Risk Estimation of Cardiovascular Disease in Qatar’s Primary Care Settings: Application of QRISK3 Algorithm and Sociodemographic Predictors

Risk Estimation of Cardiovascular Disease in Qatar’s Primary Care Settings: Application of QRISK3 Algorithm and Sociodemographic Predictors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Risk Estimation of Cardiovascular Disease in Qatar’s Primary Care Settings: Application of QRISK3 Algorithm and Sociodemographic Predictors Mohamed Ahmed Syed, Ahmed Sameer Alnuaimi, Dana Bilal El Kaissi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8608190/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Apr, 2026 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 27 You are reading this latest preprint version Abstract Background: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, with a growing burden in the Eastern Mediterranean Region. Despite high prevalence of metabolic and lifestyle-related risk factors in Qatar, no locally validated risk prediction tool exists. This study aimed to estimate 10-year CVD risk using the QRISK3 algorithm in Qatar’s primary care setting and identify key predictors of high risk. Methods: A cross-sectional study was conducted among adults (>18 years) registered with the Primary Health Care Corporation (PHCC). QRISK3 scores were calculated for participants without established CVD using demographic, clinical, and laboratory data. High risk was defined as QRISK3 ≥10%. Logistic regression was used to identify independent predictors. Results: Of 1,606 eligible participants, 24.5% were classified as high risk and 15.6% had prevalent CVD, indicating that approximately 40% of adults assessed required preventive or secondary care interventions. Age was the strongest predictor (OR=612.6 for ≥60 years vs. <40 years), followed by male sex (OR=5.8), Southern Asian nationality (OR=6.8), Class II obesity (OR=6.4), and residence in Doha (OR=2.3). The final model demonstrated 85.5% predictive accuracy. Conclusions: Nearly one-quarter of adults without established CVD in Qatar’s primary care settings are at high predicted risk, highlighting the need for targeted prevention strategies. The study further advocates the significance and utility of QRISK3 as a risk‑stratification tool for guiding prevention efforts. Its integration into routine primary care assessment may support early identification of individuals who would benefit from lifestyle counseling, risk factor modification, and targeted clinical & preventive interventions. Cardiovascular risk QRISK3 Qatar Primary Care & Prevention Introduction Cardiovascular diseases (CVDs) is an umbrella term encompassing a broad spectrum of disorders affecting the heart and blood vessels, including coronary heart disease, cerebrovascular disease, rheumatic heart disease, and several other related conditions[1]. CVDs are the leading cause of mortality globally[2]. Recent estimates indicate that approximately 21.1 million deaths—constituting nearly one-third of all global fatalities—are attributable to CVDs each year[3]. Projections indicate a substantial increase in the global burden of CVD in the next few decades. Consequently between 2025 and 2050, the global prevalence of CVD is expected to rise by 90%, with crude mortality increasing by 73.4% and crude disability-adjusted life years (DALYs) increasing by 54.7%, resulting in an estimated 35.6 million cardiovascular deaths by 2050[4]. These trends are mainly attributed to population aging and persistent exposure to modifiable risk factors.[4] The commonly associated risk factors with CVDs are mostly preventable which primarily include hypertension, diabetes mellitus, dyslipidemia, obesity, tobacco use, physical inactivity, unhealthy dietary patterns, and alcohol consumption[5, 6]. Evidence strongly suggests that management and prevention of these risk factors can reduce the morbidity, disability and mortality associated with CVDs as well as increase the years of healthy living and quality of life particularly among the growing elderly population.[7–9] Moreover, atherosclerotic processes begin years before clinical presentation, highlighting the value of early disease screening and intervention [10]. In accordance with the international best practices, global guidelines recommend the use of validated cardiovascular risk prediction tools in primary care to identify individuals at high risk and guide preventive strategies[11]. Such tools enable clinicians to systematically stratify risk using routinely collected demographic and clinical data, and designing targeted lifestyle interventions. [12] Studies from the Eastern Mediterranean Region (EMR), including Qatar, report high prevalence of metabolic and lifestyle-related CVD risk factors such as hypertension, obesity, and diabetes, contributing to substantial regional disease burden[13]. Although several CVD risk prediction algorithms exist internationally, no tool has been specifically derived or validated for the Qatari population. QRISK3, a widely used and externally validated tool incorporates diverse clinical and sociodemographic predictors and is designed for use with routinely captured electronic medical record data[14, 15]. However, the applicability of the tool in diverse healthcare settings can be ascertained with local validation studies. Considering the rising burden of CVD in Qatar and the region, integrating a validated risk prediction instrument into primary care could potentially further augment and inform the prevention strategies. This study aims to apply the QRISK3 algorithm within the Qatari primary care context to estimate 10-year cardiovascular risk across sociodemographic groups and to explore its potential utility for informing national CVD prevention strategies. Methods Settings Qatar is a peninsular Arab nation with a universal healthcare system. The Primary Health Care Corporation (PHCC) is the country’s largest publicly funded primary care provider and delivers comprehensive, integrated, and coordinated person-centered services at the community level, with a strong emphasis on disease prevention, healthy lifestyles, and wellness promotion.[16] As of 2024, approximately 1.87 million individuals were registered across its 31 health centers distributed throughout Qatar. Study design and population The study employed cross-sectional design. A detailed protocol describing the methodology has been published previously[17]. Adults aged > 18 years with a valid health card and an active phone number documented in PHCC’s electronic medical record (EMR) system were randomly selected for inclusion. Sample selection To ensure that the selected sample was representative of PHCC’s registered population, a stratified random sampling approach was utilized. A complete list of all individuals aged > 18 years registered at PHCC was retrieved and stratified into 60 strata according to age group (18–29, 30–39, 40–49, 50–59, and ≥ 60 years), gender (male, female), and nationality categories (Qatari, North African, South East Asian, Southern Asian, Western Asian, and others), based on geographic regions (see Table S2 in the supplementary file). Each stratum was assigned a fixed sample size of 100 individuals to achieve a 95% confidence interval, yielding a target sample of 6,000 individuals. Accounting for an anticipated 10% response rate, a final sample size of 60,000 individuals was determined. Data collection The target sample received a Short Message Service (SMS) communication containing study information and instructions to complete an online form if they were interested in participating. Individuals who expressed interest were scheduled for an appointment at a PHCC health center. During these visits, trained data collectors obtained written informed consent, measured vital signs, administered a structured questionnaire, and collected a blood sample[17]. Additionally, health service utilization data was extracted from the EMR for the preceding 12-month period.[17] For the purposes of the present study, only the vital sign measurements, questionnaire responses, blood test results, and EMR data required for the computation of QRISK3 scores were utilized[18]. Variables included: age; gender; ethnicity (with nationality used as a proxy); smoking status; diabetes status; diagnosis of angina or myocardial infarction in a first-degree relative < 60 years; diagnosis of chronic kidney disease (stages 3–5); diagnosis of atrial fibrillation; prescription of blood pressure medications; diagnosis of migraines; diagnosis of systemic lupus erythematosus; diagnosis of severe mental illness; prescription of antipsychotic medications; prescription of steroids; diagnosis of erectile dysfunction; cholesterol/HDL ratio; systolic blood pressure (mmHg); height (cm); and weight (kg). Data analysis QRISK3 scores were calculated using the R Project for Statistical Computing (R-4.5.1 for Windows)[18]. Basic descriptive statistics and multiple logistic regression analyses were conducted using the Statistical Package for the Social Sciences (SPSS). QRISK3 scores were computed for participants aged 25–84 years without a pre-existing CVD diagnosis. A QRISK3 score ≥ 10% over a 10-year period was classified as ‘high risk’[19]. Descriptive statistics were used to determine the proportion of individuals classified as high risk across categories of age, gender, nationality, education level, income, body mass index (BMI), duration of residence in Qatar, and physical activity level. A multivariate logistic regression model was employed to examine the association between high CVD risk and these demographic and behavioral variables. Results Distribution of QRISK3 Scores A total of 1356 individuals met the eligibility criteria for QRISK3 calculation (see table S2 supplementary file). The distribution was skewed toward lower risk categories as demonstrated in Table 1 . More than half of the participants (52.0%, n = 705) had a predicted 10-year cardiovascular risk below 5% (Table 1 ). Collectively, nearly three-quarters (71%) of the study cohort were classified as low risk according to NICE thresholds.[18, 19] This pattern suggests that while most adults in primary care settings are at low predicted risk, there is a clinically significant subgroup (n = 393) with double-digit risk scores who may benefit from targeted preventive interventions. Table 1 QRISK3-the 10 years probability of developing CVD-categories QRISK3 Scores N % Cumulative Percent < 5.0 705 52.0 52.0 5.0–9.9 258 19.0 71.0 10.0–14.9 159 11.7 82.7 15.0–19.9 88 6.5 89.2 20.0–24.9 65 4.8 94.0 25.0–29.9 33 2.4 96.5 30.0–34.9 23 1.7 98.2 35.0–39.9 13 1.0 99.1 40.0–44.9 6 0.4 99.6 45.0–49.9 2 0.1 99.7 50.0–54.9 3 0.2 99.9 55.0+ 1 0.1 100.0 Total 1356 100.0 Missing cases = 604 Overall Cardiovascular Risk Classification When grouped into clinically relevant categories (Table 2 ), 963 participants (60.0%) were classified as low risk (< 10%), while 393 (24.5%) were identified as high risk (QRISK3 ≥ 10%). Additionally, 250 participants (15.6%) had a documented diagnosis of cardiovascular disease and were excluded from QRISK3 computation. Summative the findings indicate that approximately two out of every five individuals assessed either had established CVD or were at high predicted risk. This proportion is substantial and highlights the potential burden on primary care services being delivered through PHCC primary health care centers. Table 2 the 10 years CVD risk assessment of the study sample N % Low risk 963 60.0 High risk (QRISK3 = 10+) 393 24.5 Have CVD 250 15.6 Total 1606 100.0 Missing cases = 345 High-Risk Prevalence by Sociodemographic characteristics The prevalence of adults with high QRISK3 scores varied significantly across sociodemographic and other related factors as reported in the unadjusted bivariate models in Table 3 . Age demonstrated the strongest association with cardiovascular risk. The probability of having high risk score increased from as low as 1.1% in the younger than 40 years old to as high as 79.8% among participants aged 60–84 years as reported in Table 3 . The significant age gradient strongly highlights the cumulative impact of cardiometabolic risk factors and the predictive sensitivity of QRISK3 in older populations. There were significant differences in the probability of having high QRISK score between males and females. Males had a significantly higher prevalence of high QRISK (37.8%) compared with females (18.7%) as demonstrated in Table 3 . The results also highlight substantial variation in cardiovascular risk observed across nationality groups. The highest prevalence of high risk was noticed among individuals with South-eastern Asian background (42.4%,), Southern Asians (32.6%), and Western Asians (29.1%,). On the contrary, North African participants demonstrated the lowest risk (21.0%) as highlighted in Table 3 . Body mass index (BMI) categories showed a statistically significant positive association with high risk (p = 0.038). The prevalence of High-risk score was highest among Class I and II obesity (33.1% and 34.6% respectively) as depicted in Table 3 . Socioeconomic indicators such as education and income demonstrated no consistent pattern, and physical activity (> 150 minutes/week) was not significantly associated with risk status. The duration of residence in Qatar showed a significant association with the prevalence of high QRISK score. Those living in the country for more than 10 years had a higher prevalence of high risk (32.8%) compared to shorter durations. Geographic variation was also observed, with participants residing in Doha having the highest prevalence (33.6%) compared to Al Wakra (19.3%) and other municipalities (21.7%) as shown in Table 3 . Table 3 The relative frequency of high QRISK score (10+) by selected explanatory variables. High QRisk3 score (10+) Variables Total N N % 95% confidence interval P Age group (years)- compared to (18–39) < 0.001 18–39 357 4 1.1 (0.4–2.6) 40–49 399 42 10.5 (7.8–13.8) 50–59 353 150 42.5 (37.4–47.7) 60+ 247 197 79.8 (74.4–84.4) Gender < 0.001 Female 626 117 18.7 (15.8–21.9) Male 730 276 37.8 (34.3–41.4) Nationality groups < 0.001 Qatari nationality 142 41 28.9 (21.9–36.7) Northern Africa 391 82 21 (17.2–25.2) South-eastern Asia 158 67 42.4 (34.9–50.2) Southern Asia 138 45 32.6 (25.2–40.7) Western Asia/Middle east 364 106 29.1 (24.6–33.9) Other (miscellaneous) nationality 163 52 31.9 (25.1–39.3) BMI (Kg/m2) categories 0.038 Acceptable Weight (< 25) 219 53 24.2 (18.9–30.2) Overweight (25-29.9) 552 151 27.4 (23.8–31.2) Class 1 Obesity (30-34.9) 396 131 33.1 (28.6–37.8) Class 2 Obesity (35-39.9) 136 47 34.6 (27–42.8) Class 3 (morbid) Obesity (40+) 53 11 20.8 (11.6–33) Highest level of education 0.178 Never attended school 12 7 58.3 (31.2–82) Primary school (Class 1 to 6) 40 14 35 (21.7–50.4) Secondary school (Class 7 to 12) 233 71 30.5 (24.8–36.6) Trade/technical/vocational qualification 74 25 33.8 (23.8–45) Diploma/bachelor’s degree 753 207 27.5 (24.4–30.8) Post graduate degree 238 68 28.6 (23.1–34.5) Duration of residence in Qatar < 0.001 5 to 10 years 252 46 18.3 (13.9–23.4) > 10 years 989 324 32.8 (29.9–35.7) Average household’s total income (QAR) per month 0.022 Less than 5000 165 45 27.3 (20.9–34.4) 5000–10000 359 99 27.6 (23.1–32.4) 11000–20000 299 79 26.4 (21.7–31.6) 21000–30000 131 36 27.5 (20.4–35.6) 31000–40000 63 27 42.9 (31.2–55.2) 41000–50000 35 9 25.7 (13.6–41.7) 51000–60000 22 9 40.9 (22.5–61.5) More than 60000 42 20 47.6 (33.1–62.5) Physically active (> 150 min/week) 0.84 Negative 1071 309 28.9 (26.2–31.6) Positive 285 84 29.5 (24.4–35) Current address (municipality) 0.002 Al Rayyan 493 144 29.2 (25.3–33.3) Al Wakra 114 22 19.3 (12.9–27.3) Doha 536 180 33.6 (29.7–37.7) Others (Al Dhaayen, Al Khor and Al Thakhira, Al Shamal, Al Sheehaniya, Umm Slal) 161 35 21.7 (15.9–28.6) Independent Predictors of High Risk The multivariate logistic regression analysis was used to assess the net and independent association of the previously studied explanatory variables with the probability of having a high QRISK score as the outcome variable. The model identified age, sex, nationality, BMI, and residence (municipality) as important independent predictors of high CVD risk as depicted in Table 4 . Old age was the most powerful predictor of a high QRISK3 score. Being 60 years or older significantly increased the probability of having a high QRISK score by 612 times compared to those younger than 40 years old after adjusting for the possible confounding effect of other explanatory variables included in the model (Table 4 ). Male sex significantly increased the probability of having the studied outcome by 5.8 times compared to females. This effect was adjusted for other possible confounders included in the model. Among the studied nationality groups, the Southern Asians had the highest risk (6.8) followed by Qatari nationals and South-eastern Asians (3.8 and 3.9 respectively) compared to the miscellaneous nationalities group as reported in Table 4 . Moreover, obesity was another important predictor for high QRISK scores (Table 4 ). Class 2 obesity was the strongest predictor increasing the probability of the outcome by 6.4 times compared to individuals with acceptable BMI after adjusting for the remaining explanatory variables included in the model. Living in Doha (the urban territory with highest density in Qatar significantly increased the risk of the outcome by more than two times (OR = 2.3) after adjusting for the remaining confounders). Furthermore, education, income, duration of residence, and physical activity showed no obvious or statistically significant association with the probability of having high QRISK score after adjustment for the other confounders included in the model as documented in Table 4 . The final model demonstrated strong predictive performance, with an overall accuracy of 85.5%, correctly classifying 72.8% of high-risk individuals and 90.8% of low-risk individuals (Table 4 ). Table 4 Multiple logistic regression equation predicting high risk (QRisk3 score = 10+) for developing CVD in the next 10 years by selected explanatory variables. Adjusted OR 95% confidence interval (OR) P Being a male compared to females 5.75 (3.64–9.09) < 0.001 Nationality groups compared to Other (miscellaneous) nationality < 0.001 Qatari nationality 3.75 (1.48–9.54) 0.005 Northern Africa 0.89 (0.44–1.78) 0.744[NS] South-eastern Asia 3.88 (1.77–8.5) < 0.001 Southern Asia 6.79 (2.78–16.59) < 0.001 Western Asia/Middle east 2.41 (1.23–4.73) 0.011 Age group (years) compared to (18–39) < 0.001 40–49 7.73 (2.6–22.7) < 0.001 50–59 75.13 (25.9–217.7) < 0.001 60+ 612.56 (194.8–1926.0) < 0.001 Highest level of education compared to those with less than class 7 0.689[NS] Secondary school (Class 7 to 12) 1.32 (0.44–3.92) 0.621[NS] Trade/technical/vocational qualification 0.81 (0.22–2.96) 0.754[NS] Diploma/bachelor’s degree 0.88 (0.3–2.54) 0.809[NS] Post graduate degree 0.92 (0.29–2.91) 0.892[NS] Average household’s total income (QAR) per month compared to those less than 5,000 0.847[NS] (5,000–10,000) 0.92 (0.49–1.72) 0.792[NS] (11,000–20,000) 0.99 (0.5–1.98) 0.981[NS] (21,000–30,000) 0.68 (0.29–1.56) 0.357[NS] (31,000–40,000) 0.98 (0.37–2.61) 0.971[NS] (41,000–50,000) 0.43 (0.13–1.5) 0.188[NS] (51,000–60,000) 0.78 (0.18–3.49) 0.748[NS] (More than 60,000) 0.57 (0.17–1.91) 0.363[NS] Duration of residence in Qatar compared to those who stayed for 5 years or less 0.359[NS] 6–10 years 1.60 (0.38–6.63) 0.52[NS] 11 + years 2.11 (0.55–8.11) 0.278[NS] Current address (municipality) compared to those living in Others (Al Dhaayen, Al Khor and Al Thakhira, Al Shamal, Al Sheehaniya, Umm Slal) 0.06[NS] Al Rayyan 1.92 (0.99–3.72) 0.052[NS] Al Wakra 1.10 (0.43–2.84) 0.836[NS] Doha 2.26 (1.15–4.43) 0.018 BMI (Kg/m2) categories compared to those with acceptable Weight (< 25) < 0.001 Overweight (25-29.9) 1.75 (0.94–3.25) 0.076[NS] Class 1 Obesity (30-34.9) 3.10 (1.64–5.88) < 0.001 Class 2 Obesity (35-39.9) 6.41 (2.84–14.47) 150 min/week) 0.75 (0.46–1.23) 0.256[NS] P (model) < 0.001 Overall predictive accuracy = 85.5% Predictive accuracy of high-risk individuals = 72.8% Predictive accuracy of low-risk individuals = 90.8% Discussion Principal findings of the study This study represents one of the first applications of the QRISK3 algorithm in Qatar’s primary care setting. The main findings of the study indicated that almost one-quarter (24.5%) of participants without established CVD were classified as high risk (QRISK3 ≥ 10%), and an additional 15.6% had prevalent CVD. This substantiates the fact that approximately 40% of adults assessed either require secondary prevention or meet criteria for primary prevention interventions. Age was the most powerful predictor, with odds of high risk increasing more than 600-fold among participants aged ≥ 60 years compared to those aged 18–39 years. Males were nearly six times more likely to be high risk than females. The finding suggests that men in this population carry a disproportionately higher burden of predicted CVD risk, which may be attributable to differences in risk factor profiles such as smoking, blood pressure, and lipid levels. Moreover, Southern and South-eastern Asian participants exhibited the highest adjusted odds of high risk, even after controlling for other factors. Qatari nationals also showed elevated risk compared to miscellaneous groups. Furthermore, the findings highlighted Class 2 obesity associated with more than six-fold higher odds and residence in Doha remained an independent predictor of high risk, suggesting area-level factors or population clustering. Comparison of the key findings with existing evidence The finding almost one-quarter (24.5%) of participants without established CVD were classified as high risk (QRISK3 ≥ 10%), and an additional 15.6% had prevalent CVD highlights a notable burden of a 10-year cardiovascular disease (CVD) risk in the multiethnic population included in the study. This proportion aligns with findings from other studies applying QRISK3 in diverse populations, where a large segment of adults exhibited moderate to high predicted risk levels, particularly among older individuals and males.[20, 21] As previously mentioned, age and male gender were the strongest predictors of high CVD risk, a finding that substantiates the results from the UK Biobank validation of QRISK3, which reported risk scores increased with age and that predictive performance varied by gender[22]. Additionally, findings of a QRISK3 application in India similarly documented age and sex related gradient in predicted cardiovascular risk. QRISK3 scores increased progressively with advancing age and higher risk estimates observed among male participants[23]. Moreover, a QRISK3 study conducted in Saudi Arabia reported greater predicted risk in men compared to women, with risk estimates escalating in older age groups[15]. It can be argued that these age- and gender-specific patterns may be linked to prevalent behaviors such as high tobacco consumption, unhealthy dietary habits, and physical inactivity, all of which contribute to the growing burden of CVD in these settings[24–27]. Hence, in clinical settings, QRISK3’s ability to differentiate risk by age and gender can help clinicians prioritize interventions for older adults and men who may benefit most from early preventive measures.[18] Moreover, the study also indicated notable association of years of residence in Qatar with CVD risk, particularly beyond 10 years. This finding may be associated with prolonged exposure to urbanized environments and sedentary lifestyle changes[28]. This finding is further substantiated by the municipality of residence, where individuals living in more urban areas (participants residing in Doha having the highest prevalence compared to other municipalities) demonstrated higher cardiovascular risk, highlighting the cumulative impact of urbanization patterns. Literature suggests that urbanization often brings shifts in environmental exposures, reduced opportunities for physical activity, and greater reliance on processed foods, which in turn drive lifestyle changes that elevate cardiovascular risk[29–31]. Evidence further indicates type of food consumption and unhealthy lifestyles are also significant risk factors of CVD in the GCC region[32, 33]. Similarly, population-based research of multiple Saudi and other Middle Eastern populations concluded a high prevalence of obesity, physical inactivity, and consumption of processed foods which exacerbates the established cardiovascular risk factors, including blood pressure and lipid disorders[28, 34]. Furthermore, the study reported that BMI had a weaker but statistically significant effect, as its contribution is mediated through other clinical variables such as blood pressure, glycaemic status, and lipid profile[35]. It is important to acknowledge that QRISK3 does not directly account for dietary habits or physical activity; rather, these lifestyle-related factors are indirectly captured through clinical indicators such as body mass index (BMI), blood pressure, and lipid profile. This indirect approach positions QRISK3 as a robust tool for capturing the cumulative influence of lifestyle patterns and urbanization on cardiovascular risk. On the contrary, educational levels and physical activity had no significant influence predicted risk which may be attributed to self-reporting biases and the structural limitations of QRISK3. Given that physical activity is not included as a direct variable within the algorithm, its effect is instead mediated through downstream clinical indicators such as BMI, blood pressure, and lipid profile.[36] Strengths and limitations of the study The salient features of the study include that it employed a robust sampling strategy across age, gender, and nationality strata, enhancing representativeness of the PHCC and utilized a validated risk algorithm (QRISK3) which is widely used internationally and incorporates a broad range of clinical and sociodemographic predictors. Moreover, combining biometric measurements, laboratory results, and EMR records improved accuracy of risk estimation. The study has certain limitations. Since the study employed a cross-sectional design, the results cannot establish causality or assess temporal changes in risk. It is important to mention that approximately 600 cases lacked complete information for QRISK3 calculation, which may introduce bias. Nationality was used as proxy for ethnicity in the study. It can be argued that this approach may mask heterogeneity within groups and limit generalizability. Moreover, physical activity and some lifestyle factors were self-reported, which might raise some concerns about measurement error as risk can be over- or under-estimated. Implications of the study findings For individuals with a QRISK3 score below 10%, particularly those under 40 who already exhibit cardiovascular risk factors, QRISK3 can serve as a valuable tool to guide discussions and encourage lifestyle modification. Active engagement in shared decision-making is essential, as the abstract concept of risk can be made more tangible by illustrating how interventions such as weight reduction, dietary improvements, increased physical activity, and smoking cessation can lower the predicted 10-year risk. Moreover, serial QRISK3 assessments can be employed to establish appropriate follow-up intervals and monitor changes in risk factors over time, thereby reinforcing patient motivation and enabling clinicians to track progress effectively. Conclusions This study provides one of the first applications of the QRISK3 algorithm within Qatar’s primary care settings, providing meaningful insights into the cardiovascular risk profile of a diverse population. Almost 40% of assessed adults either had established cardiovascular disease or were classified as high risk, highlighting substantial disease burden. Age, male sex, certain nationality groups, obesity, and urban residence emerged as the strongest independent predictors of high cardiovascular risk. These findings substantiate existing evidence on sociodemographic and metabolic determinants of CVD while highlighting local patterns influenced by Qatar’s demographic diversity and rapid urbanization. The study further advocates the practical utility of QRISK3 as a risk-stratification tool for guiding prevention efforts. Its integration into routine primary care assessment may support early identification of individuals who would benefit from lifestyle counseling, risk factor modification, and targeted clinical interventions. Future research should evaluate the calibration and predictive performance of QRISK3 in Qatar using longitudinal outcomes, incorporate more objective measures of lifestyle behaviors, and explore genetic or biomarker-based refinements to risk prediction. Strengthening risk-based screening and implementing culturally tailored preventive programs (particularly for high-risk demographic groups) can contribute meaningfully to national strategies aiming to reduce the growing cardiovascular burden within the region. Abbreviations BMI Body mass index CVD Cardiovascular disease EMR Electronic medical record OR Odds ratio PHCC Primary Health Care Corporation Declarations Ethics approval and consent to participate All study procedures were conducted in accordance with the Declaration of Helsinki. The study was reviewed and approved by Primary Health Care Corporation (PHCC)’s Independent Review Board (BUHOOTH-D-23-00058). Informed consent was obtained from participants aged 18 or over. Overall, the study was planned and conducted with integrity according to generally accepted ethical principles.[17] Consent for publication Not applicable Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. Competing Interests The authors declare that they have no competing interests. Authors contributions Mohamed Ahmed Syed: Conception of idea, literature review, data analysis and interpretation drafting and review of manuscript Ahmed Sameer Alnuaimi: drafting of manuscript, data analysis and interpretation and review of manuscript Dana Bilal El Kaissi: Literature review, drafting of manuscript, data analysis and interpretation Tamara Jamil Marji: Data analysis and interpretation and review of manuscript Muslim Abbas Syed: Literature review, drafting of manuscript, data analysis and interpretation and drafting and review of manuscript Funding declaration The study was funded by Primary Health Care Corporation Acknowledgements We would like to acknowledge Primary Health Care Corporation for funding this study and the data collectors who were involved in data collection. Clinical trial number: not applicable References White, H., et al., Universal MI definition update for cardiovascular disease. Current cardiology reports, 2014. 16 (6): p. 492. Mendis, S., I. Graham, and J. Narula, Addressing the global burden of cardiovascular diseases; need for scalable and sustainable frameworks. Global Heart, 2022. 17 (1): p. 48. Di Cesare, M., et al., The heart of the world. Global heart, 2024. 19 (1): p. 11. Chong, B., et al., Global burden of cardiovascular diseases: projections from 2025 to 2050. European journal of preventive cardiology, 2025. 32 (11): p. 1001-1015. Yusuf, S., et al., Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. The lancet, 2004. 364 (9438): p. 937-952. O'donnell, M.J., et al., Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. The Lancet, 2010. 376 (9735): p. 112-123. Baldassarre, D., et al., Rationale and design of the CV-PREVITAL study: an Italian multiple cohort randomised controlled trial investigating innovative digital strategies in primary cardiovascular prevention. BMJ open, 2023. 13 (7): p. e072040. Law, M.R., J.K. Morris, and N.J. Wald, Use of blood pressure lowering drugs in the prevention of cardiovascular disease: meta-analysis of 147 randomised trials in the context of expectations from prospective epidemiological studies. Bmj, 2009. 338 . Baigent, C., et al., Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet (London, England), 2010. 376 (9753): p. 1670-1681. Leong, D.P., et al., Reducing the Global Burden of Cardiovascular Disease, Part 2: Prevention and Treatment of Cardiovascular Disease. Circ Res, 2017. 121 (6): p. 695-710. Rossello, X., et al., Risk prediction tools in cardiovascular disease prevention: a report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP). European journal of preventive cardiology, 2019. 26 (14): p. 1534-1544. Hippisley-Cox, J., et al., Development and validation of a new algorithm for improved cardiovascular risk prediction. Nature Medicine, 2024. 30 (5): p. 1440-1447. Syed, M.A., et al., Prevalence of metabolic syndrome in primary health settings in Qatar: a cross sectional study. BMC Public Health, 2020. 20 (1): p. 611. Sheikh, A., et al., Risk prediction models for atherosclerotic cardiovascular disease: A systematic assessment with particular reference to Qatar. Qatar medical journal, 2021. 2021 (2): p. 42. Livingstone, S., et al., Effect of competing mortality risks on predictive performance of the QRISK3 cardiovascular risk prediction tool in older people and those with comorbidity: external validation population cohort study. The lancet Healthy longevity, 2021. 2 (6): p. e352-e361. Syed, M.A., et al., Key service delivery processes, challenges and barriers to healthcare access for managing diabetes outside target HbA1c levels in primary care settings in Qatar: a qualitative inquiry of healthcare professionals’ and service users’ perspectives. BMJ Public Health, 2025. 3 (1). Syed, M.A., et al., Health risk factors, status and service utilisation of adults in primary health care settings in Qatar: The HEALTHSIGHT study protocol. Plos one, 2024. 19 (5): p. e0304160. Hippisley-Cox, J., C. Coupland, and P. Brindle, Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. bmj, 2017. 357 . Duerden, M., N. O’Flynn, and N. Qureshi, Cardiovascular risk assessment and lipid modification: NICE guideline. The British Journal of General Practice, 2015. 65 (636): p. 378. Chan, S.K.J., Burden and determinants of cardiovascular health and outcomes in patients with cancer . 2025, University of East Anglia. Vamja, R., et al., Community-based evaluation of cardiovascular risk using QRISK® 3 in type 2 diabetes mellitus population in Gujarat, India. BMC Public Health, 2025. 25 (1): p. 1-12. Parsons, R.E., et al., Independent external validation of the QRISK3 cardiovascular disease risk prediction model using UK Biobank. Heart, 2023. 109 (22): p. 1690-1697. Sanjay Kalra, D., et al., QRISK3-Based Cardiovascular Risk Assessment Among Indian Healthcare Professionals. European Journal of Cardiovascular Medicine, 2025. 15 : p. 423-430. Turk-Adawi, K., et al., Cardiovascular disease in the Eastern Mediterranean region: epidemiology and risk factor burden. Nature Reviews Cardiology, 2018. 15 (2): p. 106-119. Sultan, Y., et al., Smoking-related disease impact in the Eastern Mediterranean region: a comprehensive assessment using global burden of disease data. Asian Pacific Journal of Cancer Prevention: APJCP, 2024. 25 (2): p. 495. Melaku, Y.A., et al., The Impact of Unhealthy Lifestyle on the Burden of Non-Communicable Diseases in the State of Qatar: A Systematic Analysis of the Global Burden of Disease Study 2021. American Journal of Lifestyle Medicine, 2025: p. 15598276251405214. Biswas, T., et al., Clustering of metabolic and behavioural risk factors for cardiovascular diseases among the adult population in South and Southeast Asia: findings from WHO STEPS data. The Lancet Regional Health-Southeast Asia, 2023. 12 . Al-Nozha, M.M., et al., Coronary artery disease in Saudi Arabia. Saudi medical journal, 2004. 25 (9): p. 1165-1171. Chan, F., et al., Projected impact of urbanization on cardiovascular disease in China. International journal of public health, 2012. 57 (5): p. 849-854. Yusuf, S., et al., Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization. Circulation, 2001. 104 (22): p. 2746-2753. Rajagopalan, S., et al., The urban environment and cardiometabolic health. Circulation, 2024. 149 (16): p. 1298-1314. Aljefree, N. and F. Ahmed, Prevalence of cardiovascular disease and associated risk factors among adult population in the Gulf region: a systematic review. Advances in Public Health, 2015. 2015 (1): p. 235101. Alibrahim, M.S., et al., Risk factors for cardiovascular disease among Saudi students: Association with BMI, current smoking, level of physical activity, and dietary habits. PLoS One, 2025. 20 (5): p. e0321206. Musaiger, A.O., Overweight and obesity in eastern mediterranean region: prevalence and possible causes. Journal of obesity, 2011. 2011 (1): p. 407237. Ng, M., et al., Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. The lancet, 2014. 384 (9945): p. 766-781. Prince, S.A., et al., A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. International journal of behavioral nutrition and physical activity, 2008. 5 (1): p. 56. Additional Declarations No competing interests reported. Supplementary Files SupplementarlyfileS2.docx Cite Share Download PDF Status: Published Journal Publication published 09 Apr, 2026 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 18 Feb, 2026 Reviews received at journal 17 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviews received at journal 16 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviews received at journal 14 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviewers invited by journal 06 Feb, 2026 Editor assigned by journal 06 Feb, 2026 Editor invited by journal 29 Jan, 2026 Submission checks completed at journal 29 Jan, 2026 First submitted to journal 29 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8608190","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589087023,"identity":"bf6a5287-684b-4c45-8104-034fd56ca612","order_by":0,"name":"Mohamed Ahmed Syed","email":"","orcid":"","institution":"Primary Health Care Corporation","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Ahmed","lastName":"Syed","suffix":""},{"id":589087024,"identity":"29bee8e2-432a-4679-be26-352604e99aef","order_by":1,"name":"Ahmed Sameer Alnuaimi","email":"","orcid":"","institution":"Primary Health Care Corporation","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"Sameer","lastName":"Alnuaimi","suffix":""},{"id":589087025,"identity":"586a8416-a5b3-45a2-b456-450a90fe7ebe","order_by":2,"name":"Dana Bilal El Kaissi","email":"","orcid":"","institution":"Primary Health Care Corporation","correspondingAuthor":false,"prefix":"","firstName":"Dana","middleName":"Bilal El","lastName":"Kaissi","suffix":""},{"id":589087026,"identity":"dbe05d51-4076-43aa-bc7a-c365cfa65dbf","order_by":3,"name":"Tamara Jamil Marji","email":"","orcid":"","institution":"Primary Health Care Corporation","correspondingAuthor":false,"prefix":"","firstName":"Tamara","middleName":"Jamil","lastName":"Marji","suffix":""},{"id":589087027,"identity":"7cf3f2c2-680e-4dad-9087-31abe0478720","order_by":4,"name":"Muslim Abbas Syed","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYNACNhsGA1K1pJGu5TAJWnRnpD+T+FB2Ps+c/fADxi8Vhxn4jh/Ar8XsRo6Z5Ixzt4ste9IMmGXOHGaQPJNAUAubNG/b7cQNN3gYmCXbgJ46QFBL+jPpv23nkLScf0BIS4KZNGPbAbAWxo9twNC+QciWM2+MLXvOJRcbnEkzOMxwxoZH8gYhW46nP7zxo8wuz+D44YcPf1RIyPGdJ2ALELBIAAmwssM8DAw8DAcI6mBg/gDTwvgDRBKhZRSMglEwCkYWAACBRkjTWO/iqgAAAABJRU5ErkJggg==","orcid":"","institution":"Primary Health Care Corporation","correspondingAuthor":true,"prefix":"","firstName":"Muslim","middleName":"Abbas","lastName":"Syed","suffix":""}],"badges":[],"createdAt":"2026-01-15 07:54:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8608190/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8608190/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-026-05823-8","type":"published","date":"2026-04-09T15:58:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":106808893,"identity":"662bfd6c-a920-4f06-9ac1-cbf969ddb0b7","added_by":"auto","created_at":"2026-04-13 16:04:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1287087,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8608190/v1/3e1b0cea-1917-45a8-8e0a-5bcf4cba1dfa.pdf"},{"id":102396625,"identity":"1638499f-2833-4f9e-8398-442072172b22","added_by":"auto","created_at":"2026-02-11 09:50:38","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":37671,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarlyfileS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8608190/v1/40adc787087416e34141e75b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk Estimation of Cardiovascular Disease in Qatar’s Primary Care Settings: Application of QRISK3 Algorithm and Sociodemographic Predictors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs) is an umbrella term encompassing a broad spectrum of disorders affecting the heart and blood vessels, including coronary heart disease, cerebrovascular disease, rheumatic heart disease, and several other related conditions[1]. CVDs are the leading cause of mortality globally[2]. Recent estimates indicate that approximately 21.1\u0026nbsp;million deaths\u0026mdash;constituting nearly one-third of all global fatalities\u0026mdash;are attributable to CVDs each year[3]. Projections indicate a substantial increase in the global burden of CVD in the next few decades. Consequently between 2025 and 2050, the global prevalence of CVD is expected to rise by 90%, with crude mortality increasing by 73.4% and crude disability-adjusted life years (DALYs) increasing by 54.7%, resulting in an estimated 35.6\u0026nbsp;million cardiovascular deaths by 2050[4]. These trends are mainly attributed to population aging and persistent exposure to modifiable risk factors.[4]\u003c/p\u003e \u003cp\u003eThe commonly associated risk factors with CVDs are mostly preventable which primarily include hypertension, diabetes mellitus, dyslipidemia, obesity, tobacco use, physical inactivity, unhealthy dietary patterns, and alcohol consumption[5, 6]. Evidence strongly suggests that management and prevention of these risk factors can reduce the morbidity, disability and mortality associated with CVDs as well as increase the years of healthy living and quality of life particularly among the growing elderly population.[7\u0026ndash;9] Moreover, atherosclerotic processes begin years before clinical presentation, highlighting the value of early disease screening and intervention [10]. In accordance with the international best practices, global guidelines recommend the use of validated cardiovascular risk prediction tools in primary care to identify individuals at high risk and guide preventive strategies[11]. Such tools enable clinicians to systematically stratify risk using routinely collected demographic and clinical data, and designing targeted lifestyle interventions. [12]\u003c/p\u003e \u003cp\u003e Studies from the Eastern Mediterranean Region (EMR), including Qatar, report high prevalence of metabolic and lifestyle-related CVD risk factors such as hypertension, obesity, and diabetes, contributing to substantial regional disease burden[13]. Although several CVD risk prediction algorithms exist internationally, no tool has been specifically derived or validated for the Qatari population. QRISK3, a widely used and externally validated tool incorporates diverse clinical and sociodemographic predictors and is designed for use with routinely captured electronic medical record data[14, 15]. However, the applicability of the tool in diverse healthcare settings can be ascertained with local validation studies.\u003c/p\u003e \u003cp\u003eConsidering the rising burden of CVD in Qatar and the region, integrating a validated risk prediction instrument into primary care could potentially further augment and inform the prevention strategies. This study aims to apply the QRISK3 algorithm within the Qatari primary care context to estimate 10-year cardiovascular risk across sociodemographic groups and to explore its potential utility for informing national CVD prevention strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSettings\u003c/h2\u003e \u003cp\u003eQatar is a peninsular Arab nation with a universal healthcare system. The Primary Health Care Corporation (PHCC) is the country\u0026rsquo;s largest publicly funded primary care provider and delivers comprehensive, integrated, and coordinated person-centered services at the community level, with a strong emphasis on disease prevention, healthy lifestyles, and wellness promotion.[16] As of 2024, approximately 1.87\u0026nbsp;million individuals were registered across its 31 health centers distributed throughout Qatar.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design and population\u003c/h3\u003e\n\u003cp\u003eThe study employed cross-sectional design. A detailed protocol describing the methodology has been published previously[17]. Adults aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years with a valid health card and an active phone number documented in PHCC\u0026rsquo;s electronic medical record (EMR) system were randomly selected for inclusion.\u003c/p\u003e\n\u003ch3\u003eSample selection\u003c/h3\u003e\n\u003cp\u003eTo ensure that the selected sample was representative of PHCC\u0026rsquo;s registered population, a stratified random sampling approach was utilized. A complete list of all individuals aged\u0026thinsp;\u0026gt;\u0026thinsp;18 years registered at PHCC was retrieved and stratified into 60 strata according to age group (18\u0026ndash;29, 30\u0026ndash;39, 40\u0026ndash;49, 50\u0026ndash;59, and \u0026ge;\u0026thinsp;60 years), gender (male, female), and nationality categories (Qatari, North African, South East Asian, Southern Asian, Western Asian, and others), based on geographic regions (see Table S2 in the supplementary file). Each stratum was assigned a fixed sample size of 100 individuals to achieve a 95% confidence interval, yielding a target sample of 6,000 individuals. Accounting for an anticipated 10% response rate, a final sample size of 60,000 individuals was determined.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003e The target sample received a Short Message Service (SMS) communication containing study information and instructions to complete an online form if they were interested in participating. Individuals who expressed interest were scheduled for an appointment at a PHCC health center. During these visits, trained data collectors obtained written informed consent, measured vital signs, administered a structured questionnaire, and collected a blood sample[17]. Additionally, health service utilization data was extracted from the EMR for the preceding 12-month period.[17]\u003c/p\u003e \u003cp\u003eFor the purposes of the present study, only the vital sign measurements, questionnaire responses, blood test results, and EMR data required for the computation of QRISK3 scores were utilized[18]. Variables included: age; gender; ethnicity (with nationality used as a proxy); smoking status; diabetes status; diagnosis of angina or myocardial infarction in a first-degree relative\u0026thinsp;\u0026lt;\u0026thinsp;60 years; diagnosis of chronic kidney disease (stages 3\u0026ndash;5); diagnosis of atrial fibrillation; prescription of blood pressure medications; diagnosis of migraines; diagnosis of systemic lupus erythematosus; diagnosis of severe mental illness; prescription of antipsychotic medications; prescription of steroids; diagnosis of erectile dysfunction; cholesterol/HDL ratio; systolic blood pressure (mmHg); height (cm); and weight (kg).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eQRISK3 scores were calculated using the R Project for Statistical Computing (R-4.5.1 for Windows)[18]. Basic descriptive statistics and multiple logistic regression analyses were conducted using the Statistical Package for the Social Sciences (SPSS). QRISK3 scores were computed for participants aged 25\u0026ndash;84 years without a pre-existing CVD diagnosis. A QRISK3 score\u0026thinsp;\u0026ge;\u0026thinsp;10% over a 10-year period was classified as \u0026lsquo;high risk\u0026rsquo;[19]. Descriptive statistics were used to determine the proportion of individuals classified as high risk across categories of age, gender, nationality, education level, income, body mass index (BMI), duration of residence in Qatar, and physical activity level. A multivariate logistic regression model was employed to examine the association between high CVD risk and these demographic and behavioral variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of QRISK3 Scores\u003c/h2\u003e \u003cp\u003e \u003cem\u003eA total of 1356 individuals met the eligibility criteria for\u003c/em\u003e QRISK3 calculation (see table S2 supplementary file). The distribution was skewed toward lower risk categories as demonstrated in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. More than half of the participants (52.0%, n\u0026thinsp;=\u0026thinsp;705) had a predicted 10-year cardiovascular risk below 5% (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Collectively, nearly three-quarters (71%) of the study cohort were classified as low risk according to NICE thresholds.[18, 19]\u003c/p\u003e \u003cp\u003eThis pattern suggests that while most adults in primary care settings are at low predicted risk, there is a clinically significant subgroup (n\u0026thinsp;=\u0026thinsp;393) with double-digit risk scores who may benefit from targeted preventive interventions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQRISK3-the 10 years probability of developing CVD-categories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQRISK3 Scores\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCumulative Percent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.0\u0026ndash;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.0\u0026ndash;14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15.0\u0026ndash;19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20.0\u0026ndash;24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25.0\u0026ndash;29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30.0\u0026ndash;34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35.0\u0026ndash;39.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40.0\u0026ndash;44.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45.0\u0026ndash;49.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50.0\u0026ndash;54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55.0+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1356\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eMissing cases\u0026thinsp;=\u0026thinsp;604\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOverall Cardiovascular Risk Classification\u003c/h3\u003e\n\u003cp\u003eWhen grouped into clinically relevant categories (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), 963 participants (60.0%) were classified as low risk (\u0026lt;\u0026thinsp;10%), while 393 (24.5%) were identified as high risk (QRISK3\u0026thinsp;\u0026ge;\u0026thinsp;10%). Additionally, 250 participants (15.6%) had a documented diagnosis of cardiovascular disease and were excluded from QRISK3 computation.\u003c/p\u003e \u003cp\u003eSummative the findings indicate that approximately two out of every five individuals assessed either had established CVD or were at high predicted risk. This proportion is substantial and highlights the potential burden on primary care services being delivered through PHCC primary health care centers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ethe 10 years CVD risk assessment of the study sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh risk (QRISK3\u0026thinsp;=\u0026thinsp;10+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHave CVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1606\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100.0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eMissing cases\u0026thinsp;=\u0026thinsp;345\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHigh-Risk Prevalence by Sociodemographic characteristics\u003c/h2\u003e \u003cp\u003eThe prevalence of adults with high QRISK3 scores varied significantly across sociodemographic and other related factors as reported in the unadjusted bivariate models in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Age demonstrated the strongest association with cardiovascular risk. The probability of having high risk score increased from as low as 1.1% in the younger than 40 years old to as high as 79.8% among participants aged 60\u0026ndash;84 years as reported in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The significant age gradient strongly highlights the cumulative impact of cardiometabolic risk factors and the predictive sensitivity of QRISK3 in older populations.\u003c/p\u003e \u003cp\u003eThere were significant differences in the probability of having high QRISK score between males and females. Males had a significantly higher prevalence of high QRISK (37.8%) compared with females (18.7%) as demonstrated in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe results also highlight substantial variation in cardiovascular risk observed across nationality groups. The highest prevalence of high risk was noticed among individuals with South-eastern Asian background (42.4%,), Southern Asians (32.6%), and Western Asians (29.1%,). On the contrary, North African participants demonstrated the lowest risk (21.0%) as highlighted in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eBody mass index (BMI) categories showed a statistically significant positive association with high risk (p\u0026thinsp;=\u0026thinsp;0.038). The prevalence of High-risk score was highest among Class I and II obesity (33.1% and 34.6% respectively) as depicted in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSocioeconomic indicators such as education and income demonstrated no consistent pattern, and physical activity (\u0026gt;\u0026thinsp;150 minutes/week) was not significantly associated with risk status. The duration of residence in Qatar showed a significant association with the prevalence of high QRISK score. Those living in the country for more than 10 years had a higher prevalence of high risk (32.8%) compared to shorter durations. Geographic variation was also observed, with participants residing in Doha having the highest prevalence (33.6%) compared to Al Wakra (19.3%) and other municipalities (21.7%) as shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe relative frequency of high QRISK score (10+) by selected explanatory variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eHigh QRisk3 score (10+)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% confidence interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (years)- compared to (18\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.4\u0026ndash;2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(7.8\u0026ndash;13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(37.4\u0026ndash;47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(74.4\u0026ndash;84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(15.8\u0026ndash;21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(34.3\u0026ndash;41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNationality groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQatari nationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(21.9\u0026ndash;36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(17.2\u0026ndash;25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouth-eastern Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(34.9\u0026ndash;50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(25.2\u0026ndash;40.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern Asia/Middle east\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(24.6\u0026ndash;33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther (miscellaneous) nationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(25.1\u0026ndash;39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (Kg/m2) categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcceptable Weight (\u0026lt;\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(18.9\u0026ndash;30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (25-29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(23.8\u0026ndash;31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 1 Obesity (30-34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(28.6\u0026ndash;37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 2 Obesity (35-39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(27\u0026ndash;42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass 3 (morbid) Obesity (40+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(11.6\u0026ndash;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest level of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever attended school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(31.2\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school (Class 1 to 6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(21.7\u0026ndash;50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school (Class 7 to 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(24.8\u0026ndash;36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrade/technical/vocational qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(23.8\u0026ndash;45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploma/bachelor\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(24.4\u0026ndash;30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost graduate degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(23.1\u0026ndash;34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of residence in Qatar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(1.2\u0026ndash;41.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 to 5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(6.8\u0026ndash;22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;5 to 10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(13.9\u0026ndash;23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(29.9\u0026ndash;35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage household\u0026rsquo;s total income (QAR) per month\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(20.9\u0026ndash;34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5000\u0026ndash;10000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e359\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(23.1\u0026ndash;32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11000\u0026ndash;20000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(21.7\u0026ndash;31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21000\u0026ndash;30000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(20.4\u0026ndash;35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31000\u0026ndash;40000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(31.2\u0026ndash;55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41000\u0026ndash;50000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(13.6\u0026ndash;41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51000\u0026ndash;60000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(22.5\u0026ndash;61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 60000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(33.1\u0026ndash;62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysically active (\u0026gt;\u0026thinsp;150 min/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(26.2\u0026ndash;31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(24.4\u0026ndash;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent address (municipality)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl Rayyan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(25.3\u0026ndash;33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl Wakra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(12.9\u0026ndash;27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(29.7\u0026ndash;37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers (Al Dhaayen, Al Khor and Al Thakhira, Al Shamal, Al Sheehaniya, Umm Slal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(15.9\u0026ndash;28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIndependent Predictors of High Risk\u003c/h2\u003e \u003cp\u003eThe multivariate logistic regression analysis was used to assess the net and independent association of the previously studied explanatory variables with the probability of having a high QRISK score as the outcome variable. The model identified age, sex, nationality, BMI, and residence (municipality) as important independent predictors of high CVD risk as depicted in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Old age was the most powerful predictor of a high QRISK3 score. Being 60 years or older significantly increased the probability of having a high QRISK score by 612 times compared to those younger than 40 years old after adjusting for the possible confounding effect of other explanatory variables included in the model (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Male sex significantly increased the probability of having the studied outcome by 5.8 times compared to females. This effect was adjusted for other possible confounders included in the model. Among the studied nationality groups, the Southern Asians had the highest risk (6.8) followed by Qatari nationals and South-eastern Asians (3.8 and 3.9 respectively) compared to the miscellaneous nationalities group as reported in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Moreover, obesity was another important predictor for high QRISK scores (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Class 2 obesity was the strongest predictor increasing the probability of the outcome by 6.4 times compared to individuals with acceptable BMI after adjusting for the remaining explanatory variables included in the model. Living in Doha (the urban territory with highest density in Qatar significantly increased the risk of the outcome by more than two times (OR\u0026thinsp;=\u0026thinsp;2.3) after adjusting for the remaining confounders). Furthermore, education, income, duration of residence, and physical activity showed no obvious or statistically significant association with the probability of having high QRISK score after adjustment for the other confounders included in the model as documented in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The final model demonstrated strong predictive performance, with an overall accuracy of 85.5%, correctly classifying 72.8% of high-risk individuals and 90.8% of low-risk individuals (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple logistic regression equation predicting high risk (QRisk3 score\u0026thinsp;=\u0026thinsp;10+) for developing CVD in the next 10 years by selected explanatory variables.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% confidence interval (OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBeing a male compared to females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.64\u0026ndash;9.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNationality groups compared to Other (miscellaneous) nationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eQatari nationality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.48\u0026ndash;9.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNorthern Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.44\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.744[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSouth-eastern Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.77\u0026ndash;8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSouthern Asia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.78\u0026ndash;16.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWestern Asia/Middle east\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.23\u0026ndash;4.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge group (years) compared to (18\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.6\u0026ndash;22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e50\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(25.9\u0026ndash;217.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e60+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e612.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(194.8\u0026ndash;1926.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHighest level of education compared to those with less than class 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.689[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSecondary school (Class 7 to 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.44\u0026ndash;3.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.621[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTrade/technical/vocational qualification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.22\u0026ndash;2.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.754[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDiploma/bachelor\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.3\u0026ndash;2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.809[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePost graduate degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.29\u0026ndash;2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.892[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAverage household\u0026rsquo;s total income (QAR) per month compared to those less than 5,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.847[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e(5,000\u0026ndash;10,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.49\u0026ndash;1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.792[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e(11,000\u0026ndash;20,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.5\u0026ndash;1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.981[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e(21,000\u0026ndash;30,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.29\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.357[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e(31,000\u0026ndash;40,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.37\u0026ndash;2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.971[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e(41,000\u0026ndash;50,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.13\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.188[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e(51,000\u0026ndash;60,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.18\u0026ndash;3.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.748[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e(More than 60,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.17\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.363[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDuration of residence in Qatar compared to those who stayed for 5 years or less\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.359[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.38\u0026ndash;6.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e11\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.55\u0026ndash;8.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.278[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCurrent address (municipality) compared to those living in Others (Al Dhaayen, Al Khor and Al Thakhira, Al Shamal, Al Sheehaniya, Umm Slal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAl Rayyan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.99\u0026ndash;3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.052[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAl Wakra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.43\u0026ndash;2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.836[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDoha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.15\u0026ndash;4.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI (Kg/m2) categories compared to those with acceptable Weight (\u0026lt;\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOverweight (25-29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.94\u0026ndash;3.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.076[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eClass 1 Obesity (30-34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.64\u0026ndash;5.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eClass 2 Obesity (35-39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.84\u0026ndash;14.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eClass 3 (morbid) Obesity (40+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.46\u0026ndash;14.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBeing physically active (\u0026gt;\u0026thinsp;150 min/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.46\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.256[NS]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eP (model)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eOverall predictive accuracy\u0026thinsp;=\u0026thinsp;85.5%\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section4\"\u003e \u003ch2\u003ePredictive accuracy of high-risk individuals\u0026thinsp;=\u0026thinsp;72.8%\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePredictive accuracy of low-risk individuals\u0026thinsp;=\u0026thinsp;90.8%\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal findings of the study\u003c/h2\u003e \u003cp\u003eThis study represents one of the first applications of the QRISK3 algorithm in Qatar\u0026rsquo;s primary care setting. The main findings of the study indicated that almost one-quarter (24.5%) of participants without established CVD were classified as high risk (QRISK3\u0026thinsp;\u0026ge;\u0026thinsp;10%), and an additional 15.6% had prevalent CVD. This substantiates the fact that approximately 40% of adults assessed either require secondary prevention or meet criteria for primary prevention interventions. Age was the most powerful predictor, with odds of high risk increasing more than 600-fold among participants aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years compared to those aged 18\u0026ndash;39 years. Males were nearly six times more likely to be high risk than females. The finding suggests that men in this population carry a disproportionately higher burden of predicted CVD risk, which may be attributable to differences in risk factor profiles such as smoking, blood pressure, and lipid levels. Moreover, Southern and South-eastern Asian participants exhibited the highest adjusted odds of high risk, even after controlling for other factors. Qatari nationals also showed elevated risk compared to miscellaneous groups. Furthermore, the findings highlighted Class 2 obesity associated with more than six-fold higher odds and residence in Doha remained an independent predictor of high risk, suggesting area-level factors or population clustering.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eComparison of the key findings with existing evidence\u003c/h2\u003e \u003cp\u003eThe finding almost one-quarter (24.5%) of participants without established CVD were classified as high risk (QRISK3\u0026thinsp;\u0026ge;\u0026thinsp;10%), and an additional 15.6% had prevalent CVD highlights a notable burden of a 10-year cardiovascular disease (CVD) risk in the multiethnic population included in the study. This proportion aligns with findings from other studies applying QRISK3 in diverse populations, where a large segment of adults exhibited moderate to high predicted risk levels, particularly among older individuals and males.[20, 21]\u003c/p\u003e \u003cp\u003eAs previously mentioned, age and male gender were the strongest predictors of high CVD risk, a finding that substantiates the results from the UK Biobank validation of QRISK3, which reported risk scores increased with age and that predictive performance varied by gender[22]. Additionally, findings of a QRISK3 application in India similarly documented age and sex related gradient in predicted cardiovascular risk. QRISK3 scores increased progressively with advancing age and higher risk estimates observed among male participants[23]. Moreover, a QRISK3 study conducted in Saudi Arabia reported greater predicted risk in men compared to women, with risk estimates escalating in older age groups[15]. It can be argued that these age- and gender-specific patterns may be linked to prevalent behaviors such as high tobacco consumption, unhealthy dietary habits, and physical inactivity, all of which contribute to the growing burden of CVD in these settings[24\u0026ndash;27]. Hence, in clinical settings, QRISK3\u0026rsquo;s ability to differentiate risk by age and gender can help clinicians prioritize interventions for older adults and men who may benefit most from early preventive measures.[18]\u003c/p\u003e \u003cp\u003eMoreover, the study also indicated notable association of years of residence in Qatar with CVD risk, particularly beyond 10 years. This finding may be associated with prolonged exposure to urbanized environments and sedentary lifestyle changes[28]. This finding is further substantiated by the municipality of residence, where individuals living in more urban areas (participants residing in Doha having the highest prevalence compared to other municipalities) demonstrated higher cardiovascular risk, highlighting the cumulative impact of urbanization patterns. Literature suggests that urbanization often brings shifts in environmental exposures, reduced opportunities for physical activity, and greater reliance on processed foods, which in turn drive lifestyle changes that elevate cardiovascular risk[29\u0026ndash;31]. Evidence further indicates type of food consumption and unhealthy lifestyles are also significant risk factors of CVD in the GCC region[32, 33]. Similarly, population-based research of multiple Saudi and other Middle Eastern populations concluded a high prevalence of obesity, physical inactivity, and consumption of processed foods which exacerbates the established cardiovascular risk factors, including blood pressure and lipid disorders[28, 34]. Furthermore, the study reported that BMI had a weaker but statistically significant effect, as its contribution is mediated through other clinical variables such as blood pressure, glycaemic status, and lipid profile[35].\u003c/p\u003e \u003cp\u003eIt is important to acknowledge that QRISK3 does not directly account for dietary habits or physical activity; rather, these lifestyle-related factors are indirectly captured through clinical indicators such as body mass index (BMI), blood pressure, and lipid profile. This indirect approach positions QRISK3 as a robust tool for capturing the cumulative influence of lifestyle patterns and urbanization on cardiovascular risk. On the contrary, educational levels and physical activity had no significant influence predicted risk which may be attributed to self-reporting biases and the structural limitations of QRISK3. Given that physical activity is not included as a direct variable within the algorithm, its effect is instead mediated through downstream clinical indicators such as BMI, blood pressure, and lipid profile.[36]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations of the study\u003c/h2\u003e \u003cp\u003eThe salient features of the study include that it employed a robust sampling strategy across age, gender, and nationality strata, enhancing representativeness of the PHCC and utilized a validated risk algorithm (QRISK3) which is widely used internationally and incorporates a broad range of clinical and sociodemographic predictors. Moreover, combining biometric measurements, laboratory results, and EMR records improved accuracy of risk estimation.\u003c/p\u003e \u003cp\u003eThe study has certain limitations. Since the study employed a cross-sectional design, the results cannot establish causality or assess temporal changes in risk. It is important to mention that approximately 600 cases lacked complete information for QRISK3 calculation, which may introduce bias. Nationality was used as proxy for ethnicity in the study. It can be argued that this approach may mask heterogeneity within groups and limit generalizability. Moreover, physical activity and some lifestyle factors were self-reported, which might raise some concerns about measurement error as risk can be over- or under-estimated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eImplications of the study findings\u003c/h2\u003e \u003cp\u003eFor individuals with a QRISK3 score below 10%, particularly those under 40 who already exhibit cardiovascular risk factors, QRISK3 can serve as a valuable tool to guide discussions and encourage lifestyle modification. Active engagement in shared decision-making is essential, as the abstract concept of risk can be made more tangible by illustrating how interventions such as weight reduction, dietary improvements, increased physical activity, and smoking cessation can lower the predicted 10-year risk. Moreover, serial QRISK3 assessments can be employed to establish appropriate follow-up intervals and monitor changes in risk factors over time, thereby reinforcing patient motivation and enabling clinicians to track progress effectively.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003e This study provides one of the first applications of the QRISK3 algorithm within Qatar\u0026rsquo;s primary care settings, providing meaningful insights into the cardiovascular risk profile of a diverse population. Almost 40% of assessed adults either had established cardiovascular disease or were classified as high risk, highlighting substantial disease burden. Age, male sex, certain nationality groups, obesity, and urban residence emerged as the strongest independent predictors of high cardiovascular risk. These findings substantiate existing evidence on sociodemographic and metabolic determinants of CVD while highlighting local patterns influenced by Qatar\u0026rsquo;s demographic diversity and rapid urbanization.\u003c/p\u003e \u003cp\u003eThe study further advocates the practical utility of QRISK3 as a risk-stratification tool for guiding prevention efforts. Its integration into routine primary care assessment may support early identification of individuals who would benefit from lifestyle counseling, risk factor modification, and targeted clinical interventions. Future research should evaluate the calibration and predictive performance of QRISK3 in Qatar using longitudinal outcomes, incorporate more objective measures of lifestyle behaviors, and explore genetic or biomarker-based refinements to risk prediction. Strengthening risk-based screening and implementing culturally tailored preventive programs (particularly for high-risk demographic groups) can contribute meaningfully to national strategies aiming to reduce the growing cardiovascular burden within the region.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCVD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic medical record\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePHCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrimary Health Care Corporation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll study procedures were conducted in accordance with the Declaration of Helsinki.\u0026nbsp;The study was reviewed and approved by Primary Health Care Corporation (PHCC)’s Independent Review Board (BUHOOTH-D-23-00058). Informed consent was obtained from participants aged 18 or over. Overall, the study was planned and conducted with integrity according to generally accepted ethical principles.[17]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors contributions\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eMohamed Ahmed Syed: Conception of idea, literature review, data analysis and interpretation drafting and review of manuscript\u003c/li\u003e\n \u003cli\u003eAhmed Sameer Alnuaimi: drafting of manuscript, data analysis and interpretation and review of manuscript\u003c/li\u003e\n \u003cli\u003eDana Bilal El Kaissi: Literature review, drafting of manuscript, data analysis and interpretation\u003c/li\u003e\n \u003cli\u003eTamara Jamil Marji: Data analysis and interpretation and review of manuscript\u003c/li\u003e\n \u003cli\u003eMuslim Abbas Syed: Literature review, drafting of manuscript, data analysis and interpretation and drafting and review of manuscript\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by Primary Health Care Corporation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge Primary Health Care Corporation for funding this study and the data collectors who were involved in data collection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWhite, H., et al., \u003cem\u003eUniversal MI definition update for cardiovascular disease.\u003c/em\u003e Current cardiology reports, 2014. \u003cstrong\u003e16\u003c/strong\u003e(6): p. 492.\u003c/li\u003e\n \u003cli\u003eMendis, S., I. Graham, and J. Narula, \u003cem\u003eAddressing the global burden of cardiovascular diseases; need for scalable and sustainable frameworks.\u003c/em\u003e Global Heart, 2022. \u003cstrong\u003e17\u003c/strong\u003e(1): p. 48.\u003c/li\u003e\n \u003cli\u003eDi Cesare, M., et al., \u003cem\u003eThe heart of the world.\u003c/em\u003e Global heart, 2024. \u003cstrong\u003e19\u003c/strong\u003e(1): p. 11.\u003c/li\u003e\n \u003cli\u003eChong, B., et al., \u003cem\u003eGlobal burden of cardiovascular diseases: projections from 2025 to 2050.\u003c/em\u003e European journal of preventive cardiology, 2025. \u003cstrong\u003e32\u003c/strong\u003e(11): p. 1001-1015.\u003c/li\u003e\n \u003cli\u003eYusuf, S., et al., \u003cem\u003eEffect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study.\u003c/em\u003e The lancet, 2004. \u003cstrong\u003e364\u003c/strong\u003e(9438): p. 937-952.\u003c/li\u003e\n \u003cli\u003eO\u0026apos;donnell, M.J., et al., \u003cem\u003eRisk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study.\u003c/em\u003e The Lancet, 2010. \u003cstrong\u003e376\u003c/strong\u003e(9735): p. 112-123.\u003c/li\u003e\n \u003cli\u003eBaldassarre, D., et al., \u003cem\u003eRationale and design of the CV-PREVITAL study: an Italian multiple cohort randomised controlled trial investigating innovative digital strategies in primary cardiovascular prevention.\u003c/em\u003e BMJ open, 2023. \u003cstrong\u003e13\u003c/strong\u003e(7): p. e072040.\u003c/li\u003e\n \u003cli\u003eLaw, M.R., J.K. Morris, and N.J. Wald, \u003cem\u003eUse of blood pressure lowering drugs in the prevention of cardiovascular disease: meta-analysis of 147 randomised trials in the context of expectations from prospective epidemiological studies.\u003c/em\u003e Bmj, 2009. \u003cstrong\u003e338\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eBaigent, C., et al., \u003cem\u003eEfficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials.\u003c/em\u003e Lancet (London, England), 2010. \u003cstrong\u003e376\u003c/strong\u003e(9753): p. 1670-1681.\u003c/li\u003e\n \u003cli\u003eLeong, D.P., et al., \u003cem\u003eReducing the Global Burden of Cardiovascular Disease, Part 2: Prevention and Treatment of Cardiovascular Disease.\u003c/em\u003e Circ Res, 2017. \u003cstrong\u003e121\u003c/strong\u003e(6): p. 695-710.\u003c/li\u003e\n \u003cli\u003eRossello, X., et al., \u003cem\u003eRisk prediction tools in cardiovascular disease prevention: a report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP).\u003c/em\u003e European journal of preventive cardiology, 2019. \u003cstrong\u003e26\u003c/strong\u003e(14): p. 1534-1544.\u003c/li\u003e\n \u003cli\u003eHippisley-Cox, J., et al., \u003cem\u003eDevelopment and validation of a new algorithm for improved cardiovascular risk prediction.\u003c/em\u003e Nature Medicine, 2024. \u003cstrong\u003e30\u003c/strong\u003e(5): p. 1440-1447.\u003c/li\u003e\n \u003cli\u003eSyed, M.A., et al., \u003cem\u003ePrevalence of metabolic syndrome in primary health settings in Qatar: a cross sectional study.\u003c/em\u003e BMC Public Health, 2020. \u003cstrong\u003e20\u003c/strong\u003e(1): p. 611.\u003c/li\u003e\n \u003cli\u003eSheikh, A., et al., \u003cem\u003eRisk prediction models for atherosclerotic cardiovascular disease: A systematic assessment with particular reference to Qatar.\u003c/em\u003e Qatar medical journal, 2021. \u003cstrong\u003e2021\u003c/strong\u003e(2): p. 42.\u003c/li\u003e\n \u003cli\u003eLivingstone, S., et al., \u003cem\u003eEffect of competing mortality risks on predictive performance of the QRISK3 cardiovascular risk prediction tool in older people and those with comorbidity: external validation population cohort study.\u003c/em\u003e The lancet Healthy longevity, 2021. \u003cstrong\u003e2\u003c/strong\u003e(6): p. e352-e361.\u003c/li\u003e\n \u003cli\u003eSyed, M.A., et al., \u003cem\u003eKey service delivery processes, challenges and barriers to healthcare access for managing diabetes outside target HbA1c levels in primary care settings in Qatar: a qualitative inquiry of healthcare professionals\u0026rsquo; and service users\u0026rsquo; perspectives.\u003c/em\u003e BMJ Public Health, 2025. \u003cstrong\u003e3\u003c/strong\u003e(1).\u003c/li\u003e\n \u003cli\u003eSyed, M.A., et al., \u003cem\u003eHealth risk factors, status and service utilisation of adults in primary health care settings in Qatar: The HEALTHSIGHT study protocol.\u003c/em\u003e Plos one, 2024. \u003cstrong\u003e19\u003c/strong\u003e(5): p. e0304160.\u003c/li\u003e\n \u003cli\u003eHippisley-Cox, J., C. Coupland, and P. Brindle, \u003cem\u003eDevelopment and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study.\u003c/em\u003e bmj, 2017. \u003cstrong\u003e357\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eDuerden, M., N. O\u0026rsquo;Flynn, and N. Qureshi, \u003cem\u003eCardiovascular risk assessment and lipid modification: NICE guideline.\u003c/em\u003e The British Journal of General Practice, 2015. \u003cstrong\u003e65\u003c/strong\u003e(636): p. 378.\u003c/li\u003e\n \u003cli\u003eChan, S.K.J., \u003cem\u003eBurden and determinants of cardiovascular health and outcomes in patients with cancer\u003c/em\u003e. 2025, University of East Anglia.\u003c/li\u003e\n \u003cli\u003eVamja, R., et al., \u003cem\u003eCommunity-based evaluation of cardiovascular risk using QRISK\u0026reg; 3 in type 2 diabetes mellitus population in Gujarat, India.\u003c/em\u003e BMC Public Health, 2025. \u003cstrong\u003e25\u003c/strong\u003e(1): p. 1-12.\u003c/li\u003e\n \u003cli\u003eParsons, R.E., et al., \u003cem\u003eIndependent external validation of the QRISK3 cardiovascular disease risk prediction model using UK Biobank.\u003c/em\u003e Heart, 2023. \u003cstrong\u003e109\u003c/strong\u003e(22): p. 1690-1697.\u003c/li\u003e\n \u003cli\u003eSanjay Kalra, D., et al., \u003cem\u003eQRISK3-Based Cardiovascular Risk Assessment Among Indian Healthcare Professionals.\u003c/em\u003e European Journal of Cardiovascular Medicine, 2025. \u003cstrong\u003e15\u003c/strong\u003e: p. 423-430.\u003c/li\u003e\n \u003cli\u003eTurk-Adawi, K., et al., \u003cem\u003eCardiovascular disease in the Eastern Mediterranean region: epidemiology and risk factor burden.\u003c/em\u003e Nature Reviews Cardiology, 2018. \u003cstrong\u003e15\u003c/strong\u003e(2): p. 106-119.\u003c/li\u003e\n \u003cli\u003eSultan, Y., et al., \u003cem\u003eSmoking-related disease impact in the Eastern Mediterranean region: a comprehensive assessment using global burden of disease data.\u003c/em\u003e Asian Pacific Journal of Cancer Prevention: APJCP, 2024. \u003cstrong\u003e25\u003c/strong\u003e(2): p. 495.\u003c/li\u003e\n \u003cli\u003eMelaku, Y.A., et al., \u003cem\u003eThe Impact of Unhealthy Lifestyle on the Burden of Non-Communicable Diseases in the State of Qatar: A Systematic Analysis of the Global Burden of Disease Study 2021.\u003c/em\u003e American Journal of Lifestyle Medicine, 2025: p. 15598276251405214.\u003c/li\u003e\n \u003cli\u003eBiswas, T., et al., \u003cem\u003eClustering of metabolic and behavioural risk factors for cardiovascular diseases among the adult population in South and Southeast Asia: findings from WHO STEPS data.\u003c/em\u003e The Lancet Regional Health-Southeast Asia, 2023. \u003cstrong\u003e12\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eAl-Nozha, M.M., et al., \u003cem\u003eCoronary artery disease in Saudi Arabia.\u003c/em\u003e Saudi medical journal, 2004. \u003cstrong\u003e25\u003c/strong\u003e(9): p. 1165-1171.\u003c/li\u003e\n \u003cli\u003eChan, F., et al., \u003cem\u003eProjected impact of urbanization on cardiovascular disease in China.\u003c/em\u003e International journal of public health, 2012. \u003cstrong\u003e57\u003c/strong\u003e(5): p. 849-854.\u003c/li\u003e\n \u003cli\u003eYusuf, S., et al., \u003cem\u003eGlobal burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization.\u003c/em\u003e Circulation, 2001. \u003cstrong\u003e104\u003c/strong\u003e(22): p. 2746-2753.\u003c/li\u003e\n \u003cli\u003eRajagopalan, S., et al., \u003cem\u003eThe urban environment and cardiometabolic health.\u003c/em\u003e Circulation, 2024. \u003cstrong\u003e149\u003c/strong\u003e(16): p. 1298-1314.\u003c/li\u003e\n \u003cli\u003eAljefree, N. and F. Ahmed, \u003cem\u003ePrevalence of cardiovascular disease and associated risk factors among adult population in the Gulf region: a systematic review.\u003c/em\u003e Advances in Public Health, 2015. \u003cstrong\u003e2015\u003c/strong\u003e(1): p. 235101.\u003c/li\u003e\n \u003cli\u003eAlibrahim, M.S., et al., \u003cem\u003eRisk factors for cardiovascular disease among Saudi students: Association with BMI, current smoking, level of physical activity, and dietary habits.\u003c/em\u003e PLoS One, 2025. \u003cstrong\u003e20\u003c/strong\u003e(5): p. e0321206.\u003c/li\u003e\n \u003cli\u003eMusaiger, A.O., \u003cem\u003eOverweight and obesity in eastern mediterranean region: prevalence and possible causes.\u003c/em\u003e Journal of obesity, 2011. \u003cstrong\u003e2011\u003c/strong\u003e(1): p. 407237.\u003c/li\u003e\n \u003cli\u003eNg, M., et al., \u003cem\u003eGlobal, regional, and national prevalence of overweight and obesity in children and adults during 1980\u0026ndash;2013: a systematic analysis for the Global Burden of Disease Study 2013.\u003c/em\u003e The lancet, 2014. \u003cstrong\u003e384\u003c/strong\u003e(9945): p. 766-781.\u003c/li\u003e\n \u003cli\u003ePrince, S.A., et al., \u003cem\u003eA comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review.\u003c/em\u003e International journal of behavioral nutrition and physical activity, 2008. \u003cstrong\u003e5\u003c/strong\u003e(1): p. 56.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular risk, QRISK3, Qatar, Primary Care \u0026 Prevention","lastPublishedDoi":"10.21203/rs.3.rs-8608190/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8608190/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, with a growing burden in the Eastern Mediterranean Region. Despite high prevalence of metabolic and lifestyle-related risk factors in Qatar, no locally validated risk prediction tool exists. This study aimed to estimate 10-year CVD risk using the QRISK3 algorithm in Qatar’s primary care setting and identify key predictors of high risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A cross-sectional study was conducted among adults (\u0026gt;18 years) registered with the Primary Health Care Corporation (PHCC). QRISK3 scores were calculated for participants without established CVD using demographic, clinical, and laboratory data. High risk was defined as QRISK3 ≥10%. Logistic regression was used to identify independent predictors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Of 1,606 eligible participants, 24.5% were classified as high risk and 15.6% had prevalent CVD, indicating that approximately 40% of adults assessed required preventive or secondary care interventions. Age was the strongest predictor (OR=612.6 for ≥60 years vs. \u0026lt;40 years), followed by male sex (OR=5.8), Southern Asian nationality (OR=6.8), Class II obesity (OR=6.4), and residence in Doha (OR=2.3). The final model demonstrated 85.5% predictive accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Nearly one-quarter of adults without established CVD in Qatar’s primary care settings are at high predicted risk, highlighting the need for targeted prevention strategies. The study further advocates the significance and utility of QRISK3 as a risk‑stratification tool for guiding prevention efforts. Its integration into routine primary care assessment may support early identification of individuals who would benefit from lifestyle counseling, risk factor modification, and targeted clinical \u0026amp; preventive interventions.\u003c/p\u003e","manuscriptTitle":"Risk Estimation of Cardiovascular Disease in Qatar’s Primary Care Settings: Application of QRISK3 Algorithm and Sociodemographic Predictors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 09:50:34","doi":"10.21203/rs.3.rs-8608190/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-18T07:44:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T18:18:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T16:50:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T16:29:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T16:10:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T15:49:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T15:27:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T15:10:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T14:51:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T14:31:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T14:25:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T14:23:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9817061290257282683805971064083485434","date":"2026-02-16T13:28:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296753426002709755102906537499383546624","date":"2026-02-15T15:52:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T14:42:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236388832673807316552024340085124166656","date":"2026-02-09T06:37:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-08T23:12:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253725125340609488578937579154652509529","date":"2026-02-08T23:01:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172478699708067445930798685925565721283","date":"2026-02-08T22:16:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57384699204826890361336446600487176601","date":"2026-02-08T17:04:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228118895471468628503209468736826395146","date":"2026-02-08T16:36:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338094211504664347543902228103171151791","date":"2026-02-06T15:35:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-06T13:49:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-06T13:46:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-29T09:34:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-29T06:08:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-01-29T05:57:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c7c160d1-6fee-4ad0-864b-91a5e5a26acb","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:01:42+00:00","versionOfRecord":{"articleIdentity":"rs-8608190","link":"https://doi.org/10.1186/s12872-026-05823-8","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2026-04-09 15:58:33","publishedOnDateReadable":"April 9th, 2026"},"versionCreatedAt":"2026-02-11 09:50:34","video":"","vorDoi":"10.1186/s12872-026-05823-8","vorDoiUrl":"https://doi.org/10.1186/s12872-026-05823-8","workflowStages":[]},"version":"v1","identity":"rs-8608190","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8608190","identity":"rs-8608190","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}