was inversely associated with consumption of pulses,rotisserie food, chocolate, sweets and physical activity during leisure time and directly associatedwith consumption of meat. Mean value of total cardiovascular risk was 4% in women and 11% inmen. One percent of women and 16% of men were at high cardiovascular risk (≥ 20% in 10 years).Normal weight persons were four times more likely to be at low risk than obese persons.
Conclusion: This study demonstrated the feasibility of a surveillance network of GPs in Italyfocusing on obesity and other CVD risk factors. It also provided information on lifestyle habits, suchas diet and physical activity.
Published: 29 September 2008
BMC Family Practice 2008, 9:53 doi:10.1186/1471-2296-9-53
Received: 4 March 2008Accepted: 29 September 2008
This article is available from: http://www.biomedcentral.com/1471-2296/9/53
© 2008 Donfrancesco et al; licensee 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.
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BackgroundOver the last decades, a slow but steady decrease can beobserved in cardiovascular disease (CVD) mortality inindustrialized countries. This is partly due to improve-ments in pharmacological treatment during both acuteand chronic phases, reduction in levels of establishedmajor risk factors – total cholesterol and hypertension –,better pharmacological control of risk factors, and reduc-tion in smoking habit [1,2]. An inverse trend can beobserved for obesity, whose prevalence is steadily increas-ing [3].
Scientific evidence accumulated over recent years hasprompted the American Heart Association to identifyobesity as the major and modifiable risk factor for CVD,the UK Government (Department of Health) to includeobesity as one of the six key priorities in the White PaperChoosing Health: Making Healthy Choices Easier and theItalian Ministry of Health to launch the Project'Guadagnare Salute' which foresees multidisciplinary inter-ventions in the field of diet and physical activity [4-6].Promotion of physical activity and healthy diet areregarded as key factors to prevent and reduce overweightand obesity epidemic. In USA, the Healthy People 2010programme identifies physical activity as one of the publichealth top priorities [7]. The Public Health Programme2003–2008 of the European Union invites countries toimplement initiatives that sustain and promote physicalactivity [8]. In 2005, the Aprifel, the French agency forresearch and information on fresh fruits and vegetables,the Italian Ministry of Health and the National Centre forDisease Prevention and Control (CCM) organized theInternational Conference on Health Benefits of Mediterra-nean Style Diet – From Scientific Evidence to Health Pre-vention Actions with the aim of highlighting the latestscientific knowledge on health benefits of Mediterraneanstyle diet and the strategic perspectives for health opera-tors [9].
In Italy, a national surveillance network for overweight,obesity and other cardiovascular risk factors does notexist. Presently, latest available data on overweight andobesity come from the multi-scopes survey performed in2005 and published in 2007 by the Italian Institute ofNational Statistics (ISTAT), collecting self-reported infor-mation on health status [10], and from the EpidemiologicCardiovascular Observatory (OEC) of the ProgettoCUORE, estimating prevalence of overweight, obesity andother CVD risk factors in about 10,000 Italian men andwomen aged 35–74 years examined between 1998 and2002. [11]. Many initiatives are taken at the regional level,but they are mainly targeted to children and adolescents[12-16]. Therefore, there is a pressing need to establish anational surveillance system for collecting reliable and
comparable data and implementing prevention proce-dures at individual and population levels.
Following the experience of other Italian studies trying toevaluate the feasibility of a surveillance network for otherrisk factors and diseases (alcohol, smoking, physical inac-tivity, renal diseases) [17-20], an Italian pilot project wasimplemented in January 2006 with the main aim of eval-uating the feasibility of a surveillance network for CVDand obesity. Secondary aims were to assess the prevalenceof cardiovascular risk conditions and distribution of over-weight and obesity in men and women aged 35–74 years,to describe lifestyle habits such as diet and physical activ-ity, and to investigate the association between lifestyle,obesity and predicted cardiovascular risk in the generalpopulation.
The study lasted about two years and involved generalpractitioners (GPs) to perform screening examinations toassess cardiovascular risk, lifestyles habits (physical activ-ity, diet and smoking) and drug use among their patients.Particular focus was laid on overweight and obese per-sons.
MethodsScreeningForty GPs from the Italian Association of General Practi-tioners (SIMG) and homogeneously distributed across thecountry were engaged. To ensure uniformity in the appli-cation of screening procedures, a training course for GPson standardization of measurements was organized.
At least one GP per region participated in the study [Figure1]. Each participating GP had the task to screen 56patients aged 35–74 years, randomly selected by theNational Institute of Health (ISS) from each GP's patientlist in order to have 7 patients in each age decade and sex(35–44, 45–54, 55–64, 65–74 years). Selected patientswere invited by letter to the ambulatory of their own GP.
The screening protocol foresaw the completion of a ques-tionnaire containing questions on anagraphical position,such as marital status and educational level, habits andlifestyles, remote pathological anamnesis (angina pec-toris, myocardial infarction, cerebrovascular accidents,TIA, intermittent claudication), current drug treatments(antihypertensives, lipid-lowering, oral antidiabetics),family history (father, mother, sisters/brothers) of prema-ture coronary heart disease, cerebrovascular accidents andfamily history of diabetes. Anthropometric (weight,height, waist circumference, hip circumference) andblood pressure measurements were performed usingstandardized methodologies (standard electronic scale,wall height ruler, tape measure, mercury sphygmoma-nometer). Laboratory analyses considered for risk assess-
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ment were: total cholesterol (TC), HDL-cholesterol (HDL-C) and glycaemia. Total cardiovascular risk (TCR) wasassessed using the CUORE Project risk function [21,22].
A data entry software intended for GPs was developed tofacilitate the process of data collection and storage duringthe examination of patients. At the end of the study, everyGP sent collected data to the ISS for processing. Data sentby the 40 GPs referred to 1,044 men and 1,046 women,that is an average of 55 patients per GP. Written informa-tion consent was obtained from each study participant.
Quality controlsThe software provided to GPs for data entry offers auto-matic checks for variables congruence (e.g. an informa-tion related to the number of daily smoked cigarettes
could not refer to a non-smoker) and alerts GPs to missingdata.
The main aim of this pilot study was to assess the feasibil-ity of a network of GPs for CVD surveillance, preventionand health promotion. For this reason, collected informa-tion were carefully evaluated for completeness and relia-bility.
For completeness analysis, the percentage of missing val-ues for each variable was calculated; for reliability analy-sis, anthropometric (height, weight, waist and hipcircumference) and blood pressure measurements wereconsidered: the distribution of the terminal digit ofrecorded blood pressure readings and the percentage ofequal consecutive readings were calculated [23]. For
Geographical distribution of general practitioners and patients involved in the studyFigure 1Geographical distribution of general practitioners and patients involved in the study.
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height, the percentage of terminal zeros was calculated aswell; for weight, waist and hip circumferences the termi-nal decimal and whole digits were considered. Homoge-neity of terminal digit distribution indicates a correctapplication of standard methodologies for risk factorsmeasurements.
Data analysisData on 2,090 patients included in the lists of 40 GPs weretransmitted to the ISS.
Demographic characteristics (age, educational level andmarital status) and the adverse conditions (moderate orlow physical activity at work and during leisure time,smoking habit, obesity, hypertension, hypercholestero-lemia and pharmacological treatment) were reported asprevalence. Definitions used in the data analysis of theOEC for hypertension, hypercholesterolemia and diabeteswere taken into consideration to make results comparable[11]. The distribution of overweight and obesity was cal-culated considering the following categories: normalweight as body mass index (BMI) 18.5–24.9 kg/m2, over-weight as BMI 25.0–29.9 kg/m2 and obesity as BMI ≥ 30.0kg/m2 or waist circumference > 88 cm in women and >102 cm in men. If not differently indicated, results andcomments hereinafter refer to BMI.
Mean values were calculated for continuous major cardio-vascular risk factors and consumption of some foods. Thefrequency of food consumption per week was recorded forthe following food items: meat, cheese, fish, pulses, choc-olate, fried food, sweet, wholemeal food, rotisserie foodand sugar drink. Daily consumption was asked for fruitsand vegetables. Alcohol intake was recorded as grams perday.
The relation of BMI and other CVD risk factors to con-sumption of some food items, alcohol intake and physicalactivity was assessed by univariate and multivariate analy-ses. For univariate analysis, age-adjusted Pearson's corre-lation coefficients (r) were calculated to assess relationsbetween couple of variables; for multivariate analysis,multivariate linear regression models were performedconsidering CVD risk factors as dependent variables andconsumption of food items, alcohol intake and physicalactivity as independent covariates (all models were alsoage and sex adjusted). In order to compare the effect ofindependent covariates, standardized coefficients ofregression models were calculated (regression coefficientmultiplied by the ratio of the standard deviation of theindependent variable to the standard deviation of thedependent variable). Only significant coefficients werereported.
The 10-year TCR was assessed by GPs through the dataentry software using the individual risk score built withinthe longitudinal study of the Italian CUORE Project [22].This score allows to estimate the probability of experienc-ing a first cardiovascular event (myocardial infarction,stroke) over the next 10 years knowing the level of eightrisk factors for CVD (age, gender, SBP, TC, HDL-C, diabe-tes, smoking habit and use of antihypertensive medica-tion).
The TCR was assessed by GPs on 45% of those patientsconsidered eligible for application of the CUORE Projectrisk function (complete information for the risk factorsincluded in the CVD risk assessment, risk factors valueswithin the reference limits, age range 35–69 years, freefrom previous cardiovascular events). For the remaining55%, TCR was assessed within the present analysis usingthe risk factors values collected by GPs.
On the basis of the TCR value, patients were classified aslow risk (TCR < 3.0%), risk to be kept under controlthrough the adoption of a healthy lifestyle (TCR 3.0–19.9%), high risk (TCR ≥ 20.0%). Age-adjusted ANOVAmodels were used to assess mean difference between nor-mal weight and obese, and the relation between BMI (orwaist and hip circumferences) and TCR.
After checking for homogeneity of variances, comparisonsamong mean values of men and women were made usingthe t-test. In absence of homogeneity, the Welch test wasused. The independence between categorical variables wasassessed using the chi-square test.
Results associated to a p-value ≤ 0.05 were considered sta-tistically significant. Quality controls and statistical analy-ses were performed using SAS software version 8.2 (SASInstitute, Cary, NC).
ResultsQuality controlAs the age distribution between men and women was sim-ilar, age adjustments were not needed to compare resultsamong sexes.
The completeness of collected information was consid-ered satisfactory. Percentages of recorded information arethe following: 98% for blood pressure measurements;94% for laboratory analyses such as TC, HDL-C and gly-caemia; 98% for anthropometric measurements. Throughthe questionnaire, smoking habit was registered for 98%of sample, physical activity at work and during leisuretime for 70% and 98% respectively, and food consump-tion frequency for 98%; information on medications usewere available for 95% of sample.
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The distribution of the terminal whole digit of blood pres-sure readings was found to be highly concentred: morethan 50% of readings had zero as terminal digit. The pro-portion of equal readings between first and second meas-urements was 50% for SBP and 40% for DBP. Also foranthropometric measurements, the distributions of theterminal whole and decimal digits were concentrated onzero.
Sample characteristics and risk conditionsTable 1 shows the main sample characteristics: more than70% of women and 80% of men were married or cohab-itant; 41% of women and 42% of men had high schooldiploma or university degree (Table 1). Ninety percent ofwomen and 78% of men declared to make low or very lowphysical activity at work (mostly sedentary work orinvolving standing/walking) (Table 1), 10% of womenand 22% of men declared to make high or very high phys-ical activity at work (work involving much walking, man-ual work involving strenuous efforts). Physical activityduring leisure time was low or very low (sedentary activityor moderate physical activity for no more than 4 hours aweek) for both men and women (Table 1); only 5% ofwomen and 11% of men declared to make high or veryhigh physical activity during leisure time (enjoying sportas a hobby, doing heavy gardening, or exercising regularlyfor agonistic activity).
Prevalence of overweight was 34% in women and 50% inmen; 23% of both men and women were obese. Waist cir-cumference exceeded the threshold for obesity in 45% ofwomen and 28% of men. Forty one percent of womenand 27% of men were normal weight. Mean value of BMIfell in the overweight category for both men and women(Table 2).
Dietary and physical activity habitsData on dietary habits collected through administeredquestionnaire showed that women ate more wholemealfood, vegetables and chocolate compared to men; menhad a higher weekly consumption of alcohol, meat, friedfood and sugar drinks than women (Table 3). Normalweight persons declared to have a higher average numberof meals eaten away from home per week compared toobese persons. No statistically significant differences werefound between normal weight and obese persons withregard to frequency of breakfast consumption away fromhome. Similar results were recorded using waist circum-ference as obesity indicator. Eight percent of normalweight women and 0.4% of obese women declared tomake high or very high physical activity during leisuretime; among men, 15% and 8% respectively.
Considering food frequency, alcohol intake and physicalactivity singly, BMI was found to be significantly and pos-
itively correlated, although not strongly, with meat con-sumption and alcohol intake (r = 0.05 and r = 0.08respectively), while significant negative correlations werefound between BMI and consumption of fish (r = -0.05),pulses (r = -0.11), wholemeal food (r = -0.05), vegetables(r = -0.04), chocolate (r = -0.07), sweets (r = -0.05), rotis-
Table 1: Demographic characteristics and conditions at risk.
WOMEN MENN % N %
Demographic characteristics
Age classes35–44 247 24 248 2445–54 260 25 250 2455–64 265 25 261 2565–74 274 26 285 27
Marital statusSingle 116 11 123 12Married or live-in-partner 770 74 867 83Separated or divorced 50 5 37 4Widowed 110 11 15 1
EducationCollege or university 113 11 131 13High school 306 30 298 29Secondary school 335 33 404 39Primary school 275 27 200 19
Conditions at risk
Physical activity at workVery low 261 33 250 38Low 451 57 262 40
Physical activity during leisure timeVery low 562 55 440 43Low 402 40 474 46
Body Mass Index, kg/m2
25.0 – 29.9 348 34 512 50> = 30.0 234 23 235 23
Waist circumference, cm> 88 for women, > 102 for men 458 45 283 28
Smoking habit 183 18 269 26Hypertension* 400 39 427 42Antihypertensive medication 365 37 378 38Hypercholesterolaemia** 332 33 326 32Lipid-lowering medication 117 12 156 16Diabetes*** 98 10 137 14Diabetes specific medication 67 7 97 10
Men and women 35–74 years.*Systolic blood pressure ≥ 160 mmHg or diastolic blood pressure ≥ 95 mmHg or use of antihypertensive medications.** Total cholesterol ≥ 6.2 mmol/l or use of lipid lowering medications.***Glycemia ≥ 7 mmol/l or use of specific medications for diabetes.
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serie food (r = -0.05) and level of physical activity duringleisure time (r = -0.19). Correlation of BMI to physicalactivity at work and other food items consumption wasnot statistically significant. Similar results were recordedusing waist circumference as obesity indicator.
Multivariate regression model confirmed that higher con-sumption of meat was associated with higher levels ofBMI, while higher consumption of pulses, chocolate,sweets and rotisserie food was associated with lower levelsof BMI (Table 4). Physical activity during leisure time hadthe strongest inverse impact on BMI (Table 4). Physicalactivity during leisure time was found to be statisticallyassociated with all risk factors considered with the excep-tion of TC (Table 4); no association was found with phys-ical activity at work.
Total cardiovascular riskThree percent of sample (48 men and 10 women)declared to have experienced a cerebrovascular or coro-nary event before entering the study. The TCR was calcu-lated for 806 women and 805 men. Mean value of TCRwas 4% in women (standard deviation was 5%) and 11%in men (standard deviation was 11%). One percent ofwomen and 16% of men were at high cardiovascular risk(TCR ≥ 20.0% in 10 years).
In both men and women the mean value of TCR was asso-ciated with BMI classes (women: 3%, 5%, 7%; men: 8%,11%, 13% for normal weight, overweight and obeserespectively); similar results were recorded consideringwaist circumference (women: 3% and 6% for non-obeseand obese respectively; men: 9% and 14%).
Thirty percent of obese women versus 78% of normalweight women, and 15% of obese men versus 44% of nor-
Table 2: Mean and standard deviation of blood pressure, blood test and anthropometric measurements.
WOMEN MENN mean standard deviation N mean standard deviation
Systolic blood pressure, mmHg 1,016 131.9 17.4 1,027 133.5 16.0Diastolic blood pressure, mmHg 1,016 79.4 8.9 1,027 81.1 9.2Serum Total Cholesterol, mmol/l 978 5.5 1.0 984 5.3 1.0Serum HDL-Cholesterol, mmol/l 978 1.5 0.4 980 1.3 0.3Glycaemia, mmol/l 978 5.4 1.3 984 5.8 1.6Weight, kg 1,016 67.5 12.7 1,027 80.7 13.0Height, cm 1,016 159.2 6.9 1,027 171.2 7.2BMI, kg/m2 1,016 26.7 5.3 1,027 27.5 4.2Waist circumference, cm 1,016 87.8 13.0 1,026 96.8 11.4Hip circumference, cm 1,002 99.0 12.3 1,007 100.9 11.2
Men and women 35–74 years.
Table 3: Food frequency per week and alcohol intake.
WOMEN MENMedian Mean Min Max Median Mean Min Max
Meat * 3 3.4 0 8 3 3.7 0 8Cheese 3 3.3 0 8 3 3.4 0 8Fish 2 1.7 0 8 2 1.8 0 8Pulses 2 2.2 0 8 2 2.1 0 8Chocolate * 1 2.3 0 8 1 1.9 0 8Fry food * 1 0.8 0 7 1 1.0 0 8Sweet 1 1.6 0 8 1 1.7 0 8Wholmeal food * 0 1.9 0 8 0 1.5 0 8Rotisserie food 0 0.7 0 8 0 0.8 0 7Sugar drink * 0 0.9 0 8 0 1.1 0 8Fruits 2 2.5 0 8 2 2.4 0 8Vegetables * 2 1.9 0 8 1 1.7 0 8Alcohol intake* 0 8.1 0 83 20 24.6 0 239
Men and women aged 35–74 years.Fruits and vegetables food frequency per day. Alcohol intake expressed in grams.* Significant difference between means of men and women (t-test; p-value ≤ 0.05).
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mal weight men were at low cardiovascular risk (Figure 2).Four percent of obese women versus 0.3% of normalweight women, and 20% of obese men versus 10% of nor-mal weight men were at high cardiovascular risk.
DiscussionObesity represents a threat to health which is peculiar,although not exclusive, to the so-called 'well-being socie-ties'. A constant increase in the availability of food on one
side and an increasingly sedentary lifestyle on the otherside appear to be the main factors behind this situation.Many degenerative diseases are associated with obesity:diabetes, hypertension, ischemic heart disease, heart fail-ure, some types of cancers (endometrial, colorectal, kid-ney, gall bladder and breast cancer in post-menopausalwomen), and osteoarticular pathologies. Obesity has arather complicated association with cardiovascular risk:some researchers argue that this association is indirect and
Table 4: Relation between food frequency, physical activity, alcohol intake and main cardiovascular risk factors.
SBP DBP TC HDL-C Glycemia BMI WaistStd Coeff Std Coeff Std Coeff Std Coeff Std Coeff Std Coeff Std Coeff
Meat 0.061* 0.052* 0.051*CheeseFish -0.063*Pulses -0.051* -0.074**Chocolate 0.122*** -0.050* -0.071** -0.077**Fry food -0.057* 0.056*Sweet -0.071** -0.056* -0.055*Wholmeal food -0.065**Rotisserie food -0.058* -0.072**Sugar drink -0.075** 0.052*Fruits -0.078**Vegetables 0.062*Physical activity at workLeisure-time physical activity -0.049* -0.074** 0.132*** -0.066** -0.187*** -0.195***Alcohol intake 0.083** 0.073** 0.146*** 0.064* 0.078**
Men and women aged 35–74 years.Variables on the top of the columns are considered as dependent variables. Food intake, physical activity and alcohol intake are considered as independent covariates in each model. The seven models are also age and sex adjusted.Std Coeff is the standardized coefficient of multivariate linear regression model.Significance: *** p < 0.001, ** p < 0.01, * p < 0.05.All food frequencies are per week except for fruits and vegetables (per day). Alcohol intake expressed in grams.SBP: systolic blood pressure (mmHg); DBP: diastolic blood pressure (mmHg); HDL-C: HDL-cholesterol (mmol/l); glycaemia: fasting plasma glucose (mmol/l); BMI: body mass index (kg/m2); waist: waist circumference (cm).
Distribution of Total Cardiovascular Risk (TCR) by Body Mass Index categoriesFigure 2Distribution of Total Cardiovascular Risk (TCR) by Body Mass Index categories. Men and women 35–69 years without previous cardiovascular disease.
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dependent on an increased prevalence of diabetes, hyper-tension and dysplipidemia, while others consider obesityas an independent risk factor for CVD [24,25].
All things considered, initiatives aiming at monitoringtrends in national obesity and promoting preventionactions at local level are of strategic importance. In 1984,the Center for Disease Control and Prevention (CDC) ofUSA developed the Behavioral Risk Factor SurveillanceSystem to collect monthly in all 50 states information onhealth risk behaviours and preventive health practices[26]. In Europe, the European Physical Activity Surveil-lance System (EUPASS) has been implemented to contrib-ute to the establishment of an appropriate, cost-effectiveand feasible Community health monitoring system to beachieved by developing and testing a surveillance system,using physical activity, being a major indicator of publichealth, as its substantive focus and starting point [27]. InUK, the Counterweight programme developed and testeda weight management intervention programme in pri-mary care [28,29].
Italy still lacks a valid national system of surveillance totrack overweight, obesity and other cardiovascular riskfactors. For this reason, a pilot project was implementedwith the aim of evaluating the feasibility of a national sur-veillance network for obesity and other cardiovascular riskfactors. Forty GPs were engaged to collect their patients'data on dietary habits, physical activity, use of medica-tions, smoking habit, major cardiovascular risk factorslevel and anthropometric measurements using standard-ized methodologies.
The completeness of collected information on routinelyperformed measurements and laboratory assays, as well asinformation collected through questionnaire such as die-tary habits, physical activity level and smoking habit wasconsidered satisfactory. Younger persons were easy toenrol, in fact the homogeneity in age distribution foreseenin the protocol was not altered. The parameters used forthe analysis of reliability of collected data referred to qual-ity controls performed in the epidemiological field, wherethe accuracy of measurements represents a crucial bondfor reliability of estimates [23]. In this study, the analyseson the distribution of terminal digit of blood pressurereadings and anthropometric measurements representeda valid self-monitoring tool for GPs using standardizedmethodologies. However, a good reliability of collectedinformation was confirmed by the significance found inknown associations (dietary habits, physical activity, BMI,waist circumference, cardiovascular risk factors) and byhomogeneity of results with those obtained through OECsurvey [11].
Given the availability of data, it was worth studying die-tary habits, physical activity and relation of these withCVD risk factors, despite this went beyond the main aimof the study. Since cross-sectional design prevents deter-mination of causality and assessment of lifestyle changes'effects (diet, physical activity) over time, analyses of datafocused on association of cardiovascular risk factors withfood frequency and overweight/obesity, underlying differ-ences between men and women. Results showed that menhad a greater tendency to be overweight compared towomen, while obesity is a problem affecting both gen-ders. Compared to men, women seemed to care moreabout their diet, eating healthy food, such as wholemealfood and vegetables, more frequently than did men. Onthe other hand, men ate food rich in saturated fats such asmeat, fried food and sugar drinks more frequently thandid women. Persons with lower BMI ate sweets, rotisseriefood and chocolate more frequently than did overweightpersons and declared to eat away from home more fre-quently. This behaviour may indicate either that personswith high BMI or waist circumference knew their weightproblems and were willing to solve them or that personsoverweight/obese had the tendency to not declare theirunhealthy dietary habits. Although fish, wholemeal foodand vegetables consumption resulted not significantwhen adjusted for all other foods consumption, a morefrequent consumption of these food items was found tobe correlated with persons having a lower BMI or waist cir-cumference. Physical activity during leisure time was abetter discriminator than physical activity at work forassessing the tendency to overweight or obesity; however,both types of physical activity were low or very low. Menmade more high or very high physical activity during lei-sure time compared to women. Alcohol intake was foundto be associated with most cardiovascular risk factors and,together with physical activity during leisure time, wasone of the most important factors within the multivariateanalyses. Alcohol consumption was higher for men thanfor women.
The level of 10-year TCR calculated through the CUOREProject function ranged in the risk category to be keptunder control through the adoption of healthy lifestyle(risk category between 3.0% and 19.9%) for both menand women. The likelihood for normal weight persons tobe low risk was almost four times higher compared toobese persons.
It is not easy to establish to what extent patients includedin this study are representative of the Italian patient pop-ulation. As for cardiovascular risk factors comparison,mean levels of risk factors and prevalence of risk condi-tions were in line with results obtained through OEC sur-vey: mean BMI, SBP, HDL-C values were quite similar tothose reported by OEC (BMI 26 kg/m2 for women and 27
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kg/m2 for men, SBP 135 mmHg for women and 132mmHg for men, HDL-C 1.5 mmol/l for women and 1.3mmol/l for men). Mean values of TC, glycemia, hyperten-sion and hypercholesterolemia were higher for both gen-ders (OEC values: TC 5.5 mmol/l for women and 5.3mmol/l for men, glycaemia 4.8 mmol/l for women and5.2 mmol/l for men, hypertension 31% for women and33% for men, hypercholesterolemia 25% for women and21% for men) [11]. The opposite was found for smokinghabit: OEC showed higher prevalence compared to thisstudy (OEC values: 21% for women and 30% for men).Physical inactivity prevalence was found to be higher inthis study, especially for women. Differences could beascribed to the different period of screening (OEC surveywas performed at the end of 1990s); this is particularlytrue for smoking prevalence, which has been changingafter the introduction of the Italian anti-smoking legisla-tion in January 2005 banning smoking in public places.
Overall, the picture that emerged from this pilot project ispositive, showing a population with mostly healthy, butcertainly improvable, dietary habits (mean consumptionof meat 3/4 times per week, fish and pulses twice perweek, two portions of vegetables and fruits per day, smallamount of fried food, acceptable mean level of alcoholintake per day) and a mean cardiovascular risk level notexceeding the threshold of high risk.
Obesity prevalence should not be underestimated and isprobably due to food quantity rather than food quality,and to a mostly sedentary lifestyle. Monitoring bodyweight and risk factors levels represent a first step towardspersuading the general population to implement preven-tive actions against the onset of diseases leading to wors-ening quality of life or even reduction in the years of life.Obesity has its roots in the common way of thinking,which must be changed through lifestyle interventionstargeted to the general population.
Little rigorous research study exist on how effective train-ing is in helping primary care teams to manage obesitymore effectively [30]; nevertheless, several studies showedthat GPs have a significant role to play in preventing anddiagnosing weight problems and in improving initialcounselling [31,32]. It is a fact that they are often con-sulted [33] and most patients believe that their GPs couldhelp them lose weight [34]. A cross sectional telephonesurvey conducted in 2002 in France on 600 GPs reportedthat most of them regarded obesity as a disease and agreedthat their role includes weight problem management butcomplain lack of time as a frequent problem [35], whichis confirmed also by other studies [36,37], and generallyknown to be a significant barrier to preventive care in gen-eral practice [38,39]. In our pilot project, a random selec-tion of GPs was not feasible and it is now impossible to
determine to what extent this choice influenced the studysuccess. Preventing overweight and obesity could avoidfurther increase of health care use and related costs: Lisetvan Dijk et al. study [40] evaluated GPs consultations inmoderate and severely overweight persons in Netherlandsand reported that obese persons, regardless of differentgender, age, social status and lifestyle, are more likely toconsult their GP.
To ensure effective surveillance of prevention of obesityand CVD, the activity of GPs should be accompanied byother initiatives. As suggested by an increasing number ofreports and as pointed out by Benjamin Caballero fromThe Johns Hopkins University also pointed out in his glo-bal epidemic of obesity overview, obesity is not only a dis-order of individual behaviour but it is highly influencedby socioeconomic environment [41]. This is why preven-tion needs to take place simultaneously at the individuallevel and at the population level. Good social support canhelp give people the emotional and practical resourcesthey need, particularly for coping with difficult life transi-tions.
The WHO Regional Office for Europe, within the Strategyfor the Prevention and Control of Noncommunicable Diseases,on the occasion of the Copenhagen meeting held on 12September 2006, indicated that effective interventions caninclude: control of the quality of consumer informationabout certain products, such as food, tobacco, alcohol;cooperation with industry through voluntary or enforcedagreements to reduce levels of added salt, fat and sugars inmanufactured foods; regulations within the built environ-ment, for example through health impact assessment oftransport and urban development proposals in order topromote walking and cycling [42]. The objectives of thestrategy should be combined with integrated action onrisk factors and their underlying determinants across sec-tors, with efforts to strengthen health systems towardimproved prevention and control.
In Italy, beside population-based interventions imple-mented within the Project 'Guadagnare Salute', the CCM,in collaboration with ISS, has launched a national train-ing plan aimed at increasing GPs' awareness about the keyrole of cardiovascular prevention, in order to implementan Italian national surveillance system following theobjectives of the pilot study under consideration. The newskills acquired through the training course will translateinto health objectives for the general population and forindividuals: lifestyle changes among individuals at highrisk and general population; reduction in the frequency ofrisk conditions and in the mean level of single risk factors(an exhaustive session is dedicated to overweight, obesity,nutrition and physical activity). The software cuore.exe,downloadable free of charge from the website of the
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CUORE Project http://www.cuore.iss.it, helps GPs to setup a data archive, to monitor cardiovascular risk and riskfactors trends in patients over time, to keep a record of theprescribed therapies and lifestyle recommendations givento patients, to collect data related to obesity and over-weight, and to print out the patient's cardiovascular riskscore, including some recommendations for lifestyle andtherapy, if prescribed. The Observatory of CardiovascularRisk (ORC), coordinated by the ISS, pools data collectedby GPs through the cuore.exe, supports GPs with qualitycontrol, and disseminate results for monitoring TCR andrisk factors by genders, age and geographical area andevaluating the efficacy of prevention action in primarycare. Preliminary data of the ORC are under evaluation.
ConclusionThe active role of GPs in the process of prevention repre-sents a potential key to success. GPs have the primary andcomplete responsibility toward their patients, contributeto the development of individual assistance plan and rec-ommend specialist visits when necessary. This study hasdemonstrated the feasibility of a GPs surveillance networkfor obesity and CVD in Italy. It may support informativecampaigns on how people can adopt healthy diet andincrease daily physical activity; it also represents a goodway to monitor overweight, obesity and CVD, and a greatstep toward assuring a good health in the future.
Competing interestsThe authors declare that they have no competing interests.
Authors' contributionsCD: performed statistical analysis, interpretation of dataand made substantial contribution in drafting the manu-script. CL: made substantial contribution to acquisition ofdata. OB: was involved in revising the manuscript criti-cally. GR: was involved in revising the manuscript criti-cally. PC: was involved in drafting the manuscript andmaking language revision. FD: made contribution toacquisition of data. LP: was involved in revising the man-uscript critically. SG: made substantial contribution inconception and design, and revised the manuscript criti-cally. All authors read and approved the final manuscript.
AcknowledgementsThe Obesity Project Research Group
Simona Giampaoli, Ovidio Brignoli, Gabriele Riccardi, Cinzia Lo Noce, Chi-ara Donfrancesco, Paola Ciccarelli, Francesco Dima, Luigi Palmieri.
General Practitioners involved in the pilot study:
Riccardo Agati, Franco Bagagli, Giuseppe Bernardi, Alberto Bozzani, Ovidio Brignoli, Bernardino Bruno, Luigi Canciani, Mario Celebrano, Giulio Corg-atelli, Antonio Pompeo Coviello, Gaetano D'Ambrosio, Vittorio Di Carlo, Simonetta Di Cristoforo, Giuliano Ermini, Alessandro Filippi, Bruno Franco Novelletto, Giuseppe Galli, Fabrizio Germini, Shafik Kurtam, Luciano Lippa,
Carlo Fedele Marulli, Massimo Mazzilli, Gerardo Medea, Giovanni Merlino, Antonio Metrucci, Raffaella Micheli, Vito Milazzo, Rosalba Murgia, Giuseppe Noberasco, Romano Paduano, Italo Paolini, Damiano Parretti, Domenico Pasculli, Salvatore Pasqualetto, Andrea Salvetti, Aurelio Sessa, Alfonso Toz-zoli, Cesare Filippo Urru, Paolo Valle, Domenico Valletta.
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