Cooccurrence of noncommunicable disease risk factors and their determinants among the Afghan population: WHO STEPS Survey 2018

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This study examined the prevalence and the cooccurrence of NCD risk factors and their sociodemographic determinants among the Afghan population. Method The 2018 Afghanistan WHO STEPS survey was analyzed to investigate the prevalence and determinants of NCD risk factors and their cooccurrence. This was a nationally representative household-based cross-sectional study that included 3955 participants. Poisson regression was employed to explore associations between the number of cooccurring risk factors and demographic characteristics. Results A high prevalence of both behavioral and metabolic risk factors were observed in this study. Smoking (8.9%), sedentary behaviour (43.8%), unhealthy diet (18.2%), hypertension (12.2%), diabetes (9.6%), and obesity (16.9%) were among the prevalent risk factors identified. A significant portion of the population exhibited multiple concurrent risks. Only 9% had no risk factors, while 40% exhibited at least 3 risk factors. The regression analysis revealed associations between demographic factors and having multiple risk factors. Notably, females, older individuals, urban residents, and married individuals exhibited a higher likelihood of cooccurring risk factors. Conclusion Our findings revealed a high prevalence of NCD risk factors in Afghanistan and explored the complex interplay between demographics and cooccurrence of NCD risk factors. These findings contribute to the understanding of NCD epidemiology in the country and underscore the importance of specific interventions to alleviate the burden of NCDs and improve population health. Noncommunicable disease Risk factors Cooccurrence Afghanistan 1. Introduction In an era of rapid globalization and urbanization, noncommunicable diseases (NCDs) have emerged as a significant global health challenge, contributing significantly to morbidity, mortality, and the overall burden on healthcare systems. According to reports from the World Health Organization (WHO) 2023, NCDs account for almost 74% of all global deaths, with cardiovascular disease (CVD), chronic respiratory disease (CRD), diabetes, and cancer accounting for 80% of all NCD-related deaths (1, 2) . NCDs can affect individuals of all age groups and backgrounds; however, trends show higher morbidity and mortality rates among older age groups and those with poor economic status. WHO data show that NCDs disproportionally affect poor people, and almost 77% of all deaths resulting from NCDs, which is equivalent to 31.4 million deaths, occur in LMICs (3) . Afghanistan, amidst historical and cultural richness, faces challenges such as armed conflict, political instability, and limited healthcare access. The country is undergoing an epidemiological transition marked by a shift from infectious diseases to a rise in NCDs (4) . Lifestyle changes, urbanization, and shifting dietary habits contribute to an increased burden of NCDs, necessitating a nuanced exploration of associated risk factors (5) . The primary component in understanding and preventing NCDs lies in identifying the associated risk factors. The WHO identifies four behavioral factors (smoking, unhealthy diet, physical inactivity, excess alcohol consumption) and four metabolic conditions (hypertension, hyperglycemia, lipid imbalance, overweight/obesity) as primary global risk factors for NCDs (6) . Behavioral risk factors, contribute to nearly 80% of all NCDs globally (6) . Smoking remains the leading risk factor, resulting in approximately 8 million deaths annually (7, 8) . Insufficient fruit and vegetable intake results in approximately 16 million DALYs and 1.7 million deaths globally (9) . Physical inactivity is responsible for approximately four to five million annual deaths worldwide (10) . Additionally, around 3 million people die due to harmful alcohol use each year, representing 5.3% of all deaths globally (11) . Data from Afghanistan highlight alarming figures in NCD risk factors. According to the Global Youth Tobacco Survey 2010, more than 16% of Afghan youths had ever smoked (12) . Almost half of the population (49.8%) is reported as being sedentary in 2018 (13) . Metabolic risk factors such as hypertension (35.5%) (14) , diabetes (12.1%) (15) , and overweight (25.5%) (16) are also prevalent among the Afghan population. NCD risk factors rarely exist in isolation; rather, they often manifest as a cluster of interconnected risk factors. The simultaneous occurrence of NCD factors not only increases the risk of developing NCDs but also amplifies the complexity of managing and preventing these conditions (17) . Recognizing and understanding the patterns of cooccurrence are essential for designing holistic interventions that address the multifaceted nature of these risk factors (18) . Globally, more than 50% of countries, exhibit a prevalence exceeding 50% for four or more NCD risk factors, with Southeast Asian countries reporting the highest rates (19) . For example, in Bangladesh 38% (20) and in India approximately 36% were reported to have 3 or more risk factors concurrently (21) . Studies in South Asian countries have shown that sex, age, education, ethnicity, and wealth are associated with the cooccurrence of NCD risk factors (21–24) . Although multiple studies, including the WHO STEP survey, have been conducted in Afghanistan to measure the prevalence of NCD risk factors, no efforts have been made to study the cooccurrence of multiple NCD risk factors in Afghanistan. Therefore, in this manuscript, we intend to bridge a significant knowledge void by systematically examining the cooccurrence of NCD risk factors and their determinants among the Afghan population. This study aims to provide a detailed understanding of the cooccurrence of NCD risk factors, offering insights that can inform evidence-based interventions and policies for the prevention and management of NCDs in Afghanistan. 2. Methodology We used data from the Afghanistan WHO STEP survey, a nationally representative cross-sectional study conducted in 2018. The survey targeted permanent residents aged 18–69 years from selected households who consented to participate. Temporary residents (those who were residents for less than 12 weeks) and those who were beyond the age limit (18–69 years) were excluded. The sample size for this study was defined as 3972 household members (males and females) across 50 randomly selected districts in 6 zones of Afghanistan, however, data was collected from 3955 participants. Data collection followed the WHO STEPwise approach to surveillance (STEPS). Detailed demographic and behavioral information were gathered using a structured questionnaire, followed by physical measurements, including weight, height, waist circumference, and blood pressure. Blood and urine samples were collected for biochemical measurements. Detailed information can be found elsewhere (25) . To ensure the representativeness of the data, sample weighting and non-response weighting were calculated. The protocol for this survey was approved by the MoPH ethics board (IRB) in Afghanistan, and informed consent was obtained from the participants prior to the interview. We utilized demographic characteristics, including age, sex, residence, region, marital status, education, and employment, as independent variables. The number of coexisting risk factors was used as the outcome variable, which was derived by counting the presence of four behavioral and five metabolic risk factors in each individual (the definition of the presence of a risk factor is given in parentheses): Tobacco use (current smokers), Alcohol consumption (current users), Unhealthy diet (eating fruits and/or vegetables less than the recommended levels of 3 times a week and stating to consume ‘often’ or ‘always’ processed salty food), Sedentary behavior (being sedentary for ≥ 8 hours per day), Hypertension (systolic/diastolic pressures ≥ 140/90 mmHg), Hyperglycemia (fasting blood glucose ≥ 126), Overweight (BMI ≥ 25), Central obesity (waist circumference > 94 cm for females, > 80 cm for males), and Hypercholesterolemia (≥ 200 mg/dL). In the first step the prevalence of the risk factors along with the sex distribution were assessed. Since the outcome variable was count data, we used the Poisson regression model to evaluate the associations between demographic characteristics and the number of cooccurring risk factors. The relative ratio (RR) for each independent variable is calculated as the exponential of the corresponding regression coefficient. Statistical significance levels were set at α = 0.05, and 95% confidence intervals were calculated with robust standard errors. Stata version 12 IC was used to perform the analysis. 3. Results Demographic characteristics and prevalence of risk factors The basic demographic characteristics of the participants, along with the prevalence of the risk factors in the sample, are described in the tables below. Table 1 Description of study participants Variables Measurement Frequency Percentage Male 2,022 51.1% Female 1,930 48.8% Missing values 3 0.1% Participants age group 64 217 5.5% Missing values 31 0.8% Education level No Education 2,584 65.3% Secondary Education 574 14.5% High School 572 14.4% Higher Education 180 4.6% Missing values 45 1.1% Employment Status Employed 2,001 50.6% Unemployed 1,755 44.4% Student 142 3.6% Retired 42 1.1% Missing value 15 0.3% Marital Status Single 619 15.7% Married 3,169 80.1% Separated / divorced / Widowed 163 4.1% Missing value 4 0.1% Place of residence Rural 1,877 47.5% Urban 2,078 52.5% Region South-Eastern 674 17.1% Southern 619 15.6% Western 643 16.3% Northern 666 16.8% North-Eastern 638 16.1% Central 715 18.1% Total 3,955 100% Table 1 : Description of study participants Table 1 presents the demographic characteristics of the participants. Fifty-one percent of the participants were male, the majority (52.5%) were living in urbanized settings, and approximately half of them (50.6%) were employed. Most of the participants were aged 18–34 years. The majority of participants were married (80.1%) and illiterate (65.3%). The prevalence of behavioral and metabolic risk factors among the included respondents is presented in the following table which is distributed by sex. Table 2: Prevalence of behavioral and metabolic risk factors Variables Total Male Female Missing Value No % N % N % Current Smoking Yes 354 8.9% 275 13.6% 79 4.1% 3 (0.1%) No 3,598 91.0% 1,747 86.4% 1,851 95.9% Passive Smoking Status Yes 1,767 44.7% 1,170 57.9% 597 30.9% 3 (0.1%) No 2,185 55.2% 852 42.1% 1,333 69.1% Alcohol Consumption Yes 33 0.8% 30 1.5% 3 0.2% 3 (0.1%) No 3,919 99.1% 1,992 98.5% 1,927 99.8% Fruit Consumption < 3 times/week 1,902 48.1% 915 45.2% 987 51.1% 3 (0.1%) ≥ 3 times/week 2,041 51.6% 1,104 54.6% 937 48.5% Don’t know 9 0.2% 3 0.2% 6 0.3% Vegetable Consumption < 3 times/week 676 17.1% 380 18.8% 296 15.3% 3 (0.1%) ≥ 3 times/week 3,268 82.6% 1,640 81.1% 1,628 84.3% Don’t know 8 0.2% 2 0.1% 6 0.3% Eating Processed Food high in Salt Always 216 5.5% 103 5.1% 113 5.8% 3 (0.1%) Often 491 12.4% 253 12.5% 238 12.3% Sometimes 1,461 36.9% 738 36.5% 723 37.5% Rarely 729 20.1% 405 20.0% 387 20.1% Never 965 24.4% 511 25.3% 454 23.5% Don’t know 27 0.7% 12 0.6% 15 0.8% Unhealthy Diet No 3,233 81.8% 1,647 81.4% 1,586 82.2% 3 (0.1%) Yes 719 18.2% 375 18.6% 344 17.8% Sedentary Lifestyle < 8 hours/day 2,220 56.2% 1,255 62.1% 965 50.1% 4 (0.1%) ≥ 8 hours/day 1,731 43.8% 767 37.9% 964 49.9% High Blood Pressure Normal 3,438 87.8% 1,750 87.3% 1,688 88.3% 39 (0.9%) High 478 12.2% 255 12.7% 223 11.7% High Blood Sugar Normal 2,899 79.5% 1,498 79.4% 1,401 79.6% 308 (7.8%) Prediabetic 398 10.9% 232 12.3% 166 9.4% Diabetic 350 9.6% 157 8.3% 193 10.9% Cholesterol Normal (< 200 mg/dL) 3,235 87.2% 1,759 91.5% 1,476 82.6% 247 (6.3%) Borderline (200 - <240 mg/dL) 353 9.5% 131 6.8% 222 12.4% High (≥ 240 mg/dL) 120 3.2% 32 1.7% 88 4.9% BMI Level Underweight 265 7.1% 109 5.4% 156 9.0% 208 (5.3%) Normal 1,774 47.3% 1,051 52.2% 723 41.7% Overweight 1,072 28.6% 606 30.1% 466 26.9% Obesity 636 16.9% 247 12.3% 389 22.4% Central Obesity Yes 1,612 43.1% 652 32.4 960 55.5% 215 (5.4%) No 2,128 56.9% 1,357 67.6% 771 44.5% Coexistence of multiple risk factors No risk factors 354 8.9% 220 62.7% 131 37.3% 1 Risk factor 1,004 25.4% 571 56.9% 433 43.1% 2 Risk factors 1,015 25.7% 515 50.7% 500 49.3% 3 Risk factors 842 21.3% 405 48.1% 437 51.9% 4 Risk factors 480 12.1% 202 42.1% 278 57.9% 5 Risk factors 203 5.1% 87 42.1% 116 57.1% 6 Risk factors 46 1.2% 16 34.8% 30 65.2% 7 Risk factors 11 0.3% 6 54.6% 5 45.5% Table 2: Prevalence of behavioral and metabolic risk factors As shown in Table 2, a small percentage (8.9%) reported current smoking, while almost half of the respondents reported being passively exposed to smoking (44.7%). A large difference was observed in the prevalence of active smoking between men and women (13.6% vs 4.1%). The prevalence of alcohol consumption was shown to be very low, as only 0.8% (1.5% of men and 0.2% of women) reported that they consumed alcohol. In terms of diet, almost half of the participants reported eating fruits fewer than three times per week (48.1%), whereas for vegetables, this percentage was 17.1%. Interestingly, the prevalence of low fruit consumption was greater among females (51.1% vs 45.2%), whereas for low vegetable consumption, men reported higher rates (18.8% vs 15.3%). A total of 5.5% of the participants reported that they always consumed salty food; however, a majority (36.9%) reported that they ate salty food sometimes. Overall, around 18% of the participants were identified as having an unhealthy diet. The table also highlights that a large percentage of the participants had a sedentary lifestyle, where 43.8% reported having more than 8 hours of sedentary behavior per day. Furthermore, the prevalence of metabolic risk factors included high blood pressure, affecting 12.1% (men reporting slightly higher rates). Furthermore, a significant proportion were prediabetic (10.91%) and diabetic (9.6%). Approximately 9.52% of the participants had borderline high blood cholesterol levels, and 3.24% had high cholesterol levels. Additionally, a considerable proportion were overweight (28.6%) or obese (16.9%). The prevalence of obesity was substantially greater among females. Central obesity was also prevalent, affecting 43.1% of the participants. The prevalence of central obesity was substantially greater among females compared to males (55.5% vs 32.4%). As table 2 shows, a substantial portion of the population is affected by at least one risk factor, and only 9% of the participants are free of any risk factor. A considerable portion of participants (25.4%) had only one identified risk factor, and an equal portion (25.7%) exhibited two risk factors. A total of 21.3% of the study participants had 3 identified risk factors, and approximately 19% exhibited at least four or more risk factors together. The findings highlight a significant difference in the distribution of multiple risk factors among males and females. Males tended to have fewer risk factors, whereas females exhibited a greater propensity for the presence of multiple risk factors. The majority of 220 risk-free individuals were males (62.7%), while among those who had three or more risk factors, a greater proportion were females. Table 3 Poisson Regression Results for the Association Between Demographic Characteristics and NCD Risk Factors Variables Relative Ratio 95% Confidence Interval (CI) P Value Sex Male ( 1 ) Reference < 0.001 Female 1.17 1.11–1.23 Age Group < 24 ( 1 ) Reference 25–34 1.28 1.20–1.37 64 1.55 1.40–1.72 Education Level No Education ( 1 ) Reference Secondary Education 1.02 0.96–1.08 0.425 High School 0.98 0.92–1.05 Higher Education 1.06 0.96–1.16 Employment status Employed ( 1 ) Reference Unemployed 0.98 0.93–1.03 0.009 Student 0.80 0.69–0.92 Retired 1.15 0.96–1.39 Marital Status Single ( 1 ) Reference < 0.001 Married 1.16 1.07–1.25 Separated/Divorced/Widowed 1.41 1.26–1.56 Place of residence Rural ( 1 ) Reference Urban 1.11 1.07–1.15 < 0.001 Region South-Eastern ( 1 ) Reference Southern 1.24 1.17–1.32 < 0.001 Western 0.95 0.89–1.01 Northern 1.03 0.97–1.11 North-Eastern 1.01 0.94–1.07 Central 0.98 0.92–1.04 _cons 1.19 1.09–1.29 Table 3 : Poisson Regression Results for the Association Between Demographic Characteristics and NCD Risk Factors The results of the Poisson regression analysis, as presented in Table 3 , revealed several significant associations between demographic characteristics and the number of cooccurring NCD risk factors. Females exhibited a 17% (RR: 1.17, CI: 1.11–1.23) higher relative ratio compared to men. As age category increased, there was a significant positive association with the relative ratio of NCD risk factors. For each unit increase in age category, individuals were 1.28 to 1.74 times more likely to have cooccurring more risk factors compared to the reference category (15–24 year). Similarly, individuals living in urban settings also exhibited higher likelihood of having multiple NCD risk factors compared to their counterparts. Additionally, marital status emerged as a significant predictor of the cooccurrence of multiple NCD risk factors, with married individuals exhibiting a nearly 16% higher likelihood of concurrent NCD risk factor occurrence, while those who were divorced, separated or widowed showed an approximately 41% elevated likelihood of experiencing multiple NCD risk factors concurrently when compared to single individuals. Conversely, employment status and education did not exhibit statistically significant associations with cooccurrence of multiple NCD risk factors. 4. Discussion This study, which builds upon the Afghanistan NCD STEP survey data, provides comprehensive insight into the prevalence of multiple NCD risk factors and embarks upon an in-depth exploration of associations that underlie the burden of NCD risk factors in the country. This analysis revealed critical information concerning behavioral and metabolic risk factors. The prevalence of smoking, unhealthy diet and physical inactivity were found to be high, while the prevalence of alcohol consumption was very low. Moreover, a substantial proportion of the participants were found to have metabolic risk factors, including hypertension, hyperglycemia, hypercholesterolemia, and obesity, which underscore the necessity for continued observation and intervention. In terms of behavioral risk factors, smoking and passive exposure to smoking represent complex scenarios. However, the prevalence of active smoking was relatively low (8.9%), and passive exposure to smoking was more pronounced, affecting nearly half of the population. There was a significant sex disparity in the prevalence of active smoking, with males representing higher rates, which is in line with global trends (26–28) . However, Afghanistan’s cultural landscape plays a pivotal role in shaping health behaviors, with smoking being a particularly complex issue in this context. A significant factor contributing to the observed gender disparities could be the societal norms and cultural taboos surrounding smoking among Afghan women. There is a possibility that this stigma attached to female smoking resulted in underreporting of the prevalence, as women might be hesitant to disclose their smoking behavior due to concerns about social judgments. Similarly, alcohol consumption emerged as a rare behavior, with only 0.84% admitting to regular alcohol intake. Notably, the majority of alcohol consumers were males. Like smoking, the consumption of alcohol is subject to cultural taboos in Afghanistan, and it is important to consider the influence of cultural and religious concerns, which may discourage women from openly disclosing such behaviors. This cultural context could result in an underestimation of alcohol consumption prevalence among the population and specifically among women. Furthermore, the study looks into sedentary behavior and unhealthy dietary patterns, revealing gender disparities in fruit and vegetable consumption. Women exhibited a greater likelihood of meeting recommended levels for vegetable consumption, whereas men were more likely to consume fruits adequately. These disparities stem from complex interplay among cultural, social, and economic factors, highlighting the need for tailored strategies to promote healthier dietary habits. Moreover, the recent sociopolitical changes in Afghanistan, particularly restrictions on women's access to work and public spaces, have raised concerns about exacerbating sedentary behavior among women. Our findings indicate that a substantial proportion of the population is affected by at least one risk factor, underscoring the pervasiveness of NCD risk factors in Afghanistan. Alarmingly, only 9% of the participants were free of any risk factors, emphasizing the need for comprehensive public health interventions. This trend is somewhat comparable to that in Nepal (14.3%) (22) but much lower than those in Myanmar (49%) (29) and Kenya (27.9%) (18) . A noteworthy finding was the gender disparity, with males tending to have fewer risk factors than females. A particular concern is the greater proportion of females presenting four or more risk factors. However, this finding is consistent with some global trends (30–32) ; particular interventions are required in the context of Afghanistan, where women are deprived of many basic services, including those for health. Potential causes of this disparity could be various cultural, social, and biological factors. The Afghan society often assigns distinct roles and responsibilities to men and women, which influence lifestyle choices. One potential explanation for the presence of multiple risk factors among women could be related to the complex interplay of socioeconomic factors, access to healthcare, and education. Women in Afghanistan often face limited access to education and work, healthcare services, and social activities. Recent government regulations imposing severe restrictions on women in Afghanistan are indeed a matter of great concern that further worsens their health status and has far-reaching implications for the health and wellbeing of Afghan women. Age also played a significant role in the coexistence of risk factors, with younger individuals exhibiting a lower likelihood of experiencing multiple risk factors. As age advanced, a clear upward trend in the confluence of risk factors became evident. This trend, which is consistent with global trends (20, 30, 33) , highlights the importance of age-targeted interventions, with a particular focus on the elderly population. Older adults, particularly those aged 45 and above, are a vulnerable group concerning NCD risk factors. They often face unique challenges, such as age-related decreases in physical activity, changes in dietary patterns, and a greater likelihood of comorbidities. Tailored interventions for this age group should consider these factors and aim to promote healthy aging through lifestyle modifications and regular assessments. Public health interventions should consider the life stage and specific risk factors relevant to different age groups. For instance, efforts to reduce smoking might focus on preventing smoking initiation among the young population, while interventions to promote physical activity should be tailored to older adults' needs. Place of residence also demonstrated a strong association with the clustering of risk factors. The greater likelihood of having multiple risk factors among urban residents can be attributed to many factors, including social preferences, changes in lifestyle, and dietary habits. Urbanization often leads to an increased sedentary lifestyle, greater consumption of processed food, reduced physical activity, and exposure to unhealthy behaviors. On the other hand, in rural areas in Afghanistan, traditional agriculture and labor-intensive lifestyles that involve regular physical activity and a diet that relies on locally sourced and unprocessed foods may lead to a lower risk of experiencing NCD risk factors. This association between place of residence and the cooccurrence of risk factors has implications for public health interventions. Addressing the unique risk factor profiles of rural and urban residents can be instrumental in mitigating the burden of NCDs. Additionally, this emphasizes the importance of considering the geographical context when designing and implementing health promotion programs. Strengths and limitations One of the main strengths of this study is the use of a national dataset, which is a reliable resource and provides a comprehensive overview of NCD risk factors at the national level in Afghanistan. The large sample size of this study enhances the statistical power of the findings, which allows us to detect significant associations even among subgroups. On the other hand, some limitations in our study need to be considered when interpreting the findings. Since we used secondary data, the quality and comprehensiveness of the data depended on the original study, and inconsistencies or missing information may have affected our analysis. The cross-sectional nature of this study limits our ability to establish a causal relationship between risk factors and their determinants. Moreover, many behavioral risk factors, such as smoking, alcohol consumption, and dietary habits, rely on self-reported data. This introduces the possibility of recall bias and social desirability bias, where participants may underreport or overreport certain behaviors due to personal preferences or some social norms. Conclusion The comprehensive analysis of NCD risk factors among the Afghan population revealed a complex interplay of sociodemographic factors and the coexistence of NCD risk factors. These findings underscore the multifaceted nature of NCDs and the urgent need for targeted public health interventions in Afghanistan. The impact of cultural norms, economic factors, and social disparities on NCD risk factors highlights the importance of tailored strategies that consider the unique challenges faced by various demographic groups. As Afghanistan navigates its path toward stability and development, addressing the growing burden of NCDs must be a priority. The insights gained from this study serve as a foundation for evidence-based policymaking and the design of targeted interventions that can reduce the burden of NCDs and their associated risk factors, which ultimately improve the health and well-being of the Afghan population. Declarations Ethical approval: Not applicable Author Contribution: ASN contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing of the original draft, review and editing, and visualization. VW supervised the project and contributed to conceptualization, methodology, analysis, review and editing. PD contributed to the conceptualization, methodology, review and editing of the manuscript. AShN and SGM contributed to the methodology, writing and editing. All authors critically reviewed and approved the final version of the manuscript, and they agreed to be accountable for all aspects of the work to ensure its integrity and accuracy. Acknowledgment: We extend our deepest appreciation to the Heidelberg Institute of Global Health for their support and collaboration during the process. Additionally, we express our gratitude to the World Health Organization for providing access to the data from the Afghanistan STEP survey. 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Prevalence of tobacco use in urban and rural areas of Pakistan; a sub-study from second National Diabetes Survey of Pakistan (NDSP) 2016-2017. Pakistan Journal of Medical Sciences. 2020;36(4):808. Mon AS, Win HH, Sandar WP, Walton P, Swe KH, Vervoort JP, et al. Co-occurrence of behavioural risk factors for non-communicable diseases among 40-year and above aged community members in three regions of Myanmar. Open Research Europe. 2023;3:77. Phaswana-Mafuya N, Peltzer K, Chirinda W, Musekiwa A, Kose Z. Sociodemographic predictors of multiple non-communicable disease risk factors among older adults in South Africa. Global health action. 2013;6(1):20680. Dumith SC, Muniz LC, Tassitano RM, Hallal PC, Menezes AM. Clustering of risk factors for chronic diseases among adolescents from Southern Brazil. Preventive medicine. 2012;54(6):393-6. Ricardo CZ, Azeredo CM, Machado de Rezende LF, Levy RB. Co-occurrence and clustering of the four major non-communicable disease risk factors in Brazilian adolescents: Analysis of a national school-based survey. Plos one. 2019;14(7):e0219370. Cham B, Scholes S, Groce NE, Badjie O, Mindell JS. High level of co-occurrence of risk factors for non-communicable diseases among Gambian adults: A national population-based health examination survey. Preventive Medicine. 2020;141:106300. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-4523447","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313973992,"identity":"c2401d1f-f498-44ce-a782-6f727f0377f9","order_by":0,"name":"Ahmad Siyar Noormal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYDACZgST8cAHIMnGToIWhoMzQFqYcSnFBg7zoBuCDci38xh+rtxhI2c+7fCDwza/tsnzMTMwfviYg1uLwWEeY8mzZ9KMZW6nGRzO7btt2MbMwCw5cxseLcw8BpKNbYcTZ0gnALX03GYEamFj5sWjRb6Zx/gnREv6h8OWPbftCWoBetkMakuOwWGGH7cTCWoxOMxWZtnYlmYsIZ1TcLC34XZyGzNjM16/yPcf3nyzsc1GTkI6feODH39u285vbz744SM+hzFwGCDYjG1gsgGfeiBgf4DE+UNA8SgYBaNgFIxIAAA3Y08dsrk1xgAAAABJRU5ErkJggg==","orcid":"","institution":"Ministry of Public Health","correspondingAuthor":true,"prefix":"","firstName":"Ahmad","middleName":"Siyar","lastName":"Noormal","suffix":""},{"id":313973993,"identity":"364d8f8c-38c3-4f62-a70d-c1c8b14c1767","order_by":1,"name":"Volker Winkler","email":"","orcid":"","institution":"Heidelberg Institute of Global Health","correspondingAuthor":false,"prefix":"","firstName":"Volker","middleName":"","lastName":"Winkler","suffix":""},{"id":313973994,"identity":"3faf66bb-2878-4f5b-93ab-51c006484426","order_by":2,"name":"Safa Marva Gulam Mokhamed","email":"","orcid":"","institution":"Istanbul University","correspondingAuthor":false,"prefix":"","firstName":"Safa","middleName":"Marva Gulam","lastName":"Mokhamed","suffix":""},{"id":313973995,"identity":"0db32a80-362e-49b6-84a8-fc1bd37db632","order_by":3,"name":"Ajmal Shekeb Noormal","email":"","orcid":"","institution":"University of Essex","correspondingAuthor":false,"prefix":"","firstName":"Ajmal","middleName":"Shekeb","lastName":"Noormal","suffix":""},{"id":313973996,"identity":"0da0bc6d-ecc9-4654-b488-3959de8af668","order_by":4,"name":"Peter Dambach","email":"","orcid":"","institution":"University Hospital Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Dambach","suffix":""}],"badges":[],"createdAt":"2024-06-03 17:36:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4523447/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4523447/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97137591,"identity":"d787709e-cf1b-4fa1-ab7e-8e495dbe8782","added_by":"auto","created_at":"2025-12-01 09:57:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1096358,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4523447/v1/f0f09f64-090e-4571-9eaa-89aaeec500c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cooccurrence of noncommunicable disease risk factors and their determinants among the Afghan population: WHO STEPS Survey 2018","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn an era of rapid globalization and urbanization, noncommunicable diseases (NCDs) have emerged as a significant global health challenge, contributing significantly to morbidity, mortality, and the overall burden on healthcare systems. According to reports from the World Health Organization (WHO) 2023, NCDs account for almost 74% of all global deaths, with cardiovascular disease (CVD), chronic respiratory disease (CRD), diabetes, and cancer accounting for 80% of all NCD-related deaths\u003csup\u003e(1, 2)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNCDs can affect individuals of all age groups and backgrounds; however, trends show higher morbidity and mortality rates among older age groups and those with poor economic status. WHO data show that NCDs disproportionally affect poor people, and almost 77% of all deaths resulting from NCDs, which is equivalent to 31.4\u0026nbsp;million deaths, occur in LMICs\u003csup\u003e(3)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAfghanistan, amidst historical and cultural richness, faces challenges such as armed conflict, political instability, and limited healthcare access. The country is undergoing an epidemiological transition marked by a shift from infectious diseases to a rise in NCDs\u003csup\u003e(4)\u003c/sup\u003e. Lifestyle changes, urbanization, and shifting dietary habits contribute to an increased burden of NCDs, necessitating a nuanced exploration of associated risk factors\u003csup\u003e(5)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe primary component in understanding and preventing NCDs lies in identifying the associated risk factors. The WHO identifies four behavioral factors (smoking, unhealthy diet, physical inactivity, excess alcohol consumption) and four metabolic conditions (hypertension, hyperglycemia, lipid imbalance, overweight/obesity) as primary global risk factors for NCDs\u003csup\u003e(6)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBehavioral risk factors, contribute to nearly 80% of all NCDs globally\u003csup\u003e(6)\u003c/sup\u003e. Smoking remains the leading risk factor, resulting in approximately 8\u0026nbsp;million deaths annually\u003csup\u003e(7, 8)\u003c/sup\u003e. Insufficient fruit and vegetable intake results in approximately 16\u0026nbsp;million DALYs and 1.7\u0026nbsp;million deaths globally\u003csup\u003e(9)\u003c/sup\u003e. Physical inactivity is responsible for approximately four to five million annual deaths worldwide\u003csup\u003e(10)\u003c/sup\u003e. Additionally, around 3\u0026nbsp;million people die due to harmful alcohol use each year, representing 5.3% of all deaths globally\u003csup\u003e(11)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eData from Afghanistan highlight alarming figures in NCD risk factors. According to the Global Youth Tobacco Survey 2010, more than 16% of Afghan youths had ever smoked\u003csup\u003e(12)\u003c/sup\u003e. Almost half of the population (49.8%) is reported as being sedentary in 2018\u003csup\u003e(13)\u003c/sup\u003e. Metabolic risk factors such as hypertension (35.5%)\u003csup\u003e(14)\u003c/sup\u003e, diabetes (12.1%)\u003csup\u003e(15)\u003c/sup\u003e, and overweight (25.5%)\u003csup\u003e(16)\u003c/sup\u003e are also prevalent among the Afghan population.\u003c/p\u003e \u003cp\u003eNCD risk factors rarely exist in isolation; rather, they often manifest as a cluster of interconnected risk factors. The simultaneous occurrence of NCD factors not only increases the risk of developing NCDs but also amplifies the complexity of managing and preventing these conditions\u003csup\u003e(17)\u003c/sup\u003e. Recognizing and understanding the patterns of cooccurrence are essential for designing holistic interventions that address the multifaceted nature of these risk factors\u003csup\u003e(18)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlobally, more than 50% of countries, exhibit a prevalence exceeding 50% for four or more NCD risk factors, with Southeast Asian countries reporting the highest rates\u003csup\u003e(19)\u003c/sup\u003e. For example, in Bangladesh 38% \u003csup\u003e(20)\u003c/sup\u003e and in India approximately 36% were reported to have 3 or more risk factors concurrently \u003csup\u003e(21)\u003c/sup\u003e. Studies in South Asian countries have shown that sex, age, education, ethnicity, and wealth are associated with the cooccurrence of NCD risk factors\u003csup\u003e(21\u0026ndash;24)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough multiple studies, including the WHO STEP survey, have been conducted in Afghanistan to measure the prevalence of NCD risk factors, no efforts have been made to study the cooccurrence of multiple NCD risk factors in Afghanistan. Therefore, in this manuscript, we intend to bridge a significant knowledge void by systematically examining the cooccurrence of NCD risk factors and their determinants among the Afghan population. This study aims to provide a detailed understanding of the cooccurrence of NCD risk factors, offering insights that can inform evidence-based interventions and policies for the prevention and management of NCDs in Afghanistan.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cp\u003eWe used data from the Afghanistan WHO STEP survey, a nationally representative cross-sectional study conducted in 2018. The survey targeted permanent residents aged 18\u0026ndash;69 years from selected households who consented to participate. Temporary residents (those who were residents for less than 12 weeks) and those who were beyond the age limit (18\u0026ndash;69 years) were excluded. The sample size for this study was defined as 3972 household members (males and females) across 50 randomly selected districts in 6 zones of Afghanistan, however, data was collected from 3955 participants. Data collection followed the WHO STEPwise approach to surveillance (STEPS). Detailed demographic and behavioral information were gathered using a structured questionnaire, followed by physical measurements, including weight, height, waist circumference, and blood pressure. Blood and urine samples were collected for biochemical measurements. Detailed information can be found elsewhere\u003csup\u003e(25)\u003c/sup\u003e. To ensure the representativeness of the data, sample weighting and non-response weighting were calculated. The protocol for this survey was approved by the MoPH ethics board (IRB) in Afghanistan, and informed consent was obtained from the participants prior to the interview.\u003c/p\u003e \u003cp\u003eWe utilized demographic characteristics, including age, sex, residence, region, marital status, education, and employment, as independent variables. The number of coexisting risk factors was used as the outcome variable, which was derived by counting the presence of four behavioral and five metabolic risk factors in each individual (the definition of the presence of a risk factor is given in parentheses): Tobacco use (current smokers), Alcohol consumption (current users), Unhealthy diet (eating fruits and/or vegetables less than the recommended levels of 3 times a week and stating to consume \u0026lsquo;often\u0026rsquo; or \u0026lsquo;always\u0026rsquo; processed salty food), Sedentary behavior (being sedentary for \u0026ge;\u0026thinsp;8 hours per day), Hypertension (systolic/diastolic pressures\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg), Hyperglycemia (fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;126), Overweight (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25), Central obesity (waist circumference\u0026thinsp;\u0026gt;\u0026thinsp;94 cm for females, \u0026gt;\u0026thinsp;80 cm for males), and Hypercholesterolemia (\u0026ge;\u0026thinsp;200 mg/dL).\u003c/p\u003e \u003cp\u003eIn the first step the prevalence of the risk factors along with the sex distribution were assessed. Since the outcome variable was count data, we used the Poisson regression model to evaluate the associations between demographic characteristics and the number of cooccurring risk factors. The relative ratio (RR) for each independent variable is calculated as the exponential of the corresponding regression coefficient. Statistical significance levels were set at α\u0026thinsp;=\u0026thinsp;0.05, and 95% confidence intervals were calculated with robust standard errors. Stata version 12 IC was used to perform the analysis.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003cb\u003eDemographic characteristics and prevalence of risk factors\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe basic demographic characteristics of the participants, along with the prevalence of the risk factors in the sample, are described in the tables below.\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\u003eDescription of study participants\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\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eMeasurement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2,022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1,930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParticipants age group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2,584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1,755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3,169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeparated / divorced / Widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMissing value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1,877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2,078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth-Eastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth-Eastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3,955\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cem\u003eDescription of study participants\u003c/em\u003e\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 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003epresents the demographic characteristics of the participants. Fifty-one percent of the participants were male, the majority (52.5%) were living in urbanized settings, and approximately half of them (50.6%) were employed. Most of the participants were aged 18\u0026ndash;34 years. The majority of participants were married (80.1%) and illiterate (65.3%). The prevalence of behavioral and metabolic risk factors among the included respondents is presented in the following table which is distributed by sex.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eTable\u0026nbsp;2: Prevalence of behavioral and metabolic risk factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMissing Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCurrent Smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e95.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePassive Smoking Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAlcohol Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e98.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFruit Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eVegetable Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e84.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eEating Processed Food high in Salt\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e3 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOften\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSometimes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRarely\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eUnhealthy Diet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSedentary Lifestyle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;8 hours/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;8 hours/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHigh Blood Pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e39 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHigh Blood Sugar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e308 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrediabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal (\u0026lt;\u0026thinsp;200 mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e247 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBorderline (200 - \u0026lt;240 mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (\u0026ge;\u0026thinsp;240 mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eBMI Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e208 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCentral Obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e215 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eCoexistence of multiple risk factors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo risk factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 Risk factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 Risk factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 Risk factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 Risk factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 Risk factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 Risk factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e65.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 Risk factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e45.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eTable\u0026nbsp;2: Prevalence of behavioral and metabolic risk factors\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;2, a small percentage (8.9%) reported current smoking, while almost half of the respondents reported being passively exposed to smoking (44.7%). A large difference was observed in the prevalence of active smoking between men and women (13.6% vs 4.1%). The prevalence of alcohol consumption was shown to be very low, as only 0.8% (1.5% of men and 0.2% of women) reported that they consumed alcohol. In terms of diet, almost half of the participants reported eating fruits fewer than three times per week (48.1%), whereas for vegetables, this percentage was 17.1%. Interestingly, the prevalence of low fruit consumption was greater among females (51.1% vs 45.2%), whereas for low vegetable consumption, men reported higher rates (18.8% vs 15.3%). A total of 5.5% of the participants reported that they always consumed salty food; however, a majority (36.9%) reported that they ate salty food sometimes. Overall, around 18% of the participants were identified as having an unhealthy diet. The table also highlights that a large percentage of the participants had a sedentary lifestyle, where 43.8% reported having more than 8 hours of sedentary behavior per day.\u003c/p\u003e \u003cp\u003eFurthermore, the prevalence of metabolic risk factors included high blood pressure, affecting 12.1% (men reporting slightly higher rates). Furthermore, a significant proportion were prediabetic (10.91%) and diabetic (9.6%). Approximately 9.52% of the participants had borderline high blood cholesterol levels, and 3.24% had high cholesterol levels. Additionally, a considerable proportion were overweight (28.6%) or obese (16.9%). The prevalence of obesity was substantially greater among females. Central obesity was also prevalent, affecting 43.1% of the participants. The prevalence of central obesity was substantially greater among females compared to males (55.5% vs 32.4%).\u003c/p\u003e \u003cp\u003eAs table 2 shows, a substantial portion of the population is affected by at least one risk factor, and only 9% of the participants are free of any risk factor. A considerable portion of participants (25.4%) had only one identified risk factor, and an equal portion (25.7%) exhibited two risk factors. A total of 21.3% of the study participants had 3 identified risk factors, and approximately 19% exhibited at least four or more risk factors together. The findings highlight a significant difference in the distribution of multiple risk factors among males and females. Males tended to have fewer risk factors, whereas females exhibited a greater propensity for the presence of multiple risk factors. The majority of 220 risk-free individuals were males (62.7%), while among those who had three or more risk factors, a greater proportion were females.\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\u003ePoisson Regression Results for the Association Between Demographic Characteristics and NCD Risk Factors\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 \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelative Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% Confidence Interval (CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.20\u0026ndash;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40\u0026ndash;1.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58\u0026ndash;1.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61\u0026ndash;1.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40\u0026ndash;1.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eEducation Level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh School\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\u003e0.92\u0026ndash;1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eEmployment status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\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\u003e0.93\u0026ndash;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.69\u0026ndash;0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u0026ndash;1.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u0026ndash;1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeparated/Divorced/Widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u0026ndash;1.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u0026ndash;1.15\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=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth-Eastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u0026ndash;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e 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align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral\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\u003e0.92\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e_cons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09\u0026ndash;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: \u003cem\u003ePoisson Regression Results for the Association Between Demographic Characteristics and NCD Risk Factors\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe results of the Poisson regression analysis, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, revealed several significant associations between demographic characteristics and the number of cooccurring NCD risk factors. Females exhibited a 17% (RR: 1.17, CI: 1.11\u0026ndash;1.23) higher relative ratio compared to men. As age category increased, there was a significant positive association with the relative ratio of NCD risk factors. For each unit increase in age category, individuals were 1.28 to 1.74 times more likely to have cooccurring more risk factors compared to the reference category (15\u0026ndash;24 year). Similarly, individuals living in urban settings also exhibited higher likelihood of having multiple NCD risk factors compared to their counterparts. Additionally, marital status emerged as a significant predictor of the cooccurrence of multiple NCD risk factors, with married individuals exhibiting a nearly 16% higher likelihood of concurrent NCD risk factor occurrence, while those who were divorced, separated or widowed showed an approximately 41% elevated likelihood of experiencing multiple NCD risk factors concurrently when compared to single individuals.\u003c/p\u003e \u003cp\u003eConversely, employment status and education did not exhibit statistically significant associations with cooccurrence of multiple NCD risk factors.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study, which builds upon the Afghanistan NCD STEP survey data, provides comprehensive insight into the prevalence of multiple NCD risk factors and embarks upon an in-depth exploration of associations that underlie the burden of NCD risk factors in the country.\u003c/p\u003e \u003cp\u003eThis analysis revealed critical information concerning behavioral and metabolic risk factors. The prevalence of smoking, unhealthy diet and physical inactivity were found to be high, while the prevalence of alcohol consumption was very low. Moreover, a substantial proportion of the participants were found to have metabolic risk factors, including hypertension, hyperglycemia, hypercholesterolemia, and obesity, which underscore the necessity for continued observation and intervention.\u003c/p\u003e \u003cp\u003eIn terms of behavioral risk factors, smoking and passive exposure to smoking represent complex scenarios. However, the prevalence of active smoking was relatively low (8.9%), and passive exposure to smoking was more pronounced, affecting nearly half of the population. There was a significant sex disparity in the prevalence of active smoking, with males representing higher rates, which is in line with global trends \u003csup\u003e(26–28)\u003c/sup\u003e. However, Afghanistan’s cultural landscape plays a pivotal role in shaping health behaviors, with smoking being a particularly complex issue in this context. A significant factor contributing to the observed gender disparities could be the societal norms and cultural taboos surrounding smoking among Afghan women. There is a possibility that this stigma attached to female smoking resulted in underreporting of the prevalence, as women might be hesitant to disclose their smoking behavior due to concerns about social judgments. Similarly, alcohol consumption emerged as a rare behavior, with only 0.84% admitting to regular alcohol intake. Notably, the majority of alcohol consumers were males. Like smoking, the consumption of alcohol is subject to cultural taboos in Afghanistan, and it is important to consider the influence of cultural and religious concerns, which may discourage women from openly disclosing such behaviors. This cultural context could result in an underestimation of alcohol consumption prevalence among the population and specifically among women.\u003c/p\u003e \u003cp\u003eFurthermore, the study looks into sedentary behavior and unhealthy dietary patterns, revealing gender disparities in fruit and vegetable consumption. Women exhibited a greater likelihood of meeting recommended levels for vegetable consumption, whereas men were more likely to consume fruits adequately. These disparities stem from complex interplay among cultural, social, and economic factors, highlighting the need for tailored strategies to promote healthier dietary habits. Moreover, the recent sociopolitical changes in Afghanistan, particularly restrictions on women's access to work and public spaces, have raised concerns about exacerbating sedentary behavior among women.\u003c/p\u003e \u003cp\u003eOur findings indicate that a substantial proportion of the population is affected by at least one risk factor, underscoring the pervasiveness of NCD risk factors in Afghanistan. Alarmingly, only 9% of the participants were free of any risk factors, emphasizing the need for comprehensive public health interventions. This trend is somewhat comparable to that in Nepal (14.3%) \u003csup\u003e(22)\u003c/sup\u003e but much lower than those in Myanmar (49%) \u003csup\u003e(29)\u003c/sup\u003e and Kenya (27.9%) \u003csup\u003e(18)\u003c/sup\u003e. A noteworthy finding was the gender disparity, with males tending to have fewer risk factors than females. A particular concern is the greater proportion of females presenting four or more risk factors. However, this finding is consistent with some global trends \u003csup\u003e(30–32)\u003c/sup\u003e; particular interventions are required in the context of Afghanistan, where women are deprived of many basic services, including those for health. Potential causes of this disparity could be various cultural, social, and biological factors. The Afghan society often assigns distinct roles and responsibilities to men and women, which influence lifestyle choices. One potential explanation for the presence of multiple risk factors among women could be related to the complex interplay of socioeconomic factors, access to healthcare, and education. Women in Afghanistan often face limited access to education and work, healthcare services, and social activities. Recent government regulations imposing severe restrictions on women in Afghanistan are indeed a matter of great concern that further worsens their health status and has far-reaching implications for the health and wellbeing of Afghan women.\u003c/p\u003e \u003cp\u003eAge also played a significant role in the coexistence of risk factors, with younger individuals exhibiting a lower likelihood of experiencing multiple risk factors. As age advanced, a clear upward trend in the confluence of risk factors became evident. This trend, which is consistent with global trends\u003csup\u003e(20, 30, 33)\u003c/sup\u003e, highlights the importance of age-targeted interventions, with a particular focus on the elderly population. Older adults, particularly those aged 45 and above, are a vulnerable group concerning NCD risk factors. They often face unique challenges, such as age-related decreases in physical activity, changes in dietary patterns, and a greater likelihood of comorbidities. Tailored interventions for this age group should consider these factors and aim to promote healthy aging through lifestyle modifications and regular assessments. Public health interventions should consider the life stage and specific risk factors relevant to different age groups. For instance, efforts to reduce smoking might focus on preventing smoking initiation among the young population, while interventions to promote physical activity should be tailored to older adults' needs.\u003c/p\u003e \u003cp\u003ePlace of residence also demonstrated a strong association with the clustering of risk factors. The greater likelihood of having multiple risk factors among urban residents can be attributed to many factors, including social preferences, changes in lifestyle, and dietary habits. Urbanization often leads to an increased sedentary lifestyle, greater consumption of processed food, reduced physical activity, and exposure to unhealthy behaviors. On the other hand, in rural areas in Afghanistan, traditional agriculture and labor-intensive lifestyles that involve regular physical activity and a diet that relies on locally sourced and unprocessed foods may lead to a lower risk of experiencing NCD risk factors. This association between place of residence and the cooccurrence of risk factors has implications for public health interventions. Addressing the unique risk factor profiles of rural and urban residents can be instrumental in mitigating the burden of NCDs. Additionally, this emphasizes the importance of considering the geographical context when designing and implementing health promotion programs.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStrengths and limitations\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eOne of the main strengths of this study is the use of a national dataset, which is a reliable resource and provides a comprehensive overview of NCD risk factors at the national level in Afghanistan. The large sample size of this study enhances the statistical power of the findings, which allows us to detect significant associations even among subgroups. On the other hand, some limitations in our study need to be considered when interpreting the findings. Since we used secondary data, the quality and comprehensiveness of the data depended on the original study, and inconsistencies or missing information may have affected our analysis. The cross-sectional nature of this study limits our ability to establish a causal relationship between risk factors and their determinants. Moreover, many behavioral risk factors, such as smoking, alcohol consumption, and dietary habits, rely on self-reported data. This introduces the possibility of recall bias and social desirability bias, where participants may underreport or overreport certain behaviors due to personal preferences or some social norms.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eThe comprehensive analysis of NCD risk factors among the Afghan population revealed a complex interplay of sociodemographic factors and the coexistence of NCD risk factors. These findings underscore the multifaceted nature of NCDs and the urgent need for targeted public health interventions in Afghanistan. The impact of cultural norms, economic factors, and social disparities on NCD risk factors highlights the importance of tailored strategies that consider the unique challenges faced by various demographic groups.\u003c/p\u003e\u003cp\u003eAs Afghanistan navigates its path toward stability and development, addressing the growing burden of NCDs must be a priority. The insights gained from this study serve as a foundation for evidence-based policymaking and the design of targeted interventions that can reduce the burden of NCDs and their associated risk factors, which ultimately improve the health and well-being of the Afghan population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Contribution:\u0026nbsp;\u003c/strong\u003eASN contributed to conceptualization, methodology, formal analysis, investigation, data curation, writing\u0026nbsp;of\u0026nbsp;the original draft, review and editing,\u0026nbsp;and\u0026nbsp;visualization. VW supervised the project\u0026nbsp;and\u0026nbsp;contributed to conceptualization, methodology, analysis, review and editing. PD contributed\u0026nbsp;to the\u0026nbsp;conceptualization, methodology, review and editing of the manuscript. AShN and SGM contributed\u0026nbsp;to the\u0026nbsp;methodology, writing and editing. All authors critically reviewed and approved the final version of the manuscript, and they agreed to be accountable for all aspects of the work to ensure its integrity and accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u003c/strong\u003e We extend our deepest appreciation to the Heidelberg Institute of Global Health for their support and collaboration during the process.\u0026nbsp;Additionally, we express our gratitude to the World Health Organization for providing access to the data from the Afghanistan STEP survey. Finally, we are thankful to all\u0026nbsp;the\u0026nbsp;colleagues, friends, and family members who provided encouragement and assistance during the course of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis project received no specific grant from any funding agency in the public commercial or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of\u0026nbsp;interest:\u0026nbsp;\u003c/strong\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization W. Noncommunicable diseases: WHO; 2023 [updated 16.Sep.2023; cited 2024 22.01]. Available from: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases.\u003c/li\u003e\n\u003cli\u003eBennett JE, Stevens GA, Mathers CD, Bonita R, Rehm J, Kruk ME, et al. 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Tobacco control. 2003;12(4):e4-e.\u003c/li\u003e\n\u003cli\u003eNargis N, Thompson ME, Fong GT, Driezen P, Hussain AG, Ruthbah UH, et al. Prevalence and patterns of tobacco use in Bangladesh from 2009 to 2012: evidence from International Tobacco Control (ITC) Study. PloS one. 2015;10(11):e0141135.\u003c/li\u003e\n\u003cli\u003eBasit A, Younus BB, Waris N, Fawwad A. Prevalence of tobacco use in urban and rural areas of Pakistan; a sub-study from second National Diabetes Survey of Pakistan (NDSP) 2016-2017. Pakistan Journal of Medical Sciences. 2020;36(4):808.\u003c/li\u003e\n\u003cli\u003eMon AS, Win HH, Sandar WP, Walton P, Swe KH, Vervoort JP, et al. Co-occurrence of behavioural risk factors for non-communicable diseases among 40-year and above aged community members in three regions of Myanmar. Open Research Europe. 2023;3:77.\u003c/li\u003e\n\u003cli\u003ePhaswana-Mafuya N, Peltzer K, Chirinda W, Musekiwa A, Kose Z. Sociodemographic predictors of multiple non-communicable disease risk factors among older adults in South Africa. Global health action. 2013;6(1):20680.\u003c/li\u003e\n\u003cli\u003eDumith SC, Muniz LC, Tassitano RM, Hallal PC, Menezes AM. Clustering of risk factors for chronic diseases among adolescents from Southern Brazil. Preventive medicine. 2012;54(6):393-6.\u003c/li\u003e\n\u003cli\u003eRicardo CZ, Azeredo CM, Machado de Rezende LF, Levy RB. Co-occurrence and clustering of the four major non-communicable disease risk factors in Brazilian adolescents: Analysis of a national school-based survey. Plos one. 2019;14(7):e0219370.\u003c/li\u003e\n\u003cli\u003eCham B, Scholes S, Groce NE, Badjie O, Mindell JS. High level of co-occurrence of risk factors for non-communicable diseases among Gambian adults: A national population-based health examination survey. Preventive Medicine. 2020;141:106300. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Noncommunicable disease, Risk factors, Cooccurrence, Afghanistan","lastPublishedDoi":"10.21203/rs.3.rs-4523447/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4523447/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eNoncommunicable diseases (NCDs) pose a significant public health challenge globally, contributing to substantial morbidity and mortality. This study examined the prevalence and the cooccurrence of NCD risk factors and their sociodemographic determinants among the Afghan population.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThe 2018 Afghanistan WHO STEPS survey was analyzed to investigate the prevalence and determinants of NCD risk factors and their cooccurrence. This was a nationally representative household-based cross-sectional study that included 3955 participants. Poisson regression was employed to explore associations between the number of cooccurring risk factors and demographic characteristics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA high prevalence of both behavioral and metabolic risk factors were observed in this study. Smoking (8.9%), sedentary behaviour (43.8%), unhealthy diet (18.2%), hypertension (12.2%), diabetes (9.6%), and obesity (16.9%) were among the prevalent risk factors identified. A significant portion of the population exhibited multiple concurrent risks. Only 9% had no risk factors, while 40% exhibited at least 3 risk factors. The regression analysis revealed associations between demographic factors and having multiple risk factors. Notably, females, older individuals, urban residents, and married individuals exhibited a higher likelihood of cooccurring risk factors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings revealed a high prevalence of NCD risk factors in Afghanistan and explored the complex interplay between demographics and cooccurrence of NCD risk factors. These findings contribute to the understanding of NCD epidemiology in the country and underscore the importance of specific interventions to alleviate the burden of NCDs and improve population health.\u003c/p\u003e","manuscriptTitle":"Cooccurrence of noncommunicable disease risk factors and their determinants among the Afghan population: WHO STEPS Survey 2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-25 08:09:08","doi":"10.21203/rs.3.rs-4523447/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3e94ff56-8471-412e-83ae-371636388a26","owner":[],"postedDate":"June 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-26T12:24:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-25 08:09:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4523447","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4523447","identity":"rs-4523447","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}